This study introduces an advanced framework for picture fuzzy linear programming problems (PFLPP), deploying picture fuzzy numbers (PFNs) to articulate diverse parameters. Integral to this approach are the three cardinal membership functions: membership, neutral, and non-membership, each contributing distinctly to the formation of the PFLPP. Emphasis is placed on employing these degrees to formulate the PFLPP in its most unadulterated form. Furthermore, the research delineates a novel optimization model, tailored specifically for the resolution of the PFLPP. A meticulous case study, accompanied by a numerical example, is presented, demonstrating the efficacy and robustness of the proposed methodology. The study culminates in a comprehensive discussion of the findings, highlighting pivotal insights and delineating potential avenues for future inquiry. This exploration not only advances the theoretical underpinnings of picture fuzzy sets but also offers practical implications for the application of linear programming in complex decision-making scenarios.
The digital age has witnessed the rampant spread of misinformation, significantly impacting the medical and financial sectors. This phenomenon, fueled by various sources, contributes to public distress and information warfare, necessitating robust countermeasures. In response, a novel model has been developed, integrating cloud computing with advanced machine learning techniques. This model prioritizes the identification and mitigation of false information through optimized classification strategies. Utilizing diverse datasets for predictive analysis, the model employs state-of-the-art algorithms, including K-Nearest Neighbors (KNN) and Random Forest (RF), to enhance accuracy and efficiency. A distinctive feature of this approach is the implementation of cloud-empowered transfer learning, providing a scalable and optimized solution to address the challenges posed by the vast, yet often unreliable, information available online. By harnessing the potential of cloud computing and machine learning, this model offers a strategic approach to combating the prevalent issue of misinformation in the digital world.
In today’s digital age, new technological tools are increasingly becoming an indispensable part of the educational process, particularly in educating children. The development of technology, including tablet computers, and mobile phones, offers numerous opportunities for enhancing teaching methods and fostering children’s development. These technologies offer access to a wide array of digital resources, interactive content, and personalized educational experiences, thereby opening up new methods in education. However, despite the benefits, many studies underscore the potential negative impacts of utilizing new technologies in children’s education. For the above reasons, a study was conducted to examine how new technologies influence children’s learning of geometric shapes. Forty children, evenly distributed by gender and place of residence, participated in the study, undergoing testing using both mobile phones and Tablet PCs. The results revealed statistically significant differences based on the respondents’ gender, place of residence, and the devices through which the tasks were presented, particularly for certain geometric shapes.
This investigation explores the intricate relationship between government support, foreign investment, and research and development (R&D) commitment in China’s strategic sectors, with a focus on the crucial role of government assistance in spurring innovation in small-scale enterprises. Utilizing advanced deep learning and data mining techniques, the impact of government subsidies and tax incentives on R&D dedication in burgeoning strategic firms is meticulously analyzed, particularly in the context of varying levels of external financing. The findings reveal a significant, multifaceted influence of government financial aid and foreign capital on R&D endeavors. A pronounced, positive correlation between government financial backing and R&D activities emerges, with the magnitude of this relationship being substantially modulated by the degree of external investment. This underscores the importance of strategic financial orchestration in nurturing research and innovation within pivotal sectors. Insights gleaned from this study are of paramount importance for policy formulators and business leaders, offering a refined comprehension of the synergistic interplay between government incentives, foreign investment, and R&D commitment. These findings are pivotal for promoting economic growth and technological progress in China’s key industries.
The development of an adaptive Lane Keeping Assistance System (LKAS) is presented, focusing on enhancing vehicular lateral stability and alleviating driver workload. Traditional LKAS with static parameters struggle to accommodate varying driver behaviors. Addressing this challenge, the proposed system integrates adaptive driver characteristics, aligning with individual driving habits and intentions. A novel lane departure decision model is introduced, employing time-space domain fusion to effectively discern driver's lane change intentions, thus informing system decisions. Further innovation is realized through the application of reinforcement learning theory, culminating in the creation of a master controller for lane departure intervention. This controller dynamically adjusts to driver behavior, optimizing lane keeping accuracy. Extensive simulations, coupled with hardware-in-the-loop experiments using a driving simulator, substantiate the system's efficacy, demonstrating marked improvements in lane keeping precision. These advancements position the system as a significant contribution to the field of driver assistance technologies.
Against the backdrop of current market demands for a variety of products in small batches, traditional single-variety assembly lines are transitioning to variable production lines to accommodate the manufacturing of multiple similar products. This paper discusses the production unit as a microcosm of the variable production line, which boasts advantages such as smaller line scale, short setup times for changeovers, and ease of product scheduling. A mathematical model for splitting variable production lines into production units is established, with solutions at two levels: resource allocation and product scheduling. The upper-level model focuses on determining the number of production units and the distribution scheme of operators and equipment across multiple channels; the lower-level model addresses the product allocation problem, which is characterized by multiple stages, divisibility, variable batch sizes, and minimum batch size constraints. The solution approaches include a branch and bound method for small-scale problems to obtain optimal solutions, and an improved particle swarm optimization (PSO) algorithm for medium to large-scale problems to find near-optimal solutions. The innovation of the paper lies in the construction of the variable production line splitting model and the optimization algorithms for resource allocation and product scheduling.
In Banda Aceh City, Indonesia, particularly in Punge Jurong Gampong, the effectiveness of child oral health service interventions is notably impacted by the level of maternal knowledge and involvement. This quasi-experimental study was designed to scrutinize the impact of maternal behaviors on the maintenance of children's dental and oral health, employing a primary verbal healthcare strategy. Utilizing a pre-test and post-test approach, the research encompassed 45 mothers in the intervention group and an equal number in the control group. The intervention primarily consisted of educating mothers about the critical importance of dental and oral health, integrating promotional and preventive measures. The findings of this study reveal that maternal influence is a pivotal factor in shaping the oral health habits of children, with such influence being modulated by variables including cultural perceptions, socioeconomic status, educational background, and information accessibility. The range of maternal activities observed varied significantly, encompassing diligent teeth brushing practices and challenges in recognizing the significance of primary teeth. The study underlines a substantial need for customized, culturally sensitive interventions tailored to the unique context of Punge Jurong Gampong. It was observed that while the average knowledge level and Hypertext Preprocessor (PHP)-M scores of mothers in both the intervention and control groups did not show a significant difference, notable variances in attitudes and behaviors related to oral health were statistically significant (p>0.05). These results highlight the criticality of context-specific, culturally informed educational programs in improving pediatric oral health outcomes. The study emphasizes the role of collaborative efforts involving healthcare professionals, community leaders, and educational institutions in creating an enabling environment for the effective implementation of primary oral healthcare strategies. Thus, this research contributes to the understanding of the multifaceted nature of maternal influence on child oral health and underscores the necessity of personalized and culturally adaptive educational interventions.
This investigation delves into the critical challenges of urban development and management, employing a comprehensive evaluation of four strategic alternatives: transit-oriented development, green infrastructure investment, smart city technologies, and community-based development. These alternatives are rigorously assessed against a set of eight meticulously chosen criteria. Distinct from conventional analyses, the study adopts the sophisticated Criteria Importance Through Inter-criteria Correlation (CRITIC)-Weighted Aggregated Sum Product Assessment (WASPAS) methodology, utilizing spherical fuzzy sets (SFS). This approach mitigates uncertainties inherent in decision-making processes, thereby refining the accuracy of the evaluation. The CRITIC-WASPAS method, with its innovative application in this context, augments the precision of the assessments, yielding a detailed appraisal of each alternative's merits and limitations. Through assigning weighted criteria and systematically ranking these alternatives, the study furnishes pivotal insights for urban planners and policymakers. This contribution is instrumental in guiding decisions that promote resilience, equity, and environmental sustainability in urban environments. The novel integration of the CRITIC-WASPAS method in this domain not only propels the field forward but also lays a robust foundation for informed and effective decision-making. The outcomes of this research are poised to significantly impact the discourse on sustainable urban development, offering a data-driven framework that is essential for sculpting the future of cities amidst evolving urban challenges.
The NVH (noise, vibration, harshness) performance of automobiles is a key issue in enhancing user comfort. However, car manufacturers and original equipment manufacturers often invest more research and development effort into the new car performance at the initial design stage, neglecting the study of whole vehicle NVH durability and reliability, and this can significantly affect the user's riding experience. This paper focuses on the phenomenon of NVH performance degradation under idle conditions. Using LMS data acquisition equipment and software, vibration acceleration and frequency at 17 points on the vehicle, including the steering wheel, seat rail, and engine mount, were collected and analyzed. By conducting comparative experiments, the causes of NVH performance degradation after long mileage were explored. This aims to provide new ideas for improving the durability and reliability of whole vehicle NVH in future research and production.
In the realm of decision-making, the delineation of uncertainty and ambiguity within data is a pivotal challenge. This study introduces a novel approach through complex intuitive hesitant fuzzy sets (CIHFS), which offers a unique multidimensional perspective for data analysis. The CIHFS framework is predicated on the concept that membership degrees reside within the unit disc of the complex plane, thereby providing a more nuanced representation of data. This method stands apart in its ability to simultaneously process and analyze data in a two-dimensional format, incorporating additional descriptive elements known as phase terms into the membership degrees. The study is bifurcated into two primary phases. Initially, a possibility degree measure is proposed, facilitating the ranking of numerical values within the CIHFS context. Subsequently, the development of innovative operational rules and aggregation operators (AOs) is undertaken. These AOs are instrumental in amalgamating diverse options within a CIHFS framework. The research dissects and deliberates on various AOs, including weighted average (WA), ordered weighted average (OWA), weighted geometric (WG), ordered weighted geometric (OWG), hybrid average (HA), and hybrid geometric (HG). Furthermore, the study extends to the realm of multi-criteria decision making (MCDM), where it proposes a methodology utilizing intricate intuitive and fuzzy information. This methodology emphasizes the objective management of weights, thereby enhancing the decision-making process. The study's findings hold significant implications for the optimization of resources and decision-making strategies, providing a robust framework for the application of CIHFS in practical scenarios.
In the era of branding, the design of plush toy brands often faces a contradiction with the needs of target user groups. Addressing the brand transformation challenges faced by small and micro enterprises in the plush toy industry, this paper proposes a method for generating creative design schemes for plush toy brands based on extension theory. This method involves introducing the theory of primitives, utilizing extension primitives to construct problem models, employing extension diamond thinking for ideation and divergence, and using extension analysis for a comprehensive description of brand design elements. Subsequently, the method involves transforming these elements through extension transformation to generate innovative brand design schemes.
Meteorological parameter modeling is imperative for predicting future atmospheric conditions. This study focuses on the Sub-Saharan region of West Africa, a region characterized by its climatic diversity and unique weather patterns, making it an ideal subject for meteorological research. The objective was to model meteorological parameters using trigonometric and polynomial functions, assessing their predictive accuracy in selected West African stations. The parameters considered include air temperature, air pressure, wind speed, rainfall, and relative humidity, with data sourced from the HelioClim satellite archive, spanning 1980 to 2022. The data, recorded in comma-separated value (CSV) format, were analyzed using descriptive statistics, specifically mean and standard deviation. Each meteorological parameter underwent modeling through both polynomial and trigonometric functions. The comparative effectiveness of these models was evaluated using the adjusted coefficient of determination and Root Mean Square Error (RMSE). The preference for the adjusted coefficient of determination over the standard coefficient of determination (R2) was due to its ability to account for biases arising from variances in the number of parameters in both model types. The results indicated that both trigonometric and polynomial models are robust in their predictive capabilities, demonstrating their utility in accurate parameter estimation and future weather prediction. These findings suggest that such models are valuable tools in climate studies, enhancing understanding and awareness of weather conditions in the Sub-Saharan West African region.
Decades of engineering practice have substantiated that the implementation of construction joints is a pivotal method for mitigating dam cracking. The integration of various joint types, notably transverse and induced joints, within roller-compacted concrete (RCC) arch dams has emerged as a promising strategy to curtail cracking and structural failure. This approach leverages the unique structural characteristics inherent to each joint type. Given the intricate, variable, and dynamic nature of thermal stress in RCC arch dams, the design process for crack prevention, particularly the configuration of induced joints, demands an accurate representation of the dam's operational conditions from construction through to service. Investigations in practical engineering contexts have revealed that the utilization of a contact unit simulation methodology, featuring an open/close iterative function for modeling the behavior of induced and transverse joints in RCC arch dams, proves effective. This method is complemented by the adoption of equivalent strength theory as the criterion for structural integrity assessment. A comprehensive process simulation encompassing the entire dam structure further enhances the efficacy of this approach. Such simulations facilitate a more granular examination of joint placement within the dam and the structural design of the joints themselves. As a result, induced joints can be optimally opened in alignment with design expectations, thereby alleviating tensile stress triggered by temperature reductions. This strategy assures superior construction quality of the dam's concrete body, contributing significantly to the longevity and safety of RCC arch dams.
Open Access
, 