The purpose of this study is to examine the connection between child mortality in Eastern Europe and ecological footprint, urbanization, education, health expenditure, and industrialization. The study acknowledges the significance of understanding how these factors influence the infant mortality rates in this region from 1993 to 2022. The Grossman Health Outcome (H-O) model investigates the theoretical framework. For the existence of the cross-sectional dependency, mixed-order unit root, and cointegration problem, the famous Cross-Sectional Autoregressive Distributed Lag (CS-ARDL) approach is applied. The research also used the Augmented Mean Group (AMG) and Common Correlated Effects Mean Group (CCEMG) to check robustness. The findings illustrated that health expenditure and education lessen the infant mortality rate in Eastern European countries. But ecological footprint, industrialization and unemployment raise the infant mortality rate. According to the CS-ARDL findings, expenditure on healthcare significantly reduces child mortality. Still, the ecological footprint significantly impacts increasing child mortality. However, the AMG and CCEMG models demonstrate that investing in education is the most effective strategy for reducing child mortality. Therefore, the government of Eastern European countries should provide more priorities in the sustainable urbanization, health expenditure, and education sectors. The robustness of the AMG and CCEMG also demonstrated the strength of the CS-ARDL findings. This research paper contributes to SDG 3 by examining the environmental and health factors that influence child mortality in Eastern Europe. Policymakers, public health professionals, and other stakeholders can use the findings to inform the development and implementation of programs that specifically target the identified causes of child mortality.

The rapid expansion of corporate sustainability reporting has increased transparency requirements while raising concerns about greenwashing driven by selective, narrative-based disclosure. This study assesses the credibility of Environmental, Social, and Governance (ESG) communication by comparing corporate sustainability reports with external media coverage for a sample of 204 large firms operating in Central and Eastern Europe in 2023. Using natural language processing techniques, the analysis constructs a Greenwashing Severity Index (GSI) that captures discrepancies between firms’ ESG self-representation and external public narratives. The index combines ESG-specific focus measures, sentiment analysis, TF–IDF-based term weighting, and topic modeling to quantify imbalances in ESG communication. Results indicate moderate but widespread greenwashing across countries, industries, and firm sizes, with substantial heterogeneity linked to differences in regulatory maturity and stakeholder scrutiny. Higher alignment between corporate disclosures and external narratives is observed among larger firms and in sectors subject to stronger public accountability, while finance, aviation, and online commerce exhibit higher greenwashing severity. A propensity score matching analysis further shows that firms with imbalanced emphasis across ESG dimensions display significantly higher GSI values, consistent with strategic disclosure behavior rather than substantive sustainability engagement. Overall, the findings demonstrate that transparency alone is insufficient to ensure credible ESG communication, highlighting the need for EU sustainability governance to move beyond disclosure-based compliance toward digitalized, data-driven monitoring frameworks that systematically integrate external information sources to curb strategic ESG misrepresentation and enhance corporate accountability under evolving regulatory regimes.

Hamstrings–quadriceps (H–Q) imbalance represents a biomechanical marker of knee instability and injury risk in running. This in silico (simulation-based) study introduces a digital machine learning framework designed to estimate H–Q imbalance using biomechanical features derived entirely from synthetic running trials and conceptually mappable to inertial-sensor domains. Key biomechanical predictors included the dynamic hamstrings-to-quadriceps ratio (H:Qdyn), the knee moment limb symmetry index (LSI), and the early-stance co-contraction index (CCI), all standard indicators of muscular balance and coordination in sports medicine. A reduced musculoskeletal framework emulating flexor–extensor balance, limb symmetry, and co-contraction patterns generated 573 synthetic running trials for 160 virtual subjects across three speeds. These interpretable features trained a calibrated gradient-boosting classifier evaluated via ROC-AUC, PR-AUC, balanced accuracy, F1, and Brier score. Across all conditions, the model achieved ROC-AUC 0.933 (95% CI 0.908–0.958), balanced accuracy 0.943 (95% CI 0.924–0.962), PR-AUC 0.918 (95% CI 0.892–0.943), F1 0.940 (95% CI 0.919–0.958), and Brier 0.056 (95% CI 0.041–0.072), outperforming the logistic baseline. Dynamic H:Q ratio and knee moment symmetry were the dominant predictors, while co-contraction provided complementary biomechanical nuance. These results demonstrate that simulation-derived frameworks can reproduce IMU-relevant biomechanical variability, enabling interpretable machine learning for transparent assessment of muscular balance in sports medicine.

Hamstrings–quadriceps (H–Q) imbalance represents a biomechanical marker of knee instability and injury risk in running. This in silico study introduces a simulation-derived machine-learning framework designed to estimate H–Q imbalance using biomechanical features conceptually mappable to inertial-sensor domains. A reduced musculoskeletal framework emulating flexor–extensor balance, limb symmetry, and co-contraction patterns generated 573 synthetic running trials for 160 virtual subjects across three speeds. These interpretable features trained a calibrated gradient-boosting classifier evaluated via ROC-AUC, PR-AUC, balanced accuracy, F1, and Brier score. Across all conditions, the model achieved ROC-AUC 0.933 (95% CI 0.908–0.958), balanced accuracy 0.943, PR-AUC 0.918, F1 0.940, and Brier 0.056, outperforming a calibrated logistic baseline. Dynamic H:Q ratio and knee-moment symmetry were the dominant predictors, while co-contraction contributed complementary nuance. These results indicate that simulation-derived digital frameworks can reproduce IMU-relevant biomechanical variability, enabling interpretable machine learning for objective assessment of muscular balance in sports medicine.

The economics of burnout is estimated at around $300 bn globally, reaching a point where the World Health Organisation could declare a pandemic in the foreseeable future. There are obviously financial losses due to burnout, however, there are also financial gains for the pharma industry and therapists. In this article we shall put some data in balance, but also look at deeper regulatory implications, for companies, hospitals and people. Along with exploring causes and financial impacts, we shall look at how non-financial reporting can provide more timely guidance and alarm signals, better social responsibility disclosure practices where medical reports are more material, not just a set of issues. The time is right to revisit the Human Resource Accounting (HRA) practice from both its traditional cost approach perspective and also from present challenges to understand how value is put at risk.

Sports forensic is a very niche segment of knowledge and activity encompassing sports, audit, legislation, chemistry and police investigation. The purpose of this article is to have a transdisciplinary approach encompassing on one hand academic frameworks of analysis and on the other qualified judgement of professional bodies and authorities. Outside its law enforcement capacity, evidence gathered by police in an attempt to find non-coercive solutions, can serve as advancements in the field of sports. The perspective of this article is an education one, identifying causes, effects as well as providing insights for prevention and anti-corruption programmes. Also, organisational risks are zoomed-in via annual reports and their subsequent non-financial reporting. Mapping out and better understanding scale and stimuli of corruption in sports while acting can play a disruptive role in other criminal activities. International cooperation and identifying best practices are a key aspect in guiding whistle-blowers as well as making sports forensic a more established filed of research, for better competitions.