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Environment, Social, and Governance Performance and Managerial Opportunism
No full textThis thesis investigates whether Environmental, Social, and Governance (ESG) practices are used opportunistically by chief executive officers (CEOs) in US-listed firms between 2010 and 2020. Drawing on upper echelons theory, it argues that observable CEO characteristics critically shape manager behaviour. Because CEOs exercise significant discretion over financial reporting and strategic decisions, they may use ESG initiatives as self-serving practices to divert attention from misconduct. Such opportunism increases risk of stock price crashes, encourages lenient trade credit policies, and impedes leverage adjustments. The thesis aims to examine how CEO traits, such as power, age, and tenure, affect the likelihood of ESG being used opportunistically, and how this, in turn, influences firm-level outcomes. It explores whether these effects vary across different stages of a CEO’s career. While younger, early-tenure, or less powerful CEOs might be more responsive to governance mechanisms, they could face career concerns that prompt opportunism. Conversely, the influence of governance may diminish for CEOs later in their careers. Hence, the impact of ESG on crash risk, trade credit provision, and leverage adjustments is contingent upon the CEO's specific career stage and traits. To address this objective, this thesis addresses three key research questions. Research Question 1 examines the role of CEO power in the relationship between ESG and stock price crash risk. Defining CEO power primarily as structural power (distinct from age or tenure), the findings show that ESG engagement increases crash risk, particularly driven by the social pillar. However, this effect is attenuated when CEOs hold greater structural power, a result explained by incentive-based compensation. Further analysis identifies real earnings management, specifically through abnormal discretionary advertising expenses, as a complementary mechanism linking ESG to increased crash risk. Research Question 2 ascertains the role of CEO age in the relationship between ESG and leverage adjustments. Given that CEO age is associated with risk aversion and conservative financial preferences, the findings indicate that ESG accelerates leverage adjustments, an effect driven by the environmental pillar. However, this effect is attenuated among older CEOs. Furthermore, under incentive-based compensation, older female CEOs appear less opportunistically motivated than their male counterparts. Channel tests reveal that ESG increases the cost of both equity and debt, though this effect weakens with older CEOs. The higher cost of equity accelerates leverage adjustment by incentivizing strategies to reduce financial distress among over-levered firms. The cost of debt, on the other hand, constrains leverage adjustments because it restricts debt servicing ability among over-levered firms as a form of competitive mediation. Overall, ESG initiatives appear to encourage a shift towards internal financing strategies.Research Question 3 investigates the role of CEO tenure in the relationship between ESG and trade credit provision. CEO tenure reflects the depth of accumulated business relationships and institutional knowledge. These relational assets are vital in shaping trade credit decisions and are grounded in trust and long-term partnerships. The findings indicate that ESG engagement enables firms to extend more trade credit, but this is attenuated among longer-tenured CEOs. Early-tenure CEOs appear to use ESG signals to justify lenient credit policies. Longer-tenured CEOs, aware of the associated risks, refrain from such behaviour. Critically, trade credit provision itself serves as a channel linking ESG to increased stock price crash risk, extending the agency perspective on ESG. Overall, the thesis demonstrates that whether ESG reflects managerial opportunism or stewardship is contingent on certain observable CEO characteristics, specifically, power, age and tenure, selected for their conceptual clarity and empirical relevance to financial reporting, crash risk, capital structure, and trade credit. To safeguard shareholder wealth, the board of directors must consider these CEO traits. Social initiatives may not harm shareholders under powerful or longer-tenured CEOs, while environmental initiatives appear more opportunistically motivated among older CEOs. Although older female CEOs exhibit reduced opportunism under incentive-based pay, they reinforce the positive effect of ESG on trade credit provision, thereby increasing crash risk. The findings reveal that managerial opportunism and stewardship in ESG coexist, fundamentally determined by these observable CEO characteristics.</p
Approach Towards the Development of Digital Twin for Structural Health Monitoring of Civil Infrastructure: A Comprehensive Review
No full textCivil infrastructure assets' contribution to countries' economic growth is significantly increasing due to the rapid population growth and demands for public services. These civil infrastructures, including roads, bridges, railways, tunnels, dams, residential complexes, and commercial buildings, experience significant deterioration from the surrounding harsh environment. Traditional methods of visual inspection and non-destructive tests are generally undertaken to monitor and evaluate the structural health of the infrastructure. However, these methods lack reliability due to the need for instrumentation calibration and reliance on subjective visual judgments. Digital twin (DT) technology digitally replicates existing infrastructure, offering significant potential for real-time intelligent monitoring and assessment of structural health. This study reviews the existing applications of DTs across various sectors. It proposes an approach for developing DT applications in civil infrastructure, including using the Internet of Things, data acquisition, and modelling, together with the platform requirements and challenges that may be confronted during DT development. This comprehensive review is a state-of-the-art review of advancements and challenges in DT technology for intelligent monitoring and maintenance of civil infrastructure.</p
Effect of the Functionality of Flour and Protein Isolate-Based Ingredients on the Protein Interactions and Structural Properties of Meat Analogue Patties
No full textMeat is a rich protein source, but environmental and health concerns have increased demand for plant-based alternatives. While soy protein isolate dominates the market, issues with allergenicity, GMOs, and monoculture highlight the need for diversified proteins. Legumes such as mung bean and cowpea show strong potential yet remain underexplored, especially in textured forms and regarding their digestibility. This thesis evaluates the physicochemical, structural, functional, and nutritional properties of mung bean, cowpea, and soy in flour and isolate forms, linking ingredient characteristics to texturisation behaviour and digestion. It also examines the ability of legume flours to partially replace SPI and assesses the in vitro digestibility of patties made with mung bean and cowpea isolates, contributing to the development of more sustainable, structurally robust, and nutritionally enhanced plant-based meat analogues. The first experimental chapter provides a comprehensive analysis of mung bean, cowpea, and soy flours and protein isolates, focusing on their potential application in plant-based meat analogues. Mung bean flour (MBF) and cowpea flour (CPF) demonstrated substantial protein content (20.55% and 20.29%, respectively), indicating their potential as rich protein sources as an alternative to soy flour (SBF). CPF exhibited the highest pasting temperature (78.75 °C) and peak viscosity (462.5 cP), reflecting enhanced starch stability and resistance. Among isolates, mung bean protein isolate (MPI) showed the greatest peak viscosity (23.00 cP). Significant differences (p Plant proteins are largely globular and lack inherent fibrous structure, so texturisation was applied to enhance their functionality. The second experimental chapter investigated how this process affects mung bean, cowpea, and soy ingredients, evaluating their suitability for producing textured vegetable proteins (TVPs) via low-moisture extrusion for use in meat analogues. The physicochemical and structural attributes of the resulting TVPs were primarily governed by the composition of the raw materials and the extrusion conditions optimised for each type of ingredient, namely mung bean flour, cowpea flour, soy flour, and soy protein isolate. Protein isolate-based TVPs exhibited more defined fibrous structures, higher expansion ratios, and stronger protein-protein interactions, resulting in enhanced water-holding and rehydration capacities. In contrast, flour-based TVPs, particularly textured mung bean flour (T MBF) and textured cowpea flour (T-CPF), demonstrated greater porosity and expansion, with starch components contributing to improved hydration and textural softness compared to that of textured soy protein isolates (T-SPI). While textured soy flour (T-SBF) showed comparatively weaker structural integrity, it retained valuable nutritional attributes. Overall, the findings highlighted the feasibility of using legume-based flours and isolates to partially replace soy protein isolate in plant-based meat formulations. The third experimental chapter examined the physicochemical, structural, textural, and nutritional properties of plant-based patties formulated with textured mung bean, cowpea, and soy flours combined with soy protein isolate. The findings demonstrated that these formulations exhibited competitive protein levels, improved cooking yield, and enhanced 2 moisture retention were observed compared with conventional meat patties and addressed sustainability concerns associated with soy dependency. Incorporating texturised vegetable proteins increased β-sheet structures and strengthened the protein network, leading to improved firmness and cooking stability. Water distribution analysis showed tighter water binding and lower cooking loss than beef patties. Mineral and amino acid profiling confirmed the nutritional viability of the formulations, with good macronutrient and essential amino acid retention, though sulphur-containing amino acids remained limited. PCA further showed that formulations with higher mung bean and cowpea flour closely aligned with the structural and functional attributes of beef and commercial plant-based patties. Overall, textured mung bean and cowpea flours emerged as promising alternatives to textured soy, capable of delivering balanced and high-performing meat analogue systems. The fourth experimental chapter reformulated two promising patty prototypes from Chapter 3, examining the impact of replacing SPI with MPI and CPI in these selected formulations. The reformulated patties exhibited higher protein content, improved cooking yield, and reduced cooking loss, leading to enhanced cooking performance and minimal shrinkage despite the absence of SPI. However, textural analysis revealed reduced hardness, gumminess, and chewiness, making the patties more comparable to beef. These changes resulted from the weaker protein-protein interactions of MPI and CPI compared with SPI, resulting in lower internal binding strength and a softer structure. The limited ability of MPI and CPI to form strong intermolecular bonds and their distinct gelation properties further contribute to these textural modifications. These findings offer valuable insights into the potential of MPI and CPI as alternative protein sources for plant-based meat analogues. The gastrointestinal behaviour of beef, chicken, commercial plant-based patties, and the developed analogues was evaluated using the INFOGEST model, revealing clear structural, physicochemical, and nutritional differences. Sodium dodecyl-sulfate polyacrylamide gel electrophoresis (SDS-PAGE), 3 confocal imaging, and free amino acid analysis confirmed progressive protein breakdown, with beef and chicken showing efficient hydrolysis and a clear shift to low-molecular-weight peptides. Plant-based patties also degraded but retained more residual peptides, indicating limited enzyme accessibility and the influence of plant anti-nutritional factors. Reduced digestibility in plant analogues was linked to their higher β-sheet content, phytate-mediated mineral and protein binding, and the effects of dietary fibre and flavour compounds, all of which restrict protease access and slow digestion. Overall, this study demonstrates that mung bean and cowpea protein isolates, especially in T CPF formulations, can effectively replace SPI while delivering strong structural and functional performance. Although these plant-based patties showed good cooking and textural properties, they exhibited lower protein hydrolysis than beef and chicken, driven by compact β-sheet structures, anti-nutritional factors, and restricted enzyme accessibility. The work advances theoretical understanding by revealing how flour-based systems with higher starch and fibre undergo distinct thermo-mechanical transitions during extrusion, and by providing mechanistic insights into how legume protein architecture and matrix compactness limit digestibility. Collectively, these findings identify key structural and compositional barriers in legume-based systems and offer guidance for designing plant-based meats with improved texture, functionality, and nutritional bioavailability. This thesis therefore delivers both practical formulation strategies and new scientific insights that extend current knowledge in food protein science.</p
Test Journal Article Record 1 Dec 2025 and 8 Jan 2026 ELE to FIG KC
No full textThis is a test journal article record with keywords, FoR Codes, UN SDGs and AIATSIS Subject Headings.</p
Enhancing Green Competitive Performance of Product–Service Systems through a Dynamic Capabilities Lens
No full textIncorporating environmental awareness into business operations while maintaining competitive performance is a significant challenge. To address this, many companies are enhancing their offerings by integrating services with products—a strategy known as product–service systems (PSS). This innovation aims to boost competitiveness and promote environmental consciousness. However, although PSS is recognized as a valuable approach for staying competitive, the interplay between PSS and its influencing capabilities remains insufficiently explored. This study examines the relationships among Organizational Learning Development (OLD), Supply Chain Integration (SCI), Supply Chain Digitalization (SCD), Supply Chain Agility and Resilience (SCAR), Green Supply Chain (GSC), and Product–Service Systems’ Green Competitive Performance (PSSGCP). Data were gathered through a structured survey involving 502 official motorcycle service partners in Indonesia and analyzed using SEM. The results confirm significant positive relationships between GSC and PSSGCP and between SCAR and PSSGCP. Moreover, OLD, SCI, and SCD each positively influence SCAR, whereas only OLD and SCD have direct positive effects on GSC. The analysis also reveals that OLD positively influences SCI, which subsequently impacts SCD—indicating that SCD mediates the influence of SCI on GSC. These findings provide practical and theoretical insights that enable managers and researchers to better align green and competitive performance goals. Furthermore, managers can assess the standardized loadings to evaluate each capability’s contribution to enhancing PSSGCP.</p
Challenges and Tensions in Digitising Living Cultural Heritage: The Case of Street Vendors in Vietnam
No full textThis paper examines the challenges and tensions involved in digitising living cultural heritage, in particular street vendors in Vietnam. Street vending, deeply embedded in Vietnam’s socioeconomic and cultural fabric, faces growing pressures from
urbanisation, gentrification, and regulatory frameworks that marginalise these practices. The digitisation of their heritage through tools like augmented reality (AR) and virtual reality (VR) holds potential for cultural preservation but raises critical questions about authenticity, representation, and power dynamics among stakeholders. This research identifies key tensions, including the exclusion of vendors from decision-making processes, regional disparities in heritage valuation, and the risk of commodifying their
practices. By addressing these complexities, the paper advocates for extended reality design to authentically reflect the hybrid and evolving nature of street vending, and participatory design methodologies that empower street vendors as active contributors to the digitisation process. Through this approach, it seeks to offer
insights into ethical and inclusive strategies for safeguarding intangible heritage in post-colonial societies.</p
Machine Learning-Based Optimization of Clausius-Mossotti Equation for Dielectric Constant Calculation
No full textIntroduction The Clausius-Mossotti (C–M) equation is one of the most fundamental theoretical models to calculate the dielectric constant of materials. As was proposed over a century ago, it has been widely applied in dielectric material research due to its relatively simple mathematical form and clear physical interpretation. The equation, derived based on the Lorentz effective field approximation and the assumption of constant polarization, establishes a relationship among the macroscopic dielectric constant and the microscopic polarizability and unit cell volume. However, with the growing understanding of dielectric materials, it becomes clear that dielectric constant is affected by a range of additional factors, such as bond length, packing density and symmetry. These factors are not captured by the conventional C–M equation, indicating the limitations of the equation. Recently, machine learning (ML) methods show a promise in improving the prediction accuracy of dielectric properties. The ML models can outperform classical equations in terms of accuracy, but they often lack interpretability, which hinders their widespread use in material design and optimization. To address these issues, this study proposed a novel bidirectional embedding approach with domain knowledge and machine learning. The ML-based correction term was integrated into the classical C–M equation, enhancing its predictive accuracy, while retaining physical interpretability. This work could explore a potential of combining physical formulas with ML methods to create more robust and explainable models for material design. Methods The data of single-phase microwave dielectric ceramics were collected from published literatures and the Materials Project database. The dataset included materials whose dielectric constants were measured by the Hakki-Coleman method in a frequencies range from 5 to 18 GHz. A machine learning model identified molecular dielectric polarizability per volume (ppv), average bond length (blm) and unit cell volume (va) as the most significant contributors to the dielectric constant in a previous study. To enhance the accuracy of the C–M equation, correction terms were introduced based on the three key features. Symbolic regression, combined with genetic algorithms, was used to derive mathematical expressions for the correction terms. The symbolic regression technique utilized evolutionary algorithms to generate and evolve potential expressions. The structures and parameters of expressions were optimized to minimize prediction errors. Hyperparameters were optimized by a grid search method to identify the best-performing models. The process was carried out iteratively. A total of 16,000 candidate mathematical expressions were generated and compared. Finally, the Pareto front analysis was used to select the optimal expression that balances accuracy and complexity. Results and Discussion The revised C–M equation results in significant improvements in its predictive performance. For the Shannon polarizability dataset, the R2 value of the revised C–M equation is increased by 42.29%, and the RMSE value is decreased by 14.72%. The correction term compensates for the inaccuracies of the original equation, particularly in cases where the molecular dielectric polarizability per volume of Shannon value (ppvs) is either overestimated or underestimated. For low-polarizability materials with ppvs values less than 0.2087, the Shannon database tends to underestimate the dielectric constant, and the correction term compensates for this error. Conversely, for high-polarizability materials, the dielectric constant is often overestimated, and the correction term reduces the overestimation. The analysis of the relationship between the correction term (Δppvs) and features reveals that the correction term is most sensitive to ppvs, followed by blm, with va having the least impact. This finding indicates that the dielectric polarizability plays a dominant role in determining the accuracy of the revised C–M equation. The influence of the features on the correction term is further explored through partial derivatives. The first-order partial derivatives show that the correction term’s contribution from ppvs is much greater than that from blm or va. The second-order partial derivatives reveal a non-linear relationship between the correction term and ppvs and blm, while the relationship with va is linear. The results indicate that the compensatory effect of the correction term gradually diminishes, and the suppression effect becomes more pronounced as ppvs increases. This behavior is consistent with the correction mechanism of “elevating the underrated, reducing the overrated”. Conclusions This study introduced a bidirectional embedding approach that could integrate machine learning with domain knowledge to enhance both the accuracy and interpretability of the Clausius-Mossotti equation. The revised equation achieved a higher prediction accuracy with a 42.29% increase in R2 and a 14.72% decrease in RMSE via incorporating the correction term derived from symbolic regression. The revised equation retained the physical meaning of the original one. The research could highlight a potential of bidirectional embedding approach to create models that could balance both accuracy and interpretability, offering a promising perspective on optimizing classical equations and material design.</p
Innovative Double Dumbbell-Shaped Flux-Switching Linear Tube Generator for Ocean Wave Energy Conversion: Design, Simulation, and Experimental Validation
No full textThis study introduces a novel double dumbbell-shaped flux-switching linear tube generator (DDFSLG) for ocean wave energy conversion. The innovative architecture features a uniquely shaped stator and translator, distinguishing it from conventional linear generators. Unlike traditional systems, the DDFSLG is housed in a cylindrical buoy. The translator oscillates axially within the stator. This eliminates the need for motion rectification and reduces mechanical friction losses in the power take-off (PTO) system. These design advancements result in high power output and improved performance. The DDFSLG’s three-phase coil circuit is another key innovation, improving electrical performance and stability in irregular wave conditions. We conducted comprehensive experimental validation using an MTS-250 kN testing system, which demonstrated strong agreement between theoretical predictions and measured results. We compared star and delta coil connections to assess how circuit configuration affects power output and efficiency. Furthermore, hydrodynamic simulations using the JONSWAP spectrum and ANSYS AQWA software (Ansys 13.0) provide detailed insight into the system’s dynamic response under realistic oceanic conditions.</p
Additive manufacturing, topology optimization of thermoelectric generators, and beyond: a comprehensive review on pioneering thermoelectric conversion for a sustainable future
No full textThermoelectric generators (TEGs) offer a promising route for sustainable energy by converting waste heat directly into electricity, addressing critical global energy efficiency challenges. However, traditional fabrication limits TEG design complexity and application scope. This comprehensive review explores the pioneering integration of additive manufacturing (AM), particularly Direct Ink Writing (DIW), and topology optimization to overcome these limitations and enhance TEG performance. We review fundamental thermoelectric principles, material advancements (with a focus on Cu2Se), TEG design strategies (planar, lateral, vertical), simulation techniques (FEM), and AM fabrication processes. Key findings highlight AM’s ability to create complex, shape-conformable TEGs adaptable to diverse heat sources, minimizing material waste, which goes beyond previous efforts in the literature. Topology optimization significantly improves material distribution and efficiency, with studies showing optimized leg shapes yielding performance gains (e.g., hourglass shapes improving efficiency > 70 % over cylindrical). AM techniques like DIW enable high-performance materials, with 3D-printed Cu2-xSe achieving zT = 1.2 at 1000 K and printed BixSbxTex reaching efficiencies of 8.7 % (ΔT = 236 °C). While DIW offers simplicity and flexibility, its limitations and challenges include achieving high density and controlling microstructure. This review concludes that the combination of AM and topology optimization offers a transformative approach to designing and manufacturing highly efficient, customizable TEGs—key to advancing sustainable energy harvesting technologies. Future work should focus on novel printable materials, multi-material printing, and enhanced simulation models.</p
Exploring 2D Graphene-Based Nanomaterials for Biomedical Applications: A Theoretical Modeling Perspective
No full textTwo-dimensional (2D) graphene-based nanomaterials (GNMs) have shown potential in biomedical applications, including diagnostics, therapeutics, and drug delivery, due to their unique combination of properties such as mechanical strength, excellent electrical and thermal conductivity as well as high adsorption capacity which, combined with the ease of their surface functionalization, enable biocompatibility and bioactivity. Theoretical molecular modeling can advance our understanding of the biomedical potential of 2D graphene-based nanomaterials by providing insights into the structure, dynamics, and interactions of these nanomaterials with biological systems, at the level of detail that experiments alone cannot currently access. This perspective highlights recent computational modeling advances and challenges in examining the interactions of 2D graphene-based nanomaterials with physiologically relevant biomolecular systems, including aqueous solutions, peptides, proteins, nucleic acids, lipid membranes, and pharmaceutical drug molecules. Examples of the theoretical contributions to design of graphene-based biomaterials and devices are also provided.</p