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Found 5152 publications
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
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
Effect of heat stress and feed restriction on performance, carcass traits, and meat quality of growing rabbits
Publication Name: Livestock Science
Publication Date: 2025-12-01
Volume: 302
Issue: Unknown
Page Range: Unknown
Description:
The effects of heat stress and feed restriction were evaluated on a total of 180 weaned rabbits divided into three experimental groups (60 animals/group): 2 groups were fed ad libitum and reared under different temperatures (20 °C – 20AD and 30 °C – 30AD), while a third group was housed under controlled temperature (20 °C) but pair-fed to 30AD rabbits, thus feed restricted (20FR). During the trial, both 30AD and 20FR groups exhibited reduced growth performance, including body weight and daily weight gain (both, P < 0.001), although feed conversion ratio improved (P = 0.016). The reference carcasses of 20FR and 30AD rabbits were lighter and leaner (both, P < 0.001) than that of 20AD rabbits, while the slaughter yield decreased only in 20FR rabbits (P = 0.001). Regarding meat physical traits, 20FR rabbits exhibited the highest pHu (P < 0.001) and the lowest total losses (P < 0.001), whereas the meat-to-bone ratio decreased in both 20FR and 30AD groups (P = 0.007). As for meat proximate composition, protein and lipid contents were lower (P = 0.008 and P = 0.0002, respectively) in 20FR and 30AD rabbits, while water content was greater (P < 0.001) compared to 20AD rabbits. At the lipid level, higher TBARS (P = 0.001) were found in both 20FR and 30AD groups. The 20FR and 30AD groups showed some differences in their carcass and meat quality traits, however the majority of changes induced by chronic heat stress were mostly attributed to the reduced feed intake.
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
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
Decision-analytics-based electric vehicle charging station location selection: A cutting-edge fuzzy rough framework
Publication Name: Energy Reports
Publication Date: 2025-12-01
Volume: 14
Issue: Unknown
Page Range: 711-735
Description:
Electric vehicles are of great significance in supporting sustainable transportation and sustainability. In parallel with the increasing demand for such vehicles worldwide, the electric vehicle charging stations (EVCSs) market has grown dramatically. The study presents a practical model for selecting EVCS sites integrating multi-criteria decision-making (MCDM), fuzzy, and rough sets. The research aims to bridge the gap in evaluating EVCS locations by leveraging the superiorities of fuzzy and rough set theories to address vagueness effectively. Firstly, assessment criteria cover the environment, economic, technology, and social drivers. Secondly, a fuzzy Defining Interrelationships Between Ranked criteria (F-DIBR) model is applied to determine the weight values of siting factors. Last, for the first time, the Mixed Aggregation by COmprehensive Normalization Technique (MACONT) with hybrid fuzzy rough numbers (FRN-MACONT) model is proposed to obtain the ranking results. Further, a new approach for defining hybrid fuzzy rough numbers is suggested, based on an improved methodology for determining rough numbers' lower and upper limits, allowing consideration of mutual relations between a set of objects and flexible representation of rough boundary intervals depending on the dynamic environmental conditions. The study's novelties reside in deciding the importance of the driving forces used in determining the EVCS site location with a novel method, F-DIBR, and selecting the optimal site with a new FRN-MACONT approach. The results show that “economy” is the most significant criterion, whereas “system reliability” is the most critical sub-criterion. The findings also indicate that the Konak territory performs the best, whereas the Cigli territory is the second best. Comprehensive sensitivity analysis verifies the proposed framework's validity, robustness, and effectiveness. As per the research findings and analyses, some managerial implications are further discussed. The approach introduced has the potential to contribute to the green transport literature.
Open Access: Yes
Halal tourism research in Indonesian context: a bibliometric analysis
Publication Name: Discover Sustainability
Publication Date: 2025-12-01
Volume: 6
Issue: 1
Page Range: Unknown
Description:
Halal tourism is a growing sector of tourism that has attracted considerable attention in recent years due to its potential for economic growth and the need to meet the demands of Muslim travelers. This study aims to provide a comprehensive overview of halal tourism research in Indonesia through the utilization of bibliometric approach. The study utilizes Scopus database to analyze the publication trends, co-authorship, and thematic analysis, as well as the future research directions on this field in the context of Indonesia spanning the years 2017 to 2024. The findings indicate that there is a disparity in the involvement of authors and affiliations from Indonesia in terms of publications. The results show consistent growth in Indonesian publications, but emphasize the need for better quality and global dissemination. Moreover, the findings suggest that Indonesia plays a key role in the development of tourism in Indonesia due to its Muslim population and integration of Islamic principles in education and tourism. These findings highlight the importance of understanding Muslims tourists’ behavior, political economy influences, and service quality in different regions of Indonesia, thereby informing policy-making, industry practices, and future research agendas in this field.
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
Comparative analysis of NLP-driven MCQ generators from text sources
Publication Name: Computers and Education Artificial Intelligence
Publication Date: 2025-12-01
Volume: 9
Issue: Unknown
Page Range: Unknown
Description:
The application of learning sciences with technology has been shown to boost learner interactions, yet the potential of advanced tool, particularly those that leverage Natural Language Processing (NLP), still very much untapped in learning contexts. This paper speaks to this age-old problem of generating quality Multiple-Choice questions (MCQs) – a prevalent but time-consuming mode of assessment – via the suggested comprehensive comparison study of template-based AI solutions. The study contrasts general-purpose Large Language Models (LLMs) with specialized MCQ-focused AI programs. The scientific approach employed was quite stringent, where each of the software applications was benchmarked using a common dataset of text across varying levels of complexity and topic. Results indicate that general-purpose LLMs, especially DeepSeek and ChatGPT, consistently present higher performance and reliability, especially when processing complex textual content. Whereas specialized tools offer distinctive formatting options, they exhibit decreasing performance as texts become more complex and signify strong operation constriction at the free versions. Developing solid and effective distractors turned out to be a complicated task for all the tested tools. We conclude the paper by presenting a standardized assessment model, making evidence-based recommendations for developers and teachers, and suggesting ways to incorporate various AI capabilities into modern educational assessment effectively.
Open Access: Yes
