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Found 6273 publications
Technological and antioxidant characteristics of milk curds coagulated with fresh fig latex under different coagulation conditions
Publication Name: Applied Food Research
Publication Date: 2026-06-01
Volume: 6
Issue: 1
Page Range: Unknown
Description:
Plant-derived milk coagulants have received growing attention as natural alternatives to rennet, yet limited information is available regarding the technological and antioxidant characteristics of curds produced with Ficus carica latex. This study evaluated the milk-clotting ability of fresh Ficus carica latex and its influence on the physicochemical, textural, and antioxidant properties of milk curds produced under different coagulation conditions (latex dosage: 200–300 µL; pH: 5.8–6.2; temperature: 32–45 °C). Coagulation time ranged from 15 min (45 °C, pH 6.2, 300 µL) to 52 min (32 °C, pH 5.8, 300 µL). Fig latex did not markedly affect final pH (6.00–6.11), and the highest curd yield (15.0 %) was achieved with 300 µL latex at 45 °C and pH 5.8, while the rennet-induced sample yielded 15.9 % under standard conditions. Latex addition significantly increased TPC values (up to 374 mg GAE/kg), whereas 200 µL resulted in lower TPC levels (149 mg GAE/kg). Fig latex–induced curds exhibited distinct mechanical properties, with generally lower hardness, cohesiveness, and chewiness values than the rennet-induced curd. Therefore, Ficus carica latex may represent a potential plant-derived milk coagulant, particularly for applications in fresh or soft-type cheeses, and the results suggest that antioxidant-related properties of curds can be enhanced without additional enrichment steps.
Open Access: Yes
A novel hybrid neutrosophic-fuzzy-uncertain data envelopment analysis model for assessment of wind farm locations
Publication Name: Energy Nexus
Publication Date: 2026-06-01
Volume: 22
Issue: Unknown
Page Range: Unknown
Description:
Renewable energy resources have got much attention in recent years. Because of climate change in the world, the countries try to develop renewable energy resources. Wind farms are renewable energy resources that can produce electricity with no negative effect on climate change. In this study, as an important topic, the location selection problem of wind farms is considered as a real case study. For the first time, a multi-criteria wind farm location selection problem with neutrosophic fuzzy and uncertain criteria at the same time is developed. As the set of criteria consists of both input and output criteria, we develop a novel hybrid neutrosophic-fuzzy-uncertain scheme of BCC DEA model for the first time to solve the problem. As solution approach, a chance-constrained programming approach based on possibility measure of the neutrosophic fuzzy constraints of the model also for the first time is proposed in this study. An extensive computational study on the case study by the proposed approach is performed. The candidate locations of the case study are prioritized where the location of Birjand city with score of 1.1656 is selected as the best location. A sensitivity analysis on the confidence levels of the chance-constrained programming approach is performed and also the obtained results are compared to the approaches of the literature.
Open Access: Yes
Optimal parameter extraction of fuel cells based on interval branch-and-bound optimization algorithm
Publication Name: Energy Reports
Publication Date: 2026-06-01
Volume: 15
Issue: Unknown
Page Range: Unknown
Description:
Fuel cells play an important role in reducing environmental impacts to produce cleaner electricity. Numerical models are used to simulate their performance and build efficient observers in real use. The accuracy of these models is a major concern, as they can be parameterized by several values. Most of the previous works study the estimation of these parameters using various metaheuristics. While these methods are stochastic and do not provide any proof of optimality, the current paper introduces a global optimization method to accurately bound the optimal root mean square error between the parameterized model and some experimental data. The proposed algorithm is based on a deterministic Interval Branch-and-Bound optimization (IBBO) framework. Interval arithmetic ensures set-based computations to safely bound the objective function value. Four types of fuel cells, with their experimental data, are used to demonstrate the efficiency of the proposed methods. IBBO results are compared with some competing optimization methods used in the literature. They show a better accuracy for the computed feasible solutions (upper bounds) and a guaranteed value of the best possible solutions (lower bounds). This last information is not possible to obtain with metaheuristic algorithms. Compared to other Branch-and-Bound algorithms, IBBO proposes a new mix of mechanisms (e.g. advanced constraint propagation, specific search heuristic and feasible point finding method). Due to the deterministic nature of IBBO, results can be repeated. Its convergence analysis is detailed on four fuel cells from which a real test system based on Scribner technology is used to demonstrate the accuracy and robustness of IBBO on several usage scenarios.
Open Access: Yes
Mapping the scholarly literature on the infodemic using topic modelling
Publication Name: Social Sciences and Humanities Open
Publication Date: 2026-06-01
Volume: 13
Issue: Unknown
Page Range: Unknown
Description:
The present study aims to map the scholarly evolution of the infodemic as a research subject through a scientometric analysis of 852 peer-reviewed articles indexed in Web of Science between 2020 and 2024 building on scientometric methods and Structural Topic Modeling (STM). Findings reveal a sharp rise in publications during the pandemic years, peaking in 2022, followed by a remarkable decline in both output and citation impact. The STM uncovered 20 distinct topics, with dominant themes centred on health communication, misinformation, social media, and institutional trust. While several themes peaked early in the pandemic, others, such as institutional or public trust, gained prominence later. Topic correlations showed dense interlinkages but low modularity suggested conceptual fragmentation and weak field consolidation. The results highlight that infodemic scholarship remains an emergent, interdisciplinary domain, however, there is a need for stable theoretical foundations.
Open Access: Yes
Mathematical Analysis of Real-Time Data Processing Methods for IoT Applications Based on Hesitant Bipolar Fuzzy Dombi Power Operators
Publication Name: Systems and Soft Computing
Publication Date: 2026-06-01
Volume: 8
Issue: Unknown
Page Range: Unknown
Description:
The rapid growth of Internet of Things (IoT) technologies has made real-time data processing a critical component for efficient monitoring, analysis, and intelligent decision-making in dynamic and large-scale environments. IoT systems continuously generate massive volumes of heterogeneous data that must be processed with minimal latency to ensure timely responses and reliable system performance. Effective real-time data processing enables IoT applications to adapt to changing conditions, enhance operational efficiency, improve safety and reliability, and support time-sensitive services in domains such as smart cities, healthcare monitoring, industrial automation, and intelligent transportation systems. This study presents a comprehensive mathematical framework for the analysis of real-time data processing methods for IoT applications based on hesitant bipolar fuzzy (HBF) Dombi power operators. The proposed model is designed to effectively capture uncertainty, hesitation, and bipolar information that naturally arise in real-world IoT environments due to incomplete, imprecise, and conflicting data sources. By incorporating a multi-criteria decision-making (MCDM) approach, multiple real-time data processing techniques are systematically evaluated and prioritized with respect to several performance-related attributes. The proposed HBF Dombi power-based framework offers a reliable and transparent mechanism for comparing competing real-time data processing strategies and selecting the most suitable method for specific IoT scenarios. The results indicate that the proposed approach improves decision accuracy and supports better alignment between data processing methods and the complex operational requirements of modern IoT systems. This work contributes both theoretical insights and practical guidance for the design and evaluation of efficient, adaptive, and intelligent real-time IoT data processing architectures.
Open Access: Yes
Psychological foundations of solo dining experience in tourism and hospitality industry. A mixed methods study
Publication Name: Acta Psychologica
Publication Date: 2026-06-01
Volume: 266
Issue: Unknown
Page Range: Unknown
Description:
Solo dining has gained substantial recognition in the restaurant business worldwide. Considering the growing trend of solo dining, it has attracted increasing interest from academia and industry. Since the current stage of knowledge on solo dining practices and their management is limited and scattered, it requires integration to provide scholars with an outline of the current state of knowledge in this field, as well as the industry expert insights. To address this issue, the current study applies a mixed-method approach to first gather insights from the existing literature by conducting a systematic literature review (SLR). Subsequently, in order to substantiate SLR findings, a qualitative study is designed to garner industry expert views on solo dining experience, enhancing restaurant practices and strategies. The SLR has revealed three distinct thematic areas, such as solo dining antecedents, solo diner's experience while dining, and the solo diner's outcome behaviour. Based on these insights, it was necessary to identify the practical takeaways for the restaurants in the hospitality industry; therefore, a qualitative study was designed following the mixed-method approach of research design. The empirical results present solo dining strategies implemented by restaurant owners and staff. The Gioia technique of qualitative analysis of data brought forward four solo dining aggregate dimensions presenting the solo diner's profile, adaptive comfort, human touch, and restaurant strategies. This study makes a novel contribution to the solo dining literature by identifying psychophysiological aspects of solo dining and offering managerial insights for restaurant industry professionals.
Open Access: Yes
Galerkin finite element analysis of trihybrid nanofluid flow in porous corrugated cavities with thermal radiation and ANN validation
Publication Name: Results in Engineering
Publication Date: 2026-06-01
Volume: 30
Issue: Unknown
Page Range: Unknown
Description:
This work tackles the issue of enhancing heat transmission and minimizing entropy formation in tiny enclosures pertinent to thermal energy storage. It looks at how magnetohydrodynamic (MHD), non-Darcian porous media, a ternary hybrid nanofluid composition (Fe3 O4 –hBN–CuO/water), and triangle corrugation work together in a corrugated rectangular cavity. The goal is to figure out how these things affect convection, entropy formation, and the overall efficiency of the thermodynamic system. Utilizing the Galerkin finite element technique (GFEM), we found numerical solutions to the mathematical models for momentum, energy, and entropy generation. The effects of the porosity parameter, ternary nanoparticle concentration, Hartmann number, Darcy number, and Rayleigh number were carefully studied for the cavities' flat and triangular corrugated walls. Artificial Neural Network (ANN) model was developed and trained to predict the average Nusselt number and total entropy generation with high precision, using fewer computational resources compared to conventional CFD approaches. It is observed that the ANN model is used mostly as an ancillary prediction instrument derived from FEM-generated data, rather than as the principal computational framework. The results show that corrugated shapes improve local heat transfer by increasing the surface area and causing flow disruptions. However, too many corrugations lower the average Nusselt numbers because they cause recirculation. Higher Rayleigh numbers make buoyancy-driven convection stronger, whereas larger magnetic fields make circulation weaker, which makes conduction-dominated transport more likely and lowers entropy generation. The porosity and Darcy number have a big effect on convective intensity and entropy formation. On the other hand, the right number of nanoparticles may boost thermal conductivity without making irreversibility too high. The ANN model showed great prediction ability (MSE≈1.12 × 10⁻⁷), which proved that it works well for quickly testing Multiphysics systems. These results show that integrating ternary nanofluids, controlling porous media, and changing the magnetic field may improve thermal performance in advanced applications, including solar collectors, cooling electronics, and thermal energy storage devices. Combining ANN prediction gives us a solid base for designing and improving next-generation heat management solutions in a way that works well.
Open Access: Yes
Who gets to use ChatGPT? A global study on digital access and inequality in higher education
Publication Name: Social Sciences and Humanities Open
Publication Date: 2026-06-01
Volume: 13
Issue: Unknown
Page Range: Unknown
Description:
This study examines how national-level digital development – measured by the ICT Development Index (IDI) – affects university students’ use of ChatGPT. Special emphasis is placed on mediating factors that may influence this relationship, including technical access, institutional and linguistic support, and individual background characteristics, particularly in relation to educational equity and sustainability (SDG 4). The analysis is based on survey data from 20,242 students across 58 countries and applies multivariate statistical methods, including logistic regression, PLS-SEM modeling, and cluster analysis. The results indicate that students in countries with higher IDI scores are more likely to use ChatGPT, primarily because of more advanced digital competencies and greater technological access. The country of study proved to be a stronger predictor than citizenship, underscoring the key role of the local educational environment. Functional access emerged as the most decisive mediating factor, while institutional and linguistic support had a more indirect effect on usage. Cluster analysis identified three distinct student profiles and highlighted that a high level of digital infrastructure alone does not ensure the widespread adoption of generative AI tools. The study proposes a multi-level interpretive framework: at the macro level, national digital infrastructure; at the meso level, institutional and linguistic support; and at the micro level, individual characteristics – connected by functional access as a mediating dimension. This context-sensitive approach contributes to a more comprehensive and practice-oriented understanding of digital inequalities and the integration of generative AI in higher education, offering guidance for promoting inclusive and sustainable technology use.
Open Access: Yes
Unveiling Marketing Competencies for MBA Graduates Through Text Mining and Machine Learning Applications
Publication Name: Applied AI Letters
Publication Date: 2026-06-01
Volume: 7
Issue: 2
Page Range: Unknown
Description:
In today's dynamic business environment, marketing professionals are expected to demonstrate a balanced mix of conceptual knowledge, technical skills, and behavioral competencies while addressing their customers. This study analyses 2000 job postings from job web portal, collected between April and June 2023, to identify the competencies most demanded from master's in business administration (MBA) graduates specializing in marketing. Using a text mining approach, including systematic preprocessing, bigram extraction, and term frequency-inverse document frequency (TF-IDF) based term weighting, the extracted competencies were categorized into knowledge, skill, and behavioral based competencies. Findings of the study reveal strong emphasis by employers on marketing research, marketing management, and sales management as core knowledge-based competencies required. While selling ability, client relationship management, and digital marketing have evolved as key skill-based competencies. Time management, teamwork, and leadership evolved as critical behavioral attributes needed for an MBA graduate specializing in the marketing domain. These insights highlight the need for management curricula to integrate conceptual learning with practical skill development and structured behavioral guidance. The results also provide accreditation bodies with evidence-based competency trends for strengthening outcome-based frameworks and guiding industry partners in aligning recruitment, training, and academic collaboration efforts. By offering data driven competencies grounded in real job market demand, the study supports the development of more industry ready MBA marketing graduates.
Open Access: Yes
DOI: 10.1002/ail2.70020
