Search Results (63,774)

Electric vehicles, known for their eco-friendliness and rechargeable–dischargeable capabilities, can serve as energy storage batteries to support the operation of the microgrid in certain scenarios. Therefore, photovoltaic-storage electric vehicle charging stations have emerged as an important solution to address the challenges posed by energy interconnection networks. However, electric vehicle charging loads exhibit notable randomness, potentially altering load characteristics during certain periods and posing challenges to the stable operation of microgrids. To address this challenge, this paper proposes a hierarchical optimal dispatching strategy based on photovoltaic-storage charging stations. The strategy utilizes a dynamic electricity pricing model and the adaptive particle swarm optimization algorithm to effectively manage electric vehicle charging loads. By decomposing the dispatching task into multiple layers, the strategy effectively solves the problems of the “curse of dimensionality” and slow convergence associated with large numbers of electric vehicles. Simulation results demonstrate that the strategy can effectively achieve peak shaving and valley filling, reducing the load variance of the microgrid by 24.93%, and significantly reduce electric vehicle charging costs and distribution network losses, with a reduction of 92.29% in electric vehicle charging costs and 32.28% in microgrid losses compared to unorganized charging. Additionally, this strategy can meet the travel demands of electric vehicle owners while providing convenient charging services. Full article

This study evaluated the effects of adding waste PET fibers on the mechanical properties and chloride ion penetration of latex-modified ultra-rapid hardening cement concrete used for emergency road pavement repairs. The primary experimental variable was the content of waste PET fibers. The mechanical properties of the concrete were evaluated through compressive strength, flexural strength, and splitting tensile strength tests. Its durability was evaluated through chloride ion penetration, surface resistivity, and abrasion resistance tests. The experimental results were compared with the quality standards for emergency repair concrete set by the Korea Expressway Corporation. As a result, this study has enhanced the strength and resistance to chloride ions of latex-modified concrete by incorporating waste PET fibers. In the mixture with 3.84 kg/m³ of waste PET fibers, the compressive strength was 29.9 MPa at 4 h and 42.5 MPa at 28 curing days. The flexural strength was 6.0 MPa at 4 curing hours and 7.0 MPa at 28 days, and the splitting tensile strength was 4.5 MPa at 28 days of curing. The chloride ion permeability amount and abrasion depth were 1081C and 0.82 mm, respectively. The mixture with 3.84 kg/m³ of waste PET fibers has superior compressive strength, flexural strength, splitting tensile strength, chloride ion penetration, and surface resistivity compared to the mixture with 7.68 kg/m³. This result means that the waste PET fibers caused poor dispersion and fiber-balling within the concrete, leading to loose internal void structures when incorporated at 3.84 kg/m³. However, the abrasion resistance test showed better results for the mixture with 7.68 kg/m³ of waste PET fibers than the 3.84 kg/m³ mixture. Therefore, the test results indicated that 3.84 kg/m³ of waste PET fibers is the most effective for latex-modified concrete used in emergency road pavement repairs. Full article

The fusion of low-resolution hyperspectral images (LR-HSI) with high-resolution multispectral images (HR-MSI) provides a cost-effective approach to obtaining high-resolution hyperspectral images (HR-HSI). Existing methods primarily based on convolutional neural networks (CNNs) struggle to capture global features and do not adequately address the significant scale and spectral resolution differences between LR-HSI and HR-MSI. To tackle these challenges, our novel FCSwinU network leverages the spectral fast Fourier convolution (SFFC) module for spectral feature extraction and utilizes the Swin Transformer’s self-attention mechanism for multi-scale global feature fusion. FCSwinU employs a UNet-like encoder–decoder framework to effectively merge spatiospectral features. The encoder integrates the Swin Transformer feature abstraction module (SwinTFAM) to encode pixel correlations and perform multi-scale transformations, facilitating the adaptive fusion of hyperspectral and multispectral data. The decoder then employs the Swin Transformer feature reconstruction module (SwinTFRM) to reconstruct the fused features, restoring the original image dimensions and ensuring the precise recovery of spatial and spectral details. Experimental results from three benchmark datasets and a real-world dataset robustly validate the superior performance of our method in both visual representation and quantitative assessment compared to existing fusion methods. Full article

Environmentally persistent free radicals (EPFRs) generated on particles under irradiation in water have attracted particular attention, and their formation mechanisms are not well understood. This study investigated the photoformation of EPFRs on both actual samples collected from an oil production plant in Panjin, Liaoning, China, and simulated Fe(III)-montmorillonite samples in water. The EPFRs detected on actual samples were not easily generated compared with those in the soil or in the air, based on the concentrations of identified PAHs. EPR signals in the range of 10¹⁷ to 10¹⁸ spin/g were detected on the simulated Fe(III)-montmorillonite samples. Their g factors were smaller than 2.0030, which indicated the generation of carbon-centered EPFRs. The primary byproducts were identified by chromatography–mass spectrometry (GC-MS), and a possible EPFR formation pathway during PAH degradation was proposed. Hydrogenation of PAHs during the photoformation of EPFRs was observed and might be due to the catalysis of the simulated particles and the interaction of the intermediates. Meanwhile, the effects of the typical anions (NO₂⁻ and Cl⁻) and the surfactant (TWEEN^® 80 and sodium dodecyl sulfate) were investigated and indicated that the phototransformation process and adsorption process would affect the formation of EPFRs. Overall, our study provided useful information to understand the photoformation of EPFRs in aqueous environments. Full article

Driven by global sustainability trends, Building Information Modelling (BIM) technology is increasingly becoming a key tool in the construction industry to improve efficiency and sustainability. This study aims to identify the key factors affecting BIM implementation in the context of Environmental, Social, and Governance (ESG) and Sustainable Development Goals (SDGs) and to construct a theoretical framework for BIM implementation based on these factors. To achieve this objective, this study used a systematic literature review (SLR) method to systematically review the relevant literature between 2009 and 2024 and identified 16 key factors from the selected 406 studies through keyword co-occurrence analysis (using VOSviewer 1.6.20) and data coding. These key factors include top management support for ESG and SDGs, alignment of SDGs, ESG integration, technical support, BIM software, BIM hardware, structural adjustment and collaboration, capacity building, change management, skill and attitude, educational training and development, incentive mechanism, roles and responsibilities, sustainable construction practices, policies and regulations, and resource efficiency. This study categorises these factors under the Strategy, Technology, Organisation, People, Environment (STOPE) framework and proposes a theoretical implementation framework for BIM accordingly. The findings not only provide a practical guiding framework for the sustainable development of construction companies in the context of ESG and SDG integration but also lay a solid theoretical foundation for future empirical research. Full article

The European Union’s policy aims for the wide-scale deployment of automated mobility by 2030, i.e., within the next programming period (2028–2034), with the deployment of autonomous road vehicles (AVs) in cities playing a key role. Researchers suggest that AV deployment will have complex impacts on urban development, which are difficult to quantify due to scarce real-life data. The present research aims to evaluate different policy pathways of AV deployment for sustainable urban development in the next EU programming period. A multicriteria analysis is conducted, combining AHP and VIKOR, with the participation of experts across Europe. Initially, the potential impacts on sustainable urban development are weighted as evaluation criteria. Then, different pathways are evaluated against these criteria, i.e., AV deployment as collective and/or private transport in specific areas and periods or in the whole Functional Urban Area (FUA) on a 24 h basis. An interesting finding is that the effect on the city’s spatial development, not thoroughly examined by literature, is highly ranked by experts. Regarding policy pathways, autonomous collective transport with 24 h service of the FUA emerged as the optimum alternative. The proposed methodology provides a tool for planners, researchers, and policy makers and a framework for an open debate with society. Full article

Mixed non-motorized traffic is largely unaffected by motor vehicle congestion, offering high accessibility and convenience, and thus serving as a primary mode of “last-mile” transportation in urban areas. To advance stochastic capacity estimation methods and provide reliable assessments of non-motorized roadway capacity, this study proposes a stochastic capacity estimation model based on power spectral analysis. The model treats discrete traffic flow data as a time-series signal and employs a stochastic signal parameter model to fit stochastic traffic flow patterns. Initially, UAVs and video cameras are used to capture videos of mixed non-motorized traffic flow. The video data were processed with an image detection algorithm based on the YOLO convolutional neural network and a video tracking algorithm using the DeepSORT multi-target tracking model, extracting data on traffic flow, density, speed, and rider characteristics. Then, the autocorrelation and partial autocorrelation functions of the signal are employed to distinguish among four classical stochastic signal parameter models. The model parameters are optimized by minimizing the AIC information criterion to identify the model with optimal fit. The fitted parametric models are analyzed by transforming them from the time domain to the frequency domain, and the power spectrum estimation model is then calculated. The experimental results show that the stochastic capacity model yields a pure EV capacity of 2060–3297 bikes/(h·m) and a pure bicycle capacity of 1538–2460 bikes/(h·m). The density–flow model calculates a pure EV capacity of 2349–2897 bikes/(h·m) and a pure bicycle capacity of 1753–2173 bikes/(h·m). The minimal difference between these estimates validates the effectiveness of the proposed model. These findings hold practical significance in addressing urban road congestion. Full article

Background/Objectives: This study aimed to investigate the effects of fish oil-derived omega-3 polyunsaturated fatty acids (omega-3 PUFAs) on gut microbiota and serum lipid metabolites in T2DM. Methods: In a three-month, randomized, double-blind, placebo-controlled study, 110 T2DM patients received either fish oil (n = 55) or corn oil (n = 55) capsules daily. Serum lipids, glycemic parameters, gut microbiota diversity, and lipidomics were assessed. Results: This study found that fish oil-derived omega-3 PUFAs intervention did not significantly lower the fasting plasma glucose levels when compared with the baseline level (p > 0.05). However, serum fasting blood glucose (p = 0.039), glycosylated hemoglobin levels (p = 0.048), HOMA-IR (p = 0.022), total cholesterol (p < 0.001), triglyceride (p = 0.034), LDL cholesterol (p = 0.048), and non-HDL levels (p = 0.046) were significantly lower in the fish oil group compared with the corn oil group after three months of intervention. Also, it altered glycerophospholipid metabolism and gut microbiota. After three months, the fish oil group showed a significantly lower abundance of Desulfobacterota compared with the corn oil control group (p = 0.003), with reduced levels of Colidextribacter (p = 0.002), Ralstonia (p = 0.021), and Klebsiella (p = 0.013). Conversely, the abundance of Limosilactobacillus (p = 0.017), Lactobacillus (p = 0.011), and Haemophilus (p = 0.018) increased significantly. In addition, relevant glycolipid metabolism indicators showed significant correlations with the altered profiles of serum lipid metabolites, intestinal bacteria, and fungi. Conclusions: This study highlights the impact of fish oil-derived omega-3 PUFAs on intestinal microbiota structure and function in patients with type 2 diabetes. The observed decrease in pathogenic bacterial species and the enhancement of beneficial species may have significant implications for gut health and systemic inflammation, both of which are pivotal in managing diabetes. Further research is warranted to comprehensively elucidate the long-term benefits and underlying mechanisms of these microbiota alterations. Full article

High-Content Crumb Rubber Asphalt (HCRA) binder improves road performance and address waste tyre pollution, yet its ageing behaviour is not fully understood. In this study, 70# neat asphalt binder and HCRA with rubber contents of 35% and 50% were selected and aged through the Thin Film Oven Test (TFOT) and Pressure Ageing Vessel (PAV) tests. FTIR (Fourier Transform Infrared Spectroscopy) and DSR (Dynamic Shear Rheometer) were employed to investigate their chemical composition and rheological properties. The FTIR results show that HCRA’s chemical test results are similar to those of 70#, but HCRA is more susceptible to ageing. I(C=C) strength decreases with age. The DSR results show that HCRA outperforms 70# neat asphalt binder in terms of viscoelasticity, high temperature performance and fatigue resistance, and exhibits greater resistance to ageing. The ageing index (AI) was obtained through a calculation using the formula, and overall, 70# neat asphalt binder is more sensitive to ageing behaviour and less resistant to ageing, and HCRA is particularly outstanding for fatigue resistance. A strong correlation is observed between chemical composition and some rheological property indicators. Therefore, we are able to predict the rheological properties using chemical composition indicators. This study provides insight into the ageing behaviour of a neat asphalt binder and an HCRA binder and demonstrates that the HCRA binder outperforms conventional asphalt in several performance areas. It also provides theoretical support for the consumption of waste tyres to prepare high content crumb rubber asphalt. Full article

Hypocrellins (HYPs), naturally occurring 3,10-xylene-4,9-anthracene derivatives sourced from Shiraia bambusicola and Hypocrella bambusae, exhibit significant photobiological activities. Despite their capability for generating a high yield of reactive oxygen species, including singlet oxygen and superoxide anion radical, their application in photodynamic therapy (PDT) is constrained. This limitation is due to their low dark phototoxicity, weak absorption within the therapeutic window of PDT (600–900 nm), and inherent hydrophobicity, which hinder their immediate use in amphipathic PDT applications. This review comprehensively discusses the research advancements in the bioactivities and biosynthesis of HYPs, alongside the reported chemical and physical modifications that enhance their water solubility and extend their therapeutic window. Additionally, it explores potential strategies for developing pharmaceuticals, photocatalytic agents, and photosensitive pesticides based on HYPs. Full article

Slow-release urea, as an environmentally friendly fertiliser, can provide a continuous and uniform supply of nutrients needed by the crop, reduce the amount and frequency of fertiliser application, and promote the uptake and utilisation of nitrogen in crops. The production of E. ferox is often dominated by the application of quick-acting fertilisers, resulting in serious problems of over-fertilisation, inappropriate periods of fertilisation, eutrophication of soil and water due to fertilisation, and difficulties in applying fertilisers. Therefore, in this study, different amounts (CK, T1, T2, T3, T4, T5) of SRU (Slow-release Urea) were first applied, and T3 (18.8 kg·667 m⁻²) was found to significantly improve both yield and quality. Further, it was found that under different SRU (CK, S1, S2, S3, S4) application period treatments, application of 18.8 kg·667 m⁻² at AFP20 (S2) period significantly increased the yield and quality of E. ferox. In the seed kernels of E. ferox, the total yield, soluble sugar content, total starch, and flavonoid content increased significantly by 10.35%, 36.40%, 5.91%, and 22.80%, respectively, compared with CK. In addition, the expression of key sugar transporter genes (EfSWEETs), flavonoid synthesis-related genes (EfPAL, EfDFR, etc.), and starch synthesis-related enzyme activities (SBE, SSS, GBSS) were significantly increased. By exploring the quantity of application and application period of SRU, this study was carried out to investigate the in-depth effect of SRU on the growth and development of E. ferox and to provide technical references for the increase in E. ferox yield, the improvement in E. ferox quality, and the simplification of fertiliser application. Full article

This study introduces an ensemble model designed for real-time monitoring of bedridden patients. The model was developed using a unique dataset, specifically acquired for this study, that captures six typical movements. The dataset was balanced using the Synthetic Minority Over-sampling Technique, resulting in a diverse distribution of movement types. Three models were evaluated: a Decision Tree Regressor, a Gradient Boosting Regressor, and a Bagging Regressor. The Decision Tree Regressor achieved an accuracy of 0.892 and an R² score of 1.0 on the training dataset, and 0.939 on the test dataset. The Boosting Regressor achieved an accuracy of 0.908 and an R² score of 0.99 on the training dataset, and 0.943 on the test dataset. The Bagging Regressor was selected due to its superior performance and trade-offs such as computational cost and scalability. It achieved an accuracy of 0.950, an R² score of 0.996 for the training data, and an R² score of 0.959 for the test data. This study also employs K-Fold cross-validation and learning curves to validate the robustness of the Bagging Regressor model. The proposed system addresses practical implementation challenges in real-time monitoring, such as data latency and false positives/negatives, and is designed for seamless integration with hospital IT infrastructure. This research demonstrates the potential of machine learning to enhance patient safety in healthcare settings. Full article

A crucial role is played by steering-angle prediction in the control of autonomous vehicles (AVs). It mainly includes the prediction and control of the steering angle. However, the prediction accuracy and calculation efficiency of traditional YOLOv5 are limited. For the control of the steering angle, angular velocity is difficult to measure, and the angle control effect is affected by external disturbances and unknown friction. This paper proposes a lightweight steering angle prediction network model called YOLOv5Ms, based on YOLOv5, aiming to achieve accurate prediction while enhancing computational efficiency. Additionally, an adaptive output feedback control scheme with output constraints based on neural networks is proposed to regulate the predicted steering angle using the YOLOv5Ms algorithm effectively. Firstly, given that most lane-line data sets consist of simulated images and lack diversity, a novel lane data set derived from real roads is manually created to train the proposed network model. To improve real-time accuracy in steering-angle prediction and enhance effectiveness in steering control, we update the bounding box regression loss function with the generalized intersection over union (GIoU) to Shape-IoU_Loss as a better-converging regression loss function for bounding-box improvement. The YOLOv5Ms model achieves a 30.34% reduction in weight storage space while simultaneously improving accuracy by 7.38% compared to the YOLOv5s model. Furthermore, an adaptive output feedback control scheme with output constraints based on neural networks is introduced to regulate the predicted steering angle via YOLOv5Ms effectively. Moreover, utilizing the backstepping control method and introducing the Lyapunov barrier function enables us to design an adaptive neural network output feedback controller with output constraints. Finally, a strict stability analysis based on Lyapunov stability theory ensures the boundedness of all signals within the closed-loop system. Numerical simulations and experiments have shown that the proposed method provides a 39.16% better root mean squared error (RMSE) score than traditional backstepping control, and it achieves good estimation performance for angles, angular velocity, and unknown disturbances. Full article

In the present study, biocomposite materials were created by incorporating biochar (BC) at rates of 1, 2.5, and 5 wt.% into a poly(butylene succinate) (PBSu) matrix using a two-stage melt polycondensation procedure in order to provide understanding of the aging process. The biocomposites in film form were exposed to UV irradiation for 7, 14, and 21 days. Photostability was examined by several methods, such as Fourier transform infrared spectroscopy (FTIR), which proved that new carbonyl and hydroxyl groups were formed during UV exposure. Moreover, Differential Scanning Calorimetry (DSC) measurements were employed to record the apparent UV effect in their crystalline morphology and thermal transitions. According to the molecular weight measurements of composites, it was apparent that by increasing the biochar content, the molecular weight decreased at a slower rate. Tensile strength tests were performed to evaluate the deterioration of their mechanical properties during UV exposure, while Scanning Electron Microscopy (SEM) images illustrated the notable surface alternations. Cracks were formed at higher UV exposure times, to a lesser extent in PBSu/BC composites than in neat PBSu. Furthermore, the mechanism of the thermal degradation of neat PBSu and its biocomposites prior to and upon UV exposure was studied by Pyrolysis–Gas Chromatography/Mass Spectrometry (Py‒GC/MS). From all the obtained results it was proved that biochar can be considered as an efficient UV-protective additive to PBSu, capable of mitigating photodegradation. Full article

To optimize the acceleration performance of independently driven electric vehicles with four in-wheel motors, this paper proposes an anti-slip regulation (ASR) strategy based on dynamic road surface observer for more efficient tracking of the optimal slip ratio and enhanced vehicle acceleration. The method uses the Unscented Kalman Filter (UKF) observer to estimate vehicle speed and calculate the actual slip ratio, while a fuzzy controller based on the Burckhardt tire model identifies road surfaces. The road’s peak adhesion coefficient and optimal slip ratio curve are fitted using a Back Propagation Neural Network (BPNN) optimized by Particle Swarm Optimization (PSO). The control strategy further refines torque management through an adaptive sliding mode control (ASMC) that integrates adaptive laws and a super-twisting sliding mode approach to track the optimal slip ratio. Joint simulations with MATLAB/Simulink and Carsim on low-adhesion, docking, and split-traction surfaces demonstrate that the strategy quickly and accurately identifies the optimal slip ratio across various road surfaces. This enables the tire slip ratio to approach the optimal value in minimal time, significantly improving vehicle dynamic performance. Compared to conventional sliding mode controllers, the optimized ASMC reduces chattering and improves control precision. Full article