Human Factors Analysis in Software Engineering

The general human factors analysis analyzes human functions, effects and influence in a system. But in a narrow sense, it analyzes human influence upon the reliability of a system, it includes traditional human reliability analysis, human error analysis, man-machine interface analysis, human character analysis, and others. A software development project in software engineering is successful or not to be completely determined by human factors. In this paper, we discuss the human factors intensions, declare the importance of human factors analysis for software engineering by listed some instances. At last, we probe preliminarily into the mentality that a practitioner in software engineering should possess. Key words human factors analysis - software engineering - software reliability CLC number TP 311.5 Foundation item: Supported by the National Natural Science Foundation of China (60173013)Biography: Xu Ren-zuo (1946-), male, Professor, research direction: software engineering, software reliability engineering, software safety and software testing.

Statistics Analysis:FDI Obsorbing in Jan-Jun

According to China's Customs' statistics,from January to May 2008,the import and export volume of foreign- invested enterprises totaled US$563.612 billion,an increase of 21.28 percent over the same period of last year,5.02 percent lower than the growth rate of the country (26.30 percent) in the same period,accounting for 55.69 percent of the total import and export of the country.(See Chart 1)

Cryo-EM Data Statistics and Theoretical Analysis of KaiC Hexamer

Cryo-electron microscopy(cryo-EM) provides a powerful tool to resolve the structure of biological macromolecules in natural state. One advantage of cryo-EM technology is that different conformation states of a protein complex structure can be simultaneously built, and the distribution of different states can be measured. This provides a tool to push cryo-EM technology beyond just to resolve protein structures, but to obtain the thermodynamic properties of protein machines. Here, we used a deep manifold learning framework to get the conformational landscape of Kai C proteins, and further obtained the thermodynamic properties of this central oscillator component in the circadian clock by means of statistical physics.

Stability and Influence Factors Analysis of Wharf Slope on Slippery Stratum

With the growing tension of port shoreline resource in Three Gorges Reservoir area, many wharfs can only be constructed on slippery stratum with poor geological condition, which means buckling failure occurs easily. FEM strength reduction method is used in analyzing slope stability of a wharf in Chongqing, and its accuracy is verified by comparing the acquired results with the computation of traditional limit equilibrium method. On this basis, the influences of reservoir water level variation, overload and backfill material behind the retaining wall, and soil share strength of wharf slope on slippery stratum are analyzed. Analysis shows that, there＇ s a most adverse water level in wharf slope, and the engineering proposals with a certain practical significance such as improve the drainage behind retaining wall, controll overload and improve the strength of backfill soil and subsoil are suggested.

Combinatorial analysis on spatial information statistics for the karst water environment in Guiyang,China

The karst groundwater system is extremely vulnerable and easily contaminated by human activities.To understand the spatial distribution of contaminants in the groundwater of karst urban areas and contributors to the contamination,this paper employs the spatial information statistics analysis theory and method to analyze the karst groundwater environment in Guiyang City.Based on the karst ground water quality data detected in 61 detection points of the research area in the last three years,we made Kriging evaluation isoline map with some ions in the karst groundwater,such as SO4 2-,Fe 3+,Mn 2+and F -,analyzed and evaluated the spatial distribution,extension and variation of four types of ions on the basis of this isoline map.The results of the analysis show that the anomaly areas of SO4 2-,Fe 3+,Mn 2+,Fand other ions are mainly located in Baba’ao,Mawangmiao and Sanqiao in northwestern Gui- yang City as well as in its downtown area by reasons of the original non-point source pollution and the contamination caused by human activities(industrial and domestic pollution).

Evaluation of Yarn Quality and Optimization of Cotton Spinning Parameter Based on Factors Analysis

In this paper, the spinning parameters are optimized by using the method of factor analysis. The yarns obtained from four different spinning parameters are evaluated by this method. Two common factors, fineness unevenness and tenacity level, are extracted from the seven yarn-quality indexes. The accumulative contribution percentage of the two factors is up to 91.813%,and much information in the yarn-quality indexes is reflected by the two factors. Then the score of each factor is calculated to evaluate the quality of yarn. Based on that, the techniques are optimized. The result is well in line with spinning practices, so it is testified feasibly to use this method to optimize spinning parameter.

Electricity Price Influence Factors Analysis Using Stochastic Matrix for Real-Time Electricity Price Forecasting

Real-time electricity price( RTEP) influence factor extraction is essential to forecasting accurate power system electricity prices. At present,new electricity price forecasting models have been studied to improve predictive accuracy,ignoring the extraction and analysis of RTEP influence factors. In this study,a correlation analysis method is proposed based on stochastic matrix theory.Firstly, an augmented matrix is formulated, including RTEP influence factor data and RTEP state data. Secondly, data correlation analysis results are obtained given the statistical characteristics of source data based on stochastic matrix theory.Mean spectral radius( MSR) is used as the measure of correlativity.Finally,the proposed method is evaluated in New England electricity markets and compared with the BP neural network forecasting method. Experimental results show that the extracted index system comprehensively generalizes RTEP influence factors,which play a significant role in improving RTEP forecasting accuracy.

Statistics and Analysis on Papers Published on Journal ‘Advanced Technology of Electrical Engineering and Energy’

The paper presented the statistics and analysis on papers published on the journal 'Advanced Technology of Electrical Engineering and Energy' from 1996 to 2008: the paper acceptance rate,the paper category,the first author's affiliations,the top 7 first authors,the top 10 coauthors and also the journal evaluation indexes of the journal.It offers details of the journal to anyone interested,especially to our editorial board and our broad readers.

Research on Determination of the Main Factors Influencing the Gas Well Post-Frac Productivity Prediction for Tight Sandstone Reservoirs Based on Factors Analysis

With the characteristics such as low porosity, low permeability and low gas saturation, tight sandstone reservoirs almost have no natural capacity and need to be fractured (or fracturing) for productivity. Therefore, fracturing capacity prediction is very necessary. However there were so many factors, and the relations between the factor and the post-frac productivity are complex. In this study, first of all factors are concluded from the gas well stable productivity formula, then I used factor analysis to look for the main factors from the logging parameters and fracturing parameters in SULIGE area. This study could provide basal references for the gas post-frac productivity prediction in tight sandstone reservoirs.

Microarray Analysis Using Rank Order Statistics for ARCH Residual Empirical Process

Statistical two-group comparisons are widely used to identify the significant differentially expressed (DE) signatures against a therapy response for microarray data analysis. We applied a rank order statistics based on an Autoregressive Conditional Heteroskedasticity (ARCH) residual empirical process to DE analysis. This approach was considered for simulation data and publicly available datasets, and was compared with two-group comparison by original data and Auto-regressive (AR) residual. The significant DE genes by the ARCH and AR residuals were reduced by about 20% - 30% to these genes by the original data. Almost 100% of the genes by ARCH are covered by the genes by the original data unlike the genes by AR residuals. GO enrichment and Pathway analyses indicate the consistent biological characteristics between genes by ARCH residuals and original data. ARCH residuals array data might contribute to refining the number of significant DE genes to detect the biological feature as well as ordinal microarray data.

基于statistics的316L不锈钢柠檬酸钝化工艺正交实验数据分析

从statistics的数据处理能力出发,运用统计学知识对316L不锈钢柠檬酸钝化工艺参数正交实验数据进行分析,找出钝化速率的主要影响因素;柠檬酸含量5wt%、双氧水含量5wt%、钝化时间60 min为最佳的实验方案;双氧水含量为钝化速率的主要影响因素。

Application of an Error Statistics Estimation Method to the PSAS Forecast Error Covariance Model

In atmospheric data assimilation systems, the forecast error covariance model is an important component. However, the paralneters required by a forecast error covariance model are difficult to obtain due to the absence of the truth. This study applies an error statistics estimation method to the Pfiysical-space Statistical Analysis System （PSAS） height-wind forecast error covariance model. This method consists of two components： the first component computes the error statistics by using the National Meteorological Center （NMC） method, which is a lagged-forecast difference approach, within the framework of the PSAS height-wind forecast error covariance model; the second obtains a calibration formula to rescale the error standard deviations provided by the NMC method. The calibration is against the error statistics estimated by using a maximum-likelihood estimation （MLE） with rawindsonde height observed-minus-forecast residuals. A complete set of formulas for estimating the error statistics and for the calibration is applied to a one-month-long dataset generated by a general circulation model of the Global Model and Assimilation Office （GMAO）, NASA. There is a clear constant relationship between the error statistics estimates of the NMC-method and MLE. The final product provides a full set of 6-hour error statistics required by the PSAS height-wind forecast error covariance model over the globe. The features of these error statistics are examined and discussed.

The k Nearest Neighbors Estimator of the M-Regression in Functional Statistics

It is well known that the nonparametric estimation of the regression function is highly sensitive to the presence of even a small proportion of outliers in the data.To solve the problem of typical observations when the covariates of the nonparametric component are functional,the robust estimates for the regression parameter and regression operator are introduced.The main propose of the paper is to consider data-driven methods of selecting the number of neighbors in order to make the proposed processes fully automatic.We use thek Nearest Neighbors procedure(kNN)to construct the kernel estimator of the proposed robust model.Under some regularity conditions,we state consistency results for kNN functional estimators,which are uniform in the number of neighbors(UINN).Furthermore,a simulation study and an empirical application to a real data analysis of octane gasoline predictions are carried out to illustrate the higher predictive performances and the usefulness of the kNN approach.

A Measurement Theoretical Foundation of Statistics

It is a matter of course that Kolmogorov’s probability theory is a very useful mathematical tool for the analysis of statistics. However, this fact never means that statistics is based on Kolmogorov’s probability theory, since it is not guaranteed that mathematics and our world are connected. In order that mathematics asserts some statements concerning our world, a certain theory (so called “world view”) mediates between mathematics and our world. Recently we propose measurement theory (i.e., the theory of the quantum mechanical world view), which is characterized as the linguistic turn of quantum mechanics. In this paper, we assert that statistics is based on measurement theory. And, for example, we show, from the pure theoretical point of view (i.e., from the measurement theoretical point of view), that regression analysis can not be justified without Bayes’ theorem. This may imply that even the conventional classification of (Fisher’s) statistics and Bayesian statistics should be reconsidered.

Inferential Statistics and Machine Learning Models for Short-TermWind Power Forecasting

The inherent randomness,intermittence and volatility of wind power generation compromise the quality of the wind power system,resulting in uncertainty in the system’s optimal scheduling.As a result,it’s critical to improve power quality and assure real-time power grid scheduling and grid-connected wind farm operation.Inferred statistics are utilized in this research to infer general features based on the selected information,confirming that there are differences between two forecasting categories:Forecast Category 1(0-11 h ahead)and Forecast Category 2(12-23 h ahead).In z-tests,the null hypothesis provides the corresponding quantitative findings.To verify the final performance of the prediction findings,five benchmark methodologies are used:Persistence model,LMNN(Multilayer Perceptron with LMlearningmethods),NARX(Nonlinear autoregressive exogenous neural networkmodel),LMRNN(RNNs with LM training methods)and LSTM(Long short-term memory neural network).Experiments using a real dataset show that the LSTM network has the highest forecasting accuracy when compared to other benchmark approaches including persistence model,LMNN,NARX network,and LMRNN,and the 23-steps forecasting accuracy has improved by 19.61%.

Looking at Electric Power Consumption from Statistics

On the basis of the historical statistics on power consumption, this paper anal）yes the power consumption trends in China by means of analyzing methods based on consumption proportion curve, fixed base curve, month-on-month increase curve and fixed base index curve. Special attentions are paid to the consumption trend in the first half of 2010, and policy strategies are suggested targeting the problems reflected by the consumption trend.

On the True Face of the Economic Statistics

In 2012, China National Ministry of Education issued a new undergraduate course catalog, economic statistics to be classified as a second level discipline of applied economics. However, what specific content should be included in the second level discipline has become a very important issue. What should be taught to students or how can they adapt to the needs of the social market aroused a wide attention. For this case, the National Ministry of Education has given a clearly provision for economic statistics＇ core courses, but whether these main courses can reflect the actual needs and characteristics of economic statistics or not still needs some considerations and discussions. This article started from the angle of the studies on China and the U.S. Journals concerning economic statistics. Using text mining by R, a recent mainstream statistical analysis software, gives a comparative analysis on the contents of economic statistics for recent decades. We created wordcloud about the contents of core statistical journals by R which can help us visually examine the course of economic statistics discipline development for the comparative study. Besides, we drew a conclusion that there were significant differences between China and America＇s economic statistics, the main difference is the United States pay more attention to the exploration of new methods and be able to adapt to market demand, the development of China＇s economic statistics are still more traditional, it need a better understanding of the multi-disciplinary knowledge of education, such as Bayesian and dynamics. Another is that the curriculum of China＇s economic statistics are corrected for China＇s actual situation, a new training program pay more attention to students＇ practical ability and social practice. There are a lot of practical problems remain untouched or unsolved which need efforts of decades probably.

The use of hydrogeochemical analyses and multivariate statistics for the characterization of thermal springs in the Constantine area, Northeastern Algeria

This paper deals with the results of a hydrogeochemistry study on the thermal waters of the Constantine area, Northeastern Algeria, using geochemical and statistical tools. The samples were collected in December2016 from twelve hot springs and were analyzed for physicochemical parameters(electric conductivity, p H,total dissolved solids, temperature, Ca, Mg, Na, K, HCO_3,Cl, SO_4, and SiO_2). The waters of the thermal springs have temperatures varying from 28 to 51 °C and electric conductivity values ranging from 853 to 5630 l S/cm. Q-mode Cluster analysis resulted in the determination of two major water types: a Ca–HCO_3–SO_4 type with a moderate salinity and a Na–K–Cl type with high salinity. The plot of the major ions versus the saturation indices suggested that the hydrogeochemistry of thermal groundwater is mainly controlled by dissolution/precipitation of carbonate minerals, dissolution of evaporite minerals(halite and gypsum), and ion exchange of Ca(and/or Mg) by Na. The Gibbs diagram shows that evaporation is another factor playing a minor role. Principal Component Analysis produced three significant factors which have 88.2% of totalvariance that illustrate the main processes controlling the chemistry of groundwaters, which are respectively: the dissolution of evaporite minerals(halite and gypsum), ion exchange, and dissolution/precipitation of carbonate minerals. The subsurface reservoir temperatures were calculated using different cation and silica geothermometers and gave temperatures ranging between 17 and 279 °C. The Na–K and Na–K-Ca geothermometers provided high temperatures(up to 279 °C), whereas, estimated geotemperatures from K/Mg geothermometers were the lowest(17–53 °C). Silica geothermometers gave the most reasonable temperature estimate of the subsurface waters overlap between 20 and 58 °C, which indicate possible mixing with cooler Mg groundwaters indicated by the Na–K–Mg plot in the immature water field and in silica and chloride mixing models. The results of stable isotope analyses(δ^(18) O and δ~2 H) suggest that the origin of thermal water recharge is precipitation, which recharged from a higher altitude(600–1200 m) and infiltrated through deep faults and fractures in carbonate formations. They circulate at an estimated depth that does not exceed 2 km and are heated by a high conductive heat flow before rising to the surface through faults that acted as hydrothermal conduits.During their ascent to the surface, they are subjected to various physical and chemical changes such as cooling by conduction and change in their chemical constituents due to the mixing with cold groundwaters.

Quantification constraints on time gap for comagmatic gold deposits:Evidence from chronological statistics

The time gap between diagenesis and mineralization (TGDM) for comagmatic gold deposits (CGD) plays an important role in confirming the genetic relationship between gold deposits and their related intrusions. With the help of preciously published isotopic ages of some typical gold deposits and their related rocks in China,the authors have discussed and quantified the distribution characteristics and scope of the TGDM. Statistical analyses and Kolmogorov tests showed that mineralizing events are either contemporaneous with or slightly postdate their cognate magma. The TGDM conforms with normal distributions at a 0.05 confidence level and clusters between 0 and 16.0 Ma with a mean of 7.0 Ma. Thus,if the TGDM of CGD is less than 16.0 Ma,it is reasonable to consider,with the aid of other evidence,the possibility of its comagmatic genetic affiliation. The authors also emphasized that to get a precise time gap it is necessary to strengthen the diagenesis-mineralization geological background of the deposits studied,and to pay attention to the study of time gap in combination with trace elements and isotope tracing.

Evaluation of Groundwater Quality in the Deep Maastrichtian Aquifer of Senegal Using Multivariate Statistics and Water Quality Index-Based GIS

A regional groundwater quality evaluation was conducted in the deep Maastrichtian aquifer of Senegal through multivariate statistical analysis and a GIS-based water quality index using physicochemical data from 232 boreholes distributed over the whole country. The aim was to 1) identify the water types and likely factors influencing the hydrochemistry, and 2) determine the suitability of groundwater for drinking and irrigation. Results showed that sodium, chloride, and fluoride are highly correlated with electrical conductivity (EC) reflecting the significant contribution of these elements to groundwater mineralization. The principal component analysis evidenced: 1) salinization processes (loaded by Na+, K+, EC, Cl-, F- and HCO3-) controlled by water/rock interaction, seawater intrusion and cation exchange reactions;2) dolomite dissolution loaded by the couple Ca2+ and Mg2+ and 3) localized mixing with upper aquifers and gypsum dissolution respectively loaded by NO3- and SO42-. The hierarchical clustering analysis distinguished four clusters: 1) freshwater (EC = 594 μs/cm) with mixed-HCO3 water type and ionic contents below WHO standard;2) brackish (Na-mixed) water type with moderate mineralization content (1310 μs/cm), 3) brackish (Na-Cl) water type depicted by high EC values (3292 μs/cm) and ionic contents above WHO and 4) saline water with Na-Cl water type and very high mineralization contents (5953 μs/cm). The mapping of the groundwater quality index indicated suitable zones for drinking accounting for 54% of the entire area. The occurrence of a central brackish band and its vicinity, which were characterized by high mineralization, yielded unsuitable groundwater for drinking and agricultural uses. The approach used in this study was valuable for assessing groundwater quality for drinking and irrigation, and it can be used for regional studies in other locations, particularly in shallow and vulnerable aquifers.