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This study examines the influence of online classes on the mental stress of Indian students amid the COVID-19 pandemic. Data from 428 respondents, spanning from class 10 to research scholars, was collected via a Google Form questionnaire. Parameters like excitement for online classes, class time gaps, teacher-centric lecture, competitive mindset, disruption of normal life, and health related issues were analysed using descriptive statistics. The findings offer insights into the potential causes of mental stress among students during online classes, with implications for improving mental health through screening strategies. These findings may also apply to offline classes. [ABSTRACT FROM AUTHOR]

Purpose: Data-overload is a generic, complicated issue, affecting data-analysis processes for many researchers. The challenge emanates from the researcher collecting volumes of data that hinder their ability to engage in higher-level data-analysis processes. Despite the existence of numerous frameworks towards understanding the world, novice qualitative researchers often become overwhelmed, disregard guidelines, and neglect to clearly explain their data-analysis processes. This paper discusses the author's experience with dataoverload and how the three-phase coding process and collaborative decision-making with other stakeholders helped overcome challenges. The aim is to offer new accounting science researchers a set of examples, to serve as benchmarks when dealing with data-overload in their qualitative research projects. Methodology/Approach: The author employs narrative reflection, to capture the interpretive thematic dataanalysis of in-depth interviews and document-based datasets derived from a completed dissertation. This paper's discussions are based on the key factors of data-overload and data-analysis concepts provided by the literature review. Charmaz's constructivist grounded theory principles and Creswell's spiral framework were used to present an open, axial and focused coding process, organising data around concepts, and forming categories and themes for theory development. Implications and Value: The three-phase coding method in qualitative data-analysis can effectively help navigate data-overload and provide reliable insights concerning the collected data. The developed frameworks can assist accounting science students and supervisors, as supplementary resources in addition to existing guidance, when they experience data-overload in their qualitative research projects. [ABSTRACT FROM AUTHOR]

Noncommunicable diseases (NCDs) or chronic diseases are responsible for 41 million deaths each year, equivalent to 71% of all worldwide deaths. Many technologies are used to aid the treatment of NCDs, and data analysis has been used as an approach to improve the understanding of human behaviour related to risk factors. This study aims to distinguish how human behaviour data analysis has been applied to support the treatment and prevention of NCDs, what technologies are currently used, and what gaps are still left unexplored. We conducted a systematic mapping study to analyse academic articles published from 2010 to September 2021. A filtering process mitigated article bias by reviewing, analysing, and classifying 41 works from 12,395 collected. The main results obtained presented that 43% applied data analysis in depression, 17% applied for general NCDs, and 12% for diabetes. Whereas, machine learning represents 60% of technologies found in the articles, mobile devices 58%, and wearables 29%. This study proposes two taxonomies obtained from the analysis of the selected articles that allow systematised guides to access the knowledge produced in the study. In addition, the taxonomies link technologies used to identify human behaviour with associated NCDs. [ABSTRACT FROM AUTHOR]

A new data‐analysis method is proposed for processing time‐force data obtained from a recently developed novel tuning‐fork test that has been utilized in hydrogen embrittlement studies. The analysis method is based on the application of suitable functions that are shown to provide a good fit to the data. The fitted functions allow for the calculation of first‐ and second‐order time derivatives from the initially scattered data. The negative peaks of the second‐order derivatives of the fitted functions can be associated with the more sudden changes that could be observed in the experimental data. Since these sudden changes are expected to be related to the operation of the mechanisms at the microstructural level, the present analysis provides a potentially useful method for obtaining knowledge of the operation of these mechanisms. [ABSTRACT FROM AUTHOR]

We wanted to determine whether we could use an automated machine learning system called Azure for the selection process and placement of conscript training in such a way that AI can make decisions for the right conscript training program individually. To test this, we had four separate datasets and access to the Microsoft Azure automated machine learning environment. According to the test sets we performed, we see that, by using an automated machine learning environment, it was possible to reach the precision level of the decisions we wanted. The main obstacle was not the used automated machine learning environment itself, but the quality of the data used for learning. We also made improvement suggestions regarding how data could be collected and what kind of data we should measure to make predictive data analysis better and be more usable in the future. [ABSTRACT FROM AUTHOR]

The human genome project galvanized the scientific community around an ambitious goal. Upon completion, the project delivered several discoveries, and a new era of research commenced. More importantly, novel technologies and analysis methods materialized during the project period. The cost reduction allowed many more labs to generate high-throughput datasets. The project also served as a model for other extensive collaborations that generated large datasets. These datasets were made public and continue to accumulate in repositories. As a result, the scientific community should consider how these data can be utilized effectively for the purposes of research and the public good. A dataset can be reanalyzed, curated, or integrated with other forms of data to enhance its utility. We highlight three important areas to achieve this goal in this brief perspective. We also emphasize the critical requirements for these strategies to be successful. We draw on our own experience and others in using publicly available datasets to support, develop, and extend our research interest. Finally, we underline the beneficiaries and discuss some risks involved in data reuse. [ABSTRACT FROM AUTHOR]

The human genome project galvanized the scientific community around an ambitious goal. Upon completion, the project delivered several discoveries, and a new era of research commenced. More importantly, novel technologies and analysis methods materialized during the project period. The cost reduction allowed many more labs to generate high-throughput datasets. The project also served as a model for other extensive collaborations that generated large datasets. These datasets were made public and continue to accumulate in repositories. As a result, the scientific community should consider how these data can be utilized effectively for the purposes of research and the public good. A dataset can be re-analyzed, curated, or integrated with other forms of data to enhance its utility. We highlight three important areas to achieve this goal in this brief perspective. We also emphasize the critical requirements for these strategies to be successful. We draw on our own experience and others in using publicly available datasets to support, develop, and extend our research interest. Finally, we underline the beneficiaries and discuss some risks involved in data reuse.

The purpose of this research data is to mention a correlation matrix of data-analytic estimates between the HEXACO personality traits and number of job interviews and job offers through job search networking behavior. The data-analytic correlations are based on 773 graduated students in universities in the North of Vietnam. This data used self-report surveys to collect the data. The data was analyzed using the AMOS software version 22. The obtained data indicated that the level of networking behavior positively affects both number of job interviews and job offers of individuals. Moreover, the acquired data revealed that the personality traits of honesty-humility, extraversion, agreeableness, conscientiousness, and openness to experience are positively related to the level of networking behavior, while emotionality is negatively associated with networking intensity.

We wanted to determine whether we could use an automated machine learning system called Azure for the selection process and placement of conscript training in such a way that AI can make decisions for the right conscript training program individually. To test this, we had four separate datasets and access to the Microsoft Azure automated machine learning environment. According to the test sets we performed, we see that, by using an automated machine learning environment, it was possible to reach the precision level of the decisions we wanted. The main obstacle was not the used automated machine learning environment itself, but the quality of the data used for learning. We also made improvement suggestions regarding how data could be collected and what kind of data we should measure to make predictive data analysis better and be more usable in the future.

[Display omitted] • Data-analysis of the Ollo de Sapo Formation reveals an input of wet mafic magma. • A combined magmatic and metasedimentary source explains the Ollo de Sapo composition. • Felsic Igneous Province of NW Gondwana is derived from a fast melting event. Cambro–Ordovician silicic magmatism in the Central Iberian Zone of the Iberian Massif (Ollo de Sapo Formation, OSF) constitutes a voluminous and geochemically atypical magmatic event that formed preceding the breakup of the northern margin of Gondwana. To date, and due to uncommon geochemical signatures, such as a high Fe, Mg content compared to anatectic melts and the departing from the calc-alkaline trends, the origin of such magmatic event is not fully understood. Herein, we report a data-analysis of geochemistry linking magmas and source compositions. The analysis of the combined data from multiple studies ascribes the geochemistry of the OSF rocks to a combination of extensive melting of Ediacaran metasiliciclastic rocks and a Ca-rich component. It is hypothesized that fluids released by crystallization of mafic magmas contributed to partial melting of a thick metasedimentary pile represented by Ediacaran siliciclastic rocks. Such melting event gave rise to a mobile nebulite or migma , which was able to extrude and form the super-eruption or "flare-up" that characterizes Cambro-Ordovician silicic magmatism at the Gondwana margin. Fast, catastrophic crustal melting with large-scale restite entrainment, triggered by the influx of mafic magma-derived fluids, are considered the main cause of the unique features of this Cambro-Ordovician atypical silicic large igneous province of Gondwana. [ABSTRACT FROM AUTHOR]

Computer vision is a consistent and advanced technique for image processing, with the propitious outcome, and enormous potential. A computer vision has been strongly adopted in the heterogeneous domain including agriculture. During the study of existing research on the role of computer vision in fruits and vegetables among various horticulture products of agriculture fields it is noticed that, the existing survey paper has not focused properly on mathematical framework, feature descriptor, defect detection on multiple datasets of fruits and vegetables elaborately. This has motivated us to undertake an extensive survey. In this paper, we examine the paper broadly related to fruits and vegetables among various horticulture products of agriculture fields, specific model, data pre-processing, data analysis method and overall value of performance accuracy by using a particular performance metric. Moreover, we study the different type of disease present in various fruit and vegetable. We have also focused on the comparison of different machine learning approach with respect to different performance metrics on the same dataset. Thus, we have found that among all existing machine learning techniques SVM give better classification accuracy. A generalized framework to grade the quality and defect detection of multiple fruits and vegetables is also proposed in this survey. This paper covers the survey of ninety-eight papers closely related to computer vision in the agricultural field. By the survey, we have found that computer vision plays an important role and has a large potential to address the challenges related to the agricultural fields.

Plate-reader based methods for high-throughput measurement of growth rate, cellular survival, and chronological lifespan are a compelling addition to the already powerful toolbox of budding yeast Saccharomyces cerevisiae genetics. These methods have overcome many of the limits of traditional yeast biology techniques, but also present a new bottleneck at the point of data-analysis. Herein, we describe SPOCK (Survival Percentage and Outgrowth Collection Kit), an R-based package for the analysis of data created by high-throughput plate reader based methods. This package allows for the determination of chronological lifespan, cellular growth rate, and survival in an efficient, robust, and reproducible fashion.

Mastitis is a complex, multifactorial disease. Pathogens, cows and farmers (via management) all play a role. It is costly and annoying for the farmer and threatens the image of the entire dairy industry. Prevention and control of mastitis is based on multiple principles that have been known for a long time. To implement them successfully, they should be put forward by a motivated and motivating advisor that transfers the existing knowledge to the farmer. When the changes are data-driven, applied by an encouraged farmer through a farm-specific implementation, prevention and control of mastitis will be successful and result in happy cows, happy farmers, happy advisors, happy consumers, and a happy industry. Nationwide projects focussing on communication and transfer of existing knowledge in prevention and control are very helpful in reaching high numbers of farmers and advisors and harmonizing the message brought by different parties. This paper gives an overview of multifactorial approach of mastitis management and prevention with a focus on milking, bedding and data-analysis.

The main contribution of this work is a method to generate datasets of pointing gestures. A person points to tracked objects within a motion capture environment and a neuronal network further processes the tracking data. We found that different input data combination has no significant effect on the performance of the networks. Therefore, it is possible to train a network and obtain correct results, regardless of the complete availability of data for head, shoulder, elbow or hand. With the presented method, we could achieve an overall accuracy of 35 mm within a 2D plane over a distance of 2 m. [ABSTRACT FROM AUTHOR]

• A workflow was established for unbiased screening of metabolites. • In vitro biotransformation was performed, using GI enzymes and fecal microflora. • An automated workflow was developed to analyze longitudinal LC–MS data. • The unbiased screening revealed the stepwise elimination of sugar moieties. • The data analysis workflow can replace laborious human data revision processing. Although some herbal remedies have been used for decades, little is known about the active compounds and the mechanism of action. Many natural products, such as glycosides, can be considered as prodrugs, which become active after biotransformation. To optimize the workflow of in vitro biotransformation followed by automated data analysis, hederacoside C was used as a model compound for saponins. Hederacoside C was subjected to gastrointestinal enzymes and fecal microflora. Samples were analyzed with UHPLC-PDA-HRMS before, during and after in vitro biotransformation, which allowed the monitoring of the relative abundances of the compound and its metabolites. The data-analysis workflow was optimized to render as much information as possible from the longitudinal LC MS data. XCMS was used to convert the raw data into features via peak-picking, followed by grouping, and EDGE was used for the extraction of significant differential profiles. To evaluate if the workflow was suitable for dynamic multiclass metabolomics data, an interactive Shiny web app was developed in R to rate the quality of the resulting features. These ratings were used to train a random forest model for predicting experts response. A performance analysis revealed that the random forest model was capable of correctly predicting the reviewers response in most cases (AUC 0.926 with 10 fold cross validation). The automated data analysis workflow was used for unbiased screening for metabolites and revealed the biotransformation of hederacoside C. As expected, a decrease in relative abundance of hederacoside C was observed over time. Additionally, the relative abundance of metabolites increased, illustrating the biotransformation of hederacoside C, especially in the colon phase, where microbial fermentation takes place. Stepwise progressive elimination of sugar moieties was the major metabolic pathway. [ABSTRACT FROM AUTHOR]

The aim of this work is to describe the situation of nautical tourism in one of the largest islands in the Mediterranean through the analysis of the marinas and the sailing habits of the boaters. After outlining the typology, characteristics and functions of the marinas in the introduction, we briefly present the quantitative-qualitative analysis methodology that was carried out through interviews to the stakeholders and subsequent processing of the original information obtained. The presentation of the results concerns the context of Italian ports and then focuses on Sardinia in terms of ports and berths. The discussion, on the other hand, is based on the original analysis of the use of Sardinian ports, the navigation mode of the boaters, including the characteristics of the boats used. In the conclusions, it is emphasized that Sardinia is at the centre of a boating area that extends to the Upper Tyrrhenian Sea, within which it represents a hub and the favourite area for the most important and rich segment of yachts. This, however, does not preclude existing structures from pursuing an improvement in the supply from the point of view of environmental compatibility, of the quality of the services offered and, above all, of a stronger connection to the tourist offer of the hinterlands. A possible development of the present study may cover other similar cases in the Mediterranean, regarding in particular the specific aspect of the degree of openness towards the boaters coming from "continental" territories.

Any knowledge generation process involves raw data comprehension, evaluation and inferential reasoning. These practices, common to different disciplines, are known as data analysis, and represent the most important set of activities in research contexts. Researchers use data analysis software methods and tools for generating new knowledge in their daily data analysis. In recent years, data analysis software has been incorporating explicit references in modelling of cognitive processes, in order to improve the assistance offered in data analysis tasks. However, data analysis software commercial suites are still resisting this inclusion, and there is little empirical work done in knowing more about how cognitive aspects inclusion in software helps researchers in analyzing data. In this paper, we evaluate the impact produced by the explicit inclusion of cognitive processes in the assistance logic of software tools design and development. We conducted an empirical experiment comparing data analysis performance using traditional software versus data analysis performance using software-assistance tools which incorporate cognitive processes in their design. The experiment is designed in terms of accuracy, efficiency, productivity and user satisfaction during the data analysis made by researchers. It allowed us to find some clear benefits of the cognitive inclusion in the software designed for research contexts, with statistically significant differences in terms of accuracy, productivity and researcher's satisfaction in support of this explicit inclusion, although some efficiency weaknesses are detected. We also discuss the implications of these results for the priority of cognitive inclusion in the software tools design for research contexts data analysis. [ABSTRACT FROM AUTHOR]

The aim of this work is to describe the situation of nautical tourism in one of the largest islands in the Mediterranean through the analysis of the marinas and the sailing habits of the boaters. After outlining the typology, characteristics and functions of the marinas in the introduction, we briefly present the quantitative-qualitative analysis methodology that was carried out through interviews to the stakeholders and subsequent processing of the original information obtained. The presentation of the results concerns the context of Italian ports and then focuses on Sardinia in terms of ports and berths. The discussion, on the other hand, is based on the original analysis of the use of Sardinian ports, the navigation mode of the boaters, including the characteristics of the boats used. In the conclusions, it is emphasized that Sardinia is at the centre of a boating area that extends to the Upper Tyrrhenian Sea, within which it represents a hub and the favourite area for the most important and rich segment of yachts. This, however, does not preclude existing structures from pursuing an improvement in the supply from the point of view of environmental compatibility, of the quality of the services offered and, above all, of a stronger connection to the tourist offer of the hinterlands. A possible development of the present study may cover other similar cases in the Mediterranean, regarding in particular the specific aspect of the degree of openness towards the boaters coming from "continental" territories. [ABSTRACT FROM AUTHOR]

A computerized solution to the sensitivity analysis of Electre 3. The multicriteria method Electre III allows sorting out actions likely to solve a decision problem, basing one arguments on several criteria. The complexity of the method renders a possible sensitivity analysis tedious. This short communication details an iterative algorithm used in the pre-existing Electre III software which allows carrying out this analysis automatically.

Outranking methods and robustness analysis. The introduction gives the arguments on which the choice of outranking methods is based: application domain, existence of a software, lack of works on the robustness of outranking methods. The reasons for performing a robustness analysis in decision aid are given: parameters nature, the technique used in order to give them a value and classical techniques used in order to study their variations. Three definitions are then suggested to understand the notion of a robust conclusion and the associated nuances. The difficulties of robustness analysis are summarized. Then, some theoretical paths are proposed: determination of the direction of result evolution according to parameter evolution, ""microscope principle"", determination of maximal variation interval for each parameter, ""star shape"" analysis, use of discrete values coming from variation interval, use of parameter families, partition in domains of the space of possible parameter values where conclusions are sufficiently robust. Some advice which could facilitate robustness analysis are given.

Afforestation in the UK has been significant and continuing since 1920 (up to 30,000 ha per year). Planting data is used to drive a dynamic process-based carbon accounting model (C-Flow) to estimate removals of atmospheric CO2 to these forests. It is assumed that the afforestation can be represented by the characteristics of Sitka spruce for conifers and beech for broadleaves. The present area of forest considered for these estimates is 1.6 millions ha. In 1990 the uptake to trees, litter, soil and products was 2.6 terragramme C, rising to 2.8 terragramme C in 1998. Deforestation is believed to be small. Supporting measurements show that the model predicts long term uptake by conifers well but that losses from planted peat shortly after establishment need further consideration. Process modelling of beech growth suggests that it is primarily dependant on atmospheric CO2 concentration and not on stomatal control per se. UK research priorities relevant to preparation of GHG (greenhouse gas) Inventories are presented.

The paper presents the total stem volume increment and total drain, as well as the corresponding carbon contents, of the trees of Finnish forests. The carbon contents are converted to carbon dioxide equivalents. The total increment is above stump stem wood volume increment including bark. The estimates come from the National Forest Inventory of Finland conducted by the Finnish Forest Research Institute. The method is briefly described. The total drain includes fellings, i.e. removals and cutting waste, as well as natural mortality of above stump stem wood volume of trees including bark. The above stump stem volumes are converted to total tree biomass, total dry matter and total carbon content using available coefficients.

Since 1995, experiences have been gathered with the calculation of National Greenhouse Gas Inventories in Germany. Because only marginal changes of the total forested area occur in Germany, relevant changes of the balance of greenhouse gases in the forest sector are related to forest management. It was found that the increment of wood by far exceeds the harvested timber volume. Therefore, forests in Germany currently represent a significant sink for carbon (8-9 Mt C per year). In the context of the Kyoto Protocol, area afforested, reforested and deforested (Afforestation Reforestation Deforestation under Intergovernemental Panel on Climate Change definitions) is small. In contrast to the high quality of data from national forest inventories, reporting of Afforestation Reforestation Deforestation areas involves high error due to lacks and inconsistencies in national land-use statistics.

The purpose of this research data is to mention a correlation matrix of data-analytic estimates between the HEXACO personality traits and number of job interviews and job offers through job search networking behavior. The data-analytic correlations are based on 773 graduated students in universities in the North of Vietnam. This data used self-report surveys to collect the data. The data was analyzed using the AMOS software version 22. The obtained data indicated that the level of networking behavior positively affects both number of job interviews and job offers of individuals. Moreover, the acquired data revealed that the personality traits of honesty-humility, extraversion, agreeableness, conscientiousness, and openness to experience are positively related to the level of networking behavior, while emotionality is negatively associated with networking intensity., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier Inc.)

Abstract: We emphasize the estimation of uncertainties of AMS results and the detection of instrumental error in the framework of commonly used methods of AMS data-analysis.The conventional methods are fast but raise the following four issues: (1) observed sampling variance has scatter that may lead to some true values being over five standard errors away from the mean. (2) Use of the Gaussian distribution for the end-result is unwarranted in several cases. (3) The standard error of the mean is slightly biased due to sampling and correlations. (4) Detection of instrumental errors could be improved. The Bayesian CAR model that we have introduced carries the calculations out with full probability distributions and uses an overall probabilistic process to describe the instrumental error. The scatter in the uncertainties given by CAR is an order of magnitude smaller than that of the sampling-based uncertainties, resulting in more reliable uncertainties. Compared to previous methods, better detection and estimation of instrumental error is also achieved. [Copyright &y& Elsevier]

Scientific workflow systems have been introduced in response to the demand of researchers from several domains of science who need to process and analyze increasingly larger datasets. The design of these systems is largely based on the observation that data analysis applications can be composed as pipelines or networks of computations on data. In this work, we present a run-time support system that is designed to facilitate this type of computation in distributed computing environments. Our system is optimized for data-intensive workflows, in which efficient management and retrieval of data, coordination of data processing and data movement, and check-pointing of intermediate results are critical and challenging issues. Experimental evaluation of our system shows that linear speedups can be achieved for sophisticated applications, which are implemented as a network of multiple data processing components. [ABSTRACT FROM AUTHOR]

Abstract: Food economists and financial researchers have long been preoccupied by the issue of evaluating the performance of agri-food firms. As the financial restructuring of the agri-business sector during the past two decades or so reflects sweeping changes that have occurred worldwide, questions have arisen regarding the application of the most suitable financial decision-making tools. This paper introduces a new financial decision aid approach, which is based on data analysis techniques in combination with a multicriteria analysis method (PROMETHEE II). Several notions and concepts forming the financial engineering methodological framework are adopted for the design of this approach. For illustrative purposes, the case of Greek agri-businesses is used. The analysis results in an overall ranking of the examined firms’ performance. Finally, given the limitations of the current study, a research agenda is proposed. [Copyright &y& Elsevier]