Search results for: low input farming

Authors: Fathi Abid, Bilel Kaffel

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The olive tree, the olive harvest during winter season and the production of olive oil better known by professionals under the name of the crushing operation have interested institutional traders such as olive-oil offices and private companies such as food industry refining and extracting pomace olive oil as well as export-import public and private companies specializing in olive oil. The major problem facing producers of olive oil each winter campaign, contrary to what is expected, it is not whether the harvest will be good or not but whether the sale price will allow them to cover production costs and achieve a reasonable margin of profit or not. These questions are entirely legitimate if we judge by the importance of the issue and the heavy complexity of the uncertainty and competition made tougher by a high level of indebtedness and the experience and expertise of speculators and producers whose objectives are sometimes conflicting. The aim of this paper is to study the formation mechanism of olive oil prices in order to learn about speculators’ behavior and expectations in the market, how they contribute by their industry knowledge and their financial alliances and the size the financial challenge that may be involved for them to build private information hoses globally to take advantage. The methodology used in this paper is based on two stages, in the first stage we study econometrically the formation mechanisms of olive oil price in order to understand the market participant behavior by implementing ARMA, SARMA, GARCH and stochastic diffusion processes models, the second stage is devoted to prediction purposes, we use a combined wavelet- ANN approach. Our main findings indicate that olive oil market participants interact with each other in a way that they promote stylized facts formation. The unstable participant’s behaviors create the volatility clustering, non-linearity dependent and cyclicity phenomena. By imitating each other in some periods of the campaign, different participants contribute to the fat tails observed in the olive oil price distribution. The best prediction model for the olive oil price is based on a back propagation artificial neural network approach with input information based on wavelet decomposition and recent past history.

Keywords: olive oil price, stylized facts, ARMA model, SARMA model, GARCH model, combined wavelet-artificial neural network, continuous-time stochastic volatility mode

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Authors: Soheila Sadeghi

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Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: resource allocation, machine learning, optimization, data-driven decision-making, project management

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Authors: Pappu Kumar, Ajai Singh, Anshuman Singh

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There are different water user sectors such as rural, urban, mining, subsistence and commercial irrigated agriculture, commercial forestry, industry, power generation which are present in the catchment in Subarnarekha River Basin and Ranchi city. There is an inequity issue in the access to water. The development of the rural area, construction of new power generation plants, along with the population growth, the requirement of unmet water demand and the consideration of environmental flows, the revitalization of small-scale irrigation schemes is going to increase the water demands in almost all the water-stressed catchment. The WEAP Model was developed by the Stockholm Environment Institute (SEI) to enable evaluation of planning and management issues associated with water resources development. The WEAP model can be used for both urban and rural areas and can address a wide range of issues including sectoral demand analyses, water conservation, water rights and allocation priorities, river flow simulation, reservoir operation, ecosystem requirements and project cost-benefit analyses. This model is a tool for integrated water resource management and planning like, forecasting water demand, supply, inflows, outflows, water use, reuse, water quality, priority areas and Hydropower generation, In the present study, efforts have been made to access the utility of the WEAP model for water supply and demand analysis for Ranchi city. A detailed works have been carried out and it was tried to ascertain that the WEAP model used for generating different scenario of water requirement, which could help for the future planning of water. The water supplied to Ranchi city was mostly contributed by our study river, Hatiya reservoir and ground water. Data was collected from various agencies like PHE Ranchi, census data of 2011, Doranda reservoir and meteorology department etc. This collected and generated data was given as input to the WEAP model. The model generated the trends for discharge of our study river up to next 2050 and same time also generated scenarios calculating our demand and supplies for feature. The results generated from the model outputs predicting the water require 12 million litter. The results will help in drafting policies for future regarding water supplies and demands under changing climatic scenarios.

Keywords: WEAP model, water demand analysis, Ranchi, scenarios

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Authors: Moustafa Osman Mohammed

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The modeling of a spatial-economic database is crucial in recitation economic network structure to social development. Sustainability within the spatial-economic model gives attention to green businesses to comply with Earth’s Systems. The natural exchange patterns of ecosystems have consistent and periodic cycles to preserve energy and materials flow in systems ecology. When network topology influences formal and informal communication to function in systems ecology, ecosystems are postulated to valence the basic level of spatial sustainable outcome (i.e., project compatibility success). These referred instrumentalities impact various aspects of the second level of spatial sustainable outcomes (i.e., participant social security satisfaction). The sustainability outcomes are modeling composite structure based on a network analysis model to calculate the prosperity of panel databases for efficiency value, from 2005 to 2025. The database is modeling spatial structure to represent state-of-the-art value-orientation impact and corresponding complexity of sustainability issues (e.g., build a consistent database necessary to approach spatial structure; construct the spatial-economic-ecological model; develop a set of sustainability indicators associated with the model; allow quantification of social, economic and environmental impact; use the value-orientation as a set of important sustainability policy measures), and demonstrate spatial structure reliability. The structure of spatial-ecological model is established for management schemes from the perspective pollutants of multiple sources through the input–output criteria. These criteria evaluate the spillover effect to conduct Monte Carlo simulations and sensitivity analysis in a unique spatial structure. The balance within “equilibrium patterns,” such as collective biosphere features, has a composite index of many distributed feedback flows. The following have a dynamic structure related to physical and chemical properties for gradual prolong to incremental patterns. While these spatial structures argue from ecological modeling of resource savings, static loads are not decisive from an artistic/architectural perspective. The model attempts to unify analytic and analogical spatial structure for the development of urban environments in a relational database setting, using optimization software to integrate spatial structure where the process is based on the engineering topology of systems ecology.

Keywords: ecological modeling, spatial structure, orientation impact, composite index, industrial ecology

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Authors: Pablo Reche

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Since 1980’s aquaculture has become the biggest economic activity in southern Chile, being Salmo salar and Oncorhynchus mykiss the main finfish species. High fish density makes both species prone to contract diseases, what drives the industry to big losses, affecting greatly the local economy. Three are the most concerning infective agents, the infectious salmon anemia virus (ISAv), the bacteria Piscirickettsia salmonis and the copepod Caligus rogercresseyi. To regulate the industry the government arranged the salmon farms within management areas named as barrios, which coordinate the fallowing periods and antibiotics treatments of their salmon farms. In turn, barrios are gathered into larger management areas, named as macrozonas whose purpose is to minimize the risk of disease transmission between them and to enclose the outbreaks within their boundaries. However, disease outbreaks still happen and transmission to neighbor sites enlarges the initial event. Salmon disease agents are mostly transported passively by local currents. Thus, to understand how transmission occurs it must be firstly studied the physical environment. In Chile, salmon farming takes place in the inner seas of the southernmost regions of western Patagonia, between 41.5ºS-55ºS. This coastal marine system is characterised by western winds, latitudinally modulated by the position of the South-Eats Pacific high-pressure centre, high precipitation rates and freshwater inflows from the numerous glaciers (including the largest ice cap out of Antarctic and Greenland). All of these forcings meet in a complex bathymetry and coastline system - deep fjords, shallow sills, narrow straits, channels, archipelagos, inlets, and isolated inner seas- driving an estuarine circulation (fast outflows westwards on surface and slow deeper inflows eastwards). Such a complex system is modelled on the numerical model MIKE3, upon whose 3D current fields particle-track-biological models (one for each infective agent) are decoupled. Each agent biology is parameterized by functions for maturation and mortality (reproduction not included). Such parameterizations are depending upon environmental factors, like temperature and salinity, so their lifespan will depend upon the environmental conditions those virtual agents encounter on their way while passively transported. CLIC (Connectivity-Langrangian–IFOP-Chile) is a service platform that supports the graphical visualization of the connectivity matrices calculated from the particle trajectories files resultant of the particle-track-biological models. On CLIC users can select, from a high-resolution grid (~1km), the areas the connectivity will be calculated between them. These areas can be barrios and macrozonas. Users also can select what nodes of these areas are allowed to release and scatter particles from, depth and frequency of the initial particle release, climatic scenario (winter/summer) and type of particle (ISAv, Piscirickettsia salmonis, Caligus rogercresseyi plus an option for lifeless particles). Results include probabilities downstream (where the particles go) and upstream (where the particles come from), particle age and vertical distribution, all of them aiming to understand how currently connectivity works to eventually propose a minimum risk zonation for aquaculture purpose. Preliminary results in Chiloe inner sea shows that the risk depends not only upon dynamic conditions but upon barrios location with respect to their neighbors.

Keywords: aquaculture zonation, Caligus rogercresseyi, Chilean Patagonia, coastal oceanography, connectivity, infectious salmon anemia virus, Piscirickettsia salmonis

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Authors: Nathalie Stephenne, Mathieu Veschkens, Stéphane Palm, Christophe Charlemagne, Jacques Defoux

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According to Europe’s first River basin Management Plans (RBMPs), 56% of European rivers failed to achieve the good status targets of the Water Framework Directive WFD. In Central European countries such as Belgium, even more than 80% of rivers failed to achieve the WFD quality targets. Although the RBMP’s should reduce the stressors and improve water body status, their potential to address multiple stress situations is limited due to insufficient knowledge on combined effects, multi-stress, prioritization of measures, impact on ecology and implementation effects. This paper describes a webGis prototype developed for the Walloon administration to improve the communication and the management of sediment dredging actions carried out in rivers and lakes in the frame of RBMPs. A large number of stakeholders are involved in the management of rivers and lakes in Wallonia. They are in charge of technical aspects (client and dredging operators, organizations involved in the treatment of waste…), management (managers involved in WFD implementation at communal, provincial or regional level) or policy making (people responsible for policy compliance or legislation revision). These different kinds of stakeholders need different information and data to cover their duties but have to interact closely at different levels. Moreover, information has to be shared between them to improve the management quality of dredging operations within the ecological system. In the Walloon legislation, leveling dredged sediments on banks requires an official authorization from the administration. This request refers to spatial information such as the official land use map, the cadastral map, the distance to potential pollution sources. The production of a collective geodatabase can facilitate the management of these authorizations from both sides. The proposed internet system integrates documents, data input, integration of data from disparate sources, map representation, database queries, analysis of monitoring data, presentation of results and cartographic visualization. A prototype of web application using the API geoviewer chosen by the Geomatic department of the SPW has been developed and discussed with some potential users to facilitate the communication, the management and the quality of the data. The structure of the paper states the why, what, who and how of this communication tool.

Keywords: sediments, web application, GIS, rivers management

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Authors: J. D. García Espinel, Paula Pérez Sánchez, Carlos Egea Ruiz, Carlos Lardín Mifsut, Andrés López-Aranguren Oliver

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This paper shortly describes three main actuations over a Waste Water Treatment Plant (WWTP) for reducing its energy consumption: Optimization of the biological reactor in the aeration stage by including new control algorithms and introducing new efficient equipment, the installation of an innovative hybrid system with zero Grid injection (formed by 100kW of PV energy and 5 kW of mini-wind energy generation) and an intelligent management system for load consumption and energy generation control in the most optimum way. This project called RENEWAT, involved in the European Commission call LIFE 2013, has the main objective of reducing the energy consumptions through different actions on the processes which take place in a WWTP and introducing renewable energies on these treatment plants, with the purpose of promoting the usage of treated waste water for irrigation and decreasing the C02 gas emissions. WWTP is always required before waste water can be reused for irrigation or discharged in water bodies. However, the energetic demand of the treatment process is high enough for making the price of treated water to exceed the one for drinkable water. This makes any policy very difficult to encourage the re-use of treated water, with a great impact on the water cycle, particularly in those areas suffering hydric stress or deficiency. The cost of treating waste water involves another climate-change related burden: the energy necessary for the process is obtained mainly from the electric network, which is, in most of the cases in Europe, energy obtained from the burning of fossil fuels. The innovative part of this project is based on the implementation, adaptation and integration of solutions for this problem, together with a new concept of the integration of energy input and operative energy demand. Moreover, there is an important qualitative jump between the technologies used and the alleged technologies to use in the project which give it an innovative character, due to the fact that there are no similar previous experiences of a WWTP including an intelligent discrimination of energy sources, integrating renewable ones (PV and Wind) and the grid.

Keywords: aeration system, biological reactor, CO2 emissions, energy efficiency, hybrid systems, LIFE 2013 call, process optimization, renewable energy sources, wasted water treatment plants

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Authors: Karen Eastman

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More youths and young adults with autism spectrum disorder (ASD) have been entering the workforce in recent years. Historically, students with ASD struggle after leaving high school and experience lower rates of employment, with social skills continuing to be the most problematic area of concern. Special education teachers may find it challenging to identify effective combinations of evidence-based practices (EBPs) and supports to best guide these students. One EBP, Peer Mediated Instruction and Intervention (PMII) has been well documented in the literature as being effective for younger students with autism but not researched as much with older students and adults, particularly in work settings. A need to combine PMII with other EBPs has been identified as a way to achieve a greater positive impact rather than any practice alone. A multiple baseline across skills design was used in this research project with two participants in different settings. PMII was combined with Visual Supports, with typical peers being trained in both practices. PMII is an evidence-based practice used to address social concerns by training peers without disabilities as to how they can provide feedback to and support, the student with ASD with social interactions in structured settings. The peers without disabilities were the instructors, while the adults facilitated the social situations and provided support to both the peers and students with ASD when needed. Because many individuals with ASD learn best with visual input, rather than using only the spoken word (verbal directions and feedback), Visual Supports were used in conjunction with PMII. Visual Supports can include written words, pictures, symbols, videos, or objects. In this project, the Visual Supports used were written social scripts, videos, Stop and Think signs, written reminder cards, a school map, and a pictorial task analysis of work tasks. Variables that may affect intervention outcomes in this project included attendance at school and school-based work settings for both the students with ASD and the peers without disabilities and behaviors and responses from others in the settings. Qualitative data was also collected from observations and surveys with peers about the process and their role. Data indicated that the students with ASD responded more positively to redirection and support from their peers than to teachers and staff and showed an increase in positive interactions with others. Those surveyed indicated a positive attitude toward and response to the use of peer interventions with visual supports.

Keywords: autism, social skills, vocational training, peer interventions

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Authors: Al-Juhaishi Mohammed, Mikael Motelica-Heino, Fabrice Muller, Audrey Guirimand-Dufour, Christian Défarge

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This study aims at improving the urban hydrological cycle of the Orléans agglomeration (France) and understanding the relationship between physical and chemical parameters of urban surface runoff and the hydrological conditions. In particular water quality parameters such as pH, conductivity, total dissolved solids, major dissolved cations and anions, and chemical and biological oxygen demands were monitored for three types of urban water discharges (wastewater treatment plant output (WWTP), storm overflow and stormwater outfall) under two hydrologic scenarii (dry and wet weather). The first results were obtained over a period of five months.Each investigated (Ormes and l’Egoutier) outfall represents an urban runoff source that receives water from runoff roads, gutters, the irrigation of gardens and other sources of flow over the Earth’s surface that drains in its catchments and carries it to the Loire River. In wet weather conditions there is rain water runoff and an additional input from the roof gutters that have entered the stormwater system during rainfall. For the comparison the results La Chilesse is a storm overflow that was selected in our study as a potential source of waste water which is located before the (WWTP).The comparison of the physical-chemical parameters (total dissolved solids, turbidity, pH, conductivity, dissolved organic carbon (DOC), concentration of major cations and anions) together with the chemical oxygen demand (COD) and biological oxygen demand (BOD) helped to characterize sources of runoff waters in the different watersheds. It also helped to highlight the infiltration of wastewater in some stormwater systems that reject directly in the Loire River. The values of the conductivity measured in the outflow of Ormes were always higher than those measured in the other two outlets. The results showed a temporal variation for the Ormes outfall of conductivity from 1465 µS cm-1 in the dry weather flow to 650 µS cm-1 in the wet weather flow and also a spatial variation in the wet weather flow from 650 µS cm-1 in the Ormes outfall to 281 μS cm-1 in L’Egouttier outfall. The ultimate BOD (BOD28) showed a significant decrease in La Corne outfall from 210 mg L-1 in the wet weather flow to 75 mg L-1 in the dry weather flow because of the nutrient load that was transported by the runoff.

Keywords: BOD, COD, the Loire River, urban hydrology, urban dry and wet weather discharges, macronutrients

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Authors: M.Sivaramakrishnaiah, D. S .Nasan, P. V. Subhanjeneyulu, J. A. Sandeep Kumar, N. Sreenivasulu, B. V. Amarnath Reddy, B. Veeralingam

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Design, numerical simulation, fabrication, and physical experimentation of the Tesla’s Bladeless centripetal turbine for generating electrical power are presented in this research paper. 29 Pressurized air combined with water via a nozzle system is made to pass tangentially through a set of parallel smooth discs surfaces, which impart rotational motion to the discs fastened common shaft for the power generation. The power generated depends upon the fluid speed parameter leaving the nozzle inlet. Physically due to laminar boundary layer phenomena at smooth disc surface, the high speed fluid layers away from the plate moving against the low speed fluid layers nearer to the plate develop a tangential drag from the viscous shear forces. This compels the nearer layers to drag along with the high layers causing the disc to spin. Solid Works design software and fluid mechanics and machine elements design theories was used to compute mechanical design specifications of turbine parts like 48 mm diameter discs, common shaft, central exhaust, plenum chamber, swappable nozzle inlets, etc. Also, ANSYS CFX 2018 was used for the numerical 2 simulation of the physical phenomena encountered in the turbine working. When various numerical simulation and physical experimental results were verified, there is good agreement between them 6, both quantitatively and qualitatively. The sources of input and size of the blades may affect the power generated and turbine efficiency, respectively. The results may change if there is a change in the fluid flowing between the discs. The inlet fluid pressure versus turbine efficiency and the number of discs versus turbine power studies based on both results were carried out to develop the 8 relationships between the inlet and outlet parameters of the turbine. The present research work obtained the turbine efficiency in the range of 7-10%, and for this range; the electrical power output generated was 50-60 W.

Keywords: tesla turbine, cohesion type bladeless turbine, boundary layer theory, cohesion type bladeless turbine, tangential fluid flow, viscous and adhesive forces, plenum chamber, pico hydro systems

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Authors: Simon Espigolé, Igor Shalyminov, Helen Hastie

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Deep machine learning techniques including deep neural networks (DNN) have been used to model language and dialogue for conversational agents to perform tasks, such as giving technical support and also for general chit-chat. They have been shown to be capable of generating long, diverse and coherent sentences in end-to-end dialogue systems and natural language generation. However, these systems tend to imitate the training data and will only generate the concepts and language within the scope of what they have been trained on. This work explores how deep neural networks can be used in a task that would normally require human creativity, whereby the human would read the movie description and/or watch the movie and come up with a compelling, interesting movie title. This task differs from simple summarization in that the movie title may not necessarily be derivable from the content or semantics of the movie description. Here, we train a type of DNN called a sequence-to-sequence model (seq2seq) that takes as input a short textual movie description and some information on e.g. genre of the movie. It then learns to output a movie title. The idea is that the DNN will learn certain techniques and approaches that the human movie titler may deploy that may not be immediately obvious to the human-eye. To give an example of a generated movie title, for the movie synopsis: ‘A hitman concludes his legacy with one more job, only to discover he may be the one getting hit.’; the original, true title is ‘The Driver’ and the one generated by the model is ‘The Masquerade’. A human evaluation was conducted where the DNN output was compared to the true human-generated title, as well as a number of baselines, on three 5-point Likert scales: ‘creativity’, ‘naturalness’ and ‘suitability’. Subjects were also asked which of the two systems they preferred. The scores of the DNN model were comparable to the scores of the human-generated movie title, with means m=3.11, m=3.12, respectively. There is room for improvement in these models as they were rated significantly less ‘natural’ and ‘suitable’ when compared to the human title. In addition, the human-generated title was preferred overall 58% of the time when pitted against the DNN model. These results, however, are encouraging given the comparison with a highly-considered, well-crafted human-generated movie title. Movie titles go through a rigorous process of assessment by experts and focus groups, who have watched the movie. This process is in place due to the large amount of money at stake and the importance of creating an effective title that captures the audiences’ attention. Our work shows progress towards automating this process, which in turn may lead to a better understanding of creativity itself.

Keywords: creativity, deep machine learning, natural language generation, movies

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Authors: J. Satya Eswari, J. Sekhar Babub, Meena Murmu, Govardhan Bhat

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Electrical Discharge Machining (EDM) is an unconventional manufacturing process based on removal of material from a part by means of a series of repeated electrical sparks created by electric pulse generators at short intervals between a electrode tool and the part to be machined emmersed in dielectric fluid. In this paper, a study will be performed on the influence of the factors of peak current, pulse on time, interval time and power supply voltage. The output responses measured were material removal rate (MRR) and surface roughness. Finally, the parameters were optimized for maximum MRR with the desired surface roughness. RSM involves establishing mathematical relations between the design variables and the resulting responses and optimizing the process conditions. RSM is not free from problems when it is applied to multi-factor and multi-response situations. Design of experiments (DOE) technique to select the optimum machining conditions for machining AISI 4140 using EDM. The purpose of this paper is to determine the optimal factors of the electro-discharge machining (EDM) process investigate feasibility of design of experiment techniques. The work pieces used were rectangular plates of AISI 4140 grade steel alloy. The study of optimized settings of key machining factors like pulse on time, gap voltage, flushing pressure, input current and duty cycle on the material removal, surface roughness is been carried out using central composite design. The objective is to maximize the Material removal rate (MRR). Central composite design data is used to develop second order polynomial models with interaction terms. The insignificant coefficients’ are eliminated with these models by using student t test and F test for the goodness of fit. CCD is first used to establish the determine the optimal factors of the electro-discharge machining (EDM) for maximizing the MRR. The responses are further treated through a objective function to establish the same set of key machining factors to satisfy the optimization problem of the electro-discharge machining (EDM) process. The results demonstrate the better performance of CCD data based RSM for optimizing the electro-discharge machining (EDM) process.

Keywords: electric discharge machining (EDM), modeling, optimization, CCRD

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Authors: Johannes Gantner, Michael Held, Matthias Fischer

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The refurbishment of the building stock in terms of energy supply and efficiency is one of the major challenges of the German turnaround in energy policy. As the building sector accounts for 40% of Germany’s total energy demand, additional insulation is key for energy efficient refurbished buildings. Nevertheless the energetic benefits often the environmental and economic performances of insulation materials are questioned. The methods Life Cycle Assessment (LCA) as well as Life Cycle Costing (LCC) can form the standardized basis for answering this doubts and more and more become important for material producers due efforts such as Product Environmental Footprint (PEF) or Environmental Product Declarations (EPD). Due to increasing use of LCA and LCC information for decision support the robustness and resilience of the results become crucial especially for support of decision and policy makers. LCA and LCC results are based on respective models which depend on technical parameters like efficiencies, material and energy demand, product output, etc.. Nevertheless, the influence of parameter uncertainties on lifecycle results are usually not considered or just studied superficially. Anyhow the effect of parameter uncertainties cannot be neglected. Based on the example of an exterior wall the overall lifecycle results are varying by a magnitude of more than three. As a result simple best case worst case analyses used in practice are not sufficient. These analyses allow for a first rude view on the results but are not taking effects into account such as error propagation. Thereby LCA practitioners cannot provide further guidance for decision makers. Probabilistic analyses enable LCA practitioners to gain deeper understanding of the LCA and LCC results and provide a better decision support. Within this study, the environmental and economic impacts of an exterior wall system over its whole lifecycle are illustrated, and the effect of different uncertainty analysis on the interpretation in terms of resilience and robustness are shown. Hereby the approaches of error propagation and Monte Carlo Simulations are applied and combined with statistical methods in order to allow for a deeper understanding and interpretation. All in all this study emphasis the need for a deeper and more detailed probabilistic evaluation based on statistical methods. Just by this, misleading interpretations can be avoided, and the results can be used for resilient and robust decisions.

Keywords: uncertainty, life cycle assessment, life cycle costing, Monte Carlo simulation

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Authors: Adel Edwar Waheeb Louka

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deeplearning, image processing, machine learningSarapin, intraarticular, chronic knee pain, osteoarthritisFNS, trauma, hip, neck femur fracture, minimally invasive surgery

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Authors: Federico Garrido, Mostafa El Hayani, Ahmed Shams

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The exploration of the potential of artificial intelligence (AI) in architecture is still embryonic, however, its latent capacity to change design disciplines is significant. 'Synthetic Classism' is a research project that questions the underlying aspects of classically organized architecture not just in aesthetic terms but also from a geometrical and morphological point of view, intending to generate new architectural information using historical examples as source material. The main aim of this paper is to explore the uses of artificial intelligence and machine learning algorithms in architectural design while creating a coherent narrative to be contained within a design process. The purpose is twofold: on one hand, to develop and train machine learning algorithms to produce architectural information of small pavilions and on the other, to synthesize new information from previous architectural drawings. These algorithms intend to 'interpret' graphical information from each pavilion and then generate new information from it. The procedure, once these algorithms are trained, is the following: parting from a line profile, a synthetic 'front view' of a pavilion is generated, then using it as a source material, an isometric view is created from it, and finally, a top view is produced. Thanks to GAN algorithms, it is also possible to generate Front and Isometric views without any graphical input as well. The final intention of the research is to produce isometric views out of historical information, such as the pavilions from Sebastiano Serlio, James Gibbs, or John Soane. The idea is to create and interpret new information not just in terms of historical reconstruction but also to explore AI as a novel tool in the narrative of a creative design process. This research also challenges the idea of the role of algorithmic design associated with efficiency or fitness while embracing the possibility of a creative collaboration between artificial intelligence and a human designer. Hence the double feature of this research, both analytical and creative, first by synthesizing images based on a given dataset and then by generating new architectural information from historical references. We find that the possibility of creatively understand and manipulate historic (and synthetic) information will be a key feature in future innovative design processes. Finally, the main question that we propose is whether an AI could be used not just to create an original and innovative group of simple buildings but also to explore the possibility of fostering a novel architectural sensibility grounded on the specificities on the architectural dataset, either historic, human-made or synthetic.

Keywords: architecture, central pavilions, classicism, machine learning

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Authors: Ricardo Torcato, Helder Morais

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The manufacture of crystal glass parts is based on obtaining the rough geometry by blowing and/or injection, generally followed by a set of manual finishing operations using cutting and grinding tools. The forming techniques used do not allow the obtainment, with repeatability, of parts with complex shapes and the finishing operations use intensive specialized labor resulting in high cycle times and production costs. This work aims to explore the digital manufacture of crystal glass parts by investigating new subtractive techniques for the automated, flexible finishing of these parts. Finishing operations are essential to respond to customer demands in terms of crystal feel and shine. It is intended to investigate the applicability of different computerized finishing technologies, namely milling and grinding in a CNC machining center with or without ultrasonic assistance, to crystal processing. Research in the field of grinding hard and brittle materials, despite not being extensive, has increased in recent years, and scientific knowledge about the machinability of crystal glass is still very limited. However, it can be said that the unique properties of glass, such as high hardness and very low toughness, make any glass machining technology a very challenging process. This work will measure the performance improvement brought about by the use of ultrasound compared to conventional crystal grinding. This presentation is focused on the mechanical characterization and analysis of the cutting forces in CNC machining of superior crystal glass (Pb ≥ 30%). For the mechanical characterization, the Vickers hardness test provides an estimate of the material hardness (Hv) and the fracture toughness based on cracks that appear in the indentation. Mechanical impulse excitation test estimates the Young’s Modulus, shear modulus and Poisson ratio of the material. For the cutting forces, it a dynamometer was used to measure the forces in the face grinding process. The tests were made based on the Taguchi method to correlate the input parameters (feed rate, tool rotation speed and depth of cut) with the output parameters (surface roughness and cutting forces) to optimize the process (better roughness using the cutting forces that do not compromise the material structure and the tool life) using ANOVA. This study was conducted for conventional grinding and for the ultrasonic grinding process with the same cutting tools. It was possible to determine the optimum cutting parameters for minimum cutting forces and for minimum surface roughness in both grinding processes. Ultrasonic-assisted grinding provides a better surface roughness than conventional grinding.

Keywords: CNC machining, crystal glass, cutting forces, hardness

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Authors: Emmanuel Ophel Gilbert, Williams Speret

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The control for fluid flow behaviours of viscous fluids and heat transfer occurrences within heated mini-channel is considered. Heat transfer and flow characteristics of different viscous liquids, such as engine oil, automatic transmission fluid, one-half ethylene glycol, and deionized water were numerically analyzed. Some mathematical applications such as Fourier series and Laplace Z-Transforms were employed to ascertain the behaviour-wave like structure of these each viscous fluids. The steady, laminar flow and heat transfer equations are reckoned by the aid of numerical simulation technique. Further, this numerical simulation technique is endorsed by using the accessible practical values in comparison with the anticipated local thermal resistances. However, the roughness of this mini-channel that is one of the physical limitations was also predicted in this study. This affects the frictional factor. When an additive such as tetracycline was introduced in the fluid, the heat input was lowered, and this caused pro rata effect on the minor and major frictional losses, mostly at a very minute Reynolds number circa 60-80. At this ascertained lower value of Reynolds numbers, there exists decrease in the viscosity and minute frictional losses as a result of the temperature of these viscous liquids been increased. It is inferred that the three equations and models are identified which supported the numerical simulation via interpolation and integration of the variables extended to the walls of the mini-channel, yields the utmost reliance for engineering and technology calculations for turbulence impacting jets in the near imminent age. Out of reasoning with a true equation that could support this control for the fluid flow, Navier-stokes equations were found to tangential to this finding. Though, other physical factors with respect to these Navier-stokes equations are required to be checkmated to avoid uncertain turbulence of the fluid flow. This paradox is resolved within the framework of continuum mechanics using the classical slip condition and an iteration scheme via numerical simulation method that takes into account certain terms in the full Navier-Stokes equations. However, this resulted in dropping out in the approximation of certain assumptions. Concrete questions raised in the main body of the work are sightseen further in the appendices.

Keywords: frictional losses, heat transfer, laminar flow, mini-channel, number simulation, Reynolds number, turbulence, viscous fluids

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Authors: Harshan Tee Pee

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Climate elements include surface temperatures, rainfall patterns, humidity, type and amount of cloudiness, air pressure and wind speed and direction. Change in one element can have an impact on the regional climate. The scientific predictions indicate that global climate change will increase the number of extreme events, leading to more frequent natural hazards. Global warming is likely to intensify the risk of drought in certain parts and also leading to increased rainfall in some other parts. Drought is a slow advancing disaster and creeping phenomenon– which accumulate slowly over a long period of time. Droughts are naturally linked with aridity. But droughts occur over most parts of the world (both wet and humid regions) and create severe impacts on agriculture, basic household welfare and ecosystems. Drought condition occurs at least every three years in India. India is one among the most vulnerable drought prone countries in the world. The economic impacts resulting from extreme environmental events and disasters are huge as a result of disruption in many economic activities. The focus of this paper is to develop a comprehensive understanding about the distributional impacts of disaster, especially impact of drought on agricultural production and income through a panel study (drought year and one year after the drought) in Raikhel village, Maharashtra, India. The major findings of the study indicate that cultivating area as well as the number of cultivating households reduced after the drought, indicating a shift in the livelihood- households moved from agriculture to non-agriculture. Decline in the gross cropped area and production of various crops depended on the negative income from these crops in the previous agriculture season. All the landholding categories of households except landlords had negative income in the drought year and also the income disparities between the households were higher in that year. In the drought year, the cost of cultivation was higher for all the landholding categories due to the increased cost for irrigation and input cost. In the drought year, agriculture products (50 per cent of the total products) were used for household consumption rather than selling in the market. It is evident from the study that livelihood which was based on natural resources became less attractive to the people to due to the risk involved in it and people were moving to less risk livelihood for their sustenance.

Keywords: climate change, drought, agriculture economics, disaster impact

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Authors: Eashan Hatti

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In software development, programmers often must make a choice between high-level programming and high-performance programs. High-level programming encourages the use of complex, pervasive abstractions. However, the use of these abstractions degrades performance-high performance demands that programs be low-level. In a compiler, the optimizer attempts to let the user have both. The optimizer takes high-level, abstract code as an input and produces low-level, performant code as an output. However, there is a problem with having the optimizer be a built-in part of the compiler. Domain-specific abstractions implemented as libraries are common in high-level languages. As a language’s library ecosystem grows, so does the number of abstractions that programmers will use. If these abstractions are to be performant, the optimizer must be extended with new optimizations to target them, or these abstractions must rely on existing general-purpose optimizations. The latter is often not as effective as needed. The former presents too significant of an effort for the compiler developers, as they are the only ones who can extend the language with new optimizations. Thus, the language becomes more high-level, yet the optimizer – and, in turn, program performance – falls behind. Programmers are again confronted with a choice between high-level programming and high-performance programs. To investigate a potential solution to this problem, we developed Peridot, a prototype programming language. Peridot’s main contribution is that it enables library developers to easily extend the language with new optimizations themselves. This allows the optimization workload to be taken off the compiler developers’ hands and given to a much larger set of people who can specialize in each problem domain. Because of this, optimizations can be much more effective while also being much more numerous. To enable this, Peridot supports metaprogramming designed for implementing program transformations. The language is split into two fragments or “levels”, one for metaprogramming, the other for high-level general-purpose programming. The metaprogramming level supports logic programming. Peridot’s key idea is that optimizations are simply implemented as metaprograms. The meta level supports several specific features which make it particularly suited to implementing optimizers. For instance, metaprograms can automatically deduce equalities between the programs they are optimizing via unification, deal with variable binding declaratively via higher-order abstract syntax, and avoid the phase-ordering problem via non-determinism. We have found that this design centered around logic programming makes optimizers concise and easy to write compared to their equivalents in functional or imperative languages. Overall, implementing Peridot has shown that its design is a viable solution to the problem of writing code which is both high-level and performant.

Keywords: optimization, metaprogramming, logic programming, abstraction

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Authors: Leigh Ann Wilson, Melanie Borrego

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The purpose of institutional learning outcomes (ILOs) is to assess what students across the university know and what they do not. The issue is gathering this information in a systematic and usable way. This presentation will explain how one institution has engineered this process for both student success and maximum faculty curriculum and course design input. At Brandman University, there are three levels of learning outcomes: course, program, and institutional. Institutional Learning Outcomes (ILOs) are mapped to specific courses. Faculty course developers write the signature assignments (SAs) in alignment with the Institutional Learning Outcomes for each course. These SAs use a specific rubric that is applied consistently by every section and every instructor. Each year, the 12-member General Education Team (GET), as a part of their work, conducts the calibration and assessment of the university-wide SAs and the related rubrics for one or two of the five ILOs. GET members, who are senior faculty and administrators who represent each of the university's schools, lead the calibration meetings. Specifically, calibration is a process designed to ensure the accuracy and reliability of evaluating signature assignments by working with peer faculty to interpret rubrics and compare scoring. These calibration meetings include the full time and adjunct faculty members who teach the course to ensure consensus on the application of the rubric. Each calibration session is chaired by a GET representative as well as the course custodian/contact where the ILO signature assignment resides. The overall calibration process GET follows includes multiple steps, such as: contacting and inviting relevant faculty members to participate; organizing and hosting calibration sessions; and reviewing and discussing at least 10 samples of student work from class sections during the previous academic year, for each applicable signature assignment. Conversely, the commitment for calibration teams consist of attending two virtual meetings lasting up to three hours in duration. The first meeting focuses on interpreting the rubric, and the second meeting involves comparing scores for sample work and sharing feedback about the rubric and assignment. Next, participants are expected to follow all directions provided and participate actively, and respond to scheduling requests and other emails within 72 hours. The virtual meetings are recorded for future institutional use. Adjunct faculty are paid a small stipend after participating in both calibration meetings. Full time faculty can use this work on their annual faculty report for "internal service" credit.

Keywords: assessment, assurance of learning, course design, institutional learning outcomes, rubrics, signature assignments

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Authors: P. Mounnarath, U. Schmitz, Ch. Zhang

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Fragility analysis is an effective tool for the seismic vulnerability assessment of civil structures in the last several years. The design of the expansion joints according to various bridge design codes is almost inconsistent, and only a few studies have focused on this problem so far. In this study, the influence of the expansion joint clearances between the girder ends and the abutment backwalls on the seismic fragility assessment of continuous integral bridge frames is investigated. The gaps (ranging from 60 mm, 150 mm, 250 mm and 350 mm) are designed by following two different bridge design code specifications, namely, Caltrans and Eurocode 8-2. Five bridge models are analyzed and compared. The first bridge model serves as a reference. This model uses three-dimensional reinforced concrete fiber beam-column elements with simplified supports at both ends of the girder. The other four models also employ reinforced concrete fiber beam-column elements but include the abutment backfill stiffness and four different gap values. The nonlinear time history analysis is performed. The artificial ground motion sets, which have the peak ground accelerations (PGAs) ranging from 0.1 g to 1.0 g with an increment of 0.05 g, are taken as input. The soil-structure interaction and the P-Δ effects are also included in the analysis. The component fragility curves in terms of the curvature ductility demand to the capacity ratio of the piers and the displacement demand to the capacity ratio of the abutment sliding bearings are established and compared. The system fragility curves are then obtained by combining the component fragility curves. Our results show that in the component fragility analysis, the reference bridge model exhibits a severe vulnerability compared to that of other sophisticated bridge models for all damage states. In the system fragility analysis, the reference curves illustrate a smaller damage probability in the earlier PGA ranges for the first three damage states, they then show a higher fragility compared to other curves in the larger PGA levels. In the fourth damage state, the reference curve has the smallest vulnerability. In both the component and the system fragility analysis, the same trend is found that the bridge models with smaller clearances exhibit a smaller fragility compared to that with larger openings. However, the bridge model with a maximum clearance still induces a minimum pounding force effect.

Keywords: expansion joint clearance, fiber beam-column element, fragility assessment, time history analysis

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Authors: Eduardas Spiriajevas

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Since the end of the 20th century, when Lithuania regained its independence, a process of demographic shrinkage started. Recently, it affects the efficiency of implementation of actions related to regional development policy and geographic scopes of created value added in the regions. The demographic structures of human resources reflect onto the regions and their economic geographic environment. Due to reshaping economies and state reforms on restructuration of economic branches such as agriculture and industry, it affects the economic significance of services’ sector. These processes influence the competitiveness of labor market and its demographic characteristics. Such vivid consequences are appropriate for the structures of human migrations, which affected the processes of demographic ageing of human resources in the regions, especially in peripheral ones. These phenomena of modern times induce the demographic shrinkage of society and its economic geographic characteristics in the actions of regional development and in regional policy. The internal and external migrations of population captured numerous regional economic disparities, and influenced on territorial density and concentration of population of the country and created the economies of spatial unevenness in such small geographically compact country as Lithuania. The processes of territorial reshaping of distribution of population create new regions and their economic environment, which is not corresponding to the main principles of regional policy and its power to create the well-being and to promote the attractiveness for economic development. These are the new challenges of national regional policy and it should be researched in a systematic way of taking into consideration the analytical approaches of regional economy in the context of economic geographic research methods. A comparative territorial analysis according to administrative division of Lithuania in relation to retrospective approach and introduction of method of location quotients, both give the results of economic geographic character with cartographic representations using the tools of spatial analysis provided by technologies of Geographic Information Systems. A set of these research methods provide the new spatially evidenced based results, which must be taken into consideration in reshaping of national regional policy in economic geographic context. Due to demographic shrinkage and increasing differentiation of economic developments within the regions, an input of economic geographic dimension is inevitable. In order to sustain territorial balanced economic development, there is a need to strengthen the roles of regional centers (towns) and to empower them with new economic functionalities for revitalization of peripheral regions, and to increase their economic competitiveness and social capacities on national scale.

Keywords: demographic shrinkage, economic geography, Lithuania, regions

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Authors: Arindam Chaudhuri

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Classification of data has been actively used for most effective and efficient means of conveying knowledge and information to users. The prima face has always been upon techniques for extracting useful knowledge from data such that returns are maximized. With emergence of huge datasets the existing classification techniques often fail to produce desirable results. The challenge lies in analyzing and understanding characteristics of massive data sets by retrieving useful geometric and statistical patterns. We propose a supervised parallel fuzzy rough support vector machine (PFRSVM) for data classification in cloud environment. The classification is performed by PFRSVM using hyperbolic tangent kernel. The fuzzy rough set model takes care of sensitiveness of noisy samples and handles impreciseness in training samples bringing robustness to results. The membership function is function of center and radius of each class in feature space and is represented with kernel. It plays an important role towards sampling the decision surface. The success of PFRSVM is governed by choosing appropriate parameter values. The training samples are either linear or nonlinear separable. The different input points make unique contributions to decision surface. The algorithm is parallelized with a view to reduce training times. The system is built on support vector machine library using Hadoop implementation of MapReduce. The algorithm is tested on large data sets to check its feasibility and convergence. The performance of classifier is also assessed in terms of number of support vectors. The challenges encountered towards implementing big data classification in machine learning frameworks are also discussed. The experiments are done on the cloud environment available at University of Technology and Management, India. The results are illustrated for Gaussian RBF and Bayesian kernels. The effect of variability in prediction and generalization of PFRSVM is examined with respect to values of parameter C. It effectively resolves outliers’ effects, imbalance and overlapping class problems, normalizes to unseen data and relaxes dependency between features and labels. The average classification accuracy for PFRSVM is better than other classifiers for both Gaussian RBF and Bayesian kernels. The experimental results on both synthetic and real data sets clearly demonstrate the superiority of the proposed technique.

Keywords: FRSVM, Hadoop, MapReduce, PFRSVM

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Authors: Moustafa Osman Mohammed

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The resilient and sustainable of construction ecology is essential to world’s socio-economic development. Environmental resilience is crucial in relating construction ecology to topology of spatial-economic model. Sustainability of spatial-economic model gives attention to green business to comply with Earth’s System for naturally exchange patterns of ecosystems. The systems ecology has consistent and periodic cycles to preserve energy and materials flow in Earth’s System. When model structure is influencing communication of internal and external features in system networks, it postulated the valence of the first-level spatial outcomes (i.e., project compatibility success). These instrumentalities are dependent on second-level outcomes (i.e., participant security satisfaction). These outcomes of model are based on measuring database efficiency, from 2015 to 2025. The model topology has state-of-the-art in value-orientation impact and correspond complexity of sustainability issues (e.g., build a consistent database necessary to approach spatial structure; construct the spatial-economic model; develop a set of sustainability indicators associated with model; allow quantification of social, economic and environmental impact; use the value-orientation as a set of important sustainability policy measures), and demonstrate environmental resilience. The model is managing and developing schemes from perspective of multiple sources pollutants through the input–output criteria. These criteria are evaluated the external insertions effects to conduct Monte Carlo simulations and analysis for using matrices in a unique spatial structure. The balance “equilibrium patterns” such as collective biosphere features, has a composite index of the distributed feedback flows. These feedback flows have a dynamic structure with physical and chemical properties for gradual prolong of incremental patterns. While these structures argue from system ecology, static loads are not decisive from an artistic/architectural perspective. The popularity of system resilience, in the systems structure related to ecology has not been achieved without the generation of confusion and vagueness. However, this topic is relevant to forecast future scenarios where industrial regions will need to keep on dealing with the impact of relative environmental deviations. The model attempts to unify analytic and analogical structure of urban environments using database software to integrate sustainability outcomes where the process based on systems topology of construction ecology.

Keywords: system ecology, construction ecology, industrial ecology, spatial-economic model, systems topology

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Authors: Ole-Christian Galbo Engstrøm, Erik Schou Dreier, Birthe Møller Jespersen, Kim Steenstrup Pedersen

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Grain quality analysis is a multi-parameterized problem that includes a variety of qualitative and quantitative parameters such as grain type classification, damage type classification, and nutrient regression. Currently, these parameters require human inspection, a multitude of instruments employing a variety of sensor technologies, and predictive model types or destructive and slow chemical analysis. This paper investigates the feasibility of applying near-infrared hyperspectral imaging (NIR-HSI) to grain quality analysis. For this study two datasets of NIR hyperspectral images in the wavelength range of 900 nm - 1700 nm have been used. Both datasets contain images of sparsely and densely packed grain kernels. The first dataset contains ~87,000 image crops of bulk wheat samples from 63 harvests where protein value has been determined by the FOSS Infratec NOVA which is the golden industry standard for protein content estimation in bulk samples of cereal grain. The second dataset consists of ~28,000 image crops of bulk grain kernels from seven different wheat varieties and a single rye variety. In the first dataset, protein regression analysis is the problem to solve while variety classification analysis is the problem to solve in the second dataset. Deep convolutional neural networks (CNNs) have the potential to utilize spatio-spectral correlations within a hyperspectral image to simultaneously estimate the qualitative and quantitative parameters. CNNs can autonomously derive meaningful representations of the input data reducing the need for advanced preprocessing techniques required for classical chemometric model types such as artificial neural networks (ANNs) and partial least-squares regression (PLS-R). A comparison between different CNN architectures utilizing 2D and 3D convolution is conducted. These results are compared to the performance of ANNs and PLS-R. Additionally, a variety of preprocessing techniques from image analysis and chemometrics are tested. These include centering, scaling, standard normal variate (SNV), Savitzky-Golay (SG) filtering, and detrending. The results indicate that the combination of NIR-HSI and CNNs has the potential to be the foundation for an automatic system unifying qualitative and quantitative grain quality analysis within a single sensor technology and predictive model type.

Keywords: deep learning, grain analysis, hyperspectral imaging, preprocessing techniques

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Authors: Jilbert Novelero, Oliver Mariano

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In a world where technological evolution and competition to create innovative products are at its peak, problems on Electronic Waste (E-Waste) are now becoming a global concern. E-waste is said to be any electrical or electronic devices that have reached the terminal of its useful life. The major issue are the volume and the raw materials used in crafting E-waste which is non-biodegradable and contains hazardous substances that are toxic to human health and the environment. The objective of this study is to gather baseline data in terms of the composition of E-waste in the solid waste stream and to determine the top 5 E-waste categories in a highly urbanized city. Recommendations in managing these wastes for its reduction were provided which may serve as a guide for acceptance and implementation in the locality. Pasig City was the chosen beneficiary of the research output and through the collaboration of the City Government of Pasig and its Solid Waste Management Office (SWMO); the researcher successfully conducted the Electronic Waste Analysis and Characterization Study (E-WACS) to achieve the objectives. E-WACS that was conducted on April 2019 showed that E-waste ranked 4th which comprises the 10.39% of the overall solid waste volume. Out of 345, 127.24kg which is the total daily domestic waste generation in the city, E-waste covers 35,858.72kg. Moreover, an average of 40 grams was determined to be the E-waste generation per person per day. The top 5 E-waste categories were then classified after the analysis. The category which ranked first is the office and telecommunications equipment that contained the 63.18% of the total generated E-waste. Second in ranking was the household appliances category with 21.13% composition. Third was the lighting devices category with 8.17%. Fourth on ranking was the consumer electronics and batteries category which was composed of 5.97% and fifth was the wires and cables category where it comprised the 1.41% of the average generated E-waste samples. One of the recommendations provided in this research is the implementation of the Pasig City Waste Advantage Card. The card can be used as a privilege card and earned points can be converted to avail of and enjoy services such as haircut, massage, dental services, medical check-up, and etc. Another recommendation raised is for the LGU to encourage a communication or dialogue with the technology and electronics manufacturers and distributors and international and local companies to plan the retrieval and disposal of the E-wastes in accordance with the Extended Producer Responsibility (EPR) policy where producers are given significant responsibilities for the treatment and disposal of post-consumer products.

Keywords: E-waste, E-WACS, E-waste characterization, electronic waste, electronic waste analysis

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Authors: Victorita Radulescu

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Introduction: The optimum exploitation of agricultural land in the presence of an aquifer polluted by the surface sources requires close monitoring of groundwater level in both periods of intense irrigation and in absence of the irrigations, in times of drought. Currently in Romania, in the south part of the country, the Baragan area, many agricultural lands are confronted with the risk of groundwater pollution in the absence of systematic irrigation, correlated with the climate changes. Basic Methods: The non-steady flow of the groundwater from an aquifer can be described by the Bousinesq’s partial differential equation. The finite element method was used, applied to the porous media needed for the water mass balance equation. By the proper structure of the initial and boundary conditions may be modeled the flow in drainage or injection systems of wells, according to the period of irrigation or prolonged drought. The boundary conditions consist of the groundwater levels required at margins of the analyzed area, in conformity to the reality of the pollutant emissaries, following the method of the double steps. Major Findings/Results: The drainage condition is equivalent to operating regimes on the two or three rows of wells, negative, as to assure the pollutant transport, modeled with the variable flow in groups of two adjacent nodes. In order to obtain the level of the water table, in accordance with the real constraints, are needed, for example, to be restricted its top level below of an imposed value, required in each node. The objective function consists of a sum of the absolute values of differences of the infiltration flow rates, increased by a large penalty factor when there are positive values of pollutant. In these conditions, a balanced structure of the pollutant concentration is maintained in the groundwater. The spatial coordinates represent the modified parameters during the process of optimization and the drainage flows through wells. Conclusions: The presented calculation scheme was applied to an area having a cross-section of 50 km between two emissaries with various levels of altitude and different values of pollution. The input data were correlated with the measurements made in-situ, such as the level of the bedrock, the grain size of the field, the slope, etc. This method of calculation can also be extended to determine the variation of the groundwater in the aquifer following the flood wave propagation in envoys.

Keywords: environmental protection, infiltrations, numerical modeling, pollutant transport through soils

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Authors: RAJ KUMAR G.C.

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There are tremendous opportunities to maximize smallholder farmers’ income from small-scale water resource development through micro irrigation and multiple-use water systems (MUS). MUS are an improved water management approach, developed and tested successfully by iDE that pipes water to a community both for domestic use and for agriculture using efficient micro irrigation. Different MUS models address different landscape constraints, water demand, and users’ preferences. MUS are complemented by micro irrigation kits, which were developed by iDE to enable farmers to grow high-value crops year-round and to use limited water resources efficiently. Over the last 15 years, iDE’s promotion of the MUS approach has encouraged government and other key stakeholders to invest in MUS for better planning of scarce water resources. Currently, about 60% of the cost of MUS construction is covered by the government and community. Based on iDE’s experience, a gravity-fed MUS costs approximately $125 USD per household to construct, and it can increase household income by $300 USD per year. A key element of the MUS approach is keeping farmers well linked to input supply systems and local produce collection centers, which helps to ensure that the farmers can produce a sufficient quantity of high-quality produce that earns a fair price. This process in turn creates an enabling environment for smallholders to invest in MUS and micro irrigation. Therefore, MUS should be seen as an integrated package of interventions –the end users, water sources, technologies, and the marketplace– that together enhance technical, financial, and institutional sustainability. Communities are trained to participate in sustainable water resource management as a part of the MUS planning and construction process. The MUS approach is cost-effective, improves community governance of scarce water resources, helps smallholder farmers to improve rural health and livelihoods, and promotes gender equity. MUS systems are simple to maintain and communities are trained to ensure that they can undertake minor maintenance procedures themselves. All in all, the iDE Nepal MUS offers multiple benefits and represents a practical and sustainable model of the MUS approach. Moreover, there is a growing national consensus that rural water supply systems should be designed for multiple uses, acknowledging that substantial work remains in developing national-level and local capacity and policies for scale-up.

Keywords: multiple-use water systems , small scale water resources, rural livelihoods, practical and sustainable model

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Authors: Dhruvit S. Berawala, Jann R. Ursin, Obrad Slijepcevic

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Shale gas is one of the most rapidly growing forms of natural gas. Unconventional natural gas deposits are difficult to characterize overall, but in general are often lower in resource concentration and dispersed over large areas. Moreover, gas is densely packed into the matrix through adsorption which accounts for large volume of gas reserves. Gas production from tight shale deposits are made possible by extensive and deep well fracturing which contacts large fractions of the formation. The conventional reservoir modelling and production forecasting methods, which rely on fluid-flow processes dominated by viscous forces, have proved to be very pessimistic and inaccurate. This paper presents a new approach to forecast shale gas production by detailed modeling of gas desorption, diffusion and non-linear flow mechanisms in combination with statistical representation of these processes. The representation of the model involves a cube as a porous media where free gas is present and a sphere (SiC: Sphere in Cube model) inside it where gas is adsorbed on to the kerogen or organic matter. Further, the sphere is considered consisting of many layers of adsorbed gas in an onion-like structure. With pressure decline, the gas desorbs first from the outer most layer of sphere causing decrease in its molecular concentration. The new available surface area and change in concentration triggers the diffusion of gas from kerogen. The process continues until all the gas present internally diffuses out of the kerogen, gets adsorbs onto available surface area and then desorbs into the nanopores and micro-fractures in the cube. Each SiC idealizes a gas pathway and is characterized by sphere diameter and length of the cube. The diameter allows to model gas storage, diffusion and desorption; the cube length takes into account the pathway for flow in nanopores and micro-fractures. Many of these representative but general cells of the reservoir are put together and linked to a well or hydraulic fracture. The paper quantitatively describes these processes as well as clarifies the geological conditions under which a successful shale gas production could be expected. A numerical model has been derived which is then compiled on FORTRAN to develop a simulator for the production of shale gas by considering the spheres as a source term in each of the grid blocks. By applying SiC to field data, we demonstrate that the model provides an effective way to quickly access gas production rates from shale formations. We also examine the effect of model input properties on gas production.

Keywords: adsorption, diffusion, non-linear flow, shale gas production

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Authors: Jessica Scher Lisa, Eric Shyman

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Autism spectrum disorder (ASD) is a neuro-developmental disorder that is characterized by persistent deficits in social communication and social interaction (i.e. deficits in social-emotional reciprocity, nonverbal communicative behaviors, and establishing/maintaining social relationships), as well as by the presence of repetitive behaviors and perseverative areas of interest (i.e. stereotyped or receptive motor movements, use of objects, or speech, rigidity, restricted interests, and hypo or hyperactivity to sensory input or unusual interest in sensory aspects of the environment). Additionally, diagnoses of ASD require the presentation of symptoms in the early developmental period, marked impairments in adaptive functioning, and a lack of explanation by general intellectual impairment or global developmental delay (although these conditions may be co-occurring). Over the past several decades, many theories have been developed in an effort to explain the root cause of ASD in terms of atypical central cognitive processes. The field of neuroscience is increasingly finding structural and functional differences between autistic and neurotypical individuals using neuro-imaging technology. One main area this research has focused upon is in visuospatial processing, with specific attention to the notion of ‘weak central coherence’ (WCC). This paper offers an analysis of findings from selected studies in order to explore research that challenges the ‘deficit’ characterization of a weak central coherence theory as opposed to a ‘superiority’ characterization of strong local coherence. The weak central coherence theory has long been both supported and refuted in the ASD literature and has most recently been increasingly challenged by advances in neuroscience. The selected studies lend evidence to the notion of amplified localized perception rather than deficient global perception. In other words, WCC may represent superiority in ‘local processing’ rather than a deficit in global processing. Additionally, the right hemisphere and the specific area of the extrastriate appear to be key in both the visual and lexicosemantic process. Overactivity in the striate region seems to suggest inaccuracy in semantic language, which lends itself to support for the link between the striate region and the atypical organization of the lexicosemantic system in ASD.

Keywords: autism spectrum disorder, neurology, visual processing, weak coherence

Procedia PDF Downloads 127