WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://gcris.khas.edu.tr/handle/20.500.12469/4465

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A New Nano-Scale Authentication Architecture for Improving the Security of Human-Computer Interaction Systems Based on Quantum Computing
(Springer, 2025) Jafari Navimipour, Nima; Zohaib, Muhammad; Navimipour, Nima Jafari; Misra, Neeraj Kumar; Rasmi, Hadi; Salahov, Huseyn; Hosseinzadeh, Mehdi; Computer Engineering
Human-Computer Interaction (HCI) is an interdisciplinary area of study focusing on the interaction of users and computers by scheming interactive computer interfaces. In addition, HCI systems need security to confirm user authentication, which is a crucial issue in these systems. Hence, user authentication is vital, allowing only authorized users to access data. Authentication is critical to the digital world since it provides security and safety for digital data. Moreover, a digital signature is an authentication method to confirm the accuracy and reliability of digital documents or communications. In addition, designing the circuit based on the complementary metal-oxide semiconductor (CMOS) technology can affect the security and safety of digital data due to the excessive heat dissipation of circuits. On the other hand, quantum-dot cellular automata (QCA) and reversible logic as alternative technologies to CMOS address these problems. Since QCA and reversible logic circuits have minimal energy dissipation, which is considered nearly zero, approaching these technologies proves extremely difficult for any hacker. This work presents an effective structure for the authenticator and human-computer interaction using QCA and IBM quantum computing with Qiskit simulations. The proposed structure has outperformed current circuits in terms of area, cell count, and latency. The paper demonstrates the QCA reversible logic layout of the proposed HCI authenticator and integrates IBM quantum computing simulations using Qiskit for validation. The implementation and testing of results are performed utilizing QCADesigner-2.0.3 and Qiskit simulation tools. The accuracy and efficiency of the proposed design are validated through simulation-derived comparison values, and energy dissipation simulations prove that the suggested circuit dissipates minimal energy.
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A New Quantum-Enhanced Approach To Ai-Driven Medical Imaging System (Vol 28 , 213 , 2024)
(Springer, 2025) Jafari Navimipour, Nima; Avval, Danial Bakhshayeshi; Darbandi, Mehdi; Navimipour, Nima Jafari; Ul Ain, Noor; Kassa, Sankit; Computer Engineering
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The Effect of Educational Leadership on Students' Achievement: a Cross-Cultural Meta-Analysis Research on Studies Between 2006 and 2024
(Sage Publications Ltd, 2025) Karadag, Engin; Sertel, Gulsum
This meta-analysis combined various leadership approaches to examine the relationship between educational leadership and student achievement. The literature review encompassed 348 independent research articles/dissertations, with a sample group of 3,659,268 study subjects. Analyses conducted using a random-effects model revealed that educational leadership had a medium-level effect on student achievement. The impact of educational leadership on student achievement was found to be more substantial in collectivist cultures (e.g. Asia) than in individualistic cultures (e.g. the USA). Among the leadership theories, instructional leadership and leadership practices demonstrated the most significant effects. The influence of educational leadership on student achievement varies according to geographical continent, national cultural background, school level, and academic achievement. As anticipated, the impact of educational leadership practices on student achievement diminished over time during exceptional circumstances such as the COVID-19 pandemic. Given the observed effect of educational leadership on student achievement, future research should investigate the influence of leadership on other school components and stakeholders.
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Miniaturized Soft Growing Robots for Minimally Invasive Surgeries: Challenges and Opportunities
(Iop Publishing Ltd, 2025) Stroppa, Fabıo; Saraç Stroppa, Mine; Sarac, Mine; Mechatronics Engineering; Computer Engineering
Advancements in assistive robots have significantly transformed healthcare procedures in recent years. Clinical continuum robots have enhanced minimally invasive surgeries, offering benefits to patients such as reduced blood loss and a short recovery time. However, controlling these devices is difficult due to their limited accuracy in three-dimensional deflections and challenging localization, particularly in confined spaces like human internal organs. Consequently, there has been growing research interest in employing miniaturized soft growing robots, a promising alternative that provides enhanced flexibility and maneuverability. In this work, we extensively investigated issues concerning their designs and interactions with humans in clinical contexts. We took insights from the open challenges of the generic soft growing robots to examine implications for miniaturization, actuation, and biocompatibility. We proposed technological concepts and provided detailed discussions on leveraging existing technologies, such as smart sensors, haptic feedback, and artificial intelligence, to ensure the safe and efficient deployment of the robots. Finally, we offer an array of opinions from a biomedical engineering perspective that contributes to advancing research in this domain for future research to transition from conceptualization to practical clinical application of miniature soft growing robots.
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Reflective Thinking Predicts Disbelief in God Across 19 Countries
(Springer, 2025) Yılmaz, Onurcan; Yilmaz, Onurcan; Isler, Ozan; Terry, Jenny; Ross, Robert M.; Psychology
In the present study, we tested three hypotheses about relationships between reflective thinking, intuitive thinking (both measured using the Cognitive Reflection Test; CRT), and belief in God or gods (BiG) in university students across 19 culturally and geographically diverse countries (n = 7,771). In support of our first hypothesis, we found a negative relationship between reflective thinking and BiG; and in support of our second hypothesis, we found a positive relationship between intuitive thinking and BiG. Contrary to our third hypothesis, we found no evidence that measuring CRT prior to measuring BiG decreased BiG. Given that this is the first large cross-cultural test of these hypotheses to have a preregistered analysis plan, the first to hold education constant across countries, and the first to use both Bayesian and frequentist methods, these results considerably bolster the evidence in support of the first two hypotheses and against the third hypothesis.
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Performance Evaluation of Operators in the Telecommunication Industry
(Springer Heidelberg, 2025) Samanlıoğlu, Funda; Samanlioglu, Funda; Sert, Yusuf Burakhan; Yu, Hao; Simic, Vladimir; Industrial Engineering
One of the pioneer technological developments for society is telecommunication. Thus, related industry is proliferating worldwide, and this acceleration forces companies to constantly increase their service quality and product variety to attract new customers. Especially in regions with high growth, losing customers to competitors (s) causes considerable costs in the long run. Therefore, companies should constantly test their products and increase service quality to continue customer loyalty and help society communicate better. This study considers almost all scenarios a customer can encounter, from the first step of being a customer to canceling the contract. Three telecommunication service operators in Turkiye are reviewed based on 15 criteria. First, the "hesitant fuzzy Analytic Hierarchy Process" (HF-AHP) is employed to compute the importance weights of criteria. Then, the telecommunication companies are assessed via hesitant fuzzy "VIsekriterijumska optimizacija i KOmpromisno Resenje" (HF-VIKOR), considering these criteria's weights. HF-TOPSIS is also applied as a comparative analysis to validate the study's outcomes. Results provide valuable outcomes and policies for the decision-makers in the telecommunication industry.
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Exploring the Potential of Virtual Reality for Motor Skills Training in Children With Special Educational Needs: Perspectives From Experts From Five Countries
(Springer, 2025) Karadag, Engin; Aydogmus, Murat; Simsek, Irfan; Ciftci, S. Koza; Karkali, Katerina; Goumas, Efthymios; Bellas, Lidia Esther Godoy
Virtual reality (VR) has emerged as a promising tool for enhancing motor skill training in children with special educational needs (SEN). This qualitative case study explored the perspectives and experiences of experts regarding the integration of VR technology into motor skill training for children with SEN. This study investigated VR's perceived benefits, challenges, and adaptability of VR in supporting motor skill development in diverse educational and therapeutic settings. Semi-structured interviews were conducted with 20 purposively sampled experts including special education teachers and occupational therapists. A thematic analysis of the interview data revealed several key themes, including the potential of VR to provide engaging, personalized, and repetitive practice opportunities; the challenges of cost, accessibility, and teacher training; and the need for adaptability to accommodate various types of SEN. The participants emphasized the importance of collaboration between educators, therapists, and technology developers in creating effective VR interventions. These findings suggest that, while VR offers unique advantages for motor skill training, its successful implementation requires careful consideration of individual needs, resource availability, and professional development.
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A New a Flow-Based Approach for Enhancing Botnet Detection Using Convolutional Neural Network and Long Short-Term Memory
(Springer London Ltd, 2025) Jafari Navimipour, Nima; Heidari, Arash; Navimipour, Nima Jafari; Computer Engineering
Despite the growing research and development of botnet detection tools, an ever-increasing spread of botnets and their victims is being witnessed. Due to the frequent adaptation of botnets to evolving responses offered by host-based and network-based detection mechanisms, traditional methods are found to lack adequate defense against botnet threats. In this regard, the suggestion is made to employ flow-based detection methods and conduct behavioral analysis of network traffic. To enhance the performance of these approaches, this paper proposes utilizing a hybrid deep learning method that combines convolutional neural network (CNN) and long short-term memory (LSTM) methods. CNN efficiently extracts spatial features from network traffic, such as patterns in flow characteristics, while LSTM captures temporal dependencies critical to detecting sequential patterns in botnet behaviors. Experimental results reveal the effectiveness of the proposed CNN-LSTM method in classifying botnet traffic. In comparison with the results obtained by the leading method on the identical dataset, the proposed approach showcased noteworthy enhancements, including a 0.61% increase in precision, a 0.03% augmentation in accuracy, a 0.42% enhancement in the recall, a 0.51% improvement in the F1-score, and a 0.10% reduction in the false-positive rate. Moreover, the utilization of the CNN-LSTM framework exhibited robust overall performance and notable expeditiousness in the realm of botnet traffic identification. Additionally, we conducted an evaluation concerning the impact of three widely recognized adversarial attacks on the Information Security Centre of Excellence dataset and the Information Security and Object Technology dataset. The findings underscored the proposed method's propensity for delivering a promising performance in the face of these adversarial challenges.
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Fault Lines and Bytes: Cybersecurity Challenges Amid Turkiye's February 2023 Earthquakes
(IEEE Computer Soc, 2025) Bıçakcı, Ahmet Salih; Gucuyener Evren, Ayhan; Bicakci, Salih; Political Science and International Relations
Natural disasters can reveal vulnerabilities that might compromise the essential tenets of cybersecurity, commonly seen as secondary concerns. This article analyzes February 2023 earthquakes in T & uuml;rkiye as a case study to assess the cyberrisks that may emerge with natural disasters.
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Leaving No One Behind: Just Energy Transition of Fossil Fuel-Producing Countries
(Frontiers Media Sa, 2025) Ediger, Şevket Volkan; Industrial Engineering
The dual challenges of mitigating climate change and safeguarding our planet, alongside eradicating inequality and poverty to ensure prosperity for all humanity, represent the defining issues of the Anthropocene. Addressing these interconnected global crises requires inclusive, equitable, and fair actions, leaving no one behind. A just and equitable energy transition from fossil fuels to renewables is thus indispensable. To date, efforts have focused mainly on reducing fossil fuel consumption, particularly in fossil fuel-importing nations, often neglecting the unique circumstances of fossil fuel-exporting countries. This study hypothesis "achieving energy transition goals necessitates the comprehensive recognition, integration, and addressing of the specific needs and challenges faced by fossil fuel-exporting nations, ensuring their full and equitable participation in the transition process." Through a critical analysis of the rights and responsibilities of fossil fuel-exporting countries within the energy law framework, the study concludes that a successful phase-out of fossil fuels will remain unattainable unless mechanisms are established to safeguard these nations' economic and social welfare. Moreover, the incentives and support to reduce fossil fuel consumption must be extended to producing and transit countries to ensure a truly inclusive and sustainable global transition.
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Using Machine Learning To Identify Key Predictors of Maternal Success in Sheep for Improved Lamb Survival
(Frontiers Media Sa, 2025) Emsen, Ebru; Odevci, Bahadir Baran; Korkmaz, Muzeyyen Kutluca
This study investigates key physiological, genetic, and environmental factors influencing maternal success in sheep to enhance lamb survival and maternal quality. Using data from native and crossbred prolific ewes in a high-altitude, cold-climate region, we applied machine learning models to predict mothering scores based on dam characteristics, birth conditions, and lamb attributes. Pregnant ewes were monitored 24 hours per day, beginning three days before parturition, with minimal human intervention. Predictor variables included dam breed, body weight, age, litter size, lamb genotype, lambing season, time of lambing, parturition duration, and lambing assistance. Several machine learning algorithms, including Random Forest, Decision Trees, Logistic Regression, and Support Vector Machines (SVM), were evaluated for predictive accuracy. The Random Forest model achieved the highest accuracy (67.2%) and demonstrated the best overall performance with a 0.41 Kappa statistic and the lowest mean absolute error (0.59). Feature importance analysis identified dam weight at birth, parturition duration, and lamb birth weight as the strongest predictors of maternal success. The Decision Tree model highlighted time of lambing, lamb genotype, and lambing assistance as key decision points for classifying mothering ability. Further analysis revealed that shorter parturition durations (<= 38 min), unassisted lambing, and smaller litter sizes were associated with higher mothering scores. Breed-specific maternal differences were also observed, with crossbred prolific ewes exhibiting stronger maternal instincts. These findings provide actionable insights for precision livestock farming, emphasizing the importance of genetic selection, birthing management, and environmental monitoring to enhance maternal efficiency and lamb survival.
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Can Reflection Mitigate Covid-19 Vaccine Conspiracy Beliefs and Hesitancy
(Taylor & Francis Ltd, 2025) Yılmaz, Onurcan; Sarıbay, Selahattin Adil; Ozman, Nagihan; Yilmaz, Onurcan; Isler, Ozan; Saribay, S. Adil; Psychology
Objective design:Periods of social turmoil, such as the COVID-19 pandemic, tend to amplify conspiracy beliefs, evidenced by increased vaccine hesitancy. Despite this trend, effective interventions targeting vaccine-related conspiracy beliefs remain scarce, partly due to underexplored cognitive processes. Three competing theoretical accounts offer differing predictions about the role of reflective thinking in supporting conspiracy beliefs: the Motivated Reasoning Account suggests reflection strengthens commitment to pre-existing attitudes; the Reflective Reasoning Account posits that reflection enhances belief accuracy; and the Reflective Doubt Account proposes reflection fosters general scepticism. Main outcome measures:Utilising open science practices and a validated technique to activate reflection, we conducted an experimental investigation with a diverse sample (N = 1483) segmented by vaccine attitudes. We investigated the impact of reflection on specific and generic COVID-19 conspiracy beliefs and vaccine-support behaviours across pro-vaccine, neutral, and vaccine-hesitant groups, while examining the moderating effects of scientific literacy, intellectual humility, and actively open-minded thinking. Results:The confirmatory analysis provided no direct support for the theoretical predictions. However, findings indicated that intellectual humility significantly moderated the effect of reflection, enhancing vaccine-support behaviour among participants with high intellectual humility, highlighting the complex interplay of cognitive style and prior attitudes in shaping responses to conspiracy beliefs and vaccine-support actions. Conclusion:The study highlights that while reflective thinking alone did not directly influence vaccine support behavior, its positive effect emerged among individuals with higher intellectual humility, emphasizing the importance of individual differences in shaping belief-related outcomes.
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The Nexus Between Migration and Environmental Degradation Based on Fundamental Climate Variables and Extreme Climate Indices for the Mena Domain
(Elsevier, 2025) Ucal, Meltem; Demiralay, Zekican; Ucal, Meltem; Kurnaz, M. Levent; Economics
Environmental migration has recently become primary source of population growth and environmental degradation from extreme events has created the environmental refugee concept with a variety of manners affecting lives. For understanding of the environmental degradation impact on migration, a hybrid approach (regional climate modelling, RegCM4.4 and statistical modelling, ordered logit) has been applied for 65 countries in the Middle East and North Africa (MENA) for the periods of 2021-2050 and 2051-2080. It is aimed to examine how climate change affect migration by applying fundamental climate variables (i.e., maximum temperature, minimum temperature, and precipitation) and the control variables (i.e., the hot days, the tropical nights, and the dry days) in the MENA. While key findings indicate an increase in the minimum temperatures (Tmin) in future in all populous cities, the water amount may further decrease in the mid-latitude and Mediterranean with temperate climates due to precipitation change. While it may pose a high risk in the regions having experienced extreme temperatures e.g., tropical nights (Tn), it may further adversely affect ones not having experienced extremes. Considering statistically significant positive relationship between Tmin, and net migration rate (NMIG), and negative relationship between precipitation and NMIG, it may encourage migration to cooler regions.
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Management Frameworks and Management System Standards in the Context of Integration and Unification: a Review and Classification of Core Building Blocks for Consilience
(Mdpi, 2025) Aydın, Mehmet Nafiz; Aydin, Mehmet Nafiz; Management Information Systems
Management frameworks (MFs) and management system standards (MSSs) are essential tools for improving organisational management practises. They inherently include a range of fundamental building blocks that facilitate the creation of structured management systems. However, these building blocks have not yet been holistically identified or unified into a consilient taxonomy. Addressing this research gap, this study conducts a comprehensive review of 415 academic papers and theses, 47 ISO MSSs, and 79 MFs sourced from scholarly databases and official publications. Utilising a novel heuristic methodology, this study integrates a literature review, clustering, text mining analytics, and an expert review to develop a Consilient Building Block Taxonomy (CBBT). This taxonomy categorises the foundational components of MFs and MSSs, presenting them as a structured framework that unifies these elements into a cohesive system. By providing a systematic classification, the CBBT serves as a foundation for the development of a Unified Singular Management System (USMS). The proposed taxonomy enhances operational coherence, strategic alignment, and efficiency by consolidating the core aspects of diverse management systems. This study concludes with insights into how the CBBT can be leveraged to achieve integration and unification in management practises, offering significant potential for both research and practical applications.
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Broad Observational Perspectives Achieved by the Accreting White Dwarf Sciences in the Xmm-Newton and Chandra Eras
(Mdpi, 2025) Balman, Solen; Orio, Marina; Luna, Gerardo J. M.
Accreting white dwarf binaries (AWDs) comprise cataclysmic variables (CVs), symbiotics, AM CVns, and other related systems that host a primary white dwarf (WD) accreting from a main sequence or evolved companion star. AWDs are a product of close binary evolution; thus, they are important for understanding the evolution and population of X-ray binaries in the Milky Way and other galaxies. AWDs are essential for studying astrophysical plasmas under different conditions along with accretion physics and processes, transient events, matter ejection and outflows, compact binary evolution, mergers, angular momentum loss mechanisms, and nuclear processes leading to explosions. AWDs are also closely related to other objects in the late stages of stellar evolution, with other accreting objects in compact binaries, and even share common phenomena with young stellar objects, active galactic nuclei, quasars, and supernova remnants. As X-ray astronomy came to a climax with the start of the Chandra and XMM-Newton missions owing to their unprecedented instrumentation, new excellent imaging capabilities, good time resolution, and X-ray grating technologies allowed immense advancement in many aspects of astronomy and astrophysics. In this review, we lay out a panorama of developments on the study of AWDs that have been accomplished and have been made possible by these two observatories; we summarize the key observational achievements and the challenges ahead.
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Processor Design and Application of Futuristic
(Univ Nis, 2025) Jafari Navimipour, Nima; Pathak, Nirupma; Bhoi, Bandan Kumar; Ahmadpour, Seyed-Sajad; Kassa, Sankit R.; Navimipour, Nima Jafari; Computer Engineering
Many devices consist of low-power processor. Quantum-dot-cellular-automata (QCA) based processor designs provide enhanced performance compared with conventional metal-oxide-semiconductor (MOS) based processors. Nanocomputing-based processors are often energy-efficient. We have developed Nanotechnology QCA-based different subcomponents of processor such as 2-to-4 decoder, 3-to-8 decoder, Delay Flip-flop (D-FF), and sequence counter. A potential energy proof has been measured in the 2-to-4 decoder design. The synthesis approach algorithm has been presented for all designs. Further, the potential energy calculation results show for 2-to-4 decoder. According to the synthesis results 2-to-4 decoder has improved 82.3% cell count, 86% area, and 85% latency over previous work. Comparing the primitive results with the prior one, results improved by 64% and 76% in terms of cell count and area in the design of the 3-to-8 decoder. Among the different components of the processor is D-FF, which has an improvement of 66.37% in cell counts and 62.5% in area over the prior design. Primitive results have improved, including latency, cell count, and area, showing the proposed processor design is comparable to lowpower devices and high speed. In terms of balance power, the proposed subcomponent of the processor will benefit low power device.
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Strategic Tour Operator Selection in the Tourism Sector Using a Quantum Picture Fuzzy Rough Set-Based Multi-Criteria Decision-Making Approach
(Pergamon-elsevier Science Ltd, 2025) Görçün, Ömer Faruk; Pamucar, Dragan; Dincer, Hasan; Yuksel, Serhat; Iyigun, Ismail; Simic, Vladimir; Business Administration
Tour operator selection is critical for ensuring high-quality services, customer satisfaction, and sustainable tourism development. However, traditional decision-making methods often fail to address the complexities and uncertainties involved in this process. This study introduces a robust decision-making framework that integrates quantum picture fuzzy rough sets (QPFR) with advanced Multi-Criteria Decision-Making (MCDM) techniques to enhance the evaluation and selection of tour operators. The methodology incorporates QPFR, the Decision-Making Trial and Evaluation Laboratory (DEMATEL), and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to assess and rank seven prominent tour operators in the Turkish tourism sector. The evaluation is based on 16 comprehensive criteria: quality, safety, environmental impact, authenticity, and economic contribution. Expert inputs and artificial intelligence techniques were utilized to ensure the model's reliability and accuracy. The findings reveal that the proposed model effectively minimizes uncertainties, provides consistent rankings, and highlights the critical importance of specific criteria in decision-making. Sensitivity analysis confirms the robustness of the results, demonstrating the model's applicability to dynamic and complex decision-making contexts. This study offers theoretical contributions and practical insights for decision-makers, emphasizing the value of integrating advanced computational methods to support sustainable tourism development.
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Investigation of the Potential Effect of Complement 5 on Transplantation Outcome by Bioinformatics Tools
(Iranian Soc Nephrolgy, 2025) Oguz, Suleyman Rustu; Kivanc, Demet; Ozdilli, Kursat; Karadeniz, Sedat; Kluge, Ekin Ece Gurer; Ciftci, Hayriye Senturk
Introduction. Activation of the complement system following transplantation may result in allograft rejection. Our study aimed to evaluate the potential relationship between factors affecting kidney transplant success and complement 5 (C5) using bioinformatic tools. Methods. GenCards and Genemania were used to provide the genetic functional information belonging to the C5 gene, and genomic browsers of STRING, UCSC, KEGG were used to reveal interactions with other genes and various pathways. MiRDB was used to specify the miRNAs that were associated with the C5 gene. The UniProt database was used to determine the tissues that expressed the C5 gene using protein-protein interactions. Results. In the bioinformatic analyses performed, high levels of C5 gene expression were found in the naiive kidney. Twenty-five genes were found to be strongly associated with C5. Fifty-four miRNAs targeting the C5 gene were specified. The C5 gene was found to be involved in biologic processes such as complement activation (FDR = 6.46e-22), complement binding (FDR = 2.20e-06), cytolysis (FDR = 4.82e-14), regulation of complement activation (FDR = 4.08e24), positive regulation of vascular endothelial growth factor production (FDR = 0.0430), regulation of macrophage chemotaxis (FDR = 0.0447), activation of the immune response (FDR = 1.26e13), leukocyte-mediated immunity (FDR = 1.41e-09), innate immune response (FDR = 3.05e-09), allograft rejection (FDR = 2.40e-12), oxidative injury response (FDR = 0.00016), and trigerring of the beginning of the complement cascade (FDR = 0.0244). Conclusions. The data obtained in this study will be used to guide future experimental investigations in the field of transplantation, and these data will give physicians with insight into allograft status following transplantation.
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A Hybrid Rough Aggregation Approach for the Selection of Artificial Intelligence-Based Industrial Cleaning Robots Used in Public Spaces From the Perspective of Urban Waste Management
(Pergamon-elsevier Science Ltd, 2025) Görçün, Ömer Faruk; Saha, Abhijit; Kumar, Pydimarri Venkata Ravi; Debnath, Bijoy Krishna; Business Administration
Waste management is becoming increasingly complex and challenging, especially in megacities with large populations. Unlike the past, when urban waste was simply collected and disposed of, modern waste management requires careful planning and execution of collection, separation, recycling, and reuse processes. Effective management of this complex system now needs more than just human effort. Integrating artificial intelligence (AI)-based systems into waste management can enhance waste reduction, reuse, and recycling effectiveness and efficiency. Selecting suitable AI-based cleaning robots (AI-ICR) for crowded public spaces, such as stations, train stations, and airports, poses complex decision-making challenges. The primary challenge is the novelty of the technology, which leads to uncertainties in selecting AI-ICRs. To address this challenge, we have developed a decision-making approach based on rough Archimedean-Dombi partitioned aggregation. This approach, termed "rough Archimedean-Dombi partitioned aggregation," combines the flexibility of Archimedean operators, the smoothness of Dombi operators, and the structured decomposition of Partitioned operators. This model is mainly chosen for its ability to handle the uncertainty and complexity inherent in multiple criteria decision-making (MCDM) processes. Leveraging rough numbers provides a robust framework for evaluating AI-ICRs under uncertain conditions. The main advantage of this model is its robustness, consistency, stability, and ability to handle complex uncertainties. We applied the proposed model to assess four AI-ICR alternatives identified through extensive research. We evaluated these alternatives using eighteen criteria established through comprehensive field studies. Based on the results, "Recycling cost (B12)" emerged as the most crucial criterion for selecting AIICRs. Additionally, the research identifies the SD45 manufactured by Peppermint Robotics Co. as the optimal AIICR candidate. Finally, the sensitivity and benchmark analyses to validate the proposed model confirm its robustness, consistency, and reliability.
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A Nano-Design of a Quantum-Based Arithmetic and Logic Unit for Enhancing the Efficiency of the Future Iot Applications
(Aip Publishing, 2025) Jafari Navimipour, Nima; Zaker, Maryam; Navimipour, Nima Jafari; Misra, Neeraj Kumar; Zohaib, Muhammad; Kassa, Sankit; Hakimi, Musawer; Computer Engineering
The Internet of Things (IoT) is an infrastructure of interconnected devices that gather, monitor, analyze, and distribute data. IoT is an inevitable technology for smart city infrastructure to ensure seamless communication across multiple nodes. IoT, with its ubiquitous application in every sector, ranging from health-care to transportation, energy, education, and agriculture, comes with serious challenges as well. Among the most significant ones is security since the majority of IoT devices do not encrypt normal data transmissions, making it easier for the network to breach and leak data. Traditional technologies such as CMOS and VLSI have the added disadvantage of consuming high energy, further creating avenues for security threats for IoT systems. To counter such problems, we require a new solution to replace traditional technologies with a secure IoT. In contrast to traditional solutions, quantum-based approaches offer promising solutions by significantly reducing the energy footprint of IoT systems. Quantum-dot Cellular Automata (QCA) is one such approach and is an advanced nano-technology that exploits quantum principles to achieve complex computations with the advantages of high speed, less occupied area, and low power consumption. By reducing the energy requirements to a minimum, QCA technology makes IoT devices secure. This paper presents a QCA-based Arithmetic Logic Unit (ALU) as a solution to IoT security problems. The proposed ALU includes more than 12 logical and arithmetic operations and is designed using majority gates, XOR gates, multiplexers, and full adders. The proposed architecture, simulated in QCADesigner 2.0.3, achieves an improvement of 60.45% and 66.66% in cell count and total occupied area, respectively, compared to the best of the existing designs, proving to be effective and efficient.

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