25 research outputs found
Estimating Passenger Car Equivalent Factors for Heterogeneous Traffic Using Occupancy-Density Linear Regression Model
No full textA variety of methods have been proposed in the existing literature for the estimation of passenger car equivalent (PCE) factors. These methods are based on the comparison of selected attributes of different vehicles. This research, for the first time, utilizes the basic notion of the linear relationship between road area occupancy and density for the estimation of PCE factors for different vehicle types in heterogeneous traffic. Aerial photographs obtained from an unmanned aerial vehicle (UAV) were analyzed to estimate the road area occupancy and the number of vehicles classified in seven selected groups. A linear least-squares regression model was developed between road area occupancy and classified vehicle count. The coefficients of the occupancy-density linear regression model were used to estimate PCE and motorcycle equivalent (MCE) factors. The comparison of the estimated set of PCE values with the values reported in the literature shows that PCE factors estimated using the proposed method are reasonable and produce a better occupancy-density relationship than the other studies. In comparison with the existing methods that rely on lane-based measurements, the proposed method is well suited for traffic with weak/no lane discipline, as it considers the entire road width and the dynamics of lateral movement of different types of vehicles. The proposed method does not need extensive traffic data of speeds, headways, flow rates, and so forth, and is applicable on aerial photographs obtained from other sources, such as satellites.Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported with funding from Exascale Open Data Analytics Lab, National Center for Big Data and Cloud Computing (NCBC) and the Higher Education Commission of Pakistan.
Acknowledgments
The authors are thankful to research students Syed Hassan Ali, Haseeb Ahmed, Zohaib Ahmed, Aqib Abbasi, Asad Rehan, Mirza Ali Haider, Syed Abbas Hasan Zaidi, and Omema for their help in this research
A comparative study of heat transfer analysis of fractional Maxwell fluid by using Caputo and Caputo-Fabrizio derivatives
No full textA comparative analysis is carried out to study the unsteady flow of a Maxwell fluid in the presence of Newtonian heating near a vertical flat plate. The fractional derivatives presented by Caputo and Caputo–Fabrizio are applied to make a physical model for a Maxwell fluid. Exact solutions of the non-dimensional temperature and velocity fields for Caputo and Caputo–Fabrizio time-fractional derivatives are determined via the Laplace transform technique. Numerical solutions of partial differential equations are obtained by employing Tzou’s and Stehfest’s algorithms to compare the results of both models. Exact solutions with integer-order derivative (fractional parameter α = 1) are also obtained for both temperature and velocity distributions as a special case. A graphical illustration is made to discuss the effect of Prandtl number Pr and time t on the temperature field. Similarly, the effects of Maxwell fluid parameter λ and other flow parameters on the velocity field are presented graphically, as well as in tabular form.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author
RELATIONSHIP BETWEEN EMPLOYEE JOB SATISFACTION AND TURNOVER INTENTION IN MICROFINANCE BANKS IN PAKISTAN
No full textLEGAL PROTECTION FOR CUSTOMERS IN ONLINE LOANS ACCORDING TO SHARIA ECONOMIC LAW
No full textThis article aims to examine the legal protection for customers in the implementation of online-based money loans or known as online loans (pinjol). The research method used is a doctrinal approach where the author will examine legal norms related to customer protection according to the Banking Law, and a number of Financial Services Authority (OJK) regulations related to pinjol. The results of this study conclude that there are several phenomena that occur in the implementation of pinjol that do not get legal protection for customers, namely: First, the determination of interest that does not refer to the interest rate provisions of Bank Indonesia, second, the maximum interest setting on online loans in fintech companies is 0.4 percent per day but the amount of real interest is not regulated in the agreement. Third, Financial Services Authority Regulation No.77/POJK.01/2016 on Information Technology-Based Money Lending and Borrowing Services OJK Regulation No.77 of 2016 is the basis for the implementation of Peer to Peer Lending business activities or online lending and borrowing which is one of the types of fintech, including the protection of customer personal data. Customer personal data collected by fintech providers must be kept confidential in accordance with applicable privacy provisions.
 
Naïve Multi-label Classification of YouTube Comments Using Comparative Opinion Mining
No full textAbstractWith the turning wheel of time, the influence of the social networking websites on the people has significantly increased. People are now connecting with each other in cyber space and show their sentiments in the form of comments in different social networking websites such as Twitter, Facebook and Google Plus. YouTube is considered as a king in the field of video sharing. It is a largest video sharing repository, where people come and share their thoughts regarding video in the form of comments. If we are able to find useful information through comment, then these unstructured comments can be useful for different purposes. Sentiment analysis is the one way to find out the feeling of people and in the case of YouTube, we can understand the behaviour and response of individuals after seeing particular video. There are situations in which opinion shared by user has comparative content. The user sees the video of comparison of two options or products and shares his/her preference based on some reasoning. Comparative opinion mining leads to situation where number of options defines the number of labels. In this paper, we have used Naïve Bayes machine learning algorithm to perform multilabel classification to find out the sentiments of the commenters for different options. In order to reduce the computational requirements, we adopted a naïve assumption that words around keywords related to particular option are enough to understand the sentiments of user. The developed classifier based on naïve assumption demonstrated slightly lower performance with the benefit of requirement of less computational power
Network-Based Modeling of the Molecular Topology of Fuchsine Acid Dye with Respect to Some Irregular Molecular Descriptors
No full textFuchsine acid is one of the supramolecular dyes used in Masson’s trichrome stain and has enormous applications in histology. It is also used in Van Gieson’s method with picric acid to show red collagen fibers and in Masson’s trichrome to show smooth muscle in contrast to collagen. In addition to these, it has several other important applications in electronic fields and photonic devices as an organic semiconductor. Therefore, it is of utmost importance to investigate and predict the complex molecular topology of fuchsine acid, which serves as a foundation for the link with its physicochemical properties. In this article, the supramolecular sheet of fuchsine acid is modeled topologically based on the edge partition, and closed formulae are derived for some of its important irregular molecular descriptors, with the ultimate object of throwing some light on the effectiveness of the computed molecular descriptors for QSAR and QSPR analyses
Assessment of national dosimetry quality audits results for teletherapy machines from 1989 to 2015
No full text(DIS)COVERING THE (IN)VISIBILITY OF THE COLORED IN THE VANISHING HALF BY BRIT BENNETT
No full textColored people are subjugated and marginalized in every possible way due to their racial identity. It is one of the burning issues not only in the contemporary American society but around the globe. The present research universalizes the phenomenon of creating binaries on the basis of race and color. It investigates how racial identities are generated to achieve certain political goals. It challenges the self-proclaimed notion of the white Americans that they are living in post-racial society. It identifies the multiple issues attached with this socially constructed phenomenon of racism. It examines the nature of systematic racism foregrounded in The Vanishing Half written by the Afro-American author Brit Bennett while employing Richard Delgado and Jean Stefancic’s concepts discussed in Critical Race Theory: An Introduction. These concepts intersect the modern forms of racism. It shows that minorities are victims of identity crises. They are the most invisible and unrepresented creatures. Multiple psychosocial issues are faced by these marginalized and colored people. They suffer under the supremacy of the white man
Comparison of Machine Learning Algorithms for Sepsis Detection
No full textSepsis is a very fatal disease, causing a lot of causalities all over the world, about 2, 70,000 die of Sepsis annually, thus early detection of Sepsis disease would be a remedy to prevent this disease and it would be a big relief to the family of sepsis patients. Different researchers have worked on sepsis disease detection and its prediction but still the need to have an improved model for Sepsis detection remains. We compared various machine learning algorithms for Sepsis detection and used the dataset publicly available for all the researchers at Physionet.org, the dataset contains many empty or Null values, we applied backward filling and forward filling techniques, and we calculated missing values of MAP using equation (1) which gives more precise results, we divided the 40,336 files of datasets A and B into 80% training set and 20% testing set. We applied the algorithms twice one time using vital signs and clinical values of patients and the second time using only vital signs of the patients; using vital signs only the training accuracy of KNN, Logistic Regression, Random Forest, MLP, and Decision Trees was 0.992, 0.999, 0.981, 0.981, and 0.981 respectively, while the testing accuracy of KNN, Logistic Regression, Random Forest, MLP, and Decision Trees was 0.987, 0.980, 0.983, 0.981, and 0.981 respectively, for Sepsis Label 0, the value of precision for KNN, Random Forest, Decision Trees, Logistic Regression, and MLP was 0.99, 0.98, 0.98, 0.98, and 0.98 respectively, while the value of recall for KNN, Random Forest, Decision Trees, Logistic Regression, and MLP was 1.00, 1.00, 1.00, 1.00, and 1.00 respectively; the comparison of all the above-mentioned algorithms showed that KNN leads over all the competitors regarding the accuracy, precision, and recall.
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Decoding the Developmental Trajectory of Energy Trading in Power Markets through Bibliometric and Visual Analytics
No full textThis research leverages bibliometric methodologies, enhanced by the visual analytics capabilities of CiteSpace, to meticulously examine the evolution and current trends in energy trading within power markets, analyzing 642 scholarly articles from the Web of Science Core Collection spanning from 1996 to 2023. The study aims to illuminate the prevailing research landscape, growth patterns, and future directions in energy trading dynamics. Key findings include: (1) A noticeable escalation in the volume of publications, especially from 2021 to 2023, indicating a burgeoning interest and rapid evolution in this research area; (2) The author and institutional collaboration networks are in a nascent stage, with a predominantly China-centric international collaboration pattern, including significant partnerships with the United States, Australia, and the United Kingdom; (3) The focal points of research are centered around themes such as “energy management”, “demand-side innovation”, “decentralized energy trading”, and “strategic optimization”, covering areas such as intelligent grid technologies, energy market dynamics, and sustainable energy solutions. The study recommends enhancing collaborative networks, fusing technological and strategic dimensions in research, increasing focus and funding for emerging technologies, and promoting wider international and cross-disciplinary collaborations to enrich the understanding of energy trading dynamics in the context of electricity markets
