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To describe the current status and expound the problems of the statistical and evaluation work on China's university libraries, and introduce the possible solutions and enforcement.

Alternative approaches for the statistical and evaluation work at China's college and university libraries, which are carried out at different levels, are introduced and analyzed to help the librarians for their library development.

Identifies the limitations and problems in current statistic and evaluation systems at China's college and university libraries and offers some suggestions how each aspect can improve.

Data are difficult to obtain and upgrade. Lack of exhaustive data makes the conclusion unilateral sometimes.

Comprehensive introduction of the status quo of the statistical and evaluation work on China's college and university libraries is useful to those who want to know the situation in China. Constructive suggestions are useful to Chinese administration and librarians.

This paper introduces statistical and evaluation work on China's college and university libraries in the round, and gives some feasible suggestions.

Discusses alternative strategies which may be employed when differences arise between achieved audit‐sampling results and planned results, which means that risk levels used in ex post decision making may be different from planned levels. Contrasts a conventional strategy — which is to fix the risk of incorrect acceptance at a planned level and to ignore the risk of incorrect rejection or to accept the minimum available level of that risk which is consistent, after the fact, with the planned level of risk of incorrect acceptance — with a theoretically appealing strategy which balances both risk levels in proportion to their perceived disutility. Reports on the results of an experiment involving these two strategies, in which all subjects were auditors with statistical audit experience. Suggests that the most important statistically significant finding is that, in certain circumstances, these auditors are more willing to base audit decisions on statistical evidence after the alternative strategy is explained and available for their use.

The purpose of this paper is to apply grey statistical model to identify and classify live fault rupture.

Based on grey statistical mode, this paper uses eight faults' ripping speed observation data from 1997 to 2001, according to the grey statistics method for analysis, and recognizes active fault rupture situation. Using the conventional methods, namely taking all faults monitoring stations' average dislocation rate to analysis and make judgment, the average results are obtained.

The results show that the results are closer to reality because the grey statistical evaluation method has considered dislocation rate and other discrete factors.

The method exposed in the paper can be used to monitor and recognize live fault rupture in earthquake prediction.

According to the fault dislocation rate, this paper advances active fault rupture identification and classification method based on grey statistical model.

The purpose of this study is to address a highly heterogeneous rift margin environment and exhibit considerable spatiotemporal hydro-climatic variations. In spite of limited, random and inaccurate data retrieved from rainfall gauging stations, the recent advancement of satellite rainfall estimate (SRE) has provided promising alternatives over such remote areas. The aim of this research is to take advantage of the technologies through performance evaluation of the SREs against ground-based-gauge rainfall data sets by incorporating its applicability in calibrating hydrological models.

Selected multi satellite-based rainfall estimates were primarily compared statistically with rain gauge observations using a point-to-pixel approach at different time scales (daily and seasonal). The continuous and categorical indices are used to evaluate the performance of SRE. The simple scaling time-variant bias correction method was further applied to remove the systematic error in satellite rainfall estimates before being used as input for a semi-distributed hydrologic engineering center's hydraulic modeling system (HEC-HMS). Runoff calibration and validation were conducted for consecutive periods ranging from 1999–2010 to 2011–2015, respectively.

The spatial patterns retrieved from climate hazards group infrared precipitation with stations (CHIRPS), multi-source weighted-ensemble precipitation (MSWEP) and tropical rainfall measuring mission (TRMM) rainfall estimates are more or less comparably underestimate the ground-based gauge observation at daily and seasonal scales. In comparison to the others, MSWEP has the best probability of detection followed by TRMM at all observation stations whereas CHIRPS performs the least in the study area. Accordingly, the relative calibration performance of the hydrological model (HEC-HMS) using ground-based gauge observation (Nash and Sutcliffe efficiency criteria [NSE] = 0.71; R² = 0.72) is better as compared to MSWEP (NSE = 0.69; R² = 0.7), TRMM (NSE = 0.67, R² = 0.68) and CHIRPS (NSE = 0.58 and R² = 0.62).

Calibration of hydrological model using the satellite rainfall estimate products have promising results. The results also suggest that products can be a potential alternative source of data sparse complex rift margin having heterogeneous characteristics for various water resource related applications in the study area.

This research is an original work that focuses on all three satellite rainfall estimates forced simulations displaying substantially improved performance after bias correction and recalibration.

This study aims to intend and implement the optimal power flow, where tuning the production cost is done with the inclusion of stochastic wind power and different kinds of flexible AC transmission systems (FACTS) devices. Here, the speed with fitness-based krill herd algorithm (SF-KHA) is adopted for deciding the FACTS devices’ optimal sizing and placement integrated with wind power. Here, the modified SF-KHA optimizes the sizing and location of FACTS devices for attaining the minimum average production cost and real power depletions of the system. Especially, the objective includes reserve cost for overestimation, cost of thermal generation of the wind power, direct cost of scheduled wind power and penalty cost for underestimation. The efficiency of the offered method over several popular optimization algorithms has been done, and the comparison over different algorithms establishes proposed KHA algorithm attains the accurate optimal efficiency for all other algorithms.

The proposed FACTS devices-based power system with the integration of wind generators is based on the accurate placement and sizing of FACTS devices for decreasing the actual power loss and total production cost of the power system.

Through the cost function evaluation of the offered SF-KHA, it was noted that the proposed SF-KHA-based power system had secured 13.04% superior to success history-based adaptive differential evolution, 9.09% enhanced than differential evolution, 11.5% better than artificial bee colony algorithm, 15.2% superior to particle swarm optimization and 9.09% improved than flower pollination algorithm.

The proposed power system with the accurate placement and sizing of FACTS devices and wind generator using the suggested SF-KHA was effective when compared with the conventional algorithm-based power systems.

This study aims to examine the potential of two artificial intelligence (AI)-based algorithms, namely, adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP), for indirect estimation of the ultimate bearing capacity (q_ult) of rock foundations, which is a considerable civil and geotechnical engineering problem.

The input-processing-output procedures taking place in ANFIS and GEP are represented for developing predictive models. The great importance of simultaneously considering both qualitative and quantitative parameters for indirect estimation of q_ult is taken into account and explained. This issue can be considered as a remarkable merit of using AI-based approaches. Furthermore, the evaluation procedure of various models from both engineering and accuracy viewpoints is also demonstrated in this study.

A new and explicit formula generated by GEP is proposed for the estimation of the q_ult of rock foundations, which can be used for further engineering aims. It is also presented that although the ANFIS approach can predict the output with a high degree of accuracy, the obtained model might be a black-box. The results of model performance analyses confirm that ANFIS and GEP can be used as alternative and useful approaches over previous methods for modeling and prediction problems.

The superiorities and weaknesses of GEP and ANFIS techniques for the numerical analysis of engineering problems are expressed and the performance of their obtained models is compared to those provided by other approaches in the literature. The findings of this research provide the researchers with a better insight to using AI techniques for resolving complicated problems.

The purpose of this paper is to explore how the sophistication of a quality costing system depends on the quality management maturity (QMM) level in Food and Beverage (F&B) enterprises. Since no previous research has taken place in this area, the paper aims at analyzing the relationships between quality costing and the specific variables that define the various maturity stages.

A structured questionnaire was used to survey 457 F&B companies. This produced 104 usable responses (23 percent response rate). Multidimensional correspondence analysis (MCA) with hierarchical cluster analysis (HCA) were used to detect and represent underlying structures in the categorical data set and to detect possible clusters between variables.

The more mature a company’s QMM, the more emphasis they placed on appraisal quality costs and effective use of quality costs information. Prevention costs have no statistically significant connection with the level of maturity. A generalized “expensive” use of quality costing, with no focus on problematic areas and possible solutions, does not always lead to the resolution of problems.

A complicated – and some think unfair – tax system, combined with limited cash liquidity constitutes an unstable environment for Greek companies, in which they have to survive and develop. This environment does not support quality costing, thus resulting in limited interest by company management in participating in the authors’ research. Furthermore, the Greek Uniform Chart of Accounts and the Greek Accounting Standards do not include specific quality-related accounts, making it difficult for companies to measure quality costs and for researchers to investigate the quality costing field.

It is the first time that QMM levels of Greek F&B companies have been reported. The research explores the characteristics that a quality costing system of Greek F&B organizations develop at the various maturity levels. The analysis uses an exploratory method – MCA – which can highlight intense correspondences of characteristics and clusters, which cannot be predicted in advance.

This paper concerns with the measurement uncertainty estimation in the analog‐to‐digital conversion‐based instruments. By using an ad hoc developed software tool, the Monte Carlo method is applied in order to assess the uncertainties associated with the measurement results, overcoming the possible inapplicability of the pure theoretical approach prescribed in the ISO – “Guide to the Expression of Uncertainty in Measurement”. By implementing the software tool in the measurement instruments, the proposed approach can be utilized in order to make the instrument itself able to auto‐estimate the measurement uncertainties.