Search Results (12,938)

High levels of photovoltaic (PV) generation in distribution networks create substantial uncertainty and voltage variability, which limits the effectiveness of conventional deterministic distribution network reconfiguration (DNR) strategies. In PV-dominated feeders, rare but severe operating conditions may considerably influence active power losses and voltage stability. To address this challenge, this paper proposes a risk-informed optimization framework for DNR that combines reinforcement learning with probabilistic performance assessment. A Deep Q-Network (DQN) agent is designed to support the selection of feasible radial switching configurations by interacting with the distribution network environment. Throughout the learning process, candidate network topologies are evaluated through radial load flow calculations, while a composite objective function incorporating active power losses and voltage deviation steers the agent toward improved configurations. The training stage is based on deterministic performance indices; however, the final reconfiguration solution is assessed under uncertainty to examine its operational robustness. For this purpose, extensive Monte Carlo simulations are performed to capture the stochastic behavior of PV generation and load demand. Tail-based risk metrics, including Value at Risk (VaR) and Conditional Value at Risk (CVaR), are computed for both loss and voltage deviation indices, providing insight into the performance of the selected configuration under unfavorable operating scenarios. The proposed framework is first validated on the IEEE 33-bus distribution system and then further investigated on the IEEE 69-bus network. The obtained results demonstrate that the proposed DQN-based reconfiguration approach can enhance voltage profiles and reduce power losses under high PV penetration. In addition, the probabilistic analysis identifies meaningful trade-offs between efficiency and voltage robustness, highlighting the importance of considering uncertainty-driven risk assessment in computational decision-making for modern active distribution networks. Full article

Natural fibres, both bio-based and mineral-derived, are increasingly investigated for use in mortar as a means of improving technical efficiency while potentially reducing reliance on synthetic fibres where performance and durability are adequate. This review synthesises mortar-focused evidence across cement-based binders, air lime and natural hydraulic lime binders, gypsum-based binders and clay-based binders, with emphasis on mix designs, fibre–matrix interactions, durability-related behaviours and engineering applications. Across binder systems, the most consistently reported benefit of fibre incorporation is improved crack control and post-crack integrity, provided that fibre dispersion, dosage, and workability are adequately controlled. Some formulations also exhibit reduced measured drying shrinkage, whereas changes in compressive and flexural strength are inconsistent, reflecting the effects of fibre type and content, water demand, density, pore structure and matrix–fibre bonding. Durability is strongly binder- and exposure-dependent. For cement-based mortars, alkaline and calcium-rich pore solution remain key limits for many plant fibres, especially under wetting–drying exposure. For lime-based, gypsum-based and clay-based mortars, chemical attack is generally less severe, but performance and property retention remain sensitive to moisture history, curing path and conditioning. Hygrothermal and hygric effects are conditional and should be considered alongside density, moisture state, pore structure and water uptake. Overall, natural fibres are most convincing when crack control, post-crack integrity, compatibility or moisture-related performance are required, rather than for universal strength or durability improvement. For that, further studies and optimisation are needed. Full article

Knowledge tracing (KT) is a core technology in intelligent tutoring systems that predicts students’ future responses by analyzing their historical interaction sequences. Although existing deep learning-based KT models achieve high predictive accuracy, their “black-box” nature severely hinders practical deployment in educational scenarios. To balance predictive performance and interpretability, this paper proposes AMDKT, an interpretable dual-stream Transformer model grounded in the Student Proficiency–Item Competency Matching (SPIM) mechanism. The model employs two parallel Transformer branches to separately model the temporal evolution of student proficiency and the competency demands of each item and defines the discrepancy between their outputs as “proficiency surplus.” A non-negative regularization loss is further introduced to constrain the proficiency surplus to be non-negative for correctly answered samples, thereby embedding pedagogical rules into the optimization objective and ensuring that the model outputs conform to educational cognitive priors. Experiments on five public datasets show that AMDKT achieves AUC performance comparable to the state-of-the-art AKT model, and obtains statistically superior results against DKT, DKVMN, DEEP-IRT, and DIMKT on most datasets, with relatively weaker performance observed on the KDD Cup 2010 dataset. Ablation studies verify the effectiveness of each component, and visualization results demonstrate that AMDKT produces smooth, coherent, and interpretable student proficiency trajectories, providing a fine-grained tool for quantifying individual learning progress. Therefore, AMDKT offers a feasible solution for applications such as weak knowledge point localization, adaptive exercise recommendation, and learning risk warning. Full article

This report focuses on cognitive gaps of the pressure dependence of melting temperature, presenting: (i) a coherent discussion on $T_m\left(P\right)$ behavior for both ‘Hard Matter’ (‘standard’ solid state) and Soft Matter systems, to reveal the importance of the strength of intermolecular interactions; (ii) a model picture linking systems with $d{T}_m/dP>0$, $d{T}_m/dP<0$, and the $T_m\left(P\right)$ curve maximum, which also shows the relevance of the negative-pressure domain; and (iii) the ultimate validation test of the recent ‘universal’ parabolic scaling of $P_m\left(T\right)$ against empirical data. The evidence for ‘Hard Matter’ focuses on systems relevant to the semiconductor industry: silicon, germanium, and gallium nitride. For Soft Matter, these include unique polymeric systems and liquid-crystalline pentylcyanobiphenyl (5CB). For the latter, symmetry-selected melting/freezing takes place. Finally, the report presents the specific case of graphite and diamond. The report also presents an innovative solution for melting-temperature and pressure detection. Full article

Protein–ligand interactions are fundamental to physiological processes and drug discovery. Based on the types of information on protein–ligand interactions provided by nuclear magnetic resonance (NMR) experiments, these NMR experiments can be categorized into three distinct classes: (i) molecular-level qualitative binding detection, (ii) residue-level mapping of binding interfaces, and (iii) atomic-level structure determination and conformational dynamics of protein–ligand complexes. This hierarchy enables a workflow that accelerates the progression from initial binding identification to structural and dynamic characterization. In this review, we discuss how these experiments characterize molecular recognition. Notably, the term “ligand” in this article refers exclusively to small molecules. Full article

This study presents a systematic literature review of spatial planning frameworks for coastal hazard mitigation, conducted in accordance with the PRISMA 2020 guidelines. A structured search of two academic databases, Scopus and Web of Science, covering the period 2016 to 2026 identified 368 records, of which 27 peer-reviewed studies were included in the final synthesis. The review pursues four interrelated objectives: analyzing global publication trends in research on spatial planning for coastal hazard mitigation; identifying and describing coastal hazard typologies and their associated mitigation approaches; examining how spatial planning frameworks are integrated with hazard mitigation strategies; and synthesizing cross-cutting constructs that structure an integrative analytical model. Findings reveal a marked intensification of scholarly output over the final third of the review period, with Asia–Pacific and Europe as the most represented regions, while sub-Saharan Africa and small island developing states remain critically underrepresented. Coastal hazards are classified into four categories: slow-onset climate and hydro-geological processes, acute hydro-meteorological events, multi-risk systemic hazard interactions, and ecological and environmental degradation. Three analytically distinct integration families are identified: geospatial and technical modeling, NbS and ecosystem-planning integration, and participatory governance and institutional integration. From these, four cross-cutting constructs emerge inductively across the evidence base, namely spatial risk assessment and geospatial modeling, land-use governance and climate-proof planning, community-based resilience and participatory governance, and ecosystem-based adaptation and nature-based solutions, which together constitute an integrative analytical framework. The discussion demonstrates that governance capacity, rather than technical sophistication, is the primary moderator of implementation effectiveness, and that socio-spatial equity in hazard planning represents a cross-regional challenge irrespective of governance capacity level. Limitations include the geographic concentration of the evidence based in high-capacity planning contexts and the predominantly projected rather than implemented nature of effectiveness evidence. Full article

Background: Manure-amended agricultural soil is a critical reservoir of antibiotic resistance genes (ARGs), posing escalating threats to environmental health and food safety. However, the temporal trajectories of ARG prevalence throughout the complete reproductive cycle of cash crops, and their mechanistic linkages with fertilization regimes and microbial community succession, remain inadequately understood. Methods: To bridge this knowledge gap, we conducted an in situ field experiment over the entire growth period of Chinese cabbage at a long-term manure-amended farm in Tianjin, China. Six contrasting fertilization strategies were evaluated: unfertilized control (CK1), unfertilized baseline control (CK2), traditional full-rate combined manure–chemical fertilization (TF), traditional half-rate combined manure–chemical fertilization (T1), half-dose sole manure fertilizer (T2), and half-dose sole chemical fertilizer only (T3). Results: Our results demonstrated that ARG abundance and associated mobile genetic elements (MGEs) exhibited a pronounced transient surge immediately post-fertilization, yet reverted to baseline levels by harvest, revealing a tangible resilience of the soil resistome. Notably, the optimized half-organic fertilization (T2) effectively curtailed the proliferation of manure-derived pathogenic taxa while preserving beneficial keystone phyla (e.g., Acidobacteria and Proteobacteria), indicating a trade-off between nutrient provisioning and ecological filtering. Co-occurrence network analysis further identified MB-A2-108, Saccharimonadales, and Rokubacteriales as pivotal hosts for multidrug-resistant ARGs, underscoring that microbial interspecific interactions—rather than taxonomic richness alone—are the primary drivers of resistome succession. Quantitative risk assessment confirmed that the T2 regimen reduced the composite ARG contamination index (CF_zone) by 25% relative to conventional full fertilization (TF), while maintaining comparable cabbage yields. Conclusions: Collectively, our findings advocate for precision organic fertilization as a nature-based solution that synchronizes nutrient supply with crop demand, curtails ARG propagation, and mitigates long-term agroecological risks. Full article

Three zinc(II) complexes based on 1-phenyl-3-methyl-4-acyl-5-pyrazolone were synthesized and characterized by elemental analysis, by X-ray crystallography and spectroscopy (UV-vis, fluorescence and IR). Crystallographic studies reveal that the complexes have a mononuclear structure in the solid state; however, intermolecular interactions combine the complexes into a 1D polymer chain. The complexes exhibit weak luminescence in the polycrystalline state and moderate emission in solutions and thin amorphous films. Thin films of the studied compounds were deposited on a glass substrate using thermal vacuum spraying, spin coating, and Langmuir-Blodgett technology and were studied using atomic force microscopy (AFM), X-ray spectroscopy, and fluorescence spectroscopy. Full article

This study investigates machine learning techniques for predicting the behavior of proton exchange membrane electrolyzers, which are vital for sustainable hydrogen production. This work addresses these challenges by integrating artificial neural networks to develop predictive models capable of capturing the performance of proton exchange membrane electrolyzers with high accuracy. This research utilizes a multi-layer perceptron network architecture, optimized through rigorous data preprocessing, parameter tuning, and error minimization strategies. The dataset used was based on published PEME numerical simulation datasets and encompasses key performance indicators, including stack voltage, water transport, and electrochemical reactions. The trained artificial neural networks models achieved mean squared error values of 3.66 × 10⁻⁵ and 9.75 × 10⁻⁶, with correlation coefficients of 0.99996 and 0.99958, demonstrating near-perfect predictive accuracy. A comparative benchmarking study against alternative regression algorithms revealed that the proposed MLP models significantly outperformed Gradient Boosting and Random Forest by several orders of magnitude, thereby establishing a higher level of persuasiveness and reliability for the developed framework. Average deviation rates of 0.11% and −0.01% further validated model reliability. The novelty of this work lies in its comprehensive approach, which goes beyond isolated metrics by addressing interactions across system parameters. This integrated framework enables enhanced prediction, control, and optimization of proton exchange membrane electrolyzer’s performance, setting a new benchmark for leveraging machine learning in hydrogen energy systems. These findings pave the way for scalable, cost-effective solutions to improve proton exchange membrane electrolyzers’ efficiency and operational reliability. Full article

This paper presents a mathematical model to investigate how waves and currents interact with a flexible perforated floating membrane in finite water depth within the framework of linear wave theory. The perforated flexible membrane is modeled based on Darcy’s law and the one-dimensional string equation. The complex dispersion relation in the presence of current velocity is derived from the Green’s function technique using a fundamental source potential solution. The dispersion curve is analyzed by comparing the phase and group velocities for different water depths. Further, a physical model associated with the effect of current on a moored finite floating perforated flexible membrane integrated with a vertical wall is formulated. Then, the theoretical solution of a physical boundary value problem near a vertical rigid wall is obtained using the matching technique and the roots of the dispersion relation derived from the Green’s function technique. Numerical simulations are provided to verify the convergence of the series solution and the accuracy of the obtained analytical findings are evaluated against previously published analytical and experimental datasets. Further, several numerical results on the membrane deflection, hydrodynamic coefficients, and horizontal force on the wall for various structural parameters, mooring stiffness, and current velocities are analyzed. It is observed that the present analysis with this perforated membrane breakwater will be helpful to coastal and marine engineers to understand the influence of current velocity. Full article

Wind is an important abiotic factor that may influence predator–prey interactions. In this paper, we propose and analyze a stochastic predator–prey model in which the predator attack rate is described by a unimodal function of a fixed wind-intensity index. We establish the global existence, uniqueness, and positivity of solutions, together with stochastic ultimate boundedness, and derive a sufficient condition for the existence of a unique ergodic stationary distribution. A key analytical feature is that the Foster–Lyapunov recurrence argument is completed without quadratic predator self-limitation by exploiting the negative contribution generated by linear predator mortality. The resulting condition $\lambda >0$ incorporates effective predation, density dependence, wind modulation, predator mortality, and population-level environmental noise, and is a sufficient rather than necessary condition. Numerical parameter sweeps based on the complete expression for $\lambda$ and simulations using a logarithmic Euler–Maruyama scheme illustrate parameter-specific changes in the sufficient-condition quantity and in the post-burn-in empirical population distributions. Full article

Flexible pressure sensors serve as core sensing components for wearable health monitoring systems, electronic skins for soft robots, and flexible human–machine interaction devices. Benefiting from the interfacial electric double-layer polarization effect, iontronic sensing delivers far higher pressure response sensitivity than conventional parallel-plate capacitive sensors, endowing it with distinctive advantages in the detection of weak physiological signals. Nevertheless, current dense ionic thin-film dielectric layers suffer from limited deformation space under compression and poor low-pressure sensing capability. Mainstream high-precision micropillar arrays are fabricated via photolithography, 3D printing, and metal etching molds, which require costly equipment and complicated fabrication procedures, making large-area mass production unfeasible. Random frosted concave-convex microstructures feature disordered dimensions, leading to severe device hysteresis and narrow linear ranges, which fail to achieve ultrahigh sensitivity alongside a wide pressure detection range simultaneously. To address the aforementioned multiple bottlenecks, this paper proposes a low-cost resin template replication process to fabricate TPU-based ionic thin-film dielectric layers with ordered micropillar array microstructures. Combined with inkjet-printed silver conductive PI flexible electrodes, an iontronic flexible pressure sensor with a sandwich layered structure is constructed. Multi-dimensional investigations including microscopic morphology characterization, electromechanical sensing performance calibration, and human wearable application tests are systematically implemented to thoroughly elucidate the synergistic enhancement mechanism of the arrayed micropillars. Test results demonstrate that the effective pressure detection range of the sensor spans 0–1038 kPa, accommodating ultra-low pressures such as pulse signals as well as medium-to-high-pressure loads including joint bending. The sensitivity reaches 23.27 kPa⁻¹ within the low-pressure range of 0–200 kPa and remains stable at 3.52 kPa⁻¹ in the high-pressure range of 200–1038 kPa, with piecewise linear fitting correlation coefficients of 0.93 and 0.96 respectively. Both the response time and recovery time of the device are 40 ms, and the hysteresis error throughout the loading-unloading cycle is merely 2.62%. After 20,000 consecutive cyclic loading-unloading tests, the peak capacitance output only decays by 5.1%, verifying outstanding mechanical fatigue resistance and electrical stability. Validations in multi-scenario applications prove that the sensor can accurately capture human physiological and motion signals including radial artery pulses, laryngeal deformation induced by multi-syllable vocalization, and multi-angle bending of fingers and elbow joints, suitable for home-based health monitoring, quantitative rehabilitation training, flexible tactile interaction and other scenarios. The entire fabrication process eliminates high-precision micro-nano processing equipment such as photolithography systems, plasma etchers and 3D printers; only general chemical raw materials and conventional laboratory instruments are adopted. The reusable templates enable low manufacturing costs and large-area coating forming, offering a novel low-cost technical solution for the engineering implementation and industrialization of high-performance iontronic flexible pressure sensors. Full article

Underground coal mining faces coupled hazards from respirable coal dust and spontaneous coal combustion. This study developed a dual-function flame-retardant dust suppressant comprising carboxymethyl cellulose (CMC), polycarbodiimide (PCDI), ammonium polyphosphate (APP), and zinc borate (ZB). A four-factor, three-level orthogonal design was used to screen formulations by penetration depth, followed by rheological, water-scour, simulated-roadway, temperature-programmed oxidation, contact-angle, Fourier Transform Infrared Spectroscopy (FTIR), and Scanning Electron Microscope (SEM) analyses. CMC and PCDI significantly affected penetration, whereas APP and ZB showed no significant effects within the tested ranges. The selected formulation (1% CMC, 12% APP, 3.5% ZB, and 1.5% PCDI) showed stable viscosity development and the lowest mass loss under repeated water scour. In simulated-roadway tests, the stock solution achieved an average dust-suppression efficiency of 59.7%. At 170 °C, a 10% treatment reduced CO release by 40.0% and increased the mean apparent activation energy of coal oxidation by 29.73%. Rapid wetting, intermolecular interactions, and formation of a continuous porous crosslinked film supported dust consolidation and oxidation inhibition. The developed material therefore offers a potential integrated approach for controlling coal dust and spontaneous combustion risks in underground mines. Full article

Wearable assistive exoskeletons require lightweight, compact, and backdrivable transmission systems with low output impedance to ensure safe and comfortable human–robot interaction. These efficient, modular actuators benefit from reduction mechanisms that minimize axial bulk while providing high motion regularity. While existing transmissions perform well on some of these metrics, their practical implementation is often constrained by geometric complexity, low backdrivability, limited reduction ratios, or standard component sizes. This paper presents a novel combination of a Rolling Element Eccentric (REE) stage and a planetary gearbox, specifically designed for wearable exoskeleton actuation. The proposed architecture integrates a bearing-based REE drive concentrically within the sun gear of a planetary transmission, reducing mechanical complexity and friction and improving regularity. Moreover, the design exploits additively manufactured bearings, enabling substantial weight reduction, reduced encumbrance, and increased design freedom without reliance on standard bearing dimensions. A prototype reducer has been designed and fabricated using additive manufacturing techniques. It was experimentally evaluated and compared with state-of-the-art transmission designs. These investigations demonstrated low friction, minimal backlash, good torsional stiffness, and sufficient backdrivability, despite the high reduction ratio, while maintaining a compact, flat form factor. The experimental results indicate that the proposed rolling element eccentric planetary transmission is a viable and effective solution for lightweight, efficient, axially compact (independently of the implemented reduction ratio), and backdrivable actuators in assistive wearable robotics. Full article

Enterotoxigenic Escherichia coli K88 (ETEC-K88) is a primary causative agent of post-weaning diarrhea in piglets, yet most in vitro infection models evaluate epithelial injury at a limited number of endpoint time points, failing to capture the temporal dynamics of host–pathogen interactions. Here, we established a time-resolved ETEC-K88 challenge model in IPEC-J2 cells from 0 h to 48 h. The data showed that ETEC-K88 adhesion significantly increased after 4 h and peaked after 24 h, with a critical response transition at 4–8 h characterized by coordinated changes in mRNA expression of tryptophan metabolism, tight junction, aquaporins, and Solute Carrier Transporters (SLC). Additionally, we also evaluated the protective effects of L-tryptophan supplementation in IPEC-J2 with ETEC-K88 infection and found that its addition significantly restored the disrupted gene expression related to tryptophan metabolism, transporter channels, aquaporins, and cell cycle. Finally, two different mouse strains, C57BL/6J and BALB/c mice, were challenged with ETEC-K88 to assess strain- and segment-specific intestinal responses. Although overt diarrhea was not clearly induced in mice, the ETEC-K88 fimbria receptor genes were induced in both mice strains. Additionally, both mice strains exhibited obvious intestinal histomorphological abnormalities and showed the strain- and segment-specific expression of intestinal stem cell marker genes (Lgr5, SOX9), goblet cell marker gene TFF3, and aquaporin genes. In conclusion, we have defined a temporal epithelial response framework for ETEC-K88 infection in IPEC-J2 cells and mice, providing a theoretical basis for developing nutritional strategies against ETEC-associated intestinal dysfunction in pig production. Full article