This article proposes a hybrid algorithm that combines Bacterial Foraging (BF) and Particle Swarm Optimization (PSO) to optimize the control parameters: Integral (I) controller plus Fuzzy-like Proportional and Derivative (Fuzzy-like PD) as the Fuzzy Scaling Factors (FSF) of the Fuzzy controller. The fitness function integral of time multiplied absolute error (ITAE) was utilized as a minima criterion to assess the control design effectiveness. The proposed controller is then applied to pilot a UAV Quadcopter model for usage in a variety of agricultural applications, including field observation, crop health monitoring, pesticide spraying, disease detection, etc. The numerical simulations perform the validity of the fast responses, stable and reliable without error.

When dealing with uncertainties and conflicting criteria in project management, complex Multi Attribute Decision Making (MADM) techniques are used. This study presents a novel approach by incorporating a Modified Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) with Multi Valued Multi Polar Neutrosophic Hypersoft Sets (MVMPNHSS). The model’s ability to manage variability and imprecise information within pseudo-realistic data, crafted to mimic actual challenges in project management, was tested. The model was designed to improve the efficiency and reliability of decision-making in the computation processes employed. Findings indicate that the developed method resolves issues posed by uncertainty through multifaceted states of membership, non-membership, and indeterminate overlap. It has shown to outperform other established methods on systematic project lapse evaluation measures by improving accuracy in assessing project alternatives. The structural comparative analysis discussed in this paper captures gaps in available literature and the value added by the proposed approach. The focus of this research was on providing specific guidelines aimed at project managers while underlining the need to implement the suggested methodology to improve decision-making efficiency and optimize project results in volatile environments.

With the rapid development of economic globalization, the political, economic and cultural exchanges between countries in the world are becoming more and more frequent, and the society's demand for Japanese professionals has greatly increased, which also puts higher demands on for college Japanese teaching, especially college Japanese translation teaching (JTT). College JTT, as the important component of the college Japanese teaching system, is affected by the traditional teaching system and model, and there are problems such as lack of teaching materials, outdated teaching approaches, unreasonable teaching curriculum settings, and single teaching implementation approaches. The teaching effect evaluation of JTT courses is multiple-attribute group decision-making (MAGDM) problem. In this work, in order to manage the MAGDM, the interval-valued intuitionistic fuzzy number CoCoSo based on the CRITIC (IVIFN-CRITIC-CoCoSo) approach is constructed under interval-valued intuitionistic fuzzy sets (IVIFSs). Finally, numerical example for teaching effect evaluation of JTT courses has been illustrated and some comparisons is employed to illustrate advantages of IVIFN-CRITIC-CoCoSo approach. This study illustrates four contributions: (1) a novel MAGDM approach based on IVIFN-CRITIC-CoCoSo approach is constructed under IVIFS. (2) The attributes weights are illustrated through CRITIC approach. (3) numerical example for teaching effectiveness evaluation of JTT courses has been illustrated and (4) some comparisons is illustrated advantages of IVIFN-CRITIC-CoCoSo approach.

In real life, we can face many things and services, which are performed regularly. Supply Chain Management (SCM) is the one that does the business. SCM refers to the management of finished products and their distribution to the final consumer. Many types of services relieve people a lot and create a lot more attachment with people. Online services are very easy for those people who cannot afford to buy equipment from cities. From this, they saved a large part of all processing income and time. The use of bipolar complex fuzzy soft sets (BCFSS) in SCM is investigated in this research. The bipolar complex fuzzy soft Hamacher weighted average (BCFSHWA), bipolar complex fuzzy soft Hamacher ordered weighted average (BCFSHOWA), bipolar complex fuzzy soft Hamacher hybrid average (BCFSHHA), bipolar complex fuzzy soft Hamacher weighted geometric (BCFSHWG), bipolar complex fuzzy soft Hamacher ordered weighted geometric (BCFSHOWG), and bipolar complex fuzzy soft Hamacher hybrid geometric (BCFSHHG) are among the operators that we present and illustrate. We utilize these operators to address bipolar complex fuzzy soft Multi-Attribute Border Approximation area Comparison (MABAC) issues and analyze their specific instances. We illustrate the efficacy and superiority of our technique over current methods using a numerical example in SCM, highlighting its potential for attaining optimal performance and solutions in SCM scenarios.

The advancement in the Internet of Things (IoT) and its broad scope of applications have led to the generation of huge volumes of data to be processed. Time-consuming operations, particularly time-critical operations, are submitted to fog nodes due to their proximity. Meanwhile, advanced operations are submitted to cloud computing centers for extensive computation and storage. However, task allocation to fog nodes lessens transmission latency and improves resource utilization. On the other hand, task offloading to cloud data centers maximizes resource utilization while increasing transmission delay because of the greater distance. The difficulty is in efficiently mapping tasks with appropriate resources that have matching requisites with tasks, which is the key problem in cloud-fog computing that needs to be addressed. In light of these challenges, this study introduces an innovative approach named Multi-objective Reptile Search Algorithm (MRSA), aimed at mitigating concerns about quality of service (QoS). This algorithm is implemented within the fog broker, a pivotal component responsible for task distribution. The simulation results demonstrate the efficacy of MRSA in enhancing resource utilization, makespan, and load balancing, substantiated through comparison with existing algorithms.

In the current electricity market environment, this paper proposes a user-side resource interactive transaction decision-making method based on scipy solver and genetic algorithm, which significantly improves the user-side resource transaction volume and reduces the risk loss of e-commerce. Compared with traditional methods, this study classifies controllable load resources more accurately by introducing fuzzy C-means clustering method, which provides more reliable data support for auxiliary decision making. At the same time, the construction and solution of the two-layer programming model not only considers the peak-valley price strategy of e-commerce, but also comprehensively coordinates the user demand response, and realizes the maximization of income expectations and the minimization of transaction risks. In addition, scipy solver is used to solve the power consumption model, which further optimizes the user-side resource transaction. After testing, the method in this paper not only significantly improved the user-side resource transaction volume, but also made the power user satisfaction as high as 0.99, which fully demonstrated the significant effect of this research method in improving user satisfaction, and provided a strong support for the intelligent and sustainable development of the power market.

In this article, we expose the theory of q-rung orthopair fuzzy (q-ROF) sets (q-ROFSs), which is the robust improvement of concepts of fuzzy sets (FSs) and intuitionistic FSs. The q-ROFS is an advanced framework that permits decision-makers to evaluate complex and unpredictable information during the decision-making process. The Hamy mean (HM) models are more powerful and effective aggregation models used to reduce the impact of different attributes and express correlation among different objects. We discussed the basic operations of Aczel Alsina operations under consideration of q-ROF environments. Some new strategies proposed by exploring the theory of Aczel Alsina aggregation expressions based on HM models, such as q-ROF Aczel Alsina Hamy mean (q-ROFAAHM) and q-ROF Aczel Alsina weighted Hamy mean (q-ROFAAWHM) operators. We also present a list of new approaches under consideration of the Dual Hamy mean (DHM) model, such as q-ROF Aczel Alsina Dual Hamy mean (q-ROFAADHM) and q-ROF Aczel Alsina weighted Dual Hamy mean (q-ROFAAWDHM) operators. Some flexible and reliable properties of our derived approaches are also discussed. A multi-attribute group decision-making (MAGDM) technique is a relatively advanced decision-making approach which is utilized to evaluate reliable optimal options by the decision maker. An appropriate algorithm for a MAGDM problem is also presented to reveal the robustness of our derived approaches. To show the flexibility and consistency of our discussed approaches, we study a practical example to choose the best option. To show the applicability and feasibility of currently discussed methodologies, we contrast the results of previously proposed aggregation operators (AOs) with the results of new methodologies.

Precise pile settlement prediction (SP) in rock-socketed foundations is vital for designing robust bridge foundations and other civil engineering structures. In this work, the behaviors of three powerful algorithms are employed, Dynamic Differential Annealed Optimization (DDAO), Runge Kutta Optimization (RKO), and Ant Lion Optimization (ALO) to improve the performance of the Adaptive Neuro-Fuzzy Inference System (ANFIS) model. In the ANFIS model, some critical input parameters include the rock's unconfined compressive strength, pile length, and pile diameter, which predict SP with high accuracy. The primary contribution of this research is its comparative study with optimization techniques applied to the ANFIS model. Results show that the ANFIS model optimized by DDAO algorithm has the lowest Root Mean Square Error (RMSE) and highest coefficient of determination (R 2 ). On the other side, even though the models optimized through RKO and ALO algorithms also have high predictive capabilities, ALO has extra power in generating a set of Pareto-optimal solutions. This will facilitate the engineers in selecting the most appropriate model given specific design requirements and site-specific constraints. The study provides essential development within the geotechnical engineering study by enhancing the SP prediction accuracy. All these can greatly improve the design and reliability of bridge foundations and other major civil engineering structures for performance and long-term stability.

With the increasingly diversified and personalized needs of users, traditional closed innovation can no longer adapt to rapidly evolving market demands. Consequently, the emergence of open innovation communities has become pivotal for enterprise innovation. Through Internet cooperative innovation community, customers actively engage in new product development, activities, co-creating value with enterprises, thereby transforming external innovation resources into the internal assets and enhancing the effectiveness of new product development processes. While existing researches focus on the influencing factors and assessment of innovation performance of open innovation community from the single perspective of customers or enterprises, there remains limited to understand the comprehensive performance evaluation from the dual perspective of customer and enterprise's co-innovation. Therefore, this study systematically analyzes the four stages of the co-innovation process within the open innovation community and their influencing factors based on the Stage-gate theory, and build a comprehensive performance evaluation model for co-innovation of open innovation community from the perspective of co-creation of customer and enterprise by using analytic hierarchy process and fuzzy comprehensive evaluation method. The model is then applied and verified through a practical case of Xiaomi open innovation community. This study not only enriches the theory of open innovation, but also provides guidance for the practice of co-innovation between enterprises and customers in the Internet environment.

Abstract In the fast-moving field of Natural Language Processing (NLP), making lexicons is still a method for many text analysis applications. This process of generating lexicons has traditionally used techniques such as semantic matches, word embeddings, and tools like EMPATH. With the arrival of Large Language Models (LLMs) including GPT-3.5, GPT-4 and Mistral 7b 0.1, we have new ways to create lexicons. This study takes a close look at how these older methods stack up against the newer options brought by LLMs. We carried out a detailed analysis, looking at how well different methods could create lexicons, focusing on their precision, scalability, and concluding on how efficiently they can be used in real-world settings. By using standard NLP tasks like document classification, emotion classification and sentiment analysis, this research prove itself on a variety of datasets to test how well the lexicons worked. This discovery, along with others from our study, aims to help professionals and researchers find the best approaches to lexicon creation today, setting the stage for more research in the NLP field.