Rebalancing Strategy for Bike-Sharing Systems Based on the Model of Level of Detail

Traveling by bike-sharing systems has become an indispensable means of transportation in our daily lives because green commuting has gradually become a consensus and conscious action. However, the problem of “difficult to rent or to return a bike” has gradually become an issue in operating the bike-sharing system. Moreover, scientific and systematic schemes that can efficiently complete the task of rebalancing bike-sharing systems are lacking. This study aims to introduce the basic idea of the k-divisive hierarchical clustering algorithm. A rebalancing strategy based on the model of level of detail in combination with genetic algorithm was proposed. Data were collected from the bike-sharing system in Ningbo. Results showed that the proposed algorithm could alleviate the problem of the uneven distribution of the demand for renting or returning bikes and effectively improve the service from the bike-sharing system. Compared with the traditional method, this algorithm helps reduce the effective time for rebalancing bike-sharing systems by 28.3%. Therefore, it is an effective rebalancing scheme.

Cell Type Hierarchy Reconstruction via Reconciliation of Multi-resolution Cluster Tree

A wealth of clustering algorithms are available for Single-cell RNA sequencing (scRNA-seq), but it remains challenging to compare and characterize the features across different scales of resolution. To resolve this challenge Multi-resolution Reconciled Tree (MRtree), builds a hierarchical tree structure based on multi-resolution partitions that is highly flexible and can be coupled with most scRNA-seq clustering algorithms. MRtree out-performs bottom-up or divisive hierarchical clustering approaches because it inherits the robustness and versatility of a flat clustering approach, while maintaining the hierarchical structure of cells. Application to fetal brain cells yields insight into subtypes of cells that can be reliably estimated.

Production and trade patterns in the world apple market

Awareness of healthy food, population growth, increasing incomes, and urbanization raise the global demand for fruit, where the second position goes to apples. However, their supply is insufficient, implying the lost revenues and exacerbating nutritional food insecurity. To help growers, traders, and consumers cope with such a challenge, this research focused on revealing some world patterns in apple production and trade detailed by groups of countries, their capacities, and prices. The explored data on fresh and processed apples derived from the Food and Agriculture Organization Statistics. The methodological framework of the study engaged divisive hierarchical clustering, analysis of interval variation series, and inequality indicators. The research findings identified two major clusters of 50 out of 96 countries specialized in production and foreign sales of 83.2% and 76.9% of apples. The study outcome comparing fair trade via two triple histograms specified the prevailing deviations between –82% and 80% around farm gate apple prices in 47 exporting countries and the same between –83% and 83% in 46 importing countries. Based on the Gini coefficient, Ratio 20/20, and Hoover index, the accomplished evaluations quantified total disparity in apple trading by 13% to 40%, calculated misbalance between 20% of the top and bottom world traders, and grounded preferable market alignments ranged from 9% to 38%.

Fuzzy Divisive Hierarchical Clustering of Solvents According to Their Experimentally and Theoretically Predicted Descriptors

The present study describes a simple procedure to separate into patterns of similarity a large group of solvents, 259 in total, presented by 15 specific descriptors (experimentally found and theoretically predicted physicochemical parameters). Solvent data is usually characterized by its high variability, different molecular symmetry, and spatial orientation. Methods of chemometrics can usefully be used to extract and explore accurately the information contained in such data. In this order, advanced fuzzy divisive hierarchical-clustering methods were efficiently applied in the present study of a large group of solvents using specific descriptors. The fuzzy divisive hierarchical associative-clustering algorithm provides not only a fuzzy partition of the solvents investigated, but also a fuzzy partition of descriptors considered. In this way, it is possible to identify the most specific descriptors (in terms of higher, smallest, or intermediate values) to each fuzzy partition (group) of solvents. Additionally, the partitioning performed could be interpreted with respect to the molecular symmetry. The chemometric approach used for this goal is fuzzy c-means method being a semi-supervised clustering procedure. The advantage of such a clustering process is the opportunity to achieve separation of the solvents into similarity patterns with a certain degree of membership of each solvent to a certain pattern, as well as to consider possible membership of the same object (solvent) in another cluster. Partitioning based on a hybrid approach of the theoretical molecular descriptors and experimentally obtained ones permits a more straightforward separation into groups of similarity and acceptable interpretation. It was shown that an important link between objects’ groups of similarity and similarity groups of variables is achieved. Ten classes of solvents are interpreted depending on their specific descriptors, as one of the classes includes a single object and could be interpreted as an outlier. Setting the results of this research into broader perspective, it has been shown that the fuzzy clustering approach provides a useful tool for partitioning by the variables related to the main physicochemical properties of the solvents. It gets possible to offer a simple guide for solvents recognition based on theoretically calculated or experimentally found descriptors related to the physicochemical properties of the solvents.

Decision Making Methods to Optimize New Dam Site Selections on the Nitra River

Grouping both existing and newly planned reservoirs based on selected measurable characteristics allows to point out issues that are relevant to area management using experience obtained from the environment of other sites. Divisive hierarchical clustering has been deployed to find similarities between dam locations. The Nitra River Basin (located in Nitra District, Nitra Region in Slovakia) with its 54 reservoirs is the model area. Profiles for 11 potential new reservoirs have been developed. Partial river basins were identified for each of the existing and new reservoirs using a digital relief model. The area size, proportion of arable land, forestland and built-up area, degree of exposure to soil erosion and the volume of surface runoff have been used as parameters for comparisons. Six clusters have been identified containing similar existing as well as new locations, one of them being a special case.

Finding specific peaks (markers) using fuzzy divisive hierarchical associative-clustering based on the chromatographic profiles of medicinal plant extracts obtained at various detection wavelengths

Advanced chemometric methods, such as fuzzy c-means (FCM), a fuzzy divisive hierarchical clustering algorithm (FDHC), and fuzzy divisive hierarchical associative-clustering (FDHAC), have been successfully applied in this study.