Search Results (13,823)

Cancer research has undergone a fundamental transformation in recent decades due to the integration of artificial intelligence (AI) models into the study of tumor biology. However, tumor evolution, driven by genetic and phenotypic alterations leading to heterogeneity, resistance and metastasis, remains a major challenge in oncology. To understand these processes is crucial for developing effective therapeutic strategies and improving patient outcomes. Conventional methods often fail to capture the complexity and dynamics of these processes. In contrast, AI tools have the ability to integrate and analyze large-scale multi-omics, imaging and clinical data, offering the capability to decode tumor complexity. AI-driven methods facilitate multi-modal data integration, enabling the recognition of patterns that connect molecular alterations with phenotypic outcomes. In functional genomics, AI tools predict the effects of genetic variants, identify regulatory elements and map dysregulated pathways, thus clarifying mechanisms underlying tumor development and resistance. In the imaging field, deep learning techniques improve tumor segmentation, characterization and longitudinal monitoring, providing more accurate insights into tumor progression and treatment response. Predictive modeling could allow the anticipation of tumor evolution and drug response, supporting adaptive therapeutic plans and real-time treatment adjustments. Moreover, AI supports biomarker discovery, patient stratification and decision support systems that can improve clinical trial design and accelerate the development of personalized therapies. However, these advances raise important ethical challenges, including data privacy, algorithmic bias and the preservation of patient autonomy. Addressing these concerns is essential to ensure the responsible deployment of AI in oncology. Full article

This study develops a theoretical framework for evaluating the dynamic response and energy performance of the EBH260 transverse-axis roadheader cutting head under horizontal swing cutting conditions. Multi-directional loads on a single pick are synthesized to predict three-directional cutting-head loads, torque, and power demand. A dynamic stability evaluation approach based on coefficients of variation is proposed, and cutting slot depth is identified as a key process parameter influencing cutting efficiency and specific energy consumption. Results indicate that the cutting slot depth strongly affects load distribution and energy consumption, with specific energy peaking around 610 mm and decreasing in the 650–750 mm range, reflecting improved multi-pick coordination. Rotational speed and horizontal feed speed exhibit a coupled effect on specific energy consumption, with speed increases from 30 to 55 r/min reducing energy by 15–25%, and feed speed increases from 1000 to 2500 mm/min increasing energy by 20–35%. Under the representative preferred condition ($d$ = 700 mm, $n$ = 50 r/min, $v$ = 1400 mm/min), the average total power is 126.44 kW and specific energy consumption is 2.90 kW·h/m³, consistent with the rated power of the EBH260 cutting system. The framework provides a theoretical reference for operational parameter selection, while full-scale experimental validation is required to assess the effects of rock heterogeneity, pick wear, and field-scale dynamics. Full article

Rural industrial integration has become a key strategic direction for promoting agricultural transformation and rural revitalization, with the economic performance of leading agricultural enterprises serving as a critical metric for evaluating its success. However, empirical evidence on the characteristics and boundary conditions of how rural industrial integration affects the economic performance of these enterprises remains limited. This study investigates this relationship using panel data from 821 leading agricultural enterprises in Jiangxi Province from 2021 to 2023, analyzed with a random-effects model. The results reveal a significant positive statistical association between the degree of rural industrial integration and firm economic performance, which remains robust across multiple reliability checks. Further analysis indicates that this positive association varies considerably across different firm characteristics, exhibiting four forms of contingent heterogeneity: “size-reverse,” “leverage-positive,” “age-U-shaped,” and “subsidy-dampening.” Based on these findings, two policy recommendations are proposed: first, implement a differentiated support strategy that prioritizes guiding small-sized enterprises and moderately leveraged firms into rural industrial integration activities, together with phase-specific support measures for firms at different life-cycle stages; second, improve the allocation efficiency of fiscal subsidy funds, alongside the supporting systems and long-term mechanisms for rural industrial integration. Full article

The open-source Python package Geoloop introduces a novel, semi-analytical model for predicting the performance of deep (>500 m depth) vertical borehole heat exchangers (BHEs), with a focus on capturing depth-dependent variations in subsurface thermal properties, i.e., geothermal gradient and thermal conductivity. Conventional computationally efficient semi-analytical models based on load-aggregation of g-functions often assume uniform subsurface thermal properties. Geoloop addresses this gap by implementing a vertically stacked approach, allowing for realistic simulation of depth-variability in both the subsurface and borehole material properties. The model is benchmarked in the shallow domain against standard depth-uniform g-function implementations (up to 100 m depth) and for deeper conditions with a numerical finite volume model, demonstrating strong agreement and validating its accuracy and efficiency. Simulations for typical Dutch conditions show that deeper BHEs (up to 2000 m) can achieve significantly higher thermal power supply than shallower systems, and results in terms of resulting inlet/outlet temperatures for given heat extraction rates can strongly deviate (>4 °C) from results obtained by depth-uniform assumptions in thermal properties. Application of the model to the Dutch context reveals a non-linear increase in heat extraction potential with depth, surpassing values assumed in common practice by Dutch industry. The results highlight the importance of considering local geological heterogeneity and depth-dependent properties for accurate deep borehole heat exchanger (BHE) performance assessment and system optimization. Geoloop thus offers a robust, versatile platform for advancing the design and analysis of deep vertical BHE systems. Full article

This paper proposes a lightweight and adaptive anomaly detection framework for Internet of Things (IoT) time-series data that progressively combines statistical filtering with reinforcement learning (RL)-based decision mechanisms. Three classical statistical filters, Hampel, interquartile range (IQR), and Z-score, are initially evaluated under controlled IoT anomaly scenarios. While fixed-parameter configurations perform well under specific conditions, their performance degrades in non-stationary and heterogeneous environments. To address this limitation, a tabular Q-learning agent is introduced to dynamically select both filtering methods and their associated parameters according to scenario-specific signal characteristics. By extending the action space to include joint filter and parameter selection, the framework improves adaptability while reducing the need for manual tuning. A multi-agent reinforcement learning (MARL) formulation is further introduced to support distributed learning across heterogeneous IoT environments. The framework is additionally evaluated using real-world IoT temperature data augmented with controlled anomaly injection, enabling reproducible benchmarking under partially realistic sensing conditions. Experimental results show that both RL and MARL maintain stable detection performance across heterogeneous sensor streams. While MARL does not systematically outperform the single-agent approach in detection accuracy, it improves scalability and supports scenario-specific policy specialization, which is particularly relevant for distributed IoT deployments. Overall, the proposed approach provides a lightweight, interpretable, and computationally efficient solution for adaptive anomaly detection in resource-constrained IoT systems. Full article

Following the global financial crisis, the transition to IFRS 9’s forward-looking Expected Credit Loss (ECL) model has introduced significant implementation complexity, particularly in emerging markets facing data limitations. This study investigates the heterogeneous ECL compliance strategies adopted within the Cambodian banking sector during a period of heightened credit stress, marked by a system-wide non-performing loan ratio of 8.6%. Utilizing a multiple-case study design and replication logic, a qualitative content analysis was conducted on the 2024 audited financial statements of 13 representative institutions, ranging from market leaders to international subsidiaries. The findings reveal a pronounced technical divide: market leaders utilize advanced internal statistical methods, such as cohort analysis, whereas international subsidiaries rely on top-down parent-group proxy models to bridge local data gaps. A “macro-correlation paradox” was identified, where certain institutions prioritize faithful representation by excluding macroeconomic variables when statistical links to historical defaults remain weak. Furthermore, a significant transparency gap exists, where granular disclosures are consistent with a signaling interpretation regarding institutional safety. These results suggest that ECL compliance in data-limited environments may be interpreted as a strategic management choice rather than a standardized technical exercise, highlighting the need for regulatory standardization of modeling assumptions to improve inter-bank comparability. Full article

West Nile virus (WNV) is a globally distributed mosquito-borne flavivirus with significant implications for both veterinary and public health. While horses are incidental dead-end hosts, their epidemiological role extends beyond clinical disease, as they can serve as effective sentinel hosts for detecting local viral circulation. Their frequent exposure to mosquito vectors, ability to mount measurable antibody responses, geographic stability, accessibility for monitoring, and the possibility of observation within managed owner–veterinarian systems make them particularly suitable for surveillance within a One Health framework. Evidence from Europe and the Americas demonstrates that equine seroprevalence and field surveillance can identify transmission hotspots, reveal silent circulation, and contribute to the understanding of spatial and temporal risk patterns. The review also addresses key limitations, including vaccination effects, flavivirus cross-reactivity, methodological heterogeneity, and challenges in interpreting serological data across different ecological contexts. Strengthening equine sentinel surveillance through standardized methodologies and integration with predictive and geospatial approaches may improve early warning capacity and support more effective control of WNV and other emerging arboviral diseases. Full article

Landslide detection has largely relied on supervised learning or DEM-based representations, which can limit rapid deployment and generalization across heterogeneous terrain. In this work, we present a zero-shot, fully unsupervised framework that identifies landslide-like geomorphic instability candidates from raw UAV-mounted LiDAR, removing the need for labeled data, pre-event baselines, or rasterized terrain abstractions. Our approach is motivated by the observation that landslides manifest as localized geometric inconsistencies in the terrain surface. We capture this through a multi-scale formulation that combines point-level and cluster-level indicators of instability. At the point level, a PCA-based residual depth metric reduces slope-induced bias and highlights surface discontinuities, while local concavity captures terrain depletion patterns. At the cluster level, geomorphometric descriptors such as curvature concentration, surface roughness, elevation discontinuity, and slope variation are extracted using density-aware 3D clustering and integrated through adaptive feature fusion. The resulting probabilistic instability field enables spatially coherent delineation of landslide scars, including rupture boundaries, displaced material, and emerging failure regions. In addition, the detected patches provide useful priors for post-event susceptibility analysis without requiring temporal observations. Experiments across diverse geomorphic settings show that the proposed method improves detection of subtle terrain disturbances compared to DEM-based pipelines and supervised learning approaches, while remaining robust to noise and terrain variability. Overall, this work demonstrates that geometry-driven, unsupervised inference on raw 3D data can serve as a practical and scalable alternative for near real-time landslide detection using UAV-based systems. Full article

Life expectancy is a key indicator of public health and sustainable development in Gulf Cooperation Council (GCC) countries, where rapid economic growth, urbanization, and fossil-fuel dependence create environmental and health challenges. This study examines the determinants of life expectancy in six Gulf Cooperation Council countries from 2000 to 2023, focusing on death rates, renewable energy consumption, gross domestic product (GDP) per capita growth, government health expenditure, and carbon dioxide (CO₂) emissions. The empirical strategy combines cross-sectional dependence and slope heterogeneity tests, second-generation panel unit root tests, panel cointegration analysis, and a dynamic System Generalized Method of Moments (System GMM) estimator, with Driscoll–Kraay fixed-effects estimates used for robustness. The results show that higher death rates significantly reduce life expectancy, whereas renewable energy consumption and government health expenditure improve longevity. GDP per capita growth has a modest positive effect, while CO₂ emissions negatively affect life expectancy, confirming the adverse public health consequences of environmental degradation. Robustness checks support the reliability of the main findings. Overall, the evidence highlights the need for integrated policies that combine clean energy transition, stronger environmental regulation, preventive healthcare investment, and sustainable urban development to improve long-term health outcomes in resource-dependent economies in the region. Full article

Background/Objectives: Chronic myeloid leukemia (CML), essential thrombocythemia (ET), primary myelofibrosis (PMF), and polycythemia vera (PV) are myeloproliferative neoplasms (MPNs) that require precise molecular characterization. Although driver mutations such as BCR-ABL1 and JAK2 are diagnostically important, they do not fully explain disease heterogeneity. The NanoString nCounter^® system enables direct multiplex gene expression analysis without RNA amplification and is suitable for degraded bone marrow specimens. This study aimed to analyze cytokine gene expression in bone marrow mononuclear cells of patients with MPNs and controls using NanoString technology, identify differentially expressed genes (DEGs) among MPN subtypes, and investigate their biological significance. Methods: Bone marrow aspirates were collected from 19 patients with MPNs (CML, ET, PMF, and PV) and 6 control patients. Mononuclear cells were isolated, and RNA expression of a 40-gene cytokine panel was analyzed using the NanoString nCounter^® system with strict quality control and normalization. DEGs were identified for each MPN subtype, followed by Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway analyses. Results: CML and PV demonstrated 20 and 12 DEGs, respectively. In contrast, ET showed only one DEG (IRAK2), and PMF showed none. Functional analyses revealed enrichment of cytokine signaling, Toll-like receptor (TLR), and JAK-STAT pathways in CML, indicating immune and inflammatory dysregulation. PV DEGs were associated with TLR signaling, IL-17 pathways, and cytokine–cytokine receptor interactions, suggesting active cytokine-mediated inflammation. Conclusions: CML and PV exhibited distinct cytokine-driven transcriptional signatures, whereas ET and PMF exhibited minimal alterations. These findings support the clinical utility of NanoString technology for bone marrow specimens and highlight disease-specific immune pathways as potential diagnostic biomarkers in MPNs. Full article

Rice is one of the world’s most important food crops, feeding over half of the global population and being crucial for food security. Accurate, timely mapping of rice fields is essential for precision agriculture, yet conventional methods relying on static samples fail to capture dynamic farmers’ planting decisions. To address this, we propose the Multi-Source Dynamic Sample Generation and Phenology-Guided Feature Selection Framework for In-Season Rice Identification (MSDF-RiceID) using multi-source remote sensing imagery. It incorporates two key innovations: (i) a rule-based sample updating mechanism based on historical rice maps and a dynamic threshold algorithm, and (ii) phenology-guided feature optimization through exponential weighting. Developed specifically to handle complex cropping patterns and high cloud cover in Hunan Province, MSDF-RiceID integrates these innovations within a grid-search-optimized Random Forest classifier to produce reliable monthly rice distribution maps. In-season samples corresponding to transplanting dates in April (DOY 100, 120), June (DOY 160), and July (DOY 184), differentiated as early-, middle-, and late-rice crops. The optimal feature set combined Sentinel-1 (PRI, VH, VH_VV), Sentinel-2 (NDYI, PSRI, NDBI, NDWI), and MODIS (NDVI, EVI, NDBI, LSWI) indices. Accuracy increased seasonally, with F1-score rising from 0.82 in May to 0.97 at harvest. Cross-region validation in Taishan (Guangdong) and Panjin (Liaoning) showed that the earliest identifiable stage (F1-score > 0.9) occurred earlier than in Hunan due to Hunan’s more complex triple-cropping phenology, highlighting the model’s strong transferability. Furthermore, MSDF-RiceID outperformed existing products (TWDTW-Rice and EARice10), increasing overall accuracy by 0.12–0.18, Kappa by 0.23–0.35, and F1-score by 0.09–0.15. These results demonstrate its effectiveness for in-season, large-scale, and dynamic rice mapping under persistent cloud cover, thereby providing direct support for precision agricultural management in heterogeneous cropping systems. Full article

Short-term wind speed forecasting plays a critical role in enabling the reliable integration of renewable energy and supporting the sustainable operation of power systems. However, traditional dual-frequency decomposition methods oversimplify wind speed dynamics by separating them into only high-frequency disturbances and low-frequency trends, making it difficult to capture intermediate-frequency transitional dynamics. Additionally, single models struggle to adapt to multi-scale temporal features, limiting forecasting performance. To address these issues, this paper proposes a tri-band frequency-aware heterogeneous expert collaboration framework. First, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is employed for signal denoising, followed by Particle Swarm Optimization-Time Varying Filtering-based Empirical Mode Decomposition (PSO-TVF-EMD) for multi-scale signal disentanglement. Then, Permutation Entropy (PE) is used to construct a tri-band structure consisting of high-, intermediate-, and low-frequency components. A frequency-aware expert routing mechanism assigns Bayesian Optimization Long Short-Term Memory (BO-LSTM), an improved Markov model, and Auto-Regressive Integrated Moving Average (ARIMA) to the corresponding frequency bands. Finally, a reliability-aware cooperative aggregation strategy integrates predictions from multiple experts. Experimental results show that representative baseline models, including BO-LSTM, Markov, ARIMA, Gated Recurrent Unit (GRU) and Convolutional Neural Network Long Short-Term Memory (CNN-LSTM), achieve MAE values ranging from 0.308 to 0.429, while the proposed framework reduces the Mean Absolute Error (MAE) to 0.193 and Root Mean Square Error (RMSE) to 0.274, with a Mean Absolute Percentage Error (MAPE) of 7.35% and R² of 0.927. Compared with the dual-frequency decomposition scheme (MAE = 0.266), the proposed tri-band framework achieves an average improvement of approximately 28.1%. The results suggest that explicitly modeling intermediate-frequency dynamics and aligning model inductive biases with multi-scale signal characteristics can effectively enhance short-term wind speed forecasting performance. Full article

GNSS signals are extremely weak at the Earth’s surface and are highly vulnerable to in-band interference, particularly high-dynamic linear frequency-modulated (LFM) jamming, which may lead to receiver loss of lock. Existing anti-jamming techniques struggle to balance real-time constraints with high-precision frequency estimation. This paper proposes a Newton-iteration-enhanced three-spectral-line RIFE algorithm implemented on a heterogeneous FPGA platform (Zynq-7000 SoC). The method performs coarse frequency estimation using the three-spectral-line RIFE to mitigate FFT fence effects, followed by Newton-based quadratic refinement, enabling high estimation accuracy with reduced FFT size. A fast–slow loop architecture is adopted, where the FPGA (PL) performs real-time interference suppression and the ARM (PS) handles system control and parameter updates. Experimental results show that, under static interference, the proposed method achieves a 10.9 dB improvement over direct estimation algorithms. Under chirp interference, it significantly outperforms both direct estimation and conventional iterative methods. In GNSS closed-loop tests, the proposed approach extends the anti-jamming margin to 82 dB J/S. Overall, the proposed method effectively balances estimation accuracy and processing latency, providing a practical solution for GNSS anti-jamming in high-dynamic environments. Full article

Ensuring that government-led large-scale online public services evolve from formal participation to substantive quality represents a key governance challenge. This study extends the Unified Theory of Acceptance and Use of Technology (UTAUT) by integrating value identity as a core value-rational driver and by taking self-reported teaching practice quality as the ultimate outcome variable. Based on a cross-sectional survey of 2226 teachers analyzed via structural equation modeling, our findings reveal a stark ‘Governance Paradox’: at the aggregate level, both Behavioral Intention (β = −0.213) and Social Influence (β = −0.098) are unexpectedly associated with lower self-reported teaching practice quality, despite Value Identity being a powerful predictor of intention (β = 0.900). We conceptualize this statistical paradox not as an anomaly, but as a diagnostic for misaligned subsystems within a complex socio-technical system. Crucially, this paradox is systematically resolved through multi-group analysis; disaggregating the sample by institutional context reveals the expected positive relationships within homogeneous subgroups. This suggests that hidden heterogeneity and contextual factors are responsible for distorting the aggregate picture. Theoretically, this research offers two contributions: it reframes statistical aggregation artifacts as a systems-diagnostic framework for governance and introduces “Motivation Fusion” as a micro-foundational mechanism to explain the institutional and psychological conditions that produce such artifacts. Practically, the study provides a micro-foundational diagnostic framework for designing more targeted and effective public service policies. Full article

Climate change remains among the most pressing environmental challenges confronting the world, exerting profound pressure on both ecological systems and socio-economic development. To advance understanding of the evolution patterns and driving mechanisms governing hydroclimatic systems in arid and semi-arid regions, this study employed an integrated framework encompassing trend testing, change-point detection, periodicity and persistence analysis, and machine learning-based attribution. Focusing on the Mu Us Sandy Land from 1982 to 2023, we systematically investigated the spatiotemporal evolution, periodic characteristics, and driving mechanisms of hydroclimatic factors. Furthermore, future climate risks were assessed using CMIP6 multi-model data. The results showed that: (1) All four variables exhibited positive slopes, but only soil moisture showed a statistically significant long-term wetting trend (β = 0.025 × 10⁻³, p = 0.0008) and a clear global abrupt change in 2011; the upward tendencies of precipitation (p = 0.3946), potential evapotranspiration (p = 0.4970), and surface runoff (p = 0.1097) did not reach the 0.05 significance level. (2) Meteorological elements showed weak periodicity and strong anti-persistence (mean Hurst index = 0.379 for precipitation and 0.222 for PET), whereas hydrological elements exhibited clear seasonal–interannual periods and more random future variability with greater spatial heterogeneity (mean Hurst index = 0.436 for runoff and 0.414 for soil moisture). (3) Monthly changes were mainly associated with local surface processes. Vegetation dynamics were key predictors of precipitation, runoff, and soil moisture, while potential evapotranspiration was dominated by atmospheric demand, with limited influence from large-scale climate indices. (4) Under high-emission scenarios, imbalanced water–heat increases may lead to a higher likelihood of drought conditions. Full article