Calf massager: Intervention for body muscle discomfort during prolonged standing

Many occupations require workers to stand for long periods of time without proper interventions, which causes discomfort in the back and lower limbs. Therefore, this study aims to assess the effectiveness in alleviating body muscle discomfort during prolonged standing through the use of a calf massager. This study was conducted among male workers at a manufacturer with production line workers and the list was obtained from the HR Department and simple random sampling was done by number categorization. A total of 100 respondents (50 respondents for both the control and the experimental groups) participated in this study. The experiment took place in a room with a similar setup for the production line. Each respondent was requested to perform the simulated task for 2 hr. For the experimental group, the calf massager was turned on every 15 min. At every 15‐min interval after turning on the calf massager, respondents from both groups were required to evaluate their discomfort level on a Borg's scale CR‐10 questionnaire. The results showed that the level of body discomfort among respondents in the experimental group reduced (20–30%) compared with that of the control group. Multivariate analysis results revealed that the discomfort rating for the lower back, knees, thighs, calves, and feet was significantly lower (p < .05) among the experimental group compared with the control group. For lower body parts, the lower back region was statistically significant (p < .05) at the 90th, 105th, and 120th min; the thigh region was statistically significant (p < .05) at the 120th min; the knee region was statistically significant (p < .05) at the 105th and 120th min; the calf region was statistically significant (p < .05) at all minute intervals except the 15th and 45th min, while the feet region, was statistically significant at the 105th and 120th min. Therefore, this study indicates that calf massage treatment is capable of reducing body muscle discomfort during prolonged standing and highlights the significance of calf massage.     

Identification of microstructural zones and thermal cycles in a weldment of modified 9Cr-1Mo steel

This paper presents the results of a study on the microstructural and microchemical variations in a multipass Gas Tungsten Arc weld (GTAW) of modified 9Cr-1Mo steel. The changes brought about in the steel due to the heating and cooling cycles during welding and the subsequent effects due to reheating effects during multipass welding are described. Detailed analytical transmission electron microscopy has been carried to study the type and composition of the primary and secondary phases in this steel. The systematic changes in microstructural parameters such as Prior Austenite Grain Size, martensite lath size, number density, size and microchemistry of carbides, have been understood based on the different transformations that the steel undergoes during the heating and cooling process. Based on the observed microstructure, an attempt has been made to identify distinct microstructural zones and possible thermal cycles experienced by different regions of the weldment.     

Useful NMR solvent mixture for polyesters: Trifluoroacetic acid-d/chloroform-d

Summary NMR spectra of high molecular weight polyesters such as polyglycolide (PG) and poly(ethylene terephthalate) (PET) have not been available because of low solubility in suitable NMR solvents. Although PG is not soluble enough at ambient temperatures for NMR spectra in either chloroform-d or trifluoroacetic acid-d alone, it dissolves readily to over 0.9% in a mixture of the two. At this concentration, ¹H and ¹³C spectra can be obtained on samples where end group signals are visible from which molecular weights can readily be determined. The solutions are stable up to at least four days when moisture is excluded. NMR spectra of PET can likewise be obtained at ambient temperatures in the solvent mixture which dissolves up to at least 0.6% of PET.     

AHW-BGOA-DNN: a novel deep learning model for epileptic seizure detection

Neural Computing and Applications - “Brain–Computer Interface” (BCI)—a real-life support system provides a way for epileptic patients to improve their quality of life. In...     

Detection of dental abnormalities using SVM and PSVM

Landmarks needed for detecting dental abnormalities in cephalometric analysis were selected from the digital image, and the angle values needed for dental analysis were calculated and stored in a database which is used for developing training dataset. Principal component analysis was applied for dimension reduction to get the desired feature vectors which are trained and tested using support vector machine and proximal support vector machine classifier to detect the dental abnormalities, the performance of the classifiers were also compared.     

Think,feel, bid: the impact of environmental conditions on the role of bidders’ cognitive and affective processes in auction bidding

Environmental conditions and the interplay of cognitive and affective processes both exert influences on bidding behavior. This paper brings the above together, considering how the (external) auction environment determines the impact of (internal) cognitive and affective processes on bidding behavior, assessed in comparison to the optimal bid. Two aspects of the auction environment were considered, namely auction dynamics (low: first-price sealed-bid auction, high: Dutch auction) and value uncertainty (low, high). In a laboratory experiment, we assess bidders’ cognitive workload and emotional arousal through physiological measurements. We find that higher auction dynamics increase the impact of emotional arousal on bid deviations, but not that of cognitive workload. Higher value uncertainty, conversely, increases the impact of cognitive workload on bid deviations, but not that of emotional arousal. Taken together, the auction environment is a critical factor in understanding the nature of the underlying decision process and its impact on bids.     

Objective evaluation of speech dysfluencies using wavelet packet transform with sample entropy

Dysfluency and stuttering are a break or interruption of normal speech such as repetition, prolongation, interjection of syllables, sounds, words or phrases and involuntary silent pauses or blocks in communication. Stuttering assessment through manual classification of speech dysfluencies is subjective, inconsistent, time consuming and prone to error. This paper proposes an objective evaluation of speech dysfluencies based on the wavelet packet transform with sample entropy features. Dysfluent speech signals are decomposed into six levels by using wavelet packet transform. Sample entropy (SampEn) features are extracted at every level of decomposition and they are used as features to characterize the speech dysfluencies (stuttered events). Three different classifiers such as k-nearest neighbor (kNN), linear discriminant analysis (LDA) based classifier and support vector machine (SVM) are used to investigate the performance of the sample entropy features for the classification of speech dysfluencies. 10-fold cross validation method is used for testing the reliability of the classifier results. The effect of different wavelet families on the classification performance is also performed. Experimental results demonstrate that the proposed features and classification algorithms give very promising classification accuracy of 96.67% with the standard deviation of 0.37 and also that the proposed method can be used to help speech language pathologist in classifying speech dysfluencies.