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Found 6515 publications
Direct Terpene extraction from aromatic herbs: Comparative evaluation of ultrasound-assisted stir bar sorptive extraction and ultrasound-assisted extraction by HPLC-PDA with AGREE/AGREEprep greenness assessment
Publication Name: Journal of Food Composition and Analysis
Publication Date: 2026-07-01
Volume: 155
Issue: Unknown
Page Range: Unknown
Description:
This study presents the development and comparison of two extraction methods: ultrasound- assisted stir bar sorptive extraction with liquid desorption (UA-SBSE-LD) and ultrasound-assisted extraction (UAE) for the isolation and quantification of six major terpenes and terpenoids (thymol, carvacrol, eugenol, p-cymene, γ-terpinene, and α-pinene) from culinary aromatic herbs. The analytes were analyzed by high-performance liquid chromatography with photodiode array detection (HPLC-PDA). For UA-SBSE-LD, the effects of key extraction parameters, including adsorption time, ionic strength, pH, desorption solvent composition, desorption time, and solid:liquid ratio, were investigated, while UAE was evaluated using a water–ethanol system without pH adjustment. Both techniques demonstrated excellent linearity (R² ≥ 0.9805), acceptable detection limits (0.21–17 µg/mL), and satisfactory recoveries (82–120%), meeting validation requirements. Applied to thyme, oregano, rosemary, and basil, UA-SBSE-LD outperformed UAE for volatile hydrophobic compounds like γ-terpinene and α-pinene, yielding higher extraction efficiencies without organic solvent pretreatment. UA-SBSE-LD achieved up to 6.89 mg/g of α-pinene from rosemary and 4.04 mg/g of eugenol from basil. The sustainability of the methods was evaluated using AGREE and AGREEprep tools, indicating a greener profile for UAE. The direct application of UA-SBSE-LD to solid plant matrices, coupled with HPLC-PDA, represents a sensitive alternative to gas chromatography-based methods for determining terpenes in food matrices.
Open Access: Yes
Understanding the psychology of knowledge sharing and experience in digital service ecosystems
Publication Name: Acta Psychologica
Publication Date: 2026-07-01
Volume: 267
Issue: Unknown
Page Range: Unknown
Description:
Drawing on service-dominant (SD) logic, which conceptualizes value as emerging through resource integration and use rather than direct technological outputs, the study examines how technology-mediated knowledge-sharing platforms (TMKSP) influence employee and employee-perceived customer experience using the DART (dialogue, access, risk assessment, transparency) framework of value co-creation. Employing a mixed-method approach, a pre-hoc qualitative study (Study A) identified key TMKSP features relevant to value co-creation, which informed the development of a DART-based survey for the quantitative phase (Study B). Data from retail employees were analyzed using PLS-SEM with two-tailed bias-correct bootstrapping. The findings show that TMKSP significantly improves employee experience via platform access and reduced perceived risk, while enhancing employee-perceived customer experience through employee-customer dialogue and platform transparency. Mediation analysis confirms the explanatory role of DART-based constructs in linking TMKSP with experience outcomes, although the mediating role of perceived platform risk was not supported. The study contributes theoretically by operationalizing SD logic within an internal service ecosystem and demonstrating how value-in-use is shared through employee-perceived co-creation conditions rather than through direct technological effects. It offers practical guidance for managers aiming to design employee and customer-centric knowledge-sharing ecosystems.
Open Access: Yes
Computational thinking and self-leadership as predictor of innovative work behavior among employees in green product firms : An explanatory sequential mixed method
Publication Name: Acta Psychologica
Publication Date: 2026-07-01
Volume: 267
Issue: Unknown
Page Range: Unknown
Description:
This study employed an explanatory sequential mixed-method design to investigate the factors influencing innovative work behavior in green product firms in Pakistan. Guided by social cognitive theory, data from 278 employees were analyzed using structural equation modeling in AMOS, followed by qualitative interviews to further explain and contextualize the quantitative findings. The findings showed that computational thinking (β = 0.62, p < 0.001) and self‑leadership (β = 0.56, p < 0.001) have a significant positive association with creative self-efficacy. Additionally, creative self-efficacy has a significant direct positive influence on innovative work behavior (β = 0.76, p < 0.001). The mediation analysis confirmed that creative self-efficacy significantly mediated the relationship between computational thinking and innovative work behavior (indirect β = 0.29) and between self‑leadership and innovative work behavior (indirect β = 0.26). Notably, knowledge sharing significantly moderated the relationship between creative self-efficacy and innovative work behavior (β = 0.32, p < 0.001) strengthening the effect of creative self-belief on innovative. Eighteen (n = 18) interviews were conducted to gain insight into how these mechanisms worked. During the thematic analysis, results revealed that knowledge sharing weakens negative effect of hierarchical constraints, enabling employees to act on their creative self-efficacy. Computational thinking is associated with a language of credibility for innovative ideas, while self‑leadership is associated with a necessary internal motivation against bureaucratic fatigue. These findings are relevant for green product firms operating in high power distance, resource-constrained contexts such as Pakistan.
Open Access: Yes
Gender and Power: Financial Independence and Women's Relational Empowerment in the Global South
Publication Name: Gender Work and Organization
Publication Date: 2026-07-01
Volume: 33
Issue: 4
Page Range: 1283-1297
Description:
This study adopts a positive and contextually grounded representation of married women in Global South (GS) countries through the theory of gender and power (TGP) and Kabeer's empowerment framework, to examine factors driving financial independence (FI) and empowerment among women in Mauritius and Zimbabwe. Drawing on 55 in-depth interviews with married women (28 in Mauritius and 27 in Zimbabwe), findings indicate that gendered power relations and institutional forces are pivotal in shaping empowerment for married women. Three interconnected themes emerged: “societal and institutional factors,” “context-embedded financial independence and autonomy,” and “women's relational empowerment.” Theoretically, we intersect Kabeer's empowerment framework with the TGP to illustrate how FI operates at the nexus of resources, agency, gendered power relations, and structural constraints, both aligning with and challenging universalized assumptions in gender, development, and empowerment research. Empirically, the paper advances scholarship by providing nuanced insights into empowerment processes within under-researched GS contexts.
Open Access: Yes
DOI: 10.1111/gwao.70123
Environmental and socio-economic factors behind data provision in 17 citizen science projects
Zoltán Csabai
Marianna Biró
Judit Vörös
László Mezőfi
Barna Páll-Gergely
Zsuzsanna Márton
Zoltán Soltész
Zsófia Horváth
Bálint Halpern
Zsóka Vásárhelyi
Kornélia Kurucz
Bálint Pernecker
Gábor Földvári
Barbara Barta
Balázs Károlyi
Ágnes Turóci
Éva Szabó
Erika Juhász
Ádám Selmeczi-Kovács
Péter Lovászi
László Zsolt Garamszegi
Publication Name: People and Nature
Publication Date: 2026-07-01
Volume: 8
Issue: 7
Page Range: 2251-2265
Description:
Citizen science approaches in ecology have recently become increasingly popular. Although many advantages, such as the cost-effective collection of vast amounts of data, outweigh the disadvantages, most projects face difficulties, such as non-random sampling, pseudo-absences or various biases, such as detection/reporting biases or participant-related biases. To unravel some of the environmental and socio-economic factors underlying data provision occurring non-randomly, we analysed the geographically tractable record-level databases of 17 separate citizen science projects in ecology and conservation in Hungary. We matched the records to an independent administrative dataset to identify those environmental and socio-economic predictors that are expected to shape participant activity, which varies widely according to the purpose, subject and other characteristics of the projects. Despite the projects' variation, we were able to identify general patterns linking population density of a given municipality and the proportion of protected areas with participant activity. Both variables were significantly associated with the number of observations. If the most urbanised and densely populated capital was left out of the analysis, both the level of education and the proportion of elderly people were positively associated with the number of observations a project received. However, the relationship between a population's socio-economic status and participant activity varied greatly across particular citizen science projects. Our results highlight that citizen science participation is shaped by both environmental context and socio-economic characteristics, revealing systematic spatial biases in data provision. Our results thus provide new insights into the methodology and design of future citizen science projects. Read the free Plain Language Summary for this article on the Journal blog.
Open Access: Yes
DOI: 10.1002/pan3.70335
Effect of Sugarcane Bagasse Ash on the sustainable performance of hot-mix asphalt: A case study of experimental and numerical analysis
Publication Name: Case Studies in Construction Materials
Publication Date: 2026-07-01
Volume: 24
Issue: Unknown
Page Range: Unknown
Description:
The growing demand for sustainable road infrastructure has intensified the interest in alternative mineral fillers that reduce natural resource consumption and environmental impacts. This study investigates the use of Sugarcane Bagasse Ash (SBA), an abundant agricultural by-product in sub-Saharan Africa, as a partial replacement for conventional mineral fillers in hot-mix asphalt (HMA). Unlike previous studies that considered SBA primarily as a minor additive, this study provides a systematic evaluation across a wide replacement range (0–16 %), combined with experimental testing and numerical validation. Marshall and indirect tensile strength (ITS) tests were conducted on HMA mixtures produced using locally sourced Nigerian aggregates and 60/70 penetration-grade bitumen. A three-dimensional finite element model (FEM) of the ITS configuration was developed to corroborate the experimental response and identify stress concentration zones. results indicate that SBA improves both mechanical and volumetric performance within an optimal replacement range of 6–10 %, with peak performance of approximately 8 % SBA. Within this range, Marshall stability increased from 7.6 kN to 9.0 kN, the Marshall quotient reached 3.3 kN/mm, bulk density increased to 2.51 g/cm³, and air voids decreased from 4.9 % to 3.5 %, remaining within standard design limits. Microstructural analyses confirmed the predominance of amorphous silica and porous SBA morphology, which promoted enhanced filler–binder interactions and mixture densification. FEM predictions of peak tensile stress agreed with laboratory ITS results within 10 % and successfully reproduced observed crack initiation zones. Excessive SBA content (> 10 %) led to reduced stability and density owing to over-filling effects. The findings demonstrate that 6–10 % SBA is a technically viable and sustainable filler replacement for HMA, particularly in sugarcane-producing regions, offering improved performance alongside waste valorization and reduced reliance on quarry-derived fillers.
Open Access: Yes
Artificial neural network analysis of chemical reaction and radiation effects on MHD ternary nanofluid flow over an exponentially accelerated inclined plate
Publication Name: South African Journal of Chemical Engineering
Publication Date: 2026-07-01
Volume: 57
Issue: Unknown
Page Range: Unknown
Description:
This investigation explores the magnetohydrodynamic (MHD) free convective heat and mass transfer characteristics of a ternary nanofluid traversing an exponentially accelerated inclined plate within a porous medium. The theoretical framework integrates the complexities of internal heat generation/absorption and fluctuating wall temperatures. Analytical solutions were rigorously derived utilizing the Laplace transform technique, while a sophisticated Artificial Neural Network (ANN) was implemented to forecast and corroborate these mathematical outcomes. Heat Transfer (Nusselt Number) evaluated against the interplay of the Prandtl number, thermal radiation parameters, and temporal progression. Mass Transfer (Sherwood Number) analyzed as a function of magnetic permeability, the Schmidt number, and time. Thermal Enhancement findings indicate that an augmentation in the nanofluid volume fraction significantly bolsters thermal conductivity, thereby elevating the temperature profile. The proposed Levenberg-Marquardt Algorithm-based Backpropagation Artificial Neural Network (LMA BANN) demonstrated exceptional predictive fidelity. The model achieved a precision threshold exceeding 99.9% for the Nusselt number and near-perfect accuracy for the Sherwood number. These results are substantiated by negligible Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) values, coupled with correlation coefficients (R) nearing unity, signifying a robust alignment between the analytical and predicted datasets.
Open Access: Yes
Artificial intelligence-driven performance analysis of carbon nanotubes hybrid nanofluid with wastewater treatment applications: an intelligent neuro-computing model
Publication Name: South African Journal of Chemical Engineering
Publication Date: 2026-07-01
Volume: 57
Issue: Unknown
Page Range: Unknown
Description:
The current study examines the properties of heat radiation on the Darcy Forchheimer flow of carbon nanotube/water based hybrid nanofluid across a Riga plate in the occurrence of oxytactic microbes, employing a novel intelligent numerical computing paradigm based on the legacy of neural networks with the intelligent Bayesian regularization (NN-IBR) method. The AI-driven neuro-computing model for improving the thermal behavior of a carbon nanotube (CNT) hybrid nanofluid in wastewater treatment has a wide range of applications. It has the potential to dramatically improve thermal management efficiency in wastewater treatment plants, improve pollutant removal through optimal heat and mass transfer, and minimize energy consumption in treatment operations. This model can also be used in sustainable water recycling, industrial effluent treatment, and smart environmental management systems, where intelligent prediction and control of nanofluid performance is critical for accomplishing environmentally friendly and cost-effective operations. The Homotopy analysis approach is used to classify the obtained equations. The concentration profile increases as the activation energy parameter values upsurge.
Open Access: Yes
Decision-Analytics-Based Stock Selection: A Fuzzy Aczel–Alsina Ordinal Priority Approach
Publication Name: International Journal of Fuzzy Systems
Publication Date: 2026-07-01
Volume: 28
Issue: 5
Page Range: 1495-1519
Description:
In today’s competitive environment, evaluating and selecting stocks for portfolio optimization is a critical challenge for investors, especially under conditions of uncertainty. Traditional approaches often fail to address the complexities of multi-criteria decision-making (MCDM) in real-world investment scenarios. This study introduces a novel fuzzy Ordinal Priority Approach based on Aczel–Alsina weighted evaluation (OPA-AAWE) to tackle the portfolio selection problem. Taking into account seven financial performance criteria, the model was applied to 374 stocks listed on the Istanbul Stock Exchange for a period of 12 months. The findings demonstrate that the proposed methodology effectively handles uncertainty, offers flexibility in decision-making, and identifies the most optimal portfolios. Sensitivity analysis further confirms the robustness and reliability of the model. These results highlight the practical applicability of the fuzzy OPA-AAWE framework in real-world investment decision-making, offering investors a comprehensive tool for improved portfolio selection.
Open Access: Yes
