Search Results (1,719)

Accurately quantifying vertical sediment transport rates in large seaward rivers is vital for estimating basin-scale water and sediment fluxes and assessing riverbed evolution. Traditional multi-point velocity and suspended sediment concentration (SSC) measurements are costly and slow, hindering long-term online monitoring. Bidirectional flows in tidal reaches further exacerbate this challenge. We propose a physics-constrained support vector machine (SVM) inversion method to estimate vertical sediment transport rates from single-point measurements. Constrained by modified logarithmic velocity and Rouse suspended sediment concentration profiles, it quantitatively relates single-point hydraulic variables to key parameters governing vertical distributions. Lower Yangtze River tidal reach field data validate the hybrid model’s successful reconstruction of vertical distributions. It accurately captures transient sediment responses across maximum flood and ebb. Inverted transport rates match measurements closely (RMSE = 0.085, NSE = 0.969, PBIAS = 2.50%) and exhibit strong cross-site generalization. Sensitivity analysis identifies 0.4 times the water depth above the riverbed as the optimal single-point sensor position. Although currently validated only in the lower Yangtze River, this low-cost, reliable method supports local basin management, flood control, and disaster mitigation by enabling continuous sediment flux monitoring. However, applying it to other river or estuarine systems may require recalibration or retraining to adapt to different local conditions. Full article

Accurate prediction of a ship’s Estimated Time of Arrival (ETA) is of great significance for port scheduling, logistics management, and navigation safety. Traditional ETA prediction approaches often rely on manual experience for parameter tuning, which tends to be inefficient and susceptible to subjective factors. To address this issue and improve prediction accuracy, this study proposes a hybrid modeling framework, integrating Bayesian Optimization (BO), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks. In this approach, Automatic Identification System (AIS) data is leveraged to predict the total voyage duration before departure, thereby deriving the vessel’s ETA. The model, referred to as BO-CNN-LSTM, utilizes BO for automatic hyperparameter tuning, employs CNN for extracting local features, and applies LSTM network to capture temporal dependencies. The model is developed using a dataset of 32,972 distinct voyage records, among which 23,947 are retained as valid samples after data cleaning. Pearson correlation analysis is conducted to select key input variables, including navigation speed, ship type, sailing distance, and deadweight tonnage. Additionally, sailing distance is processed using the Ramer–Douglas–Peucker algorithm. Experimental evaluation indicates that the BO-CNN-LSTM model achieves a coefficient of determination of 0.987, along with a mean absolute error and root mean square error of 6.078 and 8.730, respectively. These results significantly outperform comparison models such as CNN, LSTM, CNN-LSTM, random forest, AdaBoost, and Elman neural networks. Overall, this study validates the effectiveness and superiority of the proposed BO-CNN-LSTM model in ship ETA prediction, providing an efficient and effective prediction solution for intelligent maritime transportation systems. Full article

Short-term load forecasting plays a pivotal role in modern power system operations yet it remains challenging due to the complex spatiotemporal dependencies in load data. This paper proposes a dual-head attention residual network (DARNet) that significantly advances STLF through three key innovations: (1) a hybrid encoder combining 1D-CNN and GRU architectures to simultaneously capture the local load patterns and long-term temporal dependencies, achieving a 28% better locality awareness than that of conventional approaches; (2) a novel dual-head attention mechanism that dynamically models both the inter-temporal relationships and cross-variable dependencies, reducing the feature engineering requirements; and (3) an autocorrelation-adjusted recursive forecasting framework that cuts the multi-step prediction error accumulation by 33% compared to that with standard seq2seq models. Extensive experiments on real-world datasets from three Chinese cities demonstrate DARNet’s superior performance, outperforming six state-of-the-art benchmarks by 21–35% across all of the evaluation metrics (MAPE, SMAPE, MAE, and RRSE) while maintaining robust generalization across different geographical regions and prediction horizons. Full article

This study presents a techno-economic and environmental assessment of a photovoltaic–diesel–grid hybrid renewable energy system (SHER) applied to a university campus, with the aim of reducing monetary costs by implementing a methodology to mitigate energy consumption during peak hours, controlling the output of the diesel generator, and determining greenhouse gas emissions. Hourly load profiles are incorporated using billing data, local solar resource data, and grid connection rate schedules. The HOMER Pro v3.14.2 software is used to simulate and identify an off-grid scenario during peak hours, sizing the photovoltaic system at 30%, 50%, 70%, and 100% to compare the investment cost of the SHER. System performance is evaluated using key indicators, including net present cost ($6.96 million), levelized cost of energy (LCOE, $0.707/kWh), and CO₂ emissions (101,311 kg/yr.), among others. The results indicate that photovoltaic generation can cover approximately 80% of annual electricity demand, while the diesel generator operates only during critical periods, contributing to reduced operating costs and emissions. The optimal configuration has a lower LCOE than conventional supply, a renewable fraction of close to 80%, and an investment payback period of approximately five years, demonstrating the technical, economic, and environmental viability of the proposed system. Full article

Non-convex stochastic optimization presents fundamental mathematical challenges across machine learning, wireless networks, data center resource allocation, and optical wireless communication systems, where complex loss landscapes with multiple local minima and saddle points impede classical variational inference methods. This paper introduces the Quantum-Inspired Variational Inference (QIVI) framework, which systematically integrates quantum mechanical principles (superposition, entanglement, and measurement operators) into classical variational inference through rigorous mathematical formulations grounded in Hilbert space theory and operator algebras. We develop a unified optimization framework that encodes classical parameters as quantum-inspired states within finite-dimensional complex Hilbert spaces, employing unitary evolution operators and adaptive basis selection governed by gradient covariance eigendecomposition. The core mathematical contribution establishes that QIVI achieves a convergence rate of $\mathit{O}({log}^{2}T/T^{1/2})$ for $\sigma$-strongly non-convex functions, provably improving upon the classical $\mathit{O}\left(T^{-1/4}\right)$ rate, yielding a theoretical speedup factor of $1.85$–$1.96\times$. Comprehensive experiments across synthetic benchmarks, Bayesian neural networks, and real-world applications in network optimization and financial portfolio management demonstrate 23–$47\%$ faster convergence, 15–$35\%$ superior objective values, and 28–$46\%$ improved uncertainty calibration. The principal contributions include: (i) a rigorous Hilbert space-based mathematical framework for quantum-inspired variational inference grounded in operator algebras, (ii) a novel hybrid quantum–classical algorithm (QIVI) with adaptive basis selection via gradient covariance eigendecomposition, (iii) formal convergence proofs establishing provable improvement over classical methods, (iv) comprehensive empirical validation across diverse problem domains relevant to machine learning and network optimization, and (v) demonstration of the framework’s applicability to optimization problems arising in wireless networks, data center resource allocation, and network system design. Statistical validation using the Friedman test (${\chi }^2=847.3$, $p<0.001$) and post hoc Wilcoxon signed-rank tests with Holm–Bonferroni correction confirm that QIVI’s improvements over all baseline methods are statistically significant at the $\alpha =0.05$ level across all benchmark categories. The framework discovers $18.1$ out of 20 true modes in multimodal distributions versus $9.1$ for classical methods, demonstrating the potential of quantum-inspired optimization approaches for challenging stochastic problems arising in machine learning, wireless communication, and network optimization. Full article

The study presents a comprehensive first-principles investigation of the structural, electronic, and magnetic properties of rocksalt scandium nitride (ScN) and its Cr- and Mn-doped derivatives using spin-polarized density-functional theory within the GGA + U (U_Cr = 3.5 eV, U_Mn = 2.7 eV) and HSE06 frameworks. Pristine ScN crystallizes in the cubic Fm$\bar{3}$m structure and exhibits narrow-gap semiconducting behavior, with an indirect band gap of 0.82 eV obtained from hybrid-functional calculations, in excellent agreement with reported theoretical values. Substitutional doping with Cr and Mn introduces localized 3d states near the Fermi level, driving a transition toward spin-polarized metallic or half-metallic behavior accompanied by robust ferromagnetism. Density-of-states and band-structure analyses reveal that magnetism and charge transport in the doped systems are dominated by exchange-split transition-metal 3d states hybridized with N-2p orbitals. Total energy calculations confirm ferromagnetic ground states for both Cr- and Mn-doped ScN, with Mn substitution yielding stronger exchange stabilization and higher magnetic moments. Magnetocrystalline anisotropy energies, evaluated using the force-theorem approach, are found to be negligibly small, indicating weak anisotropy consistent with the moderate spin–orbit coupling strength in ScN-based nitrides. Nevertheless, symmetry breaking around dopant sites gives rise to a finite Dzyaloshinskii–Moriya interaction, leading to weak spin canting and non-collinear magnetic tendencies. The interplay between magnetic exchange coupling, spin–orbit interaction, and local inversion symmetry breaking positions of Cr- and Mn-doped ScN as promising dilute magnetic semiconductors with tunable spin polarization and chiral magnetic interactions, offering a viable platform for nitride-based spintronic and magneto-electronic applications. Full article

Titanium dioxide (TiO₂) has evolved from a conventional photocatalyst into a sophisticated nano-platform that bridges environmental sustainability and biomedicine. This paper proposes a unified interfacial redox design framework that links the electronic-structure engineering of the TiO₂ with the spatial control of its reactive oxygen species (ROS). In the environmental sector, we highlight advances in photocatalytic detoxification, such as the cleavage of organophosphates via Ag-modified TiO₂, driven by doping and metal–support interactions. In the biomedical domain, TiO₂ is framed as an active bio-interface capable of coordinative protein binding. We specifically examine the “moonlighting” protein dihydrolipoamide dehydrogenase (DLDH) as a model for stable, oriented biofunctionalization. By integrating RGD-targeting motifs, these hybrid systems enable integrin-directed, localized photodynamic effects. We further address critical toxicological considerations, emphasizing that TiO₂ behavior is context-dependent and governed by particle size, crystallinity, and surface state. By synthesizing insights from catalysis and redox biology, this manuscript outlines principles for the rational design of safer, application-specific TiO₂ technologies. This convergence supports a transition from non-selective oxidation toward predictable, spatially confined redox outcomes in both complex environmental matrices and physiological systems. This review outlines key mechanistic insights and proposes design principles for controlled and context-dependent TiO₂ activity. Full article

Semantic segmentation for autonomous driving demands balancing high-fidelity perception with real-time latency. While Transformers achieve state-of-the-art results, their quadratic complexity bottlenecks high-resolution processing. State Space Models (SSMs) like Mamba offer linear complexity but often suffer from local detail loss and inefficient scanning strategies. We introduce AutoMamba, a tailored Hybrid-SSM architecture. We propose a Hybrid-SSM block incorporating Depthwise Convolutions to inject local spatial priors and a Stage-Adaptive Mixed-Scanning strategy. This strategy prioritizes horizontal context in early stages for road layouts while only activating vertical scanning in deep layers to preserve anisotropic structures like poles. Furthermore, we reveal that unlike Transformers, Mamba architectures require Auxiliary Supervision and Online Hard Example Mining (OHEM) to address “long-tail forgetting.” Experiments on Cityscapes and BDD100K under a training-from-scratch setting demonstrate AutoMamba’s superiority. Notably, AutoMamba-B0 achieves 67.79% mIoU on Cityscapes with 31.3% fewer FLOPs than SegFormer-B0. Moreover, while the larger SegFormer-B2 fails with Out-Of-Memory errors at $2048\times 2048$ resolution, AutoMamba-B2 scales efficiently, validating its linear complexity advantage for next-generation perception systems. Full article

Free-space multi-agent path planning remains challenging at large scales. Most existing methods either offer optimality guarantees but do not scale beyond a few dozen agents or rely on grid-world assumptions that do not generalize well to continuous space. In this paper, we propose a hybrid, rule-based planning framework that combines Priority Inheritance with Backtracking (PIBT) with a novel safety-aware path smoothing method. Our approach extends PiBT to eight-connected grids and selectively applies string-pulling-based smoothing while preserving collision safety through local interaction awareness and a fallback collision resolution step based on Safe Interval Path Planning (SIPP). This design allows us to reduce overall path lengths while maintaining real-time performance. We demonstrate that our method can scale to over 500 agents in large free-space environments, outperforming existing any-angle and optimal methods in terms of runtime, while producing near-optimal trajectories in sparse domains. Our results suggest this framework is a promising building block for scalable, real-time multi-agent navigation in robotics systems operating beyond grid constraints. Full article

Background/Objectives: Oral diseases remain among the most prevalent noncommunicable conditions worldwide, with biofilm-driven dysbiosis playing a central role in dental caries, gingivitis, periodontitis, and oral candidiasis. Curcumin has attracted considerable interest because of its anti-inflammatory, antioxidant, antimicrobial, and regenerative properties. However, its clinical use remains limited by poor water solubility, chemical instability, rapid metabolism, and low bioavailability. This review aimed to provide a comprehensive analysis of curcumin-based nanoformulations for oral health applications, with emphasis on their mechanistic actions, antibiofilm activity, and translational relevance. Methods: This review examined representative nanocarrier systems developed for curcumin delivery in oral health. These included polymeric nanoparticles, nanomicelles and nanoemulsions, solid lipid nanoparticles and nanostructured lipid carriers, nanogels, hydrogels, mucoadhesive films, and metallic or hybrid nanosystems. The analysis focused on molecular mechanisms of action, antimicrobial and antibiofilm effects against major oral pathogens, and key translational challenges. Results/Findings: Across the reviewed studies, nanoformulations consistently improved curcumin solubility, stability, tissue penetration, mucosal retention, and controlled release. Mechanistically, they enhanced anti-inflammatory activity through inhibition of nuclear factor kappa B (NF-κB), strengthened antioxidant defenses via the nuclear factor erythroid 2-related factor 2/heme oxygenase-1 (Nrf2/HO-1) axis, supported tissue repair and osteogenic responses, disrupted oral biofilms, and modulated local immune responses. Antimicrobial activity was reported against Streptococcus mutans, Porphyromonas gingivalis, Aggregatibacter actinomycetemcomitans, and Candida albicans, with reduced exopolysaccharide production, impaired adhesion, and improved biofilm penetration. Conclusions: Curcumin-based nanoformulations represent promising adjunctive platforms for oral healthcare. However, their clinical translation still requires improved stability in the oral-environment standardized manufacturing and characterization, rigorous safety evaluation, and well-designed controlled clinical studies. Full article

Against frequent extreme heat, landscaped green spaces cool, humidify, and mitigate urban heat islands, also boosting thermal comfort. Classical Chinese garden “gray spaces” are transitional gathering zones with strong microclimate-regulating potential, yet systematic research on their mechanisms in Western Sichuan memorial gardens remains limited. This study first reveals their thermal characteristics; establishes a refined classification system; uncovers nonlinear links between garden elements, spatial form, and thermal comfort; and proposes optimization strategies. Key findings: (1) Gray spaces show notable microclimate regulation. Internal air temperatures drop by 0.8–4.3 °C, relative humidity rises by 2.2–22.33%, and average PET decreases by 3.1 °C, effectively relieving thermal stress. (2) Thermal comfort is closely related to gray space types, with open halls performing best due to their strong sense of enclosement and shading. (3) Plant-dominated and hybrid spaces are superior to water-dominated ones. PET is negatively correlated with 40–70% plant canopy and 20–30% water coverage, while excess water leads to stuffiness. Hybrid spaces reach ideal blue–green synergy at 50–60% canopy and 20–30% water. (4) The summer PET comfort threshold for Western Sichuan gray spaces is 29.1–31.5 °C (neutral at 30.2 °C), higher than European standards, reflecting local adaptation to a hot–humid climate and guiding microclimate-adaptive design. Full article

Gardenia jasminoides Ellis is a commercially important medicinal and ornamental plant; however, its functional genomics remain poorly understood because of the lack of efficient cell-based research tools. To address this limitation, we established an optimized method for isolating viable protoplasts from petal and mesophyll tissues of G. jasminoides and developed a polyethylene glycol (PEG)-mediated transient expression system. For petal protoplast isolation, the optimal enzyme combination consisted of 3.0% cellulase R-10 and 1.0% macerozyme R-10 supplemented with 0.5 M D-mannitol, yielding 5.26 × 10⁶ protoplasts per gram fresh weight (FW) with 80.63% viability. For mesophyll protoplast isolation, 1.5% cellulase R-10 and 0.5% macerozyme R-10 supplemented with 0.5 M D-mannitol produced 8.75 × 10⁶ protoplasts g⁻¹ FW with 84.55% viability. PEG-mediated transient transformation was optimized at 20% PEG4000 for petal protoplasts and 40% PEG4000 for mesophyll protoplasts, resulting in efficient GFP expression. This system was successfully applied to subcellular localization analyses of floral regulatory proteins (GjAP3, GjPI, and GjSEP) and defense-related proteins (GjNPR1 and GjTGA2), as well as to the validation of protein–protein interactions between GjSEP and GjPI and between GjNPR1 and GjTGA2 using bimolecular fluorescence complementation and yeast two-hybrid assays. Collectively, these results establish a reliable and species-specific protoplast-based platform for rapid functional characterization of genes in G. jasminoides, providing an effective tool for future studies on gene regulation, metabolic engineering, and molecular breeding in this horticultural plant species. Full article

Multi-agent swarm collaboration, inspired by the collective behaviors of biological swarms in nature, has wide applications in dynamic open environments. However, hybrid action spaces in multi-agent reinforcement learning (MARL) present a critical challenge: the inherent coupling between discrete and continuous actions severely undermines policy stability and convergence, especially under dynamic topologies. Existing methods fail to decouple this coupling, leading to suboptimal policies and unstable training. This paper addresses the core problem of action coupling under dynamic topologies, proposing a Parametrized Graph Convolution Reinforcement Learning (P-DGN) method. Operating within the actor–critic framework, P-DGN decouples the optimization pathways for hybrid actions, with a biomimetic observation design inspired by starling flock behaviors: each agent only observes the states of its seven nearest neighbors to achieve efficient local interaction and global collaboration. Its actor network uses multi-head attention to build dynamic relation kernels, develops temporal relation regularization (TRR) to improve policy consistency across time steps, and generates continuous actions with a Gaussian policy. Meanwhile, P-DGN’s critic network, based on deep Q-network (DQN), evaluates Q-values for discrete actions to guide optimal choices. We evaluate P-DGN in two different multi-agent cooperative environments. Experimental results show that compared with parametrized deep Q-network (P-DQN) and DQN baseline, the proposed method has faster convergence speed and stronger training stability. Moreover, with dense rewards, P-DGN agents learn emergent tactics like encirclement. Overall, P-DGN offers a new approach for optimizing hybrid action spaces in multi-agent systems within open, dynamic environments, balancing theoretical generality with practical utility, and its biomimetic design provides a biologically plausible framework for multi-agent swarm collaboration. Full article

This review surveys how nanoparticles and biomolecular nanosized structures interact with model membrane systems, and how these interfacial processes govern their performance in drug and gene delivery, antimicrobial strategies, biosensing, and nanotoxicology. The nanostructures covered include polymeric nanoparticles, lipid-based carriers, peptide nanostructures, dendrimers, and multifunctional hybrids. Model membranes span Langmuir monolayers, supported lipid bilayers, vesicles/liposomes across sizes, and emerging hybrid or asymmetric constructs that better approximate native complexity. Mechanistically, interactions follow recurrent routes—surface adsorption, bilayer insertion, pore formation, and lipid extraction/reorganization—regulated by particle size, morphology, charge, ligand architecture, and lipophilicity, in conjunction with membrane composition, phase state, curvature, and asymmetry. A multiscale toolkit links structure, mechanics, and dynamics: Langmuir troughs and Brewster Angle Microscopy map thermodynamics and mesoscale morphology; atomic force microscopy and quartz crystal microbalance with dissipation resolve nanoscale topography and viscoelasticity; fluorescence microscopy/spectroscopy reports on localization and packing; neutron and X-ray reflectometry quantify vertical structure; molecular dynamics provides atomistic pathways and design hypotheses. Historically, the field advanced from early monolayers and bilayers, through the fluid mosaic model, to raft microdomains and modern biomimetic systems, enabling increasingly realistic experiments. Key advances include cross-method integration linking experimental observations with image-based computational models; persistent debates concern the translation from simplified models to living membranes, the role of dynamic coronas, and scale/force-field limits in simulations. Future efforts should prioritize hybrid models incorporating proteins and asymmetric lipidomes, standardized reporting and reference systems, rigorous coupling of experiments with calibrated simulations and machine learning, and alignment with safety-by-design and regulatory expectations, thereby shifting interfacial measurements from descriptive observation to predictive design rules. Full article

Vehicular Ad Hoc Networks (VANETs) have become the foundation for the implementation of intelligent transportation systems and new vistas for road safety and traffic efficiency. However, these networks are still susceptible to Sybil attacks, a form of attack that requires malicious entities to create a series of fake identities in order to have an out-of-proportion influence. The present paper puts forth a new Sybil attack detection framework that combines Verifiable Delay Functions (VDFs) in synergistic cooperation with a hierarchical fog-cloud computing structure. Our method does not rely on any additional properties of VDFs but uses them to prove uniqueness computationally, deploying purposefully placed fog nodes for effective localized detection. We mathematically formulate a multi-layered detection algorithm that processes interactions between vehicles on two fog (and cloud) layers to produce suspicion scores using spatiotemporal consistency and VDF challenge-response patterns. Security analysis proves the system’s ability to resist a range of Sybil attack variants with performance evaluation outperforming at detection above 97.8% and false positives below 2.3%. The incorporation of machine learning techniques also extends detection capabilities, and our hybrid VDF-ML method proves better adaptation to the changing attack patterns. Details of implementation and detailed simulations in various traffic situations prove the feasibility and efficiency of our proposed solution to set a new level playing ground for secure VANET communications. Full article