Search Results (221)

Background: During infection with the cytomegalovirus (CMV), the membrane system of the infected cell is remodelled into a megastructure called the assembly compartment (AC). These extensive changes may involve the manipulation of the host cell proteome by targeting a pleiotropic function of the cell such as ubiquitination (Ub). In this study, we investigate whether the Ub system is required for the establishment and maintenance of the AC in murine CMV (MCMV)-infected cells Methods: NIH3T3 cells were infected with wild-type and recombinant MCMVs and the Ub system was inhibited with PYR-41. The expression of viral and host cell proteins was analyzed by Western blot. AC formation was monitored by immunofluorescence with confocal imaging and long-term live imaging as the dislocation of the Golgi and expansion of Rab10-positive tubular membranes (Rab10 TMs). A cell line with inducible expression of hemagglutinin (HA)-Ub was constructed to monitor ubiquitination. siRNA was used to deplete host cell factors. Infectious virion production was monitored using the plaque assay. Results: The Ub system is required for the establishment of the infection, progression of the replication cycle, viral gene expression and production of infectious virions. The Ub system also regulates the establishment and maintenance of the AC, including the expansion of Rab10 TMs. Increased ubiquitination of WASHC1, which is recruited to the machinery that drives the growth of Rab10 TMs, is consistent with Ub-dependent rheostatic control of membrane tubulation and the continued expansion of Rab10 TMs. Conclusions: The Ub system is intensively utilized at all stages of the MCMV replication cycle, including the reorganization of the membrane system into the AC. Disruption of rheostatic control of the membrane tubulation by ubiquitination and expansion of Rab10 TREs within the AC may contribute to the development of a sufficient amount of tubular membranes for virion envelopment. Full article

Weathering steel (WS) is widely used in bridge construction due to its high corrosion resistance, durability, and low maintenance requirements. This paper reviews the performance of WS bridges in Canadian climates, focusing on the formation of protective patina, influencing factors, and long-term maintenance strategies. The protective patina, composed of stable iron oxyhydroxides, develops over time under favorable wet–dry cycles but can be disrupted by environmental aggressors such as chlorides, sulfur dioxide, and prolonged moisture exposure. Key alloying elements like Cu, Cr, Ni, and Nb enhance corrosion resistance, while design considerations—such as drainage optimization and avoidance of crevices—are critical for performance. The study highlights the vulnerability of WS bridges to microenvironments, including de-icing salt exposure, coastal humidity, and debris accumulation. Regular inspections and maintenance, such as debris removal, drainage system upkeep, and targeted cleaning, are essential to mitigate corrosion risks. Climate change exacerbates challenges, with rising temperatures, altered precipitation patterns, and ocean acidification accelerating corrosion in coastal regions. Future research directions include optimizing WS compositions with advanced alloys (e.g., rare earth elements) and integrating climate-resilient design practices. This review highlights the need for a holistic approach combining material science, proactive maintenance, and adaptive design to ensure the longevity of WS bridges in evolving environmental conditions. Full article

The widespread integration of electric vehicles (EVs) into smart grid infrastructures necessitates intelligent and robust battery health diagnostics to ensure system resilience and performance longevity. While numerous studies have addressed the estimation of State of Health (SOH) and the prediction of remaining useful life (RUL) using machine and deep learning, most existing models fail to capture both short-term degradation trends and long-range contextual dependencies jointly. In this study, we introduce V2G-HealthNet, a novel hybrid deep learning framework that uniquely combines Long Short-Term Memory (LSTM) networks with Transformer-based attention mechanisms to model battery degradation under dynamic vehicle-to-grid (V2G) scenarios. Unlike prior approaches that treat SOH estimation in isolation, our method directly links health prediction to operational decisions by enabling SOH-informed adaptive load scheduling and predictive maintenance across EV fleets. Trained on over 3400 proxy charge-discharge cycles derived from 1 million telemetry samples, V2G-HealthNet achieved state-of-the-art performance (SOH RMSE: 0.015, MAE: 0.012, $R^2$: 0.97), outperforming leading baselines including XGBoost and Random Forest. For RUL prediction, the model maintained an MAE of 0.42 cycles over a five-cycle horizon. Importantly, deployment simulations revealed that V2G-HealthNet triggered maintenance alerts at least three cycles ahead of critical degradation thresholds and redistributed high-load tasks away from ageing batteries—capabilities not demonstrated in previous works. These findings establish V2G-HealthNet as a deployable, health-aware control layer for smart city electrification strategies. Full article

Asphalt pavement cracking represents a prevalent form of deterioration that significantly compromises road performance and safety under the combined effects of environmental factors and traffic loading. Crack sealing has emerged as a widely adopted and cost-effective preventive maintenance strategy that restores the pavement’s structural integrity and extends service life. This paper presents a systematic review of the development of crack sealing technology, conducts a comparative analysis of conventional sealing materials (including emulsified asphalt, hot-applied asphalt, polymer-modified asphalt, and rubber-modified asphalt), and examines the existing performance evaluation methodologies. Critical failure mechanisms are thoroughly investigated, including interfacial bond failure resulting from construction defects, material aging and degradation, hydrodynamic scouring effects, and thermal cycling impacts. Additionally, this review examines advanced sensing methodologies for detecting premature sealant failure, encompassing both non-destructive testing techniques and active sensing technologies utilizing intelligent crack sealing materials with embedded monitoring capabilities. Based on current research gaps, this paper identifies future research directions to guide the development of intelligent and sustainable asphalt pavement crack repair technologies. The proposed research framework provides valuable insights for researchers and practitioners seeking to improve the long-term effectiveness of pavement maintenance strategies. Full article

In high-elevation mining areas, the roadbeds of certain surface ore haul roads are predominantly composed of sandstone. These sandstones are exposed to cold climatic conditions for long periods and are highly susceptible to erosion by the effects of freeze–thaw, which can degrade their support properties. This paper investigates the mechanism of strength deterioration of sandstone containing prefabricated cracks under cyclic loading and unloading after experiencing freeze–thaw. Sandstone specimens containing prefabricated cracks were prepared and subjected to 0, 20, 40, 60, and 80 freeze–thaw cycle tests. The strength changes were tested, and the crack extension process was analyzed using numerical simulation techniques. The study results show the following: 1. The wave propagation speed within the sandstone is more sensitive to changes in the number of freeze–thaw cycles. In contrast, mass damage shows significant changes only when more freeze–thaw cycles are experienced. 2. As the number of freeze–thaw cycles increases, the frequency of energy release from the numerical model accelerates. 3. The trend of the Cumulative Strain Difference (${\epsilon }_c$) reflects that the plastic strain difference between numerical simulation and actual measurement gradually decreases with increasing stress cycle level. 4. With the increase in freeze–thaw cycles, the damage morphology of the specimen undergoes a noticeable change, which is gradually transformed from monoclinic shear damage to X-shaped conjugate surface shear damage. 5. The number of tensile cracks dominated throughout the cyclic loading and unloading process, but with the increase in freeze–thaw cycles, the percentage of shear cracks increased. As the freeze–thaw cycles increase, sandstones are more inclined to undergo shear damage. These findings are important guidelines for road design and maintenance in alpine mining areas. Full article

The increasing deployment of photovoltaic and energy storage systems (ESSs) in modern power grids has highlighted the critical challenge of component degradation, which significantly impacts system efficiency, operational costs, and long-term reliability. Conventional energy dispatch and optimization approaches fail to adequately mitigate the progressive efficiency loss in PV modules and battery storage, leading to suboptimal performance and reduced system longevity. To address these challenges, this paper proposes a quantum-enhanced degradation pathway optimization framework that dynamically adjusts operational strategies to extend the lifespan of PV storage systems while maintaining high efficiency. By leveraging quantum-assisted Monte Carlo simulations and hybrid quantum–classical optimization, the proposed model evaluates degradation pathways in real time and proactively optimizes energy dispatch to minimize efficiency losses due to aging effects. The framework integrates a quantum-inspired predictive maintenance algorithm, which utilizes probabilistic modeling to forecast degradation states and dynamically adjust charge–discharge cycles in storage systems. Unlike conventional optimization methods, which struggle with the complexity and stochastic nature of degradation mechanisms, the proposed approach capitalizes on quantum parallelism to assess multiple degradation scenarios simultaneously, significantly enhancing computational efficiency. A three-layer hierarchical optimization structure is introduced, ensuring real-time degradation risk assessment, periodic dispatch optimization, and long-term predictive adjustments based on PV and battery aging trends. The framework is tested on a 5 MW PV array coupled with a 2.5 MWh lithium-ion battery system, with real-world degradation models applied to reflect light-induced PV degradation (0.7% annual efficiency loss) and battery state-of-health deterioration (1.2% per 100 cycles). A hybrid quantum–classical computing environment, utilizing D-Wave’s Advantage quantum annealer alongside a classical reinforcement learning-based optimization engine, enables large-scale scenario evaluation and real-time operational adjustments. The simulation results demonstrate that the quantum-enhanced degradation optimization framework significantly reduces efficiency losses, extending the PV module’s lifespan by approximately 2.5 years and reducing battery-degradation-induced wear by 25% compared to conventional methods. The quantum-assisted predictive maintenance model ensures optimal dispatch strategies that balance energy demand with system longevity, preventing excessive degradation while maintaining grid reliability. The findings establish a novel paradigm in degradation-aware energy optimization, showcasing the potential of quantum computing in enhancing the sustainability and resilience of PV storage systems. This research paves the way for the broader integration of quantum-based decision-making in renewable energy infrastructure, enabling scalable, high-performance optimization for future energy systems. Full article

Long-term shutdowns caused by ice formation on wind turbine blades can lead to significant power generation losses, a persistent issue for wind farm operators. The rapid acquisition of ice mass and thickness on blades under actual meteorological conditions can facilitate the more effective adjustment of operation and maintenance strategies, enabling the selection of appropriate de-icing methods and optimal human resource allocation. This study proposes a novel approach utilizing icing simulation data across various meteorological parameters to train a Multilayer Perceptron (MLP) neural network, enabling rapid ice accretion prediction while maintaining acceptable accuracy. The results demonstrate that the MLP model achieves mean absolute percentage errors (MAPEs) of 7.13% and 7.02% for predicting rime ice mass and maximum thickness, respectively. For glaze ice prediction, the model yields MAPE values of 10.22% and 9.42% for ice mass and maximum thickness prediction, respectively. All MLP models exhibit R² values exceeding 0.95, indicating excellent model fitting. The model is used to simulate and analyze the blade icing condition of a wind farm (located at 27° N and 117° E). The results showed that during a typical icing cycle, the maximum hourly ice accumulation mass on the studied blade was 5.01 kg, and the accumulated ice accumulation mass over 24 h was 95.43 kg. The maximum hourly ice accumulation thickness was 10.38 mm, and the accumulated ice accumulation thickness over 24 h was 228.43 mm. Full article

African swine fever virus (ASFV), a highly contagious and lethal virus affecting domestic and wild pigs, has raised global concerns due to its continued spread across Europe and Asia. While traditional transmission pathways involve suids and soft ticks, this study investigates the potential role of freshwater gastropods as environmental reservoirs capable of sustaining ASFV. We analysed ASFV survival in ten gastropod species after long-term co-incubation with the virus. Viral transcriptional activity, particularly of the late gene B646L and members of the multigene family MGF505, was evaluated in snail faeces up to nine weeks post-infection. Results revealed that several gastropods, including Melanoides tuberculata, Tarebia granifera, Physa fontinalis, and Pomacea bridgesii, support long-term persistence of ASFV, accompanied by increased MGF505 gene expression. Notably, the simultaneous activation of MGF5052R and MGF50511R significantly correlated with higher B646L expression and extended viral survival, suggesting a functional role in ASFV maintenance. Conversely, antiviral (AV) activity assays showed that some gastropod faeces reduced replication of the unrelated Influenza virus, hinting at induced host defences. A negative correlation was observed between AV activity and the expression of MGF505 2R/11R, implying that ASFV may suppress antiviral responses to facilitate persistence. These findings suggest that certain gastropods may serve as overlooked environmental hosts, contributing to ASFV epidemiology via long term viral shedding. Further research is needed to clarify the mechanisms underlying ASFV–host interactions and to assess the ecological and epidemiological implications of gastropods in ASFV transmission cycles. Full article

Battery health monitoring and remaining useful life (RUL) estimation for electric vehicles face two significant challenges: battery data heterogeneity and sample imbalance. This study presents a novel approach based on Transformer architecture to specifically address these issues. We utilized the NASA lithium-ion battery cycling dataset, which contains charge-discharge and impedance measurement data under various temperature conditions. To tackle data heterogeneity, we developed a multimodal feature fusion strategy that effectively integrates battery sensor data from different sources and formats, including time-series charge-discharge sensor data and spectral impedance sensor measurements. To mitigate sample imbalance, we implemented an adaptive resampling technique and hierarchical attention mechanism, enhancing the model’s ability to recognize rare degradation patterns. Our Transformer-based model captures long-term dependencies in the battery degradation process through its self-attention mechanism. Experimental results demonstrate that the proposed solution significantly improves battery degradation prediction accuracy, achieving a 21.3% increase in accuracy when processing heterogeneous data and a 24.5% improvement in prediction capability for imbalanced samples compared to traditional methods. Additionally, through case studies, we validate the applicability of this method in actual electric vehicle battery management systems, providing reliable data support for battery preventive maintenance and replacement decisions. The findings have important implications for enhancing the reliability and economic efficiency of electric vehicle battery management systems. Full article

To accurately and reliably characterize the degradation trend of rolling bearings and predict their life cycle, this paper proposes a bearing life prediction model based on an improved transformer encoder layer and multi-scale convolution. First, time-domain, frequency-domain, and time-frequency domain features are extracted from the vibration data covering the entire lifespan of the rolling bearings and passed through the transformer encoder layer. A novel dual-layer self-attention mechanism network structure is proposed to capture global information on the lifecycle progression of rolling bearings. Next, to further extract local temporal features within the bearing’s life cycle, a multi-scale convolution module is proposed to reinforce the local information across the entire lifespan. This method fully exploits both the long-term trends and short-term dynamic variations in the health status of rolling bearings, effectively enhancing the accuracy of life predictions. Experimental results show that, even under conditions with interference features, the TransCN model outperforms mainstream advantage model in terms of prediction accuracy and generalizability. This approach offers a new solution for managing the fault risk of rotating machinery and reducing maintenance costs. Full article

Laminated Veneer Lumber (LVL) presents an alternative material for offshore wind turbine towers and blades for an energy sector whose greenhouse gas emissions are substantial. In compliance with AS/NZS 4536, this case study facilitates a specifications’ selection framework that embraces a validated, cost–benefit determination via life cycle cost analyses (LCCA) specification comparisons. A structured consultation with three key Western Australian offshore industry experts, compliant with a standard phenomenological qualitative approach, further facilitates offshore wind turbine (OWT), LCCA cost comparisons between traditional steel and fibreglass components and LVL wooden components. LVL is found to have a higher capital cost but can generate long-term savings of AUD 30,400 per comparable unit less than Traditional OWT specifications, noting a 5% lower LVL operation and maintenance cost. Where decommissioning recycling facilities exist, OWT LVL specification components are encouraged. This work argues that LVL options uptake in Western Australia (WA) is both practicable and whole-cost effective. Full article

Bridges are assets in every society, and their deterioration can have severe economic, social, and environmental consequences. Therefore, implementing effective asset management strategies is crucial to ensure bridge infrastructure’s long-term performance and safety. Roadmaps can serve as valuable tools for bridge asset managers, helping bridge engineers make informed decisions that enhance bridge safety while maintaining controlled life cycle costs. Although some bridge asset management roadmaps exist, such as the one published by the United States Federal Highway Administration (FHWA), there is a lack of structured research roadmaps that are both region-specific and adaptable as guiding frameworks for similar studies. For instance, the FHWA roadmap cannot be universally applied across diverse regional contexts. This study addresses this critical gap by developing a research roadmap tailored to Idaho, USA. The roadmap was developed using a three-phase methodological approach: (1) a comprehensive analysis of past and ongoing Department of Transportation (DOT)-funded research projects over the last five years, (2) a nationwide survey of DOT funding and research practices, and (3) a detailed assessment of Idaho Transportation Department (ITD) deficiently rated bridge inventory, including individual element condition states. In the first phase, three filtering stages were implemented to identify the top 25 state projects. A literature review was conducted for each project to provide ITD’s Technical Advisory Committee (TAC) members with insights into research undertaken by various state DOTs. Moreover, in the second phase, approximately six questionnaires were designed and distributed to other state DOTs. These questionnaires primarily covered topics related to bridge research priorities and funding allocation. In the final phase, a condition state analysis was conducted using data-driven methods. Key findings from this three-phase methodological approach highlight that ultra-high-performance concrete (UHPC), bridge deck preservation, and maintenance strategies are high-priority research areas across many DOTs. Furthermore, according to the DOT responses, funding is most commonly allocated to projects related to superstructure and deck elements. Finally, ITD found that the most deficient elements in Idaho bridges are reinforced concrete abutments, reinforced concrete pile caps and footings, reinforced concrete pier walls, and movable bearing systems. These findings were integrated with insights from ITD’s TAC to generate a prioritized list of 23 high-impact research topics aligned with Idaho’s specific needs and priorities. From this list, the top six topics were selected for further investigation. By adopting this strategic approach, ITD aims to enhance the efficiency and effectiveness of its bridge-related research efforts, ultimately contributing to safer and more resilient transportation infrastructure. This paper could be a helpful resource for other DOTs seeking a systematic approach to addressing their bridge research needs. Full article

The growing deployment of photovoltaic (PV) and energy storage systems (ESSs) in power grids has amplified concerns over component degradation, which undermines efficiency, increases costs, and shortens system lifespan. This paper proposes a quantum-enhanced optimization framework to mitigate degradation impacts in PV–storage systems through real-time adaptive energy dispatch. The framework combines quantum-assisted Monte Carlo simulation, quantum annealing, and reinforcement learning to model and optimize degradation pathways. A predictive maintenance module proactively adjusts charge–discharge cycles based on probabilistic forecasts of degradation states, improving resilience and operational efficiency. A hierarchical structure enables real-time degradation assessment, hourly dispatch optimization, and weekly long-term adjustments. The model is validated on a 5 MW PV array with a 2.5 MWh lithium-ion battery using real degradation profiles. Results demonstrate that the proposed framework reduces battery wear by 25% and extends PV module lifespan by approximately 2.5 years compared to classical methods. The hybrid quantum–classical implementation achieves scalable optimization under uncertainty, enabling faster convergence across high-dimensional solution spaces. This study introduces a novel paradigm in degradation-aware energy management, highlighting the potential of quantum computing to enhance both the sustainability and real-time control of renewable energy systems. Full article

Especially NMC-LCO 18650 cells, lithium-ion batteries are essential parts of electric vehicles (EVs), where their dependability and performance directly affect operating efficiency and safety. Predictive maintenance, cost control, and increasing user confidence in electric vehicle technology depend on accurate Remaining Useful Life (RUL) forecasting of these batteries. Using advanced machine learning models, this research uses past usage data and essential performance characteristics to forecast the RUL of NMC-LCO 18650 batteries. The work creates a scalable and web-based application for RUL prediction by utilizing predictive models like Long Short-Term Memory (LSTM), Linear Regression (LR), Artificial Neural Network (ANN), and Random Forest with Extra Trees Regressor (RF with ETR) with results in Mean Square Error (MSE) as accuracy as 96%, 97%, 98% and 99% respectively. This research also emphasizes the importance of algorithm design that can provide reliable RUL predictions even in cases when cycle count data is lacking by properly using alternative features. On further investigation, our findings highlighted that the introduction of cycle count as a feature is critical for significantly reducing the mean squared error (MSE) in all four models. When the cycle count is included as a feature, the MSE for LSTM decreases from 12,291.69 to 824.15, the MSE for LR decreases from 3363.20 to 51.86, the MSE for ANN decreases from 2456.65 to 1858.31, and finally, the RF with ETR decreases from 384.27 to 10.23, which makes it the best performing model considering these two crucial performance metrics. Apart from forecasting the remaining useful life of these lithium-ion batteries, the web application gives options for selecting a model amongst these models for prediction and further classifies battery condition and advises best use practices. Conventional approaches for battery life prediction, such as physical disassembly or electrochemical modeling, are resource-intensive, ecologically destructive, and unfeasible for general use. On the other hand, machine learning-based methods use extensive real-world data to generate scalable, accurate, and efficient forecasts. Full article

Sustainability is increasingly prioritized in infrastructure design; however, its integration into the conceptual design phase remains limited, particularly for pedestrian bridges, where structural performance plays a critical role. While existing frameworks address environmental and economic impacts in later stages, they typically fail to incorporate structural performance and sustainability holistically at the outset. To address this gap, this study introduces a quantitative decision-support framework tailored for the conceptual design of footbridges. The methodology integrates five key indicators, Global Warming Potential (GI), Total Cost (TC), Robustness (RO), Inspection (IN), and Maintenance (MA), using a Multi-Criteria Decision Making (MCDM) approach, specifically the Weighted Sum Model (WSM), supported by Pearson correlation analysis, to identify trade-offs and interdependencies among metrics. The framework is tested on two real-world case studies involving steel pedestrian bridges in different urban contexts. The results reveal a strong correlation between inspection and maintenance, suggesting that designs optimized for inspection accessibility can significantly reduce life cycle maintenance efforts and costs. Robustness appears to be largely independent from environmental impact, indicating the potential to improve structural resilience without compromising sustainability. Furthermore, cost–sustainability relationships are shown to be highly context-dependent. The practical implications of these findings are substantial: by offering a structured, data-driven tool for early-stage evaluation, the framework enables engineers, urban planners, and policymakers to make informed design choices that align with long-term sustainability goals. It offers a methodological basis for comparing design options based on quantifiable sustainability and structural metrics, contributing to evidence-based decision making in line with evolving standards for sustainable infrastructure. Full article