Search Results (1,765)

Distance learning has become essential to higher education, yet its application in military officer training presents unique academic, operational, and security challenges. For Lithuania’s future officers, remote education must foster not only knowledge acquisition but also decision-making, leadership, and operational readiness—competencies traditionally developed in immersive, in-person environments. This study addresses these challenges by integrating System Dynamics Modelling, Contemporary Risk Management Standards (ISO 31000:2022; Dynamic Risk Management Framework), and Learning Analytics to evaluate the interdependencies among twelve critical factors influencing the system resilience and effectiveness of distance military education. Data were collected from fifteen domain experts through structured pairwise influence assessments, applying the fuzzy DEMATEL method to map causal relationships between criteria. Results identified key causal drivers such as Feedback Loop Effectiveness, Scenario Simulation Capability, and Predictive Intervention Effectiveness, which most strongly influence downstream outcomes like learner engagement, risk identification, and instructional adaptability. These findings emphasize the strategic importance of upstream feedback, proactive risk planning, and advanced analytics in enhancing operational readiness. By bridging theoretical modelling, contemporary risk governance, and advanced learning analytics, this study offers a scalable framework for decision-making in complex, high-stakes education systems. The causal relationships revealed here provide a blueprint not only for optimizing military distance education but also for enhancing overall system resilience and adaptability in other critical domains. Full article

Environmental degradation and soil desertification are among the most severe environmental issues of recent decades worldwide. Over time, these processes have led to increasingly extreme and highly dynamic climatic conditions. In Brazil, the Northeast Region is characterized by semi-arid and arid areas that exhibit high climatic variability and are extremely vulnerable to environmental changes and pressures from human activities. The application of geotechnologies and geographic information system (GIS) modeling is essential to mitigate the impacts and pressures on the various ecosystems of Northeastern Brazil (NEB), where the Caatinga biome is predominant and critically threatened by these factors. In this context, the objective was to map and assess the spatiotemporal patterns of land use and land cover (LULC), detecting significant trends of loss and gain, based on surface reflectance data and precipitation data over two decades (2000–2019). Remote sensing datasets were utilized, including Landsat satellite data (LULC data), MODIS sensor data (surface reflectance product) and TRMM data (precipitation data). The Google Earth Engine (GEE) software was used to process orbital images and determine surface albedo and acquisition of the LULC dataset. Satellite data were subjected to multivariate analysis, descriptive statistics, dispersion and variability assessments. The results indicated a significant loss trend over the time series (2000–2019) for forest areas (Z_MK = −5.872; Tau = −0.958; p < 0.01) with an annual loss of −3705.853 km² and a total loss of −74,117.06 km². Conversely, farming areas (agriculture and pasture) exhibited a significant gain trend (Z_MK = 5.807; Tau = 0.947; p < 0.01), with an annual gain of +3978.898 km² and a total gain of +79,577.96 km², indicating a substantial expansion of these areas over time. However, it is important to emphasize that deforestation of the region’s native vegetation contributes to reduced water production and availability. The trend analysis identified an increase in environmental degradation due to the rapid expansion of land use. LULC and albedo data confirmed the intensification of deforestation in the Northern, Northwestern, Southern and Southeastern regions of NEB. The Northwestern region was the most directly impacted by this increase due to anthropogenic pressures. Over two decades (2000–2019), forested areas in the NEB lost approximately 80.000 km². Principal component analysis (PCA) identified a significant cumulative variance of 87.15%. It is concluded, then, that the spatiotemporal relationship between biophysical conditions and regional climate helps us to understand and evaluate the impacts and environmental dynamics, especially of the vegetation cover of the NEB. Full article

High dynamic range (HDR) imaging aims to overcome the limited dynamic range of traditional imaging systems and achieve effective restoration of the brightness and color of the real world. In recent years, single-image HDR (SI-HDR) reconstruction technology has become a research hotspot due to its simple acquisition process and applicability to dynamic scenes. This paper proposes an improved SI-HDR reconstruction method based on HDRUNet, which systematically integrates channel, spatial attention mechanism, brightness expansion, and color-enhancement branches, and constructs an adaptive multi-component loss function. This effectively enhances the detail restoration in extreme exposure areas and improves the overall color expressiveness. Experiments on public datasets such as NTIRE 2021, VDS, and HDR-Eye show that the proposed method outperforms the mainstream SI-HDR methods in terms of PSNR, SSIM, and VDP evaluation metrics. It performs particularly well in complex scenarios, demonstrating greater robustness and generalization ability. Full article

To address the technical challenges in dynamic monitoring of grout diffusion patterns under complex geological conditions, in this study, a distributed parallel grouting monitoring system based on electrical resistivity tomography was developed. The system achieves three-dimensional visualization of grout propagation through hardware architecture innovation and the integration of inversion algorithms. At the hardware level, a cascadable distributed data acquisition terminal was designed, employing a dynamic optimization strategy for electrode combinations. This breakthrough overcomes traditional serial acquisition limitations. Algorithmically, a Bayesian estimation-based geological unit merging inversion model was proposed; it dynamically calculates merging thresholds through the noise posterior probability, achieving an improvement of more than 30% in the inversion boundary resolution compared with traditional least squares methods. Numerical simulations and physical experiments demonstrated that dipole arrays with 0.5 m electrode spacing exhibit optimal sensitivity to variations in grout resistivity, accurately capturing electrical response characteristics during diffusion. In practical roadbed grouting applications, the system yielded a grout diffusion radius showing only a 0.3 m deviation from the core sampling verification results, with three-dimensional imaging clearly depicting the diffusion morphology. This system provides reliable technical support for the precise control and quality assessment of underground engineering grouting processes. Full article

The field of bacterial systems biology is rapidly advancing beyond static genomic analyses, and moving toward dynamic, integrative approaches that connect genetic variation with cellular function. This review traces the progression from genome-wide association studies (GWAS) to multi-omics frameworks that incorporate transcriptomics, proteomics, and interactome mapping. We emphasize recent breakthroughs in high-resolution transcriptomics, including single-cell, spatial, and epitranscriptomic technologies, which uncover functional heterogeneity and regulatory complexity in bacterial populations. At the same time, innovations in proteomics, such as data-independent acquisition (DIA) and single-bacterium proteomics, provide quantitative insights into protein-level mechanisms. Experimental and AI-assisted strategies for mapping protein–protein interactions help to clarify the architecture of bacterial molecular networks. The integration of these omics layers through quantitative trait locus (QTL) analysis establishes mechanistic links between single-nucleotide polymorphisms and systems-level phenotypes. Despite persistent challenges such as bacterial clonality and genomic plasticity, emerging tools, including deep mutational scanning, microfluidics, high-throughput genome editing, and machine-learning approaches, are enhancing the resolution and scope of bacterial genetics. By synthesizing these advances, we describe a transformative trajectory toward predictive, systems-level models of bacterial life. This perspective opens new opportunities in antimicrobial discovery, microbial engineering, and ecological research. Full article

The ${\chi }_{c1}\left(3872\right)$ plays a pivotal role in understanding hadronic structures, remaining one of the most extensively studied exotic particles among numerous observed unconventional hadronic states. Sustained experimental and theoretical investigations into the particle over the past two decades have propelled its study into a high-precision regime, marked by refined measurements of its decay dynamics and line shape, thereby offering critical insights for resolving longstanding debates between molecular, tetraquark, hybrid, and charmonium interpretations of this particle. Furthermore, the heavy-quark symmetry in the molecular picture predicts a series of ${\chi }_{c1}\left(3872\right)$ partners. The BESIII experiment has made seminal contributions to the study of the ${\chi }_{c1}\left(3872\right)$, leveraging its unique capabilities in high-statistics data acquisition and low-background condition, such as observations of the productions $e^{+}{e}^{-}\to \gamma {\chi }_{c1}\left(3872\right)$ and $\omega {\chi }_{c1}\left(3872\right)$ and investigations of its decays. This article gives a concise review and prospects of the study of the ${\chi }_{c1}\left(3872\right)$ from the BESIII experiment. Full article

The direct current resistivity method holds advantages such as rapid, efficient, and automatic data acquisition. It is an important geophysical exploration technology for monitoring dynamic changes in subsurface geology. However, this method has such issues as volume effect and non-uniqueness in inversion. To meet the demand for high-resolution direct current resistivity inversion of dynamic geological models characterized by discontinuous changes, this study proposed a cross-gradient constrained time-lapse inversion method, thereby enhancing inversion imaging accuracy. A cross-gradient constraint term between models was incorporated into the objective function of time-lapse inversion to constrain the structural consistency and highlight local resistivity changes. This method avoided excessively smooth imaging as often caused by over-reliance on a reference model in time-lapse inversion, thereby significantly improving both the spatial resolution and quantitative accuracy of direct current resistivity monitoring inversion images. Numerical examples confirmed that the proposed method delivers higher inversion imaging accuracy in identifying dynamic resistivity changes, evidenced by a substantially lower normalized mean-square error (MSE). Furthermore, physical model experiments and a case study confirmed the stability of this method under actual monitoring conditions. The proposed method provides a more precise and effective inversion imaging technique for refined monitoring of dynamic changes in subsurface geologic bodies. Full article

Inundation dynamics in low-lying deltas are becoming increasingly important to monitor due to the impacts of climate change and human alterations to hydrological systems, which disrupt natural inundation patterns. In the Nemunas River Delta, where seasonal and extreme floods impact agricultural and natural landscapes, we used Sentinel-1 synthetic aperture radar (SAR) imagery (2015–2019), validated with drone data, to map flood extents. SAR provides consistent, 10 m resolution data unaffected by cloud cover, while drone imagery provides high-resolution (10 cm) data at 90 m flight height for validation during SAR acquisitions. Results revealed peak inundation during spring snowmelt and colder months, with shorter, rainfall-driven summer floods. Approximately 60% of inundated areas were low-lying agricultural fields, which experienced prolonged waterlogging due to poor drainage and soil degradation. Inundation duration was shaped by lithology, land cover, and topography. A consistent 5–10-day lag between peak river discharge and flood expansion suggests discharge data can complement SAR when imagery is unavailable. This study confirms SAR’s value for flood mapping in cloud-prone, temperate regions and highlights its scalability for monitoring flood-prone deltas where agriculture and infrastructure face increasing climate-related risks. Full article

A pluripotent callus is central to genetic transformation in Cinnamomum parthenoxylon; however, the molecular and cellular mechanisms regulating callus formation and subsequent differentiation remain unelucidated, hindering progress in its genetic improvement. This study systematically investigated the dynamic changes during the in vitro regeneration of C. parthenoxylon through morphological observations, physiological assays, and transcriptomic analyses, while comparing differences in callus formation under varying induction conditions to elucidate the mechanism of its high-efficiency regeneration. The results showed that the formation of a pluripotent callus is a critical step in C. parthenoxylon regeneration, characterized by the presence of highly proliferative cell zones. Compared to an ordinary callus (P3C), a pluripotent callus (P3) exhibited higher activities of polyphenol oxidase (PPO) and indole-3-acetic acid oxidase (IAAO), as well as elevated levels of zeatin riboside (ZR) and abscisic acid (ABA). In contrast, P3 showed lower levels of soluble sugars, soluble proteins, malondialdehyde (MDA), indole-3-acetic acid (IAA), and gibberellins (GA), a reduced IAA/ZR ratio, and diminished peroxidase (POD) activity. Weighted gene co-expression network analysis (WGCNA) identified 27 hub transcription factors (TFs) strongly associated with IAA/ZR, primarily from the ERF, bHLH, MYB, WRKY, and C3H families. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed the significant enrichment of differentially expressed genes (DEGs) related to plant hormone signal transduction and cell wall metabolism during pluripotent callus acquisition. Further investigations revealed that five genes encoding a putative indole-3-acetic acid-amido synthetase GH3.1, protein TIFY 10A, a two-component response regulator ARR2-like isoform X2, and xyloglucan endotransglucosylase/hydrolase, likely promoting callus pluripotency by modulating plant hormone signaling and cell wall metabolism, thereby enhancing in vitro regeneration in C. parthenoxylon. In summary, this study provides critical insights into the molecular mechanisms of C. parthenoxylon regeneration and offers valuable germplasm resources for establishing an efficient and stable genetic transformation system via tissue culture. Full article

Low temperature severely restricts maize seedling establishment and yield in northern China, but the proteomic basis of low-temperature tolerance in maize remains unclear. This study used TMT-labeled quantitative proteomics combined with data-independent acquisition (DIA) and liquid chromatography–tandem mass spectrometry (LC-MS/MS) to analyze dynamic proteome changes in two maize inbred lines (low-temperature-tolerant B144 and low-temperature-sensitive Q319) at the three-leaf stage under 5 °C treatment. A total of 4367 non-redundant proteins were identified. For differentially expressed proteins (DEPs, fold change >2.0 or <0.5, ANOVA-adjusted p < 0.05, false discovery rate [FDR] < 0.05), B144 showed exclusive upregulation under stress (6 DEPs at 24 h; 16 DEPs at 48 h), while Q319 exhibited mixed regulation (9 DEPs at 24 h: 6 upregulated, 3 downregulated; 21 DEPs at 48 h: 19 upregulated, 2 downregulated). Functional annotation indicated that ribosomal proteins, oxidoreductases, glycerol-3-phosphate permease, and actin were significantly upregulated in both lines. Pathway enrichment analysis revealed associations with carbohydrate metabolism, amino acid biosynthesis, and secondary metabolite synthesis. Weighted gene co-expression network analysis (WGCNA) identified genotype-specific expression patterns: B144 showed differential expression of proteins related to acetyl-CoA synthetase and fatty acid β-oxidation at 24 h and of proteins related to D-3-phosphoglycerate dehydrogenase at 48 h; Q319 showed differential expression of proteasome-related proteins at 24 h and of proteins related to elongation factor 1α (EF-1α) at 48 h. Venn analysis found no shared DEPs between the two lines at 24 h but four overlapping DEPs at 48 h. These results clarify proteomic differences underlying low-temperature tolerance divergence between maize genotypes and provide candidate targets for molecular breeding of low-temperature-tolerant maize. Full article

Smart manufacturing demands ever-increasing equipment reliability and continuous availability. Traditional fault diagnosis relies on attached sensors and complex wiring to collect vibration signals. This approach suffers from poor environmental adaptability, difficult maintenance, and cumbersome preprocessing. This study pioneers the use of high-temporal-resolution dynamic visual information captured by an event camera to fine-tune a multimodal large model for the first time. Leveraging non-contact acquisition with an event camera, sparse pulse events are converted into event frames through time surface processing. These frames are then reconstructed into a high-temporal-resolution video using spatiotemporal denoising and region of interest definition. The study introduces the multimodal model Qwen2.5-VL-7B and employs two distinct LoRA fine-tuning strategies for bearing fault classification. Strategy A utilizes OpenCV to extract key video frames for lightweight parameter injection. In contrast, Strategy B calls the model’s built-in video processing pipeline to fully leverage rich temporal information and capture dynamic details of the bearing’s operation. Classification experiments were conducted under three operating conditions and four rotational speeds. Strategy A and Strategy B achieved classification accuracies of 0.9247 and 0.9540, respectively, successfully establishing a novel fault diagnosis paradigm that progresses from non-contact sensing to end-to-end intelligent analysis. Full article

This paper presents an optimised signal processing framework for contactless physiological monitoring using Frequency Modulated Continuous Wave (FMCW) radar within automotive environments. This research focuses on enhancing heart rate (HR) and heart rate variability (HRV) detection from radar signals by integrating radar placement optimisation and advanced phase-based processing techniques. Optimal radar placement was evaluated through Signal-to-Clutter Ratio (SCR) analysis, conducted with multiple human participants in both laboratory and dynamic driving simulator experimental conditions, to determine the optimal in-vehicle location for signal acquisition. An effective processing pipeline was developed, incorporating background subtraction, range bin selection, bandpass filtering, and phase unwrapping. These techniques facilitated the reliable extraction of inter-beat intervals and heartbeat peaks from the phase signal without the need for contact-based sensors. The framework was evaluated using a Walabot FMCW radar module against ground truth HR signals, demonstrating consistent and repeatable results under baseline and mild motion conditions. In subsequent work, this framework was extended with deep learning methods, where radar-derived HR and HRV were benchmarked against research-grade ECG and achieved over 90% accuracy, further reinforcing the robustness and reliability of the approach. Together, these findings confirm that carefully guided radar positioning and robust signal processing can enable accurate and practical in-cabin physiological monitoring, offering a scalable solution for integration in future intelligent vehicle and driver monitoring systems. Full article

Accurate detection of road surface information is crucial for enhancing vehicle driving safety and ride comfort. To overcome the limitation that traditional suspension systems struggle to respond to road excitations in real time due to time delays in signal acquisition and control, suspension preview control technology has attracted significant attention for its proactive adjustment capability, with efficient road surface information perception being a critical prerequisite for its implementation. This paper systematically reviews road surface information detection technologies for suspension preview, focusing on the identification of potholes and speed bumps. Firstly, it summarizes relevant publicly available datasets. Secondly, it sorts out mainstream detection methods, including traditional dynamic methods, 2D image processing, 3D point cloud analysis, machine/deep learning methods, and multi-sensor fusion methods, while comparing their applicable scenarios and evaluation metrics. Furthermore, it emphasizes the core role of elevation information (e.g., pothole depth, speed bump height) in suspension preview control and summarizes elevation reconstruction technologies based on LiDAR, stereo vision, and multi-modal fusion. Finally, it prospects future research directions such as optimizing robustness, improving real-time performance, and reducing labeling costs. This review provides technical references for enhancing the accuracy of road surface information detection and the control efficiency of suspension preview systems, and it is of great significance for promoting the development of intelligent chassis. Full article

The acquisition of plant ecological indicators, such as leaf area index and leaf area density values, typically relies on labor-intensive field sampling and measurements, which are often time-consuming and hinder large-scale application. As different plant ecological indicators are closely related to plants’ geometric characteristics, the development of dynamic correlation and prediction methods for relevant indicators has become an important research topic. However, existing 3D plant models are mainly used for visualization purposes, which cannot accurately reflect the plant’s growth process or geometric characteristics. This study presents a workflow for parametric 3D plant modeling and ecological indicator analysis, integrating dynamic plant modeling, indicator calculation, and microclimate simulation. With the established plant model, a method for calculating and analyzing ecological indicators, including the leaf area index, leaf area density, aboveground biomass, and aboveground carbon storage, was then proposed. A method for exporting the model-generated data into ENVI-met v.5.0 to simulate the microclimate environment was also established. Then, by taking Daijia Lake Park as an example, this study utilized site planting construction drawings and field survey data to perform parametric modeling of 21,685 on-site trees from 65 species at three different growth stages using Blender v.4.0 and The Grove plugin v.10. The generated plant model’s accuracy was then verified using the 3D IoU ratio between the models and on-site scanned point cloud data. Plant ecological indicators at various stages were then extracted and exported to ENVI-met for microclimate analysis. The workflow integrates the simulation of plant growth dynamics and their interactions with environmental factors. It can also be used for scenario-based predictions in planting design and serves as a basis for urban green space monitoring and management. Full article

The accurate and efficient acquisition of the spatiotemporal distribution of surface water is of vital importance for water resource utilization, flood monitoring, and environmental protection. However, deep learning models often suffer from two major limitations when applied to high-resolution remote sensing imagery: the loss of small water body features due to encoder scale differences, and reduced boundary accuracy for narrow water bodies in complex backgrounds. To address these challenges, we introduce the A²DSC-Net, which offers two key innovations. First, a multi-branch dilated convolution (MBDC) module is designed to capture contextual information across multiple spatial scales, thereby enhancing the recognition of small water bodies. Second, a Dynamic Snake Convolution module is introduced to adaptively extract local features and integrate global spatial cues, significantly improving the delineation accuracy of narrow water bodies under complex background conditions. Ablation and comparative experiments were conducted under identical settings using the LandCover.ai and Gaofen Image Dataset (GID). The results show that A²DSC-Net achieves an average precision of 96.34%, average recall of 96.19%, average IoU of 92.8%, and average F1-score of 96.26%, outperforming classical segmentation models such as U-Net, DeepLabv3+, DANet, and PSPNet. These findings demonstrate that A²DSC-Net provides an effective and reliable solution for water body extraction from high-resolution remote sensing imagery. Full article