Search results for: behavior detection

Authors: Mehari Kahsay, Krishna Murthy Kyathegowda, Temesgen Berhanu

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Aluminum matrix composites have gained focus on research and industrial use, especially those not requiring extreme loading or thermal conditions, for the last few decades. Their relatively low cost, simple processing and attractive properties are the reasons for the widespread use of aluminum matrix composites in the manufacturing of automobiles, aircraft, military, and sports goods. In this article, the microstructure, mechanical, and corrosion behaviors of the aluminum metal matrix were reviewed, focusing on the stir casting fabrication process and usage of agro/industrial waste reinforcement particles. The results portrayed that mechanical properties like tensile strength, ultimate tensile strength, hardness, percentage of elongation, impact, and fracture toughness are highly dependent on the amount, kind, and size of reinforcing particles. Additionally, uniform distribution, wettability of reinforcement particles, and the porosity level of the resulting composite also affect the mechanical and corrosion behaviors of aluminum matrix composites. The two-step stir-casting process resulted in better wetting characteristics, a lower porosity level, and a uniform distribution of particles with proper handling of process parameters. On the other hand, the inconsistent and contradicting results on corrosion behavior regarding monolithic and hybrid aluminum matrix composites need further study.

Keywords: microstructure, mechanical behavior, corrosion, aluminum matrix composite

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Authors: Naohiro Nakamura

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Analysis methods to accurately estimate the behavior of buildings when earthquakes occur is very important for improving the seismic safety of such buildings. Recently, the use of damping devices has increased significantly and there is a particular need to appropriately evaluate the behavior of buildings with such devices during earthquakes in the design stage. At present, however, the accuracy of the analysis evaluations is not sufficient. One reason is that the accuracy of current analysis methods has not been appropriately verified because there is very limited data on the behavior of actual buildings during earthquakes. Many types of shaking table test of large structures are performed at the '3-Dimensional Full-Scale Earthquake Testing Facility' (nicknamed 'E-Defense') operated by the National Research Institute of Earth Science and Disaster Prevention (NIED). In this study, simulations using 3- dimensional analysis models were conducted on shaking table test of a 5-story steel-frame structure with dampers. The results of the analysis correspond favorably to the test results announced afterward by the committee. However, the suitability of the parameters and models used in the analysis and the influence they had on the responses remain unclear. Hence, we conducted additional analysis and studies on these models and parameters. In this paper, outlines of the test are shown and the utilized analysis model is explained. Next, the analysis results are compared with the test results. Then, the additional analyses, concerning with the hysteresis curve of the dampers and the beam-end stiffness of the frame, are investigated.

Keywords: three-dimensional analysis, E-defense, full-scale experimen, vibration control damper

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Authors: Hareem Kausar

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Women of today’s world are energetic, enthusiastic and high-spirited. They tend to be the best in whatever they do and strive to accept and fulfil each challenge with utmost liveliness. The aim of the research was about studying the impact of Maternal Employment on the Child’s Behavioral Development. It was conducted as an initiative to study the impact factor in Pakistani culture and for deep insight to the subject using qualitative research methodology. The samples were interviewed through semi-structured interview method in three phases including two working mothers, two children and a day care center official and the data was collected and analyzed through content analysis. Further, it was linked with the literature from the west and the results show that children of working mothers tend to be sound mentally and physically but at some points they face the inner feeling of solitude. Overall, develop the mechanism in independence in their nature and behavior but maternal employment definitely affects the overall behavioral development of the children.

Keywords: maternal employment, child behavior- development, childhood, impact

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Authors: Vinicius G. Goecks, Gregory M. Gremillion, William D. Nothwang

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Modern approaches to train intelligent agents rely on prolonged training sessions, high amounts of input data, and multiple interactions with the environment. This restricts the application of these learning algorithms in robotics and real-world applications, in which there is low tolerance to inadequate actions, interactions are expensive, and real-time processing and action are required. This paper addresses this issue introducing CyberSteer, a novel approach to efficiently design intrinsic reward functions based on human intention to guide deep reinforcement learning agents with no environment-dependent rewards. CyberSteer uses non-expert human operators for initial demonstration of a given task or desired behavior. The trajectories collected are used to train a behavior cloning deep neural network that asynchronously runs in the background and suggests actions to the deep reinforcement learning module. An intrinsic reward is computed based on the similarity between actions suggested and taken by the deep reinforcement learning algorithm commanding the agent. This intrinsic reward can also be reshaped through additional human demonstration or critique. This approach removes the need for environment-dependent or hand-engineered rewards while still being able to safely shape the behavior of autonomous robotic agents, in this case, based on human intention. CyberSteer is tested in a high-fidelity unmanned aerial vehicle simulation environment, the Microsoft AirSim. The simulated aerial robot performs collision avoidance through a clustered forest environment using forward-looking depth sensing and roll, pitch, and yaw references angle commands to the flight controller. This approach shows that the behavior of robotic systems can be shaped in a reduced amount of time when guided by a non-expert human, who is only aware of the high-level goals of the task. Decreasing the amount of training time required and increasing safety during training maneuvers will allow for faster deployment of intelligent robotic agents in dynamic real-world applications.

Keywords: human-robot interaction, intelligent robots, robot learning, semisupervised learning, unmanned aerial vehicles

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Authors: A. H. Yehia, M. M. Rashwan, K. A. Assaf, K. Abd el Samee

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The aim of this work is to use an environmental, cheap; organic non-traditional admixture to improve the structural behavior of sustainable reinforced concrete beams contains different ratios of recycled concrete aggregate. The used admixture prepared by using wastes from vegetable oil industry. Under and over reinforced concrete beams made from natural aggregate and different ratios of recycled concrete aggregate were tested under static load until failure. Eight beams were tested to investigate the performance and mechanism effect of admixture on improving deformation characteristics, modulus of elasticity and toughness of tested beams. Test results show efficiency of organic admixture on improving flexural behavior of beams contains 20% recycled concrete aggregate more over the other ratios.

Keywords: deflection, modulus of elasticity, non-traditional admixture, recycled concrete aggregate, strain, toughness, under and over reinforcement

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Authors: Kristian Bautista, Ruben A. Idoy

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A bio-inspired metaheuristic algorithm inspired by the theory of collective animal behavior (CAB) was integrated to density-based clustering modeled as multimodal optimization problem. The algorithm was tested on synthetic, Iris, Glass, Pima and Thyroid data sets in order to measure its effectiveness relative to CDE-based Clustering algorithm. Upon preliminary testing, it was found out that one of the parameter settings used was ineffective in performing clustering when applied to the algorithm prompting the researcher to do an investigation. It was revealed that fine tuning distance δ3 that determines the extent to which a given data point will be clustered helped improve the quality of cluster output. Even though the modification of distance δ3 significantly improved the solution quality and cluster output of the algorithm, results suggest that there is no difference between the population mean of the solutions obtained using the original and modified parameter setting for all data sets. This implies that using either the original or modified parameter setting will not have any effect towards obtaining the best global and local animal positions. Results also suggest that CDE-based clustering algorithm is better than CAB-density clustering algorithm for all data sets. Nevertheless, CAB-density clustering algorithm is still a good clustering algorithm because it has correctly identified the number of classes of some data sets more frequently in a thirty trial run with a much smaller standard deviation, a potential in clustering high dimensional data sets. Thus, the researcher recommends further investigation in the post-processing stage of the algorithm.

Keywords: clustering, metaheuristics, collective animal behavior algorithm, density-based clustering, multimodal optimization

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Authors: A. Benatiallah, A. Hadjadj, D. Benatiallah, F. Abaidi, A. Harrouz

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Photovoltaic system is established as a reliable and economical source of electricity in rural and Sahara areas, especially in developing countries where the population is dispersed, has low consumption of energy and the grid power is not extended to these areas due to viability and financial problems. The production of energy by the photovoltaic system is very fluctuates and depend of meteorological conditions. Wind is a very important and often neglected parameter in the behavior of the solar module. The electric performances of a solar module to the silicon are very appreciable to the blows; in the present work we have studies the behavior of multi-crystal solar module according to the density of dust, and the principals electric feature of the solar module. An evaluation permits to affirm that a solar module under the effect of sand will collect a lower flux to the normal conditions.

Keywords: photovoltaic, multi-crystal module, experimental, effect of dust, performances

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Authors: J. J. Rickard, A. Belli, P. Goldberg Oppenheimer

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Medical diagnostics, environmental monitoring, homeland security and forensics increasingly demand specific and field-deployable analytical technologies for quick point-of-care diagnostics. Although technological advancements have made optical methods well-suited for miniaturization, a highly-sensitive detection technique for minute sample volumes is required. Raman spectroscopy is a well-known analytical tool, but has very weak signals and hence is unsuitable for trace level analysis. Enhancement via localized optical fields (surface plasmons resonances) on nanoscale metallic materials generates huge signals in surface-enhanced Raman scattering (SERS), enabling single molecule detection. This enhancement can be tuned by manipulation of the surface roughness and architecture at the sub-micron level. Nevertheless, the development and application of SERS has been inhibited by the irreproducibility and complexity of fabrication routes. The ability to generate straightforward, cost-effective, multiplex-able and addressable SERS substrates with high enhancements is of profound interest for SERS-based sensing devices. While most SERS substrates are manufactured by conventional lithographic methods, the development of a cost-effective approach to create nanostructured surfaces is a much sought-after goal in the SERS community. Here, a method is established to create controlled, self-organized, hierarchical nanostructures using electrohydrodynamic (HEHD) instabilities. The created structures are readily fine-tuned, which is an important requirement for optimizing SERS to obtain the highest enhancements. HEHD pattern formation enables the fabrication of multiscale 3D structured arrays as SERS-active platforms. Importantly, each of the HEHD-patterned individual structural units yield a considerable SERS enhancement. This enables each single unit to function as an isolated sensor. Each of the formed structures can be effectively tuned and tailored to provide high SERS enhancement, while arising from different HEHD morphologies. The HEHD fabrication of sub-micrometer architectures is straightforward and robust, providing an elegant route for high-throughput biological and chemical sensing. The superior detection properties and the ability to fabricate SERS substrates on the miniaturized scale, will facilitate the development of advanced and novel opto-fluidic devices, such as portable detection systems, and will offer numerous applications in biomedical diagnostics, forensics, ecological warfare and homeland security.

Keywords: hierarchical electrohydrodynamic patterning, medical diagnostics, point-of care devices, SERS

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Authors: V. Pizarro-Carmona, S. Castano-Solis, M. Cortés-Carmona, J. Fraile-Ardanuy, D. Jimenez-Bermejo

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The purpose of this article is to optimize the Equivalent Electric Circuit Model (EECM) of different orders to obtain greater precision in the modeling of Li-ion battery packs. Optimization includes considering circuits based on 1RC, 2RC and 3RC networks, with a dependent voltage source and a series resistor. The parameters are obtained experimentally using tests in the time domain and in the frequency domain. Due to the high non-linearity of the behavior of the battery pack, Genetic Algorithm (GA) was used to solve and optimize the parameters of each EECM considered (1RC, 2RC and 3RC). The objective of the estimation is to minimize the mean square error between the measured impedance in the real battery pack and those generated by the simulation of different proposed circuit models. The results have been verified by comparing the Nyquist graphs of the estimation of the complex impedance of the pack. As a result of the optimization, the 2RC and 3RC circuit alternatives are considered as viable to represent the battery behavior. These battery pack models are experimentally validated using a hardware-in-the-loop (HIL) simulation platform that reproduces the well-known New York City cycle (NYCC) and Federal Test Procedure (FTP) driving cycles for electric vehicles. The results show that using GA optimization allows obtaining EECs with 2RC or 3RC networks, with high precision to represent the dynamic behavior of a battery pack in vehicular applications.

Keywords: Li-ion battery packs modeling optimized, EECM, GA, electric vehicle applications

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Authors: Taher Abofol, Yaakov Kareev, Judith Avrahami, Peter M. Todd

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Are children sensitive to their relative strength in competitions against others? Performance on tasks that require cooperation or coordination (e.g. the Ultimatum Game) indicates that early precursors of adult-like notions of fairness and reciprocity, as well as altruistic behavior, are evident at an early age. However, not much is known regarding developmental changes in interactive decision-making, especially in competitive interactions. Thus, it is important to study the developmental aspects of strategic behavior in these situations. The present research focused on cognitive-developmental changes in a competitive interaction. Specifically, it aimed at revealing how children engage in strategic interactions that involve the allocation of limited resources over a number of fields of competition, by manipulating relative strength. Relative strength refers to situations in which player strength changes midway through the game: the stronger player becomes the weaker one, while the weaker player becomes the stronger one. An experiment was conducted to find out if the behavior of children of different age groups differs in the following three aspects: 1. Perception of relative strength. 2. Ability to learn while gaining experience. 3. Ability to adapt to change in relative strength. The task was composed of a resource allocation game. After the players allocated their resources (privately and simultaneously), a competition field was randomly chosen for each player. The player who allocated more resources to the field chosen was declared the winner of that round. The resources available to the two competitors were unequal (or equal, for control). The theoretical solution for this game is that the weaker player should give up on a certain number of fields, depending on the stronger opponent’s relative strength, in order to be able to compete with the opponent on equal footing in the remaining fields. Participants were of three age groups, first-graders (N = 36, mean age = 6), fourth-graders (N = 36, mean age = 10), and eleventh-graders (N = 72, mean age = 16). The games took place between players of the same age and lasted for 16 rounds. There were two experimental conditions – a control condition, in which players were of equal strength, and an experimental condition, in which players differed in strength. In the experimental condition, players' strength was changed midway through the session. Results indicated that players in all age groups were sensitive to their relative strength, and played in line with the theoretical solution: the weaker players gave up on more fields than the stronger ones. This understanding, as well as the consequent difference in allocation between weak and strong players, was more pronounced among older participants. Experience led only to minimal behavioral change. Finally, the children from the two older groups, particularly the eleventh graders adapted quickly to the midway switch in relative strength. In contrast, the first-graders hardly changed their behavior with the change in their relative strength, indicating a limited ability to adapt. These findings highlight young children’s ability to consider their relative strength in strategic interactions and its boundaries.

Keywords: children, competition, decision making, developmental changes, strategic behavior

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Authors: Aboozar Aghaei, Kamran Dehghani

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In the present work, the dissimilar Monel400 and SS316 were joined by friction stir welding (FSW). The applied rotating speed was 400 rpm, whereas the traverse speed varied between 50 and 150 mm/min. At a constant rotating speed, the sound welds were obtained at the welding speeds of 50 and 100 mm/min. However, a groove-like defect was formed when the welding speed exceeded 100 mm/min. The mechanical properties of the joints were evaluated using tensile and fatigue tests. The fatigue strength of dissimilar FSWed specimens was higher than that of both Monel400 and SS316. To study the failure behavior of FSWed specimens, the fracture surfaces were analyzed using a scanning electron microscope (SEM). The failure analysis indicates that different mechanisms may contribute to the fracture of welds. This was attributed to the dissimilar characteristics of dissimilar materials exhibiting different failure behaviors.

Keywords: frictions stir welding, stainless steel, Monel400, mechanical properties

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Authors: Birgit Heike Spohn

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Several authors (e.g., Krowatschek & Reid, 2011; Winkler, 1998) pronounce themselves in favor of the use of relaxation techniques in school because those techniques could help children to cope with stress, improve power of concentration, learning, and social behavior as well as class climate. Children with learning disabilities might profit from those techniques in a special way because they contribute to improved learning behavior. There is no study addressing the frequency of the use of relaxation techniques in special schools for children with learning disabilities in German speaking countries. The paper presents a study in which all teachers of special schools for children with learning disabilities in a district of South Germany (n = 625) were questioned about the use of relaxation techniques in school using a standardized questionnaire. Variables addressed were the use of these techniques in the classroom, aspects of their use (kind of relaxation technique, frequency, and regularity of their use), and potential influencing factors. The results are discussed, and implications for further research are drawn.

Keywords: special education, learning disabilities, relaxation training, concentration

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Authors: A. I. Khalafalla, K. A. Al-Busada, I. M. El-Sabagh

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Pox and pox-like diseases of camels are a group of exanthematous skin conditions that have become increasingly important economically. They may be caused by three distinct viruses: camelpox virus (CMPV), camel contagious ecthyma virus (CCEV) and camel papillomavirus (CAPV). These diseases are difficult to differentiate based on clinical presentation in disease outbreaks. Molecular methods such as PCR targeting species-specific genes have been developed and used to identify CMPV and CCEV, but not simultaneously in a single tube. Recently, multiplex PCR has gained reputation as a convenient diagnostic method with cost- and time–saving benefits. In the present communication, we describe the development, optimization and validation a multiplex PCR assays able to detect simultaneously the genome of the three viruses in one single test allowing for rapid and efficient molecular diagnosis. The assay was developed based on the evaluation and combination of published and new primer sets, and was applied to the detection of 110 tissue samples. The method showed high sensitivity, and the specificity was confirmed by PCR-product sequencing. In conclusion, this rapid, sensitive and specific assay is considered a useful method for identifying three important viruses in specimens from camels and as part of a molecular diagnostic regime.

Keywords: multiplex PCR, diagnosis, pox and pox-like diseases, camels

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Authors: Vivian Wu

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During the mating season, honeybee drones make mating fights to congregation areas where they face fierce competition to mate with a queen. Drones have developed distinct anatomical and functional features in order to optimize their chances of success. Flight activities of western honeybee (Apis mellifera) drones and foragers were monitored using radio frequency identification (RFID) to test if drones have also developed distinct flight behaviors. Drone flight durations showed a bimodal distribution dividing the flights into short flights and long flights while forager flight durations showed a left-skewed unimodal distribution. Interestingly, the short trips occurred prior to the long trips on a daily basis. The first trips of the day the drones made were primarily short trips, and the distribution significantly shifted to long trips as the drones made more trips. In contrast, forager trips showed no such shift of distribution. In addition, drones made short trips but no long mating trips on days associated with a significant drop in temperature and increase of clouds compared to the previous day. These findings suggest that drones may have developed a unique flight behavior making short trips first to survey the weather conditions before flying out to the congregation area to pursue a successful mating.

Keywords: apis mellifera, drone, flight behavior, weather, RFID

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Authors: Abdulsalam M. Alhawsawi

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Estimation of a radiation detector's efficiency plays a significant role in calculating the activity of radioactive samples. Detector efficiency is measured using sources that emit a variety of energies from low to high-energy photons along the energy spectrum. Some photon energies are hard to find in lab settings either because check sources are hard to obtain or the sources have short half-lives. This work aims to develop a method to determine the efficiency of a High Purity Germanium Detector (HPGe) based on the 662 keV gamma ray photon emitted from Cs-137. Cesium-137 is readily available in most labs with radiation detection and health physics applications and has a long half-life of ~30 years. Several photon efficiencies were calculated using the MCNP5 simulation code. The simulated efficiency of the 662 keV photon was used as a base to calculate other photon efficiencies in a point source and a Marinelli Beaker form. In the Marinelli Beaker filled with water case, the efficiency of the 59 keV low energy photons from Am-241 was estimated with a 9% error compared to the MCNP5 simulated efficiency. The 1.17 and 1.33 MeV high energy photons emitted by Co-60 had errors of 4% and 5%, respectively. The estimated errors are considered acceptable in calculating the activity of unknown samples as they fall within the 95% confidence level.

Keywords: MCNP5, MonteCarlo simulations, efficiency calculation, absolute efficiency, activity estimation, Cs-137

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Authors: Anjali Chouksey, Riya Nimje, Jahanvi Saraf

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Online transactions have increased dramatically in this new ‘social-distancing’ era. With online transactions, Fraud in online payments has also increased significantly. Frauds are a significant problem in various industries like insurance companies, baking, etc. These frauds include leaking sensitive information related to the credit card, which can be easily misused. Due to the government also pushing online transactions, E-commerce is on a boom. But due to increasing frauds in online payments, these E-commerce industries are suffering a great loss of trust from their customers. These companies are finding credit card fraud to be a big problem. People have started using online payment options and thus are becoming easy targets of credit card fraud. In this research paper, we will be discussing machine learning algorithms. We have used a decision tree, XGBOOST, k-nearest neighbour, logistic-regression, random forest, and SVM on a dataset in which there are transactions done online mode using credit cards. We will test all these algorithms for detecting fraud cases using the confusion matrix, F1 score, and calculating the accuracy score for each model to identify which algorithm can be used in detecting frauds.

Keywords: machine learning, fraud detection, artificial intelligence, decision tree, k nearest neighbour, random forest, XGBOOST, logistic regression, support vector machine

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Authors: Brittany Richardson, Ying Wang

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For physical therapy, exercise prescription, athlete training, and regular fitness training, it is crucial to perform health assessments or fitness assessments periodically. An accurate assessment is propitious for tracking recovery progress, preventing potential injury and making long-range training plans. Assessments include necessary measurements, height, weight, blood pressure, heart rate, body fat, etc. and advanced evaluation, muscle group strength, stability-mobility, and movement evaluation, etc. In the current standard assessment procedures, the accuracy of assessments, especially advanced evaluations, largely depends on the experience of physicians, coaches, and personal trainers. And it is challenging to track clients’ progress in the current assessment. Unlike the tradition assessment, in this paper, we present a deep learning based face recognition algorithm for accurate, comprehensive and trackable assessment. Based on the result from our assessment, physicians, coaches, and personal trainers are able to adjust the training targets and methods. The system categorizes the difficulty levels of the current activity for the client or user, furthermore make more comprehensive assessments based on tracking muscle group over time using a designed landmark detection method. The system also includes the function of grading and correcting the form of the clients during exercise. Experienced coaches and personal trainer can tell the clients' limit based on their facial expression and muscle group movements, even during the first several sessions. Similar to this, using a convolution neural network, the system is trained with people’s facial expression to differentiate challenge levels for clients. It uses landmark detection for subtle changes in muscle groups movements. It measures the proximal mobility of the hips and thoracic spine, the proximal stability of the scapulothoracic region and distal mobility of the glenohumeral joint, as well as distal mobility, and its effect on the kinetic chain. This system integrates data from other fitness assistant devices, including but not limited to Apple Watch, Fitbit, etc. for a improved training and testing performance. The system itself doesn’t require history data for an individual client, but the history data of a client can be used to create a more effective exercise plan. In order to validate the performance of the proposed work, an experimental design is presented. The results show that the proposed work contributes towards improving the quality of exercise plan, execution, progress tracking, and performance.

Keywords: exercise prescription, facial recognition, landmark detection, fitness assessments

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Authors: Ghodbane Azeddine, Saad Maarouf, Boland Jean-Francois, Thibeault Claude

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This paper proposes a new design of an active fault-tolerant flight control system against abrupt actuator faults. This overall system combines a multiple time scale sliding mode controller for fault compensation and a geometric approach for fault detection and diagnosis. The proposed control system is able to accommodate several kinds of partial and total actuator failures, by using available healthy redundancy actuators. The overall system first estimates the correct fault information using the geometric approach. Then, and based on that, a new reconfigurable control law is designed based on the multiple time scale sliding mode technique for on-line compensating the effect of such faults. This approach takes advantages of the fact that there are significant difference between the time scales of aircraft states that have a slow dynamics and those that have a fast dynamics. The closed-loop stability of the overall system is proved using Lyapunov technique. A case study of the non-linear model of the F16 fighter, subject to the rudder total loss of control confirms the effectiveness of the proposed approach.

Keywords: actuator faults, fault detection and diagnosis, fault tolerant flight control, sliding mode control, multiple time scale approximation, geometric approach for fault reconstruction, lyapunov stability

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Authors: Khalid Alhudaib

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Potato is considered as one of the most important and potential vegetable crops in Saudi Arabia. Alfalfa mosaic virus (AMV), genus Alfamovirus, family Bromoviridae is among the broad spread of viruses in potato. During spring and fall growing seasons of potato in 2015 and 2016, several field visits were conducted in the four major growing areas of potato cultivation (Riyadh-Qaseem-Hail-Hard). The presence of AMV was detected in samples using ELISA, dot blot hybridization and/or RT-PCR. The highest occurrence of AMV was observed as 18.6% in Qaseem followed by Riyadh with 15.2% while; the lowest infection rates were recorded in Hard and Hail, 8.3 and 10.4%, respectively. The sequences of seven isolates of AMV obtained in this study were determined and the sequences were aligned with the other sequences available in the GenBank database. Analyses confirmed the low variability among AMV isolated in this study, which means that all AMV isolates may originate from the same source. Due to high incidence of AMV, other economic susceptible crops may become affected by high incidence of this virus in potato crops. This requires accurate examination of potato seed tubers to prevent the spread of the virus in Saudi Arabia. The obtained results indicated that the hybridization and ELISA are suitable techniques in the routine detection of AMV in a large number of samples while RT-PCR is more sensitive and essential for molecular characterization of AMV.

Keywords: Alfamovirus, AMV, Alfalfa mosaic virus, PCR, potato

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Authors: J.-Y. Lee, H.-S. Lim, S.-E. Kim

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Recently, the use of high-strength materials is increasing as the construction of large structures and high-rise structures increases. This paper presents an analysis of the shear behavior of prestressed concrete members with various types of materials by simulating a finite element (FE) analysis. The analytical results indicated that the shear strength and shear failure mode were strongly influenced by not only the shear reinforcement ratio but also the yield strength of shear reinforcement and the compressive strength of concrete. Though the yield strength of shear reinforcement increased the shear strength of prestressed concrete members, there was a limit to the increase in strength because of the change of shear failure modes. According to the results of FE analysis on various parameters, the maximum yield strength of the steel stirrup that can be applied to prestressed concrete members was about 860 MPa.

Keywords: prestressed concrete members, high strength reinforcing bars, high strength concrete, shear behavior

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Authors: Md. Abu Sufian, Robiqul Islam, Imam Hossain Shajid, Mahesh Hanumanthu, Jarasree Varadarajan, Md. Sipon Miah, Mingbo Niu

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Coronary Heart Disease (CHD) remains a principal cause of global morbidity and mortality, characterized by atherosclerosis—the build-up of fatty deposits inside the arteries. The study introduces an innovative methodology that leverages cloud-based platforms like AWS Live Streaming and Artificial Intelligence (AI) to early detect and prevent CHD symptoms in web applications. By employing novel simulation processes and AI algorithms, this research aims to significantly mitigate the health and societal impacts of CHD. Methodology: This study introduces a novel simulation process alongside a multi-phased model development strategy. Initially, health-related data, including heart rate variability, blood pressure, lipid profiles, and ECG readings, were collected through user interactions with web-based applications as well as API Integration. The novel simulation process involved creating synthetic datasets that mimic early-stage CHD symptoms, allowing for the refinement and training of AI algorithms under controlled conditions without compromising patient privacy. AWS Live Streaming was utilized to capture real-time health data, which was then processed and analysed using advanced AI techniques. The novel aspect of our methodology lies in the simulation of CHD symptom progression, which provides a dynamic training environment for our AI models enhancing their predictive accuracy and robustness. Model Development: it developed a machine learning model trained on both real and simulated datasets. Incorporating a variety of algorithms including neural networks and ensemble learning model to identify early signs of CHD. The model's continuous learning mechanism allows it to evolve adapting to new data inputs and improving its predictive performance over time. Results and Findings: The deployment of our model yielded promising results. In the validation phase, it achieved an accuracy of 92% in predicting early CHD symptoms surpassing existing models. The precision and recall metrics stood at 89% and 91% respectively, indicating a high level of reliability in identifying at-risk individuals. These results underscore the effectiveness of combining live data streaming with AI in the early detection of CHD. Societal Implications: The implementation of cloud-based AI for CHD symptom detection represents a significant step forward in preventive healthcare. By facilitating early intervention, this approach has the potential to reduce the incidence of CHD-related complications, decrease healthcare costs, and improve patient outcomes. Moreover, the accessibility and scalability of cloud-based solutions democratize advanced health monitoring, making it available to a broader population. This study illustrates the transformative potential of integrating technology and healthcare, setting a new standard for the early detection and management of chronic diseases.

Keywords: coronary heart disease, cloud-based ai, machine learning, novel simulation techniques, early detection, preventive healthcare

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Authors: Aida Rafat, Ramazan Kahraman, Mert Atilhan

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The aggressive corrosion behavior of conventional amine solvents is one of main barriers against large scale commerizaliation of amine absorption process for carbon capture application. Novel CO2 absorbents that exhibit minimal corrosivity against operation conditions are essential to lower corrosion damage and control and ensure more robustness in the capture plant. This work investigated corrosion behavior of carbon steel CS1018 in various CO2 absrobent solvents. The tested solvents included the classical amines MEA, DEA and MDEA, piperazine activated solvents MEA/PZ, MDEA/PZ and MEA/MDEA/PZ as well as mixtures of MEA and Room Temperature Ionic Liquids RTIL, namely MEA/[C4MIM][BF4] and MEA/[C4MIM][Otf]. Electrochemical polarization technique was used to determine the system corrosiveness in terms of corrosion rate and polarization behavior. The process parameters of interest were CO2 loading and solution temperature. Electrochemical resulted showed corrosivity order of classical amines at 40°C is MDEA> MEA > DEA wherase at 80°C corrosivity ranking changes to MEA > DEA > MDEA. Corrosivity rankings were mainly governed by CO2 absorption capacity at the test temperature. Corrosivity ranking for activated amines at 80°C was MEA/PZ > MDEA/PZ > MEA/MDEA/PZ. Piperazine addition seemed to have a dual advanatge in terms of enhancing CO2 absorption capacity as well as nullifying corrosion. For MEA/RTIL mixtures, the preliminary results showed that the partial repalcement of aqueous phase in MEA solution by the more stable nonvolatile RTIL solvents reduced corrosion rates considerably.

Keywords: corrosion, amines, CO2 capture, piperazine, ionic liquids

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Authors: Wipanee Maen-In

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This research aims to study the tourism marketing and the trip behaviors profile of international tourists who visited Phuket in Thailand and study the influence of their selected demographic characters on their selected trip behaviors. The study was conducted through survey by using questionnaires asking 400 sample respondents from international tourists who visited Phuket. The result found out that type of group travel is the key variable that indicates higher and lower daily spending tourists, tourists spend more when they visit with their family. Trip arrangement is the key variables that indicate shorter and longer stay tourists. From these findings, it is recommended that both private and public sectors should make marketing to potential tourists in order to increase tourism revenue and to be a sustainable tourism, all of agencies that involves in Phuket tourism industry should coordinate to satisfy tourists to revisit and recommend Phuket to friends and relatives.

Keywords: consumer behavior, international tourists, Phuket province, tourism marketing

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Authors: JoVontae Butts

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A central and enduring theme in the study of American politics is political participation, which indicates the health of a democracy, citizen buy-in, and fair political representation. Though voting push factors have been thoroughly researched and are becoming better understood, the effect of those same push factors often varies for marginalized people. Black voters begun to cast votes at a steadily increasing rate following the 1996 election, gradually growing to its highest level in the 2012 presidential election, even surpassing white voter participation rates. The thirty-year growth period of Black voter engagement concluded in the 2016 election, with the number of participating Black voters stumbling by approximately 7% while other demographics remained roughly the same. Theories for the shift in Black voter behavior range from vote suppression to discouragement due to Barack Obama’s concluding tenure in office. Furthermore, Black voter engagement rebounded in the 2020 election, leaving turnout and race scholars to speculate even further, predicting that disapproval of Trump energized the Black voter bloc. Though there is much conjecture regarding the changes in Black voter behavior, there is truly little empirical evidence to vet those suppositions. This study engages and quantifies speculations for the changes in Black voter engagement in recent elections using 2016 and 2020 American National Election Studies Pilot Study data. Additionally, this study expands upon McGregor’s theory of political hypervigilance by exploring differences in political engagement for church-attending Black voters and those that do not.

Keywords: race, religion, evangelicalism, political engagement

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Authors: Madhushi Bandara, Dharshana Kasthurirathna, Danaja Maldeniya, Mahendra Piraveenan

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Community detection is an extremely useful technique in understanding the structure and function of a social network. Louvain algorithm, which is based on Newman-Girman modularity optimization technique, is extensively used as a computationally efficient method extract the communities in social networks. It has been suggested that the nodes that are in close geographical proximity have a higher tendency of forming communities. Variants of the Newman-Girman modularity measure such as dist-modularity try to normalize the effect of geographical proximity to extract geographically dispersed communities, at the expense of losing the information about the geographically proximate communities. In this work, we propose a method to extract geographically dispersed communities while preserving the information about the geographically proximate communities, by analyzing the ‘community network’, where the centroids of communities would be considered as network nodes. We suggest that the inter-community link strengths, which are normalized over the community sizes, may be used to identify and extract the ‘overlay communities’. The overlay communities would have relatively higher link strengths, despite being relatively apart in their spatial distribution. We apply this method to the Gowalla online social network, which contains the geographical signatures of its users, and identify the overlay communities within it.

Keywords: social networks, community detection, modularity optimization, geographically dispersed communities

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Authors: Hector Liu, Chih-Ming Tsai

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Consumer behaviors in the subscription of over-the-top (OTT) media services have substantially changed because of the COVID-19 pandemic; hence, this study aims to determine the factors affecting subscription intentions. The increased usage of OTT media, particularly in the lockdowns during the COVID-19 pandemic, has intensified the competition between both global and local streaming providers. While studies have discussed antecedents accounting for this change, they have paid limited attention to the psychological factors that shape consumer behavior in using OTT services. Given the changes in consumers’ psychological states during the pandemic, this study seeks to fill the research gap by integrating the expectancy-value model to provide insights into the key gratifications that consumers seek and obtain and that have affected their subscription to OTT services. This study proposes a theoretical model and assesses this framework on data collected from 1,068 OTT service users in Taiwan. The results strengthen the literature by indicating a clear growth in the popularity and subscription of OTT services because of the COVID-19 lockdowns as well as factors such as perceived quality and satisfaction, which influence behavioral intentions for OTT services. Most crucially, however, OTT viewers who acquired a sense of belonging, a sense of being accompanied, and a sense of reduction in anxiety due to being quarantined and in lockdown show a higher tendency to continue their subscriptions to their OTT services of choice during the pandemic. With consumer behavior trends forever changed by the COVID-19 pandemic, the implications from this study provide OTT service platforms with an opportunity to capitalize on their current and potential customers’ changing desires, demands, and factors for a continued subscription.

Keywords: consumer behavior, COVID-19, expectancy-value model, OTT media services

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Authors: Peter Modregger, Özgül Öztürk

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Glass fabric reinforced thermoplastic (GFRT) are composite materials, which combine low weight and resilient mechanical properties rendering them especially suitable for automobile construction. However, defects in the glass fabric as well as in the polymer matrix can occur during manufacturing, which may compromise component lifetime or even safety. One type of these defects is local fiber shifts, which can be difficult to detect. Recently, we have experimentally demonstrated the reliable detection of local fiber shifts by X-ray scattering based on the edge-illumination (EI) principle. EI constitutes a novel X-ray imaging technique that utilizes two slit masks, one in front of the sample and one in front of the detector, in order to simultaneously provide absorption, phase, and scattering contrast. The principle of contrast formation is as follows. The incident X-ray beam is split into smaller beamlets by the sample mask, resulting in small beamlets. These are distorted by the interaction with the sample, and the distortions are scaled up by the detector masks, rendering them visible to a pixelated detector. In the experiment, the sample mask is laterally scanned, resulting in Gaussian-like intensity distributions in each pixel. The area under the curves represents absorption, the peak offset refraction, and the width of the curve represents the scattering occurring in the sample. Here, scattering is caused by the numerous glass fiber/polymer matrix interfaces. In our recent publication, we have shown that the standard deviation of the absorption and scattering values over a selected field of view can be used to distinguish between intact samples and samples with local fiber shift defects. The quantification of defect detection performance was done by using p-values (p=0.002 for absorption and p=0.009 for scattering) and contrast-to-noise ratios (CNR=3.0 for absorption and CNR=2.1 for scattering) between the two groups of samples. This was further improved for the scattering contrast to p=0.0004 and CNR=4.2 by utilizing a harmonic decomposition analysis of the images. Thus, we concluded that local fiber shifts can be reliably detected by the X-ray scattering contrasts provided by EI. However, a potential application in, for example, production monitoring requires fast data acquisition times. For the results above, the scanning of the sample masks was performed over 50 individual steps, which resulted in long total scan times. In this paper, we will demonstrate that reliable detection of local fiber shift defects is also possible by using single images, which implies a speed up of total scan time by a factor of 50. Additional performance improvements will also be discussed, which opens the possibility for real-time acquisition. This contributes a vital step for the translation of EI to industrial applications for a wide variety of materials consisting of numerous interfaces on the micrometer scale.

Keywords: defects in composites, X-ray scattering, local fiber shifts, X-ray edge Illumination

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Authors: Inci Develioglu, Hasan Firat Pulat

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Sustainable development is one of the most important topics in today's world, and it is also an important research topic for geoenvironmental engineering. Dredging process is performed to expand the river and port channel, flood control and accessing harbors. Every year large amount of sediment are dredged for these purposes. Dredged marine soils can be reused as filling materials, road and foundation embankments, construction materials and wildlife habitat developments. In this study, geotechnical engineering properties and compressibility behavior of dredged soil obtained from the Izmir Bay were investigated. The samples with four different organic matter contents were obtained and particle size distributions, consistency limits, pH and specific gravity tests were performed. The consolidation tests were conducted to examine organic matter content (OMC) effects on compressibility behavior of dredged soil. This study has shown that the OMC has an important effect on the engineering properties of dredged soils. The liquid and plastic limits increased with increasing OMC. The lowest specific gravity belonged to sample which has the maximum OMC. The specific gravity values ranged between 2.76 and 2.52. The maximum void ratio difference belongs to sample with the highest OMC (De_11% = 0.38)_. As the organic matter content of the samples increases, the change in the void ratio has also increased. The compression index increases with increasing OMC.

Keywords: compressibility, consolidation, geotechnical properties, organic matter content, dredged soil

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Authors: Carlos H. Cuadra, Nobuhiro Shimoi

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Application of piezoelectric sensors to detect structural damage due to seismic action on building structures is investigated. Plate-type piezoelectric sensor was developed and proposed for this task. A film-type piezoelectric sheet was attached on a steel plate and covered by a layer of glass. A special glue is used to fix the glass. This glue is a silicone that requires the application of ultraviolet rays for its hardening. Then, the steel plate was set up at a steel column-beam joint of a test specimen that was subjected to bending moment when test specimen is subjected to monotonic load and cyclic load. The structural behavior of test specimen during cyclic loading was verified using a finite element model, and it was found good agreement between both results on load-displacement characteristics. The cross section of steel elements (beam and column) is a box section of 100 mm×100 mm with a thin of 6 mm. This steel section is specified by the Japanese Industrial Standards as carbon steel square tube for general structure (STKR400). The column and beam elements are jointed perpendicularly using a fillet welding. The resulting test specimen has a T shape. When large deformation occurs the glass plate of the sensor device cracks and at that instant, the piezoelectric material emits a voltage signal which would be the indicator of a certain level of deformation or damage. Applicability of this piezoelectric sensor to detect structural damages was verified; however, additional analysis and experimental tests are required to establish standard parameters of the sensor system.

Keywords: piezoelectric sensor, static cyclic test, steel structure, seismic damages

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Authors: Sari Aouatef, Larabi Amina

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First-principles calculations within density functional theory based are used to investigate the influence of doped rare earth elements on some properties of perovskite systems Dy₀.₀₆₂₅Y₀.₉₃₇₅FeO₃ and Dy₀.₁₂₅ Y₀.₈₇₅ FeO₃. The electronic and magnetic properties are studied by means of the full-potential linearized augmented plane wave method with Vasp code. The calculated densities of states presented in this work identify the semiconducting behavior for Dy₀.₁₂₅ Y₀.₈₇₅ FeO₃, and the semi-metallic behavior for Dy₀.₀₆₂₅Y₀.₉₃₇₅ FeO₃. Besides, to investigate magnetic properties of several compounds, four magnetic configurations are considered (ferromagnetic (FM), antiferromagnetic type A (A-AFM), antiferromagnetic type C (C-AFM) and antiferromagnetic type G (G-AFM). By doping the Dy element, the system shows different changes in the magnetic order and electronic structure. It is found that Dy₀.₀₆₂₅Y₀.₉₃₇₅ FeO₃ exhibits the strongest magnetic change corresponding to the transition to the ferromagnetic order with the largest magnetic moment of 4.997.

Keywords: DFT, Perovskites, multiferroic, magnetic properties

Procedia PDF Downloads 142