Online monitoring of Chinese herbal medicine production process toward lean six sigma: multimodal data fusion based on transformer architecture.

Exposing and simulating spatiotemporal patterns of varied electric vehicles travel on a large-scale network based on real-time RFID data

Advancing real-time coastal data monitoring: Bio-optical property analysis (chlorophyll-a and TSM) in the Northern Bay of Bengal using Sentinel-3 OLCI, IRS Oceansat-3, and artificial neural networks.

We conducted a qualitative evaluation of health department measles response practices from March to October 2024. Focus group sessions with public health officials from 11 jurisdictions revealed that despite resource constraints and evolving challenges, health departments enhanced surveillance and response capabilities by prioritizing community engagement, data modernization, and continuous quality improvement. Key lessons include the importance of real-time data tracking, the value of established protocols and training, and the need for standardized metrics to evaluate response effectiveness across jurisdictions. (Am J Public Health. 2026;116(4):437-442. https://doi.org/10.2105/AJPH.2025.308392).

Traditional assessments of depressive symptoms often rely on retrospective self-reports, which may be affected by cognitive and memory biases. Few studies have compared retrospective and dynamic (real-time) assessments to examine the consistency and structure of depressive symptom reporting. This study aimed to compare retrospective and dynamic assessments of depressive symptoms in youth using network analysis to explore symptom-level associations and clustering. Ninety Brazilian adolescents and young adults (mean age=18years), with and without depression, completed the Short Mood and Feelings Questionnaire (SMFQ) every other day for 14days via a smartphone-based chatbot (dynamic assessment). At the end of the 2-week period, they completed the same questionnaire retrospectively. Network analyses were conducted using Exploratory Graph Analysis (EGA) and Dynamic Exploratory Analysis (DEA) to identify symptom communities and compare network structures across both assessment methods. Both retrospective and dynamic assessments revealed three symptom communities; however, the composition and structure of these communities differed. Retrospective assessments showed stronger connections among cognitive symptoms, while dynamic assessments displayed a more balanced distribution, with stronger associations between somatic and affective symptoms. Findings highlight significant differences in depressive symptom networks between retrospective and dynamic assessments. Dynamic methods may offer less biased and more ecologically valid insights into youth depression, underscoring the importance of real-time data collection in clinical assessment and research.

Lightweight active just-in-time soft sensor model for real-time data streaming

Corrigendum to “ChatCNC: Conversational machine monitoring via large language model and real-time data retrieval augmented generation” [J Manuf Syst 79 (2025) 504–514

Railway transportation plays a crucial role in freight movement, but the manual monitoring of wagon conditions leads to higher labour requirements, longer maintenance times, and increased operational costs. Frequent inspections are necessary to track parameters such as load status, temperature, vibrations, smoke detection, and overall wagon health, which is timeconsuming and inefficient. To address these challenges, this project proposes a Smart Railway Wagon based on Programmable Logic Controller (PLC).In the proposed system, various sensors—including load, temperature, vibration, smoke,and GPS modules—are installed on the wagon to continuously monitor its operational conditions. The PLC acts as the central controller, processing real-time data from these sensors and automatically triggering alerts or actuators when abnormal conditions are detected. Through an IoTenabled communication module, the data is transmitted to a SCADA system or cloud dashboard, enabling remote monitoring and predictive maintenance from a central control room.By automating the monitoring process, the system significantly reduces the dependency omanual labour, minimizes maintenance m time, and enhances safety and operational efficiency.This integration of PLC-based automation with IoT and SCADA ensures real-time monitoring, faster decision-making, and better resource management, making it a robust and scalable solution for modern railway operations

Proper allocation of resources within the Emergency Departments (EDs) is very important in enhancing patient outcomes, wait times, and operational efficiency. The following paper introduces a Hybrid Simulation-Optimization Approach, which is a hybrid of the Discrete-Event Simulation (DES) and optimization algorithms in the allocation of resources, including medical staff, beds, and equipment dynamically. The implementation of the model was based on real-life ED data available in a hospital in a metropolitan city, and the model was compared to the traditional cloud-based and heuristic resource allocation models. The findings indicated a great improvement using the Hybrid Simulation-Optimization Model with 85 percent staff utilization, 4.5 patients treated / hour per staff member and 15 percent staff idle time, in comparison to Cloud-Based System that achieved 70 percent staff utilization and the staff-patient ratio was 1:6. More so, the Hybrid Model cut patient waiting time to 10 minutes as compared to the Cloud-Based System that recorded 15 minutes. These results pinpoint the power of the integration of Simulation and optimization to optimize the allocation of staff, manpower, and usage of resources in EDs. The staff-patient ratio was also streamlined to 1:4, which allowed enhancing the balance of work. The results of the staff utilization and patient care time improvements prove the usefulness of real-time information integration and optimization algorithms. Further studies on the subject should aim at extending the method to multiple hospitals, adding real-time data feeds, and machine learning algorithms to better predict demand and allocate resources, eventually enhancing the management of healthcare resources in a wide range of EDs.

ABSTRACT Creep deformation is a dominant failure mode in metallic materials during service, governing structural stability, reliability and design life. However, conventional creep testing is time-consuming and resource-intensive, limiting the availability of high-fidelity creep data for efficient prediction. To address this issue, this study aims to develop a material-level digital twin system capable of generating high-fidelity creep strain data under limited experimental conditions. The proposed system is demonstrated using a 7-series aluminium alloy and employs a physics-guided gated recurrent unit model that integrates real-time sensor data with dynamic updating for creep strain prediction. Results show that incorporating physical constraints improves prediction accuracy by approximately 12% compared with the GRU model. Comparison with continuum damage mechanics-based creep models further demonstrated the competitive predictive capability of the proposed approach. Furthermore, a standalone application was developed to enable real-time monitoring and prediction of the creep process, facilitating efficient experimental implementation.

Efficient route optimization is critical for municipal solid waste collection, as poorly planned routes increase operational costs, environmental impact, and resource consumption. This study compares three path optimization algorithms-Ant Colony Optimization, Dijkstra's algorithm, and the Nearest Neighbour heuristic-in the context of municipal waste collection networks. The algorithms are evaluated on simulated graphs of varied sizes to assess path cost, computational time, and solution stability. Ant Colony Optimization demonstrates higher adaptability, cost-effectiveness, while Dijkstra provides deterministic optimal pathways with lower variance. The Nearest Neighbour approach, although computationally faster, constantly produces inferior routes. Performance disparities were statistically validated using the Wilcoxon signed-rank test. The findings offer practical guidelines for selecting efficient routing algorithms in urban waste collection systems and establish a foundation for future improvements incorporating real-time data and hybrid models.

This study aimed to evaluate the real-time performance of an Electrostatic Mass Monitor (EDM) against the reference-grade Tapered Element Oscillating Microbalance (TEOM) for measuring PM2.5 mass concentrations emitted from a simulated oil/gas furnace. PM2.5 was sampled from the furnace exhaust using a dilution system under varying operating conditions, including a wide range of Air-to-Fuel Ratios (AFR) (2.5, 5.0, 7.5) and flue gas temperature setpoints (75 °C–150 °C). Experimental results confirmed that PM2.5 emission rates were overwhelmingly controlled by the AFR, with the fuel-rich condition (AFR = 2.5) generating concentrations up to 1400 g/m3 and exhibiting high temporal volatility. A linear regression analysis performed on the paired real-time data established a strong positive correlation between the two instruments (Pearson’s r = 0.962; Adj. R2= 0.963). The resulting regression slope of 0.909 suggests the EDM systematically reports mass concentrations approximately 9% lower than the TEOM for these combustion aerosols. However, the EDM demonstrated superior temporal resolution, capturing greater short-term fluctuations in the PM2.5 concentration compared to the TEOM. The high level of correlation validates the EDM as a reliable and cost-effective instrument for continuous, real-time PM2.5 emission monitoring in combustion source applications, contingent upon the application of a site-specific calibration factor derived from the TEOM.

The growing need for real-time, secure, and noise-resilient biomedical data transmission – especially in critical applications like ECG monitoring – exposes the limitations of traditional Digital Signal Processing (DSP) techniques. These methods struggle with asynchronous signal domains and lack deep learning-based security integration. To address these challenges, this study introduces a novel DSP architecture that combines Wiener Filtering with Asynchronous FIFO buffering for effective noise reduction and timing correction during ECG signal preprocessing. To ensure secure data handling without signal degradation, a Sparse Graph Quantum Hamiltonian Generative Adversarial Attention Network (SpQH-GAN), optimized via Hyperbolic Sine Optimization (HySiO), is proposed. This unified framework supports high-fidelity ECG signal regeneration while ensuring real-time performance and data confidentiality. The model achieved a PSNR of 39.7 dB, SSIM of 0.96, and a 98.7% Mean Opinion Score (MOS) agreement with clinicians, demonstrating its clinical reliability. The proposed solution offers a robust and scalable approach for secure, high-quality ECG data processing in asynchronous and noisy environments.

Malaria remains a major public health threat in Ethiopia, with more than 7.3 million confirmed cases and 1,157 deaths reported in 2024, representing the highest incidence recorded in the past seven years. Persistent regional heterogeneity in transmission, emerging insecticide resistance, and systemic health system constraints continue to undermine national malaria elimination efforts. This study assessed systemic challenges and strategic priorities in Ethiopia's malaria management across six operational domains: preparedness, detection, containment, response, recovery, and prevention. A national landscape analysis was conducted using a convergent mixed-methods approach. Peer-reviewed literature and relevant policy documents published between 2016 and 2025 were systematically searched across bibliographic databases and institutional repositories. Eligible studies were assessed using predefined inclusion criteria and synthesized descriptively across six operational domains of malaria management. In parallel, structured consultations were undertaken with malaria programme professionals at federal, regional, and facility levels to validate and contextualize findings from the evidence synthesis. These consultations were conducted as part of technical validation and did not constitute primary qualitative research. The search yielded 246 records, of which 198 unique documents were screened and 24 malaria-specific primary studies met inclusion criteria. Household surveys reported national insecticide-treated net (ITN) ownership averaging approximately 64%, with substantially lower coverage and utilization documented in several high-burden and urban-adjacent settings, particularly within Oromia and selected densely populated areas. Indoor residual spraying (IRS) was implemented in roughly half of targeted high-risk zones, with operational coverage constrained by logistical delays and reduced effectiveness in areas with documented pyrethroid resistance. Digital malaria surveillance platforms were operational in approximately 80% of health facilities; however, multiple studies reported delayed reporting, incomplete data submission, and weak feedback mechanisms, especially in remote districts. Diagnostic performance was compromised by intermittent stock-outs, expired rapid diagnostic tests (RDTs), and widespread pfhrp2/3 gene deletions affecting HRP2-based RDT sensitivity. Post-outbreak recovery capacity remained limited, with only about half of health facilities reporting timely replenishment of essential malaria commodities within three months and little evidence of routine after-action review processes. Cross-cutting constraints included delayed financing, fragmented digital systems, limited routine entomological surveillance, and insufficient community engagement in prevention activities. Ethiopia's malaria programme demonstrates foundational capacity to interrupt transmission but remains constrained by systemic, operational, and equity-related gaps. Strengthening real-time surveillance and data use, adapting vector control strategies in response to resistance patterns, institutionalizing recovery and learning mechanisms, and embedding community-centred prevention approaches are essential to support a resilient and sustainable malaria response.

With the rapid development of science and technology, various intelligent devices continuously generate large amounts of real-time location data. Efficiently utilizing this data for accurate location prediction has become a critical issue in fields such as intelligent transportation and smart logistics. To realize low-power location prediction, this paper constructs a molecular recurrent neural network (RNN) model based on DNA strand displacement (DSD) technology. The RNN model can process sequence data and accurately predict the next position. Firstly, multiple modules are designed based on DSD circuits, including dual-channel weighted summation module, dual-domain data module and Tanh activation function module. Secondly, a RNN model for processing sequence data is constructed using the above modules. Finally, the constructed RNN model successfully achieves position prediction for multiple inputs and a single output. The robustness and accuracy of the neural network are verified through data experiment. It has been demonstrated that DNA molecules can effectively process complex sequence data. This method holds significant potential in the field of path planning. This method holds significant potential in the field of path planning. This paper uses MAE and RMSE to evaluate the experimental data. The results prove that the RNN model constructed in this paper demonstrates strong accuracy and stability.

ABSTRACT This article applies the offense-defence balance (ODB) framework to evaluate the effectiveness of U.S. suppression and destruction of enemy air defences (SEAD/DEAD) in East Asia against Chinese integrated air defence systems. Specifically, it compares two force composition options: (1) stealth aircraft supported by cruise and hypersonic missiles; and (2) stealth aircraft augmented by collaborative combat aircraft (CCA) alongside the same missile suite. Drawing on recent debates over China’s anti-access/area denial (A2/AD) strategy, the article examines the penetrative potential of U.S. airpower in contested airspace and assesses how the integration of CCA – a new generation of AI-enabled, semi-autonomous platforms – may reshape the cost-exchange ratio in favour of the offense. The findings suggest that CCA-enhanced operations offer a more cost-effective solution, enabling saturation attacks that overwhelm Chinese air defences while facilitating high-precision strikes on mobile and static targets by sharing real-time target data with stealth aircraft.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has underscored an urgent need for rapid, accurate, and accessible diagnostic tools to detect infections, facilitate timely quarantine, and inform therapeutic decisions. Fabricating nanostructures on biosensors can enhance biomolecule orientation, minimize steric hindrance, and reduce non-specific binding, resulting in improved signal-to-noise ratios and high sensitivity, making them promising point-of-care diagnostic. However, clinical translation remains limited by challenges in scalable and reproducible fabrication and insufficient diagnostic validation. Here, we introduce a nanowell biosensor (NW-Biosen) fabricated via semiconductor manufacturing, enabling scalable production and yields >1000 electrodes with reproducible properties. NW-Biosen detects SARS-CoV-2 antigens from patient nasal swabs within ∼10 min, with high sensitivity and minimal interference from other coronavirus recombinant proteins, respiratory pathogens, bacteria, and environmental substances, while maintaining consistent reproducibility with different batches. We validated NW-Biosen through two independent clinical trials involving 249 retrospective and 243 prospective patient samples. In prospective cohort, NW-Biosen achieved 93.02% sensitivity, 98.73% specificity, and a Cohen's kappa of 0.927, indicating near-perfect agreement with RT-PCR and superior sensitivity compared to commercially available colorimetric kits. Thus, NW-Biosen enables rapid, highly sensitive, reproducible, and cost-effective at-home detection, with real-time data transmission to public health authorities via mobile app integrated with a miniature potentiostat.

The global maritime industry, a critical pillar of international trade, continues to face persistent challenges in ensuring the integrity, security, and transparency of containerized cargo data, particularly during ocean transport. Traditional container tracking systems at sea often lack the reliability and resilience required to prevent data tampering, cyber threats, and operational inefficiencies. As supply chains become more complex and interconnected, the demand for robust, end-to-end data security solutions becomes more pressing. A promising technological advancement in this area is the convergence of smart containers, equipped with Internet of Things (IoT) sensors for real-time condition monitoring, and blockchain technology (BCT) for secure data validation. These IoT devices facilitate continuous tracking of critical parameters such as location, temperature, humidity, tilt, and the like. However, the data they generate remains vulnerable to cyberattacks, signal disruptions, and unauthorized alterations. Blockchain’s decentralized and tamper-evident architecture addresses these vulnerabilities by enabling secure data immutability, transparent audit trails, and enhanced stakeholder trust. Despite its potential, the practical integration of blockchain with smart container systems in maritime logistics remains largely underexplored. To bridge this gap, this paper proposes a blockchain-enabled smart container monitoring system that combines container real-time data with secure physical tracking. Furthermore, to ensure scalability and efficient in data storage, hybrid on/off-chain architecture is introduced, balancing blockchain integrity with performance and resource optimization.

Stress is a key determinant of health outcomes and may influence work performance. Questionnaire-based assessments of stress are typically broad and retrospective. Daily stress measurements via smartphones offer more granular, real-time data but have adherence issues. Using an already established communication medium (WhatsApp) and a more conversational style assessment might improve adherence and help collect more detailed insights into (work) stress, underlying stressors, and countering energy sources. This study focuses on the usability and feasibility of semiautomated voice- and text-messages (with and without emojis) via WhatsApp as a method to collect daily data on experienced work stress, stressors, and energy sources. A sample of 210 workers was recruited via social media and participated in a 10-workday diary study using semiautomated WhatsApp messages to rate daily stress, stressors, and energy sources. Questions (with and without emojis) were presented by a chatbot as text messages with clickable buttons (multiple-choice questions; MC) or with instructions to answer with either a voice or a text message. The study used an experimental design with 4 groups: (1) week 1 voice, week 2 text/MC with emojis; (2) week 1 voice, week 2 text/MC without emojis; (3) week 1 text/MC, week 2 voice with emojis; (4) week 1 text/MC, week 2 voice without emojis. Pre- and poststudy web-based questionnaires assessed demographics, familiarity with voice messages, and usability, including participants' preference for research studies. Open answers were coded using artificial intelligence, and the number of stressors or energy sources was compared across the 3 collection methods (MC, voice, and text messages) to determine if the amount and quality of information collected differ per method within participants. A total of 158 workers completed at least 80% of scheduled conversations. The sample was predominantly women(170/210, 81%), highly educated (173/210, 82%), and a slight majority worked part-time (109/210, 52%). Mean adherence to the daily schedule was very high (mean of 95%). The postquestionnaire revealed a strong preference for MC and text over voice messages, mostly due to ease and convenience in a variety of situations. The number of stressors per week was approximately 3 times higher in the MC-condition than in the voice condition, even though average stress levels per week did not differ significantly within participants. The number of energy sources was comparable between open answers in the voice and text conditions, but voice messages consisted of more words. Collecting (work) stress data via semiautomatic WhatsApp messages is a feasible method with low effort for participants. Usability ratings indicated a strong preference among participants for MC and text messages over voice messages. Future research should explore usability in more diverse samples and in direct comparison to traditional assessment methods.

Abstract- The probability of a patient's survival is intimately tied to the speed of professional intervention during the critical 'golden hour'. In many current setups, however, emergency response systems function without integrated communication between mobile units and medical centers. This lack of synchronization frequently leads to transport delays, overcrowding at specific hospitals, and improper distribution of healthcare assets. This paper presents LifeLink, a unified emergency coordination system developed to facilitate active data flow among patients, paramedics, and healthcare facilities. Keywords: Emergency Coordination, Hospital Resource Optimization, Real-Time Data Exchange, Intelligent Routing, Digital Health Infrastructure