Search

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]

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

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

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

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

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

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

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

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

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

Underwater wireless sensor networks (UWSNs) have the potential to provide environmental data for various applications, including studies related to environmental changes, early warning systems, and monitoring in industry. Continuous delivery of information in these contexts is paramount. UWSNs comprise the fundamental assets in these applications. However, the peculiar characteristics of underwater require sensor nodes to rely on their limited battery reserves. Consequently, energy management in these networks becomes a critical resource allocation problem within underwater sensor networks. To address this decision making problem, cluster-based network routing protocols have been extensively explored as a technology to minimize network energy consumption. Cluster heads (CHs) are employed to aggregate data and reduce overall energy usage, thus prolonging the network's lifespan. On the other hand, the focus on harvesting energy from ambient resources underwater has gained attention as a means to extend the operational life of sensor nodes in the distributed communication network system. This paper considers the stochastic energy harvesting process at each sensor node, specifically addressing the energy-aware routing problem in underwater acoustic sensor networks (UASNs). The contribution of this work lies in proposing a novel reinforcement learning-based algorithm for determining cluster heads (CHs), which involves not only considering the nodes' positions and residual energy but also accounting for the expected harvested energy. Numerical results validate that our introduced approach significantly decreases energy consumption and substantially extends the network's operational lifetime considerably. [ABSTRACT FROM AUTHOR]

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

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

The mechanical properties of graphene reinforced composites are often hampered by challenges related to the dispersion and aggregation of graphene within the matrix. This paper explores the mechanism of cooling rate, process temperature, and process pressure's influence on the agglomeration behavior of graphene and the tensile response of composites from a computer simulation technology, namely molecular dynamics. Our findings reveal that the cooling rate exerts minimal influence on the tensile response of composites. Conversely, processing temperature significantly affects the degree of graphene aggregation, with higher temperatures leading to the formation of larger-sized graphene clusters. In contrast, processing pressure exhibits negligible impact on the degree of graphene aggregation, and increasing pressure effectively mitigates the formation of large-sized graphene clusters. Moreover, we elucidate the intrinsic factors governing the mechanical response to variations in processing parameters. Notably, we observe that the stretching process facilitates the decomposition of large-sized graphene clusters into smaller ones. This research contributes to the advancement of lightweight metal matrix composites by offering insights into optimizing processing parameters. Additionally, it provides crucial theoretical underpinnings for developing high-performance graphene-reinforced composites. [ABSTRACT FROM AUTHOR]

It is often said that election processes depend on trust for their legitimacy. Trust is therefore crucial to the sustainability of democracy. It also seems clear that patterns and forms of trust in Western democracies are changing in tandem with digitalization. This paper explores some possible inflections of the concept of trust in the 'age of digitalization' by examining its articulation by computer scientists doing research on digital voting and by contrasting these articulations with a Science and Technology Studies (STS) analysis of trust in the Danish voting process. The motivation for characterizing and discussing these views of trust, is that it matters deeply how trust is conceptualized in relation to voting, democracy and beyond, and that today computer science as an engineering, scientific and intellectual field increasingly affects how we perceive of trust – indeed, how we organize social life and come to think about the world we live in. If Digitalization appears to be implicated in the current transformation of trust, then the questions of how trust is defined, by whom, and with what implications are open and acute matters in need of more general awareness and careful attentention. [ABSTRACT FROM AUTHOR]

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

This study explored how computer anxiety and technology acceptance played roles in online learning interests among social work students. The study with cross-sectional and purposive sampling methods recruited 151 social work students in a Midwest school of social work program. Hierarchical multivariate regression revealed that a high level of computer anxiety was related to a low level of online learning interests. Conversely, a high level of technology acceptance (perceived usefulness) was associated with a high level of online learning interests. Social work programs may consider developing and providing appropriate technological training, reducing technological barriers, and obtaining feedback from students for their online learning experience to increase online learning interests by reducing computer anxiety and increasing technology acceptance. [ABSTRACT FROM AUTHOR]

Remote sensing and computer vision technologies are increasingly leveraged for rapid post-disaster building damage assessment, becoming a crucial and practical approach. In this context, the accuracy of employing various AI models in pixel-level change detection methods is significantly dependent on the consistency between pre- and post-disaster building images, particularly regarding variations in resolution, viewing angle, and lighting conditions; in object-level feature recognition methods, the low richness of semantic details of damaged buildings in images leads to a poor detection accuracy. This paper proposes a novel method, OCD-BDA (Object-Level Change Detection for Post-Disaster Building Damage Assessment), as an alternative to pixel-level change detection and object-level feature recognition methods. Inspired by human cognitive processes, this method incorporates three key steps: an efficient sample acquisition for object localization, labeling via HGC (Hierarchical and Gaussian Clustering), and model training and prediction for classification. Furthermore, this study establishes a change detection dataset based on Google Earth imagery of regions in Hatay Province before and after the Turkish earthquake. This dataset is characterized by pixel inconsistency and significant differences in photographic angles and lighting conditions between pre- and post-disaster images, making it a valuable test dataset for other studies. As a result, in the experiments of comparative generalization capabilities, OCD-BDA demonstrated a significant improvement, achieving an accuracy of 71%, which is twice that of the second-ranking method. Moreover, OCD-BDA exhibits superior performance in tasks with small sample amounts and rapid training time. With only 1% of the training samples, it achieves a prediction accuracy exceeding that of traditional transfer learning methods with 60% of samples. Additionally, it completes assessments across a large disaster area (450 km²) with 93% accuracy in under 23 min. [ABSTRACT FROM AUTHOR]

Nanotechnology and machine learning (ML) are rapidly emerging fields with numerous real-world applications in medicine, materials science, computer engineering, and data processing. ML enhances nanotechnology by facilitating the processing of dataset in nanomaterial synthesis, characterization, and optimization of nanoscale properties. Conversely, nanotechnology improves the speed and efficiency of computing power, which is crucial for ML algorithms. Although the capabilities of nanotechnology and ML are still in their infancy, a review of the research literature provides insights into the exciting frontiers of these fields and suggests that their integration can be transformative. Future research directions include developing tools for manipulating nanomaterials and ensuring ethical and unbiased data collection for ML models. This review emphasizes the importance of the coevolution of these technologies and their mutual reinforcement to advance scientific and societal goals. [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]

Aiming at the problems of insufficient navigation area recognition accuracy, fuzzy boundary of obstacle segmentation, and high consumption of computational resources in the autonomous navigation of water navigation sensors, such as USVs, this paper proposes a DMTN-Net network architecture based on DeeplabV3+ to improve the accuracy and efficiency of environment sensing. Firstly, DMTN-Net adopts the lightweight MobileNetV2 as the backbone, which reduces the amount of computation. Secondly, the innovative N-Decoder structure integrates cSE and Triplet Attention, which enhances the feature representation and improves the segmentation performance. Finally, various experiments were conducted on the MassMind dataset, Pascal VOC2007 dataset, and related sea areas. The experimental results show that DMTN-Net performs well on MassMind and Pascal VOC2007 datasets, and compared with other mainstream networks, the indexes of mIoU, mPA, and mPrecision are significantly improved, and the computational cost is greatly reduced. In addition, the offshore navigation experiments further validate its performance advantages and provide solid support for the practicalization of USV waterborne sensors. [ABSTRACT FROM AUTHOR]

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

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]

The undertaking centers around fostering a high level confirmation system to neutralize the developing danger of keylogging assaults. Keylogging, a sort of digital assault that catches keystrokes to take delicate data, represents a huge gamble to conventional confirmation techniques that depend on console input. This undertaking presents an original security approach joining two key developments: a double keypad input framework and a visual verification convention. The double keypad framework comprises of two separate information keypad (Ordinary Keypad and Virtual Keypad), each liable for an alternate part of the validation interaction. This division confuses the capacity for keyloggers to catch total confirmation arrangements, accordingly improving security. At the same time, the visual validation part presents a dynamic, graphical check process that supplements the double keypad framework. Clients interface with visual components like pictures or examples showed on a screen, which are not vulnerable to keylogging. This adds an extra layer of validation that is both easy to use and impervious to information catch by malignant programming. The mix of these two frameworks makes a diverse guard technique. The double keypad component decreases the gamble of compromised keystrokes, while the visual validation process guarantees that regardless of whether keystrokes are caught, the verification stays secure. The venture expects to convey a hearty, secure, and natural verification arrangement that improves assurance against keylogging and other digital dangers, giving a dependable method for getting delicate data in different applications. [ABSTRACT FROM AUTHOR]

The growing need for solutions that can support the computer-based and distant assessment of functional models has resulted in ad hoc implementations of various diagramming tools. These tools are typically not intended for the purpose of functional modelling and lack the flexibility and efficiency of the traditional pen-and-paper approach. This paper reports on an experimental study of 42 students who were introduced to functional modelling through either printed vocabulary materials for pen-and-paper modelling or a specifically developed software application for computer-based modelling. All participants received an identical task—model an electric citrus juicer—with a brief description of how one operates and a photograph of a commercial example. The results show no significant difference in their total scores. However, the pen-and-paper group performed significantly better when it came to the selection of appropriate functions and creating plausible function–flow pairs. These results suggest that the current version of the software alters the functional modelling process in which the students typically engage. Also, it has been hypothesised that the software tool's lack of flexibility and dynamism in presenting the predefined function vocabulary, when compared to the traditional printouts, might result in earlier fixation and the selection of less appropriate functions. On the other hand, the computer-supported approach can be better controlled and is less prone to critical errors, such as disregarding functional modelling conventions. [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]

Copyright of Revista Iberoamericana de Ciencia, Tecnologia y Sociedad is the property of Centro de Estudios sobre Ciencia, Desarrollo y Educacion Superior and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The rapid 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]

Copyright of Automotive Engineer (1674-6546) is the property of Auto Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

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]

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: 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]