Search results for: production-living-ecology spatial network

Authors: Ayat N. El-Shazly, Dina Magdy Abdo, Hamdy Maamoun Abdel-Ghafar, Xiangju Song, Heqing Jiang

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Product water recovery and draw solution (DS) reuse is the most energy-intensive stage in forwarding osmosis (FO) technology. Sucrose solution is the most suitable DS for FO application in food and beverages. However, sucrose DS recovery by conventional pressure-driven or thermal-driven concentration techniques consumes high energy. Herein, we developed a spontaneous and sustainable solar-driven evaporation process based on a photothermal membrane for the concentration and recovery of sucrose solution. The photothermal membrane is composed of multi-walled carbon nanotubes (f-MWCNTs)photothermal layer on a hydrophilic polyvinylidene fluoride (PVDF) substrate. The f-MWCNTs photothermal layer with a rough surface and interconnected network structures not only improves the light-harvesting and light-to-heat conversion performance but also facilitates the transport of water molecules. The hydrophilic PVDF substrate can promote the rapid transport of water for adequate water supply to the photothermal layer. As a result, the optimized f-MWCNTs/PVDF photothermal membrane exhibits an excellent light absorption of 95%, and a high surface temperature of 74 °C at 1 kW m−2 . Besides, it realizes an evaporation rate of 1.17 kg m−2 h−1 for 5% (w/v) of sucrose solution, which is about 5 times higher than that of the natural evaporation. The designed photothermal evaporation process is capable of concentrating sucrose solution efficiently from 5% to 75% (w/v), which has great potential in FO process and juice concentration.

Keywords: solar, pothothermal, membrane, MWCNT

Procedia PDF Downloads 98

Authors: Brandon Ko

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Whether it’s a kitchen staff or a law firm, many groups tend to have homogenous characteristics of race, gender, interests, and goals. Social groups are not typically random samples of the population and will usually have common identifiers. According to Blau, age, sex, and education all play salient roles in shaping relationships within members of society. So if there is some degree of homogeneity within groups, the question arises whether this is beneficial or harmful to a group’s effectiveness. There has been much disagreement in the scientific community as to whether the presence of homophily benefits or hinders an athletic team's cohesiveness. For this paper, a comparative study of research of soccer case studies that followed various, youth players was studied against examinations of the effects that such a culture has on athletes. The case studies were used as evidence to determine what kind of homophily existed within the soccer camps. One case study followed several European developmental clubs such as Bayern Munich and Barcelona. Another study followed eight different players, four of each gender, implementing a similar method of interviewing, observing, and questioning. The individual and team goals of each athlete were reviewed to see which teams and players were ego-oriented and which were team-oriented. Additionally, there had been little research done on the relationship between homophily and how it applies to the sport community, suggesting the need to develop this neglected problem in applied psychology. This paper argues that the benefits of an egalitarian culture and stronger relations with people of a similar socio-demographic outweigh the liabilities of cohesion like being stereotyped and a lack of network outside the group as produced by homophily in athletic competition.

Keywords: group cohesion, homophily, sports psychology, youth athletics

Procedia PDF Downloads 283

Authors: Deepa S N, Srinivasan a D, Veeramanju K T

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The power transformer is a critical equipment in the transmission and distribution network that must be managed to ensure uninterrupted power service. The liquid insulation is essential for the proper functioning of the transformer, as it serves as both coolant and insulating medium, which influences the transformer’s durability. Further, the insulating state of a power transformer has a significant impact on its reliability. Mineral oil derived from petroleum crude oil has been employed as liquid dielectrics for decades due to its superior functional characteristics, however as a resource for the same are getting depleted over the years. Research is undertaken across the globe to identify a viable substitute for mineral oil. Further, alternate insulating oils are being investigated for better environmental impact, biodegradability and economics. Several combinations of vegetable oil derived natural esters are being inspected by researchers across the globe in these domains. In this work, mineral oil is blended with soyabean oil with various proportions and dielectric properties such as dielectric breakdown voltage, relative permittivity, dissipation factor, viscosity, flash and fire point have been investigated according to international standards. A quantitative comparison is made among various samples and is observed that the blended oil sample of equal proportion of mineral oil and soyabean oil, MO50+SO50 exhibits superior dielectric properties such as breakdown voltage of 65kV, dissipation factor of 0.0044, relative permittivity of 3.1680 that are closer to the range of values recommended for power transformer applications. Also, Breakdown voltage values of all the investigated oil samples obeyed the Weibull and Normal probability distribution.

Keywords: blended oil, dielectric breakdown, liquid insulation, power transformer

Procedia PDF Downloads 87

Authors: Richa Grover, Rahul Grover, V. Balaji Rao, Kavish Kejriwal

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Selection of suppliers is a crucial problem in the supply chain management. On top of that, sustainable supplier selection is the biggest challenge for the organizations. Environment protection and social problems have been of concern to society in recent years, and the traditional supplier selection does not consider about this factor; therefore, this research work focuses on introducing sustainable criteria into the structure of supplier selection criteria. Sustainable Supply Chain Management (SSCM) is the management and administration of material, information, and money flows, as well as coordination among business along the supply chain. All three dimensions - economic, environmental, and social - of sustainable development needs to be taken care of. Purpose of this research is to maximize supply chain profitability, maximize social wellbeing of supply chain and minimize environmental impacts. Problem statement is selection of suppliers in a sustainable supply chain network by ranking the suppliers against sustainable criteria identified. The aim of this research is twofold: To find out what are the sustainable parameters that can be applied to the supply chain, and to determine how these parameters can effectively be used in supplier selection. Multicriteria decision making tools will be used to rank both criteria and suppliers. AHP Analysis will be used to find out ratings for the criteria identified. It is a technique used for efficient decision making. TOPSIS will be used to find out rating for suppliers and then ranking them. TOPSIS is a MCDM problem solving method which is based on the principle that the chosen option should have the maximum distance from the negative ideal solution (NIS) and the minimum distance from the ideal solution.

Keywords: sustainable supply chain management, sustainable criteria, MCDM tools, AHP analysis, TOPSIS method

Procedia PDF Downloads 323

Authors: Jean Berger, Nassirou Lo, Martin Noel

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Perfectly suited for natural or man-made emergency and disaster management situations such as flood, earthquakes, tornadoes, or tsunami, multi-target search path planning for a team of rescue agents is known to be computationally hard, and most techniques developed so far come short to successfully estimate optimality gap. A novel mixed-integer linear programming (MIP) formulation is proposed to optimally solve the multi-target multi-agent discrete search and rescue (SAR) path planning problem. Aimed at maximizing cumulative probability of successful target detection, it captures anticipated feedback information associated with possible observation outcomes resulting from projected path execution, while modeling agent discrete actions over all possible moving directions. Problem modeling further takes advantage of network representation to encompass decision variables, expedite compact constraint specification, and lead to substantial problem-solving speed-up. The proposed MIP approach uses CPLEX optimization machinery, efficiently computing near-optimal solutions for practical size problems, while giving a robust upper bound obtained from Lagrangean integrality constraint relaxation. Should eventually a target be positively detected during plan execution, a new problem instance would simply be reformulated from the current state, and then solved over the next decision cycle. A computational experiment shows the feasibility and the value of the proposed approach.

Keywords: search path planning, search and rescue, multi-agent, mixed-integer linear programming, optimization

Procedia PDF Downloads 370

Authors: Tannia Vindel, Carlos Mejia, Damaris Araujo, Carlos Velasquez, Darlin Trejo

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The DWDM (Dense Wavelenght Division Multiplexing) is in constant growth around the world by consumer demand to meet their needs. Since its inception in this operation arises the need for a system which enable us to expand the communication of an entire nation to improve the computing trends of their societies according to their customs and geographical location. The Honduran Company of Telecommunications (HONDUTEL), provides the internet services and data transport technology with a PDH and SDH, which represents in the Republic of Honduras C. A., the option of viability for the consumer in terms of purchase value and its ease of acquisition; but does not have the efficiency in terms of technological advance and represents an obstacle that limits the long-term socio-economic development in comparison with other countries in the region and to be able to establish a competition between telecommunications companies that are engaged in this heading. For that reason we propose to establish a new technological trend implemented in Europe and that is applied in our country that allows us to provide a data transfer in broadband as it is DWDM, in this way we will have a stable service and quality that will allow us to compete in this globalized world, and that must be replaced by one that would provide a better service and which must be in the forefront. Once implemented the DWDM is build upon the existing resources, such as the equipment used, and you will be given life to a new stage providing a business image to the Republic of Honduras C,A, as a nation, to ensure the data transport and broadband internet to a meaningful relationship. Same benefits in the first instance to existing customers and to all the institutions were bidden to these public and private need of such services.

Keywords: demultiplexers, light detectors, multiplexers, optical amplifiers, optical fibers, PDH, SDH

Procedia PDF Downloads 262

Authors: Heribert R. Kaijage, Samuel N. A. Codjoe, Simon H. D. Mamuya, Mangi J. Ezekiel

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There is strong evidence that climate has changed significantly affecting various sectors including public health. The recommended feasible solution is adopting development trajectories which combine both mitigation and adaptation measures for improving resilience pathways. This approach demands a consideration for complex interactions between climate and social-ecological systems. While other sectors such as agriculture and water have developed climate resilience indices, the public health sector in Tanzania is still lagging behind. The aim of this study was to find out how can we use Demographic and Health Surveys (DHS) linked with Remote Sensing (RS) technology and metrological information as tools to inform climate change resilient development and evaluation for the health sector. Methodological review was conducted whereby a number of studies were content analyzed to find appropriate indicators and indices for climate resilience household and their integration approach. These indicators were critically reviewed, listed, filtered and their sources determined. Preliminary identification and ranking of indicators were conducted using participatory approach of pairwise weighting by selected national stakeholders from meeting/conferences on human health and climate change sciences in Tanzania. DHS datasets were retrieved from Measure Evaluation project, processed and critically analyzed for possible climate change indicators. Other sources for indicators of climate change exposure were also identified. For the purpose of preliminary reporting, operationalization of selected indicators was discussed to produce methodological approach to be used in resilience comparative analysis study. It was found that household climate resilient index depends on the combination of three indices namely Household Adaptive and Mitigation Capacity (HC), Household Health Sensitivity (HHS) and Household Exposure Status (HES). It was also found that, DHS alone cannot complement resilient evaluation unless integrated with other data sources notably flooding data as a measure of vulnerability, remote sensing image of Normalized Vegetation Index (NDVI) and Metrological data (deviation from rainfall pattern). It can be concluded that if these indices retrieved from DHS data sets are computed and scientifically integrated can produce single climate resilience index and resilience maps could be generated at different spatial and time scales to enhance targeted interventions for climate resilient development and evaluations. However, further studies are need to test for the sensitivity of index in resilience comparative analysis among selected regions.

Keywords: climate change, resilience, remote sensing, demographic and health surveys

Procedia PDF Downloads 164

Authors: Salma El Hajjami, Jamal Malki, Alain Bouju, Mohammed Berrada

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With the expansion of machine learning and data mining in the context of Big Data analytics, the common problem that affects data is class imbalance. It refers to an imbalanced distribution of instances belonging to each class. This problem is present in many real world applications such as fraud detection, network intrusion detection, medical diagnostics, etc. In these cases, data instances labeled negatively are significantly more numerous than the instances labeled positively. When this difference is too large, the learning system may face difficulty when tackling this problem, since it is initially designed to work in relatively balanced class distribution scenarios. Another important problem, which usually accompanies these imbalanced data, is the overlapping instances between the two classes. It is commonly referred to as noise or overlapping data. In this article, we propose an approach called: One Side Behavioral Noise Reduction (OSBNR). This approach presents a way to deal with the problem of class imbalance in the presence of a high noise level. OSBNR is based on two steps. Firstly, a cluster analysis is applied to groups similar instances from the minority class into several behavior clusters. Secondly, we select and eliminate the instances of the majority class, considered as behavioral noise, which overlap with behavior clusters of the minority class. The results of experiments carried out on a representative public dataset confirm that the proposed approach is efficient for the treatment of class imbalances in the presence of noise.

Keywords: machine learning, imbalanced data, data mining, big data

Procedia PDF Downloads 130

Authors: Zeinab Haji Abolhasani, Romeo Marian, Lee Luong

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This paper and its companions (Part II, Part III) will concentrate on optimizing a class of supply chain problems known as Multi- Commodities Consumer Supply Chain (MCCSC) problem. MCCSC problem belongs to production-distribution (P-D) planning category. It aims to determine facilities location, consumers’ allocation, and facilities configuration to minimize total cost (CT) of the entire network. These facilities can be manufacturer units (MUs), distribution centres (DCs), and retailers/end-users (REs) but not limited to them. To address this problem, three major tasks should be undertaken. At the first place, a mixed integer non-linear programming (MINP) mathematical model is developed. Then, system’s behaviors under different conditions will be observed using a simulation modeling tool. Finally, the most optimum solution (minimum CT) of the system will be obtained using a multi-objective optimization technique. Due to the large size of the problem, and the uncertainties in finding the most optimum solution, integration of modeling and simulation methodologies is proposed followed by developing new approach known as GASG. It is a genetic algorithm on the basis of granular simulation which is the subject of the methodology of this research. In part II, MCCSC is simulated using discrete-event simulation (DES) device within an integrated environment of SimEvents and Simulink of MATLAB® software package followed by a comprehensive case study to examine the given strategy. Also, the effect of genetic operators on the obtained optimal/near optimal solution by the simulation model will be discussed in part III.

Keywords: supply chain, genetic algorithm, optimization, simulation, discrete event system

Procedia PDF Downloads 315

Authors: Redouane Krini, Lutz Nuhn, Hicham El Mard Cheol Woo Ha, Yoondeok Han, Kwang-Sup Lee, Dong-Yol Yang, Jinsoo Joo, Rudolf Zentel

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To use Quantum Dots (QDs) in the two photon initiated polymerization technique (TPIP) for 3D patternings, QDs were modified on the surface with photosensitive end groups which are able to undergo a photopolymerization. We were able to fabricate fluorescent 3D lattice structures using photopatternable QDs by TPIP for photonic devices such as photonic crystals and metamaterials. The QDs in different diameter have different emission colors and through mixing of RGB QDs white light fluorescent from the polymeric structures has been created. Metamaterials are capable for unique interaction with the electrical and magnetic components of the electromagnetic radiation and for manipulating light it is crucial to have a negative refractive index. In combination with QDs via TPIP technique polymeric structures can be designed with properties which cannot be found in nature. This makes these artificial materials gaining a huge importance for real-life applications in photonic and optoelectronic. Understanding of interactions between nanoparticles and biological systems is of a huge interest in the biomedical research field. We developed a synthetic strategy of polymer functionalized nanoparticles for biomedical studies to obtain hybrid systems of QDs and copolymers with a strong binding network in an inner shell and which can be modified in the end through their poly(ethylene glycol) functionalized outer shell. These hybrid systems can be used as models for investigation of cell penetration and drug delivery by using measurements combination between CryoTEM and fluorescence studies.

Keywords: biomedical study models, lithography, photo induced polymerization, quantum dots

Procedia PDF Downloads 524

Authors: M. Magrini, D. Moroni, G. Palazzese, G. Pieri, D. Azzarelli, A. Spada, L. Fanucci, O. Salvetti

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Railway crossings are complex entities whose optimal management cannot be addressed unless with the help of an intelligent transportation system integrating information both on train and vehicular flows. In this paper, we propose an integrated system named SIMPLE (Railway Safety and Infrastructure for Mobility applied at level crossings) that, while providing unparalleled safety in railway level crossings, collects data on rail and road traffic and provides value-added services to citizens and commuters. Such services include for example alerts, via variable message signs to drivers and suggestions for alternative routes, towards a more sustainable, eco-friendly and efficient urban mobility. To achieve these goals, SIMPLE is organized as a System of Systems (SoS), with a modular architecture whose components range from specially-designed radar sensors for obstacle detection to smart ETSI M2M-compliant camera networks for urban traffic monitoring. Computational unit for performing forecast according to adaptive models of train and vehicular traffic are also included. The proposed system has been tested and validated during an extensive trial held in the mid-sized Italian town of Montecatini, a paradigmatic case where the rail network is inextricably linked with the fabric of the city. Results of the tests are reported and discussed.

Keywords: Intelligent Transportation Systems (ITS), railway, railroad crossing, smart camera networks, radar obstacle detection, real-time traffic optimization, IoT, ETSI M2M, transport safety

Procedia PDF Downloads 496

Authors: Ajay Kumar Singh, Arvind Kumar, S. P. Singh

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Cellulose nanofibrils (CNF) have shown potential of their extensive use in various fields, including papermaking, due to their unique characteristics. In this study, CNF’s were prepared by fibrillating the pulp obtained from raw materials e.g. bagasse, hardwood and softwood using enzymatic pretreatment followed by mechanical refining. These nanofibrils, when examined under FE-SEM, show that partial fibrillation on fiber surface has resulted in production of nanofibers. Mixing these nanofibers with the unrefined and normally refined fibers show their reinforcing effect. This effect is manifested in observing the improvement in the physical and mechanical properties e.g. tensile index and burst index of paper. Tear index, however, was observed to decrease on blending with nanofibers. The optical properties of paper sheets made from blended fibers showed no significant change in comparison to those made from only mechanically refined pulp. Mixing of normal pulp fibers with nanofibers show increase in ºSR and consequent decrease in drainage rate. These changes observed in mechanical, optical and other physical properties of the paper sheets made from nanofibrils blended pulp have been tried to explain considering the distribution of the nanofibrils alongside microfibrils in the fibrous network. Since usually, paper/boards with higher strength are observed to have diminished optical properties which is a drawback in their quality, the present work has the potential for developing paper/boards having improved strength alongwith undiminished optical properties utilising the concepts of nanoscience and nanotechnology.

Keywords: enzymatic pretreatment, mechanical refining, nanofibrils, paper properties

Procedia PDF Downloads 352

Authors: Kamaran Fathulla

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Human creativity knows no bounds. More than a millennia ago humans have expressed their knowledge on cave walls and on clay artefacts. Using visuals such as diagrams and paintings have always provided us with a natural and intuitive medium for expressing such creativity. Making sense of human generated visualisation has been influenced by western scientific philosophies which are often reductionist in their nature. Theoretical frameworks such as those delivered by Peirce dominated our views of how to make sense of visualisation where a visual is seen as an emergent property of our thoughts. Others have reduced the richness of human-generated visuals to mere shapes drawn on a piece of paper or on a screen. This paper introduces an alternate framework where the centrality of human functioning is given explicit and richer consideration through the multi aspectual philosophical works of Herman Dooyeweerd. Dooyeweerd's framework of understanding reality was based on fifteen aspects of reality, each having a distinct core meaning. The totality of the aspects formed a ‘rainbow’ like spectrum of meaning. The thesis of this approach is that meaningful human functioning in most cases involves the diversity of all aspects working in synergy and harmony. Illustration of the foundations and applicability of this approach is underpinned in the case of humans use of diagramming for creative purposes, particularly within an educational context. Diagrams play an important role in education. Students and lecturers use diagrams as a powerful tool to aid their thinking. However, research into the role of diagrams used in education continues to reveal difficulties students encounter during both processes of interpretation and construction of diagrams. Their main problems shape up students difficulties with diagrams. The ever-increasing diversity of diagrams' types coupled with the fact that most real-world diagrams often contain a mix of these different types of diagrams such as boxes and lines, bar charts, surfaces, routes, shapes dotted around the drawing area, and so on with each type having its own distinct set of static and dynamic semantics. We argue that the persistence of these problems is grounded in our existing ways of understanding diagrams that are often reductionist in their underpinnings driven by a single perspective or formalism. In this paper, we demonstrate the limitations of these approaches in dealing with the three problems. Consequently, we propose, discuss, and demonstrate the potential of a nonreductionist framework for understanding diagrams based on Symbolic and Spatial Mappings (SySpM) underpinned by Dooyeweerd philosophy. The potential of the framework to account for the meaning of diagrams is demonstrated by applying it to a real-world case study physics diagram.

Keywords: SySpM, drawing style, mapping

Procedia PDF Downloads 236

Authors: Katerina Koliou, Scott Parr, Evangelos Kaisar

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The surface transportation of freight is particularly vulnerable to storm and hurricane disasters, while at the same time, it is the primary transportation mode for delivering medical supplies, fuel, water, and other essential goods. To better plan for commercial vehicles during an evacuation, it is necessary to understand how these vehicles travel during an evacuation and determine if this travel is different from the general public. The research investigation used Florida's statewide continuous-count station traffic volumes, where then compared between years, to identify locations where traffic was moving differently during the evacuation. The data was then used to identify days on which traffic was significantly different between years. While the literature on auto-based evacuations is extensive, the consideration of freight travel is lacking. To better plan for commercial vehicles during an evacuation, it is necessary to understand how these vehicles travel during an evacuation and determine if this travel is different from the general public. The goal of this research was to investigate the movement of vehicles by classification, with an emphasis on freight during two major evacuation events: hurricanes Irma (2017) and Michael (2018). The methodology of the research was divided into three phases: data collection and management, spatial analysis, and temporal comparisons. Data collection and management obtained continuous-co station data from the state of Florida for both 2017 and 2018 by vehicle classification. The data was then processed into a manageable format. The second phase used geographic information systems (GIS) to display where and when traffic varied across the state. The third and final phase was a quantitative investigation into which vehicle classifications were statistically different and on which dates statewide. This phase used a two-sample, two-tailed t-test to compare sensor volume by classification on similar days between years. Overall, increases in freight movement between years prevented a more precise paired analysis. This research sought to identify where and when different classes of vehicles were traveling leading up to hurricane landfall and post-storm reentry. Of the more significant findings, the research results showed that commercial-use vehicles may have underutilized rest areas during the evacuation, or perhaps these rest areas were closed. This may suggest that truckers are driving longer distances and possibly longer hours before hurricanes. Another significant finding of this research was that changes in traffic patterns for commercial-use vehicles occurred earlier and lasted longer than changes for personal-use vehicles. This finding suggests that commercial vehicles are perhaps evacuating in a fashion different from personal use vehicles. This paper may serve as the foundation for future research into commercial travel during evacuations and explore additional factors that may influence freight movements during evacuations.

Keywords: evacuation, freight, travel time, evacuation

Procedia PDF Downloads 66

Authors: Aleh Meshyk, Anastasiya Vouchak

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On the average snow covers Belarus for 75 days in the south-west and 125 days in the north-east. During the cold season snowpack often destroys due to thaws, especially at the beginning and end of winter. Over 50% of thawing days have a positive mean daily temperature, which results in complete snow melting. For instance, in December 10% of thaws occur at 4 С mean daily temperature. Stable snowpack lying for over a month forms in the north-east in the first decade of December but in the south-west in the third decade of December. The cover disappears in March: in the north-east in the last decade but in the south-west in the first decade. This research takes into account that precipitation falling during a cold season could be not only liquid and solid but also a mixed type (about 10-15 % a year). Another important feature of snow cover is its density. In Belarus, the density of freshly fallen snow ranges from 0.08-0.12 g/cm³ in the north-east to 0.12-0.17 g/cm³ in the south-west. Over time, snow settles under its weight and after melting and refreezing. Averaged annual density of snow at the end of January is 0.23-0.28 g/сm³, in February – 0.25-0.30 g/сm³, in March – 0.29-0.36 g/сm³. Sometimes it can be over 0.50 g/сm³ if the snow melts too fast. The density of melting snow saturated with water can reach 0.80 g/сm³. Average maximum of snow depth is 15-33 cm: minimum is in Brest, maximum is in Lyntupy. Maximum registered snow depth ranges within 40-72 cm. The water content in snowpack, as well as its depth and density, reaches its maximum in the second half of February – beginning of March. Spatial distribution of the amount of liquid in snow corresponds to the trend described above, i.e. it increases in the direction from south-west to north-east and on the highlands. Average annual value of maximum water content in snow ranges from 35 mm in the south-west to 80-100 mm in the north-east. The water content in snow is over 80 mm on the central Belarusian highland. In certain years it exceeds 2-3 times the average annual values. Moderate water content in snow (80-95 mm) is characteristic of western highlands. Maximum water content in snow varies over the country from 107 mm (Brest) to 207 mm (Novogrudok). Maximum water content in snow varies significantly in time (in years), which is confirmed by high variation coefficient (Cv). Maximums (0.62-0.69) are in the south and south-west of Belarus. Minimums (0.42-0.46) are in central and north-eastern Belarus where snow cover is more stable. Since 1987 most gauge stations in Belarus have observed a trend to a decrease in water content in snow. It is confirmed by the research. The biggest snow cover forms on the highlands in central and north-eastern Belarus. Novogrudok, Minsk, Volkovysk, and Sventayny highlands are a natural orographic barrier which prevents snow-bringing air masses from penetrating inside the country. The research is based on data from gauge stations in Belarus registered from 1944 to 2014.

Keywords: density, depth, snow, water content in snow

Procedia PDF Downloads 160

Authors: Md. Mahadi Hasan Sany, Mumenunnessa Keya, Sharun Khushbu, Sheikh Abujar

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Since the beginning of China's economic reform, trade between the U.S. and China has grown rapidly, and has increased since China's accession to the World Trade Organization in 2001. The US imports more than it exports from China, reducing the trade war between China and the U.S. for the 2019 trade deficit, but in 2020, the opposite happens. In international and U.S. trade, Washington launched a full-scale trade war against China in March 2016, which occurred a catastrophic epidemic. The main goal of our study is to measure and predict trade relations between China and the U.S., before and after the arrival of the COVID epidemic. The ML model uses different data as input but has no time dimension that is present in the time series models and is only able to predict the future from previously observed data. The LSTM (a well-known Recurrent Neural Network) model is applied as the best time series model for trading forecasting. We have been able to create a sustainable forecasting system in trade between China and the US by closely monitoring a dataset published by the State Website NZ Tatauranga Aotearoa from January 1, 2015, to April 30, 2021. Throughout the survey, we provided a 180-day forecast that outlined what would happen to trade between China and the US during COVID-19. In addition, we have illustrated that the LSTM model provides outstanding outcome in time series data analysis rather than RFR and SVR (e.g., both ML models). The study looks at how the current Covid outbreak affects China-US trade. As a comparative study, RMSE transmission rate is calculated for LSTM, RFR and SVR. From our time series analysis, it can be said that the LSTM model has given very favorable thoughts in terms of China-US trade on the future export situation.

Keywords: RFR, China-U.S. trade war, SVR, LSTM, deep learning, Covid-19, export value, forecasting, time series analysis

Procedia PDF Downloads 196

Authors: Vlad Andrei Raducanu, Mariana Lucia Angelescu, Ion Cinca, Vasile Danut Cojocaru, Doina Raducanu

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By using smart digital tools, such as generative design (GD) and digital fabrication (DF), problems of high actuality concerning resources optimization (materials, energy, time) can be solved and applications or products of free-form type can be created. In the new digital technology materials are active, designed in response to a set of performance requirements, which impose a total rethinking of old material practices. The article presents the design procedure key steps of a free-form architectural object - a column type one with connections to get an adaptive 3D surface, by using the parametric design methodology and by exploiting the properties of conventional metallic materials. In parametric design the form of the created object or space is shaped by varying the parameters values and relationships between the forms are described by mathematical equations. Digital parametric design is based on specific procedures, as shape grammars, Lindenmayer - systems, cellular automata, genetic algorithms or swarm intelligence, each of these procedures having limitations which make them applicable only in certain cases. In the paper the design process stages and the shape grammar type algorithm are presented. The generative design process relies on two basic principles: the modeling principle and the generative principle. The generative method is based on a form finding process, by creating many 3D spatial forms, using an algorithm conceived in order to apply its generating logic onto different input geometry. Once the algorithm is realized, it can be applied repeatedly to generate the geometry for a number of different input surfaces. The generated configurations are then analyzed through a technical or aesthetic selection criterion and finally the optimal solution is selected. Endless range of generative capacity of codes and algorithms used in digital design offers various conceptual possibilities and optimal solutions for both technical and environmental increasing demands of building industry and architecture. Constructions or spaces generated by parametric design can be specifically tuned, in order to meet certain technical or aesthetical requirements. The proposed approach has direct applicability in sustainable architecture, offering important potential economic advantages, a flexible design (which can be changed until the end of the design process) and unique geometric models of high performance.

Keywords: parametric design, algorithmic procedures, free-form architectural object, sustainable architecture

Procedia PDF Downloads 374

Authors: ChunYi Peng, Wei Hsuan CHeng, Shyh Kuang Ueng

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This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.

Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies

Procedia PDF Downloads 56

Authors: Qinglin Liu, Zhiyong Lin, Zongheng Wei, Jianfeng Wen, Congming Yi, Hai Liu

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The multi-channel blind rendezvous problem in distributed cognitive radio networks (DCRNs) refers to how users in the network can hop to the same channel at the same time slot without any prior knowledge (i.e., each user is unaware of other users' information). The channel hopping (CH) technique is a typical solution to this blind rendezvous problem. In this paper, we propose a quinary coding and matrix structure-based CH algorithm called QCMS-CH. The QCMS-CH algorithm can guarantee the rendezvous of users using only one cognitive radio in the scenario of the asynchronous clock (i.e., arbitrary time drift between the users), heterogeneous channels (i.e., the available channel sets of users are distinct), and symmetric role (i.e., all users play a same role). The QCMS-CH algorithm first represents a randomly selected channel (denoted by R) as a fixed-length quaternary number. Then it encodes the quaternary number into a quinary bootstrapping sequence according to a carefully designed quaternary-quinary coding table with the prefix "R00". Finally, it builds a CH matrix column by column according to the bootstrapping sequence and six different types of elaborately generated subsequences. The user can access the CH matrix row by row and accordingly perform its channel, hoping to attempt rendezvous with other users. We prove the correctness of QCMS-CH and derive an upper bound on its Maximum Time-to-Rendezvous (MTTR). Simulation results show that the QCMS-CH algorithm outperforms the state-of-the-art in terms of the MTTR and the Expected Time-to-Rendezvous (ETTR).

Keywords: channel hopping, blind rendezvous, cognitive radio networks, quaternary-quinary coding

Procedia PDF Downloads 91

Authors: Haidy S. Ghali, Engy Serag, A. Samer Ezeldin

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Copper is an essential raw material used in the construction industry. During the year 2021 and the first half of 2022, the global market suffered from a significant fluctuation in copper raw material prices due to the aftermath of both the COVID-19 pandemic and the Russia-Ukraine war, which exposed its consumers to an unexpected financial risk. Thereto, this paper aims to develop two ANN-LSTM price prediction models, using Python, that can forecast the average monthly copper prices traded in the London Metal Exchange; the first model is a multivariate model that forecasts the copper price of the next 1-month and the second is a univariate model that predicts the copper prices of the upcoming three months. Historical data of average monthly London Metal Exchange copper prices are collected from January 2009 till July 2022, and potential external factors are identified and employed in the multivariate model. These factors lie under three main categories: energy prices and economic indicators of the three major exporting countries of copper, depending on the data availability. Before developing the LSTM models, the collected external parameters are analyzed with respect to the copper prices using correlation and multicollinearity tests in R software; then, the parameters are further screened to select the parameters that influence the copper prices. Then, the two LSTM models are developed, and the dataset is divided into training, validation, and testing sets. The results show that the performance of the 3-Month prediction model is better than the 1-Month prediction model, but still, both models can act as predicting tools for diverse economic situations.

Keywords: copper prices, prediction model, neural network, time series forecasting

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Authors: Daud Mustafa Minhas, Raja Rehan Khalid, Georg Frey

Abstract:

The next generation power systems are equipped with abundantly available free renewable energy resources (RES). During their low-cost operations, the price of electricity significantly reduces to a lower value, and sometimes it becomes negative. Therefore, it is recommended not to operate the traditional power plants (e.g. coal power plants) and to reduce the losses. In fact, it is not a cost-effective solution, because these power plants exhibit some shutdown and startup costs. Moreover, they require certain time for shutdown and also need enough pause before starting up again, increasing inefficiency in the whole power network. Hence, there is always a trade-off between avoiding negative electricity prices, and the startup costs of power plants. To exploit this trade-off and to increase the profit of a power plant, two main contributions are made: 1) introducing retrofit technology for state of art coal power plant; 2) proposing optimal control strategy for a power plant by exploiting different flexibility features. These flexibility features include: improving ramp rate of power plant, reducing startup time and lowering minimum load. While, the control strategy is solved as mixed integer linear programming (MILP), ensuring optimal solution for the profit maximization problem. Extensive comparisons are made considering pre and post-retrofit coal power plant having the same efficiencies under different electricity price scenarios. It concludes that if the power plant must remain in the market (providing services), more flexibility reflects direct economic advantage to the plant operator.

Keywords: discrete optimization, power plant flexibility, profit maximization, unit commitment model

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Authors: Hedi Dghim, Ahmed El-Naggar, Istvan Erlich

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Power electronic converters are being introduced in low voltage (LV) grids at an increasingly rapid rate due to the growing adoption of power electronic-based home appliances in residential grid. Photovoltaic (PV) systems are considered one of the potential installed renewable energy sources in distribution power systems. This trend has led to high distortion in the supply voltage which consequently produces harmonic currents in the network and causes an inherent voltage unbalance. In order to investigate the effect of harmonic distortions, a case study of a typical LV grid configuration with high penetration of 3-phase and 1-phase rooftop mounted PV from southern Germany was first considered. Electromagnetic transient (EMT) simulations were then carried out under the MATLAB/Simulink environment which contain detailed models for power electronic-based loads, ohmic-based loads as well as 1- and 3-phase PV. Note that, the switching patterns of the power electronic circuits were considered in this study. Measurements were eventually performed to analyze the distortion levels when PV operating under different solar irradiance. The characteristics of the load-side harmonic impedances were analyzed, and their harmonic contributions were evaluated for different distortion levels. The effect of the high penetration of PV on the harmonic distortion of both positive and negative sequences was also investigated. The simulation results are presented based on case studies. The current distortion levels are in agreement with relevant standards, otherwise the Total Harmonic Distortion (THD) increases under low PV power generation due to its inverse relation with the fundamental current.

Keywords: harmonic distortion analysis, power quality, PV systems, residential distribution system

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Authors: Alisa Salimova, Jiane Zuo, Christopher Homer

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The objective of this study is to analyse the North Canal riparian zone land use with the help of remote sensing analysis in ArcGis using 30 cloudless Landsat8 open-source satellite images from May to August of 2013 and 2017. Land cover, urban construction, heat island effect, vegetation cover, and water system change were chosen as the main parameters and further analysed to evaluate its impact on the North Canal water quality. The methodology involved the following steps: firstly, 30 cloudless satellite images were collected from the Landsat TM image open-source database. The visual interpretation method was used to determine different land types in a catchment area. After primary and secondary classification, 28 land cover types in total were classified. Visual interpretation method was used with the help ArcGIS for the grassland monitoring, US Landsat TM remote sensing image processing with a resolution of 30 meters was used to analyse the vegetation cover. The water system was analysed using the visual interpretation method on the GIS software platform to decode the target area, water use and coverage. Monthly measurements of water temperature, pH, BOD, COD, ammonia nitrogen, total nitrogen and total phosphorus in 2013 and 2017 were taken from three locations of the North Canal in Tongzhou district. These parameters were used for water quality index calculation and compared to land-use changes. The results of this research were promising. The vegetation coverage of North Canal riparian zone in 2017 was higher than the vegetation coverage in 2013. The surface brightness temperature value was positively correlated with the vegetation coverage density and the distance from the surface of the water bodies. This indicates that the vegetation coverage and water system have a great effect on temperature regulation and urban heat island effect. Surface temperature in 2017 was higher than in 2013, indicating a global warming effect. The water volume in the river area has been partially reduced, indicating the potential water scarcity risk in North Canal watershed. Between 2013 and 2017, urban residential, industrial and mining storage land areas significantly increased compared to other land use types; however, water quality has significantly improved in 2017 compared to 2013. This observation indicates that the Tongzhou Water Restoration Plan showed positive results and water management of Tongzhou district had been improved.

Keywords: North Canal, land use, riparian vegetation, river ecology, remote sensing

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Authors: Fahimeh Ghorbani, Alam Saleh

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Ideology, politics and art have always been omnipresent patterns of Islam since its early age. The Islamic empire, expanded from China to Andalusia, has instrumentalized art and architecture to enhance political legitimacy of different dynasties or states throughout its history. Quranic verses utilized to convey ideological messages in the major mosques and mausoleums. Iranians had already been employing art and architecture to propagate their political legitimacy prior to Islam. The land of Iran and its art with strong civilizational pre-Islamic history has been profoundly politicized since the rise of Islam in the region. Early Islamic period in Iran has witnessed introduction of a new architectural language, new formulas for spatial configuration in built spaces, as well as new system of architectural decoration. Studying Iran’s Early Islamic architecture helps in better understanding the process of socio-political identity making of Iranian-Islamic culture, and thus art and architecture. This period also set the stage for formation of glorious architectural movements through Persianate world in later periods. During the Early Islamic period in Iran, the innovative combination of Islamic ideology and Iranian Architecture created formidable ideological tools in politicizing art in the region and beyond. As such, this paper aims to investigate the political history and architectural legacy from late Sassanid to Early Islamic period, delves into the ways in which Early Islamic architecture played role in transforming Persian concepts of kingship, administration, and social organization. In so doing, the study focuses on the Perso-Islamic architectural synthesis under the Samanids and Seljuk dynasty as case studies. The paper also explores how the newly introduced Islamic architecture has been employed to address the question of political legitimacy and to propagate states’ political agenda in early Islamic Iran (650-1250). As for the existing literature, despite its uniqueness and significance, Early Islamic architecture of Iran has received little scholarly attention. However, there exists a sizeable body of scholarship on socio-historic condition of the land of Iran during Early Islamic period which provide a solid base for the project. Methodologically speaking, the authors look into the subject through various lenses. They will conduct historic and archival research in libraries, private collections, and archives in Iran and the related neighbouring countries in Persian, Arabic and English. The methods of visual and formal analysis are applied to examine architectural features of the period. There are also a high number of intriguing, yet poorly examined, published and unpublished documents, old plans, drawings and photos of monuments preserved in Cultural Heritage of Iran Organization which will be consulted.

Keywords: Iran, Islamic architecture, early Islamic Iran, early Islamic architecture, politicized art, political legitimacy, propaganda, aesthetics

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Authors: Mayank, Navneet Kaur, Narinder Singh

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The V599 mutations in the BRAF protein are extremely oncogenic, responsible for countless of malignant conditions. Along with wild type, V599E, V599D, and V599R are the important mutated variants of the BRAF proteins. The BRAF inhibitory anticancer agents are continuously developing, and sorafenib is a BRAF inhibitor that is under clinical use. The crystal structure of sorafenib bounded to wild type, and V599 is known, showing a similar interaction pattern in both the case. The mutated 599th residue, in both the case, is also found not interacting directly with the co-crystallized sorafenib molecule. However, the IC50 value of sorafenib was found extremely different in both the case, i.e., 22 nmol/L for wild and 38 nmol/L for V599E protein. Molecular docking study and MMGBSA binding energy results also revealed a significant difference in the binding pattern of sorafenib in both the case. Therefore, to explore the role of distinctively situated 599th residue, we have further conducted comprehensive computational studies. The molecular dynamics simulation, residue interaction network (RIN) analysis, and residue correlation study results revealed the importance of the 599th residue on the therapeutic outcome and overall dynamic of the BRAF protein. Therefore, although the position of 599th residue is very much distinctive from the ligand-binding cavity of BRAF, still it has exceptional control over the overall functional outcome of the protein. The insight obtained here may seem extremely important and guide us while designing ideal BRAF inhibitory anticancer molecules.

Keywords: BRAF, oncogenic, sorafenib, computational studies

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Authors: T. Kuchukhidze, N. Jalagonia, T. Archuadze, G. Bokuchava

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The growing scientific-technological progress in modern civilization causes actuality of producing construction materials which can successfully work in conditions of high temperature, radiation, pressure, speed, and chemically aggressive environment. Such extreme conditions can withstand very few types of materials and among them, ceramic materials are in the first place. Corundum ceramics is the most useful material for creation of constructive nodes and products of various purposes for its low cost, easy accessibility to raw materials and good combination of physical-chemical properties. However, ceramic composite materials have one disadvantage; they are less plastics and have lower toughness. In order to increase the plasticity, the ceramics are reinforced by various dopants, that reduces the growth of the cracks. It is shown, that adding of even small amount of carbon fibers and carbon nanotubes (CNT) as reinforcing material significantly improves mechanical properties of the products, keeping at the same time advantages of alundum ceramics. Graphene in composite material acts in the same way as inorganic dopants (MgO, ZrO2, SiC and others) and performs the role of aluminum oxide inhibitor, as it creates shell, that gives possibility to reduce sintering temperature and at the same time it acts as damper, because scattering of a shock wave takes place on carbon structures. Application of different structural modification of carbon (graphene, nanotube and others) as reinforced material, gives possibility to create multi-purpose highly requested composite materials based on alundum ceramics. In the present work offers simplified technology for obtaining of aluminum oxide ceramics, reinforced with carbon nanostructures, during which chemical modification with doping carbon nanostructures will be implemented in the process of synthesis of final powdery composite – Alumina. In charge doping carbon nanostructures connected to matrix substance with C-O-Al bonds, that provide their homogeneous spatial distribution. In ceramic obtained as a result of consolidation of such powders carbon fragments equally distributed in the entire matrix of aluminum oxide, that cause increase of bending strength and crack-resistance. The proposed way to prepare the charge simplifies the technological process, decreases energy consumption, synthesis duration and therefore requires less financial expenses. In the implementation of this work, modern instrumental methods were used: electronic and optical microscopy, X-ray structural and granulometric analysis, UV, IR, and Raman spectroscopy.

Keywords: ceramic materials, α-Al₂O₃, carbon nanostructures, composites, characterization, hot-pressing

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Authors: Sophia Shi

Abstract:

Acute kidney injury (AKI) is the sudden onset of kidney damage in which the kidneys cannot filter waste from the blood, requiring emergency hospitalization. AKI patient mortality rate is high in the ICU and is virtually impossible for doctors to predict because it is so unexpected. Currently, there is no hybrid model predicting AKI that takes advantage of two types of data. De-identified patient data from the MIMIC-III database and de-identified kidney images and corresponding patient records from the Beijing Hospital of the Ministry of Health were collected. Using data features including serum creatinine among others, two numeric models using MIMIC and Beijing Hospital data were built, and with the hospital ultrasounds, an image-only model was built. Convolutional neural networks (CNN) were used, VGG and Resnet for numeric data and Resnet for image data, and they were combined into a hybrid model by concatenating feature maps of both types of models to create a new input. This input enters another CNN block and then two fully connected layers, ending in a binary output after running through Softmax and additional code. The hybrid model successfully predicted AKI and the highest AUROC of the model was 0.953, achieving an accuracy of 90% and F1-score of 0.91. This model can be implemented into urgent clinical settings such as the ICU and aid doctors by assessing the risk of AKI shortly after the patient’s admission to the ICU, so that doctors can take preventative measures and diminish mortality risks and severe kidney damage.

Keywords: Acute kidney injury, Convolutional neural network, Hybrid deep learning, Patient record data, ResNet, Ultrasound kidney images, VGG

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Authors: Mustafa Var, Yasin Kültiğin Yaman, Elif Berna Var, Müberra Pulatkan

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This study deals with real application of an urban transformation project in Trabzon, Turkey. It aims to highlight the significance of using native species in terms of planting design of transformation projects which will also promote sustainability of urban identity. Urban identity is a phenomenon shaped not only by physical, but also by natural, spatial, social, historical and cultural factors. Urban areas face with continuous change which can be whether positive or negative way. If it occurs in a negative way that may have some destructive effects on urban identity. To solve this problematic issue, urban renewal movements initally started after 1840s around the world especially in the cities with ports. This process later followed by the places where people suffered a lot from fires and has expanded to all over the world. In Turkey, those processes have been experienced mostly after 1980s as country experienced the worst effects of unplanned urbanization especially in 1950-1990 period. Also old squares, streets, meeting points, green areas, Ottoman bazaars have changed slowly. This change was resulted in alienation of inhabitants to their environments. As a solution, several actions were taken like Mass Housing Laws which was enacted in 1981 and 1984 or urban transformation projects. Although projects between 1990-2000 were tried to satisfy the expectations of local inhabitants by the help of several design solutions to promote cultural identity; unfortunately those modern projects has also been resulted in alienation of urban environments to the inhabitants. Those projects were initially done by TOKI (Housing Development Administration of Turkey) and later followed by the Ministry of Environment and Urbanization after 2011. Although they had significant potentials to create healthy urban environments, they could not use this opportunity in an effective way. The reason for their failure is that their architectural styles and planting designs are unrespectful to local identity and environments. Generally, it can be said that the most of the urban transformation projects implementing in Turkey nearly have no concerns about the locality. However, those projects can be used as a positive tool for enhanching the urban identity of cities by means of local planting material. For instance, Kyoto can be identified by Japanese Maple trees or Seattle can be specified by Dahlia. In the same way, in Turkey, Istanbul city can be identified by Judas and Stone Pine trees or Giresun city can be identified by Cherry trees. Thus, in this paper, the importance of conserving urban identity is discussed specificly with the help of using local planting elements. After revealing the mistakes that are made during urban transformation projects, the techniques and design criterias for preserving and promoting urban identity are examined. In the end, it is emphasized that every city should have their own original, local character and specific planting design which can be used for highlighting its identity as well as architectural elements.

Keywords: urban identity, urban transformation, planting design, landscape architecture

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Authors: Peng Chun-Yi, Chen Wei-Hsuan, Ueng Shyh-Kuang

Abstract:

This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.

Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies

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Authors: Pariya Sheykhmaleki, Debajyoti Pati

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Rural areas in the United States face numerous challenges in providing quality and assessable primary healthcare services, especially during emergencies such as natural disasters or pandemics. This study showcases a cutting-edge flexible module that aims to overcome these challenges by offering adaptable healthcare facilities capable of providing comprehensive health services in remote and disaster-prone regions. According to the Health Resources and Services Administration (HRSA), approximately 62 million Americans, or 1 in 5 individuals, live in areas designated as Health Professional Shortage Areas (HPSAs) for primary care. These areas are characterized by limited access to healthcare facilities, shortage of healthcare professionals, transportation barriers, inadequate healthcare infrastructure, higher rates of chronic diseases, mental health disparities, and limited availability of specialized care, including urgent circumstances like pandemics that can exacerbate this issue. To address these challenges, the literature study began by examining primary health solutions in very remote areas, e.g., spaceships, to identify the state-of-the-art technologies and the methods used to facilitate primary care needs. The literature study on flexibility in architecture and interior design was also adapted to develop a conceptual design for rural areas. The designed flexible module provides an innovative solution. This module can be prefabricated as all parts are standardized. The flexibility of the module allows the structure to be modified based on local and geographical requirements as well as the ability to expand as required. It has been designed to stand either by itself or work in tandem with public buildings. By utilizing sustainable approaches and flexible spatial configurations, the module optimizes the utilization of limited resources while ensuring efficient and effective healthcare delivery. Furthermore, the poster highlights the key features of this flexible module, including its ability to support telemedicine and telehealth services for all five levels of urgent care conditions, i.e., from facilitating fast tracks to supporting emergency room services, in two divided zones. The module's versatility enables its deployment in rural areas located far from urban centers and disaster-stricken regions, ensuring access to critical healthcare services in times of need. This module is also capable of responding in urban areas when the need for primary health becomes vastly urgent, e.g., during a pandemic. It emphasizes the module's potential to bridge the healthcare gap between rural and urban areas and mitigate the impact of rural health challenges.

Keywords: rural health, healthcare challenges, flexible modular design, telemedicine, telehealth

Procedia PDF Downloads 76