Search results for: tilt model

Authors: Racholsan Raj Nirmal, B. S. V. Patnaik, R. Jayaganthan

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The deformation behaviour of additively manufactured AlSi10Mg alloy under low strains, high strain rates and elevated temperature conditions is essential to analyse and predict its response against dynamic loading such as impact and thermomechanical fatigue. The constitutive relation of Johnson-Cook is used to capture the strain rate sensitivity and thermal softening effect in AlSi10Mg alloy. Johnson-Cook failure model is widely used for exploring damage mechanics and predicting the fracture in many materials. In this present work, Johnson-Cook material and damage model parameters for additively manufactured AlSi10Mg alloy have been determined numerically from four types of uniaxial tensile test. Three different uniaxial tensile tests with dynamic strain rates (0.1, 1, 10, 50, and 100 s^-1) and elevated temperature tensile test with three different temperature conditions (450 K, 500 K and 550 K) were performed on 3D printed AlSi10Mg alloy in ABAQUS/Explicit. Hexahedral elements are used to discretize tensile specimens and fracture energy value of 43.6 kN/m was used for damage initiation. Levenberg Marquardt optimization method was used for the evaluation of Johnson-Cook model parameters. It was observed that additively manufactured AlSi10Mg alloy has shown relatively higher strain rate sensitivity and lower thermal stability as compared to the other Al alloys.

Keywords: ABAQUS, additive manufacturing, AlSi10Mg, Johnson-Cook model

Procedia PDF Downloads 168

Authors: Safaa H. Sindi, Michael Roe

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This project divides supply chain (SC) models into seven Eras, according to the evolution of the market’s needs throughout time. The five earliest Eras describe the emergence of supply chains, while the last two Eras are to be created. Research objectives: The aim is to generate the two latest Eras with their respective models that focus on the consumable goods. Era Six contains the Optimal Multidimensional Matrix (OMM) that incorporates most characteristics of the SC and allocates them into four quarters (Agile, Lean, Leagile, and Basic SC). This will help companies, especially (SMEs) plan their optimal SC route. Era Seven creates an Automated Multidimensional Model (AMM) which upgrades the matrix of Era six, as it accounts for all the supply chain factors (i.e. Offshoring, sourcing, risk) into an interactive system with Heuristic Learning that helps larger companies and industries to select the best SC model for their market. Methodologies: The data collection is based on a Fuzzy-Delphi study that analyses statements using Fuzzy Logic. The first round of Delphi study will contain statements (fuzzy rules) about the matrix of Era six. The second round of Delphi contains the feedback given from the first round and so on. Preliminary findings: both models are applicable, Matrix of Era six reduces the complexity of choosing the best SC model for SMEs by helping them identify the best strategy of Basic SC, Lean, Agile and Leagile SC; that’s tailored to their needs. The interactive heuristic learning in the AMM of Era seven will help mitigate error and aid large companies to identify and re-strategize the best SC model and distribution system for their market and commodity, hence increasing efficiency. Potential contributions to the literature: The problematic issue facing many companies is to decide which SC model or strategy to incorporate, due to the many models and definitions developed over the years. This research simplifies this by putting most definition in a template and most models in the Matrix of era six. This research is original as the division of SC into Eras, the Matrix of Era six (OMM) with Fuzzy-Delphi and Heuristic Learning in the AMM of Era seven provides a synergy of tools that were not combined before in the area of SC. Additionally the OMM of Era six is unique as it combines most characteristics of the SC, which is an original concept in itself.

Keywords: Leagile, automation, heuristic learning, supply chain models

Procedia PDF Downloads 388

Authors: Upasana G. Singh, Meera Gungea

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Learning is no more confined to the traditional classroom, teacher, and student interaction. Many universities offer courses through the Open and Distance Learning (ODL) mode, attracting a diversity of learners in terms of age, gender, and profession to name a few. The ODL mode has surfaced as one of the famous sought-after modes of learning, allowing learners to invest in their educational growth without hampering their personal and professional commitments. This mode of learning, however, requires that those who ultimately choose to adopt it must be prepared to undertake studies through such medium. The purpose of this research is to assess whether students who join universities offering courses through the ODL mode are ready to embark and study within such a framework. This study will be helpful to unveil the challenges students face in such an environment and thus contribute to developing a framework to ease adoption and integration into the ODL environment. Prior to the implementation of e-learning, a readiness assessment is essential for any institution that wants to adopt any form of e-learning. Various e-learning readiness assessment models have been developed over the years. However, this study is based on a conceptual model for e-Learning Readiness Assessment which is a ‘hybrid model’. This hybrid model consists of 4 main parameters: 1) Technological readiness, 2) Culture readiness, 3) Content readiness, and 4) Demographics factors, with 4 sub-areas, namely, technology, innovation, people and self-development. The model also includes the attitudes of users towards the adoption of e-learning as an important aspect of assessing e-learning readiness. For this study, some factors and sub-factors of the hybrid model have been considered and adapted, together with the ‘Attitude’ component. A questionnaire was designed based on the models and students where the target population were students enrolled at the Open University of Mauritius, in undergraduate and postgraduate courses. Preliminary findings indicate that most (68%) learners have an average knowledge about ODL form of learning, despite not many (72%) having previous experience with ODL. Despite learning through ODL 74% of learners preferred hard copy learning material and 48% found difficulty in reading learning material on electronic devices.

Keywords: open learning, distance learning, student readiness, a hybrid model

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Authors: K. E. Oyetade, S. D. Eyono Obono

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One of the fundamental characteristics of Information and Communication Technology (ICT) has been the ever-changing nature of continuous release and models of ICTs with its impact on the academic, social, and psychological benefits of its introduction in schools. However, there seems to be a growing concern about its negative impact on students when introduced early in schools for teaching and learning. This study aims to design a model of child development factors affecting the early introduction of ICTs in schools in an attempt to improve the understanding of child development and introduction of ICTs in schools. The proposed model is based on a sound theoretical framework. It was designed following a literature review of child development theories and child development factors. The child development theoretical framework that fitted to the best of all child development factors was then chosen as the basis for the proposed model. This study hence found that the Jean Piaget cognitive developmental theory is the most adequate theoretical frameworks for modeling child development factors for ICT introduction in schools.

Keywords: child development factors, child development theories, ICTs, theory

Procedia PDF Downloads 411

Authors: Tero Huhtala, Minna Pikkarainen, Saila Saraniemi

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Information technology has long been used as an enabler of exchange for goods and services. Services are evolving from generic to personalized, and the reverse use of customer data has been discussed in both academia and industry for the past few years. This article presents the results of an empirical case study in the area of preventive health care services. The primary data were gathered in workshops, in which future personal data-based services were conceptualized by analyzing future scenarios from a business perspective. The aim of this study is to understand business model transformation in emerging personal data ecosystems. The work was done as a case study in the context of occupational healthcare. The results have implications to theory and practice, indicating that adopting personal data management principles requires transformation of the business model, which, if successfully managed, may provide access to more resources, potential to offer better value, and additional customer channels. These advantages correlate with the broadening of the business ecosystem. Expanding the scope of this study to include more actors would improve the validity of the research. The results draw from existing literature and are based on findings from a case study and the economic properties of the healthcare industry in Finland.

Keywords: ecosystem, business model, personal data, preventive healthcare

Procedia PDF Downloads 249

Authors: Bothinah Altaf, Gary Montague, Elaine B. Martin

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This study involves the modeling and monitoring of an ammonia synthesis fixed-bed reactor using partial least squares (PLS) and its variants. The process exhibits complex dynamic behavior due to the presence of heat recycling and feed quench. One limitation of static PLS model in this situation is that it does not take account of the process dynamics and hence dynamic PLS was used. Although it showed, superior performance to static PLS in terms of prediction, the monitoring scheme was inappropriate hence adaptive PLS was considered. A limitation of adaptive PLS is that non-conforming observations also contribute to the model, therefore, a new adaptive approach was developed, robust adaptive dynamic PLS. This approach updates a dynamic PLS model and is robust to non-representative data. The developed methodology showed a clear improvement over existing approaches in terms of the modeling of the reactor and the detection of faults.

Keywords: ammonia synthesis fixed-bed reactor, dynamic partial least squares modeling, recursive partial least squares, robust modeling

Procedia PDF Downloads 392

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

Procedia PDF Downloads 270

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: 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

Procedia PDF Downloads 229

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

Procedia PDF Downloads 493

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

Procedia PDF Downloads 338

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

Procedia PDF Downloads 282

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: 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

Procedia PDF Downloads 329

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: 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: 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: 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

Abstract:

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

Abstract:

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: F. Jiménez, R. Jódar, M. Martín, G. Sánchez, G. Sciavicco

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Abstract—Attribute or feature selection is one of the basic strategies to improve the performances of data classification tasks, and, at the same time, to reduce the complexity of classifiers, and it is a particularly fundamental one when the number of attributes is relatively high. Its application to unsupervised classification is restricted to a limited number of experiments in the literature. Evolutionary computation has already proven itself to be a very effective choice to consistently reduce the number of attributes towards a better classification rate and a simpler semantic interpretation of the inferred classifiers. We present a feature selection wrapper model composed by a multi-objective evolutionary algorithm, the clustering method Expectation-Maximization (EM), and the classifier C4.5 for the unsupervised classification of data extracted from a psychological test named BASC-II (Behavior Assessment System for Children - II ed.) with two objectives: Maximizing the likelihood of the clustering model and maximizing the accuracy of the obtained classifier. We present a methodology to integrate feature selection for unsupervised classification, model evaluation, decision making (to choose the most satisfactory model according to a a posteriori process in a multi-objective context), and testing. We compare the performance of the classifier obtained by the multi-objective evolutionary algorithms ENORA and NSGA-II, and the best solution is then validated by the psychologists that collected the data.

Keywords: evolutionary computation, feature selection, classification, clustering

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Authors: Haseen Siddiqui, Sanjay M. Mahajani

Abstract:

Various important reactions in chemical and metallurgical industries fall in the category of gas-solid reactions. These reactions can be categorized as catalytic and non-catalytic gas-solid reactions. In gas-solid reaction systems, heat and mass transfer limitations put an appreciable influence on the rate of the reaction. The consequences can be unavoidable for overlooking such effects while collecting the reaction rate data for the design of the reactor. Pyrolysis reaction comes in this category that involves the production of gases due to the interaction of heat and solid substance. Pyrolysis is also an important step in the gasification process and therefore, the gasification reactivity majorly influenced by the pyrolysis process that produces the char, as a feed for the gasification process. Therefore, in the present study, a non-isothermal transient 1-D model is developed for a single biomass pellet to investigate the effect of heat and mass transfer limitations on the rate of pyrolysis reaction. The obtained set of partial differential equations are firstly discretized using the concept of ‘method of lines’ to obtain a set of ordinary differential equation with respect to time. These equations are solved, then, using MATLAB ode solver ode15s. The model is capable of incorporating structural changes, porosity variation, variation in various thermal properties and various pellet shapes. The model is used to analyze the effectiveness factor for different values of Lewis number and heat of reaction (G factor). Lewis number includes the effect of thermal conductivity of the solid pellet. Higher the Lewis number, the higher will be the thermal conductivity of the solid. The effectiveness factor was found to be decreasing with decreasing Lewis number due to the fact that smaller Lewis numbers retard the rate of heat transfer inside the pellet owing to a lower rate of pyrolysis reaction. G factor includes the effect of the heat of reaction. Since the pyrolysis reaction is endothermic in nature, the G factor takes negative values. The more the negative value higher will be endothermic nature of the pyrolysis reaction. The effectiveness factor was found to be decreasing with more negative values of the G factor. This behavior can be attributed to the fact that more negative value of G factor would result in more energy consumption by the reaction owing to a larger temperature gradient inside the pellet. Further, the analytical expressions are also derived for gas and solid concentrations and effectiveness factor for two limiting cases of the general model developed. The two limiting cases of the model are categorized as the homogeneous model and unreacted shrinking core model.

Keywords: effectiveness factor, G-factor, homogeneous model, lewis number, non-catalytic, shrinking core model

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Authors: Harish Rajak, Preeti Patel

Abstract:

HDAC inhibitors can reactivate gene expression and inhibit the growth and survival of cancer cells. The 3D-QSAR and Pharmacophore modeling studies were performed to identify important pharmacophoric features and correlate 3D-chemical structure with biological activity. The pharmacophore hypotheses were developed using e-pharmacophore script and phase module. Pharmacophore hypothesis represents the 3D arrangement of molecular features necessary for activity. A series of 55 compounds with well-assigned HDAC inhibitory activity was used for 3D-QSAR model development. Best 3D-QSAR model, which is a five PLS factor model with good statistics and predictive ability, acquired Q2 (0.7293), R2 (0.9811) and standard deviation (0.0952). Molecular docking were performed using Histone Deacetylase protein (PDB ID: 1t69) and prepared series of hydroxamic acid based HDAC inhibitors. Docking study of compound 43 show significant binding interactions Ser 276 and oxygen atom of dioxine cap region, Gly 151 and amino group and Asp 267 with carboxyl group of CONHOH, which are essential for anticancer activity. On docking, most of the compounds exhibited better glide score values between -8 to -10.5. We have established structure activity correlation using docking, energetic based pharmacophore modelling, pharmacophore and atom based 3D QSAR model. The results of these studies were further used for the design and testing of new HDAC analogs.

Keywords: Docking, e-pharmacophore, HDACIs, QSAR, Suberoylanilidehydroxamic acid.

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