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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]

Optimum design of truss structures can be a challenging and difficult field of study specially if an optimum design problem is comprised of continuous and discrete decision variables such as in truss size and layout optimization problems. Usually metaheuristic approaches are selected to solve these kind of problems and there are many different metaheuristic methods that have been used in dealing with truss optimization problems, which provide optimum solutions for those problems. However, among these various proposed metaheuristics, it is very seldom to face and find a method that is based on technologies such as computer networks. This paper aims to utilize a metaheuristic algorithm which is based on some principals of connected banking systems, which is called the Connected Banking System Optimizer(CBSO) algorithm. The performance of the CBSO is tested against three benchmark truss size/layout optimum design problems namely 15, 18 and 25 bar truss structures. Besides the truss size/layout problems, CEC-BC-2017 test functions with ten dimensions and also three other constrained optimum design problems are also investigated to examine the performance of the CBSO in dealing with these standard unconstrained and constrained test functions. The results of the CBSO optimum designs are presented and compared with some of the available studies in the literature. Statistical analyses are done to have a fair evaluation of the CBSO algorithm's efficiency and its ability in dealing with the benchmark truss size/layout optimization problems. The CBSO outperforms its rivals and presents lighter weight truss structures. There is no need for configuration of extra parameters in the CBSO algorithm when solving the examined benchmark truss structures, which makes it a robust parameter-free optimization algorithm. [ABSTRACT FROM AUTHOR]

The evaluation of land suitability for agricultural production is a complex process that requires specialized information and expert analysis. This paper introduces the Soil Suitability Evaluation (SSE) model, a computer program designed to assess land suitability for agriculture. The model considers soil surface conditions (erosion, micro-relief, and slope), physical characteristics (texture and depth), and chemical properties (salinity, sodicity, fertility, and calcium carbonate). It involves calculating these soil characteristics along with their respective weights. Compared to other land suitability evaluation methods, the SSE model provides realistic and practical results including assessments of soil and crop suitability. It serves as a simple and useful tool for decision-makers to determine land quality for agricultural use and proposed some recommendations for land reclamation. [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]

This study aimed to show what role biomedical engineering can play in sexual health. A new concept of sexological ontology, an essential tool for building evidence-based models of sexual health, is proposed. This ontology should be based on properly validated mathematical models of sexual reactions identified using reliable measurements of physiological signals. This paper presents a review of the recommended measurement methods. Moreover, a general human sexual reaction model based on dynamic systems built at different levels of time × space × detail is presented, and the actual used modeling approaches are reviewed, referring to the introduced model. Lastly, examples of devices and computer programs designed for sexual therapy are described, indicating the need for legal regulation of their manufacturing, similar to that for other medical devices. [ABSTRACT FROM AUTHOR]

This review considers the use of fractal concepts to improve the development, fabrication, and characterisation of catalytic materials and supports. First, the theory of fractals is discussed, as well as how it can be used to better describe often highly complex catalytic materials and enhance structural characterisation via a variety of different methods, including gas sorption, mercury porosimetry, NMR, and several imaging modalities. The review then surveys various synthesis and fabrication methods that can be used to create catalytic materials that are fractals or possess fractal character. It then goes on to consider how the fractal properties of catalysts affect their performance, especially their overall activity, selectivity for desired products, and resistance to deactivation. Finally, this review describes how the optimum fractal catalyst material for a given reaction system can be designed on a computer. [ABSTRACT FROM AUTHOR]

Animating the relationship between affect and ideology in histories of artificial intelligence, this paper explores how the transatlantic post-war quest to engineer common sense via computational means has profoundly shaped both the social logics of machine learning systems and the sensorial politics of everyday knowledge production. Focusing on the Cyc project, a logic-based AI endeavour to 'codify human common sense' which began at the USA-based Microelectronics and Computer Technology Corporation in 1984, and making links to MIT Media Lab's Open Mind Common Sense Project inaugurated in 1999, I trace how the imperative within late twentieth-century computer science to make intelligent systems more intuitive by translating implicit human knowledge into explicit machine knowledge involved not only mathematical and technological challenges but also affective, ideological and socio-political ones. In tracking the interactions between intuition and common sense across these genealogies of machine intelligence, I tease out some of the key atmospheres, processes, and correlations via which AI technologies have become embedded with ideology, normativity and prejudice at the levels of logic, procedure and data. Through adjudicating the meanings of reason, truth and perceptibility as matters of algorithmically calibrated fit and popularity, intelligent architectures are also radically reconstituting the intelligible and the sensible - in ways, I argue, that complicate any notion of a clean epistemological or ontological break between first and second wave AI. Dwelling within these unfinished histories, however, also points to how inhabiting counter-intuitive tendencies may open up new possibilities for (un)common sense and distributed intuition within computational cultures. [ABSTRACT FROM AUTHOR]

"Weatherscapes" is a new column in Weatherwise magazine by Contributing Editor Ed Darack that delves into the use of supercomputing and artificial intelligence in atmospheric science. The article explores the evolution of AI and its practical applications in meteorology, dispelling common misconceptions about the field. It traces the historical development of AI from its conceptual origins to its current role in enhancing weather forecasting and atmospheric research. The text highlights the growing importance of AI in atmospheric science and its potential to revolutionize weather prediction methods. [Extracted from the article]

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]

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]

Agile Transformations are challenging processes for organizations that look to extend the benefits of Agile philosophy and methods beyond software engineering. Despite the impact of these transformations on organizations, they have not been extensively studied in academia. We conducted a study grounded in workshops and interviews with 99 participants from 30 organizations, including organizations undergoing transformations ("final organizations") and companies supporting these processes ("consultants"). The study aims to understand the motivations, objectives, and factors driving and challenging these transformations. Over 700 responses were collected to the question and categorized into 32 objectives. The findings show that organizations primarily aim to achieve customer centricity and adaptability, both with 8% of the mentions. Other primary important objectives, with above 4% of mentions, include alignment of goals, lean delivery, sustainable processes, and a flatter, more team-based organizational structure. We also detect discrepancies in perspectives between the objectives identified by the two kinds of organizations and the existing agile literature and models. This misalignment highlights the need for practitioners to understand with the practical realities the organizations face. [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]

Abnormal crowd behavior recognition is one of the research hotspots in computer vision. Its goal is to use computer vision technology and abnormal behavior detection models to accurately perceive, predict, and intervene in potential abnormal behaviors of the crowd and monitor the status of the crowd system in public places in real time, to effectively prevent and deal with public security risks and ensure public life safety and social order. To this end, focusing on the abnormal crowd behavior recognition technology in the computer vision system, a systematic review study of its theory and cutting-edge technology is conducted. First, the crowd level and abnormal behaviors in public places are defined, and the challenges faced by abnormal crowd behavior recognition are expounded. Then, from the dimensions based on traditional methods and based on deep learning, the mainstream technologies of abnormal behavior recognition are discussed, and the design ideas, advantages, and limitations of various methods are analyzed. Next, the mainstream software tools are introduced to provide a comprehensive reference for the technical framework. Secondly, typical abnormal behavior datasets at home and abroad are sorted out, and the characteristics of these datasets are compared in detail from multiple perspectives such as scale, characteristics, and uses, and the performance indicators of different algorithms on the datasets are compared and analyzed. Finally, the full text is summarized and the future development direction of abnormal crowd behavior recognition technology is prospected. [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 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]

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]

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]

As one of the main causes of sickness and mortality, heart disease, also known as cardiovascular disease, must be detected early in order to be prevented and treated. The rapid development of computer technology presents an opportunity for the cross-combination of medicine and informatics. A novel stacking model called SDKABL is presented in this work. It uses three classifiers, namely K-Nearest Neighbor (KNN), Decision Tree (DT), and Support Vector Machine (SVM) at the base layer and the Bidirectional Long Short-Term Memory based on Attention Mechanisms (ABiLSTM) model at the meta layer for the ultimate prediction. For lowering the temporal complexity and enhancing the model's accuracy, the dimensionality reduction approach is seen to be crucial. Principal Component Analysis (PCA) was utilized in SDKABL to minimize dimensionality and facilitate feature fusion. Using several performance measures, including precision, F1-score, accuracy, recall, and Receiver Operating Characteristic (ROC) score, the performance of SDKABL was compared to that of other independent classifiers. The experimental findings demonstrate that our proposed model combining individual classifiers with the stacking method helps improve the prediction model's accuracy. [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]

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]

In this paper we present an exploratory study on the tools, the resources and the teaching methods used at department of computer technologies of the Higher Institute of Technological Studies of Sfax, during and except the period of the lockdown. We first describe our survey object of the study, then we analyze the results from the retained answers, and finally we conclude our work by giving perspectives. [ABSTRACT FROM AUTHOR]

An additively stacked quasi-self-similarity Sierpinski Carpet-based fractal antenna embedded over a photonic band gap substrate is proposed in this paper. The proposed structure is designed for continuous in-vivo monitoring of cardiac Troponin I (cTnI) levels in the blood released from the heart muscles even with a trivial malfunction. The total size of the antenna biosensor is 0.4 λ0 × 0.33 λ0 × 0.025 λ0 and is assumed to be implanted over a leadless pacemaker. The substrate material used is Teflon (εr = 2.1) with gold as patch material to accomplish as bio-receptor. The frequency band of 0.1–3 Terahertz is considered for the design as per IEEE standard. Result validation without bio-model is performed through High Frequency Structural Simulator, Computer Simulation Technology, Advanced Design System, and COMSOL simulation software. The dielectric constant values of blood, myocardium, and other biological parameters are calculated based on the Cole–Cole model using Matrix Laboratory for further analysis. Those values are incorporated in the design to imitate a bio-model, to check the variation in frequency with regard to permittivity, in turn corresponding to troponin level change. [ABSTRACT FROM AUTHOR]

Ultraviolet (UV) irradiation has a significant impact on enhancing the performance of sensors. Currently, there is still an urgent need for devices that are simple and easy to fabricate. In this work, a cost-effective and convenient method was employed to fabricate a UV light emitting diode. A four-inch silicon based AlGaN/GaN heterojunction substrate was used. The Ni/Au (20/20 nm) layer was deposited on the AlGaN barrier layer, followed by thermal oxidation to prepare the UV diode. The diode annealed in oxygen can emit UV light at a wavelength of 365 nm and is visible, and no light is emitted from the device in nitrogen annealing. The I–V and C–V characteristics of the device are tested to explore the mechanism. Transmission electron microscopy and technology computer aided design simulation methods were used to analyze the device luminescence mechanism. [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]

Artificial intelligence has garnered significant attention in recent years as a rapidly advancing field of computer technology. With the continual advancement of computer hardware, deep learning has made breakthrough developments within the realm of artificial intelligence. Over the past few years, applying deep learning architecture in medicine and industrial anomaly inspection has significantly contributed to solving numerous challenges related to efficiency and accuracy. For excellent results in radiological, pathological, endoscopic, ultrasonic, and biochemical examinations, this paper utilizes deep learning combined with image processing to identify spinal canal and vertebral foramen dimensions. In existing research, technologies such as corrosion and expansion in magnetic resonance image (MRI) processing have also strengthened the accuracy of results. Indicators such as area and Intersection over Union (IoU) are also provided for assessment. Among them, the mean Average Precision (mAP) for identifying intervertebral foramen (IVF) and intervertebral disc (IVD) through YOLOv4 is 95.6%. Resnet50 mixing U-Net was employed to identify the spinal canal and intervertebral foramen and achieved IoU scores of 79.11% and 80.89%. [ABSTRACT FROM AUTHOR]

Breast cancer is a potentially life-threatening disease is one of the most common cancers specially for women. For fast and effective treatment early detection of the tumor is very important. But the frequent checkup is not possible for many people with the traditional way of detection due to their cost and availability. To detect the biomedical imaging terahertz (THz) frequency is a suitable range and using patch antenna in biomedical detection is cost effective compared to the traditional methods. So, to detect the early stage of breast tumor, this article has proposed a graphene based THz antenna. The primary goal of this antenna is to detect the breast tumor with the improvement of the performance and the secondary goal is to make the antenna very light weighted. The design of the antenna was done using computer simulation technology (CST) studio and the dimension of the antenna is kept 18×23 μm2 and a breast phantom model has been designed and the performance analysis of the antenna has been done for both free space and breast tissue. The return loss of the antenna in free space is -52.4422 dB at 6.96 THz. The details of the detection of tumor through the proposed patch antenna has been shown in this study. [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]

Cultivated land is the most important natural resource for human survival and development. The quality of cultivated land is closely related to grain output, and whether it can guarantee stable food supply is directly related to national food security. Cultivated land quality evaluation is an effective tool for understanding and mastering cultivated land quality. However, few studies have applied bibliometrics to quantitatively and systematically analyze this field. We used VOSviewer 1.6.19 and CiteSpace 6.3.1 software to visually analyze and construct 2478 documents related to cultivated land quality evaluation retrieved from the Web of Science core collection database from 2000 to 2023. Results show that cultivated land quality evaluation is still a popular research field. The collaboration ability among authors is weak and the distribution of institutions and countries publishing in this field is very uneven. In addition, the relevant research has been published in a variety of journals such as agriculture, environment, ecology, and computer technology. The research content is becoming more and more interdisciplinary. Keywords such as "Soil quality", "Swat", "Remote sensing", "Heavy metals" and "Ecosystem services" have become hot topics in this field. In the future, it is necessary to further deepen the connotation of cultivated land quality, develop a long time series dynamic model of cultivated land quality evaluation and monitoring, and enhance the transformation of research results into practical applications. [ABSTRACT FROM AUTHOR]

[Objective] The computer control system is a core course in the field of automation engineering, mainly focusing on the principles and methods of computers executing automated processes. This course extends the theory of automatic control to practical applications and is an important course that integrates theory with practice. Digital PID control, minimum cycle control, and Dahlquist control play significant roles in computer control system education, directly impacting the stability and response speed of engineering applications. However, current teaching practices exhibit notable shortcomings, particularly in the realm of digital PID control, with insufficient emphasis on platforms for teaching and experimenting with minimum cycle control and Dahlquist control. [Methods] To address this challenge, we proposed an innovative experimental platform based on ophthalmic surgical robots. Ophthalmic surgical robots represented typical examples of precise control systems, facing complex engineering challenges in controlling "robot--retina" contact forces. Through this platform, we planned to design multiple experimental projects covering different topics, such as contact model identification, digital PID control, minimum cycle control, and Dahlquist control. First, students will learn through experimentation and analysis how to understand and identify the complex contact models within ophthalmic surgical robot systems. This process extends beyond classroom theory, serving as crucial validation of precise control theory in practical engineering applications. By observing and measuring forces and positional relationships during robot--retina interactions, students will explore methods to accurately simulate and predict these interactions. Second, students will further explore the application of digital PID control algorithms and adjust the parameters of the PID controller, such as proportional, integral, and derivative coefficients, to achieve precise adjustment of contact forces. This practical experience not only deepens their understanding of PID control theory but also cultivates their ability to troubleshoot and optimize control systems in real-world applications. Simultaneously, minimum cycle control, as another critical component, will teach students how to maintain system stability and rapid response under strict time constraints, which is crucial in certain scenarios, such as medical device control requiring immediate dynamic adjustments. By learning minimum cycle control, students will understand how to optimize system response times within defined control cycles, ensuring stability without compromising performance. Finally, experiments with Dahlquist control will guide students in exploring and applying nonlinear control strategies to address complex dynamic characteristics within practical systems. This integration of theory and practice will not only deepen students' understanding of control theory but also foster innovative thinking and problem-solving skills essential for tackling engineering challenges. [Results] These experiments are not merely about imparting theoretical knowledge but are designed to enable students to apply their learning flexibly in practical engineering applications, addressing complex control challenges. Through interaction with ophthalmic surgical robots, students will gain invaluable practical experience, preparing them comprehensively for future work in fields requiring high-precision applications. [Conclusions] The experimental platform based on ophthalmic surgical robots fills current gaps in teaching, providing students with comprehensive and practical learning opportunities. This initiative not only enhances their understanding and achievements in technology innovation and engineering applications but also nurtures a new generation of capable professionals ready to tackle real-world engineering challenges. [ABSTRACT FROM AUTHOR]

[Objective] Disruptive intelligent technologies, such as large language models, are rapidly advancing and reshaping productivity, posing challenges to traditional knowledge-driven teaching models and setting new expectations for talent cultivation in higher education. Integrating these large language models with existing course content and designing new experiments to achieve talent cultivation goals in the intelligent era is crucial. This involves improving students' digital literacy and training various abilities. [Methods] To address these challenges, this study focuses on talent cultivation ability goals in the context of new productivity. The study analyzes the technical characteristics and applications of current large language models, explores the practical skills that students need, and sets corresponding cultivation goals. This study introduces the concept of "intelligent collaborative innovation ability," which involves cultivating students' innovation skills using artificial intelligence technologies such as large language models. This study discusses how to internalize this ability across different majors and proposes an innovative experimental design method characterized by "predetermined direction, diverse paths, and dynamic results." This method involves setting clear yet flexible goals for real engineering problems and encouraging students to seek solutions from various perspectives using diverse methods. This design tests students' innovative thinking and strengthens their adaptability to uncertainties and challenges. A dynamic result evaluation mechanism ensures comprehensive and fair assessment, promoting deeper thinking and continuous improvement. [Results] To address these challenges, this study begins with a focus on talent cultivation in the era of new productivity. The study analyzes the technical characteristics and applications of current large language models, explores the practical skills that students need, and sets corresponding cultivation goals. This study introduces the concept of "intelligent collaborative innovation ability," which involves cultivating students' innovation skills using artificial intelligence technologies such as large language models. This study discusses how to internalize this ability across different majors and proposes an innovative experimental design method characterized by "predetermined direction, diverse paths, and dynamic results." This method involves setting clear yet flexible goals for engineering problems and encouraging students to seek solutions from various perspectives using diverse methods. This design tests students' innovative thinking and strengthens their adaptability to uncertainties and challenges. A dynamic result evaluation mechanism ensures comprehensive and fair assessment, promoting deeper thinking and continuous improvement. [Conclusions] The study can transform teaching methods to focus on students and learning, effectively cultivating innovation and autonomous learning abilities. This method allows for personalized cultivation and a hierarchical evaluation. The proposed ability cultivation direction and experimental design method can serve as a reference model for innovative teaching practices across various disciplines when integrated with large language models. [ABSTRACT FROM AUTHOR]

The presented work provides a comprehensive evaluation of the current availability of education programs and courses related to of AI the field of Information Technologies and Computer Science in higher education institutions (HIEs) in the Republic of Bulgaria. More specifically, this study examines 163 bachelor's and 239 master's degree programs from 28 HEIs available during the 2023/24 academic year in four professional fields: (1) Electrical Engineering, Electronics, and Automation; (2) Communication and Computer Technologies; (3) Informatics and Computer Science; and (4) Mathematics. The conducted evaluation shows that 41.1% of evaluated BSc programs and 26.4% of MSc programs include at least one AI-dedicated course. Results indicate a significant presence of AI-focused education, particularly in degrees related to Informatics and Computer Science, where 47.8% of AI courses are concentrated. However, a notable disparity exists in the inclusion of AI subjects across other technical fields, particularly in Electrical Engineering and related degrees, which contain only 8% of the identified AI courses for BSc degree programs. The findings highlight the need for a broader and more accelerated integration of AI education to meet the evolving demands of both students and the labor market. This work underscores the importance of strategic curriculum adaptation to enhance the readiness of Bulgarian HEIs to support the development and application of AI technologies, addressing the skills gap and fostering a workforce capable of navigating the AI-driven future. [ABSTRACT FROM AUTHOR]

Aspherical surfaces, with their varying curvature, minimize aberrations and enhance clarity, making them essential in optics, aerospace, medical devices, and telecommunications. However, manufacturing these surfaces is challenging because of systematic errors in CNC equipment, tool wear, measurement inaccuracies, and environmental disturbances. These issues necessitate precise error compensation to achieve the desired surface shape. Traditional methods for spherical optics are inadequate for aspherical components, making accurate surface shape error detection and compensation crucial. This study integrates advanced metrology with optimized material removal functions in the grinding and polishing processes. By combining numerical control technology, computer technology, and data analysis, we developed CAM software (version 1) tailored for aspherical surfaces. This software uses a compensation correction algorithm to process error data and generate NC programs for machining. Our approach automates and digitizes the grinding and polishing process, improving efficiency and surface accuracy. This advancement enables high-precision mass production of rotationally symmetrical aspherical optical components, addressing existing manufacturing challenges and enhancing optical system performance. [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]

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]