Search Results (92)

Magnesium oxychloride (MOA) adhesive plywood represents a novel inorganic matrix panel technology that eliminates organic volatile compounds from the adhesive system and avoids high-temperature hot pressing, potentially offering significant carbon footprint advantages. This study presents a comparative life cycle carbon footprint assessment of conventional urea–formaldehyde (UF) plywood and MOA plywood manufactured in China, using 1 m³ of a finished panel as the functional unit under a cradle-to-grave system boundary, comprising the production stage (Modules A1–A3)—explicitly including forestry operations (silviculture, felling, extraction/forwarding, loading and log haulage) and veneer manufacture within Module A1, now reported as a disaggregated inventory and delimited in a system boundary diagram—and the end-of-life stage (Modules C2–C4), evaluated across three end-of-life (EOL) scenarios: incineration, landfill, and mechanical recycling. Foreground data (process energy, adhesive formulation, transport distances) are metered/primary data collected over a full production year at a single large-scale plywood plant in Suqian, Jiangsu; background data are from ecoinvent v3.9.1 (cut-off), characterised with IPCC AR6 GWP100. Results indicate that MOA plywood generates approximately 253 kg CO₂-e/m³ at the production stage (A1–A3), compared with 301 kg CO₂-e/m³ for UF plywood, a reduction of 15.8% (47.5 kg CO₂-e/m³). Contribution analysis attributes virtually the entire gap to process energy (steam 65.7%, electricity 34.3%), while adhesive raw materials and inbound transport cancel to within rounding, demonstrating that the advantage is a process energy rather than a green chemistry phenomenon. A parameter-specific one-at-a-time analysis and a 200,000-run Monte Carlo simulation with triangular distributions show no reversal of the UF–MOA ranking in any of the 200,000 realisations within the adopted uncertainty ranges, with an approximately 56 kg CO₂-e/m³ median advantage (5th–95th percentile of about 31–85). Under EOL incineration, MOA plywood retains a substantial advantage even after the newly quantified burden of flue gas HCl neutralisation (13.3 kg CO₂-e/m³) and inorganic residue management (0.9 kg CO₂-e/m³) arising from the chloride content of the Sorel cement binder are charged to the MOA system. Under landfill, both products behave similarly, as wood carbon dynamics dominate. A break-even analysis shows that the service life of MOA plywood would have to fall below 25.3 years (against a 30-year reference) for its cradle-to-gate advantage to be erased. These findings clarify the lifecycle trade-offs of inorganic adhesive plywood and provide actionable data for environmental product declarations and procurement frameworks. Full article

Increasing the accuracy of age-at-death estimation using two skeletal-characteristic stages can enhance confidence in biological identification using forensic science. Transition analysis and the inverse prediction method with a Bayesian approach was proposed in this study to estimate age from skeletal characteristics measured as binary variables. The Bayesian approach with adaptive rejection sampling was employed to derive the posterior distributions of the transition model parameters and the age classification threshold in order to reverse the age-at-death estimation from a binary predictor. The posterior odds ratio was proposed to assess the value of observed evidence for the age estimation. Subsequently, the efficiency of our proposed method, measured by the percentage of correct classification, was evaluated by Monte Carlo simulation and compared with the Maximum Likelihood Estimation with the inverse prediction method. The simulation results supported the advantages of our proposed method, especially when using small sample sizes. In an application involving chest X-ray images with two chest plate ossification stages, the results showed that our method could identify suitable features of the chest plate, providing good age-prediction performance with a high percentage of accuracy. Full article

Interval censoring commonly arises in clinical trials, screening studies, and longitudinal medical investigations in which event status is assessed only at scheduled examination times. Rank-based procedures provide flexible tools for comparing interval-censored (IC) event-time distributions, but inference is usually based on first-order normal approximations that may be inaccurate in small or moderately sized samples and under substantial censoring. We develop a saddlepoint approximation (SPA) to the conditional permutation distribution of a linear rank statistic derived from the proportional reversed-hazard model with IC data. Conditional on the observed group size, the permutation distribution is represented through a bivariate cumulant generating function, and Skovgaard’s approximation is used to obtain computationally efficient tail probabilities without exhaustive permutation enumeration. The finite-sample performance of the proposed method is evaluated under log-normal, Weibull, and Gompertz event-time distributions and under monitoring schemes producing predominantly left, interval, or right-censored observations. Monte Carlo (MC) permutation p-values based on ${10}^6$ random permutations are used as a numerical benchmark (not the exact permutation distribution). Across the evaluated simulation scenarios, the SPA generally produces p-values that are closer to the MC permutation benchmark than those obtained from the standard normal approximation (NA). Applications to lung tumor, HIV drug-resistance, and breast-cosmesis data illustrate the relevance of the method to biomedical event-time studies. The proposed approximation provides an accurate and computationally efficient approach to rank-based inference for IC medical data. Full article

Green methanol is widely regarded as a technically feasible low-carbon marine fuel for sustainable shipping in the medium term, but its economic viability remains constrained by fuel price and supply-scale uncertainty. This study develops a dynamic total cost of ownership (TCO) model for a methanol dual-fuel container ship with a nominal capacity of 15,000 twenty-foot equivalent units (TEUs) operating on the Ningbo–Zhoushan–Valencia green shipping corridor. The model integrates capital expenditure, operating expenditure, fuel costs, European Union Emissions Trading System (EU ETS) carbon costs, FuelEU Maritime compliance costs, and green premium revenue. It evaluates a 15-year baseline, 25- and 30-year extensions, speed scenarios, probabilistic parameter uncertainty, and purchase-versus-charter thresholds. Under baseline assumptions (carbon price of 73.5 EUR/tCO2 and green methanol price of 1500 USD/t), the 15-year present-value cost of the green methanol case is 134.4% higher than that of the very low sulfur fuel oil (VLSFO) case. Annual costs may cross in 2045, 2040, and 2037 under baseline, optimistic, and accelerated decarbonization scenarios, respectively, but cumulative discounted cost advantage is not achieved within 30 years. Across 5000 Monte Carlo simulations, the probability of cumulative methanol cost advantage is 0% at 15, 25, and 30 years; methanol price and its decline rate remain the dominant uncertainty drivers. Slower speeds reduce the absolute cost gap but do not reverse the fuel ranking. These results show that carbon pricing or green premium revenue alone cannot close the corridor-level cost gap under the tested conditions. They also provide corridor-level evidence for methanol procurement, bunkering-capacity planning, and coordination among ship operators, ports, fuel suppliers, and cargo owners. Shipowners and port planners can update the framework as fuel prices, policy parameters, and bunkering conditions change. Full article

Physical access to robotic manipulators remains constrained by cost, safety requirements, and limited laboratory availability, creating barriers to both research and education. This paper presents a computational framework that combines stochastic error modeling with Digital Twin technology to characterize positioning uncertainty in a six-degree-of-freedom manipulator without requiring physical hardware. Four independent noise sources—joint encoder noise, thermal drift, elastic link deformation, and geometric parameter tolerances—are modeled as stochastic processes and propagated through the manipulator kinematics using Monte Carlo simulation with N = 10,000 trials across 50 workspace configurations. The results reveal that elastic deformation dominates the combined positioning error by a factor of 45.94 over encoder noise, contributing 99.97% of the total root-mean-square (RMS) uncertainty. A probabilistic workspace map constructed from 3000 sampled configurations quantifies accuracy and manipulability across the reachable space, exposing a counterintuitive trade-off: configurations with higher manipulability indices tend to exhibit larger positioning errors due to gravitational loading on extended links. Two control algorithms—a reverse process-based control law (RPBCL) and sliding mode control (SMC)—are evaluated under stochastic conditions over 200 trials. SMC achieves a mean steady-state error of 0.0029 mm, representing a 48.2% reduction compared to RPBCL (0.0056 mm), with the difference confirmed statistically significant by a two-sample t-test (t = 5.066, p = 0.000002). All results are visualized through a Unity3D Digital Twin interface that renders probabilistic workspace maps, three-dimensional error ellipsoids, and a real-time sliding surface monitor. The proposed framework provides a foundation for safe, hardware-free evaluation of manipulator control strategies in engineering education and research. Full article

By the end of 2024, global photovoltaic (PV) capacity exceeded 2.2 TW, shifting planning from feasibility demonstration toward site–technology co-selection under energy, technical, economic, environmental, territorial, and socio-regulatory constraints. The existing multicriteria literature treats site and technology selection as independent problems under an implicit infinite-grid assumption, which is untenable in markets such as Chile and Peru. This study develops and validates an integrated Delphi–AHP framework with six criteria and eighteen subcriteria calibrated by twenty-eight experts from six Latin American countries. The framework underwent Delphi binary validation, AHP consistency control ($C{R}_{\mathrm{agg}}$ between 0.0013 and 0.0247; discard rate 2.6%), geometric-mean aggregation, deterministic sensitivity analysis, Monte Carlo simulation (10,000 iterations), rank-reversal testing, and nonparametric subgroup analysis. The dominant pair $\{I_2,E{c}_2\}$, consisting of grid hosting capacity and LCOE, appears as Top-2 in 84.77% of Monte Carlo iterations and is preserved across 15 of 16 leave-one-out scenarios. Grid hosting capacity surpasses useful solar resource by a factor of 3.41. A demonstrative application to 18 site–technology alternatives confirms the ranking, with an objective-weighting benchmark (entropy, CRITIC) yielding concordant results (Spearman $\rho \ge 0.89$). The findings formalize a shift in the PV planning bottleneck from solar resource to grid capacity. Full article

Forward osmosis (FO) membranes are commonly evaluated through macroscopic observables such as water flux and reverse solute flux. However, these quantities do not necessarily reveal whether water permeation and solute leakage remain governed by the same dominant transport pathways, particularly in heterogeneous nanostructured membranes where selective nanochannels and defect-mediated pores can contribute differently to solvent and solute transport. Here, we introduce a hierarchical diagnostic framework to assess transport coherence loss in heterogeneous FO membranes. The framework comprises a baseline model (BM), an extended model (EM) including chemistry–geometry coupling through accessibility loss, and a full model (FM) incorporating selective pore-size heterogeneity. The ratio of reverse solute flux to water flux $R_J=J_s/J_w$ is used as a regime-based diagnostic descriptor of transport organisation, while its normalised form maps coherence variations across the state-space defined by structural selectivity and nanochemical state. The results show that chemistry–geometry coupling produces the first clear reorganisation of the coherence landscape, whereas pore-size heterogeneity mainly broadens the response while preserving its dominant topology. Simulations based on both Monte Carlo and experimentally derived pore-size distributions show consistent trends. Overall, the BM–EM–FM hierarchy offers an interpretable framework for describing transport coherence loss and the emergence of leakage-prone regimes in heterogeneous FO membranes. Full article

This paper investigates the magnetization reversal processes in spring-exchange magnetic composites featuring irregular, dendritic structures. A disorder-based cluster Monte Carlo method combined with a Diffusion-Limited Aggregation (DLA) algorithm was used to model a fractal-like soft magnetic phase (Fe) embedded in a high-coercivity hard matrix (Fe-Nb-B-Dy). A multiparameter analysis was performed by varying the soft phase volume fraction (10–30%), intergrain exchange coupling via contact bridges (25–100%), system scale factors (1–20), surface-to-volume anisotropy ratios (K_S/K_V = 1–20), and the degree of random anisotropy contribution (RAC = 0–100%). The simulations reveal that highly branched fractal structures enhance the interfacial contact area, which accelerates the nucleation of domain reversal driven by the soft phase, paradoxically lowering the overall coercivity compared to compact morphologies. Furthermore, a lack of easy magnetization axis coherent alignment triggers a cascading reversal mechanism through local “weak links”, severely degrading the coercive field from approximately 4.2 T to below 0.4 T in extreme cases (at 30% Fe, 25% coupling and high K_S/K_V ratio). These findings suggest potentially the most important factors and their impact that should be taken into account in the design and optimization of next-generation powder-sintered permanent magnets. Full article

Conventional liquid springs enable storage of energy in the form of interfacial tension at forcibly wetted lyophobic surfaces. The pressure–volume work performed to compress the liquid into a poorly wettable porous medium is recovered during spontaneous expulsion when pressure falls below the capillary pressure characteristic of a given system. Our study explores generalizations to easily wettable materials where liquid infiltration is opposed solely by steric hindrance exerted on liquid molecules in micro-sized pores. The concept is exemplified in molecular simulations of prototypical model systems with methanol intruding narrow slits between hydrocarbon or graphene surfaces. While these materials show significant wetting propensities at macroscopic interfaces with liquid methanol, substantial compression is required to wet molecular-sized pores barely accommodating a monolayer of liquid molecules. The observed O(10³) bar intrusion pressures secure stored energy densities competitive with supercapacitors and amenable to improvement. Wall–liquid attraction and small pore diameters lead to intrusion–expulsion pathways along cooperative-adsorption isotherms. The process avoids abrupt liquid/vapor transitions and associated nucleation barriers, responsible for cycle hysteresis in experiments with water in hydrophobic capillaries. Using open ensemble (Grand Canonical) Monte Carlo sampling, we identify the range of porosities supporting reversible energy storage/recovery operation in lyophilic media; the results can assist with the design of molecular spring devices with competitive storage and power capacities in pragmatic contexts. Full article

The most widely used bankruptcy predictor, Altman’s Z-Score, assigns a positive coefficient to asset turnover; faster firms are rated safer. Under crisis conditions, that assumption reverses. We introduce the Solvency Margin (SM), a diagnostic calculable from standard financial statements that measures, in dollars, how far an organization is from the threshold where operations become impossible. Unlike static liquidity ratios, the SM yields a concrete speed limit: the maximum operating velocity at which an organization can survive a defined shock. We validated the SM against pre-crisis financial data across three crises in two domains. Regarding the automotive sector, SM computed from FY2019 filings showed directional predictive power among ten major automakers in both the 2021 semiconductor shortage (ρ = 0.50, p = 0.14) and the 2020 COVID-19 pandemic (ρ = 0.53, p = 0.12; ρ = 0.70, p = 0.036 excluding one governance-driven outlier). With reference to the 2023 U.S. banking crisis, SM augmented with a Deposit Stability Factor predicted crisis outcomes among eighteen regional banks (Spearman ρ = 0.62, p = 0.006), correctly ranking three of four failed institutions in the bottom three positions. Monte Carlo simulation (450,000+ runs) confirmed threshold behavior. We present a five-step calculation method and a three-lever decision framework for practitioners. Full article

Recently, two novel extensions of the Weibull distribution have been introduced through Manly’s exponential transformation, offering a flexible mechanism for modeling skewness, tail behavior, and complex hazard rate structures. In this study, we develop a comprehensive theoretical and inferential framework for one of these models, referred to as the Baker–T1 distribution, to establish it as a mature and practically viable lifetime model for reliability and survival analysis. While the Baker–T1 model exhibits remarkable flexibility in capturing skewness, tail behavior, and complex hazard rate shapes, its statistical properties and practical performance have not yet been systematically investigated. To bridge this gap, we derive a wide range of fundamental distributional characteristics, including reliability measures, hazard and reversed-hazard functions, quantiles, moments, skewness, kurtosis, dispersion indices, and order statistics, establishing the model’s analytical tractability and structural richness. An extensive inferential framework is introduced by implementing eight classical estimation techniques, and their finite-sample behavior is rigorously examined through a large-scale Monte Carlo simulation study under diverse parameter configurations. The practical relevance of the Baker–T1 model is further demonstrated using two genuine datasets from biomedical and engineering domains, where it consistently outperforms thirteen competing lifetime distributions according to likelihood-based and information-theoretic criteria. Full article

The magnetic properties of materials similar to Nd-Fe-B permanent magnets are highly sensitive to microstructure. Using Hybrid Monte Carlo micromagnetics simulations, we systematically investigate how grain boundary (GB) and grain crystallographic orientation affect coercivity (H_c) and remanence (M_r). A polycrystalline model with independently adjustable microstructural parameters is constructed via Voronoi tessellation. Our results show that increasing GB width from 2 nm to 10 nm reduces Hc by 32% and M_r by 16%. Grain boundary acts as both a nucleation site and pinning center: a wider GB facilitates reverse domain nucleation, especially at the triple junctions. However, domain wall propagation is underpinned by GB during the propagation process. For a thick GB, H_c decreases with increasing GB saturation magnetization (M_s′), because the thick weakly magnetic layer weakens exchange coupling between adjacent grains, shifting the reversal behavior from collective switching to more localized nucleation. Increasing the average easy-axis tilt angle reduces H_c, as the misalignment lowers the effective anisotropy component along the applied field direction, facilitating magnetization reversal. These findings confirm the importance of GB and texture control in optimizing the magnetic performance of Nd-Fe-B permanent magnets, offering references for experimental investigations. 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

This article models the Ecuadorian shrimp market as a nonlinear system with recurring latent regimes that affect margins and planning decisions. A multivariate Hidden Markov Model (HMM) with Gaussian emissions in log space is estimated via the Baum–Welch algorithm to segment the joint dynamics of pounds produced, dollars invoiced, and average price. The analysis uses monthly data from January 2017 to May 2025 (T = 101). The selected four-state specification shows strong fit and outperforms linear alternatives (log likelihood = 480.9; AIC = 859.8; BIC = 729.5). The dominant regime (State 2) concentrates high prices (~USD 2.97/lb) with intermediate production and acts as an attractor (stationary probability ≈ 1), while States 0 and 1 capture orderly expansion and oversupply conditions, and State 3 reflects episodic demand rallies. Adverse regimes (States 0–1) exhibit expected durations of 6–8 months, suggesting natural reversion toward the profitable regime. These estimates enable probabilistic regime forecasting and Monte Carlo scenario simulation to support hedging, inventory management, and financial stress testing. Overall, the proposed HMM framework provides an operational decision tool for producers, traders, and policymakers seeking to anticipate regime shifts, mitigate oversupply cycles, and stabilize margins. Full article

This study addresses key challenges in obtaining reliable infrared data for maritime ship observation and limitations of existing models, such as simplified reflectance assumptions and incomplete multi-band coverage. To improve modeling accuracy and computational efficiency, a high-precision Bidirectional Reflectance and Pseudo-random Vector Enhanced Reverse Monte Carlo Method (BP-ERMCM) is developed. By combining the Bidirectional Reflectance Distribution Function (BRDF), pseudo-random vector approaches, and improved ray-tracking algorithms with precomputed thermal radiation and MODTRAN’s atmospheric transfer model, BP-ERMCM provides multi-view infrared characteristic simulations across 3–5 μm and 8–12 μm bands. Simulations using a 3D ship model with 191 viewpoints reveal seasonal sensitivity, with summer peak intensity at 9.8 μm being 39.3% higher than in winter, and viewpoint dependency showing oblique overhead radiation 5.65 times greater than that from bow angles. Long-wave contours enhance target distinction, while mid-wave regions are dominated by reflection, increasing intensity at 3.8 μm by 56.1–85.7%. These findings highlight BP-ERMCM’s potential to inform infrared signature database construction, detector optimization, and maritime observation strategies. The findings underscore BP-ERMCM’s capability to enhance efficiency and accuracy, providing valuable insights for infrared databases, sensor selection, and maritime observation strategies, thereby advancing infrared signature analysis in maritime applications. Full article