Search Results (66,463)

Freezing effectively extends the shelf life of food and maintains product quality by inhibiting microorganisms, enzyme activity, and chemical reactions. However, issues such as ice crystal formation, protein denaturation, lipid oxidation, and the low-temperature adaptability of psychrophilic microorganisms during the freezing process can directly affect the final quality of frozen foods. Among these, the size and distribution of ice crystals are key factors determining the extent of tissue damage. Therefore, this review aims to identify innovative and optimized freezing and frozen storage strategies. In order to save energy and improve product quality, various new technologies have emerged in recent years, such as ultrasonic-assisted freezing, high-pressure freezing, and magnetic-field-assisted freezing. This study systematically discusses the principles, applications, and impact mechanisms of these technologies on frozen foods. Furthermore, this study proposes the future development trends of frozen foods, filling the gap in the current food industry where there is a lack of systematic discussion and evaluation of frozen foods. It provides technical support and research directions for continuous development and innovation in the field of frozen foods. Full article

In vitro cultured plant calli, induced through dedifferentiation, are colonized by diverse endophytes. Most of these endophytes, being substantially inherited from the mother plant and highly dependent on the host’s internal ecological niche, are termed host-dependent endophytes (HDEs). Due to their close association with their hosts, HDEs exhibit heritable characteristics. However, our current understanding of plant HDEs and their effects on the host plant is limited. In this study, we characterized the composition and potential functions of the endophytic microbiota in grapevine calli derived from different varieties and organs corresponding to Cabernet Sauvignon berry flesh (CF), Rose Honey berry flesh (RF), and Rose Honey shoot tip (RS) using high-throughput sequencing and bioinformatics. Our results showed that the genotype and organotype of the explant did not affect the alpha diversity of endophytes in callus, but were associated with differences in beta diversity and community structure of the endophytic microbiota. Different types of grapevines calli inherited distinct endophytes from their mother plants, whereas sharing a conservative core endophytic microbiota consisting of a small number of amplicon sequence variants (ASVs) with high relative abundances (bacteria: 38 ASVs ranging from 79 to 92%; fungi: 9 ASVs ranging from 32 to 58 prediction analyses using revealed conserved functional traits of the endophytic microbiota across callus types, including a core suite of bacterial adaptive phenotypes, stable central metabolism dominated by oxidative phosphorylation, and uniformly structured fungal communities dominated by saprotrophs and pathotrophs, while consistently containing yeast-form fungi. Although minor variations such as elevated trait abundance in the CF group were noted, no statistically significant functional divergence was observed, demonstrating that the endophytic microbiota of grapevine callus maintains a conserved functional profile across different types. Collectively, this study provides a methodological framework for investigating plant HDEs and offers new insights into host-endophyte interactions at the cellular level. Full article

Agricultural intelligent manipulators are essential for autonomous operations in smart agriculture. However, their industrial deployment faces critical bottlenecks, including perception failures, crop damage, and poor cost–benefit ratios in unstructured environments. Following the PRISMA guidelines, this study reviewed 22 key representative studies and 78 related studies (2015–2026). This review analyzes mechanisms for low-damage and high-precision operations across hardware (rigid–flexible structures), perception (multi-modal fusion), and decision-making (intelligent control). We compare operational efficiency and damage rates in harvesting, transplanting, and sorting, finding that rigid–flexible actuators with vision-guided force control are key to overcoming current limitations. To evaluate these technologies, we established a benchmarking framework across fruit/vegetable harvesting, seedling grafting, and precision plant protection to assess four technological trajectories. We also address engineering challenges: machinery–agronomy misalignment, high sensor costs, and limited edge computing. Notably, we introduce an economic payback period analysis to evaluate commercial feasibility. Ultimately, future research should prioritize lightweight variable-stiffness hardware, synchronous visuo-tactile perception, and digital twins to seamlessly integrate machinery and agronomy. Full article

Maritime aerial imaging is strongly affected by rapid illumination variations induced by dynamic sea conditions, which often cause conventional exposure control approaches to misinterpret intrinsic scene brightness as overexposure resulting from elevated camera settings. To overcome this issue, an adaptive exposure control framework based on a Glare-Aware Attention Network is proposed, implemented within an end-to-end dual-branch architecture. The framework utilizes an Exposure State Encoding (ESE) module to encode the current frame’s exposure parameters as conditional vectors, thereby resolving physical ambiguities in scene understanding. A Glare-Aware Spatial Attention (GASA) mechanism is further introduced, incorporating a glare prior map (GPM) generated using a “high-luminance, low-texture” heuristic to explicitly suppress sun glint effects. A Scene Difficulty-Adaptive Loss Weighting (SDAW) scheme is designed to adaptively regulate loss weights, and region-aware evaluation metrics, KREA and ISR, are defined. On a self-collected maritime aerial imaging dataset, the proposed approach significantly outperforms both traditional and deep learning-based methods in terms of full-frame and region-level performance metrics. Compared with the multi-task CNN baseline that has the closest parameter count, it achieves a 1.7 dB gain in PSNR. Cross-dataset validation on SeaDronesSee, temporal consistency analysis, and embedded platform testing further support the generalization and real-time feasibility of the proposed solution. Offering a high-accuracy, region-aware exposure control solution for aerial cameras in complex sea surface scenarios. Full article

Tooth segmentation from dental meshes is a fundamental step in clinical applications such as computer-aided orthodontics and dental implantation. Compared with mature image segmentation, deep learning-based mesh segmentation research is currently in a high-speed development stage. This study follows a dual-flow personalized feature learning scheme based on meshes and researches high-resolution mesh segmentation problems for clinical needs, proposing a dual-flow deep learning architecture called Position Shape Network (PSNet). Its basic idea includes continuously adjusting the feature map size in the network layer to enhance the model’s generalization ability and designing a reasonable branch structure to personalize the learning of position attributes represented by coordinates and shape attributes represented by surface perimeter area. In addition, it is proposed that the resolution of the validation set should be determined by comprehensively analyzing and simplifying errors to ensure the credibility of the model evaluation. Under this evaluation system, PSNet was compared with relevant authoritative methods in experiments, and the results verified the rationality and efficiency of the method and viewpoint proposed in this paper. Full article

Driven by demanding global emission regulations and the urgent requirements for sustainable mobility, Electric Vehicles (EVs) have emerged as the primary alternative to Internal Combustion Engine (ICE) vehicles. Central to this transition is the electric propulsion system (EPS), a multidisciplinary integration of power electronics, advanced motor drives, and electrochemical energy storage. This paper provides a comprehensive overview of the current landscape of power electronic drives, focusing on the evolution of high-efficiency traction motors and next-generation energy storage systems (ESSs), and advancements in ultra-fast chargers. The analysis explores the vital impact of power converters, evaluating recent breakthroughs in wide-bandgap (WBG) semiconductors and advanced control topologies that enhance energy density and thermal management. Furthermore, the study identifies critical challenges in the design, modulation, and operational reliability of converters under dynamic automotive environments. By synthesizing current research trends and technical bottlenecks, this paper offers insights into the future trajectory of power electronics in achieving high-performance, cost-effective, and carbon-neutral transportation. Full article

The trace water content in industrial oil critically affects the operational stability and service life of industrial equipment and serves as a key indicator for evaluating oil quality. Therefore, the rapid, sensitive, and visual detection of trace water in oil is of great engineering significance for equipment condition monitoring and early fault warning. Existing detection methods predominantly rely on precision instruments; although they enable quantitative analysis, their operational procedures are complicated and time-consuming, which are unsuitable for on-site real-time monitoring. Consequently, there is an urgent need for a novel trace water detection sensor that offers high sensitivity, visualization, and adaptability to oil-phase environments. Herein, a coplanar electrode alternating current electroluminescent (ACEL) sensor is developed for the visual detection of trace water in oil. The ACEL sensor features a multilayer structure comprising a substrate layer, a coplanar electrode layer, and a humidity-sensitive luminescent layer. The humidity-sensitive luminescent layer consists of humidity-sensitive hydrogel and ZnS: Cu electroluminescent powder, forming a loose and porous film that enables high-sensitivity humidity sensing and simultaneously electroluminescent visual signal output. The sensing mechanism study reveals that variations in trace water content modulate the dielectric properties of the humidity-sensitive layer, which further affect the electroluminescent intensity of the ACEL sensor. In addition, the ACEL sensor enables the rapid, naked-eye recognition of humidity changes under trace water conditions without the need for precision instruments, achieving a rapid response time of 3 s and a detection limit as low as 60 ppm, all making it applicable for different types of industrial oils. Thus, this ACEL sensor features a novel detection mechanism, excellent universality, fast response, and ease of operation, offering a new visual sensing strategy for trace water detection in industrial oil and holding broad prospects for practical applications. Full article

Adherent-invasive Escherichia coli (AIEC) is considered a central pathogen in Crohn’s disease (CD), as it was first identified in 1998 in an ileal biopsy from a CD patient and has received extensive attention worldwide, but there is currently a lack of quantitative evaluation of the literature in this field. This study aims to provide a bibliometric analysis of AIEC in CD research over the past 25 years. Publications on AIEC in CD research from 1999 to 2025 were extracted from the Web of Science Core Collection database. Bibliometric analysis was conducted using VOSviewer (1.6.18), RStudio (version 4.0), and HistCite (version 12.03.17) to visualize by author, country, institution, journal, reference, and keywords. A total of 250 articles were analyzed. Overall, the number of annual publications in this field has increased since 2007, with noticeable fluctuations. Barnich Nicolas published the most articles, 66, and has a high impact in the field of AIEC in CD research. The most cited author within this data set by global citation score was Arlette Darfeuille-Michaud. Among countries and institutions, France and Université Clermont Auvergne (UCA) had the highest number of publications. The journal with the most publications is Inflammatory Bowel Diseases. The most frequent keyword was “adhesion”. Over the past 25 years, France has led the rapid growth in AIEC research. Furthermore, according to trend topic analysis, the prevalence of AIEC in CD continues to be the central research theme. Our study provides valuable insights into AIEC research, supporting international collaboration to advance AIEC research in CD. This study provides a visual overview of the field of AIEC in CD. Full article

High-viscosity fluid mixing is a critical unit operation in chemical engineering and polymer processing. Its efficiency and uniformity directly determine product quality and production costs. This paper reviews the mixing mechanisms of high-viscosity fluids, which rely on laminar stretching and folding rather than turbulent eddies, and summarizes the corresponding CFD models and constitutive equations of fluid. It also introduces different methods to reduce numerical diffusion and summarizes high-viscosity mixers. Current challenges, such as numerical diffusion in laminar simulations and the high Weissenberg number problem, are discussed. The application of machine learning in CFD simulations is considered a prospective method to enhance the accuracy and efficiency of simulations involving high-viscosity fluid mixing. Full article

Background/Objectives: Although creatine monohydrate is widely recognized as an effective ergogenic aid in strength and power sports, its role in endurance and mixed-sport disciplines remains less clearly established. This scoping review aimed to map the current evidence regarding the effects of creatine supplementation on performance, recovery-related outcomes, and body composition in endurance and mixed-sport contexts. Methods: A scoping review of randomized controlled trials published between 1996 and 2025 was conducted. Eligible studies evaluated creatine supplementation in endurance and mixed-sport contexts, including both sport-specific and broader exercise populations when the exercise protocol, testing model, or outcomes were relevant to endurance or mixed-sport performance, recovery, or body composition. A total of 38 studies met the inclusion criteria. Outcomes were categorized into exercise performance, biochemical markers related to recovery and exercise stress, and body composition parameters. Results: Creatine supplementation was most often associated with reported favorable changes in repeated-sprint performance and high-intensity power output, particularly during intermittent, sprint-based, or power-endurance tasks. Several studies reported favorable changes in sprint performance, peak power, or total work output relative to placebo or baseline values in cycling, swimming, rowing, and canoeing/kayaking protocols, although findings were not uniform across studies and not all favorable within-group changes were placebo-superior. Some studies also reported favorable changes in end-phase sprint capacity during prolonged exercise. Findings related to recovery were less consistent. Selected studies reported reductions in inflammatory markers, including C-reactive protein (CRP) and tumor necrosis factor α (TNF-α), whereas markers of muscle damage showed mixed responses. Most supplementation protocols involved a 5–7-day loading phase of 20 g/day, occasionally followed by a maintenance dose of 2–5 g/day. Small increases in total body mass were commonly observed, while evidence regarding fat-free mass and aerobic outcomes remained limited or inconsistent. Conclusions: Current evidence suggests that creatine supplementation may be most relevant in selected endurance and mixed-sport contexts involving repeated high-intensity efforts, sprint finishes, or power-endurance demands, rather than for endurance performance broadly. In contrast, evidence for recovery-related biochemical responses, body composition changes, and aerobic adaptations remains equivocal. Further well-controlled, sport- or context-specific, and field-based studies are needed to better clarify the role of creatine in endurance and mixed-sport exercise. Full article

Background/Objectives: Carbapenem-resistant Klebsiella pneumoniae (CRKP) is of great concern because of the difficulties encountered in the management of infections it may cause. This study aims to identify possible difficulties in the management of K. pneumoniae infections in the current context of antibiotic resistance, particularly regarding carbapenem resistance. Methods: This is a retrospective, cross-sectional study that analyses epidemiological, clinical and bacteriological features identified in all patients with CRKP infections/colonization admitted during 2024 in an infectious diseases hospital. Results: Carbapenemase-producing K. pneumoniae isolates were co-harboring NDM+OXA-48 in 55.2% of cases. NDM+OXA-48-producing K. pneumoniae (116 isolates, 55.2%) was correlated with high resistance to aztreonam (100%, p = 0.01), ceftazidime–avibactam (100%, p < 0.01), trimethoprim–sulfamethoxazole (99.1%, p < 0.01), gentamycin (94.8%, p < 0.01), amikacin (93.8%, p < 0.01), colistin (79.8%, p < 0.01). OXA-48-producing K. pneumoniae (29 isolates, 13.8%) was correlated with lower resistance to ceftazidime–avibactam (11.5%, p < 0.01), amikacin (48.1%, p < 0.01), colistin (51.7%, p = 0.01), and gentamycin (65.5%, p < 0.01). We found in vitro synergistic effects of ceftazidime/avibactam + aztreonam for 32/32 CRKP isolates and of colistin + tigecycline for 12/14 CRKP isolates. Higher recurrence of CRKP infections was recorded in patients with urinary tract conditions (RR = 11.58, 95%CI: 1.58–81.91) and upper urinary tract devices (RR = 3.53, 95% CI: 1.72–7.22). In this study, adequate antibiotic treatment, compared to excessive antibiotic treatment in CRKP infections, was associated with shorter treatment duration (p = 0.02) and shorter length of hospitalization (p = 0.04). Conclusions: In our study, CRKP is frequently coharboring NDM+OXA-48, having limited treatment options. Implementing new treatment strategies, testing antibiotic synergies for older antibiotics in order to identify alternative treatment options and avoiding unnecessary carbapenem consumption are essential for decreasing the burden of CRKP infections. Full article

To minimize the influence of interface states and surface damage, by inserting a gate oxide layer, the photoelectrochemical oxidation method was utilized to directly grow the gate oxide layer while simultaneously creating the gate-recessed regions onto gallium nitride (GaN)-based single-gate and dual-gate metal-oxide-semiconductor high-electron-mobility transistors (MOS-HEMTs). Compared to the single-gate structure, the two-dimensional electron gas (2DEG) channel layer was also modulated by the auxiliary gate, in addition to being modulated by the main gate. Consequently, a wider transconductance range, larger saturation drain-source current, lower gate leakage current, and higher drain-source breakdown voltage were the benefits derived from the auxiliary gate functionality in the dual-gate devices. Moreover, the low-frequency noise characteristics of the GaN-based MOS-HEMTs could also be improved by the dual-gate structure. These experimental results demonstrated that incorporating a dual-gate structure and directly grown gate oxide layers onto GaN-based MOS-HEMTs is a promising alternative for GaN-based low-noise, high-power, and high-frequency applications. Full article

Emerging contaminants (ECs) and microplastics (MPs) are increasingly detected in surface waters, wastewaters, and drinking water, often as complex mixtures, transformation products, and particle-associated burdens that challenge routine monitoring. This critical review examines current analytical strategies for the detection and characterization of both molecular and particulate emerging contaminants in aquatic systems, with particular emphasis on their relevance to environmental and human health risk assessment. For molecular ECs, targeted LC–MS/MS and GC–MS and GC–MS/MS approaches are evaluated alongside high-resolution mass spectrometry (HRMS)-based suspect and non-target screening, retrospective data mining, and transformation-product elucidation. For MPs, particle-resolved vibrational spectroscopy including µ-FTIR and µ-Raman is critically assessed in comparison with complementary thermal analysis methods, such as pyrolysis–GC–MS and thermal extraction–desorption GC–MS (TED–GC–MS). Particular attention is given to the influence of sampling design, matrix-adapted sample preparation, analytical confidence, and method-dependent size and polymer coverage on data quality and interstudy comparability. The review further highlights the risks of ECs in relation to exposure pathways, mixture effects, and the potential carrier role of MPs for ECs, additives, and microorganisms. Finally, key priorities are identified for next-generation monitoring frameworks, including harmonized workflows, transparent confidence reporting, and stronger integration of analytical evidence with fate, exposure, and risk assessment. Full article

Introduction: Accurate detection and characterization of intracardiac masses remain a major challenge in cardiovascular imaging due to overlapping morphological features between tumors, thrombi, and vegetations, as well as the inherent limitations of echocardiography, including operator dependency and variable image quality. Although echocardiography is the first-line imaging modality for evaluating cardiac masses, diagnostic uncertainty frequently necessitates additional multimodality imaging. Artificial intelligence (AI), including machine learning and deep learning approaches, has emerged as a promising strategy to improve image interpretation, automate feature extraction, and enhance diagnostic consistency. Objective: This narrative review aims to examine current advances in AI-enhanced echocardiography for cardiac tumor detection, with a particular focus on detection, segmentation, classification, multimodal integration, and clinical translation. Methods: A narrative literature review was conducted using PubMed, Scopus, and Google Scholar databases. Relevant English-language studies published between 2016 and 2026 were identified using keywords including “artificial intelligence”, “machine learning”, “deep learning”, “echocardiography”, “cardiac tumors”, “intracardiac masses”, “multimodal imaging”, and “ultrasomics”. Original studies, reviews, and methodological papers related to AI-assisted cardiovascular imaging were evaluated. Discussion: Current evidence suggests that AI-driven techniques, including radiomics (ultrasomics), convolutional neural networks, and multimodal learning frameworks, can improve the detection, segmentation, and classification of intracardiac masses. Experimental studies have reported high diagnostic performance, with some deep learning models achieving diagnostic accuracies exceeding 95% under controlled conditions. AI-assisted systems may also reduce interobserver variability and improve workflow efficiency. Multimodal AI approaches integrating echocardiography with cardiac magnetic resonance imaging, computed tomography, electrocardiography, and clinical data appear particularly promising for improving diagnostic discrimination. However, current models remain limited by small and imbalanced datasets, insufficient external validation, data heterogeneity, and limited generalizability across institutions and imaging protocols. Additional barriers to clinical implementation include annotation variability, limited interpretability of deep learning models, and regulatory considerations. Conclusions: AI-enhanced echocardiography has substantial potential to improve the detection and characterization of intracardiac masses by augmenting diagnostic consistency and supporting clinical decision-making. Nevertheless, current evidence remains largely based on retrospective and experimental studies. Future progress will depend on large multicenter collaborations, standardized imaging datasets, explainable AI frameworks, and prospective clinical validation to enable safe and effective integration into routine cardiovascular practice. Full article

Radiation detection technology is critical in medical diagnosis, high-energy physics experiments, nuclear environmental monitoring, and radiation safety protection. Its technological iteration stems from innovations in high-performance radiation detection materials. Traditional materials often have narrow dose–response intervals, insufficient high-precision measurement capability, low spatial resolution, and poor stability, failing to meet high-precision detection requirements. Ag-doped phosphate glass (Ag-PG), based on radio-photoluminescence (RPL), effectively addresses these limitations with its comprehensive advantages: high radiation sensitivity, a wide linear dose–response range, submicron spatial resolution for radiation imaging, write-erase-rewrite capability, and visualized dose monitoring potential, and it also boasts significant fundamental research value and engineering application prospects. Specifically, while existing RPL reviews mainly provide a comprehensive analysis from the perspective of RPL and present typical RPL material systems, this paper systematically analyzes the structural characteristics of the Ag-PG matrix and the coordination configuration and site occupation of Ag ions. It clarifies RPL luminescence properties, dose–response mechanisms, and the evolution of luminescence centers, while reviewing advancements in applications such as radiation dose detection and high-resolution X-ray imaging. By summarizing the current research status, technical advantages and existing challenges of Ag-PG, this study provides theoretical references and conceptual insights to promote breakthroughs in its fundamental research and practical applications in high-precision radiation dose detection, advanced medical imaging, micro-nano-scale radiation detection, and nuclear industry non-destructive testing. Full article