Search Results (12,268)

For polar ships, navigation in ice-covered regions can lead to high risk to structural safety. To study the structural risk induced by ice loads, a risk assessment framework is proposed based on a probabilistic analysis. The fatigue failure probability is derived with the first-order second-moment (FOSM) method. Typical ice load cases are extracted as a joint probability distribution of ice thickness and ship speed, based on shipboard measurements. Equivalent fatigue stresses for each case are calculated using a coupled discrete element method (DEM) and finite element method (FEM), and fatigue failure probabilities are obtained via linear cumulative damage theory. The ultimate strength failure probability is derived from the reliability theory. The probabilistic distribution of load-carrying capacity for the bow structure, determined by the moment estimation method, is used as the structural resistance, while the ice load distribution identified from shipboard monitoring is treated as the external load. Considering both the likelihood and consequence of failure, a risk matrix is constructed to assess structural failure risk. Inspection and maintenance intervals are then proposed according to the assessed risk levels. This approach offers a quantitative basis for structural risk management, supporting safe navigation and efficient maintenance planning for polar ships. Full article

This paper introduces a new four-switch, high-voltage, high-step-up converter employing two transformers. The topology enables Zero-Voltage Switching (ZVS) across all primary switches for operating conditions ranging from no load to full load. A voltage-quadrupler and a voltage-doubler rectifier are used on the secondary sides of the transformers, enabling reduced turn-off current for the voltage-quadrupler diodes and Zero-Current Switching (ZCS) turn-off for the voltage-doubler diodes, thereby ensuring high efficiency across diverse load levels. Notably, the voltage stress experienced by the voltage-multiplier diodes is significantly lower than the output voltage, thereby rendering the converter exceptionally suitable for high-voltage applications such as electron beam welding (EBW). The voltage gain surpasses that of the conventional phase-shift full-bridge (PSFB) converter, permitting a lower transformer turns ratio and thus reducing winding resistivity. The removal of the substantial output inductor leads to a lighter and more compact design, eliminating insulation concerns associated with inductor windings. This paper details the operation of the proposed converter, supported by experimental results from a 500-W prototype with a 150-V input and 2-kV output, which confirm its high performance and operational advantages. Full article

To address the dual challenges of fossil fuel depletion and environmental pollution, developing clean, renewable alternative fuels is an urgent need. Biomass gas, including biomass syngas and biogas, offers significant potential as an internal combustion engine alternative fuel due to its widespread availability and carbon-neutral properties. This review summarizes research on biomass gas application in dual-fuel diesel engines. Firstly, biosyngas and biogas production methods, characteristics, and purification needs are detailed, highlighting gas composition variability as a key factor impacting engine performance. Secondly, dual-fuel diesel engine operating modes and their integration with advanced low-temperature combustion technologies are analyzed. The review focuses on how biomass gas affects combustion characteristics, engine performance, and emissions. Results indicate dual-fuel mode effectively reduces diesel consumption, emissions, while its carbon-neutrality lowers life-cycle CO₂ emissions and generally suppresses NO_x formation. However, challenges include potential BTE reduction and increased CO and HC emissions at low loads. Future research should prioritize gas quality standardization, intelligent combustion system optimization, and full-chain techno-economic evaluation to advance this technology. Overall, this review concludes that dual-fuel operation with biomass gases can achieve high diesel substitution rates, significantly reducing NO_x and particulate matter emissions. However, challenges such as decreased brake thermal efficiency and increased CO and HC emissions under low-load conditions remain. Future efforts should focus on gas composition standardization, intelligent combustion control, and system-level optimization. Full article

This study investigates the effects of high levels of photovoltaic (PV) generation on the unbalanced distribution network using the quasi-dynamic simulation method on DIgSILENT PowerFactory. We are motivated by the need to diversify the national energy matrix, following the power blackout that occurred in Ecuador in 2024 and the energy limitations characterized by the use of fossil fuels. For this purpose, we deployed the simulation of the PJM 13-Node Test Feeder, which is a low-voltage distribution network and mimics the U.S. system, and represents a realist distribution network with residential and commercial load profiles. We simulated realistic PV generation dynamics for a typical day, capturing stochastic solar irradiance, ambient temperature variation, and the impacts of cloud cover. In those conditions, PV generation reached 31.6% of the system total load. We found that during peak irradiance hours, the voltage levels on certain nodes, predominantly low-load buses, exceed nominal levels. The average power factor is noted to diminish by 0.90 p.u to 0.82 p.u at the feeder bus, and further drops to 0.35 p.u at the most PV-penetrated site. While distributed PV generation can effectively reduce line loading and improve energy efficiency, without reactive power compensation, the highest penetration PV generation scenario could result in deterioration of voltage stability and power quality. The prescribed quasi-dynamic framework is practical and computationally feasible, allowing for the assessment of operational performance of distribution networks with high renewables penetration. Full article

Beyond mechanical performance and aesthetics, the susceptibility of 3D-printed resins to microbial colonization and biofilm formation represent an important factor influencing dentures’ longevity. Therefore, this study evaluated Candida albicans colonization and mature biofilm formation on three different 3D-printed denture base resins (Bio Denture—BD; Denture Base Cosmos—CD; Smart Print Bio Denture—SP) and compared them to heat-curing resin (HC). Before the microbiological evaluation, the surface roughness (Sa) was assessed. Biofilm viability was determined through colony-forming units per milliliter (CFU/mL) and biofilm morphology was qualitatively examined using a scanning electron microscope (SEM). The composition of the extracellular polymeric substance (EPS) was investigated by measuring the amounts of carbohydrates (µg/mL), proteins (ng/mL), and extracellular DNA (eDNA) (fluorescence unit). One-way ANOVA was performed for eDNA and Sa and Kruskal–Wallis for the other properties (α = 0.05). Higher surface roughness mean values (standard deviation) (p < 0.05) were observed in CD [0.111 (0.013)] compared to HC [0.084 (0.018) and BD [0.078 (0.015)]. For wettability, BD [63.2 (5.2)] and SP [65.2 (3.1)] resins showed a greater wettability (p < 0.05) than HC resin [73.0 (3.5)], while SP showed lower (p < 0.01) protein levels (425 ng/mL) compared to HC (568.6 ng/mL) and BD (554.8 ng/mL) in the EPS. Despite these differences, the 3D-printed denture base resins exhibited microbial load (CFU/mL), EPS composition (carbohydrates and eDNA), and morphological features of C. albicans biofilm comparable to those of conventional heat-cured PMMA. These findings suggest that, despite resin-specific variations, 3D-printed denture base materials exhibit a similar susceptibility to C. albicans colonization and biofilm formation as conventional denture bases, thereby directing future research towards developing new 3D-printed resins with enhanced antimicrobial properties to improve clinical outcomes. Full article

Rural tropical regions face escalating threats from zoonotic AIV and dengue virus but lack sewered infrastructure for conventional wastewater surveillance. We implemented surface water-based surveillance (SWBS) in peri-urban Dhaka (Bangladesh) and Ruili (China) from July to November 2023 and coupled it with machine learning-enhanced digital epidemiology. Reverse transcription quantitative PCR (RT-qPCR) was employed to detect the M gene of AIV and to subtype H1, H5, H7, H9, and H10 in surface water. Wild bird feces (n = 40) were collected within 3 km of positive sites to source-track AIV. For the dengue virus, a serogroup-specific RT-qPCR assay targeting the CprM gene was used. Genomic sequencing of AIV and dengue virus was performed to elucidate phylogenetic relationships with local clinical strains. Clinical data related to dengue fever were also collected for correlation analysis. Meanwhile, 13 dengue-related keyword search volumes were harvested daily from Google, Bing and Baidu for four cities to reveal the relationship between dengue epidemics and the web search index. AIV H5 was detected in Dhaka city from week 38, peaking at week 39, while dengue virus was persistently detected from week 29 to week 45, aligning with clinical trends. Time-series cross-correlation analysis revealed that variations in surface water viral load led clinical case reports by approximately two weeks (max CCF = 0.572 at lag −2). In Ruili city, dengue virus was detected from week 32 to week 44. To sharpen sensitivity, 383 weekly web search series for 13 dengue keywords from four countries were screened; random-forest and XGBoost models retained five symptom queries that generated a composite index explaining 79% of variance in dengue RNA levels in an independent Ruili test set (n = 24) and reduced superfluous sampling by 35%. Phylogenetic analysis verified identity between water-derived and patient-derived DENV-2, confirming local transmission. The study demonstrates that AIV SWBS is optimally integrated with wild bird sampling for source attribution, whereas dengue SWBS achieves maximal efficiency when combined with real-time web search monitoring, providing tailored, low-cost early-warning modules for resource-constrained tropical settings. Full article

Modern power distribution systems are increasingly stressed as they operate closer to their voltage stability limits, driven by growing electricity demand, complex load behaviors, and the evolving structure of power networks. Radial distribution systems, in particular, are highly susceptible to voltage instability under critical loading conditions, where even minor load increases can trigger voltage collapse. Such events threaten the continuity and quality of power supply and can cause damage to infrastructure and sensitive equipment. While large-scale cascading failures are typically associated with transmission systems, localized cascading effects such as sequential voltage drops, feeder outages, and protective device operations can still occur in distribution networks, especially under high loading. Therefore, reliable and timely voltage stability assessment is essential to maintain system reliability and prevent disruptions. This study presents a comprehensive comparative analysis of four voltage stability indices designed for radial distribution networks. The performance of these indices is evaluated on the IEEE 33-bus and 69-bus test systems under various critical loading conditions and multiple static load models, including Constant Power Load (CPL), Constant Current Load (CIL), Constant Impedance Load (CZL), Composite Load (COML), and Exponential Load (EXL). The analysis investigates each index’s effectiveness in identifying voltage collapse points, estimating critical load levels, and calculating load margins, while also evaluating their robustness across diverse operating scenarios. The findings offer practical insights and serve as a valuable benchmark for selecting suitable voltage stability indicators to support monitoring and planning in modern distribution networks. Full article

Fractal dimension (Frac) and lacunarity (Lac) are frequently proposed as biomarkers of multiscale image complexity, but their incremental value over standardized radiomics remains uncertain. We position both measures within the Image Biomarker Standardisation Initiative (IBSI) feature space by running a fully reproducible comparison in two settings. In a baseline experiment, we analyze $N=1000$ simulated $64\times 64$ textured ROIs discretized to $N_g=64$, computing 92 IBSI descriptors together with Frac (box counting) and Lac (gliding box), for 94 features per ROI. In a wavelet-augmented experiment, we analyze $N=1000$ ROIs and add level-1 wavelet descriptors by recomputing first-order and GLCM features in each sub-band (LL, LH, HL, and HH), contributing $4\times (19+19)=152$ additional features and yielding 246 features per ROI. Feature similarity is summarized by a consensus score that averages z-scored absolute Pearson and Spearman correlations, distance correlation, maximal information coefficient, and cosine similarity, and is visualized with clustered heatmaps, dendrograms, sparse networks, PCA loadings, and UMAP and t-SNE embeddings. Across both settings a stable two-block organization emerges. Frac co-locates with contrast, difference, and short-run statistics that capture high-frequency variation; when wavelets are included, detail-band terms from LH, HL, and HH join this group. Lac co-locates with measures of large, coherent structure—GLSZM zone size, GLRLM long-run, and high-gray-level emphases—and with GLCM homogeneity and correlation; LL (approximation) wavelet features align with this block. Pairwise associations are modest in the baseline but become very strong with wavelets (for example, Frac versus GLCM difference entropy, which summarizes the randomness of gray-level differences, with $\left|r\right|\approx 0.98$; and Lac versus GLCM inverse difference normalized (IDN), a homogeneity measure that weights small intensity differences more heavily, with $\left|r\right|\approx 0.96$). The multimetric consensus and geometric embeddings consistently place Frac and Lac in overlapping yet separable neighborhoods, indicating related but non-duplicative information. Practically, Frac and Lac are most useful when multiscale heterogeneity is central and they add a measurable signal beyond strong IBSI baselines (with or without wavelets); otherwise, closely related variance can be absorbed by standard texture families. Full article

Grid security and system dispatch can be compromised by pronounced volatility in power load under extreme meteorological conditions. However, the dynamic and nonlinear interactions between power load and meteorological variables across diverse weather scenarios are not well captured by existing methods, resulting in limited accuracy and robustness. To address this gap, a short-term power load forecasting model with a dual-channel architecture is proposed. Features are extracted in parallel via dual-channel feature extraction (DCFE): the first channel employs an improved Cascaded Multiscale 2D Convolutional Network (CMCNN) to model local fluctuations and global periodicity in the load time series. The second channel derives scenario-aware variable weights using the Maximal Information Coefficient (MIC); meteorological variables are then gated and weighted before being processed by a multi-layer self-attention network to learn global dependencies. Subsequently, dynamic feature-level fusion is achieved through cross-attention, strengthening key interactions between power load and meteorological factors. The fused representation is fed into an Attention-Enhanced Bidirectional Gated Recurrent Unit (AE-BiGRU) to precisely model temporal dependencies across multiple weather scenarios. Experiments on five years of power load and meteorological data from a region in Australia indicate that the proposed method outperforms the best baseline across multiple weather conditions: RMSE, MAE, MAPE, and sMAPE decrease on average by 32.44%, 31.42%, 30.73%, and 31.05%, respectively, while R² increases by 0.034 on average, demonstrating strong adaptability and robustness. Full article

Respiratory tract infections are a major cause of morbidity and mortality. After the SARS-CoV-2 pandemic, pathogenetic mechanisms leading to more severe outcomes were investigated, including uncontrolled viral replication in the upper airways. This was only partially investigated for other respiratory viruses. We measured mucosal expression of IFN-β1, IFN-λ1, IFN-λ2/3, IL-1β, and IL-6 in patients infected by human metapneumovirus, human rhinovirus, human respiratory syncytial virus or type A influenza virus. A total of 806 nasopharyngeal swabs were collected from patients presenting at emergency departments or hospitalized. Viral load was inferred through cycle threshold determination, whereas cytokine levels were measured through mRNA detection. Each expression pattern was correlated with age, viral load, and specific infecting virus. IFN-β1 and IFN-λ2/3 showed a negative correlation with viral load, while IFN-λ1 and IL-6 exhibited the opposite trend, suggesting increased inflammation with higher viral load. This was more evident in the ≥70-year-old group, with significantly higher IL-6 levels. Higher viral load of potentially more pathogenic viruses was associated with higher IL-6 expression. Cytokine production in the upper respiratory tract is only partially influenced by age per se, with a more relevant role played by viral load and specific infecting virus. In older patients, this response is less coordinated and prone to elicit a proinflammatory response, especially when clinically impacting viruses are involved. Full article

To meet the strengthening requirements of damaged steel beams in hygrothermal environments, this study conducted four-point bending tests on nine pre-cracked steel beam specimens. The coupled effects of surface roughness, end anchorage, prestressing level of carbon fiber-reinforced polymer (CFRP), and hygrothermal aging on the flexural behavior of the strengthened beams were systematically investigated. Results show that high-grade sandblasting (Sa3) significantly enhances interfacial bond strength through a synergistic “mechanical interlock-adhesion” mechanism, increasing the cracking load of the adhesive layer by 8.2–16.8% compared with Sa2, while partially mitigating the performance degradation caused by hygrothermal aging. The use of end anchorages effectively suppresses CFRP debonding at the beam ends, improving the ultimate load capacity and deformation performance. When a prestress equivalent to 25% of the CFRP’s ultimate tensile strength was applied, the load capacity of the strengthened beams further increased by 10.5–19.3%, interfacial cracking was effectively delayed, and the CFRP utilization efficiency reached 96.8–98.5%. Although hygrothermal exposure accelerated interfacial deterioration and reduced the interfacial cracking load, its influence on the ultimate load was relatively limited. These results offer valuable scientific and engineering insights for the design and interface treatment of CFRP-strengthened steel bridges in hygrothermal regions. Full article

The landing gear is one of the key components of an aircraft, enduring significant forces during takeoff and landing, and is influenced by various uncertain factors related to its structure. Therefore, conducting strength tests on the landing gear structure to study its ultimate load capacity is of great significance for structural design and analysis. This paper proposes a visual measurement method for dynamic pose of landing gear that combines stereo vision and CAD digital model. The method first establishes a measurement reference in CAD digital model and then uses close-range photogrammetry and binocular stereo vision technology to unify the coordinate system of the physical landing gear model with the measurement coordinate system of CAD model. Finally, during the motion of the landing gear, CAD model and the physical model can be synchronized by tracking a small number of key points, thus obtaining the complete motion state of the landing gear during the test. The experimental results demonstrate that the RMSE of the angle error is less than 0.1°, and the RMSE of the trajectory error is under 0.3 mm. This level of accuracy meets the requirements for pose measurement during the landing gear retraction and extension test. Compared to existing methods, this approach offers greater environmental adaptability, effectively reducing the impact of unfavorable factors such as occlusion during testing. It allows for the retrieval of pose information for any point on the landing gear, including its centroid. Full article

Plastic pollution is a pressing environmental concern globally, especially in marine ecosystems. In this study, the evaluation of the potential ingestion of plastic, mostly in the form of microplastics (MPs), by fledglings of Cory’s shearwaters (Calonectris borealis) from the Canary Islands (Spain) was conducted. The total number of plastics found in the stomach samples was 674, primarily comprising large MPs (1–5 mm: 82%), followed by mesoplastics (>5–25 mm: 18%). The predominant morphology was threadlike (31.6%), followed by hard, irregularly shaped fragments (28.3%), microspheres (22.4%), and sheets (15.7%). Loads were found to overlap with those described for the same species in highly populated areas such as the Mediterranean Sea. Plastic counts above Cory’s threshold value may suggest poor environmental status for the Canary Current region. FTIR-ATR analysis evidenced the predominance of polyethylene (PE) (46.7%), polypropylene (PP) (24.6%) and polyamide (PA) (20.4%). This is likely linked not only to the fact that PE is the most produced plastic worldwide, but also the fact that, along with PP, it makes up the highest amount of single-use plastic products. Overall, findings provide a contamination-controlled, FTIR-verified baseline for fledglings from Tenerife; however, given the limited, single-season sample (n = 33) and opportunistic design, results are descriptive and not intended for population-level inference. Yet, the potential of Cory’s shearwater as a sentinel species to monitor plastic pollution is highlighted, emphasizing the urgent need for effective mitigation strategies to address plastic pollution in marine environments. Full article

The reuse of rubber inclusions obtained from End-of-Life Tires (ELTs) offers both environmental and technical benefits in civil engineering applications, reducing landfill disposal and enhancing the dynamic properties of geomaterials. The use of well-graded Gravel–Rubber Mixtures (wgGRMs), produced by blending well-graded gravel with granulated rubber, has been investigated for use in different geotechnical applications. The percentage of rubber inclusions included in wgGRMs significantly modifies the mechanical response of these mixtures, influencing stiffness, strength, dilatancy and dynamic properties. Due to the material heterogeneity (i.e., stiff gravel and soft rubber), the effective implementation of wgGRMs requires the development of constitutive models that can capture the non-linear stress–strain response of wgGRMs subjected to representative in situ loading conditions. In this study, a critical state-based generalized plasticity model is presented and tailored for wgGRMs. Calibration is performed using experimental data from isotropically consolidated drained triaxial tests on wgGRMs with different rubber contents. It is shown that the model accurately reproduces key features observed experimentally, including post-peak strain softening, peak strength variation, and volumetric changes across different confining pressure levels and rubber content fractions. This model represents a useful tool for predicting the behavior of wgGRMs in engineering practice, supporting the reuse of ELT-derived rubber. Full article

Maritime ferries increasingly operate under non-stationary hydro–meteorological conditions that complicate cost planning. This study investigates how short-term weather variability affects expenditures for a ferry on the Gdynia–Karlskrona route. We combine a state-based operational framework (18 discrete states) with a subsystem-level cost model covering navigation, propulsion/steering, loading/unloading, stability control, and mooring/anchoring. Direct and indirect costs are linked to subsystem activity and state duration, while weather is incorporated through hazard categories that scale hourly costs. Expert-elicited rates and observed monthly state durations provide the basis for baseline estimates and hazard scenario simulations. Results reveal a disproportionate cost structure: two open-sea states constitute over 97% of the baseline monthly cost (19,490.19 PLN). Weather hazards further amplify costs, with moderate (1st-degree) and severe (2nd-degree) scenarios producing increases of ~8% and ~20%, respectively, compared to normal conditions. By embedding weather as an endogenous factor in a probabilistic cost model based on a semi-Markov process, the approach enhances predictive fidelity and supports decision-making for climate-resilient planning. These findings suggest that adaptive routing, speed management, and targeted maintenance of the propulsion and steering subsystems during open-sea navigation offer the highest potential for cost resilience. The study provides operators and policymakers with a transparent framework for climate-resilient planning and investment in semi-enclosed maritime corridors. Full article