Search Results (6,310)

Polyphenol oxidase (PPO) is a key enzyme responsible for enzymatic browning in fresh-cut fruits and vegetables, severely compromising their quality and shelf life. This study aimed to investigate the combined inhibitory mechanism of oxyresveratrol (OXY) and epigallocatechin gallate (EGCG) on PPO through multi-spectroscopic analyses, molecular docking, TEM, and XRD, with the goal of developing a natural and effective anti-browning strategy for fresh-cut fruits. The results showed that the optimal combined effect was achieved at an OXY:EGCG ratio of 1:2, where the inhibition rate was significantly enhanced by 43.72% and 14.36% compared to using OXY or EGCG single treatment, respectively. The combined treatment exhibited enhanced chelation capacity of copper ion and DPPH radical scavenging activity, and enhanced hydrogen-bonding interactions while lowering binding energy, exhibiting characteristics of mixed inhibition kinetics. Structural characterization showed that the combined treatment drastically reduced the enzyme’s fluorescence intensity to 39.81% of that of the native enzyme, induced rearrangements in α-helix and random coil structures, triggered obvious protein aggregation, and weakened the intensity of crystal diffraction peaks. Importantly, the combined treatment effectively delayed browning in fresh-cut pear slices, demonstrating its practical application potential. These findings provide a promising natural combined approach for controlling enzymatic browning and extending the shelf life of fresh-cut produce. Full article

This paper presents a subwavelength multiband bandstop metamaterial reflector for enhancing antenna gain beyond what is achieved by conventional metallic reflectors. The unit cell was designed to generate multiband stopbands at three frequencies: the first at 1.8 GHz, the second at 2.6 GHz, and the third at 3.5 GHz. The design was implemented on a low-cost FR-4 printed circuit board with a simple design and compact size. The unit-cell structure included a transmission line configuration using the tri-section step-impedance technique, combined with a capacitive load at the end of the transmission line via a double interdigital structure. The structure could control the first, second, and third resonance frequencies to occur at the desired bands and ensured independent resonances. The double interdigital capacitive load significantly increased capacitive loading, generating a strong slow-wave effect along the transmission line structure, exceeding that achieved with conventional capacitive loading techniques. As a result, the transmission line size of the unit cell was drastically reduced from the conventional λ/2 to λ/16. To achieve metamaterial characteristics, the unit cell was modified to exhibit negative permittivity (ENG) for enhanced reflection compared to conventional materials and positive permeability. This was accomplished by adding a rod (ROD) structure behind the main structure using the tri-section step-impedance technique. This configuration generated left-handed current on the structure, resulting in negative permittivity values at all three resonance frequencies. An array of 14 × 14 unit cells was fabricated to form the metamaterial reflector with an overall size of 148 mm × 161.7 mm, which is very compact. The performance was evaluated by placing a single-frequency resonant dipole antenna, operating at the same resonance frequency as the reflector, in front of the reflector along the X-axis plane. The antenna gain increased from approximately 2 dB at all frequencies to about 8 dB. The radiation pattern exhibited directional radiation at 0 degrees. The measured results of frequency responses and antenna gains were close to the simulated results. With superior characteristics and compact size, the proposed metamaterial reflector can be applied for multiband 5G and other modern antenna systems. Full article

As the scaling of silicon-based devices approaches physical limits, two-dimensional transition-metal dichalcogenide field-effect transistors (2D TMD FETs) have emerged as promising candidates for logic devices in the post-Moore era. However, their design optimization relies heavily on computationally intensive TCAD simulations, thereby limiting efficient exploration of multidimensional parameter spaces. This paper proposes an efficient automated design framework for 2D TMD FETs under small-sample conditions and validates it using a monolayer MoS${}_2$ FET as a case study. The framework integrates device design, physics-based simulation, performance prediction, and inverse design, establishing a bidirectional mapping between device parameters and electrical performance. Target-driven closed-loop optimization is achieved through TCAD-based feedback validation. Results demonstrate that, using a dataset comprising 300 TCAD samples, the forward model achieves an average coefficient of determination (${\mathrm{R}}^2$) of 0.9503. TCAD revalidation of the inverse-designed devices yields an average mean absolute error (MAE) of 0.0464 and an average mean absolute percentage error (MAPE) of 5.46% for performance metrics. Regarding computational efficiency, while a single TCAD simulation takes approximately 25 to 50 min, the trained model performs inference in under 50 ms, achieving a speedup of at least $3\times {10}^4$ during the inference phase. Accounting for the generation of the 300 TCAD samples and the training of both forward and inverse models, the framework’s one-time computational cost ranges from 160.27 to 285.27 h. Once the cumulative number of design tasks exceeds approximately 342 to 385, the total computational cost falls below that of direct TCAD simulation, with the computational advantage becoming increasingly significant as the number of tasks grows. Consequently, this method is highly suitable for large-scale parameter sweeps, device screening, and multi-objective, high-frequency design iterations. It drastically reduces repetitive TCAD calls, offering a scalable solution for the efficient, automated design of 2D TMD FETs. Full article

Sugarcane (Saccharum spp.) is a globally vital high-biomass sugar crop with a massive demand for potassium (K). Low-K⁺ stress severely restricts its yield and stress resistance. Plasma membrane intrinsic proteins (PIPs) play pivotal roles in transmembrane water transport and ion homeostasis; however, their evolutionary characteristics and molecular mechanisms underlying nutritional stress responses in the complex polyploid sugarcane remain poorly understood. In this study, genome-wide identification in the sugarcane cultivar XTT22 yielded 149 PIP gene family members (comprising 54 PIP1s and 95 PIP2s). Phylogenetic and chromosomal localization analyses demonstrated that the sugarcane PIP family underwent drastic paralogous expansion during evolution, with tandem duplication acting as the core driving force for the dramatic expansion of the PIP2 subfamily. Spatiotemporal expression profiling unveiled significant modular functional division among PIP genes, identifying a core co-expression group driving rapid early seedling elongation and a PIP2-specific expression cluster dedicated to the physiological homeostasis of mature stems. Notably, the core member ScPIP2-70 exhibited significant early-induced responses at both transcriptional and protein levels in roots under low-K⁺ stress. Functional complementation assays in the K⁺-uptake deficient yeast strain R5421 further confirmed that the heterologous expression of ScPIP2-70 effectively rescued the growth defects of yeast under low-K⁺ conditions, demonstrating its potential transmembrane K⁺ transport activity. This study not only comprehensively elucidates the evolutionary dynamics and spatiotemporal expression profiles of the sugarcane PIP gene family but also uncovers the novel pleiotropic function of ScPIP2-70 in mediating low-K⁺ stress tolerance, providing critical theoretical support and candidate gene resources for breeding “potassium-efficient” sugarcane cultivars via modern biotechnology. Full article

This paper presents a methodology to evaluate the entire process of thermal evolution and fracture failure within stay cables subjected to sheath fires. Computer software FDS 2021 and ABAQUS 2021 are applied to build a sequentially thermos–mechanics coupled method integrating thermal, structural, and fracture dynamic analyses in stay cables under sheath fire exposure conditions. Herein, three representative fire scenarios including full-circumferential, top-side, and bottom-side ignition are reconstructed. Further, 127 individual wires, accounting for interstitial cavity radiation and contact heat transfer, are utilized to perform analysis on sectional temperature in stay cables. The results indicate that the ignition mode dictates the cross-sectional temperature gradient, with localized ignitions inducing highly asymmetric thermal fields and pronounced internal bending moments. Elevated temperatures trigger a progressive load redistribution from the degraded fire-facing wires to cooler internal layers. Ultimately, abrupt global fracture occurs when the residual ultimate load-carrying capacity intersects with the actual applied tension, resulting in a fracture morphology that closely corresponds to the spatial thermal distribution. Furthermore, the structural capacity degradation exhibits three distinct time-dependent stages: a slow degradation stage, a sharp decline stage, and a recovery stage. Among the analyzed scenarios, full-circumferential ignition induces the most drastic overall capacity reduction, while bottom-side ignition poses a markedly greater rupture risk than top-side ignition. Full article

Kaempferol, a naturally occurring dietary flavonoid abundantly found in various fruits and vegetables, has garnered significant attention due to its diverse pharmacological properties, most notably its potent anticancer activity. In the orchestration of cancer pathogenesis, the PTEN/PI3K/AKT/mTOR signaling cascade plays a pivotal role, where aberrant activation or dysregulation of this pathway drastically accelerates tumor cell proliferation, survival, and metabolic reprogramming, thereby driving tumorigenesis. Moreover, non-coding microRNAs (miRNAs) have emerged as key modulators involved in regulating this pathway, functioning as either oncogenes or tumor suppressors to determine cancer cell fate. Previous studies have demonstrated that kaempferol and its derivatives, owing to their molecular structures, suppress the PI3K/AKT/mTOR signaling pathway either directly or indirectly, thereby playing a critical role in inhibiting cancer cell proliferation. Hence, this comprehensive review elucidates the therapeutic potential of kaempferol and its derivatives, focusing specifically on their molecular mechanisms in modulating the PTEN/PI3K/AKT/mTOR pathway. Furthermore, this review highlights the crosstalk between kaempferol and the specific miRNAs targeting these signaling components in various cancer types. Full article

Cross-view geo-localization (CVGL) is a critical technology used in unmanned aerial vehicles (UAVs) and widely applied in navigation and target localization tasks. However, owing to the extreme perspective disparity between UAV oblique views and satellite vertical views, CVGL still involves significant challenges, including geometric distortion caused by viewpoint differences, drastic appearance inconsistencies, and the difficulty in bridging semantic gaps between heterogeneous data. To address these issues, we propose a novel CVGL method named dynamic-feature collaborative optimization and semantic-alignment network (DFSA), designed to extract robust feature representations and achieve fine-grained alignment. Specifically, the DFSA employs a residual-based vision transformer as the backbone to capture global context while alleviating the training instability and feature collapse often associated with standard transformers. To bridge the semantic gap between global and local features, we design a feature optimization module comprising a local feature enhancer and a global feature aggregator. This module establishes a closed-loop collaborative system that facilitates top-down semantic guidance and bottom-up detail feedback. Furthermore, we introduce a semantic segmentation and alignment module that adaptively partitions images into semantic regions based on feature response distributions, shifting the matching granularity from the global level to the semantic region level to effectively overcome feature mismatches caused by positional offsets and scale variations. Extensive experiments conducted on the University-1652 and SUES-200 datasets demonstrate the superior image retrieval performance of the proposed DFSA. Specifically, DFSA achieves a Recall@1 of 94.87% and an Average Precision (AP) of 95.32% on the University-1652 dataset and maintains highly competitive Recall@1 performances between 96.83% and 99.25% across various altitudes on the SUES-200 dataset. These results validate the model’s effectiveness in handling extreme viewpoint changes for UAV-based cross-view image retrieval tasks. Full article

Plateau environments are characterized by low temperatures, intensive radiation and drastic diurnal temperature fluctuations, all of which greatly suppress rooting and seedling establishment of tea chrysanthemum cuttings. To compare the regulatory effects of different rooting regulators on tea chrysanthemum cuttings under plateau cultivation conditions, this study conducted a 60-day plug-tray cultivation experiment from late July to late September with three biological replicates. All the cuttings, including the IBA-pre-treated control (CK), were uniformly pretreated with 500 mg·L⁻¹ indole-3-butyric acid (IBA). On this basis, four treatments, including 5-aminolevulinic acid hydrochloride (5-ALA, T1), γ-aminobutyric acid (GABA, T2), Bacillus amyloliquefaciens (T3) and Bacillus velezensis (T4), were applied to determine root morphology, seedling growth, physiological stress metabolism, photosynthetic capacity and rhizosphere substrate characteristics. The different rooting promoters exerted distinct regulatory effects on cutting performance. The GABA treatment significantly improved leaf gas exchange, seedling growth, antioxidant status and rhizosphere nutrient conditions, with net photosynthetic rate, stomatal conductance and transpiration rate improved by 316.34%, 92.31% and 168.00%, respectively, and significantly increased seedling vigor index, plant height and stem diameter by 21.74%, 60.85% and 46.08%, respectively. It also elevated the soluble sugar content and the ascorbate peroxidase (APX) activity, reduced malondialdehyde (MDA) and hydrogen peroxide (H₂O₂) accumulation, and optimized rhizosphere available nitrogen and phosphorus levels, as well as related enzyme activities. Nevertheless, the 5-ALA treatment exhibited unique advantages in improving rooting rate and seedling survival. Mantel tests confirmed that the seedling vigor index closely correlated with the root architecture, the total chlorophyll and the transpiration rate. A principal component analysis (PCA) and a cluster heatmap both identified GABA as the treatment with relatively balanced overall performance. A comprehensive D-value evaluation ranked the treatments as GABA > B. amyloliquefaciens > 5-ALA > B. velezensis > control. The treatment with 5-ALA mainly improved the antioxidant capacity, the B. amyloliquefaciens treatment favored root elongation and total nutrients, and the B. velezensis treatment only produced mild improvements. This study indicates that the different exogenous regulators target divergent growth and physiological processes, and the GABA treatment could coordinately boost root development, photosynthetic performance, antioxidant defense and rhizosphere nutrient cycling, thus presenting great application potential for tea chrysanthemum cutting propagation under plateau cultivation conditions. Full article

Background: The health sector is getting transformed with the usage of AI, be it diagnosis, treatment planning, disease prediction, and or health system management. Research in this field has picked up in the last few years, which was made possible with the emergence of machine learning, natural language processing and the increasing number of e-health records. Objectives: The study aims to investigate the current trends in the implementation of artificial intelligence (AI) applications in medical settings by investigating the global scientific output/landscape on this theme, such as the annual publication trends, country-wise contributions, and publishing patterns. Methods: The current study is based on systematic review by combining bibliometric analysis and cluster analysis using VOSviewer version 1.6.20, R software version 4.5.0, and Biblioshiny (Bibliometrix package in R) along with preferred reporting items for systematic reviews and meta analyses (PRISMA), 2020 which provides transparency and rigorous visualization to examine the articles published in English on the use of AI in healthcare, after the onset of COVID-19 till date i.e., from 2020 to 2026 on the Scopus database. Results: Using the relevant search string, 5940 documents were identified between 2020 and 2026, 1434 were included for analysis after screening and relevant filters. The publications have increased remarkably after 2020 on this theme and more than half of the publications have their roots in the discipline of Medicine. The USA, China, and the United Kingdom have contributed the most to the volume of research. Natural language processing and diagnosis are the emerging themes. The Journal of Medical Internet Research, BMC Medical Informatics and Decision Making, Computers in Biology and Medicine, IEEE Journal of Biomedical and Health Informatics, Frontiers in Public Health, and Digital Health are some of the most influential sources in the field. Li J and Liu X are among the authors with remarkable local impact. Conclusions: The work aims to assist investigators, health care professionals, and policymakers to learn about modern trends and focus on critical areas of future research and collaboration in AI-enhanced health care. The limitation of the study is that it considered only the Scopus database but it has opened up opportunities for researchers for analysis using other databases such as Dimensions, Lens, and PubMed. Also, this review is considering the publication record since the onset of COVID-19 but a comparative analysis of pre and post-pandemic studies can also be conducted to get a holistic view of drastic collaboration of research in this field. Discussions: The findings suggest that the role of artificial intelligence in health care has paramount over recent years, with other supporting technologies but a technologically hesitant population as well as low acceptance of AI due to ethical issues, cannot be ignored for ensuring efficiency in the health sector. Full article

This study investigates the time-dependent seismic fragility of reinforced concrete (RC) bridge piers exposed to the harsh saline–alkali environments of Northwest China. A comprehensive analytical framework was developed by integrating quasi-static cyclic tests, nonlinear finite element modeling, and incremental dynamic analysis (IDA). Four pier specimens were subjected to accelerated corrosion in a composite sulfate–chloride solution for up to 90 days. Experimental results reveal a critical threshold: once the actual mass loss of the longitudinal reinforcement reaches approximately 12.05% (corresponding to a stirrup mass loss of approximately 21.45%), the severe loss of core confinement triggers a fundamental failure mode transition from ductile flexural yielding to brittle flexural-shear failure. Traditional displacement-based parameters are fundamentally inadequate for capturing this brittle shift; therefore, the Krätzig hysteretic energy dissipation index was adopted to rigorously quantify structural damage. Subsequently, a time-dependent Probabilistic Seismic Demand Model (PSDM) was constructed, explicitly incorporating the experimentally calibrated reinforcement degradation laws. The fragility analysis demonstrates a distinct biphasic degradation mechanism: while short-term sulfate attack temporarily enhances initial stiffness via a “pore-filling effect,” prolonged composite exposure drastically amplifies seismic vulnerability. Notably, under a severe earthquake intensity of 1.0 g (PGA), the exceedance probability for Severe Damage reaches 50.24% after 90 days of exposure, representing a 2.7-fold increase compared to the uncorroded baseline This research provides a robust, energy-based quantitative methodology for the lifecycle seismic evaluation and maintenance of transport infrastructure in aggressive composite environments. Full article

In complex and unstructured orchard environments, accurate fruit detection is essential for yield estimation and robotic harvesting in precision agriculture. However, single-model detectors often suffer from reduced robustness and high miss rates under drastic illumination changes, severe occlusions, and dense fruit overlap. To address these challenges, this study proposes a multi-model fusion framework for robust mango detection in complex orchard environments. The proposed method employs YOLOv8n, YOLOv8s, and YOLOv8m as base detectors and applies multi-scale test-time augmentation (TTA) to obtain predictions from different augmented views. After mapping the predicted bounding boxes back to the original image coordinate system, predictions corresponding to the same target across different TTA views of each base detector are matched based on the intersection over union (IoU), yielding model-specific prediction results. Weighted Box Fusion (WBF) is then applied to determine the fused bounding-box coordinates. For candidate targets jointly detected by multiple base detectors, the confidence scores provided by the individual models are combined using Noisy-OR to obtain the fused confidence score. Finally, Gaussian Soft-NMS is applied to decay the scores of overlapping candidate boxes, thereby reducing the risk of incorrectly suppressing adjacent mangoes in densely clustered scenes. Experiments on two complementary datasets under within-dataset evaluation protocols demonstrate the effectiveness of the proposed method. On the standard dataset (Data1), Recall and mAP@0.5 reach 95.52% and 98.60%, respectively. Across five repeated random holdout splits of Data2, the proposed framework increased the mean Recall from 82.79% to 84.90% and the mean mAP@0.5 from 90.27% to 91.23%. These results indicate that the proposed framework improves detection robustness and completeness compared with single-model detectors in complex orchard environments, demonstrating its potential for offline yield estimation and orchard phenotyping. Full article

The SARS-CoV-2 pandemic has significantly impacted global health, politics, medicine, finance, and society. Since 2020, various mutations have been reported, leading to drug resistance in current treatments against different SARS-CoV-2 strains and a drastic increase in cases of long-COVID. This situation underscores the urgent need to develop targeted and effective drugs to combat the spread of SARS-CoV-2 strains and their mutants, manage long-COVID symptoms and prepare for future pandemics. Currently, the treatment of SARS-CoV-2 focuses on targeting the virus’s entry and replication mechanisms to disrupt its life cycle. This review examines approved drugs, clinical candidates, and inhibitors under development, along with their bioassay data, while highlighting associated challenges. It illustrates how inhibitors bind to active sites, providing insights and emphasizing the importance of in silico studies, such as molecular docking, molecular dynamics simulation, FEP+, WaterMap, and quantitative structure–activity relationship (QSAR) analyses, and their correlation with experimental studies in expediting the drug discovery process. The review aims to provide researchers with insights into the gaps that need to be addressed concerning mutations affecting viral entry and to prepare for future pandemics. Full article

Autonomous commercial vehicles (ACVs) frequently encounter drastic variations in payload and complex road conditions during practical operations. Consequently, effectively suppressing external disturbances caused by payload and road slope uncertainties has become a critical challenge in enhancing the robustness of their low-level control systems. To address this issue, this paper proposes a sliding mode control (SMC) strategy based on Luenberger observer disturbance compensation (LOSM), aiming to simultaneously mitigate the adverse effects of these two uncertainties on the vehicle’s speed control performance. First, according to the driving characteristics of commercial vehicles, a full-condition longitudinal dynamic model encompassing uphill, downhill, and flat road scenarios is established. Second, by deeply integrating the Luenberger observer with sliding mode control theory, an active disturbance rejection LOSM speed controller is designed. Furthermore, the boundary conditions for the closed-loop system to achieve asymptotic stability are rigorously derived and proven using Lyapunov functions. Finally, to comprehensively verify the effectiveness of the proposed strategy, eight typical testing scenarios are constructed, and three benchmark algorithms—PI control, radial basis function adaptive sliding mode (RBFSM) control, and radial basis function backstepping sliding mode (RBFBSSM) control are introduced for comparative analysis. The validation results demonstrate that although all four methods can achieve speed tracking and suppress disturbances, the proposed LOSM strategy exhibits the optimal comprehensive performance across various scenarios. Specifically, its steady-state mean error is typically maintained below 2.5%, and it yields the minimum steady-state variance in the majority of scenarios. These results demonstrate that the designed LOSM method can significantly improve the precision and smoothness of ACVs’ speed control under the dual disturbances of unknown mass and road slope. Full article

As the global energy sector and maritime industry transition toward carbon neutrality, Carnot batteries have emerged as a critical technology for flexible, long-duration energy management. Carnot batteries, which operate on a Power-to-Heat-to-Power principle, present a highly promising alternative to conventional electrochemical batteries. The present study provides a review of Carnot battery architectures, systematically evaluating their thermodynamic cycles, working fluids, Thermal Energy Storage media and key turbomachinery components. A comparative assessment of the current literature illustrates that system selection requires balancing round-trip efficiency, Energy Storage Density and Technology Readiness Level. According to the findings of the present study, high-temperature Brayton cycles offer robust baseline efficiencies of 60–80% whereas subcritical Rankine cycles benefit from commercial maturity and can achieve efficiencies exceeding 200% when integrated with cryogenic heat sinks like LNG. It should be clarified that efficiency values exceeding 100% represent “Apparent Round-Trip-Efficiencies (RTE)” resulting from the thermodynamic contribution of external exergy streams, such as LNG cryogenic cold, rather than standalone cycle efficiencies, which are strictly below 100%. In addition, volumetric energy density varies drastically based on the physical phase of the storage medium, scaling from under 1 kWh/m³ for unpressurized water to over 385 kWh/m³ for advanced thermochemical systems. Although most configurations currently remain in the prototyping phase, the technology holds transformative potential for the maritime sector. Carnot batteries can deliver a self-contained, zero-emission electrical power supply to cover the vessel’s electrical load requirements during harbor stays and transit within Emission Control Areas (ECAs) by dynamically capturing and storing shipboard waste heat during open sea transit. Full article

With the growing risks posed by extreme weather, such as cold waves, to the secure operation of power systems integrated with large-scale wind and PV power, conventional multi-energy complementary modes fail to cope with the drastic output fluctuations in renewable resources. In this study, a hydro–wind–PV joint-optimized scheduling model is established to quantify the compensation requirement of wind–PV power output fluctuations and to optimize the hydropower compensatory regulation, aiming to clarify the actual effects and inherent limitations of hydropower under cold-wave scenarios. Based on 86-year hourly operational simulation data of a practical virtual case in northwest China, the main simulation results, limited to a daily time horizon with five statistically extracted scenarios, are as follows: First, cold-wave events significantly raise the peak shaving and compensation pressure of hydropower, with the maximum fluctuation amplitude of new energy output reaching 86.76%. Second, compared with conventional operating conditions, hydropower can satisfy the above compensation demand, whereas the reservoir water level deviates from the normal range by −2.2–3.0 m after scheduling, which leads to water consumption or effective storage occupation of reservoirs. Third, restricted by the hydropower installed capacity and reservoir regulation constraints, the power deficit of 1962 MWh and water spillage of 10.59 million m³ cannot be completely resolved. This study can provide theoretical support for analyzing wind–PV fluctuation risks and revealing the multi-energy coupling operation mechanism in cold-wave environments. Full article