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Abstract The explosion in foundation poses a significant threat to people and buildings. Currently, a unified empirical prediction formula for crater in calcareous foundation has not been established. In this paper, analyzed the types and sizes of explosion crater with different scaled burial depths through field tests and numerical simulation. In field tests, revealed the influence of scaled burial depth on the type and size of explosion crater and obtained the critical scaled burial depth for three different types of explosion craters, namely ejecta-type crater, collapse-type crater and covert explosion. Through the Smooth Particle Hydrodynamic-Finite Element Method (SPH-FEM) coupling algorithm, studied the movement trajectory of sand particles around the explosive at the moment of explosion in detail. Based on the field tests and numerical simulation results, it was found that calcareous sand has a smaller specific gravity due to its own characteristics, and the size of the explosion crater is larger than that of quartz sand at the same scaled burial depth. Obtained an empirical formula for crater in calcareous sand. Which can quickly predict the size of explosion crater and provide calculation basis for explosion resistant design in calcareous sand foundations.

Pseudopoda Jäger, 2000 is currently the largest genus in the family Sparassidae Bertkau, 1872, comprising 255 species. Of these, 154 species have been recorded in China, representing 60.4% of the global species.In October 2023, a spider survey was conducted in Shennongjia National Nature Reserve, Hubei Province, China. After examination and morphological comparison, one new species of the genus Pseudopoda was identified and is described here.In this paper, a new species of Pseudopoda collected in Hubei Province, China, is proposed and named Pseudopoda guanmenshan sp. nov. A detailed description, diagnosis, photographs and distribution map of the new species are provided.

Laser powder bed fusion (LPBF) Invar 36 alloy has attracted plenty of attention in cryogenic components or precision instruments due to its low coefficient of thermal expansion in a wide range of temperature. However, the LPBF Invar 36 has a significant anisotropic mechanical property due to the unique columnar grain morphology and pore feature. In this study, the influence of scanning speed on the anisotropic tensile properties were investigated, and the melt pool dynamics were analyzed by the numerical simulation. In this respect, tensile tests of specimens parallel and perpendicular to the building direction with a scanning speed from 200 to 1400 mm/s of LPBF Invar 36 alloy were conducted, and the microstructural influential factors on the anisotropic tensile properties were analyzed. Results show that as the volumetric energy density (VED) increases, the inhomogeneity of grain morphology decreases, while the pore feature changes from the lack-of-fusion (LOF) pore perpendicular to the building direction to the bubble-shaped spherical pore, which is resulted from the collapse of the key-holes. In this case, the grain orientation and size, as well as the dislocation density in the vicinity of pores are influenced by the pore morphology due to different thermal history. On the whole, the combined morphology of dislocation and stress concentration affected by the pore morphology are the main reason of the increased level of anisotropy in tensile properties.

Abstract The rapid advancement in attractive platforms such as biomedicine and human‐machine interaction has generated urgent demands for intelligent materials with high strength, flexibility, and self‐healing capabilities. However, existing self‐healing ability materials are challenged by a trade‐off between high strength, low elastic modulus, and healing ability due to the inherent low strength of noncovalent bonding. Here, drawing inspiration from human fibroblasts, a monomer trapping synthesis strategy is presented based on the dissociation and reconfiguration in amphiphilic ionic restrictors (7000‐times volume monomer trapping) to develop a eutectogel. Benefiting from the nanoconfinement and dynamic interfacial interactions, the molecular chain backbone of the formed confined domains is mechanically reinforced while preserving soft movement capabilities. The resulting eutectogels demonstrate superior mechanical properties (1799% and 2753% higher tensile strength and toughness than pure polymerized deep eutectic solvent), excellent self‐healing efficiency (>90%), low tangential modulus (0.367 MPa during the working stage), and the ability to sensitively monitor human activities. This strategy is poised to offer a new perspective for developing high strength, low modulus, and self‐healing wearable electronics tailored to human body motion.

BackgroundCyadox (CYA) is a derivative of quinoxaline 1,4-dioxide and a safe and effective synthetic antibacterial agent.ObjectiveThis study aimed to explore the drug transport in blood, distribution, depletion and hepatotoxicity of drugs in animals.MethodsThe transport of CYA in blood was studied using fluorescence, circular dichroism (CD) and molecular docking methods. Tissue distribution and depletion of CYA in rats were evaluated following oral administration of [3H]-CYA at different doses. Hepatotoxicity of drugs evaluated by transcriptomics.ResultsDuring transport in the bloodstream, the drug binds to bovine serum albumin (BSA) by hydrogen bonding and has only one binding site. Hydrogen bonds were formed between O (2) of CYA and ARG208, O (3) of CYA and LEU480, VAL481. The secondary protein conformation of BSA changed after binding with an increase in α-helix and a decrease in β-strand. After a single oral administration of [3H]-CYA, it was excreted rapidly within 7 days, with 34.81% from the urine and 60.25% from the feces. Higher and sustained levels of radioactivity were detected in the liver during the post-dose period, suggesting that the drug may concentrate in the liver. The transcriptomic data indicates that CYA exhibits low hepatotoxicity. However, there are indications that it may have an impact on steroid biosynthesis.ConclusionThis study could serve as a basis for conducting further studies on the use of CYA in food animals and improving the pharmacologic, pharmacokinetic, and toxicologic effects of CYA on food animals.

IntroductionCrop height and above-ground biomass (AGB) serve as crucial indicators for monitoring crop growth and estimating grain yield. Timely and accurate acquisition of wheat crop height and AGB data is paramount for guiding agricultural production. However, traditional data acquisition methods suffer from drawbacks such as time-consuming, laborious and destructive sampling.MethodsThe current approach to estimating AGB using unmanned aerial vehicles (UAVs) remote sensing relies solely on spectral data, resulting in low accuracy in estimation. This method fails to address the ill-posed inverse problem of mapping from two-dimensional to three-dimensional and issues related to spectral saturation. To overcome these challenges, RGB and multispectral sensors mounted on UAVs were employed to acquire spectral image data. The five-directional oblique photography technique was utilized to construct the three-dimensional point cloud for extracting crop height.Results and DiscussionThis study comparatively analyzed the potential of the mean method and the Accumulated Incremental Height (AIH) method in crop height extraction. Utilizing Vegetation Indices (VIs), AIH and their feature combinations, models including Random Forest Regression (RFR), eXtreme Gradient Boosting (XGBoost), Gradient Boosting Regression Trees (GBRT), Support Vector Regression (SVR) and Ridge Regression (RR) were constructed to estimate winter wheat AGB. The research results indicated that the AIH method performed well in crop height extraction, with minimal differences between 95% AIH and measured crop height values were observed across various growth stages of wheat, yielding R2 ranging from 0.768 to 0.784. Compared to individual features, the combination of multiple features significantly improved the model’s estimate accuracy. The incorporation of AIH features helps alleviate the effects of spectral saturation. Coupling VIs with AIH features, the model’s R2 increases from 0.694-0.885 with only VIs features to 0.728-0.925. In comparing the performance of five machine learning algorithms, it was discovered that models constructed based on decision trees were superior to other machine learning algorithms. Among them, the RFR algorithm performed optimally, with R2 ranging from 0.9 to 0.93.ConclusionIn conclusion, leveraging multi-source remote sensing data from UAVs with machine learning algorithms overcomes the limitations of traditional crop monitoring methods, offering a technological reference for precision agriculture management and decision-making.

Abstract Objective To determine the optimal scan duration for ultrafast DCE-MRI in effectively differentiating benign from malignant breast lesions. Methods The study prospectively recruited participants who underwent breast ultrafast DCE-MRI from September 2021 to March 2023. A 30-phase breast ultrafast DCE-MRI on a 3.0-T MRI system was conducted with a 4.5-s temporal resolution. Scan durations ranged from 40.5 s to 135.0 s, during which the analysis is performed at three-phase intervals, forming eight dynamic sets (scan duration [SD]40.5s: 40.5 s, SD54s: 54.0 s, SD67.5s: 67.5 s, SD81s: 81.0 s, SD94.5s: 94.5 s, SD108s: 108.0 s, SD121.5s: 121.5 s, and SD135s: 135.0 s). Two ultrafast DCE-MRI parameters, maximum slope (MS) and initial area under the curve in 60 s (iAUC), were calculated for each dynamic set and compared between benign and malignant lesions. Areas under the receiver operating characteristic curve (AUCs) were used to assess their diagnostic performance. Results A total of 140 women (mean age, 47 ± 11 years) with 151 lesions were included. MS and iAUC from eight dynamic sets exhibited significant differences between benign and malignant lesions (all p 0.05). Conclusions Ultrafast DCE-MRI with a 67.5-s scan duration appears optimal for effectively differentiating malignant from benign breast lesions. Critical relevance statement By evaluating scan durations (40.5–135 s) and analyzing two ultrafast DCE-MRI parameters, we found a scan duration of 67.5 s optimal for discriminating between these lesions and offering a balance between acquisition time and diagnostic efficacy. Key Points Ultrafast DCE-MRI can effectively differentiate malignant from benign breast lesions. A minimum of 67.5-sec ultrafast DCE-MRI scan duration is required to differentiate benign and malignant lesions. Extending the scan duration beyond 67.5 s did not significantly improve diagnostic accuracy. Graphical Abstract

A d10-Cd cluster containing sandwich-type arsenotungstate [C3H12N2]6[Cd4Cl2(B-α-AsW9O34)2] was synthesized and its structure characterized through elemental analyses, X-ray powder diffraction (XRPD), IR spectroscopy, X-ray photoelectron spectroscopy (XPS), and single-crystal X-ray diffraction. The X-ray analysis revealed that the molecular unit of the compound consists of a captivating tetra-Cd-substituted sandwich-type polyoxoanion, accompanied by six elegantly protonated 1,2-diaminopropane as counter ions. The further novelty of the tetranuclear cadmium cluster lies in its occupied chlorine atom sites. This makes it highly susceptible to coordinate reactions with nitrogen on polycyclic aromatic hydrocarbons, thereby exhibiting different fluorescent signals that facilitate the identification and detection of these carcinogenic substances in methanol.

Assessing the quality of maize seedlings is crucial for field management and germplasm evaluation. Traditional methods for evaluating seedling quality mainly rely on manual field surveys, which are not only inefficient but also highly subjective, while large-scale satellite detection often lacks sufficient accuracy. To address these issues, this study proposes an innovative approach that combines the YOLO v8 object detection algorithm with Voronoi spatial analysis to rapidly evaluate maize seedling quality based on high-resolution drone imagery. The YOLO v8 model provides the maize coordinates, which are then used for Voronoi segmentation of the field after applying the Convex Hull difference method. From the generated Voronoi diagram, three key indicators are extracted: Voronoi Polygon Uniformity Index (VPUI), missing seedling rate, and repeated seedling rate to comprehensively evaluate maize seedling quality. The results show that this method effectively extracts the VPUI, missing seedling rate, and repeated seedling rate of maize in the target area. Compared to the traditional plant spacing variation coefficient, VPUI performs better in representing seedling uniformity. Additionally, the R2 for the estimated missing seedling rate and replanting rate based on the Voronoi method were 0.773 and 0.940, respectively. Compared to using the plant spacing method, the R2 increased by 0.09 and 0.544, respectively. The maize seedling quality evaluation method proposed in this study provides technical support for precision maize planting management and is of great significance for improving agricultural production efficiency and reducing labor costs.

Vapor pressure deficit (VPD) shows significant spatial and temporal variability in the context of global climate change, which is important for studying the implications of climate change on the structure and function of ecosystems to analyze the effects of VPD on vegetation dynamics. Spatial patterns of vegetation sensitivity to VPD have been recently investigated, however, the feedback of different vegetation types to VPD may vary depending on physiological characteristics, it is unclear how different types influence the sensitivity to VPD. In this study, the ERA5-Land reanalysis time-series dataset was used to analyze the spatial and temporal trends of VPD under different vegetation types. It was found that VPD showed an increasing trend in vegetated areas over the past 20 years with large spatial heterogeneity, generally increasing with drying conditions. On this basis, the spatial patterns of vegetation sensitivity to VPD and temporal trends in sensitivity were evaluated over the past 20 years in China using the enhanced vegetation index (EVI) and near-infrared reflectance of vegetation (NIRv) which can describe vegetation dynamics. The results show that the sensitivities under the two indices have high spatial consistency, with northeastern and central China showing positive sensitivities and southern China showing negative sensitivities, respectively. The positive sensitivities are relatively high for Deciduous Broadleaf Forests (DBF), Deciduous Needleleaf Forests (DNF), Grasslands (GL), and Croplands (CL) types, while the negative sensitivities are larger for Shrublands (SL) and Savannas (SA) types. Under different climatic zones, the sensitivity of CL and GL are independent of climatic zones (both showing positive), while the sensitivity of SL is negative in the Humid zone and positive in the Semi-Arid zone. Temporally, the sensitivity showed a slow increasing trend over the last 20 years. In terms of vegetation types, sensitivities of Evergreen Broadleaf Forests (EBF), DBF, GL and CL types showed a significant increasing trend (p

Accurately assessing maize crop height (CH) and aboveground biomass (AGB) is crucial for understanding crop growth and light-use efficiency. Unmanned aerial vehicle (UAV) remote sensing, with its flexibility and high spatiotemporal resolution, has been widely applied in crop phenotyping studies. Traditional canopy height models (CHMs) are significantly influenced by image resolution and meteorological factors. In contrast, the accumulated incremental height (AIH) extracted from point cloud data offers a more accurate estimation of CH. In this study, vegetation indices and structural features were extracted from optical imagery, nadir and oblique photography, and LiDAR point cloud data. Optuna-optimized models, including random forest regression (RFR), light gradient boosting machine (LightGBM), gradient boosting decision tree (GBDT), and support vector regression (SVR), were employed to estimate maize AGB. Results show that AIH99 has higher accuracy in estimating CH. LiDAR demonstrated the highest accuracy, while oblique photography and nadir photography point clouds were slightly less accurate. Fusion of multi-source data achieved higher estimation accuracy than single-sensor data. Embedding structural features can mitigate spectral saturation, with R2 ranging from 0.704 to 0.939 and RMSE ranging from 0.338 to 1.899 t/hm2. During the entire growth cycle, the R2 for LightGBM and RFR were 0.887 and 0.878, with an RMSE of 1.75 and 1.76 t/hm2. LightGBM and RFR also performed well across different growth stages, while SVR showed the poorest performance. As the amount of nitrogen application gradually decreases, the accumulation and accumulation rate of AGB also gradually decrease. This high-throughput crop-phenotyping analysis method offers advantages, such as speed and high accuracy, providing valuable references for precision agriculture management in maize fields.

A timely and comprehensive understanding of winter wheat maturity is crucial for deploying large-scale harvesters within a region, ensuring timely winter wheat harvesting, and maintaining grain quality. Winter wheat maturity prediction is limited by two key issues: accurate extraction of wheat planting areas and effective maturity prediction methods. The primary aim of this study is to propose a method for predicting winter wheat maturity. The method comprises three parts: (i) winter wheat planting area extraction via phenological characteristics across multiple growth stages; (ii) extraction of winter wheat maturity features via vegetation indices (VIs, such as NDVI, NDRE, NDII1, and NDII2) and box plot analysis; and (iii) winter wheat maturity data prediction via the selected VIs. The key findings of this work are as follows: (i) Combining multispectral remote sensing data from the winter wheat jointing-filling and maturity-harvest stages can provide high-precision extraction of winter wheat planting areas (OA = 95.67%, PA = 91.67%, UA = 99.64%, and Kappa = 0.9133). (ii) The proposed method can offer the highest accuracy in predicting maturity at the winter wheat flowering stage (R2 = 0.802, RMSE = 1.56 days), aiding in a timely and comprehensive understanding of winter wheat maturity and in deploying large-scale harvesters within the region. (iii) The study’s validation was only conducted for winter wheat maturity prediction in the North China Plain wheat production area, and the accuracy of harvesting progress information extraction for other regions’ wheat still requires further testing. The method proposed in this study can provide accurate predictions of winter wheat maturity, helping agricultural management departments adopt information-based measures to improve the efficiency of monitoring winter wheat maturation and harvesting, thus promoting the efficiency of precision agricultural operations and informatization efforts.

Heat shock proteins (HSPs) are molecular chaperones that play essential roles in plant development and in response to various environmental stresses. Understanding R. delavayi HSP genes is of great importance since R. delavayi is severely affected by heat stress. In the present study, a total of 76 RdHSP genes were identified in the R. delavayi genome, which were divided into five subfamilies based on molecular weight and domain composition. Analyses of the chromosome distribution, gene structure, and conserved motif of the RdHSP family genes were conducted using bioinformatics analysis methods. Gene duplication analysis showed that 15 and 8 RdHSP genes were obtained and retained from the WGD/segmental duplication and tandem duplication, respectively. Cis-element analysis revealed the importance of RdHSP genes in plant adaptations to the environment. Moreover, the expression patterns of RdHSP family genes were investigated in R. delavayi treated with high temperature based on our RNA-seq data, which were further verified by qRT-PCR. Further analysis revealed that nine candidate genes, including six RdHSP20 subfamily genes (RdHSP20.4, RdHSP20.8, RdHSP20.6, RdHSP20.3, RdHSP20.10, and RdHSP20.15) and three RdHSP70 subfamily genes (RdHSP70.15, RdHSP70.21, and RdHSP70.16), might be involved in enhancing the heat stress tolerance. The subcellular localization of two candidate RdHSP genes (RdHSP20.8 and RdHSP20.6) showed that two candidate RdHSPs were expressed and function in the chloroplast and nucleus, respectively. These results provide a basis for the functional characterization of HSP genes and investigations on the molecular mechanisms of heat stress response in R. delavayi.

The continuous retrieval of clear-sky land surface temperature (LST) holds paramount importance in monitoring vegetation temperature and assessing water stress conditions. Nonetheless, the extensive cloud cover results in a widespread lack of LST data, posing challenges in accurately forecasting LST in regions characterized by diverse vegetation types and complex terrains. Therefore, this paper proposes a synthetic aperture radar (SAR)- and digital elevation model (DEM)-integrated LST reconstruction model (SDX-LST) to generate realistic and high-spatial-resolution (30 m) clear-sky LST data. To assess the practicality and robustness of the SDX-LST model, this paper selects the study areas of Loess Plateau (LP), Qinghai-Tibet Plateau, Northeast China Plain, Nanling Mountains, and North China Plain in China, Desert Rock, Nevada in America, spanning a wide range of longitude and latitude and having obvious differences in topography, landforms, and vegetation. The analysis of the reconstruction results in different spatial location distributions, vegetation cover types, and multidate and time distributions throughout the year indicate that the SDX-LST model achieves excellent performance and high stability (with a mean absolute error lower than 2K). The SDX-LST predictions demonstrate a commendable level of consistency with the ERA5-hourly product and in situ data. We conclude that the integration of SAR and DEM within the SDX-LST model enables precise predictions of LST for various vegetation types and intricate terrains. The study quantitatively analyzes the effects of SAR and DEM on LST and introduces novel insights for exploring SAR-based reconstruction of LST.

IntroductionObtaining wheat growth information accurately and efficiently is the key to estimating yields and guiding agricultural development.MethodsThis paper takes the precision agriculture demonstration area of Jiaozuo Academy of Agriculture and Forestry in Henan Province as the research area to obtain data on wheat biomass, nitrogen content, chlorophyll content, and leaf area index. By using the coefficient of variation method, a Comprehensive Growth Monitoring Indicator (CGMI) was constructed to perform fractional derivative processing on drone spectral data, and correlation analysis was performed on the fractional derivative spectra with a single indicator and CGMI, respectively. Then, grey correlation analysis was carried out on differential spectral bands with high correlation, the grey correlation coefficients between differential spectral bands were calculated, and spectral bands with high correlation were screened and taken as input variables for the model. Next, ridge regression, random forest, and XGboost models were used to establish a wheat CGMI inversion model, and the coefficient of determination (R2) and root mean squared error (RMSE) were adopted for accuracy evaluation to optimize the wheat optimal growth inversion model.Results and discussionThe results of the study show that: using the data of wheat biomass, nitrogen content, chlorophyll content and leaf area index to construct the comprehensive growth monitoring indicators, the correlation between the wheat growth monitoring indicators and the spectra was calculated, and the results showed that the correlation between the comprehensive growth monitoring indicators and the single indicator correlation had different degrees of increase, and the growth rate could reach 82.22%. The correlation coefficient between the comprehensive growth monitoring indexes and the differential spectra reached 0.92 at the flowering stage, and compared with the correlation coefficient with the original spectra at the same period, the correlation coefficients increased to different degrees, which indicated that the differential processing of spectral data could effectively enhance the spectral correlation. The three models of Random Forest, Ridge Regression and XGBoost were used to construct the wheat growth inversion model with the best effect at the flowering stage, and the XGBoost model had the highest inversion accuracy when comparing in the same period, with the training and test sets reaching 0.904 and 0.870, and the RMSEs were 0.050 and 0.079, so that the XGBoost model can be used as an effective method of monitoring the growth of wheat. To sum up, this study demonstrates that the combination of constructing comprehensive growth monitoring indicators and differential processing spectra can effectively improve the accuracy of wheat growth monitoring, bringing new methods for precision agriculture management.

The antioxidant activity of chitosan (CS) and three water-soluble derivatives was analyzed comparatively by in vitro and in vivo experiments, including hydroxypropyl chitosan (HPCS), quaternary ammonium salt of chitosan (HACC), and carboxymethyl chitosan (CMCS). The results show that chitosan and its water-soluble derivatives have a scavenging ability on DPPH radicals, superoxide radicals, and hydroxyl radicals, and a reducing ability. A remarkable difference (p < 0.05) was found for HACC and HPCS compared with CS on DPPH radicals, hydroxyl radicals, and reducing ability. The antioxidant ability of the four chitosan samples was in the order of HPCS > HACC > CMCS > CS. Furthermore, antioxidant activity of all samples increased gradually in a concentration-dependent manner. The in vivo result indicates that oral CS and its derivatives samples result in a decrease in lipid peroxides (LPO) and free fatty acids (FFA) levels in serum with an increase in superoxide dismutase (SOD) activity. Especially for the HPCS and HACC groups, the LPO, FFA, and SOD activity in serum was different significantly in comparison with the high-fat controlgroup (HF) (p < 0.05). These results indicate that chitosan and its derivatives can be used as good antioxidants, and the antioxidant activity might be related to the molecular structure of chitosan derivatives.

Remifentanil has anti-tumor effects, but its effects on colorectal cancer are unclear. Therefore, the present study investigated whether remifentanil mediates colorectal cancer cell malignant behaviors through the modulation of chemokine ligand 2-C-C chemokine receptor 2 signaling. SW480 cells were divided into control, low remifentanil dose (0.5 ng/ml), medium remifentanil dose (5.0 ng/ml), high remifentanil dose (50.0 ng/ml), and high remifentanil dose+GW0742 (1.0 µM) groups. Detection of SW480 cell proliferation, apoptosis, and migration was performed by cell counting kit-8, flow cytometry, and wound-healing assays. Bcl-2-associated X protein, proliferating cell nuclear antigen, programmed death-ligand 1, B-cell lymphoma 2, chemokine ligand 2, and C-C chemokine receptor 2 protein levels were examined by Western blot. Remifentanil inhibited SW480 cell proliferation and migration, and facilitated SW480 cell apoptosis in a dose-dependent pattern, accompanied by a striking decrease in proliferating cell nuclear antigen, programmed death-ligand 1, and B-cell lymphoma 2 protein levels and an overt elevation in Bcl-2-associated X protein levels. Bindarit had similar effects as remifentanil, and GW0742 attenuated remifentanil-mediated effects on SW480 cell proliferation, migration, and apoptosis. Remifentanil and bindarit inhibited chemokine ligand 2 and C-C chemokine receptor 2 protein levels, but GW0742 attenuated remifentanil-mediated effects on chemokine ligand 2 and C-C chemokine receptor 2 protein levels. Remifentanil may inhibit colorectal cancer malignant behaviors by inhibiting the chemokine ligand 2/C-C chemokine receptor 2 signaling. [ABSTRACT FROM AUTHOR]

Abstract Rumen microbiotas are known to influence the fat deposition (FD) in sheep, but controversy over causality remains unresolved. Here, we performed microbiome-wide association studies (MWAS), microbiome genome-wide association analysis (mbGWAS) and bidirectional mendelian randomization (MR) analyses on 1,150 sheep with genotype data from whole-genome resequencing, 16S rRNA sequencing and multilevel FD-traits data. We quantified the proportion of individual variation in FD-traits explained by host genetics, rumen microbiota, and their interaction effects. We identified 32 rumen microbiota biomarkers including Bifidobacterium that were associated with FD-traits (Padj

Abstract Human–robot collaboration (HRC) is a novel manufacturing paradigm designed to fully leverage the advantage of humans and robots, efficiently and flexibly accomplishing customized manufacturing tasks. However, existing HRC systems lack the transfer and generalization capability for environment perception and task reasoning. These limitations manifest in: (1) current methods rely on specialized models to perceive scenes; and need retraining the model when facing unseen objects. (2) current methods only address predefined tasks, and cannot support undefined task reasoning. To avoid these limitations, this paper proposes a novel HRC approach based on Foundation Models (FMs), including Large Language models (LLMs) and Vision Foundation Models (VFMs). Specifically, a LLMs-based task reasoning method is introduced, utilizing prompt learning to transfer LLMs into the domain of HRC tasks, supporting undefined task reasoning. A VFMs-based scene semantic perception method is proposed, integrating various VFMs to achieve scene perception without training. Finally, a FMs-based HRC system is developed, comprising perception, reasoning, and execution modules for more flexible and generalized HRC. The superior performances of FMs in perception and reasoning are demonstrated by extensive experiments. Furthermore, the feasibility and effectiveness of the FMs-based HRC system are validated through an part assembly case involving a satellite component model.

Abstract Shale oil development in Ordos Basin, China, primarily relies on the displacement of crude oil during the post-fracturing shut-in stage (PFSIS) of horizontal wells. Reservoir wettability significantly influences the shut-in duration and even the development approach. However, due to strong heterogeneity and super tight characteristics, the reservoir usually shows an mixed wettability, and it was usually hard to differentiate the wettability in different pore sizes. With this in mind, this study focuses on core samples from shale oil reservoir in the Longdong region of the Ordos basin to quantitatively analyze the reservoir wettability. Amott method combined with nuclear magnetic resonance is adopted in the paper to meed this end. And the optimal post-fracturing shut-in duration for Huachi and Heshui areas in the Longdong region are determined based on both wettability and field practice analysis as well as numerical simulations. Qualitative wettability evaluation reveals that the reservoir in the Longdong region is weakly oil-wet (oil-wet pores account for 58.9% and water-wet pores for 41.1%), and that larger pores are more water-wet, while smaller pores are more oil-wet. Field practice observes a noticeable two-stage decline in wellhead pressure, with pressure drop rates and water content decline rates following the order of neutral reservoir > weakly oil-wet reservoir > oil-wet reservoir during the post-fracturing shut-in stage. Numerical simulations indicate that the determination of the optimal post-fracturing shut-in duration for horizontal wells should consider reservoir properties, wettability, and injection volume. The final optimal shut-in durations for the Huachi and Heshui areas in the reservoir are determined to be 36 days and 43 days, respectively. Our study qualitatively distinguishes the wettability in different pores sizes and thus determines reasonable post-fracturing shut-in durations in different areas in Longdong region. The research has major implication for building a realistic method of wettability analysis in shale or tight oil reservoir.

Abstract Despite the growing interest of archiving information in synthetic DNA to confront data explosion, quantitatively querying the data stored in DNA is still a challenge. Herein, we present Search Enabled by Enzymatic Keyword Recognition (SEEKER), which utilizes CRISPR-Cas12a to rapidly generate visible fluorescence when a DNA target corresponding to the keyword of interest is present. SEEKER achieves quantitative text searching since the growth rate of fluorescence intensity is proportional to keyword frequency. Compatible with SEEKER, we develop non-collision grouping coding, which reduces the size of dictionary and enables lossless compression without disrupting the original order of texts. Using four queries, we correctly identify keywords in 40 files with a background of ~8000 irrelevant terms. Parallel searching with SEEKER can be performed on a 3D-printed microfluidic chip. Overall, SEEKER provides a quantitative approach to conducting parallel searching over the complete content stored in DNA with simple implementation and rapid result generation.

Xinjiang is a major province of sheep breeding in China, which plays an important role in meeting people’s needs for meat products, increasing farmers’ income and sustainable development of animal husbandry. However, the genetic differentiation relationship between breeds was not clear, and most sheep had low fecundity, which seriously restricted the efficient development of sheep industry. Therefore, this study used the whole genome resequencing to detect the genetic variation of Dexin mutton and fine-wool sheep, explored the selected regions and important genes of the litter size traits, analyzed the genetic mechanism of reproductive traits, and provided new insights for the high fecundity breeding of sheep. A total of 5,236.338 G genome data and 35,884,037 SNPs were obtained. Furthermore, we identified 39 selection signals spanning candidate genes, 99 genes were significantly associated related to growth, reproduction and immunity, among which, BRIP1, BMPR1B, BMP4, NGF, etc. genes, and MAKP signaling pathway, Fanconi anemia pathway and Thyroid hormone signaling pathway and other signaling pathways were significantly correlated with litter size trait. Among them, we identified NGF, TrKA and BRIP1 genes was the important genes for sheep litter size traits and the mutation frequencies of 9 SNPs in BRIP1 gene were significantly different in domestic sheep in the world. The research provided new insights for the breeding of self-cultivated meat fine-wool sheep.

Objective: This meta-analysis aims to explore the dose–response relationship of aerobic exercise or aerobic combined resistance exercise on cardiopulmonary function in maintenance hemodialysis（MHD）, with the goal of aiding in the formulation of precise exercise prescriptions. Methods: A literature search up to August 18, 2023, was conducted in databases including Web of Science, among others, focusing on the effects of exercise interventions on cardiopulmonary function in hemodialysis patients.Two researchers independently conducted literature screening, data extraction, and an assessment of study methodology quality. A dose–response meta-analysis was carried out using a one-stage cubic spline mixed-effects model, followed by stratified analyses based on intervention period, intervention method, and exercise environment. Results: A nonlinear dose–response relationship was observed between exercise and 6-minute walk test (6WMT) as well as peak oxygen uptake (VO2Peak) in hemodialysis patients. The optimal exercise dose for the 6WMT across the full exposure range was 922 METs-min/week, with VO2Peak increasing with the dose. The effects were influenced by the type of exercise, intervention period, and exercise environment. An exercise dose of 500 METs-min/week and 619 METs-min/week was found sufficient to achieve the minimal clinically important differences (MCID) for 6WMT and VO2Peak, respectively. Conclusion: There is a significant association between the dose of exercise and its effects. With appropriate adjustment of variables, even low-dose exercise can lead to clinically significant improvements in cardiopulmonary function.

Abstract Simple, sensitive, and accurate molecular diagnostics are critical for preventing rapid spread of infection and initiating early treatment of diseases. However, current molecular detection methods typically rely on extensive nucleic acid sample preparation and expensive instrumentation. Here, a simple, fully integrated, lab‐in‐a‐magnetofluidic tube (LIAMT) platform is presented for “sample‐to‐result” molecular detection of virus. By leveraging magnetofluidic transport of micro/nano magnetic beads, the LIAMT device integrates viral lysis, nucleic acid extraction, isothermal amplification, and CRISPR detection within a single engineered microcentrifuge tube. To enable point‐of‐care molecular diagnostics, a palm‐sized processor is developed for magnetofluidic separation, nucleic acid amplification, and visual fluorescence detection. The LIAMT platform is applied to detect SARS‐CoV‐2 and HIV viruses, achieving a detection sensitivity of 73.4 and 63.9 copies µL−1, respectively. Its clinical utility is further demonstrated by detecting SARS‐CoV‐2 and HIV in clinical samples. This simple, affordable, and portable LIAMT platform holds promise for rapid and sensitive molecular diagnostics of infectious diseases at the point‐of‐care.

Coronary artery disease (CAD) is an irreversible and fatal disease. It necessitates timely and precise diagnosis to slow CAD progression. Electrocardiogram (ECG) and phonocardiogram (PCG), conveying abundant disease-related information, are prevalent clinical techniques for early CAD diagnosis. Nevertheless, most previous methods have relied on single-modal data, restricting their diagnosis precision due to suffering from information shortages. To address this issue and capture adequate information, the development of a multi-modal method becomes imperative. In this study, a novel multi-modal learning method is proposed to integrate both ECG and PCG for CAD detection. Along with deconvolution operation, a novel ECG-PCG coupling signal is evaluated initially to enrich the diagnosis information. After constructing a modified recurrence plot, we build a parallel CNN network to encode multi-modal information, involving ECG, PCG and ECG-PCG coupling deep-coding features. To remove irrelevant information while preserving discriminative features, we add an autoencoder network to compress feature dimension. Final CAD classification is conducted by combining support vector machine and optimal multi-modal features. The experiment is validated on 199 simultaneously recorded ECG and PCG signals from non-CAD and CAD subjects, and achieves high performance with accuracy, sensitivity, specificity and f1-score of 98.49%, 98.57%,98.57% and 98.89%, respectively. The result demonstrates the superiority of the proposed multi-modal method in overcoming information shortages of single-modal signals and outperforming existing models in CAD detection. This study highlights the potential of multi-modal deep-coding information, and offers a wider insight to enhance CAD diagnosis.

Resource-based cities often face problems such as resource scarcity and insufficient electricity to achieve complex high-quality growth. At present, there is relatively little research on the impact on the high-quality development of such cities. To study the key variables that affect the high-quality growth of resource-based cities, we adopt entropy weighted TOPSIS technology, spatial correlation analysis, and spatial econometric models. The main conclusions are as follows: (1) The overall high-quality development of resource-based cities in China is on the rise year by year; The cities with the highest growth rates are those that are mature, rejuvenated, growing, and declining. (2) Resource-based cities have a positive geographical correlation in high-quality development, and different numbers of clusters are displayed by changing the Moran I index score. (3) High quality development is strongly influenced by human capital, urbanization, technological innovation, and global market openness. There are significant differences in the ways in which these variables affect various types of resource-based cities. Policy makers who strive to reduce regional inequality and encourage high-quality growth in resource-based communities may benefit greatly from the insights provided by this study.

Since decoration is an essential part of buildings, the carbon emissions generated by decoration work should not be ignored. In recent years, prefabricated decoration has attracted much attention as efforts are made to pursue green, low-carbon, and waste-reducing buildings. However, research on carbon emissions assessment of prefabricated buildings has focused mainly on the structural aspect of prefabricated buildings, with few studies having considered prefabricated decoration. This study therefore focuses on assessing the carbon emissions of prefabricated decoration from the life cycle perspective of a case study residential building and explores the potential for reducing carbon emissions by decorating buildings with prefabricated components. The results show that using prefabricated decoration in the case study building reduced carbon emissions by 29.08% at the building material production stage compared to traditional decoration, and using an optimized design of prefabricated decoration, the building’s energy consumption over its design life could reduce carbon emissions by 1046 kgCO2/m2. These findings demonstrate the benefits of prefabrication decoration for reducing carbon emissions. This study provides decoration companies with robust data and insights to guide future decisions and practices, helping to transform and achieve the carbon neutrality goal for the building decoration industry.