A Hybrid Deep Convolutional Neural Network Approach for Predicting the Traffic Congestion Index

Traffic congestion is one of the most important issues in large cities, and the overall travel speed is an important factor that reflects the traffic status on road networks. This study proposes a hybrid deep convolutional neural network (CNN) method that uses gradient descent optimization algorithms and pooling operations for predicting the short-term traffic congestion index in urban networks based on probe vehicles. First, the input data are collected by the probe vehicles to calculate the traffic congestion index (output label). Then, a CNN that uses gradient descent optimization algorithms and pooling operations is applied to enhance its performance. Finally, the proposed model is chosen on the basis of the R-squared (R2) and root mean square error (RMSE) values. In the best-case scenario, the proposed model achieved an R2 value of 98.7%. In addition, the experiments showed that the proposed model significantly outperforms other algorithms, namely the ordinary least squares (OLS), k-nearest neighbors (KNN), random forest (RF), recurrent neural network (RNN), artificial neural network (ANN), and convolutional long short-term memory (ConvLSTM), in predicting traffic congestion index. Furthermore, using the proposed method, the time-series changes in the traffic congestion status can be reliably visualized for the entire urban network

Influential factors in the out-of-class activities of Korean college students

This study aimed to explore who participates in what kinds of out-of-class activities in Korea\u27s universities. Therefore, the researchers examine whether differences exist in the pattern of out-of-class experiences according to the individual characteristics of the students, including gender, grade, household income level, high school performance and major. The researchers also aimed to examine the empirical evidence to determine the relationships between the patterns in out-of-class activities and the institutional characteristics of the university that the student attends. In terms of the institutional characteristics, this study is concerned with the location and size of the university. To explore these questions, the researchers analyzed K-NSSE data with hierarchical linear modeling. In sum, the findings of the statistical analysis of this study support the results of the preceding research in which different personal and institutional characteristics are related to five types of out-of-class activities. (DIPF/Orig.

Improving Traffic Efficiency in a Road Network by Adopting Decentralised Multi-Agent Reinforcement Learning and Smart Navigation

In the future, mixed traffic flow will consist of human-driven vehicles (HDVs) and connected autonomous vehicles (CAVs). Effective traffic management is a global challenge, especially in urban areas with many intersections. Much research has focused on solving this problem to increase intersection network performance. Reinforcement learning (RL) is a new approach to optimising traffic signal lights that overcomes the disadvantages of traditional methods. In this paper, we propose an integrated approach that combines the multi-agent advantage actor-critic (MA-A2C) and smart navigation (SN) to solve the congestion problem in a road network under mixed traffic conditions. The A2C algorithm combines the advantages of value-based and policy-based methods to stabilise the training by reducing the variance. It also overcomes the limitations of centralised and independent MARL. In addition, the SN technique reroutes traffic load to alternate paths to avoid congestion at intersections. To evaluate the robustness of our approach, we compare our model against independent-A2C (I-A2C) and max pressure (MP). These results show that our proposed approach performs more efficiently than others regarding average waiting time, speed and queue length. In addition, the simulation results also suggest that the model is effective as the CAV penetration rate is greater than 20%

The relationship between participation in out-of-class activities and cognitive and social outcomes of Korean college students

In the era of the 4th Industrial Revolution, higher education institutions should change practices of educational programs and services, which are mainly based on traditional classroom-based instructions, to allow students to have more diverse experiences. Since college students spend relatively more time engaged in out-of-class activities than attending regular courses, it is necessary to examine how participating in out-of-class programs is related to cultivation of the competencies that the future demands. This study explores the relationship between out-of-class activity participation and perceived change in cognitive and social outcomes of Korean college students. Five out-of-class activities were examined: learning community, undergraduate research, service learning, internship, and residential college programs. K-NSSE (Korea-National Survey of Student Engagement) data were analyzed using hierarchical linear model analysis. The study findings are consistent with the results of previous research that demonstrated a positive association between participating in out-of-class activities and students\u27 cognitive and social outcomes. (DIPF/Orig.

Copper nanofiber-networked cobalt oxide composites for high performance Li-ion batteries

We prepared a composite electrode structure consisting of copper nanofiber-networked cobalt oxide (CuNFs@CoOx). The copper nanofibers (CuNFs) were fabricated on a substrate with formation of a network structure, which may have potential for improving electron percolation and retarding film deformation during the discharging/charging process over the electroactive cobalt oxide. Compared to bare CoOxthin-film (CoOxTF) electrodes, the CuNFs@CoOxelectrodes exhibited a significant enhancement of rate performance by at least six-fold at an input current density of 3C-rate. Such enhanced Li-ion storage performance may be associated with modified electrode structure at the nanoscale, improved charge transfer, and facile stress relaxation from the embedded CuNF network. Consequently, the CuNFs@CoOxcomposite structure demonstrated here can be used as a promising high-performance electrode for Li-ion batteries

Changes in Cytokine Expression after Electroacupuncture in Neuropathic Rats

The production of proinflammatory cytokines including interleukin-1 (IL-1), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) plays a key role in chronic pain such as neuropathic pain. We investigated changes in cytokine expression in injured peripheral nerves and dorsal root ganglia (DRG) following electroacupuncture (EA) treatment. Neuropathic pain was induced by peripheral nerve injury to the left hind limb of Sprague-Dawley rats under pentobarbital anesthesia. Two weeks later, the nerve-injured rats were treated by EA for 10 minutes. The expression levels of IL-1β, IL-6, and TNF-α in peripheral nerves and DRG of neuropathic rats were significantly increased in nerve-injured rats. However, after EA, the cytokine expression levels were noticeably decreased in peripheral nerves and DRG. These results suggest that EA stimulation can reduce the levels of proinflamtory cytokines elevated after nerve injury

Associations between Organochlorine Pesticides and Vitamin D Deficiency in the U.S. Population

Background: Recently low dose organochlorine (OC) pesticides have been strongly linked to various chronic diseases including diabetes and cardiovascular diseases. Both field and animal studies have suggested a possibility that persistent lipophilic chemicals like OC pesticides can cause vitamin D deficiency, but there have been no human studies of exposure to any chemical as a possible cause of vitamin D deficiency. This study was performed to examine if serum concentrations of OC pesticides were associated with serum concentrations of 25-hydroxyvitamin D (25(OH)D) in the U.S. general population. Methodology/Principal Findings: Cross-sectional associations of serum OC pesticides with serum 25(OH)D were investigated in 1,275 subjects aged $20 in the National Health and Nutrition Examination Survey(NHANES), 2003–2004. We selected 7 OC pesticides detectable in$80 % of participants. Among the 7 OC pesticides, p,p9-DDT (b = 20.022, P,0.01), p,p9-DDE (b = 20.018, P = 0.04), and b-hexachlorocyclohexane (b = 20.022, P = 0.02) showed significant inverse associations with serum 25(OH)D levels. When study subjects were stratified by age, race, and the presence of various chronic diseases, p,p9-DDT showed consistent inverse associations in all subgroups, although stronger associations tended to be observed among subjects with old age, white race, or chronic diseases. Conclusion/Significance: The current study suggests that the background exposure to some OC pesticides leads to vitamin D deficiency in human. Considering the importance of vitamin D deficiency in the development of chronic diseases