New aggregation operators (AOs) for interval-valued intuitionistic fuzzy sets (IVIFS) have been developed, offering advancements in multi-attribute group decision-making (MAGDM). IVIFS employs intervals for membership and non-membership grades, providing a robust framework to handle uncertainties inherent in real-world scenarios. This study introduces operational laws for interval-valued intuitionistic fuzzy values (IVIFVs), formulated on the Frank T-norm and T-conorm, and presents a generalization of the Maclaurin symmetric mean (MSM) operator tailored for these values. Named the interval-valued intuitionistic fuzzy Frank weighted MSM (IVIFFWMSM) and interval-valued intuitionistic fuzzy Frank MSM (IVIFFMSM), these operators incorporate new operational principles that enhance the aggregation process. The effectiveness of these operators is demonstrated through their application to a MAGDM problem, where they are compared with existing operators. This approach not only enriches the theoretical landscape of fuzzy decision-making models but also provides practical insights into the optimization of market risk.
Through the deployment of bibliometric techniques and network visualizations, this analysis synthesizes the evolution and trajectories of autonomous driving research from 2002 to May 2024, as captured in the Scopus database encompassing 342 scholarly documents. This study was conducted to delineate the developmental contours, thematic emphases, and the expansive growth trajectory within this field, particularly noting a surge in scholarly outputs since 2017. Such growth has been primarily facilitated by breakthroughs in artificial intelligence and sensor technologies, along with burgeoning interdisciplinary collaborations and escalating academic and industrial investments. A meticulous examination of publication trends, document types, subject areas, and geographic distributions elucidates the multidisciplinary and international nature of this burgeoning field. Key thematic clusters identified—comprising foundational technologies, environmental sustainability, safety measures, regulatory frameworks, user experience, connectivity, and technological innovations—underscore the prevailing research directions and emerging focal areas poised to shape future autonomous mobility solutions. Notably, increased emphasis on environmental sustainability and regulatory frameworks has been observed, highlighting their critical roles in advancing and integrating autonomous driving systems. This study provides pivotal insights for researchers, policymakers, and industry stakeholders, fostering a nuanced understanding of the field’s dynamics and facilitating strategic alignments and innovations in autonomous mobility. The emergent research domains and collaborative networks revealed herein not only map the current landscape but also guide future scholarly endeavors in autonomous driving systems globally.
A bibliometric analysis has been conducted to contextualize the research on economic development in urban areas and clarify its scholarly purpose. Data were retrieved from the Scopus database, covering publications from 1973 to May 2024, using the keywords "Economic Development" and "City," resulting in the identification of 475 documents for analysis. The findings reveal longitudinal trends in the literature on economic development in cities, demonstrating a significant upward trend since 2000, interspersed with fluctuations. The study examines various aspects, including publication types, subject areas, leading publishing platforms, global perspectives, organizational contributions, prominent authors, and thematic regions, providing a comprehensive overview of the scholarly engagement with this domain. The dynamic interplay between academic inquiry and contextual factors is underscored, advocating for continued scholarly vigilance and adaptive methodologies to effectively navigate the evolving discourse on urban economic development. The insights gained contribute to a nuanced understanding of the interdisciplinary nature of urban economic development research, highlighting the importance of diverse publication formats, interdisciplinary collaboration, and international knowledge exchange in addressing complex urban challenges and fostering sustainable urban futures.
Computer-Aided Design (CAD) is employed extensively to facilitate design processes through software tools, serving as an indispensable component in Reverse Engineering (RE) across various sectors. This study elucidates the integration of RE and CAD in constructing generic product models for the manufacturing industry, particularly through the enhancement of the Feature-Based Design (FBD) approach. The Characteristic Product Features (CPF) methodology, pivotal in this research, enhances FBD by enabling the creation of parametrically defined generic features. Such features encapsulate a range of parameters including geometrical dimensions, topological constraints, and requirements for material properties and functionality, all dictated by the parametric model established. The methodology affords mechanical engineers enhanced capabilities to devise specific or customized manufacturing processes, applicable in domains spanning CAD, Computer-Aided Manufacturing (CAM), and Computer-Aided Engineering (CAE). The practical application of CPF within CAD is exemplified through the development of a three-dimensional geometrical model of an extruder screw utilized in polymer extrusion, illustrating the potential for tailored process innovation in manufacturing.
In this study, an economical prototype of a uniaxial shake table, named the Eastern Mediterranean University (EMU) shake table, was developed using an Arduino platform for the simulation of sinusoidal waves and scaled earthquake data. The table incorporates a ball-screw mechanism actuated by a stepper motor. Simulations were conducted using sinusoidal signals and earthquake data for three distinct seismic events, recorded at discrete timestamps. The performance of the shake table was assessed by analyzing the discrepancies between the input signals and the table's outputs.In sinusoidal mode, a feedforward gain was computed to achieve the desired output amplitude values. Furthermore, a decreasing trend in the error between input and output acceleration values was observed. The table, without any payload, achieved an acceleration of 0.8 g at a frequency of 14.5 Hz and an amplitude of 1 mm. However, the effectiveness of earthquake simulations was constrained by the storage capacity of the Arduino Uno and the motor's performance capacity. Iterative methods were necessary for each earthquake simulation to determine the minimal timestep size that the motor could optimally handle. The methodology for simulating earthquakes was elaborated, identifying limitations and suggesting areas for future enhancement. The major constraints of the project were cost, time, and resource availability.
In the quest to secure energy supply and mitigate dependence on imported fossil fuels, nations are diversifying into renewable energy sources (RES). This study investigates the impact of renewable electricity production on economic growth, alongside the interplay with research and development (R&D) expenditures, through a comparative lens focusing on Norway and Brazil—both pioneers in the renewable energy arena. Analysis incorporates per capita R&D expenditures to gauge the nexus between renewable energy initiatives and R&D investment, employing data spanning from 2003 to 2014. The investigation reveals a notable divergence between the two nations. In Norway, no significant link was identified between the volume of renewable energy produced and per capita R&D expenditures. Nonetheless, a causal connection between economic growth and R&D investment was observed, with a robust correlation suggesting a profound influence of economic expansion on R&D activities. Contrarily, Brazil's scenario delineates a unidirectional causal relationship where economic growth positively influences the renewable energy sector, with no discernible association between R&D expenditures per capita and economic growth. These findings underscore the variegated impacts of renewable energy policies and R&D investments on economic dynamics within the context of Norway and Brazil, highlighting the necessity for tailored approaches in leveraging renewable energy for sustainable development.
In the pursuit of sustainable urban development, the implementation of cleaner propulsion systems in public transportation emerges as a critical strategy to reduce urban pollution and emissions. This study focuses on the City of Niš, where conventional propulsion vehicles, predominantly buses, contribute significantly to environmental degradation. The necessity to adopt alternative propulsion systems is underscored by the myriad of limitations and uncertainties that accompany such a transition. To address this complexity, the criteria importance through intercriteria correlation (CRITIC) method was employed to derive weight coefficients, while the evaluation based on distance from average solution (EDAS) method was utilized to select optimal propulsion systems. These methodologies facilitated a comprehensive evaluation of alternatives, including buses, electric trolleybuses, and trams, for both city and suburban public transport. The integration of these multi-criteria decision-making techniques enabled a systematic analysis of each alternative against established criteria, thereby assisting in the identification of the most advantageous propulsion systems. This approach not only aids in making informed decisions that align with sustainability objectives but also contributes significantly to mitigating the environmental impact of urban transport. The findings from this study provide a foundational framework that supports decision-makers in the strategic implementation of environmentally sustainable transport solutions in urban settings.
The rapid advancement of technology has correspondingly escalated the sophistication of cyber threats. In response, the integration of artificial intelligence (AI) into cybersecurity (CS) frameworks has been recognized as a crucial strategy to bolster defenses against these evolving challenges. This analysis scrutinizes the effects of AI implementation on CS effectiveness, focusing on a case study involving company XYZ's adoption of an AI-driven threat detection system. The evaluation centers on several pivotal metrics, including False Positive Rate (FPR), Detection Accuracy (DA), Mean Time to Detect (MTTD), and Operational Efficiency (OE). Findings from this study illustrate a marked reduction in false positives, enhanced DA, and more streamlined security operations. The integration of AI has demonstrably fortified CS resilience and expedited incident response capabilities. Such improvements not only underscore the potential of AI-driven solutions to significantly enhance CS measures but also highlight their necessity in safeguarding digital assets within a continuously evolving threat landscape. The implications of these findings are profound, suggesting that leveraging AI technologies is imperative for effectively mitigating cyber threats and ensuring robust digital security in contemporary settings.
Indonesia, known for its abundant renewable resources, especially solar energy, presents a substantial potential for developing solar-powered solutions to meet its increasing electricity demands. This study explores the feasibility of a Solar Power Plant (PLTS) as the energy source for a personal Electric Vehicle Charging Station (SPKL), facilitating the transition from fuel-based to electric vehicles. Using a simulation-based approach, a hypothetical daily electricity load of 12,711 kW was considered. The simulations indicate that an On-Grid PLTS is the most economically viable option, offering significant investment returns. The annual energy output of the PLTS was calculated to be 30,767 kWh. Financial projections suggest a substantial profit by the 25th year, amounting to IDR 374,450,204.39. This research underscores the strategic importance of integrating hybrid technologies in developing renewable energy infrastructures, particularly in regions like Indonesia, where solar irradiance is high. The findings advocate for broader implementation of such systems aligned with national energy sustainability and economic efficiency goals.
This study investigates the application of Multi-Criteria Decision Analysis (MCDA) methods to the classification of research papers within a Systematic Literature Review (SLR). Distinctions are drawn between compensatory and non-compensatory MCDA approaches, which, despite their distinctiveness, have often been applied interchangeably, leading to a need for clarification in their usage. To address this, the methods of Entropy Weight Method (EWM), Analytic Hierarchy Process (AHP), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) were utilized to determine the parameters for ranking papers within an SLR portfolio. The source of this ranking comprised publications from three major databases: Scopus, ScienceDirect, and Web of Science. From an initial yield of 267 articles, a final portfolio of 90 articles was established, highlighting not only the compensatory and non-compensatory classifications but also identifying methods that incorporate features of both. This nuanced categorization reveals the complexity and necessity of selecting an appropriate MCDA method based on the dataset characteristics, which may exhibit attributes of both approaches. The analysis further illuminated the geographical distribution of publications, leading contributors, thematic areas, and the prevalence of specific MCDA methods. This study underscores the importance of methodological precision in the application of MCDA to systematic reviews, providing a refined framework for evaluating academic literature.
Open Access
