Implementation Of Monitoring System Research Articles

Influence of Particulate Matter and Carbon Dioxide on Students’ Emotions in a Smart Classroom

The effects of air quality on health and cognition are well documented, but few studies have focused on its impact on emotions, leaving this area underexplored. This study investigates the influence of environmental factors—specifically particulate matter (PM1, PM2.5, and PM10) and carbon dioxide (CO2)—on students’ basic emotions in secondary school classrooms. For the collection of environmental data, we used low-cost sensors, which were carefully calibrated to ensure acceptable accuracy for monitoring air quality variables, despite inherent precision limitations compared to traditional sensors. Emotions were recorded via camera and analyzed using a custom-developed code. Based on these data, we found significant but modest correlations, such as the negative correlation between PM levels and happiness, and positive correlations of CO2 concentrations with fear and disgust. The regression models explained between 36% and 62% of the variance in emotions like neutrality, sadness, fear, and happiness, highlighting nonlinear relationships in some cases. These findings underscore the need for improved classroom environmental management, including the implementation of real-time air quality monitoring systems. Such systems would enable schools to mitigate the negative emotional effects of poor air quality, contributing to healthier and more conducive learning environments. Future research should explore the combined effects of multiple environmental factors to further understand their impact on student well-being.

Soil Health Metric: IoT-based Agriculture Monitoring System

Agriculture has been an essential component of human civilization, experiencing considerable evolution over the centuries. In contemporary society, technological advancements, particularly the Internet of Things (IoT), have emerged as transformative forces, facilitating precision farming and enhancing resource management efficiency. This research examines the development and implementation of an IoT-based agricultural monitoring system. By utilizing components such as sensors, wireless networks, and data analytics, this system offers real-time insights into soil moisture, temperature, humidity, and water levels. The automation will be integrated into optimal resource utilization, minimization of waste, and increased productivity. This paper outlining the operation, parts, and benefits of IoT-based farming systems with a perspective on its transformation of agriculture and the problem of sustainability and its relation to the environment.

Analysis of the Electric Power Monitoring System in Wind Power Plants Based on Arduino Uno

The Wind Power Plant is a renewable energy solution that can reduce dependence on fossil fuels and reduce greenhouse gas emissions. However, to ensure operational efficiency and reliability, an effective monitoring system is needed to monitor and manage the electrical power produced. This resear chaims to develop and analyzean electrical power monitoring system at PLT Busing the Arduino Uno platform. The proposed monitoring system consist sofsophisti cated sensors integrated with Arduino Unotocollect real-time data regarding wind speed, output voltage, current and turbine operational conditions. The data collectedby these sensor sissentto the Arduino Uno, which then processes andstores thedata for further analysis. Additionally,this data canbe transmitted toot her devices for remote monitoring via a wire less communication module. Algorithms were developed to analyze and display critical information about turbine performance, including anomaly detection and failure prediction. System testing was carried out on a small-scale PLTB model, and the results showed that this system was able to monitor changes in electrical power with high accuracy and fast response time. Implementation of this Arduino Uno-based monitoring system also enable searly detection of potential problems, so that preventive action can be taken to reduce downtime and increase operational efficiency. The results of this research showthat using Arduino Uno as a platform for the electrical power monitoring system at PLTBis not only effective in collecting andanalyzing data,butis also economical andeasy to implement.This system canbeeasily adaptedand expanded to suit the specific needs of various types of PLTB. In conclusion, the Arduino Uno-based monitoring system offers an innovative and practical solution to improve the performance and reliability of PLTB.

Thermal damage diagnosis in micro grinding processes based on raw acoustic emission signals and convolution neural networks

A micro-grinding process is usually the last machining operation among the various processes employed in the manufacturing of metallic components. It is generally the first option when the manufactured products require a combination of low surface roughness values and narrow dimensional tolerances. However, one of the main challenges of a micro-grinding process is avoiding high specific energy and the corresponding intense heat generation, as this can damage the workpiece. The energy consumed during micro-grinding is almost entirely converted into heat, making the component susceptible to thermal damage. As the occurrence of this damage compromises the service life of manufactured components, the implementation of monitoring systems is essential to guarantee the quality of the manufactured products improving process efficiency. In this study, a new approach based on convolution neural networks (CNNs) is proposed to predict the occurrence of thermal damage in the micro-grinding process. This approach uses raw acoustic emission (AE) signals as inputs into CNN, which is a more technically feasible approach to performing real-time monitoring. To obtain the AE data, micro-grinding experiments were conducted on N2711 grade steel (which is susceptible to thermal damage) under different cutting conditions (ae = 5–50 µm). The obtained AE signals were labeled based on the offline analyses performed on the workpieces, which identified the occurrence of thermal damage through visual inspections, microhardness, and microstructural analysis. For the conditions employed in this work, thermal damage was obtained after grinding with depth of cut values > 10 µm. Total 3960 AE signals were obtained of which 2200 of them were associated with thermal damage. The results depict that the proposed CNN model was able to successfully classify normal and thermal damage AE signals reaching an accuracy of 98.6% over the dataset (0.4% false positives and 1.0% false negatives).

Implementation of Wastewater Monitoring System using Low-Cost Water Sensor and IoT-based SCADA

Wastewater monitoring is crucial for ensuring the wastewater quality is in the allowable range. This paper presents the implementation of a wastewater monitoring system using low-cost water sensors and Internet of Things (IoT)-based Supervisory Control and Data Acquisition (SCADA). The low-cost water sensor comprises the pH, temperature, turbidity, and Dissolved Oxygen (DO) sensors. The sensors are connected to the ESP32 microcontroller that is equipped with the WiFi module for communicating with the master unit, namely the Haiwell CBOX. Since the Haiwell CBOX is an IoT-based device, it is connected to the IoT cloud server. Thus, it can be accessed remotely through the Internet. The experimental results show that the user-friendly graphical interface is easy to develop and provides the user with useful and helpful information for monitoring and analyzing wastewater parameters. Further, the communication time interval of four seconds between the ESP32 and CBOX achieves a stable connection.

Wind Turbine Gearbox Anomaly Detection Using Signal-to-Image Processing Algorithms and Convolutional Autoencoder

Abstract In this study, signal-to-image conversion techniques coupled with a convolutional auto encoder (CAE) are used for the detection of anomalies in the wind turbine (WT) gearbox system. Firstly, the time series data is converted to images using six different algorithms. Thereafter, these images are stacked into the multi-dimensional structures known as “data cubes”, which is finally fed into CAE for the anomaly identification. The results of this study demonstrate the enhanced efficacy of the method specially in the Gramian Angular Field model in detecting anomalies accurately, suggesting a viable path towards the implementation of dependable and affordable WT monitoring systems. This will open the door for the renewable energy industry’s condition monitoring procedures to become more automated and digitalized.

Wireless Sensing Network for Implementation of Air Quality Monitoring System and Indoor Air Quality Index Application

Indoor air quality significantly impacts respiratory health and mental activity. This study utilizes a wireless sensing network (WSN) based on the Internet of Things (IoT) to monitor indoor air quality, referred to as an indoor air quality monitoring system. The system was installed and applied on campus at the University of Baghdad. The present study aims to monitor air quality parameters continuously within laboratories. Carbon monoxide, sulfur dioxide, nitrogen dioxide, ammonia, and particulate objects are the pollutants chosen to be monitored by the installed system in this study. These pollutants were selected because they affect indoor facilities' comfort, health, and working conditions. Colored coded data was employed in the monitoring system; defined ranges for each pollutant were also integrated. Sensor nodes, wireless modules that connect to the IoT server, and user applications are the main components of the IAQMS system. LCDs, mobile applications, the ThingSpeak web server, and the LabView platform are examples of techniques used to present data collected by the system. Additionally, the system includes a notification function that alerts students and lab personnel when indoor air quality index IAQI values signal unhealthy indoor air quality. The proactive approach ensures a regulated standard for indoor air quality and pollutants.

Effects of pH on Enzyme Activity in Pakistan

Purpose: The aim of the study was to assess the effects of pH on enzyme activity in Pakistan. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study indicated that effects of pH on enzyme activity are significant because enzymes are highly sensitive to the pH levels in their environment. Each enzyme has an optimal pH at which it functions most efficiently. Deviations from this optimal pH can lead to a decrease in enzyme activity. At extreme pH levels, enzymes may become denatured, altering their structure and impairing their ability to bind to substrates. This is because changes in pH can affect the ionic bonds and hydrogen bonds within the enzyme, altering its active site. As a result, enzyme activity typically increases as the pH approaches the optimal level, and decreases when the pH moves away from this point. Therefore, maintaining proper pH levels is crucial for the effective functioning of enzymes in biological systems. Implications to Theory, Practice and Policy: Lock and key model, induced fit theory and chemical equilibrium theory may be used to anchor future studies on the effects of pH on enzyme activity in Pakistan. In practical applications, industries utilizing enzymes should prioritize the implementation of rigorous pH monitoring and control systems to maintain optimal conditions for enzymatic activity. From a policy perspective, it is crucial for policymakers to establish clear guidelines governing the use of enzymes in industrial applications, with a particular emphasis on the importance of pH control in achieving optimal performance.

CONTEMPORARY CHALLENGES IN CIVIL PROTECTION AND DEFENSE

The addressed problem is the need to enhance public policies and regulations in disaster management in Brazil, due to the increasing frequency and severity of these events. The hypothesis posits that integrating public policies with emerging technologies and community participation can significantly improve disaster management. The methodology involves a literature review of existing legislation, case study analysis, and the implementation of real-time hydrometeorological monitoring systems. Key results indicate that, despite advancements in prevention and mitigation policies, significant gaps remain in inter-institutional coordination and practical application of regulations. The main conclusion is that effective integration of public policies and emerging technologies is essential for risk reduction and the effective protection of vulnerable communities.

Implementing data governance in financial systems: Strategies for ensuring compliance and security in multi-source data integration projects

This study explores the complexities of implementing data governance within financial systems, focusing on strategies to ensure compliance and security in multi-source data integration projects. The primary aim was to identify challenges and establish a comprehensive framework to enhance data integrity, confidentiality, and regulatory adherence in the financial sector. Using a mixed-methods approach, the research included an extensive literature review, case studies, and expert interviews to gain insights into current practices, challenges, and emerging trends in data governance. Key findings reveal that while multi-source data integration offers significant opportunities for enhanced decision-making, it also presents substantial risks, including data breaches and regulatory non-compliance. The study underscores the pivotal role of data governance frameworks in mitigating these risks through the establishment of clear data policies, the implementation of robust cybersecurity measures, and the promotion of a compliance-focused organizational culture. Additionally, the analysis highlights the necessity for harmonized global data privacy laws to address the diverse regulatory environments that financial institutions operate within. The research concludes that effective data governance is crucial for financial institutions to safeguard their data assets, ensure regulatory compliance, and maintain stakeholder trust. Recommendations include the adoption of advanced technologies like artificial intelligence and blockchain to enhance data governance capabilities, the provision of ongoing employee training to foster a culture of compliance, and the implementation of dynamic compliance monitoring systems to keep pace with evolving regulatory demands. By embracing these strategies, financial institutions can develop a resilient data governance framework that not only ensures compliance and security but also drives operational excellence, competitive advantage, and sustained trust in the increasingly digital financial landscape. Keywords: Data Governance, Financial Systems, Multi-Source Data Integration, Regulatory Compliance, Cybersecurity, Data Privacy.

Unlocking potential: exploring the interplay between entrepreneurship and research competence across diverse academic disciplines

This study aims to identify and analyze the self-assessment of entrepreneurship sub-competences among students at Latvian higher education institutions. The research questions addressed include: What is the self-assessment of students’ entrepreneurship competence at different levels of education? How and to what extent does the self-assessment of students’ entrepreneurship competence vary across different thematic groups? How and to what extent does the self-assessment of students’ entrepreneurship competence differ between academic and professional programs? Do bachelor’s and Master’s students’ entrepreneurship competences improve during the study process? Additionally, the study explores the correlation between students’ self-assessment of their entrepreneurship competence and their research competence. Data were collected using an assessment tool for students’ transversal competences developed in the ESF project “Development and implementation of the education quality monitoring system”, which consists of an online survey data and were analyzed with SPSS Statistics v.21. The results show that students evaluated their entrepreneurship competence just above average. Bachelor-level students improved their entrepreneurship competences during their studies, but there were no statistically significant differences between Master’s students’ evaluations in their first and last study years. Network analysis indicates that the entrepreneurship sub-competence “Identification, mobilization, and efficient use of internal and external resources” is connected with most research competence dimensions, and improving this sub-competence will therefore have the most effect on influencing research competence.

Trust and distrust coexistence and change in the adoption of an emerging technology in organizations

Emerging technologies challenge organizations by introducing information systems that transform processes and introduce a series of risks and uncertainties for stakeholders. These conditions make trust and distrust important in the adoption and implementation of technology. Most of the trust in Information Systems literature has focused on building the constructs of trust and distrust between an artifact and the final user, but there is a gap in the analysis of changes in trust and distrust beliefs and their possible coexistence across time. This case study illustrates how trust and distrust manifest across a mobile health monitoring system implementation: the attitudes, objects of trust/distrust, and the changes they exhibit across four stages. The results point at the objects of trust/distrust corresponding to the technology frames that shift during the implementation from an artifact-centric to a more encompassing technology involving other users, programmers, and themselves. The actual technology use leads to changes in perceived risks and expectations, which are fed by the interaction of all users with the system. Behaviors are shaped by coexisting trust and distrust in technology beliefs, which usually compensate for the elements of the technology they distrust. The study concludes that the coexistence of trust and distrust in technology is a constantly shifting phenomenon, leading to a complex yet stabilized use of technology.

Implementation of home OSAHS screening device based on sleep sounds

Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) is a common chronic sleeping respiratory condition. Screening for OSAHS approaches based on sleep breathing sounds has received much attention since it can provide fully noncontact monitoring at home. Deploying a diagnostic sound-based OSAHS system on a smartphone not only interferes with regular phone use but may also pose privacy problems, as well as suffer from decreasing accuracy owing to device variances. This work describes a local OSAHS monitoring system implementation designed for a dedicated device to estimate the apnea-hypopnea index and screening for OSAHS while doing away with concerns about the abovementioned issue. The OSAHS screening system is deployed on a Kendryte K510 (Canaan Inc.), a low-power RISC-V 64-bit CPU that has 2.5 TFLOPS, to analyze sleep breath sounds in a 9-s window. The design results are relatively low-cost and efficient for screening severe OSAHS. The equipment can be further used as a platform to evaluate the performance of the OSAHS diagnosis model.

An IoT Based Real-Time Environmental Monitoring System for Developing Areas

Rapid urbanization and industrialization have raised concerns about environmental quality and sustainability in recent years. The Internet of Things (IoT) has played an important role in monitoring physical phenomena by generating data that can be sent and preserved in the cloud. This work explores an IoT-based environmental monitoring system's potential, using an Arduino-based device for real-time tracking of environmental parameters including sound levels, humidity, dust concentration, total volatile organic compounds (TVOC), carbon dioxide (CO2), and carbon monoxide (CO). Real-time data are collected from various semi-residential and marketplace locations named in Pach raster More, Tomaltola, DowamoyiMore, Fojdarimore, and station road in Jamalpur district of Mymensingh Division, Bangladesh on non-holiday days, providing a representative snapshot of typical environmental conditions. The collected data is stored in a cloud server named firebase database. The implemented monitoring system offers several key features including accuracy and reliability, real-time monitoring data analytics alerts and notifications historical data as well as it can lead to various benefits and impacts of Improved Air and Water Quality Healthier Urban Environment (IAWUE) to enable local authorities and individuals to make educated decisions for a healthier and more sustainable urban environment. Graphical representations of the data revealed distinct patterns and trends, offering valuable insights into air quality variations across different areas. Interestingly, the results showed sound levels slightly below the standard range, indicating a relative control of noise pollution in the sampled areas. The findings of the work will serve as a vital resource for further research and guide policy-making for environmental improvement and sustainable practices in urban settings.

Balancing plant safety and efficiency through innovative engineering practices in oil and gas operations

In the oil and gas industry, balancing plant safety with operational efficiency is a critical challenge that requires innovative engineering practices. This paper explores how advanced engineering techniques can enhance both safety and efficiency in oil and gas operations. By integrating cutting-edge technologies and methodologies, companies can achieve a harmonious balance between protecting personnel, equipment, and the environment, while also optimizing productivity and resource utilization. Key innovative engineering practices discussed include the implementation of real-time monitoring systems and predictive analytics to enhance safety and operational efficiency. Real-time monitoring, powered by Internet of Things (IoT) sensors, allows for continuous tracking of plant conditions, detecting anomalies before they escalate into significant issues. Predictive analytics, utilizing machine learning algorithms, further aids in forecasting potential failures and scheduling maintenance proactively, thereby minimizing downtime and operational disruptions. The paper also examines the role of risk-based approaches and safety management systems in improving plant safety. Techniques such as quantitative risk assessment (QRA) and safety integrity level (SIL) analysis are pivotal in identifying potential hazards and designing mitigation strategies. These practices ensure that safety measures are proportionate to the actual risk, avoiding over-engineering while addressing critical safety concerns. Moreover, the integration of automation and digital twin technology is discussed as a means to enhance both safety and efficiency. Automation reduces human error and improves precision in operations, while digital twins provide a virtual replica of physical assets, enabling simulation and optimization of plant operations in a risk-free environment. Case studies illustrate successful implementations of these practices, highlighting their impact on reducing incidents and improving operational performance. The paper concludes by emphasizing the need for ongoing innovation and collaboration between engineers, safety experts, and operational managers to continuously advance safety standards and operational efficiency in oil and gas operations.

Design and Implementation of an IoT-Based Water Level Monitoring System with Automated Pump Control and Real-Time Notifications

Design and Implementation of an IoT-Based Water Level Monitoring System with Automated Pump Control and Real-Time Notifications

Practical design and implementation of IoT-based occupancy monitoring systems for office buildings: A case study

This study introduces a scalable, cloud-based approach to occupancy monitoring designed to optimize HVAC operations in office buildings. It addresses the challenges of developing and implementing a multi-parameter IoT-based occupancy monitoring system by integrating various off-the-shelf sensors—CO2, infrared (IR), motion (PIR), and door status detection—into a cohesive system. Leveraging wireless LoRaWAN and novel cloud technologies, the system ensures easy installation, efficient maintenance, and robust data management. CO2-based occupancy detection models were trained using data from a reference office and validated in another office environment. Among the various models evaluated, the four best-performing ones—Decision Trees, Random Forest, LightGBM, and K-Nearest Neighbors—were selected for integration into a multi-parameter detection system. To further enhance system performance and identify optimal sensor combinations and configurations for cost-effective and accurate occupancy detection, a data fusion methodology was employed. This methodology, validated with ground-truth data from a test bed, tested the monitoring system in different office settings, ranging from single to quadruple-occupant rooms. Integration of additional parameters into the developed data fusion approach significantly improved system performance, achieving a True Positive Rate (TPR) of 95% compared to 81% with a simple baseline data fusion method. This approach also reduced false detections during unoccupied periods, as tested in multiple rooms within the studied building, thereby enhancing the system's reliability for integration into occupancy-aware HVAC control strategies.

Trade Effects of Non-Tariff Barriers in Tanzania: A Case of the Dar es Salaam-Namanga Busy Border Trade Post

The main objective of this study is to examine and quantify the effects of Non-Tariff Barriers (NTBs) on cargo trade movement in Tanzania, focusing on the Dar es Salaam-Namanga Border Post. Specifically, the study aims to identify the main administrative or technical requirements along this trade route (both old and new), analyze the regularity of these requirements based on respondents’ feedback, and provide recommendations for mitigating NTBs. The study utilized an empirical survey design, collecting mostly qualitative data on border trade performance and perceived non-tariff barriers. The research was guided by economic theory and targeted formal and informal cross-border traders, transport companies, truck drivers, weighbridge attendants, and customs and revenue authorities. Data was collected through desk research, interviews, questionnaires, and stakeholder consultations. The Dar es Salaam-Namanga route was chosen for its importance as the busiest commercial corridor in the East African Community (EAC). The study employed both probability and non-probability sampling techniques, restricted to the Arusha-Namanga roadway. Four data collection methods were used: desk research, interviews, questionnaires, and stakeholder consultations. The data were analyzed using thematic analysis for qualitative information, and an econometric model based on Newton's gravitational equation (a modified gravity model) was used to quantify trade effects. Statistical Package for Social Sciences (SPSS) was employed for stepwise multiple linear regression analysis. The model's predictive power was tested using an F-test, and it was verified to meet assumptions of linearity, homoscedasticity, and multicollinearity. The results revealed that roadblocks, customs clearance, permits, and road infrastructure significantly affected trade performance. Unethical practices at weighbridges, frequent police checks, and customs procedures lead to monetary losses and time delays for cross-border traders. Bribery, police checks, and unloading processes during inspections were identified as factors contributing to the perception of NTBs as costly and time-consuming. On a positive note, improved road conditions have reduced transportation costs by lowering vehicle repair expenses and decreasing travel time. The study concludes that there is a need for regional cooperation within the EAC to eliminate existing NTBs. It recommends streamlining administrative procedures and working hours at border points, and implementing monitoring systems to ensure the effective removal of unnecessary barriers to trade. These measures are essential to improving trade efficiency and promoting the goals of the re-established East African Community.

Design and implementation of a scalable IoT-based real-time environmental monitoring and alarm system: an experimental study

Digital transformation is crucial for organizations to survive, be competitive, and grow in the modern age. IoT is the key to enabling this transformation by connecting devices and optimizing processes. This paper presents the design and implementation of a generic IoT-based real-time environmental monitoring and alarm system. The platform is validated by applying it to a manufacturing plant scenario, where various sensors simulate industrial conditions. Scalable message distribution systems such as MQTT and Apache Kafka facilitate reliable data transmission. A microservice architecture is constructed for the backend services to ensure uninterrupted and high throughput services in the application domain. Instead of deploying a real WSN, traffic generation services were chosen to minimize costs, provide greater control and flexibility, and facilitate faster, scalable testing in a controlled environment. The platform also features an integrated alarm system with an event definition module, which allows users to define custom action rules. This flexible, scalable, and resilient architecture can be used across a wide range of application domains that require digital transformation. The experimental study demonstrates the platform's capabilities and great potential for broader IoT applications.

Implementation of Inclusive Education in a Selected School in Malé, Maldives: Teachers’ Experiences

The basis of inclusive education is not only related to equal human rights, but also as a means to improve and achieve quality education for all. In this respect, teachers play an important role in the successful implementation of inclusive education. However, some research has shown that teachers have inadequate knowledge, skills, and training to implement inclusive education. This study aims to gain a deeper understanding of teachers’ experiences in the implementation of inclusive education at a school in Malé, Maldives, including challenges faced. The study was guided by both the principles of constructivist theory and ecological theory to unveil these challenges. To attain the objectives of the study, related literature on international policies and concepts of inclusive education was reviewed in detail. Adopting a qualitative approach, data was also collected through interviews and observations with nine teachers who were selected purposefully. The data gathered from the field was analysed and described in a narrative form. Findings support the importance of teacher training, appropriate teacher-student ratio, proper resources, and an effective monitoring system for the successful implementation of inclusive education.