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In order to study the influence of different base oils on the lubrication of planar double enveloping hourglass worm drive，running in test is carried out. It is found that，when using polyethylene glycol synthetic oil，the transmission efficiency increases by 4.5% and the temperature rise is 15 ℃ lower than that of poly-alpha-olefins synthetic oil. When mineral oil is used，the transmission efficiency will be reduced by about 10%，and the temperature rise will increase by more than 58 ℃. Polyethylene glycol synthetic oil has the better performance for long-term continuous working plane double enveloping hourglass worm reducer. Mineral oil is only suitable for discontinuous operation and low load applications. When heat dissipation measures are taken，poly-alpha-olefins synthetic oil can be used. Further research shows that the minimum film thickness is not enough to fully reflect the difference of transmission efficiency and temperature rise caused by different base oils，but it can be used to evaluate the lubrication state.

Feature selection plays a key role in many machine learning problems. Especially as an important data preprocessing method, robust and pragmatic feature selection methods can be applied to extract meaningful features and eliminate redundant ones. As we all known, many feature selection methods select features by using some certain feature evaluation criteria to obtain the corresponding score for every feature, such that we can select high score features. Unfortunately, correlated features usually connect with each other, which may result in large correlations between top ranked features, such that the redundancy among the selected features is brought about. To solve this problem, we introduce the redundancy matrix A in the AGRM (a novel auto-weighted feature selection framework via global redundancy minimization) framework. Meanwhile, we introduce the adaptive redundancy matrix S and treat the redundancy matrix S as an optimizing variable, rather than setting the redundant matrix S as a prior. In addition, we propose a robust algorithm to efficiently address the constrained optimization problem. Finally, extensive experiments on six datasets show the superiority of our proposed method.

Binary butterfly optimization approach (bBOA) is a recent high performing feature selection algorithm presented in 2018 which is based on the food foraging behavior of butterflies. This paper tries to improve the structure of the bBOA to enhance its classification accuracy, dimension reduction and reliability in feature selection task for who are interested in the fields of data mining and pattern recognition. The new initialization strategy and differential evolution strategy are applied to reduce the randomness of bBOA's initialization and local search process. Then, a new parameter is added to make the bBOA's transfer function more adaptive to the change of exploration and exploitation. Besides, evolution population dynamics (EPD) mechanism is employed as an extension of bBOA. The new method called optimization and extension of binary butterfly optimization approaches (OEbBOA) is tested with the K nearest neighbor classier in which twenty UCI datasets and seven recent algorithms are utilized to assess the performance of the OEbBOA algorithm. The experimental results and nonparametric Wilcoxons rank sum test confirm the efficiency of the proposed OEbBOA in maximizing classification accuracy while minimizing the number of features selected.

Feature selection in classification has always been an important but difficult problem.This kind of problem requires that feature selection algorithms can not only help classifiers to improve the classification accuracy,but also reduce the redundant features as much as possible.Therefore,in order to solve feature selection in the classification problems better,a new wrapped feature selection algorithm XGBSFS was proposed.The thought process of building trees in XGBoost was used for reference,and the importance of features from three importance metrics was measured to avoid the limitation of single importance metric.Then the improved sequential floating forward selection (ISFFS) was applied to search the feature subset so that it had high quality.Compared with the experimental results of eight datasets in UCI,the proposed algorithm has good performance.

Feature selection is an important method to reduce the number of attributes of high-dimensional data and an essential preprocess work in classification. It eliminates irrelevant, redundant, and noisy features improves the performance of the model and reduces the computational burden. Fruit fly optimization algorithm is a new algorithm proposed in recent years, which imitates the foraging behavior of fruit fly. To the best of our knowledge, it has not been systematically applied to feature selection. This paper uses the fruit fly optimization algorithm as a search strategy and designs a wrapper-based feature selection method, named binary improved fruit fly optimization algorithm (BIFFOA). Besides, four different strategies based on evolutionary population dynamics (EPD) and new mutation operators are employed to enhance the BIFFOA. The extensive experiments on 25 datasets (see Table 1) show that the performance of the BIFFOA is better than several state-of-the-art algorithms.

Constraint Programming (CP) is an efficient technique for solving combinatorial (optimization) problems. In modern constraint solver, a CP Model is defined over reversible variables that take values in domains and propagators which filter the domains of the variables. Constraint propagation scheme schedules the propagators. A reasonable constraint propagation algorithm can improve the efficiency of solving CP problems. In this paper, we propose two efficient parallel propagation schemes based on multi-thread technique for table constraint. First, we give the formal definition of the parallel consistency and prove that the parallel propagation scheme is equivalent to the classic serial propagation scheme. Then, we propose two parallel propagation schemes: static submission and dynamic submission, which exploit work stealing thread pool and atomic operations to parallelize the classic propagation of table constraint. Finally, extensive experiments on various types of problems show that the two parallel schemes outperform their original serial version on a large number of instances. The results demonstrate the competitiveness of parallel propagation algorithms on solving extensional constraints.

Variable ordering heuristic plays a central role in solving constraint satisfaction problems. Many heuristics have been proposed and well-studied. In order to take advantage of the fact that many generic variable ordering heuristics work well for different problems, we propose a novel method in this paper, namely ParetoHeu, to combine variable ordering heuristics. At each node of the search tree, a set of candidate variables is generated by a new strategy based on Pareto optimality and a variable is selected from the set randomly. The method is easy to be implemented in constraint solvers. The experiments on various benchmark problems show that ParetoHeu is more efficient than both the participant heuristics which are popular in constraint solvers. It is also more robust than some classical strategies which have been used to combine variable ordering heuristics.

Market basket prediction, which is the basis of product recommendation systems, is the concept of predicting what customers will buy in the next shopping basket based on analysis of their historical shopping records. Although product recommendation systems develop rapidly and have good performance in practice, state-of-the-art algorithms still have plenty of room for improvement. In this paper, we propose a new algorithm combining pattern prediction and preference prediction. In pattern prediction, sequential rules, periodic patterns and association rules are mined and probability models are established based on their statistical characteristics, e.g., the distribution of periods of a periodic pattern, to make a more precise prediction. Products that have a higher probability will have priority to be recommended. If the quantity of recommended products is insufficient, then we make a preference prediction to select more products. Preference prediction is based on the frequency and tendency of products that appear in customers’ individual shopping records, where tendency is a new concept to reflect the evolution of customers’ shopping preferences. Experiments show that our algorithm outperforms those of the baseline methods and state-of-the-art methods on three of four real-world transaction sequence datasets.

A concise synthesis of 3-(1-alkenyl)isoindolin-1-ones and 5-(1-alkenyl)pyrrol-2-ones has been achieved by the coupling reactions of N-acyliminium ions produced from 3-hydroxyisoindol-1-ones or 5-hydroxy-1-pyrrol-2-ones with unactivated olefins in the presence of BF3·OEt2 at room temperature. For most of the olefins, the reactions afforded the Csp3–Csp2 cross-coupling products, but for the α-methylstyrene and 1-hexene, the Csp3–Csp3 cross-coupling products were obtained.

Crying serves as the primary means through which infants communicate, presenting a significant challenge for new parents in understanding its underlying causes. This study aims to classify infant cries to ascertain the reasons behind their distress. In this paper, an efficient graph structure based on multi-dimensional hybrid features is proposed. Firstly, infant cries are processed to extract various speech features, such as spectrogram, mel-scaled spectrogram, MFCC, and others. These speech features are then combined across multiple dimensions to better utilize the information in the cries. Additionally, in order to better classify the efficient graph structure, a local-to-global convolutional neural network (AlgNet) based on convolutional neural networks and attention mechanisms is proposed. The experimental results demonstrate that the use of the efficient graph structure improved the accuracy by an average of 8.01% compared to using standalone speech features, and the AlgNet model achieved an average accuracy improvement of 5.62% compared to traditional deep learning models. Experiments were conducted using the Dunstan baby language, Donate a cry, and baby cry datasets with accuracy rates of 87.78%, 93.83%, and 93.14% respectively. [ABSTRACT FROM AUTHOR]

Bidirectional singleton arc consistency (BiSAC) which is an extended singleton arc consistency (SAC) has been proposed recently. The first contribution of this paper is to propose and prove two theorems of BiSAC theoretically (one is a property of BiSAC and the other is the property of allowing the deletion of some BiSAC-inconsistent values). Secondly, based on these properties we present two algorithms, denoted by BiSAC-DF and BiSAC-DP, to enforce BiSAC. Also, we prove their correctness and analyze the space and time complexity of them in detail. Besides, for special circumstances, we show that BiSAC-DF admits a worst-case time complexity in O(en²d4) and a best one in O(en²d³) when the problem is an already BiSAC, while BiSAC-DP also has the same best one when the tightness is small. Finally, experiments on a wide range of CSP instances show BiSAC-DF and BiSAC-DP are usually around one order of magnitude faster than the existing BiSAC-1. For some special instances, BiSAC-DP is about two orders of magnitude efficient. [ABSTRACT FROM AUTHOR]