Search Results (1,801)

This study conducted a 1:3 scale model test to investigate the improvement mechanism of damaged steel–concrete transition segments strengthened by UHPC. Meanwhile, a void region was introduced at the bottom of the transition segment to simulate the grouting defect in practical engineering. Then, static and fatigue tests on these transition segments were carried out on different parameters, including non-strengthening, UHPC strengthening and UHPC strengthening combined with void repair. Digital image correlation (DIC) was employed to characterize the global strain field of the transition segment. The experimental results show that UHPC strengthening reduced the relative displacement by 0.06 mm (46.2%), while UHPC strengthening combined with void repair achieved a reduction of 0.13 mm (96%). The average strain at critical points of the transition segment decreased by 76.2% after UHPC strengthening, while a greater reduction of 86.5% was achieved when UHPC strengthening was combined with void repair. In addition, crack propagation was effectively inhibited following UHPC strengthening. The refined finite element analysis results indicated that the predicted damage state at 1.0 P was in good agreement with the experimental observations, and under the 1.3 P overload condition, the difference between calculated and measured loads at the same displacement level was only 2.5%, and most of the stresses remained below the tensile and compressive strengths of UHPC. Finally, the proposed predictive method for the circumferential tensile stress of the transition segment exhibited a prediction error of 5%, indicating satisfactory accuracy. Full article

Research Purpose: This study investigates the role of digital investor behavior, measured through Google Trends, alongside technical indicators such as RSI and Bollinger Bands, in forecasting volatility in the Dubai Financial Market. The aim is to develop a hybrid analytical framework that integrates behavioral and technical dimensions to enhance predictive accuracy in emerging markets. Study Methodology: Daily data from 2020 to 2025 were collected, covering both crisis and post-crisis periods. Digital attention was quantified using Google Trends search indices, while technical indicators included RSI and Bollinger Bands calculated over a 7-day horizon. Volatility was modeled using ARCH, GARCH, and EGARCH frameworks, with Max Drawdown employed as a complementary risk metric to capture extreme market movements. Findings: Digital investor attention shows a predictive association with volatility, particularly when combined with technical indicators. Models incorporating both behavioral and technical variables demonstrated superior predictive performance. The EGARCH model successfully captured the asymmetric impact of negative shocks (γ < 0, p < 0.05), while Max Drawdown provided additional insights into risk exposure during periods of heightened market stress, achieving an R² of 95.36%. Scientific value: This study positions digital attention as a complementary variable that improves forecasting, moving beyond conventional price-based models in volatility modeling; by integrating Google Trends with technical analysis, the research introduces a hybrid forecasting framework that can be adapted to other emerging markets. Practical Implications: The findings offer practical value for policymakers and investors. Regulators can use digital attention measures as early warning signals to anticipate volatility, while investors can integrate behavioral and technical indicators to improve risk management and trading strategies. From a foresight perspective, the study contributes to building more resilient financial systems by embedding behavioral data into predictive tools. Full article

Chronic obstructive pulmonary disease (COPD) imposes a substantial physical and psychosocial burden, yet the role of loneliness remains under-recognised in clinical practice. Loneliness, defined as a subjective discrepancy between desired and actual social relationships, has emerged as a clinically relevant determinant of patient outcomes. This narrative review synthesises current evidence on the epidemiology, mechanisms, and clinical consequences of loneliness in COPD, and evaluates its implications for disease management. Available evidence indicates that loneliness affects a considerable proportion of individuals with COPD, with prevalence estimates ranging from approximately 18% to over 30%, particularly among patients with greater symptom burden, functional limitation, and oxygen dependence. Dyspnoea and advancing disease severity reduce social participation and increase vulnerability to perceived social disconnection. Loneliness influences COPD outcomes through interconnected behavioural, biological, and healthcare engagement pathways, including systemic inflammation, neuroendocrine stress responses, physical inactivity, impaired self-management, and reduced engagement with healthcare services. These mechanisms contribute to poorer clinical trajectories, as loneliness is consistently associated with reduced health-related quality of life, increased exacerbations, higher healthcare utilisation, greater risk of hospitalisation, and elevated mortality, independent of depression and anxiety. Despite this, loneliness is rarely assessed in routine respiratory care, and targeted interventions remain limited. Emerging strategies, including pulmonary rehabilitation, peer support, and digital health interventions, show promise in reducing loneliness and improving outcomes. Loneliness represents a modifiable and clinically actionable risk factor in COPD, and its integration into routine assessment and management may enhance patient engagement, optimise treatment effectiveness, and reduce healthcare burden. Addressing loneliness represents a critical opportunity to advance more effective and comprehensive COPD care. Full article

The transition to a low-carbon economy is the cornerstone of global sustainability, requiring high-emission enterprises to shift from carbon-intensive production to genuine green innovation. However, this study uncovers a significant structural impediment to this transition: the “defensive greenwashing” response to climate stress. Focusing on listed companies in China’s high-emission industries (2009–2024), we employ a Debiased Machine Learning (DML) framework and Causal Forest analysis to capture the non-linear impacts of multi-dimensional climate risks. Our findings reveal a robust “threshold-trigger” mechanism: once climate pressures—whether physical shocks or policy-induced transition risks—exceed corporate endurance levels, firms aggressively pivot toward strategic “information arbitrage” rather than substantive decarbonization. We identify a profound “capability paradox” in sustainability governance, where firms with higher digital maturity and resource slack leverage their technical prowess to “calibrate” sophisticated narratives, thereby widening the monitoring gap and distorting green asset pricing. Furthermore, CEO risk preference acts as a psychological accelerator, amplifying strategic decoupling, particularly under transition-risk-induced uncertainty. By demonstrating how climate stress inadvertently incentivizes symbolic compliance over sustainable transformation, this research offers critical micro-level insights for policymakers. These findings are vital for refining sustainability oversight and ensuring that capital allocation fosters a resilient, equitable transition toward true ecological and economic decoupling. Full article

The Niger Delta region of Nigeria experiences multiple environmental stresses due to intensive oil exploration and pervasive gas flaring, both of which contribute to local and regional climate changes, extreme weather events, and excessive and erratic rainfall. Consequently, flooding remains a recurrent natural disaster, disproportionately impacting the low-lying states of Delta, Bayelsa, and Rivers. This study employs remotely sensed geospatial data and a GIS-based weighted overlay analysis to delineate flood-prone areas on a regional scale in the central Niger Delta states. Flood susceptibility was determined through a weighted overlay of digital elevation model (DEM), slope, proximity to streams, rainfall, and LULC data, among others. Weights of criteria were derived through an analytical hierarchy process (AHP) with a very good consistency ratio of 2.5%. Land use and land cover (LULC) and rainfall data were further analyzed to detect trends of changes between 2012 and 2022. The results show that relatively 77% of the study region is prone to flooding. Areas prone to very high flooding are about 16%, high is 29%, moderate is 32%, while low and very low flood-prone areas cover 18% and 5% of the study region, respectively. There is also a notable increase in average annual rainfall and land cover changes. Average rainfall increased by 58.1% between 2012 and 2017, and by 11.5% between 2017 and 2022. Land cover change analysis further indicates that approximately 1.3% of the study area was converted predominantly to flooded zones and water bodies from 2017 to 2022. The results of this study could be useful for urban regional planning, flood mitigation, and resettlement policies aimed at reducing flood vulnerability and enhancing resilience in the central Niger Delta, as well as other places where similar challenges exist. Full article

The next generation of naval and defense systems will strain current naval ship cooling systems. Throughout its life-cycle, this strain will alter the behavior of the physical system, and any virtual representation of the system will become outdated due to component aging. Digital twins are a trending tool that can assimilate real-time sensor data to tailor a virtual representation to its physical counterpart. The online faithful virtual representation of the physical system provided by digital twins can be used for real-time system optimizations and proactive fault detection, diagnostics, and control adjustments, alleviating the stress of component aging. To support these complex power systems throughout their lifecycles, data-driven solutions for digital twin tuning will become essential. This paper investigates the application of a parameter-tuning digital twin framework to enhance the performance of a multi-physics model. The digital twin framework comprises a digital twin tuning scheme, a physical testbed designed to emulate the cooling system of a ship, and a multi-physics representation of that system. The digital twin tuning scheme leverages a swarm of particles and online sensor data to evaluate permutations of parameters to update the digital representation periodically. The digital twin framework was applied to a physical system to provide experimental data results demonstrating the usefulness of the tuning system. The physical system was designed and constructed to emulate the heat generation and dissipation from 6 liquid-cooled power converters under loads ranging from 10–15 kW at 99% efficiency. Two scenarios were applied to evaluate the performance of the digital twin framework. Results demonstrate that the digital twin framework can adapt to system changes in real-time and improve the accuracy of the related virtual representation by more than 90% when measured at four points of the system under test. Full article

Hard ceramic coatings are essential for extending the performance of metal parts under the extreme heat and stress found in aerospace and defense environments. There is a major knowledge gap regarding this topic in the current literature. While there has been significant research on individual fabrication methods or specific coating materials separately, no previous review has combined experimental lifecycle data with a broad computational design approach that covers the entire design-to-deployment process. This review fills that gap by offering a unified roadmap from integrated computational materials engineering (ICME) to machine learning (ML). This roadmap speeds up the rational design of coatings for next-generation aerospace systems. The practical importance of this framework is its clear use in gas turbine engine qualification, hypersonic vehicle thermal protection, and landing gear surface engineering. It can cut down on experimental trial-and-error cycles by allowing ML-guided composition screening and condition-based maintenance through digital twin integration. The main ceramic material systems, tungsten carbide (WC), boron nitride (BN), boron carbide (B₄C), silicon carbide (SiC), alumina (Al₂O₃), and zirconia (ZrO₂), are examined for their protective roles in aerospace-grade alloys. A key contribution is the multiscale computational framework that includes density functional theory, molecular dynamics, finite element analysis, and ML-driven inverse design. Together, these methods improve predictions for thermal breakdown, multi-axial stress responses, and coating lifetime. Future research should focus on ultra-high-temperature ceramics, multifunctional self-healing coatings, and surface engineering methods driven by data. Full article

This study examines the relationship between Sudanese audiences’ exposure to digital media coverage of the civil war and their psychological immunity and post-traumatic stress disorder (PTSD) symptoms while also investigating the mediating role of psychological immunity. Data were collected through an online survey from a sample of 774 Sudanese respondents residing both inside and outside Sudan. The findings indicate a relatively high level of exposure to digital news coverage, particularly through social media platforms such as Facebook, reflecting the growing centrality of digitally mediated news environments in conflict reporting. The results also reveal moderate levels of psychological immunity and relatively high levels of PTSD symptoms, especially in the hyperarousal dimension. Statistical analysis shows a negative association between media exposure and psychological immunity, and a positive association between exposure and PTSD symptoms, with psychological immunity partially mediating this relationship. Beyond these findings, this study highlights important implications for journalism practice and media responsibility. The intensity and emotional nature of digital war coverage raise critical concerns regarding ethical news production, the circulation of distressing content, and the potential psychological impact on audiences. These findings underscore the need for more responsible digital journalism practices, including content moderation, trauma-sensitive reporting, and the promotion of media literacy, in order to mitigate the potential harm associated with repeated exposure to crisis-related news content. Full article

This study examined the stress corrosion of Alloy 625 in Cl⁻ + S₂O₃²⁻ solutions using digital holography in combination with electrochemical methods. Without elastic tensile stress, intergranular corrosion (IGC) occurred, due to the higher activity of grain boundaries compared to the grain interior and to preferential adsorption of sulfur (produced by S₂O₃²⁻ decomposition) at these boundaries. Digital holography observations showed that IGC initiated at certain grain boundaries and propagated to adjacent boundaries, even in the absence of elastic tensile deformation. Applying elastic tensile stress (260 MPa, ~46% σy) increased the defect density within the oxide film, thereby enhancing corrosion and anodic currents, and inducing river-like cracks. Although elastic tensile stress suppressed IGC, it simultaneously promoted stress corrosion cracking (SCC), as the stress exerted a stronger accelerating effect on corrosion than the grain-boundary did. Digital holography allowed in situ monitoring of the stress corrosion process in Alloy 625, demonstrating that cracks initiated via localized corrosion/IGC and subsequently propagated along the direction of the applied stress. Full article

The demand for lower-impact materials in mobility has increased interest in the lightweight composite structures for electric vehicles (EVs). This study presents an extended and revised dataset for biaxial non-crimp fabric (NCF) composite laminates intended for two-wheeled EV chassis applications, building on earlier published results by repeating all mechanical tests and recalculations and by adding a full stress–strain analysis, a repeatability assessment across multiple specimens, and a digital image correlation (DIC)-based strain evaluation. Three material families, represented by four laminate conditions, were investigated: carbon/epoxy composites post-cured for 4 h and 10 h, glass-fiber composites, and linen (flax) composites. The tensile and flexural behaviors were characterized according to ISO 527-4 and ISO 14125, respectively, while a GOM ARAMIS optical system was used to obtain the axial strain, transverse strain, and Poisson’s ratio. Carbon laminates showed the highest performance, with the 10 h post-cure condition reaching 1126 MPa tensile strength, up to 60 GPa Young’s modulus, 696 MPa flexural strength, and 43 GPa flexural modulus. Glass laminates provided intermediate properties, whereas flax laminates showed lower strength but higher compliance and deformation capacity. The obtained results show that the biaxial NCF composites studied in this work offer weight-saving potential for micro-mobility chassis and provide a standard-based benchmark for future durability and life-cycle studies. Full article

In this study, the structural, thermal, and mechanical properties of nanocomposites obtained by adding zinc oxide (ZnO) nanoparticles (NPs), produced by phyto-mediated synthesis using Dianthus chinensis plant extract, to a PMMA-based photopolymer resin at different ratios (0.05%, 0.10%, 0.15%, 0.20%, and 0.25%, by weight) were evaluated. The prepared composite resins were produced in different test geometries using a DLP (digital light processing)-based 3D printer (Asiga Ultra). Following the structural characterization of ZnO nanoparticles, tensile, compressive, and flexural mechanical tests were performed on the resulting composites, as well as FTIR, TGA, DSC, and DMA analyses. The FTIR results showed that ZnO NPs were physically integrated into the matrix. TGA and DSC analyses revealed that the addition of ZnO NPs, particularly at an addition rate of 0.15%, increased thermal stability. DMA analyses showed an increase in storage modulus and glass transition temperature as the addition rate increased. In mechanical tests, the highest modulus of elasticity and maximum strength values were obtained at additive ratios of 0.10–0.15%. The highest tensile strength (55.31 MPa) and compressive strength (388.53 MPa) were obtained at ZnO contents of 0.10–0.15 wt%, while the maximum flexural strength reached 125.94 MPa at 0.15 wt% ZnO. In addition, the storage modulus increased from 1.469 × 10⁹ Pa for the control resin to 1.872 × 10⁹ Pa for the composite containing 0.15 wt% ZnO, indicating improved stiffness and thermomechanical stability. The stress–strain curves show that improvements in ductility and deformation capacity of the material are achieved at these additive ratios. The findings demonstrate that green-synthesized ZnO nanoparticles are an effective and sustainable additive material for improving the mechanical and thermal performance of DLP-based photopolymer dental resins. Full article

This paper proposes a digital twin framework for robust parallel control of the mobile gin pole in ultra-high voltage (UHV) transmission line construction, aiming to improve safety and operational efficiency under uncertain conditions. The new framework integrates kinetic analysis, machine learning models, and multi-objective optimization algorithms to address the challenges of heavy-lifting operations in complex terrains. The method conducts finite-element kinetic analysis based on the actual structure of the mobile gin pole. A Tyrannosaurus Rex Optimization Algorithm (TROA) is employed to enhance the performance of the Extra Randomized Trees (ET) model for predicting key parameters such as maximum axial stress and shear stress. The framework leverages the Non-Dominated Sorting Genetic Algorithm III (NSGA-III) to optimize safety and efficiency metrics by adjusting key control parameters. A digital twin system for the mobile gin pole was constructed to validate the proposed approach. Results indicate that: (1) The proposed prediction model achieved performance improvements with R², RMSE, and MSE of 0.9642, 19.6, and 7.42, respectively. Compared with baseline machine learning models, the proposed model achieved significant improvements of 21.5%, 19.2%, and 5.1% in R², RMSE, and MSE, respectively. (2) Experiments confirm that the proposed model maintains high prediction accuracy under noise interference and missing data scenarios, indicating strong robustness. (3) Under various operation conditions, the method reduces safety risks by up to 32.30% and improves operational efficiency by up to 42.73%. Case studies further verify the effectiveness of the proposed framework, demonstrating superior prediction accuracy, noise resistance, and computational efficiency compared to conventional control methods. The core methodological novelty of this study lies in integrating TROA, ET, NSGA-III, and digital twin technology into a unified framework for mobile gin poles. This framework adopts TROA-ET to convert finite-element-based kinetic analysis into a behavior–mechanics surrogate model. It further embeds the constructed surrogate model into an NSGA-III-driven digital twin parallel control architecture. In this way, the study contributes an integrated and computationally efficient solution for safety–efficiency co-optimization of mobile gin pole operations under uncertainty. Full article

This study examines the determinants of bilateral trade asymmetries between Türkiye and its three main EU partners—Germany, Bulgaria, and Romania—over 2002–2024. Within the gravity framework, bilateral symmetry in trade data implies that reported exports should equal partner imports (Xᵢⱼ = Mⱼᵢ). Deviations from this condition reflect systematic distortions caused by valuation practices, institutional gaps, and crisis-induced disruptions. This study employs a fixed-effects panel framework to identify the structural and contextual determinants of mirror−data asymmetries in Türkiye–EU trade. Using HS2−level mirror statistics from TÜİK and Eurostat, three asymmetry measures—the Bilateral Asymmetry Index (BAI), Absolute Logarithmic Difference (ALD), and Relative Symmetry Index (RSI)—are estimated through a fixed-effects panel model. Results show that a one−unit improvement in logistics performance (LPI) reduces asymmetry by approximately 0.17 points (p < 0.01). Maritime connectivity (LSCI) shows a small but statistically significant positive coefficient, while exchange rate volatility remains insignificant. The effects of global crises are heterogeneous: the 2008 financial crisis significantly increases asymmetry (+0.07, p < 0.01), whereas COVID−19 is associated with a reduction in asymmetry (−0.04, p < 0.01). The interaction between LPI and crisis periods is negative and significant (−0.03, p < 0.05), confirming that a stronger logistics capacity buffers crisis-induced reporting gaps. Country-specific results reveal that Romania drives much of the variation (within−R² = 0.26), while Germany remains largely insulated from crisis effects. The findings highlight that deviations from bilateral symmetry are driven by structural and institutional factors rather than random error. Policy recommendations stress harmonized customs valuation, digital logistics integration, and enhanced Türkiye–EU statistical coordination to strengthen trade data reliability and crisis resilience. Full article

Concrete and cementitious composites exhibit brittle failure under shear stress, limiting their resilience in seismic and high-load applications; this study investigates whether crimped NiTi shape memory alloy (SMA) fibers can enhance pure shear strength and ductility of mortar specimens, with particular focus on the effect of thermal activation. Z-shaped mortar specimens were prepared with SMA fiber volume fractions of 0%, 1.0%, and 1.25%, tested under both non-heated and heated conditions using a Universal Testing Machine, with deformation monitored via LVDTs and Digital Image Correlation. SMA fiber reinforcement increased peak shear strength by 13% and 14.5% for 1.0% and 1.25% fiber volumes, respectively, under ambient conditions, reaching up to 22% enhancement after thermal activation due to recovery-stress-induced prestressing; the 1.0% fiber volume achieved the highest ductility index of 4.05 compared to 1.03 for plain mortar, while SMA fibers had negligible influence on initial shear modulus but substantially improved post-cracking response and crack bridging. These findings demonstrate that crimped SMA fibers effectively improve shear resilience of cementitious composites, with 1.0% fiber content offering the optimal balance between strength and ductility, though activation protocols require careful calibration to minimize thermal degradation of the matrix. Full article

Although secondary control of direct current (DC) microgrids has been widely studied, traditional static current sharing may still cause severe voltage sag under large-signal constant power load (CPL) steps, and many distributed schemes rely on global topology information while showing limited transient disturbance rejection. To address these issues, this paper proposes an observer-assisted, stability-margin-driven prescribed-time distributed secondary control strategy for islanded DC microgrids. A dynamic CPL risk evaluation function updates current-sharing ratios according to converter operating margins, while a distributed prescribed-time observer estimates disturbance envelopes and alleviates high-frequency chattering. Local adaptive gains remove the explicit dependence of controller tuning on global Laplacian eigenvalue information. MATLAB R2024a-based numerical studies show that, under a 6000 W CPL stress scenario, the proposed method limits the maximum voltage drop to 3.37 V, compared with 24.60 V for the conventional virtual current derivative (VCD) method. Under heterogeneous line impedances and a non-ideal digital benchmark, the proposed method yields a normalized current-sharing error of 0.72%, whereas the VCD method exhibits milder voltage transients. These results support the algorithmic effectiveness and numerical robustness of the proposed strategy within the adopted validation environment. Full article