Search results for: balance training

Authors: Tebogo Emma Makaba, Barnabas Ndlovu Gatsheni

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The cities of Johannesburg and Pretoria both located in the Gauteng province are separated by a distance of 58 km. The traffic queues on the Ben Schoeman freeway which connects these two cities can stretch for almost 1.5 km. Vehicle traffic congestion impacts negatively on the business and the commuter’s quality of life. The goal of this paper is to identify variables that influence the flow of traffic and to design a vehicle traffic prediction model, which will predict the traffic flow pattern in advance. The model will unable motorist to be able to make appropriate travel decisions ahead of time. The data used was collected by Mikro’s Traffic Monitoring (MTM). Multi-Layer perceptron (MLP) was used individually to construct the model and the MLP was also combined with Bagging ensemble method to training the data. The cross—validation method was used for evaluating the models. The results obtained from the techniques were compared using predictive and prediction costs. The cost was computed using combination of the loss matrix and the confusion matrix. The predicted models designed shows that the status of the traffic flow on the freeway can be predicted using the following parameters travel time, average speed, traffic volume and day of month. The implications of this work is that commuters will be able to spend less time travelling on the route and spend time with their families. The logistics industry will save more than twice what they are currently spending.

Keywords: Bagging ensemble methods, confusion matrix, multi-layer perceptron, vehicle traffic flow.

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Authors: R. Ramli, M. I. Ghazali, H. Ibrahim, M. M. Kasim, F. M. Kamal, S.Vikneswari

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Teachers form the backbone of any educational system, hence selecting qualified candidates is very crucial. In Malaysia, the decision making in the selection process involves a few stages: Initial filtering through academic achievement, taking entry examination and going through an interview session. The last stage is the most challenging since it highly depends on human judgment. Therefore, this study sought to identify the selection criteria for teacher candidates that form the basis for an efficient multi-criteria teacher-candidate selection model for that last stage. The relevant criteria were determined from the literature and also based on expert input that is those who were involved in interviewing teacher candidates from a public university offering the formal training program. There are three main competency criteria that were identified which are content of knowledge, communication skills and personality. Further, each main criterion was divided into a few subcriteria. The Analytical Hierarchy Process (AHP) technique was employed to allocate weights for the criteria and later, integrated a Simple Weighted Average (SWA) scoring approach to develop the selection model. Subsequently, a web-based Decision Support System was developed to assist in the process of selecting the qualified teacher candidates. The Teacher-Candidate Selection (TeCaS) system is able to assist the panel of interviewers during the selection process which involves a large amount of complex qualitative judgments.

Keywords: Analytic Hierarchy Process, Simple Weighted Average, Decision Support System, Multi-criteria decision making problem.

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Authors: G. Sandhya Devi, G. Suvarna Kumar, S. Chandini

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This research work aims to develop a system that will analyze and identify students who indulge in malpractices/suspicious activities during the course of an academic offline examination. Automated Video Surveillance provides an optimal solution which helps in monitoring the students and identifying the malpractice event immediately. This work is organized into three modules. The first module deals with performing an impersonation check using a PCA-based face recognition method which is done by cross checking his profile with the database. The presence or absence of the student is even determined in this module by implementing an image registration technique wherein a grid is formed by considering all the images registered using the frontal camera at the determined positions. Second, detecting such facial malpractices in which a student gets involved in conversation with another, trying to obtain unauthorized information etc., based on the threshold range evaluated by considering his/her mouth state whether open or closed. The third module deals with identification of unauthorized material or gadgets used in the examination hall by training the positive samples of the object through various stages. Here, a top view camera feed is analyzed to detect the suspicious activities. The system automatically alerts the administration when any suspicious activities are identified, thereby reducing the error rate caused due to manual monitoring. This work is an improvement over our previous work published in identifying suspicious activities done by examinees in an offline examination.

Keywords: Impersonation, image registration, incrimination, object detection, threshold evaluation.

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Authors: Moustafa Osman Mohammed

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Construction industry, as one of the main contributor in depletion of natural resources, influences climate change. This paper discusses incremental and evolutionary development of the proposed models for optimization of a life-cycle analysis to explicit strategy for evaluation systems. The main categories are virtually irresistible for introducing uncertainties, uptake composite structure model (CSM) as environmental management systems (EMSs) in a practice science of evaluation small and medium-sized enterprises (SMEs). The model simplified complex systems to reflect nature systems’ input, output and outcomes mode influence “framework measures” and give a maximum likelihood estimation of how elements are simulated over the composite structure. The traditional knowledge of modeling is based on physical dynamic and static patterns regarding parameters influence environment. It unified methods to demonstrate how construction systems ecology interrelated from management prospective in procedure reflects the effect of the effects of engineering systems to ecology as ultimately unified technologies in extensive range beyond constructions impact so as, - energy systems. Sustainability broadens socioeconomic parameters to practice science that meets recovery performance, engineering reflects the generic control of protective systems. When the environmental model employed properly, management decision process in governments or corporations could address policy for accomplishment strategic plans precisely. The management and engineering limitation focuses on autocatalytic control as a close cellular system to naturally balance anthropogenic insertions or aggregation structure systems to pound equilibrium as steady stable conditions. Thereby, construction systems ecology incorporates engineering and management scheme, as a midpoint stage between biotic and abiotic components to predict constructions impact. The later outcomes’ theory of environmental obligation suggests either a procedures of method or technique that is achieved in sustainability impact of construction system ecology (SICSE), as a relative mitigation measure of deviation control, ultimately.

Keywords: Sustainability, constructions ecology, composite structure model, design structure matrix, environmental impact assessment, life cycle analysis, climate change.

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Authors: Abobaker Mohammed Qasem Farhan, Shangming Yang, Mohammed Al-Nehari

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Pneumonia is a kind of lung disease that creates congestion in the chest. Such pneumonic conditions lead to loss of life due to the severity of high congestion. Pneumonic lung disease is caused by viral pneumonia, bacterial pneumonia, or COVID-19 induced pneumonia. The early prediction and classification of such lung diseases help reduce the mortality rate. We propose the automatic Computer-Aided Diagnosis (CAD) system in this paper using the deep learning approach. The proposed CAD system takes input from raw computerized tomography (CT) scans of the patient's chest and automatically predicts disease classification. We designed the Hybrid Deep Learning Algorithm (HDLA) to improve accuracy and reduce processing requirements. The raw CT scans are pre-processed first to enhance their quality for further analysis. We then applied a hybrid model that consists of automatic feature extraction and classification. We propose the robust 2D Convolutional Neural Network (CNN) model to extract the automatic features from the pre-processed CT image. This CNN model assures feature learning with extremely effective 1D feature extraction for each input CT image. The outcome of the 2D CNN model is then normalized using the Min-Max technique. The second step of the proposed hybrid model is related to training and classification using different classifiers. The simulation outcomes using the publicly available dataset prove the robustness and efficiency of the proposed model compared to state-of-art algorithms.

Keywords: CT scans, COVID-19, deep learning, image processing, pneumonia, lung disease.

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Authors: Peyman Shadman Heidari, Roohollah Ahmady Jazany, Mahmood Reza Mehran, Pouya Shadman Heidari, Mohammad khorasani

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Modeling of Panel Zone (PZ) seismic behavior, because of its role in overall ductility and lateral stiffness of steel moment frames, has been considered a challenge for years. There are some studies regarding the effects of different doubler plates thicknesses and geometric properties of PZ on its seismic behavior. However, there is not much investigation on the effects of number of provided continuity plates in case of presence of one triangular haunch, two triangular haunches and rectangular haunch (T shape haunches) for exterior columns. In this research first detailed finite element models of 12tested connection of SAC joint venture were created and analyzed then obtained cyclic behavior backbone curves of these models besides other FE models for similar tests were used for neural network training. Then seismic behavior of these data is categorized according to continuity plate-s arrangements and differences in type of haunches. PZ with one-sided haunches have little plastic rotation. As the number of continuity plates increases due to presence of two triangular haunches (four continuity plate), there will be no plastic rotation, in other words PZ behaves in its elastic range. In the case of rectangular haunch, PZ show more plastic rotation in comparison with one-sided triangular haunch and especially double-sided triangular haunches. Moreover, the models that will be presented in case of triangular one-sided and double- sided haunches and rectangular haunches as a result of this study seem to have a proper estimation of PZ seismic behavior.

Keywords: Continuity plate, FE models, Neural network, Panel zone, Plastic rotation, Rectangular haunch, Seismic behavior

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Authors: Do Thi Ha, Khanitta Nuntaboot

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Background: Competency of nurses is vital to safe nursing practice as well as essential component to drive quality of nursing services. There exists little up to date information concerning actual competency among Vietnamese nurses. Purposes: The purpose of this study is to identify the actual nursing competency among nurses in clinical settings in Vietnam. Methods: A qualitative study, ethnographic method, comprised of the participant-observation, in-depth interview, and focus group discussion with multidisciplinary groups of nurses employing in Cho Ray hospital, Vietnam, managers/administrators, nurse teachers, medical doctors, other health care providers, patients and family members which derived from purposeful sampling technique. Content analysis was used for data analysis. Results: Five essential themes of nursing competencies among nurses were identified include (1) knowledge, (2) skills, (3) attitude and value-based nursing practice, (4) legal and ethical competencies, and (5) transcultural competencies. Basic and advanced knowledge were identified as further two dimensions of knowledge. There were five sub themes identified as further dimensions of skills include technical skills, communication skills, organizing and management skills, teamwork and interrelationship, and critical thinking skills. Conclusions: The findings from this study provide valuable information and understanding of the actual competency among nurses in clinical settings in Vietnam. It is expected that this understanding would assist in developing a guide to nursing education and training, nursing practice and relevant policy regulation used for promoting nursing competency among nurses.

Keywords: Nursing competency, qualitative design, ethnographic method, Vietnam.

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Authors: Achela K. Fernando, Xiujuan Zhang, Peter F. Kinley

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A feed-forward, back-propagation Artificial Neural Network (ANN) model has been used to forecast the occurrences of wastewater overflows in a combined sewerage reticulation system. This approach was tested to evaluate its applicability as a method alternative to the common practice of developing a complete conceptual, mathematical hydrological-hydraulic model for the sewerage system to enable such forecasts. The ANN approach obviates the need for a-priori understanding and representation of the underlying hydrological hydraulic phenomena in mathematical terms but enables learning the characteristics of a sewer overflow from the historical data. The performance of the standard feed-forward, back-propagation of error algorithm was enhanced by a modified data normalizing technique that enabled the ANN model to extrapolate into the territory that was unseen by the training data. The algorithm and the data normalizing method are presented along with the ANN model output results that indicate a good accuracy in the forecasted sewer overflow rates. However, it was revealed that the accurate forecasting of the overflow rates are heavily dependent on the availability of a real-time flow monitoring at the overflow structure to provide antecedent flow rate data. The ability of the ANN to forecast the overflow rates without the antecedent flow rates (as is the case with traditional conceptual reticulation models) was found to be quite poor.

Keywords: Artificial Neural Networks, Back-propagationlearning, Combined sewer overflows, Forecasting.

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Authors: Mannan Mridha, Usama Gazay, Kosovare V. Aslani, Hugo Linder, Alice Ravizza, Carmelo de Maria

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In most developing countries, although the vast majority of the people are living in the rural areas, the qualified medical doctors are not available there. Health care workers and paramedics, called village doctors, informal healthcare providers, are largely responsible for the rural medical care. Mishaps due to wrong diagnosis and inappropriate medication have been causing serious suffering that is preventable. While innovators have created many devices, the vast majority of these technologies do not find applications to address the needs and conditions in low-resource settings. The primary motive is to address the acute lack of affordable medical technologies for the poor people in low-resource settings. A low cost smart medical device that is portable, battery operated and can be used at any point of care has been developed to detect breathing rate, electrocardiogram (ECG) and arterial pulse rate to improve diagnosis and monitoring of patients and thus improve care and safety. This simple and easy to use smart medical device can be used, managed and maintained effectively and safely by any health worker with some training. In order to empower the health workers and village doctors, our device is being further developed to integrate with ICT tools like smart phones and connect to the medical experts wherever available, to manage the serious health problems.

Keywords: Healthcare for low resources settings, health awareness education, improve patient care and safety, smart and affordable medical device.

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Authors: Ronald F. Gonzales, Gordon W. Romney, Pradip Peter Dey, Mohammad Amin, Bhaskar Raj Sinha

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A virtualized and virtual approach is presented on academically preparing students to successfully engage at a strategic perspective to understand those concerns and measures that are both structured and not structured in the area of cyber security and information assurance. The Master of Science in Cyber Security and Information Assurance (MSCSIA) is a professional degree for those who endeavor through technical and managerial measures to ensure the security, confidentiality, integrity, authenticity, control, availability and utility of the world-s computing and information systems infrastructure. The National University Cyber Security and Information Assurance program is offered as a Master-s degree. The emphasis of the MSCSIA program uniquely includes hands-on academic instruction using virtual computers. This past year, 2011, the NU facility has become fully operational using system architecture to provide a Virtual Education Laboratory (VEL) accessible to both onsite and online students. The first student cohort completed their MSCSIA training this past March 2, 2012 after fulfilling 12 courses, for a total of 54 units of college credits. The rapid pace scheduling of one course per month is immensely challenging, perpetually changing, and virtually multifaceted. This paper analyses these descriptive terms in consideration of those globalization penetration breaches as present in today-s world of cyber security. In addition, we present current NU practices to mitigate risks.

Keywords: Cyber security, information assurance, mitigate risks, virtual machines, strategic perspective.

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Authors: Amah Nnachi Lofty, Oyefeso Olufemi, Ibiam Udu Ama

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New and more powerful communications technologies continue to emerge at a rapid pace and their uses in education are widespread and the impact remarkable in the developing societies. This study investigates Mobile Collaboration Learning Technique (MCLT) on learners’ outcome among students in tertiary institutions of developing nations (a case of Nigeria students). It examines the significance of retention achievement scores of students taught using mobile collaboration and conventional method. The sample consisted of 120 students using Stratified random sampling method. Five research questions and hypotheses were formulated, and tested at 0.05 level of significance. A student achievement test (SAT) was made of 40 items of multiple-choice objective type, developed and validated for data collection by professionals. The SAT was administered to students as pre-test and post-test. The data were analyzed using t-test statistic to test the hypotheses. The result indicated that students taught using MCLT performed significantly better than their counterparts using the conventional method of instruction. Also, there was no significant difference in the post-test performance scores of male and female students taught using MCLT. Based on the findings, the following submissions was made that: Mobile collaboration system be encouraged in the institutions to boost knowledge sharing among learners, workshop and training should be organized to train teachers on the use of this technique, schools and government should consistently align curriculum standard to trends of technological dictates and formulate policies and procedures towards responsible use of MCLT.

Keywords: Education, communication, learning, mobile collaboration, technology.

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Authors: Hussain Syed Asad, Richard Kwok Kit Yuen, Gongsheng Huang

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Real-time optimization has been considered an effective approach for improving energy efficient operation of heating, ventilation, and air-conditioning (HVAC) systems. In model-based real-time optimization, model mismatches cannot be avoided. When model mismatches are significant, the performance of the real-time optimization will be impaired and hence the expected energy saving will be reduced. In this paper, the model mismatches for chiller plant on real-time optimization are considered. In the real-time optimization of the chiller plant, simplified semi-physical or grey box model of chiller is always used, which should be identified using available operation data. To overcome the model mismatches associated with the chiller model, hybrid Genetic Algorithms (HGAs) method is used for online real-time training of the chiller model. HGAs combines Genetic Algorithms (GAs) method (for global search) and traditional optimization method (i.e. faster and more efficient for local search) to avoid conventional hit and trial process of GAs. The identification of model parameters is synthesized as an optimization problem; and the objective function is the Least Square Error between the output from the model and the actual output from the chiller plant. A case study is used to illustrate the implementation of the proposed method. It has been shown that the proposed approach is able to provide reliability in decision making, enhance the robustness of the real-time optimization strategy and improve on energy performance.

Keywords: Energy performance, hybrid adaptive modeling, hybrid genetic algorithms, real-time optimization, heating, ventilation, and air-conditioning.

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Authors: Rao Fu, Jie Chen, Shutian Zeng, Yiping Zhuang, Agus Sudjianto

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Generative Adversarial Net (GAN) has proved to be a powerful machine learning tool in image data analysis and generation. In this paper, we propose to use Conditional Generative Adversarial Net (CGAN) to learn and simulate time series data. The conditions include both categorical and continuous variables with different auxiliary information. Our simulation studies show that CGAN has the capability to learn different types of normal and heavy-tailed distributions, as well as dependent structures of different time series. It also has the capability to generate conditional predictive distributions consistent with training data distributions. We also provide an in-depth discussion on the rationale behind GAN and the neural networks as hierarchical splines to establish a clear connection with existing statistical methods of distribution generation. In practice, CGAN has a wide range of applications in market risk and counterparty risk analysis: it can be applied to learn historical data and generate scenarios for the calculation of Value-at-Risk (VaR) and Expected Shortfall (ES), and it can also predict the movement of the market risk factors. We present a real data analysis including a backtesting to demonstrate that CGAN can outperform Historical Simulation (HS), a popular method in market risk analysis to calculate VaR. CGAN can also be applied in economic time series modeling and forecasting. In this regard, we have included an example of hypothetical shock analysis for economic models and the generation of potential CCAR scenarios by CGAN at the end of the paper.

Keywords: Conditional Generative Adversarial Net, market and credit risk management, neural network, time series.

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Authors: Eman A. Embaby, Amira A. A. Abdallah

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Background: Occupational standing is associated with low back pain (LBP) development. Yet, trunk and gluteus-medius muscles’ fatigability has not been extensively studied during occupational standing. This study examined and correlated the rectus abdominus (RA), erector-spinae (ES), external oblique (EO), and gluteus-medius (GM) muscles’ fatigability on both sides while standing in a confined area for 30min Methods: Median frequency EMG data were collected from 15 female clinical instructors with chronic LBP (group A) and 15 asymptomatic controls (group B) (mean age 29.53±2.4 vs 29.07±2.4years, weight 63.6±7 vs 60±7.8kg, and height 162.73±4 vs 162.8±6cm respectively) using a spectrum analysis program. Data were collected in the first and last 5min of the standing task. Results: Using Mixed three-way ANOVA, group A showed significantly (p<0.05) lower frequencies for the right and left ES, and right GM in the last 5min and significantly higher frequencies for the left RA in the first and last 5min than group B. In addition, the left ES and right EO, ES and GM in group B showed significantly higher frequencies and the left ES in group A showed significantly lower frequencies in the last 5min compared with the first. Moreover, the right RA showed significantly higher frequencies than the left in the last 5min in group B. Finally, there were significant (p<0.05) correlations among the median frequencies of the tested four muscles on the same side and between both sides in both groups. Discussion/Conclusions: Clinical instructors with LBP are more liable to have higher trunk and gluteus-medius muscle fatigue than asymptomatic individuals. Thus, endurance training for these muscles should be included in the rehabilitation of such patients.

Keywords: EMG, Fatigability, Gluteus-medius, LBP, Standing, Trunk.

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Authors: I. Lucas, N. Schomberg, F-A. Couturier

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Most empirical studies have analyzed how liquidity risks faced by individual institutions turn into systemic risk. Recent banking crisis has highlighted the importance of grasping and controlling the systemic risk, and the acceptance by Central Banks to ease their monetary policies for saving default or illiquid banks. This last point shows that banks would pay less attention to liquidity risk which, in turn, can become a new important channel of loss. The financial regulation focuses on the most important and “systemic” banks in the global network. However, to quantify the expected loss associated with liquidity risk, it is worth to analyze sensitivity to this channel for the various elements of the global bank network. A small bank is not considered as potentially systemic; however the interaction of small banks all together can become a systemic element. This paper analyzes the impact of medium and small banks interaction on a set of banks which is considered as the core of the network. The proposed method uses the structure of agent-based model in a two-class environment. In first class, the data from actual balance sheets of 22 large and systemic banks (such as BNP Paribas or Barclays) are collected. In second one, to model a network as closely as possible to actual interbank market, 578 fictitious banks smaller than the ones belonging to first class have been split into two groups of small and medium ones. All banks are active on the European interbank network and have deposit and market activity. A simulation of 12 three month periods representing a midterm time interval three years is projected. In each period, there is a set of behavioral descriptions: repayment of matured loans, liquidation of deposits, income from securities, collection of new deposits, new demands of credit, and securities sale. The last two actions are part of refunding process developed in this paper. To strengthen reliability of proposed model, random parameters dynamics are managed with stochastic equations as rates the variations of which are generated by Vasicek model. The Central Bank is considered as the lender of last resort which allows banks to borrow at REPO rate and some ejection conditions of banks from the system are introduced.

Liquidity crunch due to exogenous crisis is simulated in the first class and the loss impact on other bank classes is analyzed though aggregate values representing the aggregate of loans and/or the aggregate of borrowing between classes. It is mainly shown that the three groups of European interbank network do not have the same response, and that intermediate banks are the most sensitive to liquidity risk.

Keywords: Systemic Risk, Financial Contagion, Liquidity Risk, Interbank Market, Network Model.

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Authors: Justyna Humięcka-Jakubowska

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Background in music analysis: Traditionally, when we think about a composer’s sketches, the chances are that we are thinking in terms of the working out of detail, rather than the evolution of an overall concept. Since music is a “time art,” it follows that questions of a form cannot be entirely detached from considerations of time. One could say that composers tend to regard time either as a place gradually and partially intuitively filled, or they can look for a specific strategy to occupy it. It seems that the one thing that sheds light on Stockhausen’s compositional thinking is his frequent use of “form schemas,” that is often a single-page representation of the entire structure of a piece. Background in music technology: Sonic Visualiser is a program used to study a musical recording. It is an open source application for viewing, analyzing, and annotating music audio files. It contains a number of visualisation tools, which are designed with useful default parameters for musical analysis. Additionally, the Vamp plugin format of SV supports to provide analysis such as for example structural segmentation. Aims: The aim of paper is to show how SV may be used to obtain a better understanding of the specific musical work, and how the compositional strategy does impact on musical structures and musical surfaces. It is known that “traditional” music analytic methods don’t allow indicating interrelationships between musical surface (which is perceived) and underlying musical/acoustical structure. Main Contribution: Stockhausen had dealt with the most diverse musical problems by the most varied methods. A characteristic which he had never ceased to be placed at the center of his thought and works, it was the quest for a new balance founded upon an acute connection between speculation and intuition. In the case with Mikrophonie I (1964) for tam-tam and 6 players Stockhausen makes a distinction between the “connection scheme,” which indicates the ground rules underlying all versions, and the form scheme, which is associated with a particular version. The preface to the published score includes both the connection scheme, and a single instance of a “form scheme,” which is what one can hear on the CD recording. In the current study, the insight into the compositional strategy chosen by Stockhausen was been compared with auditory image, that is, with the perceived musical surface. Stockhausen’s musical work is analyzed both in terms of melodic/voice and timbre evolution. Implications: The current study shows how musical structures have determined of musical surface. The general assumption is this, that while listening to music we can extract basic kinds of musical information from musical surfaces. It is shown that interactive strategies of musical structure analysis can offer a very fruitful way of looking directly into certain structural features of music.

Keywords: Automated analysis, composer's strategy, Mikrophonie I, musical surface, Stockhausen.

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Authors: Aziz, Nur Mardhiyah, Salleh, Hafez

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Implementing Information Technology/ Information System (IT/IS) is critical for every industry as its potential benefits have been to motivate many industries including the Malaysian construction industry to invest in it. To successfully implement IT/IS has become the major concern for every organisation. Identifying the critical success factors (CSFs) has become the main agenda for researchers, academicians and practitioners due to the wide number of failures reported. This research paper seeks to identify the CSFs that influence the successful implementation of IT/IS in construction industry in Malaysia. Limited factors relating to people issue will be highlighted here to showcase some as it becomes one of the major contributing factors to the failure. Three (3) organisations have participated in this study. Semi-structured interviews are employed as they offer sufficient flexibility to ensure that all relevant factors are covered. Several key issues contributing to successful implementations of IT/IS are identified. The results of this study reveal that top management support, communication, user involvement, IT staff roles and responsibility, training/skills, leader/ IT Leader, organisation culture, knowledge/ experience, motivation, awareness, focus and ambition, satisfaction, teamwork/ collaboration, willingness to change, attitude, commitment, management style, interest in IT, employee behaviour towards collaborative environment, trust, interpersonal relationship, personal characteristic and competencies are significantly associated with the successful implementations of IT/IS. It is anticipated that this study will create awareness and contribute to a better understanding amongst construction industry players and will assist them to successfully implement IT/IS.

Keywords: critical success factors, construction industry , IT/IS, people

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Authors: Shinji Kajiwara

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The basic ability of a vehicle is to “run”, “turn” and “stop”. The safeness and comfort during a drive on various road surfaces and speed depends on the performance of these basic abilities of the vehicle. Stability and maneuverability of a vehicle are vital in automotive engineering. The stability of a vehicle is the ability of the vehicle to revert back to a stable state during a drive when faced with crosswinds and irregular road conditions. Maneuverability of a vehicle is the ability of the vehicle to change direction during a drive swiftly based on the steering of the driver. The stability and maneuverability of a vehicle can also be defined as the driving stability of the vehicle. Since the fossil fueled vehicle is the main type of transportation today, the environmental factor in automotive engineering is also vital. By improving the fuel efficiency of the vehicle, the overall carbon emission will be reduced, thus reducing the effect of global warming and greenhouse gas on the Earth. Another main focus of the automotive engineering is the safety performance of the vehicle, especially with the worrying increase of vehicle collision every day. With better safety performance of a vehicle, every driver will be more confident driving every day. Next, let us focus on the “turn” ability of a vehicle. By improving this particular ability of the vehicle, the cornering limit of the vehicle can be improved, thus increasing the stability and maneuverability factor. In order to improve the cornering limit of the vehicle, a study to find the balance between the steering systems, the stability of the vehicle, higher lateral acceleration and the cornering limit detection must be conducted. The aim of this research is to study and develop a new suspension system that will boost the lateral acceleration of the vehicle and ultimately improving the cornering limit of the vehicle. This research will also study environmental factor and the stability factor of the new suspension system. The double wishbone suspension system is widely used in a four-wheel vehicle, especially for high cornering performance sports car and racing car. The double wishbone designs allow the engineer to carefully control the motion of the wheel by controlling such parameters as camber angle, caster angle, toe pattern, roll center height, scrub radius, scuff, and more. The development of the new suspension system will focus on the ability of the new suspension system to optimize the camber control and to improve the camber limit during a cornering motion. The research will be carried out using the CAE analysis tool. Using this analysis tool we will develop a JSAE Formula Machine equipped with the double wishbone system and also the new suspension system and conduct simulation and conduct studies on the performance of both suspension systems.

Keywords: Automobile, Camber Thrust, Cornering force, Suspension.

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Authors: Efua Ehiaguina, Haruna Moda

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Interest in the development of appropriate safety culture in the oil and gas industry has taken centre stage among stakeholders in the industry. Human behaviour has been identified as a major contributor to occupational accidents, where abnormal activities associated with safety management are taken as normal behaviour. Poor safety culture is one of the major factors that influence employee’s safety behaviour at work, which may consequently result in injuries and accidents and strengthening such a culture can improve workers safety performance. Nigeria oil and gas industry has contributed to the growth and development of the country in diverse ways. However, in terms of safety and health of workers, this industry is a dangerous place to work as workers are often exposed to occupational safety and health hazard. To ascertain the impact of employees’ safety and how it impacts health and safety compliance within the local industry, online safety culture survey targeting frontline workers within the industry was administered covering major subjects that include; perception of management commitment and style of leadership; safety communication method and its resultant impact on employees’ behaviour; employee safety commitment and training needs. The preliminary result revealed that 54% of the participants feel that there is a lack of motivation from the management to work safely. In addition, 55% of participants revealed that employers place more emphasis on work delivery over employee’s safety on the installation. It is expected that the study outcome will provide measures aimed at strengthening and sustaining safety culture in the Nigerian oil and gas industry.

Keywords: Oil and gas safety, safety behaviour, safety culture, safety compliance.

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Authors: Shahlan Surat, Saemah Rahman, Saadiah Kummin

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When teachers reflect and evaluate whether their teaching methods actually have an impact on students’ learning, they will adjust their practices accordingly. This inevitably improves their students’ learning and performance. The approach in meta-teaching can invigorate and create a passion for teaching. It thus helps to increase the commitment and love for the teaching profession. This study was conducted to determine the level of metacognitive thinking of teachers in the process of teaching and learning in the classroom. Metacognitive thinking teachers include the use of metacognitive knowledge which consists of different types of knowledge: declarative, procedural and conditional. The ability of the teachers to plan, monitor and evaluate the teaching process can also be determined. This study was conducted on 377 graduate teachers in Klang Valley, Malaysia. The stratified sampling method was selected for the purpose of this study. The metacognitive teaching inventory consisting of 24 items is called InKePMG (Teacher Indicators of Effectiveness Meta-Teaching). The results showed the level of mean is high for two components of metacognitive knowledge; declarative knowledge (mean = 4.16) and conditional (mean = 4.11) whereas, the mean of procedural knowledge is 4.00 (moderately high). Similarly, the level of knowledge in monitoring (mean = 4.11), evaluating (mean = 4.00) which indicate high score and planning (mean = 4.00) are moderately high score among teachers. In conclusion, this study shows that the planning and procedural knowledge is an important element in improving the quality of teachers teaching in the classroom. Thus, the researcher recommended that further studies should focus on training programs for teachers on metacognitive skills and also on developing creative thinking among teachers.

Keywords: Metacognitive thinking skills, procedural knowledge, conditional knowledge, declarative knowledge, meta-teaching and regulation of cognitive.

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Authors: Alireza Mortezaei, Kimia Mortezaei

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Recordings from recent earthquakes have provided evidence that ground motions in the near field of a rupturing fault differ from ordinary ground motions, as they can contain a large energy, or “directivity" pulse. This pulse can cause considerable damage during an earthquake, especially to structures with natural periods close to those of the pulse. Failures of modern engineered structures observed within the near-fault region in recent earthquakes have revealed the vulnerability of existing RC buildings against pulse-type ground motions. This may be due to the fact that these modern structures had been designed primarily using the design spectra of available standards, which have been developed using stochastic processes with relatively long duration that characterizes more distant ground motions. Many recently designed and constructed buildings may therefore require strengthening in order to perform well when subjected to near-fault ground motions. Fiber Reinforced Polymers are considered to be a viable alternative, due to their relatively easy and quick installation, low life cycle costs and zero maintenance requirements. The objective of this paper is to investigate the adequacy of Artificial Neural Networks (ANN) to determine the three dimensional dynamic response of FRP strengthened RC buildings under the near-fault ground motions. For this purpose, one ANN model is proposed to estimate the base shear force, base bending moments and roof displacement of buildings in two directions. A training set of 168 and a validation set of 21 buildings are produced from FEA analysis results of the dynamic response of RC buildings under the near-fault earthquakes. It is demonstrated that the neural network based approach is highly successful in determining the response.

Keywords: Seismic evaluation, FRP, neural network, near-fault ground motion

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Authors: Derek Wardle, Tarik Al-Shemmeri, Neil Packer

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This paper presents a small tube/wire type electrostatic precipitator (ESP). In the ESPs present form, particle charging and collecting voltages and airflow rates were individually varied throughout 200 ambient temperature test runs ranging from 10 to 30 kV in increments on 5 kV and 0.5 m/s to 1.5 m/s, respectively. It was repeatedly observed that, at input air velocities of between 0.5 and 0.9 m/s and voltage settings of 20 kV to 30 kV, the collection efficiency remained above 95%. The outcomes of preliminary tests at combustion flue temperatures are, at present, inconclusive although indications are that there is little or no drop in comparable performance during ideal test conditions. A limited set of similar tests was carried out during which the collecting electrode was grounded, having been disconnected from the static generator. The collecting efficiency fell significantly, and for that reason, this approach was not pursued further. The collecting efficiencies during ambient temperature tests were determined by mass balance between incoming and outgoing dry PM. The efficiencies of combustion temperature runs are determined by analysing the difference in opacity of the flue gas at inlet and outlet compared to a reference light source. In addition, an array of Leit tabs (carbon coated, electrically conductive adhesive discs) was placed at inlet and outlet for a number of four-day continuous ambient temperature runs. Analysis of the discs’ contamination was carried out using scanning electron microscopy and ImageJ computer software that confirmed collection efficiencies of over 99% which gave unequivocal support to all the previous tests. The average efficiency for these runs was 99.409%. Emissions collected from a woody biomass combustion unit, classified to a diameter of 100 µm, were used in all ambient temperature trials test runs apart from two which collected airborne dust from within the laboratory. Sawdust and wood pellets were chosen for laboratory and field combustion trials. Video recordings were made of three ambient temperature test runs in which the smoke from a wood smoke generator was drawn through the precipitator. Although these runs were visual indicators only, with no objective other than to display, they provided a strong argument for the device’s claimed efficiency, as no emissions were visible at exit when energised.  The theoretical performance of ESPs, when applied to the geometry and configuration of the tested model, was compared to the actual performance and was shown to be in good agreement with it.

Keywords: Electrostatic precipitators, air quality, particulates emissions, electron microscopy, ImageJ.

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Authors: Akrem Sellami, Imed Riadh Farah

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Hyperspectral imagery (HSI) typically provides a wealth of information captured in a wide range of the electromagnetic spectrum for each pixel in the image. Hence, a pixel in HSI is a high-dimensional vector of intensities with a large spectral range and a high spectral resolution. Therefore, the semantic interpretation is a challenging task of HSI analysis. We focused in this paper on object classification as HSI semantic interpretation. However, HSI classification still faces some issues, among which are the following: The spatial variability of spectral signatures, the high number of spectral bands, and the high cost of true sample labeling. Therefore, the high number of spectral bands and the low number of training samples pose the problem of the curse of dimensionality. In order to resolve this problem, we propose to introduce the process of dimensionality reduction trying to improve the classification of HSI. The presented approach is a semi-supervised band selection method based on spatial hypergraph embedding model to represent higher order relationships with different weights of the spatial neighbors corresponding to the centroid of pixel. This semi-supervised band selection has been developed to select useful bands for object classification. The presented approach is evaluated on AVIRIS and ROSIS HSIs and compared to other dimensionality reduction methods. The experimental results demonstrate the efficacy of our approach compared to many existing dimensionality reduction methods for HSI classification.

Keywords: Hyperspectral image, spatial hypergraph, dimensionality reduction, semantic interpretation, band selection, feature extraction.

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Authors: Somayeh Komeylian

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The direction of arrival (DoA) estimation is the crucial aspect of the radar technologies for detecting and dividing several signal sources. In this scenario, the antenna array output modeling involves numerous parameters including noise samples, signal waveform, signal directions, signal number, and signal to noise ratio (SNR), and thereby the methods of the DoA estimation rely heavily on the generalization characteristic for establishing a large number of the training data sets. Hence, we have analogously represented the two different optimization models of the DoA estimation; (1) the implementation of the decision directed acyclic graph (DDAG) for the multiclass least-squares support vector machine (LS-SVM), and (2) the optimization method of the deep neural network (DNN) radial basis function (RBF). We have rigorously verified that the LS-SVM DDAG algorithm is capable of accurately classifying DoAs for the three classes. However, the accuracy and robustness of the DoA estimation are still highly sensitive to technological imperfections of the antenna arrays such as non-ideal array design and manufacture, array implementation, mutual coupling effect, and background radiation and thereby the method may fail in representing high precision for the DoA estimation. Therefore, this work has a further contribution on developing the DNN-RBF model for the DoA estimation for overcoming the limitations of the non-parametric and data-driven methods in terms of array imperfection and generalization. The numerical results of implementing the DNN-RBF model have confirmed the better performance of the DoA estimation compared with the LS-SVM algorithm. Consequently, we have analogously evaluated the performance of utilizing the two aforementioned optimization methods for the DoA estimation using the concept of the mean squared error (MSE).

Keywords: DoA estimation, adaptive antenna array, Deep Neural Network, LS-SVM optimization model, radial basis function, MSE.

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Authors: Trina Isabel D. Santiago, Daniel C. Chua, Jumee F. Tayaban, Joseph Daniel S. Timbol, Joshua M. Yanes

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Numerous instances of human rights violations on healthcare workers have been reported during the COVID-19 pandemic in the Philippines. This paper aims to explore these civil and political rights violations and propose recommendations to address these. Our review shows that a wide range of civic and political human rights violations have been committed by individual citizens and government agencies on individual healthcare workers and health worker groups. These violations include discrimination, red-tagging, evictions, illegal arrests, and acts of violence ranging from chemical attacks to homicide. If left unchecked, these issues, compounded by the pandemic, may lead to the exacerbations of the pre-existing problems of the Philippine healthcare system. Despite all pre-existing reports by human rights groups and public media articles, there still seems to be a lack of government action to condemn and prevent these violations. The existence of government agencies which directly contribute to these violations with the lack of condemnation from other agencies further propagate the problem. Given these issues, this policy brief recommends the establishment of an interagency task force for the protection of human rights of healthcare workers as well as the expedited passing of current legislative bills towards the same goal. For more immediate action, we call for the establishment of a dedicated hotline for these incidents with adequate appointment and training of point persons, construction of clear guidelines, and closer collaboration between government agencies in being united against these issues.

Keywords: COVID-19 pandemic, healthcare workers, human rights violations, Philippines.

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Authors: Sama’ila Idi Ningi, Abubakar Yusuf Dutse

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The banking sector poses a lot of problems in Nigeria in general and the non-oil export sector in particular. The banks' lack effectiveness in handling small, medium or long-term credit risk (lack of training of loan officers, lack of information on borrowers and absence of a reliable credit registry) results in non-oil exporters being burdened with high requirements, such as up to three years of financial statements, enough collateral to cover both the loan principal and interest (including a cash deposit that may be up to 30% of the loans' net present value), and to provide every detail of the international trade transaction in question. The stated problems triggered this research. Consequently, information on bank financing of non-oil exports was collected from 100 respondents from the 20 Deposit Money Banks (DMBs) in Nigeria. The data was analysed by the use of descriptive statistics correlation and regression. It is found that, Nigerian banks are participants in the financing of non-oil exports. Despite their participation, the rate of interest for credit extended to non-oil export is usually high, ranging between 15-20%. Small and medium sized non-oil export businesses lack the credit history for banks to judge them as reputable. Banks also consider the non-oil export sector very risky for investment. The banks actually do grant less credit than the exporters may require and therefore are not properly funded by banks. Banks grant very low volume of foreign currency loan in addition to, unfavorable exchange rate at which Naira is exchanged to the Dollar and other currencies in the country. This makes importation of inputs costly and negatively impacted on the non-oil export performance in Nigeria.

Keywords: Supply Side Factors, Bank Financing, Non-Oil Exports.

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Authors: Dina Tareq Ismail, Alexandria A. Proff

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The United Arab Emirates (UAE) is a nation seeking to advance in all fields, particularly education. One area of focus for UAE 2021 agenda is to restructure UAE schools and universities by equipping them with highly developed technology. The agenda also advises educational institutions to prepare students with applicable and transferrable Information and Communication Technology (ICT) skills. Despite the emphasis on ICT and computer literacy skills, there exists limited empirical data on the use of M-Learning in the literature. This qualitative study explores the motivation of higher primary Arabic teachers in private schools toward implementing and integrating M-Learning apps in their classrooms. This research employs a phenomenological approach through the use of semistructured interviews with nine purposefully selected Arabic teachers. The data were analyzed using a content analysis via multiple stages of coding: open, axial, and thematic. Findings reveal three primary themes: (1) Arabic teachers with high levels of procedural knowledge in ICT are more motivated to implement M-Learning; (2) Arabic teachers' perceptions of self-efficacy influence their motivation toward implementation of M-Learning; (3) Arabic teachers implement M-Learning when they possess high utility and/or intrinsic value in these applications. These findings indicate a strong need for further training, equipping, and creating buy-in among Arabic teachers to enhance their ICT skills in implementing M-Learning. Further, given the limited availability of M-Learning apps designed for use in the Arabic language on the market, it is imperative that developers consider designing M-Learning tools that Arabic teachers, and Arabic-speaking students, can use and access more readily. This study contributes to closing the knowledge gap on teacher-motivation for implementing M-Learning in their classrooms in the UAE.

Keywords: ICT Skills, M-Learning, self-efficacy, teachermotivation.

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Authors: Mohammad T. Uddin, Aurup R. Dhar

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The study was conducted to assess the impact of conservation agriculture practice on farmers’ socioeconomic condition and soil environmental quality in Bangladesh. A total of 450 (i.e., 50 focal, 150 proximal and 250 control) farmers from five districts were selected for this study. Descriptive statistics like sum, averages, percentages, etc. were calculated to evaluate the socioeconomic data. Using Enyedi’s crop productivity index, it was found that the crop productivity of focal, proximal and control farmers was increased by 0.9, 1.2 and 1.3 percent, respectively. The result of DID (Difference-in-difference) analysis indicated that the impact of conservation agriculture practice on farmers’ average annual income was significant. Multidimensional poverty index (MPI) indicates that poverty in terms of deprivation of health, education and living standards was decreased; and a remarkable improvement in farmers’ socioeconomic status was found after adopting conservation agriculture practice. Most of the focal and proximal farmers stated about increased soil environmental condition where majority of control farmers stated about constant environmental condition in this regard. The Probit model reveals that minimum tillage operation, permanent organic soil cover, and application of compost and vermicompost were found significant factors affecting soil environmental quality under conservation agriculture. Input support, motivation, training programmes and extension services are recommended to implement in order to raise the awareness and enrich the knowledge of the farmers on conservation agriculture practice.

Keywords: Conservation agriculture, crop productivity, socioeconomic status, soil environment quality.

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Authors: Saba Gachpaz, Hamid Reza Heidari

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The sharp increase in population growth leads to more pressure on agricultural areas to satisfy the food supply. This necessitates increased resource consumption and underscores the importance of addressing sustainable agriculture development along with other environmental considerations. Land-use management is a crucial factor in obtaining optimum productivity. Machine learning is a widely used technique in the agricultural sector, from yield prediction to customer behavior. This method focuses on learning and provides patterns and correlations from our data set. In this study, nine physical control factors, namely, soil classification, electrical conductivity, normalized difference water index (NDWI), groundwater level, elevation, annual precipitation, pH of water, annual mean temperature, and slope in the alluvial plain in Khuzestan (an agricultural hotspot in Iran) are used to decide the best agricultural land use for both rainfed and irrigated agriculture for 10 different crops. For this purpose, each variable was imported into Arc GIS, and a raster layer was obtained. In the next level, by using training samples, all layers were imported into the python environment. A random forest model was applied, and the weight of each variable was specified. In the final step, results were visualized using a digital elevation model, and the importance of all factors for each one of the crops was obtained. Our results show that despite 62% of the study area being allocated to agricultural purposes, only 42.9% of these areas can be defined as a suitable class for cultivation purposes.

Keywords: Land suitability, machine learning, random forest, sustainable agriculture.

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Authors: Marjan Mahdavi-Roshan, Arsalan Salari, Mahbobeh Gholipour

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Hypertension is one of the important reasons of morbidity and mortality in countries, including Iran. It has been shown that hypertension is a consequence of the interaction of genetics and environment. Nutrients have important roles in the controlling of blood pressure. We assessed dietary habit and anthropometric status in patients with hypertension in the north of Iran, and that have special dietary habit and according to their culture. This study was conducted on 127 patients with newly recognized hypertension and the 120 normotensive participants. Anthropometric status was measured and demographic characteristics, and medical condition were collected by valid questionnaires and dietary habit assessment was assessed with 3-day food recall (two weekdays and one weekend). The mean age of participants was 58 ± 6.7 years. The mean level of energy intake, saturated fat, vitamin D, potassium, zinc, dietary fiber, vitamin C, calcium, phosphorus, copper and magnesium was significantly lower in the hypertensive group compared to the control (p < 0.05). After adjusting for energy intake, positive association was observe between hypertension and some dietary nutrients including; Cholesterol [OR: 1.1, P: 0.001, B: 0.06], fiber [OR: 1.6, P: 0.001, B: 1.8], vitamin D [OR: 2.6, P: 0.006, B: 0.9] and zinc [OR: 1.4, P: 0.006, B: 0.3] intake. Logistic regression analysis showed that there was not significant association between hypertension, weight and waist circumference. In our study, the mean intake of some nutrients was lower in the hypertensive individuals compared to the normotensive individual. Health training about suitable dietary habits and easier access to vitamin D supplementation in patients with hypertension are cost-effective tools to improve outcomes in Iran.

Keywords: Hypertension, dietary intake, weight, waist circumference, North of Iran.

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