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Single-cell RNA sequencing (scRNA-seq) technology is one of the most cost-effective and efficacious methods for revealing cellular heterogeneity and diversity. Precise identification of cell types is essential for establishing a robust foundation for downstream analyses and is a prerequisite for understanding heterogeneous mechanisms. However, the accuracy of existing methods warrants improvement, and highly accurate methods often impose stringent equipment requirements. Moreover, most unsupervised learning-based approaches are constrained by the need to input the number of cell types a prior, which limits their widespread application. In this paper, we propose a novel algorithm framework named WLGG. Initially, to capture the underlying nonlinear information, we introduce a weighted distance penalty term utilizing the Gaussian kernel function, which maps data from a low-dimensional nonlinear space to a high-dimensional linear space. We subsequently impose a Lasso constraint on the regularized Gaussian graphical model to enhance its ability to capture linear data characteristics. Additionally, we utilize the Eigengap strategy to predict the number of cell types and obtain predicted labels via spectral clustering. The experimental results on 14 test datasets demonstrate the superior clustering accuracy of the WLGG algorithm over 16 alternative methods. Furthermore, downstream analysis, including marker gene identification, pseudotime inference, and functional enrichment analysis based on the similarity matrix and predicted labels from the WLGG algorithm, substantiates the reliability of WLGG and offers valuable insights into biological dynamic biological processes and regulatory mechanisms. [ABSTRACT FROM AUTHOR]

The location of level crossings for pedestrian circulation is a safety issue due to potential human losses and material damage. This problem has become a concern in urban and industrial environments due to the characteristics of rail systems and unsafe pedestrian behaviour. Research reveals that psychological and infrastructural factors influence pedestrians' decision-making when crossing railroad lines. One of these is the convenience that leads pedestrians to cross at the nearest point. However, investigations to date only study the impact of location on the risk and severity of crashes and pedestrian rule violations. The current research is quasi-experimental, i.e., no optimisation tool has been developed to determine which crossing points pedestrians should follow. Therefore, this research develops a mathematical model for locating level crossings at railways to minimise the total weighted-walked distance. A genetic algorithm was proposed for solving the model, especially for large-size problems. The algorithm parameters were calibrated using the design of experiments and ten instances based on the characteristics of the area under study. The obtained results provide (1) the location of the level crossings and (2) the route pedestrians should follow for each origin-destination pair based on the facilities adjacent to the rail lines. A sensitivity analysis was performed using the ten instances to determine how the location of the level crossings changes with modifications in the facilities' location. Results showed that the model provides alternative solutions according to the problem's size. This study delivers a methodology for practitioners and stakeholders of transportation systems.

The location of level crossings for pedestrian circulation is a safety issue due to potential human losses and material damage. This problem has become a concern in urban and industrial environments due to the characteristics of rail systems and unsafe pedestrian behaviour. Research reveals that psychological and infrastructural factors influence pedestrians' decision-making when crossing railroad lines. One of these is the convenience that leads pedestrians to cross at the nearest point. However, investigations to date only study the impact of location on the risk and severity of crashes and pedestrian rule violations. The current research is quasi-experimental, i.e., no optimisation tool has been developed to determine which crossing points pedestrians should follow. Therefore, this research develops a mathematical model for locating level crossings at railways to minimise the total weighted-walked distance. A genetic algorithm was proposed for solving the model, especially for large-size problems. The algorithm parameters were calibrated using the design of experiments and ten instances based on the characteristics of the area under study. The obtained results provide (1) the location of the level crossings and (2) the route pedestrians should follow for each origin-destination pair based on the facilities adjacent to the rail lines. A sensitivity analysis was performed using the ten instances to determine how the location of the level crossings changes with modifications in the facilities' location. Results showed that the model provides alternative solutions according to the problem's size. This study delivers a methodology for practitioners and stakeholders of transportation systems. • A model for locating level crossing in railway lines to minimise walked distance. • A genetic algorithm was developed and calibrated for solving any problem size. • Sensitivity analysis shows the solution flexibility under changes in the system. • A useful tool for stakeholders of any transportation system. [ABSTRACT FROM AUTHOR]

The gas content in the oil is used as the fault input characteristic for the power transformer. Still, the accuracy of the diagnosis results is not ideal, and such a model is unstable. This research proposes a hybrid intelligent fault diagnosis method based on the improved grey wolf algorithm and an optimized probabilistic neural network. Firstly, a strategy of three nonlinear control factors is introduced to fit the grey wolves’ search process. The weighted distance was modified to update the position information of grey wolf elements to avoid the algorithm falling into the local optimum. Secondly, the performance of the improved grey wolf algorithm was tested through six commonly used functions. The results show that the improved grey wolf algorithm has high convergence accuracy and stability in both multimodal and unimodal functions. Finally, the improved grey wolf algorithm and the probabilistic neural network were combined to diagnose the oil-immersed power transformer through hybrid intelligent algorithms. As a result, the fault diagnosis model proved valid for transformer fault diagnosis.

Designing appropriate similarity metrics (distance) and estimating the optimal number of clusters have been two important issues in cluster analysis. This study proposed an improved k-means clustering algorithm involving a Weighted Distance and a novel Internal Validation index (WeDIV). The weighted distance, EP_dis, was designed by considering the relative contribution between Euclidean and Pearson distances with a weighted strategy. This strategy can effectively capture information reflecting the globally spatial correlation and locally variable trend simultaneously in high-dimensional space. The new internal validation index,RCH, inspired by the Calinski-Harabasz (CH) index and the analysis of variance, was developed to automatically estimate the optimal number of clusters. The EP_dis was proved reliable in mathematics and was validated on two simulated datasets. Four simulated datasets representing different properties were used to validate the effectiveness of RCH. Furthermore, We compared the clustering performance of WeDIV with 12 prevailing clustering algorithms on 16 UCI datasets. The results demonstrated that WeDIV outperforms the others regardless of specifying the number of clusters or not.

Designing appropriate similarity metrics (distance) and estimating the optimal number of clusters have been two important issues in cluster analysis. This study proposed an improved k -means clustering algorithm involving a Weighted Distance and a novel Internal Validation index (WeDIV). The weighted distance, EP _ dis , was designed by considering the relative contribution between Euclidean and Pearson distances with a weighted strategy. This strategy can effectively capture information reflecting the globally spatial correlation and locally variable trend simultaneously in high-dimensional space. The new internal validation index, RCH , inspired by the Calinski-Harabasz (CH) index and the analysis of variance, was developed to automatically estimate the optimal number of clusters. The EP _ dis was proved reliable in mathematics and was validated on two simulated datasets. Four simulated datasets representing different properties were used to validate the effectiveness of RCH. Furthermore, We compared the clustering performance of WeDIV with 12 prevailing clustering algorithms on 16 UCI datasets. The results demonstrated that WeDIV outperforms the others regardless of specifying the number of clusters or not. [ABSTRACT FROM AUTHOR]

Fuzzy queuing frameworks shed data on openness and fluctuation in each and every domain. In this detailed study, we present a model for assessing system characteristics in a tri-cum biserial bulk queuing systems with a server that exists commonly in an ambiguous environment. The estimates of two distinct ranking prototypes of a given queuing model are compared in this research. For the model's validity, we acquired numerical illustrations in the tabular form, showing that a fuzzy queue can be more realistic than a crisp queue. [ABSTRACT FROM AUTHOR]

Context: To analyze the scales at which landscape structure influences ecological processes, two approaches with different underlying ecological assumptions exist; the usual threshold method and the weighted-distance method. Objectives: We used abundance of species to test if the combination of weighted-distance and threshold approaches improves the explained variance of landscape metrics. Methods: We developed a workflow using the two approaches to calculate metrics computed at multiple scales. The latter was developed using weighted metrics based on different weighted-distance functions, and one metric could be selected for more than one spatial scale. Then, we tested the explained variance of species distribution (the activity-density of Abax parallelepipedus) by these two approaches applied independently and then together in modeling a specific ecological response. Results: The combination of metrics computed at multiple scales calculated by both weighted-distance and threshold method improved the predictive performance of the models. More precisely, adding metrics derived from the weighted-distance method to the threshold method significantly increased the explained variance when using the same environmental variables. The mean R2 values of the selected model for the threshold method was 0.34 ± 0.10, 0.49 ± 0.11 with the weighted-distance method, and reached 0.71 ± 0.07 with the two methods combined. These results demonstrate the importance of combining metrics using the weighted-distance method and the threshold method. In addition, activity-density was better explained by metrics selected at multiple scales. Conclusions: This study highlights the importance of combining threshold and weighted-distance method at several scales to improve the explanation of ecological responses based on species abundance. [ABSTRACT FROM AUTHOR]

Human emotion plays a major role in expressing their feelings through speech. Emotional speech recognition is an important research field in the human–computer interaction. Ultimately, the endowing machines that perceive the users' emotions will enable a more intuitive and reliable interaction.The researchers presented many models to recognize the human emotion from the speech. One of the famous models is the Gaussian mixture model (GMM). Nevertheless, GMM may sometimes have one or more of its components as ill-conditioned or singular covariance matrices when the number of features is high and some features are correlated. In this research, a new system based on a weighted distance optimization (WDO) has been developed for recognizing the emotional speech. The main purpose of the WDO system (WDOS) is to address the GMM shortcomings and increase the recognition accuracy. We found that WDOS has achieved considerable success through a comparative study of all emotional states and the individual emotional state characteristics. WDOS has a superior performance accuracy of 86.03% for the Japanese language. It improves the Japanese emotion recognition accuracy by 18.43% compared with GMM and k -mean. [ABSTRACT FROM AUTHOR]

Industrial networks are complex and diverse. Among existing intrusion prevention systems available, several of them have problems such as low detection accuracy rate, high false positive (FP) rate, and low real-time performance for impersonation attacks. To address such issues, it is proposed in this article an industrial network intrusion detection algorithm based on multifeature data clustering optimization model, where the weighted distances and security coefficients of data are classified based on the priority threshold of data attribute feature for each node in the network, given that the data modules in the industrial network environment are diverse and easy to diagnose, restore, and rebuild. The proposed algorithm can effectively improve the detection rate and real-time performance of detecting abnormal behavior for the multifeature data in industrial networks. The novel features are twofold, to rapidly select a node with high-security coefficient as the cluster center, and match the multifeature data around the center into a cluster. Experimental results show that the proposed algorithm has good superiority in terms of detection rate and time compared to other algorithms. In the industrial network, the detection accuracy of abnormal data reaches 97.8%, and the FP of detection is decreased by 8.8%. [ABSTRACT FROM AUTHOR]

The ability to accurately and reliably predict annual electricity demand is essential in modern society for effective planning, economic development, and to ensure the sustainability of the electricity supply. Considering the correlation between annual electricity demand and economic development, as well as annual electricity demand under low-carbon-economy targets, this paper proposes an improved quantum clustering algorithm (particle swarm optimization–weighted distance quantum clustering, PSO-WDQC) as a power demand forecasting model. This method can not only improve the accuracy of predictions but also accurately evaluate the economic development of a region. To demonstrate this ability, the paper applies the proposed method to low-dimensional Iris data as well as high-dimensional Wine data in order to verify the effectiveness of the method. Then, the method is combined with ridge regression to predict the demand for electricity under the low-carbon-economy target of China. The experimental results show that the method can accurately predict annual power demand with a relative error of 0.1674%. Moreover, the model accurately reflects that the Chinese economy has entered a new normal state since 2012, meaning that the economic growth rate has changed from high-speed to medium-high-speed. [ABSTRACT FROM AUTHOR]

In this paper, the effect of the initial clustering center selection on the performance of the K-means algorithm is studied, and the performance of the algorithm is enhanced through better initialization techniques. In the K-means clustering process, when calculating the density of a data set by using a weighted distance density calculation method, significant improvement in the defects of poor clustering results caused by the local optimum and large intra-cluster variance in the traditional K-means clustering algorithm has been found. Experimental results show that by using the improved method proposed in this paper, the intra-cluster variance of clustering results is reduced by 15.5% compared with the traditional method, which makes great improvement in the performance of the algorithm. [ABSTRACT FROM AUTHOR]

Given a set P of n points in the plane and a multiset W of k weights with k ≤ n , we assign each weight in W to a distinct point in P to minimize the maximum weighted distance from the weighted center of P to any point in P. In this paper, we present an algorithm which takes O (k 2 n 2 log 3 ⁡ n) time for the problem. We also consider the case that all weights in W are at most 1, and present an O (k 5 n log 3 ⁡ k + k n log 3 ⁡ n) -time algorithm. For a constant k , it takes only O (n log 3 ⁡ n) time, which is near linear. [ABSTRACT FROM AUTHOR]

Aiming at the problems of the high time complexity and poor detection quality of current outlier detection algorithms, we propose a new outlier detection algorithm based on the improved OPTICS clustering and LOPW. Firstly, the original data set is preprocessed by the improved OPTICS clustering algorithm and the preliminary outlier dataset is obtained by filtering the reachability graph of clustering results. Then, we use the newly defined local outlier factor based on P-weight (LOPW) to calculate the degree of outliers of the objects in the primary outlier dataset. When distances calculated, the leave-one partition information entropy gain is introduced to determine the weight of features, thus improving the precision of outlier detection. Experimental results show that the improved algorithm can improve the computational efficiency and the precision of outlier detection. [ABSTRACT FROM AUTHOR]

In this article the author discusses the problem of defuzzification by minimizing the weighted distance between the two fuzzy quantities. Also, this study obtains the nearest point with respect to a fuzzy number showing that this point is unique and relative to the weighted distance. Through applying this point, this method is presented for effectively ranking of the fuzzy numbers and their images to deal with and overcome the deficiencies of the previous techniques. Finally, several numerical examples following the procedure indicate the ranking results are valid. [ABSTRACT FROM AUTHOR]

Counting the number of shortest paths in various graphs is an important and interesting combinatorial problem, especially in weighted graphs with various applications. We consider a specific infinite graph here, namely the honeycomb grid. Changing to its dual, the triangular grid, paths between triangle pixels (we abbreviate this term to trixels) are counted. The number of shortest weighted paths between any two trixels of the triangular grid is discussed. For each trixel, there are three different types of neighbor trixels, 1-, 2- and 3-neighbours, depending the Euclidean distance of their midpoints. When considering weighted distances, the positive values α, β and γ are assigned to the ‘steps’ to various neighbors. We gave formulae for the number of shortest weighted paths between any two trixels in various cases by the respective weight values. The results are nicely connected to various numbers well-known in combinatorics, e.g., to binomial coefficients and Fibonacci numbers.

Remote sensing image retrieval (RSIR) is a fundamental task in remote sensing. Most content-based RSIR approaches take a simple distance as similarity criteria. A retrieval method based on weighted distance and basic features of convolutional neural network (CNN) is proposed in this letter. The method contains two stages. First, in offline stage, the pretrained CNN is fine-tuned by some labeled images from the target data set, then used to extract CNN features, and labeled the images in the retrieval data set. Second, in online stage, we use the fine-tuned CNN model to extract the CNN feature of the query image and calculate the weight of each image class and apply them to calculate the distance between the query image and the retrieved images. Experiments are conducted on two RSIR data sets. Compared with the state-of-the-art methods, the proposed method is simplified but efficient, significantly improving retrieval performance. [ABSTRACT FROM AUTHOR]