Search Results (289)

Alfalfa is a high-quality forage crop whose viscoelastic properties strongly influence the performance of baling, pickup, and stacking operations. In this study, small alfalfa block specimens were tested using a universal testing machine to investigate stress relaxation and creep behaviors under different moisture contents (12%, 15%, 18%), densities (100, 150, 200 kg/m³), and maximum compressive stresses (8, 12, 16 kPa). Experimental data were fitted using viscoelastic models for parameter analysis. Results indicated that the relaxation response consisted of a rapid attenuation followed by a slow stabilization phase. The five-element Maxwell model achieved a higher fitting accuracy (coefficient of determination, R² > 0.997) than the three-element model. The creep process exhibited three stages, including instantaneous elastic deformation, decelerated creep, and steady-state deformation, and it was accurately represented by the five-element Kelvin model (R² > 0.998). Increasing moisture content reduced stiffness, while moderate moisture improved viscosity and shape retention. Higher density enhanced blocks compactness, stiffness, and damping characteristics, resulting in smaller deformation. The viscoelastic response to compressive stress showed moderate enhancement followed by attenuation under overload, with the best recovery and deformation resistance observed at 12 kPa. These findings elucidate the viscoelastic behavior of alfalfa blocks and provide theoretical support and engineering guidance for evaluating bale stability and optimizing pickup–clamping parameters. Full article

Net-shaped casting processes in the automotive industry have proved to be difficult to simulate due to the complexities of the interactions amongst thermal, fluid, and solute transport regimes in the solidifying domain, along with the interface. The existing casting simulation software lacks the necessary real-time estimation of thermophysical properties (thermal diffusivity and thermal conductivity) and the interfacial heat-transfer coefficient (IHTC) to evaluate the thermal resistances in a casting process and solve the temperature in the solidifying domain. To address these shortcomings, an axial directional solidification experiment setup was developed to map the thermal data as the melt solidifies unidirectionally from the chill surface under unsteady-state conditions. A Dilute Eutectic Cast Aluminum (DECA) alloy, Al-5Zn-1Mg-1.2Fe-0.07Ti, Eutectic Cast Aluminum (ECA) alloys (A365 and A383), and pure Al (P0303) were used to demonstrate the validity of the experiments to evaluate the thermal diffusivity (α) of both the solid and liquid phases of the solidifying metal using an inverse heat-transfer analysis (IHTA). The thermal diffusivity varied from 0.2 to 1.9 cm²/s while the IHTC changed from 9500 to 200 W/m²K for different alloys in the solid and liquid phases. The heat flux was estimated from the chill side with transient temperature distributions estimated from IHTA for either side of the mold–metal interface as an input to compute the interfacial heat-transfer coefficient (IHTC). The results demonstrate the reliability of the axial solidification experiment apparatus in accurately providing input to the casting simulation software and aid in reproducing casting numerical simulation models efficiently. Full article

A precast concrete sandwich panel (PCSP), consisting of inner and outer wythes, an insulation layer, and connectors, relies heavily on the shear behavior of these connectors, which governs the structural performance of the entire system. Owing to their low thermal conductivity, excellent durability, and high strength, fiber-reinforced polymer (FRP) connectors offer strong potential for widespread application. This study introduces a novel cross-shaped FRP rod connector designed to provide improved anchorage performance, bidirectional shear resistance, and ease of installation. However, concern remains about the specific influence of embedment depth, outer-wythe thickness, and insulation-layer thickness on its shear performance. Moreover, no calculation model for shear capacity or shear–slip model has been established considering the shear-bending interaction within the connector. To evaluate its shear behavior, six groups of push-out tests were conducted, with key parameters including embedment depth, outer-wythe thickness, and insulation-layer thickness. The specimens exhibited two primary failure modes: connector fracture and concrete anchorage failure. The measured shear capacity per connector ranged from 5.63 kN to 14.19 kN, increasing with longer embedment depths, decreasing with increasing insulation thickness, and showing no clear dependence on outer-wythe thickness. Guided by test results and the Hashin failure criterion for composite materials, analytical formulas to estimate the shear capacity of FRP connectors were developed. The mean ratio of calculated to experimental values is 0.97, with a standard deviation of 0.06, indicating good agreement between the predicted and measured shear capacities. Furthermore, a theoretical shear–slip model was established. The correlation coefficients between the experimental and calculated load–slip curves for all specimens are greater than 0.98, indicating a high consistency in curve shape and variation trend. Full article

Polyetheretherketone (PEEK) is a high-performance engineering plastic widely used in aerospace, automotive, and other industries due to its heat resistance and mechanical strength. However, its high friction coefficient and low thermal conductivity limit its use in heavy-load environments. Existing studies have extensively explored the individual effects of thermal processing or irradiation on PEEK. However, the synergistic mechanism between the initial microstructure formed by mold temperature and subsequent irradiation modification remains unclear. This paper investigates the coupled effects of injection molding temperature and electron beam irradiation on the tribology of carbon fiber-reinforced PEEK composites, with the aim of identifying process conditions that improve friction and wear performance under high load by controlling the crystal morphology and cross-linking network. Carbon fiber (CF) particles were mixed with PEEK particles at a 1:2 mass ratio, and specimens were prepared at injection molding temperatures of 150 °C, 175 °C, and 200 °C. Some specimens were irradiated with an electron beam dose of 200 kGy. The friction coefficient, wear rate, surface shape, and crystallinity of the material were obtained using friction and wear tests, white-light topography, SEM, and XRD. The results show that the injection molding temperature of the material influences the friction performance. Optimal performance is obtained at 175 °C with a friction coefficient of 0.12 and wear rate of 9.722 × 10⁻⁶ mm³/(N·m). After irradiation modification, the friction coefficient decreases to 0.10. This improvement is due to the moderate melt fluidity, adequate fiber infiltration, and dense crystallization at this temperature. In addition, cross-linking of chains occurs, and surface transfer films are created at this temperature. However, irradiation leads to a slight increase in wear rate to 1.013 × 10⁻⁵ mm³/(N·m), suggesting that chain segment fracture and embrittlement effects are enhanced at this dose. At 150 °C, there is weak interfacial bonding and microcrack development. At 200 °C, excessive thermal motion reduces crystallinity and adds residual stress, increasing wear sensitivity. Overall, while irradiation reduces the friction coefficient, the wear rate is affected by the initial microstructure at molding. At non-optimal temperatures, embrittlement tends to dominate the wear mode. This study uncovers the synergistic and competitive dynamics between the injection molding process and irradiation modification, offering an operational framework and a mechanistic foundation for applying CF/PEEK under heavy-load conditions. The present approach can be extended in future work to other reinforcement systems or variable-dose irradiation schemes to further optimize overall tribological performance. Full article

Accurate prediction of road traffic crash severity is essential for developing data-driven safety strategies and optimizing resource allocation. This study presents a predictive modeling framework that utilizes Random Forest (RF), Gradient Boosting (GB), and K-Nearest Neighbors (KNN) to estimate segment-level frequencies of fatalities, serious injuries, and slight injuries on Hungarian roadways. The model integrates an extensive array of predictor variables, including roadway geometric design features, traffic volumes, and traffic composition metrics. To address class imbalance, each severity class was modeled using resampled datasets generated via the Synthetic Minority Over-sampling Technique (SMOTE), and model performance was optimized through grid-search cross-validation for hyperparameter optimization. For the prediction of serious- and slight-injury crash counts, the Random Forest (RF) ensemble model demonstrated the most robust performance, consistently attaining test accuracies above 0.91 and coefficient of determination (R²) values exceeding 0.95. In contrast, for fatalities count prediction, the Gradient Boosting (GB) model achieved the highest accuracy (0.95), with an R² value greater than 0.87. Feature importance analysis revealed that heavy vehicle flows consistently dominate crash severity prediction. Horizontal alignment features primarily influenced fatal crashes, while capacity utilization was more relevant for slight and serious injuries, reflecting the roles of geometric design and operational conditions in shaping crash occurrence and severity. The proposed framework demonstrates the effectiveness of machine learning approaches in capturing non-linear relationships within transportation safety data and offers a scalable, interpretable tool to support evidence-based decision-making for targeted safety interventions. Full article

Damage to structural members or joints can change the load transfer path of the structure. Additionally, structures may experience severe damage or even collapse due to the impact of aftershocks. To investigate the effects of beam-column joint damage, link damage, and aftershocks on the seismic performance of K-shaped eccentrically braced steel frame (K-EBF) structures, incremental dynamic analysis, fragility analysis, and collapse resistance evaluation were conducted using examples of 12-story and 18-story K-EBF structures. The results showed that considering beam-column joint damage, link damage, and aftershocks compared to not considering them, and the maximum inter-story drift ratio (θ_max) of the 12-story and 18-story K-EBF structures increased by 11.1% and 20.1%, respectively, under fortification earthquakes, and by 30.0% and 56.7%, respectively, under rare earthquakes. The failure probability of the severe damage limit state of the 12-story and 18-story K-EBF structures increased by 1.0% and 3.0%, respectively, under fortification earthquakes, and by 15.3% and 24.0%, respectively, under rare earthquakes. Additionally, the minimum collapse margin ratios (CMR_{P = 10%}) of the two structures decrease by 27.8% and 32.3%, respectively. The influence of aftershocks on the structural seismic response tends to intensify as the intensity of ground motion increases, and the beam-column joint damage and link damage further increases the failure probability of different damage limit states, leading to a decrease in the minimum collapse resistance coefficient of the structure. Therefore, in the seismic performance analysis of K-EBF structures, the effects of beam-column joint damage, link damage, and aftershocks should be fully considered to accurately reflect the response of structures under seismic actions. Overall, the impact of link damage, as well as aftershocks, on the structural collapse resistance is greater than that of beam-column joint damage. Full article

This study analyzes long-term deterioration patterns in 1378 Maryland steel bridges using annual Bridge Health Index (BHI) records from 1995–2021. Missing observations were addressed through linear interpolation combined with forward/backward filling, after which feature-wise z-score standardization was applied to ensure comparability across annual trajectories. Euclidean K-means clustering (k-means++ initialization, 10 restarts) was implemented to identify deterioration archetypes, with K = 6 selected using the elbow method and the silhouette coefficient. Cluster-internal stability was evaluated using bridge-level Root Mean Squared Error (RMSE), and uncertainty in median deterioration profiles was quantified using 2000-iteration percentile-based bootstrap confidence intervals. To interpret structural and contextual drivers within each group, Principal Component Analysis (PCA) was performed on screened and standardized geometric, structural, and traffic-related attributes. Results revealed strong imbalance in cluster membership (757, 503, 35, 33, 44, and 6 bridges), reflecting substantial diversity in long-term BHI behavior. Cluster-median RMSE values ranged from 2.69 to 22.66, while wide confidence bands in smaller clusters highlighted elevated uncertainty due to limited sample size. PCA indicated that span length, deck width, truck percentage, and projected future ADT were the most influential differentiators of deteriorating clusters, while stable clusters were distinguished by consistently high BHI component values and limited geometric complexity. Missing rehabilitation records prevented definitive attribution of U-shaped or recovering trajectories to specific intervention events. Overall, this study establishes a scalable, statistically supported framework for deterioration-trajectory profiling and provides actionable insight for proactive inspection scheduling, rehabilitation prioritization, and long-term asset management planning for state-level bridge networks. Full article

The efficient pre-incubation selection of duck eggs is essential to ensuring stable hatchability, but most existing optoelectronic and machine vision systems have been calibrated for chicken eggs and cannot be directly used for duck eggs because of their larger size, stronger reflectivity and wider morphological variability. This study proposes an optoelectronic method specifically adapted to Adigel duck eggs that combines load cell weighing, infrared distance sensing and dual-projection image processing in a single stationary setup. A total of 300 eggs were measured manually and automatically, and the results were statistically compared. The developed algorithm uses adaptive Gaussian thresholding (51 × 51, C = 2) and a median 5 × 5 filter to stabilize contour extraction on glossy and spotted shells, followed by ellipsoid-based volume estimation with a breed-specific correction factor (K_v = 0.637). The automatic system showed high agreement with manual measurements (r > 0.95 for mass and linear dimensions) and a mean relative error below 2%. Density, the shape index (If) and the shape coefficient (K₁) were computed to classify eggs into “suitable”, “borderline” and “unsuitable” categories. A preliminary incubation trial (n = 60) of eggs classified as “suitable” resulted in 92% hatchability, thus confirming the predictive value of the proposed criteria. Unlike chicken-oriented systems, the presented solution provides breed-specific calibration and can be implemented in small and medium hatcheries for the reproducible, non-destructive sorting of hatching duck eggs. Full article

We establish a general, device-oriented procedure to extract absolute pump-band metrics from room-temperature UV–Vis (ultraviolet–visible) absorbance—including the absorption coefficient α(λ), per-active-ion cross-section ${\sigma }_{\mathrm{e}\mathrm{f}\mathrm{f}}\left(\lambda \right)$, the effective per-active-ion absorption cross section ${\sigma }_{\mathrm{e}\mathrm{f}\mathrm{f}}\left(\lambda \right)$ and derivative-based line-shape descriptors. As a representative case study, the procedure is applied to nanocrystalline Er³⁺/Ho³⁺:Y₃Ga₅O₁₂ over the 350–700 nm spectral range. After baseline correction and line-shape inspection assisted by the numerical second derivative of the absorbance, we extract conservative peak positions and the full width at half maximum across the visible 4f–4f manifolds. At the technologically relevant pump wavelengths near 406 nm (Er-addressing) and 473 nm (Ho-addressing) bands, resulting absorption coefficients are α = 0.313 ± 0.047 cm⁻¹ and α = 0.472 ± 0.071 cm⁻¹, respectively. The corresponding per-active-ion ${\sigma }_{\mathrm{e}\mathrm{f}\mathrm{f}}$ of (3.62 ± 0.54) × 10⁻²² cm² and (5.46 ± 0.82) × 10⁻²² cm², referenced to the measured optical path length L = 0.22 ± 0.03 mm (approximately 15% propagated relative uncertainty; explicit 1/L rescaling). Cross sections are reported per total active-ion density (Er³⁺ + Ho³⁺). The spectra exhibit Stark-type substructure only partially resolved at room temperature; the second derivative highlights hidden components, and we report quantitative descriptors (component count, mean spacing, curvature-weighted prominence, and pump detuning) that link line-shape structure to absolute pump response. These device-grade metrics enable rate-equation modelling (pump thresholds, detuning tolerance), optical design choices (path length, single/multi-pass or cavity coupling), and host-to-host benchmarking at 295 K. The procedure is general and applies to any rare-earth-doped material given an absorbance spectrum and path length. Full article

Rapid and accurate phenotypic screening of rice germplasms is crucial for identifying potential sources of rice sheath blight resistance. However, visual and/or caliper-based estimations of coalescing, necrotic, diseased lesions of rice sheath blight (ShB)-infected plants are time-consuming, labor-intensive, and subject to human rater subjectivity. Here, we propose the use of RGB images and image processing techniques to quantify ShB disease progression in terms of lesion height and diseased area. To be specific, we developed a Pixel Color- and Coordinate-based K-Means Clustering (PCC-KMC) algorithm utilizing the Mahalanobis distance metric, aimed at accurately segmenting symptomatic and non-symptomatic regions within rice stem images. The performance of PCC-KMC, combined with manual classification of the segmented regions, was evaluated using Lin’s concordance correlation coefficient (${\rho }_c$) by comparing its results to visual measurements of ShB lesion height (cm) and to lesion/diseased area (cm₂) measured using ImageJ. Low bias (C_b) and high precision (r) were observed for absolute lesion height (C_b = 0.93, r = 0.94) and absolute symptomatic area (C_b = 0.98, r = 0.97) studies. Furthermore, to automatically classify the segmented regions produced by the PCC-KMC algorithm, we employed a convolutional neural network (CNN). Unlike conventional CNNs that require fixed-size image inputs, our CNN is designed to take the RGB histogram of each segmented region (a 1000 by 3 representation) as input and determine whether the region corresponds to ShB infection. This design effectively handles the arbitrary sizes and irregular shapes of segmentation regions generated by PCC-KMC. Our CNN was trained based on an 85%:15% composition for the training and testing dataset from a total of 168 ShB-infected stem sample images, recording 92% accuracy and 0.21 loss. PCC-KMC-CNN also showed high accuracy and precision for the absolute lesion height (C_b = 0.86, r = 0.90) and absolute diseased area (C_b = 0.99, r = 0.97) studies, indicating that PCC-KMC combined with automatic CNN-based classification performs very effectively. These results demonstrate that the potential of our methodology to serve as an alternative to the traditional visual-based ShB disease severity assessment and can be considered to be utilized for lab-scale, high-throughput phenotyping of rice ShB. Full article

The compressive strength is the primary parameter used for the design, control, and performance assessment of cementitious materials. However, this value is strongly influenced by specimen geometry, which has led to the introduction of shape coefficients to convert compressive strength results between different specimen types, particularly between cubes and cylinders. While this topic has been extensively investigated in concrete, very limited research has addressed the shape coefficient in soil-cement or cement-treated base materials, despite their widespread use in pavement construction. Aiming to bridge this gap, this study systematically analyzes the unconfined compressive strength (UCS) of soil-cement specimens with different geometries. Two soil-cement mixtures with distinct physical and chemical characteristics were tested at various curing ages (7, 28, and 90 days) using cylindrical specimens (150 mm diameter × 180 mm height) and cubic specimens (150 mm edge). The results show that the UCS in cylindrical specimens (UCS_cyl) was consistently higher than that of cubic specimens (UCS_cub), although the difference decreased with increasing compressive strength. By combining all datasets, a single conversion factor of 1.04 was derived, resulting in an equation, UCS_cyl = 1.04·UCS_cub, with an excellent determination coefficient (R² = 0.99), enabling reliable conversion between cubic and cylindrical UCS results for soil-cement. Full article

Accurate simulation of boundary layer wind field structures is essential for evaluating tropical cyclone (TC) wind hazards and supporting engineering design in coastal regions. However, existing models often assume radially symmetric and homogeneous surface conditions, leading to limited accuracy near landfall where surface roughness varies significantly. This study conducts a comprehensive evaluation of four representative TC boundary layer models of M95, K01, Y21a, and Y21b, under both idealized and real TC case conditions. The idealized experiments are used to clarify the role of vertical advection and turbulent diffusion in shaping the TC boundary layer, while the landfalling case of Typhoon Mangkhut (2018) is simulated to examine the impacts of surface roughness parameterization. Results show that Y21a, which incorporates nonlinear vertical advection, produces stronger and more realistic super-gradient phenomenon than linear models of M95 and K01. Furthermore, the model of Y21b, which accounts for spatially varying drag coefficients and using a terrain-following coordinate system, successfully reproduces the asymmetric wind patterns observed in the WRF simulations during landfall, achieving the highest correlation (R = 0.93). When the spatially varying drag coefficients incorporated into the linear models, their correlation with WRF improved markedly by about 37%. These findings highlight the necessity of incorporating nonlinear advection, dynamic turbulence, and surface heterogeneity for physically consistent TC boundary layer simulations. The results provide valuable guidance for improving parametric wind field models and enhancing TC wind hazard assessments over complex coastal terrains. Full article

Imidazolium-based ionic liquids bearing a homologous series of oxychlorine anions—1-butyl-3-methylimidazolium chlorite, chlorate, and perchlorate—were synthesized and characterized to relate anion oxygenation to density, thermal expansivity, viscosity, electrical and molar conductivity, ionicity, and antimicrobial performance. Temperature-dependent measurements were carried out from 293.15 to 323.15 K: density and viscosity were recorded and modeled to obtain thermal expansion coefficients; electrical and molar conductivities were measured under identical conditions; and activation parameters were extracted by Arrhenius analysis for viscous flow and for conductivity. Ionicity was assessed from Walden plots and quantified by vertical deviation from the potassium-chloride reference (Angell approach). Complementary DFT calculations provided optimized ion-pair geometries, noncovalent contact patterns, molecular electrostatic potential maps, and frontier-orbital descriptors. In silico ADMET properties were computed to contextualize pharmacokinetic and safety flags. Antimicrobial activity was evaluated by broth microdilution against Escherichia coli, Staphylococcus aureus, Bacillus cereus, and Candida quilliermondii; [Bmim]Cl was included as a comparator to isolate the effect of anion oxygenation. The combined experimental–computational workflow delineates how chlorite, chlorate, and perchlorate shape physicochemical behavior, ionicity, and bioactivity in [Bmim] ionic liquids, providing design guidance for future applications. Full article

The degree of urbanization and the uncontrolled expansion of the built environment play a defining role in shaping contemporary society, contributing significantly to abrupt temperature fluctuations and a declining quality of life. This study aims to analyze land use and land cover (LULC) patterns in the municipality of Deva, located in the central part of Hunedoara County, Romania (45°52′ N, 22°54′ E). The analysis covers the period from March 2022 to March 2023 and is based on open-source datasets. Supervised classification of LULC was performed using two GIS software platforms: ArcGIS Pro and QGIS. Sentinel-2A satellite imagery, with spatial resolutions of 10 m, 20 m, and 60 m, was processed using two different classification algorithms—the Minimum Distance classifier (via the Semi-Automatic Classification Plugin in QGIS) and the k-Nearest Neighbor (k-NN) algorithm in ArcGIS Pro. The comparative accuracy assessment indicated that the k-NN classifier in ArcGIS Pro performed better, achieving an overall accuracy of 89.7% and a Kappa coefficient of 0.86, while the Minimum Distance classifier in QGIS obtained an overall accuracy of 81.2% and a Kappa coefficient of 0.78. The outputs of both classification workflows were compared, and an accuracy assessment was conducted during the post-processing stage. The best results were obtained using the k-NN algorithm. The classification maps generated in this study can serve as a valuable foundation for local authorities to monitor environmental changes and support urban planning initiatives in Deva. Full article

Against the backdrop of the global energy crisis and the “dual carbon” goals (carbon peaking and carbon neutrality), the passive energy-saving design of substation buildings in cold regions faces severe challenges. This study systematically conducts a decomposed analysis of the shape coefficient, thermal performance of the building envelope (including external walls, internal walls, roofs, and external windows), and window-to-wall ratio of substation buildings in cold regions, quantifies the degree of influence of each factor, and proposes corresponding energy-saving design strategies. This study took a 110 kV substation in Yuhua District, Shijiazhuang City, Hebei Province, as the research object. A building energy consumption model was established based on DeST (2023) software, and the influence of the building shape coefficient, U-values of the envelope structure (external walls, internal walls, roofs, external windows), and window-to-wall ratio on the building’s cooling and heating loads was analyzed using the numerical simulation and control variable methods. Leveraging a rigorously validated, high-resolution simulation framework, we quantitatively dissect the marginal energy penalties and payoffs of every passive design variable governing fully indoor substations in cold-climate zones. The resultant multidimensional response surfaces are distilled into a deterministic, climate-specific passive energy-saving protocol that secures heating-energy savings of up to 43% without compromising electrical safety or operational accessibility. (1) Reducing the shape coefficient can significantly lower the heat load, and it is recommended to control it at 0.35–0.40; (2) The thermal performance of the envelope structure has a differential effect: the energy-saving effect is optimal when the U-value of external walls is 0.20–0.30 W/(m²·K) and the U-value of roofs is ≤0.25 W/(m²·K). A U-value of 2.4 W/(m²·K) is recommended for external windows, while the internal wall exerts a weak influence; (3) The window-to-wall ratio should be controlled by orientation: east-facing/north-facing ≤ 0.20, south-facing ≤ 0.35, and west-facing ≤ 0.30. Based on the above results, a comprehensive energy-saving strategy of “compact form–high-efficiency envelope–limited window-to-wall ratio” is proposed, which provides theoretical support and technical pathways for the energy-saving design of substation buildings in cold areas. Compared with existing substation buildings, the recommended parameters yield a significant reduction in total life-cycle carbon emissions and hold important practical significance for realizing the “dual carbon” goals (carbon peaking and carbon neutrality) of the power system. Full article