Search Results (11,829)

With the advancement of systematic ecological protection and restoration, ecosystem coordination assessment and multi-scale differentiation analysis have become increasingly important for regional ecological governance. In this context, this study develops a multi-scale coupling coordination evaluation framework for the mountain–water–forest–farmland–lake (MWFFL) system in Dangtu County, Anhui Province. The framework integrates 14 indicators across five subsystems, uses a combined weighting method based on the Entropy Weight Method and Analytic Hierarchy Process, and applies the coupling coordination degree (CCD) model and trend analysis to characterize inter-system coordination and its spatiotemporal patterns at the regional and ecosystem scales. The results indicate that land use is dominated by arable land, with water bodies forming the structural backbone and construction land distributed in clusters. From 2020 to 2024, the mean CCD remained stable around 0.675, indicating that the overall coupling coordination level was relatively stable. Spatially, the CCD pattern remained higher in the southwest and lower in the northwest, with a new high-value clustering zone emerging in the south. At the ecosystem scale, the four ecological restoration units showed distinct spatiotemporal patterns of coupling coordination. This multi-scale MWFFL evaluation framework supports regional ecological monitoring and provides a reference for restoration effectiveness assessment in similar regions under the life community concept. Full article

Apnea of prematurity (AOP) remains a critical challenge in neonatal care, with caffeine citrate serving as the cornerstone of pharmacological intervention. However, the current standardized dosing schedule fails to account for significant inter-individual variability in caffeine pharmacokinetics and clinical response. This narrative review explores the transformative potential of integrating wearable sensor technologies and multi-modal data analytics into a closed-loop framework for personalized caffeine therapy. Based on a synthesis of current monitoring literature, we propose a theoretical, comprehensive monitoring system utilizing the area under the respiratory curve (rAUC) as a continuous proxy metric, alongside waveform amplitude analysis aligned with pediatric polysomnography standards. By incorporating emerging metrics such as respiratory rate variability (RRV) and hypoxic burden, the framework enables the objective quantification of respiratory stability. Furthermore, the integration of established neonatal intensive care unit (NICU) parameters for bradycardia and oxygen saturation detection provides a critical cross-validation layer to minimize artifact-induced false alarms. This conceptual model bridges the gap between advanced signal processing and clinical oversight, offering a scalable pathway toward precision dosing. By shifting from reactive to predictive neonatology, sensor-driven optimization can enhance therapeutic efficacy, reduce alarm fatigue, and ultimately improve developmental outcomes for preterm infants. Full article

Dental fluorosis (DF) remains a global public health challenge traditionally attributed to elevated water fluoride F⁻. However, the Halo Effect and environmental factors now complicate this dose–response relationship. Following PRISMA 2020 guidelines, this systematic review identified 20 observational studies (n = 21,780) via PubMed, Scopus, and Web of Science. Inclusion logic utilized the PICOS framework, specifically selecting human studies that reported quantitative water F⁻ levels alongside environmental or dietary confounders. Quality was assessed via the Newcastle–Ottawa Scale. Synthesis revealed that in optimal fluoridated areas (0.7 mg/L), mild DF prevalence reached 15–20% in cohorts with high “Halo Effect” exposure (infant formula, processed beverages) a twofold increase over historical benchmarks. High altitude (>2000 m) and arid climates further exacerbated toxicity by altering renal clearance. These factors sustain systemic fluoride levels that inhibit protease activity (MMP-20/KLK4) and induce endoplasmic reticulum stress during enamel maturation, causing hypomineralization. Current water-centric monitoring is insufficient for modern risk assessment. A transition toward Total Daily Intake (TDI) models and context-specific standards accounting for altitude and dietary diffusion is essential to balance caries prevention with systemic safety. Full article

This study presents a low-cost edge-IoT-based structural health monitoring (SHM) architecture for crack monitoring in operational underground tunnels. The system integrates crack-displacement sensing, temperature measurement, ESP32-based edge processing, LoRa/MQTT communication, AES/ECDSA-based data protection, and cloud-based data management. The architecture was validated through a 30-day field campaign at two representative cracks in the Hai Van Tunnel, with measurements acquired at 60 s sampling intervals. The results show that edge-based wavelet–Kalman processing improved measurement stability and reduced high-frequency noise, while the implemented security mechanism introduced only minor latency relative to the monitoring cycle. The two monitored cracks exhibited small micrometer-scale fluctuations associated with temperature variation and showed no cumulative widening trend during the observation period. This study demonstrates the feasibility of campaign-based tunnel crack monitoring using a trustworthy edge-sensing architecture, while longer deployments with more sensing nodes are needed to fully evaluate scalability and operational durability. Full article

To address the key challenges in GB-RAR monitoring of super-tall buildings—namely, complex noise interference (transient pulse disturbances coupled with high-frequency random fluctuations), the difficulty of distinguishing normal wind-induced vibrations from hazardous deformations, and the propensity of single-algorithm prediction models to converge prematurely—this paper proposes an integrated monitoring data processing workflow that combines status assessment and deformation early warning, using Wuhan Greenland Center as a case study. A denoising method combining Median Absolute Deviation outlier removal and Savitzky–Golay filtering was designed for preprocessing, quantitatively validated through signal-to-noise ratio analysis. Based on filtered data, a spatio-temporal trajectory model was established to visualize and evaluate building movement. Furthermore, a GB-RAR-oriented residual-driven warning framework was developed by coupling a PSO-GA-BP deformation prediction model with adaptive sliding-window thresholding and finite-state warning decisions. Simulation results demonstrate that the PSO-GA-BP model outperforms other neural network models in prediction accuracy, and the derived early warning system exhibits strong feasibility and sensitivity. This workflow proves suitable for GB-RAR deformation monitoring of super-tall buildings, offering valuable reference for future research. Full article

Accurate and automated extraction of solid waste sites from remote-sensing imagery constitutes a pivotal demand for contemporary environmental regulation and risk mitigation. However, in high-resolution remote-sensing imagery, solid waste sites are typically represented as a single semantic image object (SIO), which is composed of multiple physical image parcels (PIPs) exhibiting significant variations in scale, morphology, and spectral properties. This intrinsic heterogeneity substantially increases the complexity and uncertainty of multi-class site identification. To address this challenge, this paper proposes WasteMOE Net (WMN), which is developed based on the core concept of modeling the SIO–PIP relationship. WMN adopts a heterogeneous expert selection mechanism combined with a nested mixture-of-experts architecture. It thus enables adaptive perception of complex PIPs across diverse scenarios and their integrated discrimination at the SIO level. In addition, by incorporating the explicit nonlinear representation capability of the KAN network, WMN effectively improves multi-class recognition accuracy while maintaining computational efficiency. Furthermore, this study constructs a high-resolution solid waste site dataset in accordance with the SIO–PIP-aware annotation principle, encompassing five representative categories: tailings ponds (TP), construction spoil sites (CSS), landfill sites (LS), garbage dump sites (GDS), and excavation sites (ES). Experimental results show that WMN achieves mAP50 values of 74.2% (GDS), 63.5% (CSS), 80.9% (ES), 85.4% (TP), and 83.1% (LS) in detection tasks, and 75.4%, 64.1%, 83.0%, 86.7%, and 84.1% for the corresponding categories in segmentation tasks. It achieves competitive performance compared with state-of-the-art methods in both tasks. Further, in a real-world application over Loudi City, China, WMN completed the processing of a 490.67 km² area within 1.34 h. The recognition accuracies for GDS and ES reached 54.8% and 65.3%, respectively. Finally, the proposed method has been successfully integrated into a GIS-based solid waste pollution risk prevention system, which markedly boosts the overall efficiency of environmental monitoring and on-site inspections. Full article

Aqueous precipitation and crystallization are central to impurity removal, product formation, and resource recovery in mineral and chemical processing, but robust inline monitoring remains challenging because supersaturation is not measured directly and conductivity signals are affected by temperature, composition drift, bubbles, solids, polarization, and fouling. Electrical conductivity (EC) is attractive as a low-cost, rugged process analytical tool, yet its usefulness depends on mechanistic interpretation: EC reflects charge-carrier concentration and mobility rather than supersaturation itself. This review organizes the literature into a layered framework covering (i) measurement integrity and deployment, (ii) bulk-signal extraction in multiphase media, (iii) estimation of latent variables such as dissolved concentration or supersaturation proxies, and (iv) control readiness based on conductivity-derived targets. Frequency-aware conductivity extraction, event-anchored verification, and observer-based estimation are treated as optional, complementary modules. A Ca-carbonate/CaCO₃ system is used as an illustrative case because its coupling among conductivity, pH/speciation, supersaturation, and precipitation is especially transparent, although the framework is intended for broader processing systems, including complex liquors and slurries. Opportunities are also highlighted for nanomaterials to improve both precipitation control and EC information content. Full article

In embodied-intelligence Industrial Internet of Things (IIoT), multi-AGV intelligent warehousing requires continuous processing of latency-sensitive tasks, such as environmental perception, inventory monitoring, and anomaly detection. Due to limited onboard computing capability and energy capacity, purely local execution can hardly satisfy real-time requirements, whereas fully cloud-based processing may incur excessive transmission delay and backhaul overhead. To address this issue, this paper investigates the joint optimization of AGV service-point migration and task offloading under a cloud-edge-end collaborative architecture. Considering the impact of service-point selection on wireless access, MEC resources, movement delay, and energy consumption, as well as the effect of offloading decisions on transmission, computation, and AGV-side energy cost, a dual-time-scale optimization model is formulated to minimize the long-term accumulated system delay while satisfying task latency and AGV energy constraints. To solve the resulting mixed discrete problem, a DPSO-MAPPO algorithm is proposed, where DPSO searches service-point plans satisfying movement and conflict constraints at the slow time scale, and MAPPO learns coordinated multi-AGV offloading policies at the fast time scale. The delay and energy feedback further enables coordination between the two types of decisions. Simulation results show that the proposed algorithm converges stably, reduces system delay by 13.55% compared with benchmark algorithms, and improves total energy consumption and energy-violation control. Full article

The development of new technologies enabling rapid, frequent, and reagent-free monitoring of kidney function is recognized as being of paramount importance. In this work, mid-(MIR) and near-infrared (NIR) spectroscopy were compared for the prediction of key renal biomarkers—creatinine, urea and albumin—using 54 serum solutions mimicking the biochemical profiles of five stages of chronic kidney disease (CKD). MIR spectra were acquired in a high-throughput microplate platform after a simple dehydration step, while the NIR spectra were obtained directly from liquid serum using a fiber optic probe. After evaluating several spectral pre-processing methods and targeted spectral regions, excellent regression models (R² > 0.9 for the best models) were obtained for the three biomarkers. MIR provided highly accurate urea predictions, whereas optimized NIR sub-regions enabled excellent estimation of creatinine and albumin. Both MIR and NIR, associated with supervised classification methods, enabled us to successfully distinguish healthy from diseased profiles and to identify the diseases state with AUC > 0.93. These findings highlight the complementary value of MIR and NIR spectroscopy for kidney disease assessment and their potential integration into point-of-care diagnostic systems. Full article

As tunnel engineering continues to advance toward deeper, longer, and more complex projects, the risks encountered during the construction phase have evolved into a combination of various disaster types and the accumulation of multiple contributing factors. Traditional empirical and semi-empirical risk management methods are increasingly revealing shortcomings in terms of timeliness, accuracy, and the ability to process multi-source data. In recent years, driven by advancements in computing power and sensor technology, artificial intelligence algorithms (AI algorithms) such as machine learning and deep learning have been rapidly adopted in tunnel construction risk management. This paper retrieved relevant literature from the Web of Science database covering the period from 2010 to 2025. After rigorous screening, 96 highly relevant papers were selected for bibliometric analysis. This paper systematically reviews research progress from two perspectives: algorithmic models and engineering applications. The review indicates that, in terms of algorithmic models, traditional machine learning, convolutional neural network, recurrent neural network, generative adversarial network, Transformer, and graph neural network constitute a multi-level technical framework encompassing feature representation, risk perception, and intelligent decision-making. In terms of applications, AI algorithms have been widely integrated into typical scenarios such as geological hazard identification and prediction, surrounding rock stability and deformation prediction, rock burst assessment and early warning, lining defect detection and structural safety assessment, construction-induced ground settlement prediction, and tunnel gas and fire hazard prediction, significantly enhancing risk identification and early warning capabilities. However, several challenges remain, including the scarcity of high-quality datasets, the prevalence of noisy, incomplete, and heterogeneous monitoring data, insufficient coupling between model interpretability and engineering mechanisms, limited cross-project transferability, and the lack of integrated management systems for multi-hazard lifecycle control. Based on this, this paper proposes future research directions in areas such as data infrastructure development, integration of mechanism constraints, and multi-hazard collaborative modeling, aiming to provide guidance for the further development of intelligent risk management in tunnel construction. Full article

Under the dual constraints of global food security and ecological protection, conventional agriculture is hampered by low resource efficiency and sluggish environmental response. Crop digital twin technology establishes a dynamic virtual reality system that integrates crops, environment, and water to enable real-time interaction and optimization. Based on the existing literature, this paper reviews the concept, architecture, and core modules of this technology and summarizes its applications in precision irrigation and crop monitoring. There are three major bottlenecks that persist, including limited high-frequency multi-source sensing and spatiotemporal fusion, insufficient parameter calibration and dynamic updating, and weak cross-scale integration from plant to watershed. Water is increasingly recognized as the key constraint and control variable and acting as both the central physiological driver of crop growth and the mass-flow link that connects the soil–plant–atmosphere continuum. The spatiotemporal dynamics of crop water deficit, compensatory root water uptake, evapotranspiration feedback, and the hydraulic behavior of irrigation-district canal systems constitute the core hydrological processes that must be simulated within the digital twin. Synchronizing crop water demand, soil moisture dynamics, atmospheric evapotranspiration, and irrigation scheduling within a unified spatiotemporal framework establishes a complete sensing, diagnosis, prediction and regulation technical chain. This chain offers a core pathway for alleviating agricultural water scarcity, improving irrigation efficiency, and ensuring food security. Full article

Robotic belt grinding is an effective and widely adopted finishing method for superalloys, offering notable advantages such as high material removal capability, low heat input, and reduced workpiece damage. In addition, robots can readily integrate multiple sensors—such as infrared radiation cameras, force sensors, and high-speed cameras—which facilitate real-time monitoring of the grinding process and thereby enhance grinding quality control. With the establishment and continuous advancement of large-scale artificial intelligence (AI) data models, new breakthroughs have emerged in the optimization of robotic grinding processes. Owing to its dexterous workspace and advantages in high flexibility and cost-effectiveness, robotic belt grinding has become a critical process for the precision forming of complex curved components such as aero-engine blades and blisks. However, factors such as the limited absolute accuracy of industrial robots, time-varying grinding contact states, and significant transient boundary effects make it difficult for the current constant-parameter open-loop machining mode to simultaneously meet the demands for high material removal efficiency and high surface integrity on complex profiles. This paper systematically reviews the technologies for precision control and process optimization of robotic belt grinding aimed at pointwise precise material removal. First, the structural composition of the robotic belt grinding system and the material removal mechanism are analyzed. Then, centered on the compensation concept, a hierarchical progressive technical framework is outlined, covering geometric calibration compensation, force/position hybrid online compensation, transient entry boundary compensation, and system-level comprehensive compensation of multi-source errors, with a comparison of the applicable scenarios and the effects on shape and property control at each level. Furthermore, under the support of effective compensation, the collaborative optimization methods of material removal modeling, multi-objective optimization of process parameters, force-constrained trajectory planning, and intelligent adaptive processes are elaborated. Finally, current technical bottlenecks are summarized, and future trends in next-generation adaptive grinding technology driven by digital twins and embodied intelligence are envisioned. This review aims to provide a systematic theoretical reference for the high-precision and intelligent upgrading of robotic precision grinding systems. Full article

In Mediterranean small-scale forestry, the adoption of highly mechanized CTL systems remains limited by fragmented forest lots, variable stand conditions, and high machine costs. This case study evaluated the operational productivity and economic feasibility of harvester-based felling and processing in two Mediterranean conifer stands in Southern Italy. A harvester was monitored in Calabrian pine and silver fir stands using a time-motion approach. Processing represented the dominant productive phase, while moving accounted for about one-third of productive machine time. Under the observed site conditions, the Calabrian pine showed higher gross productivity and lower unit time consumption than silver fir. The economic analysis indicated that feasibility was strongly dependent on gross productivity, benchmark motor-manual costs, and harvested lot volume, with more favourable break-even conditions in Calabrian pine. Full article

The present integrative umbrella review aims to provide a comprehensive overview of the evidence and practices related to mental health and career transitions in elite sport toward the implementation of service provision through digital interventions. Following PRIO guidelines, an extensive search across five databases (2015–2025) identified 52 eligible manuscripts (e.g., conceptual, review, and position studies). Data extraction focused on mental health, dual-career pathways, career transition challenges and needs, and identity-related issues among high-performance athletes. The findings revealed a strong consensus that athlete well-being is shaped by the dynamic interaction of mental health symptoms, sport-specific stressors, identity processes, and structural conditions across the athletic lifespan. Mental health vulnerabilities (e.g., anxiety, depression, disordered eating, and distress) were consistently reported, particularly during injury, deselection, and retirement. Dual-career engagement, diversified identities, and proactive career planning emerged as key protective factors, while stigma, limited literacy, and uneven access to psychological services remained persistent barriers. Five main thematic areas (Matrix 1) operationalized in ten higher-order intervention domains (e.g., Matrix 2, screening, monitoring, literacy, and others) and 14 potential online implementation strategies (Matrix 3) were identified. However, the evidence highlights fragmented implementation and a lack of scalable, cross-national tools to support athletes during and beyond their competitive careers. Therefore, a harmonized, evidence-based, multidimensional framework for the development and implementation of digital support resources has been proposed. This integrative review underscores the need for integrated, culturally sensitive, and digitally enabled support systems to promote sustainable transitions and long-term athlete well-being. Full article

Background: Cardiac telemetry monitoring represents an important aspect of in-hospital patient safety in both telemetry and critical care settings. Despite technological advancements, telemetry effectiveness may be diminished due to systemic failures including operational processes, instructional policies, and human factors. Alarm fatigue, recognized by the Joint Commission as a leading contributor to serious patient harm, lies at the forefront of these failures. Objective: This narrative review utilized and synthesized sources indexed through PubMed, PubMed Central, MEDLINE, Web of Science, Google Scholar, Directory of Open Access Journals (DOAJ), and Scopus to illustrate the factors involved in hospital related monitoring failures. We purport that alarm fatigue and telemetry monitoring failures are the result of complex systemic failures comprising technological and human failures. Through this narrative, we propose an evidence-based framework known as the Safer Telemetry Architecture (STA) to pinpoint redundancies and promote closed-loop communication regarding alarm management. Conclusions: Monitored in-hospital environments represent a key area of preventable morbidity and mortality due to systemic design flaws. Our STA framework addresses such flaws via improvements in nurse-driven protocols, alarm routing, mandatory coverage standards for backup, and increased performance auditing. Systemic improvements via such a framework may represent an important institutional strategy for hospitals with cardiac monitoring, but requires further prospective validation. Managing redundancies in alerts and sounds, improving backup and nursing telemetry protocols, and promoting closed or continuous loops targeting alarm response times and telemetry utilization are key to effectively improving patient safety. Full article