Search Results (2,613)

This study investigated a nonlinear tristable electromagnetic vibration energy harvester enhanced with adjustable mechanical stoppers. The stoppers were designed to limit yoke displacement and prevent sticking in the outer stable positions. The research aimed to extend the operational frequency bandwidth and improve the stability of energy conversion under variable excitation conditions. A comprehensive experimental setup based on a PXI measurement platform was developed to analyze the system’s electrical and mechanical responses. The study examined the influence of stopper spacing, vibration acceleration, and electrical load on the frequency–voltage characteristics and beam strain. The results showed that introducing adjustable stoppers eliminated the sticking phenomenon and broadened the effective operational bandwidth to a total range of 14 Hz under excitation frequencies between 10 and 50 Hz, while maintaining structural safety. The findings confirmed that the proper selection of stopper distance enabled adaptive tuning of the harvester’s dynamic behavior and power output, increasing the average voltage by approximately 50% compared with the configuration without stoppers. The proposed approach improved the autonomy and reliability of self-powered sensor nodes and other low-power electronic systems for Internet of Things (IoT) and condition monitoring applications. Full article

Electrochemiluminescence (ECL) has evolved into a powerful analytical technique due to its ultra-high sensitivity, low background noise, and precise electrochemical control. The development of efficient ECL emitters is central to advancing this technology for practical applications. Covalent organic frameworks (COFs) have recently emerged as promising candidates for constructing high-performance ECL systems. The tunable porosity, ordered π-conjugated structures, and versatile modular functionalities of COFs provide fast massive transport, effective electron transfer, rapid interfacial electrochemical reaction, and enhanced ECL emission performance. This review provides a comprehensive overview of the rational design strategies and structural engineering for COF-based ECL materials at the molecular level. Linkage chemistry, monomer selection (luminophores and π-conjugated non-ECL motifs), precise framework regulation, post-synthetic modification, composite formation, and other ECL enhancement strategies were discussed for developing COF-based ECL emitter. Both the incorporation of aggregation-induced emission and intramolecular charge transfer mechanisms are included to enhance ECL efficiency. Donor–acceptor conjugation, heteroatom element content, isomerism, substitution, and dimensional direction were regarded as effective strategies to regulate the electronic structure and band diagrams for designing high-performance ECL systems. The role of COFs as both active emitters and functional scaffolds for signal amplification is critically examined. Furthermore, their diverse analytical applications across biosensing, food safety, environmental monitoring, and chiral recognition are highlighted. By correlating structural features with ECL performance, this review offers insights into the design principles of next-generation reticular ECL materials and outlines future directions for their practical deployment in sensitive and selective sensing platforms. Full article

Background: Chest radiography (CXR) is the most frequently performed radiological exam worldwide, but reporting backlogs, caused by a shortage of radiologists, remain a critical challenge in emergency care. Artificial intelligence (AI) triage systems can help alleviate this challenge by differentiating normal from abnormal studies and prioritizing urgent cases for review. This study aimed to externally validate TRIA, a commercial AI-powered CXR triage algorithm (NeuralMed, São Paulo, Brazil). Methods: TRIA employs a two-stage deep learning approach, comprising an image segmentation module that isolates the thoracic region, followed by a classification model trained to recognize common cardiopulmonary pathologies. We trained the system on 275,399 CXRs from multiple public and private datasets. We performed external validation retrospectively on 1045 CXRs (568 normal and 477 abnormal) from a teaching university hospital that was not used for training. We established ground truth using a large language model (LLM) to extract findings from original radiologist reports. An independent radiologist review of a 300-report subset confirmed the reliability of this method, achieving an accuracy of 0.98 (95% CI 0.978–0.988). We compared four ensemble decision strategies for abnormality detection. Performance metrics included sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUROC) with 95% CI. Results: The general abnormality classifier achieved strong performance (AUROC 0.911). Individual pathology models for cardiomegaly, pneumothorax, and effusion showed excellent results (AUROC of 0.968, 0.955, and 0.935, respectively). The weighted ensemble demonstrated the best balance, with an accuracy of 0.854 (95% CI, 0.831–0.874), a sensitivity of 0.845 (0.810–0.875), a specificity of 0.861 (0.830–0.887), and an AUROC of 0.927 (0.911–0.940). Sensitivity-prioritized methods achieving sensitivity >0.92 produced lower specificity (<0.69). False negatives were mainly subtle or equivocal cases, although many were still flagged as abnormal by the general classifier. Conclusions: TRIA achieved robust and balanced accuracy in distinguishing normal from abnormal CXRs. Integrating this system into clinical workflows has the potential to reduce reporting delays, prioritize urgent cases, and improve patient safety. These findings support its clinical utility and warrant prospective multicenter validation. Full article

This paper constructs a numerical simulation model for the fire and fire-fighting system of an all-electric vehicle ro-ro passenger ship to study the influence of fire characteristics and fire-fighting system layout parameters on the fire-extinguishing system. The simulation results show that the fire can spread to the upper deck within 52 s, and the smoke will fill the main deck within 57 s. The study found that the battery capacity has a super-linear relationship with the fire hazard, and the fire thermal spread radius of a 240 Ah battery can reach 3.5 m. The high-expansion foam system has a low applicability in quickly suppressing battery fires due to its response delay and limited cooling capacity for deep-seated fires; the fire-extinguishing efficiency of fine water mist has spatial dependence: 800 µm droplets achieve effective cooling in the core area of the fire source with stronger penetrating power, while 200 µm droplets show better environmental cooling ability in the surrounding area; at the same time, the large-angle nozzles with an angle of 80–120° have a wider coverage range and perform better in overall temperature control and smoke containment than small-angle nozzles. The study also verified the effectiveness of fire curtains in forming fire compartments through physical isolation, which can reduce the heat radiation range by approximately 3 m. This research provides an innovative solution for improving the fire safety level of transporting all-electric vehicles on ro-ro passenger ships. Full article

Achieving long-term stable optimization in complex industrial processes (CIPs) is notoriously challenging due to their unclear physical/chemical reaction mechanisms, fluctuating operating conditions, and stringent regulatory constraints. A significant gap persists between promising artificial intelligence (AI) algorithms developed in academic research and their practical deployment in industrial actual processes. To bridge this gap, this article introduces the AI- and security-empowered end–edge–cloud modular platform (AISE³CMP). It consists of four systems such as whole-process AI modeling, end-side basic loop and AI-assisted decision-making, edge-side security isolation and AI control, and cloud-side security transmission and AI optimization. The data isolation collection module of the platform was deployed at a municipal solid waste incineration (MSWI) power plant in Beijing, where it collected multimodal data from real-world industrial sites. The platform’s functionality and effectiveness were validated through the software and hardware developed at the Smart Environmental Protection Beijing Laboratory. The experimental results show efficient and reliable signal transmission between the systems, confirming the platform’s ability to meet the computational demands of AI-based optimization and control algorithms. Compared to previous platforms, AISE³CMP features a dual-security transmission mechanism to mitigate data exchange risks and a modular design to enhance integration efficiency. To the best of our knowledge, this platform is the first prototype of a portable, end-to-end cloud platform with a dual-layer security mechanism for CIPs. While the platform effectively addresses data transmission security, further strengthening of cloud-side data protection and ensuring operational safety on the end-side remain significant challenges for the future. Additionally, utilizing this architecture to enable multi-region and multi-plant data sharing, in order to develop industry-specific large language models, represents a key research direction. Full article

The global water sector faces unprecedented challenges from climate change, rapid urbanisation, and ageing infrastructure, necessitating a shift towards proactive, digital strategies. Historically characterised as “data rich but information poor,” the sector struggles with underutilised and siloed operational data. Traditional machine learning (ML) models have provided a foundation for smart water management, and subsequently deep learning (DL) approaches utilising algorithmic breakthroughs and big data have proved to be even more powerful under the right conditions. This paper explores and reviews the transformative potential of Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs), enabling a paradigm shift towards data-centric thinking. GenAI, particularly when augmented with Retrieval-Augmented Generation (RAG) and agentic AI, can create new content, facilitate natural language interaction, synthesise insights from vast unstructured data (of all types including text, images and video) and automate complex, multi-step workflows. Focusing on the critical area of drinking water quality, we demonstrate how these intelligent tools can move beyond reactive systems. A case study is presented which utilises regulatory reports to mine knowledge, providing GenAI-powered chatbots for accessible insights and improved water quality event management. This approach empowers water professionals with dynamic, trustworthy decision support, enhancing the safety and resilience of drinking water supplies by recalling past actions, generating novel insights and simulating response scenarios. Full article

This study characterized leaf extracts of Cymbopogon flexuosus (Ryukyu Lemongrass Corporation, Okinawa, Japan) and evaluated the bioaccessibility and bioactivities of phenolic compounds following a simulated in vitro gastrointestinal model of digestion (in vitro GID) of plant material. Undigested (controls, AqC, EtC) and digested aqueous (AqD) and ethanolic (EtD) extracts were analyzed. Control extracts contained higher total phenolics and flavonoids than digested ones, with EtC showing the highest values. UHPLC-QToF-MS (ultra-high-performance liquid chromatography system coupled to a quadrupole time-of-flight mass spectrometer) identified 32 compounds, including phenolic acids, flavone aglycones, C-glycosides, and derivatives. Hydroxybenzoic acids, coumaric acid, caffeic esters, flavones, tricin derivatives, vitexin, and isoorientin exhibited reduced recovery, while coumaric acid hexoside, ferulic acid hexoside, and isoschaftoside/schaftoside exceeded 100% recovery, suggesting release from the matrix. Some compounds were absent from AqD, and many were found in the pellet, indicating potential colonic metabolism. Antioxidant activity (DPPH, reducing power, β-carotene/linoleic acid) was stronger in controls but always weaker than BHT/ascorbic acid. Extracts mildly inhibited α-amylase but more strongly inhibited α-glucosidase as shown with applied enzyme inhibition assays, especially EtD (76.93% at a concentration of 10 mg/mL), which showed stronger activity than controls but remained below acarbose (87.74% at 1 mg/mL). All extracts promoted HaCaT keratinocyte growth and reduced HCT-116 colon cancer cell viability at 250 µg/mL, with the strongest effects in AqC and AqD. Overall, GID decreased antioxidant activity but enhanced antidiabetic potential, confirming the safety and selective anticancer effects of C. flexuosus extracts. Full article

This study presents a time-dependent probabilistic risk assessment of Station Blackout (SBO) for a newcomer nuclear program, in this case Uganda’s prospective first nuclear power plant, with emphasis on the role of Flexible and Diverse Coping Strategies (FLEX) in mitigating AC power loss. The model developed incorporates exponentially distributed fail-to-run rates for Emergency Diesel Generators (EDGs) and Alternate AC (AAC) sources and applies a Weibull distribution to model offsite power recovery, reflecting the country’s infrastructural constraints and grid reliability challenges. Two operational scenarios are analyzed: (1) reliance solely on AC-based power sources and (2) inclusion of a Turbine-Driven Pump (TDP) as a non-AC coping mechanism. In both cases, FLEX is assumed to be integrated after initial commercial operation and functions as a portable AC supply. Results indicate that timely FLEX deployment substantially reduces SBO risk, with the largest benefit at longer mission times. Sensitivity analysis reveals that equipment reliability dominates deployment timing effects, with high-reliability systems achieving up to 75% risk reduction, while low-reliability equipment provides limited improvement below 25%. These findings provide a quantified risk perspective for Uganda’s nuclear safety planning and support evidence-based decisions on post-startup integration, infrastructure investment, and emergency preparedness. Full article

Electric Submersible Pumps (ESPs) are widely deployed in high-flowrate wells but are constrained by frequent failures and the need for rig-based interventions. This study presents the development and field validation of a rigless cable-deployed ESP (CDESP) system designed to enhance operational uptime and reduce intervention costs. The system features a corrosion-resistant metal-jacketed power cable, an inverted ESP configuration that eliminates the motor lead extension (MLE), and a vertical cable hanger spool (VCHS) for surface integration without removing the production tree. A field trial in a high-H₂S well demonstrated successful rigless deployment using coiled tubing (CT), achieving over two years of continuous runtime. Post-retrieval inspection revealed minimal wear, validating the system’s mechanical durability and reusability. Operational performance demonstrated reduced non-productive time (NPT), enhanced safety, and cost savings, with deployment completed in under 24 h, compared to the typical 10–14 days for rig-based methods. The CDESP system’s compatibility with digital monitoring and its potential for redeployment across wells positions it as a transformative solution for offshore and mature field operations. These findings support the broader adoption of CDESP as a scalable, efficient, and safer alternative to conventional ESP systems. Full article

Battery fault detection is crucial for maintaining the safety and reliability of large-scale lithium-ion battery systems, especially in demanding applications like electric vehicles and energy storage power stations. However, existing research primarily addresses either temporal patterns or spatial variations in isolation. This paper presents a comprehensive review of fault detection from a spatio-temporal perspective, with a specific focus on AI-driven methods that integrate temporal dynamics with spatial sensor data. The contributions of this review include an in-depth analysis of advanced techniques such as transfer learning, foundation models, and physics-informed neural networks, emphasizing their potential for modeling complex spatio-temporal dependencies. On the engineering side, this review surveys the practical application of these methods for early fault detection and diagnostics in large-scale battery systems, supported by case studies and real-world deployment examples. The findings of this review provide a unified perspective to guide the development of robust and scalable spatio-temporal fault detection methods for EV batteries, highlighting key challenges, promising solutions, and future research directions. Full article

Left ventricular thrombus (LVT) remains a clinically significant complication following acute myocardial infarction (MI). Although its incidence has declined in the era of primary percutaneous coronary interventions (PCIs), the best treatment remains unclear. For decades, vitamin K antagonists (VKAs) such as warfarin have been the mainstay of therapy, supported by guidelines recommendations. However, the limitations of warfarin, including a narrow therapeutic range, the need for frequent monitoring, and food/drug interactions, have spurred interest in direct oral anticoagulants (DOACs). This review summarizes the available evidence on anticoagulation strategies for LVT after MI, focusing on observational studies and recent randomized controlled trials. A total of 12 studies were included in this review: 9 retrospective cohorts and 3 randomized controlled trials. Patient populations ranged from small single-center cohorts to large multicenter registries. DOACs, compared with warfarin, were associated with a higher rate of thrombus resolution, a lower rate of stroke and systemic embolism, and a similar mortality. The usage of DOACs marginally reduced the rate of major bleeding compared with warfarin. The current evidence indicates that DOACs may offer comparable efficacy and potentially improved safety relative to warfarin, although most randomized trials remain small and underpowered for definitive conclusions. Larger, adequately powered studies are still required before DOACs can be routinely considered equivalent alternatives. The RIVAWAR randomized trial provides the strongest evidence to date regarding the use of DOACs in LVT after MI, but further large-scale randomized studies are required to establish definitive guidance. Until then, anticoagulation therapy including DOACs should be individualized, balancing the thromboembolic risk, bleeding risk, and practical considerations of anticoagulant use. Full article

Nanotechnology offers powerful new tools to enhance food quality monitoring and safety assurance. In the food industry, nanoscale materials (e.g., metal, metal oxide, carbon, and polymeric nanomaterials) are being integrated into sensory systems to detect spoilage, contamination, and intentional food tampering with unprecedented sensitivity. Nanosensors can rapidly identify foodborne pathogens, toxins, and chemical changes that signal spoilage, overcoming the limitations of conventional assays that are often slow, costly, or require expert operation. These advances translate into improved food safety and extended shelf-life by allowing early intervention (for example, via antimicrobial nano-coatings) to prevent spoilage. This review provides a comprehensive overview of the types of nanomaterials used in food sensory applications and their mechanisms of action. We examine current applications in detecting food spoilage indicators and adulterants, as well as recent innovations in smart packaging and continuous freshness monitoring. The advantages of nanomaterials—including heightened analytical sensitivity, specificity, and the ability to combine sensing with active preservative functions—are highlighted alongside important toxicological and regulatory considerations. Overall, nanomaterials are driving the development of smarter food packaging and sensor systems that promise safer foods, reduced waste, and empowered consumers. However, realizing this potential will require addressing safety concerns and establishing clear regulations to ensure responsible deployment of nano-enabled food sensing technologies. Representative figures of merit include Au/AgNP melamine tests with LOD 0.04–0.07 mg L⁻¹ and minute-scale readout, a smartphone Au@carbon-QD assay with LOD 3.6 nM, Fe₃O₄/DPV detection of Sudan I at 0.001 µM (linear 0.01–20 µM), and a reusable Au–Fe₃O₄ piezo-electrochemical immunosensor for aflatoxin B1 with LOD 0.07 ng mL⁻¹ (≈15 × reuse), alongside freshness labels that track TVB-N/amine in near-real time and e-nose arrays distinguishing spoilage stages. Full article

Topical tretinoin (all-trans-retinoic acid) is a first-generation vitamin A derivative with well-established efficacy in acne vulgaris and photoaging. Owing to its pleiotropic effects on epidermal differentiation, collagen synthesis, and skin pigmentation, numerous off-label uses have been proposed across dermatology. This narrative review summarizes current evidence on the efficacy and safety of topical tretinoin for multiple dermatological conditions, based on studies published between January 2000 and July 2025. Robust data from randomized clinical trials (RCTs) and systematic reviews support its benefit in acne and photoaging, whereas smaller RCTs and prospective studies indicate potential efficacy for melasma, postinflammatory hyperpigmentation, striae distensae, flat warts, alopecia areata, androgenetic alopecia, hypertrophic scars and keloids, and actinic keratosis and as pretreatment before chemical peels or laser resurfacing. However, high-quality, adequately powered trials with standardized outcome measures are still needed to establish clinical guidelines. Regarding cutaneous oncology, a large RCT demonstrated no preventive effect of tretinoin on keratinocyte carcinomas. Adverse events are typically mild, localized, and transient, and available evidence does not support an association with systemic adverse effects. Full article

To accommodate the high penetration of intermittent renewable energy sources like wind and solar power into the grid, coal-fired units are required to operate with enhanced deep peak-shaving and variable load capabilities. This study develops a dynamic model of the boiler–turbine coordinated control system (BTCCS) for ultra-supercritical once-through boiler (OTB) coal-fired units operating under wet conditions. A mechanistic model framework is established based on mass and energy conservation. In case of missing steady-state data, this work proposes a mechanism-integrated parameter identification method that determines model parameters using only dynamic running data while incorporating physical constraints. Model validation demonstrates that the proposed approach accurately reproduces the variable-load operation of the BTCCS within the range of 50–350 MW. Mean relative errors of output variables are all less than 7.5%, and root mean square errors of output variables are less than 0.3 MPa, 1.4 kg/s, 0.25 m, and 20.7 MW, respectively. Open-loop simulations further confirm that the model captures the essential dynamic characteristics of the system, making it suitable for simulation studies and control system design aimed at improving operational flexibility and safety of OTB coal-fired units under wet conditions. Full article

Under the drive of the “dual carbon” goals, the insufficient frequency regulation capability of nuclear power as a baseload source and the dynamic demand of integrating a high proportion of renewable energy into the grid have increasingly highlighted conflicts. The inherent minute-level regulation inertia of nuclear power units struggles to cope with second-level frequency fluctuations in the grid, leading to an increased risk of system instability. There is an urgent need for energy storage technologies to fill the millisecond-level power support gap for nuclear power frequency regulation. This paper, focusing on nuclear power frequency regulation scenarios, constructs a “Technology–Economy–Policy” multidimensional energy storage evaluation system for the first time. Through a systematic analysis of 11 key indicators, such as response time and safety, the paper selects energy storage technologies suitable for nuclear power frequency regulation scenarios and proposes a hybrid energy storage optimization strategy. The research provides a systematic evaluation framework and empirical support for the selection of energy storage for nuclear power frequency regulation, with significant practical value in improving grid dynamic stability and promoting the construction of new power systems under the “dual carbon” goals. Full article