Search results for: seismic random noise attenuation

Authors: Iman A. Saad, Loay E. George

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Iris pattern is an important biological feature of human body; it becomes very hot topic in both research and practical applications. In this paper, an algorithm is proposed for iris recognition and a simple, efficient and fast method is introduced to extract a set of discriminatory features using first order gradient operator applied on grayscale images. The gradient based features are robust, up to certain extents, against the variations may occur in contrast or brightness of iris image samples; the variations are mostly occur due lightening differences and camera changes. At first, the iris region is located, after that it is remapped to a rectangular area of size 360x60 pixels. Also, a new method is proposed for detecting eyelash and eyelid points; it depends on making image statistical analysis, to mark the eyelash and eyelid as a noise points. In order to cover the features localization (variation), the rectangular iris image is partitioned into N overlapped sub-images (blocks); then from each block a set of different average directional gradient densities values is calculated to be used as texture features vector. The applied gradient operators are taken along the horizontal, vertical and diagonal directions. The low order norms of gradient components were used to establish the feature vector. Euclidean distance based classifier was used as a matching metric for determining the degree of similarity between the features vector extracted from the tested iris image and template features vectors stored in the database. Experimental tests were performed using 2639 iris images from CASIA V4-Interival database, the attained recognition accuracy has reached up to 99.92%.

Keywords: Iris recognition, contrast stretching, gradient features, texture features, Euclidean metric.

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Authors: Isabela Moreira Queiroz

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Slope stability analyses are largely carried out by deterministic methods and evaluated through a single security factor. Although it is known that the geotechnical parameters can present great dispersal, such analyses are considered fixed and known. The probabilistic methods, in turn, incorporate the variability of input key parameters (random variables), resulting in a range of values of safety factors, thus enabling the determination of the probability of failure, which is an essential parameter in the calculation of the risk (probability multiplied by the consequence of the event). Among the probabilistic methods, there are three frequently used methods in geotechnical society: FOSM (First-Order, Second-Moment), Rosenblueth (Point Estimates) and Monte Carlo. This paper presents a comparison between the results from deterministic and probabilistic analyses (FOSM method, Monte Carlo and Rosenblueth) applied to a hypothetical slope. The end was held to evaluate the behavior of the slope and consequent risk analysis, which is used to calculate the risk and analyze their mitigation and control solutions. It can be observed that the results obtained by the three probabilistic methods were quite close. It should be noticed that the calculation of the risk makes it possible to list the priority to the implementation of mitigation measures. Therefore, it is recommended to do a good assessment of the geological-geotechnical model incorporating the uncertainty in viability, design, construction, operation and closure by means of risk management. 

Keywords: Probabilistic methods, risk assessment, risk management, slope stability.

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Authors: B. L. Ho, L. Shi, D. F. Wang, V. C. T. Mok

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The brain’s functional connectivity while temporally non-stationary does express consistency at a macro spatial level. The study of stable resting state connectivity patterns hence provides opportunities for identification of diseases if such stability is severely perturbed. A mathematical model replicating the brain’s spatial connections will be useful for understanding brain’s representative geometry and complements the empirical model where it falls short. Empirical computations tend to involve large matrices and become infeasible with fine parcellation. However, the proposed analytical model has no such computational problems. To improve replicability, 92 subject data are obtained from two open sources. The proposed methodology, inspired by financial theory, uses multivariate regression to find relationships of every cortical region of interest (ROI) with some pre-identified hubs. These hubs acted as representatives for the entire cortical surface. A variance-covariance framework of all ROIs is then built based on these relationships to link up all the ROIs. The result is a high level of match between model and empirical correlations in the range of 0.59 to 0.66 after adjusting for sample size; an increase of almost forty percent. More significantly, the model framework provides an intuitive way to delineate between systemic drivers and idiosyncratic noise while reducing dimensions by more than 30 folds, hence, providing a way to conduct attribution analysis. Due to its analytical nature and simple structure, the model is useful as a standalone toolkit for network dependency analysis or as a module for other mathematical models.

Keywords: Functional magnetic resonance imaging, multivariate regression, network hubs, resting state functional connectivity.

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Authors: Fl. Loury, P. Sablonière

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In this paper a stochastic scenario-based model predictive control applied to molten salt storage systems in concentrated solar tower power plant is presented. The main goal of this study is to build up a tool to analyze current and expected future resources for evaluating the weekly power to be advertised on electricity secondary market. This tool will allow plant operator to maximize profits while hedging the impact on the system of stochastic variables such as resources or sunlight shortage.

Solving the problem first requires a mixed logic dynamic modeling of the plant. The two stochastic variables, respectively the sunlight incoming energy and electricity demands from secondary market, are modeled by least square regression. Robustness is achieved by drawing a certain number of random variables realizations and applying the most restrictive one to the system. This scenario approach control technique provides the plant operator a confidence interval containing a given percentage of possible stochastic variable realizations in such a way that robust control is always achieved within its bounds. The results obtained from many trajectory simulations show the existence of a ‘’reliable’’ interval, which experimentally confirms the algorithm robustness.

Keywords: Molten Salt Storage System, Concentrated Solar Tower Power Plant, Robust Stochastic Model Predictive Control.

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Authors: Shiuh-Jer Huang, Chen-Zon Yan, C. K. Huang, Chun-Chien Ting

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Since the vision system application in industrial environment for autonomous purposes is required intensely, the image recognition technique becomes an important research topic. Here, deep learning algorithm is employed in image system to recognize the industrial object and integrate with a 7A6 Series Manipulator for object automatic gripping task. PC and Graphic Processing Unit (GPU) are chosen to construct the 3D Vision Recognition System. Depth Camera (Intel RealSense SR300) is employed to extract the image for object recognition and coordinate derivation. The YOLOv2 scheme is adopted in Convolution neural network (CNN) structure for object classification and center point prediction. Additionally, image processing strategy is used to find the object contour for calculating the object orientation angle. Then, the specified object location and orientation information are sent to robotic controller. Finally, a six-axis manipulator can grasp the specific object in a random environment based on the user command and the extracted image information. The experimental results show that YOLOv2 has been successfully employed to detect the object location and category with confidence near 0.9 and 3D position error less than 0.4 mm. It is useful for future intelligent robotic application in industrial 4.0 environment.

Keywords: Deep learning, image processing, convolution neural network, YOLOv2, 7A6 series manipulator.

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Authors: Aref Aasi, Sahar Ebrahimi Bajgani, Erfan Aasi

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Breast cancer (BC) continues to be the most frequent cancer in females and causes the highest number of cancer-related deaths in women worldwide. Inspired by recent advances in studying the relationship between different patient attributes and features and the disease, in this paper, we have tried to investigate the different classification methods for better diagnosis of BC in the early stages. In this regard, datasets from the University Hospital Centre of Coimbra were chosen, and different machine learning (ML)-based and neural network (NN) classifiers have been studied. For this purpose, we have selected favorable features among the nine provided attributes from the clinical dataset by using a random forest algorithm. This dataset consists of both healthy controls and BC patients, and it was noted that glucose, BMI, resistin, and age have the most importance, respectively. Moreover, we have analyzed these features with various ML-based classifier methods, including Decision Tree (DT), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) along with NN-based Multi-Layer Perceptron (MLP) classifier. The results revealed that among different techniques, the SVM and MLP classifiers have the most accuracy, with amounts of 96% and 92%, respectively. These results divulged that the adopted procedure could be used effectively for the classification of cancer cells, and also it encourages further experimental investigations with more collected data for other types of cancers.

Keywords: Breast cancer, health diagnosis, Machine Learning, biomarker classification, Neural Network.

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Authors: Anil Ranjitbhai Patel, Clement John Shaji, Peter Liggesmeyer

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Safety and security of Autonomous Vehicles (AVs) is a growing concern, first, due to the increased number of safety-critical functions taken over by automotive embedded systems; second, due to the increased exposure of the software-intensive systems to potential attackers; third, due to dynamic interaction in an uncertain and unknown environment at runtime which results in changed functional and non-functional properties of the system. Frequently occurring environmental uncertainties, random component failures, and compromise security of the AVs might result in hazardous events, sometimes even in an accident, if left undetected. Beyond these technical issues, we argue that the safety and security of AVs against accidental and deliberate faults are poorly understood and rarely implemented. One possible way to overcome this is through a well-known diversity approach. As an effective approach to increase safety and security, diversity has been widely used in the aviation, railway, and aerospace industries. Thus, paper proposes fault-tolerance by diversity model taking into consideration the mitigation of accidental and deliberate faults by application of structure and variant redundancy. The model can be used to design the AVs with various types of diversity in hardware and software-based multi-version system. The paper evaluates the presented approach by employing an example from adaptive cruise control, followed by discussing the case study with initial findings.

Keywords: Autonomous vehicles, diversity, fault-tolerance, adaptive cruise control, safety, security.

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Authors: Revoti Prasad Bora, Nikita Katyal

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Promotion is a key element in the retail business. Thus, analysis of promotions to quantify their effectiveness in terms of Revenue and/or Margin is an essential activity in the retail industry. However, measuring the sales/revenue uplift is based on estimations, as the actual sales/revenue without the promotion is not present. Further, the presence of Halo and Cannibalization in a multiple parallel promotions’ scenario complicates the problem. Calculating Baseline by considering inter-brand/competitor items or using Halo and Cannibalization's impact on Revenue calculations by considering Baseline as an interpretation of items’ unit sales in neighboring nonpromotional weeks individually may not capture the overall Revenue uplift in the case of multiple parallel promotions. Hence, this paper proposes a Machine Learning based method for calculating the Revenue uplift by considering the Halo and Cannibalization impact on the Baseline and the Revenue. In the first section of the proposed methodology, Baseline of an item is calculated by incorporating the impact of the promotions on its related items. In the later section, the Revenue of an item is calculated by considering both Halo and Cannibalization impacts. Hence, this methodology enables correct calculation of the overall Revenue uplift due a given promotion.

Keywords: Halo, cannibalization, promotion, baseline, temporary price reduction, retail, elasticity, cross price elasticity, machine learning, random forest, linear regression.

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Authors: Yi-Hsiang Pan

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The purpose of this study was to develop a “teachers’ self-efficacy scale for high school physical education teachers (TSES-HSPET)” in Taiwan. This scale is based on the self-efficacy theory of Bandura [1], [2]. This study used exploratory and confirmatory factor analyses to test the reliability and validity. The participants were high school physical education teachers in Taiwan. Both stratified random sampling and cluster sampling were used to sample participants for the study. 350 teachers were sampled in the first stage and 234 valid scales (male 133, female 101) returned. During the second stage, 350 teachers were sampled and 257 valid scales (male 143, female 110, 4 did not indicate gender) returned. The exploratory factor analysis was used in the first stage, and it got 60.77% of total variance for construct validity. The Cronbach’s alpha coefficient of internal consistency was 0.91 for sumscale, and subscales were 0.84 and 0.90. In the second stage, confirmatory factor analysis was used to test construct validity. The result showed that the fit index could be accepted (χ2 (75) =167.94, p <.05, RMSEA =0.07, SRMR=0.05, GFI=0.92, NNFI=0.97, CFI=0.98, PNFI=0.79). Average variance extracted of latent variables were 0.43 and 0.53, which composite reliability are 0.78 and 0.90. It is concluded that the TSES-HSPET is a well-considered measurement instrument with acceptable validity and reliability. It may be used to estimate teachers’ self-efficacy for high school physical education teachers.

Keywords: teaching in physical education, teacher's self-efficacy, teacher's belief

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Authors: Walid MOUDANI

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The healthcare environment is generally perceived as being information rich yet knowledge poor. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. In fact, valuable knowledge can be discovered from application of data mining techniques in healthcare system. In this study, a proficient methodology for the extraction of significant patterns from the Coronary Heart Disease warehouses for heart attack prediction, which unfortunately continues to be a leading cause of mortality in the whole world, has been presented. For this purpose, we propose to enumerate dynamically the optimal subsets of the reduced features of high interest by using rough sets technique associated to dynamic programming. Therefore, we propose to validate the classification using Random Forest (RF) decision tree to identify the risky heart disease cases. This work is based on a large amount of data collected from several clinical institutions based on the medical profile of patient. Moreover, the experts- knowledge in this field has been taken into consideration in order to define the disease, its risk factors, and to establish significant knowledge relationships among the medical factors. A computer-aided system is developed for this purpose based on a population of 525 adults. The performance of the proposed model is analyzed and evaluated based on set of benchmark techniques applied in this classification problem.

Keywords: Multi-Classifier Decisions Tree, Features Reduction, Dynamic Programming, Rough Sets.

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Authors: Fereshteh Afkari, Davood Bagheri

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The effect of teaching method on learning assistance Dunn Review .The study, to compare the effects of collaboration on teaching mathematics learning courses, including writing, science, experimental girl students by other methods of teaching basic first paid and the amount of learning students methods have been trained to cooperate with other students with other traditional methods have been trained to compare. The survey on 100 students in Tehran that using random sampling ¬ cluster of girl students between the first primary selections was performed. Considering the topic of semi-experimental research methods used to practice the necessary information by questionnaire, examination questions by the researcher, in collaboration with teachers and view authority in this field and related courses that teach these must have been collected. Research samples to test and control groups were divided. Experimental group and control group collaboration using traditional methods of mathematics courses, including writing and experimental sciences were trained. Research results using statistical methods T is obtained in two independent groups show that, through training assistance will lead to positive results and student learning in comparison with traditional methods, will increase also led to collaboration methods increase skills to solve math lesson practice, better understanding and increased skill level of students in practical lessons such as science and has been writing.

Keywords: method of teaching, learning, collaboration

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Authors: Wojciech Górecki, Anna Sowiżdżał, Grzegorz Machowski, Tomasz Maćkowski, Bartosz Papiernik, Michał Stefaniuk

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The article shows results of the project which aims at evaluating possibilities of effective development and exploitation of natural gas from argillaceous series of the Autochthonous Miocene in the Carpathian Foredeep. To achieve the objective, the research team develop a world-trend based but unique methodology of processing and interpretation, adjusted to data, local variations and petroleum characteristics of the area. In order to determine the zones in which maximum volumes of hydrocarbons might have been generated and preserved as shale gas reservoirs, as well as to identify the most preferable well sites where largest gas accumulations are anticipated a number of task were accomplished. Evaluation of petrophysical properties and hydrocarbon saturation of the Miocene complex is based on laboratory measurements as well as interpretation of well-logs and archival data. The studies apply mercury porosimetry (MICP), micro CT and nuclear magnetic resonance imaging (using the Rock Core Analyzer). For prospective location (e.g. central part of Carpathian Foredeep – Brzesko-Wojnicz area) reprocessing and reinterpretation of detailed seismic survey data with the use of integrated geophysical investigations has been made. Construction of quantitative, structural and parametric models for selected areas of the Carpathian Foredeep is performed on the basis of integrated, detailed 3D computer models. Modeling are carried on with the Schlumberger’s Petrel software. Finally, prospective zones are spatially contoured in a form of regional 3D grid, which will be framework for generation modelling and comprehensive parametric mapping, allowing for spatial identification of the most prospective zones of unconventional gas accumulation in the Carpathian Foredeep. Preliminary results of research works indicate a potentially prospective area for occurrence of unconventional gas accumulations in the Polish part of Carpathian Foredeep.

Keywords: Autochthonous Miocene, Carpathian Foredeep, Poland, shale gas.

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Authors: S.Shanmugam, V.G. Srisanthi, S.Ramachandran

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Reinforced concrete has good durability and excellent structural performance. But there are cases of early deterioration due to a number of factors, one prominent factor being corrosion of steel reinforcement. The process of corrosion sets in due to ingress of moisture, oxygen and other ingredients into the body of concrete, which is unsound, permeable and absorbent. Cracks due to structural and other causes such as creep, shrinkage, etc also allow ingress of moisture and other harmful ingredients and thus accelerate the rate of corrosion. There are several interactive factors both external and internal, which lead to corrosion of reinforcement and ultimately failure of structures. Suitable addition of mineral admixture like silica fume (SF) in concrete improves the strength and durability of concrete due to considerable improvement in the microstructure of concrete composites, especially at the transition zone. Secondary reinforcement in the form of fibre is added to concrete, which provides three dimensional random reinforcement in the entire mass of concrete. Reinforced concrete beams of size 0.1 m X 0.15 m and length 1m have been cast using M 35 grade of concrete. The beams after curing process were subjected to corrosion process by impressing an external Direct Current (Galvanostatic Method) for a period of 15 days under stressed and unstressed conditions. The corroded beams were tested by applying two point loads to determine the ultimate load carrying capacity and cracking pattern and the results of specimens were compared with that of the companion specimens. Gravimetric method is used to quantify corrosion that has occurred.

Keywords: Carbonation, Corrosion, Cracking, Spalling.

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Authors: Yu Zhang, Ye Tian, Fang Ye Yixuan Kang

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The capacity of conventional cellular networks has reached its upper bound and it can be well handled by introducing femtocells with low-cost and easy-to-deploy. Spectrum interference issue becomes more critical in peace with the value-added multimedia services growing up increasingly in two-tier cellular networks. Spectrum allocation is one of effective methods in interference mitigation technology. This paper proposes a game-theory-based on OFDMA downlink spectrum allocation aiming at reducing co-channel interference in two-tier femtocell networks. The framework is formulated as a non-cooperative game, wherein the femto base stations are players and frequency channels available are strategies. The scheme takes full account of competitive behavior and fairness among stations. In addition, the utility function reflects the interference from the standpoint of channels essentially. This work focuses on co-channel interference and puts forward a negative logarithm interference function on distance weight ratio aiming at suppressing co-channel interference in the same layer network. This scenario is more suitable for actual network deployment and the system possesses high robustness. According to the proposed mechanism, interference exists only when players employ the same channel for data communication. This paper focuses on implementing spectrum allocation in a distributed fashion. Numerical results show that signal to interference and noise ratio can be obviously improved through the spectrum allocation scheme and the users quality of service in downlink can be satisfied. Besides, the average spectrum efficiency in cellular network can be significantly promoted as simulations results shown.

Keywords: Femtocell networks, game theory, interference mitigation, spectrum allocation.

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Authors: Patrick Cummings, Laura Cassani, Deirdre Kelliher

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In this paper we present findings from a research effort to apply a hybrid collaborative-context approach for a system focused on Marine Corps civil affairs data collection, aggregation, and analysis called the Marine Civil Information Management System (MARCIMS). The goal of this effort is to provide operators with information to make sense of the interconnectedness of entities and relationships in their area of operation and discover existing data to support civil military operations. Our approach to build a recommendation engine was designed to overcome several technical challenges, including 1) ensuring models were robust to the relatively small amount of data collected by the Marine Corps civil affairs community; 2) finding methods to recommend novel data for which there are no interactions captured; and 3) overcoming confirmation bias by ensuring content was recommended that was relevant for the mission despite being obscure or less well known. We solve this by implementing a combination of collective matrix factorization (CMF) and graph-based random walks to provide recommendations to civil military operations users. We also present a method to resolve the challenge of computation complexity inherent from highly connected nodes through a precomputed process.

Keywords: Recommendation engine, collaborative filtering, context based recommendation, graph analysis, coverage, civil affairs operations, Marine Corps.

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Authors: Veruska C. Dutra, Mary L. G. S. Senna, Afonso R. Aquino

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Tourists bring many benefits to a local community, encouraging it to be involved in that activity; however, it may also have detrimental effects like garbage, noise, violence, external culture and the damaging of the natural environment among others, which may promote community dissatisfaction. The contact between the tourist and the local community is a concern, especially when the community is located near protected areas. In this case, the community must know the tourist destination well, so it can collaborate in the tourism development without harming the environment. In this context, the present article aims to demonstrate the results of a research study conducted as part of a doctorate program in Sciences from the University of Sao Paulo, Brazil. It had as an objective to elaborate a methodology proposal to measure the local community satisfaction indicator, with applicability on a case study in the Mateiros community located in the surrounding area of the Parque Estadual do Jalapão –PEJ conservation unit in the state of Tocantins, Brazil. This is a study of an interdisciplinary nature that had the deductive method as its guide. The indicator result is going to be presented in this study. It pointed out as negative factors: there is no involvement between the local community and the tourism sector, and there is also dissatisfaction with regard to the town’s basic services. The study showed as positive the local community knowledge about the various attractions in the surrounding area and that the group recognizes the importance of the tourism for the town and life. Concerning the methodology that was used, the results showed that it can collaborate in seeking actions of improvement and involvement of the community in the planning and development of the local tourism. It comes out as an efficient analysis tool, thus enabling the perceiving of the local community point of view.

Keywords: Satisfaction indicator, tourism, community, Jalapão.

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Authors: Izian Abd. Karim, Kachalla Mohammed, Nora Farah A. A. Aziz, Law Teik Hua

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The economic use and ease of construction of profiled deck composite slab is marred with the complex and un-economic strength verification required for the serviceability and general safety considerations. Beside these, albeit factors such as shear span length, deck geometries and mechanical frictions greatly influence the longitudinal shear strength, that determines the ultimate strength of profiled deck composite slab, and number of methods available for its determination; partial shear and slope-intercept are the two methods according to Euro-code 4 provision. However, the complexity associated with shear behavior of profiled deck composite slab, the use of these methods in determining the load carrying capacities of such slab yields different and conflicting values. This couple with the time and cost constraint associated with the strength verification is a source of concern that draws more attentions nowadays, the issue is critical. Treating some of these known shear strength influencing factors as random variables, the load carrying capacity violation of profiled deck composite slab from the use of the two-methods defined according to Euro-code 4 are determined using reliability approach, and comparatively studied. The study reveals safety values from the use of m-k method shows good standing compared with that from the partial shear method.

Keywords: Composite slab, first order reliability method, longitudinal shear, partial shear connection, slope-intercept.

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Authors: Lule Herman, E. Ovuga, M. Mshilla, S. Ojara, G. Kimbugwe, A. P. Adrawa, N. Mahuro

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Adolescents in Northern Uganda are at risk of teenage pregnancies, unsafe abortions and sexually transmitted infections (STIs). There is silence on sex both at home and school. This cross sectional descriptive analytical study interviews a random sample of 827 students and 13 teachers on knowledge, perception and acceptability to a comprehensive adolescent sexual and reproductive health education in “O” and “A” level secondary schools in Gulu District. Quantitative data was analyzed using SPSS 16.0. Directed content analysis of themes of transcribed qualitative data was conducted manually for common codes, sub-categories and categories. Of the 827 students; 54.3% (449) reported being in a sexual relationship especially those aged 15-17 years. Majority 96.1% (807) supported the teaching of a comprehensive ASRHE, citing no negative impact 71.5% (601). Majority 81.6% (686) agreed that such education could help prevention of STIs, abortions and teenage pregnancies, and that it should be taught by health workers 69.0% (580). Majority 76.6% (203) reported that ASRHE was not currently being taught in their schools. Students had low knowledge levels and misconceptions about ASRHE. ASRHE was highly acceptable though not being emphasized; its success in school settings requires multidisciplinary culturally sensitive approaches amongst which health workers should be frontiers.

Keywords: Acceptability, ASRHE, Knowledge, Perception.

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Authors: Salako Emmanuel Adekunle

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Computer education is referred to as the knowledge and ability to use computers and related technology efficiently, with a range of skills covering levels from basic use to advance. Computer continues to make an ever-increasing impact on all aspect of human endeavours such as education. With numerous benefits of computer education, what are the insights of students on computer education? This study investigated the perception of senior secondary school students on computer education in Federal Capital Territory (FCT), Abuja, Nigeria. A sample of 7500 senior secondary schools students was involved in the study, one hundred (100) private and fifty (50) public schools within FCT. They were selected by using simple random sampling technique. A questionnaire [PSSSCEQ] was developed and validated through expert judgement and reliability coefficient of 0.84 was obtained. It was used to gather relevant data on computer education. Findings confirmed that the students in the FCT had positive perception on computer education. Some factors were identified that affect students’ perception on computer education. The null hypotheses were tested using t-test and ANOVA statistical analyses at 0.05 level of significance. Based on these findings, some recommendations were made which include competent teachers should be employed into all secondary schools. This will help students to acquire relevant knowledge in computer education, technological supports should be provided to all secondary schools; this will help the users (students) to solve specific problems in computer education and financial supports should be provided to procure computer facilities that will enhance the teaching and the learning of computer education.

Keywords: Computer education, perception, secondary school, students.

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Authors: Salako Emmanuel Adekunle

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With numerous advantages of ICT in teaching such as using images to improve the retentive memory of students, academic staff is yet to deliver instructions adequately and effectively due to no power supply, lack of technical supports and non-availability of functional ICT tools. This study was conducted to investigate the attitudes of academic staff towards the use of information communication technology as a pedagogical tool for effective teaching in FCT College of Education, Zuba-Abuja, Nigeria. A sample of 200 academic staff from five schools/faculties was involved in the study. The respondents were selected by using simple random sampling technique (SRST). A questionnaire was developed and validated by the experts in Measurement and Evaluation, and reliability co-efficient of 0.85 was obtained. It was used to gather relevant data from the respondents. This study revealed that the respondents had positive attitudes towards the use of ICT as a pedagogical tool for effective teaching. Also, the uses of ICT by the academic staff included: to encourage closer relationship for attainment of higher academic, and to deliver instructions effectively. The study also revealed that there is a significant relationship between the attitudes and the uses of ICT by the academic staff. Based on these findings, some recommendations were made which include: power supply should be provided to operate ICT facilities for effective teaching, and technical assistance on ICT usage for effective delivery of instructions should be provided among other recommendations.

Keywords: Academic staff, attitudes, information communication technology, pedagogical tool, teaching and use.

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Authors: A. S. Haruna, M. U. Tambawal, A. A. Salawu

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The psychological impact of peer influence on its individual group members, can make them resist HIV/AIDS counselling and testing. This study investigated the correlate of peer influence and resistance to HIV/AIDS counselling and testing among students in tertiary institutions in Kano state, Nigeria. To achieve this, three null hypotheses were postulated and tested. Cross- Sectional Survey Design was employed in which 1512 sample was selected from a student population of 104,841.Simple Random Sampling was used in the selection. A self-developed 20-item scale called Peer Influence and Psychological Resistance Inventory (PIPRI) was used for data collection. Pearson Product Moment Correlation (PPMCC) via test-retest method was applied to estimate a reliability coefficient of 0.86 for the scale. Data obtained was analyzed using t-test and PPMCC at 0.05 level of confidence. Results reveal 26.3% (397) of the respondents being influenced by their peer group, while 39.8% showed resistance. Also, the t-tests and PPMCC statistics were greater than their respective critical values. This shows that there was a significant gender difference in peer influence and a difference between peer influence and resistance to HIV/AIDS counselling and testing. However, a positive relationship between peer influence and resistance to HIV/AIDS counselling and testing was shown. A major recommendation offered suggests the use of reinforcement and social support for positive attitudes and maintenance of safe behaviour among students who patronize HIV/AIDS counselling.

Keywords: Peer influence, HIV/AIDS counselling and testing, Resistance.

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Authors: Aya Salama

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Digital Twin has emerged as a compelling research area, capturing the attention of scholars over the past decade. It finds applications across diverse fields, including smart manufacturing and healthcare, offering significant time and cost savings. Notably, it often intersects with other cutting-edge technologies such as Data Mining, Artificial Intelligence, and Machine Learning. However, the concept of a Human Digital Twin (HDT) is still in its infancy and requires further demonstration of its practicality. HDT takes the notion of Digital Twin a step further by extending it to living entities, notably humans, who are vastly different from inanimate physical objects. The primary objective of this research was to create an HDT capable of automating real-time human responses by simulating human behavior. To achieve this, the study delved into various areas, including clustering, supervised classification, topic extraction, and sentiment analysis. The paper successfully demonstrated the feasibility of HDT for generating personalized responses in social messaging applications. Notably, the proposed approach achieved an overall accuracy of 63%, a highly promising result that could pave the way for further exploration of the HDT concept. The methodology employed Random Forest for clustering the question database and matching new questions, while K-nearest neighbor was utilized for sentiment analysis.

Keywords: Human Digital twin, sentiment analysis, topic extraction, supervised machine learning, unsupervised machine learning, classification and clustering.

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Authors: Y. Zheng, W. Sun

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This paper describes a new approach which can be used to interpret the experimental creep deformation data obtained from miniaturized thin plate bending specimen test to the corresponding uniaxial data based on an inversed application of the reference stress method. The geometry of the thin plate is fully defined by the span of the support, l, the width, b, and the thickness, d. Firstly, analytical solutions for the steady-state, load-line creep deformation rate of the thin plates for a Norton’s power law under plane stress (b→0) and plane strain (b→∞) conditions were obtained, from which it can be seen that the load-line deformation rate of the thin plate under plane-stress conditions is much higher than that under the plane-strain conditions. Since analytical solution is not available for the plates with random b-values, finite element (FE) analyses are used to obtain the solutions. Based on the FE results obtained for various b/l ratios and creep exponent, n, as well as the analytical solutions under plane stress and plane strain conditions, an approximate, numerical solutions for the deformation rate are obtained by curve fitting. Using these solutions, a reference stress method is utilised to establish the conversion relationships between the applied load and the equivalent uniaxial stress and between the creep deformations of thin plate and the equivalent uniaxial creep strains. Finally, the accuracy of the empirical solution was assessed by using a set of “theoretical” experimental data.

Keywords: Bending, Creep, Miniature Specimen, Thin Plate.

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Authors: Yuekun Chen, Yousef Sardahi, Salam Hajjar, Christopher Greer

Abstract:

This paper presents a multi-objective optimal design of a cascade control system for an underactuated mechanical system. Cascade control structures usually include two control algorithms (inner and outer). To design such a control system properly, the following conflicting objectives should be considered at the same time: 1) the inner closed-loop control must be faster than the outer one, 2) the inner loop should fast reject any disturbance and prevent it from propagating to the outer loop, 3) the controlled system should be insensitive to measurement noise, and 4) the controlled system should be driven by optimal energy. Such a control problem can be formulated as a multi-objective optimization problem such that the optimal trade-offs among these design goals are found. To authors best knowledge, such a problem has not been studied in multi-objective settings so far. In this work, an underactuated mechanical system consisting of a rotary servo motor and a ball and beam is used for the computer simulations, the setup parameters of the inner and outer control systems are tuned by NSGA-II (Non-dominated Sorting Genetic Algorithm), and the dominancy concept is used to find the optimal design points. The solution of this problem is not a single optimal cascade control, but rather a set of optimal cascade controllers (called Pareto set) which represent the optimal trade-offs among the selected design criteria. The function evaluation of the Pareto set is called the Pareto front. The solution set is introduced to the decision-maker who can choose any point to implement. The simulation results in terms of Pareto front and time responses to external signals show the competing nature among the design objectives. The presented study may become the basis for multi-objective optimal design of multi-loop control systems.

Keywords: Cascade control, multi-loop control systems, multi-objective optimization, optimal control.

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Authors: Imali T. Jayamanne, K. P. Asoka Ramanayake

Abstract:

Transition from tertiary level education to employment is one of the challenges that many fresh university graduates face after graduation. The transition period or the waiting time to obtain the first employment varies with the socio-economic factors and the general characteristics of a graduate. Compared to other fields of study, Arts graduates in Sri Lanka, have to wait a long time to find their first employment. The objective of this study is to identify the determinants of the transition from higher education to employment of these graduates using survival models. The study is based on a survey that was conducted in the year 2016 on a stratified random sample of Arts graduates from Sri Lankan universities who had graduated in 2012. Among the 469 responses, 36 (8%) waiting times were interval censored and 13 (3%) were right censored. Waiting time for the first employment varied between zero to 51 months. Initially, the log-rank and the Gehan-Wilcoxon tests were performed to identify the significant factors. Gender, ethnicity, GCE Advanced level English grade, civil status, university, class received, degree type, sector of first employment, type of first employment and the educational qualifications required for the first employment were significant at 10%. The Cox proportional hazards model was fitted to model the waiting time for first employment with these significant factors. All factors, except ethnicity and type of employment were significant at 5%. However, since the proportional hazard assumption was violated, the lognormal Accelerated failure time (AFT) model was fitted to model the waiting time for the first employment. The same factors were significant in the AFT model as in Cox proportional model.

Keywords: AFT model, first employment, proportional hazard, survey design, waiting time.

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Authors: Arash Esmaeili, Zhibang Liu, Yang Xiang, Jimmy Yun, Lei Shao

Abstract:

High pressure carbon dioxide (CO2) absorption from a specific off-gas in a conventional column has been evaluated for the environmental concerns by the Aspen HYSYS simulator using a wide range of single absorbents and piperazine (PZ) blended solutions to estimate the outlet CO2 concentration, CO2 loading, reboiler power supply and regeneration heat duty to choose the most efficient solution in terms of CO2 removal and required heat duty. The property package, which is compatible with all applied solutions for the simulation in this study, estimates the properties based on electrolyte non-random two-liquid (E-NRTL) model for electrolyte thermodynamics and Peng-Robinson equation of state for vapor phase and liquid hydrocarbon phase properties. The results of the simulation indicate that PZ in addition to the mixture of PZ and monoethanolamine (MEA) demand the highest regeneration heat duty compared with other studied single and blended amine solutions respectively. The blended amine solutions with the lowest PZ concentrations (5wt% and 10wt%) were considered and compared to reduce the cost of process, among which the blended solution of 10wt%PZ+35wt%MDEA (methyldiethanolamine) was found as the most appropriate solution in terms of CO2 content in the outlet gas, rich-CO2 loading and regeneration heat duty.

Keywords: Absorption, amine solutions, Aspen HYSYS, CO2 loading, piperazine, regeneration heat duty.

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Authors: Daniele Losanno, Giorgio Serino

Abstract:

This paper focuses on parametric analysis of reinforced concrete structures equipped with supplemental damping braces. Practitioners still luck sufficient data for current design of damper added structures and often reduce the real model to a pure damper braced structure even if this assumption is neither realistic nor conservative. In the present study, the damping brace is modelled as made by a linear supporting brace connected in series with the viscous/hysteretic damper. Deformation capacity of existing structures is usually not adequate to undergo the design earthquake. In spite of this, additional dampers could be introduced strongly limiting structural damage to acceptable values, or in some cases, reducing frame response to elastic behavior. This work is aimed at providing useful considerations for retrofit of existing buildings by means of supplemental damping braces. The study explicitly takes into consideration variability of (a) relative frame to supporting brace stiffness, (b) dampers’ coefficient (viscous coefficient or yielding force) and (c) non-linear frame behavior. Non-linear time history analysis has been run to account for both dampers’ behavior and non-linear plastic hinges modelled by Pivot hysteretic type. Parametric analysis based on previous studies on SDOF or MDOF linear frames provide reference values for nearly optimal damping systems design. With respect to bare frame configuration, seismic response of the damper-added frame is strongly improved, limiting deformations to acceptable values far below ultimate capacity. Results of the analysis also demonstrated the beneficial effect of stiffer supporting braces, thus highlighting inadequacy of simplified pure damper models. At the same time, the effect of variable damping coefficient and yielding force has to be treated as an optimization problem.

Keywords: Brace stiffness, dissipative braces, non-linear analysis, plastic hinges, reinforced concrete.

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Authors: M. R. Homaeinezhad, E. Tavakkoli, M. Habibi, S. A. Atyabi, A. Ghaffari

Abstract:

This paper describes a new supervised fusion (hybrid) electrocardiogram (ECG) classification solution consisting of a new QRS complex geometrical feature extraction as well as a new version of the learning vector quantization (LVQ) classification algorithm aimed for overcoming the stability-plasticity dilemma. Toward this objective, after detection and delineation of the major events of ECG signal via an appropriate algorithm, each QRS region and also its corresponding discrete wavelet transform (DWT) are supposed as virtual images and each of them is divided into eight polar sectors. Then, the curve length of each excerpted segment is calculated and is used as the element of the feature space. To increase the robustness of the proposed classification algorithm versus noise, artifacts and arrhythmic outliers, a fusion structure consisting of five different classifiers namely as Support Vector Machine (SVM), Modified Learning Vector Quantization (MLVQ) and three Multi Layer Perceptron-Back Propagation (MLP–BP) neural networks with different topologies were designed and implemented. The new proposed algorithm was applied to all 48 MIT–BIH Arrhythmia Database records (within–record analysis) and the discrimination power of the classifier in isolation of different beat types of each record was assessed and as the result, the average accuracy value Acc=98.51% was obtained. Also, the proposed method was applied to 6 number of arrhythmias (Normal, LBBB, RBBB, PVC, APB, PB) belonging to 20 different records of the aforementioned database (between– record analysis) and the average value of Acc=95.6% was achieved. To evaluate performance quality of the new proposed hybrid learning machine, the obtained results were compared with similar peer– reviewed studies in this area.

Keywords: Feature Extraction, Curve Length Method, SupportVector Machine, Learning Vector Quantization, Multi Layer Perceptron, Fusion (Hybrid) Classification, Arrhythmia Classification, Supervised Learning Machine.

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Authors: Florin Leon, Silvia Curteanu

Abstract:

Developing complete mechanistic models for polymerization reactors is not easy, because complex reactions occur simultaneously; there is a large number of kinetic parameters involved and sometimes the chemical and physical phenomena for mixtures involving polymers are poorly understood. To overcome these difficulties, empirical models based on sampled data can be used instead, namely regression methods typical of machine learning field. They have the ability to learn the trends of a process without any knowledge about its particular physical and chemical laws. Therefore, they are useful for modeling complex processes, such as the free radical polymerization of methyl methacrylate achieved in a batch bulk process. The goal is to generate accurate predictions of monomer conversion, numerical average molecular weight and gravimetrical average molecular weight. This process is associated with non-linear gel and glass effects. For this purpose, an adaptive sampling technique is presented, which can select more samples around the regions where the values have a higher variation. Several machine learning methods are used for the modeling and their performance is compared: support vector machines, k-nearest neighbor, k-nearest neighbor and random forest, as well as an original algorithm, large margin nearest neighbor regression. The suggested method provides very good results compared to the other well-known regression algorithms.

Keywords: Adaptive sampling, batch bulk methyl methacrylate polymerization, large margin nearest neighbor regression, machine learning.

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Authors: Kadkhodaie A., Bagheri M.

Abstract:

Evaluation of crop plants resistance to environmental stresses specially in germination stage is a critical factor in their selection in different conditions of cultivation. Therefore use of a procedure in controllable situation can help to evaluate plants reaction to stress quickly and precisely. In order to study germination characteristics of flax in water and salinity stress conditions were conducted two laboratories experimental. The two experimental were conducted in 4-replicant completing random design for salinity and water stress. The treatment, for salinity and water stress was three potential (zero, 40, 80 mM) of NaCl and three potential (zero, -2, -4 bar) of PEG respectively. Germination percentage and rate, in addition to Radical and plumule length and dry-weight and plumule/Radical ration were measured. All of characteristics reduce under water stress conditions. salinity stress significant reduce germination rate and Radical and plumule length of flax seeds. Hydropriming and osmopriming significant increased germination rate, plumule length and plumule/Radical ration ration of flax seeds. But germination percentage and Radical and plumule dry weight significant increased only in hydropriming treat. Hydropriming and osmopriming could not be used to improved germination under saline and drought stress. But has more tolerance in salinity and drought stress in flax by less reduce in Radical and plumule length under saline and drought stress.

Keywords: linseed, salt stress, water stress, seed treatment, Germination

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