Background: This study aimed to investigate the effect size (ES) of air temperature on the executive functions of human brain and body physiological responses. Methods: In this empirical study, the participants included 35 male students who were exposed to 4 air temperature conditions of 18°C, 22°C, 26°C and 30°C in 4 separate sessions in an air conditioning chamber. The participants were simultaneously asked to take part in the N-backtest. The accuracy, electrocardiogram (ECG) signals and the respiration rate were recorded to determine the effect of air temperature. Results: Compared to moderate air temperatures (22°C), high (30°C) and low (18°C) air temperatures had a much more profound effect on changes in heart beat rate, the accuracy of brain executive functions and the response time to stimuli. There were statistically significant differences in the accuracy by different workload levels and various air temperature conditions(P<0.05). Although the heart beat rate index, the ratio between low frequency and high frequency (LF/HF), and the respiratory rate were more profoundly affected by the higher and lower air temperatures than moderate air temperatures (P0.05). Conclusion: The results confirmed that the unfavorable air temperatures may considerably affect the physiological responses and the cognitive functions among indoor employees.Therefore, providing them with thermal comfort may improve their performance within indoor environments.

Forecasting the time of future outbreaks would minimize the impact of diseases by taking preventive steps including public health messaging and raising awareness of clinicians for timely treatment and diagnosis. The present study investigated the accuracy of support vector machine, artificial neural-network, and random-forest time series models in influenza like illness (ILI) modeling and outbreaks detection. The models were applied to a data set of weekly ILI frequencies in Iran. The root mean square errors (RMSE), mean absolute errors (MAE), and intra-class correlation coefficient (ICC) statistics were employed as evaluation criteria. It was indicated that the random-forest time series model outperformed other three methods in modeling weekly ILI frequencies (RMSE = 22.78, MAE = 14.99 and ICC = 0.88 for the test set). In addition neural-network was better in outbreaks detection with total accuracy of 0.889 for the test set. The results showed that the used time series models had promising performances suggesting they could be effectively applied for predicting weekly ILI frequencies and outbreaks.

The purpose of this study was to investigate the physiological and mental health effects caused by exposure to low-frequency noise in typical control rooms and office-like areas. The participants were 35 male students who were exposed to noise at levels of 55, 65, 70, and 75 dBA. The N-back test was used at three cognitive performance loads (low workload (n = 1), medium workload (n = 2), and high workload (n = 3) to evaluate working memory simultaneously in an air conditioning chamber in four sessions with a constant level. The electroencephalography, electrocardiogram, and electrooculography were measured using Nexus 4 by Bio traces software (Mind Media Co.). For evaluation of mental fatigue, fatigue visual analog scale, and psycho-physiological indices were also used. The results showed that the losses of physiological and mental health were rapidly increased with exposure to noise levels of 65–75 dBA. The results showed that mental fatigue significantly affected heart rate, low- to high-frequency ratios, and electroencephalogram indices such as theta, alpha, as well as eye activities and working memory. The findings confirmed that the mental fatigue caused by low-frequency noise significantly impacted the employees’ psycho-physiological and working memory responses. Implementation of the effective interventions to overcome employees’ mental fatigue in typical control rooms and office-like areas can improve the health and acoustic comfort and, consequently, the cognitive performance.

Background. Parkinson’s disease (PD) is a neurological disorder that is marked by the deficit of neurons in the midbrain that changes motor and cognitive function. In the substantia nigra, the selective demise of dopamine‐producing neurons was the main cause of this disease. The purpose of this research was to discover genes involved in PD development. Methods. In this study, the microarray dataset (GSE22491) provided by GEO was used for further analysis. The Limma package under R software was used to examine and assess gene expression and identify DEGs. The DAVID online tool was used to accomplish GO enrichment analysis and KEGG pathway for DEGs. Furthermore, the PPI network of these DEGs was depicted using the STRING database and analyzed through the Cytoscape to identify hub genes. Support vector machine (SVM) classifier was subsequently employed to predict the accuracy of genes. Result. PPI network consisted of 264 nodes as well as 502 edges was generated using the DEGs recognized from the Limma package under the R software. Moreover, three genes were identified as hubs: GNB5, GNG11, and ELANE. By using 3‐gene combination, SVM found that prediction accuracy of 88% can be achieved. Conclusion. According to the findings of the study, the 3 hub genes GNB5, GNG11, and ELANE may be used as PD detection biomarkers. Moreover, the results obtained from SVM with high accuracy can be considered as PD biomarkers in further investigations.