Search Results (2,398)

Heart failure (HF) remains a global health challenge with high rates of hospitalization and mortality, particularly among the elderly. Many episodes of worsening HF occur before symptoms arise, underscoring the need for sensitive monitoring tools. Respiratory Stability Time (RST) is a novel index that quantifies the duration of stable respiration during sleep, reflecting pulmonary congestion and circulatory status. RST can be measured continuously and non-invasively using a contactless under-mattress sensor. Observational cohort studies show that low RST predicts poor prognosis, while its improvement parallels recovery from decompensation. Importantly, recent prospective multicenter observations involving 100 patients demonstrated that sustained RST decline often precedes HF readmission, probably enabling early intervention. A multicenter trial (ITMETHOD-HF III), involving 80 patients, is currently testing whether RST-guided therapy can reduce HF readmissions. RST might substantially enhance current HF management by enabling us to provide proactive therapeutic intervention, though further validation is warranted. Full article

Low-cost sensors have demonstrated their advances in acquiring hyper-localized data compared to traditional, high-maintenance air quality monitoring stations. The study aims to leverage the mobility of participants equipped with low-cost wearable monitors (LWMs) by comparing their exposure to particulate matter (PM) across indoor-home, outdoor-walking, and hybrid-commuting micro-environments. The LWMs would be calibrated first through field co-location and the multiple linear regression models. The coefficient of determination (R²) of PM_1.0 and PM_2.5 increased to over 0.85 after calibration, along with the reduced root mean square error of 2.25 and 3.46 , respectively. The 26-day PM data collection with geographic locations could identify individual exposure patterns, local source contributions, and hotspot maps. Commuting constituted a small fraction of daily time (4–8%) but contributed a disproportionate impact, accounting for 11% of individual PM exposure. Indoor-home PM_2.5 exposure varied significantly among the urban districts. Based on the PM_2.5 hotspot map, the elevated concentration was mainly concentrated in dense residential areas and historical industrial areas, as well as interchanges of major roads and the highway system. LWMs acting as non-regulatory instruments can complement monitoring stations to provide missing short-term and hyper-localized air pollution data. Future studies should integrate long-term monitoring and citizen science across seasons and geographical regions to address pollutant spatiotemporal variability for building and city sustainability. Full article

Emerging fungal plant pathogens are significant biotic stresses to crops that threaten global food security, biodiversity, and agricultural sustainability. Historically, these pathogens cause devastating crop losses and continue to evolve rapidly due to climate change, international trade, and intensified farming practices. Recent advancements in diagnostic technologies, including remote sensing, sensor-based detection, and molecular techniques, are transforming disease monitoring and detection. These tools, when combined with data mining and big data analysis, facilitate real-time surveillance and early intervention strategies. There is a need for extension and digital advisory services to empower farmers with actionable insights for effective disease management. This manuscript presents an inclusive review of the socioeconomic and historical impacts of fungal plant diseases, the mechanisms driving the emergence of these pathogens, and the pressing need for global surveillance and reporting systems. By analyzing recent advancements and the challenges in the surveillance and diagnosis of fungal pathogens, this review advocates for an integrated, multidisciplinary approach to address the growing threats posed by these emerging fungal diseases. Fostering innovation, enhancing accessibility, and promoting collaboration at both national and international levels are crucial for the agricultural community to protect crops from these emerging biotic stresses, ensuring food security and supporting sustainable farming practices. Full article

This innovative study introduces an eco-conscious and cost-effective approach to synthesizing gelatin-based carbon dots (CDs) via two distinctive methods: hydrothermal processing in a muffle furnace (CDs-MF) and domestic microwave (CDs-MW). Both strategies harness natural, low-cost materials and prioritize simplicity, sustainability, and environmental friendliness, culminating in effective fluorescent sensing of the pesticide thiamethoxam (TMX). For the hydrothermal route, the investigation explores two purification approaches—ultracentrifugation (CDs-MF-C) and 0.22 µm syringe filtration (CDs-MF-F)—while the microwave-derived CDs (CDs-MW) undergo dialysis alone. This study aims to investigate how synthesis and purification impact the CDs structural, morphological, and photophysical characteristics. The difference in size was obtained from transmission electron microscopy (TEM): 30–40 nm for CDs-MF-C, 12–15 nm for CDs-MF-F, and 3–6 nm for CDs-MW. Fluorescence emission performance reveals that CDs-MF-F performs a fluorescence quantum yield of 27%, CDs-MF-C at 23%, and CDs-MW at a modest 3%. All variants exhibit TMX detection via fluorescence quenching through the inner filter effect (IFE). Analytically, CDs-MF-C stands out with the lowest detection limit (LOD = 0.396 ppm) and quantification limit (LOQ = 1.317 ppm), followed by CDs-MF-F (LOD = 0.475 ppm; LOQ = 1.585 ppm) and CDs-MW (LOD = 0.549 ppm; LOQ = 1.831 ppm). These findings emphasize the unique interplay between the synthesis pathway, purification strategy, and functional performance, demonstrating the critical importance of tuning structural properties for optimizing carbon-dot sensors. Full article

Lubricating oils are utilised in equipment and machinery to reduce friction and enhance material utilisation. The utilisation of oil leads to an increase in its thickness and density over time. Current methods for assessing oil life are slow, expensive, and complex, and often only applicable in laboratory settings and unsuitable for real-time or field use. This leads to unexpected equipment failures, unnecessary oil changes, and economic and environmental losses. A comprehensive review of the extant literature revealed no studies and no national or international patents on neural network algorithm-based oil life modelling and classification using green sensors. In order to address this research gap, this study, for the first time in the literature, provides a green conductivity sensor with high-accuracy prediction of oil life by integrating real-time field measurements and artificial neural networks. This design is based on analysing resistance change using a relatively low-cost, three-dimensional, eco-friendly sensor. The sensor is characterised by its simplicity, speed, precision, instantaneous measurement capability, and user-friendliness. The MLP and LVQ algorithms took as input the resistance values measured in two different oil types (diesel, bench oil) after 5–30 h of use. Depending on their degradation levels, they classified the oils as ‘diesel’ or ‘bench oil’ with 99.77% and 100% accuracy. This study encompasses a sensing system with a sensitivity of 50 µS/cm, demonstrating the proposed methodologies’ efficacy. A next-generation decision support system that will perform oil life determination in real time and with excellent efficiency has been introduced into the literature. The components of the sensor structure under scrutiny in this study are conducive to the creation of zero waste, in addition to being environmentally friendly and biocompatible. The developed three-dimensional green sensor simultaneously detects physical (resistance change) and chemical (oxidation-induced polar group formation) degradation by measuring oil conductivity and resistance changes. Measurements were conducted on simulated contaminated samples in a laboratory environment and on real diesel, gasoline, and industrial oil samples. Thanks to its simplicity, rapid applicability, and low cost, the proposed method enables real-time data collection and decision-making in industrial maintenance processes, contributing to the development of predictive maintenance strategies. It also supports environmental sustainability by preventing unnecessary oil changes and reducing waste. Full article

The integration of deep learning (DL) and nanozyme-based biosensing has emerged as a transformative strategy for next-generation medical diagnostics. This review explores how DL architectures enhance nanozyme design, functional optimization, and predictive modeling by elucidating catalytic mechanisms such as dual-atom active sites and substrate-surface interactions. Key applications include disease biomarker detection, medical imaging enhancement, and point-of-care diagnostics aligned with the ASSURED criteria. In clinical contexts, advances such as wearable biosensors and smart diagnostic platforms leverage DL for real-time signal processing, pattern recognition, and adaptive decision-making. Despite significant progress, challenges remain—particularly the need for standardized biomedical datasets, improved model robustness across diverse populations, and the clinical translation of artificial intelligence (AI)-enhanced nanozyme systems. Future directions include integration with the Internet of Medical Things, personalized medicine frameworks, and sustainable sensor development. The convergence of nanozymes and DL offers unprecedented opportunities to advance intelligent biosensing and reshape precision diagnostics in healthcare. Full article

The convergence of genomic selection and artificial intelligence (AI) is redefining precision breeding in dairy cattle, enabling earlier, more accurate, and multi-trait selection for health, fertility, climate resilience, and economic efficiency. This review critically examines how advanced genomic tools—such as genome-wide association studies (GWAS), genomic breeding values (GEBVs), machine learning (ML), and deep learning (DL) models to accelerate genetic gain for complex, low heritability traits. Key applications include improved resistance to mastitis and metabolic diseases, enhanced thermotolerance, reduced enteric methane emissions, and increased milk yield. We discuss emerging computational frameworks that combine sensor-derived phenotypes, omics datasets, and environmental data to support data-driven selection decisions. Furthermore, we address implementation challenges related to data integration, model interpretability, ethical considerations, and access in low-resource settings. By synthesizing interdisciplinary advances, this review provides a roadmap for developing AI-augmented genomic selection pipelines that support sustainable, climate-smart, and economically viable dairy systems. Full article

This study presents a comprehensive assessment of long-term nutrient dynamics in the northern South China Sea (NSCS), a region that hosts the world’s largest marine ranching cluster and serves as a cornerstone of China’s “Blue Granary” initiative. By integrating multi-sensor satellite remote sensing data (Landsat and Sentinel-2, 2002–2024) with in situ observations, we developed robust retrieval algorithms for total nitrogen (TN) and total phosphorus (TP), achieving high accuracy (TN: R² = 0.82, RMSE = 0.09 mg/L; TP: R² = 0.94, RMSE = 0.0071 mg/L; n = 63). Results showed that TP concentrations increased significantly faster than TN, leading to a decline in the TN:TP ratio (NP) from 19.2 to 13.2 since 2013. This shift indicates a transition from phosphorus (P) limitation to nitrogen (N) limitation, driven by warming sea surface temperatures (SST) (about 1.16 °C increase) and increased anthropogenic phosphorus inputs (about 27.84% increase). The satellite-based framework offers a scalable, cost-effective solution for monitoring aquaculture water quality. When integrated with artificial intelligence (AI) technologies, these near-real-time nutrient anomaly data can support early warning of harmful algal blooms (HABs), offering key insights for ecosystem-based management and climate adaptation. Overall, our findings highlight the utility of remote sensing in advancing sustainable marine resource governance amid environmental change. Full article

This paper analyzes the long-term thermal effect of newly applied fiber-reinforced concrete link slabs on an existing steel bridge for a rehabilitation project of the Maryland Transportation Authority. To enhance structural resilience, thermal movement is one of the major concerns in concrete link slab design. To accommodate the global thermal expansion of a full bridge, the existing fixed bearings were modified to expansion bearings to release the longitudinal thermal movement of the super-structure. Their movements were measured by the installed LVDT devices. In this pilot study for the Maryland Transportation Authority (MDTA), engineered cementitious composite (ECC) and ultra-high-performance concrete (UHPC) were selected as candidate materials for link slabs to replace traditional steel expansion joints. To evaluate the performances of ECC and UHPC, built-in strain gauges were implemented for long-term field monitoring to ensure the durability of link slabs. For comparison, the measured data were collected over two full years to study their thermal effects in order to evaluate their sustainability. The novelty of the study is in comparing the performance of different materials side-by-side using true sensor measurements and numerical simulation. Thermal movement performance, including thermal cracking, will be one of the major selection criteria for the link slab material. Full article

This paper presents the research outcomes of the Golden Seal project, which addresses the omnipresent issue of plastic pollution in coastal areas while enhancing their touristic value through the deployment of Internet of Things (IoT) technologies integrated into a gamified recycling framework. The developed system employs an IoT-enabled Wireless Sensor Network (WSN) to systematically collect, transmit, and analyze environmental data. A centralized, cloud-based platform supports real-time monitoring and data integration from Unmanned Aerial and Surface Vehicles (UAV and USV) equipped with sensors and high-resolution cameras. The system also introduces the Beach Cleanliness Index (BCI), a composite indicator that integrates quantitative environmental metrics with user-generated feedback to assess coastal cleanliness in real time. A key innovation of the project’s architecture is the incorporation of a Serious Game (SG), designed to foster public awareness and encourage active participation by local communities and municipal authorities in sustainable waste management practices. Pilot implementations were conducted at selected sites characterized by high tourism activity and accessibility. The results demonstrated the system’s effectiveness in detecting and classifying plastic waste in both coastal and terrestrial settings, while also validating the potential of the Golden Seal initiative to promote sustainable tourism and support marine ecosystem protection. Full article

Artificial intelligence (AI) and big data analytics are transforming the fight against climate change by enabling advanced monitoring, predictive modeling, and actionable insights. This study aims to examine how AI-driven analytics enhance the understanding of climate systems, support mitigation strategies, and inform policy decisions. By processing vast datasets from satellites, sensors, and climate models, AI algorithms identify patterns, predict extreme weather events, and quantify the impacts of human activities on ecosystems. Applications, such as real-time greenhouse gas monitoring, precision agriculture, and energy optimization, showcase AI’s potential to reduce emissions and enhance sustainability. Challenges, including data gaps, algorithmic biases, and ethical considerations, must be addressed to fully realize AI’s transformative potential. AI and big data contribute to the accelerating global efforts to mitigate climate change and build resilience against its adverse effects. Full article

Spatial variability in vine vigour and yield components is a major challenge for vineyard management and consistent grape quality, particularly in hilly landscapes. This study evaluates the impact of on-the-go variable-rate fertilisation (VRA) in reducing within-vineyard variability in an 8.5 hectares commercial vineyard in the Conegliano Valdobbiadene Prosecco DOCG region (Italy). Over two growing seasons, a proximal NDVI sensor (GreenSeeker) guided real-time fertiliser applications without prescription maps. Vine vigour, yield components, and grape quality were evaluated using geostatistical analysis and coefficient of variation (CV) metrics. VRA reduced total spatial variability (sill) by 55% and erratic variance (nugget effect) by 39% for NDVI measurements. Variability in yield components also decrease (−21.1% for cluster number, −6.25% for cluster weight), while grape composition parameters (total soluble solids, titratable acidity, and pH) was not significantly altered despite a slightly higher variability (in titratable acidity and pH), indicating that fertiliser modulation did not compromise grape quality. Nitrogen input was reduced by 50%, highlighting economic and environmental benefits (−302 kg CO₂). These results show that simplified, sensor-based, on-the-go VRA is a practical and sustainable precision viticulture tool, even in small and heterogeneous vineyards typical of the Conegliano Valdobbiadene Prosecco DOCG area. Full article

In recent years, there have been significant advances in the application of technology in professional kitchens. This evolution of “smart kitchens” has transformed the food processing sector, ensuring higher standards of food safety through continual microbial monitoring, quality control, and hygiene improvements. This review critically discusses the recent developments in technology in commercial kitchens, focusing on their impact on microbial safety, operational efficiency, and sustainability. The literature was sourced from peer-reviewed journals, industry publications, and regulatory documents published between 2000 and 2025, selected for their relevance to the assurance of food safety using emerging technologies especially for use in commercial kitchens. Some of the most significant of these technologies currently being employed in smart kitchens include the following: smart sensors and IoT devices, artificial intelligence and machine learning systems, blockchain-based traceability technology, robotics and automation, and wearable monitoring devices. The review evaluated these technologies against criteria such as adherence to existing food safety regulations, ease of integration, cost factors, staff training requirements, and consumer perception. It is shown that these innovations will significantly enhance hygiene control, reduce the levels of waste, and increase business revenue. However, they are constrained by high installation costs, integration complexity, lack of standardized assessment measures, and the need for harmonizing automation with human oversight. Thus, for the widespread and effective uptake of these technologies, there is a need for better collaboration between policymakers, food experts, and technology innovators in creating scalable, affordable, and regulation-compliant solutions. Overall, this review provides a consolidated evidence base and practical insights for stakeholders seeking to implement advanced microbial safety technologies in professional kitchens, highlighting both current capabilities and future research opportunities. Full article

Sensor arrays serve a crucial role in various fields such as environmental monitoring, industrial process control, and medical diagnostics, yet their reliability remains challenged by sensor drift and noise contamination. This study presents a novel framework for real-time data error correction and long-term drift compensation utilizing an iterative random forest-based error correction algorithm paired with an Incremental Domain-Adversarial Network (IDAN). The iterative random forest algorithm leverages the collective data from multiple sensor channels to identify and rectify abnormal sensor responses in real time. The IDAN integrates domain-adversarial learning principles with an incremental adaptation mechanism to effectively manage temporal variations in sensor data. Experiments utilizing the metal oxide semiconductor gas sensor array drift dataset demonstrate that the combination of these approaches significantly enhances data integrity and operational efficiency, achieving a robust and good accuracy even in the presence of severe drift. This study underscores the efficacy of integrating advanced artificial intelligence techniques for the ongoing evolution of sensor array technology, paving the way for enhanced monitoring systems capable of sustaining high levels of performance over extended time periods. Full article

Understanding mixing behavior at river confluences is essential for effective watershed management in response to increasing environmental issues such as algal blooms and chemical pollution. This study focused on the confluence of the Nakdong and Geumho Rivers, employing high-resolution field measurements using an ADCP (M9) and YSI EXO sensors. Water temperature (°C) and electrical conductivity (μS/cm) data were collected under three representative conditions, including flow ratios of 0.91, 0.45, and 0.29, as well as 0.05, with a maximum temperature difference of up to 6 °C. Mixing behavior was three-dimensionally analyzed by integrating cross-sectional and longitudinal data, and the accuracy of visualization was evaluated using IDW and Kriging spatial interpolation techniques. The analysis revealed that under low flow ratio conditions, vertical mixing was delayed; the thermal stratification persisted up to approximately 3 km downstream from the confluence (Line 3), and complete mixing was not achieved until about 7 km downstream (Line 5) due to density currents. Quantitative comparison indicated that IDW (R² = 0.901, RMSE = 31.522) outperformed Kriging (R² = 0.79, RMSE = 35.458). This study provides a quantitative criterion for identifying the mixing completion zone, thereby addressing the limitations of previous studies that relied on numerical models or limited field data, and offering practical evidence for water quality monitoring and sustainable river management. Full article