Search Results (33,191)

The renewed interest in hydrogen peroxide-based space propulsion systems has highlighted the persistent issue of catalyst degradation during long-term operation. Although several studies have investigated the underlying causes of this phenomenon, effective regeneration techniques capable of restoring catalytic activity have not yet been clearly demonstrated. This study investigates the mechanisms responsible for performance degradation and proposes a viable regeneration strategy for palladium-based catalysts. Experimental analyses were conducted on a batch of commercial Al${}_2$O${}_3$/Pd pellets subjected to multiple firing cycles in a 10 N-class hybrid mini-thruster. Monitoring of the propulsive performance revealed a progressive decline in catalytic activity, ultimately preventing ignition of the hybrid rocket engine. To characterize the degradation mechanisms, the pellets were examined through visual inspection, static hydrogen peroxide decomposition tests, and Temperature Programmed Reduction (TPR) analysis. The results indicated significant surface oxidation of palladium, leading to reduced decomposition efficiency. A chemical regeneration procedure based on sodium borohydride (NaBH${}_4$) treatment was subsequently developed to restore catalytic performance. The regenerated pellets were tested under the same experimental conditions that had previously led to ignition failure. Their propulsive performance was then compared with both the degraded pellets and a new batch of equivalent catalysts. The results demonstrate that the regeneration process successfully restored the catalytic activity to levels comparable with the original state, enabling stable and efficient hybrid combustion. These findings confirm the role of surface oxidation in catalyst degradation and demonstrate that targeted chemical treatment can significantly extend catalyst lifetime. The proposed regeneration strategy offers a practical method to reduce costs of ground-based experimental campaigns and support the future deployment of hydrogen peroxide-based propulsion systems in space applications by providing insights into the mechanisms that can degrade the performance of palladium catalysts. Full article

Terminal lakes in arid regions face severe degradation due to the dual pressures of climate change and anthropogenic water consumption. Traditional single-threshold methods for defining ecological water requirements often fail to balance ecosystem stability with water scarcity. To address this, this study constructs a comprehensive framework coupling “Morphometric Stability–Ecological Security Reliability–Resource Use Efficiency” to delineate the suitable ecological interval for Ebinur Lake, the largest saltwater lake in Xinjiang. Using multi-source remote sensing data (Landsat, Sentinel, ICESat, CryoSat), we reconstruct the long-term hydrological dynamics from 2001 to 2023. Results indicate a significant shrinking trend in the lake area, driven primarily by reduced inflow. We jointly consider the lake morphometric breakpoint, the ecological security baseline, and the lower bound of ecosystem service water use efficiency (ESWUE) to determine a minimum suitable ecological area of 500 km²; the regulation upper limit is set at 740 km² based on the marginal peak of ESWUE. However, monitoring data reveal that the lake falls below the minimum 500 km² baseline in approximately 40% of months, highlighting a severe ecological deficit risk. Furthermore, ESWUE analysis shows a peak in April (10 CNY/m³), suggesting that, under current climate conditions, a “Spring Surplus and Autumn Deficit” regulation strategy—advancing the replenishment window to the spring windy season—can maximize dust suppression benefits at a lower evaporative cost. This study provides a theoretical basis and methodological paradigm that will contribute to the sustainable management of shrinking terminal lakes globally. Full article

Background/Objectives: Automatic segmentation and identification of vertebrae in spinal CT are essential for assisting diagnosis of spinal disorders and for preoperative planning. The task is challenging due to the high structural similarity between adjacent vertebrae and the morphological variability of vertebrae. Most existing methods rely on fully supervised deep learning and, constrained by limited annotations, struggle to remain robust in complex scenarios. Methods: We propose a semi-supervised approach built on a dual-branch 3D U-Net. Mamba modules are inserted between the encoder and decoder to model long-range dependencies along the cranio–caudal axis. The identification branch employs a 3D convolutional block attention module (3D-CBAM) to enhance class discriminability. A unified semi-supervised objective is formulated via teacher–student consistency: for each unlabeled sample, weakly and strongly augmented views are generated, and cross-branch consistency is enforced, together with confidence-based filtering and class-frequency reweighting. In addition, a connected-component analysis is used to enforce anatomically plausible sequential continuity of vertebral indices in the outputs. Results: Experiments on VerSe 2019 and 2020 show that, on the public VerSe 2019 test set (with VerSe 2020 scans used as unlabeled training data), the supervised baseline achieved a Dice score of 89.8% and an identification accuracy of 92.3%. Incorporating unlabeled data improved performance to 91.6% Dice and 97.5% identification accuracy (relative gains of +1.8 and +5.2 percentage points). Compared with competing methods, the proposed semi-supervised model attains higher or comparable segmentation accuracy and the highest identification accuracy. Conclusions: Without additional annotation cost, the proposed method markedly improves the overall performance of vertebra segmentation and identification, offering more robust automated support for clinical workflows. Full article

Anomaly detection on tabular data is widely used in fraud detection, predictive maintenance, and medical screening. While heterogeneous ensembles combining multiple detection paradigms achieve strong performance, their computational cost limits deployment in latency-sensitive or resource-constrained environments. We propose KD-AnomalyNet, a teacher–student framework that distills anomaly knowledge from a high-capacity ensemble into a lightweight neural model for efficient inference. Beyond performance replication, we study how anomaly representations transfer during distillation. To this end, we introduce a noise perturbation analysis that serves as a diagnostic probe for representation stability without introducing additional trainable components. Experiments on ten benchmark datasets show that the distilled model preserves up to 98.5% of the teacher’s AUC-ROC on the nine capacity-sufficient datasets (84.7% mean retention across all ten datasets) while achieving 26–181× inference speedups. Our analysis reveals which forms of anomaly knowledge transfer reliably—global outliers (78% transfer) and isolation-based detection (88% retention)—and which degrade under compression—local outliers (20% transfer) and neighborhood-based detection (76% retention)—providing practical guidance for deploying distilled anomaly detectors. Full article

Artificial-intelligence-driven wayside monitoring has become a promising solution for early identification of railway wheel flats, enabling safer operations and more efficient maintenance planning. This study introduces a comparative investigation of supervised and unsupervised machine learning strategies for wheel flat identification, with particular emphasis on real-time applicability and sensor cost reduction. Support Vector Machines (SVMs) and k-means clustering are evaluated as representative supervised and unsupervised approaches using vibration data obtained from numerically simulated train–track interactions under realistic operating conditions, including train speeds of 120 km/h and 200 km/h and multiple wheel flat severities. A key contribution of this work is the proposal of a simplified supervised classification framework that directly exploits Auto-Regressive features extracted from rail-mounted accelerometers, eliminating the need for feature normalization and multi-sensor data fusion. This simplification significantly reduces computational effort, making the approach suitable for real-time deployment in operational railway environments. In parallel, a systematic sensitivity analysis is conducted to assess the influence of sensor placement and to identify the minimum sensor configuration required to achieve reliable damage classification. The outputs from the current study show that an SVM emerges with more accurate defect classification than the k-means clustering, allowing a wayside system with fewer sensors. Full article

Anthropogenic pressures and climate change are accelerating the degradation of seagrass ecosystems and the ecological services they provide. In temperate systems, the decline of eelgrass (Zostera marina) has raised noticeable concern, particularly as restoration actions (e.g., transplantation) require accurate, nondestructive estimates of leaf biomass. Allometric power-law models can provide such proxies, but their applied value depends on whether fitted parameters remain transferable across sites and sampling periods. Here, using two extensive and independently collected datasets from San Quintín Bay (SQ) and Punta Banda estuary (PB), we evaluate three formulations: M1 (biomass–length), M2 (biomass–length–width), and M3 (biomass–area surrogate). All three models produced consistent fits in both datasets, and parameter-comparison tests detected no significant between-site differences. Reciprocal cross-projections of monthly mean leaf biomass showed high concordance, supporting practical parameter stability within the SQ–PB domain. A model-selection analysis based on goodness of fit and parsimony further identified the bivariate model M2 as the best-performing proxy across sites. Taken together, these results support a practical interpretation in which eelgrass may express phenotypic plasticity through shifts in trait distributions (length and width), while the scaling relation linking morphology to biomass remains effectively stable. For applied restoration-comparison purposes, we therefore recommend using M2—preferably with site-fitted parameters, or pooled/mean parameters when supported by reproducibility tests—to estimate aerial production non-destructively and cost-effectively. Full article

In this study, a techno-economic and carbon footprint (GHG, CO₂-equivalent) analysis was conducted on two alternative biofuels, green diesel and bio-jet fuel, produced from renewable lipids. The focus of the work is the comparison of various lipid feedstocks, including waste cooking oil, and four types of vegetable oils: cardoon, soybean, palm, and sunflower. Process optimization and design were performed to minimize production costs by using the process simulation software Aspen Plus^®. Green diesel and bio-jet fuel were obtained via hydrodeoxygenation and hydroisomerization/hydrocracking, respectively. Sensitivity analyses confirmed consistent results across the tested vegetable oils. Hydrodeoxygenation achieved triglyceride molar conversions exceeding 97%, with overall mass yields into the diesel fraction surpassing 79%. Conversely, hydroisomerization/hydrocracking of green diesel resulted in over 90% conversion of n-paraffins and more than 50% overall mass yield. The economic analysis showed that the primary cost factor influencing the payback selling price of the biofuels is the price of the lipid feedstocks. Biofuels are economically viable only when lipid prices are below 1000 €/ton and hydrogen prices are below 3000 €/ton. An important aspect is also represented by the combined-cycle energy recovery system, which strongly affects the overall capital cost and increases internal power generation efficiency. The carbon footprint calculated over a cradle-to-grave boundary showed shows net GHG reductions versus the fossil reference fuels for all scenarios. Net avoided emissions range from 1.74 to 3.63 kgCO₂-eq/kg green diesel and from 0.80 to 3.70 kgCO₂-eq/kg bio-jet fuel across the investigated feedstocks, approximately 40–84% and 20–95% of the respective savings relative to the fossil reference fuels under the stated background and logistics assumptions. Results are expressed per kg of produced fuel as a functional unit, using literature-derived upstream emission factors for oil supply and background inputs (hydrogen, Italian grid electricity and transport). For the bio-jet configuration, co-product burdens were partitioned by mass; the Discussion section highlights the sensitivity of the GD vs. BJF comparison to co-product handling and allocation choices. In this context, the choice of feedstock is essential in establishing the resulting GHG intensity of the two biofuels. From both economic and climate change perspectives, waste cooking oil emerges as the most promising option, particularly given its classification as waste-derived feedstock in the system boundary, unlike the virgin oil sources. Full article

The transportation sector is a major contributor to global greenhouse gas emissions, making electric vehicle (EV) adoption critical for decarbonization. This study investigates EV adoption determinants in Turkey using explainable machine learning, focusing on economic, infrastructure, and attitudinal factors while exploring driver behavior and fuel-efficiency awareness. Data from 304 participants were collected; after excluding undecided responses, the final analytical sample comprised 232 participants. Multiple algorithms (Random Forest, XGBoost, Logistic Regression, and SVM) were evaluated, addressing class imbalance via SMOTETomek. SHAP analysis identified policy-relevant predictors. Results reveal that EV adoption intentions are primarily driven by perceived cost impact, EV knowledge, and charging infrastructure accessibility, showing substantially stronger effects than driver behavior. Exploratory analysis indicates that aggressive driving correlates with lower fuel-efficiency awareness, whereas maintenance and eco-driving support higher awareness. The best-performing Random Forest model achieved 89.36% accuracy and a 0.9348 F1-score. Rather than claiming novelty in ML application, this study contributes an interpretable framework and emerging-market evidence contrasting economic/infrastructure factors against behavioral variables. Findings provide actionable insights for policy, highlighting cost-focused incentives, infrastructure deployment, and targeted awareness campaigns. Full article

Ventilation systems in agricultural settings are designed to deliver specific air exchange rates, which are often not achievable using natural ventilation. In this study, we analyzed 105 agricultural ventilation fans tested between 2015 and 2025 at the Bioenvironmental and Structural Systems (BESS) Laboratory, including 0.6, 0.9, 1.2, and 1.5 m diameter fans operating at static pressures ranging from 0 to 75 Pa. The main objective of the study is to develop and introduce guidelines to help select the most suitable ventilation fans to improve the sustainability of agricultural operations. Two web-based interactive calculators were developed to visualize fan performance relative to low- and high-performing fans of the same diameter. Our findings indicated that the ventilation efficiency ratio (VER) of the fans ranged from 2 to 50 m³ h⁻¹ W⁻¹, and larger fans consistently showed higher efficiency at typical operating pressures of 12.5 to 37.5 Pa. In general, variable-speed fans operated at 85%, rather than full capacity, achieved higher efficiency. Two cost comparison scenarios were examined. In the first scenario, the fan with a higher purchasing cost but also 35% higher efficiency resulted in a payback period of 4.1 years. In the second scenario, the difference in fan efficiencies was less than 3.5%, which did not help with recovering higher purchase costs during the 10-year analysis period. It was concluded that selecting fans solely based on purchase price can lead to higher long-term costs. To improve the sustainability of agricultural fans, VER and operating conditions need to be evaluated together and integrated into automated control strategies. Future studies can focus on integrating fans with high efficiencies into sensor-based automated ventilation control systems to quantify long-term energy savings in livestock buildings and other agricultural operations. Full article

This research investigates the colonnade of Tomb 7 at the UNESCO World Heritage site of the Tombs of the Kings in Paphos, Cyprus. Specifically, a multi-drum column located at the south-east corner of the tomb is examined from both geometric and structural perspectives. Being the only standing element to support the entablature on that side of the tomb, the column is crucial for maintaining the structural stability of the monument. Numerical structural analyses are performed on the column via the finite element method (FEM), supported by close-range recording techniques—particularly terrestrial laser scanning (TLS)—to generate finite element (FE) models. Several modelling strategies capable of converting point cloud data into reliable structural models are developed and compared with the aim of identifying the most effective and cost-efficient approach. Each method is analyzed in detail to evaluate its workflow, assumptions, strengths, and limitations in the context of heritage structures with complex irregular geometries. Linear static and dynamic analyses are performed on five different FE models to assess the column’s mechanical response and to understand how differences in geometric representation affect the structural behaviour. The results indicate that all approaches adequately capture the general structural response. The comparison of the different modelling strategies highlights the trade-offs between geometric accuracy, computational efficiency, and practical usability. These outcomes indicate the potential and the current limitations of exploiting point cloud data for structural analysis and contribute to the development of more robust and accurate scan-to-FEM methodologies for the conservation and assessment of cultural heritage structures. Full article

In the analysis and design of wind turbines, aeroelastic analysis is required that considers elastic structure and control in addition to aerodynamic characteristics. In recent years, with the increase in size and reduction in the cost of wind turbines, problems have emerged that cannot be addressed with the standard analysis methods. The accuracy of the Blade Element and Momentum (BEM) theory, which is the most common aerodynamic analysis and design theory, is reduced in conditions where three-dimensional effects such as radial flow are not negligible. Furthermore, full-model Computational Fluid Dynamics (CFD), which is commonly used for complex aerodynamic problems, is not applicable for the design calculation of wind turbines considering itscomputational demands. To address these challenges, the Actuator Line Model (ALM) can be utilized as practical load analysis methods that account for structural elasticity, control, and fluctuating winds—offering a level of fidelity between both approaches. However, the conventional ALM does not account for the stall delay (SD) observed in the inboard section of rotor. In this study, an ALM based on CFD is developed by incorporating Snel’s stall delay model, which was developed for BEM. Additionally, the use of the NREL 5 MW reference wind turbine rotor results in the load distribution of the inboard section of this developed model to be comparable to that of the full-model CFD; however, a similar observation is not made for the conventional BEM. Full article

The transition towards circular economy is now a key strategy to address the environmental issues we are facing. Within this framework, biochar, a carbon-rich material derived from residual agricultural pyrolysis, can represent a sustainable and circular solution. This paper aims at evaluating the possibility of implementing a local biochar-production system as part of an economic and social strategy of the redevelopment of an abandoned rural site, Borgo di Perolla, in Tuscany, Italy. A cost–benefits analysis (CBA) was conducted to evaluate the economic feasibility of three different scenarios of production and strategies: Scenario 1 considers revenues solely from the production and sale of biochar and wood vinegar; Scenario 2 additionally includes potential income from the sale of voluntary carbon credits; and Scenario 3 incorporates biochar credits within the European Union Emission Trading System (EU ETS). For each scenario, three indicators were calculated: Net-Present Value (NPV), Internal Rate of Return (IRR), and Breakeven point (BEP). The most evident result that emerged is that the sale of biochar and its by-products alone is not sufficient to ensure the project’s economic sustainability, mainly due to high production costs. Only through carbon-credit-trading markets biochar becomes not only an environmentally strategic tool but also an economically rewarding one. In this sense, market infrastructures, such as the ETS, are essential for the dissemination of circular models, like biochar, that generate both environmental and economic benefits. Previous studies on biochar have largely focused on its application and associated benefits, while cost–benefit analyses have primarily examined its economic feasibility through the commercialization of biochar as a soil amendment, particularly within the United States context. The present work contributes to this literature in three main ways. First, it provides a site-specific and replicable CBA framework applied to a real territorial regeneration project (Borgo di Perolla), grounded in primary data collected through field surveys, stakeholder interviews, and expert validation. Second, the study explicitly compares multiple market-access scenarios within the same analytical framework, ranging from biochar-only sales to voluntary carbon markets, allowing for a clear identification of the economic thresholds at which biochar becomes financially sustainable. Third, and most importantly, the main contribution of this work lies in the explicit modeling of biochar integration into the EU Emissions Trading System. This paper extends the analysis to a regulated carbon market scenario, assuming the recognition of biochar-based carbon removals within the EU ETS framework. From a methodological perspective, the study quantitatively assesses how ETS price dynamics affect the profitability, internal rate of return, and break-even point of a biochar project over a long-term horizon. From a policy perspective, the analysis anticipates recent regulatory developments, such as the EU Regulation 2024/3012, on establishing a Union certification framework for permanent carbon removals, carbon farming, and carbon storage in products, by showing how biochar could function as a fully market-integrated climate technology. Full article

Globalization has reorganized industrial spatial patterns, embedding regional economies into complex global production systems. However, the existing literature primarily focuses on the national level, leaving the “global-national-local” multi-scale linkages of sub-national regions underexplored. Focusing on Guangdong, which is China’s most open economic gateway, this study constructs a nested Multi-Regional input–output (MRIO) model to systematically reveal its industrial linkage paths across multiple scales. The results demonstrate that Guangdong features “strong local services and extensive global connections.” Specifically, the network is led by the high-R&D-intensity category and supported by energy and low-R&D categories, highlighted by two core supply paths, which are non-metallic mineral supply for construction and metal product support for optical–electrical manufacturing. Four heterogeneous modes are identified: resource security, innovation-driven dual circulation, cost-competitive regional division, and export-oriented service support. Crucially, the provincial “domestic intermediate chains plus international core chains” logic underscores Guangdong’s role as a bridge connecting Global and Domestic Value Chains. Theoretically, this work enriches the local dimension of Global Production Network theory. Methodologically, it provides an operational tool for nested analysis. Practically, it offers policy evidence for open economies to optimize industrial layouts and enhance supply chain resilience. Full article

Fine-tuning a BERT-Base model for specific tasks, such as sentiment analysis, has become resource-intensive and often requires high computational power and memory. This paper introduces SCALE, a novel resource-efficient fine-tuning method that targets the most critical transformer layers, which reduces computational costs without sacrificing performance. By dynamically profiling transformer layers via activation magnitudes and attention entropy, SCALE selects and adapts only the most influential layers with lightweight adapter modules. The proposed method outperforms traditional fine-tuning techniques, achieving a 2.3% improvement in accuracy on the IMDB dataset and reducing training time by 56.3% compared to full-model fine-tuning. Experiments across various sentiment analysis benchmarks demonstrate SCALE’s effectiveness in optimizing fine-tuning for the BERT-base model in resource-constrained environments, achieving up to 99% of the performance of full-model fine-tuning while using only 40% of the parameters. The empirical validation in this study is restricted to binary and multi-class sentiment classification. The evaluation specifically reflects effectiveness in sentiment analysis text classification tasks. Full article

Polylactic acid (PLA) is one of the most widely used materials for fused filament fabrication (FFF) or fused deposition modeling (FDM), being recognized for its low carbon footprint, relatively low costs and good mechanical properties. Improving the mechanical and technological properties of PLA with various additives has led to the production of different types of PLA-based filaments, such as hyper PLA (HPLA), PLA, PLA+ and PLA Lite. Studies on the mechanical properties of HPLA are scarce; therefore, the objective of this paper was to determine the mechanical properties of 3D-printed HPLA under tensile and bending stress conditions and to obtain numerical models that depend on the raster pattern orientation. The principal component analysis (PCA) reveals very different results for bending compared with tension, with outcomes varying significantly depending on the orientation of the raster angle. Full article