Search Results (909)

Concrete is an essential material in the construction industry due to its strength and versatility. However, its quality can be compromised by environmental factors during its fresh and early-age states. To address this vulnerability, various sensors have been implemented to monitor critical parameters. While high-precision sensors (e.g., piezoelectric and fiber optic) offer accurate measurements, their cost and fragility limit their widespread use in construction environments. In response, this study proposes a cost-effective, Arduino-based wireless monitoring system to track temperature and humidity in fresh and early-age concrete elements. The system was validated through laboratory tests on cylindrical specimens and industrial applications on self-compacting concrete New Jersey barriers. The sensors recorded temperature variations between 15 °C and 35 °C and relative humidity from 100% down to 45%, depending on environmental exposure. In situ monitoring confirmed the system’s ability to detect thermal gradients and evaporation dynamics during curing. Additionally, the presence of embedded sensors caused a tensile strength reduction of up to 37.5% in small specimens, highlighting the importance of sensor placement. The proposed solution demonstrates potential for improving quality control and curing management in precast concrete production with low-cost devices. Full article

Machining chromium–nickel alloy steel is challenging due to its material properties, such as high strength and toughness. These properties often lead to tool damage and degradation of tool life, which overall impacts the production time, cost, and quality of the product. Therefore, it is essential to investigate early signs of tool damage to determine the effective machining conditions for chromium–nickel alloy steel, thereby increasing tool life and improving product quality. In this study, the early signs of tool wear were observed in a physical vapor deposition (PVD) carbide-coated tool (Seco Tools, Björnbacksvägen, Sweden) during the machining of X5CrNi18-10 steel under both dry and wet conditions. A finish turning operation was performed on the outer diameter (OD) of the workpiece with a 0.4 mm nose radius tool. At the early stage, the tool was examined from the functional side (f–side) and the passive side (p–side). The results indicate that dry machining leads to increased coating removal, more heat generation, and visible damage, such as pits and surface scratches. By comparison, wet machining helps reduce heat and wear, thereby improving tool life and machining quality. These findings suggest that a coolant must be used when machining chromium–nickel alloy steel with a PVD carbide-coated tool. Full article

Tea cultivation faces significant threats from red spider mite (RSM: Oligonychus coffeae) infestations, which reduce yields and economic viability in major tea-producing regions. Current automated detection methods rely on supervised deep learning models requiring extensive labeled data, limiting scalability for smallholder farmers. This article proposes AIM-Net (AI-based Infestation Mapping Network) by evaluating SwAV (Swapping Assignments between Views), a self-supervised learning framework, for classifying RSM infestation severity (Mild, Moderate, Severe) using a geo-referenced, field-acquired dataset of RSM infested tea-leaves, Cam-RSM. The methodology combines SwAV pre-training on unlabeled data with fine-tuning on labeled subsets, employing multi-crop augmentation and online clustering to learn discriminative features without full supervision. Comparative analysis against a fully supervised ResNet-50 baseline utilized 5-fold cross-validation, assessing accuracy, F1-scores, and computational efficiency. Results demonstrate SwAV’s superiority, achieving 98.7% overall accuracy (vs. 92.1% for ResNet-50) and macro-average F1-scores of 98.3% across classes, with a 62% reduction in labeled data requirements. The model showed particular strength in Mild_RSM-class detection (F1-score: 98.5%) and computational efficiency, enabling deployment on edge devices. Statistical validation confirmed significant improvements (p < 0.001) over baseline approaches. These findings establish self-supervised learning as a transformative tool for precision pest management, offering resource-efficient solutions for early infestation detection while maintaining high accuracy. Full article

This study presents a simulation-based damage modeling and fatigue risk assessment of a reusable ceramic matrix composite thruster designed for short-duration, green bipropellant propulsion systems. The thruster is constructed from a fiber-reinforced ultra-high temperature ceramic matrix composite composed of zirconium diboride, silicon carbide, and carbon fibers. Time-resolved thermal and structural simulations are conducted on a validated thruster geometry to characterize the severity of early-stage thermal shock, stress buildup, and potential degradation pathways. Unlike traditional fatigue studies that rely on empirical fatigue constants or Paris-law-based crack-growth models, this work introduces a simulation-derived stress-margin envelope methodology that incorporates ±20% variability in temperature-dependent material strength, offering a physically grounded yet conservative risk estimate. From this, a normalized risk index is derived to evaluate the likelihood of damage initiation in critical regions over the 0–10 s firing window. The results indicate that the convergent throat region experiences a peak thermal gradient rate of approximately 380 K/s, with the normalized thermal shock index exceeding 43. Stress margins in this region collapse by 2.3 s, while margin loss in the flange curvature appears near 8 s. These findings are mapped into green, yellow, and red risk bands to classify operational safety zones. All the results assume no active cooling, representing conservative operating limits. If regenerative or ablative cooling is implemented, these margins would improve significantly. The framework established here enables a transparent, reproducible methodology for evaluating lifetime safety in ceramic propulsion nozzles and serves as a foundational tool for fatigue-resilient component design in green space engines. Full article

Background/Objectives: Melanoma is an aggressive type of skin cancer that poses serious health risks if not detected in its early stages. Although early diagnosis enables effective treatment, delays can result in life-threatening consequences. Traditional diagnostic processes predominantly rely on the subjective expertise of dermatologists, which can lead to variability and time inefficiencies. Consequently, there is an increasing demand for automated systems that can accurately classify melanoma lesions and retrieve visually similar cases to support clinical decision-making. Methods: This study proposes a transfer learning (TL)-based deep learning (DL) framework for the classification of melanoma images and the enhancement of content-based image retrieval (CBIR) systems. Pre-trained models including DenseNet121, InceptionV3, Vision Transformer (ViT), and Xception were employed to extract deep feature representations. These features were integrated using a weighted fusion strategy and classified through an Ensemble learning approach designed to capitalize on the complementary strengths of the individual models. The performance of the proposed system was evaluated using classification accuracy and mean Average Precision (mAP) metrics. Results: Experimental evaluations demonstrated that the proposed Ensemble model significantly outperformed each standalone model in both classification and retrieval tasks. The Ensemble approach achieved a classification accuracy of 95.25%. In the CBIR task, the system attained a mean Average Precision (mAP) score of 0.9538, indicating high retrieval effectiveness. The performance gains were attributed to the synergistic integration of features from diverse model architectures through the ensemble and fusion strategies. Conclusions: The findings underscore the effectiveness of TL-based DL models in automating melanoma image classification and enhancing CBIR systems. The integration of deep features from multiple pre-trained models using an Ensemble approach not only improved accuracy but also demonstrated robustness in feature generalization. This approach holds promise for integration into clinical workflows, offering improved diagnostic accuracy and efficiency in the early detection of melanoma. Full article

The combined application of fibers and lightweight aggregates (LWAs) represents an effective approach to achieving high-strength, lightweight concrete. To enhance the predictability of the mechanical properties of fiber-reinforced lightweight aggregate concrete (LWAC), this study conducts an in-depth investigation into its hydration characteristics. In this study, high-strength LWAC was developed by incorporating low water absorption LWAs, various volume fractions of basalt fiber (BF) (0.1%, 0.2%, and 0.3%), and a ternary cementitious system consisting of 70% cement, 20% fly ash, and 10% silica fume. The hydration-related properties were evaluated through isothermal calorimetry test and high-temperature calcination test. The results indicate that incorporating 0.1–0.3% fibers into the cementitious system delays the early hydration process, with a reduced peak heat release rate and a delayed peak heat release time compared to the control group. However, fitting the cumulative heat release over a 72-h period using the Knudsen equation suggests that BF has a minor impact on the final degree of hydration, with the difference in maximum heat release not exceeding 3%. Additionally, the calculation model for the final degree of hydration in the ternary binding system was also revised based on the maximum heat release at different water-to-binder ratios. The results for chemically bound water content show that compared with the pre-wetted LWA group, under identical net water content conditions, the non-pre-wetted LWA group exhibits a significant reduction at three days, with a decrease of 28.8%; while under identical total water content conditions it shows maximum reduction at ninety days with a decrease of 5%. This indicates that pre-wetted LWAs help maintain an effective water-to-binder ratio and facilitate continuous advancement in long-term hydration reactions. Based on these results, influence coefficients related to LWAs for both final degree of hydration and hydration rate were integrated into calculation models for degrees of hydration. Ultimately, this study verified reliability of strength prediction models based on degrees of hydration. Full article

Maritime corrosion is a global problem often retarded through protective coatings containing corrosion inhibitors (CIs). ZnAl layered double hydroxides (LDH) have been used to immobilize CIs, which can reduce their early leaching and, thus, foster long-term corrosion protection. However, the environmental behavior of these nanomaterials remains largely unknown, particularly in the context of global changes. The present study aims to assess the environmental behavior of four anti-corrosion nanomaterials in an ocean acidification scenario (IPCC SSP3-7.0). Three different concentrations of the nanostructured CIs (1.23, 11.11, and 100 mg L⁻¹) were prepared and maintained at 20 °C and 30 °C in artificial salt water (ASW) at two pH values, with and without the presence of organic matter. The nanomaterials’ particle size and the release profiles of Al³⁺, Zn²⁺, and anions were monitored over time. In all conditions, the hydrodynamic size of the dispersed nanomaterials confirmed that the high ionic strength favors their aggregation/agglomeration. In the presence of organic matter, dissolved Al³⁺ increased, while Zn²⁺ decreased, and increased in the ocean acidification scenario at both temperatures. CIs were more released in the presence of humic acid. These findings demonstrate the influence of the tested parameters in the nanomaterials’ environmental behavior, leading to the release of metals and CIs. Full article

This study presents a progressive strength prediction model for cement paste based on the hypothesis that compressive strength is governed by the microstructural compactness of hydration products. A three-stage modeling framework was developed: (1) a semi-empirical model for pure cement paste incorporating water-to-cement ratio and paste density; (2) a density-corrected effective water–cement ratio ${\left(w/c\right)}_{eff}$ that accounts for the physical effects of mineral additives including fly ash, slag, and limestone powder; and (3) a hydration-informed strength model incorporating curing age and temperature through an equivalent hydration degree $\alpha \left(t_e\right)$. Experimental validation using over 60 cement paste mixes demonstrated high predictive accuracy, with coefficients of determination up to 0.97. The proposed model unifies the influence of binder composition, packing density, and curing conditions into a physically interpretable and practically applicable formulation. It enables early-age strength prediction of blended cementitious systems using only routine mix and density parameters, supporting performance-based mix design and optimization. The methodology provides a robust foundation for extending compactness-based modeling to more complex cementitious materials and structural applications. Full article

This work aims to reveal the full-field strain evolution characteristics and failure mechanisms of anisotropic coal samples under creep loading. A series of compression tests combined with digital image correlation (DIC) monitoring were employed to characterize the strain evolution process of coal specimens with bedding angles of 0°, 30°, 60°, and 90°. Testing results show that the peak strength, peak strain, and the creep loading stage of coal are significantly influenced by the bedding angle. The peak strength initially decreases and then increases as the bedding angle increases. In addition, the creep failure of coal manifests as a process of instantaneous deformation, decelerating creep, steady-state creep, accelerating creep, and failure. Under graded creep loading conditions, coal specimens exhibit distinct creep characteristics at high stress levels. Moreover, the bedding angle significantly influences the strain field evolution of the coal samples. Finally, for coal specimens with bedding angles of 0° and 90°, the final macroscopic fracture pattern upon failure is characterized by longitudinal tensile splitting. In contrast, coal samples with bedding angles of 30° and 60° tend to exhibit failure along the bedding interfaces, forming tensile-shear fractures. The results of this study will provide theoretical guidance for the prevention, early warning, and safety management of coal mine disasters. Full article

This study looked at the performance requirements of repair materials for concrete structures in cold regions, systematically analyzing the effects of steel fiber dosage (0.7–2.1%), early-strength agent PRIORITY dosage (6–10%), and their coupling effects on the workability, interfacial bond strength, and freeze–thaw resistance of rapid-hardening ultra-high-performance concrete (UHPC). Through fluidity testing, bond interface failure analysis, freeze–thaw cycle testing, and pore analysis, the mechanism of steel fibers and early-strength agent on the multi-dimensional performance of fast-hardening UHPC was revealed. The results showed that when the steel fiber dosage exceeded 1.4%, the flowability was significantly reduced, while a PRIORITY dosage of 8% improved the flowability by 20.5% by enhancing the paste lubricity. Single addition of steel fibers decreased the interfacial bond strength, but compound addition of 8% PRIORITY offset the negative impact by optimizing the filling effect of hydration products. Under freeze–thaw cycles, excessive steel fibers (2.1%) exacerbated the mass loss (1.67%), whereas a PRIORITY dosage of 8% increased the retention rate of relative dynamic elastic modulus by 10–15%. Pore analysis shows that the synergistic effect of 1.4% steel fiber and 8% PRIORITY can reduce the number of pores, optimize the pore distribution, and make the structure denser. The study determined that the optimal compound mixing ratio was 1.4% steel fibers and 8% PRIORITY. This combination ensures construction fluidity while significantly improving the interfacial bond durability and freeze–thaw resistance, providing a theoretical basis for the design of concrete repair materials in cold regions. Full article

Swine infectious diseases, often caused by multiple co-infecting agents, pose severe global threats to pig health and industry economics. Conventional single-plex testing assays, whether relying on pathogen antigens or nucleic acids, exhibit limited efficacy in the face of co-infection events. The modern nucleic acid-based multiplex testing (NAMT) methods demonstrate substantial strengths in the simultaneous detection of multiple pathogens involving co-infections owing to their remarkable sensitivity, exceptional specificity, high-throughput, and short turnaround time. The development, commercialization, and application of NAMT assays in swine infectious disease surveillance would be advantageous for early detection and control of pathogens at the onset of an epidemic, prior to community transmission. Such approaches not only contribute to saving the lives of pigs but also aid pig farmers in mitigating or preventing substantial economic losses resulting from infectious disease outbreaks, thereby alleviating unwanted pressure on animal and human health systems. The current literature review provides an overview of some modern NAMT methods, such as multiplex quantitative real-time PCR, multiplex digital PCR, microarrays, microfluidics, next-generation sequencing, and their applications in the diagnosis of swine infectious diseases. Furthermore, the strengths and weaknesses of these methods were discussed, as well as their future development and application trends in swine disease diagnosis. Full article

Objectives: To identify clinical, functional, laboratory, and patient-reported parameters associated with medium-term risk of hospitalization or death among older adults attending a multidisciplinary outpatient clinic, and to assess the predictive performance of these measures for individual risk stratification. Methods: In this cohort study, 350 adults aged ≥65 years were assessed at baseline and followed for an average of 8 months. The primary outcome was a composite of hospitalization or all-cause mortality. Parameters assessed included frailty and comorbidity measures, functional parameters, such as gait speed and grip strength, laboratory biomarkers, and patient-reported measures, such as quality of life (QoL, assessed on a Likert scale) and the presence of depressive symptoms. Predictive performance was evaluated using univariable logistic regression and multivariable modeling. Discriminative ability was assessed via area under the ROC curve (AUC), and selected models were internally validated using repeated k-fold cross-validation. Results: Overall, 40 participants (11.4%) experienced hospitalization or death. Traditional clinical risk indicators, including frailty and comorbidity scores, were significantly associated with the outcome. Patient-reported QoL (AUC = 0.74) and Geriatric Depression Scale (GDS) scores (AUC = 0.67) demonstrated useful overall discriminatory ability, with high specificities at optimal cut-offs, suggesting they could act as “red flags” for adverse outcomes. However, the limited sensitivities of individual predictors underscore the need for more comprehensive screening instruments with improved ability to identify at-risk individuals earlier. A multivariable model that incorporated several predictors did not outperform QoL alone (AUC = 0.79), with cross-validation confirming comparable discriminative performance. Conclusions: Patient-reported measures—particularly quality of life and depressive symptoms—are valuable predictors of hospitalization or death and may enhance traditional frailty and comorbidity assessments in outpatient geriatric care. Future work should focus on developing or integrating screening tools with greater sensitivity to optimize early risk detection and guide preventive interventions. Full article

Circulating fluidized bed fly ash (CFBFA) stockpiles release alkaline dust, high-pH leachate, and secondary CO₂/SO₂—an environmental burden that exceeds 240 Mt yr⁻¹ in China alone. Yet, barely 25% is recycled, because the high f-CaO/SO₃ contents destabilize conventional cementitious products. Here, we presents a pressurized flue gas heat curing (FHC) route to bridge this scientific deficit, converting up to 85 wt% CFBFA into structural lightweight gravel. The gypsum dosage was optimized, and a 1:16 (gypsum/CFBFA) ratio delivered the best compromise between early ettringite nucleation and CO₂-uptake capacity, yielding the highest overall quality. The optimal mix reaches 9.13 MPa 28-day crushing strength, 4.27% in situ CO₂ uptake, 1.75 g cm⁻³ bulk density, and 3.59% water absorption. Multi-technique analyses (SEM, XRD, FTIR, TG-DTG, and MIP) show that FHC rapidly consumes expansive phases, suppresses undesirable granular-ettringite formation, and produces a dense calcite/needle-AFt skeleton. The FHC-treated CFBFA composite gravel demonstrates 30.43% higher crushing strength than JTG/TF20-2015 standards, accompanied by a water absorption rate 28.2% lower than recent studies. Its superior strength and durability highlight its potential as a low-carbon lightweight aggregate for structural engineering. A life-cycle inventory gives a cradle-to-gate energy demand of 1128 MJ t⁻¹ and a process GWP of 226 kg CO₂-eq t⁻¹. Consequently, higher point-source emissions paired with immediate mineral sequestration translate into a low overall climate footprint and eliminate the need for CFBFA landfilling. Full article

Aluminium alloy 6082 is widely used in the automotive and aerospace industries due to its high strength-to-weight ratio. However, its structural integrity can sometimes be affected by an early fatigue failure. This study investigates the fatigue performance of extruded 6082-T6 samples through a series of fatigue tests conducted at varying stress levels. The material showed significant variability under identical fatigue conditions, suggesting the presence of microstructural defects. Scanning electron microscopy with energy-dispersive spectroscopy (SEM/EDS) and scanning transmission electron microscopy (S/TEM) were used to identify the nature and location of the defects and evaluate the underlying mechanisms influencing the fatigue performance. Computer tomography (CT) also confirmed the presence of oxide inclusions on the fracture surface and near the edges of the samples. These oxide inclusions are distributed throughout the material heterogeneously and in the form of broken oxide films, suggesting that they might have originated during the material’s early processing stages. These oxides acted as stress concentrators, initiating microcracks that led to catastrophic and unpredictable early failure, ultimately reducing the fatigue life of micro-oxide-containing samples. These results highlight the need for better casting control and improved post-processing techniques to minimise the effect of oxide presence in the final components, thus enhancing their fatigue life. Full article

To reveal the long-term deformation behavior of high-volume fly ash-based backfill under continuous mining and backfilling, a fly ash–cement backfill material with 73.0% fly ash content was developed, and creep characteristic tests considering initial damage were conducted. The results demonstrate that: (1) A calculation method for the initial damage of backfill based on stress–strain hysteresis loop cycles is proposed, with cumulative characteristics of initial damage across mining phases analyzed; (2) Creep behaviors of backfill affected by initial damage are investigated, revealing the weakening effect of initial damage on long-term bearing capacity; (3) An enhanced, nonlinear plastic damage element is developed, enabling the construction of an HKBN constitutive model capable of characterizing the complete creep behavior of backfill materials. The research establishes a theoretical framework for engineering applications of backfill materials with early-age strength below 5 MPa, while significantly enhancing the utilization efficiency of coal-based solid wastes. Full article