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Acadlore Transactions on Applied Mathematics and Statistics
ATAIML
Acadlore Transactions on Applied Mathematics and Statistics (ATAMS)
ATG
ISSN (print): 2959-4057
ISSN (online): 2959-4065
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2023: Vol. 1
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Acadlore Transactions on Applied Mathematics and Statistics (ATAMS) is a peer-reviewed, open access academic journal on applied mathematics and statistics. It is published quarterly online by Acadlore. The publication dates of the four issues usually fall in March, June, September, and December each year.

  • Professional service - All articles submitted go through rigorous yet rapid peer review and editing, following the strictest publication standards.

  • Fast publication - All articles accepted are quickly published, thanks to our expertise in organizing peer-review, editing, and production.

  • Open access - All articles published are immediately available to global audience, and freely sharable anywhere, anytime.

  • Additional benefits - All articles accepted enjoy free English editing, and face no length limit or color charges.

Editor(s)-in-chief（2）
bisera andrić gušavac
University of Belgrade, Serbia
bisera.andric.gusavac@fon.bg.ac.rs | website
Research interests: Mathematical Modelling; Optimization; Industrial Engineering; Performance Analytics
milena popović
University of Belgrade, Serbia
milena.popovic@fon.bg.ac.rs | website
Research interests: Data Envelopment Analysis; Quantitative Models and Methods; Mathematical Modelling; Optimization; Business Analytics and Performance Analytics

Aims & Scope

Aims

Acadlore Transactions on Applied Mathematics and Statistics (ATAMS) (ISSN:) aims to provide a platform for researchers, academics, and professionals to publish original research articles, reviews, and technical notes in the multidisciplinary areas of applied mathematics and statistics.

The scope of this journal covers a wide range of topics related to the application of mathematical and statistical theories in various fields such as engineering, physical sciences, social sciences, economics and finances, and more. The journal welcomes papers that emphasize the development of new mathematical and statistical models, as well as the application of existing models to real-world problems.

The journal is committed to the highest standards of academic rigor and strives to publish papers that are both innovative and impactful. It provides a forum for the exchange of ideas and encourages interdisciplinary collaborations.

We hope that this journal will become a valuable resource for researchers, academics, and professionals who are interested in the application of mathematics and statistics to solve real-world problems.

The aim of ATAMS is to encourage scientists to publish their theoretical and experimental results in as much detail as possible. Therefore, the journal has no restrictions regarding the length of papers. Full details should be provided so that the results can be reproduced. In addition, the journal has the following features:

  • Authors from emerging countries enjoy the same high-quality services as those from the developed world.

  • Following a rigorous review process, the accepted papers are published quickly to minimize the time from submission to publication.

  • The published papers have maximum exposure under our open access policy.

Scope

The scope of the journal covers, but is not limited to the following topics:

  • Mathematical modeling: Using mathematical methods to describe and solve practical problems.

  • Statistical theory: Exploring new statistical methods, researching their properties and applications.

  • Data analysis and mining: Applying statistical and mathematical methods to analyze and mine data to derive useful conclusions.

  • Numerical computation and computational methods: Developing new numerical computation methods and algorithms and performing numerical simulations and computations.

  • Interdisciplinary fields of applied mathematics and statistics: Such as financial mathematics, medical statistics, biostatistics, environmental statistics, etc.

Articles
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Abstract

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This study aims to identify efficient Information Technology (IT) candidates for a specific position and highlight areas for improvement using Data Envelopment Analysis (DEA). By streamlining the selection process and reducing costs, the findings can assist companies in making better-informed hiring decisions. Additionally, the results provide candidates with valuable feedback on areas for development, increasing their chances of securing employment in their desired company. The DEA model offers a unique advantage in this context by generating reference units for each candidate, enabling precise determination of the necessary changes in inputs or outputs for achieving efficiency. The Charnes, Cooper, and Rhodes (CCR) model served as the baseline, with parallel comparisons drawn against the Banker, Charnes, and Cooper (BCC) and categorical models to identify the most effective approach. The findings reveal the efficient candidates based on the assessed criteria, demonstrating that less experienced candidates can be evaluated as efficient compared to their more experienced counterparts. The hypothesis that the BCC model, with its more flexible efficiency frontier, results in poorer candidate differentiation was confirmed. This study highlights the value of adopting the DEA method in evaluating the employment efficiency of IT candidates, offering practical implications for both hiring organizations and job-seekers.

Abstract

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Historically, infectious diseases have greatly impacted human health, necessitating a robust understanding of their trends, processes, and transmission. This study focuses on the COVID-19 pandemic, employing mathematical, statistical, and machine-learning methods to examine its time-series data. We quantify data irregularity using approximate entropy, revealing higher volatility in the U.S., Italy, and India compared to China. We employ the Dynamic Time Warping algorithm to assess regional similarity, finding a strong correlation between the U.S. and Italy. The Seasonal Trend Decomposition using the LOESS algorithm illuminates strong trend degrees in all observed regions, but China's prevention measures show marked effectiveness. These tools, whilst already valuable, still present opportunities for development in both theory and practice.

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