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Human-Computer Interaction (HCI) research has extensively employed eye-tracking technologies in a variety of fields. Meanwhile, the ongoing development of Internet Protocol TV (IPTV) has significantly enriched the TV customer experience, which is of great interest to researchers across academia and industry. A previous study was carried out at the BT Ireland Innovation Centre (BTIIC), where an eye tracker was employed to record user interactions with a Video-on-Demand (VoD) application, the BT Player. This paper is a complementary and subsequent study of the analysis of eye-tracking data in our previously published introductory paper. Here, we propose a method for integrating layout information from the BT Player with mining the process of customer eye movement on the screen, thereby generating HCI and Industry-relevant insights regarding user experience. We incorporate a popular Machine Learning model, a discrete-time Markov Chain (DTMC), into our methodology, as the eye tracker records each gaze movement at a particular frequency, which is a good example of discrete-time sequences. The Markov Model is found suitable for our study, and it helps to reveal characteristics of the gaze movement as well as the user interface (UI) design on the VoD application by interpreting transition matrices, first passage time, proposed 'most likely trajectory' and other Markov properties of the model. Additionally, the study has revealed numerous promising areas for future research. And the code involved in this study is open access on GitHub. [ABSTRACT FROM AUTHOR]

The stop signal task (SST) paradigm with its original roots in 1948 has been proposed to study humans' response inhibition. Several statistical software codes have been designed by researchers to simulate SST data in order to study various theories of modeling response inhibition and their assumptions. Yet, there has been a missing standalone statistical software package to enable researchers to simulate SST data under generalized scenarios. This paper presents the R statistical software package "SimSST", available in Comprehensive R Archive Network (CRAN), to simulate stop signal task (SST) data. The package is based on the general non-independent horse race model, the copulas in probability theory, and underlying ExGaussian (ExG) or Shifted Wald (SW) distributional assumption for the involving go and stop processes enabling the researchers to simulate sixteen scenarios of the SST data. A working example for one of the scenarios is presented to evaluate the simulations' precision on parameter estimations. Package limitations and future work directions for its subsequent extensions are discussed. [ABSTRACT FROM AUTHOR]

Conservation agriculture has recently been a hot topic of agricultural research and has generated significant global interest. Conservation agriculture has three core principles: minimal soil disturbance, permanent soil cover, and crop rotations. The research on this topic has also witnessed a boom in recent years as the number of peer-reviewed literature on conservation agriculture is rising exponentially. This study critically examines all the peer-reviewed documents published on conservation agriculture from the 1990s to 2021 and indexed in the web of science core database. The search returned 3023 documents, which were then processed in the R-based bibliometric package for annual scientific production trend, source, author, document, citation, keyword analysis, and co-occurrence networking using VOSviewer. Our findings show remarkable growth in conservation agriculture research in recent times, although it witnessed a shortfall in 2021. Notably, 15 core source journals contribute the most to the field, while 8692 researchers have authored or co-authored at least a document on conservation agriculture. While the USA, India, and Australia are front runners in conservation agriculture research, the spread of the topic is worldwide. [ABSTRACT FROM AUTHOR]

Separating urban land and structure values is important for national accounts and for analysis of real estate risk over time. A large part of the literature on urban land valuation uses the land residual method, which relies on the assumption that structures are easily replaced. But urban land value depends on accessibility to nearby land uses, implying that infrastructure and the slowly changing built environment are the most important components of land value. Investments in structures are only slowly reversible, implying that land and structure function as a bundled good whereas land residual theory severs the connection between land value and structure value over time. We develop a simple theoretical model that includes option value and compare to a nested land residual model before and after a shock to values. Cross-sectionally our model shows that land residual theory overestimates structure value. Over time almost all of any change in property value is allocated to land residuals. Data from Maricopa county, AZ, 2012–2018 strongly support option value models when nested within a general model that also includes land residuals. FHFA estimates use entirely different cost estimation methods: our analysis of FHA data suggest that our conclusions generalize to the U.S. as a whole, and that high and rising land value ratios over 50 years (the "hockey stick" pattern found in the literature) are likely an artifact of the residual model. [ABSTRACT FROM AUTHOR]

We developed an interactive online textbook that interleaves R programming activities with text as a way to facilitate students' understanding of statistical ideas while minimizing the cognitive and emotional burden of learning programming. In this exploratory study, we characterize the attitudes and experiences of 672 undergraduate students as they used our online textbook as part of a 10-week introductory course in statistics. Students expressed negative attitudes and concerns related to R at the beginning of the course, but most developed more positive attitudes after engaging with course materials, regardless of demographic characteristics or prior programming experience. Analysis of a subgroup of students revealed that change in attitudes toward R may be linked to students' patterns of engagement over time and students' perceptions of the learning environment. [ABSTRACT FROM AUTHOR]

The employability of young graduates has gained increasing significance in the labour market of the 21st century. Universities turn out millions of graduates annually, but at the same time, employers highlight their lack of the requisite skills for sustainable employment. We live today in a world of data, and therefore courses that feature numerical and computational tools to gather and analyse data are to be sourced for and integrated into life sciences' curricula as they provide a number of benefits for both the students and faculty members that are engaged in teaching the courses. The lack of this teaching in undergraduate Microbiology curricula is devastating and leaves a knowledge gap in the graduates that are turned out. This results in an inability of the emerging graduates to compete favourably with their counterparts from other parts of the world. There is a necessity on the part of life science educators to adapt their teaching strategies to best support students' curricula that prepare them for careers in science. Bioinformatics, Statistics and Programming are key computational skills to embrace by life scientists and the need for training beginning at undergraduate level cannot be overemphasized. This article reviews the need to integrate computational skills in undergraduate Microbiology curricula in developing countries with emphasis on Nigeria. [ABSTRACT FROM AUTHOR]

In cultural heritage, many projects execute Named Entity Linking (NEL) through global Linked Open Data (LOD) references in order to identify and disambiguate entities in their local datasets. It allows users to obtain extra information and contextualise the data with it. Thus, the aggregation and integration of heterogeneous LOD are expected. However, such development is still limited partly due to data quality issues. In addition, analysis on the LOD quality has not sufficiently been conducted for cultural heritage. Moreover, most research on data quality concentrates on ontology and corpus level observations. This paper examines the quality of the eleven major LOD sources used for NEL in cultural heritage with an emphasis on instance-level connectivity and graph traversals. Standardised linking properties are inspected for 100 instances/entities in order to create traversal route maps. Other properties are also assessed for quantity and quality. The outcomes suggest that the LOD is not fully interconnected and centrally condensed; the quantity and quality are unbalanced. Therefore, they cast doubt on the possibility of automatically identifying, accessing, and integrating known and unknown datasets. This implies the need for LOD improvement, as well as the NEL strategies to maximise the data integration. [ABSTRACT FROM AUTHOR]

Copyright of Methods in Ecology & Evolution is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper considers the unfolding of Smart Specialisation Strategy (RIS3) in one of Europe's innovation-leading metropolitan regions: Stockholm, Sweden. Theoretically, it contributes to debates around change agency and reflects more broadly, which implications arise for metropolitan innovation-leader regions and which are of more generic nature. It argues that actor endowments, dense networks, experience with participatory governance processes and good governance are not sufficient for a successful implementation of RIS3. It finds that transformative regional innovation policy is contingent upon the adequate empowerment of change agents rather than on general regional preconditions. [ABSTRACT FROM AUTHOR]

The use of stable isotopes to characterize ecosystem dynamics and infer leaf gas exchange processes has become increasingly prevalent over the last few decades within the ecological community. While advancements in theory and our understanding of the physiological processes controlling isotopic signatures in plants has been well‐documented, no standardized tool currently exists to facilitate the computation of common isotope‐derived plant physiological indices. Here, we present isocalcR, an R package intended to facilitate the use of stable isotope data from plant tissues by providing an integrated collection of functions and recommended reference data. The isocalcR R package contains a suite of functions that compute leaf carbon isotope discrimination (∆13C), leaf intercellular [CO2], the ratio of leaf intercellular to atmospheric [CO2], the difference between atmospheric and leaf intercellular [CO2], and intrinsic water use efficiency from carbon isotope signatures in leaf or wood tissue with minimal inputs from the user. isocalcR also implements and provides recommended input atmospheric [CO2] (ppm) and atmospheric δ13CO2 (‰) data for the period 0–2021 C.E. A major goal of isocalcR is to provide a standardized, open‐source tool to streamline the calculation of reproducible physiological indices from stable isotope signatures in plant tissues, incorporating the most up‐to‐date theory, while simultaneously eliminating potential errors associated with complex calculations. isocalcR can be used for any location globally as long as the user provides information regarding temperature and elevation to the main workhorse functions. [ABSTRACT FROM AUTHOR]

A newly emerging field in statistics is distributional regression, where not only the mean but each parameter of a parametric response distribution can be modelled using a set of predictors. As an extension of generalized additive models, distributional regression utilizes the known link functions (log, logit, etc.), model terms (fixed, random, spatial, smooth, etc.) and available types of distributions but allows us to go well beyond the exponential family and to model potentially all distributional parameters. Due to this increase in model flexibility, the interpretation of covariate effects on the shape of the conditional response distribution, its moments and other features derived from this distribution is more challenging than with traditional mean-based methods. In particular, such quantities of interest often do not directly equate the modelled parameters but are rather a (potentially complex) combination of them. To ease the post-estimation model analysis, we propose a framework and subsequently feature an implementation in R for the visualization of Bayesian and frequentist distributional regression models fitted using the bamlss, gamlss and betareg R packages. [ABSTRACT FROM AUTHOR]

Risk and risk–benefit assessments of food are complex exercises, in which access to and use of several disconnected individual stand‐alone databases is required to obtain hazard and exposure information. Data obtained from such databases ideally should be in line with the FAIR principles, i.e. the data must be Findable, Accessible, Interoperable and Reusable. However, often cases are encountered when one or more of these principles are not followed. In this project, we set out to assess if existing commonly used databases in risk assessment are in line with the FAIR principles. We also investigated how access, interoperability and reusability of data could be improved. We used the OpenFoodTox and the Seafood database as examples and showed how commonly used freely available open‐source tools and repositories can be implemented in the data extraction process of risk assessments to increase data reusability and crosstalk across different databases. [ABSTRACT FROM AUTHOR]

Background: Infant mortality is defined as the death of a child at any time after birth and before the child's first birthday. Sub-Saharan Africa has the highest infant and child mortality rate in the world. Infant and child mortality rates are higher in Ethiopia. A study was carried out to estimate the risk factors that affect infant mortality in Ethiopia.Method: The EDHS- 2016 data set was used for this study. A total of 10,547 mothers from 11 regions were included in the study's findings. To estimate the risk factors associated with infant mortality in Ethiopia, several count models (Poisson, Negative Binomial, Zero-Infated Poisson, Zero-Infated Negative Binomial, Hurdle Poisson, and Hurdle Negative Binomial) were considered.Result: The average number of infant deaths was 0.526, with a variance of 0.994, indicating over-dispersion. The highest mean number of infant death occurred in Somali (0.69) and the lowest in Addis Ababa (0.089). Among the multilevel log linear models, the ZINB regression model with deviance (17,868.74), AIC (17,938.74), and BIC (1892.97) are chosen as the best model for estimating the risk factors affecting infant mortality in Ethiopia. However, the results of a multilevel ZINB model with a random intercept and slope model revealed that residence, mother's age, household size, mother's age at first birth, breast feeding, child weight, contraceptive use, birth order, wealth index, father education level, and birth interval are associated with infant mortality in Ethiopia.Conclusion: Infant deaths remains high and infant deaths per mother differ across regions. An optimal fit was found to the data based on a multilevel ZINB model. We suggest fitting the ZINB model to count data with excess zeros originating from unknown sources such as infant mortality. [ABSTRACT FROM AUTHOR]

In an increasingly competitive global market, companies have steadily increased their production, logistics, and innovation capacities. However, the ability to develop competitive advantages afforded by technology is becoming increasingly important. In the paradigm where competition and the speed of technological development grow exponentially, companies must be able to obtain a competitive advantage through the adoption of technologies that would allow them to improve their products, services, strategies, or production processes, among others. To this end, the present study aims to identify the characteristics of the technology to enable competitive advantages. This is achieved through a systematic review of the literature and analyzing its results with a statistical analysis known as Multiple Corresponds Analysis (MCA) developed in the R language. Based on the results, we identify four independent clusters linked to the characteristics of the technology to enable competitive advantages. The first of these clusters is focused on specialization, integration, compatibility, cost and scalability variables; the second includes flexibility, applicability, demand and innovation; the third is related to sustainability, productivity, energy and resources; finally, the fourth one is composed by complexity, utility and connectivity. The implications of the study show how the characteristics of the technology can be applied to enable competitive advantages. The paper concludes with a discussion of the stateof-the-art in relation to the characteristics of technology to enable competitive advantage and formulation of 30 future research questions to be explored in further research. [ABSTRACT FROM AUTHOR]

Nowadays, exploratory and confirmatory factor analyses are two important consecutive steps in an overall analysis process. The overall analysis should start with an exploratory factor analysis that explores the data and establishes a hypothesis for the factor model in the population. Then, the analysis process should be continued with a confirmatory factor analysis to assess whether the hypothesis proposed in the exploratory step is plausible in the population. To carry out the analysis, researchers usually collect a single sample, and then split it into two halves. As no specific splitting methods have been proposed to date in the context of factor analysis, researchers use a random split approach. In this paper we propose a method to split samples into equivalent subsamples similar to one that has already been proposed in the context of multivariate regression analysis. The method was tested in simulation studies and in real datasets. [ABSTRACT FROM AUTHOR]

In this paper, we propose an advanced scripting approach using Python and R for satellite image processing and modelling terrain in Côte d'Ivoire, West Africa. Data include Landsat 9 OLI/TIRS C2 L1 and the SRTM digital elevation model (DEM). The EarthPy library of Python and 'raster' and 'terra' packages of R are used as tools for data processing. The methodology includes computing vegetation indices to derive information on vegetation coverage and terrain modelling. Four vegetation indices were computed and visualised using R: the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index 2 (EVI2), Soil-Adjusted Vegetation Index (SAVI) and Atmospherically Resistant Vegetation Index 2 (ARVI2). The SAVI index is demonstrated to be more suitable and better adjusted to the vegetation analysis, which is beneficial for agricultural monitoring in Côte d'Ivoire. The terrain analysis is performed using Python and includes slope, aspect, hillshade and relief modelling with changed parameters for the sun azimuth and angle. The vegetation pattern in Côte d'Ivoire is heterogeneous, which reflects the complexity of the terrain structure. Therefore, the terrain and vegetation data modelling is aimed at the analysis of the relationship between the regional topography and environmental setting in the study area. The upscaled mapping is performed as regional environmental analysis of the Yamoussoukro surroundings and local topographic modelling of the Kossou Lake. The algorithms of the data processing include image resampling, band composition, statistical analysis and map algebra used for calculation of the vegetation indices in Côte d'Ivoire. This study demonstrates the effective application of the advanced programming algorithms in Python and R for satellite image processing. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Financial, Accounting & Managerial Studies is the property of Journal of Financial, Accounting & Managerial Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ginsenoside Rg1 is one of the major ginsenosides found in roots of Panax ginseng and Panax notoginseng. Ginsenoside Rg1 is known to possess various biological activities including immunity enhancement activity. However, it is not clear whether the regulation of immune function by Rg1 is related to the intestinal microbiota. In the present study, the immuno-modulatory and gut microbiota-reshaping effects of ginsenoside Rg1 were evaluated. Ginsenoside Rg1 acts as an immune-enhancing agent to increase spleen index and the number of T, B and dendritic cells in dexamethasone (Dex)-treated mice. Ginsenoside Rg1 also increased the production of sIgA and regulated the expression of interleukin 2 (IL-2), IL-4, IL-10 and IFN-γ. Meanwhile, Rg1 administration regulated the structure of intestinal microbiota. The relative abundance of mouse intestinal microbial groups, such as Alistipes, Ruminococcaceae, Lachnospiraceae, and Roseburia were increased by Rg1 administration, whereas a decrease in the potential pathogens like Helicobacteraceae, Dubosiella, Mycoplasma, Alloprevotella, Allobaculum was observed. Moreover, Rg1 metabolites of Lachnospiraceae bacterium enhanced the proliferation of CD4+ T cells and T regulatory (Treg) cells. Ginsenoside Rg1 improved the inflammatory condition of the colonic tissue and repaired the destructed mucosal barrier. This study suggested that Rg1 strengthens immunity with regulating the homeostasis of intestinal microbiota in mice. [ABSTRACT FROM AUTHOR]