Search Results (23,256)

Ferritic lightweight steels are an emerging class of low-density steels (LDSs) with promising mechanical properties. The study aimed to develop two ferritic lightweight steels with different Mn concentrations. Al was incorporated to achieve the lightweighting effect due to its relatively low atomic mass of substitutional solutions. The C concentration was kept at a minimum level to avoid the precipitation of carbides and the Mn addition was intended to increase solid solution strengthening. Thermodynamic calculations (Thermo-Calc) were employed to design the composition, analyze the phase constituents, and predict the phase transformation behavior. Microstructural investigation and hardness tests were conducted to experimentally verify the calculations. Both produced alloys exhibited a fully ferritic microstructure. Compared to industrially produced DP980 steel, a density reduction of about 7.2% and 8.3% was attained for the Fe-0.04C-5.5Al-1.6Mn-0.075Nb and Fe-0.04C-5.6Al-5.5Mn-0.08Nb steels, respectively. The steel with the higher Mn content showed increased hardness attributed to its solution strengthening effect. An increase in the hardness values was also measured with the progress in hot-rolling thickness reductions for both alloys. The alloying elements influenced the microstructural characteristics, phase transformation behavior, density, and hardness of the newly designed lightweight steels. Full article

With the expansion of offshore and subsea infrastructure in Arctic and sub-Arctic regions, concerns are rising, driven by climate change and global warming, over the risk of drifting icebergs colliding with these structures in cold waters. Traditional methods for estimating iceberg underwater height and assessing subgouge soil properties, such as costly and time-consuming underwater surveys or centrifuge tests, are still used, but the industry continues to seek faster and more cost-efficient solutions. In this study, the extra tree regression (ETR) algorithm was employed for the first time to simultaneously model iceberg drafts and subgouge soil properties in both sandy and clay seabeds. The ETR approach first predicted the iceberg draft, then simulated subgouge soil reaction forces and deformations. A total of 22 ETR models were developed, incorporating parameters relevant to both iceberg draft estimation and subgouge soil characterization. The best-performing ETR models, along with the most influential input variables, were identified through a combination of sensitivity, error, discrepancy, and uncertainty analyses. The ETR model predicted iceberg draft with a high level of accuracy (R = 0.920, RMSE = 1.081), while the superior model for vertical reaction force in sand achieved an RMSE of 43.95 with 70% of predictions within 16% error. The methodology demonstrated improved prediction capacity over traditional techniques and can serve early-stage iceberg risk management. Full article

Traditional crosslinked-polyethylene (XLPE) insulation suffers from high recycling costs and low efficiency due to its thermosetting properties. In contrast, thermoplastic polypropylene (PP), with advantages of melt recyclability, low energy consumption, and excellent comprehensive performance, has emerged as an ideal alternative to XLPE. This study conducts a comparative analysis of low-voltage cables insulated with PP, silane-crosslinked XLPE (XLPE-S), and UV-crosslinked XLPE (XLPE-U), focusing on production processes, mechanical properties, thermal stability, and electrical performance. Tensile test results show that PP exhibits the highest elongation at break (>600%) before aging, and its tensile strength (>20 MPa) after aging outperforms that of XLPE, indicating superior flexibility and anti-aging capability. PP exhibits a lower thermal elongation (<50%) at 140 °C compared to XLPE, and its high-crystallinity molecular structure endows better heat-resistant deformation performance. The volume resistivity of PP reaches 9.2 × 10¹⁵ Ω·m, comparable to that of XLPE-U (3.9 × 10¹⁵ Ω·m) and significantly higher than XLPE-S (3.0 × 10¹⁴ Ω·m). All three materials pass the 4-h voltage withstand test, confirming their satisfied insulation reliability. PP-insulated low-voltage cables demonstrate balanced performance in production efficiency, energy consumption cost, mechanical toughness, and electrical insulation. Notably, their recyclability significantly surpasses traditional XLPE, showing potential to promote green upgrading of the cable industry and providing a sustainable insulation solution for low-voltage power distribution systems. Full article

Poly(3-hydroxybutyrate) is a biobased and biodegradable polymer, produced via bacterial fermentation and characterized by an isotactic structure and mechanical properties similar to those of polyethylene and polypropylene. However, its brittleness—due to high crystallinity (~70%) and thermal degradation, starting at a temperature range of 180–190 °C near its melting point (175 °C)—makes its processing difficult and limits its applications. Most recent studies on modifying P3HB involved solution casting, typically using chloroform, which raises sustainability concerns. In this study blends of isotactic poly(3-hydroxybutyrate) (i-P3HB) with atactic poly(3-hydroxybutyrate) (a-P3HB) and poly(3-hydroxybutyrate-co-4-hydroxybutyrate) (P34HB) were prepared through solvent-free extrusion, and the thermal and mechanical properties of these blends were characterized. The obtained blends showed an extended processing window with reduced processing temperatures (150–160 °C), which were significantly lower than the onset of the decomposition temperature of i-P3HB, thereby avoiding thermal degradation. Furthermore, the crystallinity of these blends could be varied between 17 and 70%, depending on the polymer ratio, which allows for tailormade materials with tunable mechanical properties and an elongation at break up to 600%. Based on the results, the obtained blends in this study are promising candidates for various applications and processing techniques, such as injection molding, extrusion, and fiber spinning, offering a sustainable alternative to conventional plastics. Full article

Mica is a naturally layered material recognized for its superior insulation and exceptional barrier properties; however, it is prone to agglomeration, and its compatibility with resin remains to be resolved. In this work, phosphate butynediol ethoxylate (PBEO), synthesized by the reaction of a commercial corrosion inhibitor, butynediol ethoxylate, with phosphorus pentoxide, was employed to modify mica nanosheets (MNs), as evidenced by FTIR, Raman, and XPS. The obtained MN@PBEO demonstrated improved water dispersibility and enhanced compatibility with acrylic latex. EIS measurements revealed that the impedance (|Z|_0.01Hz) for the waterborne acrylic coating with 0.5 wt% MN@PBEO was approximately an order of magnitude greater than that of the pure waterborne acrylic coating after 28 days of immersion in a 3.5 wt% NaCl solution. Additionally, compared to the pure waterborne acrylic coating, the 0.5 wt% MN@PBEO/acrylic nanocomposite coating on Q235 carbon steel exhibited a water diffusion coefficient that was roughly ten times lower, demonstrating substantially enhanced corrosion protection, attributable to its superior barrier properties. Full article

Traditional hydraulic-fracturing models are restricted by low computational efficiency, insufficient field data, and complex physical mechanisms, causing evaluation delays and failing to meet practical engineering needs. To address these challenges, this study innovatively develops a dynamic hydraulic-fracturing monitoring method that integrates machine learning with numerical simulation. Firstly, this study uses GOHFER 9.5.6 software to generate 12,000 sets of fracture geometry data and constructs a big dataset for hydraulic fracturing. In order to improve the efficiency of the simulation, a macro command is used in combination with a Python 3.11 code to achieve the automation of the simulation process, thereby expanding the data samples for the surrogate model. On this basis, a parameter sensitivity analysis is carried out to identify key input parameters, such as reservoir parameters and fracturing fluid properties, that significantly affect fracture geometry. Next, a neural-network surrogate model is established, which takes fracturing geological parameters and pumping parameters as inputs and fracture geometric parameters as outputs. Data are preprocessed using the min–max normalization method. A neural-network structure with two hidden layers is chosen, and the model is trained with the Adam optimizer to improve its predictive accuracy. The experimental results show that the efficiency of automated numerical simulation for hydraulic fracturing is significantly improved. The surrogate model achieved a prediction accuracy of over 90% and a response time of less than 10 s, representing a substantial efficiency improvement compared to traditional fracturing models. Through these technical approaches, this study not only enhances the effectiveness of fracturing but also provides a new, efficient, and accurate solution for oilfield fracturing operations. Full article

The observed increase in the diversity and level of pollutant content in the water environment forces the development of more effective technologies for their removal. Using nanomaterials in water and wastewater treatment offers numerous opportunities to remove organic and inorganic contaminants that are hardly removable in conventional processes. In this group, carbon-based nanomaterials, mainly carbon nanotubes (CNTs), graphene (Gr), and graphene oxide (GO), are very popular. This review aims to present the directions and diversity of applications of carbon-based nanomaterials (CNMs) in water and wastewater technology, as well as the challenges and environmental dangers that new solutions entail. Authors also present the results of the research on the changes in properties of GO produced in the laboratory as water suspension and a freeze-dried product over time. The results confirm the significant influence of the form of graphene oxide and its storage time on the structural properties, hydrophilicity, and stability of GO. Therefore, they should be considered when selecting an adsorbent or reaction catalyst in environmental applications for developing new greener and sustainable methods of treatment and purification, which use fewer reagents and release safer products. Full article

The application of conductive polymers in wound dressings presents great potential for accelerated wound healing since their high electrical conductivity and biocompatibility facilitate the delivery of external electrical stimuli to cells and tissues, promoting cell differentiation and proliferation. Electrospinning is a very straightforward method for the preparation of polymeric wound dressings capable of mimicking the extracellular matrix of skin, promoting hemostasis, absorbing wound exudate, allowing atmospheric oxygen permeation and maintaining an appropriately moist environment. In this work, in situ chemically polymerized poly(3,4-ethylenedioxythiophene) (PEDOT) was achieved through hyaluronic acid-doping. The synthesized PEDOT was used for the production of conductive and biodegradable chitosan (CS)/gelatin (GEL)/PEDOT electrospun meshes. Additionally, the randomly aligned meshes were crosslinked with a 1,4-butanediol diglycidyl ether and their physicochemical and mechanical properties were investigated. The results show that the incorporation of a conductive polymer led to an increase in conductivity of the solution, density and fiber diameter that influenced porosity, water uptake, and dissolvability and biodegradability of the meshes, while maintaining appropriate water vapor permeation values. Due to their intrinsic similarity to the extracellular matrix and cell-binding sequences, CS/GEL/PEDOT electrospun nanofibrous meshes show potential as conductive nanofibrous structures for electrostimulated wound dressings in skin tissue engineering applications. Full article

Driven by the increasing global population and rapid urbanization, aircraft noise pollution has emerged as a significant environmental challenge, impeding the sustainable development of the aviation industry. Traditional noise prediction methods are limited by incomplete datasets, insufficient spatiotemporal consistency, and poor adaptability to complex meteorological conditions, making it difficult to achieve precise noise management. To address these limitations, this study proposes a novel noise prediction framework based on a hybrid Convolutional Neural Network–Bidirectional Long Short-Term Memory–Attention (CNN–BiLSTM–Attention) model. By integrating multi-source data, including meteorological parameters (e.g., temperature, humidity, wind speed) and aircraft trajectory data (e.g., altitude, longitude, latitude), the framework achieves high-precision prediction of aircraft noise. The Haversine formula and inverse distance weighting (IDW) interpolation are employed to effectively supplement missing data, while spatiotemporal alignment techniques ensure data consistency. The CNN–BiLSTM–Attention model leverages the spatial feature extraction capabilities of CNNs, the bidirectional temporal sequence processing capabilities of BiLSTMs, and the context-enhancing properties of the attention mechanism to capture the spatiotemporal characteristics of noise. The experimental results indicate that the model’s predicted mean value of 68.66 closely approximates the actual value of 68.16, with a minimal difference of 0.5 and a mean absolute error of 0.89%. Notably, the error remained below 2% in 91.4% of the prediction rounds. Furthermore, ablation studies revealed that the complete CNN–BiLSTM–AM model significantly outperformed single-structure models. The incorporation of the attention mechanism was found to markedly enhance both the accuracy and generalization capability of the model. These findings highlight the model’s robust performance and reliability in predicting aviation noise. This study provides a scientific basis for effective aviation noise management and offers an innovative solution for addressing noise prediction problems under data-scarce conditions. Full article

The global transition toward plant-based diets has intensified the search for sustainable protein alternatives, positioning hemp-based meat analogs (HBMAs) as a promising solution due to their exceptional nutritional profile and environmental benefits. This comprehensive review critically examines hemp protein research, focusing on extraction technologies, nutritional excellence, functional innovation, and sustainable processing approaches for meat analog development. Hemp seeds contain 25–30% protein, primarily consisting of highly digestible edestin and albumin proteins that provide a complete amino acid profile comparable to soy and animal proteins. The protein exhibits superior digestibility (>88%) and generates bioactive peptides with demonstrated antioxidant, antihypertensive, and anti-inflammatory properties, offering significant health benefits beyond basic nutrition. Comparative analysis reveals that while alkaline extraction-isoelectric precipitation remains the industrial standard due to cost-effectiveness ($2.50–3.20 kg⁻¹), enzymatic extraction and ultrasound-assisted methods deliver superior functional properties despite higher costs. Hemp protein demonstrates moderate solubility and good emulsifying properties, though its gelation capacity requires optimization through enzymatic hydrolysis, high-pressure processing, or strategic blending with complementary proteins. Processing innovations, particularly high-moisture extrusion combined with protein blending strategies, enable fibrous structures closely mimicking conventional meat texture. Hemp protein can replace up to 60% of soy protein in high-moisture meat analogs, with formulations incorporating wheat gluten or chickpea protein showing superior textural attributes. Despite advantages in nutritional density, sustainability, and functional versatility, HBMAs face challenges including sensory limitations, regulatory barriers, and production scaling requirements. Hemp cultivation demonstrates 40–50% lower carbon footprint and water usage compared with conventional protein sources. Future research directions emphasize techniques and action processes, developing novel protein modification techniques, and addressing consumer acceptance through improved sensory properties for successful market adoption. Full article

Accidental surface surcharge will generate additional load in the stratum, which then leads to unfavorable impacts on the underlying shield tunnel. This paper proposes a probabilistic analysis method to address this problem. In this framework, an improved soil–tunnel interaction model considering the nonlinearity of the subgrade is established at first, and the Newton–Raphson iterative solution algorithm is employed to acquire tunnel responses. Then, the random field models of the initial stiffness and the ultimate reaction of the subgrade are constructed to realize the spatial variability of soil properties. Finally, with the aid of the Monte Carlo Simulation method, the probabilistic analyses on tunnel responses are performed by combining the improved soil–tunnel interaction model and the random field model of subgrade parameters. The applicability and the superiority of the improved soil–tunnel interaction model are validated by a historical case from Shanghai Metro Line 9. The results prove that the traditional linear foundation model will overestimate the bearing capacity of the subgrade, thereby leading to overly optimistic assessments of surcharge-induced tunnel responses. This shortcoming could be addressed by the improved nonlinear soil–tunnel interaction model. The influences of spatial variability of soil properties on tunnel responses are nonnegligible. The stronger the uncertainties of subgrade parameters, in terms of the initial stiffness and the ultimate reaction concerned in this work, the higher the failure risk of the shield tunnel subjected to the surcharge. The failure modes of the tunnel subjected to the surcharge are controlled by the longitudinal curvature radius of the tunnel within the current assessment criteria, which means if this evaluation indicator can be restricted within the allowable value, then the opening of the circumferential joint and the longitudinal settlement can also meet the requirements. Compared with the influences of the uncertainty of the subgrade ultimate reaction, the spatial variability of the subgrade initial stiffness has greater influences on tunnel failure risk under the same conditions. An increase in the range of surcharge will raise the risk of tunnel failure, while the influence of tunnel burial depth is just the opposite. Full article

Water pollution poses serious threats to public health and the environment, therefore requiring efficient and scalable monitoring solutions. This paper presents a cyber–physical system (CPS) that integrates paper-based biosensors with an edge IoT architecture and long-range wide area network (LoRaWAN) for real-time assessment of water quality. The biosensors detect pollutants such as arsenic, lead, and nitrates with a detection limit of 0.5 ppb. The system proposed was compared with existing LSTM systems based on two performance metrics: detection accuracy and latency. Paper-based biosensors were fabricated using silver nanoparticle-functionalized substrates to show high sensitivity and low-cost pollutant detection. TCN algorithm deployment at the edge allows for real-time processing for time-series data analysis due to its high accuracy and low latency properties compared with LSTM models, which were mainly chosen due to their usage in most applications dealing with time-series-based analysis. Experimentation was carried out by deploying the developed CPS in controlled environments, simulating pollutants at different levels, and executing the models to test their accuracy in detecting pollutants and the latency of data processing. The TCN framework achieved a detection accuracy of 98.7%, which surpassed LSTM by 92.4%. In addition, TCN reduced latency in processing by 38% to enable fast data analysis and decision making. LoRaWAN allowed for perfect packet transmission of up to 15 km, while the loss rate stayed as low as 2.1%. These results establish the proposed CPS as reliable, efficient, and scalable for real-time water contamination monitoring. Thus, this research introduces the integration of paper-based biosensors with advanced computational frameworks. Full article

The poor quality of recycled coarse aggregate (RCA), particularly its high water absorption and low strength, has long restricted the development of recycled aggregate concrete (RAC). In this study, a novel combined spraying treatment method integrating cement slurry and a methyl sodium silicate (MSS) solution was proposed to improve the comprehensive performance of RCA. The effects of the treatment on RCA properties, including crushing value, water absorption, dynamic water absorption, apparent density, micromorphology, and contact angle, were systematically investigated. Furthermore, the treated RCA was incorporated into concrete to evaluate the mechanical strength, water absorption, and interfacial transition zone (ITZ) properties of the resulting RAC. The results indicated that cement slurry treatment alone significantly reduced the crushing value of the RCA by 30.1% but had little effect on water absorption. Conversely, MSS solution treatment reduced RCA water absorption by 29.6% without affecting its strength. The combined spraying method successfully enhanced both strength and water absorption performance. When applied in the RAC, cement slurry-treated RCA improved compressive and splitting tensile strengths, while MSS-treated RCA notably reduced water absorption. RAC prepared with combined-treated RCA achieved further strength improvement, and although its water absorption was not as low as that of MSS-only treated RAC, it still showed a substantial decrease compared to untreated RCA. Nanoindentation and microstructural analyses revealed that MSS enhanced the ITZ by forming a hydrophobic molecular film and reacting with new mortar, inhibiting water transport and improving RAC durability. An optimal MSS concentration of 10% was identified for achieving the best combined performance in strength and durability. Full article

This article investigates the precipitation behavior of the phases in metastable β-type titanium alloys (Ti-38644) and their significant impact on the mechanical properties. By manipulating various solid solution aging parameters, the morphology, quantity, and distribution of the αs phase can be optimized. After one hour of solid solution treatment at 760 °C, the alloy is predominantly composed of the β phase, with a higher concentration of aluminum at the grain boundaries compared to the interior of the grains. Subsequently, after ten hours of aging treatment at 450 °C and 470 °C, the needle-shaped αs phase preferentially precipitated at the grain boundaries. As the aging temperature increased to 470 °C, the area percentage of the αs phase rose from 42.36% to 57.34%, while its yield strength (σs) increased from 967 MPa at 450 °C to 1211 MPa at 470 °C. This increase in σs results from the combined effects of dislocation strengthening (${\sigma }_{\rho }$) and precipitation hardening. This article provides a comprehensive theoretical analysis of the various factors that influence σs, offering valuable theoretical support for the development of heat treatment processes for the Ti-38644 titanium alloy. Full article

Traditional educational assessment systems suffer from inefficient question selection strategies that fail to optimally probe student knowledge while requiring extensive testing time. We present a novel hierarchical probabilistic neural framework that integrates Bayesian inference with symmetric deep neural architectures to enable adaptive, efficient knowledge assessment. Our method models student knowledge as latent representations within a graph-structured concept dependency network, where probabilistic mastery states, updated through variational inference, are encoded by symmetric graph properties and symmetric concept representations that preserve structural equivalences across similar knowledge configurations. The system employs a symmetric dual-network architecture: a concept embedding network that learns scale-invariant hierarchical knowledge representations from assessment data and a question selection network that optimizes symmetric information gain through deep reinforcement learning with symmetric reward structures. We introduce a novel uncertainty-aware objective function that leverages symmetric uncertainty measures to balance exploration of uncertain knowledge regions with exploitation of informative question patterns. The hierarchical structure captures both fine-grained concept mastery and broader domain understanding through multi-scale graph convolutions that preserve local graph symmetries and global structural invariances. Our symmetric information-theoretic method ensures balanced assessment strategies that maintain diagnostic equivalence across isomorphic concept subgraphs. Experimental validation on large-scale educational datasets demonstrates that our method achieves 76.3% diagnostic accuracy while reducing the question count by 35.1% compared to traditional assessments. The learned concept embeddings reveal interpretable knowledge structures with symmetric dependency patterns that align with pedagogical theory. Our work generalizes across domains and student populations through symmetric transfer learning mechanisms, providing a principled framework for intelligent tutoring systems and adaptive testing platforms. The integration of probabilistic reasoning with symmetric neural pattern recognition offers a robust solution to the fundamental trade-off between assessment efficiency and diagnostic precision in educational technology. Full article