Search Results (861)

The construction of resource interface protocol templates is a key prerequisite for the unified integration of live, virtual, and constructive (LVC) resources in complex simulation and test environments. However, real-world protocol documents are usually heterogeneous in format, inconsistent in description, and rich in nested structures and implicit semantics, which makes manual analysis inefficient and error-prone. To address this issue, this paper proposes an intelligent construction method for LVC resource interface protocol templates based on large language models. First, raw protocol documents are converted into a unified Markdown representation, and a semantic understanding module is used for main-table identification, minimum-unit splitting, and auxiliary-table association. Then, a protocol item type identification expert module is designed to recognize complex structures such as frame headers, ordinary items, dynamic items, struct items, branch items, sub-protocol items, and checksum items. Finally, the extracted information is integrated into structured intermediate results for automatic XML template generation. Experiments on a representative test set composed of 20 protocol tables from real-world LVC resource interface documents show that the proposed method achieves a main-table extraction accuracy of 0.9761, a type recognition F1-score of 0.9769, an XML generation success rate of 1.0, and a node consistency of 0.9478. These results demonstrate that the proposed method can effectively improve the automation and engineering applicability of protocol template construction. Full article

Composed Video Retrieval (CoVR) enables flexible video search by retrieving a target video that reflects a specified modification to a query video. The triplet datasets—consisting of query videos, query text, and target videos—required for model training have been collected manually. Recent studies have explored automatic construction of training triplets for CoVR; however, most existing approaches rely heavily on caption similarity. This limitation is particularly problematic in soccer videos, where identical or highly similar captions can correspond to visually distinct situations, making it difficult to construct triplets with appropriate relationships. To address this issue, this paper proposes a multimodal triplet construction framework specialized for soccer videos. The key idea is to explicitly incorporate visual similarity alongside textual similarity. Specifically, candidate target videos are selected by combining visual similarity with commentary caption filtering, enabling the identification of videos that are visually similar yet semantically different. The semantic difference between videos is then generated as query text using a large language model (LLM) without manual annotation. Furthermore, a multimodal large language model (MLLM) is introduced to estimate whether the generated modification is visually and semantically consistent with the video pair. Rather than replacing human verification, this step provides an automated screening signal to identify potentially unreliable triplets. The experiments show that the proposed framework automatically constructs triplets with reasonable validity under limited human validation. These results demonstrate the potential of scalable triplet construction for CoVR in soccer videos. Full article

The buildup of non-biodegradable plastic waste and poor management of agro-industrial by-products have caused a major environmental crisis. The present research addresses the development of novel materials supporting the circular bioeconomy. This study aimed to develop and characterize bio-composite films derived from native Oxalis tuberosa starch and keratin microparticles (KMPs) extracted from cattle horn waste. The experimental methodology employed a 2³ factorial design and involved the characterization of the films included the evaluation of physical and optical properties and the identification of functional groups via spectroscopy, mechanical tests, and thermogravimetric analysis (TGA). The results revealed significant interactions (p ≤ 0.05). Higher processing temperatures were the main reason for the drop in water activity (a_w) and moisture content (MC) levels. Concurrently, the incorporation of KMPs reduced water solubility, increased opacity, and enhanced thermal stability. FTIR analysis confirmed the existence of intermolecular interactions between the hydroxyl and amide functional groups. In conclusion, bio-composites composed based on Oxalis tuberosa starch and keratin microparticles represent a sustainable alternative to mitigate the use of conventional plastics in the industry. Full article

Small-disturbance damping characteristics have become a critical concern in renewable-dominated power systems under low short circuit ratio (SCR) conditions. In heterogeneous systems composed of grid-following photovoltaic (GFL-PV) and grid-forming energy storage system (GFM-ESS) units, strong dynamic coupling may weaken the damping of critical oscillation modes, thereby complicating stability analysis and coordinated parameter tuning. This paper proposes a damping enhancement strategy for a low-SCR GFL-PV/GFM-ESS system. The main innovation is an integrated damping-oriented framework that links detailed small-disturbance modeling, dominant-mode identification, participation-factor analysis, parameter-sensitivity evaluation, and coordinated optimization. First, a dynamic model including GFL-PV, GFM-ESS, and their coupling is established, and the corresponding linearized model is verified. Then, eigenvalue, modal, participation-factor, and sensitivity analyses are performed to identify weakly damped modes, key state variables, and sensitive parameters. Furthermore, a Joint Opposite Selection-enhanced particle swarm optimization (JOS-PSO) strategy is proposed to tune multiple coupled parameters. Simulation results under different operating conditions show that the proposed method improves damping characteristics, small-disturbance stability, and dynamic performance. Full article

Quick response codes are widely used as anti-counterfeiting labels in the field of product packaging, but they are easily illegally copied. Thus, this paper introduces a quick response code verification method that combines an anti-counterfeiting pattern with a deep feature fusion network. Firstly, a specialized anti-counterfeiting quick response code is designed, composed of a standard quick response code and an anti-counterfeiting pattern, which is essentially a fine-grained random texture distribution sensitive to copying. Next, the anti-counterfeiting patterns are overlapped and divided into blocks during the data processing, which effectively expands the data volume and avoids the interference of pattern content on the authenticity identification. Then, a convolutional self-learning preprocessing layer is employed to initially learn the feature information that represents the difference between authenticity and forgery. Finally, a multi-feature fusion convolutional neural network is proposed to identity the authenticity of anti-counterfeiting patterns. The proposed network comprises two branches, facilitating multi-scale feature extraction and fusion. The effectiveness of the proposed approach is evaluated on a self-constructed quick response code dataset, and the experimental results demonstrate that the proposed approach outperforms traditional knowledge engineering methods and similar deep learning methods. Full article

The identification of dyes in ancient textiles is crucial for provenance research and scientific conservation. However, the extremely significant value of these cultural relics necessitates the use of non-destructive analytical techniques. To establish a non-destructive, in-situ, accurate, and rapid method for identifying natural dyes in ancient silk fabric samples, we employed desorption electrospray ionization high-resolution mass-spectrometry imaging (DESI-MSI). By optimizing key instrumental parameters—including sample pretreatment method, DESI spray solvent composition, and DESI heated transfer line (HTL) temperature—we determined the optimal mass-spectrometry imaging conditions. The optimal conditions for achieving the highest mass-spectrometry ion peak signal intensity and the best imaging quality were as follows: employing sample pretreatment using double-sided adhesive tape; a spray solvent composed of methanol (100%, v/v) with 0.1% formic acid and 0.1 μg/mL of leucine enkephalin; and an HTL temperature of 400 °C. The characteristic compound in the G42 silk fabric sample was successfully separated. Based on the characteristic mass-to-charge ratio of the major component, the compound was preliminarily identified as berberine. This result was further verified by tandem mass-spectrometry imaging and tandem mass spectra and finally confirmed by comparison with the mass spectrum of a reference standard. Consequently, the source of the dye in the sample was determined to be amur cork tree. The experiments confirmed the applicability and accuracy of the DESI-MSI method for the non-destructive analysis of precious textiles. This work underscores the urgent need to use such non-destructive techniques to provide technical support for the identification of high-value, inaccessible, or fragile silk artifacts and guide the historical tracing and preservation of these cultural relics. Full article

In this study, we systematically analyzed the dynamic changes in chromatin accessibility and the transcriptional responses in the spleen of largemouth bass (Micropterus salmoides) following infection with iridovirus (LMBV) using the assay for transposase-accessible chromatin with sequencing (ATAC-seq) and transcriptome sequencing (RNA-seq). Based on post-infection survival status, largemouth bass were classified into a resistant group (SR) and a susceptible group (SS). A total of 11,317 differentially accessible regions were identified between the two groups, among which the chromatin accessibility of core promoter regions was entirely increased in the SR group, suggesting that chromatin remodeling in these regions may directly participate in the transcriptional regulation of immune-related genes. Functional enrichment analysis revealed that genes associated with differentially accessible regions were significantly enriched in immune-related pathways such as autophagy, apoptosis, Toll-like receptor signaling, and NOD-like receptor signaling. Motif analysis further identified that transcription factors significantly enriched in the SR group included CTCF and heterodimers composed of multiple members of the ETS and FOX transcription factor families. Through integrative analysis, seven transcription factors (CTCF, Spi1, ETV2::FOXI1, FOXJ2::ELF1, FOXO1::ELK1, SPIC, and FOXO1::ELF1) were found to be significantly enriched in core promoter regions. To further screen for differentially expressed genes directly regulated by chromatin accessibility changes, an overlapping analysis was performed between 629 predicted target genes and 2656 differentially expressed genes (DEGs), resulting in the identification of 71 candidate genes. Among these, three immune-related genes (irf4a, btk, and nfil3-2) belonging to the ETS and FOX families were identified. This study reveals the dynamic chromatin accessibility landscape of largemouth bass in response to LMBV infection and demonstrates that increased chromatin accessibility in core promoter regions is closely associated with the resistant phenotype. Heterodimers of ETS and FOX family transcription factors may participate in antiviral immune responses by regulating the expression of key immune genes such as irf4a, btk, and nfil3-2, providing potential epigenetic molecular markers for disease resistance breeding in fish. Full article

Computational chemistry has played a central role in early-stage drug discovery by accelerating target selection, hit identification, and lead optimization. This review summarizes recent developments in molecular docking, pharmacophore modeling, molecular dynamics (MD), and virtual screening (VS), with a focus on their application in practical drug discovery workflows. Advances in docking protocols, including consensus scoring, physics-based rescoring, and ensemble approaches, addressed the challenges of receptor flexibility. Both ligand-based and structure-based pharmacophore models facilitated scaffold hopping and guided library prioritization. MD simulations were used to assess binding pose stability, identify cryptic binding pockets, and characterize solvent interactions. These simulations also supported free-energy calculations using endpoint and alchemical methods. Large-scale VS campaigns employed curated compound libraries, often composed of make-on-demand molecules, and relied on high-performance computing or cloud infrastructure to screen up to 10⁹ compounds. Hits were validated using orthogonal biophysical assays and filtered by absorption, distribution, metabolism, excretion, and toxicity (ADMET) predictions. Integrated pipelines combining pharmacophore modeling, docking, MD, and free-energy calculations improved enrichment rates and reduced the number of compounds requiring synthesis. Several case studies demonstrated the identification of nanomolar-affinity leads from ultra-large screening campaigns. The review also addressed ongoing challenges, such as inconsistent scoring of binding affinity, protonation, and tautomeric errors, dataset bias, and reproducibility issues. Strategies to mitigate these limitations included standardized library preparation, adherence to FAIR (Findable, Accessible, Interoperable, and Reusable) data principles, and the use of prospective benchmarking protocols. The review discussed emerging trends, including the use of quantum chemistry for electronic structure refinement, ensemble docking guided by cryo-electron microscopy (cryo-EM) data, and the integration of computational tools with automated synthesis and high-throughput screening in closed-loop discovery systems. These approaches have the potential to accelerate the design–make–test cycle, increase hit novelty, and improve decision-making in early drug development programs. Full article

Background/Objectives: Inpatient clinical deterioration is a major contributor to adverse hospital outcomes, such as unplanned intensive care unit (ICU) admissions and death. Rapid response systems aim to address this challenge by enabling early identification and intervention in at-risk patients. This study evaluated the impact of implementing a mobile intensive care team on clinical outcomes in surgical patients. Methods: A retrospective observational cohort study was conducted in a tertiary care hospital, comparing two consecutive periods: a pre-intervention phase (PRETIM) and a post-intervention phase (TIM). The study included 17,156 adult surgical patients. The TIM consisted of a proactive outreach team composed of one attending intensivist and two resident physicians, focusing on post-ICU monitoring and early identification of clinical deterioration on surgical wards. The primary outcome was in-hospital mortality. Secondary outcomes included ICU readmission and length of stay. Multivariable logistic regression adjusted for age, sex and surgical section was performed, along with subgroup and sensitivity analyses excluding early non-modifiable deaths. Results: Baseline characteristics were comparable between groups. In-hospital mortality decreased significantly following implementation of the TIM (8.0% vs. 5.3%; p < 0.001), corresponding to an absolute risk reduction of 2.7% and a number needed to treat of 37. ICU readmission rates did not differ significantly between groups. Sensitivity analysis excluding early deaths confirmed the mortality reduction. Subgroup analysis demonstrated consistent effects across surgical specialties, with the largest reductions observed in neurosurgery and general surgery. Conclusions: The implementation of a mobile intensive care team was associated with a significant and clinically meaningful reduction in in-hospital mortality among surgical patients. The findings support the role of proactive post-ICU monitoring and early intervention strategies in improving patient outcomes in high-risk hospital populations. Full article

Background: Xinshubao tablet (XSB), a traditional Chinese medicine (TCM) formula composed of five medicinal herbs, is used clinically to alleviate cardiovascular diseases. This study aimed to investigate the coronary vasodilatory effects of XSB and its individual herbs, exploring its active constituents and the underlying mechanisms. Methods: The vasorelaxant effects of XSB and its individual herbal intestinal absorption solutions (IASs) were evaluated by ex vivo coronary artery ring assays. The chemical constituents of the best active herbal IAS were qualitatively identified using ultra-performance liquid chromatography–quadrupole time-of-flight mass spectrometry (UPLC–Q-TOF-MS). Molecular docking and ex vivo assays were used to predict and validate the bioactive constituents and mechanisms responsible for coronary vasorelaxation. Results: Vasodilation experiments revealed that XSB-IAS and its individual herb IAS exhibited varying degrees of vasodilatory effects, in the range of 0.8–18 g raw materials/mL. At 6, 12, and 18 mg of raw materials/mL, Crataegus pinnatifida Bge (Shanzha) exhibited vasodilation rates of 26.45% ± 1.8%, 36.57% ± 3.5%, and 45.16% ± 6.3%, which were obviously higher than those of the other individual herbs. Fifty constituents in Shanzha IAS were identified by UPLC-Q-TOF-MS. Vasodilation-related protein–protein interaction (PPI) network revealed NOS3 as a core regulatory target. Molecular docking demonstrated that among the identified constituents, isochlorogenic acid B, betulin, etc., displayed binding affinity to NOS3. Isochlorogenic acid B was further validated to exhibit vasodilatory effects in the ranges of 0.05–2.5 mM. Mechanistic results showed that isochlorogenic acid B improved vasodilation by inhibiting Ca²⁺ influx through both voltage-dependent and receptor-operated Ca²⁺ channels, activating K⁺ channels, and exhibiting endothelium-dependent vasorelaxation. Conclusions: This study provides insights into the material basis and mechanisms underlying the vasorelaxant effects of XSB. Isochlorogenic acid B was firstly found to exert the coronary vasodilatory effect. This study can also contribute to the identification of efficacy-related quality markers in TCM. Full article

Sustainable development results from the harmonious integration of economic growth, social equity, and environmental sustainability. Building on available risk analysis capacities, this study employs risk perception as a diagnostic tool to evaluate the adequacy of decision-making regarding environmental sustainability in vulnerable human settlements under a changing climate in the Dominican Republic. Using the perceived risk profile approach and a specially designed questionnaire, the research explores issues related to climate change and sustainability, targeting a population composed of decision-makers and professionals engaged in risk assessment. The findings reveal a systematic underestimation of risk across most perception variables, as well as a generally low collective risk perception. The study’s methodological framework enables the identification of proactive measures to strengthen knowledge and performance among decision-makers and stakeholders involved in advancing sustainable development in Dominican human settlements. Full article

Coated ferromagnetic conductors (CFCs) are widely used in the engineering field, such as transportation, petrochemicals, energy, etc. Owing to long-term exposure to harsh and corrosive environments, involving large temperature differences, cyclic loading and humidity, hidden corrosion occurring under the coatings of CFCs has been found to be one of the most critical defects posing a severe threat to the structural strength and safety of CFCs. Therefore, it is important to conduct rapid imaging and quantitative evaluation of this hidden corrosion via Non-Destructive Evaluation (NDE) techniques. A magnetic field-viewing film (MFVF) characterizes magnetic fields by displaying corresponding color shifts, offering a direct visual representation of the magnetic field intensity. In light of this, this paper proposes an MFVF-based probe composed of multiple micro-sensor units for fast imaging of hidden corrosion in CFCs. An image-processing technique based on the modified Canny algorithm is subsequently proposed for identification of corrosion opening profiles in MFVF images. Based on the identification results, an assessment of hidden corrosion parameters is conducted. It is inferred from the experimental results that the opening area, depth and volume of hidden corrosion can be quantitatively evaluated, with an average accuracy of 86.1%. Full article

This study presents an E-nose system for the identification and classification of volatile compounds in traditional Filipino alcoholic beverages, Basi, Bignay, Lambanog, and Tapuy. The system utilizes a gas sensor array composed of MQ3, MQ6, MQ8, MQ135, and MQ136 sensors, and implements a Support Vector Machine (SVM) algorithm with principal component analysis for classification and dimensionality reduction. The experimental process involves three main phases: absorption, data acquisition, and desorption. A total of 225 training samples per class and a total of 20 testing samples were used, evenly distributed among all classes. The SVM model achieved an accuracy of 85%, highlighting its effectiveness in distinguishing between the beverages. This work contributes to the advancement of low-cost, sensor-based solutions for quality control, standardization, and the cultural preservation of traditional Filipino wines. Full article

Nabkhas are a common type of biogenic aeolian landform in arid and semi-arid regions. Their morphological characteristics, surface sediment grain size composition, and spatial distribution patterns can, to some extent, be associated with the interactions between vegetation and the aeolian environment. In this study, nabkhas formed around Caragana tibetica shrubs in the desert steppe of Damao Banner, Inner Mongolia, were selected as the research object. Based on field investigations, UAV image identification, grain size analysis, and spatial point pattern analysis, the characteristics of nabkhas were comparatively analyzed among a control plot without shrubs (CK) and three shrub-covered plots: a low coverage plot (LCP), a medium coverage plot (MCP), and a high coverage plot (HCP). The results showed that (1) some morphological parameters of nabkhas varied among plots with different vegetation cover, but the responses of various indicators were not entirely consistent. The MCP exhibited relatively higher values in indicators such as shrub long axis (L_g), short axis (W_g), and windward slope length (L_y). (2) The surface sediments of nabkhas were mainly composed of silt and fine sand, followed by very fine sand. Compared with the CK, the silt content was generally lower in the shrub-covered plots, whereas the contents of fine sand and very fine sand were higher. The mean grain size (Mz, Φ value) tended to decrease, while the skewness (SK_G) and kurtosis (K_G) tended to increase, and the sorting coefficient (σ_G) showed relatively limited variation. (3) In the LCP, MCP, and HCP, the fractal dimension (D) was significantly positively correlated with the Mz and σ_G (p < 0.05), and significantly negatively correlated with the SK_G and K_G (p < 0.01), suggesting that the D may be associated with variations in sediment grain size structure. (4) Overall, the nabkhas around Caragana tibetica shrubs exhibited a spatial distribution pattern characterized by aggregation at small scales and randomness at large scales, with small-scale clustering being more evident in the MCP and HCP. In general, nabkhas around Caragana tibetica shrubs under different vegetation cover conditions showed observable differences in morphological characteristics, surface sediment grain size composition, and spatial distribution patterns, providing a comparative case reference for the study of nabkhas in desert steppe areas. Full article

Preeclampsia remains a major cause of maternal and perinatal morbidity and mortality worldwide, yet progress in biomarker discovery and predictive modeling has translated only modestly into clinically meaningful risk stratification. Over the past two decades, numerous biomarkers and predictors reflecting placental–angiogenic dysfunction, maternal cardiovascular maladaptation, and inflammatory–metabolic stress have been proposed, alongside increasingly sophisticated statistical and machine learning approaches. However, many predictive strategies continue to treat preeclampsia as a single disease entity and rely on static thresholds applied at isolated gestational time points. Accumulating biological and clinical evidence instead suggests that preeclampsia represents a heterogeneous syndrome composed of partially overlapping mechanistic phenotypes whose relative contributions vary across pregnancy and across individuals. In this narrative review, we argue that further progress in prediction is likely to depend less on the identification of additional biomarkers and more on how biological heterogeneity and temporal dynamics are integrated into predictive frameworks. We synthesize current evidence supporting multimarker approaches, phenotype-informed frameworks, and longitudinal risk trajectories that conceptualize prediction as a dynamic process rather than a binary classification task. We also examine the complementary roles of classical statistical models and machine learning, emphasizing that calibration, external validation, interpretability, transportability, and clinical usability are essential, alongside discrimination, for successful clinical implementation. Finally, we outline key research priorities for the next generation of predictive studies, including mechanistically grounded phenotyping, dynamic risk updating across gestation, rigorous evaluation across diverse populations, and explicit linkage of risk stratification to preventive interventions and clinical decision-making. Together, these directions support a shift toward an integrative, longitudinal, and clinically anchored approach to preeclampsia prediction. Full article