Texas A&M University – Commerce

In this study, a three-dimensional nonlinear finite element (FE) model was developed using Abaqus/Explicit to simulate the effects of internal blasts. The numerical model was validated against two previously published numerical and experimental works, demonstrating strong agreement in deformation results. A parametric study was carried out to evaluate the influence of several key factors on the deformation of the receiver tunnel subjected to an explosion in the adjacent donor tunnel. The investigation considered critical variables such as lining material, tunnel inner diameter, cross-sectional shape, spacing between tunnels, and TNT charge weight. The results clearly indicate that expanded polystyrene (EPS) foam, across various densities, demonstrates superior capacity for absorbing blast waves compared to polyurethane and aluminum foams. Furthermore, it was found that lower-density EPS foam provides enhanced mitigation of deformation in tunnel linings. The findings also revealed that damage to the tunnel walls is more strongly correlated with the tunnel shape where the circular tunnel exhibited the best performance. It showed the lowest deformation and delayed peak response. In addition, tunnel deformation increases markedly with higher TNT charge weights. A blast of 1814 kg produced approximately five times the deformation compared to a 454 kg charge. Moreover, it is seen that increasing the spacing between donor and receiver tunnels from 1.5 D to 2.5 D led to a 38.7% reduction in maximum deformation.

Purpose This paper examines the extent, quality, and tone of Sustainable Development Goals (SDG) disclosures by UK FTSE 100 firms, assessing whether these reflect genuine commitment or serve impression management purposes. Design/methodology/approach A meaning-oriented content analysis of SDG-related disclosures from 75 FTSE 100 company reports is conducted. Drawing on the SDG Compass and KPMG's SDG reporting criteria, we develop a multidimensional analytical framework to assess disclosure scope, quality (semantic attributes and managerial orientation), and tone (thematic manipulation and structural emphasis). Findings SDG reporting is widespread but predominantly symbolic. Most firms emphasise a narrow set of high-profile goals (notably SDGs 8, 12, 13), with disclosures often framed in overly optimistic language, reinforcing favourable corporate narratives. Crucially, our targeted analysis reveals that many SDG sub-targets are poorly aligned with existing sustainability reporting frameworks, limiting their operational relevance. We demonstrate that these structural deficiencies, coupled with managerial apathy, foster superficial engagement and constrain meaningful accountability. Practical implications The paper calls for the development of more specific, business-relevant SDG sub-targets and performance indicators, enhanced assurance practices and stronger alignment of post-2030 frameworks with established sustainability reporting standards. Originality/value This study reframes symbolic SDG engagement as the outcome of both managerial choices and structural limitations within the SDG architecture. It contributes to SDG disclosure literature by developing a composite framework that integrates disclosure scope, quality and narrative tone–offering a multidimensional lens for evaluating sustainability reporting. By extending the analytical lens beyond headline goals, it offers fresh insights into the institutional and managerial conditions that legitimise symbolic reporting. These insights are particularly salient as deliberations on the post-2030 sustainable development agenda gather momentum.

While many studies have examined the impact of oil price changes on stock market returns, most overlook the asymmetric impact on disaggregated sectoral indices. This study addresses this gap by examining the sector-specific impact of oil price changes in India, one of the largest oil-importing economies. Using monthly data from April 2008 to July 2025 collected from the Bloomberg database, we employed both linear and Non-linear ARDL models to explore the asymmetric impact in short- and long-run relationships. The findings reveal significant heterogeneity in the sectoral responses to oil price changes. While the FMCG, media and pharma sectors do not exhibit cointegration with oil prices, other sectors, namely banking, auto, metal, energy, IT, financial services, and real estate, asymmetrically responded to oil price changes. The negative oil price changes cause stronger and short-run sectoral responses than the positive changes, as confirmed by the Wald test and GIRFs. The error correction terms are negative and statistically significant for all the sectors, which confirms a long-run equilibrium and mean-reverting behaviour. This establishes that sectors react differently to positive and negative oil price changes in the long run. Investors must account for the non-linear relationship between these variables and take appropriate action when forming portfolio strategies. The results suggest that policymakers should monitor and find alternative energy sources to avoid sector-specific vulnerabilities during oil price fluctuations.

This paper examines how education and Human Resource Management (HRM) in the education sector together can catalyze socio-economic development in Dwarahat district (Uttarakhand), a predominantly rural Himalayan district in India. Using secondary data from census reports, district education records, and state/national policy documents, the study maps Dwarahat’s education indicators, identifies systemic HRM challenges within local educational institutions, and proposes an integrated policy and operational roadmap. Principal findings indicate that while Dwarahat’s literacy averages are comparable to state levels, significant challenges persist in gender parity, teacher distribution, retention, and infrastructure. Dwarahat’s overall literacy rate (79.1%) nearly matches the Uttarakhand average (79.6%) and surpasses the national figure (74.0%). Male literacy (88.2%) is above both state and national averages, while female literacy (70.3%) lags behind the state (70.9%) and contributes to a higher gender literacy gap (17.9%). Primary enrollment is strong (96.5%), but secondary enrollment (72.4%) falls below the state average (75.6%) with dropout rates (27.6%) exceeding both state and national levels. HRM-related concerns are pronounced: the pupil–teacher ratio (32:1) is higher than the state average (28:1), the share of female teachers (28.7%) is lower than both state (35.2%) and national averages (37.4%), and ICT facility coverage (22.5%) remains inadequate. Vocational training opportunities are scarce, with only two institutes compared to 54 across Uttarakhand. The paper concludes by recommending HRM-driven educational reforms tailored to the socio-cultural context of Dwarahat, emphasizing skill development, gender equity, improved teacher management, and community-based engagement as pathways for sustainable socio-economic development.

Proficiency in machine learning (ML) and the associated computational math foundations have become critical skills for engineers. Required areas of proficiency include the ability to use available ML tools and the ability to develop new tools to solve engineering problems. Engineers also need to be proficient in using generative artificial intelligence (AI) tools in a variety of contexts, including as an aid to learning, research, writing, and code generation. Using these tools properly requires a solid understanding of the associated computational math foundation. Without this foundation, engineers will struggle with developing new tools and can easily misuse available ML/AI tools, leading to poorly designed systems that are suboptimal or even harmful to society. Teaching (and learning) these skills can be difficult due to the breadth of skills required. One contribution of this paper is that it approaches teaching this topic within an industrial engineering human factors framework. Another contribution is the detailed case study narrative describing specific pedagogical challenges, including implementation of teaching strategies (successful and unsuccessful), recent observed trends in generative AI, and student perspectives on learning this topic. Although the primary methodology is anecdotal, we also include empirical data in support of anecdotal results.

ABSTRACT This study investigated the complex relationship between forgiveness and racial trauma among Black Americans and focused on how experiences of racial discrimination affect mental health and the capacity to forgive. Drawing on theories of race‐based traumatic stress and forgiveness, we used the Racial Trauma Scale (RTS) and the Forgiveness Reconciliation Inventory (FRI) to analyze responses from 196 Black participants. A canonical correlation analysis revealed a moderate relationship ( r c = 0.39) between forgiveness and racial trauma symptoms, with psychological safety and coping mechanisms contributing more to collaborative exploration and remorse/change of the offender. The results emphasize the importance of culturally responsive and trauma‐informed counseling approaches, prioritizing emotional safety and coping strategies over forgiveness as a universal therapeutic goal. This study also supports the validity of the RTS and FRI scores, highlighting their usefulness in assessing racial trauma and forgiveness within clinical and research contexts.

On Wednesday, 25 June 2025, the Federal Reserve voted to make changes to the Tier 1 Capital requirements for eight global systemically important banks. The changes include a cut to the enhanced supplementary leverage ratio (eSLR). The purpose of this study is to examine the immediate impact of this announcement on the stock prices of these eight systemically important banks. Using event study analysis and controlling for interest rate movements and general market conditions, we find that most of these banks generated superior returns during the event period. When compared to the KBW Index, however, results are mixed. Some banks do have superior returns even on pre-event day. The heterogeneous effects among banks emphasize that the benefits of capital regulation changes depend on a bank’s size, structure, and scope of operations.

Nontoxic, lead-free perovskites have gained significant attention in recent years due to their potential environmental benefits. This numerical study investigates the application of a novel perovskite material, KSnI 3 , as the absorber layer in solar cell devices due to its high absorption coefficient in the visible photon energy range. Four different electron transport layers (ETLs), including CdZnS, PC[Formula: see text]BM, SnS 2 and SrTiO 3 (STO), along with ten different hole transport layers (HTLs), including PTAA, CNTS, C 6 PcH 2 , MoO 3 , MoS 2 , C6TBTAPH 2 , GaAs, TiO 2 , Cu 2 Te and D-PBTTT-14, are investigated to develop various distinct combinations. The study begins by optimizing the HTL, after which four distinct combinations using the selected HTLs and the ETLs were made for further numerical analysis. Several device parameters, including HTL acceptor density, absorber layer thickness, defect density and donor density, were optimized to enhance device efficiency. Following optimization, the device configuration of FTO/PC[Formula: see text]BM/KSnI 3 /D-PBTTT-14/Au was identified as the champion, with a power conversion efficiency (PCE) of 22.28%, an open-circuit voltage ([Formula: see text] of 1.382[Formula: see text]V, a short-circuit current density ([Formula: see text] of 18.435[Formula: see text]mA/cm 2 and a fill factor (FF) of 87.44%. Additionally, the effects of temperature, series resistance, shunt resistance, generation and recombination rates, JV characteristics, capacitance, Mott-Schottky analysis and quantum efficiency (QE) are observed following the device parameter optimization. These significant findings from this theoretical study are expected to guide future experimental research on KSnI 3 perovskite, potentially leading to the development of KSnI 3 -based perovskite solar cells.

This study examines the transformative impact of fintech on the financial services landscape for Indian start-ups, highlighting the role of digital innovation in redefining traditional financial models. By evaluating how fintech enhances financial inclusion and provides start-ups with accessible, efficient, and cost-effective solutions, the research sheds light on the unique challenges faced by start-ups, including high collateral requirements and stringent credit assessments. Utilizing a mixed-methods approach, the study combines quantitative analysis through a structured Likert scale questionnaire administered to start-up founders, employees, and financial professionals, with qualitative insights from a comprehensive literature review. The findings reveal significant benefits of fintech adoption, such as improved accessibility and reduced operational costs, particularly among younger, educated professionals in tech-centric industries. However, the study also highlights the trade-offs associated with fintech integration, including cybersecurity and regulatory risks. These insights underscore the need for targeted educational initiatives, robust regulatory frameworks, and enhanced trust-building measures to support the sustainable growth of fintech in the Indian start-up ecosystem.