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The purpose of this study is to highlight and demonstrate how the study of stress and related responses in construction can best be measured and benchmarked effectively.

A range of perceptual and physiological measures are obtained across different time periods and during different activities in a fieldwork setting. Differences in the empirical results are analysed and implications for future studies of stress discussed.

The results of this study strongly support the use of multiple psychometrics and biosensors whenever biometrics are included in the study of stress. Perceptual, physiological and environmental factors are all shown to act in concert to impact stress. Strong conclusions on the potential drivers of stress should then only be considered when consistent results apply across multiple metrics, time periods and activities.

Stress is an incredibly complex condition. This study demonstrates why many current applications of biosensors to study stress in construction are not up to the task and provides empirical evidence on how future studies can be significantly improved.

To the best of the author’s knowledge, this is the first study to focus explicitly on demonstrating the need for multiple research instruments and settings when studying stress or related conditions in construction.

Drowsiness is a common cause of severe road accidents. Therefore, numerous drowsiness detection methods were developed and explored in recent years, especially concepts using physiological measurements achieved promising results. Nevertheless, existing systems have some limitations that hinder their use in vehicles. To overcome these limitations, this paper aims to investigate the development of a low-cost, non-invasive drowsiness detection system, using physiological signals obtained from conventional wearable devices.

Two simulator studies, the first study in a low-level driving simulator (N = 10) to check feasibility and efficiency, and the second study in a high-fidelity driving simulator (N = 30) including two age groups, were conducted. An algorithm was developed to extract features from the heart rate signals and a data set was created by labelling these features according to the identified driver state in the simulator study. Using this data set, binary classifiers were trained and tested using various machine learning algorithms.

The trained classifiers reached a classification accuracy of 99.9%, which is similar to the results obtained by the studies which used intrusive electrodes to detect ECG. The results revealed that heart rate patterns are sensitive to the drivers’ age, i.e. models trained with data from one age group are not efficient in detecting drowsiness for another age group, suggesting to develop universal driver models with data from different age groups combined with individual driver models.

This work investigated the feasibility of driver drowsiness detection by solely using physiological data from wrist-worn wearable devices, such as smartwatches or fitness trackers that are readily available in the consumer market. It was found that such devices are reliable in drowsiness detection.

Wearables are gaining prominence in the health-care industry and their use is growing. The elderly and other patients can use these wearables to monitor their vitals at home and have them sent to their doctors for feedback. Many studies are being conducted to improve wearable health-care monitoring systems to obtain clinically relevant diagnoses. The accuracy of this system is limited by several challenges, such as motion artifacts (MA), power line interference, false detection and acquiring vitals using dry electrodes. This paper aims to focus on wearable health-care monitoring systems in the literature and provides the effect of MA on the wearable system. Also presents the problems faced while tracking the vitals of users.

MA is a major concern and certainly needs to be suppressed. An analysis of the causes and effects of MA on wearable monitoring systems is conducted. Also, a study from the literature on motion artifact detection and reduction is carried out and presented here. The benefits of a machine learning algorithm in a wearable monitoring system are also presented. Finally, distinct applications of the wearable monitoring system have been explored.

According to the study reduction of MA and multiple sensor data fusion increases the accuracy of wearable monitoring systems.

This study also presents the outlines of design modification of dry/non-contact electrodes to minimize the MA. Also, discussed few approaches to design an efficient wearable health-care monitoring system.

The purpose of this study was to identify the role of two kinds of leisure activities (i.e. casual and serious leisure) in reducing psychological and physiological stresses and, specifically, to investigate the differences between pre- and post-psychological and physiological stresses.

The data analyses were conducted using the Statistical Package for the Social Sciences 20.0 program. Descriptive analyses were calculated to identify the characteristics of the sample, including gender, education and age. Because of the small sample size (n < 30), this study uses a nonparametric test. The Wilcoxon signed-rank test was used to examine the differences between pre- and post-stresses of psychological and physiological approaches. The Mann-Whitney U test was used to examine the differences of stresses between the casual and serious leisure groups. In addition, the Kendall rank correlation coefficient was used to measure the association between leisure experiences and stresses.

The findings from this study indicated that pre-overall affective stress was significantly higher than post-overall affective stress. There were significant differences between pre- and post-physiological stresses during serious leisure. Research findings also suggested that serious leisure experiences have a significant and negative relationship with cognitive stress and physiological stress.

The data were obtained from two different types of leisure setting, and hence, the generalizability of the study findings to other regions needs to be explored in future studies. Research across other leisure settings also might permit the validation of more stable relationships between leisure and stresses. Future research is needed to investigate other important antecedents of individuals’ psychological and physiological stresses in the leisure setting and may identify the complex nature of leisure participants’ perceptions and their relationships with experiences.

Despite the growth of stress and leisure research, physiological-based analyses in this area are limited. Numerous studies have focused on leisure coping with negative life events based on social psychological perspectives. The finding of this study would be helpful to leisure practitioners to manifest the strengths and opportunities of experiences and performances associated with the leisure activity.

Current commercial rapid prototyping systems can be used for fabricating layered models for subsequent creation of fully‐dense metal parts using investment casting. Due to increased demand for shortened product development cycles however, there exists a demand to rapidly fabricate functional fully‐dense metal parts without hard tooling. A possible solution to this problem is direct layered rapid manufacturing of such parts, for example, via laser‐beam fusion of the metal powder. The rapid manufacturing process discussed herein is based on this approach. It involves selective laser‐beam scanning of a predeposited metal‐powder layer, forming fully‐dense claddings as the basic building block of individual layers. This paper specifically addresses only one of the fundamental issues of the rapid manufacturing process under investigation at the University of Toronto, namely the fabrication of single claddings. Our theoretical investigation of the influence of the process parameters on cladding’s geometrical properties employed thermal modeling and computer process simulation. Numerous experiments, involving fabrication of single claddings, were also carried out with varying process parameters. Comparisons of the process simulations and experimental results showed good agreement in terms of overall trends.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

With continuous scaling of digital circuit CMOS technology, the vulnerability of these circuits are significantly increasing against the soft errors. On the other hand, the effects of process variation in the electrical properties of nano-scale circuits, have introduced the statistical methods as an unavoidable choice for the soft error rate (SER) estimation. The purpose of this paper is to provide a statistical soft error rate (SSER) estimation approach for combinational circuits in the presence of process variation.

In this paper a new method is proposed for the SSER estimation of combinational circuits based on the Bayesian networks (BNs). This allows to factor the joint probability distributions over variables in a circuit graph. The distribution of the initial transient fault pulse is estimated by the pre-characterization tables. Timing signals are propagated by BN theory and the probability distribution of electrical and timing masking are calculated.

Simulation results for some benchmark circuits show that the proposed method is accurate with 3.7 percent difference with the Monte-Carlo SPICE simulation and with orders of magnitude improvement in runtime.

The proposed framework is the scheme giving the low estimation time with plausible accuracy compared to other schemes. The comparison exhibits that the designer can save its estimation time in terms of performance and complexity. The deterministic-based methods also are able to evaluate the SER of combinational circuit, yet in an unacceptable time.

Economic globalization is leading large companies to focus on international strategic management. Nowadays, the assets referred to as “corporate intangibles,” such as corporate reputation, are becoming increasingly important because they are considered a key factor for the viability of an organization, and companies therefore need to incorporate them into their scorecards for management. The problem is that their measurement is subjective and latent. These two characteristics impede direct international comparison and require demonstrating the accuracy of comparison via a minimum of two tests – construct equivalence and metric equivalence. As regards corporate reputation, construct equivalence was verified by Naomi Gardberg (2006). However, the subsequent studies did not address metric equivalence. Based on the results of a survey provided by the Reputation Institute (n = 5,950, 50 firms evaluated in 17 countries in the Americas, Europe, Asia and Australia), the degree of RepTrak metric equivalence has been tested, using two different methodologies, multigroup analysis (structural equation model), and a new technique from 2016, the Measurement Invariance of Composite Model procedure from the Partial Least Square Path Modeling family. As one would expect from other cross-cultural studies, reputation metrics do not meet the full metric equivalence, which is why they require standardization processes to ensure international comparability. Both methodologies have identified the same correction parameters, which have allowed validation of the mean and variance of response style by country.

Ti_49.4Ni_50.6 (at. %) shape memory alloy (SMA) is a unique class of smart materials because of unbeatable property which given a wide variety of their applications across a broad range of fields including an orthopedic implant. It plays a very important role in the constructions of novel orthopedic implants application (like dynamic compression plate) because of lower Young’s modulus compared to other biomedical implant materials, high mechanical strength, excellent corrosion resistance and unique property like shape memory effect. Conventional machining of Ti-Ni yields poor surface finish and low dimensional accuracy of the machined components. Hence, wire electro-discharge machining (WDEM) of Ti-Ni has been performed. The purpose of this paper is to investigate the effect of variation of five process parameters, namely, a pulse-on time, pulse-off time, spark gap set voltage (SV), wire feed and wire tension on the material removal rate, surface roughness (SR), kerf width (KW) and dimensional deviation (DD), in the WDEM of Ti_49.5Ni_50.6 SMA.

The effect of machining parameters on Ti_49.4Ni_50.6 has been fully explored using WEDM with zinc coated brass wire as an electrode. In this work, L18 orthogonal array based on Taguchi method has been used to conduct a series of experiments and statically evaluate the experimental data by the use of the method of analysis of variance. Scanning electron microscope images of the machined surface, at the highest and lowest pulse-on time, have been taken to evaluate the quality of surface in terms of their SR values.

For the highest pulse-on time, it is observed that blow holes, cracks, melted droplets and craters have been formed on the machined surface with an SR of 2.744 µm, while for the lowest pulse-on time, these are not formed with an SR of 0.862 µm. It is seen that the pulse-on time is the most significant process parameter for MRR, SR and KW, while the DD is significantly affected by spark gap SV. The optimal values of the process parameters have been obtained by the method of analysis of mean and the confirmatory experiments have been carried out to validate results of optimization. Energy dispersive spectroscopy analysis of the machined surface of Ti_49.4Ni_50.6 has shown a certain amount of deposition of material on the machined surface.

This is an original paper.

The purpose of this paper is to improve the performance of control loop suffering from control valve stiction. Control valve stiction is considered as of one of the main causes of oscillation in process variables, which require performing costly unplanned maintenance and process shutdown. An adaptive solution to handle valve stiction while maintaining safety and quality until next planned maintenance is highly desirable to save considerable cost and effort.

This paper implements a new stiction compensation method built using adaptive inverse model techniques and intelligent control theories. Finite impulse response (FIR) model, which is known to be robust, as a compensator for stiction. The parameters of FIR model are tuned in an adaptive way using differential evolution (DE) technique. The performance of proposed method is compared with other two compensation techniques.

The new method showed excellent performance of the DE–FIR compensator compared to other dynamic inversion methods in terms of minimizing process variability, energy saving and valve stem aggressiveness.

The compensation ability for all compensators reduces with the increase of stiction severity, thus the over shoot case always shows the worst result. In future works, other optimization techniques will be explored to find the appropriate technique that can extend the FIR model size with smallest computation time that can improve the performance of the compensator in over shoot case. In addition, the estimation of the valve residual life based on the level of stiction and effort required by the controller should be considered.

The presented approach represents an original contribution to the literature. It performs stiction compensation without a need for a prior knowledge on the process or the valve models and guarantees a smooth control of the stem movement with a low control effort. The proposed approach differs from previous adaptive methods as it uses stable FIR models and DE to find the appropriate parameters of the inverse model and handle nonlinear behavior of stiction.