Lower Monitoring Costs Research Articles

A study into strain sensor of cement-based material using CPW transmission lines

Abstract Based on the theory of coplanar waveguide (CPW) transmission line, a novel microwave non-destructive strain monitoring sensor specifically designed for cement-based material structures is presented in this study. The aim is to establish the relationship between the variation of the S11 phase parameter of the CPW strain sensor and the structural strain, utilizing a linear analysis. The feasibility of the strain monitoring by the CPW sensor is validated through simulations and experiments. The obtained results demonstrate a strong linear correlation between the phase change of the S11 parameter and the strain, with a goodness of fit of 0.987. The simulated strain sensor exhibits a sensitivity of 48.83 ppm/με, while the experimental measurement sensor shows a sensitivity of 65.82 ppm/με. These findings highlight the potential significance of the proposed method, offering a new approach that is characterized by high sensitivity, low cost, and simplicity for strain monitoring in concrete structures. Among them, the sensor cement mortar matrix made in this study was mixed with the recycled material made of waste glass steel FRP after a certain treatment process. The development of this method holds promise for the advancement of health monitoring in concrete structures.

Retrieval of Soil Heavy Metal Content for Environment Monitoring in Mining Area via Transfer Learning

Monitoring environmental pollution sources is an ongoing issue that must be addressed to reduce risks to public health, food safety, and the environment. However, retrieving topsoil heavy metal content at a low cost for environmental monitoring in mining areas is challenging. Therefore, this study proposes a network model based on transfer learning theory and a back propagation (BP) network optimized by a genetic algorithm (GA), taking the Daxigou mining area in Shaanxi Province, China, as a case study. Firstly, visible and near-infrared spectrum data from Landsat8 satellite images, digital elevation models, and geochemical data from field-collected soil samples were used to extract environmental factor candidates indicating the content and spatial distribution of certain heavy metals, including copper (Cu) and lead (Pb). Secondly, each element was correlated with environmental factors and a multicollinearity test was performed to determine the optimal factor set. Then, the BP network optimized by GA was pre-trained with sample data collected in 2017 and retrained with minimal sample data from 2019 using the parameter transfer learning method, allowing spatial distribution mapping of the Cu and Pb content in topsoil of the Daxigou mining area in 2019. From the validation results using field-collected data, the root mean square error (RMSE) and mean relative error (MRE) values using the proposed model, respectively, reduced by 4.688 mg/kg and 1.533 mg/kg for Cu and reduced by 1.586 mg/kg and 1.232 mg/kg for Pb compared to the traditional GA-BP model. Thus, conclusions can be drawn that our proposed Tr-GA-BP network performs well, requiring 16 training samples collected in 2019. In addition, the content of Cu is the highest; Pb is the second highest in the study area. Both of them were spatially distributed mainly in the exploitation, slag stacking, roadside, etc., consistent with field investigation results.

Cost-effectiveness analysis of HIV pre-exposure prophylaxis in Japan

BackgroundWhile global efforts have been made to prevent transmission of HIV, the epidemic persists. Men who have sex with men (MSM) are at high risk of infection. Despite evidence of its cost-effectiveness in other jurisdictions, pre-exposure prophylaxis (PrEP) for MSM is neither approved nor reimbursed in Japan.MethodThe cost-effectiveness analysis compared the use of once daily PrEP versus no PrEP among MSM over a 30-year time horizon from a national healthcare perspective. Epidemiological estimates for each of the 47 prefectures informed the model. Costs included HIV/AIDS treatment, HIV and testing for sexually transmitted infections, monitoring tests and consults, and hospitalization costs. Analyses included health and cost outcomes, as well as the incremental cost-effectiveness ratio (ICER) reported as the cost per quality-adjusted life year (QALY) for all of Japan and each prefecture. Sensitivity analyses were performed.FindingsThe estimated proportion of HIV infections prevented with the use of PrEP ranged from 48% to 69% across Japan, over the time horizon. Cost savings due to lower monitoring costs and general medical costs were observed. Assuming 100% coverage, for Japan overall, daily use of PrEP costs less and was more effective; daily use of PrEP was cost-effective at a willingness to pay threshold of ¥5,000,000 per QALY in 32 of the 47 prefectures. Sensitivity analyses found that the ICER was most sensitive to the cost of PrEP.InterpretationCompared to no PrEP use, once daily PrEP is a cost-effective strategy in Japanese MSM, reducing the clinical and economic burden associated with HIV.

Wearable devices for remote monitoring of hospitalized patients with COVID-19 in Vietnam.

Patients with severe COVID-19 disease require monitoring with pulse oximetry as a minimal requirement. In many low- and middle- income countries, this has been challenging due to lack of staff and equipment. Wearable pulse oximeters potentially offer an attractive means to address this need, due to their low cost, battery operability and capacity for remote monitoring. Between July and October 2021, Ho Chi Minh City experienced its first major wave of SARS-CoV-2 infection, leading to an unprecedented demand for monitoring in hospitalized patients. We assess the feasibility of a continuous remote monitoring system for patients with COVID-19 under these circumstances as we implemented 2 different systems using wearable pulse oximeter devices in a stepwise manner across 4 departments.

Adaptive transfer learning for multimode process monitoring and unsupervised anomaly detection in steam turbines

Through condition-based maintenance strategy, engineers can monitor the health states of equipment and take actions based on the sensor data. Limited by the low failure frequency and high monitoring costs, it is difficult to obtain sufficient historical data of all fault types for condition monitoring (CM). In the steam turbine operation, environmental factors, varying power consumption and manual adjustments can lead to a multimode process, which consists of multiple normal and abnormal conditions. This paper proposes a framework for online unsupervised CM and anomaly detection, not relying on expert knowledge or labeled historical data. Since there are often few monitoring data at the beginning of a new incoming operating mode, an adaptive self-transfer learning algorithm based on Gaussian processes is developed to model the monitoring data with uncertainty information, and to capture the cross-correlations between the different normal modes. A two-hierarchical identification criterion based on the predicted posterior intervals is introduced to first identify the change-points in the observations, and second to decide whether it is an anomaly or a transition between normal modes. The proposed framework is tested on a real steam turbine. The results illustrate its high effectiveness.

A Low-Cost, Low-Power, Multisensory Device and Multivariable Time Series Prediction for Beehive Health Monitoring.

We present a custom platform that integrates data from several sensors measuring synchronously different variables of the beehive and wirelessly transmits all measurements to a cloud server. There is a rich literature on beehive monitoring. The choice of our work is not to use ready platforms such as Arduino and Raspberry Pi and to present a low cost and power solution for long term monitoring. We integrate sensors that are not limited to the typical toolbox of beehive monitoring such as gas, vibrations and bee counters. The synchronous sampling of all sensors every 5 min allows us to form a multivariable time series that serves in two ways: (a) it provides immediate alerting in case a measurement exceeds predefined boundaries that are known to characterize a healthy beehive, and (b) based on historical data predict future levels that are correlated with hive's health. Finally, we demonstrate the benefit of using additional regressors in the prediction of the variables of interest. The database, the code and a video of the vibrational activity of two months are made open to the interested readers.

A novel label-free electrochemical immunosensor for the detection of heat shock protein 70 of lung adenocarcinoma cell line following paclitaxel treatment using l-cysteine-functionalized Au@MnO2/MoO3 nanocomposites.

The future trend in achieving precision medicine involves the development of non-invasive cancer biomarker sensors that offer high accuracy, low cost, and time-saving benefits for risk clarification, early detection, disease detection, and therapeutic monitoring. A facile approach for the synthesis of MoO3 nanosheets was developed by thermally oxidizing MoS2 nanosheets in air followed by thermal annealing. Subsequently, Au@MnO2 nanocomposites were prepared using a combined hydrothermal process and in situ chemical synthesis. In this study, we present a novel immunosensor design strategy involving the immobilization of antiHSP70 antibodies on Au@MnO2/MoO3 nanocomposites modified on a screen-printed electrode (SPE) using EDC/NHS chemistry. This study establishes HSP70 as a potential biomarker for monitoring therapeutic response during anticancer therapy. Impedance measurements of HSP70 on the Au@MnO2/MoO3/SPE immunosensor using EIS showed an increase in impedance with an increase in HSP70 concentration. The electrochemical immunosensor demonstrated a good linear response in the range of 0.001 to 1000 ng mL-1 with a detection limit of 0.17 pg mL-1 under optimal conditions. Moreover, the immunosensor was effective in detecting HSP70 at low concentrations in a lung adenocarcinoma cell line following Paclitaxel treatment, indicating its potential for early detection of the HSP70 biomarker in organ-on-a-chip and clinical applications.

Design and performance evaluation of electromechanical impedance instrumented quantitative corrosion measuring probe based on conical rods

Previous study has proved that using electromechanical impedance instrumented bar-type corrosion measuring probe can realize the quantitative assessment of the corrosion amount. To gain more insights into the working mechanism and design better probes, this work examined a new type of corrosion measuring probe based on the conical rod, and evaluated their performance. Theoretical model of this type of new probes was established based on one dimensional piezo-elasticity theory, and the electrical impedance was derived to obtain first resonant and anti-resonant frequencies in longitudinal vibration mode. Two experiments were performed to validate the feasibility of the probe for corrosion measurement, including the artificial uniform corrosion experiment and the accelerated corrosion test. Comparisons between the theoretical predictions and the experimental results from the artificial uniform corrosion experiment were made, and good agreement was found. Effects of piezoelectric patch thickness and cone angle on first resonant and anti-resonant frequencies were also analyzed. In addition, a wireless impedance measurement system was preliminarily realized, which is very promising in developing the low cost and high accuracy online real-time monitoring technology for the pipeline corrosion monitoring.

A metal-free voltammetric sensor for sensitive determination of Rhodamine B using carboxyl-functionalized carbon nanomaterials

Rhodamine B (RhB) is a synthetic xanthine dye that is harmful to environment and human health. Therefore, the development of low cost and efficient analytical techniques for RhB monitoring is very critical for environmental protection and food safety. Herein, a novel metal-free voltammetric sensor was constructed for sensitive detection of RhB using carboxyl-functionalized carbon nanomaterials. To understand the role of carboxyl functionalization in RhB electrooxidation, the electrochemical properties and voltammetric responses of carboxyl-functionalized multi-walled carbon nanotube (f-MWCNT), carboxyl-functionalized reduced graphene oxide (f-RGO), multi-walled carbon nanotube (MWCNT) and reduced graphene oxide (RGO) were compared. Amongst, the f-MWCNT/GCE exhibited the lowest charge transferred resistance and the largest electroactive surface area. The f-MWCNT/GCE showed extraordinary electrocatalytic activity for RhB oxidation, which is primarily attributed to the unique 3D structure and the carboxyl functionalization. The anodic peak current of RhB is linearly correlated to RhB concentration from 0.05 to 100 μM with a low limit of detection (LOD, 2.6 nM). In addition, the f-MWCNT/GCE achieved the selective identification and detection of RhB in presence of common metal ions and other red dyes, and retained a robust and stable voltammetric responses for at least a week. The f-MWCNT/GCE was successfully applied to detect RhB concentration in tomato sauce samples with good recovery.

Wearable devices for remote monitoring of hospitalized patients with COVID-19 in Vietnam

Patients with severe COVID-19 disease require monitoring with pulse oximetry as a minimal requirement. In many low- and middle- income countries, this has been challenging due to lack of staff and equipment. Wearable pulse oximeters potentially offer an attractive means to address this need, due to their low cost, battery operability and capacity for remote monitoring. Between July and October 2021, Ho Chi Minh City experienced its first major wave of SARS-CoV-2 infection, leading to an unprecedented demand for monitoring in hospitalized patients. We assess the feasibility of a continuous remote monitoring system for patients with COVID-19 under these circumstances as we implemented 2 different systems using wearable pulse oximeter devices in a stepwise manner across 4 departments.

Adaptive incentive-based demand response with distributed non-compliance assessment

Traditional residential incentive-based demand response (DR) programs use fixed incentive structures that do not incorporate closed-loop feedback to compensate for non-compliance by participants. In practice, such programs may not reliably meet their event goals. To address this challenge, real-time feedback can be used to adaptively modify the participants’ incentives, an approach which has not been proposed before. This paper proposes a flexible monitoring framework to detect potential non-compliance, whereby a second DR event is adaptively scheduled with higher incentives. In this context, constraints are presented to prevent over-compensation and gaming of the DR system by the participants. This novel dual-event design is implemented using a distributed event-stream monitoring framework to preserve scalability and ensure low monitoring costs. The merits of the proposed DR design are demonstrated at a utility-scale for 100,000 residents, while also considering the adoption of residential electric vehicles that are poised to increase the flexibility of the demand in the distribution system.

Artificial Intelligence-Aided Low Cost and Flexible Graphene Oxide-Based Paper Sensor for Ultraviolet and Sunlight Monitoring

The adverse effect of ultraviolet (UV) radiation on human beings has sparked intense interest in the development of new sensors to effectively monitor UV and solar exposure. This paper describes a novel low-cost and flexible graphene oxide (GO)-based paper sensor capable of detecting the total amount of UV or sun energy delivered per unit area. GO is incorporated into the structure of standard printing paper, cellulose, via a low-cost fabrication technique. The effect of UV and solar radiation exposure on the GO paper-based sensor is investigated using a simple color change analysis. As a result, users can easily determine the amount of ultraviolet or solar energy received by the sensor using a simple color analysis application. A neural network (ANN) model is also explored to learn the relation between UV color intensity and exposure time, then digitally display the results. The accuracy for the developed ANN reached 96.83%. The disposable, cost-effective, simple, biodegradable, safe, and flexible characteristics of the paper-based UV sensor make it an attractive candidate for a variety of sensing applications. This work provides new vision toward developing highly efficient and fully disposable GO-based photosensors.Graphical

Spatial water quality analysis of the watercourses in Northwestern New Territories (Hong Kong)

Abstract River water is an important source for drinking water supply in Northwestern New Territories of Hong Kong. Thus, there is no denying the fact that monitor the quality of river water is a must for the locals. In this study, a mixed multivariate analysis method was used to lower monitoring costs by optimizing the layout of water quality monitoring stations. To this purpose, the data from a period of five years and over 36,000 observations was evaluated in this article. The cluster analysis approach was also used to categorize monitoring stations into three groups. What's more, three latent factors that predominantly influence the river water quality were assessed using factor analysis: anthropogenic pollution, seawater intrusion and geological processes, and the nitrification process. A spatial pattern using the three latent factor scores was plotted and six redundant monitoring stations were identified by this pattern. Finally, discriminant analysis was used to extract seven significant parameters. The results showed that the surface water-monitoring program of the watercourses in the Northwestern New Territories (Hong Kong) could be adjusted by reducing the monitoring stations to 18 and the measured chemical parameters to seven to ensure the detection of water quality and reduce the cost.

DeepPulse: An Uncertainty-aware Deep Neural Network for Heart Rate Estimations from Wrist-worn Photoplethysmography.

Wearable Photoplethysmography (PPG) has gained prominence as a low cost, unobtrusive and continuous method for physiological monitoring. The quality of the collected PPG signals is affected by several sources of interference, predominantly due to physical motion. Many methods for estimating heart rate (HR) from PPG signals have been proposed with Deep Neural Networks (DNNs) gaining popularity in recent years. However, the "black-box" and complex nature of DNNs has caused a lack of trust in the predicted values. This paper contributes DeepPulse, an uncertainty-aware DNN method for estimating HR from PPG and accelerometer signals, with aims of increasing trust of the predicted HR values. To the best of the authors' knowledge no PPG HR estimation method has considered aleatoric and epistemic uncertainty metrics. The results show DeepPulse is the most accurate method for DNNs with smaller network sizes. Finally, recommendations are given to reduce epistemic uncertainty, validate uncertainty estimates, improve the accuracy of DeepPulse as well as reduce the model size for resource-constrained edge devices.

Singlemode-Multimode-Singlemode Optical Fiber Sensor for Accurate Blood Pressure Monitoring

A dual-channel single-mode-multi-mode-single-mode (SMS) fiber optic sensor encapsulated by polydimethylsiloxane (PDMS) was proposed for the first time, for the simultaneous monitoring of the brachial and radial arteries for accurate blood pressure prediction. With the help of the machine learning algorithm Support Vector Regression (SVR), the SMS fiber sensor can continuously and accurately monitor the systolic and diastolic blood pressure. Commercial sphygmomanometers are used to calibrate the accuracy of blood pressure measurement. Compared with the single-channel system, this system can extract more pulse wave features for blood pressure prediction, such as radial artery transit time (RPTT), brachial artery transit time (BPTT), and the transit time difference between the radial artery and the brachial artery (DBRPTT). The results show that the performance of dual-channel blood pressure monitoring is more accurate than that of single-channel blood pressure monitoring in terms of the absolute value of the correlation coefficient (R) and the average value of the difference between SBP and DBP. In addition, both the single-channel and dual-channel blood pressure monitoring are in line with the Association for the Advancement of Medical Devices (AAMI), but the average deviation (DM, 0.06 mmHg) and standard deviation (SD, 1.54 mmHg) of dual-channel blood pressure monitoring are more accurate. The blood pressure monitoring system has the characteristics of low cost, high sensitivity, non-invasive and capability for remote real time monitoring, which can provide effective solution for intelligent health monitoring in the era of artificial intelligence in the future.

Long-term mass-balance monitoring and evolution of ice in caves through structure from motion–multi-view stereo and ground-penetrating radar techniques

This study investigates the application of a terrestrial structure from motionmulti-view stereo (SfM-MVS) approach combined with ground-penetrating radar (GPR) surveys for monitoring the surface topographic change of two permanent ice deposits in caves located in the Julian Alps (south-eastern European Alps). This method allows accurate calculation of both seasonal and annual mass balance, estimating the amount of ice inside caves. The ground-based SfM approach represents a low-cost workflow with very limited logistical problems of transportation and human resources and a fast acquisition time, all key factors in such extreme environments. Under optimal conditions, SfM-MVS allows sub-centimetric resolution results, comparable to more expensive and logistically demanding surveys such as terrestrial laser scanning (TLS). Fourteen SfM acquisitions were made between the 2017–2020 ablation seasons (i.e. July–October) while 2 GPR surveys were acquired in 2012. The obtained dense point clouds and digital terrain models (DTMs) made possible a reliable calculation of topographic changes and mass balance rates during the analysed period. The integration of SfM-MVS products with GPR surveys provided comprehensive imaging of the ice thickness and the total ice volume present in each of the caves, proving to be a reliable, low cost and multipurpose methodology ideal for long-term monitoring.

Partograph adherence and its barriers in a tertiary care hospital: A mixed-method study

Background: Partograph is a low cost, effective and mandatory tool for intrapartum monitoring. However, the partograph has been grossly underutilised. Aims and Objectives: The present study aims to find out the extent of the use of a partograph and the barriers to effectively using the same in a tertiary care facility. Materials and Methods: This cross-sectional study comprises quantitative and qualitative methods. The quantitative component consisted of a retrospective review of case sheets of patients undergoing uncomplicated vaginal delivery between 1 January 2020 and 15 February 2020. The percentage of cases where a partograph was initiated and completed was assessed. For the qualitative part, staff nurses on duty in the labour room during the study period and responsible for recording the partograph were invited to participate in a focused group discussion (FGD). Sister-in-charges, supposed to supervise the staff nurses were invited to participate an In-depth interview (IDI). Results: Out of 131 case sheets, a partograph was initiated in 64 (48.85%) cases and completed in 4 out of 64 partographs (6.25%). The 'Cervical dilatation' parameter was plotted in all 64 cases, while the 'amniotic fluid condition' was the least plotted parameter (12 [18.75%]). Data regarding the 'time of rupture of membranes' (15.63%) and 'condition at birth' (4.69%) were also neglected parameters. The FGD and IDI revealed 'shortage of staff and heavy workload', 'presentation in advanced labour', 'poor training and supervision', 'incomplete documentation of clinical information by doctors' and 'policy level lacunae' as the potential barriers to partograph adherence and completeness. Conclusion: Partograph adherence and completeness appeared inadequate in this tertiary care centre.

Model of monitoring of oil soil pollution and its termination

The assessment of impact of oil production economic activities on land pollution in Russia contributes to evolutionary management decision making. Oil industrial pollution affects negatively flora and fauna. Thus, its important to identify the level of its exposure and danger, the site of contamination. A system approach is needed. When studying the environment, its necessary to consider the presence of risk situations and stochastic irreversible changes. Its essential to identify the nature and type of soil contamination with petroleum products using high-tech tools, intellectual procedures. The work considers modeling of such situation, forecasting and identification of oil contaminants. The submodel of optimal termination of monitoring is also considered. Ending monitoring of environmental optimization will result in lower monitoring costs, since monitoring oilcontaminated environments is an expensive and complex technological mechanism, often requiring satellite data. The proposed algorithm for modeling and system analysis is based on situational modeling. Evolutionary modeling allows to adapt the procedure (methodology) of forecasting and assessment to environmental risk factors. It increases the accuracy (formalization and evidence) and completeness of conclusions, the efficiency of situation analysis, which affects manageability of risk both for the oil complex and for individual enterprise in the industry. The results of the research may be used for development of software tools, in particular expert and predictive systems. Situational models are needed when oil companies are solving multi-criteria and multifactor problems.

A Modified Fully Convolutional Network for Crack Damage Identification Compared with Conventional Methods

Large-scale structural health monitoring and damage detection of concealed underwater structures are always the urgent and state-of-art problems to be solved in the field of civil engineering. With the development of artificial intelligence especially the combination of deep learning and computer vision, greater advantages have been brought to the concrete crack detection based on convolutional neural network (CNN) over the traditional methods. However, these machine learning (ML) methods still have some defects, such as it being inaccurate or not strong, having poor generalization ability, or the accuracy still needs to be improved, and the running speed is slow. In this article, a modified fully convolutional network (FCN) with more robustness and more effectiveness is proposed, which makes it convenient and low cost for long-term structural monitoring and inspection compared with other methods. Meanwhile, to improve the accuracy of recognition and prediction, innovations were conducted in this study as follows. Moreover, differed from the common simple deconvolution, it also includes a subpixel convolution layer, which can greatly reduce the sampling time. Then, the proposed method was verified its practicability with the overall recognition accuracy reaching up to 97.92% and 12% efficiency improvement.

The Power of Renegotiation and Monitoring in Software Outsourcing: Substitutes or Complements?

Monitoring and contract renegotiation are two common solutions for addressing information asymmetry and uncertainty between a client and a vendor of software outsourcing services. Monitoring is mostly applied in time-and-materials contracts, as a basis for inspecting and reimbursing the vendor’s efforts in system development. Renegotiation, by contrast, is deployed in fixed-price and time-and-materials contracts to mitigate the loss of surplus from uncertainty after system development. We investigate the interaction between monitoring and renegotiation and examine the corresponding contract choice problem. We find that the client benefits from renegotiation based on two effects: an uncertainty-resolution effect and a post-development incentive effect, which incentivizes the vendor to exert additional effort in system development. Monitoring does not resolve uncertainty, although it does encourage the vendor to exert additional effort, a pre-development incentive effect. Our analysis shows that the choice of renegotiation or monitoring depends on the interactions of the above effects, which are moderated by the renegotiation cost, monitoring cost, and bargaining power in renegotiation. When renegotiation cost is low: if the client has high bargaining power and low monitoring cost, monitoring and renegotiation are complements and both are selected; otherwise, the two instruments are substitutes and contract renegotiation is preferred. When renegotiation cost is high: monitoring substitutes for renegotiation and the client only chooses monitoring if the cost to do it is low; or else neither is used. Overall, this research shows that four appropriate contract strategies should be used under somewhat different circumstances. We further analyze the impacts of some other key aspects of software outsourcing and extend the base model to address two alternative situations to show the robustness of our findings. The results apply to a range of software reliability growth models, including when machine learning or cloud computing are used.