Search Results (1,410)

Aimed at addressing the issue of frequent short-duration ICE start–stop events in P2-configuration plug-in hybrid electric vehicles (PHEVs) employing conventional instantaneous optimization-based Equivalent Consumption Minimization Strategy (ECMS)—and the resulting deterioration of vehicle smoothness, NVH performance, fuel economy, and emission performance—this paper proposes a novel energy management strategy, designated ECMS-ISS, which integrates instantaneous optimization with an engine unnecessary start suppression algorithm. A multilayer perceptron (MLP) neural network is first constructed as an online identifier to recognize high-frequency intervals of frequent start–stop events in real time. A dedicated penalty function is then embedded within these identified intervals, with the penalty intensity adaptively adjusted according to the accumulated count of short-duration start–stop events, enabling zoned and targeted intervention without affecting engine torque output during normal operating intervals. Simulation results under NEDC and WLTC driving cycles demonstrate that, compared with the conventional A-ECMS, ECMS-ISS reduces engine start–stop events by 35.48% and 32.31%, respectively, and reduces comprehensive fuel consumption by 2.13% and 5.40%, while significantly decreasing CO, NOx, and HC emissions. Compared with RB-EMS, ECMS-ISS also exhibits superior fuel economy and emission reductions, with the final SOC maintained within a reasonable range throughout. The proposed strategy demonstrates distinct advantages in reconciling multiple objectives, including start–stop rationality, fuel economy, emission performance, and battery health, thereby providing a practical and adaptive solution to the frequent engine start–stop problem in P2-configuration PHEVs. Full article

This study evaluates the structural dynamics of the landscape within the Sierra de San Pedro and Embalse de Cornalvo Special Areas of Conservation between 2006 and 2018 to determine the effect of land-use spatial configuration on ecosystem stability. Methodologically, Corine Land Cover cartography was geoprocessed in QuantumGIS utilising the LecoS plugin to calculate five landscape metrics. The results reveal marked stability within the agroforestry matrix of both protected areas, demonstrating the effectiveness of their conservation status against drastic land-use changes. Nonetheless, contrasting internal trajectories were identified: the Sierra de San Pedro experienced a process of silent reforestation and the unification of natural habitats (characterised by a reduction in patch numbers and an increase in mean patch size), whereas the Cornalvo landscape exhibited strong structural homogeneity dominated by human activity. It is concluded that while the Sierra de San Pedro is evolving towards forest maturation and robust internal connectivity, Cornalvo maintains a structural inertia of absolute stability. These findings demonstrate the importance of incorporating spatial metrics into environmental management to strengthen ecosystem resilience to change. Full article

This study investigated the application of Covariant Hamiltonian Optimization for Motion Planning (CHOMP) in the MATLAB programming environment to an industrially relevant Universal Robots UR5e six-DOF manipulator. The parameters were set to be equal to those of the standard MoveIt2 CHOMP plugin, and the obstacle cost was computed directly in MATLAB using the Robotics System Toolbox’s forward kinematics function to obtain the end-effector position at each trajectory waypoint, which was then evaluated against a piecewise potential field defined over three spherical obstacles in the workspace. We executed five distinct picking task examples and one task over thirty trials, together with a sensitivity analysis over the weight parameter defining the optimization smoothness (i.e., weight/gamma). The mixed empirical results exposed major drawbacks of vanilla CHOMP under our parameter configuration. We achieved a collision-free result for only two of the five tasks, T-03 and T-05, with T-03 converging quickly in five iterations (0.16 s) and T-05 requiring 156 iterations and hitting the planning timeout limit of 10 s. Three tasks did not yield any collision-free result under the 10 s time limit. One of those three tasks, T-01, when running 30 random trial simulations after adding a tiny amount of noise to the start/end poses, yielded 0%, so all trials timed out on its planning 200-iteration limit with an invalid collision result. We analyzed the movement profile (position, velocity, and acceleration over time) of the trajectories generated during the experiments. Several examples exceed the UR5e velocity limit (180 deg/s) and the UR5e acceleration limit (400 deg/s²) by an order of magnitude, and peak values on T-02 reached up to 4731.92 deg/s². With these chosen parameters, basic CHOMP is not industrially suitable for the UR5e robot or for the implementation of the empirical evaluation of CHOMP discussed in this paper. We also identified the modes of failure of basic CHOMP under these parameters and discuss relevant changes. Full article

Information analysis recommendation differs from conversational recommender systems (CRS) because relevance changes with the decision phase. The same event may support observation, interpretation, option selection, or action feedback, yet most large language model (LLM)-agent CRS represent dialogue state as intent and preference. This study examines whether explicit decision-stage awareness improves recommendation and whether it can be added independently of the LLM backbone. We propose Stage-Aware Conversational Recommender System (SA-CRS), a plug-in layer guided by the Observe–Orient–Decide–Act cycle. It decouples stage detection from LLM reasoning and uses detected stages to guide dialogue strategy and candidate re-ranking. We evaluate SA-CRS on an information analysis recommendation dataset from event-structured reports, using multi-turn simulated dialogues and four LLM backbones. Oracle stage injection improves Hit@5 by 3.0 percentage points (pp), showing that decision stage provides a signal beyond topic matching. With a prompt-based detector, SA-CRS improves Hit@5 by 9.0 pp on a strong backbone; with an independent Bidirectional Encoder Representations from Transformers (BERT) detector and probabilistic re-ranking, gains range from 6.5 to 15.5 pp. Negative controls with uniform or random stage signals fail to reproduce the improvements and may reduce efficiency. The results suggest that, within the evaluated single-domain information-analysis setting, decoupled decision-stage awareness is practical for decision-intensive CRS. Full article

Traditional rural landscape evaluations have generally relied on ground-level photographs or videos. However, these approaches have limitations in spatial continuity, depth cues, and interactivity. Unmanned Aerial Vehicle (UAV) photogrammetry and immersive virtual reality (VR) were integrated into a comparative rural landscape evaluation framework to assess landscape aesthetic quality. UAV-derived 3D village models were generated and deployed on PICO 4 headsets through Unity 3D and the Cesium plugin, providing evaluators with spatially continuous and 6DoF-enabled immersive representations of village scenes. The evaluation included ten landscape feature factors, including color harmony, vegetation richness, building layout harmony, openness of view, and sense of spatial depth. Ratings were collected from 75 valid participants across 17 villages, with village-level mean scores serving as the primary unit of inference. Paired-samples t-tests, subgroup sensitivity analysis, expert-only presentation-order sensitivity analysis, Pearson correlations, Steiger tests for dependent correlations, stepwise multiple linear regression, nested leave-one-village-out cross-validation (LOOCV), and bootstrap variable-selection stability analysis were conducted to examine differences between the 2D photo-based and VR-based conditions. The results showed that: (1) overall satisfaction was significantly higher in the VR-based condition than in the 2D photo-based condition (3.46 vs. 3.24); (2) the condition-specific regression models retained different landscape feature factors: sense of spatial depth and color harmony in the 2D photo-based model, and vegetation distribution pattern and environmental comfort in the VR-based model; and (3) the VR-based regression model had a higher condition-specific internal $R^2$ than the 2D photo-based model ($R^2=0.784$ vs. 0.569). Within the present dataset, the VR-based model also showed lower SD-normalized prediction error under nested LOOCV, while bootstrap resampling showed higher selection frequencies for the predictors retained in the VR-based model. Overall, the findings demonstrate the potential of UAV-derived immersive VR for rural landscape evaluation and provide new evidence on how presentation conditions influence landscape perception and evaluation. Full article

Accurate localization of bounding boxes is a prerequisite for enabling vision-driven precision agriculture pipelines, as downstream tasks such as morphological feature extraction, growth monitoring, and digital-twin synchronization depend directly on the geometric quality of the detected boxes. Distance-IoU (DIoU) and Complete-IoU (CIoU) improve upon simple overlap-based objectives by incorporating a normalized center-distance term into the regression loss, along with the overlap and, for CIoU, an aspect-ratio penalty, but that term remains embedded in a single composite formulation with an implicit, non-adjustable weight. We propose a Directional Alignment Penalty (DAP), an auxiliary localization regularizer that introduces an independently weighted normalized center-displacement term into the bounding-box regression objective without modifying the detector architecture. The proposed DAP-YOLOv8 increased mAP@0.5:0.95 from 51.69% to 53.76% and mAP@0.5 from 80.03% to 80.36% while preserving precision and recall; repeated-seed experiments further showed that the improvement in fine-grained localization was consistent across random initializations on a purpose-built oyster-mushroom (Pleurotus ostreatus) dataset collected from a real-world smart greenhouse. Results show that explicitly modeling the center-distance factor as an independent and tunable component can improve fine-grained localization without sacrificing the computational efficiency of the base detector, thereby providing a lightweight plug-in extension for agricultural detection and digital-twin applications. Full article

To comply with stringent emission regulations, the deployment of hybridized powertrains is continuously expanding. However, architectures such as plug-in and parallel hybrids intrinsically reduce the overall runtime of the internal combustion engine (ICE). Because the battery state-of-charge (SOC) dictates intermittent engine activation, this operational strategy inevitably induces frequent cold-start events. This study investigates the thermal dynamics of commercial catalyst geometries (300–1200 cpsi, 2–8 mil) via 1D numerical simulations under real-world driving conditions. Without active heating, high-thermal-mass substrates unexpectedly outperform ultra-thin-wall variants by buffering against convective quenching during prolonged idling. However, integrating start–stop functionality halts cold exhaust flow, elevating mean temperatures and marginalizing geometric disparities. Evaluating electrically heated catalysts (EHCs) reveals that discrete preheating is highly inefficient due to rapid heat dissipation. Conversely, continuous closed-loop heating coupled with start–stop functionality sustains operational temperatures for over 90% of the cycle. Under continuous heating, substrate geometry ceases to dictate thermal performance; instead, it governs electrical efficiency. Low-thermal-mass monoliths minimize cumulative energy demand to 213 kJ (versus 277 kJ for high-mass variants), incurring a negligible CO₂ penalty. Consequently, future hybrid architectures must integrate lightweight EHCs to ensure sustainable emission control. Full article

End-of-life electric-vehicle and plug-in hybrid (EV/PHEV) battery packs pose a recurrent decision: refurbish, redeploy in second-life storage, recycle, or reject. Technical condition, safety, economics, regulation, traceability, and environmental benefit interact, making pathway selection a systems-level decision problem. This paper develops a risk-informed multi-criteria framework for the conditional viability of refurbished batteries under data-scarce conditions. Failure mode, effects, and criticality analysis (FMECA) supplies a pathway-specific residual-risk penalty; multi-criteria decision analysis (weighted-sum and the Technique for Order of Preference by Similarity to Ideal Solution, TOPSIS) orders four alternatives on six benefit criteria; and a screening-level avoided-burden indicator, not a life-cycle assessment, positions the environmental criterion. An Integrated Viability Index (IVI) offsets weighted benefits against the risk penalty through one tunable coefficient. All inputs are illustrative and literature-informed; the demonstration tests decision logic, not empirical pathway performance. Preference is conditional: refurbishment leads under economic and technical priority with credible risk mitigation, second-life reuse under environmental priority, and recycling under safety, regulatory, and infrastructure constraints, while rejection never leads. As the risk penalty rises, leadership migrates traceably toward recycling, and IVI–TOPSIS divergence localizes exactly where the risk treatment changes the decision. A proposed Refurbished-Battery Suitability Index (RBSI) would couple measured diagnostics to the IVI; its calibration remains future work. Full article

Non-uniform illumination in tunnel environments severely degrades image quality, posing substantial challenges to visual monitoring and intelligent transportation systems. While histogram equalization (HE) remains prevalent due to its computational simplicity, its non-linear pixel transformations frequently induce over-enhancement, artifacts, and structural distortions. This paper proposes Prior-Guided Histogram Equalization (PGHE), a lightweight enhancement framework that integrates conventional HE with Retinex-based illumination priors. Within the Retinex decomposition paradigm, PGHE constructs a contrast illumination map from the ratio between the HE-enhanced image and the original input. A Prior Correction Module (PCM) subsequently refines this map via relative total variation regularization, thereby restoring spatial coherence and alleviating local discontinuities introduced by HE. The corrected map is then applied to the original image to obtain the final enhanced result. Extensive evaluation on the LOL low-light benchmarks and a proprietary tunnel dataset comprising 247 real-world frames shows that PGHE offers favorable trade-offs among contrast enhancement, structural fidelity, and brightness preservation: it is particularly strong in brightness preservation and Entropy, while its PSNR/SSIM on LOL and its NIQE on the tunnel dataset are comparable to, but not always the best among, the compared methods. Furthermore, the proposed PCM functions as a plug-in module that improves existing HE variants with measurable gains in Structural Similarity and perceived naturalness at a modest cost in Absolute Mean Brightness Error. Full article

Maintaining frequency stability in islanded microgrids (MGs) has become increasingly challenging due to the growing penetration of renewable energy sources, particularly photovoltaic systems, wind turbine generators (WTGs), and plug-in hybrid electric vehicles (PHEVs). The intermittent nature of renewable generation and continuous load variations introduces significant power imbalances, resulting in frequency deviations and degraded system stability. Although the classical Ziegler–Nichols (ZN) tuning method is attractive because of its simplicity and ease of implementation, it is generally limited to conventional proportional–integral–derivative (PID) controllers and is often inadequate for renewable-dominated MGs. To overcome these limitations, this paper proposes a modified ZN-based tuning strategy for a novel multistage PID (MPID) controller. Unlike the conventional ZN method, the proposed approach extends its applicability to the MPID structure by introducing an additional proportional gain (KPP), enabling the tuning of five controller parameters while preserving low computational complexity and practical implementation. The proposed controller is implemented and validated using a detailed MATLAB/Simulink model of an isolated MG comprising PV systems, WTG, diesel generators, and PHEVs. Its performance is comprehensively evaluated under multi-step load disturbances, renewable power fluctuations, combined disturbances, and different PHEV charging/discharging modes and battery state-of-charge levels. Furthermore, the proposed controller is benchmarked against conventional ZN-PID, ZN-FOPID, and both PID- and MPID-based controllers tuned using Particle Swarm Optimization, Cuckoo Search Algorithm, Moth–Flame Optimization, and Grasshopper Optimization Algorithm. Simulation results demonstrate that the proposed ZN-MPID controller achieves the best overall dynamic performance, with a settling time of 4.109 s, zero overshoot, a maximum frequency undershoot of 1.801 × 10⁻⁴ Hz, and the lowest error indices (ISE = 3.073 × 10⁻⁶, ITSE = 0.697 × 10⁻⁶, and ITAE = 3.40 × 10⁻⁴). Compared with the investigated metaheuristic-based PID controllers, the proposed controller reduces the settling time by up to 86.1% and the error indices by up to 95.5%. It also consistently outperforms all investigated MPID tuning methods, confirming the effectiveness of the proposed modified ZN tuning strategy. Overall, the proposed methodology provides an efficient, low-complexity, and practical solution for frequency regulation in renewable-dominated isolated MGs. Full article

Background/Objective: Cinnamaldehyde (CA) is a naturally occurring bioactive compound derived from the leaves, bark, roots, and flowers of the Chinese medicinal plant Cinnamomum cassia. It exhibits a broad spectrum of pharmacological properties, encompassing antioxidant, antibacterial, anti-diabetic, antifungal, and anticancer activities. Notably, it has shown potential therapeutic benefits in the management of type 2 diabetes mellitus (T2DM) and non-alcoholic fatty liver disease (NAFLD). Ochratoxin A (OTA), a common contaminant found in foods such as cereals, coffee, and raisins, is also present in traditional Chinese medicinal materials, including Astragalus and liquorice. T2DM and NAFLD share intertwined pathophysiological pathways, including insulin resistance, dyslipidaemia, chronic low-grade inflammation and oxidative stress, with insulin resistance serving as the common pathological hub for both conditions. Consequently, they frequently co-occur and exacerbate each other. OTA exerts dual-targeted toxicity to the pancreas and liver, which may synergistically drive the development of the comorbidity of T2DM and NAFLD. These two processes are mutually causal and together constitute the pathological basis of metabolic comorbidity. Methods: Network toxicology employs toxicological data, gene expression, and protein–protein interaction (PPI) networks to predict the targets of toxins, while network pharmacology, based on systems biology principles, reveals how drugs exert regulatory effects through multiple targets and pathways. In this study, we employed an integrated network toxicology and network pharmacology approach to jointly decipher the potential mechanisms by which CA intervenes in OTA-induced comorbid T2DM-NAFLD. First, a network toxicology approach was employed to preliminarily screen for core toxicological targets responsible for OTA’s pathogenicity. Subsequently, network pharmacology was used to identify potential targets of CA-mediated intervention in the disease. Finally, the common overlap among the CA intervention targets, OTA toxicity targets, and disease targets was defined as the final set of potential targets for CA-mediated intervention in OTA-induced T2DM-NAFLD comorbidity. A PPI network was constructed using the STRING database, and topological analysis was performed with Cytoscape. Core targets were selected using the median values of six parameters—betweenness centrality, closeness centrality, degree centrality, eigenvector centrality, LAC (local average connectivity) score, and network centrality—as cut-off thresholds, and the top 10 key genes were further identified using the cytoHubba plugin. Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted via the DAVID database, and the results were visualized on the CNSknowall platform. Lastly, molecular docking of the core targets was performed using the CB-DOCK2 platform to validate binding affinity. Results: Based on an integrated analysis of network toxicology, network pharmacology, and molecular docking, 10 key targets were systematically identified. These may serve as potential mediators of cinnamaldehyde in the treatment of OTA-induced T2DM-NAFLD comorbidity. Among these, six targets—albumin (ALB), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), interleukin-6 (IL-6), tumor necrosis factor (TNF), actin beta (ACTB), and estrogen receptor 1 (ESR1)—possess crystal structures amenable to molecular docking. KEGG enrichment analysis revealed that CA and OTA jointly participate in key pathological processes such as the cancer pathway, the lipid and atherosclerosis pathway, the advanced glycation end-products–receptor for advanced glycation end-products (AGE-RAGE) signaling pathway, the phosphatidylinositol 3-kinase–protein kinase B (PI3K-Akt) signaling pathway, the TNF signaling pathway, and the interleukin-17 (IL-17) signaling pathway. OTA exacerbates inflammatory responses, impairs insulin signaling, promotes hepatic steatosis, and disrupts systemic metabolic homeostasis, ultimately contributing to T2DM-NAFLD comorbidity. Conversely, cinnamaldehyde counteracts these pathological processes through multiple mechanisms, including antioxidant and anti-inflammatory effects as well as regulation of glucose and lipid metabolism, thereby restoring metabolic homeostasis. Conclusions: This study has preliminarily identified the toxicological targets of OTA and the potential intervention targets of CA, offering new avenues for preventing and intervening in OTA-induced metabolic toxicity. Furthermore, it provides a theoretical basis for CA as a potential multi-target therapeutic agent and presents novel insights worthy of further investigation into the prevention of T2DM-NAFLD comorbidity. Full article

Believing that electric vehicles (EVs) benefit the environment may be associated with EV intentions, but current car owners and non-car owners answer different practical questions. This cross-sectional online survey of 328 adults residing in Canada therefore analyzes the groups separately. The focal item—“The use of EVs will help protect the environment”—is treated as a perceived environmental benefit of EVs rather than as a validated general environmental-concern scale. For 226 car owners with complete focal variables, an ordered logistic model including personal environmental responsibility produced an odds ratio (OR) of 1.82 per one-category increase in perceived environmental benefit (95% confidence interval [CI] 1.51–2.19; p < 0.001). The association remained positive in the available demographic sensitivity model (OR 2.05, 95% CI 1.68–2.51). For 72 non-car owners, the parsimonious exploratory model produced an OR of 1.47 (95% CI 1.04–2.08; p = 0.029), but the estimate was attenuated after broader demographic adjustment (OR 1.39, 95% CI 0.96–2.02; p = 0.080). Cluster-robust stacked cumulative-logit diagnostics found no evidence against proportional odds in the primary models. Because owners reported five-category purchase likelihood for a plug-in electric vehicle and non-owners reported seven-category stated EV preference, no formal group comparison was conducted. The findings are associational, based on single items and a non-probability sample, and are consistent only with selected propositions of Value–Belief–Norm theory. Evidence is strongest for a positive owner–context association and suggestive, but less stable, for non-car owners. Full article

Collision statistics provide a finite-resolution view of information by measuring how often independent samples fall on the same state and form the basis of integer-order Rényi entropies. Here, we use low-order Rényi entropies to characterize finite-resolution approximations to Shannon entropy and mutual information. Specifically, we determine what population information is captured by finite collision moments, we quantify how the resulting targets differ from their Shannon counterparts, and we analyze how accurately they can be estimated from finite samples. We use the interpolation remainder to identify structural approximation error induced by extrapolating from integer-order Rényi entropies to the Shannon point. We separate this deterministic error from finite-sample estimation error: increasing sample size improves estimation of a finite-resolution target but does not eliminate its deterministic difference from Shannon entropy or mutual information. Finally, we show that finite collision moments do not generally identify Shannon entropy, and that increasing collision order shifts sensitivity toward high-probability events. Our numerical experiments illustrate the approximation–estimation trade-off and evaluate collision-based approximations alongside plug-in and Miller–Madow estimators. Together, these results provide a principled way to use low-order coincidence structure as finite-resolution information, while making explicit what finite collision moments can and cannot reveal about Shannon entropy and mutual information. Full article

Amid global energy demand escalation, renewable energy intermittency, and electric vehicle (EV) charging demand concentration exacerbating power grid supply–demand contradictions, Vehicle-to-Grid (V2G) emerges as a solution, yet EV users’ V2G acceptance and participation willingness lack in-depth exploration. This study aims to fill this research gap by investigating Chongqing EV users’ V2G acceptance, behavioral intention, and influencing mechanisms to provide support for V2G promotion. It targets EV owners in Chongqing’s downtown areas, collecting 295 valid questionnaires, covering users’ demographics, travel-charging habits, and subjective attitudes. Based on technology acceptance and usage theories, it constructs a structural equation model (SEM) with perceived usefulness, ease of use, economic viability, and technological risk as latent variables to analyze their impacts on behavioral intention. Results show that perceived usefulness, perceived ease of use, and economic benefits positively affect behavioral intention, while technology risk perception exerts a negative effect; users with fixed commutes, low-range anxiety, and home charging piles are more receptive, and 70% support V2G but worry about battery wear and plug-in duration. Its innovation lies in integrating EV charging–discharging and travel patterns into the analysis, and its findings enrich new energy technology acceptance theory and provide a theoretical basis for transportation-energy system coordinated planning and V2G development. Full article

The decarbonisation of the automotive sector requires product innovation, circular end-of-life management and energy-system transformation to be treated as interdependent strategic choices. This study proposes a decision-oriented life cycle assessment (LCA) framework for evaluating sustainable product innovation in sport utility vehicles (SUVs), focusing on how powertrain selection and post-consumer management support the low-carbon transition. Six SUV powertrain technologies—petrol, diesel and CNG internal combustion engine vehicles (ICEVs), petrol plug-in hybrid electric vehicles (PHEVs), battery electric vehicles (BEVs) and fuel cell electric vehicles (FCEVs)—were assessed for 2025–2050 using ReCiPe 2016, IPCC 2021, Cumulative Energy Demand, CML-IA and Ecological Scarcity 2021. Landfilling and recycling scenarios were combined with fuel- and energy-cycle modelling, including well-to-tank (WTT) and tank-to-wheel (TTW) emissions and a Paris Agreement-compatible 2050 pathway. Recycling generally outperformed landfilling, reducing greenhouse gas emissions by 26–35%, cumulative energy demand by 28–59%, carcinogenic air emissions by 27–43% and heavy-metal impacts on soil by 62–80%, although eutrophication revealed category-specific trade-offs. BEV and FCEV configurations were particularly sensitive to material recovery and energy-supply decarbonisation, whereas ICEV impacts remained dominated by fuel use. The findings show that sustainable SUV design requires strategic alignment of product architecture, circular supply chains, recycling technologies and low-carbon energy policy. Full article