Search Results (1,948)

With the advancement of modern engineering structures, traditional cement concrete is increasingly unable to meet the mechanical performance requirements under complex conditions. To overcome the performance limitations of materials, modified concrete has become a focal point of research. By incorporating modifying materials such as fibers, polymers, or mineral admixtures, the properties of concrete can be significantly enhanced. Among these, rubberized concrete has attracted considerable attention due to its unique performance advantages. This study conducted fracture tests on rubberized concrete using non-standard concrete three-point bending beam specimens of varying dimensions to evaluate its fracture performance. Employing conventional concrete fracture theoretical models, the fracture toughness parameters of rubberized concrete were calculated, and a comparative analysis was performed regarding the applicability of various theoretical calculation formulas to rubberized concrete. The results indicated that the fracture performance of rubberized concrete varied significantly with changes in specimen size. The initial toughness exhibited a consistent size-dependent variation across different theoretical models. The fracture toughness corresponding to crack height ratios between 0.05 and 0.25 showed contradictory trends; however, for crack height ratios between 0.3 and 0.5, the fracture toughness became consistent. This study integrated boundary effect theory and employed Guinea’s theory to propose an optimization coefficient γ for the double-K fracture toughness formula, yielding favorable optimization results. Full article

The entanglement entropy of a random tensor network (RTN) is studied using tools from free probability theory. Random tensor networks are simple toy models that help in understanding the entanglement behavior of a boundary region in the anti-de Sitter/conformal field theory (AdS/CFT) context. These can be regarded as specific probabilistic models for tensors with particular geometry dictated by a graph (or network) structure. First, we introduce a model of RTN obtained by contracting maximally entangled states (corresponding to the edges of the graph) on the tensor product of Gaussian tensors (corresponding to the vertices of the graph). The entanglement spectrum of the resulting random state is analyzed along a given bipartition of the local Hilbert spaces. The limiting eigenvalue distribution of the reduced density operator of the RTN state is provided in the limit of large local dimension. This limiting value is described through a maximum flow optimization problem in a new graph corresponding to the geometry of the RTN and the given bipartition. In the case of series-parallel graphs, an explicit formula for the limiting eigenvalue distribution is provided using classical and free multiplicative convolutions. The physical implications of these results are discussed, allowing the analysis to move beyond the semiclassical regime without any cut assumption, specifically in terms of finite corrections to the average entanglement entropy of the RTN. Full article

Based on an integrated analysis, this study summarized the current status of soil quality in Yantai apple orchards, developed a multivariate regulation model for key soil physicochemical properties, and proposed optimized fertilization strategies to improve soil quality in the region. The study analyzed the physicochemical properties of the topsoil (0–30 cm) in 19 representative apple orchards across Yantai, including indicators like pH, organic matter (OM), major nutrient ions, and salinity indicators, using standardized measurements and multivariate statistical methods, including descriptive statistics analysis, frequency distribution analysis, canonical correlation analysis, stepwise regression equation analysis, and regression fit model analysis. The results demonstrated that in apple orchards across the Yantai region, reductions in pH were significantly mitigated under the combined increased OM and exchangeable calcium (Ca). Exchangeable potassium (EK) rose in response to the joint elevation of OM and available nitrogen (AN), and AN was also positively influenced by EK, while OM also exhibited a promotive effect on Olsen phosphorus (OP). Furthermore, Ca increased with higher pH. AN and EK jointly contributed to the increases in electrical conductivity (EC) and chloride ions (Cl), while elevated exchangeable sodium (Na) and soluble salts (SS) were primarily driven by EK. Accordingly, enhancing organic and calcium source fertilizers is recommended to boost OM and Ca levels, reduce acidification, and maintain EC within optimal limits. By primarily reducing potassium’s application, followed by nitrogen and phosphorus source fertilizers, the supply of macronutrients can be optimized, and the accumulation of Na, Cl, and SS can be controlled. Collectively, the combined analysis of soil quality status and the multivariate regulation model clarified the optimized fertilization strategies, thereby establishing a solid theoretical and practical foundation for recognizing the necessity of soil testing and formula fertilization, the urgency of improving soil quality, and the scientific rationale for nutrient input management in Yantai apple orchards. Full article

The consumption of fresh fruit and vegetables is essential for a healthy diet as they contain a diverse composition of vitamins, minerals, fibre, and bioactive compounds. However, cross-contamination during harvest and post-harvest poses a high risk of microbial contamination. Therefore, handling fruit and vegetables during processing and contact with wet equipment and utensil surfaces is an ideal environment for microbial contamination and foodborne illness. Nevertheless, less attention has been paid to some emerging pathogens that are now increasingly recognised as transmissible to humans through contaminated fruit and vegetables, such as Arcobacter and Cronobacter species in various products, which are the main risk in fruit and vegetables. Cronobacter and Arcobacter spp. are recognised food-safety hazards because they pose a risk of foodborne disease, especially in vulnerable groups such as newborns and immunocompromised individuals. Cronobacter spp. have been linked to severe infant conditions—notably meningitis and sepsis—most often traced to contaminated powdered infant formula. Although Arcobacter spp. have been less extensively studied, they have also been associated with foodborne disease, chiefly from dairy products and meat. With this in mind, this review provides an overview of the main chemical and physical sanitisation methods in terms of their ability to reduce the contamination of fresh fruit and vegetable products caused by two emerging pathogens: Arcobacter and Cronobacter. Emerging chemical (organic acid compounds, extracts, and essential oils) and physical methods (combination of UV-C with electrolysed water, ultrasound, and cold atmospheric plasma) offer innovative and environmentally friendly alternatives to traditional approaches. These methods often utilise natural materials, less toxic solvents, and novel techniques, resulting in more sustainable processes compared with traditional methods that may use harsh chemicals and environmentally harmful processes. This review provides the fruit and vegetable industry with a general overview of possible decontamination alternatives to develop optimal and efficient processes that ensure food safety. Full article

Blood analogs are widely employed in in vitro experiments such as particle image velocity (PIV) to secure hemodynamics, assisting pathophysiological diagnoses of neurovascular and cardiovascular diseases, as well as pre-surgical planning and intraoperative orientation. To obtain accurate physical parameters, which are critical for diagnosis and treatment, blood analogs should exhibit realistic non-Newtonian shear-thinning features. In this study, two types of blood analogs working under room temperature (293.15 K) were created to mimic the steady-state shear-thinning features of blood over a temperature range of 295 to 312 K and a shear range of 1~250 s⁻¹ at a hematocrit of ~40%. Type I was a general-purpose analog composed of deionized (DI) water and xanthan gum (XG) powder, while Type II was specially designed for PIV tests, incorporating DI water, XG, and fluorescent microspheres. By minimizing the root mean square deviation between generated blood analogs and an established viscosity model, formulas for both blood analogs were successfully derived for the designated temperatures. The results showed that both blood analogs could replicate the shear-thinning viscosities of real blood, with the averaged relative discrepancy < 5%. Additionally, a strong linear correlation was observed between body temperature and XG concentration in both blood analogs (coefficient of determination > 0.96): for Type I, 295–312 K correlates with 140–520 ppm, and for Type II, 295–315 K correlates with 200–560 ppm. This work bridges the gap between idealized steady-state non-Newtonian viscosity models of blood and the complexities of real-world physiological conditions, offering a versatile platform for advancing particle image velocimetry tests and hemodynamics modeling, optimizing therapeutic interventions, and enhancing biomedical technologies in temperature-sensitive environments. Full article

The research and development of the next generation of supersonic transports (SSTs) meets economic and environmental problems. An SST encounters critical challenges, including the need for low fuel consumption, low noise, and low gas emissions. Currently, the feasibility of developing SSTs is increasing through the application of cutting-edge technologies, such as composite materials, advanced electric systems, sustainable aviation fuel, and innovative design methodologies. The object of this study was to perform the conceptual design of a 50-seat supersonic transport utilizing general conceptual design methods. In estimating weight and flight performance, statistical formulae were correlated with data from civil supersonic and subsonic jet transports. For wing sizing, carpet plots were created to explore the optimal combination of wing aspect ratio and wing loading. The results suggested that by utilizing advanced technologies, such as the use of a composite material for the structure, the maximum takeoff weight can potentially be reduced while still meeting design requirements. The constraint of climb gradient largely affects the maximum takeoff weight, and it is anticipated that flight performance at low speeds will be improved. Full article

This study develops two machine learning models to predict the medal performance of countries at the 2028 Olympic Games while systematically analyzing and quantifying the impacts of the host effect and exceptional coaching on medal gains. The dataset encompasses records of total medals by country, event categories, and athletes’ participation from the Olympic Games held between 1896 and 2024. We use K-means clustering to analyze medal trends, categorizing 234 nations into four groups (${\alpha }_1$, ${\alpha }_2$, ${\alpha }_3$, ${\alpha }_4$). Among these, ${\alpha }_1$, ${\alpha }_2$, ${\alpha }_3$ represent medal-winning countries, while ${\alpha }_4$ consists of non-medal-winning nations. For the $\alpha$1, $\alpha$2, and $\alpha$3 groups, 2–3 representative countries from each are selected for trend analysis, with the United States serving as a case study. This study extracts ten factors that may influence medal wins from the dataset, including participant data, the number of events, and medal growth rates. Factor analysis is used to reduce them into three principal components: Factor analysis condenses ten influencing factors into three principal components: the event scale factor (F1), the medal trend factor (F2), and the gender and athletic ability factor (F3). An ARIMA model predicts the factor coefficients for 2028 as 0.9539, 0.7999, and 0.2937, respectively. Four models (random forest, BP Neural Network, XGBoost, and SVM) are employed to predict medal outcomes, using historical data split into training and testing sets to compare their predictive performance. The research results show that XGBoost is the optimal medal predicted model, with the United States projected to win 57 gold medals and a total of 135 medals in 2028. For non-medal-winning countries (${\alpha }_4$), a three-layer fully connected neural network (FCNN) is constructed, achieving an accuracy of 85.5% during testing. Additionally, a formula to calculate the host effect and a Bayesian linear regression model to assess the impact of exceptional coaching on athletes’ medal performance are proposed. The overall trend of countries in the $\alpha$1 group is stable, but they are significantly affected by the host effect; the trend in the $\alpha$2 group shows an upward trend; the trend in the $\alpha$3 group depend on the athletes’ conditions and whether the events they excel in are included in that year’s Olympics. In the $\alpha$4 group, the probabilities of the United Arab Republic (UAR) and Mali (MLI) winning medals in the 2028 Olympic Games are 77.47% and 58.47%, respectively, and there are another four countries with probabilities exceeding 30%. For the eight most recent Olympic Games, the gain rate of the host effect is 74%. Great coaches can bring an average increase of 0.2 to 0.5 medals for each athlete. The proposed models, through an innovative integration of clustering, dimensionality reduction, and predictive algorithms, provide reliable forecasts and data-driven insights for optimizing national sports strategies. These contributions not only address the gap in predicting first-time medal wins for non-medal-winning nations but also offer guidance for policymakers and sports organizations, though they are constrained by assumptions of stable historical trends, minimal external disruptions, and the exclusion of unknown athletes. Full article

Freeze–thaw damage is a critical durability challenge in cold climates that leads to surface spalling, cracking, and degradation of structural performance. In northern China, the severity of winter conditions further accelerates the degradation of concrete infrastructure. This study investigates the reinforcement of frost-damaged concrete using engineered cementitious composites (ECC) prepared with Yellow River sedimentary sand (YRS), employed as a 100% mass replacement for quartz sand to promote sustainability. The interface bonding performance of ECC-C40 specimens was evaluated by testing the impact of various surface roughness treatments, freeze–thaw cycles, and interface agents. A multi-factor predictive formula for determining interface bonding strength was created, and the bonding mechanism and model were examined through microscopic analysis. The results show that ECC made with YRS significantly improved the interface bonding performance of ECC-C40 specimens. Specimens treated with a cement expansion slurry as the interface agent and those subjected to the splitting method for surface roughness achieves the optimal reinforced condition, exhibited a 27.57%, 35.17%, 43.57%, and 42.92% increase in bonding strength compared to untreated specimens under 0, 50, 100, and 150 cycles, respectively. Microscopic analysis revealed a denser interfacial microstructure. Without an interface agent, the bond interface followed a dual-layer, three-zone model; with the interface agent, a three-layer, three-zone model was observed. Full article

As a classical statistical method, multiple regression is widely used for forecasting tasks in power, medicine, finance, and other fields. The rise of machine learning has led to the adoption of neural networks, particularly Long Short-Term Memory (LSTM) models, for handling complex forecasting problems, owing to their strong ability to capture temporal dependencies in sequential data. Nevertheless, the performance of LSTM models is highly sensitive to hyperparameter configuration. Traditional manual tuning methods suffer from inefficiency, excessive reliance on expert experience, and poor generalization. Aiming to address the challenges of complex hyperparameter spaces and the limitations of manual adjustment, an enhanced sparrow search algorithm (ISSA) with adaptive parameter configuration was developed for LSTM-based multivariate regression frameworks, where systematic optimization of hidden layer dimensionality, learning rate scheduling, and iterative training thresholds enhances its model generalization capability. In terms of SSA improvement, first, the population is initialized by the reverse learning strategy to increase the diversity of the population. Second, the mechanism for updating the positions of producer sparrows is improved, and different update formulas are selected based on the sizes of random numbers to avoid convergence to the origin and improve search flexibility. Then, the step factor is dynamically adjusted to improve the accuracy of the solution. To improve the algorithm’s global search capability and escape local optima, the sparrow search algorithm’s position update mechanism integrates Lévy flight for detection and early warning. Experimental evaluations using benchmark functions from the CEC2005 test set demonstrated that the ISSA outperforms PSO, the SSA, and other algorithms in optimization performance. Further validation with power load and real estate datasets revealed that the ISSA-LSTM model achieves superior prediction accuracy compared to existing approaches, achieving an RMSE of 83.102 and an $R^2$ of 0.550 during electric load forecasting and an RMSE of 18.822 and an $R^2$ of 0.522 during real estate price prediction. Future research will explore the integration of the ISSA with alternative neural architectures such as GRUs and Transformers to assess its flexibility and effectiveness across different sequence modeling paradigms. Full article

Reinforced concrete structures are prone to the development of microcracks during service. In this study, a composite-modified epoxy potting adhesive was formulated using nano-TiO₂, carboxyl-terminated butadiene nitrile liquid rubber (CTBN), and the reactive diluent D-669. The mechanical properties and effectiveness of this composite adhesive in repairing oblique cracks were systematically evaluated and compared with those of single-component-modified epoxy adhesives. Key material parameters influencing the performance of oblique crack repair were identified, and the underlying repair mechanisms were analyzed. Based on these findings, a theoretical formula for calculating the shear-bearing capacity of beams with repaired web reinforcement was proposed. Experimental results demonstrated that compared to single-component-modified epoxy resin, the optimally formulated composite adhesive improved the tensile strength, elongation at break, and bond strength by 4.07–21.16 MPa, 13.28–20.4%, and 1.05–3.79 MPa, respectively, while reducing the viscosity by 48–872 mPa·s. The viscosity of the adhesive was found to play a critical role in determining the repair effectiveness, with toughness enhancing the crack resistance and bond strength contributing to the structural stiffness recovery. The adhesive effectively penetrated the steel–concrete interface, forming a continuous bonding layer that improved energy dissipation and significantly enhanced the load-bearing capacity of the repaired beams. Full article

Evidence based practice enhances healthcare delivery and prevents unsafe procedures. While competency based assessments of resident cataract surgery are standard, evidence based analysis of refractive outcomes remains underutilized in educational curricula. This retrospective single center study evaluated refractive outcomes from 21 novice ophthalmology resident surgeons. Three independent groups were compared based on formal constant optimization for intraocular lens (IOL) calculation: non-optimized Haigis (n = 216), a0-optimized (n = 94), and a0/a1/a2-optimized (n = 121). All surgeries were supervised by a single attending surgeon. Mean absolute error (MAE) and the percentage of eyes within ±0.25 D and ±0.50 D of predicted spherical equivalent (SEQ) were calculated. Also, systematic bias in effective lens position (ELP) was analyzed to update manufacturer IOL constants. MAE improved from 0.44 D (non-optimized) to 0.35 D (a0-optimized p = 0.009) and 0.19 D (a0/a1/a2-optimized p < 0.001). The percentage within ±0.50 D increased from 65.7% to 74.4% to 95.0%, respectively. With ELP bias correction, updated A constant and ACD were 119.266 and 5.755 mm. a0/a1/a2-optimized outcomes were comparable to ELP bias correction for the Barrett UII, Kane, and Hill-RBF formulas. Evidence based optimization of IOL constants significantly enhances novice resident surgical outcomes, achieving parity with prediction models. A formal curriculum on IOL calculation and optimization is warranted. Full article

The implementation of a carbon tax in the transportation sector aims to reduce carbon emissions and encourage the transition to sustainable mobility amid increasing urbanization. The transportation sector is one of the largest contributors of carbon emissions in Indonesia, requiring effective policies to reduce its environmental impacts. Therefore, this study aims to find a more optimal carbon tax formula that is in accordance with Indonesia’s socio-economic conditions. The approach used includes analysis of transportation emission data, the economic impact of different carbon tax schemes, and tax revenue allocation strategies to support green infrastructure and sustainable transportation. The results of the study indicate that an adaptive carbon tax formula in the transportation sector is able to balance the economic burden, emission reduction targets, social justice, behavioral changes, and revenue allocation for green infrastructure, thus ensuring a just and sustainable transition. A progressive carbon tax, based on vehicle emission levels and fuel types, can encourage the transition to low-emission vehicles without excessively burdening low-income communities. With this approach, carbon tax policy functions not only as a fiscal instrument but also as a transformative strategy in creating an environmentally friendly and equitable transportation system. Full article

This paper introduces a groundbreaking decentralized approach for real-time bus monitoring and geo-location, leveraging advanced geo-statistical and multivariate statistical methods. The proposed long short-term memory (LSTM) model predicts bus arrival times with confidence intervals and reconstructs missing positioning data, offering cities an accurate, resource-efficient tracking solution within typical infrastructure limits. By employing decentralized data processing, our system significantly reduces network traffic and computational load, enabling data sharing and sophisticated analysis. Utilizing the Haversine formula, the system estimates pessimistic and optimistic arrival times, providing real-time updates and enhancing the accuracy of bus tracking. Our innovative approach optimizes real-time bus tracking and arrival time estimation, ensuring robust performance under varying traffic conditions. This research demonstrates the potential of integrating advanced statistical techniques with decentralized computing to revolutionize public transit systems. Full article

Aiming at the problems of insufficient natural productivity and large seepage resistance in low-permeability oil and gas reservoirs, a nano relative permeability improver based on nano SiO₂ was developed in this study. The nano relative permeability improver was prepared by the reversed-phase microemulsion method, and the formula was optimized (nano SiO₂ 5.1%, Span-80 33%, isobutanol 18%, NaCl 2%), so that the minimum median particle size was 4.2 nm, with good injectivity and stability. Performance studies showed that the improvement agent had low surface tension (30–35 mN/m) and interfacial tension (3–8 mN/m) as well as significantly reduced the rock wetting angle (50–84°) and enhanced wettability. In addition, it had good temperature resistance, shear resistance, and acid-alkali resistance, making it suitable for complex environments in low-permeability reservoirs. Full article

Bio-nanocomposite films were prepared using chitosan, gelatin, and varying concentrations (0, 0.5, 1.0, 2.0, and 5.0 wt%) of titanium dioxide (TiO₂) nanoparticles in acetic acid via a casting method. The incorporation of TiO₂ nanoparticles into the bio-chitosan matrix enhanced ultraviolet (UV) absorption and improved the films’ physical, mechanical, and electrical properties. Additionally, the TiO₂-loaded films exhibited antimicrobial activity, contributing to the extended preservation of packaged products by inhibiting microbial growth. Notably, the bio-nanocomposite films containing 1.0 wt% TiO₂ exhibited an electroactive response, bending under relatively low electric field strength (250 V/mm), whereas the control film without TiO₂ required higher field strength (550 V/mm) to achieve bending. This indicates potential applications in electroactive actuators requiring precise movement control. Among the tested concentrations, films containing 0.5 wt% and 1.0 wt% TiO₂ (Formulas 7 and 8) demonstrated optimal performance. These films presented a visually appealing appearance with no tear marks, low bulk density (0.91 ± 0.04 and 0.85 ± 0.18 g/cm³), a satisfactory electromechanical response at 250 V/m (17.85 ± 2.58 and 61.48 ± 6.97), low shrinkage percentages (59.95 ± 3.59 and 54.17 ± 9.28), high dielectric constant (1.80 ± 0.07 and 8.10 ± 0.73), and superior UV absorption compared with pure bio-chitosan films, without and with gelatin (Formulas 1 and 6). Full article