Search Results (7,037)

Photovoltaic desert control (PVDC), an innovative model integrating clean energy development and desertification control, faces complex coordination challenges among local governments, local communities, and photovoltaic enterprises. This study constructs a tripartite evolutionary game model to identify the conditions that drive PVDC toward coordinated governance. The model defines a three-dimensional strategy space: government regulatory intensity (Strong vs. Lax), community willingness to cooperate (Active Cooperation vs. Passive Resistance), and enterprise ecological integration (Active Ecological Integration vs. Passive Land Occupation). Replicator dynamic equations are derived to characterize nonlinear interactions, and the stability conditions of eight pure-strategy equilibrium points are identified through Jacobian matrix eigenvalue analysis. Numerical simulations are conducted using a baseline parameter set that satisfies the Evolutionary Stable Strategy conditions for the ideal equilibrium E8, namely Strong Regulation, Active Cooperation, and Active Ecological Integration. The results show that the system can converge to E8 when higher-level rewards cover government regulation, subsidy, and community-support costs; when community cooperation benefits exceed livelihood opportunity costs and compensation incentives from resistance; and when enterprises’ effective ecological integration costs are lower than the combined benefits of subsidies, avoided fines, and long-term returns. Sensitivity analysis further indicates that government subsidies, fines, community support, cooperation income, and enterprise long-term benefits are key drivers of system evolution, while excessive regulation costs, high opportunity costs, and high ecological integration costs may hinder coordination. Qualitative evidence from four PVDC-related cases in Xinjiang provides practical illustrations broadly consistent with the model mechanisms. This study offers a dynamic analytical framework for designing incentive-compatible governance mechanisms in PVDC and similar multi-stakeholder ecological restoration projects. Full article

Accurate forecasting of oil well production via field monitoring systems is significantly restricted by a structural conflict in modeling, where temporal dependency learning and nonlinear feature representation are closely coupled. Such coupling forces a trade-off between capturing long-term temporal dependencies and retaining sensitivity to short-term sensor fluctuations, while amplified local sensitivity easily increases noise interference and weakens model robustness under complex non-stationary sensor dynamics. To solve this problem, this study proposes a novel sensor-driven hybrid framework named Temporal Augmented Residual Network (TAR-Net), which adopts a decoupled paradigm to separate global temporal modeling and local fluctuation compensation explicitly. A multi-scale dilated Temporal Convolutional Network (TCN) extracts long-range temporal patterns from multi-source sensor data, and a LightGBM-based residual module conducts targeted error correction. Meanwhile, multi-scale temporal features and adaptive multi-fidelity Bayesian optimization are applied to enhance model adaptability. Validated on real sensor data from the Volve oilfield, TAR-Net surpasses 13 benchmark models with an R² of 0.9832 and a MAPE of 7.8%. Residual and trajectory analyses verify its balance between global trend consistency and local fluctuation sensitivity. This framework offers a robust sensor-aware solution for complex multi-scale temporal modeling in industrial production systems. Full article

Motor imagery (MI)-based brain–computer interfaces (BCIs) have led to great interest as a result of their potential use in neurorehabilitation, assistive robotics, and human–computer interaction. However, decoding electroencephalographic (EEG) signals with high accuracy continues to be a difficult task due to the weak signal-to-noise ratio, differences among subjects, and the complicated temporal–spectral–spatial neural dynamics. Deep learning methods recently developed, such as convolutional neural networks, recurrent architectures, graph neural networks, and adversarial transfer learning, have enhanced MI decoding performance, yet many models are still concentrating on a single representation domain or they need costly adaptation phases in terms of computation. To tackle these shortcomings, we present NeuroCrossNet, a unified tri-modal deep learning model that is able to learn the temporal, spectral, and spatial EEG features jointly for robust and calibration-free MI decoding. The suggested network combines a Temporal HyperMixer Block for capturing long-range temporal dependencies, a wavelet transformer for learning localized time–frequency representation, and a Graph Attention Network for EEG topology-aware spatial reasoning. Additionally, a Dynamic Residual Attention Gate (DRAG) has been developed to adaptively merge heterogeneous feature streams, and a compact subject-aware normalization (SAN) method enhances cross-subject generalization without the use of labeled target-domain calibration data. Our proposed model was tested following the rigorous leave-one-subject-out (LOSO) approach on BCI Competition IV-2a and High-Gamma datasets. NeuroCrossNet reached a classification accuracy of 91.30%, surpassing several strong benchmark methods, including CNN-LSTM, EEGNet, DeepConvNet, spectral CNN, and graph-based EEG decoding frameworks. Furthermore, a large number of ablation studies reveal that the integration of temporally, spectrally, and spatially complementary representations considerably boosts robustness and inter-subject consistency. Full article

Accurate Structural Health Monitoring of offshore wind turbines is critical for ensuring their long-term operational safety in harsh marine environments. Although displacement is a fundamental metric for assessing structural deformation and stress distribution, its direct measurement in open-ocean conditions is severely hindered by environmental interference and the absence of stable spatial references. Consequently, reconstructing displacement from structural acceleration through double integration is widely adopted, yet it suffers from severe baseline drift. Furthermore, existing drift-mitigation techniques often rely on empirical parameter selection and are limited to single-point reconstructions, failing to capture the full three-dimensional (3D) spatial attitude of the structure. To address these limitations, this paper proposes a novel 3D spatial attitude reconstruction framework based on advanced drift removal and spatial interpolation. First, an improved drift removal algorithm is developed to accurately eliminate baseline errors from acceleration signals, ensuring the physical fidelity of the reconstructed local displacements. Subsequently, cubic spline interpolation is utilized to extrapolate these discrete local measurements into a comprehensive full-field attitude profile of the entire turbine structure. The performance and robustness of the proposed method are systematically verified through numerical simulations and finite element analysis. Finally, its engineering applicability and accuracy are further validated via laboratory experiments and field measurements. The proposed framework effectively mitigates noise sensitivity and significantly enhances the accuracy of full-field attitude reconstruction, providing a reliable foundation for refined structural health assessments of OWTs. Full article

Energy-harvesting (EH) AIoT systems enable long-term autonomous operation but suffer from time-varying energy availability, which makes stable learning difficult. In such environments, federated learning (FL) is prone to energy depletion (blackout), while continual learning is required to handle evolving data distributions, leading to a trade-off between energy stability and catastrophic forgetting. In this paper, we propose a stability-controlled continual federated learning framework that jointly regulates local training intensity and rehearsal usage based on the residual energy state. The proposed method is derived from a Lyapunov drift-plus-penalty formulation and implemented as a lightweight mode-based control policy. Simulation results using real solar energy traces show that the proposed method significantly reduces blackout while improving accuracy and mitigating forgetting compared to existing approaches. These results demonstrate the effectiveness of energy-aware joint control for stable continual federated learning in EH-AIoT systems. Full article

This study presents a short-term, IoT-enabled sensor-based assessment of treated municipal water and decentralized groundwater in Bragança, northeastern Portugal. Two drinking-water supply contexts were compared: treated surface-water-derived municipal water from the public supply system and groundwater from a decentralized supply system serving part of a higher education campus. Five sampling points were monitored during three campaigns between January and March 2026. At each point, pH, electrical conductivity, temperature, oxidation–reduction potential, and total dissolved solids were recorded at 10 s intervals over approximately 10 min monitoring windows using a multiparameter probe integrated into an IoT-enabled data acquisition workflow. Microbiological analyses were performed on groundwater samples as complementary information. Treated municipal water showed lower mineralization, narrower parameter ranges, and higher oxidation–reduction potential, reflecting source-water characteristics, treatment, and operational control. Groundwater showed higher mineralization, lower oxidation–reduction potential, and greater variability among sampling points and campaigns, consistent with stronger local hydrogeochemical and operational influences. The repeated short-interval readings provided more detailed physicochemical profiles than isolated spot measurements, although the short monitoring windows do not represent continuous long-term high-frequency monitoring. Overall, the results support standardized IoT-enabled sensor-based monitoring as a complementary tool for short-term water-quality assessment and indicate the need for longer seasonal datasets and laboratory confirmation. Full article

Accurate carbon price forecasting is critical for trading decisions, risk management and policy formulation in carbon markets. However, mainstream decomposition-ensemble models suffer from two key drawbacks: point-wise modeling fails to capture long-term temporal dependencies, while independent modeling of decomposed trend and seasonal components leads to serious information loss. To address these limitations, this paper proposes a novel Dual-Path Interactive Attention Network (DPIANet) for carbon price time series forecasting, whose dual-parallel architecture consists of a Dual Interaction Attention (DIA) Block and a Decomposition–Subsequence Interaction Attention (DSIA) Block. First, DPIANet employs a patch-wise partitioning strategy to extract local temporal semantic information inaccessible to traditional point-wise segmentation. The DIA Block jointly captures temporal dependencies between different patches within the same sequence and inter-feature dependencies within the same time step. In parallel, the DSIA Block extracts interactive features between decomposed trend and seasonal subsequences, fusing these features with original subsequences to enhance representation and mitigate decomposition-induced information loss. A dual-layer feature selection method (PMI and XGBoost-SHAP) is adopted to identify key driving factors. Experiments on four representative Chinese regional carbon trading markets covering 2014-2020 show that DPIANet achieves superior prediction performance over state-of-the-art models in terms of MSE and MAE, with competitive robustness across different market characteristics, providing practical decision support for carbon market stakeholders. Full article

Digital camera sensors are designed to capture a wide range of incident illuminants, enabling the creation of high-quality images. However, these sensors lack the capability to differentiate between the color of the source illuminant and the actual color (or original color) of the object being captured. For this reason, the computational color constancy (CCC) was introduced and has been developed over decades. The CCC is an approach to modeling the color perception of the human visual system (HVS) by ensuring accurate object color determination under varying source illuminant conditions. At the core of human visual perception (HVP)-based CCC is attaining higher accuracy in scene illuminant estimation. The emergence of deep convolutional neural networks (DCNNs) was a recent innovation in accurate illuminant estimation, fundamentally transforming the CCC research landscape. Nevertheless, accurate illuminant estimation still remains a huge challenge for both traditional and state-of-the-art (SOTA) approaches. To further advance precision in illuminant estimation, this article presents a novel learning-based illumination color cast estimation approach to HVP-based CCC. Most importantly, the proposed approach is intended to integrate informative features into both channel and spatial regions while preserving long-term dependency feature information with the use of dense skip connections. To achieve these objectives, the proposed Dense Dual Connection Aggregated Transform Network (DDCATNet) architecture is designed to comprise several modules: shallow feature extraction, channel-wise and spatial feature-based Dense Dual Connection (DDC), fusion of the dense channel-wise attention (CA) and spatial attention (SA) branches through a gate mechanism (GM) unit, and aggregate transform. It is worth noting that both the CA blocks and the SA blocks in the DDC module are characterized by dense and cascading connections, meant to preserve long-term feature information and modulate different-level feature information at both global and local scales. The densely connected CA branch (DCA) and the densely connected SA branch (DSA) are also highly effective in securing high-contribution information while suppressing redundant data. The GM unit is integrated at the back of the DDC module, fusing the two DCA and DSA branches to ensure the adaptive merging of useful hierarchical feature information and the extraction of more valuable feature information. As a result, the proposed DDCATNet architecture significantly enhanced precision in illuminant estimation, thereby improving performance. In rigorous experiments on a wide range of datasets, the proposed DDCATNet approach outperformed its SOTA counterparts, validating the efficacy and generalization capabilities, as well as robust camera-invariance, across diverse, single- and multi-illuminant datasets and model architectures. Full article

Indoor temperature time series in district-heating buildings are often contaminated by anomalies embedded in nonstationary, multiscale thermal dynamics. This study proposes a hybrid Empirical Wavelet Transform and Long Short-Term Memory (EWT-LSTM) framework for adaptive anomaly detection and localized reconstruction. Evaluated on 15 min interval data from 45 residential units over a 112-day heating season, the framework operates via a highest-frequency branch for anomaly detection and a full-modal branch for signal repair. Quantitative results show that the EWT Highest-Frequency LSTM (EWT(HF)-LSTM) achieved the best anomaly discrimination among decomposition variants with an average F1-score of 0.531. For anomaly repair, the full EWT-LSTM produced the highest fidelity with a localized Root Mean Square Error ($RMS{E}_a$) of 0.818 °C. Furthermore, thermal comfort validation demonstrated that EWT-LSTM successfully prevented the severe comfort degradation of up to −82% in Exceeded Degree-Hours caused by unstable Empirical Mode Decomposition (EMD)-based reconstructions. These concrete results confirm that the proposed framework effectively provides clean, physically coherent temperature data for downstream district heating operations. Full article

Background: Tirbanibulin 1% ointment has demonstrated short-term efficacy and excellent tolerability in the treatment of actinic keratosis (AK) on the face and scalp. However, data on long-term efficacy are still lacking. Materials and Methods: This prospective, single-center, 12-month extension study included patients with facial and scalp AKs previously treated with tirbanibulin 1% ointment once daily for 5 consecutive days. Long-term analysis was restricted to lesions that had achieved complete clinical and dermoscopic clearance at the 2-month follow-up. At 12 months, the treated areas were reassessed clinically and dermoscopically. High-resolution images obtained at baseline, 2 months, and 12 months were compared lesion by lesion to distinguish sustained clearance, recurrence at the same anatomical site, and the development of new AKs within the treated field. Results: Thirty-seven patients were reassessed at 12 months. Of the 228 AKs treated at baseline, 116 lesions had achieved complete clearance at 2 months and were therefore eligible for long-term evaluation. At 1 year, 70/116 lesions (60.3%) remained free of recurrence, whereas 46/116 (39.7%) relapsed. Sustained clearance was observed in 35/51 grade 1 lesions (68.6%), 32/57 grade 2 lesions (56.1%), and 3/8 grade 3 lesions (37.5%). In addition, 35 new AKs developed within the previously treated field. No delayed local or systemic adverse events and no progression to invasive cSCC were observed during follow-up. Patient-reported satisfaction was high, and 94% of patients stated they would be willing to repeat the treatment. Conclusions: Tirbanibulin was associated with sustained lesion clearance at one year, particularly in lower-grade AKs. While recurrence remains relatively common—especially in thicker lesions—the treatment was well tolerated and associated with no delayed adverse effects. Its short application regimen and excellent safety profile support tirbanibulin’s role in the long-term management of field cancerization. Full article

Predicting high-resolution Normalized Difference Vegetation Index (NDVI) in mountainous ecosystems is challenging due to topographic complexity and climate heterogeneity. Existing methods often struggle to balance fine-grained spatial patterns with multi-scale meteorological drivers. This paper introduces the Hierarchical Multi-Modal Fusion Network (HMMFN), which employs a conditioned reconstruction strategy to decouple spatial learning from environmental forcing. The architecture utilizes a dual-stream encoder to process NDVI imagery and meteorological data in parallel. A Transformer module captures long-term temporal dependencies, while a multi-level fusion decoder integrates climate semantics with local vegetation details. The model is optimized using a hybrid loss function that combines Mean Squared Error and Structural Similarity Index Measure to ensure both numerical precision and spatial fidelity. Evaluated in the Liupan Mountains, HMMFN consistently outperforms baseline models across multiple lead times. For prediction horizons ranging from one to five months, the model maintains high accuracy with $R^2$ values between 0.9123 (1-month horizon) and 0.8195 (5-month horizon), achieving a 10.1% and 3.6% reduction in RMSE compared to the optimal baseline model, respectively. These results demonstrate that HMMFN effectively preserves fine-scale spatial structures while maintaining accurate temporal trends across various time steps. Full article

Traditional wooden granaries in rural Türkiye are disappearing at an accelerating rate due to agricultural abandonment, rural depopulation, and the absence of systematic documentation and conservation frameworks. In the Sındırgı district of Balıkesir, one of the richest concentrations of vernacular granary architecture in the Marmara Region, these structures remain largely unprotected and unstudied within a sustainable design framework, constituting an urgent conservation challenge. This study aims to assess the current preservation status of Sındırgı granaries, classify their typological diversity, and evaluate their sustainability performance against a defined set of ecological design criteria. A mixed methods approach was employed, combining a systematic literature review with extensive fieldwork across 33 neighborhoods. In total, 1411 granaries were identified and grouped into five typologies: evli, Simav, kabak, sandık, and üstü örtülü sandık. These typologies were systematically compared to five parameters: spatial distribution across neighborhoods, plan and section geometry, construction system and structural elements, material selection and condition, and preservation status. This comparison revealed that typological variation is not incidental but directly reflects differences in land ownership, agricultural production capacity, topography, and distance from the district center. Representative examples from each typology were documented through onsite measurements, photogrammetry, technical drawings, and interviews with local craftsmen. The sustainability performance of the granaries was then assessed across seven ecological design criteria: spatial organization, building form design, structural element design, material use and conservation, design with nature, urban design area planning, and nature interaction. The findings demonstrate that the long-term durability of these structures depends on an interrelated system of climate-responsive design decisions rather than any single factor. The study concludes by proposing a holistic conservation model comprising typology-based inventory, roof water moisture-focused intervention, periodic monitoring, and transmission of vernacular building knowledge, a framework applicable to comparable rural granary heritage across the region. Full article

Three-dimensional (3D) bioprinting has rapidly evolved into a controlling platform for the fabrication of patient-specific biomedical implants, with growing importance in advanced drug delivery systems. Beyond structural tissue engineering, bioprinted constructs now function as programmable therapeutic depots capable of localized, sustained, and stimuli-responsive drug release. This review focuses on recent biomaterial design strategies that enable precise control over drug encapsulation, retention, and release kinetics within 3D bioprinted architectures. The physicochemical and mechanical properties of bioinks, including crosslinking density, porosity, degradation behavior, viscoelasticity, and swelling characteristics, directly influence drug loading efficiency and release dynamics under physiological conditions. The rational tuning of these parameters allows the development of constructs that provide spatially controlled and temporally regulated therapeutic delivery. Recent advances in predictive modeling, such as finite element modeling (FEM), data-driven machine learning approaches, and ML, have significantly improved the ability to correlate material composition, printing parameters, and structural geometry with drug diffusion and degradation-mediated release mechanisms. These tools facilitate the optimization of printing variables including extrusion pressure, nozzle diameter, and layer resolution to ensure structural fidelity while maintaining therapeutic functionality. Emerging strategies incorporating multi-material printing, gradient architectures, and stimuli-responsive biomaterials have expanded the potential of 3D bioprinting for combination therapies and personalized medicine. This review discusses key challenges in translating bioprinted drug delivery systems into clinical applications, including the standardization of drug release characterization methods, and long-term stability assessment. Full article

Industrial transformation in resource-based regions (RBRs) is a global challenge. Shanxi is a typical resource-based province in China. The long-term exploitation of coal resources has posed huge challenges to its ecological protection and high-quality development. Breaking away from the single-city perspective, this study focuses on the regional scale and comparative analysis and attempts to construct a novel three-dimensional analytical framework, namely, “industrial characteristics, industrial layout, and industrial policies”, to explore the industrial transformation path of typical RBRs. The results indicate the following: (1) Shanxi does not have obvious advantages in terms of resource endowment, with a severely heavy industrial structure and strategic emerging industries still in the initial stage of development. At the national strategic level, it is still necessary to strengthen the application of the “pioneer and pilot” policies and mechanisms for innovation. (2) In the context of high-quality development, Shanxi needs to clarify the industrial transformation orientation. For agriculture, the focus should be placed on characteristic and efficient development. For industrial development, priority should be given to upgrading advantageous industries and cultivating emerging industries. For the tertiary industry, it is necessary to form a development pattern of “new producer services + characteristic tourism”. In terms of regional development layout, Shanxi should establish a macro-pattern to promote inter-regional coordinated development. (3) In the new period, Shanxi should accelerate the construction of transportation systems to improve the convenience of inter-regional cooperation. It is essential to increase investment in education and scientific research so as to enhance the overall social innovation capacity. Meanwhile, differentiated regional development policies should be adequately supplied to drive the high-quality evolution of local industries. Focusing on the regional scale, the new logical analysis paradigm can provide theoretical references for RBRs to clarify the direction of industrial transformation and formulate transformation policies. Full article

Background: Persistent lymph node metastases after neoadjuvant radiochemotherapy (RCT) for locally advanced rectal cancer indicate poor response to treatment. This study evaluated the long-term prognosis of patients with residual nodal disease following neoadjuvant RCT and total mesorectal excision (TME) in comparison with patients who underwent upfront TME without adjuvant radiotherapy. Methods: Consecutive patients with rectal adenocarcinoma and histopathologically confirmed lymph node metastases after TME were identified from the prospectively maintained database of the colorectal unit at Dresden-Friedrichstadt General Hospital. Patients with distant metastases, in-hospital mortality, or postoperative radiotherapy were excluded. The two groups were comprehensively compared regarding patient-, tumor-, and treatment-related characteristics. Cumulative local recurrence, time to recurrence, cancer-specific survival, and overall survival were analyzed using the Kaplan–Meier method. Results: Between 1996 and 2021, 155 eligible patients were identified, including 101 patients in the RCT group and 54 in the upfront surgery group. Baseline characteristics were largely comparable, except for a higher median age (70.5 vs. 64 years, p < 0.001) and a higher proportion of lymphovascular invasion (36.0% vs. 15.2%, p = 0.004) in the upfront surgery group. Ten-year local recurrence rates were similar between groups (21.0% [95% CI: 10.4–31.6] vs. 20.8% [95% CI: 8.5–33.1], p = 0.609). No significant differences were observed in time to recurrence or cancer-specific survival. Overall survival was lower in the upfront surgery group, most likely reflecting the substantially higher age of these patients. Conclusions: Despite more intensive treatment, patients with a persistent ypN-positive category have outcomes no better than untreated patients with node-positive disease after TME, indicating a biologically high-risk subgroup. Non-response is therefore a sign of a negative selection. These patients may lose the opportunity for optimal local tumor control during prolonged neoadjuvant treatment, underscoring the urgent need for reliable predictive markers to identify non-responders and guide individualized treatment strategies. Full article