Search results for: equation model

Authors: Shayan Mohajer Hamidi

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Federated learning has emerged as a promising approach for distributed model training while preserving data privacy. However, the challenges of communication overhead, limited network resources, and slow convergence hinder its widespread adoption. On the other hand, knowledge distillation has shown great potential in compressing large models into smaller ones without significant loss in performance. In this paper, we propose an innovative framework that combines federated learning and knowledge distillation to address these challenges and enhance the efficiency of distributed learning. Our approach, called Federated Knowledge Distillation (FKD), enables multiple clients in a federated learning setting to collaboratively distill knowledge from a teacher model. By leveraging the collaborative nature of federated learning, FKD aims to improve model compression while maintaining privacy. The proposed framework utilizes a coded teacher model that acts as a reference for distilling knowledge to the client models. To demonstrate the effectiveness of FKD, we conduct extensive experiments on various datasets and models. We compare FKD with baseline federated learning methods and standalone knowledge distillation techniques. The results show that FKD achieves superior model compression, faster convergence, and improved performance compared to traditional federated learning approaches. Furthermore, FKD effectively preserves privacy by ensuring that sensitive data remains on the client devices and only distilled knowledge is shared during the training process. In our experiments, we explore different knowledge transfer methods within the FKD framework, including Fine-Tuning (FT), FitNet, Correlation Congruence (CC), Similarity-Preserving (SP), and Relational Knowledge Distillation (RKD). We analyze the impact of these methods on model compression and convergence speed, shedding light on the trade-offs between size reduction and performance. Moreover, we address the challenges of communication efficiency and network resource utilization in federated learning by leveraging the knowledge distillation process. FKD reduces the amount of data transmitted across the network, minimizing communication overhead and improving resource utilization. This makes FKD particularly suitable for resource-constrained environments such as edge computing and IoT devices. The proposed FKD framework opens up new avenues for collaborative and privacy-preserving distributed learning. By combining the strengths of federated learning and knowledge distillation, it offers an efficient solution for model compression and convergence speed enhancement. Future research can explore further extensions and optimizations of FKD, as well as its applications in domains such as healthcare, finance, and smart cities, where privacy and distributed learning are of paramount importance.

Keywords: federated learning, knowledge distillation, knowledge transfer, deep learning

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Authors: Mohsen Farhadloo, Majid Farhadloo

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Aspect-level opinion mining that aims at discovering aspects (aspect identification) and their corresponding ratings (sentiment identification) from customer reviews have increasingly attracted attention of researchers and practitioners as it provides valuable insights about products/services from customer's points of view. Instead of addressing aspect identification and sentiment identification in two separate steps, it is possible to simultaneously identify both aspects and sentiments. In recent years many graphical models based on Latent Dirichlet Allocation (LDA) have been proposed to solve both aspect and sentiment identifications in a single step. Although LDA models have been effective tools for the statistical analysis of document collections, they also have shortcomings in addressing some unique characteristics of opinion mining. Our goal in this paper is to address one of the limitations of topic models to date; that is, they fail to directly model the associations among topics. Indeed in many text corpora, it is natural to expect that subsets of the latent topics have higher probabilities. We propose a probabilistic graphical model called focus-LDA, to better capture the associations among topics when applied to aspect-level opinion mining. Our experiments on real-life data sets demonstrate the improved effectiveness of the focus-LDA model in terms of the accuracy of the predictive distributions over held out documents. Furthermore, we demonstrate qualitatively that the focus-LDA topic model provides a natural way of visualizing and exploring unstructured collection of textual data.

Keywords: aspect-level opinion mining, document modeling, Latent Dirichlet Allocation, LDA, sentiment analysis

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Authors: Palwinder Singh, Munish Sandhir, Tejinder Singh

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The ordinary differential equations (ODE) represent a natural framework for mathematical modeling of many real-life situations in the field of engineering, control systems, physics, chemistry and astronomy etc. Such type of differential equations can be solved by analytical methods or by numerical methods. If the solution is calculated using analytical methods, it is done through calculus theories, and thus requires a longer time to solve. In this paper, we compare the numerical accuracy of the solutions given by the three main types of one-step initial value solvers: Taylor’s Series Method, Euler’s Method and Runge-Kutta Fourth Order Method (RK4). The comparison of accuracy is obtained through comparing the solutions of ordinary differential equation given by these three methods. Furthermore, to verify the accuracy; we compare these numerical solutions with the exact solutions.

Keywords: Ordinary differential equations (ODE), Taylor’s Series Method, Euler’s Method, Runge-Kutta Fourth Order Method

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Authors: Sajed Sarabandi, Mostafa Rostampour Vajari

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Breast cancer is the most frequent form of cancer among women and the fifth-leading cause of cancer-related deaths. A common feature of cancer cells is their ability to survive and evade apoptosis. Understanding the mechanisms of these pathways and their regulatory factors can lead to the development of effective treatment strategies. In this study, we aim to model the effect of key miRNAs, which are significant regulatory factors in breast cancer. We designed a Petri net focusing on two crucial pathways, proliferation, and apoptosis, and identified the role of miRNAs in these pathways. Our analysis indicates that the upregulation of miRNAs 99a and 372 can effectively increase apoptosis and decrease proliferation. Moreover, we demonstrate that miRNA-600, previously reported as a potential candidate for treatment, may not be a suitable target due to its dual activity in proliferation. Therefore, further research is required to investigate the potential of this miRNA in cancer treatment. Our model shows that a combination of miRNA upregulation and knockdown can efficiently influence key genes such as MDM2 and PTEN, leading to the activation of apoptosis in cancer cells. Ultimately, our model successfully simulates the connection between regulatory miRNAs and key genes in breast cancer.

Keywords: breast cancer, microRNAs, bio-modeling, Petri net

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Authors: Roghayyeh Motallebzadeh, Shahin Hajizadeh, Mohammad Reza Ghasemi

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Laminar mixed convection heat transfer of a nanofluid with prescribed constant heat flux on the inner wall of horizontal annular tube has been studied numerically based on two-phase mixture model in different Rayleigh numbers and Azimuth angles. Effects of applying of different volume fractions of Al2O3 nanoparticles in water as a base fluid on hydrodynamic and thermal behaviours of the fluid flow such as axial velocity, secondary flow, temperature, heat transfer coefficient and friction coefficient at the inner and outer wall region, has been investigated. Conservation equations in elliptical form has been utilized and solved in three dimensions for a steady flow. It is observed that, there is a good agreement between results in this work and previously published experimental and numerical works on mixed convection in horizontal annulus. These particles cause to increase convection heat transfer coefficient of the fluid, meanwhile there is no considerable effect on friction coefficient.

Keywords: buoyancy force, laminar mixed convection, mixture model, nano-fluid, two-phase

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Authors: Sandrine Andriantsimietry, Hantanirina Ravaosendrasoa

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Madagascar, in 2018, set up the first best available technology, autoclave, to treat the health care waste in public hospitals according the best environmental practices in health care waste management. Incineration of health care waste, frequently through open burning is the most common practice of treatment and elimination of health care waste across the country. Autoclave is a best available technology for non-incineration of health care waste that permits recycling of treated waste and prevents harm in environment through the reduction of unintended persistent organic pollutants from the health sector. A Global Environment Fund project supported the introduction of the non-incineration treatment of health care waste to help countries in Africa to move towards Stockholm Convention objectives in the health sector. Two teaching hospitals in Antananarivo and one district hospital in Manjakandriana were equipped respectively with 1300L, 250L and 80L autoclaves. The capacity of these model hospitals was strengthened by the donation of equipment and materials and the training of the health workers in best environmental practices in health care waste management. Proper segregation of waste in the wards to collect the infectious waste that was treated in the autoclave was the main step guaranteeing a cost-efficient non-incineration of health care waste. Therefore, the start-up of the switch of incineration into non-incineration treatment was carried out progressively in each ward with close supervision of hygienist. Emissions avoided of unintended persistent organic pollutants during these four months of autoclaves use is 9.4 g Toxic Equivalent per year. Public hospitals in low income countries can be model in best environmental practices in health care waste management but efforts must be made internally for sustainment.

Keywords: autoclave, health care waste management, model hospitals, non-incineration

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Authors: Badli Shah Mohd Yusoff, Khairur Rijal Jamaludin, Rozetta Dollah

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The emerging of reverse logistics and product recovery management is an important concept in reconciling economic and environmental objectives through recapturing values of the end of life product returns. End of life products contains valuable modules, parts, residues and materials that can create value if recovered efficiently. The main objective of this study is to explore and develop a model to recover as much of the economic value as reasonably possible to find the optimality of return acquisition and sorting to meet demand and maximize profits over time. In this study, the benefits that can be obtained for remanufacturer is to develop demand forecasting of used products in the future with uncertainty of returns and quality of products. Formulated based on a generic disassembly tree, the proposed model focused on three reverse logistics activity, namely refurbish, remanufacture and disposal incorporating all plausible means quality levels of the returns. While stricter sorting policy, constitute to the decrease amount of products to be refurbished or remanufactured and increases the level of discarded products. Numerical experiments carried out to investigate the characteristics and behaviour of the proposed model with mathematical programming model using Lingo 16.0 for medium-term planning of return acquisition, disassembly (refurbish or remanufacture) and disposal activities. Moreover, the model seeks an analysis a number of decisions relating to trade off management system to maximize revenue from the collection of use products reverse logistics services through refurbish and remanufacture recovery options. The results showed that full utilization in the sorting process leads the system to obtain less quantity from acquisition with minimal overall cost. Further, sensitivity analysis provides a range of possible scenarios to consider in optimizing the overall cost of refurbished and remanufactured products.

Keywords: core acquisition, end of life, reverse logistics, quality uncertainty

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Authors: Dorit Alt, Nirit Raichel

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Educational practice is continually subjected to renewal needs, due mainly to the growing proportion of information communication technology, globalization of education, and the pursuit of quality. These types of renewal needs require developing updated instructional and assessment practices that put a premium on adaptability to the emerging requirements of present society. However, university instruction is criticized for not coping with these new challenges while continuing to exemplify the traditional instruction. In order to overcome this critical inadequacy between current educational goals and instructional methods, the LLAF consortium (including 16 members from 8 countries) is collaborating to create a curricular reform for lifelong learning (LLL) in teachers' education, health care and other applied fields. This project aims to achieve its objectives by developing, and piloting models for training students in LLL and promoting meaningful learning activities that could integrate knowledge with the personal transferable skills. LLAF has created a practical guide for teachers containing updated pedagogical strategies and assessment tools based on the constructivist approach for learning. This presentation will be limited to teachers' education only and to the contribution of a pre-pilot research aimed at providing a scale designed to measure constructivist activities in higher education learning environments. A mix-method approach was implemented in two phases to construct the scale: The first phase included a qualitative content analysis involving both deductive and inductive category applications of students' observations. The results foregrounded eight categories: knowledge construction, authenticity, multiple perspectives, prior knowledge, in-depth learning, teacher- student interaction, social interaction and cooperative dialogue. The students' descriptions of their classes were formulated as 36 items. The second phase employed structural equation modeling (SEM). The scale was submitted to 597 undergraduate students. The goodness of fit of the data to the structural model yielded sufficient fit results. This research elaborates the body of literature by adding a category of in-depth learning which emerged from the content analysis. Moreover, the theoretical category of social activity has been extended to include two distinctive factors: cooperative dialogue and social interaction. Implications of these findings for the LLAF project are discussed.

Keywords: constructivist learning, higher education, mix-methodology, lifelong learning

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Authors: Rajesh Singh, Kailash Kale

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In this paper, the Binomial process type occurrence of software failures is considered and failure intensity has been characterized by one parameter Rayleigh class Software Reliability Growth Model (SRGM). The proposed SRGM is mathematical function of parameters namely; total number of failures i.e. η-0 and scale parameter i.e. η-1. It is assumed that very little or no information is available about both these parameters and then considering non-informative priors for both these parameters, the Bayes estimators for the parameters η-0 and η-1 have been obtained under square error loss function. The proposed Bayes estimators are compared with their corresponding maximum likelihood estimators on the basis of risk efficiencies obtained by Monte Carlo simulation technique. It is concluded that both the proposed Bayes estimators of total number of failures and scale parameter perform well for proper choice of execution time.

Keywords: binomial process, non-informative prior, maximum likelihood estimator (MLE), rayleigh class, software reliability growth model (SRGM)

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Authors: Muthana A. M. Jameel Al-Jaboori

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In this paper, we will present a mathematical model to design a system able to generate electricity from ocean-sea waves. We will use the basic principles of the transfer of the energy potential of waves in a chamber to force the air inside a vertical or inclined cylindrical column, which is topped by a wind turbine to rotate the electric generator. The present mathematical model included a high number of variables such as the wave, height, width, length, velocity, and frequency, as well as others for the energy cylindrical column, like varying diameters and heights, and the wave chamber shape diameter and height. While for the wells wind turbine the variables included the number of blades, length, width, and clearance, as well as the rotor and tip radius. Additionally, the turbine rotor and blades must be made from the light and strong material for a smooth blade surface. The variables were too vast and high in number. Then the program was run successfully within the MATLAB and presented very good modeling results.

Keywords: water wave, models, Wells turbine, MATLAB program

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Authors: Jonathan Heng, Yoong Cheah Huei

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A strategic model that does not trigger any false alarms to detect anomalies in Secure Water Treatment (SWaT) test bed is presented. This model uses machine learning invariants formulated from streamlining the general form of Auto-Regressive models with eXogenous input. A creative generalized CUSUM algorithm to integrate the invariants and the detection strategy technique is successfully developed and tested in the SWaT Programmable Logic Controllers (PLCs). Three steps to fine-tune parameters, b and τ in the generalized algorithm are stated and an example used to demonstrate the tuning process is discussed. This approach can swiftly and effectively detect various scopes of cyber-attacks such as multiple points single stage and multiple points multiple stages in SWaT. This technique can be applied in water treatment plants and other cyber physical systems like power and gas plants too.

Keywords: machine learning invariants, generalized CUSUM algorithm with invariants and detection strategy, scope of cyber attacks, strategic model, tuning parameters

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Authors: Fahad Alsharari, Mohd Salmi Md. Noorani

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A Motzkin shift is a mathematical model for constraints on genetic sequences. In terms of the theory of symbolic dynamics, the Motzkin shift is nonsofic, and therefore, we cannot use the Perron-Frobenius theory to calculate its topological entropy. The Motzkin shift M(M,N) which comes from language theory, is defined to be the shift system over an alphabet A that consists of N negative symbols, N positive symbols and M neutral symbols. For an x in the full shift AZ, x is in M(M,N) if and only if every finite block appearing in x has a non-zero reduced form. Therefore, the constraint for x cannot be bounded in length. K. Inoue has shown that the entropy of the Motzkin shift M(M,N) is log(M + N + 1). In this paper, we find a new method of calculating the topological entropy of the Motzkin shift M(M,N) without any measure theoretical discussion.

Keywords: entropy, Motzkin shift, mathematical model, theory

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Authors: Rahma Fitriani, Eni Sumarminingsih, Suci Astutik

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In a fast growing region, conversion of agricultural lands which are surrounded by some new development sites will occur sooner than expected. This phenomenon has been experienced by many regions in Indonesia, especially the fringe of Jakarta (BoDeTaBek). Being Indonesia’s capital city, rapid conversion of land in this area is an unavoidable process. The land conversion expands spatially into the fringe regions, which were initially dominated by agricultural land or conservation sites. Without proper control or growth management, this activity will invite greater costs than benefits. The current land use is the use which maximizes its value. In order to maintain land for agricultural activity or conservation, some efforts are needed to keep the land value of this activity as high as possible. In this case, the knowledge regarding the functional relationship between land value and its driving forces is necessary. In a fast growing region, development externalities are the assumed dominant driving force. Land value is the product of the past decision of its use leading to its value. It is also affected by the local characteristics and the observed surrounded land use (externalities) from the previous period. The effect of each factor on land value has dynamic and spatial virtues; an empirical spatial dynamic land value model will be more useful to capture them. The model will be useful to test and to estimate the extent of land use externalities on land value in the short run as well as in the long run. It serves as a basis to formulate an effective urban growth management’s policy. This study will apply the model to the case of land value in the fringe of Jakarta Metropolitan. The model will be used further to predict the effect of externalities on land value, in the form of prediction map. For the case of Jakarta’s fringe, there is some evidence about the significance of neighborhood urban activity – negative externalities, the previous land value and local accessibility on land value. The effects are accumulated dynamically over years, but they will fully affect the land value after six years.

Keywords: growth management, land use externalities, land value, spatial panel dynamic

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Authors: Sam Weber, Deepak Mishra, Susan Wilde, Elizabeth Kramer

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The frequency and severity of cyanobacteria harmful blooms (CyanoHABs) have been increasing over time, with point and non-point source eutrophication and shifting climate paradigms being blamed as the primary culprits. Excessive nutrients, warm temperatures, quiescent water, and heavy and less regular rainfall create more conducive environments for CyanoHABs. CyanoHABs have the potential to produce a spectrum of toxins that cause gastrointestinal stress, organ failure, and even death in humans and animals. To promote enhanced, proactive CyanoHAB management, risk modeling using geospatial tools can act as predictive mechanisms to supplement current CyanoHAB monitoring, management and mitigation efforts. The risk maps would empower water managers to focus their efforts on high risk water bodies in an attempt to prevent CyanoHABs before they occur, and/or more diligently observe those waterbodies. For this research, exploratory spatial data analysis techniques were used to identify the strongest predicators for CyanoHAB blooms based on remote sensing-derived cyanobacteria cell density values for 771 waterbodies in the Georgia Piedmont and landscape characteristics of their watersheds. In-situ datasets for cyanobacteria cell density, nutrients, temperature, and rainfall patterns are not widely available, so free gridded geospatial datasets were used as proxy variables for assessing CyanoHAB risk. For example, the percent of a watershed that is agriculture was used as a proxy for nutrient loading, and the summer precipitation within a watershed was used as a proxy for water quiescence. Cyanobacteria cell density values were calculated using atmospherically corrected images from the European Space Agency’s Sentinel-2A satellite and multispectral instrument sensor at a 10-meter ground resolution. Seventeen explanatory variables were calculated for each watershed utilizing the multi-petabyte geospatial catalogs available within the Google Earth Engine cloud computing interface. The seventeen variables were then used in a multiple linear regression model, and the strongest predictors of cyanobacteria cell density were selected for the final regression model. The seventeen explanatory variables included land cover composition, winter and summer temperature and precipitation data, topographic derivatives, vegetation index anomalies, and soil characteristics. Watershed maximum summer temperature, percent agriculture, percent forest, percent impervious, and waterbody area emerged as the strongest predictors of cyanobacteria cell density with an adjusted R-squared value of 0.31 and a p-value ~ 0. The final regression equation was used to make a normalized cyanobacteria cell density index, and a Jenks Natural Break classification was used to assign waterbodies designations of low, medium, or high risk. Of the 771 waterbodies, 24.38% were low risk, 37.35% were medium risk, and 38.26% were high risk. This study showed that there are significant relationships between free geospatial datasets representing summer maximum temperatures, nutrient loading associated with land use and land cover, and the area of a waterbody with cyanobacteria cell density. This data analytics approach to CyanoHAB risk assessment corroborated the literature-established environmental triggers for CyanoHABs, and presents a novel approach for CyanoHAB risk mapping in waterbodies across the greater southeastern United States.

Keywords: cyanobacteria, land use/land cover, remote sensing, risk mapping

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Authors: Terence Soule, Tami Al Ghamdi

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To study how the partial knowledge transfer may affect the Genetic Algorithm (GA) performance, we model the Transfer Learning (TL) process using GA as the model solver. The objective of the TL is to transfer the knowledge from one problem to another related problem. This process imitates how humans think in their daily life. In this paper, we proposed to study a case where the knowledge transferred from the S problem has less information than what the T problem needs. We sampled the transferred population using different strategies of TL. The results showed transfer part of the knowledge is helpful and speeds the GA process of finding a solution to the problem.

Keywords: transfer learning, partial transfer, evolutionary computation, genetic algorithm

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Authors: Kamaludeen Samaila, Khashyaullah Abdulfattah, Fahimi Ahmad Bin Amir

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The study was conducted to examine the relationship between selected factors (performance expectancy, effort expectancy, social influence and facilitating condition) and students’ intention to use the learning management system (LMS), as well as investigating the factors predicting students’ intention to use the LMS. The study was specifically conducted at the Faculty of Educational Study of University Putra Malaysia. Questionnaires were distributed to 277 respondents using a random sampling technique. SPSS Version 22 was employed in analyzing the data; the findings of this study indicated that performance expectancy (r = .69, p < .01), effort expectancy (r=.60, p < .01), social influence (r = .61, p < .01), and facilitating condition (r=.42, p < .01), were significantly related to students’ intention to use the LMS. In addition, the result also revealed that performance expectancy (β = .436, p < .05), social influence (β=.232, p < .05), and effort expectancy (β = .193, p < .05) were strong predictors of students’ intention to use the LMS. The analysis further indicated that (R2) is 0.054 which means that 54% of variation in the dependent variable is explained by the entire predictor variables entered into the regression model. Understanding the factors that affect students’ intention to use the LMS could help the lecturers, LMS managers and university management to develop the policies that may attract students to use the LMS.

Keywords: LMS, postgraduate students, PutraBlas, students’ intention, UPM, UTAUT model

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Authors: Anastasia Rita Widiarti, Agus Harjoko, Marsono, Sri Hartati

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This paper presents the results of a study to test whether the Javanese character manuscript image preprocessing model that have been more widely applied, can also be applied to segment of the Batak characters manuscripts. The treatment process begins by converting the input image into a binary image. After the binary image is cleaned of noise, then the segmentation lines using projection profile is conducted. If unclear histogram projection is found, then the smoothing process before production indexes line segments is conducted. For each line image which has been produced, then the segmentation scripts in the line is applied, with regard of the connectivity between pixels which making up the letters that there is no characters are truncated. From the results of manuscript preprocessing system prototype testing, it is obtained the information about the system truth percentage value on pieces of Pustaka Batak Podani Ma AjiMamisinon manuscript ranged from 65% to 87.68% with a confidence level of 95%. The value indicates the truth percentage shown the initial processing model in Javanese characters manuscript image can be applied also to the image of the Batak characters manuscript.

Keywords: connected component, preprocessing, manuscript image, projection profiles

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Authors: Ravi Prasher

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In nanostructured material thermal transport is dominated by contact resistance. Theoretical models describing thermal transport at interfaces assume perfectly flat surface whereas in reality surfaces can be rough with roughness ranging from sub-nanoscale dimension to micron scale. Here we introduce a model which includes both nanoscale contact mechanics and nanoscale heat transfer for rough nanoscale surfaces. This comprehensive model accounts for the effect of phonon acoustic mismatch, mechanical properties, chemical properties and randomness of the rough surface.

Keywords: adhesion and contact resistance, Kaptiza resistance of rough surfaces, nanoscale thermal transport

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Authors: Mazen Zaki, Sherwat E. Ibrahim

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Renewable energy has become a very important and critical topic all around the world due to the limited resources that led to shifting to the trend of renewable energy and its integration with the conventional ones. This paper investigates the adoption of the solar energy technology for up-scale residential sector in Cairo, Egypt. The technology acceptance model uses several stakeholder points’ of views to develop vignettes to be used in examining the intention and attitude of the householders to adopt the solar energy technology.

Keywords: solar energy, technology acceptance model, TAM, stakeholder analysis, vignette, residential sector

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Authors: Nermin Mahmoud Gohar, Aisha Tarek Noour

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Learner satisfaction has been a vital field of interest in the literature. Accordingly, the paper will explore the reasons behind individual differences in motivation and satisfaction. This study examines the effect of both; Herzberg’s and Maslow’s theories on learners satisfaction. A self-administered questionnaire was used to collect data from learners who were geographically widely spread around the College of Maritime Transport and Technology (CMTT) at the Arab Academy for Science, Technology and Maritime Transport (AAST&MT) in Egypt. One hundred and fifty undergraduates responded to a questionnaire survey. Respondents were drawn from two branches in Alexandria and Port Said. The data analysis used was SPSS 22 and AMOS 18. Factor analysis technique was used to find out the dimensions under study verified by Herzberg’s and Maslow’s theories. In addition, regression analysis and structural equation modeling were applied to find the effect of the above-mentioned theories on maritime transport learners’ satisfaction. Concerning the limitation of this study, it used the available number of learners in the CMTT due to the relatively low population in this field.

Keywords: motivation, satisfaction, needs, education, Herzberg’s and Maslow’s theories

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Authors: Ming-Hwi Yao, Su-Szu Yang

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Typhoons are one of the major types of disasters that affect Taiwan each year and that cause severe damage to agriculture. Indeed, the damage exacted during a typical typhoon season can be up to $1 billion, and is responsible for nearly 75% of yearly agricultural losses. However, there is no consensus on how to reduce the damage caused by the strong winds and heavy precipitation engendered by typhoons. One suggestion is the use of windbreak nets, which are a low-cost and easy-to-use disaster mitigation strategy for crop production. In the present study, we conducted an evaluation to determine the optimal conditions of a windbreak net by using a computational fluid dynamics (CFD) model. This model may be used as a reference for crop protection. The results showed that CFD simulation validated windbreak nets of different mesh sizes and heights in the experimental area; thus, CFD is an efficient tool for evaluating the effectiveness of windbreak nets. Specifically, the effective wind protection length and height were found to be 6 and 1.3 times the length and height of the windbreak net, respectively. During a real typhoon, maximum wind gusts of 18 m s-1 can be reduced to 4 m s-1 by using a windbreak net that has a 70% blocking rate. In short, windbreak nets are significantly effective in protecting typhoon-affected areas.

Keywords: computational fluid dynamics, disaster, typhoon, windbreak net

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Authors: Azizah Hashim, Rohana Ngah

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Transformation of Economic Development is aimed to transform Malaysia to become a high-income developed nation with a knowledge-based economy by 2020. To achieve this national agenda, the country needs to further strengthen its economic development, growth and well-being. Therefore, this study aspires to examine the relationship between market orientation, human resource management and adoption of information communication technology and SMEs performance and cash management as a mediator. This study will employ quantitative approaches. Questionnaires will be distributed to managers and owners in service sectors. The data collected will be analyzed using SPSS and Structural Equation Modelling. Resource Based Theory (RBT) adopts as an integral part of management literature that explains the performance of organizations through building resources and implement of their strategies.

Keywords: small medium enterprises (SMEs), market orientation, human resource management, adoption of information communication technology

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Authors: Ebrahim H. Kapeel, Ehab Safwat, Hossam Hendy, Ahmed M. Kamel, Yehia Z. Elhalwagy

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Motivated by the problem of the availability of high-fidelity flight simulation models for small unmanned aerial vehicles (UAVs). This paper focuses on the geometric-mass inertia modeling and the actuation system modeling for the small fixed-wing UAVs. The UAV geometric parameters for the body, wing, horizontal and vertical tail are physically measured. Pendulum experiment with high-grade sensors and data analysis using MATLAB is used to estimate the airplane moment of inertia (MOI) model. Finally, UAV’s actuation system is modeled by estimating each servo transfer function by using the system identification, which uses experimental measurement for input and output angles through using field-programmable gate array (FPGA). Experimental results for the designed models are given to illustrate the effectiveness of the methodology. It also gives a very promising result to finalize the open-loop flight simulation model through modeling the propulsion system and the aerodynamic system.

Keywords: unmanned aerial vehicle, geometric-mass inertia model, system identification, Simulink

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Authors: Maurice Ntahobari, Levin Kuhlmann, Mario Boley, Zhinoos Razavi Hesabi

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For machine learning based epileptic seizure prediction, it is important for the model to be implemented in small implantable or wearable devices that can be used to monitor epilepsy patients; however, current state-of-the-art methods are complex and computationally intensive. We use Shapley Additive Explanation (SHAP) to find relevant intracranial electroencephalogram (iEEG) features and improve the computational efficiency of a state-of-the-art seizure prediction method based on the extra trees classifier while maintaining prediction performance. Results for a small contest dataset and a much larger dataset with continuous recordings of up to 3 years per patient from 15 patients yield better than chance prediction performance (p < 0.004). Moreover, while the performance of the SHAP-based model is comparable to that of the benchmark, the overall training and prediction time of the model has been reduced by a factor of 1.83. It can also be noted that the feature called zero crossing value is the best EEG feature for seizure prediction. These results suggest state-of-the-art seizure prediction performance can be achieved using efficient methods based on optimal feature selection.

Keywords: machine learning, seizure prediction, extra tree classifier, SHAP, epilepsy

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Authors: Tarek S. Duzan, Fisal Ben Ammer, Mohamed Sula

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In some places within Sirt Basin-Libya, it has been noticed that seismic data below pre-upper cretaceous unconformity (PUK) is hopeless to resolve the large-scale structural features and is unable to fully determine reservoir delineation. Seismic artifacts (multiples) are observed in the reservoir zone (Nubian Formation) below PUK, which complicate the process of seismic interpretation. The nature of the unconformity and the structures below are still ambiguous and not fully understood which generates a significant gap in characterizing the geometry of the reservoir, the uncertainty accompanied with lack of reliable seismic data creates difficulties in building a robust geological model. High resolution dipmeter is highly useful in steeply dipping zones. This paper uses FMl and OBMl borehole images (dipmeter) to analyze the structures below the PUK unconformity from two wells drilled recently in the North Gialo field (a mature reservoir). In addition, borehole images introduce new evidences that the PUK unconformity is angular and the bedding planes within the Nubian formation (below PUK) are significantly titled. Structural dips extracted from high resolution borehole images are used to construct a new geological model by the utilization of latest software technology. Therefore, it is important to use the advance well logs technology such as FMI-HD for any future drilling and up-date the existing model in order to minimize the structural uncertainty.

Keywords: FMI (formation micro imager), OBMI (oil base mud imager), UBI (ultra sonic borehole imager), nub sandstone reservoir in North gialo

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Authors: Muhammad Sajjad-ur-Rehman

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Pakistan was meant to be a progressive modern Muslim nation state since its inception in 1947. Its birth was a big hope for the Muslims of Sub-continent to transform their societies on Islamic lines—the promise which made them unite and vote for Pakistan during independence movement. This was the vision put forwarded by Allama Iqbal and Muhammad Ali Jinnah—the two founding fathers of Pakistan. Dwelling on interpretive/ analytical approach, this paper analyzes the thoughts and reflections of Iqbal and Jinnah to understand the issues of collective identity formation in Pakistan. It argues that there may be traced two distinct identity models in the thoughts and reflections of these two leading figures of Pakistan movement: First may be called as ‘faith-based identity model’ while the other may be named as ‘interests-based identity model’. These can also be entitled as ‘Islam-as-faith model’ and ‘Islam-as-ideology model’. Former seeks the diffusion of power by cultural/ faith based means and thus society remains independent in determining its change. While the later goes on to open and expand the power realm by maximizing the role of state in determining the social change. With the help of these models, it can better be explained that what made Pakistani society fail in the collective political identity construction, hindering thus the political potential of the society to be utilized for initiating state formation and societal growth. As a result, today, we see a state that is often rebelled and resisted on the name of ethnicity, religion and sectarianism on one hand and by the ordinary folk when and wherever possible.

Keywords: idealogy, Iqbal, Jinnah, identity

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Authors: Joel A. Gordon, Nazmiye Balta-Ozkan, Seyed Ali Nabavi

Abstract:

Hydrogen-fueled technologies are rapidly advancing as a critical component of the low-carbon energy transition. In countries historically reliant on natural gas for home heating, such as the UK, hydrogen may prove fundamental for decarbonizing the residential sector, alongside other technologies such as heat pumps and district heat networks. While the UK government is set to take a long-term policy decision on the role of domestic hydrogen by 2026, there are considerable uncertainties regarding consumer preferences for ‘hydrogen homes’ (i.e., hydrogen-fueled appliances for space heating, hot water, and cooking. In comparison to other hydrogen energy technologies, such as road transport applications, to date, few studies have engaged with the social acceptance aspects of the domestic hydrogen transition, resulting in a stark knowledge deficit and pronounced risk to policymaking efforts. In response, this study aims to safeguard against undesirable policy measures by revealing the underlying relationships between the factors of domestic hydrogen acceptance and their respective dimensions: attitudinal, socio-political, community, market, and behavioral acceptance. The study employs an online survey (n=~2100) to gauge how different UK householders perceive the proposition of switching from natural gas to hydrogen-fueled appliances. In addition to accounting for housing characteristics (i.e., housing tenure, property type and number of occupants per dwelling) and several other socio-structural variables (e.g. age, gender, and location), the study explores the impacts of consumer heterogeneity on hydrogen acceptance by recruiting respondents from across five distinct groups: (1) fuel poor householders, (2) technology engaged householders, (3) environmentally engaged householders, (4) technology and environmentally engaged householders, and (5) a baseline group (n=~700) which filters out each of the smaller targeted groups (n=~350). This research design reflects the notion that supporting a socially fair and efficient transition to hydrogen will require parallel engagement with potential early adopters and demographic groups impacted by fuel poverty while also accounting strongly for public attitudes towards net zero. Employing a second-order multigroup confirmatory factor analysis (CFA) in Mplus, the proposed hydrogen acceptance model is tested to fit the data through a partial least squares (PLS) approach. In addition to testing differences between and within groups, the findings provide policymakers with critical insights regarding the significance of knowledge and awareness, safety perceptions, perceived community impacts, cost factors, and trust in key actors and stakeholders as potential explanatory factors of hydrogen acceptance. Preliminary results suggest that knowledge and awareness of hydrogen are positively associated with support for domestic hydrogen at the household, community, and national levels. However, with the exception of technology and/or environmentally engaged citizens, much of the population remains unfamiliar with hydrogen and somewhat skeptical of its application in homes. Knowledge and awareness present as critical to facilitating positive safety perceptions, alongside higher levels of trust and more favorable expectations for community benefits, appliance performance, and potential cost savings. Based on these preliminary findings, policymakers should be put on red alert about diffusing hydrogen into the public consciousness in alignment with energy security, fuel poverty, and net-zero agendas.

Keywords: hydrogen homes, social acceptance, consumer heterogeneity, heat decarbonization

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Authors: Sakhon Woothipatanapan, Surasit Prakobkit

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This paper presents a model for analysis the induced voltage of transmission lines (energized) acting on neighboring distribution lines (de-energized). From environmental restrictions, 22 kV distribution lines need to be installed under 115 kV transmission lines. With the installation of the two parallel circuits like this, they make the induced voltage which can cause harm to operators. This work was performed with the ATP-EMTP modeling to analyze such phenomenon before field testing. Simulation results are used to find solutions to prevent danger to operators who are on the pole.

Keywords: transmission system, distribution system, induced voltage, off-line operation

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Authors: Ahmed El-Banbi

Abstract:

Several PVT models exist to represent how PVT properties are handled in sub-surface and surface engineering calculations for oil and gas production. The most commonly used models include black oil, modified black oil (MBO), and compositional models. These models are used in calculations that allow engineers to optimize and forecast well and reservoir performance (e.g., reservoir simulation calculations, material balance, nodal analysis, surface facilities, etc.). The choice of which model is dependent on fluid type and the production process (e.g., depletion, water injection, gas injection, etc.). Based on close to 2,000 reservoir fluid samples collected from different basins and locations, this paper presents some conclusions on the occurrence of reservoir fluids. It also reviews the common methods used to classify reservoir fluid types. Based on new criteria related to the production behavior of different fluids and economic considerations, an updated classification of reservoir fluid types is presented in the paper. Recommendations on the use of different PVT models to simulate the behavior of different reservoir fluid types are discussed. Each PVT model requirement is highlighted. Available methods for the calculation of PVT properties from each model are also discussed. Practical recommendations and tips on how to control the calculations to achieve the most accurate results are given.

Keywords: PVT models, fluid types, PVT properties, fluids classification

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Authors: Audrius Dedele, Aukse Miskinyte

Abstract:

Air pollution is a major problem, especially in large cities, causing a variety of environmental issues and a risk to human health effects. In order to observe air quality, to reduce and control air pollution in the city, municipalities are responsible for the creation of air quality management plans, air quality monitoring and emission inventories. Atmospheric dispersion modelling systems, along with monitoring, are powerful tools, which can be used not only for air quality management, but for the assessment of human exposure to air pollution. These models are widely used in epidemiological studies, which try to determine the associations between exposure to air pollution and the adverse health effects. The purpose of this study was to determine the concentration of particulate matter smaller than 10 μm (PM10) in different districts of Kaunas city during winter season. ADMS-Urban dispersion model was used for the simulation of PM10 pollution. The inputs of the model were the characteristics of stationary, traffic and domestic sources, emission data, meteorology and background concentrations were entered in the model. To assess the modelled concentrations of PM10 in Kaunas districts, geographic information system (GIS) was used. More detailed analysis was made using Spatial Analyst tools. The modelling results showed that the average concentration of PM10 during winter season in Kaunas city was 24.8 µg/m3. The highest PM10 levels were determined in Zaliakalnis and Aleksotas districts with are the highest number of individual residential properties, 32.0±5.2 and 28.7±8.2 µg/m3, respectively. The lowest pollution of PM10 was modelled in Petrasiunai district (18.4 µg/m3), which is characterized as commercial and industrial neighbourhood.

Keywords: air pollution, dispersion model, GIS, Particulate matter

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