Search results for: H₂-optimal model reduction

Authors: F. S. Al-Anzi, M. Sarfraz, A. Elmi, A. R. Khan

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Researchers are responding to the environmental challenges of Kuwait in localized, innovative, effective and economic ways. One of the vital and significant examples of the natural challenges is lack or water and desertification. In this research, the project team focuses on redesigning a prototype, using Value Engineering Methodology, which would provide similar functionalities to the well-known technology of Waterboxx kits while reducing the capital and operational costs and simplifying the process of manufacturing and usability by regular farmers. The design employs used tires and recycled plastic sheets as raw materials. Hence, this approach is going to help not just fighting desertification but also helping in getting rid of ever growing huge tire dumpsters in Kuwait, as well as helping in avoiding hazards of tire fires yielding in a safer and friendlier environment. Several alternatives for implementing the prototype have been considered. The best alternative in terms of value has been selected after thorough Function Analysis System Technique (FAST) exercise has been developed. A prototype has been fabricated and tested in a controlled simulated lab environment that is being followed by real environment field testing. Water and soil analysis conducted on the site of the experiment to cross compare between the composition of the soil before and after the experiment to insure that the prototype being tested is actually going to be environment safe. Experimentation shows that the design was equally as effective as, and may exceed, the original design with significant savings in cost. An estimated total cost reduction using the VE approach of 43.84% over the original design. This cost reduction does not consider the intangible costs of environmental issue of waste recycling which many further intensify the total savings of using the alternative VE design. This case study shows that Value Engineering Methodology can be an important tool in innovating new designs for reducing costs.

Keywords: desertification, functional analysis, scrap tires, value engineering, waste recycling, water irrigation rationing

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Authors: Kaddi Mohammed, A. Benatiallah, D. Benatiallah

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Data grid is a technology that has full emergence of new challenges, such as the heterogeneity and availability of various resources and geographically distributed, fast data access, minimizing latency and fault tolerance. Researchers interested in this technology address the problems of the various systems related to the industry such as task scheduling, load balancing and replication. The latter is an effective solution to achieve good performance in terms of data access and grid resources and better availability of data cost. In a system with duplication, a coherence protocol is used to impose some degree of synchronization between the various copies and impose some order on updates. In this project, we present an approach for placing replicas to minimize the cost of response of requests to read or write, and we implement our model in a simulation environment. The placement techniques are based on a cost model which depends on several factors, such as bandwidth, data size and storage nodes.

Keywords: response time, query, consistency, bandwidth, storage capacity, CERN

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Authors: Christina Wainikka, Besrat Tesfaye

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Several of the countries in the Baltic Sea region are ranked high in international innovations rankings, such as the Global Innovation Index and European Innovation Scoreboard. There are however some concerns in the performance of different countries. For example, there is a widely spread notion about “The Swedish Paradox”. Sweden is ranked high due to investments in R&D and patent activity, but the outcome is not as high as could be expected. SMEs in Sweden are also below EU average when it comes to registering intellectual property rights such as patents and trademarks. This study is concentrating on the protection of utility model. This intellectual property right does not exist in Sweden, but in for example Finland and Germany. The utility model protection is sometimes referred to as a “patent light” since it is easier to obtain than the patent protection but at the same time does cover technical solutions. In examining statistics on patent activities and activities in registering utility models it is clear that utility model protection is scarcely used in the countries that have the protection. In Germany 10 577 applications were made in 2021. In Finland there were 259 applications made in 2021. This can be compared with patent applications that were 58 568 in Germany in 2021 and 1 662 in Finland in 2021. In Sweden there has never been a protection for utility models. The only protection for technical solutions is patents and business secrets. The threshold for obtaining a patent is high, due to the legal requirements and the costs. The patent protection is there for often not chosen by SMEs in Sweden. This study examines whether the protection of utility models in other countries in the Baltic region provide SMEs in these countries with better options to protect their innovations. The legal methodology is comparative law. In order to study the effects of the legal differences statistics are examined and interviews done with SMEs from different industries.

Keywords: baltic sea region, comparative law, SME, utility model

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Authors: M. L. Seretlo-Rangata, T. Sodi, S. Govender

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Despite interventions by various governments to regulate water demand and address water scarcity, literature shows that billions of people across the world continue to struggle with access because not everyone contributes equally to conservation efforts. Behavioral factors such as individual and collective aspects of cognition and commitment have been found to play an important role in water conservation. The aim of the present study was to explore the perceptions of community members in the Lephalale area, Limpopo province, towards water conservation with a view to developing an explanatory psychological model on water conservation. Twenty (20) participants who relied on communal taps to access water in Lephalale Local Municipality, Limpopo province, were selected through purposeful sampling. In-depth, semi-structured, individual face-to-face interviews were used to gather data and were analyzed utilizing thematic content analysis (TCA). The research findings revealed that there are various psychological effects of water scarcity on communities, such as emotional distress, interpersonal conflicts and disruptions of daily activities of living. Additionally, the study results showed that the coping strategies developed by participants to deal with water scarcity included adopting alternative water use behaviors as well as adjusting current behaviors and lifestyles. Derived from the study findings, a psychological model of water conservation was developed. The model incorporates some ideas from the Value-Belief-Norm (VBN) theory and the Afrocentric theory. The model suggests that people’s worldviews, including their values, beliefs and culture, are significant determinants of their pro-environmental behaviors. The study concludes by recommending that authorities and policymakers should consider psychological factors when developing water management programs, strategies and interventions with the consultation of psychology experts.

Keywords: water conservation, psychological model, pro-environmental behaviour, conservation psychology, water-use behaviour

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Authors: Adam J. Carinci

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Background: The opioid epidemic is the most pressing healthcare crisis of our time. There is increasing recognition that opioids have limited long-term efficacy and are associated with hyperalgesia, addiction, and increased morbidity and mortality. Therefore, alternative strategies to combat chronic pain are paramount. We initiated a multicenter retrospective case series to review the efficacy of DRG stimulation in facilitating opioid tapering, opioid discontinuation and as a viable alternative to chronic opioid therapy. Purpose: The dorsal root ganglion (DRG) plays a key role in the development and maintenance of pain. Recent innovations in neuromodulation, specifically, dorsal root ganglion stimulation, offers an effective alternative to opioids in the treatment of chronic pain. This retrospective case series demonstrates preliminary evidence that DRG stimulation facilitates opioid tapering, opioid discontinuation and presents a viable alternative to chronic opioid therapy. Procedure: This small multicenter retrospective case series provides preliminary evidence that DRG stimulation facilitates opioid weaning, opioid tapering and is a viable option to opioid therapy in the treatment of chronic pain. A retrospective analysis was completed. Visual analog scale pain scores and pain medication usage were collected at the baseline visit and after four weeks, 3 months and 6 months of treatment. Ten consecutive patients across two study centers were included. The pain was rated 7.38 at baseline and decreased to 1.50 at the 4-week follow-up, a reduction of 79.5%. All patients significantly decreased their opioid pain medication use with an average > 30% reduction in morphine equivalents and four were able to discontinue their medications entirely. Conclusion: This Retrospective case series demonstrates preliminary evidence that DRG stimulation facilitates opioid tapering, opioid discontinuation and presents a viable alternative to chronic opioid therapy.

Keywords: dorsal root ganglion, neuromodulation, opioid sparing, stimulation

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Authors: A. Zahedi Yeganeh, A. Khademolhosseini, R. Mokhtari Malekabadi

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Undoubtedly one of the most far-reaching and controversial topics considered in the past few decades, has been globalization. Globalization lies in the essence of the modern culture. It is a complex and rapidly expanding network of links and mutual interdependence that is an aspect of modern life; though some argue that this link existed since the beginning of human history. If we consider globalization as a dynamic social process in which the geographical constraints governing the political, economic, social and cultural relationships have been undermined, it might not be possible to simply describe its impact on the urban fabric. But since in this phenomenon the increase in communications of societies (while preserving the main cultural - regional characteristics) with one another and the increase in the possibility of influencing other societies are discussed, the need for more studies will be felt. The main objective of this study is to grade based on some globalization factors on urban fabric applying the TOPSIS model. The research method is descriptive - analytical and survey. For data analysis, the TOPSIS model and SPSS software were used and the results of GIS software with fourteen cities are shown on the map. The results show that the process of being influenced by the globalization of the urban fabric of fourteen zones of Isfahan was not similar and there have been large differences in this respect between city zones; the most affected areas are zones 5, 6 and 9 of the municipality and the least impact has been on the zones 4 and 3 and 2.

Keywords: grading, globalization, urban fabric, 14 zones of Isfahan, TOPSIS model

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Authors: A. P. Joshi, H. V. Warrior, J. P. Panda

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This paper is a continuation of the work carried out by various turbulence modelers in Oceanography on the topic of oceanic turbulent mixing. It evaluates the evolution of ocean water temperature and salinity by the appropriate modeling of turbulent mixing utilizing proper prescription of eddy viscosity. Many modelers in past have suggested including terms like shear, buoyancy and vorticity to be the parameters that decide the slow pressure strain correlation. We add to it the fact that dissipation anisotropy also modifies the correlation through eddy viscosity parameterization. This recalibrates the established correlation constants slightly and gives improved results. This anisotropization of dissipation implies that the critical Richardson’s number increases much beyond unity (to 1.66) to accommodate enhanced mixing, as is seen in reality. The model is run for a couple of test cases in the General Ocean Turbulence Model (GOTM) and the results are presented here.

Keywords: Anisotropy, GOTM, pressure-strain correlation, Richardson critical number

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Authors: Xiangtuo Chen, Paul-Henry Cournéde

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Crop yield prediction is a paramount issue in agriculture. The main idea of this paper is to find out efficient way to predict the yield of corn based meteorological records. The prediction models used in this paper can be classified into model-driven approaches and data-driven approaches, according to the different modeling methodologies. The model-driven approaches are based on crop mechanistic modeling. They describe crop growth in interaction with their environment as dynamical systems. But the calibration process of the dynamic system comes up with much difficulty, because it turns out to be a multidimensional non-convex optimization problem. An original contribution of this paper is to propose a statistical methodology, Multi-Scenarios Parameters Estimation (MSPE), for the parametrization of potentially complex mechanistic models from a new type of datasets (climatic data, final yield in many situations). It is tested with CORNFLO, a crop model for maize growth. On the other hand, the data-driven approach for yield prediction is free of the complex biophysical process. But it has some strict requirements about the dataset. A second contribution of the paper is the comparison of these model-driven methods with classical data-driven methods. For this purpose, we consider two classes of regression methods, methods derived from linear regression (Ridge and Lasso Regression, Principal Components Regression or Partial Least Squares Regression) and machine learning methods (Random Forest, k-Nearest Neighbor, Artificial Neural Network and SVM regression). The dataset consists of 720 records of corn yield at county scale provided by the United States Department of Agriculture (USDA) and the associated climatic data. A 5-folds cross-validation process and two accuracy metrics: root mean square error of prediction(RMSEP), mean absolute error of prediction(MAEP) were used to evaluate the crop prediction capacity. The results show that among the data-driven approaches, Random Forest is the most robust and generally achieves the best prediction error (MAEP 4.27%). It also outperforms our model-driven approach (MAEP 6.11%). However, the method to calibrate the mechanistic model from dataset easy to access offers several side-perspectives. The mechanistic model can potentially help to underline the stresses suffered by the crop or to identify the biological parameters of interest for breeding purposes. For this reason, an interesting perspective is to combine these two types of approaches.

Keywords: crop yield prediction, crop model, sensitivity analysis, paramater estimation, particle swarm optimization, random forest

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Authors: Qais H. Alsafasfeh

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The aim of this paper is to provide a forecast of the peak demand for the next 15 years for electrical distribution companies. The proposed methodology provides both the peak demand and its location for the next 15 years. This paper describes the Spatial Load Forecasting model used, the information provided by electrical distribution company in Jordan, the workflow followed, the parameters used and the assumptions made to run the model. The aim of this paper is to provide a forecast of the peak demand for the next 15 years for electrical distribution companies. The proposed methodology provides both the peak demand and its location for the next 15 years. This paper describes the Spatial Load Forecasting model used, the information provided by electrical distribution company in Jordan, the workflow followed, the parameters used and the assumptions made to run the model.

Keywords: load forecast, peak demand, spatial load, electrical distribution

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Authors: Ioannis Manikas

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The increased consumer demand for environmentally friendly production and distribution practices and the stricter environmental regulations turned environmental aspects into important criteria in business decision-making. On the other hand, Food and Nutrition Security (FNS) has evolved dramatically during the last decades in theory and practice serving as a reference point for exchanging experiences among all agents involved in programs and projects to fostering policy and strategy development. Global pressures make it more important than ever to gain a better understanding of the contribution that agrifood businesses make to FNS and to examine ways to make them more resilient in an increasingly globalized and uncertain world. This study extends the standard three-dimensional model of sustainability to include two more dimensions: A technological dimension and a policy/political dimension. Apart from the economic, environmental and social dimensions regularly used in sustainability literature, the extended model will accurately represent the measures and policies addressing food and nutrition security.

Keywords: food and nutrition security, sustainability, food safety, resilience

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Authors: H. Barati, M. Wu, A. Kharicha, A. Ludwig

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A transient model for nozzle clogging has been developed and successfully validated against a laboratory experiment. Key steps of clogging are considered: transport of particles by turbulent flow towards the nozzle wall; interactions between fluid flow and nozzle wall, and the adhesion of the particle on the wall; the growth of the clog layer and its interaction with the flow. The current paper is to investigate the mesh (size and type) sensitivity of the model in both two and three dimensions. It is found that the algorithm for clog growth alone excluding the flow effect is insensitive to the mesh type and size, but the calculation including flow becomes sensitive to the mesh quality. The use of 2D meshes leads to overestimation of the clog growth because the 3D nature of flow in the boundary layer cannot be properly solved by 2D calculation. 3D simulation with tetrahedron mesh can also lead to an error estimation of the clog growth. A mesh-independent result can be achieved with hexahedral mesh, or at least with triangular prism (inflation layer) for near-wall regions.

Keywords: clogging, continuous casting, inclusion, simulation, submerged entry nozzle

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Authors: Shuo Tian, Xuepiao Bai, Jianqin Shang, Pengtao Gai, Yuansong Zeng

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Shot peen forming is an essential forming process for aircraft metal wing panel. With the development of computer simulation technology, scholars have proposed a numerical simulation method of shot peen forming based on stress field. Three shot peen forming indexes of crater diameter, shot speed and surface coverage are required as simulation parameters in the stress field method. It is necessary to establish the relationship between simulation and experimental process parameters in order to simulate the deformation under different shot peen forming parameters. The shot peen forming tests of the 2024-T351 aluminum alloy workpieces were carried out using uniform test design method, and three factors of air pressure, feed rate and shot flow were selected. The second-order response surface model between simulation parameters and uniform test factors was established by stepwise regression method using MATLAB software according to the results. The response surface model was combined with the stress field method to simulate the shot peen forming deformation of the workpiece. Compared with the experimental results, the simulated values were smaller than the corresponding test values, the maximum and average errors were 14.8% and 9%, respectively.

Keywords: shot peen forming, process parameter, response surface model, numerical simulation

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Authors: Omotoyosi Bilikies Ilori, Adekunle Saheed Ajisebiyawo

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After the Second World War, there was an emergence of a new stage of capitalist globalization with its Neo-liberal ideology. There were global economic and political restructurings that affected third-world countries like Nigeria. Neo-liberalism is the driving force of globalization, which is the latest manifestation of imperialism that engenders endemic poverty in Nigeria. Poverty is severe and widespread in Nigeria. Poverty entails a situation where a person lives on less than one dollar per day and has no access to basic necessities of life. Poverty is inhuman and a breach of human rights. The Nigerian government initiated some strategies in the past to help in poverty reduction. Neo-liberalism manifested in the Third World, such as Nigeria, through the privatization of public enterprises, trade liberalization, and the rollback of the state investments in providing important social services. These main ideas of Neo-liberalism produced poverty in Nigeria and also encouraged the abandonment of the social contract between the government and the people. There is thus a gap in the provision of social services and subsidies for the masses, all of which Neo-liberal ideological positions contradict. This paper is a qualitative study which draws data from secondary sources. The theoretical framework is anchored on the market theory of capitalist globalization and public choice theory. The objectives of this study are to (i) examine the impacts of Neo-liberalism on poverty in Nigeria as a typical example of a Third World country and (ii) find out the effects of Neo-liberalism on the provision of social services and subsidies and employment. The findings from this study revealed that (i) the adoption of the Neo-liberal ideology by the Nigerian government has led to increased poverty and poor provision of social services and employment in Nigeria; and (ii) there is an increase in foreign debts which compounds poverty situation in Nigeria. This study makes the following recommendations: (i) Government should adopt strategies that are pro-poor to eradicate poverty; (ii) The Trade Unions and the masses should develop strategies to challenge Neo-liberalism and reject Neo-liberal ideology.

Keywords: neo-liberalism, poverty, employment, poverty reduction, structural adjustment programme

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Authors: Aber Salem Aboalgasm, Rupert Ward

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This study investigated some results of the use of digital art tools by junior school children in order to discover if these tools could promote artistic ability and creativity. The study considers the ease of use and usefulness of the tools as well as how to assess artwork produced by digital means. As the use of these tools is a relatively new development in Art education, this study may help educators in their choice of which tools to use and when to use them. The study also aims to present a model for the assessment of students’ artistic development and creativity by studying their artistic activity. This model can help in determining differences in students’ creative ability and could be useful both for teachers, as a means of assessing digital artwork, and for students, by providing the motivation to use the tools to their fullest extent. Sixteen students aged nine to ten years old were observed and recorded while they used the digital drawing tools. The study found that, according to the students’ own statements, it was not the ease of use but the successful effects the tools provided which motivated the children to use them.

Keywords: artistic ability, creativity, drawing digital tool, TAM model, psychomotor domain

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Authors: H. Aripin, I. Made Joni, Edvin Priatna, Nundang Busaeri, Svilen Sabchevski

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In this investigation, manganese has been produced from medium grade manganese ore from Karangnunggal mine (West Java, Indonesia). The ores were grinded using a jar mill to pass through a 150 mesh sieve. The effects of keeping it at a temperature of 1200 °C in methane gas on the structural properties have been studied. The material’s properties have been characterized on the basis of the experimental data obtained using X-ray fluorescence (XRF), X-ray diffraction (XRD), Scanning Electron Microscopy (SEM), and Fourier transform infrared (FTIR) spectroscopy. It has been found that the ore contains MnO₂ as the main constituents at about 46.80 wt.%. It can be also observed that the ore particles are agglomerated forming dense grains with different texture and morphology. The irregular-shaped grains with dark contrast, the large brighter grains, and smaller grains with bright texture and smooth surfaces are associated with the presence of manganese, calcium, and quartz, respectively. From XRD patterns, MnO₂ is reduced to hausmannite (Mn₃O₄), manganosite (MnO) and manganese carbide (Mn₇C₃). At a temperature of 1200°C the keeping time does not have any effect on the formation of crystals and the crystalline phases remain almost unchanged in the time range from 15 to 90 minutes. An increase of the keeping time up to 45 minutes during the sintering process leads to an increase of the MnO concentration, while at 90 minutes, the concentration decreases. At longer keeping times the excess reaction of the methane gas and manganese oxide in the ore causes an increase of carbon deposition. As a result, it blocks the particle surface and then hinders the reduction process of manganese oxide. From FTIR spectrum allows one to explain that the appearance of C=O stretching mode arises from absorption of atmospheric methane and manganese oxide of the ore. The intensity of this band increases with increasing the keeping time, indicating an increase of carbon deposition on the surface of manganese oxide.

Keywords: manganese, medium grade manganese ore, structural properties, keeping the temperature, carbon deposition

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Authors: E. A. Kiridi, A. O. Ogunlela

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Pollutants from aquacultural practices constitute environmental problems and phytoremediation could offer cheaper environmentally sustainable alternative since equipment using advanced treatment for fish tank effluent is expensive to import, install, operate and maintain, especially in developing countries. The main objective of this research was, therefore, to develop a mathematical model for phytoremediation by aquatic plants in aquaculture wastewater. Other objectives were to evaluate the retention times on phytoremediation rates using the model and to measure the nutrient level of the aquaculture effluent and phytoremediation rates of three aquatic macrophytes, namely; water hyacinth (Eichornia crassippes), water lettuce (Pistial stratoites) and morning glory (Ipomea asarifolia). A completely randomized experimental design was used in the study. Approximately 100 g of each macrophyte were introduced into the hydroponic units and phytoremediation indices monitored at 8 different intervals from the first to the 28th day. The water quality parameters measured were pH and electrical conductivity (EC). Others were concentration of ammonium–nitrogen (NH₄⁺ -N), nitrite- nitrogen (NO₂⁻ -N), nitrate- nitrogen (NO₃⁻ -N), phosphate –phosphorus (PO₄³⁻ -P), and biomass value. The biomass produced by water hyacinth was 438.2 g, 600.7 g, 688.2 g and 725.7 g at four 7–day intervals. The corresponding values for water lettuce were 361.2 g, 498.7 g, 561.2 g and 623.7 g and for morning glory were 417.0 g, 567.0 g, 642.0 g and 679.5g. Coefficient of determination was greater than 80% for EC, TDS, NO₂⁻ -N, NO₃⁻ -N and 70% for NH₄⁺ -N using any of the macrophytes and the predicted values were within the 95% confidence interval of measured values. Therefore, the model is valuable in the design and operation of phytoremediation systems for aquaculture effluent.

Keywords: aquaculture effluent, macrophytes, mathematical model, phytoremediation

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Authors: Jackson Simon Lusagalika, Lai Hsin-Chih, Dai Yu-Tung

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Particles with an aerodynamic diameter of 2.5 meters or less are referred to as "fine particulate matter" (PM₂.₅) are easily inhaled and can go deeper into the lungs than other particles in the atmosphere, where it may have detrimental health consequences. In this study, we use a hybrid model that combined CMAQ and AERMOD as well as initial meteorological fields from the Weather Research and Forecasting (WRF) model to study the impact of power plant PM₂.₅ emissions in South Taiwan since it frequently experiences higher PM₂.₅ levels. A specific date of March 3, 2022, was chosen as a result of a power outage that prompted the bulk of power plants to shut down. In some way, it is not conceivable anywhere in the world to turn off the power for the sole purpose of doing research. Therefore, this catastrophe involving a power outage and the shutdown of power plants offers a great occasion to evaluate the impact of air pollution driven by this power sector. As a result, four numerical experiments were conducted in the study using the Continuous Emission Data System (CEMS), assuming that the power plants continued to function normally after the power outage. The hybrid model results revealed that power plants have a minor impact in the study region. However, we examined the accumulation of PM₂.₅ in the study and discovered that once the vortex at 925hPa was established and moved to the north of Taiwan's coast, the study region experienced higher observed PM₂.₅ concentrations influenced by meteorological factors. This study recommends that decision-makers take into account not only control techniques, specifically emission reductions, but also the atmospheric and meteorological implications for future investigations.

Keywords: PM₂.₅ concentration, powerplants, hybrid air quality model, CEMS, Vorticity

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Authors: Lin Li, P. G. R. N. I. Pussella

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When representing spatial data and their attributes on different types of maps, the scale plays a key role in the process of map generalization. The process is consisted with two main operators such as selection and omission. Once some data were selected, they would undergo of several geometrical changing processes such as elimination, simplification, smoothing, exaggeration, displacement, aggregation and size reduction. As a result of these operations at different levels of data, the geometry of the spatial features such as length, sinuosity, orientation, perimeter and area would be altered. This would be worst in the case of preparation of small scale maps, since the cartographer has not enough space to represent all the features on the map. What the GIS users do is when they wanted to analyze a set of spatial data; they retrieve a data set and does the analysis part without considering very important characteristics such as the scale, the purpose of the map and the degree of generalization. Further, the GIS users use and compare different maps with different degrees of generalization. Sometimes, GIS users are going beyond the scale of the source map using zoom in facility and violate the basic cartographic rule 'it is not suitable to create a larger scale map using a smaller scale map'. In the study, the effect of map generalization for GIS analysis would be discussed as the main objective. It was used three digital maps with different scales such as 1:10000, 1:50000 and 1:250000 which were prepared by the Survey Department of Sri Lanka, the National Mapping Agency of Sri Lanka. It was used common features which were on above three maps and an overlay analysis was done by repeating the data with different combinations. Road data, River data and Land use data sets were used for the study. A simple model, to find the best place for a wild life park, was used to identify the effects. The results show remarkable effects on different degrees of generalization processes. It can see that different locations with different geometries were received as the outputs from this analysis. The study suggests that there should be reasonable methods to overcome this effect. It can be recommended that, as a solution, it would be very reasonable to take all the data sets into a common scale and do the analysis part.

Keywords: generalization, GIS, scales, spatial analysis

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Authors: Marco Avila Lopez, Hasnae Ait-Douchi, Silvia De Los Santos, Badr Eddine Lebrouhi, Pamela Ramírez Vidal

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In the context of the energy transition, fuel cell technology has emerged as a solution for harnessing hydrogen energy and mitigating greenhouse gas emissions. An in-depth study was conducted on a PEMFC-type fuel cell, with an initiation of an analysis of its operational principles and constituent components. Subsequently, the modelling of the fuel cell was undertaken using the Python programming language, encompassing both steady-state and transient regimes. In the case of the steady-state regime, the physical and electrochemical phenomena occurring within the fuel cell were modelled, with the assumption of uniform temperature throughout all cell compartments. Parametric identification was carried out, resulting in a remarkable mean error of only 1.62% when the model results were compared to experimental data documented in the literature. The dynamic model that was developed enabled the scrutiny of the fuel cell's response in terms of temperature and voltage under varying current conditions.

Keywords: fuel cell, modelling, dynamic, thermal model, PEMFC

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Authors: Dongping Fang, Lian Duan, Xiaojing Yuan, Mike Xu, Allyn Klunder, Kevin Tan, Suiting Cao, Yeqing Ji

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Accurate prediction of a medical condition with straight clinical evidence is a long-sought topic in the medical management and health insurance field. Although great progress has been made with machine learning algorithms, the medical community is still, to a certain degree, suspicious about the model's accuracy and interpretability. This paper presents an innovative hierarchical attention deep learning model to achieve good prediction and clear interpretability that can be easily understood by medical professionals. This deep learning model uses a hierarchical attention structure that matches naturally with the medical history data structure and reflects the member’s encounter (date of service) sequence. The model attention structure consists of 3 levels: (1) attention on the medical code types (diagnosis codes, procedure codes, lab test results, and prescription drugs), (2) attention on the sequential medical encounters within a type, (3) attention on the medical codes within an encounter and type. This model is applied to predict the occurrence of stage 3 chronic kidney disease (CKD3), using three years’ medical history of Medicare Advantage (MA) members from a top health insurance company. The model takes members’ medical events, both claims and electronic medical record (EMR) data, as input, makes a prediction of CKD3 and calculates the contribution from individual events to the predicted outcome. The model outcome can be easily explained with the clinical evidence identified by the model algorithm. Here are examples: Member A had 36 medical encounters in the past three years: multiple office visits, lab tests and medications. The model predicts member A has a high risk of CKD3 with the following well-contributed clinical events - multiple high ‘Creatinine in Serum or Plasma’ tests and multiple low kidneys functioning ‘Glomerular filtration rate’ tests. Among the abnormal lab tests, more recent results contributed more to the prediction. The model also indicates regular office visits, no abnormal findings of medical examinations, and taking proper medications decreased the CKD3 risk. Member B had 104 medical encounters in the past 3 years and was predicted to have a low risk of CKD3, because the model didn’t identify diagnoses, procedures, or medications related to kidney disease, and many lab test results, including ‘Glomerular filtration rate’ were within the normal range. The model accurately predicts members A and B and provides interpretable clinical evidence that is validated by clinicians. Without extra effort, the interpretation is generated directly from the model and presented together with the occurrence date. Our model uses the medical data in its most raw format without any further data aggregation, transformation, or mapping. This greatly simplifies the data preparation process, mitigates the chance for error and eliminates post-modeling work needed for traditional model explanation. To our knowledge, this is the first paper on an interpretable deep-learning model using a 3-level attention structure, sourcing both EMR and claim data, including all 4 types of medical data, on the entire Medicare population of a big insurance company, and more importantly, directly generating model interpretation to support user decision. In the future, we plan to enrich the model input by adding patients’ demographics and information from free-texted physician notes.

Keywords: deep learning, interpretability, attention, big data, medical conditions

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Authors: Sepideh Mohammadi

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Iran is a country that boasts of ethnic diversity, comprising various groups such as Turks, Lors, Arabs, Baluchs, Persians, Kurds, Gliks, Azaris, and Tabaris. The majority of people in Iran are Persians and as such, the Persian language is the official language of the country. However, it is also a common language among different ethnic groups. It is worth noting that there is a longstanding history of coexistence and cultural relations between the Turkic and Lor ethnic groups. The purpose of this article is to study the range of intercultural sensitivities of Turks and Lor peoples to identify the state of intercultural competence and reduce conflicts in the direction of cultural policy. It is important to gain insight into the mutual perceptions of Lor and Turkic people towards each other. Understanding these perceptions can greatly aid in fostering stronger relationships and promoting effective communication between the two ethnic groups. The study employed a qualitative content analysis approach to gather data using a semi-structured interview tool. The participants consisted of ten individuals from the Lor ethnic and ten individuals from the Turk ethnic. According to Milton Bennett's six-stage model, our findings reveal that the Turkish and Lor ethnic groups tend to exhibit higher intercultural sensitivity in the second stage, which consists of defense against differences. Both groups tend to emphasize the differences between them, and the notion of "us and the other" holds significant importance for them. It is important to acknowledge that both the Turk and Lor ethnicities consist of various clans, which significantly shape intercultural relations between them. A common stereotype in this regard is that the Turks of Tabriz province often do not recognize the Turks of other provinces of the country as their own. Moreover, our study indicates that an increase in interaction and communication between the Lor and Turk ethnic groups may lead to a reduction in cultural sensitivities between them.

Keywords: intercultural communication, intercultural sensitivity, Iran, Lor, Turk

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Authors: Francesco Marotti de Sciarra, Raffaele Barretta

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Starting from nonlocal continuum mechanics, a thermodynamically new nonlocal model of Euler-Bernoulli nanobeams is provided. The nonlocal variational formulation is consistently provided and the governing differential equation for transverse displacement are presented. Higher-order boundary conditions are then consistently derived. An example is contributed in order to show the effectiveness of the proposed model.

Keywords: Bernoulli-Euler beams, nanobeams, nonlocal elasticity, closed-form solutions

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Authors: Kathaleeya Chanda, Panupong Chanplin, Suppara Charoenpoom

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This purposes of the development of creative instruction model through digital media are to: 1) enable learners to learn from instruction media application; 2) help learners implementing instruction media correctly and appropriately; and 3) facilitate learners to apply technology for searching information and practicing skills to implement technology creatively. The sample group consists of 130 cases of secondary students studying in Bo Kluea School, Bo Kluea Nuea Sub-district, Bo Kluea District, Nan Province. The probability sampling was selected through the simple random sampling and the statistics used in this research are percentage, mean, standard deviation and one group pretest – posttest design. The findings are summarized as follows: The congruence index of instruction media for occupation and technology subjects is appropriate. By comparing between learning achievements before implementing the instruction media and learning achievements after implementing the instruction media, it is found that the posttest achievements are higher than the pretest achievements with statistical significance at the level of .05. For the learning achievements from instruction media implementation, pretest mean is 16.24 while posttest mean is 26.28. Besides, pretest and posttest results are compared and differences of mean are tested, the test results show that the posttest achievements are higher than the pretest achievements with statistical significance at the level of .05. This can be interpreted that the learners achieve better learning progress.

Keywords: teaching learning model, digital media, creative instruction model, Bo Kluea school

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Authors: Abeer F. Almarzouki, Christopher A. Brown, Richard J. Brown, Anthony K. P. Jones

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Although relief is commonly assumed to be a direct reflection of pain reduction, it seems to be driven by complex emotional interactions in which pain reduction is only one component. For example, termination of a painful/aversive event may be relieving and rewarding. Accordingly, in this study, whether terminating an aversive negative prediction of pain would be reflected in a greater relief experience was investigated, with a view to separating apart the effects of the manipulation on pain and relief. We use aversive conditioning paradigm to investigate the perception of relief in an aversive (threat) vs. positive context. Participants received positive predictors of a non-painful outcome which were presented within either a congruent positive (non-painful) context or an incongruent threat (painful) context that had been previously conditioned; trials followed by identical laser stimuli on both conditions. Participants were asked to rate the perceived intensity of pain as well as their perception of relief in response to the cue predicting the outcome. Results demonstrated that participants reported more pain in the aversive context compared to the positive context. Conversely, participants reported more relief in the aversive context compares to the neutral context. The rating of relief in the threat context was not correlated with pain reports. The results suggest that relief is not dependant on pain intensity. Consistent with this, relief in the threat context was greater than that in the positive expectancy condition, while the opposite pattern was obtained for the pain ratings. The value of relief in this study is better appreciated in the context of an impending negative threat, which is apparent in the higher pain ratings in the prior negative expectancy compared to the positive expectancy condition. Moreover, the more threatening the context (as manifested by higher unpleasantness/higher state anxiety scores), the more the relief is appreciated. The importance of the study highlights the importance of exploring relief and pain intensity in monitoring separately or evaluating pain-related suffering. The results also illustrate that the perception of painful input may largely be shaped by the context and not necessarily stimulus-related.

Keywords: aversive context, pain, predictions, relief

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Authors: J.P. Henriques, E. R. Neto, G. Almeida, G. Ribeiro, J.V. Coutinho, A.B. Lugli

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Industry 4.0, or the Fourth Industrial Revolution, is transforming the relationship between people and machines. In this scenario, some technologies such as Cloud Computing, Internet of Things, Augmented Reality, Artificial Intelligence, Additive Manufacturing, among others, are making industries and devices increasingly intelligent. One of the most powerful technologies of this new revolution is the Digital Twin, which allows the virtualization of a real system or process. In this context, the present paper addresses the linear and nonlinear dynamic study of a didactic level plant using Digital Twin. In the first part of the work, the level plant is identified at a fixed point of operation, BY using the existing method of least squares means. The linearized model is embedded in a Digital Twin using Automation Studio® from Famous Technologies. Finally, in order to validate the usage of the Digital Twin in the linearized study of the plant, the dynamic response of the real system is compared to the Digital Twin. Furthermore, in order to develop the nonlinear model on a Digital Twin, the didactic level plant is identified by using the method proposed by Hammerstein. Different steps are applied to the plant, and from the Hammerstein algorithm, the nonlinear model is obtained for all operating ranges of the plant. As for the linear approach, the nonlinear model is embedded in the Digital Twin, and the dynamic response is compared to the real system in different points of operation. Finally, yet importantly, from the practical results obtained, one can conclude that the usage of Digital Twin to study the dynamic systems is extremely useful in the industrial environment, taking into account that it is possible to develop and tune controllers BY using the virtual model of the real systems.

Keywords: industry 4.0, digital twin, system identification, linear and nonlinear models

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Authors: Burak Oksuzoglu, Savas Yildirim, Ferhat Kutlu

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Opinion mining (OM) is one of the natural language processing (NLP) problems to determine the polarity of opinions, mostly represented on a positive-neutral-negative axis. The data for OM is usually collected from various social media platforms. In an era where social media has considerable control over companies’ futures, it’s worth understanding social media and taking actions accordingly. OM comes to the fore here as the scale of the discussion about companies increases, and it becomes unfeasible to gauge opinion on individual levels. Thus, the companies opt to automize this process by applying machine learning (ML) approaches to their data. For the last two decades, OM or sentiment analysis (SA) has been mainly performed by applying ML classification algorithms such as support vector machines (SVM) and Naïve Bayes to a bag of n-gram representations of textual data. With the advent of deep learning and its apparent success in NLP, traditional methods have become obsolete. Transfer learning paradigm that has been commonly used in computer vision (CV) problems started to shape NLP approaches and language models (LM) lately. This gave a sudden rise to the usage of the pretrained language model (PTM), which contains language representations that are obtained by training it on the large datasets using self-supervised learning objectives. The PTMs are further fine-tuned by a specialized downstream task dataset to produce efficient models for various NLP tasks such as OM, NER (Named-Entity Recognition), Question Answering (QA), and so forth. In this study, the traditional and modern NLP approaches have been evaluated for OM by using a sizable corpus belonging to a large private company containing about 76,000 comments in Turkish: SVM with a bag of n-grams, and two chosen pre-trained models, multilingual universal sentence encoder (MUSE) and bidirectional encoder representations from transformers (BERT). The MUSE model is a multilingual model that supports 16 languages, including Turkish, and it is based on convolutional neural networks. The BERT is a monolingual model in our case and transformers-based neural networks. It uses a masked language model and next sentence prediction tasks that allow the bidirectional training of the transformers. During the training phase of the architecture, pre-processing operations such as morphological parsing, stemming, and spelling correction was not used since the experiments showed that their contribution to the model performance was found insignificant even though Turkish is a highly agglutinative and inflective language. The results show that usage of deep learning methods with pre-trained models and fine-tuning achieve about 11% improvement over SVM for OM. The BERT model achieved around 94% prediction accuracy while the MUSE model achieved around 88% and SVM did around 83%. The MUSE multilingual model shows better results than SVM, but it still performs worse than the monolingual BERT model.

Keywords: BERT, MUSE, opinion mining, pretrained language model, SVM, Turkish

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Authors: Harish Rajak, Swati Singh

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A series of novel pyrimidine based semicarbazone were designed and synthesized on the basis of semicarbazone based pharmacophoric model to satisfy the structural prerequisite crucial for antiepileptic activity. The semicarbazones based pharmacophoric model consists of following four essential binding sites: (i) An aryl hydrophobic binding site with halo substituent; (ii) A hydrogen bonding domain; (iii) An electron donor group and (iv) Another hydrophobic-hydrophilic site controlling the pharmacokinetic features of the anticonvulsant. The aryl semicarbazones has been recognized as a structurally novel class of compounds with remarkable anticonvulsant activity. In the present study, all the test semicarbazones were subjected to molecular docking using Glide v5.8. Some of the compounds were found to interact with ARG192, GLU270 and THR353 residues of 1OHV protein, present in GABA-AT receptor. The chemical structures of the synthesized molecules were characterized by elemental and spectral (IR, 1H NMR, 13C NMR and MS) analysis. The anticonvulsant activities of the compounds were investigated using maximal electroshock seizure (MES) and subcutaneous pentylenetrtrazole (scPTZ) models. The neurotoxicity was evaluated in mice by the rotorod test. The attempts were also made to establish structure-activity relationships among synthesized compounds. The results of the present study confirmed that the pharmacophore model with four binding sites is essential for antiepileptic activity.

Keywords: pyrimidine, semicarbazones, anticonvulsant activity, neurotoxicity

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Authors: Yaru Meng

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The study contributes to the small but growing literature around computerized approaches to dynamic assessment (C-DA), wherein individual items are accompanied by mediating prompts. Mediation in the current computerized dynamic listening assessment (CDLA) was informed by an attribute-based mediation model (AMM) that identified the underlying L2 listening cognitive abilities and associated descriptors. The AMM served to focus mediation during C-DA on particular cognitive abilities with a goal of specifying areas of learner difficulty. 86 low-intermediate L2 English learners from a university in China completed three listening assessments, with an experimental group receiving the CLDA system and a control group a non-dynamic assessment. As an assessment, the use of the AMM in C-DA generated detailed diagnoses for each learner. In addition, both within- and between-group repeated ANOVA found greater gains at the level of specific attributes among C-DA learners over the course of a 5-week study. Directions for future research are discussed.

Keywords: computerized dynamic assessment, effectiveness, English as foreign language listening, attribute-based mediation model

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Authors: Omnia M.Kandil, Noha M. F. Hassan, Doaa Sedky, Hatem A. Shalaby, Heba M. Ashry, Nadia M. T. Abu El Ezz, Sahar M. Kandeel, Mohamed S. Abdelfattah Ying L, Ebtesam M. Al-Olayan

Abstract:

The cestode, Taenia saginata is a zoonotic tapeworm that it’s larval stage which known as Cysticercus bovis cause cyst formation in cattle’s organs such as heart, lung, liver, tongue, esophagus and diaphragm muscle, despite the infected cattle may show no clinical signs. In view of considerable interest in developing cysticidal drugs including those from medicinal plants, because of their consideration as eco-friendly and biodegradable as well as having multiple bioactive compounds that may translate to multiple mechanisms in killing the parasites. This study was achieved to evaluate, for the first time, the efficacy of methanolic extract of Balanites aegyptiaca fruits and Moringa oleifera seeds against metacestode larval stage of the cestode Taenia saginata in BALB/c mice compared with commonly used anthelmintic albendazole and assigning the level of tumor necrosis factor (TNF-α) to monitor immune and inflammatory response of experimentally infected animals. The results revealed a marked decrease in the numbers of cysticerci found in all treated mice groups and up to 88% reduction was achieved in the B. aegyptiaca treated group; higher than that was recorded in both M. oleifera (72.23%) and albendazole treated ones (80.56%). The cysts of the treated groups were smaller of the control one. Besides, the mean concentration of TNF-α following treatment with Balanites and Moringa extracts, was higher but not significant difference than that in the untreated infected control one (P<0.05), evidence for inflammation and cyst damage. It can be concluded that the in vivo efficacy of M. oleifera extract was comparable to a commercial anthelmintic, and the B. aegyptiaca extract was superior in the reduction of cysticerci numbers.

Keywords: Balanites aeggyptica, Moringa oleifera, cysticercosis, BALB/C mice

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Authors: Merkineh Markos Lorato, Gedefaw Diress Alene

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Introduction: Visual impairment is any condition of the eye or visual system that results in loss/reduction of visual functioning. It significantly influences the academic routine and social activities of children, and the effect is severe for low-income countries like Ethiopia. So, this study aimed to determine the pooled prevalence of visual impairment among school children in Ethiopia. Methods: Databases such as Medical Literature Analysis and Retrieval System Online, Excerpta Medica dataBASE, World Wide Web of Science, and Cochrane Library searched to retrieve eligible articles. In addition, Google Scholar and a reference list of the retrieved eligible articles were addressed. Studies that reported the prevalence of visual impairment were included to estimate the pooled prevalence. Data were extracted using a standardized data extraction format prepared in Microsoft Excel and analysis was held using STATA 11 statistical software. I² was used to assess the heterogeneity. Because of considerable heterogeneity, a random effect meta-analysis model was used to estimate the pooled prevalence of visual impairment among school children in Ethiopia. Results: The result of 9 eligible studies showed that the pooled prevalence of visual impairment among school children in Ethiopia was 7.01% (95% CI: 5.46, 8.56%). In the subgroup analysis, the highest prevalence was reported in South Nations Nationalities and Tigray region together (7.99%; 3.63, 12.35), while the lowest prevalence was reported in Addis Ababa (5.73%; 3.93, 7.53). Conclusion: The prevalence of visual impairment among school children is significantly high in Ethiopia. If it is not detected and intervened early, it will cause a lifetime threat to visually impaired school children, so that school vision screening program plan and its implementation may cure the life quality of future generations in Ethiopia.

Keywords: visual impairment, school children, Ethiopia, prevalence

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