Search Results (229)

The increasing global demand for marine energy resources, coupled with the deployment of offshore oil and gas pipelines and submarine power cables, highlights the requirement for reliable subsea infrastructure. To protect these assets from environmental hazards and anthropogenic disturbances, seabed burial via trenching is widely adopted, with submarine trenchers serving as the main installation equipment. Trenching involves excavating a trench on the seabed to place pipelines, cables, or other subsea infrastructure. These operations involve complex soil–tool interactions that fundamentally govern cutting resistance, trench-wall stability, and overall equipment performance. Specifically, distinct engineering challenges arise across different trencher configurations: plough trenchers often encounter complex seabed structures, jet-type trenchers are prone to trench sidewall collapse, and mechanical trenchers face cutting difficulties in hard clay. A thorough understanding of these interactions is therefore critical for resolving operational challenges and optimizing trencher efficiency in engineering practice. To deeply understand these type-specific issues, this review summarizes the geomechanical problems associated with various trenching technologies, synthesizes recent research advances from analytical frameworks, physical experiments, and numerical simulations, and identifies existing knowledge gaps. By consolidating these findings, the paper provides a reference for addressing trencher-related engineering challenges, supporting equipment optimization, and facilitating the deployment of offshore energy transmission networks. 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

This study empirically aims to analyze the impact of primary monetary policy stance and transmission mechanisms of the European Central Bank (ECB)—such as the total assets of the ECB, long-term interest rate based on the government bond yields, and the EURUSD exchange rate—on major volatile cryptocurrencies like Bitcoin and Ethereum, as well as the leading stablecoin Tether. To this end, the study employs the linear Autoregressive Distributed Lag (ARDL) and the Bootstrap ARDL (BA-ARDL) procedures, robust approaches with limited data in time series analysis. The dataset consists of monthly data over the period from January 2019 to December 2025. We summarize the novel and robust primary empirical results of our study as follows: First, (i) it is revealed that the ECB’s balance sheet expansion has encouraged Bitcoin and Ethereum, yet has also, to a limited extent, suppressed Tether. Secondly, (ii) while the ECB’s long-term interest rate negatively impacts the prices of Bitcoin, Ethereum, and Tether, the negative impact on Tether is relatively weaker. Finally, (iii) the EURUSD exchange rate positively affects Ethereum, while its effect on Bitcoin is not statistically significant. On the other hand, at a 10% significance level, EURUSD has a weak negative effect on Tether. In conclusion, the empirical evidence demonstrates that the primary monetary policy stance and transmission mechanisms of the ECB influence the leading digital assets in distinct ways. Taking our findings into account is crucial for designing the digital euro in terms of financial stability and regulatory framework. Finally, we offer sound policy implications for the ECB based on empirical findings. Full article

The essential role of electricity supply for public and private services highlights the need to monitor the stability of power transmission networks during, or immediately after, hazardous events. In the aftermath of calamities, traditional field inspections may be impractical or unsafe, leaving operators without timely information on the condition of critical assets. In this paper, we present and discuss the performance of two automatic Artificial Intelligence (AI)-based models (Multi-Layer Perceptron (MLP) neural network architectures and Support Vector Machine (SVM) model) designed to automatically assess the status of high-voltage transmission towers and power lines through multi-temporal spaceborne Synthetic Aperture Radar (SAR) image analysis. Model development and testing rely on real COSMO-SkyMed Stripmap observations of damaged towers and power lines affected by documented hazardous events across Italy, complemented by simulated tower data generated with a physics-guided, signature-based SAR simulator designed to preserve the observed target-to-background contrast and spatial footprint patterns of real SAR tower signatures. Results indicate that the MLP, trained on either real or simulated data, achieved 100% Overall Accuracy (OA) with no observed false positives or false negatives within the considered visibility-screened real test set, while providing inference times on the order of tenths of milliseconds per target… Computational performance characteristics, operational advantages, and the potential pathway toward satellite on-board porting are discussed to enhance situational awareness and support the prioritisation of interventions during critical events. Full article

Dengue virus infection is a febrile illness caused by the Orthoflavivirus Dengue, which is transmitted by the mosquitoes Aedes aegypti or Aedes albopictus. Despite the fact that Dengue virus (DENV) is present in tropical and subtropical areas, climate change with global warming has been associated with the spread of Aedes aegypti and Aedes albopictus mosquitoes in several other regions worldwide. Notably, as the presence of Aedes albopictus has been confirmed in Southern Europe, already locally transmitted cases of Dengue virus infection have been reported in Europe. Apart from Europe, Australia has reported DENV cases in the 21st century that have been associated with the transmission of Aedes aegypti in the neighboring islands. Climate change, namely increasing temperatures, higher humidity and rainfalls, together with the development of urban heat islands, uncontrollable deforestation and urbanization, travelling and trade, has contributed significantly to the spread of DENV infection. Modern diagnosis based upon the advent of “multi-omics” techniques and machinery learning programs will be of the utmost importance for the early and accurate diagnosis of DENV infection. Finally, preventive measures for controlling Dengue virus infection, such as the use of repellents, educational programs, and improvement in water storage and waste management at the community levels would be very useful. Regarding climate change, the One Health Approach by integrating collaboration of various sectors and raising public awareness seems to be of the utmost importance in this context. Further investigations regarding the development of antiviral agents and vaccines will be an important asset in our armamentarium against DENV infection. Full article

This study examines whether conventional and Islamic real estate indices are associated with different patterns of financial connectedness and long-memory behavior in selected eco-resource sectors. The analysis focuses on four resource-related financial markets—water, food, agriculture and livestock, and reduced-energy sector exposure—and evaluates how the inclusion of different real estate indices changes the connectedness structure of this system. Bayesian Time-Varying Parameter Vector Autoregression (TVP-VAR) is used to estimate time-varying connectedness and spillover dynamics, while Long Short-Term Memory Neural Networks (LSTM-NN) are applied as a complementary tool to assess long-memory and forecasting-related patterns in the connectedness series. Compared with using either method alone, this design captures both the evolving network structure of market-based risk transmission and the persistence of connectedness patterns over time. Using market data from 20 September 2016 to 9 January 2026, the results show that conventional real estate indices are generally associated with stronger connectedness in the eco-resource financial network, suggesting greater potential for market-based risk transmission. In contrast, Islamic real estate indices exhibit comparatively lower connectedness and more persistent long-memory behavior in the examined sample. These findings indicate that real estate asset heterogeneity matters for understanding financial connectedness among selected sustainability-related sectors. The study contributes to sustainable finance by showing how conventional and Islamic real estate assets may play different roles in the financial connectedness of resource-related markets. Full article

Islamic banking is increasingly expected to align Sharia-based intermediation with sustainability objectives, yet empirical evidence remains limited on how sustainability disclosure links financing structures with bank performance. This study examines whether Islamic Sustainable Banking (ISB) functions as a mediating mechanism between profit-sharing financing, debt-based financing, and financial performance in Islamic banks in Indonesia and Malaysia. ISB is measured using an Islamic Sustainable Banking Disclosure Index that integrates Maqasid al-Shariah principles with SDG-oriented disclosure indicators. Using panel data from 23 Islamic banks over 2018–2023 and applying partial least squares structural equation modeling, mediation analysis, PLS-MGA, and permutation tests, the study finds that both profit-sharing and debt-based financing are negatively associated with ISB disclosure, while ISB is positively associated with net profit margin but not return on assets. The mediation results indicate statistically significant negative indirect associations through ISB, suggesting that sustainability disclosure operates as a conditional transmission mechanism rather than an automatic performance driver within the specified PLS-SEM model. Cross-country tests reveal significant differences between Indonesia and Malaysia, particularly in the associations between financing structures and profitability. The study contributes to Islamic sustainable finance by clarifying how Maqasid-oriented disclosure connects financing composition, governance capacity, and profitability, while offering practical implications for bank managers, regulators, and policymakers seeking to integrate sustainability into Islamic banking governance and financing decisions. 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

In dollarised economies, the absence of autonomous monetary policy shifts the burden of macroeconomic adjustment onto the banking system, where deposits and credit constitute the principal channel through which liquidity is conveyed to commercial activity. The literature has documented this relationship using aggregate national data, yet its behaviour at the monthly provincial scale remains underexplored for Latin America, particularly in fully dollarised economies and over recent periods marked by severe shocks. This article addresses that gap for Ecuador using a monthly panel of its 24 provinces over 2019–2025, estimated as a Panel VAR by two-step GMM, with monthly sales declared to the Internal Revenue Service used as a high-frequency indicator of provincial economic activity. The pandemic is incorporated as an exogenous control. The theoretical framework combines the supply-leading hypothesis, the credit-channel literature on transmission lags arising from financial frictions, and financial intermediation theory on liquidity and asset transformation. The system exhibits a predominantly supply-leading dynamic: deposits and credit retain predictive capacity over provincial sales, with no robust evidence of reverse feedback. Transmission speed is heterogeneous across channels. Deposits affect sales with a one-period lag, whereas credit requires an additional period—a pattern consistent with the differential role of each channel in banks’ asset-transformation function. The provincial-scale evidence for a dollarised economy shows that the macroeconomic relevance of financial intermediation depends on the heterogeneous transmission speeds of its components, with implications for territorial policy. Full article

Under growing geopolitical uncertainty and rising expectations for responsible development, regional governance increasingly functions as a cross-border signal that shapes how region brands are evaluated in international markets. Drawing on moral legitimacy theory, this study examines whether morally legitimatized regional governance is associated with region brand reputation and, in turn, host-country consumer trust. We conceptualize morally legitimatized regional governance through three dimensions—governance vision altruism, governance procedural transparency, and governance structural compatibility—and test the proposed model using survey data from 975 consumers who had purchased or intended to purchase foreign brands. Structural equation modeling shows that all three dimensions are positively associated with region brand reputation, which is subsequently associated with higher host-country consumer trust. Among the three governance dimensions, procedural transparency shows the strongest association with region brand reputation, followed by structural compatibility and vision altruism. Multi-group analyses further show that perceived economic distance and cultural distance significantly condition the associations between morally legitimatized regional governance and region brand reputation. These findings indicate that responsible regional governance is not only a public governance issue but also a sustainability-relevant intangible asset associated with reputation spillovers in international markets. The study extends moral legitimacy theory to the regional governance context, clarifies the reputational transmission mechanism from governance to host-country consumer trust, and shows that the effectiveness of governance signals depends on host-country context. The results also suggest that regions seeking to build reputation in international markets should move beyond symbolic sustainability narratives and invest in verifiable transparency, governance capability, and context-sensitive communication. Full article

This paper integrates the Elastic Net-TVP-VAR-BK framework and constructs a high-dimensional multilayer frequency-domain network, including short-, medium-, and long-term layers, to investigate extreme risk spillovers among 56 industries in the Chinese stock market. We examine the topology of the multilayer network at the system, cross-sector, and industry levels, as well as from both static and dynamic perspectives. Using daily data on 56 industry indices from 1 March 2007 to 30 September 2024, our empirical results show that: (1) All multilayer network topologies, including edge structures, node characteristics, and spillover strengths, exhibit significant frequency heterogeneity, and the dynamic topology of the three-layer network shows fluctuations and directional differences during critical periods. (2) In most periods, the short-term layer exhibits stronger average spillover intensity and denser inter-industry linkages, suggesting that short-horizon risk transmission plays a more prominent role in rapid contagion. However, the medium- and long-term layers remain important for identifying persistent and structural risk transmission. (3) At the industry level, capital markets and textiles, apparel, and luxury goods within the short-term layer, food products, household products, and road and rail in the medium-term layer as well as construction and engineering, industrial conglomerates, trading companies and distributors, metals and mining, and distributors in the long-term layer, all demonstrate high cross-industry systemic importance and total systemic importance, thereby establishing themselves as key nodes within their respective frequency domains. The findings provide theoretical support for policymakers in formulating strategies to address market risks and offer important references for investors in asset allocation and risk management decisions. Full article

Outliers in financial time series can reveal latent inter-asset relationships that are often missed by traditional dependence measures and average dynamic models. To address this gap, we propose Outlier-Driven Network Inference (ODNI), a framework for reconstructing directed lagged Tail Outlier-Triggering Networks from financial time series. ODNI first converts multivariate return series into upper and lower tail outlier indicators using empirical quantiles, then applies a bivariate EM-based attribution model to infer lagged triggering relationships across tail channels, and finally constructs a directed weighted network by combining baseline-corrected excess activation with EM attribution weights. For controlled evaluation, we simulate multivariate time series with volatility clustering and cross-variable spillovers from a known directed interaction template using a cross-GARCH(1,1) model. Across extensive experiments, ODNI achieves the best reconstruction performance among CoVaR, a Clayton copula tail-dependent network, and DCC-GARCH, with especially strong precision. Robustness tests show stable behavior across regimes, with systematic improvement as sample length increases and true coupling becomes more identifiable. Applications to major foreign exchange rates and global stock indices further reveal clear regional structure and asymmetric sender–receiver roles across tail-triggering channels. ODNI provides a practical tool for uncovering latent risk transmission pathways driven by tail outliers. Full article

This study investigates time-varying market efficiency and cross-market correlations in cryptocurrency markets across South Korea, the United States, and Japan. Using rolling-window multifractal detrended fluctuation analysis (MF-DFA) and dynamic conditional correlation–generalized autoregressive conditional heteroskedasticity (DCC-GARCH), we analyze 11 cryptocurrency–fiat pairs—Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Bitcoin Cash (BCH) denominated in Korean Won (KRW), US Dollar (USD), and Japanese Yen (JPY)—from January 2018 to September 2025. MF-DFA results confirm persistent multifractality and significant time-variation in market efficiency across all markets, consistent with the Adaptive Market Hypothesis (AMH). DCC-GARCH estimates reveal a structural divergence between return integration and efficiency correlations: return-based correlations for same-asset cross-fiat pairs are exceptionally high (mean dynamic conditional correlation of approximately 0.96–0.98), whereas efficiency-based correlations are far more heterogeneous, with cross-asset pairs approaching near-zero synchronization. We interpret the Kimchi Premium as a product of institutional frictions that impede price-level arbitrage while leaving volatility transmission largely unaffected. These findings suggest that cryptocurrency market integration is multidimensional—globally synchronized in risk dynamics, yet locally segmented in the structural quality of information processing. Full article

With digital assets increasingly comprising a significant portion of personal wealth, the secure management and transfer of digital legacies have emerged as a pressing concern. Secret sharing offers a solution to this problem. However, distributing shares containing the unique private key for digital assets poses significant risks of theft or tampering, potentially leading to the illegal appropriation of user assets. This paper presents a leakage-resistant digital inheritance distribution scheme based on sparse-matrix secret sharing. It employs an efficient thresholding scheme that uses sparse matrices, achieving near-linear complexity for share reconstruction via a random striped matrix. Reconstruction time is significantly reduced compared to traditional polynomial interpolation methods. To address the realistic scenario where an asset owner holds multiple independent digital accounts, we propose a multi-account blinding and aggregation mechanism. This mechanism allows the dealer to establish isolated group keys for each account in a single round of communication, while preventing adversaries from linking different accounts to the same owner. A key-derivation and encrypted-transmission mechanism is then designed based on the aggregated group keys. Group keys are established by consensus among heirs, from which each heir derives a unique session key. Authenticated encryption ensures the confidentiality, integrity, and identity-bound transmission of shares. Through security proofs and experimental performance evaluation, it is demonstrated that the proposed scheme satisfies adaptive security requirements with the hash function H modeled as a random oracle, while all other cryptographic primitives (PRF, AES-GCM, HMAC) are assumed to be secure under standard computational assumptions. Full article

Linear assets (LAs), such as conveyor systems, road networks, pipelines, and power transmission lines, are typically maintained through localized, segment-based interventions. While such approaches effectively address spatially heterogeneous degradation, they often neglect the system-level consequences of repeated local actions. In particular, improvements in segment condition may be accompanied by increased structural complexity, leading to reduced reliability and higher lifecycle costs. This paper proposes a unified engineering framework that integrates segment-level condition assessment with system-level structural effects. The framework is based on a dual representation of asset condition, distinguishing between material state (MS) and structural state (SS), which correspond to material aging (MA) and structural aging (SA), respectively. A key contribution is the introduction of the fragmentation penalty (FP), capturing the negative impact of increasing segmentation and interface density on system performance. The framework incorporates multi-threshold decision logic, enabling differentiation between operational, refurbishment, and replacement regimes, and interprets maintenance actions as transformations affecting both condition and structure. A formal model is developed to represent the asset as a dynamic system of segments and interfaces. It provides a basis for future empirical calibration and structure-aware optimization. Although the model is developed using conveyor belt loops as a reference case, its broader relevance is discussed for other classes of linear assets with repeated local intervention and evolving structural heterogeneity. A simple worked example is included to demonstrate the operational meaning of the proposed fragmentation-aware perspective. The results show that maintenance decisions may change when structural side effects are considered together with local condition improvement, and they provide a basis for future empirical calibration and structure-aware optimization of maintenance strategies. Full article