Search Results (256)

Background: Eustachian tube dysfunction (ETD) is associated with several common otologic conditions but lacks standardized diagnostic thresholds in Taiwan. This study aimed to evaluate the diagnostic performance of the GSI TympStar Pro pressure-swallow test and to identify a ROC-derived maximal peak pressure difference (MPD) cutoff for distinguishing clinically diagnosed obstructive Eustachian tube dysfunction (oETD) from healthy controls. Methods: A total of 152 subjects were enrolled, including 100 healthy controls and 52 patients with clinically diagnosed oETD, confirmed by otolaryngologist assessment and supported by ETDQ-7 symptom scoring. Tympanometry and tympanometry-based Eustachian tube function testing using the GSI TympStar Pro ETF–Intact pressure-swallow module, a modified three-tympanogram pressure-swallow protocol, were performed. Group comparisons were conducted using Mann–Whitney U and Chi-square tests. Diagnostic performance was assessed using receiver operating characteristic (ROC) curve analysis. Results: The median MPD was 11 daPa (IQR 6–17) in the control group, compared to 0 daPa (IQR 0–2) in the ETD group (p < 0.001). ROC analysis demonstrated that a cutoff value of ≤4 daPa yielded a sensitivity of 100.0% and specificity of 91.0% in the per-person analysis, and a sensitivity of 97.5% and specificity of 95.5% in the per-ear analysis. In the Type A-only per-ear analysis, MPD retained discriminatory performance, with an AUC of 0.978 (95% CI, 0.961–0.994). Conclusions: When interpreted in conjunction with ETDQ-7 symptom assessment and routine clinical evaluation, pressure-swallow testing may provide complementary objective information for the functional assessment of patients with suspected obstructive Eustachian tube dysfunction, particularly when resting tympanometry is unremarkable. In this single-center Taiwanese cohort, lower MPD values may serve as an adjunctive indicator of impaired Eustachian tube pressure equalization, and the ROC-derived cutoff of ≤4 daPa showed discriminatory value for differentiating symptomatic obstructive ETD from healthy controls. This cutoff should be interpreted as an exploratory, protocol-specific threshold and requires prospective validation in larger multicenter populations before broader clinical application. Full article

This paper tests whether reinforcement learning improves monthly commodity-sector rotation once the experiment is reconstructed from investable instruments, strong active benchmarks, realistic costs, and strictly chronological validation. Total returns for energy (DBE), gold (GLD), agriculture (DBA), and base metals (DBB) are obtained from CRSP; GSG and DBC are investable broad-commodity benchmarks. Twelve lagged market-state features generate month-$t+1$ decisions. The initial training sample contains 132 targets through December 2018, the original holdout contains 72 months through December 2024, and a frozen-policy temporal extension adds 17 months through May 2026. All active results deduct 10 basis points per unit of drift-adjusted turnover, and Sharpe ratios use contemporaneous Treasury-bill returns. Six-month momentum earns an 18.6% CAGR and 1.112 excess Sharpe; a fixed 10-seed PPO ensemble earns 9.6% and 0.438. An expanding supervised expert selector earns 16.0% and 0.755. In an explicitly exploratory memory-window sensitivity, a 60-month rolling selector reaches 23.4% and 1.033, but its mean advantage over momentum is not statistically established ($p=0.323$). The pattern is consistent with time variation, but it neither identifies an optimal window nor establishes that older observations are harmful. PPO’s temporal-extension surge is concentrated in March 2026 and reverses when that month is removed. Direct deep RL therefore does not robustly dominate; constrained expert selection remains a research candidate whose memory sensitivity requires prospective confirmation. Full article

This paper identifies and characterizes the background driving Lévy process (BDLP) associated with the Generalized Tempered Stable (GTS) distribution, a flexible seven-parameter family of infinitely divisible distributions with applications in physics and quantitative finance. We show that the corresponding BDLP is a finite-variation, infinite-activity Type B Lévy process and derive its explicit background driving characteristic exponent function (BDCEF). The resulting BDLP provides a unified representation that encompasses several important special cases, including the bilateral stable, bilateral Gamma, and Variance Gamma distributions. Building on these results, we develop a simulation framework based on a stationary Ornstein–Uhlenbeck (OU)-type process driven by the GTS BDLP. The mean-reversion speed parameter of the OU process is calibrated using maximum likelihood estimation applied to daily return data from the SPY ETF and Ethereum over the period 2010–2024. The proposed simulation methodology produces realistic daily cumulative return trajectories, and comprehensive numerical error analyses demonstrate the accuracy and efficiency of the resulting discretization scheme. These findings provide both a theoretical extension of Lévy-driven OU models and a practical framework for simulating complex financial return dynamics. Full article

Commodity prices are notoriously hard to forecast, and whether the returns of commodity exchange-traded funds (ETFs) can be predicted remains an open question. We compare three deep learning models, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer, for forecasting the returns of six Deutsche Bank commodity ETFs covering agriculture (DBA), base metals (DBB), broad commodities (DBC), energy (DBE), oil (DBO), and precious metals (DBP). Using daily price data from January 2007 to December 2025, we predict daytime returns (open to close) and overnight returns (previous close to open) separately, over five horizons of 1, 5, 30, 60, and 180 trading days. Each model sees a 20-day window of price-based features, returns, rolling averages and volatilities, momentum, and recent lags, built from all six ETFs. All models are trained on a strict chronological split and judged by two simple, decision-oriented measures: how often they call the direction correctly, and the risk-adjusted return (annualized Sharpe ratio) of a stylized long–short strategy that ignores transaction costs. Formal significance tests with HAC corrections for overlapping targets, bootstrap confidence intervals, and comparisons with ARIMA, random forest, and simpler benchmarks corroborate strong predictability in overnight DBP and daytime DBB at medium horizons. Predictability turns out to be highly specific to the asset, the trading session, and the horizon. Overnight returns of the precious metals ETF (DBP) are by far the most predictable: the correct direction is called 71.6% of the time at 60 days and 76.7% at 180 days, with Sharpe ratios reaching about 15. Base metals (DBB) daytime returns are predictable at 30 days and oil (DBO) daytime returns at 180 days, whereas one-day-ahead forecasts and agricultural returns (DBA) stay essentially unpredictable. The Transformer has a slight edge at longer horizons and the GRU at shorter ones. Key directional accuracy and Sharpe ratio results are confirmed by Newey–West HAC significance tests and Diebold–Mariano forecast comparison tests with the Harvey–Leybourne–Newbold small-sample correction; HAC standard errors at the 180-day horizon exceed naïve OLS errors by a factor of approximately 7.4, and we explicitly flag results that do not survive this correction. A three-fold expanding walk-forward validation scheme corroborates the main findings, with DBP overnight and DBO daytime predictability persisting across all evaluation windows. Deep learning architectures statistically and economically outperform logistic regression, ridge regression, and momentum baselines on the most predictable configurations. An anomalous failure of all models on DBA daytime returns at the 180-day horizon is diagnosed as a regime-driven artefact associated with post-2021 commodity inflation, not a general feature of agricultural return dynamics. The broader lesson is that splitting returns into daytime and overnight components exposes predictable structure that conventional close-to-close returns hide. Full article

Across the diverse family of flavoenzymes, the isoalloxazine cofactor was found to display extremely diverse redox properties, both with respect to the absolute value of the potential regime wherein it operates and to its redox cooperativity, that is, the relative positioning of its individual 1-electron transitions. Taking together electrochemical data and 3D structural information reported for selected representatives of the flavoenzyme family, we assessed the contribution of pK value modifications at the three protonatable nitrogens of the isoalloxazine moiety. While the absolute value of the redox regime appears only weakly dependent on such pK modifications, the diversity of redox cooperativity is readily rationalized by (protein-induced) stabilization/destabilization of the proton primarily on N5 and to lesser degrees on N1 and N3. The mathematical formalism underlying the interdependence of pK values and redox midpoint potentials is subsequently extended to representatives of the family featuring extremely positive redox cooperativity (i.e., the electron bi/confurcating flavoenzymes). Observed structural idiosyncrasies of these cases were found to rationalize the extremely strong inversion (ΔE ≪ −800 mV) of 1-electron midpoint potentials in the framework of this formalism. Full article

Digital transformation is reshaping industries, business models, and investment opportunities, creating new challenges for international portfolio management. The European communication services sector has become a strategic component of the digital economy, driven by advances in artificial intelligence (AI), digital platforms, 5G infrastructure, cloud computing, cybersecurity, and data-driven business models. Despite its importance, limited evidence exists regarding the effectiveness of portfolio optimisation strategies within digitally transforming sectors. This study investigates international portfolio optimisation using constituent firms of the MSCI Europe Communication Services 35/20 Capped Index. Drawing upon Modern Portfolio Theory and the Treynor–Black framework, an actively managed portfolio is constructed and evaluated against the SPDR^® MSCI Europe Communication Services UCITS ETF and an equal-weight portfolio. Using daily market data, the analysis estimates asset returns, alpha and beta coefficients, portfolio weights, and risk-adjusted performance measures, including the Sharpe and Treynor ratios. Paired-samples t-tests are employed to assess the statistical significance of performance differences among investment strategies. The findings show that the Treynor–Black portfolio generated the highest annual return (27.32%), outperforming both the benchmark and equal-weight portfolios, and the highest percentage of Sharpe ratios (1.2159), suggesting that diversification benefits outweighed the advantages of active security selection. Hypothesis testing indicates no statistically significant difference between the Treynor–Black and equal-weight portfolios, and no statistically significant difference exists between the proposed and benchmark portfolios. The study extends the international portfolio management literature by applying the Treynor–Black model to a digitally transforming sector. The findings suggest that portfolio performance is influenced not only by firm-level financial characteristics but also by broader digital and institutional environments. Firms operating within digitally advanced and well-governed economies appear better positioned to exploit technological innovation and generate sustainable long-term value. Overall, the results demonstrate that successful international portfolio optimization requires balancing active security selection with diversification while recognizing the role of digital transformation, governance quality, and innovation ecosystems in shaping investment performance within the digital economy. Full article

Carbon price instability within the EU Emissions Trading System (EU ETS) is associated with financial stress that propagates across green finance markets, yet the system-level dynamics linking carbon allowance instruments, clean energy equities, green bonds, and oil volatility remain insufficiently characterized over the turbulent 2021–2026 period. This paper applies the DCC-GARCH $R^2$ decomposed connectedness framework to five exchange-traded funds and one volatility index spanning the principal channels through which EU ETS regulatory shocks propagate to financial markets, and derives a novel Connectedness-Based Hierarchy Index (CBHI) that translates the transmitter–receiver hierarchy into a time-varying portfolio desirability index. Five key findings emerge. First, the carbon allowance futures ETF (KRBN) and the Paris-aligned equity ETF (CARB) form a near-closed systemic bloc within this asset universe: bilateral connectedness reaches $0.867$, with to and from directional connectedness values both approaching 80%. Second, this co-transmitter structure is highly contingent on the joint inclusion of both instruments; excluding CARB raises KRBN’s CBHI from $0.251$ to $22.204$, reclassifying it as a structural diversifier and reducing the mean Total Connectedness Index (TCI) from 52.18% to 30.81%. Third, system-wide connectedness averages 52.18% but surges to nearly 73% during EU ETS regulatory crises. Fourth, connectedness-aware portfolios outperform the minimum-variance benchmark across all risk-adjusted metrics, yielding an annualized Sharpe ratio of $0.148$ vs. $-1.066$ ($T=1035$ observations, daily rebalancing, zero transaction costs); this improvement is driven by reallocation toward crude oil volatility (OVX), the sole non-ETF and most peripheral instrument (to = 5.55%, $\mathrm{CBHI}=14.310$), which the CBHI identifies as the system’s dominant structural diversifier and whose weight rises from 0.4% in the benchmark to 31.6%. Fifth, the CBHI uncovers a structural tension in Paris-aligned mandates: CARB records the second-lowest CBHI ($0.254$), indicating that climate alignment and systemic risk minimization are partially conflicting objectives. Full article

ETFE (EthylenTetraFluoroEthylen) cushion facade systems are increasingly adopted in contemporary architecture due to their lightweight properties and design flexibility. However, their thin, meltable structures present persistent uncertainties in fire safety. Specifically, the quantitative effects of fire origin, facade location, and passive–active fire protection measures on structural integrity, toxicity, and secondary fire risks remain underexplored. This study evaluates the fire performance of 15 ETFE cushion facade typologies under 135 scenarios, including fires originating externally, internally, and within the cushion, across middle, corner, and recessed facade locations. Simulations are conducted using artificial intelligence-based code generation to address the limitations of conventional fire modeling. Fire behavior is quantified via time to structural failure, burning duration, CO toxic gas production, dripping onset and mass, and normalized fire and dripping performance indices. Results show that passive measures provide limited structural delay and often increase burning duration and toxicity. Conversely, active systems demonstrate more balanced, scenario-dependent performance, reducing fire intensity, toxic gas emission, and melt-induced secondary risks. These findings highlight that effective fire safety in ETFE cushion facades requires holistic, location-sensitive and scenario-sensitive integration of passive and active measures rather than reliance on singular strategies, ensuring property protection and life safety in buildings. Full article

Monitoring equity drawdown risk requires real-time indicators that can be implemented without look-ahead bias and that may add information beyond standard volatility measures. This study develops a leakage-safe residual-stress indicator from cross-sectional PCA reconstruction errors in U.S. sector excess returns. Using daily adjusted prices for SPY and 11 U.S. sector ETFs, sector excess returns are computed relative to SPY, the common component is estimated with principal component analysis (PCA), and residual stress is defined as the cross-sectional root-mean-square magnitude of out-of-sample reconstruction residuals. The PCA mapping is estimated using information available only through $t-1$, the stress score is computed at t, and high-stress regimes are defined using rolling train-only quantile thresholds shifted forward by one trading day. The results show that realized volatility remains the stronger standalone benchmark in overall early-warning classification performance. Residual stress is therefore not proposed as a replacement for volatility. Instead, it is most useful as a complementary indicator of cross-sectional market dislocation. In the baseline sample, residual-stress spikes cluster around several drawdown-onset episodes, and conditional regime analysis shows that when volatility is low, high residual stress is associated with a higher probability of a drawdown onset within the next $H=21$ trading days than the low-stress/low-volatility regime. Event-overlap and lead-time diagnostics suggest that residual stress can identify some onset episodes not captured by a simple volatility-threshold rule, although its main incremental value lies in conditional risk stratification rather than systematically earlier triggering. The contribution of the paper is to develop a leakage-safe and interpretable residual-stress diagnostic for conditional drawdown-risk monitoring. The evidence supports a balanced interpretation: residual stress adds state-dependent information beyond standard volatility measures, especially in otherwise low-volatility states, but it does not dominate realized volatility as a standalone predictor. Full article

Portfolio optimization demands simultaneous consideration of multiple conflicting criteria under uncertainty, yet prevailing approaches either rely on some black-box machine learning (ML) models that sacrifice interpretability or employ classical multi-criteria decision-making (MCDM) methods lacking predictive foresight. This paper proposes a two-stage framework integrating a Transformer encoder for multi-output financial forecasting with the Peer-Prediction Trees for MCDM (PPT-MCDM) method for dynamic asset ranking and portfolio construction. The Transformer generates forward-looking predictions of next-period return, volatility, and maximum drawdown, while PPT-MCDM ranks assets by their excess performance index (EPI), measuring how much each asset’s multi-criteria profile exceeds data-driven peer expectations. The framework is validated on 28 sector and thematic exchange-traded funds (ETFs) over a 51-month out-of-sample period from January 2022 to March 2026. The PPT-MCDM portfolio achieves an annualized return of 11.99% with a Sharpe ratio of 0.589 and maximum drawdown of 18.80%, compared to the S&P 500 benchmark delivering 9.16% return, Sharpe ratio of 0.391, and maximum drawdown of 20.25%. An ablation study confirms that Transformer predictions improve the Sharpe ratio by 39.9% relative to using only observed backward-looking criteria. The main contributions of this work are three-fold: first, the development of a two-stage framework integrating deep learning forecasting with interpretable MCDM-based portfolio ranking; second, the first application of PPT-MCDM method to dynamic portfolio optimization with expanding-window retraining; third, empirical evidence that the framework outperforms the S&P 500 on both return and risk-adjusted metrics during a period encompassing both bear and bull market conditions. Full article

This study investigates the residual web crippling behavior of pultruded glass fiber-reinforced polymer (P-GFRP) hollow sections after exposure to elevated temperatures. The primary objective is to evaluate the combined influence of temperature and loading configuration on web crippling capacity, failure mechanisms, and structural performance, and to develop practical prediction models for engineering applications. A total of twenty pultruded GFRP hollow section specimens were exposed to temperatures of 24 °C, 200 °C, 250 °C, 300 °C, and 350 °C and tested under four loading configurations: End Ground (EG), Interior Ground (IG), End Two Flange (ETF), and Interior Two Flange (ITF). In addition to web crippling tests, tensile, SEM-EDS, TGA-DSC, DMA, and FT-IR analyses were conducted to investigate the mechanical, thermal, and microstructural degradation mechanisms. The results showed that elevated temperatures significantly reduced the web crippling capacity, with strength losses reaching up to 80% at 350 °C due to matrix degradation, fiber–matrix debonding, and loss of structural integrity. Among the investigated loading configurations, IG exhibited the highest load-carrying performance, whereas ETF experienced the greatest capacity reduction. A temperature-dependent reduction factor and unified empirical prediction equations were developed and demonstrated good agreement with the experimental results, with experimental-to-predicted ratios ranging from 0.97 to 1.15. The findings provide valuable insight into the post-fire behavior of pultruded GFRP hollow sections and offer practical guidance for the design, assessment, and fire safety evaluation of GFRP structural members exposed to elevated-temperature environments. Full article

ETFE cushions are applied to building facades in a wide range of geometric forms and sizes. However, information on how cushion geometry and dimensions affect bulging behavior, thickness and area values, structural strength, thermal conductivity, and energy performance remains limited. Therefore, this study investigates cushion typology in eight geometries (isosceles and equilateral triangle, square, rectangle, rhombus, pentagon, hexagon, circular) with side lengths or radius values between 1 and 10 m, covering 115 variations. Geometric/physical mathematical area calculations, the parabolic dome model, elastic plate bending theory, the empirical thickness model, and thermal-resistance and degree day-based energy calculation approaches are used to obtain planar area, inflated curved surface area, maximum and average thickness, R and U values, and heating, cooling, and total energy consumption for each typology. The use of AI in numerical calculations provides fast and efficient design solutions in architecture and enables various geometric and performance scenarios to be produced rapidly. Circular, hexagon, and pentagon cushions lower U values and provide energy savings due to their high bulging capacity and deformation homogeneity; square, rhombus, and rectangle cushions show medium-level performance; isosceles and equilateral triangles limit energy savings because they produce higher U values. In conclusion, an increase in average bulging thickness and characteristic length reduces the number of cushions required to cover the facade, decreases the U value, reduces total heating and cooling energy consumption, and improves thermal performance. When a facade is covered with cushions of different geometries and sizes, it provides up to approximately 99.24% energy savings. Full article

The rapid growth of environmental, social, and governance (ESG) investing has intensified the debate regarding whether ESG-oriented investment strategies exhibit performance patterns that differ from those of conventional investments, particularly during periods of market disruption. This study examines the risk-adjusted performance of ESG-oriented and non-ESG exchange-traded funds (ETFs) across market regimes surrounding the COVID-19 shock. The analysis classifies 28 passively managed ETFs into four sustainability-based categories and evaluates their performance using factor-based asset pricing models derived from the Fama–French framework. Additional analyses assess benchmark-relative performance using the S&P 500 and MSCI World indices and consider alternative ETF classifications based on investment mandates. The study estimates regime-specific regressions for the pre-COVID, COVID, and post-COVID periods. The results indicate that performance patterns vary across market regimes and ETF categories. Non-ESG ETFs tend to underperform on a risk-adjusted basis during the pre-COVID period, although this effect disappears thereafter. ESG-oriented ETFs generally exhibit limited evidence of abnormal performance, while factor exposures vary across regimes, reflecting changes in sector composition and macro-financial conditions. The findings suggest that, in addition to ESG orientation, market regimes and sectoral exposures play an important role in explaining differences in ETF performance. Full article

This study examines whether retail social media sentiment and community attention explain daily net capital flows into U.S. spot Bitcoin exchange-traded funds (ETFs), and whether issuer brand visibility conditions that relationship. We construct a balanced panel of $N=10$ ETFs over $T=514$ trading days (January 2024 to January 2026) and combine it with 162,819 cleaned Reddit posts to derive three AI-driven discourse variables: engagement-weighted sentiment, community attention, and a novel issuer-specific BrandScore. Entity fixed-effects regressions show that neither aggregate sentiment nor BrandScore level alone significantly predicts fund-level flows; however, the Sentiment $\times$ BrandScore interaction is significant ($\widehat{\beta }=2.930$, $p=0.038$), indicating that sentiment becomes economically meaningful only when attached to a visible issuer. This interaction survives two-way (entity + date) fixed effects ($p=0.012$) and winsorization ($p=0.004$). Panel quantile regressions reveal distributional heterogeneity in the brand-sentiment channel. Rolling 90-day window estimation confirms the mechanism is episodic, with the interaction achieving significance in 62.8% of subsample windows. These results provide suggestive evidence for a brand-filtered sentiment transmission mechanism in digital asset markets. Full article

The assumption that highly correlated financial assets share identical risk profiles often overlooks crucial distributional asymmetries. This study introduces a Goodness-of-Fit (GoF) framework to evaluate stochastic symmetry and structural alignment of equity returns. Moving beyond linear correlation, we apply non-parametric GoF tests—Kolmogorov–Smirnov, permutation-based Anderson–Darling, and Epps–Singleton—complemented by Energy Distance metrics, Extreme Value Theory (EVT) for 1% and 5% tail asymptotics, and robust L-moments to quantify tail asymmetry. We analyze major stocks against market indices and sectoral ETFs using ARMA-GARCH filtered innovations to isolate IID components. Our findings reveal a significant decoupling between correlation and stochastic symmetry; highly correlated assets frequently exhibit tail asymmetry and structural drift. Energy Distance decomposition isolates shape-driven deviations from scale-driven volatility. Furthermore, hierarchical clustering categorizes assets into distinct risk profiles, bridging structural divergence and left-tail risk. A 1000-iteration bootstrapped backtest shows that integrating our GoF framework with tail-risk penalties improves risk-adjusted performance, evidenced by superior Sharpe ratios (outperforming 80.3% of random allocations). In conclusion, high linear correlation does not guarantee distributional symmetry. The proposed framework offers deeper insights into asymmetric asset behavior than conventional second moment metrics, providing a robust tool for portfolio risk management under non-Gaussian market conditions. Full article