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IntroductionThis study aims to investigate the impact of human activities on the evolution of coastal wetlands in the Liaohe River Delta. Understanding these impacts is crucial for the management, protection, and sustainable development of the ecological environment in the region.MethodsQuantitative analysis of the evolution of coastal wetlands along the Liaohe River from 1995 to 2020 using Landsat series remote sensing images. This study examines the changes in different landscapes and explores the impact of human activities on wetlands through methods such as land transfer matrix, landscape pattern index, and human activity hotspots.ResultsThe results show that the area of coastal wetlands in the Liaohe River Delta exhibited a fluctuating downward trend from 1995 to 2020. The eastern and western parts of the delta experienced greater disturbance to wetland landscapes due to human activities, while the northern and southern parts were mostly waters and reed lands with a smaller degree of human disturbance. Human activities have led to significant changes in farmland, ponds, rice paddies, construction land, and reed fields, thereby promoting the evolution of coastal wetlands.DiscussionThis article discusses the characteristics of landscape pattern evolution in the study area, as well as the impact of human interference, economy, policies, and other factors on it, and compares it with the evolution patterns of other coastal wetlands in China. The changes in the landscape pattern of the study area are mainly reflected in the reduction and fragmentation of natural wetland areas, which are closely related to human activities, especially the development of construction and agriculture. The main economic manifestation is that the increase in land demand due to economic and urban development has led to the transformation of natural wetlands into construction land and other artificial landscapes. The implementation of regulations such as the Wetland Protection Law has contributed to the rational use and ecological restoration of wetlands, and also reflects the influence of policy factors. Compared with other studies, coastal wetlands in China generally face issues of degradation and loss, mainly caused by human activities, but also affected by natural factors such as rising sea levels, changes in coastlines, and climate change.

Automated guided vehicles (AGVs) have emerged as crucial machinery in enterprise production and transportation as intelligent factories have gained traction. This study proposes a multi-source data enhancement fusion convolutional neural network (ME-CNN) approach for the mechanical fault diagnosis of heavy-duty AGVs. Specifically, an improved stacked autoencoder (SAE) was employed to augment the available data and address the issue of insufficient data samples during fault diagnosis. Subsequently, the augmented data were processed using a continuous wavelet transform (CWT). This method employs a combination of diverse measurement signals and data sources to facilitate a more precise and resilient diagnosis of heavy-duty AGVs. In addition, to further promote the coverage of smart factories, this method is combined with digital mirroring, and the virtual platform receives data to monitor the status of heavy-loaded AGVs in real-time and determine the type of fault. Following testing, the system response time was less than 500 ms, allowing for the timely diagnosis of faults. Finally, the validity of the method was verified by comparing a factory-heavy load AGV with various other algorithms, resulting in an accuracy rate of 97.0% for fault diagnosis.

The safe operation of high-speed rail running gear is crucial, as fault diagnosis can effectively prevent potential risks and ensure the smooth operation of the train. The Belief Rule Base (BRB) method has demonstrated excellent performance in complex system modeling. However, during the optimization process, BRB may lead to a “combinatorial explosion” of rules within the model, resulting in a loss of model interpretability and an increase in complexity. To address this, a Multidimensional Belief Rule Base (MBRB) fault diagnosis method is proposed. By optimizing the structure and parameters, the interpretability of the model is enhanced, and its complexity is reduced. Specifically, the model inputs are decomposed into multiple dimensions for analysis, and then the MBRB rules are updated using the Projection Covariance Matrix Adaption Evolution Strategy (P-CMA-ES), increasing the model’s interpretability and accuracy. Finally, the effectiveness of this method is validated through an example of high-speed rail running gear.

Industrial robots are prone to failure due to harsh working environments, which affects movement accuracy. The fault diagnosis of industrial robots has become an indispensable part of robot collaborative maintenance in intelligent manufacturing. Most existing diagnostic methods only use a single data source, and the diagnostic accuracy will be affected due to signal acquisition errors and noise interference. This paper proposes a multi-source data fusion and channel attention convolutional neural network (MD-CA-CNN) for fault diagnosis of multi-joint industrial robots. The network takes the time domain data and time-frequency domain data of the vibration signal, torque signal, and current signal of the six joints of the robot as input. Then, we realize the diagnosis of the faults by using a Softmax Classifier layer after the two parts of feature extraction and feature fusion. In addition, a channel attention mechanism is developed. It acts on the two parts of feature extraction and feature fusion, respectively. It assigns weights to different source data and weights to time-domain and time-frequency domain features. Finally, we established a test bench to compare the proposed method with the deep learning algorithm that only uses multi-data source fusion, the deep learning algorithm that only uses a single data source, and the commonly used machine learning algorithm. The results show that the MD-CA-CNN model proposed in this paper has the highest accuracy and stability, reaching 95.8% $\pm ~0.39$ %, which verifies the method’s effectiveness.

This paper introduces an enhanced Whale Optimization Algorithm, named the Multi-Swarm Improved Spiral Whale Optimization Algorithm (MISWOA), designed to address the shortcomings of the traditional Whale Optimization Algorithm (WOA) in terms of global search capability and convergence velocity. The MISWOA combines an adaptive nonlinear convergence factor with a variable gain compensation mechanism, adaptive weights, and an advanced spiral convergence strategy, resulting in a significant enhancement in the algorithm’s global search capability, convergence velocity, and precision. Moreover, MISWOA incorporates a multi-population mechanism, further bolstering the algorithm’s efficiency and robustness. Ultimately, an extensive validation of MISWOA through “simulation + experimentation” approaches has been conducted, demonstrating that MISWOA surpasses other algorithms and the Whale Optimization Algorithm (WOA) and its variants in terms of convergence accuracy and algorithmic efficiency. This validates the effectiveness of the improvement method and the exceptional performance of MISWOA, while also highlighting its substantial potential for application in practical engineering scenarios. This study not only presents an improved optimization algorithm but also constructs a systematic framework for analysis and research, offering novel insights for the comprehension and refinement of swarm intelligence algorithms.

The limited availability of thermal infrared (TIR) training samples leads to suboptimal target representation by convolutional feature extraction networks, which adversely impacts the accuracy of TIR target tracking methods. To address this issue, we propose an unsupervised cross-domain model (UCDT) for TIR tracking. Our approach leverages labeled training samples from the RGB domain (source domain) to train a general feature extraction network. We then employ a cross-domain model to adapt this network for effective target feature extraction in the TIR domain (target domain). This cross-domain strategy addresses the challenge of limited TIR training samples effectively. Additionally, we utilize an unsupervised learning technique to generate pseudo-labels for unlabeled training samples in the source domain, which helps overcome the limitations imposed by the scarcity of annotated training data. Extensive experiments demonstrate that our UCDT tracking method outperforms existing tracking approaches on the PTB-TIR and LSOTB-TIR benchmarks.

Summary: A micro turbine engine’s thrust relies on combustion chamber efficiency, closely tied to the design of its evaporation tube. This study thoroughly investigates evaporation and atomization processes within the tube, introducing a pioneering bionic-inspired structure. Integrating a honeycomb sheet into the traditional tube, both configurations undergo a comparative analysis. Results show a direct correlation between elevated air temperatures and reduced fuel droplet diameters, leading to increased fuel evaporation rates. The bionic tube, with a 1mm-thick honeycomb sheet, 0.6 mm aperture diameter, and 3 sheets, significantly improves fuel droplet atomization and evaporation compared to the conventional design. This research holds broader significance in understanding and enhancing micro turbine engine performance.

The Mengshan granitoid complex is located in the central part of Jiangnan Orogen and belongs to the western part of Jiangxi Province, where several phases of granitic magmatism record the crustal evolution of the late-Indosinian. However, its petrogenesis remains uncertain, largely due to controversies over its origin, evolutionary process and tectonic setting during intrusion. In this study, the lithological features and contact relationships observed in the systematic field geological investigations indicate that the late magmatic phases of the Mengshan granitoid complex are mainly composed of medium–fine-grained biotite monzogranite and fine-grained monzogranite, both of which developed primary fine-grained muscovite. Petrological, petrographic, geochemical and zircon U-Pb-Hf isotopic studies were further carried out on these rocks. Zircon U-Pb isotopic analyses suggest that the medium–fine-grained biotite monzogranite mainly formed at 220.7 ± 1.0 Ma to 218.0 ± 0.8 Ma, and that the fine-grained monzogranite formed at 211.5 ± 2.9 Ma to 212.9 ± 1.0 Ma. Whole-rock geochemical analysis results suggest that these rocks are rich in Cs, Rb, and U, and deficient in Ba, Sr, and Ti, and that they have properties characteristic of rocks with high silica, low P content and high K calc-alkali. Mineralogical and geochemical analysis results suggest that they are S-type granites. The εHf(t) values of the early-stage medium–fine-grained biotite monzogranite and late-stage fine-grained monzogranite range from –4.7 to 0.3 and from –3.2 to 0.7, respectively. Geochemical and isotopic data suggest that these granitoids were derived from the partial melting of Proterozoic continent basement rocks, and that minor mantle materials were involved during their generation. The presence of the early Mesozoic Mengshan granitoid complex reflects a reduplicated far-field converge effect of the collision of the North China and South China blocks and the subduction of the Palaeo-Pacific plate into the South China block. The thickening of the Earth’s crust facilitated crustal delamination, underplating of mantle-derived magma, and crustal heating, triggering intense partial melting of the lithosphere and magma enrichment.

The running gear mechanism is a critical component of high-speed trains, essential for maintaining safety and stability. Malfunctions in the running gear can have severe consequences, making it imperative to assess its condition accurately. Such assessments provide insights into the current operational status, facilitating timely maintenance and ensuring the reliable and safe operation of high-speed trains. Traditional evidential reasoning models for assessing the health of running gear typically require the integration of multiple characteristic indicators, which are often challenging to obtain and may lack comprehensiveness. To address these challenges, this paper introduces a novel assessment model that combines evidential reasoning with multidimensional fault conclusions. This model synthesizes results from various fault diagnoses to establish a comprehensive health indicator system for the running gear. The diagnostic outcomes serve as inputs to the model, which then assesses the overall health status of the running gear system. To address potential inaccuracies in initial model parameters, the covariance matrix adaptation evolution strategy (CMA-ES) algorithm is utilized for parameter optimization. Comparative experiments with alternative methods demonstrate that the proposed model offers superior accuracy and reliability in assessing the health status of high-speed train running gear.

Mobile robots play an important role in the industrial Internet of Things (IIoT); they need effective mutual communication between the cloud and themselves when they move in a factory. By using the sensor nodes existing in the IIoT environment as relays, mobile robots and the cloud can communicate through multiple hops. However, the mobility and delay sensitivity of mobile robots bring new challenges. In this paper, we propose a dynamic cooperative transmission algorithm with mutual information accumulation to cope with these two challenges. By using rateless coding, nodes can reduce the delay caused by retransmission under poor channel conditions. With the help of mutual information accumulation, nodes can accumulate information faster and reduce delay. We propose a two-step dynamic algorithm, which can obtain the current routing path with low time complexity. The simulation results show that our algorithm is better than the existing heuristic algorithm in terms of delay.

Abstract The micro turbine engine (MTE) plays a vital role in the aerospace sector. However, traditional research and design models no longer suffice to accommodate the highly complex operating conditions of the MTE. Therefore, this paper briefly analyzes the concept and development process of digital twin technology and integrates it into the research and design of the MTE. This integration takes into account the recent leapfrog developments in information technology and digital intelligence technology. Utilizing digital twin technology to model the core components of the MTE allows for effective structural optimization, online monitoring, and timely problem detection in support of extending the overall lifespan of the engine. Multiple algorithms are used throughout the development, design, and usage phases of each component to merge into a cohesive whole, providing robust support for the MTE's overall development, production, and manufacturing. Digital twin technology facilitates accurate performance prediction, reliability evaluation, and advanced design optimization for the MTE. This, in turn, reduces the design cycle and research design cost more effectively. Lastly, we propose the future application and development trend of digital twin technology in the core components of the MTE.

ABSTRACTUsing a continuous laser to soften glass locally is a novel way of selective glass forming. Due to gaussian distribution of laser power density, a temperature gradient will be generated on glass treated with localized irradiation by laser, which brings about the coexistence of a glass state and a viscoelastic state. This situation creates a bulge on the surface of the softened area. Based on this phenomenon, we propose a measurement approach and a calculation model for the softening zone width of glass irradiated locally by laser and verify the calculation model through simulations and experiments. The maximum temperature deviation of the proposed temperature field prediction model is 4.7°C, while the minimum deviation is 0.53°C; making the deviation between the calculation model of the softening zone width and the experimental measurement result less than 0.319%.

Background Improvement on the updating equation of an algorithm is among the most improving techniques. Due to the lack of search ability, high computational complexity and poor operability of equilibrium optimizer (EO) in solving complex optimization problems, an improved EO is proposed in this article, namely the multi-strategy on updating synthetized EO (MS-EO). Method Firstly, a simplified updating strategy is adopted in EO to improve operability and reduce computational complexity. Secondly, an information sharing strategy updates the concentrations in the early iterative stage using a dynamic tuning strategy in the simplified EO to form a simplified sharing EO (SS-EO) and enhance the exploration ability. Thirdly, a migration strategy and a golden section strategy are used for a golden particle updating to construct a Golden SS-EO (GS-EO) and improve the search ability. Finally, an elite learning strategy is implemented for the worst particle updating in the late stage to form MS-EO and strengthen the exploitation ability. The strategies are embedded into EO to balance between exploration and exploitation by giving full play to their respective advantages. Result and Finding Experimental results on the complex functions from CEC2013 and CEC2017 test sets demonstrate that MS-EO outperforms EO and quite a few state-of-the-art algorithms in search ability, running speed and operability. The experimental results of feature selection on several datasets show that MS-EO also provides more advantages.

Abstract O‐GlcNAcylation functions as a cellular nutrient and stress sensor and participates in almost all cellular processes. However, it remains unclear whether O‐GlcNAcylation plays a role in the establishment and maintenance of cell polarity, because mice lacking O‐GlcNAc transferase (OGT) are embryonically lethal. Here, a mild Ogt knockout mouse model is constructed and the important role of O‐GlcNAcylation in establishing and maintaining cell polarity is demonstrated. Ogt knockout leads to severe pulmonary fibrosis and dramatically promotes epithelial‐to‐mesenchymal transition. Mechanistic studies reveal that OGT interacts with pericentriolar material 1 (PCM1) and centrosomal protein 131 (CEP131), components of centriolar satellites required for anchoring microtubules to the centrosome. These data further show that O‐GlcNAcylation of PCM1 and CEP131 promotes their centrosomal localization through phase separation. Decrease in O‐GlcNAcylation prevents PCM1 and CEP131 from localizing to the centrosome, instead dispersing these proteins throughout the cell and impairing the microtubule‐centrosome interaction to disrupt centrosome positioning and cell polarity. These findings identify a previously unrecognized role for protein O‐GlcNAcylation in establishing and maintaining cell polarity with important implications for the pathogenesis of pulmonary fibrosis.

Mineral image segmentation based on computer vision is vital to realize automatic mineral analysis. However, current image segmentation methods still cannot effectively solve the problem of sandstone grains that are adjoined and concealed by leaching processes, and the segmentation performance of small and irregular grains still needs to be improved. This investigation explores and designs a Mask R-CNN-based sandstone image segmentation model, including a hybrid attention mechanism, loss function construction, and receptive field enlargement. Simultaneously, we propose a high-quality sandstone dataset with abundant labels named SMISD to facilitate comprehensive training of the model. The experimental results show that the proposed segmentation model has excellent segmentation performance, effectively solving adhesion and overlap between adjacent grains without affecting the classification accuracy. The model has comparable performance to other models on the COCO dataset, and performs better on SMISD than others.

In smart cities, large amounts of multi-source data are generated all the time. A model established via machine learning can mine information from these data and enable many valuable applications. With concerns about data privacy, it is becoming increasingly difficult for the publishers of these applications to obtain users’ data, which hinders the previous paradigm of centralized training through collecting data on a large scale. Federated learning is expected to prevent the leakage of private data by allowing users to train models locally. The existing works generally ignore architectures designed in real scenarios. Thus, there still exist some challenges that have not yet been explored in federated learning applied in smart cities, such as avoiding sharing models with improper parties under privacy requirements and designing satisfactory incentive mechanisms. Therefore, we propose an efficient attribute-based participant selecting scheme to ensure that only someone who meets the requirements of the task publisher can participate in training under the premise of high privacy requirements, so as to improve the efficiency and avoid attacks. We further extend our scheme to encourage clients to take part in federated learning and provide an audit mechanism using a consortium blockchain. Finally, we present an in-depth discussion of the proposed scheme by comparing it to different methods. The results show that our scheme can improve the efficiency of federated learning by enabling reliable participant selection and promote the extensive use of federated learning in smart cities.

Geochemical survey of stream sediments is an effective technology for mineral prospecting. The extraction of geochemical anomalies in stream sediments based on catchment basins has always been one of the research hotspots.The catchment basin analysis method is proposed based on Sample Catchment Basin Analysis (SCBA)in this paper, and it is used to identify geochemical anomalies in the Wulasitai region, East Kunlun Orogenic Belt in Qinghai Province.Catchment basins are divided by a high-precision DEM, and the Au, Ag, and Pb factor loadings of 1∶50000 stream sediments are measured by the catchment basin analysis. During the extraction process, a variety of geomorphic parameters such as main stream slope, relief ratio, catchment basin area, are used as the sediment delivery ratios for residual correction calculation, and the geochemical background and anomalies are separated by the C-A fractal modeling. The anomaly extraction results show that the area of the catchment basin is the most suitable parameter as the sediment delivery ratio to participate in the downstream attenuation correction. The catchment basin method can effectively identify and extract geochemical anomaly information.Anomalies correspond well to the spatial location of ore deposits, and can provide beneficial information for the next step of mineral prospecting.

Abstract All life forms require pH homeostasis. The disorder of pH homeostasis is associated with many severe diseases, suggesting the significance of pH‐detection for theranostic diseases. Photoacoustic imaging (PAI) technology has piqued great research interests in spatiotemporal detection of pH in vivo due to its non‐invasiveness and non‐ionization, as well as the high resolution and deep tissue penetration. This mini‐review outlines the most recent advances in activatable organic PA probes for pH‐detection with respect to molecular design, response mechanisms, and their in vivo applications. The basic understanding of PA probes is clarified. The pH‐response mechanisms of the PA probes at the molecular level are particularly emphasized to elucidate the structural design strategies. Finally, the challenges and perspectives, mainly focusing on quantitative detection, are discussed. This review aims to pave the way for further exploration of responsive organic PA probes for in vivo pH‐detection.

As human–robot interaction and teleoperation technologies advance, anthropomorphic control of humanoid arms has garnered increasing attention. However, accurately translating sensor-detected arm motions to the multi-degree freedom of a humanoid robotic arm is challenging, primarily due to occlusion issues with single-sensor setups, which reduce recognition accuracy. To overcome this problem, we propose a human-like arm control strategy based on multi-sensor fusion. We defined the finger bending angle to represent finger posture and employed a depth camera to capture arm movement. Consequently, we developed an arm movement tracking system and achieved anthropomorphic control of the imitation human arm. Finally, we verified our proposed method’s effectiveness through a series of experiments, evaluating the system’s robustness and real-time performance. The experimental results show that this control strategy can control the motion of the humanoid arm stably, and maintain a high recognition accuracy in the face of complex situations such as occlusion.

The Cretaceous lithospheric extension in South China is very distinct and expressed by systematic extension-related geological records which are concentrated in the coastal terrane, Shi-Hang Rift, and Jiangnan Massif. The Cretaceous extension in the coastal terrane and Shi-Hang Rift began almost simultaneously during 145–140 Ma, while there is a lack of reliable magmatic indication for tectonic extension in the Jiangnan Massif during 145–140 Ma. This paper reports a newly discovered Baihesi granite with a concordant age of 142.52 ± 0.57 Ma (MSWD = 1.2) on the southeast margin of the eastern Jiangnan Massif with evidence from petrology, geochronology, petrochemistry and Hf isotopes. The Baihesi granite is a monzogranite and classified as a peraluminous, high-K calc-alkaline series. The trace element compositions are characterized by relatively flat rare earth element patterns; strong depletion of Eu; slightly enriched light rare earth elements; enrichment of Cs, Rb, Th, U, and Pb; and depletion of Ba, Sr, P, and Ti. Combined with other petrochemistry characteristics, such as high SiO2 content, high differentiation index, low Zr/Hf and Nb/Ta ratios, low ratio between light and heavy REEs, low P2O5 content, and high zirconium saturation temperature, Baihesi granite is proven to be highly evolved aluminous A-type granite. The εHf(t) values of Baihesi granite range from −5.57 to −2.37, with two-stage model ages (TDM2) ranging from 1,551 to 1,345 Ma, suggesting a similar source to Neoproterozoic arc-related magmatic rocks in the Shuangqiaoshan Group. We argue that Baihesi granite has originated mainly from partial melting of a reworked juvenile crust related to Neoproterozoic arc-related magmatism in the Jiangnan Massif, which was controlled by subduction along the South China active continental margin. Furthermore, we constrain the onset of Cretaceous extension in the eastern Jiangnan Massif to be 145–140 Ma and highlight the Early Cretaceous reactivation of the northeastern Jiangxi Province fault zone and the structural inheritance in intraplate tectonics.

Hydrocolloids have been widely used to adjust properties of natural starches, but related research on kudzu starch is still rare. In this study, we investigated the effects of gum arabic (AG), sodium alginate (SA), locust bean gum (LG), and guar gum (GG) on kudzu starch from the perspective of its particle size, pasting, texture, rheology, dehydration rate, thermal properties, microstructure, and sensitivity to amyloglucosidase. Results showed that GG significantly increased the particle size of starch. Addition of AG led to lower peak-, final- and holding-viscosity. SA increased the retention viscosity of kudzu starch, while LG and GG increased its peak viscosity. Addition of hydrocolloids increased the hardness, chewiness, and cohesiveness of starch-hydrocolloid complexes, and reduced the dehydration rate of complex gels. Dynamic rheological data showed that the energy storage modulus (G′) was significantly higher than the loss modulus (G″). The magnitude of modulus increased with frequency, and elastic properties were better than viscous properties. Thermal analysis showed that hydrocolloids increased the starting temperature (To), and the final temperature (Tc). With addition of each of these four hydrocolloids, a more regular and porous thick-wall dense structure was formed, which effectively lowered kudzu starch's sensitivity to amyloglucosidase. It indicated that the binding of hydrocolloid to starch may slow down glucose release into blood during digestion. These results will help understand effects of natural hydrocolloid on kudzu starch, as well as expanding its application in food industry.

ObjectiveTo explore the effect of independently developed transcranial pulsed current stimulation (tPCS) on alleviating athlete’s mental fatigue.MethodsA total of 60 college athletes were randomly divided into the active stimulation group (current intensity:1.5 mA, lasting for 15 min) and the sham stimulation group. Subjective questionnaires, behavior test, and functional near-infrared spectroscopy (fNIRS) test were conducted before and after the experiment. Two-way ANOVA with repeated measures was used to compare the differences in mental fatigue indexes before and after the two experimental conditions.ResultsAfter 7 days of exercise training, there was a significant difference in the main effect of the time factor in all indexes of the two groups (p < 0.05). The scores of rated perceived exertion (RPE) scale, positive and negative affect schedule (PANAS), critical flicker frequency (CFF), and reaction time (RT), in the tPCS treatment group, were better than those in the sham stimulation group (p < 0.05). After 7 days of exercise training, all the subjects had different degrees of athlete’s mental fatigue; the subjects in the active stimulation group have a good evaluation of the tPCS developed by the research group without adverse actions.ConclusiontPCS intervention can improve emotional state, reduce the subjective evaluation of fatigue, improve behavioral levels such as attention and reaction time and increase cerebral prefrontal blood flow and oxygen supply.

With the continuous progress and application of robotics technology, the importance of mobile robots capable of adapting to specialized work environments is gaining prominence. Among them, achieving precise and stable control of AGVs (Automated Guided Vehicles) stands as a paramount task propelling the advancement of mobile robotics. Consequently, this study devises a control system that enables AGVs to attain stable and accurate motion through equipment connection and debugging, kinematic modeling of the four-wheel steering AGV, and a selection and comparative analysis of motion control algorithms. The effectiveness of the Stanley-PID control algorithm in guiding the motion of a four-wheel steering AGV is validated through MATLAB 2021a simulation software. The simulation results illustrate the outstanding stability and precise control capabilities of the Stanley-PID algorithm.

Porcine Circovirus-like (PCL) virus, a new emerging virus, has been widely detected in Guangdong, Guangxi, and Anhui provinces in China, which may be a novel agent causing severe diarrhea in newborn piglets and tending to spread widely. Evidence suggests that the virus is related to hemorrhagic enteritis and diarrhea, and many newborn piglets were emaciated to death after infection. Therefore, a sensitive, quick, and accurate detection system for virus detection and epidemiological investigation is necessary. In this study, we developed a real-time quantitative PCR assay based on SYBR green for the detection of PCL virus. The ORF4 conserved region of PCL virus was found by the alignment of the uploaded genome sequences to design specific primers, and the primers were tested and showed good specificity, sensitivity, and reproducibility. Approximately, 138 fecal samples were obtained from diarrheal pigs in South China from June to December 2021. Approximately, 22.46% (31/138) of the samples and 40% (8/20) of the pig farms were positive for PCL virus, respectively, by using this method. Moreover, it is worth noting that the virus was first detected in Hainan and Jiangxi Provinces of China, which means that the virus may spread widely in China. Through evolutionary tree analysis and partial sequence comparison, there are some differences of virus genes in each province, suggesting that there is a risk of variation, and the four PCL virus strains showed a sequence similarity of 86.7%–87.8% for the rep gene and 92.2%–92.9% for the Rep protein, respectively, with Bo-Circo-like virus that is detected in bovine, which further demonstrates a close relationship between the two viruses that originated from different animals. In conclusion, our study provides a useful diagnostic approach to PCL virus detection and epidemiological inquiry. Meanwhile, the epidemic data using this real-time qPCR assay provide evidence for the widespread variations and epidemic of the virus in South China, and warn the appropriate measures for prevention, and control of porcine circovirus-like virus infection should be under consideration in pig production.

Robot dynamics model uncertainty and unpredictable external perturbations are important factors that influence control accuracy and stability. To accurately compensate for the dynamics model in sliding mode control (SMC), a new parallel network (PCR) is proposed in this paper. The network parallelizes the radial basis function and convolutional neural network, which gives it the advantage of making full use of one-dimensional data fitting results and two-dimensional data feature information, realizing the deep learning of multidimensional data and improving the model’s compensation accuracy and anti-interference ability. Meanwhile, based on the integration of adaptive control techniques and gradient descent, a new weight update algorithm is designed to realize the online learning of PCR networks under loss-free functions. Then, a new sliding mode controller (PCR-SMC) is established. The model-free intelligent control of the robot is accomplished without knowledge of the predetermined upper bounds. Additionally, the stability analysis of the control system is proved by the Lyapunov theorem. Lastly, robot tracking control simulations are performed on two trajectories. The results demonstrate the high-precision tracking performance of this controller in comparison with the RBF-SMC controller.

The low-rank models have gained remarkable performance in the field of remote sensing image denoising. Nonetheless, the existing low-rank-based methods view residues as noise and simply discard them. This causes denoised results to lose many important details, especially the edges. In this paper, we propose a new denoising method named EPLRR-RSID, which focuses on edge preservation to improve the image quality of the details. Specifically, we considered the low-rank residues as a combination of useful edges and noisy components. In order to better learn the edge information from the low-rank representation (LRR), we designed multi-level knowledge to further distinguish the edge part and the noise part from the residues. Furthermore, a manifold learning framework was introduced in our proposed model to better obtain the edge information, as it can find the structural similarity of the edge part while suppressing the influence of the non-structural noise part. In this way, not only the low-rank part is better learned, but also the edge part is precisely preserved. Extensive experiments on synthetic and several real remote sensing datasets showed that EPLRR-RSID has superior advantages over the compared state-of-the-art (SOTA) approaches, with the mean edge protect index (MEPI) values reaching at least 0.9 and the best values in the no-reference index BRISQUE, which represents that our method improved the image quality by edge preserving.

This study analysed the impact of turning rate, the centre of mass height, and road adhesion coefficient on the rollover stability of heavy-duty automated guided vehicles (AGVs) using a multi-body dynamics simulation model. The lateral deflection angle of the centre of mass was used as a metric to evaluate the rollover behaviour of the AGVs, and the results were obtained quantitatively. The findings showed that the turning rate had the largest impact on AGV rollover stability, followed by the centre of mass height, while the road adhesion coefficient had the least impact. Despite having the lowest impact, the road adhesion coefficient was one of the key factors contributing to AGV slippage, and severe slippage could easily lead to rollover incidents. To further evaluate the rollover behaviour of the AGVs, a lateral-load transfer rate (LTR) index was derived from the change in wheel load generated by the lateral tilt angle during steering. The range of LTR values was determined for different ranges of turning rate, the centre of mass height, and road adhesion coefficient. The results indicated that the range of LTR values for turning rate was 0.23–0.45, for the centre of mass height was 0.323–0.393, and the minimum value of 0.337 was obtained for a road adhesion coefficient of 0.6.

The African swine fever virus (ASFV) is a huge and complex DNA virus that can lead to the acute death of pigs and cause huge losses to the global swine industry. The CD2v protein is a transmembrane protein encoded by the ASFV’s EP402R gene, which can effectively inhibit the bystander lymphocyte proliferation in response to mitogens and mediate the absorption of red blood cells to ASFV-infected cells. The CD2v protein contains repetitive amino acid sequences ([KPCPPP]3 labeled as RAAS), which is reported as a genetic marker and an epitope. However, the specific biological function of the RAAS is unknown. Here, we have found that the truncated CD2v protein with RAAS can enter Chinese hamster ovary cells, but the truncated CD2v protein without RAAS cannot enter the cells. Also, the RAAS can carry the macromolecular protein EGFP to enter various cells through multiple endocytic processes that are dependent on time, concentration, and location. Besides, the RAAS enter the cells via the macropinocytosis or the clathrin-mediated endocytosis. These results indicate that the RAAS can function as a cell-penetrating peptide that provides a new insight for ASFV research and has potential application value as a tool for drug delivery.

To advance the calculation performance of the battle royale optimization algorithm (BRO), a hybrid improved BRO algorithm (HBC) is proposed in this paper. The level mechanism of the chicken swarm optimization algorithm (CSO) is integrated into the BRO algorithm to divide all into elite players and ordinary players, and the level relationship of different players is established. Then, an elite player update method of random exploring and directional update in a small range is proposed to improve the development ability. The update method of ordinary players improved, the update mechanism of elite random guidance is introduced to make full use of the excellent location information in the population. The performance verification experiment of the HBC algorithm is carried out on 20 benchmark functions and a practical project. Comparing with several other algorithms, the computational performance of the HBC algorithm is the best. Furthermore, the HBC algorithm is applied to solve the inverse kinematics of the 7R 6DOF robot. The experimental results show that the HBC algorithm effectively improves the average convergence accuracy and reduces the running time, compared with the BRO algorithm. This fully shows that the HBC algorithm is more competitive in stability, calculation accuracy, and speed.

The effects of three-stage homogenization on recrystallization and fatigue crack growth behavior of 7020 aluminum alloy were investigated. It was shown that a large amount of fine Al3Zr dispersoids form in the grain and near the grain boundaries of the alloy after three-stage homogenization. The ratio of volume fraction to radius for the Al3Zr dispersoids after three-stage homogenization increased by 171.2% compared with the one-stage homogenization. The recrystallization fraction was reduced from 29.5% for the one-stage homogenized alloy to 19.1% for the three-stage homogenized alloy. If the fatigue crack propagation encounters small recrystallized grains with high misorientation, the crack is easy to grow quickly along grain boundaries. When the stress intensity factor at the crack tip is 10MPa·m1/2, the fatigue crack growth rate (FCGR) of the one-stage homogenized alloy is 30.6% faster than that of the three-stage homogenized alloy.

Based on an electrochemical method, three-dimensional arrayed nanopore structures are machined onto a Mg surface. The structured Mg surface is coated with a thin gold (Au) film, which is used as a surface-enhanced Raman scattering (SERS) substrate. A rhodamine 6G (R6G) probe molecule is used as the detection agent for the SERS measurement. Different sizes of arrayed micro/nanostructures are fabricated by different treatment time using the electrochemical process. The topographies of these micro/nanostructures and the thickness of the Au film have an influence on the Raman intensity of the Mg substrate. Furthermore, when the thickness of Au film coating is held constant, the Raman intensity on the structured Mg substrates is about five times higher after a treatment time of 1 min when compared with other treatment times. The SERS enhancement factor ranges from 106 to 1.75 × 107 under these experimental conditions. Additionally, a 10−6 mol·L−1 solution of lysozyme was successfully detected using the Mg–Au nanopore substrates. Our low-cost method is reproducible, homogeneous, and suitable for the fabrication of SERS substrates.

Although there has been extensive research on the factors that influence sonochemical reactions in solid particle suspensions, the role that solid particles play in the process remains unclear. Herein, the effect of monodisperse silica particles (10–100 μm, 0.05–10 vol%) on the sonochemical activity (20 kHz) was investigated using triiodide formation monitoring and luminol tests. The results demonstrate that, in the particle size range considered, the sonochemical yields were enhanced in dilute suspensions (0.05–1 vol%), while further particle addition in semi-dilute suspensions (1–10 vol%) decreased the yields. Two regimes, namely the site-increasing regime and sound-damping regime, are identified in respect of the enhancing and inhibiting effects of the particles, respectively, and their dependence on particle characteristics is analyzed. Both regimes are confirmed based on the cavitation erosion test results or cavitation noise analysis. The clarification of the two regimes provides a better understanding of the dominant factors controlling sonochemistry in the presence of solid particles, as well as a guide for sonochemical efficiency prediction.

The surface accuracy and quality of the mirror blank determine the performance of the optical mirror after coating; therefore, it is crucial to seek a simple and effective method for mirror blank processing. The gas jet forming provides a new idea, which employs a gas jet to impinge on a liquid pool, forms a specific curved surface and solidifies it. However, due to the interface instability that occurred with a specific range of jet velocity, periodical fluctuations appear on the interface and affect the forming of the mirror blank. In this study, the amplitude and transfer period of surface waves in the air gas/dimethyl silicone oil system are taken as the research objects. After conducting orthogonal experiments, this study uses a high-speed camera to record the generation of the surface wave and the fluctuation of the gas–liquid interface during its transfer. Subsequently, this study analyses the influences of jet velocity and viscosity of dimethyl silicone oil on the amplitude and transfer period of the surface wave. In the following experiments, multiple groups of parameters were selected in the range of jet velocity of 11~13 m/s and dimethyl silicone oil viscosity of 500~2000 Pa·s. This study measures the interface morphology with different parameters using the method of image recognition. Finally, using the measured data, this study establishes the calculation model of surface wave amplitude and transfer period, realizing the study of the characteristics of surface waves on the unstable interface. This research can be used to enhance the forming accuracy of the gas jet-forming method.

In recent years, a variety of data-driven evolutionary algorithms (DDEAs) have been proposed to solve time-consuming and computationally intensive optimization problems. DDEAs are usually divided into offline DDEAs and online DDEAs, with offline DDEAs being the most widely studied and proven to display excellent performance. However, most offline DDEAs suffer from three disadvantages. First, they require many surrogates to build a relatively accurate model, which is a process that is redundant and time-consuming. Second, when the available fitness evaluations are insufficient, their performance tends to be not entirely satisfactory. Finally, to cope with the second problem, many algorithms use data generation methods, which significantly increases the algorithm runtime. To overcome these problems, we propose a brand-new DDEA with radial basis function networks as its surrogates. First, we invented a fast data generation algorithm based on clustering to enlarge the dataset and reduce fitting errors. Then, we trained radial basis function networks and carried out adaptive design for their parameters. We then aggregated radial basis function networks using a unique model management framework and demonstrated its accuracy and stability. Finally, fitness evaluations were obtained and used for optimization. Through numerical experiments and comparisons with other algorithms, this algorithm has been proven to be an excellent DDEA that suits data optimization problems.

Porcine reproductive and respiratory syndrome (PRRS) is a vertically transmitted reproductive disorder that is typically characterized by miscarriage, premature birth, and stillbirth in pregnant sows after infection. Such characteristics indicate that PRRSV can infect and penetrate the porcine placental barrier to infect fetus piglets. The porcine trophoblast is an important component of the placental barrier, and secretes various hormones, including estrogen and progesterone, to maintain normal pregnancy and embryonic development during pregnancy. It is conceivable that the pathogenic effects of PRRSV infection on porcine trophoblast cells may lead to reproductive failure; however, the underlying detailed mechanism of the interaction between porcine trophoblast (PTR2) cells and PRRSV is unknown. Therefore, we conducted genome-wide mRNA and long non-coding RNA (lncRNA) analysis profiling in PRRSV-infected PTR2. The results showed that 672 mRNAs and 476 lncRNAs were significantly different from the control group after viral infection. Target genes of the co-expression and co-location of differential mRNAs and lncRNAs were enriched by GO (gene ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis, revealing that most of the pathways were involved in cell nutrient metabolism, cell proliferation, and differentiation. Specifically, the estrogen signaling pathway, the PI3K (PhosphoInositide-3 Kinase)-Akt (serine/threonine kinase) signaling pathway, and the insulin secretion related to embryonic development were selected for analysis. Further research found that PRRSV inhibits the expression of G-protein-coupled estrogen receptor 1 (GPER1), thereby reducing estrogen-induced phosphorylation of AKT and the mammalian target of rapamycin (mTOR). The reduction in the phosphorylation of AKT and mTOR blocks the activation of the GPER1- PI3K-AKT-mTOR signaling pathway, consequently restraining insulin secretion, impacting PTR2 cell proliferation, differentiation, and nutrient metabolism. We also found that PRRSV triggered trophoblast cell apoptosis, interrupting the integrity of the placental villus barrier. Furthermore, the interaction network diagram of lncRNA, regulating GPER1 and apoptosis-related genes, was constructed, providing a reference for enriching the functions of these lncRNA in the future. In summary, this article elucidated the differential expression of mRNA and lncRNA in trophoblast cells infected with PRRSV. This infection could inhibit the PI3K-AKT-mTOR pathway and trigger apoptosis, providing insight into the mechanism of the vertical transmission of PRRSV and the manifestation of reproductive failure.

Nanostructures have been widely employed in surface-enhanced Raman scattering (SERS) substrates. Recently, in order to obtain a higher enhancement factor at a lower detection limit, hierarchical structures, including nanostructures and nanoparticles, appear to be viable SERS substrate candidates. Here we describe a novel method integrating the nanoindentation process and chemical redox reaction to machine a hierarchical SERS substrate. The micro/nanostructures are first formed on a Cu(110) plane and then Ag nanoparticles are generated on the structured copper surface. The effect of the indentation process parameters and the corrosion time in the AgNO3 solution on the Raman intensities of the SERS substrate with hierarchical structures are experimentally studied. The intensity and distribution of the electric field of single and multiple Ag nanoparticles on the surface of a plane and with multiple micro/nanostructures are studied with COMSOL software. The feasibility of the hierarchical SERS substrate is verified using R6G molecules. Finally, the enhancement factor using malachite green molecules was found to reach 5.089 × 109, which demonstrates that the production method is a simple, reproducible and low-cost method for machining a highly sensitive, hierarchical SERS substrate.

This study investigated the the gas jet forming process for optical aspherical mirror blanks. The trend of the influence of the gas jet parameters on the surface shape of the mirror blanks was inferred by analysing the variation in the morphology of the gas jet stream. Based on the theoretical analysis, the inference was verified by numerical simulation. The experimental results were compared with the simulation predictions, and it was found that the average prediction deviation for the diameter was 1.07 mm, while the average prediction deviation for the principal curvature was 0.03665 mm−1, which is challenging to correct in simulation. Therefore, we developed a dimensionless prediction model of the surface curvature and surface diameter of the mirror blanks by considering the jet parameters using experimental data. The model’s average prediction error for the surface diameter of the formed surface was 0.3192 mm, and the average prediction error of the principal curvature for the formed surface was 0.00269 mm−1.

The multi-laminated controlled release system has been widely used in the modern pharmaceutical industry because of its simple structure and safety, which can control the drug release behavior by regulating its internal parameters. To achieve three desired drug release behaviors of constant rate, linear reduction, and nonlinearity, which can be controlled by the optimization of the controllable parameters in the controlled release system, such as initial concentration and diffusion coefficient, a new regularization cuckoo search algorithm based on the cuckoo search algorithm and Tikhonov regularization method is proposed. The numerical examples confirm the stability, effectiveness, and feasibility of the proposed method. The effect of the number of model layers on the optimization results is also analyzed.

In order to improve the optimization efficiency of the biogeography-based optimization (BBO) algorithm, an improved BBO algorithm, that is, worst opposition learning and random-scaled differential mutation BBO (WRBBO), is presented in this paper. First, BBO's mutation operator is deleted to reduce the computational complexity and a more efficient random-scaled differential mutation operator is merged into BBO's migration operator to obtain global search ability. Second, in order to balance exploration and exploitation, the BBO's migration operator is replaced with a dynamic heuristic crossover to enhance the local search ability. Finally, a worst opposition learning is merged into the improved algorithm to avoid trapping into local optima. A large number of experiments are made on 18 various kinds of classic benchmark functions and some complex functions from the CEC-2013 test set. In addition, WRBBO is applied to clustering optimization and medical image segmentation. The experimental results show that WRBBO has better optimization efficiency on benchmark function optimization, clustering optimization, and medical image segmentation than quite a few state-of-the-art BBO variants and other algorithms.

We investigate a broadcast scenario that a mobile sink disseminating broadcast packets while moving in asynchronous wireless sensor networks (WSNs), to prolong the network lifetime and reduce the broadcast delay. Such a scenario may yield multiple logical sink nodes in WSNs and the moving speed and trajectory of the mobile sink have the significant impact on the speed and efficiency of the broadcast. Thus, the conventional broadcast protocols may not fit for the mobile sink scenario. We propose a fast and efficient broadcast (FEB) protocol with a mobile sink in asynchronous WSNs. We analyze the moving pattern as well as the travel speed of the mobile sink. We jointly consider the broadcast process, the node location, and the neighbor coverage information within two-hops and then propose an efficient broadcast scheme together with a heuristic moving strategy for the mobile sink. The proposed broadcast scheme needs to exchange node coverage information before the transmission begins, thus to avoid redundant packet transmissions, and the proposed moving pattern strategy decreases the network broadcast delay by moving the mobile sink to monitoring areas, where fewer nodes are covered by the broadcast process. The simulation results show that our proposed protocol significantly reduces the broadcast delay and energy consumption.

Lévy flight Shuffle Frog Leaping Algorithm (LSFLA) is a SFLA variant and enhances the performance of SFLA largely, however, it still has some defects, such as poor convergence and low efficiency. So an improved LSFLA, namely, LSFLA based on Differential perturbation and Quasi-Newton search (DQLSFLA), is proposed in this paper. Firstly, the way of updating only one solution which is the worst one at every sub-iteration in LSFLA is replaced with an all-solution updating way in the subgroup to improve the probability of obtaining the best solution, to omit one sorting step at every sub-iteration and the sub-iteration number parameter setting, to reduce the computational complexity and to enhance the optimization efficiency. Secondly, a random differential perturbation approach and an improved Lévy flight updating one are created and used in the subgroup updating to form DLSFLA, that is, the first half frogs in the subgroup use the random differential perturbation updating approach to improve the global search ability, and the last half adopt the improved Lévy flight updating method to keep the advantage of the Lévy flight updating method and overcome its defects. Finally, quasi-Newton local search is integrated into DLSFLA near the end of the algorithm to improve the convergence speed. Experimental results on the complex functions from CEC-2014 show that DQLSFLA's convergence quality and optimization efficiency are much better than LSFLA's and that compared with quite a few other state-of-the-art algorithms, DQLSFLA has better performance. The results on medical image enhancement and Quadratic Assignment Problem (QAP) also show that DQLSFLA can solve the real word optimization problems better than LSFLA can.

This paper studies the scenario of data collection in a rechargeable sensor network (RSN), which is powered by directional wireless power transfer (DWPT). DWPT can overcome the limit of broadcast property of energy waves of omnidirectional wireless power transfer (WPT) by providing directional energy beams with higher antenna gain, therefore, help to promote energy efficiency. We formulate an energy efficiency maximization optimization problem, with buffer and delay constraints and propose a (M + 1)-approximation algorithm, where M is the number of sectors that a power beacon (PB)contains. Simulation results illustrate the benefit of our proposed scheme over the benchmark.

In energy-harvesting wireless sensor networks (EH-WSNs), node can harvest energy from the environment. However, the energy harvested is usually insufficient, time varying and unbalanced. In order to make full use of the energy harvested from the environment, each node can dynamically adjust its transmission power which may affect the associated link quality. Thus, the link quality between two same nodes is dynamic. For the forwarding node, the transmission cost of it and its one hop candidate relay node may change at any time. In this scenario, for the design of opportunistic routing, the size, members and priorities in the relay set need to be updated dynamically. In this paper, we propose an opportunistic routing in EH-WSNs with dynamic transmission power (ORDTP). ORDTP can help the sending node collect information of neighbor nodes in time, accurately estimate the transmission cost, and dynamically update the size, members and priorities in the relay set. The simulation results show that ORDTP can effectively reduce the delay, improve the transmission success rate, and reduce the packet redundancy rate.

We reviewed the records of 337 confirmed cases of tuberculosis patients in Monrovia, the capital of Liberia, 2015. The risk factors affecting the survival and multidrug-resistance of tuberculosis patients were examined. Kaplan-Meier analysis and the log-rank test were used to assess the differences in survival among the patients, while Cox regression model was used for multivariate analysis. The qualitative data was tested with chi-square test in the single factor analysis of multidrug-resistant TB. Multivariate analysis was performed using binary logistic regression analysis. The significance level for all the tests were set at 0.05. The mean period of the follow-up of patients was 10 months. In the 337 patients, 33 (9.8%) died, the 21-month survival rate was 90.2%. The results of multivariate Cox regression analysis show that overcrowding (HR = 7.942, 95% CI 3.258-19.356), former smoking (HR = 3.773, 95% CI 1.601-8.889), current smoking (HR = 3.546, 95% CI 1.195-10.521), multidrug-resistance tuberculosis (HR = 4.632, 95% CI 1.913-11.217) were risk factors for death during anti-tuberculosis treatment in TB patients in Liberia. The results of binary logistic regression analysis show that extra-pulmonary (OR = 2.032, 95% CI 1.133-3.644), family history of TB (OR = 2.387, 95% CI 1.186-4.807) and current smoking (OR = 3.436, 95% CI 1.681-7.027) were risk factors for multidrug-resistant tuberculosis. These results can provide insights on local tuberculosis early intervention, increase public health awareness, and strengthen the control of factors that may affect the survival and multidrug-resistance of tuberculosis patients.

Using soft pneumatic actuator is a feasible solution in the complex unstructured environment, owing to their inherent compliance, light weight, and safety. However, due to the limitations of soft actuators' materials and structures, they fall short of motion accuracy and load capacity, or need large-size, bulky compressors. Meanwhile, in order to gain better control, it is essential for them to sense the environments as well. This leads to high-price sensors or a complicated manufacture technique. Here, a self-sensing vacuum soft actuation structure is proposed, aiming at acquiring good balance among precision, output force, and actuation pressure. The actuator mainly comprises a flexible membrane and a compression spring. When actuated, the flexible membrane outside the actuator compresses the internal spring skeleton, realizing large contractile motion in axial direction. Its built-in force sensor can indirectly measure the absolute displacement of the actuator with certain accuracy (about 5% F.S.). Besides, it does not require high actuation pressure to generate enough output force. The actuator is quite easy to manufacture with low cost, and there are a variety of materials to choose from. We established quasi-static models for actuators built of two different kinds of membrane materials, and tested their accuracy and output force. In addition, to break through the limits of vacuum actuation, a method of positive-negative pressure combined actuation has been proposed, which lowers the requirements for air source equipments, increases actuation pressure, and reduces potential safety threats at the same time. This kind of soft actuators can also effectively resist and detect impacts. The design of a two-finger dexterous robot hand and robot joint based on this soft actuator illustrates its broad application prospects in the fields of mobile robots, wearable devices, and human–robot interaction.

Fuel injection nozzles are a key component of electronic injection engines, and their inner surface roughness affects the performance of the nozzles and restricts the working efficiency of the engine. Therefore, the polishing technology for a nozzle’s inner surface is particularly important. At present, abrasive flow polishing technology is commonly used to treat the inner surfaces of the nozzles. This study investigated the magnetic particles in the abrasive flow working medium. Due to the external magnetic field, magnetic particles are affected by the magnetic field force and change the polishing performance of the abrasive flow working medium. Through a numerical analysis and contrast experimental research, we can see that the choice of different grinding grain sizes, kinematic viscosity, magnetic field intensity, and process parameters, such as inlet pressure, with magnetic particles in a solid–liquid two-phase abrasive flow for polishing, can effectively improve the quality of the injection nozzle’s inner surface. The study also reveals that the influence of the nozzle’s inner surface polishing quality is significant and creates a mechanism for process parameters.

By exploring the effects of energy and velocity under different inlet speeds and abrasive concentrations, different inlet boundary conditions were selected for numerical analysis, and the intrinsic properties of the solid–liquid two-phase abrasive flow research and development calibre pipes were explored. The influence of law and process characteristics of solid–liquid two-phase abrasive flow is discussed. The fourth-order variable diameter pipe is taken as the research object. It is concluded that the inlet velocity is increased, the total energy and kinetic energy of the abrasive grains are also increased, and the total energy and kinetic energy are increased. The more intensely the abrasive grains collide with the workpiece wall, the more favourable is the effect of the abrasive flow on the wall surface finishing, and the increase of the abrasive concentration. The number of collisions of the disordered abrasive grains on the wall surface is increased, which is more conducive to the wall surface. The finishing process provides theoretical reference and technical support for the engineering application of solid–liquid two-phase abrasive flow.

Laplacian Biogeography-Based Optimization (LxBBO) is a BBO variant which improves BBO’s performance largely. When it solves some complex problems, however, it has some drawbacks such as poor performance, weak operability, and high complexity, so an improved LxBBO (ILxBBO) is proposed. First, a two-global-best guiding operator is created for guiding the worst habitat mainly to enhance the exploitation of LxBBO. Second, a dynamic two-differential perturbing operator is proposed for the first two best habitats’ updating to improve the global search ability in the early search phase and the local one in the late search one, respectively. Third, an improved Laplace migration operator is formulated for other habitats’ updating to improve the search ability and the operability. Finally, some measures such as example learning, mutation operation removing, and greedy selection are adopted mostly to reduce the computation complexity of LxBBO. A lot of experimental results on the complex functions from the CEC-2013 test set show ILxBBO obtains better performance than LxBBO and quite a few state-of-the-art algorithms do. Also, the results on Quadratic Assignment Problems (QAPs) show that ILxBBO is more competitive compared with LxBBO, Improved Particle Swarm Optimization (IPSO), and Improved Firefly Algorithm (IFA).

Aluminum (Al) is a metal material commonly used in industry, but its surface is easily corroded. The superhydrophobic surface has great self-cleaning and anti-corrosion properties, and it is an ideal method to construct a functional aluminum surface. Here, a simple method based on cyclic chemical etching was proposed to achieve the superhydrophobic Al surface with honeycomb structures. The surface of the sample etched eight times comprised micro/nano-scale honeycomb cavities, while exhibiting a water contact angle (WCA) of 135°. After being treated with an octadecanethiol (C _18 H _38 S) methanol solution, this sample demonstrated a WCA of 153.1°. A self-cleaning test was performed on the superhydrophobic Al surface, showing the excellent self-cleaning property. Finally, the electrochemical anti-corrosion test demonstrated that the above-mentioned superhydrophobic Al surface had great corrosion resistance property. Overall, this work has enriched the theory and technology for fabricating aluminum to achieve superhydrophobic.

Region-level traffic information can characterize dynamic changes of urban traffic at the macro level. Real-time region-level traffic prediction help city traffic managers with traffic demand analysis, traffic congestion control, and other activities, and it has become a research hotspot. As more vehicles are equipped with GPS devices, remote sensing data can be collected and used to conduct data-driven region-level-based traffic prediction. However, due to dynamism and randomness of urban traffic and the complexity of urban road networks, the study of such issues faces many challenges. This paper proposes a new deep learning model named TmS-GCN to predict region-level traffic information, which is composed of Graph Convolutional Network (GCN) and Gated Recurrent Unit (GRU). The GCN part captures spatial dependence among regions, while the GRU part captures the dynamic change of traffic within the region. Model verification and comparison are carried out using real taxi GPS data from Shenzhen. The experimental results show that the proposed model outperforms both the classic time series prediction model and the deep learning model at different scales.