Search results for: network devices

Authors: Kalaya Sribuddhachart, Mayuree Pumipaiboon, Mayuva Youngsabanant-Areekijseree

Abstract:

The ultrastructure of 20 species of pheasant eggshells in Thailand, (Simese Fireback, Lophura diardi), (Silver Pheasant, Lophura nycthemera), (Kalij Pheasant, Lophura leucomelanos crawfurdii), (Kalij Pheasant, Lophura leucomelanos lineata), (Red Junglefowl, Gallus gallus spadiceus), (Crested Fireback, Lophura ignita rufa), (Green Peafowl, Pavo muticus), (Indian Peafowl, Pavo cristatus), (Grey Peacock Pheasant, Polyplectron bicalcaratum bicalcaratum), (Lesser Bornean Fireback, Lophura ignita ignita), (Green Junglefowl, Gallus varius), (Hume's Pheasant, Syrmaticus humiae humiae), (Himalayan Monal, Lophophorus impejanus), Golden Pheasant, Chrysolophus pictus, (Ring-Neck Pheasant, Phasianus sp.), (Reeves’s Pheasant, Syrmaticus reevesi), (Polish Chicken, Gallus sp.), (Brahma Chicken, Gallus sp.), (Yellow Golden Pheasant, Chrysolophus pictus luteus), and (Lady Amhersts Pheasant, Chrysolophus amherstiae) were studied by Secondary electron imaging (SEI) and Energy dispersive X-ray analysis (EDX) detectors of scanning electron microscope. Generally, all pheasant eggshells showed 3 layers of cuticle, palisade, and mammillary. The total thickness was ranging from 190.28±5.94-838.96±16.31µm. The palisade layer is the most thickness layer following by mammillary and cuticle layers. The palisade layer in all pheasant eggshells consisted of numerous vesicle holes that were firmly forming as network thorough the layer. The vesicle holes in all pheasant eggshells had difference porosity ranging from 0.44±0.11-0.23±0.05 µm. While the mammillary layer was the most compact layer with a variable shape (broad-base V and U-shape) connect to shell membrane. Elemental analysis by of 20 specie eggshells showed 9 apparent elements including carbon (C), oxygen (O), calcium (Ca), phosphorous (P), sulfur (S), magnesium (Mg), silicon (Si), aluminum (Al), and copper (Cu) at the percentage of 28.90- 8.33%, 60.64-27.61%, 55.30-14.49%, 1.97-0.03%, 0.08-0.03%, 0.50-0.16%, 0.30-0.04%, 0.06-0.02%, and 2.67-1.73%, respectively. It was found that Ca, C, and O showed highest elemental compositions, which essential for pheasant embryonic development, mainly presented as composited structure of calcium carbonate (CaCO3) more than 97%. Meanwhile, Mg, S, Si, Al, and P were major inorganic constituents of the eggshells which directly related to an increase of the shell hardness. Finally, the percentage of heavy metal copper (Cu) has been observed in 4 eggshell species. There are Golden Pheasant (2.67±0.16%), Indian Peafowl (2.61±0.13%), Green Peafowl (1.97±0.74%), and Silver Pheasant (1.73±0.11%), respectively. A non-significant difference was found in the percentages of 9 elements in all pheasant eggshells. This study is useful to provide the information of biology and taxonomic of pheasant study in Thailand for conservation.

Keywords: pheasants eggshells, secondary electron imaging (SEI) and energy dispersive X-ray analysis (EDX), morphology, Thailand

Procedia PDF Downloads 235

Authors: Riddhi Chopra

Abstract:

Alfred Thayer Mahan described the sea as a ‘great common,’ which would serve as a medium for communication, trade, and transport. The seas of Asia are witnessing an intriguing historical anomaly – rise of an indigenous maritime power against the backdrop of US domination over the region. As China transforms from an inward leaning economy to an outward-leaning economy, it has become increasingly dependent on the global sea; as a result, we witness an evolution in its maritime strategy from near seas defense to far seas deployment strategies. It is not only patrolling the international waters but has also built a network of civilian and military infrastructure across the disputed oceanic expanse. The paper analyses the reorientation of China from a naval power to a blue water navy in an era of extensive globalisation. The actions of the Chinese have created a zone of high alert amongst its neighbors such as Japan, Philippines, Vietnam and North Korea. These nations are trying to align themselves so as to counter China’s growing brinkmanship, but China has been pursuing claims through a carefully calibrated strategy in the region shunning any coercive measures taken by other forces. If China continues to expand its maritime boundaries, its neighbors – all smaller and weaker Asian nations would be limited to a narrow band of the sea along its coastlines. Hence it is essential for the US to intervene and support its allies to offset Chinese supremacy. The paper intends to provide a profound analysis over the disputes in South China Sea and East China Sea focusing on Philippines and Japan respectively. Moreover, the paper attempts to give an account of US involvement in the region and its alignment with its South Asian allies. The geographic dynamics is said the breed a national coalition dominating the strategic ambitions of China as well as the weak littoral states. China has conducted behind the scenes diplomacy trying to persuade its neighbors to support its position on the territorial disputes. These efforts have been successful in creating fault lines in ASEAN thereby undermining regional integrity to reach a consensus on the issue. Chinese diplomatic efforts have also forced the US to revisit its foreign policy and engage with players like Cambodia and Laos. The current scenario in the SCS points to a strong Chinese hold trying to outspace all others with no regards to International law. Chinese activities are in contrast with US principles like Freedom of Navigation thereby signaling US to take bold actions to prevent Chinese hegemony in the region. The paper ultimately seeks to explore the changing power dynamics among various claimants where a rival superpower like US can pursue the traditional policy of alliance formation play a decisive role in changing the status quo in the arena, consequently determining the future trajectory.

Keywords: China, East China Sea, South China Sea, USA

Procedia PDF Downloads 241

Authors: Roberto M. G. Velásquez, Jonas R. Gazoli, Nelson Ponce Jr, Valério L. Borges, Alessandro Sete, Fernanda M. C. Tomé, Julian D. Hunt, Heitor C. Lira, Cristiano L. de Souza, Fabio T. Bindemann, Wilmar Wounnsoscky

Abstract:

Nowadays, society is working toward reducing energy losses and greenhouse gas emissions, as well as seeking clean energy sources, as a result of the constant increase in energy demand and emissions. Energy loss occurs in the gas pressure reduction stations at the delivery points in natural gas distribution systems (city gates). Installing pressure reduction turbines (PRT) parallel to the static reduction valves at the city gates enhances the energy efficiency of the system by recovering the enthalpy of the pressurized natural gas, obtaining in the pressure-lowering process shaft work and generating electrical power. Currently, the Brazilian natural gas transportation network has 9,409 km in extension, while the system has 16 national and 3 international natural gas processing plants, including more than 143 delivery points to final consumers. Thus, the potential of installing PRT in Brazil is 66 MW of power, which could yearly avoid the emission of 235,800 tons of CO2 and generate 333 GWh/year of electricity. On the other hand, an economic viability analysis of these energy efficiency projects is commonly carried out based on estimates of the project's cash flow obtained from several variables forecast. Usually, the cash flow analysis is performed using representative values of these variables, obtaining a deterministic set of financial indicators associated with the project. However, in most cases, these variables cannot be predicted with sufficient accuracy, resulting in the need to consider, to a greater or lesser degree, the risk associated with the calculated financial return. This paper presents an approach applied to the technical-economic viability analysis of PRTs projects that explicitly considers the uncertainties associated with the input parameters for the financial model, such as gas pressure at the delivery point, amount of energy generated by TRP, the future price of energy, among others, using sensitivity analysis techniques, scenario analysis, and Monte Carlo methods. In the latter case, estimates of several financial risk indicators, as well as their empirical probability distributions, can be obtained. This is a methodology for the financial risk analysis of PRT projects. The results of this paper allow a more accurate assessment of the potential PRT project's financial feasibility in Brazil. This methodology will be tested at the Cuiabá thermoelectric plant, located in the state of Mato Grosso, Brazil, and can be applied to study the potential in other countries.

Keywords: pressure reduction turbine, natural gas pressure drop station, energy efficiency, electricity generation, monte carlo methods

Procedia PDF Downloads 113

Authors: George R. Puno, Eric N. Bruno

Abstract:

The natural landscape of the Philippine archipelago plus the current realities of climate change make the country vulnerable to flood hazards. Flooding becomes the recurring natural disaster in the country resulting to lose of lives and properties. Musimusi is among the rivers which exhibited inundation particularly at the inhabited floodplain portion of its watershed. During the event, rescue operations and distribution of relief goods become a problem due to lack of high resolution flood maps to aid local government unit identify the most affected areas. In the attempt of minimizing impact of flooding, hydrologic modelling with high resolution mapping is becoming more challenging and important. This study focused on the analysis of flood extent as a function of different geomorphologic characteristics of Musimusi watershed. The methods include the delineation of morphometric parameters in the Musimusi watershed using Geographic Information System (GIS) and geometric calculations tools. Digital Terrain Model (DTM) as one of the derivatives of Light Detection and Ranging (LiDAR) technology was used to determine the extent of river inundation involving the application of Hydrologic Engineering Center-River Analysis System (HEC-RAS) and Hydrology Modelling System (HEC-HMS) models. The digital elevation model (DEM) from synthetic Aperture Radar (SAR) was used to delineate watershed boundary and river network. Datasets like mean sea level, river cross section, river stage, discharge and rainfall were also used as input parameters. Curve number (CN), vegetation, and soil properties were calibrated based on the existing condition of the site. Results showed that the drainage density value of the watershed is low which indicates that the basin is highly permeable subsoil and thick vegetative cover. The watershed’s elongation ratio value of 0.9 implies that the floodplain portion of the watershed is susceptible to flooding. The bifurcation ratio value of 2.1 indicates higher risk of flooding in localized areas of the watershed. The circularity ratio value (1.20) indicates that the basin is circular in shape, high discharge of runoff and low permeability of the subsoil condition. The heavy rainfall of 167 mm brought by Typhoon Seniang last December 29, 2014 was characterized as high intensity and long duration, with a return period of 100 years produced 316 m3s-1 outflows. Portion of the floodplain zone (1.52%) suffered inundation with 2.76 m depth at the maximum. The information generated in this study is helpful to the local disaster risk reduction management council in monitoring the affected sites for more appropriate decisions so that cost of rescue operations and relief goods distribution is minimized.

Keywords: flooding, geomorphology, mapping, watershed

Procedia PDF Downloads 230

Authors: Christine Böckmann, Julia Rosemann

Abstract:

We present an inversion algorithm that is used in the European Aerosol Lidar Network for the inversion of data collected with multi-wavelength Raman lidar. These instruments measure backscatter coefficients at 355, 532, and 1064 nm, and extinction coefficients at 355 and 532 nm. The algorithm is based on manually controlled inversion of optical data which allows for detailed sensitivity studies and thus provides us with comparably high quality of the derived data products. The algorithm allows us to derive particle effective radius, volume, surface-area concentration with comparably high confidence. The retrieval of the real and imaginary parts of the complex refractive index still is a challenge in view of the accuracy required for these parameters in climate change studies in which light-absorption needs to be known with high accuracy. Single-scattering albedo (SSA) can be computed from the retrieve microphysical parameters and allows us to categorize aerosols into high and low absorbing aerosols. From mathematical point of view the algorithm is based on the concept of using truncated singular value decomposition as regularization method. This method was adapted to work for the retrieval of the particle size distribution function (PSD) and is called hybrid regularization technique since it is using a triple of regularization parameters. The inversion of an ill-posed problem, such as the retrieval of the PSD, is always a challenging task because very small measurement errors will be amplified most often hugely during the solution process unless an appropriate regularization method is used. Even using a regularization method is difficult since appropriate regularization parameters have to be determined. Therefore, in a next stage of our work we decided to use two regularization techniques in parallel for comparison purpose. The second method is an iterative regularization method based on Pade iteration. Here, the number of iteration steps serves as the regularization parameter. We successfully developed a semi-automated software for spherical particles which is able to run even on a parallel processor machine. From a mathematical point of view, it is also very important (as selection criteria for an appropriate regularization method) to investigate the degree of ill-posedness of the problem which we found is a moderate ill-posedness. We computed the optical data from mono-modal logarithmic PSD and investigated particles of spherical shape in our simulations. We considered particle radii as large as 6 nm which does not only cover the size range of particles in the fine-mode fraction of naturally occurring PSD but also covers a part of the coarse-mode fraction of PSD. We considered errors of 15% in the simulation studies. For the SSA, 100% of all cases achieve relative errors below 12%. In more detail, 87% of all cases for 355 nm and 88% of all cases for 532 nm are well below 6%. With respect to the absolute error for non- and weak-absorbing particles with real parts 1.5 and 1.6 in all modes the accuracy limit +/- 0.03 is achieved. In sum, 70% of all cases stay below +/-0.03 which is sufficient for climate change studies.

Keywords: aerosol particles, inverse problem, microphysical particle properties, regularization

Procedia PDF Downloads 343

Authors: Andrea Cretu, Calin Cadar, Maria Miclaus, Lucian Barbu-Tudoran, Siegfried Stapf, Ioan Ardelean

Abstract:

Additives and mineral admixtures have become an integral part of cement-based materials. It is common practice to add silica fume to cement based mixes in order to produce high-performance concrete. There is still a lack of scientific understanding regarding the effects that silica fume has on the microstructure of hydrated cement paste. The aim of the current study is to develop high-performance materials with low permeability and high resistance to flexural stress using silica fume and an organosilane. Organosilane bonds with cement grains and silica fume, influencing both the workability and the final properties of the mix, especially the pore size distributions and pore connectivity. Silica fume is a known pozzolanic agent which reacts with the calcium hydroxide in hydrated cement paste, producing more C-S-H and improving the mechanical properties of the mix. It is believed that particles of silica fume act as capillary pore fillers and nucleation centers for C-S-H and other hydration products. In order to be able to design cement-based materials with added silica fume and organosilane, it is necessary first to understand the formation of the porous network during hydration and to observe the distribution of pores and their connectivity. Nuclear magnetic resonance (NMR) methods in low-fields are non-destructive and allow the study of cement-based materials from the standpoint of their porous structure. Other methods, such as XRD and SEM-EDS, help create a comprehensive picture of the samples, along with the classic mechanical tests (compressive and flexural strength measurements). The transverse relaxation time (T₂) was measured during the hydration of 16 samples prepared with two water/cement ratios (0.3 and 0.4) and different concentrations or organosilane (APTES, up to 2% by mass of cement) and silica fume (up to 6%). After their hydration, the pore size distribution was assessed using the same NMR approach on the samples filled with cyclohexane. The SEM-EDS and XRD measurements were applied on pieces and powders prepared from the samples that were used in mechanical testing, which were kept under water for 28 days. Adding silica fume does not influence the hydration dynamics of cement paste, while the addition of organosilane extends the dormancy stage up to 10 hours. The size distribution of the capillary pores is not influenced by the addition of silica fume or organosilane, while the connectivity of capillary pores is decreased only when there is organosilane in the mix. No filling effect is observed even at the highest concentration of silica fume. There is an apparent increase in flexural strength of samples prepared only with silica fume and a decrease for those prepared with organosilane, with a few exceptions. XRD reveals that the pozzolanic reactivity of silica fume can only be observed when there is no organosilane present and the SEM-EDS method reveals the pore distribution, as well as hydration products and the presence or absence of calcium hydroxide. The current work was funded by the Romanian National Authority for Scientific Research, CNCS – UEFISCDI, through project PN-III-P2-2.1-PED-2016-0719.

Keywords: cement hydration, concrete admixtures, NMR, organosilane, porosity, silica fume

Procedia PDF Downloads 161

Authors: Yasmine Salehy, Yann Leroy, Francois Cluzel, Hong-Minh Hoang, Laurence Fournaison, Anthony Delahaye, Bernard Yannou

Abstract:

Taking into consideration all the life cycle of a product is now an important step in the eco-design of a product or a technology. Life cycle assessment (LCA) is a standard tool to evaluate the environmental impacts of a system or a process. Despite the improvement in refrigerant regulation through protocols, the environmental damage of refrigeration systems remains important and needs to be improved. In this paper, the environmental impacts of refrigeration systems in a typical supermarket are compared using the LCA methodology under different conditions. The system is used to provide cold at two levels of temperature: medium and low temperature during a life period of 15 years. The most commonly used architectures of supermarket cold production systems are investigated: centralized direct expansion systems and indirect systems using a secondary loop to transport the cold. The variation of power needed during seasonal changes and during the daily opening/closure periods of the supermarket are considered. R134a as the primary refrigerant fluid and two types of secondary fluids are considered. The composition of each system and the leakage rate of the refrigerant through its life cycle are taken from the literature and industrial data. Twelve scenarios are examined. They are based on the variation of three parameters, 1. location: France (Paris), Spain (Toledo) and Sweden (Stockholm), 2. different sources of electric consumption: photovoltaic panels and low voltage electric network and 3. architecture: direct and indirect refrigeration systems. OpenLCA, SimaPro softwares, and different impact assessment methods were compared; CML method is used to evaluate the midpoint environmental indicators. This study highlights the significant contribution of electric consumption in environmental damages compared to the impacts of refrigerant leakage. The secondary loop allows lowering the refrigerant amount in the primary loop which results in a decrease in the climate change indicators compared to the centralized direct systems. However, an exhaustive cost evaluation (CAPEX and OPEX) of both systems shows more important costs related to the indirect systems. A significant difference between the countries has been noticed, mostly due to the difference in electric production. In Spain, using photovoltaic panels helps to reduce efficiently the environmental impacts and the related costs. This scenario is the best alternative compared to the other scenarios. Sweden is a country with less environmental impacts. For both France and Sweden, the use of photovoltaic panels does not bring a significant difference, due to a less sunlight exposition than in Spain. Alternative solutions exist to reduce the impact of refrigerating systems, and a brief introduction is presented.

Keywords: eco-design, industrial engineering, LCA, refrigeration system

Procedia PDF Downloads 189

Authors: Omwoyo Bosire Onyancha

Abstract:

The Journal Citation Reports (JCR) of the Thomson Reuters has, for decades, provided the data for bibliometrically assessing the impact of journals. In their criticism of the journal impact factor (JIF), a number of scholars such as Priem, Taraborelli, Groth and Neylon (2010) observe that the “JIF is often incorrectly used to assess the impact of individual articles. It is troubling that the exact details of the JIF are a trade secret, and that significant gaming is relatively easy”. The emergence of alternative metrics (Altmetrics) has introduced another dimension of re-assessing how the impact of journals (and other units such as articles and even individual researchers) can be measured. Altmetrics is premised upon the fact that research is increasingly being disseminated through social network sites such as ResearchGate, Mendeley, Twitter, Facebook, LinkedIn, and ImpactStory, among others. This paper adopts informetrics (including altmetrics) techniques to report on the findings of a study conducted to investigate and compare the social media impact of 274 South Africa Post Secondary Education (SAPSE)-accredited journals, which are recognized and accredited by the Department of Higher Education and Training (DHET) of South Africa (SA). We used multiple sources to extract data for the study, namely Altmetric.com and the Thomson Reuters’ Journal Citation Reports. Data was analyzed in order to determine South African journals’ presence and impact on social media as well as contrast the social media impact with Thomson Reuters’ citation impact. The Spearman correlation test was performed to compare the journals’ social media impact and JCR citation impact. Preliminary findings reveal that a total of 6360 articles published in 96 South African journals have received some attention in social media; the most commonly used social media platform was Twitter, followed by Mendeley, Facebook, News outlets, and CiteULike; there were 29 SA journals covered in the JCR in 2008 and this number has grown to 53 journals in 2014; the journals indexed in the Thomson Reuters performed much better, in terms of their altmetrics, than those journals that are not indexed in Thomson Reuters databases; nevertheless, there was high correlation among journals that featured in both datasets; the journals with the highest scores in Altmetric.com included the South African Medical Journal, African Journal of Marine Science, and Transactions of the Royal Society of South Africa while the journals with high impact factors in JCR were South African Medical Journal, Onderstepoort: Journal of Veterinary Research, and Sahara: Journal of Social Aspects of HIV-AIDS; and that Twitter has emerged as a strong avenue of sharing and communicating research published in the South African journals. Implications of the results of the study for the dissemination of research conducted in South Africa are offered. Discussions based on the research findings as well as conclusions and recommendations are offered in the full text paper.

Keywords: altmetrics, citation impact, journal citation reports, journal impact factor, journals, research, scholarly publishing, social media impact, South Africa

Procedia PDF Downloads 204

Authors: Ilana Singer, Anastasia Lučić, Julie Leclerc

Abstract:

Introduction: Tics, characterized by repetitive, sudden, non-voluntary motor movements or vocalizations, are prevalent in chronic tic disorder (CT) and Tourette Syndrome (TS). These neurodevelopmental disorders often coexist with various psychiatric conditions, leading to challenges and reduced quality of life. While medication in conjunction with behavioral interventions, such as Habit Reversal Training (HRT), Exposure Response Prevention (ERP), and Comprehensive Behavioral Intervention for Tics (CBIT), has shown efficacy, a significant proportion of patients experience persistent tics. Thus, innovative treatment approaches are necessary to improve therapeutic outcomes, such as mindfulness-based approaches. Nonetheless, the effectiveness of mindfulness-based interventions in the context of CT and TS remains understudied. Objective: The objective of this scoping review is to provide an overview of the current state of research on mindfulness-based interventions for CT and TS, identify knowledge and evidence gaps, discuss the effectiveness of mindfulness-based interventions with other treatment options, and discuss implications for clinical practice and policy development. Method: Using guidelines from Peters (2020) and the PRISMA-ScR, a scoping review was conducted. Multiple electronic databases were searched from inception until June 2023, including MEDLINE, EMBASE, PsychInfo, Global Health, PubMed, Web of Science, and Érudit. Inclusion criteria were applied to select relevant studies, and data extraction was independently performed by two reviewers. Results: Five papers were included in the study. Firstly, we found that mindfulness interventions were found to be effective in reducing anxiety and depression while enhancing overall well-being in individuals with tics. Furthermore, the review highlighted the potential role of mindfulness in enhancing functional connectivity within the Default Mode Network (DMN) as a compensatory function in TS patients. This suggests that mindfulness interventions may complement and support traditional therapeutic approaches, particularly HRT, by positively influencing brain networks associated with tic regulation and control. Conclusion: This scoping review contributes to the understanding of the effectiveness of mindfulness-based interventions in managing CT and TS. By identifying research gaps, this review can guide future investigations and interventions to improve outcomes for individuals with CT or TS. Overall, these findings emphasize the potential benefits of incorporating mindfulness-based interventions as a smaller subset within comprehensive treatment strategies. However, it is essential to acknowledge the limitations of this scoping review, such as the exclusion of a pre-established protocol and the limited number of studies available for inclusion. Further research and clinical exploration are necessary to better understand the specific mechanisms and optimal integration of mindfulness-based interventions with existing behavioral interventions for this population.

Keywords: scoping reviews, Tourette Syndrome, tics, mindfulness-based, therapy, intervention

Procedia PDF Downloads 83

Authors: Afsheen Aman, Zainab Bibi, Shah Ali Ul Qader

Abstract:

Introduction: Xylan is a heteropolysaccharide composed of xylose monomers linked together through 1,4 linkages within a complex xylan network. Owing to wide applications of xylan hydrolytic products (xylose, xylobiose and xylooligosaccharide) the researchers are focusing towards the development of various strategies for efficient xylan degradation. One of the most important strategies focused is the use of heat tolerant biocatalysts which acts as strong and specific cleaving agents. Therefore, the exploration of microbial pool from extremely diversified ecosystem is considerably vital. Microbial populations from extreme habitats are keenly explored for the isolation of thermophilic entities. These thermozymes usually demonstrate fast hydrolytic rate, can produce high yields of product and are less prone to microbial contamination. Another possibility of degrading xylan continuously is the use of immobilization technique. The current work is an effort to merge both the positive aspects of thermozyme and immobilization technique. Methodology: Geobacillus stearothermophilus was isolated from soil sample collected near the blast furnace site. This thermophile is capable of producing thermostable endo-β-1,4-xylanase which cleaves xylan effectively. In the current study, this thermozyme was immobilized within a synthetic and a non-synthetic matrice for continuous production of metabolites using entrapment technique. The kinetic parameters of the free and immobilized enzyme were studied. For this purpose calcium alginate and polyacrylamide beads were prepared. Results: For the synthesis of immobilized beads, sodium alginate (40.0 gL-1) and calcium chloride (0.4 M) was used amalgamated. The temperature (50°C) and pH (7.0) optima of immobilized enzyme remained same for xylan hydrolysis however, the enzyme-substrate catalytic reaction time raised from 5.0 to 30.0 minutes as compared to free counterpart. Diffusion limit of high molecular weight xylan (corncob) caused a decline in Vmax of immobilized enzyme from 4773 to 203.7 U min-1 whereas, Km value increased from 0.5074 to 0.5722 mg ml-1 with reference to free enzyme. Immobilized endo-β-1,4-xylanase showed its stability at high temperatures as compared to free enzyme. It retained 18% and 9% residual activity at 70°C and 80°C, respectively whereas; free enzyme completely lost its activity at both temperatures. The Immobilized thermozyme displayed sufficient recycling efficiency and can be reused up to five reaction cycles, indicating that this enzyme can be a plausible candidate in paper processing industry. Conclusion: This thermozyme showed better immobilization yield and operational stability with the purpose of hydrolyzing the high molecular weight xylan. However, the enzyme immobilization properties can be improved further by immobilizing it on different supports for industrial purpose.

Keywords: immobilization, reusability, thermozymes, xylanase

Procedia PDF Downloads 374

Authors: Moanaro Ao, Nzanthung Ngullie

Abstract:

Municipal solid waste management (MSWM) has unique challenges in hilly urban settlements. Efforts have been taken by municipalities, private players, non-governmental organizations, etc. for managing solid waste by preventing its generation, reusing, and recovering them into useful products to the extent possible, thereby minimizing its impact on the environment and human health. However, there are many constraints that lead to inadequate management of solid waste. Kohima is an unplanned hill station city in the North Eastern Region of India. The city is facing numerous issues due to the mismanagement of the MSW generated. Kohima Municipal Council (KMC) is the Urban Local Body (ULB) responsible for providing municipal services. The present MSWM system in Kohima comprises of collection, transportation, and disposal of waste without any treatment. Several efforts and experimental projects on waste management have been implemented without any success. Waste management in Kohima city is challenging due to its remote location, difficult topography, dispersed settlements within the city, sensitive ecosystem, etc. Furthermore, the narrow road network in Kohima with limited scope for expansion, inadequate infrastructure facilities, and financial constraints of the ULB add up to the problems faced in managing solid waste. This hill station also has a unique system of traditional local self-governance. Thus, shifting from a traditional system to a modern system in implementing systematic and scientific waste management is also a challenge in itself. This study aims to analyse the existing situation of waste generation, evaluate the effectiveness of the existing management system of MSW, and evolve a strategic approach to achieve a sustainable and resilient MSWM system. The results from the study show that a holistic approach, including social aspects, technical aspects, environmental aspects, and financial aspects, is needed to reform the MSWM system. Stringent adherence to source segregation is required by encouraging public participation through awareness programs. Active involvement of community-based organizations (CBOs) has brought a positive change in sensitizing the public. A waste management model was designed to be adopted at a micro-level such as composting household biodegradable waste and incinerator plants at the community level for non-biodegradable waste. Suitable locations for small waste stations were identified using geographical information system (GIS) tools for waste recovery and recycling. Inculcating the sense of responsibility in every waste generator towards waste management by implementing incentive-based strategies at the Ward level was explored. Initiatives based on the ‘polluters pay principle’ were also explored to make the solid waste management model “self-sustaining”.

Keywords: municipal solid waste management, public participation, source segregation, sustainable

Procedia PDF Downloads 68

Authors: Lucie Speyer, Vincent Lecocq, Séverine Humbert, Antoine Hugon

Abstract:

Mesoporous alumina is commonly used as a catalyst support for the hydrotreating of heavy petroleum cuts. The process of fabrication usually involves: the synthesis of the boehmite AlOOH precursor, a kneading-extrusion step, and a calcination in order to obtain the final alumina extrudates. Alumina is described as a complex porous medium, generally agglomerates constituted of aggregated nanocrystallites. Its porous texture directly influences the active phase deposition and mass transfer, and the catalytic properties. Then, it is easy to figure out that each step of the fabrication of the supports has a role on the building of their porous network, and has to be well understood to optimize the process. The synthesis of boehmite by precipitation of aluminum salts was extensively studied in the literature and the effect of various parameters, such as temperature or pH, are known to influence the size and shape of the crystallites and the specific surface area of the support. The calcination step, through the topotactic transition from boehmite to alumina, determines the final properties of the support and can tune the surface area, pore volume and pore diameters from those of boehmite. However, the kneading extrusion step has been subject to a very few studies. It generally consists in two steps: an acid, then a basic kneading, where the boehmite powder is introduced in a mixer and successively added with an acid and a base solution to form an extrudable paste. During the acid kneading, the induced positive charges on the hydroxyl surface groups of boehmite create an electrostatic repulsion which tends to separate the aggregates and even, following the conditions, the crystallites. The basic kneading, by reducing the surface charges, leads to a flocculation phenomenon and can control the reforming of the overall structure. The separation and reassembling of the particles constituting the boehmite paste have a quite obvious influence on the textural properties of the material. In this work, we are focused on the influence of the kneading step on the alumina catalysts supports. Starting from an industrial boehmite, extrudates are prepared using various kneading conditions. The samples are studied by nitrogen physisorption in order to analyze the evolution of the textural properties, and by synchrotron small-angle X-ray scattering (SAXS), a more original method which brings information about agglomeration and aggregation of the samples. The coupling of physisorption and SAXS enables a precise description of the samples, as same as an accurate monitoring of their evolution as a function of the kneading conditions. These ones are found to have a strong influence of the pore volume and pore size distribution of the supports. A mechanism of evolution of the texture during the kneading step is proposed and could be attractive in order to optimize the texture of the supports and then, their catalytic performances.

Keywords: alumina catalyst support, kneading, nitrogen physisorption, small-angle X-ray scattering

Procedia PDF Downloads 253

Authors: O. A. Adeyemi, M. O. Idris, W. A. Oke, O. T. Olorode, S. O. Alayande, A. E. Adeoye

Abstract:

The purpose of this paper is to investigate the process of capability building at subsidiary level and the challenges to such process. The relevance of external factors for capability development, have not been explicitly addressed in empirical studies. Though, internal factors, acting as enablers, have been more extensively studied. With reference to external factors, subsidiaries are actively influenced by specific characteristics of the host country, implying a need to become fully immersed in local culture and practices. Specifically, in MNCs, there has been a widespread trend in management practice to increase subsidiary autonomy,  with subsidiary managers being encouraged to act entrepreneurially, and to take advantage of host country specificity. As such, it could be proposed that: P1: The degree at which subsidiary management is connected to the host country, will positively influence the capability development process. Dynamic capabilities reside to a large measure with the subsidiary management team, but are impacted by the organizational processes, systems and structures that the MNC headquarter has designed to manage its business. At the subsidiary level, the weight of the subsidiary in the network, its initiative-taking and its profile building increase the supportive attention of the HQs and are relevant to the success of the process of capability building. Therefore, our second proposition is that: P2: Subsidiary role and HQ support are relevant elements in capability development at the subsidiary level. Design/Methodology/Approach: This present study will adopt the multiple case studies approach. That is because a case study research is relevant when addressing issues without known empirical evidences or with little developed prior theory. The key definitions and literature sources directly connected with operations of western subsidiaries in emerging markets, such as China, are well established. A qualitative approach, i.e., case studies of three western subsidiaries, will be adopted. The companies have similar products, they have operations in China, and both of them are mature in their internationalization process. Interviews with key informants, annual reports, press releases, media materials, presentation material to customers and stakeholders, and other company documents will be used as data sources. Findings: Western Subsidiaries in Emerging Market operate in a way substantially different from those in the West. What are the conditions initiating the outsourcing of operations? The paper will discuss and present two relevant propositions guiding that process. Practical Implications: MNCs headquarter should be aware of the potential for capability development at the subsidiary level. This increased awareness could induce consideration in headquarter about the possible ways of encouraging such known capability development and how to leverage these capabilities for better MNC headquarter and/or subsidiary performance. Originality/Value: The paper is expected to contribute on the theme: drivers of subsidiary performance with focus on emerging market. In particular, it will show how some external conditions could promote a capability-building process within subsidiaries.

Keywords: case studies, dynamic capability, emerging market, subsidiary

Procedia PDF Downloads 122

Authors: Ismene Ithai Bras-Ruiz

Abstract:

Learning Analytics (LA) is employed by universities not only as a tool but as a specialized ground to enhance students and professors. However, not all the academic programs apply LA with the same goal and use the same tools. In fact, LA is formed by five main fields of study (academic analytics, action research, educational data mining, recommender systems, and personalized systems). These fields can help not just to inform academic authorities about the situation of the program, but also can detect risk students, professors with needs, or general problems. The highest level applies Artificial Intelligence techniques to support learning practices. LA has adopted different techniques: statistics, ethnography, data visualization, machine learning, natural language process, and data mining. Is expected that any academic program decided what field wants to utilize on the basis of his academic interest but also his capacities related to professors, administrators, systems, logistics, data analyst, and the academic goals. The Open and Distance University System (SUAYED in Spanish) of the University National Autonomous of Mexico (UNAM), has been working for forty years as an alternative to traditional programs; one of their main supports has been the employ of new information and communications technologies (ICT). Today, UNAM has one of the largest network higher education programs, twenty-six academic programs in different faculties. This situation means that every faculty works with heterogeneous populations and academic problems. In this sense, every program has developed its own Learning Analytic techniques to improve academic issues. In this context, an investigation was carried out to know the situation of the application of LA in all the academic programs in the different faculties. The premise of the study it was that not all the faculties have utilized advanced LA techniques and it is probable that they do not know what field of study is closer to their program goals. In consequence, not all the programs know about LA but, this does not mean they do not work with LA in a veiled or, less clear sense. It is very important to know the grade of knowledge about LA for two reasons: 1) This allows to appreciate the work of the administration to improve the quality of the teaching and, 2) if it is possible to improve others LA techniques. For this purpose, it was designed three instruments to determinate the experience and knowledge in LA. These were applied to ten faculty coordinators and his personnel; thirty members were consulted (academic secretary, systems manager, or data analyst, and coordinator of the program). The final report allowed to understand that almost all the programs work with basic statistics tools and techniques, this helps the administration only to know what is happening inside de academic program, but they are not ready to move up to the next level, this means applying Artificial Intelligence or Recommender Systems to reach a personalized learning system. This situation is not related to the knowledge of LA, but the clarity of the long-term goals.

Keywords: academic improvements, analytical techniques, learning analytics, personnel expertise

Procedia PDF Downloads 128

Authors: Solanki Ravirajsinh

Abstract:

In the software testing life cycle, failing to conduct thorough performance testing can result in significant losses for an organization due to application crashes and improper behavior under high user loads in production. Simulating large volumes of requests, such as 5 million within 5-10 minutes, is challenging without a scalable performance testing framework. Leveraging cloud services to implement a performance testing framework makes it feasible to handle 5-10 million requests in just 5-10 minutes, helping organizations ensure their applications perform reliably under peak conditions. Implementing a scalable performance testing framework using cloud services and tools like JMeter, EC2 instances (Virtual machine), cloud logs (Monitor errors and logs), EFS (File storage system), and security groups offers several key benefits for organizations. Creating performance test framework using this approach helps optimize resource utilization, effective benchmarking, increased reliability, cost savings by resolving performance issues before the application is released. In performance testing, a master-slave framework facilitates distributed testing across multiple EC2 instances to emulate many concurrent users and efficiently handle high loads. The master node orchestrates the test execution by coordinating with multiple slave nodes to distribute the workload. Slave nodes execute the test scripts provided by the master node, with each node handling a portion of the overall user load and generating requests to the target application or service. By leveraging JMeter's master-slave framework in conjunction with cloud services like EC2 instances, EFS, CloudWatch logs, security groups, and command-line tools, organizations can achieve superior scalability and flexibility in their performance testing efforts. In this master-slave framework, JMeter must be installed on both the master and each slave EC2 instance. The master EC2 instance functions as the "brain," while the slave instances operate as the "body parts." The master directs each slave to execute a specified number of requests. Upon completion of the execution, the slave instances transmit their results back to the master. The master then consolidates these results into a comprehensive report detailing metrics such as the number of requests sent, encountered errors, network latency, response times, server capacity, throughput, and bandwidth. Leveraging cloud services, the framework benefits from automatic scaling based on the volume of requests. Notably, integrating cloud services allows organizations to handle more than 5-10 million requests within 5 minutes, depending on the server capacity of the hosted website or application.

Keywords: identify crashes of application under heavy load, JMeter with cloud Services, Scalable performance testing, JMeter master and slave using cloud Services

Procedia PDF Downloads 27

Authors: Robert Caulk

Abstract:

A generalized framework for adaptive machine learning deployments in algorithmic trading is introduced, tested, and released as open-source code. The presented software aims to test the hypothesis that recent data contains enough information to form a probabilistically favorable short-term price prediction. Further, the framework contains various adaptive machine learning techniques that are geared toward generating profit during strong trends and minimizing losses during trend changes. Results demonstrate that this adaptive machine learning approach is capable of capturing trends and generating profit. The presentation also discusses the importance of defining the parameter space associated with the dynamic training data-set and using the parameter space to identify and remove outliers from prediction data points. Meanwhile, the generalized architecture enables common users to exploit the powerful machinery while focusing on high-level feature engineering and model testing. The presentation also highlights common strengths and weaknesses associated with the presented technique and presents a broad range of well-tested starting points for feature set construction, target setting, and statistical methods for enforcing risk management and maintaining probabilistically favorable entry and exit points. The presentation also describes the end-to-end data processing tools associated with FreqAI, including automatic data fetching, data aggregation, feature engineering, safe and robust data pre-processing, outlier detection, custom machine learning and statistical tools, data post-processing, and adaptive training backtest emulation, and deployment of adaptive training in live environments. Finally, the generalized user interface is also discussed in the presentation. Feature engineering is simplified so that users can seed their feature sets with common indicator libraries (e.g. TA-lib, pandas-ta). The user also feeds data expansion parameters to fill out a large feature set for the model, which can contain as many as 10,000+ features. The presentation describes the various object-oriented programming techniques employed to make FreqAI agnostic to third-party libraries and external data sources. In other words, the back-end is constructed in such a way that users can leverage a broad range of common regression libraries (Catboost, LightGBM, Sklearn, etc) as well as common Neural Network libraries (TensorFlow, PyTorch) without worrying about the logistical complexities associated with data handling and API interactions. The presentation finishes by drawing conclusions about the most important parameters associated with a live deployment of the adaptive learning framework and provides the road map for future development in FreqAI.

Keywords: machine learning, market trend detection, open-source, adaptive learning, parameter space exploration

Procedia PDF Downloads 88

Authors: Fabio N. Moreno, Letícia B. Marinho, Beatriz D. Ruiz, Maria Helena R. B. Martins

Abstract:

Brazil is a top consumer of pesticides worldwide, and the São Paulo State is one of the highest consumers among the Brazilian federative states. However, representative data about the occurrence of pesticides in surface waters of the São Paulo State is scarce. This paper aims to present the results of pesticides monitoring executed within the Water Quality Monitoring Network of CETESB (The Environmental Agency of the São Paulo State) between the 2018-2022 period. Surface water sampling points (21 to 25) were selected within basins of predominantly agricultural land-use (5 to 85% of cultivated areas). The samples were collected throughout the year, including high-flow and low-flow conditions. The frequency of sampling varied between 6 to 4 times per year. Selection of pesticide molecules for monitoring followed a prioritizing process from EMBRAPA (Brazilian Agricultural Research Corporation) databases of pesticide use. Pesticides extractions in aqueous samples were performed according to USEPA 3510C and 3546 methods following quality assurance and quality control procedures. Determination of pesticides in water (ng L-1) extracts were performed by high-performance liquid chromatography coupled with mass spectrometry (HPLC-MS) and by gas chromatography with nitrogen phosphorus (GC-NPD) and electron capture detectors (GC-ECD). The results showed higher frequencies (20- 65%) in surface water samples for Carbendazim (fungicide), Diuron/Tebuthiuron (herbicides) and Fipronil/Imidaclopride (insecticides). The frequency of observations for these pesticides were generally higher in monitoring points located in sugarcane cultivated areas. The following pesticides were most frequently quantified above the Aquatic life benchmarks for freshwater (USEPA Office of Pesticide Programs, 2023) or Brazilian Federal Regulatory Standards (CONAMA Resolution no. 357/2005): Atrazine, Imidaclopride, Carbendazim, 2,4D, Fipronil, and Chlorpiryfos. Higher median concentrations for Diuron and Tebuthiuron in the rainy months (october to march) indicated pesticide transport through surface runoff. However, measurable concentrations in the dry season (april to september) for Fipronil and Imidaclopride also indicates pathways related to subsurface or base flow discharge after pesticide soil infiltration and leaching or dry deposition following pesticide air spraying. With exception to Diuron, no temporal trends related to median concentrations of the most frequently quantified pesticides were observed. These results are important to assist policymakers in the development of strategies aiming at reducing pesticides migration to surface waters from agricultural areas. Further studies will be carried out in selected points to investigate potential risks as a result of pesticides exposure on aquatic biota.

Keywords: pesticides monitoring, são paulo state, water quality, surface waters

Procedia PDF Downloads 59

Authors: Julia Córdoba

Abstract:

People are not completely autonomous, as we live in society; therefore, people could be defined as relationally dependent. The lack, decrease or loss of physical, psychological and/or social interdependence due to a disability situation is known as dependence. This is related to the need for help from another person in order to carry out activities of daily living. This population group lives with major social limitations that significantly reduce their participation and autonomy. They have high levels of stigma and invisibility from private environments (family and close networks), as well as from the public order (environment, community). The importance of this study lies in the fact that the lack of support and adjustments leads to what authors call the circle of exclusion. This circle describes how not accessing services - due to the difficulties caused by the disability situation impacts biological, social and psychological levels. This situation produces higher levels of exclusion and vulnerability. This study will focus on the process of autonomy and dependence of adults with disability from the model of disability proposed by the International Classification of Functioning, Health and Disability (ICF). The objectives are: i) to write down the relationship between autonomy and dependence based on socio-health variables and ii) to determine the relationship between the situation of autonomy and dependence and the expectations and interests of the participants. We propose a study that will use a survey technique through a previously validated virtual questionnaire. The data obtained will be analyzed using quantitative and qualitative methods for the details of the profiles obtained. No less than 200 questionnaires will be administered to people between 18 and 64 years of age who self-identify as having some degree of dependency due to disability. For the analysis of the results, the two main variables of autonomy and dependence will be considered. Socio-demographic variables such as age, gender identity, area of residence and family composition will be used. In relation to the biological dimension of the situation, the diagnosis, if any, and the type of disability will be asked. For the description of these profiles of autonomy and dependence, the following variables will be used: self-perception, decision-making, interests, expectations and life project, care of their health condition, support and social network, and labor and educational inclusion. The relationship between the target population and the variables collected provides several guidelines that could form the basis for the analysis of other research of interest in terms of self-perception, autonomy and dependence. The areas and situations where people state that they have greater possibilities to decide and have a say will be obtained. It will identify social (networks and support, educational background), demographic (age, gender identity and residence) and health-related variables (diagnosis and type of disability, quality of care) that may have a greater relationship with situations of dependency or autonomy. It will be studied whether the level of autonomy and/or dependence has an impact on the type of expectations and interests of the people surveyed.

Keywords: life project, disability, inclusion, autonomy

Procedia PDF Downloads 67

Authors: Salwa Karboune, Amanda Waglay

Abstract:

Recent studies indicate that health-promoting functional ingredients and nutraceuticals can help support and improve the overall public health, which is timely given the aging of the population and the increasing cost of health care. The development of novel ‘natural’ functional ingredients is increasingly challenging. Biocatalysis offers powerful approaches to achieve this goal. Our recent research has been focusing on the development of innovative biocatalytic approaches towards the isolation of protein isolates from potato by-products and the generation of peptides. Potato is a vegetable whose high-quality proteins are underestimated. In addition to their high proportion in the essential amino acids, potato proteins possess angiotensin-converting enzyme-inhibitory potency, an ability to reduce plasma triglycerides associated with a reduced risk of atherosclerosis, and stimulate the release of the appetite regulating hormone CCK. Potato proteins have long been considered not economically feasible due to the low protein content (27% dry matter) found in tuber (Solanum tuberosum). However, potatoes rank the second largest protein supplying crop grown per hectare following wheat. Potato proteins include patatin (40-45 kDa), protease inhibitors (5-25 kDa), and various high MW proteins. Non-destructive techniques for the extraction of proteins from potato pulp and for the generation of peptides are needed in order to minimize functional losses and enhance quality. A promising approach for isolating the potato proteins was developed, which involves the use of multi-enzymatic systems containing selected glycosyl hydrolase enzymes that synergistically work to open the plant cell wall network. This enzymatic approach is advantageous due to: (1) the use of milder reaction conditions, (2) the high selectivity and specificity of enzymes, (3) the low cost and (4) the ability to market natural ingredients. Another major benefit to this enzymatic approach is the elimination of a costly purification step; indeed, these multi-enzymatic systems have the ability to isolate proteins, while fractionating them due to their specificity and selectivity with minimal proteolytic activities. The isolated proteins were used for the enzymatic generation of active peptides. In addition, they were applied into a reduced gluten cookie formulation as consumers are putting a high demand for easy ready to eat snack foods, with high nutritional quality and limited to no gluten incorporation. The addition of potato protein significantly improved the textural hardness of reduced gluten cookies, more comparable to wheat flour alone. The presentation will focus on our recent ‘proof-of principle’ results illustrating the feasibility and the efficiency of new biocatalytic processes for the production of innovative functional food ingredients, from potato by-products, whose potential health benefits are increasingly being recognized.

Keywords: biocatalytic approaches, functional ingredients, potato proteins, peptides

Procedia PDF Downloads 379

Authors: Nasreen Abdulmannan

Abstract:

Fetal abnormalities are categorized as a structural abnormality, non-structural abnormality, or a combination of both. Fetal structural abnormalities (FSA) include, but are not limited, to Down syndrome, congenital diaphragmatic hernia, and cleft lip and palate. These abnormalities can be detected in the first weeks of pregnancy, which is almost around 9 - 20 weeks gestational. Etiological factors for FSA are unknown; however, transmitted genetic risk can be one of these factors. Consanguineous marriage often referred to as inbreeding, represents a significant risk factor for FSA due to the increased likelihood of deleterious genetic traits shared by both biological parents. In a country such as the Kingdom of Saudi Arabia (KSA), consanguineous marriage is high, which creates a significant risk of children being born with congenital abnormalities. Historically, the practice of consanguinity occurred commonly among European royalty. For example, Great Britain’s Queen Victoria married her German first cousin, Prince Albert of Coburg. Although a distant blood relationship, the United Kingdom’s Queen Elizabeth II married her cousin, Prince Philip of Greece and Denmark—both of them direct descendants of Queen Victoria. In Middle Eastern countries, a high incidence of consanguineous unions still exists, including in the KSA. Previous studies indicated that a significant gap exists in understanding the lived experiences of Saudi women dealing with an FSA-complicated pregnancy. Eleven participants were interviewed using a semi-structured interview format for this qualitative phenomenological study investigating the lived experiences of pregnant Saudi women carrying a child with FSA. This study explored the gaps in current literature regarding the lived experiences of pregnant Saudi women whose pregnancies were complicated by FSA. In addition, the researcher acquired knowledge about the available support and resources as well as the Saudi cultural perspective on FSA. This research explored the lived experiences of pregnant Saudi women utilizing Giorgi’s (2009) approach to data collection and data management. Findings for this study cover five major themes: (1) initial maternal reaction to the FSA diagnosis per ultrasound screening; (2) strengthening of the maternal relationship with God; (3) maternal concern for their child’s future; (4) feeling supported by their loved ones; and (5) lack of healthcare provider support and guidance. Future research in the KSA is needed to explore the network support for these mothers. This study recommended further clinical nursing research, nursing education, clinical practice, and healthcare policy/procedures to provide opportunities for improvement in nursing care and increase awareness in KSA society.

Keywords: fetal structural abnormalities, psychological distress, health provider, health care

Procedia PDF Downloads 155

Authors: Yiea Funk Te, Daniel Mueller, Irena Pletikosa Cvijikj

Abstract:

Small and medium enterprises (SMEs) play an important role in the economy of many countries. When the overall world economy is considered, SMEs represent 95% of all businesses in the world, accounting for 66% of the total employment. Existing studies show that the current business environment is characterized as highly turbulent and strongly influenced by modern information and communication technologies, thus forcing SMEs to experience more severe challenges in maintaining their existence and expanding their business. To support SMEs at improving their competitiveness, researchers recently turned their focus on applying data mining techniques to build risk and growth prediction models. However, data used to assess risk and growth indicators is primarily obtained via questionnaires, which is very laborious and time-consuming, or is provided by financial institutes, thus highly sensitive to privacy issues. Recently, web mining (WM) has emerged as a new approach towards obtaining valuable insights in the business world. WM enables automatic and large scale collection and analysis of potentially valuable data from various online platforms, including companies’ websites. While WM methods have been frequently studied to anticipate growth of sales volume for e-commerce platforms, their application for assessment of SME risk and growth indicators is still scarce. Considering that a vast proportion of SMEs own a website, WM bears a great potential in revealing valuable information hidden in SME websites, which can further be used to understand SME risk and growth indicators, as well as to enhance current SME risk and growth prediction models. This study aims at developing an automated system to collect business-relevant data from the Web and predict future growth trends of SMEs by means of WM and data mining techniques. The envisioned system should serve as an 'early recognition system' for future growth opportunities. In an initial step, we examine how structured and semi-structured Web data in governmental or SME websites can be used to explain the success of SMEs. WM methods are applied to extract Web data in a form of additional input features for the growth prediction model. The data on SMEs provided by a large Swiss insurance company is used as ground truth data (i.e. growth-labeled data) to train the growth prediction model. Different machine learning classification algorithms such as the Support Vector Machine, Random Forest and Artificial Neural Network are applied and compared, with the goal to optimize the prediction performance. The results are compared to those from previous studies, in order to assess the contribution of growth indicators retrieved from the Web for increasing the predictive power of the model.

Keywords: data mining, SME growth, success factors, web mining

Procedia PDF Downloads 267

Authors: Susan Thurstans, Natalie Sessions, Carmel Dolan, Kate Sadler, Bernardette Cichon, Shelia Isanaka, Dominique Roberfroid, Heather Stobagh, Patrick Webb, Tanya Khara

Abstract:

For many years, wasting and stunting have been viewed as separate conditions without clear evidence supporting this distinction. In 2014, the Emergency Nutrition Network (ENN) examined the relationship between wasting and stunting and published a report highlighting the evidence for linkages between the two forms of undernutrition. This systematic review aimed to update the evidence generated since this 2014 report to better understand the implications for improving child nutrition, health and survival. Following PRISMA guidelines, this review was conducted using search terms to describe the relationship between wasting and stunting. Studies related to children under five from low- and middle-income countries that assessed both ponderal growth/wasting and linear growth/stunting, as well as the association between the two, were included. Risk of bias was assessed in all included studies using SIGN checklists. 45 studies met the inclusion criteria- 39 peer reviewed studies, 1 manual chapter, 3 pre-print publications and 2 published reports. The review found that there is a strong association between the two conditions whereby episodes of wasting contribute to stunting and, to a lesser extent, stunting leads to wasting. Possible interconnected physiological processes and common risk factors drive an accumulation of vulnerabilities. Peak incidence of both wasting and stunting was found to be between birth and three months. A significant proportion of children experience concurrent wasting and stunting- Country level data suggests that up to 8% of children under 5 may be both wasted and stunted at the same time, global estimates translate to around 16 million children. Children with concurrent wasting and stunting have an elevated risk of mortality when compared to children with one deficit alone. These children should therefore be considered a high-risk group in the targeting of treatment. Wasting, stunting and concurrent wasting and stunting appear to be more prevalent in boys than girls and it appears that concurrent wasting and stunting peaks between 12- 30 months of age with younger children being the most affected. Seasonal patterns in prevalence of both wasting and stunting are seen in longitudinal and cross sectional data and in particular season of birth has been shown to have an impact on a child’s subsequent experience of wasting and stunting. Evidence suggests that the use of mid-upper-arm circumference combined with weight-for-age Z-score might effectively identify children most at risk of near-term mortality, including those concurrently wasted and stunted. Wasting and stunting frequently occur in the same child, either simultaneously or at different moments through their life course. Evidence suggests there is a process of accumulation of nutritional deficits and therefore risk over the life course of a child demonstrates the need for a more integrated approach to prevention and treatment strategies to interrupt this process. To achieve this, undernutrition policies, programmes, financing and research must become more unified.

Keywords: Concurrent wasting and stunting, Review, Risk factors, Undernutrition

Procedia PDF Downloads 127

Authors: Ali Kazemi

Abstract:

Those within the volatile and interconnected international economic markets, appropriately predicting market trends, hold substantial fees for traders and financial establishments. Traditional device mastering strategies have made full-size strides in forecasting marketplace movements; however, monetary data's complicated and networked nature calls for extra sophisticated processes. This observation offers a groundbreaking method for monetary marketplace prediction that leverages the synergistic capability of Graph Convolutional Networks (GCNs) and Long Short-Term Memory (LSTM) networks. Our suggested algorithm is meticulously designed to forecast the traits of inventory market indices and cryptocurrency costs, utilizing a comprehensive dataset spanning from January 1, 2015, to December 31, 2023. This era, marked by sizable volatility and transformation in financial markets, affords a solid basis for schooling and checking out our predictive version. Our algorithm integrates diverse facts to construct a dynamic economic graph that correctly reflects market intricacies. We meticulously collect opening, closing, and high and low costs daily for key inventory marketplace indices (e.g., S&P 500, NASDAQ) and widespread cryptocurrencies (e.g., Bitcoin, Ethereum), ensuring a holistic view of marketplace traits. Daily trading volumes are also incorporated to seize marketplace pastime and liquidity, providing critical insights into the market's shopping for and selling dynamics. Furthermore, recognizing the profound influence of the monetary surroundings on financial markets, we integrate critical macroeconomic signs with hobby fees, inflation rates, GDP increase, and unemployment costs into our model. Our GCN algorithm is adept at learning the relational patterns amongst specific financial devices represented as nodes in a comprehensive market graph. Edges in this graph encapsulate the relationships based totally on co-movement styles and sentiment correlations, enabling our version to grasp the complicated community of influences governing marketplace moves. Complementing this, our LSTM algorithm is trained on sequences of the spatial-temporal illustration discovered through the GCN, enriched with historic fee and extent records. This lets the LSTM seize and expect temporal marketplace developments accurately. Inside the complete assessment of our GCN-LSTM algorithm across the inventory marketplace and cryptocurrency datasets, the version confirmed advanced predictive accuracy and profitability compared to conventional and opportunity machine learning to know benchmarks. Specifically, the model performed a Mean Absolute Error (MAE) of 0.85%, indicating high precision in predicting day-by-day charge movements. The RMSE was recorded at 1.2%, underscoring the model's effectiveness in minimizing tremendous prediction mistakes, which is vital in volatile markets. Furthermore, when assessing the model's predictive performance on directional market movements, it achieved an accuracy rate of 78%, significantly outperforming the benchmark models, averaging an accuracy of 65%. This high degree of accuracy is instrumental for techniques that predict the course of price moves. This study showcases the efficacy of mixing graph-based totally and sequential deep learning knowledge in economic marketplace prediction and highlights the fee of a comprehensive, records-pushed evaluation framework. Our findings promise to revolutionize investment techniques and hazard management practices, offering investors and economic analysts a powerful device to navigate the complexities of cutting-edge economic markets.

Keywords: financial market prediction, graph convolutional networks (GCNs), long short-term memory (LSTM), cryptocurrency forecasting

Procedia PDF Downloads 65

Authors: Nutcha Taneepanichskul, Helen C. Hailes, Mark Miodownik

Abstract:

Plastic waste has emerged as a critical global environmental challenge, primarily driven by the prevalent use of conventional plastics derived from petrochemical refining and manufacturing processes in modern packaging. While these plastics serve vital functions, their persistence in the environment post-disposal poses significant threats to ecosystems. Addressing this issue necessitates approaches, one of which involves the development of biodegradable plastics designed to degrade under controlled conditions, such as industrial composting facilities. It is imperative to note that compostable plastics are engineered for degradation within specific environments and are not suited for uncontrolled settings, including natural landscapes and aquatic ecosystems. The full benefits of compostable packaging are realized when subjected to industrial composting, preventing environmental contamination and waste stream pollution. Therefore, effective sorting technologies are essential to enhance composting rates for these materials and diminish the risk of contaminating recycling streams. In this study, it leverage hyperspectral imaging technology (HSI) coupled with advanced machine learning algorithms to accurately identify various types of plastics, encompassing conventional variants like Polyethylene terephthalate (PET), Polypropylene (PP), Low density polyethylene (LDPE), High density polyethylene (HDPE) and biodegradable alternatives such as Polybutylene adipate terephthalate (PBAT), Polylactic acid (PLA), and Polyhydroxyalkanoates (PHA). The dataset is partitioned into three subsets: a training dataset comprising uncontaminated conventional and biodegradable plastics, a validation dataset encompassing contaminated plastics of both types, and a testing dataset featuring real-world packaging items in both pristine and contaminated states. Five distinct machine learning algorithms, namely Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Logistic Regression, and Decision Tree Algorithm, were developed and evaluated for their classification performance. Remarkably, the Logistic Regression and CNN model exhibited the most promising outcomes, achieving a perfect accuracy rate of 100% for the training and validation datasets. Notably, the testing dataset yielded an accuracy exceeding 80%. The successful implementation of this sorting technology within recycling and composting facilities holds the potential to significantly elevate recycling and composting rates. As a result, the envisioned circular economy for plastics can be established, thereby offering a viable solution to mitigate plastic pollution.

Keywords: biodegradable plastics, sorting technology, hyperspectral imaging technology, machine learning algorithms

Procedia PDF Downloads 79

Authors: Kaviyarasan D., Satish Kumar S. R.

Abstract:

India being vast in area and population with great scope of international business, roadways and railways network connection within the country is expected to have a big growth. There are numerous rail-cum-road bridges constructed across many major rivers in India and few are getting very old. So there is more possibility of repairing or coming up with such new bridges in India. Analysis and design of such bridges are practiced through conventional procedure and end up with heavy and uneconomical sections. Such heavy class steel bridges when subjected to high seismic shaking has more chance to fail by stability because the members are too much rigid and stocky rather than being flexible to dissipate the energy. This work is the collective study of the researches done in the truss bridge and steel concrete composite truss bridges presenting the method of analysis, tools for numerical and analytical modeling which evaluates its seismic behaviour and collapse mechanisms. To ascertain the inelastic and nonlinear behaviour of the structure, generally at research level static pushover analysis is adopted. Though the static pushover analysis is now extensively used for the framed steel and concrete buildings to study its lateral action behaviour, those findings by pushover analysis done for the buildings cannot directly be used for the bridges as such, because the bridges have completely a different performance requirement, behaviour and typology as compared to that of the buildings. Long span steel bridges are mostly the truss bridges. Truss bridges being formed by many members and connections, the failure of the system does not happen suddenly with single event or failure of one member. Failure usually initiates from one member and progresses gradually to the next member and so on when subjected to further loading. This kind of progressive collapse of the truss bridge structure is dependent on many factors, in which the live load distribution and span to length ratio are most significant. The ultimate collapse is anyhow by the buckling of the compression members only. For regular bridges, single step pushover analysis gives results closer to that of the non-linear dynamic analysis. But for a complicated bridge like heavy class steel bridge or the skewed bridges or complicated dynamic behaviour bridges, nonlinear analysis capturing the progressive yielding and collapse pattern is mandatory. With the knowledge of the postelastic behaviour of the bridge and advancements in the computational facility, the current level of analysis and design of bridges has moved to state of ascertaining the performance levels of the bridges based on the damage caused by seismic shaking. This is because the buildings performance levels deals much with the life safety and collapse prevention levels, whereas the bridges mostly deal with the extent damages and how quick it can be repaired with or without disturbing the traffic after a strong earthquake event. The paper would compile the wide spectrum of modeling to analysis of the steel concrete composite truss bridges in general.

Keywords: bridge engineering, performance based design of steel truss bridge, seismic design of composite bridge, steel-concrete composite bridge

Procedia PDF Downloads 185

Authors: M. Marín-Ferrer, M. A. Hernández, T. Tollefsen, S. Vanzo, E. Nweke, P. V. Tognoli, M. De Cort

Abstract:

Under the terms of Article 36 of the Euratom Treaty, European Union Member States (MSs) shall periodically communicate to the European Commission (EC) information on environmental radioactivity levels. Compilations of the information received have been published by the EC as a series of reports beginning in the early 1960s. The environmental radioactivity results received from the MSs have been introduced into the Radioactivity Environmental Monitoring database (REMdb) of the Institute for Transuranium Elements of the EC Joint Research Centre (JRC) sited in Ispra (Italy) as part of its Directorate General for Energy (DG ENER) support programme. The REMdb brings to the scientific community dealing with environmental radioactivity topics endless of research opportunities to exploit the near 200 millions of records received from MSs containing information of radioactivity levels in milk, water, air and mixed diet. The REM action was created shortly after Chernobyl crisis to support the EC in its responsibilities in providing qualified information to the European Parliament and the MSs on the levels of radioactive contamination of the various compartments of the environment (air, water, soil). Hence, the main line of REM’s activities concerns the improvement of procedures for the collection of environmental radioactivity concentrations for routine and emergency conditions, as well as making this information available to the general public. In this way, REM ensures the availability of tools for the inter-communication and access of users from the Member States and the other European countries to this information. Specific attention is given to further integrate the new MSs with the existing information exchange systems and to assist Candidate Countries in fulfilling these obligations in view of their membership of the EU. Article 36 of the EURATOM treaty requires the competent authorities of each MS to provide regularly the environmental radioactivity monitoring data resulting from their Article 35 obligations to the EC in order to keep EC informed on the levels of radioactivity in the environment (air, water, milk and mixed diet) which could affect population. The REMdb has mainly two objectives: to keep a historical record of the radiological accidents for further scientific study, and to collect the environmental radioactivity data gathered through the national environmental monitoring programs of the MSs to prepare the comprehensive annual monitoring reports (MR). The JRC continues his activity of collecting, assembling, analyzing and providing this information to public and MSs even during emergency situations. In addition, there is a growing concern with the general public about the radioactivity levels in the terrestrial and marine environment, as well about the potential risk of future nuclear accidents. To this context, a clear and transparent communication with the public is needed. EURDEP (European Radiological Data Exchange Platform) is both a standard format for radiological data and a network for the exchange of automatic monitoring data. The latest release of the format is version 2.0, which is in use since the beginning of 2002.

Keywords: environmental radioactivity, Euratom, monitoring report, REMdb

Procedia PDF Downloads 443

Authors: Stefan Fuchs

Abstract:

Against the backdrop of the proliferation of shopping malls in Japan during the last two decades, this paper presents the results of an ethnography conducted at a recently built suburban shopping mall in Western Tokyo. Through the analysis of the lived experiences of local residents, mall customers and the mall management this paper evaluates the benefits and disadvantages of the Business Improvement District (BID) model, which was implemented as urban redevelopment strategy in the area surrounding the shopping mall. The results of this research project show that while the BID model has in some respects contributed to the economic prosperity and to the perceived convenience of the area, it has led to gentrification and the redevelopment shows some deficiencies with regard to the inclusion of the elderly population as well as to the democratization of the decision-making process within the area. In Japan, shopping malls have been steadily growing both in size and number since a series of deregulation policies was introduced in the year 2000 in an attempt to push the domestic economy and to rejuvenate urban landscapes. Shopping malls have thereby become defining spaces of the built environment and are arguably important places of social interaction. Notwithstanding the vital role they play as factors of urban transformation, they have been somewhat overlooked in the research on Japan; especially with respect to their meaning for people’s everyday lives. By examining the ways, people make use of space in a shopping mall the research project presented in this paper addresses this gap in the research. Moreover, the research site of this research project is one of the few BIDs of Japan and the results presented in this paper can give indication on the scope of the future applicability of this urban redevelopment model. The data presented in this research was collected during a nine-months ethnographic fieldwork in and around the shopping mall. This ethnography includes semi-structured interviews with ten key informants as well as direct and participant observations examining the lived experiences and perceptions of people living, shopping or working at the shopping mall. The analysis of the collected data focused on recurring themes aiming at ultimately capturing different perspectives on the same aspects. In this manner, the research project documents the social agency of different groups within one communal network. The analysis of the perceptions towards the urban redevelopment around the shopping mall has shown that mainly the mall customers and large businesses benefit from the BID redevelopment model. While local residents benefit to some extent from their neighbourhood becoming more convenient for shopping they perceive themselves as being disadvantaged by changing demographics due to rising living expenses, the general noise level and the prioritisation of a certain customer segment or age group at the shopping mall. Although the shopping mall examined in this research project is just an example, the findings suggest that in future urban redevelopment politics have to provide incentives for landowners and developing companies to think of other ways of transforming underdeveloped areas.

Keywords: business improvement district, ethnography, shopping mall, urban redevelopment

Procedia PDF Downloads 136

Authors: Muhammad Naveed Tahir, Wang Yingkuan, Huang Wenjiang, Raheel Osman

Abstract:

Numerous models used in prediction and decision-making process but most of them are linear in natural environment, and linear models reach their limitations with non-linearity in data. Therefore accurate estimation is difficult. Artificial Neural Networks (ANN) found extensive acceptance to address the modeling of the complex real world for the non-linear environment. ANN’s have more general and flexible functional forms than traditional statistical methods can effectively deal with. The link between information technology and agriculture will become more firm in the near future. Monitoring crop biophysical properties non-destructively can provide a rapid and accurate understanding of its response to various environmental influences. Crop chlorophyll content is an important indicator of crop health and therefore the estimation of crop yield. In recent years, remote sensing has been accepted as a robust tool for site-specific management by detecting crop parameters at both local and large scales. The present research combined the ANN model with satellite-derived chlorophyll indices from LANDSAT 8 imagery for predicting real-time wheat chlorophyll estimation. The cloud-free scenes of LANDSAT 8 were acquired (Feb-March 2016-17) at the same time when ground-truthing campaign was performed for chlorophyll estimation by using SPAD-502. Different vegetation indices were derived from LANDSAT 8 imagery using ERADAS Imagine (v.2014) software for chlorophyll determination. The vegetation indices were including Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Chlorophyll Absorbed Ratio Index (CARI), Modified Chlorophyll Absorbed Ratio Index (MCARI) and Transformed Chlorophyll Absorbed Ratio index (TCARI). For ANN modeling, MATLAB and SPSS (ANN) tools were used. Multilayer Perceptron (MLP) in MATLAB provided very satisfactory results. For training purpose of MLP 61.7% of the data, for validation purpose 28.3% of data and rest 10% of data were used to evaluate and validate the ANN model results. For error evaluation, sum of squares error and relative error were used. ANN model summery showed that sum of squares error of 10.786, the average overall relative error was .099. The MCARI and NDVI were revealed to be more sensitive indices for assessing wheat chlorophyll content with the highest coefficient of determination R²=0.93 and 0.90 respectively. The results suggested that use of high spatial resolution satellite imagery for the retrieval of crop chlorophyll content by using ANN model provides accurate, reliable assessment of crop health status at a larger scale which can help in managing crop nutrition requirement in real time.

Keywords: ANN, chlorophyll content, chlorophyll indices, satellite images, wheat

Procedia PDF Downloads 146

Authors: Reza Nematirad, Anil Pahwa, Balasubramaniam Natarajan

Abstract:

Time series data consist of successive data points collected over a period of time. Accurate prediction of future values is essential for informed decision-making in several real-world applications, including electricity load demand forecasting, lifetime estimation of industrial machinery, traffic planning, weather prediction, and the stock market. Due to their critical relevance and wide application, there has been considerable interest in time series forecasting in recent years. However, the proliferation of sensors and IoT devices, real-time monitoring systems, and high-frequency trading data introduce significant intricate temporal variations, rapid changes, noise, and non-linearities, making time series forecasting more challenging. Classical methods such as Autoregressive integrated moving average (ARIMA) and Exponential Smoothing aim to extract pre-defined temporal variations, such as trends and seasonality. While these methods are effective for capturing well-defined seasonal patterns and trends, they often struggle with more complex, non-linear patterns present in real-world time series data. In recent years, deep learning has made significant contributions to time series forecasting. Recurrent Neural Networks (RNNs) and their variants, such as Long short-term memory (LSTMs) and Gated Recurrent Units (GRUs), have been widely adopted for modeling sequential data. However, they often suffer from the locality, making it difficult to capture local trends and rapid fluctuations. Convolutional Neural Networks (CNNs), particularly Temporal Convolutional Networks (TCNs), leverage convolutional layers to capture temporal dependencies by applying convolutional filters along the temporal dimension. Despite their advantages, TCNs struggle with capturing relationships between distant time points due to the locality of one-dimensional convolution kernels. Transformers have revolutionized time series forecasting with their powerful attention mechanisms, effectively capturing long-term dependencies and relationships between distant time points. However, the attention mechanism may struggle to discern dependencies directly from scattered time points due to intricate temporal patterns. Lastly, Multi-Layer Perceptrons (MLPs) have also been employed, with models like N-BEATS and LightTS demonstrating success. Despite this, MLPs often face high volatility and computational complexity challenges in long-horizon forecasting. To address intricate temporal variations in time series data, this study introduces Times2D, a novel framework that parallelly integrates 2D spectrogram and derivative heatmap techniques. The spectrogram focuses on the frequency domain, capturing periodicity, while the derivative patterns emphasize the time domain, highlighting sharp fluctuations and turning points. This 2D transformation enables the utilization of powerful computer vision techniques to capture various intricate temporal variations. To evaluate the performance of Times2D, extensive experiments were conducted on standard time series datasets and compared with various state-of-the-art algorithms, including DLinear (2023), TimesNet (2023), Non-stationary Transformer (2022), PatchTST (2023), N-HiTS (2023), Crossformer (2023), MICN (2023), LightTS (2022), FEDformer (2022), FiLM (2022), SCINet (2022a), Autoformer (2021), and Informer (2021) under the same modeling conditions. The initial results demonstrated that Times2D achieves consistent state-of-the-art performance in both short-term and long-term forecasting tasks. Furthermore, the generality of the Times2D framework allows it to be applied to various tasks such as time series imputation, clustering, classification, and anomaly detection, offering potential benefits in any domain that involves sequential data analysis.

Keywords: derivative patterns, spectrogram, time series forecasting, times2D, 2D representation

Procedia PDF Downloads 42

Authors: Evangelos Kaisar, Anika Tabassum, Taraneh Ardalan, Majed Al-Ghandour

Abstract:

The economy of any country is dependent on its ability to accommodate the movement and delivery of goods. The demand for goods movement and services increases truck traffic on highways and inside the cities. The livability of most cities is directly affected by the congestion and environmental impacts of trucks, which are the backbone of the urban freight system. Better operation of heavy vehicles on highways and arterials could lead to the network’s efficiency and reliability. In many cases, roundabouts can respond better than at-level intersections to enable traffic operations with increased safety for both cars and heavy vehicles. Recently emerged, the concept of turbo-roundabout is a viable alternative to the two-lane roundabout aiming to improve traffic efficiency. The primary objective of this study is to evaluate the operation and performance level of an at-grade intersection, a conventional two-lane roundabout, and a basic turbo roundabout for freight movements. To analyze and evaluate the performances of the signalized intersections and the roundabouts, micro simulation models were developed PTV VISSIM. The networks chosen for this analysis in this study are to experiment and evaluate changes in the performance of the movement of vehicles with different geometric and flow scenarios. There are several scenarios that were examined when attempting to assess the impacts of various geometric designs on vehicle movements. The overall traffic efficiency depends on the geometric layout of the intersections, which consists of traffic congestion rate, hourly volume, frequency of heavy vehicles, type of road, and the ratio of major-street versus side-street traffic. The traffic performance was determined by evaluating the delay time, number of stops, and queue length of each intersection for varying truck percentages. The results indicate that turbo-roundabouts can replace signalized intersections and two-lane roundabouts only when the traffic demand is low, even with high truck volume. More specifically, it is clear that two-lane roundabouts are seen to have shorter queue lengths compared to signalized intersections and turbo-roundabouts. For instance, considering the scenario where the volume is highest, and the truck movement and left turn movement are maximum, the signalized intersection has 3 times, and the turbo-roundabout has 5 times longer queue length than a two-lane roundabout in major roads. Similarly, on minor roads, signalized intersections and turbo-roundabouts have 11 times longer queue lengths than two-lane roundabouts for the same scenario. As explained from all the developed scenarios, while the traffic demand lowers, the queue lengths of turbo-roundabouts shorten. This proves that turbo roundabouts perform well for low and medium traffic demand. The results indicate that turbo-roundabouts can replace signalized intersections and two-lane roundabouts only when the traffic demand is low, even with high truck volume. Finally, this study provides recommendations on the conditions under which different intersections perform better than each other.

Keywords: At-grade intersection, simulation, turbo-roundabout, two-lane roundabout

Procedia PDF Downloads 149