Search Results (15,204)

This study extends the application and validation of the Gibbs Free Energy Gradient Method (GMM) for modelling gasification processes, focusing on scaling up from pilot to commercial operations. The model simulates syngas production using poplar wood and olive pomace briquettes under various gasification conditions. Experimental data from a downdraft gasifier were employed to refine the model parameters, achieving accurate predictions of syngas composition with an average error below 8%. Sensitivity analyses highlight the impact of operating conditions, particularly temperature and air flow rates, on the syngas calorific value, hydrogen yield, and energy efficiency. These results emphasize the potential of biomass residues, such as olive pomace, for sustainable energy production in a circular economy context. The findings demonstrate the robustness of the GMM for predicting gasification performance and offer practical guidance for scaling up biomass-to-energy systems while optimizing efficiency and reducing waste. Full article

Mitigating ground vibrations from sources like vehicles and construction operations poses significant challenges, often relying on computationally intensive numerical methods such as Finite Element Methods (FEM) or Boundary Element Methods (BEM) for analysis. This study addresses these limitations by developing and evaluating Machine Learning (ML) methodologies for the rapid and accurate prediction of Insertion Loss (IL), a critical parameter for assessing the effectiveness of open trenches as vibration barriers. A comprehensive database was systematically generated through high-fidelity numerical simulations, capturing a wide range of geometric, elastic, and physical configurations of a stratified geotechnical system. Three distinct ML strategies—Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forests (RF)—were initially assessed for their predictive capabilities. Subsequently, a Meta-RF stacking ensemble model was developed, integrating the predictions of these base methods. Model performance was rigorously evaluated using complementary statistical metrics (RMSE, MAE, NMAE, R), substantiated by in-depth statistical analyses (normality tests, Bootstrap confidence intervals, Wilcoxon tests) and an analysis of input parameter sensitivity. The results clearly demonstrate the high efficacy of Machine Learning (ML) in accurately predicting IL across diverse, realistic scenarios. While all models performed strongly, the RF and the Meta-RF stacking ensemble models consistently emerged as the most robust and accurate predictors. They exhibited superior generalization capabilities and effectively mitigated the inherent biases found in the ANN and SVM models. This work is intended to function as a proof-of-concept and offers promising avenues for overcoming the significant computational costs associated with traditional simulation methods, thereby enabling rapid design optimization and real-time assessment of vibration mitigation measures in geotechnical engineering. Full article

Optimizing the siting and servicing of urban facilities is a core operations research problem that must reconcile heterogeneous demand, spatial constraints, and network-realistic travel. We present GD-ARISE, a GIS-integrated and data analytics pipeline that maintains a pedestrian–road network metric from demand inference through siting to routing. The workflow has three modules: (i) GIS integration that unifies spatial layers on one network and distance metric; (ii) data analytics that builds multi-criteria suitability via the Analytic Hierarchy Process (AHP) and maps scores to adaptive service radii; (iii) optimal location-and-routing that selects nonoverlapping sites with a transparent greedy rule (SCASS) and computes depot-to-depot routes via simulated annealing on the same metric. A case study in Seoul’s Gangnam District yields a high-coverage portfolio and feasible collection routes. We add a theoretical framework that casts SCASS as a conflict-graph problem, document the AHP elicitation with consistency checks, and report robustness analyses including sensitivity to AHP weights and to radius bounds. Results indicate that core hotspots remain stable to weighting, whereas mid-range corridors shift as criteria priorities or spatial parameters change. Full article

Degenerative cervical myelopathy (DCM) is a common cause of spinal cord dysfunction in adults and is frequently accompanied by pain, a symptom that remains under-recognised despite its profound impact on quality of life. Conventional magnetic resonance imaging (MRI) is indispensable for identifying structural spinal cord compression; however, it is unable to detect early microstructural alterations, particularly those that may contribute to pain pathophysiology. This narrative review critically appraises the limitations of standard MRI in the diagnostic assessment of DCM and examines the expanding role of advanced imaging modalities—most notably diffusion tensor imaging (DTI)—in evaluating spinal cord integrity. DTI-derived parameters, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD), demonstrate sensitivity to axonal and myelin injury. For example, reductions in FA and AD have been linked to axonal disruption in sensory pathways, while elevations in RD suggest demyelination, a hallmark of neuropathic pain. Despite this potential, the widespread implementation of DTI is constrained by technical heterogeneity, limited accessibility, and the absence of standardised protocols. Future research priorities include the incorporation of pain-specific imaging endpoints, longitudinal validation across diverse cohorts, and integration with artificial intelligence frameworks to enable automated analysis and predictive modelling. Collectively, these advances hold promise for enabling earlier diagnosis, refined symptom stratification, and more personalised therapeutic strategies in DCM. Full article

As a large-scale flexible structure, the free-vibration characteristics of a horizontal single-axis solar tracking bracket (HSSTB) hold significance for its dynamic optimization design. However, due to material fabrication, construction processes, and harsh field service environments, structural parameters such as the elastic modulus inevitably exhibit uncertainty, leading to discrepancies between actual free-vibration characteristics and design values. This study considers the randomness of the steel elastic modulus and conducts a global sensitivity analysis of a real-life five-column HSSTB. First, the Kriging method is employed to build a surrogate model to describe the natural frequencies of the HSSTB and its stochastic parameters, which enables efficient evaluation of the statistical characteristics of the HSSTB’s natural frequencies. Further, the Sobol indices are utilized to quantify the influence of parameter randomness on the natural frequencies. The results indicate that the mean values of the first five natural frequencies are slightly lower than the design values. The first, fourth, and fifth natural frequencies of the five-column HSSTB are predominantly influenced by the middle three columns, while the second and third natural frequencies are more susceptible to the two edge columns. Full article

Arid-region water resource management is hindered by severely inadequate river discharge monitoring, with effective observations of hydrological processes particularly lacking in narrow river channels. To overcome this bottleneck, this study proposes an integrated multi-model remote sensing retrieval framework and systematically evaluates the applicability of Manning’s equation, the At-Many-Stations Hydraulic Geometry (AHG) model, and the AHG’s relaxed form (AMHG) in typical arid-region rivers on the northern slope of the Kunlun Mountains. Runoff was estimated by integrating multi-source remote sensing imagery (Sentinel-2, Landsat-8, and Gaofen-1) on the Google Earth Engine platform and combining it with genetic algorithms for parameter optimization. The results indicate that Manning’s equation performed the best overall (RMSE = 21.78 m³/s, NSE = 0.94) and was highly robust to river width extraction errors, with Manning’s roughness coefficient having a significantly greater impact than the hydraulic slope. The AHG model can construct long-term discharge series based on limited measured data but is sensitive to the accuracy of river width extraction. Although the AMHG model improved the retrieval performance, its effectiveness was constrained by systematic biases in proxy variables. The study also found that the AHG exponent b in the rivers of this region exhibits high stability (coefficient of variation < 0.09), providing a theoretical basis for constructing a sustainable discharge monitoring system. The integrated method developed in this study offers a reliable technical pathway for dynamic hydrological monitoring and quantitative water resource management in data-scarce arid regions. Full article

This study presents an integrated framework for spatiotemporal mapping of carbon stocks in rubber plantations in Rayong Province, Eastern Thailand—an area undergoing rapid agricultural transformation and rubber expansion. Unlike most existing assessments that rely on Tier 1 IPCC defaults or coarse plantation age classes, our framework combines annual plantation age derived from Landsat time series, age-specific allometric growth models, and Tier 2 soil organic carbon (SOC) accounting. This enables fine-scale, age- and site-sensitive estimation of both tree and soil carbon. Results show that tree biomass dominates the carbon pool, with mean tree carbon stocks of 66.94 ± 13.1% t C ha⁻¹, broadly consistent with national field studies. SOC stocks averaged 45.20 ± 0.043% t C ha⁻¹, but were overwhelmingly inherited from pre-conversion land use (43.7 ± 0.042% t C ha⁻¹). Modeled SOC changes ($\mathsf{\Delta }\mathrm{SOC}$) were modest, with small gains (2.06 t C ha⁻¹) and localized losses (−9.96 t C ha⁻¹), producing a net mean increase of only 1.44 t C ha⁻¹. These values are substantially lower than field-based estimates (5–15 t C ha⁻¹), reflecting structural limitations of the global empirical ΔSOC model and reliance on generalized default parameters. Uncertainties also arise from allometric assumptions, generalized soil factors, and Landsat resolution constraints in smallholder landscapes. Beyond carbon, ecological trade-offs of rubber expansion—including biodiversity loss, soil fertility decline, and hydrological impacts—must be considered. By integrating methodological innovation with explicit acknowledgment of uncertainties, this framework provides a conservative but policy-relevant basis for carbon accounting, subnational GHG reporting, and sustainable land-use planning in tropical agroecosystems. Full article

This study investigates how global monetary and oil shocks propagate across advanced and pegged oil economies, focusing on the United States, Germany, the United Kingdom, Saudi Arabia, and the United Arab Emirates over the period 2015–2023. It examines which transmission channels—liquidity, credit, or equity—serve as the dominant conduits of spillovers under fixed exchange rate regimes. To address this question, this paper develops a hybrid causal–computational framework that integrates high-frequency identification of monetary and oil shocks with econometric benchmarks (Local Projections and Time-Varying Parameter VARs) and a Graph Neural Network-based Causal Shock Network (GNN-CSN) enhanced with SHAP explainability. The results show that global monetary shocks significantly raise interbank funding costs in Saudi Arabia and the UAE, while sovereign credit spreads remain largely stable, indicating that liquidity—not credit—constitutes the main transmission channel. Equity markets absorb much of the external adjustment, reflecting sectoral sensitivity to global cycles. By combining causal identification, dynamic estimation, and explainable machine learning, the framework improves predictive accuracy and transparency, offering new evidence on how external shocks shape financial dynamics in resource-dependent, dollar-pegged economies. Full article

This paper presents a genetic algorithm (GA) approach to optimize key structural parameters of the torque motor used in a direct-drive slide knife gate valve. The optimization aims at enhancing the performance of the torque motor by improving the output torque, minimizing the overshoot, and reducing the response time. A mathematical model based on these performance indicators is formulated to guide the optimization process. Compared to the original design, the optimized design is shown to achieve a 26.4% increase in output torque, a 0.14 ms reduction in response time, and a 9% decrease in overshoot. Additionally, AMESim simulations confirm that the optimized motor significantly improves valve control accuracy, dynamic response, and flow stability, while also decreasing sensitivity to pressure fluctuations under high-current conditions. Finally, experimental results are provided to corroborate the simulation findings, validating the accuracy and effectiveness of the proposed optimization methodology. This study provides novel theoretical insights and practical guidance for the design of high-performance torque motors used in direct-drive electro-hydraulic servo valves within aerospace applications. Full article

The development of sensitive and specific diagnostic methods for secondary bacterial meningitis remains an urgent challenge in neurosurgical and intensive care units. A combination of various clinical and biochemical parameters, as well as biomarkers and metabolites in cerebrospinal fluid (CSF), can be considered for constructing multivariate diagnostic models. In this study, 96 CSF samples from 53 patients with suspected secondary meningitis were analyzed. The first cohort, consisting of patients with sequelae of severe brain damage, included 7 patients (21 CSF samples) with and 29 patients (56 CSF samples) without secondary bacterial meningitis. The second cohort comprised patients after neurosurgical interventions, including 10 patients (12 CSF samples) with and 7 patients (7 CSF samples) without secondary bacterial meningitis. Combined group 1 with 33 CSF samples from patients with secondary bacterial meningitis and combined group 2 with 63 CSF samples from patients without secondary bacterial meningitis had statistically different cell and biochemical compositions and higher CSF concentrations of biomarkers (interleukin-6 and S100 protein) and lactate-containing aromatic metabolites in group 1. Univariate prognostic models constructed on 4-hydroxyphenyllactic, phenyllactic, and indole-3-lactic acids demonstrated outstanding AUC-ROC of more than 0.91. A multivariate model built on all biomarkers and metabolites resulted in AUC-ROC = 0.94 with a sensitivity of 0.94 and specificity of 0.86, and was found to be the most accurate method for the diagnosis of secondary bacterial meningitis. Full article

Background/Objectives: Allergen-specific immunotherapy remains the only disease-modifying treatment for allergic diseases, and the use of recombinant hypoallergenic derivatives is a promising therapeutic approach. Among these, BTH2 has previously shown efficacy in an acute murine model of allergy induced by Blomia tropicalis. The present study aimed to evaluate both the efficacy and safety of BTH2 in a chronic asthma model induced by B. tropicalis. Methods: A/J male mice (n = 6) were sensitized and chronically challenged with B. tropicalis extract over four months. One group repeatedly received subcutaneous doses of BTH2 (25 µg) for three months (65 doses). Parameters of allergic airway inflammation, antibody profiles, cytokine levels, and markers of AIT success were evaluated in bronchoalveolar lavage fluid, lung tissue, serum, and splenocyte cultures. Results: Repeated BTH2 administration was well tolerated, with no signs of systemic toxicity. BTH2 significantly reduced neutrophilic and eosinophilic airway inflammation, while increasing lymphocytes and regulatory cytokines in the lungs. It suppressed IgE against B. tropicalis allergens, while inducing mucosal IgA responses and systemic IgG, which may be linked to the observed blocking antibody activity in BTH2-treated mice. The treatment also led to downregulation of Th2 cytokines and enhanced expression of regulatory and Th1-associated cytokines, especially IL-10, TGF-β and IFN-γ. Correlation matrix analyses indicated that regulatory cytokines were correlated with beneficial antibody responses and reduced inflammation. Conclusions: BTH2 shows strong therapeutic and immunomodulatory effects in a chronic asthma model induced by B. tropicalis, with a favorable safety profile. These findings support its potential for future clinical trials, including those involving patients with allergic asthma. Full article

The Loess Plateau is one of the most landslide-prone regions in China, where rainfall-induced shallow loess landslides severely constrain regional economic and social development. Therefore, investigating the stability of shallow loess slopes under rainfall conditions is of great significance. Taking Yuzhong County in Gansu Province as an example, this study uses the SINMAP model (Version 2.0) to assess slope stability. The areas of unstable zones under different rainfall intensities were identified, and the spatial distribution of hazard sites was analyzed to evaluate the applicability of this deterministic physical model in the study area. Furthermore, a Personnel Risk Level (PRL) determined by combining population density with the Stability Index (SI, defined as the probability that the factor of safety exceeds 1: SI = Prob (FS > 1)) was proposed and applied to assess the potential impact of landslides on local residents. The novelty of this study lies in three aspects: (1) targeting Yuzhong County (a loess region with scarce comprehensive landslide risk assessments) to fill the regional research gap, (2) quantifying PRL through a modified hazard index (HI = population density × (1/SI)) to achieve spatialized risk mapping for vulnerable populations, and (3) systematically analyzing the dynamic response of slope stability to five gradient rainfall intensities (from light rain to severe rainstorm) and verifying model sensitivity to key parameters. The results show that as rainfall intensity increases, stable areas gradually decrease while unstable areas expand, with stable zones progressively transforming into unstable ones. Greater rainfall intensity also leads to an increase in the number of landslides within unstable zones. The proposed PRL helps delineate the severity of hazards in different townships, providing new references for mitigating casualties and property losses caused by landslides. Full article

This review surveys theoretical frameworks developed to describe rubber contact and friction on rough surfaces, with a particular focus on tire–road interaction. It begins with classical continuum approaches, which provide valuable foundations but show limitations when applied to viscoelastic materials and multiscale roughness. More recent formulations are then examined, including the Klüppel–Heinrich model, which couples fractal surface descriptions with viscoelastic dissipation, and Persson’s theory, which applies a statistical mechanics perspective and later integrates flash temperature effects. Grosch’s pioneering experimental work is also revisited as a key empirical reference linking friction, velocity, and temperature. A comparative discussion highlights the ability of these models to capture scale-dependent contact and energy dissipation while also noting practical challenges such as calibration requirements, parameter sensitivity, and computational costs. Persistent issues include the definition of cutoff criteria for roughness spectra, the treatment of adhesion under realistic operating conditions, and the translation of detailed power spectral density (PSD) data into usable inputs for predictive models. The review emphasizes progress in connecting material rheology, surface characterization, and operating conditions but also underscores the gap between theoretical predictions and real tire–road performance. Bridging this gap will require hybrid approaches that combine physics-based and data-driven methods, supported by advances in surface metrology, in situ friction measurements, and machine learning. Overall, the paper provides a critical synthesis of current models and outlines future directions toward more predictive and application-oriented tire–road friction modeling. Full article

With increasing attention paid to the development of natural gas hydrates, various mining methods have been studied. CO₂-CH₄ hydrate replacement has become one of the key research topics in the field of natural gas hydrate mining because it can overcome the disadvantage of traditional mining methods that easily lead to reservoir collapse and realize CO₂ sequestration while extracting CH₄. However, complex heat and mass transfer, as well as fluid migration, are involved in CO₂-CH₄ hydrate in situ replacement, and this method has the drawbacks of slower reaction rates and a lower replacement efficiency compared to traditional methods. Therefore, a substantial amount of experimental and simulation research is still needed to advance this method. This paper reviews the current research on CH₄ recovery from CH₄ hydrate by CO₂ replacement. The main CO₂-CH₄ hydrate replacement mechanisms are summarized according to whether the hydrate cage structure is disrupted. Numerical simulation studies based on the above replacement mechanisms are introduced and compared in detail. The effects of various replacement methods, such as soaking replacement and dynamic replacement, as well as factors including the presence of initial water, reservoir permeability, temperature, and pressure on the replacement reaction, are summarized. Additionally, existing pore-scale replacement studies are reviewed, highlighting the necessity of pore-scale research on CO₂-CH₄ hydrate replacement reactions, pointing out the shortcomings of current pore-scale studies, and proposing suggestions for future research directions. This work provides a reference for the development of the CO₂-CH₄ hydrate replacement method and the realization of its industrial applications. Full article

Background/Objectives: Parkinson’s disease (PD) affects both articulatory and phonatory subsystems, leading to characteristic speech changes known as hypokinetic dysarthria. However, few studies have jointly analyzed these subsystems within the same participants using interpretable deep-learning-based measures. Methods: Speech data from the PC-GITA corpus, including 50 Colombian Spanish speakers with PD and 50 age- and sex-matched healthy controls were analyzed. We combined phonological feature posteriors—probabilistic indices of articulatory constriction derived from the Phonet deep neural network—with harmonics-to-noise ratio (HNR) as a laryngeal measure. Linear mixed-effects models tested how these measures related to disease severity (UPDRS, UPDRS-speech, and Hoehn and Yahr), age, and sex. Results: PD participants showed significantly higher [continuant] posteriors, especially for dental stops, reflecting increased spirantization and articulatory weakening. In contrast, [sonorant] posteriors did not differ from controls, indicating reduced oral constriction without a shift toward more open, approximant-like articulations. HNR was predicted by vowel height and sex but did not distinguish PD from controls, likely reflecting ON-medication recordings. Conclusions: These findings demonstrate that deep-learning-derived articulatory features can capture early, subphonemic weakening in PD speech—particularly for coronal consonants—while single-parameter laryngeal indices such as HNR are less sensitive under medicated conditions. By linking spectral energy patterns to interpretable phonological categories, this approach provides a transparent framework for detecting subtle articulatory deficits and developing feature-level biomarkers of PD progression. Full article