Search results for: input randomization

Authors: Jose Daniel Braga Alves, Joaquim Gois, Alexandre Leite

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This abstract presents the responsible and sustainable mining case study of a Portuguese mine operation, highlighting how mine exploitation can sustainably exist in balance with the environment, aligned with all stakeholders. The mining operation is remotely located in a United Nations (UN) biodiversity reserve, away from major industrial centers or logistical ports, and presents an interesting investigation to assess the balanced mine operation in alignment with all key stakeholders, which presents unique opportunities as well as challenges. Based on the sustainable mining framework, it is intended to detail examples of best practices from Sociedade Mineira de Neves-Corvo (SOMINCOR), demonstrating social acceptance by the local community, health, and safety at work, reduction of environmental impacts and management of mining waste, which directly influence the acceptance and recognition of a sustainable operation. The case study aims to present the SOMINCOR approach to sustainable mining, focusing on social responsibility, considering materials provided by Lundin Mining Corporation (LMC) and SOMINCOR and the socially responsible approach of the mining operations., referencing related international guidelines, UN Sustainable Development Goals. The researchers reviewed LMC's annual Sustainability Reports (2019, 2020 and 2021) and updated information regarding material topics of the most significant interest to internal and external stakeholders. These material topics formed the basis of the corporation-wide sustainability strategy. LMC's Responsible Mining Policy (RMP) was reviewed, focusing on the commitment that guides the approach to responsible operation and management of the Company's business. Social performance, compliance, environmental management, governance, human rights, and economic contribution are principles of the RMP. The Human Rights Risk Impact Assessment (HRRIA), based on frameworks including UN Guiding Principles (UNGP), Voluntary Principles on Security and Human Rights, and a community engagement program implemented (SLO index), was part of this research. The program consists of ongoing surveys and perceptions studies using behavioural science insights, data from which was not available within the timeframe of completing this research. LMC stakeholder engagement standards and grievance mechanisms were also reviewed. Stakeholder engagement and the community's perception are key to this operation to ensure social license to operate (SLO). Preliminary surveys with local communities provided input data for the local development strategy. After the implementation of several initiatives, subsequent surveys were performed to assess acceptance and trust from the local communities and changes to the SLO index. SOMINCOR's operation contributes to 12 out of 17 sustainable development goals. From the assessed and available data, local communities and social engagement are priorities to SOMINCOR. Experience to date shows that the continual engagement with local communities and the grievance mechanisms in place are respected and followed for all concerns presented by any stakeholder. It can be concluded that this underground mine in Portugal complies with applicable regulations and goes beyond them with regard to sustainable development and engagement with key stakeholders.

Keywords: sustainable mining, development goals, portuguese mining, zinc copper

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Authors: Ankit Khurana

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The sufficient heat storage capacity or ability to dissipate heat is the most decisive parameter to have an effective and efficient functioning of Friction-based Brake Disc systems. The primary aim of the research was to analyse the effect of multiple coatings on lightweight disk rotors surface which not only alleviates the mass of vehicle & also, augments heat transfer. This research is projected to aid the automobile fraternity with an enunciated view over the thermal aspects in a braking system. The results of the project indicate that with the advent of modern coating technologies a brake system’s thermal curtailments can be removed and together with forced convection, heat transfer processes can see a drastic improvement leading to increased lifetime of the brake rotor. Other advantages of modifying the surface of a lightweight rotor substrate will be to reduce the overall weight of the vehicle, decrease the risk of thermal brake failure (brake fade and fluid vaporization), longer component life, as well as lower noise and vibration characteristics. A mathematical model was constructed in MATLAB which encompassing the various thermal characteristics of the proposed coatings and substrate materials required to approximate the heat flux values in a free and forced convection environment; resembling to a real-time braking phenomenon which could easily be modelled into a full cum scaled version of the alloy brake rotor part in ABAQUS. The finite element of a brake rotor was modelled in a constrained environment such that the nodal temperature between the contact surfaces of the coatings and substrate (Wrought Aluminum alloy) resemble an amalgamated solid brake rotor element. The initial results obtained were for a Plasma Electrolytic Oxidized (PEO) substrate wherein the Aluminum alloy gets a hard ceramic oxide layer grown on its transitional phase. The rotor was modelled and then evaluated in real-time for a constant ‘g’ braking event (based upon the mathematical heat flux input and convective surroundings), which reflected the necessity to deposit a conducting coat (sacrificial) above the PEO layer in order to inhibit thermal degradation of the barrier coating prematurely. Taguchi study was then used to bring out certain critical factors which may influence the maximum operating temperature of a multi-coated brake disc by simulating brake tests: a) an Alpine descent lasting 50 seconds; b) an Autobahn stop lasting 3.53 seconds; c) a Six–high speed repeated stop in accordance to FMVSS 135 lasting 46.25 seconds. Thermal Barrier coating thickness and Vane heat transfer coefficient were the two most influential factors and owing to their design and manufacturing constraints a final optimized model was obtained which survived the 6-high speed stop test as per the FMVSS -135 specifications. The simulation data highlighted the merits for preferring Wrought Aluminum alloy 7068 over Grey Cast Iron and Aluminum Metal Matrix Composite in coherence with the multiple coating depositions.

Keywords: lightweight brakes, surface modification, simulated braking, PEO, aluminum

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Authors: Abhisek Chakrabarty, Subhraprakash Mandal

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Flash flood is one of the catastrophic natural hazards in the mountainous region of India. The recent flood in the Mandakini River in Kedarnath (14-17th June, 2013) is a classic example of flash floods that devastated Uttarakhand by killing thousands of people.The disaster was an integrated effect of high intensityrainfall, sudden breach of Chorabari Lake and very steep topography. Every year in Himalayan Region flash flood occur due to intense rainfall over a short period of time, cloud burst, glacial lake outburst and collapse of artificial check dam that cause high flow of river water. In Sikkim-Derjeeling Himalaya one of the probable flash flood occurrence zone is Teesta Watershed. The Teesta River is a right tributary of the Brahmaputra with draining mountain area of approximately 8600 Sq. km. It originates in the Pauhunri massif (7127 m). The total length of the mountain section of the river amounts to 182 km. The Teesta is characterized by a complex hydrological regime. The river is fed not only by precipitation, but also by melting glaciers and snow as well as groundwater. The present study describes an attempt to model surface runoff in upper Teesta basin, which is directly related to catastrophic flood events, by creating a system based on GIS technology. The main object was to construct a direct unit hydrograph for an excess rainfall by estimating the stream flow response at the outlet of a watershed. Specifically, the methodology was based on the creation of a spatial database in GIS environment and on data editing. Moreover, rainfall time-series data collected from Indian Meteorological Department and they were processed in order to calculate flow time and the runoff volume. Apart from the meteorological data, background data such as topography, drainage network, land cover and geological data were also collected. Clipping the watershed from the entire area and the streamline generation for Teesta watershed were done and cross-sectional profiles plotted across the river at various locations from Aster DEM data using the ERDAS IMAGINE 9.0 and Arc GIS 10.0 software. The analysis of different hydraulic model to detect flash flood probability ware done using HEC-RAS, Flow-2D, HEC-HMS Software, which were of great importance in order to achieve the final result. With an input rainfall intensity above 400 mm per day for three days the flood runoff simulation models shows outbursts of lakes and check dam individually or in combination with run-off causing severe damage to the downstream settlements. Model output shows that 313 Sq. km area were found to be most vulnerable to flash flood includes Melli, Jourthang, Chungthang, and Lachung and 655sq. km. as moderately vulnerable includes Rangpo,Yathang, Dambung,Bardang, Singtam, Teesta Bazarand Thangu Valley. The model was validated by inserting the rain fall data of a flood event took place in August 1968, and 78% of the actual area flooded reflected in the output of the model. Lastly preventive and curative measures were suggested to reduce the losses by probable flash flood event.

Keywords: flash flood, GIS, runoff, simulation model, Teesta river basin

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Authors: Caroline Smith, Professor Debi Bhattacharya, Sion Scott

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Introduction: Oropharyngeal Dysphagia (OD) effects around 15% of older people, however it is often unrecognised and under diagnosed until they are hospitalised. There is a need for primary care healthcare practitioners (HCPs) to assume a proactive role in identifying and managing OD to prevent adverse outcomes such as aspiration pneumonia. Understanding the determinants of primary care HCPs undertaking this new behaviour provides the intervention targets for addressing. This realist review, underpinned by the Theoretical Domains Framework (TDF), aims to synthesise relevant literature and develop programme theories to understand what interventions work, how they work and under what circumstances to facilitate HCPs to prevent harm from OD. Combining realist methodology with behavioural science will permit conceptualisation of intervention components as theoretical behavioural constructs, thus informing the design of a future behaviour change intervention. Furthermore, through the TDF’s linkage to a taxonomy of behaviour change techniques, we will identify corresponding behaviour change techniques to include in this intervention. Methods & analysis: We are following the five steps for undertaking a realist review: 1) clarify the scope 2) Literature search 3) appraise and extract data 4) evidence synthesis 5) evaluation. We have searched Medline, Google scholar, PubMed, EMBASE, CINAHL, AMED, Scopus and PsycINFO databases. We are obtaining additional evidence through grey literature, snowball sampling, lateral searching and consulting the stakeholder group. Literature is being screened, evaluated and synthesised in Excel and Nvivo. We will appraise evidence in relation to its relevance and rigour. Data will be extracted and synthesised according to its relation to Initial programme theories (IPTs). IPTs were constructed after the preliminary literature search, informed by the TDF and with input from a stakeholder group of patient and public involvement advisors, general practitioners, speech and language therapists, geriatricians and pharmacists. We will follow the Realist and Meta-narrative Evidence Syntheses: Evolving Standards (RAMESES) quality and publication standards to report study results. Results: In this ongoing review our search has identified 1417 manuscripts with approximately 20% progressing to full text screening. We inductively generated 10 IPTs that hypothesise practitioners require: the knowledge to spot the signs and symptoms of OD; the skills to provide initial advice and support; and access to resources in their working environment to support them conducting these new behaviours. We mapped the 10 IPTs to 8 TDF domains and then generated a further 12 IPTs deductively using domain definitions to fulfil the remaining 6 TDF domains. Deductively generated IPTs broadened our thinking to consider domains such as ‘Emotion,’ ‘Optimism’ and ‘Social Influence’, e.g. If practitioners perceive that patients, carers and relatives expect initial advice and support, then they will be more likely to provide this, because they will feel obligated to do so. After prioritisation with stakeholders using a modified nominal group technique approach, a maximum of 10 IPTs will progress to test against the literature.

Keywords: behaviour change, deglutition disorders, primary healthcare, realist review

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Authors: Mario Tauzene Afonso Matangue, Ivan Andres Sanchez Ortiz

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The Metropolitan area of Maputo (Mozambique Capital City) is located in semi-arid zone (800 mm annual rainfall) with 1101170 million inhabitants. On the west side, there are the flatlands of Infulene where the Mulauze River flows towards to the Indian Ocean, receiving at this site, the storm water contaminated with sanitary sewage from Maputo, transported through a concrete open channel. In Infulene, local communities grow salads crops such as tomato, onion, garlic, lettuce, and cabbage, which are then commercialized and consumed in several markets in Maputo City. Lettuce is the most daily consumed salad crop in different meals, generally in fast-foods, breakfasts, lunches, and dinners. However, the risk of infection by several pathogens due to the consumption of lettuce, using the Quantitative Microbial Risk Assessment (QMRA) tools, is still unknown since there are few studies or publications concerning to this matter in Mozambique. This work is aimed at determining the annual risk arising from the consumption of lettuce grown in Infulene valley, in Maputo, using QMRA tools. The exposure model was constructed upon the volume of contaminated water remaining in the lettuce leaves, the empirical relations between the number of pathogens and the indicator of microorganisms (E. coli), the consumption of lettuce (g) and reduction of pathogens (days). The reference pathogens were Vibrio cholerae, Cryptosporidium, norovirus, and Ascaris. The water quality samples (E. coli) were collected in the storm water channel from January 2016 to December 2018, comprising 65 samples, and the urban lettuce consumption data were collected through inquiry in Maputo Metropolis covering 350 persons. A non-parametric bootstrap was performed involving 10,000 iterations over the collected dataset, namely, water quality (E. coli) and lettuce consumption. The dose-response models were: Exponential for Cryptosporidium, Kummer Confluent hypergeomtric function (1F1) for Vibrio and Ascaris Gaussian hypergeometric function (2F1-(a,b;c;z) for norovirus. The annual infection risk estimates were performed using R 3.6.0 (CoreTeam) software by Monte Carlo (Latin hypercubes), a sampling technique involving 10,000 iterations. The annual infection risks values expressed by Median and the 95th percentile, per person per year (pppy) arising from the consumption of lettuce are as follows: Vibrio cholerae (1.00, 1.00), Cryptosporidium (3.91x10⁻³, 9.72x 10⁻³), nororvirus (5.22x10⁻¹, 9.99x10⁻¹) and Ascaris (2.59x10⁻¹, 9.65x10⁻¹). Thus, the consumption of the lettuce would result in greater risks than the tolerable levels ( < 10⁻³ pppy or 10⁻⁶ DALY) for all pathogens, and the Vibrio cholerae is the most virulent pathogens, according to the hit-single models followed by the Ascaris lumbricoides and norovirus. The sensitivity analysis carried out in this work pointed out that in the whole QMRA, the most important input variable was the reduction of pathogens (Spearman rank value was 0.69) between harvest and consumption followed by water quality (Spearman rank value was 0.69). The decision-makers (Mozambique Government) must strengthen the prevention measures related to pathogens reduction in lettuce (i.e., washing) and engage in wastewater treatment engineering.

Keywords: annual infections risk, lettuce, non-parametric bootstrapping, quantitative microbial risk assessment tools

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Authors: Sergio Celaschi, Henrique Canavarro de Alencar, Claaudecir Biazoli

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Effluent quality is of the highest priority for compliance with the permit limits of environmental protection agencies and ensures the protection of their local water system. Of the pollutants monitored, the biochemical oxygen demand (BOD) posed one of the greatest challenges. This work presents a solution for wastewater treatment plants - WWTP’s ability to react to different situations and meet treatment goals. Delayed BOD5 results from the lab take 7 to 8 analysis days, hindered the WWTP’s ability to react to different situations and meet treatment goals. Reducing BOD turnaround time from days to hours is our quest. Such a solution is based on a system of two BOD bioreactors associated with Digital Twin (DT) and Machine Learning (ML) methodologies via an Internet of Things (IoT) platform to monitor and control a WWTP to support decision making. DT is a virtual and dynamic replica of a production process. DT requires the ability to collect and store real-time sensor data related to the operating environment. Furthermore, it integrates and organizes the data on a digital platform and applies analytical models allowing a deeper understanding of the real process to catch sooner anomalies. In our system of continuous time monitoring of the BOD suppressed by the effluent treatment process, the DT algorithm for analyzing the data uses ML on a chemical kinetic parameterized model. The continuous BOD monitoring system, capable of providing results in a fraction of the time required by BOD5 analysis, is composed of two thermally isolated batch bioreactors. Each bioreactor contains input/output access to wastewater sample (influent and effluent), hydraulic conduction tubes, pumps, and valves for batch sample and dilution water, air supply for dissolved oxygen (DO) saturation, cooler/heater for sample thermal stability, optical ODO sensor based on fluorescence quenching, pH, ORP, temperature, and atmospheric pressure sensors, local PLC/CPU for TCP/IP data transmission interface. The dynamic BOD system monitoring range covers 2 mg/L < BOD < 2,000 mg/L. In addition to the BOD monitoring system, there are many other operational WWTP sensors. The CPU data is transmitted/received to/from the digital platform, which in turn performs analyses at periodic intervals, aiming to feed the learning process. BOD bulletins and their credibility intervals are made available in 12-hour intervals to web users. The chemical kinetics ML algorithm is composed of a coupled system of four first-order ordinary differential equations for the molar masses of DO, organic material present in the sample, biomass, and products (CO₂ and H₂O) of the reaction. This system is solved numerically linked to its initial conditions: DO (saturated) and initial products of the kinetic oxidation process; CO₂ = H₂0 = 0. The initial values for organic matter and biomass are estimated by the method of minimization of the mean square deviations. A real case of continuous monitoring of BOD wastewater effluent quality is being conducted by deploying an IoT application on a large wastewater purification system located in S. Paulo, Brazil.

Keywords: effluent treatment, biochemical oxygen demand, continuous monitoring, IoT, machine learning

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Authors: Ousmane Sarr

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The Senegalese coasts have high productivity of fishery resources due to the frequency of intense up-welling system that occurs along its coast, caused by the maritime trade winds making its waters nutrients rich. Fishing plays a primordial role in Senegal's socioeconomic plans and food security. However, a global diagnosis of the Senegalese maritime fishing sector has highlighted the challenges this sector encounters. Among these concerns, some significant stocks, a priority target for artisanal fishing, need further assessment. If no efforts are made in this direction, most stock will be overexploited or even in decline. It is in this context that this research was initiated. This investigation aimed to apply a multi-modal approach (LBB, Catch-only-based CMSY model and its most recent version (CMSY++); JABBA, and JABBA-Select) to assess the stock of Atlantic bonito, Sarda sarda (Bloch, 1793) in the Senegalese Exclusive Economic Zone (SEEZ). Available catch, effort, and size data from Atlantic bonito over 15 years (2004-2018) were used to calculate the nominal and standardized CPUE, size-frequency distribution, and length at retentions (50 % and 95 % selectivity) of the species. These relevant results were employed as input parameters for stock assessment models mentioned above to define the stock status of this species in this region of the Atlantic Ocean. The LBB model indicated an Atlantic bonito healthy stock status with B/BMSY values ranging from 1.3 to 1.6 and B/B0 values varying from 0.47 to 0.61 of the main scenarios performed (BON_AFG_CL, BON_GN_Length, and BON_PS_Length). The results estimated by LBB are consistent with those obtained by CMSY. The CMSY model results demonstrate that the SEEZ Atlantic bonito stock is in a sound condition in the final year of the main scenarios analyzed (BON, BON-bt, BON-GN-bt, and BON-PS-bt) with sustainable relative stock biomass (B2018/BMSY = 1.13 to 1.3) and fishing pressure levels (F2018/FMSY= 0.52 to 1.43). The B/BMSY and F/FMSY results for the JABBA model ranged between 2.01 to 2.14 and 0.47 to 0.33, respectively. In contrast, The estimated B/BMSY and F/FMSY for JABBA-Select ranged from 1.91 to 1.92 and 0.52 to 0.54. The Kobe plots results of the base case scenarios ranged from 75% to 89% probability in the green area, indicating sustainable fishing pressure and an Atlantic bonito healthy stock size capable of producing high yields close to the MSY. Based on the stock assessment results, this study highlighted scientific advice for temporary management measures. This study suggests an improvement of the selectivity parameters of longlines and purse seines and a temporary prohibition of the use of sleeping nets in the fishery for the Atlantic bonito stock in the SEEZ based on the results of the length-base models. Although these actions are temporary, they can be essential to reduce or avoid intense pressure on the Atlantic bonito stock in the SEEZ. However, it is necessary to establish harvest control rules to provide coherent and solid scientific information that leads to appropriate decision-making for rational and sustainable exploitation of Atlantic bonito in the SEEZ and the Eastern Atlantic Ocean.

Keywords: multi-model approach, stock assessment, atlantic bonito, healthy stock, sustainable, SEEZ, temporary management measures

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Authors: K. Ghosh, S. N. Tripathi, Manish Joshi, Y. S. Mayya, Arshad Khan, B. K. Sapra

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We have developed a CFD coupled aerosol microphysics model in the context of aerosol generation from a glowing wire. The governing equations can be solved implicitly for mass, momentum, energy transfer along with aerosol dynamics. The computationally efficient framework can simulate temporal behavior of total number concentration and number size distribution. This formulation uniquely couples standard K-Epsilon scheme with boundary layer model with detailed aerosol dynamics through residence time. This model uses measured temperatures (wire surface and axial/radial surroundings) and wire compositional data apart from other usual inputs for simulations. The model predictions show that bulk fluid motion and local heat distribution can significantly affect the aerosol behavior when the buoyancy effect in momentum transfer is considered. Buoyancy generated turbulence was found to be affecting parameters related to aerosol dynamics and transport as well. The model was validated by comparing simulated predictions with results obtained from six controlled experiments performed with a laboratory-made hot wire nanoparticle generator. Condensation particle counter (CPC) and scanning mobility particle sizer (SMPS) were used for measurement of total number concentration and number size distribution at the outlet of reactor cell during these experiments. Our model-predicted results were found to be in reasonable agreement with observed values. The developed model is fast (fully implicit) and numerically stable. It can be used specifically for applications in the context of the behavior of aerosol particles generated from glowing wire technique and in general for other similar large scale domains. Incorporation of CFD in aerosol microphysics framework provides a realistic platform to study natural convection driven systems/ applications. Aerosol dynamics sub-modules (nucleation, coagulation, wall deposition) have been coupled with Navier Stokes equations modified to include buoyancy coupled K-Epsilon turbulence model. Coupled flow-aerosol dynamics equation was solved numerically and in the implicit scheme. Wire composition and temperature (wire surface and cell domain) were obtained/measured, to be used as input for the model simulations. Model simulations showed a significant effect of fluid properties on the dynamics of aerosol particles. The role of buoyancy was highlighted by observation and interpretation of nucleation zones in the planes above the wire axis. The model was validated against measured temporal evolution, total number concentration and size distribution at the outlet of hot wire generator cell. Experimentally averaged and simulated total number concentrations were found to match closely, barring values at initial times. Steady-state number size distribution matched very well for sub 10 nm particle diameters while reasonable differences were noticed for higher size ranges. Although tuned specifically for the present context (i.e., aerosol generation from hotwire generator), the model can also be used for diverse applications, e.g., emission of particles from hot zones (chimneys, exhaust), fires and atmospheric cloud dynamics.

Keywords: nanoparticles, k-epsilon model, buoyancy, CFD, hot wire generator, aerosol dynamics

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Authors: Ririh Rahma Ratinghayu, Jayu Pramudya, Nur Aini Masruroh, Shi-Woei Lin

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This research studies pricing strategy in cooperative setting of hotel duopoly selling perishable product under fixed capacity constraint by using the perspective of managers. In hotel revenue management, competitor’s average room rate and occupancy rate should be taken into manager’s consideration in determining pricing strategy to generate optimum revenue. This information is not provided by business intelligence or available in competitor’s website. Thus, Information Sharing (IS) among players might result in improved performance of pricing strategy. IS is widely adopted in the logistics industry, but IS within hospitality industry has not been well-studied. This research put IS as one of cooperative game schemes, besides Mutual Price Setting (MPS) scheme. In off-peak season, hotel manager arranges pricing strategy to offer promotion package and various kinds of discounts up to 60% of full-price to attract customers. Competitor selling homogenous product will react the same, then triggers a price war. Price war which generates lower revenue may be avoided by creating collaboration in pricing strategy to optimize payoff for both players. In MPS cooperative game, players collaborate to set a room rate applied for both players. Cooperative game may avoid unfavorable players’ payoff caused by price war. Researches on horizontal cooperative game in logistics show better performance and payoff for the players, however, horizontal cooperative game in hotel revenue management has not been demonstrated. This paper aims to develop hotel revenue management models under duopoly cooperative schemes (IS & MPS), which are compared to models under non-cooperative scheme too. Each scheme has five models, Capacity Allocation Model; Demand Model; Revenue Model; Optimal Price Model; and Equilibrium Price Model. Capacity Allocation Model and Demand Model employs self-hotel and competitor’s full and discount price as predictors under non-linear relation. Optimal price is obtained by assuming revenue maximization motive. Equilibrium price is observed by interacting self-hotel’s and competitor’s optimal price under reaction equation. Equilibrium is analyzed using game theory approach. The sequence applies for three schemes. MPS Scheme differently aims to optimize total players’ payoff. The case study in which theoretical models are applied observes two hotels offering homogenous product in Indonesia during a year. The Capacity Allocation, Demand, and Revenue Models are built using multiple regression and statistically tested for validation. Case study data confirms that price behaves within demand model in a non-linear manner. IS Models can represent the actual demand and revenue data better than Non-IS Models. Furthermore, IS enables hotels to earn significantly higher revenue. Thus, duopoly hotel players in general, might have reasonable incentives to share information horizontally. During off-peak season, MPS Models are able to predict the optimal equal price for both hotels. However, Nash equilibrium may not always exist depending on actual payoff of adhering or betraying mutual agreement. To optimize performance, horizontal cooperative game may be chosen over non-cooperative game. Mathematical models can be used to detect collusion among business players. Empirical testing can be used as policy input for market regulator in preventing unethical business practices potentially harming society welfare.

Keywords: horizontal cooperative game theory, hotel revenue management, information sharing, mutual price setting

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Authors: Helena Pera, Arcadi Sanmartin, Albert Falques, Rafael Rebolo, Xavier Ametller, Heiko Zillgen, Cecile Prum, Boris Even, Eric Kapornyai

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Sheet piles system can be an interesting solution when dealing with harbors or quays designs. However, current design methods lead to conservative approaches due to the lack of specific basis of design. For instance, some design features still deal with pseudo-static approaches, although being a dynamic problem. Under this concern, the study particularly focuses on hydrodynamic water pressure definition and stability analysis of sheet pile system under seismic loads. During a seismic event, seawater produces hydrodynamic pressures on structures. Currently, design methods introduce hydrodynamic forces by means of Westergaard formulation and Eurocodes recommendations. They apply constant hydrodynamic pressure on the front sheet pile during the entire earthquake. As a result, the hydrodynamic load may represent 20% of the total forces produced on the sheet pile. Nonetheless, some studies question that approach. Hence, this study assesses the soil-structure-fluid interaction of sheet piles under seismic action in order to evaluate if current design strategies overestimate hydrodynamic pressures. For that purpose, this study performs various simulations by Plaxis 2D, a well-known geotechnical software, and CFD models, which treat fluid dynamic behaviours. Knowing that neither Plaxis nor CFD can resolve a soil-fluid coupled problem, the investigation imposes sheet pile displacements from Plaxis as input data for the CFD model. Then, it provides hydrodynamic pressures under seismic action, which fit theoretical Westergaard pressures if calculated using the acceleration at each moment of the earthquake. Thus, hydrodynamic pressures fluctuate during seismic action instead of remaining constant, as design recommendations propose. Additionally, these findings detect that hydrodynamic pressure contributes a 5% to the total load applied on sheet pile due to its instantaneous nature. These results are in line with other studies that use added masses methods for hydrodynamic pressures. Another important feature in sheet pile design is the assessment of the geotechnical overall stability. It uses pseudo-static analysis since the dynamic analysis cannot provide a safety calculation. Consequently, it estimates the seismic action. One of its relevant factors is the selection of the seismic reduction factor. A huge amount of studies discusses the importance of it but also about all its uncertainties. Moreover, current European standards do not propose a clear statement on that, and they recommend using a reduction factor equal to 1. This leads to conservative requirements when compared with more advanced methods. Under this situation, the study calibrates seismic reduction factor by fitting results from pseudo-static to dynamic analysis. The investigation concludes that pseudo-static analyses could reduce seismic action by 40-50%. These results are in line with some studies from Japanese and European working groups. In addition, it seems suitable to account for the flexibility of the sheet pile-soil system. Nevertheless, the calibrated reduction factor is subjected to particular conditions of each design case. Further research would contribute to specifying recommendations for selecting reduction factor values in the early stages of the design. In conclusion, sheet pile design still has chances for improving its design methodologies and approaches. Consequently, design could propose better seismic solutions thanks to advanced methods such as findings of this study.

Keywords: computational fluid dynamics, hydrodynamic pressures, pseudo-static analysis, quays, seismic design, steel sheet pile

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Authors: M. Shahab Uddin, Pennung Warnitchai

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Currently more than half of the world’s populations are living in cities, and the number and sizes of cities are growing faster than ever. Cities rely on the effective functioning of complex and interdependent critical infrastructures networks to provide public services, enhance the quality of life, and save the community from hazards and disasters. In contrast, complex connectivity and interdependency among the urban critical infrastructures bring management challenges and make the urban system prone to the domino effect. Unplanned rapid growth, increased connectivity, and interdependency among the infrastructures, resource scarcity, and many other socio-political factors are affecting the typical state of an urban system and making it susceptible to numerous sorts of diversion. In addition to internal vulnerabilities, urban systems are consistently facing external threats from natural and manmade hazards. Cities are not just complex, interdependent system, but also makeup hubs of the economy, politics, culture, education, etc. For survival and sustainability, complex urban systems in the current world need to manage their vulnerabilities and hazardous incidents more wisely and more interactively. Coordinated management in such systems makes for huge potential when it comes to absorbing negative effects in case some of its components were to function improperly. On the other hand, ineffective management during a similar situation of overall disorder from hazards devastation may make the system more fragile and push the system to an ultimate collapse. Following the quantum, the current research hypothesizes that a hazardous event starts its journey as an emergency, and the system’s internal vulnerability and response capacity determine its destination. Connectivity and interdependency among the urban critical infrastructures during this stage may transform its vulnerabilities into dynamic damaging force. An emergency may turn into a disaster in the absence of effective management; similarly, mismanagement or lack of management may lead the situation towards a catastrophe. Situation awareness and factual decision-making is the key to win a battle. The current research proposed a contextual decision support system for an urban critical infrastructure system while integrating three different models: 1) Damage cascade model which demonstrates damage propagation among the infrastructures through their connectivity and interdependency, 2) Restoration model, a dynamic restoration process of individual infrastructure, which is based on facility damage state and overall disruptions in surrounding support environment, and 3) Optimization model that ensures optimized utilization and distribution of available resources in and among the facilities. All three models are tightly connected, mutually interdependent, and together can assess the situation and forecast the dynamic outputs of every input. Moreover, this integrated model will hold disaster managers and decision makers responsible when it comes to checking all the alternative decision before any implementation, and support to produce maximum possible outputs from the available limited inputs. This proposed model will not only support to reduce the extent of damage cascade but will ensure priority restoration and optimize resource utilization through adaptive and collaborative management. Complex systems predictably fail but in unpredictable ways. System understanding, situation awareness, and factual decisions may significantly help urban system to survive and sustain.

Keywords: disaster prevention, decision support system, emergency response, urban critical infrastructure system

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Authors: Chandu Rathnayake, Isuri Anuradha

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Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

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Authors: Lassi Lehto, Jaakko Kahkonen, Juha Oksanen, Tapani Sarjakoski

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The use of raster-formatted data sets in geospatial analysis is increasing rapidly. At the same time, geographic data are being introduced into disciplines outside the traditional domain of geoinformatics, like climate change, intelligent transport, and immigration studies. These developments call for better methods to deliver raster geodata in an efficient and easy-to-use manner. Data cube technologies have traditionally been used in the geospatial domain for managing Earth Observation data sets that have strict requirements for effective handling of time series. The same approach and methodologies can also be applied in managing other types of geospatial data sets. A cloud service-based geodata cube, called GeoCubes Finland, has been developed to support online delivery and analysis of most important geospatial data sets with national coverage. The main target group of the service is the academic research institutes in the country. The most significant aspects of the GeoCubes data repository include the use of multiple resolution levels, cloud-optimized file structure, and a customized, flexible content access API. Input data sets are pre-processed while being ingested into the repository to bring them into a harmonized form in aspects like georeferencing, sampling resolutions, spatial subdivision, and value encoding. All the resolution levels are created using an appropriate generalization method, selected depending on the nature of the source data set. Multiple pre-processed resolutions enable new kinds of online analysis approaches to be introduced. Analysis processes based on interactive visual exploration can be effectively carried out, as the level of resolution most close to the visual scale can always be used. In the same way, statistical analysis can be carried out on resolution levels that best reflect the scale of the phenomenon being studied. Access times remain close to constant, independent of the scale applied in the application. The cloud service-based approach, applied in the GeoCubes Finland repository, enables analysis operations to be performed on the server platform, thus making high-performance computing facilities easily accessible. The developed GeoCubes API supports this kind of approach for online analysis. The use of cloud-optimized file structures in data storage enables the fast extraction of subareas. The access API allows for the use of vector-formatted administrative areas and user-defined polygons as definitions of subareas for data retrieval. Administrative areas of the country in four levels are available readily from the GeoCubes platform. In addition to direct delivery of raster data, the service also supports the so-called virtual file format, in which only a small text file is first downloaded. The text file contains links to the raster content on the service platform. The actual raster data is downloaded on demand, from the spatial area and resolution level required in each stage of the application. By the geodata cube approach, pre-harmonized geospatial data sets are made accessible to new categories of inexperienced users in an easy-to-use manner. At the same time, the multiresolution nature of the GeoCubes repository facilitates expert users to introduce new kinds of interactive online analysis operations.

Keywords: cloud service, geodata cube, multiresolution, raster geodata

Procedia PDF Downloads 139

Authors: Andreas Rudisch, Ralf Lampert, Andreas Kolbitsch

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It is a well-known fact among the engineering community that earthquakes with comparatively low magnitudes can cause serious damage to nonstructural components (NSCs) of buildings, even when the supporting structure performs relatively well. Past research works focused mainly on NSCs of nuclear power plants and industrial plants. Particular attention should also be given to architectural façade elements of old masonry buildings (e.g. ornamental figures, balustrades, vases), which are very vulnerable under seismic excitation. Large numbers of these historical nonstructural components (HiNSCs) can be found in highly frequented historical city centers and in the event of failure, they pose a significant danger to persons. In order to estimate the vulnerability of acceleration sensitive HiNSCs, the peak horizontal floor acceleration (PHFA) is used. The PHFA depends on the dynamic characteristics of the building, the ground excitation, and induced nonlinearities. Consequently, the PHFA can not be generalized as a simple function of height. In the present research work, an extensive case study was conducted to investigate the influence of induced nonlinearity on the PHFA for old masonry buildings. Probabilistic nonlinear FE time-history analyses considering three different hazard levels were performed. A set of eighteen synthetically generated ground motions was used as input to the structure models. An elastoplastic macro-model (multiPlas) for nonlinear homogenized continuum FE-calculation was calibrated to multiple scales and applied, taking specific failure mechanisms of masonry into account. The macro-model was calibrated according to the results of specific laboratory and cyclic in situ shear tests. The nonlinear macro-model is based on the concept of multi-surface rate-independent plasticity. Material damage or crack formation are detected by reducing the initial strength after failure due to shear or tensile stress. As a result, shear forces can only be transmitted to a limited extent by friction when the cracking begins. The tensile strength is reduced to zero. The first goal of the calibration was the consistency of the load-displacement curves between experiment and simulation. The calibrated macro-model matches well with regard to the initial stiffness and the maximum horizontal load. Another goal was the correct reproduction of the observed crack image and the plastic strain activities. Again the macro-model proved to work well in this case and shows very good correlation. The results of the case study show that there is significant scatter in the absolute distribution of the PHFA between the applied ground excitations. An absolute distribution along the normalized building height was determined in the framework of probability theory. It can be observed that the extent of nonlinear behavior varies for the three hazard levels. Due to the detailed scope of the present research work, a robust comparison with code-recommendations and simplified PHFA distributions are possible. The chosen methodology offers a chance to determine the distribution of PHFA along the building height of old masonry structures. This permits a proper hazard assessment of HiNSCs under seismic loads.

Keywords: nonlinear macro-model, nonstructural components, time-history analysis, unreinforced masonry

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Authors: Claudio Loconsole, Daniele Leonardis, Antonio Brunetti, Gianpaolo Francesco Trotta, Nicholas Caporusso, Vitoantonio Bevilacqua

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Unfortunately, many people still do not have access to communication, with specific regard to reading and writing. Among them, people who are blind or visually impaired, have several difficulties in getting access to the world, compared to the sighted. Indeed, despite technology advancement and cost reduction, nowadays assistive devices are still expensive such as Braille-based input/output systems which enable reading and writing texts (e.g., personal notes, documents). As a consequence, assistive technology affordability is fundamental in supporting the visually impaired in communication, learning, and social inclusion. This, in turn, has serious consequences in terms of equal access to opportunities, freedom of expression, and actual and independent participation to a society designed for the sighted. Moreover, the visually impaired experience difficulties in recognizing objects and interacting with devices in any activities of daily living. It is not a case that Braille indications are commonly reported only on medicine boxes and elevator keypads. Several software applications for the automatic translation of written text into speech (e.g., Text-To-Speech - TTS) enable reading pieces of documents. However, apart from simple tasks, in many circumstances TTS software is not suitable for understanding very complicated pieces of text requiring to dwell more on specific portions (e.g., mathematical formulas or Greek text). In addition, the experience of reading\writing text is completely different both in terms of engagement, and from an educational perspective. Statistics on the employment rate of blind people show that learning to read and write provides the visually impaired with up to 80% more opportunities of finding a job. Especially in higher educational levels, where the ability to digest very complex text is key, accessibility and availability of Braille plays a fundamental role in reducing drop-out rate of the visually impaired, thus affecting the effectiveness of the constitutional right to get access to education. In this context, the Braille Lab project aims at overcoming these social needs by including affordability in designing and developing assistive tools for visually impaired people. In detail, our awarded project focuses on a technology innovation of the operation principle of existing assistive tools for the visually impaired leaving the Human-Machine Interface unchanged. This can result in a significant reduction of the production costs and consequently of tool selling prices, thus representing an important opportunity for social entrepreneurship. The first two assistive tools designed within the Braille Lab project following the proposed approach aims to provide the possibility to personally print documents and handouts and to read texts written in Braille using refreshable Braille display, respectively. The former, named ‘Braille Cartridge’, represents an alternative solution for printing in Braille and consists in the realization of an electronic-controlled dispenser printing (cartridge) which can be integrated within traditional ink-jet printers, in order to leverage the efficiency and cost of the device mechanical structure which are already being used. The latter, named ‘Braille Cursor’, is an innovative Braille display featuring a substantial technology innovation by means of a unique cursor virtualizing Braille cells, thus limiting the number of active pins needed for Braille characters.

Keywords: Human rights, social challenges and technology innovations, visually impaired, affordability, assistive tools

Procedia PDF Downloads 275

Authors: Tehrrie Konig, Cassiano Bortolozo, Daniel Metodiev, Rodolfo Mendes, Marcio Andrade, Marcio Moraes

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Gravitational mass movements are recurrent events in Brazil, usually triggered by intense rainfall. When these events occur in urban areas, they end up becoming disasters due to the economic damage, social impact, and loss of human life. To identify the landslide-susceptible areas, it is important to know the geotechnical parameters of the soil, such as cohesion, internal friction angle, unit weight, hydraulic conductivity, and hydraulic diffusivity. The measurement of these parameters is made by collecting soil samples to analyze in the laboratory and by using geophysical methodologies, such as Vertical Electrical Survey (VES). The geophysical surveys analyze the soil properties with minimal impact in its initial structure. Statistical analysis and mathematical models of physical basis are used to model and calculate the Factor of Safety for steep slope areas. In general, such mathematical models work from the combination of slope stability models and hydrological models. One example is the mathematical model TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope- Stability Model) which calculates the variation of the Factor of Safety of a determined study area. The model relies on changes in pore-pressure and soil moisture during a rainfall event. TRIGRS was written in the Fortran programming language and associates the hydrological model, which is based on the Richards Equation, with the stability model based on the principle of equilibrium limit. Therefore, the aims of this work are modeling the slope stability of Campos do Jordão with TRIGRS, using geotechnical and geophysical methodologies to acquire the soil properties. The study area is located at southern-east of Sao Paulo State in the Mantiqueira Mountains and has a historic landslide register. During the fieldwork, soil samples were collected, and the VES method applied. These procedures provide the soil properties, which were used as input data in the TRIGRS model. The hydrological data (infiltration rate and initial water table height) and rainfall duration and intensity, were acquired from the eight rain gauges installed by Cemaden in the study area. A very high spatial resolution digital terrain model was used to identify the slopes declivity. The analyzed period is from March 6th to March 8th of 2017. As results, the TRIGRS model calculates the variation of the Factor of Safety within a 72-hour period in which two heavy rainfall events stroke the area and six landslides were registered. After each rainfall, the Factor of Safety declined, as expected. The landslides happened in areas identified by the model with low values of Factor of Safety, proving its efficiency on the identification of landslides susceptible areas. This study presents a critical threshold for landslides, in which an accumulated rainfall higher than 80mm/m² in 72 hours might trigger landslides in urban and natural slopes. The geotechnical and geophysics methods are shown to be very useful to identify the soil properties and provide the geological characteristics of the area. Therefore, the combine geotechnical and geophysical methods for soil characterization and the modeling of landslides susceptible areas with TRIGRS are useful for urban planning. Furthermore, early warning systems can be developed by combining the TRIGRS model and weather forecast, to prevent disasters in urban slopes.

Keywords: landslides, susceptibility, TRIGRS, vertical electrical survey

Procedia PDF Downloads 174

Authors: Adewale Tolulope Irewale, Elias Emeka Elemike, Christian O. Dimkpa, Emeka Emmanuel Oguzie

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As the global population steadily approaches the 10billion mark, the world is currently faced with two major challenges among others – accessing sustainable and clean energy, and food security. Accessing cleaner and sustainable energy sources to drive global economy and technological advancement, and feeding the teeming human population require sustainable, innovative, and smart solutions. To solve the food production problem, producers have relied on fertilizers as a way of improving crop productivity. Commercial inorganic fertilizers, which is employed to boost agricultural food production, however, pose significant ecological sustainability and economic problems including soil and water pollution, reduced input efficiency, development of highly resistant weeds, micronutrient deficiency, soil degradation, and increased soil toxicity. These ecological and sustainability concerns have raised uncertainties about the continued effectiveness of conventional fertilizers. With the application of nanotechnology, plant biomass upcycling offers several advantages in greener energy production and sustainable agriculture through reduction of environmental pollution, increasing soil microbial activity, recycling carbon thereby reducing GHG emission, and so forth. This innovative technology has the potential for a circular economy and creating a sustainable agricultural practice. Nanomaterials have the potential to greatly enhance the quality and nutrient composition of organic biomass which in turn, allows for the conversion of biomass into nanofertilizers that are potentially more efficient. Water hyacinth plant harvested from an inland water at Warri, Delta State Nigeria were air-dried and milled into powder form. The dry biomass were used to prepare biochar at a pre-determined temperature in an oxygen deficient atmosphere. Physicochemical analysis of the resulting biochar was carried out to determine its porosity and general morphology using the Scanning Transmission Electron Microscopy (STEM). The functional groups (-COOH, -OH, -NH2, -CN, -C=O) were assessed using the Fourier Transform InfraRed Spectroscopy (FTIR) while the heavy metals (Cr, Cu, Fe, Pb, Mg, Mn) were analyzed using Inductively Coupled Plasma – Optical Emission Spectrometry (ICP-OES). Impregnation of the biochar with nanonutrients were achieved under varied conditions of pH, temperature, nanonutrient concentrations and resident time to achieve optimum adsorption. Adsorption and desorption studies were carried out on the resulting nanofertilizer to determine kinetics for the potential nutrients’ bio-availability to plants when used as green fertilizers. Water hyacinth (Eichhornia crassipes) which is an aggressively invasive aquatic plant known for its rapid growth and profusion is being examined in this research to harness its biomass as a sustainable feedstock to formulate functionalized nano-biochar fertilizers, offering various benefits including water hyacinth biomass upcycling, improved nutrient delivery to crops and aquatic ecosystem remediation. Altogether, this work aims to create output values in the three dimensions of environmental, economic, and social benefits.

Keywords: biochar-based nanofertilizers, eichhornia crassipes, greener agriculture, sustainable ecosystem, water hyacinth

Procedia PDF Downloads 65

Authors: Danyi Wang, Andrew Schriefer, Dennis O'Rourke, Brajendra Kumar, Yang Liu, Fei Zhong, Juergen Scheuenpflug, Zheng Feng

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Purpose: It has been recognized that the microbiome plays critical roles in disease pathogenesis, including cancer, autoimmune disease, and multiple sclerosis. To develop a clinical-grade assay for exploring microbiome-derived clinical biomarkers across disease areas, a two-phase approach is implemented. 1) Identification of the optimal sample preparation reagents using pre-mixed bacteria and healthy donor stool samples coupled with proprietary Sigma-Aldrich® bioinformatics solution. 2) Exploratory analysis of patient samples for enabling precision medicine. Study Procedure: In phase 1 study, we first compared the 16S sequencing results of two ATCC® microbiome standards (MSA 2002 and MSA 2003) across five different extraction kits (Kit A, B, C, D & E). Both microbiome standards samples were extracted in triplicate across all extraction kits. Following isolation, DNA quantity was determined by Qubit assay. DNA quality was assessed to determine purity and to confirm extracted DNA is of high molecular weight. Bacterial 16S ribosomal ribonucleic acid (rRNA) amplicons were generated via amplification of the V3/V4 hypervariable region of the 16S rRNA. Sequencing was performed using a 2x300 bp paired-end configuration on the Illumina MiSeq. Fastq files were analyzed using the Sigma-Aldrich® Microbiome Platform. The Microbiome Platform is a cloud-based service that offers best-in-class 16S-seq and WGS analysis pipelines and databases. The Platform and its methods have been extensively benchmarked using microbiome standards generated internally by MilliporeSigma and other external providers. Data Summary: The DNA yield using the extraction kit D and E is below the limit of detection (100 pg/µl) of Qubit assay as both extraction kits are intended for samples with low bacterial counts. The pre-mixed bacterial pellets at high concentrations with an input of 2 x106 cells for MSA-2002 and 1 x106 cells from MSA-2003 were not compatible with the kits. Among the remaining 3 extraction kits, kit A produced the greatest yield whereas kit B provided the least yield (Kit-A/MSA-2002: 174.25 ± 34.98; Kit-A/MSA-2003: 179.89 ± 30.18; Kit-B/MSA-2002: 27.86 ± 9.35; Kit-B/MSA-2003: 23.14 ± 6.39; Kit-C/MSA-2002: 55.19 ± 10.18; Kit-C/MSA-2003: 35.80 ± 11.41 (Mean ± SD)). Also, kit A produced the greatest yield, whereas kit B provided the least yield. The PCoA 3D visualization of the Weighted Unifrac beta diversity shows that kits A and C cluster closely together while kit B appears as an outlier. The kit A sequencing samples cluster more closely together than both the other kits. The taxonomic profiles of kit B have lower recall when compared to the known mixture profiles indicating that kit B was inefficient at detecting some of the bacteria. Conclusion: Our data demonstrated that the DNA extraction method impacts DNA concentration, purity, and microbial communities detected by next-generation sequencing analysis. Further microbiome analysis performance comparison of using healthy stool samples is underway; also, colorectal cancer patients' samples will be acquired for further explore the clinical utilities. Collectively, our comprehensive qualification approach, including the evaluation of optimal DNA extraction conditions, the inclusion of positive controls, and the implementation of a robust qualified bioinformatics pipeline, assures accurate characterization of the microbiota in a complex matrix for deciphering the deep biology and enabling precision medicine.

Keywords: 16S rRNA sequencing, analytical validation, bioinformatics pipeline, metagenomics

Procedia PDF Downloads 170

Authors: Edward Shizha, Edward Makwarimba

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Newcomer and refugee youth feel excluded in the education system in Canada, and the formal education environment does not fully cater for their learning needs. The objective of this study was to build knowledge and understanding of the educational needs and experiences of these youth in Canada and how available afterschool programs can most effectively support their learning needs and academic outcomes. The Employment and Social Development Canada (ESDC), which funded this research, enables and empowers students to advance their educational experience through targeted investments in services that are delivered by youth-serving organizations outside the formal education system through afterschool initiatives. A literature review and a provincial/territorial internet scan were conducted to determine the availability of services and programs that serve the educational needs and academic outcomes of newcomer youth in 10 provinces and 3 territories in Canada. The goal was to identify intersectional factors (e.g., gender, sexuality, culture, social class, race, etc.) that influence educational outcomes of newcomer/refugee students and to recommend ways the ESDC could complement settlement services to enhance students’ educational success. First, data was collected through a literature search of various databases, including PubMed, Web of Science, Scopus, Google docs, ACADEMIA, and grey literature, including government documents, to inform our analysis. Second, a provincial/territorial internet scan was conducted using a template that was created by ESDC staff with the input of the researchers. The objective of the web-search scan was to identify afterschool programs, projects, and initiatives offered to newcomer/refugee youth by service provider organizations. The method for the scan included both qualitative and quantitative data gathering. Both the literature review and the provincial/territorial scan revealed that there are gender disparities in educational outcomes of newcomer and refugee youth. High school completion rates by gender show that boys are at higher risk of not graduating than girls and that girls are more likely than boys to have at least a high school diploma and more likely to proceed to postsecondary education. Findings from literature reveal that afterschool programs are required for refugee youth who experience mental health challenges and miss out on significant periods of schooling, which affect attendance, participation, and graduation from high school. However, some refugee youth use their resilience and ambition to succeed in their educational outcomes. Another finding showed that some immigrant/refugee students, through ethnic organizations and familial affiliation, maintain aspects of their cultural values, parental expectations and ambitious expectations for their own careers to succeed in both high school and postsecondary education. The study found a significant combination of afterschool programs that include academic support, scholarships, bursaries, homework support, career readiness, internships, mentorship, tutoring, non-clinical counselling, mental health and social well-being support, language skills, volunteering opportunities, community connections, peer networking, culturally relevant services etc. These programs assist newcomer youth to develop self-confidence and prepare for academic success and future career development. The study concluded that advantages of afterschool programs are greatest for youth at risk for poor educational outcomes, such as Latino and Black youth, including 2SLGBTQI+ immigrant youth.

Keywords: afterschool programs, educational outcomes, newcomer youth, refugee youth, youth-serving organizations

Procedia PDF Downloads 76

Authors: Farideh Namvar, Rosfarizan Mohamed

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In the field of nanotechnology, the use of various biological units instead of toxic chemicals for the reduction and stabilization of nanoparticles, has received extensive attention. This use of biological entities to create nanoparticles has designated as “Green” synthesis and it is considered to be far more beneficial due to being economical, eco-friendly and applicable for large-scale synthesis as it operates on low pressure, less input of energy and low temperatures. The lack of toxic byproducts and consequent decrease in degradation of the product renders this technique more preferable over physical and classical chemical methods. The variety of biomass having reduction properties to produce nanoparticles makes them an ideal candidate for fabrication. Metal oxide nanoparticles have been said to represent a "fundamental cornerstone of nanoscience and nanotechnology" due to their variety of properties and potential applications. However, this also provides evidence of the fact that metal oxides include many diverse types of nanoparticles with large differences in chemical composition and behaviour. In this study, iron oxide nanoparticles (Fe3O4-NPs) were synthesized using a rapid, single step and completely green biosynthetic method by reduction of ferric chloride solution with brown seaweed (Sargassum muticum) water extract containing polysaccharides as a main factor which acts as reducing agent and efficient stabilizer. Antimicrobial activity against six microorganisms was tested using well diffusion method. The resulting S-IONPs are crystalline in nature, with a cubic shape. The average particle diameter, as determined by TEM, was found to be 18.01 nm. The S-IONPs were efficiently inhibited the growth of Listeria monocytogenes, Escherichia coli and Candida species. Our favorable results suggest that S-IONPs could be a promising candidate for development of future antimicrobial therapies. The nature of biosynthesis and the therapeutic potential by S-IONPs could pave the way for further research on design of green synthesis therapeutic agents, particularly nanomedicine, to deal with treatment of infections. Further studies are needed to fully characterize the toxicity and the mechanisms involved with the antimicrobial activity of these particles. Antioxidant activity of S-IONPs synthesized by green method was measured by ABTS (2, 2'-azino-bis (3-ethylbenzothiazoline-6-sulphonic acid) (IC50= 1000µg) radical scavenging activity. Also, with the increasing concentration of S-IONPs, catalase gene expression compared to control gene GAPDH increased. For anti-angiogenesis study the Ross fertilized eggs were divided into four groups; the control and three experimental groups. The gelatin sponges containing albumin were placed on the chorioalantoic membrane and soaked with different concentrations of S-IONPs. All the cases were photographed using a photo stereomicroscope. The number and the lengths of the vessels were measured using Image J software. The crown rump (CR) and weight of the embryo were also recorded. According to the data analysis, the number and length of the blood vessels, as well as the CR and weight of the embryos reduced significantly compared to the control (p < 0.05), dose dependently. The total hemoglobin was quantified as an indicator of the blood vessel formation, and in the treated samples decreased, which showed its inhibitory effect on angiogenesis.

Keywords: anti-angiogenesis, antimicrobial, antioxidant, biosynthesis, iron oxide (fe3o4) nanoparticles, sargassum muticum, seaweed

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Authors: Muhammad Bilal Shaikh

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Mineral processing plants play a pivotal role in extracting valuable minerals from raw ores, contributing significantly to various industries. However, the optimization of plant throughput remains a complex challenge, necessitating innovative approaches for increased efficiency and productivity. This research paper investigates the application of Multimodal Artificial Intelligence (MAI) techniques to address this challenge, aiming to improve overall plant throughput in mineral processing operations. The integration of multimodal AI leverages a combination of diverse data sources, including sensor data, images, and textual information, to provide a holistic understanding of the complex processes involved in mineral extraction. The paper explores the synergies between various AI modalities, such as machine learning, computer vision, and natural language processing, to create a comprehensive and adaptive system for optimizing mineral processing plants. The primary focus of the research is on developing advanced predictive models that can accurately forecast various parameters affecting plant throughput. Utilizing historical process data, machine learning algorithms are trained to identify patterns, correlations, and dependencies within the intricate network of mineral processing operations. This enables real-time decision-making and process optimization, ultimately leading to enhanced plant throughput. Incorporating computer vision into the multimodal AI framework allows for the analysis of visual data from sensors and cameras positioned throughout the plant. This visual input aids in monitoring equipment conditions, identifying anomalies, and optimizing the flow of raw materials. The combination of machine learning and computer vision enables the creation of predictive maintenance strategies, reducing downtime and improving the overall reliability of mineral processing plants. Furthermore, the integration of natural language processing facilitates the extraction of valuable insights from unstructured textual data, such as maintenance logs, research papers, and operator reports. By understanding and analyzing this textual information, the multimodal AI system can identify trends, potential bottlenecks, and areas for improvement in plant operations. This comprehensive approach enables a more nuanced understanding of the factors influencing throughput and allows for targeted interventions. The research also explores the challenges associated with implementing multimodal AI in mineral processing plants, including data integration, model interpretability, and scalability. Addressing these challenges is crucial for the successful deployment of AI solutions in real-world industrial settings. To validate the effectiveness of the proposed multimodal AI framework, the research conducts case studies in collaboration with mineral processing plants. The results demonstrate tangible improvements in plant throughput, efficiency, and cost-effectiveness. The paper concludes with insights into the broader implications of implementing multimodal AI in mineral processing and its potential to revolutionize the industry by providing a robust, adaptive, and data-driven approach to optimizing plant operations. In summary, this research contributes to the evolving field of mineral processing by showcasing the transformative potential of multimodal artificial intelligence in enhancing plant throughput. The proposed framework offers a holistic solution that integrates machine learning, computer vision, and natural language processing to address the intricacies of mineral extraction processes, paving the way for a more efficient and sustainable future in the mineral processing industry.

Keywords: multimodal AI, computer vision, NLP, mineral processing, mining

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Authors: Kuldeep Rawat, Michael O'Shea, Maureen McGough

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The paper will discuss a National Institute of Justice (NIJ) funded project to provide cost-effective aviation technologies and research to support counter-poaching operations related to endangered, protected, and/or regulated wildlife. The goal of this project is to provide cost-effective aviation technology and research support to Kenya Wildlife Service (KWS) in their counter-poaching efforts. In pursuit of this goal, Elizabeth City State University (ECSU) is assisting the National Institute of Justice (NIJ) in enhancing the Kenya Wildlife Service’s aviation technology and related capacity to meet its counter-poaching mission. Poaching, at its core, is systemic as poachers go to the most extreme lengths to kill high target species such as elephant and rhino. These high target wildlife species live in underdeveloped or impoverished nations, where poachers find fewer barriers to their operations. In Kenya, with fifty-nine (59) parks and reserves, spread over an area of 225,830 square miles (584,897 square kilometers) adequate surveillance on the ground is next to impossible. Cost-effective aviation surveillance technologies, based on a comprehensive needs assessment and operational evaluation, are needed to curb poaching and effectively prevent wildlife trafficking. As one of the premier law enforcement Air Wings in East Africa, KWS plays a crucial role in Kenya, not only in counter-poaching and wildlife conservation efforts, but in aerial surveillance, counterterrorism and national security efforts as well. While the Air Wing has done, a remarkable job conducting aerial patrols with limited resources, additional aircraft and upgraded technology should significantly advance the Air Wing’s ability to achieve its wildlife protection mission. The project includes: (i) Needs Assessment of the KWS Air Wing, to include the identification of resources, current and prospective capacity, operational challenges and priority goals for expansion, (ii) Acquisition of Low-Cost Aviation Technology to meet priority needs, and (iii) Operational Evaluation of technology performance, with a focus on implementation and effectiveness. The Needs Assessment reflects the priorities identified through two site visits to the KWS Air Wing in Nairobi, Kenya, as well as field visits to multiple national parks receiving aerial support and interviewing/surveying KWS Air wing pilots and leadership. Needs Assessment identified some immediate technology needs that includes, GPS with upgrades, including weather application, Night flying capabilities, to include runway lights and night vision technology, Cameras and surveillance equipment, Flight tracking system and/or Emergency Position Indicating Radio Beacon, Lightweight ballistic-resistant body armor, and medical equipment, to include a customized stretcher and standard medical evacuation equipment. Results of this assessment, along with significant input from the KWS Air Wing, will guide the second phase of this project: technology acquisition. Acquired technology will then be evaluated in the field, with a focus on implementation and effectiveness. Results will ultimately be translated for any rural or tribal law enforcement agencies with comparable aerial surveillance missions and operational environments, and jurisdictional challenges, seeking to implement low-cost aviation technology. Results from Needs Assessment phase, including survey results and our ongoing technology acquisition and baseline operational evaluation will be discussed in the paper.

Keywords: aerial surveillance mission, aviation technology, counter-poaching, wildlife protection

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Authors: Maria Areias, Diogo Simões, Ana Figueiredo, Anishur Rahman, Filipa Figueiredo, João Nunes

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It is expected that close to 7 billion people will live in urban areas by 2050. In order to improve the sustainability of the territories and its transition towards circular economy, it’s necessary to understand its metabolism and promote and guide the entrepreneurship answer. The study of a macroeconomic metabolism involves the quantification of the inputs, outputs and storage of energy, water, materials and wastes for an urban region. This quantification and analysis representing one opportunity for the promotion of green entrepreneurship. There are several methods to assess the environmental impacts of an urban territory, such as human and environmental risk assessment (HERA), life cycle assessment (LCA), ecological footprint assessment (EF), material flow analysis (MFA), physical input-output table (PIOT), ecological network analysis (ENA), multicriteria decision analysis (MCDA) among others. However, no consensus exists about which of those assessment methods are best to analyze the sustainability of these complex systems. Taking into account the weaknesses and needs identified, the CiiM - Circular Innovation Inter-Municipality project aims to define an uniform and globally accepted methodology through the integration of various methodologies and dynamic approaches to increase the efficiency of macroeconomic metabolisms and promoting entrepreneurship in a circular economy. The pilot territory considered in CiiM project has a total area of 969,428 ha, comprising a total of 897,256 inhabitants (about 41% of the population of the Center Region). The main economic activities in the pilot territory, which contribute to a gross domestic product of 14.4 billion euros, are: social support activities for the elderly; construction of buildings; road transport of goods, retailing in supermarkets and hypermarkets; mass production of other garments; inpatient health facilities; and the manufacture of other components and accessories for motor vehicles. The region's business network is mostly constituted of micro and small companies (similar to the Central Region of Portugal), with a total of 53,708 companies identified in the CIM Region of Coimbra (39 large companies), 28,146 in the CIM Viseu Dão Lafões (22 large companies) and 24,953 in CIM Beiras and Serra da Estrela (13 large companies). For the construction of the database was taking into account data available at the National Institute of Statistics (INE), General Directorate of Energy and Geology (DGEG), Eurostat, Pordata, Strategy and Planning Office (GEP), Portuguese Environment Agency (APA), Commission for Coordination and Regional Development (CCDR) and Inter-municipal Community (CIM), as well as dedicated databases. In addition to the collection of statistical data, it was necessary to identify and characterize the different stakeholder groups in the pilot territory that are relevant to the different metabolism components under analysis. The CIIM project also adds the potential of a Geographic Information System (GIS) so that it is be possible to obtain geospatial results of the territorial metabolisms (rural and urban) of the pilot region. This platform will be a powerful visualization tool of flows of products/services that occur within the region and will support the stakeholders, improving their circular performance and identifying new business ideas and symbiotic partnerships.

Keywords: circular economy tools, life cycle assessment macroeconomic metabolism, multicriteria decision analysis, decision support tools, circular entrepreneurship, industrial and regional symbiosis

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Authors: Caroline Spencer, Suzanne Cross, Dudley McArdle, Frank Archer

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Objectives: The evolution of the Victorian Compendium of Community-Based Resilience Building Case Studies and its capacity to help communities implement activities that encourage adaptation to disaster risk reduction and promote community resilience in rural and urban locations provide this paper's objectives. Background: Between 2012 and 2019, community groups presented at the Monash University Disaster Resilience Initiative (MUDRI) 'Advancing Community Resilience Annual Forums', provided opportunities for communities to impart local resilience activities, how to solve challenges and share unforeseen learning and be considered for inclusion in the Compendium. A key tenet of the Compendium encourages compiling and sharing of grass-roots resilience building activities to help communities before, during, and after unexpected emergencies. The online Compendium provides free access for anyone wanting to help communities build expertise, reduce program duplication, and save valuable community resources. Identifying case study features across the emergency phases and analyzing critical success factors helps communities understand what worked and what did not work to achieve success and avoid known barriers. International exemplars inform the Compendium, which represents an Australian first and enhances Victorian community resilience initiatives. Emergency Management Victoria provided seed funding for the Compendium. MUDRI matched this support and continues to fund the project. A joint Steering Committee with broad-based user input and Human ethics approval guides its continued growth. Methods: A thematic analysis of the Compendium identified case study features, including critical success factors. Results: The Compendium comprises 38 case studies, representing all eight Victorian regions. Case studies addressed emergency phases, before (29), during (7), and after (17) events. Case studies addressed all hazards (23), bushfires (11), heat (2), fire safety (1), and house fires (1). Twenty case studies used a framework. Thirty received funding, of which nine received less than $20,000 and five received more than $100,000. Twenty-nine addressed a whole of community perspective. Case studies revealed unique and valuable learning in diverse settings. Critical success factors included strong governance; board support, leadership, and trust; partnerships; commitment, adaptability, and stamina; community-led initiatives. Other success factors included a paid facilitator and local government support; external funding, and celebrating success. Anecdotally, we are aware that community groups reference Compendium and that its value adds to community resilience planning. Discussion: The Compendium offers an innovative contribution to resilience research and practice. It augments the seven resilience characteristics to strengthen and encourage communities as outlined in the Statewide Community Resilience Framework for Emergency Management; brings together people from across sectors to deliver distinct, yet connected actions to strengthen resilience as a part of the Rockefeller funded Resilient Melbourne Strategy, and supports communities and economies to be resilient when a shock occurs as identified in the recently published Australian National Disaster Risk Reduction Framework. Each case study offers learning about connecting with community and how to increase their resilience to disaster risks and to keep their community safe from unexpected emergencies. Conclusion: The Compendium enables diverse communities to adopt or adapt proven resilience activities, thereby preserving valuable community resources and offers the opportunity to extend to a national or international Compendium.

Keywords: case study, community, compendium, disaster risk reduction, resilience

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Authors: Carlotta D’Alessandro, Giuseppe Ioppolo, Katarzyna Szopik-Depczyńska

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— The growing scarcity of resources and the mounting pressures of climate change, water pollution, and chemical contamination have prompted societies, governments, and businesses to seek ways to minimize their environmental impact. To combat climate change, and foster sustainability, Industrial Symbiosis (IS) offers a powerful approach, facilitating the shift toward a circular economic model. IS has gained prominence in the European Union's policy framework as crucial enabler of resource efficiency and circular economy practices. The essence of IS lies in the collaborative sharing of resources such as energy, material by-products, waste, and water, thanks to geographic proximity. It can be exemplified by eco-industrial parks (EIPs), which are natural environments for boosting cooperation and resource sharing between businesses. EIPs are characterized by group of businesses situated in proximity, connected by a network of both cooperative and competitive interactions. They represent a sustainable industrial model aimed at reducing resource use, waste, and environmental impact while fostering economic and social wellbeing. IS, combined with Artificial Intelligence (AI)-driven technologies, can further optimize resource sharing and efficiency within EIPs. This research, supported by the “CE_IPs” project, aims to analyze the potential for IS and AI, in advancing circularity and sustainability at Fosso Imperatore. The Fosso Imperatore Industrial Park in Nocera Inferiore, Italy, specializes in agriculture and the industrial transformation of agricultural products, particularly tomatoes, tobacco, and textile fibers. This unique industrial cluster, centered around tomato cultivation and processing, also includes mechanical engineering enterprises and agricultural packaging firms. To stimulate the shift from a traditional to a circular economic model, an AI-powered Local Development Plan (LDP) is developed for Fosso Imperatore. It can leverage data analytics, predictive modeling, and stakeholder engagement to optimize resource utilization, reduce waste, and promote sustainable industrial practices. A comprehensive SWOT analysis of the AI-powered LDP revealed several key factors influencing its potential success and challenges. Among the notable strengths and opportunities arising from AI implementation are reduced processing times, fewer human errors, and increased revenue generation. Furthermore, predictive analytics minimize downtime, bolster productivity, and elevate quality while mitigating workplace hazards. However, the integration of AI also presents potential weaknesses and threats, including significant financial investment, since implementing and maintaining AI systems can be costly. The widespread adoption of AI could lead to job losses in certain sectors. Lastly, AI systems are susceptible to cyberattacks, posing risks to data security and operational continuity. Moreover, an Analytic Hierarchy Process (AHP) analysis was employed to yield a prioritized ranking of the outlined AI-driven LDP practices based on the stakeholder input, ensuring a more comprehensive and representative understanding of their relative significance for achieving sustainability in Fosso Imperatore Industrial Park. While this study provides valuable insights into the potential of AIpowered LDP at the Fosso Imperatore, it is important to note that the findings may not be directly applicable to all industrial parks, particularly those with different sizes, geographic locations, or industry compositions. Additional study is necessary to scrutinize the generalizability of these results and to identify best practices for implementing AI-driven LDP in diverse contexts.

Keywords: artificial intelligence, climate change, Fosso Imperatore, industrial park, industrial symbiosis

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Authors: Kevin F. Hsu, Alvin Chua, Sarah X. Lin

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As a multilingual society, Singaporean names for different parts of the city have changed over time. Residents included Indigenous Malays, dialect-speakers from China, European settler-colonists, and Tamil-speakers from South India. Each group would name locations in their own languages. Today, as ancestral tongues are increasingly supplanted by English, contemporary Singaporeans’ understanding of once-common place names is disappearing. After demolition or redevelopment, some urban places will only exist in archival records or in human memory. United Nations conferences on the standardization of geographic names have called attention to how place names relate to identity, well-being, and a sense of belonging. The Singapore Place-Naming Project responds to these imperatives by capturing past and present place names through digitizing historical maps, mining archival records, and applying selective crowd-sourcing to trace the evolution of place names throughout the city. The project ensures that both formal and vernacular geographical names remain accessible to historians, city planners, and the public. The project is compiling a gazetteer, a geospatial archive of placenames, with streets, buildings, landmarks, and other points of interest (POI) appearing in the historic maps and planning documents of Singapore, currently held by the National Archives of Singapore, the National Library Board, university departments, and the Urban Redevelopment Authority. To create a spatial layer of information, the project links each place name to either a geo-referenced point, line segment, or polygon, along with the original source material in which the name appears. This record is supplemented by crowd-sourced contributions from civil service officers and heritage specialists, drawing from their collective memory to (1) define geospatial boundaries of historic places that appear in past documents, but maybe unfamiliar to users today, and (2) identify and record vernacular place names not captured in formal planning documents. An intuitive interface allows participants to demarcate feature classes, vernacular phrasings, time periods, and other knowledge related to historical or forgotten spaces. Participants are stratified into age bands and ethnicity to improve representativeness. Future iterations could allow additional public contributions. Names reveal meanings that communities assign to each place. While existing historical maps of Singapore allow users to toggle between present-day and historical raster files, this project goes a step further by adding layers of social understanding and planning documents. Tracking place names illuminates linguistic, cultural, commercial, and demographic shifts in Singapore, in the context of transformations of the urban environment. The project also demonstrates how a moderated, selectively crowd-sourced effort can solicit useful geospatial data at scale, sourced from different generations, and at higher granularity than traditional surveys, while mitigating negative impacts of unmoderated crowd-sourcing. Stakeholder agencies believe the project will achieve several objectives, including Supporting heritage conservation and public education; Safeguarding intangible cultural heritage; Providing historical context for street, place or development-renaming requests; Enhancing place-making with deeper historical knowledge; Facilitating emergency and social services by tagging legal addresses to vernacular place names; Encouraging public engagement with heritage by eliciting multi-stakeholder input.

Keywords: collective memory, crowd-sourced, digital heritage, geospatial, geographical names, linguistic heritage, place-naming, Singapore, Southeast Asia

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Authors: Essam Gooda, Chadi Abdallah, Hamdi Seif, Safaa Baydoun, Rouya Hdeib, Hilal Obeid

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Floods are common natural disaster occurring in semi-arid regions in Lebanon. This results in damage to human life and deterioration of environment. Despite their destructive nature and their immense impact on the socio-economy of the region, flash floods have not received adequate attention from policy and decision makers. This is mainly because of poor understanding of the processes involved and measures needed to manage the problem. The current understanding of flash floods remains at the level of general concepts; most policy makers have yet to recognize that flash floods are distinctly different from normal riverine floods in term of causes, propagation, intensity, impacts, predictability, and management. Flash floods are generally not investigated as a separate class of event but are rather reported as part of the overall seasonal flood situation. As a result, Lebanon generally lacks policies, strategies, and plans relating specifically to flash floods. Main objective of this research is to improve flash flood prediction by providing new knowledge and better understanding of the hydrological processes governing flash floods in the East Catchments of El Assi River. This includes developing rainstorm time distribution curves that are unique for this type of study region; analyzing, investigating, and developing a relationship between arid watershed characteristics (including urbanization) and nearby villages flow flood frequency in Ras Baalbeck and Fekha. This paper discusses different levels of integration approach¬es between GIS and hydrological models (HEC-HMS & HEC-RAS) and presents a case study, in which all the tasks of creating model input, editing data, running the model, and displaying output results. The study area corresponds to the East Basin (Ras Baalbeck & Fakeha), comprising nearly 350 km2 and situated in the Bekaa Valley of Lebanon. The case study presented in this paper has a database which is derived from Lebanese Army topographic maps for this region. Using ArcMap to digitizing the contour lines, streams & other features from the topographic maps. The digital elevation model grid (DEM) is derived for the study area. The next steps in this research are to incorporate rainfall time series data from Arseal, Fekha and Deir El Ahmar stations to build a hydrologic data model within a GIS environment and to combine ArcGIS/ArcMap, HEC-HMS & HEC-RAS models, in order to produce a spatial-temporal model for floodplain analysis at a regional scale. In this study, HEC-HMS and SCS methods were chosen to build the hydrologic model of the watershed. The model then calibrated using flood event that occurred between 7th & 9th of May 2014 which considered exceptionally extreme because of the length of time the flows lasted (15 hours) and the fact that it covered both the watershed of Aarsal and Ras Baalbeck. The strongest reported flood in recent times lasted for only 7 hours covering only one watershed. The calibrated hydrologic model is then used to build the hydraulic model & assessing of flood hazards maps for the region. HEC-RAS Model is used in this issue & field trips were done for the catchments in order to calibrated both Hydrologic and Hydraulic models. The presented models are a kind of flexible procedures for an ungaged watershed. For some storm events it delivers good results, while for others, no parameter vectors can be found. In order to have a general methodology based on these ideas, further calibration and compromising of results on the dependence of many flood events parameters and catchment properties is required.

Keywords: flood risk management, flash flood, semi arid region, El Assi River, hazard maps

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Authors: Murat Arıbaş, Uğur Özcan

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Due to the increasing number of universities and academics, the fund of the universities for research activities and grants/supports given by government institutions have increased number and quality of academic research projects. Although every academic research project has a specific purpose and importance, limited resources (money, time, manpower etc.) require choosing the best ones from all (Amiri, 2010). It is a pretty hard process to compare and determine which project is better such that the projects serve different purposes. In addition, the evaluation process has become complicated since there are more than one evaluator and multiple criteria for the evaluation (Dodangeh, Mojahed and Yusuff, 2009). Mehrez and Sinuany-Stern (1983) determined project selection problem as a Multi Criteria Decision Making (MCDM) problem. If a decision problem involves multiple criteria and objectives, it is called as a Multi Attribute Decision Making problem (Ömürbek & Kınay, 2013). There are many MCDM methods in the literature for the solution of such problems. These methods are AHP (Analytic Hierarchy Process), ANP (Analytic Network Process), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation), UTADIS (Utilities Additives Discriminantes), ELECTRE (Elimination et Choix Traduisant la Realite), MAUT (Multiattribute Utility Theory), GRA (Grey Relational Analysis) etc. Teach method has some advantages compared with others (Ömürbek, Blacksmith & Akalın, 2013). Hence, to decide which MCDM method will be used for solution of the problem, factors like the nature of the problem, types of choices, measurement scales, type of uncertainty, dependency among the attributes, expectations of decision maker, and quantity and quality of the data should be considered (Tavana & Hatami-Marbini, 2011). By this study, it is aimed to develop a systematic decision process for the grant support applications that are expected to be evaluated according to their scientific adequacy by multiple evaluators under certain criteria. In this context, project evaluation process applied by The Scientific and Technological Research Council of Turkey (TÜBİTAK) the leading institutions in our country, was investigated. Firstly in the study, criteria that will be used on the project evaluation were decided. The main criteria were selected among TÜBİTAK evaluation criteria. These criteria were originality of project, methodology, project management/team and research opportunities and extensive impact of project. Moreover, for each main criteria, 2-4 sub criteria were defined, hence it was decided to evaluate projects over 13 sub-criterion in total. Due to superiority of determination criteria weights AHP method and provided opportunity ranking great number of alternatives TOPSIS method, they are used together. AHP method, developed by Saaty (1977), is based on selection by pairwise comparisons. Because of its simple structure and being easy to understand, AHP is the very popular method in the literature for determining criteria weights in MCDM problems. Besides, the TOPSIS method developed by Hwang and Yoon (1981) as a MCDM technique is an alternative to ELECTRE method and it is used in many areas. In the method, distance from each decision point to ideal and to negative ideal solution point was calculated by using Euclidian Distance Approach. In the study, main criteria and sub-criteria were compared on their own merits by using questionnaires that were developed based on an importance scale by four relative groups of people (i.e. TUBITAK specialists, TUBITAK managers, academics and individuals from business world ) After these pairwise comparisons, weight of the each main criteria and sub-criteria were calculated by using AHP method. Then these calculated criteria’ weights used as an input in TOPSİS method, a sample consisting 200 projects were ranked on their own merits. This new system supported to opportunity to get views of the people that take part of project process including preparation, evaluation and implementation on the evaluation of academic research projects. Moreover, instead of using four main criteria in equal weight to evaluate projects, by using weighted 13 sub-criteria and decision point’s distance from the ideal solution, systematic decision making process was developed. By this evaluation process, new approach was created to determine importance of academic research projects.

Keywords: Academic projects, Ahp method, Research projects evaluation, Topsis method.

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Authors: Mariza Kaskara, Stella Girtsou, Maria Prodromou, Alexia Tsouni, Christodoulos Mettas, Stavroula Alatza, Kyriaki Fotiou, Marios Tzouvaras, Charalampos Kontoes, Diofantos Hadjimitsis

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Natural hazard assessment and monitoring are crucial in managing the risks associated with fires, floods, and geohazards, particularly in regions prone to these natural disasters, such as Greece and Cyprus. Recent advancements in technology, developed by the BEYOND Center of Excellence of the National Observatory of Athens, have been successfully applied in Greece and are now set to be transferred to Cyprus. The implementation of these advanced technologies in Greece has significantly improved the country's ability to respond to these natural hazards. For wildfire risk assessment, a scalar wildfire occurrence risk index is created based on the predictions of machine learning models. Predicting fire danger is crucial for the sustainable management of forest fires as it provides essential information for designing effective prevention measures and facilitating response planning for potential fire incidents. A reliable forecast of fire danger is a key component of integrated forest fire management and is heavily influenced by various factors that affect fire ignition and spread. The fire risk model is validated by the sensitivity and specificity metric. For flood risk assessment, a multi-faceted approach is employed, including the application of remote sensing techniques, the collection and processing of data from the most recent population and building census, technical studies and field visits, as well as hydrological and hydraulic simulations. All input data are used to create precise flood hazard maps according to various flooding scenarios, detailed flood vulnerability and flood exposure maps, which will finally produce the flood risk map. Critical points are identified, and mitigation measures are proposed for the worst-case scenario, namely, refuge areas are defined, and escape routes are designed. Flood risk maps can assist in raising awareness and save lives. Validation is carried out through historical flood events using remote sensing data and records from the civil protection authorities. For geohazards monitoring (e.g., landslides, subsidence), Synthetic Aperture Radar (SAR) and optical satellite imagery are combined with geomorphological and meteorological data and other landslide/ground deformation contributing factors. To monitor critical infrastructures, including dams, advanced InSAR methodologies are used for identifying surface movements through time. Monitoring these hazards provides valuable information for understanding processes and could lead to early warning systems to protect people and infrastructure. Validation is carried out through both geotechnical expert evaluations and visual inspections. The success of these systems in Greece has paved the way for their transfer to Cyprus to enhance Cyprus's capabilities in natural hazard assessment and monitoring. This transfer is being made through capacity building activities, fostering continuous collaboration between Greek and Cypriot experts. Apart from the knowledge transfer, small demonstration actions are implemented to showcase the effectiveness of these technologies in real-world scenarios. In conclusion, the transfer of advanced natural hazard assessment technologies from Greece to Cyprus represents a significant step forward in enhancing the region's resilience to disasters. EXCELSIOR project funds knowledge exchange, demonstration actions and capacity-building activities and is committed to empower Cyprus with the tools and expertise to effectively manage and mitigate the risks associated with these natural hazards. Acknowledgement:Authors acknowledge the 'EXCELSIOR': ERATOSTHENES: Excellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment H2020 Widespread Teaming project.

Keywords: earth observation, monitoring, natural hazards, remote sensing

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Authors: Jocelyn Doucet, Jean-Philippe Laviolette, Ali Eslami

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The end-of-life management and recycling of polymer wastes remains a key environment issue in on-going efforts to increase resource efficiency and attaining GHG emission reduction targets. Half of all the plastics ever produced were made in the last 13 years, and only about 16% of that plastic waste is collected for recycling, while 25% is incinerated, 40% is landfilled, and 19% is unmanaged and leaks in the environment and waterways. In addition to the plastic collection issue, the UN recently published a report on chemicals in plastics, which adds another layer of challenge when integrating recycled content containing toxic products into new products. To tackle these important issues, innovative solutions are required. Chemical recycling of plastics provides new complementary alternatives to the current recycled plastic market by converting waste material into a high value chemical commodity that can be reintegrated in a variety of applications, making the total market size of the output – virgin-like, high value products - larger than the market size of the input – plastic waste. Access to high-quality feedstock also remains a major obstacle, primarily due to material contamination issues. Pyrowave approaches this challenge with its innovative nano-recycling technology, which purifies polymers at the molecular level, removing undesirable contaminants and restoring the resin to its virgin state without having to depolymerise it. This breakthrough approach expands the range of plastics that can be effectively recycled, including mixed plastics with various contaminants such as lead, inorganic pigments, and flame retardants. The technology allows yields below 100ppm, and purity can be adjusted to an infinitesimal level depending on the customer's specifications. The separation of the polymer and contaminants in Pyrowave's nano-recycling process offers the unique ability to customize the solution on targeted additives and contaminants to be removed based on the difference in molecular size. This precise control enables the attainment of a final polymer purity equivalent to virgin resin. The patented process involves dissolving the contaminated material using a specially formulated solvent, purifying the mixture at the molecular level, and subsequently extracting the solvent to yield a purified polymer resin that can directly be reintegrated in new products without further treatment. Notably, this technology offers simplicity, effectiveness, and flexibility while minimizing environmental impact and preserving valuable resources in the manufacturing circuit. Pyrowave has successfully applied this nano-recycling technology to decontaminate polymers and supply purified, high-quality recycled plastics to critical industries, including food-contact compliance. The technology is low-carbon, electrified, and provides 100% traceable resins with properties identical to those of virgin resins. Additionally, the issue of low recycling rates and the limited market for traditionally hard-to-recycle plastic waste has fueled the need for new complementary alternatives. Chemical recycling, such as Pyrowave's microwave depolymerization, presents a sustainable and efficient solution by converting plastic waste into high-value commodities. By employing microwave catalytic depolymerization, Pyrowave enables a truly circular economy of plastics, particularly in treating polystyrene waste to produce virgin-like styrene monomers. This revolutionary approach boasts low energy consumption, high yields, and a reduced carbon footprint. Pyrowave offers a portfolio of sustainable, low-carbon, electric solutions to give plastic waste a second life and paves the way to the new circular economy of plastics. Here, particularly for polystyrene, we show that styrene monomer yields from Pyrowave’s polystyrene microwave depolymerization reactor is 2,2 to 1,5 times higher than that of the thermal conventional pyrolysis. In addition, we provide a detailed understanding of the microwave assisted depolymerization via analyzing the effects of microwave power, pyrolysis time, microwave receptor and temperature on the styrene product yields. Furthermore, we investigate life cycle environmental impact assessment of microwave assisted pyrolysis of polystyrene in commercial-scale production. Finally, it is worth pointing out that Pyrowave is able to treat several tons of polystyrene to produce virgin styrene monomers and manage waste/contaminated polymeric materials as well in a truly circular economy.

Keywords: nanorecycling, nanomaterials, plastic recycling, depolymerization

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