The relevance of the study is determined by systemic problems in land use and the need to ensure the country's food security. The research focuses on the agricultural lands of five federal subjects in Asian Russia: Republic of Tuva, Republic of Buryatia, Tyumen Oblast, Novosibirsk Oblast, and Khabarovsk Krai. The aim is to identify the key reasons for the withdrawal of land from agricultural use and to develop measures for optimizing its utilization. The study employed methods of comparative and statistical analysis of state land registry and monitoring data for the period 2021-2025. The results revealed a significant variation in the proportion of unused land, ranging from 3.07% in the Republic of Tuva to 24-30% in the other studied regions. Environmental challenges in agriculture are intensifying under current conditions. Advances in biological and chemical processes have significantly expanded the possibilities for agricultural activity. This has led to an increase in geo-ecological problems: aridity, flooding, soil salinization, waterlogging, permafrost, desertification, dust storm formation, mudflows, wildfires, etc. It was established that the primary reasons are natural and climatic constraints (droughts, waterlogging, permafrost), organizational and economic factors (low profitability, machinery shortages), the consequences of the 1990s land reform, and imperfections in the legislative framework. Key proposed measures include: improving the monitoring system, developing land reclamation, providing economic incentives for agricultural producers, conducting a land inventory, and strengthening liability for the misuse of land. The practical significance of the work lies in the fact that its conclusions can be used by state and municipal authorities to develop targeted programs for bringing abandoned lands back into economic circulation and enhancing the efficiency of the macro-region's agricultural industrial complex.

Abstract Background Campus mental health services face real challenges such as rapidly growing demand, lagging risk identification, and uneven allocation of intervention resources. Early identification and continuous monitoring of mental health problems are crucial for reducing the risk of symptom exacerbation and improving intervention effectiveness. With the development of educational big data technology, the comprehensive analysis of student behavior, learning, and psychological data offers new possibilities for optimizing the campus mental health service system. This study aims to explore the operational effectiveness of a campus mental health service system driven by educational big data and analyze its practical value in improving students’ mental state and enhancing their risk identification capabilities. Methods The study employed a quasi-experimental design, selecting 420 undergraduate students from two universities as research subjects. One university implemented a mental health service system supported by educational big data as the intervention group, while the other university used a conventional mental health service model as the control group. The educational big data-driven service system mainly includes a psychological risk early warning system integrating multi-source data, dynamic tracking of individual mental state, and a tiered intervention delivery mechanism. Mental state assessment used the anxiety and depression dimensions of the Symptom Checklist-90 (SCL-90), and psychological stress level was assessed using the Perceived Stress Scale (PSS). The intervention period was one semester, with assessments conducted at the beginning and end of the semester. Statistical analysis used paired t-tests and independent samples t-tests, controlling for variables such as grade and gender. Results End-of-term assessment results showed that the SCL-90 anxiety dimension score in the intervention group decreased from 2.12 ± 0.51 to 1.58 ± 0.47, while that in the control group decreased from 2.09 ± 0.49 to 1.92 ± 0.50, with a significant difference between the groups (t = 4.27, p<.001). The depression dimension score decreased from 2.06 ± 0.48 to 1.54 ± 0.45 in the intervention group, while the decrease in the control group was only from 2.03 ± 0.46 to 1.87 ± 0.47, with a statistically significant difference (t = 3.89, p<.001). The PSS score decreased significantly more in the intervention group than in the control group (t = 3.45, p=.001). Further analysis revealed a significant negative correlation between the number of risk warnings triggered by educational big data and the degree of improvement in psychological symptoms (r = -0.36, p<.001), suggesting that data-driven services have a positive role in psychological state regulation. Discussion A campus mental health service system driven by big data in education can effectively improve students’ anxiety and depression levels and enhance the timeliness of psychological risk identification and intervention. From a psychotherapy perspective, this model helps to achieve early detection and tiered intervention for mental health problems, compensating for the shortcomings of traditional service systems in dynamic monitoring and precise support. Future research can further incorporate longitudinal tracking data to assess the system’s impact on the prevention of serious mental health problems and long-term recovery of psychological function, while exploring sustainable optimization paths for campus mental health services within the framework of data ethics and privacy protection.

Coronary computed tomography angiography (CTA) plays a pivotal role in the diagnosis of coronary heart disease in elderly hypertensive patients. This study aimed to investigate the hemodynamic changes in terms of dynamic characteristics, magnitude, and duration following intravenous bolus injection of iodinated contrast media. Thirty-two medicated hypertensive patients and 32 nonhypertensive patients underwent Iopromide 370-mediated coronary CTA examination (registration number: ChiCTR2300070704). Hemodynamic parameters were determined at 6 predefined time points from 1 minute before injection to 30 minutes postinjection, using a noninvasive hemodynamic monitoring system. Continuous variables were compared using an independent-samples t test. Categorical data were compared using the χ2 test or a nonparametric test based on data distribution. Repeated measures data were analyzed using repeated measures analysis of variance. The patterns of hemodynamic change were comparable between groups, with most indicators showing a transient increase followed by recovery to baseline. The magnitude of change was more pronounced in the hypertensive group, with most indicators fluctuating within 10% of baseline values. All hemodynamic values during injection correlated well with baseline levels in both groups, with Pearson correlation coefficients ranging from 0.61 to 0.87. No acute allergic-like reactions were observed, and there was no statistically significant difference in physiological responses between groups. Our findings indicate that Iopromide 370 has favorable safety and tolerability. Greater clinical caution may be warranted for elderly hypertensive patients with cardiovascular comorbidities.

To address coverage gaps in high-latitude space weather monitoring caused by constraints in energy, bandwidth, and labeled samples, this study presents a systematic solution deployed in Hailar, China. We constructed a Cloud–Edge–Terminal system featuring wind–solar hybrid energy and RK3588-based edge computing, achieving six months of stable ionospheric–geomagnetic observation under −40 °C. Furthermore, we propose a Dual-Adversarial Recurrent Autoencoder (DA-RAE) for anomaly detection. Utilizing a single-source domain strategy, the model learns physical manifolds from quiet-day data, enabling zero-shot anomaly perception in the unsupervised target domain. Field tests in March 2025 demonstrated superior generalized anomaly detection capabilities, successfully identifying both transient space weather events and environmental equipment faults (baseline drifts). This work validates the value of edge intelligence for autonomous operations in extreme environments, providing a reproducible paradigm for global ground-based networks.

The purpose of the study is analysis of cyber threats exploiting vulnerabilities in network equipment due to untimely software updates, development of a process model for centralized update management that provides rapid response to emerging cyber threats in accordance with the requirements of regulatory documents and information security standards (FSTEC, GOST, ISO/IEC 27001/27002). The methodological basis of the study consists of an analysis of regulatory requirements and vulnerability management practices, a process approach (PDCA), as well as a set of scanning methods: SNMPv3 surveys for version and inventory control, authenticated SSH/CLI audit, checks for updates and compliance with policies. The results of the study. The results of the study. An original process model for centralized updating of network equipment software has been substantiated and described, built around the cycle «identification — prioritization — installation — verification» and integrated with cyber threat monitoring systems. The proposed approach is the first to combine the principles of centralized management with adaptive update prioritization based on current threat analysis, which significantly reduces response time to cyber-attacks and simplifies update management on distributed network resources. Research perspectives. Development is envisaged in terms of deep integration with SCAP/CMDB/SIEM and further automation of updates; application of the results in the public sector/CII requires formalized update installation plans and certification reports.

Investigating the long-term performance of building materials, such as drying shrinkage, moisture expansion, creep, and others, usually requires long-lasting tests with a high number of specimens. Given the initial costs, required data acquisition systems, and the time allocated, conventional sensors like LVDTs become costly for such long-term experimental studies. This article proposes an innovative cost-effective solution combining optical microscopy imaging, 3D printed sliding rulers, and Python-based artificial vision to overcome these limitations. The 3D printed rulers establish a local physical reference frame, while the artificial vision system uses contour detection and point tracking of optical targets to quantify displacements. Unlike continuous monitoring systems, the proposed solution utilises a discontinuous point-tracking approach, allowing a single USB microscope to monitor an unlimited number of specimens while maintaining the possibility for moisture exchange between the material surface and the environment. The system was metrologically validated against a laser interferometer, achieving an expanded instrumental uncertainty of 0.0042 mm (4.2 µm), determined through strict calibration. These results demonstrate that the proposed solution delivers accuracy comparable to conventional sensors but with significantly higher scalability and lower cost, making it highly suitable for extensive long-term experimental programmes.

Flotation, also known as froth flotation, is a method for separating minerals from powdered materials by altering their floatability through the use of flotation reagents. This paper proposes a flotation process control system for mineral processing based on machine learning. Addressing the issue of lack of precise detection methods in the flotation process of iron concentrate, a neural network regression method is used to predict the amount of reagents and the grade of the flotation concentrate. The flotation data in this paper come from the Key Laboratory of Multitechnology Resource Utilization of Bayan Obo Mine, Inner Mongolia Autonomous Region. The preprocessed data form the dataset used to create the production prediction model. The neural network model is constructed using the PyTorch deep learning framework. Finally, based on the established model, a comprehensive flotation dosing monitoring system is developed using the Django framework, which includes functions such as production indicator large screens, workshop personnel safety monitoring large screens, flotation reagent usage processing, flotation reagent procurement platform.

The purpose of this study was to explore how General Information Technology (GIT) is implemented in the General Certificate of Education (G.C.E.) Advanced Level (A/L) curriculum in Sri Lanka. The specific objectives were to examine the factors affecting the implementation of GIT as an innovation at school level, to understand the views of students and teachers on implementing GIT, and to explore the strengths and weaknesses of implementing GIT as an innovation. The study is exploratory, constructivist, and qualitative in nature. The multiple-case study design was used to have an in-depth study. A purposive sample of two urban schools was selected within the Kandy Educational Zone, where there are Computer Learning Centres (CLCs) that currently teach GIT. Data were collected using semi-structured interviews, observations, and document analysis. The theoretical framework was informed by Fullan’s Interactive Factors Affecting Implementation Model. The following themes emerged in the analysis: lack of sufficient awareness of aims and objectives of implementing GIT by teachers and students, poor physical resources and infrastructure in the CLC, less enthusiasm among students due to rules and regulations in the CLC, poor teacher professional development programmes, weak administration in the school, poor monitoring systems of the Ministry of Education, Provincial Department of Education, Zonal Education Office, and the school, and lack of adequate support from the family for the students to learn GIT. Due to the above reasons, both teachers and students were less enthusiastic about the implementation of GIT. Measures such as providing required physical resources and maintenance from the Ministry of Education, revising the syllabus to fulfil students’ needs that align with the National Vocational Qualification Framework (NVQF) guidelines, and establishing a proper monitoring system are recommended.

Laser measurement technology is widely used for deformation or pose monitoring of the dual-reflector antenna systems. However, conventional models of surface temperature variation with altitude fail to accurately characterise the temperature gradients between the main reflector and the subreflector of the large-aperture antennas, due to the complex near-ground environment, the antenna’s dual-reflector structural properties, and the antenna’s own rotation changes. This temperature modelling discrepancy significantly influences the laser atmospheric propagation deflection characteristics, ultimately leading to a decrease in the accuracy of antenna attitude measurements. To address these issues, this paper proposes a theory of air stratification within large-aperture antennas and utilizes this theory to optimize the temperature gradient between the antenna’s dual reflectors. Secondly, a coupled heat-fluid dynamics model for the dual-reflector surfaces is established using Computational Fluid Dynamics to simulate the atmospheric stratification under different rotational positions of the antenna. Finally, the effectiveness and feasibility of the proposed theory were verified through experiments in the antenna model and the China Nanshan 25 m non-rotatable antenna. This research provides an original theoretical and practical basis for precision environmental modelling in antenna measurements, offering prior assurance for improving the accuracy of laser-based antenna attitude measurement.

Abstract Offshore fish farms have been developed for providing large quantities of improved-quality aquaculture products. Critical components such as nets/tendons of the fish cages’ net systems in offshore farms may become damaged due to severe environmental conditions. Fish escape through the damaged nets with dire economic/biological consequences. Thus, early detection of these damages is important. Currently, structural health monitoring (SHM) in the net systems is costly, time-consuming, and sporadic as it is conducted via divers and remote operating vehicles. SHM in the tendons can also be achieved by checking the force signals acquired via load sensors, with only failures such as broken tendons successfully detected and not incipient damages such as degradation. The present case study investigates the detection of a single damaged vertical tendon in a cage’s net system via an automated vibration-based SHM method. The method’s novelty lies in integrating vector autoregressive models identified based on simulated displacement data from two spatial measurement points on the fish cage under changing wave and current conditions, thus allowing the accurate detection of degraded tendons and enabling a remote, continuous, cost-effective monitoring with a constant stream of integrity data. Test cases for the healthy and damaged cage are examined, with degradation (fatigue damage) considered along the whole tendon and at specific points in the tendon. The degradation is simulated by stiffness reduction, and the method successfully detects all 184 test cases for the damaged cage and 34 of 36 test cases for the healthy cage.

This research examines the juridical clash between the mandatory authority of banks to block suspicious accounts and the constitutional rights of customers to legal certainty and property protection. Utilizing a normative legal research method with statutory and conceptual approaches, this study analyzes the implementation of Law Number 8 of 2010 and POJK Number 8 of 2023. The findings reveal that heavy reliance on automated Artificial Intelligence (AI) monitoring systems creates a significant risk of "false positive" identifications, which often lack transparent verification mechanisms. Although banks possess the legal basis for account blocking as an ex-officio obligation, the current "block first, verify later" procedure frequently ignores the prudential guarantees of the Banking Law and consumer protection principles. This research concludes that a reconstruction of internal banking regulations is essential to integrate a "Right to be Heard" mechanism, ensuring that preventive measures comply with the due process of law. Implementing standardized, transparent procedures and human oversight is crucial to balancing financial system stability with the protection of well-intentioned customers.

Purpose: This study aims to construct an evaluation system for the coordinated development of quantity and quality of employment among China's new-generation migrant workers. Based on five core dimensions’ employment capacity, environment, security, remuneration, and labor relations it establishes comprehensive indicators to dynamically measure the coordinated level of employment quality at the national and provincial levels from 2011 to 2023. The study seeks to reveal the evolutionary laws and structural contradictions of this system, identify regional heterogeneity, dynamic polarization, and short-board bottlenecks, and provide theoretical and policy support for addressing the coexisting dilemma of "difficulty in recruiting workers" and "difficulty in finding jobs" while promoting high-quality and full employment. Materials and Methods: This study employs the CRITIC objective weighting method to determine indicator weights and introduces a multi-system coordination index model along with kernel density estimation to calculate static/dynamic coordinated development indices. Findings: The comprehensive evaluation of employment quality reveals significant static regional heterogeneity, with a persistent development gap between high- and low-performing regions. Dynamic evolution analysis indicates that overall employment quality is improving, but this improvement is accompanied by local polarization. Policy intervention has driven short-term convergence and long-term differentiation in resilience. The coordination index evaluation reveals multidimensional structural weaknesses, among which employment security and labor remuneration have become key bottlenecks restricting overall coordinated development. Implications to Theory, Practice, and Policy: Theoretically, it reveals the impact mechanism of the five-dimensional synergy of employment ability, environment, security, compensation, and relationship on the coordination of quantity and quality of employment for the new generation of migrant workers, enriching the theoretical explanation of the "quantity and quality imbalance" in structural employment contradictions. At the policy level, it is recommended to establish a dynamic monitoring system, coordinate the five-dimensional improvement policies, implement regional differentiated interventions, and address the shortcomings in employment security and labor remuneration. In practice, providing quantitative guidance for government planning and enterprise manpower adjustment, alleviating the contradiction between skill mismatch and insufficient guarantees, and promoting high-quality and full employment.

The rapid spread of diseases like COVID-19 highlights the need for adaptable monitoring systems to support public health measures such as mask compliance and social distancing. This study presents the CB-OWL-ViT framework: a Cluster-Based Open-World Localization Vision Transformer for mask detection and social distance estimation. It incorporates homography-based distance estimation for effective deployment with monocular cameras. The innovative integration of open-world vision-language detection with a clustering-based strategy enhances mask-wearing assessments, enabling adaptability without retraining. Evaluations on datasets including Kaggle, Roboflow, and a new dataset from the University of Waterloo show that CB-OWL-ViT improves mask detection precision by 0.37 and F1-score by 0.2 compared to the baseline. The homography module achieves a Mean Absolute Error of 0.1116 in distance estimation, and real-world tests demonstrate a recall of 0.98 for detecting noncompliance in the “Without Mask” class. This framework is a practical solution for large-scale disease monitoring across various settings.

Introduction: Vital sign monitoring is essential to the management of critically ill and injured patients. Recent advances in patient monitoring systems have the potential to improve outcomes by providing real-time data and predictive insights, which are particularly valuable in prehospital and resource-limited settings. We conducted a systematic review of the literature to assess the capabilities, performance, and clinical impact of patient monitoring technologies designed for these environments. Methods: In accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we conducted a systematic review using PubMed and Scopus search engines on studies published between 2018-2022 that proposed or tested novel patient monitorint systems with utility in prehospital or resource-limited settings. Two reviewers independently screened studies, and discrepancies were resolved by a senior author. Of 217 studies identified in the search, 40 met the proposed inclusion criteria. Results: Compared to standard platforms, wearable and contactless systems for patient monitoring demonstrated high accuracy but with delayed responsiveness and less reliable temperature measurements. Artificial intelligence (AI)-based platforms consistently outperformed well-accepted scoring systems in predicting outcomes such as mortality, intensive care unit (ICU) admission, and clinical decompensation. In this review we summarize proposals for prototypes of integrated patient monitoring systems that combine biosensors, AI algorithms, global positioning system, and wireless communication designed to facilitate triage in prehospital settings, and we then compare their components. Various platforms were piloted and demonstrated minimal disruption to workflow and positive user feedback, although most lacked comprehensive cost analyses. Conclusions: Emerging patient monitoring system technologies may enhance remote triage and care delivery, particularly in resource-limited settings. However, significant barriers remain, including cost, limited testing in real-world environments, and the lack of higher tiers of evidence. Future efforts should prioritize field-based testing, usability in low-resource settings, and cost-effectiveness analyses to guide clinical adoption.

The real-time monitoring and control of multi-jet electrospinning is significant to promote the quality of produced nanofiber membranes. In this paper, a multi-jet electrospinning system with coaxial laser was established to promote the jet visibility, which was beneficial for the accurate identification of jet features. A multi-jet feature recognition algorithm including the modules of image pre-processing, the jet feature segmentation and the jet feature identification was designed, and the processing time for the image processing to detect the multi-jet features of Taylor cone areas and visible jet lengths could be shortened to within 40ms. Then, the judgement rule of multi-jet ejection mode was designed and the multi-jet ejection mode was divided into four states according to the recognized jet feature parameters. Based on the identified multi-jet features, a closed-loop control system was designed with the applied voltage as the adjustment parameter. Through experimental verification, nanofiber membranes of good quality with uniform fiber diameter distribution were successfully obtained. This work has significant potential to promote the quality of produced nanofiber membranes and to accelerate the industrial applications of electrospinning technology.

Safe exploitation of the marine natural gas hydrate (NGH) resource is essential to meet the demand of the future energy requirement. To enable real-time monitoring of methane leakage during the production test of NGH, an ocean stereoscopic monitoring system based on underwater single-point mooring structure is developed, which supports in situ monitoring of marine environment at the sea-air interface, the euphotic zone, and the seabed boundary layer. Numerical simulations were conducted to evaluate the effect of mooring configuration, cable lengths, and buoyancy settings on the mooring stability of the system against the current and waves. Based on the simulation result, an optimized segmented inverse-catenary mooring configuration is developed to achieve a balance between the performance and cost. The designed submersible relay buoy isolates the upper dynamic S-shaped cable from the lower static straight electro-optical-mechanical (EOM) cable, thereby improving system stability. The monitoring system based on the optimized mooring structure is successfully deployed at the NGH zone in the northern South China Sea at the water depth of 1330 m confirming its working stability in harsh sea conditions.

The Dalat Nuclear Research Reactor (DNRR), Vietnam’s primary nuclear research facility, requires modernized instrumentation to support its expanding role in radioisotope production and scientific research. This work presents a novel, independent field-programmable gate array (FPGA)–based real-time monitoring system to complement the existing ASUZ-14 R control and protection system—which uses six separate calculation units to process signals from three independent detector assemblies. The proposed system demonstrates significant improvements in computational speed, critical for the DNRR’s frequent operational transients. Implemented on a single Xilinx Artix-7 FPGA board, this solution consolidates the computation of all critical parameters (period, power, and reactivity) for all three detector channels into one unified board. The system performs simultaneous real-time sampling of neutron flux signals, applies digital filtering, and executes reactor point kinetics equations. Experimental validation at the DNRR confirms comparable accuracy and superior response time compared to the existing monitoring feature. This integrated architecture represents a significant consolidation of hardware, replacing multiple units with a single, highly cost-effective platform. Coupled with a signal isolation unit, the FPGA board ensures complete noninterference with normal reactor operations. This work provides a valuable tool for enhancing operational awareness and contributes to the strategic diversification of instrumentation.

The global shipping industry faces escalating sustainability risks arising from geopolitical disruptions, operational instability, and tightening environmental regulations. These risks often first emerge in qualitative market narratives, limiting the effectiveness of conventional backward-looking indicators. This study proposes a sustainability-oriented natural language processing (NLP) framework for the early detection of sustainability-critical stress in global shipping. Using 155 weekly expert-curated shipping market reports published between 2022 and 2025, the framework integrates topic modeling and domain-tuned sentiment analysis to extract sustainability-relevant signals from unstructured text. Critical-to-Quality (CTQ) factors are reconceptualized as sustainability-critical performance dimensions encompassing economic sustainability (freight rate stability), operational sustainability (schedule reliability, lead time, vessel utilization, and equipment availability), and environmental sustainability (eco-efficiency). Topic–sentiment interactions are quantified using network analysis and ElasticNet-based estimation to construct composite CTQScores, which capture the intensity and persistence of sustainability stress. Empirical validation using observed market performance indicators demonstrates that the CTQScores exhibit strong directional accuracy and systematically precede market adjustments, supporting their role as early warning indicators rather than predictive forecasts. The framework is operationalized as a Sustainability Risk Radar, enabling proactive monitoring of economic, operational, and environmental risks. The findings demonstrate how NLP-based analytics can support ESG-aligned sustainability risk monitoring and resilience-oriented decision-making in global shipping systems.

We are doing a project on webcam based student attention monitoring system for privacy compliant & e learning while attending online classes .

This study develops an Internet of Things (IoT)-based monitoring system to measure water levels in gallon containers using a load cell as the primary weight sensor. The system enables users to monitor remaining water in real time and prevent unexpected shortages during daily activities. The method involves assembling hardware consisting of a load cell and an ESP32 microcontroller, followed by a calibration process to ensure accurate weight measurements. Programmed through the Arduino IDE, the ESP32 wirelessly transmits processed sensor data to a cloud platform such as Google Sheets or a hosting service for storage and management. Water levels are visualized through a web-based dashboard that presents data in a clear and accessible format. A notification feature alerts users when the gallon reaches a predefined minimum threshold. Experimental results show that the sensor consistently detects weight changes with stable performance throughout the monitoring process. The dashboard successfully displays real-time data reflecting the actual gallon condition. Overall, the system offers an effective and practical solution for automated water-level monitoring in households, offices, and small businesses.