Hybrid Support Research Articles

Multi-Domain Digital Twin and Real-Time Performance Optimization for Marine Steam Turbines

The digital twin model, which serves as a virtual counterpart symmetric to the physical entity, enables high-fidelity simulation and real-time monitoring. However, digital twin implementation for marine steam turbines (MSTs) faces dual multi-domain simulation fidelity and computational efficiency challenges. This study establishes a MST digital twin modeling methodology through two interconnected innovations: (1) a Modelica-based modular architecture enabling cross-domain coupling across mechanical, thermodynamic, and hydrodynamic systems via hierarchical decomposition, ensuring bidirectional symmetry between physical components and their virtual representations; and (2) a hybrid support vector regression-bidirectional long short-term memory (SVR-BiLSTM) surrogate model combining Gaussian radial basis function-supported SVR for steady-state mapping with Bi-LSTM networks for dynamic error compensation. Experimental validation demonstrates: (a) the SVR component achieves <1.57% absolute error under step-load conditions with 85% computational time reduction versus physics-based models; and (b) Bi-LSTM integration improves transient prediction accuracy by 14.85% in maximum absolute error compared to standalone SVR, effectively resolving static–dynamic discrepancies in telemetry simulation. This dual-approach innovation successfully bridges the critical trade-off between real-time computation and predictive accuracy while maintaining symmetric consistency between the physical turbine and its digital counterpart, providing a validated technical foundation for the intelligent operation and maintenance of MSTs.

The Development of Social Entrepreneurship in Remote Communities: A Case Study in Sidenreng Rappang

Background. Social entrepreneurship has emerged as a strategic approach to address socio-economic disparities, especially in remote communities where access to economic opportunities and institutional support is limited. Purpose. This study aims to explore the development of social entrepreneurship in the remote region of Sidenreng Rappang, Indonesia, focusing on community-driven initiatives and their socio-economic impacts. Method. Employing a qualitative case study methodology, data were collected through in-depth interviews, participant observation, and document analysis involving local entrepreneurs, community leaders, and development agencies. Results. The findings reveal that successful social entrepreneurship in the region is driven by strong local leadership, the utilization of indigenous knowledge, and adaptive strategies that align with the community’s cultural and ecological context. Challenges identified include limited access to capital, insufficient training in entrepreneurial skills, and weak institutional support. Conclusion. The study concludes that fostering social entrepreneurship in remote areas requires a hybrid support system involving education, mentorship, policy facilitation, and community empowerment. This research contributes to the growing body of literature on inclusive development by highlighting the contextual dynamics of entrepreneurial ecosystems in marginalized areas.

Prediction of Shear Strength of Steel Fiber-Reinforced Concrete Beams with Stirrups Using Hybrid Machine Learning and Deep Learning Models

The shear behavior of beams cast with steel fiber reinforced concrete and provided with stirrups is a complex phenomenon that depends on various factors. In the present research effort, a hybrid support vector regression model combined with a particle swarm optimization algorithm is provided, to explore the relationship between the material and dimensional characteristics of a concrete beam and its shear strength. A database with diverse material properties associated with the shear strength of a steel fiber reinforced concrete beam was established from numerous reliable published research articles and was utilized for the development and evaluation of the model. The obtained results from the hybrid support vector regression model were then validated through the results of the artificial neural network and convolutional neural network models combined with the particle swarm optimization algorithm. In conclusion, the adopted hybrid support vector regression approach was proven to be a successful engineering technique that can be used in structural and construction engineering problems.

Prediction of diabetes using hybrid support vector machines with Artificial Bee Colony

Prediction of diabetes using hybrid support vector machines with Artificial Bee Colony

In situ synthesis of Nb-modified AFI topology molecular sieve and its modulation on the formantion of high-efficiency catalyst for 4,6-dimethyldibenzothiophene hydrodesulfurization

In situ synthesis of Nb-modified AFI topology molecular sieve and its modulation on the formantion of high-efficiency catalyst for 4,6-dimethyldibenzothiophene hydrodesulfurization

Classification of brain stroke based on susceptibility-weighted imaging using machine learning

Magnetic resonance imaging (MRI) is used to identify brain disorders, particularly strokes. Rapid treatment, often referred to as "time is brain," is emphasized in recent studies, stressing the significance of early intervention within six hours of stroke onset to save lives and enhance outcomes. The traditional manual diagnosis of brain strokes by neuroradiologists is both subjective and time-intensive. To tackle this challenge, this study introduces an automated method for classify brain stroke from MRI images based on pre- and post-stroke patients. The technique employs machine learning, with a focus on susceptibility weighted imaging (SWI) sequences, and involves four stages: preprocessing, segmentation, feature extraction, classification and performance evaluation. The paper proposes classification and performance evaluation to determine stroke region according to three types of categories, those are poor improvement, moderate improvement and good improvement stroke patients based on pre and post patients. Then, performance evaluation is verified using accuracy, sensitivity and specificity. Results indicate that the hybrid support vector machine and bagged tree (SVMBT) yields the best performance for stroke lesion classification, achieving the highest accuracy which is 99% and showing significant improvement for stroke patients. In conclusion, the proposed stroke classification technique demonstrates promising potential for brain stroke diagnosis, offering an efficient and automated tool to assist medical professionals in timely and accurate assessments.

Modeling the discharge coefficient of labyrinth sluice gates using hybrid support vector regression and metaheuristic algorithms

Gates and weirs are frequently used hydraulic structures employed for controlling water flow rates in irrigation and drainage networks. Therefore, accurately estimating the discharge coefficient (Cd) is important for precise flow measurement. The present study used intelligent predictive models for modeling Cd in labyrinth sluice gates. For this purpose, key dimensionless parameters and reliable experimental datasets were used. The support vector regression (SVR) model was hybridized with particle swarm optimization (PSO) and genetic algorithms (GA). The statistical metrics and graphical plots evaluated the performance of the generated models. Three commonly used statistical indicators, namely root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2), were used for quantitatively evaluating the performance of the proposed models. The SVR-PSO model achieved the lowest values of RMSE (0.0287) and MAE (0.0209) and the highest value of R2 (0.9732), indicating that it was more accurate than SVR-GA (RMSE = 0.0324, MAE = 0.0257, R2 = 0.9685) and SVR (RMSE = 0.0575, MAE = 0.0468, R2 = 0.8958) on the testing data. The findings revealed that the hybrid SVR methods were more accurate than the standalone SVR model. In addition, regarding the value of the objective function criterion (OBF), the SVR-PSO (OBF = 0.0245) and SVR-GA (OBF = 0.0273) had lower OBF values and provided more precise estimates of the Cd compared to existing nonlinear regression-based formulas and existing data-driven approaches. Finally, sensitivity and SHapley Additive exPlanations (SHAP) analyses determined the relative importance of each input variable for the prediction of Cd.

Hybrid work arrangements: Challenges and the way forward in the private sector

Working from home, teleworking, and hybrid work arrangements are not new concepts, but they have become the norm in response to the post-pandemic environment and are increasingly recognised for promoting a healthy work-life balance. Advances in technology have also transformed how employees manage their professional and personal lives. This study explores the work-life balance of employees in hybrid work arrangements within a private organisation in Sarawak, examining the challenges, impacts, and strategies used to balance work and personal responsibilities. Face-to-face interviews were conducted with five managers, each of whom had at least one child. The findings reveal that long working hours and heavy workloads are the main challenges employees face, negatively affecting both their family time and work performance. Time management and maintaining a positive attitude were identified as key strategies for achieving work-life balance. The study also shows that employees are generally satisfied with current hybrid work arrangement policies and support, with many expressing a desire to continue working under these arrangements until retirement. It is recommended that the organisation continue offering hybrid work options, enabling employees to optimise performance without compromising their well-being.

Artificial Intelligence Revolution for Enhancing Modern Education Using Zone of Proximal Development Approach

Artificial Intelligence has the potential to revolutionize modern education by providing personalized learning experiences, automating administrative tasks, facilitating communication between educators and students, and enabling new forms of assessment. This paper illustrates the integration of Vygotsky's Zone of Proximal Development (ZPD) with modern education methods, integrating human instruction and artificial intelligence (AI). The ZPD model describes the terrain between what a learner can accomplish independently and what they can be taught with the assistance of an instructor. The learner is at the center of this model, whose learning trajectory is directed by the instructor and modern AI tools. The convergence of these forces within the ZPD creates a highly efficient learning process where support is adaptive and tailored to the individual. Scaffolding, the key construct in ZPD, is provided via expert instructor instruction and AI-generated personal feedback. While the instructor provides pedagogical expertise and emotional support, AI platforms provide data-informed, real-time feedback tailored to personal learning needs. This hybrid support structure facilitates incremental skill acquisition and confidence build-up, resulting in measurable progress in learning. The outcome is a transition toward self-directed learning, which is a key proof of education advancement and mental development. By incorporating the technologies into the traditional teacher-student model, the suggested model identifies the future of learner-centered instruction. It aligns teaching approaches with individualized, technology-driven learning to enable sustainable academic achievement. The model also reaffirms the role of teachers as learning facilitators, leveraging AI to optimize learning paths.

Hybrid support systems: A shift in managing infarct-related cardiogenic shock

Hybrid support systems: A shift in managing infarct-related cardiogenic shock

A new prediction strategy for dynamic multi-objective optimization using hybrid Fuzzy C-Means and support vector machine

A new prediction strategy for dynamic multi-objective optimization using hybrid Fuzzy C-Means and support vector machine

Reactive metal–support interaction of In2O3/crystalline carbon hybrid support for highly durable and efficient oxygen reduction reaction electrocatalyst

Reactive metal–support interaction of In2O3/crystalline carbon hybrid support for highly durable and efficient oxygen reduction reaction electrocatalyst

SVM-RideNN: hybrid support vector machine-rider neural network based sentiment analysis using product review

ABSTRACT Nowadays, shopping on online sites has become most mainstream for users. Moreover, the social media comments and reviews serve a huge role in online shopping. The satisfactions of users have also been improved with the utilization of sentiment analysis (SA). In addition, the sentiment analysis determines the digital text, in which the messages are positive, negative, or neutral. SA has difficulty in examining sentiments that are expressed in different languages, dialects, or colloquialisms. Moreover, it faces trouble with slang, abbreviations, and emojis. To bridge this gap, a hybrid Support Vector Machine Rider Optimization Neural Network (SVM-RideNN)-based sentimental analysis is proposed in this research. This method aims to examine the type and identification of text representation, and performance of sentiment analysis. The input product reviews are obtained from the acquired dataset, and the essential features are refined. From the refined features, the sentimental analysis process is done using the SVM-RideNN. In addition, the metrics, namely precision, recall, F1-score, and rouge, are employed to validate the performance of the model, and it has attained the finest output of 91.8%, 94.9%, 93.3%, and 86.9%

Let´s meet: the communication dynamics of value propositions of convention bureaus

ABSTRACT This study responds to calls for more knowledge regarding the dynamics of value propositions. The study develops knowledge about the communication dynamics of VPs by exploring the marketing communication of convention bureaus in post-COVID times. The data includes 120 marketing communication items from 12 Swedish convention bureaus. The data is analysed inductively using content analysis. Subsequently, discourse theory is applied as an approach to analyse value proposition dynamics. The findings reveal three value proposition themes constructed as communicated benefits, emphasising personal interactions in a special place, hybrid meetings technology and support of sustainable development. This study highlights the performative role that discourses play in shaping VPs in times of change. Place, flexibility and ethics emerge as distinct features of contemporary discourse. The study contributes to the theorising of VP dynamics by proposing that benefits are continuously constituted in communication. It showcases how sociocultural norms permeate marketing communication and expands the concept of value proposition dynamics with a time- and place-sensitive communication approach. Further, the study provides guidance for marketing professionals' responses to adverse events, innovations, and long-term changes in society.

Review of intermediate representations for quantum computing

Intermediate representations (IRs) are fundamental to classical and quantum computing, bridging high-level quantum programming languages and the hardware-specific instructions required for execution. This paper reviews the development of quantum IRs, focusing on their evolution and the need for abstraction layers that facilitate portability and optimization. Monolithic quantum IRs, such as QIR (Lubinski et al. in Front Phys 10:940293, 2022. https://doi.org/10.3389/fphy.2022.940293), QSSA (Peduri et al. in Proceedings of the 31st ACM SIGPLAN international conference on compiler construction. CC 2022. Association for Computing Machinery, New York, 2022), or Q-MLIR (McCaskey and Nguyen in Proceedings-2021 IEEE International Conference on Quantum Computing and Engineering, QCE, 2021), their effectiveness in handling abstractions, and their hybrid support between quantum-classical operations are evaluated. However, a key limitation is their inability to address qubit locality, an essential feature for distributed quantum computing (DQC). To overcome this, InQuIR (Nishio and Wakizaka in InQuIR: Intermediate Representation for Interconnected Quantum Computers, 2023. https://arxiv.org/abs/2302.00267) was introduced as an IR specifically designed for distributed systems, providing explicit control over qubit locality and inter-node communication. While effective in managing qubit distribution, InQuIR’s dependence on manual manipulation of communication protocols increases complexity for developers. NetQIR (Vázquez-Pérez et al. in NetQIR: An Extension of QIR for Distributed Quantum Computing, 2024. https://arxiv.org/abs/2408.03712), an extension of QIR for DQC, emerges as a solution to achieve the abstraction of quantum communications protocols. This review emphasizes the need for further advancements in IRs for distributed quantum systems, which will play a crucial role in the scalability and usability of future quantum networks.

Coupling Beams’ Shear Capacity Prediction by Hybrid Support Vector Regression and Particle Swarm Optimization

Coupling Beams’ Shear Capacity Prediction by Hybrid Support Vector Regression and Particle Swarm Optimization

Forecasting Natural Gas Futures Prices Using Hybrid Machine Learning Models During Turbulent Market Conditions: The Case of the Russian–Ukraine Crisis

ABSTRACTRecently, many researchers have shown keen interest in natural gas price prediction using machine learning and hybrid architectures. Our research forecasts natural gas future prices with different hybrid machine learning models using over a hundred technical indicators. The hybrid deep cross‐network model outperformed the single‐stage deep cross‐network regression and hybrid support vector machine models with 33% and 46% lower mean absolute error and 22% and 1.2 times better directional hit rate during 11 months of turbulent market circumstances due to the Russia–Ukraine crisis. The hybrid deep cross‐network model is 14, 5, and 6 times more profitable than the hybrid support vector machine, the benchmark passive buy‐and‐hold strategy, and the single‐stage deep cross‐network regression models. The hybrid deep cross‐network model is resilient during low‐ and high‐volatility periods. Deep cross‐network algorithm technical indicator interactions are more statistically significant than support vector machine polynomial kernel interactions. Energy traders and policymakers can exploit our findings.

Validating a digital depression prevention program for adolescents in Jordan: cultural adaptation and user testing in a randomized controlled trial.

Digital health interventions (DHIs) offer scalable solutions for improving mental health care access in underserved settings. This study is part of a multi-phased project aimed at adapting a depression prevention DHI for Jordanian adolescents. It evaluated the feasibility, cultural acceptability, and effectiveness of the translated and culturally adapted DHI, named Al-Khaizuran, with comparisons to school-based group CBT. A two-arm, single-blind randomized controlled trial with a mixed-methods design was conducted among 109 Jordanian adolescents aged 15-17 years experiencing mild to moderate depression. Participants were randomly assigned to either Al-Khaizuran DHI (n=55) or school-based group CBT (n=54). The adaptation of Al-Khaizuran DHI components was guided by the Ecological Validity Framework, while the procedural adaptation followed Barrera and Castro's Heuristic Framework, incorporating iterative refinement based on user feedback and contextual considerations. Al-Khaizuran DHI was found to be a culturally relevant and acceptable intervention for Jordanian adolescent. Over half of the participants reported that the intervention was effective, empowering, and easy to use, with 51% expressing satisfaction and willingness to recommend it. However, challenges such as limited access to personal devices, privacy concerns, and participants' reliance on shared family resources emerged as significant barriers to consistent engagement. Participants showed a preference for individualized, blended interventions, with a significant reduction in support for group CBT. No significant difference was found in depression scores between the two groups. However, the Al-Khaizuran DHI group demonstrated higher post-intervention resilience scores (p=0.026). Beliefs about the effectiveness of the intervention significantly predicted behavioral intention (p=0.022), while perceived difficulty was a barrier to adherence (p=0.015). Al-Khaizuran DHI exemplifies the potential of culturally adapted digital interventions in bridging mental health care gaps in resource-limited settings. However, its effectiveness is contingent upon addressing barriers to access, enhancing program interactivity, and integrating hybrid support systems that combine digital tools with in-person guidance. Future implementations should consider strategies to actively engage parents to foster a supportive environment that promotes the well-being of adolescents. https://doi.org/10.1186/ISRCTN14751844, identifier ISRCTN14751844.

Improving the electrocatalytic activity of Pd nanoparticles through electronic coupling interaction with a Ni2P–MoS2 hybrid support for ethanol electro-oxidation in an alkaline medium

Pd/Ni2P–MoS2 hybrid shows improved electrocatalytic activity for ethanol electrooxidation. The interaction between Pd and the support enhances reaction kinetics and stability in alkaline environments, leading to better overall performance.

Investigating glutaraldehyde cross-linked starch as a hybrid support for immobilizing pectinase and xylanase for pectic-oligosaccharides production

Investigating glutaraldehyde cross-linked starch as a hybrid support for immobilizing pectinase and xylanase for pectic-oligosaccharides production