Search Results (6,432)

Background/Objectives: Leishmaniasis is a group of diseases caused by obligate intracellular parasites of the Leishmania genus and is classified by the World Health Organization as a category I neglected tropical disease. Leishmania infantum predominantly affects children under five years of age and shows an increasing incidence of cutaneous and visceral forms. The development of new therapeutic alternatives remains challenging, making in silico approaches valuable for accelerating antileishmanial drug discovery. This study aimed to identify new compounds with potential activity against Leishmania infantum amastigotes using artificial intelligence-based classification models. Methods: A curated database of compounds with reported biological activity was constructed. Molecular representation employed zero- to two-dimensional descriptors calculated with Dragon software (v 7.0.10). Unsupervised k-means cluster analysis was applied to define training and external prediction sets. Supervised models were developed on the WEKA platform using IBk, J48, multilayer perceptron, and sequential minimal optimization algorithms. Model performance was assessed through internal cross-validation and external validation procedures. Results: All models achieved classification accuracies above eighty percent for both training and prediction sets, indicating consistent predictive performance and good generalization ability. The validated models were applied to virtual screening of the DrugBank database and a collection of synthetic compounds. This screening campaign enabled the identification of one hundred twenty compounds with potential activity against the amastigote form of Leishmania infantum. Conclusions: Artificial intelligence-based QSAR models proved to be useful tools for prioritizing antileishmanial candidates. The integration of molecular descriptors, machine learning, and virtual screening offers an efficient strategy for drug discovery. Full article

Wine bottle aging is governed by complex redox reactions involving phenolic compounds, oxygen transfer and storage conditions, which collectively determine the evolution of wine composition and sensory properties. This review critically examines the main oxidative mechanisms responsible for bottle aging and evaluates traditional and emerging strategies aimed at modulating the evolution of wine. Particular attention is paid to oxygen management, cork type, temperature and light exposure, as well as alternative approaches such as accelerated aging techniques and underwater storage. The available evidence suggests that most accelerated aging technologies fail to replicate the chemical pathways of natural in-bottle aging, often resulting in different aromatic profiles. Attention is paid to underwater aging, an emerging practice that combines specific conditions of temperature, light and limited oxygen availability. The results of the available studies indicate that underwater aging does not significantly alter the basic chemical parameters of wine, but can modulate its phenolic, chromatic and sensory evolution, suggesting a slowdown in oxidative processes compared to traditional aging in the cellar. Full article

Satellite remote sensing has become essential for water quality assessment across inland and coastal environments, with rapid improvements in recent years. Significant advances have been made in detecting optically active parameters (such as chlorophyll-a, suspended matter, and turbidity), showing consistently strong performance across multiple studies. Specifically, the median validation performance (R²) derived from the quantitative synthesis indicates R² = 0.82 for chlorophyll-a (interquartile range—IQR: 0.75–0.90), R² = 0.80 for total suspended matter (IQR: 0.78–0.85), and R² = 0.88 for turbidity (IQR: 0.85–0.90). Conversely, the retrieval of optically inactive parameters (such as nutrients like total phosphorus and total nitrogen) remains more context dependent. It exhibits moderate, more variable results, with median R² = 0.68 (IQR: 0.64–0.74) for total phosphorus and R² = 0.75 (IQR: 0.70–0.80) for total nitrogen. These findings clearly illustrate the varying success of retrievals of optically active and inactive parameters and underscore the inherent difficulties of indirect estimation methods. However, high reported accuracy has yet to translate into transferable, uncertainty-informed, and operational monitoring systems. This gap stems from structural issues in validation design, physics integration, uncertainty management, and multi-sensor compatibility rather than data limitations alone. We present a PRISMA-guided, distribution-aware quantitative synthesis of 152 peer-reviewed studies (1980–2025), based on a systematic search protocol, to evaluate satellite-based retrievals of both optically active and inactive parameters. Instead of simply averaging performance, we analyse the empirical distributions of validation metrics, considering the validation protocol, sensor type, parameter category, degree of physics integration, and uncertainty quantification. The synthesis demonstrates that validation strategy often influences reported results more than the algorithm class itself, with accuracy inflated under non-independent cross-validation methods and notable variability between studies concealed by mean-based reports. Across four decades, four persistent structural challenges remain: limited transferability across sites and sensors beyond calibration areas; weak or implicit physical integration in many data-driven models; lack of or inconsistency in uncertainty quantification; and fragmented multi-sensor harmonisation that restricts operational scalability. To address these issues, we introduce two evidence-based coding frameworks: a physics-integration taxonomy (P0–P4) and an uncertainty-quantification hierarchy (U0–U4). Applying these frameworks shows that most studies remain focused on low-to-moderate levels of physics integration and primarily consider uncertainty at the prediction stage, with limited attention to upstream sources throughout the observation and inference process. Building on this structured synthesis, we propose a transferable, physics-informed, and uncertainty-aware conceptual framework that links model architecture, validation robustness, and probabilistic uncertainty to well-founded design principles. By shifting satellite water quality modelling from isolated algorithm demonstrations towards integrated, evidence-based system design, this study promotes scalable, decision-grade environmental monitoring amid the accelerating impacts of climate change. Full article

Controlling radical selectivity within nanoreactors remains a formidable challenge due to the inherent high reactivity and short half-lives of reactive species. Herein, we report a novel size-matched nanoconfinement strategy using a cobalt-nickel-layered double hydroxide (CoNi-LDH) nanoreactor for the highly selective generation and stabilization of sulfate radicals (SO₄^∙−) via piezoelectric activation of peroxymonosulfate (PMS). By precisely tailoring the LDH interlayer spacing to 5.27 Å to match the kinetic diameter of SO₄^∙−, the nanoreactor effectively suppresses non-selective side reactions and radical quenching. Consequently, the CoNi-LDH achieves an unprecedented reaction rate (k_obs = 0.40 min⁻¹) and superior defluorination efficiency (78.9%) for fluoroquinolone antibiotics, significantly outperforming non-size-confined counterparts. Mechanistic insights reveal a synergistic pathway where piezo-generated hot electrons, mediated by Ni sites, accelerate the Co²⁺/Co³⁺ redox cycle to ensure long-term catalytic stability. The robustness of this nanoconfined system is further demonstrated by its exceptional tolerance to complex water matrices and its practical operability in a continuous-flow reactor. This study provides a pioneering approach for spatial radical control at the nanoscale to achieve efficient and targeted environmental remediation. Full article

The present paper proposes an adaptive steering weight allocation strategy based on a non-cooperative Stackelberg game and Model Predictive Control (MPC) for dynamic steering authority allocation in human–machine shared control of intelligent vehicles. First, the human–machine steering interaction is modelled as a Stackelberg game, and the steering control problem is formulated as an MPC optimization problem. The optimal control sequences of the driver and the Advanced Driver Assistance System (ADAS) under game equilibrium are then derived through backward induction. Subsequently, driver behaviour is classified as aggressive, moderate, or conservative according to lateral preview error and lateral acceleration, and the driver state is quantified using parametric indicators. Furthermore, by integrating potential field-based driving risk assessment with human–machine conflict intensity, a fuzzy logic-based dynamic weight adjustment mechanism is constructed. Simulation results show that when the steering intentions of the driver and the ADAS are highly consistent, the proposed strategy can effectively reduce driver workload and improve driving safety. In high-risk driving situations, the strategy automatically transfers more steering authority to the ADAS to enhance safety, whereas under low-risk conditions with strong human–machine steering conflict, greater driver authority is preserved to ensure that the vehicle follows the intended path. Hardware-in-the-loop experiments in lane-changing assistance scenarios further verify the effectiveness of the proposed strategy under different driving styles. Quantitative results show that, compared with manual driving, the proposed strategy reduces the maximum lateral overshoot by 98.75%, 85.54%, and 98.58% for aggressive, moderate, and conservative drivers, respectively. In addition, the peak yaw rate and driver control effort are significantly reduced, indicating smoother vehicle dynamic response and lower steering workload. These results demonstrate that the proposed strategy can effectively improve lane-change stability, reduce driver burden, and maintain safe and coordinated human–machine shared control. Full article

Repairing maxillofacial bone defects remains a major clinical challenge due to inadequate vascularisation and poor integration with host tissue. While bioactive scaffolds have shown promise in supporting osteogenesis and angiogenesis, achieving robust and synchronised dual regenerative outcomes is still elusive. This study presents a multifunctional, cell-free magnetic hydrogel platform designed to biomimetically coordinate osteogenic and angiogenic processes for effective maxillofacial bone regeneration. The composite poly(vinyl alcohol)-vaterite (PVA-Vat) hydrogel scaffold incorporates tuneable magnetic nanoparticles (MNPs) composed of single-domain superparamagnetic iron oxide (Fe₃O₄). By harnessing magneto-mechanical cues to orchestrate bilateral communication between human bone mesenchymal stem cells and endothelial cells, this platform provides a deeper mechanistic understanding of coupled tissue regeneration and delivers superior dual-regenerative performance for maxillofacial bone repair. Under magnetic stimulation, a coculture system demonstrated strong osteogenesis-angiogenesis coupling mediated by reciprocal VEGFA-BMP2 signalling. This reciprocal crosstalk was evidenced by a synergistic amplification of VEGFA and BMP2 expression in coculture compared to monocultures, where MNP-stimulated osteoprogenitors secreted VEGFA to drive endothelial capillary-like network formation, while endothelial cells reciprocally enhanced endogenous BMP2 levels to accelerate osteoblastic mineralisation. These findings establish MNP-integrated hydrogels as a cell-free, multifunctional platform capable of synchronising dual regenerative pathways, offering a biomimetic strategy to overcome vascularisation and integration barriers in maxillofacial bone repair. Full article

This study investigated the influence of hyperparameter selection within an EEMD–AR–SVM framework for bearing fault diagnosis under constant- and variable-speed operating conditions. Two preprocessing configurations, namely, Method 1, in which EEMD was applied after segmentation, and Method 2, in which EEMD preceded segmentation, were evaluated under three rotational regimes—constant speed, acceleration (Test A), and deceleration (Test B)—while number of Intrinsic Mode Functions (N), autoregressive model order (L), and segment length were systematically varied towards identifying combinations that maximized classification accuracy. The results showed the methods achieved 100% accuracy under constant-speed operation. However, Method 2 consistently outperformed Method 1 under nonstationary regimes, reaching 94.12% accuracy during acceleration and 95.00% during deceleration. The outer race remained the most challenging fault type, although its separability substantially improved when EEMD was performed prior to segmentation. The findings demonstrated, in a clear and interpretable manner, that the empirical choice of N and L directly affects classifier accuracy in stationary and nonstationary scenarios and the order of preprocessing steps plays a decisive role in diagnostic reliability. Such contributions provide a reproducible methodological basis for advancing vibration-based fault diagnosis and support the development of interpretable, high-performance predictive maintenance strategies for industrial environments. Full article

As large language models (LLMs) pursue higher accuracy, their model sizes have surged, substantially increasing GPU memory consumption. Prior work mitigates this issue by distributing the memory burden across multiple GPUs. However, on clusters interconnected via Ethernet, the resulting computational intensity is insufficient to hide the significant network latency. Achieving a favorable compute-to-communication ratio is further constrained by the memory required to cache the massive activations generated during the forward pass. PyAO, proposed in this paper, effectively offloads activations, selects offloading strategies based on their offloading efficiency, and minimizes data-movement bottlenecks, thereby enabling larger micro-batch sizes. In Ethernet-interconnected cluster environments, experiments on popular models—including OPT-1.3B, GPT-0.8B, and Llama-1.2B—demonstrate that PyAO reduces peak GPU memory by up to 1.94× at the same micro-batch size, enables up to 2.5× larger batch sizes, and accelerates training by up to 3.63× relative to the baseline. Full article

This study evaluated thermal and thermo-alkaline post-treatment of digested cattle manure (DCM) as a strategy to increase methane recovery and improve the flexibility of biogas systems within hybrid renewable energy alternatives. A 10 L mesophilic CSTR was operated for 311 days, producing lignin-rich digestate that was subjected to a statistically designed range of post-treatment conditions varying temperature (50–90 °C), pH (8–12), and contact time (6–24 h). Biomethane potential assays and lignocellulosic fractionation were used to determine changes in solubilization, biodegradability, and methane production kinetics. Thermal treatment provided modest improvements, reaching 84 mg SCOD g⁻¹ PCOD solubilization and a 26 mL CH₄ g⁻¹ COD increase in methane yield. Thermo-alkaline treatment produced substantially higher enhancements, with the most severe condition (90 °C-pH 12–24 h) achieving 493 mg SCOD g⁻¹ PCOD solubilization, 66% removal of structural carbohydrates, and a 60.2 mL CH₄ g⁻¹ COD increase in methane yield, corresponding to a 16% rise in biodegradability and a twofold increase in methane production rate. Gompertz modeling indicated accelerated kinetics and minimal lag time. A strong linear correlation (R² = 0.90) between severity index and solubilization supported predictable scalability. These results demonstrate that thermo-alkaline hydrolysis can significantly enhance post-digestion methane recovery and strengthen the role of agricultural biogas in integrated renewable energy systems. The techno-economic analysis revealed that, despite higher operating costs for thermo-alkaline post-treatment than for the control, the main drivers are chemical costs and the price of renewable energy, and thus the application of post-treatment as a sustainable solution for animal manure treatment will likely improve as renewable energy prices increase in the future. Full article

Residential air conditioning systems are a major contributor to household electricity consumption in tropical regions, where environmental factors such as climate variability and particulate pollution (PM10) can further increase cooling demand and accelerate equipment degradation. This study proposes an Enhanced Deep Q-Network (Enhanced DQN) for energy-efficient and maintenance-aware control of residential split-type air conditioners under dynamic environmental conditions. The proposed method integrates several stability-oriented reinforcement learning mechanisms, including Double Q-learning, a dueling architecture, prioritized experience replay, multi-step returns, Bayesian-style regularization via Monte Carlo dropout, and entropy-aware exploration. The framework is evaluated through a two-stage process consisting of a diagnostic benchmark on LunarLander-v3 to assess learning stability, followed by a realistic 365-day simulation driven by Thai weather and PM10 data. Compared with a fixed 25 °C baseline, the proposed controller reduced annual electricity consumption from 5116.22 kWh to as low as 4440.03 kWh, corresponding to a saving of 13.22%. The learned policy also exhibited environmentally adaptive behavior under high PM10 conditions, indicating maintenance-aware characteristics. These findings demonstrate that reinforcement learning can provide robust, adaptive, and sustainable control strategies for residential cooling systems in tropical environments. Full article

Episodic future thinking (EFT) has been confirmed as a promising cognitive intervention for enhancing prospective memory (PM), yet emerging evidence suggests its effects may depend on the specificity of induction. The current study investigated this issue by dichotomizing EFT into two distinct methods: specific (researcher-guided detailed mental simulations) versus non-specific (participants’ self-guided imagination), implemented through differentially structured future thinking instructions. We also analyzed the distinct cognitive strategies mainly employed under each EFT condition based on the Dynamic Multiprocess Framework. The latent profile analysis (LPA) was further conducted to characterize individual variability in responsiveness to EFT manipulations. Behavioral results revealed comparable PM accuracy improvements across both EFT methods relative to the control group; moreover, specific EFT uniquely accelerated response times for both PM and ongoing task execution. The LPA further identified three distinct EFT response patterns—self-competent, proactive, and reactive—each exhibiting unique state-dependent cognitive characteristics. These findings provide a refined understanding of the EFT-PM relationship: (1) specific EFT facilitates more automatic retrieval of PM intentions, whereas non-specific EFT predominantly engages strategic monitoring; (2) individual differences in baseline mental images influence the effectiveness of EFT methods, suggesting the potential benefits of personalized intervention approaches for PM enhancement. Full article

The development of high-performance Fortran code for large-scale scientific simulations is inherently challenging: direct Fortran implementation demands substantial expertise in numerical methods, optimization and system architecture. Manual derivation of numerical schemes is error-prone and time-consuming. This paper advocates a four-stage development methodology involving Python prototyping and symbolic derivation. Systematic validation at each step of incremental transition from symbolic specification to Fortran code produces numerically correct maintainable code faster than by direct manual implementation without sacrificing the resultant performance or code quality. Large Language Models effectively accelerate Python prototyping and boilerplate generation but require rigorous verification of the generated Fortran code. We suggest practical implementation guidelines including validation strategies. Python prototyping and symbolic code generation provide effective instruments for developing efficient production-ready Fortran implementations. Full article

Resource-constrained IoT systems face a fundamental conflict between cryptographic security and energy efficiency, particularly in critical infrastructure monitoring requiring long-term autonomous operation. This study presents a hybrid framework integrating signal-adaptive compression with hardware-accelerated authenticated encryption to resolve this trade-off. The Dynamic Payload Compression with Selective Encryption framework classifies sensor data into three SNR regimes and applies adaptive compression strategies: 24.15-fold compression for low-SNR backgrounds, 1.77-fold for transitional states, and no compression for high-SNR leak detection events. Experimental validation using 2714 acoustic sensor samples demonstrates 5.91-fold average payload reduction with 100% detection accuracy. The integration with STM32L5 hardware AES acceleration reduces power–data correlation from 0.820 to 0.041, increasing differential power analysis attack complexity from 500 to over 221,000 required traces. Compression-induced timing variance provides additional side-channel masking, burying cryptographic signals beneath a 0.00009 signal-to-noise ratio. Projected on 19,200 mAh lithium thionyl chloride batteries, the system achieves 14-year operational lifetime under realistic duty cycles, exceeding industrial requirements for critical infrastructure protection while maintaining robust security against physical attacks. Full article

Artificial intelligence (AI) has become a transformative tool in the field of molecular biosensing, enabling data-driven optimization in sensor design, signal processing, and real-time monitoring. AI promotes the discovery of biomarkers, the design of high-affinity receptors, and the rational engineering of sensing materials, thereby enhancing sensitivity, specificity, and detection accuracy. In the development of biosensors, AI-assisted strategies have accelerated the identification of novel molecular targets, guided the design of proteins and aptamers with enhanced binding performance, and optimized plasmonic and nanophotonic structures through forward prediction and inverse design frameworks. The integration of artificial intelligence has significantly enhanced the performance of various biosensing platforms, including optical, electrochemical, and microfluidic biosensors. It also enabled automatic feature extraction, noise reduction, dimensionality reduction, and multimodal data fusion, overcoming the challenges posed by complex signals, environmental interference, and device variations. These capabilities are particularly crucial for wearable molecular biosensors, as low signal strength, motion artifacts, and fluctuations in physiological conditions impose strict requirements on robustness and real-time reliability. This review systematically summarizes the latest advancements in AI-assisted molecular biosensors, highlighting representative sensing strategies and algorithms for wearable and real-time monitoring, and discusses the current challenges and future development opportunities of intelligent biosensing technologies. Full article

Molecular docking is a foundational technique in computational drug discovery, widely used to generate binding hypotheses, prioritize compounds, and support target-selectivity studies. The continued growth of open-source docking resources, together with improvements in scoring functions, sampling strategies, and hardware acceleration, has substantially lowered barriers to teaching, early-stage hit identification, and reproducible research. Beyond standalone docking engines, the open-source ecosystem now encompasses browser-accessible tools, preparation and analysis utilities, integrative modeling platforms, and AI-augmented methods for pose prediction, rescoring, and virtual screening. These developments have made docking workflows more accessible, customizable, and transparent across diverse research settings. This review examines open-source docking from a workflow-centered perspective, spanning study design, structural-data acquisition, binding-site definition, receptor and ligand preparation, docking execution, and post-docking validation. It further evaluates how open AI methods are being incorporated into these stages to expand structural coverage, improve screening efficiency, and support contemporary structure-based drug design. Collectively, this review outlines a practical and evidence-based framework for the effective use of open-source docking and virtual-screening pipelines in modern drug discovery. Full article