Stress Monitoring Research Articles

Design of stress monitoring system for planetary gear shaft

A remote sensing gear shaft stress monitoring system is studied to deal with the stress balance of transmission gear, which realizes the real-time dynamic monitoring of the planetary gear shaft stress. The design of the whole system structure, the installation position of the basic system and the special working environment of the gear are carried out; the circuit of each function unit is designed, which is based on the overall index requirement and installation space limitation; the software program design is carried out on the basis of the hardware circuit; The main components of the system and the principle of dynamic monitoring system are described; each functional unit and the whole of the system are tested and analyzed in detail. The operation results show that the design is reliable, the actual functions of each unit can meet the design index, and has a certain contribution in the field of gear shaft stress monitoring.

Depression Monitoring System via Social Media Data using Machine Learning frameworkk

Abstract: Stress and Depression is one of the most widely recognized and handicapping mental issue that relevantly affects society. Automatic health monitoring systems could be crucial and important to improve depression and stress detection system using social networking. Sentiment Analysis alludes to the utilization of natural language processing and content mining approaches planning to recognize feeling or opinion. Full of feeling Computing is the examination and advancement of frameworks and gadgets that can perceive, decipher, process, and mimic human effects. Sentiment Analysis and deep learning techniques could give powerful algorithms and frameworks to a target appraisal and observing of mental issue and, specifically of depression and stress. In this paper, the application of sentiment analysis and deep learning methodologies to depression and stress detection and monitoring are discussed. In addition, a fundamental plan of an incorporated multimodal framework for stress and depression checking, that incorporates estimation investigation and full of feeling processing strategies, is proposed. In particular, the paper traces the fundamental issues and moves comparative with the structure of such a framework.

Global Stress Detection Framework Combining a Reduced Set of HRV Features and Random Forest Model.

Approximately 65% of the worldwide adult population has experienced stress, affecting their daily routine at least once in the past year. Stress becomes harmful when it occurs for too long or is continuous (i.e., chronic), interfering with our performance, attention, and concentration. Chronic high stress contributes to major health issues such as heart disease, high blood pressure, diabetes, depression, and anxiety. Several researchers have focused on detecting stress through combining many features with machine/deep learning models. Despite these efforts, our community has not agreed on the number of features to identify stress conditions using wearable devices. In addition, most of the reported studies have been focused on person-specific training and testing. Thanks to our community's broad acceptance of wearable wristband devices, this work investigates a global stress detection model combining eight HRV features with a random forest (RF) algorithm. Whereas the model's performance is evaluated for each individual, the training of the RF model contains instances of all subjects (i.e., global training). We have validated the proposed global stress model using two open-access databases (the WESAD and SWELL databases) and their combination. The eight HRV features with the highest classifying power are selected using the minimum redundancy maximum relevance (mRMR) method, reducing the training time of the global stress platform. The proposed global stress monitoring model identifies person-specific stress events with an accuracy higher than 99% after a global training framework. Future work should be focused on testing this global stress monitoring framework in real-world applications.

A method for characterization of stress concentration degree of coal mine roadway surrounding rock

Abstract An accurate assessment of the stress concentration of the surrounding rock is crucial for ensuring safety in underground coal mines with a high potential for rockburst accidents. Traditional methods for measuring the stress of the surrounding rock use borehole stress-monitoring equipment and drilling and cutting methods. However, these methods do not always yield accurate and reliable data. In this study, we aim to explore the feasibility of a novel approach for characterizing stress concentration in surrounding rock using monitoring while drilling technology during large-diameter drilling. This study combined microseismic monitoring to collect vibration signals during the drilling process and collection of pulverized coal samples at each drilling stage. We analyzed the amplitude, number of coal vibration events and proportion of coarse pulverized coal. By integrating these indices, we characterized the stress concentration in the surrounding rock. Our approach was validated by comparing accurate, stable and representative data from deep- and shallow-hole stress gauges installed in similar locations and data from the conventional drilling cutting method. Our findings indicate that the proposed method provides a reliable and effective alternative to traditional techniques during large-diameter drilling. The proposed approach can significantly enhance safety management in underground coal mines prone to rockburst accidents.

Working Stress Measurement of Prestressed Rebars Using the Magnetic Resonance Method

Prestressed rebars are usually used to apply vertical prestress to concrete to prevent web cracking. The reduction of working stress will affect the durability of the structure. However, the existing working stress detection methods for prestressed rebars still need to be improved. To monitor the working stress of rebars, a magnetic resonance sensor was introduced to carry out experimental research. The correlation between rebar stress and the sensor’s induced voltage was theoretically analyzed using the magnetoelastic effect and magnetic resonance theory. A working stress monitoring method for prestressed rebars based on magnetic resonance was proposed. Working stress monitoring experiments were carried out for 16 mm, 18 mm, and 20 mm diameter rebars. The results showed that the induced voltage peak-to-peak value and the rebar prestress were nonlinearly correlated under different working conditions. Correlations between the characteristic indicators and the rebar working stress were obtained using nonlinear and linear fit. The cubic polynomial segmented fit outperformed the gradient overall linear fit, with the goodness of fit R2 greater than 0.96. The average relative error values of working stress monitoring were less than 5% under different working conditions. This provides a new method for working stress measurement of vertical prestressed rebars.

Alveoli-Like Multifunctional Scaffolds for Optical and Electrochemical In Situ Monitoring of Cellular Responses from Type II Pneumocytes.

While breathing, alveoli are exposed to external irritants, which contribute to the pathogenesis of lung disease. Therefore, in situ monitoring of alveolar responses to stimuli of toxicants under in vivo environments is important to understand lung disease. For this purpose, 3D cell cultures are recently employed for examining cellular responses of pulmonary systems exposed to irritants; however, most of them have used ex situ assays requiring cell lysis and fluorescent labeling. Here, an alveoli-like multifunctional scaffold is demonstrated for optical and electrochemical monitoring of cellular responses of pneumocytes. Porous foam with dimensions like the alveoli structure is used as a backbone for the scaffold, wherein electroactive metal-organic framework crystals, optically active gold nanoparticles, and biocompatible hyaluronic acid are integrated. The fabricated multifunctional scaffold allows for label-free detection and real-time monitoring of oxidative stress released in pneumocytes under toxic-conditions via redox-active amperometry and nanospectroscopy. Moreover, cellular behavior can be statistically classified based on fingerprint Raman signals collected from the cells on the scaffold. The developed scaffold is expected to serve as a promising platform to investigate cellular responses and disease pathogenesis, owing to its versatility in monitoring electrical and optical signals from cells in situ in the 3D microenvironments.

Novel cementitious materials with mechanoluminescence for the application of visible stress monitoring and recording

The development of real-time and accurate visual stress detection is crucial for the field of building engineering. Herein, a new strategy is explored for the development of novel cementitious materials by hierarchical aggregation smart luminescent material and resin-based material. The cementitious material with such layered structure is inherently capable of visualization of stress monitoring and recording by converting the stress to visible light. The specimen fabricated by the novel cementitious material could repetitively emit green visible light under excitation of a mechanical pulse for 10 cycles, suggesting that the cementitious material shows highly reproducible performance. Moreover, the numerical simulations and analysis for the models of stress indicate that the luminescent time is synchronous with the stress and the emission intensity is proportional to the value of stress. To the best of our knowledge, this is the first study that the cementitious material realizes visible stress monitoring and recording, which supplies new insights for exploring modern multi-functional building materials.

Mechanism and Empirical Study of Rockburst in the Adjacent Area of a Fully Mechanized Top-Coal Caving Face Based on Microseismic Technology

Monitoring and providing warnings for coal mine rockburst disasters is a worldwide problem. Several rockburst accidents have occurred in a 1301 belt transport chute near a 1300 fully mechanized caving mine face. To address this issue, an empirical study of the occurrence mechanism of rockbursts in the adjacent area of the fully mechanized top-coal caving face was carried out. This paper mainly addresses the following issues: (1) based on microseismic monitoring technology, the distribution characteristics of the host-rock-supported pressure of the 1300 working face were measured, and the evolution and distribution of the deep-well caving working face host-rock-supported pressure were analyzed. It is revealed that the occurrence mechanism of rockburst in the adjacent area is actually caused by the evolution and superposition of the lateral abutment pressure of the 1300 stope, and the stress of the original rock along the 1301 belt transport down chute; (2) a theoretical calculation model of dynamic and static abutment pressure in longwall stope is built, and an example is tested. The results show that the peak position of lateral abutment pressure of the coal body outside the 1300 goaf is around 63 m, and the peak value of abutment pressure is around 47 MPa; (3) coal body stress monitoring, bolt dynamometer detection, and other means are compared and analyzed. At the same time, with the help of CT geophysical prospecting and drilling cutting measurements, it is concluded that the 1301 belt transport down chute is in the bearing pressure influence zone (superimposed zone), which further verifies the validity of microseismic analysis results and the accuracy of the above theoretical model. Based on this, the early warning system and prevention measures for rockburst based on microseismic monitoring are proposed. The engineering practice shows that the dynamic and static bearing pressure distribution and evolution law of the working face can be dynamically obtained by using microseismic technology, which provides a basis for the accurate prediction and treatment of rockbursts.

Development of the Force Detecting Liner for Total Knee Arthroplasty

With the increase in the prevalence of knee arthritis, the market for liners with force detection devices has expanded. The force detection device assists the doctor to adjust the installation position angle of the prosthesis liner during the operation. Pressure sensors made of flexible materials which are attractive for use in force detection due to their high sensitivity, fast response time, and low cost. Here, the pressure-sensing device with bendable and disposable flexible sensor arrays for force detection is fabricated. The spatial resolution of force detection is greatly improved by designing the sensor array layout through finite element simulation. The device also achieves synchronous dynamic monitoring of axial stress and tangential stress inside the knee joint, filling in the research gap of tangential stress inside the knee joint. The pressure sensors offer a dynamically stable, wide range, high sensitivity, and great consistency avenue for knee arthroplasty.

Research progress on the application of hyperspectral imaging techniques in tea science

AbstractHyperspectral imaging technology combines two‐dimensional imaging and spectral technology, which can simultaneously obtain spatial and spectral information of the object to be measured and is an advanced technical method. With the development of science and technology, the detection of tea has also been continuously improved, and it has developed in the direction of being nondestructive, fast, real‐time, and accurate. In this paper, the principle of hyperspectral imaging technology is introduced, and according to research on hyperspectral imaging technology in the nondestructive rapid detection of tea in the past 5 years, the application of hyperspectral imaging technology in the detection of tea biochemical components, accurate classification, determination of mildew degree, and stress monitoring and the application progress in planting production management are analyzed. Additionally, the main challenges existing in the current research are analyzed, and future application prospects are proposed to provide a reference for the application and promotion of hyperspectral imaging technology in the actual production of tea.

Armored steel wire stress monitoring strategy of a flexible hose in LNG tandem offloading operation

Cryogenic flexible hose is the key equipment for tandem offloading operation of Floating Liquefied Natural Gas (FLNG). The harsh marine environment leads hulls motion responses and complex hose motion characteristics during the tandem offloading, thus causing various failure behaviors of the entire hose. In this paper, a combined global-local monitoring strategy for armored steel wire stress distributions in cryogenic flexible hose is proposed. High-precision flexible hose governing equations are established by using the function to fit the relationship between the vertical force and the hose length. A reasonable monitoring strategy is given based on the pipeline configuration variations and sensor accuracy. Real-time monitoring of the flexible hose from the global (hose) to the local (armored steel wire) during the tandem offloading is carried out by installing sensors in specified locations on hulls and hose segments. The tandem offloading operation under different (static, calm and harsh) sea states is modelled by Orcaflex software. An armored steel wire model is developed using ABAQUS software. Comparisons of the pipeline configurations, curvatures, axial forces and maximum wire stresses are made with the simulation results to verify the feasibility of the monitoring strategy. The results show that the proposed monitoring strategy in this paper can accurately and effectively obtain the hose motion characteristics and maximum stress states of armored steel wires under different sea states, and can evaluate the hose structural safety in real time during the tandem offloading operation.

A wearable sweat electrochemical aptasensor based on the Ni–Co MOF nanosheet-decorated CNTs/PU film for monitoring of stress biomarker

Monitoring cortisol, a hormone released by the adrenal cortex in response to stress, is essential to evaluate the endocrine response to stress stimuli. While the current cortisol sensing methods require large laboratory settings, complex assay, and professional personnel. Herein, a novel flexible and wearable electrochemical aptasensor based on a Ni–Co metal-organic frameworks (MOF) nanosheet-decorated carbon nanotubes (CNTs)/polyurethane (PU) film is developed for rapid and reliable detection of cortisol in sweat. First, the CNTs/PU (CP) film was prepared by a modified wet spinning technology, and the CNTs/polyvinyl alcohol (PVA) solution was thermally deposited on the surface of CP film to form the highly flexible CNTs/PVA/CP (CCP) film with excellent conductivity. Then aminated Ni–Co MOF nanosheet prepared by a facile solvothermal method was conjugated with streptavidin and modified on the CCP film. Biofunctional MOF can effectively capture cortisol aptamer due to its excellent specific surface area. In addition, the MOF with peroxidase activity can catalytic oxidization of hydroquinone (HQ) by hydrogen peroxide (H2O2), which could amplify the peak current signal. The catalytic activity of Ni–Co MOF was substantially suppressed in the HQ/H2O2 system due to the formation of the aptamer-cortisol complex, which reduced the current signal, thereby realizing highly sensitive and selective detection of cortisol. The sensor has a linear range of 0.1–100 ng/mL and a detection limit of 0.032 ng/mL. Meanwhile, the sensor showed high accuracy for cortisol detection under mechanical deformation conditions. More importantly, the prepared MOF/CCP film based three-electrode was assembled with the polydimethylsiloxane (PDMS) substrate, and the sweat-cloth was used as the sweat collection channel to fabricate a wearable sensor patch for monitoring of cortisol in volunteers’ sweat in the morning and evening. This flexible and non-invasive sweat cortisol aptasensor shows great potential for quantitative stress monitoring and management.

The tetrazolium test: reviving an old but useful stress marker.

Adverse environmental conditions often compromise plant survival. Thus, stress monitoring with accurate and cost-effective technologies must be a priority to anticipate environmental threats, but processing time and the associated costs are still important limitations. Revisiting research technologies such as biochemical markers can provide straightforward data to foresee the effects of plant stressors. The tetrazolium test is based on the ability of living tissues to reduce a colorless salt, 2,3,5-triphenyl tetrazolium chloride (TTC), into the red compound formazan. It is not only useful for seed viability tests, but also for tissue damage assessments, as initially proposed by Larcher et al. (1969).

Cheap, cost-effective, and quick stress biomarkers for drought stress detection and monitoring in plants.

The detection and monitoring of drought stress in plants growing in their natural habitat are essential for the study of plant stress physiology. However, with the advent of plant phenotyping and new -omics technologies, the application of simple, cheap, cost-effective, quick, and practical methods to assess drought stress in plants seems more challenging than ever, particularly in low-income countries. Here, currently available methods that do not require specialized equipment, but reliably detect and monitor drought stress in plants at low cost will be discussed. This will not only boost research on plant stress physiology in low-income countries but will also help several laboratories with very limited resources around the globe to perform high-quality research.

Stress Detection Based on Naïve Bayes Algorithm

Abstract: Stress is a prevalent issue that affects individuals' mental and physical well-being, leading to various health problems. The use of machine learning (ML) has been gaining popularity as a tool for stress detection. ML techniques have shown promising results in identifying patterns and features from various physiological and behavioral data sources such as heart rate, blood pressure, and speech signals. The primary goal of stress detection using ML is to provide accurate, non-invasive, and costeffective methods for early stress detection and intervention. Overall, stress detection using ML holds great promise in providing an objective, efficient, and scalable approach for stress monitoring and intervention. Further research is required to address the challenges associated with data collection, feature extraction, and model generalization to diverse populations and contexts.

Digital image correlation method for detecting strain in wood drying process

Stress and strain monitoring is of great significance for wood drying. Using digital image correlation (DIC), this study measured displacement and strain changes during wood drying in real time. The results showed that when wood is dried below the fiber saturation point, the difference of the radial and tangential shrinkage ratio gradually increased. An analysis of tangential and radial shrinkage ratio revealed that in the early stage of drying, the tangential and radial shrinkage ratio was relatively flat; in the middle stage of drying, the shrinkage ratio began to gradually increase; and in the later stage of drying, the tangential shrinkage ratio was approximately twice as large as that in the radial direction. Further, regarding the tangential and radial strain distribution, the radial strain distribution was more scattered than that in the tangential direction. The radial strain distribution exhibited compressive strain on both ends of the specimen, and the tensile strain was in the center. The tangential strain distribution was tensile strain on the left side of the specimen and compression strain on the right. Overall, DIC is stable and reliable, and it can be used to monitor well the stress and strain changes during wood drying.

Sensitivity and specificity of in vivo COVID-19 screening by detection dogs: Results of the C19-Screendog multicenter study

Trained dogs can recognize the volatile organic compounds contained in biological samples of patients with COVID-19 infection. We assessed the sensitivity and specificity of in vivo SARS-CoV-2 screening by trained dogs.We recruited five dog-handler dyads. In the operant conditioning phase, the dogs were taught to distinguish between positive and negative sweat samples collected from volunteers' underarms in polymeric tubes. The conditioning was validated by tests involving 16 positive and 48 negative samples held or worn in such a way that the samples were invisible to the dog and handler. In the screening phase the dogs were led by their handlers to a drive-through facility for in vivo screening of volunteers who had just received a nasopharyngeal swab from nursing staff. Each volunteer who had already swabbed was subsequently tested by two dogs, whose responses were recorded as positive, negative, or inconclusive. The dogs’ behavior was constantly monitored for attentiveness and wellbeing.All the dogs passed the conditioning phase, their responses showing a sensitivity of 83–100% and a specificity of 94–100%. The in vivo screening phase involved 1251 subjects, of whom 205 had a COVID-19 positive swab and two dogs per each subject to be screened. Screening sensitivity and specificity were respectively 91.6–97.6% and 96.3–100% when only one dog was involved, whereas combined screening by two dogs provided a higher sensitivity. Dog wellbeing was also analyzed: monitoring of stress and fatigue suggested that the screening activity did not adversely impact the dogs’ wellbeing. This work, by screening a large number of subjects, strengthen recent findings that trained dogs can discriminate between COVID-19 infected and healthy human subjects and introduce two novel research aspects: i) assessement of signs of fatigue and stress in dogs during training and testing, and ii) combining screening by two dogs to improve detection sensitivity and specificity.Using some precautions to reduce the risk of infection and spillover, in vivo COVID-19 screening by a dog-handler dyad can be suitable to quickly screen large numbers of people: it is rapid, non-invasive and economical, since it does not involve actual sampling, lab resources or waste management, and is suitable to screen large numbers of people.

Can corporate social responsibility enhance corporate competitiveness? An empirical analysis based on listed companies in China's pharmaceutical manufacturing industry

AbstractThe current study explores the influence mechanism of corporate social responsibility on corporate competitiveness. From 2011 to 2020, a research sample was collected from 108 A‐share pharmaceutical manufacturing companies listed on Shanghai and Shenzhen stock exchanges. Findings depict that the pharmaceutical manufacturing enterprises significantly improved competitiveness by fulfilling social responsibility. Good internal control showed a mediating effect on the influence of corporate social responsibility. Moreover, a perfect equity incentive reflects the moderating effect in the relationship between corporate social responsibility and enterprise competitiveness. The study not only provides an empirical test for the relationship between corporate social responsibility and corporate competitiveness but also analyzes the transmission mechanism between internal control and executive equity incentive variables. The study findings stress monitoring stakeholders and improving the enterprise's internal control system. Moreover, it is necessary to establish a reasonable and effective executive equity incentive mechanism.

A New Method for Fractionation and Characterization of Polyphenols and Tannins from Grapevine Leaf Tissue.

Plants accumulate different types of phenolic material in their tissue as a response to biotic as well as abiotic stress. Monomeric polyphenols and smaller oligomers can serve as protection against ultraviolet radiation or prevent oxidative tissue damage, while larger molecules such as tannins can be the plant's reaction to an infection or physical damage. Therefore, characterization, profiling, and quantification of diverse phenolics can provide valuable information about the plant and the stress status at any given time. A method was developed that allows the extraction of polyphenols and tannins from leaf tissue, followed by fractionation and quantification. Extraction was performed with liquid nitrogen and 30% acetate-buffered ethanol. The method was tested with four cultivars under varying extraction conditions (solvent strength and temperature) and showed great improvements of the chromatography that would otherwise be impacted by tannins. The separation of tannins from smaller polyphenols was achieved by bovine serum albumin precipitation and resuspension in a urea-triethanolamine buffer. Tannins were reacted with ferric chloride and analyzed spectrophotometrically. Monomeric non-protein-precipitable polyphenols were then analyzed via HPLC-DAD from the supernatant of the precipitation sample. This way, a more complete spectrum of compounds can be analyzed from the same plant tissue extract. With the fractionation suggested here, hydroxycinnamic acids and flavan-3-ols can be separated and quantified with good accuracy and precision. Possible applications include the assessment of plant stress and response monitoring using the total concentrations of polyphenols and tannins, as well as the ratios between those compound classes.

Abaxial leaf surface-mounted multimodal wearable sensor for continuous plant physiology monitoring.

Wearable plant sensors hold tremendous potential for smart agriculture. We report a lower leaf surface-attached multimodal wearable sensor for continuous monitoring of plant physiology by tracking both biochemical and biophysical signals of the plant and its microenvironment. Sensors for detecting volatile organic compounds (VOCs), temperature, and humidity are integrated into a single platform. The abaxial leaf attachment position is selected on the basis of the stomata density to improve the sensor signal strength. This versatile platform enables various stress monitoring applications, ranging from tracking plant water loss to early detection of plant pathogens. A machine learning model was also developed to analyze multichannel sensor data for quantitative detection of tomato spotted wilt virus as early as 4 days after inoculation. The model also evaluates different sensor combinations for early disease detection and predicts that minimally three sensors are required including the VOC sensors.