Search Results (109)

Recent breakthroughs in large language models have made it feasible to effectively summarize cross-lingual dialogue information, proving essential for the global communication context. However, existing methodologies encounter difficulties in maintaining factual consistency across multiple dialogue exchanges and lack clear explanations of the summarization process. This paper presents a novel factually consistent prompting technology with large language models to address these challenges in cross-lingual dialogue summarization. First, we propose a factual replacement mechanism to enhance information analysis by incorporating noise information into summarization candidates. We adopt a self-guidance framework to enforce factual consistency, enhancing information flow tracking in cross-lingual hybrid dialogue scenarios with the assistance of GPT-based models. Furthermore, we introduce a view-aware chain-of-thought-driven architecture to improve the interpretability and transparency of the cross-lingual dialogue summarization process. Comprehensive experimental evaluations on cross-lingual summarization tasks, spanning English, French, Spanish, Russian, Chinese, and Arabic, and hybrid cross-lingual tasks substantiate that the proposed model achieves superior performance relative to state-of-the-art baselines. Full article

Standing equine computed tomography (CT) acquisitions are susceptible to residual postural sway, which can introduce view-inconsistent motion and degrade image quality. External optical tracking based on ChArUco fiducials is a promising, low-cost strategy to enable projection-wise motion compensation, yet quantitative guidance on how camera–marker geometry affects pose-estimation performance remains limited. This CT-motivated benchtop study characterizes how the relative camera–ChArUco configuration influences both the accuracy (bias with respect to ground truth) and the precision (repeatability) of pose estimates obtained from RGB images using OpenCV ChArUco detection and reprojection-error minimization to estimate the rigid camera-to-board transformation. Controlled experiments systematically varied acquisition protocol (continuous repeated estimates at fixed pose versus cyclic repositioning), viewing angle over a wide angular range at two working distances, and camera-to-board distance over multiple depth settings. Ground truth for angular configurations was defined by a stepper-motor rotation stage, while distance ground truth was obtained by ruler measurements. Performance was summarized via mean absolute error and standard deviation across repeated measurements, complemented by variance-based statistical testing with multiple-comparison correction. Cyclic repositioning did not yield evidence of increased variability relative to continuous acquisitions, supporting view-by-view sampling. Viewing angle induced a consistent accuracy–precision trade-off for rotations: frontal views minimized mean error but exhibited higher variability, whereas oblique views reduced jitter at the expense of increased bias. Increasing working distance reduced repeatability, most prominently for depth-related components. Overall, these findings provide pre-clinical guidance for selecting camera/marker placement (moderately oblique viewpoints, limited working distance, sufficient image footprint) before in-scanner and in-vivo validation for standing equine CT motion compensation. Full article

Multiple sclerosis (MS) and its animal model experimental autoimmune encephalomyelitis (EAE) are autoimmune inflammatory demyelinating diseases of the central nervous system (CNS). For decades, oligodendrocytes were regarded as passive targets of autoimmune inflammation in these conditions. However, recent studies challenge this view, revealing that oligodendrocytes are active participants—not just passive targets—in the pathogenesis of MS and EAE. In this review, we summarize recent research that highlights the active and dynamic roles of oligodendrocytes in these diseases. Full article

Wearable multi-modal body fluid monitoring enables continuous, non-invasive, and context-aware assessment of human physiology. By integrating biochemical and physical information across multiple modalities, wearable systems overcome the limitations of single-marker sensing and provide a more holistic view of dynamic health states. This review offers a system-level overview of recent advances in multi-modal body fluid monitoring, structured into three hierarchical dimensions. We first examine sensing-combination strategies such as multi-marker analysis within single fluids, coupling biochemical signals with bioelectrical, mechanical, or thermal parameters, and emerging multi-fluid acquisition to improve analytical accuracy and physiological relevance. Next, we discuss platform-integration mechanisms based on biochemical, physical, and hybrid sensing principles, along with monolithic and modular architectures enabled by flexible electronics, microfluidics, microneedles, and smart textiles. Finally, the data-processing patterns are analyzed, involving cross-modal calibration, machine learning inference, and multi-level data fusion to enhance data reliability and support personalized and predictive healthcare. Beyond summarizing technical advances, this review establishes a comprehensive framework that moves beyond isolated signal acquisition or simple metric aggregation toward holistic physiological interpretation. It guides the development of next-generation wearable multi-modal body fluid monitoring systems that overcome the challenges of high integration, miniaturization, and personalized medical applications. Full article

This paper presents a case study framework for the operational reliability monitoring of a grid-connected photovoltaic (PV) inverter using SCADA measurements collected during February–April 2025. The workflow combines correlation-based drift analysis, probabilistic outputs from established machine learning models (XGBoost and LSTM), and temporal consistency modeled through a hidden Markov model (HMM). The resulting evidence is summarized into two interpretable composite indicators: a Health Index (HI), intended to capture short-term deviations, and a Reliability Score (RS), intended to provide a smoother reliability-oriented summary over time. A time-aware evaluation protocol is employed to reduce temporal leakage and to assess predictive utility under rare-event conditions, complemented by baseline comparisons and sensitivity checks for key thresholds and modeling settings. Within the analyzed dataset, the results suggest that HI is responsive to transient disturbances, while RS supports trend monitoring and maintenance prioritization by consolidating multiple weak signals into a consistent operational view. The proposed indicators are positioned as data-driven risk summaries for decision support rather than direct physical measures of deviation patterns. Generalization to other inverters and sites requires further validation on longer horizons and with additional operational/maintenance records. Full article

Nano- and submicron particles (NSPs) with folate for targeting are actively used for the treatment and diagnosis of cancer and inflammatory diseases. Albumin-containing systems have enhanced biocompatibility, circulation time, and colloidal stability, which are important for medical applications. The outstanding binding properties of albumin allow the transport of numerous therapeutic and/or imaging agents. This review summarizes multiple aspects of binding a folate residue (or folic acid) to NSPs and the functioning of folate-albumin-NSPs. Special attention in the review is given to the types of bonds between folic acid and albumin, i.e., covalent and non-covalent, and to the confirmation and quantification of binding by different physicochemical methods. The process of binding, the qualitative and quantitative characteristics of binding and forming product, and its functioning are interconnected with the binding conditions; thus, an analysis of reaction conditions is provided. For the proper functioning of folate-albumin-NSPs, the state of albumin within them is important; thus, considerable focus in the review is placed on the features of structure modification of serum albumin in folate-albumin binding, i.e., the amino acid residues involved in this process and the conformational state of the protein. The stability and the functioning of the protein within folate-albumin-NSPs are discussed. Also, the effectiveness of targeting by folate is viewed as dependent on many characteristics of folate-albumin-NSPs, particularly on the peculiarities of binding between the folic acid residue and albumin. Furthermore, the authors discussed and suggested solutions concerning the shortcomings highlighted in the studies devoted to obtaining folate-modified albumin-containing NSPs. Full article

Cell membrane receptors play key roles in physiological and pathological processes, yet the mechanisms governing their expression and distribution across the plasma membrane remain not completely understood. Broadly, membrane receptors can be categorized into phasic and tonic receptors. Tonic GABA-A receptors have attracted considerable interest due to their distinct molecular composition and their capacity to mediate highly sensitive, sustained inhibitory responses in the presence of ambient GABA. Traditionally, these receptors have been described as residing in peri- and extrasynaptic regions, where they are thought to sense GABA spillover and generate tonic inhibition. However, evidence accumulated over several decades has challenged this canonical view. Multiple studies have reported activity-dependent and pathology-associated relocalization of tonic GABA-A receptor subunits from their typical peri- and extrasynaptic domains into the synaptic cleft. This phenomenon has been documented in both in vivo and in vitro models, yet major questions remain regarding its occurrence, underlying mechanisms, functional significance, and adaptive value. This review synthesizes current evidence and highlights ongoing controversies surrounding the ultrastructural localization of tonic GABA-A receptors. Based on an exhaustive search of the PubMed database, this review summarizes key findings from studies investigating the subcellular distribution of these receptors and discusses emerging perspectives on their potential synaptic presence. Full article

Circulating tumor DNA (ctDNA) analysis has emerged as a powerful and minimally invasive approach for genomic profiling of metastatic castration-resistant prostate cancer (mCRPC), enabling real-time detection of tumor-derived mutations that guide therapy. Approximately 20% of mCRPC patients harbor alterations in homologous recombination repair (HRR) genes, most commonly BRCA1/2 and ATM, which are actionable with different poly-(ADP-ribose) polymerase inhibitors (PARPIs) used as monotherapy or in combination with androgen receptor signaling inhibitors (ARSIs). A smaller subset of patients with mismatch repair deficiency (MMRd) or microsatellite instability-high (MSI-high) tumors may benefit from immune checkpoint blockade with pembrolizumab. Different FDA-approved liquid biopsy assays detect these actionable alterations when tissue biopsies are unavailable or insufficient. This review summarizes current evidence on ctDNA-based genotyping in mCRPC, highlighting clinically actionable mutations, corresponding targeted therapies, and technical and analytical considerations for clinical implementation. By capturing DNA shed from multiple metastatic sites, ctDNA profiling provides a comprehensive view of tumor heterogeneity and enables serial monitoring of molecular evolution. Overall, ctDNA analysis represents a transformative advance in precision oncology, supporting personalized treatment selection and ongoing assessment of therapeutic response in mCRPC. Full article

This review summarizes recent progress and emerging trends in multiparameter optical fiber sensing, emphasizing techniques that enable the simultaneous measurement of temperature, strain, acoustic waves, pressure, and other environmental quantities within a single sensing network. Such capabilities are increasingly important for structural health monitoring, environmental surveillance, industrial diagnostics, and geophysical observation, where multiple stimuli act on the fiber simultaneously. The paper outlines the physical principles and architectures underlying these systems and focuses on strategies for compensating and decoupling cross-sensitivity among measured parameters. Special attention is devoted to advanced distributed sensing schemes based on coherent optical frequency-domain reflectometry (C-OFDR), coherent phase-sensitive time-domain reflectometry (Φ-OTDR), and Brillouin optical time-domain reflectometry (BOTDR). Their theoretical foundations, their signal-processing algorithms, and the design modifications that improve parameter discrimination and accuracy are analyzed and compared. The review also highlights the roles of polarization and mode diversity and the growing application of machine-learning techniques in the interpretation and calibration of data. Finally, current challenges and promising directions for the next generation of fiber-optic multiparameter sensors are outlined, with a view toward high-resolution, low-cost, and field-deployable solutions for real-world monitoring applications. Full article

Global climate change exerts a systematic threat to the yield stability, nutritional quality, pest and disease control, and supply chain security of the fruit and vegetable industry via multiple ways, including altering temperature, carbon dioxide concentration, rainfall, ocean acidification, and soil deterioration. To tackle climate change, actions like carbon pricing and low-carbon technologies not only promote emission reduction but also impose pressure and economic difficulties on farmers, producers, logistics, transport, etc. This review, from an integrated view of “policy–technology relationship”, begins by summarizing the impacts of the aforementioned climate factors and systematically analyzes the influence of climate, policy, and technology on the fruit and vegetable industry. The research shows that the solution lies in the strategic cooperation between policies and technologies: technological innovation (e.g., controlled environment agriculture) offers potential for establishing resilient production systems, yet its successful implementation largely relies on forward—looking policy support and infrastructure investment, particularly the initial investment in renewable energy. Therefore, this paper puts forward an integrated framework intended for designing “resilient” fruit and vegetable systems, offering new theoretical foundations and path options for the coordinated advancement of climate mitigation and global nutrition security goals. This work offers an integrated framework for designing antifragile fruit and vegetable systems, harmonizing climate mitigation (SDG 13) with nutritional security (SDG 2) through strategically coordinated policy and technology interventions. Full article

As a fundamental element in industrial fluid transportation, the main pump fulfills an irreplaceable function in critical infrastructure, including the energy, water conservancy, petrochemical, and sewage treatment industries. As the core component of key power equipment, its operating condition is intrinsically connected to the safety, stability, and reliability of the entire system. This paper provides a systematic review of the latest advances in fault mechanism analysis and diagnosis methods for main pump systems. First, the typical structural composition and functional characteristics of the main pump system are examined, and the occurrence mechanisms and evolution rules of typical faults, such as mechanical malfunctions and performance degradation caused by hydraulic imbalance, are discussed in detail. Second, the main technical approaches to fault diagnosis are summarized and reviewed, including diagnosis methods based on signal processing, modeling, data-driven techniques, and multi-source information fusion. The advantages, limitations, and application scopes of these approaches are comparatively analyzed. On this basis, the development trends in main pump fault diagnosis technology and the key challenges faced—such as strong noise, small sample size, and multiple fault coupling—are identified and discussed. Finally, future research prospects are put forward in view of the limitations of current research. This review aims to provide theoretical insights and technical support for advancing condition monitoring, fault diagnosis, and health management of main pump systems. Full article

The evaluation of the fracturing effect of coalbed methane (CBM) wells is crucial for the efficient development of CBM reservoirs. Currently, studies focusing on the evaluation of the hydraulic fracture stimulation effect of coal seams and the integrated analysis of “drilling-fracturing-monitoring” are relatively insufficient. Therefore, this paper takes three drainage and production wells in the coalbed methane block on the northwest wing of the Xiangxia anticline in the Bijie Experimental Zone of Guizhou Province as the research objects. In view of the complex geological characteristics of this area, such as multiple and thin coal seams, high gas content, and high stress and low permeability, the paper systematically summarizes the results of drilling and fracturing engineering practices of the three drainage and production wells in the area, including the application of key technologies such as a two-stage wellbore structure and the “bentonite slurry + low-solid-phase polymer drilling fluid” system to ensure wellbore stability, low-solid-phase polymer drilling fluid for wellbore protection, and staged temporary plugging fracturing. On this basis, a study on microseismic signal acquisition and tomographic energy inversion based on a ground dense array was carried out, achieving four-dimensional dynamic imaging and quantitative interpretation of the fracturing fractures. The results show that the fracturing fractures of the three drainage and production wells all extend along the direction of the maximum horizontal principal stress, with azimuths concentrated between 88° and 91°, which is highly consistent with the results of the in situ stress calculation from the previous drilling engineering. The overall heterogeneity of the reservoir leads to the asymmetric distribution of fractures, with the transformation intensity on the east side generally higher than that on the west side, and the maximum stress deformation influence radius reaching 150 m. The overall transformation effect of each well is good, with the effective transformation volume ratio of fracturing all exceeding 75%, and most of the target coal seams are covered by the fracture network, significantly improving the fracture connectivity. From the perspective of the transformed planar area per unit fluid volume, although there are numerical differences among the three wells, they are all within the effective transformation range. This study shows that microseismic fracture monitoring technology can provide a key basis for the optimization of fracturing technology and the evaluation of the production increase effect, and offers a solution to the problem of evaluating the hydraulic fracture stimulation effect of coal seams. Full article

Background: Cardiovascular disease (CVD) is the leading global cause of death and is shaped by interacting biological, environmental, lifestyle, and social factors. Traditional models often treat risk factors in isolation and may miss dependencies among exposures and biomarkers. Objective: To map interdependencies among environmental, social, behavioral, and biological predictors of CVD risk using Bayesian network models. Methods: A cross-sectional analysis was conducted using NHANES 2017–2018 data. After complete-case procedures, the analytic sample included 601 adults and 22 variables: outcomes (systolic/diastolic blood pressure, total/LDL/HDL cholesterol, triglycerides) and predictors (BMI, C-reactive protein (CRP), allostatic load, Dietary Inflammatory Index, income, education, age, gender, race, smoking, alcohol, and serum lead, cadmium, mercury, and PFOA). Spearman’s correlations summarized pairwise associations. Bayesian networks were learned with two approaches: Grow–Shrink (constraint-based) and Hill-Climbing (score-based, Bayesian Gaussian equivalent score). Network size metrics included number of nodes, directed edges, average neighborhood size, and Markov blanket size. Results: Correlation screening reproduced expected patterns, including very high systolic–diastolic concordance (p ≈ 1.00), strong LDL–total cholesterol correlation (p = 0.90), inverse HDL–triglycerides association, and positive BMI–CRP association. The final Hill-Climbing network contained 22 nodes and 44 directed edges, with an average neighborhood size of ~4 and an average Markov blanket size of ~6.1, indicating multiple indirect dependencies. Across both learning algorithms, BMI, CRP, and allostatic load emerged as central nodes. Environmental toxicants (lead, cadmium, mercury, PFOS, PFOA) showed connections to sociodemographic variables (income, education, race) and to inflammatory and lipid markers, suggesting patterned exposure linked to socioeconomic position. Diet and stress measures were positioned upstream of blood pressure and triglycerides in the score-based model, consistent with stress-inflammation–metabolic pathways. Agreement across algorithms on key hubs (BMI, CRP, allostatic load) supported network robustness for central structures. Conclusions: Bayesian network modeling identified interconnected pathways linking obesity, systemic inflammation, chronic stress, and environmental toxicant burden with cardiovascular risk indicators. Findings are consistent with the view that biological dysregulation is linked with CVD and environmental or social stresses. Full article

In recent years, unmanned aerial vehicle (UAV) quantitative remote sensing technology has demonstrated significant advantages in fields such as agricultural monitoring and ecological environment assessment. However, achieving the goal of quantification still faces major challenges due to the angle effect. This effect, caused by the bidirectional reflectance distribution function (BRDF) of surface targets, leads to significant spectral response variations at different observation angles, thereby affecting the inversion accuracy of physicochemical parameters, internal components, and three-dimensional structures of ground objects. This study systematically reviewed 48 relevant publications from 2000 to the present, retrieved from the Web of Science Core Collection through keyword combinations and screening criteria. The analysis revealed a significant increase in both the number of publications and citation frequency after 2017, with research spanning multiple disciplines such as remote sensing, agriculture, and environmental science. The paper comprehensively summarizes research progress on the angle effect in UAV quantitative remote sensing. Firstly, its underlying causes based on BRDF mechanisms and radiative transfer theory are explained. Secondly, multi-angle data acquisition techniques, processing methods, and their applications across various research fields are analyzed, considering the characteristics of UAV platforms and sensors. Finally, in view of the current challenges, such as insufficient fusion of multi-source data and poor model adaptability, it is proposed that in the future, methods such as deep learning algorithms and multi-platform collaborative observation need to be combined to promote theoretical innovation and engineering application in the research of the angle effect in UAV quantitative remote sensing. This paper provides a theoretical reference for improving the inversion accuracy of surface parameters and the development of UAV remote sensing technology. Full article

The integration of artificial intelligence into healthcare has advanced rapidly in recent years, with multimodal approaches emerging as promising tools for improving diagnostic accuracy and clinical decision making. These approaches combine heterogeneous data sources such as medical images, electronic health records, physiological signals, and clinical notes to better capture the complexity of disease processes. Despite this progress, only a limited number of studies offer a unified view of multimodal AI applications in medicine. In this review, we provide a comprehensive and up-to-date analysis of machine learning and deep learning-based multimodal architectures, fusion strategies, and their performance across a range of diagnostic tasks. We begin by summarizing publicly available datasets and examining the preprocessing pipelines required for harmonizing heterogeneous medical data. We then categorize key fusion strategies used to integrate information from multiple modalities and overview representative model architectures, from hybrid designs and transformer-based vision-language models to optimization-driven and EHR-centric frameworks. Finally, we highlight the challenges present in existing works. Our analysis shows that multimodal approaches tend to outperform unimodal systems in diagnostic performance, robustness, and generalization. This review provides a unified view of the field and opens up future research directions aimed at building clinically usable, interpretable, and scalable multimodal diagnostic systems. Full article