Search Results (4,184)

To meet the comfort and health needs of the elderly in daily activity environments, a refined temporal and zonal thermal environment design across diverse spaces must align with dynamic changes in their daily activity spatiotemporal trajectories. This constitutes a research gap in the existing literature. This study focused on elderly individuals in rural Xi’an, integrating on-site subjective daily activity questionnaires, thermal comfort field surveys, and continuous thermal environment monitoring to evaluate summer thermal environments based on spatiotemporal activity differentiation. The key conclusions are as follows: (1) Elderly people primarily engage in activities in indoor and outdoor spaces, with considerably fewer activities occurring in semi-outdoor areas. Summer outdoor activities occur between 6:00 and 9:00 and 17:00–21:00, while indoor activities dominate other times. (2) The established adaptive thermal response models indicate indoor and outdoor neutral temperatures are 23.8 °C (Operative temperature) and 28.8 °C (UTCI). Indoor 80% acceptability upper limit is 27.5 °C and outdoor 80% acceptability upper limit is 34.1 °C. These results exhibit distinct differences from those observed in alternative climate zones and urban areas in the same climate zone. (3) The thermal environment of outdoor shaded areas remains within the acceptable range for a longer duration than that of indoors, and kitchens have the worst indoor thermal quality. This evaluation provides supplementary insights into current spatiotemporal thermal environment research. Full article

Soil drought impact on irrigation areas is not merely a single reduction in crop yields, but rather a chain reaction that occurs from multiple dimensions including crop growth, water resource allocation, soil environment, operation of irrigation area projects, agricultural economy and ecosystems. The changing trend and mutation characteristics of soil drought are unclear in the People’s Victory Canal Irrigation District (PVCID). The Standardized Soil Moisture Index (SSMI) and the breaks for additive seasons and trend (BFAST) decomposition algorithm were adopted, combined with the eXtreme Gradient Boosting (XGBoost) model, to explore spatio-temporal evolution characteristics, driving factors and response to meteorological drought of soil drought. During the research period, the area percentage of SSMI showing a downward trend was 97.30%. The most severe soil drought occurred in 2019. In addition, the optimal trivariate combination is precipitation, evapotranspiration, and air temperature. This study has clarified the spatio-temporal evolution laws and driving mechanisms of soil drought in the PVCID, providing an important theoretical basis for the early warning, prevention and control of soil drought and the adaptive management of the ecosystem. Full article

Background: Temozolomide (TMZ) resistance represents a major therapeutic challenge in glioblastoma treatment, where autophagy has emerged as a key adaptive survival mechanism. Although numerous studies have implicated autophagy in TMZ resistance, most have assessed this process at a single point, thereby overlooking its dynamic and time-dependent nature. Methods: In this study, we systematically investigated the temporal regulation of autophagy-related gene expression in two human glioblastoma cell lines with distinct MGMT methylation status and TMZ sensitivities (T98G and U87) following TMZ treatment. Cells were exposed to TMZ and harvested at defined time points (0 h, 6 h, 24 h, and 48 h). The expression levels of genes representing distinct stages of the autophagy pathway, including initiation, nucleation, elongation, selective autophagy, lysosomal function, and transcriptional regulation, were analyzed using RT-qPCR. Relative gene expression was calculated using the 2^−ΔΔCT method with GAPDH as the reference gene. Results: Our results reveal a time-dependent and phase-specific transcriptional reprogramming of the autophagy machinery in response to TMZ-induced stress. Early time points were characterized by modulation of autophagy initiation and nucleation genes, whereas intermediate and late phases showed prominent regulation of genes associated with autophagosome elongation, selective autophagy, autophagic flux, and transcriptional control. Conclusions: Collectively, these findings demonstrate that autophagy in TMZ-treated glioblastoma cells is not a static response but a dynamically regulated, multi-phase program. Specifically, in TMZ-resistant T98G cells, this process matures into a sustained adaptive program with robust late-phase lysosomal integration, while in TMZ-sensitive U87 cells, the early autophagic response is transient and fails to support long-term lysosomal coordination. This temporal perspective provides new insights into the role of autophagy in TMZ tolerance and underscores the importance of time-resolved analyses when targeting autophagy to overcome chemoresistance in glioblastoma. Full article

Civil aviation VHF communication is safety-critical, yet operational links are routinely disturbed by atmospheric effects, aging hardware, and electromagnetic interference. The resulting anomalies are typically weak, intermittent, and extremely rare, which makes real-time detection difficult under strong temporal dependence and severe class imbalance. We propose an end-to-end framework that couples (i) a learnable kernel projection for adaptive nonlinear feature extraction, (ii) a differentiable relevance–redundancy objective for feature refinement, and (iii) conditional temporal generation to augment minority anomaly patterns. A lightweight CNN–LSTM head is used for streaming inference. Training uses a mixture of operational anomalies and simulated degradation scenarios, while evaluation is conducted using operational data only. Experiments on 1.2 million VHF frames collected from real flight operations and ground station monitoring achieve an F1-score of 0.947, ROC-AUC of 0.972, and PR-AUC of 0.968, with an average inference latency of 34.7 ms. Full article

Accurate electricity load forecasting has become increasingly challenging in Türkiye due to rapid structural changes in the power system driven by renewable energy expansion. Between 2016 and 2022, solar capacity increased by 130% and wind generation by 83%, resulting in renewable-induced variability exceeding 160%. To assess how different forecasting approaches respond to this evolving environment, Facebook Prophet, XGBoost, and Deep Neural Networks (DNNs) were evaluated using more than 55,000 hourly load observations under a strictly chronological out-of-sample validation framework. The comparative analysis reveals substantial differences in model performance. XGBoost achieved the highest forecasting accuracy, with a Mean Absolute Error of 981.48 MWh, a Root Mean Squared Error of 1344.15 MWh, and a Mean Absolute Percentage Error of 2.72%, while effectively capturing rapid intraday variations and maintaining peak deviations within ±1100 MWh. DNN models delivered competitive overall accuracy (MAE: 997.82 MWh; MAPE: 2.77%) but exhibited a tendency to smooth temporal variations, leading to an underestimation of extreme winter peaks by up to 4100 MWh. In contrast, Prophet showed limited adaptability to the observed structural volatility, producing errors nearly seven times higher than XGBoost (MAE: 7041.79 MWh; RMSE: 8718.14 MWh). Based on these findings, a layered forecasting framework is proposed, employing XGBoost for short-term operational dispatch and reserving statistical models for long-term planning and policy analysis. Full article

This study presents a comprehensive investigation of exact fractional wave solutions and bifurcation analysis for the (3 + 1)-dimensional extended shallow water wave (3D-eSWW) equation with $\beta$-derivative, which models nonlinear wave phenomena in fluid dynamics and coastal engineering. Leveraging the flexibility of the fractional derivative, the model provides a more generalized and adaptable framework for describing shallow water wave propagation. The Modified Extended Direct Algebraic Method (MEDAM) is systematically employed to derive a broad spectrum of novel exact analytical solutions. These include the following: dark solitary waves, singular solitons, singular periodic waves, periodic solutions expressed via trigonometric and Jacobi elliptic functions, polynomial solutions, hyperbolic wave patterns, combined dark–singular structures, combined hyperbolic–linear waves, and exponential-type wave profiles. Each solution family is presented with explicit parameter constraints that ensure both mathematical consistency and physical relevance, thereby offering a robust classification of wave regimes under diverse conditions. A thorough bifurcation analysis is conducted on the reduced dynamical system to examine parametric dependence and stability transitions. Critical bifurcation thresholds are identified, and distinct solution branches are mapped in the parameter space spanned by wave numbers, nonlinear coefficients, external forcing, and the fractional order $\beta$. The analysis reveals how solution dynamics undergo qualitative transitions—such as the emergence of solitary waves from periodic patterns or the appearance of singular structures—driven by the interplay of nonlinearity, dispersion, and fractional-order effects. These insights are crucial for understanding wave stability, predictability, and the onset of extreme events in shallow water contexts. Graphical representations of selected solutions validate the analytical results and illustrate the influence of $\beta$ on wave morphology, propagation, and stability. The simulations demonstrate that varying the fractional order can significantly alter wave profiles, highlighting the role of fractional calculus in capturing complex real-world behaviors. This work demonstrates the efficacy of the MEDAM technique in handling high-dimensional fractional nonlinear PDEs and provides a systematic framework for predicting and classifying wave regimes in real-world shallow water environments. The findings not only enrich the solution inventory of the 3D-eSWW equation but also advance the analytical toolkit for studying complex spatio-temporal dynamics in fractional mathematical physics and fluid mechanics. Ultimately, this research contributes to the development of more accurate models for coastal protection, tsunami forecasting, and marine engineering applications. Full article

Predicting the future impact of technical economic documents at release time is challenging due to delayed supervision signals, long-tailed label distributions, and time- and domain-dependent shifts in language and topics. Moreover, similarity graphs derived from text embeddings can be noisy due to boilerplate and evolve under temporal drift, making robustness and leakage-free evaluation essential. We formulate grant-time patent impact prediction as a node classification and within-domain ranking problem on a large-scale semantic similarity document graph built from patent text embeddings, avoiding any future citation leakage. The document graph is constructed via ANN Top-K retrieval and similarity thresholding, enabling scalable and reproducible sparsification on hundreds of thousands of nodes. We propose GraphGPT-Patent, which adapts a reversible graph-to-sequence foundation backbone to local subgraphs extracted from the similarity network. The model incorporates time- and domain-conditioned edge reliability to suppress drift-induced and template-driven pseudo-similarity, and optimizes a joint objective coupling high-impact classification with ranking consistency within comparable groups. Experiments on USPTO granted patents (2000–2022) across three high-volume CPC domains and three evaluation horizons show consistent gains over text-only and GNN baselines, achieving up to 0.94 recall for the positive class and improved macro-average recall across nine settings. Temporal shift analyses further quantify the effect of training-data freshness, while explanation subgraphs provide auditable structural evidence of model decisions. The proposed framework offers an effective graph-based learning pipeline for scalable impact prediction and downstream triage under strict information constraints. Full article

The Urban Heat Island (UHI) effect represents a major barrier to sustainable urban development, amplifying energy demand, public health risks, and climate vulnerability. This study provides an advanced geospatial assessment of UHI dynamics in Southampton, UK, using Landsat 8 and 9 imagery (2017–2023) to evaluate seasonal and interannual variations relevant to climate-resilient urban planning. This study integrates spatial techniques, including Land Surface Temperature estimation, NDVI-based emissivity modelling, hotspot analysis, and urban–rural gradient profiling, to identify persistent UHI hotspots concentrated in high-density commercial and industrial zones, with intensities reaching 2–3 °C above the citywide mean. It combines seasonal UHI mapping, hotspot analysis, and urban–rural gradient profiling to provide a comprehensive assessment of Southampton’s thermal landscape. The findings reveal persistent UHI hotspots in the city centre and industrial zones, with intensity peaks of 2–3 °C above the mean. Temporal analysis reveals winter-intensified UHI patterns, consistent with climate-sensitive processes observed in temperate coastal environments. Green spaces demonstrate measurable cooling benefits (up to ~1 °C), underscoring their role as sustainable nature-based mitigation strategies. By delivering a replicable, data-driven framework for continuous environmental monitoring, the research directly supports sustainable urban design, targeted greening interventions, and climate-adaptation policies. The findings provide practical tools for reducing heat stress, enhancing energy efficiency, and strengthening long-term urban resilience in medium-sized coastal cities. Full article

Emotions and affective responses are core intervention targets in music therapy. Through acoustic elements, music can evoke emotional responses at physiological and neurological levels, influencing cognition and behavior while providing an important dimension for evaluating therapeutic efficacy. However, emotions are inherently abstract and difficult to represent directly. Artificial intelligence models therefore provide a promising tool for modeling and quantifying such abstract affective states from physiological signals. In this paper, we propose a structured and explicitly factorized multi-modal representation learning framework for joint affective state and preference inference. Instead of entangling heterogeneous dynamics within monolithic encoders, the framework decomposes representation learning into cross-channel interaction modeling and intra-channel temporal–spectral organization modeling. The framework integrates electroencephalography (EEG), peripheral physiological signals (GSR, BVP, EMG, respiration, and temperature), and eye-movement data (EOG) within a unified temporal modeling paradigm. At its core, a Dynamic Token Feature Extractor (DTFE) transforms raw time series into compact token representations and explicitly factorizes representation learning into (i) explicit channel-wise cross-series interaction modeling and (ii) temporal–spectral refinement via learnable frequency-domain gating. These complementary structural modules are implemented through Cross-Series Intersection (CSI) and Intra-Series Intersection (ISI), which perform low-rank channel dependency learning and adaptive spectral modulation, respectively. A hierarchical cross-modal fusion strategy integrates modality-level tokens in a representation-consistent and interaction-aware manner, enabling coordinated modeling of neural, autonomic, and attentional responses. The entire framework is optimized under a unified multi-task objective for valence, arousal, and liking prediction. Experiments on the DEAP dataset demonstrate consistent improvements over state-of-the-art methods. The model achieves 98.32% and 98.45% accuracy for valence and arousal prediction, 97.96% for quadrant classification in single-task evaluation, and 92.8%, 91.8%, and 93.6% accuracy for valence, arousal, and liking in joint multi-task settings. Overall, this work establishes a structure-aware and factorized multi-modal representation learning framework for robust affective decoding and intelligent music therapy systems. Full article

To address the challenges commonly encountered in civil aircraft operating under multi-mode, strongly coupled closed-loop control—namely scarce fault samples, pronounced distribution shift, and root-cause explanations that are easily confounded by covariates—this paper proposes a root-cause analysis method that integrates ensemble learning with constraint-guided reasoning by large language models (LLMs). First, for Full Authority Digital Engine Control (FADEC) monitoring sequences, a feature system comprising environment-normalized ratios, mechanism-informed mixing indices, and multi-scale temporal statistics is constructed, thereby improving cross-mode comparability and enhancing engineering-semantic expressiveness. Second, in the anomaly detection stage, a cost-sensitive LightGBM model is adopted and a validation-set-based adaptive thresholding strategy is introduced to achieve robust identification under highly imbalanced fault conditions. Furthermore, for Root Cause Analysis (RCA), a “computation–reasoning decoupling” framework is developed: Shapley Additive exPlanations (SHAP) are used to generate segment-level contribution evidence, while causal chains, engineering prohibitions, and structured output templates are injected into prompts to constrain the LLM, enabling it to infer root-cause candidates and produce structured explanations under mechanism-consistency constraints. Experiments on real flight data demonstrate that our method yields an anomaly detection F1-score of 0.9577 and improves overall RCA accuracy to 97.1% (versus 62.3% for a pure SHAP baseline). Practically, by translating complex high-dimensional data into actionable natural language diagnostic reports, the proposed method provides reliable and interpretable decision support for rapid RCA. Full article

Mobile ad hoc networks (MANETs) are highly vulnerable to denial-of-service (DoS) attacks because their decentralized operation, rapidly changing topology, and constrained node resources limit the use of heavyweight security mechanisms. This paper presents an Adaptive Clustering and Random-Forest-based Intrusion Detection System (ACRF-IDS), a lightweight intrusion detection framework that combines mobility-aware adaptive clustering with supervised learning to detect network-layer DoS behaviors. Cluster heads are elected using a multi-metric utility (residual energy, link stability, and mobility) to stabilize observations under node movement. Within fixed monitoring windows, cluster heads aggregate routing-, forwarding-, and energy-related features and classify nodes using a Random Forest model; a temporal voting scheme further suppresses transient mobility-induced alarms. Using ns-2.35 simulations with Ad hoc On-Demand Distance Vector (AODV) and both flooding and blackhole DoS scenarios, ACRF-IDS is compared with (i) a static clustering-based threshold IDS, (ii) a non-clustered Support Vector Machine (SVM)-based IDS, and (iii) AIFAODV, which specializes in flooding. Across the evaluated network sizes (4–50 nodes), the proposed method achieves a higher detection rate and F1-score while maintaining a lower false positive rate than the baseline techniques. We additionally quantify network-level impact using PDR, throughput, and routing overhead, showing that ACRF-IDS improves availability under DoS while adding bounded overhead. Future work will extend the evaluation to more diverse attack behaviors and validate the approach in real-world MANET testbeds. Full article

Situated at the core of the Asian arid zone, the Tarim River Basin (TRB) serves as a critical indicator of regional hydroclimatic responses to global warming. Utilizing 27 extreme climate indices recommended by the Expert Team on Climate Change Detection and Indices, this study analyzes the spatiotemporal evolution of climate extremes in the TRB from 1960 to 2022 and explores their correlations with primary large-scale atmospheric circulation factors. The results indicate that, at the temporal scale, extreme warm indices (TX90P, TN90P, SU25, TR20) and most extreme precipitation indices (except for CDD) exhibited increasing trends, accompanied by pronounced abrupt changes and periodic characteristics. The changes were characterized by stronger warming at low temperatures than at high temperatures, greater nighttime warming than daytime warming, and larger increases in warm days than cold days. Extreme temperature and precipitation indices underwent abrupt changes in the mid-to-late 1990s and 1980s, respectively. The former exhibits pronounced “cold-warm” oscillations at 10–15-year and 25–35-year scales, while the latter displays distinct “wet-dry” cyclic alternations at 8–9-year and 30–32-year scales. Spatially, extreme temperature indices showed consistent warming across most stations. In contrast, the change trends of extreme precipitation indices displayed obvious spatial heterogeneity, with growth rates generally decreasing from west to east. Further analyses demonstrate that most extreme climate indices exhibit significant linear correlations with the AMO, PDO, NAO, and NOI. Notably, the AMO emerges as the dominant driver of variations in both extreme temperature and precipitation. In the context of accelerated global warming, these insights are pivotal for enhancing regional climate risk management and water resource adaptability. Full article

Against the backdrop of a gradual shift in the focus of cultural heritage (CH) conservation and utilization toward the integrated system formed by CH and its surrounding environment as well as regional systems, research on the coordinated protection of nature and culture to promote regional high-quality development has become a new trend. However, systematic summaries of the spatial–temporal distribution of CH in cross-regional typical geomorphic units at the river basin scale and their correlation with the natural environment remain insufficient. This study takes 387 Cultural Relics Protection Units in the Three Gorges of the Yangtze River (the Three Gorges region) as the research objects, utilizing GIS spatial analysis technology to examine the impact of the natural environment on CH across different periods and types. The theory of time-depth is introduced to reveal the layering mechanisms and underlying cultural logics. Coupled with the Minimum Cumulative Resistance (MCR) model, this study constructs a cultural corridor network and proposes spatial planning strategies. The findings are as follows: (1) The absolute core area for the distribution of CH across all periods remains the gentle slope zone near the river, characterized by elevations below 500 m, slopes within 25°, and distances from water systems within 1 km. However, the adaptive scope exhibits a diachronic evolution from core accumulation to peripheral expansion. (2) Different types of CH exhibited distinct natural adaptation strategies and vertical accumulation. Settlement Sites in the Before Qin Dynasty Period formed the foundational layer of survival rationality, while Ordinary Tombs in the Qin–Yuan Dynasty Period reinforced sedentism. Ancient Architecture in the Ming–Qing Dynasty Period underwent a transformation from “adapting to nature” to “reconstructing nature” as a product of environmental construction. Modern and Contemporary Significant Historical Sites and Representative Buildings in the After Qing Dynasty Period are characterized by a ruptured insertion on steep slopes, inscribing revolutionary memory onto space. The main stream of the Yangtze River serves as the core area of continuous deposition, while the extremely steep slopes form a distinctive stratigraphic accumulation of precipitous terrain. (3) Based on these distribution patterns, the study further proposes a spatial framework for CH called “One Corridor, Three Wings.” This framework uses the main stream of the Yangtze River as the spatial–temporal axis, linking the four core overlapping nodes of Fengjie, Wushan, Badong, and Xiling, supplemented by three secondary cultural clusters of the red heritage sites in southern Badong, the ancient town along the Daning River in Wushan, and the fortress sites in the Xiling–Yiling area. This research not only reveals the evolutionary path of CH in the Three Gorges region, but also provides a scientific basis for the systematic conservation and differentiated utilization of regional CH. Furthermore, it serves as a planning foundation and strategic reference for planning the Yangtze River National Cultural Park, as well as for the integrated preservation and utilization of river basin CH and linear CH with the aim of coordinated natural and cultural conservation. Full article

Autonomous aerial refueling is a key technology for enhancing the endurance of unmanned aerial vehicles; however, the wingtip vortices generated by the tanker create a strong three-dimensional wake-vortex flow field, whose downwash and lateral airflow can impose significant rolling moments on the follower Unmanned Aerial Vehicle (UAV), posing a serious threat to flight safety. To address this issue, this study proposes an integrated framework that combines wake-vortex risk-field modeling with optimal path planning. The classical Hallock–Burnham (HB) model is first employed to predict vortex descent and lateral transport, while a two-phase model is used to characterize the temporal decay of vortex circulation. The predicted vortex parameters are then coupled with the UAV’s aerodynamic characteristics, and the rolling-moment coefficient (RMC) is introduced as a risk metric to compute its spatiotemporal distribution in three dimensions, thereby transforming the invisible wake-vortex disturbance into a visualizable and quantifiable dynamic three-dimensional risk map. On this basis, a wake-vortex-aware path-planning algorithm based on particle swarm optimization (PSO) is developed, incorporating adaptive weighting and elitist mutation strategies. A multi-objective cost function considering path length, safety, and smoothness is further constructed to search for an optimal safe path under wake-vortex influence. Simulation results indicate that, compared with the classical A* and Rapidly-Exploring Random Tree (RRT) algorithms, the proposed method reduces cumulative risk exposure by approximately 90% and 75%, respectively, while limiting the increase in path length to about 8% (significantly lower than the increases of 40% for A* and 44% for RRT). In addition, the maximum turning angle is constrained within 10°, and the computation time remains around 0.052 s, satisfying real-time requirements. These results demonstrate that the proposed method can generate safe, efficient, and dynamically feasible paths for UAV aerial refueling and provide a valuable reference for wake-vortex avoidance in similar aerospace missions. Full article

Multimodal human–AI systems generally consider facial expressions and body motions as separate input streams, leading to disjointed interpretations and diminished emotional coherence. To overcome this issue, we offer the Engagement-Safe Expressive Alignment (ESEA) paradigm and the Unified Visual Synchrony (UVS) framework as its computational implementation. UVS models the coherence between facial expressions and gestures, offering an interpretable visual synchrony signal that can function as adaptive feedback in human–AI interactions. The framework’s key component is the Consistency Index for Affective Synchrony ($CIAS$), which correlates brief visual segments with scalar synchrony scores through a common latent representation. Facial and gestural signals are processed by modality-specific projection networks into a unified latent space, and $CIAS$ is derived from the similarity and short-term temporal consistency of these latent trajectories. The synchrony index is regarded as an estimation of affective visual coherence within the ESEA paradigm. We formalize the UVS/$CIAS$ framework and conduct a comparative experimental evaluation utilizing matched and mismatched face–gesture segments derived from rendered dialog footage. Utilizing ROC analysis, score distribution comparisons, temporal visualizations, and negative control tests, we illustrate that $CIAS$ effectively captures structured face–gesture alignment that surpasses similarity-based baselines, while also delivering a persistent, time-resolved synchronization signal. These findings establish $CIAS$ as a principled and interpretable feedback signal for future affect-aware, engagement-focused multimodal agents. Full article