Search Results (654)

The objective of this study is to develop a methodological approach to the integral assessment of the investment attractiveness of metallurgical enterprises to improve the efficiency of investment activities and the implementation of projects and ensure sustainable development. The metallurgy industry faces the challenge of balancing efficiency goals and sustainable objectives (ESG) and risks. Our approach takes into account the relationship between investment potential, realized opportunities, and the level of risk. Based on a systematic analysis of theoretical approaches, an integral investment attractiveness index is proposed that aggregates investment potential (consisting of seven sub-potentials), an assessment of the results of project implementation, and an aggregated risk index. Assessing investment attractiveness is important for ensuring the sustainable implementation of effective projects and determining their priority. A panel dataset was constructed using data from two metallurgy companies. The relationship between investment attractiveness and classical indicators (ROIC, EVA, MVA, Tobin’s Q, and P/BV) is examined through panel regression with fixed effects, cross-correlation analysis of the temporal structure of relationships, a CUSUM test for model stability, and decomposition of investment attractiveness changes. Decomposition of investment attractiveness changes makes it possible to quantify the contribution of potential, opportunities, and risk to the dynamics of investment attractiveness across various periods, including crisis and post-crisis ones describing the specifics of the metallurgic industry. The presented methodology is relevant for increasing the efficiency of project implementation within the framework of an integral company policy and contributes to the acceleration of industrial implementation of sustainable projects in the metallurgy sector. Full article

Energy security has become a central policy challenge because decarbonisation must be achieved without weakening the reliability, affordability and resilience of national energy systems. This article examines whether and how energy transition policies contribute to national energy security, with particular attention to aggregate policy stringency, individual policy instruments, and renewable electricity deployment. The analysis uses a panel of 49 countries over 23 observed years between 2000 and 2023, excluding 2002, comprising 1127 country-year observations, and applies two-way fixed-effects models with Driscoll–Kraay standard errors. The aggregate Energy Policy Stringency Index has a positive but statistically insignificant coefficient in the contemporaneous model (0.421) and remains insignificant with one-, two- and three-year lags (0.187, 0.128 and –0.026, respectively). Renewable electricity generation is consistently positive and significant, with coefficients ranging from 0.071 to 0.090, indicating that actual renewable deployment is more closely associated with energy security than formal policy stringency. Policy-instrument decomposition shows that fossil fuel excise taxes have the strongest positive association, with coefficients from 1.554 to 1.082 in full-instrument models and from 1.614 to 1.077 in one-by-one robustness checks. Air emission standards have delayed positive effects, while some renewable-support and cross-sectoral tools show mixed results, indicating dependence on design and system readiness. Full article

Water scarcity is redefining the limits of industrial development across the Middle East and North Africa (MENA), yet the regional dynamics of industrial water withdrawal remain poorly quantified. In this study, industrial water withdrawal was examined for 18 MENA countries over the period 1995–2022 using four seven-year periods, integrating spatio-temporal assessment, hierarchical clustering, Tapio decoupling, and Logarithmic Mean Divisia Index (LMDI) decomposition. The results showed that industrial water withdrawal per capita was highly concentrated: Iraq exceeded 300 m³/person/year between 1995 and 2001, and then declined to roughly 150–200 m³/person/year by 2002–2008, while Lebanon rose to 150–200 m³/person/year and became the highest-intensity case in the period 2009–2015 and 2016–2022. In addition, clustering identified four groups, with Egypt and Iraq forming a distinct pair and Saudi Arabia remaining structurally unique. Decoupling analysis presented favorable decoupling states most clearly in the 2002–2008 period, but became mixed after 2009. Decomposition further showed that population growth and economic development were the most persistent positive drivers of industrial water withdrawal, whereas technical change was the most variable counterforce. These results show that industrial water withdrawal in MENA is concentrated, structurally uneven, and increasingly shaped by country-specific interactions between growth, demography, technology, and industrial structure. The findings provide actionable evidence for policymakers, industrial planners, and water managers seeking to align industrial growth with water security in the MENA region. Full article

We present a 42-year (1984–2025) Landsat consistent satellite vegetation trajectory for coastal wetlands in the Shell Beach area in the Breton Sound estuary, Louisiana. We applied the Controlled Interrupted Time Series (CITS) analysis to the satellite record to quantify the causal effect of the 2009 Mississippi River Gulf Outlet (MRGO) closure on the coastal wetland vegetation. The analysis used NDVI, kNDVI, and NDII across 88 vegetation transect plots located within five Coastal Reference and Monitoring Systems (CRMS) stations in the Shell Beach wetlands. Vegetation communities identified included Saline, Brackish, Freshwater, and Intermediate marsh. Sentinel-2 data from 2015 to 2025 were retained as an independent parallel record for NDRE analysis only. Quarterly median composites were decomposed using the Seasonal-Trend decomposition using LOESS (STL) to isolate de-seasonalized vegetation anomalies. The CITS design used segmented Ordinary Least Squares (OLS) regression with Newey–West HAC standard errors (lag = 3) at the study area. Northern Barataria Bay was used as an untreated regional control site to remove concurrent climate and sea level rise confounders. Whilst Hurricane Katrina and subsequent years (2005–2008) were excluded from the models, the single group ITS identified significant negative post-closure slope change across three indices. These were NDVI (β3 = −0.0034 yr⁻¹, p = 0.000), NDII (β3 = −0.0032 yr⁻¹), and kNDVI (β3 = −0.0016 yr⁻¹). These values indicated continued site-level decline relative to the pre-closure trend. Community-stratified ITS analysis showed a distinct divergent pattern with Freshwater marshes demonstrating significant recovery, with NDVI β3 = +0.0190 yr⁻¹, p = 0.000, whilst Saline, Brackish, and Intermediate communities continued to decline. CITS Difference-in-Differences (DiD) confirmed that site-level NDII and kNDVI declines were MRGO-specific. The DiD findings were that NDII = −0.00313 yr⁻¹, p < 0.001; kNDVI = −0.00123 yr⁻¹, p = 0.008. These findings isolated that physiological water stress and the non-linear biomass losses were a result of the MRGO-closure. The Freshwater DiD for NDVI (+0.02071 yr⁻¹, p = 0.000) was the strongest evidence of MRGO-specific recovery. Barataria Freshwater declined, whilst the Shell Beach Freshwater recovered. The results demonstrated that multi-index decadal Landsat monitoring with seasonal decomposition and full inter-sensor harmonization is essential for restoration trajectory assessment in managed coastal wetlands. Full article

This study addresses the challenge of separating local damage information from full-field structural responses under complex environmental and noise conditions by proposing a structural damage localization method that integrates piecewise denoising, Robust Principal Component Analysis (RPCA), and a differential damage index. First, full-field responses obtained from vision-based measurement are processed through piecewise denoising and continuous displacement extraction, and then organized into a structural spatiotemporal response matrix. RPCA is subsequently employed to separate low-rank global response components from sparse local anomalies, and a damage index is constructed by differencing sparse-component statistical features between healthy and damaged states. Moving-load tests on a simply supported beam show that the DI peak in the damaged region is approximately 28 times higher than the non-damaged background level, and the identified DI peak accurately falls within the actual damage region. Compared with RMS, kurtosis, curvature index, wavelet energy, PCA residual, and RPCA sparse-energy indicators, the proposed method is the only one that achieves zero regional localization error. Under noise levels of 40–20 dB, all 30 repeated trials achieve a 100% localization success rate, and the success rate remains 93.33% even at 10 dB. Moreover, the localization results remain stable when $\lambda /{\lambda }_0$ varies from 0.50 to 1.50. Even when the number of spatial measurement points is reduced from 3401 to 128, the method maintains zero mean localization error and a 100% localization success rate. These results demonstrate that the synergy among piecewise denoising, RPCA decomposition, and state-difference enhancement effectively highlights damage-induced local anomalies, providing a robust and physically interpretable framework for full-field-response-based structural damage localization. Full article

Irregular migration across the Mediterranean Sea causes thousands of deaths annually, mostly due to shipwrecks involving structurally inadequate vessels navigating under severe meteo-marine conditions. The forensic investigation of human remains recovered in such contexts is particularly challenging due to advanced decomposition and the absence of documentary evidence linking victims to a specific departure event. In the present study, a methodology is developed and validated for reconstructing the most probable departure location of human remains recovered at sea, through the integration of backward Lagrangian drift simulations with large-scale oceanographic and atmospheric datasets provided by the Copernicus Marine Service (CMEMS). The methodology was applied to five bodies recovered in the Aeolian Islands area (Sicily, Italy) between March and June 2024. Simulations were performed using the OpenDrift Leeway model, with an ensemble of several drifters released across five temporal offsets per recovery site. Results were synthesised through a drift probability metric $P_d$ and a newly proposed Hydrodynamic Connectivity Index (HCI), cross-referenced with documented shipwreck incidents and complemented by a wave climate analysis. The methodology successfully identified the port of Bizerte (Tunisia) and the shipwreck event of 5–6 February 2024 as the most probable origin, in full agreement with independent forensic findings, demonstrating the reliability of the proposed approach for forensic reconstruction of shipwreck events in the central Mediterranean and the possibility of being used as aid in recovering further remains. Full article

Poland’s energy transition represents a structurally complex case of decarbonization in a coal-dependent economy, where declining hard coal consumption, increasing renewable energy production, growing natural gas use, and continued economic expansion interact within the same energy–economic system. This study assesses the evolution of emission pressure in Poland between 2000 and 2023 using a Fuzzy Multi-Criteria Evaluation (FMCE) framework. The analysis integrates four system-level variables: gross domestic product, hard coal consumption, natural gas consumption, and renewable electricity production, the latter transformed into an inverse fuzzy variable representing insufficient renewable energy penetration. The FMCE-based emission pressure index was constructed using min–max normalization, continuous fuzzy membership degrees, weighted aggregation, and component-level decomposition. The results show that Poland’s emission pressure was highest in the early phase of the analyzed period, especially in 2000–2007, when the energy system remained strongly shaped by coal dependence. The years 2008–2013 formed an unstable transitional phase, while 2014–2018 showed a more stable moderate-pressure configuration. After 2018, the index declined markedly, indicating a shift toward lower emission pressure; however, only selected years reached the formal low-pressure category, which suggests that a stable low-emission regime has not yet been fully established. The decomposition confirms that hard coal was the dominant contributor to emission pressure for most of the period, although its relative contribution declined over time. Renewable energy development increasingly weakened emission pressure, while natural gas played an ambiguous transitional role by partly replacing coal but maintaining fossil-fuel dependence. The study contributes to energy-transition research by proposing an interpretable fuzzy composite index for tracking structural emission pressure over time. The findings underline the need for continued coal phase-down, accelerated renewable energy integration, grid modernization, and careful governance of natural gas as a transitional fuel in Poland’s pathway toward a lower-emission energy system. Full article

Municipal organic waste management remains a major challenge for sustainable urban development, particularly in regions requiring decentralized treatment alternatives that reduce landfill dependency and promote circular resource recovery. This study compared Takakura composting and vermicomposting for the stabilization of municipal organic waste under decentralized operational conditions in the Ecuadorian Amazon and developed a Composite Circular Resource Recovery and Process Performance Index (CRRPPI) to evaluate resource recovery efficiency. Municipal organic waste was treated through Takakura composting, vermicomposting, and uncontrolled decomposition (control). Operational performance was assessed using material conversion efficiency, process productivity, nutrient recovery efficiency, nutrient productivity, and final physicochemical characteristics. These indicators were integrated into the CRRPPI framework to provide a multidimensional assessment of circular resource recovery performance. Takakura composting showed the highest operational efficiency, achieving material conversion efficiencies of up to 0.80, process productivity values of 1.23 kg day⁻¹, and superior nutrient recovery efficiencies for nitrogen (0.835), phosphorus (0.730), and potassium (0.880). The highest CRRPPI values were obtained for Takakura treatments (0.835–0.842), while vermicomposting showed intermediate performance, and the control treatment presented the lowest resource recovery efficiency (0.216). Sensitivity analysis confirmed ranking stability under ±20% weighting variations, and ANOVA followed by Tukey’s HSD test identified significant differences among treatments (p < 0.05). The results indicate that Takakura composting is an effective strategy for decentralized municipal organic waste valorization and nutrient recirculation. Furthermore, the proposed CRRPPI provides a practical exploratory framework for integrated evaluation of biological stabilization technologies by simultaneously considering operational performance and circular resource recovery. Full article

Accurately evaluating how aero-engine performance supports upper-level capability remains a challenging issue in the digital planning, demonstration, and design of complex equipment systems-of-systems. Existing studies mainly rely on two-level analyses at the subsystem and system-of-systems levels, which are insufficient to characterize the cross-level transmission relationships among the aero-engine, aircraft performance, and overall capability. To address this limitation, this paper proposes a multilayer network-based contribution evaluation method for aero-engines oriented toward digital equipment planning and demonstration. First, a three-layer evaluation index system is constructed, including the overall capability layer, the aircraft performance layer, and the aero-engine performance layer, based on the OODA loop concept and aviation physical constraints. This provides a structured and traceable basis for cross-level requirement decomposition and scheme evaluation. Second, by integrating expert prior judgment with mechanism-based sensitivity analysis, the interrelationships among indicators at different layers are quantified, and a multilayer evaluation index network is established. Third, topological structure analysis is employed to identify key indicators in the aero-engine layer, and a cascading propagation model is introduced to evaluate the supporting roles and contribution rates of both individual indicators and the overall aero-engine layer with respect to the overall capability layer. Simulation results show that the proposed method can effectively reveal the structural characteristics, propagation paths, and dynamic influence patterns of aero-engine-layer indicators within the multilayer network. The proposed method provides methodological support for digital equipment planning, scheme demonstration, design optimization, and capability-oriented decision-making of aero-engines. Full article

Natural lakes in urbanizing regions face compounding climatic and anthropogenic pressures. Despite their socio-ecological importance, the dual vulnerability of these urban lakes to both long-term areal shrinkage and the shifting frequencies of extreme water events remains a critical research gap, often overlooked in favor of large, remote lake systems. We investigated surface area dynamics, extreme events, and climatic attribution of 7320 natural lakes across China’s five major urban agglomerations (Jing-Jin-Ji, Yangtze River Delta, Greater Bay Area, Chengdu-Chongqing, and Middle Yangtze) from 2001 to 2023. Using a satellite area product, we assessed long-term trends via Seasonal-Trend decomposition by Loess (STL). Regional climate shifts were detected via multi-scale Standardized Precipitation–Evapotranspiration Index (SPEI) breakpoint analysis, and climate attribution was performed by correlating detrended lake areas with SPEI. Results show 59.4% of lakes exhibit significant trends, with shrinkage (50%) vastly outpacing expansion (9.4%), most severely in Jing-Jin-Ji (−0.28%/year). Despite all agglomerations transitioning toward wetter conditions (2008–2013), extreme event responses diverged markedly regionally. Climate-driven lakes (14.5%) displayed stronger shrinkage and greater sensitivity to extremes than lakes with low climate sensitivity, particularly in Jing-Jin-Ji and Chengdu-Chongqing. These findings reveal pronounced spatial heterogeneity in urban lake vulnerability, providing an evidence base for sensitivity-stratified management strategies. Full article

Background: Electroencephalographic (EEG) functional connectivity analysis requires multiple signal-processing, source-modelling, and statistical steps that can limit its adoption in clinician-led randomised controlled trials (RCTs). NeuroStat was developed as a prototype research tool to integrate this workflow; formal usability validation with clinician end-users has not yet been conducted. Methods: NeuroStat is an open-source Python/PyQt6 desktop application that integrates automated artefact removal (a Generalised Eigenvalue Decomposition for Artefact Identification [GEDAI] pathway and a traditional Artefact Subspace Reconstruction (ASR)/Independent Component Analysis (ICA)/ICLabel pathway), boundary element model (BEM) source localisation using the Desikan–Killiany atlas (68 cortical regions), Phase Lag Index (PLI) connectivity estimation across five canonical frequency bands, and RCT-oriented statistical analysis. Evaluation separated sensor-space and source-space claims: a sensor-level simulation (repeated across five independent random seeds) tested preprocessing robustness, a repeated source-space simulation tested recovery of a known cortical parcel-pair contrast after forward projection and inverse reconstruction, a PhysioNet benchmark tested posterior Desikan–Killiany alpha PLI in 20 healthy adults, and an illustrative application to 20 sessions from a published chiropractic RCT demonstrated real-world workflow applicability. Results: In the sensor-level simulation benchmark, the Traditional pathway achieved a mean absolute error of 0.168 ± 0.017 PLI units and root mean squared error of 0.219 ± 0.045 (mean ± SD across five independent random seeds) across all artefact conditions. In the source-space simulation, reconstructed alpha PLI for the known bilateral lateral-occipital parcel pair exceeded anterior control edges across 60 repeated condition runs (mean known-control difference = 0.105 PLI units, 95% CI 0.096–0.114; t(59) = 22.61, p < 0.001). In the PhysioNet source-space benchmark, posterior Desikan–Killiany alpha PLI was higher during eyes-closed than eyes-open rest (Cohen’s d = 0.85, p = 0.001; 16/20 subjects showing the expected direction) after ICLabel-enabled preprocessing. In the pilot RCT application, all 20 sessions completed processing without manual intervention, with default-mode network alpha PLI showing a pre-to-post change of +0.071 in the intervention group versus +0.015 in the active control group. Conclusions: NeuroStat integrates preprocessing, source-space construction, connectivity estimation, and statistical reporting within a parameter-logged desktop workflow for EEG functional connectivity studies. Current evidence supports initial technical feasibility, sensor-level preprocessing robustness for one pathway in controlled simulations, source-space recovery of a known parcel-level contrast, source-space sensitivity to an expected posterior alpha resting-state contrast, and error-free processing across 20 real RCT sessions in a pilot workflow demonstration. Formal usability testing, test–retest reliability analysis, participant-specific source-model validation, and clinical-population validation remain necessary before clinician-facing or trial-deployment claims can be made. Full article

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

The study investigates the state-dependent dynamics of overconfidence in the Bangladesh equity market by exploring the relationship between market returns and trading volume within a nonlinear information-theoretic framework. Building up on the traditional return–volume literature, the study differentiates between total market returns and unexpected returns, with the latter representing unexpected information shocks obtained using the Market Index Model. Transfer Entropy with bootstrap inference estimates the directional and asymmetric information flows across five different market states, namely: bullish, bearish, crisis, extended crisis, and COVID-19. The evidence suggests that the overconfidence biases in aggregate market returns are small and intermittent and are reflected in poor and unstable information flow between market returns and trading volume. In comparison, unexpected market returns have a directionally significant impact on trading behavior, which supports the behavior of state-dependent overconfidence. The findings also reveal that overconfidence is higher in normal and bullish market situations but drops significantly in crisis-based situations. The asymmetric analysis indicates increased trading responses to negative returns shocks, as it is more evident that investors are more sensitive to losses and recovery expectations. The research adds to behavioral finance literature on frontier markets through an unexpected return decomposition with nonlinear causality model. The results have serious implications on market surveillance, assessment of investor behavior and design of regulatory policies. Full article

Terrestrial ecosystems are facing increasingly severe threats driven by the dual pressures of climate change and anthropogenic activities. However, current remote sensing-based ecological research still exhibits notable deficiencies in the integration of multi-source data. This study develops a Critical Transition Index (CTI) for Northeast China. The CTI integrates four remotely sensed vegetation variables (LAI, NDVI, SIF, and VOD) with time series decomposition (STL), multiple early-warning signals (ar1, variance, skewness, and kurtosis), consistency scoring, and Mahalanobis distance. The framework systematically assesses vegetation resilience and its spatiotemporal responses to climatic stressors. Results reveal pronounced differences among variables: the structural indicator LAI identified the highest proportion of high-risk areas (60.8%, CTI ≥ 0.8), whereas the functional indicator SIF showed relatively high stability, with a mean CTI of 0.619 and a high-risk proportion of only 16.0%. High-risk areas are primarily concentrated in cropland–grassland mosaics, while forested regions maintain lower risk. Temporal analysis of land cover composition within high-risk areas shows a clear “structural diffusion” trend: the proportion of deciduous broadleaf forests in the high-risk category increased from being negligible in early periods (2003–2007) to approximately 20% in later periods (2013–2017) for both SIF and VOD indicators. This study underscores the necessity of multi-indicator frameworks for detecting critical transitions and provides quantitative, spatially explicit scientific insights for ecosystem early-warning and regional management strategies. Full article

TiVNbZrCr high-entropy intermetallic alloy was investigated as a deuterium storage material using gravimetric sorption measurements, thermogravimetric analysis, and temperature-resolved synchrotron X-ray diffraction during deuterium desorption. The as-prepared alloy had an experimentally determined composition of Ti₁₇V₁₉Zr₁₉Nb₂₂Cr₂₃ and a density of 6.59 g·cm⁻³. Empirical alloy-design parameters indicate that the alloy is not a single-phase bcc solid solution, but rather a compositionally complex intermetallic alloy. The calculated hydrogen-affinity descriptors suggest a strong thermodynamic driving force for deuteride formation. Under 5 MPa D₂, the alloy absorbed 3.28 wt.% D, corresponding to D/M = 1.1. After ex situ deuteration, additional diffraction reflections were indexed using tetragonal deuteride reference structures corresponding to ZrV₂D_2.35 and TiD₂, while the Cr-rich bcc phase remained comparatively stable. Thermal desorption released 2.28 wt.% D up to 600 °C in three partially overlapping steps. These results demonstrate that deuterium storage in TiVNbZrCr is governed by phase-selective deuteride formation and decomposition rather than by homogeneous bcc lattice expansion. Full article