Gulf University for Science & Technology (GUST) is the first private university established in Kuwait. It has a dual-enrollment agreement with the University of Missouri–St. Louis.
Adopting multiple theoretical perspectives, this study separately and jointly examines the impacts of reporting Environmental, Social, and Governance (ESG) initiatives and gender diversity in boardrooms on corporate stock liquidity. Using a sample of non-financial firms listed on the Frankfurt CDAX from 2010 to 2023, the study investigates whether ESG reporting enhances stock liquidity and how gender diversity moderates this relationship. To assess ESG reporting, we utilise Refinitiv Workspace ESG scores, while gender diversity and stock liquidity are measured through various indices. The results demonstrate that firms with higher ESG reporting experience higher stock liquidity, with governance disclosure having the most substantial impact. Likewise, firms with greater gender-diverse boards exhibit higher stock liquidity. Besides, gender diversity in the boardroom positively moderates the effect of ESG reporting and its components on stock liquidity, implying that as gender diversity increases within a company, the influence of ESG reporting on enhancing stock liquidity becomes more pronounced. The results signify the roles of ESG reporting gender diversity in enhancing stock liquidity and investor confidence, and provide theoretical and practical implications for firms, investors, and policymakers to promote sustainable corporate practices and enhance market liquidity.
Context: In recent years, advanced Artificial Intelligence (AI) tools, such as Generative Pretrained Transformers (GPTs), have shown remarkable potential across diverse application areas, including academic research. One of the promising applications of using these tools for conducting Systematic Literature Reviews (SLRs), which are effort intensive and time-consuming process in software engineering research. Objective: This study investigates the use of GPT-4o to support SLR activities in the context of software engineering. Methods: We employed a mixed research method to explore the significance of GPT-4o in conducting sub-SLR activities, such as formulating research questions, generating Boolean strings, and performing quality assessments. Four benchmark SLR studies were selected and used to replicate the SLR activities using GPT-4o, which were further assessed by six domain experts. Results: In the results, GPT-4o demonstrated consistency in generating Boolean search strings, identifying themes, and quality assessment criteria across all the benchmark studies, and showed close alignment with expert evaluations. The model effectively automated key SLR activities, including literature search string formulation, validation of primary studies, quality assessment, thematic analysis, and taxonomy development. However, GPT-4o was less accurate in identifying relevant studies across databases, occasionally missing domain-specific literature. Conclusions: The findings indicate that GPT-4o can effectively support several SLR tasks in software engineering when integrated within a domain expert-in-the-loop workflow. However, the study evaluates selected activities under controlled settings rather than a full end-to-end SLR execution. Future research should extend this evaluation to additional datasets and alternative LLMs to further assess generalizability.
Graphene and iron oxide nanocomposite materials attracted significant attention in different disciplines including optoelectronics, catalysis, and energy conversion/storage devices. Despite the extreme potential, a major obstacle had been the lack of effective and environmentally benign production techniques for mass-producing iron oxide-graphene nanocomposites. To overcome the obstacle, we opted for an efficient, facile, and eco-friendly hydrothermal synthesis route for the synthesis of iron oxide-graphene nanocomposites. The technique involved the homogenous mixing of metal salt precursor (iron chloride), and graphene oxide (GO) followed by a hydrothermal reaction under normal conditions. The synthesized nanocomposites were systematically investigated for structural, morphological, thermal, optical, and magnetic characteristics using XRD, Raman, SEM, TGA, UV–Vis, PL, and VSM techniques. The XRD and Raman studies confirmed the formation of α-Fe2O3-RGO and Fe3O4-RGO nanocomposites. The SEM images disclosed the anchoring of metal oxide nanoparticles to graphene nanosheets. The nanocomposite exhibited enhanced thermal stability compared to the pristine GO sample. The optical studies corroborated the better charge transfer response of nanocomposites and Hall effect measurements affirmed these nanocomposites as charge transport materials. The VSM measurements confirmed the magnetic behavior of the samples. Therefore, these nanocomposite materials could be a viable option for optoelectronics and energy conversion/storage devices.
Heat stress poses a significant threat to cotton, affecting crucial developmental stages from fertilization to boll development and thereby reducing both yield and quality. As global climate change advances, the likelihood of severe heat waves increases, presenting a serious challenge to agricultural production and our ability to feed a growing population. Cotton’s resilience to heat involves a suite of physiological and biochemical responses, including adjustments in water management and protective mechanisms at the cellular level, such as the synthesis of heat shock proteins (HSPs) and antioxidants. These adaptations are crucial for maintaining cellular integrity and overall plant health under thermal stress. Recent research highlighted specific proteins and enzymes within the plant that help combat stress-related damage. However, the exact molecular mechanisms facilitating these protective responses are not fully delineated. Further research is needed to identify and validate additional molecular mechanisms underlying the plant’s response to heat stress. Additionally, exploring new breeding strategies for the development of more resilient cultivars. In addition, field trials and testing under real-world conditions will be essential to assess the effectiveness of the proposed strategies for mitigating the negative effects of heat stress on crop yields.
The low thermal conductivity of air poses a significant challenge to the efficiency of solar air heaters, and this requires sophisticated heat transfer enhancement techniques. This study investigates the performance of a tubular solar air heater integrated with twisted tape inserts to address this limitation. A hybrid approach using 3D computational fluid dynamics combined with deep neural networks was adopted for systematic analysis of five dimensionless parameters-twisted tape length (lt /Dh), twisted tape diameter (dt /Dh), twisted tape thickness (tt /Dh), twisted tape pitch (pt /Dh), and Reynolds number. Quantitative results have shown that tape diameter increment coupled with pitch reduction are the major promoters for thermal enhancement, as an increase in (dt /Dh) from 0.322 to 0.483 increases the Nusselt number by 30%. On the other hand, tighter pitch ratio (pt /Dh) has resulted in a 4.7-fold increase in friction factor at Re = 10,000. The surrogate model with the DNN showed highly accurate prediction results with an average deviation of approximately 1-2% relative to the CFD results. For the global optimization experiment with the DNN, the maximum value of the thermo-hydraulic performance factor of 1.45 was found at Re = 10,000, (dt /Dh) = 0.48, and (pt /Dh) = 2.68, making it superior to the state-ofthe-art tubular swirl generators reported in the literature. The originality and creativity in the thesis lie in the conversion of a machine learning model from a prediction model to a design engine to expose the non-linear inter-relationship between the parameters. This hybrid approach involving CFD and ML not only captures the complex physical phenomenon associated with the twisted tape geometry in terms of affecting the flow and heat transfer mechanisms in an accurate manner, but it also generates a faster and more stable surrogate model for the performance predictions and optimizations with equal efficacy.
