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Trends of surface water quality of the Krishna river, India during the urbanization process

ENVIrONMENTAL QUALITY MANAGEMENT 2023年第3期32卷 111-121页

作者： rentachintala, Lakshmi raghu Nagendra Prasad Gangireddy, Muni reddy Mutukuru Mohapatra, Pranab Kumar Andhra Univ Dept Civil Engn AU Coll Engn A Visakhapatnam 530003 Andhra Pradesh India Indian Inst Technol Gandhinagar Gandhinagar Gujarat India

In the present study, Amaravati, the proposed city of India is considered to assess the impacts of urbanization on water quality of the Krishna river in the vicinity. Long-term surface water quality data of various parameters of Krishna river are obtained from Central Water Commission (CWC). Trends of various parameters are analyzed using a modified version of Mann-Kendall (M-K) test;bootstrapped M-K trend test with optional bias corrected pre-whitening and r programming are used affecting water quality prior to and at the commencement of urbanization. During the onset of urbanization, minimum BOD is decreased from 0.49 to 0.2 mg/L, while the maximum BOD is increased by 67.7%. Dissolved oxygen's (DO) minimum value is decreased by 29.33% while maximum DO value is decreased by 4.47% at the beginning of urbanization. During the process of urbanization, total coliform's minimum count is increased to 330 from 2 MPN/100 ml while the maximum count of total coliforms is increased to 16,000 from 2400 MPN/100 ml. Faecal coilform's minimum count is increased from 2 to 80 MPN/100 ml while maximum count is increased to 16,000 from 800 MPN/100 ml during urbanization. It is found that due to urbanization, pH, DO values exceed the allowable limit.

关键词： Mann-Kendall test r programming urbanization water quality

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The role of Big Data in the Media and Entertainment Industry 4

The Role of Big Data in the Media and Entertainment Industry

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International Conference on Computational Intelligence and Communication Technology (CICT)

作者： Suri, Mehul Singh, Shailendra Narayan Amity Univ Uttar Pradesh Dept Comp Sci & Engn Noida India

ISBN: (纸本)9781538608869

Media and entertainment industries are advancing at an unprecedented speed every day, governed by dual requirements to minimize costs while at the same time generating more revenues from a highly competitive and uncertain market. Media companies are among the first to adopt the great bigdata technology, enabling them to increase digital transformation, leveraging not only the data already available but also new data sources both in and out of the organization. This paper is a synthesis of the art of great data analysis in the media and entertainment industry and of the essential role that big data plays in this sector of the world market. Finally, the research is distilled into a full set of future big data issues in the next generation to support the media and entertainment industry and help them keep their customers happy.

关键词： data forecasting r programming big data media and entertainment industry statistics

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Integrating Bifactor Models into a Generalizability Theory Based Structural Equation Modeling Framework

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JOUrNAL OF EXPErIMENTAL EDUCATION 2023年第4期91卷 718-738页

作者： Vispoel, Walter P. Lee, Hyeryung Xu, Guanlan Hong, Hyeri Univ Iowa Iowa City IA 52242 USA Pearson Psychometr & Res Serv Dept Iowa City IA USA

Although generalizability theory (GT) designs have traditionally been analyzed within an ANOVA framework, identical results can be obtained with structural equation models (SEMs) but extended to represent multiple sources of both systematic and measurement error variance, include estimation methods less likely to produce negative variance components, and correct for score coarseness. In research reported here, we integrated principles of bifactor modeling into a GT-SEM framework to partition systematic variance for subscale and composite scores into general and group factor effects and measurement error into multiple components. GT-bifactor modeling allowed for partitioning of variance at different levels of aggregation, clearer definitions of constructs within sampled domains, extended indices of score consistency, insights into composite and subscale score viability, and markers for best enhancing score consistency. results for domain and facet scores from the recently updated form of the Big Five Inventory highlighted the importance of taking all sources of measurement error into account and the diagnostic benefits of GT-bifactor designs over conventional univariate and multivariate GT designs. Findings further revealed that corrections for scale coarseness noticeably enhanced overall score consistency by reducing specific-factor and random-response measurement error. We provide code in r for applying all illustrated designs in a detailed online supplement.

关键词： Bifactor model Big Five Inventory generalizability theory r programming structural equation modeling

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A Novel Technique to Design Optimum Bituminous Mix Designs Based on r Studio and Autograph with Integration 9

A Novel Technique to Design Optimum Bituminous Mix Designs B...

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9th International Conference of Materials Processing and Characterization (ICMPC)

作者： Soujanya, Ch. Priya, G. Akhila Penkiramu Kumar, C. Naveen VNRVJIET Hyderbad Telangana India VNRVJIET Dept Civil Engn Hyderbad Telangana India

road has become an entangled part of construction. The construction of pavement involves immense expense of speculation. Different methods are involved to obtain required optimum gradation mix in which Trial and Error method is categorized as prior. The sequence of this method leads to Time consumption, Wastage of materials and most likely it is not precised. To overcome the above limitations a new tool in r-programming is adopted to generate accurate results. This paper emphasis on traditional methods like Midpoint gradation, Trial and Error, Excel solver and compared with new optimisation technique developed by r programming used in Bituminous Mix Design for Dense Bituminous Macadam (Grade-II). Aggregate gradation is said to be optimum when it reaches maximum stability in which the particle arrangement is proper where the smaller are packed with larger particles which reduces the voids and particle to particle contact is achieved. This paper studies on calculating the aggregate gradation area by methods like Trapezoidal and Simpsons rule and has observed that r programming with least gradation area of 4.66 has obtained and by calculating gradation area with integration r programming has got least area of 7.892 compared with different methods. This implies that aggregate gradation mix of r programming is said to be optimum as it is more stable and dense (C) 2019 Published by Elsevier Ltd.

关键词： r programming Autograph tool Optimisation Aggregate blending

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Can you learn it? Probably! Developing Learning Analytics Tools in r 16

Can you learn it? Probably! Developing Learning Analytics To...

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16th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL)

作者： Di Nunzio, Giorgio Maria Univ Padua Dept Informat Engn Via Gradenigo 6-A I-35131 Padua Italy

ISBN: (纸本)9781450342292

Automatic text categorization is an effective way to organize large text datasets in Digital Libraries (DL). However, most of the available machine learning tools are complex and go beyond the scope of what a digital library curator need or is able to do in order to classify the objects of a DL. Drawing inspiration from the field of Learning Analytics and Interactive Machine Learning, we design and implement visual interactive classifiers that are intuitive to train and easy to use. In this poster, we present an interactive Web application in r that allows users to use text classifier in an innovative way.

关键词： Automated Text Classification Naive Bayes Interactive Machine Learning Learning Analytics r programming

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Technical Debt in the Peer-review Documentation of r Packages: a rOpenSci Case Study 18

Technical Debt in the Peer-Review Documentation of R Package...

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29th IEEE/ACM International Conference on Program Comprehension (ICPC) / 18th IEEE/ACM International Conference on Mining Software repositories (MSr)

作者： Codabux, Zadia Vidoni, Melina Fard, Fatemeh H. Univ Saskatchewan Saskatoon SK Canada RMIT Univ Melbourne Vic Australia Univ British Columbia Vancouver BC Canada

ISBN: (纸本)9781728187105

Context: Technical Debt (TD) is a metaphor used to describe code that is "not quite right." Although TD studies have gained momentum, TD has yet to be studied as thoroughly in non-Object-Oriented (OO) or scientific software such as r. r is a multi-paradigm programming language, whose popularity in data science and statistical applications has amplified in recent years. Due to r's inherent ability to expand through user-contributed packages, several community-led organizations were created to organize and peer-review packages in a concerted effort to increase their quality. Nonetheless, it is well-known that most r users do not have a technical programming background, being from multiple disciplines. Objective: The goal of this study is to investigate TD in the documentation of the peer-review of r packages led by rOpenSci. Method: We collected over 5,000 comments from 157 packages that had been reviewed and approved to be published at rOpenSci. We manually analyzed a sample dataset of these comments posted by package authors, editors of rOpenSci, and reviewers during the review process to investigate the types of TD present in these reviews. results: The findings of our study include (i) a taxonomy of TD derived from our analysis of the peer-reviews (ii) documentation debt as being the most prevalent type of debt (iii) different user roles are concerned with different types of TD. For instance, reviewers tend to report some types of TD more than other roles, and the types of TD they report are different from those reported by the authors of a package. Conclusion: TD analysis in scientific software or peer-review is almost non-existent. Our study is a pioneer but within the context of r packages. However, our findings can serve as a starting point for replication studies, given our public datasets, to perform similar analyses in other scientific software or to investigate the rationale behind our findings.

关键词： Technical Debt r programming rOpensci Technical Debt Taxonomy Mining Software repositories

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Defining Spatial Entropy from Multivariate Distributions of Co-occurrences 1

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9th International Conference on Spatial Information Theory

作者： Leibovici, Didier G. Univ Nottingham Ctr Geospatial Sci Nottingham NG7 2RD England

ISBN: (数字)9783642038327

ISBN: (纸本)9783642038310

Finding geographical patterns by analysing the spatial configuration distribution of events, objects or their attributes has a long history in geography, ecology and epidemiology. Measuring the presence of patterns;clusters, or comparing the spatial organisation for different attributes, symbols within the same map or for different maps, is often the basis of analysis. Landscape ecology has provided a long list of interesting indicators;e.g. summaries of patch size distribution. Looking at content information, the Shannon entropy is also a measure of a distribution providing insight into the organisation of data, and has been widely used for example in economical geography. Unfortunately, using the Shannon entropy on the bare distribution of categories within the spatial domain does not describe the spatial organisation itself. Particularly in ecology and geography, some authors have proposed integrating some spatial aspects into the entropy: using adjacency properties or distances between and within categories. This paper goes further with adjacency, emphasising the use of co-occurences of categories at multiple orders, the adjacency being seen as a particular co-occurence of order 2 with a distance of collocation null, and proposes a spatial entropy measure framework. The approach allows multivariate data with covariates to be accounted for, and provides the flexibility to design a wide range of spatial interaction models between the attributes. Generating a multivariate multinomial distribution of collocations describing the spatial organisation, allows the interaction to be assessed via ail entropy formula. This spatial entropy is dependent on the distance of collocation used, which can be seen as a scale factor in the spatial organisation to be analysed.

关键词： spatial information entropy co-occurrences spatial statistics Multivariate data spatial point process r programming

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Does e-retailer's country of origin influence consumer privacy, trust and purchase intention?

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JOUrNAL OF CONSUMEr MArKETING 2023年第2期40卷 248-259页

作者： Bhattacharya, Saptarshi Sharma, rajendra Prasad Gupta, Ashish Indian Inst Foreign Trade Dept Mkt Kolkata India Indian Inst Foreign Trade Dept Mkt New Delhi India

Purpose - Consumers are worried about sharing their sensitive information during online shopping due to the e-tailer's unethical practices and hacking-related concerns. Prior research has established the country of origin (COO) as a trust-building cue;however, it requires empirical testing in the online retailing context. The present study aims to examine the e-tailer COO's effect on consumer privacy, trust and purchase intention. Design/methodology/approach - An online survey floated a seven-point Likert scale questionnaire and invited the receivers to participate in the investigation over e-mails and text messages. A total of 355 usable responses were analyzed using r programming. Findings - This study empirically validated a proposed conceptual model examining the influence of COO on consumer privacy, trust and purchase intention. The findings suggest that COO influences consumer privacy, trust and purchase intention. This study further found that the privacy practices of online retailers positively impact consumer trust. Trust acts as a mediating factor in influencing purchase intention. Practical implications - This study offers valuable insights for advancing the research agenda and actionable inputs to e-commerce managers for alleviating consumer privacy concerns in emerging economies. Future researchers can test the proposed model in other demographic and e-commerce settings. Originality/value - This study contributes to the present knowledge on consumer privacy in online retailing in the Indian context. This paper also examines the relationship of COO with consumer privacy, trust and purchase intention, an underexplored research area in emerging markets.

关键词： Privacy Trust Purchase intention Online retailing (e-retailing) Country of origin (COO) r programming

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Development of Machine Learning-based Predictive Models for Air Quality Monitoring and Characterization

Development of Machine Learning-based Predictive Models for ...

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IEEE-region-10 Conference (IEEE TENCON)

作者： Amado, Timothy M. Dela Cruz, Jennifer C. Mapua Univ Sch Elect Elect & Comp Engn Manila Philippines

ISBN: (纸本)9781538654576

One of the biggest environmental problems right now is air pollution. Air quality is needed to be consistently monitored and assessed to ensure better living conditions. The U.S. Environmental Protection Agency (EPA) uses the air quality index (AQI) to standardize the air quality. However, AQI requires precise and accurate sensor readings and complex calculations, making it not feasible for portable air quality monitoring devices. The aim of this paper is to find an alternative way of monitoring and characterizing air quality through the use of integrated gas sensors and building predictive models using machine learning algorithms that can be used to obtain data-driven solutions to mitigate the risk of air pollution. The proposed methodology is implemented by building a prototype for the integrated sensors using DHT 11 temperature and relative humidity sensor, MQ2, MQ5 and MQ135 gas sensors. Five predictive models are developed in the study, k-nearest neighbors (KNN), support vector machine (SVM), Naive-Bayesian classifier, random forest and neural network. results show that the researchers are able to obtain an accuracy of 98.67%, 97.78%, 98.67%, 94.22%, and 99.56% for all the five models respectively, having the neural network to be the best performing model.

关键词： air quality characterization AQI machine learning integrated sensors r programming

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Digital Filter Implementation in Hadoop Data Mining System 22nd

Digital Filter Implementation in Hadoop Data Mining System

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22nd International Conference on Computer Networks (CN)

作者： Czerwinski, Dariusz Lublin Univ Technol PL-20618 Lublin Poland

ISBN: (纸本)9783319194196;9783319194189

Paper presents Savitzky-Golay digital filter implementation using the r software environment in private Hadoop Data Mining System. The idea of the research was the implementation of the digital filter to handle big data came from measurements. The study focused on proper filter implementation and comparison of the results with National Instruments Diadem - enterprise class system for data management and mining.

关键词： Digital signal processing Savitzky-Golay filter Data mining system Cloudera Hadoop r programming

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