Search Results (10,998)

Shot peening is a widely used surface treatment method for improving fatigue life by inducing surface compressive residual stresses. In critical automotive components such as parabolic leaf springs, shot peening under pre-tension (stress shot peening) can further enhance durability. This study presents a finite element model simulating stress peening in high-strength spring steels, incorporating realistic boundary conditions, material degradation due to decarburization, and stochastic shot properties, offering a high-accuracy yet computationally efficient alternative to extensive experimental testing. Results show that both below- and above-yield pre-stressing produce beneficial residual stresses, while the consideration on decarburization effects significantly alters surface stress fields. The model offers a reliable, time-efficient alternative to experiments for process and fatigue life optimization. Full article

Harvest-disrupting rain events (HDREs) are prolonged cloudy–rainy spells during winter wheat maturity that impede harvesting and drying, induce pre-harvest sprouting and grain mould, and threaten food security in the Huang–Huai–Hai Plain (HHHP), China’s core winter wheat region. Using daily meteorological records (1960–2019), remote sensing-derived land-use data and topography, we develop a hazard–exposure–vulnerability framework to quantify HDRE risk and its drivers at 1 km resolution. Results show that HDRE risk has increased markedly over the past six decades, with the area of medium-to-high risk rising from 26.9% to 73.1%. The spatial pattern evolved from a “high-south–low-north” structure to a concentrated high-risk belt in the central–northern HHHP, and the risk centroid migrated from Fuyang (Anhui) to Heze (Shandong), with an overall displacement of 124.57 km toward the north–northwest. GeoDetector analysis reveals a shift from a “humidity–temperature dominated” mechanism to a “sunshine–humidity–precipitation co-driven” mechanism; sunshine duration remains the leading factor (q > 0.8), and its interaction with relative humidity shows strong nonlinear enhancement (q = 0.91). High-risk hot spots coincide with low-lying plains and river valleys with dense winter wheat planting, indicating the joint amplification of meteorological conditions and underlying surface features. The results can support regional decision-making for harvest-season early warning, risk zoning, and disaster risk reduction in the HHHP. Full article

Maritime vision systems for unmanned surface vehicles confront challenges in small-object detection, specular reflections and low-light conditions. This paper introduces WA-YOLO, a water-aware training framework that incorporates lightweight attention modules (ECA/CBAM) to enhance the model’s discriminative capacity for small objects and critical features, particularly against cluttered water ripples and glare backgrounds; employs advanced bounding box regression losses (e.g., SIoU) to improve localization stability and convergence efficiency under wave disturbances; systematically explores the efficacy trade-off between high-resolution input and tiled inference strategies to tackle small-object detection, significantly boosting small-object recall (APS) while carefully evaluating the impact on real-time performance on embedded devices; and introduces physically inspired data augmentation techniques for low-light and strong-reflection scenarios, compelling the model to learn more robust feature representations under extreme optical variations. WA-YOLO achieves a compelling +2.1% improvement in mAP@0.5 and a +6.3% gain in APS over YOLOv8 across three test sets. When benchmarked against the advanced RT-DETR model, WA-YOLO not only surpasses its detection accuracy (0.7286 mAP@0.5) but crucially maintains real-time performance at 118 FPS on workstations and 17 FPS on embedded devices, achieving a superior balance between precision and efficiency. Our approach offers a simple, reproducible and readily deployable solution, with full code and pre-trained models publicly released. Full article

COVID-19 lockdown announcements triggered global waves of panic buying, leading to widespread panic buying of essential goods and supply chain disruptions. Although the acute phase of the pandemic has passed, panic buying continues to emerge during natural disasters, extreme weather events, and other crisis-related disruptions, highlighting the ongoing need for evidence-based strategies to address its psychological drivers. Social cognition constructs, including willingness, intentions, attitudes, subjective norms, and risk perceptions, have been identified as modifiable psychological predictors of panic buying. However, few studies have experimentally tested theory-driven interventions aimed at modifying these mechanisms. This study evaluated the effectiveness of a brief, online intervention based on integrated social cognition models in reducing panic-buying-related cognitions during a hypothetical lockdown scenario. A pre-registered randomized controlled trial was conducted with Australian grocery shoppers (N = 140), who were randomly allocated to an intervention or control condition. Participants completed self-report measures assessing their willingness, intentions, attitudes, subjective norms, and risk perceptions at both pre- and post-intervention times. The hypotheses were partially supported. Compared with the control condition, the intervention group reported greater reductions across targeted psychological constructs. For hygiene products, significant decreases were observed across all five constructs, and for non-perishable foods, willingness, intention, and attitudes significantly decreased. For cleaning products, reductions were evident for attitudes, subjective norms, and intentions. These findings suggest that theory-informed, scalable interventions can effectively modify the social cognition processes underlying panic buying. This study extends existing research and demonstrates the potential for brief, theory-based communication strategies to reduce panic-buying-related cognitions. Future research should evaluate these interventions in real-world settings and explore mechanisms to target automatic cognitive processes. Full article

Microfluidic cell culture systems and organ-on-a-chip platforms provide powerful tools for modeling physiological processes, disease progression, and drug responses under controlled microenvironmental conditions. These technologies rely on diverse cell culture methodologies, including 2D and 3D culture formats, spheroids, scaffold-based systems, hydrogels, and organoid models, to recapitulate tissue-level functions and generate rich, multiparametric datasets through high-resolution imaging, integrated sensors, and biochemical assays. The heterogeneity and volume of these data introduce substantial challenges in pre-processing, feature extraction, multimodal integration, and biological interpretation. Artificial intelligence (AI), particularly machine learning and deep learning, offers solutions to these analytical bottlenecks by enabling automated phenotyping, predictive modeling, and real-time control of microfluidic environments. Recent advances also highlight the importance of technical frameworks such as dimensionality reduction, explainable feature selection, spectral pre-processing, lightweight on-chip inference models, and privacy-preserving approaches that support robust and deployable AI–microfluidic workflows. AI-enabled microfluidic and organ-on-a-chip systems now span a broad application spectrum, including cancer biology, drug screening, toxicity testing, microbial and environmental monitoring, pathogen detection, angiogenesis studies, nerve-on-a-chip models, and exosome-based diagnostics. These platforms also hold increasing potential for precision medicine, where AI can support individualized therapeutic prediction using patient-derived cells and organoids. As the field moves toward more interpretable and autonomous systems, explainable AI will be essential for ensuring transparency, regulatory acceptance, and biological insight. Recent AI-enabled applications in cancer modeling, drug screening, etc., highlight how deep learning can enable precise detection of phenotypic shifts, classify therapeutic responses with high accuracy, and support closed-loop regulation of microfluidic environments. These studies demonstrate that AI can transform microfluidic systems from static culture platforms into adaptive, data-driven experimental tools capable of enhancing assay reproducibility, accelerating drug discovery, and supporting personalized therapeutic decision-making. This narrative review synthesizes current progress, technical challenges, and future opportunities at the intersection of AI, microfluidic cell culture platforms, and advanced organ-on-a-chip systems, highlighting their emerging role in precision health and next-generation biomedical research. Full article

Potato (Solanum tuberosum) is an important crop globally, being a starchy, energy-dense food source rich in several micronutrients and bioactive compounds. Achieving food security for everyone is highly challenging in the context of growing populations and climate change. As a highly adaptable crop, potatoes can significantly contribute to food security for vulnerable populations and have outstanding commercial relevance. Specific pre- and post-harvest parameters influence potato quality. It is vital to understand how these factors interact to shape potato quality, minimizing post-harvest losses, ensuring consumer safety, and enhancing marketability. This review highlights how pre-harvest (cultivation approaches, agronomic conditions, biotic and abiotic stresses) and post-harvest factors impact tuber’s microbial stability, physiological behaviour, nutritional, functional attributes and frying quality. Quality parameters, such as moisture content, dry matter, starch, sugar, protein, antioxidants, and color, are typically measured using both traditional and modern assessment methods. However, advanced non-destructive techniques, such as imaging and spectroscopy, enable rapid, high-throughput quality inspection from the field to storage. This review integrates recent advancements and specific findings to identify factors that contribute to substantial quality degradation or enhancement, as well as current challenges. It also examines how pre- and post-harvest factors collectively impact potato quality. It proposes future directions for quality maintenance and enhancement across the field and storage, highlighting research gaps in the pre- and post-harvest linkage. Full article

Climate change induces widespread effects on crop production, influencing multiple developmental stages and associated agronomic outcomes. Using long-term meteorological data from Jilin Province, Northeast China, this study examined temporal and spatial variations in climatic conditions through trend analysis, Mann–Kendall tests, and inverse distance weighting interpolation. A fuzzy comprehensive evaluation model was applied to classify maize cultivation suitability into four levels across major production areas, with Level I representing the most suitable regions, Level II highly suitable regions, Level III moderately suitable regions, and Level IV low suitable regions. Changes in suitable areas were analyzed before and after abrupt climatic shifts. From 1976 to 2020, Jilin Province experienced a significant rise in annual mean temperature, a marked decline in sunshine duration, and a slight increase in precipitation. The area of Level I suitability remained stable, while Level II expanded to approximately 1.3 times its original area. Conversely, Level III and IV areas decreased by 4.59% and 28.77%, respectively, compared with the pre-transition period. Spatially, the most suitable maize cultivation areas shifted from central to northern and eastern Jilin due to climatic warming. Although rising temperatures enhanced thermal conditions for maize production, reduced sunshine and variable precipitation constrained further expansion. These findings provide a scientific basis for optimizing maize variety selection and cropping structure in high-latitude regions, supporting yield improvement and sustainable development of the maize industry under a changing climate. Full article

Effective situation awareness relies on the robust processing of high-dimensional data streams generated by onboard sensors. However, the application of deep generative models to extract features from complex UAV sensor data (e.g., GPS, IMU, and radar feeds) faces two fundamental challenges: critical training instability and the difficulty of representing multi-modal distributions inherent in dynamic flight maneuvers. To address this, this paper proposes a novel unsupervised sensor data processing framework to overcome these issues. Our core innovation is a deep generative model, VAE-WRBM-MDN, specifically engineered for stable feature extraction from non-linear time-series sensor data. We demonstrate that while standard Variational Autoencoders (VAEs) often struggle to converge on this task, our introduction of Weighted-uncertainty Restricted Boltzmann Machines (WRBM) for layer-wise pre-training ensures stable learning. Furthermore, the integration of a Mixture Density Network (MDN) enables the decoder to accurately reconstruct the complex, multi-modal conditional distributions of sensor readings. Comparative experiments validate our approach, achieving 95.69% classification accuracy in identifying situational patterns. The results confirm that our framework provides robust enabling technology for real-time intelligent sensing and raw data interpretation in autonomous systems. Full article

Adoptive cell therapy (ACT) with T cells modified with a chimeric antigen receptor (CAR T cells) has dramatically improved outcomes in hematologic cancers. However, its efficacy in solid tumors, such as melanoma, is hampered by several factors. These include heterogeneous expression of tumor-associated antigens (TAA) and an immunosuppressive, profibrotic tumor microenvironment (TME), which restricts cytotoxic CAR T cells trafficking into the tumor, as well as their persistence and cytolytic activity. As a result, responses to CAR T cell monotherapy in melanoma and other solid tumors are typically weak, transient or even absent. Emerging evidence suggests that combining traditional chemotherapy with CAR T cell therapy can enhance the antitumor activity of CAR T cells in solid malignancies. Partial tumor cell killing by chemotherapy improves access to TAA and disrupts the TME by affecting the global structure of the tumor tissue. Here, we developed an immunocompetent syngeneic B16 melanoma mouse model to test a combination of classical dacarbazine (DTIC) chemotherapy with ACT with murine CAR T cells. B16-F10 (next as B16) melanoma cells were modified to express a human/murine hybrid epidermal growth factor receptor (EGFR) recognized by a murine CAR bearing a single-chain variable fragment (scFv) derived from cetuximab, an anti-EGFR monoclonal antibody approved for the treatment of colorectal and certain other solid tumors. Prior to CAR T cells administration, cyclophosphamide (CPA) pre-conditioning was used. We demonstrated that DTIC therapy followed by infusion of murine CAR T cells targeting the human/murine hybrid EGFR (EGFR mCAR T cells) provided superior tumor control and prolonged survival compared to monotherapy with either DTIC or EGFR mCAR T cells alone. These findings support the potential feasibility of a combined therapeutic strategy for human melanoma involving DTIC treatment followed by EGFR CAR T cells infusion after CPA pre-conditioning. Full article

Background: Oxidative stress is a key contributor to many chronic diseases. Natural biocompounds with antioxidant activity are of growing therapeutic interest. Brassica macrocarpa, a plant from the Brassicaceae family, has shown in vitro safety and antioxidant potential due to its rich content of glucosinolates and phenolics. However, in vivo, its effects remain poorly characterized. This study aimed to evaluate the in vivo safety and biological effects of Brassica macrocarpa leaf extract in zebrafish embryos and to assess its potential to counteract copper sulphate (CuSO₄)-induced oxidative stress. Methods: Zebrafish embryos were exposed to Brassica macrocarpa extract at concentrations from 125 to 2000 µg/mL. Embryonic mortality and malformations were monitored daily to determine sub-lethal concentrations (125–500 µg/mL) for further behavioural and biochemical analysis. Antioxidant properties were tested in a CuSO₄-induced oxidative stress model. Results: No teratogenic effects were observed over 96 h. Larvae showed normal swimming and no behavioural changes. Pre-treatment with the extract significantly reduced CuSO₄-induced ROS and NO production, modulated antioxidant enzyme (SOD, CAT) activity, and lowered lipid peroxidation and protein oxidation, slightly affecting DNA damage. Conclusions: Brassica macrocarpa extract in vivo appears safe at sub-lethal doses and shows promising antioxidant effects, suggesting its potential role in managing oxidative stress-related conditions. Full article

This study presents a one-dimensional numerical simulation of particulate matter (PM) oxidation and regeneration behavior in gasoline particulate filters (GPFs) under Worldwide Harmonized Light Vehicles Test Cycle (WLTC) conditions. The model incorporates both catalyst activity—represented by activation energy (E) and pre-exponential factor (A)—and exhaust control strategies involving forced fuel cut (FC). PM deposition and oxidation were simulated based on solid-state and gas-phase reactions, with the effects of oxygen concentration and temperature analyzed in detail. The results show that under high catalyst activity (E = 100 kJ mol⁻¹, A = 6.2 × 10⁷), PM oxidation proceeds efficiently even during medium-speed phases, achieving a 98.8% oxidation rate after one WLTC. Conversely, conventional catalysts (E = 120 kJ mol⁻¹, A = 6.2 × 10⁶) exhibited limited regeneration, leaving 0.11 g of residual PM. Introducing forced FC effectively enhanced oxidation by increasing oxygen concentration to 20% and sustaining heat release. A single continuous 100 s FC yielded the highest oxidation (96% reduction), while split FCs reduced peak PM accumulation. These findings demonstrate that optimizing the balance between catalyst activity and FC control can significantly improve GPF regeneration performance, providing a practical strategy for PM reduction in GDI vehicles under real driving conditions. Full article

Background/Objectives: Situations previously paired with drug use can become conditioned stimuli (i.e., physical stress or psychosocial stress) that elicit intense craving and relapse, even after prolonged abstinence. Previous studies have shown that pharmacological disruption of reconsolidation after memory reactivation could be promising for reducing pathological fear and stress-related responses. For this reason, the aim of this research was to examine the role of β-AR in the retrieval of aversive memories through the potential of β-AR antagonism to mitigate the effects of exposure to stressful stimuli. Methods: This question was addressed using a model to assess the re-emergence of an aversive contextual memory induced by both physical stressors (restraint and tail-pinch) and psychosocial stress (social defeat) in morphine- or saline-treated mice previously subjected to a conditioned place aversion (CPA) paradigm, in which naloxone was administered to precipitate opioid withdrawal. To assess the effects of propranolol on aversive memories related to opioid addiction, the number of chamber crossings and the time spent in the naloxone-paired compartment were measured. Results: Our results showed that morphine-treated mice spent significantly less time in the naloxone-paired chamber than saline mice during the post-test and after exposure to stressful stimuli, than during the pre-test, showing an effect for aversive memories in addiction. In contrast, when propranolol was administered intraperitoneally 30 min before the exposure to both social and physical stress, the time spent enhanced significantly (p < 0.01), supporting a role for propranolol in addiction-related memories. Conclusions: These results suggest that propranolol could attenuate the aversive memories that may contribute to relapse to opioid addiction. Full article

The integration of large-scale offshore wind power, facilitated by Flexible Fractional Frequency Transmission Systems (FFFTS), presents significant challenges for traditional transmission line protection. The fault current fed by the Modular Multilevel Matrix Converter (M3C) exhibits weak-infeed and controlled characteristics during faults, severely degrading the sensitivity of conventional current differential protection. Moreover, the stringent synchronization requirement for data from both line ends further compromises reliability. To address this issue, this paper proposes a novel differential protection scheme based on the Dynamic Time Warping (DTW) algorithm. The method leverages the DTW algorithm to quantify and compare the variation trends of current waveforms on both sides of the line before and after a fault. By utilizing the pre-fault current as a reference sequence, the scheme constructs a protection criterion that is inherently insensitive to synchronization errors. A key innovation is its capability for fault identification and phase selection under weak synchronization conditions. Simulation results demonstrate that the proposed scheme operates correctly within 0.5 ms, exhibits high sensitivity with a DTW ratio significantly greater than 2.0 during internal faults, and remains stable during external faults. It also shows strong robustness against high transition resistance, noise interference, and current transformer sampling errors. Full article

Polylactic acid (PLA) is a widely used bio-based polymer, although its application is limited by mechanical brittleness and low thermal resistance. PLA-based biocomposites reinforced with waste materials are gaining attention due to their sustainability, but their durability under degradation conditions remains a key concern. In this work, PLA biocomposites containing 0, 1, and 3% wt. of Olive-stone Biomass Ash (OBA) were manufactured and characterized both (1) after manufacture and (2) after laboratory-accelerated weathering (including UV exposure, heat, and humidity). The results obtained were analyzed to evaluate the influence of ash incorporation on degradation resistance (measured through Carbonyl Indices, CI), mechanical properties (tensile strength), thermal (Thermogravimetric Analysis—Differential Scanning Calorimetry, TGA-DSC), structure (Fourier Transform Infrared Spectroscopy, FT-IR), morphology (Scanning Electron Microscopy, SEM) and appearance (colorimetry and gloss). Key quantitative findings include a 35% reduction in tensile strength for raw PLA after 1000 h weathering exacerbated to 48% and 50% with 1% and 3% OBA incorporation, respectively. Degradation indices showed increased hydroxyl formation, with HI values ranging from 0.38 to 2.80 for PLA, while for biocomposites HI rose up to 5.85 for PLA with 3% OBA. Subsequently, a solid-state reaction was model-fitted from experimental data obtained by means of TGA analysis for determining the kinetic triplet (pre-exponential factor, the activation energy, and the reaction mechanism). Finally, the Acceleration Factor (AF), which combines the effects of radiation, temperature, and humidity to predict long-term material performance, is addressed analytically. Full article

Introduction: Malignant pericardial effusion (MPE) represents a relatively rare complication in various types of solid tumors. Its management is often challenging. One solution can be represented by surgical approaches, including a pericardio-peritoneal window (PPW), which allows draining the fluid into the abdominal cavity. The aim of this study is to investigate the efficacy and long-term outcomes of the PPW procedure as a definitive therapeutic strategy for MPE. Materials and methods: We retrospectively and prospectively observed pre-, peri-, and postoperative data of patients undergoing pericardio-peritoneal window creation from 2010 to December 2023 at the European Institute of Oncology (IEO), including the surgical procedures needed, total and specific postoperative complications, 30-day mortality rate, relapse rate, and the treatment of possible relapses. Results: A total of 44 consecutive patients underwent a pericardio-peritoneal window. In 28 patients (63.8%) PPW was associated with mono or bilateral videothoracoscopy for pleural biopsies/talc poudrage. In 23 cases, pre-operative percutaneous pericardial drainage (usually 1–2 days before surgery) was performed. No intraoperative deaths were observed. The 30-day mortality was 9% (four patients). We observed pericardial effusion recurrence in three patients at two months and in five patients at six months. In only two cases we treated this condition because of a pre-tamponade condition, treated by percutaneous pericardial drainage. The success rate of the PPW regarding pericardial relapse requiring further procedures was 95.5%. Conclusions: Patients presenting with a favorable short-term prognosis benefit from the pericardio-peritoneal window as a safe and effective method for resolving malignant pericardial effusion. Conversely, pericardial drainage is recommended as the most appropriate therapy for those with a less favorable prognosis. Full article