Search Results (22,660)

This study developed a sustainable high-strength coal gangue backfill material for underground mining applications using coal gangue, fly ash, and cement as primary raw materials, with red mud (RM) as an alternative alkali activator. The mechanical properties of the backfill material were systematically optimized by adjusting coal gangue particle size and alkali activator dosage. The optimized formulation (coal gangue/fly ash/cement = 5:4:1, 3–6 mm coal gangue particle size, 5% RM, which named BF-6-5RM) achieved superior compressive strengths of 8.23 MPa (7 days) and 10.5 MPa (28 days), significantly exceeding conventional backfill requirements and outperforming a CaO-activated reference system (coal gangue/fly ash/cement = 5:4:1, 3–6 mm coal gangue particle size, 2% CaO, which named BF-6-2CaO). Microstructural and physicochemical analyses revealed that both formulations produced calcium silicate hydrate gels (C-S-H gels) and ettringite (AFt) as key hydration products, though BF-6-5RM exhibited a denser microstructure with well-developed ettringite networks and no detectable portlandite (CH), explaining its enhanced early-age strength. Environmental assessments confirmed effective heavy metal immobilization via encapsulation, adsorption, precipitation and substitution, except for arsenic (As), which exceeded Class III groundwater thresholds (DZ/T 0290-2015) due to elevated raw material content, displaying “surface wash-off, diffusion and depletion” leaching behavior. The findings confirm that red mud-based alkali activation is a viable technology for underground backfilling, provided it is coupled with arsenic control strategies like chemical stabilization or the selection of low-arsenic raw materials. This approach not only enables the resource utilization of hazardous industrial waste but also facilitates the production of backfill materials that combine both mechanical strength and environmental compatibility, thereby delivering dual economic and ecological benefits for sustainable mining practices. Full article

As cities develop and resource demands rise, the water sector faces crucial challenges to deliver reliable, sustainable, and efficient services. Digital Twins (DTs), virtual replicas of physical systems, offer a promising tool to transform how we manage water infrastructure. Originally developed in the aerospace industry, DTs are now gaining traction in the water sector, enabling real-time monitoring, simulation, and predictive control of water and wastewater treatment, collection and distribution networks, and water reclamation and reuse systems. While still emerging in the water sector, DTs have shown potential to enhance operational efficiency, reduce environmental impacts, and support smarter, more resilient water management. This review study provides a comprehensive overview of current DT applications in the water sector, highlighting successful case studies, technical challenges, and knowledge gaps. It also explores how DTs can help bridge the water–energy nexus by optimizing resources utilized across interconnected systems. By synthesizing recent advances and identifying future research directions, this paper illustrates how DTs can play a central role in building sustainable, adaptive, and digitally-enabled water infrastructure. Full article

B-cell acute lymphoblastic leukemia (B-ALL) is characterized by the abnormal proliferation of B-lineage lymphocytes in the bone marrow (BM). The roles of immune cells within the BM microenvironment remain incompletely understood. Single-cell RNA sequencing (scRNA-seq) provides the potential for groundbreaking insights into the pathogenesis of B-ALL. In this study, scRNA-seq was conducted on BM samples from 17 B-ALL patients (B-ALL cohorts) and 13 healthy controls (HCs). Bioinformatics analyses, including clustering, differential expression, pathway analysis, and gene set variation analysis, systematically identified immune cell types and assessed T-cell prognostic and metabolic heterogeneity. A metabolic-feature-based machine learning model was developed for B-ALL subtyping. Furthermore, T-cell–monocyte interactions, transcription factor (TF) activity, and drug enrichment analyses were performed to identify therapeutic targets. The results indicated significant increases in Pro-B cells, alongside decreases in B cells, NK cells, monocytes, and plasmacytoid dendritic cells (pDCs) among B-ALL patients, suggesting immune dysfunction. Clinical prognosis correlated significantly with the distribution of T-cell subsets. Metabolic heterogeneity categorized patients into four distinct groups (A–D), all exhibiting enhanced major histocompatibility class I (MHC-I)-mediated intercellular communication. The metabolic-based machine learning model achieved precise classification of B-ALL groups. Analysis of TF activity underscored the critical roles of MYC, STAT3, and TCF7 within the B-ALL immunometabolic network. Drug targeting studies revealed that dorlimomab aritox and palbociclib specifically target dysregulation in ribosomal and CDK4/6 pathways, offering novel therapeutic avenues. This study elucidates immunometabolic dysregulation in B-ALL, characterized by altered cellular composition, metabolic disturbances, and abnormal cellular interactions. Key TFs were identified, and targeted drug profiles were established, demonstrating the significant clinical potential of integrating immunological mechanisms with metabolic regulation for the treatment of B-ALL. Full article

Introduction. Post-traumatic stress disorder (PTSD) remains one of the best-described yet also one of the most heterogeneous psychiatric disorders. Existing neuroimaging studies point to key changes in the hippocampus, amygdala, and prefrontal cortex, but the role of PTSD duration in modulating these changes has not been fully explained. Objectives. The aim of the study was to assess the impact of PTSD duration (≤5 years vs. >5 years) on volumetric and microstructural brain parameters, using multiple Magnetic Resonance Imaging (MRI) sequences (3D Ax BRAVO, Cube T2 FLAIR, Diffusion Tensor Imaging—DTI) and a set of macroscopic morphometric measurements. Methods. The study included 92 participants: 33 with PTSD of ≤5 years duration, 31 with PTSD > 5 years, and 28 healthy controls. Volume and diffusion parameters of six Regions of Interest (ROIs) (hippocampus, amygdala, prefrontal cortex—right and left) were evaluated, along with their associations with nine brain measurements (including width of the third ventricle, corpus callosum, and lateral fissures). Statistical analyses included the Kruskal–Wallis test with Compact Letter Display (CLD) correction and Spearman correlations. Results. (1) The volume of the right hippocampus was significantly greater in the PTSD > 5 years group compared to controls (p = 0.006), with intermediate values in the PTSD ≤ 5 years group. (2) In the left amygdala, an increase in Fractional Anisotropy (FA) and related anisotropy measures was observed in PTSD > 5 years (p ≈ 0.02), without volumetric changes. (3) In the left prefrontal cortex, diffusivity was reduced in PTSD ≤ 5 years (p = 0.035), partially normalizing after >5 years. (4) Correlation analysis revealed that chronic PTSD strengthens the negative associations between hippocampal microstructure and both the width of the amygdala and the interhemispheric fissure, indicating a progressive reorganization of fronto-limbic networks. Conclusions. PTSD induces region- and time-dependent brain changes: (a) adaptive/hypertrophic protection of the right hippocampus after many years of illness, (b) cumulative microstructural reorganization of the left amygdala, and (c) transient impairment of diffusion in the left prefrontal cortex in early PTSD. These findings highlight the necessity of considering the temporal dimension in planning therapeutic interventions and in the search for biomarkers of PTSD progression. Full article

Heavy-duty diesel trucks (HDDTs) are recognized as significant sources of air pollutants and greenhouse gases (GHGs) along freight corridors in port cities. Despite their impact, few studies have provided detailed spatiotemporal insights into their emissions within port-adjacent highway systems. This study presents a high-resolution, hourly emission inventory at the road-segment level for six major expressways in Shanghai, one of China’s leading port cities. The emission estimates are derived using a locally adapted COPERT V model, calibrated with HDDT GPS trajectory data and detailed road network information from OpenStreetMap. The inventory quantifies emissions of CO₂, NO_x, CO, PM, and VOCs, highlighting distinct temporal and spatial variation patterns. Weekday emissions consistently exceed those of weekends, with three prominent traffic-related peaks occurring between 5:00–7:00, 10:00–12:00, and 14:00–16:00. Spatial analysis identifies the G1503 and S20 expressways as major emission corridors, with S20 exhibiting particularly high emission intensity relative to its length. Combined spatiotemporal patterns reveal that weekday emission hotspots are more concentrated, reflecting typical freight activity cycles such as morning dispatch and afternoon return. The findings provide a scientific basis for formulating more precise emission control measures targeting HDDT operations in urban port environments. Full article

Floods are among the most destructive natural disasters, and accurate flood susceptibility mapping (FSM) is crucial for disaster prevention and mitigation amid climate change. The Poyang Lake basin, characterized by complex flood formation mechanisms and high spatial heterogeneity, poses challenges for the application of FSM models. Currently, the use of machine learning models in this field faces several bottlenecks, including unclear model applicability, limited sample quality, and insufficient machine interpretation. To address these issues, we take the 2020 Poyang Lake flood as a case study and establish a high-precision flood inundation sample database. After feature screening, the performance of three hybrid models optimized by Particle Swarm Optimization (PSO)—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Convolutional Neural Network (CNN) is compared. Furthermore, the Shapley Additive exPlanations (SHAP) framework is employed to interpret the contributions and interaction effects of the driving factors. The results demonstrate that the ensemble learning models exhibit superior performance, indicating their greater applicability for flood susceptibility mapping in complex basins such as Poyang Lake. The RF model has the best predictive performance, achieving an area under the receiver operating characteristic curve (AUC) value of 0.9536. Elevation is the most important global driving factor, while SHAP local interpretation reveals that the driving mechanism has significant spatial heterogeneity, and the susceptibility of local depressions is mainly controlled by the terrain moisture index. A nonlinear phenomenon is observed where the SHAP value was negative under extremely high late rainfall, which is preliminarily attributed to the “spatial transfer that is prone to occurrence” mechanism triggered by the backwater effect, highlighting the complex nonlinear interactions among factors. The proposed “high-precision sampling, model comparison, SHAP explanation” framework effectively improves the accuracy and interpretability of FSM. These research findings can provide a scientific basis for smart flood control and precise flood risk management in basins. Full article

With the increasing demand for sustainable food production, the facility agriculture is progressively developing towards automation and intelligence. Traditional control techniques such as PID, fuzzy logic, and model predictive control have been widely applied in greenhouse planting for years. Existing greenhouse management systems still face challenges such as limited adaptability to fluctuating outdoor climates, and difficulties in maintaining both productivity and cost-effectiveness. Recently, with the development of greenhouse systems towards comprehensive environmental perception and intelligent decision-making, a large number of intelligent control and modeling technologies have provided new opportunities for the technological update of greenhouse management systems. This review systematically summarizes recent progress in greenhouse regulation and crop growth control technologies, emphasizing applications of intelligent techniques, involving adaptive strategies, neural networks, and reinforcement learning. Special attention is given to how these methods improve system robustness and control performance in terms of environmental stability, crop productivity, and energy efficiency, which are key performance indicators of greenhouse systems. Their advantages over conventional strategies in agricultural greenhouse systems are also analyzed in detail. Furthermore, the integration of intelligent technologies with greenhouse system modeling is examined, covering both greenhouse environmental models and crop growth models. The strengths and weaknesses of different techniques, such as mechanism, computational fluid dynamics (CFD), and data-driven models, are analyzed and discussed in terms of accuracy, computational cost, and applicability. Finally, future challenges and research opportunities are discussed, emphasizing the need for real-time adaptability, sustainability, and cluster intelligence. Full article

Air source heat pumps (ASHPs) have become a promising alternative to conventional heating and cooling systems, making accurate performance prediction increasingly important. This study presents a comparative analysis of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) models for evaluating the ASHP performance under varying ambient conditions, examining the symmetry or asymmetry of prediction behavior across cold and hot regimes. Two experimental campaigns were carried out in a controlled climate room: the first primarily covering moderate to high temperatures ($-3 °\mathrm{C}$ to $36 °\mathrm{C}$), and the second mainly covering negative and low ambient temperatures ($-16 °\mathrm{C}$ to $18 °\mathrm{C}$). Performance data were collected to capture system behavior under diverse thermal conditions, making predictions more challenging. Both models were optimized, ANFIS through grid partitioning and ANN via architecture selection. Results demonstrate that ANN models achieved a superior overall accuracy, with mean absolute errors of 0.061 to 0.064 for cold and hot ambient conditions, respectively, showing a particularly strong performance under cold conditions. ANFIS demonstrated remarkable robustness in low-temperature predictions, maintaining less than 3% deviation across variations in water inlet temperature. Both approaches revealed temperature-dependent characteristics: cold-condition modeling required more complex architectures but yielded higher precision, whereas warm-condition modeling performed reliably with simpler configurations but showed slightly reduced accuracy. Full article

Reliable DC cable insulation is crucial for photovoltaic (PV) systems and high-voltage DC (HVDC) networks. However, conventional materials such as cross-linked polyethylene (XLPE) and polyvinyl chloride (PVC) face challenges under prolonged DC stress—notably space charge buildup, dielectric losses, and thermal aging. Cross-linked polyolefin (XLPO) has emerged as a halogen-free, thermally stable alternative, but its comparative DC performance remains underreported. Methods: We evaluated the insulations of virgin XLPO, XLPE, and PVC PV cables under ±1 kV DC using time-domain indices (IR, DAR, PI, Loss Index), supported by MATLAB and FTIR. Multi-layer cable geometries were modeled in MATLAB to simulate radial electric field distribution, and Fourier-transform infrared (FTIR) spectroscopy was employed to reveal polymer chemistry and functional groups. Results: XLPO exhibited an IR on the order of 10⁸–10⁹ Ω, and XLPE (IR ~ 10⁸ Ω) and PVC (IR ~ 10⁷ Ω, LI ≥ 1) at 60 s, with favorable polarization indices under both polarities. Notably, they showed high insulation resistance and low-to-moderate loss indices (≈1.3–1.5) under both polarities, indicating controlled relaxation with limited conduction contribution. XLPE showed good initial insulation resistance but revealed polarity-dependent relaxation and higher loss (especially under positive bias) due to trap-forming cross-linking byproducts. PVC had the lowest resistance (GΩ-range) and near-unit DAR/PI, dominated by leakage conduction and dielectric losses. Simulations confirmed a uniform electric field in XLPO insulation with no polarity asymmetry, while FTIR spectra linked XLPO’s low polarity and PVC’s chlorine content to their electrical behavior. Conclusions: XLPO outperforms XLPE and PVC in resisting DC leakage, charge trapping, and thermal stress, underscoring its suitability for long-term PV and HVDC applications. This study provides a comprehensive structure–property understanding to guide the selection of advanced, polarity-resilient cable insulation materials. Full article

The high proportion of renewable energy connected to the grid has resulted in insufficient consumption capacity in distribution networks, while the construction of new-type power distribution systems has imposed higher reliability requirements. With its flexible power synchronization control capabilities, grid-forming energy storage systems possess the ability to both promote the consumption of distributed energy resources in new-type distribution networks and enhance their reliability. However, current control methods are still hindered by drawbacks such as high computational complexity and a singular optimization objective. To address this, this paper proposes an optimized strategy for unified active–reactive power coordinated control in high-proportion renewable energy distribution networks with the participation of multiple grid-forming energy storage systems. Firstly, to optimize the parameters of grid-forming energy storage systems more accurately, this paper employs an improved iterative self-organizing data analysis technique algorithm to generate typical scenarios consistent with the scheduling time scale. Quantile regression (QR) and Gaussian mixture model (GMM) clustering are utilized to generate typical scenarios for renewable energy output. Subsequently, considering operational constraints and equipment state constraints, a unified active–reactive power coordinated control model for the distribution network is established. Meanwhile, to ensure the optimality of the results, this paper adopts an improved northern goshawk optimization (NGO) algorithm to solve the model. Finally, the effectiveness and feasibility of the proposed method are validated and illustrated through an improved IEEE-33 bus test system tested on MATLAB 2024B. Through analysis, the proposed method can reduce the average voltage fluctuation by 6.72% and increase the renewable energy accommodation rate by up to 8.64%. Full article

Heat exchangers are widely used process equipment in industrial sectors, making the study of their temperature dynamics particularly appealing due to the nonlinearities involved. Model-free approaches enable the use of input and output data to generate specific and accurate estimations for each proposed system. In this work, a model-free identification strategy is proposed using a convolutional neural network to estimate the system’s behavior. Notably, the model does not rely on direct temperature measurements; instead, temperature is inferred from other system signals such as reference, flow, and control inputs. This data-driven approach offers greater specificity and adaptability, often outperforming manufacturer-provided coefficients whose performance may vary from design expectations. The results yielded an $R^2$ index of 0.9951 under nominal conditions and 0.9936 when the system was subjected to disturbances. Full article

Since its introduction, the construct of executive functions (EFs) has been associated with a set of tests to assess these functions and a brain network centered in the associative frontal brain regions. While the majority of perspectives have endorsed these associations, some studies have started casting doubts on them. In this article, the association between the construct of EFs, the tests used to assess them, and the involvement of frontal regions is examined. A sample of 28 patients with brain injuries was divided into three subgroups according to the region of the injury (anterior, posterior, antero-posterior). Patients were assessed with a battery of tests, including 25 measures of EFs and 6 control measures. A series of regression models revealed no significant differences in performance across the three groups. Findings indicate that the EF tests are not specific enough to differentiate EFs and brain injuries. The alleged reference of EFs to the frontal areas of the brain should attribute a higher role to other associative areas. The present study provides recommendations about how the EFs concept could be improved through methodological refinements and/or its dissemination. Full article

Against the backdrop of a high proportion of distributed renewable energy sources being integrated into the power grid, distribution networks are confronted with issues of grid-wide and decentralized harmonic pollution and voltage deviation, rendering traditional point-to-point governance methods inadequate for meeting collaborative governance requirements. To address this problem, this paper proposes a source-tracing-oriented harmonic mitigation and optimization control strategy for distribution networks. Firstly, it identifies regional dominant harmonic source mitigation nodes based on harmonic and reactive power sensitivity indices as well as comprehensive voltage sensitivity indices. Subsequently, with the optimization objectives of reducing harmonic power loss and suppressing voltage fluctuation in the distribution network, it configures the quantity and capacity of voltage-detection-based active power filters (VDAPFs) and Static Var Generators (SVGs) and solves the model using an improved Spider Jump algorithm (SJA). Finally, the effectiveness and feasibility of the proposed method are validated through testing on an improved IEEE-33 standard node test system. Through analysis, the proposed method can reduce the voltage fluctuation rate and total harmonic distortion (THD) by 2.3% and 2.6%, respectively, achieving nearly 90% equipment utilization efficiency with the minimum investment cost. Full article

This paper innovatively achieves finite-time modified function projection synchronization (MFPS) for different fractional-order chaotic systems. By leveraging the advantages of radial basis function (RBF) neural networks in nonlinear approximation, this paper proposes a novel fractional-order sliding-mode controller. It is designed to address the issues of system model uncertainty and external disturbances. Based on Lyapunov stability theory, it has been demonstrated that the error trajectory can converge to the equilibrium point along the sliding surface within a finite time. Subsequently, the finite-time MFPS of the fractional-order hyperchaotic Chen system and fractional-order chaotic entanglement system are realized under conditions of periodic and noise disturbances, respectively. The effects of the neural network parameters on the performance of the MFPS are then analyzed in depth. Finally, a color image encryption scheme is presented integrating the above MFPS method and exclusive-or operation, and its effectiveness and security are illustrated through numerical simulation and statistical analysis. In the future, we will further explore the application of fractional-order chaotic system MFPS in other fields, providing new theoretical support for interdisciplinary research. Full article

In the field of water process engineering, accurately predicting the total nitrogen (TN) concentration distribution in the Semi-Enclosed Bay area is of great importance for water quality assessment, pollution control, and scientific management. Due to the coupling of multiple influencing factors, the pollution process is complex, and traditional monitoring methods struggle to achieve large-scale, long-term real-time observation. Although numerical simulations can reproduce TN transport processes, they are computationally expensive and have low prediction efficiency. To address this, this study develops a deep learning hybrid model that integrates a Temporal Convolutional Network (TCN) and a Long Short-Term Memory (LSTM) network, referred to as the TCN-LSTM-Hybrid Model, to predict the spatiotemporal distribution of TN concentration fields in Shenzhen Bay. Comparative experiments show that this model outperforms traditional models such as TCN, LSTM, GRU, and MLP in terms of prediction accuracy and spatial generalization, offering higher computational efficiency and breaking through the limitations of “point-based prediction” by achieving “field-based prediction,” thereby providing a new path for pollutant simulation in complex ocean environments, supporting more informed decision making in ocean and coastal management. Full article