Social media and student academic performance: A cross-country analysis using PISA 2018

The pervasive use of social media is reshaping how the emerging generation communicates, learns, and thinks. As such, examining its impact on academic performance has grown increasingly important. The purpose of this study is to investigate the effects of social media usage on students’ learning outcomes, using data from the Programme for International Student Assessment (PISA) 2018 database. In order to eliminate selection bias and assess the causal effect of using social media on learning, this research used propensity score matching (PSM) as an approach. By conducting analyses in each participating country, we were able to observe how the effects of social media use for school learning vary in different social, cultural, and political contexts. After obtaining the average treatment effects of each country, we find that the effects of social media use on learning varied significantly by country. In countries such as Mexico and Turkey, a positive relationship was observed between social media usage for academic purposes and student performance. Conversely, in countries such as the US and UK, a negative relationship was evident. Although the reasons behind these contrasting outcomes across countries remain outside the scope of this paper, the conclusions and practical implications are presented with caution, acknowledging the limitations of our research and indicating potential areas for further exploration

A remaining useful life prediction and maintenance decision optimal model based on Gamma process

Aiming at the practical problem of maintenance decision-making, the remaining useful life (RUL) prediction method and the maintenance decision optimization model are studied emphatically. Firstly, the condition space model based on Gamma degradation process is established, according to the characteristics of the degradation process of the equipment condition. Then the RUL expectancy is predicted by this model, and the RUL probability density function of the equipment can be got. Finally, this model is validated by the data obtained from the roller bearing life test. The maintenance decision model is established with the minimum cost as the objective, the maintenance decision is optimized, and the RUL prediction and maintenance decision are realized. the example proves the validity and feasibility of this model

Design of a reconfigurable FFT processor using multi-objective genetic algorithm

This paper describes the implementation of Multi-objective Genetic Algorithm (MOGA) in a 16-point Radix-4 Single Path Delay Feedback (R4SDF) pipelined Fast Fourier Transform (FFT) processor in Verilog. The role of MOGA is to optimize the wordlength of the FFT coefficient and at the same time make sure the processor operates at acceptable Signal to Noise Ratio (SNR). Reducing the wordlength of FFT coefficient will contribute to lower Switching Activity (SA), thus lower power consumption is required for the operation of FFT processor

The NN phase shifts in the extended quark-delocalization, color-screening model

An alternative method is applied to the study of nucleon-nucleon(NN) scattering phase shifts in the framework of extended quark delocalization, color-screening model(QDCSM), where the one-pion-exchange(OPE) with short-range cutoff is included.Comment: 5 pages, 3 figures, two-colum

Energy shift of the three-particle system in a finite volume

Using the three-particle quantization condition recently obtained in the particle-dimer framework, the finite-volume energy shift of the two lowest three-particle scattering states is derived up to and including order $L^{-6}$. Furthermore, assuming that a stable dimer exists in the infinite volume, the shift for the lowest particle-dimer scattering state is obtained up to and including order $L^{-3}$. The result for the lowest three-particle state agrees with the results from the literature, and the result for the lowest particle-dimer state reproduces the one obtained by using the Luescher equation.Comment: Final version published in Phys. Rev. D. Corrected typos: factor of 2 in Eq. (115) [previously Eq. (114)] and factor 6 in Eq. (120) [previously Eq. (119)

Experimentally-determined characteristics of radiant systems for office buildings

Radiant heating and cooling systems have significant energy-saving potential and are gaining popularity in commercial buildings. The main aim of the experimental study reported here was to characterize the behavior of radiant cooling systems in a typical office environment, including the effect of ceiling fans on stratification, the variation in comfort conditions from perimeter to core, control on operative temperature vs. air temperature and the effect of carpet on cooling capacity. The goal was to limit both the first cost and the perceived risk associated with such systems. Two types of radiant systems, the radiant ceiling panel (RCP) system and the radiant slab (RS) system, were investigated. The experiments were carried out in one of the test cells that constitute the FLEXLAB test facility at the Lawrence Berkeley National Laboratory in March and April 2016. In total, ten test cases (five for RCP and five for RS) were performed, covering a range of operational conditions. In cooling mode, the air temperature stratification is relatively small in the RCP, with a maximum value of 1.6 K. The observed stratification effect was significantly greater in the RS, twice as much as that in the RCP. The maximum increase in dry bulb temperature in the perimeter zone due to solar radiation was 1.2 K for RCP and 0.9 K for RS – too small to have a significant impact on thermal comfort. The use of ceiling fans was able to reduce any excess stratification and provide better indoor comfort, if required. The use of thin carpet requires a 1 K lower supply chilled water temperature to compensate for the added thermal resistance, somewhat reducing the opportunities for water-side free cooling and increasing the risk of condensation. In both systems, the difference between the room operative temperature and the room air temperature is small when the cooling loads are met by the radiant systems. This makes it possible to use the air temperature to control the radiant systems in lieu of the operative temperature, reducing both first cost and maintenance costs

On Reinforcement Learning for Full-length Game of StarCraft

StarCraft II poses a grand challenge for reinforcement learning. The main difficulties of it include huge state and action space and a long-time horizon. In this paper, we investigate a hierarchical reinforcement learning approach for StarCraft II. The hierarchy involves two levels of abstraction. One is the macro-action automatically extracted from expert's trajectories, which reduces the action space in an order of magnitude yet remains effective. The other is a two-layer hierarchical architecture which is modular and easy to scale, enabling a curriculum transferring from simpler tasks to more complex tasks. The reinforcement training algorithm for this architecture is also investigated. On a 64x64 map and using restrictive units, we achieve a winning rate of more than 99\% against the difficulty level-1 built-in AI. Through the curriculum transfer learning algorithm and a mixture of combat model, we can achieve over 93\% winning rate of Protoss against the most difficult non-cheating built-in AI (level-7) of Terran, training within two days using a single machine with only 48 CPU cores and 8 K40 GPUs. It also shows strong generalization performance, when tested against never seen opponents including cheating levels built-in AI and all levels of Zerg and Protoss built-in AI. We hope this study could shed some light on the future research of large-scale reinforcement learning.Comment: Appeared in AAAI 201