Real-time Monitoring System Research Articles

Prioritising elements of digitalisation for lean and green SME operations: an ISM-MICMAC study in the Indian context

PurposeThe purpose of this paper is to identify the key elements of digitalization for lean and green operations and develop a conceptual framework for their implementation. The paper focuses on small and medium-sized enterprises (SMEs) and aims to explore the role of digitalisation in enhancing their operational efficiency and sustainability. By identifying key factors and metrics related to digitalisation, the paper seeks to provide insights for strategic management to improve lean and green practices in SMEs.Design/methodology/approachInterpretive structural modeling (ISM) and Matrix of Cross-Impact Multiplication Applied to a Classification (MICMAC) approaches were deployed to classify the major dimensions of digitalisation. These methods were used to analyse the direct and indirect relationships among the identified elements of digitalisation. A comprehensive literature review and expert consultations were conducted to identify 13 key elements relevant to lean and green operations. The experts also assisted in determining the contextual relationships between the variables for the ISM model.FindingsThe analysis classified the 13 identified elements of digitalisation into different levels according to their driving power and dependence. The results from the ISM model indicated three levels of classifications. At level-1, Internet of things (IoT) and smart sensors (IoT & SS), automation and robotics directly influence lean and green operations. At level-2, real-time monitoring and control system and at level-3 fundamental elements of digitalisation such as big data analytics, predictive maintenance, cloud computing, energy management systems (EMSs), additive manufacturing, blockchain, digital workflow automation and digital collaboration platform.Originality/valueAll elements are interrelated and essential for making strategic decisions. This study emphasis the significance of prioritising these attributes to attain long-term excellence through digitalisation. For the industries that seek the reward of lean and green operations for their growth, this paper has great practical utility. Identifying the key factors of digitalisation would help strategic managers in handling lean and green environment of SMEs through these aspects.

Real-time paddy grain drying and monitoring system using long range-internet of things

Grain drying environmental parameters are an important issue throughout the paddy grain production process. A real-time monitoring system requires rapid, online, and accurate measurement results. In the paddy grain drying process, the heated air velocity, temperature, relative humidity, and moisture content have to be carefully monitored and maintained to ensure product quality and safety. This study aimed to propose a real-time paddy grain drying and monitoring system using a long-range internet of things (LoRa-IoT). The real-time monitoring system consisted of sensors, LoRa, and IoT platforms. The LoRa end node and gateway were utilized as a wireless radio communication platform of IoT for long-distance signal transmission. From the experiment, the gateway received data from the end node at a distance of 2 km with a time on air (ToA) of 981 ms. As a result, the proposed monitoring system succeeded in measuring and recording the heated air velocity, temperature, and relative humidity data during the paddy grain drying process from 25% moisture content down to 14%. Regarding moisture content, the accuracy of real-time monitoring information was confirmed with a direct measurement method, resulting in a root mean square error (RMSE) of 6.17%.

A real time monitoring system for accurate plant leaves disease detection using deep learning

A real time monitoring system for accurate plant leaves disease detection using deep learning

Energy-efficient Methods for Controlling Biofouling in Water Treatment Membranes and Future Directions

This paper aims to address the critical issue of biofouling in water treatment membrane systems, which decreases operational efficiency and increases energy consumption. The study explores energy-efficient biofouling control methods, focusing on established and emerging technologies. The review examines various approaches, including surface modification, antimicrobial nanomaterials, and photocatalytic membranes. Techniques using zwitterionic polymers, amphiphilic coatings, silver nanoparticles (nAg), and nanodiamonds (UDD) are analyzed for their effectiveness in mitigating biofouling. Photocatalytic membranes employing Metal-Organic Frameworks (MOFs) are also evaluated for their sustainable microbial inhibition capabilities. Surface modification techniques demonstrated the potential to alter membrane hydrophilicity, effectively preventing microbial adhesion and biofilm formation. Incorporating antimicrobial nanomaterials such as nAg and UDD disrupted microbial cell membranes and enhanced hydrophilicity, significantly increasing biofouling resistance. Photocatalytic membranes utilizing MOFs produced reactive oxygen species (ROS) under light exposure, providing a sustainable and energy-efficient approach to biofouling control. However, the integration of real-time monitoring systems and AI-based predictive models remains necessary to further optimize membrane performance and reduce energy consumption. The development of multifunctional membranes that combine biofouling resistance with resource recovery capabilities is essential to tackle the global water crisis and ensure access to clean water. Continued research in integrating advanced technologies such as AI and sustainable materials will be pivotal in advancing energy-efficient biofouling control.

Combatting fraud in real-time payments: Strategies and technologies for securing instant payment systems

This comprehensive article explores the evolution and security challenges of real-time payment systems in the modern financial landscape. The article examines advanced fraud prevention strategies, including machine learning algorithms, artificial intelligence implementation, and real-time monitoring systems. It explores the transformation of Know Your Customer (KYC) processes through AI-powered identity verification and discusses the critical balance between security measures and user experience. The article further delves into emerging technologies such as quantum-resistant cryptography and edge computing, highlighting their role in shaping the future of payment security. The article demonstrates how financial institutions are adapting to new threats while maintaining transaction efficiency and regulatory compliance.

Design and Implementation of a Low-cost IoT based real-time emission monitoring system for a Thermoelectric Generator-integrated Biomass Cookstove

Design and Implementation of a Low-cost IoT based real-time emission monitoring system for a Thermoelectric Generator-integrated Biomass Cookstove

Banking Frauds and Their Impact: Harnessing Technology for Early Detection

Fraud in the banking sector poses significant risks, including financial losses, operational disruptions, increased compliance costs, and reputational damage, particularly in India’s rapidly digitalizing financial landscape. This research examines the impact of fraud on banks and highlights the role of advanced technologies like artificial intelligence (AI), machine learning (ML), and big data analytics in mitigating these risks. By leveraging early warning systems for real-time monitoring, predictive analytics, and automated fraud detection, banks can enhance their resilience against evolving threats. The study offers actionable insights through case studies and industry practices to strengthen fraud management frameworks, safeguard assets, and rebuild customer trust in an increasingly digital environment.

Concentration of traffic air pollutants and influencing metrological factors in Hawassa City roadways, Ethiopia

IntroductionThe traffic air pollution caused by transportation is a growing global problem that contributes to millions of deaths each year. Despite its importance, information on pollutant concentration is limited in many developing cities, especially in Ethiopia. This study aimed to determine the concentration levels and spatial and temporal variations of traffic air pollutants in Hawassa and to investigate the influence of metrological parameters on the concentration of traffic air pollutants.MethodsA real-time monitoring system of Aero-Qual Series 300/500 was used to monitor pollutants, and 24 monitoring sites were included on both heavy and low-traffic volume roads. The study monitored morning and afternoon times over 24 days to comprehensively characterize the temporal variations.ResultsThe results showed that the mean PM2.5 concentration on heavy- and low-traffic volume roads was 161.6 ± 26.1 μg/m3 and 95 ± 14.2 μg/m3, respectively, whereas the PM10 concentration was 178.7 ± 20.3 μg/ m3 and 102.3 ± 17.6 μg/m3, respectively. Similarly, the mean NO2 concentrations on roads with heavy and low traffic volumes were 86.4 ± 14.4 μg/m3 and 61.7 ± 14.2 μg/m3, respectively. Significantly higher, concentrations were recorded on traffic light roads, followed by main asphalt roads, for both types of traffic air pollutants. The ratio of PM2.5/PM10 was higher (0.924), in which the pollution sources attributed to anthropogenic sources. Kendall’s tau-b correlation analysis suggested that Meteorological parameters (temperature and relative humidity) were positively correlated with traffic air pollutants. Likewise, stepwise multiple linear regression analysis confirms that the concentrations of traffic air pollutants had a positive relationship with metrological parameters.ImplicationsThe findings of this study therefore showed the need for regular air quality monitoring of the urban areas to copping out the adverse public health impacts. And, it highlighted an urgent need for long-term monitoring of traffic air pollution and the development of emission control programs that can be readily implemented to decrease the emissions from anthropogenic sources. Also, it brings a sense of collaboration among stakeholders to tackle the effects of air pollution by providing an inclusive and sustainable development agenda for Hawassa.

Development of Maintenance Management System for Real-Time Monitoring of Power Transformer to Improve Availability Performance: The Case of Tagamenda TANESCO Grid Substation

Power transformers are critical assets in TANESCO's grid substations, playing a vital role in ensuring a reliable and uninterrupted power supply. However, the growing challenges of ageing infrastructure, increasing energy demand, and reactive maintenance practices often lead to unplanned outages, higher operational costs, and reduced transformer availability. This dissertation focuses on the Development of a Maintenance Management System for Real-Time Monitoring of Power Transformers at TANESCO grid substations, with the goal of improving availability performance. The study explores the design and implementation of a Real-Time Monitoring Management System (RTMMS), leveraging advanced sensors, data analytics, and predictive maintenance techniques. The RTMMS continuously monitors critical parameters such as temperature, oil levels, dissolved gases, and load conditions, providing real-time insights into transformer health. By integrating predictive analytics, the system identifies potential faults early, enabling timely interventions and reducing downtime. Additionally, it supports data-driven maintenance planning, enhances operational reliability, and extends transformer lifespan. The proposed system addresses challenges such as high initial costs, data management complexity, and resistance to technological change through phased deployment, secure cloud solutions, and comprehensive training programs. The expected benefits include improved transformer availability, cost efficiency, enhanced grid sustainability, and a shift from reactive to proactive maintenance practices. This research underscores the transformative potential of real-time monitoring systems in modernizing maintenance management and aligns TANESCO with global best practices in energy sector innovation and operational excellence.

Identification of gas outburst precursors based on outburst percolation theory

Uniaxial compression experiments were conducted on coal rock utilizing a computed tomography (CT) scanning system for real-time monitoring to explain the issue of gas volume significantly exceeding reservoir capacity during coal and gas outbursts. A percolation factor a which can make a significant contribution to the research on premonitory information of gas outbursts is introduced to determine whether percolation occurs in coal rock, and supports the outburst percolation theory. It was found that percolation probability and correlation length increase with greater porosity, and that the number of pore clusters decreases as porosity increases. All three parameters exhibit explosive increases or decreases before the porosity reaches the percolation threshold. The percolation threshold for the coal varies at different stress levels; as the coal approaches its stress limit, the percolation threshold decreases, making percolation transformation more likely to occur and increasing the risk of a coal and gas outburst. When a > 1, percolation transformation occurs within the coal, and large-scale gas migration channels are formed. This stage completes the latent phase of the outburst, significantly elevating the likelihood of an outburst incident. This approach establishes quantitative relationships between porosity and various percolation parameters at different stress levels. It contributes to the research on precursory information of coal and gas outbursts.

Optimization and sensitivity analysis for developing a real-time non-contact physiological parameters measurement and monitoring system using IPPG signal for biomedical applications

Optimization and sensitivity analysis for developing a real-time non-contact physiological parameters measurement and monitoring system using IPPG signal for biomedical applications

Creating a Halotolerant Degrader for Efficient Mineralization of p-Nitrophenol-Substituted Organophosphorus Pesticides in High-Saline Wastewater.

The bioaugmentation performance is severely reduced in the treatment of high-saline pesticide wastewater because the growth and degradation activity of pesticide degraders are significantly inhibited by high salt concentrations. In this study, a heterologous biodegradation pathway comprising the seven genes mpd/pnpABCDEF responsible for the bioconversion of p-nitrophenol (PNP)-substituted organophosphorus pesticides (OPs) into β-oxoadipate and the genes encoding Vitreoscilla hemoglobin (VHb) and green fluorescent protein (GFP) were integrated into the genome of a salt-tolerant chassis Halomonas cupida J9, to generate a genetically engineered halotolerant degrader J9U-MP. RT-PCR assays demonstrated that the nine exogenous genes are successfully transcribed to mRNA in J9U-MP. Gas chromatography analysis of methyl parathion (MP) and its intermediates demonstrated that the expressed MP hydrolase and PNP-degrading enzymes PnpABCD show obvious degradation activity toward the specific substrates in J9U-MP. Stable isotope analysis showed that J9U-MP is able to efficiently convert 13C6-PNP into 13CO2, demonstrating the complete mineralization of MP in high-salt media. J9U-MP is genetically stable during passage culture, and genomic integration of exogenous genes does not negatively influence the growth of J9U-MP. Under oxygen-limited conditions, VHb-expressing J9U-MP does not show obvious growth inhibition and a significant reduction in the MP degradation rate. A real-time monitoring system with enhanced GFP is used to track the motion and activity of J9U-MP during bioremediation. Moreover, 50 mg/L MP and its intermediates (i.e., PNP and HQ) were completely degraded by J9U-MP within 12 h in wastewater supplemented with 60 g/L NaCl. After 3 days of incubation, 25 mg/L 13C6-PNP was converted into 13CO2 by J9U-MP in wastewater supplemented with 60 g/L NaCl. Our results highlight the power of synthetic biology for creating new halotolerant pollutant-mineralizing strains. The strong competitive advantages of J9U-MP in high-salinity and low-oxygen environments make this degrader suitable for in situ bioaugmentation of OP wastewater.

Wearable sensor-based real time monitoring system for physical education teaching and training

The comprehensive progress of mental and physical quality is majorly influenced by the college physical education, which is considered a part of the educational system. Developing a scientific and efficient evaluation index is significant to compute physical education teaching quality. The traditional methods of assessing performance in physical education often rely on subjective evaluations or delayed feedback from manual data collection. To overcome these challenges, a design based on a real-time monitoring system is introduced that leverages wearable biosensor technology to improve physical education teaching and training. Initially, some physiological indicators, namely heart rate, respiration rate, body temperature, and motion activity, were recorded and analysed to provide individualized insights into physical performance. Data pre-processing is performed using a median filter to reduce noise and Z-score normalization to standardize the input dataset. Key features are extracted using Fast Fourier Transform (FFT), enabling the identification of critical performance metrics. A Rat Swarm Optimized Efficient Random Forest (RSO-ERF) algorithm was introduced to enhance classification accuracy and optimize system performance. Experimental results demonstrate the proposed system’s effectiveness in providing real-time feedback, identifying individual fitness levels, and supporting adaptive teaching strategies. The increased system’s analysis tendency allows for customized training regimens, ongoing feedback, and improved physical health metrics monitoring. It also gives educators the ability to make data-driven decisions, encourage safety, and improve the educational experience for athletes and students. The findings underscore the potential of wearable biosensor technology combined with advanced algorithms in transforming physical education methodologies for improved engagement and performance outcomes.

Real-time detection of powder bed defects in laser powder bed fusion using deep learning on 3D point clouds

ABSTRACT Powder bed defects are critical factors affecting the print quality and stability in Laser Powder Bed Fusion (LPBF). However, traditional 2D image-based powder bed defect monitoring methods are limited by sensitivity to lighting conditions and insufficient data capture. This study proposes a real-time defect monitoring system based on 3D point cloud data and deep learning approach. The system uses binocular vision to capture point cloud data in real time, enabling high-precision defect segmentation with advanced deep learning models. However, direct deep learning on point clouds can result in the loss of small defect features during downsampling. To address this, an indirect point cloud deep learning method based on 2D projection is introduced, which improves segmentation accuracy for small defects while reducing inference time. By deploying the trained model, this study establishes a closed-loop control system for powder bed defect detection and conducts real-world printing tests, demonstrating effective defect remediation capabilities. Although larger-scale industrial testing is still required, this research illustrates the significant potential of 3D point cloud-based deep learning in enhancing defect detection and quality control in additive manufacturing.

IoT-Based Real-Time Water Quality Monitoring and Sensor Calibration for Enhanced Accuracy and Reliability

The increasing water pollution levels and the growing demand for clean water necessitate advanced monitoring solutions. This study aims to develop an Internet of Things (IoT)-based real-time water quality monitoring system that addresses the limitations of traditional methods, which are often time-consuming and lack continuous monitoring capabilities. The system integrates pH, temperature, and turbidity sensors with an Arduino microcontroller and NodeMCU for Wi-Fi connectivity, transmitting data to a cloud platform for real-time access and analysis. The methodology involved interfacing these sensors with the microcontroller, displaying LCD data, and providing LED alerts. The findings indicate that the prototype’s readings closely align with laboratory measurements, with minor deviations in pH and temperature values and slightly more significant differences in turbidity readings. This demonstrates the prototype’s reliability and accuracy in real-time environmental monitoring. The conclusion emphasises the system’s potential to enhance water resource management through continuous and accurate tracking, enabling timely interventions. Future recommendations include improving sensor calibration, expanding the range of monitored parameters, and integrating advanced data analytics for predictive capabilities. The system’s real-time data logging capabilities significantly enhance water management by providing timely alerts and enabling proactive responses to water quality issues. The impact of this system is substantial, offering a scalable and cost-effective solution for continuous monitoring, thus contributing to improved water resource management and public health protection.

Identification and Mitigation of Risk Factors in the Implementation of the Probolinggo-Banyuwangi Toll Road Project Package 2

This study focuses on identifying and mitigating risks associated with constructing the Probolinggo-Banyuwangi Toll Road Project Package 2, a vital segment of Indonesia's Trans-Java Toll Network. The background highlights the project's significance in enhancing regional connectivity and supporting economic growth alongside the challenges it faces from technical, managerial, financial, social, and environmental factors. This research aims to identify critical risks and develop effective mitigation strategies. The methodology involves data collection through interviews, surveys, and document analysis, prioritizing risks based on their probability and impact. Key findings reveal that delayed delivery, rising material prices, and lack of skilled labor are the most significant risks, demanding targeted mitigation strategies. The conclusion emphasizes the importance of implementing real-time risk monitoring systems, improving logistics coordination, and fostering stakeholder collaboration, particularly with government agencies. By prioritizing critical risks and developing comprehensive mitigation strategies, this research contributes valuable insights for managing complex infrastructure projects, ensuring project sustainability and success.

Development and Optimization of a Real-Time Monitoring System of Small-Scale Multi-Purpose Juice Extractor.

According to the concept of smart postharvest management, an information and communication technology sensor-based monitoring system is required in the juicing process to reduce losses and improve process efficiency. Such technologies are considered economically burdensome and technically challenging for small-scale enterprises to adopt. From this perspective, this study aimed to develop a smart monitoring system for the juicing processes in small-scale enterprises and to identify the optimal operating conditions based on the monitoring data. The system developed is equipped with two weight sensors attached to the twin-screw juice extractor, allowing for the automatic measurement of the weight of the raw material and the resulting juice product. The measured data are automatically transmitted and stored on a computer. Additionally, the system was designed to remotely control the speeds of the juicing and feeding screws, which are the primary controlling factors of the twin-screw juicer. Juice yield and processing time were optimized using carrots and pears. The optimal juicing and feeding speeds for pear yield were found to be 167.4 rpm and 1557 rpm, respectively; carrots achieved an optimal yield at a juicing speed of 502.2 rpm and feeding speed of 1211 rpm. In contrast, the processing time was minimized at juicing-feeding speeds of 6-6 and 7-5 for pears and carrots, respectively. Consequently, it was challenging to determine the optimal conditions for simultaneously optimizing the yield and processing time. This also suggests that the juicing process is affected by the properties of the fruits and vegetables being processed. By developing a system capable of accumulating the data necessary for the digitization of postharvest management and food processing, this research offers a valuable platform for the smart monitoring and optimization of the juicing process.

Materials Design and Structural Health Monitoring of Horizontal Axis Offshore Wind Turbines: A State-of-the-Art Review.

In recent decades, Offshore Wind Turbines (OWTs) have become crucial to the clean energy transition, yet they face significant safety challenges due to harsh marine conditions. Key issues include blade damage, material corrosion, and structural degradation, necessitating advanced materials and real-time monitoring systems for enhanced reliability. Carbon fiber has emerged as a preferred material for turbine blades due to its strength-to-weight ratio, although its high cost remains a barrier. Structural Health Monitoring Systems (SHMS) play a vital role in detecting potential faults through real-time data on structural responses and environmental conditions. Effective monitoring approaches include vibration analysis and acoustic emission detection, which facilitate early identification of anomalies. Additionally, robust data transmission technologies are essential for SHMS effectiveness. This paper reviews material design strategies, data acquisition methods, and safety assessment techniques for OWTs, addressing current challenges and future directions in the field.

Transmission line trip faults under extreme snow and ice conditions: a case study

Extreme weather events, particularly snow and ice storms, present significant threats to the stability and reliability of high-voltage transmission lines, leading to substantial disruptions in power supply. This study delves into the causes and consequences of transmission line trip faults that occur under severe winter conditions, with a focused case study in Inner Mongolia—an area frequently impacted by snow and ice hazards. By systematically analyzing field data collected during critical periods of ice accumulation, this research identifies and examines key factors contributing to faults, such as conductor galloping, insulator degradation, and structural fatigue. These issues are often exacerbated by prolonged exposure to low temperatures and high wind speeds, which further compromise the integrity of transmission infrastructure. In addition to field observations, comprehensive testing of the affected insulators and components reveals mechanical and electrical vulnerabilities that play a significant role in the occurrence of trip faults. To combat these challenges, the paper proposes a series of mitigation and prevention strategies. These include enhancing design specifications to ensure resilience against increased ice and wind loads, deploying real-time monitoring systems capable of detecting early indicators of conductor galloping and ice accumulation, and employing advanced de-icing technologies to reduce the risk of ice-related failures. Moreover, the integration of unmanned aerial vehicles (UAVs) and artificial intelligence (AI)-based fault detection tools presents promising opportunities for improving remote monitoring capabilities and enabling proactive maintenance interventions. By leveraging these innovative technologies, the resilience of transmission lines in harsh climates can be significantly enhanced. The findings of this study not only provide a comprehensive framework for minimizing the impact of extreme weather on transmission infrastructure but also contribute valuable insights toward fostering a more reliable and resilient power grid capable of withstanding the challenges posed by an increasingly volatile climate.

The Resurging of Hydrocarbon Gas as Early Sign of Battery Rollover Degradation.

Gas analysis offers real-time critical insights into the various processes occurring within batteries. However, monitoring battery degradation through gas formation remains relatively underexplored. Traditional coin cell setups pose challenges for long-cycle experiments and do not accurately reflect real-life battery usage. In this study, online electrochemical mass spectrometry (OEMS) was used to monitor gas evolution in a 4500 mAh NMC/graphite 21700 cylindrical battery throughout its cycling life. By tracking gas species and quantities, we identified distinct phases of gas evolution corresponding to different stages of degradation. Notably, the resurgence of hydrocarbon gases such as C2H4 emerged as an early indicator of rollover degradation. Furthermore, the continuous dissolution and reformation of the solid-electrolyte interphase (SEI) layer was identified as the primary driver of battery degradation. This study demonstrates the potential for integrating miniature gas sensors into battery systems for real-time health monitoring and early degradation detection.