Search Results (1,181)

This paper studies the informational hierarchy of individual stocks in the Colombian Stock Exchange (BVC), with particular attention to the role of cross-listed securities. The paper addresses a gap in the literature on small emerging markets, where evidence on intra-market information and return transmission remains scarce, particularly in the presence of illiquidity, cross-listing, and external risk exposure. Using daily data for 2015–2024, we estimate a five-asset vector autoregression VAR (3) with exogenous global controls and compute generalized forecast error variance decompositions within the Diebold–Yilmaz connectedness framework, with residual-bootstrap inference and CBOE Volatility Index (VIX)-based regime analysis. The VIX regimes are used to distinguish low-, medium-, and high-global-risk environments because global risk appetite is a key channel through which external shocks affect emerging equity markets. Three results stand out. First, total connectedness is moderate in the full sample, at 25.2%, but rises sharply with global risk, from 17.5% in low-VIX periods to 28.4% in high-VIX periods. Second, Ecopetrol’s American Depositary Receipt listed on the New York Stock Exchange (EC, NYSE) emerges as the dominant net transmitter of return innovations, and its informational leadership becomes stronger as global uncertainty increases. Third, when the local Ecopetrol share is excluded, leadership shifts to Bancolombia’s ADR (CIB), suggesting that directional spillover leadership is associated not only with firm identity but also with the offshore trading venue. These findings document a regime-dependent and venue-driven informational hierarchy, consistent with ADR-listed securities acting as dominant transmitters of return innovations to the domestic Colombian equity system. For portfolio managers, the results imply that diversification across local Colombian equities may overstate the number of independent information sources, especially during high-risk periods, and that monitoring ADRs, global volatility, oil prices, and exchange-rate conditions may improve hedging and risk management. Full article

In the context of an increasingly complex and dynamic digital ecosystem, accurately assessing the value of Specialized, Refined, Differentiated, and Innovative (SRDI) enterprises is crucial for making effective decisions. Traditional valuation methods struggle to effectively address issues such as the high R&D expenditures and significant operational risks associated with these enterprises. This study proposes an interpretable intelligent decision-support framework for valuing SRDI enterprises listed on the Beijing Stock Exchange (BSE), constructing a multidimensional indicator system that encompasses solvency, profitability, and R&D capabilities. Feature importance screening using the XGBoost algorithm was conducted to identify key indicators as input variables for a backpropagation (BP) neural network. Concurrently, the Particle Swarm Optimization (PSO) algorithm was applied to the neural network to optimize initial weights and thresholds, thereby modeling nonlinear valuation relationships. Empirical analysis of 770 SRDI firms listed on the Beijing Stock Exchange from 2020 to 2024 indicates that the XGBoost-PSO-BPNN model achieved a coefficient of determination of 0.8083 on the test set, outperforming traditional linear models and benchmark models such as single-tree models. SHAP explainability analysis further reveals that current asset turnover, return on assets, and equity concentration are the primary value drivers. This study employs various clustering methods to further classify enterprises into three categories and proposes recommendations for differentiated regulatory policies, providing intelligent decision support for enterprises operating within complex digital ecosystems. Full article

Climate change and low-carbon transition policies affect sustainable development by changing firms’ financing costs and investors’ capital allocation. This paper investigates whether and how climate-related information is priced in China’s equity market, focusing on firm-level carbon intensity and exposure to climate policy uncertainty (CPU). First, univariate-sorted portfolio tests confirm the existence of a carbon premium, as firms with high carbon intensity earn significantly higher average returns. However, the unconditional relation between CPU exposure and stock returns is insignificant. Bivariate-sorted portfolios reveal a strong interaction between the carbon premium and the CPU premium. The carbon premium is higher for firms with high exposure to CPU, whereas a significant and negative CPU premium appears among low-carbon firms and, in sector-level tests, is concentrated in non-energy firms. Further analysis demonstrates clear differences between energy and non-energy sectors, which may be attributable to cash flow risks and uncertainty in growth options. The findings contribute to climate-related asset pricing and sustainable finance research by showing that transition-risk pricing depends on the interaction between carbon exposure and policy uncertainty. Full article

Background/Objectives: This updated systematic review and meta-analysis provide a focused synthesis of contemporary evidence on clinical outcomes reported after the Latarjet procedure and Free Bone Block (FBB) techniques for anterior shoulder instability, focusing on recurrence, patient-reported outcome measures (PROMs), return to sport, complications, and osteoarthritis progression. Given that most available studies report single-procedure cohorts, between-technique comparisons were interpreted in the context of indirect evidence and study-level heterogeneity. Methods: A systematic review and meta-analysis were conducted according to PRISMA 2020 guidelines. The updated search included studies published from 2019 to May 2024 and was integrated with 70 studies from the previous review that were re-screened according to the same eligibility criteria. Eligible studies reported outcomes for the Latarjet or FBB procedure, with a minimum 2-year follow-up and at least five patients in the relevant treatment cohort. Risk of bias was assessed using the RoB 2 and MINORS tools. Outcomes were synthesized using random-effects meta-analysis. Pooled estimates were calculated separately for each procedure, and between-technique contrasts were treated as exploratory and descriptive when appropriate. Heterogeneity was assessed using I². Results: Ninety-eight studies with 6043 patients and 6071 shoulders were included: 72 on Latarjet, 23 on FBB, and 3 direct comparative studies. Both procedures were associated with low recurrence rates, improved PROMs, and comparable return-to-sport rates. Recurrence was 7% for Latarjet and 5% for FBB. Return to sport was 66% after Latarjet and 65% after FBB. Complication rates were 5% for Latarjet and 8% for FBB, while osteoarthritis progression was 12% and 9%, respectively. PROMs improved after both techniques, although differences between procedures should be interpreted cautiously because of the indirect nature of most comparisons and substantial heterogeneity across studies. Conclusions: Both Latarjet and FBB procedures were associated with generally favorable outcomes for anterior shoulder instability with bone loss in the included studies. The available evidence suggests broadly comparable clinical outcomes, with possible differences in complication profile, recurrence pattern, and osteoarthritis progression. However, these findings should be interpreted considering differences in patient characteristics, follow-up duration, surgical technique, graft type, and outcome definitions across studies. Current evidence does not allow definitive conclusions regarding the superiority of one technique over the other, but it provides useful descriptive outcome profiles to inform clinical decision-making and guide future direct comparative research. Full article

This paper studies an optimal consumption–investment problem in a multi-asset financial market where risky assets returns incorporate returns history. Preferences are modelled using Epstein–Zin recursive utility, allowing a separation between risk aversion and intertemporal substitution. Using the well-known martingale optimality principle and forward–backward stochastic differential equations (FBSDEs), we obtain explicit closed-form solutions for the optimal strategy and value function. A sensitivity analysis illustrates the dependence of optimal policies and value function on key parameters, including risk aversion, elasticity of intertemporal substitution (EIS), memory horizon, learning intensity, and wealth-history parameters. The findings provide new insights into the interaction between behavioural features and dynamic portfolio choice in a multi-asset setting. Full article

This study investigates whether short-term reversal exists in an emerging government bond market and whether its returns are consistent with a risk-based explanation. Using Indonesian government bonds from January 2010 to December 2025, the results show that loser portfolios outperform winner portfolios in terms of excess returns relative to the benchmark. A long–short reversal strategy produces statistically significant excess returns and remains highly persistent across rolling 10-year windows, although the evidence is weaker over shorter 5-year horizons. Further analysis indicates that the strategy experiences statistically significant losses during bad times while delivering positive average returns over the full sample, broadly aligned with a risk-based interpretation of short-term reversal. Transaction cost analysis further supports the strategy’s practical feasibility, as observed bid–ask spreads for on-the-run Indonesian government bonds remain below the estimated breakeven threshold. Overall, this study provides rare evidence on short-term reversals, their state-dependent performance, and their practical feasibility in an emerging government bond market. Full article

Background: Cognitive changes after minor surgery may affect patient safety, functional recovery, and readiness for hospital discharge, even after low-risk procedures with early discharge protocols. In this regard, patients require neuropsychological assessment at discharge, which may have important clinical implications for return to daily activities and postoperative decision-making. Our study investigated postoperative cognitive changes after minor surgery under general anesthesia using a neuropsychological assessment and a non-surgical group. Methods: Patients undergoing laparoscopic cholecystectomy received propofol or sevoflurane anesthesia. A non-surgical control group was included. Cognitive performance was assessed at baseline and discharge using the Computerized Visual Attention Test (CVAT), the controlled oral word association test (COWAT), and the symbol digit modalities test (SDMT). Relative change scores were calculated as ((baseline − postoperative performance)/baseline). Group differences were analyzed using two-tailed multivariate analysis of variance (MANOVA), followed by ANOVAs and Bonferroni-adjusted pairwise comparisons. Results: A total of 105 participants were included (37 non-surgical, 34 propofol, 34 sevoflurane). MANOVA showed a significant group effect (Pillai’s trace = 0.332, p < 0.001, η² = 0.196). The ANOVAs revealed significant differences in sustained attention (CVAT), verbal fluency (COWAT) and executive function (SDMT). The propofol group showed evident decline in sustained attention compared to non-surgical. In verbal fluency, non-surgical improved at day after, whereas both surgical groups showed no improvement, indicating worse performance. In SDMT the sevoflurane group had worse performance. Conclusions: Minor surgery under general anesthesia may lead to transient impairments in attention and learning at discharge, supporting the need for postoperative cognitive monitoring and individualized discharge decisions. Full article

The beneficial utilization of small and medium floods requires a clear flood-control safety boundary before floodwater can be moderately stored and regulated as a water resource. For the Three Gorges Reservoir, a large river-type reservoir with long-distance backwater effects and tributary blocking, this boundary cannot be determined solely from the dam-front water level. This study developed a one-dimensional unsteady hydrodynamic model with dynamic roughness calibration to investigate the risk-constrained flood-season operating water level of the Three Gorges Reservoir. Typical flood events and the 20-year return period design flood were used to examine the responses of the maximum dam-front flood-regulation water level, excess flood volume, longitudinal water levels, and exceedance risk at key reservoir-area sections under different initial regulation water levels and release-discharge conditions. The results show that the Changshou reach is the main control section for high-water-level inundation risk under the study scenarios. When the initial regulation water level is at or below 155 m, the dam-front flood-regulation water level, the peak water level at Changshou, and the exceedance duration generally vary only slightly. When the initial regulation water level exceeds 155 m, these risk indicators increase markedly, indicating a reduced flood-control safety margin. Perturbation analysis further shows that the dam-front flood-regulation indicators are relatively insensitive to small roughness and dam-front boundary perturbations, whereas the Changshou water level and exceedance duration are more sensitive to roughness and flood-volume perturbations. Therefore, 155 m should be interpreted as a conservative operational reference boundary under the current design-flood framework, existing operation rules, and the assumption of no forecast-based pre-release, rather than as an absolute safety threshold. Increasing release discharge can reduce high-water-level risk in the reservoir area under preset release limits, but its practical application must remain conditional on downstream flood-control constraints and real-time flood-conveyance capacity. The results provide a hydrodynamic basis for risk-constrained flood-season operation of large river-type reservoirs. Full article

Digital technologies are increasingly promoted as enablers of decarbonization and environmental, social, and governance (ESG) compliance in shipping, yet adoption remains constrained by high upfront costs, uncertain returns, supply–demand mismatch, and the risk of symbolic ESG disclosure and greenwashing. This study develops a collaborative governance framework to explain how technology provision, enterprise adoption, and public regulation co-evolve under ESG constraints. We construct a tripartite evolutionary game involving technology providers, shipping enterprises, and the government, incorporating ESG-driven market preference, technology matching efficiency, supply- and demand-side subsidies, regulatory intensity, greenwashing detection and penalties, and system-wide ESG benefits. Replicator dynamics and equilibrium stability analysis are used to derive convergence conditions, and numerical simulations together with system dynamics are employed to examine adjustment paths and convergence speed under alternative policy scenarios. Results indicate that a high-compliance equilibrium emerges when the net benefits of supply and adoption are positive and regulatory benefits offset enforcement and subsidy costs. Matching efficiency is identified as a key friction that slows diffusion and delays convergence even under favorable ESG market signals. Subsidies reduce cost pressure on both supply and demand sides, while greenwashing penalties and effective detection strengthen compliance incentives and accelerate convergence. Overall, the findings suggest that policy packages combining targeted incentives with credible enforcement are more effective than single-instrument approaches, and that improving technology–business fit is essential for transforming ESG pressure from external compliance into sustained internal adoption. Full article

Assessing drought risk under evolving climate conditions is critical for adaptation planning, yet it remains challenged by projection uncertainty and methodological complexity. This study presents a global probabilistic framework for estimating self-calibrating Palmer Drought Severity Index (scPDSI) drought return periods by integrating observational climate data with statistical modeling. We combined the scPDSI with a stochastic weather generator and generalized extreme value (GEV) analysis to evaluate drought duration extremes at a 2.5° × 2.5° global resolution. The weather generator creates 1000 synthetic time series per grid cell to enable probabilistic assessment, reproducing observed variability, persistence, and long-term trends. To project future risk, we derived return periods by scaling synthetic series using regional temperature change factors from a multi-model CMIP6 ensemble. Results indicate broad agreement with climate model ensembles but highlight regions where nonlinear dynamics drive divergences. We explicitly address critical methodological limitations raised in the recent literature, including the use of scPDSI as a sole indicator, the assumption of stationary variance in the stochastic generator, and the statistical challenges of modeling discrete drought durations with GEV distributions. This framework offers a spatially explicit, observationally grounded tool for decision-makers, while underscoring the necessity of multi-index validation in future global assessments. Full article

Background: Percutaneous coronary intervention (PCI) during extracorporeal cardiopulmonary resuscitation (ECPR) before sustained return of spontaneous circulation (ROSC) is increasingly performed, yet most published reports fail to document ROSC status at the time of intervention—leaving this specific clinical phenotype poorly characterized. We aimed to clarify this ambiguity by systematically separating studies with explicit no-ROSC documentation from those in which the phenotype is only inferred and to describe selection, feasibility, and outcomes for the resulting cohorts. Methods: PubMed, Embase, and Cochrane CENTRAL were searched on 30 January 2026. Studies were pre-classified as DEFINITE (explicit no-sustained-ROSC documentation at PCI) or PROBABLE (workflow strongly implying no sustained ROSC). The 13 DEFINITE studies served as the primary analysis population; the 14 PROBABLE studies provided supportive evidence. Risk of bias was assessed using ROBINS-I (DEFINITE, primary) and JBI checklists (all studies). Sensitivity analyses excluded overlapping registries (ELSO, SAVE-J). Data were synthesized descriptively along three axes—selection, feasibility, and outcomes—without meta-analysis. Registered in PROSPERO (CRD420251252255); PRISMA 2020 compliant. Results: Twenty-seven studies encompassing 12,882 patients were included. In the DEFINITE primary cohort (13 studies, N = 3320), median survival to discharge was 30.3% (IQR 26.5–40.8; range 21.0–69.0; and n = 11) and favourable neurological outcome (CPC 1–2) 33.5% (IQR 16.8–45.8; range 10.4–92.0; and n = 12). Exclude-overlap sensitivity analysis (19 studies, N = 2741) yielded concordant estimates (survival 31.1%, IQR 27.2–37.0). PCI rates spanned 24–100% and post-procedural TIMI 3 flow 62.4–84.0%. ROBINS-I rated 9/13 DEFINITE studies at serious overall risk of bias and 4/13 at moderate (none low), predominantly from confounding by indication and selection bias—substantially more stringent than the JBI appraisal. Conclusions: PCI without sustained ROSC under ECPR is technically feasible, but the practice is widespread while remaining insufficiently standardized in ROSC reporting. Descriptive benchmarks from DEFINITE studies provide realistic outcome ranges for shared decision-making; no inference regarding comparative effectiveness is possible from observational data. Standardized documentation of ROSC status at PCI initiation is an immediate priority for future ECPR research. Full article

Growing awareness of low-carbon environmental protection necessitates incorporating the dynamic evolution of consumer preferences into supply chain management. Unlike prior research focusing on single financing or static preferences, this paper uniquely integrates financing choice under mixed-financing conditions and government incentives under dynamic information updating. We investigate a low-carbon supply chain where a capital-constrained retailer combines low-carbon credit financing (LCF) and trade credit financing (TCF) and under the updating of consumption preferences during the lead time, and build a carbon reduction optimization model. The manufacturer’s abatement and production, the retailer’s financing balancing LCF and TCF under bankruptcy risk, and ordering are modeled within a two-period newsvendor framework with demand information updating. Result analysis reveals the following: (i) Under preference updating and equal subsidy amounts, LCF consistently induces higher carbon reduction efficiency than promotion allowances. (ii) Counterintuitively, promotion allowances dynamically enhance the supply chain’s total profit more effectively than LCF, exposing a critical divergence between environmental efficiency and economic performance. (iii) Across all incentive schemes (single or combined), the marginal gain in carbon reduction efficiency declines with increasing subsidy intensity. These findings imply that policymakers should flexibly align the choice of incentive instrument with the prioritized objective—carbon reduction or supply chain profitability—given the diminishing marginal returns. Full article

Gold mining firms operate in an environment characterized by substantial commodity price volatility, capital intensity, and long investment horizons. Traditional deterministic financial planning frameworks are insufficient to capture the nonlinear and asymmetric risks associated with gold price fluctuations. This study develops a simulation-based scenario planning framework for gold mining firms, integrating deterministic scenario analysis with stochastic price modeling. Using a stylized and benchmark-calibrated financial model intended for methodological illustration rather than firm-specific forecasting, the study evaluates the impact of gold price uncertainty on key financial indicators, including EBITDA, free cash flow, and net present value. Monte Carlo simulations indicate substantial dispersion in financial outcomes, with approximately 28% of simulated realizations producing negative Net Present Value outcomes under baseline assumptions. The results further demonstrate that volatility significantly amplifies downside exposure despite positive expected returns, thereby highlighting the limitations of deterministic planning approaches. The findings suggest that probabilistic scenario-based financial planning provides a more comprehensive framework for evaluating financial resilience and tail-risk exposure in commodity-dependent industries. Full article

This study analyzes the scouring characteristics of the Mahakam River section to support bridge design and safety assessments. Using a 100-year return period, the design rainfall was determined to be 1246 mm via the Log Pearson III method, resulting in a peak design flood discharge (Q₁₀₀) of 77,984 m³/s. Hydraulic analysis using a rating curve indicates a flood water level elevation of 32.578 m from the riverbed. Scouring calculations, including construction and stream scouring, were performed using Laursen’s and empirical methods. The results show a total scouring depth of 4.8 cm/year, primarily driven by stream scouring, as construction scouring was zero under current existing conditions. These findings emphasize the necessity of bank protection, such as gabions, to mitigate erosion risks for future infrastructure. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly being integrated into dental diagnostics, promising improved detection, efficiency, and patient communication. While these developments offer potential clinical benefits, emerging commercial applications raise important ethical concerns. This study explores how providers of diagnostic AI systems frame their technologies in marketing materials, with particular attention to features designed to influence patient acceptance and increase revenue. Methods: An exploratory qualitative thematic analysis was conducted on publicly available promotional content from leading dental AI companies between September and October 2025. Materials were analyzed for recurring rhetorical strategies related to commercialization, persuasion, technological authority, and representations of objectivity. Ethical interpretation was guided by principlism, professional ethics, and virtue-based perspectives. Results: The findings show that AI is frequently marketed not only as a diagnostic aid but also as a tool for boosting case acceptance, return on investment, and practice expansion. Visualizations and performance metrics are used rhetorically to position AI as authoritative and objective, encouraging patient compliance while downplaying uncertainty and potential harm. These practices risk undermining patient autonomy, promoting diagnostic inflation and overtreatment, and compromising professional integrity by shifting attention from patient welfare toward commercial outcomes. Conclusion: Pervasive marketing of diagnostic AI amplifies existing tensions between professional integrity and commercial incentives in dentistry. Without appropriate safeguards, AI risks reinforcing a transactional model of care in which patients are treated as consumers and diagnostics become instruments of persuasion. To preserve trust and ethical practice, dentists and professional organizations must ensure that AI remains a supportive clinical tool rather than a commercial device, prioritizing transparency, informed consent, and patient-centered care. Full article