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Data objects with both numeric and categorical attributes are prevalent in many real-world applications. However, most of the partitional clustering algorithms dealing with such data may trap into local optima. To further promote the performance, a novel clustering algorithm, called ABC-K-Prototypes (Artificial Bee Colony clustering based on K-Prototypes), is presented on the basis of the K-Prototypes algorithm, the search strategy of the artificial bee colony, and the chaos theory. In the presented approach, the one-step k-prototypes procedure is first given, and then this procedure is combined with the search strategy of the artificial bee colony to address the mixed numeric and categorical data. In the search process of scout bees, the chaotic map is utilized to generate chaotic sequences for substituting the random numbers. To accelerate the convergence of the ABC-K-Prototypes algorithm, the multi-source search is adopted in the search process of scout bees. Finally, the performance of the ABC-K-Prototypes algorithm is demonstrated by a series of experiments on mixed numeric and categorical data in comparison with that of the other popular algorithms. [ABSTRACT FROM AUTHOR]

Most of the initialization approaches are dedicated to the partitional clustering algorithms which process categorical or numerical data only. However, in real-world applications, data objects with both numeric and categorical features are ubiquitous. The coexistence of both categorical and numerical attributes make the initialization methods designed for single-type data inapplicable to mixed-type data. Furthermore, to the best of our knowledge, in the existing partitional clustering algorithms designed for mixed-type data, the initial cluster centers are determined randomly. In this paper, we propose a novel initialization method for mixed data clustering. In the proposed method, both the distance and density are exploited together to determine initial cluster centers. The performance of the proposed method is demonstrated by a series of experiments on three real-world datasets in comparison with that of traditional initialization methods. [ABSTRACT FROM AUTHOR]

Data with categorical attributes are ubiquitous in the real world. However, existing partitional clustering algorithms for categorical data are prone to fall into local optima. To address this issue, in this paper we propose a novel clustering algorithm, ABC-K-Modes (Artificial Bee Colony clustering based on K-Modes), based on the traditional k-modes clustering algorithm and the artificial bee colony approach. In our approach, we first introduce a one-step k-modes procedure, and then integrate this procedure with the artificial bee colony approach to deal with categorical data. In the search process performed by scout bees, we adopt the multi-source search inspired by the idea of batch processing to accelerate the convergence of ABC-K-Modes. The performance of ABC-K-Modes is evaluated by a series of experiments in comparison with that of the other popular algorithms for categorical data. [ABSTRACT FROM AUTHOR]

Abstract: Data objects with mixed numeric and categorical attributes are commonly encountered in real world. The k-prototypes algorithm is one of the principal algorithms for clustering this type of data objects. In this paper, we propose an improved k-prototypes algorithm to cluster mixed data. In our method, we first introduce the concept of the distribution centroid for representing the prototype of categorical attributes in a cluster. Then we combine both mean with distribution centroid to represent the prototype of the cluster with mixed attributes, and thus propose a new measure to calculate the dissimilarity between data objects and prototypes of clusters. This measure takes into account the significance of different attributes towards the clustering process. Finally, we present our algorithm for clustering mixed data, and the performance of our method is demonstrated by a series of experiments on four real-world datasets in comparison with that of traditional clustering algorithms. [Copyright &y& Elsevier]

Abstract: In many applications, data objects are described by both numeric and categorical features. The k-prototype algorithm is one of the most important algorithms for clustering this type of data. However, this method performs hard partition, which may lead to misclassification for the data objects in the boundaries of regions, and the dissimilarity measure only uses the user-given parameter for adjusting the significance of attribute. In this paper, first, we combine mean and fuzzy centroid to represent the prototype of a cluster, and employ a new measure based on co-occurrence of values to evaluate the dissimilarity between data objects and prototypes of clusters. This measure also takes into account the significance of different attributes towards the clustering process. Then we present our algorithm for clustering mixed data. Finally, the performance of the proposed method is demonstrated by a series of experiments on four real world datasets in comparison with that of traditional clustering algorithms. [Copyright &y& Elsevier]

Background: Acetylation on lysine is a widespread post-translational modification which is reversible and plays a crucial role in some biological activities. To better understand the mechanism, it is necessary to identify acetylation sites in proteins accurately. Computational methods are popular because they are more convenient and faster than experimental methods. In this study, we proposed a new computational method to predict acetylation sites in human by combining sequence features and structural features including physicochemical property (PCP), position specific score matrix (PSSM), auto covariation (AC), residue composition (RC), secondary structure (SS) and accessible surface area (ASA), which can well characterize the information of acetylated lysine sites. Besides, a two-step feature selection was applied, which combined mRMR and IFS. It finally trained a cascade classifier based on SVM, which successfully solved the imbalance between positive samples and negative samples and covered all negative sample information. Results: The performance of this method is measured with a specificity of 72.19% and a sensibility of 76.71% on independent dataset which shows that a cascade SVM classifier outperforms single SVM classifier. Conclusions: In addition to the analysis of experimental results, we also made a systematic and comprehensive analysis of the acetylation data. [ABSTRACT FROM AUTHOR]

The earth-to-air heat exchanger (EAHE) is a remarkable technology that taps into the Earth's subsurface heat to either heat or cool the air circulating within buried underground pipes, which can effectively reduce building energy consumption. In cold-arid regions of northwest China, outdoor climate parameters vary considerably throughout the year, and shallow soil temperatures also fluctuate dramatically. This dynamic thermal environment significantly impacts the practical application of the EAHE system. Furthermore, the operation efficiency of the EAHE system can be influenced by the configuration of system parameters. Therefore, this paper takes an EAHE project in Lanzhou as the research object to investigate the operation performance, energy consumption and economy of EAHE in the cold-arid regions of Northwest China. The research results show that heat exchange potential is high in summer and winter, and the outlet temperature varies more gently in winter. During the summer, the cooling capacity of the system is sufficient to the cooling demands of the building, without using auxiliary equipment for cooling; compared to an air-cooled unit with a COP of 3.2, the system reduces cooling energy consumption by 2063.4 kWh. Moreover, we determined the influence degree and optimal scheme of each factor under different evaluation indexes through orthogonal simulation. The specific parameters of the optimal scheme are as follows: pipe length 20 m, pipe diameter 100 mm, buried depth 4 m, and airflow velocity 7 m/s. Compared with the original scheme, the heat transfer efficiency and heat transfer power of this scheme are increased by 37.91 % and 17.6 %, respectively, and the cost per unit heat transfer power is 11,700 CNY/kW, which is 8000 CNY/kW lower than the original scheme. [ABSTRACT FROM AUTHOR]

• The motor behavior of droplet on tilted superhydrophobic dust surface was observed. • A dynamic model of droplet rolling dust removal was established. • The droplet cleaning effect can be controlled by the drop height and tilt angle. This study proposes a method for adjusting the mass of droplet dust removal based on droplet height and photovoltaic tilt angle, utilizing a model of droplet motion on inclined superhydrophobic surfaces. A droplet's motion on dusty and inclined hydrophobic surfaces is analyzed using a high-speed digital imaging system. The impact of drop height and photovoltaic tilt angle on the mass of dust removed by the droplet is experimentally investigated. The findings reveal that the mass of droplet dust removal increases quickly with the rise of drop height and tilt angle. The modification of the tilt angle yields a more pronounced enhancement in dust removal efficiency. At an angle of 40°, the drop height rises from 0.5 cm to 10 cm, leading to an extra 6.6 mg of dust removal per drop. At a height of 0 cm, the mass increased by 10.62 mg after raising the angle from 10° to 40°. Droplet impact on the surface is prone to shattering. The emitted minute droplets will adhere to the surface and form a cohesive ash band, with a visible light transmission rate of merely 67 %. When a droplet rolls on a tilted superhydrophobic surface, there's a dust removal threshold. Once exceeded, the rolling shape tends to become ellipsoidal. The experimental and model predictions of droplet dust removal mass are of the same order of magnitude and differ by a few micrograms. Therefore, the theoretical model can be used to improve the efficiency of droplet dust removal by adjusting the drop height and photovoltaic angle. [ABSTRACT FROM AUTHOR]

Highlights • A novel method is proposed for the prediction of N6-methyladenosine sites. • We construct the combination of deep features and the original features to make the results better. • M6A-PXGB first uses particle swarm optimization to optimize parameters in XGBoost model. • Overall accuracy of the M6A-PXGB is 77.16% to sensitivity value 76.38%, specificity value 75.98%, AUC value 0.839 and MCC value 0.535. Abstract N6-methyladenosine (m6A) is the one of the most important RNA modifications, playing the role of splicing events, mRNA exporting and stability to cell differentiation. Because of wide distribution of m6A in genes, identification of m6A sites in RNA sequences has significant importance for basic biomedical research and drug development. High-throughput laboratory methods are time consuming and costly. Nowadays, effective computational methods are much desirable because of its convenience and fast speed. Thus, in this article, we proposed a new method to improve the performance of the m6A prediction by using the combined features of deep features and original features with extreme gradient boosting optimized by particle swarm optimization (PXGB). The proposed PXGB algorithm uses three kinds of features, i.e., position-specific nucleotide propensity (PSNP), position-specific dinucleotide propensity (PSDP), and the traditional nucleotide composition (NC). By 10-fold cross validation, the performance of PXGB was measured with an AUC of 0.8390 and an MCC of 0.5234. Additionally, PXGB was compared with the existing methods, and the higher MCC and AUC of PXGB demonstrated that PXGB was effective to predict m6A sites. The predictor proposed in this study might help to predict more m6A sites and guide related experimental validation. [ABSTRACT FROM AUTHOR]

Palmitoylated proteins are important proteins that regulate several biological processes in cells, including protein localization, protein trafficking, and protein-protein interactions. The identification of palmitoylated proteins may therefore contribute to our understanding of important molecular mechanisms and biological functions. In this paper, a simple but effective computational method named MRMD-Palm is proposed for identifying palmitoylated proteins. In developing the method, first, palmitoylated and non-palmitoylated proteins were collected to construct a benchmark dataset. Next, some typical sequence-based feature-encoding schemes related to the amino acid composition, pseudo amino acid composition and physico-chemical properties were extracted. To avoid noise and reduce information redundancy, a feature selection technique named MRMD (Max-Relevance-Max-Distance) was introduced to obtain the optimal feature subset. Finally, the random forest algorithm was utilized as the predictor. Under 10-fold cross-validation, the method achieved a sensitivity of 97.1%, a specificity of 95.9% and a MCC of 73.7%, representing better performance than most current counterparts. Additionally, the experimental results showed that the use of hydrophobicity to encode palmitoylated sequences can improve the performance of the predictor. This finding is consistent with the biological phenomenon that palmitoylation is highly hydrophobic. It is believed that the results of this study will lead to the determination of the protein palmitoylation mechanism. • Novel analytical method is developed to analysis and identify palmitoylated proteins. • A feature selection technique named MRMD (Max-Relevance-Max-Distance) was introduced to obtain the optimal feature subset. • Results showed that the hydrophobicity feature can improve the performance of the predictor. [ABSTRACT FROM AUTHOR]