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Nanoporous anion-conducting membranes have gained considerable interest for their potential to reduce resistance in electrochemical devices1-4. Current pore-forming methods, such as backbone engineering through polymers of intrinsic microporosity5,6 or covalent organic and metal-organic frameworks7,8, however, suffer from limited structural control, mechanical fragility or demanding synthesis. Here we establish a supramolecular strategy that overcomes these limitations by constructing uniform, dynamic nanopores. Co-assembly of the rigid macrocyclic host cucurbit[7]uril with the cationic polymer guest quaternized poly(piperidinium-terphenyl) yields a robust network of nanometre-scale channels while simultaneously enhancing mechanical and chemical stability. The dynamic host-guest interactions allow the pore structure to fluctuate on picosecond and angstrom scales. This transient environment supports low-friction hydroxide migration through a Grotthuss mechanism, producing a marked enhancement in ionic conductivity. This bottom-up design principle provides a versatile new tool for molecularly engineering transport pathways and promises to advance electrochemical reactors with respect to energy efficiency, operational stability and the production of high-purity products.

Inter-basin water transfer is a commonly adopted measure for improving hydrological and ecological conditions within watersheds. However, systematically quantifying the actual improvement effects of water diversion projects on watershed runoff conditions remains a weak link in current hydraulic engineering evaluation. This study conducts complex modeling of the Changjiang-Hanjiang Water Diversion Project (CHWD), the Middle Route of South-to-North Water Diversion Project (MR-SNWD), the Dongjing River diversion, and the cascade reservoirs in the middle and lower reaches of the Hanjiang River with Soil and Water Assessment Tool (SWAT). Based on this modeling framework, the daily runoff process is simulated in the lower Hanjiang River under a scenario with no CHWD, and the impact of the CHWD on the runoff process is investigated in the lower Hanjiang River. The results indicate that: (1) the water supplementation from the CHWD is insufficient to balance the reduction impact on downstream Hanjiang River runoff caused by water withdrawal from the MR-SNWD; (2) the water supplementation from the CHWD is primarily utilized to supply the Dongjing River diversion, with limited effect on increasing runoff in the Hanjiang River section downstream of Dongjing River; (3) the water supplementation from the CHWD contributes to enhancing the stability of the runoff process in the lower Hanjiang River. This study yields a significant insight that gravity-flow water conveyance is suboptimal for inter-basin water transfer in flat terrain regions. These findings offer valuable implications for the planning and design of such projects globally.

To assess longitudinal changes in subfoveal choroidal thickness (SFCT) and mean choroidal thickness (MCT) in intermediate uveitis, eyes with at least one follow-up visit were included. These eyes were stratified into clinically worsened, stable, or improved based on changes in clinical parameters including Standardization of Uveitis Nomenclature (SUN) classification, to evaluate their prognostic value for future best-corrected visual acuity (BCVA) and central retinal thickness (CRT). Spectral domain optical coherence tomography (Heidelberg Engineering, Germany) was used to image the central macula. SFCT, MCT, and CRT were measured manually within the central 1 mm. Mixed-effects regression analysis controlling for age and sex was used to evaluate the prognostic value of SFCT and MCT regarding future BCVA and CRT. A total of 91 eyes from 52 patients were included in the analysis. While 12 eyes worsened, 62 remained stable, and 17 improved. Choroidal thickness remained stable over time, with no significant differences in SFCT or MCT change between clinical groups (p > 0.5 for all). When controlling for age and sex, both the baseline SFCT (estimate = -0.35 × 10- ³ logMAR per µm, p = 0.040) and MCT (estimate = -0.42 × 10-³ logMAR per µm, p = 0.018) were prognostic for future BCVA. MCT, but not SFCT, was significantly associated with future CRT (estimate = -0.15 μm per µm, p = 0.010 vs. -0.13 μm per µm, p = 0.140). Choroidal thickness in terms of baseline SFCT and MCT is prognostic of future BCVA and could serve as a prognostic structural biomarker for intermediate uveitis.

To address the core bottlenecks in the evolutionary performance evaluation of automobile door stamping processes, namely insufficient adaptation to dynamic uncertain information and the lack of systematic methodological support in existing methods, this study establishes a complete evaluation methodological framework covering the indicator system, weight algorithm, and evaluation model. First, based on the PDCA cycle logic, a multi-stage indicator system integrating static benchmarks and dynamic evolution dimensions is designed, covering the entire process of planning, execution, inspection, and optimization. Second, an entropy weight-information cloud coupled weighting algorithm is proposed. The entropy weight method is used to extract objective data features, and the fuzzy-random modeling capability of the information cloud model is combined to achieve robust weight allocation of evolutionary indicators. Finally, to solve the problem that the traditional TOPSIS method cannot distinguish the advantages and disadvantages of schemes near the ideal solution, JS divergence is introduced to improve the traditional TOPSIS algorithm, and the distance between the process scheme and the positive/negative ideal solutions is quantified to realize accurate performance ranking. Through case validation on automobile door stamping processes, five typical door stamping process schemes are evaluated. The results show that the comprehensive performance score ranges from 0.1765 to 0.8689, and the performance ranking is highly consistent with the actual production logic, verifying the effectiveness and industrial adaptability of the proposed methodology. This study provides a systematic quantitative tool for the evolutionary performance evaluation of processes in complex manufacturing scenarios, and has important theoretical reference and engineering application value.

Credit card fraud detection is a difficult applied machine learning problem. It combines extreme class imbalance, temporal non-stationarity, and a sharp cost gap between missed fraud and false alarms. This paper presents an end-to-end experimental framework for fraud detection on the benchmark European transaction dataset (284,807 transactions; fraud prevalence 0.173%). A strict no-data-leakage protocol is enforced throughout. The data are split chronologically into training (70%), validation (15%), and test (15%) sets, and every preprocessing step - feature scaling, SHapley Additive exPlanations (SHAP)-based feature selection, and oversampling - is fitted only on the training partition. Two domain-informed features are engineered from the raw timestamp (sinusoidal hour encoding and log-transformed amount), and SHAP analysis reduces the 33-dimensional feature space to 15 features. Six oversampling strategies - SMOTE, BorderlineSMOTE, SVMSMOTE, ADASYN, SMOTEENN, and SMOTETomek - are compared across 12 classical classifiers, 3 multilayer perceptron (MLP) architectures, a purpose-built deep neural network (FraudNet), and 7 ensemble methods, giving 85 model-sampler combinations. Decision thresholds are tuned on the validation set using the [Formula: see text]-score, and all final metrics are reported on the held-out temporal test set. To characterise temporal drift explicitly, we measure distributional shift between splits using the Population Stability Index (PSI), Kolmogorov-Smirnov (KS) tests, and Jensen-Shannon divergence on the SHAP-selected features. We also report 1000-replicate bootstrap 95% confidence intervals for the leading configurations. The MLP (128-64-32) without oversampling reaches the highest individual [Formula: see text] of 0.7722 (95% CI: [0.6712, 0.8420]). The Soft Voting ensemble attains the best Matthews Correlation Coefficient (MCC) of 0.8060 (95% CI: [0.7045, 0.8807]) and an AUC of 0.9703. LightGBM under SMOTEENN shows the largest gain from oversampling, with [Formula: see text] rising from 0.0745 to 0.7588. ADASYN consistently underperforms, and no single oversampling method dominates across all model families. The drift analysis confirms measurable but modest distributional shift between splits. Because the dataset spans only 48 hours, this shift reflects short-horizon, mostly intra-day variation rather than the long-horizon concept drift seen in production; we therefore treat the chronological protocol as a methodological lower bound and emphasise this limit throughout. Together, these findings give practical guidance for designing production-grade fraud detection systems under strict temporal and data-integrity constraints.

Accurate monitoring of agricultural land is a cornerstone of sustainable land management, particularly in arid regions like Saudi Arabia, where water resources are scarce. Traditional Land Use Land Cover (LULC) classification methods, dependent on manually engineered features, often lack robustness across diverse environmental conditions. While deep learning models like Convolutional Neural Networks (CNNs) automate feature extraction and enhance generalization, their computational complexity can be prohibitive. This research investigates a hybrid methodology to optimize this balance, integrating the powerful feature learning of DenseNet121 with the computational efficiency of advanced machine learning classifiers, specifically Decision Trees (DT) and XGBoost. The objective was to develop a precise and efficient tool for mapping key land covers-especially agricultural areas-in Najran City using 2020 Landsat 8 imagery. The proposed framework extracts complementary spatial and spectral features, which are then classified. Experimental results demonstrated that the DenseNet121-XGBoost hybrid model achieved superior performance, with an overall accuracy of 98.82% and a Kappa coefficient of 0.9638, significantly outperforming the standalone CNN. This study confirms the efficacy of hybrid deep learning for reliable agricultural land monitoring, providing a valuable decision-support tool for promoting sustainable practices in arid environments.

Speech Emotion Recognition (SER) plays a significant role in human-computer interaction by enabling intelligent systems to recognize and respond to human emotional states. Accurate emotion recognition from speech signals is essential for applications such as mental health assessment, healthcare monitoring, virtual assistants, and human-robot interaction. However, traditional SER approaches often rely on manually engineered acoustic features and suffer from limited generalization due to dataset imbalance, speaker variability, and environmental noise. To address these challenges, this study proposes a Conv1D-based deep learning framework for robust speech emotion recognition using complementary acoustic features and data augmentation techniques. The proposed framework utilizes Mel-Frequency Cepstral Coefficients (MFCCs), Zero-Crossing Rate (ZCR), and Root Mean Square Energy (RMSE) extracted from frame-level speech signals to capture spectral, temporal, and energy-related emotional characteristics. The extracted acoustic descriptors are sequentially concatenated into a unified feature representation and provided as input to the Conv1D architecture for hierarchical temporal feature learning. Experiments were conducted using a combined multi-dataset setup consisting of RAVDESS, SAVEE, CREMA-D, and TESS datasets. The dataset was divided into training, validation, and testing subsets, and data augmentation techniques including noise injection, time shifting, pitch modification, and time stretching were applied exclusively to the training samples to improve robustness and class balance. Experimental evaluation across seven emotional categories demonstrates that the proposed framework achieves a test accuracy of 94.91% with a Macro F1-score of 0.94, showing strong recognition capability across diverse emotional speech conditions. The results indicate that the integration of discriminative acoustic feature extraction, sequential feature representation, and Conv1D-based temporal learning significantly improves speech emotion recognition performance. The proposed framework provides an effective and computationally efficient solution suitable for practical real-world SER applications.

Large-scale artificial intelligence (AI) models achieve notable performance in computer vision but require substantial computational resources, limiting their deployment on edge devices1,2. Optical neural networks (ONNs) promise reduced latency and energy consumption by making use of the inherent parallelism of light3. However, present ONNs struggle to scale and are confined to simple tasks, owing to the challenges of replicating exact algebraic operations of digital models using physical (analogue) systems. This work introduces a new paradigm that directly embeds core computer vision principles, including similarity-based recognition, attention-guided perception and detail-context fusion, into a large-scale optical metasurface. By unifying optical physics with these computer vision fundamentals, we develop a photonic-electronic engine that overcomes scalability and generality barriers, enabling high-accuracy, general-purpose computer vision at the edge. The resulting system combines a 41-million-parameter optical metasurface front end with a co-designed, ultraefficient 87,000-parameter digital back end, outperforming many digital models with tens of millions of parameters across object detection, segmentation, 3D reconstruction and video understanding. We build a deployable prototype and demonstrate real-time edge visual processing in natural scenes. This work represents a path towards practical optical computing for general vision tasks in complex natural environments, enabling a new paradigm for low-energy, low-latency, real-time on-device vision intelligence.

Sleep disturbances are a debilitating feature of psoriasis, but whether they arise from itching or from direct effects of inflammation on the brain remains unclear. Here we show, using clinical data and a mouse model of psoriasis-like inflammation, that affected animals exhibit marked wakefulness and fragmented non-rapid eye movement sleep, even when itching is eliminated. This sleep disruption is linked to overactivity of wake-promoting neurons in a brain region called the anterior hypothalamic area. We find evidence of a local inflammatory response in this region, including activation of microglia and elevated levels of the signaling protein tumor necrosis factor-alpha. Directly delivering an inhibitor of this protein into the anterior hypothalamic area significantly restores normal sleep. These findings reveal a direct pathway from skin inflammation to sleep-regulating brain circuits and identify tumor necrosis factor-alpha as a potential target for treating insomnia in psoriasis.

Gravitational water vortex turbines (GWVTs) have emerged as promising hydrokinetic technology for energy extraction in riverine systems characterized by low flow velocity and ultra-low head, where conventional hydropower solutions are not feasible. In this study, a three-dimensional computational fluid dynamics (CFD) model is developed to investigate turbine performance within a conical basin configuration. The numerical framework, validated against published reference data, is used to systematically evaluate the influence of airfoil axial spacing, chord sizing, and airfoil profile, on vortex structure and power coefficient. The results demonstrate that basin-induced flow control plays a critical role in enhancing tangential momentum transfer to the rotor. Among the investigated cases, a configuration employing a NACA0024 ducted airfoil with a chord size of 35 mm and an axial spacing of 50 mm yielded the highest power coefficient of 0.419 compared to the baseline basin power coefficient of 0.313 and reached a maximum value of 0.736 at higher inlet velocities. Performance analysis based on tip speed ratio (TSR) further identified an optimal low-TSR operating range consistent with gravitational water vortex turbine characteristics. Compared with previous GWVT studies that focus on turbine geometry, the present work highlights the effectiveness of basin wall airfoil design as an impactful strategy for performance enhancement in low-head hydrokinetic applications.

A flexible, lightweight and low-cost enzymatic bioanode was developed using a screen-printed silver conductive transparency sheet for enzymatic biofuel cell (EBFC) applications. In this work, indole was electrochemically polymerized directly onto the conductive substrate to form a polyindole (PIn) matrix capable of simultaneously entrapping glucose oxidase (GOx) and redox mediator vitamin K3 (VK3). The study integrates a disposable transparency-sheet platform with an electroactive PIn network that promotes efficient enzyme immobilization and enhanced electron transfer for glucose bioelectrocatalysis. The synergistic interaction between PIn, VK3 and GOx significantly improved charge-transfer kinetics and stabilized the bioelectrocatalytic interface, resulting in enhanced electrochemical performance. The fabricated PIn/VK3/GOx bioanode exhibited a current density of 1.18 mA cm- 2 in 40 mM glucose solution, demonstrating efficient glucose-dependent electrocatalytic activity. The conductive PIn framework facilitated rapid electron transport between the buried active sites of GOx and the electrode surface, while VK3 acted as an efficient and biocompatible electron shuttle. The bioanode also displayed good electrical stability, semiconducting behavior, and favorable electrochemical characteristics, highlighting its suitability for flexible and wearable bioelectronic applications.

This study proposes a data-driven experimental decision framework to identify the most suitable sustainable supplementary materials for green concrete, aiming to reduce cement usage, industrial waste burden, and environmental impacts in the construction sector. It experimentally evaluates compressive strength, split tensile strength, flexural strength, and ultrasonic pulse velocity (UPV) of green concrete incorporating waste materials including silica fume, GGBS, metakaolin, granite dust, rice husk ash, ceramic waste, marble powder, coconut shell powder, plastic waste, and bottom ash. A hybrid methodology integrating Pearson correlation, Analytical Hierarchy Process (AHP), and k-means clustering was developed to capture complex interrelationships. Correlation-based dependency analysis was incorporated into AHP to generate objective performance weightages, where compressive strength was ranked highest (37%), followed by flexural strength (25%), UPV (22%), and split tensile strength (16%). K-means clustering then categorized materials into best and worst performance groups. The findings revealed silica fume as the most optimal and balanced material, achieving 48.5&#xa0;MPa compressive strength, 4.0&#xa0;MPa split tensile strength, 7.5&#xa0;MPa flexural strength, and 4400&#xa0;m/s UPV, indicating superior structural performance and durability potential. ANOVA confirmed strong statistical distinction between clusters (p&#x2009;<&#x2009;0.0001), validating the robustness of the classification. The main contribution of this work lies in introducing a scalable machine-learning-assisted multi-criteria framework that objectively ranks sustainable cement replacement materials, enabling reliable selection for high-performance green concrete design.

Deep learning has significantly advanced brain-computer interface (BCI) technology. However, most deep learning models operate as black boxes, limiting their clinical applicability and scientific interpretability. This lack of transparency makes it difficult to determine whether predictions are driven by genuine neural activity or artifacts. To address this limitation, we propose Analformer, a novel Transformer-based architecture designed to achieve both high predictive performance and neuroscientific interpretability. The core component of Analformer is an Analytical Patch Embedding module, which employs fixed, non-trainable Morlet wavelet kernels to extract explainable spatio-temporal-frequency features from raw EEG signals. This structure enables standard neurophysiological analyses-including time-frequency analysis, topography, and F-value time-frequency (FTF) analysis-to be derived directly from the model's internal representations. Furthermore, by analyzing attention weights over these interpretable features, Analformer provides attention-based connectivity that may reflect functional relationships between brain regions. We evaluated Analformer on two large public datasets covering three representative BCI paradigms: Motor Imagery (MI), event-related potentials (ERP), and steady-state visually evoked potentials (SSVEP). Experimental results demonstrate that Analformer achieves competitive performance across all paradigms while producing analysis outputs consistent with established neuroscientific findings. These results suggest that Analformer provides a unified framework that bridges high-performance BCI decoding with interpretable, data-driven scientific analysis.

This study presents an advanced PV-enabled heat generation system with precise thermal power regulation for resistive heating applications. Conventional solar thermal systems often rely on direct PV-resistor coupling, which leads to poor energy utilization, or MPPT-based operation, which maximizes electrical extraction but provides limited control over chamber temperature. To address these limitations, the proposed system integrates photovoltaic panels with a high-efficiency synchronous Boost converter and a hybrid power regulation algorithm, enabling stable and controlled thermal power delivery under variable solar conditions. Experimental validation was conducted under several operating scenarios. Under MPPT operation, the converter achieved efficiencies between 86.4% and 88%, but the chamber temperature varied significantly with irradiance, highlighting the lack of thermal control. In a constant-power experiment, the system regulated 100 W despite irradiance fluctuations from 560 W/m2 to 770 W/m2, maintaining efficiencies above 88.6%. The chamber temperature increased from 30.2&#xa0;&#xb0;C to 60-80&#xa0;&#xb0;C, with a heating time constant of approximately 880 s and a cooling time constant of 1100 s. In a staircase power test, the system successfully regulated 50 W, 100 W, and 150 W, with a maximum achievable power of 165 W under available solar conditions. The thermal response exhibited consistent first-order dynamics, yielding a mean thermal resistance of 0.390&#xa0;&#xb0;C/W and a thermal capacitance of approximately 2256 J/&#xb0;C. These results demonstrate stable and controllable PV-powered heat generation suitable for solar dryers and heat chambers.

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Amino acids are the building blocks of proteins and thus among the macronutrients to feed humankind. They have extensive industrial applications as animal feed and food additives like dietary supplements, and flavor enhancers and moreover as chemical precursors. We present the design of a synthetic enzyme cascade system for synthesizing a series of amino acids from methanol. This acts as a case study on how to contribute to the sustainable, renewable energy-based supply of food and feed. The modular nature of the cascade allows for plug-and-play module swapping and fine tuning of enzyme composition to customize the target compound. The one-pot enzymatic systems, capable of utilizing methanol, ammonia, and, partially, carbon dioxide, were employed to synthesize glycine, serine, L-aspartic acid, L-valine, L-glutamic acid, and L-proline. Considering the increasing availability of methanol produced from carbon dioxide through thermocatalytic and even electrocatalytic and photocatalytic processes, methanol represents a key intermediate in future CO2-based value chains. In this context, our study provides a pathway for amino acid synthesis and food and feed production based on methanol and CO2 as carbon building blocks with reduced environmental impact.