Abstract Accurate measurement of key parameters in permanent magnet synchronous motor (PMSM) is of paramount importance for motor control and monitoring. To reduce costs and overcome the limitations of sensor accuracy, there has been increasing attention and research on establishing key parameter prediction models using easily accessible data. However, the existing soft sensor models are established based on the measured data, neglecting the known mechanism knowledge. To this end, an improved soft sensor modelling framework combining the virtual output of the mechanism model is proposed to enhance the prediction accuracy. In this work, the mechanistic model of PMSM is briefly introduced. The fusion model, using the Transformer as an example, is denoted as F-Transformer. Then F-Transformer is established based on the easily measured variables and the virtual output of the mechanism model. The modelling performance and advantages of the proposed model are verified by a semi-physical experimental platform. The average root mean square error of the F-Transformer was reduced by approximately 87.73% and 88.51% compared to the mechanism model and data-driven model(Transformer) respectively, fully demonstrating its advantages in high prediction accuracy and strong generalization capability under various operating conditions.

Toward ethical provenance tracking: The GA4GH model data access agreement (DAA).

This article presents a data-driven control method to address the local asymptotic stabilization problem of discrete-time neural networks (DNNs) under input saturation. To reduce communication load, a memory-type event-triggered mechanism (MEM) is first designed to mitigate the superfluous triggers. Then, a memory-dependent Lyapunov function (MLF) is constructed to accommodate the memory term introduced by the MEM. Based on the designed MEM, the MLF and two data-based system representations, a data-based stabilization criterion is developed, and an estimated region of attraction (ERA) is determined. Simultaneously, the feedback gain and the trigger matrix are co-designed to guarantee the local stability of the closed-loop system. A notable feature of the proposed approach is that the proposed stabilization criteria rely solely on accessible data, without necessitating full knowledge of the system matrices. It makes the approach well-suited for practical applications where precise modeling is difficult or infeasible. Furthermore, a hybrid optimization scheme combining the linear objective minimization method and the particle swarm optimization (PSO) algorithm is presented to maximize the size of the ERA. Finally, two numerical simulations are given to validate the effectiveness of the proposed optimization algorithm, illustrate the influence of data size, and demonstrate the advantages of the designed MEM in stabilizing DNNs.

Neuroscience education is increasingly important in today's rapidly evolving educational landscape, as understanding the brain's functions and learning processes can significantly enhance teaching practices. Moreover, the rapid evolution of technological equipment in brain studies, combined with the abundance of digitally available data, enables more accurate brain-based analyses in education, offering better insights into optimizing learning outcomes and tailoring teaching methods. However, there remains a lack of clarity regarding which strategies and tools are most effective in supporting teacher Professional Development (PD) in this specialized field. This study is motivated by the need to explore and assess the current strategies, both pedagogical and technological, that shape PD programs for neuroscience educators. Through this review, we aim to map out existing strategies and tools, identify gaps in the literature, and provide insights that can guide the future design and implementation of teacher development programs. The main findings highlight the limited number of specific strategies informing teacher PD programs in neuroscience and the minimal inclusion of technology, particularly neurotechnologies, despite their promised benefits. The increasing accessibility of brain-based data, along with the potential outcomes in personalized education, underscores the necessity for developing more neuroscience-driven strategies in teaching practices.

Multi-annotation agreement and prediction consistency networks: Improving semi-supervised segmentation of medical images with ambiguous boundaries.

Stop writing repetitive code! Scaffolding a semantic data access layer to abstract developers from semantic technologies

Mind the (widening) gap: why public health must engage with AI now.

The development of the Human Health Exposure Analysis Resource (HHEAR) Data Repository for environmental epidemiology research.

Management of student assessments in Islamic boarding schools, especially at the Madrasah I’dadiyah level, is still largely manual and fragmented, making it difficult to monitor students’ academic development and character effectively (Hidayat, 2019). This study aims to design an Enterprise Architecture for the student assessment system using the TOGAF (The Open Group Architecture Framework) method, known as a standard framework for structured enterprise architecture design (The Open Group, 2018; Lankhorst, 2017). The research approach was carried out through a case study at the Madrasah I’dadiyah Islamic Boarding School Salafiyah Syafi’iyah Sukorejo, which combines business needs analysis, application architecture design, data, and technology (Erl, Khattak, & Buhler, 2017; Nurhadi & Susanto, 2020). The results of the study indicate that the application of TOGAF ADM can produce an integrated system architecture, enabling more efficient, accurate, and easily accessible assessment data management by Islamic boarding school managers. The proposed system also supports data-driven decision-making, facilitates the evaluation of students' academic and moral development, and strengthens educational management within the I'dadiyah environment. This research contributes to the development of an enterprise architecture-based educational information system in Islamic boarding schools.

Aim: The study aims to examine the security landscape of embedded analytics applications and to identify key threats and vulnerabilities associated with the integration of Business Intelligence and data visualization within business applications. Methods: The research adopts an analytical review approach to assess security risks in embedded analytics environments. It evaluates common embedded BI architectures and examines existing security controls, governance mechanisms, and regulatory considerations relevant to data-driven business applications. Results: The analysis identifies increased exposure to security risks arising from extensive use of embedded BI, including data breaches, unauthorized access, regulatory noncompliance, and unethical data use. The findings highlight gaps in security implementation and governance practices within embedded analytics deployments. Conclusion: Embedded analytics significantly enhances real time decision making but introduces substantial security challenges. Addressing these challenges requires a structured approach that aligns technical safeguards with governance frameworks and regulatory requirements. Recommendation: Organizations should implement robust technical controls, strengthen data governance practices, and align embedded analytics deployments with applicable regulatory frameworks to mitigate security risks and ensure secure and ethical use of Business Intelligence solutions.

Abstract Active, responsive, non-equilibrium materials–at the forefront of materials engineering–offer dynamical restructuring, mobility and other complex life-like properties. Yet, this enhanced functionality comes with significant amplification of the size and complexity of the datasets needed to characterize their properties, thereby challenging conventional approaches to analysis. To meet this need, we present BARCODE: Biomaterial Activity Readouts to Categorize, Optimize, Design and Engineer, an open-access software that automates high throughput screening of microscopy video data to enable non-equilibrium material optimization and discovery. BARCODE produces a unique fingerprint or ‘barcode’ of performance metrics that visually and quantitatively encodes dynamic material properties with minimal file size. Using three complementary material-agnostic analysis branches, BARCODE significantly reduces data dimensionality and size, while providing rich, multiparametric outputs and rapid tractable characterization of activity and structure. We analyze a series of datasets of cytoskeleton networks and cell monolayers to demonstrate BARCODE’s abilities to accelerate and streamline screening and analysis, reveal unexpected correlations and emergence, and enable broad non-expert data access, comparison, and sharing.

The proliferation of mobile payments has brought about increasingly severe security challenges, including data breaches and identity forgery, which pose a significant threat to user assets and privacy. To meet the stringent security requirements of China’s Multi-Level Protection Scheme (MLPS) Level 3 for financial systems, this study proposes an innovative privacyenhancing protection scheme for mobile banking payments. This scheme is designed to provide comprehensive security throughout the entire lifecycle, from payment authentication to subsequent auditing. Specifically, our solution introduces two core mechanisms: the Privacy-Preserving Authentication (PPA) protocol, which ensures the privacy of user identities and transaction data during the payment process by combining the private data access characteristics of Oblivious RAM (ORAM) with zero-knowledge proof technology; and the Distributed Ledger Audit Mechanism (DLAM), which utilizes the decentralized and immutable features of blockchain, supplemented by ORAM, to guarantee the integrity of system logs and the privacy of the auditing process.

Cloud computing might seem like another new technology to anyone looking at it for the first time. But cloud computing is like old wine in new bottle. Cloud Computing is a compilation of existing techniques and technologies, packaged within a new infrastructure paradigm that offers improved scalability, elasticity, business agility, faster startup time, reduced management costs and just-in-time availability of resources. Cloud Computing is a technology that allows you to store and access data and applications over the internet instead of using your computer’s hard drive or a local server. At the present time the demand for cloud computing services are increasing with respect to that demand for cloud computing skills is also increasing. This paper provides the brief information about the cloud computing, its security issues and what is to be done to remove the security threats.

Community service activities were carried out at the Pratama Buana Mekar Clinic located at Jl. Raya Laswi No. 56, Baleendah, Baleendah District, Bandung Regency, West Java. This activity was carried out in the form of implementing a Management Information System from design, operation to monitoring its implementation. The purpose of this activity is to implement an integrated Hospital Information System at the Pratama Buana Mekar Clinic, provide training to clinic staff so they are able to operate the system independently, increase the efficiency of administrative processes and patient services through digitalization, strengthen accurate, secure, and easily accessible health data management as needed. The method used is an SDLC (System Development Life Cycle) based implementation with a mentoring and training approach, which begins with analyzing clinic needs, designing modules (registration, billing, cashier, medical records, pharmacy and warehouse), system installation & configuration, clinic staff training, evaluation of use and improvement. Community Service will be carried out from June 11, 2025, to August 23, 2025. Suggestions that can be recommended are to do routine data backups (daily/weekly), update the system if there are bug fixes or feature improvements, record any problems that arise and immediately communicate them for improvement, use reports from the system to see patient trends, the most used services, and the clinic's financial performance so that this data can help management improve the quality of service.

In recent years, artificial intelligence (AI) has become an important technology that enhances the competitive advantages for businesses. This study investigates the application of artificial intelligence and how it can optimize reverse logistics for the aquatic industry in the Mekong Delta. It also explores the current situation in applying AI, its benefits, and challenges when using AI in reverse logistics for aquatic enterprises. The research uses qualitative and quantitative methods to collect data from interviewing managers, logistics staff, and technicians to deliver a survey to 41 seafood businesses. Results show AI applications in forecasting, storage, and recycling can cut operational costs by over 10% for 46.3% of firms and improve recovery time by over 10% for 56.1%. Benefits also include higher operational efficiency and better environmental performance. However, challenges persist in system integration, data access, and workforce readiness. The study provides practical recommendations, including enhancing AI workforce training, system integration, and collaboration with technology providers, to help seafood companies overcome barriers and maximize the benefits of AI in reverse logistics.

ObjectiveThe Coronavirus disease (COVID-19) pandemic affected millions of people worldwide and caused hundreds of thousands of deaths. The CHA₂DS₂-VASc score is a scoring system used to determine the indication for anticoagulation in patients with atrial fibrillation (AF) and determines the risk of stroke. Previous studies have shown that it predicts mortality in COVID-19 patients well. New guidelines simplified the score as the CHA₂DS₂-VA score, which is free of sex factor. In this study, we planned to investigate the ability of this simplified score in predicting mortality and intensive care unit (ICU) admission in COVID-19 patients.Materials and methodsAll patients who were diagnosed with COVID-19 between January 2021 and January 2022 were screened, and patients with accessible data were enrolled. A total of 838 patients were included. The baseline characteristics of the patients and CHA₂DS₂-VA scores were recorded, and their relationship with poor outcomes was investigated. Mann-Whitney U and T-test were used for continuous variables, while logistic regression and ROC analysis were performed to identify predictors of 1-year mortality and ICU admission.ResultsThe mean age of the study population was 53.8 ± 18.5, and 53.6% of them (n = 449) were male. Intensive care unit (ICU) admission was present in 177 (21.1%) patients. 1-year mortality was present in 86 (10.3%) patients. Univariable regression analysis revealed that hypertension, diabetes mellitus, coronary artery disease, heart failure, atrial fibrillation, COPD, CHA2DS2-VA score, glomerular filtration rate, and albumin level were predictors of 1-year mortality. In multivariate regression analysis, only the CHA₂DS₂-VA score was predictive of 1-year mortality (OR = 1.63, 95% CI: 1.05–2.55; p = 0.029). Cut-off value of CHA2DS2-VA score for predicting 1-year mortality was found to be ≥ 3 (AUC:0.863, p < 0.001) with 75% sensitivity and 81% specificity. A CHA₂DS₂-VA score of ≥ 2 (AUC = 0.725, p < 0.001) constituted the cut-off value for intensive care admission with 61% sensitivity and 74% specificity.ConclusionsAs a result of our study, we found that the CHA₂DS₂-VA score is an independent predictor of 1-year mortality following COVID-19 disease. Cut-off values of the CHA2DS2-VA score may help identify patients with an increased likelihood of ICU admission and 1-year mortality, although its predictive value may be limited in lower-risk populations.

Ocular trauma is a major cause of monocular blindness. Early and timely reporting is essential for favorable visual outcomes. This study examines health-seeking behaviors in ocular trauma cases, with a focus on traumatic cataracts. Single-center study in Western Central India. This retrospective study included all first-time traumatic cataract surgeries (previously operated cases excluded) performed between 2009 and 2022. Data from electronic medical records, including demographic and clinical details, were entered into pre-tested online forms, exported to Excel, and analyzed using Statistical Package for Social Sciences version 22. Descriptive statistics and cross-tabulation were applied, with P < 0.05 considered significant. Best corrected visual acuity (BCVA). A total of 2,093 eyes of 2,093 patients. Of the cohort, 1,473 (70.3%) were male and 620 (29.6%) female, median age 25 years; 39.4% were pediatric cases. The median interval between trauma and first consultation was 15 days; only 17.2% presented within 24 hours. Reporting interval significantly affected visual outcomes (P = 0.000). Most patients (91%) were from rural areas, and 86.3% were from low socioeconomic backgrounds. Pre- and post-surgical vision showed significant improvement, though delayed reporting reduced the gains. Main reasons for late presentation were lack of awareness, underestimation of severity, ignorance of primary care availability, and financial barriers. Delayed reporting after ocular trauma leads to poorer visual outcomes. Community counseling and awareness programs for patients, caregivers, and healthcare workers are essential. Promoting early reporting can substantially reduce preventable blindness due to ocular trauma. Data Access Statement: Research data supporting this publication are available from the NN repository located at www.NNN.org/download.

Vintage prints are crucial to preserve the cultural, historical, and artistic heritage and although traditional techniques of restoration are important challenges, physical deterioration, including fading, stains, ripping, and noise are major obstacles to preserve printed images. Manual conservation and classical methods of digital inpainting can be time-consuming, subjective and unable to match the level of fine textuality and stylistic fidelity. This paper presents a GAN-based reconstruction model of the high-quality reconstruction of the damaged vintage prints with the deep generative learning and style-conscious constraints. The suggested method uses an adversarial learning paradigm where a generator network aims at restoring missing structures, textures and tonal continuity and a discriminator network is used to assess realism, stylistic consistency and historical plausibility. The extensive art collection maintained in museums, libraries, and personal collections is filtered, including various patterns of degradation and printing styles. The high-level preprocessing, such as noise normalization, contrast enhancement, degradation-sensitive annotation, and others, facilitates the powerful training. The model considers content similarity preserving loss functions, similarity of perception, and consistency of style as content preserving goals in order to retain artistic integrity. Massive experiments indicate that the suggested structure significantly improves the performance of standard restoration and baseline deep learning structures in terms of structural and perceptual quality and visual authenticity. The effectiveness of the reconstructed outputs as the art historians and painting experts confirm the effectiveness of these measures in preserving original aesthetic character also through qualitative evaluations. The findings in the article suggest that GAN-based reconstruction is a scalable, customizable, and culturally aware way to conserve digital data and allow long-term preservation, accessibility of archival data, and scholarly study of delicate vintage prints.

Rapid growth in the number of devices connected to the Internet of Things (IoT) and the exponential surge in data usage clearly suggest that the development of big data is inextricably linked with the IoT. In an ever-expanding network, big data raises concerns regarding data access efficiency. This study critically reviews IoT data analytics, tools, techniques, and challenges in extracting meaningful information from IoT device-generated massive data sets. IoT data analysis approaches, including real-time analysis, predictive analysis, and anomalous behavior analysis, are discussed in detail. How big data platforms and cloud computing can tackle IoT data and why IoT data preprocessing, integration, and storage matter are explored in this paper. Additionally, it covers issues and future research directions in IoT data analytics, including data security, scalability, and privacy.

This study examines the transition from traditional paper‑based systems to advanced digital document and records management frameworks, emphasizing the growing strategic importance of AI‑enabled processing, cloud‑based platforms, workflow automation, and blockchain‑supported integrity controls. These technologies collectively foster more intelligent, scalable, and flexible document workflows. The analysis highlights key benefits, including enhanced operational efficiency, improved information security and regulatory compliance, reduced costs through decreased paper dependency and cloud scalability, and strengthened governance supported by accurate and accessible data. The paper also identifies critical challenges such as cybersecurity risks, integration complexities with legacy infrastructure, change‑management obstacles, and budgetary limitations—that organizations must address to achieve a successful digital transformation. Strategically, the study underscores the need for robust governance mechanisms to ensure that digital initiatives align with institutional goals and regulatory requirements. Insights from sectors such as healthcare and finance illustrate how modern digital document management solutions deliver measurable improvements in efficiency, compliance, and service quality. Ongoing developments in AI, cloud computing, and blockchain technologies are expected to further shape the field, making organizational agility and innovation essential for future competitiveness.