Search Results (397)

Accurate identification of road roughness is pivotal for optimizing vehicle suspension control and enhancing passenger comfort. However, existing data-driven methods often struggle to balance classification accuracy with the strict computational constraints of real-time onboard monitoring. To address this challenge, this paper proposes a lightweight and robust road roughness classification framework utilizing a single sprung mass accelerometer. First, to overcome the scarcity of labeled real-world data and the limitations of linear models, a high-fidelity co-simulation platform combining CarSim and Simulink is established. This platform generates physically consistent vibration datasets covering ISO A–F roughness levels, effectively capturing nonlinear suspension dynamics. Second, we introduce DB-MLP, a novel Dual-Branch Multi-Layer Perceptron architecture. In contrast to computationally intensive Transformer or RNN-based models, DB-MLP employs a dual-branch strategy with multi-resolution temporal projection to efficiently capture multi-scale dependencies, and integrates dual-domain (time and position-wise) feature transformation blocks for robust feature extraction. Experimental results demonstrate that DB-MLP achieves a superior accuracy of 98.5% with only 0.58 million parameters. Compared to leading baselines such as TimeMixer and InceptionTime, our model reduces inference latency by approximately 20 times (0.007 ms/sample) while maintaining competitive performance on the specific road classification task. This study provides a cost-effective, high-precision solution suitable for real-time deployment on embedded vehicle systems. Full article

Tropical cyclones can rapidly alter watershed chemistry by shifting hydrologic pathways and mobilizing stored nutrients, yet these disturbances often remain undetected when storms cause little visible flooding or geomorphic damage. During Hurricane Helene 2024, intense rainfall across the Oconee River watershed in Georgia generated sharp increases in discharge that triggered substantial nutrient export despite minimal physical alteration to the landscape. High-frequency measurements of nitrate, phosphate, and sulfate in urban, forested, and recreational settings revealed pronounced and synchronous post-storm increases in all three solutes. Nitrate showed the strongest and most persistent response, with mean concentrations increasing from approximately 1–3 mg/L during pre-storm conditions to 6–14 mg/L post-storm across sites, and remaining elevated for several months after hydrologic conditions returned to baseline. Phosphate concentrations increased sharply during the post-storm period, rising from pre-storm means of ≤0.3 mg/L to a post-storm average of 1.5 mg/L, but declined more rapidly during recovery, consistent with sediment-associated mobilization and subsequent attenuation. Sulfate concentrations also increased substantially across the watershed, with post-storm mean values commonly exceeding 20 mg/L and maximum concentrations reaching 41 mg/L, indicating sustained dissolved-phase release and enhanced temporal variability. Recovery trajectories differed by solute: phosphate returned to baseline within weeks, nitrate declined gradually, and sulfate remained elevated throughout the winter. These findings demonstrate that substantial chemical perturbations can occur even in the absence of visible storm impacts, underscoring the importance of event-based, high-resolution monitoring to detect transient but consequential shifts in watershed biogeochemistry. They also highlight the need to better resolve solute-specific pathways that govern nutrient mobilization during extreme rainfall in mixed-use watersheds with legacy nutrient stores and engineered drainage networks. Full article

Background: Exposure to environmental pollutants, especially endocrine-disrupting chemicals, disproportionately affects vulnerable populations like pregnant women, lactating mothers, and preterm infants. This study aimed to assess the detection patterns of DiNP-, DEP-, and DEHP-related metabolites in maternal urine and breast milk, examine agreement between matrices, and explore maternal factors associated with phthalate exposure. Methods: Fifty-five mothers who delivered at ≤32 gestational weeks and whose infants were hospitalized in the Neonatal Intensive Care Unit (NICU) were enrolled. Breast milk and urine samples were analyzed using a validated isotope-dilution LC–MS/MS method. Urinary phthalate metabolite concentrations were adjusted for specific gravity. Linear mixed-effects models with a random intercept for mother were used to examine associations between urinary and breast milk phthalate metabolite concentrations, assess temporal changes, and evaluate the influence of breast milk lipid content. Results: DEHP and DiNP metabolites were detected in nearly all maternal urine samples. Breast milk contained predominantly primary metabolites (MEHP and MiNP), while secondary oxidative metabolites were rarely detected. Urine concentrations consistently exceeded breast milk concentrations. Urinary and breast milk phthalate concentrations were not correlated across sampling periods, indicating limited matrix concordance. Conclusions: Mothers of very preterm infants experience sustained phthalate exposure in the postpartum period; however, limited metabolite transfer to breast milk indicates that maternal urine remains the preferred biomonitoring matrix for assessing systemic phthalate exposure. Breast milk phthalate profiles exhibit compound-specific temporal changes and appear largely independent of concurrent urinary exposure biomarkers. Full article

The Linfen–Yuncheng Basin is located on the southern edge of the Fenwei Fault Zone, influenced by intense tectonic activity, thick Quaternary sedimentation, and anthropogenic disturbance, it exhibits prominent characteristics of ground subsidence and fissure development. However, uncertainties still exist regarding the primary controlling factors of subsidence. This study employs multi-temporal InSAR data, combined with small baseline subset (SBAS–InSAR) technology to invert the high-precision ground line of sight deformation fields, and conducts time-series decomposition analysis using the Seasonal Trend Decomposition (STL) method. The results show that from 2017 to 2025, subsidence was mainly concentrated in the central and southern regions of the basin, with a maximum cumulative subsidence exceeding 200 mm and an average annual subsidence rate of −40 mm/year. Its spatial distribution is highly consistent with major structural zones such as the Zhongtiao Mountain Front Fault and the Linyi Fault, indicating that fault activity exerts a significant controlling effect on subsidence patterns. Groundwater level fluctuations are positively correlated with overall ground subsidence, and the response rate of different monitoring points is constrained by differences in aquifer depth and permeability. Groundwater aquifer points exhibit rapid and reversible subsidence response, while confined aquifer points are affected by low-permeability or compressible layers, showing a significant lag effect. The research results indicate that time-series analysis based on InSAR can not only effectively reveal the subsidence evolution process at different scales, but also provide a scientific basis for groundwater resource regulation, geological disaster prevention and control, and sustainable regional land utilization. Full article

This study examines how the sense of home for people with dementia is shaped not only by physical settings but by dynamic atmospheric compositions emerging through memory, sensation, and everyday practices. Building on a preliminary literature mapping that identified three dimensions of home in later-life care environments—safe space, small world, and connection—we developed a multisensory co-design toolkit combining key-element cards and curated olfactory prompts. The study was conducted in a dementia-friendly residential care facility in Italy. Nine residents with mild–moderate dementia (aged 75–84) participated in two group sessions and six individual sessions, facilitated by two design researchers with care staff present. Data consist of audio-recorded and transcribed interviews, guided olfactory sessions, and researcher fieldnotes. Across sessions, participants articulated “small worlds” as micro-environments composed of meaningful objects, bodily comfort, routines, and sensory cues that supported emotional regulation and identity continuity. Olfactory prompts, administered through a low-intensity and participant-controlled protocol, supported scene-based autobiographical recall for some participants, often eliciting memories of domestic rituals, places, and relationships. Rather than treating home-like design as a fixed architectural style, we interpret home as continuously re-made through situated sensory–temporal patterns and relational practices. We translate these findings into atmospheric design directions for dementia care: designing places of self and refuge, staging accessible material memory devices, embedding gentle olfactory micro-worlds within daily routines, and approaching atmosphere as an ongoing process of co-attunement among residents, staff, and environmental conditions. The study contributes a methodological and conceptual framework for multisensory, narrative-driven approaches to designing home-like environments in long-term care. Full article

Land subsidence constitutes a critical hazard to coastal megacities globally, amplifying flood risks and damaging infrastructure. Taking Tianjin—a major port city underlain by compressible sediments and affected by groundwater over-exploitation—as a case study, we address two key research gaps: the absence of a quantitative framework coupling groundwater extraction with construction land expansion, and the inadequate separation of seasonal and long-term subsidence drivers. We developed an integrated remote-sensing-based approach: high-resolution subsidence time series (2016–2023) were derived via Small BAseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) using Sentinel-1 Synthetic Aperture Radar (SAR) imagery, validated against leveling measurements (R > 0.885, error < 20 mm). This subsidence dataset was fused with groundwater level records and annual construction land maps. Seasonal-Trend Decomposition using Loess (STL) isolated trend, seasonal, and residual components, which were input into a Random Forest (RF) model to quantify the relative contributions of subsidence drivers. Dynamic Time Warping (DTW) and Cross-Wavelet Transform (CWT) were further employed to characterize temporal patterns and lag effects between subsidence and its drivers. Our results reveal a distinct shifting subsidence pattern: “areal expansion but intensity weakening.” Groundwater control policies mitigated five historical subsidence funnels, reducing areas with severe subsidence from 72.36% to <5%, while the total subsiding area expanded by 1024.74 km², with new zones emerging (e.g., northern Dongli District). The RF model identified the long-term groundwater level trend as the dominant driver (59.5% contribution), followed by residual (23.3%) and seasonal (17.2%) components. Cross-spectral analysis confirmed high coherence between subsidence and long-term groundwater trends; the seasonal component exhibited a dominant resonance period of 12 months and a consistent subsidence response lag of 3–4 months. Construction impacts were conceptualized as a “load accumulation-soil compression-time lag” mechanism, with high-intensity engineering projects inducing significant local subsidence. This study provides a robust quantitative framework for disentangling the complex interactions between subsidence, groundwater, and urban expansion, offering critical insights for evidence-based hazard mitigation and sustainable urban planning in vulnerable coastal environments worldwide. Full article

We present the design and characterization of a fully automated free-standing liquid crystal (FSLC) film holder, enabling remote and precise control of liquid crystal (LC) volume release, wiping speed, and temperature. Using 4-octyl-4′-cyanobiphenyl (8CB) as a test material, we systematically investigated the influence of formation parameters on the resulting film thickness and temporal evolution. Thickness measurements performed by monitoring the difference in optical path lengths of two arms of a standard optical intensity autocorrelation setup reveal that the wiping speed is the dominant factor determining both the initial film thickness and the subsequent annealing dynamics, while temperature becomes relevant only at the highest wiping speeds. Faster wiping speeds consistently produce thinner and more uniform FSLC films on the order of 3 µm, due to reduced LC mass deposition. Time-resolved optical and X-ray scattering measurements confirm the presence of an annealing phase following film formation, which can last for between 1 s and 10 min time scales, until a stable smectic configuration is reached. The holder provides a reliable and fully remote tool for generating high-quality FSLC films at rates up to 1 Hz, suitable for optical to hard X-ray experiments where direct access to the sample environment is limited. Full article

Diffuse aurora is a widespread and long-lasting auroral emission that plays an important role in diagnosing magnetosphere-ionosphere coupling and magnetospheric plasma transport. Despite its scientific significance, diffuse aurora remains challenging to identify automatically in all-sky imager (ASI) observations due to its weak optical intensity, indistinct boundaries, and gradual temporal evolution. These characteristics, together with frequent cloud contamination, limit the effectiveness of conventional keogram-based or morphology-driven detection approaches and hinder large-scale statistical analyses based on long-term optical datasets. In this study, we propose an automated framework for the identification and temporal segmentation of diffuse aurora from untrimmed all-sky auroral videos. The framework consists of a frame-level coarse identification module that combines weak morphological information with inter-frame temporal dynamics to detect candidate diffuse-auroral intervals, and a snippet-level segmentation module that dynamically aggregates temporal information to capture the characteristic gradual onset-plateau-decay evolution of diffuse aurora. Bidirectional temporal modeling is employed to improve boundary localization, while an adaptive mixture-of-experts mechanism reduces redundant temporal variations and enhances discriminative features relevant to diffuse emission. The proposed method is evaluated using multi-year 557.7 nm ASI observations acquired at the Arctic Yellow River Station. Quantitative experiments demonstrate state-of-the-art performance, achieving 96.3% frame-wise accuracy and an Edit score of 87.7%. Case studies show that the method effectively distinguishes diffuse aurora from cloud-induced pseudo-diffuse structures and accurately resolves gradual transition boundaries that are ambiguous in keograms. Based on the automated identification results, statistical distributions of diffuse aurora occurrence, duration, and diurnal variation are derived from continuous observations spanning 2003–2009. The proposed framework enables robust and fully automated processing of large-scale all-sky auroral images, providing a practical tool for remote sensing-based auroral monitoring and supporting objective statistical studies of diffuse aurora and related magnetospheric processes. Full article

Northern Thailand experiences recurrent seasonal haze driven by biomass burning (BB), which results in hazardous PM2.5 exposure and elevated environmental health risks. To address the need for timely and spatially resolved emission information, this study developed and evaluated an operational near-real-time (NRT) biomass-burning PM2.5 emission estimation system using the Fire INventory from NCAR version 2.5 (FINNv2.5). The objectives of this study are threefold: (1) to construct a high-resolution (≤1 km) NRT biomass-burning PM2.5 emission inventory for Northern Thailand; (2) to assess its temporal and spatial consistency with ground-based PM2.5 measurements and satellite fire observations; and (3) to examine its potential utility for informing environmental health risk management. The developed system captured short-lived, high-intensity burning episodes that defined the haze crisis, revealing a distinct peak period from late February to early April. Cumulative emissions from January to April 2024 exceeded 250,000 tons, dominated by Chiang Mai (25.8%) and Mae Hong Son (25.5%), which together contributed 51.3% of regional emissions. Strong correspondence with MODIS/VIIRS FRP (r = 0.79) confirmed the reliability of the NRT emission signal, while regression against observed PM2.5 concentrations indicated a substantial background burden (intercept = 40.41 μg m⁻³) and moderate explanatory power (R² = 0.448), reflecting additional meteorological and transboundary influences. Translating these relationships into operational metrics, an Emission Control Threshold of 1518 tons day⁻¹ was derived to guide targeted burn permitting and reduce population exposure during peak-risk periods. This NRT biomass-burning PM2.5 emission estimation framework offers timely emissions information that may support decision makers in environmental health risk management, including the development of early warnings, adaptive burn-permit strategies, and more coordinated responses across Northern Thailand. Full article

We present a new method for mapping global gas flaring using a multiyear spatio-temporal database of VIIRS Nightfire (VNF) nighttime infrared detections from the Suomi NPP, NOAA-20, and NOAA-21 satellites. The method is designed to resolve closely spaced industrial combustion sources and to produce a stable, physically meaningful flare catalog suitable for long-term monitoring and emissions analysis. The method combines adaptive spatial aggregation of high-temperature detections with a hierarchical clustering that super-resolves individual flare stacks within oil and gas fields. Post-processing yields physically consistent flare footprints and attraction regions, allowing separation of closely spaced sources. Flare clusters are assigned to operational categories (e.g., upstream, midstream, LNG) using prior catalogs combined with AI-assisted expert interpretation. In this step, a multimodal large language model (LLM) provides contextual classification suggestions based on geospatial information, high-resolution daytime imagery, and detection time-series summaries, while final attribution is performed and validated by domain experts. Compared with annual flare catalogs commonly used for national flaring estimates, the new catalog demonstrates substantially improved performance. It is more selective in the presence of intense atmospheric glow from large flares, identifies approximately twice as many active flares, and localizes individual stacks with ~50 m precision, resolving emitters separated by ~400–700 m. For the well-defined class of downstream flares at LNG export facilities, the catalog achieves complete detectability. These improvements support more accurate flare inventories, facility-level attribution, and policy-relevant assessments of gas flaring activity. Full article

Invasions by alien plants are major global drivers of ecosystem changes and loss of biodiversity. Guangxi is an ecological barrier in southern China that is increasingly being affected by invasive alien plant species. We comprehensively reviewed the literature, compiling and analyzing the long-term changes in species composition, native range, life forms, municipal-scale patterns, and correlates of invasive alien plant richness in Guangxi at three time points (1973, 2010, and 2023). Over the 50-year period, the number of invasive alien plant species markedly increased from 31 species in 1973 to 84 in 2010 and 158 in 2023; the number of families, genera, and species increased 2.05-, 3.75-, and 5.10-fold, respectively. Species native to North America consistently dominated the invasive flora, followed by those native to Africa. The number of species native to South America and Asia increased in the records from 2010 to 2023. Annual herbaceous plants accounted for the largest proportion of invasive species throughout the study period and showed the largest absolute increase in species number. However, no substantial temporal shifts in the overall life-form composition were detected. At the municipal scale, the invasive alien plant richness exhibited pronounced spatial heterogeneity. The invasive alien plant richness was highest in Guilin and Baise in 1973, in Guilin in 2023, followed by Nanning and Baise. Correlation analyses based on 2023 data revealed a significant positive association between invasive alien plant richness and tourism intensity, whereas relationships between population size, gross domestic product, and climatic variables were weak or nonsignificant. Overall, our results document the continued expansion and the spatial differentiation of invasive alien plants in Guangxi over the 50-year period of 1973–2023. These patterns primarily reflect the accumulation in the number of recorded invasive species under a consistent classification framework and should be interpreted with caution given the potential variation in survey effort among periods and cities. The results provide a descriptive baseline for the provincial-scale monitoring, risk assessment, and management of invasive alien plants. Full article

This follow-up study investigates the long-term environmental sustainability and remediation outcomes of the UPPER (‘Urban Productive Parks for Sustainable Urban Regeneration’-UIA04-252) project in Latina, Italy, focusing on Nature-Based Solutions (NbS) applied to urban green infrastructure. By integrating proximal and satellite-based remote sensing methodologies, the research evaluates persistent improvements in vegetation health, soil moisture dynamics, and overall environmental quality over multiple years. Building upon the initial monitoring framework, this case study incorporates updated data and refined techniques to quantify temporal changes and assess the ecological performance of NbS interventions. In more detail, ground-based data from meteo-climatic, air quality stations and remote satellite data from the Sentinel-2 mission are adopted. Ground-based measurements such as temperature, humidity, radiation, rainfall intensity, PM10 and PM2.5 are carried out to monitor the overall environmental quality. Updated satellite imagery from Sentinel-2 is analyzed using advanced band ratio indices, including the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI) and the Normalized Difference Moisture Index (NDMI). Comparative temporal analysis revealed consistent enhancements in vegetation health, with NDVI values significantly exceeding baseline levels (NDVI 2022–2024: +0.096, p = 0.024), demonstrating successful vegetation establishment with larger gains in green areas (+27.0%) than parking retrofits (+11.4%, p = 0.041). However, concurrent NDWI decline (−0.066, p = 0.063) indicates increased vegetation water stress despite irrigation infrastructure. NDMI improvements (+0.098, p = 0.016) suggest physiological adaptation through stomatal regulation. Principal Component Analysis (PCA) of meteo-climatic variables reveals temperature as the dominant environmental driver (PC2 loadings > 0.8), with municipality-wide NDVI-temperature correlations of r = −0.87. These multi-scale findings validate sustained NbS effectiveness in enhancing vegetation density and ecosystem services, yet simultaneously expose critical water-limitation trade-offs in Mediterranean semi-arid contexts, necessitating adaptive irrigation management and continued monitoring for long-term urban climate resilience. The integrated monitoring approach underscores the critical role of continuous, multi-scale assessment in ensuring long-term success and adaptive management of NbS-based interventions. Full article

Respiratory diseases pose a significant threat to intensive pig farming, and cough recognition serves as a key indicator for early intervention. However, its practical application is constrained by the scarcity of labeled samples and the complex acoustic conditions of farm environments. To address these challenges, this study proposes a lightweight pig cough recognition method based on a pre-trained model. By freezing the backbone of a pre-trained audio neural network and fine-tuning only the classifier, our approach achieves effective knowledge transfer and domain adaptation with very limited data. We further enhance the model’s ability to capture temporal–spectral features of coughs through a time–frequency dual-stream module. On a dataset consisting of 107 cough events and 590 environmental noise clips, the proposed method achieved an accuracy of 94.59% and an F1-score of 92.86%, significantly outperforming several traditional machine learning and deep learning baseline models. Ablation studies validated the effectiveness of each component, with the model attaining a mean accuracy of 96.99% in cross-validation and demonstrating good calibration. The results indicate that our framework can achieve high-accuracy and well-generalized pig cough recognition under small-sample conditions. The main contribution of this work lies in proposing a lightweight fine-tuning paradigm for small-sample audio recognition in agricultural settings, offering a reliable technical solution for early warning of respiratory diseases on farms. It also highlights the potential of transfer learning in resource-limited scenarios. Full article

Background/Objectives: Healthcare-associated infections (HAIs), particularly those caused by multidrug-resistant (MDR) bacteria, remain a major challenge for Romanian hospitals. This study aimed to evaluate the epidemiological burden of MDR-related HAIs and to characterize the distribution of MDR bacterial isolates and their antimicrobial resistance patterns over four consecutive semesters in a Romanian tertiary care hospital. Methods: A retrospective study was conducted using data from the Electronic Registry of HAIs, clinical observation sheets, and microbiology laboratory records. An epidemiological analysis was performed on patients diagnosed with MDR-related HAIs, while a separate microbiological analysis included all MDR bacterial isolates identified during the study period. Descriptive and comparative statistical analyses were applied to assess temporal trends, pathogen distribution, and resistance profiles. Results: Of the 327 HAIs identified, 56 cases (17.13%) were caused by MDR bacteria. Most MDR-HAIs originated from the Intensive Care Unit (≈60%), with Acinetobacter baumannii and Klebsiella spp. as the predominant pathogens. Overall mortality among patients with MDR-HAIs was high (51.79%), particularly in infections caused by A. baumannii and K. pneumoniae. Microbiological analysis of MDR isolates (n = 406) revealed consistently high resistance rates to ciprofloxacin, cefepime, and ceftazidime, exceeding 95% in 2023–2024, while resistance to carbapenems surpassed 90% by the end of the study period. Temporal variability in MDR burden was observed across semesters, suggesting an influence of clinical and institutional factors. Conclusions: MDR-related HAIs represent a significant and persistent problem in Romanian acute-care hospitals, particularly in Intensive Care Units. The dominance of carbapenem-resistant A. baumannii and extended-spectrum beta-lactamase-producing and carbapenem-resistant Klebsiella spp. highlights the urgent need for strengthened antimicrobial stewardship, enhanced microbiological surveillance, and reinforced infection prevention strategies. Full article

With the widespread application of high-resolution remote sensing imagery and unmanned aerial vehicle technologies in agricultural scenarios, accurately characterizing spatial pest diffusion from multi-temporal images has become a critical issue in intelligent agricultural management. To overcome the limitations of existing machine learning approaches that focus mainly on static recognition and lack effective spatio-temporal diffusion modeling, a UAV-based pest diffusion prediction and simulation framework is proposed. Multi-temporal UAV RGB and multispectral imagery are jointly modeled using a graph-based representation of farmland parcels, while temporal modeling and environmental embedding mechanisms are incorporated to enable simultaneous prediction of diffusion intensity and propagation paths. Experiments conducted on two real agricultural regions, Bayan Nur and Tangshan, demonstrate that the proposed method consistently outperforms representative spatio-temporal baselines. Compared with ST-GCN, the proposed framework achieves approximately 17–22% reductions in MAE and MSE, together with 8–12% improvements in PMR, while maintaining robust classification performance with precision, recall, and F1-score exceeding 0.82. These results indicate that the proposed approach can provide reliable support for agricultural information systems and diffusion-aware decision generation. Full article