Search Results (370)

A major connectivity gap arises from the misalignment between the forward-looking climate disclosures required by IFRS S2 and the historically rooted asset valuations mandated by IAS 36. This misalignment can cause the overvaluation of carbon-intensive assets and disrupt capital allocation decisions. This research specifically examines transition risks, such as carbon pricing, regulatory shocks, and technological disruption, and quantifies the financial externality using a combination of deterministic impairment testing and stochastic climate scenarios. We create a constructive framework and develop a model of a Synthetic Representative Firm, calibrated to major integrated steel producers in Europe. To generate nonlinear Green Swan shocks for Value-in-Use, the process combines Monte Carlo simulation with the Merton Jump-Diffusion model. This comparison shows the difference between the steady Management View and the volatile Market View. Empirical results reveal a material Sustainability Discount, representing a substantial erosion in the recoverable amount under IFRS S2 transition risk scenarios compared to the IAS 36 Deterministic Baseline. Simulations show a strong probability of asset stranding due to restricted cost pass-through, indicating that older assets may face elevated impairment risks under disorderly transition scenarios. Traditional deterministic models may not fully capture aspects of Double Materiality, potentially leaving balance sheets less responsive to transition risks. Integrating digitalization and the Circular Carbon Economy (CCE) framework presents a strategic method for averting value destruction. Therefore, this research supports the integration of stochastic transition risk modeling into impairment testing to achieve faithful financial representation. Full article

As the key technology of space exploration, space power has been a major area of international research focus. A lot of research work has been carried out around the world for the space nuclear reactor using the heat pipe, liquid metal and gas cooling methods. With the development of molten salt reactor in the Generation IV reactor system, molten salt dissolving fissile material and acting as a coolant at the same time has become a new cooling scheme, which provides new ideas for the design of space nuclear reactors. In this study, a novel reactor, the liquid-solid dual-fuel space nuclear reactor (LSSNR) was preliminarily proposed, combining the molten salt fuel and cross-shaped spiral solid fuel to achieve the design goals of 30-year lifetime and an active core weight of less than 200 kg. Monte Carlo neutron transport code OpenMC based on ENDF/B-VII.1 library was employed for neutronics design in the aspect of fuel type, cladding material, reflector material and the spectral shift absorber. Then, the thickness of the control drum absorber was optimized to meet the requirement of the sufficient shutdown margin, lower solid fuel enrichment, and 30-effective-full power-years (EFPY) operation lifetime. Finally, UC solid fuel with U-235 enrichment of 80.98 wt.% and B₄C thickness of 0.75 cm were adopted in LSSNR, and BeO was adopted as the reflector and the matrix material of the control drum. A spectral shift absorber Gd₂O₃ was used to avoid the subcritical LSSNR returning to criticality in a launch accident. The k_eff with the control drum in the innermost position is 0.954949, and the k_eff reaches 1.00592 after 30 EFPY of operation. The total mass of the active core is 158.11 kg. In addition, the thermal-hydraulic feasibility of LSSNR using cross-shaped spiral fuel was analyzed based on a 4/61 reactor core model. The structure of cross-shaped spiral fuel achieves enhanced heat transfer by generating turbulence, which leads to a uniform temperature distribution of the coolant flow field and reduces local temperature peaks. Based on the LSSNR scheme, some neutronic characteristics were analyzed. Results demonstrate that the LSSNR has strongly negative reactivity coefficients due to the thermal expansion of liquid fuel, and the fission gas-induced pressure meets safety requirements. One hundred years after the end of core life, the total radioactivity of reactor core is reduced by 99% and is 7.1305 Ci. Full article

The sustainable management of waste is a significant problem facing humanity, especially in regions with low recycling rates and a lack of infrastructure. For example, Romania has a recycling rate of only 12%, a long way from meeting the European Union’s target of 42%. This article proposes a framework for sustainable waste management, called CETHTB-Chain, by combining the circular economy, Triple Helix Twins collaboration, and blockchain technology. To test the viability of this framework, a Monte Carlo simulation with 10,000 iterations and system dynamics modelling with a 10-year simulation period was conducted. The Monte Carlo simulation revealed that CETHTB-Chain can improve recycling rates by a mean of 45.6% (95% CI, 38.6–52.6%), material recovery rates by 62.7% (95% CI, 54.4–70.0%), cost savings by 18.53 euros per ton, and CO₂ reduction by 629 kg per ton of waste. System dynamics modelling revealed that CETHTB-Chain is feasible for implementation, following S-curve growth, with recycling rates of 38.6% in 7–10 years. Sensitivity analysis revealed that blockchain technology adoption (ρ = 0.612) and citizen participation (ρ = 0.379) were key drivers of CETHTB-Chain performance. By combining Monte Carlo simulation and system dynamics modelling, this article has shown CETHTB-Chain to be a statistically significant and temporally feasible blueprint for transitioning from a linear economy to a circular economy in waste management. By engaging academia, industry, and government in a collaborative relationship facilitated by blockchain technology, CETHTB-Chain has provided valuable evidence for strategic planning in waste management in the European Union. Full article

In the realm of computational biomechanics, quantifying the reliability of surface-engineered implants is critical yet challenging due to material anisotropy and experimental limitations. Standard deterministic approaches often fail to capture the failure probability of brittle coatings, compromising the accuracy of lifespan predictions. This study’s originality lies in a stochastic framework that addresses titanium boride data scarcity using a geometric decision node (GDN). By autonomously switching between Palmqvist and Radial-Median regimes, the GDN eliminates deterministic bias and provides a failure-probability-based reliability assessment, thereby surpassing the limitations of conventional models. The evaluation was carried out on powder-pack borided Ti6Al4V layers produced at 1000 °C (10, 15, and 20 h). By combining instrumented Berkovich nanoindentation (N = 14, hardness scatter 17.6–34.8 GPa) with a Monte Carlo simulation algorithm (n = 10,000), we successfully modeled the stochastic brittle failure of the coating. The computational model, governed by a multivariate joint probability density function (JPDF), revealed a mixed-mode fracture mechanism where 77.9% of the virtual population developed radial cracks while 22.1% re mained in the Palmqvist regime. Weibull statistical analysis yielded a characteristic toughness of 2.25 MPa·m^1/2 and a low modulus of m = 1.58. This low modulus mathematically quantifies the coating’s sensitivity to microstructural defects, demonstrating that probabilistic algorithms—rather than mean-value deterministic calculations—are essential for ensuring the structural integrity of borided components in biomechanical design applications. Full article

Three-edge bearing (TEB) tests and a crack-width-dependent load-carrying model were used to assess the combined effects of recycled glass fiber-reinforced polymer (rGFRP) short fibers and glass fiber-reinforced polymer (GFRP) bars in concrete pipes. Using the force method, a circumferential statically indeterminate ring analysis was formulated to obtain internal forces at critical sections and the neutral-axis position. Fiber distribution was simulated by means of Monte Carlo sampling, and single-filament pull-out tests were fitted to relate embedded length to pull-out force, enabling calculation of the fiber-bridging contribution at cracked sections. Ten specimen types with different bar/fiber schemes were tested under external pressure to validate the model. Predicted cracking and ultimate loads agreed with measurements, with most errors within ±20%. Adding 1% (vol.) rGFRP fibers increased the cracking load by 11.81% and the ultimate load by 0.45%. Without fibers, replacing steel bars with equal-area GFRP bars increased the cracking load by 1.35% but reduced the ultimate load by 35.45%. For all specimens, the load–maximum crack-width relation was strongly linear (R² > 0.93). The proposed approach and dataset support engineering use of recycled GFRP materials for crack control and load-carrying design of concrete pipes. Full article

Nitridation of different materials using ion implantation is of considerable interest for many applications. As electronic components, oxynitride (SiO_xN_y) layers exhibit beneficial properties such as precise compositional variability, refractive index tunability, oxidation resistance, and low mechanical stress. In the present study we investigate nanoscale SiO_xN_y synthesized using ion implantation methods. To introduce N⁺ ions into a shallow Si subsurface region, both conventional ion beam implantation and plasma immersion ion implantation with subsequent high-temperature treatment in dry O₂ are used. The optical and morphological properties and chemical bonding of formed SiO_xN_y layers were studied by applying spectroscopic ellipsometry in the range of VIS-Near IR (SE) and IR (IR-SE), Raman spectroscopy and Atomic Force Microscopy (AFM). Monte Carlo modeling of implant profiles contributed to understanding physical and chemical processes and predicted different influences of the incorporated N⁺ ions on the oxidation mechanism, confirmed by the thickness dependence of SiO_xN_y/Si layers obtained from the SE data analysis. IR-SE spectral analysis established the formation of Si-O, Si-N, Si-N-O and Si-Si chemical bonds in the grown layers. The occurrence of amorphization of the Si crystal lattice due to incorporation of high-energy N⁺ ions into the Si lattice is confirmed by the Raman and ellipsometry results. The free Si atoms can congregate, forming nanocrystalline clusters. AFM imaging revealed that both implantation methods left the surface of the resulting SiO_xN_y layers considerably smooth with similar roughness parameter values. The results of the studies imply that the technological approaches used allow the production of high-quality nanoscale silicon oxynitride films with appropriate tunable composition and properties for possible application in advanced electronic devices for nanoelectronics, optoelectronics and sensor applications. Full article

In this study, the Monte Carlo (MC) method is employed to generate the diameter and relative positional distributions of carbon nanotubes (CNTs). Based on this, we develop a three-layer thermal model for a copper-carbon nanotube (Cu-CNT) through-silicon via (TSV). By integrating Gauss–Hermite quadrature with the Law of Large Numbers (LLN), an analytical expression for thermal conductivity is derived, enabling efficient and accurate estimation of the thermal conductivity of Cu-CNT-filled TSV. Contrary to expectations, the thermal conductivity of TSV does not increase significantly with CNT volume fraction, primarily due to the interfacial thermal resistance at Cu-CNT and CNT-CNT junctions. Through calibration against previously reported experimental data, the effective Cu-CNT interfacial thermal resistance is estimated to be on the order of 10⁻⁷ m²K/W. Comparison with previously reported effective thermal conductivity data of Cu-CNT composites shows that the model maintains an error below 2% when the CNT volume fraction is below 10%. The model is therefore most suitable for low CNT volume fractions, where the assumed spatial distribution and structural simplifications remain physically valid. Furthermore, this study investigates the influence of TSV length on thermal performance, predicts the variation in thermal conductivity of Cu-CNT composites under different volume fractions, and the extracted thermal conductivity values are further used as material inputs for device-level electro-thermal COMSOL 6.1 simulations. Full article

Crumb rubber concrete (CRC), as a heterogeneous multiphase composite material composed of coarse aggregate, rubber particles, cement mortar, pores, and other constituents, is frequently regarded as a homogeneous material in engineering applications. This study employs numerical homogenization to compute equivalent mechanical parameters for CRC. By establishing a two-dimensional parametric random aggregate model combined with Monte Carlo simulations and finite element computations, it systematically analyzes the influence of rubber content (0%, 5%, 10%, 15%) and specimen size (50–150 mm) on CRC’s macroscopic equivalent elastic modulus. The research reveals that stable homogenization results, usable as macroscopic equivalent material parameters, are attained when the Representative Volume Element (RVE) size of the CRC model is ≥5 times the maximum aggregate particle size (dₘₐₓ). The equivalent modulus E decreases rapidly initially with increasing size, followed by a decelerated decline toward stabilization. A predictive model based on the fitted formula ln Eᵣ = kᵣ ln L + bᵣ (where Eᵣ denotes reduced modulus) enables elastic modulus prediction for large-scale components up to 600 mm. This study elucidates the macro-mesoscopic linkage mechanism governing CRC’s equivalent elastic parameters, providing a theoretical foundation for engineering structural design. Full article

As emerging nonvolatile memory devices, ferroelectric field-effect transistors (FeFETs) have attracted significant attention for memory applications. However, due to the stochastic nature of fabrication processes and material properties, FeFETs exhibit pronounced device-to-device (DTD) variations, leading to threshold voltage dispersion and inconsistency in memory window (MW), which severely constrain array-level performance and reliability. In this study, a compact model-based variability analysis methodology for FeFETs has been proposed. Specifically, the Preisach ferroelectric (FE) hysteresis model was combined with the MIT Virtual Source (MVS) physical compact model to establish a macro-model for FeFETs, and statistical simulations were performed to evaluate device-level variations. Using the proposed framework, how fluctuations in two key FE parameters, film thickness (t_FE) and coercive field (E_C), affect FeFET transfer characteristics, threshold voltage (V_TH), and MW was systematically investigated. Monte Carlo (MC) simulations were further conducted to quantify the distribution width and statistical features of V_TH under different variability scenarios. The results indicate that random fluctuations in process-related parameters broaden the FeFET I_d-V_g characteristics, induce shifts in high/low threshold voltages, and cause MW variations. Moreover, when t_FE and E_C fluctuate simultaneously, the dispersions of V_TH and MW become significantly larger than those induced by a single-parameter fluctuation. The proposed compact-modeling framework and variability analysis approach enables the efficient evaluation of parameter tolerance and performance margin in FeFET arrays, providing guidance for storage-array design. Full article

Rising atmospheric CO₂ levels have increased the demand for robust, scalable adsorbents for practical CO₂ capture and separation. Porous organic polymers (POPs) are attractive candidates because their pore architecture and binding site properties can be precisely tuned via building blocks and linkage formation. This review summarizes experimental and computational studies of azo-linked POPs and, more broadly, nitrogen–nitrogen (N–N) linked systems, emphasizing how synthetic routes, building blocks, and framework topology govern CO₂ uptake. We highlight key synthetic strategies and representative systems, including porphyrin–azo networks, and discuss the relatively sparse experimental literature on alternative N–N linked POPs incorporating azoxy and azodioxy motifs. Emphasis is placed on reversible nitroso/azodioxide chemistry as a potential pathway to ordered porous organic materials. Computational studies provide a practical route to connect structure with adsorption behavior in largely amorphous or partially ordered networks. We review hierarchical workflows combining periodic DFT and electrostatic potential properties, grand canonical Monte Carlo (GCMC) simulations, and binding energy calculations to rationalize trends and identify favorable binding environments. Computational findings demonstrate that pore accessibility and stacking models can strongly influence predicted CO₂ adsorption. This review provides guidelines for designing POPs with enhanced CO₂ adsorption, offering an outlook and discussing challenges for future studies. Full article

The prediction of material lifetime is central to nanomaterial engineering and reliability analysis. We propose the Marshall–Olkin Power Half-Logistic (MOPHL) distribution, obtained by applying a Marshall–Olkin transform to the Power Half-Logistic baseline. We derive core properties—including moments, hazard rate characterization, and Rényi entropy—and develop inference under generalized progressive hybrid censoring. Estimation is carried out via maximum likelihood and Bayesian methods using a Metropolis–Hastings sampler. Asymptotic results, Fisher information, and corresponding confidence/credible intervals are provided. A Monte Carlo study assesses bias, the mean squared error, and coverage across censoring scenarios and hazard regimes. In a case study on hydroxylated fullerene degradation, MOPHL outperforms nine competing models in goodness-of-fit and predictive reliability. We also report the mean time to failure and mean residual life to support engineering decision-making. The proposed framework offers a tractable and robust tool for degradation analysis under censored data, with applicability to materials, mechanical components, biomedical devices, and environmental monitoring. Full article

The dangerous potential of chemical warfare requires immediate development of new materials capable of detecting and efficiently adsorbing the toxic nerve agents VX and Novichok (A-234). The current adsorbents fail to achieve sufficient detection efficiency and specific binding capabilities. Our research, conducted through advanced computational modeling, predicts that carbon nanocones (CNCs) could function as effective molecular traps for these toxic substances. The research combines density functional theory (DFT) with molecular dynamics (MD) and Monte Carlo (MC) simulations to explain the basic principles of molecular trapping by these agents. The nanocone shape produces two distinct and selective binding areas. MC shows preferential trapping VX molecules within the internal concave surface (P1), while A-234 molecules are strongly adsorbed on the external convex surface (P2). Docking results complement this by showing that A-234 exhibits stronger single-molecule binding on the more open surface, consistent with its preference for P2. The nanocone captures molecules through van der Waals forces, which produce measurable electronic changes that modify its electronic signature. The research demonstrates that carbon nanocones represent a promising candidate material for the future development of chemical defense systems, potentially including sensitive detection systems and advanced filtration technologies. Full article

Topological optimization has established itself as an efficient tool for the design of highly complex structures and the rational use of materials, especially in problems involving multiple constraints and conflicting objectives. This work presents a new multi-material topological optimization approach based on the ESO smoothing method (SESO), formulated as a multi-objective optimization problem in a MATLAB R2021a environment. The multi-objective formulation simultaneously considers the minimization of the maximum von Mises equivalent stress (or minimum principal stress) and the maximum displacement, which are fundamental criteria for structural engineering design. The proposed methodology also incorporates a reliability analysis using the First-Order Reliability Method (FORM), modeling uncertainties associated with the applied force, volume fraction, and modulus of elasticity through normal and lognormal probability distributions, with a target reliability index of ${\beta }_{target}=3.0$. The consistency of the reliability analysis was evaluated using Monte Carlo simulations, validating the reliability indices obtained via FORM. The approach was applied to two classical three-dimensional numerical examples: a cantilever beam under base and center loads and an MBB beam, considering two widely used engineering materials, steel and concrete. The results indicate improved multi-material distribution in the design domain and greater structural robustness against unfavorable loading planes, variations in the modulus of elasticity, and volume constraints imposed by FORM. Furthermore, the minimum yield stress of steel (${\sigma }_y^{min}$) and the compressive strength of concrete ($f_{ck}^{min}$) were calibrated, representing the minimum material strengths required to resist the maximum von Mises stress in steel and the minimum principal stress (${\sigma }_3$) in concrete, ensuring the target reliability index is achieved. This method, thus, highlights the integration of SESO with multi-material, multi-objective, and reliability-based optimization as a consistent, robust, and practically relevant strategy with potential for future applications in structural engineering projects. Full article

Reliability is a fundamental aspect of modern structural engineering due to the inherent randomness of materials, loads, and environmental conditions. However, as system complexity increases, a substantial computational cost is typically required to evaluate the failure probability, often involving 10⁵–10⁶ limit state function evaluations in a conventional Monte Carlo simulation. To address this challenge, this study presents an active-learning-driven deep neural network (ALDNN) meta model algorithm to improve both efficiency and accuracy in reliability analysis. To substantially reduce the computational costs, a multi-phase active learning framework incorporating weighted sampling and adaptive threshold-based candidate filtering is implemented by iteratively selecting more important points and adaptively training deep neural networks. Thresholds for candidate sample points and training datasets are gradually adjusted based on feedback from estimated responses. The proposed method reduces the number of true limit state evaluations to the order of 10² in the benchmark problems considered, while maintaining high accuracy. Its performance is assessed using widely referenced benchmark problems, and finite-element-method-based implicit examples for frame structures are further employed to verify applicability. The results demonstrate the high efficiency, accuracy, and scalability of the ALDNN meta model as system complexity increases. Full article

Guaranteed annuity options (GAOs) allow policyholders to convert accumulated funds into life annuities at maturity at a guaranteed minimum rate. Thus, insurers are exposed to both investment and longevity risks. Accurate valuation of these long-term, survival-contingent contracts is essential for solvency assessment and risk management. Many existing approaches assume independence between interest rate and mortality risks. This paper develops a computationally efficient pricing framework for GAOs that jointly models interest and mortality rates as correlated stochastic processes with regime-switching dynamics governed by a finite-state continuous-time Markov chain. Model parameters are estimated using U.S. interest rates and cohort mortality data via quasi-maximum likelihood estimation. A semi-analytic valuation formula is derived based on the joint distribution of the underlying processes. Numerical results show that incorporating correlation and regime-switching materially increases GAO prices relative to conventional one-state models. The proposed semi-analytic approach delivers substantial computational advantages over standard Monte Carlo simulations. Sensitivity analysis further identifies the parameters most relevant for long-horizon pricing and solvency considerations. This highlights the practical relevance of the framework for managing longevity-linked guarantees under economic and demographic uncertainty. Full article