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This letter presents a novel self‐supervised learning strategy to improve the robustness of a monocular depth estimation (MDE) network against motion blur. Motion blur, a common problem in real‐world applications like autonomous driving and scene reconstruction, often hinders accurate depth perception. Conventional MDE methods are effective under controlled conditions but struggle to generalise their performance to blurred images. To address this problem, we generate blur‐synthesised data to train a robust MDE model without the need for preprocessing, such as deblurring. By incorporating self‐distillation techniques and using blur‐synthesised data, the depth estimation accuracy for blurred images is significantly enhanced without additional computational or memory overhead. Extensive experimental results demonstrate the effectiveness of the proposed method, enhancing existing MDE models to accurately estimate depth information across various blur conditions. [ABSTRACT FROM AUTHOR]

The advancement of electromagnetic interference (EMI) shielding materials provides reliable protection for the communication technology. However, the performance of conventional EMI shielding materials is significantly diminished in high‐temperature conditions. Herein, an efficient method is proposed to prepare conductive fabrics with temperature‐strengthened EMI shielding performance via chemical plating technique. The as‐prepared polyethylene/polyester‐metallized fabric (PMF) coated with Ni–W–P alloy exhibits outstanding EMI shielding effectiveness (SE) in X band, which is strengthened from 16 to 59 dB with temperature raising from room temperature to 130 °C. Particularly, the corresponding temperature‐strengthened shielding behaviors are investigated through simulations in Computer Simulation Technology. Additionally, PMF presents excellent corrosion resistance (corrosion voltage [Ecoor] of −0.324 V and corrosion current [Icoor] of −6.760 A·cm−2) and admirable Joule heating performance, reaching maximum temperature of 74 °C at only 1.5 V. Therefore, the as‐prepared PMF endows enormous potential in EMI protection under high temperature and harsh environments. [ABSTRACT FROM AUTHOR]

One of the key elements of vertical high‐voltage GaN‐based devices is a properly designed junction termination extension (JTE) structure. One of the approaches to the fabrication of JTE structures is the use of p‐type epitaxial layers and their appropriate shaping to obtain high values of breakdown voltage. In this work, optimization of two‐zone (TZ) step‐etched JTE structures for vertical GaN power devices using technology computer aided design simulations is presented. Two constructions of the device are used, with a single‐zone (SZ‐JTE) and TZ‐JTE (TZ‐JTE) structure. The dependence of the breakdown voltage of SZ‐JTE structure is very sensitive to its thickness. The maximum breakdown voltage of 1654 V is obtained for the SZ‐JTE thickness of 110 nm. The use of a TZ‐JTE with an appropriate segment thickness ratio allows for a breakdown voltage above 75% of the theoretical value (≈1500 V) for a much wider range of JTE thicknesses and to achieve higher values of the breakdown voltage, reaching ≈96% of the theoretical value (1887 V). Finally, the performed simulations allow to map the full dependence on the thickness of the TZ‐JTE areas (2D‐map) and to determine the process window leading to the breakdown voltage above 1500 V. [ABSTRACT FROM AUTHOR]

Electrical circuits are present in a variety of technologies, making their design an important part of computer aided engineering. The growing number of parameters that affect the final design leads to a need for new approaches to quantify their impact. Machine learning may play a key role in this regard; however, current approaches often make suboptimal use of existing knowledge about the system at hand. In terms of circuits, their description via modified nodal analysis is well‐understood. This particular formulation leads to systems of differential‐algebraic equations (DAEs), which bring with them a number of peculiarities, for example, hidden constraints that the solution needs to fulfill. We use the recently introduced dissection index that can decouple a given system of DAEs into ordinary differential equations, only depending on differential variables, and purely algebraic equations, that describe the relations between differential and algebraic variables. The idea is to then only learn the differential variables and reconstruct the algebraic ones using the relations from the decoupling. This approach guarantees that the algebraic constraints are fulfilled up to the accuracy of the nonlinear system solver, and it may also reduce the learning effort as only the differential variables need to be learned. [ABSTRACT FROM AUTHOR]

Summary: The application of computer technologies associated with sensors and artificial intelligence (AI) in the quantification and qualification of quality parameters of meat products of various domestic species is an area of research, development, and innovation of great relevance in the agri‐food industry. This review covers the most recent advances in this area, highlighting the importance of computer vision, artificial intelligence, and ultrasonography in evaluating quality and efficiency in meat products' production and monitoring processes. Various techniques and methodologies used to evaluate quality parameters such as colour, water holding capacity (WHC), pH, moisture, texture, and intramuscular fat, among others related to animal origin, breed and handling, are discussed. In addition, the benefits and practical applications of the technology in the meat industry are examined, such as the automation of inspection processes, accurate product classification, traceability, and food safety. While the potential of artificial intelligence associated with sensor development in the meat industry is promising, it is crucial to recognise that this is an evolving field. This technology offers innovative solutions that enable efficient, cost‐effective, and consumer‐oriented production. However, it also underlines the urgent need for further research and development of new techniques and tools such as artificial intelligence algorithms, the development of more sensitive and accurate multispectral sensors, advances in computer vision for 3D image analysis and automated detection, and the integration of advanced ultrasonography with other technologies. Also crucial is the development of autonomous robotic systems for the automation of inspection processes, the implementation of real‐time monitoring systems for traceability and food safety, and the creation of intuitive interfaces for human‐machine interaction. In addition, the automation of sensory analysis and the optimisation of sustainability and energy efficiency are key areas that require immediate attention to address the current challenges in this agri‐food and agri‐industrial sector, highlighting and emphasising the importance of ongoing innovation in the field. [ABSTRACT FROM AUTHOR]

Designs for computer experiments in quantitative factors use columns with many levels. Filling the experimental space is their most important property, and there are many criteria that assess aspects of space‐filling. Recently, Tian and Xu proposed a stratification pattern for assessing the stratification‐related space‐filling properties of designs for quantitative experimental variables whose number of levels is a power of a – usually small – integer. Such designs have been named GSOAs, in generalization of the earlier proposal of strong – or stratum – orthogonal arrays (SOAs). Latin hypercube designs (LHDs) with a suitable number of levels are special cases of GSOAs. Tian and Xu proposed to use the stratification pattern as a means to ranking (G)SOAs. They reported a small simulation study in which arrays that fared well in that ranking performed well in predicting an unknown function. Shi and Xu refined the criterion and also demonstrated success of a design that fares well on their refined criterion. This paper explains the ideas behind the stratification pattern and the related ranking criteria. A practical example and several toy examples aid the illustration. The stratification pattern can be calculated using the R package SOAs, which does not only provide the pattern itself but also provides more detail in a dimension by weight table, in the spirit of the refinement by Shi and Xu. [ABSTRACT FROM AUTHOR]

Serious games can play a crucial role in developing competencies for the job market, offering an innovative and engaging approach to learning. This study uses FLIGBY to develop employability skills among computer engineering graduates. FLIGBY is a serious game that simulates a virtual company environment where players make strategic decisions, emphasizing the principles of flow and positive psychology. Immersion in realistic scenarios provides practical experience, contributing to the development of market‐ready skills. A quantitative methodology based on descriptive statistics and hypothesis testing was adopted to measure the development of competencies for the job market using the Systems Engineering Competency Framework. The results show that competence development occurs mainly in the professional and managerial dimensions. In contrast, there is no development of core and technical competencies. Furthermore, the perception of the development of these competencies occurs mainly for students with more years of professional experience. The experiential nature of FLIGBY allows users to develop practical knowledge, promoting adaptability and resilience. This gamified approach accelerates the learning curve, preparing individuals for real‐world challenges in the computer engineering workplace. [ABSTRACT FROM AUTHOR]

The trend for an increasingly ubiquitous and cyber‐physical world has been leveraging the use and importance of microcontrollers (μC) to unprecedented levels. Therefore, microcontroller programming (μCP) becomes a paramount skill for electrical and computer engineering students. However, μCP poses significant challenges for undergraduate students, given the need to master low‐level programming languages and several algorithmic strategies that are not usual in "generic" programming. Moreover, μCP can be time‐consuming and complex even when using high‐level languages. This article samples the current state of μCP education in Portugal and unveils the potential support of natural language processing (NLP) tools (such as chatGPT). Our analysis of μCP curricular units from seven representative Portuguese engineering schools highlights a predominant use of AVR 8‐bit μC and project‐based learning. While NLP tools emerge as strong candidates as students' μC companion, their application and impact on the learning process and outcomes deserve to be understood. This study compares the most prominent NLP tools, analyzing their benefits and drawbacks for μCP education, building on both hands‐on tests and literature reviews. By providing automatic code generation and explanation of concepts, NLP tools can assist students in their learning process, allowing them to focus on software design and real‐world tasks that the μC is designed to handle, rather than on low‐level coding. We also analyzed the specific impact of chatGTP in the context of a μCP course at ISEP, confirming most of our expectations, but with a few curiosities. Overall, this work establishes the foundations for future research on the effective integration of NLP tools in μCP courses. [ABSTRACT FROM AUTHOR]

Summary: Multivalued logic (MVL) offers higher information density as compared with binary logic systems for an equal number of digits. In a ternary microprocessor, memory access is the most time‐ and power‐consuming action. In order to design a ternary computer, the possibilities for designing ternary encoders and decoders must be examined. This paper presents the designs of 9:2 unbalanced ternary encoder (TENC) and 2:9 unbalanced ternary line address decoder (TDEC) based on novel designs of standard ternary inverter (STI), ternary NAND (TNAND), and ternary NOR (TNOR) logic gates using resistive random‐access memory (RRAM) and carbon nanotube field effect transistor (CNTFET) technology. The simulation results indicate an improvement in performance parameters such as power consumption, delay, power–delay product (PDP), and component count of the proposed circuits in comparison with the different existing counterparts. Moreover, a detailed analysis is carried out on the impact of different process, voltage, and temperature (PVT) variations on the performance metrics, including propagation delay, power consumption, and PDP of the 9:2 unbalanced ternary encoder and 2:9 unbalanced ternary line address decoder. The proposed ternary encoder circuit shows an improvement of 62% in delay, 71% in power consumption, 88.6% improvement in PDP, and 53% reduction in CNTFET count, whereas the ternary decoder circuit exhibits an improvement of 6.6% and 94% in delay and 43% and 94% improvement in power consumption, respectively. Moreover, the CNTFET count is reduced by 16.07% and 46%. [ABSTRACT FROM AUTHOR]

Background: Postdoctoral training holds an increasingly important place in preparation for leading academic and research positions. While little empirical research has described postdoctoral training beyond the sciences, across all fields, "misaligned expectations" are often touted as a key source of postdoctoral strife. Purpose/Hypothesis: This article describes mentorship competency beliefs within engineering and computer science fields, which increasingly engage in postdoctoral training. Design/Method: An embedded mixed‐methods design was used to quantitatively identify mentorship profiles from survey data using latent profile analysis (LPA) from a sample of n = 118 postdoctoral scholars and n = 165 postdoctoral supervisors. Qualitative thematic analysis of interviews with n = 29 postdoctoral scholars and n = 20 postdoctoral supervisors was used to identify meaning in the differences between quantitative profiles. The combination of LPA with thematic analysis enabled the triangulation of distinct postdoctoral mentorship profile definitions. Results: LPA identified six postdoctoral fellow profiles and four supervisor profiles, which became clearly definable through thematic analysis. Postdoc profiles included Technical Manager, Autonomy Focused Advisor, Stretched Mentor, Well‐Rounded Mentor, Exemplar Mentor, and Leader‐Mentor, while supervisor profiles included Autonomous Mentor, Reflective Mentor, Research Lab Mentor, and Confident Leader‐Mentor. Some of these are aligned, but several are not, giving insight into the phenomenon of "misaligned expectations" in postdoctoral literature. Conclusions: The mentorship profiles illustrate the misalignment in expectations, which leads to negative mentorship experiences for many postdoctoral scholars. [ABSTRACT FROM AUTHOR]

One of the most common methods to protect electronic and telecommunication systems in a high‐power electromagnetic (HPEM) environment is using a conductive enclosure. Amplifiers are the vital parts in transmitter and receiver systems as the main components of a telecommunication system, so investigating the effects of HPEM waveform on their performance is very important. The Presence of apertures in the body of the conductive enclosure, as well as non‐linear elements such as transistors in amplifiers, adds to the complexity of examining the coupling of electromagnetic fields on the performance of amplifiers. Considering the importance of the topic, in this study, the impact of the HPEM waveform on the power amplifier inside the conductive enclosure has been investigated. By using the finite difference delay modelling (FDDM) numerical method as a stable method followed by the vector fitting (VF) method, the electromagnetic problem has become a circuit problem, which allows examining the response of the power amplifier and any circuit, including non‐linear elements against the interference field. Responses of bipolar linear and non‐linear components in the perforated conductive enclosure by the proposed method have been compared with the results obtained by computer simulation technology microwave studio (CST‐MWS) for verification. [ABSTRACT FROM AUTHOR]

In this paper, we develop an approach for analyzing the increasingly important strand of research that deals with automated systems of governing. Such systems, which figure prominently in public policy and regulation, are designed to utilize the rapid advancement in computer technology, like artificial intelligence, with the purpose of governing something or someone. Drawing on a large sample of articles we present a comprehensive analysis of scholarly works where these systems are studied as political, rather than neutral, instruments of governing. We find that the current state of the art articulates the politics of automated systems of governing in three ways. Namely, as part of ontological, epistemological and ideological questions. We conclude that future research should investigate the complex forms of marketization nested in these systems, that it should move from theoretical examples to detailed empirical studies and that political science should get more involved with the issue. [ABSTRACT FROM AUTHOR]

Through the enrolment of big data, deep learning, sensor fusion, and computer vision technologies, Amazon Go and similar shops pursue the automated management of retail subjects, goods, and transactions. Tracing the logics of automated shop technology, the paper makes two contributions. First, it proposes a theory of “retail transparency” to attend to how automated shops reimagine space as a series of pockets of excess (actions that escape circuits of capitalist valuation) to be countered through acts of making‐transparent (datafication for integration into digital systems of control). Retail transparency is underpinned by interventions aimed at perceiving, incorporating, and productivising excess. Second, we argue that logics of deep learning raise important challenges to traditional conceptions of resistance in digital geographies, as these tend to rely on a celebration or cultivation of excess. Instead, we offer a speculative reflection outlining a politics of “circuit‐breaking” which refuses to engage algorithmic logics on their own terms. [ABSTRACT FROM AUTHOR]

Recent applications like mmWave technologies require antenna characteristics such as high gain and wide bandwidth for smooth operation with high speed. Hence, this paper presents a novel pi‐shaped patch antenna framed within a square structure for mmWave applications at 28 GHz. In the process of designing a four‐element linear‐planar antenna array (LPAA), a thin single antenna element is designed on a 0.275 mm Rogers 5880 substrate with a dielectric constant of 2.2. The proposed single‐element antenna provides a gain of 3 dBi at 28 GHz and a wide impedance bandwidth ranging from 22.482 GHz to 40.511 GHz. Then, this proposed structure is transformed into a four‐element LPAA with a compact dimension of 0.263 mm × 18.55 mm × 23.99 mm. To enhance and predict the impedance bandwidth of the proposed LPAA, mathematical modeling using response surface methodology and constrained numerical optimization is applied. In the optimization process of the antenna, independent factors such as substrate height (Hs) and interelement spacing (d) that influence the antenna's responses such as impedance bandwidth (BW) and operating frequency (Fr) are considered. These factors are varied repeatedly and simulated using computer simulation technology (CST) suite 2019 for preparing the dataset, which is used as an input for mathematical modeling. Then, response surface methodology (RSM) is employed to relate the responses with independent factors. Models are validated using analysis of variance (ANOVA). The optimum parameters determined by applying constrained numerical optimization are substrate height (Hs) and interelement spacing (d) of values 0.263 mm and 5.61 mm, respectively. The optimized LPAA provides a wide bandwidth of 11 GHz and a peak gain of 9.25 dBi. The antenna also gives radiation efficiency of ≥98.5% and VSWR of less than 2 in its operating frequency range. The results included here are simulated using the CST EM solver and validated using the Ansys High‐Frequency Simulation Software (HFSS). The proposed antenna provides high gain and wide bandwidth, which makes it a good candidate for 5G wireless communication applications. [ABSTRACT FROM AUTHOR]

Exploiting simulation modeling of graphene synaptic field‐effect transistors is extremely important for helping researchers to construct carbon‐based neuromorphic computing systems. Here, lateral‐ionic‐gated graphene synaptic FETs with different gate lengths are fabricated, and they are modeled by using basic physic models combined with the ions migration‐diffusion model and graphene material model. The feasibility and accuracy of the proposed modeling are validated by showing an excellent agreement between simulations and experimental results. The slicing technique of the modeling is proposed to analyze the influence of ionic concentration and diffusion coefficient on the ions movement to reveal their working mechanism. The effect of key parameters about gate length, ionic concentration, and diffusion coefficient on synaptic behavior such as short‐term plasticity, and long‐term plasticity is simulated and discussed. In addition, three kinds of spike‐timing‐dependent plasticity are obtained by the device modeling. This research opens up promising avenues for the development of artificial synapse modeling and paths to new opportunities for the construction of carbon‐based neuromorphic networks. [ABSTRACT FROM AUTHOR]

Gadolinium contrast is an important agent in magnetic resonance imaging (MRI), particularly in neuroimaging where it can help identify blood–brain barrier breakdown from an inflammatory, infectious, or neoplastic process. However, gadolinium contrast has several drawbacks, including nephrogenic systemic fibrosis, gadolinium deposition in the brain and bones, and allergic‐like reactions. As computer hardware and technology continues to evolve, machine learning has become a possible solution for eliminating or reducing the dose of gadolinium contrast. This review summarizes the clinical uses of gadolinium contrast, the risks of gadolinium contrast, and state‐of‐the‐art machine learning methods that have been applied to reduce or eliminate gadolinium contrast administration, as well as their current limitations, with a focus on neuroimaging applications. Evidence Level: 3 Technical Efficacy: Stage 1 [ABSTRACT FROM AUTHOR]

The Industrial Internet has emerged as a key technology in the field of industrial automation, revolutionizing traditional manufacturing processes and enabling advanced control systems by integrating machines, sensors, and software systems through network connectivity, allowing for real‐time data exchange, analysis, and decision‐making in industrial environments. Programmable Logic Controllers (PLCs) play a critical role in industrial automation systems, which are specialized digital computers designed to control various manufacturing processes and machinery. As a promising language, Structured Text (ST) is of paramount importance for industrial Internet, and it is essential to translate ST into C, which is widely used in various industries for implementing control systems. However, due to differences in syntax, data types, and programming paradigms between the two languages, the translation process is facing several challenges. In this paper, we present a domain‐specific translation tool that automates the process of converting ST code to C code. This tool incorporates advanced AI techniques, particularly leveraging the capabilities of NL‐PL LLMs, to facilitate accurate and efficient translation while generating comprehensive documentation to assist developers. Finally, the evaluation and case studies conducted validate the effectiveness of the tool, demonstrating its practical applicability and benefits in real‐world industrial settings. [ABSTRACT FROM AUTHOR]

The article discusses the development and applications of memristors in electronic circuits, neural networks, and neuromorphic computing. Memristors, first proposed in 1971, have since been extensively researched and recognized for their potential in future electronics, computing, and AI due to their small size, low power consumption, and dual memory-processing functions. The special issue covers various topics related to memristors, including non-linear dynamics, chaotic circuits, synaptic applications, and in-memory neuromorphic systems, showcasing the significant impact of memristors in advancing technology. Researchers and industry professionals worldwide continue to focus on memristive technologies, highlighting their importance in shaping the future of electronics and computing. [Extracted from the article]

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]

Interest in the study of computer technology in schools (often in the form of Computer Science subjects or coding initiatives) is seen to have undergone two waves of interest, one in the late 1970s/early 1980s and the other more recently since about 2010. Through a historical exploration of the rationales for the study of computer technology in schools in both the first wave and the second wave, this paper aimed to explore the various curriculum ideologies underpinning their justification. In doing so, the paper highlights the chameleon‐like nature of this change agenda in aligning with contemporary trends to advance the study of computer technology on the curriculum. The paper argues that current manifestations of the study of computer technology in schools are at odds with broader curriculum reform goals and international policies and raises questions about students' access to such knowledge. [ABSTRACT FROM AUTHOR]

A novel Quasi‐Yagi‐Uda antenna on a flexible material is designed and demonstrated in this paper. The antenna is developed on an ultra‐thin polyimide substrate to support dual‐frequency bands centered at 3.8 and 5.2 GHz for 5G and WiFi‐enabled wireless communications. The proposed Quasi‐Yagi‐Uda has a twin‐F‐shaped driven element which is excited using a microstrip line feed. The arms of the driven element are suitably designed to meet the frequency specifications of the proposed antenna. The design of the antenna is carried out using Computer Simulation Technology Microwave Studio while the antenna optimization is performed using the Fire‐Fly algorithm. The optimum geometrical details of the proposed antenna are obtained with a higher rate of convergence. The proposed dual‐band antenna provides 205 and 195 MHz bandwidth in both frequency bands guaranteeing high‐speed wireless communications in the 5G domain. The Quasi‐Yagi‐Uda provides a gain greater than 4.5 dBi in both operating bands with an efficiency greater than 80%. The prototype antenna optimized using the Fire‐Fly algorithm is fabricated and the simulation results are validated. [ABSTRACT FROM AUTHOR]

The field of computer vision is characterized by computationally intensive algorithms and techniques with strict real‐time requirements. Field programmable gate arrays (FPGAs) are based on a concurrent paradigm which allows the design of efficient hardware architectures and has positioned FPGAs as an ideal device for implementing compute‐intensive applications. For this reason, FPGA technology has had a great impact in areas such as computer vision, where one of the main objectives for researchers working in this field is to create efficient automatic object recognition systems. Therefore, the need to provide undergraduates with the necessary skills to design FPGA‐based object recognition systems is evident. With this aim in mind, it is essential that specialization courses related to the design of these systems include the required resources for the student to apply the theoretical knowledge in solving practical problems. In this article, we present a development tool designed to help students, teachers, and researchers during the design‐modeling‐implementation process of object recognition systems based on FPGAs. The proposed tool operates under a modular approach as this facilitates the working on any of the phases of a recognition system and it is considered as a hybrid because the other phases can be developed using a software language. An empirical evaluation involving undergraduates enrolled in a Computer Engineering program was conducted to create a hardware architecture for the DAISY descriptor that uses the homogeneous features of objects immersed in images to produce an efficient representation. By considering similar descriptors such as Scale‐Invariant Feature Transform (SIFT) and Histogram of Oriented Gradients (HOG), DAISY is computed by convolving orientation maps instead of using weighted sums of gradient norms, which offers the same kind of invariance at a lower computational cost for the dense case. The results obtained during such an evaluation indicated that students consider this FPGA‐based tool to be an alternative to receiving practical training on designing systems for solving problems related to the area of object recognition. [ABSTRACT FROM AUTHOR]

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Automated cameras (including camera traps) are an established observation tool, allowing, for example the identification of behaviours and monitoring without harming organisms. However, limitations including imperfect detection, insufficient data storage and power supply restrict the use of camera traps, making inexpensive and customizable solutions desirable. We describe a camera system and evaluation toolset based on Raspberry Pi computers and YOLOv5 that can overcome those shortcomings with its modular properties. We facilitate the set‐up and modification for researchers via detailed step‐by‐step guides.A customized camera system prototype was constructed to monitor fast‐moving organisms on a continuous schedule. For testing and benchmarking, we recorded mason bees (Osmia cornuta) approaching nesting aids on 20 sites. To efficiently process the extensive video material, we developed an evaluation toolset utilizing the convolutional neural network YOLOv5 to detect bees in the videos.In the field test, the camera system performed reliably for more than a week (2 h per day) under varying weather conditions. YOLOv5 detected and classified bees with only 775 original training images. Overall detection reliability varied depending on camera perspective, site and weather conditions, but a high average detection precision (78%) was achieved, which was confirmed by a human observer (80% of algorithm‐based detections confirmed).The customized camera system mitigates several disadvantages of commercial camera traps by using interchangeable components and incorporates all major requirements a researcher has for working in the field including moderate costs, easy assembly and an external energy source. We provide detailed user guides to bridge the gap between ecology, computer science and engineering. [ABSTRACT FROM AUTHOR]

The present study examines the lateral thermal vibrations of a nanobeam subjected to an axial motion while being influenced by a sinusoidal thermal load. The partial differential equation describing the system was obtained using the extended Hamiltonian principle. Also considered were the Euler‐Bernoulli (EB) beam, the nonlocal couple stress theory, and Eringen's nonlocal elasticity model. This type of axially moving beam has multiple applications in design, including industrial, civil, structural, chemical, and computer engineering. The Laplace transform technique is utilized to translate partial differential equations into a thermoelastic differential equation of the sixth order. This study investigates the impact of nanobeam size and velocity on thermo‐mechanical characteristics. To explore the impacts of axial velocity, pulse width, nonlocal index, material length scale coefficient, and phase lag coefficients on the examined studied fields, such as lateral vibration and temperature change for the moving nanobeam are calculated. The specified factors were discovered to impact the flexibility and dynamic response of the nanobeam substantially. [ABSTRACT FROM AUTHOR]

Clóvis Caesar Gonzaga, a renowned figure in interior point methods, made significant contributions to the field of operational research. His analysis of path following methods in linear programming, presented in the late 1980s, remains the best complexity bound in the field. Born in Brazil in 1944, Gonzaga obtained degrees in electronic engineering and systems engineering and computer science. He held various academic positions, including professorships at COPPE UFRJ and UFSC. Gonzaga's work revolutionized the optimization community worldwide, and his legacy continues to inspire researchers in Brazil and beyond. [Extracted from the article]

In this article, a continuously microfluidically tunable filter using two microstrip resonators is proposed. By injecting different amount of water into the tubes, the resonant frequency as well as coupling intensity between two resonators can be controlled. As a result, the fractional bandwidth (FBW) of the proposed band can keep constant when tuning the frequency. As far as the authors know, most of the state‐of‐the‐art microfluidics tunable filters are discontinuous, and the tuning range is limited. In this article, the wide tuning capacity of the frequency is realized by placing some tubes between the microstrip resonators and the ground. By increasing or decreasing the amount of water in the pipes, the local dielectric constant and the equivalent electrical length of the transmission line are changed simultaneously. Therefore, it plays a key role in resonant frequency tuning. In the other hand, the coupling coefficient can be controlled by injecting the amount of water into the tube between the two resonators. Finally, through the analysis of computer simulation technology simulation and measurement, it can be seen that the tuning range of resonant frequency is up to 41.3%, with the central frequency varying from 1.33 to 1.88 GHz. The insertion loss is less than 3.09 dB and the return loss is higher than 15 dB, while the FBW is 3.4% ± 0.2%. The measurement matches well with the simulation results, verifying the proposed design methodology. [ABSTRACT FROM AUTHOR]

In the present work, we investigate the impact of structure dimensional parameters on the short channel effects which occurs especially below 20 nm regime particularly gate induced drain leakage (GIDL) current. Using technology computer aided design simulation (TCAD), we have examined the GIDL for SiGe as source/drain in NTJLFET. The structural dimensional parameters such as the nanotube thickness, core and outer gates thickness and gate electrode work function shows the significant impact on the band to band tunneling in lateral direction (L‐BTBT) which induced GIDL current. It is analyzed that increase in the nanotube thickness and physical oxide thickness increase the GIDL current, while increasing the gate electrode work function, core gate and outer gate thicknesses gives reduced GIDL current. The SiGe S/D NTJLFET produce a remarkable high ION/IOFF ratio ~ 1011. A compact model for GIDL current is also developed which shows the dependency of structure parameters on leakage current. The SiGe has been incorporated as source and drain in NTJLFET which creates the energy band discontinuity. Furthermore, SiGe S/D NTJLFET is fairly compared with the conventional NT JLFET and nanowire (NW) JLFET and shows an improved performance. [ABSTRACT FROM AUTHOR]

Military program management and system engineering re quire the expression of costs and delay trade‐off with respect to system architecture. If architecture frameworks (AF) such as NATO (NAF) were designed to fill this common need, their current state is essentially descriptive. As it turns out, building defense systems architectures using those frameworks in a properly anticipated cost/delay budget envelope would require to have all system engineering already solved, because the architecture frameworks are designed to provide an explicit representation of the operational domain that can be used in analysis, for articulation of issues and requirements, as support to planning, and as a means of solution design and validation, among other things. Thus Quality‐Resource‐Time optimality in a regularly evolving environment cannot be represented in acceptable delay without automated optimization assistance. Our contribution in this article explores coupling architecture framework with operation research (OR) models to enable computer assisted design and evaluation of heterogeneous views in NATO Architecture Framework (NAF). Our illustrative example is a Linear Programming based bridge between program management and system engineering to anticipate optimal trade‐offs. This article presents promising results, with which we hope to show how OR and AF will be indivisible in architecture evaluation process. [ABSTRACT FROM AUTHOR]

Strong orthogonal arrays are suitable designs for computer experiments because of stratification in low‐dimensional projections. However, strong orthogonal arrays may be very expensive for a moderate number of factors. In this article, we develop a method for constructing space‐filling designs with more economical run sizes. These designs are not only column‐orthogonal but also enjoy a large proportion of low‐dimensional stratification properties that strong orthogonal arrays ought to have. Moreover, a class of proposed designs can be 3‐orthogonal. In addition, some theoretical results on regular fractional factorial designs are obtained as a by‐product. [ABSTRACT FROM AUTHOR]

The rapid development and widespread use of computer technology in today's society have brought significant benefits and convenience to individuals and organizations. The study presents a concept for constructing a research framework in the realm of computer ethics impact. This involves the introduction of an information fusion model and the utilization of a Dempster–Shafer (D–S) evidence theory algorithm. The information fusion model aims to integrate multiple sources of evidence related to computer ethics, including expert opinions, public attitudes, and legal regulations. Furthermore, the experimental results show that the platform's impact perception was effective in reducing the number of individuals who consider computer ethics unnecessary from 40% to 3%. This finding highlights the potential of this platform for enhancing public awareness and understanding of computer ethics. In conclusion, the proposed information fusion model and D–S evidence theory algorithm offer a valuable tool for conducting early research on the construction of computer ethics. It is of utmost importance to delve into the uncontrollable facets of building computer ethics and to navigate the subject–object dilemma from the standpoint of computer users. This approach is essential in addressing the ethical dilemmas that have arisen due to the rapid advancement of computer technology in today's world. The novelty lies in the research methodology used in this study, substantial public perception change, ethical risk reduction, and subject–object issue in computer ethics resolution all substantially contribute to the field's influence on people, organizations, and societies. [ABSTRACT FROM AUTHOR]

The training room of electrical engineering plays an important role in implementing learners' theoretical electrical knowledge and enhancing their operational skills. When operating in an electrical training room, learners often configure complex equipment with a set of multiple wires, components, and tools, and then debug them to ensure normal operation. It requires a large amount of practice to acquire the skills to configure the devices, and it is hard for learners to configure the device without any experience. Furthermore, it is also difficult to have hands‐on practice when there is not enough physical equipment on site or learners cannot be there in person. By applying virtual reality technology, it is possible to offer a greater learning experience for the learners. A VR‐based wiring learning platform (VWLP), in this study, is proposed, which targets at offering a resource of assisted learning regarding the electrical equipment. The result of the study reveals that the VWLP constructed in this research can promote learning effectiveness in electrical engineering, especially in the area of wiring skills, and the blended learning mode (a mix of both conventional instructor‐led practice activities and VR‐based learning platform) enhances the learning effect. Besides this, the result shows that VWLP has a significantly positive effect regarding participants' learning cognition and motivation. By communicating with the virtual objects in the VR environment, the participants can demonstrate a promoted comprehension of the training equipment. Meanwhile, participants in the experimental team also perceive an increase in their learning confidence due to increased practice opportunities, which in turn reduces cognitive load. [ABSTRACT FROM AUTHOR]

In this work, an original Si/SiGe heterojunction tunneling field‐effect transistor which has a double‐stacked heterogeneous oxide gate (HfO2/Al2O3) structure (DSG–HJ–TFET) is designed and investigated by Sentaurus technology computer aided design (TCAD) simulation software. To ensure good interface quality, a stacked oxide gate dielectric of Al2O3 and HfO2 is proposed. The on‐state current (Ion) is increased though improving the carrier mobility degradation caused by the poor quality of interface between the high‐κ dielectric and semiconductor. Moreover, a heterojunction with SiGe (source)/Si (channel) and pocket layer which is inserted between the source region and the channel is adopted to reduce the tunneling barrier and improve the Ion. Therefore, the higher Ion is obtained by the proposed DSG–HJ–TFET. In the simulation results, it is shown that Ion of DSG–HJ–TFET is increased by three orders of magnitude compared with that of the conventional high‐κ gate dielectric TFET structure. In addition, the off‐state current (Ioff) of 4.91 × 10−11 μA μm−1, the minimum subthreshold swing of 14 mV dec−1, the Ion/Ioff ratio of 1.13 × 1012, transconductance of 260 μS μm−1, fT of 49.8 GHz, and gain bandwidth product (GBW) of 7.2 GHz are obtained. The propose DSG–HJ–TFET is favored in ultralow‐power applications for the rather good performance. [ABSTRACT FROM AUTHOR]

This document is an editorial summarizing the Applied Geometric Algebras in Computer Science and Engineering (AGACSE) conference held in Brno, Czech Republic in September 2021. The conference aimed to promote the use of geometric algebra in fields such as image processing, robotics, and quantum computing. The conference proceedings were published in the journal Mathematical Methods in the Applied Sciences. The editorial provides a list of accepted papers, covering topics such as applied geometry, technological applications, algebra, and quantum phenomena. One specific paper explores the use of geometric algebra in teaching rotations through neural networks. The document is a compilation of research papers showcasing the applications of geometric algebra in various fields, including robotics, control systems, image processing, cryptography, and physics. Each paper presents a specific problem or application and proposes a unique approach or solution using geometric algebra. The authors compare their methods with existing techniques and provide mathematical analysis to support their claims. Overall, the papers demonstrate the versatility and effectiveness of geometric algebra in different domains. [Extracted from the article]

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]

Microwave imaging (MI) is a noninvasive and nonionizing procedure for detection of cancerous cells in healthy body tissues using radiofrequency (RF) and microwaves. The procedure involves the use of Ultra Wide Band (UWB) antennas for sensing purposes. Therefore, this research article presents the design, development, and testing of a low‐profile UWB Archimedean spiral microstrip‐patch antenna (ASMA) for detection of skin cancer using monostatic radar‐based microwave imaging. The proposed ASMA consists of a spiral resonator with a defective ground structure and a slotted microstrip feed line with dimensions of 38 × 38 × 0.87 mm3. The proposed antenna shows an impedance bandwidth for the frequency range of 2.2–13.9 GHz, with a peak gain of 6.8 dB at 7.8 GHz. In silico analysis of the proposed ASMA for MI is carried out with Gaustav model using Computer Simulation Technology Microwave Studio. To validate the performance of the ASMA as a sensor for MI, a prototype of the same is fabricated and a four‐layered bio‐phantom of the human forearm is prepared for in vitro and in vivo testing of the proposed procedure. The validation of ASMA radiation properties is done using a Vector Network Analyser (E‐5063A) (VNA) and an anechoic chamber with the fabricated antenna at 10 mm away from the prepared bio‐phantom. The recorded S parameter data with bio‐phantom and the VNA is processed using different beamforming algorithms like Delay and Sum and Coherent Factor‐Delay Multiply and Sum (CF‐DMAS) to reconstruct the image of the scanned area. The reconstructed images are 97%–98% accurate. The proposed ASMA sensor is also safe for human exposure as it has a specific absorption rate of 0.0546 W/Kg at 5 GHz that complies with the safety guidelines of the Federal Communications Commission to minimize potential health risks associated with exposure to RF and microwave radiation. [ABSTRACT FROM AUTHOR]

After offering great promise and attracting substantial investment in technology development, fields emission did not succeed in the competition against liquid crystal technology for computer screens and plasma technology for large flat panel television. We review and analyze some of the challenges that prevented field emission from becoming a mayor information display technology. W e report the development of a novel modelling tool that can be used to support the design of field emitters optimized to the application requirements. The previous lack of such a simulation tool that incorporates the range of phenomena that affect field emission has been a limiting factor in emitter performance and data analysis. The new model enables more accurate and effective design of field emitters including the emission dynamics and thermal behaviour for a wide range of geometries and materials. It provides a powerful tool with high accessibility to support engineers to make informed decisions as part of the product design process. This increases opportunities for field emission to be used in a range of non‐display applications. [ABSTRACT FROM AUTHOR]

Using deep learning (DL) to recognize building and infrastructure damage via images is becoming popular in vision‐based structural health monitoring (SHM). However, many previous studies solely work on the existence of damage in the images and directly treat the problem as a single‐attribute classification or separately focus on finding the location or area of the damage as a localization or segmentation problem. Abundant information in the images from multiple sources and intertask relationships are not fully exploited. In this study, the vision‐based SHM problem is first reformulated into a multiattribute multitask setting, where each image contains multiple labels to describe its characteristics. Subsequently, a general multiattribute multitask detection framework, namely ϕ‐NeXt, is proposed, which introduces 10 benchmark tasks including classification, localization, and segmentation tasks. Accordingly, a large‐scale data set containing 37,000 pairs of multilabeled images is established. To pursue better performance in all tasks, a novel hierarchical framework, namely multiattribute multitask transformer (MAMT2) is proposed, which integrates multitask transfer learning mechanisms and adopts a transformer‐based network as the backbone. Finally, for benchmarking purposes, extensive experiments are conducted on all tasks and the performance of the proposed MAMT2 is compared with several classical DL models. The results demonstrate the superiority of the MAMT2 in all tasks, which reveals a great potential for practical applications and future studies in both structural engineering and computer vision. [ABSTRACT FROM AUTHOR]

Background: This study examines post‐surgical outcomes of maxillary position using virtual surgical planning (VSP) with computer designed and manufactured surgical splints, without the use of costly patient specific implants (PSI), in the treatment of routine nonsyndromic orthognathic patients. The cost of these personalized medical devices and their impact in the setting of cranio‐maxillofacial surgery is currently under review by The Department of Health and Aged Care in Australia. Methods: This is a single‐centre retrospective analysis of 49 patients who underwent bimaxillary orthognathic surgery by a single surgeon at Epworth Richmond Hospital (Victoria, Australia) over a period spanning 2016 to 2020. Patients were included in the study provided their surgery was facilitated using VSP with manufacture of computer designed occlusal splints. Results: Use of computer designed and manufactured splints were highly reliable in reproducing the virtual surgical plan, when using palatal plane, upper incisor angulation, and anterior upper facial height. Conclusion: Use of computer designed and manufactured splints provide a method of leveraging the accuracy of VSP methods, without the additional costs associated with PSI. These findings may assist in appropriate resource allocation and case stratification in patients undergoing orthognathic surgery. [ABSTRACT FROM AUTHOR]

Extended reality (XR), including augmented reality (AR), mixed reality (MR), and virtual reality (VR), can make complex information more intuitively understandable. However, it still needs to be determined which XR technology is the most suitable for reviewing performance‐based building design, during which architects check whether design strategies increase energy efficiency, indoor environment quality and so forth. It requires comprehensive design and simulation information to be vividly represented. The authors develop three XR apps (AR, MR, and VR) to interactively visualize façade fenestration geometries and indoor illuminance simulations. Then XR technologies are assessed by 120 students and young architects, from task performance and engagement level two aspects. The task performance is measured by correct rate and time consumption two indicators, while the engagement level is measured by usability and interest two indicators. Evaluation results show that compared to AR and VR, MR is the best XR technology for this aim. VR outperforms AR on three indicators except for usability. By exposing three different XR technologies' performances in aiding fenestration design, this study increases the practical value of applying XR to the building design field. [ABSTRACT FROM AUTHOR]

Summary: As an important technology in computer vision, video‐based human action recognition has a great commercial value, which has attracted extensive attention in the field of computer vision and pattern recognition in both academia and industry. To date, there are a wide variety of applications of human action recognition, such as surveillance, robotics, health care, video searching, and human–computer interaction. However, there are many challenges involved in human action recognition in videos, such as cluttered backgrounds, occlusions, viewpoint variation, execution rate, and camera motion. However, data redundancy and single feature were largely limited the accuracy of human action recognition. In this article, adopting the key frame extraction and multifeature fusion techniques, a novel action recognition method was proposed, which can improve the recognition accuracy. The main works are as follows: 1) in order to solve the problem of data redundancy, a key frame extraction method based on node contribution weighting is proposed to extract video key frames; 2) different kinds of information flows are extracted from the obtained key frame sequences, and different convolutional neural networks are used to obtain corresponding classification results and merge, so as to better complement the information in different flows. Lastly, the experimental results show that our method improves the accuracy of action recognition. [ABSTRACT FROM AUTHOR]

Due to the improvement in computing power and the development of computer technology, deep learning has pene‐trated into various fields of the medical industry. Segmenting lesion areas in medical scans can help clinicians make accurate diagnoses. In particular, convolutional neural networks (CNNs) are a dominant tool in computer vision tasks. They can accurately locate and classify lesion areas. However, due to their inherent inductive bias, CNNs may lack an understanding of long‐term dependencies in medical images, leading to less accurate grasping of details in the images. To address this problem, we explored a Transformer‐based solution and studied its feasibility in medical imaging tasks (OstT). First, we performed super‐resolution reconstruction on the original MRI image of osteosarcoma and improved the texture features of the tissue structure to reduce the error caused by the unclear tissue structure in the image during model training. Then, we propose a Transformer‐based method for medical image segmentation. A gated axial attention model is used, which augments existing architectures by introducing an additional control mechanism in the self‐attention module to improve segmentation accuracy. Experiments on real datasets show that our method outper‐forms existing models such as Unet. It can effectively assist doctors in imaging examinations. [ABSTRACT FROM AUTHOR]

Correspondences between geometric domains (mappings) are ubiquitous in computer graphics and engineering, both for a variety of downstream applications and as core building blocks for higher level algorithms. In particular, mapping a shape to a convex or star‐shaped domain with simple geometry is a fundamental module in existing pipelines for mesh generation, solid texturing, generation of shape correspondences, advanced manufacturing etc. For the case of surfaces, computing such a mapping with guarantees of injectivity is a solved problem. Conversely, robust algorithms for the generation of injective volume mappings to simple polytopes are yet to be found, making this a fundamental open problem in volume mesh processing. VOLMAP is a large scale benchmark aimed to support ongoing research in volume mapping algorithms. The dataset contains 4.7K tetrahedral meshes, whose boundary vertices are mapped to a variety of simple domains, either convex or star‐shaped. This data constitutes the input for candidate algorithms, which are then required to position interior vertices in the domain to obtain a volume map. Overall, this yields more than 22K alternative test cases. VOLMAP also comprises tools to process this data, analyze the resulting maps, and extend the dataset with new meshes, boundary maps and base domains. This article provides a brief overview of the field, discussing its importance and the lack of effective techniques. We then introduce both the dataset and its major features. An example of comparative analysis between two existing methods is also present. [ABSTRACT FROM AUTHOR]

In the wave of the Internet era created by computer and communication technology, flexible sensors play an important role in accurately collecting information owing to their excellent flexibility, ductility, freeform bending or folding, and versatile structural shapes. By endowing elastomeric polymers with conductivity, researchers have recently devoted extensive efforts toward developing high-performance flexible sensors based on elastomeric conductive layers and exploring their potential applications in diverse fields ranging from project manufacturing to daily life. This review reports the recent advancements in elastomeric polymers used to make conductive layers, as well as the relationships between elastomeric polymers and the performance and application of flexible sensors are comprehensively summarized. First, the principles and methods for using elastomeric polymers to construct conductive layers are provided. Then, the fundamental design, unique properties, and underlying mechanisms in different flexible sensors (pressure/strain, temperature, humidity) and their related applications are revealed. Finally, this review concludes with a perspective on the challenges and future directions of high-performance flexible sensors. [ABSTRACT FROM AUTHOR]

Recently, automated decision‐making (ADM) has been increasingly introduced in for example, the public sector potentially ensuring efficiency and more just decision‐making. The increasing use of ADM has been reflected by a growing interest by scholarly research. While initially mainly researchers within law and computer sciences engaged with ADM, there has also been a growing engagement by social science and humanities‐oriented researchers. This article traces the emergence and evolution of ADM research beyond computer sciences and engineering with a specific focus on social sciences and humanities by identifying central concerns and methods while outlining a stable baseline for future research. Based on a systematic mapping of publications, we outline the contours of ADM as an area of research engaging with an emerging empirical phenomenon. Drawing on findings from the mapping, we discuss ways ahead for ADM research as part of the subfield of digital sociology and suggest that sociological media and communication studies have a crucial role to play in developing future research avenues. Drawing on advances made in audience research, we suggest a radically contextualized and people‐centered approach to ADM. Such an approach would help to develop ADM and ground it alongside people's divergent capabilities and contextual arrangements. [ABSTRACT FROM AUTHOR]

Efficient management of water resources is an important task given the significance of water in daily lives and economic growth. Water resource management is a specific field of study which deals with the efficient management of water resources towards fulfilling the needs of society and preventing from water‐related disasters. Many activities within this domain are getting benefitted with the recent technological advancements. Within many others, computer vision‐based solutions have emerged as disruptive technologies to address complex real‐world problems within the water resource management domain (e.g., flood detection and mapping, satellite‐based water bodies monitoring, monitoring and inspection of hydraulic structures, blockage detection and assessment, drainage inspection and sewer monitoring). However, there are still many aspects within the water resource management domain which can be explored using computer vision technologies. Therefore, it is important to investigate the trends in current research related to these technologies to inform the new researchers in this domain. In this context, this paper presents the bibliometric analysis of the literature from the last two decades where computer vision technologies have been used for addressing problems within the water resource management domain. The analysis is presented in two categories: (a) performance analysis demonstrating highlighted trends in the number of publications, number of citations, top contributing countries, top publishing journals, top contributing institutions and top publishers and (b) science mapping to demonstrate the relation between the bibliographic records based on the co‐occurrence of keywords, co‐authorship analysis, co‐citation analysis and bibliographic coupling analysis. Bibliographic records (i.e., 1059) are exported from the Web of Science (WoS) core collection database using a comprehensive query of keywords. VOSviewer opensource tool is used to generate the network and overlay maps for the science mapping of bibliographic records. Results highlighted important trends and valuable insights related to the use of computer vision technologies in water resource management. An increasing trend in the number of publications and focus on deep learning/artificial intelligence (AI)‐based approaches has been reported from the analysis. Further, flood mapping, crack/fracture detection, coastal flood detection, blockage detection and drainage inspections are highlighted as active areas of research. [ABSTRACT FROM AUTHOR]

Achieving low contact resistance (RC) is one of the major challenges in producing 2D FETs for future CMOS technology applications. In this work, the electrical characteristics for semimetal (Sb) and normal metal (Ti) contacted MoS2 devices are systematically analyzed as a function of top and bottom gate‐voltages (VTG and VBG). The semimetal contacts not only significantly reduce RC but also induce a strong dependence of RC on VTG, in sharp contrast to Ti contacts that only modulate RC by varying VBG. The anomalous behavior is attributed to the strongly modulated pseudo‐junction resistance (Rjun) by VTG, resulting from weak Fermi level pinning (FLP) of Sb contacts. In contrast, the resistances under both metallic contacts remain unchanged by VTG as metal screens the electric field from the applied VTG. Technology computer aided design simulations further confirm the contribution of VTG to Rjun, which improves overall RC of Sb‐contacted MoS2 devices. Consequently, the Sb contact has a distinctive merit in dual‐gated (DG) device structure, as it greatly reduces RC and enables effective gate control by both VBG and VTG. The results offer new insight into the development of DG 2D FETs with enhanced contact properties realized by using semimetals. [ABSTRACT FROM AUTHOR]

Modeling of complex systems requires large and expensive computational systems. Costs and processing time can be reduced by more than half in a well‐managed cloud environment. Modeling the flow of ethylene propylene diene monomer (EPDM) through a Garvey die was achieved by employing the latest cloud computer technology commercially available at a modest cost. The description of the stress–strain behavior for EPDM was done using the Otswald‐de Waele model. The results demonstrate that at higher velocities, the flow lines do not follow a smooth profile upon exiting the die, which indicates instabilities that may affect on the finished surface. Prediction of extrudate flow instabilities gives a computational option to make an initial estimate of permissible extrusion speeds. [ABSTRACT FROM AUTHOR]

With the progress and development of computer technology, applying machine learning methods to cancer research has become an important research field. To analyze the most recent research status and trends, main research topics, topic evolutions, research collaborations, and potential directions of this research field, this study conducts a bibliometric analysis on 6206 research articles worldwide collected from PubMed between 2011 and 2021 concerning cancer research using machine learning methods. Python is used as a tool for bibliometric analysis, Gephi is used for social network analysis, and the Latent Dirichlet Allocation model is used for topic modeling. The trend analysis of articles not only reflects the innovative research at the intersection of machine learning and cancer but also demonstrates its vigorous development and increasing impacts. In terms of journals, Nature Communications is the most influential journal and Scientific Reports is the most prolific one. The United States and Harvard University have contributed the most to cancer research using machine learning methods. As for the research topic, "Support Vector Machine," "classification," and "deep learning" have been the core focuses of the research field. Findings are helpful for scholars and related practitioners to better understand the development status and trends of cancer research using machine learning methods, as well as to have a deeper understanding of research hotspots. [ABSTRACT FROM AUTHOR]