Students’ performance is an important factor for the evaluation of teaching quality in colleges. The aim of this study is to propose a novel intelligent approach to predict students’ performance ...using support vector regression (SVR) optimized by an improved duel algorithm (IDA). To the best of our knowledge, few research studies have been developed to predict students’ performance based on student behavior, and the novelty of this study is to develop a new hybrid intelligent approach in this field. According to the obtained results, the IDA-SVR model clearly outperformed the other models by achieving less mean square error (MSE). In other words, IDA-SVR with an MSE of 0.0089 has higher performance than DT with an MSE of 0.0326, SVR with an MSE of 0.0251, ANN with an MSE of 0.0241, and PSO-SVR with an MSE of 0.0117. To investigate the efficacy of IDA, other parameter optimization methods, that is, the direct determination method, grid search method, GA, FA, and PSO, are used for a comparative study. The results show that the IDA algorithm can effectively avoid the local optima and the blindness search and can definitely improve the speed of convergence to the optimal solution.
We derive generalization bounds for learning algorithms based on their robustness: the property that if a testing sample is “similar” to a training sample, then the testing error is close to the ...training error. This provides a novel approach, different from complexity or stability arguments, to study generalization of learning algorithms. One advantage of the robustness approach, compared to previous methods, is the geometric intuition it conveys. Consequently, robustness-based analysis is easy to extend to learning in non-standard setups such as Markovian samples or quantile loss. We further show that a weak notion of robustness is both sufficient and necessary for generalizability, which implies that robustness is a fundamental property that is required for learning algorithms to work.
A metal- and reagent-free, electrochemical intramolecular oxidative amination reaction of tri- and tetrasubstituted alkenes has been developed. The electrosynthetic method proceeds through radical ...cyclization to form the key C–N bond, allowing a variety of hindered tri- and tetrasubstituted olefins to participate in the amination reaction. The result is the efficient synthesis of a host of alkene-bearing cyclic carbamates and ureas and lactams.
As a clean and efficient technology for the degradation of organic contaminants, sulfate radical based advanced oxidation processes (SR-AOPs) have attracted more and more attention in the past ...decades. Cobalt is regarded as the most reactive and efficient non-noble metal catalyst for the activation of persulfate including peroxymonosulfate (PMS) and peroxydisulfate (PDS) to produce sulfate radicals. Due to the limitations of homogeneous catalytic systems, the heterogeneous cobalt-containing catalysts have been emerged and rapidly developed. Various strategies have been schemed to further enhance the activation ability of persulfate by heterogeneous cobalt-containing catalysts. This paper provides an overview on the recent progress in enhancement strategies for the highly efficient activation of persulfate by heterogeneous cobalt-containing catalysts. With a brief introduction on the chemistry and feature of sulfate radical reactions catalyzed by homogeneous Co2+/Co3+ species, the main strategies for enhancing persulfate activation by heterogeneous cobalt-containing catalysts are summarized, such as surface and morphology design, multiple reactive centers design, organic-inorganic hybrids and heterostructure composites. Future perspectives of heterogeneous SR-AOPs systems catalyzed by cobalt-containing catalysts are outlined.
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•Enhancement strategies for persulfate activation are generalized and summarized.•The potential mechanisms of heterogenous cobalt-containing catalysts are discussed.•A future blueprint for the heterogeneous cobalt-containing catalysts is described.
Electrochemical 1,2‐hydroxydifluoromethylation and C−H difluoromethylation of acrylamides were developed by using CF2HSO2NHNHBoc as the source of the CF2H group. These electricity‐powered oxidative ...alkene functionalization reactions do not need transition‐metal catalysts or chemical oxidants. The reaction outcome, 1,2‐difuntionalization or C−H functionalization, is determined by the substituents on the amide nitrogen atom of the acrylamides instead of by the reaction conditions.
Turn up the power: Electrochemical 1,2‐hydroxydifluoromethylation and C−H difluoromethylation of acrylamides is developed by using CF2HSO2NHNHBoc as the source of the CF2H group. These electricity‐powered oxidative alkene functionalization reactions do not need transition‐metal catalysts or chemical oxidants.
High-carbohydrate diets (HCD) can induce the occurrence of nonalcoholic fatty liver disease (NAFLD), characterized by dramatic accumulation of hepatic lipid droplets (LDs). However, the potential ...molecular mechanisms are still largely unknown. In this study, we investigated the role of autophagy in the process of HCD-induced changes of hepatic lipid metabolism, and to examine the process of underlying mechanisms during these molecular contexts. We found that HCD significantly increased hepatic lipid accumulation and activated autophagy. Using primary hepatocytes, we found that HG increased lipid accumulation and stimulated the release of NEFA by autophagy-mediated lipophagy, and that lipophagy significantly alleviated high glucose (HG)-induced lipid accumulation. Oxidative and endoplasmic reticulum (ER) stress pathways played crucial regulatory roles in HG-induced lipophagy activation and HG-induced changes of lipid metabolism. Further investigation found that HG-activated lipophagy and HG-induced changes of lipid metabolism were via enhancing carbohydrate response element-binding protein (ChREBP) DNA binding capacity at PPARγ promoter region, which in turn induced transcriptional activation of the key genes related to lipogenesis and autophagy. The present study, for the first time, revealed the novel mechanism for lipophagy mediating HCD-induced changes of lipid metabolism by oxidative stress and ER stress, and ChREBP/PPARγ pathways. Our study provided innovative evidence for the direct relationship between carbohydrate and lipid metabolism via ChREBP/PPARγ pathway.
Cobalt nanowires with different shapes and sizes were synthesized by reduction of carboxylate salts of Co
II
in 1, 2-butanediol using a solvothermal chemical process. The well-crystallized Co ...nanowires with hexagonal close-packed (hcp) phase are observed and the (002) crystalline direction is along the long axis of nanowires. The morphology control is strongly dependent on the reaction parameters. By varying the amount of capping agent in proper ranges, the effect of reaction parameters on controlling the size and shape of Co nanowires is demonstrated. With the amount of capping agent increasing, the aspect ratio of Co nanowires increases remarkably. However, the magnetic measurement of cobalt nanowires shows that the coercivity of the Co nanocrystals does not increase with the increase in aspect ratio monotonously, which suggests that the tip shape and microstructure also play an important role in the magnetization reversal process of the Co nanocrystals, and the aspect ratio plays a much less role as the ratio value exceeds 11. To further understand the effect of size on the magnetic properties in the Co nanowires, micromagnetic simulations were performed, which confirms that the magnetic properties are barely affected by the aspect ratio larger than 10. The highest coercivity of 624 kA·m
−1
is obtained for ellipsoid nanowires with a mean length of 200 nm, which also displays a strong magnetic anisotropy. As a result, the highest energy product of the wires reaches 248 kJ·m
−3
.
This technical note studies Markov decision processes under parameter uncertainty. We adapt the distributionally robust optimization framework, assume that the uncertain parameters are random ...variables following an unknown distribution, and seek the strategy which maximizes the expected performance under the most adversarial distribution. In particular, we generalize a previous study which concentrates on distribution sets with very special structure to a considerably more generic class of distribution sets, and show that the optimal strategy can be obtained efficiently under mild technical conditions. This significantly extends the applicability of distributionally robust MDPs by incorporating probabilistic information of uncertainty in a more flexible way.
Students' performance is an important factor for the evaluation of teaching quality in colleges. The prediction and analysis of students' performance can guide students' learning in time. Aiming at ...the low accuracy problem of single model in students' performance prediction, a combination prediction method is put forward based on ant colony algorithm. First, considering the characteristics of students' learning behavior and the characteristics of the models, decision tree (DT), support vector regression (SVR) and BP neural network (BP) are selected to establish three prediction models. Then, an ant colony algorithm (ACO) is proposed to calculate the weight of each model of the combination prediction model. The combination prediction method was compared with the single Machine learning (ML) models and other methods in terms of accuracy and running time. The combination prediction model with mean square error (MSE) of 0.0089 has higher performance than DT with MSE of 0.0326, SVR with MSE of 0.0229 and BP with MSE of 0.0148. To investigate the efficacy of the combination prediction model, other prediction models are used for a comparative study. The combination prediction model with MSE of 0.0089 has higher performance than GS-XGBoost with MSE of 0.0131, PSO-SVR with MSE of 0.0117 and IDA-SVR with MSE of 0.0092. Meanwhile, the running speed of the combination prediction model is also faster than the above three methods.
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