Search Results (36,033)

Aiming to enable intelligent vehicles to achieve autonomous charging under low-battery conditions, this paper presents a navigation system for autonomous charging that integrates an improved bidirectional A* algorithm for path planning and an optimized YOLOv11n model for visual recognition. The system utilizes the improved bidirectional A* algorithm to generate collision-free paths from the starting point to the charging area, dynamically adjusting the heuristic function by combining node–target distance and search iterations to optimize bidirectional search weights, pruning expanded nodes via a greedy strategy and smoothing paths into cubic Bézier curves for practical vehicle motion. For precise localization of charging areas and piles, the YOLOv11n model is enhanced with a CAFMFusion mechanism to bridge semantic gaps between shallow and deep features, enabling effective local–global feature fusion and improving detection accuracy. Experimental evaluations in long corridors and complex indoor environments showed that the improved bidirectional A* algorithm outperforms the traditional improved A* algorithm in all metrics, particularly in that it reduces computation time significantly while maintaining robustness in symmetric/non-symmetric and dynamic/non-dynamic scenarios. The optimized YOLOv11n model achieves state-of-the-art precision (P) and mAP@0.5 compared to YOLOv5, YOLOv8n, and the baseline model, with a minor 0.9% recall (R) deficit compared to YOLOv5 but more balanced overall performance and superior capability for small-object detection. By fusing the two improved modules, the proposed system successfully realizes autonomous charging navigation, providing an efficient solution for energy management in intelligent vehicles in real-world environments. Full article

The aim of this study was to evaluate the influence of genotype and diet on geese from crossbreeding meat lines Tapphorn (T) and Eskildsen (E). This study was conducted on 240 crossbred geese assigned to two dietary groups: an SBM diet group fed a standard soybean-based diet and an LPS diet group fed a yellow lupin-based diet. Birds were reared under identical management conditions and slaughtered at 17 weeks of age. The following traits were recorded: meat colour (CIELab), pH₂₄, cooking loss, breast and thigh muscle texture (shear force and energy), and sensory traits. The results showed a significant effect of both genotype and diet on meat quality. The LPS diet lowered shear force and energy (by ~11%, p < 0.001), reduced cooking loss in breast muscles (by ~5%, p < 0.001), and improved the juiciness and flavour of thigh muscles. The ET genotype positively influenced the meat colour intensity (lower L*, higher a*), while the lupin-based diet improved technological parameters, especially the water-holding capacity. The results confirm that replacing soybean meal with yellow lupin protein is an effective nutritional strategy that can improve goose meat quality and sustainability without compromising the sensory quality. These outcomes support developing soy-free feeding strategies in goose production to meet consumer expectations and reduce reliance on imported feed. Full article

With the expansion of offshore oil and gas resources to deepwater areas, the problem of the vortex-induced vibration of marine risers, as a key structure connecting offshore platforms and subsea wellheads, has become increasingly prominent. At present, there are few reviews on the vortex-induced vibration of flexible risers. This review provides a detailed discussion of vortex-induced vibration in marine risers. This review begins with the engineering background. It then systematically analyzes the key factors that influence VIV response. These factors include the riser’s structural parameters, such as aspect ratio and mass ratio. They also include the external fluid environment. Next, this review evaluates current VIV suppression strategies by analyzing specific experimental results. It compares the effectiveness and trade-offs of passive techniques. It also examines the potential and limitations of active methods, which often use smart materials, like piezoelectrics. This study highlights the major challenges in VIV research today. These challenges relate to prediction accuracy and suppression efficiency. Key problems include model uncertainty at high Reynolds numbers and the practical implementation of suppression devices in engineering systems. Finally, this paper presents an outlook on the future directions. It concludes that an intelligent, full-lifecycle integrity management system is the best path forward. Full article

Access to clean water is a pressing global concern and membrane technologies play a vital role in addressing this challenge. Thin-film composite membranes prepared via interfacial polymerization (IPol) using meta-phenylenediamine (MPD) and trimesoyl chloride (TMC) exhibit excellent separation performance, but face limitations such as fouling and low hydrophilicity. This study investigated the interaction between MPD and sulfonated zinc phthalocyanine, Zn(SO₂⁻)₄Pc, as a potential strategy for enhancing membrane properties. Using Density Functional Theory (DFT) and Time-Dependent DFT (TD-DFT), we analyzed the optimized geometries, electronic structures, UV–Vis absorption spectra, FT-IR vibrational spectra, and molecular electrostatic potentials of MPD, Zn(SO₂⁻)₄Pc, and their complexes. The results show that MPD/Zn(SO₂⁻)₄Pc exhibits reduced HOMO-LUMO energy gaps and enhanced charge delocalization, particularly in aqueous environments, indicating improved stability and reactivity. Spectroscopic features confirmed strong interactions via hydrogen bonding and π–π stacking, suggesting that Zn(SO₂⁻)₄Pc can act as a co-monomer or additive during IPol to improve polyamide membrane functionality. A conformational analysis of MPD/Zn(SO₂⁻)₄Pc was conducted using density functional theory (DFT) to evaluate the impact of dihedral rotation on molecular stability. The 120° conformation was identified as the most stable, due to favorable π–π interactions and intramolecular hydrogen bonding. These findings offer computational evidence for the design of high-performance membranes with enhanced antifouling, selectivity, and structural integrity for sustainable water treatment applications. Full article

Background/Objectives: Hot-melt extrusion (HME) has gained prominence for the manufacture of sustained-release oral dosage forms, yet the application of wax-based matrices and their resilience to alcohol-induced dose dumping (AIDD) remains underexplored. This study aimed to develop and characterise wax-based sustained-release felodipine formulations, with a particular focus on excipient functionality and robustness against AIDD. Methods: Felodipine sustained-release formulations were prepared via HME using Syncrowax HGLC as a thermally processable wax matrix. Microcrystalline cellulose (MCC) and lactose monohydrate were incorporated as functional fillers and processing aids. The influence of wax content and filler type on mechanical properties, wettability, and drug release behaviour was systematically evaluated. Ethanol susceptibility testing was conducted under simulated co-ingestion conditions (4%, 20%, and 40% v/v ethanol) to assess AIDD risk. Results: MCC-containing tablets demonstrated superior sustained-release characteristics over 24 h, showing better wettability and disintegration. In contrast, tablets formulated with lactose monohydrate remained structurally intact during dissolution, overly restricting drug release. This limitation was effectively addressed through granulation, where reduced particle size significantly improved surface accessibility, with 0.5–1 mm granules achieving a satisfactory release profile. Ethanol susceptibility testing revealed divergent behaviours between the two filler systems. Unexpectedly, MCC-containing tablets showed suppressed drug release in ethanolic media, likely resulting from inhibitory effect of ethanol on filler swelling and disintegration. Conversely, formulations containing lactose monohydrate retained their release performance in up to 20% v/v ethanol, with only high concentrations (40% v/v) compromising matrix drug-retaining functionality and leading to remarkably increased drug release. Conclusions: This study highlights the pivotal role of excipient type and constitutional ratios in engineering wax-based sustained-release formulations. It further contributes to the understanding of AIDD risk through in vitro assessment and offers a rational design strategy for robust, alcohol-resistant oral delivery systems for felodipine. Full article

The integration of renewable energy sources (RESs) into power systems, particularly in microgrids, is becoming a prominent trend aimed at reducing dependence on traditional energy sources. Replacing conventional synchronous generators with grid-connected RESs through power electronic converters has significantly reduced the inertia of microgrids. This reduction negatively impacts the dynamics and operational performance of microgrids when confronted with uncertainties, posing challenges to frequency and voltage stability, especially in a standalone operating mode. To address this issue, this research proposes enhancing microgrid stability through frequency control based on virtual inertia (VI). Additionally, the Iterative Learning Control (ILC) method is employed, leveraging iterative learning strategies to improve the quality of output response control. Accordingly, the ILC-VI control method is introduced, integrating the iterative learning mechanism into the virtual inertia controller to simultaneously enhance the system’s inertia and damping coefficient, thereby improving frequency stability under varying operating conditions. The effectiveness of the ILC-VI method is evaluated in comparison with the conventional VI (C-VI) control method through simulations conducted on the MATLAB/Simulink platform. Simulation results demonstrate that the ILC-VI method significantly reduces the frequency nadir, the rate of change of frequency (RoCoF), and steady-state error across iterations, while also enhancing the system’s robustness against substantial variations from renewable energy sources. Furthermore, this study analyzes the effects of varying virtual inertia values, shedding light on their role in influencing response quality and convergence speed. This research underscores the potential of the ILC-VI control method in providing effective support for low-inertia microgrids. Full article

Background/Objectives: Colorectal cancer (CRC) is among the most prevalent malignant neoplasms globally. Chemotherapeutic treatment strategies have demonstrated minimal improvement over the past decade. Combination therapies, including those with nutraceuticals, are currently being investigated as promising alternatives to enhance therapeutic efficacy. The organosulfur garlic extract diallyl disulfide (DADS) has demonstrated anti-tumoral activity in several types of cancer. This study aimed to investigate the effects of DADS and 5-fluorouracil (5-FU), both individually and in combination, on the human CRC cell lines Caco-2 and HT-29. Methods: Caco-2, HT-29, and non-tumoral human umbilical vein endothelial cells (HUVEC) were exposed to DADS (25–600 µM) and 5-FU (5–100 µM), either individually or in simultaneous combination (DADS 100 µM + 5-FU 100 µM), for 24 h. Cytotoxicity was evaluated in all three cell lines. In addition, the effects of these treatments on oxidative stress, cell migration, genotoxicity, cell death, global DNA methylation, and gene–nutraceutical interactions were assessed in both tumor cell lines. Results: DADS demonstrated cytotoxic effects at high concentrations in Caco-2, HT-29, and HUVECs and induced DNA damage in both colorectal cancer cell lines. The combination of DADS and 5-FU significantly promoted apoptotic cell death, increased genotoxicity, elevated global DNA methylation, and inhibited cell migration, with these effects being particularly pronounced in HT-29 cells. Conclusions: We provide evidence that DADS combined with 5-FU is potentially useful in the therapy of CRC. However the combination of nutraceuticals and chemotherapy must consider the distinct molecular and phenotypic characteristics of each tumor cell line. Full article

Background/Objectives: Peripheral artery disease (PAD) impacts more than 200 million individuals globally and leads to mortality and morbidity secondary to progressive limb dysfunction and amputation. However, clinical management of PAD remains suboptimal, in part because of the lack of standardized biomarkers to predict patient outcomes. Growth differentiation factor 15 (GDF15) is a stress-responsive cytokine that has been studied extensively in cardiovascular disease, but its investigation in PAD remains limited. This study aimed to use explainable statistical and machine learning methods to assess the prognostic value of GDF15 for limb outcomes in patients with PAD. Methods: This prognostic investigation was carried out using a prospectively enrolled cohort comprising 454 patients diagnosed with PAD. At baseline, plasma GDF15 levels were measured using a validated multiplex immunoassay. Participants were monitored over a two-year period to assess the occurrence of major adverse limb events (MALE), a composite outcome encompassing major lower extremity amputation, need for open/endovascular revascularization, or acute limb ischemia. An Extreme Gradient Boosting (XGBoost) model was trained to predict 2-year MALE using 10-fold cross-validation, incorporating GDF15 levels along with baseline variables. Model performance was primarily evaluated using the area under the receiver operating characteristic curve (AUROC). Secondary model evaluation metrics were accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). Prediction histogram plots were generated to assess the ability of the model to discriminate between patients who develop vs. do not develop 2-year MALE. For model interpretability, SHapley Additive exPlanations (SHAP) analysis was performed to evaluate the relative contribution of each predictor to model outputs. Results: The mean age of the cohort was 71 (SD 10) years, with 31% (n = 139) being female. Over the two-year follow-up period, 157 patients (34.6%) experienced MALE. The XGBoost model incorporating plasma GDF15 levels and demographic/clinical features achieved excellent performance for predicting 2-year MALE in PAD patients: AUROC 0.84, accuracy 83.5%, sensitivity 83.6%, specificity 83.7%, PPV 87.3%, and NPV 86.2%. The prediction probability histogram for the XGBoost model demonstrated clear separation for patients who developed vs. did not develop 2-year MALE, indicating strong discrimination ability. SHAP analysis showed that GDF15 was the strongest predictive feature for 2-year MALE, followed by age, smoking status, and other cardiovascular comorbidities, highlighting its clinical relevance. Conclusions: Using explainable statistical and machine learning methods, we demonstrated that plasma GDF15 levels have important prognostic value for 2-year MALE in patients with PAD. By integrating clinical variables with GDF15 levels, our machine learning model can support early identification of PAD patients at elevated risk for adverse limb events, facilitating timely referral to vascular specialists and aiding in decisions regarding the aggressiveness of medical/surgical treatment. This precision medicine approach based on a biomarker-guided prognostication algorithm offers a promising strategy for improving limb outcomes in individuals with PAD. Full article

Underwater acoustic (UWA) communication supports many critical applications but still faces several physical-layer signal processing challenges. In response, recent advances in machine learning (ML) and deep learning (DL) offer promising solutions to improve signal detection, modulation adaptability, and classification accuracy. These developments highlight the need for a systematic evaluation to compare various ML/DL models and assess their performance across diverse underwater conditions. However, most existing reviews on ML/DL-based UWA communication focus on isolated approaches rather than integrated system-level perspectives, which limits cross-domain insights and reduces their relevance to practical underwater deployments. Consequently, this systematic literature review (SLR) synthesizes 43 studies (2020–2025) on ML and DL approaches for UWA communication, covering channel estimation, adaptive modulation, and modulation recognition across both single- and multi-carrier systems. The findings reveal that models such as convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and generative adversarial networks (GANs) enhance channel estimation performance, achieving error reductions and bit error rate (BER) gains ranging from ${10}^{-3}$ to ${10}^{-6}$. Adaptive modulation techniques incorporating support vector machines (SVMs), CNNs, and reinforcement learning (RL) attain classification accuracies exceeding 98% and throughput improvements of up to 25%. For modulation recognition, architectures like sequence CNNs, residual networks, and hybrid convolutional–recurrent models achieve up to 99.38% accuracy with latency below 10 ms. These performance metrics underscore the viability of ML/DL-based solutions in optimizing physical-layer tasks for real-world UWA deployments. Finally, the SLR identifies key challenges in UWA communication, including high complexity, limited data, fragmented performance metrics, deployment realities, energy constraints and poor scalability. It also outlines future directions like lightweight models, physics-informed learning, advanced RL strategies, intelligent resource allocation, and robust feature fusion to build reliable and intelligent underwater systems. Full article

Incorporating health-promoting resveratrol into food products is challenging, primarily due to its poor solubility. Covalent conjugation is a promising, low-energy, and environmentally friendly strategy to overcome this limitation. This study compared the effectiveness of free radical grafting and alkaline methods for covalently conjugating whey protein isolate (WPI) with resveratrol. Conjugates were evaluated for molecular weight, structural characteristics, functional properties, and antioxidant activities. Both methods yielded conjugates with enhanced solubility relative to native resveratrol, with fold increases from 7.6 to 21.7 for the free radical grafting and from 8.1 to 23.6 for the alkaline method. Conjugates prepared via free radical grafting exhibited greater increases in molecular weight (10–100 kDa range), higher resveratrol incorporation (up to 17.6%), and superior functional properties compared to the alkaline conjugates (p < 0.05). Specifically, emulsifying activity, foaming capacity, and foaming stability improved by up to 64.7%, 45.8%, and 220.9%, respectively, compared to WPI. The antioxidant activities of the free radical grafting conjugates were 1.3- to 3.6-fold higher than those of alkaline conjugates. These findings highlight free radical grafting of WPI as a promising approach for incorporating resveratrol and improving the functionality of protein-based ingredients in functional food products. Full article

Alzheimer’s disease (AD) involves the accumulation of amyloid-β (Aβ) plaques, whose quantification plays a central role in understanding disease progression. Automated segmentation of Aβ deposits in histopathological micrographs enables large-scale analyses but is hindered by the high cost of detailed pixel-level annotations. Weakly supervised learning offers a promising alternative by leveraging coarse or indirect labels to reduce the annotation burden. We evaluated a weakly supervised approach to segment and analyze thioflavin-S-positive parenchymal amyloid pathology in AD and age-matched brains. Our pipeline integrates three key components, each designed to operate under weak supervision. First, robust preprocessing (including retrospective multi-image illumination correction and gradient-based background estimation) was applied to enhance image fidelity and support training, as models rely more on image features. Second, class activation maps (CAMs), generated by a compact deep classifier SqueezeNet, were used to identify, and coarsely localize amyloid-rich parenchymal regions from patch-wise image labels, serving as spatial priors for subsequent refinement without requiring dense pixel-level annotations. Third, a patch-based convolutional neural network, U-Net, was trained on synthetic data generated from micrographs based on CAM-derived pseudo-labels via an extensive object-level augmentation strategy, enabling refined whole-image semantic segmentation and generalization across diverse spatial configurations. To ensure robustness and unbiased evaluation, we assessed the segmentation performance of the entire framework using patient-wise group k-fold cross-validation, explicitly modeling generalization across unseen individuals, critical in clinical scenarios. Despite relying on weak labels, the integrated pipeline achieved strong segmentation performance with an average Dice similarity coefficient (≈0.763) and Jaccard index (≈0.639), widely accepted metrics for assessing segmentation quality in medical image analysis. The resulting segmentations were also visually coherent, demonstrating that weakly supervised segmentation is a viable alternative in histopathology, where acquiring dense annotations is prohibitively labor-intensive and time-consuming. Subsequent morphometric analyses on automatically segmented Aβ deposits revealed size-, structural complexity-, and global geometry-related differences across brain regions and cognitive status. These findings confirm that deposit architecture exhibits region-specific patterns and reflects underlying neurodegenerative processes, thereby highlighting the biological relevance and practical applicability of the proposed image-processing pipeline for morphometric analysis. Full article

Mobile Charging Robots (MCRs), essentially high-voltage batteries mounted on mobile platforms, offer a flexible solution for electric vehicle (EV) charging, particularly in environments like supermarket parking lots with photovoltaic (PV) generation. Unlike fixed charging stations, MCRs must be strategically dispatched and recharged to maximize operational efficiency and revenue. This study investigates a Model Predictive Control (MPC) approach using Mixed-Integer Linear Programming (MILP) to coordinate MCR charging and movement, accounting for the additional complexity that EVs can park at arbitrary locations. The performance impact of EV arrival and demand forecasts is evaluated, comparing perfect foresight with data-driven predictions using long short-term memory (LSTM) networks. A slack variable method is also introduced to ensure timely recharging of the MCRs. Results show that incorporating forecasts significantly improves performance compared to no prediction, with perfect forecasts outperforming LSTM-based ones due to better-timed recharging decisions. The study highlights that inaccurate forecasts—especially in the evening—can lead to suboptimal MCR utilization and reduced profitability. These findings demonstrate that combining MPC with predictive models enhances MCR-based EV charging strategies and underlines the importance of accurate forecasting for future smart charging systems. Full article

The dual environmental challenges of karst areas lie in organic solid waste’s (OSW) massive generation scale and diffuse dispersion, which accelerate bedrock exposure and soil contamination, while simultaneously representing an underutilized resource for soil amendments through optimized composting. Bio-enhanced composting of multi-source OSW yields compounds with dual redox/adsorption capabilities, effectively improving soil quality and restoring ecological balance. The recycling and circular utilization of OSW resources become particularly critical in karst regions with vulnerable soil ecosystems, where sustainable resource management is urgently needed to maintain ecological balance. This review elucidates the ecological impacts of multi-source OSW compost applications on soil environments in ecologically fragile karst regions, specifically elucidating the mechanisms of heavy metals (HMs) migration–transformation and organic contaminant degradation (with emphasis on emerging pollutants), and the functional role of microbial carbon pumps in these processes. Furthermore, establishing a sustainable “multi-source OSW−compost−organic matter (adsorption and redox sites)−microorganisms−pollution remediation” cycle creates a green, low-carbon microenvironment for long-term soil remediation. Finally, this study evaluates the application prospects of the refined composting technology utilizing multi-objective regulation for OSW resource recycling and utilization in karst areas. This review provides critical insights for optimizing soil remediation strategies in karst ecosystems through organic waste valorization. Full article

The Vesical Imaging Reporting and Data System (VI-RADS) is used to detect muscle-invasive bladder cancer, with emerging prognostic implications. Integrating imaging parameters with molecular biomarkers may improve risk stratification in bladder cancer. This study evaluated whether combining VI-RADS scores with serum cytokeratin fragment 19 (CYFRA 21-1) levels—a clinically relevant biomarker for bladder cancer—could improve overall survival (OS) prediction. We retrospectively analyzed 134 patients who underwent transurethral resection of bladder tumors, magnetic resonance imaging, and postoperative serum CYFRA 21-1 measurements. In total, 15 cancer-specific deaths were observed during follow-up. Receiver operating characteristic curve analysis identified optimal prognostic cut-off values: VI-RADS score ≥ 4 and CYFRA 21-1 level ≥ 1.8 ng/mL. The 1-, 2-, and 3-year OS in patients with both high VI-RADS scores and CYFRA 21-1 levels were 42.9%, 16.7%, and 8.3%, respectively, significantly lower than those in other groups (p < 0.001, 0.002, and 0.003, respectively). Multivariate Cox proportional hazards analysis demonstrated that such patients had the poorest OS (hazard ratio: 7.51; p = 0.002). This suggests that combining VI-RADS and CYFRA 21-1 improves prognostic accuracy in bladder cancer, demonstrating potential clinical utility by informing individualized treatment strategies; however, limitations include the retrospective study design and absence of external validation. Full article

Near-field acoustic holography (NAH) provides an effective way to achieve wide-band, high-resolution visualization measurement of the sound insulation performance of building components. However, based on Green’s function, the microphone array’s inherent amplitude and phase mismatch errors will exponentially amplify the sound field inversion process, significantly reducing the measurement accuracy. To systematically evaluate this problem, this study combines numerical simulation with actual measurements in a soundproof room that complies with the ISO 10140 standard, quantitatively analyzes the influence of array system errors on NAH reconstructed sound insulation and acoustic images, and proposes an error correction strategy based on channel transfer function normalization. The research results show that when the array amplitude and phase mismatch mean values are controlled within 5% and 5°, respectively, the deviation of the weighted sound insulation measured by NAH can be controlled within 1 dB, and the error in the key frequency band of building sound insulation (200–1.6k Hz) does not exceed 1.5 dB; when the mismatch mean value increases to 10% and 10°, the deviation of the weighted sound insulation can reach 2 dB, and the error in the high-frequency band (≥1.6k Hz) significantly increases to more than 2.0 dB. The sound image shows noticeable spatial distortion in the frequency band above 250 Hz. After applying the proposed correction method, the NAH measurement results of the domestic microphone array are highly consistent with the weighted sound insulation measured by the standard method, and the measurement difference in the key frequency band is less than 1.0 dB, which significantly improves the reliability and applicability of low-cost equipment in engineering applications. In addition, the study reveals the inherent mechanism of differential amplification of system errors in the propagating wave and evanescent wave channels. It provides quantitative thresholds and operational guidance for instrument selection, array calibration, and error compensation of NAH technology in building sound insulation detection. Full article