Search Results (14,840)

To address the challenges associated with fatigue damage monitoring in load-bearing composite structures, we developed a method that utilizes Lamb wave propagation and partial least squares regression (PLSR) for effective monitoring. Initially, we extracted diverse characteristics from both the time and frequency domains of the Lamb wave signal to capture the essence of the damage. Subsequently, we constructed a PLSR model, leveraging Lamb wave multi-feature fusion, specifically tailored for in-service fatigue damage monitoring. The efficacy of our proposed approach in quantitatively monitoring fatigue damage was thoroughly validated through rigorous standard fatigue tests. In practical applications, our model effectively mitigated the impact of multicollinearity among feature variables on model accuracy. Furthermore, the PLSR model demonstrated superior accuracy compared to the PCR model, given an equal number of principal components. To strike a harmonious balance between efficiency and precision, we optimized the size of the feature variable. The results show that the optimized PLSR model achieved an R-squared value exceeding 97% in predicting the in-service damage area. This underscores the robustness and reliability of our method in accurately monitoring fatigue damage in load-bearing composite structures. Full article

This paper investigates the inland container transportation problem with a focus on multi-size containers, fuel consumption, and carbon emissions. To reflect a more realistic situation, the depot’s initial inventory of empty containers is also taken into consideration. To linearly model the constraints imposed by the multiple container sizes and the limited number of empty containers, a novel graphical representation is presented for the problem. Based on the graphical representation, a mixed-integer programming model is presented to minimize the total transportation cost, which includes fixed, fuel, and carbon emission costs. To efficiently solve the model, a tailored branch-and-price algorithm is designed, which is enhanced by improvement schemes including a heuristic label-setting algorithm, decremental state-space relaxation, and the introduction of a high-quality upper bound. Results from a series of computational experiments on randomly generated instances demonstrate that (1) the proposed branch-and-price algorithm demonstrates a superior performance compared to the tabu search algorithm and the genetic algorithm; (2) each additional empty container in the depot reduces the total transportation cost by less than 1%, with a diminishing marginal effect; (3) the rational configuration of different types of trucks improves scheduling flexibility and reduces fuel and carbon emission costs as well as the overall transportation cost; and (4) extending customer time windows also contributes to lower the total transportation cost. These findings not only deepen the theoretical understanding of inland container transportation optimization but also provide valuable insights for logistics companies and policymakers to improve efficiency and implement more sustainable operational practices. Additionally, our research paves the way for future investigations into the integration of dynamic factors and emerging technologies in this field. Full article

The achievement of rapid and reliable image object tracking has long been crucial and challenging for the advancement of image-guided technology. This study investigates real-time object tracking by offering an image target based on nuclear correlation tracking and detection methods to address the challenge of real-time target tracking in complicated environments. In the tracking process, the nuclear-related tracking algorithm can effectively balance the tracking performance and running speed. However, the target tracking process also faces challenges such as model drift, the inability to handle target scale transformation, and target length. In order to propose a solution, this work is organized around the following main points: this study dedicates its first part to the research on kernelized correlation filters (KCFs), encompassing model training, object identification, and a dense sampling strategy based on a circulant matrix. This work developed a scale pyramid searching approach to address the shortcoming that a KCF cannot forecast the target scale. The tracker was expanded in two stages: the first stage output the target’s two-dimensional coordinate location, and the second stage created the scale pyramid to identify the optimal target scale. Experiments show that this approach is capable of resolving the target size variation problem. The second part improved the KCF in two ways to meet the demands of a long-term object tracking task. This article introduces the initial object model, which effectively suppresses model drift. Secondly, an object detection module is implemented, and if the tracking module fails, the algorithm is redirected to the object detection module. The target detection module utilizes two detectors, a variance classifier and a KCF. Finally, this work includes trials on object tracking experiments and subsequent analysis of the results. Initially, this research provides a tracking algorithm assessment system, including an assessment methodology and the collection of test videos, which helped us to determine that the suggested technique outperforms the KCF tracking method. Additionally, the implementation of an evaluation system allows for an objective comparison of the proposed algorithm with other prominent tracking methods. We found that the suggested method outperforms others in terms of its accuracy and resilience. Full article

Nickel manganese oxide (NiMnO₃) combines magnetic and dielectric properties, making it a promising material for sensor and supercapacitor applications, as well as for catalytic water splitting. The efficiency of its utilization is notably influenced by particle size. In this study, we investigate the influence of thermal treatment parameters on the phase composition of products from alkali co-precipitation of nickel and manganese (II) ions and identify optimal conditions for synthesizing phase-pure nickel manganese oxide. Ultrafine nanoparticles of NiMnO₃ (with sizes as small as 2 nm) are obtained via liquid-phase ultrasonic dispersion, exhibiting a narrow size distribution. A systematic exploration of the solvent nature (water, N-methyl-2-pyrrolidone, dimethyl sulfoxide, dimethylformamide) on the efficiency of ultrasonic dispersion of NiMnO₃ nanoparticles is provided. It is demonstrated that particle size is influenced not only by absorbed acoustic power, dependent on the physical properties of the used solvent (boiling temperature, gas solubility, viscosity, density) but also by the chemical stability of the solvent under prolonged ultrasonic treatment. Our findings provide insights for designing ultrasonic treatment protocols for nanoparticle dispersions with tailored particle sizes. Full article

(1) Background: Accurate inversion of crop water content is key to making an intelligent irrigation decision. However, little effort has been devoted to accurately estimating the crop water content of winter wheat in the North China Plain. (2) Method: The crop water content of winter wheat was measured at jointing, flowering and grain-filling stages, respectively. UAV-based multispectral remote sensing images were used to calculate thirteen vegetation indices, including SAVI, EVI, R-M, NDRE, OSAVI, GOSAVI, REOSAVI, GBNDVI, NDVI, RVI, DVI, GNDVI, and TVI. Five machine learning (ML) algorithms (i.e., MLR, RF, PLSR, ElasticNet, and ridge regression) were adopted to estimate the crop water content of winter wheat at the three growth stages. The benchmark datasets, which include CWC as well as vegetation indices calculated based on spectral indices, were adopted to validate the performance of the ML models. (3) Results: The correlation coefficients ranged from 0.64 to 0.82 at different growth stages. The optimal vegetation indices were GNDVI for the jointing stage, NDRE for the flowering and the grain-filling stage, respectively. Among the five machine learning methods, random forest (RF) showed the best performance across the three growth stages, with its coefficient of determination (R²) of 0.80, or an increase by 20.1% than those of other models. In addition, the RMSE and RPD of the RF model at the flowering stage were 3.00% and 2.01, which significantly outperformed other models and growth stages. (4) Conclusion: This study may provide theoretical support and technical guidance for monitoring current water status in wheat crops, which is useful to develop a precise irrigation prescription map for local farmers. (5) Limitation: The main limitation of this study is that the sample size is relatively small and may not fully reflect the characteristics of the target groups. At the same time, subjectivity and bias may exist in the data collection, which may have a certain impact on the accuracy of the results. Future studies could consider expanding sample sizes and improving data collection methods to overcome these limitations. Full article

Robotically selective broccoli harvesting requires precise lightweight detection models to efficiently detect broccoli heads. Therefore, this study introduces a lightweight and high-precision detection model named LBDC-YOLO (Lightweight Broccoli Detection in Complex Environment—You Look Only Once), based on the improved YOLOv8 (You Look Only Once, Version 8). The model incorporates the Slim-neck design paradigm based on GSConv to reduce computational complexity. Furthermore, Triplet Attention is integrated into the backbone network to capture cross-dimensional interactions between spatial and channel dimensions, enhancing the model’s feature extraction capability under multiple interfering factors. The original neck network structure is replaced with a BiFPN (Bidirectional Feature Pyramid Network), optimizing the cross-layer connection structure, and employing weighted fusion methods for better integration of multi-scale features. The model undergoes training and testing on a dataset constructed in real field conditions, featuring broccoli images under various influencing factors. Experimental results demonstrate that LBDC-YOLO achieves an average detection accuracy of 94.44% for broccoli. Compared to the original YOLOv8n, LBDC-YOLO achieves a 32.1% reduction in computational complexity, a 47.8% decrease in parameters, a 44.4% reduction in model size, and a 0.47 percentage point accuracy improvement. When compared to models such as YOLOv5n, YOLOv5s, and YOLOv7-tiny, LBDC-YOLO exhibits higher detection accuracy and lower computational complexity, presenting clear advantages for broccoli detection tasks in complex field environments. The results of this study provide an accurate and lightweight method for the detection of broccoli heads in complex field environments. This work aims to inspire further research in precision agriculture and to advance knowledge in model-assisted agricultural practices. Full article

In this work, a comprehensive characterization of microplastic samples collected from unique geographical locations, including the Mediterranean Sea, Strait of Gibraltar, Western Atlantic Ocean and Bay of Biscay utilizing advanced hyperspectral imaging (HSI) techniques working in the short-wave infrared range (1000–2500 nm) is presented. More in detail, an ad hoc hierarchical classification approach was developed and applied to optimize the identification of polymers. Morphological and morphometrical attributes of microplastic particles were simultaneously measured by digital image processing. Results showed that the collected microplastics are mainly composed, in decreasing order of abundance, by polyethylene (PE), polypropylene (PP), polystyrene (PS) and expanded polystyrene (EPS), in agreement with the literature data related to marine microplastics. The investigated microplastics belong to the fragments (86.8%), lines (9.2%) and films (4.0%) categories. Rigid (thick-walled) fragments were found at all sampling sites, while film-type microplastics and lines were absent in some samples from the Mediterranean Sea and the Western Atlantic Ocean. Rigid fragments and lines are mainly made of PE, whereas PP is the most common polymer for the film category. Average Feret diameter of microplastic fragments decreases from EPS (3–4 mm) to PE (2–3 mm) and PP (1–2 mm). The setup strategies illustrate that the HSI-based approach enables the classification of the polymers constituting microplastic particles and, at the same time, to measure and classify them by shape. Such multiple characterization of microplastic samples at the individual level is proposed as a useful tool to explore the environmental selection of microplastic features (i.e., composition, category, size, shape) and to advance the understanding of the role of weathering, hydrodynamic and other phenomena in their transport and fragmentation. Full article

Bands of interdendritic porosity and positive macrosegregation are commonly observed in pressure die castings, with previous studies demonstrating their close relation to dilatant shear bands in granular materials. Despite recent technological developments, the micromechanism governing dilatancy in the high-pressure die casting (HPDC) process for alloys between liquid and solid temperature regions is still not fully understood. To investigate the influence of fluid flow and the size of externally solidified crystals (ESCs) on the evolution of dilatant shear bands in HPDC, various filling velocities were trialled to produce HPDC samples of Al8SiMnMg alloys. This study demonstrates that crystal fragmentation is accompanied by a decrease in dilatational concentration, producing an indistinct shear band. Once crystal fragmentation stagnates, the enhanced deformation rate associated with a further increase in filling velocity (from 2.2 ms⁻¹ to 4.6 ms⁻¹) localizes dilatancy into a highly concentrated shear band. The optimal piston velocity is 3.6 ms⁻¹, under which the average ESC size reaches the minimum, and the average yield stress and overall product of strength and elongation reach the maximum values of 144.6 MPa and 3.664 GPa%, respectively. By adopting the concept of force chain buckling in granular media, the evolution of dilatant shear bands in equiaxed solidifying alloys can be adequately explained based on further verification with DEM-type modeling in OpenFOAM. Three mechanisms for ESC-enhanced dilation are presented, elucidating previous reports relating the presence of ESCs to the subsequent shear band characteristics. By applying the physics of granular materials to equiaxed solidifying alloys, unique opportunities are presented for process optimization and microstructural modeling in HPDC. Full article

This paper presents an analysis and optimization of a polygeneration power-production system that integrates a concentrating solar tower, a supercritical CO₂ Brayton cycle, a double-flash geothermal Rankine cycle, and an internal combustion engine. The concentrating solar tower is analyzed under the weather conditions of the Mexicali Valley, Mexico, optimizing the incident radiation on the receiver and its size, the tower height, and the number of heliostats and their distribution. The integrated polygeneration system is studied by first and second law analyses, and its optimization is also developed. Results show that the optimal parameters for the solar field are a solar flux of 549.2 kW/m², a height tower of 73.71 m, an external receiver of 1.86 m height with a 6.91 m diameter, and a total of 1116 heliostats of 6 m × 6 m. For the integrated polygeneration system, the optimal values of the variables considered are 1437 kPa and 351.2 kPa for the separation pressures of both flash chambers, 753 °C for the gasification temperature, 741.1 °C for the inlet temperature to the turbine, 2.5 and 1.503 for the turbine pressure ratios, 0.5964 for the air–biomass equivalence ratio, and 0.5881 for the CO₂ mass flow splitting fraction. Finally, for the optimal system, the thermal efficiency is 38.8%, and the exergetic efficiency is 30.9%. Full article

The encapsulation of essential oils (EOs) in nanostructured lipid carriers (NLCs) represents a modern and sustainable approach within the agrochemical industry. This research evaluated the colloidal properties and insecticidal activity of NLCs loaded with thyme essential oil (TEO-NLC) and rosemary essential oil (REO-NLC) against three common arthropod pests of ornamental flowers: Frankliniella occidentalis, Myzus persicae, and Tetranychus urticae. Gas chromatography–mass spectrometry (GC-MS) analysis identified the major chemical constituents of the EOs, with TEO exhibiting a thymol chemotype and REO exhibiting an α-pinene chemotype. NLCs were prepared using various homogenization techniques, with high shear homogenization (HSH) providing the optimal particle size, size distribution, and surface electrical charge. A factorial design was employed to evaluate the effects of EO concentration, surfactant concentration, and liquid lipid/solid lipid ratio on the physicochemical properties of the nanosuspensions. The final TEO-NLC formulation had a particle size of 347.8 nm, a polydispersity index of 0.182, a zeta potential of −33.8 mV, an encapsulation efficiency of 71.9%, and a loading capacity of 1.18%. The REO-NLC formulation had a particle size of 288.1 nm, a polydispersity index of 0.188, a zeta potential of −34 mV, an encapsulation efficiency of 80.6%, and a loading capacity of 1.40%. Evaluation of contact toxicity on leaf disks showed that TEO-NLC exhibited moderate insecticidal activity against the western flower thrips and mild acaricidal activity against the two-spotted spider mite, while REO-NLC demonstrated limited effects. These findings indicate that TEO-NLCs show potential as biopesticides for controlling specific pests of ornamental flowers, and further optimization of the administration dosage could significantly enhance their effectiveness. Full article

Cell micromanipulation is an important technique in the field of biomedical engineering. Microgrippers play a crucial role in connecting macroscopic and microscopic objects in micromanipulation systems. However, since the operated biological cells are deformable, vulnerable, and typically distributed in sizes ranging from micrometers to millimeters, it poses a huge challenge to microgripper performance. To solve this problem, this paper develops a dual-driven piezoelectric microgripper with a high displacement amplification ratio, large stroke, and parallel gripping. By adopting modular configuration, three kinds of flexure-based mechanisms, including the lever mechanism, Scott–Russell mechanism, and parallelogram mechanism are connected in series to realize three-stage amplification, which effectively makes up for the shortage of small output displacement of the piezoelectric actuator. At the same time, the use of the parallelogram mechanism also isolates the parasitic rotation movement, and realizes the parallel movement of the gripping jaws. In addition, the kinematics, statics, and dynamics models of the microgripper are established by using the pseudo-rigid body and Lagrange methods, and the key geometric parameters are also optimized. Finite element simulation and experimental tests verify the effectiveness of the developed microgripper. The results show that the developed microgripper allows an amplification ratio of 46.4, a clamping stroke of 2180 $\mathsf{\mu }$m, and a natural frequency of 203.1 Hz. Based on the developed microgripper, the nondestructive micromanipulation of zebrafish embryos is successfully realized. Full article

Nuclear energy is increasingly being considered for such targeted energy applications as data centers in light of their high capacity factors and low carbon emissions. This paper focuses on assessing the tradeoffs between economies of scale versus mass production to identify promising reactor sizes to meet data center demands. A framework is then built using the best cost estimates from the literature to identify ideal reactor power sizes for the needs of the given data center. Results should not be taken to be deterministic but highlight the variability of ideal reactor power output against the required demand. While certain advocates claim that with the gigawatts of clean, firm energy needed, large plants are ideal, others advocate for SMRs that can be deployed in large quantities and reap the benefits from learning effects. The findings of this study showcase that identifying the optimal size for a reactor is likely more nuanced and dependent on the application and its requirements. Overall, the study does show potential economic promise for coupling nuclear reactors to data centers and industrial heat applications under certain key conditions and assumptions. Full article

Fly ash washing wastewater was carbonated with carbon dioxide (CO₂) to remove calcium (Ca) by forming a calcium carbonate (CaCO₃) precipitate. An investigation of the factors affecting carbonation showed that Ca removal was highly dependent on the initial pH of the wastewater. The Ca removal was 10%, 61%, 91% and more than 99% at initial wastewater pH levels of 11.8, 12.0, 12.5 and 13.0, respectively. The optimal conditions for carbonation were initial pH of 13.0, carbonation time of 30 min and CO₂ flow rate of 30 mL/min. The Ca concentration in the wastewater decreased to <40 mg/L, while 73 g of CaCO₃ precipitate was produced per liter of wastewater. However, heavy metals, specifically Pb and Zn, co-precipitated during carbonation, which resulted in a CaCO₃ product that contained as much as 0.61 wt% of Pb and 0.02 wt% of Zn. Activated carbon modified by a quaternary ammonium salt was used to selectively adsorb the Pb and Zn first. The Pb- and Zn-free water was then carbonated. By combining adsorption with carbonation, the Ca concentration in the treated wastewater was decreased to about 28 mg/L, while the Na, Cl and K were retained. The wastewater thus treated was ready for NaCl and KCl recovery. In addition, the precipitate had a Ca content of more than 38 wt% and almost no heavy metals. The average particle size of the precipitate was 47 μm, with a uniform cubic shape. The quality of the precipitate met the requirements for the industrial reuse of CaCO₃. In summary, adsorption and carbonation combined were able to remove pollutants from wastewater while recovering useful resources. Full article

Transitioning from petrol or gas vehicles to electric vehicles (EVs) poses significant challenges in reducing emissions, lowering operational costs, and improving energy storage. Wireless charging EVs offer promising solutions to wired charging limitations such as restricted travel range and lengthy charging times. This paper presents a comprehensive approach to address the challenges of wireless power transfer (WPT) for EVs by optimizing coupling frequency and coil design to enhance efficiency while minimizing electromagnetic interference (EMI) and heat generation. A novel coil design and adaptive hardware are proposed to improve power transfer efficiency (PTE) by defining the optimal magnetic resonant coupling WPT and mitigating coil misalignment, which is considered a significant barrier to the widespread adoption of WPT for EVs. A new methodology for designing and arranging roadside lanes and facilities for dynamic wireless charging (DWC) of EVs is introduced. This includes the optimization of transmitter coils (TCs), receiving coils (RCs), compensation circuits, and high-frequency inverters/converters using the partial differential equation toolbox (pdetool). The integration of wireless charging systems with smart grid technology is explored to enhance energy distribution and reduce peak load issues. The paper proposes a DWC system with multiple segmented transmitters integrated with adaptive renewable photovoltaic (PV) units and a battery system using the utility main grid as a backup. The design process includes the determination of the required PV array capacity, station battery sizing, and inverters/converters to ensure maximum power point tracking (MPPT). To validate the proposed system, it was tested in two scenarios: charging a single EV at different speeds and simultaneously charging two EVs over a 1 km stretch with a 50 kW system, achieving a total range of 500 km. Experimental validation was performed through real-time simulation and hardware tests using an OPAL-RT platform, demonstrating a power transfer efficiency of 90.7%, thus confirming the scalability and feasibility of the system for future EV infrastructure. Full article

The lack of dense random-access memory is one of the main obstacles to the development of digital superconducting computers. It has been suggested that AVRAM cells, based on the storage of a single Abrikosov vortex—the smallest quantized object in superconductors—can enable drastic miniaturization to the nanometer scale. In this work, we present the numerical modeling of such cells using time-dependent Ginzburg–Landau equations. The cell represents a fluxonic quantum dot containing a small superconducting island, an asymmetric notch for the vortex entrance, a guiding track, and a vortex trap. We determine the optimal geometrical parameters for operation at zero magnetic field and the conditions for controllable vortex manipulation by short current pulses. We report ultrafast vortex motion with velocities more than an order of magnitude faster than those expected for macroscopic superconductors. This phenomenon is attributed to strong interactions with the edges of a mesoscopic island, combined with the nonlinear reduction of flux-flow viscosity due to the nonequilibrium effects in the track. Our results show that such cells can be scaled down to sizes comparable to the London penetration depth, ∼100 nm, and can enable ultrafast switching on the picosecond scale with ultralow energy per operation, ∼${10}^{-19}$ J. Full article