Statistical Background Research Articles

Methodological Features of the Management of Modern Labour Protection in the Construction

In the recent ten years, there has been a steady decline in industrial injuries and occupational diseases in the global economy. At the same time, against the background of favorable statistics on accidents, an increase in the overall incidence is observed, which are called «work related diseases». By decision of the Government of the Russian Federation to improve the production environment should ensure the reform of the management system of labour protection, focused on the prevention of the manifestation of dangerous and harmful factors. In this article there is analyse of the methodological aspects of occupational risk management, which imply a continuous and consistent cycle of activity from four stages, characterized as «the Deming-Shewhart cycle».

INFLUENCE OF SOLAR ACTIVITIES ON CLIMATE CHANGE

The solar energy received by the atmosphere of the Earth is universally distributed, which constitutes the atmospheric dynamics. The changes observed in the climate of the Earth are due to solar output variations. Some detailed reviews have accounted for the influence of solar activities on the climate using sunspot number derived from peak Sun hours, but has gaps in analyzing critical statistical background information, seasonal, latitudinal and solar activity year validation. A number of statistical concepts are explained in order to enhance our scientific knowledge of climate response to solar activity variation. The influence of geomagnetism to climate response is also examined. This review will serve as analysis and research methodology discourse which could be an application of scientific method. Additionally, this review discusses the influence of the solar activity on climate change based on the existing literature, by focusing on solar activities other than sunspot number alone. Here, we also validate methods using forward and inverse modeling, mean percentage difference, and relative variability. Results indicated that the total solar irradiance (TSI) has remained the main focus of Sun-climate interaction with somewhat negligent on energetic particle precipitation resulting from geomagnetic disturbance reaching the atmosphere. However, statistical concept remains asset to investigate the solar activity influence on climate in both local and global latitudes. We therefore recommend provision of solar quantities data to researchers for further study.

Flood risk assessment and management for urban areas in Brazilian Pantanal using free and open source GIS software

The present work provides city managers a tool to strategically deploy a flood risk assessment and management, with statistical, technological and graphical approaches. For risk assessment, the return period of the years between 1977 and 2017 was analysed, comprising a historical series of 40 years of maximum quotas and maximum flows. Researchers used the QuantumGis 2.18 platform to deploy flood risk mapping. Maps with flood risk levels within the urban area of study are presented, built as 3D models. The calculation of the return period estimation for flood risk is a reliable tool to understand the dynamics of a water body inserted in an urban area. Thus, it is possible to deploy scenarios with statistical background to aid decision making with regard to risk assessment. Technology-based information is key to support decision making, helping cities to raise funds to deploy risk management actions. The use of a free and open source GIS software such as QuantumGis presents a low cost, efficient and interface-friendly platform to solve flood management issues.

Flood risk assessment and management for urban areas in Brazilian Pantanal using free and open source GIS software

The present work provides city managers a tool to strategically deploy a flood risk assessment and management, with statistical, technological and graphical approaches. For risk assessment, the return period of the years between 1977 and 2017 was analysed, comprising a historical series of 40 years of maximum quotas and maximum flows. Researchers used the QuantumGis 2.18 platform to deploy flood risk mapping. Maps with flood risk levels within the urban area of study are presented, built as 3D models. The calculation of the return period estimation for flood risk is a reliable tool to understand the dynamics of a water body inserted in an urban area. Thus, it is possible to deploy scenarios with statistical background to aid decision making with regard to risk assessment. Technology-based information is key to support decision making, helping cities to raise funds to deploy risk management actions. The use of a free and open source GIS software such as QuantumGis presents a low cost, efficient and interface-friendly platform to solve flood management issues.

Design of a Hard Expectation-Maximization-Based Normalized Matched Filter (EM-NMF) for the Detection of Chemical Warfare Agents Under Background Contamination

We propose a hard expectation-maximization-based normalized matched filter (EM-NMF) for the detection of chemical warfare agent (CWA) clouds under background contamination. The NMF, which is one of the most powerful detectors, requires background statistics calculated from a background training dataset. However, in practice, because the training dataset is likely to contain CWA-on background pixels, the performance of the NMF is severely degraded. This phenomenon is referred to as background contamination. To address this issue, we propose an algorithm that estimates the posterior probability of each pixel belonging to either the background or the CWA class. The optimal posterior probabilities are obtained by maximizing the log-likelihood of a contaminated dataset using the EM algorithm. Based on the posterior probability, we extract CWA-free background pixels from the contaminated dataset and design a hard EM-NMF with extracted CWA-free background pixels. We demonstrate that the proposed algorithm is an effective solution for background contamination, via experimental results conducted with actual CWA data measured by a Bruker HI–90 instrument in an outdoor setting as well as synthetic CWA data.

Nonparametric Spectral-Spatial Anomaly Detection

Due to abundant spectral information contained in the hyperspectral images, they are suitable data for anomalous targets detection. The use of spatial features in addition to spectral ones can improve the anomaly detection performance. An anomaly detector, called nonparametric spectral-spatial detector (NSSD), is proposed in this work which utilizes the benefits of spatial features and local structures extracted by the morphological filters. The obtained spectral-spatial hypercube has high dimensionality. So, accurate estimates of the background statistics in small local windows may not be obtained. Applying conventional detectors such as Local Reed Xiaoli (RX) to the high dimensional data is not possible. To deal with this difficulty, a nonparametric distance, without any need to estimate the data statistics, is used instead of the Mahalanobis distance. According to the experimental results, the detection accuracy improvement of the proposed NSSD method compared to Global RX, Local RX, weighted RX, linear filtering based RX (LF-RX), background joint sparse representation detection (BJSRD), Kernel RX, subspace RX (SSRX) and RX and uniform target detector (RX-UTD) in average is 47.68%, 27.86%, 13.23%, 29.26%, 3.33%, 17.07%, 15.88%, and 44.25%, respectively.

A tale of two niches: methods, concepts, and evolution

Being snapshots in time, species ranges may fall short of representing all of the geographic or environmental space that they are able to occupy. This has important implications for niche studies yet most comparative studies overlook the transient nature of species distributions and assume that they are at equilibrium. We review the methods most widely used for niche comparisons today and suggest a modified framework to describe and compare niches based on snapshot species range data. First, we introduce a new environmental space-based Niche Equivalence Statistic to test niche similarity between two species, which explicitly incorporates the spatial distribution of environments and their availability into statistical tests. We also introduce a new Background Statistic to measure the ability of this Niche Equivalence Statistic to detect differences based on the available environmental-space. These metrics enable fair comparisons between different geographies when the ranges of species are out of equilibrium. Based on distinct parameterizations of the new Equivalence and Background statistics, we then propose a Niche Divergence Test and a Niche Overlap Test, which allow assessment of whether differences between species emerge from true niche divergences. These methods are implemented in a new R package, ‘humboldt’ and applied to simulated species with pre-defined niches. The new methods improve accuracy of niche similarity and associated tests – consistently outperforming other tests. We show that the quantification of niche similarity should be performed only in environmental space, which is less sensitive than geographic space to the spatial abundance of key environmental variables. Further, our methods characterize the relationships between non-analogous and analogous climates in the species’ distributions, something not available previously. These improvements allow assessment of whether the different environmental spaces occupied by two taxa emerge from true niche evolution, as opposed to differences in life history and biological interactors, or differences in the variety and configuration of environments accessible to them.

An End-to-End Hyperspectral Scene Simulator with Alternate Adjacency Effect Models and Its Comparison with CameoSim

In this research, we developed a new rendering-based end to end Hyperspectral scene simulator CHIMES (Cranfield Hyperspectral Image Modelling and Evaluation System), which generates nadir images of passively illuminated 3-D outdoor scenes in Visible, Near Infrared (NIR) and Short-Wave Infrared (SWIR) regions, ranging from 360 nm to 2520 nm. MODTRAN TM (MODerate resolution TRANsmission), is used to generate the sky-dome environment map which includes sun and sky radiance along with the polarisation effect of the sky due to Rayleigh scattering. Moreover, we perform path tracing and implement ray interaction with medium and volumetric backscattering at rendering time to model the adjacency effect. We propose two variants of adjacency models, the first one incorporates a single spectral albedo as the averaged background of the scene, this model is called the Background One-Spectra Adjacency Effect Model (BOAEM), which is a CameoSim like model created for performance comparison. The second model calculates background albedo from a pixel’s neighbourhood, whose size depends on the air volume between sensor and target, and differential air density up to sensor altitude. Average background reflectance of all neighbourhood pixel is computed at rendering time for estimating the total upwelled scattered radiance, by volumetric scattering. This model is termed the Texture-Spectra Incorporated Adjacency Effect Model (TIAEM). Moreover, for estimating the underlying atmospheric condition MODTRAN is run with varying aerosol optical thickness and its total ground reflected radiance (TGRR) is compared with TGRR of known in-scene material. The Goodness of fit is evaluated in each iteration, and MODTRAN’s output with the best fit is selected. We perform a tri-modal validation of simulators on a real hyperspectral scene by varying atmospheric condition, terrain surface models and proposed variants of adjacency models. We compared results of our model with Lockheed Martin’s well-established scene simulator CameoSim and acquired Ground Truth (GT) by Hyspex cameras. In clear-sky conditions, both models of CHIMES and CameoSim are in close agreement, however, in searched overcast conditions CHIMES BOAEM is shown to perform better than CameoSim in terms of ℓ 1 -norm error of the whole scene with respect to GT. TIAEM produces better radiance shape and covariance of background statistics with respect to Ground Truth (GT), which is key to good target detection performance. We also report that the results of CameoSim have a many-fold higher error for the same scene when the flat surface terrain is replaced with a Digital Elevation Model (DEM) based rugged one.

Moving object detection using statistical background subtraction in wavelet compressed domain

Moving object detection is a fundamental task and extensively used research area in modern world computer vision applications. Background subtraction is one of the widely used and the most efficient technique for it, which generates the initial background using different statistical parameters. Due to the enormous size of the video data, the segmentation process requires considerable amount of memory space and time. To reduce the above shortcomings, we propose a statistical background subtraction based motion segmentation method in a compressed transformed domain employing wavelet. We employ the weighted-mean and weighted-variance based background subtraction operations only on the detailed components of the wavelet transformed frame to reduce the computational complexity. Here, weight for each pixel location is computed using pixel-wise median operation between the successive frames. To detect the foreground objects, we employ adaptive threshold, the value of which is selected based on different statistical parameters. Finally, morphological operation, connected component analysis, and flood-fill algorithm are applied to efficiently and accurately detect the foreground objects. Our method is conceived, implemented, and tested on different real video sequences and experimental results show that the performance of our method is reasonably better compared to few other existing approaches.

Low-Rank and Sparse Matrix Decomposition With Orthogonal Subspace Projection-Based Background Suppression for Hyperspectral Anomaly Detection

Although the low-rank and sparse matrix decomposition (LRaSMD)-based anomaly detectors can effectively extract the low-rank structure as the background component and the sparse structure as the anomaly component for anomaly detection (AD) while simultaneously considering the additive noise, the background interferences in the sparse component remain a serious problem that will increase the false alarm rate and influence the detection of real anomalies. To alleviate this issue, a novel LRaSMD with orthogonal subspace projection (OSP)-based background suppression and adaptive weighting for hyperspectral AD is proposed in this letter. Based on the fact that the background interferences in the sparse component are mainly some sparse objects with slight spectral differences from the main background, the OSP is employed to project the sparse component into the background orthogonal subspace that is estimated from the low-rank component to suppress the background interferences and highlight the anomalies. Furthermore, the low-rank component provides an effective estimation of the background statistics, which can be used to adaptively weigh the detection results. Experiments on both synthetic and real hyperspectral data sets demonstrate the effectiveness of the proposed algorithm.

Hyperspectral anomaly detection via density peak clustering

In the last few years, a density peak clustering algorithm (DP) has demonstrated its advantages in hyperspectral data analysis and processing. In this letter, we take the benefits of the DP algorithm to the hyperspectral anomaly detection, to circumvent two negative aspects which affect the detection performance: The untenable supposition of the Gaussian distribution and the contamination of the background statistics caused by anomalies. Specifically, the proposed DP-based hyperspectral anomaly detection method is implemented as follows: A hyperspectral image (HSI) is first divided into local windows to address computationally expensive density computations. In each local window, the DP is performed to calculate the density of each pixel. Finally, we detect anomalies using the obtained density map, based on that anomalies are generally with low probability of existence in the image and thus have low densities. Experimental results obtained on four real hyperspectral datasets demonstrate that the detection performance of the proposed method is superior to some widely used anomaly detection methods.

If numbers could “feel”: How well do executives trust their intuition?

Purpose In the business and data analytics community, intuition has not been discussed widely in terms of its application to executive decision-making. However, the purpose of this paper is to focus on new global research that combines intuition, trust and analytics in terms of how well C-level executives trust their intuition. Design/methodology/approach Our Fulbright research, as described in this paper and performed by colleagues from the United States, Canada, Poland and Italy, examines executives’ as well as other less experienced employees’ preferences for different types of intuition versus data analysis. This study set out to better understand the degree to which executives prefer intuition versus analysis and the relationship between these approaches to decision-making. Our research combines elements of a review, a cross-cultural/cross-company survey study and a biometrics study in interoception. The research team has a multidisciplinary background in business, information technology, strategy, trust management, statistics and neuroscience. Findings Based on our research, the main findings are as follows. The use of and preference for intuition types change as employees gain more experience. However, there may be intuition styles that are more static and trait-like, which are linked to roles, differentiating managers from leaders. Using “inferential intuition” and “seeing the big picture” go hand in hand. Listening to your body signals can promote improved intuition. Cross-cultural differences may impact executive decision-making. Executives often prefer to use their intuition over analysis/analytics. Research limitations/implications This research could be expanded to have a larger sample size of C-level executives. We had 172 responses with 65% C-level executives and 12% directors. However, a recent survey by the Economist Intelligence Unit on intuition used by executives had a sample of 174 executives around the world, which is comparable with our sample size. Practical implications From our research, executives should continue to apply their experiential learning through intuition to complement their use of data in making strategic decisions. We have often discounted the use of intuition in executive decision-making, but our research highlights the importance of making it a critical part of the executive decision-making process. Originality/value Based on the results of our survey and biometrics research, executives apply their intuition to gain greater confidence in their decision-making. Listening to their body signals can also improve their intuitive executive awareness. This complements their use of data and analytics when making executive decisions.

The effect of corruption and culture on corporate social performance: an empirical study

PurposeThe study aims to examine the simultaneous influence of corruption and culture on corporate social and environmental performance of selected companies.Design/methodology/approachTheoretical propositions on how corruption and culture influence corporate social responsibility performance were developed and empirically tested. Corruption is measured using Transparency International’s Corruption Perception Index and Schwartz (2008) cultural dimension is used as a measure of culture. Descriptive analysis was performed to provide the background statistics of the variables examined. This was followed by regression analysis which forms the main data analysis.FindingsThe multiple regression analysis results indicated that corruption and two of the three cultural dimensions (embeddedness and Mastery) are significantly related to corporate social responsibility performance.Originality/valueThe study contributes to the corporate social responsibility literature by revealing that corruption and culture are key determinants of corporate social responsibility performance.

Automatically Identifying Relevant Variables for Linear Regression with the Lasso Method: A Methodological Primer for its Application with R and a Performance Contrast Simulation with Alternative Selection Strategies

ABSTRACT The abundance of available digital big data has created new challenges in identifying relevant variables for regression models. One statistical problem that gained relevance in the era of big data is high-dimensional statistical inference, when the number of variables greatly exceeds the number of observations. Typically, prediction errors in linear regression skyrocket when the number of included variables gets close to the number of observations, and ordinary least squares (OLS) regression no longer works in a high-dimensional scenario. Regularized estimators as a feasible solution include the Least Absolute Shrinkage and Selection Operator (Lasso), which we introduce to communication scholars here. We will include the statistical background of this technique that combines estimation and variable selection simultaneously and helps identify relevant variables for regression models in high-dimensional scenarios. We contrast the Lasso with two alternative strategies of selecting variables for regression models, namely, a theory-based “subset selection” of variables and a nonselective “all in” strategy. The simulation shows that the Lasso produces lower and more relatively stable prediction errors than the two alternative variable selection strategies, and it is therefore recommended to use, especially in high-dimensional settings typical in times of big data analysis.

Improved detection of faint streaks based on a streak-like spatial filter

This paper presents an algorithm to detect very faint object streaks on CCD images acquired with an optical system. The proposed detection method uses image filters simulating the geometrical form and orientation of possible streaks on the CCD image. The method searches for a matching between streak and filter evaluating the convolutions of the image with all possible filters. Based on the statistics of the image background a threshold is applied in order to accept, respectively reject, the possible streak candidates. The detection probabilities and the effect of different parameter settings are estimated with tests on simulated images, while subframes of real images are used to evaluate the applicability of the algorithm to real cases. The detection probability of this method depends on the length and on the signal-to-noise ratio of the streak. For long streaks, a detection for signal-to-noise values around 0.5 is achieved. The further characterization of the detected streak (e.g. centroid and length), which is not performed in the current algorithm, and the reduction of the computation time, which is relatively high for full acquired frames, as well as possible improvements are briefly addressed.

Statistics Anxiety of Graduate Students

Commonly described by psychologists; anxiety is a psychological construct, as a state of apprehension, a vague fear that is only indirectly associated with an object (Scovel, 1991). A small amount of anxiety is believed to be needed for learners since it improves performance which is known as facilitating anxiety. Statistics anxiety is a feeling of anxiety when taking a statistics course or doing statistical analysis (Cruise, Cash, & Bolton, 1985). In general, statistics anxiety is a debilitating anxiety which negatively affect students’ performance. Statistics is one of the compulsory courses in most graduate programs. Students entering to these graduate programs especially in social sciences such as education, usually do not have any math and statistical background which creates a negative perception towards statistics related courses. These negative feelings and perceptions are thought to be affect their level of success. The aim of this study is, therefore, to determine the anxiety related views of graduate students towards statistics course. Qualitative content analysis with purposeful sampling is used in the study. To this end, the research data were obtained with the help of 26 graduate students studying in Department of Educational Sciences at Çanakkale Onsekiz Mart University in the academic years of 2017-2018. A semi-structured interview form was used for data collection which aims to determine the anxiety related views of graduate students. Analysis of the results revealed five themes of statistics anxiety; (1) importance of the course, (2) math skills, (3) computer skills, (4) exam stress, and (5) foreign language skills. It can be concluded that defining and understanding the source of anxiety related behaviors will help to conduct more effective and efficient statistics courses.

214 12-Month Post Marketing Safety Surveillance Data of Chewable Inulin Fiber

INTRODUCTION: Most Americans consume only half of the daily fiber recommended by the National Academy of Medicine, which is 25 grams per day of fiber for women and 38 grams per day for men. Fiber has been shown to promote bowel regularity, lower cholesterol, control blood sugar, and maintain body weight. To fill the “fiber gap” many patients use fiber supplements. There is a perception that fiber supplementation can lead to bloating and excess gas production, among other adverse events. This study aimed to examine whether or not a pattern of adverse events was associated with chewable inulin fiber (CIF). METHODS: A call-in number for reporting adverse events (AEs) was provided on bottles of a specific brand of CIF. An independent call center with pharmacovigilance-trained health care personnel in accordance with the FDA and global regulatory guidelines on properly reporting AEs was retained to receive and record customer AEs. The AEs for this study were collected and processed from January 1, 2018 to December 31, 2018. RESULTS: An estimated 809,259 individual patients consumed CIF during the surveillance. An analysis of the data showed that there were no serious AEs reported. Additionally, the self-reporting rate of non-serious AEs was low, with only 23 non-serious events reported by 21 individuals; a rate of 2.8 per 100,000 users. The top reported, non-serious AEs were flatulence, diarrhea, and constipation; however, the rates for these were extremely low (4.9, 3.7, and 1.2 per million, respectively). Two events were reported that were classified as musculoskeletal and connective tissue disorders; however, these were considered to not be related, having no basis for biological plausibility. CONCLUSION: 12 months of monitoring real-world use of CIF in a population of over 800,000 patients demonstrates an extremely low rate of self-reported AEs. Contrary to conventional thinking, the rate of flatulence was remarkably low (4.9 per million users), and can most likely be considered statistical background noise. These data, combined with previous RCTs, indicates that CIF is a safe and effective option for fiber supplementation.

A Better Approach to Generating Random Numbers

The term random number has been used by many scholars to explain the behaviour of a stochastic system. Many of such scholars with statistical or mathematical background view it as an organized set of numbers produced by a function in a numerical way in which the next number to be produced is unknown or unpredictable. This paper produced software that generates a sequence of random number and also compared the algorithm with the commonly used method of random number generator. The three most common methods selected were the Mid Square method, Fibonacci method and Linear Congruential Generator Method (LCG). The result shows that the LCG provides a more acceptable result in terms of speed, long cycle, uniformity and independence Applications of this random numbers can be seen in Monte Carlo simulations, simulation or modelling, password generation, cryptography and online games.

Utah Valley University: A Continuing Culture of Transformation

Utah Valley University is a large, regional, open-admission institution with growing enrollments and changing demographics. It has a history of transformation and change, beginning as a trade school in 1941 and evolving to a university in 2008. This article illustrates how the university has engaged in on-going strategic planning to anticipate and effectively manage threats and opportunities. The article begins with background information and statistics about the university and the Woodbury School of Business, explains how the university has leveraged its elective Carnegie classification as a community engaged institution, and shares examples of transformations in teaching and learning to enable student success. It then illustrates how the university’s Woodbury School of Business has paralleled the directions of the university to build capacity among its faculty for engaged learning and pedagogical innovations. The article ends with a summary of key outcomes and thoughts on the sustainability of transformation.

Fundamentals of Clinical Prediction Modeling for the Neurosurgeon.

Clinical prediction models in neurosurgery are increasingly reported. These models aim to provide an evidence-based approach to the estimation of the probability of a neurosurgical outcome by combining 2 or more prognostic variables. Model development and model reporting are often suboptimal. A basic understanding of the methodology of clinical prediction modeling is needed when interpreting these models. We address basic statistical background, 7 modeling steps, and requirements of these models such that they may fulfill their potential for major impact for our daily clinical practice and for future scientific work.