Search Results (178)

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT), known as Artificial Intelligence of Things (AIoT), has emerged as a transformative paradigm for enabling intelligent, data-driven, and context-aware decision-making in urban environments to reduce the carbon footprint of mobility and industry. This review examines the conceptual foundations, and state-of-the-art developments of AIoT, with a particular emphasis on its applications in smart cities and its relevance to climate change management. AIoT integrates sensing, connectivity, and intelligent analytics to provide optimized solutions in transportation systems, energy management, waste collection, and environmental monitoring, directly influencing urban sustainability. Beyond urban efficiency, AIoT can play a critical role in addressing the global challenges and management of climate change by (a) precise measurements and autonomously remote monitoring; (b) real-time optimization in renewable energy distribution; and (c) developing prediction models for early warning of climate disasters. This paper performs a literature review and bibliometric analysis to identify the current landscape of AIoT research in smart city contexts. Over 1885 articles from Web of Sciences and over 1854 from Scopus databases, published between 1993 and January 2026, were analyzed. The results reveal a strong and accelerating growth in research activity, with publication output doubling in the most recent two years compared to 2023. Waste management and air quality monitoring have emerged as leading application domains, where AIoT-based optimization and predictive models demonstrate measurable improvements in operational efficiency and environmental impact. Altogether, these support faster and more effective decisions for reducing greenhouse gas emissions and ensuring the sustainable use of resources. The reviewed studies reveal rapid advancements in edge intelligence, federated learning, and secure data sharing through the integration of AIoT with blockchain technologies. However, significant challenges remain regarding scalability, interoperability, privacy, ethical governance, and the effective translation of research outcomes into policy and citizen-oriented tools such as climate applications, insurance models, and disaster alert systems. By synthesizing current research trends, this article highlights the potential of AIoT to support sustainable, resilient, and citizen-centric smart city ecosystems while identifying both critical gaps and promising directions for future investigations. Full article

Prolonged +Gz acceleration during high-performance flight exposes pilots to the risk of G-induced loss of consciousness (G-LOC), a dangerous condition that compromises operational safety. To enable early detection without intrusive sensors, we present a vision-based warning system that analyzes facial action units (AUs) as physiological indicators of impending G-LOC. Our approach combines computer vision with physiological modeling to capture subtle facial microexpressions associated with cerebral hypoxia using widely available RGB cameras. We propose a novel Graph Convolution-Augmented Vision Transformer (GC-ViT) network architecture that effectively captures dynamic AU variations in pilots under G-LOC conditions by integrating global context modeling with vision Transformer. The proposed framework integrates a vision–semantics collaborative Transformer for robust AU feature extraction, where EfficientNet-based spatiotemporal modeling is enhanced by Transformer attention mechanisms to maintain recognition accuracy under high-G stress. Building upon this, we develop a graph-based physiological model that dynamically tracks interactions between critical AUs during G-LOC progression by learning the characteristic patterns of AU co-activation during centrifugal training. Experimental validation on centrifuge training datasets demonstrates strong performance, achieving an AUC-ROC of 0.898 and an AP score of 0.96, confirming the system’s ability to reliably identify characteristic patterns of AU co-activation during G-LOC events. Overall, this contact-free system offers an interpretable solution for rapid G-LOC detection, or as a complementary enhancement to existing aeromedical monitoring technologies. The non-invasive design demonstrates significant potential for improving safety in aerospace physiology applications without requiring modifications to current cockpit or centrifuge setups. Full article

Background: Mosquito-borne viral diseases are a growing global health threat, and artificial intelligence (AI) and machine learning (ML) are increasingly proposed as forecasting tools to support early-warning and response. However, the available evidence is fragmented across pathogens, settings and modelling approaches. This review provides, to the best of our knowledge, the first comprehensive comparative assessment of AI/ML models forecasting mosquito-borne viral diseases in human populations, jointly synthesising predictive performance across model families and appraising both methodological quality and operational readiness. Methods: Following PRISMA 2020, we searched PubMed, Embase and Scopus up to August 2025. We included studies applying AI/ML or statistical models to predict arboviral incidence, outbreaks or temporal trends and reporting at least one quantitative performance metric. Given the substantial heterogeneity in outcomes, predictors and time–space scales, we conducted a descriptive synthesis. Risk of bias and applicability were evaluated using PROBAST. Results: Ninety-eight studies met the inclusion criteria, of which 91 focused on dengue. The forecasts spanned national to city-level settings and annual-to-weekly resolutions. Across classification tasks, tree-ensemble models showed the most consistent performance, with accuracies typically above 0.85, while classical ML and deep-learning models showed wider variability. For regression tasks, errors increased with temporal horizon and spatial aggregation: short-term, fine-scale forecasts (e.g., weekly city level) often achieved low absolute errors, whereas long-horizon national models frequently exhibited very large errors and unstable performance. PROBAST assessment indicated that most studies (63/98) were at high risk of bias, with only 24 judged at low risk and limited external validation. Conclusions: AI/ML models, especially tree-ensemble approaches, show strong potential for short-term, fine-scale forecasting, but their reliability drops substantially at broader spatial and temporal scales. Most remain research-stage, with limited external validation and minimal operational deployment. This review clarifies current capabilities and highlights three priorities for real-world use: standardised reporting, rigorous external validation, and context-specific calibration. Full article

Passenger-gathering behavior often triggers safety incidents such as stampedes due to overcrowding, posing significant challenges to public order maintenance and passenger safety. Traditional early warning algorithms for passenger-gathering behavior typically perform only global modeling of image appearance, neglecting the analysis of individual passenger actions in practical 3D physical space, leading to high false-alarm and missed-alarm rates. To address this issue, we decompose the modeling process into two stages: human pose estimation and gathering behavior recognition. Specifically, the pose of each individual in 3D space is first estimated from images, and then fused with global features to complete the early warning. This work focuses on the former stage and aims to develop an accurate and efficient human pose estimation model capable of real-time inference on resource-constrained devices. To this end, we propose a 3D human pose estimation framework that integrates a hybrid spatio-temporal Transformer with three collaborative agents. First, a reinforcement learning-based architecture search agent is designed to adaptively select among Global Self-Attention, Window Attention, and External Attention for each block to optimize the model structure. Second, a feedback optimization agent is developed to dynamically adjust the search process, balancing exploration and convergence. Third, a quantization agent is employed that leverages quantization-aware training (QAT) to generate an INT8 deployment-ready model with minimal loss in accuracy. Experiments conducted on the Human3.6M dataset demonstrate that the proposed method achieves a mean per joint position error (MPJPE) of 42.15 mm with only 4.38 M parameters and 19.39 GFLOPs under FP32 precision, indicating substantial potential for subsequent gathering behavior recognition tasks. Full article

Background/Objectives: The rapidly evolving crisis of Novel Synthetic Opioids (NSOs) poses a serious and growing threat for global public health. NSOs include prescription/non-prescription opioids (fentanyl, non-fentanyl analogues), herbal derivatives, and other emerging analogues that are of critical concern due to their high potency, misuse potential, and addiction and intoxication risk. There remains an important gap in real-time, systematic monitoring of NSOs emergence, especially in online communities where these substances appear for the first time. This study aimed to employ the NPSfinder^® automated web-crawling tool to detect, monitor, analyse, and evaluate the evolving NSOs scene. Methods: Data were collected during two time-periods, i.e., 2017–2019 and 2023, from selected high traffic psychonaut online platforms to better understand trends in opioids market evolution and adaptability and compare NPSfinder^® findings with other well-known Early Warning Systems (EWSs) databases to assess detection overlap and early identification capacity. Results: Within the selected time-periods, a total of 446 NSOs were detected by NPSfinder^®; fentanyl analogues (n = 249) were dominant, with a notable rise in non-fentanyl analogues, especially nitazene-like compounds, in 2023. Over 57% of these NSOs were not captured by any of the other EWSs, confirming the tool’s strong capacity to identify early threats. Conclusions: Overall, the low overlap across EWS databases underscores the global challenges in comprehensive opioid detection and surveillance. Future studies should integrate web-crawler findings with real-world datasets. It will be vital to combine these efforts with toxicological, mortality, and clinical outcome analyses, especially for emerging research compounds, to inform targeted harm-reduction strategies. Full article

Inter-basin water transfer projects are core infrastructure for achieving sustainable water resource allocation and addressing regional water scarcity, and pumping station units, as their critical energy-consuming and operation-controlling components, are vital to the projects’ sustainable performance. With the growing complexity and scale of these projects, pumping station units have become more intricate, leading to a gradual rise in failure rates. However, existing fault diagnosis methods are relatively backward, failing to promptly detect potential faults—this not only threatens operational safety but also undermines sustainable development goals: equipment failures cause excessive energy consumption (violating energy efficiency requirements for sustainability), unplanned downtime disrupts stable water supply (impairing reliable water resource access), and even leads to water waste or environmental risks. To address this sustainability-oriented challenge, this paper focuses on the fault characteristics of pumping station units and proposes a comprehensive and accurate fault diagnosis model, aiming to enhance the sustainability of water transfer projects through technical optimization. The model utilizes advanced algorithms and data processing technologies to accurately identify fault types, thereby laying a technical foundation for the low-energy, reliable, and sustainable operation of pumping stations. Firstly, continuous wavelet transform (CWT) converts one-dimensional time-domain signals into two-dimensional time-frequency graphs, visually displaying dynamic signal characteristics to capture early fault features that may cause energy waste. Next, the multi-head attention mechanism (MHA) segments the time-frequency graphs and correlates feature-location information via independent self-attention layers, accurately capturing the temporal correlation of fault evolution—this enables early fault warning to avoid prolonged inefficient operation and energy loss. Finally, the improved convolutional neural network (CNN) layer integrates feature information and temporal correlation, outputting predefined fault probabilities for accurate fault determination. Experimental results show the model effectively solves the difficulty of feature extraction in pumping station fault diagnosis, considers fault evolution timeliness, and significantly improves prediction accuracy and anti-noise performance. Comparative experiments with three existing methods verify its superiority. Critically, this model strengthens sustainability in three key ways: (1) early fault detection reduces unplanned downtime, ensuring stable water supply (a core sustainable water resource goal); (2) accurate fault localization cuts unnecessary maintenance energy consumption, aligning with energy-saving requirements; (3) reduced equipment failure risks minimize water waste and environmental impacts. Thus, it not only provides a new method for pumping station fault diagnosis but also offers technical support for the sustainable operation of water conservancy infrastructure, contributing to global sustainable development goals (SDGs) related to water and energy. Full article

This study assessed the effectiveness of vegetation index change metrics (ΔVI = Post − Pre) derived from Sentinel-2 imagery for detecting landslide-affected areas and evaluating their relationship with rainfall intensity, thereby enhancing the early-warning potential. The analysis focused on Sancheong-gun, Gyeongsangnam-do, South Korea, where intense rainfall in July 2025 triggered multiple landslides. Pre- and post-event Sentinel-2 Level-2A images (10 m spatial resolution) were used to compute changes in the Normalized Difference Vegetation Index (ΔNDVI), Soil-Adjusted Vegetation Index (ΔSAVI), Modified Soil-Adjusted Vegetation Index (ΔMSAVI), Normalized Difference Moisture Index (ΔNDMI), and Global Vegetation Moisture Index (ΔGVMI) over the landslide-affected post-disaster (PD) and non-damaged (ND) areas. Sensitivity was assessed based on the differences in mean ΔVI between the PD and ND areas, Welch’s t-statistics, and Cohen’s d values. All indices exhibited significant differences between the PD and ND areas (p < 0.001), with ΔMSAVI showing the highest sensitivity (MSAVI > GVMI ≈ SAVI > NDVI > NDMI). Correlation analysis revealed that ΔMSAVI had the strongest positive association with rainfall accumulation (72 h: r = 0.54; 7 days: r = 0.49), indicating that greater rainfall corresponded to stronger vegetation degradation signals. These findings highlight ΔMSAVI as a robust and responsive indicator of rainfall-triggered landslides, supporting its integration into satellite-based early-warning and rapid damage detection systems for improved landslide monitoring and response. Full article

Background: Antimicrobial resistance is a growing global health threat, with beta-lactamases playing a central role in resistance to beta-lactam antibiotics. Building on our previous survey of 2340 putative beta-lactamases, we conducted an in-depth analysis of 129 prioritized candidates (70–98.5% amino acid identity to characterized enzymes) detected in 102 bacterial genera across 13 phylogenetic classes from environmental, animal, and human sources worldwide. Methods: We applied a motif-centric assessment of class-defining catalytic residues, evaluated the genomic context using a heuristic Index of Proximal Mobility (IPM) derived from the two immediately adjacent open reading frames, and examined the phylogenetic placement. AI-based substrate predictions were generated at a restricted scope as exploratory evidence. Results: Candidates spanned all Ambler classes (A–D); preservation of catalytic motifs was common and consistent with potential catalytic activity. Twelve of 129 (9.3%) loci had nearby mobile-element types (e.g., insertion sequences, integrases, transposases) and scored High IPM, indicating genomic contexts compatible with horizontal gene transfer. We also observed near-identical class A enzymes across multiple genera and continents, frequently adjacent to mobilization proteins. Conclusions: We propose a reproducible, bias-aware, early warning framework that prioritizes candidates based on motif integrity and mobility context. The framework complements existing surveillance (GLASS/EARS-Net) and aligns with a One Health approach integrating human, animal, and environmental reservoirs. Identity thresholds and IPM are used for inclusion and contextual prioritization, rather than as proof of function or mobility; AI-based predictions serve as hypothesis-generating tools. Experimental studies will be essential to confirm enzymatic activity, mobility, and clinical relevance. Full article

Global warming is recognized as a climate crisis that extends beyond a mere increase in the Earth’s temperature, triggering rapid and widespread climatic changes worldwide. In particular, the frequency and intensity of extreme rainfall events have increased in Korea and the Association of Southeast Asian Nations (ASEAN) region, leading to a significant increase in flood damage. The growing number of large-scale hydrological disasters underscores the urgent need for accurate and rapid flood-forecasting systems that can support disaster preparedness and mitigation. Compared with conventional physics-based forecasting systems, artificial intelligence (AI) models can provide faster predictions using limited observational data. In this study, a river water-level prediction model was constructed using real-time observation data and a long short-term memory (LSTM) algorithm, which is a recurrent neural network-based deep learning approach suitable for hydrological time-series forecasting. A repeated k-fold cross-validation technique was applied to enhance model generalization and prevent overfitting. In addition, water-level differencing was employed to convert nonstationary water-level data into stationary time-series inputs, thereby improving the prediction stability. Water-level observation stations in the Philippines, Indonesia, and the Republic of Korea were selected as study sites, and the model performance was evaluated at each location. The differenced LSTM model achieved a root mean square error of 0.13 m, coefficient of determination (R²) of 0.866, Nash–Sutcliffe efficiency (NSE) of 0.844, and Kling–Gupta efficiency of 0.893, thus outperforming the non-differenced baseline by approximately 17%. The repeated k-fold validation approach was particularly effective when the training data period was short or the number of input variables was limited. These results confirm that ensuring temporal stationarity and applying repeated cross-validation can significantly enhance the predictive accuracy of real-time flood forecasting. The proposed framework exhibits strong potential for implementation in regional early warning systems across data-limited flood-prone areas in the ASEAN region. Ongoing studies that apply and verify this approach in diverse hydrological contexts are expected to further improve and expand AI-based flood prediction models. Full article

This study investigated Cenchrus purpureus in the southern part of the Gaoligong Mountains and quantified its invasion risk using an integrated approach. We combined the Drivers–Pressures–State–Impacts–Responses (DPSIR) model, Analytic Hierarchy Process (AHP), Structural Equation Modeling (SEM), and Traditional Ecological Knowledge (TEK). We adopted non-random sampling techniques to conduct a survey on the cognition, hazards, utilization and management of C. purpureus among 402 respondents from 25 villages. Our results classify C. purpureus as a medium-risk species (Level II). We identified a central socio-ecological dilemma: while 36.1% of communities use it for fodder, 54% report that it causes soil degradation, signaling potential long-term agricultural losses. SEM analysis confirmed that the willingness to manage the invasion is directly influenced by these usage patterns and risk perceptions. The traditional ecological knowledge of Cenchrus purpureus was highly consistent with scientific assessment, validating its use as an early warning indicator. Therefore, our study validates a multidisciplinary framework that integrates models (DPSIR, AHP, SEM) with traditional knowledge for a holistic assessment of C. purpureus invasion. This approach offers a replicable strategy for ecosystem management in global biodiversity hotspots in the mountainous regions. Full article

Nowadays, information obtained through Global Navigation Satellite Systems (GNSSs) is widely employed in modern geodesy. The Precise Point Positioning (PPP) approach, which leverages signals from multiple GNSS constellations (e.g., GPS, GLONASS, Galileo, and BeiDou), enables high-precision positioning—crucial for seismic monitoring and early tsunami warning systems (EEWs). Recent advances, such as increased satellite availability and additional frequency bands, have significantly improved PPP performance, particularly in terms of positioning accuracy and convergence time. This study focuses on the Rete Integrata Nazionale GNSS (RING) network, managed by the Istituto Nazionale di Geofisica e Vulcanologia (INGV), which comprises dual-frequency GNSS receivers distributed across the Italian peninsula and parts of the Mediterranean Basin. We evaluate the performance of the RING data (GPS and GNSS) acquired in a period of three weeks between 19 January 2024 and 9 February 2024 and analyzed in real time by using different PPP strategies: standard PPP and PPP with Regional Augmentation (PPP-RA). The preliminary results show that the PPP-RA approach enhances positioning accuracy and reduces convergence time, especially when comparing GPS-only datasets with those incorporating full multi-GNSS configurations. For the daily solution, in the optimal setup (i.e., full GNSS with RA), real-time solutions exhibit average accuracies of 2.05, 1.73, and 4.35 cm for the North, East, and vertical components, respectively. Sub-daily accuracies’ analysis, using 300 s sliding windows, showed even better uncertainties, exhibiting median values of 0.41, 0.32, and 0.9 cm for the North, East and vertical components, respectively. Based on the outcomes for network-wide sub-daily accuracies, 84% of the stations demonstrate average errors within 2 cm for North and East components and 3 cm for the vertical one. The analysis on the convergence time after data gaps occurred during the investigation period shows that 87% of the RING stations experienced convergence times lower than five minutes in the GNSS PPP-RA solution. These findings underscore the potential of RT-GNSS RING data for enhancing seismic monitoring and early warning systems, particularly in tectonically active regions. Full article

Biological recolonization after cleaning remains a major challenge for the conservation of stone cultural heritage. As recolonization can start within months to a few years following intervention, developing rapid, field-deployable diagnostic approaches is crucial to better monitor microbial reappearance and to assess treatment performance in real time. Traditional evaluation methods lack the capacity to take into consideration non-cultivable microorganisms or assess functional traits relevant to recolonization. To bypass this gap, we applied on-site direct Whole-Genome Sequencing (Oxford Nanopore^® MinION™ sequencer) coupled with colorimetric analysis to understand the microbiome, resistome, and metabolic traits of subaerial biofilms present in untreated and treated (recolonized) areas of stone statues at the “Largo da Porta Férrea” (Coimbra’s UNESCO World Heritage site). Colorimetric analysis (ΔE of 32–40 in recolonized vs. 19–43 in untreated areas) and genomic data pointed out that the applied treatment provided only a short-term effect (roughly 4–5 years), with a marked decline in fungi (1–2% vs. 7–18%), coupled with an increased recolonization mainly by Cyanobacteriota (circa 35–45%) and several stress-resistant Bacteria (globally ~95% of reads vs. 73–79% in controls). Antimicrobial resistance profiles significantly differed between sites, with treated areas showing distinct and unique resistance genes, and plasmids containing the blaTEM-116 gene, which can indicate potential adaptive shifts in the resistomes profiles after intervention. Metabolic pathways analysis revealed that untreated areas retained more complete nitrogen and sulfur cycling gene sets, whereas treated areas showed reduced biogeochemical gene contents, consistent with earlier-stage recolonization steps. Given the current recolonization detection and the ongoing biofilm formation, routine monitoring efforts (e.g., every 6 months) are recommended. Overall, this study demonstrates the first on-site genomic characterization of recolonization events on heritage stone, providing a practical prompt-warning tool for conservation monitoring and future biofilm management strategies. Full article

With the rapid development of autonomous vehicles and intelligent transportation systems, vehicle detection and distance estimation have become critical technologies for ensuring driving safety. However, real-world in-vehicle environments impose strict constraints on computational resources, making it impractical to deploy high-end GPUs. This implies that even highly accurate algorithms, if unable to run in real time on embedded platforms, cannot fully meet practical application demands. Although existing deep learning-based detection and stereo vision methods achieve state-of-the-art accuracy on public datasets, they often rely heavily on massive computational power and large-scale annotated data. Their high computational requirements and limited cross-scenario generalization capabilities restrict their feasibility in real-time vehicle-mounted applications. On the other hand, traditional algorithms such as Semi-Global Block Matching (SGBM) are advantageous in terms of computational efficiency and cross-scenario adaptability, but when used alone, their accuracy and robustness remain insufficient for safety-critical applications. Therefore, the motivation of this study is to develop a stereo vision-based collision warning system that achieves robustness, real-time performance, and computational efficiency. Our method is specifically designed for resource-constrained in-vehicle platforms, integrating a lightweight YOLOv8n detector with SGBM-based depth estimation. This approach enables real-time performance under limited resources, providing a more practical solution compared to conventional deep learning models and offering strong potential for real-world engineering applications. Full article

Traditional risk models often fail to capture extreme losses in interconnected global stock markets. This study introduces a novel approach, Expectile Regression Neural Network with Modified LASSO regularization (ERNN-mLASSO), to model nonlinear systemic risk. By analyzing five major stock indices (JKSE, GSPC, GDAXI, FTSE, N225), we identify distinct market roles: developed markets, such as the GSPC, act as risk spreaders, while emerging markets, like the JKSE, act as risk takers. Our network systemic risk index, SNRI, accurately captures systemic shocks during the COVID-19 crisis. More importantly, the model projects increasing global financial fragility through 2025, providing an early warning signal for policymakers and risk managers of potential future instability. Full article

Cropland abandonment, a global challenge, necessitates comprehensive monitoring to achieve the zero hunger goal. Prior monitoring approaches to cropland abandonment often face constraints in resolution, time series, drivers, prediction, or a combination of these. Here, we proposed an artificial intelligence framework to comprehensively monitor cropland abandonment and tested the framework in Hengyang City, China. Specifically, we first mapped land cover at 30 m resolution from 1985 to 2023 using Landsat, stable sample points, and a machine learning model. Subsequently, we constructed the extent, time, and frequency of cropland abandonment from 1986 to 2022 by analyzing pixel-level land-use trajectories. Finally, we quantified the drivers of cropland abandonment using machine learning models and predicted the spatial distribution of cropland abandonment risk from 2032 to 2062. Our results indicated that the abandonment maps achieved overall accuracies of 0.88 and 0.78 for identifying abandonment locations and timing, respectively. From 1986 to 2022, the proportion of cropland abandonment ranged between 0.15% and 4.06%, with an annual average abandonment rate of 1.32%. Additionally, the duration of abandonment varied from 2 to 38 years, averaging approximately 14 years, indicating widespread cropland abandonment in the study area. Furthermore, 62.99% of the abandoned cropland experienced abandonment once, 27.17% experienced it twice, and only 0.23% experienced it five times or more. Over 50% of cropland abandonment remained unreclaimed or reused. During the study period, tree cover, soil pH, soil total phosphorus, potential crop yield, and the multiresolution index of valley bottom flatness emerged as the five most important environmental covariates, with relative importances of 0.087, 0.074, 0.068, 0.050, and 0.043, respectively. Temporally, cropland abandonment in 1992 was influenced by transportation inaccessibility and low agricultural productivity, soil quality degradation became an additional factor by 2010, and synergistic effects of all three drivers were observed from 2012 to 2022. Notably, most cropland had a low abandonment risk (mean: 0.36), with only 0.37% exceeding 0.7, primarily distributed in transitional zones between cropland and non-cropland. Future risk predictions suggested a gradual decline in both risk values and the spatial extent of cropland abandonment from 2032 to 2062. In summary, we developed a comprehensive framework for monitoring cropland abandonment using artificial intelligence technology, which can be used in national or regional land-use policies, warning systems, and food security planning. Full article