Search Results (45)

Wearable inertial sensors offer a portable alternative to laboratory-grade force plates for postural stability assessment; however, their validity across progressively challenging balance tasks remains under-explored. This study evaluated the reliability and concurrent validity of inertially sensed metrics compared with force-plate-derived postural sway metrics across a five-level spectrum of unstable surfaces (Floor, Foam Pad, Rotating Disc, Air Disc, Bosu). Twenty-five healthy young women (22.1 ± 3.6 years, 1.64 ± 0.04 m, 58.44 ± 8.21 kg) performed five trials of single-leg standing (40 s each) on each surface. Postural sway was computed from antero-posterior (AP) and medio-lateral (ML) center of pressure (CoP) recordings using a force plate (Kistler, 9286 AA, Winterthur, Switzerland, sampling at 500 Hz) in synchronization with a lateral shank-mounted inertial sensor (Bionomadix BN-ACCL3, Biopac Systems, Inc., Santa Barbara, CA, USA, sampling at 100 Hz). In addition to reliability, a two-tiered analysis evaluated global concordance (unstandardized slopes) and method agreement (standardized z-scores). Intraclass correlation coefficients (ICCs) for the inertial sensor were excellent (range: 0.95–0.96), surpassing the force plate (range: 0.85–0.92) as trials accumulated. Analysis revealed moderate-to-good global concordance in the AP direction (r = 0.60, p = 0.001) and good-to-excellent in the ML one (r = 0.85, p < 0.001), validating the progressive intensifying effect of the surface graduation. Individual ranking agreement—evaluated via standardized z-scores—was also significant in both the AP (r = 0.61, p < 0.001) and the ML (r = 0.85, p < 0.001) directions, indicating a convergence into how the two modalities rank individual performance. Bland–Altman plots confirmed high absolute agreement between standardized scores, though a predictable proportional bias was observed in raw units, where the inertial sensor’s underestimation of sway magnitude increased linearly with task difficulty. The five-level postural challenge graduation is a highly reliable framework for balance assessment. While the shank-mounted sensor exhibits proportional underestimation of sway magnitude compared to the CoP at extreme intensities, its high internal stability and sensitivity to task difficulty make it a valid and robust tool for longitudinal clinical monitoring. Full article

The most widely used bankruptcy predictor, Altman’s Z-Score, assigns a positive coefficient to asset turnover; faster firms are rated safer. Under crisis conditions, that assumption reverses. We introduce the Solvency Margin (SM), a diagnostic calculable from standard financial statements that measures, in dollars, how far an organization is from the threshold where operations become impossible. Unlike static liquidity ratios, the SM yields a concrete speed limit: the maximum operating velocity at which an organization can survive a defined shock. We validated the SM against pre-crisis financial data across three crises in two domains. Regarding the automotive sector, SM computed from FY2019 filings showed directional predictive power among ten major automakers in both the 2021 semiconductor shortage (ρ = 0.50, p = 0.14) and the 2020 COVID-19 pandemic (ρ = 0.53, p = 0.12; ρ = 0.70, p = 0.036 excluding one governance-driven outlier). With reference to the 2023 U.S. banking crisis, SM augmented with a Deposit Stability Factor predicted crisis outcomes among eighteen regional banks (Spearman ρ = 0.62, p = 0.006), correctly ranking three of four failed institutions in the bottom three positions. Monte Carlo simulation (450,000+ runs) confirmed threshold behavior. We present a five-step calculation method and a three-lever decision framework for practitioners. Full article

The objective of this study was to explore the impact of exchange rate volatility on corporate financial stability in European corporations, with particular emphasis on the Eurozone and non-Eurozone. The data set of this study consisted of 80 publicly listed non-financial corporations in eight European countries over the period of 2010–2024. The model was able to capture the impact of various macroeconomic changes that affected European corporations in the past few years. The macroeconomic changes that were captured in this study were the European sovereign debt crisis, the COVID-19 pandemic in the world, and the conflict in Ukraine. The financial stability was measured by the Altman Z-score, the leverage ratio, and the current ratio. In this study, the financial impact of the exchange rate was measured by the rolling standard deviations and the conditional volatility with the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. The fixed effects model estimation with the System Generalized Method of Moments (GMM) was used in this study. The results of this study showed that the exchange rate volatility was negatively correlated with financial stability in terms of the leverage ratio. However, the Eurozone provides protection against the financial impact of the exchange rate volatility in terms of the leverage ratio. The diagnostic tests in this study were carried out with the Hansen Test and the Arellano-Bond Test. The diagnostic tests confirmed that the results were valid. The significance of this study was that it provided longitudinal data on the impact of the exchange rate on the financial stability of European corporations with particular emphasis on the Eurozone and non-Eurozone. The study also provided new insights on the exchange rate in corporate finance. The Eurozone provides protection against the financial impact of the exchange rate. Full article

Background/Objectives: The Global Lung Initiative (GLI) 2022 race-neutral spirometry reference equations were introduced to improve interpretability across populations; however, their performance in Middle Eastern adolescents remains insufficiently validated. This study evaluated the applicability of GLI-2022 among healthy Jordanian adolescents. Methods: Healthy adolescents were recruited from secondary schools across multiple Jordanian cities (July–November 2025). Spirometry was performed according to ATS/ERS standards using a single device and standardized procedures. GLI-2022 predicted values and z-scores were derived for forced expiratory volume in one second (FEV₁), forced vital capacity (FVC), and FEV₁/FVC. Calibration was assessed using mean (SD) z-scores and the proportion below the lower limit of normal (LLN; z < −1.645). Agreement between measured and predicted values was examined using Bland–Altman methods. LLN-based pattern classifications were compared with those obtained using the local reference equation and GLI-2012. Results: A total of 921 adolescents (482 males, 439 females; mean age 15.7–16.0 years) were included. GLI-2022 produced positive mean z-scores for FEV1 (0.51–0.73) and FVC (0.51–0.69), with low proportions below LLN for both indices (<2% in each sex), indicating underestimation of predicted lung volumes. Exact binomial testing confirmed that the observed proportions below LLN for FEV1 and FVC were significantly lower than the expected 5% in both sexes (all p < 0.001). The FEV1/FVC ratio showed smaller deviations (mean z 0.07–0.19), with 4.1% of females and 5.8% of males below LLN, and these proportions did not differ significantly from 5% (female p = 0.444; male p = 0.402). Mean observed-minus-predicted biases for FEV1 were +0.185 L in females and +0.306 L in males, and for FVC were +0.224 L and +0.351 L, respectively; FEV1/FVC bias was −0.15 percentage points in females and +0.60 percentage points in males. LLN-based pattern classification showed 98.7% overall agreement with the local equation and 99.7% with GLI-2012; concordance for obstructive and possible restrictive patterns was 93.5% and 100.0%, respectively. Conclusions: In healthy Jordanian adolescents, GLI-2022 appears to underestimate predicted FEV₁ and FVC, yielding upward-shifted z-scores and fewer volume indices below LLN, while the ratio is less affected. Although LLN-based pattern classification was largely preserved, population-specific validation remains necessary before routine clinical adoption of GLI-2022 in Jordanian adolescents; extrapolation to other Middle Eastern adolescent populations should await additional regional validation. Full article

This study examines the impact of working capital management policy (WCMP), including working capital investment policy (WCIP), working capital financing policy (WCFP), and cash holding policy (CHP) on financial distress (FD) among non-financial firms listed on the Egyptian Stock Exchange during 2010–2024. FD is proxied by the Altman Z-score, where higher values indicate lower distress risk. The study further investigates whether economic policy uncertainty (EPU) moderates the relationship between WCMP and FD. Using panel data analysis and the Fixed Effects Model, the results show that conservative WCIP and higher cash holdings significantly reduce FD risk, whereas greater reliance on short-term financing increases firms’ vulnerability to distress. The findings also reveal that EPU amplifies the effects of WCMP on FD. Overall, the study highlights the strategic importance of prudent liquidity management in enhancing firms’ financial resilience in emerging market environments characterized by macroeconomic uncertainty. Full article

Background/Objectives: Serum neurofilament light chain (sNfL) is an early and sensitive biomarker of polyneuropathy. This study compared the UmanDiagnostics enzyme-linked immunosorbent assay (ELISA), and Meso Scale Discovery (MSD) R-PLEX assay with the current gold-standard single-molecule array (Simoa) assay for sNfL measurement. Methods: sNfL levels were measured with Simoa, ELISA, and MSD R-PLEX in 330 serum samples from 73 individuals with a pathogenic transthyretin gene variant (TTRv) and in 165 healthy controls (HC) with ELISA and MSD R-PLEX. Results: Median sNfL levels, assessed in serum samples from TTRv individuals, differed across all assays (all p < 0.001). Passing–Bablok regression slopes were 1.01 (Simoa–ELISA), 1.00 (Simoa–MSD R-PLEX), and 1.02 (MSD R-PLEX-ELISA), with very strong correlations (all r > 0.8). Bland–Altman analysis showed mean differences of 0.1 ± 0.2 pg/mL (Simoa–ELISA), 0.7 ± 0.1 pg/mL (Simoa–MSD R-PLEX), and −0.6 ± 0.2 pg/mL (MSD R-PLEX-ELISA). In HC, sNfL levels positively correlated with age. Z-score normalization allowed for inter-assay comparison. Conclusions: The ELISA and MSD R-PLEX assays provide suitable alternatives for the Simoa assay to measure sNfL levels in carriers of a pathogenic TTR-gene variant. The differences in concentrations defined by the assays directly relate to the internal standard provided with the assays. Full article

Nowadays, the European economy faces significant global challenges that threaten the continuity of economic growth, especially in the German manufacturing sector, which is under strain from financial turmoil, resulting in numerous layoffs and firm closures. In this respect, FinTech significantly contributes to addressing these issues by providing data-driven analytical tools that improve the assessment and monitoring of firms’ financial position. However, in the literature, we have not found any paper that uses machine learning (ML) algorithms to assess the financial distress of German manufacturing firms, highlighting methodological and sectoral gaps that need to be bridged. Therefore, this study aims to develop an econometric and ML-based financial distress scoring model for German manufacturing firms by estimating contemporaneous Altman Z-scores that provide better insights into the financial distress determinants, enabling better financial management. The econometric findings revealed that the regression model has an adjusted R-squared value of 86%, confirming that the selected firm-specific and macroeconomic factors play a substantial role in explaining financial distress. The findings recommend that German manufacturing businesses retain more earnings rather than distributing them as dividends, while reducing their debt in capital structures to enhance financial stability. Moreover, the ML results found that Gradient Boosting and Random Forest have the highest accuracy scores among the ML methods, suggesting that these models provide strong capability for assessing financial distress and supporting more effective financial risk management, allowing firms to effectively respond to the threats of a dynamic environment and thereby better support the growth of the German and European economies. Full article

This study investigates the impact of ESG practices on the financial stability in a multisector sample of 86 publicly listed Brazilian firms, focusing on the Weighted Average Cost of Capital (WACC) and Altman Z-Score (AZS) as a proxy for insolvency risk. Using Bloomberg data from 2010 to 2021, this research applies advanced econometric methods, including Ordinary Least Squares (OLS), Vector Autoregression (VAR) and Fully Modified Ordinary Least Squares (FMOLS), to capture both short- and long-term effects. The findings reveal a financial learning curve: in the short term, ESG adoption can temporarily increase WACC and insolvency risk due to initial implementation costs, whereas in the long term, it reduces financial risk, enhances operational efficiency, and strengthens corporate resilience. These results underscore ESG practices as a strategic determinant of long-term value creation and financial stability. This study offers actionable insights for policymakers, investors, and corporate leaders aiming to align sustainability initiatives with financial performance in emerging market contexts. Full article

Background: Financial sustainability remains a central challenge for U.S. hospitals as rising operating costs, shifting federal reimbursement, and policy uncertainty intensify economic pressures. This study estimates the prevalence and recent changes in financial distress among U.S. short-term acute care hospitals. Methods: We conducted a national longitudinal analysis of all U.S. short-term acute care hospitals from 2021 to 2023 using financial and operational data from Medicare cost reports linked with community-level data from the American Community Survey. Financial distress was measured using the Altman Z-score, with severe distress defined as Z ≤ 1.8. Logistic regression models were used to identify organizational, operational, and market characteristics associated with distress. Results: The proportion of hospitals classified as severely financially distressed increased from 18.6% in 2021 to 22.0% in 2023. Operating margins and returns on assets declined significantly over the study period, while mean Z-scores showed a modest but non-significant downward trend. In adjusted models, urban hospitals had higher odds of distress (OR 1.27, 95% CI 1.15–1.40, p < 0.001), as did hospitals with longer average lengths of stay (OR 1.07 per day, 95% CI 1.04–1.09, p < 0.001) and higher debt-to-equity ratios (OR 1.05 per unit, 95% CI 1.05–1.06, p < 0.001). Higher occupancy rates were protective (OR 0.31, 95% CI 0.25–0.40, p < 0.001). Larger market population was also associated with increased distress risk (OR 1.61, 95% CI 1.21–2.14, p = 0.001), while other market characteristics were not significant. Conclusions: Financial distress remains widespread and appears to be increasing among U.S. acute care hospitals. Operational efficiency, capital structure, and local market scale are key drivers of financial vulnerability, highlighting the need for targeted strategies to strengthen hospital resilience and preserve access to essential acute care services. Full article

This study tests the connectionbetween corporate governance structures and firm value, incorporating financial distress as a mediating mechanism among construction companies listed in ASEAN markets. Utilizing a sample of 58 firms drawn from an initial population of 169 companies over the 2018–2021 period, this study measures governance mechanisms through managerial ownership, institutional ownership, independent commissioners, audit committees, and litigation risk. Firm value is proxied by Tobin’s Q, while financial distress is assessed utilizing the Altman Z-Score. Panel data regression is employed to test the direct connections, and the Sobel test is used to evaluate the mediating role of financial distress. The outcome describes that managerial ownership and audit committees have a favorable effect on firm value, whereas independent commissioners and litigation risk exert a negative influence. Institutional ownership shows no significant association with firm value. Moreover, institutional ownership significantly affects financial distress, whereas the other governance mechanisms show no significant association with financial distress, although financial distress itself has a detrimental impact on firm value. The mediation analysis describes that financial distress mediates only the connection between institutional ownership and firm value. These outcomes help clarify prior inconsistencies in the literature and underscore the importance of strengthening managerial ownership and audit committees, optimizing the role of independent commissioners, and mitigating litigation risk to sustain firm value. Full article

Background: The increasing integration of Environmental, Social, and Governance (ESG) factors into financial decision-making has prompted debate over their impact on corporate credit risk. While many studies suggest that ESG performance may enhance firms’ resilience, empirical evidence remains mixed due to data inconsistency and methodological heterogeneity and differences in time horizons over which ESG effects materialise. Methods: The study investigates the relationship between ESG performance and credit risk using a panel of European firms from 2020 to 2024, a phase highly characterised by substantial macroeconomic shocks. The Altman Z-score serves as a proxy for default risk, while ESG data are sourced from Refinitiv Eikon. Four fixed-effects panel regressions are estimated: a baseline model using aggregate ESG scores, an extended model with financial controls, and disaggregated and sector-specific models. Results: The findings indicate that ESG scores—either aggregated or by pillar—show limited statistical significance in explaining variations in the Z-score. In contrast, financial variables such as solvency, liquidity, and cash flow ratios display strong, positive, and significant effects on credit stability. Some heterogeneous sectoral effects emerge: social factors are positive in technology, while governance has a negative impact in basic materials. Conclusions: ESG initiatives may not yield immediate improvements in default risk metrics, particularly over short and crisis-dominated periods, but could enhance financial resilience over time. Combining ESG information with traditional financial ratios remains essential; the results underscore the importance of consistent and high-quality ESG disclosure to reduce measurement error and enhance comparability across firms. Full article

Climate change observed in recent decades has, in most cases, negatively impacted on the operations of non-financial and agricultural enterprises. Filling a gap in the economic literature, this article presents the results of a study on the impact of rising temperature on the resilience to bankruptcy risk of over four thousand agricultural enterprises operating in Poland between 2016 and 2023, taking into account temperature and macroeconomic conditions of regions of their operation and assessing resilience with Altman (Z-score) and Zmijewski (X-score) methods. Using panel regression, it was demonstrated that temperature changes have a significant nonlinear (parabolic) effect on enterprise resilience. An increase in annual average temperatures above the long-term average weakens enterprise resilience. A generally similar, although individually variable relationship occurs for changes in average temperatures in spring, autumn, and winter. In the summer, this relationship is ambiguous. Furthermore, the resilience to bankruptcy risk improves growth in regional GDP and agricultural production, as well as enterprise’s assets, profitability and the share of equity in the financing structure. The conclusions can be used by agricultural enterprises in preparing contingency plans in the event of potential temperature shocks, and public administration for developing programs to protect agriculture against temperature shocks and food security plans. Full article

This study analyzes the ability of machine-learning algorithms to detect financial statement fraud using four financial ratios as inputs: the Altman Z-Score, Beneish M-Score, Montier C-Score, and Dechow F-Score. It also evaluates whether the Wirecard AG scandal of 2020 could have been detected by the model developed in this study. Financial statement data was obtained from the financial data vendor Bloomberg L.P. The dataset consists of 2,014,827 firm years between 1988–2019, from companies across the globe, of which 1145 firm years were identified as fraudulent. A balanced dataset of 1046 fraudulent firm years and 1046 randomly selected firm years was used to train and evaluate multiple machine-learning algorithms via an automated pipeline search. The selected model is an ensemble combining gradient boosting and k-nearest neighbors. On the held-out test set, it correctly classified 82.03% of the manipulated and 89.88% of the non-manipulated firm years, with an overall accuracy of 85.69%. Applied retrospectively to Wirecard AG, the model identified 7 of 17 firm years as fraudulent. Full article

Individuals often lack field-based tools to monitor exercise effectiveness. New sensing methods may allow for an improved measurement of the individualized response to exercise by monitoring oxygen kinetics directly in muscle tissue. This study aimed to validate a non-invasive wearable sensor capable of measuring muscle oxygen saturation (SmO₂) using continuous-wave near-infrared spectroscopy (CW-NIRS) against a laboratory-validated frequency-domain NIRS (FDNIRS) device. Ten physically fit adults performed an incremental cycling test until voluntary exhaustion. Devices were placed on contralateral rectus femoris muscles. SmO₂ was simultaneously measured continuously for the duration of the protocol. Time series alignment was performed using linear interpolation to enable direct comparison between devices at matched time points. Z-score normalization accounted for inter-individual differences in a group-level analysis. Individual subject validation showed strong correlations between the two devices (r = 0.792, range: 0.69–0.88, p < 0.001) with an RMSD < 5% for most subjects, a mean bias of 0.005 and low proportional bias (−0.199) between all paired measurements. Group-level analysis demonstrated a correlation of r = 0.788. Bland–Altman analysis revealed that 95% of all measurements fell between −8.1% and 7.6% SmO₂. The CW-NIRS device delivered reliable performance compared to the FDNIRS device, offering potential applications for real-time physiological monitoring during exercise and performance assessment. Full article

Background/Objectives: Evaluating coronary artery abnormalities (CAAs) in Kawasaki disease (KD) is essential for treatment decisions and long-term management and prognosis. Accurate diagnosis is challenging due to differing criteria across guidelines. This study aimed to assess the variability in CAA prevalence using Japanese Ministry of Health (JMH) criteria and Z score formulas and identify the formula pair with the highest CAA diagnostic agreement. Methods: Echocardiographic data from 309 patients with acute KD were collected. CAA prevalence was evaluated using JMH criteria and Z score formulas of Kobayashi, de Zorzi, Kurotobi, McCrindle, Olivieri and Dallaire. Prevalence differences were analyzed using McNemar’s t-tests, Z score values with paired samples t-test, and agreement between Z score formula pairs with Cohen’s Kappa (κ) coefficients and Bland–Altman plots. Results: The CAA prevalence varied significantly across definitions. For the right CA, prevalence was lower by JMH criteria than by Z scores (32.7% vs. 37.2–39.8%). For the left main CA, JMH (47.6%) and Kobayashi (44.8%) showed higher prevalence compared to other formulas (25.8–42.9%). Variability was greater at higher Z score values (>5 mm, medium/large aneurysm). Overall, the Kobayashi–Dallaire and McCrindle–Dallaire pairs showed the highest agreement (κ = 0.745–0.831 and 0.569–0.870, respectively); however, the McCrindle–Dallaire reached only moderate agreement for the left main CA (κ = 0.569). Conclusions: The Kobayashi and Dallaire formulas appear most suitable for evaluating CAA in predominantly Caucasian populations. Larger validation studies are warranted to refine diagnostic criteria and optimize global KD care. Full article