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By comparing management in 1987 with today, this column examines the differences for library managers as technology including computers and the Internet has become an integral part of their lives. Technology has made completing many managerial functions easier and erased the barriers of time and space. Communication has become almost instantaneous. Opportunities for learning are now global. On the other hand, the time required to manage and effectively use technology has reduced time devoted to other tasks. The work-free weekend has disappeared; products must have a slick professional look; and form has sometimes become more important than content. Contemporary library managers must focus both for themselves and for their staff on ways to use technology effectively in meeting library goals. Returning to an idyllic fictionalized past is impossible, especially now that artificial intelligence will likely bring another cycle of radical changes. Library managers will need flexibility, experimentation, and harsh critical evaluation of results to achieve success. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM), owing to its unique manufacturing approach, can drive manufacturing towards higher levels of sophistication, flexibility, and intelligence within the context of Industry 4.0. However, the complexity of AM systems and their nature as data-intensive manufacturing domains present challenges in producing high-quality products. With the advancement of digital and computer technologies, data-driven machine learning (ML) has been widely applied in AM, as it provides effective methods for quality control, process optimization, and complex system modeling. This paper succinctly summarizes the various phases of utilizing ML to assist in AM processes. It elucidates the advantages of using ML over traditional methods in each phase, starting from the pre-processing phase of design for additive manufacturing (DfAM) and parameter optimization, through the processing phase of defect detection, to the post-processing phase of part quality assessment. The objective of DfAM is to optimize product design, taking into account the interactions between multiple variables. The nonlinear capability of ML can be effectively utilized in DfAM. Defect detection aims to ensure the repeatability, durability, and reliability of parts. Combining sensors with ML can guarantee part quality. Lastly, assessing part quality and inspecting various subsystems of AM are conducted from the perspective of the part's microstructure. This review explores the application of ML in addressing numerous issues related to the AM process workflow. It provides a comprehensive and systematic summary of the application of various ML models at different phases, investigates the potential of newer ML technologies to assist AM in the future, and concludes with the limitations of current ML applications in AM as well as future development directions. By intersecting these two dynamic fields, it aims to enrich the AM research domain. [ABSTRACT FROM AUTHOR]

This study determined the impact of the use of classroom technology among teachers to the academic performance of pupils in Select Elementary School for the school year 2023-2024. Specifically, this study answered the following: 1.) the profile of participants in terms of frequency use of these classroom technology: Computer and. Projector. 2.) the academic performance of pupils in terms of general average grades in the 2nd grading period. 3.) the relationship between the use of classroom technology and the academic performance of the participants. This study used a descriptive survey research design. The researcher examined the phenomenon through observations and through statistical analysis. Along with questionnaires that was given out to respondents, interview with the respondents and a few experts in this field were also conducted. The respondents of this study were the forty pupils enrolled in the school year 2023-2024. It was randomly selected from 169 learners in every grade level and their academic performance during the 2nd quarter period with the used of classroom technology of teachers. The data revealed that in terms of using computer, there were twenty eight (28) or seventy eight percent (78%) of the respondents answered that their teachers used computer most of the time. This indicated that the teachers of this respondents were most likely used the computer as part of their teaching. In contrast, nine (9) or twenty-five point five percent (22.5%) of the respondents answered that their teachers always use computers. Furthermore, three (3) or seven point five percent (7.5%) Seldom had used computers and no respondents answered on category of never. This study disclosed that the use of computer and projector greatly affected their academic performance. The classroom technologies used by teachers like; Computer and Projector greatly affect the Academic Performance of the pupils. Majority of the respondents obtained a Very Satisfactory grade, and only few obtained Outstanding, however, none got below 80% academic grade. Based on the findings, there was a significant relationship between the academic performance and the used of classroom technology such as computer and projector. Therefore, null hypothesis was rejected. [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]

Photogrammetry is a technique for studying and defining objects' shape, dimension, and position in a three-dimensional space using measurements obtained from two-dimensional photographs. It has gained popularity following the development of computer graphics technologies and has been applied to various branches of medicine. In this study, the authors present a method for low-cost photorealistic documentation of corpses during autopsy using single-camera photogrammetry with a mobile phone. Besides representing the body by demonstrating the injured and non-injured body parts as control, evidencing the body parts on a 3D reconstruction allows easy explanation to nonmedical experts such as lawyers. [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]

The rapid evolution of computer technology has significantly impacted the field of medicine, particularly in the utilization of information and image evidence. In the realm of sports medicine, this technological advancement plays a crucial role in ensuring sports safety, especially in the context of injury recovery following sports-related activities. The necessity to interpret and utilize a vast amount of sports medical data effectively has emerged as a pivotal research avenue. This paper delves into the challenges associated with extracting, studying, and the accuracy training of complex algorithms essential for analyzing critical sporting medical data. Central to this discussion is introducing an Optimized Convolutional Neural Network (OCNN) model, which is based on deep learning principles. This model is designed to enhance the detection and risk assessment of diseases related to sport medicine. It incorporates a novel Self-Adjustment Resizing algorithm (SAR), augmented by a self-coding method of convolution (SCM). The proposed OCNN model comprises two convolutional layers, two pooling layers, a fully connected layer, and a SoftMax structure. This architecture is tailored for the classification and analysis of sport-related medical data. [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]

Computer tools allow to ease the daily life of everyone, especially for people with disabilities. The optimal choice of pointing interfaces or types of settings can be difficult to achieve. Although there exist a few tests to compare and evaluate the performance of computer access technology (CAT), most of them only provide results as qualitative terms. Besides, the current choice is based mainly on clinical observations or non-standardized tests. To objectify the recommendations in CAT, support funding for people with disabilities and measure the functional repercussions of a therapeutic action, it is necessary to carry out comparative tests with measurable criteria. This paper presents the development of a personalized, free, and dedicated evaluation platform of pointing interfaces and aids. The quantitative evaluation process is described in detail, including the configuration of different exercises with settable parameters that permits to build a customized evaluation process with increased difficulties, the definition of performance indicators and the statistical analysis methods for quantified comparative tests. Finally, we present the assessment results of four people with different disabilities using multiple pointing interfaces, which verify the effectivity of this evaluation platform with the help of occupational therapists. [ABSTRACT FROM AUTHOR]

The advancement of novel technologies has positioned mobile wireless networks as the central focus of computer technology research. The selection process for the subsequent relay node in message forwarding holds paramount importance and is deemed critical. Drawing inspiration from Social Mobility theory, this paper presents a pioneering node classification model that categorizes all nodes into three distinct types. Furthermore, we introduce the concept of Active Entropy, where the numerical value of Active Entropy serves as a metric for message forwarding. Consequently, the routing algorithm transforms into a limited set of strategies between nodes belonging to different types. This routing algorithm offers numerous advantages including enhanced delivery rate, reduced network overhead ratio and decreased transmission delay. We evaluate and compare the performance of our proposed algorithm with that of DFDL algorithm and Prophet algorithm on the ONE simulation platform. Experimental results demonstrate that our proposed algorithm achieves a delivery rate at least 15% higher compared to the other two algorithms while also reducing overhead ratio. [ABSTRACT FROM AUTHOR]

Infectious diseases caused by pathogenic microorganisms pose a serious threat to human health. Despite advances in molecular biology, genetics, computation, and medicinal chemistry, infectious diseases remain a significant public health concern. Addressing the challenges posed by pathogen outbreaks, pandemics, and antimicrobial resistance requires concerted interdisciplinary efforts. With the development of computer technology and the continuous exploration of artificial intelligence(AI)applications in the biomedical field, the automatic morphological recognition and image processing of microbial images under microscopes have advanced rapidly. The research team of Institute of Microbiology, Chinese Academy of Sciences has developed a single cell microbial identification technology combining Raman spectroscopy and artificial intelligence. Through laser Raman acquisition system and convolutional neural network analysis, the average accuracy rate of 95.64% has been achieved, and the identification can be completed in only 5 min. These technologies have shown substantial advantages in the visible morphological detection of pathogenic microorganisms, expanding anti-infective drug discovery, enhancing our understanding of infection biology, and accelerating the development of diagnostics. In this review, we discuss the application of AI-based machine learning in image analysis, genome sequencing data analysis, and natural language processing (NLP) for pathogen identification, highlighting the significant role of artificial intelligence in pathogen diagnosis. AI can improve the accuracy and efficiency of diagnosis, promote early detection and personalized treatment, and enhance public health safety. [ABSTRACT FROM AUTHOR]

AbstractIn customer-driven design of systems or products, one has performance targets in mind and would like to determine values for product or system parameters that meet such targets. Engineering computer simulation models predict performance given design parameter values; meeting a target is done iteratively through an optimization search procedure, typically by optimizing a regression, neural network or other type of approximation metamodel of the computer model. We pose the thesis that, since forward metamodel construction is a key part of this strategy, constructing an inverse metamodel directly is advantageous. The inverse metamodel obviates the need for optimization in many settings. One can design experiments that allow simultaneous fitting of forward and inverse metamodels. We discuss the potential for this strategy, the connection with the calibration problem, and some of the issues that must be resolved to make the approach practical. [ABSTRACT FROM AUTHOR]

This editorial explores the limitations of artificial intelligence (AI) in cancer clinical research. It discusses the risks of bias in AI models and issues such as inadequate sample size and overfitting in the literature. The article argues that AI lacks the cognitive ability for understanding and meaning, as well as the capacity for insight and discovery that humans possess. It emphasizes the importance of human intelligence, creativity, and ethical considerations in utilizing AI appropriately, and highlights the need for careful scrutiny and cautious application of AI in research and decision-making. [Extracted from the article]

Care and nursing training (CNT) refers to developing the ability to effectively respond to patient needs by investigating their requests and improving trainees' care skills in a caring environment. Although conventional CNT programs have been conducted based on videos, books, and role-playing, the best approach is to practice on a real human. However, it is challenging to recruit patients for continuous training, and the patients may experience fatigue or boredom with iterative testing. As an alternative approach, a patient robot that reproduces various human diseases and provides feedback to trainees has been introduced. This study presents a patient robot that can express feelings of pain, similarly to a real human, in joint care education. The two primary objectives of the proposed patient robot-based care training system are (a) to infer the pain felt by the patient robot and intuitively provide the trainee with the patient's pain state, and (b) to provide facial expression-based visual feedback of the patient robot for care training. [ABSTRACT FROM AUTHOR]

Introduction Methods Results Conclusion Various catheter designs are appearing for Pulsed Field Ablation (PFA). It is unclear if they differ in terms of safety and efficiency. PFA studies have reported hemolysis, kidney injury, high troponin, among other side effects.Using a CT‐derived computer model, we compared catheter designs using two metrics: (1) efficiency: power delivered to an atrial wall target, expressed as a percent of total generator power; and (2) safety: electric current to achieve 90% transmurality (since more energy causes more collateral effects), as well as the corresponding electrode current density (ECD), a heat and bubble metric. The following catheter designs were compared: penta‐spline basket, Nitinol spheres (focal 9 mm and large 1‐shot), circular, balloon, and flex‐circuit. Target was a 6 × 47 mm circumferential segment of atrial wall at LPV antrum. Transmurality was defined as percent of target having >600 volts per centimeter (V/cm) electric field needed for electroporation.Efficiency was 0.9, 1.4, 2.7, 5.9, 10, and 12% for the large 1‐shot and 9 mm Nitinol spheres, penta‐spline, circular, flex spline, and balloon catheters, respectively. Regarding safety, currents for 90% transmurality were 70, 39,36,12.5, 5.3, and 4 Amps for the same respective catheters, with less being safer. ECD was 124, 25, 74, 83, 41, and 31 A/cm2, respectively.Computer models demonstrated a remarkable range in efficiency among catheters studied. Those having less atrial blood exposure had the highest efficiencies, with factors of up to 13X more efficiency compared to exposed ones. Higher efficiency designs have less collateral current and are safer. Confirmatory in‐vivo studies are required. [ABSTRACT FROM AUTHOR]

Introduction: This study aimed to investigate medical students' perceptions of telehealth in Pakistan and assess the relationship between students' proficiency in computers and technology, gender, and age. Methods: A cross-sectional study was conducted on 330 medical students from various universities in Karachi, Pakistan. Data on demographic characteristics, computer proficiency, and perceptions of telehealth were collected using a self-administered questionnaire. Descriptive statistics, chi-square tests, and Spearman's rank correlation coefficient were utilized for data analysis. Results: The study included 330 participants, predominantly from public universities (83.3%), with a mean age of 21.40 ± 4.24 years. Most participants were pursuing MBBS (67%). In terms of computer proficiency, 41.8% fell into the middle category. Gender correlated significantly with proficiency (p < 0.05), with females more often in the middle category and males in the not-that-amateur category. Age demonstrated a small positive correlation with proficiency (p < 0.05). A majority (77.6%) expressed a general interest in technology. Regarding healthcare apps, 51.8% had 1–3 apps, and 33.6% had none. Most participants believed telehealth would enhance healthcare (72.1%) and make it cheaper (63.3%). Additionally, 56.7% agreed that Pakistan should invest in telehealth, while only 2.7% disagreed. Conclusion: The study revealed that medical students in Pakistan exhibit an interest in technology and perceive telehealth as beneficial. These findings suggest that telehealth holds the potential to enhance healthcare opportunities in Pakistan. Therefore, investing in and expanding telehealth services may represent a viable solution for improving access to healthcare in the country. [ABSTRACT FROM AUTHOR]

Artificial intelligence embedded cloud computing technology can serve as a reliable storage for digital records in the fifth industrial revolution in South Africa. Some cloud storage facilities are embedded in the artificial intelligence powered electronic records management to ensure that digital archives and records are effectively managed and can be accessed anytime and anywhere as long as there is access to the Internet. Digital archives refer to the digitalised archival records and born digital archives that can be managed through artificial intelligence cloud computing technology. Artificial intelligence embedded cloud computing technology enables organisations to access and store information without managing their own physical devices or information technology infrastructure. Artificial intelligence refers to the use of intelligent robotic machines and AI powered software and applications to manage digital archives through artificial intelligence powered cloud storage facilities. Cloud computing as technology means using Internet servers for storage, management, and processing of digital archives and records for the entire life cycle. This is a qualitative study, where content analysis was used as a research methodology. Themes such as artificial intelligence, cloud computing technology and digital archives were used to search for literature. The findings indicate that artificial intelligence embedded cloud computing technology can store digital archives and records effectively and they can be protected through encrypted passwords and security codes for security purposes. The digital archives can also be accessed anytime and anywhere through the use of smart computer technology. The study recommends a framework on how artificial intelligence embedded cloud computing technology can be used to manage the digital archives and ensure easy access to such digital archives in South Africa. It is hoped that organisations in South Africa can adopt the framework to utilise artificial intelligence cloud computing technology to manage their digital archives effectively and efficiently. [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]

Occlusion presents a major obstacle in the development of pedestrian detection technologies utilizing computer vision. This challenge includes both inter-class occlusion caused by environmental objects obscuring pedestrians, and intra-class occlusion resulting from interactions between pedestrians. In complex and variable urban settings, these compounded occlusion patterns critically limit the efficacy of both one-stage and two-stage pedestrian detectors, leading to suboptimal detection performance. To address this, we introduce a novel architecture termed the Attention-Guided Feature Enhancement Network (AGFEN), designed within the deep convolutional neural network framework. AGFEN improves the semantic information of high-level features by mapping it onto low-level feature details through sampling, creating an effect comparable to mask modulation. This technique enhances both channel-level and spatial-level features concurrently without incurring additional annotation costs. Furthermore, we transition from a traditional one-to-one correspondence between proposals and predictions to a one-to-multiple paradigm, facilitating non-maximum suppression using the prediction set as the fundamental unit. Additionally, we integrate these methodologies by aggregating local features between regions of interest (RoI) through the reuse of classification weights, effectively mitigating false positives. Our experimental evaluations on three widely used datasets demonstrate that AGFEN achieves a 2.38% improvement over the baseline detector on the CrowdHuman dataset, underscoring its effectiveness and potential for advancing pedestrian detection technologies. [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]

With the fetishisation of computer technologies in education, the older sense of technology as pertaining to skill, what the Greeks termed 'technē', seems to have slipped from view. Technology is generally equated with the object itself rather than the facility to use it. A skill such as writing, for example, is rarely considered a technology and yet it is a vital tool for communication that aids understanding. Writing is also a technology that is framed in particular ways in relation to boys and young men. With constant concerns over 'boys' literacy', it is generally viewed as a 'feminine' activity. Drawing on interviews with a group of young men – all proficient writers reflecting on their schooling – this article examines their perspectives on writing and broader engagement with different technologies. In particular, it considers how writing by hand can promote forms of embodied cognition and the affordances this provides. [ABSTRACT FROM AUTHOR]

Sketches are fundamental in design and crucial representations for ideation, problem-solving, and communication. In the realm of architecture, sketches encapsulate the evolution of ideas from conceptualization to construction. Hand-drawn sketches, characterized by their open-ended, ambiguous nature and rapid production, are indispensable in bridging the gap between abstract concepts and tangible designs, guiding the progression from early design stages to final product realization. However, despite their significant potential and pivotal role in the design process, hand sketches have often been overlooked and swiftly abandoned in the ongoing discourse surrounding traditional versus digital design methodologies, particularly with the widespread integration of computer technologies. This study endeavours to unlock the wealth of information embedded within hand sketches, from initial design concepts to intricate manufacturing details, using a hybrid digital form-finding tool. By employing swarm algorithms in the quest for form, it is anticipated that the boundaries of conceptual ideas delineated by hand sketches will be expanded. This is facilitated by an algorithm developed in Processing using the Java coding language, complemented by an intuitive interface. The research commences with a comprehensive literature review encompassing biomimetics, sketching techniques, and tools for transitioning hand sketches into digital realms. Subsequently, a thorough elucidation of the algorithm, crafted within the Processing environment, is provided. The tool's efficacy is assessed through experimentation involving adjustments of various parameters on identical sketches and application to seven architectural sketches representing built designs, with subsequent interpretation of the outcomes. It is posited that the adaptability of the algorithm's core logic, coupled with the development-friendly environment of Processing, holds immense potential for empowering designers to steer sketches in desired directions through tailored enhancements. [ABSTRACT FROM AUTHOR]

With teachers continuing to report challenges in classroom management and difficulties in implementing scientific inquiry, the current manner in which science practical work is conducted in schools suggests the need for added teacher support. In this regard, we can leverage computer vision to provide instructional support by relieving teachers of the need to carry out mundane observations and perform basic interpretations of student activity. However, to our knowledge, little is known about the noticing practices of teachers during practical work, and the support preferences of such a computer vision system have not been studied before. To this end, we recruited 17 science educators with different teaching expertise for a qualitative investigation into the noticing practices and support preferences of science teachers. Results revealed seven major categories and 36 minor categories of student activity that teachers typically observe, which enabled us to derive observation routines that can emulate quality teacher noticing for computer vision input. Our obtained list of observation categories represents a first-of-its-kind list which takes into account concrete noticing practices of science teachers and remains applicable across all types of practical tasks. From participants' ranking of computer vision models, we further understood the type of computer vision output that teachers prefer for instructional support. To our best of knowledge, no prior research has examined the connection between teacher noticing and computer vision in such detail. Using these findings, we can then pursue the development of computer vision for instructional support in science practical work in an informed manner, taking into account the realities of science laboratories and proclivities of science teachers. [ABSTRACT FROM AUTHOR]

This paper presents the findings about implementing a machine learning (ML) technique to optimize the performance of 5 G mm wave applications utilizing multiple-input multiple-output (MIMO) antennas operating at the 28 GHz frequency band. This article examines various methodologies, including simulation, measurement, and the utilization of an RLC-equivalent circuit model, to evaluate the appropriateness of an antenna for its intended applications. In addition to its compact dimensions, the proposed design exhibits a maximum gain of 10.34 dBi, superior isolation exceeding 26 dB, and a broad bandwidth of 16.56 % Centered at 28 GHz and spanning from 25.905 to 30.544 GHz. Another supervised regression machine learning technique is utilized to predict the antenna's gain accurately. Machine learning (ML) models can be assessed by several measures, such as the variance score, R square, mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and Mean Absolute Percentage Error (MAPE). Among the six machine learning models considered, it is seen that the Gaussian Process Regression (GPR) model exhibits the lowest error and achieves the highest level of accuracy in forecasting gain. The antenna under consideration has promising qualities for its intended use in high-band 5 G applications. This is evidenced by the modelling findings obtained from Computer Simulation Technology (CST) and Advanced Design System (ADS)and the measured and projected results derived using machine learning methodologies. [ABSTRACT FROM AUTHOR]

As computer graphics technology continues to advance, Collision Detection (CD) has emerged as a critical element in fields such as virtual reality, computer graphics, and interactive simulations. CD is indispensable for ensuring the fidelity of physical interactions and the realism of virtual environments, particularly within complex scenarios like virtual assembly, where both high precision and real-time responsiveness are imperative. Despite ongoing developments, current CD techniques often fall short in meeting these stringent requirements, resulting in inefficiencies and inaccuracies that impede the overall performance of virtual assembly systems. To address these limitations, this study introduces a novel algorithm that leverages the capabilities of a Backpropagation Neural Network (BPNN) to optimize the structural composition of the Hybrid Bounding Volume Tree (HBVT). Through this optimization, the research proposes a refined Hybrid Hierarchical Bounding Box (HHBB) framework, which is specifically designed to enhance the computational efficiency and precision of CD processes. The HHBB framework strategically reduces the complexity of collision detection computations, thereby enabling more rapid and accurate responses to collision events. Extensive experimental validation within virtual assembly environments reveals that the proposed algorithm markedly improves the performance of CD, particularly in handling complex models. The optimized HBVT architecture not only accelerates the speed of collision detection but also significantly diminishes error rates, presenting a robust and scalable solution for real-time applications in intricate virtual systems. These findings suggest that the proposed approach offers a substantial advancement in CD technology, with broad implications for its application in virtual reality, computer graphics, and related fields. [ABSTRACT FROM AUTHOR]

Brain tumor is a global issue due to which several people suffer, and its early diagnosis can help in the treatment in a more efficient manner. Identifying different types of brain tumors, including gliomas, meningiomas, pituitary tumors, as well as confirming the absence of tumors, poses a significant challenge using MRI images. Current approaches predominantly rely on traditional machine learning and basic deep learning methods for image classification. These methods often rely on manual feature extraction and basic convolutional neural networks (CNNs). The limitations include inadequate accuracy, poor generalization of new data, and limited ability to manage the high variability in MRI images. Utilizing the EfficientNetB3 architecture, this study presents a groundbreaking approach in the computational engineering domain, enhancing MRI-based brain tumor classification. Our approach highlights a major advancement in employing sophisticated machine learning techniques within Computer Science and Engineering, showcasing a highly accurate framework with significant potential for healthcare technologies. The model achieves an outstanding 99% accuracy, exhibiting balanced precision, recall, and F1-scores across all tumor types, as detailed in the classification report. This successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors, marking a notable improvement over current methods. The integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency, paving the way for wider application. This research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology. [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]

Thin elastic ribbons represent a class of intermediary objects lying in-between thin elastic plates and thin elastic rods. Although the two latter families of thin structures have received much interest from the Computer Graphics community over the last decades, ribbons have seldom been considered and modelled numerically so far, in spite of a growing number of applications in Computer Design. In this article, starting from the reduced developable ribbon models [Sadowsky 1929; Wunderlich 1962] recently popularised in Soft Matter Physics, we propose a both accurate and efficient algorithm for computing the statics of thin elastic ribbons. Inspired by the super-clothoid model for thin elastic rods, our method relies on compact ribbon elements whose normal curvature varies linearly with respect to arc length s, while their geodesic torsion is quadratic in s. In contrast, however, for the sake of efficiency, our algorithm avoids building a fully reduced kinematic chain and instead treats each element independently, gluing them only at the final solving stage through well-chosen bilateral constraints. Thanks to this mixed variational strategy, which yields a banded Hessian, our algorithm recovers the linear complexity of low-order models while preserving the high-order convergence of curvature-based models. As a result, our approach is scalable to a large number of elements, and suitable for various boundary conditions and unilateral contact constraints, making it possible to handle challenging scenarios such as confined buckling experiments or Möbius bands with contact. Remarkably, our mixed algorithm proves an order of magnitude faster compared to Discrete Elastic Ribbon models of the literature while achieving, in a few seconds only, high accuracy levels that remain out of reach for such low-order models. Additionally, our numerical model can incorporate various ribbon energies, including the RibExt model for quasi-developable ribbons recently introduced in Physics [Audoly and Neukirch 2021], which allows to transition smoothly between a rectangular Kirchhoff rod and a (developable) Sadowsky ribbon. Our numerical scheme is carefully validated against demanding experiments of the Physics literature, which demonstrates its accuracy, efficiency, robustness, and versatility. Our Merci code is publicly available at https://gitlab.inria.fr/elan-public-code/merci for the sake of reproducibility and future benchmarking. [ABSTRACT FROM AUTHOR]

Anisotropic hyperelastic distortion energies are used to solve many problems in fields like computer graphics and engineering with applications in shape analysis, deformation, design, mesh parameterization, biomechanics, and more. However, formulating a robust anisotropic energy that is low order and yet sufficiently non-linear remains a challenging problem for achieving the convergence promised by Newton-type methods in numerical optimization. In this article, we propose a novel analytic formulation of an anisotropic energy that is smooth everywhere, low order, rotationally invariant, and at least twice differentiable. At its core, our approach utilizes implicit rotation factorizations with invariants of the Cauchy-Green tensor that arises from the deformation gradient. The versatility and generality of our analysis is demonstrated through a variety of examples, where we also show that the constitutive law suggested by the anisotropic version of the well-known As-Rigid-As-Possible energy is the foundational parametric description of both passive and active elastic materials. The generality of our approach means that we can systematically derive the force and force-Jacobian expressions for use in implicit and quasistatic numerical optimization schemes, and we can also use our analysis to rewrite, simplify, and speed up several existing anisotropic and isotropic distortion energies with guaranteed inversion safety. [ABSTRACT FROM AUTHOR]

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

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]

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]

The slowdown of Moore's Law necessitates an exploration of novel computing methodologies, new materials, and advantages in chip design. Thus, carbon-based materials have promise for more energy-efficient computing systems in the future. Moreover, sustainability emerges as a new concern for the semiconductor industry. The production and recycling processes associated with current chips present huge environmental challenges. Electronic waste is a major problem, and sustainable solutions in computing must be found. In this review, we examine an alternative chip design based on nanocellulose, which also features semiconductor properties and transistors. Our review highlights that nanocellulose (NC) is a versatile material and a high-potential composite, as it can be fabricated to gain suitable electronic and semiconducting properties. NC provides ideal support for ink-printed transistors and electronics, including green paper electronics. Here, we summarise various processing procedures for nanocellulose and describe the structure of exclusively nanocellulose-based transistors. Furthermore, we survey the recent scientific efforts in organic chip design and show how fully automated production of such a full NC chip could be achieved, including a Process Design Kit (PDK), expected variation models, and a standard cell library at the logic-gate level, where multiple transistors are connected to perform basic logic operations—for instance, the NOT-AND (NAND) gate. Taking all these attractive nanocellulose features into account, we envision how chips based on nanocellulose can be fabricated using Electronic Design Automation (EDA) tool chains. [ABSTRACT FROM AUTHOR]

Data transparency plays a critical role in understanding IoT privacy practices and making informed decisions. To gain a comprehensive understanding of transparency in the IoT environment, a systematic literature review of 58 academic articles is conducted to investigate the progress and status of existing data transparency studies from a design perspective. Data transparency was identified as a signifier to bridge the connection between user behavior and privacy risks. The level of transparency achieved was shaped by users' privacy perceptions, which in turn influenced their privacy behavior. GUI-based transparency design has been widely used in IoT, but it is not sufficient to provide users with accessible, understandable, and unified transparency information. A conceptual transparency design is proposed based on the extracted design opportunities and practices. This paper provides an important resource on transparency issues in the IoT environment, and will benefit the design and computer science communities. [ABSTRACT FROM AUTHOR]

This article proposes the use of the Karhunen Loève expansion in the design of computer experiments when the Gaussian Process (GP) model is used to model the output, Y(x). We start by finding the K-L expansion in the case of the univariate and multivariate processes. When the analytical solution does not exist, we apply several methods of approximation, such as Haar wavelet functions and Fourier expansions. Numerical examples are presented to illustrate the idea of the approximations. We use the approximated model to select the maximum entropy sampling design. [ABSTRACT FROM AUTHOR]

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While there is a growing body of research that explores the integration of music and coding in learning environments, much of this work has either emphasised the technical aspects of computer language design or music as a motivational context within which to learn computer science concepts. In this paper, we report on a study in which five undergraduate students with experience in both music and coding completed two creative musical tasks: one using conventional instruments and tools and one using Python code in an online music + coding environment. Inspired by the work of Christopher Small (1998. Musicking: The Meanings of Performing and Listening. University Press of New England), we describe music + coding as a set of interlocking processes which we call computational musicking and explore how practices from both domains are reimagined in this new hybrid context. We introduce semiotic theories of translation and transcription to make sense of the computational musicking process and describe strategies that participants devised in their creative process. [ABSTRACT FROM AUTHOR]

AbstractOne of the most common strategies to achieve optimum designs for physical and computer high-dimensional experiments with no model pre-specification and limited budget is to uniformly spread the design points over the experimental domain, which are called uniform designs. A uniform design provides good design space coverage (space-filling), resulting in more accurate estimates using fewer points. Algorithmic search is commonly used for finding such designs, but this approach becomes ineffective for large problems. Though the construction of uniform designs theoretically is challenging, intriguing and encouraging findings have recently been accomplished for highly specialized cases. This paper gives a new theoretical construction method for high-dimensional experiments with a mixture of four-level and sixteen-level factors via an adjusted version of the multiple quadrupling algorithm (Elsawah, Communication in Mathematics and Statistics 10:623-652, 2022). The new technique (called AMQA) is simple to use since it only needs small suitable four-level designs, which may be easily generated theoretically or by algorithmic search. The quality of the small designs ensures the quality of the large designs. The effectiveness of the AMQA is investigated both theoretically and computationally. The main findings demonstrate that, compared with other widely used techniques, the AMQA provides more accurate results for high-dimensional screening using a variety of models, including polynomial, spline, and kriging models, as well as variable selection techniques, including AIC, BIC, RIC, SCAD, and LASSO. To further improve performance, a hybrid algorithm that combines the AMQA with the iterative threshold accepting algorithm is proposed. [ABSTRACT FROM AUTHOR]

This research proposes a new image encryption method to improve the security of image encryption by integrating a multi-chaotic system and mixed pixel-bit level encryption. In the face of the growing use of communications and computer technology, as well as the need to protect sensitive information, this method utilizes chaotic algorithms to increase encryption security. Experimental results show increased security and resistance to various attacks, validating the method's effectiveness in protecting digital images. The measurement tools used include histograms, information entropy, adjacent pixel correlation coefficient, key sensitivity analysis, and robustness testing. In conclusion, this method offers an effective and robust approach to image encryption, providing an important contribution to the field of information security. [ABSTRACT FROM AUTHOR]

Embedded computer systems refer to specialized computer systems that integrate operating systems and functional software into computer hardware systems, are application centric, based on computer technology, and have tailorable software and hardware. They have strict requirements for real-time performance, reliability, cost, volume, and power consumption. The performance and security evaluation of embedded systems is an important aspect of the development, development, and integration process of embedded systems, which can provide various quantitative analysis basis for the development and technical transformation of embedded products. The performance and security evaluation of embedded systems is a very complex issue, and evaluators may have different understandings of the problem due to their different starting points and observation perspectives. The embedded computers' performance evaluation is a classical multiple-attribute decision-making (MADM) issue. In such paper, the generalized weighted geometric Bonferroni mean (GWGBM) operator is built for MADM with single-valued neutrosophic sets (SVNSs). Then, the single-valued neutrosophic number generalized power geometric BM (SVNNGPGBM) operator is built and then the MADM decision methods are proposed based on the SVNNGPGBM operator and power geometric (PG) operator. Finally, an example about embedded computers' performance evaluation and some comparative analysis were given to demonstrate the SVNNGPGBM method. [ABSTRACT FROM AUTHOR]

The improvement in the reliability and lifespan of insulated gate bipolar transistor (IGBT) devices and the enhancement of system efficiency under high-frequency pulse conditions are receiving increasingly wide attention, and all of these are closely related to the evaluation of the power loss of the IGBT. The traditional method for assessing the power loss of the IGBT is through curve fitting of the dual-pulse experimental results, without delving into the conduction process of the IGBT and without considering the IGBT model itself and the parasitic parameters present in the test circuit. In response to the condition of a pulse duration of 100 ns for IGBT single devices, a universal test circuit has been designed to measure the collector-emitter voltage waveform and the collector current waveform. A method based on Computer Simulation Technology for field-circuit co-simulation to evaluate the power loss of the IGBT has been proposed, which takes into account the parasitic parameters in the actual circuit. In this method, the 3D model of the IGBT device, as well as the circuit model of the test circuit, is established. The power loss curve of the IGBT was obtained from the evaluation model, and the results agree well with the power loss curve obtained from experiments. This method provides guidance for seeking lower power loss in order to improve the reliability and lifespan of the IGBT, as well as enhance system efficiency. It also provides theoretical guidance for further research on the thermal damage mechanism of the IGBT. [ABSTRACT FROM AUTHOR]

To ensure the safety of coal mine drilling operation and reduce losses caused by accidents, this study proposes a fuzzy-reasoning-based early prediction method for in-hole accidents during underground coal mine drilling processes. First, the mechanism of in-hole accidents during underground drilling in coal mines was analyzed, and the changes in different accident-related parameters were summarized. Second, based on the suddenness of different accidents, they were distinguished into sudden-change and slow-change accidents, and the corresponding features were extracted from short- and long-timescale information, respectively. Subsequently, a rule base was constructed based on the analysis of field data and manual experience, and different accident occurrence probabilities were determined using fuzzy reasoning to realize the early prediction of in-hole accidents in the coal mine drilling process. Finally, WinCC was used to design the upper computer interface for in-hole accident prediction in underground drilling processes in coal mines. It displays the results of three types of in-hole accident prediction: drill bit failure, stuck pipe, and bit drop. [ABSTRACT FROM AUTHOR]

Fire is a serious threat to people's lives and property. Be aimed at the low detection accuracy of the current fire detection system, a fire detection system based on one-chip computer is proposed in this work. To achieve fire detection and warning, the system collects these signals such as smoke and temperature from the surrounding environment, and processes them with a one-chip computer. The simulation experiments of the system are conducted with Proteus, and the experimental results indicate that the system can detect accurately. [ABSTRACT FROM AUTHOR]

Herbal medicines are frequently blended in the form of multi-drug combinations primarily based on the precept of medicinal compatibility, to achieve the purpose of treating diseases. However, due to the lack of appropriate techniques and the multi-component and multi-target nature of Chinese medicine compounding, it is tough to explain how the drugs interact with each other. As a rising discipline, cyber pharmacology has formed a new approach characterized by using holistic and systematic "network targets" via the cross-fertilization of computer technology, bioinformatics, and different multidisciplinary disciplines. It can broadly screen the active ingredients of traditional Chinese medicine, enhance the effective utilization of drugs, and elucidate the mechanism of drug action. We will overview the principles of Chinese medicine compounding and dispensing, the research methods of network pharmacology, and the software of network pharmacology in the lookup of compounded Chinese medicines, aiming to supply thoughts for the better application of network pharmacology in the research of Chinese medicines. [ABSTRACT FROM AUTHOR]