Search Results (27)

Replacing peat in green roof substrates with sustainable alternatives while maintaining plant performance and ecosystem services remains a critical challenge. We studied biochar-substrate interactions across four commercial green roof formulations (based on the type of organic component) in a greenhouse experiment: pure vermicompost, vermicompost + fen peat, fen peat, and mixed fen/high-moor peat. Substrates were amended with straw biochar, pine bark biochar, or left unamended (5% v/v, n = 4 replicates) and planted with a grass seed mixture mimicking early green roof establishment. Plant growth, nutrient contents (nitrate and phosphate contents), and microbial indicators (microbial biomass carbon (MBC), qCO₂, and enzyme activities) were measured 30 days after the experiment began. Straw biochar in vermicompost boosted nitrate (90.8 mg kg⁻¹) and root N (3.1%) compared to the control, while pine bark biochar in mixed peat released phosphate (+375%) and maximized MBC (874 µg g⁻¹). Biochar intensified substrate effects, suppressing CO₂ in peat through liming effects (pH from 4.6 to 6.5–7.1) but priming respiration in vermicompost via labile C supply. PCA explained 63% of the variance, with nitrate, plant N, and microbial parameters driving substrate separation. These short-term greenhouse results demonstrate critical biochar-substrate specificity for green roof substrate development, emphasizing formulation-specific matching over universal biochar application. Full article

This study focuses on a 155 mm 32CrNi3MoV steel barrel and presents a thermochemically coupled phase transformation and diffusion dynamics model. The model leverages the significant disparity between radial and axial temperature gradients to simplify the heat conduction problem to a one-dimensional transient formulation. The temperature field distribution during firing sequences is solved analytically, accounting for the dynamic shift in critical phase transformation temperatures under high heating rates. The evolution of the martensitic layer thickness under repeated thermal shock is subsequently calculated. A numerical model for the pulsed diffusion of C and N is established based on Fick’s second law, incorporating the competitive diffusion–phase transformation mechanisms that govern martensite/austenite interface migration. To quantitatively evaluate the synergistic contribution of C and N to austenite stabilization, a carbon equivalent (Ceq) model is introduced, with the weight coefficient of N relative to C determined to be 0.68 and the critical Ceq required to lower the martensite start temperature below 25 °C calculated as 1.15 wt%. Concurrently, the microstructure and elemental distribution within the austenite layer of the retired barrel are systematically characterized using multi-scale techniques. The results indicate that the austenite layer on the inner bore surface arises from the synergistic effects of cyclic thermal-shock-induced phase transformation and elemental diffusion. Based on the Ceq criterion, the austenite layer thickness increases rapidly during the initial ~100 firing cycles, after which the growth rate slows significantly: it reaches approximately 1.27 μm after the first cycle and 2.94 μm after 1000 cycles, with only 0.2 μm of additional thickening between 100 and 1000 cycles—consistent with the experimentally observed range of 1.52–4.16 μm. The martensitic layer formed during the first firing cycle exhibits low thermal conductivity, which impedes subsequent heat transfer and leads to stabilization of its thickness at a characteristic depth. Grain refinement induced by repeated thermal shock provide short-circuit diffusion paths for elemental diffusion, accelerating compositional homogenization within the austenite layer and resulting in a stepped concentration profile at the interface. This study provides a representative example of non-equilibrium coupled phase transformation–diffusion phenomena under extreme transient loading. The established thickness prediction model can provide guidance for service life assessment of large-caliber barrels, offering both theoretical foundations and practical engineering guidance for their material design and performance optimization. Full article

Offshore wind turbines (OWTs) are subjected to long-term coupled wind–wave loads, and frequently endure extreme loads under wind speeds exceeding the cut-out speed during service. This paper uses the OpenFAST v4.0.0 to conduct a detailed numerical analysis of an offshore monopile wind turbine, investigating its aerodynamic loads, tower deformation, displacement, acceleration, and foundation reactions under cut-in, rated and cut-out conditions, and further explores the influence of reference wind speed. Distinct response discrepancies are identified between directions and operating conditions. Fore–aft (F-A) responses are dominated by axial thrust and the first-order bending mode, reaching their peak under the rated condition. Side–side (S-S) responses are controlled by lateral turbulence; under cut-out conditions, the sharply reduced aerodynamic damping triggers significant higher-order mode participation, resulting in the maximum S-S responses. With increasing reference wind speed, F-A responses rise monotonically, while S-S displacement tends to plateau above a critical wind speed. The aerodynamic loads differ sharply across cut-in, rated and cut-out conditions; F-A thrust fluctuates between 0.25 × 10³ and 0.75 × 10³ kN at the rated condition and nears zero at the cut-out condition. The nacelle’s F-A acceleration peaks at 0.503 m/s² under the rated condition, while S-S acceleration peaks at 1.32 m/s² under the cut-out condition. The OWT’s tower F-A displacement peaks at 0.689 m under the rated condition, while S-S displacement peaks at 0.429 m under the cut-out condition. Full article

This study investigates the influence of H13 steel substrate surface roughness on the corrosion behavior of CrAlN coatings in a 3.5 wt.% NaCl solution. The interfacial structure of the coatings and the evolution of corrosion products were characterized using electrochemical techniques, X-ray photoelectron spectroscopy (XPS), and scanning electron microscopy (SEM). Results indicate that reducing the substrate surface roughness from 0.235 μm to 0.167 μm resulted in a proportional decrease in the coating’s critical load (Lc1), from 23.3 N to 17.3 N. Concurrently, the corrosion potential (Ecorr) shifted positively, the charge transfer resistance (Rct) increased significantly, and the corrosion current density (Icorr) decreased markedly. After 14 days of immersion, the most substantial positive shift in Ecorr was observed, moving from −1.038 V to −0.803 V (ΔE = 0.235 V). Rct increased dramatically from 2360 Ω·cm² to 2.772 × 10⁶ Ω·cm², representing an enhancement of two orders of magnitude. Icorr decreased from 7.003 × 10⁻⁵ A·cm⁻² to 1.182 × 10⁻⁶ A·cm⁻², corresponding to a reduction of 98%. Following 20 days of immersion, the sample with a substrate roughness of 0.214 μm exhibited corrosion damage to the underlying substrate. In contrast, the coating on the sample with a lower roughness (0.167 μm) remained relatively intact. Surface roughness directly governs collision, adsorption, and diffusion processes during coating deposition. While higher roughness enhances coating-substrate interfacial adhesion, it concomitantly increases surface porosity, ultimately compromising corrosion resistance. Therefore, practical applications necessitate a comprehensive optimization of coating adhesion strength and corrosion resistance, tailored to specific service environments. Full article

As a critical component of scraper conveyors, the middle trough operates under harsh conditions for extended periods, making it prone to failure and thus reducing the overall service life of the equipment. To address this issue and extend its service life, this study incorporated different amounts of CeO₂ into Cr₃C₂/Fe-based composite coatings. It investigated the effects of CeO₂ on the coating’s phase composition, microstructural evolution, wear resistance and corrosion resistance. Results show that CeO₂ addition did not alter the coating’s phase composition. The composition remained α-Fe, M₂₃C₆ (M: Fe, Cr) and vanadium carbides. However, CeO₂ promoted the transformation from columnar grains to equiaxed grains and refined the grains. With increasing CeO₂ content, the composite coating’s mechanical properties gradually improved. The Ce2 coating exhibited the highest microhardness (923.08 HV_0.5), the lowest friction coefficient (0.31) and the lowest wear rate (0.00217 mm³/N·m). Its dominant wear mechanisms were abrasive wear and mild adhesive wear. In 3.5% NaCl solution, the Ce2 coating showed the highest corrosion potential (−0.82 V) and the lowest corrosion current density (2.04 × 10⁻⁶ A/cm²), indicating excellent corrosion resistance. This study provides theoretical support for preparing high-performance Cr₃C₂/Fe-based composite coatings. It clarifies the key mechanism by which CeO₂ regulates coating properties. The developed composite coating has broad application potential due to its excellent combined wear and corrosion resistance. It can be widely used for surface strengthening of vulnerable components in mining machinery such as scraper conveyors, offering important theoretical and technical support for improving the service life of scraper conveyor middle troughs. Full article

The rapid proliferation of connected vehicles equipped with both Vehicle-to-Vehicle (V2V) sidelink and cellular interfaces creates new opportunities for real-time vehicular applications, yet achieving ultra-reliable communication without prohibitive cellular costs remains challenging. This paper addresses reliable inter-vehicle video streaming for safety-critical applications such as See-Through for Passing and Obstructed View Assist, which require stringent Service Level Objectives (SLOs) of 50 ms latency with 99% reliability. Through measurements in Seoul urban environments, we characterize the complementary nature of V2V and Vehicle-to-Network-to-Vehicle (V2N2V) paths: V2V provides ultra-low latency (mean 2.99 ms) but imperfect reliability (95.77%), while V2N2V achieves perfect reliability but exhibits high latency variability ($P_{99}$: 120.33 ms in centralized routing) that violates target SLOs. We propose a hybrid framework that exploits V2V as the primary path while selectively retransmitting only lost packets via V2N2V. The key innovation is a dual loss detection mechanism combining gap-based and timeout-based triggers leveraging Real-Time Protocol (RTP) headers for both immediate response and comprehensive coverage. Trace-driven simulation demonstrates that the proposed framework achieves a 99.96% packet reception rate and 99.71% frame playback ratio, approaching lossless transmission while maintaining cellular utilization at only 5.54%, which is merely 0.84 percentage points above the V2V loss rate. This represents a $7\times$ cost reduction versus PLR Switching (4.2 GB vs. 28 GB monthly) while reducing video stalls by $10\times$. These results demonstrate that packet-level selective redundancy enables cost-effective ultra-reliable V2X communication at scale. Full article

Soil corrosion is a critical durability and cost factor for metallic foundations in photovoltaic (PV) power plants, yet it is still addressed with fragmented criteria compared with atmospheric corrosion. This paper reviews the main soil corrosivity drivers relevant to PV installations—moisture and aeration dynamics, electrical resistivity, pH and buffer capacity, dissolved ions (notably chlorides and sulfates), microbiological activity, hydro-climatic variability and geological heterogeneity—highlighting their coupled and non-linear effects, such as differential aeration, macrocell formation and corrosion localization. Building on this mechanistic basis, an engineering-oriented methodological roadmap is proposed to translate soil characterization into durability decisions. The approach combines soil corrosivity classification according to DIN 50929-3 and DVGW GW 9, tiered estimation of hot-dip galvanized coating consumption using AASHTO screening, resistivity–pH correlations and ionic penalty factors, and verification against conservative NBS envelopes. When coating life is insufficient, a traceable steel thickness allowance based on DIN bare-steel corrosion rates is introduced to meet the target service life. The framework provides a practical and auditable basis for durability design and risk control of PV foundations in heterogeneous soils. The proposed framework shows that, for soils exceeding AASHTO mild criteria, zinc corrosion rates may increase by a factor of 1.3–1.7 when chloride and sulfate penalties are considered, potentially reducing coating service life by more than 40%. The methodology proposed enables designers to estimate the penalty factors for sulfates ($f_p\left({SO}_4^{2-}\right)$) and chlorides ($f_p\left({Cl}^{-}\right)$) in each specific project, calculating the appropriate values of $K_{{SO}_4^{2-}}$ and $K_{{Cl}^{-}}$ using electrochemical techniques—ER/LPR and EIS—to estimate the effect of the soluble salts content in the ${Zn}_{Corr Rate}$, not properly catch by the proxy indicator $V_{corr}\left(ER, pH\right)$ when sulfate and chloride content are over AAHSTO limits for mildly corrosive soils. Full article

Reliable traffic-signal information delivery is critical for safe navigation through signalized intersections, particularly for low-cost autonomous vehicles that rely on Vehicle-to-Network (V2N) communication rather than on-board HD maps or expensive perception sensors. Ensuring this selective delivery requires accurate infrastructure-side map-matching, which becomes challenging when vehicles operate with only Standard Definition (SD) maps and noisy GNSS measurements. This study comparatively evaluates five infrastructure-side map-matching algorithms under varying GNSS errors and road-network densities using real trajectories from Jeju Island with controlled Gaussian perturbations. The framework includes geometric matching, Extended Kalman Filtering (EKF), route-constrained filtering, grid-based spatial indexing, and a hybrid route–EKF fallback mechanism, executed in real time on a cloud-hosted Kafka pipeline. The hybrid route–EKF algorithm exhibited consistently high and stable link-matching accuracy (0.99308–0.96546 across GPS error groups; 0.9887–0.9777 across density groups) together with strong signal-matching accuracy (0.99394–0.96950; 0.9865–0.9790). Route-constrained and Kalman-based approaches also performed well, while heading-based matching showed clear limitations. These results indicate that infrastructure-side map-matching provides a scalable foundation for cloud-assisted traffic-signal information services and supports the feasibility of delivering reliable traffic-signal information to low-cost autonomous platforms. Full article

Heterostructure (HS) refers to a class of structural materials composed of two or more different chemical components or crystal structures. Integration of Inconel 625 (IN625) nickel-based superalloy and Ti6Al4V (TC4) titanium alloy to a HS material offers a promising strategy to achieve graded thermo-mechanical properties, extended service temperature ranges, and significant weight reduction, which are highly desirable in aerospace applications. However, obtaining a better metallurgical bonding between the two alloys remains a critical challenge. In this study, laser directed energy deposition (L-DED) technology was employed to fabricate IN625/TC4 HS materials with a nonlinear gradient transition, following systematic investigations into the phase composition and crack sensitivity of IN625/TC4 gradient layers prepared from mixed powders of varying compositions. In addition, microstructure, phase distribution, and mechanical properties of HS materials at room temperature were characterized. The metallurgical defect-free IN625/TC4 HS material was successfully prepared, featuring a smooth transition of microstructure, reduced cracking sensitivity, and reliable metallurgical bonding. Furthermore, a novel design concept and illustrative reference for the L-DED fabrication of N625/TC4 HS material with excellent comprehensive performance was presented, while providing a theoretical metallurgical basis and data support for the potential applications of IN625/TC4 HS materials in the field of aerospace. Full article

The Vehicular Internet of Things (V-IoT) has emerged as a cornerstone of next-generation intelligent transportation systems (ITSs), enabling applications ranging from safety-critical collision avoidance and cooperative awareness to infotainment and fleet management. These heterogeneous services impose stringent quality-of-service (QoS) demands for latency, reliability, and fairness while competing for limited and dynamically varying spectrum resources. Conventional schedulers, such as round-robin or static priority queues, lack adaptability, whereas deep reinforcement learning (DRL) solutions, though powerful, remain computationally intensive and unsuitable for real-time roadside unit (RSU) deployment. This paper proposes a lightweight and interpretable reinforcement learning (RL)-based spectrum management framework for Vehicular Internet of Things (V-IoT) networks. Two enhanced Q-Learning variants are introduced: a Value-Prioritized Action Double Q-Learning with Constraints (VPADQ-C) algorithm that enforces reliability and blocking constraints through a Constrained Markov Decision Process (CMDP) with online primal–dual optimization, and a contextual Q-Learning with Upper Confidence Bound (Q-UCB) method that integrates uncertainty-aware exploration and a Success-Rate Prior (SRP) to accelerate convergence. A Risk-Aware Heuristic baseline is also designed as a transparent, low-complexity benchmark to illustrate the interpretability–performance trade-off between rule-based and learning-driven approaches. A comprehensive simulation framework incorporating heterogeneous traffic classes, physical-layer fading, and energy-consumption dynamics is developed to evaluate throughput, delay, blocking probability, fairness, and energy efficiency. The results demonstrate that the proposed methods consistently outperform conventional Q-Learning and Double Q-Learning methods. VPADQ-C achieves the highest energy efficiency (≈$8.425\times {10}^7$ bits/J) and reduces interruption probability by over $60\%$, while Q-UCB achieves the fastest convergence (within ≈190 episodes), lowest blocking probability (≈0.0135), and lowest mean delay (≈0.351 ms). Both schemes maintain fairness near $0.364$, preserve throughput around 28 Mbps, and exhibit sublinear training-time scaling with $\mathcal{O}\left(1\right)$ per-update complexity and $\mathcal{O}\left(N^2\right)$ overall runtime growth. Scalability analysis confirms that the proposed frameworks sustain URLLC-grade latency (<0.2 ms) and reliability under dense vehicular loads, validating their suitability for real-time, large-scale V-IoT deployments. Full article

Food and nutrition services are critical to the health of long-term care home (LTCH) residents, yet little is known about how regulatory inspections detect non-compliance with Food, Nutrition, and Hydration (FNH) standards. We conducted a cross-sectional study of administrative inspection data from all licensed LTCHs in Ontario, Canada. One inspection report was randomly selected per LTCH, yielding a sample of 623 LTCHs. The data were collected for the period spanning 1 January 2024 to 31 December 2024. The primary exposure was use of the FNH inspection protocol, and the outcome was FNH non-compliance, defined as at least one Written Notification or Compliance Order. Statistical analyses included chi-square tests for categorical variables and independent samples t-tests (including Welch’s t-tests where appropriate) for continuous variables, with effect sizes (Φ, Cramer’s V, Cohen’s d) reported to complement p-values. This study did not require research ethics review under Western University policy, consistent with Canada’s Tri-Council Policy Statement (TCPS 2, Article 2.2) regarding use of publicly available data. FNH non-compliance was identified in 12.2% (n = 76) of all LTCHs, and in 43.7% of those using the FNH protocol. Use of the FNH protocol was associated with a higher likelihood of detecting FNH non-compliance compared with other inspection protocols (p < 0.001, Φ = 0.55). LTCH ownership and inspection type were also associated with detection patterns. This exploratory study provides the first province-wide analysis of FNH non-compliance in Ontario LTCHs. Findings suggest that inspection protocols influence detection of FNH issues, underscoring the need for further comparative and qualitative research to understand the organizational factors underlying non-compliance. Full article

Gallium nitride (GaN) power devices have attracted extensive attention due to their superior performance in high-frequency and high-power applications. However, the reliability and lifetime prediction of these devices under various operating conditions remain critical challenges. In this study, a hybrid approach combining finite element simulation and deep learning is proposed to predict the lifetime of GaN power devices. COMSOL Multiphysics (V6.3) is employed to simulate the thermal and mechanical stress behavior of GaN devices under different power and frequency conditions, while capturing key degradation indicators such as temperature cycles and stress concentrations. The variation in temperature over time can reflect the degradation of the device and also reveal the fatigue damage caused by the long-term accumulation of thermal stress on the chip. LSTM performs exceptionally well in extracting features from time series data, effectively capturing the long-term and short-term dependencies within the time series. By using simulation data to establish a connection between the chip temperature and its service life, the temperature data and the lifespan data are combined into a dataset, and the LSTM neural network is used to explore the impact of temperature changes over time on the lifespan. The method mentioned in this paper can make preliminary predictions of the results when sufficient experimental data cannot be obtained in a short period of time. The prediction results have a certain degree of reliability. Full article

Vehicle-to-vehicle (V2V) and vehicle-to-network (V2N) communications are two key components of intelligent transport systems (ITSs) that can share spectrum resources through in-band overlay. V2V communication primarily supports traffic safety, whereas V2N primarily focuses on infotainment and information exchange. Achieving reliable V2V transmission alongside high-rate V2N services in resource-constrained, dynamically changing traffic environments poses a significant challenge for resource allocation. To address this, we propose a novel reinforcement learning (RL) framework, termed Graph Attention Network (GAT)-Advantage Actor–Critic (GAT-A2C). In this framework, we construct a graph based on V2V links and their potential interference relationships. Each V2V link is represented as a node, and edges connect nodes that may interfere. The GAT captures key interference patterns among neighboring vehicles while accounting for real-time mobility and channel variations. The features generated by the GAT, combined with individual link characteristics, form the environment state, which is then processed by the RL agent to jointly optimize the resource blocks allocation and the transmission power for both V2V and V2N communications. Simulation results demonstrate that the proposed method substantially improves V2N rates and V2V communication success ratios under various vehicle densities. Furthermore, the approach exhibits strong scalability, making it a promising solution for future large-scale intelligent vehicular networks operating in dynamic traffic scenarios. Full article

Background/Objectives: The COVID-19 pandemic exposed critical vulnerabilities in healthcare systems, especially in ensuring continuity of care for patients with chronic diseases. Rehabilitation services, essential for recovery following SARS-CoV-2 infection, were among the most disrupted. This exploratory study aimed to assess Romanian patients’ perceptions of the accessibility and quality of post-COVID-19 rehabilitation services, focusing on individuals with chronic conditions. Methods: This exploratory cross-sectional study was conducted over a 12-month period in 2024. Data were collected from 76 adult patients diagnosed with at least one chronic condition (hypertension, diabetes mellitus, ischemic heart disease, cancer, or chronic obstructive pulmonary disease) and with confirmed prior SARS-CoV-2 infection. Most participants were recruited during outpatient specialty consultations, with a smaller number included from hospital settings, all located in Bucharest. A structured questionnaire was administered by the principal investigator after obtaining informed consent. Quantitative data were analyzed using non-parametric methods following confirmation of non-normal distribution via the Shapiro–Wilk test (p < 0.05). Satisfaction scores were reported as medians with interquartile ranges (IQR), and group comparisons were performed using the Mann–Whitney U test. A mixed-methods approach was employed, including thematic analysis of open-ended responses. Results: Patient satisfaction with rehabilitation services was consistently low. The median satisfaction scores [IQR] were accessibility 1.0 [0.0–2.0], quality of services 0.0 [0.0–4.0], staff empathy 0.0 [0.0–5.0], and perceived effectiveness 0.0 [0.0–5.0]. The median score for perceived difficulties in access was 1.0 [1.0–2.0], indicating widespread barriers. No statistically significant differences were observed between urban and rural participants or across chronic disease categories. Thematic analysis (n = 65) revealed key concerns including lack of publicly funded services, cost barriers, limited physician referral, service scarcity in rural areas, and demand for home-based rehabilitation options. Conclusions: Romanian patients with chronic illnesses and previous SARS-CoV-2 infection continue to face substantial barriers in accessing post-COVID-19 rehabilitation services. These findings highlight the need for more equitable and integrated recovery programs, especially for vulnerable populations in underserved settings. Full article

The decommissioning of the Zion Nuclear Power Plant (NPP) provided a unique opportunity to harvest and study service-aged reactor pressure vessel (RPV) beltline materials. This work, conducted through the U.S. Department of Energy’s Light Water Reactor Sustainability (LWRS) Program, aims to improve the understanding of radiation-induced embrittlement to support extended nuclear plant operations. Material segments containing the Linde 80 flux, wire heat 72105 (WF-70) beltline weld and the A533B Heat B7835-1 base metal, obtained from the intermediate shell region with a peak fluence of 0.7 × 10¹⁹ n/cm² (E > 1.0 MeV), were extracted, cut into blocks, and machined into test specimens for mechanical and microstructural characterization. The segmentation process involved oxy-propane torch-cutting, followed by precision machining using wire saws and electrical discharge machining (EDM). A chemical composition analysis confirmed the expected variations in alloying elements, with copper levels being notably higher in the weld metal. The harvested specimens enable a detailed evaluation of through-wall embrittlement gradients, a comparison with the existing surveillance data, and the validation of predictive embrittlement models. This study provides critical data for assessing long-term reactor vessel integrity, informing aging-management strategies, and supporting regulatory decisions to extend the life of nuclear plants. This article is a revised and expanded version of a paper entitled, “Current Status of the Characterization of RPV Materials Harvested from the Decommissioned Zion Unit 1 Nuclear Power Plant”, PVP2017-65090, which was accepted and presented at the ASME 2017 Pressure Vessels and Piping Conference, Waikoloa, HI, USA, 16–20 July 2017. Full article