Applied Research Advances (ARA) is an international journal that publishes high-quality scientific original research and review papers. The scope of the ARA journal covers the following areas of interest: Machine learning (ML) and artificial intelligence (AI) research advances with application topics related to decision-making, automation, predictive analytics, natural language processing, autonomous systems, robotics, predictive maintenance, fault detection, digital twins, additive manufacturing, Industrial engineering research advances with application topics related to smart manufacturing, analytics in operations, big data, human factors engineering, workforce 5.0, sustainable lean manufacturing, supply chain engineering, data-driven six sigma methodologies, closed-loop models, smart factories, eco-design, performance assessment, materials flow analysis, service systems, etc. Environmental engineering research advances with application topics related to recycling, waste management, environmental monitoring, climate change mitigation, wastewater treatment, air pollution monitoring, etc. Automation research advances with application topics related to human-robot interactions, smart manufacturing, industrial automation, healthcare automation, collaborative systems, swarm robotics, Multi-agent systems, robotic process automation, etc. Information and communication technology (ICT) research advances with application topics related to edge computing, cloud computing, cybersecurity, Blockchain, Internet of Things, smart infrastructure, quantum computing & communication, decentralized technologies, etc. Health and biomedical research advances with application topics related to biomaterials, drug delivery systems, healthcare, imaging technologies, health informatics, telemedicine, etc. Manufacturing research advances with application topics related to additive manufacturing, nanotechnology, manufacturing process optimization, smart materials, production systems, Industry 4.0/5.0, industrial Internet of Things, green manufacturing, machining, etc. Process engineering research advances with application topics related to sustainable processes, process safety, multi-objective optimization, process control, process modeling, separation technologies, risk management, sustainable processes, renewable energy processes, data-driven process engineering, food process engineering, exergy analysis, etc. Smart cities research advances with application topics related to intelligent transportation systems, urban mobility, energy management, smart grids, sensor-based infrastructure monitoring, Energy storage, connected vehicles, Mobility-as-a-Service platforms, Traffic flow optimization, net-zero buildings, etc. Agricultural research advances with application topics related to sustainable farming, smart agriculture, food safety, genetic engineering, precision agriculture, digital agriculture, disease management, climate-smart agriculture, soil conservation, and other critical agricultural problems. Education research advances with application topics related to accessibility, adaptive learning systems, digital learning environments, inclusive education, education leadership, virtual/augmented reality, interactive technologies, online & hybrid learning models, personalized learning, social learning, digital inclusion, education policy, etc. General Journal guidelines for authors Applied Research Advances publishes research articles, reviews, short communications, and case studies. Research articles must include: motivation for the work, an adequate overview of the representative work in the field including up-to-date references, a clear statement of the novelty in the presented research, suitable theoretical background, one or more examples to demonstrate and discuss the presented ideas and, finally, conclusions. Short communications are usually 4-7 pages long, research articles and case studies 8-14 pages, while reviews can be longer. Page number limits are not strict and, with appropriate reasoning, the submitted articles can also be longer or shorter. Authors are requested to follow the ARA guidelines and strictly format their manuscripts as per the article template that is available here. If extensions of previously published conference papers are submitted, Editors will check if sufficient new material has been added to fulfill the journal standards and qualify the submission for the review process. The added material must not have been previously published. New results are desired but not necessarily required; however, the submission should contain expansions of key ideas, examples, elaborations, etc., of the conference submission. ARA’s acceptance rate is 23%. In 2025, the median time from submission to acceptance for all articles was 64 days, and 9 days from acceptance to online publication in the ONLINE FIRST section. The ONLINE FIRST section of DMA lists the papers accepted for publication and copy-edited but not yet assigned to an issue. Aims and Scope: The principal aim of the journal is to bring together the latest research and development in various fields of decision science. We would like to highlight that papers should refer to Aims and scope, but they are not limited to. Publication Frequency: One issue per year is published online, but processed and accepted papers, with full bibliographic data, are added to the issue continuously over the whole year. Open Access: This is an open-access journal which means that all content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles in this journal without asking prior permission from the publisher or the author. Publication fee: There is no submission charge or publication fee. Publication in ARA is free of charge for the authors. The Applied Research Advances is annually classified by the Ministry of Education, Science and Technological Development of the Republic of Serbia.
Center for Open Research and Education (CORE)
An Introduction of the Mellin Transformation under Interval Uncertainty
Mellin transformation has potential applications in signal processing, quantum mechanics, and numerous domains of knowledge. Data collection and its interpretations must go through impreciseness in almost every real field of decision-making or modelling. In this paper, we contribute an alternative approach for the Mellin transformation under imprecision. The interval-valued Mellin transformation is introduced in this paper. We discuss the linearity, scaling, and sifting properties of the interval-valued transformation. Also, the Mellin transformation of the generalized Hukuhara derivative of the interval-valued function is addressed. One possible application of interval-valued Mellin transformation to solve an uncertain Euler differential equation is manifested, and several more consequent applications are hinted.
Copyright (c) 2026 Musaraf Hossain, Mostafijur Rahaman, Shariful Alam (Author) https://creativecommons.org/licenses/by/4.0
Meta-Graphs and Iterated Meta-Graphs: A Novel Framework for Hierarchical Network Analysis and Complex System Modeling
Graph theory provides a fundamental framework for representing and analyzing relationships among interconnected entities. In this paper, we introduce the concepts of Meta-Graphs and iterated Meta-Graphs as hierarchical structures that extend classical graph representations to multiple levels of abstraction. A Meta-Graph is defined as a graph whose vertices are themselves graphs, while edges represent specified relations between those graphs. Building on this concept, an iterated Meta-Graph is obtained recursively by constructing graphs whose vertices are objects from the preceding level, thereby enabling the representation of complex systems with nested organizational structures. To formalize these ideas, we provide rigorous definitions of Meta-Graphs, relation lifting mechanisms, and iterated universes, together with illustrative examples motivated by real-world networked systems. We further extend the framework by introducing the Temporal Iterated Meta-Graph, which models the evolution of hierarchical graph structures through time-indexed snapshots, and the edge-valued fuzzy iterated Meta-Graph, which enriches certified meta-relations with membership degrees that quantify uncertainty, confidence, or interaction strength. Several practical examples from microservice architectures, dependency analysis, and organizational systems are presented to demonstrate the applicability of the proposed framework. The introduced concepts establish a foundation for studying higher-order and dynamic graph structures, opening new directions for modeling, analysis, and decision-making in complex interconnected environments.
Copyright (c) 2026 Takaaki Fujita, Ajoy Kanti Das, Suman Das (Author) https://creativecommons.org/licenses/by/4.0
The Impact of Smart Packaging on the Digitalization of Supply Chains
The food supply chain (FSC) plays an extremely important role in society and the global economy, while at the same time facing numerous challenges, among which packaging occupies a key position. Traditional forms of packaging often contribute to increased losses, higher costs, and reduced overall food supply chain efficiency. This paper analyzes the application of smart packaging, namely active packaging, intelligent packaging, and QR code-based packaging, as alternative solutions that can contribute to extending product shelf life, preserving quality, and improving product traceability throughout food supply chains. These alternatives are compared based on a defined set of nine criteria using a hybrid model that integrates the fuzzy best-worst method and the fuzzy combined compromise solution method. Special emphasis is placed on the importance of smart packaging in the digital transformation of the food supply chain, particularly through its connection with modern technologies. The obtained results indicate that the analyzed solutions contribute to increasing the transparency, efficiency, and long-term sustainability of food supply chains. The paper shows that future development in this field is directed toward integrating digital technologies with environmentally friendly materials to reduce waste, preserve freshness, and enhance the resilience of food supply chains.
Copyright (c) 2026 Mladen Božić, Svetlana Dabić-Miletić, Nikola Pavlov (Author) https://creativecommons.org/licenses/by/4.0
Targeted Intersection Safety in Data-Sparse Cities: A Discrete-Time Microsimulation and Decision-Making Framework Applied to a Hazardous Urban Junction in Libya
Urban safety policy choices in low-data contexts frequently face both high uncertainty and pressing timelines. This paper suggests a lightweight and reproducible framework that combines a discrete-time microsimulation of a single, high-risk four-leg intersection with multi-criteria decision-making. In the model, vehicles and pedestrians are generated via Poisson arrival processes, while behavioral variability such as speeding, red-light running, jaywalking, and driver yielding is modeled using probabilistic parameters calibrated to local Libyan traffic conditions. Four low-cost interventions (i.e., speed bumps, a red-light camera, an improved pedestrian crosswalk, and a one-lane roundabout) are evaluated against the baseline. Each strategy is evaluated using four main simulation-based criteria (i.e., reduction in accidents, effect on vehicle delay, cost of implementation, and a pedestrian safety measure) that combine near-miss and waiting-time changes. These outputs are fed into a Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) analysis under safety-first, cost-sensitive, and pedestrian-focused stakeholder perspectives. Outcomes exhibit clear, mechanism-consistent trends. By reporting uncertainty explicitly and providing scenario-dependent rankings, the framework translates limited local data into clear, defensible decision guidance. The contribution is both practical and methodological; i.e., a sparse-data pipeline that urban areas can readily implement to prioritize first-step safety spending at high-risk intersections.
Technique for Order Preference by Similarity to the Ideal Solution
Copyright (c) 2026 Ibrahim Badi, Mouhamed Bayane Bouraima, Sam Aberi Okemwa (Author) https://creativecommons.org/licenses/by/4.0
Enhancing Decision-Making in Banking Systems: A Picture Fuzzy Rough Decision-Making Approach with Schweizer-Sklar Prioritized Aggregation Operators
Decision-making in the banking industry frequently requires navigating ambiguity and imprecise information. Choices are frequently made in ambiguous and imprecise circumstances. This research aims to offer a more advanced method for dealing with these kinds of uncertainties. The paper investigates the use of picture fuzzy rough sets (PFRSs) in conjunction with the Schweizer-Sklar t-norm and t-conorm, which provide an organized method for managing data ambiguity and can improve the banking decision-making process. The Schweizer-Sklar framework is used to introduce new prioritized aggregation operators that are specially designed for managing uncertainty with PFRSs. Important properties, like idempotency, monotonicity, and boundedness, for the derived aggregation operators are proved. With an emphasis on tasks like loan evaluation and credit scoring, the suggested operators are applied in the banking industry. This illustrates their capacity to control uncertainty and produce trustworthy decision results. The PFRSS-based approach's efficacy and resilience are validated by comparison with current methodologies, which makes it a useful instrument for intricate decision-making in the banking industry.
Copyright (c) 2026 Mehwish Sarfraz, Tahira Karamat (Author) https://creativecommons.org/licenses/by/4.0
A Pythagorean Fuzzy Multiple-Attribute Decision-Making Model for Smart Governance Research Applications
This applied research examines multiple-attribute decision-making issues that involve Pythagorean fuzzy (PyF) information. An advanced method for multiple-attribute decision-making, which employs arithmetic and geometric operations to create aggregation operators on Pythagorean fuzzy sets, is introduced. More detailed, Pythagorean prioritized fuzzy power Aczel-Alsina geometric and Pythagorean prioritized fuzzy power Aczel-Alsina averaging operators are proposed. Several associated properties of the novel prioritized aggregation operators are addressed. A multiple-attribute decision-making model is constructed based on the proposed prioritized aggregation operators. A numerical study demonstrates smart governance research applications of the developed aggregation operators. Finally, the significant advances of the developed prioritized aggregation operators are confirmed through comparison analyses.
Prioritized Aggregation Operator Advances
Copyright (c) 2026 Tahira Karamat, Mehwish Sarfraz (Author) https://creativecommons.org/licenses/by/4.0
Unveiling the Challenges of Artificial Intelligence Use in Auditing: A Holistic Multi-Criteria Decision-Making Approach
This study adopts the Fermatean fuzzy Stepwise Weight Assessment Ratio Analysis (FF-SWARA) method to systematically assess the challenges associated with the use of artificial intelligence (AI) in auditing. Data were collected from four domain experts who evaluated seven identified challenges, after which the proposed method was applied to determine their relative severity. The results reveal that transparency and explainability, robustness and reliability, and data privacy are the three most critical challenges related to the use of AI in auditing. The study makes a meaningful contribution to the decision sciences and management literature by providing practical insights for policymakers and professionals in the auditing domain. It concludes by outlining clear directions for future research.
Copyright (c) 2026 Mouhamed Bayane Bouraima (Author) https://creativecommons.org/licenses/by/4.0
Large Language Models in Multi Criteria Decision Making: A Systematic Review, Taxonomy, and Future Research Agenda
This study is a systematic review, bibliometric analysis and taxonomy building of Large Language Models (LLM) and Multi-Criteria Decision-Making (MCDM) integration. A total of 63 publications from the Scopus database were analysed using PRISMA guidelines and VOSviewer tools. The findings show this field is highly nascent, with a rapid rise since 2025. Bibliometric analysis shows that this field is widely practiced globally, with 37 countries involved and China and India contributing most publications. This study introduces a new classification system based on the roles of LLM, the integration stages and the application areas. Comparative studies show LLM-enhanced MCDM improves automation, scalability, and unstructured data processing, but remains prone to biases, lack of interpretability and sensitivity to prompts. The research concludes with gaps and opportunities to advance this field towards robust, explainable and hybrid intelligent decision-making systems.
Copyright (c) 2026 Sushil Kumar Sahoo, Bibhuti Bhusan Choudhury, Prasant Ranjan Dhal, Supriya Sahu, Sudhakar Majhi, Ipsita Dhar (Author) https://creativecommons.org/licenses/by/4.0
Experimental Validation of Internal Ballistic Mathematical Model for a Small Caliber Weapon
This paper examines the influence of barrel length on pressure and the initial velocity of the projectile. The theoretical analysis is conducted using the Drozdov analytical solution. An experimental method is used to investigate the ballistic characteristics of different barrel lengths from 720 mm to 600 mm, and also to validate the analytical results. The obtained deviations between analytical and measured values are less than 3.5%, 2%, and 6% for barrel maximum pressure, initial velocity, and firing time, respectively. The study findings show the reliability of the used model in determining internal ballistic characteristics for small-caliber weapons.
Copyright (c) 2026 Aleksa Aničić, Vučko Vučković, Luka Đorđević, Lazar Blagojević, David Tošić (Author) https://creativecommons.org/licenses/by/4.0
Identifying Suitable Location for Electric Vehicles Charging by Using Fermatean Fuzzy-based Personalized Decision Model
Electric vehicles (EVs) are gaining a lot of attention in the transport industry. With global leaders combating climate change and enforcing green habits, EVs are viable options for transport. One crucial challenge is charging EVs and identifying apt locations to set stations to charge EVs. The location identification problem is considered a multi-criteria decision problem (MCDP) considering diverse criteria. Previous studies on location selection for EV charging could not model uncertainty effectively, handle subjective randomness, capture experts' hesitation, and consider personalized grading of locations. Motivated by these gaps, in this paper, authors put forward an integrated framework that comprises Fermatean fuzzy preferences, entropy measure, and modified WASPAS with Copeland approach for modeling uncertainty with reduced subjective randomness, effective capturing of hesitation of experts, and obtaining both individualistic and cumulative grades for locations to charge EVs. The practicality of the framework is testified via a case example of location grading in Tamil Nadu, India. Results infer that ‘Singanallur’ and ‘Peelamedu’ are top two locations for setting charging points and criteria viz., pollution, cost, and eco-impact are relatively important for location identification. The paper presents a novel combination that methodically calculates weights and ranks locations both in the personalized and combined contexts. Its applicability to location selection is unique with the locations being considered novel for the study. The study faces following limitations viz., (i) partial information cannot be modelled, (ii) experts’ weights are not methodically obtained. and (iii) GIS-based selection process is not followed.
Copyright (c) 2026 Kattur Soundarapandian Ravichandran, Mithun Karthik, Dhruva Sundararajan (Author) https://creativecommons.org/licenses/by/4.0
AI-Powered Threats and Solutions: A Theoretical Analysis of Risks, Governance, and Ethical Safeguards
Artificial Intelligence (AI) represents one of the most transformative technological paradigms of the 21st century, revolutionizing fields such as healthcare, finance, defense, and communication. However, the same capabilities that enable innovation also introduce substantial risks when exploited maliciously or left unregulated. This study presents a theoretical analysis of the emerging threats posed by AI—including deepfakes, autonomous cyberattacks, data manipulation, misinformation propagation, and algorithmic bias—and explores the countermeasures designed to address these risks. Drawing upon contemporary research, the paper emphasizes the dual-use nature of AI, the ethical and governance challenges it poses, and the pressing need for global cooperation. The findings reveal that sustainable and responsible AI development depends on integrating explainable AI, adversarial defense systems, robust governance frameworks, and international regulatory coordination. The paper concludes by proposing a multidimensional framework that aligns AI innovation with human safety, transparency, and accountability.
Copyright (c) 2026 Arkyadeep Sarkar, Shankha Shubhra Goswami, Sushil Kumar Sahoo (Author) https://creativecommons.org/licenses/by/4.0
A Sustainable Deteriorating Inventory Model with Carbon Emission Reduction Policies
In inventory models, profit can be hugely improved by assuming demand according to the requirements of the market situation. In this study, demand is assumed to be time dependent, stock dependent or price dependent for a realistic view of market patterns. The production rate is also considered demand dependent and deterioration is taking time dependent. To address environmental concerns, carbon emissions from inventory holding and replenishment activities are integrated into the decision framework. The model is compared with and without a permissible delay in payment. Numerical examples, theorems, and graphs support the existence of the proposed model. Industrial applications and managerial insights significantly contribute to the novelty of the proposed model. Lastly, the study is concluded with future remarks.
Permissible delay in payments; deterioration
Copyright (c) 2026 Diptesh Kumar Roy, Magfura Pervin, Shashi Bajaj Mukharjee, Kamal Hossain Gazi, Sankar Prasad Mondal (Author) https://creativecommons.org/licenses/by/4.0
Selection of Optimal Sites for New Airport in Urban Areas: An Intuitionistic Fuzzy based Decision Making Study
Airports play an important part in elevating regional communication, resulting in better economic value and enhanced accessibility. Choosing the best location for building a new airport involves considering multiple, interrelated, and complex criteria, including economic activity, population density, land availability, geographic suitability, strategic importance, and accessibility. A decision-making framework has been proposed, involving criteria and suitable regions for establishing a new airport station in urban areas, utilizing Multi-Criteria Decision-Making (MCDM) procedures. The Fuzzy Analytic Hierarchy Process (AHP) is employed to determine the objective balance for the evaluation criteria, and the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used to rate the alternative locations. Intuitionistic Fuzzy Sets are incorporated into the model to support uncertainty and inaccuracy in the experts' judgments. A comparative analysis is also conducted to ensure the stability and feasibility of the final output. The proposed model aims to assist urban researchers, policymakers, and aviation authorities in making detailed, data-driven decisions for the development of national infrastructure.
Copyright (c) 2026 Prodip Bhaduri, Kamal Hossain Gazi, Sankar Prasad Mondal, Aditi Biswas, Tetiana Zhyber, Arijit Ghosh (Author) https://creativecommons.org/licenses/by/4.0
E-Waste and the Telecommunication Industry: A 15-Year Bibliometric Mapping of Research Progress
The rapid growth of e-waste resulting from technological advancements in telecommunications has intensified research on efficient management strategies, particularly in recycling, environmental impact, and sustainable disposal methods. To provide a comprehensive bibliometric analysis, this study employs Bibliometrix and VOSviewer 1.6.20 to graphically map key insights into the telecom sector’s role in e-waste management between 2000 and 2024. The findings reveal that: (1) publications increased exponentially, peaking at 95 in 2024 after nearly doubling from 38 in 2019 to 61 in 2020; (2) Zeng Xianlai emerged as the most influential author, averaging 149.63 citations per article across eight publications; and (3) the National Environmental Engineering Research Institute (NEERI) demonstrated the greatest research influence, followed by the Academy of Scientific and Innovative Research (128) and the State Key Joint Laboratory of Environment Simulation and Pollution Control (135.38). Overall, the study underscores the growing urgency of addressing e-waste in telecommunications and emphasizes the need for sustainable solutions to minimize environmental impact and advance the 2030 SDGs
Copyright (c) 2025 Ajay Kumar, Sunita (Author) https://creativecommons.org/licenses/by/4.0
HyperRough Number and SuperHyperRough Number with Applications
Rough set theory provides a systematic framework for modeling uncertainty by approximating subsets through lower and upper approximations induced by an equivalence relation. The rough number, defined as the interval between the mean attribute values of these approximations, offers a numerical measure of uncertainty. HyperRough Sets extend this framework by incorporating multiple attributes into the approximation process, while SuperHyperRough Sets further generalize it via iterated powerset constructions. However, numerical counterparts of these extended structures—the HyperRough Number and the SuperHyperRough Number—have not yet been formally introduced. In this paper, we define HyperRough Numbers and SuperHyperRough Numbers, establish their fundamental properties, and illustrate their usefulness in multi-attribute decision-making problems. The author contends that these numerical constructs provide a basis for future research on sophisticated applications across a wide range of applied sciences.
Copyright (c) 2026 Takaaki Fujita, Arif Mehmood (Author) https://creativecommons.org/licenses/by/4.0
Applied Multi-Attribute Decision-Making with Complex Pythagorean Fuzzy Data based on Prioritized Aczel–Alsina Aggregation Operators: A Case for a Software Company
Complex Pythagorean fuzzy (C-PF) information is an extended form of fuzzy set theory. In this paper, we aim to compute the theory of prioritized aggregation operators based on Aczel-Alsina t-norm and t-conorm for managing the theory of C-PF information, such as C-PF prioritized Aczel-Alsina averaging, C-PF prioritized Aczel-Alsina ordered averaging, C-PF prioritized Aczel-Alsina geometric, and C-PF prioritized Aczel-Alsina ordered geometric operators. Three basic properties of the derived theory are also examined. Additionally, we illustrate the proposed multi-attribute decision-making technique for evaluating real-life problems related to software companies. Finally, we provide some examples to illustrate the contrast between derived work and previous or current information in order to demonstrate the ability and proficiency of the novel approach.
Prioritized Aczel-Alsina Aggregation Operators
Copyright (c) 2025 Tahira Karamat, Mehwish Sarfraz (Author) https://creativecommons.org/licenses/by/4.0
A System Dynamics Framework for Analyzing and Ranking Factors Influencing Life Expectancy
Life expectancy is a key measure of public health and development, but its determinants are complex and interdependent, making accurate prediction and policymaking challenging. This study adopts a system dynamics modeling approach to identify, rank, and simulate the factors influencing life expectancy, with a focus on healthcare clinics in Iran. Based on an extensive literature review and expert consultations, 25 indicators are selected and validated through a structured questionnaire, yielding a Cronbach’s alpha of 0.74, confirming acceptable internal consistency. Simulation experiments are carried out by Vensim software under baseline, inflationary, and deflationary conditions, and complemented by sensitivity analysis. The findings indicate that inflation has the most substantial negative impact, reducing life expectancy by more than four years under a 10% annual increase, while improvements in environmental quality, healthcare access, and nutrition exert positive and reinforcing effects. The study contributes by demonstrating the value of system dynamics in capturing feedback relationships often overlooked in conventional approaches, thereby providing policymakers with integrated insights to design effective strategies for improving life expectancy.
Copyright (c) 2025 Mehdi Faridzad, Mahdi Yousefi Nejad Attari, Ali Ala (Author) https://creativecommons.org/licenses/by/4.0