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Amid increasingly stringent global environmental regulations, marine engines are undergoing an essential transition from conventional fossil fuels to alternative fuels to meet escalating regulatory requirements. This study evaluates the effects of injection pressure, the timing of ammonia injection, and the pre-injection of ammonia on combustion and emissions, aiming to identify optimal operational parameters for low-speed marine engines. A three-dimensional model of a large-bore, low-speed marine engine in a high-pressure diffusion mode was developed based on computational fluid dynamics (CFD). Simulations were conducted under 25%, 50%, 75% and 100% loads with a high ammonia energy substitution rate of 95%. The results indicate that, compared to traditional pure diesel operation, adjusting the injection pressure and the ammonia injection timing, along with employing appropriate pre-injection strategies, significantly enhances in-cylinder pressure and temperature, improves thermal efficiency, and reduces specific fuel consumption. Additionally, the dual-fuel strategy using diesel and ammonia effectively reduces nitrogen oxide emissions by up to 37.5% and carbon dioxide emissions by 93.7%. [ABSTRACT FROM AUTHOR]

The use of nursing transfer robots is a vital solution to the problem of daily mobility difficulties for semi-disabilities. However, the fact that care-receivers have different physical characteristics leads to force concentration during human–robot interaction, which affects their comfort. To address this problem, this study installs an array of double wedge-shaped airbags onto the end-effector of a robot, and analyses airbag mechanical properties. Firstly, this study performed the mechanical testing and data collection of the airbag, including its external load and displacement, at various gas masses. Then, the performance of the Back Propagation (BP) neural network is improved using chaos (C) theory and simulated annealing particle swarm optimization (SAPSO), resulting in the establishment of the CSAPSO-BP neural network. By this method, a fitting model is developed to determine the mechanical parameters of the wedge-shaped airbag stiffness, and the fitting relation of external load–displacement is obtained. Data analyses show that the wedge-shaped airbag stiffness increases quadratically, linearly, and with a constant rate as the gas mass increases. The airbag stiffness regulation and model describe its three distinct phases with quadratic, linear, and linear invariant characteristics as the gas mass changes. These findings contribute to the structural optimization of airbags. [ABSTRACT FROM AUTHOR]

Microbial activity plays a crucial role in upholding the functional stability of vegetation–soil ecosystems. Nevertheless, there exists a paucity of studies concerning the impact of sand-fixing vegetation (Haloxylon ammodendron) on the structure and functional attributes of soil microbial communities. We employed Illumina high-throughput sequencing and PICRUSt2 functional prediction technology to investigate the characteristics of soil bacterial community structure, diversity, and metabolic functions in an artificial H. ammodendron forest, and RDA analysis and the Mantel test were used to reveal the main environmental factors affecting the structure and ecological functions of soil bacterial communities. The findings revealed a significant increase in the principal nutrient contents (organic matter, total nitrogen, total phosphorus) in the H. ammodendron forest soil compared to the mobile dune soil, while a reduction of 17.17% in the surface soil water content was observed. The H. ammodendron forest exhibited a significant enhancement in the diversity and richness index of soil bacteria. Specifically, Actinobacteria (24.94% ± 11.85%), Proteobacteria (29.99% ± 11.56%), and Chloroflexi (11.14% ± 4.55%) emerged as the dominant bacterial phyla, with Actinobacteria displaying significantly higher abundance compared to the mobile dune soil. PICRUSt2 analyses revealed that the predominant secondary metabolic functions of soil bacteria were carbohydrate metabolism, amino acid metabolism, and the metabolism of cofactors and vitamins. Additionally, the tertiary metabolic pathways exhibited greater activity in relation to enzyme function, nucleotide metabolism, energy metabolism, and antibiotics. The RDA results demonstrated that SOM, AK, and pH collectively accounted for 82.4% of the cumulative contribution, significantly influencing the bacterial community. Moreover, the Mantel test revealed that the metabolic function of soil bacteria primarily relied on five environmental factors, namely SOM, TN, AK, pH, and EC. This study significantly advances our understanding of the structural and functional changes in soil bacterial communities during the reclamation of sandy land through the establishment of artificial H. ammodendron forests. [ABSTRACT FROM AUTHOR]

Static frequency converters (SFCs) are very important for starting the connection of synchronous capacitors to the power grid, which is beneficial for ensuring the impact of electric vehicle connection on the inertia of the power grid. In the traditional sensorless initial rotor position detection method, the signal-to-noise ratio of the induced voltage at the machine terminal is small, making it difficult to accurately extract the rotor position. In this study, a reliable initial position detection method for a sensorless-controlled synchronous machine drive is proposed. A step excitation voltage was applied to the excitation circuit before the motor was started, and the three-phase induction voltage at the terminals was sampled in real time. The sampling signal was processed in two ways: digital filter processing and stator flux calculation. The accuracy of the initial rotor position is determined by comparing the differences between the two results. This algorithm does not depend on additional hardware circuits and has fewer setting parameters; therefore, it is easy to apply in engineering applications. Finally, a comparative experiment was conducted using a real-time digital system (RTDS) to verify the feasibility and effectiveness of the proposed method. The proposed rotor position detection method can effectively improve the detection reliability and ensure the start-up reliability of SFCS. [ABSTRACT FROM AUTHOR]

This study aims to enhance surgical safety and facilitate patient recovery through the investigation of vibration-assisted micro-milling technology for bone-material removal. The primary objective is to reduce cutting force and improve surface quality. Initially, a predictive model is developed to estimate the cutting force during two-dimensional (2D) vibration-assisted micro-milling of bone material. This model takes into account the anisotropic structural characteristics of bone material and the kinematics of the milling tool. Subsequently, an experimental platform is established to validate the accuracy of the cutting-force model for bone material. Micro-milling experiments are conducted on bone materials, with variations in cutting direction, amplitude, and frequency, to assess their impact on cutting force. The experimental results demonstrate that selecting appropriate machining parameters can effectively minimize cutting force in 2D vibration-assisted micro-milling of bone materials. The insights gained from this study provide valuable guidance for determining cutting parameters in vibration-assisted micro-milling of bone materials. [ABSTRACT FROM AUTHOR]

The presence of oily wastewater poses a significant threat to both the ecological environment and public health. In order to solve this problem, the design and preparation of an efficient oil–water separation membrane is very important. Metal–organic frameworks (MOFs) are currently a promising material for oil–water separation due to their tunable wettability, adjustable pore size and also low density, high porosity, and high surface area. Therefore, MOFs-based membranes show great potential in the field of oil–water separation. In this paper, we first introduce the oil–water separation mechanism and then comprehensively summarize the common preparation methods of MOFs-based oil–water separation membranes and the research progress of different MOFs-based membranes, including the ZIF series, UiO series, MIL series, etc. Finally, we also analyze the challenges faced by MOFs-based membranes in oil–water separation and provide an outlook on their future development and application. [ABSTRACT FROM AUTHOR]

The diaphragm is a key component of the lithium-ion battery and largely determines its performance. Currently, commercial diaphragms suffer from poor thermal stability, low porosity, and low liquid absorption rate. In this study, we prepared a polyurethane/polyacrylonitrile (PU/PAN) lithium-ion battery diaphragm using a centrifugal spinning method with PU as the main substrate and PAN as the additive. The results showed that the PU/PAN nanofiber diaphragm prepared by centrifugal spinning had a 3D porous structure, and when using 18% PU:PAN = 7:3, the porosity of the fiber diaphragm was 83.9%, the liquid absorption rate was 493%, and the ionic conductivity was 1.79 mS/cm. The battery system had good electrochemical performance and thermal stability, with an electrochemical stability window of 5.2 V. The diaphragm did not shrink when heated at 160 °C. In a lithium-ion battery system with lithium iron phosphate (LiFePO4) as the cathode material, the capacity remained at 147.1 mAh/g after 50 cycles at a 0.2 C rate, with a capacity retention rate of 95.8%. This indicated excellent cycle stability and a multiplicative performance with good application potential. [ABSTRACT FROM AUTHOR]

Triaxial tests were employed to investigate palm-fiber-reinforced sand under consolidated drained conditions in this study. Sixteen series of triaxial tests were carried out to investigate the properties of palm-fiber-reinforced sand. One series of pure sand was also employed for comparison. The deviator stress, stress path, shear strength, volume change, void ratio, and enhanced coefficient of fiber-reinforced sand were studied with different fiber lengths varying from 8 mm to 20 mm and fiber contents varying in mass from 0.3% to 0.9%. The test results indicate that palm fibers were beneficial for enhancing the shear strength of the sand. Compared to the peak shear strength increase of about 10% to 20%, the critical shear strength increased much more, by a little over 100%. Therefore, the fibers played a key role in enhancing the critical shear strength of the sand but not the peak shear strength of the sand. The addition of fiber to sand resulted in prolongation of the axial strain required to reach the critical void ratio and improved the sand's ability to resist larger deformations, enhancing its toughness. Furthermore, the critical shear strength of the sand was positively correlated with both fiber content and fiber length, and the axial strain required to reach the critical shear strength increased with increasing fiber content and length. This study provides valuable experimental data and serves as a reference for temporary reinforcement in geotechnical engineering. [ABSTRACT FROM AUTHOR]

With the development of technologies such as big data and artificial intelligence, digital technology and finance are becoming increasingly intertwined. At present, digital finance has altered the competitive environment of commercial banks, and the traditional competitive edges in service scenarios and channels, customer information, and capital have been challenged. Based on perfect competition and technology spillover effects, this study attempted to measure the impact of digital finance on commercial banks' total factor productivity (TFP) and its mediating and moderating mechanisms. We have used the data envelopment analysis-based Malmquist productivity index to measure the total factor productivity of 132 commercial banks in China between 2011 and 2019. The results show that (a) digital finance significantly enhances the TFP of commercial banks; (b) risk taking partially mediates the relationship between digital finance and TFP. The study further tests the effect of the nature of property rights and the moderating effect of diversification. The findings suggest that digital finance significantly improves the TFP of non-state-owned commercial banks but has no significant effect on the TFP of state-owned commercial banks. Additionally, the implementation of diversification can strengthen the effect of digital finance on TFP. [ABSTRACT FROM AUTHOR]

The uncertainty of target sizes and the complexity of backgrounds are the main reasons for the poor detection performance of small infrared targets. Focusing on this issue, this paper presents a robust and accurate algorithm that combines multiscale kurtosis map fusion and the optical flow method for the detection of small infrared targets in complex natural scenes. The paper has made three main contributions: First, it proposes a structure for infrared small target detection technology based on multiscale kurtosis maps and optical flow fields, which can well represent the shape, size and motion information of the target and is advantageous to the enhancement of the target and the suppression of the background. Second, a strategy of multi-scale kurtosis map fusion is presented to match the shape and the size of the small target, which can effectively enhance small targets with different sizes as well as suppress the highlighted noise points and the residual background edges. During the fusion process, a novel weighting mechanism is proposed to fuse different scale kurtosis maps, by means of which the scale that matches the true target is effectively enhanced. Third, an improved optical flow method is utilized to further suppress the nontarget residual clutter that cannot be completely removed by multiscale kurtosis map fusion. By means of the scale confidence parameter obtained during the multiscale kurtosis map fusion step, the optical flow method can select the optimal neighborhood that matches best to the target size and shape, which can effectively improve the integrity of the detection target and the ability to suppress residual clutter. As a result, the proposed method achieves a superior performance. Experimental results on eleven typical complex infrared natural scenes show that, compared with seven state-of-the-art methods, the presented method outperforms in terms of subjective visual effect, as well as some main objective evaluation indicators such as BSF, SCRG and ROC, etc. [ABSTRACT FROM AUTHOR]

Precision spraying can significantly reduce herbicide input for turf weed management. A major challenge for autonomous precision herbicide spraying is to accurately and reliably detect weeds growing in turf. Deep convolutional neural networks (DCNNs), an important artificial intelligent tool, demonstrated extraordinary capability to learn complex features from images. The feasibility of using DCNNs, including various image classification or object detection neural networks, has been investigated to detect weeds growing in turf. Due to the high level of performance of weed detection, DCNNs are suitable for the ground-based detection and discrimination of weeds growing in turf. However, reliable weed detection may be subject to the influence of weeds (e.g., biotypes, species, densities, and growth stages) and turf factors (e.g., turf quality, mowing height, and dormancy vs. non-dormancy). The present review article summarizes the previous research findings using DCNNs as the machine vision decision system of smart sprayers for precision herbicide spraying, with the aim of providing insights into future research. [ABSTRACT FROM AUTHOR]

In the field of computer vision, training a well-performing model on a dataset with a long-tail distribution is a challenging task. To address this challenge, image resampling is usually introduced as a simple and effective solution. However, when performing instance segmentation tasks, there may be multiple classes in one image. Hence, image resampling alone is not enough to obtain a sufficiently balanced distribution at the level of target data volume. In this paper, we propose an improved instance segmentation method for long-tail datasets based on Mask R-CNN. Specifically, an object-centric memory bank is used to establish an object-centric storage strategy that can solve the imbalance problem with respect to categories. In the testing phase, a post-processing calibration is used to adjust each class logit to change the confidence score, which improves the prediction score of tail classes. A discrete cosine transform-based mask is used to obtain high-quality masks, which improves segmentation accuracy. The evaluation of the proposed method on the LVIS dataset demonstrates its effectiveness. The proposed method improves the AP performance of EQL by 2.2%. [ABSTRACT FROM AUTHOR]

The hydrostatic bearing is the core component of ultra-precision computer numerical control (CNC) machine tools. Because the temperature rise in the oil film of hydrostatic bearings seriously affects the working accuracy of the bearings, it is important to study the flow and heat transfer characteristics of the oil film. Based on the physical model of an incompressible viscous fluid flowing in a flat microfluidic boundary layer, velocity, temperature and heat flux distribution equations of oil film are constructed by theories of heat transfer and hydrodynamics. Then, the effects of several parameters on velocity distribution, temperature distribution and heat flux distribution are analyzed, such as the upper plate velocity, the channel length, and so on. The results show that the dimensionless velocity of the oil film decreases with the increase in the upper plate velocity and the channel length. The oil film temperature distribution can be divided into three zones: the increasing zone, stabilizing zone and decreasing zone. The heat flux decreases linearly with the increase in the plate thickness, and increases linearly with the increase in the temperature difference. [ABSTRACT FROM AUTHOR]

Stochastic models play a crucial role in global navigation satellite systems (GNSS) data processing. Many studies contribute to the stochastic modeling of GNSS observation noise, whereas few studies focus on the stochastic modeling of process noise. This paper proposes a method that is able to jointly estimate the variances of observation noise and process noise. The method is flexible since it is based on the least-squares variance component estimation (LS-VCE), enabling users to estimate the variance components that they are specifically interested in. We apply the proposed method to estimate the variances for the dual-frequency GNSS observation noise and for the process noise of the receiver code bias (RCB). We also investigate the impact of the stochastic model upon parameter estimation, ambiguity resolution, and positioning. The results show that the precision of GNSS observations differs in systems and frequencies. Among the dual-frequency GPS, Galileo, and BDS code observations, the precision of the BDS B3 observations is highest (better than 0.2 m). The precision of the BDS phase observations is better than two millimeters, which is also higher than that of the GPS and Galileo observations. For all three systems, the RCB process noise ranges from 0.5 millimeters to 1 millimeter, with a data sampling rate of 30 s. An improper stochastic model of the observation noise results in an unreliable ambiguity dilution of precision (ADOP) and position dilution of precision (PDOP), thus adversely affecting the assessment of the ambiguity resolution and positioning performance. An inappropriate stochastic model of RCB process noise disturbs the estimation of the receiver clock and the ionosphere delays and is thus harmful for timing and ionosphere retrieval applications. [ABSTRACT FROM AUTHOR]

A tagged or reporter astrovirus can be a valuable tool for the analysis of various aspects of the virus life cycle, and to aid in the development of genetically engineered astroviruses as vectors. Here, transposon-mediated insertion mutagenesis was used to insert a 15-nucleotide (nt) sequence into random sites of open reading frame 1a (ORF1a) based on an infectious full-length cDNA clone of porcine astrovirus (PAstV). Five sites in the predicted coiled-coil structures (CC), genome-linked protein (VPg), and hypervariable region (HVR) in ORF1a of the PAstV genome were identified that could tolerate random 15 nt insertions. Incorporation of the commonly used epitope tags, His, Flag, and HA, into four of the five insertion sites permitted the production of infectious viruses and allowed recognition by specifically tagged monoclonal antibodies. The results of immuno-fluorescent assays showed that Flag-tagged ORF1a protein overlapped partially with capsid and ORF2b proteins in the cytoplasm. Improved light-oxygen-voltage (iLOV) gene was also introduced at the insertion sites of CC, VPg, and HVR. Only one viable recombinant reporter PAstV expressing iLOV inserted in HVR was recovered. Biological analysis of the reporter virus showed that it displayed similar growth characteristics, and yet produced less infectious virus particles, when compared with the parental virus. The recombinant virus carrying the iLOV fused with the HVR of ORF1a protein maintained its stability and showed green fluorescence after 15 passages in cell cultures. The resultant fluorescently tagged virus could provide a promising tool for the rapid screening of antiviral drugs as well as allowing the visualization of PAstV infection and replication in living cells.