Abstract: The paper aims to clarify the relationship between product involvement level and consumer tendency toward online review. It proposes the products in two classes and examines the level of user attention and significant difference between attribute-based areas and experience-based areas in each category. It uses an eye-tracking experiment to simulate the experience of online shopping behavior in order to view the consumers' shopping behavior. Thus, a scenario was designed, and 23 participants were asked step by step to purchase some products and add them to their shopping cart. The fixation durations are used to examine the amount of visual attention of the user in each area of interest (AOI) determined considering two classes of high involvement and low involvement products, and paired sample T-test was used to examine the effect of the product’s types on the online review content. The study results explained that users of high involvement products consider the attribute-based points more highly than the experience-based points.
Abstract: Nowadays, the increasing applicability of internet of things (IoT) systems has changed the way that the world around is perceived. The massive interconnection of systems by means of sensing, processing and communication, allows multitude of data to be at our fingertips. In this way, countless advances have been made in different fields such as personal care, predictive maintenance in industry, quality control in production processes, security, and in everything imaginable. However, all these electronic systems have in common the need to be electrically powered. In this context, batteries and wires are the most commonly used solutions, but they are not a definitive solution in some applications, because of the attainability, the serviceability, or the performance requirements. Therefore, the need arises to look for other types of solutions based on energy harvesting and long-life electronics. Energy Harvesting can be defined as the action of capturing energy from the environment and store it for an instantaneous use or later use. Among the materials capable of harvesting energy from the environment, such as thermoelectrics, electromagnetics, photovoltaics or triboelectrics, the most suitable is the piezoelectric material. The phenomenon of piezoelectricity is one of the most powerful sources for energy harvesting, ranging from a few micro wats to hundreds of wats, depending on certain factors such as material type, geometry, excitation frequency, mechanical and electrical configurations, among others. In this research work, an exhaustive study is carried out on how different types of piezoelectric materials and electrical configurations influence the maximum power that a bimorph harvester is able to extract from mechanical vibrations. A series of experiments has been carried out in which the manufactured bimorph specimens are excited under fixed inertial vibrational conditions. In addition, in order to evaluate the dependence of the maximum transferred power, different load resistors are tested. In this way, the pure active power that achieves the maximum power transfer can be approximated. In this paper, we present the design of low-cost energy harvesting solutions based on piezoelectric smart materials with tunable frequency. The results obtained show the differences in energy extraction between the PZT materials studied and their electrical configurations. The aim of this work is to gain a better understanding of the behavior of piezoelectric materials, and the design process of bimorph PZT harvesters to optimize environmental energy extraction.
Abstract: This paper is intended to evaluate the structural performance of welded electronic road signs under various damage scenarios (DSs) using a finite element (FE) model calibrated with full-scale ultimate load testing results. The tested electronic road sign specimen was built with a back skin made of 5052 aluminum and two channels and a frame made of 6061 aluminum, where the back skin was connected to the frame by welding. The size of the tested specimen was 1.52 m long, 1.43 m wide, and 0.28 m deep. An actuator applied vertical loads at the center of the back skin of the specimen, resulting in a displacement of 158.7 mm and an ultimate load of 153.46 kN. Using these testing data, generation and calibration of a FE model of the tested specimen were executed in ABAQUS, indicating that the difference in the ultimate load between the calibrated model simulation and full-scale testing was only 3.32%. Then, six different DSs were simulated where the areas of the welded connection in the calibrated model were diminished for the DSs. It was found that the corners at the back skin-frame joint were prone to connection failure for all the DSs, and failure of the back skin-frame connection occurred remarkably from the distant edges.
Abstract: To accelerate the remanufacturing process of electronic waste products, this study designs a partial disassembly line with the multi-robotic station to effectively dispose of excessive wastes. The multi-robotic partial disassembly line is a technical upgrade to the existing manual disassembly line. Balancing optimization can make the disassembly line smoother and more efficient. For partial disassembly line balancing with the multi-robotic station (PDLBMRS), a mixed-integer programming model (MIPM) considering the robotic efficiency differences is established to minimize cycle time, energy consumption and hazard index and to calculate their optimal global values. Besides, an enhanced NSGA-II algorithm (HNSGA-II) is proposed to optimize PDLBMRS efficiently. Finally, MIPM and HNSGA-II are applied to an actual mixed disassembly case of two types of computers, the comparison of the results solved by GUROBI and HNSGA-II verifies the correctness of the model and excellent performance of the algorithm, and the obtained Pareto solution set provides multiple options for decision-makers.
Abstract: To reflect the influence of after-treatment system retrofit and natural gas-fueled vehicle replace on exhaust emissions emitted by urban buses, a portable emission measurement system (PEMS) was employed herein to conduct real driving emission measurements. This study investigated the differences in particle number (PN), particle mass (PM), and nitrogen oxides (NOx) emissions from a China IV diesel bus retrofitted by catalyzed diesel particulate filter (CDPF), a China IV diesel bus, and a China V natural gas bus. The results show that both tested diesel buses possess markedly advantages in NOx emission control when compared to the lean-burn natural gas bus equipped without any NOx after-treatment system. As to PN and PM, only the DOC+CDPF retrofitting diesel bus exhibits enormous benefits on emission control related to the natural gas bus, especially the normal diesel bus. Meanwhile, the differences in PM and PN emissions between retrofitted and normal diesel buses generally increase with the increase in vehicle specific power (VSP). Furthermore, the differences in PM emissions, especially those in the higher VSP ranges, are more significant than those in PN. In addition, the maximum peak PN particle size (32 nm) of the retrofitted diesel bus was significantly lower than that of the normal diesel bus (100 nm). These phenomena indicate that the CDPF retrofitting can effectively reduce diesel bus exhaust particle emissions, especially those with large particle sizes.
Abstract: The classification of periodontal diseases has changed significantly in favor of simplifying the protocol of diagnosis and periodontal treatment. This review study aims to highlight the latest publications in the new periodontal disease classification, talking about the most significant differences versus the old classification with the tendency to express the advantages or disadvantages of clinical application. The aim of the study also includes the growing tendency to link the way of classification of periodontal diseases with predetermined protocols of periodontal treatment of the diagnoses included in the classification. The new classification of periodontal diseases is rather comprehensive in its subdivisions, as the disease is viewed in its entirety, with the biological dimensions of the disease, the degree of aggravation and progression of the disease, in relation to risk factors, predisposition to patient susceptibility and impact of periodontal disease to the general health status of the patient.
Abstract: Creating favourable conditions for students’ comprehension of mathematical content is one of the primary problems in teaching mathematics in secondary school. The fact of comprehension includes the ability to build a working situational model and thus becomes an important means of solving mathematical problems. This paper describes a holistic approach to teaching mathematics designed to address the primary challenges of such teaching; specifically, the challenge of students’ comprehension. Essentially, this approach consists of (1) establishing links between the attributes of the notion: the sense, the meaning, and the term; (2) taking into account the components of student’s subjective experience—value-based emotions, contextual, procedural and communicative—during the educational process; (3) linking together different ways to present mathematical information; (4) identifying and leveraging the relationships between real, perceptual and conceptual (scientific) mathematical spaces by applying real-life situational modelling. The article describes approaches to the practical use of these foundational concepts. Identifying how proposed methods and techniques influence understanding of material used in teaching mathematics was the primary goal. The study included an experiment in which 256 secondary school students took part: 142 in the study group and 114 in the control group. All students in these groups had similar levels of achievement in math and studied math under the same curriculum. In the course of the experiment, comprehension of two topics — “Derivative” and “Trigonometric functions”—was evaluated. Control group participants were taught using traditional methods. Students in the study group were taught using the holistic method: under teacher’s guidance, they carried out assignments designed to establish linkages between notion’s characteristics, to convert information from one mode of presentation to another, as well as assignments that required the ability to operate with all modes of presentation. Identification, accounting for and transformation of subjective experience were associated with methods of stimulating the emotional value component of the studied mathematical content (discussions of lesson titles, assignments aimed to create study dominants, performing theme-related physical exercise ...) The use of techniques that forms inter-subject notions based on linkages between, perceptual real and mathematical conceptual spaces proved to be of special interest to the students. Results of the experiment were analysed by presenting students in each of the groups with a final test in each of the studied topics. The test included assignments that required building real situational models. Statistical analysis was used to aggregate test results. Pierson criterion x2 was used to reveal statistics significance of results (pass-fail the modelling test). Significant difference of results was revealed (p < 0.001), which allowed to conclude that students in the study group showed better comprehension of mathematical information than those in the control group. The total number of completed assignments of each student was analysed as well, with average results calculated for each group. Statistical significance of result differences against the quantitative criterion (number of completed assignments) was determined using Student’s t-test, which showed that students in the study group completed significantly more assignments than those in the control group (p = 0.0001). Authors thus come to the conclusion that suggested increase in the level of comprehension of study material took place as a result of applying implemented methods and techniques.
Abstract: Rheumatoid Arthritis patients have swelling and pain in joints of the hand. The regions where the patient feels pain also show increased body temperature. Thermal cameras can be used to detect the rise in temperature of the affected regions. To monitor the progression of Rheumatoid Arthritis, patients must visit the clinic regularly for scanning and examination. After scanning and evaluation, the dosage of the medicine is regulated accordingly. To monitor the disease progression over time, the correlation of the images between different visits must be established. It has been observed that the thermal measurements do not remain the same over time, even within a single scanning, when low-cost thermal cameras are used. In some situations, temperatures can vary as much as 2◦C within the same scanning sequence. In this paper, it has been shown that although the absolute temperature varies over time, the relative difference between different regions remains similar. Results have been computed over four scanning sequences and are presented.
Abstract: Outcome in any learning process should target three goals – propelling the underachiever’s engagement in the learning process, enhancing the drive to achieve, and modifying attitudes and beliefs in his/her capabilities. An intervention study with a three-pronged approach incorporating self-regulatory training targeting three categories of strategies – cognitive, metacognitive and motivational – was designed adopting the before and after control-experimental group design. The evaluation of the training process was based on pre- and post-intervention measures obtained through three indices of measurement – academic scores based on grades on school examinations and comprehension tests, affective variables scores and level of strategy use obtained through responses on scales and questionnaires, and content analysis of subjective responses to open-ended probes. The evaluation relied on three sources – student, teacher and parent. The t-test results for the experimental and control groups on the pre- and post-intervention measurements indicate a significant increase on comprehension tasks for the experimental group. Though statistically significant difference was not found on the school examination scores for the experimental group, there was considerable decline in performance for the control group. Analysis of covariance (ANCOVA) was applied on the scores obtained on affective variables, namely, self-esteem, personal achievement goals, personal ego goals, personal task goals, and locus of control. The experimental group showed increase in personal achievement goals and personal ego goals as compared to the control group. Responses given by the experimental group to the open-ended probes on causal attributions indicated a considerable shift from external to internal causes when moving from the pre- to post-intervention stage. ANCOVA results revealed significantly higher use of learning strategies inclusive of mental learning strategies, behavioral learning strategies, self-regulatory strategies, and an improvement in study orientation encompassing study habits and study attitudes among the experimental group students. Parents and teachers reported significant progressive transformation towards constructive engagement with study material and self-imposed regulation. The implications of this study are three-fold: firstly, strategies training (cognitive, metacognitive and motivational) should be embedded into daily classroom routine; secondly, scaffolding by teachers through activities based on curriculum will eventually enable students to rely more on their own judgements of effective strategy use; thirdly, enhanced confidence will radiate to the affective aspects with enduring effects on other domains of life as well. The cyclic nature of the interaction between utilizing one’s resources, managing effort and regulating emotions forms the foundation for academic achievement.
Abstract: This systematic literature review sought to explore the dimensions of diversity that can affect classroom learning. This review is significant as it can aid educators in reaching more of their diverse student population and creating supportive classrooms for teachers and students. For this study, peer-reviewed articles were found and compiled using Google Scholar. Key terms used in the search include student individuality, classroom equality, student development, teacher development, and teacher individuality. Relevant educational standards such as Common Core and Partnership for the 21st Century were also included as part of this review. Student and teacher individuality and equality is discussed as well as methods to grow both within educational settings. Embracing student and teacher individuality was found to be key as it may affect how each person interacts with given information. One method to grow individuality and equality in educational settings included drafting and employing revised teaching standards which include various Common Core and US State standards. Another was to use educational theories such as constructivism, cognitive learning, and Experiential Learning Theory. However, barriers to growing individuality, such as not acknowledging differences in a population’s dimensions of diversity, still exist. Studies found preserving the dimensions of diversity owned by both teachers and students yielded more positive and beneficial classroom experiences.
Abstract: Social media applications are increasingly being used in our everyday communications. These applications utilise end-to-end encryption mechanisms which make them suitable tools for criminals to exchange messages. These messages are preserved in the volatile memory until the device is restarted. Therefore, volatile forensics has become an important branch of digital forensics. In this study, the WormHex tool was developed to inspect the memory dump files for Windows and Mac based workstations. The tool supports digital investigators by enabling them to extract valuable data written in Arabic and English through web-based WhatsApp and Twitter applications. The results confirm that social media applications write their data into the memory, regardless of the operating system running the application, with there being no major differences between Windows and Mac.
Abstract: Digital divide, the gap in the access to the world of digital technologies and the socio-economic opportunities that they create is an important phenomenon of the XXI century. This gap may exist between countries, regions within a country or socio-demographic groups, creating the classes of “digital have and have nots”. While the 1st-level divide (the difference in opportunities to access the digital networks) was demonstrated to diminish with time, the issues of 2nd level divide (the difference in skills and usage of digital systems) and 3rd level divide (the difference in effects obtained from digital technology) may grow. The paper offers a systemic review of literature on the measurement of the digital divide, noting the certain conceptual stagnation due to the lack of effective instruments that would capture the complex nature of the phenomenon. As a result, many important concepts do not receive the empiric exploration they deserve. As a solution the paper suggests a composite Digital Life Index, that studies separately the digital supply and demand across seven independent dimensions providing for 14 subindices. The Index is based on Internet-borne data, a distinction from traditional research approaches that rely on official statistics or surveys. The application of the model to the study of the digital divide between Russian regions and between cities in China have brought promising results. The paper advances the existing methodological literature on the 2nd level digital divide and can also inform practical decision-making regarding the strategies of national and regional digital development.
Abstract: Machine learning has evolved from an area of academic research to a real-world applied field. This change comes with challenges, gaps and differences exist between common practices in academic environments and the ones in production environments. Following continuous integration, development and delivery practices in software engineering, similar trends have happened in machine learning (ML) systems, called MLOps. In this paper we propose a framework that helps to streamline and introduce best practices that facilitate the ML lifecycle in an industrial setting. This framework can be used as a template that can be customized to implement various machine learning experiments. The proposed framework is modular and can be recomposed to be adapted to various use cases (e.g. data versioning, remote training on Cloud). The framework inherits practices from DevOps and introduces other practices that are unique to the machine learning system (e.g.data versioning). Our MLOps practices automate the entire machine learning lifecycle, bridge the gap between development and operation.
Abstract: With the advent of three-dimension (3D) technology, there are lots of research in converting 2D images to 3D images. The main difference between 2D and 3D is the visual illusion of depth in 3D images. In the recent era, there are more depth estimation techniques. The objective of this paper is to convert 2D images to 3D images with less computation time. For this, the input image is divided into blocks from which the depth information is obtained. Having the depth information, a depth map is generated. Then the 3D image is warped using the original image and the depth map. The proposed method is tested on Make3D dataset and NYU-V2 dataset. The experimental results are compared with other recent methods. The proposed method proved to work with less computation time and good accuracy.
Abstract: This paper considers the advent of 5G and the use of it in a Based Control System (BCS), posing as a basic concept the question of what the real differences and practical improvements are compared to 4G. To this purpose, a testbed hardware simulator has been designed and built where identical machines with the same sensors and management systems will communicate with different radio access network connections. This allows an objective statistical comparison of performance on the real functioning and improvement of the infrastructure with the Industrial Internet of Things (IIoT) connected to it.
Abstract: The coalbed methane (CBM) extraction industry (an unconventional energy source) has not established guidelines for experimental evaluation of sensitivity damage for coal samples. The existing experimental process of previous researches mainly followed the industry standard for conventional oil and gas reservoirs (CIS). However, the existing evaluation method ignores certain critical differences between CBM reservoirs and conventional reservoirs, which could inevitably result in an inaccurate evaluation of sensitivity damage and, eventually, poor decisions regarding the formulation of formation damage prevention measures. In this study, we propose improved experimental guidelines for evaluating seepage sensitivity damage of CBM reservoirs by leveraging on the shortcomings of the existing methods. The proposed method was established via a theoretical analysis of the main drawbacks of the existing methods and validated through comparative experiments. The results show that the proposed evaluation technique provided reliable experimental results that can better reflect actual reservoir conditions and correctly guide the future development of CBM reservoirs. This study is pioneering the research on the optimization of experimental parameters for efficient exploration and development of CBM reservoirs.
Abstract: Background: Stroke is a disabling neurological disease. Rehabilitative therapies are important treatment methods. This clinical trial was done to compare the effects of virtual reality (VR) beside conventional rehabilitation versus conventional rehabilitation alone on the spasticity and motor function in stroke patients. Materials and methods: In this open-label randomized controlled clinical trial, 40 consecutive patients with stable first-ever ischemic stroke in the past three to 12 months that were referred to a rehabilitation clinic in Tehran, Iran in 2020 were enrolled. After signing the informed written consent form, subjects were randomly assigned by block randomization of five in each block as cases with 1:1 into two groups of 20 cases; conventional plus VR therapy group: 45-minute conventional therapy session plus 15-minute VR therapy, and conventional group: 60-minute conventional therapy session. VR rehabilitation is designed and developed with different stages. Outcomes were Modified Ashworth scale, Recovery Stage score for motor function, range of motion (ROM) of shoulder abduction/wrist extension, and patients’ satisfaction rate. Data were compared after study termination. Results: The satisfaction rate among the patients was significantly better in combination group (P = 0.003). Only wrist extension was varied between groups and was better in combination group. The variables generally had statistically significant difference (P < 0.05). Conclusion: VR plus conventional rehabilitation therapy is superior versus conventional rehabilitation alone on the wrist and elbow spasticity and motor function in patients with stroke.
Abstract: Gender statuses are inherently unequal; it is difficult to establish equality between men and women in the light of traditional inequalities across the world. This research focuses on the similarities and differences among women from different generations, different kinds of educational backgrounds and highlights the conflict experiences of the characters in Githa Hariharan’s novel “The Thousand Faces of Night”. The purpose is to show how women are suffering and are being humiliated in a male-dominated society. The paper depicts how women in India grapple from male domination aggressiveness as well as the cultural, social and religious controlling in the society they live in. The paper also seeks to explore the importance of knowledge as a powerful component which produces positive effects at the level of desire. The paper is based on the theories of Simone Beauvoir, Pierre Bourdieu, Edward Said, Rene Descartes and Amy Bhatt. Finally, the research emphasizes survival against hegemonic regimes and hope of Indian women for better life.
Abstract: This paper presents the results of the study on the estimation of fly ash, slag and cement contents in blended and composite cements by selective dissolution method. Types of cement samples investigated include Ordinary Portland Cement (OPC) with fly ash as performance improver, OPC with slag as performance improver, Portland Pozzolana Cement (PPC), Portland Slag Cement (PSC) and composite cement confirming to respective Indian Standards. Slag and OPC contents in PSC were estimated by selectively dissolving OPC in stage 1 and selectively dissolving slag in stage 2. In the case of composite cement sample, the percentage of cement, slag and fly ash were estimated systematically by selective dissolution of cement, slag and fly ash in three stages. In the first stage, cement is dissolved and separated by leaving the residue of slag and fly ash, designated as R1. The second stage involves gravimetric estimation of fractions of OPC, residue and selective dissolution of fly ash and slag contents. Fly ash content, R2 was estimated through gravimetric analysis. Thereafter, the difference between the R1 and R2 is considered as slag content. The obtained results of cement, fly ash and slag using selective dissolution method showed 10% of standard deviation with the corresponding percentage of respective constituents. The results suggest that this selective dissolution method can be successfully used for estimation of OPC and Supplementary Cementitious material (SCM) contents in different types of cements.
Abstract: Early detection of anomalies in data centers is important to reduce downtimes and the costs of periodic maintenance. However, there is little research on this topic and even fewer on the fusion of sensor data for the detection of abnormal events. The goal of this paper is to propose a method for anomaly detection in data centers by combining sensor data (temperature, humidity, power) and deep learning models. The model described in the paper uses one autoencoder per sensor to reconstruct the inputs. The auto-encoders contain Long-Short Term Memory (LSTM) layers and are trained using the normal samples of the relevant sensors selected by correlation analysis. The difference signal between the input and its reconstruction is then used to classify the samples using feature extraction and a random forest classifier. The data measured by the sensors of a data center between January 2019 and May 2020 are used to train the model, while the data between June 2020 and May 2021 are used to assess it. Performances of the model are assessed a posteriori through F1-score by comparing detected anomalies with the data center’s history. The proposed model outperforms the state-of-the-art reconstruction method, which uses only one autoencoder taking multivariate sequences and detects an anomaly with a threshold on the reconstruction error, with an F1-score of 83.60% compared to 24.16%.