Predicting student success in Arcadia University’s math courses

This project examines the relative efficacy of Arcadia’s math placement test and math SAT scores in predicting student success, and explores whether SAT scores alone might suffice for certain courses

Water Quality Prediction Method Based on OVMD and Spatio-Temporal Dependence

Water quality changes at one monitoring spot are not only related to local historical data but also spatially to the water quality of the adjacent spots. Additionally, the non-linear and non-stationary nature of water quality data has a significant impact on prediction results. To improve the accuracy of water quality prediction models, a comprehensive water quality prediction model has been initially established that takes into account both data complexity and spatio-temporal dependencies. The Optimal Variational Mode Decomposition (OVMD) technology is used to effectively decompose water quality data into several simple and stable time series, highlighting short-term and long-term features and enhancing the model\u27s learning ability. The component sequence and spot adjacency matrix are used as the input of Graph Convolutional Network (GCN) to extract the spatial characteristics of the data, and the spatio-temporal dependencies of water quality data at different spots are obtained by combining GCN into the neurons of Gated Recurrent Unit (GRU). The attention model is added to automatically adjust the importance of each time node to further improve the accuracy of the training model and obtain a multi-step prediction output that more closely aligns with the characteristics of water quality change. The proposed model has been validated with real monitoring data for ammonia nitrogen (NH3-N) and total phosphorus (TP), and the results show that the proposed model is better than ARIMA, GRU and GCN+GRU models in terms of prediction results and it shows obvious advantages in the benchmark comparison experiment, which can provide reliable evidence for water pollution source traceability or early warning

Power Efficient User Cooperative Computation to Maximize Completed Tasks in MEC Networks

In this paper, the user cooperative task computation is explored by sharing the computing capability of the user equipments (UEs) so as to enhance the performance of mobile edge computing (MEC) networks. The number of completed tasks is maximized while minimizing the total power consumption of the UEs by jointly optimizing the user task offloading decision, the computational speed for the offloaded task and the transmit power for task offloading. An iterative algorithm based on the linear programming relaxation is proposed to solve the formulated mixed integer non-linear problem. The simulation results show that the proposed user cooperative computation scheme can achieve a higher completed tasks ratio than the noncooperative scheme

On consideration of content preference and sharing willingness in D2D assisted offloading

Device-to-device (D2D) assisted offloading heavily depends on the participation of human users. The content preference and sharing willingness of human users are two crucial factors in the D2D assisted offloading. In this paper, with consideration of these two factors, the optimal content pushing strategy is investigated by formulating an optimization problem to maximize the offloading gain measured by the offloaded traffic. Users are placed into groups according to their content preferences, and share content with intergroup and intragroup users at different sharing probabilities. Although the optimization problem is nonconvex, the closed-form optimal solution for a special case is obtained, when the sharing probabilities for intergroup and intragroup users are the same. Furthermore, an alternative group optimization (AGO) algorithm is proposed to solve the general case of the optimization problem. Finally, simulation results are provided to demonstrate the offloading performance achieved by the optimal pushing strategy for the special case and AGO algorithm. An interesting conclusion drawn is that the group with the largest number of interested users is not necessarily given the highest pushing probability. It is more important to give high pushing probability to users with high sharing willingness

A meta-analysis of the effects of levothyroxine dose adjustment on maternal and infant outcomes in pregnant women with hypothyroidism

Objective·To evaluate the effects of levothyroxine (L-T4) dose adjustment according to the level of thyroid stimulating hormone (TSH) on maternal and infant outcomes in the pregnant women with hypothyroidism by meta-analysis.Methods·China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP), Wanfang Data Knowledge Service Platform, PubMed, Cochrane Library and Embase were retrieved to collect all the controlled studies on the treatment of pregnant women with hypothyroidism by adjusting the dose of L-T4 according to TSH level from the establishment of the databases to April 9, 2022. The references were also traced. Literature screening, data extraction, and quality evaluation were performed independently by two researchers. Cochrane evaluation was used to evaluate the quality of the included literature. Outcome indicators included gestational hypertension, gestational diabetes, postpartum hemorrhage, delivery mode, preterm birth, fetal death, neonatal asphyxia, and low birth weight infants. RevMan 5.3 was used for meta-analysis.Result·A total of 1 268 articles were retrieved from 6 databases, and 8 were included in the study, including 4 Chinese articles and 4 English articles. The overall risk of study bias was at a moderate level. Compared with the control group, the OR of gestational diabetes risk was 0.61 (95%CI 0.44‒0.86, P=0.004) and the OR of fetal death risk was 0.38 (95%CI 0.18‒0.81, P=0.010) in the experimental group with L-T4 dose adjusted according to the TSH level of the pregnant women with hypothyroidism, which were both statistically significant. However, the treatment method of adjusting L-T4 dose did not affect the risks of vaginal delivery [OR=1.82 (95%CI 0.75‒4.40, P=0.180)], gestational hypertension [OR=0.77 (95%CI 0.53‒1.12, P=0.170)], postpartum hemorrhage [OR=1.20 (95%CI 0.50‒2.92, P=0.680)], preterm birth [OR=0.72 (95%CI 0.48‒1.06, P=0.100)], low birth weight infants [OR=1.00 (95%CI 0.65‒1.54, P=0.999)], or neonatal asphyxia [OR=0.50 (95%CI 0.20‒1.27, P=0.150)] significantly.Conclusion·Adjusting the L-T4 therapeutic dose according to the TSH level may help reduce the risks of gestational diabetes and fetal death in the pregnant women with hypothyroidism