This paper first discusses a fractional-order Liu system of order as low as 2.7 and shows its chaotic characteristics by carrying out numerical simulations such as Lyapunov exponents, bifurcation diagrams and phase portraits. Then, by using the topological horseshoe theory and computer-assisted proof, the existence of chaos in the system is verified theoretically. Finally, the fractional-order system is implemented on a Field Programmable Gate Array (FPGA) and the results obtained show that the fractional-order Liu system is indeed chaotic.

Single particle analysis with cryogenic electron microscopy (cryo-EM) can determine high resolution structures of macromolecular assemblies. To reconstruct final three-dimension(3D) density map, a reliable initial model is essential and ab-initio reconstruction usually can avoid model bias. The grand challenge for ab-initio reconstruction is to alignment two-dimension(2D) low signal-to-noise ratio(SNR) of particle images with random and unknown parameters. Here we formulate a fast ab initio reconstruction method(named AlignFM) based on Fourier Mellin transform and particle-swarm intelligence optimization(PSO) algorithm. It enables simultaneous 2D alignment and 3D refinement, which can fast generate a reliable 3D map. After experiments on several real datasets. AlignFM shows high performance on fast ab initio reconstruction with a small amount of data.

The endothelium is a monolayer of cells lining the inner blood vessels. Endothelial cells (ECs) play indispensable roles in angiogenesis, homeostasis, and immune response under normal physiological conditions, and their dysfunction is closely associated with pathologies such as cardiovascular diseases. Abnormal EC metabolism, especially fatty acid (FA) dysfunctional metabolism, contributes to the development of many diseases including pulmonary hypertension (PH). In this review, we focus on discussing the latest advances in FA metabolism in ECs under normal and pathological conditions with an emphasis on PH. We also highlight areas of research that warrant further investigation.

The outbreak of recently identified 2019 novel coronavirus (2019-nCOV) infection has become a world-wide health threat. Currently, more information is needed for further understanding the transmission, clinical characteristics, and infection control procedures of 2019-nCOV. Recently, the role of the eye in transmitting 2019-nCOV has been intensively discussed. Previous investigations about other high infectious human COVs, that is, severe acute respiratory syndrome coronavirus (SARS-CoV) and the Middle East respiratory syndrome coronavirus (MERS-CoV), may provide helpful information. In this review, we describe the genomics and morphology of human CoVs, the epidemiology, systemic and ophthalmic manifestations, mechanisms of human CoVs infection, and infection control procedures. The role of the eye in the transmission of SARS-CoV and 2019-nCOV is discussed. Although the conjunctiva is directly exposed to extraocular pathogens, and the mucosa of ocular surface and upper respiratory tract is connected by nasolacrimal duct and share same entry receptors for some respiratory viruses. The eye is rarely involved in human CoVs infection, conjunctivitis is quite rare in patients with SARS-CoV and 2019-nCoV infection, and COV RNA positive rate by RT-PCR test in tears and conjunctival secretions from patients with SARS-CoV and 2019-nCoV infection is also very low, which imply that the eye is neither a preferred organ of human COVs infection, nor is a preferred gateway of entry for human COVs to infect respiratory tract. However, pathogens exposed to the ocular surface might be transported to nasal and nasopharyngeal mucosa by constant tear rinsing through lacrimal duct, and then cause respiratory tract infection. Considering close doctor-patient contact is quite common in ophthalmic practice which are apt to transmit human COVs by droplets and fomites, hand hygiene and personal protection are still highly recommended for health care workers to avoid hospital-related viral transmission during ophthalmic practice.

The main objective of this study is to develop a Hapke photometric model that is suited for Chang’E-1 (CE-1) Interference Imaging Spectrometer (IIM) data. We first divided the moon into three areas including ‘maria’, ‘new highland’ and old ‘highland’ with similar photometry characteristic based on the Hapke parameters of the moon derived from Lunar Reconnaissance Orbiter Camera (LROC) Wide Angle Camera (WAC) multispectral data. Then, we selected the sample data in the ‘maria’ area and obtained a new set of Hapke model’s parameters that can best fit these data. Result shows that photometric correction using Hapke model with these new derived parameters can eliminate the effect of variations in viewing and luminating geometry, especially ‘opposition surge’, more efficiently than the empirical model. The corrected mosaic shows no significant artifacts along the tile boundaries, and more detailed information of the image can be exhibited due to a better correction of ‘opposition surge’ at small phase angle (g <15°).

Abstract: The Micro-Electro-Mechanical System (MEMS) LiDAR point cloud in autonomous vehicles has a large deflection range, which results in slow registration speed and poor applicability. To maximize speed, an improved NDT (Normal Distribution Transform) method that integrates point cloud density features has been proposed. First, the point cloud is reduced using a modified voxel filter and a pass-through filter. Next, the ISS (Intrinsic Shape Signature) algorithm is utilized to analyze the point cloud features and extract keypoints, the 4PCS (4-Point Congruent Sets) algorithm is then employed to calculate the initial pose under the constraints of the keypoint set to complete the coarse registration. Finally, the self-adaptive segmentation model is constructed by using KD-tree to obtain the density features of keypoints, and the NDT algorithm is combined with this model to form an SSM-NDT algorithm, which is used for fine registration. Each algorithm was compared on the autonomous vehicle dataset PandaSet and actual collected data sets, the results show that the novel method increases the speed by at least 60% and takes into account good registration accuracy and strong anti-interference.

Air quality is one of the most concerning problems in major industrialized cities in the world. Prediction of future air quality is highly relevant to public health. In some big cities, multiple air quality measurement stations are deployed at different locations to monitor air pollutants, such as NO2, CO, PM 2.5 and PM 10, over time. At every monitoring time stamp t, we observe one station×feature matrix xt of the pollutant data, which represents a spatio–temporal process. Traditional methods on prediction of air quality typically use data from one station or can only predict a single pollutant (such as PM 2.5) at a time, which ignores the spatial correlation among different stations. Moreover, the air pollution data are typically highly nonstationary, We propose a de-trending graph convolutional LSTM (long short term memory) to continuously predict the whole station×feature matrix in the next 1 to 48 hours, which not only captures the spatial dependency among multiple stations by replacing an inner product with convolution, but also incorporates the de-trending signals (transform a nonstationary process to a stationary one by differencing the data) into our model. Experiments on the air quality data of the city Chengdu and multiple major cities in China demonstrate the feasibility of our method and show promising results.

Transmembrane protein 100 (TMEM100) plays an important role in angiogenesis, vascular morphogenesis, integrity and cardiovascular development. TMEM100 is a downstream target of the BMP9/10 and BMPR2/ALK1 signaling pathways. Our recent study demonstrates TMEM100 is a lung endothelium enriched gene. Endothelial-specific deletion of Tmem100 impairs lung endothelial cells regeneration. Activation of Tmem100 signaling represents a novel strategy for lung vascular repair and regeneration. It is interesting and important to understand the roles of TMEM100 in the physiological and pathological conditions. In this review, we summarized the current knowledge of TMEM100.

Increasing green total factor productivity is the key to achieving green development in agriculture. This study measured the green total factor productivity of Jiangxi’s agriculture, and its regional and temporal evolution characteristics were examined. The fixed-effects model was then used to investigate the model’s fundamental components empirically. The study’s findings reveal the following: During the period under review, technical change was the primary element driving the rise in the green total factor productivity of agriculture. A rising “U”-shaped trend with notable regional variances characterizes the spatial and temporal evolution. The primary factors that affect changes in green total factor productivity in agriculture include the ease of transportation, the per capita disposable income of rural residents, the level of agricultural mechanization, the degree of urbanization, the level of financial support for agriculture, and the percentage of workers in secondary industries. As a result, it is suggested that, in order to substantially increase agricultural green total factor productivity, agricultural green technological support should be strengthened, more fully developed, and promoted in a multi-pronged approach.

The performance of the absolute atom gravimeters used on moving platforms, such as vehicles, ships and aircrafts, is strongly affected by the vibration noise. To suppress its influence, we summarize a vibration compensation method utilizing data measured by a classical accelerometer. The measurements with the accelerometer show that the vibration noise in the vehicle can be 2 order of magnitude greater than that in the lab during daytime, and can induce an interferometric phase fluctuation with a standard deviation of 16.70π. With the compensation method, our vehicle-mounted atom gravimeter can work normally in these harsh conditions. Comparing the Allan standard deviations before and after the vibration noise correction, we find a suppression factor of 22.74 can be achieved in static condition with an interrogation time of T = 20 ms, resulting a sensitivity of 1.35 mGal/Hz1/2, and a standard deviation of 0.5 mGal with an average time of 10 s. We also demonstrate the first test of an atom gravimeter in a moving vehicle, in which a suppression factor of 50.85 and a sensitivity of 60.88 mGal/Hz1/2 were realized with T = 5 ms.

Few studies have paid attention to the industrial lands along the Chinese Grand Canal from a cultural per-spective, but cities along the route like Hangzhou have relied on the Canal system to promote industrializa-tion over the past hundred years, and relocated, transformed, or demolished the factories to construct cul-tural space or products mostly after industrial recession at the beginning of 21st century. To deeply analyze this process is quite helpful to understand the typical cultural change and its gain and loss along the Canal especially after cultural and creative industries have been established as the pillar industries of China's na-tional economy. Therefore, along the Hangzhou section of the Canal, we dynamically monitored the land use of the selected 10 industrial lands, with the analysis of high-resolution satellite imageries and the in-dustrial past, the areas of different uses in 2022 were compared. Meanwhile, along with archival data, fieldwork, and experts’ consultation, we designed a mark sheet with factors and scores to comprehensively assess cultural changes over those lands, and its reasons and influence also have been argued, and the prob-lems and suggestions have been offered. This study can contribute to a better understanding of public poli-cy choices in China regarding cities along the Grand Canal after the industrial decline, and provide new perspectives for cultural research, heritage conservation, and urban planning of canals or waterfront spaces.

This paper presents an improved droop control strategy for grid-connected inverter power stability and power quality under distorted with consideration of grid fluctuation and inter-harmonics. An instantaneous frequency without PLL and amplitude of capacitor voltage feed-forward control strategy is given to power stability control, meanwhile a grid current feedback control is given by an incomplete derivation with a high-pass filter, so that the harmonics and inter-harmonics current can be suppressed. These approaches can provide both good active and reactive power dynamic response under fluctuation of frequency, and rejection ability against harmonic and inter-harmonic voltage. Based on model of inverter, the proposed control strategies are designed in detail. Simulations and experiments are present to validate the effectiveness of proposed method.

Concrete-filled steel tube (CFST) members have been widely used in the field of civil engineering due to their advanced superior mechanical properties. However, internal defects such as concrete core voids and interface debonding are likely to weaken the load-carrying capacity and stiffness of these members, which affects safety and serviceability of CFST structures. Visualizing the inner defects of concrete core in CFST members have been a critical need in civil engineering construction, a travel time tomography (TTT) is introduced to quantitatively identify and visualize the sizes and positions of CFST members in this paper. Moreover, a parameter analysis is performed to investigate the relationship between TTT imaging qualities and influence factors, e.g. inversion parameters, defect sizes and positions. The effectiveness and accuracy of the TTT algorithm are verified by several numerical examples and the results demonstrate that TTT can identify the sizes and positions of concrete core void defects in CFST members efficiently and several inversion parameters including model weighting matrix and inversion grid size really pose a significant impact on the imaging results of CFST members. In addition, several optimum parameters are recommended to benefit the future study of the promising TTT approach for CFST members.

The present study aims to develop an efficient numerical method for computing the diffraction and radiation of water waves with horizontal long cylindrical structures, such as floating breakwaters. A higher-order scheme is used to discretize geometry of the structure as well as the relevant physical quantities. As the kernel of this method, Wehausen’s free-surface Green function is calculated by a newly-developed Gauss-Kronrod adaptive quadrature algorithm after elimination of its Cauchy-type singularities. To improve computational efficiency, a Chebyshev approximation approach is applied to a fast calculation of the Green function that needs evaluation thousands of times. In addition, OpenMP parallel technique is used to the formation of influence coefficient matrix, which significantly reduces CPU time. Finally, computations are performed on wave exciting forces and hydrodynamic coefficients for the long cylindrical structures, either floating or submerged. Comparison with other numerical and analytical methods demonstrates good performance of the present method.

The interpretation of land use and land cover (LULC) is an important issue in the fields of high-resolution remote sensing (RS) image processing and land resource management. Fully training a new or existing convolutional neural network (CNN) architecture for LULC classification requires a large amount of remote sensing images. Thus, fine-tuning a pre-trained CNN for LULC detection is required. To improve the classification accuracy for high resolution remote sensing images, it is necessary to use another feature descriptor and to adopt a classifier for post-processing. A fully connected conditional random fields (FC-CRF), to use the fine-tuned CNN layers, spectral features, and fully connected pairwise potentials, is proposed for image classification of high-resolution remote sensing images. First, an existing CNN model is adopted, and the parameters of CNN are fine-tuned by training datasets. Then, the probabilities of image pixels belong to each class type are calculated. Second, we consider the spectral features and digital surface model (DSM) and combined with a support vector machine (SVM) classifier, the probabilities belong to each LULC class type are determined. Combined with the probabilities achieved by the fine-tuned CNN, new feature descriptors are built. Finally, FC-CRF are introduced to produce the classification results, whereas the unary potentials are achieved by the new feature descriptors and SVM classifier, and the pairwise potentials are achieved by the three-band RS imagery and DSM. Experimental results show that the proposed classification scheme achieves good performance when the total accuracy is about 85%.

Several preclinical and clinical reports have demonstrated that levels of circulating high mobility group box 1 protein (HMGB1) are increased early after trauma and are associated with systemic inflammation and clinical outcomes. However, the mechanisms of the interaction between HMGB1 and inflammatory mediators that lead to the development of remote organ damage after trauma remain obscure. HMGB1 and inflammatory mediators were analyzed in plasma from 54 combat casualties, collected on admission to a military hospital in Iraq, and at 8 and 24 hours after admission. Forty-five (83%) of these patients had traumatic brain injury (TBI). Nine healthy volunteers were enrolled as controls. HMGB1 plasma levels were significantly increased in the first 8 hours after admission, and were found to be associated with systemic inflammatory responses, injury severity score, and presence of TBI. These data provided the rationale for designing experiments in rats subjected to blast injury and hemorrhage, to explore the effect of HMGB1 inhibition by CX-01. Animals were cannulated, then recovered for 5-7 days before blast injury in a shock tube and volume-controlled hemorrhage. Blast injury and hemorrhage induced an early increase in HMGB1 plasma levels that coincided with severity of tissue damage and mortality. CX-01 inhibited systemic HMGB1 release, decreased local and systemic inflammatory responses, significantly reduced tissue and organ damage, and tended to increase survival. These data suggest that CX-01 has potential as an adjuvant treatment for traumatic hemorrhage.

In an effort to enhance the efficiency and safety of open-pit mines, this study explores the optimization of end slope road parameters and slope structures, specifically focusing on unmanned driving lanes.A significant aspect of the study is the development of a truck trajectory offset model, which considers the different reaction times between automated sensors and human drivers in adapting to environmental changes. To test these concepts, the study uses numerical simulations to confirm the stability of the proposed end slope designs. Using Victory West Mine No. 1 as a case study, the research determines the optimized width for unmanned driving lanes and the maximum angle for safe steepening of end slopes. The findings indicate that the optimized unmanned lane width for NTE240 mining dump trucks is 1743 mm, allowing for a 2-degree increase in the slope angle at the south end slope. This optimization leads to a steep mining stripping volume of 3.2735 million m³ and a coal output of 2.49628 million tons, maintaining a stripping ratio of 1.31m³/t. These results demonstrate that the unmanned driving road width optimization not only ensures slope safety but also significantly boosts the economic benefits of steep mining in open-pit mines.

Co-free Li-rich oxide is a promising cathode for low cost high energy density Li-ion battery but presents poor cyclic stability. To address this issue, CeO2 is coated on Co-free Li-rich oxide through a low-temperature aged process, resulting in a novel CeO2-coated Li-rich oxide compo-site. Due to the low-temperature aged process, a uniform CeO2 coating layer of 6 nm in thickness is successfully coated on Co-free Li-rich oxide. With this uniform coating, the resulting CeO2-coated Li-rich oxide composite presents improved cyclic stability as well as rate capability as the cathode of Li-ion battery. The capacity retention of the resulting CeO2-coated Li-rich oxide is enhanced from 67% to 85% after 100 cycles and its capacity retention of 5C/0.05C is enhanced from 10% to 23%, compared with the uncoated sample. Such significant improvements enable this low-temperature aged process to be helpful for the practical application of Li-rich oxide materials, not limited to Co-free Li-rich oxide.

Zinc is mostly extracted from zinc oxide and sulfide minerals, and this process involves flotation as a key step. While it is easier to float the sulfide mineral, its consumption and depletion has led to an increased reliance on zinc oxide minerals, including smithsonite; hence the development of efficient ways of collecting smithsonite by flotation is an important objective. Herein, we describe the use of 2-(hexadecanoylamino)acetic acid (HAA), a novel surfactant, as a collector during smithsonite flotation. The mechanism and flotation performance of HAA during smithsonite flotation were investigated by total organic carbon (TOC) content studies, zeta potential measurements, FTIR spectroscopy, and XPS analyses, combined with micro-flotation experiments. The flotation results revealed that HAA is an excellent collector in pulp over a wide pH range (9–12) and at a relatively low concentration (2 × 10^‒4 mol/L), at which a recovery of close to 90% of the smithsonite mineral was obtained. TOC-content studies reveal that the good flotation recovery is ascribable to large amounts of collector molecule adsorbed on the smithsonite surface, while zeta potential measurements show that the HAA is chemically adsorbed onto the smithsonite. FTIR and XPS analyses reveal that the HAA-collector molecules adsorb onto the smithsonite surface as zinc-HAA complexes involving carboxylate moieties and Zn sites on the smithsonite surface in alkaline solution.

In this study, we aim to evaluate how cyclodextrin molecules affect the hydrophilicity and selec-tivity of PVDF membrane. Herein, we prepared PVDF-PVP(1-x) β-CDx membranes, with x rang-ing from 0 to 1 wt%. The membranes were deeply characterized by means of X-ray diffraction (XRD), attenuated total reflectance infrared (ATR-IR), Raman spectroscopies, Thermal gravimet-ric analysis (TGA), Contact angle measurement (CA), scanning electron microscopy (SEM) and atomic force microscopy (AFM). The study unequivocally demonstrates the effectiveness of the addition of β-CD molecules on membranes' structural, morphological, and dielectric properties. Adding 0.25wt% β-CD enhanced hydrophilicity, with CA decreasing from 72.6° to 56° and roughness decreasing from 81 to 29 nm. The PVDF-PVP(1-x) β-CDx membranes were then fully characterized using electrochemical impedance spectroscopy (EIS) to determine their dielectric properties, which clearly showed that the presence of β-CD induced polarization of the mem-brane. The separation of various dyes with different sizes and charges was utilized. These dyes included methylene blue (MB, positive, 0.504 nm), methyl red (MR, neutral, 0.487 nm), rose Bengal (RB, negative, 0.588 nm), and brilliant blue (BB, negative, 0.798 nm). Based on our re-search, we found that the membrane's selectivity is determined by both exclusion factors (such as size and dielectric properties) and inclusion inside the β-CD cage. We found that using alpha-CD instead of beta-CD led to the disappearance of dye inclusion. These findings strongly sug-gest that β-CD molecules can be a viable solution for dye removal from wastewater.

Understanding how industrial agglomeration affects agricultural green production efficiency is essential for green agricultural development. This study uses the super-efficient EBM-GML to measure and analyze the spatial and temporal evolution characteristics and core sources of dynamics of agricultural green production efficiency in China by using panel data from 30 Chinese provinces from 2006 to 2020. It also empirically investigates the relationships between industrial agglomeration, land transfer, and agricultural production efficiency. By using fixed, intermediary, and threshold effects models, the internal links between the industrial agglomeration, land transfer, and agricultural green production efficiency are examined. The findings indicate that the green production efficiency of Chinese agriculture exhibits the regional characteristics of being “high in the west and low in the east, high in the south and low in the north” in terms of space; in terms of time series, the overall trend is that of growing, with an average annual growth rate of 11.45%, and the growth primarily depends on the “single-track drive” of green technological progress. By promoting land transfer, industrial agglomeration can increase the agricultural green production efficiency and decomposition index. Land transfer has a double-threshold effect on the influence of industrial agglomeration on agricultural green production efficiency. As a result, this study suggests adhering to the idea of synergistic development, promoting agricultural green development, strengthening the development of industrial agglomerations, promoting the quality and efficiency of industry, improving land-transfer mechanisms, and placing a focus on resource efficiency improvements, as well as other policy recommendations.

The deformation monitoring information of concrete dams contains some high-frequency com-ponents, and the high-frequency components are strongly nonlinear, which reduces the accuracy of dam deformation prediction. In order to solve such problems, this paper proposes a concrete dam deformation monitoring model based on empirical mode decomposition (EMD) combined with wavelet threshold noise reduction and sparrow search algorithm (SSA) optimization of long short-term memory network (LSTM). The model uses EMD combined with wavelet threshold to decompose and denoise the measured deformation data. On this basis, the LSTM model based on SSA optimization is used to mine the nonlinear function relationship between the reconstructed monitoring data and various influencing factors. The example analysis shows that the model has good calculation speed, fitting and prediction accuracy and it can effectively mine the date char-acteristics inherent in the measured deformation, and reduce the influence of noise components on the modeling accuracy.

Background: Uncontrolled hemorrhage is a major preventable cause of death in patients with trauma. However, the majority of large animal models of hemorrhage have utilized controlled hemorrhage rather than uncontrolled hemorrhage to investigate the impact of immunopathy and coagulopathy on multi-organ failure (MOF) and mortality. This study evaluates these alterations in a severe porcine-controlled and uncontrolled hemorrhagic shock (HS) model. Methods: Anesthetized female swine underwent controlled hemorrhage (22 ml/kg) and uncontrolled hemorrhage by partial splenic resection followed with or without lactated Ringer solution (LR) or Voluven® resuscitation. Blood chemistry, physiologic variables, systemic and tissue levels of complement proteins and cytokines, coagulation parameters, organ function, and damage were recorded and assessed. Results: HS resulted in systemic and local complement activation, cytokine release, metabolic acidosis, hyperkalemia, MOF, and no animal survival. Resuscitation with LR and Voluven® after HS improved hemodynamic parameters (MAP and SI), metabolic acidosis, hyperkalemia, and survival but resulted in increased complement activation and worse coagulopathy. Compared with the LR group, the HS animal treated with Voluven® had worse dilutional anemia, coagulopathy, renal and hepatic dysfunction, increased myocardial complement activation and renal damage, and decreased survival rate. Conclusions: Hemorrhagic shock triggers early immunopathy and coagulopathy that appear associated with MOF and death. This study indicates that immunopathy and coagulopathy are therapeutic targets that may be addressed with a high-impact adjunctive treatment to conventional resuscitation.

Degraded soils causing from natural and human affects are universal in arid and semi-arid regions all over the world. Bentonite and humic acid (BHA) are increasingly being tested to remediate these degraded lands with potential benefits on crop production and soil health. The objective of this paper was to determine the residual effects four to five years after a one-time BHA application at six rates on (i) dynamic changes in soil properties, and (ii) oat crop productivity parameters, in a dryland farming ecosystem. With increasing rates of one-time BHA application, soil profile water storage displayed a piecewise linear increase plus plateau, whereas soil electrical conductivity, pH and bulk density were all reduced significantly (P < 0.05) in the 0-20 cm and 20-60 cm layers. The improved soil environments gave rise to an increased activity of soil enzymes urease, invertase and catalase that respectively reached the peak values of 97%, 37% and 32% at the rates of 21 to 24 Mg BHA ha-1. These conversely boosted soil nutrient turnover, leading to a 40% higher soil available P. Compared with the control treatment, application of BHA at the estimated optimum rate (roughly 24 Mg ha-1) increased grain yield by 20%, protein yield by 62%, water use efficiency by 41%, and partial factor productivity of N by 20%. Results of this study showed for the first time that a one-time BHA application would be a new and effective strategy to combat land degradation, drought, and promote a sustainable soil micro-ecological environment in dryland agroecosystem under a varying climate scenario.

This paper presents a novel approach for semantic segmentation of building roof in dense urban environment with Deep Convolution Neural Network (DCNN) using imagery acquired by a Chinese Very High Resolution (VHR) satellite mission, i.e. GaoFen-2 (GF-2). To provide an operational end-to-end work flow for accurate build roof mapping with feature extraction as well as image segmentation, a fully convolutional DCNN with both convolutional and deconvolutional layers is designed to perform the VHR image analysis for labeling pixels. Since the diverse urban patterns and building styles in large areas, sample image data sets of building roof and non-building roof are collected over different metropolitan regions in China. We selected typical cities with dense urban environment in each metropolitan region as study areas for collecting training and test samples. High performance cluster with GPU-mounted workstations is employed to perform the model training and optimization. With the building roof samples collected over different cities, the predictive model with multiple NN layers is developed for building roof labeling. The validation of the building roof map shows that the overall accuracy(OA) and the mean Intersection Over Union( mIOU) of DCNN based segmentation are 94.67%, 0.85 respectively, while CRF-refined segmentation achieved OA of 94.69% and mIOU of 0.83. The results suggest that the proposed approach is a promising solution for building roof mapping with VHR images over large areas across different urban and building patterns. With the operational acquisition of GF2 VHR imagery, it is expected to develop an automated pipeline for operational built-up area monitoring and timely update of building roof map over large areas.

The aim of this study was to investigate the physicochemical characterization and biological activities of chitosan (CH) based composite films with the incorporation of chestnut flower essential oil (CFEO). The composite films were prepared by casting method and characterized in terms of structural, morphological and mechanical properties via FT-IR, XRD, UV, SEM and TGA. The results proved the significantly increased water vapor permeability (WVP), heat resistance, antibacterial and antioxidant capabilities of CH-CFEO films. The incorporation of CH and CFEO enhanced the UV blocking, which made the film shield almost all UV light. Films with the tensile strength of 6.37 ± 0.41 MPa and elongation at break of 22.57 ± 0.35% were got with 6 mg mL-1 CFEO. Subsequently, the fruit preservation experiments also confirmed that the composite films could effectively extend the shelf life of bananas through their excellent water vapor and oxygen barrier. These desirable performances enable our newly developed composite films to be a remarkable packaging material to solve the fresh fruit preservation dilemma.

Autonomous target recognition (ATR) plays a crucial role in maintaining situational awareness during environmental monitoring. Unmanned aerial vehicles (UAVs) equipped with autonomous target recognition technology can gather and analyze real-time information about targets, including their locations, sizes, and types. However, UAV-captured images in complex real-world environments often display significant variations in perspective and scale due to changes in UAV altitude and distance. Existing methods for autonomous target recognition on UAVs struggle to capture targets from large field-of-view and multi-scale images, resulting in low recognition accuracy and high false-positive rates. This paper introduces two novel Slimmable neural network models, namely SE-YOLOv5s and ST-YOLOv5s, which are based on the YOLOv5s architecture. These models incorporate the Squeeze and Excitation and Swin-Transformer mechanisms to enhance the ability to extract features from large field-of-view and multi-scale images. To evaluate their performance, experiments were conducted on the Visdrone19 aerial dataset. Compared to the state-of-the-art YOLOv5s algorithm, the utilization of SE-YOLOv5s and ST-YOLOv5s for autonomous target recognition on low-altitude drones resulted in improvements in both accuracy and false-positive rates. These proposed methods combine Slimmable neural network design with feature enhancement mechanisms, addressing the challenges posed by complex real-world environments in UAV missions. The advancements in autonomous target recognition on low-altitude drones will significantly contribute to enhancing situational awareness in future environmental monitoring.

Arc sensors have been used in seam tracking and widely studied since the 80s; commercial arc sensing products for T and V shaped grooves have been developed. However, it is difficult to use these arc sensors in narrow gap welding because arc stability and sensing accuracy are not satisfactory. Pulse gas melting arc welding (P-GMAW) has been successfully applied in narrow gap welding and all position welding processes, so it is worthwhile to research P-GMAW arc sensing technology. In this paper, we derived a linear mathematical P-GMAW model for arc sensing, and the assumptions for the model are verified through experiments and finite element methods. Finally, the linear characteristics of the mathematical model were investigated. In torch height changing experiments, uphill experiments, and groove angle changing experiments the P-GMAW arc signals all satisfied the linear rules. In addition, the faster the welding speed, the higher the arc signal sensitivities; the smaller the groove angle, the greater the arc sensitivities. The arc signal variation rate needs to be modified according to the welding power, groove angles, and swing or rotate speed.

Abnormal connections in brain networks of healthy people always bring the problems of cognitive impairments and degeneration of specific brain circuits, which may finally result in Alzheimer’s disease (AD). Exploring the development of the brain from normal controls (NC) to AD is an essential part of human research. Although connections changes have been found in the development, the connection mechanism that drives these changes remain incompletely understood. The purpose of this study is to explore the connection changes in brain networks in the process from NC to AD, and uncover the underlying connection mechanism that shapes the topologies of AD brain networks. In particular, we propose a model named MINM from the perspective of topology-based mutual information to achieve our aim. MINM concerns the question of estimating the connection probability between two cortical regions with the consideration of both the mutual information of their observed network topologies and their Euclidean distance in anatomical space. In addition, MINM considers establishing and deleting connections, simultaneously, during the networks modeling from the stage of NC to AD. Experiment results show that MINM is sufficient to capture an impressive range of topological properties of real brain networks such as characteristic path length, network efficiency, and transitivity, and it also provides an excellent fit to the real brain networks in degree distribution compared to experiential models. Thus, we anticipate that MINM may explain the connection mechanism for the formation of the brain network organization in AD patients.

This study aims to develop an in-situ field repair approach, special for aircraft composite structures, to enhance the interlaminar toughness of plain woven composites (PWCs) by adding multi-walled carbon nanotubes (MWCNTs). MWCNTs are dispersed at each interface between prepreg layers by means of solvent spraying with the density is 1.58 g/m2. And then, the layers are stacked with the predefined sequence and cured at 120℃ and 1 bar pressure using the heat repairing instrument. Moreover, double cantilever beam (DCB) standard test is used to investigate the interlaminar toughening effect due to the MWCNTs. For comparison, original samples are also prepared, the results indicate that the introduction of MWCNTs can favorably enhance the interlaminar toughness of PWCs at field repair approach and the Mode I fracture energy release rate GIC increases by 102.92%. Based on finite element method (FEM) of continuum damage mechanics, the original and MWCNTs toughening specimen under DCB Mode I fracture are modeled and analyzed. The simulation and experiment are in good agreement. Finally, the toughening mechanism of MWCNTs is explored by scanning electron microscope (SEM), it is founded that a large amount of Fiber-matrix (F-M) interface debonding and matrix cracking of mountain shape are the major modes of fracture accompanied with few fiber breakage and matrix peeling off for the MWCNTs toughening specimens.

In this paper, a novel permanent magnet-assisted synchronous reluctance machine (PMASynRM) with rare-earth PMs and ferrite magnets is proposed. The performance of PMASynRM is discussed with respected to the different magnet ratio of rare-earth PMs and ferrite magnets. Some characteristics including the flux density, output torque, cogging torque, output power, power factor, torque ripple, loss, efficiency, and demagnetization are calculated by 2-D finite element analysis (FEA). The analysis results show that the excellent performance can be obtained by using hybrid magnet of rare-earth PMs and ferrite magnets with the suitable magnet ratio, and provide some desirable cost-performance trade-off.

Air pollution is related to the development of people's livelihood. As one of the main pollutants in the air, formaldehyde affects people's physical and mental health. In order to remove formaldehyde and better protect the health of residents, it is very necessary to develop efficient adsorption materials. In this study, a cellulose composite aerogel Microcrystalline Cellulose/ N-(2- aminoethyl) -3- aminopropyl methyl dimethoxysilane (MCC/APMDS) modified by APMDS was explored. The pore volume and specific surface area of the modified aerogel decreased, the average pore diameter increased, and the adsorption capacity of formaldehyde was stronger. The adsorption experiment shows that the sample prepared under the conditions of APMDS dosage of 6 wt%, reaction time of 4 h and reaction temperature of 90℃ has the largest average pore size (14.56 nm) and the largest formaldehyde adsorption capacity (9.52 mg/g). The excellent adsorption of the composite aerogel is due to high porosity and rich active amino groups on the surface. This study provides valuable information for the preparation of adsorption materials with high formaldehyde adsorption capacity for air purification.

Wearable technology has been proposed as a potential tool to change the way of human life, such as the smart bracelet and the Google Glass. In the wearable technology, the inertial sensor has great significance in tracking the object movements. The paper focused on detecting the movements of user’s finger based on the inertial sensor to give the control signals. Firstly, the attitude matrix, which represented the transformation relation of carrier coordinate system and the navigation coordinate system, was obtained. Secondly, the attitude matrix was expressed based on the quaternions. Thirdly, the finger gesture was processed by the attitude matrix to get the attitude angle. Finally, the robot was controlled by attitude angle to make the moving action. The experimental results showed the detection of the finger movement is effective.

The centrifugal force field in the hydrocyclone was affected by the concave-wall curvature radius R0, the mechanism of droplet deformation was closely related to the mass transfer efficiency. Numerical simulation and experimental data were collected to reveal the deformation characteristics and mechanism of single droplet crossing concave-wall jet. Normalized interfacial energy γ and stretching performance were provided to investigate the droplet deformation process. The results showed that the droplet was stretched along the streamwise and shrank along the spanwise in the concave-wall jet. The droplet interfacial energy and deformation were the largest when the droplet crossed the jet boundary at t = 0.20 s. Maximum γ value increased with the increase of R0 by 57.3% to 71.4%, and the distance between droplet and concave-wall increased with R0. Q-criterion was exported to show the vortex strength increasing as the decrease of R0 in the jet boundary. The pressure distribution inside the droplet showed that the pressure decreased as R0 increased, while the pressure difference increased along the streamwise and wall-normal direction. The study suggested that the droplet breakup was more difficult for the smaller R0, which was beneficial for liquid-liquid heterogeneous separation.

.(1) Background: Groundwater resources management in dry lands, characterized by climate var-iability and population growth, is difficult. Exploration and exploitation of groundwater, due to inadequate surface water is very costly. The present study employed Analytic Hierarchy Process (AHP) and GIS to identify Ground-Water Potential (GWP) areas in a semi-arid region of Nigeria; (2) Methods: Land-use-land-cover, drainage density, slope, rainfall, static water level, soil media, vadose media and aquifer media were selected for GWP analysis. Parameters weights were de-termined using AHP and ranked based on their contribution to GWP by experts. The parameters were then integrated using weighted overlay tool in ArcGIS 10.5 to produce GWP map of the study area. Boreholes yield data from 245 wells were collected to determine the model accuracy and model validation; (3) Results: Results classified the study area into very high GWP (1.9%), high GWP (8.8%), moderate GWP (62%), low GWP (20.70%) and very low GWP (6.6%). Validation of the AHP model with boreholes yield data shows a correlation coefficient of 71.3% giving a good prediction; (4) Conclusions: AHP and GIS can be used to successfully map GWP areas which could serve as an exploration guide for sustainable management of groundwater resources in semi-arid areas.

Globally, weeds interfere with agricultural production activities and have a very serious impact on agriculture and animal husbandry. Identifying a safe and reliable weed control strategy might increase yield, production net income, and improve crop quality. A field experiment was carried out to explore the effects of weeds interference frequency on the yield and quality of Glycyrrhiza uralensis Fisch in an arid and semi-arid area of northwest China. The experiment consisted of seven treatments and were (1) no weeding, marked as WF0; (2)-(7) artificial weeding by hoe once every 1, 2, 4, 6, 8, 10 weeks after emergence, marked as WF1, WF2, WF4, WF6, WF8 and WF10, respectively. We found that the higher weeding frequency had a higher the plant height, photosynthesis, yield and quality. The highest yield was obtained in the WF1 treatment, while the cost of weeding was the highest among all treatments. The concentration of liquiritin and glycyrrhizic acid were increased by 53.24% and 36.57%, with the highest nitrogen metabolism enzymatic activities and quality in WF4 treatment. WF4 treatment exhibited the largest increases in the net income among all treatments in both growing seasons, with respective increases up to 71.39 % and 78.81%. These findings suggested that weeding once every 4 weeks would be an effective and sustainable measure to control weeds in an arid and semi-arid area.

Background and Objectives: The existing recommendations for female pelvic magnetic susceptibility weighted imaging MRI (FP-SWI) have not been optimized to capture the disease characteristics of the female pelvis. Therefore, it is imperative to explore acquisition parameters before conducting larger-scale studies. To establish optimized flip angle (FA) for acquiring FP-SWI to improve the image quality of female pelvic lesion sites. Materials and Methods: To evaluate signal quality and lesion conspicuity, regions of interest (ROIs) were manually drawn within the lesion on SWI sequences. Signal intensity was measured within the ROIs. Additionally, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated for SWI sequences acquired at three FA: 10°, 15°, and 20°. Finally, a qualitative assessment of anatomical detail visualization, geometric distortion extent, artifacts, and lesion clarity was performed using a four-point scale. The Wilcoxon rank sum test was employed to compare the qualitative evaluation parameters. The intra-class correlation coefficient (ICC) was calculated to determine the consistency of subjective ratings between two observers. Results: FA=10° showed significantly higher SNR than FA=15° and 20° in most anatomical regions (P&lt;0.05), while the CNR was significantly higher than FA=15° and 20° (P&lt;0.05). Compared with FA=15° and 20°, the image quality of FA=10° was significantly higher, with less distortion and ghosting, and better image contrast (P&lt;0.05). Lesions with FA=10° exhibited higher clarity compared to FA=15° and 20° (P&lt;0.05). Conclusions: The image quality of FP-SWI images at FA=10° exceeds that at FA=15° and 20°. We recommend that when conducting SWI examination of the female pelvic cavity, the FA value should be set at 10° to stabilize the SWI sequence and improve image quality.

Autonomous aerial refueling technology is an effective solution to extend flight duration of unmanned aerial vehicles, and also a great challenge due to its high risk. For autonomous probe-and-drogue refueling tasks, relative navigation to provide relative position between the receiver aircraft and the refueling drogue is the first and essential step, and vision-based method is the most frequently used. A new monocular vision navigation sensor with image processing strategy consisting of the drogue detection method and the tracking method is developed for autonomous aerial refueling in this paper. In the drogue detection method, thresholding and mathematical morphology method are adopted to eliminate image interference, and contours extraction method is applied to obtain all contours, which are then subsequently checked to achieve target contour of drogue. In the tracking method, a rectangle of interest (ROI) of current frame image is determined by positioning results of last frame, and then processed by the previous drogue detection method. Finally, the proposed image processing strategy in monocular vision navigation sensor is validated using real flight images, which are captured from an autonomous aerial refueling testbed using a micro six-rotor aircraft as receiver aircraft.

To investigate the repair mechanism of umbilical cord mesenchymal stem cells (UC-MSCs) in hydrogel on pancreatic β-cells in diabetes, MIN-6 damaged by streptozotocin (STZ) in vitro was used in co-culture with UC-MSCs in hydrogel (UC-MSCs + hydrogel). It was found that UC-MSCs + hydrogel had a significant repair effect on injured MIN-6 cells, which was better than the use of UC-MSCs alone (without hydrogel). After repair, the expression of superoxide dismutase (SOD) and catalase (CAT) as well as the total antioxidant capacity (T-AOC) of the repaired MIN-6 cells were increased, effectively reducing the oxidative stress caused by STZ. In addition, UC-MSCs + hydrogel were able to curb the inflammatory response by promoting the expression of anti-inflammatory factor IL-10 and reducing inflammatory factor IL-1β. In addition, the expression of both nuclear antigen Ki67 for cell proliferation and insulin-related genes such as Pdx1 and MafA was increased in the repaired MIN-6 cells by UC-MSCs + hydrogel, suggesting that the repair effect promotes the proliferation of the injured MIN-6 cells. Compared with the use of UC-MSCs alone, UC-MSCs + hydrogel exhibited longer survival time of UC-MSCs, because the porous structure and hydrophilic properties of the hydrogel could affect the growth of cells and slow down their metabolic activities, resulting in a better repair effect on the injured MIN-6 cells.

Rapid and accurate detection of road cracks is of great significance for road health monitoring, but currently this work is mainly completed through manual site surveys. Low-altitude UAV remote sensing can obtain images with centimeter or even subcentimeter ground resolution, which provides a new efficient and economical approach for rapid crack detection. Nevertheless, crack detection networks face challenges such as edge blurring and misidentification due to the heterogeneity of road cracks and the complexity of the background. To address these issues, we propose a real-time edge reconstruction crack detection network (ERNet) which adopts multi-level information aggregation to reconstruct crack edges and improve the accuracy of segmentation between the target and background. To capture global dependencies across spatial and channel levels, we propose an efficient bilateral decomposed convolutional attention module (BDAM) that combines depth separable convolution and dilated convolution to capture global dependencies across spatial and channel levels. To enhance the accuracy of crack detection, we use a coordinate-based fusion module that integrates spatial, semantic, and edge reconstruction information. In addition, we propose an automatic measurement of crack information for extracting the crack trunk and its corresponding length and width. Experimental results demonstrate that our network achieves the best balance between accuracy and inference speed.

Studies on the detection of layers with elevated black carbon aerosol (BC) concentrations and the formation conditions of these layers help understand the vertical distribution of BC concentrations, which will provide a basis for the assessment of climate effects and early BC pollution warnings. By using the Weather Research and Forecasting with Chemistry (WRF-Chem) numerical model, we performed a numerical simulation analysis on the authenticity of strong elevated BC concentration layers that were detected by an aircraft in the mixing layer over Harbin, China, which is a high-emission area, on a clear sunny afternoon in the early heating period of 2016. We then discuss possible problems and solutions when non-vertical paths are used to detect the vertical distribution of BC concentrations. Finally, we discuss the favorable conditions for the formation of elevated BC concentration layers by weak vertical flow. The results show that the horizontal variability of BC concentration in the mixing layer in the observation area in Harbin was sufficiently large during the measurement. This produced a false elevated layer, as detected by the aircraft during one round of spiral flight in the mixing layer. The root mean square of the horizontal distribution of BC concentration did not change with height in the mixing layer during the daytime, but it decreased with the thickness of the mixing layer and was higher in the mixing layer than in the free atmosphere. Therefore, the thinner the mixing layer, in which the vertical distribution of the BC concentration is detected in an inclined path, the stronger interference of the horizontal variability on the detected results. When a spiral flight detection path is used, the aircraft should fly at least two rounds in the mixing layer. In the daytime, due to strong turbulence in the mixing layer, weak vertical uplift is not favorable for the occurrence of elevated BC concentration layers in the mixing layer. In the nighttime, if weak vertical uplift is well matched with the BC concentration or its vertical gradient, elevated BC concentration layers can be formed in the atmosphere. Compared with upper layers far from the ground, nighttime elevated layers are easier to form in lower layers near the ground because high BC concentrations or large vertical gradients are more likely to occur in the lower layers. Both cases facilitate the occurrence of large vertical upward transport rates of BC.

As noted in the introductory quotation, an ideal map was long ago seen as the map of the map, the map of the map, of the map, and so on endlessly. This recursive perspective on maps, however, has received little attention in cartography. Cartography, as a scientific discipline, is essentially founded on Euclidean geometry and Gaussian statistics, which deal with respectively regular shapes, and more or less similar things. It is commonly accepted that geographic features – such as rivers, cities, streets and building – are not regular and that the Earth’s surface is full of fractal or scaling or living phenomena with far more small things than large ones at different levels of scale. This paper argues for a new paradigm in mapping, based on fractal or living geometry and Paretian statistics, and – more critically – on the new conception of space, conceived and developed by Christopher Alexander, that space is neither lifeless nor neutral, but a living structure capable of being more living or less living. The fractal geometry is not limited to Benoit Mandelbrot’s framework, but is extended towards Christopher Alexander’s living geometry and based upon the third definition of fractal: A set or pattern is fractal if the scaling of far more small things than large ones recurs multiple times. Paretian statistics deals with far more small things than large ones, so it differs fundamentally from Gaussian statistics, which deals with more or less similar things. Under the new paradigm, I make several claims about maps and mapping: (1) Topology of geometrically coherent things – in addition to that of geometric primitives – enables us to see a scaling or fractal or living structure; (2) Under the third definition, all geographic features are fractal or living, given the right perspective and scope; (3) Exactitude is not truth – to paraphrase Henri Matisse – but the living structure is; and (4) Töpfer’s law is not universal, but scaling law is. All these assertions are supported by evidence, drawn from a series of previous studies. This paper demands a monumental shift in perspective and thinking from what we are used to on the legacy of cartography and GIS.

Deep convolutional neural networks (CNNs) have been widely used in various medical imaging tasks. However, due to the intrinsic locality of convolution operation, CNNs generally cannot model long-range dependencies well, which are important for accurately identifying breast cancer from unregistered multi-view mammograms. This motivates us to leverage the architecture of Vision Transformers to capture long-range relationships of multiple mammograms from the same patient. For this purpose, we employ local Transformer blocks to separately learn patch relationships within a specific view (CC/MLO) of one side (right/left) of mammogram. The outputs from different views and sides are concatenated and fed into global Transformer blocks, to jointly learn patch relationships between two different views of the left and right breasts. To evaluate the proposed model, we retrospectively assembled a dataset involving 949 sets of CC and MLO view mammograms, which include 470 malignant cases and 479 normal or benign cases. We trained and evaluated the model using a five-fold cross-validation method. Without any arduous preprocessing steps (e.g., optimal window cropping, chest wall or pectoral muscle removal, two-view image registration, etc.), our two-view Transformer-based model achieves lesion classification accuracy of 77.0% and area under ROC curve (AUC = 0.814), outperforming state-of-the-art multi-view CNNs by 3.1% and 3%, respectively. Meanwhile, the new two-view model improves mammographic case classification accuracies of two single-view models by 7.4% (CC) and 4.5% (MLO), respectively. The promising results unveil the great potential of using Transformers to develop high-performing computer-aided diagnosis (CADx) schemes of mammography.

Flooding is one major environmental problem facing urban cities causing varying degree of damages to properties and disruptions to socio-economic activities. Nigeria is the most populated African country and Kano metropolis is the second largest urban center in Nigeria and the most populated in Northern Nigeria. The aim of the paper is to conduct flood risk assessment of Kano metropolis. The city is divided into two hydrological basins; Challawa and Jakara Basins. Flood frequency analysis for 2 to 100-year return periods were carried out for both the basins using Log-Pearson Type III distribution and flood inundation and hazard mappings were carried out. Social vulnerability to flood of both basins were carried out using MOVE framework. Assessment of flood risk were carried out using map algebra spatial analyst tool in ArcGIS 10.7. The result shows that an area of 50.91 and 40.56 km2 were vulnerable to a 100-year flood. The flood risk map for the two basins show that 10.50 km2 and 14.23 km2 of land in Challawa and Jakara basins respectively are affected by the risk of a 100-year flood out of which 11.48 km2 are built-up area. As the city is densely populated, with a population density of well over 20,000 persons per square kilometer in the highly built-up locations, this means that much more than 230,000 persons will be affected by the flood risk in the two basins

As Christopher Alexander conceived and defined through his life’s work – The Nature of Order – wholeness is a recursive structure that recurs in space and matter and is reflected in human minds and cognition. Based on the definition of wholeness, a mathematical model of wholeness, together with its topological representation, has been developed, and it is able to address not only why a structure is beautiful, but also how much beauty the structure has. Given the circumstance, this paper is attempted to argue for the wholeness as the scientific foundation of sustainable urban design and planning, with the help of the mathematical model and topological representation. We start by introducing the wholeness as a mathematical structure of physical space that pervasively exists in our surroundings, along with two fundamental laws – scaling law and Tobler’s law – that underlie the 15 properties for characterizing and making living structures. We argue that urban design and planning can be considered to be wholeness-extending processes, guided by two design principles of differentiation and adaptation, to transform a space – in a piecemeal fashion – into a living or more living structure. We further discuss several other urban design theories and how they can be justified by and placed within the theory of wholeness. With the wholeness as the scientific foundation, urban design can turn into a rigorous science with creation of living structures as the primary aim.

Construction safety is especially important because the construction industry is so important to a country's development. Significant research and practice have been conducted to mitigate potential risks during construction and improve worker efficiency. With the rapid advancement of cognitive neuroscience and the incorporation of medical technology in recent years, various wearable monitoring devices have been widely used in the construction field for real-time monitoring of workers' physical and mental status. Among these, the use of EEG (Electroencephalogram) in construction environment research allows researchers to gain insight into the physical and mental states of construction workers while performing construction tasks. This review introduces EEG technology and portable EEG devices, followed by an overview of their use in both monitoring workers' adverse reactions and identifying hazards on construction sites, providing an effective guide for EEG research in the construction field and on-site safety management.

To facilitate the promotion and application of the whole process engineering consulting (WPEC) model, the transmission mechanism of influencing factors in WPEC's promotion and application is investigated. It identifies and categorizes the influencing factors within the framework of "external environment-project factors-stakeholders", systematically organizing and summarizing ten primary categories and thirty-eight secondary influencing factors of WPEC's promotion and ap-plication. Utilizing the complex network analysis method, a network of influencing factors in the promotion and application of WPEC is constructed. The network is then converted into three binary networks based on weight, which are subjected to structural analysis and motif analysis. On this basis, the transmission mechanism of influencing factors in WPEC's promotion and applica-tion and its causations are explored.

Water splitting technology is an efficient approach to generate hydrogen (H2) energy, which can well address the problems of environmental deterioration and energy shortage, as well as establishment of a clean and sustainable hydrogen economy powered by renewable energy sources due to the green reaction of H2 with O2. While the efficiency of H2 production by water splitting technology is intimately related with the reactions on electrode. Nowadays, the efficient electrocatalysts in water splitting reactions are the precious metal-based materials, i.e., Pt/C, RuO2 and IrO2. Ni (Co, Fe)-based layered double hydroxides (LDH) 2D materials are the typical non-precious metal-based materials in water splitting with advantages of low cost, excellent electrocatalytic performance and simple preparation methods, which exhibits a great potential for the substitution for precious metal-based materials. This review summarizes the recent progress of Ni (Co, Fe)-based LDH 2D materials for water splitting, which mainly focuses on discussing and analyzing the different strategies to modify LDH materials towards high electrocatalytic performance. We also discuss the recent achievements including their electronic structure, electrocatalytic performance, catalytic center, preparation process and catalytic mechanism. Furthermore, the characterization progress in revealing electronic structure and catalytic mechanism of LDH is highlighted in this review. Finally, we put forward some future perspectives related to design and explore advanced LDH catalysts in water splitting.

Conceived and developed by Christopher Alexander through his life’s work: The Nature of Order, wholeness is defined as a mathematical structure of physical space in our surroundings. Yet, there was no mathematics, as Alexander admitted then, that was powerful enough to capture his notion of wholeness. Recently, a mathematical model of wholeness, together with its topological representation, has been developed that is capable of addressing not only why a space is good, but also how much goodness the space has. This paper develops a structural perspective on goodness of space – both large- and small-scale – in order to bridge two basic concepts of space and place through the very concept of wholeness. The wholeness provides a de facto recursive definition of goodness of space from a holistic and organic point of view. A space is good, genuinely and objectively, if its adjacent spaces are good, the larger space to which it belongs is good, and what is contained in the space is also good. Eventually, goodness of space – sustainability of space – is considered a matter of fact rather than of opinion under the new view of space: space is neither lifeless nor neutral, but a living structure capable of being more living or less living, or more sustainable or less sustainable. Under the new view of space, geography or architecture will become part of complexity science, not only for understanding complexity, but also for making and remaking complex or living structures.

Fire is a common natural disturbance in forest ecosystems and plays an important role in subsequent vegetation patterns. Based on the spatial sequence method instead of the time successional sequence method, this study selected burned areas in different locations in the Anning River Basin, which contains typical dry valleys. Quadrat surveys and quantitative classification were used to identify the vegetation classification, distribution pattern, and environmental interpretation during the natural restoration process after forest fire. The results showed that: (1) the vegetation community in the early stage of natural recovery after forest fire disturbance could be divided into seven community types, and Quercus guyavaefolia H. Leveille (Qg) was the dominant species in the community; (2) vegetation samples could be divided into five ecological types, and the classification and distribution pattern of community types in this region changed most obviously with altitude; and (3) detrended correspondence analysis could clearly classify vegetation community types, and detrended canonical correspondence analysis could well reveal the relationships between species and environmental factors. This study provides a scientific basis guiding the restoration of ecosystem structural stability and biodiversity in burned areas.

The task of weakly supervised point cloud semantic segmentation has received widespread attention and also has been widely used in autonomous driving, robotics, and modern industries. Due to the high dimensionality and large volume of point cloud data, many technical difficulties appeal to weakly supervised semantic processing, especially segmentation. However, weakly-supervised schemes usually provide only partial labelling information of the underlying point cloud data and thus need to effectively extract local features, geometric information, and contextual relationships only using these limited labelled data for supervised learning. To improve weakly-supervised semantic segmentation, we propose a novel segmentation network through the boundary-based feature aggregation based on a K-NN algorithm with down-sampling operation, and also introduce the smoothness loss and Siamese loss for effective segmentation. The experiments on public datasets demonstrate that our presented segmentation method exceeds most of the existing fully supervised and weakly-supervised methods in terms of mIoU. Specifically, our network has high segmentation accuracy on the labels of objects with similar geometrical structures, such as ceiling, wall, floor, chair and table, reaching 91.2%, 98.8%, 83.3%, 75.3% and 80.2%, respectively. Extensive experiments also illustrate its robustness, effectiveness, and generalization of the proposed weakly supervised segmentation network.

This article presents a method for extracting neural signal features to identify the imagination of left and right hand grasping movements. A functional magnetic resonance imaging (fMRI) experiment is employed to identify four brain regions with significant activations during motor imagery(MI) and the effective connections between these regions of interest (ROIs) were calculated using Dynamic Window-level Granger Causality (DWGC). Then, a real-time fMRI(rt-fMRI) classification system for left and right hand MI is developed using the Open-NFT platform. The experimental results show that incorporating effective connections can enhance the average accuracy of real-time three-class classification (rest, left hand and right hand) by 3% in comparison to traditional multivoxel pattern classification analysis(MVPA). Moreover, it significantly improves classification accuracy during the initial stage of MI tasks while reducing the latency effects in real-time decoding. The study suggests that the effective connections obtained through the DWGC method serve as valuable features for real-time decoding of MI using fMRI. Moreover, they exhibit higher sensitivity to changes in brain states. This research offers theoretical support and technical guidance for extracting neural signal features in the context of fMRI-based studies.

A simulation test system for the two-dimensional (2D) fuze overload loading process was designed to address the loading issues of recoil overload and centrifugal overload in fuze safety and arming (S&amp;A) devices. By incorporating centrifuge rotation energy storage, impact acceleration simulation, and equivalent centrifugal rotation simulation, a block equipped with a fuze S&amp;A device was accelerated instantly upon being impacted by a centrifuge-driven impact hammer, simulating recoil overload loading. The impact hammer was retracted instantaneously by adopting an electromagnetic brake, which resulted in the centrifugal rotation of the block around its track, to simulate the centrifugal overload loading. The dynamic equations of the simulation test system and the equations of impact hammer motions were established, whereby the rotation speed of the centrifuge and the braking force of the electromagnetic brake were calculated and selected. A dynamic model of the collision between the impact hammer and block was established using ANSYS/LS-DYNA software for simulation analysis. The acceleration curves of the recoil overload and centrifugal overload with variations in the centrifuge speed, cushion material, and buffer thickness were obtained, which verified the feasibility of the proposed loading simulation method. 2D overload loading simulation tests were performed using the developed simulation system, and the acceleration curves of the recoil overload and centrifugal overload were measured. The test results indicated that the proposed system can accomplish 2D overload loading simulations for a recoil overload of several 10000 g and centrifugal overload of several 1000 g.

Following our previous success in identifying new steroid-based anticancer agents, we herein disclosed the structural requirements for retaining high potency against cancer cells and associated modes of action. The structurally novel steroidal dimer by001 inhibited growth of different esophageal cancer cells and colony formation at low micromolar levels, elevated cellular ROS levels and caused mitochondrial dysfunction. Mechanistic studies showed that by001 induced cell death through the mitochondria and death receptor-mediated apoptotic pathways and autophagy induction, as well as inhibited migration.

We present a generic and open-source framework for the numerical modeling of the expected transport and storage mechanisms in unconventional gas reservoirs. These unconventional reservoirs typically contain natural fractures at multiple scales. Considering the importance of these fractures in shale gas production, we perform a rigorous study on the accuracy of different fracture models. The framework is validated against an industrial simulator and is used to perform a history-matching study on the Barnett shale. This work presents an open-source code that leverages cutting-edge numerical modeling capabilities like automatic differentiation, stochastic fracture modeling, multi-continuum modeling and other explicit and discrete fracture models. We modified the conventional mass balance equation to account for the physical mechanisms that are unique to organic-rich source rocks. Some of these include the use of an adsorption isotherm, a dynamic permeability-correction function, and an embedded discrete fracture model (EDFM) with fracture-well connectivity. We explore the accuracy of the EDFM for modeling hydraulically-fractured shale-gas wells, which could be connected to natural fractures of finite or infinite conductivity, and could deform during production. Simulation results indicates that although the EDFM provides a computationally efficient model for describing flow in natural and hydraulic fractures, it could be inaccurate under these three conditions: 1. when the fracture conductivity is very low. 2. when the fractures are not orthogonal to the underlying Cartesian grid blocks, and 3. when sharp pressure drops occur in large grid blocks with insufficient mesh refinement. Each of these results are very significant considering that most of the fluids in these ultra-low matrix permeability reservoirs get produced through the interconnected natural fractures, which are expected to have very low fracture conductivities. We also expect sharp pressure drops near the fractures in these shale gas reservoirs, and it is very unrealistic to expect the hydraulic fractures or complex fracture networks to be orthogonal to any structured grid. In conclusion, this paper presents an open-source numerical framework to facilitate the modeling of the expected physical mechanisms in shale-gas reservoirs. The code was validated against published results and a commercial simulator. We also performed a history-matching study on a naturally-fractured Barnett shale-gas well considering adsorption, gas slippage & diffusion and fracture closure as well as proppant embedment, using the framework presented. This work provides the first open-source code that can be used to facilitate the modeling and optimization of fractured shale-gas reservoirs. To provide the numerical flexibility to accurately model stochastic natural fractures that are connected to hydraulically-fractured wells, it is built atop other related open-source codes. We also present the first rigorous study on the accuracy of using EDFM to model both hydraulic fractures and natural fractures that may or may not be interconnected.

Augment reality (AR) is crucial for immersive human-computer interaction (HCI) and vision of artificial intelligence (AI). Labeled data drove object recognition in AR. However, manual annotating data is expensive and labor-intensive, and furthermore, scanty labeled data limits the application of AR. Aiming at solving the problem of insufficient training data in AR object recognition, an automated vision data synthesis method called BAGAN is proposed in this paper based on the 3D modeling and GAN algorithm. Our approach has been validated to have better performance than other methods through image recognition task on natural image database ObjectNet3D. This study can shorten the algorithm development time of AR and expand the application scope of AR, which is of great significance to immersive interactive systems.

Background: Atherosclerosis (AS), a key pathological contributor of cardiovascular diseases (CVDs), was characterized by formation of atherosclerotic plaque in aortic wall and caused high mortality and morbidity worldwide. Factors contributing to the initiation and progression of AS included vascular endothelial dysfunction, dyslipidemia, inflammation, and oxidative stress. Shexiang Baoxin Pill (SBP) was a traditional Chinese medicine that had been widely applied in clinic and proved to be effective for patients with CVDs. However, the mechanisms underlying its anti-atherosclerotic effects have not been completely elucidated. The study aimed to investigate the protective effects of SBP on AS and its potential mechanisms. Methods: Different dose of SBP was orally administered in apolipoprotein E-deficient (ApoE-/-) mice treated with a high-fat diet (HFD). Histopathological and immunohistochemical analysis, ELISA, untargeted metabolomics analysis, 16S rRNA sequence analysis, and spearman analysis were applied to identify the protective effects and mechanisms of SBP in HFD-induced AS. Results: SBP significantly alleviated HFD-induced atherosclerotic lesion both in aorta and aortic sinus with a reduction in serum levels of total cholesterol (TC), triglycerides (TG) and low-density lipoprotein cholesterol (LDL-C), as well as interleukin-6 (IL-6) and interleukin-1β (IL-1β), and increased high-density lipoprotein cholesterol (HFD-C) level with no significant change of body weight in HFD-induced ApoE-/- mice. Mechanismly, SBP markedly reshaped the gut microbiota composition and associated metabolomic characteristics. 2 differential intestinal floras at phylum level and 8 differential microbes at genus level, 5 metabolic pathways and 9 differential fecal metabolites related to CVDs were figured out. Besides, intensive correlation between 8 differential microbes and 9 fecal metabolites was observed. Conclusions: Our results showed that SBP could improve HFD-triggered serum lipid disorder, systematic inflammation, and contribute to the alleviation of atherosclerotic lesion. The antiatherogenic properties of SBP might partly result from the reshaping of the gut microbiota and fecal metabolic profile.

Inosine 5'-monophoaphate (IMP) allows animals to sense umami. Intake of IMP in C57/KsJ-db/db (db/db) mice induced lipohyperplasia causing the liver cirrhosis, however, how to that of in normal mammals injure health is still unclear. Thus, we had investigated that intake of IMP in C57BL/6J mice effected its metabolic functions. We found that intake of 255 M/d IMP in C57BL/6J mice for 4 months induced hyperlipidemia and body fat rate raised. In mechanism, the expressions of ACC1 and phosphorylated ACC2 in hepatocytes were increased though IMP promoting phosphorylation of AMPK. The increased ACC1 promoted the conversion of acetyl-CoA into TG. These TG were transported out of hepatocytes to avoid NAFLD, causing a deficiency of acetyl-CoA in liver, and then the increased phosphorylated ACC2 promoted cytoplasm fatty acids into mitochondria to convert into acetyl-CoA though the fatty acids β-oxidation pathway, causing a deficiency of fatty acids. Therefore, liver enhanced the absorption of exogenous fatty acids, which were converted into TG caused lipohyperplasia. Moreover, intake of IMP in normal mice induced complement system weaken in liver causing mild inflammation. Our data not only alerted that humans avoid excessive intake of IMP, but also provided novel insights into the adipose of metabolic dysfunctions.

Permeable asphalt pavement refers to an asphalt mixture layer with an air void content of more than 18% and internal water permeability and drainage capabilities, which can quickly drain away water on the road surface, improve rainy day travel safety and ride comfort. This paper aims to explore the optimal asphalt mixture mix design for long longitudinal slope roads. By using CT scanning technology to analyze the air void content of different rotated and compacted asphalt mixture specimens, and extensively testing and evaluating the performance of permeable pavement mixtures, the following conclusions are drawn: Based on the research philosophy of functional integration, a new asphalt mixture gradation suitable for long longitudinal slope roads is proposed, with the optimal key factor composition being: 0.075mm passing rate of 7%, 2.36mm passing rate of 20%, 9.5mm passing rate of 55%, and oil-stone ratio of 4.8%. The FAM mixture was divided into three parts for air void analysis, with the upper part having a slightly higher air void content than the lower part. The air void distribution diagram of the FAM mixture is concave, with higher air void rate curves on both sides and a lower middle curve. Compared with laboratory dry measurement method, CT scanning test showed slightly higher air void content in specimens. Through dynamic modulus testing, the strength requirement for road asphalt mixture in pavement structure design was evaluated. It was found that at high temperature conditions (50℃), the minimum dynamic modulus value of the FAM mixture was 323 MPa, with a peak value of 22746MPa at a temperature of -10℃ and a frequency of 25HZ. The dynamic modulus value at high temperature conditions is lower than at low temperature conditions, while the dynamic modulus value at high frequency conditions is higher than at low frequency conditions. This study provides useful information and experimental data for the design of new asphalt mixtures for long longitudinal slope roads, and has conducted in-depth research on the air void distribution and performance of the mixture, providing strong support for related research fields and practical applications.

In this paper, we study the well-posedness and asymptotics of a one-dimensional thermoelastic laminated beam system either with or without structural damping, where the heat conduction is given by Fourier's law effective in the rotation angle displacements. We show that the system is well-posed by using Lumer-Philips theorem, and prove that the system is exponentially stable if and only if the wave speeds are equal, by using the perturbed energy method and Gearhart-Herbst-Prüss-Huang theorem. Furthermore, we show that the system with structural damping is polynomially stable provided that the wave speeds are not equal, by using the second-order energy method.

In this study, based on a new quantum integral identity, the authors establish some quantum estimates of Hermite-Hadamard type inequalities for convex functions. These results generalize and improve some known results given in literatures.

Cadmium (Cd) contamination in rice grains is posing a significant threat to global food security. To restrict the transport of Cd in the soil-rice system, an efficient way is to use the ionomics strate-gy. Since calcium (Ca) and Cd have similar ionic radii, their uptake and translocation may be linked in multiple aspects in rice. However, the underlying antagonistic mechanisms are still not fully understood. Therefore, we first summarized the current knowledge on the physiological and molecular footprints of Cd translocation in plants and then explored the potential antagonistic points between Ca and Cd in rice, including exchange adsorption on roots, plant cell wall compo-sition, co-transporter gene expression, and transpiration inhibition. This review provides sugges-tions for the Ca/Cd interacting study in rice and introduces the ionomics research to better control the accumulation of Cd in plants.

Concrete is more stable than filler as a false roof in a downward approach type infill quarry, but the mechanical model of a concrete false roof is not clear. As a result, there has been limited application. Therefore, it is necessary to establish the mechanical model of a concrete artificial false roof to enhance the stability of the downward approach infill quarry. This paper combines theoretical analysis, model derivation, finite element analysis and field application methods. Firstly, the basic assumptions of thin plate theory and elasticity mechanics are used as the premise to establish the differential equations of the deflection curve of the concrete thin plate false roof. Then, the three types of bending moment generation of the concrete thin plate false roof are explored. Secondly, the relationship between the location of dangerous surface and dangerous point in hard-supported weak-slab structure and soft-supported weak-slab structure is analyzed, and the type of uniform load and calculation model of concrete thin-slab false roof are improved. Finally, according to the established mechanical model of thin slab false roof, finite element analysis and field application were carried out. The results show that there are three types of deflection curve differential equations in the concrete thin slab false roof perpendicular to the strike, and three extreme points exist in the middle section of the thin slab and on both sides of the surrounding rock. The hazardous surface of the hard-supported weak-slab structure is present at the intersection of the encircling rock and the thin-slab fake ceiling, with the perilous point situated on the top of this surface. The hazardous surface of the soft-supported weak-slab structure can be found in the centre section of the thin-slab fake ceiling, with the perilous point being located at the bottom of this surface. Moreover, the damage mode for both structures is that of maximum tensile stress damage. Upon conducting a finite element analysis, the results indicate that the concrete's maximum tensile stress is significantly high.

Hexabromocyclododecane (HBCD) was most important flame retardant in Expanded Polystyrene foam and Extruded Polystyrene foam in the past forty years in the world. China was the major producer and user of HBCD, and the total HBCD production was about 0.3 million tons. Although HBCD has been completely banned in China in 2021 because of its long-range transport, bioaccumulation and toxicity, there's still a lot of residues in environment. Therefore, we reviewed multiple studies concerning the distribution of HBCD in diverse environmental matrices, such as air, dust, soil, water, sediment and biota. Results revealed that HBCD levels in different environments in China present a geographical variation and were at high level compared with other countries. In all environmental media, relatively high HBCD concentrations have been found in industrial and urban areas. Industrialization and urbanization are two important factors that influence the concentration and distribution of HBCD in the environment. In terms of isomer, γ-HBCD was the dominant isomer in soil, water, and sediment, while in the biota, α-HBCD was the predominant isomer.

In recent years, laser engraving has received widespread attention as a convenient, efficient, and programmable method, which has enabled the obtaining of high-quality porous graphene from various precursors. Laser engraving is often used to fabricate the dielectric layer with micro-structure for capacitive pressure sensors, however, the usual choice of electrodes remains poorly flexible metal electrodes, which greatly limits the overall flexibility of the sensors. In this work, we propose a flexible capacitive pressure sensor made entirely of thermoplastic polyurethane (TPU) and laser-induced graphene (LIG) derived from wood. The capacitive pressure sensor consisted of a flexible LIG/TPU electrode (LTE), a LIG/TPU electrode with micro hole array, and dielectric layer of TPU with micro-cone array molded from laser-engraved hole array on wood, which provided high sensitivity (0.11 kPa-1), ultra-wide pressure detection range (100Pa to 1.4MPa), fast response (~300 ms) and good stability (&gt;4000 cycles, at 0-35 kPa). We believe that our research makes a significant contribution to the literature because the easily available materials derived from wood and overall consistent flexibility meet the requirements of flexible electronic devices.

Practical applications of CsPbX3 nanocrystals (NCs) are limited by their poor stability. The formation of heterojunction between CsPbX3 NCs and oxides is an effective means to protect perovskite from polar solvents and other external factors. Significantly improving the stability and photocatalytic properties of the core/shell perovskite is very important for its application in photoelectric and photocatalytic technology. Here, we report the synthesis of asymmetrical CsPbBr3/TiO2 core-shell heterostructure NCs at single particle level by hot injection liquid phase synthesis and sol-gel method, where each CsPbBr3 NCs is partially covered by titanium dioxide. It is shown that the type II arrangement is generated at the heterogeneous interface, which greatly facilitates the separation of electron-hole pairs and increases the carrier transport efficiency. More crucial, due to the protection of titanium dioxide shell, the product has higher long-term stability in humid air compared with bare CsPbBr3 NCs. The asymmetrical core-shell heterostructure prepared in this study not only improves the stability of CsPbX3 NCs, but also provides some ideas for optoelectronic device applications and TiO2-based photocatalysts.

In this paper, we consider a fourth-order suspension bridge equation with nonlinear damping term |u_t|^m-2u_t and source term |u|^p-2u. We give necessary and sufficient condition for global existence and energy decay results without considering the relation between m and p. Moreover, when p>m, we give sufficient condition for finite time blow-up of solutions. The lower bound of the blow-up time T_max is also established. It worth to mention that our obtained results extend the recent results of Wang (J. Math. Anal. Appl., 2014) to the nonlinear damping case.

Currently, in some domestic and foreign mines, the backfill drilling pipeline experience a rupture phenomenon even when the wear degree is low. This results in a delay in production due to the filling becoming 'sick'. This paper presents, for the first time, the damage mechanism from a mechanical perspective and re-derives the anti-extrusion strength model of the backfill drilling pipeline. We investigate the influence of law on the anti-extrusion strength of pipelines from the perspective of stratum and cement ring. We then verify the theoretical and simulation results through engineering examples. The results demonstrate that the Mises stress criterion is a suitable modification principle for the anti-extrusion strength model of the backfill drilling pipeline. The anti-extrusion strength of the pipeline is related to the elastic modulus and Poisson's ratio of the stratum, and the thickness of the cement ring. It is negatively affected by the depth of the stratum. For hard strata, a cement ring with a smaller elastic modulus is suitable, while for soft stratum, a cement ring with a larger elastic modulus is recommended. When the missing angle of the cement ring is less than 60°, the stress concentration factor increases up to 2.2. The stress unloading capacity of the cement ring ranges from 32.7% to 37.8%, and optimal performance of the cement ring is achieved when it has high strength and low rigidity. The backfill filling pipeline of a copper mine abroad was destroyed due to external extrusion force exceeding its anti-extrusion strength value. The modified pipeline anti-extrusion strength model is 18.2% higher than the casing API strength value. This finding can inform the design of the backfill filling pipeline for China's kilometer-deep wells in the future.

Amid escalating urbanization, devising rational commercial space layouts is a critical challenge. Leveraging machine learning, this study uses a Back-propagation (BP) neural network to optimize commercial spaces in Weinan City's central urban area. The results indicate an increased number of commercial facilities with a trend of multi-centered agglomeration and outward expansion. Based on these findings, we propose a strategic framework for rational commercial space development emphasizing aggregation centers, development axes, and spatial guidelines. This strategy provides valuable insights for urban planners in small and medium-sized cities in the Yellow River Basin and metropolitan areas, ultimately showcasing the power of machine learning in enhancing urban planning.

The petrogenesis of high Ba-Sr granitoids provide a great significance to penetrate the Proto-Tethys evolution in the North Qilian orogenic belt. This paper presents a combination of zircon U-Pb age, whole-rock major and trace element concentrations, and Sr-Nd-Hf isotopic data for Caowa high Ba-Sr dioritic intrusion from the eastern part of the North Qilian orogenic belt, aiming to decipher its petrogenesis and tectonic setting. LA-ICP-MS zircon U-Pb dating yields an emplacement age of 447±3 Ma for the Caowa intrusion, indicating a magmatic activity of the late Ordovician. The Caowa quartz diorites contain moderate contents of SiO2, MgO, Mg# and resultant high concentrations of Na2O+K2O, Fe2O3T and Al2O3, displaying calc-alkaline and metaluminous characteristics. Their relatively elevated Ba (up to 1165 ppm) and Sr (561 to 646 ppm) contents, with obvious enrichment in LILEs (e.g. Ba、Th、U) and depletion in HFSEs (e.g. Nb、Ta、Ti) resemble those of typical high Ba-Sr granitoids in subduction zone. Together with enriched Sr-Nd isotopic compositons[(87Sr/86Sr)i=0.7082−0.7086, εNd(t)= -5.1 to -4.9], and relatively extensive εHf(t) values (-13.2 to +8.5) of zircons, it suggests that these high Ba-Sr quartz diorites were derived from a mixture magma source between the ancient crust materials and the enriched lithospheric mantle metasomatised by fluid was released from subducted oceanic crust or sediment. Taking into account the ophiolites, high pressure metamorphic rocks and arc magmatic rocks in the region, we infer that affected by the northward subduction of the Qilian Proto-Tethys ocean, the Laohushan oceanic crust of the North Qilian back-arc basin was subducted during the Late Ordovician and resulted in extensive metasomatism of lithospheric mantle by fluids derived from oceanic crust or sediments, and the Caowa high Ba-Sr quartz diorites generated in the process of crust-mantle interaction during the Late Ordovician.

This paper addresses the input coupling problem for the shape memory alloy (SMA) actuated parallel platform with completely unknown nonlinear dynamics. The problem means that in a parallel platform with multiple SMA actuators acting together, the displacement of one of the actuators will simultaneously affect the remaining actuators to change. Due to the coupling problem and nonlinear characteristics of the SMA actuators, it is not easy to control the platform to achieve bi-directional swing with completely unknown nonlinear dynamics. Therefore, a model-free adaptive sliding mode control method is proposed to improve the control accuracy of the complex motion of the platform, avoiding the complex modeling process of the SMA actuated parallel platform. Based on the model-free adaptive control (MFAC) theory, a multiple input and multiple output (MIMO) dynamic linearized mathematical model of the parallel platform is established using the online input and output data of the system. Then, a sliding mode controller is designed based on the dynamic model. A rigorous theoretical analysis proves that the proposed model-free adaptive sliding mode controller is able to stably control the SMA actuated parallel platform, and its tracking error is uniformly ultimately bounded (UUB). Finally, experiments are conducted on the established SMA actuated parallel platform. The results show that the proposed model-free controller is feasible and effective, and can control the system well to achieve the complex motion of bi-directional swing.

Promoting green development has become an important way to optimize the ecological and economic structure and promote sustainable development. The Yangtze River Economic Belt is an important economic corridor in China, and it remains to be examined whether the implementation of environmental regulations has enhanced the green total factor productivity (GTFP) of cities. This paper calculates the green total factor productivity of 107 cities within the Yangtze River Economic Belt by using the super-efficient SBM model and the GML index with Chinese city panel data from 2007-2019 and measures the intensity of environmental regulations through textual analysis. The bidirectional fixed-effects model constructed in this paper suggests an inverted U-shaped relationship between environmental regulation and GTFP, which is confirmed by a series of robustness tests. Green technology innovation and advanced industrial structure play a mechanism role in this. In regions with lower levels of green technology and industrial structure, the inverted U-shaped relationship between environmental regulation and green total factor productivity is more significant. In addition, the phenomenon of GTFP rising and then falling because of environmental regulation is more obvious in cities in the middle and upper reaches of the Yangtze River and outside the urban agglomerations. Therefore, when formulating environmental regulation policies, it is necessary to balance the relationship between economic development and environmental protection and harmonize the environment and economic development of the entire Yangtze River Basin.

Underwater wireless optical communication (UWOC) is acknowledged as a useful way to transmit data in ocean for short distance application. Carrying UWOC device on mobile platform is quite practical in ocean engineering, which is helpful to exploit its advantages. In application, such platform needs camera to observe surroundings and guide its action. Since the majority of ocean is always dark, active illumination is necessary to imaging. Once UWOC worked in such environment, its performance is affected by the illumination light noise. In this paper, we study the influence of underwater LED illumination on bidirectional UWOC with Monte Carlo method. We simulate forward noise from the LED illumination to the opposite receiver in the cooperative terminal, and the backscattering noise on the adjacent receiver in the same terminal. The results show that the forward noise is reduced with the increase of absorption coefficient, scattering coefficient, transmitting distance, and separated distance between receiver and the optical axis of LED. But it becomes greater with FOV of receiver. The backscattering noise is reduced with the increase of absorption coefficient and separated distance between receiver and LED. But it becomes greater with FOV and scattering coefficient, while it has little relation with transmitting distance. In order to reduce such two kind noises, besides inserting optical filter in the receivers and narrow their field, the optical axis of LED light should keep away from the receivers. The results in this paper is helpful for UWOC application.

The teaching and learning environment have changed significantly over the last decade with the integration of an increasing amount of information and communication technologies, especially in distance education due to global pandemics, total lockdowns, isolation, and quarantine periods. The number of numerous virtual and non-virtual tools involved in educational processes is growing day by day. In terms of technological innovations used in learning environments, many researchers and educators are trying their best to bring more creative ideas into the technology- based learning environments. Although higher educational institutions have greatly benefited from the growing number of hardware and software resources in the educational process, they have also faced some setbacks that have harmed the learning processes. These technologies have in turn complicated some of the processes in the learning atmosphere. Therefore, we take Spanish teaching course as an example to present the challenging smarter teaching and learning systems that higher educational institutions face today.

This study aims to test a model linking destination image, perceived value, tourist satisfaction, and tourist loyalty. Based on a sample of 300 tourists travelling by car from the World Natural Heritage Site of Tianchi, China, a new model of destination image was explored and data were analysed using partial least squares structural equation modelling (PLS-SEM). The results show that perceived value and satisfaction are direct antecedents of destination loyalty. Above all, perceived value and tourist satisfaction mediate the relationship between destination image and loyalty. Finally, this study discusses the theoretical and management implications of the findings in order to boost the tourism industry in the context of car trips.

In this paper, we study the semilinear pseudo-parabolic equations $\displaystyle u_{t} - \triangle_{\mathbb{B}}u - \triangle_{\mathbb{B}}u_{t} = \left|u\right|^{p-1}u$ on a manifold with conical singularity, where $\triangle _{\mathbb{B}}$ is Fuchsian type Laplace operator investigated with totally characteristic degeneracy on the boundary $x_{1} = 0$. Firstly, we discuss the invariant sets and the vacuum isolating behavior of solutions with the help of a family of potential wells. Then, we derive a threshold result of existence and nonexistence of global weak solution: for the low initial energy $J(u_{0})<d$, the solution is global in time with $I(u_{0}) >0$ or $\displaystyle\Vert \nabla_{\mathbb{B}}u_{0}\Vert_{L_{2}^{\frac{n}{2}}(\mathbb{B})} = 0$ and blows up in finite time with $I(u_{0}) < 0$; for the critical initial energy $J(u_{0}) = d$, the solution is global in time with $I(u_{0}) \geq0$ and blows up in finite time with $I(u_{0}) < 0$. The decay estimate of the energy functional for the global solution and the estimates of the lifespan of local solution are also given.

The search algorithm based on symbiotic organisms’ interactions is a relatively recent bio-inspired algorithm of the swarm intelligence field for solving numerical optimization problems. It is meant to optimize applications based on the simulation of the symbiotic relationship among the distinct species in the ecosystem. The modified SOS algorithm is developed to solve independent task scheduling problems. This paper proposes a modified symbiotic organisms search based scheduling algorithm for efficient mapping of heterogeneous tasks to access cloud resources of different capacities. The significant contribution of this technique is the simplified representation of the algorithm's mutualism process, which uses equity as a measure of relationship characteristics or efficiency of species in the current ecosystem to move to the next generation. These relational characteristics are achieved by replacing the original mutual vector, which uses an arithmetic mean to measure the mutual characteristics with a geometric mean that enhances the survival advantage of two distinct species. The modified symbiotic organisms search algorithm (G_SOS) aimed to minimize the task execution time (Makespan), response, degree of imbalance and cost and improve the convergence speed for an optimal solution in an IaaS cloud. The performances of the proposed technique have been evaluated using a Cladism toolkit simulator, and the solutions are found to be better than the existing standard (SOS) technique and PSO.

There are unsolved problems related to inflation, gravity, dark matter, dark energy, missing antimatter, and the birth of the universe. Some of them can be better answered by assuming the existence of aether and hypoatoms. Both were created during the inflation in the very early universe. While aether forms vacuum, hypoatoms, composed of both matter and antimatter and believed to be neutrinos, form all observable matter. In vacuum, aether exists between the particle-antiparticle dark matter form and the dark energy form in a dynamic equilibrium: A + A-bar = gamma + gamma. The same reaction stabilizes hypoatoms and generates a 3-dimensional sink flow of aether that causes gravity. Based on the hypoatom structure, the singularity does not exist inside a black hole; the core of the black hole is a hypoatom star or neutrino star. By gaining enough mass, ca. 3 X 10²² M_sun, to exceed neutrino degeneracy pressure, the black hole collapses or annihilates into the singularity, thus turning itself into a white hole or a Big Bang. The universe is anisotropic. Its center, or where the Big Bang happened, is at 0.66^+0.03_-0.01 times the radius of the observable universe at Galactic coordinates (l, b) = (286⁺¹⁰_-10, -43⁺⁷_-6). If we look from the Local Group to the center of the universe, the universe is rotating clockwise.

Taiwan’s auditors have suffered from processing excessive audit data, including drawing audit evidence. This study advances sampling techniques by integrating machine learning with sampling. This machine learning integration helps avoid sampling bias, keep randomness and variability, and target the risker samples. We first apply a Naive Bayes classifier to classify data into some classes. Next, a user-based, item-based, or hybrid approach is employed to draw audit evidence. The representativeness index is the primary metric for measuring the representativeness of audit evidence. The user-based approach denotes the selection of samples between two percentiles in a class as audit evidence. It may be equivalent to a combination of monetary and variable sampling methods. The item-based approach represents the choice of risky samples as audit evidence. It may be identical to a combination of non-statistical and monetary sampling methods. Auditors can hybridize those user-based and item-based approaches to balance representativeness and riskiness in selecting audit evidence. Three experiments show that sampling using machine learning integration has the benefits of drawing unbiased samples, handling complex patterns, correlations, and unstructured data, and improving efficiency in sampling big data. However, the limitations are the classification accuracy output by machine learning algorithms and the range of prior probabilities.

Background: The major histocompatibility complex (MHC) plays a key role in the adaptive immune response to pathogens due to its extraordinary polymorphism. However, the spatial patterns of MHC variation in the striped hamster remain unclear, particularly regarding the relative contribution of balancing selection in shaping MHC population and diversity compared to neutral forces. Methods: In this study, we investigated the immunogenic variation of the striped hamster in four wild populations in Inner Mongolia, which experience heterogeneous parasitic stress. Our goal was to identify local adaptation by comparing the genetic structure at MHC with that at seven microsatellite loci, taking into account neutral processes. Results: We observed significant variation in parasite pressure among sites, with parasite burden showing a correlation with temperature and precipitation. Molecular analysis revealed a similar co-structure between MHC and microsatellite loci. We observed lower genetic differentiation at MHC loci compared to microsatellite loci, and no correlation was found between the two. Conclusions: Overall, these results suggest a complex interplay between neutral evolutionary forces and balancing selection in shaping the spatial patterns of MHC variation. Local adaptation was not detected at a small scale but may be applicable at a larger scale.

The future index prices are viewed as a critical issue for any trader and investor. For this craving, various models have shown up in the literature. The Geometric Brownian motion (GBM) is one of the popular models. This work examines four types of GBM as per the presence of memory or kind of volatility. These models include classical GBM with memoryless and constant volatility assumptions, SVGBM with memoryless and stochastic volatility assumption, GFBM with memory and constant volatility assumption, and SVGFBM with memory and stochastic volatility assumption. These models are utilized in an empirical study to forecast the future index price of Energy Sector in the Saudi Stock Exchange Market. The assessment was led by utilizing two error standards, mean square error (MSE) and mean absolute percentage error (MAPE). The outcomes showed that the SVGFBM occupies the highest level of accuracy according to smallest values of MSE and MAPE. While the accuracy of GBM come in the tail of the list models under study. These results have affirmed the positive affection of combining memory and stochastic volatility assumption into the GBM model, which agreed findings of numerous earlier works. Furthermore, the findings showed the GFBM models are more accurate than GBM models regardless of the type of volatility. While, under the same type of memory, the models with stochastic volatility assumption are more accurate than the corresponding models of constant volatility assumption. In general, all models considered in this work uncovered high accuracy through the value of MAPE ≤ 10%. This indicates of the ability of applying these models in a real financial environment. Through this empirical study, we can say that the Energy Sector in Saudi Arabia is predictable and stable and afterward we urge financial investors and stockholder to trade and invest in this sector.

There are rich proper noun derivative semantic relationships in geographic entity vector data. The identification of traditional proper noun derivative semantic relationships usually requires toponym researchers to examine the source of derived toponyms by consulting a large number of toponym documents. This method cannot adapt to the mining of proper noun derivative semantic relationships for large-scale geographical entity. In this regard, this paper proposes a method of identifying the proper noun derivative semantic relationships for geographical entity by combining prompt learning and XGBoost model. This method transforms the extraction problem of proper noun derivative semantic relationships into a binary classification problem by analyzing the characteristics of the derivation relationship of toponyms in the form of toponyms and the spatial and temporal distribution of geography. Firstly, prompt learning and gaussian mixture model are used to extract and cluster the derivation words of toponyms, so as to realize the recognition of derived toponyms collections. Then, enumerate the possible proper noun derivations in the toponym set, and then the XGBoost model is used to identify the semantic relations derived from the proper noun of geographical entities. In the experiment, the recognition method of proper noun derivative semantic relationships based on prompt learning and XGBoost proposed in this paper has achieved good relationship recognition effect in the experiment. This method has certain practical value in the fields of toponym management and toponym translation.

Authorities define cities – or human settlements in general – through imposing top-down rules in terms of whether buildings belong to cities. Emerging geospatial big data makes it possible to define cities from the bottom up, i.e., buildings determine themselves whether they belong to a city based on the notion of natural cities that is defined based on head/tail breaks, a classification and visualization tool for data with a heavy-tailed distribution. In this paper, we used 125 million building locations – all building footprints of America (mainland) or their centroids more precisely – to derive 2.1 million natural cities in the country (http://lifegis.hig.se/uscities/). These natural cities – in contrast to government defined city boundaries – constitute a valuable data source for city-related research.

In this study, the effects of music therapy on anxiety for patients undergoing regional anaesthesia in an operating room was succinctly investigated. This investigation was largely based on the adapted Hospital Anxiety and Depression Score (HADS) and Spielberger State-Trait Anxiety Inventory (STAI-S), of patients undergoing regional anaesthesia in an operating room. A randomized control trial was performed on 90 patients due for surgery. The selected patients for regional anaesthesia were allocated to either the music therapy group who listened to music using headphones for the entire surgery or the no-treatment control group. Based on the findings, it has been conclusively demonstrated that music can decrease the patient's anxiety level. According to the socio-demographic evaluation, elderly patients have the highest stress hormones levels when compared to young patients. Although elderly patients are more likely to choose religious songs to help them relax, cortisol analysis revealed an increase in cortisol levels among the elderly compared with younger patients. As a result, music is especially important to be delivered to elderly patients. Nonetheless, there is no restriction against administering music to elderly patients because evidence from the Hospital Anxiety and Depression Score (HADS) and State-Trait Anxiety Inventory (STAI-S) has shown that music helps to shift their attention away from pain and complications and makes them feel tranquil. Similarly, the HADS and modified Spielberger STAI (STAI-S) analyses demonstrate a substantial outcome for both groups, with respondents responding positively. The study found that listening to music during regional anaesthesia might help people feel less worried.

Endothelial autocrine signaling is essential to maintain vascular hemostasis. There is limited in-formation about the role of endothelial autocrine signaling in regulating severe pulmonary vas-cular remodeling during the onset of pulmonary arterial hypertension (PAH). In this study, we employed the first severe PAH mouse model, Egln1Tie2Cre (Tie2Cre-mediated disruption of Egln1) mice, to identify the novel autocrine signaling mediating the pulmonary vascular endothelial cells (PVECs) hyperproliferation and the pathogenesis of PAH. PVECs isolated from Egln1Tie2Cre lung expressed upregulation of many growth factors or angiocrine factors such as CXCL12, and exhib-ited hyperproliferative phenotype in coincident with upregulation of proliferation specific tran-scriptional factor FoxM1. Treatment of CXCL12 on PVECs increased FoxM1 expression, which was blocked by CXCL12 receptor CXCR4 antagonist AMD3100 in culture human PVECs. Endo-thelial specific deletion of Cxcl12 (Egln1/Cxcl12Tie2 Cre) or AMD3100 treatment in Egln1Tie2Cre mice downregulated FoxM1 expression in vivo. We then generated and characterized a novel mouse model with endothelial specific FoxM1 deletion in Egln1Tie2Cre mice (Egln1/Foxm1Tie2Cre), and found that endothelial FoxM1 deletion reduced pulmonary vascular remodeling and right ventricular systolic pressure. Together, our study identified a novel mechanism of endothelial autocrine sig-naling in regulating PVECs hyperproliferation and pulmonary vascular remodeling in PAH.

System-of-systems (SoS) evolution is a complex and unpredictable process. Although various principles to facilitate collaborative SoS evolution have been proposed, there is a lack of experimental data validating their effectiveness. To address these issues, we present an Agent-Based Model (ABM) for SoS evolution in the Internet of Vehicles (IoV) , serving as a quantitative analysis tool for SoS research. By integrating multiple complex and rational behaviors of individuals, we aim to simulate real-world scenarios as accurately as possible. To simulate the SoS evolution process, our model employs multiple agents with autonomous interactions and incorporates external environmental variables. Furthermore, we propose three evaluation metrics: evolutionary time, degree of variation, and evolutionary cost, to assess the performance of SoS evolution. Our study demonstrates that enhanced information transparency significantly improves the evolutionary performance of distributed SoS. Conversely, the adoption of uniform standards only brings limited performance enhancement to distributed SoSs. Although our proposed model has limitations, it stands out from other approaches that utilize Agent-Based Modeling to analyze SoS theories. Our model focuses on realistic problem contexts and simulates realistic interaction behaviors. This study enhances the comprehension of SoS evolution processes and provides valuable insights for the formulation of effective evolutionary strategies.

It is generally believed that the universe emerged from the Big Bang, before which space and time did not exist. However, Penrose proposed his theory of conformal cyclic cosmology, in which an aeon existed preceding this universe. Based on the cosmological principle, the universe is homogeneous and isotropic, expanding without center, but recently more and more deviations have been observed. By studying the fundamental particles and reactions of nature, we initiated a model that the Big Bang is the result of the collapse of a neutrino star within a black hole. We therefore re-analyze the cosmic microwave background (CMB) and find that the Center of the universe where the Big Bang occurred was at 0.66 ^+0.03 _-0.01 times the radius of the surface of last scattering away from the Local Supercluster (LS) and at Galactic coordinates (l, b) = (286^o +/- 10^o, -43^{o +7} _-6). If we look from the LS to the Center, the universe is spinning clockwise. Our findings are supported by Penrose’s conformal cyclic cosmology, low-variance circle sets in the CMB, and many other independent observational evidence of cosmic inhomogeneities, spatial anisotropies, and time variations.

Sustainable urban design or planning is not a LEGO-like assembly of prefabricated elements, but an embryo-like growth with persistent differentiation and adaptation towards a coherent whole. The coherent whole has a striking character – called living structure – that consists of far more small substructures than large ones. To detect the living structure, natural streets or axial lines have been previously adopted to be topologically represent an urban environment as a coherent whole. This paper develops a new approach to detecting the underlying living structure of urban environments. The approach takes an urban environment as a whole and recursively decomposes it into meaningful subwholes at different levels of hierarchy or scale ranging from the largest to the smallest. We compared the new approach to natural street and axial line approaches and demonstrated, through four case studies, that the new approach is better and more powerful. Based on the study, we further discuss how the new approach can be used not only for understanding, but also for effectively designing or planning the living structure of an urban environment to be more living or more livable.

In this paper, we study the global existence and exponential decay for a dynamic contact problem between a Timoshenko beam with second sound and two rigid obstacles, of which the heat flux is given by Cattaneo's law instead of the usual Fourier's law. The main difficulties arise from the irregular boundary terms, from the low regularity of the weak solution and from the weaker dissipative effects of heat conduction induced by Cattaneo's law. By considering related penalized problems, proving some a priori estimates and passing to the limit, we prove the global existence of the solutions. By considering the approximate framework, constructing some new functionals and applying the perturbed energy method, we obtain the exponential decay result for the approximate solution, and then prove the exponential decay rate to the original problem by utilizing the weak lower semicontinuity arguments.

Agricultural activities are the second largest source of greenhouse gas emissions, and carbon emissions from agricultural land use (CEALU) have become a hot issue across the world. However, few scholars explored the impact of agricultural land policies on carbon emissions, such as the High-standard farmland construction(HSFC) in China.Thus，by relying on provincial panel data for China for the period 2005-2017, the effect of the high-standard basic farmland construction policy on carbon emissions from agricultural land use per unit area and its regional differences were quantitatively analyzed using the difference-in-difference (DID) model. The results showed that: 1) China's CEALU per unit area presented a fluctuating upward trend during the period 2005-2017, from 392.58 kg/ha to 457.72 kg/ha, with an average annual growth rate of 1.31%. 2) The high-standard farmland construction(HSFC) policy produced a significant carbon emission reduction effect in agricultural land use, and reduced the CEALU per unit area by 10.80% on average. With the promotion of this policy, its carbon emission reduction effect in agricultural land use presented an overall increasing trend. 3) The carbon emission reduction effect of the high-standard farmland construction policy in agricultural land use was significant in central China, but non-significant in eastern China and western China.

The development of using mitochondrial transplantation in medical therapy are currently discussed. According to the endosymbiotic theory, the movement of mitochondria between different cells is possible. Therefore, an idea of transferring the mitochondria purified from the cells with a limited lifespan to shorten the lifespan of immortal cancer cells was generated. Human umbilical vein endothelial cell (HUVEC) is a kind of primary cell with a limited lifespan. In the present study, the mitochondria purified from HUVEC were applied to alter the malignance of B16F10 melanoma cell. In the comparison of the basic function of mitochondria, HUVEC mitochondria exhibited a higher mitochondrial membrane potential (MMP), while the lower levels in ATP, mitochondrial DNA (mt-DNA) and mitochondrial protein (mt-protein) as compared to melanoma B16F10. After transplanted the B16F10 with HUVEC mitochondria, the migration, invasion, and the proteins involved in cancer stemness were decreased, however, the proteins involved in cell proliferation, such as cyclin D1 and cyclin E1 were increased. In addition, the levels of ATP, mt-DNA and mt-protein of B16F10 were also reduced. As for the dynamics of mitochondria, the reduced expressions of DRP1 and LC3 II revealed that transferred mitochondria can attenuate the mitophagy of B16F10. In mice, inoculated the B16F10 carrying the transplanted HUVEC mitochondria resulted in the larger size of tumor, which is corresponded to the higher levels of cyclin D1 and E1. Interestingly, the lower expressions of N-cadherin, α-SMA and MMP-9 reveled the tumor were decreased in mesenchymal characteristics. Therefore, despite the HUVEC mitochondria did not inhibit the proliferation of B16F10, the metastatic characteristic was reduced.

Depth-sensing technology has led to broad applications of inexpensive depth cameras that can capture human motion and scenes in 3D space. Background subtraction algorithms can be improved by fusing color and depth cues, thereby allowing many issues encountered in classical color segmentation to be solved. In this paper, we propose a new fusion method that combines depth and color information for foreground segmentation based on an advanced color-based algorithm. First, a background model and a depth model are developed. Then, based on these models, we propose a new updating strategy that can eliminate ghosting and black shadows almost completely. Extensive experiments have been performed to compare the proposed algorithm with other, conventional RGB-D algorithms. The experimental results suggest that our method extracts foregrounds with higher effectiveness and efficiency.

Due to the long-term deformation settlement of foundations, issues such as damage and functional failure of buildings and structures have long been of concern in the engineering field. The creep of soil is one of the primary causes leading to long-term deformation of foundations. In this paper, the consolidation deformation, creep characteristics, and creep model of reconstituted saturated silty clay were studied by isotropic compression test and triaxial shear creep test. The results show that for the isotropic compression test, although the applied load adopts different stages of loading, as long as the final applied confining pressure is the same, the number of stages applied by the confining pressure has little effect on the final isotropic consolidation deformation of the sample and the triaxial undrained shear strength after creep. However, for the triaxial shear creep test, it is found that under the same final deviatoric stress, the final deviatoric strain of the sample is closely related to the number of loading stages of deviatoric stress. It shows that the more loading stages of the sample loaded with the same deviatoric stress, the smaller the final deviatoric strain, and the triaxial undrained shear strength of the sample after creep will increase. In addition, it is reasonable to determine the pore pressure dissipation of the sample to 95%((u0-u0)/u0=95%) as the main consolidation end time (tEOP) of the sample. The isotropic consolidation creep curves and the triaxial shear creep curves show certain nonlinearity. Then the logarithmic model and the hyperbolic model were used to fit the creep curves of the samples. It is found that the hyperbolic model has a better fitting effect than the logarithmic model, but for the triaxial shear creep test, the creep parameters of the sample change greatly. Therefore, studying the creep characteristics of soil under different pre-loading steps is of significant engineering importance for evaluating the long-term deformation of underground structures.

This study investigates the impact of specimen size on the mechanical properties of wood, specifically focusing on compression strength, elastic modulus, and Poisson's ratio. Compression tests were conducted using three different specimen sizes (20mm×20mm×30mm, 40mm×40mm×60mm, 60mm×90mm×90mm) in the longitudinal, radial, and tangential directions. Mechanical parameters, failure mechanisms, load-displacement curves, and stress-strain relationships were systematically analyzed for each size. The study also evaluated the influence of specimen size on the accuracy of finite element numerical simulations by utilizing the obtained mechanical parameters. The results reveal a significant correlation between compressive strength and specimen size, indicating a decrease in compressive strength with increasing specimen size. Conversely, elastic modulus and Poisson's ratio exhibit less sensitivity to variations in specimen size. Notably, parameters derived from small-sized specimens (20mm×20mm×30mm) exhibited substantial errors, while those obtained from medium-sized (40mm×40mm×60mm) and large-sized specimens (60mm×90mm×90mm) demonstrated greater reliability, providing precise results in finite element numerical simulations.

Promoting the sustainable development of resource-based cities is of great significance to the ecological protection and high-quality development of the Yellow River Basin. Combining the environmental status and economic development, we objectively evaluate the ecological safety level of resource-based cities in the Yellow River Basin, compare and analysis the main factors affecting the ecological safety of different resource-based cities, and formulate precise and differentiated ecological safety protection measures in river basins to achieve high-quality development. Taking the Yellow River Basin as an example, this paper analyses the ecological level of the Yellow River Basin based on the theory of sustainable development, calculates the ecological safety level of 30 resource-based cities in the Yellow River Basin from 2006 to 2020 by TOPSIS model, and makes convergence analysis by classification. Through the DID empirical test of sustainable development planning policy on ecological safety factors, distinguish the development characteristics of different resource-based cities. The results show that: (1) the ecological safety level of resource-based cities in the Yellow River Basin is generally developing well, and there are differences among different types of resource-based cities; (2) the kinetic energy of sustainable development of resource-based cities in the Yellow River Basin is mainly industrial transformation, and the power of science and technology is insufficient; (3) local governments pay limited attention to environmental protection, and the sustainable development planning of resource-based cities in 2013 mainly promotes the adjustment of industrial structure of resource-based cities.

This paper seeks to model the crack propagation in of AAs (aluminum-alloys) 2524-T3 and 7050-T7452 subjected to fatigue loading at low temperature. Fatigue crack growth tests were performed on AAs 2524-T3 and 7050-T7452 subjected to constant-amplitude and actual random-spectra loading histories at room temperature of about 25 °C and at cryogenic temperature of -70 °C, respectively, to determine their crack growth properties and residual lives. The interaction mechanisms between cryogenic temperature and the fatigue load were deduced on the basis of the results of fractographic analysis. Temperature-dependent residual lives under actual random-spectrum load history were modeled based on a modified accumulation damage rule by accounting for the load interaction. Good correlation was achieved between the predictions and actual experiments, demonstrating the practical and effective use of the proposed method.

Coarse-grained molecular dynamics simulations are employed to investigate the spatiotemporal evolution of vesicles (polymersomes) by the self-assembly of randomly distributed amphiphilic BAB triblock copolymers with hydrophilic A and hydrophobic B blocks in an aqueous solution. The vesiculation pathway consists of several intermediate structures, such as an interconnected network of copolymer aggregates, a cage of cylindrical micelles and a lamellar cage. The cage-to-vesicle transition occurs at a constant aggregation number and practically eliminates the hydrophobic interfacial area between the B block and solvent. Molecular reorganization under-lying the sequence of morphology transitions from a cage-like aggregate to a vesicle is nearly isentropic. The end-to-end distances of isolated copolymer chains in solution and those within a vesicular assembly follow log normal probability distributions. This is attributed to the pre-ponderance of folded chain configurations in which the two hydrophobic end groups of a given chain stay close to each other. However, the probability distribution of end-to-end distances is broader for chains within the vesicle as compared to that of a single chain. This is due to the swelling of the folded configurations within the hydrophobic bilayer. Increasing the hydropho-bicity of the B block reduces the vesiculation time without qualitatively altering the self-assembly pathway.

This study investigated and classified typical structures in rural village and analyzed the vulnerability of various typical types of structures. Based on the statistics of earthquake damages with magnitudes above 5 from 1996 to 2013 in China, the damage matrixes of different types of structures in rural village are obtained. And The vulnerability index and the vulnerability equation of structure are crucial to assess the earthquake losses of typical structures under different magnitudes earthquakes. According to the seismic loss of different types of structures under different earthquake magnitudes, there are possible to improve the seismic resilience of the buildings in rural village. Moreover, the regional vulnerability is analyzed by β probability distribution function, and the comprehensive seismic performance index of different types of agricultural buildings in the region is obtained. The main research is to predict the loss of different types of structures under different earthquake magnitudes in the future, and to provide technical support for different types of building in rural village reinforcement.

This chapter aims to construct a relatively complete, universal, and highly reliable road surface state monitoring and perception system through the research of the A1 contact road reconstruction project of an international airport (hereinafter referred to as the “airport”). The system design is based on on-the-spot investigation, theoretical analysis, and engineering practice, using vibrating string type and resistive strain sensors as sensing means, real-time high-frequency signal acquisition technology as the guide, 4G router for wireless communication technology as the media, and storage technology for monitoring system based on server storage terminals. In combination with engineering background, aircraft motion state, main landing gear configuration, sensor selection and evaluation, and other major factors, the actual monitoring information needs of the plane are fully taken into account, as well as the difference in sensor information types, and the system design is carried out by stratification and classification.

Restoration experience is a positive response against the ever-increasing problem of stress. It is recovery of attentional capacity, clearing random thoughts and feelings of relaxation and calmness. Although there is a body of research on investigating the impact of open green space characteristics on psycho- logical restoration through attention restoration theory and supportive environment theory, there is little knowledge on how the interaction among these characteristics could promote restorative outcomes. To address this problem, using Perceived Sensory Dimension (PSD) for open green space qualities and Perceived Restorativeness (PR) as the property of restorative environment, this study assessed the extent to which the PSD and PR impact on restoration experience. Using 444 screened surveys, a Partial Least Square Structural Equation Model (PLS-SEM) was developed and the validity and reliability of it, was demonstrated. Extensive analysis of the results showed how environmental qualities can promote restoration experience using a sample of university students. These results provide information for landscape architecture and planning researchers to promote the development of open spaces as a resource of psychological restoration and stress relief.