Search results for: green infrastructure network

Authors: Manel Hammami, Gabriele Grandi

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In recent years, multilevel inverters have become more attractive for single-phase photovoltaic (PV) systems, due to their known advantages over conventional H-bridge pulse width-modulated (PWM) inverters. They offer improved output waveforms, smaller filter size, lower total harmonic distortion (THD), higher output voltages and others. The most common multilevel converter topologies, presented in literature, are the neutral-point-clamped (NPC), flying capacitor (FC) and Cascaded H-Bridge (CHB) converters. In both NPC and FC configurations, the number of components drastically increases with the number of levels what leads to complexity of the control strategy, high volume, and cost. Whereas, increasing the number of levels in case of the cascaded H-bridge configuration is a flexible solution. However, it needs isolated power sources for each stage, and it can be applied to PV systems only in case of PV sub-fields. In order to improve the ratio between the number of output voltage levels and the number of components, several hybrids and asymmetric topologies of multilevel inverters have been proposed in the literature such as the FC asymmetric H-bridge (FCAH) and the NPC asymmetric H-bridge (NPCAH) topologies. Another asymmetric multilevel inverter configuration that could have interesting applications is the cascaded asymmetric H-bridge (CAH), which is based on a modular half-bridge (two switches and one capacitor, also called level doubling network, LDN) cascaded to a full H-bridge in order to double the output voltage level. This solution has the same number of switches as the above mentioned AH configurations (i.e., six), and just one capacitor (as the FCAH). CAH is becoming popular, due to its simple, modular and reliable structure, and it can be considered as a retrofit which can be added in series to an existing H-Bridge configuration in order to double the output voltage levels. In this paper, an original and effective method for the analysis of the DC-link voltage ripple is given for single-phase asymmetric H-bridge multilevel inverters based on level doubling network (LDN). Different possible configurations of the asymmetric H-Bridge multilevel inverters have been considered and the analysis of input voltage and current are analytically determined and numerically verified by Matlab/Simulink for the case of cascaded asymmetric H-bridge multilevel inverters. A comparison between FCAH and the CAH configurations is done on the basis of the analysis of the DC and voltage ripple for the DC source (i.e., the PV system). The peak-to-peak DC and voltage ripple amplitudes are analytically calculated over the fundamental period as a function of the modulation index. On the basis of the maximum peak-to-peak values of low frequency and switching ripple voltage components, the DC capacitors can be designed. Reference is made to unity output power factor, as in case of most of the grid-connected PV generation systems. Simulation results will be presented in the full paper in order to prove the effectiveness of the proposed developments in all the operating conditions.

Keywords: asymmetric inverters, dc-link voltage, level doubling network, single-phase multilevel inverter

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Authors: Rashid S. Mohammad, Shicheng Zhang, Xinzhe Zhao

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In tight oil reservoirs, wettability alteration has generally been considered as an effective way to remove fracturing fluid retention on the surface of the fracture and consequently improved oil production. However, there is a lack of a reliable productivity prediction model to show the relationship between the wettability and oil production in tight oil well. In this paper, a new oil productivity prediction model of immiscible oil-water flow and miscible CO₂-oil flow accounting for wettability is developed. This mathematical model is established by considering two different length scales: nonporous network and propped fractures. CO₂ flow diffuses in the nonporous network and high velocity non-Darcy flow in propped fractures are considered by taking into account the effect of wettability alteration on capillary pressure and relative permeability. A laboratory experiment is also conducted here to validate this model. Laboratory experiments have been designed to compare the water saturation profiles for different contact angle, revealing the fluid retention in rock pores that affects capillary force and relative permeability. Four kinds of brines with different concentrations are selected here to create different contact angles. In water-wet porous media, as the system becomes more oil-wet, water saturation decreases. As a result, oil relative permeability increases. On the other hand, capillary pressure which is the resistance for the oil flow increases as well. The oil production change due to wettability alteration is the result of the comprehensive changes of oil relative permeability and capillary pressure. The results indicate that wettability is a key factor for fracturing fluid retention removal and oil enhancement in tight reservoirs. By incorporating laboratory test into a mathematical model, this work shows the relationship between wettability and oil production is not a simple linear pattern but a parabolic one. Additionally, it can be used for a better understanding of optimization design of fracturing fluids.

Keywords: wettability, relative permeability, fluid retention, oil production, unconventional and tight reservoirs

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Authors: E. N. R. de Silva

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The main purpose of this research is to set a policy framework for establishing a legal, institutional and social infrastructure that enhances the welfare, health, safety and economic interest of the consumers in Sri Lanka. It will help to develop an approach to continuously and successfully advocate for a consumer protection legal reform agenda and also it is significant as it gives directions to create national consumer protection associations in Sri Lanka. The methodology adopted for this research is purely a qualitative approach and it is generally and specifically categorized. Generally, part of this research looked at the existing laws, regulations and how effective they are in order to protect consumers. It will analyze the consumer protection framework and specially, consumer protection enhanced by the public organizations in Japan. This research offers a model with methods and legal instruments to enforce advocacy group to enhance consumer welfare, also brings out reforms to be made in the national legal framework on consumer welfare.

Keywords: consumer protection association, consumer protection law, consumer welfare, legal framework

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Authors: Tomas Premoli, Sareh Rowlands

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In this study, we investigate the impact of various convolutional neural network (CNN) architectures on the accuracy of diagnosing Alzheimer’s disease (AD) using patient MRI scans. Alzheimer’s disease is a debilitating neurodegenerative disorder that affects millions worldwide. Early, accurate, and non-invasive diagnostic methods are required for providing optimal care and symptom management. Deep learning techniques, particularly CNNs, have shown great promise in enhancing this diagnostic process. We aim to contribute to the ongoing research in this field by comparing the effectiveness of different CNN architectures and providing insights for future studies. Our methodology involved preprocessing MRI data, implementing multiple CNN architectures, and evaluating the performance of each model. We employed intensity normalization, linear registration, and skull stripping for our preprocessing. The selected architectures included VGG, ResNet, and DenseNet models, all implemented using the Keras library. We employed transfer learning and trained models from scratch to compare their effectiveness. Our findings demonstrated significant differences in performance among the tested architectures, with DenseNet201 achieving the highest accuracy of 86.4%. Transfer learning proved to be helpful in improving model performance. We also identified potential areas for future research, such as experimenting with other architectures, optimizing hyperparameters, and employing fine-tuning strategies. By providing a comprehensive analysis of the selected CNN architectures, we offer a solid foundation for future research in Alzheimer’s disease diagnosis using deep learning techniques. Our study highlights the potential of CNNs as a valuable diagnostic tool and emphasizes the importance of ongoing research to develop more accurate and effective models.

Keywords: Alzheimer’s disease, convolutional neural networks, deep learning, medical imaging, MRI

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Authors: Bhakti Chitale

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Mumbai city, which is the business capital of India and one of the most crowded cities in the world, holds the biggest slum in Asia. The Mumbai Metropolitan Region (MMR) occupies an area of 4035 sq.km. with a population of 22.8 million people. This population is mostly urban with 91% of this population living in areas of Municipal Corporations and Councils. Another 3% live in Census Towns. The region has 9 Municipal Corporations, 8 Municipal councils, and around 1000 villages. On the one hand MMR reflects the highest contribution to the Nations overall economy and on the other hand it shows the horrible and intolerable picture of about 2 million people, who are living in slums/without even slum with totally unhygienic conditions and with total loss of hope. The generations are about to get affected adversely if the solution is not worked out. This study is an attempt towards working out the solution. Mumbai Metropolitan Region Development Authority (MMRDA) is state government's authority, specially formed to govern the development of MMR. MMRDA is engaged in long term planning, promotion of new growth centres, implementation of strategic projects and financing infrastructure development. While preparing the master plan for MMR for next 20 years MMRDA conducted a detail study regarding Housing scenario in MMR and possible options for improvement. The author was the in charge officer for the said assignment. This paper puts light on the interesting outcomes of the research study, which ranges from the adverse effects of government policies, automatic responses of housing market, effects on planning processes, and overall changing needs of housing patterns in the world due to changes in the social mechanism. It alarms the urban planners who usually focus on smart infrastructure development, about allied future dangers. This housing study will explain the complexities, realities and needs of innovations in the housing policies all over the world. The paper will explain further few success stories and failure stories of government initiatives with reasons. It gives the clear idea about the differences in needs of housing for people from different economic groups and direct and indirect market pressures on low cost housing. Magical phenomenon came in front like a large percentage of vacant houses is present in spite of the huge need. Housing market gets affected by the developments or any other physical and financial changes taking place in the nearby areas or cities, also by changes in cities which are located far from the region and also by the international investments or policy changes. Instead of just depending on governments actions in case of generation of affordable housing, it becomes equally important to make the housing markets automatically generate such stock and still make them sustainable is the aim of all the movement. In summary, we may say that the paper will sequentially elaborate the complete dynamics of housing in one of the most crowded urban area in the world that is Mumbai Metropolitan Region, with a lot of data, analysis, case studies, and recommendations.

Keywords: Mumbai India, slum housing, region planning, market recommendations

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Authors: Marina Shakleina, Konstantin Shaklein, Stanislav Yakiro

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The digitalization of modern human life over the last decade has facilitated the acquisition, storage, and processing of data, which are used to detect changes in consumer preferences and to improve the internal efficiency of the production process. This emerging trend has attracted academic interest in the use of big data in research. The study focuses on modeling the social comfort of the Russian population for the period 2010-2021 using big data. Big data provides enormous opportunities for understanding human interactions at the scale of society with plenty of space and time dynamics. One of the most popular big data sources is Google Trends. The methodology for assessing social comfort using big data involves several steps: 1. 574 words were selected based on the Harvard IV-4 Dictionary adjusted to fit the reality of everyday Russian life. The set of keywords was further cleansed by excluding queries consisting of verbs and words with several lexical meanings. 2. Search queries were processed to ensure comparability of results: the transformation of data to a 10-point scale, elimination of popularity peaks, detrending, and deseasoning. The proposed methodology for keyword search and Google Trends processing was implemented in the form of a script in the Python programming language. 3. Block and summary integral indicators of social comfort were constructed using the first modified principal component resulting in weighting coefficients values of block components. According to the study, social comfort is described by 12 blocks: ‘health’, ‘education’, ‘social support’, ‘financial situation’, ‘employment’, ‘housing’, ‘ethical norms’, ‘security’, ‘political stability’, ‘leisure’, ‘environment’, ‘infrastructure’. According to the model, the summary integral indicator increased by 54% and was 4.631 points; the average annual rate was 3.6%, which is higher than the rate of economic growth by 2.7 p.p. The value of the indicator describing social comfort in Russia is determined by 26% by ‘social support’, 24% by ‘education’, 12% by ‘infrastructure’, 10% by ‘leisure’, and the remaining 28% by others. Among 25% of the most popular searches, 85% are of negative nature and are mainly related to the blocks ‘security’, ‘political stability’, ‘health’, for example, ‘crime rate’, ‘vulnerability’. Among the 25% most unpopular queries, 99% of the queries were positive and mostly related to the blocks ‘ethical norms’, ‘education’, ‘employment’, for example, ‘social package’, ‘recycling’. In conclusion, the introduction of the latent category ‘social comfort’ into the scientific vocabulary deepens the theory of the quality of life of the population in terms of the study of the involvement of an individual in the society and expanding the subjective aspect of the measurements of various indicators. Integral assessment of social comfort demonstrates the overall picture of the development of the phenomenon over time and space and quantitatively evaluates ongoing socio-economic policy. The application of big data in the assessment of latent categories gives stable results, which opens up possibilities for their practical implementation.

Keywords: big data, Google trends, integral indicator, social comfort

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Authors: Norbert Magyar, Jozsef Kovacs, Peter Tanos, Balazs Trasy, Tamas Garamhegyi, Istvan Gabor Hatvani

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Water is one of the most important common resources, and as a result of urbanization, agriculture, and industry it is becoming more and more exposed to potential pollutants. The prevention of the deterioration of water quality is a crucial role for environmental scientist. To achieve this aim, the operation of monitoring networks is necessary. In general, these networks have to meet many important requirements, such as representativeness and cost efficiency. However, existing monitoring networks often include sampling sites which are unnecessary. With the elimination of these sites the monitoring network can be optimized, and it can operate more economically. The aim of this study is to illustrate the applicability of the CCDA (Combined Cluster and Discriminant Analysis) to the field of water quality monitoring and optimize the monitoring networks of a river (the Danube), a wetland-lake system (Kis-Balaton & Lake Balaton), and two surface-subsurface water systems on the watershed of Lake Neusiedl/Lake Fertő and on the Szigetköz area over a period of approximately two decades. CCDA combines two multivariate data analysis methods: hierarchical cluster analysis and linear discriminant analysis. Its goal is to determine homogeneous groups of observations, in our case sampling sites, by comparing the goodness of preconceived classifications obtained from hierarchical cluster analysis with random classifications. The main idea behind CCDA is that if the ratio of correctly classified cases for a grouping is higher than at least 95% of the ratios for the random classifications, then at the level of significance (α=0.05) the given sampling sites don’t form a homogeneous group. Due to the fact that the sampling on the Lake Neusiedl/Lake Fertő was conducted at the same time at all sampling sites, it was possible to visualize the differences between the sampling sites belonging to the same or different groups on scatterplots. Based on the results, the monitoring network of the Danube yields redundant information over certain sections, so that of 12 sampling sites, 3 could be eliminated without loss of information. In the case of the wetland (Kis-Balaton) one pair of sampling sites out of 12, and in the case of Lake Balaton, 5 out of 10 could be discarded. For the groundwater system of the catchment area of Lake Neusiedl/Lake Fertő all 50 monitoring wells are necessary, there is no redundant information in the system. The number of the sampling sites on the Lake Neusiedl/Lake Fertő can decrease to approximately the half of the original number of the sites. Furthermore, neighbouring sampling sites were compared pairwise using CCDA and the results were plotted on diagrams or isoline maps showing the location of the greatest differences. These results can help researchers decide where to place new sampling sites. The application of CCDA proved to be a useful tool in the optimization of the monitoring networks regarding different types of water bodies. Based on the results obtained, the monitoring networks can be operated more economically.

Keywords: combined cluster and discriminant analysis, cost efficiency, monitoring network optimization, water quality

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Authors: Rahul Malhotra

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Fiber based access networks can deliver performance that can support the increasing demands for high speed connections. One of the new technologies that have emerged in recent years is Passive Optical Networks. This paper is targeted to show the simultaneous delivery of triple play service (data, voice and video). The comparative investigation and suitability of various data rates is presented. It is demonstrated that as we increase the data rate, number of users to be accommodated decreases due to increase in bit error rate.

Keywords: BER, PON, TDMPON, GPON, CWDM, OLT, ONT

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Authors: Seyed Mohammad Mousavi Shalheh, Elham Rostami, Seyed Majid Mousavi, Somayeh Shirin Jani

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The aim of this research was to evaluate the potential of Rogbeh village located in Khanafereh, Shadegan city functions in Khuzestan also is the feasibility to build infrastructure and appropriate spaces to attract tourists as well as creating jobs and transforming the village institute of ecotourism in the region. It seems that the village has the potential for developing rural tourism with careful planning, and with regard to job creation and economic recovery programs, social-cultural and environmental-ecological accompanied will be welcomed by the people. Therefore, we can provide a strategy for developing tourism and achieving sustainable advancement of rural tourism. Based on researches carried out and according to regional climate differences and the position of the Rogbeh Village toward the lagoon, this research can be used by other researchers to develop and manage tourism.

Keywords: Shadegan Lagoon, Iranian villages, tourism industry, local architecture, Rogbeh village, landscape design, ecology

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Authors: Ghouas Halima, Haddou Boumedienne, Jean Peal Cancelier, Cristophe Gourdon, Ssaka Collines

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This work concerns the coacervate extraction of industrial dye, namely BezanylGreen - F2B, from an aqueous solution by nonionic surfactant “Lutensol AO7 and TX-114” (readily biodegradable). Binary water/surfactant and pseudo-binary (in the presence of solute) phase diagrams were plotted. The extraction results as a function of wt.% of the surfactant and temperature are expressed by the following four quantities: percentage of solute extracted, E%, residual concentrations of solute and surfactant in the dilute phase (Xs,w, and Xt,w, respectively) and volume fraction of coacervate at equilibrium (Фc). For each parameter, whose values are determined by a design of experiments, these results are subjected to empirical smoothing in three dimensions. The aim of this study is to find out the best compromise between E% and Фc. E% increases with surfactant concentration and temperature in optimal conditions, and the extraction extent of TA reaches 98 and 96 % using TX-114 and Lutensol AO7, respectively. The effect of sodium sulfate or cetyltrimethylammonium bromide (CTAB) addition is also studied. Finally, the possibility of recycling the surfactant is proved.

Keywords: extraction, cloud point, non ionic surfactant, bezanyl green

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Authors: Varsha Kheradiya, Ganga Prasad Pandey

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The use of wearable antennas is rising because wireless devices become small. The wearable antenna is part of clothes used in communication applications, including energy harvesting, medical application, navigation, and tracking. In current years, Antennas embroidered on clothes, conducting antennas based on fabric, polymer embedded antennas, and inkjet-printed antennas are all attractive ways. Also shows the analysis required for wearable antennas, such as wearable antennae interacting with the human body. The primary requirements for the antenna are small size, low profile minimizing radiation absorption by the human body, high efficiency, structural integrity to survive worst situations, and good gain. Therefore, research in energy harvesting, biomedicine, and military application design is increasingly favoring flexible wearable antennas. Textile materials that are effectively used for designing and developing wearable antennas for body area networks. The wireless body area network is primarily concerned with creating effective antenna systems. The antenna should reduce their size, be lightweight, and be adaptable when integrated into clothes. When antennas integrate into clothes, it provides a convenient alternative to those fabricated using rigid substrates. This paper presents a study of U slot loaded wearable textile antenna. U slot patch antenna design is illustrated for wideband from 1GHz to 6 GHz using textile material jeans as substrate and pure copper polyester taffeta fabric as conducting material. This antenna design exhibits dual band results for WLAN at 2.4 GHz and 3.6 GHz frequencies. Also, study U slot position horizontal and vertical shifting. Shifting the horizontal positive X-axis position of the U slot produces the third band at 5.8 GHz.

Keywords: microstrip patch antenna, textile material, U slot wearable antenna, wireless body area network

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Authors: Kemal Comakli, Meryem Terhan

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In this study, the potential of heat recovery from waste flue gas was examined in 60 MW district heating system of a university, and fuel saving was aimed by using the recovered heat in the system as a source again. Various scenarios are intended to make use of waste heat. For this purpose, actual operation data of the system were taken. Besides, the heat recovery units that consist of heat exchangers such as flue gas condensers, economizers or air pre-heaters were designed theoretically for each scenario. Energy analysis of natural gas-fired boiler’s exhaust flue gas in the system, and economic analysis of heat recovery units to predict payback periods were done. According to calculation results, the waste heat loss ratio from boiler flue gas in the system was obtained as average 16%. Thanks to the heat recovery units, thermal efficiency of the system can be increased, and fuel saving can be provided. At the same time, a huge amount of green gas emission can be decreased by installing the heat recovery units.

Keywords: heat recovery from flue gas, energy analysis of flue gas, economical analysis, payback period

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Authors: Santosh Ghosh, Vinod Kumar Yadav, Vivekananda Mukherjee, Ishta Garg

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India, akin to the rest of the world today, is grappling with balancing act between ever increasing demand for energy and alarmingly high level of green house gas emission, which is inevitable corollary of energy production in the conventional way. Researchers and energy policy makers around the world are now focusing on renewable energy (RE) technologies to find solution to this crisis. In India various agencies at both national and state level has been set up and bestowed with responsibility of development of renewable energy technologies, viz. Ministry of New Renewable Energy (MNRE), National Vidyut Vyapar Nigam Ltd. (NVVNL), Indian Renewable Energy Development Agency Limited (IREDA) and RE Development Agencies in respective states. In the present work, the preparedness of India in terms of forming institutional and policy frame work briefly discussed. Status of implementation of RE technologies state wise and of India as a whole, critically reviewed.

Keywords: energy policy, energy sustainability, renewable energy, IREDA

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Authors: Soha Saad, Jean Bigeon, Florence Ossart, Etienne Sourdille

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Developing better solutions for train rescheduling problems has been drawing the attention of researchers for decades. Most researches in this field deal with minor incidents that affect a large number of trains due to cascading effects. They focus on timetables, rolling stock and crew duties, but do not take into account infrastructure limits. The present work addresses electric infrastructure incidents that limit the power available for train traction, and hence the transportation capacity of the railway system. Rescheduling is needed in order to optimally share the available power among the different trains. We propose a rescheduling process based on dynamic multiphysics railway simulations that include the mechanical and electrical properties of all the system components and calculate physical quantities such as the train speed profiles, voltage along the catenary lines, temperatures, etc. The optimization problem to solve has a large number of continuous and discrete variables, several output constraints due to physical limitations of the system, and a high computation cost. Our approach includes a phase of sensitivity analysis in order to analyze the behavior of the system and help the decision making process and/or more precise optimization. This approach is a quantitative method based on simulation statistics of the dynamic railway system, considering a predefined range of variation of the input parameters. Three important settings are defined. Factor prioritization detects the input variables that contribute the most to the outputs variation. Then, factor fixing allows calibrating the input variables which do not influence the outputs. Lastly, factor mapping is used to study which ranges of input values lead to model realizations that correspond to feasible solutions according to defined criteria or objectives. Generalized Sobol indexes are used for factor prioritization and factor fixing. The approach is tested in the case of a simple railway system, with a nominal traffic running on a single track line. The considered incident is the loss of a feeding power substation, which limits the power available and the train speed. Rescheduling is needed and the variables to be adjusted are the trains departure times, train speed reduction at a given position and the number of trains (cancellation of some trains if needed). The results show that the spacing between train departure times is the most critical variable, contributing to more than 50% of the variation of the model outputs. In addition, we identify the reduced range of variation of this variable which guarantees that the output constraints are respected. Optimal solutions are extracted, according to different potential objectives: minimizing the traveling time, the train delays, the traction energy, etc. Pareto front is also built.

Keywords: optimization, rescheduling, railway system, sensitivity analysis, train timetable

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Authors: Maryam Kheirollahpour, Mahmoud Danaee, Amir Faisal Merican, Asma Ahmad Shariff

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Background: The presence of nonlinearity among the risk factors of eating behavior causes a bias in the prediction models. The accuracy of estimation of eating behaviors risk factors in the primary prevention of obesity has been established. Objective: The aim of this study was to explore the potential of a hybrid model of structural equation modeling (SEM) and Artificial Neural Networks (ANN) to predict eating behaviors. Methods: The Partial Least Square-SEM (PLS-SEM) and a hybrid model (SEM-Artificial Neural Networks (SEM-ANN)) were applied to evaluate the factors affecting eating behavior patterns among university students. 340 university students participated in this study. The PLS-SEM analysis was used to check the effect of emotional eating scale (EES), body shape concern (BSC), and body appreciation scale (BAS) on different categories of eating behavior patterns (EBP). Then, the hybrid model was conducted using multilayer perceptron (MLP) with feedforward network topology. Moreover, Levenberg-Marquardt, which is a supervised learning model, was applied as a learning method for MLP training. The Tangent/sigmoid function was used for the input layer while the linear function applied for the output layer. The coefficient of determination (R²) and mean square error (MSE) was calculated. Results: It was proved that the hybrid model was superior to PLS-SEM methods. Using hybrid model, the optimal network happened at MPLP 3-17-8, while the R² of the model was increased by 27%, while, the MSE was decreased by 9.6%. Moreover, it was found that which one of these factors have significantly affected on healthy and unhealthy eating behavior patterns. The p-value was reported to be less than 0.01 for most of the paths. Conclusion/Importance: Thus, a hybrid approach could be suggested as a significant methodological contribution from a statistical standpoint, and it can be implemented as software to be able to predict models with the highest accuracy.

Keywords: hybrid model, structural equation modeling, artificial neural networks, eating behavior patterns

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Authors: Mahdi Alkaeed, Adeel Ehsan

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Enterprise information systems (EIS) act as the backbone of organizations that belong to different domains. These systems not only play a major role in the efficient usage of resources and time but also throw light on the future roadmap for the enterprise. In today's rapidly expanding world of business and technology, enterprise systems from various heterogenous environments have to exchange information at some point, be it within the same organization or between different organizations. This reality strengthens the importance of interoperability between these systems, which is one of the key enablers of systems collaboration. Both information technology infrastructure and business processes have to be aligned with each other to achieve this effect. This will be difficult to attain if traditional tightly coupled architecture is used. Instead, a more loosely coupled service-oriented architecture has to be used. That would enable an effective interoperability level between different EIS. This paper discusses and presents the current work that has been done in the field of EIS interoperability. Along the way, it also discusses the challenges, solutions to tackle those challenges presented in the studied literature, and limitations, if any.

Keywords: enterprise systems interoperability, collaboration and integration, service-based architecture, open system architecture

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Authors: Nurunnahar Akter, Elena Kulinskaya, Nicholas Steel, Ilyas Bakbergenuly

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Although Hormone Replacement Therapy (HRT) is an effective treatment in ameliorating menopausal symptoms, it has mixed effects on different health outcomes, increasing, for instance, the risk of breast cancer. Because of this, many symptomatic women are left untreated. Untreated menopausal symptoms may result in other health issues, which eventually put an extra burden and costs to the health care system. All-cause mortality analysis may explain the net benefits and risks of the HRT therapy. However, it received far less attention in HRT studies. This study investigated the impact of HRT on all-cause mortality using electronically recorded primary care data from The Health Improvement Network (THIN) that broadly represents the female population in the United Kingdom (UK). The study entry date for this study was the record of the first HRT prescription from 1984, and patients were followed up until death or transfer to another GP practice or study end date, which was January 2017. 112,354 HRT users (cases) were matched with 245,320 non-users by age at HRT initiation and general practice (GP). The hazards of all-cause mortality associated with HRT were estimated by a parametric Weibull-Cox model adjusting for a wide range of important medical, lifestyle, and socio-demographic factors. The multilevel multiple imputation techniques were used to deal with missing data. This study found that during 32 years of follow-up, combined HRT reduced the hazard ratio (HR) of all-cause mortality by 9% (HR: 0.91; 95% Confidence Interval, 0.88-0.94) in women of age between 46 to 65 at first treatment compared to the non-users of the same age. Age-specific mortality analyses found that combined HRT decreased mortality by 13% (HR: 0.87; 95% CI, 0.82-0.92), 12% (HR: 0.88; 95% CI, 0.82-0.93), and 8% (HR: 0.92; 95% CI, 0.85-0.98), in 51 to 55, 56 to 60, and 61 to 65 age group at first treatment, respectively. There was no association between estrogen-only HRT and women’s all-cause mortality. The findings from this study may help to inform the choices of women at menopause and to further educate the clinicians and resource planners.

Keywords: hormone replacement therapy, multiple imputations, primary care data, the health improvement network (THIN)

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Authors: Oloniyo Kemisola Eunice, Odere Oladunni Oluwafeyikemi

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Technological change and globalization have created a new global economy with Information and Communication Technology (ICT) occupying a complex position in relation to globalization. The emergence of this new global economy has serious implications on the nature and purpose of educational institutions. The paper is basically a theoretical discourse. Data for analysis were obtained from secondary sources. The paper found that significant challenges confront the integration of ICTs in education in the areas of educational policy and planning, infrastructure, language and content, capacity building and financing in Nigeria. The paper concluded that business education needs to be well equipped to anticipate and respond to opportunities created by ICTs in order to participate productively and equitably in an increasingly technology-rich and knowledge-driven world. The paper recommended, among others, that the investments in ICTs should be used to promote the development of basic skills, problem-solving and communication skills and the professional development of teachers.

Keywords: information, communication, technology, business, education

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Authors: Marianna Maiaru, Gregory M. Odegard

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During the processing of high-performance thermoset polymer matrix composites, chemical reactions occur during elevated pressure and temperature cycles, causing the constituent monomers to crosslink and form a molecular network that gradually can sustain stress. As the crosslinking process progresses, the material naturally experiences a gradual shrinkage due to the increase in covalent bonds in the network. Once the cured composite completes the cure cycle and is brought to room temperature, the thermal expansion mismatch of the fibers and matrix cause additional residual stresses to form. These compounded residual stresses can compromise the reliability of the composite material and affect the composite strength. Composite process modeling is greatly complicated by the multiscale nature of the composite architecture. At the molecular level, the degree of cure controls the local shrinkage and thermal-mechanical properties of the thermoset. At the microscopic level, the local fiber architecture and packing affect the magnitudes and locations of residual stress concentrations. At the macroscopic level, the layup sequence controls the nature of crack initiation and propagation due to residual stresses. The goal of this research is use molecular dynamics (MD) and finite element analysis (FEA) to predict the residual stresses in composite laminates and the corresponding effect on composite failure. MD is used to predict the polymer shrinkage and thermomechanical properties as a function of degree of cure. This information is used as input into FEA to predict the residual stresses on the microscopic level resulting from the complete cure process. Virtual testing is subsequently conducted to predict strength allowables. Experimental characterization is used to validate the modeling.

Keywords: molecular dynamics, finite element analysis, processing modeling, multiscale modeling

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Authors: Soheila Sadeghi

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Accurate cost prediction is essential for effective project management and decision-making in the construction industry. This study aims to develop a cost prediction model for construction projects using Machine Learning techniques and Particle Swarm Optimization (PSO). The research utilizes a comprehensive dataset containing project cost estimates, actual costs, resource details, and project performance metrics from a road reconstruction project. The methodology involves data preprocessing, feature selection, and the development of an Artificial Neural Network (ANN) model optimized using PSO. The study investigates the impact of various input features, including cost estimates, resource allocation, and project progress, on the accuracy of cost predictions. The performance of the optimized ANN model is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. The results demonstrate the effectiveness of the proposed approach in predicting project costs, outperforming traditional benchmark models. The feature selection process identifies the most influential variables contributing to cost variations, providing valuable insights for project managers. However, this study has several limitations. Firstly, the model's performance may be influenced by the quality and quantity of the dataset used. A larger and more diverse dataset covering different types of construction projects would enhance the model's generalizability. Secondly, the study focuses on a specific optimization technique (PSO) and a single Machine Learning algorithm (ANN). Exploring other optimization methods and comparing the performance of various ML algorithms could provide a more comprehensive understanding of the cost prediction problem. Future research should focus on several key areas. Firstly, expanding the dataset to include a wider range of construction projects, such as residential buildings, commercial complexes, and infrastructure projects, would improve the model's applicability. Secondly, investigating the integration of additional data sources, such as economic indicators, weather data, and supplier information, could enhance the predictive power of the model. Thirdly, exploring the potential of ensemble learning techniques, which combine multiple ML algorithms, may further improve cost prediction accuracy. Additionally, developing user-friendly interfaces and tools to facilitate the adoption of the proposed cost prediction model in real-world construction projects would be a valuable contribution to the industry. The findings of this study have significant implications for construction project management, enabling proactive cost estimation, resource allocation, budget planning, and risk assessment, ultimately leading to improved project performance and cost control. This research contributes to the advancement of cost prediction techniques in the construction industry and highlights the potential of Machine Learning and PSO in addressing this critical challenge. However, further research is needed to address the limitations and explore the identified future research directions to fully realize the potential of ML-based cost prediction models in the construction domain.

Keywords: cost prediction, construction projects, machine learning, artificial neural networks, particle swarm optimization, project management, feature selection, road reconstruction

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Authors: Dibyendu Adak, Saroj Mandal

Abstract:

The fly ash based geopolymer concrete generally requires heat activation after casting, which has been considered as an important limitation for its practical application. Such limitation can be overcome by a modification in the process at the time of mixing of ingredients (fly and activator fluid) for geopolymer concrete so that curing can be made at ambient temperature. This process modified geopolymer concrete shows an appreciable improvement in structural performance compared to conventional heat cured geopolymer concrete and control cement concrete. The improved durability performance based on water absorption, sulphate test, and RCPT is also noted. The microstructural properties analyzed through Field Emission Scanning Electron Microscope (FESEM) with Energy Dispersive X-ray Spectroscopy (EDS) and X-ray Diffraction (XRD) techniques show the better interaction of fly ash and activator solution at early ages for the process modified geopolymer concrete. This accelerates the transformation of the amorphous phase of fly ash to the crystalline phase.

Keywords: fly ash, geopolymer concrete, process modification, structural properties, durability, micro-structures

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Authors: Chao-Ton Su, Li-Fei Chen

Abstract:

The parameter design is crucial to improving the uniformity of a product or process. In the product design stage, parameter design aims to determine the optimal settings for the parameters of each element in the system, thereby minimizing the functional deviations of the product. In the process design stage, parameter design aims to determine the operating settings of the manufacturing processes so that non-uniformity in manufacturing processes can be minimized. The parameter design, trying to minimize the influence of noise on the manufacturing system, plays an important role in the high-tech companies. Taiwan has many well-known high-tech companies, which show key roles in the global economy. Quality remains the most important factor that enables these companies to sustain their competitive advantage. In Taiwan however, many high-tech companies face various quality problems. A common challenge is related to root causes and defect patterns. In the R&D stage, root causes are often unknown, and defect patterns are difficult to classify. Additionally, data collection is not easy. Even when high-volume data can be collected, data interpretation is difficult. To overcome these challenges, high-tech companies in Taiwan use more advanced quality improvement tools. In addition to traditional statistical methods and quality tools, the new trend is the application of powerful tools, such as neural network, fuzzy theory, data mining, industrial engineering, operations research, and innovation skills. In this study, several examples of optimizing the parameter settings for the manufacturing process in Taiwan’s tech companies will be presented to illustrate proposed approach’s effectiveness. Finally, a discussion of using traditional experimental design versus the proposed approach for process optimization will be made.

Keywords: quality engineering, parameter design, neural network, genetic algorithm, experimental design

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Authors: Ibrahim Isa Ali (Pantami), Abdu Jaafaru Bambale, Abimbola Alale, Danjuma Ibrahim Ndihgihdah, Muhammad Alkali, Adamu Idris Umar, Moshood Kareem, Samson Olufunmilayo Abodunrin, Muhammad Dokko Zubairu

Abstract:

The National Digital Economy Policy and Strategy, NDEPS document developed by Nigeria’s Federal Ministry of Communications & Digital Economy (FMoCDE) is anchored on 8 pillars for the acceleration of the National Digital Economy for a Digital Nigeria. NIGCOMSAT-1R and the planned HTS communication Satellite are critical assets for supporting the pillars in the drive for sustainable growth and development. This paper discusses on the gains and contribution of the strategy as a solid infrastructure. The paper also highlights these assets’ contribution as platform for Indigenous Content Development & Adoption, Digital Literacy & Skills, and Digital Services Development & Promotion.

Keywords: FMoCDE, HTS, NDEPS, nigcomsat!R, pillars

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Authors: N. Ouslimani, M. T. Abadlia, S. Yakoub, F. Tebbani

Abstract:

Water has always been synonymous with life and growth. Blue gold is first essential to the survival of the human being whose body consists of more than 65% with the development of industrialization and consumption patterns; volumes of wastewater discharges have increased considerably whether industrial or domestic, waste water must be purified before discharge. Treatment, therefore, aims to reduce the pollution load which contain. The resources in Algeria are limited and unevenly distributed. Thus, to meet all the water needs of the country and to preserve the waters of good quality drinking water supply, one solution would be to use them according to their quality and to irrigate crops for the food or be directed to the irrigation of green areas or sports complex. The purification performance of this STEP has been established since the pH analyzed pollution criteria (7.36) and temperature (16°C), MES (10 mg / l), electrical conductivity (1122 / µs / cm), DBO5 (6mg / l), DCO (15mg / l) meet the discharge standards. Arguably the purified water discharged out of the boumerdes STEP comply with Algerian regulations and can be reused in agriculture. COD biodegradability of the coefficient / BOD5 is 2.5 (less than 3) indicates that of the effluent are biodegradable hence their urban origin.

Keywords: irrigation, recovery, treated, wastewater

Procedia PDF Downloads 259

Authors: Daniela Tapusi, Adrian Andronic, Naomi Tufan, Ruxandra Erbașu, Ioana Teodorescu

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The proposed theme of this article falls within the policy of reducing carbon emissions imposed by the ‘Green New Deal’ by replacing structural elements made of energy-intensive materials with ecological materials. In this sense, wood has many qualities (high strength/mass and stiffness/mass ratio, low specific gravity, recovery/recycling) that make it competitive with classic building materials. The topological optimization of the linear glulam elements, resulting from different types of analysis (Finite Element Method, simple regression on metamodels), tests on models or by Monte-Carlo simulation, leads to a material reduction of more than 10%. This article proposes a method of obtaining topologically optimized shapes for different types of glued laminated timber beams. The results obtained will constitute the database for AI training.

Keywords: timber, glued laminated timber, artificial-intelligence, environment, carbon emissions

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Authors: Hossein Bazmara, Kaamran Raahemifar, Mostafa Sefidgar, Madjid Soltani

Abstract:

Angiogenesis is formation of new blood vessels from existing vessels. Due to flow of blood in vessels, during angiogenesis, blood flow plays an important role in regulating the angiogenesis process. Multiple mathematical models of angiogenesis have been proposed to simulate the formation of the complicated network of capillaries around a tumor. In this work, a multi-scale model of angiogenesis is developed to show the effect of blood flow on capillaries and network formation. This model spans multiple temporal and spatial scales, i.e. intracellular (molecular), cellular, and extracellular (tissue) scales. In intracellular or molecular scale, the signaling cascade of endothelial cells is obtained. Two main stages in development of a vessel are considered. In the first stage, single sprouts are extended toward the tumor. In this stage, the main regulator of endothelial cells behavior is the signals from extracellular matrix. After anastomosis and formation of closed loops, blood flow starts in the capillaries. In this stage, blood flow induced signals regulate endothelial cells behaviors. In cellular scale, growth and migration of endothelial cells is modeled with a discrete lattice Monte Carlo method called cellular Pott's model (CPM). In extracellular (tissue) scale, diffusion of tumor angiogenic factors in the extracellular matrix, formation of closed loops (anastomosis), and shear stress induced by blood flow is considered. The model is able to simulate the formation of a closed loop and its extension. The results are validated against experimental data. The results show that, without blood flow, the capillaries are not able to maintain their integrity.

Keywords: angiogenesis, endothelial cells, multi-scale model, cellular Pott's model, signaling cascade

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Authors: Nikolaos Georgoulopoulos, Alkis Hatzopoulos, Konstantinos Karamitsios, Konstantinos Kotrotsios, Alexandros I. Metsai

Abstract:

In modern server systems, business critical applications run in different types of infrastructure, such as cloud systems, physical machines and virtualization. Often, due to high load and over time, various hardware faults occur in servers that translate to errors, resulting to malfunction or even server breakdown. CPU, RAM and hard drive (HDD) are the hardware parts that concern server administrators the most regarding errors. In this work, selected RAM, HDD and CPU errors, that have been observed or can be simulated in kernel ring buffer log files from two groups of Linux servers, are investigated. Moreover, a severity characterization is given for each error type. Better understanding of such errors can lead to more efficient analysis of kernel logs that are usually exploited for fault diagnosis and prediction. In addition, this work summarizes ways of simulating hardware errors in RAM and HDD, in order to test the error detection and correction mechanisms of a Linux server.

Keywords: hardware errors, Kernel logs, Linux servers, RAM, hard disk, CPU

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Authors: Marijana Pantić, Saša Milijić

Abstract:

In Serbia as a continental country, the tourism industry relies on city-break, spa and mountain tourism, where ski resorts have primacy during the winter season. Even though the number of tourists has recently increased, the share of domestic tourists remained predominant. It is also noticed that tourists from Serbia eagerly travel abroad, which was so far researched in the context of summer holidays but not in the framework of ski resorts. Therefore, this paper examines the competitiveness of ski resorts in Serbia from the perspective of domestic tourists. A survey was used as a data collection method, covering various competitiveness dimensions. The aim is to recognize the main motives of consumers when choosing a ski resort in Serbia or abroad. The results showed that the choices of Serbian tourists are predominantly shaped by the cost of an offer – of accommodation above all others. They are attentive by estimating the value for money, which is the most common reason to choose a ski resort abroad over a domestic one. The crowd at ski resorts and ski runs appears to be a result of unbalanced accommodation capacities on the one hand and ski infrastructure on the other, which is currently the most notable competitiveness drawback of ski resorts in Serbia.

Keywords: mountain tourism, Serbia, ski resorts, tourism competitiveness

Procedia PDF Downloads 138

Authors: Miraz Hafiz Rossy

Abstract:

As part of planning to address shortfalls in fresh water supply for the world, Sea water can be a huge source of fresh water. But Desalinating sea water to get fresh water could require a lots of fossil fuels. To save the fossil fuel in terms of save the green world but meet the up growing need for fresh water, a very useful but energy efficient method needs to be introduced. Vacuum desalination of sea water using only the Renewable energy can be an effective solution to this issue. Taking advantage of sensitivity of water's boiling point to air pressure a vacuum desalination water treatment plant can be designed which would only use sea water as feed water and solar energy as fuel to produce fresh drinking water. The study indicates that reducing the air pressure to a certain value water can be boiled at very low temperature. Using solar energy to provide the condensation and the vacuum creation would be very useful and efficient. Compared to existing resources, desalination is considered to be expensive, but using only renewable energy the cost can be reduced significantly. Despite its very few drawbacks, it can be considered a possible solution to the world's fresh water shortages.

Keywords: desalination, scarcity of fresh water, water purification, water treatment

Procedia PDF Downloads 396

Authors: Aditya Verma

Abstract:

E-voting using blockchain provides us with a distributed system where data is present on each node present in the network and is reliable and secure too due to its immutability property. This work compares various blockchain consensus algorithms used for e-voting applications in the past, based on performance and node scalability, and chooses the optimal one and improves on one such previous implementation by proposing solutions for the loopholes of the optimally working blockchain consensus algorithm, in our chosen application, e-voting.

Keywords: blockchain, parallel bft, consensus algorithms, performance

Procedia PDF Downloads 171