Organizations adopt process automation software to support digital transformation, but leaders managing diverse Information Systems (IS) face challenges in developing automation capabilities that deliver both strategic advantage and operational resilience. This study examines how organizations build and sustain process automation competencies through effective Information Technology (IT) governance, strategic alignment, and systematic capability development. Drawing on Technology-to-Performance Chain (TPC) theory and the Dynamic Capabilities framework, this research analyses IS management practices to demonstrate how process automation initiatives can evolve from tactical implementations into sustainable and resilient sources of competitive advantage. A multi-method research design was employed, combining survey data from 286 IT and business leaders across manufacturing, retail, logistics, financial services, and healthcare organizations in the Nordic region, together with 18 qualitative interviews with Chief Information Officers (CIOs), IT directors, and automation leaders. The study identifies pathways for developing three critical capability dimensions: (1) technical capabilities through platform selection and integration practices, (2) governance capabilities through portfolio management and performance measurement mechanisms, and (3) organisational capabilities through change management and continuous learning. The analysis shows that governance capabilities explain 61% of return on investment variance, while balanced capability development across all three dimensions produces returns 2.5 times higher than single-dimension focus. Organizations with mature capabilities establish governance before scaling these initiatives, adopt hybrid governance models, and invest approximately 35% in governance, 35% in technical, and 30% in organisational capabilities. The study provides a capability development framework, empirical evidence of development and performance outcomes, as well as practical guidance for building automation capabilities.
The convergence of Quantum Computing (QC) and Artificial Intelligence (AI) ushers in a transformative era of computational capability, with profound implications for science and industry alongside significant technical, ethical, and environmental challenges. This article explores quantum-enhanced AI (QAI), focusing on foundational quantum principles—superposition, entanglement, and quantum parallelism—and their applications in molecular simulation, financial optimization, and climate modeling. While quantum algorithms offer exponential speedups for Machine Learning (ML) and optimization, persistent barriers such as qubit decoherence, scalability limitations, and high energy demands hinder progress. Hybrid quantum-classical architectures are identified as critical transitional tools, combining quantum processors for specialized tasks like combinatorial optimization with classical systems for error correction and data preprocessing. Near-term commercial healthcare, finance, and logistics applications are highlighted, including pilot projects using quantum neural networks (QNNs) for drug discovery, portfolio risk analysis, and supply chain optimization. However, dual-use risks—such as quantum-powered surveillance and threats to classical encryption—emphasize the need for post-quantum cryptography (PQC) and ethical frameworks. Environmental concerns, particularly the carbon footprint of superconducting qubits, drive calls for sustainable innovations like photonic qubit designs and renewable-powered quantum data centers. Anticipated milestones include fault-tolerant QNNs, standardized benchmarks for quantum advantage, and democratized cloud access. Achieving these goals requires addressing interdisciplinary talent shortages, algorithmic biases, and global disparities in quantum infrastructure. This work advocates for governance models integrating sustainability metrics and equity-focused policies to steer QAI development responsibly. By harmonizing technical innovation with ethical foresight, the quantum-AI synergy can advance solutions to global challenges while mitigating misuse and ecological harm risks.
Interval-valued Pythagorean fuzzy sets (IVPFSs) serve as a formidable tool for addressing uncertainty in information, resulting in their extensive adoption in the realm of decision making. The challenge of accurately measuring the similarity between IVPFSs remains a pressing issue. In this paper, we present some novel similarity measures based on trigonometric function and their weighted forms considering membership, non-membership and hesitancy degrees, respectively. These similarity measures adhere to key properties, which are demonstrated through numerical experiments. Subsequently, the proposed similarity measures tailored for IVPFSs are employed to address medical diagnosis and multicriteria decision-making (MCDM) problems within the context of interval-valued Pythagorean fuzzy environments. The results conclusively evidence that the proposed similarity measures result in substantially more efficient outcomes.
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.
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.
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 advent of digital economy era, the high-quality development of cultural industry has received people's attention. How to take advantage of the favorable opportunities of rapid development of the digital economy to further promote the innovative development of cultural industry is an important problem that needs to be solved in development process of Chinese cultural industries. The quality evaluation of cultural industry development in the context of the digital economy is MAGDM. The single-valued neutrosophic sets (SVNSs) is useful tool to cope with uncertain information during the quality evaluation of cultural industry development in the context of the digital economy. In this paper, the single-valued neutrosophic number EDAS (SVNN-EDAS) model based on single-valued neutrosophic number cosine similarity measure (SVNNCSM) and SVNN cosine function similarity measure (SVNNCFSM) is formed to cope with the MAGDM. The CRITIC model is administrated to obtain the weight numbers in light with the SVNNCSM and SVNNCFSM under SVNSs. Finally, numerical example and different comparative analysis for quality evaluation of cultural industry development in the context of the digital economy is administrated to validate SVNN-EDAS model. The main contributions of this study are formed: (1) the CRITIC model is formed to obtain the weight numbers in light with SVNNCSM and SVNNCFSM; (2) the SVNN-EDAS model is formed in light with SVNNCSM and SVNNCFSM under SVNNs; (3) Finally, numerical example and comparative analysis for quality evaluation of cultural industry development in the context of the digital economy is administrated to validate SVNN-EDAS model.
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.
In recent years, in the context of the rapid development of “Internet plus”, the traditional classroom of college English courses has been unable to meet the learning needs of contemporary college students. Education informatization has become an inevitable transformation of the teaching structure of colleges and universities. Online and offline hybrid teaching methods have emerged at the historic moment and continue to deepen into the classroom of higher vocational colleges. By further improving the teaching mode, traditional offline classrooms and emerging online learning are deeply integrated. After several teaching practices, it has been found that this teaching model can effectively enrich teaching methods and optimize the learning experience of students. However, there are also some problems, such as poor effective integration and integration of online and offline teaching content, and the need to improve teaching quality. The blended English teaching quality evaluation in vocational colleges in the new era is a multiple-attribute decision-making (MADM) problem. Recently, the Logarithmic TODIM (LogTODIM) technique and GRA technique has been utilized to cope with MADM issues. The double-valued neutrosophic sets (DVNSs) are utilized as a technique for characterizing uncertain information during the blended English teaching quality evaluation in vocational colleges in the new era. In this paper, the double-valued neutrosophic number Logarithmic TODIM-GRA (DVNN-LogTODIM-GRA) technique is conducted to solve the MADM under DVNSs. In the end, a numerical case study for the blended English teaching quality evaluation in vocational colleges in the new era is employed to validate the proposed technique. The main contribution of this paper is managed:(1) The entropy technique is managed to obtain weight values under DVNSs; (2) an integrated DVNN-LogTODIM-GRA technique is conducted to manage the MADM; (3) A numerical example for the blended English teaching quality evaluation in vocational colleges in the new era has been accomplished to verify the DVNN-LogTODIM-GRA technique.
China's Industrial Internet has progressed to a phase of deep implementation, finding extensive application across numerous vital sectors of the national economy. The Industrial Internet is proving to be an effective catalyst for high-quality economic and social development, increasingly serving as a pivotal force in the digital transformation of the manufacturing industry. At the heart of this transformation is the Industrial Internet platform, which acts as a transformative tool for enabling digital enhancements in manufacturing. The evaluation of the capability of these platforms to energize digital transformation is approached as a multi-attribute group decision-making (MAGDM) problem. In this context, the use of double hierarchy hesitant fuzzy linguistic term sets (DHHFLTSs) provides a precise representation of the significance in real MAGDM scenarios. Consequently, this paper introduces the double hierarchy hesitant fuzzy linguistic TOPSIS (DHHFL-TOPSIS) method, tailored specifically for MAGDM within a DHHFLTSs environment, and applies it to evaluate the capability of Industrial Internet platforms. Additionally, information entropy is utilized to determine the weights of the targets. A practical case study is also included to showcase the application of our method, and a comparative analysis is conducted to affirm its feasibility and effectiveness. The main contributions of this research are outlined as follows: (1) TOPSIS method is adapted based on DHHFLTSs; (2) PDHL-TOPSIS approach is utilized to address the MAGDM problem under DHHFLTSs; (3) A practical case study demonstrates the operational steps of our method; (4) A comparative analysis confirms the feasibility and effectiveness of the approach.
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 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.
In venture capital, fuzzy information can be effectively represented using single-valued neutrosophic sets, and TOPSIS and TODIM are commonly used methods in multiattribute decision making. Distance measurement plays a crucial role in these methods. This paper introduces a novel parameter distance measure for single-valued neutrosophic sets and an improved method based on TODIM and TOPSIS. First, the proposed distance measure addresses limitations of existing metrics and is rigorously proven to satisfy axiomatic distance definitions. Secondly, to consider the decision-maker’s risk attitude and simplify the calculation process, we propose a new method with the known parameter of risk attitude based on TODIM and TOPSIS. Finally, a case study of venture capital application is provided to showcase the effectiveness and practicality of the proposed distance measure and decision-making method in addressing multi-attribute decision-making problems within SVNS environments. Furthermore, sensitivity analysis of the distance measure parameters demonstrates consistent ranking results across various parameter values, thereby validating the robustness of the proposed approach. Comparative evaluations with other decision-making methods in identical scenarios underscore the rationality and superiority of our proposed methodology. The proposed distance measure and decision-making method offer significant contributions to the theory of single-valued neutrosophic sets and the resolution of venture capital problems.
The tourism industry is the business card of a city and an important source of financial revenue. As tourism is becoming increasingly popular, tourism satisfaction has become a key indicator for Yangzhou tourism. As an important business model, e-commerce has developed into a branch of mobile e-commerce. Mobile e-commerce can be combined with the tourism industry to provide services such as information access and city tours for tourism at the city level. The tourism mobile e-commerce service quality evaluation is multiple-attribute group decision-making (MAGDM). In recent times, Exponential TODIM (ExpTODIM) and VIKOR methods were employed to tackle the challenges of MAGDM. To handle uncertain data during the evaluation of tourism mobile e-commerce service quality, Probabilistic Linguistic Term Sets (PLTSs) have been adopted as a valuable tool. This paper presents the Probabilistic Linguistic ExpTODIM-VIKOR (PL-ExpTODIM-VIKOR) method as solution for MAGDM under PLTSs. Additionally, numerical case study is included to validate the proposed method within the context of evaluating the quality of tourism mobile e-commerce services. The primary contributions of this study can be summarized: (1) extension of ExpTODIM technique, based on VIKOR to PLTSs; (2) utilization of information entropy to derive weights based on expected values and deviation degree under PLTSs; (3) introduction of the PL-ExpTODIM-VIKOR technique to effectively manage MAGDM under PLTSs; and (4) execution of numerical study and comparative analysis to verify the effectiveness of PL-ExpTODIM-VIKOR in evaluating the quality of tourism mobile e-commerce services.
College students' mental health education refers to the educational activities aimed at cultivating college students' good psychological quality and promoting the harmonious development of college students' body and mind by taking reasonable educational means and using scientific educational methods on the premise of respecting the laws and characteristics of college students' physical and psychological development. The stage of higher education is the key stage to cultivate students' optimistic and upward psychological quality. Most college students at this stage have some psychological health problems, such as lack of security, reluctance to contact with the outside world, timidity, inferiority complex, etc. Therefore, it is very necessary to strengthen the psychological education and guidance for college students. The mental health education (MHE) quality evaluation of colleges and university in the new era is a multiple attribute decision making (MADM). The single-valued neutrosophic sets (SVNSs) is a useful tool to represent the uncertain information during the MHE) quality evaluation of colleges and university. In such paper, the single-valued neutrosophic number Aczel-Alsina power geometric (SVNNAAPG) operator and the single-valued neutrosophic number Aczel-Alsina power weighted geometric (SVNNAAPWG) operator are produced based on the Aczel-Alsina operations and classical power geometric (PG) operator under SNNs. The SVNNAAPWG operator is constructed for MADM. Eventually, numerical example of MHE quality evaluation of colleges and university in the new era and some selected comparative analysis were employed to depict the advantage of SVNNAAPWG approach. The major contribution is produced: (1) some PG operators based on the Aczel-Alsina operations and classical PG operator under SVNNs; (2) the SVNNAAPG operator and the SVNNAAPWG operator are produced based on the Aczel-Alsina operations and classical PG operator under SNNs; (3) The SVNNAAPWG operator is constructed for MADM; (4) Eventually, an example about MHE quality evaluation of colleges and university in the new era and some selected comparative studies were constructed.
This study used a radial function neural network (RBFNN) to create a novel system for calculating high-performance concrete's (HPC) compressive strength (CS) modified with fly ash and blast furnace slag. These admixtures could affect the mechanical and physical properties of HPC, and determining it definitely requires experimental efforts and costs. Herein, alternative methods such as machine learning algorithms named RBFNN could be useful to address these questions. The SSA (Salp swarm algorithm) and the artificial hummingbird algorithm (AHA) were utilized in this work to find optimal values of hyperparameters of the RBFNN approach that can be tuned. The suggested models were assessed utilizing a comprehensive dataset including 1030 data rows. Finally, the findings were compared to those documented in the literature. The findings of the calculations, which took into account evaluation metrics, depict that both hybrid SSA-RBFNN and AHA-RBFNN analysis might astonishingly perform good productivity during estimating, with R2 values of 0. 8955 and 0.8608 for SSA-RBFNN and 0.8987 and 0.8643 for AHA-RBFNN, respectively, related to the test and train segments. In conclusion, the AHA-RBFNN model created for predicting the CS of HPC amended with BFS and FA could be identified as the proposed model to be applied in practical applications.
