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Although smart construction tools are already assisting the construction sector, still major reforms are considerably seen in the coming years with Artificial Intelligence of Things (AIoT). The purpose of this work was to develop a down-to-earth reliable edge-computing design and plan, which can be utilized to bolster savvy development situations with high quality of service. The number of devices connected to industrial processes is growing in tandem with the advancement of sophisticated technologies and the use of the AIoT. Big data are created as gadgets grow more integrated and need more computing power. However, as the amount of big data generated grows, processing and analytical issues arise. Cloud computing technologies are now being used to solve processing and analytical challenges. However, by using edge computing technologies, greater emphasis is placed on executing calculations as near to the device as feasible. As a result of these facts, this article suggests the arrangement plan, which depends on the most recent cloud and computer program designing approaches and advances, and gives flexibility, interoperability, and adjustment to construction companies' particular needs. [ABSTRACT FROM AUTHOR]

This study aims to enhance the intelligence level of power grid fault analysis to address increasingly complex fault scenarios and ensure grid stability and security. To this end, an intelligent information system for power grid fault analysis, leveraging improved computer communication technology, is proposed and developed. The system incorporates a novel fault diagnosis model, combining advanced communication technologies such as distributed computing, real-time data transmission, cloud computing, and big data analytics, to establish a multi-layered information processing architecture for grid fault analysis. Specifically, this study introduces a fusion model integrating Transformer self-attention mechanisms with graph neural networks (GNNs) based on conventional fault diagnosis techniques. GNNs capture the complex relationships between different nodes within the grid topology, effectively identifying power transmission characteristics and fault propagation paths across grid nodes. The Transformer's self-attention mechanism processes time-series operational data from the grid, enabling precise identification of temporal dependencies in fault characteristics. To improve system response speed, edge computing moves portions of fault data preprocessing and analysis to edge nodes near data sources, significantly reducing transmission latency and enhancing real-time diagnosis capability. Experimental results demonstrate that the proposed model achieves superior diagnostic performance across various fault types (e.g., short circuits, overloads, equipment failures) in simulation scenarios. The system achieves a fault identification and location accuracy of 99.2%, an improvement of over 10% compared to traditional methods, with an average response time of 85 milliseconds, approximately 43% faster than existing technologies. Moreover, the system exhibits strong robustness in complex scenarios, with an average fault prediction error rate of just 1.1% across multiple simulations. This study provides a novel solution for intelligent power grid fault diagnosis and management, establishing a technological foundation for smart grid operations. [ABSTRACT FROM AUTHOR]

A novel method for solving the multiple-attribute decision-making problem is proposed using the complex Diophantine interval-valued Pythagorean normal set (CDIVPNS). This study aims to discuss aggregating operations and how they are interpreted. We discuss the concept of CDIVPN weighted averaging (CDIVPNWA), CDIVPN weighted geometric (CDIVPNWG), generalized CDIVPN weighted averaging (CGDIVPNWA) and generalized CGDIVPN weighted geometric (CGDIVPNWG). This study aimed to examine several aggregation operators using complex Diophantine interval-valued Pythagorean normal sets. We calculated the weighted average and geometric distance based on an aggregating model. We demonstrate that complex Diophantine interval-valued Pythagorean normal sets satisfy algebraic structures such as associative, distributive, idempotent, bounded, commutative and monotonic properties. In this study, we discuss the mathematical properties of the score and accuracy values. We provide an example of how enhanced score and accuracy values are used in the real world. Machine tool technology and computer science play essential roles in robots. To evaluate robotic systems, four factors must be considered such as tasks, precision, speed and completion of the work. Consequently, it is evident that the models are significantly influenced by the natural number . To further demonstrate the effectiveness of the suggested approach, flowchart based multi-criteria decision-making is provided and applied to a numerical example. Additionally, a comparative study has been carried out to demonstrate the better results that the proposed approach provides when compared to current approaches. [ABSTRACT FROM AUTHOR]

As a common method for design and analysis of computer experiments (DACE), optimal Latin hypercube designs (OLHD) offer excellent space-filling properties but are computationally intensive with larger datasets. Techniques like translational propagational LHD (TPLHD) and improved translational propagational algorithm (iTPA) that compose near-optimal LHDs reduce computational cost with adequate space-filling properties but struggle with large, high-dimensional datasets as well. This study introduces cyclic latin hypercube design (CLHD) to address these challenges by arranging elements at regular intervals, applying a roll operation, and utilizing an enhanced stochastic evolutionary (ESE) algorithm for efficient near-optimal LHD construction. Performance comparisons with OLHD, iTPA, random LHD, and periodic LHD across various dataset sizes show that CLHD successfully reduces computational time and enhances space-filling properties, up to 49.4 % better than random LHD, 30.0 % better than iTPA, and 4.2 % better than periodic LHD. Consequently, CLHD is an efficient alternative for large, high-dimensional datasets where OLHD is infeasible. [ABSTRACT FROM AUTHOR]

Nowadays, open-pit mining is the large-scale extraction of valuable ore materials from the surface with the use of modern mining equipment. If not operated properly, various unexpected events, such as equipment breakdown, slope collapse, hazardous gas emission and land pollution, would occur. With the rapid development of computer technology and big-data science, emerging applications of machine learning could significantly improve mining predictability, feasibility, efficiency and sustainability. However, there is still a lack of up-to-date systematic literature reviews on applications of machine learning to open-pit mining. To address this issue, this study reviews over 200 relevant papers mainly published in the last five years. In this review, we initially conduct a descriptive statistical analysis of these papers according to different phases in open-pit mining. Consequently, we classify their research findings into four main categories: exploration, exploitation, production and reclamation. In addition, each main category is further divided into some sub-categories, namely, feasibility evaluation and mine design planning in exploration; mine block sequencing in exploitation; drilling, blasting, haulage and processing in production; waste control and environmental protection in reclamation. Based on such a bi-level classification, we systematically summarise promising machine learning techniques (i.e. reinforcement learning and deep reinforcement learning) and potential research opportunities (e.g. integration of machine learning and simulation for mining equipment scheduling) in real-world implementations for the mining industry. [ABSTRACT FROM AUTHOR]

This exploration aims to provide a kind of interactive electronic music composition thinking based on visual learning and visual sensing technology to enhance the thinking process of electronic music composition. Initially, it summarizes the functionalities of the physics components in Unity and analyzes the principles of visual interaction implementation using Virtual Reality (VR) devices and Leap Motion in Unity. Subsequently, the composition of interactive electronic music works is designed and implemented based on ultrasonic sensor technology. Lastly, this exploration focuses on the complete visual design of the audio‐visual integration of Musical Instrument Digital Interface (MIDI) music to represent Cymatics' images of the emotional content of the music. MIDI data can be assigned to provide various mapping possibilities between images and content in music visualization composition. This exploration also designs experiments based on sensory aftereffects and audio‐visual synesthesia to further determine the counterpoint law between the shape of dynamic Cymatics' images and the timbre's audio‐visual synesthetic results. The results indicate that Bass and Kick are distributed in the mid‐low frequency and sub‐low frequency range (20–160 Hz), Vocal and Lead are distributed in the mid‐high frequency range (1280–2560 Hz), and Hihat is distributed in the high‐frequency range (2560–5120 Hz). This exploration utilizes computer technology to create a music visualization method that conforms to the visual expression and aesthetic style of a multi‐sensory experience. [ABSTRACT FROM AUTHOR]

With the rapid development of the economy and the transformation and upgrading of industrial structure, universities have increased their focus on theoretical knowledge and application, actively providing practical platforms for students, enriching the connection between theoretical knowledge and practice, deepening the reform of quality education, constructing a modern education system, and ensuring that students can adapt their professional skills to the requirements of industry technology in the field of computer information. Cultivating high-quality computer information talents can not only promote better employment for students, but also promote the development of computer information education in universities. The talent cultivation quality evaluation in practical aspects of computer information major is looked as the multiple-attribute decision-making (MADM) issue. In this paper, we extended the weighted dual Hamy mean (WDHM) operator and prioritized aggregation (PA) operator to 2-tuple linguistic neutrosophic sets (2TLNSs) to propose the 2-tuple linguistic neutrosophic prioritized WDHM (2TLNPWDHM) operator. Finally, a numerical example for talent cultivation quality evaluation in practical aspects of computer information major is employed to verify the 2TLNPWDHM operator. The main contributions of this study are summarized: (1) the 2TLNPWDHM operator is built; (2) the 2TLNPWDHM operator is designed to cope with the MADM with 2TLNNs; (3) a numerical example for talent cultivation quality evaluation in practical aspects of computer information major is supplied to proof the designed method; (4) some comparative studies are utilized to verify the rationality of the 2TLNPDHM. [ABSTRACT FROM AUTHOR]

The aim of this study is to design and develop an augmented reality based mobile application (HardwARe teaching material) based on the "Computer Systems" subject outcomes in the 5th grade curriculum. In the design and development phase of the HardwARe teaching material, the design-based research (DBR) method, which is frequently preferred in the design and development processes of a product, was employed (Kuzu et al., Anadolu Journal of Educational Sciences International 1(1:)19–35, 2011). In this method, which consists of cycles until the design is optimized, the research is carried out with the presence of the researcher and the target group in the environment at the same time (Wang & Hannafin, Educational Technology Research and Development, 53(4):5-23, 2005). The design and development stages of the HardwARe instructional material were carried out in five cycles with eight ICT teachers, two academicians specialized in the field of ICT and 15 students representing the target group. At the end of the process, an augmented reality supported (hardwARe) teaching material was developed for the targeted subject outcomes. In the process of developing the first prototype of the teaching material, three categories were identified: content, design and interaction. All subsequent cycles were based on these categories. After the completion of all the cycles, a design framework that is a suggestion for new AR applications to be developed for similar purposes was revealed. In this framework, within the scope of the content category, results such as meeting the subject outputs, the writing language used should be appropriate to the level of the target audience, and the multimedia elements used should contain sufficient information were obtained. Within the scope of the design category, abstract concepts within the scope of the subject should be concretized, button and text sizes, instructions and voiceovers used for informative purposes should be appropriate for the target group level. Finally, within the scope of the interaction category, results such as it should be easy to use, there should be instant feedback, buttons should fully fulfill their functions were reached. [ABSTRACT FROM AUTHOR]

In this report, we present two new computer simulations designed to elicit and develop students' systems thinking about the rock cycle, addressing the Next Generation Science Standards crosscutting concept Systems and Systems Models. The first simulation presents an interactive diagram of the rock cycle as a "cooking machine" that allows the students to examine the relationships between the materials and processes of the rock cycle. The second allows students to explore the cyclic path of rock named Bob in a cross-section of the Earth's crust. We present how these simulations can be implemented as part of a lesson in a middle school classroom and discuss probing questions that can be used to support students' explorations. [ABSTRACT FROM AUTHOR]

Highlights High‐performance electromagnetic absorption (EMA) materials were becoming increasingly in demand as radar technology advances. One significant way to increase the performance of EMA composite structures was through flexible design. In this work, three‐dimensional (3D) woven preforms with varying absorption layers and structures were woven on an ordinary loom. Subsequently, the vacuum‐assisted resin transfer molding (VARTM) molding method was used to prepare 3D woven EMA materials with good EMA capabilities. Then, experimental research examined the impact of varying fiber yarns, absorption layers, and incidence angles (θ) on the EMA characteristics of the 3D woven EMA materials. Lastly, Computer Simulation Technology (CST) electromagnetic simulation was used to simulate the optimal experimental result and Radar Cross Section (RCS). The results demonstrated that changing fiber yarn structure of 3D woven EMA materials can significantly improve the EMA capabilities of the 3D woven EMA materials. Simultaneously, the EMA capabilities of the 3D woven EMA materials was influenced considerably by the number of the absorption layers and the incidence angle of the electromagnetic wave. For example, the six‐layer 3D woven EMA materials (A(A2C)3C) with carbon fiber yarns introduced in the Z‐direction yarns achieved an effective absorption bandwidth of up to 8.34 GHz at a thickness 9 mm. At an incidence angle of 90°, the most excellent reflection loss value of −44.25 dB could be attained. At the same time, the shortcomings of the experiments were further optimized by CST, and RCS understood the suitability of the EMA materials. This study provided a vital research methodology for designing and developing high‐performance EMA materials. 3D preforms were woven on an ordinary loom. The VARTM was used to prepare 3D woven EMA materials. Introduction of carbon fiber yarns to improve EMA capabilities. The shortcomings of the experiments were further optimized by Computer Simulation Technology. Understood the suitability of the EMA materials by Radar Cross Section. [ABSTRACT FROM AUTHOR]

IoT device security has become a major concern as a result of the rapid expansion of the Internet of Things (IoT) and the growing adoption of cloud computing for central monitoring and management. In order to provide centrally managed services each IoT device have to connect to their respective High-Performance Computing (HPC) clouds. The ever increasing deployment of Internet of Things (IoT) devices linked to HPC clouds use various medium such as wired and wireless. The security challenges increases further when these devices communicate over satellite links. This Satellite-Based IoT-HPC Cloud architecture poses new security concerns which exacerbates this problem. An intrusion detection technology integrated in the central cloud is suggested as a potential remedy to monitor and detect aberrant activity within the network in order to allay these worries. However, the enormous amounts of data generated by IoT devices and their constrained computing power dose not allow to implement IDS techniques at source and renders towards typical central Intrusion Detection Systems (IDS) ineffectiveness. Moreover, to protect these systems, powerful intrusion detection techniques are required due to the inherent vulnerabilities of IoT devices and the possible hazards during data transmission.During the course of literature survey it is revealed that the research work has been done to detect few types of attacks by using the old school model of IDS. The computational expensiveness in terms of processing time is also an important parameter to be considered. This work introduces a novel Embedded Hybrid Deep Learning-based intrusion detection technique (EHID) based on embedded hybrid deep learning that is created specifically for IoT devices linked to HPC clouds via satellite connectivity. Two Deep Learning (DL) algorithms are integrated in the proposed method to improve detection abilities with decent accuracy while considering the processing time and number of trainable parameters to detect 14 types of threats. It segregates among the normal and attack traffic. We also modify the conventional IDS approach and propose architectural change to harness the processing power of central server of cloud. This hybrid approach effectively detects threats by harnessing the computing power available at HPC cloud along with leveraging the power of AI. Additionally, the proposed system enables real-time monitoring and detection of intrusions while providing monitoring and management services through HPC using IoT-generated data. Experiments on Edge-IIoTset Cyber Security Dataset of IoT & IIoT indicate improved detection accuracy, reduced false positives, and efficient computational performance. [ABSTRACT FROM AUTHOR]

The most relevant intersections in society include the relationship between universities and companies for a projection toward the sustainable employability of future graduates. Among the possible intersections, Service-learning (SL) is an educational proposition that may help university students to develop their personal skills, offering them opportunities to learn and practice civic commitment, improving their sense of social and citizen responsibility, and combining academic and community-service learning in a constructed programme where participants train by working on real needs of the environment to optimize and transform the latter. The development of SL programmes in university departments related to technical areas is posing a challenge to faculty members and students, thus it is important to explore this lack of programmes. The main aim of the present study was to identify SL projects and their topics through a systematic review, following the guidelines of the «Preferred Reporting Items for Systematic Reviews and Meta-Analyses» (PRISMA) declaration in the knowledge areas of Architecture, Computer Science, Environmental Engineering, Software Engineering, Computer Engineering, Artificial Intelligence, and Computer Languages and Systems, from the year 2008 to the year 2023. This review includes 128 articles, which were analyzed with ATLAS. Ti 22. The categorical system employed in this work emerged from the topics of the programmes identified in the selected articles, which were verified by experts in the mentioned fields of knowledge. The agreed categories were: accessibility, learning, social groups, courses, devices, infrastructure, games, environment, landscaping, heritage, software and web. The most relevant conclusions highlight that most of the articles refer to theoretical aspects of SL, showing a lack of data on the practical development of SL programmes and their impact on employability. The largest number of SL programmes are developed in the areas of Architecture, Computer Science and Software Engineering. Regarding the topics that are addressed in research, most of the articles refer to social groups, software, learning and accessibility. [ABSTRACT FROM AUTHOR]

In the domain of education, the integration of Artificial Intelligence (AI) has ushered in a paradigm shift towards a more technologically-driven landscape, demonstrating its efficacy as an emergent strategy. The pervasive influence of computer technology has catalyzed a surge in online learning within the country, yielding positive educational outcomes. Despite these advancements, a considerable number of educational institutions in Nigeria have yet to leverage AI technologies. Recognizing the expanding significance of AI in education, this study seeks to align with this trajectory by aggregating instances of AI implementation in education from developed countries. The methodology employed involves a comprehensive review of current advancements in AI applications within the Nigerian educational context. The review process, spanning papers retrieved from four digital libraries published between 2008 and 2022, culminated in the inclusion of 74 papers. These selected papers demonstrated the utilization of AI software tools and technologies, adhering to predefined exclusion and inclusion criteria. The findings of the study reveal a prevalent use of AI technologies in education in Nigeria, encompassing evolutionary software modelling, student performance prediction, multimedia e-learning platforms and frameworks, and the incorporation of Moodle learning. This discernible trend indicates a growing demand for the application of AI technology in the educational landscape of Nigeria. However, the study highlights a discrepancy: sophisticated AI techniques like intelligent tutoring systems, learnable robots, web-based educational systems, and advanced learning management systems are infrequently applied in Nigeria. The study suggests that Nigerian educational institutions should adopt AI practices from advanced nations to enhance student learning and bridge the gap in AI integration. [ABSTRACT FROM AUTHOR]

Device simulation plays a crucial role in complementing experimental device characterisation by enabling deeper understanding of internal physical processes. However, for simulations to be trusted, experimental validation is essential to confirm the accuracy of the conclusions drawn. In the framework of semiconductor detector characterisation, one powerful tool for such validation is the Two Photon Absorption-Transient Current Technique (TPA-TCT), which allows for highly precise, three-dimensional spatially-resolved characterisation of semiconductor detectors. In this work, the TCAD framework Synopsys Sentaurus is used to simulate depth-resolved TPA-TCT data for both p-type pad detectors (PINs) and Low Gain Avalanche Detectors (LGADs). The simulated data are compared against experimentally measured TPA-TCT results. Through this comparison, it is demonstrated that TCAD simulations can reproduce the TPA-TCT measurements, providing valuable insights into the TPA-TCT itself. Another significant outcome of this study is the successful simulation of the gain reduction mechanism, which can be observed in LGADs with increasing densities of excess charge carriers. This effect is demonstrated in an p-type LGAD with a thickness of approximately 286 µm. The results confirm the ability of TCAD to model the complex interaction between carrier dynamics and device gain. [ABSTRACT FROM AUTHOR]

The efficiency of remanufacturing systems is closely related to the modular design. This paper proposes a sustainable design concept aiming at active remanufacturing. This paper adopts the modular method related to parameter flow to decompose and construct the product's function. The author applies the clustering method to the parameter flow correlation determination to realize the quantitative division of modules. This paper combines sustainable design principles with the life cycle characteristics of products. In this paper, the sustainable design is divided into modules based on the degree of component association. Finally, this paper uses an example to demonstrate that the model in this paper can divide products into modules in a sustainable way. [ABSTRACT FROM AUTHOR]

The development of computer technology and wireless communication has introduced the wireless body area network (WBAN) to the world. In WBAN, the patient's vital signs are monitored by small sensors embedded in the body. Sensor nodes work with a limited energy source, so energy consumption is a major issue in these networks. The increase in temperature caused by data transmissions can cause serious damage to body tissue. This paper proposes an Energy-Temperature Aware Routing (ETAR) protocol to solve this problem. In ETAR, routing is done directly and multi-hop using relay nodes. Multi-hop data forwarding plays a significant role in reducing energy consumption. In the proposed method, relay nodes are selected using a fuzzy inference system. Energy, temperature, and distance parameters are defined as the inputs of the fuzzy system. Therefore, in each round, a node with more remaining energy, lower temperature, and less distance from its neighbors is selected as the relay node. The proposed protocol reduces the adverse effects of temperature on the body by setting temperature limits for sensors. The performance of ETAR was evaluated for homogeneous and heterogeneous networks. In homogenous network, this protocol improves energy consumption by 44% and 55% compared to THE and EEMR. Network lifetime is enhanced by 46% and 55% compared to THE and EEMR. The throughput is improved by 40% compared to THE and 34% compared to EEMR, respectively. In a heterogeneous network, this protocol improves energy consumption by 47% and 52% compared to THE and EEMR. Network lifetime is enhanced by 62% and 65% compared to THE and EEMR, respectively. The throughput is improved by 100% compared to THE and 97% compared to EEMR. [ABSTRACT FROM AUTHOR]

This study presents a novel method for automatically analyzing archery shooting time using AI and computer vision technologies, with a particular focus on the critical anchoring to release phase, which directly influences performance. The proposed approach detects the start of the anchoring phase using pose estimation and accurately measures the shooting time by detecting the bowstring within the athlete's facial bounding box, utilizing Canny edge detection and the probabilistic Hough transform. To ensure stability, low-pass filtering was applied to both the facial bounding box and pose estimation results, and an algorithm was implemented to handle intermittent bowstring detection due to various external factors. The proposed method was validated by comparing its results with expert manual measurements obtained using Dartfish software v2022 achieving a mean absolute error (MAE) of 0.34 s and an R 2 score of 0.95. This demonstrates a significant improvement compared to the bowstring-only method, which resulted in an MAE of 1.4 s and an R 2 score of 0.89. Previous research has demonstrated a correlation between shooting time and arrow accuracy. Therefore, this method can provide real-time feedback to athletes, overcoming the limitations of traditional manual measurement techniques. It enables immediate technical adjustments during training, which can contribute to overall performance improvement. [ABSTRACT FROM AUTHOR]

ICT integration in classroom teaching is one of the challenges that teachers face nowadays. Thus, this study aimed to determine the teachers' professional perspectives on computer technology and their process of integration in classroom teaching. The method used in this study was the descriptive-correlational research design to obtain the data needed specifically using survey questionnaire. Random sampling was used in selecting teachers per school as participants in the study for kindergarten to grade 6 Teachers in West 2 District, Division of Iligan City. Technology Implementation Questionnaire (TIQ) Version II tool was used. It was an adopted and modified instrument and developed by Wozney et al. (2006). Findings revealed that there is no significant difference in the factors affecting the respondent's professional perspective on the use of computer technology in the classroom. The results from regression analysis for the significant relationship between the respondents' professional view and their ICT process integration in terms of instructional, informative, communicative, evaluative, and organizational shows that there is a positive relationship among the variables mentioned except the informative ICT process integration. This implies that the professional perspective of the teachers has a positive influence on their ICT process integration in the classroom. Results showed that all the mentioned demographic profiles are not predictors of their ICT process integration as indicated in their pvalues greater than 0.05 level of significance. The findings suggests that teachers should change their perspective about ICT integration, from traditional way of learning to more interactive learning. [ABSTRACT FROM AUTHOR]

Monitoring the greenhouse environment is crucial for regulating physical variables such as temperature, humidity, CO2 concentration and luminosity. VANET (Vehicular Ad-hoc Network) represents a particular case of multi-hop wireless network, characterized by rapid topology changes due to the high mobility of nodes. With an increasing number of vehicles equipped with computer technologies and wireless communication devices, vehicular communication emerges as a promising domain for research, standardization, and development. In the forthcoming era, VANET is positioned to become an integral part of smart cities, offering diverse applications. This article presents the outcomes of simulating a VANET network comprising two mobile nodes within a greenhouse, alongside the design and Lyapunov analysis of mobile node control within that network. [ABSTRACT FROM AUTHOR]

Biomedical antennas play a crucial role in implantable medical telemetry, improving patient monitoring and overall well-being. This study presents a compact antenna designed for a lead-free pacemaker, with dimensions of 3.3×4×0.5 mm³. Miniaturization required open cuts in the ground plane and radiating patch, as well as a localized resistance element. The antenna features an exceptional bandwidth of 1646.8 MHz (1.0602-2.7070) GHz, guaranteeing constant performance in the human body. It covers the industrial, scientific and medical bands (ISM, 2.4-2.48 GHz) and ultra-high frequencies (UHF, 0.3-13 GHz). Simulation in a phantom produced a gain of -19.78 dBi at 2.45 GHz and -34.44 dBi at 1.2 GHz. Safety was confirmed by a specific absorption rate (SAR) study using a cardiac model. The antenna's low SAR (10 g-Avg) enabled maximum input powers to be established: 20.2 mW at 1.2 GHz and 21.23 mW at 2.45 GHz. Comparative simulations using high-frequency structure simulator (HFSS) and computer simulation technology (CST) highlighted the antenna's superiority over recent systems, demonstrating its effectiveness in the human heart. This antenna represents an advanced solution for improving patient care. Consequently, this antenna emerges as an advanced solution for addressing infectious diseases and cardiovascular conditions in the realm of medical advancements. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) can collect earth surface information with synthetic aperture radar (SAR) sensors during night, day, and bad weather. However, UAVs have only limited volume and weight for the SAR payload. A very light dual circularly polarized pyramid horn antenna was developed in order to improve sense capability in UAV dual polarimetry SAR images. The pyramid horn was designed and simulated in computer simulation technology (CST) 2022. The horn was capable of producing two circular polarizations right-hand circular polarization (RHCP) and left-hand circular polarization (LHCP) alternately using a three stepped septum polarizer and coaxial probe feed on two sides of the waveguide. The results show that the horn antenna was capable of having a wide S11 and S22 bandwidth from 4 GHz to 6.7 GHz. The horn was capable of producing two circular polarizations with an axial ratio below 3 dB from 4.4 GHz to 6.4 GHz. This gain horn antenna was relatively stable in the range of 15 dBiC with a reasonably narrow 3 dB beamwidth of 28° both RHCP and LHCP polarisation in E and H fields respectively. [ABSTRACT FROM AUTHOR]

Single-cell printing technology has arisen as a potent instrument for investigating cell biology and disease pathophysiology. Nonetheless, current single-cell printing methodologies are hindered by restricted throughput, a limited field of view, and diminished efficiency. We present an innovative single-cell printing chip that utilizes thermal inkjet technology for single-cell printing, therefore addressing these constraints. We have accomplished high-throughput, wide-field, and efficient single-cell printing by merging a high-density thermal foam-based inkjet nozzle array on a chip with high-speed cameras and computer vision technologies for optical image capture and single-cell identification training. We have shown the efficacy and adaptability of the printing chip by printing various concentrations of Chinese hamster ovary cells and human embryonic kidney 293 cells. The printing of a single 96-well plate is accomplished in 2–3 min, facilitating one-time loading and uninterrupted multi-plate paving. Our thermal bubble single-cell printing chip serves as a viable platform for high-throughput single-cell analysis applications. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence in orthodontics is emerging as a tool for localizing cephalometric points in two-dimensional X-rays. AI systems are being evaluated for their accuracy and efficiency compared to conventional methods performed by professionals. The main objective of this study is to identify the artificial intelligence algorithms that yield the best results for cephalometric landmark localization, along with their learning system. A literature search was conducted across PubMed-MEDLINE, Cochrane, Scopus, IEEE Xplore, and Web of Science. Observational and experimental studies from 2013 to 2023 assessing the detection of at least 13 cephalometric landmarks in two-dimensional radiographs were included. Studies requiring advanced computer engineering knowledge or involving patients with anomalies, syndromes, or orthodontic appliances, were excluded. Risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Newcastle–Ottawa Scale (NOS) tools. Of 385 references, 13 studies met the inclusion criteria (1 diagnostic accuracy study and 12 retrospective cohorts). Six were high-risk, and seven were low-risk. Convolutional neural networks (CNN)-based AI algorithms showed point localization accuracy ranging from 64.3 to 97.3%, with a mean error of 1.04 mm ± 0.89 to 3.40 mm ± 1.57, within the clinical range of 2 mm. YOLOv3 demonstrated improvements over its earlier version. CNN have proven to be the most effective AI system for detecting cephalometric points in radiographic images. Although CNN-based algorithms generate results very quickly and reproducibly, they still do not achieve the accuracy of orthodontists. [ABSTRACT FROM AUTHOR]

The recognition and classification of building shapes are the prerequisites and foundation for building simplification, matching, and change detection, which have always been important research problems in the field of cartographic generalization. Due to the ambiguity and uncertainty of building shape outlines, it is difficult to describe them using unified rules, which has always limited the quality and automation level of building shape recognition. In response to the above issues, by introducing object detection technology in computer vision, this article proposes a building shape recognition and classification method based on the YOLO object detection model. Firstly, for different types of buildings, four levels of building training data samples are constructed, and YOLOv5, YOLOv8, YOLOv9, and YOLOv9 integrating attention modules are selected for training. The trained models are used to test the shape judgment of buildings in the dataset and verify the learning effectiveness of the models. The experimental results show that the YOLO model can accurately classify and locate the shape of buildings, and its recognition and detection effect have the ability to simulate advanced human visual cognition, which provides a new solution for the fuzzy shape recognition of buildings with complex outlines and local deformation. [ABSTRACT FROM AUTHOR]

The end-user paradox and the illusion of digital prosperity reveal the contradictory situation in which both non-professional and professional computer scientists and engineers seem satisfied with digital development but unaware of the magnitude of waste generated by end-users and their digital artifacts. To measure this waste and to reveal end-users' problem-solving strategies, our research group set up an objective measuring system that can calculate the entropy of digital texts (EDT). To calculate EDT, a testing process of 53 participants was launched where erroneous and correct natural language digital texts were modified according to the requirements of the tasks. It was found that erroneous documents require more time and information to be modified, which implies that waste is generated by handling these documents. It was also found that when the problem-solving processes are broken down into atomic steps, EDT can reveal uncertainty and idleness, which further increases waste. The goals of the present paper are to call attention to (1) the hidden waste generated by billions of end-users and its consequences, (2) educational approaches and general ignorance which have led to these low-level results, and (3) the need to set up a standard evaluation system for further analyses. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNNs) are the prevalent technology in computer vision and have become increasingly popular for medical imaging data classification and analysis. In this field, due to the scarcity of medical data, pretrained ResNets on ImageNet can be considered a suitable first approach. This paper examines the medical imaging classification accuracy of conventional basic custom CNNs compared to ImageNet pretrained ResNets on various medical datasets in an effort to give more information about the importance of medical data and its preprocessing techniques for disease studies. Microscope-extracted cytological images were examined along with chest X-rays, MRI brain scans, and melanoma photographs. The medical images were examined in various sets, class combinations, and resolutions. Augmented image datasets and asymmetrical training and validation splits among the classes were also examined. Models were developed after they were tested and fine-tuned with respect to their network size, parameter values and network methods, image resolution, size of dataset, multitude, and genre of class. Overfitting was also examined, and comparative studies regarding the computational cost of different models were performed. The models achieved high accuracy in image classification that varies depending on the dataset and can be easily incorporated in future over-the-internet medical decision-supporting (telemedicine) environments. In addition, it appeared that conventional basic custom CNN overperformed ImageNet pretrained ResNets. The obtained results indicate the importance of utilizing medical image data as a testbed for improvements in CNN classification performance and the possibility of using CNNs and data preprocessing techniques for disease studies. [ABSTRACT FROM AUTHOR]

In this paper, we address the issue of the design and deployment of VR-oriented pedagogical scenarios by teachers as designers. To provide practical and theoretical solutions, we analyse the various existing models and tools for assisting teachers in the design and deployment of virtual reality learning environments (VRLEs). We identify a need for a model that covers the pedagogical needs of teachers, including the modelling of pedagogical structuring and the consideration of the specificities of the VR environment. Our main contributions are a pedagogical scenario model and the development of an editor called VR-PEAS (Virtual Reality PEdAgogical Scenarisation tool), which provides a solution that can support and help teacher-designers to design, adapt and reuse pedagogical scenarios oriented towards VR. We proposed a literature review to identify the main concepts of pedagogical scenario design and developed the computer solution according to an agile approach. To validate the basic concepts of our contribution and the impact of our implementation, we define a case study evaluation based on four different pedagogical situations using a qualitative method based on questionnaires. Our findings indicated that teachers could design VRLEs that were aligned with their pre-defined scenario and operate them successfully. [ABSTRACT FROM AUTHOR]

The effectiveness of digital game-based language learning (DGBLL) has been recognized by scholars. With the development of computer technology and multimedia learning environments, computer agents have been widely used in game systems to provide learning guidance or assistance. A computer agent is a virtual character created in a digital learning system to achieve instructional goals. However, in traditional teaching systems, computer agents are designed to perform a single role, such as a teacher or a student. Computer agents with a single interactive logic often lack the functionality required to understand students' conditions and needs from various perspectives, and cannot adapt for better learning support. Therefore, this study proposed an adaptive role-switching strategy that allows computer agents to adjust their roles and functions according to students' needs in digital games to promote their learning achievement. An adaptive computer agent-based digital game was developed to investigate the impact of this model on English vocabulary learning achievement, motivation, self-efficacy, and English anxiety among EFL (English as Foreign Language) students. A quasi-experiment was designed and 56 middle school students in four classes were recruited. Two classes (n = 30) were arranged to be the experimental group which used an adaptive computer agent-based digital game (adaptive computer agent-based digital game, ACA-DG), while two classes (n = 26) were arranged to be the control group which used the conventional computer agent-based digital game (conventional computer agent-based digital game, CCA-DG). The results showed that students in the experimental group had significantly higher learning achievement and self-efficacy than those in the control group. On the other hand, students in the experimental group had significantly lower English anxiety than those in the control group. There was no significant difference between the two groups regarding learning motivation. [ABSTRACT FROM AUTHOR]

Several studies have shown that involving users in design processes is a key factor in understanding user needs and essential for designing computer systems capable of providing valuable user experiences. However, many practitioners do not emphasize user needs sufficiently and neglect user involvement in software design and development. The UCD Sprint is a recently proposed, step-by-step, cost-effective process that focuses on involving users in design activities. This article reports a mixed-method study in which, for the first time, practitioners working in companies used the UCD Sprint; the study's main objective was to investigate the impact of the UCD Sprint on practitioners' design practices. The results show that, among the various activities associated with this process, those that involved users were particularly appreciated by practitioners. Thus, the UCD Sprint can enhance the innovativeness of company design practices; it enables practitioners to obtain sufficient information to create systems that fit users' needs. [ABSTRACT FROM AUTHOR]

Balanced sliced orthogonal arrays (BSOAs) are useful for designing sliced computer experiments with qualitative and quantitative factors or multiple models and nested computer experiments with two codes of different levels of accuracy. In this article, we propose a systematic method to construct BSOAs by making use of the A + B design structure and its modifications. A general method for construction of asymmetric BSOAs is also presented based on an orthogonal array with orthogonal partition. These methods are flexible for strength and factors. Numerous new symmetric and asymmetric BSOAs including a lot of ones with high strength can thus be constructed. Compared with the existing BSOAs, the constructed BSOAs have more number of columns. Moreover, these methods can also be used to construct BSOAs whose strength is different from the strength of their slices. Some selective BSOAs are tabulated for practical uses. [ABSTRACT FROM AUTHOR]

Computer technology has long been touted as a means of increasing the effectiveness of voluntary self-exclusion schemes – especially in terms of relieving gaming venue staff of the task of manually identifying and verifying the status of new customers. This paper reports on the government-led implementation of facial recognition technology as part of an automated self-exclusion program in the city of Adelaide in South Australia—one of the first jurisdiction-wide enforcements of this controversial technology in small venue gambling. Drawing on stakeholder interviews, site visits and documentary analysis over a two year period, the paper contrasts initial claims that facial recognition offered a straightforward and benign improvement to the efficiency of the city's long-running self-excluded gambler program, with subsequent concerns that the new technology was associated with heightened inconsistencies, inefficiencies and uncertainties. As such, the paper contends that regardless of the enthusiasms of government, tech industry and gaming lobby, facial recognition does not offer a ready 'technical fix' to problem gambling. The South Australian case illustrates how this technology does not appear to better address the core issues underpinning problem gambling, and/or substantially improve conditions for problem gamblers to refrain from gambling. As such, it is concluded that the gambling sector needs to pay close attention to the practical outcomes arising from initial cases such as this, and resist industry pressures for the wider replication of this technology in other jurisdictions. [ABSTRACT FROM AUTHOR]

• A model for predicting the performance of rotor-only axial fans, given the tangential flow velocity distribution, was proposed. • Using this model, a screening of possible solutions was carried out to find a design that provides maximum efficiency, and another with maximum flow. • The results were compared with the free-swirl methodology. • The designed fans were prototyped through 3D printing and their results were compared with a commercially used model. • The fan for maximum efficiency obtained a gain of 11 % while the one for maximum flow obtained a gain of 10 %. The paper puts forward a computer methodology for designing low-pressure axial fan blades for small-capacity refrigeration applications. Based on the blade element theory (BET), the airfoil efficiency of the airfoil, the principles of mass and momentum conservation together with empirical correlations for the flow irreversibilities, a mathematical model was devised for screening the blade geometric parameters (e.g., radial chord and pitch variation, and hub radius) by varying the induction coefficient distribution for a given fan diameter, motor speed, and airflow system characteristic curve. The best blade configuration is selected by means of a tailor-made optimization algorithm and undergoes a series of linear transformations for translating the fan parametrization into a CAD drawing. Two new fan blades were designed, one for maximum blade efficiency (MBE) and another for maximum airflow rate (MAR). In comparison with the free-swirl design approach, a standard procedure adopted in the open literature, the proposed blades showed an efficiency and an airflow by 20 % (MBE) and 14 % (MAR) higher than the reference. The airflow characteristics of the new designs were also assessed by means of wind-tunnel testing, which confirmed an increase of 11 % in the case of MBE design, while an enhancement of 10 % was observed in the case of MAR design. [ABSTRACT FROM AUTHOR]

Rational algebraic curves have been intensively studied in the last decades, both from the theoretical and applied point of view. In applications (e.g. level curves, linear homotopy deformation, geometric constructions in computer aided design, image detection, algebraic differential equations, etc.), there often appear unknown parameters. It is possible to adjoin these parameters to the coefficient field as transcendental elements. In some particular cases, however, the curve has a different behavior than in the generic situation treated in this way. In this paper, we show when the singularities and thus the (geometric) genus of the curves might change. More precisely, we give a partition of the affine space, where the parameters take values, so that in each subset of the partition the specialized curve is either reducible or its genus is invariant. In particular, we give a Zariski-closed set in the space of parameter values where the genus of the curve under specialization might decrease or the specialized curve gets reducible. For the genus zero case, and for a given rational parametrization, a finer partition is possible such that the specialization of the parametrization parametrizes the specialized curve. Moreover, in this case, the set of parameters where Hilbert's irreducibility theorem does not hold can be identified. We conclude the paper by illustrating these results by some concrete applications. [ABSTRACT FROM AUTHOR]

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In this paper, a compact stepped slot antenna, for Global Navigation Satellite System (GNSS) applications, is proposed. The latter one, which operates in the frequency range from 1.49 GHz to 1.66 GHz, is designed to cover five GNSS bands namely GPS-L1, Galileo-E1, GLONASS-G1, BEIDOU-B1 and EGNOS-L1. The proposed composite design is carried out in three phases. Firstly, a compact slot antenna, having a size of 58.0 × 52.0 × 1.6 mm, is designed on FR4 substrate to generate the five GNSS bands. Secondly, a compact Frequency Selective Surface (FSS) reflector, with 18.0 × 18.0 × 1.6 mm unit size, is designed to produce a stop band response matching all these bands. Finally, a single layer FSS, consisting of 9 × 9 units, is combined with the optimized antenna to achieve a high-gain directional antenna structure. The final proposed combination has been investigated using the High Frequency Structure Simulator (HFSS) and validated by Computer Simulation Technology (CST) Microwave Studio. Besides, a prototype antenna is fabricated and validated by measurements. The measurement results, which illustrate a good agreement with those corresponding to the simulations, have shown that the proposed composite antenna exhibits a directional radiation pattern with a high peak gain of 7.3 dBi and a maximum gain improvement of 5.65 dBi at 1.57 GHz. Furthermore, it offers good radiation efficiency in the operating bands, which makes it a good candidate for multisystems GNSS receivers, especially to reduce the different interferences by enhancing the antenna radiation characteristics. [ABSTRACT FROM AUTHOR]

Copyright of Kulturstudier is the property of Dansk Historisk Faellesraad and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper explores the integration of computer vision technologies to enhance traffic safety through the effective detection of car-pedestrian interactions. As urban environments become more congested, pedestrian safety remains a critical concern. The system's performance was evaluated using real-life footage from vehicle-mounted cameras, as well as images and videos sourced from online platforms. These real-world scenarios enabled a detailed assessment of the system's accuracy and efficiency in practical conditions. The study highlights the potential for significant improvements in traffic safety, particularly in Bosnia and Herzegovina, where over 38% of registered vehicles are older than 23 years, and nearly 62% exceed 14 years. The aging vehicle fleet heightens the risk of accidents, underscoring the need for advanced detection methods. The proposed system automates the identification of hazardous situations on roads, allowing timely responses from relevant authorities. [ABSTRACT FROM AUTHOR]

Copyright of International Journal on Humanities & Social Sciences is the property of International Journal on Humanities & Social Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The rapid development of quantum computer technology poses an increasing threat to conventional encryption algorithms, and accordingly, more advanced security practices need to be developed. The current paper presents an innovative quantum cryptographic mechanism that combines classical encryption techniques with quantum principles such as superposition, entanglement, and uncertainty to enhance data security in digital communication. The proposed scheme, defined as Enhanced Quantum Key Distribution (EQKD), demonstrates superior performance in key metrics, including Quantum Bit Error Rate (QBER), fidelity, key distribution rate, and resilience to eavesdropping. In particular, EQKD achieves lower QBER and higher fidelity over longer distances while also enhancing key generation efficiency and increasing the probability of detecting eavesdropping attempt. These findings highlight the effectiveness of EQKD in improving the security and reliability of quantum cryptographic systems. [ABSTRACT FROM AUTHOR]

Copyright of Anadolu University Journal of Art & Design / Sanat & Tasarım is the property of Anadolu University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Three-dimensional (3D) animation techniques have appeared as a powerful tool for teaching and learning across many education levels. 3D animations enable educators to create visual and interactive learning material. Understanding the concept of 3D and how it improves student learning is in demand. This qualitative research study aims to explore the types of 3D animations and computer technology that are integrated into teaching in higher education settings. Moreover, the study intends to examine the way 3D animations support student learning and engagement. The challenges of implementing 3D are also included in the purpose of the study. Through a one-on-one interview, a sample of forty-two college instructors replied to the research inquiries. Thematic analysis revealed that 3D animations and many technologies, such as virtual reality, are in practice in the teaching environment. The study results uncovered the challenges of implementing 3D technologies and presented some recommendations for practice to enhance teaching on higher education platforms. [ABSTRACT FROM AUTHOR]

The first books in Polish about the Pascal programming language appeared in the late 1970s, and were soon followed by a Polish translation of Niklaus Wirth's book Algorithms + Data Structures = Programs. At that time, many efforts were made to prepare teachers to teach informatics in schools, and Pascal was one of the topics taught, often with no access to computers. The situation improved significantly after the first informatics curriculum was approved for schools by the Ministry of National Education (MEN) in 1985, in response to the increasing number of microcomputers in schools and students' interest in programming. As a confirmation of the conclusions drawn from the discussion in the ABACUS magazine (1984) on "Which is the best programming language for a first course in computer science," Pascal very quickly became the basic programming language in Polish schools as a tool for practicing algorithmics. The first informatics textbook was published in 1988 and in it, after a short Logo course, Pascal took the main place as a language for developing solutions to algorithmic problems in the form of computer programs. In the following years, many teaching aids were developed for programming education in schools, as well as other aids, e.g., an authoring system, programmed in Pascal. Pascal was the primary programming language in informatics education until the end of the first decade of the 21st century, subsequently replaced by Python. It is interesting that Polish winners of national and international Olympiads in Informatics for many years used Pascal, demonstrating the enormous professional values of this language. In this article we present in detail, among others things: the role and influence of Pascal on the development and level of informatics education in schools in Poland, as well as other scientific activities and initiatives supported by this language. Many students educated in this technical and mental environment found their place in professional development teams in the best computer and technology corporations, and evidently most of them joined the national working force. [ABSTRACT FROM AUTHOR]

Latin hypercube designs (LHDs) are commonly used in designing complex computer models. With the added property of orthogonality, a special type of orthogonal Latin hypercube designs called Sliced Orthogonal Latin Hypercube Designs (SOLHD) are now a days gaining much importance in the field of computer experiments. SOLHD's are a special type of OLHDs that can be further partitioned into different slices and act as batches of smaller orthogonal Latin hypercube designs. In this article, we have proposed methods of construction for Sliced orthogonal Latin hypercube designs of equal as well as unequal batch size. The proposed methods are for obtaining SOLHD's with two or three slices. [ABSTRACT FROM AUTHOR]

As blockchain technology and smart contracts develop, computer technology is constantly integrating with smart chemical plants. Due to the continuous development of intelligent chemical plants, their systems have gradually become large and dispersed, posing a threat to safety management. In order to improve the performance of intelligent security management systems, the study first explores the principles of blockchain and smart contract technology, and then combined with the requirements of intelligent chemical plant security management systems, designs an intelligent security management system based on blockchain and smart contract technology. The experimental results showed that compared to systems without smart contract support, the communication success rate between nodes was lower. The error rates of blockchain-based encryption systems, deep learning-based encryption systems and improved data encryption systems proposed in the study were 0.22, 0.07 and 0.09, respectively. The packet loss rates were 0.13, 0.04 and 0.05, respectively. The lower the bit error rate and packet loss rate of the encryption system, the clearer the illegal eavesdropping information. The experimental results indicate that the intelligent security management system designed in this study has good encryption performance and a higher communication success rate. The results have certain reference value in security management application in intelligent chemical plants. [ABSTRACT FROM AUTHOR]

Depression is a prevalent mental health issue affecting individuals of all age groups globally. Similar to other mental health disorders, diagnosing depression presents significant challenges for medical practitioners and clinical experts, primarily due to societal stigma and a lack of awareness and acceptance. Although medical interventions such as therapies, medications, and brain stimulation therapy provide hope for treatment, there is still a gap in the efficient detection of depression. Traditional methods, like in-person therapies, are both time-consuming and labor-intensive, emphasizing the necessity for technological assistance, especially through Artificial Intelligence. Alternative to this, in most cases it has been diagnosed through questionnaire-based mental status assessments. However, this method often produces inconsistent and inaccurate results. Additionally, there is currently a lack of a comprehensive diagnostic framework that could be effective achieving accurate and robust diagnostic outcomes. For a considerable time, researchers have sought methods to identify symptoms of depression through individuals' speech and responses, leveraging automation systems and computer technology. This research proposed MDD which composed of multimodal data collection, preprocessing, and feature extraction (utilizing the T5 model for text features and the WaveNet model for speech features). Canonical Correlation Analysis (CCA) is then used to create correlated projections of text and audio features, followed by feature fusion through concatenation. Finally, depression detection is performed using a neural network with a sigmoid output layer. The proposed model achieved remarkable performance, on the Distress Analysis Interview Corpus-Wizard (DAIC-WOZ) dataset, it attained an accuracy of 92.75%, precision of 92.05%, and recall of 92.22%. For the E-DAIC dataset, it achieved an accuracy of 91.74%, precision of 90.35%, and recall of 90.95%. Whereas, on CD-III dataset (Custom Dataset for Depression), the model demonstrated an accuracy of 93.05%, precision of 92.12%, and recall of 92.85%. These results underscore the model's robust capability in accurately diagnosing depressive disorder, demonstrating the efficacy of advanced feature extraction methods and improved classification algorithm. [ABSTRACT FROM AUTHOR]

This paper introduces a comparative study between two methods, standard oven drying method and microstrip moisture sensor (MMS), for calculating moisture content (MC) of alluvial soil. This paper aims to reduce the time of calculating moisture content of soil to few hours from MMS in comparison with 24 h from standard oven drying method, which is presently a standard test in construction industry for determination of moisture content of soil. Therefore, for this purpose, a microstrip moisture sensor is designed and fabricated using computer simulation technology (CST) software. While, measurement of MC in soil performed by sensor using vector network analyser (VNA). Hence, the result obtained from MMS for soil is compared with standard oven drying method. In this way, sensor is found to be accurate with error of 1.15% only, which is negligible. Therefore, sensor can be adopted for measurement of moisture content of soil. In this way, efficiency of construction industry can be increased. Also this paper's another objective is to analyse the phenomenon of bulking of sand from designed sensor. It is found that sensor is able to detect bulking of sand accurately. Though, for study three sample of alluvial soil and sand is taken from three different construction sites. [ABSTRACT FROM AUTHOR]

The World Wide Web and the advancement of computer technology in the 1960s and 1990s respectively set the ground for a substantial and simultaneous change in many facets of our life, including medicine, health care, and medical education. The traditional didactic approach has shifted towards more dynamic and interactive methods, leveraging technologies such as simulation tools, virtual reality, and online platforms. At the forefront is the remarkable evolution that has revolutionized how medical knowledge is accessed, disseminated, and integrated into pedagogical practices. The COVID-19 pandemic also led to rapid and large-scale adoption of e-learning and digital resources in medical education because of widespread lockdowns, social distancing measures, and the closure of medical schools and healthcare training programs. This review paper examines the evolution of medical education from the Flexnerian era to the modern digital age, closely examining the influence of the evolving WWW and its shift from Education 1.0 to Education 4.0. This evolution has been further accentuated by the transition from the static landscapes of Web 2D to the immersive realms of Web 3D, especially considering the growing notion of the metaverse. The application of the metaverse is an interconnected, virtual shared space that includes virtual reality (VR), augmented reality (AR), and mixed reality (MR) to create a fertile ground for simulation-based training, collaborative learning, and experiential skill acquisition for competency development. This review includes the multifaceted applications of the metaverse in medical education, outlining both its benefits and challenges. Through insightful case studies and examples, it highlights the innovative potential of the metaverse as a platform for immersive learning experiences. Moreover, the review addresses the role of emerging technologies in shaping the post-pandemic future of medical education, ultimately culminating in a series of recommendations tailored for medical institutions aiming to successfully capitalize on revolutionary changes. [ABSTRACT FROM AUTHOR]