Hasil untuk "Electronic computers. Computer science"

C. Free, G. Phillips, Leandro Galli et al.

BackgroundThe application of mobile computing and communication technology is rapidly expanding in the fields of health care and public health. This systematic review will summarise the evidence for the effectiveness of mobile technology interventions for improving health and health service outcomes (M-health) around the world.FindingsTo be included in the review interventions must aim to improve or promote health or health service use and quality, employing any mobile computing and communication technology. This includes: (1) interventions designed to improve diagnosis, investigation, treatment, monitoring and management of disease; (2) interventions to deliver treatment or disease management programmes to patients, health promotion interventions, and interventions designed to improve treatment compliance; and (3) interventions to improve health care processes e.g. appointment attendance, result notification, vaccination reminders.A comprehensive, electronic search strategy will be used to identify controlled studies, published since 1990, and indexed in MEDLINE, EMBASE, PsycINFO, Global Health, Web of Science, the Cochrane Library, or the UK NHS Health Technology Assessment database. The search strategy will include terms (and synonyms) for the following mobile electronic devices (MEDs) and a range of compatible media: mobile phone; personal digital assistant (PDA); handheld computer (e.g. tablet PC); PDA phone (e.g. BlackBerry, Palm Pilot); Smartphone; enterprise digital assistant; portable media player (i.e. MP3 or MP4 player); handheld video game console. No terms for health or health service outcomes will be included, to ensure that all applications of mobile technology in public health and health services are identified. Bibliographies of primary studies and review articles meeting the inclusion criteria will be searched manually to identify further eligible studies. Data on objective and self-reported outcomes and study quality will be independently extracted by two review authors. Where there are sufficient numbers of similar interventions, we will calculate and report pooled risk ratios or standardised mean differences using meta-analysis.DiscussionThis systematic review will provide recommendations on the use of mobile computing and communication technology in health care and public health and will guide future work on intervention development and primary research in this field.

Miaopeng Yu, Miaopeng Yu, Haonan Liu et al.

Cloud-native microservices improve development velocity and elasticity, but they also create complex and dynamic service dependencies. Resource contention, queue buildup, and downstream slowdowns can propagate through call chains, amplifying end-to-end tail latency (e.g., p95/p99) and increasing Service Level Objective (SLO) violation risks. While many studies focus on post-hoc anomaly detection and root-cause analysis, industrial operations increasingly demand proactive capabilities, like predicting performance risks before a request finishes, issuing early warnings from partial trace prefixes, and producing actionable signals for mitigation. This mini-review synthesizes recent progress on trace-driven proactive SLO management. We summarize problem formulations and evaluation protocols for SLO violation and tail-quantile prediction, prefix early warning under precision constraints, and actionable intermediate outputs such as bottleneck candidate ranking and what-if estimation. We then survey modeling approaches spanning feature-based baselines, sequence models, graph neural networks, sequence-graph fusion, and multimodal/causal extensions, highlighting practical issues such as class imbalance, sampling-induced missing spans, and topology drift. Finally, we survey commonly used public benchmarks and traces, and discuss open challenges toward deployable, trustworthy proactive SLO management.

Francesco Ricci, L. Rokach, Bracha Shapira et al.

Smith Salifu, Peter Apata Olubambi

ABSTRACT High entropy alloys (HEAs) have gained attention as effective reinforcements for enhancing the properties of metal matrix composites (MMCs), thanks to their distinct properties in contrast to traditional reinforcement particles. In view of that, this study develops HEA‐reinforced aluminum matrix composites (AMCs) consolidated through the pulse electric current sintering (PECS) technique and examines how the HEA reinforcement influences the microstructural, tribological, and nanomechanical properties of these consolidated composites. Appropriate thermodynamic and phase identification equations were used to determine a suitable combination of elements for the development of the HEA reinforcement, and an optimized sintering process was used to achieve effective bonding within the matrix. The resulting composites exhibited enhanced densification, with Laves phase, BCC, and FCC HEA phases present. Furthermore, incorporating HEA reinforcement greatly improved the mechanical properties such as wear resistance, microhardness, and nanoindentation characteristics of the composites such that the composite with 10% HEA displayed about a 191% increase in microhardness, with a significantly lower average coefficient of friction (ACOF) and higher wear resistance as compared to the unreinforced aluminum matrix.

Jan Philip Wahle, Krishnapriya Vishnubhotla, Bela Gipp et al.

Work in Computational Affective Science and Computational Social Science explores a wide variety of research questions about people, emotions, behavior, and health. Such work often relies on language data that is first labeled with relevant information, such as the use of emotion words or the age of the speaker. Although many resources and algorithms exist to enable this type of labeling, discovering, accessing, and using them remains a substantial impediment, particularly for practitioners outside of computer science. Here, we present the ABCDE dataset (Affect, Body, Cognition, Demographics, and Emotion), a large-scale collection of over 400 million text utterances drawn from social media, blogs, books, and AI-generated sources. The dataset is annotated with a wide range of features relevant to computational affective and social science. ABCDE facilitates interdisciplinary research across numerous fields, including affective science, cognitive science, the digital humanities, sociology, political science, and computational linguistics.

Lukas Hörmann, Wojciech G. Stark, Reinhard J. Maurer

Nanoscale design of surfaces and interfaces is essential for modern technologies like organic LEDs, batteries, fuel cells, superlubricating surfaces, and heterogeneous catalysis. However, these systems often exhibit complex surface reconstructions and polymorphism, with properties influenced by kinetic processes and dynamic behavior. A lack of accurate and scalable simulation tools has limited computational modeling of surfaces and interfaces. Recently, machine learning and data-driven methods have expanded the capabilities of theoretical modeling, enabling, for example, the routine use of machine-learned interatomic potentials to predict energies and forces across numerous structures. Despite these advances, significant challenges remain, including the scarcity of large, consistent datasets and the need for computational and data-efficient machine learning methods. Additionally, a major challenge lies in the lack of accurate reference data and electronic structure methods for interfaces. Density Functional Theory, while effective for bulk materials, is less reliable for surfaces, and too few accurate experimental studies on interface structure and stability exist. Here, we will sketch the current state of data-driven methods and machine learning in computational surface science and provide a perspective on how these methods will shape the field in the future.

Mazyar Taghavi, Rahman Farnoosh

This paper investigates the utilization of Quantum Computing and Neuromorphic Computing for Safe, Reliable, and Explainable Multi_Agent Reinforcement Learning (MARL) in the context of optimal control in autonomous robotics. The objective was to address the challenges of optimizing the behavior of autonomous agents while ensuring safety, reliability, and explainability. Quantum Computing techniques, including Quantum Approximate Optimization Algorithm (QAOA), were employed to efficiently explore large solution spaces and find approximate solutions to complex MARL problems. Neuromorphic Computing, inspired by the architecture of the human brain, provided parallel and distributed processing capabilities, which were leveraged to develop intelligent and adaptive systems. The combination of these technologies held the potential to enhance the safety, reliability, and explainability of MARL in autonomous robotics. This research contributed to the advancement of autonomous robotics by exploring cutting-edge technologies and their applications in multi-agent systems. Codes and data are available.

Atharva Rasane

Computer science research spans a diverse array of topics, with scholars exploring numerous subfields. This paper examines the self-reported research interests of the top 3,260 most cited computer science authors on Google Scholar. Using the scholarly Python library, we systematically retrieved and classified their interests into predefined categories based on the Computer Science Ontology (CSO). The analysis highlights a hierarchy of primary research areas, including Artificial Intelligence, Software Engineering, Data Mining, and Computer Systems. Additionally, it investigates the distribution of these interests, identifying emerging trends, established fields, and areas with relatively less attention. These findings provide a current snapshot of research priorities and serve as a foundation for guiding future studies in computer science.

Shifeng Peng, Xuefeng Hou, Zhishu Shen et al.

Satellite computing has emerged as a promising technology for next-generation wireless networks. This innovative technology provides data processing capabilities, which facilitates the widespread implementation of artificial intelligence (AI)-based applications, especially for image processing tasks involving deep neural network (DNN). With the limited computing resources of an individual satellite, independently handling DNN tasks generated by diverse user equipments (UEs) becomes a significant challenge. One viable solution is dividing a DNN task into multiple subtasks and subsequently distributing them across multiple satellites for collaborative computing. However, it is challenging to partition DNN appropriately and allocate subtasks into suitable satellites while ensuring load balancing. To this end, we propose a collaborative satellite computing system designed to improve task processing efficiency in satellite networks. Based on this system, a workload-balanced adaptive task splitting scheme is developed to equitably distribute the workload of DNN slices for collaborative inference, consequently enhancing the utilization of satellite computing resources. Additionally, a self-adaptive task offloading scheme based on a genetic algorithm (GA) is introduced to determine optimal offloading decisions within dynamic network environments. The numerical results illustrate that our proposal can outperform comparable methods in terms of task completion rate, delay, and resource utilization.

Christoph Heinzl, Renata Georgia Raidou, Kristi Potter et al.

Materials science has a significant impact on society and its quality of life, e.g., through the development of safer, more durable, more economical, environmentally friendly, and sustainable materials. Visual computing in materials science integrates computer science disciplines from image processing, visualization, computer graphics, pattern recognition, computer vision, virtual and augmented reality, machine learning, to human-computer interaction, to support the acquisition, analysis, and synthesis of (visual) materials science data with computer resources. Therefore, visual computing may provide fundamentally new insights into materials science problems by facilitating the understanding, discovery, design, and usage of complex material systems. This seminar is considered as a follow-up of the Dagstuhl Seminar 19151 Visual Computing in Materials Sciences, held in April 2019. Since then, the field has kept evolving and many novel challenges have emerged, with regard to more traditional topics in visual computing, such as topology analysis or image processing and analysis, to recently emerging topics, such as uncertainty and ensemble analysis, and to the integration of new research disciplines and exploratory technologies, such machine learning and immersive analytics. With the current seminar, we target to strengthen and extend the collaboration between the domains of visual computing and materials science (and across visual computing disciplines), by foreseeing challenges and identifying novel directions of interdisciplinary work. We brought visual computing and visualization experts from academia, research centers, and industry together with domain experts, to uncover the overlaps of visual computing and materials science and to discover yet-unsolved challenges, on which we can collaborate to achieve a higher societal impact.

Huimin Jiang, Xianhui Wu, Farzad Sabetzadeh et al.

Abstract In online sales platforms, product design attributes influence consumer preferences, and consumer preferences also have a significant impact on future product design optimization and iteration. Online review data are the most intuitive feedback from consumers on products. Using the value of online review information to explore consumer preferences is the key to optimize the products, improve consumer satisfaction and meet consumer requirements. Therefore, the study of consumer preferences based on online reviews is of great importance. However, in previous research on consumer preferences based on online reviews, few studies have modeled consumer preferences. The models often suffer from the nonlinear structure and the fuzzy coefficients, making it challenging to build explicit models. Therefore, this study adopts a fuzzy regression approach with a nonlinear structure to model consumer preferences based on online reviews to provide reference and insight for subsequent studies. First, smartwatches were selected as the research object, and the sentiment scores of product reviews under different topics were obtained by text mining on the product online data. Second, a polynomial structure between product attributes and consumer preferences was generated to investigate the association between them further. Afterward, based on the existing polynomial structure, the fuzzy coefficients of each item in the structure were determined by the fuzzy regression approach. Finally, the mean relative error and mean systematic confidence of the fuzzy regression with nonlinear structure method were numerically calculated and compared with fuzzy least squares regression, fuzzy regression, adaptive neuro fuzzy inference system (ANFIS) and K-means-based ANFIS, and it was found that the proposed method was relatively more effective in modeling consumer preferences.

YANG Ye, WU Weizhi, ZHANG Jiaru

Granular computing imitates human being's thinking.It shows great promise as a new way for data mining and know-ledge discovery in the context of big data.To solve the problem of knowledge acquisition in inconsistent generalized decision multi-scale ordered information systems,by employing evidence theory,the optimal scale combination and rule extraction in inconsistent generalized decision multi-scale ordered information systems are studied.Dominance relations are first introduced into decision multi-scale information systems,and some basic concepts in decision multi-scale ordered information systems are introduced.With reference to the notion of scale combinations in inconsistent generalized decision multi-scale ordered information systems,representations of information granules as well as lower and upper approximations of sets under different scale combinations are presented and their relationships are examined.With different scales of decisions,several types of optimal scale combinations in inconsistent generalized decision multi-scale ordered information systems are further defined and their relationships are clarified.Finally,a method of discernibility matrix attribute reduction and rule acquisition based on generalized dominance decision functions are explored.

Courtney Hutton Pospick, Evan Suma Rosenberg

Mixed reality offers unique opportunities to situate complex tasks within spatial environments. One such task is the creation and manipulation of intricate, three-dimensional paths, which remains a crucial challenge in many fields, including animation, architecture, and robotics. This paper presents an investigation into the possibilities of spatially situated path creation using new virtual and augmented reality technologies and examines how these technologies can be leveraged to afford more intuitive and natural path creation. We present a formative study (n = 20) evaluating an initial path planning interface situated in the context of augmented reality and human-robot interaction. Based on the findings of this study, we detail the development of two novel techniques for spatially situated path planning and manipulation that afford intuitive, expressive path creation at varying scales. We describe a comprehensive user study (n = 36) investigating the effectiveness, learnability, and efficiency of both techniques when paired with a range of canonical placement strategies. The results of this study confirm the usability of these interaction metaphors and provide further insight into how spatial interaction can be discreetly leveraged to enable interaction at scale. Overall, this work contributes to the development of 3DUIs that expand the possibilities for situating path-driven tasks in spatial environments.

Zhihan Xiao, Yawei Qian, Yi Liu et al.

Abstract Association between alcohol intake and Coronavirus disease 2019 (COVID-19) risk has been explored in several observational studies, but the results are still controversial. These associations may be biased by reverse causation or confounded by other environmental exposures. To avoid potential biases, we used Mendelian randomization (MR) method to evaluate whether alcohol intake is the causal risk factor for COVID-19. Two-sample MR analyses were performed utilizing summary data from the UK Biobank with 38,984 COVID-19 patients and 1,644,784 control participants. Both inverse-variance weighted (IVW) and genetic risk score (GRS) methods were applied to estimate the relationship including COVID-19 vs. general population, hospitalized COVID-19 vs. not hospitalized COVID-19, hospitalized COVID-19 vs. general population, and severe COVID-19 vs. general population. Additionally, we conducted various sensitivity analyses to evaluate the impact of assumptions on the findings and ensure the robustness of the results. Using 80 single nucleotide polymorphisms as instrumental variables, we found that alcohol intake was not significantly associated with the occurrence of COVID-19 in both IVW and GRS methods (IVW: beta = 0.0372; 95% CI − 0.1817 to 0.2561; P = 0.74; GRS: beta = 0.0372, 95% CI − 0.1737 to 0.2481, P = 0.73). Furthermore, similar results were also observed in comparison hospitalized COVID-19 with not hospitalized COVID-19 (IVW: beta = − 0.3625; 95% CI − 1.4151 to 0.6900; P = 0.50; GRS: beta = − 0.3625, 95% CI − 1.3633 to 0.6383, P = 0.48), hospitalized COVID-19 with general population (IVW: beta = − 0.1203; 95% CI − 0.5997 to 0.3591; P = 0.62; GRS: beta = − 0.1203, 95% CI − 0.5352 to 0.2946, P = 0.57), and severe COVID-19 with general population (IVW: beta = 0.2963; 95% CI − 0.3682 to 0.9607; P = 0.38; GRS: beta = 0.2963, 95% CI − 0.3240 to 0.9166, P = 0.35). Besides, the heterogeneity and sensitivity tests suggested absence of bias due to pleiotropy. Our results highlight no evidence to support the causal role of alcohol consumption in COVID-19 risk. Further large-scale prospective studies are warranted to replicate our findings.

Elijah Cole, Suzanne Stathatos, Björn Lütjens et al.

Computer vision can accelerate ecology research by automating the analysis of raw imagery from sensors like camera traps, drones, and satellites. However, computer vision is an emerging discipline that is rarely taught to ecologists. This work discusses our experience teaching a diverse group of ecologists to prototype and evaluate computer vision systems in the context of an intensive hands-on summer workshop. We explain the workshop structure, discuss common challenges, and propose best practices. This document is intended for computer scientists who teach computer vision across disciplines, but it may also be useful to ecologists or other domain experts who are learning to use computer vision themselves.

ADCAIJ editorial Team

Fan Chao, Zhiqin He, Renkuan Feng et al.

Tradition wireless sensor networks (WSNs) transmit data by single or multiple hops. However, some sensor nodes (SNs) close to a static base station forward data more frequently than others, which results in the problem of energy holes and makes networks fragile. One promising solution is to use a mobile node as a mobile sink (MS), which is especially useful in energy-constrained networks. In these applications, the tour planning of MS is a key to guarantee the network performance. In this paper, a novel strategy is proposed to reduce the latency of mobile data gathering in a WSN while the routing strategies and tour planning of MS are jointly optimized. First, the issue of network coverage is discussed before the appropriate number of clusters being calculated. A dynamic clustering scheme is then developed where a virtual cluster center is defined as the MS sojourn for data collection. Afterwards, a tour planning of MS based on prediction is proposed subject to minimizing the traveling distance to collect data. The proposed method is simulated in a MATLAB platform to show the overall performance of the developed system. Furthermore, the physical tests on a test rig are also carried out where a small WSN based on an unmanned aerial vehicle (UAV) is developed in our laboratory. The test results validate the feasibility and effectiveness of the method proposed.

Vojdan Kjorveziroski, Sonja Filiposka, Vladimir Trajkovik

Serverless computing is a new concept allowing developers to focus on the core functionality of their code, while abstracting away the underlying infrastructure. Even though there are existing commercial serverless cloud providers and open-source solutions, dealing with the explosive growth of new Internet of Things (IoT) devices requires more efficient bandwidth utilization, reduced latency, and data preprocessing closer to the source, thus reducing the overall data volume and meeting privacy regulations. Moving serverless computing to the edge of the network is a topic that is actively being researched with the aim of solving these issues. This study presents a systematic mapping review of current progress made to this effect, analyzing work published between 1 January 2015 and 1 September 2021. Using a document selection methodology which emphasizes the quality of the papers obtained through querying several popular databases with relevant search terms, we have included 64 entries, which we then further categorized into eight main categories. Results show that there is an increasing interest in this area with rapid progress being made to solve the remaining open issues, which have also been summarized in this paper. Special attention is paid to open-source efforts, as well as open-access contributions.

Cesar Munoz-Ausecha, Juan Ruiz-Rosero, Gustavo Ramirez-Gonzalez

Radio frequency identification (RFID) is widely used in several contexts, such as logistics, supply chains, asset tracking, and health, among others, therefore drawing the attention of many researchers. This paper presents a review of the most cited topics regarding RFID focused on applications, security, and privacy. A total of 62,685 records were downloaded from the Web of Science (WoS) and Scopus core databases and processed, reconciling the datasets to remove duplicates, resulting in 40,677 unique elements. Fundamental indicators were extracted and are presented, such as the citation number, average growth rate, and average number of documents per year. We extracted the top topics and reviewed the relevant indicators using a free Python tool, ScientoPy. The results are discussed in the following sections: the first is the Applications Section, whose subsections are the Internet of Things (IoT), Supply Chain Management, Localization, Traceability, Logistics, Ubiquitous Computing, Healthcare, and Access Control; the second is the Security and Privacy section, whose subsections are Authentication, Privacy, and Ownership Transfer; finally, we present the Discussion section. This paper intends to provide the reader with a global view of the current status of trending RFID topics and present different analyses from different perspectives depending on motivations or background.

Billyanto Hendrik, Bernard Renaldy Suteja

Agile is a system development life cycle methodology that focuses on development interactions that involve the user with the development team led by the project manager as an intermediary between the client and the development team, with the project manager as the project leader, it is expected that this role can carry out project planning by making estimates and designing. project. The worst thing that can happen if the application fails to meet client expectations is the additional development time called maintenance, this risk will create losses to the company even though maintenance is an additional service, but this risk tends to be negative because it can have a negative impact on the company and members of the development team. responsible for the project, the project manager must be able to identify risks earlier during the sprint, so in this study we will discuss the analysis and risk identification of maintenance programs in agile-based project management, as a research analyst method will use a classification tree to group them so that It can be found at the sprint stage how much risk has started to be made, so that the project manager can make corrections at the next sprint to reduce maintenance risk