Monitoring System Research Articles

The Nigerian Correctional Service (NCoS): Challenges and Opportunities in Deradicalization and Reintegration

This article examines the role of the Nigerian Correctional Service (NCoS) in deradicalization programs in Nigeria. Its objectives are to analyze the programs and services provided by NCoS, identify the challenges encountered, and evaluate NCoS’s efforts in safeguarding the human rights of program participants. The study adopts a qualitative approach with a case study design. Data were collected through literature reviews, interviews with NCoS officers, program participants, and community leaders, as well as observations in several correctional facilities. The findings reveal that NCoS has developed comprehensive deradicalization programs but faces significant challenges, including overcrowding in correctional facilities, limited funding, and a shortage of professional staff. While NCoS has made efforts to protect the human rights of program participants, stigma and discrimination remain substantial hurdles. Community involvement in the reintegration of former extremists is also crucial, though it requires more effective strategies. This article provides recommendations to enhance the effectiveness of deradicalization programs in Nigeria. These include improving correctional facility capacity, developing programs and curricula, enhancing the competence of personnel, and strengthening monitoring and evaluation systems.

Real-Time Monitoring and Image Recognition System for Abnormal Activities in Financial Markets Based on Deep Learning

Real-Time Monitoring and Image Recognition System for Abnormal Activities in Financial Markets Based on Deep Learning

Mask and Social Distancing Detector for Enhanced Public Safety

This paper presents an enforcement of safety measures such as wearing face masks and maintaining physical distancing, which has become essential in various public places to ensure public health and safety. Despite the significance of these measures, many individuals fail to comply, necessitating automated systems for monitoring and regulation. This work explores using state-of-the-art deep learning models to create an effective face mask and social distancing detection system. The study evaluates, compares, and contrasts various object detection frameworks to identify the most suitable models for these tasks. Techniques such as Convolutional Neural Network (CNN) architectures MobileNetV2 are analyzed alongside singleshot detectors and multiple iterations of the YOLOv4 framework.

A Milestone Vision on The Applied Methodologies for Concrete Dam Health Monitoring

This research reviews the applied various methodologies for concrete dams’ health monitoring and assessment. The main type of dams focused on this review is the embankment fill dams that serves as an important function in water supply, irrigation, and hydroelectric power. The emphasis is devoted on the integration and assessment of static and dynamic methods for dams monitoring in order to improve safety and increase the life span of dams. Static methods are usually monitor changes that are prolonged over time and are critical for the assessment of the enduring structural health. Whereas, dynamic methods are considered as real-time data crucial to address immediate changes in the environment and operation. Mathematical models and structural health monitoring techniques were surveyed that improves the encompass of hydraulic, thermal, crack, and time (HTCT) model for extreme conditions. In addition, the use of artificial intelligence (AI) was explored and particularly for internal erosion monitoring. Comparison of such analyses reveals that static monitoring is efficient in its approach to assessment over the long term, while dynamic monitoring performs better in coping with instant changes. Based on the review findings, therefore, it could be concluded that an integrated approach with the use of static and dynamic methods, supported by advanced computational models, would enhance the effectiveness of monitoring systems. The future work shall focus on the development of such integrated systems to increase the resilience of dam infrastructures, while the monitoring technologies have to be adapted continuously to the environmental challenges and the complexity of dam systems.

EFFECTIVENESS OF THE REGULATORY ENFORCEMENT FOR AML IN UKRAINE: IS IT ALL QUIET ON THE FINANCIAL FRONT?

The relevance of studying the effectiveness of the existing financial monitoring system, particularly the adequacy of its regulatory framework, is driven by the intensification of processes related to the legalization of criminal proceeds involving financial institutions. This includes the entrenchment of shadow schemes in payment transactions, expansion of money laundering channels, and terrorism financing. Regulatory gaps have also emerged in overseeing the cryptocurrency market and digital financial services. This study aims to systematize the legal and regulatory framework and evaluate the effectiveness of the financial monitoring system, outlining prospects for further research. The research methodology involves balanced summarization, specification, systematization, generalization, and statistical analysis. Macroeconomic prerequisites for enhancing financial monitoring include socio-economic, political, and moral-ethical factors. The study notes that the effectiveness of anti-money laundering and counter-terrorist financing efforts is not always clear-cut, such as the unclear relationship between the intensity of measures and their effectiveness in preventing regulatory violations and the lack of accurate information about potential offences before disclosure. An objective understanding of financial monitoring effectiveness can be achieved through integral assessments. A methodology for calculating an integral indicator of effectiveness is proposed, which identifies weaknesses in the existing system. The need for new measures to strengthen the coordination of the financial monitoring system, especially under martial law, is emphasized. Based on the research results, recommendations for improving the effectiveness of the financial monitoring mechanism are formulated, including the introduction of artificial intelligence technologies for identifying financial transactions; the use of machine learning technologies; enhancement of monitoring in the cryptocurrency market; strengthening the oversight of transactions related to international terrorism; and developing an effective mechanism to combat financial crimes. These proposals may be useful for financial monitoring and economic security agencies in Ukraine.

Research on the construction of biosensor-assisted mental health monitoring system and talent training strategy

In recent years, mental health monitoring has become increasingly crucial due to the rising awareness of mental health issues and the demand for effective interventions. In this field, a biosensor-assisted mental health monitoring system is an important development that utilizes technology to distribute real-time information on physiological reactions connected to psychological and emotional conditions. The study intends to increase a talent training strategy and a biosensor-assisted mental health monitoring system using deep learning (DL) techniques. This investigation contains 453 participants enrolled in talent training programs that incorporate problem-solving games and theoretical understanding. After the training programs, the data is gathered from biosensors to monitor mental health. The sensor data is preprocessed using bandpass filtering to eliminate noise from the obtained data. The preprocessed data features are extracted using a Convolutional Neural Network (CNN). This study proposed an innovative Refined Prairie Dog-Optimized Poly-Kernel Support Vector Machine (RPDO-PSVM) model to predict mental health after talent training programs. RPDO optimizes the features selected from data, and PSVM predicts mental health. In a comparative analysis, the research determines the different evaluation metrics like accuracy (96%), precision (93.8%), recall (92.1%), and F1-score (94.4%). The conclusion indicates that the suggested method performs better than the forecast for monitoring mental health. The research highlights that the combination of advanced biosensor technology and strategic training offers a promising pathway for improving mental health outcomes.

Machine Learning Techniques for Robust Security in Wireless Sensor Networks

Abstract: The security of wireless sensor networks (WSNs) is critical for ensuring the reliability and accuracy of real-time monitoring systems. A key vulnerability in WSNs is the risk of system breakdown or erroneous decision-making, which can lead to adverse consequences. Given the sensitivity of data handled by these networks, robust protection against various attacks and intrusions is imperative. However, existing security algorithms often fall short when applied to large-scale WSNs due to challenges such as high energy consumption, limited throughput, and excessive computational overhead. To address these challenges, this research introduces an intelligent middleware layer designed to enhance security in WSNs. The proposed solution utilizes Generative Adversarial Networks (GANs), an unsupervised machine learning technique, featuring generator (G) and discriminator (D) networks working adversarially to enhance WSN security. This intelligent middleware acts as a protective layer between WSNs and end-users, addressing vulnerabilities and mitigating threats. The approach ensures robust security while maintaining energy efficiency and scalability for real-world applications. By integrating advanced machine learning techniques, this research establishes a foundation for creating resilient and trustworthy wireless sensor networks capable of handling modern challenges effectively.

Legal Mechanisms for Social Reintegration: An Analysis of Rehabilitation Laws Aimed at Transforming Drug Addicts into Productive Citizens

This study examines the legal mechanisms for the social reintegration of drug addicts, focusing on rehabilitation as a transformative approach to enabling individuals to re-enter society as productive citizens. Using a normative juridical analysis, the research explores the alignment of national legal frameworks with international human rights standards and evaluates the challenges in implementing rehabilitation-focused policies. Findings reveal that while laws and policies supporting rehabilitation exist, their effectiveness is hindered by societal stigma, inconsistent application, and resource limitations. The study emphasizes the need for policy alignment with international standards, expansion of community-based programs, public awareness campaigns, and strengthened monitoring systems. These measures are essential for creating a supportive legal and social environment conducive to the successful reintegration of drug addicts into society.

Qualify-as-you-go: sensor fusion of optical and acoustic signatures with contrastive deep learning for multi-material composition monitoring in laser powder bed fusion process

ABSTRACT Growing demand for multi-material Laser Powder Bed Fusion (LPBF) faces process control and quality monitoring challenges, particularly in ensuring precise material composition. This study explores optical and acoustic emission signals during LPBF processes with multiple materials, addressing challenges in process control and ensuring accurate material composition. Experimental data from processing five powder compositions were collected using a custom-built monitoring system in a commercial LPBF machine. The research categorised signals from LPBF processing various compositions, enhancing prediction accuracy by combining optical with acoustic data and training convolutional neural networks using contrastive learning. Latent spaces of trained models using two contrastive loss functions, clustered acoustic and optical emissions based on similarities, aligning with five compositions. Contrastive learning and sensor fusion were found to be essential for monitoring LPBF processes involving multiple materials. This research advances the understanding of multi-material LPBF, highlighting sensor fusion strategies’ potential for improving quality control in additive manufacturing.

The Impact of Bullying on the Social Development of Adolescents at SMA Negeri 1 Sungai Ambawang, Kubu Raya Regency

The aim of this research is to determine the role of schools in minimizing the impact of bullying on the social development of students at SMA Negeri 1 Sungai Ambawang. The location of this research is SMA Negeri 1 Sungai Ambawang, Kubu Raya Regency. The research subjects were members of the Violence Prevention and Handling Team (TPPK) of SMA Negeri 1 Sungai Ambawang. This research uses qualitative research. Data collection techniques in this research were observation, interviews and documentation. The data analysis techniques used are data collection, data reduction, data presentation, verification. The results of this research are the role of SMA Negeri 1 Sungai Ambawang school in creating Anti-bullying Programs and Policies; the school formed a special team, namely the Violence Prevention and Handling Team (TPPK); The monitoring system implemented is in collaboration with all school officials to monitor and pay attention to bullying in the classroom. There is a school community approach at SMA Negeri 1 Sungai Ambawang, namely teachers and the PPK Team play a role in providing guidance, direction, support and protection to students; provide an understanding of the dangers of bullying towards students by providing outreach; ascertain where bullying occurs by seeking information about acts of bullying that students often experience at school; handling of bullying cases is carried out through discussions with parents, teachers and third parties, namely the police as mediation intermediaries, providing guidance and supervision to students; The school will collaborate with the Kubu Raya Regency Women's Empowerment and Child Protection Service.

Rancang Bangun Prototype Sistem Kontrol Suhu dan Monitoring PH Air pada Aquarium Benih Ikan Lele Dumbo Menggunakan Arduino UNO dan NodeMcu Berbasis Internet Of Things (IoT)

Cultivation of Dumbo Catfish requires optimal water quality management to ensure healthy fry growth. Key parameters to monitor are water temperature and pH, as imbalances can negatively affect the health and growth of the fish. This study aims to design and develop a prototype water quality monitoring system based on the Internet of Things (IoT) using Arduino UNO and NodeMCU. In ideal cultivation conditions, the optimal water acidity (pH) ranges between 6–9, and the ideal water temperature is 26–30 °C. Using Arduino UNO and NodeMCU IoT technology, fish farmers can more easily control temperature and monitor water pH in the fry nursery aquariums. Arduino is the microcontroller, while the NodeMCU Wi-Fi module connects the microcontroller to the Blynk application as the user interface. Temperature regulation is achieved through water heaters and Peltier elements. Test results indicate that the system can maintain an average temperature of 26 °C and an average pH of 6, consistent with manual measurements using a water thermometer and litmus paper. The heating and cooling systems function optimally, with an average delay time of 13.5884298 milliseconds, significantly lower than the 150-millisecond threshold. Thus, the system operates effectively, facilitating fish farmers in real-time monitoring and controlling aquarium water quality.

The Importance of Automatic Counters for Sustainable Management in Rural Areas: The Case of Hiking Trails in Historic Villages of Portugal

The dynamics of territorial planning, the management of its tourism products, and the monitoring of demand flows and their impact on the territorial structure (social, economic and environmental) require tools that support the acquisition of reliable quantitative data, as far as possible in real time, that are easy to manage and allow immediate analysis. In the case of structures and equipment anchored in the nature tourism segment, in particular hiking trails, in addition to determining the demand indices in a network of hiking trails and understanding their territorial and temporal dynamics, the data collected through automatic counters is a crucial tool to support territorial management and evaluate the patterns and flows of tourist demand. Based on these assumptions, this research seeks to analyse demand data observed on eleven hiking trails in the Historic Villages of Portugal, collected through automatic monitoring systems (counters). In four years, between 2020 and 2023, the trails analysed generated a demand of almost 190,000 passages, which translates into an annual average of 47,500 passages in the tourism product “Historic Villages of Portugal” (more than 4800 passages for each trail), mostly in the spring and autumn months, mainly on weekends.

STRUCTURE OF THE SYSTEM FOR MONITORING THE CONDITION OF NITROCELLULOSE POWDER DURING THEIR STORAGE PERIOD BETWEEN PRODUCTION AND DISPOSAL

The stability of powder charges based on nitrocellulose plays a decisive role in determining their service life. As the research analysis shows, in almost all countries of the world there is a problem of maintaining the properties of powder charges at the guarantee level. The properties of nitrocellulose powder charges are constantly changing, and a large part of them is in the post-warranty stage of storage. Changes in the properties of nitrocellulose powder charges can be such that they lead to man-made accidents and disasters, both when using them for their intended purpose and when they are stored in bases and warehouses. Thus, the task of assessing the state and forecasting the properties of nitrocellulose powder charges at the relevant stages of storage becomes relevant. This task is complex and can be solved using a well-known approach to solving such tasks, namely a monitoring system. In general, any monitoring system includes three main components: measurement of informative parameters; assessment of the current state based on the performed measurements; forecast. It is shown that the problem of storage and accumulation of a large amount of ammunition, namely powder charges, is characteristic not only for Ukraine, but also for such developed NATO countries as the USA, Germany, France, and the Czech Republic. There is evidence that in some countries a system of continuous monitoring of the state of ammunition has been introduced, but the research methods are imperfect and dangerous. To control the state of nitrocellulose powder charges, it is proposed to create a monitoring system. A structural diagram of the monitoring system is presented, which includes both already developed elements and those that require additional research and development. It is shown that for the effective operation of the monitoring system, informative indicators are necessary, which allow determining the state of powder charges without the use of complex diagnostic equipment. In addition, it will allow the creation of a database of powder charges for further monitoring of their condition throughout the entire life cycle. An approach to defining and substantiating new constituent elements of the oil refinery condition monitoring system has been developed.

Research and Application of LoRaWAN-Based IoT System for Water Quality Monitoring

Water is a valuable resource that serves various purposes for humans. However, with the rapid increase in industrial zones, urban areas, and population, water pollution has become a significant concern due to human activities such as production and exploitation. Recognizing the importance of water resources, this paper explores an IoT-based system for monitoring and LoRaWAN communication technology to issue pollution level alerts for water sources. The system consists of sensors and LoRa wireless technology to transmit and process data on a web server, enabling users to monitor water quality. The wireless sensors (measurement stations) can measure parameters such as electrical conductivity, temperature, turbidity, total dissolved solids (TDS), pH levels, and dissolved oxygen (DO) concentration in water.

Leakage analysis and leakage monitoring system design for LNG tanker filling process

During the filling process of LNG tank trucks, due to the long-term operation of filling equipment in low temperature and high-pressure conditions, the sealing parts in the equipment are prone to failure, leading to leaks. The reasons for the leakage of LNG filling equipment were analyzed, and the diffusion of LNG after different equivalent leakage hole diameters and different wind speeds were numerically analyzed. A gas leak monitoring system suitable for LNG filling stations was established based on TDLAS technology. The analysis results show that when the equivalent leakage hole diameter is greater than 4 mm, the height of natural gas diffusion will exceed 5 m within 2 s without wind; the concentration of natural gas above the leakage point is inversely proportional to the height, and the decrease in concentration at a height of 5 m is between 50% and 60% compared with a height of 0.5 m; the wind will bend the natural gas diffusion shape downwind, and the degree of bending decreases as the equivalent leakage hole diameter increases; the optimal laser incidence angle of the laser gas sensor is between 30° and 50°, and the theoretical effective monitoring area width of the sensor when the installation height is 5 m is 4.46 m within the optimal laser incidence angle range.

Fine-grained fault diagnosis of photovoltaic systems based on DMAD-GAN and IFD-FGIF

Abstract In the field of photovoltaic (PV) system monitoring, fault detection faces two critical challenges: data imbalance and fault diversity, as well as incomplete complex fault information. To tackle these issues, this paper proposes a dual-mechanism anomaly detection with generative adversarial network (DMAD-GAN) and an integrated fault diagnosis with fine-grained information fusion (IFD-FGIF). DMAD-GAN utilizes GAN to integrate dual mechanisms for anomaly detection in PV datasets, with coordinate-space attention enhancing the perception of subtle features and differences in PV panels. The anomaly scoring mechanism utilizes an improved loss function to compute anomaly scores, assessing the degree of anomaly for each sample. In the IFD-FGIF method, t-SNE is used to visualize features for fault pre-classification to determine the presence of new faults. A fine-grained information fusion module is designed, leveraging ResNet50 to extract features from fault key areas and original images. This module integrates fine-grained features, original features, and fine-grained attributes. Fault attributes and categories are determined using an attribute classifier and Euclidean distance. If a new fault is identified during pre-classification, the network undergoes transfer learning to recognize and adapt to the new fault. The experimental results demonstrate that the proposed method outperforms other networks, achieving an anomaly detection accuracy of 95.86%. The fault fine-grained recognition accuracy is 95.62%. The accuracy of fine-grained information fusion has improved by 4%, and unsupervised learning of new faults has been successfully achieved. The proposed method can enhance the intelligent operation and maintenance capability of PV power plants, reduce false alarm rates in fault detection, and minimize operational risks caused by potential faults, thus effectively shortening downtime and lowering maintenance costs.

Improvement of the information system of remote monitoring of the technical condition of gas turbine engines

Improvement of the information system of remote monitoring of the technical condition of gas turbine engines

Simulations of Overhead Irrigation in a Greenhouse Setting Reveal the Potential for Splash Dispersal of Fusarium oxysporum Microconidia

Management of Fusarium wilt of Watermelon, caused by the fungus Fusarium oxysporum f. sp. niveum, or Fon, requires pathogen monitoring in watermelon production systems. This includes checking for inoculum dispersal in production greenhouses. Fon microconidia were subjected to simulated overhead irrigation to test their capacity for dispersal in a greenhouse setting and evaluate the effect of irrigation drop height on the dispersal patterns. In all trials, a single overhead irrigation event for a single contaminated seedling tray resulted in contamination of all adjacent trays and indicated a potential for microconidia dispersal of over 70 cm. No difference was detected in spore dispersal patterns based on drop height. The findings of these studies provide possible guidance for scouting protocols and a framework for studies in similar plant disease systems for greenhouse settings.

The role of primary healthcare system in the prevention and management of chronic kidney diseases in rural Nigeria

Chronic Kidney Disease (CKD) is a key source of morbidity and mortality in rural Nigeria due to its prevalence at 19.9% Caused by factors like hypertension, diabetes, infections, and environment. This research emergently appraised the role of the primary healthcare (PHC) system in the prevention and management of CKD within rural communities, including assets and liabilities. The approach followed the qualitative research design; articles were reviewed, favoring 16 peer-reviewed journals between 2015 and 2024. These studies were chosen because they addressed the topic of CKD prevention and management, PHC care delivery systems, the development of screening programs and educational initiatives, system preparedness, and policy changes. Study design, interventions, outcomes, and levels of evidence were summarised to describe trends and gaps in the effectiveness of PHC. The analysis showed that PHC systems were central in preventing CKD through screening, health promotion, lifestyle changes, and environmental modification. Nonetheless, gaps that had been noted included poor health facilities, scarcity of skilled workers, and poor availability of basic drugs. The other barriers to the implementation of EBHC included cultural belief in the use of conventional medicine and ill-health literacy. However, such barriers as poor NGO partnerships and dismal policy revision illuminated possibilities of enhancing CKD through increased provision of resources combined with efficient coordination of services. The study highlighted a need to scale up investment in PHC, especially in facilities and equipment, engaging particular audiences in culturally appropriate and targeted education to prevent and manage CKD; and an array of workforce development programs. Enhancing the monitoring and evaluation systems also puts much focus on improving and sustaining quality health care for vulnerable groups of people. These gaps may be alleviated by identifying strategies that could help interventionists strengthen or develop PHC systems to effectively tackle CKD and the despairing health status in rural Nigeria.

The Development of an IoT-Based Palm Solid Waste Counting System for Empty Fruit Bunches in the Palm Oil Industry

The recent application of palm empty fruit bunches as solid waste directly on soil or as organic fertilizer has enhanced palm farms. Moreover, the co-products of this palm oil mill constitute a dependable metric for assessing mill processing performance, specifically regarding quantity and weight. The quantity of palm empty fruit bunches is usually ascertained through manual counting using a hand counter during processing. The imprecision in calculating palm empty fruit bunches is a limitation of this method, as operators sometimes perform erroneous calculations, overlook quantities, and produce contradictory results for mill management. To address this issue, the sector requires enhanced execution of a monitoring and counting system methodology. The aim of this community service project is to create an empty fruit bunches count station using an IoTbased palm solid waste counting system for use in palm mills. The Ministry of Industry - Politeknik Teknologi Kimia Industri (PTKI) Medan will engage in and oversee the advancement of Industry 4.0 within the industrial sector. The initiative was executed at Tanah Gambus Estate in Batubara Regency, North Sumatra. Assistance was provided by a palm mill partner associated with PT. Socfin Indonesia. The implementation of the empty fruit bunches palm oil counting system was conducted efficiently, facilitating optimal operations, real–time online monitoring, uninterrupted functionality exceeding 24 hours daily, swift assessment of the empty fruit bunches volume, immediate identification of traditional counting inaccuracies by operators and management, and its station provides benefits to the palm industry.