Set Of Statistical Measures Research Articles

Ethical Leadership and Its Impact on Sustainable Performance – An Exploratory study of the Opinions of Employees at Al-Mustaqbal University

The research aimed to reveal the nature of the role that ethical leadership can play in achieving sustainable performance. The importance of the research was that it was applied in one of the most important service sectors in Iraq in general, which is the higher education sector, as well as to address a realistic problem represented in the investigation of the research sample organisations About achieving the highest levels of sustainable performance, as the research came to fill a knowledge gap represented by the scarcity of Arab or foreign studies to the knowledge of the researcher, which dealt with the relationship between research variables in one hypothetical model. The gradient was distributed to the university’s employees, who numbered (110) individuals. (86) audited and measurable questionnaires were retrieved. The descriptive analytical method was used in analysing the data based on the statistical analysis program spss v.26 and SmartPLS. The researchers also used a set of statistical measures in order to analyse the data and the reach results. The research has reached a set of results, most notably the existence of an Impact and connection relationship between ethical leadership and sustainable performance. The results indicate that paying attention to the development of ethical leadership capabilities can achieve sustainable performance in government and private institutions. Additionally, the research sample college seeks to benefit from ethical leadership behaviours to achieve sustainable performance, and its top leaders are fully aware of the necessity of following the ethical leadership approach in managing the college to reach high levels of sustainable performance.

A new approach based on integration of random subspace and C4.5 decision tree learning method for spatial prediction of shallow landslides

The research approaches a new machine learning ensemble which is a hybridization of Random subspace (RS) and C4.5, named RandSub-DT, for improving the performance of the landslide susceptibility model. This is based on the GIS database, including 170 landslide polygons and ten predisposing landslide factors, i.e., slope, aspect, curvature, TWI, land use, distance to road, distance to the river, soil type, distance to fault, and lithology. We carried out this study in the Halong and Cam Pha City areas which are important economic centers in the Quang Ninh province, Vietnam, where landslides seriously influence the daily life of the citizen causing economic damage. We then used a GIS database to construct and validate the proposed RandSub-DT model. The model performance was assessed using a confusion matrix and a set of statistical measures. The result showed that the RandSub-DT model with the classification accuracy of 90.34% in the training dataset and the prediction capability of 77.48% had a high performance for landslide prediction. This research proved that an ensemble of the C4.5 and RS provided a highly accurate estimate of landslide susceptibility in the research area.

A study of “forced-ad resistance” leading to “Skip Ad” on YouTube

The current policy of YouTube forces viewers to watch ads for at least 5 seconds before appearing “Skip Ad” button to continue watching the video. Recent studies show that a large percentage of watchers skip YouTube ads whenever possible but the factors behind such viewers behavior has not been analyzed so far. This paper presents two concepts aiming understanding of YouTube users’ behavior and maximizing the ad effectiveness by exploring; “forced-ad resistance” (FAR) and “ad-characteristics resistance” (ACR) factors. Current study aims to discuss to what extent ad-skipping behavior is affected by the influence of each one of these two factors, and to explore the significant impact of four demographic characteristics: age, gender, education, and income. The data were collected from 2531 of YouTube users through an online questionnaire. The validity and reliability of the results were tested by using a set of statistical measures through SPSS. Based on our findings, recommendations for academics and practitioners in the field of advertising are made.

Trend analysis of evapotranspiration over Iran based on NEX‐GDDP high‐resolution dataset

AbstractEstimation of evapotranspiration, representing a highly sensitive variable to global warming, is often required in water resources and agricultural studies. This study explores the possible effects of climate change on evapotranspiration (ETo) across Iran. The ETo was computed using the Penman‐Monteith Temperature (PMT) method that requires only minimum and maximum temperature (Tmin and Tmax), provided by the NASA Earth Exchange Global Daily Downscaled Projections dataset (NEX‐GDDP) with a 0.25° × 0.25° spatial resolution. Accuracy of the NEX‐GDDP Tmin and Tmax were evaluated against ground truth observations at 41 synoptic weather stations distributed across the country using a set of statistical measures, including the coefficient of determination (R2) and Nash‐Sutcliffe Efficiency (NSE). Similarly, the PMT estimated evapotranspiration based on the NEX‐GDDP Tmin and Tmax (ETo‐GDDP) were statistically evaluated against the ETo computed with observed variables at selected stations (ETo‐obs). Then, at each grid cell, the 45‐years ETo‐GDDP time series was partitioned into three 15‐years subperiods such that the differences between three subperiods were computed and inter‐compared. Furthermore, annual, seasonal, and monthly ETo time series trends of each cell were evaluated using the Mann–Kendall trend test (MK) while the corresponding change rates were estimated using Theil–Sen's slope estimator (TSSE). The results demonstrated a good agreement between ETo‐GDDP and ETo‐obs with R2 greater than 0.8 and NSE greater than 0.7 in 90.2% and 65.8% of the stations, respectively. The maps of differences between the three subperiods showed negligible changes in ETo between 1975–1990 and 1950–1975 subperiods. The spatial patterns of annual and seasonal MK statistics showed a significant increasing trend in most parts of Iran. On seasonal scale, the highest and lowest ETo changes were observed in spring (0.00035–0.0045) and in autumn (0.0005–0.0015), respectively.

A statistics-based descriptor for automatic classification of scatterers in seismic sections

Discontinuities and small structures induce diffractions on seismic or ground-penetrating radar (GPR) acquisitions. Therefore, diffraction images can be used as a tool to access valuable information concerning subsurface scattering features, such as pinch outs, fractures, and edges. Usually, diffraction-imaging methods operate on diffraction events previously detected. Pattern-recognition methods are efficient to detect, image, and characterize diffractions. The use of this kind of approach, though, requires a numerical description of image points on a seismic section or radargram. We have investigated a new descriptor for seismic/GPR data that distinguishes diffractions from reflections. The descriptor consists of a set of statistical measures from diffraction operators sensitive to kinematic and dynamic aspects of an event. We develop experiments in which the proposed descriptor was incorporated into a pattern-recognition routine for diffraction imaging. The obtained method is useful for performing the automatic classification of image points using supervised and unsupervised algorithms, as a complementary step to Kirchhoff imaging. We also develop a new type of filtering, designed to address anomalies on the diffraction operators caused by interfering events. We evaluate the method using synthetic seismic data and real GPR data. Our results indicate that the descriptor correctly discriminates diffractions and shows promising results for low signal-to-noise-ratio situations.

Enhancing Prediction Performance of Landslide Susceptibility Model Using Hybrid Machine Learning Approach of Bagging Ensemble and Logistic Model Tree

The objective of this research is introduce a new machine learning ensemble approach that is a hybridization of Bagging ensemble (BE) and Logistic Model Trees (LMTree), named as BE-LMtree, for improving the performance of the landslide susceptibility model. The LMTree is a relatively new machine learning algorithm that was rarely explored for landslide study, whereas BE is an ensemble framework that has proven highly efficient for landslide modeling. Upper Reaches Area of Red River Basin (URRB) in Northwest region of Viet Nam was employed as a case study. For this work, a GIS database for the URRB area has been established, which contains a total of 255 landslide polygons and eight predisposing factors i.e., slope, aspect, elevation, land cover, soil type, lithology, distance to fault, and distance to river. The database was then used to construct and validate the proposed BE-LMTree model. Quality of the final BE-LMTree model was checked using confusion matrix and a set of statistical measures. The result showed that the performance of the proposed BE-LMTree model is high with the classification accuracy is 93.81% on the training dataset and the prediction capability is 83.4% on the on the validation dataset. When compared to the support vector machine model and the LMTree model, the proposed BE-LMTree model performs better; therefore, we concluded that the BE-LMTree could prove to be a new efficient tool that should be used for landslide modeling. This research could provide useful results for landslide modeling in landslide prone areas.

Designing key performance indicators for higher education programs in engineering and technology

Higher education plays a crucial role in the economic and social development. In order to fulfill its role, mission, and vision, as well as enhance competitive advantages in the global integration context, universities have to put quality as the top priority. Recently, many universities nationwide have been developing quality assurance system, conducting self assessment, and pursuing accreditation in both program and institutional levels. This process aims at developing suitable action plan for quality improvement based on determined strengths and weaknesses. However, this process requires a huge effort to collect and analyze data. In order to enhance the effectiveness and efficiency, the key performance indicators (KPIs) are proposed to evaluate the quality of higher education programs in many aspects. Those KPIs can be considered as a set of statistical measures of how higher education programs are performing. This paper, therefore, provides some general definitions and proposes some KPIs in order to measure the quality of higher education programs in engineering and technology. However, findings of this study can be used as references for other programs.

Acoustic-Based Non-Destructive Estimation of Wood Quality Attributes within Standing Red Pine Trees

The relationship between acoustic velocity (vd) and the dynamic modulus of elasticity (me), wood density (wd), microfibril angle, tracheid wall thickness (wt,), radial and tangential diameters, fibre coarseness (co) and specific surface area (sa), within standing red pine (Pinus resinosa Ait.) trees, was investigated. The data acquisition phase involved 3 basic steps: (1) random selection of 54 sample trees from 2 intensively-managed 80-year-old plantations in central Canada; (2) attainment of cardinal-based vd measurements transecting the breast-height position on each sample tree; and (3) felling, sectioning and obtaining cross-sectional samples from the first 5.3 m sawlog from which Silviscan-based area-weighted mean attribute estimates were determined. The data analysis phase consisted of applying graphical and correlation analyses to specify regression models for each of the 8 attribute-acoustic velocity relationships. Results indicated that viable relationships were obtained for me, wd, wt, co and sa based on a set of statistical measures: goodness-of-fit (42%, 14%, 45%, 27% and 43% of the variability explained, respectively), lack-of-fit (unbiasedness) and predictive precision (±12%, ±8%, ±7%, ±8% and ±6% error tolerance intervals, respectively). Non-destructive approaches for estimating the prerequisite wd value when deploying the analytical framework were also empirically evaluated. Collectively, the proposed approach and associated results provide the foundation for the development of a comprehensive and precise end-product segregation strategy for use in red pine management.

Assessing Beijing's PM 2.5 pollution: severity, weather impact, APEC and winter heating

By learning the PM 2.5 readings and meteorological records from 2010–2015, the severity of PM 2.5 pollution in Beijing is quantified with a set of statistical measures. As PM 2.5 concentration is highly influenced by meteorological conditions, we propose a statistical approach to adjust PM 2.5 concentration with respect to meteorological conditions, which can be used to monitor PM 2.5 pollution in a location. The adjusted monthly averages and percentiles are employed to test if the PM 2.5 levels in Beijing have been lowered since China's State Council set up a pollution reduction target. The results of the testing reveal significant increases, rather than decreases, in the PM 2.5 concentrations in the years 2013 and 2014 as compared with those in year 2012. We conduct analyses on two quasi-experiments—the Asia-Pacific Economic Cooperation meeting in November 2014 and the annual winter heating—to gain insight into the impacts of emissions on PM 2.5 . The analyses lead to a conclusion that a fundamental shift from mainly coal-based energy consumption to much greener alternatives in Beijing and the surrounding North China Plain is the key to solving the PM 2.5 problem in Beijing.

Analysis of nuclear microsatellites reveals limited differentiation between Colchic and Hyrcanian populations of the wind-pollinated relict tree Zelkova carpinifolia (Ulmaceae).

• The Caucasus represents one of the world's biodiversity hotspots and includes the climatic refugia Hyrcan on the southern coast of the Caspian Sea and Colchis on the eastern coast of the Black Sea, where different species survived during the Quaternary climatic oscillations. We evaluated the genetic diversity of the relict tree Zelkova carpinifolia shared between the two refugia and distributed throughout the Caucasus and adjacent areas.• Specimens were collected from 30 geographical sites in Azerbaijan, Georgia, Iran, and Turkey and screened for variability at eight nuclear microsatellite loci. The genetic diversity among and within populations was assessed using a set of statistical measures.• We detected 379 different genotypes from a total of 495 individuals with varying degrees of clonal reproduction at the different sites. Low to intermediate levels of genetic diversity were observed at all sites, and strong differentiation between sampling sites was absent. In addition, we observed no clear genetic differentiation between the Colchis and Hyrcan. Bayesian clustering of the genotypes revealed three populations with high levels of admixture between the sampling sites.• The lack of strong genetic structure of studied populations of Z. carpinifolia contrasts with a previous study based on chloroplast markers and suggests that long-distance pollen dispersal is an important factor of gene flow among populations of Z. carpinifolia. The present study does not reveal any particular site with particularly isolated genotypes that would deserve more attention for conservation purposes than others, although some sites should be considered for further investigation.

Construction of two- and three-dimensional statistically similar RVEs for coupled micro-macro simulations

In this paper a method is presented for the construction of two- and three-dimensional statistically similar representative volume elements (SSRVEs) that may be used in computational two-scale calculations. These SSRVEs are obtained by minimizing a least-square functional defined in terms of deviations of statistical measures describing the microstructure morphology and mechanical macroscopic quantities computed for a random target microstructure and for the SSRVE. It is shown that such SSRVEs serve as lower bounds in a statistical sense with respect to the difference of microstructure morphology. Moreover, an upper bound is defined by the maximum of the least-square functional. A staggered optimization procedure is proposed enabling a more efficient construction of SSRVEs. In an inner optimization problem we ensure that the statistical similarity of the microstructure morphology in the SSRVE compared with a target microstructure is as high as possible. Then, in an outer optimization problem we analyze mechanical stress---strain curves. As an example for the proposed method two- and three-dimensional SSRVEs are constructed for real microstructure data of a dual-phase steel. By comparing their mechanical response with the one of the real microstructure the performance of the method is documented. It turns out that the quality of the SSRVEs improves and converges to some limit value as the microstructure complexity of the SSRVE increases. This converging behavior gives reason to expect an optimal SSRVE at the limit for a chosen type of microstructure parameterization and set of statistical measures.

Genealogy, Migration, and the Intertwined Geographies of Personal Pasts

Genealogical data have tremendous potential to reveal the geographic web of past family connections. In this article we propose a genealogical geography paradigm that utilizes queries of a database containing more than 800 million names to analyze historical migration patterns. Our conceptual model considers hourglass-shaped community ancestry and descendancy hinterlands that spread out from a local place and incorporates the ideas of diaspora, gathering, community stability, and genealogical mixing. In the process we introduce a set of statistical measures that define the migration geographies of multigenerational movements of related people. Three case studies utilizing these methods give an indication of what can be done with this analytical approach. The first case study examines the ancestry of Midwestern U.S. cities of 1900, illustrating multiple migration trajectories projecting eastward over four generations. The second considers patterns of migration to the Gold Rush–impacted areas of northern California (1850–1900) and highlights increasing community stability and shifting migration distances among mining and commercial centers. The third analyzes the geographic congruence of Fischer's (1989) four cultural pathway origins of people who migrated to Colonial America and lends mixed support of his claims. This research approach enriches our understanding of the migration and family processes that formed the geographically interconnected settlements and regional structures of the world.

A Face Recognition System Based on BDIP and DCT Pyramid

In this paper, an efficient feature extraction method based on block difference of inverse probabilities (BDIP) and DCT pyramid for face recognition is proposed. The BDIP is first computed in a face image in order to overcome the variation of illumination. The extracted BDIP image is then decomposed using DCT pyramid. The DCT pyramid decomposes the BDIP image into an approximation subband and a set of reversed L-shape blocks containing the high frequency coefficients of the DCT pyramid. A set of simple block-based statistical measures is calculated from the extracted DCT pyramid subbands. This set of statistical measures is an efficient way of reducing the dimensionality of the feature vectors. Experimental results on the standard ORL and FERET databases show that the proposed method achieves more accurate face recognition than the wavelet-based methods and the other well known methods such as the PCA and the block-based DCT with the zigzag scanning.

CLASSIFICADOR TEXTURAL PARA IDENTIFICAÇÃO DE PLANTAS

No presente trabalho, objetivou-se desenvolver um classificador textural em Matlab, para identificação de quatro espécies de plantas. A textura foi definida por meio de um conjunto de medidas estatísticas, descrevendo as variações espaciais de intensidade de pixels ou cor da parte aérea das plantas. As medidas foram calculadas utilizando-se matrizes de coocorrência. Os resultados indicaram que a classificação textural para identificação de plantas é uma das mais simples possíveis, sendo os resultados das classificações extremamente relacionados com as texturas analisadas.

Low-memory requirement and efficient face recognition system based on DCT pyramid

Face recognition (FR) is a challenging issue due to variations in pose, illumination, and expression. In this paper, a face recognition system with low-memory requirement and accurate recognition is presented. It is based on extraction of features with the DCT pyramid, in contrast to the conventional method of wavelet decomposition. The DCT pyramid performed on each face image decomposes it into an approximation subband and the reversed L-shape blocks containing the high frequency coefficients of the DCT pyramid. A set of simple block-based statistical measures is calculated from the extracted DCT pyramid subbands. This set of statistical measures is an efficient way of reducing the dimensionality of the feature vectors. Experimental results on the standard ORL and FERET databases show that the proposed method achieves more accurate face recognition than the wavelet-based methods. Moreover, it outperforms the other well known methods such as PCA and the block-based DCT with the zigzag scanning in terms of accuracy and memory requirement.

Artificial neural networks and high and low flows in various climate regimes

An algorithm coupling linear least squares and simplex optimization (LLSSIM) is used to examine the ability of a three-layer feedforward artificial neural network (ANN) to simulate the high and low flows in various climate regimes over a mountainous catchment (the Mesochora catchment in central Greece). The plot of the long-term annual catchment pseudo-precipitation (rain plus snowmelt) simulated by the snow accumulation and ablation model (SAA) of the US National Weather Service (US NWS) showed trends of three climatically distinct periods, described by clearly descending, rising and moderately descending segments in pseudo-precipitation. The ANN model was calibrated for each of the three climate types and each was validated against the others. A set of statistical measures and graphs adapted for high and low flows showed the robustness of the ANN model under various climates and transient conditions. The ANN model proved capable of simulating well the daily high and low flows when it is calibrated for increasing pseudo-precipitation and validated for moderately decreasing pseudo-precipitation. For the entire period, the ANN model provided a better simulation of high and low flows than the conceptual soil moisture accounting (SMA) model of the US NWS, which was also employed in this study. Because the ANN is not a physically-based model, it is by no means a substitute for the SMA model. However, it is concluded that the ANN approach is an effective alternative for daily high- and low-flow simulation and forecasting in climatically varied regimes, particularly in cases where the internal dynamics of the catchment do not require an explicit representation.

‘Prodigious Riches’: The Wealth of Jamaica Before the American Revolution

W hen eighteenth-century Britons contemplated their possessions in W the West Indies what struck them most was the wealth of these small tropical islands. They were particularly impressed by the wealth of the largest British colony in the Caribbean, Jamaica. Despite Jamaica's well-deserved reputation as a white person's graveyard, Europeans flocked to the island in order to acquire great fortunes. Both for individuals and for the British government the wealth of Jamaica was its greatest attraction. As Charles Leslie wrote in the mid-eighteenth century, 'Jamaica is a Constant Mine, whence Britain draws prodigious riches.' Its wealth and strategic importance in the Caribbean made it, as Marshall notes, the most valuable colony in the eighteenth-century empire, and the colony whose loss the British could have least afforded.' This article reports new estimates about how much wealth Europeans possessed in Jamaica on the eve of the American Revolution. Historians have been interested in this question before, but mainly in order to assess the contribution of the West Indies to British economic development. In an article published in this journal in 1965, Sheridan presented data on the wealth of Jamaica supporting Williams's thesis that the economic contribution of the British West Indian slave colonies to British prosperity in the late eighteenth century was considerable. Sheridan's contentions elicited a fierce response from Thomas who used a counterfactual model derived from Sheridan's own data to deny that Jamaica had made a positive contribution to British economic development. The theoretical arguments as to whether or not Williams was correct will not be addressed in this article. I agree with the recent findings of Eltis and Engerman that sugar cultivation and the slave trade did not form an especially large part of the British economy, although, as Solow has shown, profits from the slave trade comprised nearly 8 per cent of total British investment in 1770 and a remarkable 39 per cent of commercial and industrial investment, assuming that all profits went into these channels. What this article concentrates on is the economic importance of Jamaica within the British American empire before the American Revolution. It revises Sheridan's set of statistical measures about the wealth of Jamaica before the American Revolution, empirical data that have hitherto been accepted

A machine vision approach to detect and categorize hydrocarbon fires in aircraft dry bays and engine compartments

In this paper, a machine approach is applied to detect hydrocarbon fires in aircraft dry bays and engine compartments. The inputs to the machine vision system consist of a set of statistical measures derived from the histogram and image subtraction analyses of successive image frames. Specifically, heuristic rules based on the median, standard deviation and normalized first-order moment statistical measures of histogram data and the mean statistical measure of image subtraction data of successive frames are used to compute the likelihood of a fire event. This machine vision system is also tested for false alarms such as those due to flashlights and high-power halogen lights.