Media Multitasking is an increasing phenomenon in our daily lives and has accumulated considerable research in recent years. This article is based on dual motivational systems and motivation conflict theories to explore the best volume of media Multitasking on its primary working efficiency and emotional gratification. A stochastic model of task performance and volumes by combining motivation conflict normal distribution model is proposed. The empirical data is conducted for parameters estimation and model calibration. Finally, the conclusion is useful and helpful for future applications.

Background: The integrity of the autonomic nervous system (ANS) is essential for keeping physiological processes stable, even under stress. Since there is growing interest in heart rate variability (HRV) analysis for the noninvasive assessment of the ANS in sickle cell anemia (SCA) patients, we studied the behavior of the ANS in the presence of a stressor that simulates daily-life Multitasking, the Glittre ADL test (GA-T). Objectives: To evaluate the involvement of the ANS using HRV in adults with SCA during the GA-T and to quantify the strength of the correlation of HRV with lung and muscle functions. Methods: In this cross-sectional study, 16 adults with SCA and 12 healthy controls without sickle cell disease underwent HRV assessment during the GA-T, pulmonary function tests (spirometry, diffusing capacity for carbon monoxide (DLCO), and respiratory muscle testing). Peripheral muscle function [handgrip strength (HGS) and quadriceps strength (QS)] were also measured. Results: Compared to the healthy controls, adults with SCA showed lower HRV, with worse parasympathetic modulation due to reductions in the following indices: the root-mean-square difference of successive normal iRRs (iRR) (RMSSD); the percentage of pairs of consecutive iRRs whose difference is > 50m(pNN50); the high-frequency component of heart rate variability (HF); and the standard deviation of instantaneous beat-to-beat variability (SD1) (P < 0. 001 for all). Compared to healthy controls, individuals with SCA showed greater sympathovagal imbalance (higher ratio between low-frequency and HF components) and lower complexity of the ANS (lower approximate entropy). The GA-T time was correlated with parasympathetic activity indices: RMSSD (rs =-0. 650, P < 0. 01); pNN50(rs=-0. 932, P< 0. 0001), HF(rs=-0. 579, P< 0. 01), andSD1 (rs=-0. 814, P< 0. 0001). Correlations between parasympathetic activity indices and DLCO, HGS, and QS measures were also significant. Conclusions: Adults with SCA have low HRV, with low parasympathetic activity, sympathovagal imbalance, and abnormal ANS complexity. In addition, lower HRV is associated with longer GA-T time, greater impairment of pulmonary diffusion, and greater muscle strength dysfunction.

Glaucoma stands out as a prevalent ocular ailment in the elderly population, causing substantial harm to the optic nerves and eventual vision impairment. Fundus photography plays a pivotal role in the clinical assessment of glaucoma, facilitating the exploration of associated morphological alterations. Computational algorithms, capable of processing fundus images, have emerged as indispensable tools in this diagnostic domain. Hence, the imperative development of an automated diagnostic system leveraging image processing techniques is underscored. In this study, a novel approach to the segmentation and classification of retinal optic nerve head images is introduced. This method concurrently executes both tasks through a deep learning framework, thereby enhancing the learning speed within the Network. The proposed Network encompasses approximately 29 million parameters and demonstrates an efficiency of 2.5 seconds for segmenting and classifying retinal images. Central to this strategy is a multi-task deep learning Network, harmonizing segmentation and classification processes, and leveraging information from both tasks to optimize learning efficacy. Validation of the proposed method is conducted using the publicly available ORIGA dataset. The attained performance metrics for accuracy, sensitivity, specificity, and F1-score are 99.461, 93.46, 100, and 98.7006, respectively. These results collectively affirm the substantial advancement achieved by the proposed method in comparison to existing methodologies.