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This paper explores the artificial intelligence (AI) concept for complex engineering design processes in the shipping industry. It is driven by the computer technologies advancement for fast and concurrent tasks processing, machine learnability, and data-centric approach. While AI has been adopted in many industries, it is still lacking the structured approaches for practical implementation. This is especially on the generality of the methodologies and explaining AI to the non-technical members and their preparedness. Therefore, this work proposed a conceptual framework to systematically extract, represent and visualize the ship design knowledge, to develop and deploy the machine learning (ML) models, and to demonstrate the AI-based ship design processes. Comparisons to the generic ship design model were made and discussed to highlight the improvements observed. It is found that while the conventional algorithmic approach procedures were faster in terms of execution time, the stepwise empirical models were often limited by the dataset and the design assumptions with restricted estimation capabilities for solving the nonlinear ship design problems. The findings presented the impact in improving the existing processes and effectively reducing its cycle. Additionally, the approach emphasised on the validated ship design data thus its generalization for fast and wide adoptions at scales. [ABSTRACT FROM AUTHOR]

Purpose: The number of citations has been widely used to measure the significance of a paper. However, there is a need in introducing another index to determine superiority or inferiority of papers with the same number of citations. We determine superiority or inferiority of papers by using the ranking based on the number of citations and PageRank. Design/methodology/approach: We show the positive linear correlation between Citation Rank (the ranking of the number of citation) and PageRank. On this basis, we identify high-quality, prestige, emerging, and popular papers. Findings: We found that the high-quality papers belong to the subjects of biochemistry and molecular biology, chemistry, and multidisciplinary sciences. The prestige papers correspond to the subjects of computer science, engineering, and information science. The emerging papers are related to biochemistry and molecular biology, as well as those published in the journal "Cell." The popular papers belong to the subject of multidisciplinary sciences. Research limitations: We analyze the Science Citation Index Expanded (SCIE) from 1981 to 2015 to calculate Citation Rank and PageRank within a citation network consisting of 34,666,719 papers and 591,321,826 citations. Practical implications: Our method is applicable to forecast emerging fields of research subjects in science and helps policymakers to consider science policy. Originality/value: We calculated PageRank for a giant citation network which is extremely larger than the citation networks investigated by previous researchers. [ABSTRACT FROM AUTHOR]

A quantitative analysis supports the argument that very low acceptance rates of conference papers are more likely to impede publication of bold and innovative research results than to indicate the chosen papers' prestige and elite status. [ABSTRACT FROM AUTHOR]

Honeypots are computer systems that deceive cyber attackers into believing they are ordinary computer systems designed for invasion, when in fact they are primarily designed to collect data about attack methods, resulting in better protection and defense against malicious actors. As a result, developing reliability metrics for measuring the performance, strength, and effectiveness of honeypot deception is advantageous. Despite extensive and mature research on honeynet system, reliability modeling, analysis and performance prediction and evaluation, based on copula techniques for accurately testing, estimating and optimizing the overall performance of honeynet systems remain lacking. To start, a copula approach for analyzing and optimizing the performance of honeynet systems was proposed. Any honeynet system’s performance can be classified based on its availability, dependability and profit generated. As a result, the current paper sought to investigate the performance of a multi-state honeynet system in terms of availability, dependability and expected profit. This paper examines two types of repairs. Type I repairs are known as general repairs and they are used to recover from a partial or non-lethal failure to a perfect state, whereas Type II repairs are known as copula repairs they are used to recover from a complete or lethal failure to a perfect state. For the sake of generality, the supplementary variable technique and Laplace transforms were used to develop the performance models that are essential to this research, such as availability, reliability, mean time to failure (MTTF), sensitivity and profit function. The models’ numerical validation was fully carried out. The results are shown in tables and figures, enabling us to draw the conclusion that Type II repair is a superior repair policy. Type II repair, according to the findings, can more accurately portray system structure and states while still allowing for efficient assessment. [ABSTRACT FROM AUTHOR]

This document is a guest editorial from the journal IET Computer Vision, discussing the topic of advanced image restoration and enhancement. The editorial highlights the challenges faced in this field, such as the complexity of degradation models for real-world low-quality images and the difficulty of acquiring paired data. It also introduces a special issue of the journal that includes five accepted papers, which focus on video reconstruction and image super-resolution. The editorial concludes by providing brief summaries of each accepted paper. The guest editors of the special issue are also mentioned, along with their research interests and affiliations. [Extracted from the article]

With the rapid development of computer technology, communication technology, and control technology, cyber-physical systems (CPSs) have been widely used and developed. However, there are massive information interactions in CPSs, which lead to an increase in the amount of data transmitted over the network. The data communication, once attacked by the network, will seriously affect the security and stability of the system. In this paper, for the data tampering attack existing in the linear system with multiple binary observations, in the case where the estimation algorithm of the defender is unknown, the optimization index is constructed based on information entropy from the attacker's point of view, and the problem is modeled. For the problem of the multi-parameter optimization with energy constraints, this paper uses particle swarm optimization (PSO) to obtain the optimal data tampering attack solution set, and gives the estimation method of unknown parameters in the case of unknown parameters. To implement the real-time improvement of online implementation, the BP neural network is designed. Finally, the validity of the conclusions is verified through numerical simulation. This means that the attacker can construct effective metrics based on information entropy without the knowledge of the defense's discrimination algorithm. In addition, the optimal attack strategy implementation based on PSO and BP is also effective. [ABSTRACT FROM AUTHOR]

This research paper deals with a directional high gain PCB 1 × 8 antenna array for 2.4 ISM band utilizations. To achieve this antenna array, a well-matched equal-split 9 dB-power splitter is designed and integrated with the suggested antenna array. It exhibits a wide operating range of 506 MHz (2.022–2.528 GHz) and splits the feed power to 8 equal-in-phase output quantities. Then eight identical patch elements with good reflection coefficient, high gain and excellent radiation efficiency are connected at the eight-output ports of the 1 × 8-power divider in order to obtain an array antenna consisting of eight radiating elements. A quarter-wave impedance adapter is utilized to obtain a perfect matching of impedance between patches and the power divider. The suggested directional antenna resonates at 2.4 GHz with good impedance matching via offering reflection coefficient S 1 1 = − 2 4. 3 4 dB and voltage standing wave ratio (VSWR) of 1.16. Moreover, it offers good radiation traits like the enhanced gain of 14.76 dB, an excellent radiating efficiency equals about 98.86% and a directional radiation pattern. Initially, the proposed planar directional antenna array has been designed and simulated utilizing high-frequency structure simulator (HFSS) EM simulation tool, the results are validated with another simulation tool via computer simulation technology (CST) software. This printed antenna array is a potential candidate to operate at around 2.4 GHz for RFID reader utilizations due to its outstanding impedance and radiation characteristics. [ABSTRACT FROM AUTHOR]

The innovative application of Crowd Intelligent Devices (CIDS) in edge networks has garnered attention due to the rapid development of artificial intelligence and computer technology. This application offers users more reliable and low-latency computing services through computation offloading technology. However, the dynamic nature of network terminals and the limited coverage of edge servers pose challenges, such as data loss and service interruption. Furthermore, the high-speed mobility of intelligent terminals in the dynamic edge network environment further complicates the design of computation offloading and service migration strategies. To address these challenges, this paper explores the computation offloading model of cluster intelligence collaboration in a heterogeneous network environment. This model involves multiple intelligences collaborating to provide computation offloading services for terminals. To accommodate various roles, a switching strategy of split-cluster group collaboration is introduced, assigning the cluster head, the alternate cluster head, and the ordinary user are assigned to a group with different functions. Additionally, the paper formulates the optimal offloading strategy for group smart terminals as a Markov decision process, taking into account factors such as user mobility, service delay, service accuracy, and migration cost. To implement this strategy, the paper utilizes the deep reinforcement learning-based CCSMS algorithm. Simulation results demonstrate that the proposed edge network service migration strategy, rooted in groupwise cluster collaboration, effectively mitigates interruption delay and enhances service migration efficiency. [ABSTRACT FROM AUTHOR]

Livestock's live body dimensions are a pivotal indicator of economic output. Manual measurement is labor-intensive and time-consuming, often eliciting stress responses in the livestock. With the advancement of computer technology, the techniques for livestock live body dimension measurement have progressed rapidly, yielding significant research achievements. This paper presents a comprehensive review of the recent advancements in livestock live body dimension measurement, emphasizing the crucial role of computer-vision-based sensors. The discussion covers three main aspects: sensing data acquisition, sensing data processing, and sensing data analysis. The common techniques and measurement procedures in, and the current research status of, live body dimension measurement are introduced, along with a comparative analysis of their respective merits and drawbacks. Livestock data acquisition is the initial phase of live body dimension measurement, where sensors are employed as data collection equipment to obtain information conducive to precise measurements. Subsequently, the acquired data undergo processing, leveraging techniques such as 3D vision technology, computer graphics, image processing, and deep learning to calculate the measurements accurately. Lastly, this paper addresses the existing challenges within the domain of livestock live body dimension measurement in the livestock industry, highlighting the potential contributions of computer-vision-based sensors. Moreover, it predicts the potential development trends in the realm of high-throughput live body dimension measurement techniques for livestock. [ABSTRACT FROM AUTHOR]

With the deepening of industrial technology, the monitoring of the thermodynamic state of complex equipment systems has become a key to ensuring production continuity and safety. The thermodynamic state not only reflects the immediate working performance of the equipment but is also decisive in preventing overheating failures. The application of real-time remote monitoring technology provides a new solution for equipment health management, significantly reducing unexpected downtime, preventing failures, thus improving production efficiency and equipment lifespan. However, existing research still shows limitations in real-time data analysis, diversity of equipment adaptability, and accurate fault prediction. This paper proposes three innovative algorithms for the real-time remote monitoring of the thermodynamic state of complex equipment systems, and a method for mining overheating early warning information. The first algorithm, based on reconstruction error, is suitable for equipment with a large amount of normal operation data, using machine learning technology to accurately simulate the normal state to identify anomalies. The second single-class monitoring algorithm, suitable for situations with only normal operating data, can effectively detect deviations from the normal thermodynamic parameters. The third algorithm, based on statistical quantities, uses the statistical characteristics of equipment operation data for fault warning. In addition, the paper explores the application of the Bucket Sorting Fpgrowth algorithm in the mining of overheating early warning information of the thermodynamic state, analyzing potential fault modes and association rules through efficient data mining technology. These methods not only enhance the applicability and predictive accuracy of monitoring algorithms but also provide valuable decision support for equipment managers. [ABSTRACT FROM AUTHOR]

Various concrete bridges have been built across oceans, valleys, and mountains; however, the settlement displacement of bridge piers caused by environmental changes or self-weight during construction phases often leads to uneven stresses, cracking, and eventual collapse. To address the labor-intensive and high-cost issues of pier displacement monitoring using contact-type sensors, this paper proposes an automatic vision-based method for measuring pier settlement displacement under complex construction environments, such as complex image backgrounds, varying ambient light, and camera movement. In the proposed method, a deep learning network was first employed to eliminate the adverse effect of complex image backgrounds and varying ambient light on the accuracy of target detection; then, an adaptive displacement extraction algorithm without a human-computer interaction process was developed to automatically extract the center coordinates of targets attaching to the bridge piers and reference platform; finally, the pier settlement displacement was calculated by using the relative displacements obtained by a dual camera system to eliminate the measurement error caused by camera translation and rotation movements. Laboratory tests of a cantilever beam and field tests of a continuous multispan concrete girder highway bridge under construction have successfully validated the effectiveness and robustness of the developed methodology. The results obtained in this paper can provide some insights for engineers in applying computer vision technology for the real-time monitoring of bridge displacements. [ABSTRACT FROM AUTHOR]

Generally, Least Squares (LS) Method treats only random errors of observation vector in adjustment function models. However, both observation vector and elements of coefficient matrix of adjustment function model contain random errors. Therefore, there is no guarantee that the result of adjustment by LS method is the global optimal solution. Total Least Square (TLS) method is a primary estimation method that treats random errors of observation vector and coefficient matrix in adjustment functional models. Since TLS method take into account both random errors of observation vector and coefficient matrix based on errors-in-variables model, it is possible to improve the accuracy compared with the result of LS method. So TLS method has been applied to different fields of science and technology including signal and image processing, computer vision,communication engineering and geodesy. However, weighted total least square (WTLS) method has been not applied in geodetic network adjustment problem compared with other fields widely. So the purpose of this paper is to summarize the algorithm of WTLS briefly and to propose an application method in adjustment of triangulation network. Key problem in application of WTLS to adjustment of geodetic network is to determine the weight matrix (or cofactor matrix) for elements of coefficient matrix in adjustment function model. In this paper proposed a method to determine cofactor matrix for errors of coefficient matrix in triangulation network, and verifies the effectiveness of suggested method through example applied to triangulation network. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles in the issue on the topics including demonstrates exciting results based on self-imitation learning within a cross-modal setting; and providing advances in controllable generation and interpolation across attributes.

This research studied the strategies that players use in sequential adversarial games. We took the Rock-Paper-Scissors (RPS) game as an example and ran players in two experiments. The first experiment involved two humans, who played the RPS together for 100 times. Importantly, our payoff design in the RPS allowed us to differentiate between participants who used a random strategy from those who used a Nash strategy. We found that participants did not play in agreement with the Nash strategy, but rather, their behavior was closer to random. Moreover, the analyses of the participants' sequential actions indicated heterogeneous cycle-based behaviors: some participants' actions were independent of their past outcomes, some followed a well-known win-stay/lose-change strategy, and others exhibited the win-change/lose-stay behavior. To understand the sequential patterns of outcome-dependent actions, we designed probabilistic computer algorithms involving specific change actions (i.e., to downgrade or upgrade according to the immediate past outcome): the Win-Downgrade/Lose-Stay (WDLS) or Win-Stay/Lose-Upgrade (WSLU) strategies. Experiment 2 used these strategies against a human player. Our findings show that participants followed a win-stay strategy against the WDLS algorithm and a lose-change strategy against the WSLU algorithm, while they had difficulty in using an upgrade/downgrade direction, suggesting humans' limited ability to detect and counter the actions of the algorithm. Taken together, our two experiments showed a large diversity of sequential strategies, where the win-stay/lose-change strategy did not describe the majority of human players' dynamic behaviors in this adversarial situation. [ABSTRACT FROM AUTHOR]

The subject matter of surfaces in computer aided geometric design (CAGD) is the depiction and design of surfaces in the computer graphics arena. Due to their geometric features, modeling of Bézier curves and surfaces with their shape parameters is the most well-liked topic of research in CAGD/computer-aided manufacturing (CAM). The primary challenges in industries such as automotive, shipbuilding, and aerospace are the design of complex surfaces. In order to address this issue, the continuity constraints between surfaces are utilized to generate complex surfaces. The parametric and geometric continuities are the two metrics commonly used for establishing connections among surfaces. This paper proposes continuity constraints between two generalized Bézier-like surfaces (gBS) with different shape parameters to address the issue of modeling and designing surfaces. Initially, the generalized form of C3 and G3 of generalized Bézier-like curves (gBC) are developed. To check the validity of these constraints, some numerical examples are also analyzed with graphical representations. Furthermore, for a continuous connection among these gBS, the necessary and sufficient G1 and G2 continuity constraints are also developed. It is shown through the use of several geometric designs of gBS that the recommended basis can resolve the shape and position adjustment problems associated with Bézier surfaces more effectively than any other basis. As a result, the proposed scheme not only incorporates all of the geometric features of curve design schemes but also improves upon their faults, which are typically encountered in engineering. Mainly, by changing the values of shape parameters, we can alter the shape of the curve by our choice which is not present in the standard Bézier model. This is the main drawback of traditional Bézier model. [ABSTRACT FROM AUTHOR]

Traditional digital image processing techniques face problems such as complex feature extraction and weak robustness when dealing with surface defects of multiple categories of electronic components. Deep learning is widely used in industrial defect detection. However, the performance of electronic component defect detection at the pixel segmentation level needs to be improved. For pixel-level defect detection, this paper constructs a defect detection model (ECSDDNet) for electronic component surface defects in computer vision engineering management system. To improve the segmentation accuracy and detection effect, three stages of experiments are conducted to address mis-segmentation problems and the shortcomings of the Unet network structure. Firstly, a classification network that can perform weight transfer is used to replace the encoding structure in the Unet network. Secondly, a simplified version of the feature fusion is proposed and added to the skip connection of the Unet network. Finally, label smoothing is used to optimize the loss and improve the generalization of the network. After the optimization experiment, some noisy contours and small defect contours that are mis-segmented are removed. Experimental results show that ECSDDNet has good segmentation effects on electronic component surface defects and can meet the segmentation and detection needs of electronic component surface defects. [ABSTRACT FROM AUTHOR]

Recently, with more portable diagnostic devices being moved to people anywhere, point-of-care (PoC) imaging has become more convenient and more popular than the traditional "bed imaging". Instant image segmentation, as an important technology of computer vision, is receiving more and more attention in PoC diagnosis. However, the image distortion caused by image preprocessing and the low resolution of medical images extracted by PoC devices are urgent problems that need to be solved. Moreover, more efficient feature representation is necessary in the design of instant image segmentation. In this paper, a new feature representation considering the relationships among local features with minimal parameters and a lower computational complexity is proposed. Since a feature window sliding along a diagonal can capture more pluralistic features, a Diagonal-Axial Multi-Layer Perceptron is designed to obtain the global correlation among local features for a more comprehensive feature representation. Additionally, a new multi-scale feature fusion is proposed to integrate nonlinear features with linear ones to obtain a more precise feature representation. Richer features are figured out. In order to improve the generalization of the models, a dynamic residual spatial pyramid pooling based on various receptive fields is constructed according to different sizes of images, which alleviates the influence of image distortion. The experimental results show that the proposed strategy has better performance on instant image segmentation. Notably, it yields an average improvement of 1.31% in Dice than existing strategies on the BUSI, ISIC2018 and MoNuSeg datasets. [ABSTRACT FROM AUTHOR]

Abstract: Combining the techniques of paper prototyping and card sorting into a single session has the benefits of helping users to understand a new technology on the one hand, and of gaining insight into the users’ mental models of that technology on the other hand. However, acquainting users with a new technology via a paper prototype might affect their mental models, as assessed with the card sorting technique. The aim of this paper was to explore the possibility of combining the two techniques in a single research session. Thirty-seven users participated in a study concerning a payment system based on Near Field Communication (NFC). Eight group sessions were organized, including both a paper prototyping exercise and a card sorting exercise. The order of the exercises was alternated. The findings of this case study seem to suggest that the paper prototyping exercise resulted into deeper insights into the participants’ mental models resulting from the card sorting exercise. At the same time, paper prototyping seemed to prevent participants to come up with new names for their card sorting categories. [Copyright &y& Elsevier]

The aim of this paper is to examine and illustrate how the animistic ontology present in neopaganism allows embodied and sensuous interactions with virtual worlds. By considering animism as a strategy with which to rethink human cohabitation with the techno-digital otherness, I will show how neopagans who use computer technology for spiritual purposes experience the online context as an environment where lived religious practices can occur. To do so, I will particularly focus on religious practices taking place in digital games and 3D social virtual platforms due to their ability to induce immersive and interactive experiences. Because neopaganism recognizes the material living world as a central aspect of spiritual experiences, I will explore the ways that the spatial and material dimensions are articulated in neopagan's online performances, the actions they make possible, and how they enable a more intimate relationship with virtual platforms. I will accompany the theoretical reflection with case studies and interviews with technopagan practitioners experiencing their religion with and within computer technology. This paper also aims to show how this new conception of animism connects to what Mikhail Bakhtin calls "dialogism", a condition that recognizes the multiplicity of perspectives and voices and denies the possibility of not getting involved with the otherness. For such reasons, approaching the digital through an animistic ontology can help us acknowledge the convergence of humans with the techno-digital otherness and explore, on deeper levels, sensuous and embodied experiences taking place in the religious context. [ABSTRACT FROM AUTHOR]

Computer vision is a powerful tool for healthcare applications since it can provide objective diagnosis and assessment of pathologies, not depending on clinicians' skills and experiences. It can also help speed-up population screening, reducing health care costs and improving the quality of service. Several works summarise applications and systems in medical imaging, whereas less work is devoted to surveying approaches for healthcare goals using ambient intelligence, i.e., observing individuals in natural settings. Even more, there is a lack of papers providing a survey of works exhaustively covering computer vision applications for children's health, which is a particularly challenging research area considering that most existing computer vision technologies have been trained and tested only on adults. The aim of this paper is then to survey, for the first time in the literature, the papers covering children's health-related issues by ambient intelligence methods and systems relying on computer vision. [ABSTRACT FROM AUTHOR]

THE PAPER STARTS OUT by reviewing the so-called "library crisis" and the extensive literature on the determinants of journal prices. It discusses the impact of the recent merger activity among journal publishers and notes, among the possible remedies that have been suggested, the possibility that electronic publications may slow down the increase in journal prices. It next discusses the "productivity puzzle," i.e., the question of why the substantial improvements in computer technology may not have been translated into productivity increases at a faster rate. While the longer-term impact on productivity is not as unfavorable as initial approaches may have suggested, the paper argues that the cost savings in producing electronic rather than paper journals tend to be overestimated, particularly because the costs of archiving are not adequately dealt with in many approaches to this problem. While much attention has been devoted to how electronic approaches can affect the costs of producing journals, relatively few people have dealt with the even more important question of how these approaches affect productivity in teaching, learning, and research. The final substantive section of the paper deals with pricing and related issues, with particular attention to price discrimination and the bundling of journals. [ABSTRACT FROM AUTHOR]

This paper aims to highlight the life of computer technologies to understand what kind of 'technological intentionality' is present in computers based upon the phenomenological elements constituting the objects in general. Such a study can better explain the effects of new digital technologies on our society and highlight the role of digital technologies by focusing on their activities. Even if Husserlian phenomenology rarely talks about technologies, some of its aspects can be used to address the actions performed by the digital technologies by focusing on the objects' inner 'life' thanks to the analysis of passive synthesis and phenomenological horizons in the objects. These elements can be used in computer technologies to show how digital objects are 'alive.' This paper focuses on programs developed through high-order languages like C++ and unsupervised learning techniques like 'Generative Adversarial Model.' The phenomenological analysis reveals the computer's autonomy within the programming stages. At the same time, the conceptual inquiry into the digital system's learning ability shows the alive and changeable nature of the technological object itself. [ABSTRACT FROM AUTHOR]

With the rapid development of the geometric modeling industry and computer technology, the design and shape optimization of complex curve shapes have now become a very important research topic in CAGD. In this paper, the Hybrid Artificial Hummingbird Algorithm (HAHA) is used to optimize complex composite shape-adjustable generalized cubic Ball (CSGC–Ball, for short) curves. Firstly, the Artificial Hummingbird algorithm (AHA), as a newly proposed meta-heuristic algorithm, has the advantages of simple structure and easy implementation and can quickly find the global optimal solution. However, there are still limitations, such as low convergence accuracy and the tendency to fall into local optimization. Therefore, this paper proposes the HAHA based on the original AHA, combined with the elite opposition-based learning strategy, PSO, and Cauchy mutation, to increase the population diversity of the original algorithm, avoid falling into local optimization, and thus improve the accuracy and rate of convergence of the original AHA. Twenty-five benchmark test functions and the CEC 2022 test suite are used to evaluate the overall performance of HAHA, and the experimental results are statistically analyzed using Friedman and Wilkerson rank sum tests. The experimental results show that, compared with other advanced algorithms, HAHA has good competitiveness and practicality. Secondly, in order to better realize the modeling of complex curves in engineering, the CSGC–Ball curves with global and local shape parameters are constructed based on SGC–Ball basis functions. By changing the shape parameters, the whole or local shape of the curves can be adjusted more flexibly. Finally, in order to make the constructed curve have a more ideal shape, the CSGC–Ball curve-shape optimization model is established based on the minimum curve energy value, and the proposed HAHA is used to solve the established shape optimization model. Two representative numerical examples comprehensively verify the effectiveness and superiority of HAHA in solving CSGC–Ball curve-shape optimization problems. [ABSTRACT FROM AUTHOR]

This paper presents a super wideband and high-gain log periodic dipole array (LPDA) antenna. The overall structure of the antenna was constructed using microwave studio computer simulation technology. The optimal sizes of the planned antenna are 39 × 10× 0.254 mm3. The engineered antenna arrangement is implemented on an RT5880 substrate as a dielectric medium. The LPDA is arranged in four arms that are equally spaced on both lines. The main 50Ω feeder line is partially grounded at the back of the substrate. A combination of circular director units is being studied and tuned in a regular pattern at a predefined distance from the antenna. An improvement in gain of 3 dBi is the response of the director units. The Conformist LPDA is adjusted to achieve a wide range of millimeter wave bands ranging from 40 to over 70 GHz. The antenna resonates at 60 GHz, where the maximum realized gain of 14.97 dBi is attained. The antenna was tested for utilization in the V-band involving wireless personal area network (WPAN) applications recommended by IEEE 802.11ad and IEEE 802.15.3c. The outcomes of the constructed antenna elements' tests and simulations agree fairly well. The proposed layout works better than previous efforts in this field. [ABSTRACT FROM AUTHOR]

This article explores the concept of the circular economy and its connection to emerging technologies like the Internet of Things (IoT) and Industry 4.0. It emphasizes the significance of the circular economy in promoting sustainability and the reuse of materials. The article also discusses the potential benefits of IoT technologies in implementing circular economy practices. It presents various research papers that delve into the challenges, risks, and advantages of utilizing the IoT for the circular economy. The overall aim of the research is to contribute to the existing knowledge on the circular economy and inspire further progress in the field. [Extracted from the article]

The shortest path problem is a classic optimization problem in graph theory and computer technology. It involves identifying the shortest path between two nodes in a graph, where each edge has a numerical weight. In this paper, we put our effort into examining the use of the dynamic programming method to evaluate the shortest path (SP) between the two specified nodes in a multistage network where the parameter is a multi-value neutrosophic number (MVNN). Firstly, we propose an algorithm based on the forward and backward approach in an uncertain environment and also implement our approach in the Python-3 programming language. Furthermore, a numerical illustration has been provided to showcase the effectiveness and robustness of the novel model. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the properties of timelike and spacelike shifted-knots Bézier surfaces in Minkowski space- E13. These surfaces are commonly used in mathematical models for surface formation in computer science for computer-aided geometric design and computer graphics, as well as in other fields of mathematics. Our objective is to analyze the characteristics of timelike and spacelike shifted-knots Bézier surfaces in Minkowski space- E13. To achieve this, we compute the fundamental coefficients of shifted-knots Bézier surfaces, including the Gauss-curvature, mean-curvature, and shape-operator of the surface. Furthermore, we present numerical examples of timelike and spacelike bi-quadratic (m = n = 2) and bi-cubic (m = n = 3) shifted-knots Bézier surfaces in Minkowski space- E13 to demonstrate the applicability of the technique in Minkowski space. [ABSTRACT FROM AUTHOR]

Metamaterials have gained much attention due to their exciting characteristics and potential uses in constructing valuable technologies. This paper presents a double negative square resonator shape metamaterial sensor to detect the material and its thickness. An innovative double-negative metamaterial sensor for microwave sensing applications is described in this paper. It has a highly sensitive Q-factor and has good absorption characteristics approximately equal to one. For the metamaterial sensor, the recommended measurement is 20 by 20 mm. Computer simulation technology (C.S.T.) microwave studios are used to design the metamaterial structure and figure out its reflection coefficient. Various parametric analyses have been performed to optimize the design and size of the structure. The experimental and theoretical results are shown for a metamaterial sensor that is attached to five different materials such as, Polyimide, Rogers RO3010, Rogers RO4350, Rogers RT5880, and FR-4. A sensor's performance is evaluated using three different thicknesses of FR-4. There is a remarkable similarity between the measured and simulated outcomes. The sensitivity values for 2.88 GHz and 3.5 GHz are 0.66% and 0.19%, respectively, the absorption values for both frequencies are 99.9% and 98.9%, respectively, and the q-factor values are 1413.29 and 1140.16, respectively. In addition, the figure of merit (FOM) is analyzed, and its value is 934.18. Furthermore, the proposed structure has been tested against absorption sensor applications for the purpose of verifying the sensor's performance. With a high sense of sensitivity, absorption, and Q-factor, the recommended sensor can distinguish between thicknesses and materials in various applications. [ABSTRACT FROM AUTHOR]

In the current enterprise competition, the core of the management information system is the financial management information system. Building a more comprehensive and efficient financial management information system can effectively establish a more efficient innovative enterprise and promote the transformation and development of the enterprise. This paper first designs a set of financial management system templates, including general ledger, accounts receivable, and accounts payable. Second, according to the characteristics of wireless sensor network technology, this paper proposes a financial management system based on the wireless sensor network and designs a load balancing topology control algorithm (LHTCA algorithm), which balances the energy consumption of network nodes and helps to improve the network life cycle. Finally, the management system is tested and validated. The test results show that the financial management system can realize the electronicization of financial data and paperless vouchers, improve the efficiency of financial work, and promote the sharing and integration of resources. [ABSTRACT FROM AUTHOR]

In recent years, cellular neural networks (CNNs) have become a popular apparatus for simulations in neuroscience, biology, medicine, computer sciences and engineering. In order to create more adequate models, researchers have considered memory effects, reaction–diffusion structures, impulsive perturbations, uncertain terms and fractional-order dynamics. The design, cellular aspects, functioning and behavioral aspects of such CNN models depend on efficient stability and control strategies. In many practical cases, the classical stability approaches are useless. Recently, in a series of papers, we have proposed several extended stability and control concepts that are more appropriate from the applied point of view. This paper is an overview of our main results and focuses on extended stability and control notions including practical stability, stability with respect to sets and manifolds and Lipschitz stability. We outline the recent progress in the stability and control methods and provide diverse mechanisms that can be used by the researchers in the field. The proposed stability techniques are presented through several types of impulsive and fractional-order CNN models. Examples are elaborated to demonstrate the feasibility of different technologies. [ABSTRACT FROM AUTHOR]

The winner of the 2020 TPS Best Paper Award has been selected (please refer to our TPS home page for details about the award at https://ieee-npss.org/publications/transactions-on-plasma-science/). This year is the second year that the award is being given, and I am pleased to announce that Prof. Chandrashekhar Joshi, Department of Electrical and Computer Engineering, University of California at Los Angeles, Los Angeles, CA, USA, is the winner for his article “Laser-driven plasma accelerators operating in the self- guided, blowout regime.” This article appeared in IEEE TPS, vol. 45, pp. 3134–3146, in December 2017. The award plaque, certificate, and cheque will be presented to Prof. Joshi at an upcoming International Conference on Plasma Science in South Lake Tahoe, CA, USA, in June 2021. This is an open-access article and is freely available to our readers. Congratulations to Prof. Chan Joshi on this accomplishment. [ABSTRACT FROM AUTHOR]

The ability to distinguish between the abdominal conditions of adult female mosquitoes has important utility for the surveillance and control of mosquito-borne diseases. However, doing so requires entomological training and time-consuming manual effort. Here, we design computer vision techniques to determine stages in the gonotrophic cycle of female mosquitoes from images. Our dataset was collected from 139 adult female mosquitoes across three medically important species—Aedes aegypti, Anopheles stephensi, and Culex quinquefasciatus—and all four gonotrophic stages of the cycle (unfed, fully fed, semi-gravid, and gravid). From these mosquitoes and stages, a total of 1959 images were captured on a plain background via multiple smartphones. Subsequently, we trained four distinct AI model architectures (ResNet50, MobileNetV2, EfficientNet-B0, and ConvNeXtTiny), validated them using unseen data, and compared their overall classification accuracies. Additionally, we analyzed t-SNE plots to visualize the formation of decision boundaries in a lower-dimensional space. Notably, ResNet50 and EfficientNet-B0 demonstrated outstanding performance with an overall accuracy of 97.44% and 93.59%, respectively. EfficientNet-B0 demonstrated the best overall performance considering computational efficiency, model size, training speed, and t-SNE decision boundaries. We also assessed the explainability of this EfficientNet-B0 model, by implementing Grad-CAMs—a technique that highlights pixels in an image that were prioritized for classification. We observed that the highest weight was for those pixels representing the mosquito abdomen, demonstrating that our AI model has indeed learned correctly. Our work has significant practical impact. First, image datasets for gonotrophic stages of mosquitoes are not yet available. Second, our algorithms can be integrated with existing citizen science platforms that enable the public to record and upload biological observations. With such integration, our algorithms will enable the public to contribute to mosquito surveillance and gonotrophic stage identification. Finally, we are aware of work today that uses computer vision techniques for automated mosquito species identification, and our algorithms in this paper can augment these efforts by enabling the automated detection of gonotrophic stages of mosquitoes as well. [ABSTRACT FROM AUTHOR]

With the rapid development of computer technology, urban informatics, as a new discipline in the field of urban planning, has gradually attracted academic attention. The rise of urban informatics puts new pressures on urban planning, but it also provides a new perspective of analysis. This paper is a summary of a panel discussion among scholars in urban informatics held at the 2020 International Association for China Planning(IACP).In this context, the panel outlines the definition of urban informatics, and the difference between urban informatics and urban analytic and computing, and found that urban informatics pays more attention to end user. This indicates that urban informatics has been more than a supporting role in urban planning or design, and is increasingly integrated with urban planning. The panel also discusses the connotation of urban informatics and its wide application in practice, and illustrates with examples. At the same time, the team identifies the difficulties of its development mainly reflected in the two aspects of resources and talents, and the learners of urban planning discipline have natural advantage in learning urban informatics. Finally, the panel discusses how to improve teaching, and concludes that the promotion of good cases, discipline integration, training data thinking rather than focusing too much on methods and other concepts. All in all, this panel's report contributes to the wider discussion about the role of urban informatics plays in urban planning. [ABSTRACT FROM AUTHOR]

The new way of thinking science from Newtonian determinism to nonlinear unpredictability and the dawn of advanced computer science and technology can be summarized in the words of the theoretical physicist Michel Baranger, who, in 2000, said in a conference: "Twenty-first-century theoretical physics is coming out of the chaos revolution; it will be about complexity and its principal tool will be the computer.". This can be extended to natural sciences in general. Modelling and predicting ionosphere variables have been considered since many decades as a paramount objective of research by scientists and engineers. The new approach to natural sciences influenced also ionosphere research. Ionosphere as a part of the solar–terrestrial environment is recognized to be a complex chaotic system, and its study under this new way of thinking should become an important area of ionospheric research. After discussing the new context, this paper will try to review recent advances in the exploration of ionosphere parameter time series in terms of chaos theory and the use of machine-learning algorithms. [ABSTRACT FROM AUTHOR]

Multiple choice exams appear as the most common method used to measure student success in education systems in Turkey and around the world. Evaluation of multiple choice exams is usually done by optical form reading systems using expensive optical forms and optical scanners. The most important reason for using optical form reading systems is to reduce the error rate to zero in the evaluation process. In this study, an alternative system is proposed for the evaluation of multiple choice exams. The designed system is a web-based software with high accuracy on evaluating that contains optical form design module, session planning module and evaluation module. The form in which the designed template will be printed on A4 paper instead of a special optical form and the system in which standard scanners will be used instead of the optical scanner, uses the image processing algorithms in the OpenCV library in the evaluation process which is an intermediate library developed in C#. The proposed system is coded to run in parallel to speed up the evaluation process. In order to determine the performance of the proposed system, the optical forms filled by 208 students studying at Karamanoğlu Mehmetbey University, Department of Computer Engineering were evaluated. The accuracy rate of the system has been determined as 99.97%. It has been determined that 1 optical evaluation time, calculated by dividing the total time obtained by running in parallel, by the number of evaluated optical forms, varies between 1.7 seconds and 15 seconds, depending on the scanning resolution. [ABSTRACT FROM AUTHOR]

The world's population is expected to exceed 9 billion people by 2050, necessitating a 70% increase in agricultural output and food production to meet the demand. Due to resource shortages, climate change, the COVID-19 pandemic, and highly harsh socioeconomic predictions, such a demand is challenging to complete without using computation and forecasting methods. Machine learning has grown with big data and high-performance computers technologies to open up new data-intensive scientific opportunities in the multidisciplinary agri-technology area. Throughout the plant's developmental period, diseases and pests are natural disasters, from seed production to seedling growth. This paper introduces an early diagnosis framework for plant diseases based on fog computing and edge environment by IoT sensors measurements and communication technologies. The effectiveness of employing pre-trained CNN architectures as feature extractors in identifying plant illnesses has been studied. As feature extractors, standard pre-trained CNN models, AlexNet are employed. The obtained in-depth features are eliminated by proposing a revised version of the grey wolf optimization (GWO) algorithm that approved its efficiency through experiments. The features subset selected were used to train the SVM classifier. Ten datasets for different plants are utilized to assess the proposed model. According to the findings, the proposed model achieved better outcomes for all used datasets. As an average for all datasets, the accuracy of the proposed model is 93.84 compared to 85.49, 87.89, 87.04 for AlexNet, GoogleNet, and the SVM, respectively. [ABSTRACT FROM AUTHOR]

Solid pancreatic lesions (SPLs) encompass a variety of benign and malignant diseases and accurate diagnosis is crucial for guiding appropriate treatment decisions. Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) serves as a front-line diagnostic tool for pancreatic mass lesions and is widely used in clinical practice. Artificial intelligence (AI) is a mathematical technique that automates the learning and recognition of data patterns. Its strong self-learning ability and unbiased nature have led to its gradual adoption in the medical field. In this paper, we describe the fundamentals of AI and provide a summary of reports on AI in EUS-FNA/B to help endoscopists understand and realize its potential in improving pathological diagnosis and guiding targeted EUS-FNA/B. However, AI models have limitations and shortages that need to be addressed before clinical use. Furthermore, as most AI studies are retrospective, large-scale prospective clinical trials are necessary to evaluate their clinical usefulness accurately. Although AI in EUS-FNA/B is still in its infancy, the constant input of clinical data and the advancements in computer technology are expected to make computer-aided diagnosis and treatment more feasible. [ABSTRACT FROM AUTHOR]

The present study uses bio-bibliometrics to examine the contributions made by Prof. K.R. Venugopal, the Vice-Chancellor of Bangalore University. He was the Principal of the University Visvesvaraya College of Engineering (UVCE) for 15 years and Chairman of the Department of Electronics and Communications, Computer Science Engineering, and Information Science Engineering at UVCE. He has an illustrious, distinguished, and brilliant academic career with eleven degrees, including two Ph.Ds. He was a post-doctoral research scholar and visiting professor at the University of Southern California, USA. It is essential to study his contributions to understand the magnitudes and contributions in a particular domain. Data has been collected based on his curriculum vitae, available on the website of Bangalore University. Further, the paper highlights Prof. K.R. Venugopal's author productivity, year-wise distribution, collaboration, communications channels, highly cited documents and research papers, and most downloaded books. [ABSTRACT FROM AUTHOR]

Video anomaly event detection (VAED) is one of the key technologies in computer vision for smart surveillance systems. With the advent of deep learning, contemporary advances in VAED have achieved substantial success. Recently, weakly supervised VAED (WVAED) has become a popular VAED technical route of research. WVAED methods do not depend on a supplementary self-supervised substitute task, yet they can assess anomaly scores straightway. However, the performance of WVAED methods depends on pretrained feature extractors. In this paper, we first address taking advantage of two pretrained feature extractors for CNN (e.g., C3D and I3D) and ViT (e.g., CLIP), for effectively extracting discerning representations. We then consider long-range and short-range temporal dependencies and put forward video snippets of interest by leveraging our proposed temporal self-attention network (TSAN). We design a multiple instance learning (MIL)-based generalized architecture named CNN-ViT-TSAN, by using CNN- and/or ViT-extracted features and TSAN to specify a series of models for the WVAED problem. Experimental results on publicly available popular crowd datasets demonstrated the effectiveness of our CNN-ViT-TSAN. [ABSTRACT FROM AUTHOR]

Achieving timely access to data objects is a major challenge in big distributed systems like the Internet of Things (IoT) platforms. Therefore, minimizing the data read and write operation time in distributed systems has elevated to a higher priority for system designers and mechanical engineers. Replication and the appropriate placement of the replicas on the most accessible data servers is a problem of NP-complete optimization. The key objectives of the current study are minimizing the data access time, reducing the quantity of replicas, and improving the data availability. The current paper employs the Olympiad Optimization Algorithm (OOA) as a novel population-based and discrete heuristic algorithm to solve the replica placement problem which is also applicable to other fields such as mechanical and computer engineering design problems. This discrete algorithm was inspired by the learning process of student groups who are preparing for the Olympiad exams. The proposed algorithm, which is divide-and-conquer-based with local and global search strategies, was used in solving the replica placement problem in a standard simulated distributed system. The 'European Union Database' (EUData) was employed to evaluate the proposed algorithm, which contains 28 nodes as servers and a network architecture in the format of a complete graph. It was revealed that the proposed technique reduces data access time by 39% with around six replicas, which is vastly superior to the earlier methods. Moreover, the standard deviation of the results of the algorithm's different executions is approximately 0.0062, which is lower than the other techniques' standard deviation within the same experiments. [ABSTRACT FROM AUTHOR]

In order to improve the universality and security of the data management of the Internet of Things and avoid the single point of failure problem in the traditional management architecture, this paper proposes a massive connection management architecture based on blockchain. The blockchain is reconstructed according to the hierarchical structure of blockchain technology, and with reference to the attribute-based access control mechanism, a data exchange model is designed using gossip mode and TLS networking mode to ensure the safe transmission of data. The peer computer network technology, data signature, and consensus mechanism are used to complete the design of the data exchange network layer and realize the design of the blockchain-based massive connection management architecture, so as to effectively avoid the single point of failure problem in the traditional management architecture and greatly reduce the burden of the management center. The access requests of massive devices in 5G application scenarios were simulated, and the results showed that the blockchain-based access control architecture designed in this article can quickly process received requests, with information leakage below 10%, improving the security and processing performance of data connections, achieving efficient processing and management of large-scale data, and providing reliable guarantees for the development of the Internet era. [ABSTRACT FROM AUTHOR]

Object detection and tracking in camera images is a fundamental technology for computer vision and is used in various applications. In particular, object tracking using high-speed cameras is expected to be applied to real-time control in robotics. Therefore, it is required to increase tracking speed and detection accuracy. Currently, however, it is difficult to achieve both of those things simultaneously. In this paper, we propose a tracking method that combines multiple methods: correlation filter-based object tracking, deep learning-based object detection, and motion detection with background subtraction. The algorithms work in parallel and assist each other's processing to improve the overall performance of the system. We named it the "Mutual Assist tracker of feature Filters and Detectors (MAFiD method)". This method aims to achieve both high-speed tracking of moving objects and high detection accuracy. Experiments were conducted to verify the detection performance and processing speed by tracking a transparent capsule moving at high speed. The results show that the tracking speed was 618 frames per second (FPS) and the accuracy was 86% for Intersection over Union (IoU). The detection latency was 3.48 ms. These experimental scores are higher than those of conventional methods, indicating that the MAFiD method achieved fast object tracking while maintaining high detection performance. This proposal will contribute to the improvement of object-tracking technology. [ABSTRACT FROM AUTHOR]

Presently, computer experiments with multiple levels of accuracy are widely applied in science and engineering. This paper introduces a class of nested maximum entropy designs for such computer experiments. A multi-layer DETMAX algorithm is proposed to construct nested maximum entropy designs. Based on nested maximum entropy designs, we also propose an integer-programming procedure to specify the sample sizes in multi-fidelity computer experiments. Simulated annealing techniques are used to tackle complex optimization problems in the proposed methods. Illustrative examples show that the proposed nested entropy designs can yield better prediction results than nested Latin hypercube designs in the literature and that the proposed sample-size determination method is effective. [ABSTRACT FROM AUTHOR]

In this paper, two sector beam scanning approaches (BSAs) based on element position perturbations (EPPs) in the azimuth plane are introduced. In EPP-BSA, the elements' excitations are kept constant and the elements' positions in the direction normal to the array line are changed according to a predetermined EPP pattern. The magnitude and repetition rate of the selected EPP pattern determines the steering angle of the main beam. However, EPP-BSA results in a wide scanning range with a significant increase in the side lobe level (SLL). To mitigate this drawback, a reduction in the SLL of the array pattern is firstly performed using the single convolution/genetic algorithm (SC/GA) technique and then perturbing the elements' positions in the azimuth plane. This combination between SLL reduction and EPP-BSA (SLL/EPP-BSA) results in a smaller scanning range with a relatively constant half power beamwidth (HPBW) and a much lower SLL. In addition, keeping the synthesized excitation coefficients constant without adding progressive phase shifters facilitates the manufacturing process and reduces the cost of the feeding network. Furthermore, a planar antenna array thinning approach is proposed to realize the EPP-BSA. The results are realized using the computer simulation technology (CST) microwave studio software package, which provides users with an optimized modeling environment and results in realizable and realistic designs. [ABSTRACT FROM AUTHOR]

Artificial intelligence technologies such as computer vision (CV), machine learning, Internet of Things (IoT), and robotics have advanced rapidly in recent years. The new technologies provide non-contact measurements in three areas: indoor environmental monitoring, outdoor environ-mental monitoring, and equipment monitoring. This paper summarizes the specific applications of non-contact measurement based on infrared images and visible images in the areas of personnel skin temperature, position posture, the urban physical environment, building construction safety, and equipment operation status. At the same time, the challenges and opportunities associated with the application of CV technology are anticipated. [ABSTRACT FROM AUTHOR]

Face detection is a computer technology being used in a variety of applications that identify human faces in digital images. In many face recognition challenges, Convolutional Neural Networks (CNNs) are regarded as a problem solver. Occlusion is determined as the most common challenge of face recognition in realistic applications. Several studies are undergoing to obtain face recognition without any challenges. However, the occurrence of noise and occlusion in the image reduces the achievement of face recognition. Hence, various researches and studies are carried out to solve the challenges involved with the occurrence of occlusion and noise in the image, and more clarification is needed to acquire high accuracy. Hence, a deep learning model is intended to be developed in this paper using the meta-heuristic approach. The proposed model covers four main steps: (a) data acquisition, (b) pre-processing, (c) pattern extraction and (d) classification. The benchmark datasets regarding the face image with occlusion are gathered from a public source. Further, the pre-processing of the images is performed by contrast enhancement and Gabor filtering. With these pre-processed images, pattern extraction is done by the optimal local mesh ternary pattern. Here, the hybrid Whale–Galactic Swarm Optimization (WGSO) algorithm is used for developing the optimal local mesh ternary pattern extraction. By inputting the pattern-extracted image, the new deep learning model namely "CNN with Gated Recurrent Unit (GRU)" network performs the recognition process to maximize the accuracy, and also it is used to enhance the face recognition model. From the results, in terms of accuracy, the proposed WGSO- CNN + GRU model is better by 4.02%, 3.76% and 2.17% than the CNN, SVM and SRC, respectively. The experimental results are presented by performing their comparative analysis on a standard dataset, and they assure the efficiency of the proposed model. However, many challenging problems related to face recognition still exist, which offer excellent opportunities to face recognition researchers in the future. [ABSTRACT FROM AUTHOR]