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Found 6374 publications
Microalgae–bacteria interaction: a catalyst to improve maize (Zea mays L.) growth and soil fertility
Publication Name: Cereal Research Communications
Publication Date: 2025-06-01
Volume: 53
Issue: 2
Page Range: 1037-1049
Description:
Biofertilisers harbouring living organisms hold allure due to their prospective favourable influence on plant growth, coupled with a diminished environmental footprint and cost-effectiveness in contrast to conventional mineral fertilisers. The purpose of the present study was to evaluate the capacity of a specific microalga (MACC-612, Nostoc linckia) biomass and plant growth-promoting bacteria (PGPB) separately and together to improve crop growth and promote soil health. The research used a factorial design within a completely randomised block framework, featuring four replications for three consecutive years across different fields. The experiment utilised three levels of microalga (control, 0.3 g/L of N. linckia, MACC-612, and 1 g/L of N. linckia, MACC-612) and three levels of bacterial strains (control, Azospirillum lipoferum and Pseudomonas fluorescens). The result demonstrated that the use of N. linckia and PGPB separately or jointly as soil treatment resulted in a substantial improvement in chlorophyll, plant biomass, soil humus, and nitrogen, depending on the environmental conditions of the years. The combined use of N. linckia and PGPB results in an improvement in dry leaf weight by 35.6–107.3% at 50 days after sowing (DAS) and 29.6–49.8% at 65 DAS, compared to the control group. Furthermore, the studies show that the synergistic application of N. linckia at 0.3 g/L, in conjunction with A. lipoferum, significantly improved total nitrogen and (NO3 − + NO2 −)-nitrogen, registering increases of 20.7–40% and 27.1–59.2%, respectively, during the study period. The most effective synergistic combination was identified through the application of 0.3 g/L of N. linckia along with A. lipoferum. Hence, application of biofertilisers through synergistic combinations of two or more microorganisms, such as microalgae and bacteria, holds promise in improving crop chlorophyll, growth, and soil nitrogen.
Open Access: Yes
How does intergenerational transmission affect green innovation? Evidence from Chinese family businesses
Publication Name: Structural Change and Economic Dynamics
Publication Date: 2025-06-01
Volume: 73
Issue: Unknown
Page Range: 158-169
Description:
Green innovation in family businesses is a significant yet underexplored area of research, particularly with regard to the influence of dynamic succession characteristics on intergenerational inheritance and its impact on innovation. This study, integrating the social-emotional wealth theory (SEW) and the agency theory, examines 505 Chinese listed family firms spanning from 2011 to 2020. Employing the Difference-in-Differences (DID) method, we investigate how intergenerational inheritance affects green innovation investment over time. Our findings reveal that initially, intergenerational transmission tends to inhibit green innovation investment in family businesses; however, this effect diminishes as the intergenerational process unfolds, indicative of the maturation of the second generation. Notably, we observe that a higher education level among second-generation heirs weakens the inhibitory effect of intergenerational inheritance on green innovation investment. This study addresses a gap in green innovation research by considering intergenerational transmission dynamics in family businesses, thus enhancing our understanding of innovation behaviors within this context. By synthesizing SEW and agency theory, this research offers novel insights into the varying impacts of intergenerational inheritance on firm innovation, shedding light on approaches to reconcile the willingness-ability paradox in family business innovation and promoting effective governance of succession processes.
Open Access: Yes
Optimal parameter extraction of equivalent circuits for single- and three- phase Power transformers based on arctic puffin algorithm accomplished with experimental verification
Publication Name: Results in Engineering
Publication Date: 2025-06-01
Volume: 26
Issue: Unknown
Page Range: Unknown
Description:
The power transformer is a critical device in power systems. This paper addresses one of the major problems which hopes to enhance the accuracy of estimation of parameters, which is critical in power transformer modeling, maintenance, and operating efficiency. In that context, this work estimates the parameters of single- and three-phase power transformers by a new optimizer called Arctic Puffin Optimization Algorithm (APO). The algorithm is intended to improve estimation of transformer parameters with the goal of reducing the error incurred between the estimated and actual values of the parameters. To verify the accuracy of the APO, experimental measurements were conducted on single- and three-phase transformers. The assessment of the algorithm's effectiveness was performed against the effectiveness of other commonly used estimating methods. The results have shown that APO increases the accuracy of estimation of the parameters of both single- and three-phase transformers to considerable levels. Dependability of the APO was established by experimental verification, which disclosed an ultimate connection between the resultant quantities and actual measurements. The study also confirmed APO can be useful for transformer parameter estimation because APO converges more rapidly and more precisely compared with traditional methods of the literature.
Open Access: Yes
Integrating Artificial Intelligence into Fuzzy Decision Analytics: A Novel Approach to Mitigating Stereotype Threat in Sustainable Business Environments
Publication Name: Journal of Fuzzy Extension and Applications
Publication Date: 2025-06-01
Volume: 6
Issue: 2
Page Range: 371-390
Description:
Preventing the threat of stereotyping is critical for business performance improvements. Because of this situation, businesses must take the necessary precautions. However, these actions have an impact on cost increase for the businesses. The number of studies in the literature performing priority analysis for these factors is quite limited. This situation increases the need for a new study that prioritizes the analysis of these variables. Accordingly, this study aims to evaluate the factors against the stereotype threat in the sustainable business environment. An artificial intelligence model is implemented in the first stage to weigh the experts. In the following stage, selected criteria are evaluated with the help of T-Spherical fuzzy DEMATEL. Thirdly, a comparative analysis was performed using different values. Finally, selected industries are ranked by Spherical Fuzzy RATGOS with respect to the stereotype threat. The weights of the experts can be identified in the analysis process. This situation has a strong contribution to the effectiveness of the findings. It is concluded that training activities are critical to minimizing the threat of stereotypes in companies.
Open Access: Yes
Global trade of medicinal and aromatic plants. A review
Publication Name: Journal of Agriculture and Food Research
Publication Date: 2025-06-01
Volume: 21
Issue: Unknown
Page Range: Unknown
Description:
Medicinal and aromatic plants (MAPs) are essential natural resources with applications in pharmaceuticals, food, cosmetics, and pesticides. With growing consumer preferences for natural products, the global trade of MAPs (HS code: 1211) has grown significantly. This study analyzed global MAP market trends using export and import data from the International Trade Center (ITC) from 2010 to 2023. During this period, global export and import values surged by 97.8 % and 98.1 %, reaching $4.18 billion and $4.25 billion, respectively, in 2023. China and India emerged as key exporters, with India achieving a 240 % growth in export value, while the United States, Germany, and Japan were leading importers due to high domestic demand and advanced processing infrastructure. HS 121190, comprising plants for perfumery, pharmacy, and pest control, accounted for over 90 % of total trade value, ranking as the 976th most traded product globally in 2022. MAPs prices vary by origin, with vanilla ($115–255.39/kg) as the most expensive and arugula ($0.12/kg) the cheapest. Certifications like WHO-GACP and GMP are critical for quality assurance, traceability, and market competitiveness. Challenges include overharvesting, habitat destruction, trade barriers, and inconsistent quality control, necessitating sustainable cultivation, advanced processing technologies, and harmonized regulations. While Asia-Pacific, led by China and India, dominates production due to biodiversity and supportive policies, Europe and North America focus on value-added re-export. This study underscores the pivotal role of MAPs in global trade. It also provides actionable insights for stakeholders to optimize strategies, embrace sustainability, and capitalize on the expanding demand for these versatile plants.
Open Access: Yes
Driving Social Entrepreneurship Among Students: Investigating Through PLS-SEM and fsQCA Approaches in Emerging Economies
Publication Name: Emerging Science Journal
Publication Date: 2025-06-01
Volume: 9
Issue: 3
Page Range: 1591-1609
Description:
This study aims to identify the relationship between social self-efficacy, social innovation, resilience, and proactive personality concerning university students’ behavioral intention to engage in social entrepreneurship, particularly in emerging economies, like Bangladesh. A structured questionnaire was utilized to collect quantitative data from 540 students in various disciplines of study as part of the study's quantitative research methodology using partial least squares-Structural Equation Modelling (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA). The analysis reveals that proactive personality traits are associated with the social entrepreneurship intention (SEI) and that leadership orientation is also significant to SEI. The study also demonstrates that social entrepreneurial activities tend toward higher social self-efficacy and resilience, making it crucial to focus on such characteristics while facing social risk and bearing innovations. This study's novelty lies in its focus on the unique combination of psychological traits—social self-efficacy, social innovation, resilience, and proactive personality—and their impact on university students' intention to engage in social entrepreneurship in emerging economies. Additionally, the research emphasizes the importance of integrating leadership skills and social innovation into academic curricula and policy development to foster social entrepreneurship. Practical implications indicate that leadership skills and social innovation should be included in the curricula of educational institutions, and supportive policies should be developed to create available resources for prospective social entrepreneurs.
Open Access: Yes
Dem-driven investigation and AutoML-Enhanced prediction of Macroscopic behavior in cementitious composites with Variable frictional parameters
Publication Name: Materials and Design
Publication Date: 2025-06-01
Volume: 254
Issue: Unknown
Page Range: Unknown
Description:
This study presents a numerical investigation and predictive modeling framework to evaluate the influence of microscale frictional parameters on the mechanical behavior and failure mechanisms of cementitious composites. In the first phase, discrete element modeling (DEM) was employed to analyze the effects of bonded friction angle and non-bonded friction coefficient on the stress–strain response, failure evolution, and macro-scale properties. The results revealed a distinct transition from tensile to shear-dominated failure modes beyond a critical friction angle, accompanied by notable changes in compressive strength and deformation characteristics. Additionally, the role of non-bonded friction coefficient in post-failure behavior was identified, emphasizing its influence on load-redistribution. In the second phase, an AutoML-driven artificial neural network (ANN) was optimized via grid search, selecting an optimal four-layer model to predict macroparameters from microscale DEM inputs. The proposed ANN demonstrated high predictive accuracy, effectively capturing nonlinear dependencies while significantly reducing the need for additional numerical simulations. This integration of DEM and AI-based predictive modeling provides a computationally efficient, scalable solution for material characterization, enabling faster, data-driven insights into cementitious composite behavior without reliance on extensive simulation campaigns.
Open Access: Yes
Young Adults’ Feelings and Knowledge of Climate Anxiety
Publication Name: Journal of Sustainability Research
Publication Date: 2025-06-01
Volume: 7
Issue: 2
Page Range: Unknown
Description:
This study investigates the impact of climate anxiety on young adults’ consumer and social behaviour. Data were collected via a questionnaire survey among 696 university students from Széchenyi István University, Budapest Metropolitan University, and Neumann János University. The survey focused on various aspects of climate anxiety, including its frequency, intensity, perceived life impact, emotional responses, and management strategies. The analysis, supported by AI tools, identified two distinct clusters: one with moderate anxiety levels and a strong interest in learning about climate change, and another with higher anxiety levels but less desire for further information. Various statistical models, including Naive Bayes, logistic regression, and random forests, were employed to identify behavioural patterns, with decision trees showing the lowest classification error. The study highlights the significant influence of climate anxiety on the shift towards sustainable consumption and active engagement in climate action. Recommendations for future research include the broader application of deep learning models and extending the study to other demographic groups. Longitudinal data collection is also suggested to track long-term trends and inform effective public policy and communication strategies. The findings emphasise the need for comprehensive approaches to understanding and addressing climate anxiety’s societal impacts.
Open Access: Yes
DOI: 10.20900/jsr20250025
Soil Moisture Content Prediction Using Gradient Boosting Regressor (GBR) Model: Soil-Specific Modeling with Five Depths
Publication Name: Applied Sciences Switzerland
Publication Date: 2025-06-01
Volume: 15
Issue: 11
Page Range: Unknown
Description:
Monitoring soil moisture content (SMC) remains challenging due to its spatial and temporal variability. Accurate SMC prediction is essential for optimizing irrigation and enhancing water use efficiency. In this research, a Gradient Boosting Regressor (GBR) model was developed and validated to predict SMC in two soil textures, loam and silt loam, using meteorological data from Internet of Things (IoT) sensors and gravimetric SMC field measurements collected from five different depths. The statistical analysis revealed significant variation in SMC across depths in loam soil (p < 0.05), while silt loam exhibited more stable moisture distribution. The GBR model demonstrated high performance in both soil textures, achieving R2 values of 0.98 and 0.94 for silt loam and loam soils, respectively, with low prediction errors (RMSE 0.85 and 0.97, respectively). Feature importance analysis showed that precipitation and humidity were the most influential features in loam soil, while solar radiation had the highest impact on prediction in silt loam soil. Soil depth also showed a significant contribution to SMC prediction in both soils. These results highlight the necessity for soil-specific modeling to enhance SMC prediction accuracy, optimize irrigation systems, and support water resources management approaches aligning with SDG6 objectives.
Open Access: Yes
DOI: 10.3390/app15115889
