In this paper, we examine how a semester-long sustainability-oriented entrepreneurship course shapes sustainable entrepreneurial intention (SEI) through individual sustainability orientation (ISO). Grounded in an adaptation of the Theory of Planned Behaviour, ISO is conceptualized as a multidimensional construct encompassing sustainability-oriented (SO) values, SO motivation, awareness of sustainability issues, and SO self-efficacy, dimensions expected to mediate educational effects on SEI with different temporal sensitivities. We implemented a quasi-experimental design across two graduate introductory entrepreneurship courses at two Canadian universities: one redesigned around sustainability mechanisms (treatment, N = 112) and one covering standard content (comparison, N = 47). Pre- and post-test surveys were conducted. Results show no direct effect of the treatment on SEI and no change in SO values, which were high at baseline. However, the treatment significantly increased students’ sustainability awareness, self-efficacy, and, unexpectedly, motivation. Among these, changes in awareness and self-efficacy were positively associated with changes in SEI. These findings indicate that semester-long interventions can reliably influence proximal cognitions (awareness and self-efficacy), while deeper dispositions such as values, and to a lesser extent motivation, likely require longer-term or more immersive exposure. Theoretically, this study conceptualizes ISO as a temporally layered construct that helps explain why prior findings on education–SEI links are mixed. Methodologically, it demonstrates the value of coupling quasi-experimental designs with pedagogical reporting. Practically, it highlights workable mechanisms, such as sustainability-anchored projects, sustainable business modeling, and impact-oriented pitches, that strengthen awareness and SO self-efficacy. These insights encourage moving beyond outcome-level evaluations toward understanding how entrepreneurship education can foster SEI.
In this paper, we propose a new inference approach designed for binary regression facing challenges such as imbalanced data, which determines the optimal predictor by leveraging Huber-type and expectile loss functions within a copula-based framework. This approach models the predictive probability of success through copulas, as investigated by Mesfioui et al. (Statistical Papers 1–29, 2023), offering greater flexibility than traditional techniques such as logistic and probit regression by transcending linear assumptions and monotonicity constraints. Unlike the method proposed by Manski and Thompson (Journal of Econometrics 40(1): 97–123 1989), which depends on the single-crossing condition, our approach relaxes this restriction and effectively captures complex, nonlinear dependencies between covariates and the response variable using copulas. Furthermore, we develop an estimator for the proposed optimal predictor based on sample data and establish the asymptotic properties of the proposed estimator. To validate the effectiveness of our method, we conduct a comprehensive simulation study and illustrate its application to real-world data, particularly in scenarios involving imbalanced datasets.
Process engineering of biomass residues to develop cost-effective renewable nanomaterials for energy storage with high porosity, good ionic conductivity, and excellent stability is a necessary step toward a circular economy. In this study, we report a sustainable approach to creating bio-based nanoarchitecture from biofuel industry byproducts, i.e., microbe-treated groundnut shells. These carbon nanostructures were explored as electrode materials, demonstrating their potential as high-performance supercapacitors. A morphology investigation revealed that the microbial fortification of biomass acted as a natural porogen, leading to the formation of mesonanopores with a high specific surface area as well as a high degree of graphitization, as validated by Raman spectroscopy. Galvanostatic charge-discharge curves exhibited quasi-triangular, symmetric shapes, confirming ideal capacitive behavior and high electrochemical reversibility. Electrochemical Impedance studies disclosed negligible IR drop and outstanding electronic conductivity with excellent capacitance performance due to the efficient distribution of electrolyte ions. Carbon nanostructures from spent substrates synthesized from groundnut shells displayed a high specific capacitance of 576 Fg-1. This work offers a practical and evolutionary approach to advancing the development of biomass-based carbons for supercapacitor applications.
This study presents a novel, to the best of our knowledge, ultra-wideband nanobiosensor based on a double-negative (DNG) metamaterial perfect absorber for early cancer detection through exosomal biomarker analysis. Our biosensor operates across a broad frequency range from 70 THz to 3 PHz, exhibiting near-unity absorption, i.e., exceeding 99%, and angular and polarization insensitivity, i.e., providing polarization-independent absorption across the full spectrum of polarization angles (0° to 90°), ensuring stable performance under both transverse electric (TE) and transverse magnetic (TM) polarized waves. Of particular interest is its performance in the near-infrared (NIR) region (70–400 THz), where the sensor’s DNG characteristics manifest through simultaneously negative permittivity and permeability, enhancing field confinement and sensitivity. This spectral window is especially conducive to label-free, non-invasive detection of circulating exosomes, critical indicators of early stage oncogenesis. The sensor is constructed using a tri-layer metal–insulator–metal (MIM) architecture comprising nickel (Ni) layers and a silicon dioxide (SiO 2 ) dielectric spacer. The design leverages the plasmonic and thermal stability properties of Ni and the low optical attenuation of SiO 2 to achieve optimal absorption and structural robustness. Electromagnetic simulations demonstrate strong electric and magnetic resonances, producing significant near-field enhancements. These improve the detection of subtle dielectric changes associated with exosomal binding events. The sensor maintains high absorption efficiency across oblique incidence angles and various polarization states, making it suitable for real-world biomedical diagnostic applications. By focusing on the NIR regime where tissue transparency and molecular vibrational modes intersect, the proposed biosensor enables the discrimination between cancer-derived exosomes and their normal counterparts, as confirmed through spectral and field distribution analyses. The demonstrated performance highlights the sensor’s promise for next-generation photonic platforms targeting early cancer diagnostics, with potential extension to environmental monitoring and energy harvesting technologies.
This work presents a comprehensive review of hydrogen-based hybrid renewable energy systems (HRESs), covering mathematical models, simulation and artificial intelligence (AI)-driven optimization approaches. Emphasizing the potential of hydrogen as an energy carrier to deepen renewable energy integration, especially in solar and wind HRESs, this review systematically details mathematical models for various renewable generation and storage systems, serving as a structured reference for researchers. Given the complexity of HRES modeling, this work provides insights into different modeling software and optimization algorithms, with a particular focus on artificial intelligence methods. The integration of software and artificial intelligence promises to solve complex modeling and optimization challenges with potential applications in different environments. Future directions suggest that the physical model-assisted AI framework, which embeds physical principles within AI models, holds promise for enhancing prediction accuracy and reliability in HRES applications. This framework, especially when combined with stochastic optimization, offers a potential pathway to address challenges in data availability and computational complexity, supporting the effective design and optimization of hydrogen-based HRESs for real-world applications. The overall findings will help improve the design and optimization of hydrogen-based hybrid renewable energy systems for practical implementation.
