Search Results (85)

This study examines the contributions of renewable energy transition (RET) and environmental innovation (EI) to environmental performance in G7 countries from 2003 to 2021, with a focus on the transmission channels of green finance and environmental governance. Using the Augmented Mean Group (AMG) estimator and confirming robustness through the Dynamic Common Correlated Effects Mean Group (DCCE-MG) method, the study explores both direct and indirect effects of RET and EI on two key environmental indicators: the Environmental Performance Index and the Load Capacity Factor. The results reveal that both RET and EI have a significant impact on environmental performance. Moreover, green finance and environmental governance serve as crucial channels through which RET and EI exert their influence. These findings underscore the importance of developing effective financial instruments and robust regulatory frameworks to translate energy and innovation policies into tangible environmental benefits. By highlighting the interplay between technological advancement, financial capacity, and institutional quality, this study provides novel insights into the environmental policy landscape of advanced economies and offers guidance for designing integrated strategies to achieve long-term sustainability goals. Full article

In Tunisia, rabies is endemic and represents a significant public health issue. The objectives of our study were to describe the epidemiological and clinical profiles of human rabies cases and report the risk factors associated with their occurrence. We conducted a retrospective, descriptive, and analytical study of human rabies cases confirmed at the Rabies Laboratory of the Pasteur Institute in Tunis from January 2000 to November 2022. Temporal–spatial, sociodemographic, and clinical variables and factors related to the exposure context, post-exposure, and response were collected for each patient. A total of 58 human rabies cases were identified. The governorates of Kairouan and Nabeul were the most affected, with a predominance of rural areas (77%, 34/44). The highest number of cases was recorded between May and November (74%, 43/58). The cases predominantly involved males, with the most affected age group being individuals aged from 31 to 59 years (30%, 17/57). Rabies transmission was primarily due to dogs (86%, 43/50) and a single bite (55%, 32/58). After an average incubation period of 60.3 days, hydrophobia and behavioral disturbances were the most common symptoms. This study demonstrates that the risk of human rabies remains present in Tunisia, highlighting the need to improve awareness and post-exposure prophylaxis practices. Full article

This paper investigates the link between commodity price volatility and stock market indices in Nigeria, Ghana, and Côte d’Ivoire, focusing on commodities such as oil, cocoa, and gold over a daily period from 2 January 2020 to 31 December 2021. In order to conduct this study, the BEKK-GARCH process is applied to test the volatility transmission across commodity and stock markets, while focusing on the asymmetry in the conditional variances of these markets. The analysis reveals a 30% increase in volatility spillovers during the COVID-19 period, highlighting significant asymmetry in conditional variances between African stock markets and global commodity markets. Furthermore, the findings demonstrate that conditional variances in stock and commodity markets are asymmetrical. This study advances the literature on volatility transmission by providing novel evidence on asymmetric spillovers between African stock markets and global commodity prices, particularly during COVID-19. It offers insights into the unique role of emerging African markets in global financial interconnectedness. Full article

Pain estimation is a critical aspect of healthcare, particularly for patients who are unable to communicate discomfort effectively. The traditional methods, such as self-reporting or observational scales, are subjective and prone to bias. This study proposes a novel system for non-invasive pain estimation using eye-tracking technology and advanced machine learning models. The methodology begins with preprocessing steps, including resizing, normalization, and data augmentation, to prepare high-quality input face images. DeepLabV3+ is employed for the precise segmentation of the eye and face regions, achieving 95% accuracy. Feature extraction is performed using VGG16, capturing key metrics such as pupil size, blink rate, and saccade velocity. Multiple machine learning models, including Random Forest, SVM, MLP, XGBoost, and NGBoost, are trained on the extracted features. XGBoost achieves the highest classification accuracy of 99.5%, demonstrating its robustness for pain level classification on a scale from 0 to 5. The feature analysis using SHAP values reveals that pupil size and blink rate contribute most to the predictions, with SHAP contribution scores of 0.42 and 0.35, respectively. The loss curves for DeepLabV3+ confirm rapid convergence during training, ensuring reliable segmentation. This work highlights the transformative potential of combining eye-tracking data with machine learning for non-invasive pain estimation, with significant applications in healthcare, human–computer interaction, and assistive technologies. Full article

Hematology plays a critical role in diagnosing and managing a wide range of blood-related disorders. The manual interpretation of blood smear images, however, is time-consuming and highly dependent on expert availability. Moreover, it is particularly challenging in remote and resource-limited settings. In this study, we present an AI-driven system for automated blood cell anomaly detection, combining computer vision and machine learning models to support efficient diagnostics in hematology and telehealth contexts. Our architecture integrates segmentation (YOLOv11), classification (ResNet50), transfer learning, and zero-shot learning to identify and categorize cell types and abnormalities from blood smear images. Evaluated on real annotated samples, the system achieved high performance, with a precision of 0.98, recall of 0.99, and F1 score of 0.98. These results highlight the potential of the proposed system to enhance remote diagnostic capabilities and support clinical decision making in underserved regions. Full article

This study introduces a novel framework leveraging Rough Set Theory (RST)-based feature selection—MLReduct, MLSpecialReduct, and MLFuzzyRoughSet—to enhance machine learning performance on uncertain data. Applied to a private cardiovascular dataset, our MLSpecialReduct algorithm achieves a peak Random Forest accuracy of 0.99 (versus 0.85 without feature selection), while MLFuzzyRoughSet improves accuracy to 0.83, surpassing our MLVarianceThreshold (0.72–0.77), an adaptation of the traditional VarianceThreshold method. We integrate these RST techniques with preprocessing (discretization, normalization, encoding) and compare them against traditional approaches across classifiers like Random Forest and Naive Bayes. The results underscore RST’s edge in accuracy, efficiency, and interpretability, with MLSpecialReduct leading in minimal attribute reduction. Against baseline classifiers without feature selection and MLVarianceThreshold, our framework delivers significant improvements, establishing RST as a vital tool for explainable AI (XAI) in healthcare diagnostics and IoT systems. These findings open avenues for future hybrid RST-ML models, providing a robust, interpretable solution for complex data challenges. Full article

This study investigates the impact of environmental variables, such as carbon emissions and temperature anomalies, on cryptocurrency returns. While existing research has primarily focused on economic and financial determinants, the influence of environmental factors remains underexplored. Using Dynamic Conditional Correlation GARCH (DCC-GARCH) and Time-Varying Coefficients Vector Autoregression (TVC-VAR) models, this study provides empirical evidence that environmental variables significantly affect the volatility and returns of Bitcoin, Ethereum, and Tether. The results show that Bitcoin and Ethereum are highly sensitive to CO₂ emissions and temperature fluctuations, while Tether demonstrates a more moderate response. Moreover, the impact of these environmental factors evolves over time, underscoring their dynamic nature in cryptocurrency valuation. These findings highlight the importance of incorporating environmental variables into forecasting models to enhance risk management and investment strategies. This study contributes to the literature by bridging the gap between environmental concerns and cryptocurrency market behavior, offering valuable insights for investors, regulators, and policymakers. Full article

Reinforced concrete (RC) columns are structural components that carry loads and are vulnerable to damage and possible failure under blast loads. Understanding how damage accumulates and cracks propagate in these structural members is essential for improving their resilience and designing blast-resistant buildings. This study introduces an experimental approach to mitigate the fireball and fumes generated by an explosion, allowing for a more precise structural response assessment. With the help of high-speed cameras, this study experimentally investigates the real-time damage progression and crack formation in RC columns. To explore these failure mechanisms, laboratory-scale RC columns with a low reinforcement ratio are intentionally designed to experience significant damage, providing deeper insights into concrete-specific failure patterns. The tested columns are 1800 mm long and have a 100 mm diameter. Each specimen is reinforced with 3 mm longitudinal reinforcement bars and 2 mm transverse bars. An explosive driven shock tube (EDST) is used to apply blast loads, targeting the mid-height of the columns. High-speed digital image correlation (DIC) tracks the overall structural response. A numerical simulation is developed in LS-DYNA and compared with experimental data for validation. The findings demonstrate that the proposed FE model accurately simulates both the applied blast load and the resulting failure patterns. The difference between the mid-span lateral displacement predicted by the numerical simulation and the average experimental measurements remains within 15%. Full article

The adoption of biopesticides in Mediterranean agriculture is shaped by environmental, economic, and socio-cultural factors. This study explores the push and pull factors influencing farmers’ decisions in Spain’s Ebro Delta, Tunisia’s Nabeul region, and Turkey’s Adana province. Through qualitative fieldwork and comparative analysis, key barriers to adoption are identified, including high costs, limited market availability, skepticism about efficacy, and reliance on conventional pesticides. However, this study also highlights opportunities driven by regulatory changes, increasing market demand for sustainable products, and the potential of biopesticides to improve ecological sustainability. The research follows a comparative case-study approach and was conducted between January and November 2024. The methodology included a literature review, two rounds of qualitative interviews with farmers, and thematic analysis to identify barriers and enabling factors, ensuring methodological rigor and cross-validation. Findings indicate that farmers’ professional ethos and economic conditions significantly limit biopesticide adoption. Perceived inefficacy, high production costs, and low profit margins reinforce reluctance. Spain struggles with skepticism, Tunisia faces economic and informational barriers, and Turkey’s reliance on traditional practices slows innovation. Despite these obstacles, key drivers facilitate adoption, including improved agricultural education, cooperative support, and increasing consumer demand for sustainable products. Legal frameworks, particularly the EU’s “Farm to Fork” strategy, play a crucial role, though top-down policies risk local resistance. This study outlines a model for biopesticide adoption based on seven key factors, with legal frameworks and farm structure emerging as primary drivers. Addressing economic and educational barriers is crucial for widespread adoption. By implementing targeted policies, Mediterranean agriculture can become a model for sustainable practices, balancing productivity and environmental stewardship. Full article

Couchsurfing, a non-commercial form of accommodation, has become a way of life for travellers who want to open to other cultures and exchange with the inhabitants of the countries or regions they are visiting in a more active and authentic way. Despite the growing number of studies on couchsurfing recently, there is not to date an instrument for understanding the perceived value and experience of those travellers. This research fills a gap in research on the couchsurfing phenomenon by developing and validating a scale to assess the perceived value of a couchsurfing experience. The research adopted a mixed-method approach through qualitative and quantitative phases of studies. Both phases were undertaken with couchsurfers who had recently engaged in a couchsurfing experience. The results enabled us to obtain a reliable and valid scale for measuring the perceived value of a couchsurfing experience with three dimensions and nine items measuring economic value, exploration value and socio-cultural value. The measure could be used by service providers to develop appropriate tourism experiences, which impacts tourism development, particularly in rural areas. Full article

Climate change and global warming have been shown to increase the frequency and intensity of extreme weather events. Concurrently, substantial efforts are being directed toward fostering the transition to a low-carbon economy. These concurrent trends result in the emergence of both physical and transition climate risks. This study investigates the impacts of climate risks, both physical and transition, on the return of major industrial metals (aluminum, copper, iron, lead, tin, nickel, and zinc) between January 2005 and December 2023. Employing the novel multivariate quantile-on-quantile regression (m-QQR) approach, this study examines how climate risks affect metal markets under different market conditions and risk levels. The results reveal that transition risks exert a more significant adverse impact on metal returns during bearish markets conditions, particularly for metals linked to high-emission industries, while physical risks affect metal returns across a wider range of quantiles, often increasing volatility during extreme market conditions. Furthermore, copper and nickel, both of which are crucial for renewable energy development, demonstrate resilience at higher quantiles, highlighting their role in the transition to a low-carbon economy. Finally, these two metals may serve as effective hedges against losses in other metals that are more vulnerable to transition risks, like aluminum and lead. Full article

Like many other sectors, women in higher education have had negative experiences with sexual harassment. This study examines the coping mechanisms used by female lecturer/researchers and their impact on burnout. Based on Occupational Stress Theory, this research specifically analyzes the mediating role of three coping strategies, i.e., problem-focused coping, emotion-focused coping, and avoidant coping. A quantitative survey was conducted among 800 Tunisian women teacher-researchers, with 613 complete responses that are valid for analysis. The results revealed that under the influence of sexual harassment, women in Tunisian higher education institutions suffer increased burnout. Structural equation analysis shows that emotion-focused coping has a partial mediation effect, while avoidant coping fully mediates the link between harassment and burnout. This research adds to the literature on sexual harassment and suggests implications for the prevention and support of victims in higher education institutions. Full article

This study examines the structural relationship between a destination’s image (DI), social influence (SI), and tourists’ brand trust (BT) and brand loyalty (BL) in the destination of Tozeur, a Tunisian town located at the gateway to the Sahara and rooted in the Atlas Mountains, where George Lucas set scenes for the Star Wars saga. The structural correlations between the variables in the model were tested through structural equation modeling (SEM). Data from 1405 tourists, who had visited Tozeur, were analyzed through SEM using AMOS software (version 25). The results showed that DI significantly affects BT (β = 0.924, p < 0.001) and significantly affects BL (β = 0.481, p < 0.01). Additionally, SI significantly affects BT (β = 0.274, p < 0.001) and significantly affects BL (β = 0.234, p < 0.001). Furthermore, BT significantly affects the BL (β = 0.461, p < 0.01). Tourist’s trust in a brand was found to act as a partial mediator on the link between destination image and brand loyalty and between social influence and brand loyalty. The findings demonstrate the importance of the tourism destination as well as social influence in boosting tourism trust and increasing destination loyalty among tourists. The results have many practical implications for destination marketers. Full article

This paper uses data from the 24 governorates in Tunisia over the period 2012–2020 to study the relationship between job insertion of higher education graduates into the formal labor market and a number of independent variables, namely active labor supply, labor demand, an active labor market policy program (named the CIVP program), and the waiting time for job insertion. The balanced panel, which includes 216 observations for each variable, was the basis of different tests and estimations. The results of the tests allowed the assessment of a fixed effects model and a long-term relationship using FMOLS and VECM models. Results show that, in the long term, active labor supply and the CIVP program have positive effects on the job insertion of higher education graduates. In contrast, the results in the short term do not appear significant, with a negative effect of the CIVP program that reflects the fact that companies exploit most of the benefits of this wage subsidy program on job insertion before final recruitment. Using the ARDL model, the individual results by governate show specific differences across areas. Full article

This study aimed to enhance antioxidant extraction from the aquatic plant Cymodocea nodosa for cosmetic formulation through optimized ultrasound-assisted extraction using response surface methodology. The optimized conditions—30 min of extraction time, 30% ultrasonic power, and 25% hydro-ethanolic solvent—resulted in a high total phenolic content of 113.07 mg EAG/g DM and antioxidant activity of 67.02%. Chromatographic analysis revealed a rich profile of phenolic compounds, including sinapic acid (0.741 mg/g), myricetin (0.62 mg/g), and quercetin-3-O-rutinoside (0.3 mg/g), demonstrating the extract’s potent therapeutic properties. While the extract exhibited limited anti-inflammatory activity, it showed no cytotoxic effects on RAW 267.4 cells, ensuring its safety for cosmetic applications. The formulated cream maintained stable pH (6.58 to 6.6), consistent viscosity (5966.38 to 5980.6 cp), and minimal color changes over a 30-day period, indicating robust stability across various temperatures (4 °C, 25 °C, and 40 °C). These results confirm the potential of C. nodosa extracts to develop effective, stable, and eco-friendly cosmetic products, offering substantial benefits for skin health and emphasizing the importance of sustainable extraction processes in the cosmetics industry. Full article