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Research on workplace stress measurements varied without much accuracy and effectiveness. The purpose of this paper is to introduce a new quantitative assessment tool emWave Pro Plus (Institute of HeartMath) and compare heart rate variability (HRV) results with the Personal and Organizational Quality Assessment (POQA) and the Perceived Stress Scale (PSS).

This research opted for a correlational study which involves 85 full-time employees who were working at least 40 h per week in a large corporation participated in this study. The POQA and PSS were used to correlate with HRV.

Astonishing findings emerged in this study. Significant positive correlations were found between emotional stress and HRV, and between intention to quit and HRV. In other words, the researchers have to make sense the following surprising findings: the higher the emotional stress an employee faces, the healthier they are. Healthier employees may have higher intentions of quitting their jobs. The surprising results may be attributed to personality, culture, emotional regulation and age among others.

This research fulfills an identified need to validate quantifiable stress measurements especially in a corporate environment. The research also shows promising results, and future studies should continue to tap into HRV as an objective measure of mental health and workplace stress.

The sustainable green movement is significantly gaining momentum around the globe and South Africa needs to follow suit. However, such a movement needs to be significantly tested. It is therefore essential to present both foundation and supplementary research in the primary concepts within this topic in order to lay the groundwork for future analysis. The purpose of this paper is to analyse the cost‐effectiveness of the heat recovery ventilation (HRV) technology incorporated within Lincoln on the Lake, against a direct‐expansion (DX) ducted system of conventional practice utilising the life cycle cost analysis (LCCA) to determine if the sustainable option is the better choice.

This paper is a case study, based on a green building in KwaZulu‐Natal, South Africa using a ten step life cycle cost analysis.

In terms of the LCCA performed at Lincoln on the Lake, this case study has found that sustainable measures were far more cost effective over the 20 year study period than that of the comparable conventional system. The life‐cycle cost analysis tool has provided a simple, uniform and predetermined manner for which the life‐cycle costs of sustainable designs can be successfully quantified.

The value which sustainable building practices can pose, has not been fully realised among clients and professionals within the South African construction industry due to lack of proof that value incentives do exist. This paper, therefore, emphasizes that savings can be made over the long term by going the sustainable route.

The purpose of this paper is to report on how honour-related violence (HRV) is understood and managed by professionals in Finland, emphasising the need to consider collectivity as an influential factor. Therefore, this paper introduces the concept of “collective violence”. By investigating the level of awareness and recognition of these violence phenomena, this paper discusses both preventative and punitive measures that Finnish authorities are able to work with.

A total of 111 Finnish anti-violence professionals completed a survey that aimed to qualitatively investigate their perceptions of HRV and collectivity.

The findings of this study indicated that collective violence is generally poorly recognised among professionals in Finland. At present, both victim services and criminal justice system lack adequate structures to deal with issues of collective violence. These findings indicate that authorities need further education on HRV and collectivity, as well as debates on whether the criminal code should be amended to meet international requirements.

As this violence has been researched only sporadically in the Finnish context, this study provides new insight to under-researched area of honour-related and collective violence in Finland. These findings may assist other European countries dealing with similar issues as well as guiding preventative and punitive measures within the Finnish context.

The purpose fo this paper is to Monitor and sense the sysmptoms of COVID-19 as a preliminary measure using electronic wearable devices. This variability is sensed by electrocardiograms observed from a multi-parameter monitor and electronic wearable. This field of interest has evolved into a wide area of investigation with today’s advancement in technology of internet of things for immediate sensing and processing information about profound pain. A window span is estimated and reports of profound pain data are used for monitoring heart rate variability (HRV). A median heart rate is considered for comparisons with a diverse range of variable information obtained from sensors and monitors. Observations from healthy patients are introduced to identify how root mean square of difference between inter beat intervals, standard deviation of inter-beat intervals and mean heart rate value are normalized in HRV analysis.

The function of a human heart relates back to the autonomic nervous system, which organizes and maintains a healthy maneuver of inter connected organs. HRV has to be determined for analyzing and reporting the status of health, fitness, readiness and possibilities for recovery, and thus, a metric for deeming the presence of COVID-19. Identifying the variations in heart rate, monitoring and assessing profound pain levels are potential lives saving measures in medical industries.

Experiments are proposed to be done in electrical and thermal point of view and this composition will deliver profound pain levels ranging from 0 to 10. Real time detection of pain levels will assist the care takers to facilitate people in an aging population for a painless lifestyle.

The presented research has documented the stages of COVID-19, symptoms and a mechanism to monitor the progress of the disease through better parameters. Risk factors of the disease are carefully analyzed, compared with test results, and thus, concluded that considering the HRV can study better in the presence of ignorance and negligence. The same mechanism can be implemented along with a global positioning system (GPS) system to track the movement of patients during isolation periods. Despite the stringent control measurements for locking down all industries, the rate of affected people is still on the rise. To counter this, people have to be educated about the deadly effects of COVID-19 and foolproof systems should be in place to control the transmission from affected people to new people. Medications to suppress temperatures, will not be sufficient to alter the heart rate variations, and thus, the proposed mechanism implemented the same. The proposed study can be extended to be associated with Government mobile apps for regular and a consortium of single tracking. Measures can be taken to distribute the low-cost proposal to people for real time tracking and regular updates about high and medium risk patients.

The purpose of this paper is to clarify the relationship between dynamic clothing pressure of women's sports bras and heart rate variation indexes during playing basketball.

Totally, 35 healthy females aged between 20 and 24 were employed as subjects. Their heart rate variability (HRV) data and the clothing pressure values when wearing sports bras of different bust sizes during playing basketball were measured using Polar RS800CX heart rate monitor and Flexi Force201 thin film type pressure sensor with MFF series pressure test system. Their subjective comfort evaluations were conducted by five-point Likert scale. Grey correlation analysis is used to determine the correlation degree of heart rate variation indexes and subjective comfort evaluations to identify the indexes preferably associated with subjective comfort. Multiple regression analysis is applied to investigate multiple correlations between pressure of test points and the indexes selected above.

Combined subjective comfort evaluation with objective HRV evaluation the heart rate variation indexes preferably associated with subjective comfort of these bras were identified, which includes mHR, mRR, RMSSD, pNN50. By the relationship between dynamic clothing pressure and the indexes above, this study finds that the clothing pressure of the chest area during basketball exercise should be controlled in the range of 2.01-4.74 kPa.

The subjects of different body type should be taken into account. Further, the mathematic models from this study show low R2-value so the models should be provided more reliable prediction.

The present study indicates that there is an inevitable connection between heart rate variation indexes and subjective comfort of basketball sports bras and creatively points out that heart rate variation indexes can partly assess the comfortable clothing pressure. The study can offer theoretical basis for sports apparel industry to develop the comfortable basketball sports bra.

To add to the existing body of knowledge on the relationship between stress and job performance in policing, we monitored police officers' physiology using Hexoskin shirts while they responded to simulated scenarios.

We employed mixed repeated measures (baseline, intervention, post-intervention), between groups (treatment vs control group) design. Using this approach, our aims were (1) to determine whether an individualized physiological stress profile—a combination of heart rate (HR), heart rate variability (HRV), sympathetic nervous system (SNS) index and parasympathetic nervous system (PNS) index—could be developed for each participant; (2) to investigate the association between physiological stress and scenario performance and (3) to pilot test an intervention for decreasing physiological stress in real time.

We found that it was possible to individualize physiological stress profiles for each participant that alerted us when the participant was becoming stressed. We also found that physiological stress was significantly and negatively/inversely associated with scenario performance. However, our intervention to try and decrease participants' stress in real time was not successful. Several key lessons can be taken from our attempt that could inform future efforts in this area.

This was a small pilot study, precluding generalizability of results. Furthermore, our intervention was simplistic and potentially affected by an experimenter effect. Future research should explore better ways to intervene when officers are becoming physiologically stressed to help them overcome stress in real time and safeguard against the cumulative effects of stress on health and performance.

This research adds to the body of knowledge on physiological stress and job-task performance in police officers.

The purpose of this paper is to design a robust angle-of-attack (AOA) tracking control system for the hypersonic reentry vehicle (HRV) based on the linear parameter varying (LPV) theory, as the aerodynamic coefficients of the hypersonic vehicle vary quickly during the reentry phase.

First, longitudinal moment trim is done along the desired flight trajectory. The linearized system at each trim point is built and the dynamic characteristics analysis is made. Then the LPV control law with parameter-dependent quadratic Lyapunov function (PDQLF-LPV) is applied to design the AOA tracking autopilot at each trim point. Frequency performance of the autopilot is assessed and the step response simulation is conducted to validate the effectiveness of the control system. Finally, actual AOA command tracking simulations based on the time-varying nonlinear model are carried out to test the correctness and robustness of the PDQLF-LPV autopilot.

Analysis results demonstrate that the PDQLF-LPV control system can track the AOA command perfectly during the whole flight envelop with dynamics parameter variation or disturbances, which indicates that it is effective to integrate the PDQLF-LPV control theory with a parameter-varying reference model for control system design of HRV.

A reference model with varying parameters is utilized to guarantee the transient performance of the autopilot, and induced L2-norm analysis is introduced to describe and guarantee the robust stability of the autopilot.

The identification of fatigue status and early intervention to mitigate fatigue can reduce the risk of workplace injuries. Off-the-shelf wearable sensors capable of assessing multiple parameters are available. However, using numerous variables in the fatigue prediction model can elicit data issues. This study aimed at identifying the most relevant variables for measuring occupational fatigue among entry-level construction workers by using common wearable sensor technologies, such as electrocardiogram and actigraphy sensors.

Twenty-two individuals were assigned different task workloads in repeated sessions. Stepwise logistic regression was used to identify the most parsimonious fatigue prediction model. Heart rate variability measurements, standard deviation of NN intervals and power in the low-frequency range (LF) were considered for fatigue prediction. Fast Fourier transform and autoregressive (AR) analysis were employed as frequency domain analysis methods.

The log-transformed LF obtained using AR analysis is preferred for daily fatigue management, whereas the standard deviation of normal-to-normal NN is useful in weekly fatigue management.

This study was conducted with entry-level construction workers who are involved in manual material handling activities. The findings of this study are applicable to this group.

This is the first study to investigate all major measures obtainable through electrocardiogram and actigraphy among current mainstream wearables for monitoring occupational fatigue in the construction industry. It contributes knowledge on the use of wearable technology for managing occupational fatigue among entry-level construction workers engaged in material handling activities.

In light of the growing interest in neuroscience within the managerial and organizational cognition (MOC) scholarly domain at large, this chapter advances current knowledge on core neuroscience methods. It does so by building on the theoretical analysis put forward by Healey and Hodgkinson (2014, 2015), and by offering a thorough – yet accessible – methodological framework for a better understanding of key cognitive and social neuroscience methods. Classifying neuroscience methods based on their degree of resolution, functionality, and anatomical focus, the chapter outlines their features, practicalities, advantages and disadvantages. Specifically, it focuses on functional magnetic resonance imaging, electroencephalography, magnetoencephalography, heart rate variability, and skin conductance response. Equipped with knowledge of these methods, researchers will be able to further their understanding of the potential synergies between management and neuroscience, to better appreciate and evaluate the value of neuroscience methods, and to look at new ways to frame old and new research questions in MOC. The chapter also builds bridges between researchers and practitioners by rebalancing the hype and hopes surrounding the use of neuroscience in management theory and practice.

Cardiac arrest is a severe heart anomaly that results in billions of annual casualties. Smoking is a specific hazard factor for cardiovascular pathology, including coronary heart disease, but data on smoking and heart death not earlier reviewed. The Heart Rate Variability (HRV) parameters used to predict cardiac arrest in smokers using machine learning technique in this paper. Machine learning is a method of computing experience based on automatic learning and enhances performances to increase prognosis. This study intends to compare the performance of logistical regression, decision tree, and random forest model to predict cardiac arrest in smokers. In this paper, a machine learning technique implemented on the dataset received from the data science research group MITU Skillogies Pune, India. To know the patient has a chance of cardiac arrest or not, developed three predictive models as 19 input feature of HRV indices and two output classes. These model evaluated based on their accuracy, precision, sensitivity, specificity, F1 score, and Area under the curve (AUC). The model of logistic regression has achieved an accuracy of 88.50%, precision of 83.11%, the sensitivity of 91.79%, the specificity of 86.03%, F1 score of 0.87, and AUC of 0.88. The decision tree model has arrived with an accuracy of 92.59%, precision of 97.29%, the sensitivity of 90.11%, the specificity of 97.38%, F1 score of 0.93, and AUC of 0.94. The model of the random forest has achieved an accuracy of 93.61%, precision of 94.59%, the sensitivity of 92.11%, the specificity of 95.03%, F1 score of 0.93 and AUC of 0.95. The random forest model achieved the best accuracy classification, followed by the decision tree, and logistic regression shows the lowest classification accuracy.