Z Distance Function for KNN Classification

This paper proposes a new distance metric function, called Z distance, for KNN classification. The Z distance function is not a geometric direct-line distance between two data points. It gives a consideration to the class attribute of a training dataset when measuring the affinity between data points. Concretely speaking, the Z distance of two data points includes their class center distance and real distance. And its shape looks like "Z". In this way, the affinity of two data points in the same class is always stronger than that in different classes. Or, the intraclass data points are always closer than those interclass data points. We evaluated the Z distance with experiments, and demonstrated that the proposed distance function achieved better performance in KNN classification

How to trigger employees’ proactivity in competitive climates? A test of competing hypotheses

Employees’ proactive behaviors are increasingly important for organizational development and they are highly influenced by the employees’ working atmosphere, such as its level of competitiveness. However, the implications of competitive climates for employees’ proactive behaviors remain unclear to date. This study aims to examine whether employees will engage in more or less proactive behaviors in a competitive working environment. To add more nuance to this investigation, I (a) differentiate the outcome of employees’ proactive behavior as self-beneficial (e.g., self-development) or organizational-beneficial; and (b) explore whether the relationship between competitive climates and proactive behavior is moderated by individual (learning goal orientation) and organizational factors (procedural justice). The results of this research show that competitive climates can indeed increase employees’ proactivity, for the benefit of both the organization and the individuals themselves. As expected, employees with high perceptions of procedural justice engaged in more organizational beneficial proactivity under high competitive climate, instead of more proactive behaviors targeting their own development. Contrary to expectations, the results indicate that employees with a high learning goal orientation become less motivated to engage in proactive behavior aimed at both the organization and themselves under competitive climates compared to employees with a low learning orientation. These results and their implications are discussed to encourage more research as well as supportive organizational practices for increased proactivity

The effect of PID control scheme on the course-keeping of ship in oblique stern waves

Sailing in oblique stern waves causes a ship to make sharp turns and uncontrollable course deviation, which is accompanied by a large heel and sometimes leads to capsizing. Studying the control algorithm in oblique stern waves is imperative because an excellent controller scheme can improve the ship’s course-keeping stability. This paper uses the Maneuvering Modelling Group (MMG) method based on hydrodynamic derivatives and the Computational Fluid Dynamics (CFD)-based self-navigation simulation to simulate ship navigation in waves. This study examines the effect of proportion-integral-derivative (PID) controller schemes on the stability of course maintenance based on hydrodynamic derivatives and 3DOF MMG methods. Then, the optimized PID control parameters are used to simulate the ship’s 6DOF self-propulsion navigation in oblique waves using the CFD method. The nonlinear phenomena during the process, such as side-hull emergency, slamming, and green water, are considered. This study found that the range of the control bandwidth should be optimized based on the ship\u27s heading and wave parameters

An Embedding-based Approach to Inconsistency-tolerant Reasoning with Inconsistent Ontologies

Inconsistency handling is an important issue in knowledge management. Especially in ontology engineering, logical inconsistencies may occur during ontology construction. A natural way to reason with an inconsistent ontology is to utilize the maximal consistent subsets of the ontology. However, previous studies on selecting maximum consistent subsets have rarely considered the semantics of the axioms, which may result in irrational inference. In this paper, we propose a novel approach to reasoning with inconsistent ontologies in description logics based on the embeddings of axioms. We first give a method for turning axioms into distributed semantic vectors to compute the semantic connections between the axioms. We then define an embedding-based method for selecting the maximum consistent subsets and use it to define an inconsistency-tolerant inference relation. We show the rationality of our inference relation by considering some logical properties. Finally, we conduct experiments on several ontologies to evaluate the reasoning power of our inference relation. The experimental results show that our embedding-based method can outperform existing inconsistency-tolerant reasoning methods based on maximal consistent subsets.Comment: 9 pages,1 figur

Numerical simulation of the influence of building‑tree arrangements on wind velocity and PM2.5 dispersion in urban communities

Airflow behavior and outdoor PM(2.5) dispersion depend significantly on the building-tree layouts and orientation towards the prevailing wind conditions. To investigate this issue, the present work evaluates the aerodynamic effect of different building-tree layouts on the outdoor PM(2.5) dispersions in the urban communities of Shijiazhuang City, China. The adopted numerical CFD technique was based on the standard k–ε model and the Disperse Phase Model (DPM). For this study, ten different building-tree arrangements were conceptualized and all these configurations were simulated by using Ansys Fluent software to quantify the implications on the outdoor PM(2.5) dispersion due to their presence. The results have shown that: (1) a wide building interval space could benefit the air ventilation and thus decrease PM(2.5) concentrations, however, this effectiveness is highly influenced by the presence of the trees; (2) the trees on the leeward side of a building tend to increase the local wind velocity and decrease the pedestrian-level PM(2.5) concentrations, while those on the windward side tend to decrease the wind velocity. The small distance with trees in the central space of the community forms a wind shelter, hindering the particle dispersion; and (3) the configuration of parallel type buildings with clustered tree layouts in the narrow central space is most unfavorable to the air ventilation, leading to larger areas affected by excessive PM(2.5) concentration

NUMERICAL STUDY ON PROPULSIVE FACTORS IN REGULAR HEAD AND OBLIQUE WAVES

This paper applies Reynolds-averaged Navier-Stokes (RANS) method to study propulsion performance in head and oblique waves. Finite volume method (FVM) is employed to discretize the governing equations and SST k-ω model is used for modeling the turbulent flow. The free surface is solved by volume of fluid (VOF) method. Sliding mesh technique is used to enable rotation of propeller. Propeller open water curves are determined by propeller open water simulations. Calm water resistance and wave added resistances are obtained from towing computations without propeller. Self-propulsion simulations in calm water and waves with varying loads are performed to obtain self-propulsion point and thrust identify method is use to predict propulsive factors. Regular head waves with wavelengths varying from 0.6 to 1.4 times the length of ship and oblique waves with incident directions varying from 0° to 360° are considered. The influence of waves on propulsive factors, including thrust deduction and wake fraction, open water, relative rotative, hull and propulsive efficiencies are discussed

Unsupervised Feature Selection Algorithm via Local Structure Learning and Kernel Function

In order to reduce dimensionality of high-dimensional data, a series of feature selection algorithms have been proposed. But these algorithms have the following disadvantages: (1) they do not fully consider the nonlinear relationship between data features (2) they do not consider the similarity between data features. To solve the above two problems, we propose an unsupervised feature selection algorithm based on local structure learning and kernel function. First, through the kernel function, we map each feature of the data to the kernel space, so that the nonlinear relationship of the data features can be fully exploited. Secondly, we apply the theory of local structure learning to the features of data, so that the similarity of data features is considered. Then we added a low rank constraint to consider the global information of the data. Finally, we add sparse learning to make feature selection. The experimental results show that the proposed algorithm has better results than the comparison methods

Targeting the complex I and III of mitochondrial electron transport chain as a potentially viable option in liver cancer management

Abstract Liver cancer is one of the most common and lethal types of oncological disease in the world, with limited treatment options. New treatment modalities are desperately needed, but their development is hampered by a lack of insight into the underlying molecular mechanisms of disease. It is clear that metabolic reprogramming in mitochondrial function is intimately linked to the liver cancer process, prompting the possibility to explore mitochondrial biochemistry as a potential therapeutic target. Here we report that depletion of mitochondrial DNA, pharmacologic inhibition of mitochondrial electron transport chain (mETC) complex I/complex III, or genetic of mETC complex I restricts cancer cell growth and clonogenicity in various preclinical models of liver cancer, including cell lines, mouse liver organoids, and murine xenografts. The restriction is linked to the production of reactive oxygen species, apoptosis induction and reduced ATP generation. As a result, our findings suggest that the mETC compartment of mitochondria could be a potential therapeutic target in liver cancer