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Methylphenidate and sertraline had similar effects on depressive symptoms. However, methylphenidate seemed to be more beneficial in improving cognitive function and maintaining daytime alertness. Methylphenidate also offered a better tolerability than sertraline.

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This paper studies a deep learning (DL) framework to solve distributed non-convex constrained optimizations in wireless networks where multiple computing nodes, interconnected via backhaul links, desire to determine an efficient assignment of their states based on local observations. Two different configurations are considered: First, an infinite-capacity backhaul enables nodes to communicate in a lossless way, thereby obtaining the solution by centralized computations. Second, a practical finitecapacity backhaul leads to the deployment of distributed solvers equipped along with quantizers for communication through capacity-limited backhaul. The distributed nature and the nonconvexity of the optimizations render the identification of the solution unwieldy. To handle them, deep neural networks (DNNs) are introduced to approximate an unknown computation for the solution accurately. In consequence, the original problems are transformed to training tasks of the DNNs subject to non-convex constraints where existing DL libraries fail to extend straightforwardly. A constrained training strategy is developed based on the primal-dual method. For distributed implementation, a novel binarization technique at the output layer is developed for quantization at each node. Our proposed distributed DL framework is examined in various network configurations of wireless resource management. Numerical results verify the effectiveness of our proposed approach over existing optimization techniques.Index Terms-Deep neural network, distributed deep learning, primal-dual method, wireless resource management.

In this paper, we investigate the outage performance for simultaneous wireless information and power transfer (SWIPT) relaying systems in the presence of direct link between the source and the destination. For the SWIPT relaying system, we employ a power splitter (PS) at the relay, which splits the received signal into the information transmission and the energy harvesting parts. First, we provide an analysis of the outage probability as a closed-form based on a high signal-to-noise ratio approximation. From the analysis, it is recognized that the diversity order of the SWIPT relaying systems equals that of the non-SWIPT cases regardless of the energy harvesting efficiency. The closed-form outage expression also enables us to obtain a simple expression for the PS factor which minimizes the outage probability. Simulation results demonstrate the accuracy of the derived analysis and the efficiency of the proposed PS scheme.

The COVID-19 pandemic has forced tourism practitioners to create efficient strategies to attract travelers. Using three theoretical frameworks, such as tourist trust (political, destination, and interactional trust), travel constraint (intrapersonal, interpersonal, and “social distancing” structural constraint), and extended theory of planned behavior (travel attitude, perceived behavioral control, subjective norm, perceived health risk, past travel experience), we develop a comprehensive framework to explain the impact of travel promoting, restricting, and attitudinal factors on travel decision during and after the pandemic. Data was obtained through an extensive survey conducted on 1451 Korean travelers and was analyzed using probabilistic choice models and count models. The results show the specific factors that determine travel decisions during the pandemic (whether to travel and frequency) and travel intention after the pandemic. This study provides important theoretical and practical insights into how to develop successful COVID-19 recovery strategies in the tourism industry.

OBJECTIVECommon genetic variants in GCK and TCF7L2 are associated with higher fasting glucose and type 2 diabetes in nonpregnant populations. However, their associations with glucose levels from oral glucose tolerance tests (OGTTs) in pregnancy have not been assessed in a large sample. We hypothesized that these variants are associated with quantitative measures of glycemia in pregnancy.RESEARCH DESIGN AND METHODSWe analyzed the associations between variants rs1799884 (GCK) and rs7903146 (TCF7L2) and OGTT outcomes at 24–32 weeks' gestation in 3,811 mothers of European (U.K. and Australia) and 1,706 mothers of Asian (Thailand) ancestry from the HAPO cohort. We also tested associations with offspring birth anthropometrics.RESULTSThe maternal GCK variant was associated with higher fasting glucose in Europeans (P = 0.001) and Thais (P < 0.0001), 1-h glucose in Europeans (P = 0.001), and 2-h glucose in Thais (P = 0.005). It was also associated with higher European offspring birth weight, fat mass, and skinfold thicknesses (P < 0.05). The TCF7L2 variant was associated with all three maternal glucose outcomes (P = 0.03, P < 0.0001, and P < 0.0001 for fasting and 1-h and 2-h glucose, respectively) in the Europeans but not in the Thais (P > 0.05). In both populations, both variants were associated with higher odds of gestational diabetes mellitus according to the new International Association of Diabetes and Pregnancy Study Groups recommendations (P = 0.001–0.08).CONCLUSIONSMaternal GCK and TCF7L2 variants are associated with glucose levels known to carry an increased risk of adverse pregnancy outcome in women without overt diabetes. Further studies will be important to determine the variance in maternal glucose explained by all known genetic variants.

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