Search Results (275)

Efficient multimodal transportation from a passenger’s doorstep to the airport is critical for ensuring timely arrivals, reducing travel uncertainty, and optimizing overall travel experience. However, coordinating different modes of transport—such as walking, public transit, ride-hailing, and private vehicles—poses significant challenges due to varying schedules, traffic conditions, and transfer times. Traditional route planning methods often fail to account for real-time disruptions, leading to delays and inefficiencies. As air travel demand grows, optimizing these multimodal routes becomes increasingly important to minimize delays, improve passenger convenience, and enhance transport system resilience. To address this challenge, we propose an optimization framework combining Mixed-Integer Linear Programming (MILP) and Dynamic Programming (DP) to generate optimal travel routes from a passenger’s location to the airport gate. MILP is used to model and optimize multimodal trip decisions, considering time windows, cost constraints, and transfer dependencies. Meanwhile, DP allows for adaptive, real-time adjustments based on changing conditions such as traffic congestion, transit delays, and service availability. By integrating these two techniques, our approach ensures a robust, efficient, and scalable solution for multimodal transport routing, ultimately enhancing reliability and reducing travel time variability. The results demonstrate that the MILP solver converges within 20 iterations, reducing the objective value from 15.2 to 7.1 units with an optimality gap of 8.5%; the DP-based adaptation maintains feasibility under a 2 min disruption; and the multimodal analysis yields a total travel time of 9.0 min with a fare of 3.0 units, where the bus segment accounts for 6.5 min and 2.2 units of the total. In the multimodal transport evaluation, DP adaptation reduced cumulative delays by more than half after disruptions, while route selection demonstrated balanced trade-offs between cost and time across walking, bus, and train segments. Full article

The optimal scheduling of vehicles at signal-free intersections under the connected and automated vehicle (CAV) environment has become a research hotspot in the intelligent transportation field. However, existing studies often oversimplify the intersection’s conflict area and fail to adequately address the spatiotemporal sparsity of conflict points, with little attention given to bus priority requirements. To address these gaps, this paper first establishes an intersection coordinate system and constructs a conflict area analysis model based on the coordinates of key conflict points and vehicle trajectories. Subsequently, an optimal scheduling model for automated vehicles at signal-free intersections with bus priority is developed, which considers the set of vehicles influencing decisions within a time window and uses vehicle entry times and lateral lane changes as decision variables. To enhance computational speed while preserving convergence accuracy, a search space reduction method based on available gaps for conflict point traversal constraints is designed. The model is then solved using an improved double-layer multi-population particle swarm optimization (PSO) algorithm. Simulation results, compared against traditional signal control, rule-driven signal-free, and dynamic-optimization-based signal-free algorithms demonstrate that the proposed method achieves a favorable balance between computational cost and efficiency. It significantly reduces the average vehicle delay. Moreover, incorporating bus priority reduces the average per capita delay by 18.95% compared to the non-priority scenario, effectively proving the validity of the proposed method. Full article

This study investigated the impact of green tea addition on microbial community dynamics during Daqu fermentation, a critical process in traditional baijiu production. Four Daqu variants (0%, 10%, 20%, 30% tea) were analyzed across six fermentation periods using 16S rRNA/ITS sequencing, coupled with STR, TDR, Sloan neutral model, and phylogenetic analyses. Results showed time-dependent increases in bacterial/fungal richness, with 30% tea maximizing species richness. Tea delayed bacterial shifts until day 15 but accelerated fungal reconstruction from day 6, expanding the temporal response window. While stochastic processes dominated initial assembly (77–94% bacteria, 88–99% fungi), deterministic processes intensified with tea concentration, particularly in fungi (1% → 12%). Tea increased bacterial dispersal limitation and reduced phylogenetic conservatism of endogenous factors. This work proposed a framework for rationally engineering fermentation ecosystems by decoding evolutionary-ecological rules of microbial assembly. It revealed how plant-derived additives can strategically adjust niche partitioning and ancestral constraints to reprogram microbiome functionality. These findings provided a theoretical foundation in practical strategies for optimizing industrial baijiu production through targeted ecological interventions. Full article

Microbially induced carbonate precipitation (MICP), as an eco-friendly biomineralization technology, has opened up an innovative path for the green and low-carbon development of the mining industry. Unlike conventional methods, its in situ solidification minimizes environmental disturbances and reduces carbon emissions during construction. This article reviews the research on MICP technology in various scenarios within the mining industry, summarizes the key factors influencing the application of MICP, and proposes a future research direction to fill the gap of the lack of systematic guidance for the application of MICP in this field. Specifically, it elaborates on the solidification mechanism of MICP and its current application in the solidification and storage of tailings, heavy metal immobilization, waste resource utilization, carbon sequestration, and field-scale deployment, establishing a technical foundation for broader implementation in the mining sector. Key influencing factors that affect the solidification effect of MICP are discussed, along with critical engineering challenges such as the attenuation of microbial activity and the low uniformity of calcium carbonate precipitation under extreme conditions. Proposed solutions include environmentally responsive self-healing technologies (the stimulus-responsive properties of the carriers extend the survival window of microorganisms), a one-phase low-pH injection method (when the pH = 5, the delay time for CaCO₃ to appear is 1.5 h), and the incorporation of auxiliary additives (the auxiliary additives provided more adsorption sites for microorganisms). Future research should focus on in situ real-time monitoring of systems integrated with deep learning, systematic mineralization evaluation standard system, and urea-free mineralization pathways under special conditions. Through interdisciplinary collaboration, MICP offers significant potential for integrated scientific and engineering solutions in mine waste solidification and sustainable resource utilization. Full article

Dock door congestion is an essential and persistent concern within the realm of outbound warehouse logistics. The inability to accommodate outbound vehicles at the loading docks, especially during peak hours, disrupts internal warehouse operations, leads to bottlenecks, and contributes to substantial additional costs and delays. This paper addresses the critical issue of dock door congestion by proposing an integrated forecast–optimization framework for its prediction and management. The framework uses advanced forecasting methods and optimization techniques to increase warehouse throughput, boost operational efficiency, and predict potential congestion events using historical and real-time data. It combines two proven methodologies, maximum entropy bootstrap (MEB) and ensemble learning via bagging, with scenario-based stochastic optimization. This hybrid approach significantly improves upon traditional models by capturing the complex, non-monotonic components and multi-seasonality inherent in warehouse throughput data. Through a detailed real-world case study, we demonstrate how the proposed approach can accurately predict the number of trucks that can be serviced within specific time windows. This information is crucial for making operational decisions, such as whether to expand the warehouse. The approach can be generalized beyond the specific case study and offers valuable insights for any logistics or supply chain operation requiring the integration of stochastic optimization with predictive modeling. Full article

Floating solar technology has gained significant attention as part of the global expansion of renewable energy due to its potential for installation in underutilized water bodies. Several countries, including the Netherlands, have initiated efforts to extend this technology from inland freshwater applications to open offshore environments, particularly within offshore wind farm areas. This development is motivated by the synergistic benefits of increasing site energy density and leveraging the existing offshore grid infrastructure. The deployment of offshore floating photovoltaic (OFPV) systems involves assembling multiple modular units in a marine environment, introducing operational risks that may give rise to safety concerns. To mitigate these risks, weather windows must be considered prior to the task execution to ensure continuity between weather-sensitive activities, which can also lead to additional time delays and increased costs. Consequently, optimizing marine logistics becomes crucial to achieving the cost reductions necessary for making OFPV technology economically viable. This study employs a simulation-based approach to estimate the installation duration of a 5 MWp OFPV plant at a Dutch offshore wind farm site, started in different months and under three distinct risk management scenarios. Based on 20 years of hindcast wave data, the results reveal the impacts of campaign start months and risk management policies on installation duration. Across all the scenarios, the installation duration during the autumn and winter period is 160% longer than the one in the spring and summer period. The average installation durations, based on results from 12 campaign start months, are 70, 80, and 130 days for the three risk management policies analyzed. The result variation highlights the additional time required to mitigate operational risks arising from potential discontinuity between highly interdependent tasks (e.g., offshore platform assembly and mooring). Additionally, it is found that the weather-induced delays are mainly associated with the campaigns of pre-laying anchors and platform and mooring line installation compared with the other campaigns. In conclusion, this study presents a logistics modeling methodology for OFPV systems, demonstrated through a representative case study based on a state-of-the-art truss-type design. The primary contribution lies in providing a framework to quantify the performance of OFPV installation strategies at an early design stage. The findings of this case study further highlight that marine installation logistics are highly sensitive to local marine conditions and the chosen installation strategy, and should be integrated early in the OFPV design process to help reduce the levelized cost of electricity. Full article

Hemorrhage is the leading cause of preventable death in trauma care, requiring rapid and accurate detection to guide effective interventions. Hemorrhagic shock can be masked by underlying compensatory mechanisms, which may lead to delayed decision-making that can compromise casualty care. In this proof-of-concept study, we aimed to develop and evaluate machine learning models to predict Percent Estimated Blood Loss from a photoplethysmography waveform, offering non-invasive, field deployable solutions. Different model types were tuned and optimized using data captured during a hemorrhage and resuscitation swine study. Through this optimization process, we evaluated different time-lengths of prediction windows, machine learning model architectures, and data normalization approaches. Models were successful at predicting Percent Estimated Blood Loss in blind swine subjects with coefficient of determination values exceeding 0.8. This provides evidence that Percent Estimated Blood Loss can be accurately derived from non-invasive signals, improving its utility for trauma care and casualty triage in the pre-hospital and emergency medicine environment. Full article

The use of 3D printing holds significant promise to transform the construction industry by enabling automation and customization, although key challenges remain—particularly the control of fresh-state rheology. This study presents a novel formulation that combines potassium-rich biomass fly ash (BFAK) with an air-entraining plasticizer (APA) to optimize the rheological behavior, hydration kinetics, and structural performance of mortars tailored for extrusion-based 3D printing. The results demonstrate that BFAK enhances the yield stress and thixotropy increases, contributing to improved structural stability after extrusion. In parallel, the APA adjusts the viscosity and facilitates material flow through the nozzle. Isothermal calorimetry reveals that BFAK modifies the hydration kinetics, increasing the intensity and delaying the occurrence of the main hydration peak due to the formation of secondary sulfate phases such as Aphthitalite [(K₃Na(SO₄)₂)]. This behavior leads to an extended setting time, which can be modulated by APA to ensure a controlled processing window. Flowability tests show that BFAK reduces the spread diameter, improving cohesion without causing excessive dispersion. Calibration cylinder tests confirm that the formulation with 1.5% APA and 2% BFAK achieves the maximum printable height (35 cm), reflecting superior buildability and load-bearing capacity. These findings underscore the novelty of combining BFAK and APA as a strategy to overcome current rheological limitations in digital construction. The synergistic effect between both additives provides tailored fresh-state properties and structural reliability, advancing the development of a sustainable SMC and printable cementitious materials. Full article

Forest fire detection is an essential application in environmental surveillance since wildfires cause devastating damage to ecosystems, human life, and property every year. The effective and accurate detection of fire is necessary to allow for timely response and efficient management of disasters. Traditional techniques for fire detection often experience false alarms and delayed responses in various environmental situations. Therefore, developing robust, intelligent, and real-time detection systems has emerged as a central challenge in remote sensing and computer vision research communities. Despite recent achievements in deep learning, current forest fire detection models still face issues with generalizability, lightweight deployment, and accuracy trade-offs. In order to overcome these limitations, we introduce a novel technique (FireNet-KD) that makes use of knowledge distillation, a method that maps the learning of hard models (teachers) to a light and efficient model (student). We specifically utilize two opposing teacher networks: a Vision Transformer (ViT), which is popular for its global attention and contextual learning ability, and a Convolutional Neural Network (CNN), which is esteemed for its spatial locality and inductive biases. These teacher models instruct the learning of a Swin Transformer-based student model that provides hierarchical feature extraction and computational efficiency through shifted window self-attention, and is thus particularly well suited for scalable forest fire detection. By combining the strengths of ViT and CNN with distillation into the Swin Transformer, the FireNet-KD model outperforms state-of-the-art methods with significant improvements. Experimental results show that the FireNet-KD model obtains a precision of 95.16%, recall of 99.61%, F1-score of 97.34%, and mAP@50 of 97.31%, outperforming the existing models. These results prove the effectiveness of FireNet-KD in improving both detection accuracy and model efficiency for forest fire detection. Full article

Accurately estimating the energy consumption of unmanned aerial vehicles (UAVs) in real-world delivery scenarios remains a critical challenge, particularly when UAVs operate in complex urban environments and are coupled with public transportation systems. Most existing models rely on oversimplified assumptions or static mission profiles, limiting their applicability to realistic, scalable drone-based logistics. In this paper, we propose a physically-grounded and scenario-aware energy sizing methodology for UAVs operating as part of a last-mile delivery system integrated with a city’s bus network. The model incorporates detailed physical dynamics—including lift, drag, thrust, and payload variations—and considers real-time mission constraints such as delivery execution windows and infrastructure interactions. To enhance the realism of the energy estimation, we integrate computational fluid dynamics (CFD) simulations that quantify the impact of surrounding structures and moving buses on UAV thrust efficiency. Four mission scenarios of increasing complexity are defined to evaluate the effects of delivery delays, obstacle-induced aerodynamic perturbations, and early return strategies on energy consumption. The methodology is applied to a real-world transport network in Belfort, France, using a graph-based digital twin. Results show that environmental and operational constraints can lead to up to 16% additional energy consumption compared to idealized mission models. The proposed framework provides a robust foundation for UAV battery sizing, mission planning, and sustainable integration of aerial delivery into multimodal urban transport systems. Full article

Background: Vitamin D is increasingly recognized not only for its role in skeletal development but also for its immunomodulatory and gastrointestinal effects. Maternal and neonatal vitamin D deficiency (VDD) has been associated with alterations in gut microbiota, impaired intestinal barrier integrity, and increased susceptibility to inflammatory conditions in neonates. However, the exact mechanisms linking perinatal vitamin D status to neonatal gastrointestinal morbidity remain incompletely understood. Methods: This review synthesizes current evidence (2015–2024) from clinical studies, animal models, and mechanistic research on the impact of VDD during pregnancy and the neonatal period on gastrointestinal health. Databases such as PubMed, Scopus, and Web of Science were systematically searched using keywords, including “vitamin D”, “neonate”, “gut microbiome”, “intestinal barrier”, and “necrotizing enterocolitis”. Results: Emerging data suggest that VDD in utero and postnatally correlates with dysbiosis, increased intestinal permeability, and elevated inflammatory responses in neonates. Notably, low 25(OH)D levels in mothers and newborns have been linked with a higher incidence of necrotizing enterocolitis (NEC), delayed gut maturation, and altered mucosal immunity. Vitamin D appears to modulate the expression of tight junction proteins, regulate antimicrobial peptides, and maintain microbial diversity through the vitamin D receptor (VDR). Conclusions: Understanding the gastrointestinal implications of early-life VDD opens a potential window for preventive strategies in neonatal care. Timely maternal supplementation and targeted neonatal interventions may mitigate gut-related morbidities and improve early-life health outcomes. Further longitudinal and interventional studies are warranted to clarify causality and optimal intervention timing. Full article

Low-latency and robust beamforming are vital for sustaining signal quality and spectral efficiency in emerging high-mobility 5G and future 6G wireless networks. Conventional beam management approaches, which rely on periodic Channel State Information feedback and static codebooks, as outlined in 3GPP standards, are insufficient in rapidly varying propagation environments. In this work, we propose a Dominance-Enforced Adaptive Clustered Sliding Window Regression (DE-ACSW-R) framework for predictive beamforming in O-RAN Split 7-2x architectures. DE-ACSW-R leverages a sliding window of recent angle of arrival (AoA) estimates, applying in-window change-point detection to segment user trajectories and performing both Linear Regression (LR) and curvature-adaptive Support Vector Regression (SVR) for short-term and non-linear prediction. A confidence-weighted fusion mechanism adaptively blends LR and SVR outputs, incorporating robust outlier detection and a dominance-enforced selection regime to address strong disagreements. The Open Radio Unit (O-RU) autonomously triggers localised MUSIC scans when prediction confidence degrades, minimising unnecessary full-spectrum searches and saving delay. Simulation results demonstrate that the proposed DE-ACSW-R approach significantly enhances AoA tracking accuracy, beamforming gain, and adaptability under realistic high-mobility conditions, surpassing conventional LR/SVR baselines. This AI-native modular pipeline aligns with O-RAN architectural principles, enabling scalable and real-time beam management for next-generation wireless deployments. Full article

The exponential growth of Distributed Denial-of-Service (DDoS) attacks in high-speed networks presents significant real-time detection and mitigation challenges. The existing detection frameworks are categorized into flow-based and packet-based detection approaches. Flow-based approaches usually suffer from high latency and controller overhead in high-volume traffic. In contrast, packet-based approaches are prone to high false-positive rates and limited attack classification, resulting in delayed mitigation responses. To address these limitations, we propose a real-time DDoS detection architecture that combines hardware-accelerated statistical preprocessing with GPU-accelerated deep learning models. The raw packet header information is transformed into multivariate time series data to enable classification of complex traffic patterns using Temporal Convolutional Networks (TCN), Long Short-Term Memory (LSTM) networks, and Transformer architectures. We evaluated the proposed system using experiments conducted under low to high-volume background traffic to validate each model’s robustness and adaptability in a real-time network environment. The experiments are conducted across different time window lengths to determine the trade-offs between detection accuracy and latency. The results show that larger observation windows improve detection accuracy using TCN and LSTM models and consistently outperform the Transformer in high-volume scenarios. Regarding model latency, TCN and Transformer exhibit constant latency across all window sizes. We also used SHAP (Shapley Additive exPlanations) analysis to identify the most discriminative traffic features, enhancing model interpretability and supporting feature selection for computational efficiency. Among the experimented models, TCN achieves the most balance between detection performance and latency, making it an applicable model for the proposed architecture. These findings validate the feasibility of the proposed architecture and support its potential as a real-time DDoS detection application in a realistic high-speed network. Full article

Logistics efficiency is critical to operational success in manufacturing, especially for corrugated carton manufacturers. The challenges of this type of manufacturing include optimizing truck utilization, without which high costs, resource waste, and customer dissatisfaction can occur. Transportation consolidation can reduce trips, increase vehicle capacity, and lower carbon emissions. This study proposes a delivery optimization model using genetic algorithms within the Multi-Objective Evolutionary Algorithm (MOEA) framework. The results show that the model significantly improves fleet utilization from 75% to 100% and reduces delivery delays by adhering to predefined time windows, thereby improving cost efficiency and customer satisfaction. Full article

As populations age, particularly in countries like Japan, mobility impairments related to ankle joint dysfunction, such as foot drop, instability, and reduced gait adaptability, have become a significant concern. Active ankle–foot orthoses (AAFO) offer targeted support during walking; however, most existing systems rely on rule-based or threshold-based control, which are often limited to sagittal plane movements and lacking adaptability to subject-specific gait variations. This study proposes an approach driven by neuromuscular activation using surface electromyography (EMG) and a Gated Recurrent Unit (GRU)-based deep learning model to predict plantar pressure distributions at the heel, midfoot, and toe regions during gait. EMG signals were collected from four key ankle muscles, and plantar pressures were recorded using a customized sandal-integrated force-sensitive resistor (FSR) system. The data underwent comprehensive preprocessing and segmentation using a sliding window method. Root mean square (RMS) values were extracted as the primary input feature due to their consistent performance in capturing muscle activation intensity. The GRU model successfully generalized across subjects, enabling the accurate real-time inference of critical gait events such as heel strike, mid-stance, and toe off. This biomechanical evaluation demonstrated strong signal compatibility, while also identifying individual variations in electromechanical delay (EMD). The proposed predictive framework offers a scalable and interpretable approach to improving real-time AAFO control by synchronizing assistance with user-specific gait dynamics. Full article