Real-time Monitoring Technology Research Articles

Effectiveness of Risk Management through Risk Maturity Measurement: A Study at PT Pelabuhan Indonesia

Research aims: This study aims to evaluate the effectiveness of risk management at Pelindo post-merger and measure its risk management maturity level. Moreover, the study intends to offer relevant recommendations for the company in developing risk management strategies and implementation Design/Methodology/Approach: This study adopts a qualitative descriptive approach with a case study strategy, wherein the researcher analyzes an organization as the unit of analysis in a non-contrived setting, reflecting a natural environment without manipulation. Data collection techniques include interviews and document analysis, while data analysis follows the Miles and Huberman model with four main components: data collection, data reduction, data presentation, and conclusion drawing. Research findings: The study's findings indicate that the risk management maturity level at Pelindo post-merger is at the good practice phase, scoring 2.94 from the dimension and performance aspects assessment. Recommendations for improvement include enhancing risk culture, improving the effectiveness of risk management functions, and aligning risk strategy with the ERM transformation plan to increase the effectiveness and compliance of risk management. Theoretical Contribution/Originality: This research makes a theoretical contribution by assessing the level of risk management maturity in the context of mergers among state-owned enterprises (SOEs) in Indonesia's maritime industry. It uses measurement methods tailored to regulations from the Ministry of SOEs, providing a reference for other companies seeking to develop effective risk management strategies. Practitioners/Policy Implications: This research suggests that organizations should strengthen risk culture responsibilities in job descriptions at all levels, as well as adopt technology and automation for real-time risk monitoring to enhance risk management effectiveness and ensure alignment with corporate strategy. Research Limitations/Implications: The limitations of this study primarily stem from restricted access to internal and sensitive data, which constrains the depth of analysis and hampers comparison with industry best practices.

Zero-Dimensional Copper(I) Halide Microcrystals as Highly Efficient Scintillators for Flexible X-ray Imaging.

Commercially available rare-earth-doped inorganic oxide materials have been widely applied as X-ray scintillators, but the fragile characteristics, high detection limit, and harsh preparation condition seriously restrict their wide applications. Furthermore, it remains a huge challenge to realize X-ray flexible imaging technology for real-time monitoring of the curving interface of complex devices. To address these issues, we herein report two isostructural cuprous halides of zero-dimensional (0D) [AEPipz]CuX3·X·H2O (AEPipz = N-aminoethylpiperazine, X = Br and I) with controllable size to nanosize crystal as highly efficient scintillators toward flexible X-ray imaging. These cuprous halides exhibit highly efficient cyan photoluminescence and radioluminescence emissions with the highest quantum yield of 92.1% and light yield of 62,400 photons MeV-1, respectively, surpassing most of the commercially available inorganic scintillators. Meanwhile, the ultralow detection limit of 95.7 nGyair s-1 was far below the X-ray dose required for diagnosis (5.5 μGyair s-1). More significantly, the flexible film is facilely assembled with excellent foldability and high crack resistance, which further acts as a scintillation screen achieving a high spatial resolution of 17.4 lp mm-1 in X-ray imaging, demonstrating the potential application in wearable radiation radiography. The combined advantages of high light yield, low detection limit, and excellent flexibility promote these 0D cuprous halides as the most promising X-ray scintillators.

Leveraging the Integration of IoT Devices for Energy Optimization in Office Blocks

The integration of Internet of Things (IoT) devices in office buildings has emerged as a promising solution for enhancing energy efficiency and optimizing resource utilization. In the context of modern workplaces, the adoption of smart technologies offers a transformative approach towards achieving energy optimization goals. Internet connected smart devices are ubiquitous and hence opens new opportunities for optimising energy consumption within office blocks. This research paper explores the integration of IoT devices within an office environment to improve energy efficiency and sustainability. The research paper aims to review a simple and low-cost smart workplace model that employs IoT technology, cloud storage, and data analytics for real-time monitoring and analysis of energy consumption patterns within an office environment. The study examines the implementation of an integration of IoT sensors within an office environment enabling HVAC and lighting systems to dynamically adjust energy use based on real-time conditions such as occupancy levels, ambient temperature, and daylight availability. Through the identification of trends, anomalies, and opportunities for optimization, the study seeks to qualify adaptive control strategies that can effectually reduce energy waste and improve general operational productivity in office buildings. The findings from this research underwrite to the growing field of smart offices by demonstrating the potential of integrating IoT devices for energy optimization in modern office blocks.

Advancements in Real-Time Monitoring of Enteric Methane Emissions from Ruminants

The agricultural sector is responsible for a significant proportion of global anthropogenic methane (CH4) emissions, with enteric CH4 produced from ruminant livestock representing approximately 28% of the total. The development of effective mitigation strategies necessitates the accurate and actionable monitoring of CH4 emissions. However, a considerable research gap remains concerning real-time monitoring techniques capable of supporting on-farm enteric CH4 mitigation strategies. To bridge this research gap, this study explores the current status of real-time enteric CH4 emission monitoring techniques and technologies for ruminants. The study achieves this by reviewing key biomarkers and proxies for ruminant emissions, examining established animal-based measurement techniques, exploring emerging technologies, and critically assessing technological limitations and opportunities. By shedding light on this research area, this study aims to assist stakeholders in developing a viable pathway for on-farm emission monitoring, with the hope of facilitating a meaningful reduction in GHG emissions from the livestock sector.

Innovative landslide disaster monitoring: unmanned aerial vehicle-deployed GNSS technology

Real-time monitoring technologies of surface deformation represented by the global navigation satellite system (GNSS) are essential for landslides early warning. Monitoring methods such as GNSS require on-site manual installation. Therefore, it becomes nearly impossible to deploy surface monitoring equipment when landslides are located in high mountain valleys that are challenging for personnel to access, and/or in hazardous situations. We propose an intelligent real-time monitoring and early-warning technology that employs an unmanned aerial vehicle (UAV) as a carrier to deploy GNSS equipment. A system was initially designed with five components: an adaptive sampling GNSS receiver, an intelligent cooperative network transmission module, UAV-dropped GNSS equipment, special delivery UAV, and an intelligent monitoring and early warning cloud platform. This new technology was applied to a real landslide in Gansu Province, China, in 2020. The mean absolute error of 30 days in the east, north, and upward directions were 1.2, 1.3, and 2.9 mm/d, respectively, comparing the deformation velocity between the traditional monitoring station and the UAV-dropped monitoring station. This observation facilitated the successful prediction of a landslide hazard on 27 January 2021, allowing for a timely alert to be issued. Collectively, the results of this study established that UAV-deployed GNSS technology can accomplish unmanned deployment of GNSS equipment for landslide monitoring to ensure early warning issuance in inaccessible and/or high-risk areas.

A Framework of Digital Twins for Improving Respiratory Health and Healthcare Measures

The investigation describes an inventive use of digital twin technology and LSTM-based machine learning models for real-time patient lung disease monitoring and nutrition planning. The suggested application uses various patient healthcare data, treatment processes, dietary habits, and real-time sensor information to construct digital twins, which are virtual reproductions of specific patients. The LSTM model is trained on this large dataset to predict patient health improvements and dietary needs. For each patient’s digital twin, the program provides personalized treatment plans and nutritional advice, enabling proactive interventions and optimizing patient care. Using performance measures, the trained LSTM model achieves high scores for accuracy (92%), precision (89%), recall (93%), and F1 score (91%), proving its usefulness in generating credible health predictions. Patient feedback on the program shows that patients (98.8%) agree on the accuracy and importance of health feedback, as well as the convenience of access to health information (95.4%). The application’s response rate study reveals an average response rate of 85.87%, assuring prompt feedback. To secure patient information, the study emphasizes data privacy and security, adopting multilayered authentication and data encryption. The outcomes of this study demonstrate the application’s potential to revolutionize patient-centered healthcare by providing data-driven, personalized solutions to patients and healthcare professionals.

In-beam PET monitoring of proton therapy: a method for filtering prompt radiation events

Objective. In-beam positron emission tomography (PET) is a promising technology for real-time monitoring of proton therapy. Random coincidences between prompt radiation events and positron annihilation photon pairs can deteriorate imaging quality during beam-on operation. This study aimed to improve the PET image quality by filtering out the prompt radiation events. Approach. We investigated a prompt radiation event filtering method based on the accelerator radio frequency phase and assessed its performance using various prompt gamma energy thresholds. An in-beam PET prototype was used to acquire the data when the 70 MeV proton beam irradiated a water phantom and a mouse. The signal-to-background ratio (SBR) indicator was utilized to evaluate the quality of the PET reconstruction image. Main results. The selection of the prompt gamma energy threshold will affect the quality of the reconstructed image. Using the optimal energy threshold of 580 keV can obtain a SBR of 1.6 times for the water phantom radiation experiment and 2.0 times for the mouse radiation experiment compared to those without background removal, respectively. Significance. Our results show that using this optimal threshold can reduce the prompt radiation events, enhancing the SBR of the reconstructed image. This advancement contributes to more accurate real-time range verification in subsequent steps.

Wireless Intelligent Patch for Closed-loop In Situ Wound Management.

Wound infections pose a major healthcare issue, affecting the well-being of millions of patients worldwide. Effective intervention and on-site detection are important in wound management. However, current approaches are hindered by time-consuming analysis and a lack of technology for real-time monitoring and prompt therapy delivery. In this study, a smart wound patch system (SWPS) designed for wireless closed-loop and in-situ wound management is presented. The SWPS integrates a microfluidic structure, an organic electrochemical transistor (OECT) based sensor, an electrical stimulation module, and a miniaturized flexible printed circuit board (FPCB). The OECT incorporates a bacteria-responsive DNA hydrogel-coated gate for continuous monitoring of bacterial virulence at wound sites. Real-time detection of OECT readings and on-demand delivery of electrical cues to accelerate wound healing is facilitated by a mobile phone application linked with an FPCB containing low-power electronics equipped with parallel sensing and stimulation circuitry. In this proof-of-concept study, the functionality of the SWPS is validated and its application both in vitro and in vivo is demonstrated. This proposed system expands the arsenal of tools available for effective wound management and enables personalized treatment.

Performance study of novel metal corrosion probes based on the adjust method of series and parallel connections

Rod-type metal corrosion probes based on electromechanical impedance (EMI) technique can quantitatively assess the amount of corrosion, which have a promising application in pipeline corrosion monitoring and have received much more attention. In the field of structural health monitoring, the EMI technique based on series and parallel connections has been widely studied and is very promising, but it is yet to be used in the probe design. In this work, a kind of novel metal corrosion probe with two piezoelectric elements in series and parallel connections was proposed, and its performance was investigated comprehensively by combining theoretical analysis with experiment research. Theoretical models of the proposed probe in the longitudinal vibration mode for both series and parallel connections were established, and verified by the experimental results. Influences of the thicknesses of piezoelectric layer and ceramic layer on the probe performance were analyzed. Artificial quantitative corrosion tests and accelerated corrosion tests were designed to evaluate the quantitative corrosion monitoring capacity. Temperature sensitivity tests were conducted to study the temperature dependence. The wireless impedance measurement was performed to confirm the online monitoring ability. Comparisons for the probe performance between the series and parallel connections were also presented. This work provided a comprehensive understanding for this kind of novel probe, which is beneficial to develop low-cost, high-precision, high-stability pipeline corrosion real-time monitoring technology.

Long-Term Impacts of Glacier Retreat on Downstream Water Availability in Ethiopia

Purpose: The aim of the study was to examine Long-Term Impacts of Glacier Retreat on Downstream Water Availability in Ethiopia. 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 revealed that as glaciers continue to shrink due to climate change, the hydrological systems in mountainous regions of Ethiopia, such as the Simien Mountains and Bale Mountains, face disruptions in water supply, affecting both local communities and downstream users. The reduction in glacial meltwater contributes to decreased streamflow, altered river regimes, and increased variability in water availability, exacerbating water stress in a country already prone to droughts and water scarcity. Moreover, glacier retreat affects ecosystems, biodiversity, and livelihoods dependent on freshwater resources, further amplifying socio-economic vulnerabilities in the region. Unique Contribution to Theory, Practice and Policy: Climate Change Theory, Hydrological Cycle Theory & Systems Theory may be used to anchor future studies on Long-Term Impacts of Glacier Retreat on Downstream Water Availability in Ethiopia. Implement cutting-edge technologies for real-time monitoring of glacier melt and downstream water flow. Use satellite imagery, remote sensing, and IoT sensors to gather data that can inform water management practices in real time. Develop and enforce policies that ensure sustainable water use and prioritize the maintenance of water quality and ecosystem health. This includes setting limits on water withdrawals based on sustainability criteria and enhancing the legal frameworks protecting water rights.

Advanced risk management solutions for mitigating credit risk in financial operations

Advanced risk management solutions are essential for mitigating credit risk in financial operations, particularly in today's volatile economic environment. This Review explores the innovative approaches and technologies being utilized to enhance credit risk management and safeguard financial institutions against potential losses. Credit risk, the possibility that a borrower will default on their obligations, poses a significant threat to financial stability. Traditional methods of assessing and managing credit risk, such as credit scoring and historical data analysis, are no longer sufficient to address the complexities of modern financial markets. Advanced risk management solutions offer more robust and dynamic tools for identifying, assessing, and mitigating credit risk. One of the key advancements in this field is the integration of big data and machine learning algorithms. By analyzing vast amounts of data from diverse sources, financial institutions can gain deeper insights into borrower behavior, market trends, and economic indicators. Machine learning models can predict default probabilities with greater accuracy, allowing for proactive risk mitigation strategies. Additionally, real-time risk monitoring systems have become increasingly prevalent. These systems provide continuous oversight of credit portfolios, enabling financial institutions to detect early warning signs of potential defaults and take timely action. Advanced analytics and visualization tools facilitate the identification of emerging risks and the implementation of targeted interventions. Another crucial component of advanced risk management solutions is the use of stress testing and scenario analysis. These techniques simulate various economic conditions and assess the impact on credit portfolios, helping financial institutions prepare for adverse scenarios and develop contingency plans. Furthermore, blockchain technology is emerging as a promising tool for enhancing transparency and reducing fraud in credit transactions. By providing a secure and immutable record of credit histories and transactions, blockchain can improve trust and reliability in credit assessments. In conclusion, advanced risk management solutions are transforming the way financial institutions mitigate credit risk. By leveraging big data, machine learning, real-time monitoring, stress testing, and blockchain technology, financial institutions can achieve more accurate risk assessments and implement more effective risk mitigation strategies. These innovations not only enhance the stability and resilience of financial operations but also contribute to a more secure and trustworthy financial system.

Energy yield database management system based on solar photovoltaic cell using internet of things technology

This study investigates the performance of solar cell electric power generation, focusing on data collected from Prince of Songkla University, Surat Thani Campus, analyzing the Mean Squared Error (MSE) for each phase consisting of Phase A: 0.00133, Phase B: 0.00137, and Phase C: 0.00216. Through linear regression analysis, we establish the correlation between current and electrical energy in each phase, facilitating the prediction of photovoltaic power generation system performance and timely maintenance alerts. This predictive analysis aids in mitigating greenhouse gas emissions, preventing potential damages from short circuits, and ensuring system safety during installation. Our findings, which are accessible through a user-friendly dashboard interface, address a gap in previous studies by providing empirical data specifically from Prince of Songkla University, Surat Thani Campus, and by integrating IoT technology for real-time monitoring and analysis, underscoring the novelty of our approach.

Vaccine process technology-Adecade of progress.

In the past decade, new approaches to the discovery and development of vaccines have transformed the field. Advances during the COVID-19 pandemic allowed the production of billions of vaccine doses per year using novel platforms such as messenger RNA and viral vectors. Improvements in the analytical toolbox, equipment, and bioprocess technology have made it possible to achieve both unprecedented speed in vaccine development and scale of vaccine manufacturing. Macromolecular structure-function characterization technologies, combined with improved modeling and data analysis, enable quantitative evaluation of vaccine formulations at single-particle resolution and guided design of vaccine drug substances and drug products. These advances play a major role in precise assessment of critical quality attributes of vaccines delivered by newer platforms. Innovations in label-free and immunoassay technologies aid in the characterization of antigenic sites and the development of robust in vitro potency assays. These methods, along with molecular techniques such as next-generation sequencing, will accelerate characterization and release of vaccines delivered by all platforms. Process analytical technologies for real-time monitoring and optimization of process steps enable the implementation of quality-by-design principles and faster release of vaccine products. In the next decade, the field of vaccine discovery and development will continue to advance, bringing together new technologies, methods, and platforms to improve human health.

Advanced Safety Monitoring System for Coal Mines Using IOT

The Coal Mine Safety and Monitoring Project revolutionizes safety practices in coal mining through its innovative approach, leveraging modern technology for real-time monitoring and cloud-based systems. By continuously tracking critical parameters such as temperature, humidity, gas concentration, and miners' heart rates, potential hazards are swiftly identified, allowing for proactive interventions. Utilizing cloud- based platform, data collected from sensors is seamlessly transmitted and analysed in real-time, enabling immediate responses to mitigate risks and ensure miners' safety. This initiative represents a significant advancement in safety protocols, not only minimizing the risk of accidents but also enhancing operational efficiency and productivity within coal mines. By providing a comprehensive framework for safety enhancement, this project sets a new standard for the industry, prioritizing the well-being of miners while contributing to overall sustainability and success in the coal mining sector.

Review of the Real-Time Monitoring Technologies for Lithium Dendrites in Lithium-Ion Batteries.

Lithium-ion batteries (LIBs) have the advantage of high energy density, which has attracted the wide attention of researchers. Nevertheless, the growth of lithium dendrites on the anode surface causes short life and poor safety, which limits their application. Therefore, it is necessary to deeply understand the growth mechanism of lithium dendrites. Here, the growth mechanism of lithium dendrites is briefly summarized, and the real-time monitoring technologies of lithium dendrite growth in recent years are reviewed. The real-time monitoring technologies summarized here include in situ X-ray, in situ Raman, in situ resonance, in situ microscopy, in situ neutrons, and sensors, and their representative studies are summarized. This paper is expected to provide some guidance for the research of lithium dendrites, so as to promote the development of LIBs.

Laser-Induced Graphene-Based Sensors in Health Monitoring: Progress, Sensing Mechanisms, and Applications.

The rising global population and improved living standards have led to an alarming increase in non-communicable diseases, notably cardiovascular and chronic respiratory diseases, posing a severe threat to human health. Wearable sensing devices, utilizing micro-sensing technology for real-time monitoring, have emerged as promising tools for disease prevention. Among various sensing platforms, graphene-based sensors have shown exceptional performance in the field of micro-sensing. Laser-induced graphene (LIG) technology, a cost-effective and facile method for graphene preparation, has gained particular attention. By converting polymer films directly into patterned graphene materials at ambient temperature and pressure, LIG offers a convenient and environmentally friendly alternative to traditional methods, opening up innovative possibilities for electronic device fabrication. Integrating LIG-based sensors into health monitoring systems holds the potential to revolutionize health management. To commemorate the tenth anniversary of the discovery of LIG, this work provides a comprehensive overview of LIG's evolution and the progress of LIG-based sensors. Delving into the diverse sensing mechanisms of LIG-based sensors, recent research advances in the domain of health monitoring are explored. Furthermore, the opportunities and challenges associated with LIG-based sensors in health monitoring are briefly discussed.

Machine vision and artificial intelligence for plant growth stress detection and monitoring: A review

The agricultural sector faces increasing challenges in ensuring food security and optimizing crop yield, necessitating innovative solutions for early detection and mitigation of plant growth stress. The integration of advanced imaging technologies with artificial intelligence (AI) has emerged as a powerful tool for non-invasive, real-time monitoring of plant health. The objective of this paper was to review the application of machine vision and AI in identifying and classifying plant growth stress, with a focus on stressors, datasets, and the use of intelligent algorithms. The significance of plant growth stress induced by environmental variables, including temperature, light, nutrient deficiencies, and water supply were addressed and the conventional stress detection methodologies, underscores their inherent limitations, and establishes the groundwork for the exploration of state-of-the-art technologies in stress assessment. Various sensor technologies were explored, encompassing traditional RGB cameras, multispectral and hyperspectral sensors, and thermal imaging, each capable of capturing distinct stress signatures. Machine vision, leveraging high-resolution imaging and spectroscopy, offers detailed insights into plant physiological responses. Coupled with AI approaches such as deep learning, neural networks, and pattern recognition, machine vision enables the automated analysis of vast datasets, enhancing the accuracy and speed of stress detection. The recent advancements in image processing techniques tailored for plant stress identification were focused and discussed the role of feature extraction, classification, and predictive modelling in achieving robust results. The potentials of AI in plant stress physiology and its role in overcoming the limitations of traditional methods, and the use of unsupervised identification of visual symptoms to quantify stress severity, allowing for the identification of different types of plant stress were studied. Moreover, the potentials of machine vision technology and AI for real-time monitoring and decision support systems in precision agriculture were discussed. The findings of this review would contribute to the growing field of agricultural technology, offering insights into the development of automated tools that could aid farmers and researchers in mitigating the impact of abiotic stressors on crop/plant health and productivity.

HUMAN RESOURCE MANAGEMENT STRATEGIES FOR SAFETY AND RISK MITIGATION IN THE OIL AND GAS INDUSTRY: A REVIEW

The oil and gas industry is renowned for its inherent operational risks and complex safety challenges, necessitating robust Human Resource Management (HRM) strategies for effective safety measures and risk mitigation. This comprehensive review explores the evolving landscape of HRM practices within the oil and gas sector, focusing on their pivotal role in enhancing workplace safety and reducing operational risks. The review delves into the dynamic nature of the oil and gas industry, characterized by hazardous work environments, intricate technological processes, and a global workforce. Analyzing existing literature and case studies, the paper underscores the critical need for HRM strategies that prioritize safety culture, employee training, and proactive risk management. Key aspects include recruitment and selection processes tailored to identify candidates with a strong safety mindset, ongoing training programs to enhance competencies and awareness, and the establishment of a safety-centric organizational culture. Furthermore, the review examines the integration of technology and data analytics in HRM practices within the oil and gas sector. The utilization of advanced technologies for personnel training, real-time monitoring, and predictive analytics is discussed as a means to pre-emptively identify potential safety risks and proactively address them. Additionally, the paper highlights the importance of fostering communication and collaboration among employees, emphasizing the role of HRM in facilitating a transparent and open reporting culture. The findings of this review contribute to a deeper understanding of the multifaceted role played by HRM in promoting safety and mitigating risks within the oil and gas industry. As the industry continues to evolve, the adoption of innovative HRM strategies becomes imperative for organizations seeking to maintain a secure and resilient operational environment while safeguarding the well-being of their workforce.
 Keywords: HR, Management, Safety, Risk Mitigation, Oil and Gas, Industry, Review.

In Situ Surface Defect Detection in Polymer Tube Extrusion: AI-Based Real-Time Monitoring Approach.

While striving to optimize overall efficiency, smart manufacturing systems face various problems presented by the aging workforce in modern society. The proportion of aging workers is rapidly increasing worldwide, and visual perception, which plays a key role in quality control, is significantly susceptible to the impact of aging. Thus it is necessary to understand these changes and implement state-of-the-art technologies as solutions. In this study, we conduct research to mitigate the negative effects of aging on visual recognition through the synergistic effects of real-time monitoring technology combining cameras and AI in polymer tube production. Cameras positioned strategically and with sophisticated AI within the manufacturing environment promote real-time defect detection and identification, enabling an immediate response. An immediate response to defects minimizes facility downtime and enhances the productivity of manufacturing industries. With excellent detection performance (approximately 99.24%) and speed (approximately 20 ms), simultaneous defects in a tube can be accurately detected in real time. Finally, real-time monitoring technology with adaptive features and superior performance can mitigate the negative impact of decreased visual perception in aging workers and is expected to improve quality consistency and quality management efficiency.

Review on Laser Welding Technology on Aluminum and Copper Dissimilar Materials for Secondary Batteries Assembly

Electric vehicles have recently taken center stage, driven by growing environmental concerns, with a key focus on ensuring the reliability of their batteries. The materials used in the electrical connections of secondary batteries predominantly consist of Al and Cu alloys, necessitating the joining of both similar and dissimilar materials. This paper presents a technical review on laser welding technology applied to the challenging Al/Cu dissimilar metal combination. The complexity arises from the formation of brittle phases in intermetallic compounds and the low absorption of laser beams by the base materials. The characteristics of various laser welding modes and their effective monitoring through deep learning are explained in detail. The development of real-time laser welding monitoring technology is crucial to increase productivity and enable the mass production of reliable electric vehicle battery systems.