Real-time Monitoring Technology Research Articles

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.

Employing hybrid deep learning for near-real-time forecasts of sensor-based algal parameters in a Microcystis bloom-dominated lake

Harmful cyanobacterial blooms (CyanoHABs) are increasingly impacting the ecosystem of lakes, reservoirs and estuaries globally. The integration of real-time monitoring and deep learning technology has opened up new horizons for early warnings of CyanoHABs. However, unlike traditional methods such as pigment quantification or microscopy counting, the high-frequency data from in-situ fluorometric sensors display unpredictable fluctuations and variability, posing a challenge for predictive models to discern underlying trends within the time-series sequence. This study introduces a hybrid framework for near-real-time CyanoHABs predictions in a cyanobacterium Microcystis-dominated lake - Lake Dianchi, China. The proposed model was validated using hourly Chlorophyll-a (Chl a) concentrations and algal cell densities. Our results demonstrate that applying decomposition-based singular spectrum analysis (SSA) significantly enhances the prediction accuracy of subsequent CyanoHABs models, particularly in the case of temporal convolutional network (TCN). Comparative experiments revealed that the SSA-TCN model outperforms other SSA-based deep learning models for predicting Chl a (R2 = 0.45–0.93, RMSE = 2.29–5.89 μg/L) and algal cell density (R2 = 0.63–0.89, RMSE = 9489.39–16,015.37 cells/mL) at one to four steps ahead predictions. The forecast of bloom intensities achieved a remarkable accuracy of 98.56 % and an average precision rate of 94.04 % ± 0.05 %. In addition, scenarios involving various input combinations of environmental factors demonstrated that water temperature emerged as the most effective driver for CyanoHABs predictions, with a mean RMSE of 2.94 ± 0.12 μg/L, MAE of 1.55 ± 0.09 μg/L, and R2 of 0.83 ± 0.01. Overall, the newly developed approach underscores the potential of a well-designed hybrid deep-learning framework for accurately predicting sensor-based algal parameters. It offers novel perspectives for managing CyanoHABs through online monitoring and artificial intelligence in aquatic ecosystems.

SnS2 nanosheets/Ag2S quantum dots heterojunction supported by YSZ nanofibers enable real-time room-temperature NO2 gas sensor

Air pollution has emerged as a critical global issue, necessitating the development of effective real-time gas monitoring technologies. However, conventional gas sensors face limitations such as high energy consumption and sluggish response/recovery kinetics. In this study, we report a novel SnS2/Ag2S heterojunction supported by yttria-stabilized zirconia nanofibers (YSZ@SnS2/Ag2S) through a facile electrospinning, hydrothermal and subsequent cation-exchange method. This hierarchical composite nanofiber consists of well-distributed Ag2S quantum dots (QDs) attached to vertical anchored SnS2 nanosheets, forming an interconnected network supported by YSZ nanofibers. The integrated sensor exhibited high response of 3.27 and swift response/recovery times of 12/27 s to 1 ppm NO2 at room temperature, respectively. Benefited from highly permeable three-dimensional continuous conductive nanofibers, along with the hierarchical nanosheets/QDs structure, the gas transmission/diffusion and number of reactive sites were ensured. Additionally, the interfacial built-in electric field of SnS2/Ag2S heterojunction further enhanced the electron transfer, carrier migration and gas-sensitive reaction kinetics. This study provides insights into the development of functional composite nanofibers and paves the way for the exploitation of real-time room-temperature gas sensing applications.

Hydrogen evolution reaction activity enhancement from active site turnover mechanism

Although heteroatom doping is an effective way to improve the catalytic activity of transition metal phosphides (TMPs), the mechanism of activity enhancement needs to be further refined. To this end, we synthesized a Co-doped Ni2P catalyst as a research model and found that the introduction of heterogeneous Co reconstructed the charge distribution around the P site, which effectively enhanced the hydrogen evolution reaction (HER) activity of the pure Ni2P. Based on in-situ Raman real-time monitoring technology, we monitored for the first time that Co doping triggered a switch of the active site (from the original Co-active site to the P-active site), which promoted the adsorption of H2O to enhance the HER activity. The density functional theory (DFT) calculations indicated that the P site of Co-Ni2P expressed the highest activity and the Ni site of pure Ni2P expressed the highest activity, which further confirms the in-situ Raman monitoring results. The active site turnover mechanism discovered in this study will undoubtedly provide more rational and targeted ideas for future catalyst design.

Development and Validation of an Instrument for Wave Height Measurement with Encoder Sensors and Accelerometer

Ocean waves play a significant role in coastal dynamics and the management of coastal development activities. However, there are limited instruments to measure the wave height, particularly in developing countries. This study introduces a novel instrument design for simple and cost-effective wave height measurement. The proposed instrument combines a Hall-effect sensor and an accelerometer to capture the rise and fall of a buoy in response to wave movements. The Hall-effect sensor as a rotary encoder detects the rotation of the buoy while the accelerometer measures the tilt of the instrument caused by ocean waves. The instrument is constructed using PVC pipes and incorporates long-range (LoRa) communication technology for real-time monitoring. Experimental tests were conducted at a study site in Banyuwangi Regency, Indonesia, where the performance of the instrument was compared with an ultrasonic sensor-based instrument. The results validate the effectiveness of the proposed instrument design for wave height measurement. The collected data were transmitted via the LoRa communication system, enabling convenient monitoring and analysis of sea-level changes. The valuable contribution of the instrument to the field stems from its precision in measuring the wave height, adaptability to diverse conditions, and potential applicability in shallow waters.

Upper-bound vibration response spectrum computation for deployable membrane structures with uncertainty effects

Real-time monitoring technology is essential for establishing a control technology for large-sized membrane space structures. However, numerous measurement points are necessary to capture the maximum vibration response, which renders the real-time monitoring technology impractical. Thus, this study proposed an upper-bound vibration spectrum computation approach using a few measurement points samples on a deployable membrane structure. The proposed method exhibits characteristics of applying the standardized upper-bound spectrum, including uncertainty effects for the computations. The experiment results indicated that the upper-bound spectrum calculated from only 30 measured data points appropriately covered the measured spectra, thus confirming the effectiveness of the proposed method.

Serological Exosome Metabolic Biopsy of Hepatocellular Carcinoma via Designed Core-Shell Nanoparticles.

Exosome metabolite-based liquid biopsy is a promising strategy for large-scale application in practical clinics toward precise medicine. Given the current challenges in successive isolation and analysis of exosomes and their metabolites in this field, we established a low-cost, high-throughput, and rapid platform for serological exosome metabolic biopsy of hepatocellular carcinoma (HCC) via designed core-shell nanoparticles. It starts with the efficient extraction of high-quality serum exosomes and exosome metabolic features, based on which significantly obvious sample clusters are observed by unsupervised cluster analysis. The following integration of feature selection and supervised machine learning enables the identification of six key metabolites and achieves high-performance prediction between HCC, liver cirrhosis, and healthy controls. Specifically, both sensitivity and accuracy achieve 100% among any pairwise intergroup discrimination in a blind test. The quality and reliability of six key metabolites are further evaluated and validated by using different machine learning algorithms and pathway exploration. Our platform contributes to the future growth of new liquid biopsy technologies for precision diagnosis and real-time monitoring of HCC, among other conditions.

Energy Management System of Luminosity Controlled Smart City Using IoT

INTRODUCTION: With the escalating rates of urbanization, there is a pressing need for enhanced urban services. The concept of smart cities, leveraging digital technologies, offers a promising solution to elevate urban living. The integration of Internet-of-Things (IoT) in urban infrastructure, particularly on highways, opens avenues for novel services and cross-domain applications through Information and Communication Technologies. However, the efficient functioning of an IoT-enabled smart city necessitates careful energy resource management.
 OBJECTIVES: Propose a Highway Lighting System (HWLS) integrating IoT technologies to enhance urban services, focusing on significant energy savings and real-time environmental parameter monitoring.
 METHODS: To achieve the objective of enhancing urban services through the proposed Highway Lighting System (HWLS), the system was designed and implemented by integrating cutting-edge sensors, communication links, and the Blynk IoT app. The deployment involved incorporating IoT technologies for real-time monitoring of air quality, air moisture, and soil moisture, alongside a fault identification system using GSM and GPS modules.
 RESULTS: The proposed HWLS demonstrates significant energy savings, consuming only 37.6% of the original power consumption. The incorporation of IoT technologies facilitates real-time monitoring of environmental parameters, enabling informed decision-making for urban service optimization. The fault-finding system, utilizing GSM and GPS modules, enhances the reliability of the lighting system.
 CONCLUSION: In conclusion, the Highway Lighting System (HWLS) represents a novel approach to smart city infrastructure, particularly in the context of urban lighting. The integration of IoT technologies not only contributes to energy savings but also enhances the overall efficiency of urban services. The proposed system's ability to monitor environmental parameters and identify faults demonstrates its potential for sustainable urban development and improved quality of life.

Review Paper: “Quality Control and Assurance Strategy for Improved Operational Logistics”

Abstract - his project explores the implementation of a robust Quality Control and Assurance (QA/QC) strategy within operational logistics to enhance efficiency and elevate the overall quality of services. Through a comprehensive analysis of end-to-end logistics processes, we identified key areas for improvement, including bottlenecks in workflows, gaps in standardized procedures, and opportunities for technology integration. The strategy involves the adoption of real-time monitoring technologies, the development of standardized operating procedures, and the establishment of a continuous improvement culture. Training programs for employees and closer collaboration with suppliers are integral components, ensuring that the workforce is equipped to implement and adhere to quality standards. The project emphasizes the importance of data analytics for informed decision-making, regulatory compliance, and risk management. By adopting a phased roadmap for technology integration, the strategy aims to optimize logistics operations while minimizing disruptions. Regular audits, customer feedback mechanisms, and the definition of key performance indicators (KPIs) ensure ongoing evaluation and adherence to quality standards. This comprehensive QA/QC strategy, tailored to the organization's needs, is poised to drive efficiency, reduce errors, and enhance overall customer satisfaction in the operational logistics landscape.

Real-Time sanitary sewer blockage detection system using IoT

Sewer blockages and overflows have significant economic and environmental repercussions on communities. Thus, it is crucial to detect and remove sewer blockages prior to the occurrence of overflows. With the improvement in mobile networks and the development of high-quality and low-power sensors and loggers, wastewater network operators can now adopt monitoring devices enabled by the “Internet of Things” (IoT) technology for real-time monitoring. To this end, this paper studies the current state-of-the-art in sewer blockage management and introduces a novel methodology to monitor sanitary sewer blockages to prevent sanitary sewer overflows (SSO) with the least human interaction. The proposed system incorporates low-power level sensors and 4G telemetry for real-time monitoring of the manhole's sewage level to detect sewer blockages. The blockage detection methodology encompasses a set of decision rules and time series analysis to identify blockage events. The final blockage detection model offers a versatile capability to be implemented on sanitary sewer networks of different types with minimum computational costs.

Research Review Inquisitive on Indoor Air Quality Monitoring System Facilitate with Internet of Things

In recent days, Indoor Air Quality (IAQ) has become a matter of interest for researchers from both scientific and industrial community. During pandemic situation public settle more time at indoor in place of outdoor environment. With rapid growth in science and technology people have progressive necessity for active facility parameters with respect to indoor temperature, humidity and wind movement etc. Asthma, lung cancer, hypertension, cardiovascular issues, and respiratory illnesses are just a few of the chronic health issues that have been linked to frequent exposure to hazardous material in enclosed spaces. However, real-time monitoring technologies are being adopted in smart city projects to identify and remedy such unfavourable conditions, ultimately improving the general quality of life. This paper surveyed the current proposed IAQ monitoring systems in terms of Internet of Things (IoT). In addition, the research work highlights composition of diverse monitoring systems, research summary, challenges together implying future directions for the researchers. The current level of knowledge on the close relationship between IoT and IAQ is presented in this research.

Interactive Oil Spill Management, Operation and Administration System

This paper presents an oil spill management system that is interactive and real-time. The system uses geo-database and application development modules that are integrated with internet communication systems. It is designed to cover all stages of oil spill management, including before, during, and after an accident. The system can be linked with various satellites, airborne monitoring systems, and ground devices through communication systems to facilitate oil spill early warning, situational analysis, risk analysis, damage analysis, monitoring, and management in real-time. The components of the system include a database, central repository, disaster models, command and control, and communication schemes. This paper illustrates the conjunction of computer software and hardware with a variety of models and technologies for real-time oil spill monitoring and management.

Real-Time Monitoring of Neonatal Incubator Parameters Via Iot-Integrated Device: Design, Implementation, and Evaluation

The Internet of Things (IoT) has revolutionized healthcare solutions’ design, construction, and deployment. This research project focuses on developing an external device incorporating Internet of Things (IoT) technology for real-time monitoring of neonatal incubator parameters in sub-Saharan Africa. This IoT is intended to monitor real-time vital signs such as body temperature, humidity, pulse rate, oxygen concentration, air quality, and the sound level of the immediate environment and transmit the data via a mobile application. The Internet of Things has a micro controller for storing temporal sensor data. Temperature, humidity, and heart rate sensors monitor temperature, humidity, pulse oximetry, and heart rate, respectively, and a gas sensor detects harmful gases. The mic sensor detects abnormal sound levels around the incubator, while the Wi-Fi module provides the micro controller access to a Wi-Fi network. The sensor data is sent to a cloud-based platform that houses the data, analysis, and retrieval. This research paper contains the details of system design, implementation, and performance evaluation. The evaluation findings reveal the device to be trus table and accurate, providing neonate caregivers with valuable information.

Three-dimensional (3D) knitted spacer textile materials for advanced healthcare solutions: A comprehensive review

The integration of advanced textile materials is crucial to enhance the quality of healthcare solutions. Three-dimensional (3D) knitted spacer fabrics (KSFs) have emerged as a revolutionary class of medical textiles distinguished by their unique ability to provide high breathability, moisture management, and customizable structural properties. This comprehensive review systematically examined the multifaceted applications of 3D KSFs in the healthcare sector. It begins with an overview of various design and manufacturing techniques, including warp and weft knitting processes, that facilitate the production of innovative fabrics. The review then details the distinct structural and functional features of 3D KSFs, emphasizing their cushioning effects, porosity, and adaptability, which enhance their effectiveness in the medical context. Subsequent sections explore diverse applications, including wound care, where their moisture-wicking capabilities promote healing, pressure ulcer prevention, leveraging their ability to evenly distribute pressure, and orthopedic supports, which benefit from their comfort and supportive characteristics. This paper also discusses the potential of 3D KSFs in scaffold development for tissue engineering, as well as their use in assistive devices for enhanced patient mobility. Emerging trends, such as the incorporation of smart technologies for real-time monitoring and commitment to sustainability through washable and reusable designs, are critically analyzed. In turn, this review provides an in-depth resource for healthcare professionals, textile researchers, and product developers, highlighting the transformative potential of 3D KSFs for advancing healthcare solutions and improving patient outcomes.

In Vivo Transcranial Acoustoelectric Brain Imaging of Different Deep Brain Stimulation Currents.

Deep brain stimulation (DBS) is an effective treatment for neurologic disease and its clinical effect is highly dependent on the DBS leads localization and current stimulating state. However, standard human brain imaging modalities could not provide direct feedback on DBS currents spatial distribution and dynamic changes. Acoustoelectric brain imaging (AEBI) is an emerging neuroimaging method that can directly map current density distribution. Here, we investigate in vivo AEBI of different DBS currents to explore the potential of DBS visualization using AEBI. According to the typical DBS stimulus parameters, four types of DBS currents, including time pattern, waveform, frequency, and amplitude are designed to implement AEBI experiments in living rat brains. Based on acoustoelectric (AE) signals, the AEBI images of each type DBS current are explored and the resolution is quantitatively analyzed for performance evaluation. Furtherly, the AE signals are decoded to characterize DBS currents from multiple perspectives, including time-frequency domain, spatial distribution, and amplitude comparation. The results show that in vivo transcranial AEBI can accurately locate the DBS contact position with a millimeter spatial resolution (< 2 mm) and millisecond temporal resolution (< 10 ms). Besides, the decoded AE signal at DBS contact position is capable of describing the corresponding DBS current characteristics and identifying current pattern changes. This study first validates that AEBI can localize in vivo DBS contact and characterize different DBS currents. AEBI is expected to develop into a noninvasive DBS real-time monitoring technology with high spatiotemporal resolution.