Search Results (5,673)

Machine vision (MV) is reshaping numerous industries by giving machines the ability to understand what they “see” and respond without human intervention. This review brings together the latest developments in deep learning (DL), image processing, and computer vision (CV). It focuses on how these technologies are being applied in real operational environments. We examine core methodologies such as feature extraction, object detection, image segmentation, and pattern recognition. These techniques are accelerating innovation in key sectors, including healthcare, manufacturing, autonomous systems, and security. A major emphasis is placed on the deepening integration of artificial intelligence (AI) and machine learning (ML) into MV. We particularly consider the impact of convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformer architectures on the evolution of visual recognition capabilities. Beyond surveying advances, this review also takes a hard look at the field’s persistent roadblocks, above all the scarcity of high-quality labeled data, the heavy computational load of modern models, and the unforgiving time limits imposed by real-time vision applications. In response to these challenges, we examine a range of emerging fixes: leaner algorithms, purpose-built hardware (like vision processing units and neuromorphic chips), and smarter ways to label or synthesize data that sidestep the need for massive manual operations. What distinguishes this paper, however, is its emphasis on where MV is headed next. We spotlight nascent directions, including edge-based processing that moves intelligence closer to the sensor, early explorations of quantum methods for visual tasks, and hybrid AI systems that fuse symbolic reasoning with DL, not as speculative futures but as tangible pathways already taking shape. Ultimately, the goal is to connect cutting-edge research with actual deployment scenarios, offering a grounded, actionable guide for those working at the front lines of MV today. Full article

Non-suicidal self-injury (NSSI) has become an increasingly salient mental health concern among adolescents, and it commonly co-occurs with depression, anxiety, borderline personality disorder, substance use, and childhood maltreatment, forming a complex psychological risk structure. Despite a growing body of literature, a systematic understanding of the structural links between NSSI and psychiatric comorbidities remains limited. This study uses bibliometric and visualization methods to map the developmental trajectory and knowledge structure of the field and to identify research hotspots and frontiers. Drawing on the Web of Science Core Collection, we screened 1562 papers published between 2005 and 2024 on adolescent NSSI and comorbid psychological problems. Using CiteSpace 6.3.R1, VOSviewer 1.6.20, and R 4.3.3, we constructed knowledge graphs from keyword co-occurrence, clustering, burst-term detection, and co-citation analyses. The results show an explosive growth of research in recent years. Hotspots center on comorbidity mechanisms of mood disorders, the impact of childhood trauma, and advances in dynamic assessment. Research has evolved from describing behavioral features toward integrative mechanisms, with five current emphases: risk factor modeling, diagnostic standard optimization, cultural sensitivity, stratified intervention strategies, and psychological risks in special populations. With big data and AI applications, the field is moving toward dynamic prediction and precision intervention. Future work should strengthen cross-cultural comparisons, refine comorbidity network theory, and develop biomarker-informed differentiated interventions to advance both theory and clinical practice. Full article

Effective communication is a core competence in sustainable policing, yet training programs often fail to prepare officers for the emotional and relational complexity of real-world encounters. This study explored how police officers from Lithuania, the Czech Republic, and Romania (n = 109) evaluate their communication training and identify the interactions they find most difficult. Using a convergent mixed-methods design, the research integrated quantitative assessments of training coverage with qualitative analysis of officers’ narratives. Findings reveal consistent gaps in emotional regulation, empathy, negotiation, and de-escalation skills, especially in encounters with intoxicated or mentally distressed individuals, and in internal communication within hierarchical structures. Viewed through the lens of organizational sustainability, communication competence emerges as a key form of human capital that enhances officer well-being, reduces operational risks, and strengthens public trust. The study highlights the need to embed experiential, scenario-based learning into police curricula to align training with the emotional realities of field practice. Full article

Fruit size is a key trait in small-fruit breeding, yet its measurement remains labor intensive and prone to human error. To address this, we developed a non-destructive, automated size measurement system based on machine vision and LabVIEW, designed for small fruits such as cherry, blueberry, and walnut. The system integrates a modular architecture, including flat-field correction, calibration, and pattern matching sub-VIs, to ensure user-friendly operation. These sub-VIs also enable the system’s core data analysis, such as real-size conversion through calibration and noise reduction for data accuracy through flat-field correction. An optimized image processing pipeline (grayscale conversion, Canny edge detection, morphological operations) enables precise contour extraction, even for fruits with stems or irregular surfaces. The system supports multi-species adaptation through lightweight parameter adjustments, without hardware modification. Experiments involved 15 samples per species (cherry ‘Tieton’, blueberry ‘Northland’, walnut ‘Xiangling’). A gold-standard protocol was established using a pre-calibrated digital caliper operated by two experienced technicians, with the mean of six replicates per fruit defined as the true value. Results demonstrated low root mean square errors, with coefficients of determination (R²) exceeding 0.98. Paired t-tests confirmed no significant differences from the gold standard. The system achieved a measurement speed of 0.4 s per fruit, six times faster than manual methods, and complied with the precision requirements of GB/T 26906-2024 (Sweet Cherry). This system offers a cost-effective, high-throughput solution for fruit breeding and phenotyping, effectively overcoming the limitations of manual measurement. Full article

The red-crowned crane (Grus japonensis) is a flagship species for wetland biodiversity in East Asia. The Songnen Plain is a vital wetland and habitat for rare and endangered birds in Northeast China. However, rapid land use changes have raised urgent concerns about habitat loss and the survival of these populations. We combined 30 years (1990–2020) of field surveys with ensemble species distribution models (SDMs) to analyze the spatio-temporal changes in suitable habitats for all three key life stages—spring migration, breeding, and autumn migration—across the Songnen Plain. We also assessed how well the current protected-area (PA) network covers suitable habitats and identified conservation gaps. Land use type was the most significant predictor of habitat suitability. Over this period, suitable habitats decreased sharply by 60% (spring migration), 72% (breeding), and 76% (autumn migration), with severe fragmentation and a clear northward shift. Core suitable areas are now mainly found within a few nature reserves, including Zhalong, Wuyu’er River, and Xianghai. We identified three significant conservation gaps: Lindian–Anda, Tailai–Dumeng, and Meilisi Daur–Fuyu. Our results show widespread habitat reduction and demonstrate the inadequacy of the current PA network in supporting the long-term survival of red-crowned crane populations. We recommend expanding protections and restoring wetland connectivity within these gaps to maintain critical habitats and improve landscape resilience for this endangered species. Full article

Financial Technology (FinTech) is fundamentally reshaping the accounting profession, accelerating the shift from routine transactional activities to more strategic, data-driven functions. This transformation demands advanced digital competencies, yet the scholarly understanding of these skills remains fragmented. To provide conceptual and analytical clarity, this study defines FinTech as an ecosystem of enabling technologies, including artificial intelligence, data analytics, and blockchain, that collectively drive this professional transition. Addressing the lack of systematic synthesis, the study employs a systematic literature review (SLR) guided by the PRISMA 2020 framework, complemented by bibliometric analysis, to map the intellectual landscape. The review focuses on peer-reviewed journal articles published between January 2020 and June 2025, thereby capturing the accelerated digital transformation of the post-pandemic era. The analysis identifies four dominant thematic clusters: (1) the professional context and digital transformation; (2) the educational response and curriculum development; (3) core competencies and their technological drivers; and (4) ethical judgement and professional responsibilities. Synthesising these themes reveals critical research gaps in faculty readiness, curriculum integration, ethical governance, and the empirical validation of institutional strategies. By offering a structured map of the field, this review contributes actionable insights for educators, professional bodies, and firms, and advances a forward-looking research agenda to align professional readiness with the realities of the FinTech era. Full article

Spatial metabolomics is a rapidly advancing field offering powerful insights into metabolic heterogeneity in biological tissues. However, its widespread adoption is hindered by fragmented tools and the lack of comprehensive, open-source GUI software covering the full analytical workflow (quality control, preprocessing, identification, pattern, and differential analysis). To address this, we developed SMAnalyst, an open-source, integrated web-based platform. SMAnalyst consolidates core functionalities, including multi-dimensional data quality assessment (background consistency, intensity, missing values), a comprehensive metabolite annotation scoring system (mass accuracy, isotopic similarity, adduct evidence), and dual-dimension spatial pattern discovery (metabolite co-expression and pixel clustering). It also offers flexible differential analysis (cluster- or user-defined regions). With its intuitive GUI and modular workflow, SMAnalyst significantly lowers the analysis barrier, by providing a unified solution that eliminates the need for tool switching and advanced computational skills. Tested with a mouse brain dataset, SMAnalyst efficiently handles large-scale data (e.g., >14,000 pixels, >3000 ion peaks), effectively filling a critical gap in integrated analytical solutions for spatial metabolomics. Full article

The rapid development of Intelligent Connected Vehicles (ICVs) and the Internet of Vehicles (IoV) has paved the way for new real-time monitoring and control systems. However, most existing telemetry solutions remain limited by high costs, reliance on cellular networks, lack of modularity, and insufficient field validation in competitive scenarios. To address this gap, this study presents the design, implementation, and real-world validation of a low-cost telemetry platform for electric race vehicles. The system integrates an ESP32-based data acquisition unit, LoRaWAN long-range communication, and real-time visualization via Node-RED on a Raspberry Pi gateway. The platform supports multiple sensors (voltage, current, temperature, Global Positioning System (GPS), speed) and uses a FreeRTOS multi-core architecture for efficient task distribution and consistent data sampling. Field testing was conducted during Colombia’s 2024 National Electric Drive Vehicle Competition (CNVTE), under actual race conditions. The telemetry system achieved sensor accuracy exceeding 95%, stable LoRa transmission with low latency, and consistent performance throughout the competition. Notably, teams using the system reported up to 12% improvements in energy efficiency compared to baseline trials, confirming the system’s technical feasibility and operational impact under real race conditions. This work contributes to the advancement of IoV research by providing a modular, replicable, and cost-effective telemetry architecture, field-validated for use in high-performance electric vehicles. The architecture generalizes to urban e-mobility fleets for energy-aware routing, predictive maintenance, and safety monitoring. Full article

Ultra-high-voltage direct current (UHVDC) wall bushings are critical components in DC transmission systems, ensuring insulation integrity and operational reliability. In recent years, surface discharge incidents induced by charge accumulation at the gas–solid interface have become increasingly prominent. A comprehensive understanding of the electric field distribution and charge accumulation behavior of wall bushings under UHVDC is therefore essential for improving their safety and stability. In this work, an electrostatic field model of a ±800 kV UHVDC wall bushing core was developed using COMSOL Multiphysics 6.3. Based on this, a geometrically scaled model of the bushing core was further established to investigate charge distribution characteristics along the gas–solid interface under varying voltage amplitudes, application durations, and practical operating conditions. The results reveal that the maximum surface charge density occurs near the geometric corner of the core, with charge accumulation increasing as the applied voltage amplitude rises. Over time, the accumulation exhibits a saturation trend, approaching a steady state after approximately 480 min. Moreover, under actual operating conditions, the charge accumulation at the gas–solid interface increases by approximately 40%. These findings provide valuable insights for the design optimization of UHVDC wall bushings, thereby contributing to improved insulation performance and enhanced long-term operational reliability of DC transmission systems. Full article

Understanding the thermal behaviour of radioisotope generators under Martian conditions is essential for the safe and efficient operation of planetary exploration rovers. This study investigates the heat transfer and flow mechanisms around a simplified full-scale model of the Multi-Mission Radioisotope Thermoelectric Generator (MMRTG) by means of Computational Fluid Dynamics (CFD) simulations performed with ANSYS Fluent 2023 R1. The model consists of a central cylindrical core and eight radial fins, operating under pure CO₂ at a pressure of approximately 600 Pa, representative of the Martian atmosphere. Four cases were simulated, varying both the reactor surface temperature (373–453 K) and the ambient temperature (248 to 173 K) to reproduce typical diurnal and seasonal scenarios on Mars. The results show the formation of a buoyancy-driven plume rising above the generator, with peak velocities between 1 and 3.5 m/s depending on the thermal load. Temperature fields reveal that the fins generate multiple localized hot spots that merge into a single vertical plume at higher elevations. The calculated dimensionless numbers (Grashof ≈ 10⁵, Rayleigh ≈ 10⁵, Reynolds ≈ 10², Prandtl ≈ 0.7, Nusselt ≈ 4) satisfy the expected range for natural convection in low-density CO₂ atmospheres, confirming the laminar regime. These results contribute to a better understanding of heat dissipation processes in Martian environments and may guide future design improvements of thermoelectric generators and passive thermal management systems for space missions. Full article

The article examines the relationship between Industry 4.0 (I4.0) and the circular economy (CE), which are modern and widely used in various scientific disciplines as well as in interdisciplinary and transdisciplinary fields. It was taken into account that a modern resource for knowledge and innovation in a scientific area consists precisely in exploring conceptual interoperability, both for the purpose of clarifying aspects of the theory specific to that discipline and also in terms of offering new, less explored perspectives that are valuable for the practice of everyday economic activities. The main methodological component used is bibliometric analysis, starting from the construction of a database of existing approaches to the two key concepts considered, at the level of the Web of Science Core Collection (WoS). This research shows that there is an increase in the theoretical and practical scope of use of the two concepts, a characteristic observed through the consistency and diversification manifested within the considered frame of reference. The main conclusion of the study is that AI-driven servitization, IoT, and big data are facilitators of the implementation of the CE. The contribution lies in consolidating an updated bibliometric overview of this interdisciplinary field and in highlighting new directions. Full article

In the industrial internet environment, the operation and control of production equipment have become increasingly complex, and their performance directly affects the efficiency, benefits and sustainable development of manufacturing enterprises. From the three-dimensional perspective of “asset-application-maintenance”, this paper constructs a performance analysis framework for the operation and control of production equipment, systematically identifies the combination of core factors affecting performance, and fills the research gap in the current lack of empirical analysis from the configuration perspective in this field. On the basis of data from 82 manufacturing enterprises, the fsQCA method was used to identify three performance improvement paths: the high-load output mode, the lean management and control mode, and the low-failure operation mode. These paths clarify the equivalent approaches to achieve high performance in the operation and control of production equipment under the interaction of multiple factors. On this basis, the study demonstrates the operability and effectiveness of the proposed strategies in actual industrial scenarios through empirical verification in a manufacturing workshop of aero-engine transmission units. In contrast to existing studies, this study introduces the fsQCA method in the field of industrial equipment management and control for the first time to reveal the influencing paths; its originality and methodology have significant innovative significance. The research results provide new ideas and methodological guidance for enterprise managers to improve the performance of production equipment operations and controls in the industrial internet environment, which helps to enhance the sustainable development capability of manufacturing enterprises. Full article

Background: Mangrove plants are a core component of coastal ecosystems, directly influencing biodiversity and shoreline stability. However, in recent years, due to the combined pressures of human activities and climate change, nearly half of the mangrove species in China are endangered and require urgent conservation measures. This study analyzed the population dynamics and stress factors affecting four rare and endangered mangrove species—Lumnitzera racemosa, Ceriops tagal, Barringtonia racemosa, and Heritiera littoralis—on the Leizhou Peninsula, providing scientific evidence for their conservation. Methods: Field surveys and plot investigations were conducted, with population dynamics and structure quantified using static life tables, survival rates, mortality rates, and disappearance curves. Additionally, the MaxEnt species distribution model and GIS technology were applied to predict the potentially suitable distribution areas. Results: The findings revealed that the population of L. racemosa exhibits an atypical pyramid structure, with few seedlings and constraining population growth potential. The C. tagal population follows an irregular pyramid structure, with abundant seedlings but fewer mature individuals, suggesting a rapid decline followed by stability. The B. racemosa population also follows an irregular pyramid structure, with many seedlings and a greater proportion of middle-aged and older individuals, facing the risk of early mortality. The H. littoralis population is also in decline, with few seedlings and a severe risk of local extinction. MaxEnt model predictions indicated that temperature is the primary environmental factor, with Area Under the Curve (AUC) values for all species exceeding 0.8, indicating strong predictive ability. The predicted potential suitable areas showed an expanded distribution range compared to current distribution points, providing valuable references for species introduction and propagation. Conclusions: This study described the population structure of the four mangrove species on the Leizhou Peninsula and assessed their primary stress factors. The results provided a theoretical basis for the conservation and restoration of endangered mangrove species and offer important guidance for developing effective conservation strategies in southern China. Full article

The flow and solidification inside the mould are crucial to the quality of the casting billet during continuous casting. In this work, a three-dimensional coupled model of flow and solidification was established, and the flow field and temperature distribution characteristics of molten steel were deeply explored. The results indicated that the molten steel streams out of the SEN at a defined degree and enters the mould in the form of an impact stream, and then impacts the narrow surface. The eddy core position in the upper recirculation region of the flow field is (0.565 m, −0.179 m), and eddy core position in the lower recirculation region is (0.524 m, −0.455 m). Within the range of 100–400 mm from the liquid surface, the main stream and upper ring flow of molten steel have a significant impact on the solidification of the casting billet, and the distribution and longitudinal variation in the liquid phase ratio at different height sections are very obvious. At the exit of the mould, the average thickness of the inner arc and outer arc shells is 15.2 mm and 14.5 mm, respectively. The model can provide guidance for enhancing and optimizing the quality of continuous casting billets. Full article

Gallium (Ga) enrichment in sphalerite has been widely recognized; however, its enrichment mechanisms remain insufficiently understood. The South Hunan district, located at the intersection of the Nanling Region and the Qin-Hang Metallogenic Belt in South China, is characterized by abundant Jurassic magmatic-hydrothermal Pb–Zn deposits, which typically host Ga-depleted sphalerite. Recently, Ga-enriched sphalerite (up to 385 ppm by LA-ICP-MS) has been identified in newly drilled cores at Zhaojinci, adding complexity to the regional Pb–Zn metallogenic framework. EPMA elemental mapping and LA-ICP-MS time-resolved spectra indicate that Ga is homogeneously distributed within sphalerite, excluding the presence of micron-scale Ga-bearing mineral inclusions. A strong positive correlation between Ga and Cu concentrations suggests that Ga incorporation is facilitated by the coupled substitution of Zn²⁺ by Cu⁺. Sphalerite geothermometry yields formation temperatures of 118–138 °C (average 126 °C for GGIMF is and ~129 °C for SPRFT), accompanied by intermediate sulfur fugacity conditions (lg fS₂ = −22.9 to −21.2), which appear to favor Ga enrichment in sphalerite. The trace element geochemistry of the Zhaojinci sphalerite (Ga-Ge-Cd-enriched and Mn-In-Sn-Co-depleted), combined with its formation under low-temperature (120–180 °C) and intermediate fS₂ conditions (within the pyrite stability field), is consistent with MVT-like mineralization. This interpretation is supported by multiple lines of geological evidence, including the strict confinement of stratabound Pb–Zn mineralization to the Devonian Xikuangshan Formation limestone, structural control by syn-sedimentary normal faults, pervasive dolomitization of the host rocks, and the absence of genetic relationship to magmatic activity. Moreover, the sphalerite geochemical signature, corroborated by an XGBoost-based machine learning classifier, reinforce the MVT-like affinity for the Zhaojinci mineralization. This study not only emphasizes the importance of low-temperature and intermediate-fS₂ conditions in Ga enrichment within sphalerite, but also highlights the significance of discovering MVT-like sphalerite for Pb–Zn resource exploration in the South Hunan district, providing valuable new insights and directions for mineral prospecting in this geologically important region of South China. Full article