Search Results (501)

Detecting marine oil spills is vital for protecting the marine environment, ensuring maritime traffic safety, supporting marine development, and enabling effective emergency response. The dual-polarimetric (DP) synthetic aperture radar (SAR) system represents an evolution from single to full polarization (FP), which has become an essential tool for oil spill detection with the growing availability of open-source and shared datasets. Recent research has focused on enhancing DP information structures to better exploit this data. This study introduces improved DP models’ structure with modified the scattering vector coefficients to ensure consistency with the corresponding components of the FP system, enabling comprehensive comparison and analysis with traditional DP structure, includes theoretical and quantitative evaluations of simulated data from FP system, as well as validation using real DP scenarios. The results showed the following: (1) The polarimetric entropy H_L obtained through the improved DP scattering matrix C_L can achieve higher information consistency results closely aligns with FP system and better performance, compared to the typical two DP scattering structures. (2) For multiple polarimetric features from DP scattering matrix (both traditional feature and combination feature), the improved DP scattering matrix C_L can be used for oil spill extraction effectively with prominent results. (3) For oil spill extraction, the polarimetric features-based C_L tend to have relatively high contribution, especially the H_A feature combination, leading to substantial gains in improved classification performance. This approach not only enriches the structural information of the DP system under VV–VH mode but also improves oil spill identification by integrating multi-structured DP features. Furthermore, it offers a practical alternative when FP data are unavailable. Full article

As the deployment of wind energy systems continues to rise globally, ensuring the reliability and efficiency of grid-connected Doubly Fed Induction Generator (DFIG) wind turbines has become increasingly critical. Two core challenges faced by these systems include fault diagnosis in power electronic converters and accurate prediction of wind conditions for adaptive power control. Recent advancements in artificial intelligence (AI) have introduced powerful tools for addressing these challenges. This study presents the first unified comparative performance analysis of two deep learning-based models: (i) a Convolutional Neural Network-Long Short-Term Memory CNN-LSTM with Variational Mode Decomposition for real-time Grid Side Converter (GSC) fault diagnosis, and (ii) an Incremental Generative Adversarial Network (IGAN) for wind attribute prediction and adaptive droop gain control, applied to grid-integrated DFIG wind turbines. Unlike prior studies that address fault diagnosis and wind forecasting separately, both models are evaluated within a common MATLAB/Simulink framework using identical wind profiles, disturbances, and system parameters, ensuring fair and reproducible benchmarking. Beyond accuracy, the analysis incorporates multi-dimensional performance metrics such as inference latency, robustness to disturbances, scalability, and computational efficiency, offering a more holistic assessment than prior work. The results reveal complementary strengths: the CNN-LSTM achieves 88% accuracy with 15 ms detection latency for converter faults, while the IGAN delivers more than 95% prediction accuracy and enhances frequency stability by 18%. Comparative analysis shows that while the CNN-LSTM model is highly suitable for rapid fault localization and maintenance planning, the IGAN model excels in predictive control and grid performance optimization. Unlike prior studies, this work establishes the first direct comparative framework for diagnostic and predictive AI models in DFIG systems, providing novel insights into their complementary strengths and practical deployment trade-offs. This dual evaluation lays the groundwork for hybrid two-tier AI frameworks in smart wind energy systems. By establishing a reproducible methodology and highlighting practical deployment trade-offs, this study offers valuable guidance for researchers and practitioners seeking explainable, adaptive, and computationally efficient AI solutions for next-generation renewable energy integration. Full article

The spatiotemporal uneven distribution of water resources conflicts sharply with human demands, with pumping stations facing efficiency decline due to aging infrastructure and complex hydraulic interactions. This study employs numerical simulation to investigate operational mechanisms in a parallel pump system at the Yanhuanding Yanghuang Cascade Pumping Station. Using ANSYS Fluent 2024 R1 and the SST k-ω turbulence model, we demonstrate that variable-speed control expands the adjustable flow range to 1.17–1.26 m³/s while maintaining system efficiency at 83–84% under head differences of 77.8–79.8 m. Critically, energy losses (δH) at the 90° outlet pipe junction escalate from 3.8% to 18.2% of total energy with increasing flow, while Q-criterion vortex analysis reveals a 63% vortex area reduction at lower speeds. Furthermore, a dual-mode energy dissipation mechanism was identified: at 0.90n₀ speed, turbulent kinetic energy surges by 115% with minimal dissipation change, indicating large-scale vortex dominance, whereas at 0.80n₀, turbulent dissipation rate increases drastically by 39%, signifying a shift to small-scale viscous dissipation. The novelty of this work lies in the first systematic quantification of junction energy losses and the revelation of turbulent energy transformation mechanisms in parallel pump systems. These findings provide a physics-based foundation for optimizing energy efficiency in high-lift cascade pumping stations. Full article

To enhance the accuracy and robustness of lithium-ion battery state-of-health (SOH) prediction, this study proposes a dual-mode intelligent optimization BP neural network model (AO–AVOA–BP) which integrates the Aquila Optimizer (AO) and the African Vulture Optimization Algorithm (AVOA). The model leverages the global search capabilities of AO and the local exploitation strengths of AVOA to achieve efficient and collaborative optimization of network parameters. In terms of feature construction, eight key health indicators are extracted from voltage, current, and temperature signals during the charging phase, and the optimal input set is selected using gray relational analysis. Experimental results demonstrate that the AO–AVOA–BP model significantly outperforms traditional BP and other improved models on both the NASA and CALCE datasets, with MAE, RMSE, and MAPE maintained within 0.0087, 0.0115, and 1.095%, respectively, indicating outstanding prediction accuracy and strong generalization performance. The proposed method demonstrates strong generalization capability and engineering adaptability, providing reliable support for lifetime prediction and safety warning in battery management systems (BMS). Moreover, it shows great potential for wide application in the health management of electric vehicles and energy storage systems. Full article

Environmental emissions from the maritime sector, including CO₂, NO_x, and SO_x, contribute significantly to global air pollution and climate change. The International Maritime Organization (IMO) has set a target to reduce greenhouse gas emissions from international shipping to reach zero GHG by 2050 compared to 2008 levels. To meet these goals, the IMO strongly encourages the transition to alternative fuels, such as hydrogen, ammonia, and biofuels, as part of a broader decarbonization strategy. This study presents a comparative analysis of converting conventional diesel engines to dual-fuel systems utilizing alternative fuels such as methanol or natural gas. The methodology of this research is based on theoretical calculations to estimate various types of emissions produced by conventional marine fuels. These results are then compared with the emissions generated when using methanol and natural gas in dual-fuel engines. The analysis is conducted using the EVER ALOT container ship as a case study. The evaluation focuses on both environmental and economic aspects of engines operating in natural gas–diesel and methanol–diesel dual-fuel modes. The results show that using 89% natural gas in a dual fuel engine reduces nitrogen oxides (NO_x), sulfur oxides (SO_x), carbon dioxide (CO₂), particulate matter (PM), and carbon monoxide (CO) pollutions by 77.69%, 89.00%, 18.17%, 89.00%, and 30.51%, respectively, while the emissions percentage will be 77.78%, 91.00%, 54.67%, 91.00%, and 55.90%, in order, when using methanol as a dual fuel with percentage 91.00% Methanol. This study is significant as it highlights the potential of natural gas and methanol as viable alternative fuels for reducing harmful emissions in the maritime sector. The shift toward these cleaner fuels could play a crucial role in supporting the maritime industry’s transition to low-emission operations, aligning with global environmental regulations and sustainability goals. Full article

Background/Objectives: Pulmonaria officinalis L., commonly known as lungwort, is a medicinal plant traditionally used for respiratory ailments, but its biological activities have not yet been sufficiently researched. The aim of this study was to investigate the antioxidant and dose-dependent genotoxic/antigenotoxic properties of a 70% ethanolic extract. Methods: Quantification of polyphenols and GC–MS analysis were performed in order to chemically characterize the extract. Antioxidant activity was evaluated through DPPH, PFRAP, total antioxidant capacity (TAC), and ferrous ion chelating assay (FIC). MTT and alkaline comet assay were used for investigation of cytotoxicity and geno/antigenotoxicity on normal fetal fibroblast cells (MRC-5). Results: The chemical analysis of the extract showed that the extract is rich in polyphenolics and that phytol is the most abundant compound, accompanied by terpenoids, fatty acids, alcohols, polyketides, and alkaloids. In addition, notable antioxidant capacity was detected in all tests applied. The extract reduced cell viability only at the highest concentration tested (33.7%). Furthermore, a dual dose-dependent effect was recorded since the genotoxic effect of the tested extract was observed at higher concentrations, while non-genotoxic concentrations showed protective effects against oxidative damage of DNA. Namely, pretreatment with lungwort extract reduced the DNA damage induced by H₂O₂, with the highest protective effect at the lowest tested concentration, indicating a hormetic mode of action. Conclusions: These results provide a solid foundation for future research into this medicinal plant, with the aim of its potential therapeutic use in the prevention of diseases associated with oxidative stress. Full article

Research articles are valuable resources for Information Retrieval and Natural Language Processing (NLP) tasks, offering opportunities to analyze key components of scholarly content. This study investigates the presence of methodological terminology in psychology research over the past 30 years (1995–2024) by applying a novel NLP and Machine Learning pipeline to a large corpus of 85,452 abstracts, as well as the extent to which this terminology forms distinct thematic groupings. Combining glossary-based extraction, contextualized language model embeddings, and dual-mode clustering, this study offers a scalable framework for the exploration of methodological transparency in scientific text via deep semantic structures. A curated glossary of 365 method-related keywords served as a gold-standard reference for term identification, using direct and fuzzy string matching. Retrieved terms were encoded with SciBERT, averaging embeddings across contextual occurrences to produce unified vectors. These vectors were clustered using unsupervised and weighted unsupervised approaches, yielding six and ten clusters, respectively. Cluster composition was analyzed using weighted statistical measures to assess term importance within and across groups. A total of 78.16% of the examined abstracts contained glossary terms, with an average of 1.8 term per abstract, highlighting an increasing presence of methodological terminology in psychology and reflecting a shift toward greater transparency in research reporting. This work goes beyond the use of static vectors by incorporating contextual understanding in the examination of methodological terminology, while offering a scalable and generalizable approach to semantic analysis in scientific texts, with implications for meta-research, domain-specific lexicon development, and automated scientific knowledge discovery. Full article

We report a low-cost protocol and platform for whole-abdomen 3D dynamic contrast-enhanced ultrasound (DCE-US) imaging in mice using a clinical matrix-array transducer. Background/Objectives: This platform addresses common limitations of preclinical ultrasound systems. In particular, these systems often lack real-time volumetric and molecular imaging capabilities. Methods: Using a modified silicone cup and water bath configuration, mice with dual subcutaneous tumors were imaged in vivo on a clinical EPIQ 7 system equipped with an X6-1 transducer. Results: Intravenous administration of targeted microbubbles enabled high-resolution, contrast-mode 3D imaging at multiple time points. Volumetric reconstructions captured both tumors and surrounding anatomy in a single scan, while time–intensity curves and Differential Targeted Enhancement (DTE) analysis revealed greater microbubble uptake in irradiated tumors, consistent with elevated P-selectin expression. Conclusions: This standardized imaging platform enables whole-abdomen molecular DCE-US in preclinical studies, facilitating intra-animal comparisons of vascular and molecular features across lesions or organs. Full article

Accurate postharvest quality evaluation of soybeans is essential for preserving product value and meeting industry standards. Traditional inspection methods are often inconsistent, labor-intensive, and unsuitable for high-throughput operations. This study presents a non-destructive soybean classification approach using a simplified reflectance-mode hyperspectral imaging system equipped with a single light source, eliminating the complexity and maintenance demands of dual-light configurations used in prior studies. A spectral–spatial data fusion strategy was developed to classify harvested soybeans into four categories: normal, split, diseased, and foreign materials such as stems and pods. The dataset consisted of 1140 soybean samples distributed across these four categories, with spectral reflectance features and spatial texture attributes extracted from each sample. These features were combined to form a unified feature representation for use in classification. Among multiple machine learning classifiers evaluated, Linear Discriminant Analysis (LDA) achieved the highest performance, with approximately 99% accuracy, 99.05% precision, 99.03% recall and 99.03% F1-score. When evaluated independently, spectral features alone resulted in 98.93% accuracy, while spatial features achieved 78.81%, highlighting the benefit of the fusion strategy. Overall, this study demonstrates that a single-illumination HSI system, combined with spectral–spatial fusion and machine learning, offers a practical and potentially scalable approach for non-destructive soybean quality evaluation, with applicability in automated industrial processing environments. Full article

Against the backdrop of the growing popularity of blockchain technology, this study investigates blockchain adoption strategies for the fresh food supply chain (FFSC) under a carbon cap-and-trade (CAT) policy. Taking a two-echelon supply chain consisting of a supplier and a retailer as an example, we designed four blockchain adoption modes based on the supplier’s strategy (adopt or not) and the retailer’s strategy (adopt or not). Combining influencing factors such as consumers’ low-carbon preference, consumers’ freshness preference, and carbon trading price (CTP), we established four game-theoretic models. Using backward induction, we derived the equilibrium strategies for the supplier and retailer under different modes and analyzed the impact of key factors on these equilibrium strategies. The analysis yielded four key findings: (1) BB mode (both adopt blockchain) is the optimal adoption strategy for both FFSC parties when carbon prices are high, and consumers exhibit strong dual preferences. It most effectively mitigates the negative price impact of rising carbon prices by synergistically enhancing emission reduction efforts and freshness preservation efforts, thereby increasing overall profits and achieving a Pareto improvement in the benefits for both parties. (2) Consumers’ low-carbon preference and freshness preference exhibit an interaction effect. These two preferences mutually reinforce each other’s incentive effect on FFSC efforts (emission reduction/freshness preservation). Blockchain’s information transparency makes these efforts more perceptible to consumers, forming a synergistic “emission reduction-freshness preservation” cycle that further drives sales and profit growth. (3) The adoption of blockchain by either the supplier or the retailer significantly lowers the cost threshold for the other party to adopt blockchain, thereby increasing their willingness to adopt. (4) CAT and consumer preferences jointly influence the adoption strategies of suppliers and retailers. Additionally, the adoption strategies of FFSC participants are also affected by the other party’s blockchain adoption status. Drawing on the above conclusions, this study provides actionable guidance for suppliers and retailers in selecting optimal blockchain adoption strategies. Full article

Promoting low-carbon travel modes is crucial for China’s transportation sector to achieve the dual carbon goals. When exploring the mechanisms behind individuals’ travel decisions, the relationships between factors such as the built environment and transportation policies are often derived from prior experience or subjective judgment, rather than being grounded in a solid theoretical foundation. In this paper, we build on and integrate the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM) by introducing built environment perception (BEP), encouraging policy perception (EPP), and restrictive policy perception (RPP) as either perceived ease of use (PEOU) or perceived usefulness (PU). The integration aims to explain how the latent variables in TPB and TAM jointly affect low-carbon travel intention. We conduct a traveler survey in Shanghai, China to obtain the data and employ a structural equation modeling (SEM) approach to characterize the latent mechanisms. The SEM results show that traveler attitude is the most critical variable in shaping low-carbon travel intentions. Perceived ease of use has a significant positive effect on perceived usefulness, and both constructs directly or indirectly influence attitude. As for transportation policies, encouraging policies are more effective in fostering voluntary low-carbon travel intentions than restrictive ones. Considering the heterogeneity of the traveling population, differentiated policy recommendations are proposed based on machine learning modeling and SHapley Additive exPlanations (SHAP) analysis, offering theoretical support for promoting low-carbon travel strategies. Full article

Aim: To investigate the synergistic effects of exercise training and Brassica oleracea var. italica (broccoli sprout) supplementation on Apolipoprotein A-I, B-100, and J levels in men with Type 2 diabetes mellitus (T2DM). Methods: Forty-four males with T2DM were randomly assigned to four groups: Control (CG), Supplement (SG), Training (TG), and Training + Supplement (TSG) groups. Participants in the supplement groups (SG and TSG) received 10 g of broccoli supplement after meals for 12 weeks, while those in the training groups (TG and TSG) participated in a structured exercise program (resistance and aerobic), performed three times per week for 12 weeks, at intensities of 60–70% one-repetition maximum (1RM) for resistance training and 60–70% peak oxygen uptake (VO_2peak) for aerobic training. Results: Circulating levels of apolipoproteins improved after 12 weeks in the TSG, TG, and SG groups. However, the TSG group exhibited the most pronounced improvements across metabolic and lipoprotein markers, reflecting an additive effect of both interventions. Specifically, the TSG group demonstrated absolute reductions in ApoB-100 (−48.30 ± 7.20 mg/dL) and ApoJ (−44.05 ± 5.76 mg/dL), along with an increase in ApoA-I (+44.92 ± 6.05 mg/dL). Main effect analysis revealed that exercise training elicited the most substantial improvements across metabolic and lipoprotein markers, with large effect sizes for glucose (η²p = 0.787), insulin (η²p = 0.640), HOMA-IR (η²p = 0.856), ApoA-I (η²p = 0.685), ApoB-100 (η²p = 0.774), ApoJ (η²p = 0.848), and HDL-C (η²p = 0.535). Supplementation showed moderate effects, particularly on HOMA-IR (η²p = 0.370), ApoA-I (η²p = 0.383), and ApoB-100 (η²p = 0.334), supporting an additive but exercise-dominant benefit. The combined intervention group (TSG) showed the most pronounced improvements across all measured outcomes, with large effect sizes for ApoA-I (η²p = 0.883), glucose (η²p = 0.946), insulin (η²p = 0.881), HOMA-IR (η²p = 0.904), and ApoJ (η²p = 0.852). Conclusions: The effects of combining training and broccoli sprout supplementation on apolipoprotein levels are likely to result from the activation of two separate pathways, one from training and the other from supplementation. This dual-modality intervention could serve as an effective complementary strategy in managing metabolic and cardiovascular risk factors for individuals with T2DM. However, the magnitude of change induced by the combination of exercise training and broccoli supplementation was largely driven by the training component, with supplementation providing complementary but less consistent benefits. Full article

Rapid population aging in developing countries has intensified demand for accessible nursing home services, yet spatial disparities in service distribution remain insufficiently examined in secondary cities. This study investigates spatial distribution and multi-dimensional accessibility of nursing homes in Kunming, China, using comprehensive spatial analytical methods to inform sustainable urban development. We analyzed 205 nursing homes with 47,600 beds, evaluating spatial distribution patterns, economic accessibility, and spatial accessibility across different transportation modes. Our analysis reveals a pronounced monocentric pattern with nursing resources concentrated within central urban districts, creating a “primary core-multiple satellite” structure and spatial mismatch between service supply and older adult population needs. A distinct institutional dichotomy exists between publicly and privately operated facilities, establishing a dual-track system with different accessibility implications for social equity. Economic accessibility analysis demonstrates significant barriers in central urban and tourism-oriented districts dominated by higher-priced private facilities, where minimum prices frequently exceed average monthly pension. Spatial accessibility remains inadequate across all transportation modes, with only 24.3% of communities achieving normal or higher accessibility via private car, 21.5% via public bus, and merely 13.9% via walking. These limitations primarily stem from insufficient service capacity (34 beds per 1000 older adults) relative to demographic needs rather than transportation constraints. We recommend three sustainable interventions: implementing demand-based planning mechanisms, establishing progressive pricing policies, and developing older adult-friendly transportation networks. This framework supports sustainable urbanization by promoting spatial equity and efficient resource allocation, providing valuable insights for secondary cities pursuing sustainable development goals. Full article

The incentive mechanism functions as a core safeguard to ensure the efficient execution of consortium-based Public–Private Partnership (PPP) projects and the realization of value-added outcomes. The heterogeneity of consortium members, their reciprocal preferences, and the collaborative dynamics of the team collectively contribute to the formation of project alliances characterized by resource synergy, complementary advantages, and risk sharing. However, these same factors also contribute to the multi-layered structure of principal–agent relationships and the inherent complexity of incentive pathways and mechanisms in consortium-based PPP settings. Drawing upon the team collaboration effect and reciprocal preferences among consortium members, this study incorporated the member heterogeneity and developed three incentive models for such projects, such as the Dual-Performance (DP) mode, the Total-Performance (TP) mode, and the Individual-Performance (IP) mode. This study examined the conditions under which these incentive modes were established, the relationship between incentive intensity and optimal effort levels of consortium members, and the influence of reciprocal preferences on incentive effectiveness. Further, the selection criteria and appropriate application scenarios for each of the three incentive models were analyzed according to a comparative analysis, thereby putting forward effective suggestions for improving the effort levels of private investors in consortium-based PPP projects. The study results indicate that team synergy effects play an imperative role in improving the optimal effort levels under all three modes, whereas reciprocity preferences exhibit a negative relationship with effort in the DP and TP modes. When reciprocity remains within a moderate range, the DP mode achieves highest aggregate effort levels, whereas the IP mode induces positive incentive effects only under extreme reciprocity conditions. Thus, the application of dual incentive coefficients can enhance operational adaptability and allocative efficiency and governments should establish a multidimensional collaborative incentive for consortium-based PPP projects to strengthen effectiveness and project quality. This comprehensive evaluation provides crucial insights for policymakers, emphasizing the strategic selection of incentive mechanisms to enhance the sustainability and effectiveness of consortium-based PPP Projects. Full article

This paper presents a literature review of low-power hybrid wind–photovoltaic (PV) systems and introduces a 3 m diameter prototype rotor featuring twelve PV-coated pivoting blades stiffened by a peripheral rim. Existing solutions—foldable umbrella concepts, Darrieus rotors with PV-integrated blades, and morphing blades—are surveyed, and current gaps in simultaneous wind + PV co-generation on a single moving structure are highlighted. Key performance indicators such as power coefficient (C_p), DC ripple, cell temperature difference (ΔT), and levelised cost of energy (LCOE) are defined, and an integrated assessment methodology is proposed based on blade element momentum (BEM) and computational fluid dynamics (CFD) modelling, dynamic current–voltage (I–V) testing, and failure modes and effects analysis (FMEA) to evaluate system performance and reliability. Preliminary results point to moderate aerodynamic penalties (ΔC_p ≈ 5–8%), PV output during rotation equal to 15–25% of the nominal PV power (P_PV), and an estimated 70–75% reduction in blade–root bending moment when the peripheral ring converts each blade from a cantilever to a simply supported member, resulting in increased blade stiffness. Major challenges include the collective pitch mechanism, dynamic shading, and wear of rotating components (slip rings); however, the suggested technical measures—maximum power point tracking (MPPT), string segmentation, and redundant braking—keep performance within acceptable limits. This study concludes that the concept shows promise for distributed microgeneration, provided extensive experimental validation and IEC 61400-2-compliant standardisation are pursued. This paper has a dual scope: (i) a concise literature review relevant to low-Re flat-blade aerodynamics and ring-stiffened rotor structures and (ii) a multi-fidelity aero-structural study that culminates in a 3 m prototype proposal. We present the first evaluation of a hybrid wind–PV rotor employing untwisted flat-plate blades stiffened by a peripheral ring. Using low-Re BEM for preliminary loading, steady-state RANS-CFD (k-ω SST) for validation, and elastic FEM for sizing, we assemble a coherent load/performance dataset. After upsizing the hub pins (Ø 30 mm), ring (50 × 50 mm), and spokes (Ø 40 mm), von Mises stresses remain < 25% of the 6061-T6 yield limit and tip deflection ≤ 0.5%·R acrosscut-in (3 m s⁻¹), nominal (5 m s⁻¹), and extreme (25 m s⁻¹) cases. CFD confirms a broad efficiency plateau at λ = 2.4–2.8 for β ≈ 10° and near-zero shaft torque at β = 90°, supporting a three-step pitch schedule (20° start-up → 10° nominal → 90° storm). Cross-model deviations for C_p, torque, and pressure/force distributions remain within ± 10%. This study addresses only the rotor; off-the-shelf generator, brake, screw-pitch, and azimuth/tilt drives are intended for later integration. The results provide a low-cost manufacturable architecture and a validated baseline for full-scale testing and future transient CFD/FEM iterations. Full article