Search Results (5,490)

Globally, Central Asian wild fruit forests are critical repositories of wild fruit germplasm resources and provide essential ecosystem services. However, their habitats are facing escalating degradation risks driven by climate warming, shifting precipitation regimes, and intensifying anthropogenic disturbances. Accurately quantifying climate-driven spatiotemporal variations in habitat suitability for keystone wild fruit tree species is therefore an essential prerequisite for formulating targeted conservation and management strategies in arid and semi-arid landscapes. In this study, we applied the maximum entropy (MaxEnt) model to simulate the current (2000–2020 baseline) and future (2030s, 2050s, 2070s) potential suitable habitats of two dominant wild fruit tree species, Malus sieversii (Ledeb.) M.Roem. and Prunus cerasifera Ehrh., in the Ili Valley, a core distribution area of Central Asian wild fruit forests in northwestern China. We integrated rigorously screened species occurrence records with key environmental predictors and characterized future climate conditions using three Shared Socioeconomic Pathways (SSPs; SSP126, SSP245, and SSP585) spanning low to high radiative forcing levels. The model exhibited excellent predictive performance (AUC > 0.85), confirming the robustness and reliability of our habitat suitability simulations. Elevation and annual precipitation were identified as the dominant environmental variables governing habitat suitability for both species, highlighting the critical role of terrain–hydroclimate interactions in maintaining viable dryland refugia for wild fruit forests. Under the baseline climate scenario, the total area of suitable habitats reached 24.014 × 10³ km² for Malus sieversii and 18.990 × 10³ km² for Prunus cerasifera. Future climate projections revealed a consistent and significant contraction trend in suitable habitats for both species, with the magnitude of habitat loss escalating with increasing radiative forcing and longer projection time horizons. Specifically, under the high-emission SSP585 scenario by the 2070s, the suitable habitat area is projected to decline by 7.579 × 10³ km² for Malus sieversii and 9.883 × 10³ km² for Prunus cerasifera relative to the baseline. Our findings delineate climate-vulnerable hotspots of wild fruit forests and provide a robust spatial scientific basis for prioritizing in situ conservation, targeted habitat restoration, and anthropogenic disturbance regulation to support the long-term persistence of these irreplaceable wild fruit germplasm resources under accelerating global climate change. Full article

Cross-domain scene classification aims to mitigate the distribution discrepancy between domains through domain adaptation techniques. With the rapid advancement of Vision–Language Models (VLMs), utilizing them for cross-domain scene classification has emerged as a promising research direction. Current methods utilize domain-specific prompts to facilitate domain adaptation through the CLIP model. However, for remote sensing images, the considerable differences in visual features across domains pose significant challenges for learning domain-specific prompts, leading to suboptimal cross-domain performance. In addition, they cannot reduce the domain shift that exists between the source domain and the target domain. To address the above challenges, we propose a novel cross-domain scene classification method, DIPVR (Domain-Invariant Prompts and Visual Representations), which enhances model performance by learning domain-invariant features for both prompts and visual representations. Specifically, we propose learning domain-invariant prompts and introducing prior knowledge to guide the prompt-learning process. To learn domain-invariant visual representations, we propose a Visual Invariant Learning module that adaptively extracts the shared features between the source and target domains. Finally, visual features are matched with context features to align the domain distributions between the source and target domains. The experimental results on the cross-domain scene classification datasets demonstrate that our proposed method outperforms the baseline methods, achieving optimal cross-domain transfer performance. Full article

Candida species can shift from commensal organisms to opportunistic pathogens. Both Candida albicans and non-albicans Candida (NAC) species colonize oral biofilms and periodontal pockets, where they may contribute to inflammation and the progression of periodontal disease. This study aimed to determine the prevalence and antifungal susceptibility profiles of Candida species in individuals with different stages of periodontal disease. A cross-sectional study was conducted in 100 participants whose periodontal status was clinically evaluated. Saliva samples were cultured on chromogenic agar for yeast isolation, species identification was confirmed by MALDI-TOF MS, and antifungal susceptibility to fluconazole, clotrimazole, nystatin, and amphotericin B was assessed. Candida spp. was detected in 35% of participants, where C. albicans was the most prevalent species, followed by Nakaseomyces glabratus (formerly Candida glabrata), Candida parapsilosis, Candida dubliniensis, and Candida tropicalis. Species distribution varied according to periodontal status, with N. glabratus predominating in early periodontitis and C. albicans appeared more frequently in higher severe stages of periodontitis. Susceptibility testing showed resistance of C. albicans to clotrimazole (63.6%) and nystatin (22.7%), whereas amphotericin B and fluconazole remained effective. NAC species, particularly N. glabratus, exhibited resistance to nystatin and variable resistance to clotrimazole but remained susceptible to amphotericin B. These findings underscore the importance of early detection and personalized antifungal strategies for managing periodontal disease complicated by Candida colonization. Full article

This study investigated injury severity in 18,528 police-reported vehicle-to-pedestrian crashes involving elderly pedestrians in legally classified local areas of South Korea during 2012–2021. Injury severity was coded into four ordered categories: fatal, serious, minor, and reported injury. To stabilize scenario extraction from a categorical crash database, an integrated screening workflow was applied, including near-zero-variance filtering, redundancy control among overlapping roadway encodings, representative-variable selection within redundant groups, and chi-square association checks. Classification and regression tree (CART) modeling was then used to identify rule-based combinations of environmental, roadway, driver, pedestrian, and vehicle factors associated with elevated severity, while tree complexity was controlled through cost-complexity pruning and 10-fold cross-validation. A scenario-based sensitivity analysis was further conducted to evaluate counterfactual shifts in severity distributions under targeted control of key conditions within representative high-risk scenarios. The results showed that severe outcomes were concentrated in stacked-risk combinations rather than in single factors alone. A dominant pathway involved nighttime conditions combined with maneuver-related driving contexts and speeding-related violations. High-fatality scenarios persisted even when speed-related predictors were excluded, underscoring the roles of nighttime exposure, visibility limitations, conflict-prone roadway settings, heavy-vehicle involvement, and pedestrian exposure behaviors. The proposed framework translates administrative crash records into concise, operationally interpretable scenarios and intervention-relevant evidence for local-area safety. Full article

Enterococci are widely used indicators of fecal contamination because they originate in the gastrointestinal tracts of warm-blooded animals, and species-level identification can support source attribution. This study evaluated the temporal abundance and species composition of enterococci in Quebrada Mondongo, southwestern Puerto Rico, a stream influenced by wastewater treatment plant (WWTP) effluent and nonpoint-source inputs. Five sampling campaigns for species distribution and fourteen for population quantification were conducted over approximately one year at the WWTP effluent discharge and at upstream and downstream stations. Enterococci concentrations exceeded the regulatory threshold for surface waters. Among the confirmed isolates, E. faecium dominated upstream and in the effluent, occurring approximately twofold more frequently than E. faecalis. Downstream, E. faecalis increased in relative abundance, shifting the species ratio of E. faecium/E. faecalis from 2.3–3.2 to 0.89. E. casseliflavus was detected at low frequency, and E. gallinarum was not observed. Virulence-associated genes (esp, gelE) were identified in ~75% of E. faecalis isolates, consistent with enhanced environmental persistence. Although upstream and effluent patterns reflected a strong human fecal signal, the downstream enrichment of E. faecalis suggests additional secondary inputs and/or naturalization. This study provides empirical evidence of species shifts in a tropical stream, with an increase in E. faecalis downstream of a WWTP despite E. faecium dominance in the effluent highlighting the likely influence of other nonpoint fecal sources within the watershed. Overall, these results suggest that the WWTP effluent did not contribute substantially to enterococci concentrations nor significantly influence the species composition of enterococci downstream in Quebrada Mondongo, highlighting the likely influence of other nonpoint fecal sources within the watershed. Full article

Understanding Alzheimer’s Disease (AD) progression using resting-state functional Magnetic Resonance Imaging (rs-fMRI) remains an open challenge. Variational Autoencoders (VAEs) provide compact representations of high-dimensional neuroimaging data but lack mechanisms to highlight disease-relevant features. We propose KS-VAE, a novel framework that integrates the Kolmogorov–Smirnov test into the latent space of VAEs to identify statistically significant variables discriminating healthy from pathological brain states. This integration enables measurement of latent space shifts associated with cognitive decline, offering a quantitative approach to neurodegenerative processes. By modifying the most relevant variables, KS-VAE generates synthetic samples that simulate transitions between clinical conditions while preserving anatomical plausibility. The method enhances the modeling of temporal and distributional dynamics underlying disease progression and provides interpretable analysis of class-relevant features. Applied to rs-fMRI scans of 220 subjects from the ADNI cohort, KS-VAE demonstrated robust class separation between cognitively normal and Alzheimer’s disease subjects, achieving a classification accuracy of 84.5% and an F1-score of 84.5%, and clinically consistent synthetic transitions. KS-VAE thus offers a statistically grounded and clinically interpretable framework for understanding Alzheimer’s disease progression. Full article

Accurate multivariate long-term time series forecasting (LTSF) is critical for smart grid operations. However, non-stationary distribution shifts frequently induce compounding error accumulation in conventional architectures. This study proposes the Mamba-LSTM-Attention (MLA) framework, a distribution-aware architecture engineered for forecasting stability. The pipeline integrates Reversible Instance Normalization (RevIN) to neutralize statistical drift. To address computational bottlenecks, the architecture utilizes a linear-time Selective State Space Model (Mamba) to capture global trend dynamics, cascaded with a single-layer gated Long Short-Term Memory (LSTM) unit to model localized non-linear residuals. A terminal information bottleneck structurally bounds cross-step error propagation. Empirical results across standard ETT and Electricity benchmarks reveal a precision–stability trade-off. By prioritizing structural resilience, the MLA framework limits error accumulation on highly volatile datasets, yielding MSEs of 0.210 and 0.128 on ETTh2 and ETTm2 at the T = 96 horizon. This structural bottleneck inherently smooths high-frequency periodic patterns, yielding lower absolute accuracy on stationary benchmarks such as ETTh1 and ETTm1. Ultimately, the architecture establishes a computationally efficient, structurally stable baseline tailored for non-stationary anomaly tracking in smart grids. Full article

By combining hanging-drop self-assembly with melt infiltration and selective inversion, we fabricate millimetric and free-standing curved photonic heterostructures that integrate infiltrated-opal, inverse-opal, embossed, and white-scattering 2.5D metasurface domains within a single continuous body. These architectures enable configurations inaccessible to planar fabrication, including naturally formed concavities within convex inverse-opal films and alternating ordered/single-layer regions that preserve local coherence while introducing disorder at larger scales. Across these heterogeneous curved landscapes, we observe optical phenomena absent in flat photonic structures—spectrally selected lateral collimation, geometry-shifted ghost images, and transmission-derived valleys shaped by curvature-mediated Bragg extraction. Their origin lies in the geometric constraints inherent to curved assemblies, where spatially varying normals, non-parallel lattice orientations, and topologically required defects couple order and disorder into a distributed-coherence regime. This coupling expands the accessible photonic state space, establishing curvature as an active functional degree of freedom rather than a geometric constraint, positioning the self-assembled photonic heterostructures as a scalable route toward multifunctional 3D metasurfaces and new regimes of light–matter interaction. Full article

Chrysaora pacifica, a scyphozoan jellyfish widely distributed in East Asian waters, has recently shown signs of range expansion along the coasts of Korea, Japan, and China. However, ecological information on its early planktonic stage, the ephyra, remains limited. In this study, we experimentally investigated the effects of seawater temperature on the growth, feeding, and survival of C. pacifica ephyrae under controlled laboratory conditions. Five temperature treatments (12, 16, 20, 24, and 28 °C) were selected based on the species’ natural occurrence period. The results showed that ephyrae exhibited stable growth and feeding at 20–24 °C, with a high survival rate of approximately 90%, indicating that this range represents the optimal thermal condition for the ephyra stage. At 28 °C, growth and feeding were highest among all treatments; however, survival declined sharply to 22.5%, suggesting that elevated temperature may impose physiological stress. In contrast, at 12 °C, both growth and feeding activity were markedly reduced, and survival decreased to 32.5%. These findings demonstrate that temperature is a key environmental factor influencing the physiological performance and survival of C. pacifica ephyrae. This study provides essential baseline data for understanding the early life-stage ecology of this species and contributes to improving predictions of jellyfish population dynamics and potential distribution shifts in East Asian marine ecosystems under future environmental change. Full article

Operating multi-unit combined heat and power (MUCHP) plants involves determining unit commitment (UC) and coupled heat and power dispatch under demand uncertainty and progressive equipment degradation. This paper proposes a reinforcement learning approach to jointly optimize UC, dispatch, and preventive maintenance (PM). Specifically, we develop a Proximal Policy Optimization (PPO)-based policy that shifts the computational burden to offline training, enabling near-real-time decisions during operation. The trained agent is evaluated on an hourly five-unit CHP system model based on operational data from a district heating plant in the Republic of Korea, using a full-year simulation. The robustness of the proposed method is assessed against demand forecast noise and structural system shifts covering reduced, expanded, homogeneous, and heterogeneous unit configurations. The experiments indicate that the proposed approach reduced the total operating cost by 4.69 to 8.35 percent compared to three heuristic baselines across the evaluated scenarios. Moreover, it mitigates supply shortages during high-volatility seasons through proactive pre-commitment and preserves asset health by distributing production loads evenly. These results indicate that integrating PM into operational planning improves both the economic efficiency and operational stability of MUCHP systems. Full article

Urban trees deliver multiple ecosystem services. However, rapid climate change may alter species-specific growth suitability, necessitating climate-informed planting and management. We developed 1 km grid-based ensemble species distribution models (ensemble SDMS) for 18 tree species widely planted in South Korean cities and projected growth suitability under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 across four future periods (2021–2040, 2041–2060, 2061–2080, 2081–2100) relative to a historical baseline (2000–2019). We quantified multidimensional redistribution signals from SDM outputs, including binary suitable area changes, centroid displacement, latitudinal boundary shifts, and mean suitability changes, using multivariate climatic predictors and complementary environmental variables. These indicators were integrated to classify species responses into four management-relevant types: Stable, Northward Expansion, Poleward Shift, Range Contraction. Model performance was generally high (AUC = 0.74–0.97). Although the median change in suitable area remained near 0%, interspecific variability increased toward later periods and under stronger forcing, with the largest dispersion under SSP3-7.0 (2041–2060). Stable type was most frequent overall (36.8–63.2%), but Northward Expansion increased to 42.1% under late-century SSP3-7.0, and Range Contraction reached 36.8% under mid-century SSP3-7.0. This indicator-based typology provides a practical basis for decision-support tools to prioritize climate-adaptive urban tree selection, replacement, and monitoring. Full article

Against the backdrop of intensifying global population aging, ensuring the sustainable provision of age-friendly spaces has become an important domain of urban policy intervention. A close examination of the supply–demand matching of age-friendly spaces is therefore essential for policymakers seeking to achieve social and environmental sustainability in an aging society. However, existing approaches to assessing this alignment primarily rely on quantitative analyses of geographical spatial distribution, lacking methods to evaluate the structural alignment of spatial functional types. To address this gap, this study proposes and validates a type-based quantitative approach to examining the alignment between policy supply and everyday demands for age-friendly spaces. By integrating policy text analysis, questionnaire surveys, activity logs, and behavior snapshots, the study identifies the types of age-friendly spaces mentioned by policies and those demanded in daily life, and quantitatively evaluates their alignment using a matching model. The results show that the older adults’ spatial demands shift progressively from life-oriented spaces to survival-oriented spaces as age increases and health declines. More importantly, a significant structural imbalance is evident: survival-oriented spaces are oversupplied, while life-oriented spaces remain in short supply. This study provides a diagnostic method for assessing the provision of age-friendly spaces and provides practical implications for local governments in formulating more balanced, responsive, and sustainable supply strategies. Full article

The safe and efficient operation of deep-sea towing systems is heavily governed by the highly nonlinear dynamic interaction between the flexible towing cable and complex seabed topographies. While existing studies accurately predict cable dynamics in mid-water or over flat seabeds, the transient responses—such as local stress concentrations and extreme tension fluctuations—induced by discontinuous topographies (e.g., stepped or 3D irregular seabeds) remain inadequately quantified. In this study, we develop an advanced 3D dynamic numerical model combining the lumped-mass finite element formulation with a modified non-linear penalty-based seabed-contact mechanics algorithm. This framework systematically evaluates the tension distribution, bending curvature, and spatial configuration shifts in the cable during the touchdown and detachment phases across inclined, stepped, and 3D seabeds. Quantitative validation against established benchmarks demonstrates robust accuracy. Results indicate that steeper seabed inclinations linearly reduce detachment time but exponentially amplify initial contact tension. Over-stepped terrains, “point-to-line” transient collisions trigger sudden tension spikes exceeding steady-state values by up to 45%. Furthermore, 3D irregular seabeds induce severe multi-directional spatial deformations, precipitating destructive whiplash effects at high towing speeds (e.g., V > 2.2 m/s). These findings provide critical physical insights and a quantitative reference for optimizing tugboat maneuvering strategies and designing fatigue-resistant cables in complex sub-sea environments. Full article

Objectives: The aim of this study was to evaluate the psychometric properties of the Pain Catastrophizing Scale (PCS) in patients with carpometacarpal osteoarthritis of the thumb. Methods: In this cross-sectional register-based study of 253 patients with carpometacarpal osteoarthritis of the thumb, a two-parameter item response theory analysis was used to evaluate the items’ difficulty and discrimination parameters. Results: Of 253 patients, 245 (57%) were women. The mean age was 56.0 (SD 16.5) years. The mean total PCS score was 14.0 (SD 10.5) points. Difficulty estimates were distributed fairly evenly across the item score scale, with a slight shift towards higher scores. Discrimination of both total and subscale scores was perfect, varying from 1.91 to 2.84. Conclusions: PCS was able to discriminate well between different levels of catastrophizing. PCS performed slightly more accurately when the catastrophizing level was above average in the studied sample. PCS can be recommended for clinical use when assessing catastrophizing in patients with carpometacarpal osteoarthritis of the thumb. Full article

Estimating remaining useful life (RUL) and predicting bearing faults based on data-driven models have become central components of modern Prognostics and Health Management (PHM) systems. Although deep learning models have demonstrated strong performance under controlled and stationary operating conditions, their reliability in real-world industrial and marine environments is limited. In practice, operating conditions, sensor properties, and degradation mechanisms evolve continuously over time, leading to non-stationary and shifting data distributions that violate the assumptions of conventional static learning approaches. To address these challenges, two research areas have gained increasing attention: Domain Adaptation (DA), which aims to mitigate distribution discrepancies across operating conditions or machines, and Continual Learning (CL), which enables models to learn sequentially while mitigating catastrophic forgetting. However, existing studies often examine these paradigms in isolation, limiting their effectiveness in long-term deployments, where domain shifts and temporal evolution coexist. This paper presents a comprehensive and systematic review of data-driven methods for bearing fault prognosis and remaining useful life (RUL) prediction under evolving data distributions, adopting the framework of Domain-Adaptive Continual Learning (DACL). By jointly examining the DA and CL methods, this review analyses how these approaches have been individually and implicitly combined to cope with non-stationarity, knowledge retention, and limited label availability in practical PHM scenarios. We categorised existing methods, highlighted their underlying assumptions and limitations, and critically assessed their applicability to long-term, real-world monitoring systems. Furthermore, key open challenges, including scalability, robustness under sequential domain shifts, uncertainty handling, and plasticity–stability trade-offs, are identified, and research directions are outlined based on the identified limitations and practical deployment requirements of the proposed method. This review aims to establish a structured and critical reference framework for understanding the role of domain-adaptive CL in data-driven prognostics, clarifying current research trends, limitations, and open challenges in evolving data distributions. Full article