Comprehensive meta-analysis of integrity test validities: Findings and implications for personnel selection and theories of job performance.

Conducted a comprehensive meta-analysis based on 665 validity coefficients across 576,460 data points to investigate whether integrity test validities are generalizable and to estimate differences in validity due to potential moderating influences. Results indicate that integrity test validities are substantial for predicting job performance and counterproductive behaviors on the job, such as theft, disciplinary problems, and absenteeism. The estimated mean operational predictive validity of integrity test for predicting supervisory ratings of job performance is .41. Results from predictive validity studies conducted on applicants and using external criterion measures (i.e., excluding self-reports) indicate that integrity tests predict the broad criterion of organizationally disruptive behaviors better than they predict employee theft alone. Despite the influence of moderators, integrity test validities are positive across situations and settings. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Information cut for clustering using a gradient descent approach

We introduce a new graph cut for clustering which we call the Information Cut. It is derived using Parzen windowing to estimate an information theoretic distance measure between probability density functions. We propose to optimize the Information Cut using a gradient descent-based approach. Our algorithm has several advantages compared to many other graph-based methods in terms of determining an appropriate affinity measure, computational complexity, memory requirements and coping with different data scales. We show that our method may produce clustering and image segmentation results comparable or better than the state-of-the art graph-based methods.     

A new deep intuitionistic fuzzy time series forecasting method based on long short-term memory

The Journal of Supercomputing - In recent years, deep artificial neural networks can have better forecasting performance than many other artificial neural networks. The long short-term memory...     

A combo technology of autotrophic and heterotrophic denitrification processes for groundwater treatment

In this study,a sequential process (heterotrophic up-flow column and completely mixed membrane bioreactors) was proposed combining advantages of the both processes.The system was operated for 249 days with simulated and real groundwater for nitrate removal at concentrations varying from 25 to 145 mg·L-1 NO3-N.The contribution of heterotrophic process to total nitrate removal in the system was controlled by dozing the ethanol considering the nitrate concentration.By this way,sulfur based autotrophic denitrification rate was decreased and the effluent sulfate concentrations were controlled.The alkalinity requirement in the autotrophic process was produced in the heterotrophic reactor,and the system was operated without alkalinity supplementation.Throughout the study,the chemical oxygen demand in the heterotrophic reactor effluent was (23.7 ± 22) mg·L-1 and it was further decreased to(7.5 ± 7.2) mg·L-1 in the system effluent,corresponding to a 70％ reduction.In the last period of the study,the real groundwater containing 145 mg·L-1 NO3-N was completely removed.Membrane was operated without chemical washing in the first 114 days.Between days 115-249 weekly chemical washing was required.     

A modified genetic algorithm for forecasting fuzzy time series

Fuzzy time series approaches are used when observations of time series contain uncertainty. Moreover, these approaches do not require the assumptions needed for traditional time series approaches. Generally, fuzzy time series methods consist of three stages, namely, fuzzification, determination of fuzzy relations, and defuzzification. Artificial intelligence algorithms are frequently used in these stages with genetic algorithms being the most popular of these algorithms owing to their rich operators and good performance. However, the mutation operator of a GA may cause some negative results in the solution set. Thus, we propose a modified genetic algorithm to find optimal interval lengths and control the effects of the mutation operator. The results of applying our new approach to real datasets show superior forecasting performance when compared with those obtained by other techniques.     

Leaching of a copper flotation concentrate with ammonium persulfate in an autoclave system

The leaching behavior of a copper flotation concentrate was investigated using ammonium persulfate （APS） in an autoclave systee. The decomposition products of APS, active oxygen, and acidic medium were used to extract metals from the concentrate. Leaching experiments were performed to compare the availability of APS as an oxidizing agent for leaching of the concentrate under atmospheric conditions and in an autoclave system. Leaching temperature and APS concentration were found to be important parameters in both leaching systems. Atmospheric leaching studies showed that the metal extractions increased with the increase in APS concentration and temperature （up to 333 K）. A similar tendency was determined in the autoclave studies up to 423 K. It was also determined that the metal extractions decreased at temperatures above 423 K due to the passivation of the particle surface by molten elemental sulfur. The results showed that higher copper extractions could be achieved using an autoclave system.     

Classification of semantic relations between nominals

The NLP community has shown a renewed interest in deeper semantic analyses, among them automatic recognition of semantic relations in text. We present the development and evaluation of a semantic analysis task: automatic recognition of relations between pairs of nominals in a sentence. The task was part of SemEval-2007, the fourth edition of the semantic evaluation event previously known as SensEval. Apart from the observations we have made, the long-lasting effect of this task may be a framework for comparing approaches to the task. We introduce the problem of recognizing relations between nominals, and in particular the process of drafting and refining the definitions of the semantic relations. We show how we created the training and test data, list and briefly describe the 15 participating systems, discuss the results, and conclude with the lessons learned in the course of this exercise.     

The Training of Pi-Sigma Artificial Neural Networks with Differential Evolution Algorithm for Forecasting

Computational Economics - Looking at the artificial neural networks’ literature, most of the studies started with feedforward artificial neural networks and the training of many feedforward...