Self-Admitted Technical Debt (SATD) is a particular case of Technical Debt (TD) where developers explicitly acknowledge their sub-optimal implementation decisions. Though previous studies have demonstrated that SATD i...
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ISBN:
(纸本)9781728187105
Self-Admitted Technical Debt (SATD) is a particular case of Technical Debt (TD) where developers explicitly acknowledge their sub-optimal implementation decisions. Though previous studies have demonstrated that SATD is common in software projects and negatively impacts their maintenance, they have mostly approached software systems coded in traditional object-oriented programming (OOP), such as Java, C++ ***. This paper studies SATD in r packages, and reports results of a three-part study. The first part mined more than 500 r packages available on GitHub, and manually analysed more than 164k of comments to generate a dataset. The second part administered a crowd-sourcing to analyse the quality of the extracted comments, while the third part conducted a survey to address developers' perspectives regarding SATD comments. The main findings indicate that a large amount of outdated code is left commented, with SATD accounting for about 3% of comments. Code Debt was the most common type, but there were also traces of Algorithm Debt, and there is a considerable amount of comments dedicated to circumventing CrAN checks. Moreover, package authors seldom address the SATD they encounter and often add it as self-reminders.
Urbanization affects the local wind and water cycle by changing the natural surface and atmospheric conditions, which further changes the local climate and climate system. Assessment of built-up-area changes in a rapi...
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Urbanization affects the local wind and water cycle by changing the natural surface and atmospheric conditions, which further changes the local climate and climate system. Assessment of built-up-area changes in a rapidly growing urban area within a short time is a prime factor for administrators for better environmental assessment and sustainable development of urban areas. Traditional survey approaches, on the other hand, are unable to meet the demand forrapid urban land management development, and there is a pressing need to develop new methods to address the limitations of existing ones. This study compares various urban spectral indices to other existing approaches in order to determine which index provides a betterrepresentation of the impervious area in the urban watershed. Landsat 8 OLI (Operational Land Imager) satellite images acquired on 15 March 2014 and 31 March 2020 are used in the present study. Indices, namely Modified Built-up Index (MBUI), Swired Index (Swired), and Enhanced Built-up and Bareness Index (EBBI), were utilized to extract impervious areas. Thresholding of indices is done by comparing them with 1000 reference points taken from Google Earth imagery of the respective years. The accuracy of the urban indices is assessed by comparing the results with high-resolution Google Earth Satellite Images. The impervious area is extracted from spectral indices and otherremote sensing techniques such as maximum likelihood classification and support vector machine classification techniques. The overall accuracy of SVM, MLC, MBUI, EBBI, and Swired for the 2014 dataset was found to be 95.1%, 90.8%, 83.9%, 78.9%, and 87.3%, respectively, and the overall accuracy of SVM, MLC, MBUI, EBBI, and Swired was found to be 96%, 89.2%, 89.1%, 85.5%, and 92.6%, respectively. Impervious areas in the heterogeneous urban environment can be monitored in a better way and within lesser time by using spectral indices generated using Landsat 8 OLI (Operational Land Imager)
rapid land use change has taken place in many neighboring provinces of the capital of Vietnam such as Thai Nguyen province over the past 2 decades due to urbanization and industrialization. Deriving accurate and updat...
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rapid land use change has taken place in many neighboring provinces of the capital of Vietnam such as Thai Nguyen province over the past 2 decades due to urbanization and industrialization. Deriving accurate and updated land cover and land-use change information is essential for the environmental monitoring, evaluation and management. In this study, a robust classification algorithm, random Forest (rF) was employed in r programming to map and monitor temporal and spatial characteristics of urban expansion and land-use change in Thai Nguyen province, Vietnam. The results showed that there has been a substantial and uneven urban growth and a significant loss of forest and cropland between 2000 and 2016. Most of the conversion of agriculture and forest into built-up and mining uses were largely detected in rural regions and suburbs of Thai Nguyen. Further GIS analysis revealed that rapid urban and industrial expansion was spatially occurred in the southern rural portions and central area of the province. This study also demonstrates the potential of Landsat data and combination of r programming language and GIS to provide a timely, accurate and economical means to map and analyze temporal land cover and land use changes for future national and local land development planning. (C) 2018 National Authority forremote Sensing and Space Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/bync-nd/4.0/).
The social network Twitter has become a way of promoting opinions that have not gone unnoticed in current societies' social and political events. This research focuses on performing sentiment analysis of the publi...
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The social network Twitter has become a way of promoting opinions that have not gone unnoticed in current societies' social and political events. This research focuses on performing sentiment analysis of the publications on the social network Twitter from March 2021 to August 2021 to analyze users' feelings, reactions, and perceptions regarding the 2021 National Strike in Cali-Colombia, through techniques based on Text Mining and Machine Learning. A five stages methodology is proposed, which is composed of: data set identification, data extraction, data pre-processing, data processing, and results. This work intends to serve as a basic methodological instrument for future research implementing the use of computational tools, particularly sentiment analysis, to identify and analyze behavior patterns based on information related to citizens' reactions in social networks.
The paper intends to understand the research trends in "Covid-19 and SME" through a Systematic Literature review (SLr) and extract themes to explore the most affected areas of SMEs during the Covid-19 pandem...
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The paper intends to understand the research trends in "Covid-19 and SME" through a Systematic Literature review (SLr) and extract themes to explore the most affected areas of SMEs during the Covid-19 pandemic. Subsequently, the study attempts to know the struggles of SME during Covid-19 crisis in a developing country . Furthermore, the study provides a critical dynamic resilience strategy framework to manage the SMEs in the crisis period. The authors extracted data from Scopus and Web of science to conduct a Systematic Literature review (SLr), Extracted data from both databases were merged using r programming get the same tag in r programming. The study adopts a bibliometric analysis to present the research corpus in the domain of "Covid-19 and SMEs". The cluster method of r programming has been used to usher the significantly affected areas of SMEs. Based on the cluster theme, an open-ended questionnaire was developed and used to interview 23 SMEs in Bangladesh for the case study. NVIVO-13.00 was used to extract the topic from the transcriptions of the interviews. The study reveals that Cash flow shortages and Supply Chain Disruptions are the critical constraints of SMEs. On the contrary, Digital transformation has gained momentum during this crisis. Enterprises that made the best use of digital platforms through technology, digital marketing, and innovations secured the peak of success and profitability. The study also recommends a critical dynamic, resilient strategy model to adopt in the "new normal" for successful navigation of SME business in the future. The study is the first of its kind that integrates SLr and a case study on the hurdles of SME owners during the Covid 19 crisis. Thus, it helps advance the understanding of the subject matter and enables the formulation of resilient strategies by policymakers and SME owners to navigate the business in any potential crisis in the future. The study has significant methodological contribution as it presents how
Mongolian territory is 1.5 million square kilometers, there are 1.439 kindergartens that include 263.333 children. Mongolia has 5 regional zones. The research was made on selected provinces such as Sukhbaatar district...
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Mongolian territory is 1.5 million square kilometers, there are 1.439 kindergartens that include 263.333 children. Mongolia has 5 regional zones. The research was made on selected provinces such as Sukhbaatar district from Ulaanbaatarregion, Huvsgul aimag from Khangai region, Umnugobi from Central region, Dornod aimag from Eastern region and Bayan-Ulgii from Western region. Total 450 preschool children at age of 3 - 5 years old (30 children at every 3, 4 and 5 years old) were selected randomly, they performed 5 tasks of Math according to the curriculum. The classification of age, sex and region was made under cluster analyses of children’s mathematical ability using research method. The purpose of the research is classification of province zone, determination of inequality and difference between rural and urban areas. It is made support for developer’s policy and decision makers of education under the base of financing and sharing kindergarten budget, specialization, retraining of teachers and children, developing, elaboration and planning curriculum.
Stack Overflow (SO) is a popular platform among developers seeking advice on various software-related topics, including privacy and security. As for many knowledge-sharing websites, the value of SO depends largely on ...
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Stack Overflow (SO) is a popular platform among developers seeking advice on various software-related topics, including privacy and security. As for many knowledge-sharing websites, the value of SO depends largely on users' engagement, namely their willingness to answer, comment or post technical questions. Still, many of these questions (including cybersecurity-related ones) remain unanswered, putting the site's relevance and reputation into jeopardy. Hence, it is important to understand users' participation in privacy and security discussions to promote engagement and foster the exchange of such expertise. Objective: Based on prior findings on online social networks, this work elaborates on the interplay between users' engagement and their privacy practices in SO. Particularly, it analyses developers' self-disclosure behaviourregarding profile visibility and their involvement in discussions related to privacy and security. Method: We followed a mixed-methods approach by (i) analysing SO data from 1239 cybersecurity-tagged questions along with 7048 user profiles, and (ii) conducting an anonymous online survey (N=64). results: About 33% of the questions we retrieved had no answer, whereas more than 50% had no accepted answer. We observed that proactive users tend to disclose significantly less information in their profiles than reactive and unengaged ones. However, no correlations were found between these engagement categories and privacy-related constructs such as perceived control or general privacy concerns. Implications: These findings contribute to (i) a better understanding of developers' engagement towards privacy and security topics, and (ii) to shape strategies promoting the exchange of cybersecurity expertise in SO.
Bone morphogenetic protein(BMP),belongs to transforming growth factor-b(TGF-b)superfamily except *** BMP into muscular tissues induces ectopic bone formation at the site of implantation,which provides opportunity for ...
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Bone morphogenetic protein(BMP),belongs to transforming growth factor-b(TGF-b)superfamily except *** BMP into muscular tissues induces ectopic bone formation at the site of implantation,which provides opportunity for the treatment of bone *** human BMP-2(rhBMP-2)has been used clinically,but the lack of standard methods for quantifying rhBMP-2 biological activity greatly hindered the progress of *** this article,we describe an in vitro rhBMP-2 quantification method,as well as the data analyzation pipeline through logistic regression in *** studies indicated that alkaline phosphatase(ALP)activity of C2C12 cells was significantly increased when exposed to rhBMP-2,and showed dose-dependent effects in a certain concentration range of ***,we chose to quantify ALP activity as an indicator of rhBMP-2 bioactivity in vitro.A sigmoid relationship between the ALP activity and concentration of rhBMP-2 was ***,there are tons of regression models for such a non-linear *** has always been a major concern forresearchers to choose a proper model that not only fit data accurately,but also have parameters representing practical ***,to fit ourrhBMP-2 quantification data,we applied two logistic regression models,three-parameter log-logistic model and four-parameter log-logistic *** four-parameter log-logistic model(adj-r2>0.98)fits better than three-parameter log-logistic model(adj-r2>0.75)for the sigmoid ***,ourresults indicate rhBMP-2 quantification in vitro can be accomplished by detecting ALP activity and fitting four-parameter log-logistic ***,we also provide a highly adaptable r script for any additional logistic models.
Effective data visualisation is vital for data exploration, analysis and communication in research. In ecology and evolutionary biology, data are often associated with various taxonomic entities. Graphics of organisms...
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The automated classification of ambient air pollutants is an important task in air pollution hazard assessment and life quality research. In the current study, machine learning (ML) algorithms are used to identify the...
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The automated classification of ambient air pollutants is an important task in air pollution hazard assessment and life quality research. In the current study, machine learning (ML) algorithms are used to identify the inter-correlation between dominant air pollution index (API) for PM10 percentile values and other major air pollutants in order to detect the vital pollutants' clusters in ambient monitoring data around the study area. Two air quality stations, CA0016 and CA0054, were selected for this research due to their strategic locations. Non-linearrPart and Tree model of Decision Tree (DT) algorithm within the r programming environment were adopted for classification analysis. The pollutants' respective significance to PM10 occurrence was evaluated using random forest (rF) of DT algorithms and K means polar cluster function identified and grouped similar features, and also detected vital clusters in ambient monitoring data around the industrial areas. results show increase in the number of clusters did not significantly alterresults. PM10 generally shows a reduction in trend, especially in SW direction and an overall minimal reduction in the pollutants' concentration in all directions is observed (less than 1). Fluctuations were observed in the behaviors of CO and NOx during the day while NOx displayed relative stability. results also show that a direct and positive linearrelationship exists between the PM10 (target pollutant) and CO, SO2, which suggests that these pollutants originate from the same sources. A semi-linearrelationship is observed between the PM10 and others (O-3 and NOx) while humidity shows a negative linearity with PM10. We conclude that most of the major pollutants show a positive trend toward the industrial areas in both stations while trac emissions dominate this site (CA0016) for CO and NOx. Potential applications of nuggets of information derived from these results in reducing air pollution and ensuring sustainability within the city a
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