Comprehensive Monitoring System Research Articles

ClinicalGAN: powering patient monitoring in clinical trials with patient digital twins

Conducting clinical trials is becoming increasingly challenging lately due to spiraling costs, increased time to market, and high failure rates. Patient recruitment and retention is one of the key challenges that impact 90% of the trials directly. While a lot of attention has been given to optimizing patient recruitment, limited progress has been made towards developing comprehensive clinical trial monitoring systems to determine patients at risk and potentially improve patient retention through the right intervention at the right time. Earlier research in patient retention primarily focused on using deterministic frameworks to model the inherently stochastic patient journey process. Existing generative approaches to model temporal data such as TimeGAN or CRBM , face challenges and fail to address key requirements such as personalized generation, variable patient journey, and multi-variate time-series needed to model patient digital twin. In response to these challenges, current research proposes ClinicalGAN to enable patient level generation, effectively creating a patient’s digital twin. ClinicalGAN provides capabilities for: (a) patient-level personalized generation by utilizing patient meta-data for conditional generation; (b) dynamic termination prediction to enable pro-active patient monitoring for improved patient retention; (c) multi-variate time-series training to incorporate relationship and dependencies among different tests measures captured during patient journey. The proposed solution is validated on two Alzheimer’s clinical trial datasets and the results are benchmarked across multiple dimensions of generation quality. Empirical results demonstrate that the proposed ClinicalGAN outperforms the SOTA approach by 3–4×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document} on average across all the generation quality metrics. Furthermore, the proposed architecture is shown to outperform predictive methods at the task of drop-off prediction significantly (5–10% MAPE scores).

Regulation of the Novel MSTN-GAL3-NOX1 Axis Reverses Obesity-Induced Skeletal Muscle Quality Decline Resulting in Improved Glycemic Indices and Restored Angiogenesis Following Ischemia

Objective: To test the hypothesis that restored muscle quality achieved through myostatin KO improves angiogenesis in obese mice through the resolution of GAL3-NOX1-mediated endothelial dysfunction.Methods: db/db mice, a well-described model of obesity, were crossed with mice lacking myostatin (MSTN KO), a myokine that negatively regulates muscle differentiation and growth, galectin-3 (GAL3 KO), a pro-inflammatory/atherogenic RAGE (receptor for advanced glycation endproducts), and NADPH oxidase-1 (NOX1 KO), a TGF-b-stimulated primary producer of the reactive oxygen species (ROS) O2−, to generate db+/−-MSTN+/−, db+/−-GAL3+/−, and db+/−-NOX1+/− mice. We utilized nuclear magnetic resonance (NMR) spectroscopy to assess skeletal muscle (SKM) lean/fat content in db+/−-MSTN+/− mice. Muscle and plasma lipid content were analyzed via mass spectrometry. Mice were placed in metabolic cages and monitored with CLAMS (Comprehensive Lab Animal Monitoring System) in order to determine their whole-body metabolism. Insulin metabolism indices were determined via appropriate blood and plasma testing kits. Morphology of SKM was determined via microscopy of H&E and Mason’s Trichrome-stained gastrocnemius cross sections. Femoral artery ligation was used to stimulate in vivo angiogenesis in response to hind limb ischemia (HLI); whereas 1mm aortic rings cultured in ECBM (2% FBS) were used to study ex vivo angiogenesis. Gene expression was assessed by RNA-Seq and RT-qPCR. Results: MSTN deletion results in significantly increased SKM mass and restored SKM quality (i.e. morphology and metabolic function) without altering whole-body mass, fat percentage, or activity in obese (db−) mice. We observed that obesity inhibits and MSTN KO restores angiogenesis in both HLI and aortic ring assay models. Previous studies in our lab revealed that MSTN deletion was suffcient to restore endothelial function in db/db mice. Additionally, our lab has shown that GAL3 or NOX1 KO in db/db mice phenocopied the vascular improvements seen in db−-MSTN− mice without restoring SKM size or quality. RNA-Seq and RT-qPCR analysis of SKM and endothelial cells (ECs) revealed significant upregulation of GAL3 and NOX1 expression in db−-MSTN+ mice but not in db−-MSTN− and db−-GAL3− mice. Finally, either GAL3 or NOX1 deletion results in amended angiogenesis in obese animals. Conclusion: In summary, restoration of muscle quality in obese individuals provides potent protection of vascular and metabolic health. A correlate of this improvement is the attenuation of a novel MSTN-GAL3-NOX1 axis, which we have shown to mediate vascular inflammation/redox signaling in obesity. These data suggest that pharmacological targeting of upstream MSTN, downstream GAL3 and/or NOX1 could be effective in treating cardiovascular disease in the context of obesity and metabolic syndrome. A. C. Speese is supported by NIH 1F31HL165858-01A1. D.W. Stepp and D. J. Fulton are supported by NIH 1R01HL147159. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Scalable-produced micro-elastic triboelectric sensing ground for all-weather large-scale applications

Large-scale smart floor will be one potential form for a human–machine interface, but is also facing many difficulties and challenges in the materials science and information engineering. Here, we present a straightforward method for fabricating large-scale micro-elastic triboelectric nanogenerator (ME-TENG) arrays, which can be assembled as triboelectric sensing ground for real-time interface interaction and all-weather monitoring. ME-TENG unit of 6 by 6 arrays offers a maximum power density of 5.84 mW·m−2 under a load resistance of 70 MΩ. It displays a sensitivity of 132.48 mV/kPa in the pressure range of 0 to 180 kPa. The electrical output signals have remained stable in the durability tests over 10 days with various weather conditions. Furthermore, successfully demonstrated a regional comprehensive security monitoring (RCSM) system for enabling all-weather monitoring of the features, position, and movement trajectories of an object. Even more gratifying is that the ME-TENG unit of 30 cm by 30 cm prepared by a simple and fast “all-in-one” method, takes costs of 14.84 dollars/m2 and the production period of 2 h, compared with the most cost-effective piezoelectric sensors, the cost and duration were reduced by 98.67 % and 42.86 %, respectively. This work will greatly contribute to the development and implementation of large-scale sensing and the multidimensional security system.

SocialVis: Dynamic social visualization in dense scenes via real‐time multi‐object tracking and proximity graph construction

AbstractTo monitor and assess social dynamics and risks at large gatherings, we propose “SocialVis,” a comprehensive monitoring system based on multi‐object tracking and graph analysis techniques. Our SocialVis includes a camera detection system that operates in two modes: a real‐time mode, which enables participants to track and identify close contacts instantly, and an offline mode that allows for more comprehensive post‐event analysis. The dual functionality not only aids in preventing mass gatherings or overcrowding by enabling the issuance of alerts and recommendations to organizers, but also allows for the generation of proximity‐based graphs that map participant interactions, thereby enhancing the understanding of social dynamics and identifying potential high‐risk areas. It also provides tools for analyzing pedestrian flow statistics and visualizing paths, offering valuable insights into crowd density and interaction patterns. To enhance system performance, we designed the SocialDetect algorithm in conjunction with the BYTE tracking algorithm. This combination is specifically engineered to improve detection accuracy and minimize ID switches among tracked objects, leveraging the strengths of both algorithms. Experiments on both public and real‐world datasets validate that our SocialVis outperforms existing methods, showing improvement in detection accuracy and reduction in ID switches in dense pedestrian scenarios.

Analysis of Anthropogenic Waste Heat Emission from an Academic Data Center

The rapid growth in computing and data transmission has significant energy and environmental implications. While there is considerable interest in waste heat emission and reuse in commercial data centers, opportunities in academic data centers remain largely unexplored. In this study, real-time onsite waste heat data were collected from a typical academic data center and an analysis framework was developed to determine the quality and quantity of waste heat that can be contained for reuse. In the absence of a comprehensive computer room monitoring system, real-time thermal data were collected from the data center using two arrays of thermometers and thermo-anemometers in the server room. Additionally, a computational fluid dynamics model was used to simulate temperature distribution and identify “hot spots” in the server room. By simulating modification of the server room with a hot air containment system, the return air temperature increased from 23 to 46 °C and the annual waste heat energy increased from 377 to 2004 MWh. Our study emphasizes the importance of containing waste heat so that it can be available for reuse, and also, that reusing the waste heat has value in not releasing it to the environment.

Smart spirits: an integrated IoT-based liquor and health monitoring system

This project entails the development of a comprehensive health monitoring system utilizing Arduino Uno, a range of sensors (including alcohol, pulse, and DHT11), an LCD display, and a GSM module for data transmission to the Thingspeak application. Its objective is to monitor vital health parameters such as temperature, humidity, pulse rate, and alcohol levels in real-time, accessible via the Thingspeak mobile app. Key components include an Arduino Uno microcontroller, DHT11 sensor for temperature and humidity, pulse sensor for heart rate, alcohol sensor for detecting alcohol levels, LCD display for immediate feedback, and a GSM module for remote data transmission. The user-friendly Thingspeak app serves as an interface for seamless health metric monitoring. The system operates by collecting sensor data, displaying it locally on the LCD screen, and simultaneously transmitting it to the Thingspeak app via GSM communication. Users can then access the app to visualize and monitor their health metrics in real-time, allowing for prompt interventions or medical assistance if needed. The code implementation integrates sensor readings, LCD display control, and Thingspeak functionality. Leveraging Thingspeak's cloud-based platform enhances usability and accessibility, making remote health monitoring feasible. This system offers a versatile solution for individuals looking to efficiently track and manage their well-being, thanks to the integration of Arduino Uno, sensors, Thingspeak features, and GSM communication, making it suitable for both local and remote health monitoring applications.

Normalized Pulse Volume as a Superior Predictor of Respiration Recovery and Quantification of Nociception Anti-nociception Balance Compared to Opioid Effect Site Concentration: A Prospective, Observational Study

Background Quantifying pain and the balance between nociception and anti-nociception (NANB) in sedated patients is challenging. Traditional opioid titration methods overlook individual differences, while existing indices like the Noxious Stimulation Response Index (NSRI) lack correlation with effect-site concentration (Ce). The Normalized Pulse Volume (NPV), used in polygraphs, has potential for pain quantification but is underexplored. This study aimed to assess NPV’s efficacy as a pain monitoring tool compared to Ce and to explore its potential in various clinical settings. Methods The study included 39 patients undergoing surgery under total intravenous anesthesia from July 2013 to May 2014. Selection criteria were an American Society of Anesthesiologists physical status classification system (ASA score) of 1 or 2 and surgeries with minimal fluid resuscitation or blood loss. Exclusion criteria were significant posture changes, massive hemorrhage, and high perfusion index variation. NPV and Ce were measured using the Masimo SET adult SpO2 sensor. Results Out of 39 patients, 9 were excluded. NPV at recovery of spontaneous respiration (RoR) was 2.62 (95% CI: 2.26–2.98) with a coefficient of variation (CoV) of 36.3%, while total Ce was 1.48 ng/ml (95% CI: 1.14–1.84) with a CoV of 62.4%. NPV showed a narrower CoV than Ce (p < 0.05, 1.93*10−5), indicating less variability. NPV outperformed Ce in predicting RoR, suggesting a more accurate reflection of NANB balance. Its superiority in stable measurement underlines its potential as a reliable pain indicator. The study’s limitations include temporal differences in NPV and Ce calculations, affecting comparative analysis. Conclusion NPV demonstrates promise as an objective, reliable indicator of pain or NANB, showing a strong correlation with Ce. Its application could improve pain assessments in clinical settings, optimizing patient care and analgesic administration. Future research should integrate NPV with other vital signs for a comprehensive pain monitoring system.

Research on a Comprehensive Monitoring System for Tunnel Operation based on the Internet of Things and Artificial Intelligence Identification Technology

This article proposes a comprehensive monitoring system for tunnel operation to address the risks associated with tunnel operations. These risks include safety control risks, increased traffic flow, extreme weather events, and movement of tectonic plates. The proposed system is based on the Internet of Things and artificial intelligence identification technology. The monitoring system will cover various aspects of tunnel operations, such as the slope of the entrance, the structural safety of the cave body, toxic and harmful gases that may appear during operation, excessively high and low-temperature humidity, poor illumination, water leakage or road water accumulation caused by extreme weather, combustion and smoke caused by fires, and more. The system will enable comprehensive monitoring and early warning of fire protection systems, accident vehicles, and overheating vehicles. This will effectively improve safety during tunnel operation.

Combined frequency domain near-infrared spectroscopy and diffuse correlation spectroscopy system for comprehensive metabolic monitoring of inspiratory muscles during loading.

Mechanical ventilation (MV) is a cornerstone technology in the intensive care unit as it assists with the delivery of oxygen in critically ill patients. The process of weaning patients from MV can be long and arduous and can lead to serious complications for many patients. Despite the known importance of inspiratory muscle function in the success of weaning, current clinical standards do not include direct monitoring of these muscles. The goal of this project was to develop and validate a combined frequency domain near-infrared spectroscopy (FD-NIRS) and diffuse correlation spectroscopy (DCS) system for the noninvasive characterization of inspiratory muscle response to a load. The system was fabricated by combining a custom digital FD-NIRS and DCS system. It was validated via liquid phantom titrations and a healthy volunteer study. The sternocleidomastoid (SCM), an accessory muscle of inspiration, was monitored during a short loading period in fourteen young, healthy volunteers. Volunteers performed two different respiratory exercises, a moderate load and a high load, which consisted of a one-minute baseline, a one-minute load, and a six-minute recovery period. The system has low crosstalk between absorption, reduced scattering, and flow when tested in a set of liquid titrations. Faster dynamics were observed for changes in blood flow index (), and metabolic rate of oxygen () compared with hemoglobin + myoglobin (Hb+Mb) based parameters after the onset of loads in males. Additionally, larger percent changes in , and were observed compared with Hb+Mb parameters in both males and females. There were also sex differences in baseline values of oxygenated Hb+Mb, total Hb+Mb, and tissue saturation. The dynamic characteristics of Hb+Mb concentration and blood flow were distinct during loading of the SCM, suggesting that the combination of FD-NIRS and DCS may provide a more complete picture of inspiratory muscle dynamics.

An IoT enabled computational model and application development for monitoring cardiovascular risks

Technological advancement in the rise of the Internet of Things (IoT) and computational modelling and application development has revolutionized medical care. Monitoring cardiovascular risks is faster, easier, and more accurate than ever. Using IoT-enabled computational modelling and application development, medical professionals can detect, monitor, and predict certain conditions more efficiently. That paper explores the Internet of Things (IoT) enabled computational model and application development that can be used to monitor cardiovascular risks effectively. Cardiovascular diseases are the leading cause of death globally, and currently, there is a lack of comprehensive and reliable monitoring systems to assess the risk of such diseases. IoT enables the integration of different data sources, such as physical activity, diet, BMI and the environmental context, to form a comprehensive tracking tool that can provide accurate cardiovascular risk assessment. The developed application can offer personalized health coaching, leveraging machine learning algorithms to identify patterns and adapt a user's healthcare journey. Ultimately, this paper assesses the potential of IoT technology for monitoring cardiovascular risks and integrating it into current healthcare systems.

Modeling Urban-Rural health disparities using a Taxi-Based mobile atmospheric monitoring system

The disparities of pollutant exposures and health burdens exists among regions. To capture the spatio-temporal variability of PM2.5 and analyze the inequity between urban and rural areas, this study developed a health impact assessment (HIA) model, which incorporates hyper-localized roadside PM2.5 concentration collected by a taxi-based mobile monitoring system. High-resolution pollutant concentrations, health risks, and PM2.5-related deaths were mapped to illustrate the spatio-temporal patterns among regions. The results showed that the average PM2.5 concentrations in some rural counties were higher than urban areas. 7.85%, 3.72%, 8.74%, and 0.97% of individuals were exposed to environment higher than the maximum concentration of central districts in winter, spring, summer, and autumn, respectively. The application of refined HIA model was discussed and grid cells with high risks of all-cause mortality, cardiovascular disease, respiratory disease, and lung cancer were identified. Establishing the comprehensive pollutant monitoring system facilitates targeted interventions and realization of health equity.

Auckland Council Food Scraps Project

This research project delves into the complexities of Auckland City Council's food scraps collection rollout, spanning from kerbside collection to the Anaerobic Digestion Facility in Reporoa, Rotorua; where collected food scraps are transformed into environmentally friendly fertilisers and renewable gases. The project comprises two key components: assisting in the creation of a dashboard to display weekly and monthly collection and disposal data to monitor ongoing participation rates and collected tonnage requirements are met; and assisting with the continuation of the food scraps rollout initiative in wider Auckland regions and help increase consumer knowledge and involvement. This is in conjunction of aligning with Auckland Council's aim for zero waste by 2040. The initiative aims to keep food scraps out of landfills, minimising harmful greenhouse gas emissions and return valuable nutrients to the land through biofertiliser production. This project required me to learn about Auckland's circular economy solution which emphasises prevention of food waste, supporting redistribution initiatives, and promoting home and community composting. As part of helping with the rollout and helping create an easy way for residents to separate their food scraps from general rubbish, individuals have gained a raised awareness of wastage, leading to considered meal planning and cost savings. The government's commitment to nationwide food scraps services by 2030 underscores the urgency of sustainable waste management practices which it why it was crucial for me to be familiar with the whole process and sectors within the rollout to assist Auckland Council to meet this commitment. This project has also meant that I was able to understand the whole process of the Anaerobic Digestion Facility's innovative technology which processes organic material into biogas and nutrient-liquid fertiliser, contributing to New Zealand's sustainability goals. Through comprehensive monitoring and filtration systems, the facilities uphold environmental standards while pioneering large-scale food scraps processing in the country.
 Supervised by: Sarah Nicholls
 Scholarship Project Funded by: Auckland City Council

Analysis of Potential Environmental Risks in the Hydraulic Fracturing Operation in the “La Luna” Formation in Colombia

This article presents an in-depth analysis of potential environmental risks associated with hydraulic fracturing operations within the “La Luna” formation in Colombia. Using the Conesa methodology, it assesses the environmental impacts of unconventional reservoir production in Colombia, including water usage, chemical additives, air emissions, and the potential for groundwater contamination. This study incorporates comprehensive data on geological characteristics, operational procedures, and environmental conditions specific to the region. This analysis highlights the need for a proactive approach to managing potential environmental risks associated with hydraulic fracturing in Colombia. The findings underscore the importance of implementing robust regulatory measures, comprehensive monitoring systems, and the industry’s best practices to mitigate and prevent adverse environmental impacts. This research contributes to the ongoing global dialogue on the environmental implications of hydraulic fracturing in regions with sensitive ecological conditions. The environmental impacts of unconventional reservoirs are widely recognized as a general consensus; however, the absence of applied studies with the rigor of an explicit methodology in Andean countries highlights the need for specific research in this region. This article addresses this gap by proposing a detailed and structured methodology to assess and mitigate environmental impacts in unconventional reservoirs. Emphasizing the importance of knowing the characteristics of reservoir fluids, this research highlights that this critical information is only revealed by drilling exploration wells and PVT (pressure, volume, and temperature) analysis. Obtaining this data is crucial for shaping specific mitigation measures, thus allowing for the formulation of a robust environmental management plan tailored to the conditions of the reservoirs in the Andean region. This precise and contextualized approach contributes to closing the knowledge gap and promotes more sustainable practices in the exploitation of unconventional reservoirs in this specific geographical context. The methodology used in this study proved its effectiveness by accurately quantifying the risks associated with each of the environmental alterations inherent to the hydraulic fracturing process in the La Luna formation. The results obtained allowed for the identification of the critical points most susceptible to environmental impacts, serving as a solid basis for the elaboration of an environmental management plan. This strategic approach not only enabled the delineation of specific mitigation measures but also facilitated the selection of the most appropriate locations for the implementation of the plan, maximizing the effectiveness of corrective actions. It is noteworthy that the successful application of the Conesa methodology in this unconventional reservoir context evidences the versatility and applicability of this approach, consolidating it as a valuable tool for environmental assessment and effective planning in the hydraulic fracturing industry.

Digital Technology: A Step to Protect Cave Art of Indian Subcontinent as National Heritage – A Review

Abstract The integration of digital technology, particularly virtual reality, is proving to be a pivotal tool in preserving and sharing the rich cultural heritage found within Cave art sites. This article deliberates on the use and application of digital technology in the field of cave painting conservation that provides an authentic and effective method of Cave art preservation for the future. Employing advanced techniques of photogrammetry, terrestrial laser scanning, structured light scanning, and high-resolution photography can digitally preserve the intricate details of Cave art, while the utilization of humidity, temperature, and CO2 sensors may provide a comprehensive digital monitoring system to track the condition of Cave art over time. This paper deals with the evolving landscape of technologies and their application to safeguard Cave art from environmental degradation and anthropogenic factors. The digitization of cave art has the potential to accurately conserve a site if used in an effective manner while also allowing visitors to experience this art form in ways never before feasible. The application of advanced digital technology in cave art conservation is not merely a choice but a necessity, considering the impermanent nature of these paintings in their natural environment. This paper underscores the urgency and significance of leveraging digital tools to ensure the enduring legacy of Cave art, an example of how digital technologies, as they evolve, will play an increasingly essential role in the conservation and dissemination of our world’s extraordinary cultural heritage.

Survey on Sericulture: An IOT-based Cocoon Worms Monitoring for Disease Identification and Management for Quality Silk Rearing.

The sericulture industry in India, plays a vital role in the economy, with silk production contributing significantly to the country's textile sector. However, the industry faces challenges due to the detrimental impact of diseases on silk production. To address this issue, this project proposes a comprehensive monitoring system that integrates sensors for temperature, humidity, and light levels, alongside machine learning algorithms for the early detection of common silkworm diseases such as pebrine, flacherie, etc. By ensuring optimal rearing conditions and proactively managing disease outbreaks, the model aims to minimize production losses and enhance silk yield. Given the importance of silk in India's textile industry, increasing yield is crucial for meeting domestic demand, promoting exports, and bolstering economic growth. Through the adoption of advanced technologies and proactive disease management strategies, this project seeks to strengthen the sericulture sector and secure a sustainable future for silk production in India

Temporal and Spatial Thermal Performance of A Ground Heat Exchanger in Response to Bridge Solar Recharging and Deicing Operations: A Pilot Full-Scale Study

A full-scale mock-up geothermal bridge was built in Texas, USA, to evaluate the field performance of a novel external geothermal bridge de-icing system. The geothermal bridge system comprises a single U-loop ground heat exchanger (GHE) 131 m deep, a geothermal heat pump, four circulation pumps, and hydronic heating loops attached to the bridge deck. A comprehensive monitoring system was implemented to monitor the spatial ground temperature response. The geothermal bridge system was operated and monitored for solar collector and bridge de-icing operations totaling 99 days between August 2019 and April 2020. An average heat injection of 5.7 W/m was observed during the solar collector operation. Based on the defined control volume, approximately 48.9–380.8 kWh of energy, corresponding to 3.7-28.5% of the total injected energy, was available at the end of the solar-collector operation. Moreover, test results showed that 31.7-39.4% of stored thermal energy was preserved for utilization in the first winter de-icing test. The radius of the influence zone of the GHE operation was found to be 3.45 m. The outcome of the field experimental studies and further data collection performed over a year of operations showed that the heat injection into the soil by bridge solar collector was 54% more effective than heat extraction from the ground during the several de-icing tests performed from 2019 to 2020. 

Review on mechanism and remediation strategies of dissolved oxygen abnormal in surface water.

Dissolved oxygen (DO) is an important index to evaluate the quality of surface water environments. In recent years, anomalies in DO level have emerged as a major contributor to the decline of surface water quality. These anomalies have triggered several ecological and environmental challenges such as biodiversity loss, the degradation of water environmental quality, intensification of eutrophication, and an exacerbation of the greenhouse effect. Understanding the mechanisms underlying DO anomalies and devising targeted remediation strategies holds paramount importance in the scientific pursuit of water pollution control and aquatic ecosystem restoration. We explored and summarized the fluctuations and abnormal mechanism of DO concentration in surface water, focusing on factors like oxygen solubility, reoxygenation rates, and oxygen consumption by water bodies. We compiled a range of approaches for addressing DO anomalies, including pollution source management, artificial oxygenation, and the reconfiguration of aquatic ecosystems. Ultimately, we underscored the emerging significance of monitoring and regulating DO level in surface waters. Future research in this realm should encompass the establishment of distinct quality standards for surface water, the development of a comprehensive real-time spatial monitoring system for DO levels across watersheds, and the formulation of standardized procedures and technical norms.

Detection and Diagnostics of Bearing and Gear Fault under Variable Speed and Load Conditions Using Heterogeneous Signals

In industrial applications, rotating machines operate under real-time variable speed and load regimes. In the presence of faults, the degradation of critical components is accelerated significantly. Therefore, robust monitoring algorithms able to identify these faults become crucial. In the literature, it is hard to find comprehensive monitoring systems that include variable speed and load regimes with combined gearbox faults using electrical and vibration signals. For this purpose, a novel signal processing methodology including a geometric classification technique is proposed. This methodology is based on using different types of sensors such as current, voltage and vibration sensors with a regime normalization, which allows the grouping of different regimes belonging to the same health state. It consists of reducing dispersion between the class observations and separating other classes representing different health states including the variation in speed and load. Then, a peripheral threshold is proposed in our classifier to diagnose new health states. To verify the effectiveness of the methodology, current, voltage and vibration data from a gearbox system are collected under variable speed and load levels.

Layer-Dependent Sensing Performance of WS2-Based Gas Sensors.

Two-dimensional (2D) materials, such as tungsten disulfide (WS2), have attracted considerable attention for their potential in gas sensing applications, primarily due to their distinctive electrical properties and layer-dependent characteristics. This research explores the impact of the number of WS2 layers on the ability to detect gases by examining the layer-dependent sensing performance of WS2-based gas sensors. We fabricated gas sensors based on WS2 in both monolayer and multilayer configurations and methodically evaluated their response to various gases, including NO2, CO, NH3, and CH4 at room temperature and 50 degrees Celsius. In contrast to the monolayer counterpart, the multilayer WS2 sensor exhibits enhanced gas sensing performance at higher temperatures. Furthermore, a comprehensive gas monitoring system was constructed employing these WS2-based sensors, integrated with additional electronic components. To facilitate user access to data and receive alerts, sensor data were transmitted to a cloud-based platform for processing and storage. This investigation not only advances our understanding of 2D WS2-based gas sensors but also underscores the importance of layer engineering in tailoring their sensing capabilities for diverse applications. Additionally, the development of a gas monitoring system employing 2D WS2 within this study holds significant promise for future implementation in intelligent, efficient, and cost-effective sensor technologies.

Power system monitoring for electrical disturbances in wide network using machine learning

Due to infrastructure developments, wide disturbances have occurred in the power system. There is a need for intelligent monitoring systems across wide power networks for the stability and security of systems. A significant challenge in a comprehensive power monitoring system is identifying the noises in electrical measurements and oscillatory errors. In this research, the disturbances in the power system are monitored using principal component analysis with a Support vector machine and Extreme Learning Machine (ELM) for analyzing the monitored data. In this work, PCA has been used to reduce the curse of dimensionality of the original data. Then, SVM was used to select the relevant and essential features from the disturbance signals. These selected features are fed as input into the Extreme learning machine to classify the power quality events. This machine learning advantage is that it can analyze many wide-area variables in real time and reduce the masking effect of the oscillatory trends and noise on disturbances. Compared to the existing feature selection and classification of PQ disturbance data, the proposed model secured an improved accuracy of 99.16%, and the comparison results prove the model's effectiveness.