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Found 5152 publications
Integration of MULTIMOORA algorithm combined with circular q-rung orthopair fuzzy information for optimizing player positioning
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
The following paper presents a new analytical framework for the optimization of player positioning, a methodology with significant practical implications. The method implements the multi-objective optimization by ratio analysis with full multiplicative form (MULTIMOORA) in a decision-making context in which several non-commensurable performance variables have to be combined. The application of Dombi operationalizes the framework by prioritizing weighted aggregation operators coupled with circular q-rung orthopair fuzzy sets (Cq-ROFSs). The Cq-ROFSs allow multidimensional representation of uncertainty, and allow dynamic actions upon the fuzzy parameter q, such that both intuitionistic fuzzy sets and Pythagorean fuzzy sets are subsets. Two Dombi prioritized operators on Cq-ROFSs are thereby devised a Cq-ROFSs Dombi prioritized weighted averaging operator (Cq-ROFSDPWA) and a Cq-ROFSs Dombi prioritized weighted geometric operator (Cq-ROFSDPWG). Results from empirical experiments are reported that demonstrate the performance of the resulting methodology, highlighting its practical relevance. The fundamental properties of these operators are also examined. The proposed aggregation operators are applied within the MULTIMOORA technique to assess their effectiveness. Numerical examples demonstrate that the methods yield logical and consistent results across different decision-making scenarios. Comparative analyses further highlight the advantages of the Cq-ROFSDPWA and Cq-ROFSDPWG operators over existing approaches.
Open Access: Yes
Understanding patient perception of digital value co-creation in electronic health record through clustering approach
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
Patients are central to healthcare services, and comprehending their perceptions is crucial for fostering effective value co-creation. This study aimed to investigate the user characteristics and perceptions of value co-creation within the context of Mobile Electronic Health Records (EHR). Using a questionnaire collected from 422 patients, the study employed the K-modes clustering algorithm in R-Studio to group users based on shared characteristics and perceptions of value co-creation. The analysis revealed three distinct user clusters, which are high familiarity-positive perception, low familiarity-positive perception and high familiarity-neutral to negative perception. These clusters characterized by unique attributes such as socio-economic, history of medical visit, intention to use, technological familiarity, and different perception of value co-creation in Mobile EHR systems. Descriptive statistics were used to further interpret the clusters, revealing differences in user characteristics and perception across cluster. The findings emphasize the importance of alignment between user expectations and system interactions. Effective alignment fosters value co-creation through resource access, sharing, integration, and recombination, while misalignment may result in value destruction. This study highlights the need to design and implement Mobile EHR systems that align with the diverse characteristics and of their users to enhance engagement and promote value co-creation.
Open Access: Yes
Flower fertilization optimization algorithm with application to adaptive controllers
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
This article presents the Flower Fertilization Optimization Algorithm (FFO), a novel bio-inspired optimization technique inspired by the natural fertilization process of flowering plants. The FFO emulates the behavior of pollen grains navigating through the search space to fertilize ovules, effectively balancing exploration and exploitation mechanisms. The developed FFO is theoretically introduced through the article and rigorously evaluated on a diverse set of 32 benchmark optimization problems, encompassing unimodal, multimodal, and fixed-dimension functions. The algorithm consistently outperformed 14 state-of-the-art metaheuristic algorithms, demonstrating superior accuracy, convergence speed, and robustness across all test cases. Also, exploitation, exploration, and parameter sensitivity analyses were performed to have a comprehensive understanding of the new algorithm. Additionally, FFO was applied to optimize the parameters of a Proportional-Integral-Derivative (PID) controller for magnetic train positioning—a complex and nonlinear control challenge. The FFO efficiently fine-tuned the PID gains, enhancing system stability, precise positioning, and improved response times. The successful implementation underscores the algorithm’s versatility and effectiveness in handling real-world engineering problems. The positive outcomes from extensive benchmarking and practical application show the FFO’s potential as a powerful optimization tool. In applying multi-objective PID controller parameter optimization, FFO demonstrated superior performance with a sum of mean errors of 190.563, outperforming particle swarm optimization (250.075) and dynamic differential annealed optimization (219.629). These results indicate FFO’s ability to achieve precise and reliable PID tuning for control systems. Furthermore, FFO achieved competitive results on large-scale optimization problems, demonstrating its scalability and robustness.
Open Access: Yes
Web crippling behavior of cold-formed steel built-up I-sections with stiffened and unstiffened perforated webs
Publication Name: Results in Engineering
Publication Date: 2025-12-01
Volume: 28
Issue: Unknown
Page Range: Unknown
Description:
This study investigates the web crippling behaviour of cold-formed steel (CFS) back-to-back built-up U-shaped sections with perforated webs. In this research, the web opening was positioned at the mid-height of the web directly beneath the bearing plate. Initially, the geometrically and materially nonlinear finite element (FE) models were validated against 24 experimental tests from the literature, demonstrating excellent agreement. Specifically, the mean FE-to-experimental strength ratios (PFE /PEXP ) were 1.002 and 1.001 for the Interior-Two-Flange (ITF) and Interior-Loading (IL) conditions, respectively. Subsequently, the verified nonlinear FEM models were employed to conduct an extensive parametric study comprising 198 built-up I-sections. Moreover, this extensive investigation systematically examined the effects of various parameters, including hole size, the presence of unstiffened and stiffened holes, as well as different hole shapes such as rectangular, slotted, circular, and square openings, on web crippling performance. Furthermore, the results indicate that unstiffened holes can reduce the web crippling strength by as much as 54 % compared to plain webs. In contrast, edge-stiffened holes can enhance the web crippling strength by up to 42 % relative to plain webs. These findings highlight the significant impact of web perforation geometry and stiffening on the web-crippling behaviour of CFS built-up sections.
Open Access: Yes
Qualitative analysis of the sustainability of local attachment and identity based on in-depth interviews conducted in two scattered farmstead settlements
Publication Name: Discover Sustainability
Publication Date: 2025-12-01
Volume: 6
Issue: 1
Page Range: Unknown
Description:
Based on qualitative research, the paper focuses on the transformation of the traditional lifestyle of scattered farmsteads in recent decades, primarily driven by economic shifts and demographic changes. The challenging conditions of agriculture and farming, and the rise of alternative economic activities, such as tourism, recreation, and small-scale business, have reshaped these rural settlements, contributing to a different social landscape. Besides, another important trend is the influx of newcomers, including professionals, entrepreneurs, and pensioners, who adopt alternative, often non-agricultural lifestyles while maintaining a strong sense of place attachment and identity. In contrast, younger generations of native inhabitants tend to migrate elsewhere due to limited prospects, better education and job opportunities, leading to a generational shift in the population base. This study investigates two farmstead settlements exemplifying the tendencies, highlighting how sustainability is increasingly supported not by the continuation of traditional practices, but by the emergence of new lifestyle patterns such as agritourism and rural commuting. The process of change reflects a transition in the meaning and practice of rural living, where sustainability is redefined by changing forms of identity and attachment supporting the survival of farmsteads in a postmodern world.
Open Access: Yes
A hybrid physics-informed neural and explainable AI approach for scalable and interpretable AQI predictions
Publication Name: Methodsx
Publication Date: 2025-12-01
Volume: 15
Issue: Unknown
Page Range: Unknown
Description:
Air Pollution is a critical environmental issue affecting public health, climate, and ecosystems. However, accurately predicting and classifying Air Quality Index (AQI) levels across different regions remains a challenging task due to the complex nature of air pollution patterns. Conventional and ensemble ML and DL models often fail to capture the physical laws goverming the air pollution, which leads to inaccurate predictions. This study addresses these issues by introducing an approach that employs Physics-Informed Neural Networks (PINN) with Explainable AI (XAI) techniques for AQI classification (AirSense-X). The proposed approach utilizes PINN for regression, along with mapping for classification and XAI for interpretation. PINN ensures that the model learns from physical laws governing air quality rather than relying solely on data. The dataset utilized in this study is a publicly available dataset containing the AQI data at daily levels from various stations across multiple cities in India. The proposed AirSense-X approach achieves an accuracy of 98 %, with 97 % precision, 95 % recall, and an F1 score of 0.96, ensuring reliability. Similarly, the confusion matrix for the proposed approach indicated that the model correctly classified 21,306 and misclassified 268 instances. The key focuses of this study include: • Introducing a novel approach, AirSense-X, which employs PINN for accurate AQI prediction and XAI for enhanced interpretability. Additionally, the study also involves comparative analysis with conventional and ensemble ML and DL models. • Employing structure mapping technique for classification based on the predicted AQI values. • Integrating physical laws governing air pollution using a PINN model enhances prediction accuracy and ensures that the model learns beyond relying on data-driven insights.
Open Access: Yes
Optimal harmonics prediction for distribution systems powered by multi-energy sources using bidirectional long-short term memory combined with data sequence
Publication Name: Applied Soft Computing
Publication Date: 2025-12-01
Volume: 184
Issue: Unknown
Page Range: Unknown
Description:
A multi-energy resource aims to maintain a balance between energy output and load consumption and to ensure power continuity during different operating conditions. The harmonic distortions can be estimated from the output current of a harmonic source, which may not fully reflect its true harmonic distortions due to the interactions between the state changes at the power network level and the harmonic sources. System operators monitor each system's harmonic performance under different conditions of operation to find the actual contribution of grid-connected systems to harmonic-related issues. Development of machine learning algorithms leads to effective progress in the harmonic prediction and computation. In this paper, the combined data sequencing, and Bidirectional Long-Short Term Memory (Bi-LSTM) network has been exploited for the real-time harmonic prediction of future events in multi-energy sources. The validity of the proposed Model including the applications of ANFIS, ANNs, MLRA and LSTM is conducted on the two standard systems as IEEE 9-bus and IEEE 34-bus multi energy resources system that is associated with PV systems. The simulation results, based on climate changes of solar irradiance and ambient temperature in PV systems, demonstrate that the proposed methods can accurately forecast changes in total harmonic distortion (THD) as well as the voltage profile at the point of common coupling. The performance of Bi-LSTM, original LSTM, Machine Linear Regression (MLR), and Artificial Neural Networks (ANNs) techniques were assessed. These findings provide valuable insights. Four performance validation indices, RMSE, R-squared and MSE are considered to assess the performance of the competitive learning algorithms. The results showed that in the model IEEE 9-bus, Bi-LSTM outperformed all the applied methods as its RMSE value was 0.000019 while its MSE value was 3.61e-10 and finally, the Bi-LSTM had a higher value squared error (R2) was equal 1 which indicates the effectiveness of Bi-LSTM for predicting sequential total harmonic distortion. On the other hand, in case study of IEEE 34-bus, the RMSE, MSE and R2 are 0, 3.276e-30 and 1 using Bi-LSTM which means that the Bi-LSTM leads to the best performance validation indices compared to other competitive algorithms for the tested multi-energy systems.
Open Access: Yes
A novel numerical investigation of fiber Bragg gratings with dispersive reflectivity having polynomial law of nonlinearity
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
Fiber Bragg gratings represent a pivotal advancement in the field of photonics and optical fiber technology. The numerical modeling of fiber Bragg gratings is essential for understanding their optical behavior and optimizing their performance for specific applications. In this paper, numerical solutions for the revered optical fiber Bragg gratings that are considered with a cubic-quintic-septic form of nonlinear medium are constructed first time by using an iterative technique named as residual power series technique (RPST) via conformable derivative. The competency of the technique is examined by several numerical examples. By considering the suitable values of parameters, the power series solutions are illustrated by sketching 2D, 3D, and contour profiles. The results obtained by employing the RPST are compared with exact solutions to reveal that the method is easy to implement, straightforward and convenient to handle a wide range of fractional order systems in fiber Bragg gratings. The obtained solutions can provide help to visualize how light propagates or deforms due to dispersion or nonlinearity.
Open Access: Yes
The impact of asbestos cement pollution in irrigation water on physiological and germination characteristics of Trifolium pratense, Medicago sativa, and Solanum lycopersicum seeds
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
This paper investigates how plants respond to stress caused by asbestos cement products in irrigation water. It presents a thorough evaluation of the exposure and risk factors for plants, water, and soil when exposed to these materials. The experimental results provide empirical evidence of plant stress responses based on physiological and germination parameters. The research is motivated by concerns about environmental contamination from asbestos cement in irrigation water, which can be toxic to plants and lead to soil pollution, negatively impacting vegetation and soil quality. When exposed to asbestos in water, plants experience toxic stress that can inhibit photosynthesis, nutrient uptake, and germination. Asbestos can also adversely affect cell division and metabolism, risking plant growth, reproduction, and overall health, as well as making them more susceptible to disease and pests under environmental stress. The paper examines the impact on germination and physiological parameters of Trifolium pratense, Medicago sativa, and Solanum lycopersicum, particularly how they were affected by pre-established concentrations of irrigation water mixed with asbestos cement during a controlled germination experiment. The research methodology was developed in the absence of established global practices, standards, and methods, creating an opportunity for further methodological advancement. The findings could serve as a situational analysis for professionals in environmental plant protection and analytical fields.
Open Access: Yes
