In geographically isolated regions, where infrastructure limitations and remote locations pose significant challenges, a mobile application has been developed to facilitate an efficient emergency response system. This system, designed to bridge the gap in emergency support, employs a multi-faceted strategy that combines human expertise with advanced machine learning (ML) technologies. Upon activation through the application, a coordinated mechanism is triggered, dispatching local mechanics equipped with the necessary tools, resources, vehicles, and spare parts to the site of the emergency. This immediate on-site assistance is essential for addressing mechanical failures and ensuring timely support for individuals in remote areas.At the heart of the application lies a sophisticated ML model, trained on an extensive dataset comprising a wide array of emergencies likely to occur in rural settings. This model, characterized by its convolutional neural network (CNN) architecture and optimized for mobile deployment through TensorFlow Lite (TFLite), demonstrates an impressive diagnostic accuracy rate of 98%. Such precision significantly enhances the application’s capacity to diagnose issues accurately, prioritize response efforts, and optimize resource allocation.Moreover, the application leverages data-driven insights not only to streamline the emergency response process but also to facilitate predictive maintenance. By continuously learning from incoming data, the ML model can predict potential problems and suggest preventative measures to users, thereby minimizing the likelihood of future breakdowns. This predictive capability underscores the application’s role in promoting resilience within rural communities.Community engagement is further encouraged through the inclusion of local mechanics in the emergency response network. This initiative not only expands the pool of available skilled professionals but also fosters a sense of community solidarity, crucial for enhancing the system’s overall effectiveness.In summary, the development of this mobile application represents a significant advancement in emergency assistance for rural communities. By integrating real-time response capabilities with sophisticated ML models, the system not only addresses the immediate challenges of emergency support in remote areas but also contributes to the creation of a more resilient and interconnected community fabric.
This investigation delves into the noise attenuation capabilities of an innovatively designed muffler, which integrates additional piping and perforation to augment sound reflection. The enhanced muffler's design was rigorously simulated using the Helmholtz equation through the application of COMSOL Multiphysics software, aiming to delineate its acoustic performance relative to conventional models. The analysis underscored the superior efficacy of the optimized model in elevating transmission loss, diminishing acoustic pressure, and concurrently attenuating noise and frequency levels. A comparative evaluation of the transmission loss between the traditional and the novel muffler revealed a significant amelioration in the latter, highlighting its advanced noise reduction capabilities. The study further illuminated that exhaust pressure and back pressure contribute to acoustic wave generation, prompting the optimization of the muffler design to mitigate pressure, thereby circumventing potential damage. Notably, despite the analytical complexity, the construction of the proposed muffler remains straightforward, representing a pivotal advantage. This research contributes to the acoustic engineering field by presenting a muffler design that not only significantly reduces noise pollution but also demonstrates an ease of construction, making it a viable solution for widespread application. The findings advocate for the muffler's potential in enhancing acoustic comfort and environmental compliance in automotive and industrial settings.
In the context of traffic safety, the interplay between the road environment and the human factor emerges as a critical determinant of the severity of road crash consequences. This study was designed to explore the perceptions of professional drivers regarding the road environment, with a particular focus on the elements that either contribute to or mitigate safety risks. A comprehensive survey was conducted, wherein 118 professional drivers from the Republic of Serbia were asked to rate photographs depicting various road environments in terms of safety. The investigation aimed to elucidate the extent to which these drivers recognize and assess road hazards, as well as to examine potential variations in their evaluations based on demographic characteristics. The findings underscore the significant impact of the road environment on traffic safety, particularly highlighting the role of solid obstacles such as trees, pillars, and masonry objects. When vehicles veer off the road, collisions with these obstacles frequently result in exacerbated outcomes of road crashes. The methodology employed in this research involved a quantitative analysis of the survey responses, ensuring a systematic evaluation of the drivers' perceptions. The study contributes to the existing body of knowledge by offering insights into the evaluative processes of professional drivers concerning the road environment, thereby informing strategies aimed at enhancing driver safety.
Glued laminated timber (glulam), a composite material fabricated by bonding multiple wood layers, is engineered to support specific loads, offering reduced product variability and diminished sensitivity to inherent wood characteristics, such as knots. This technology facilitates a wide array of architectural designs, rendering it a popular choice for load-bearing elements across diverse construction projects, including residential structures, storage facilities, and pedestrian overpasses. Over time, exposure to various environmental conditions leads to the degradation of these structural components, necessitating periodic reinforcement to maintain their strength properties. Recent advancements have seen the adoption of fiber-reinforced polymer (FRP) for the reinforcement of columns and beams, a departure from traditional strengthening methods. This study focuses on the connection of column-beam joints using an array of steel fasteners, subsequently reinforced with FRP. Rotational tests were conducted on these fabricated connections, followed by a comprehensive analysis using the finite element method (FEM). Results indicate that connections reinforced with FRP exhibit a significant enhancement in load-carrying capacity, energy dissipation, and stiffness compared to their unreinforced counterparts. Specifically, the load-carrying capacity showed an increase of 25-39%, energy dissipation capacity augmented by 64-69%, and stiffness values rose by 2-7%. These findings underscore the efficacy of FRP reinforcement in improving the structural integrity and performance of glulam column-beam connections, offering valuable insights for the design and renovation of wood-based construction elements.
This study introduces novel aggregation operators aimed at enhancing data analysis and decision-making processes through the induction of confidence levels into complex polytopic fuzzy systems. Specifically, the induced confidence complex polytopic fuzzy ordered weighted averaging aggregation (ICCPoFOWAA) operator and the induced confidence complex polytopic fuzzy hybrid averaging aggregation (ICCPoFHAA) operator are proposed. By integrating confidence levels into the aggregation process, these operators facilitate a more nuanced interpretation of fuzzy data, allowing for the incorporation of expert judgment and uncertainty in decision-making frameworks. A practical demonstration is provided to validate the efficacy and proficiency of these innovative techniques. Through a comprehensive example, the ability of the ICCPoFOWAA and ICCPoFHAA operators to enhance decision-making accuracy and reliability is substantiated, showcasing their potential as powerful tools in the realms of data analysis and complex decision-making scenarios. The incorporation of confidence levels into fuzzy aggregation processes represents a significant advancement in the field, offering a sophisticated approach to handling uncertainty and expert opinions in multi-criteria decision-making problems. This work not only introduces groundbreaking aggregation operators but also sets a new standard for research in fuzzy decision-making, underscoring the importance of confidence levels in the analytical process.
The Spherical Fuzzy Set (SFS) framework extends the Picture Fuzzy Set (PFS) concept, offering enhanced precision in handling data characterized by conflict and uncertainty. Furthermore, similarity measures (SMs) are crucial for determining the extent of resemblance between pairs of fuzzy values. While existing SMs evaluate similarity by measuring the distance between values, they sometimes yield results that are illogical or unreasonable, due to certain properties and operational complexities. To address these anomalies, this paper introduces a parametric similarity measure based on three adjustable parameters ($\alpha_1, \alpha_2, \alpha_3$), allowing decision-makers to fine-tune the measure to suit various decision-making styles. This paper also scrutinizes existing SMs from a mathematical standpoint and demonstrates the efficacy of the proposed SM through mathematical modeling. Finally, we apply the proposed SM to tackle Multi-Attribute Decision-Making (MADM) problems. Comparative analysis reveals that our proposed SM outperforms certain existing SMs in the context of SFS-based applications.
High-entropy alloy (HEA) is currently regarded as materials with the most superior comprehensive properties, possessing capabilities not found in traditional alloys. This is particularly attributed to the characteristic presence of multiple principal elements, endowing the alloys with exceptional performance across various aspects, thus becoming a focal point of both current and future research endeavors. The performance of HEA is derived from phase transition. This review summarizes the intrinsic phase transition of HEA itself and the enhancement of HEA performance through the addition of particulate phases. Starting from the definition of HEA, the common definitions are introduced, leading to the design principles of HEA and the prediction of solid solution phases. The influence of different elements on the structural changes of HEA solid solution phases is explained through lattice distortion phase transition and segregation phase transition methods. The patterns of phase transition induced by large atomic elements are summarized, and the development process of segregation phase transition by small atomic elements is presented, offering references for future research on HEA. Furthermore, the concept of solubility of elements in HEA is introduced, based on the phase transition caused by large and small atomic elements, providing a more accurate basis for the design and preparation of HEA. The common hard particles used to enhance the performance of HEA are discussed, revealing how direct addition of particles can lead to decomposition and the uncertainty of the effects of elements on HEA performance. The significance of encapsulation techniques in enhancing the performance of high-quality HEA is proposed.
In the field of graph theory, the exploration of connectivity patterns within various graph families is paramount. This study is dedicated to the examination of the neighbourhood degree-based topological index, a quantitative measure devised to elucidate the structural complexities inherent in diverse graph families. An initial overview of existing topological indices sets the stage for the introduction of the mathematical formulation and theoretical underpinnings of the neighbourhood degree-based index. Through meticulous analysis, the efficacy of this index in delineating unique connectivity patterns and structural characteristics across graph families is demonstrated. The utility of the neighbourhood degree-based index extends beyond theoretical graph theory, finding applicability in network science, chemistry, and social network analysis, thereby underscoring its interdisciplinary relevance. By offering a novel perspective on topological indices and their role in deciphering complex network structures, this research makes a significant contribution to the advancement of graph theory. The findings not only underscore the versatility of the neighbourhood degree-based topological index but also highlight its potential as a tool for understanding connectivity patterns in a wide array of contexts. This comprehensive analysis not only enriches the theoretical landscape of graph descriptors but also paves the way for practical applications in various scientific domains, illustrating the profound impact of graph theoretical studies on understanding the intricacies of networked systems.
The integration of Artificial Intelligence (AI) into sustainable energy management presents a transformative opportunity to elevate the sustainability, reliability, and efficiency of energy systems. This article conducts an exhaustive analysis of the critical aspects concerning the AI-sustainable energy nexus, encompassing the challenges in technological integration and the facilitation of intelligent decision-making processes pivotal for sustainable energy frameworks. It is demonstrated that AI applications, ranging from optimization algorithms to predictive analytics, possess a revolutionary capacity to bolster intelligent decision-making in sustainable energy. However, this integration is not without its challenges, which span technological complexities and socio-economic impacts. The article underscores the imperative for deploying AI in a manner that is transparent, equitable, and inclusive. Best practices and solutions are proposed to navigate these challenges effectively. Additionally, the discourse extends to recent advancements in AI, including edge computing, quantum computing, and explainable AI, offering insights into the evolving landscape of sustainable energy. Future research directions are delineated, emphasizing the importance of enhancing explainability, mitigating bias, advancing privacy-preserving techniques, examining socio-economic ramifications, exploring models of human-AI collaboration, fortifying security measures, and evaluating the impact of emerging technologies. This comprehensive analysis aims to inform academics, practitioners, and policymakers, guiding the creation of a resilient and sustainable energy future.
The exploration of stakeholder perceptions concerning the elements and values underpinning the cooperative concept in Malta forms the core objective of this investigation. Employing semi-structured interviews, primary data was gathered from a diverse group of participants, including thirteen representatives from cooperatives, four from cooperative institutional bodies, and five experts within the cooperative field. The analysis reveals a notable deficiency among Maltese cooperative stakeholders in comprehending the foundational elements and values of the cooperative model. This lack of understanding is attributed to ongoing challenges such as persistent misconceptions regarding the adaptability of cooperatives to social objectives, gaps in pertinent education and training, and inadequate promotion of the cooperative paradigm. The findings suggest a critical need for stakeholders to accord greater priority to the socially relevant components of cooperatives—those designed to be integral to the concept—beyond the mere generation of annual financial surpluses. Such a shift in focus is posited as essential for fostering a deeper appreciation and application of cooperative values, benefiting not only individual entities but the broader cooperative movement. Moreover, the insights gleaned from the Maltese context offer valuable lessons for cooperative movements in other small European states, highlighting the universal applicability and potential of cooperative principles for economic development and social cohesion. This study contributes to the dialogue on cooperative development by elucidating the gaps in understanding and application of cooperative values among stakeholders, thereby offering a foundation for targeted educational and promotional strategies to enhance the cooperative model's implementation and perception.
Inducing variables are the parameters or conditions that influence the membership value of an element in a fuzzy set. These variables are often linguistic in nature and represent qualitative aspects of the problem. Thus, the objective of this paper is introduce some aggregation operators based on inducing variable, such as induced complex Polytopic fuzzy ordered weighted averaging aggregation operator (I-CPoFOWAAO) and induced complex Polytopic fuzzy hybrid averaging aggregation operator (I-CPoFHAAO). Induced aggregation operators in decision-making process are indispensable tools for managing uncertainty, integrating multiple criteria, facilitating consensus, and providing a formal and flexible framework for modeling and solving complex decision problems. At the end of the paper, we make an illustrative example to prove the ability and efficiency of the novel proposed aggregation operators.
In response to the prevalent high water cut challenge in horizontal wells of the Bohai A Oilfield, this study introduces an innovative approach for pinpointing water production points in horizontal wells. The methodology leverages a comprehensive evaluation that integrates techniques such as curve identification, dynamic analysis, numerical simulation, and seepage model calculations. In conjunction, a novel hydraulic control-based balanced oil production process has been developed. This process utilizes a specialized water plugging string to effectively seal water production points in horizontal wells. Additionally, a hydraulic control system for horizontal well oil production has been implemented, facilitating staged extraction and thus achieving balance in oil production. Field application, particularly in Well X1, demonstrates a marked improvement post-implementation: the comprehensive water cut in Well X1 decreased from an initial 98.1% to 87.3%, and the production pressure differential escalated from 0.55 MPa to 2.01 MPa. This substantial enhancement in reservoir utilization indicates a notable reduction in water cut within the crude oil. The application of this balanced production technology in horizontal wells has led to a decrease in water cut and liquid production, significantly alleviating surface processing pressures. Consequently, there has been an improvement in well productivity and the overall development effectiveness of the oilfield. These findings suggest that the balanced oil production technique offers a promising solution for enhancing oil recovery in horizontal wells, particularly in fields grappling with high water cuts.
Open Access
