Search Results (573)

The Longmenshan Fault Zone, as the steep boundary on the eastern edge of the Qinghai–Xizang Plateau, is the seismogenic structure that includes strong earthquakes such as the 7.9 magnitude Wenchuan earthquake in 2008 and the 6.6 magnitude Lushan earthquake in 2013. Based on the U.S. Geological Survey (M ≥ 2.5) earthquake catalogue from 2000 to 2025, this study systematically analyzed the spatio-temporal evolution of seismic activities in this area. We determined the completeness of the seismic magnitude by time periods and drew a spatial B-value distribution map using the maximum likelihood estimation method to reveal its variation characteristics. The analysis is divided into three intervals: 2000–2007 (pre-Wenchuan), 2008–2012 (co- and post-Wenchuan), and 2013–2025 (long-term postseismic stage; the 2008–2025 interval includes an observational and forecast assessment window). Low b values persist in the central and southern parts of the LMSF, indicating that the degree of stress concentration in these two regions is relatively high. After 2008, the b value of the Wenchuan Fault Zone rose briefly. After 2013, the b value gradually declined. This fluctuation confirmed the re-accumulation process of regional stress. The analysis results of the Z-value rate change show that there is obvious stillness in the central fault zone (Z > 2), while there is slight activation in some southern areas of the LMSF (Z ≈ −0.5 to 0). These patterns are roughly consistent with the spatial B-value structure, and our research results also provide diagnostic conclusions for interpreting the long-term seismic activity evolution and stress heterogeneity of the LMSF. Full article

Precise spatiotemporal assessment of habitat suitability is essential for crop pest and disease risk warning and food security. However, most existing approaches focus on disease occurrence, overlook spatial heterogeneity and time series information, and therefore, struggle to capture the habitat dynamics from occurrence to epidemic. We propose a dynamic framework for rice sheath blight (RSB) habitat suitability assessment that integrates rice phenology and disease time series to reveal fine-grained intra-annual spatiotemporal variability via a spatiotemporally adaptive strategy beyond the reach of traditional static models. Remote sensing and meteorological time series data, together with RSB survey data and crowdsourced records from southern China, are integrated in this study. First, the study area is partitioned into sub-regions and sensitive time windows (STWs) based on climate and rice-cropping systems. MaxEnt, combined with natural breaks, is then used to assess RSB occurrence suitability and delineate multi-level suitable areas. Geographical and temporal weighted regression (GTWR) and the coefficient of variation (CV) are finally applied within moderate-to-high occurrence-suitability areas to reconstruct time series of intra-STW epidemic potential dynamics and quantify their temporal variation. Results indicate the optimal phenology-based scheme yields one STW for single-cropping sub-regions and three STWs for double- and mixed-cropping sub-regions. MaxEnt AUC ranges from 0.610 to 0.768, with natural-break thresholds at 0.312 and 0.473. GTWR produces generally robust intra-STW fits (most local R² > 0.4), and the CV highlights localized, time-varying high-fluctuation zones within occurrence-suitable areas that static maps do not reveal. Overall, our method extends crop disease habitat suitability assessment from a static paradigm to a spatiotemporally adaptive, dynamic one, providing useful habitat background constraints for monitoring, early warning, and forecasting of crop pests and diseases under complex cropping systems. Full article

Mangrove prop root epifaunal communities support coastal biodiversity by providing habitat and refuge for associated sessile species and contributing to larval supply. Yet, regional surveys across latitudinal gradients in southeast Florida remain scarce. Understanding how these communities vary spatially and temporally is essential for forecasting biodiversity responses to environmental change. This study tested how macrobenthic assemblage composition and beta diversity on red mangrove prop roots vary from Key West to Melbourne, Florida. Presence-absence data from 32 sites in four zones were analyzed using beta diversity partitioning, indicator species analysis, and temporal turnover metrics. A strong biogeographic boundary near Palm Beach County delineated a species-rich southern pool, formed by the Florida Keys and Miami-Dade and characterized by tropical sponges. Of the 41 indicator species, 34 were associated with these two southern zones, while neither northern zone had exclusive indicators. Northern beta diversity was dominated by nested subsets of a larger species pool rather than species replacement. Over the three years, only Broward-Palm Beach gained species, while the other three zones lost species, though this was not statistically significant. These findings highlight the ecological significance of the potential biogeographic ecotones and provide a baseline study from which to gain insight into how climate-driven mangrove range expansion may impact coastal biodiversity and ecosystem resilience. Full article

Next-generation wireless systems are becoming increasingly complex, and there is a growing need for intelligent mobility management mechanisms that can ensure service continuity while making efficient use of network resources. In 5G and future 6G networks, dense small-cell deployments, heterogeneous architectures, and highly mobile users mean that frequent handovers (HOs), uneven traffic distribution, and variable network conditions often lead to degraded user experience, higher signalling overhead, and inefficient use of resources. Because user movement continuously redistributes traffic across cells, effective mobility management is inseparable from load balancing, and the HO process serves as the primary mechanism through which the network manages both. Recent advances in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), offer an opportunity to transform mobility management from reactive to predictive, since data-driven solutions can forecast user movement, fine-tune HO execution, and dynamically allocate radio resources. This paper presents a comprehensive survey of AI-enabled mobility management strategies for 5G, Beyond 5G, and upcoming 6G networks, with particular attention to HO optimization and load balancing. The surveyed literature is organized around the complete lifecycle of AI-enabled mobility management, from mobility prediction and HO decision-making through parameter optimization and execution to KPI monitoring and model updating. This structure is used to classify existing frameworks according to their architectures, learning approaches, and optimization goals. The survey then examines how intelligent HO schemes address critical issues such as load balancing, interference mitigation, connection reliability, and quality-of-service maintenance, and compares conventional and AI-based methods against standardized key performance indicators for mobility robustness, resource efficiency, and service continuity. Finally, the paper discusses unresolved problems and emerging trends, including federated learning, multi-connectivity, and non-terrestrial integration, that will shape the evolution of autonomous mobility management solutions for future wireless networks. Full article

Dam displacement forecasting is important for assessing whether long-term structural responses remain consistent with established operational behavior. In multi-point monitoring systems, however, irregular survey-line layouts and unequal numbers of monitoring points make it difficult to organize long-term records while preserving their engineering meaning. This study develops a field-oriented forecasting framework by reconstructing daily observations into a structured displacement-field object defined by survey-line order, monitoring-point alignment, and three displacement components. A valid-position-aware protocol is introduced to distinguish actual monitoring locations from structural padding, ensuring that model training and evaluation remain restricted to the same physical monitoring definition. Using long-term operational records from the Tianshengqiao First Dam, four representative models, namely SimVP, SimVPv2, PatchTST, and TimesNet, are evaluated under the same chronological split, causal forward-fill-only preprocessing, input window, prediction horizon, and evaluation boundary. All four models achieve strong predictive performance, with R² values above 0.97 in the X direction and above 0.99 in the Y and Z directions. No single trained model or forecasting route exhibits a consistent advantage across all displacement components and evaluation metrics. Under the present single-dam, case-specific setting, the relative ranking varies with displacement direction and forecasting horizon and should not be interpreted as evidence of general direction-specific suitability for any particular architecture. At the route level, the field-based route retains a slight advantage in Y-direction forecasting and overall MAE, whereas the sequence-based route remains competitive for Z-direction displacement and longer-horizon X-direction prediction. The proposed framework provides a practical and physically consistent digital representation for organizing irregular monitoring records, comparing forecasting routes, and supporting deployment-oriented model selection and subsequent model adaptation. Full article

Modern data centers are consuming more energy than ever before due to the rapid growth of cloud services, artificial intelligence (AI), and large-scale digital applications. As energy demand continues to rise, accurate load forecasting has become an important tool for improving energy management and operational planning. However, predicting data-center power demand remains challenging because computing workloads, cooling systems, and facility operations are closely connected and constantly changing. This review examines the use of artificial intelligence (AI) and machine learning (ML) techniques for data-center load forecasting. The surveyed literature covers traditional statistical methods, supervised learning algorithms, deep learning models, probabilistic forecasting techniques, and physics-informed hybrid approaches. Important topics such as forecasting horizons, feature selection, performance evaluation, and practical deployment challenges are also discussed. The reviewed studies indicate that advanced approaches, including long short-term memory (LSTM), gated recurrent unit (GRU), transformer-based models, and digital twin (DT) frameworks, can improve forecasting performance and support more energy-efficient operation. These methods can also assist carbon-aware and grid-interactive data-center management. Several challenges remain. These include limited data availability, poor generalization, interpretability issues, and real-time implementation constraints. In this paper, the reviewed studies are classified and analyzed to provide a clear reference for researchers working in AI-based data-center energy forecasting. Full article

Bird’s-eye-view (BEV) perception has become the dominant paradigm for camera-centric scene understanding in autonomous driving, as well as road occupancy prediction, which involves the dense estimation of which regions of space are occupied and by what has emerged as its most expressive form. Between 2020 and 2026, the field underwent three overlapping transitions: from two-dimensional BEV semantic map segmentation to dense three-dimensional voxel-based 3D semantic occupancy, catalyzed by the 2022 industrial adoption of “occupancy networks,” and, most recently, to efficient, generative, and four-dimensional forecasting formulations. This survey organizes the literature along six orthogonal axes output representation, view-transformation mechanism, input modality, supervision paradigm, temporal scope, and efficiency strategy and uses the representation lineage as a primary spine connecting the 2020 BEV-segmentation works to the 2026 Gaussian and 4D frontier. Alongside the ego-centric mainstream, we review the parallel multi-view and infrastructure-side lineage from multi-view pedestrian occupancy to roadside traffic occupancy, which shares the BEV occupancy-map output and contributes generalization tools the ego-centric thread has yet to absorb. We review the canonical methods at each stage, summarize the standard datasets (CARLA, GMVD, MultiviewX, WildTrack, nuScenes, SemanticKITTI, Occ3D, OpenOccupancy) and evaluation metrics (MODA, mIoU, RayIoU, RayPQ), and consolidate reported results on the Occ3D-nuScenes benchmark into a single comparison. We close by identifying open problems in label efficiency, robustness, temporal forecasting, and deployment. Our intent is to bridge the historically separate BEV-segmentation and 3D-occupancy literatures within a single taxonomy. Full article

Expressway travel demand climbs steeply during public holidays, and the severe congestion that follows has become a principal constraint on both network operating efficiency and the quality of travel services. Dependable forecasting of holiday congestion, coupled with the practical realization of proactive management, accordingly carries considerable theoretical and practical weight for improving how expressways are operated and administered and for safeguarding efficient and safe public travel. Organized around the central theme of congestion prediction and proactive management for expressways over holiday periods, this paper reviews the progress of research across five interrelated areas: traffic speed and flow prediction methods; congestion state identification and forecasting; holiday travel characteristic analysis together with demand prediction; multi-source data fusion and congestion propagation mechanisms; and expressway traffic control together with traveler behavior guidance. Having surveyed the theoretical underpinnings, core technologies, and representative methods of each area, the paper concentrates in particular on the shortcomings and difficulties that existing studies encounter in coping with the hallmark features of holiday traffic, namely demand surges of short duration combined with pronounced spatiotemporal heterogeneity, and proceeds to outline promising avenues for subsequent research. Overall, the review seeks to provide wide-ranging literature support and a theoretical reference for developing technologies that predict and proactively manage congestion on expressways during public holidays. Full article

Artificial intelligence (AI) is used in logistics, but the mechanisms linking AI utilization to firm performance remain insufficiently differentiated. Drawing on the information technology business value perspective and dynamic capabilities theory, this study examines whether managers’ perceptions of logistics-oriented AI utilization are associated with perceived firm performance through innovation capability and logistics efficiency, with managerial support treated as a secondary boundary condition. Cross-sectional survey data from 254 middle- and senior-level managers in Chinese logistics firms were analyzed using IBM SPSS Statistics 27 and IBM SPSS Amos 29 (IBM Corp., Armonk, NY, USA), and the PROCESS macro version 4.2 (Andrew F. Hayes, Calgary, AB, Canada), with Model 83 and 5000 bootstrap samples. Perceived AI utilization was positively associated with innovation capability, logistics efficiency, and perceived firm performance. Both mediators showed significant indirect effects, and their sequential indirect effect was also significant. The two individual indirect effects did not differ significantly, but both exceeded the sequential indirect effect. The proposed sequential, reverse-sequence, and parallel-mediation models produced identical fit indices, whereas the restricted direct-effects model showed weaker fit. Neither the AI utilization–managerial support interaction nor the moderated mediation indices was significant. Exploratory item-level analyses showed differentiated associations for demand forecasting and order allocation and for AI infrastructure; the pattern remained stable among 194 respondents involved in AI- or digital transformation-related activities. Innovation capability and logistics efficiency appear to function as complementary mechanisms, with a smaller capability-to-process pathway. Their relative ordering cannot be determined from the cross-sectional data. As the data are self-reported, the findings represent associations among managerial perceptions rather than objective causal effects. Sustainability implications are limited to operational efficiency because environmental outcomes were not directly measured. Full article

Artificial Intelligence (AI) is increasingly reshaping customer relationship management (CRM) practices in service industries, yet its perceived effectiveness within emerging regional tourism economies remains underexplored. This study examined respondents’ perceptions of how AI-enabled capabilities influence CRM effectiveness within the tourism sector in Mthatha, in the Eastern Cape, South Africa. Existing AI–CRM research is largely concentrated in developed economies, limiting contextual understanding of its strategic value in resource-constrained and relational tourism environments. A positivist, quantitative explanatory design was adopted, and data were collected through a structured survey administered to managers and staff of tourism enterprises across the Eastern Cape (n = 121). Partial Least Squares Structural Equation Modelling was employed to assess the measurement model and test the hypothesized relationships. The model explained 63.2% of the variance in perceived CRM effectiveness. Sales forecasting and lead scoring exerted the strongest positive influence, followed by sentiment and feedback analysis, while personalization and automation showed positive but statistically insignificant effects. The findings suggest that tourism enterprises may achieve stronger relationship outcomes by prioritizing predictive and analytical AI tools while integrating automation within human-centered service strategies. The study extends AI–CRM theory to an emerging African tourism context and demonstrates that AI effectiveness is context dependent rather than universally transferable. Full article

This paper proposes a hybrid, interpretable machine learning framework for the preliminary screening of soil macronutrients using Sentinel-2 and AgroLens data. This study aims not to replace laboratory analysis, but to test the feasibility of obtaining a useful proxy signal for estimating nitrogen (N), phosphorus (P), and potassium (K) content using a limited set of remote sensing and agricultural features. The developed pipeline includes data auditing, leakage control, feature engineering, train-only normalization, group-aware partitioning, baseline/SOTA model comparison, hybrid regression modeling, SHAP interpretation, and uncertainty assessment. The experiment used 4471 AgroLens observations and 126 features derived from Sentinel-2 spectral aggregates, vegetation indices, temporal characteristics, and crop-related parameters. The evaluation indicated that the proposed approach consistently improves forecasting quality relative to baseline models under reduced-input conditions. Linear relationships between target variables ranged from 0.14 to 0.17, while nonlinear relationships reached 0.23. SHAP analysis revealed significant contributions from vegetation indices, crop-specific interactions, and Sentinel-2 spectral channels. The findings support the applicability of the proposed framework for preliminary monitoring, prioritizing field surveys, and decision support in digital agriculture. Although an additional AgroLens control segment was used to assess the robustness of the study, the study did not include independent external validation of the data collected across different geographic or agro-climatic conditions. Full article

Inflation expectations are central to monetary policy transmission, yet relatively little is known about whether individuals’ own investment experiences are associated with how they form such expectations across different forecast horizons. This study examines the association between self-reported past investment performance and horizon-specific expected cumulative consumer price changes at the one-, three-, and five-year horizons using a large-scale online survey of 157,523 active Japanese investors. Because the survey asks respondents how consumer prices will change over each horizon, the three- and five-year responses are interpreted as expected cumulative price changes rather than annualized inflation rates. Ordered probit models are estimated while controlling for demographic, socioeconomic, and behavioral characteristics. The results show a horizon-dependent conditional association: self-reported investment performance is not significantly associated with one-year expectations in the full specification, whereas it is positively and significantly associated with three- and five-year expectations. Formal stacked OLS interaction tests indicate that the association differs significantly across horizons, and additional threshold-specific probit models show that the pattern is most evident for moderate inflation-expectation thresholds. The economic magnitudes are statistically precise but modest. Heterogeneity analyses further suggest that the association is weaker among respondents with higher financial literacy and higher assets, and stronger among respondents with a more myopic view of the future. Because the analysis relies on cross-sectional observational data and subjective performance measures, the findings should be interpreted as conditional associations rather than causal effects. Full article

This paper presents “Quantifying Airline Reputation from Multilingual Online Content: Artificial Intelligence and a Practical Application in a Lightweight Reputation-Intelligence Framework.” Airline reputation is increasingly shaped by multilingual digital narratives that evolve faster than conventional survey cycles, creating a need for timely and interpretable monitoring tools. This study develops and evaluates a lightweight reputation-intelligence framework that integrates brand-safe retrieval, multilingual transformer-based sentiment inference, zero-shot natural-language-inference aspect categorization, TF–IDF/KMeans topic induction, and short-horizon forecasting. The framework formalizes document-level outputs into managerial indicators, including a Net Sentiment Index, polarity shares, aspect scores, topic summaries, and projected sentiment trajectories. On a 3990-document annotated sentiment subset, the multilingual transformer achieved 0.9015 accuracy, 0.9048 macro-F1, 0.9050 weighted-F1, a Cohen’s kappa of 0.8492, and a Net Sentiment Index of 48.5%, while errors were concentrated between adjacent polarity classes. Aspect evaluation showed that supervised in-domain learning substantially outperformed zero-shot inference, clarifying the trade-off between cold-start portability and benchmark accuracy. The results support the framework as a pilot decision-support architecture for airline reputation sensing rather than as a definitive large-scale benchmark. The approach offers scalable and CPU-friendly monitoring capability for airlines, airports, consultants, and public-sector users, while future work should expand multilingual annotation and domain adaptation. Full article

This study examines how investors’ perceptions of artificial intelligence (AI) are associated with investor behavior, focusing on short-term and long-term investment strategies and their implications for investor resilience. While prior research has primarily emphasized the technical capabilities of AI in financial decision making, less is known about how investors perceive these technologies in practice. Using survey data from 221 individual investors and partial least squares structural equation modeling (PLS-SEM), the study analyzes how different dimensions of AI perception affect investment strategies. The results show that perceived efficiency and positive expectations regarding the future role of AI positively influence both short-term and long-term investment strategies. In contrast, perceived accuracy is associated with lower engagement in short-term strategies, suggesting greater reliance on AI-generated recommendations. Most notably, perceived forecasting ability is negatively related to long-term investment strategies, indicating that stronger belief in AI’s predictive capabilities may encourage a shift toward shorter investment horizons. The findings demonstrate that different dimensions of AI perception are associated with investment behavior in different ways. While some AI-related perceptions may support more disciplined and potentially resilient investment behavior, others may encourage greater dependence on automated forecasts and reduced long-term orientation. The study contributes to understanding the behavioral implications of AI in financial decision making. Full article

Precision agriculture increasingly relies on machine learning applied to high-resolution data acquired by unmanned aerial vehicles (UAVs) to support crop monitoring, stress detection, and yield forecasting. This survey presents a structured review of machine learning methods for UAV-enabled precision agriculture and organizes over 100 peer-reviewed studies within a unified four-dimensional taxonomy defined by sensing modality, data type, model family, and analytical task. The taxonomy enables systematic comparison across RGB, multispectral, hyperspectral, LiDAR, and IoT data sources and across classical machine learning, deep learning, hybrid sequential models, and emerging transformer-based architectures. We analyze how modeling choices interact with data characteristics to influence robustness, cross-environment generalization, computational efficiency, and deployment feasibility on UAV and edge platforms. Recurring challenges include limited labeled data, domain shift across seasons and fields, multimodal heterogeneity, occlusion, and real-time processing constraints. We identify emerging research directions, including data-efficient learning, representation-level multimodal fusion, domain adaptation, lightweight architectures for embedded deployment, and uncertainty aware decision support. By formalizing the landscape through a unified taxonomy, this survey provides a foundation for designing scalable, robust, and deployable machine learning systems for next-generation precision agriculture. Full article