Hasil untuk "Information technology"

L. Deng, Dong Yu

This monograph provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks. The application areas are chosen with the following three criteria in mind: (1) expertise or knowledge of the authors; (2) the application areas that have already been transformed by the successful use of deep learning technology, such as speech recognition and computer vision; and (3) the application areas that have the potential to be impacted significantly by deep learning and that have been experiencing research growth, including natural language and text processing, information retrieval, and multimodal information processing empowered by multi-task deep learning.

R. Watson

Introductory information systems textbooks often present the topic in somewhat of a vacuum. That is, they focus on information systems without really succeeding in showing how IS is integrated in organizations, how knowledge workers are supported, and how important IS is for an organizations success. Many undergraduate students do not understand why they are required to take an IS course since they are not IS majors. Many also expect the introductory course to focus on personal productivity software. This textbook will teach students how to exploit IS in a technology-rich environment. It will emphasize why, no matter what their major, information and communications technologies (ICT) are, and increasingly will be, a critical element in their personal success and the success of their organizations. In other words, they need to be introduced to concepts, principles, methods, and procedures that will be valuable to them for years to come in thinking about existing organization systems, proposing new systems, and working with IS professionals in implementing new systems.

L. Diamond

B. Khoshnevis

D. Valentine, G. Stebbins

J. R. Quinlan

M. Weiser

Heng Xu, H. Teo, B. Tan et al.

Youngjin Yoo, O. Henfridsson, K. Lyytinen

Fatimah Alghamdi, Fuad Bajaber

Wireless Body Area Networks (WBANs) enable real-time health monitoring essential for timely clinical intervention, yet their performance is frequently hindered by sensor degradation, noise interference, and strict low-latency constraints in resource-limited environments. Conventional preprocessing approaches indiscriminately reprocess all incoming data, including uncorrupted samples, thereby increasing computational overhead, introducing latency, and potentially distorting valid physiological trends. This study introduces a unified real-time monitoring framework tailored for WBAN systems. The key contributions include: (1) an adaptively gated multi-stage preprocessing pipeline that selectively restores corrupted samples while preserving clean data, (2) an overlap-aware sliding-window mechanism enabling low-latency operation, and (3) a clinically informed risk assessment strategy for early-warning support. By avoiding unnecessary modification of intact signals, the framework maintains physiological integrity while substantially improving reconstruction and predictive reliability. Across multiple vital signs, the proposed approach achieves substantial reconstruction gains, with Mean Squared Error (MSE) reductions ranging from 53% to 67% under strong degradation conditions. An adaptive ARIMA-based forecasting layer captures short-term physiological dynamics with directional accuracies of approximately 65–70% for one-step (10 s) ahead prediction. Early-warning behavior is intentionally conservative, prioritizing false alarm suppression over aggressive alerting. Per-signal evaluation reveals high sensitivity for blood pressure signals, whereas glucose and certain high-variability modalities exhibit conservative sensitivity under modality-specific thresholds. Importantly, the aggregated multi-modal risk decision achieves strong overall system-level performance, with sensitivity and specificity of 0.89 and 0.92, respectively. Overall, the proposed framework establishes a robust, low-latency, and computationally efficient foundation for dependable physiological monitoring in WBAN environments, leveraging selective processing to optimize both resource utilization and clinical reliability.

Wanxin Wu, Chun Pan

IntroductionIn the evolving landscape of disaster response, integrating advanced digital technologies is critical to enhancing the efficiency and effectiveness of public health systems. Traditional anomaly detection methods often fall short due to their inability to handle the dynamic, heterogeneous, and real-time nature of disaster-related data. These methods typically rely on static models that struggle with integrating continuous data streams from diverse sources like hospitals, emergency services, social media, and environmental sensors. As a result, they often fail to capture sudden shifts in disease patterns, environmental conditions, or population movements, leading to delayed risk identification and suboptimal decisions. The increasing frequency and complexity of natural disasters and pandemics underscore the need for flexible, adaptive systems capable of learning from evolving data. Recent advances in machine learning, artificial intelligence, and big data analytics offer promising tools to address these limitations by enabling real-time, high-dimensional data analysis. In recent years, the integration of advanced digital technologies has become essential for improving public health disaster response.MethodsThis study proposes a deep learning-based framework for anomaly detection and early risk identification during digital disaster response scenarios, leveraging data from hospitals, emergency services, social media, and environmental sensors. The objective of the study is to enhance real-time decision-making and situational awareness in public health crises.Results and discussionExperimental results across multiple datasets (EM-DAT, FEMA, UNOSAT, Earthquake) demonstrate that our proposed model improves anomaly detection performance by 23% in precision and reduces false alarms by 31% compared to baseline models. The method combines LSTM and transformer-based architectures to effectively analyze spatiotemporal data, offering both high accuracy and interpretability for public health experts.

Guangli Wang, Weijun Li, Jiazhan Wu et al.

<i>Bactrocera dorsalis</i> (Hendel) is an invasive fruit and vegetable pest, infesting citrus, mango, carambola, etc. We observed that the posterior thoracic scutella of some <i>B. dorsalis</i> adults are yellow, some light yellow, and some white in China. Compared with the <i>B. dorsalis</i> races with a yellow scutellum (YS) and white scutellum (WS), the race with a light-yellow scutellum (LYS) is dominant in citrus and carambola orchards. To reveal genetic correlates among the three races, the genomes of 22 samples (8 with YS, 7 with LYS, and 7 with WS) were sequenced by high-throughput sequencing technology. Single-nucleotide polymorphism (SNP) annotation showed that there were 17,580 non-synonymous mutation sites located in the exonic region. Principal component analysis based on independent SNP data revealed that the SNPs with LYS were more similar to that with YS when compared with WS. Most genes associated with scutellum color variation were involved in three pathways: oxidative phosphorylation, porphyrin and chlorophyll metabolism, and terpenoid backbone biosynthesis. By comparing the sequences among the three races, we screened out 276 differential genes (DGs) in YS vs. WS, 185 DGs in LYS vs. WS, and 104 DGs in YS vs. LYS. Most genes determining color variation in <i>B. dorsalis</i> scutella were located on chromosomes 2–5. Biochemical analysis showed that β-carotene content in YS and LYS was significantly higher than that in WS at any stage of adult days 1, 10, and 20. No significant differences were observed in cytochrome P450 or melanin content in YS, LYS, or WS. Our study provides results on aspects of scutellum color variation in <i>B. dorsalis</i> adults, providing molecular and physiological information for revealing the adaptation and evolution of the <i>B. dorsalis</i> population.

Md Javeed Khan, Mohammed Raahil Ahmed, Mohammed Abdul Aziz Taha et al.

Tshepo Sithole, Vasudeva Rao Veerdhi, Thembelani Sithebe

This paper presents a comprehensive review of static transfer switch (STS) applications in AC power systems, with a focus on enhancing reliability and fault tolerance. The review outlines the fundamental requirements for effective STS deployment, including the necessity of two truly independent and nominally synchronized AC power sources, optimal placement of the STS near protected loads, and proper grounding practices to minimize single points of failure. The analysis synthesizes recent literature on STS topologies, control mechanisms, and integration with uninterruptible power supplies (UPS), highlighting the importance of redundancy and the persistent challenges of achieving source independence and synchronization. Empirical studies and case analyses are discussed to demonstrate the impact of STS design and deployment on minimizing risks to sensitive loads. The paper concludes by providing practical recommendations and identifying future research directions for further improving STS solutions in resilient AC power systems.

Metwally Rashad, Doaa M. Alebiary, Mohammed Aldawsari et al.

The expressions on human faces reveal the emotions we are experiencing internally. Emotion recognition based on facial expression is one of the subfields of social signal processing. It has several applications in different areas, specifically in the interaction between humans and computers. This study presents a simple CCNN-SVM automated model as a viable approach for FER. The model combines a Convolutional Neural Network for feature extraction, certain image preprocessing techniques, and Support Vector Machine (SVM) for classification. Firstly, the input image is preprocessed using face detection, histogram equalization, gamma correction, and resizing techniques. Secondly, the images go through custom single Deep Convolutional Neural Networks (CCNN) to extract deep features. Finally, SVM uses the generated features to perform the classification. The suggested model was trained and tested on four datasets, CK+, JAFFE, KDEF, and FER. These datasets consist of seven primary emotional categories, which encompass anger, disgust, fear, happiness, sadness, surprise, and neutrality for CK+, and include contempt for JAFFE. The model put forward demonstrates commendable performance in comparison to existing facial expression recognition techniques. It achieves an impressive accuracy of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>99.3</mn><mo>%</mo></mrow></semantics></math></inline-formula> on the CK+ dataset, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>98.4</mn><mo>%</mo></mrow></semantics></math></inline-formula> on the JAFFE dataset, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>87.18</mn><mo>%</mo></mrow></semantics></math></inline-formula> on the KDEF dataset, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>88.7</mn><mo>%</mo></mrow></semantics></math></inline-formula> on the FER.

Nassmah Y. Al-Matari, Ammar T. Zahary, Asma A. Al-Shargabi

Abstract The emergence of 6G cognitive radio IoT networks introduces both opportunities and complexities in spectrum access and security. Blockchain technology has emerged as a viable solution to address these challenges, offering enhanced security, transparency, and efficiency in spectrum management. This survey paper offers a thorough analysis of recent advancements in blockchain-enabled security mechanisms specifically for spectrum access within 6G cognitive radio IoT networks. Covering literature from 2019 to the present, the paper highlights significant contributions and developments in integrating blockchain technology with cognitive radio and IoT systems. It reviews spectrum access security and shows how blockchain’s decentralized approach can solve related issues. Key areas of focus include secure authentication systems, tamper-resistant spectrum sensing, decentralized databases, and smart contracts for spectrum management. The paper also addresses ongoing challenges like interoperability, scalability, and the need for comprehensive security frameworks. Future research directions are proposed, emphasizing the development of advanced blockchain protocols, integration with machine learning, and addressing regulatory and standardization concerns. This paper provides valuable insights for researchers and practitioners aiming to leverage blockchain technology, alongside ML/AI, to enhance security and efficiency in next-generation cognitive radio IoT networks.

Joseph Heeley, Samuel Boobier, Jonathan D. Hirst

Abstract Selecting greener solvents during experiment design is imperative for greener chemistry. While many solvent selection guides are currently used in the pharmaceutical industry, these are often paper-based guides which can make it difficult to identify and compare specific solvents. This work presents a stand-alone version of the solvent flashcards that were developed as part of the AI4Green electronic laboratory notebook. The functionality is an intuitive and interactive interface for the visualisation of data from CHEM21, a pharmaceutical solvent selection guide that categorises solvents according to “greenness”. This open-source software is written in Python, JavaScript, HTML and CSS and allows users to directly contrast and compare specific solvents by generating colour-coded flashcards. It can be installed locally using pip, or alternatively the source code is available on GitHub: https://github.com/AI4Green/solvent_flashcards . The documentation can also be found on GitHub or on the corresponding Python Package Index webpage: https://pypi.org/project/solvent-guide/ . Scientific Contribution This simple and easy-to-use digital tool provides a visualisation of solvent greenness data through a novel intuitive interface and encourages green chemistry. It offers numerous advantages over traditional solvent selection guides, allowing users to directly customise the solvent list and generate side-by-side comparisons of only the most important solvents. The release as a standalone package will maximise the benefit of this software. Graphical Abstract

Lichong WANG, Chenhua SUN, Weisong ZHAO et al.

At present, the frequency of non-geostationary orbit(NGSO) mobile satellite service is becoming more and more tense.Aimed at the complicated problem of frequency coordination of mobile direct connection to NGSO satellite service, firstly, an analysis was conducted on the current frequency usage status and trends of mobile direct connection to NGSO satellite service.Secondly, the frequency coordination scheme between mobile direct connection to NGSO satellite service and other same frequency space services in extended L-band was analyzed and studied.By analyzed the frequency coordination of mobile direct connection to NGSO satellite service in extended L-band, the space services that need to carry out frequency coordination work with mobile direct connection to NGSO satellite service were determined.The basic frequency coordination scheme of mobile direct connection to NGSO satellite service with each space service was studied and the coordination scheme suggestions were given, at the same time, the interference analysis and calculation involved in the specific coordination scheme were listed for example.The above research can be used as reference for the research of frequency coordination of mobile direct connection to NGSO satellite service system, as well as for the next step research of key technologies related to mobile direct connection to NGSO satellite service.

Takeshi Yoshida, Yuki Onishi, Takuya Kawahara et al.

Abstract In this study, we propose a method to automate fruit harvesting with a fruit harvesting robot equipped with robotic arms. Given the future growth of the world population, food shortages are expected to accelerate. Since much of Japan’s agriculture is dependent on imports, it is expected to be greatly affected by this upcoming food shortage. In recent years, the number of agricultural workers in Japan has been decreasing and the population is aging. As a result, there is a need to automate and reduce labor in agricultural work using agricultural machinery. In particular, fruit cultivation requires a lot of manual labor due to the variety of orchard conditions and tree shapes, causing mechanization and automation to lag behind. In this study, a dual-armed fruit harvesting robot was designed and fabricated to reach most of the fruits on joint V-shaped trellis that was cultivated and adjusted for the robot. To harvest the fruit, the fruit harvesting robot uses sensors and computer vision to detect and estimate the position of the fruit and then inserts end-effectors into the lower part of the fruit. During this process, there is a possibility of collision within the robot itself or with other fruits depending on the position of the fruit to be harvested. In this study, inverse kinematics and a fast path planning method using random sampling is used to harvest fruits with robot arms. This method makes it possible to control the robot arms without interfering with the fruit or the other robot arm by considering them as obstacles. Through experiments, this study showed that these methods can be used to detect pears and apples outdoors and automatically harvest them using the robot arms.

Chien-Chang Liao, Meng-Hsiang Chen, Chun-Yen Yu et al.

<i>Background:</i> Since the advent of a new generation of inflow-sensitive inversion recovery (IFIR) technology, three-dimensional non-contrast-enhanced magnetic resonance angiography is being used to obtain hepatic vessel images without applying gadolinium contrast agent. The purpose of this study was to explore the diagnostic efficacy of non-contrast-enhanced magnetic resonance angiography (non-CE MRA), contrast-enhanced magnetic resonance angiography (CMRA), and computed tomography angiography (CTA) in the preoperative evaluation of living liver donors. <i>Methods:</i> A total of 43 liver donor candidates who were evaluated for living donor liver transplantation completed examinations. Donors’ age, gender, renal function (eGFR), and previous CTA and imaging were recorded before non-CE MRA and CMRA. CTA images were used as the standard. <i>Results:</i> Five different classifications of hepatic artery patterns (types I, III, V, VI, VIII) and three different classifications of portal vein patterns (types I, II, and III) were identified among 43 candidates. The pretransplant vascular anatomy was well identified using combined non-CE MRA and CMRA of hepatic arteries (100%), PVs (98%), and hepatic veins (100%) compared with CTA images. Non-CE MRA images had significantly stronger contrast signal intensity of portal veins (<i>p</i> < 0.01) and hepatic veins (<i>p</i> < 0.01) than CMRA. No differences were found in signal intensity of the hepatic artery between non-CE MRA and CMRA. <i>Conclusion:</i> Combined non-CE MRA and CMRA demonstrate comparable diagnostic ability to CTA and provide enhanced biliary anatomy information that assures optimum donor safety.