Li₂MnO₃ is critical component in the well-studied Li-excess cathode materials xLi₂MnO₃•(1- x)LiMO₂ for achieving high lithium storage capacity. In this article, the diffusion of Li ions in Li₂MnO₃ is studied using molecular dynamics (MD) simulation with well-behaved empirical force fields obtained by fitting against the crystal structure from experiment and phonons calculated using Density Function Theory (DFT). We have found two possible tetrahedral hopping channels, 0-TM and 1-TM(Mn⁴⁺) channel, which are differentiated by the face sharing octahedral cations. Simulation results show that the 0-TM channel is active for Li hopping, while 1-TM(Mn4+) channel is inactive. During the delithiation process, the Li ions in the TM layered are firstly removed, then those in the Li layer. However, the Li ions will be trapped in the tetrahedral 0-TM channels as long as the four face sharing octahedral sites are cleared. Up to x=1.0 for Li₂-xMnO₃, almost all the Li ions are located at the tetrahedral sites, forming a regular array along a axis. The de-intercalation of tetrahedral Li ions requires a high voltage (>5.2 V vs. Li/Li+), limiting the practical capacities measured in lab. The diffusion of Mn ions into the Li layers is observed in a deeper delithiated structure (x=1.2 for Li₂-xMnO₃), indicating an initial phase transformation to a spinel-like structure. However, the Mn ions are mainly trapped in the tetrahedral sites in the Li layer, instead of the octahedral sites fin spinel-like structure. A few of Mn ions diffusing into the octahedral sites in Li layers have no face sharing tetrahedral Li ions, revealing a further Li de-intercalation is imperative for the complete phase transformation. Our model is not stable for x≥1.4 in Li₂-xMnO₃. Other charge compensation mechanism should be considered in this high delithiation stage, eg. oxygen release.

In this study, Li-Al-OH layered double hydroxide (LDH), which was prepared by solvent-free one-step mechanochemical reaction of LiOH and Al(OH)₃, was applied to remove boron from aqueous solution. Dry-grinding for 3 h at a rotational speed of 500 rpm, Li/Al molar 1/2 was the optimum condition to prepare highly crystalline of Li-Al LDH phase with no evident impure phases. Two milling products with Li/Al molar ratio at 1/2 and 2/2 were evaluated for boron adsorption. The results confirmed that Li/Al molar ratio 2/2 sample showed high boron adsorption capacity due to the physical adsorption of Li-Al-OH LDH and chemical synergism of phase gel Al(OH)₃. The adsorption isotherms, described by the Langmuir model, indicated maximum monolayer boron uptake capacity 45.45 mg/g, implying competitive adsorption capacity of the material in our experiment.

Information encryption and anti-counterfeiting play an important role in many aspects of daily life, such as in minimizing economic losses, protecting secure communication and public security, and so on. Owing to the high information capacity and ease of operation, luminescent materials for advanced information encryption and anti-counterfeiting are essential to meet the increasing demand on encryption security. Herein, we summarize two emerging luminescent materials for information encryption and anti-counterfeiting, the AIE materials and room-temperature phosphorescent materials. At last, we discuss the opportunities and anticipations of these two information encryption and anti-counterfeiting materials.

In this paper, the research progress on dynamic behaviors of RC structural members is reviewed from experimental, theoretical and numerical perspectives. Firstly, the basic overview, measurement methods, main conclusions and current limitations of available dynamic loading tests are presented. Then the theoretical studies on the dynamic constitutive models of RC materials, the dynamic increase factor (DIF) model for concrete and reinforcing steel and the proposed modified models of dynamic behavior parameter at the structural member level are summarized. Finally, the available modelling approach and the method for incorporating the dynamic effect in numerical simulation of RC structures are reviewed. Moreover, a brief introduction is made to the dynamic hysteretic model established on the experimental data, which provides an alternative approach to consider the dynamic effect beside the commonly used DIF method. This paper provides valuable reference for experimental studies and numerical simulations on the dynamic behaviors of RC structures, and also puts forward some issues that need to be solved in the future research works..

Zirconium dioxide (ZrO2) possesses numerous advantages such as high mechanical strength, low friction coefficient, excellent optical properties, and extended lifespan. Consequently, ZrO2 has a broad research foundation and practical significance in functional films and wear-resistant coatings. However, it suffers from brittleness and low ductility when used as a bio-coating material. In this study, a Ta/ZrO2 film was fabricated on Si (100) and titanium alloy substrates using a magnetron sputtering system, with tantalum (Ta) serving solely as a transition layer. Subsequently, Zr and Nb ions were implanted into the film at varying doses but with consistent energy levels. The analysis focused on the film's microstructure, mechanical properties, hydrophilicity, and corrosion resistance. The results demonstrated a significant improvement in the hydrophilicity and corrosion resistance of the ZrO2 film following the implantation of Zr and Nb ions. First-principles calculations based on density functional theory (DFT) principles indicated that, with increasing doping concentrations of Zr and Nb in the ZrO2 model, the stability of the model increased gradually, thereby enhancing its corrosion resistance.

This paper presents an optimized energy management strategy for Li-ion power batteries used on electric vehicles (EVs) at low temperatures. Under low-temperature environments, EVs suffer a sharp driving range loss resulted from the energy and power capability reduction of the battery. Simultaneously, because of Li plating, battery degradation becomes an increasing concern as temperature drops. All these factors could greatly increase the total vehicle operation cost. Prior to battery charging and vehicle operating, preheating battery to a battery-friendly temperature is an approach to promote energy utilization and reduce total cost. Based on the proposed LiFePO4 battery model, the total vehicle operation cost under certain driving cycles is quantified in the present paper. Then given a certain ambient temperature, a target temperature of preheating is optimized under the principle of minimizing total cost. As for the preheating method, a liquid heating system is also implemented on an electric bus. Simulation results show that the preheating process becomes increasingly necessary with a decreasing ambient temperature; however, the preheating demand declines as driving range grows. Vehicle tests verify that the preheating management strategy proposed in this paper is able to save total vehicle operation cost.

This paper proposes a position recognition method for the receiver coil by the secondary coil current. Based on the practical value of the current of the receiver coil, the position recognition of the receiver coil in the WPT system is realized. This paper proposes a 3×3 array multi-transmitter coil grouping and control logic. The mathematical model of mutual inductance between the receiver and transmitter coil at different positions on the X-Y plane is established. A method for identifying the position of the receiver coil according to the current of the receiver coil is proposed. Compared with the traditional position recognition method of the receiver coil, this method does not need to add a detection coil and position sensor and can realize the position recognition of the receiver coil on the 2D plane.

A low-cost bio-mass-derived carbon substrate has been employed to synthesize MoS₂@carbon composites through a hydrothermal method. Carbon fibers derived from natural cotton provide a three-dimensional and open framework for the uniform growth of MoS₂ nanosheets, thus constructing hierarchically coaxial architecture. The unique structure could synergistically benefit fast Li-ion and electron transport from the conductive carbon scaffold and porous MoS₂ nanostructures. As a result, the MoS₂@carbon composites, when served as anodes for Li-ion batteries, exhibit a high reversible specific capacity of 820 mAh g^-1, high-rate capability (457 mAh g^-1 at 2 A g^-1), and excellent cycling stability. The superior electrochemical performance makes the MoS₂@carbon composites to be low-cost and promising anode materials for Li-ion batteries.

p53 is among the most frequently mutated tumor suppressor genes given its prevalence in >50% of all human cancers, including high grade serous endometrial cancers and ovarian cancers. In addition to loss of tumor suppression function, many mutated p53 (Mutp53) proteins acquire gain-of-function (GOF) activities as oncogenes to promote cancer progression, which manifest through aberrant expression of p53. As we have come to see, statins induce CHIP-mediated degradation of mutp53 by blocking the interaction between mutp53 and DNAJA1. Therefore, targeting critical downstream pathways of mutp53 provides an alternative strategy for treating cancers expressing mutp53. In this review, we summarize recent advances with Wee1 inhibitors and mevalonate pathway inhibitors, particularly statins, regarding their use in gynecological cancers with p53 mutations.

Pancreatic neuroendocrine tumors (PNETs) are characterized by dysregulated signaling pathways that are crucial for tumor formation and progression. The efficacy of traditional therapies is limited, particularly in treatment of PNETs at advanced stage. Epigenetic alterations profoundly impact the activity of signaling pathways in cancer development, offering potential opportunities for drug development. There is currently a lack of extensive research on epigenetic regulation in PNETs. To fill this gap, we first summarize major signaling events which are involved in PNET development. Then, we discuss the epigenetic regulation of these signaling pathways in the context of both PNET and commonly occurring, and therefore more extensively studied, malignancies. Finally, we will offer perspective on the future research direction of PNET epigenome and its potential applications in patient care.

The safe and efficient mining of coal seams with low porosity, low permeability and high inhomogeneity under complex geological conditions is a major challenge, among which the permeability of coal seams is crucial for coal mine gas extraction. The development of coal seam permeability enhancement technology can help coal mines to produce safely and efficiently, and the extracted coal bed methane can be utilized as a green energy. In order to study the effect of freezing and thawing on the evolution of mechanical and permeability properties of coal, triaxial permeability tests were carried out on low-permeability coal under two kinds of surrounding pressures. At the same time, dry, saturated and freeze-thaw coal were set up for comparison, and the effects of water and freeze-thaw were separated from each other. The triaxial mechanics and percolation laws of dry, saturated and freeze-thaw coal rocks were obtained, and the mechanism of the influence of freeze-thaw on the permeability evolution law of coal was revealed. The research results can provide theoretical guidance for the gas extraction technology of low permeability coal seams with increased permeability.

The present study provides a detailed analysis of the chloroplast genome of Microula sikkimensis. The genome was consisted of altogether 149,428bp and distinct regions, including large single-copy (81329bp), small single-copy (17261bp), and inverted repeat (25419bp). The genome contained 112 genes, including 78 protein-coding genes, 30 tRNA genes, and 4 rRNA genes, some exhibited duplication in the IR region. The chloroplast genome displayed different GC content across regions, with the IR region exhibiting the highest. Codon usage analysis and the identification of simple sequence repeats (SSRs) offer valuable genetic markers. Comparative analysis with other Boraginaceae species highlighted conservation and diversity in coding and noncoding regions. Phylogenetic analysis placed M. sikkimensis within the Boraginaceae family, revealing its distinct relationship with specific species.

To reduce the redundant counting of TDC in high-speed applications and thus reduce power consumption, a TDC architecture is proposed to quantify the difference between two adjacent rows of pixel signals. This structure can remove the identical part between two rows of pixel signals by adjusting the start and stop signal of the TDC, which will reduce the number of flipping of D flip-flops in the TDC. In the 110nm CMOS process, simulation results show that this design achieves an effective number of bits (ENOB) of 4.72 bits and a Figure of Merit (FoM) of 104.7 fJ/step - 162.3 fJ/step with a power consumption ranging from 60 µW to 93 µW. Compared with traditional counting methods, the proposed TDC can reduce counting power consumption by 30%.

Large aluminum alloy ring forgings are the core components of heavy-duty rocket fuel storage tanks, and the large residual stress inside the rings leads to poor shape accuracy of large thin-walled parts. The initial stress of the 2219 aluminum alloy ring blank was tested using the drilling method, and the creep constitutive coefficient of the 2219 aluminum alloy was determined through stress relaxation tests. The numerical simulation processes of thermal stress relief (TSR), vibration stress relief (VSR), and thermal-vibration stress relief (TVSR) were compared and established. Through the correlation analysis between the actual measurement results of residual stress and the simulation results, it can be seen that the strong correlation in three directions at each measurement point accounts for over 37.5%, and the moderate correlation accounts for over 62.5%. This indicates that the numerical simulation model of 2219 aluminum alloy ring containing initial residual stress can accurately reflect the size and distribution of residual stress inside the actual ring. The simulation results show that the derived constitutive model can describe the stress relaxation process of TVSR by combining a single thermal time effect stress relaxation constitutive theory with a VSR plastic deformation material model. The simulation models established above were used to calculate the residual stress homogenization ability of three types of aging. The results showed that VSR, TSR, and TVSR can homogenize and reduce the residual stress field inside the ring, improve the distribution of residual stress inside the ring, and have a better overall homogenization ability of TVSR. The VSR control has a certain effect on reducing and homogenizing residual stress, but compared with TSR and TVSR, the reduction and homogenization ability of residual stress control is limited. The homogenization control effect TVSR>TSR>VSR, and the maximum equivalent stress homogenization rates of VSR, TSR, and TVSR are 52.8%, 80.6%, and 82.2%, respectively. Then, numerical simulation technology is used to study how the initial residual stress in the blank will cause the deformation of the ring during the thin-walled machining process. The roundness error theory of the minimum containment area method is applied to evaluate the deformation degree during the thin-walled numerical machining process, and the TVSR method is used for stress regulation. The deformation law of the thin-walled machining of the ring under different aging parameters is studied.

Research on the development of new lightweight Al-Li alloys using selective laser melting pro-cess has great potential for industrial applications. This paper reports on the development of novel aluminum-lithium alloys using selective laser melting technology. Al-Cu-Li-Mg-Ag-Sc-Zr pre-alloyed powders with lithium contents of 1 wt.%, 2 wt.% and 3 wt.%, respectively, were prepared by inert gas atomization. After SLM process optimization, the microstructure and me-chanical properties of the as-formed specimens were investigated. The densification of the three newly developed alloys are 99.51 %, 98.96 % and 92.01 %, respectively. They all have good form-ability, with the lithium loss rate about 15 %. The as-formed alloy with 1% Li content presents good comprehensive properties with a yield strength of 412.6 ± 16.0 MPa, an ultimate tensile strength of 461.1 ± 11.7 MPa, and an elongation of 13.6 ± 1.1%. The three alloys exhibit a layered molten pool stacking morphology and have a typical heterostructure. The columnar crystals and equiaxed fine grains are alternately arranged, and most of the precipitated phases are enriched at the grain boundaries. The change of Li content mainly affects the precipitation of Cu-containing phase. When the Li content is 1 wt.%, there are θ phase, T1 phase and TB phase. When Li increased to 2 wt.%, T1 and T2 phases precipitated together. When Li reaches 3 wt.%, δ' phase precipitates with T2 phase. This study provides useful guidance for the future SLM form-ing of new crack-free and high-strength Al-Li alloys.

With the popularization of intelligent sensing and the improvement of modern medical technology, intelligent medical sensing technology has emerged as the times require. This technology combines basic disciplines such as physics, mathematics, and materials with modern technologies such as semiconductors, integrated circuits, and artificial intelligence, and has become one of the most promising focuses in the medical field. The core of intelligent medical sensor technology is to make existing medical sensors intelligent, portable, and wearable with full consideration of ergonomics and sensor power consumption issues, for conforming to the current trends in cloud medicine, personalized medicine, and health monitoring. With the development of automation and intelligence in measurement and control systems, it is required that sensors have high accuracy, reliability, stability, as well as certain data processing capabilities, self-checking, self-calibration, and self-compensation，while traditional medical sensors cannot meet such requirements. In addition, in order to manufacture high-performance sensors, it is also difficult to improve the material process alone, and it is necessary to combine computer technology with sensor technology to make up for its performance shortcomings. Intelligent medical sensing technology combines medical sensors with microprocessors to produce powerful intelligent medical sensors. On the basis of the original sensor functions, intelligent medical sensors also have functions such as self-compensation, self-calibration, self-diagnosis, numerical processing, two-way communication, information storage, and digital output. This review focuses on the application of intelligent medical sensing technology in biomedical sensing detection from three aspects: physical sensor, chemical sensor, and biosensor.

Alzheimer’s Disease (AD) is a neurodegenerative condition that currently has no known cure. The principles of the expanding field of network medicine (NM) have recently been applied to AD research. The main principle of NM proposes that diseases are much more complicated than one mutation in one gene, and incorporate different genes, connections between genes, and pathways that may include multiple diseases to create full scale disease networks. AD research findings as a result of the application of NM principles have suggested that functional network connectivity, myelination, myeloid cells, and genes and pathways may play an integral role in AD progression, and may be integral to the search for a cure. Different aspects of the AD pathology could be potential targets for drug therapy to slow down or stop the disease from advancing, but more research is needed to reach definitive conclusions. Additionally, the holistic approaches of network pharmacology in traditional Chinese medicine (TCM) research may be viable options for the AD treatment, and may lead to an effective cure for AD in the future.

Most natural language processing (NLP) tasks operate an input sentence as a sequence with token-level embeddings and features, despite its clausal structures. Taking Abstract Meaning Representation (AMR) parsing as an example, recent parsers are empowered by Transformers and pre-trained language models, but long-distance dependencies (LDDs) introduced by long sequences are still open problems. We argue that LDDs are not superficially blamed on the sequence length but are essentially related to the internal clause hierarchy. Typically, non-verb words in a clause cannot depend on words outside, and verbs from different but related clauses have much longer dependencies than those in the same clause. With this intuition, we introduce a type of clausal feature, hierarchical clause annotation (HCA), into AMR parsing and propose two HCA-based approaches, HCA-based self-attention (HCA-SA) and HCA-based curriculum learning (HCA-CL), to integrate HCA trees of complex sentences for addressing LDDs. We conduct extensive experiments on two in-distribution (ID) AMR datasets (AMR 2.0 and AMR 3.0) and three out-of-distribution (OOD) ones (TLP, New3, and Bio). Experimental results show that our HCA-based approaches achieve significant and explainable improvements against the baseline model and outperform the state-of-the-art (SOTA) model when encountering sentences with complex clausal structures that introduce most LDD cases.

Some traditional robots are based on offline programming reciprocal motion, and with the continuous upgrading of vision technology, more and more tasks are replaced by machine vision. For the current problem of insufficient accuracy of robot target localization based on binocular vision, and an improved random sampling consistency algorithm is proposed to complete parallel robot target localization and grasping under the guidance For the current problem of insufficient accuracy of robot target localization based on binocular vision, an improved random sampling consistency algorithm is proposed to complete parallel robot target localization and grasping under the guidance of multi Firstly, the RANSAC algorithm is improved based on the SURF algorithm; then the parallax gradient method is applied to iterate the matched point pairs several times to further optimize the data; then the 3D reconstruction is completed by the program technique; finally the obtained data is finally the obtained data is input into the robot arm and the camera internal and external parameters are obtained by the calibration method so that the robot can accurately locate and The experiments show that the improved algorithm has advantages in recognition accuracy and grasping success rate under multi- The experiments show that the improved algorithm has advantages in recognition accuracy and grasping success rate under multi-vision system.

This study aims to evaluate the combined assimilation of AGRI infrared radiance and ground-based MWR (Microwave Radiometer) data for predicting short-duration heavy rainfall. The WRFDA 4.3 framework is utilized to establish the data assimilation interface. The structural characteristics of this joint assimilation approach are analyzed using a typical heavy rainfall event in the Kaifeng region of Central China. The findings indicate the following: (1) The joint assimilation of FY-4A AGRI and ground-based MWR data effectively corrects the initial moisture content in model simulations. Data assimilation significantly improves the simulation of 24-hour accumulated rainfall. Furthermore, the joint assimilation of AGRI radiance and MWR data outperforms assimilating either of them individually; (2) In the heavy rainfall stage, from the low level to the high level, strong upward movement occurs in the center of the rainstorm. The water vapor in the process of this rainstorm mainly comes from the westward air transport in front of the high trough. The water vapor is continuously transported to the heavy rainfall belt through this channel, providing sufficient water vapor for the maintenance of the rainstorm; (3) The combination of the interaction between high and low level vorticity and divergence in the atmosphere and the vertical upward movement, the emergence of low level jet and the generation of unstable energy in the atmospheric environment jointly lead to the occurrence of the rainstorm process; and (4) The terrain-sensitive experiment results show that, the existence of the topography of Taihang Mountain changes the precipitation in Kaifeng area, and reducing the terrain height will lead to a 50%-60% decrease in the overall precipitation of the precipitation center, and the precipitation range will also be greatly reduced. An increase of more than 50% in terrain height will lead to an increase in precipitation center, precipitation range and precipitation intensity, and an increase of 10%-20% in overall precipitation; An increase of more than 75% in the terrain height will cause the rain belt to shift eastward by about 0.5°E, and the precipitation center will shift significantly eastward. When the terrain height increased by more than 100%, the precipitation did not continue to increase, which was basically the same as the control test. This paper provides a valuable basis for further improving the application of FY-4A AGRI radiance and ground-based microwave radiometer in numerical weather models.

To ensure consumers can purchase high-quality rice, accurate identification of rice varieties is particularly important. Methods: This article conducts research based on machine learning algorithms from the perspectives of image, spectrum, and spectrogram fusion. Six types of hyperspectral image data of rice were preprocessed using convolutional smoothing (SG) and multiple scatter correction (MSC). Texture information of the images was extracted using gray-level co-occurrence matrix. Spectra, texture, and spectrogram data were fused into a new matrix. Spectral, texture, and spectrogram fusion data were used as inputs for the model, and support vector machines (SVM), logistic regression (LR), and K-nearest neighbors (KNN) classification models were constructed and compared. Results: From the classification results, the spectrogram fusion classification performance was better than classification models using only spectra or texture. Conclusion: The research results showed that the accuracy of SVM and LR classification models exceeded 90%, and the LR model performed the best, effectively classifying rice varieties.

In recent years, nocturnal ozone enhancement(NOE) events have become a hot research topic in the field of atmospheric environment. By using statistically-based data analysis, we made a comparative investigation about nocturnal ozone concentrations and NOE events in 2022 and 2023, and further explored the effects of NOE events on O3 and PM2.5 at the night of the same day and the next day. The results showed that from 2022 to 2023 in Dongying, the annual average nocturnal ozone concentrations increased from 51 μg/m3 to 59 μg/m3, and the frequency of NOE events was higher in spring, summer and autumn, and lower in winter. NOE events not only had an obvious promoting effect on nocturnal O3 and Ox, and the daily maximum 8 h average concentration of O3 (MDA8-O3) of the same day (more apparent in summer and autumn), and nocturnal PM2.5 and PM2.5-bounded NO3- and SO42- (more apparent in winter), but also had a distinct influence on O3, Ox and MDA8-O3 of the next day (more apparent in summer). The results could strengthen the understanding of the phenomena of NOE events, and provide a scientific basis for the collaborative control of PM2.5 and ozone in urban areas in the Yellow River Delta, China.

Lithium rich layered oxides (LRLO) are a wide class of innovative active materials for positive electrodes in lithium-ion (LIB) and lithium-metal secondary batteries (LMB). LRLOs are over-stoichiometric layered oxides rich in lithium and manganese, with general formula Li1+xTM1-xO2, where TM is a blend of transition metals comprising Mn (main constituent), Ni, Co, Fe and others. Due to the very variable composition and the extended defectivity their structural identity is still debated among researchers being likely an unresolved hybrid between a monoclinic (mC24) and a hexagonal lattice (hR12). Once casted in composite positive electrode films and assembled in LIBs or LMBs, LRLOs can delivery reversible specific capacities above 220-240 mAhg-1, thus beyond any other available intercalation cathode material for LIBs, with mean working potential above 3.3-3.4 V vs Li for hundreds of cycles in liquid aprotic commercial electrodes. In this review we critically outline the recent advancements in the fundamental understanding of the physico-chemical properties of LRLO as well as the most exciting innovations in their battery performance. We focus in particular on the elusive structural identity of these phases, on the complexity of the reaction mechanism in batteries as well as on practical strategies to minimize or remove cobalt from the lattice while preserving the outstanding performance upon cycling.

The special affine Fourier transform (SAFT) is a useful and powerful analyzing tool in signal processing, optics and communications. In this paper, we mainly discuss the randomized nonuniform sampling and reconstruction for random signals bandlimited in the SAFT domain. First, we show that the nonuniform sampling is identical to the uniform sampling after a pre-filter in the sense of second order statistic characters. Then, we propose an approximate reconstruction based on sinc interpolation for the nonuniform sampling of random signals bandlimited in the SAFT domain. Finally, we give the mean square error estimate for the proposed approximate recovery approach.

Iron tailings are solid waste generated after the mining of iron mines, which have caused serious damage to the surrounding ecology and environment and have attracted widespread attention. By analyzing the formation causes, main components, and environmental effect mechanisms of iron tailings, the article systematically elaborates on the degree and mechanism of the harm of iron tailings to water bodies, atmosphere, soil, and ecosystems, and evaluates its impact on human health and sustainable development of society. On this basis, specific prevention and control measures have been proposed to reduce iron tailings emissions, realize resource recycling, and restore ecosystems. Research shows that iron tailings containing heavy metals can widely pollute the environment through dissolution and migration, and have significant biological toxicity effects. The research results can provide a theoretical reference for decision-making departments to formulate scientific and reasonable iron tailings disposal and ecological protection policies. It is recommended to strengthen specific research on the infiltration of toxic and harmful components in tailings in the future, and develop efficient and stable solidification technology, and vegetation-microbial joint restoration models to further reduce the ecological and environmental risks of iron tailings.

In this study, seventeen faceted gem-quality chrysoberyls exhibiting an attractive canary yellow color were investigated by a series of gemological, spectral, and chemical methods. Microscopic observation reveals distinct growth lines and inclusions, including CO2 fluids, carbon, and mineral crystals such as calcite, quartz, sillimanite, and mica, identified by the Raman spectrum. The FTIR spectrum exhibits the 2405 and 2160 cm-1 feature peaks and a 3223 cm-1 peak in all samples, which can be accompanied by the 3112 cm-1 shoulder, 3301, and 3412 and 3432 cm-1 peaks. The UV-Vis spectrum shows a Fe-related peak at 440 nm, along with the 650–660 nm band and the absorption band in the blue zone of the visible light. The chemical results of EDXRF reveal a V-poor, Cr-poor, and Fe-rich feature. The spectral and chemical results could contribute to explaining the origin of the canary yellow color, which originates from the abundant amount of Fe with very little influence from Cr or V.

Most natural language processing (NLP) tasks suffer performance degradation when encountering long complex sentences, such as semantic parsing, syntactic parsing, machine translation, and text summarization. Previous works address the issue with an intuition of decomposing complex sentences and linking simple ones, such as RST-style discourse parsing, split-and-rephrase (SPRP), text simplification (TS), simple-sentence-decomposition (SSD), etc. However, these works are not applicable for semantic parsing like abstract meaning representation (AMR) parsing and semantic dependency parsing due to misalignments to semantic relations and unavailabilities to preserve original semantics. Following the same intuition and avoiding deficiencies of previous works, we propose a novel framework, hierarchical clause annotation (HCA), based on the linguistic research of clause hierarchy. With the HCA framework, we annotate a large HCA corpus to explore the potentialities of integrating HCA structural features into semantic parsing with complex sentences. Moreover, we decompose HCA into two subtasks, i.e., clause segmentation and clause parsing, and provide neural baseline models for more silver annotations.

The raw material purchasing (RMP) problem is to determine the purchasing quantities of raw materials in given periods or cycles. Raw material purchasing optimization is crucial for large-scale steel plants because it is closely related to the supply of raw materials and cost savings. The raw material purchasing of large-scale steel enterprises is characterized by many varieties, large quantities, and high costs. The RMP objective is to minimize the total purchasing cost consisting of the price of raw materials, purchasing set-up costs, and inventory costs and meet product demand. We present a mixed integer linear programming (MILP) model and a column generation (CG) solution for the resulting optimization problem. The column generation algorithm is the generalization of the branch & bound algorithm with solving the linear programming (LP) relaxation of MILP using column generation (CG), and its advantage is to handle large-sized MILPs. Experimental results show the effectiveness and efficiency of the solution.

Cotton cultivation and sustaining its productivity is challenging in temperate arid regions around the globe. Exploring suitable cotton cultivation areas to improve productivity in such climatic regions is essential. Thus, this study explores the ecologically suitable areas of cotton cultivation using the Maxent model, having 375 distribution points of long-staple cotton and various factors, including 19 climatic factors, 2 terrain factors, and 6 soil factors in Xinjiang. The area under the curve (AUC) of the predicted results was greater than 0.9, indicating that the model's predictions had fairly high accuracy. However, the main environmental factors that affect cotton's growth are the lowest temperature in the coldest month, the hottest month, the precipitation in the driest season, and the monthly average temperature difference. Further, the temperature factors contribute 71%, while the contribution ratio of terrain and soil factors is only 22%. The research shows that the current planting area is consistent with the predicted area in many areas of the study. Still, some areas, such as the Turpan region, northwest of Bayingolin Mongol Autonomous Prefecture, are supposed to be suitable for planting cotton, but it is not planted. The current potential distribution area of long-staple cotton is mainly located in Aksu Prefecture and the northern part of the Kashgar Prefecture region. The climatic prediction shows that the growing area of long-staple cotton may expand to southern Altay, central Aksu, and Bortala Mongol Autonomous Prefecture. This study will be helpful for cotton cultivation suitability areas in Xinjiang and other regions with similar environments.

At the nanoscale, metals exhibit special electrochemical and optical properties, which play an important role in nanobiosensing. In particular, the surface plasmon resonance based on precious metal nanoparticles, as a kind of tag free biosensor technology, has brought high sensitivity, high reliability and convenient operation to the sensor detection. By applying an electrochemical excitation signal on the nano plasma device, modulating its surface electron density and realizing electrochemical coupling surface plasmon resonance, it can effectively complete the joint transmission of electrical signals and optical signals, increase the resonance shift of the spectrum, and further improve the sensitivity of the designed biosensor. In addition, smartphone is playing an increasingly important role in portable mobile sensor detection systems. These systems typically connect sensing devices to smartphone to perceive different types of information, from optical signals to electrochemical signals, providing ideas for the portability and low-cost design of these sensing systems. Among them, electrochemiluminescence, as a special electrochemical coupled optical technology, has good application prospects in mobile sensing detection due to its strong anti-interference ability, which is not affected by background light. In this review, the surface plasmon resonance is introduced from nanoparticles, and its response process is analyzed theoretically. Then, the mechanism and sensing application of electrochemistry coupled surface plasmon resonance and electrochemiluminescence are emphatically introduced. Finally, it extends to the related research of electrochemical coupled optical sensing on mobile detection platforms.

(1) Background: The typical transporter protein, P-glycoprotein (P-gp), has been proven to be associated with multidrug resistance (MDR). Pyxinol and 24S-Pyxinol amino acid ester derivatives had been demonstrated to be promising P-gp inhibitors for reversal of tumor MDR in KBV cells, however, their roles in MDR reversal in other tumor-resistant cells are still unclear. (2) Methods: In the present study, we synthesized 40 amino acid ester derivatives of Pyxinol and 24S-Pyxinol. The MDR reversal effects of these derivatives were evaluated in A549/Tax, MCF-7/ADR, and HCT-8/VCR cells using the CCK-8 assay. Subsequently, western blot assay, intracellular Rhodamine123 (Rh123) accumulation assay and P-gp ATPase activity assay were utilized to investigate the effects of active compound on P-gp. Molecular docking was used to predict the possible binding patterns of active compound to P-gp. Moreover, based on UPLC-Q/TOF-MS cellular metabolomics technology to identify endogenous differential metabolites and metabolic pathways associated with tumor MDR effects of the active compound. (3) Results: The results of the CCK-8 assay showed that compounds 8, 14, 16, 17, 18, 23, 25, 34, 36, 37 exhibited strong MDR reversal effect in A549/Tax, MCF-7/ADR and HCT-8/VCR cells, which were screened by reversal fold (RF) greater than 5. Among them, 10 μM of derivative 37 (new compound) showed the outstanding activity (RF: 15.31) in reversing MDR in A549/Tax cells. Compound 37 could act as a substrate for P-gp and inhibit the efflux function of P-gp by competitively binding to the binding site of antitumor drugs. Meanwhile, the molecular docking results confirmed that compound 37 was identified to under-go high-affinity binding to the drug binding site of P-gp. In addition, 14 endogenous differential metabolites and 6 metabolic pathways involved closely associated with the reversal of tumor MDR effects of compound 37 were identified. (4) Conclusions: Overall, a new candidate compound 37 (24S-3-L-threonyl-Pyxinol) could be provided for reversing tumor MDR by this study.

The influence of pH, temperature, biopolymer ratio, total concentration, and ionic concentration on the interaction between egg white protein (EWP) and chitosan (CS) was investigated through turbidity, zeta potential, and state diagram in our research. In addition, phase behavior was observed under various conditions. The turbidity of EWP remained low (turbidity<0.03) and basically unchanged at a wide range of pH (4.0-8.0), while the turbidity of CS was slightly higher (turbidity<0.2) after pH 7.0 than before. Moreover, under the same conditions, a sharply rising peak pattern was observed for the complex between EWP and CS. The maximum turbidity value was observed at 55°C, and the temperature had a mild effect on turbidity. The optimum EWP to CS ratio was found to be 12:1 based on the experiment of the turbidity curves and state diagrams influenced by different biopolymer mixing ratios. With the enhanced concentrations of total biopolymer, the maximum turbidity rose insignificantly above 0.1%.

The Internet of Things (IoT) aims to connect all objects and facilitate data interchange, with data accuracy being paramount. While IoT has seen extensive adoption in conventional sectors, its application in the petroleum industry, especially with downhole equipment, poses challenges due to the extreme high-temperature and high-pressure conditions, making accurate measurements elusive. This Paper we aim to enhance the accuracy of downhole pressure measurements in slim-hole drilling parameter monitoring, mitigate the temperature influences on strain gauges, and surmount the intrinsic limitations of the traditional BP neural network, such as sluggish convergence speeds, high sensitivity to initial weights and biases, unstable learning rates, and a tendency to fall into local minima. This study introduces a temperature compensation model using a chaotic-initiated adaptive whale optimization algorithm (C-I-WOA) to optimize a BP neural network, which is henceforth referred to as the C-I-WOA-BP model. Initially, we harnessed chaotic mapping techniques to refine the initialization process of the whale optimization algorithm (WOA) population, thereby bolstering the heterogeneity of the algorithm. Subsequently, we integrated an adaptive weight recalibration mechanism, refined the encompassing methodology of the WOA, and bolstered its capabilities for expansive global scouting and meticulous local refinement in the solution arena. Subsequently, the WOA was used to optimize the weight coefficients of the BP neural network. Finally, we conducted a detailed comparative analysis by juxtaposing the performance metrics of the BP, C-WOA-BP, I-WOA-BP, and C-I-WOA-BP networks. The experimental results indicate that the C-I-WOA-BP model demonstrates faster convergence speed, superior global search capabilities, and enhanced local exploitation potential compared to the other models. Additionally, it provides a more stable and accurate reduction in temperature-induced errors in drilling pressure parameter measurements.

Anomaly detection plays a significant role in helping gas turbines run reliably and economically. Considering collective anomalous data and both sensitivity and robustness of the anomaly detection model, a sequential symbolic anomaly detection method is proposed and applied to the gas turbine fuel system. A structural Finite State Machine is to evaluate posterior probabilities of observing symbolic sequences and most probable state sequences they may locate. Hence an estimating based model and a decoding based model are used to identify anomalies in two different ways. Experimental results indicates that these two models have both ideal performance overall, and estimating based model has a strong ability in robustness, while decoding based model has a strong ability in accuracy, particularly in a certain range of length of sequence. Therefore, the proposed method can well facilitate existing symbolic dynamic analysis based anomaly detection methods especially in gas turbine domain.

The unique characteristics of frescoes on overseas Chinese buildings can attest to the integration and historical background of Chinese and Western cultures. Reasonable analysis and preservation of overseas Chinese frescoes can provide sustainable development for culture and history. This research adopts the image analysis technology based on artificial intelligence, and proposes a ResNet-34 model and method integrating transfer learning. This deep learning model can identify and classify the source of the frescoes of the emigrants, and can effectively deal with the problems such as the small number of fresco images on the emigrants' buildings, poor quality, difficulty in feature extraction, and similar pattern text and style. The experimental results show that the training process of the model proposed in this article is stable. On the constructed Jiangmen and Haikou fresco JHD datasets, the final accuracy is 98.41%, and the recall rate is 98.53%. The above evaluation indicators are superior to classic models such as AlexNet, GoogLeNet, and VGGNet. It can be seen that the model in this article has strong generalization ability and is not prone to overfitting. It can effectively identify and classify the cultural connotations and regions of frescoes.

A novel fluorescent probe (C460@Tb-MOFs) was designed and synthesized through encapsulating the fluorescent dye 7-diethylamino-4-methyl coumarin into terbium-based metal-organic framework by a simple ultrasonic impregnation method. It is impressive that this dye-modified metal-organic framework can specifically detect styrene and temperature upon luminescence quenching. The sensing platform of this material exhibit great selectivity, fast response and good cyclability toward styrene detection. It is worth mentioning that the sensing process undergoes a distinct color change from blue to colourless, providing conditions for accurate visual detection of styrene liquid and gas. The significant fluorescence quenching mechanism of styrene toward C460@Tb-MOFs is explored in detail. Moreover, the dye-modified metal-organic framework can also achieve temperature sensing from 298 to 498 K with high relative sensitivity at 498 K. The preparation of functionalized MOFs composites by fluorescent dyes provides an effective strategy for the construction of sensors for multifunctional applications.

The narrow parking space will result in difficulties in path planning and spot turning caused by sudden changes and discontinuities in path curvature. To address the problem, this paper investigates the performance of three path planning algorithms and proposes a path optimization algorithm. First, the narrow parking space is defined based on single-step parking of the arc-line combination parking algorithm. Second, to compare the arc-line combination algorithm, Hybrid A* algorithm, and particle swarm optimization parking algorithm under different narrow parking spaces, a multi-objective evaluation function is proposed, including three evaluation indicators, namely, the path length, the number of positive and negative conversions of vehicle speed, and the smoothness of the path. Their performance is compared by simulation conducted in MATLAB, the results show that the Hybrid A* algorithm is the better than others for narrow parking spaces. Third, to smooth the planning path, a path optimization algorithm based on the cubic B-spline curve and gradient descent is proposed. Finally, the simulation is conducted between the proposed algorithm and Hybrid A* algorithm, the result shows that the proposal can effectively eliminate the curvature mutation point and constrain the path curvature to meet the requirements of a smooth path.

Ethylene is an ideal CO2 product in an electrocatalytic CO2 reduction reaction (CO2RR), which has high economic value. In this paper, Al–doped octahedral Cu2O (Al–Cu2O) catalyst was synthesized by a simple wet chemical method. The selectivity of CO2RR products was improved by doping Al onto the surface of octahedral Cu2O by regulating the Al content. The Al–Cu2O was used as an efficient electrocatalyst for CO2RR with selective ethylene production. The Al–Cu2O exhibited a high Faradic efficiency (FEC2H4) of 44.9% at -1.23 V (vs. RHE) in CO2 saturated 0.1 M KHCO3 electrolyte. Charge transferring from Al atom to Cu atom take place after Al doping in Cu2O, thereby optimizing the electronic structure, which facilitates CO2RR to ethylene production. The DFT calculation showed that the Al–Cu2O catalyst can effectively reduce the adsorption energy of the *CHCOH intermediate and promote the mass transfer of charges, thus improving the FEC2H4. After Al doping into Cu2O, the center of d orbitals shift positively, which makes the d–band closer to the Fermi level. Furthermore, the density of electronic states increases, which was conducted to the interaction between Cu atoms and intermediates, thus accelerating the electrochemical CO2 reduction process. This work proved that the metal doping strategy can effectively improve the catalytic properties of Cu2O, thus providing a useful way for CO2 cycling and green production of C2H4.

Underwater image often exhibit detail blurring and color distortion due to light scattering, impurities, and other influences, obscuring essential textures and details. This presents a challenge for existing super-resolution techniques in identifying and extracting effective features, making high-quality reconstruction difficult. This research aims to innovate underwater image super-resolution technology to tackle this challenge. Initially, an underwater image degradation model was created by integrating random subsampling, Gaussian blur, mixed noise, and suspended particle simulation to generate a highly realistic synthetic dataset, thereby training the network to adapt to various degradation factors. Subsequently, to enhance the network's capability to extract key features, improvements were made based on the symmetrically structured Blind Super-Resolution Generative Adversarial Network (BSRGAN) model architecture. An attention mechanism based on energy functions was introduced within the generator to assess the importance of each pixel, and a weighted fusion strategy of adversarial loss, reconstruction loss, and perceptual loss was utilized to improve the quality of image reconstruction. Experimental results demonstrate that the proposed method achieved significant improvements in Peak Signal-to-Noise Ratio (PSNR) and Underwater Image Quality Measure (UIQM) by 0.85 dB and 0.19, respectively, significantly enhancing the visual perception quality and indicating its feasibility in super-resolution applications.

A millimeter-wave microstrip leaky-wave antenna array based on the substrate-integrated waveguide (SIW) and the complementary split-ring resonator (CSRR) is proposed in this paper. The slow wave characteristics of the SIW are enhanced by etching two rows of symmetrical CSRR-type slots on the surface of the SIW layer. The magnetic coupling between the CSRR-SIW layer and equally spaced circular patches array on the upper layer realizes the radiation of electromagnetic energy. In the 23~30 GHz (relative bandwidth 26.4%) operating frequency band, the horizontal polarization radiation beam scanning from -62° to -2° is realized, with a peak gain of 9.6~11.7 dBi, and the cross-polarization ratio is more than 43.8 dB.

Plasma microRNAs are considered to be potential diagnostic biomarkers for endometriosis. Increasing evidence has shown that a huge amount of miRNAs are abnormally expressed in endometriosis plasma and play irreplaceable roles in diagnosis. The aim of the our study was to identify the differential expression of circular miRNA by reviewing the PubMed, ScienceDirect, and Cochrane databases between normal women and women with endometriosis and analyzing the miRNA data downloaded from the GEO database. Because of the differential miRNA expression in this review, we evaluated the diagnostic values of the differentially expressed miRNAs, particularly during the menstrual phases. According to the cut-off criteria with |log 2 FC|>1.0 and P < 0.05, 36 differentially expressed miRNAs were identified, including 13 upregulated miRNAs and 23 downregulated miRNAs. We developed miR-155, miR-574, miR-23a, and miR-520d via a Venn diagram. Functional enrichment analysis considered that the target miRNAs might be involved in various pathways related to endometriosis, including neurotrophin, Hippo, oocyte meiosis, ubiquitin mediated proteolysis, HTLV-Infection, FoxO, and Rap1 signaling pathways. CTNNB1, MYC, and ES R1 of transcription factors were related to the differentially expressed miRNAs. In summary, our study suggested that a four-miRNA could be included as a prognostic marker in endometriosis.

The worldwide pandemic of coronavirus disease 2019 (COVID-19) has become an unprecedented challenge to global public health. With the intensification of the COVID-19 epidemic, the development of vaccines and therapeutic drugs against the etiological agent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are also widespread. To prove the effectiveness and safety of these preventive vaccines and therapeutic drugs, available animal models that faithfully recapitulate clinical hallmarks of COVID-19 are urgently needed. Currently, animal models including mice, golden hamsters, ferrets, nonhuman primates and other susceptible animals have been involved in the study of COVID-19. 92 vaccine candidates have entered clinical trials after the primary evaluation in animal models, of which inactivated vaccines, subunit vaccines, virus-vectored vaccines and mRNA vaccines are promising vaccine candidates. In this review, we summarize the landscape of animal models and advanced vaccines with efficacy range from about 50% to more than 95%. In addition, we point out future directions for animal models and vaccine development, aiming at providing valuable information and accelerating the breakthroughs confronting SARS-CoV-2.

Land cover data are important basic data for earth system science and other fields. Multi-source remote sensing images have become the main data source for land cover classification. There are still many uncertainties in the scale effect of image spatial resolution on land cover classification. Since it is difficult to obtain multiple spatial resolution remote sensing images of the same area at the same time, the main current method to study the scale effect of land cover classification is to use the same image resampled to different resolutions, however errors in the resampling process lead to uncertainty in the accuracy of land cover classification. To study the land cover classification scale effect of different spatial resolutions of multi-source remote sensing data, we selected 1 m and 4 m of GF-2, 6 m of SPOT-6, 10 m of Sentinel-2 and 30 m of Landsat-8 multi-sensor data, and explored the scale effect of image spatial resolution on land cover classification from two aspects of mixed image element decomposition and spatial heterogeneity. For the study area, we compared the classification obtained from GF-2, SPOT-6, Sentinel-2, and Landsat-8 images at different spatial resolutions based on GBDT and RF. The results show that (1) GF-2 and SPOT-6 had the best classification results, and the optimal scale based on this classification accuracy was 4–6 m; (2) the optimal scale based on linear decomposition depended on the study area; (3) the optimal scale of land cover was related to spatial heterogeneity, i.e., the more fragmented and complex was the space, the smaller the scale needed; and (4) the resampled images were not sensitive to scale and increase the uncertainty of the classification. These findings have implications for land cover classification and optimal scale selection, scale effects and landscape ecology uncertainty studies.

Group A rotaviruses (RVA) have been identified as the leading cause of neonatal calf diarrhea (NCD) In Yunnan, China, despite the limited documentation on this issue within the country. Given the significant impact on animal health and welfare, further attention is warranted to comprehend the prevalence and epidemiology of this disease. In this study, we isolated RVA strains from NCD clinical samples to support RVA epidemic study and further disease control in Yunnan. Material and Methods: Ten fecal samples were obtained from calves with a single episode of neonate calf diarrhea from a farmer during a severe NCD outbreak. For the diagnosis of Group A rotavirus (RVA) infection, fecal reverse transcription-polymerase chain reaction (RT-PCR) was employed as the primary method. Subsequently, RVA isolation was conducted on MA104 cells, with inoculates being pretreated with trypsin TPCK to enhance viral infectivity. The isolated RVA strain was further confirmed through RT-PCR and immunofluorescence assay. Results: RVA virus was isolated from clinical samples following six passages depended on trypsin treatment of inoculates. The sign of CPE detected consisted of increased cell granularity, obscure cell boundaries, cell rounding, and eventual degeneration and detachment of cells. Analysis of sequencing revealed that field isolates was of group A serotype. Phylogenetic analysis based on the ORF sequences of the VP4 and VP7 sequences segments indicated that the isolate was classified as the P[1] genotype. VP4 gene of strain YN-1 shares 75.2%-99.7% nucleotide identity of and 81.6%–99.7% amino acid identity with the complete VP4 sequences of the P[1]. The complete VP7 gene of YN-1 is 981 nucleotide long and encode 326 amino acids. The RotaC v2.0 analysis classified the sequence as the G8 genotype. The VP7 gene of strain YN-1 shares 88.5%-97.7% nucleotide identity and 94.2%-99.1% amino acid identity. Conclusion: This is the first study in China which reports the circulation of Group A NCD on cattle farms in the field. Our study constitutes a crucial and a necessary step allowing preventive and veterinary medicine to support RVA disease controls in Yunnan province, China.

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.

Intercropping systems can flexibly use resources such as sunlight, heat, water, and nutrients in time and space, improve crop yield and land utilization rates, effectively reduce continuous cropping obstacles and the occurrence of diseases and insect pests, and control the growth of weeds. Thus, intercropping is a safe and efficient ecological planting mode. The legume–cereal intercropping system is the most common planting combination. Legume crops fix nitrogen from the atmosphere through their symbiotic nitrogen fixation abilities, and the fixed nitrogen can be transferred to and utilized by cereal crops in various ways. The symbiotic nitrogen fixation efficiency of legume crops was improved by reducing the inhibition of soil nitrogen on nitrogenase activity through competitive absorption of soil nitrogen. However, the effects of nitrogen transformation and distribution in intercropping systems, and microbial community structure characteristics on nitrogen transfer need to be further explored. In this review, ⅰ) we present the transformation and distribution of nitrogen in the legume–cereal intercropping system; ⅱ) we describe the soil microbial community characteristics in intercropping systems; and ⅲ) we discuss the advantages of using modern biological molecular techniques to study soil microorganisms. We conclude that intercropping can increase the diversity of soil microorganisms, and the interaction between different plants has an important impact on the diversity and composition of the bacterial and fungal communities. The extensive application of modern biological molecular techniques in soil microbial research and the great contribution of intercropping systems to sustainable agriculture are particularly emphasized in this review.

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

The Laser Thermal-Joule Heating Composite Process was studied by orthogonal tests based on an analysis of fabrication parameters such as the laser power, wire feeding speed, and electric current. Temperature profiles and the geometric morphology of deposited layers under different process parameters were analyzed, and the overlaps between the layers and the substrate were observed. Results show that when the temperature at the bottom layer of the additive manufacturing is higher than the melting point of the substrate, and the highest temperature at the top layer does not exceed the over-firing temperature, good morphology and close bonding with the substrate can be obtained. Finally, appropriate process parameters were identified and verified to print multiple layers continuously.

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

In the context of the rural revitalization strategy, it is urgent to accurately grasp the spatial differentiation characteristics and influencing factors of rural settlements in underdeveloped arid inland river basins. Taking the Shiyang River Basin as an example, the rural settlements from 2000 to 2019 were obtained by visual interpretation using satellite remote sensing data and official statistical data. Following the logical of "state characteristics - evolutionary patterns - influence factors - layout optimization", the average nearest index, gravity-center migration model, spatial statistical analysis and other methods were used combine with GIS. The spatiotemporal pattern evolution characteristics of rural settlements in the past 20 years have been analyzed. The results revealed the following: 1) The distribution pattern of rural settlements in the study area is sheet-like and strip-shaped. The projects in the southeast are mostly distributed in a patchy pattern with high density, while the characteristics in the west and north are exactly opposite. The objects in the south are distributed in the alluvial area of rivers, while settlements in the north are located in the oasis area. 2) From 2000 to 2019, the scale of rural settlements in the Shiyang River Basin, where simultaneous occurrence of new-built and disappearing phenomena, shows an expansion trend that fast firstly and then slow. Spatially, rural settlements in the basin show a trend of clustering towards the southwest. 3) The distribution characteristics of rural settlements are close to water and roads, greatly influenced by urban-rural integration and ecological migration. The results will provide scientific basis for accelerating the modernization of rural areas, and the construction of new rural areas according to local conditions.

As the largest accretionary orogen in the world, the Central Asian Orogenic Belt (CAOB) has continuous juvenile crustal growth in the Phanerozoic. The northern margin of the North China Craton(NCC) and its adjacent area is the eastern segment of the CAOB, and is a key area to study the geological evolution of Paleo-Asian Ocean (PAO). In the Permian, the west of the northern margin of NCC belongs to a post-collision extensional environment, and the east belongs to a subduction stage. But, As a connecting area, the Permian evolution of PAO in the middle of northern margin of NCC has not been systematically studied. In order to fill the blank and understand the temporal and spatial continuous evolution process of PAO, this paper focused on the Permian granitic rocks in the Chifeng area, zircon U-Pb dating and the geochemical analysis of the whole rock main and trace elements were conducted in order to build a granite chronological framework, discuss the genesis and tectonic background of the granite rocks as well as the magma-tectonic evolution history in the Chifeng area. Zircon U-Pb dating results of 8 samples are 269±1 Ma, 268±3 Ma, 260±4 Ma, 260±1 Ma, 260±1 Ma, 255±2 Ma, 254±2 Ma, 256±1Ma, which showed that the Permian granitic rocks had undergone three stages of emplacement: (1) The monzogranite and syenite (294-284 Ma); (2) The monzogranite (269-260Ma) and (3) The monzogranite and syenite (256-254Ma). The Middle Permian magmatism (269-260Ma) was represented by the monzogranite assemblages with different grain sizes. The geochemical characteristics showed that they were high-potassium calc-alkaline-potassium dossonite series of granites formed in compressional environment, indicating there was a collision between the Xing’an-Mongolian Orogenic Belt (XMOB) and the North China Craton (NCC). During the Late Permian-Early Triassic (256-248Ma), the granites in the Chifeng area are dominated by the A-type and I-type granites of high-potassium calc-alkaline series formed under an extensional environments, which constituted typical "bimodal" rock assemblage combined with the coeval basic rocks, suggesting the study area was in an extensional environment where the subducting slab is fragmented during the collision between the XMOB and the NCC. According to emplacement time and occurrence location, the plutons are interpreted to have been generated by the subduction-collision of the Paleo-Asian oceanic crust beneath the NCC. This study provides strong evidence for Permian tectonic evolution and the characterization of the eventual closure of the Paleo-Asian Ocean in the Chifeng City at the northern margin of the NCC.

The shortwave infrared Ge-Si photodetector will become the core device of the LiDAR optical receiver. In order to meet the urgent demand for photodetectors in the LiDAR field, we have designed and produced a 32×32 pixel Ge-Si photodetector array proposed and developed to meet the performance requirements of the detector array. A dark current detection system for fast scanning and detecting large-scale Ge-Si detector arrays is proposed and developed to achieve rapid detection of dark current in each pixel of the detector. The system was used to validate the main performance indicators of the detector array we designed, achieving rapid discrimination of array performance and rapid localization of damaged pixels. The scanning test results show that the average dark current of the detector array chip we designed is at the nano ampere level, and the proportion of bad points is less than 1%. The consistency of the array chip is high, which can meet the requirements of light detection at the receiving end of the LiDAR. This work laid the foundation for our subsequent development of a LiDAR prototype system.

As a member of smart materials, electrorheological elastomers (EREs) formed by pre-treating active electrorheological particles are attracting more and more attention. In this work, four Mg-doped strontium titanate particles with spherical, dendritic, flake-like and pinecone-like morphorlogies were obtained by hydrothermal and low-temperature co-precipitation. XRD, SEM, Raman and FT-IR were used to characterize the obtained products. The results showed that the sizes of Mg-doped strontium titanates are about 1.5‒2.0 μm, and the phase structure are dominated by cubic crystal. These Mg-doped strontium titanates were dispersed in a hydrogel composite elastic medium. Then, Mg-doped strontium titanate/glycerol/gelatin electrorheological composite hydrophilic elastomers were obtained with or without electric field. The electric field response properties of Mg-doped strontium titanate composite elastomers were investigated. It concluded that dendritic Mg-doped strontium titanate composite elastomers is high-performance EREs. And the maximum value of energy storage modulus of Mg-doped strontium titanate composite elastomers has achieved 8.70 MPa. This high electrorheological performance of the products is helpful for their applications in vibration control, force transducers, smart structures, dampers, and other fields.

The lateral pressure generated by liquefied soil on pile is a critical parameter in the analysis of soil-pile interaction in liquefaction-susceptible sites. Previous studies have shown that liquefied sand behaves like a non-Newton fluid, and its effect on piles has rate-dependent properties. In this study, a simplified pseudo-static method for liquefiable soil-pile interaction analysis is proposed by treating the liquefied soil as a thixotropic fluid, which considers the rate-dependent behavior. The viscous shear force generated by the relative movement between the viscous fluid (whose viscosity coefficient varies with excess pore pressure and shear strain rate) and the pile was assumed to be the lateral load on the pile. The results from the simplified analysis show that the distribution of bending moment is in good agreement with experiments data. Besides, the effects of various parameters, including relative density, thickness ratio of non-liquefiable layer to liquefiable layer, and frequency of input ground motion, on the pile-soil rate-dependent interaction were discussed in detail.

The meniscus is a meniscus-shaped fibrocartilage tissue located between the femur and tibia，it is mainly composed of meniscus cells and related extracellular matrix.The synovial limbus area of the knee joint capsule near the meniscus is divided into red areas with rich blood vessels and white areas with less blood supply according to the distribution of blood vessels,there is a transition zone called the red and white zone between the two;Red zone has better self-repair ability,The injury in this area can be treated by conservative treatment or surgical suture;Once the white area of the meniscus is torn and involves the free edge area,It is often necessary to partially remove the damaged meniscus.When most of the entire meniscus is severely torn and involved,not only the course of the disease is very long, but it cannot be repaired by sutures,Often a subtotal or total meniscus resection is required,whether it is a partial meniscus resection, a subtotal meniscus resection or a complete resection.In the later period, it may cause quadriceps atrophy and osteoarthritis (OA）.OA is a refractory multi-system disease,involve the patient’s peripheral joints,it has high disability and teratogenicity, and is very harmful to human health.Chondrocyte pyrolysis, degradation, and inflammation play a vital role in the destruction of OA articular cartilage and chondrocyte apoptosis.Meniscus stem cells have strong proliferation and differentiation ability,has become one of the hot spots in the field of meniscus repair,this article studies the role of meniscal stem cells in the development of OA.

With the continuous breakthrough of natural language processing, the application of intelligent question answering technology in electric power system has attracted wide attention. However, at present, the traditional question answering system has poor performance and is difficult to be applied in engineering practice. This paper proposes an improved BERTserini algorithm for intelligent answering of electric power regulations based on a BERT model. The proposed algorithm is implemented in two stages. The first stage is the text segmentation stage, where a multi-document long text preprocessing technique is utilized that accommodates the rules and regulations text, and then Anserini is used to extract paragraphs with high relevance to the given question. The second stage is answer generation and source retrieval stage, where a two-step fine-tuning based on the Chinese BERT model is applied to generate precise answers based on given questions, while the information regarding documents, chapters, and page numbers of these answers are also output simultaneously. The algorithm proposed in this paper eliminates the necessity for manual organization of professional question-answer pairs, thereby effectively reducing the manual labor cost compared to traditional question answering systems. Additionally, this algorithm exhibits a higher degree of exact match rate and a faster response time for providing answers.

The coupling of AC and DC power will impact the protective actions on the AC side and pose a threat to the stable operation of the interconnection system. Therefore, a new longitudinal protection method is proposed based on the comprehensive distance similarity of voltage waveforms. Initially, the measured voltage and current data are extracted to calculate the reference voltage, and the voltage waveform fitting is optimized. Subsequently, the Euclidean dynamic time warp (DTW) distance and entropy weight method are utilized to process the voltage waveform, enabling the calculation of its comprehensive distance similarity. This similarity is adopted to determine fault location. A hybrid DC multi-feed AC/DC interconnection system, incorporating line commutated converter-voltage source converter (LCC-VSC) and line commutated converter-modular multilevel converter (LCC-MMC), was established in PSCAD, and fault data was simulated and output. The effectiveness of the protection scheme was validated using MATLAB. Simulation results demonstrate that the proposed method can accurately distinguish between faults inside and outside a region. When compared to existing protection methods, it demonstrates superior performance in resisting transition resistance and noise interference, while also mitigating the impact of data asynchronicity. The speed and reliability of the method are further enhanced.

In the current pandemic of COVID-19, students and faculty are subject to social distancing and online learning. How to test students in this unprecedented environment is a new educational challenge with immediate and global impacts. The main contribution of this paper is to establish the feasibility that by a clever design we can control the average gain (which is referred to as the g-factor) from cheating behaviors to a degree as small as pre-specified so that accurate and reliable online exams can be administered. It is underlined that even after the pandemic the methods and systems in the spirit of our proposal are still valuable for cost-effective exams to promote open courses and internet-based education.

The influence of polysialic acid (PSA) and sialic acid (SA) on the gut microbial community composition and metabolites in healthy human was investigated using a bionic gastrointestinal reactor. The results indicated that PSA and SA significantly changed the gut microbiota and metabolites in different degrees. PSA can increase the relative abundances of Faecalibacterium and Allisonella, whereas SA can increase those of Bifidobacterium and Megamonas. Both can significantly increase the content of short-chain fatty acids. The results of metabolome analysis showed that PSA can upregulate ergosterol peroxide and gallic acid and downregulate the harmful metabolite N-acetylputrescine. SA can upregulate 4-pyridoxic acid and lipoic acid. PSA and SA affect gut microbiota and metabolites in different ways and have positive effects on human health. These results will provide reference for the further development of PSA- and SA-related functional foods and health products.

Macrobrachium rosenbergii is a valuable freshwater prawn in Asian aquaculture. In recent years, a new symptom that was generally called as ‘white head’ caused high mortality in M. rosenbergii farms in China. Samples of M. rosenbergii, M. nipponense, Procambarus clarkii, M. superbum, Penaeus vannamei, and Cladocera from a farm suffering from ‘white head’ in Jiangsu Province were collected and analyzed in this study. Pathogen detection showed that all samples were positive for Shrimp hemocyte iridescent virus (SHIV). Histopathological examination revealed dark eosinophilic inclusions and pyknosis in hematopoietic tissue, hepatopancreas and gills of M. rosenbergii and M. nipponense. Blue signals of in situ DIG-labeled LAMP (ISDL) appeared in hematopoietic tissue, hemocytes, hepatopancreatic sinus, and antennal gland. TEM of ultrathin sections showed a large number of SHIV particles with a mean diameter about 157.9 nm. The virogenic stromata and budding virions were observed in hematopoietic cells. Quantitative detection by TaqMan probe based real-time PCR of different tissues in natural infected M. rosenbergii showed that hematopoietic tissue contained the highest SHIV load with a relative abundance of (25.4±16.9)%. Hepatopancreas and muscle contained the lowest SHIV load with a relative abundance at (2.44±1.24)% and (2.44±2.16)%, respectively. Above results verified that SHIV is the pathogen causing ‘white head’ in M. rosenbergii, and M. nipponense and Pr. clarkii are also the susceptible species of SHIV.

A series of novel chiral nonmetallocene pincer-type rare earth metal dialkyl complexes bearing the chiral monoanionic tridentate C2-symmetric 1,3-bis(oxazolinymethylidene)isoindoline (BOXMI) ligand (BOXMI)Ln(CH2SiMe3)2 1-3 (1, Ln = Sc, yield = 57%; 2, Ln = Lu, yield = 55%; 3, Ln = Y, yield = 62%) have been prepared in moderate yields via the acid-base reaction between the BOXMI ligand and rare earth metal tri(trimethylsilylmethyl) complexes. The X-ray diffractions show that both of the complexes 1 and 2 contain one Boxmi ligand and two trimethylsilylmethyl ligands, adopting a distorted-trigonal-bipyramidal configuration. In the presence of cocatalyst such as borate and AlR3, these complexes 1-3 exhibit high activities up to 6.8 × 104 (g of polymer)/(molLn h) and high cis-1,4-selectivities up to 97%) in the polymerization of isoprene in toluene, yielding the cis-1,4-polyisoprenes with high molecular weights (Mn up to 710000 g/mol) and bimodal molecular weight distributions (Mw/Mn = 2.0-4.5).

At present, farmland abandonment（FA）is a severe phenomenon in China, imposing a serious restriction on agricultural production. In this context, it is of great significance to explore the logical relationship between high-standard farmland construction (HSFC) and FA to optimize land resource allocation and guarantee national food security. Based on 838 samples of farmers in the main rice production area of the Yangtze River Basin in China, this paper employs the Tobit model, the mediating effect model, and the moderated mediating effect model to analyze the impact of HSFC on FA at the micro level. The results show that (1) HSFC inhibits FA, and FA proportion decreases by 1.14% for every 1% increase in HSFC proportion, and the robustness test and endogeneity treatment also yield consistent conclusions. (2) Agricultural socialization services (ASS) play a positive mediating role in the influence path. HSFC promotes farmers' purchase of ASS, which in turn inhibits FA. (3) The agricultural labor transfer distance (ALTD) plays a positive moderating role in the relationship between HSFC and FA. The farther the distance, the more likely HSFC can promote farmers' purchase of ASS and inhibit FA. The results provide enlightenment on how to precisely implement HSFC policy, that is, to inhibit FA by improving the construction of high-standard farmland and the post-construction management as well as protection system. Moreover, this study helps guiding the development of ASS and optimizing the institutional environment for agricultural labor transfer, creating conditions for agricultural scale management.

The data assimilation method to improve sea fog forecast over the Yellow Sea is usually three-dimensional variational assimilation (3DVAR), whereas ensemble Kalman filter (EnKF) has not yet been applied on this weather phenomenon. In this paper, two sea fog cases over the Yellow sea, one spread widely and the other spread narrowly along the coastal area, are studied in detail by a series of numerical experiments with 3DVAR and EnKF based on the Grid-point Statistical Interpolation (GSI) system and the Weather Research and Forecasting (WRF) model. The results show that the assimilation effect of EnKF outperforms that of 3DVAR: for the widespread-fog case, the probability of detection and equitable threat scores of the forecasted sea fog area get improved respectively by ~57.9% and ~55.5%; the sea fog of the other case completely mis-forecasted by 3DVAR is produced successfully by EnKF. These improvements of EnKF relative to 3DVAR are benefited from its flow-dependent background error, resulting in more realistic depiction of sea surface wind for the widespread-fog case and better moisture distribution for the other case in the initial conditions. More importantly, the correlation between temperature and humidity in the background error of EnKF plays a vital role in the response of moisture to the assimilation of temperature, which leads to a great improvement on the initial moisture conditions for sea fog forecast.

Cycloid drives are generally used in precision machinery requiring high reduction ratios, such as robot joint (RV) reducers, in which the contact load and stress property of the internal meshing pairs greatly affect the dynamic transmission performance of the reducers. Therefore, in this study, Isogeometric Analysis (IGA) was employed to explore the multi-tooth contact problem of the cycloid pinwheel drive. Based on the NURBS curved surface generation method, the NURBS tooth profile of the cycloid gear was reconstructed. In addition, the NURBS surface of the cycloid gear - pin tooth - output pin was generated via the element splicing method. A geometrical analysis model of cycloid pinwheel drive was established to solve the contact force of the meshing pair under different input angles, and compared with the finite element method in terms of convergence, resultant accuracy, and solving timeliness. The results show that the Isogeometric analysis has higher accuracy and efficiency than the finite element method in calculating the contact stress and contact force. This method can improve the contact simulation accuracy of the cycloid drive and provides a reference for the design evaluation of RV reducer.

The airborne and satellite-based synthetic aperture radar enables the acquisition of high-resolution SAR oceanographic images in which even the outlines of ships can be identified. The detection of ship targets from SAR images has a wide range of applications, such as the military, where the dynamic grasp of enemy targets can help improve the early warning capability of naval defence, and the civilian detection of illegal fishing vessels can help improve the level of maritime management. Due to the density of ships in SAR images, the extreme imbalance between foreground and background clutter, and the diversity of target sizes, achieving lightweight and highly accurate multi-scale ship target detection remains a great challenge. To this end, this paper proposes an attention mechanism for multiscale receptive fields convolution block (AMMRF). AMMRF not only makes full use of the location information of the feature map to accurately capture the regions in the feature map that are useful for detection results, but also effectively captures the relationship between the feature map channels, so as to better learn the relationship between the ship and the background. Based on this, a new YOLOv7-based ship target detection method, You Only Look Once SAR Ship Identification (YOLO-SARSI), is proposed, which acquires the abstract semantic information extracted from the high-level convolution while retaining the detailed semantic information extracted from the low-level convolution. Compared to the deep learning detection methods proposed by previous authors, our model is more lightweight, only 18.43M. We examined the effectiveness of our method on two SAR image public datasets: the High-Resolution SAR Images Dataset (HRSID) and the Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-V1.0). The results show that the average accuracy AP50 of the detection method YOLO-SARSI proposed in this paper on the HRSID and LS-SSDD-V1.0 datasets is 4.9% and 5% higher than that of YOLOv7, respectively.

The relative position of the orchard robot to the rows of fruit trees is an important parameter for achieving autonomous navigations. The current methods for estimating the position parameters between rows of orchard robots obtain low parameter accuracy, and to address this problem, this paper proposes a machine vision-based method for detecting the relative position of orchard robots and fruit tree rows. Firstly, the fruit tree trunk is identified based on the improved YOLOv4 model; secondly, the camera coordinates of the tree trunk are calculated from the principle of binocular camera triangulation, and the ground projection coordinates of the tree trunk are obtained through coordinate conversion; finally, the midpoints of the projection coordinates of different sides are combined and the navigation path is obtained by linear fitting with the least squares method, and the position parameters of the orchard robot are obtained through calculation. The experimental results show that the average accuracy and average recall of the improved YOLOv4 model for fruit tree trunk detection are 97.05% and 95.42%, respectively, which are 5.92 and 7.91 percentage points higher than those of the original YOLOv4 model. The average errors of heading angle and lateral deviation estimates obtained based on the method in this paper are 0.57° and 0.02 m. The method can accurately calculate heading angle and lateral deviation values at different positions between rows, and can provide a reference for autonomous visual navigation of orchard robots.

Accurate estimation of livestock carrying capacity (LCC) and implementing an appropriate actual stocking rate (ASR) is key to sustainable management of grazing adapted alpine grassland ecosystems. Reliable determination of aboveground biomass is fundamental to these determinations. Peak aboveground biomass (AGBP) captured from satellite data at the peak of the growing season (POS) is widely used as a proxy for annual aboveground biomass (AGBA) to estimate LCC of grasslands. Here we demonstrate limitations of this approach and highlight the ability of POS in the estimation of ASR. We develop and trail new approaches that incorporate remote sensing phenology timings of grassland response to grazing activity, considering relations between biomass growth and consumption dynamics, in efforts to support more accurate and reliable estimation of LCC and ASR. Results show that based on averaged values from large-scale studies of alpine grassland on the Qinghai-Tibet Plateau (QTP), differences between AGBP and AGBA underestimate LCC by about 31%. Findings from a smaller-scale study that incorporate phenology timings into estimation of annual aboveground biomass reveal that Haibei alpine meadows were overgrazed by 11.5% during the study period from 2000-2005. The methods proposed can be extended to map grassland grazing pressure by predicting LCC and tracking ASR, thereby improving sustainable resource use in alpine grasslands.

The capacitated multi-item lot-sizing problem abbreviated as CLSP, is to determine the lot sizes of products in each period in a given planning horizon of finite periods meeting the product demand and resource limits in each period, and minimize the total cost consisting of production, inventory holding, and setup costs. CLSPs are often encountered in industry production settings and thus the solution is significant. In this paper, we propose a Lagrange relaxation (LR) approach for the solution of its. The approach decomposes the CLSP into several uncapacitated single-item lot-sizing problems each of which can be easily solved by dynamic programming. The feasible solutions are achieved by solving the resulting transportation problems with a proposed stepping-stone algorithm and a fix-up procedure. The Lagrange multipliers are updated by using subgradient optimization. Experimental results show that the LR approach explores high-quality solutions and has better applicability compared with other commonly used solution approaches in the literature.

Abstract Background: Numerous studies have demonstrated that the amalgamation of populations belonging to the same breed or closely related breeds leads to enhanced accuracies in genomic predictions (GP). Extensive experimentation with diverse Bayesian and Genome-enabled best linear unbiased prediction (GBLUP) models has been conducted to explore multi-breed genomic selection (GS) in livestock, ultimately establishing they as successful approaches for predicting genomic estimated breeding value (GEBV). This study aimed to examine the efficacy of BayesR and GBLUP model with different weighted genomic relationship matrices (GRM) in making genomic predictions for three distinct beef cattle breeds. Subsequently, we conducted a comparative analysis of the predictive accuracy pertaining to various marker densities and genetic correlations across three distinct beef cattle breeds. This investigation aimed to identify the optimal approach for enhancing the predictive accuracy of multi-breed genomic selection in beef cattle. Results：Genetic relationship matrices revealed moderate similarities between YL and the other breeds, with a striking genetic similarity of 0.87 between WG and HX. In HX cattle, BayesR demonstrated an enhancement in prediction accuracy, achieving 0.52 with HD and 0.46 with WGS, a marked improvement over 0.41 with HD and 0.42 with WGS in GBLUP. In WG and YL breeds, both methods showed comparable accuracies with HD, but BayesR slightly outperformed GBLUP with WGS. Further, multi-breed GS analysis indicated that BayesR consistently surpassed GBLUP in prediction accuracy, particularly with WGS data. For instance, in a combined HX and WG reference population, BayesR achieved a superior accuracy of 0.53 with WGS in HX cattle, a significant enhancement over GBLUP models. The study also underscores the advantage of incorporating multiple breeds in the reference population, which improved prediction accuracy, underscoring the value of broad-based genomic selection strategies. Conclusion: The results show that accuracy of multi-breed genomic predictions was higher with BayesR than with GBLUP, especially for the distantly genetic relationship between reference and validation breeds. Further improvements of multi-breed accuracy of genomic predictions could be achieved by increasing the density of the SNP marker. These findings underscore that BayesR providing a substantial improvement in genomic prediction and the importance of considering genetic relationships in the development of GS strategies for multi-breed cattle populations. Further research is warranted to optimize GRM construction and to explore alternative models for genomic prediction across breeds.

In this study, the effects of various beryllium (Be) addition (Be-free, 0.1, 0.25, 0.50 wt.%) on the microstructure, mechanical properties, and corrosion corrosion of the Al-Mg-Li alloys were systematically investigated. The best performance was obtained in alloy with 0.1 wt.% Be addition. Trace Be addition promotes the grain refinement of the as-cast alloy ingot and contributes positive effects to the recrystallization, bringing improvement of tensile property. Meanwhile, the best anti-corrosion behavior is also presented at 0.1 wt.% Be addition, due to its potential to reduce the width of precipitates free zone (PFZ). As the Be content increase to an excessive level, the comprehensive performance decreases. Therefore, it is strongly recommended that adding trace Be element into Al-Mg-Li alloys has a positive effect on the comprehensive service performance.

Accurate lithium battery diagnosis and prognosis is critical to increase penetration of electric vehicles and grid-tied storage systems. They are both complex due to the intricate, nonlinear, and path-dependent nature of battery degradation. Data-driven models are anticipated to play a significant role in the behavioral prediction of dynamical systems such as batteries. However, they are often limited by the amount of training data available. In this work, we generated the first big data comprehensive synthetic datasets to train diagnosis and prognosis algorithms. The proof-of-concept datasets are over three orders of magnitude larger than what is currently available in the literature. With benchmark datasets, results from different studies could be easily equated, and the performance of different algorithms can be compared, enhanced, and analyzed extensively. This will expend critical capabilities of current AI algorithms, tools, and techniques to predict scientific data.

This study aimed to identify whether gyrate atrophy of the choroid and retina (GACR) heterozygous individuals have possible clinical manifestations and to explore the potential pathogenic mechanism. In this retrospective study, we surveyed a two-generation pedigree of an individual diagnosed with GACR. Two family members underwent ophthalmological, hematologic, and genetic tests. An arginine-restricted diet with vitamin B6 supplementation was implemented; clinical assessments were repeated every 3 months during follow-up. Relative OAT mRNA expression was determined using real-time quantitative polymerase chain reaction. The 19-year-old compound heterozygous daughter (OAT: c.1186C&gt;T; c.748C&gt;T), had bilateral high myopia, posterior staphyloma, chorioretinal atrophy, macular abnormalities, and elevated hematologic ornithine. The 54-year-old heterozygous mother (OAT: c.1186C&gt;T), presented with bilateral severe myopia, asymmetric posterior staphyloma, retina and choroidal capillary layer atrophy, retinal pigment epithelium abnormalities, and mildly elevated hematologic ornithine. Compared to normal individuals, the daughter and mother had 29% and 46% relative OAT mRNA expression, respectively (P&lt;0.001). We believe that this is the first report of a carrier of one OAT variant allele with mild phenotypes, suggesting that family members should be aware of the possible involvement of autosomal recessive conditions. Additional data suggests nonsense mediated decay-initiated mRNA degradation may cause GACR.

Excitation-inhibition imbalance of GABAergic interneurons is predisposed to develop chronic temporal lobe epilepsy (TLE). We have previously shown that virtually every neuronal nitric oxide synthase (nNOS)-positive cell is a GABAergic inhibitory interneuron in the denate gyrus. The present study was designed to quantify the number of nNOS-containing hilar interneurons using stereology in pilocapine- and kainic acid (KA)-exposed transgenic adult mice that expressed GFP under the nNOS promoter. In addition, we studied the properties of miniature excitatory postsynaptic current (mEPSC) and paired-pulse response ratio (PPR) of evoked EPSC in nNOS interneurons using whole cell recording techniques. Results showed that there were fewer nNOS-immunoreactive interneurons of chronically epileptic animals. Importantly, patch-clamp recordings revealed reduction in mEPSC frequency, indicating diminished global excitatory input. In contrast, PPR of evoked EPSC following the granule cell layer stimulation was increased in epileptic animals suggesting reduced neurotransmitter release from granule cell input. In summary, we propose that impaired excitatory drive onto hippocampal nNOS interneurons may be implicated in the development of refractory epilepsy.

Surface-enhanced Raman spectroscopy (SERS) can boost the pristine Raman signal by ~ 108 times that could be exploited for producing innovative sensing devices with advanced properties. However, the inherent complexity of SERS systems restricts their further applications in rapid detection, especially in situ detection in narrow aera. Here, we construct an efficient and flexible SERS-based LOP sensor by integrating Ag/Au nanocap arrays obtained by Ag/Au coating polystyrene nanospheres on the optical fiber face. We obtain rich “hotspots” at the nanogaps between neighboring nanocaps and further achieve the excellent SERS performance with the assistance of laser induced thermophoresis on the metal film that can achieve high efficiency aggregation of detected molecules. We achieve a high Raman enhancement with a low detection limitation of 10-7 mol/L for the most efficient samples based on the above sensor. This sensor also exhibits good repeatability and stability under multiple detections, revealing the potential application in situ detection based on the reflexivity of optical fiber.

The low-temperature environment significantly inhibits the growth and metabolism of denitrifying bacteria, leading to an excessive concentration of ammonia nitrogen and total nitrogen in sewage treatment plants during the cold season. In this study, an efficient denitrifying strain of heterotrophic nitrification–aerobic denitrification(HN-AD) bacteria named HS2 was isolated and screened from industrial sewage of a chemical factory in Inner Mongolia at 8°C. The strain was confirmed to be Achromobacter spiritinus, a colorless rod-shaped bacterium. When cultured with sodium succinate as the carbon source, a carbon-to-nitrogen ratio of 20-30, a shaking rate of 150-180 r/min, and the initial pH of 6-10, the Strain HS2 exhibited excellent nitrogen removal at 8°C. Through the results of whole-genome sequencing, gene amplification, and gas product detection, the Strain HS2 was determined to possess key enzyme genes in both nitrification and denitrification pathways, suggesting a HN-AD pathway of NH4+-N→NH2OH→NO2--N→NO→N2O→N2. At 8°C, the Strain HS2 could completely remove ammonia nitrogen from industrial sewage with an initial concentration of 127.23 mg/L. Microbial species diversity analysis of the final sewage confirmed Achromobacter sp. as the dominant genus, this indicated that the low-temperature denitrifying strain HS2 plays an important role in nitrogen removal of actual low-temperature sewage.

The Internet's core network, crucial for key services, lacks a clear definition, hindering Internet development and security research. This study analyzes important Autonomous Systems (ASes) and proposes a rule-filtering, top-down method to infer the Internet's core network. It identifies two core memberships: Tier-1 ASes and Regional Tier-1 ASes. Despite a gap, Regional Tier-1 ASes show potential to join the core. Our proposed core structure, unlike traditional methods that randomly select TOP-K nodes, offers improved rationality and interpretability. Historical data analysis reveals the Internet's core structure as a tightly interconnected network of large provider ASes. Despite some changes, this structure shows remarkable stability and growing influence, potentially encouraging other ASes to establish direct connections with the core ASes, accelerating the trend towards a flattened Internet structure. Furthermore, the core structure may be evolving from a majority to a more minority-centric structure.

Background： Sepsis-induced acute lung injury (ALI) is characterized by disruption of the epithelial barrier and activation of alveolar macrophages (AMs), which leads to uncontrolled pulmonary inflammation. However, effective treatments for ALI are unavailable. This study aimed to discover potential diagnostic molecular biomarkers based on bioinformatics, which will benefit the diagnosis and treatment of sepsis-induced ALI. Methods: GSE10474 was analyzed for differentially expressed genes (DEGs) in sepsis patients with ALI (sepsis + ALI) compared with sepsis patients without ALI. Functional enrichment analysis and protein-protein interaction (PPI) network were performed. on the DEGs via R package “clusterProfiler”, and visualized via Cytoscape. Prediction analysis of microarrays (PAM) was performed to identify diagnostic biomarkers and the diagnostic ability of diagnostic biomarkers was accessed via receiver operating characteristic (ROC) curves. Moreover, interactions among diagnostic biomarkers were analyzed via GeneMANIA. We also analyzed the function of diagnostic biomarkers and predicted their corresponding drugs via Cytoscape plugin BiNGO and web tool DGIdb. At last, we analyzed the transcriptional regulation of the diagnostic biomarkers via the web tool miRNet. Results: 71 genes were found to be differentially expressed in the sepsis + ALI group, mainly involved in immune-related biological processes and pathways. STRING database indicated 31 DEGs have protein-protein interactions. In addition, the PAM identified 6 diagnostic biomarkers, including HIST1H4H, CDKN1A, HMOX1, NQO2, RHOB, and TREM1, from these 31 DEGs. Conclusion: In conclusion, through bioinformatics analyses, we identified 6 potential diagnostic biomarkers and targets for sepsis-induced ALI.

Electric field assisted combustion is an important means to improve fuel combustion efficiency. This paper conducts extensive research on flame characteristics under different forms and different application methods of electric fields, emission of soot particles and simulation status. Different flame parameter measurement methods will lead to different degrees of error, and perfect numerical simulation can make simple predictions on experimental data. Most of the current numerical simulations are in two dimensions, and it is necessary to develop a complete and accurate three-dimensional model to simulate and predict the characteristics of the flame under an electric field. The emission of soot particles is also affected by the electric field, and reasonable electric field parameters can greatly reduce the emission of soot particles. It is recommended to conduct centralized measurement of different fuels under the electric field under high pressure and temperature conditions, so as to be able to develop a wider and more accurate flame dynamics and chemical model under the electric field.

This study aimed to use spatiotemporal tensor data to measure the level of employment quality in China's provinces and analyzed the magnitude and direction of its influencing factors in the spatiotemporal dimension. Taked 30 provinces, autonomous regions and municipalities directly under the central government in China from 2011 to 2020 as the research object, the employment quality evaluation system was constructed from six dimensions of employment environment, employment status, employability, labor remuneration, social security, and labor relations. The employment quality index data was expressed as a three-order high-dimensional tensor spatio-temporal data form, and the employment quality of China's provinces was measured from the spatio-temporal perspective by using principal tensor analysis, and the visual analysis of the development and change process of employment quality was carried out. The spatial autocorrelation analysis of employment quality was carried out, and the time-space dual fixed effect model of the spatial Dubin model was selected to analyze the direction and magnitude of the influence factors of employment quality on itself and its neighboring provinces. The research showed that: (1) The overall level of employment quality in China was not high and the employment quality varies greatly among provinces, and the employment quality development gap among provinces showed a trend of widening. (2) The development of employment quality in western China was relatively fast, while the development of employment quality in central China showed insufficient stamina. (3) Sichuan Province had a strong radiation effect on the development of employment quality in neighboring provinces, and Beijing and Tianjin had a strong siphon effect on the development of employment quality in neighboring provinces. (4) The level of industrialization and informatization promoted the development of employment quality in China's provinces, while the industrial structure had a significant negative effect on the development of employment quality.

A series of novel sulfonylurea benzothiazoline were designed by splicing active groups and bioisosterism. A solvent-free synthetic route was developed for the sulfonylurea benzothiazoline derivatives via the cyclization and carbamylation. All the compounds were characterized by IR, 1H-NMR, 13C-NMR, HRMS. The biological activity tests indicated the compounds could protect maize against the injury caused by chlorsulfuron to some extent. The molecular docking result showed that the new compound competed with chlorosulfuron to bind with the herbicide target enzyme active site to attain detoxification.

Neonicotinoids (NEOs) have become the most widely used insecticides in the world since the mid-1990s. According to Chinese dietary habits, rice and water are usually heated before being consumed, but the information about the alteration through the heat treatment process is very limited. In this study, the parents of NEOs (p-NEOs) accounted for >99% of the total NEOs mass (∑NEOs) in both uncooked (median: 66.8 ng/g) and cooked (median: 41.4 ng/g) rice samples from Guangdong Province, China, while the metabolites of NEOs (m-NEOs) involved in this study accounted for less than 1%. We aimed to reveal the concentration changes of NEOs through heat treatment process, thus, several groups of rice and water samples from Guangdong were cooked and boiled, respectively. Significant (p < 0.05) reductions in acetamiprid, imidacloprid (IMI), thiacloprid, and thiamethoxam (THM) have been observed after the heat treatment of the rice samples. In water samples, the concentrations of THM and dinotefuran decreased significantly (p < 0.05) after the heat treatment. These results indicate the degradation of p-NEOs and m-NEOs during the heat treatment process. However, the concentrations of IMI increased significantly in tap water samples (p < 0.05) after heat treatment process, which might be caused by the potential IMI precursors in those industrial pesticide products. The concentrations of NEOs in rice and water can be shifted by the heat treatment process, so this process should be considered in relevant human exposure studies.

Cadmium (Cd) is a major heavy metal pollutant and a significant cause of abiotic stress in the environment. Organisms have developed various pathways to protect themselves against Cd stress. MYB protein is a large transcription factor family in plants. Many members of the family have been found to regulate developmental processes and stress responses. However, the function and mechanism of MYB116 are far from clear. In a recent study, we found that MYB116 of Chinese cabbage enhances Cd tolerance in yeast. BrMYB116 is transiently inducible by Cd treatment in Chinese cabbage. When expressed in yeast cells, it enhances tolerance against Cd stress and reduces Cd accumulation in the cells. Further analysis shows that BrMYB116 protein enhances Cd tolerance in yeast cells by activating one target gene that encodes the facilitator of iron transport 3 (FIT3). ScFIT3 is activated through specific binding with its promoter. More importantly, the expression of ScFIT3, leads to a substantial reduction of Cd accumulation in both yeast and Chinese cabbage. This finding implies that a similar network may exist in other biological systems, which presents a promising avenue for enhancing Cd tolerance in plants and developing Cd-resistant crops.

The increasing demand for higher power in rocket launchers is driving the development of higher power nozzles, achieved essentially by increasing the expansion ratio. However, this can lead to flow separation and resulting unsteady, asymmetrical forces, called side loads, which can limit the life of both the nozzle itself and other engine components. If this problem can be overcome, engine performance can be significantly increased. Therefore, various separation control methods have been proposed, but none have been fully implemented to date due to the uncertainties associated with modeling and predicting the flow phenomena. A numerical study of high area ratio rocket engine is presented to analyze the aeroelastic performance of the structure under the condition of flow separation. The finite volume method, the 2nd order upwind scheme and the extended wall function are used to analyze the flow inside the rocket nozzle and the transition of the Free Shock Separation (FSS) to Restricted Shock Separation (RSS) separation pattern ) to be discussed in detail. Since the location of the flow separation point depends heavily on the flow.

Poverty alleviation is a hallmark of post-revolution Chinese policymaking. Since 1978, the Communist Party of China (CPC) has implemented successive waves of poverty alleviation policies whose effects have become the focus of an ever-increasing body of academic literature. This paper reviews this diverse but limited literature that evaluates the impact of the CPC’s poverty reduction programs through four major channels, namely fiscal investment programs, social safety nets, rural governance on the village-, county- and provincial level, and the relocation of rural populations from destitute regions. This paper aims to synthesize results and evaluate whether and how the abovementioned poverty alleviation programs have had distinct positive or negative impacts on regional development outcomes. Furthermore, I highlight contradictions in empirical findings to motivate the discussion about contextual importance when designing and implementing future poverty alleviation programs. Finally, I suggest that an exhaustive and critical appraisal of the empirical strategies used in this literature would further the development and application of more accurate and informative methodologies.

The effect of H2S with high concentrations on catalytic dry reforming of methane (DRM) process has seldom been focused on previously. Herein, a thermodynamic analysis of the DRM reactions in the presence of H2S with the concentration varying in 0-20 vol% was conducted firstly. Several typical types of catalysts including MgO, NiO/MgO and LaNiO3 in different states were prepared for executing DRM at 800 C, 0.1 MPa in the feed of 20 vol% CO2 and 20 vol% CH4 balanced with N2. The catalytic performance of each catalyst for DRM process under conditions of absence and presence of H2S was compared. A promotion effect of increasing H2S concentration on both the conversions of CO2 and CH4 and the molar yields of CO and H2 was observed on all the catalysts especially the MgO and the pristine NiO/MgO. While a significant decline in catalytic activity of either the reduced NiO/MgO or the reduced LaNiO3 catalyst after adding H2S, moderate reactant conversions still sustained. The results of process analysis and catalyst structure characterization suggest that H2S participation can contribute to the increment in CO2 and CH4 conversion, and active S-adsorbed species may play the key role of catalysis in the reactions involving H2S.

Thin twin-roll cast strips from a model Al-Cu-Mg-Li-Zr alloy with a small addition of Sc were prepared. A combination of a fast solidification rate and a favorable effect of Sc microalloying impacts the size and distribution of primary phase particles and the dimension of eutectic cells. Small interdendritic spacing in the range of 10-15 um enables substitution of the initial long-term homogenization annealing by a two-step annealing at 300 and 450 °C and a short solution treatment at 530 °C before quenching and the final age-hardening procedure. A dense dispersion of small Al3(Sc,Zr) precipitates intensively inhibiting recrystallization forms during this treatment. Further refining and fragmentation of the structure were performed using constrained groove pressing before the solution treatment. Small pre-straining of quenched specimens and subsequent age-hardening simulating T8 temper were used to promote the formation of a homogeneous distribution of strengthening particles and to suppress preferential heterogeneous precipitation on subgrain and grain boundaries. Microstructural features and microhardness evolution during annealing were critically examined and confronted with microstructural characteristics observed on reference mold cast alloy or alloys without Sc alloying. The proposed treatment represents a model near net shape procedure for manufacturing thicker sheets for the aerospace industry.

Molecular dynamics (MD) simulations present a sophisticated nano-scale computational approach that can play a critical role in material design for next generation batteries. One critical piece of information needed for MD simulations is the non-bonded potential parameters, which can be obtained through quantum mechanics (QM) calculations or experimental methods. However, experimental data is not available for exploratory and novel materials to derive the potential parameters. Also, the QM approach needs significant computational power and is too time consuming for large systems. On the other hand, current Artificial Intelligence techniques are faster, but they cannot be generalized across molecules and properties. As a result, there is a significant barrier to discovering and exploring new materials. Overcoming this barrier, in this work, we propose a Machine Learning (ML) based technique that can learn inter-atomic potential parameters for various particle-particle interactions employing QM calculations. This ML model can be used as an alternative for QM calculations for predicting non-bonded interactions in a computationally efficient manner. Using these parameters as input to MD simulations, we can predict a diverse range of properties, enabling researchers to design new and novel materials suitable for various applications in the absence of experimental data. We employ our ML-based technique to learn the Buckingham potential, a non-bonded interatomic potential. Subsequently, we utilize these predicted values to compute the densities of four distinct molecules, achieving an accuracy exceeding 93%. This serves as a strong demonstration of the efficacy of our proposed approach.

Coseismic landslides pose significant threats, causing widespread destruction of buildings, roads, pipelines, and leading to numerous casualties. In recent years, the frequency of earthquakes has increased, prompting a growing interest in regional-scale assessment techniques for coseismic landslides. The infinite slope model proposed by Newmark is widely used to evaluate coseismic landslide hazard. However, the infinite slope model fails to reflect the impact of rock mass structure on the stability of slopes. This paper proposes a novel approach for mapping the hazards of coseismic landslides by considering the roughness of the potential slide surfaces in the inner slope. The proposed method is validated using data from the 2013 Lushan earthquake. The datasets, including geological units, peak ground acceleration (PGA), and high-resolution digital elevation models of topography, are rasterized at a grid spacing of 30 meters. They are then combined within an infinite slope model based on Newmark permanent-deformation analysis, enabling the estimation of coseismic landslide displacement in each grid area resulting from the Lushan earthquake. The modeled displacements are compared with the inventory of landslides triggered by the Lushan earthquake, allowing the derivation of a confidence level function that relates predicted displacement to the spatial variation of coseismic landslides. Ultimately, a hazard map of coseismic landslides is generated based on the established confidence level function. This map serves as a valuable tool for predicting the hazard zone of seismic regions and offers essential insights for decision-making related to infrastructure development and post-earthquake construction.

The working environment of mining electric locomotives is wet and muddy coal mine 1 roadway. There may be idling or slipping between the wheels and rails of mining electric locomotives 2 due to low friction between the wheel and rail and insufficient utilization of creep rate. Therefore, it 3 is necessary to control the creep rate within a reasonable range. In this paper, the autonomous control 4 algorithm for mining electric locomotives based on improved ε-greedy is theoretically proven to be 5 convergent and effective firstly. Secondly, after analyzing the contact state between the wheel and rail 6 under wet and slippery road conditions, it was concluded that the value of creep rate is an important 7 factor affecting the autonomous driving of mining electric locomotives. Therefore, the autonomous 8 control method for mining electric locomotives based on creep control is proposed in this paper. 9 Finally, the effectiveness of the proposed method was verified through simulation. The problem of 10 wheel slipping and idling caused by insufficient friction of mining electric locomotives in coal mining 11 environments is effectively suppressed. Autonomous operation of vehicles with optimal driving 12 efficiency can be achieved through quantitative control and utilization of the creep rate between 13 wheels and rails

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

As a radiant light source within the dynamic range of most spacecraft payloads, the moon pro-vides an excellent reference for on-orbit radiometric calibration. This research hinges on the pre-cise simulation of lunar spectral irradiances and the Earth-based Moon observation geometry. The paper leverages the Hapke model to simulate the temporal changes in lunar spectral irradi-ances, utilizing datasets obtained from Lunar Reconnaissance Orbiter Camera (LROC). The re-search also details the transformation process from the lunar geographic coordinate system to the instantaneous projection coordinate system, thereby delineating the necessary observational geometry. The insights offered by this study have the potential to enhance future in-orbit space-craft calibration procedures, thereby boosting the fidelity of data gathered from satellite obser-vations.

SAA is an acute phase protein that elevates under inflammatory circumstance. The serum level of SAA was associated with the progression of inflammation in numerous diseases. However, little attention was paid to the correlation between SAA and ONFH. In this study, SAA was found risen in the femoral head of osteonecrosis patients through proteomics analysis and further confirmed by ELISA. Furthermore, SAA was proven affecting bone metabolism in rBMSCs. It facilitated the proliferation of rBMSCs whereas it suppressed the osteogenic differentiation of rBMSCs and accelerated the adipogenic differentiation of rBMSCs. Thus, we deem that serum amyloid A, which is a vital acute phase protein in inflammation, affected bone metabolis and plays an imperative role in the pathophysiological process of ONFH.

A switchable and tunable terahertz (THz) metamaterial based on photosensitive silicon and Vanadium dioxide (VO2) was proposed. By using finite-difference time-domain (FDTD) method, the transmission and reflective properties of the metamaterial were investigated theoretically. The results imply that, the metamaterial can realize a dual electromagnetically induced transparency (EIT) or two narrow-band absorption depending on the temperature of the VO2. Additionally, the magnitude of the EIT and two narrow-band absorption can be tuned by varying the conductivity of photosensitive silicon (PSi) via pumping light. Correspondingly, the slow light effect accompanying the EIT can also be adjusted.

The mammalian or mechanistic target of rapamycin (mTOR) integrates multiple intracellular and extra-cellular upstream signals involved in the regulation of anabolic and catabolic processes in cells, and plays a key regulatory role in cell growth and metabolism. Activation of the mTOR signaling pathway has been reported to be associated with a wide range of human diseases. A growing number of in vivo and in vitro studies have demonstrated that the gut microbes and its complex metabolites can regulate host metabolic and immune responses through the mTOR pathway, and result in disorders of host phys-iological functions. In this review, we summarize the regulatory mechanisms of gut microbes and mTOR in different diseases, and discuss the crosstalk between gut microbes and their metabolites and mTOR in the disorders in gastrointestinal tract, liver, heart and other organs. We also discuss the promising appli-cation of multiple potential drugs that can adjust the gut microbiota and mTOR signal pathways. Despite the limited findings between gut microbes and mTOR, elucidating their relationship may provide new clues for the prevention and treatment of various diseases.

As Artificial Intelligence (AI) technology, particularly Machine Learning algorithms, becomes increasingly ubiquitous, our abilities to understand and interpret AI and machine learning algorithms are becoming increasingly desirable. Visualization has been used as a common tool to view and understand complex machine learning processes. In this paper, we will focus on developing a general framework for the visualization of machine learning models as a scalar valued multi-dimensional function to help users understand how the models behave over different viewing spaces. We first give a formal definition of the visualization problem. Interpolation-based morphing and subspace sampling techniques are applied to generate various renderings through projections and cross-sections of the model space as 3D surfaces or heatmap images. This method will be applied to two real-world datasets and applications: the diagnosis of Alzheimer's Disease (AD) using a human brain networks dataset and a real-world benchmark dataset for predicting home credit default risks. The visualizations show that different machine learning algorithms can behave quite differently under different conditions.

Recognizing highly occluded objects is believed to arises from the interaction between the brain's vision and cognition controlling areas, although supporting neuroimaging data is currently limited. To explore the neural mechanism during this activity, we conducted an occlusion object recognition experiment using functional magnetic resonance imaging (fMRI). During magnet resonance examinations, 66 subjects engaged in object recognition tasks with three different occlusion degrees. Generalized linear model (GLM) analysis showed that the activation degree of occipital lobe (inferior occipital gyrus, middle occipital gyrus, and occipital fusiform gyrus) and dorsal anterior cingulate cortex (dACC) was related to the occlusion degree of the objects. Multivariate pattern analysis (MVPA) further unearthed a considerable surge in classification precision when dACC activation was incorporated as a feature. This suggested the combined role of dACC and occipital lobe in occluded object recognition tasks. Moreover, psychophysiological interaction (PPI) analysis disclosed that functional connectivity (FC) between the dACC and the occipital lobe was enhanced with increased occlusion, highlighting the necessity of FC between these two brain regions in effectively identifying exceedingly occluded objects. In conclusion, these findings contribute to understanding the neural mechanisms of highly occluded objects recognition, augmenting our appreciation of how the brain manages incomplete visual data.

SLEUTH Model (slope, landuse, exclusion, urban extent, transportation and hillshade) is an important tool for landuse planning and land policy. To evaluate the performance of SLEUTH model, implementation of Sensitivity Analysis (SA) is essential. The main limitation of SA in SLEUTH application is a lack of insight into model input self-modification parameters (SMPs) variation, namely, uncertainty involved in the model transfer metrics and model presumptions, which often misled the decision makers and model users. To address this issue, this study divided the forward process into two stages. Firstly, during the transfer process ①, the contribution scores of five SMPs were drawn, and parameters highly sensitive to model output were given. Apart from that, the recommended initial value for SMPs of 0.11, 0.2, 0.87, 1.13, 15, 1.01, 0.49 were found to be subordinated to such a heterogeneous urban area simulation. Secondly, during the transfer process ②, SMP caused imagery metrics indicated the disparity between parameters with Fixed Reference and with Successive Reference. Reversely, it derives reasonable threshold for the best fit values of five prediction coefficients’ initialization by comparing the real image with the predicted one. The framework of SLEUTH model transfer mechanism not only could distinguish highly sensitive SMPs with higher contribution scores, but also could give parametric analysis for simulation imagery based on metrics. The study was found to be a practical tool for quantization response of model input variables for modelling complex urban systems. So, this insight can help geographic information scientists decide how to find out the inner forward transfer mechanism of SLEUTH model for further make good use of it and improve the model.

Baduanjin exercise is a traditional Chinese health-Qigong routine, created by an ancient physician for health promotion. Its mild-to-moderate exercise intensity is suitable for individuals with medical conditions. Recently, a large number of trials were conducted to investigate the effects of Baduanjin exercise in patients with chronic obstructive pulmonary disease (COPD). It needs to be determined whether Baduanjin exercise prescription is beneficial for the management of COPD patients. Thus, we conducted a systematic review to objectively evaluate the existing literature on this topic. We searched six databases (PubMed, Web of Science, Cochrane Library, Scopus, CNKI, and Wanfang) from its inception till early May, 2018. The adapted PEDro scale was used for study quality assessment of all randomized controlled trials (RCT). The pooled effect size (Hedge’s g) of exercise capability (6-Minute Walking Test, 6-MWT), lung function parameters (forced expiratory volume in first second, FEV1; forced volume vital capacity, FVC; FEV1/FVC ratio), and quality of life were calculated based on the random-effects model. Twenty RCTs (n = 1975 COPD patients) were included in this review, with sum scores of the adapted PEDro scale between 5 and 9. Study results of meta-analysis indicate that Baduanjin is effective in improving exercise capability (Hedge’s g = 0.69, CI 0.44 to 0.94, p < 0.001, I2 = 66%), FEV1 (Hedge’s g = 0.47, CI 0.22 to 0.73, p < 0.001, I2 = 68.01%), FEV1% (Hedge’s g = 0.38, CI 0.21 to 0.56, p < 0.001, I2 = 54.74%), FVC (Hedge’s g = 0.39, CI 0.22 to 0.56, p < 0.001, I2 = 14.57%), FEV1/FVC (Hedge’s g = 0.5, CI 0.33 to 0.68, p < 0.001, I2 = 53.49%), and the quality of life of COPD patients (Hedge’ s g = - 0.45, CI -0.77 to - 0.12, p < 0.05, I2 = 77.02%), as compared to control groups. Baduanjin exercise as an adjunctive treatment may potentially improve exercise capability and pulmonary function of COPD patients as well as quality of life. Baduanjin exercise could be tentatively prescribed for COPD in combination with the conventional rehabilitation program to quicken the process of recovery. To confirm the positive effects of Baduanjin exercise for COPD patients, future researchers need to consider our suggestions mentioned in this article.

Taking Korla fragrant pear as the research object, single factor testing and water and fertilizer coupling testing were conducted. Under the premise of the drip irrigation mode, the effects of water, nitrogen, and phosphorus on the growth, fruit quality, and yield of Korla Fragrant Pear were studied to provide a theoretical basis for efficient water and fertilizer management. Among them, the single factor test set water, nitrogen and phosphorus as the three factors, and each factor was set at five levels, namely, water (W1, 5460 m3·hm−2;W2, 5880 m3·hm−2;W3,6300 m3·hm−2; W4, 6720 m3·hm−2; W5, 7140 m3·hm−2), nitrogen (N1, 150 kg N·hm−2;N2, 225 kg N·hm−2; N3, 300 kg N·hm−2; N4, 375 kg N·hm−2; N5, 450 kg N·hm−2), phosphorus (P1,75 kg P2O5·hm−2; P2, 150 kg P2O5·hm−2; P3, 225 kg P2O5·hm−2; P4, 300 kg P2O5·hm−2; P5, 375 kg P2O5·hm−2). The results showed that the total nitrogen and total phosphorus contents in leaves under the W3, W4, N3, N4, P3, and P4 treatments were significantly higher than those under other treatments, and the fruit quality was better. Therefore, the W3, W4, N3, N4, P3, and P4 treatments were selected as the water and fertilizer inputs for the water–fertilizer coupling test for Korla fragrant pear. Eight treatments were set up in the water and fertilizer coupling test: Treatment 1 (T1, W3N3P3), Treatment 2 (T2, W3N3P4), Treatment 3 (T3, W3N4P3), Treatment 4 (T4,W3N4P4), Treatment 5 (T5, W4N3P3), Treatment 6 (T6, W4N3P4), Treatment 7 (T7, W4N4) P3), and Treatment 8 (T8, W4N4P4). The results showed that the leaf area index of the T1, T2, T3, and T4 treatments was significantly higher than that of the other treatments at maturity. The T3 treatment resulted in the highest fruit yield, single fruit weight, and primary fruit rate. The results of the grey correlation degree analysis of fruit quality showed that T3 treatment had the highest degree of correlation and ranking of each fruit quality index, indicating that T3 treatment had the highest fruit quality. Detailed investigation of pear tree growth and fruit quality showed that the T3 treatment (375 kg·hm−2 nitrogen application, 225 kg·hm−2 phosphorus application and 6300 m3·hm−2 irrigation) had the best fruit growth and development performance, and the sweet pear fruit quality was the best.

Taking Korla fragrant pear as the research object, single factor testing and water and fertilizer coupling testing were conducted. Under the premise of the drip irrigation mode, the effects of water, nitrogen, and phosphorus on the growth, fruit quality, and yield of Korla Fragrant Pear were studied to provide a theoretical basis for efficient water and fertilizer management. Among them, the single factor test set water, nitrogen and phosphorus as the three factors, and each factor was set at five levels, namely, water (W1, 5460 m3·hm−2;W2, 5880 m3·hm−2;W3,6300 m3·hm−2; W4, 6720 m3·hm−2; W5, 7140 m3·hm−2), nitrogen (N1, 150 kg N·hm−2;N2, 225 kg N·hm−2; N3, 300 kg N·hm−2; N4, 375 kg N·hm−2; N5, 450 kg N·hm−2), phosphorus (P1,75 kg P2O5·hm−2; P2, 150 kg P2O5·hm−2; P3, 225 kg P2O5·hm−2; P4, 300 kg P2O5·hm−2; P5, 375 kg P2O5·hm−2). The results showed that the total nitrogen and total phosphorus contents in leaves under the W3, W4, N3, N4, P3, and P4 treatments were significantly higher than those under other treatments, and the fruit quality was better. Therefore, the W3, W4, N3, N4, P3, and P4 treatments were selected as the water and fertilizer inputs for the water–fertilizer coupling test for Korla fragrant pear. Eight treatments were set up in the water and fertilizer coupling test: Treatment 1 (T1, W3N3P3), Treatment 2 (T2, W3N3P4), Treatment 3 (T3, W3N4P3), Treatment 4 (T4,W3N4P4), Treatment 5 (T5, W4N3P3), Treatment 6 (T6, W4N3P4), Treatment 7 (T7, W4N4) P3), and Treatment 8 (T8, W4N4P4). The results showed that the leaf area index of the T1, T2, T3, and T4 treatments was significantly higher than that of the other treatments at maturity. The T3 treatment resulted in the highest fruit yield, single fruit weight, and primary fruit rate. The results of the grey correlation degree analysis of fruit quality showed that T3 treatment had the highest degree of correlation and ranking of each fruit quality index, indicating that T3 treatment had the highest fruit quality. Detailed investigation of pear tree growth and fruit quality showed that the T3 treatment (375 kg·hm−2 nitrogen application, 225 kg·hm−2 phosphorus application and 6300 m3·hm−2 irrigation) had the best fruit growth and development performance, and the sweet pear fruit quality was the best.