Hepatic carboxylesterase 1(CES1) metabolizes many prodrugs into active ingredients or direct-acting drugs into inactive metabolites. We aim to develop a semi-physiologically based pharmacokinetic model(Semi-PBPK model) to simultaneously predict pharmacokinetics of CES1 substrates and their active metabolites in liver cirrhosis(LC) patients. Six prodrugs(enalapril, benazepril, cilazapril, temocapril, perindopril and oseltamivir) and three direct-acting drugs (flumazenil, pethidine and remimazolam) were selected. The parameters including organ blood flows, plasma binding protein concentrations, functional liver volume, hepatic enzymatic activity, glomerular filtration rate(GFR) and gastrointestinal transit rate were introduced into the simulation. Pharmacokinetic profiles of these drugs and their active metabolites were simulated in 100 virtual subjects. The developed semi-PBPK model, following validation in healthy subjects, was extrapolated to LC patients. Most of the observations are within the 5% and 95% quantile of simulations from 100 virtual patients. The estimated AUC and Cmax are within 0.5-2-fold of observation. The sensitivity analysis showed that the decreased plasma exposure of active metabolite due to the decreased CES1 was partly attenuated by the decreased GFR. Conclusion: The developed PBPK model has successfully predicted pharmacokinetics of CES1 substrates and their metabolites in healthy subjects and LC patients, which assists in tailoring dosages of CES1 substrates in LC patients.

Microbial community succession in raw milk determines its quality and storage period. In this study, carbon dioxide (CO2) at 2000 ppm was used to treat raw milk to investigate the mechanism of extending the shelf life of raw milk by CO2 treatment from the viewpoint of microbial colonies and metabolites. The results showed that the shelf life of CO2-treated raw milk was extended to 16 days at 4 ℃, while that of the control raw milk was only 6 days. Microbiomics analysis identified 221 Amplicon Sequence Variants (ASVs) in raw milk, and the alpha diversity of microbial communities increased (P < 0.05) with the extension of storage time. Among them, Pseudomonas, Actinobacteria and Serratia were the major microbial genus responsible for the deterioration of raw milk, with a percentage of 85.7%. A combined metagenomics and metabolomics analysis revealed that microorganisms altered the levels of metabolites, such as pyruvic acid, glutamic acid, 5'- cmp, arginine, 2-propenoic acid, and phenylalanine in the raw milk through metabolic activities, such as ABC transporters, pyrimidine metabolism, arginine and proline metabolism and phenylalanine metabolism, and reduced the shelf life of the raw milk. CO2 treatment prolonged the shelf life of the raw milk by inhibiting the abundance of Gram-negative aerobic bacteria, such as Acinetobacter guillouiae, Pseudomonas fluorescens, Serratia liquefaciens and Pseudomonas simiae.

To solve the problem of deformation and stress in segments when a full-face hard rock tunnel boring machine (TBM) tunnel for an urban subway passes through existing buildings, a three-dimensional mechanical calculation model of TBM tunneling under existing buildings was established based on numerical analysis to comprehensively study the spatial attributes of tunnel segments during TBM tunneling. The Qingdao Metro Line 1 TBM tunnel in the hard rock stratum of Xiaobei District passes through existing buildings. The results indicated that (1) the stress and deformation of the segments were different at different positions. The horizontal and vertical stress and displacement of the segments in front of the building were significantly affected by the building. When the segment was not in the area below the horizontal plane of the building, the vertical stress of the segment was significantly greater than the two stresses in the horizontal direction. (2) The vertical displacements of the vault and the arch bottom were the largest, and the influence of vertical accessories was stronger when the segment was closer to the building.(3) When the tunnel was excavated to approximately one tunnel diameter in front of the building, the building was affected. If the surrounding rock is unstable, the excavation face may collapse and the ground surface may sink, possibly leading to inclination, cracking, and uneven settlement of the building.

The determination and classification of seafloor sediment types are crucial for the exploitation of marine resources, construction of marine engineering, and maintenance of marine ecological en-vironments. Automatic classification of seafloor sediment based on acoustic telemetry data is an important method to quickly understand the type of a large range of sediment. Currently, most studies on sediment classification are based on multi-beam backscattering intensity data, which is a relatively single data type. Besides, the low-dimensional details of standard U-Net gradually weaken in the propagation process, limiting the accuracy of sediment classification. Therefore, this study proposes an automatic classification method of seafloor sediment types based on an im-proved U-Net and K-means clustering algorithm, using multi-beam water depth, sub-bottom profile, and sample test data in the Northern Slope of the South China Sea. Six sediment types, including gravelly muddy sand, sand, silty sand, less muddy silt, muddy silt, and silty mud, were identified in the study area. Additionally, the study area was divided into four sedimentary en-vironment zones, including the shelf sedimentary area, the upper shelf slope break sedimentary area, the lower shelf slope break mixed sedimentary area, and the slope deposition area, based on the results of sediment classification and geological background. The sedimentary environment zones were found to be distributed along the trend of the shelf slope break line. The results of this study not only provide an important supplement to the existing classification methods of seafloor sediments but also contribute to the understanding of the sedimentary environment and process of the Northern Slope of the South China Sea.

A large amount of steel slag (SS) and CO2 are generated in the steelmaking process. The indirect CO2 capture using SS is a promising way for co-treatment of the wastes. Ammonium salt solution is widely used to extract Ca2+ from the SS since it is recyclable. Several works have focus on improving the carbonation rate by adjusting various parameters (e.g., temperature and pH). However, there is little detail information about the associating behaviors and interaction strength between the various ions in the ammonia solution during the carbonation process. In this work, the Ca2+–CO32-–NH4+–Cl-–H2O system was established by using Material studio software. The effects of temperature and concentration of CO32- on CaCO3 growth were explored at the atomic scale by calculating the binding energy, mean square displacement, and diffusion coefficient between particles. Furthermore, the microstructure, bonding characteristics, and occurrence behavior of each particle were studied though molecular dynamics simulation methods. The results showed that with the increase of temperature (20–80 ℃), the binding ability and diffusion coefficients of Ca2+ and CO32- increase in the system, which is beneficial to the formation of CaCO3 clusters. With the increase of the concentration of CO32- (15–25 vol.%), the binding ability and diffusion coefficient of Ca2+ and CO32- in the system are enhanced, which is beneficial to the formation of CaCO3 clusters.

Real-time fluorescent quantitative PCR (RT-qPCR) is the most classic and widely used technology for evaluating the expression level of target gene. In order to select the proper internal reference genes for RT-qPCR analysis in Amorphophallus konjac (Araceae), eight candidate internal reference genes, including 25S ribosomal RNA gene (25S rRNA), 18S ribosomal RNA gene (18S rRNA), actin gene (ACT), glyceraldehyde-3-phosphate dehydrogenase gene (GAPDH), Ubiquitin gene (UBQ), β-tubulin gene (β-TUB), eukaryotic elongation factor 1-αgene(eEF-1α), and eukaryotic translation initiation factor 4α-1 gene (eIF-4α) were selected and tested the corresponding expression level in different tissues at different growing stages. The results showed that 25S rRNA, 18S rRNA, and ACT at the reproductive periods, eEF-1α and eIF-4α at the nutritional periods, and eEF-1α, UBQ, and ACT at different leaf developmental periods had a stable level of gene expression, respectively. These results might be useful for the study of gene function in A. konjac.

Understanding maturation stress and wood properties of poplar tension wood are critical for improving lumber yields and utilization ratio. In this study, Released Longitudinal Maturation Strains (RLMS), anatomical features, physical and mechanical properties, and nano-mechanical properties of the cell wall were analyzed at different peripheral positions and heights in nine inclined, 12-year-old poplar (Populus×euramericana ‘Zhonglin46’) trees. The correlations between RLMS and wood properties were determined. The results showed that there were mixed effects of artificial inclination on wood quality and properties. The upper sides of inclined stems had higher RLMS, proportion of G-layer, bending modulus of elasticity, and elastic modulus of cell wall but lower microfibril angle than the lower sides. At heights between 0.7 m and 2.2 m, only the double wall thickness increased with height, RLMS and other wood properties such as fiber length and basic density fluctuated or changed little with height. RLMS was a good indicator of wood properties in the tension wood area and at heights between 0.7 m and 1.5 m. The results of this study present opportunities to identify and select better quality wood in poplar trees.

Based on the survey data of 5 large grain-producing provinces in China, we studied the promo-tion effect of the agricultural production trusteeship on high quality production of grain by using a propensity score matching method. The empirical results show that the high-quality production of grain level increased by 0.292, with an increase of 87.4% after participating in agricultural production trusteeship. Among them, the level of high efficiency, premiumization, greenization and branding of grain production has respectively increased by 0.234, 0.373, 0.208, and 0.158. However, there are differences in the facilitation effects of different trusteeship services, with the best promotion effect of agricultural material supply services, followed by post-harvest services, and then land management services and cultivating and harvesting services, the ATT is 0.287, 0.230, 0.158, and 0.139, respectively. Meanwhile, there are differences in the promotion effects for farm households with different factor endowments. The promotion effect is better for small farm households with 3 laborers or less, a land management scale of 10 mus or less, and 1 type of ag-ricultural machinery or less. Therefore, in order to give full play to the promotion effect of agri-cultural production trusteeship on high quality production of grain, the government should vigorously support its development and guide more smallholders to choose agricultural produc-tion trusteeship.

Malignancy is a fatal disease with an increasing incidence with each passing year, and the early diagnosis of malignancy can significantly improve the cure rate of patients, which has profound implications for saving patients' lives. The changes of miRNA are closely related to the occurrence of malignant tumors. The expression of miRNA in many malignant tumors is obviously different and unique from that in normal tissues. In this study, a strategy for amplifying microRNA (miRNA) signal was designed using duplex specific nuclease (DSN), which was then combined with Peptide nucleic acid (PNA) probe hybridization technology to prepare a sensitive and efficient paper-based lateral flow biosensor (LFB) for the detection of pancreatic cancer (PC) associated microRNA-10b. The performance evaluation results of the LFB showed that the detection limit of miR-NA-10b is 5 nM/mL, and carries demonstrated well specificity, stability, reproducibility. The biosensor prepared in this paper can provide accurate and rapid detection of miRNA-10b. The detection results play a guiding role in the early diagnosis of PC. Meeting the clinical need for largescale screening of individuals at high risk of PC. Providing new avenues to achieve early mass screening in individuals at high risk of PC.

For the soil in different regions, the nutrient fertility contained in it is different, and the detection and zoning management of soil nutrients before tillage every year can improve grain yield. In this paper, an integrated learning strategy model based on black soil hyperspectral data is designed for rapid classification of organic matter content classification of black soil. Soil hyperspectral image dataset of Xiangyang Experimental Base was collected; by changing the internal structure of the stacking model, an LSVM-stacking model with (MLP, SVC, DTree, XGBl, kNN) five classifiers as the L1 layer was built, and the simulated annealing algorithm was used for hyperparameter optimization. Compared to other stacking models, the LSVM-stacking metrics are significantly improved. The accuracy rate of hyperparameter optimization is improved by 38.6515%, the accuracy rate of the independent test data set is 0.9488, and the comparison of individual learners can improve the recognition classification accuracy of label"1" to 1.0.

A solid structure, such as a road, building wall or envelop, used as a solar collector is considered an effective and new way to use renewable energy. This paper focused on the temperature characteristics of four structures exposed to sunshine: asphalt, red brick, composite cement and concrete road slab. Furthermore, the collected heat based on a hydraulic system was investigated experimentally. For the four structure slabs, their temperature differences are due to solar radiation absorption varied greatly by the material’s heat absorptance and color. Through the test, asphalt slab attained the highest temperature and had the weakest reflection among the structures. Compared with the others, the temperature of the asphalt slab was greater by 8.1%, 14.9% and 16.4% than the brick, composite cement and concrete, respectively. The reflection intensity growth ratio was defined and indicates the growth potential for absorbing radiation in the solid slab surface. From the experiments, it was concluded that a suitable selection of road materials can greatly improve the thermal absorption, conduction and penetration into the solid slab. The collected heat capability was approximately 250 W/m2 to 350 W/m2 in the natural summer condition. A black coating or a surface modification can collect more heat, reaching greater than 250 W/m2. The solar collecting heat efficiency with a surface configuration of the road slab can reach above 30% in the summer time.

An experimental ultra wideband ground penetrating radar based on Golay complementary sequences is proposed to locate underground pipes. Golay complementary sequences with the code length of 1024 and frequency of 1 GHz are used as the probe signals. Two-dimensional image of the buried pipes is achieved by correlation method and back projection algorithm. The experimental results show that both the plastic pipe and metallic pipe can be located with a range resolution of 10 cm. Furthermore, as the Golay complementary sequences are a pair of complementary sequences, the sum of their correlation function yields twice the value of the peak at the target position and zero elsewhere. Thus, compared with the stepped frequency signal radar or chaotic signal radar, the Golay-based radar can significantly improve the signal noise ratio and has capability of deep detection.

Type2 diabetes(T2D) is a pervasive modern disease, classified as a metabolic disorder marked by hyperglycemia, hyperinsulinemia, and life-long threatening cardiovascular morbidities. Subclinical inflammation stands as the primary pathogenetic factor propelling T2D progression, correlated with heightened levels of proinflammatory cytokines. Immune cells serve as the prominent sources of cytokine production, and under diabetic conditions, they tend toward a proinflammatory phenotype, fostering chronic inflammation. This review zeroes in on the disrupted homeostasis characteristic of diabetic conditions, encompassing cytokines, hormones, and their interplay with altered immune function, all contributing to the onset of diabetic cardiovascular complications.

In the context of the coordinated development of digital economy and environmental protection, we take the strategy of "Broadband China" as a quasi-natural experiment, adopts the panel data of 284 cities from 2007 to 2020, and evaluates the policy effect of digital economy on total factor energy efficiency through the progressive DID model. The results show that: The construction of "Broadband China" demonstration city has significantly promoted the improvement of the total factor energy efficiency of the demonstration city, but there is also significant urban heterogeneity, which is manifested in the significant improvement of the total factor energy efficiency of the eastern city, the southern city, the big city and the non-resource-based city. It has no significant effect on the total factor energy efficiency of central and western cities, northern cities, small and medium-sized cities and resource-based cities. The construction of "Broadband China" demonstration cities can improve total factor energy efficiency by promoting technological progress and strengthening environmental regulations. Therefore, we should further strengthen the construction of digital infrastructure and give full play to its positive effects. Different cities should seek differentiated development paths based on their own resource endowments and development conditions, so that the digital economy can effectively improve the total factor energy efficiency.

To study the coupling mechanism and dynamic response of the end-face movable gear trans-mission system under complex excitation, a specific configuration of end-meshing movable gear reduction mechanism was used to achieve predetermined rigid thrust transmission and mis-matched gear meshing functions, which solved the inherent defects of traditional harmonic gear mechanism thin-wall flexible wheel easy to be damaged by fatigue. Considering the phenomenon of elastic deformation of live teeth accompanied by significant changes in meshing characteristics in the transmission process of end-meshing harmonic reducer, the influence of dynamic meshing parameters, live tooth deformation, time-varying stiffness of tooth meshing, and time-varying backlash on nonlinear dynamic performance was explored, as well as the mechanism of mul-ti-parameter coupling on transmission performance. The nonlinear dynamics model of the end-meshing harmonic reducer is established to solve the chattering prediction problem. Finally, a comprehensive test bed for the transmission system of harmonic reducer with meshing type with adjustable characteristic end was built to verify the correctness of the theoretical model and provide the theoretical and technical basis for exploring the optimal parameter selection of pas-sive vibration suppression problem.

As the most important component of soft robot, the pneumatic actuator can drive robot movement. However, the effect of the actuator pneumatic network geometry on the actuator load capacity is insufficient. In this paper, the finite element analysis method is applied to calculate the load force and stability torque of the pneumatic actuator under a wide range of length and height dimensions of the pneumatic network. The effect of the network size on the structure rigidity, load capacity and deformation of the actuator is analyzed. Based on the calculation results, the effect of network structure size on the deformation and load capacity is analyzed, and the optimal geometric results with the maximum load capacity are obtained. This study provides theoretical guidance for the structural design and optimization of the pneumatic actuator.

This paper studied the dynamic characteristics of the deployment process of the replaceable interface mast and the vibration generated at the end of the mast. The interface is discretized by the absolute nodal coordinate thin shell element with gradient reduction, and the three-dimensional motion of the reconfigurable rigid body module is described by the natural coordinate method. The dynamic equations of the interface and the reconfigurable module without external constraints are established by the principle of virtual work. Finally, the dynamic models of each part are assembled into the dynamic model of the mast system deployment process through the system constraint equation. According to the established dynamic model of the mast system deployment process, the dynamic behavior changes of the replaceable interface mast are analyzed. At the same time, the differences in the deployment behavior of the replaceable interface mast under different system configuration schemes, flexible interface geometric parameters, and different driving laws are studied and compared. It provides guidance for the scheme configuration of the mast system, the structural design of the interface, and the deployment motion planning of the mast. According to the physical prototype and the assembly of the mast system model, the deployment process of the replaceable interface mast is experimentally analyzed. It shows that the established dynamic model of the mast system can correctly analyze the dynamic characteristics of the deployment behavior of the replaceable interface mast, which provides a reference for the design and behavior analysis of the mast system.

Coarse-grained molecular dynamics simulations are employed to investigate spatiotemporal evolution of vesicles (polymersomes) via self-assembly of randomly distributed amphiphilic diblock copolymers in water. Vesiculation pathway consists of several intermediate structures such as spherical/rodlike aggregates, wormlike micelles, lamellae, and cavities. Lamella to vesicle transition occurs at a constant aggregation number and is accompanied by a reduction in the solvent accessible surface area. Simulation predictions are in qualitative agreement with the mechanism of vesicle formation in which the unfavorable hydrophobic interactions between water molecules and polymer segments along the edge of the lamella are eliminated at the expense of gaining curvature energy. However, rod-lamella-vesicle transition is accompanied by an increase in copolymer packing density. Hence, the change in the surface area accompanying vesiculation predicted by the simulations is significantly lower than theoretical estimates. Changes in information entropy, quantified by the expectation of the logarithm of the probability distribution function of the segmental stretch parameter s, defined as the difference between the maximum and instantaneous segmental extension, are statistically insignificant along the vesiculation pathway. For rods, lamellae and polymersomes, s follows a log normal distribution. This is explained based on the configurational dynamics of a single diblock chain in water.

This study addresses the escalating threat of data theft ransomware, a form of cyberattack that exfiltrates sensitive information from victim networks and demands ransom for its non-disclosure. Unlike traditional crypto-ransomware, data theft ransomware leverages various infiltration techniques to gain unauthorized access and control over critical data assets. The study introduces a novel Moving Target Defense (MTD) framework, specifically designed for the Windows operating system environment, to counter these sophisticated attacks. MTD increases the unpredictability of the system by dynamically altering its configurations, thereby disrupting the adversary's ability to execute successful attacks. Our research includes the development and empirical evaluation of the MTD framework, demonstrating its effectiveness in reducing ransomware operational capabilities through quantitative analysis, qualitative observations, and statistical significance testing. We also explore the challenges and complexities involved in implementing MTD in real-world scenarios and propose strategies to overcome these barriers. The study concludes with recommendations for future research directions and the potential of MTD in enhancing cybersecurity defense mechanisms.

Although corporate social responsibility (CSR) initiatives have gained increased significance within the restaurant industry during the pandemic, there is a lack of understanding of how customers’ perceived risks influence their dining intentions for effective crisis response. This study investigates the relationship between perceived CSR, restaurant image, and dining intentions during the crisis. In addition, the study examines three types of perceived risks (quality, health, and environmental) associated with restaurants and their influence on perceived CSR, restaurant image, and dining intentions during this period. The results demonstrate that perceived CSR positively impacts a restaurant’s image and concurrently reduces perceived risks among consumers during the coronavirus disease 2019 (COVID-19) pandemic. Furthermore, perceived health risks have a negative influence on customers’ dining intentions. This study offers valuable insights into the theoretical foundations and managerial implications of CSR’s effects and risk management, particularly in the context of future pandemics within the restaurant industry.

Leucyl-aminopeptidase (LAP), an important metallopeptidase, hydrolyses amino acid residues from the N-terminal of polypeptides and proteins, acting preferentially on the peptide bond formed by N- terminus leucine. A new leucyl-aminopeptidase was found in Bacillus cereus CZ. Its gene (bclap) contained a 1485 bp ORF encoding 494 amino acids with a molecular weight of 54 kDa. The bcLAP protein was successfully expressed in E. coli BL21(DE3). Optimal activity is obtained at pH 9.0 and 58°C. The bcLAP displays a moderate thermostability and an alkaline pH adaptation range. Enzymatic activity is dramatically enhanced by Ni2+. EDTA significantly inhibits the enzymatic activity, and bestatin and SDS also show strong inhibition. The three-dimensional model of bcLAP monomer and homohexamer is simulated by Phyre2 server and SWISS-MODEL server. The docking of bestatin, Leu-Trp, Asp-Trp and Ala-Ala-Gly to bcLAP is performed using AutoDock4.2.5 , respectively. Molecular docking results show that the residues Lys260, Asp265, Lys272, Asp283, Asp342, Glu344, Arg346, Gly372, His437 are involved in the hydrogen bonding with the ligands and zinc ions. There may be two nucleophilic catalytic mechanisms in bcLAP, one involving His 437 or Arg346 and the other involving His437 and Arg346. The bcLAP can hydrolyse the peptide bonds in Leu-Trp, Leu-Trp and Ala-Ala-Gly.

Yijing possesses inherent principles and logic, and it has achieved consensus as an auxiliary decision-making function. Research on Yijing has been validated in the field of superpsychology, primarily focusing on individuals. However, the universal value of Yijing extends beyond individual divination or telepsychological induction, and there are untapped scientific potential values associated with it. Consequently, many scholars studying Yijing, including those in Chinese medicine, attempt to decipher its internal scientific mechanism through quantitative methods. In summary, scientific quantitative analysis of Yijing can be categorized into several approaches. These include quantification based on Yijing itself (binary and hexagram order), as well as quantification based on mathematical statistics, probability, and calculus. Another approach involves quantification based on expert knowledge, as well as the quantification of timewave curves and Yi-globle. Significant progress has been made in all of these quantitative research areas within the scientific paradigm. Nevertheless, future quantification of Yijing may explore rule-based evolutionary analysis, incorporating artificial intelligence technology, and conduct quantitative analysis based on the evolutionary process and results. There is even potential to deepen this research with tools such as ChatGPT. Additionally, the focus of Yijing divination may shift from individual events to group events, accompanied by space-time modeling and analysis through the construction of a new coordinate system.

Germanium (Ge) nanostrip is embedded in polymer and studied as a waveguide. Measurement reveals that this new type of semiconductor/polymer heterogeneous waveguide exhibits strong absorption for the TE mode from 1500 nm to 2004 nm, while the propagation loss for the TM mode declines from 20.56 dB/cm at 1500 nm to 4.89 dB/cm at 2004 nm. The transmission characteristics serves as an essential tool to verify the optical parameters (n-κ) of the thin strips, addressing to the ambiguity raised by spectroscopic ellipsometry regarding highly absorbing materials. Furthermore, the observed strong absorption for the TE mode at 2004 nm is well beyond the cut-off wavelength of the crystalline bulk Ge (~1850 nm at room temperature). This redshift is modeled to manifest the narrowing of the Tauc-fitted bandgap due to the grain order effect in the amorphous Ge layer. The accurate measurement of the nanometer-scale light-absorbing strips in a waveguide form is a crucial step toward the accurate design of integrated photonic devices that utilize such components.

The debate surrounding the scientific interpretation of Yijing has been ongoing, yet thus far, a systematic, comprehensive, and objective explanation of its scientization remains elusive. The intricate nature of Yijing' scientific content makes it challenging to gain universal acceptance. The emergence of ChatGPT presents a new opportunity to delve into the scientific aspects of Yijing. Leveraging the support of ChatGPT, this paper explores the potential for scientization of Yijing and the possibility of establishing a new scientific research paradigm through in-depth dialogue with ChatGPT. It examines various aspects including the evolution of scientific paradigms, perspectives from detractors and opponents of Yijing, as well as existing challenges and approaches to its scientization. The ideas presented in this paper may offer enlightening insights for further exploration of Yijing.

Vectorial optical fields have sparked considerable interest with potential applications such as optical nano-fabrication, optical micromachining, quantum information processing, optical imaging, and so on. Traditional compact vectorial optical generators with amplitude modulation perform poorly in terms of diffraction effect reduction. In order to address this issue, the refractive 4f system in amplitude modulation is longitudinally aligned using an optimization approach presented in this research. The phase images used for longitudinal alignment are loaded into the liquid crystal spatial light modulator (SLM), and the distance between the lens and the mirror in the reflective 4f system is adjusted for longitudinal alignment by compensating for the neglected phase in the integrated module of the compact vectorial optical field generator. The spot images collected by the CCD are processed using the improved eight-direction Sobel operator, and the longitudinal alignment in the reflective 4f system is determined by the sharpness of the image. The sharpness of the lines' edges and the overall image are both enhanced after optimization compared to before optimization. The results show that the proposed method can significantly reduce the longitudinal alignment error of the reflective 4f system in the amplitude modulation of the compact vectorial optical field generator, lessen the diffraction effect, and improve the performance of the system.

The root system plays an irreplaceable role in plant growth. Its improvement can increase crop productivity. However, such system is still mysterious for us. The underlying mechanism has not been fully uncovered. The investigation on proteins related to the root system is an important means to complete this task. In the previous time, lack of root-related proteins makes it impossible to adopt machine learning methods for designing efficient models for the discovery of novel root-related proteins. Recently, a public database on root-related proteins was set up and machine learning methods can be applied in this field. In this study, we proposed a machine learning based model, named Graph-Root, for identification of root-related proteins. The features derived from protein sequences and one network were extracted, where the former features were processed by graph convolutional neural network and multi-head attention, and the later features abstracted the linkage between proteins. These features were fed into the fully connected layer to make prediction. The 5-fold cross-validation and independent tests suggested its good performance. It also outperformed the only one previous model, SVM-Root. Furthermore, the importance of each feature type and component in the proposed model was investigated.

As an important means of controlling the spread of cities, large-scale urban green belts have been widely used in the planning and construction of green space systems in various mega-size cities around the world. In previous studies, there has been more research on the overall isolation effect of green belts, while there has been less research on changes in their internal structure. There have been more perspectives on the land-use change (LUC) caused by urban expansion, and less systematic research on the dual driving factors of LUC. In this paper, we constructed a multi-dimensional index system to measure the "contraction-expansion" mechanism of land use in the overlapping vegetation areas of large green belts under the dual influence of urban expansion and urban greening, from the perspectives of quantity, space, and connectivity during different construction periods. Taking the boundary of two green belts in Beijing as an example, we hope to provide scientific support for the construction and ecological protection of Beijing's green belts.

Patient value is an important factor in clinical decision-making, but conventionally, it is not incorporated in the decision processes. In this paper, a value-based therapy recommendation comprehensive model is proposed. A literature analysis is conducted to collect value-based evidence. Categorized values and candidate therapies are used in combination as filtering keywords to build this evidence. Then a formalism model is put forward to integrate the value-based evidence with clinical evidence. The relative importance of two evidence can be adjusted dynamically by decision-makers during the decision-making processes. A prototype system was implemented using a case study for breast cancer and validated for feasibility and effectiveness.

Cropland area is closely related to food production. More previous focuses were paid on impacts of extreme events on food production, but less on cropland dynamics. This study took the Farming-Pastoral Ecotone of Northern China (FPEN) as a case area, to investigate its cropland area dynamics and driving factors in view of perspectives of extreme events, environmental conditions, socioeconomic development, urban expansion, and ecological construction. We used ridge regression approach to quantify contributions of these drivers to cropland area dynamics. Results showed that cropland area increased significantly at a rate of 333.5km2/a during 1992−2020 and were spatially clustered in east of the FPEN. Impact extent and size each driving factor on cropland trend presented large spatiotemporal differences, but ecological construction (EC) had overall the greatest impact on cropland area changes, followed by urban expansion (UE). In comparison, TL10p has the smallest. UE-dominated areas increased 41.9% since 2010s, but still less than EC. Furthermore, we found that extreme events effects on cropland area trend evidently increased. Particularly, TH90p displayed the most increase (~99.4%). Cropland area changes dominated by extreme temperature events in 2010−2020 increased nearly six times than that in 1992−2010. These findings suggest that increasing impacts of extreme weather events on cropland area changes should be cautioned.

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.

This paper aims to explore the development of the field of Intangible Cultural Heritage Design. It seeks to understand the global trends in this field and provide insights for its growth and development. The objectives are achieved through a mixed-methods approach that combines bibliometric methods and content analysis. This approach allows for a quantitative and qualitative analysis of the scientific literature related to Intangible Cultural Heritage Design. The findings of the work include an analysis of the most productive countries/regions, institutions, journals, and authors in the field of Intangible Cultural Heritage Design. It also includes citation and co-citation analysis, which helps identify influential scholars and popular journals in the field, as well as patterns of col-laboration within the field. The outcomes and implications for practice include a better understanding of the global trends in Intangible Cultural Heritage Design and insights for its growth and development. The research find-ings have important theoretical and practical implications for the field, and future research directions are pro-posed. The value of the paper lies in its contribution to a better understanding of the global trends in Intangible Cultural Heritage Design. It provides insights and assistance for its growth and development, mak-ing it valuable to researchers and practitioners in the field.

Compared with the traditional bundle pricing strategy with fixed products, this paper proposes a more flexible interactive bundle pricing strategy (IBPS) for agricultural e-retailers. Since more and more consumers tend to buy more environmentally friendly and healthy organic agricultural products, we consider consumers' organic preferences and build a mixed bundle pricing model for organic and inorganic agricultural products. A free shipping strategy is introduced to maximize retailer profit and optimize consumer surplus. Through numerical analysis, we find: (1) Compared with the traditional online retailing method, IBPS can effectively stimulate consumption and higher retailer profit; (2) As the free shipping threshold increases, the total profit shows a trend of first increasing and then decreasing, indicating that a suitable free shipping threshold can optimize the retailer profit; (3) As the consumer green preference increases, the total sales of agricultural products decrease, but the organic sales improve, while total profit rises; (4) The higher the level of consumption, the higher the sales volume of organic products and there is an upward trend in the profits of e-retailers.

Since the discovery of graphene, due to its excellent optical, thermal, mechanical and electrical properties, it has a broad application prospect in energy, materials, biomedicine, electromagnetism and other fields. A great quantity of researches on the physical mechanism of graphene has been applied to engineering in electromagnetism and optics. To study the properties of graphene, different kinds of numerical methods have been developed for simulating the electromagnetic field effects of graphene, and equivalent boundary conditions have been employed to replace graphene in the computational domain. In this work, the numerical methods with equivalent boundary conditions are reviewed, and some examples are provided to illustrate their applicability.

In an $m$-person symmetric game, all players are identical and indistinguishable. In this paper, we find that the payoff tensor of the player $k$ in an $m$-person symmetric game is $k$-mode symmetric, and the payoff tensors of two different individuals are the transpose of each other. Furthermore, we reformulate the $m$-person symmetric game as a tensor complementary problem and demonstrate that locating a symmetric Nash equilibrium is equivalent to finding a solution to the resulting tensor complementary problem. Finally, we use the hyperplane projection algorithm to solve the resulting tensor complementary problem, and we present some numerical results to find the symmetric Nash equilibrium.

Many problems in the fields of finance and actuarial science can be transformed into the problem of solving backward stochastic differential equations (BSDE) and partial differential equations (PDE) with jumps, which are often difficult to solve in high-dimensional cases. To solve this problem, this paper applies the deep learning algorithm to solve a class of high-dimensional nonlinear partial differential equations with jump terms and their corresponding backward stochastic differential equations (BSDE) with jump terms. Using the nonlinear Feynman-Kac formula, the problem of solving this kind of PDE is transformed into the problem of solving the corresponding backward stochastic differential equations with jump terms, and the numerical solution problem is turned into a stochastic control problem. At the same time, the gradient and jump process of the unknown solution are separately regarded as the strategy function and they are approximated respectively by using two multilayer neural networks as function approximators. Thus, deep learning-based method is used to overcome the “curse of dimensionality” caused by high-dimensional PDE with jump, and the numerical solution is obtained. In addition, this paper proposes a new optimization algorithm based on the existing neural network random optimization algorithm, and compares the results with the traditional optimization algorithm, and achieves good results. Finally, the proposed method is applied to three practical high-dimensional problems: Hamilton-Jacobi-Bellman equation, bond pricing under the jump Vasicek model and high-dimensional option pricing model with default risk. The proposed numerical method has obtained satisfactory accuracy and efficiency. The method has important application value and practical significance in investment decision-making, option pricing, insurance and other fields.

This paper proposes a data-driven approach to the calculation of ship overtaking boundary conditions in narrow channels based on ship overtaking characteristics by the Automatic Identification System (AIS) data. First, we collect the ship tracking process data in a narrow channel by processing the original AIS data, followed by a normality test and correlation analysis, and finally, the boundary conditions are obtained using a weighted K-means clustering algorithm. to identify the relevant data. We applied the proposed approach to estimate the overtaking boundary conditions of ships in the straight channel section of Shanghai South Passage. The method yields boundary conditions that significantly reduce collision risk when a ship engages in pursuit behavior in narrow waterways, providing a scientific basis for the overtaking of Marine Autonomous Surface Ships (MASS) in autonomous navigation within such environments, further promoting the automation level of USVs and autonomous vessels.

A total internal reflection photonic crystal fiber (PCF) based on hexagonal core is proposed for gas sensing in a specific wavelength range. The higher sensitivity and lower confinement loss were realized by the structure of the proposed PCF consists of two layers with circular holes rotated hexagonally around a core region and six slotted air-hole in the cladding based on numerical analysis. The simulation results show that the enhancement of the relative sensitivity has been done by enhancing the diameter of the hexagonal shape air-hole cladding (d1) and the hexagonal arranged holes around the central solid core (d0) in this design. Also, the confinement loss has been reduced by enhancing the ratio of length to width of slotted holes (l/w) and decreasing the cladding air-hole diameters (d1). As the refractive index increases, the wavelength shifts toward the long wavelength. And in a certain temperature range, the transmission characteristics of the device does not change with the temperature. The results are helpful for designing high performance PCF for gas sensing applications.

Background Cancer is one of the most concerning public health issues in the world. One of cancer hallmarks wildly accepted is sustaining proliferative signaling, which involved most of the cell cycle biological activities. Centromeric histone, Centromere Protein A (CENPA), a variant of canonical histone H3, plays an essential role in selective chromosome segregation in the cell cycle. However, so far, a systematic pan-cancer bioinformatic analysis has not been done yet. Methods We accessed genome, transcriptome, and clinical information from open databases. The genetic alteration, mRNA expression, functional enrichment, stemness, mutation association, expression in cell populations and cellular locations, cell cycle association, survival association of CENPA, and immune association were analyzed. A prognostic model for glioma patients was constructed as an example application of CENPA as a biomarker. Drugs targeting CENPA in cancer cells were also screened and predicted by the CENPA correlation of drug sensitivity and protein-ligand docking. Results CENPA had low gene mutation in cancers. CENPA was overexpressed in almost all cancer types in TCGA compared to their normal control. CENPA was highly expressed in the nucleus of malignant cells. CENPA was associated with the cell cycle of cancer cells. CENPA is a biomarker for the cell cycle G2 phase in cancer cells. CENPA was a diagnostic and prognostic biomarker across multiple cancer types. The prognosis of glioma with CENPA was reliable and can be applied with other prognostic factors. CENPA was associated with the immune microenvironment. Drugs CD-437, 3-Cl-AHPC, Trametinib, BI-2536, and GSK461364 were predicted to target CENPA in cancer cells. Conclusion CENPA was a cell cycle biomarker in cancers with diagnostic and prognostic value.

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.

At present, diversified and highly concurrent businesses in the Internet industry often require heterogeneous databases formed by multiple databases to meet the needs. This report introduces database kernel SG-ColBase we developed. After achieving read and write concurrency control, data rollback, atomic log writing, and downtime data redo to ensure complete transaction support. The parallelism of database kernel execution is extended through field level locks and snapshot reads. Use the Bloom filter, resource cache pool, memory pool, skip list, non blocking log cache, and asynchronous data writing mechanism to improve the overall execution efficiency of the system. In terms of data storage, column storage, logical key and LSM-tree are introduced. While improving the data compression ratio and reducing data gaps, all disk data operations are written in incremental order. With the characteristics of asynchronous batch operation, the data writing speed is greatly improved. Thanks to the continuous feature of vertical data brought by column storage, the disk scanning brought by vertical traversal is reduced, which is a qualitative leap in efficiency compared with traditional relational databases in the big data analysis scenario. SG-ColBase can reduce the use of heterogeneous databases in business and improve R&D efficiency.

The construction of sustainable urban forests follows the principle that well-being in people is promoted when exposed to tree population. Facial expression is the direct representation of inner emotion that can be used to assess real-time perception in urban forests. The emergence and change of facial expressions for forest visitors are in an implicit process. The reserved character of oriental races strengthens the requirement for the accuracy to recognize expressions through instrument rating. In this study, a dataset was established with 2,886 randomly photographed faces from visitors in a constructed urban forest park and a promenade at summertime in Shenyang City at Northeast China. Six experts were invited to choose 160 photos in total with 20 images representing one of eight typical expressions as angry, contempt, disgusted, happy, neutral, sad, scared, and surprised emotions. The FireFACE ver. 3.0 software was used to test hit-ratio validation as the accuracy (ac.) to match machine-recognized photos with those identified by experts. According to Kruskal-Wallis test on the difference from averaged scores in 20 recently published papers, contempt (ac.=0.40%, P=0.0038) and scared (ac.=25.23%, P=0.0018) expressions cannot pass the validation test. Therefore, the FireFACE can be used as an instrument to analyze facial expression from oriental people in urban forests but contempt and scared expressions cannot be identified.

Slow-roll inflation may simultaneously solve the horizon problem and generate a near scale-free fluctuation spectrum $P(k)$. These two processes are intimately connected via the initiation and duration of the inflationary phase. But a recent study based on the latest {\it Planck} release suggests that $P(k)$ has a hard cutoff, $k_{\rm min}\not=0$, inconsistent with this conventional picture. Here we demonstrate quantitatively that most---perhaps all---slow-roll inflationary models fail to accommodate this minimum cutoff. We show that the small parameter $\epsilon$ must be $\gtrsim 0.9$ throughout the inflationary period to comply with the data, seriously violating the slow-roll approximation. Models with such an $\epsilon$ predict extremely red spectral indices, at odds with the measured value. We also consider extensions to the basic picture (suggested by several earlier workers) by adding a kinetic-dominated or radiation-dominated phase preceding the slow-roll expansion. Our approach differs from previously published treatments principally because we require these modifications to---not only fit the measured fluctuation spectrum, but to simultaneously also---fix the horizon problem. We show, however, that even such measures preclude a joint resolution of the horizon problem and the missing correlations at large angles.

We have presented a controllable and human-readable polynomial neural network (CR-PNN) that is the first human-readable neural network. One can imagine its influence on system identification. Subsequently, we developed a relation spectrum in a medical application, which is likely to stand alongside the Fourier spectrum. However, the system analysis methodology is incomplete in contrast to signal processing methodology. Here, we presented the system filters for the first time. In this paper, we used the simulation system to verify the availability of the system analysis methodology. The system analysis methodology showed great properties in system identification and filter. The contribution of this paper is the system analysis methodology: transform method (CR-PNN), relation spectrum, and system filter design.

Abstract In order to reduce the failure accidents of conventional packers, a kind of sealing structure of retrievable packers is designed, which can provide reference for optimizing the sealing performance and pressure capacity of packers. And theoretical calculation and experimental research on its sealing performance and pressure capacity are carries out through the finite element analysis on the contact with large deformation based on Abaqus and experimental. The results show that when the rubber cylinder of retrievable packers in the sealing process, the upper packer is subjected to greater stress and mainly plays a sealing role. In order to improve the sealing performance of packer, the height of upper and lower packer should be preferentially selected about 60 mm, the height of middle rubber cylinders should be about 50 mm, and the thickness of packer should be about 21 mm. The newly designed rubber cylinders can meet 150 ℃. Sealing and pressure-bearing function under the condition of 10000 Psi. The rubber cylinder of newly designed can satisfy the function of sealing and pressure-bearing under 140 ℃ and 10000 Psi. The whole experiment shows that the capacity of bearing pressure and sealing is stable, which can better meet the requirements of field use.

We consider the conjecture of the relationship between the entanglement entropy of spacetime, matter and the action in Einstein's general relativity. Our analysis suggests the possibility of regarding the entanglement entropy of spacetime and matter as the dimensionless action in general relativity by using Planck units. In this case, the action principle originates from the maximization of the entanglement entropy. We also show that the fundamental property of entanglement entropy leads to attractive characteristic of gravitational force for classical particles.

We consider the quantum thermodynamic origin of the gravitational force of matter by applying the spacetime entanglement entropy and the Unruh effect originating from vacuum quantum fluctuations. By analyzing both the local thermal equilibrium and quasi-static processes of a system, we may get both the magnitude and direction of Newton's gravitational force in our theoretical model. Our work shows the possibility that the elusive Unruh effect has already shown its manifestation through gravitational force.

The increasing use of energy resources recovered from the subsurface environments and the resulting carbon imbalance in the environment has motivated the need to develop thermodynamically downhill pathways to convert and store CO₂ as water insoluble calcium or magnesium carbonates. While previous studies extensively explored aqueous routes to produce calcium and magnesium carbonates from CO₂, there has been a limited scientific understanding of the phase evolution and textural changes during the direct gas-solid conversion routes to produce calcium carbonate from calcium hydroxide, which is one of the abundant constituents of alkaline industrial residues. With increasing interest in developing integrated pathways for capturing, converting and storing CO₂ from dilute flue gases, understanding the compositions of the product phases as they evolve is essential for evaluating the efficacy of a given processing route. Therefore, in this study, we investigate the phase evolution and the corresponding textural changes as calcium hydroxide is converted to calcium carbonate under the continuous flow of CO₂ at an ambient pressure of 1 atm and on heating continuously from 30 °C to 500 °C using in-operando wide angle X-ray scattering (WAXS), small angle X-ray scattering (SAXS), and ultra-small angle X-ray scattering (USAXS) measurements.

It is important for researchers in material domain to reconstruct and visualize the microstructure of alloy, which helps discover morphologies and properties that can only be found in three dimensional and microscopic space. However, because of the limitation of state-of-the-art sensors, the internal microstructure of the material is not easy to obtain. In this paper, a novel automatic 3D reconstruction method is proposed using diffraction contrast tomography technology to capture the 3D microstructure of Al-La alloy. The pipeline of the proposed method starts from the imaging of Al-La alloy with the LabDCT technique from Carl Zeiss AG, which produces a sequence of 2D microstructure sections. Then, a segmentation algorithm based on superpixel and lifted multicut is proposed to extract the 2D microstructure in an image section. Finally, 2D segmentations are joined together to reconstruct and visulize the 3D microstructure. As a result, a novel morphology of Al-La alloy is recovered with both dendrite and lamellar morphologies. The proposed method has the advantage of the ability to losslessly recover the internal microstructure.

Vibration measurement serves as the basis for various engineering practices such as natural frequency or resonant frequency estimation. As image acquisition devices become cheaper and faster, vibration measurement and frequency estimation through image sequence analysis continue to receive increasing attention. In the conventional photogrammetry and optical methods of frequency measurement, vibration signals are first extracted before implementing the vibration frequency analysis algorithm. In this work, we demonstrated that frequency prediction can be achieved using a single feed-forward convolutional neural network. The proposed method is verified using a vibration signal generator and excitation system, and the result obtained was compared with that of an industrial contact vibrometer in a real application. Our experimental results demonstrate that the proposed method can achieve acceptable prediction accuracy even in unfavorable field conditions.

In recent years, many algorithms based on end-to-end deep networks have been proposed to deal with the target detection problem of videos. However, the deep network models usually consume a lot of computing resources during the procedure of analysis of videos with complex dynamic backgrounds. In this paper, a new method of object detection based on information theory is presented. Firstly, each frame in a video is converted into an effective information map by using the Harris corner detection method. Secondly, the sensitive areas in the frame are extracted by using the context information and the effective information maps of the consecutive video frames. The sensitive areas in the video frame are the candidate areas where the target objects would be appeared at high probabilities. Thirdly, the information entropy features of each sensitive area are extracted to form the feature matrix, based on which, an SVM model is trained for selecting the target areas from the sensitive areas. Finally, the locations of the objects are detected based on the target areas in the video with a complex dynamic background. As a lightweight video detection framework, the method presented in this paper can save a lot of computing resources. Experimental results show that this method can achieve good results in the benchmark of CDnet 2014.

China is facing huge pressure on CO2 emissions reduction. The heavy industry accounts for over 60% of China’s total energy consumption, and thus lead to a large number of energy-related carbon emissions. This paper adopts the Log Mean Divisia Index (LMDI) method based on the extended Kaya identity to explore the influencing factors of CO2 emissions from China’s heavy industry; we calculate the trend of decoupling by presenting a theoretical framework for decoupling. The results show that labor productivity, energy intensity, and industry scale are the main factors affecting CO2 emissions in the heavy industry. The improvement of labor productivity is the main cause of the increase in CO2 emissions, while the decline in energy intensity leads to CO2 emissions reduction, and the industry scale has different effects in different periods. Results from the decoupling analysis show that efforts made on carbon emission reduction, to a certain extent, achieved the desired outcome but still need to be strengthened.

A novel verifiable privacy preservation scheme for outsourcing medical image to cloud through ROI based crypto-watermarking is proposed in the paper. In the proposed scheme, data owner firstly carries out substitution of S-box for the region of interest (ROI) of medical image, and then separates the image into 4 most significant bits (MSBs) plane and 4 least significant bits (LSBs) plane images. Secondly, the hash value of ROI is embedded into the two separated bit plane images using reversible watermarking algorithm. Lastly, some selected hash values are transformed into the initial parameters of chaotic maps, and the two sharing secrets, which are produced through chaos based encryption algorithm, are finally outsourced to two different cloud servers. For authorized users, they can get shares from different cloud servers, and then can losslessly recover the original medical image through a series of decryption operations and extraction of watermarking. Furthermore, the users can verify whether the original image is completely reconstructed or not, they even can locate the tampered parts inside ROI if anyone of the sharing secrets is damaged. Some experiments analyses and comparisons are given to show the security and effectiveness of proposed scheme.

This paper mainly used database technology, machine learning, thermodynamic calculation, experimental verification, etc., on integrated computational materials engineering. The interaction between different alloying elements and the strengthening effect of precipitated phases are investigated mainly for martensitic ageing steels. Modelling and parameter optimization were performed by machine learning, and the highest prediction accuracy was 98.58%. Investigated the influence of composition fluctuation on performance and correlation tests to analyze the influence of elements from multiple perspectives. Furthermore, screen out the three-component composition process parameters with composition and performance with high contrast. Thermodynamic calculations studied the effect of alloying element content on the nano-precipitation phase, Laves phase, and austenite in the material. The heat treatment process parameters of the new steel grade were also developed based on the phase diagram. A new type of martensitic ageing steel was prepared by selected vacuum arc melting. The sample with the highest overall mechanical properties had a yield strength of 1887 MPa, a tensile strength of 1907 MPa, and a hardness of 58 HRC. The sample with the highest plasticity had an elongation of 7.8%. The machine learning process for the accelerated design of new ultra-high tensile steels was found to be generalizable and reliable.

Abstract Objectives: The purpose of this study was to determine whether breast cancer and diabetes status are related in adult Americans. Methods: We conducted a cross-sectional study of 7,599 individuals from the National Health and Nutrition Examination Survey (NHANES). Diabetes was classified as type 2 diabetes and pre-diabetes. Both prediabetes and diabetes were diagnosed according to ADA 2014 guidelines. Multiple logistic regression analysis was used to explore the relationship between diabetes status and breast cancer. Results: We found that prediabetes (OR = 0. 60, 95% CI:(0. 40, 0. 88), P= 0. 009613) and non-diabetes (OR = 0.05.3,95% CI: (0.34, 0.83), P = 0. 006014) were associated with a reduced risk of breast cancer in comparison to Type 2 diabetes (literature). Prediabetes in non-Hispanic blacks was associated with a reduced risk of breast cancer (OR=0. 55,95%CI:0. 40-0. 75, P<0. 001). Using two segmented linear regression models to fit the relationship between BMI and breast cancer, we found that the relationship between BMI and breast cancer was nonlinear, but there was a threshold effect. The threshold effect analysis found that BMI affcted breast cancer at an inflection point 26. 3 Kg/m2. Adjusted OR (95% CI) on both sides of the turning point was 1. 0799 ( 1. 0029, 1. 1629 ) and 0. 9873 ( 0. 9638, 1. 0115 ), respectively. Conclusions: Diabetes status is associated with the risk of breast cancer development. Moreover, the risk of developing breast cancer steadily increased from nondiabetes to prediabetes and type 2 diabetes. In addition, the prevalence of breast cancer showed a gradual increase withincreasing BMI up to 26. 3 Kg/m2 with the highest prevalence of breast cancer. There was an inverse U-shaped relationship between BMI and the breast cancer prevalence.

Alcohol abuse can lead to alcoholic liver disease, becoming a major global burden. Hovenia dulcis fruit peduncle polysaccharides (HDPs) has the potential to alleviate alcoholic liver injury may play essential roles in treating for alcohol-exposed liver disease, however, the hepatoprotective effects and mechanisms remain unclear. In this study, the hepatoprotective effects of HDPs and its potential mechanisms were investigated in alcohol-exposed mice by liver metabolomics and gut microbiome. The results found that HDPs reduced medium-dose alcohol-caused dyslipidemia (significantly elevated T-CHO, TG, LDL-C), elevated liver glycogen levels and inhibited intestinal-hepatic inflammation (significantly decreased IL-4, IFN-γ and TNF-α), consequently reversing hepatic pathological changes. By applying gut microbiome analysis, HDPs showed significant decreases in Proteobacteria while significant increases in Firmicutes at the phylum level, and increased Lactobacillus abundance while decreased Enterobacteria abundance, maintaining the composition of gut microbiota. Further hepatic metabolomics analysis revealed that HDPs had a regulatory effect on hepatic fatty acid metabolism, by increasing the major metabolic pathways including arachidonic acid metabolism and glycerophospholipid metabolism and identified two important metabolites C00157 and C04230 involved in the above metabolism. Overall, HDPs reduced intestinal dysbiosis and hepatic fatty acid metabolism disorders in alcohol-exposed mice, suggesting that HDPs has a beneficial effect on alleviating alcohol-induced hepatic metabolic disorders.

Geospatial knowledge in massive academic papers can provide knowledge services such as location-based research hotspot analysis, spatio-temporal data aggregation, research results recommendation, etc. However, geospatial knowledge often exists implicitly in literature resources in unstructured form, which is difficult to be directly accessed and mined and utilized for rapid production of massive thematic maps. In this paper, we take the geospatial knowledge of the area studied as an example and introduce its extraction method in detail. An integrated feature template matching and random forest classification algorithm is proposed for accurately identifying research areas from the abstract texts of academic papers and producing thematic maps. Firstly, the precise recognition of geographical names is achieved step by step based on BiLSTM-CRF algorithm and improved heuristic disambiguation method; then, the area studied is extracted by the designed integrated feature recognition template of area studied using random forest classification algorithm, and a fast thematic map is designed for the knowledge of area studied, topic and literature. The experimental results show that the area studied recognition accuracy can reach 97%, the F-value is 96%, and the recall rate reaches 96%, achieving high accuracy and high efficiency of area studied extraction in text. Based on the geospatial knowledge, the thematic map can achieve the effect of fast map formation and accurate expression.

Medium-deep Borehole Heat exchanger (MBHE) has been widely paid attention for building heating. In order to avoid the thermal interference of adjacent MBHEs, temperature distribution characteristics of medium-deep rock-soil was investigated in this work. The evolution on maximum rock-soil thermal affected radius (MTAR) in full life cycle was further analyzed. The results showed that the rock-soil thermal affected area (RTAA) continuously expands in both radial and vertical directions during a heating season. Several factors of thermal extraction load, fluid velocity, geothermal gradient and pipe length have main impacts on the RTAA in vertical direction. Rock-soil thermal conductivity affects the RTAA in both radial and vertical directions. In non-heating season, temperature difference of rock-soil in radial direction dominates the temperature recovery in the first year while geothermal flow nearly dominates temperature recovery after 20 years. The thermal affected radius (TAR) in the 30 th year could be determined as an effective MTAR in full life cycle. In order to decrease installed distance of adjacent MBHEs, a new well layout form by deflecting borehole was proposed. The recommended deflection angle is equal to or larger than 4 times TAR angle. This work provides scientific reference for promoting application of multiple MBHE arrays.

We study the influence of high-order twisting phases on polarization states and optical angular momentum of a vector light field with locally linear polarization and a hybrid state of polarization (SoP). The initial SoP of a twisted vector optical field (TVOF) modulated by the high-order twisting phase possesses various symmetric distributions. The propagating properties of a high-order TVOF with locally linear polarization and hybrid SoP are explored, including the intensity compression, expansion, and conversion between the linear and circular polarization components. In particular, orbital angular momentum (OAM) appears in a high-order TVOF during propagation where no OAM exists in the initial field. The variation of OAM distribution in cross-section becomes more frequent with the increase of the twisting phase order. In addition, a non-symmetric OAM distribution appears in an anisotropic TVOF, leading to the rotation of the beam around the propagation axis during propagation. These results provide a new approach for optical field manipulation in a high-order TVOF.

In the process of metal cutting, the anti-wear performance of the tool determines the life of the tool and affects the surface quality of the workpiece. The finite element simulation method can directly show the tool wear state and morphology, but due to the limitations of the simulation time and complex boundary conditions, it has not been commonly used in tool life prediction. Based on this, a tool wear model was established on the platform of a finite element simulation software for the cutting process of titanium alloy TC4 by end milling. The key technique is to embed different types of tool wear models into the finite element model in combination with the consequent development technology. The effectiveness of the tool wear model is validated by comparing the experimental results with the simulation results. At the same time, in order to quickly predict the tool life, an empirical prediction formula of tool wear was established, and the change course of tool wear under time change was obtained.

Remote sensing has been used as an important tool for disaster monitoring and disaster scope extraction, especially for the analysis of temporal and spatial disasters patterns of large-scale long time series. In order to find out a rapid and effective method to monitor disaster in a wide range, based on the Google Earth Engine cloud platform, this study used MODIS vegetation index products of 250 meter spatial resolution synthesized in 16 days during the year 2005-2019 and three kinds of disaster monitoring and scope extraction models are proposed: normalized vegetation index median time standardization (RNDVI_TM(i)) model, the normalized vegetation index median phenology Standardization(RNDVI_AM(i)(j)) model, normalized vegetation index median spatiotemporal Standardization (RNDVI_ZM(i)(j)) model. The optimal threshold of disaster extraction for each model in different time phases was determined by Otsu method, and the extraction results were verified by Medium resolution image and ground measured data of the same or quasi-same period. Finally, the disaster scope of cultivated land in Heilongjiang province from 2010 to 2019 was extracted and the temporal and spatial pattern of disasters was analyzed based on the meteorological data. It shows that the three above-mentioned models have high disaster monitoring and range extraction capabilities with the verification accuracy of RNDVI_TM(i) 97.46%, RNDVI_AM(i)(j) 96.90%, and RNDVI_ZM(i)(j) 96.67% respectively. The spatial and temporal distribution of disasters is consistent with the disaster of the insured plots and meteorological data in the whole province. Meanwhile, it turns out that different monitoring and extraction methods are used in different disasters, among which wind hazard and insect disasters often need to be delayed for 16 days to observe. Each model also has various sensitivity and applicability to different disasters. Compared with other methods, this method is fast, and convenient, which allows it to be used for large-scale agricultural disaster monitoring and is easy to be applied into other research areas. The research provides a new idea for large-scale agricultural disaster monitoring.

Predicting and evaluating the vitality level of public spaces of urban design is crucial before new construction and renewal, especially for developing countries, to avoid failures like "ghost neighborhood" and reduce resource waste. However, existing assessment methods for the urban vitality are either static and ignore changes in the time dimension, or rely on historical big data and are unable to predict unbuilt projects. This study combines previous methods and crowds’ characteristics observation in public space, puts forward the crowd-frequency hypothesis and constructs an algorithm, and establishes a time-dimensional urban vitality dynamic prediction model. Taking the Rundle Mall neighborhood in Adelaide, Australia, as an example, the effec-tiveness of the prediction model was proved through on-site observation sampling comparative verification method. In addition, the decision tree model was used to conduct machine learning on the case data, and a set of algorithm programs were obtained that can directly input some basic information to obtain the urban vitality level (high-medium-low) in a certain period. It can be used as a tool to support the design and decision-making processes of urban planners and government officials, providing a low-cost way to achieve sustainable urban vitality construction of late-developing cities directly.

In this paper we overview all analytical engineering solutions for corrosion problems in pressure vessels delivered over the past 60 years. We briefly detail strengths and weaknesses of existing approaches, thus demonstrating the need for novel uniform corrosion analysis methods for both current and new applications. To complement the review, we also present a new analytical model allowing to estimate the lifetime of pressured elastic vessels with mechanically assisted corrosion. Both internal/external pressure and internal/external corrosion are captured and dissolution driven corrosion is also considered. The readily implemented method improves existing analytical approaches and is shown to be effective for thin and thick shells, as well as various loading and corrosion intensity and geometrical conditions.

Bundle recommendation is provided to users by combining related items into a bundle, enhancing the user's shopping experience and increase the merchant's sales revenue. Despite the success of existing solutions based on Graph Neural Networks(GNN), there are still some significant challenges: (1)It is demanding to explicitly model multiple complex associations using standard graph neural networks. (2)Numerous additional nodes and edges are introduced to approximate higher-order associations. (3)The user-bundle historical interaction data is highly sparse. In this work, we propose a Global Structural Hypergraph Convolution model for Bundle Recommendation (SHCBR) to address the above problems. Specifically, we jointly incorporate multiple complex interactions between users, items, and bundles(three entities) into a relational hypergraph without introducing additional nodes and edges. Higher-order associations are already involved in the hypergraph structure which alleviate the training burden of neural networks and the dilemma of scarce data. In addition, we design a special matrix propagation rule that captures non-pairwise complex relationships between entities. Using item nodes as links, hypergraph convolution propagation between user and bundle nodes generates the learned representations. Experimental results on two real-world datasets demonstrate that SHCBR outperforms the state-of-the-art baselines by 11.07%-25.66\% on Recall and 16.81%-33.53% on NDCG. We publicly release the codes and datasets at https://github.com/Lxtdzh/SHCBR-master/.

Smart farming, as a branch of the Internet of Things (IoT), combines the recognition of agricultural economic competencies, the progress of data and information collected from connected devices with statistical analysis to characterize the essentials of the assimilated information, allowing farmers to make intelligent conclusions that will maximize the harvest benefit. However, the integration of advanced technologies requires the adoption of high-tech security approaches. In this paper, we present a framework that promises to enhance the security and privacy of smart farms by leveraging the decentralized nature of blockchain technology. The framework stores and manages data acquired from IoT devices installed in smart farms using a distributed ledger architecture, which provides secure and tamper-proof data storage and ensures the integrity and validity of the data. The study uses the AWS cloud, ESP32, the smart farm security monitoring framework, and the Ethereum Rinkeby smart contract mechanism, which enables automated execution of pre-defined rules and regulations. As a result of a proof-of-concept implementation, the system can detect and respond to security threats in real time, and the results illustrate its usefulness in improving the security of smart farms.

Currently, major e-commerce platforms are competing to improve their return services, while merchants are experiencing abuse of return policies by consumers. The effectiveness of lenient return policies is examined in helping e-commerce platforms gain a competitive advantage in this paper. A dual oligopoly model is established consisting of one platform that offers a lenient return policy with services like Instant Refunds and the other does not provide similar ones. The scenario where consumers are single-homing, while merchants are partial multi-homing is analyzed. In the scenario, many factors are examined to assess their impact on the scale of the bilateral user base and the profits for both platforms, including the proportion of consumer abusing return policies, the benefits derived from abusive returns, penalties. and costs. The findings indicate that: (1) With an increasing proportion of consumer abusing return policies, the multi-homing merchants on platform that offer lenient return service will choose to be exclusively affiliated with rival platform, accounting for a decrease in consumer base and lower platform profits compared to competitors. (2) Platform that provides lenient return policies should restrict the amount of returns to mitigate the impact of consumer abusing return policies on its profits. (3) When the proportion of consumers abusing return policies is relatively low, platform should choose moderate levels of penalty to increase the scale of both sides of users without excessively increasing costs. However. when this proportion is high, platforms should cease to offer lenient return and refund services.

In resent years, China’s digital economy has achieved significant global recognition for its instrumental role in supporting sustainable economic and social development. Based on the panel data from China's provinces between 2013-2021, this paper investigates the impact and transmission mechanism of financial deepening on digital economy development. The study finds that financial deepening significantly improves digital economy development. Heterogeneity analysis indicates that financial deepening offers greater support to digital economy development in central and western regions than in the east. Further analysis shows that financial deepening improves technological innovation, which in turn promotes the digital economy development. After applying a test of robustness, the results proved significant.

Antibody-Drug Conjugates (ADCs) are a powerful therapeutic modality for cancer treatment. ADCs are multi-functional biologics in which a disease-targeting antibody is conjugated to an effector payload molecule through a linker. The success of currently used ADCs has been largely attributed to the development of linker systems which allow targeted release of cytocidal payload drugs inside cancer cells. Many lysosomal proteases are over-expressed in human cancers. They can effectively cleave a variety of peptide sequences, which can be exploited in the design of ADC linker systems. As a well-established linker, valine-citrulline-p-aminobenzyl carbamate (ValCitPABC) is used in many ADCs that are already approved or under preclinical and clinical development. Although ValCitPABC and related linkers are readily cleaved by cathepsins in the lysosome while remaining reasonably stable in human plasma, many studies have shown that they are susceptible to carboxylesterase 1C (Ces1C) in mouse and rat plasma, which hinders preclinical evaluation of the ADCs. Furthermore, neutropenia and thrombocytopenia, two of the most commonly observed dose-limiting adverse effects of ADCs, are believed to result from the premature hydrolysis of ValCitPABC by human neutrophil elastase. In addition to cathepsin-cleavable linkers, there is also growing interest in legumain-sensitive linkers for ADC development. Increasing plasma stability while maintaining lysosomal cleavability of ADC linkers is an objective of intensive current research. This review reports recent advances in the design and structure-activity relationship studies of various peptide/peptidomimetic linkers in this field.

Oil-water separation using special wettability materials has received much attention due to its low energy consumption and high separation efficiency. Herein, a fluorine-free superhydrophobic cotton fabric(PDMS/STA -coated cotton fabric) was successfully prepared by a simple impregnation method using hydroxyl-capped polydimethylsiloxane (PDMS-OH), tetraethoxysilane (TEOS) and stearic acid (STA) as precursors. The Investigation found that the cross-linking reactions between the hydroxyl groups form PDMS-OH, hydrolyzed TEOS made a strong interact of PDMS-OH and cotton fabric. Besides, a suitable roughness surface of coated cotton fabric was established by introducing STA due to its long chain-structure. The contact angle of this composite can reach 158.7°under optical condition due to its low surface energy and desired roughness. The oil/water separation efficiency of PDMS/STA-coated cotton fabric is higher than 90% even after 10 cycles of oil-water separation, the oil flux can reach 11862.42 L m-2 h-1. In addition, PDMS/STA-coated cotton fabric exhibits excellent chemical stability and durability under extreme conditions such as strong acid (HCl, pH=1~2) and alkali (NaOH, pH=13~14), the hydrophobicity of PDMS/STA-coated cotton fabric was decreased to 147° even after 350 cycles of abrasion testing.

The link of fracture analysis of large ships is an essential part of the structural safety of ships. However, the finite element analysis of large structure according to the traditional method not only has high requirements on the computer, but also the influence of the mesh encryption at special locations makes further requirements on the accuracy of the ship. In this paper, based on the extended finite element method (XFEM) and the multi-point constraint method (MPC), the dynamic crack expansion process of the rectangular plate containing cracks under the multi-scale model is comparatively studied by using ABAQUS simulation software Finally, the applicability of the method is verified by applying the multi-scale model of ship nodes. The results show that the computational effort of the multi-scale model is significantly reduced with the same accuracy and allowable error, which proves the accuracy and efficiency of the method.

To detect aberrant human behaviors from large volume of passive data collected by smartphones in real time, we propose an online anomaly detection method using Hotelling’s T-squared test. The test statistic is a weighted average, with more weight on the between-individual component when there are little data available for the individual and more weight on the within-individual component when the data are adequate. The algorithm takes only O(1) run time in each update and the required memory usage is fixed after a pre-specified number of updates. The performance of the proposed method, in terms of accuracy, sensitivity and specificity, is consistently better than or equal to the offline method that it builds upon depending on the sample size of the individual data.

As a potential motivation, psychological empowerment stimulates employees' work behaviors, and it determines the degree of effort and duration of employees' work. Only when employees are psychologically empowered, will they have an impact on their behavior when they believe that they are trusted. This paper chose to set the independent variable as the employee's perceived trust and the dependent variable as the company's work performance, and explored the mediating role of psychological empowerment in the two. The psychological empowerment of employees had a positive impact on work performance. Employees with high psychological empowerment tended to be proactive in their work, and had more input in the work, which in turn encouraged employees to have higher work performance. The four dimensions of psychological empowerment can positively affect employee task performance, and the ability and influence of psychological empowerment had a positive impact on relationship performance. Psychological empowerment as a whole perception played a part of the mediating role between the perception of superior dependency and task performance, and it played a part of the mediating role between perception of superior dependency and relationship performance. As a whole perception, psychological empowerment played a part of mediating role between perceived information disclosure and task performance, and part of mediating role between perceived information disclosure and relationship performance. In the study of perceived trust and work performance, this article focused on the mediating role of psychological empowerment, and further understood the internal mechanism of perceived trust.

Flexible implementations of Monte Carlo Tree Search (MCTS), combined with domain specific knowledge and hybridization with other search algorithms, can be a very powerful for the solution of problems in complex planning. We introduce mctreesearch4j, a standard MCTS implementation written as a standard JVM library following key design principles of object oriented programming. We define key class abstractions allowing the MCTS library to flexibly adapt to any well defined Markov Decision Process or turn-based adversarial game. Furthermore, our library is designed to be modular and extensible, utilizing class inheritance and generic typing to standardize custom algorithm definitions. We demon- strate that the design of the MCTS implementation provides ease of adaptation for unique heuristics and customization across varying Markov Decision Process (MDP) domains. In addition, the implementation is reasonably performant and accurate for standard MDP’s. In addition, via the implementation of mctreesearch4j, the nuances of different types of MCTS algorithms are discussed.

Infrared sensors are applied more and more widely in industrial production applications. Based on the theory of thermal radiation, this paper discusses the system design principle, temperature calibration method and thermal image analysis method in detail. The system has passed the measurement unit certification, showing that the field of view is 180°, the number of scanning points is 2048, the linear velocity is 10-100Hz, the spatial resolution is 2.5mrad, and the precision is ±1℃. On-line monitoring test has been done in the steelmaking plant of Bao Steel. The results show that the system has strong anti-interference ability, stability and reliability, and meets the application requirements of online monitoring.

To realize precise control of the quantum dots (Qdots) device, multi-channel precision bias source plays the key role. In this paper, the 16-channel high precision bias source with 18-bit resolution for Qdots device was designed. The prototype was made and its performance was tested. The short time fluctuations can reach 50μV. The step response time is less than 3μs. The resolution, stability, linearity and dynamic range of the bias source exhibits good performance. What's more, the bias source can be controlled locally and online. The results show that it is one effective and feasible topology for experiments in Qdots device application.

Metal organic frameworks (MOFs) composed of metal ions and multifunctional organic ligands are one of the most attractive porous materials. Their potential applications in gas storage, separation, catalysis, biomedicine and many other fields have attracted much attention. In this study, Nano-Mn-MOF-74 with nano size were successfully synthesized by adding acetic acid and characterized it by SEM, TEM and XRD. Furthermore, a new method to trace the degradation process of nanoMOFs through detection of ligand concentration under physiological conditions was developed.

Metal organic frameworks (MOFs) composed of metal ions and multifunctional organic ligands are one of the most attractive porous materials. Their potential applications in gas storage, separation, catalysis, biomedicine and many other fields have attracted much attention. In this study, Nano-Mn-MOF-74 with nano size were successfully synthesized by adding acetic acid and characterized it by SEM, TEM and XRD. Furthermore, a new method to trace the degradation process of nanoMOFs through detection of ligand concentration under physiological conditions was developed.

Understanding how the deformed bed topography induced by near-bank vegetation impacts the hydrodynamics is significant for understanding the maintenance condition of bed morphology and further fluvial evolution. This issue has rarely been addressed by current studies. This study with a 2D hydro-morphological model investigates the hydrodynamics over flat and deformed beds with a near-bank vegetation patch. By varying the patch density, the generalized results show that the hydrodynamics for the deformed bed differs a lot from those for the flat bed. It is found that deformed bed topography leads to an apparent decrease in longitudinal velocity and bed shear stress in the open region and longitudinal surface gradient for the entire vegetated reach. However, the transverse flow motion and transverse surface gradient in the region of the leading edge and trailing edge is enhanced or maintained, suggesting the strengthening of secondary flows. Interestingly, the deformed bed topography tends to alleviate the turbulent effect caused by the junction-interface horizontal coherent vortices, indicating that the turbulence-induced flow mixing is highly inhibited by the deformed bed. Alternatively, the enhanced secondary flows might provide compensation for the flow mixing for the deformed bed, confirmed by a faster recovery of the redistributed water discharge for the vegetated and open regions to the normal value (50%). The interior flow adjustment through the patch for the deformed bed requires a shorter distance, which links the vegetative drag length with a logarithmic relation. The tilting bed topographic effect in the open region to accelerate the flow may account for the faster flow adjustment.

(1) Background: This study examines the intention to behave actively prevent COVID-19 among local tourism practitioners by adopting an empirically validated norm activation model (NAM) of Schwartz and merging it with the Expectancy theory of Vroom; (2) Methods: The study aims to develops a theoretical framework for understanding the formation and predicting the change of personal protective intention to prevent COVID-19. Based on 514 valid responses from the field surveys; (3) Results: The author develops the refined model including 7 constructs and 26 observational items, and the results showed that the refined model has enjoys a better predictive accuracy of protective intention than the original NAM; and (4) Conclusions: The intention of preventing COVID-19 should needs wider public support and advocacy, and recognizing the change rule of improving behavioral intentions of preventing COVID-19 to maintain the safe tourism image of tourist attractions in Zhangjiajie is also benefits for local tourism practitioners.

There are abundant coal, oil and natural gas resources in "Golden Triangle" energy region. Energy resources development, coal chemical industry, coal power and other industries play an important role in promoting regional economic growth, but facing the insufficient water supply. Under the condition of marginal equilibrium of water resources, the paper firstly analyzes the water utilization, and calculates the potential water-saving amount. Then, the paper constructs a cooperative game model and allocates the newly increased GDP by using the weight distribution method. Finally, the paper puts forward some policy suggestions to reduce the efficiency gap of water utilization among regions.

Light and heat resources are ample in Xinjiang plain oasis region. Planting structure is single in irrigation area, which leads to severe seasonal water shortages due to high concentration of water utilization. Therefore, how to make full use of light and heat resources to develop oasis agriculture without expanding planting scale has become the focus of research. The biannual cropping patterns are possible solutions to the problem. Manas River Irrigation area, which is located on the northern slope of Tianshan Mountain in Xinjiang in China is regarded as a typical case study to analyze the suitable planting structure and planting scale for biannual cropping pattern. The effects of optimizing planting patterns on alleviating the contradiction between water supply and water need，and the efficiency of water resources utilization was also studied. The results of the study show that：(1) the suitable planting scales of Manas River irrigation area in wet year (P = 25%), normal year (P = 50%), and low flow year (P = 75%) are about 72.66%, 78.73% and 83.91% of the current planting scale. (2) After the planting structure was optimized, the water use process in the irrigation area extends from May to August at present to April to November. The water use ratio at the peak period of water use decreased from 0.847 to 0.601, and the water use peak was significantly dispersed. (3) Economic benefits per cubic metre of water of the biannual cropping patterns increased from 8.65 yuan·m⁻³ at present to 9.45 yuan·m⁻³, 10.31 yuan·m⁻³ and 11.43 yuan·m⁻³ in wet year (P = 25%), in normal year (P = 50%) and low flow year (P = 75%) respectively, which proved that replanting crops could improve the efficiency of water resources utilization in irrigated areas. Therefore, the optimal planting pattern can be implemented to improve the utilization efficiency of soil and water resources in irrigation area in the arid oasis with single planting pattern with excessive concentration of water use peak and serious water shortage.

The urban heat island (UHI) becomes more and more serious with the acceleration of urbanization. Many researchers have shown interest in studying the UHI by using remote sensing data. But these studies rarely examine the mountainous cities. The studies on UHI in mountainous cities often used empirical parameters to estimate the land surface temperature (LST), and lacked satellite-ground synchronous experiment to test the accuracy. This paper revised the parameters in mono-window algorithm used to retrieve the LST according to the characteristics of mountainous cities. This study examined the spatial and temporal patterns of the UHI intensity in Chongqing, a typical mountainous city, and its relationship with land cover from 2007 to 2011 based on the Landsat TM data and the improved method. The accuracy of the LST derivation increased by about 1°C compared to the traditional method. The high LST areas increased and extended from the downtown to suburban area each year, but the rate of change decreased. The UHI is dramatically impacted by the rivers. There is a good relationship between the urban sprawl and the UHI. The LST was reduced by about 1°C within a 300m distance from large urban fringe green spaces. The urban landscape parks had a strong effect relieving the UHI at a 100m distance. The LST was reduced by about 0.5°C. The study greatly improves the accuracy of LST derivation, and provides a reliable parameters for the UHI researched in mountainous city.

In this paper, we investigate the stabilization of a one-dimensional thermoelastic laminated beam with structural damping, coupled to a heat equation modeling an expectedly dissipative effect through heat conduction governed by Gurtin-Pipkin thermal law. Under some assumptions on the relaxation function g, we establish the well-posedness for the problem. Furthermore, we prove the exponential stability and lack of exponential stability for the problem. To achieve our goals, we make use of the semigroup method, the perturbed energy method and Gearhart-Herbst-Prüss-Huang theorem.

For improving the tracking accuracy and model switching speed of maneuvering target tracking in nonlinear systems, a new algorithm named interacting multiple model fifth-degree spherical simplex-radial cubature filter (IMM5thSSRCKF) is proposed in this paper. The new algorithm is a combination of the interacting multiple model (IMM) filter and fifth-degree spherical simplex-radial cubature filter (5thSSRCKF). The proposed algorithm makes use of Markov process to describe the switching probability among the models, and uses 5thSSRCKF to deal with the state estimation of each model. The 5thSSRCKF is an improved filter algorithm, which utilizes the fifth-degree spherical simplex-radial rule to improve the filtering accuracy. Finally, the tracking performance of the IMM5thSSRCKF is evaluated by simulation in a typical maneuvering target tracking scenario. Simulation results show that the proposed algorithm has better tracking performance and quicker model switching speed when disposing maneuver models compared with IMMUKF, IMMCKF and IMM5thCKF.

The accumulation of high levels of lead or lead ions in a human body is harmful, particularly to children. Its neurotoxic effect is profound, damaging the central and peripheral nervous systems, resulting in stunted growth, behavioral problems and learning disabilities. The major source of lead or lead ions comes from the drinking water and tap water. The assessment of the water quality, including lead or lead ion content, is usually completed by a regional water department professional. This assessment is time-consuming and requires expensive instruments and skilled operators. Therefore, there is a need to produce a simple-use and relatively inexpensive method to detect lead or lead ions in water samples. This research has developed a simple-use, cost effective sensor system for the detection of lead ions in tap water. An under-potential deposited bismuth sub-layer on a thin gold film based electrochemical sensor was designed, manufactured and evaluated. Differential pulse voltammetry (DPV) measurement technique was employed in this detection. Tap water from the Cleveland, Ohio, USA regional water district was the test medium. Concentrations of lead ion in the range of 8 X 10^-8 M to 8 x 10^-4 M were evaluated. This DPV detection system required 3 -6 minutes to complete the detection measurement. A longer measurement time of 6 minutes was used for the lower lead ion concentration. The selectivity of this lead ion sensor was very good, and Fe III, Cu II, Ni II and Mg II at a concentration level of 5×10^-4 M did not interfere with the lead ion measurement.

Recently, the composite materials consisting of ionic liquids (ILs) and metal-organic frameworks (MOFs) have attracted a great of attention due to their fantastic properties. Many theoretical studies have been performed towards its special structures and applications. Yet, the mechanism for the diffusion of ILs inside MOFs channels still remain unclear. Here, the DFT calculations together with frontier orbital analysis, natural charge analysis, and energy decomposition analysis were performed to investigate the diffusion behavior of a typical IL, [C4mim][PF6], into the ZIF-8 SOD cage. The potential energy surface (PES) profiles indicate that it is quite difficult for the cation [C4min]+ to diffuse into the cage of ZIF-8 through the pristine pores because of its large imidazole steric hindrance. Moreover, the PES reveals that a success diffusion could be obtained by the thermal contributions, by which the pore size is enlarged through the swing effects at a higher temperature. Subsequently, electronic structure analyses reveal that the main interactions between [PF6]-or [C4mim]+ and ZIF-8 is the steric repulsion interactions. Finally, the effects of amounts of [C4mim][PF6]on the ZIF-8 were investigated, and the results show that two pairs of [C4mim][PF6] per SOD cage is more reasonable in terms of interaction energies and structural changes.

Recent studies have discovered that functional connections are impaired in patients with Parkinson's disease (PD) accompanied by hallucinations(PD-H), even at the preclinical stage. The cerebellum has been implicated as playing a role in cognitive processes. However, the functional connectivity (FC) between cognitive sub-regions of the cerebellum of PD patients with hallucinations needs to be further clarified. Resting-state functional magnetic resonance imaging (rs-fMRI) data from three groups (17 PD-H, 13 no hallucinations accompanying the (PD-NH), and 26 healthy controls (HC) ) was collected in this study to explore the role of cerebellar FC changes in the cognitive performance. Additionally, we define cerebellar FC as a training feature for classifying all subjects using Support Vector Machine(SVM). We found that PD-H patients' cerebellum(Vermis_4_5 ) FC had increased within the left side of the precuneus(PCUN) compared to HC, cerebellum(Vermis_1_2) had increased within bilateral opercular part of the inferior frontal gyrus(IFGoprec) and triangular part of the inferior frontal gyrus(IFCtriang), left side of postcentral gyrus(PoCG), Inferior parietal lobe(IPL), and PCUNs compared to PD-NH. In the training results of machine learning, cerebellar FC has also been proven to be an effective biomarker feature, with a recognition rate of over 90% for PD-H. These findings indicate that cortico-cerebellar FC in PD-H and PD-NH patients were significantly disrupted with different distributions. The proposed pipeline offers a promising low-cost alternative for the diagnosis of preclinical PD-H and can be useful for other degenerative brain disorders.

The theory of electron spin has been proposed for a century, but the study of quantum effects in biological molecules is still in its infancy. Chirality-induced spin selectivity (CISS) is a very modern theory that can explain many biochemical phenomena. In this paper, we propose a new theoretical model based on CISS theory and quantum chemistry theory, which can well explain the theoretical explanation of the chiral selectivity of chiral proteins. Moreover, this theory can predict the spin state of corresponding chiral molecules. Taking L-DOPA and AADC enzyme as examples, this theoretical model solves the theoretical explanation of AADC enzyme's chiral catalysis selectivity problem and successfully predicts the spin state of L-DOPA and D-DOPA's valence electrons.

To prevent CO2 leakage and ensure the safety of long-term CO2 storage, it is essential to investigate the flow mechanism of CO2 in complex pore structures at the pore scale. This study focuses on reviewing the experimental, theoretical, and numerical simulation studies on the microscopic flow of CO2 in complex pore structures during the last decade. For example, advanced imaging techniques, such as X-ray computed tomography (CT) and nuclear magnetic resonance (NMR), are used to reconstruct the complex pore structures of rocks. Mathematical methods, such as Darcy's law, Young–Laplace’s law, and the Navier-Stokes equation, are used to describe the microscopic flow of CO2. Numerical methods, such as the lattice Boltzmann method (LBM) and pore network (PN) model, are used for numerical simulation. The application of these experimental and theoretical models and numerical simulation studies is discussed, considering the effect of complex pore structures. Finally, future research is suggested to focus on: (1) Conducting real-time CT scanning experiments of CO2 displacement combined with the developed real-time CT scanning clamping device to realize real-time visualization and provide quantitative description of the flow behavior of CO2 in complex pore structures; (2) The effect of pore structures change on the CO2 flow mechanism caused by the chemical reaction between CO2 and the pore surface, the flow theory of CO2 considering wettability and damage theory in a complex pore structures; (3) The flow mechanism of multi-phase CO2 in complex pore structures; (4) The flow mechanism of CO2 in the pore structures at multiscale and the scale upgrade from microscopic to mesoscopic to macroscopic. Generally, this study focuses on reviewing the research progress of CO2 flow mechanisms in complex pore structures at the pore scale and affords an overview of potential advanced developments to enhance the current understanding of CO2 microscopic flow mechanisms.

Aim: Aging is companied by deteriorations of insulin resistance (IR) and insulin secretion. However, little is known about the roles of aging on different phases of insulin secretion (ISEC); i.e., the first- and second-phase of insulin secretion (FPIS, SPIS) and glucose effectiveness (GE). Methods: Totally 169 individuals (43 men and 126 women) with the similar fasting plasma glucose (FPG: 90 mg/dL) and BMI (men: 23 kg/m2, women 22 kg/m2) were enrolled. IR, FPIS, SPIS, and GE were estimated using our previously developed equations shown below. Pearson correlation analysis was conducted to assess the correlations between age and four diabetes factors (DFs: IR, FPIS, SPIS and GE). The equations that are used to calculate the DF in the present study were built and published by our group. Results: The age of the participants ranged from 18- to 78-year-old. Men had higher FPIS but lower HDL-C levels than women (2.067±0.159, 1.950±0.186 μU/min and 1.130±0.306, 1.348±0.357 mmol/dl, accordingly). The results of Pearson correlation revealed that age was negatively related to IR and GE in both genders (IR: r = -0.39, p < 0.001 for men, r = -0.24, p < 0.003 for women; GE: r = 0.66, p < 0.001 for men, r = 0.78, p < 0.001 for women). In the meanwhile, FPIS was also only found to be negatively correlated with age in female (r = -0.238, p = 0.003). But there was no different with SPIS and age in both of genders. Conclusions: We have found that in Chinese with normal FPG level (90 mg/dl) and body mass index (men: 23 kg/m2, women: 22: kg/m2), age is negatively related to IR and GE in both genders. At the same time, only FPIS was found to be negatively related to age in women. The tightness of their relationships, from the highest to the lowest are, GE, FPIS and IR accordingly. The importance of GE should be re-evaluated. These results should be interpreted with caution because of the small sample size.

The thermal degradation of brakes has a significant impact on the stability of pure electric commercial vehicles during long downhill braking. Based on road slope and using fuzzy control method, design strategies for motor regenerative braking force and friction braking force distribution, with the aim of reducing friction braking force, improving braking stability, and recovering braking energy. By establishing driving conditions on roads with different slopes and using numerical analysis methods to verify the proposed control strategy. the results show that under the 6% constant slope driving condition, the vehicle maintains a constant speed of 30km/h downhill driving, and the braking energy recovery rate reaches 50.93%; Under the condition of 3% -6% gradient driving, the vehicle speed fluctuates slightly, but it can still be maintained at a constant speed of 30km/h for downhill driving through the adjustment of the motor's reverse drag characteristic, with a braking energy recovery rate of 49.97%; During the process of driving downhill at a constant speed, the frictional braking force does not participate in braking; The braking energy recovery rate is determined by the slope and braking deceleration.

JIS SUJ2 is most widely used in bearing steels. The advantages are good hardenability, excellent fatigue, wear resistance and comprehensive mechanical properties. For wear resistance and fatigue resistance of the steel are getting more and more attention, the residual stress state and its distribution on the surface of the heat affect zone are critical factors affecting the fatigue life and wear resistance of the parts. In this study, using the SUJ2 as the material to talk about the surface residual stress and retained austenite distribution. Doing the quenching and tempering treatment to obtain different microstructures, using the induction method to re-quench the case region. After the heat treatment, measuring the residual stress and retained austenite volume on the surface by X-ray diffraction, observing the microstructure and comparing the hardness. The results show that the microstructure after heat treatment contains unsolidified carbides, tempered martensite and retained austenite. The residual stress on the surface is the tensile state. In the re-hardened area after induction quenching, the residual stress is all compressive, and the values are more than -800 MPa. In conclusion, in the same output power condition can be observed that the microstructure of the specimens before induction hardening has a significant impact on the effective case depth. The surface residual stress has changed from a tensile to a compressive state. In the re-hardened area, the maximum of the residual compressive stress increased with the austenitized temperature increase.

In the realm of artificial intelligence, knowledge graphs have become a fascinating area of research. Relationships between entities are depicted through a structural framework in knowledge graphs. In this paper, we propose to build a domain-specific medicine dictionary (DSMD) based on the principles of knowledge graphs. Our dictionary is composed of structured triples, where each entity is defined as a concept, and these concepts are interconnected through relationships. This comprehensive dictionary boasts more than 348,000 triples, encompassing over 20,000 medicine brands and 1,500 generic medicines. It offers a groundbreaking approach to storing and accessing medical data. Our dictionary facilitates various functionalities, including medicine brand information extraction, brand-specific queries, as well as queries involving two words or question answering. We anticipate that our dictionary will serve a broad spectrum of users, catering to both human users, such as diverse range of healthcare professionals as well as AI applications.

Tao and Ming (name) or Tao and Yan (language or speech) is an inseparable pair of important concepts in traditional Chinese language philosophy. In LaoTze’s opinion, Tao could not be spoken out, and language or speech could not reach Tao’s essence. Yan and Tao are separated from each other, and they are paradoxical. All the confusion derives from Yan regardless of how delicate they are. Tao can be expressed in words, but it will never be expressed through common languages. In this article, Tao is regarded as a human being’s thoughts or intentions and is even considered to be the origin of the real world. Language can express people’s thought, although it has some limitations. There exist some contradictions between the real word and the world expressed in words. To enable people to better understand the existence of the real world from multiple perspectives at the language or linguistic level without being constrained by words, this article studies LaoTze’s linguistic philosophy reflected in his opinion on the relationship between Tao and Yan and makes a comparative analysis of modern Western language philosophers’ stand on language (or speech) and existence.

A comprehensive understanding of the connotation and basic characteristics of campus culture in vocational colleges is a prerequisite for ensuring their sustainable development. Campus culture in vocational colleges is a concentrated reflection of their educational philosophy, ideal beliefs, and value pursuits. It possesses basic characteristics such as practicality, vocational orientation, ethics, contemporaneity, integration, and regionality.

Transformative learning is an emerging and unique learning theory for adults. Foreign adult education scholars, represented by Mezirow, have produced significant research results in this field. However, research in this area in China is relatively lacking. This paper interprets the personal story of an English teacher through six stages and combines transformative learning theory to position the roles of adult learners in transformative learning. The author believes that adult learners today must take on six missions and play the roles of discerners, reflectors, communicators, integrators, aspirers, and resilient individuals in the transformative learning process.

This investigation delves into an examination of the growth dynamics observed in Central Taiwan Science Park (CTSP) and Silicon Glen in Scotland, with a specific focus on their energy, environmental, and economic management. The aim is to predict and evaluate the latest advancements and applications of technology. In terms of energy, CTSP focuses on Taiwan's supply security and green transformation challenges, while Silicon Glen emphasizes Scotland's wind energy generation technologies. Both regions are committed to the development of renewable energy and smart grids. Regarding environmental aspects, CTSP and Silicon Glen prioritize environmental protection and sustainable development by implementing measures for environmental monitoring. In terms of economic management, CTSP, and Silicon Glen serve as key technology industry hubs This investigation delves into an examination of the growth dynamics observed in Central Tai-wan Science Park (CTSP) and Silicon Glen in Scotland, with a specific focus on their energy, environmental, and economic management. The aim is to predict and evaluate the latest advancements and applications of technology. In terms of energy, CTSP focuses on Taiwan's supply security and green transformation challenges, while Silicon Glen emphasizes Scotland's wind energy generation technologies. Both regions are committed to the development of renewable energy and smart grids. Regarding environmental aspects, CTSP and Silicon Glen prioritize environmental protection and sustainable development by implementing measures for environmental monitoring. In terms of economic management, CTSP, and Silicon Glen serve as key technology industry hubs in Taiwan and Scotland, respectively, attracting numerous high-tech and startup enterprises. Various means facilitate the growth of industries and foster innovations in different industrial spheres. Important factors contributing to this growth include policy support, availability of research materials, and provision of infrastructure. This research paper compares the successful case studies of two industrial parks, along with their environmental and economic management strategies. It explores the management principles that ensure the sustainable development and economic growth of industrial parks, aiming to assist decision-makers and relevant stakeholders in evaluating the sustainable development planning of industrial parks.

The expansion of university enrollment and the implementation of tuition fees have become focal points of discussion in both current educational practices and theories. While increased university enrollment offers more opportunities for higher education, the high cost of tuition fees has pushed economically disadvantaged students to the brink of discontinuing their studies, prompting widespread concerns about educational equality.

This study aims to exploit the distinctive properties of carbon nanotube materials, which are particularly pronounced at the microscopic scale, by deploying fabrication techniques that allow their features to be observed macroscopically. Specifically, we aim to create a semiconductor device that exhibits flexibility and the ability to modulate its electromagnetic wave absorption frequency by means of biasing. Initially, we fabricate a sheet of carbon nanotubes through a vacuum filtration process. Subsequently, phosphorus and boron elements are separately doped into the nanotube sheet, enabling it to embody the characteristics of a PN diode. Measurements indicate that, in addition to the fundamental diode's current-voltage relationship, the device also demonstrates intriguing transmission properties under the TEM mode of electromagnetic waves. It exhibits a frequency shift of approximately 2.3125 GHz for each volt of bias change. The final result is a lightweight and flexible carbon-based semiconductor microwave filter, which can conform to curved surfaces. This feat underscores the potential of such materials for innovative and effective electromagnetic wave manipulation.

At present paper, we aim to study the long-time behavior of a stochastic semi-linear degenerate parabolic equation on bounded or unbounded domain and driven by a nonlinear noise and defined. Since the theory of pathwise random dynamical systems can not be applied directly to the equation with nonlinear noise, first, we establish the existence of weak pullback mean random attractors for the equation by applying the theory of mean-square random dynamical systems; then, we prove the existence of (pathwise) pullback random attractors for the Wong-Zakai approximate system of the equation. In addition, we establish the upper semicontinuity of pullback random attractors for the Wong-Zakai approximate system of the equation under consideration driven by a linear multiplicative noise.

With the advancement of the economy and society, policymakers acknowledge the significance of improving rural households' well-being as a crucial factor in promoting sustainable economic and social development. Based on the matching data of China Household Finance Survey and Digital Rural County Index, this study empirically examines the effect and mechanisms of digital village construction on subjective well-being of rural households. The results revealed that digital village construction has a significant improvement in their subjective well-being. The impact of digital village construction is particularly significant in rural areas located in the western regions of China, as well as among households possessing higher levels of human capital. The process through which digital village construction enhances the subjective well-being of rural households involves the augmentation of household income, the promotion of non-farm employment opportunities, and the enhancement of rural governance. This study provides robust micro-empirical evidence highlighting the positive welfare effects of digital rural construction. This study provides insights into the digital village construction within the happiness economy framework, offering a new perspective. The imperative to strengthen rural digital infrastructure and improve farmers' proficiency in modern information skills is a strategic policy aimed at enhancing well-being of rural households.