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Over 90 % of the world's rice is produced in the Asia–Pacific region. Synthetic-aperture radar (SAR) enables all-day and all-weather observations of rice distribution in tropical and subtropical regions. The complexity of rice cultivation patterns in tropical and subtropical regions makes it difficult to construct a representative data-relevant rice crop model, increasing the difficulty in extracting rice distributions from SAR data. To address this problem, a rice area mapping method for large regional tropical or subtropical areas based on time-series Sentinel-1 SAR data is proposed in this study. Based on the analysis of rice backscattering characteristics in mainland Southeast Asia, the combination of spatiotemporal statistical features with good generalization ability was selected and then input into the U-Net semantic segmentation model, combined with WorldCover data to reduce false alarms, finally the 20 m resolution rice area map of five countries in mainland Southeast Asia in 2019 was obtained. The proposed method achieved an accuracy of 92.20 % on the validation sample set, and the good agreement was obtained when comparing our rice area map with statistical data and other rice area maps at the national and provincial levels. The maximum coefficient of determination R2 was 0.93 at the national level and 0.97 at the provincial level. These results demonstrate the advantages of the proposed method in rice area mapping with complex cropping patterns and the reliability of the generated rice area maps. The 20 m annual paddy rice area map for mainland Southeast Asia is available at https://doi.org/10.5281/zenodo.7315076 (Sun et al., 2022b).

The on-orbit processing of massive satellite-native data relies on powerful computing power. Satellite computing has started to gain attention, with researchers proposing various algorithms, applications, and simulation testbeds. Unfortunately, a practical platform for deploying satellite computing is currently lacking. As a result, the industry needs to make relentless efforts to achieve this goal. We suggest using cloud-native technology to enhance the computing power of LEO satellites. The first main satellite of the Tiansuan constellation, BUPT-1, is a significant example of a cloud-native satellite. Prior to delving into the details of BUPT-1, we define the essential concepts of cloud-native satellites, i.e., the cloud-native load and cloud-native platform. Afterwards, we present the design scheme of cloud-native satellites, including the architecture of BUPT-1 and the experimental subjects it can support. Two validation tests are shown to reflect the operation and capability of BUPT-1. Besides, we predict possible research fields that could shape the future of satellites in the next decade.

The transport properties of electrons in graphene $p$-$n$ junction with uniform Kekul\'e lattice distortion have been studied using the tight-binding model and the Landauer-B\"uttiker formalism combined with the nonequilibrium Green's function method. In the Kekul\'e-ordered graphene, the original $K$ and $K^{\prime}$ valleys of the pristine graphene are folded together due to the $\sqrt{3} \times \sqrt{3}$ enlargement of the primitive cell. When the valley coupling breaks the chiral symmetry, special transport properties of Dirac electrons exist in the Kekul\'e lattice. In the O-shaped Kekul\'e graphene $p$-$n$ junction, Klein tunneling is suppressed, and only resonance tunneling occurs. In the Y-shaped Kekul\'e graphene $p$-$n$ junction, the transport of electrons is dominated by Klein tunneling. When the on-site energy modification is introduced into the Y-shaped Kekul\'e structure, both Klein tunneling and resonance tunneling occur, and the electron tunneling is enhanced. In the presence of a strong magnetic field, the conductance of O-shaped and on-site energy-modified Y-shaped Kekul\'e graphene $p$-$n$ junctions is non-zero due to the occurrence of resonance tunneling. It is also found that the disorder can enhance conductance, with conductance plateaus forming in the appropriate range of disorder strength. The ideal plateau value is found only in the Kekul\'e-Y system., Comment: 8 pages, 7 figures

Bayesian inference has many advantages in robotic motion planning over four perspectives: The uncertainty quantification of the policy, safety (risk-aware) and optimum guarantees of robot motions, data-efficiency in training of reinforcement learning, and reducing the sim2real gap when the robot is applied to real-world tasks. However, the application of Bayesian inference in robotic motion planning is lagging behind the comprehensive theory of Bayesian inference. Further, there are no comprehensive reviews to summarize the progress of Bayesian inference to give researchers a systematic understanding in robotic motion planning. This paper first provides the probabilistic theories of Bayesian inference which are the preliminary of Bayesian inference for complex cases. Second, the Bayesian estimation is given to estimate the posterior of policies or unknown functions which are used to compute the policy. Third, the classical model-based Bayesian RL and model-free Bayesian RL algorithms for robotic motion planning are summarized, while these algorithms in complex cases are also analyzed. Fourth, the analysis of Bayesian inference in inverse RL is given to infer the reward functions in a data-efficient manner. Fifth, we systematically present the hybridization of Bayesian inference and RL which is a promising direction to improve the convergence of RL for better motion planning. Sixth, given the Bayesian inference, we present the interpretable and safe robotic motion plannings which are the hot research topic recently. Finally, all algorithms reviewed in this paper are summarized analytically as the knowledge graphs, and the future of Bayesian inference for robotic motion planning is also discussed, to pave the way for data-efficient, explainable, and safe robotic motion planning strategies for practical applications.

In the context of label-efficient learning on video data, the distillation method and the structural design of the teacher-student architecture have a significant impact on knowledge distillation. However, the relationship between these factors has been overlooked in previous research. To address this gap, we propose a new weakly supervised learning framework for knowledge distillation in video classification that is designed to improve the efficiency and accuracy of the student model. Our approach leverages the concept of substage-based learning to distill knowledge based on the combination of student substages and the correlation of corresponding substages. We also employ the progressive cascade training method to address the accuracy loss caused by the large capacity gap between the teacher and the student. Additionally, we propose a pseudo-label optimization strategy to improve the initial data label. To optimize the loss functions of different distillation substages during the training process, we introduce a new loss method based on feature distribution. We conduct extensive experiments on both real and simulated data sets, demonstrating that our proposed approach outperforms existing distillation methods in terms of knowledge distillation for video classification tasks. Our proposed substage-based distillation approach has the potential to inform future research on label-efficient learning for video data.

Pneumatic seed metering devices are widely used in high-speed and precise corn seeding operations. The objective of this study was to analysis the high-speed seeding performance of a pneumatic precision seed metering device at seeding speed ≥12 km/h. In this study, full-factor experiments were conducted of an air-suction rotary-hub corn precision seed metering device at the early stage of development. Seed leaking was the primary failure mode, and the experimental factors had no evident regular influence on the repeat index. The negative pressure of 3-7 kPa had significantly positive and negative correlations with the qualified index and the leak index, whereas the seeding speed of 12-20 km/h was significantly negatively and positively correlated with the qualified index and the leak index, respectively. The leak index reached its greatest value of 32.48%, 15.61% and 66.77% when the negative pressure was 3 kPa, and it was always less than 10% when the negative pressure was greater than 6 kPa for the three corn seed varieties. The corn seed varieties with smaller sizes and regular shapes were more suitable for high-speed precision seeding operations above 12 km/h, and the negative pressure could be increased to improve the qualified index and reduce the leak index.

To address the problem that the data related to the relative motion angular velocity of the target encapsulated in the fire control system cannot be obtained in real time during training, this paper proposes to use a laser transmitter to emit multiple concentric laser pulses to form a laser information field, measure the position of the vehicle in the information field by detecting the movement of the vehicle in the information field and the laser frequency value, and then calculate the relative angular velocity of the target. This method simulates the effect of real operation training and is used to measure the relative angular velocity of the target movement in the laser information field to determine the tracking smoothness and aiming accuracy of the operator in the process of tracking the target, and can be applied to the design of a training system for operator training. &nbsp

The single diamond (SD) network, discovered in beetle and weevil skeletons, is the 'holy grail' of photonic materials with the widest complete bandgap known to date. This structure affects the propagation of electromagnetic waves at defined frequencies and is significant in photonic crystals, light-harvesting applications, optical waveguides, laser resonators, etc. However, prior work has not provided a start-to-finish understanding of the processes used to produce unbalanced single-network scaffolds in natural organisms, and the thermodynamical instability of SD means it is extremely difficult to make by self-assembly compared to the energetically favoured bicontinuous double diamond and other easily formed lattices; thus, artificial fabrication of this photonic structure by self-assembly has long been a formidable challenge. Herein, we report an unprecedented bottom-up fabrication of an SD titania scaffold through a one-pot co-folding scenario employing the diblock copolymer poly(ethylene oxide)-block-polystyrene as a soft template and titanium diisopropoxide bis(acetylacetonate) as an inorganic precursor in a mixed solvent, in which the inorganic species was selectively organized in one of the skeletons enclosed by the diamond minimal surface of the polymer matrix in a simultaneous assembly process. Electron crystallography investigations revealed the tetrahedral connected SD frameworks with the space group Fd-3m in the polycrystalline anatase form. A photonic bandgap calculation showed that the resulting SD structure has a wide and complete photonic bandgap. This work provides a straightforward solution to a complex synthetic challenge and offers a new approach for biologically relevant materials, next-generation optical devices, etc.

Magnetic reconnection has been extensively shown to be a promising approach to extract spinning black hole energy. In this paper, we focus on extracting spinning wormhole energy via such mechanism. The study shows that it is indeed possible to extract rotating energy from a spinning wormhole with small regularization parameter $\ell$ of the central singularity. The efficiency and power of the energy extraction are also evaluated. Quite different from the Kerr black hole, the spin of the wormhole can take arbitrarily large value. However, the increasing of wormhole spin not always improves the efficiency and power of energy extraction. By further comparing with the Kerr black hole, we find the wormhole is more efficient when the magnetic reconnection happens within radial distance $r/M, 15 pages and 8 figures, references added

The super app paradigm, exemplified by platforms such as WeChat and AliPay, has revolutionized the mobile app landscape by enabling third-party developers to deploy add-ons within these apps. These add-ons, known as miniapps, leverage user data hosted by the super app platforms to provide a wide range of services, such as shopping and gaming. With the rise of miniapps, super apps have transformed into "operating systems", offering encapsulated APIs to miniapp developers as well as in-app miniapp stores for users to explore and download miniapps. In this paper, we provide the first systematic study to consolidate the current state of knowledge in this field from the security perspective: the security measures, threats, and trade-offs of this paradigm. Specifically, we summarize 13 security mechanisms and 10 security threats in super app platforms, followed by a root cause analysis revealing that the security assumptions still may be violated due to issues in underlying systems, implementation of isolation, and vetting. Additionally, we also systematize open problems and trade-offs that need to be addressed by future works to help enhance the security and privacy of this new paradigm.

Accurate information on surface soil moisture (SSM) content at a global scale under different climatic conditions is important for hydrological and climatological applications. Machine learning (ML) based systematic integration of in-situ hydrological measurements, complex environmental and climate data and satellite observation facilitate to generate the best data products to monitor and analyse the exchanges of water, energy and carbon in the Earth system at a proper space-time resolution. This study investigates the estimation of daily SSM using eight optimised ML algorithms and ten ensemble models (constructed via model bootstrap aggregating techniques and five-fold cross-validation). The algorithmic implementations were trained and tested using the international soil moisture network (ISMN) data collected from 1722 stations distributed across the World. The result showed that K-neighbours Regressor (KNR) performs best on “test_random” set, while Random Forest Regressor (RFR) performs best on “test_temporal” and “test_independent-stations”. Independent evaluation on novel stations across different climate zones was conducted. For the optimised ML algorithms, the median RMSEs were below 0.1 cm3/cm3. GradientBoosting (GB), Multi-layer Perceptron Regressor (MLPR), Stochastic Gradient Descent Regressor (SGDR), and Random Forest Regressor (RFR) achieved a median r score of 0.6 in twelve, eleven, nine and nine climate zones, respectively, out of fifteen climate zones. The performance of ensemble models improved significantly with the median value of RMSE below 0.075 cm3/cm3 for all climate zones . All voting regressors achieved the r scores of above 0.6 in thirteen climate zones except BSh and BWh because of the sparse distribution of training stations. The metrical evaluation showed that ensemble models can improve the performance of single ML algorithms and achieve more stable results. Based on the results computed for three different test sets, the ensemble model with KNR, RFR and XB performed the best. Overall, our investigation shows that ensemble machine learning algorithms have a greater capability for predicting SSM compared to the optimised, or base ML algorithms, and indicates their huge potential applicability in estimating water cycle budgets, managing irrigation and predicting crop yields.

Downstream hydraulic geometry (DHG) for multiple discharge frequencies remains poorly investigated. This paper seeks to clarify the DHG relations of different discharge frequencies and proposes the definition, mathematical expression, and geomorphological interpretation of multifrequency DHG (MFDHG). It also verifies the existence of DHG and MFDHG in the six major exoreic rivers located in the Qinghai–Tibet Plateau. MFDHG can be depicted with (1) log-linear plots between DHG coefficients and exponents for multiple discharge frequencies and (2) independent DHG curves intersecting near congruent discharge, width, depth, or velocity. The results show that rivers in the study area exhibit strong DHG relations. The variations in the DHG coefficients and exponents usually exhibit opposite trends with increasing discharge frequency. The MFDHG of a river reach is generally stronger than that of a river basin. Congruent hydraulics, as indices of geometric variability and hydraulic self-similarity, reflect consistent changes in hydraulic variables downstream. MFDHG is a novel geomorphic phenomenon that bridges spatiotemporal dimensions in HG systems and provides a basis for establishing an overall HG relationship.

Acoustic Event Classification (AEC) has been widely used in devices such as smart speakers and mobile phones for home safety or accessibility support. As AEC models run on more and more devices with diverse computation resource constraints, it became increasingly expensive to develop models that are tuned to achieve optimal accuracy/computation trade-off for each given computation resource constraint. In this paper, we introduce a Once-For-All (OFA) Neural Architecture Search (NAS) framework for AEC. Specifically, we first train a weight-sharing supernet that supports different model architectures, followed by automatically searching for a model given specific computational resource constraints. Our experimental results showed that by just training once, the resulting model from NAS significantly outperforms both models trained individually from scratch and knowledge distillation (25.4% and 7.3% relative improvement). We also found that the benefit of weight-sharing supernet training of ultra-small models comes not only from searching but from optimization., Comment: Accepted by ICASSP 2023

BackgroundColon cancer is one of the common cancers, and its prognosis remains to be improved. The role of cuproptosis as a newly discovered form of cell death in the development of colon cancer has not been determined.MethodsBased on 983 colon cancer samples in the TCGA database and the GEO database, we performed a comprehensive genomic analysis to explore the molecular subtypes mediated by cuproptosis-related genes. Single-sample gene set enrichment analysis (ssGSEA) was utilized to quantify the relative abundance of each cell infiltrate in the TME. A risk score was established using least absolute shrinkage and selection operator regression (LASSO), and its predictive ability for colon cancer patients was verified to explore its guiding value for treatment.ResultsWe identified two distinct cuproptosis-related molecular subtypes in colon cancer. These two distinct molecular subtypes can predict clinicopathological features, prognosis, TME activity, and immune-infiltrating cells. A risk model was developed and its predictive ability was verified. Compared with patients in the high-risk score group, patients in the low-risk score group were characterized by lower tumor microenvironment score, higher stem cell activity, lower tumor mutational burden, lower microsatellite instability, higher sensitivity to chemotherapeutics, and better immunotherapy efficacy.ConclusionThis study contributes to understanding the molecular characteristics of cuproptosis-related subtypes. We demonstrate a critical role for cuproptosis genes in colon cancer s in the TME. Our study contributes to the development of individualized treatment regimens for colon cancer.

People usually have different intents for choosing items, while their preferences under the same intent may also different. In traditional collaborative filtering approaches, both intent and preference factors are usually entangled in the modeling process, which significantly limits the robustness and interpretability of recommendation performances. For example, the low-rating items are always treated as negative feedback while they actually could provide positive information about user intent. To this end, in this paper, we propose a two-fold representation learning approach, namely Double Disentangled Collaborative Filtering (DDCF), for personalized recommendations. The first-level disentanglement is for separating the influence factors of intent and preference, while the second-level disentanglement is performed to build independent sparse preference representations under individual intent with limited computational complexity. Specifically, we employ two variational autoencoder networks, intent recognition network and preference decomposition network, to learn the intent and preference factors, respectively. In this way, the low-rating items will be treated as positive samples for modeling intents while the negative samples for modeling preferences. Finally, extensive experiments on three real-world datasets and four evaluation metrics clearly validate the effectiveness and the interpretability of DDCF.

The goal of document-grounded dialogue (DocGD) is to generate a response by grounding the evidence in a supporting document in accordance with the dialogue context. This process involves four variables that are causally connected. Recently, task-specific pre-training has greatly boosted performances on many downstream tasks. Existing DocGD methods, however, continue to rely on general pre-trained language models without a specifically tailored pre-training approach that explicitly captures the causal relationships. To tackle this issue, we are the first to present a causally-complete dataset construction strategy for building million-level DocGD pre-training corpora. To better capture causality, we further propose a causally-perturbed pre-training strategy, which introduces causal perturbations on the variables and optimizes the overall causal effect. Experiments on three benchmark datasets demonstrate that our causal pre-training achieves considerable and consistent improvements under fully-supervised, low-resource, few-shot, and zero-shot settings., Work in progress

ObjectiveThe metabolic score for insulin resistance (MetS-IR) is an emerging surrogate marker for insulin resistance (IR). This study aimed to investigate the association and sex differences between MetS-IR and prediabetes risk in a Chinese population.MethodsThis cohort study included 100,309 adults with normoglycemia at baseline and had followed longitudinally for 5 years, and with prediabetes, defined according to the 2018 American Diabetes Association (ADA) recommended diagnostic criteria, as the outcome of interest. Multivariate Cox proportional hazards regression and restricted cubic spline (RCS) regression models were used to assess the association between MetS-IR and prediabetes risk.ResultsDuring an observation period of 312,843 person-years, 7,735 (14.84%) men and 4,617 (9.57%) women with pre-diabetes onset were recorded. After fully adjusting for confounders, we found an independent and positive correlation between MetS-IR and the risk of prediabetes in the Chinese population, and the degree of correlation was stronger in women than in men (HR: 1.24 vs 1.16, P-interactionConclusionIn this large cohort study, our results supported that MetS-IR was independently and positively associated with the risk of prediabetes in the Chinese population, with the association being stronger in women than in men.

INTRODUCTION: Thousands of genetic variants have been associated with human diseases and traits through genome-wide association studies (GWASs). Translating these discoveries into improved therapeutics requires discerning which variants among hundreds of candidates are causally related to disease risk. To date, only a handful of causal variants have been confirmed. Here, we leverage 100 million years of mammalian evolution to address this major challenge. RATIONALE: We compared genomes from hundreds of mammals and identified bases with unusually few variants (evolutionarily constrained). Constraint is a measure of functional importance that is agnostic to cell type or developmental stage. It can be applied to investigate any heritable disease or trait and is complementary to resources using cell type– and time point–specific functional assays like Encyclopedia of DNA Elements (ENCODE) and Genotype-Tissue Expression (GTEx). RESULTS: Using constraint calculated across placental mammals, 3.3% of bases in the human genome are significantly constrained, including 57.6% of coding bases. Most constrained bases (80.7%) are noncoding. Common variants (allele frequency ≥ 5%) and low-frequency variants (0.5% ≤ allele frequency < 5%) are depleted for constrained bases (1.85 versus 3.26% expected by chance, P < 2.2 × 10(−308)).Pathogenic ClinVar variants are more constrained than benign variants (P < 2.2 × 10(−16)). The most constrained common variants are more enriched for disease single-nucleotide polymorphism (SNP)–heritability in 63 independent GWASs. The enrichment of SNP-heritability in constrained regions is greater (7.8-fold) than previously reported in mammals and is even higher in primates (11.1-fold). It exceeds the enrichment of SNP-heritability in nonsynonymous coding variants (7.2-fold) and fine-mapped expression quantitative trait loci (eQTL)–SNPs (4.8-fold). The enrichment peaks near constrained bases, with a log-linear decrease of SNP-heritability enrichment as a function of the distance to a constrained base. Zoonomia constraint scores improve functionally informed fine-mapping. Variants at sites constrained in mammals and primates have greater posterior inclusion probabilities and higher per-SNP contributions. In addition, using both constraint and functional annotations improves polygenic risk score accuracy across a range of traits. Finally, incorporating constraint information into the analysis of noncoding somatic variants in medulloblastomas identifies new candidate driver genes. CONCLUSION: Genome-wide measures of evolutionary constraint can help discern which variants are functionally important. This information may accelerate the translation of genomic discoveries into the biological, clinical, and therapeutic knowledge that is required to understand and treat human disease.

Using satellite observations from 2003 to 2020 and cruise observations from 2019 and 2021, this study reveals an unexpected minor role of upwelling in seasonal and interannual variations in chlorophyll a (Chl a) concentrations in the coastal upwelling region east of Hainan Island (UEH) in the northwestern South China Sea (NWSCS). The results show strong seasonal and interannual variability in the Chl a concentration in the core upwelling area of the UEH. Different from the strongest upwelling in summer, the Chl a concentration in the UEH area reaches a maximum of 1.18 mg m−3 in autumn and winter, with a minimum value of 0.74 mg m−3 in summer. The Chl a concentration in summer increases to as high as 1.0 mg m−3 with weak upwelling, whereas the maximum Chl a concentration in October increases to 2.5 mg m−3. The analysis of environmental factors shows that, compared to the limited effects of upwelling, the along-shelf coastal current from the northern shelf and the increased precipitation are crucially important to the Chl a concentration variation in the study area. These results provide new insights for predicting marine productivity in upwelling areas, i.e., multiple mechanisms, especially horizontal advection, should be considered in addition to the upwelling process.

Ischemic stroke is the most common cerebrovascular disease, and vascular obstruction is an important cause of this disease. As the main method for the management of carotid artery stenosis, carotid endarterectomy (CEA) is an effective and preventive treatment measure in ischemic cerebrovascular disease. This study aims to propose the application of a new enhanced recovery after surgery (ERAS) nutritional support regimen in CEA, which can significantly improve the perioperative nutritional status of patients. A total of 74 patients who underwent CEA were included and randomly divided into two groups: 39 patients received nutritional therapy with the ERAS protocol (ERAS group) and 35 patients received routine perioperative nutritional support (control group). Our results showed that the levels of major clinical and biochemical parameters (albumin, hemoglobin, creatinine, calcium and magnesium levels, etc.) in the ERAS group were significantly higher than those in the control group after surgery (p p

Introns, as important vectors of biological functions, can influence many stages of mRNA metabolism. However, in recent research, post-spliced introns are rarely considered. In this study, the optimal matched regions between introns and their mRNAs in nine model organism genomes were investigated with improved Smith–Waterman local alignment software. Our results showed that the distributions of mRNA optimal matched frequencies were highly consistent or universal. There are optimal matched frequency peaks in the UTR regions, which are obvious, especially in the 3′-UTR. The matched frequencies are relatively low in the CDS regions of the mRNA. The distributions of the optimal matched frequencies around the functional sites are also remarkably changed. The centers of the GC content distributions for different sequences are different. The matched rate distributions are highly consistent and are located mainly between 60% and 80%. The most probable value of the optimal matched segments is about 20 bp for lower eukaryotes and 30 bp for higher eukaryotes. These results show that there are abundant functional units in the introns, and these functional units are correlated structurally with all kinds of sequences of mRNA. The interaction between the post-spliced introns and their corresponding mRNAs may play a key role in gene expression.

Context. Massive stars are generally believed to form in supersonic turbulent environment. However, recent observations have challenged this traditional view. High spatial and spectral resolution observations of the Orion Molecular Cloud and an infrared dark cloud G35.39 show a resolution-dependent turbulence, and that high-mass stars are forming exclusively in subsonic to transonic cores in those clouds. These studies demand a re-evaluation of the role of the turbulence in massive star formation. Aims. We aim to study the turbulence in a typical massive star-forming region G35.20-0.74 N with a sufficient spatial resolution to resolve the thermal Jeans length, and a spectral resolution to resolve the thermal linewidth. Methods. We use the ALMA dust continuum emission to resolve fragmentation, JVLA 1.2 cm continuum to trace ionized gas, and JVLA NH3 (1,1) to (7,7) inversion transition lines to trace linewidth, temperature, and dynamics. We fit those lines and remove line broadening due to channel width, thermal pressure, and velocity gradient to obtain a clean map of intrinsic turbulence. Results. We find that (1) the turbulence in G35.20 is overall supersonic, with mean and median Mach numbers 3.7 and 2.8, respectively. (2) Mach number decreases from 6-7 at 0.1 pc scale to, 13 pages, 6 figures, 1 table

Nowadays the ultrasonic radar, infrared detection, laser scanning radar or other perception technologies have been widely used in the automatic docking conditions. However, it’s not absolutely applicable in severe weather such as heavy rain and snow days, especially in the operation not allowed to fail, for example, the docking of the future unmanned towbarless towing vehicle (TLTV) with the expensive civil plane. A novel mechanism is proposed for relatively position and posture measuring between the TLTV and the aircraft nose tire during the automatic docking operation. The geometrical relationship between the towing vehicle and the nose tire is obtained by mathematical calculation. The position and posture of the nose tire have been obtained by splice angles and the length of rotatable rods connected with the vehicle during the docking operation.

Background: Given the limited treatment options available for kidney disease, a significant number of patients turn to alternative therapies, including traditional Chinese medicine. Among these therapies, the Fufang Shenhua tablet (SHT) has garnered attention for its effectiveness in addressing the most common deficiency of Qi and Yin in chronic glomerulonephritis. Notably, the sovereign drug of SHT is Astragali Radix (AR), with the most abundant and effective component being Astragaloside IV (AS-IV). AS-IV has been shown to possess anti-inflammatory and immunomodulatory properties, and it is extensively used in treating kidney diseases. Nevertheless, the molecular mechanisms underlying its action are numerous and intricate, and a comprehensive understanding is yet to be achieved.Aim of the review: Thus, we have thoroughly examined the existing research and outlined the advancements made in investigating the anti-inflammatory and immunomodulatory mechanisms of SHT, AR and its active component AS-IV, in relation to kidney health. This serves as a dependable foundation for conducting more comprehensive investigations, evaluating efficacy, and making further improvements in the future.Materials and methods: We conducted a comprehensive literature search utilizing multiple globally recognized databases, including Web of Science, Google Scholar, PubMed, ScienceDirect, Wiley, ACS, Springer, and CNKI. The search keywords used in this study were “Fufang Shenhua tablet,” “Astragali Radix,” “Astragaloside IV,” and “Anti-inflammatory” or “Immunity.”Results: The mechanism of inflammation inhibition by SHT, AR and its active component AS-IV is mainly related to the signaling pathways such as NF-κB, TLRs, PI3K/AKT, Wnt/β-catenin, and JAK-STAT. Immunomodulation exerts not only activating, stimulating, and regulating effects on macrophages and dendritic cells, but also on immune organs, T-lymphocytes, B-lymphocytes, and a myriad of cytokines. Moreover, the SHT, AR and its active component AS-IV also demonstrate regulatory effects on renal cells, including glomerular mesangial cells, tubular epithelial cells, and podocytes.Conclusion: To summarize, SHT, AR and its active component AS-IV, exhibit notable therapeutic effects in kidney-related ailments, and their molecular mechanisms for anti-inflammatory and immunomodulatory effects have been extensively explored. However, further standard clinical trials are necessary to evaluate their safety and efficacy in the adjunctive treatment of kidney-related diseases. Moreover, in-depth studies of unverified chemical components and regulatory mechanisms in SHT are required. It is our belief that with continued research, SHT, AR and its active component AS-IV are poised to pave the way for enhancing therapeutic outcomes in kidney-related ailments.

Within the framework of perturbative QCD factorization, we investigate the nonfactorizable contributions to the factorization-forbidden quasi-two-body decays $ B_{(s)}\rightarrow h\chi_{c0}\rightarrow h\pi^+\pi^-(K^+K^-) $ with $ B_{(s)}\rightarrow h\chi_{c0}\rightarrow h\pi^+\pi^-(K^+K^-) $ . We compare our predicted branching ratios for the $ B_{(s)}\rightarrow h\chi_{c0}\rightarrow h\pi^+\pi^-(K^+K^-) $ decay with available experiment data as well as predictions by other theoretical studies. The branching ratios of these decays are consistent with data and other theoretical predictions. However, in the Cabibbo-suppressed decays $ B_{(s)}\rightarrow h\chi_{c0}\rightarrow h\pi^+\pi^-(K^+K^-) $ with $ B_{(s)}\rightarrow h\chi_{c0}\rightarrow h\pi^+\pi^-(K^+K^-) $ , the values of the branching ratios are of the order of $ B_{(s)}\rightarrow h\chi_{c0}\rightarrow h\pi^+\pi^-(K^+K^-) $ and $ B_{(s)}\rightarrow h\chi_{c0}\rightarrow h\pi^+\pi^-(K^+K^-) $ . The ratio $ B_{(s)}\rightarrow h\chi_{c0}\rightarrow h\pi^+\pi^-(K^+K^-) $ between the decays $ B_{(s)}\rightarrow h\chi_{c0}\rightarrow h\pi^+\pi^-(K^+K^-) $ $ B_{(s)}\rightarrow h\chi_{c0}\rightarrow h\pi^+\pi^-(K^+K^-) $ and $ B_{(s)}\rightarrow h\chi_{c0}\rightarrow h\pi^+\pi^-(K^+K^-) $ and the distribution of branching ratios for different decay modes in invariant mass are considered in this study.

Wusheng Deng,1,&ast; Yun Jiang,2,&ast; Jiaoxia Qin,1,&ast; Gang Chen,3,&ast; Yongjie Lv,1 Yanmei Lei,1 Jing Luo,1 Kangkang Hong,1 Bing Huang,1 Luhai Qin,4 Xiujia Tang,1 Liumei Ye,1 Yuhai Dang,1 Chao Wang,1 Feiyang Long,1 Ke Wang,1 Jinliang Kong1 1Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People’s Republic of China; 2Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Shaoyang University, Shaoyang, Hunan, People’s Republic of China; 3Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People’s Republic of China; 4Department of Pulmonology, Hechi Traditional Chinese Medicine Hospital, Hechi, Guangxi, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Jinliang Kong; Ke Wang, Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, 530021, People’s Republic of China, Email kjl071@163.com; keewang@hotmail.comBackground: Aspergillus fumigatus is an opportunistic fungal pathogen, which is commonly found in lungs and rarely causes infections in mediastinum. Mediastinal Aspergillus abscess is a serious infectious condition, and is characterized by difficult diagnosis due to its clinical manifestations being nonspecific.Case Presentation: Here, we report a case of a mediastinal Aspergillus fumigatus abscess in an immunocompetent patient. The patient was a 45-year-old woman who presented with a 20-day history of sore throat without any underlying diseases. Chest computed tomography (CT) showed a mass in the anterior superior mediastinum. Metagenomic next-generation sequencing (mNGS) identified Aspergillus fumigatus sequences in endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) tissue, indicating the mediastinal Aspergillus fumigatus infection of this patient. The following mediastinal biopsy histological analysis and tissue fungi culture also suggested Aspergillus fumigatus infection, confirming the mNGS detection. The patient was diagnosed with mediastinal aspergillosis caused by Aspergillus fumigatus. After timely voriconazole treatment, the patient was discharged with good condition.Conclusion: Our study presented a rare case with mediastinal Aspergillus fumigatus abscess in an immunocompetent patient. As a new clinical diagnostic method, mNGS could assist timely diagnosis and precise treatment of Aspergillus infection.Keywords: mediastinal abscess, Aspergillus fumigatus, metagenomic next-generation sequencing, diagnosis, case report

The recent report of near-ambient superconductivity in nitrogen doped lutetium hydride has triggered a worldwide fanaticism and raised major questions about the latest claims. An intriguing phenomenon of color changes in pressurized samples from blue to pink to red was observed and correlated with the claimed superconducting transition, but the origin and underlying physics of these color changes have yet to be elucidated. Here we report synthesis and characterization of high-purity nitrogen doped lutetium hydride LuH$_{2\pm\text{x}}$N$_y$ with the same structure and composition as in the main phase of near-ambient superconductor1. We find a new purple phase of LuH$_{2\pm\text{x}}$N$_y$ between blue and pink phase, and reveal that the sample color changes likely stem from pressure-driven redistribution of nitrogen and its interaction with the LuH$_2$ framework. No superconducting transition is found in all blue, purple, pink and red phases at temperatures 1.8-300 K and pressures 0-30 GPa. Instead, we identify a notable temperature-induced resistance anomaly of structural and/or electronic origin in LuH$_{2\pm\text{x}}$N$_y$, which is most pronounced in the pink phase and may have been erroneously interpreted as a sign of superconducting transition. This work establishes key benchmarks for nitrogen doped lutetium hydrides, allowing an in-depth understanding of the novel pressure-induced phase changes., 17 pages and 5 figures in the main text. 9 pages and 12 figures in the Supplementary Material. Any valuable comments and suggestions are warmly welcomed

We improve QCD calculations of the $B_{d, s} \to \pi, K$ form factors at large hadronic recoil by implementing the next-to-leading-logarithmic resummation for the obtained leading-power light-cone sum rules in the soft-collinear effective theory (SCET) framework. Additionally, we endeavour to investigate a variety of the subleading-power contributions to these heavy-to-light form factors at ${\cal O}(\alpha_s^0)$, by including the higher-order terms in the heavy-quark expansion of the hard-collinear quark propagator, by evaluating the desired effective matrix element of the next-to-leading-order term in the ${\rm SCET_{I}}$ representation of the weak transition current, by taking into account the off-light-cone contributions of the two-body heavy-quark effective theory matrix elements as well as the three-particle higher-twist corrections from the subleading bottom-meson light-cone distribution amplitudes, and by computing the twist-five and twist-six four-body higher-twist effects with the aid of the factorization approximation. Having at our disposal the SCET sum rules for the exclusive $B$-meson decay form factors, we further explore in detail numerical implications of the newly computed subleading-power corrections by employing the three-parameter model for both the leading-twist and higher-twist $B$-meson distribution amplitudes. Taking advantage of the customary Bourrely-Caprini-Lellouch parametrization for the semileptonic $B_{d, s} \to \pi, K$ form factors, we then determine the correlated numerical results for the interesting series coefficients, by carrying out the simultaneous fit of the exclusive $B$-meson decay form factors to both the achieved SCET sum rule predictions and the available lattice QCD results., Comment: 74 pages, 15 figures

Quantum coherence will undoubtedly play a fundamental role in understanding of the dynamics of quantum many-body systems, thereby to reveal its genuine contribution is of great importance. In this paper, we specialize our discussions to the one-dimensional transverse field quantum Ising model initialized in the coherent Gibbs state. After quenching the strength of the transverse field, the effects of quantum coherence are studied by the rate function of quantum work distribution. We find that quantum coherence not only recovers the quantum phase transition destroyed by thermal fluctuations, but also generates some entirely new singularities both in the static state and dynamics. It can be manifested that these singularities are rooted in spin flips causing the sudden change of the domain boundaries of spin polarization. This work sheds new light on the fundamental connection between quantum critical phenomena and quantum coherence., 8 pages, 4 figures, welcome the comments. arXiv admin note: text overlap with arXiv:1810.04614, arXiv:cond-mat/0701768 by other authors

Recently the field of Human-Robot Interaction gained popularity, due to the wide range of possibilities of how robots can support humans during daily tasks. One form of supportive robots are socially assistive robots which are specifically built for communicating with humans, e.g., as service robots or personal companions. As they understand humans through artificial intelligence, these robots will at some point make wrong assumptions about the humans' current state and give an unexpected response. In human-human conversations, unexpected responses happen frequently. However, it is currently unclear how such robots should act if they understand that the human did not expect their response, or even showing the uncertainty of their response in the first place. For this, we explore the different forms of potential uncertainties during human-robot conversations and how humanoids can, through verbal and non-verbal cues, communicate these uncertainties.

Supplementary materials for the paper "AutoML from Software Engineering Perspective: Landscapes and Challenges".

The microbial requirement for nutrient resources can be estimated by soil extracellular enzyme stoichiometry (EES) and their stoichiometries. Implementing the Grain for Green Program has significantly impacted land use and soil nutrient management in the China Danxia. However, drivers of soil microbial nutrient limitation changes in abandoned cropland (AC) remained unclear after vegetation restoration. Here, according to vector analysis, we evaluated microbial nutrient limitation by studying soil EES across vegetation restoration types (naturally restored secondary forests (NF) and artificially planted forests (AF)) with AC as a control. Results showed both NF and AF soils averaged higher C- and P- acquiring enzyme, indicating rapid C and P turnover rates after vegetation restoration. However, vegetation restoration resulted in higher C requirement for microorganisms with higher enzyme C:N and vector length. In addition, microorganisms shifted from N- (< 45°) to P-limited (> 45°) conditions with enzyme N:P less than 1 after vegetation restoration, and NF exacerbated microbial P limitation compared to AF. Decreased N limitation following vegetation restoration could be contributed to improving soil ecosystem multifunctionality. The greater variation of EES was explained by the interaction of pH, soil nutrient, and microbial biomass than by any one of these factors alone, suggesting that both abiotic and biotic factors regulate microbial nutrient limitation and microbial process. Overall, our results revealed vegetation restoration could alleviate N limitation in the China Danxia, and thus enhance soil ecosystem by regulating lower microbial N limitation, which provide insight into nutrient management strategies under ecological restoration of degraded areas.

OBJECTIVE: To analyze the Doppler ultrasound blood flow of Renying (carotid artery) pulse, Cunkou (radial artery) pulse, and Fuyang (anterior tibial artery) pulse in the normal group and the functional constipation with gastrointestinal heat (FCGH) group, and to compare and explore the differences of Renying, Cunkou and Fuyang pulses. METHODS: Sixty normal subjects and 60 patients with gastrointestinal heat constipation were collected in the department of ultrasound, Beijing Anzhen Hospital, Capital Medical University. Doppler ultrasound was used to observe the blood flow indexes including maximum systolic velocity (Vp), maximum diastolic velocity (Vd), mean velocity (Vm), pulse index (PI), resistance index (RI), vascular diameter (D), and circulation blood flow cycle (ET) of Renying pulse, Cunkou pulse and Fuyang pulse in the normal group and patients with gastrointestinal heat constipation. The differences of these three pulses were compared between the normal group and the FCGH group. RESULTS: The PI, Vp, Vm, RI, and D of the three pulses in the normal group were statistically significant different (P < 0.01). In the comparison of Doppler flow diagram of three pulses in the FCGH group, there were significant differences in PI, Vd, Vp, Vm, RI, and D (P < 0.01). CONCLUSIONS: Under normal conditions, the indexes of Renying pulse Doppler flow diagram, Cunkou pulse Doppler flow diagram, and Fuyang pulse Doppler flow diagram are significantly different. In the FCGH group, most of the indexes of Renying pulse Doppler flow diagram, Cunkou pulse Doppler flow diagram, and Fuyang pulse Doppler flow diagram are different. This result verified the necessity of simultaneous examinations of all Renying, Cunkou, and Fuyang pulses in modern Chinese medicine.

A long-standing topic in artificial intelligence is the effective recognition of patterns from noisy images. In this regard, the recent data-driven paradigm considers 1) improving the representation robustness by adding noisy samples in training phase (i.e., data augmentation) or 2) pre-processing the noisy image by learning to solve the inverse problem (i.e., image denoising). However, such methods generally exhibit inefficient process and unstable result, limiting their practical applications. In this paper, we explore a non-learning paradigm that aims to derive robust representation directly from noisy images, without the denoising as pre-processing. Here, the noise-robust representation is designed as Fractional-order Moments in Radon space (FMR), with also beneficial properties of orthogonality and rotation invariance. Unlike earlier integer-order methods, our work is a more generic design taking such classical methods as special cases, and the introduced fractional-order parameter offers time-frequency analysis capability that is not available in classical methods. Formally, both implicit and explicit paths for constructing the FMR are discussed in detail. Extensive simulation experiments and an image security application are provided to demonstrate the uniqueness and usefulness of our FMR, especially for noise robustness, rotation invariance, and time-frequency discriminability.

Gram-negative bacteria are major pathogens that can cause illnesses in giant pandas. Lipopolysaccharides (LPS), components of Gram-negative bacteria, can activate immune responses in mammals (i.e., humans and mice) through recognition by toll-like receptors (TLRs). However, the giant pandas’ immune response to LPS stimulation and the differences between the giant panda and other mammals are not fully known. In this study, we administrated peripheral blood mononuclear cells (PBMCs) from giant pandas, humans, C57BL/6 mice, and rhesus monkeys by LPS treatment at 6 h followed by RNA sequencing (RNA-seq), respectively, with control of non-stimulation. KEGG analyses of differentially expressed genes (DEGs) pathways indicated that LPS could activate the classic signaling pathway of NF-κB in PBMCs from those four tested species. Thus, similar to the other three species, NF-κB is an LPS-responsive regulator of innate immune responses in giant pandas. Furthermore, the expression patterns of adapter genes, inflammatory cytokine genes, chemokines, interferon genes, cytokine genes related to cell growth and development, costimulatory molecules, Th1/Th2 cytokine genes, Th17 cytokine genes, Th9, and Th22 cytokine genes were compared among giant pandas and three other species. Our data indicated that in addition to the similar expression patterns of certain genes among giant pandas and other species, the unique expression pattern response to LPS in giant pandas was also discovered. Furthermore, Th9, Th17, and Th22 cells might be involved in the response to LPS in giant pandas at this tested time point. This study reveals that LPS-induced immune responses have different sensitivities and response timelines in giant pandas compared with other mammals. This study facilitates further understanding of the role of the TLR signaling pathway and the immune system in giant pandas, which might be helpful for disease prevention and protection.

Ovarian cancer (OV) is the most lethal form of gynecological malignancy worldwide, with limited therapeutic options and high recurrence rates. However, research focusing on prognostic patterns of ferroptosis-related genes (FRGs) in ovarian cancer is still lacking. From the 6,406 differentially expressed genes (DEGs) between TCGA-OV (n = 376) and GTEx cohort (n = 180), we identified 63 potential ferroptosis-related genes. Through the LASSO-penalized Cox analysis, 3 prognostic genes, SLC7A11, ZFP36, and TTBK2, were finally distinguished. The time-dependent ROC curves and K-M survival analysis performed powerful prognostic ability of the 3-gene signature. Stepwise, we constructed and validated the nomogram based on the 3-gene signature and clinical features, with promising prognostic value in both TCGA (p-value < .0001) and ICGC cohort (p-value = .0064). Gene Set Enrichment Analysis elucidated several potential pathways between the groups stratified by 3-gene signature, while the m6A gene analysis implied higher m6A level in the high-risk group. We applied the CIBERSORT algorithm to distinct tumor immune microenvironment between two groups, with less activated dendritic cells (DCs) and plasma cells, more M0 macrophages infiltration, and higher expression of key immune checkpoint molecules (CD274, CTLA4, HAVCR2, and PDCD1LG2) in the high-risk group. In addition, the low-risk group exhibited more favorable immunotherapy and chemotherapy responses. Collectively, our findings provided new prospects in the role of ferroptosis-related genes, as a promising prediction tool for prognosis and immune responses, in order to assist personalized treatment decision-making among ovarian cancer patients.

Additional file 6. Cell behaviors in fetal bovine serum (n=11), and HF patient serum (n=11) before and after high-intensity interval training (HIIT).

We present a "one-step" approach to modify ZnO electron transport layers (ETLs) used in organic solar cells. This approach involves adding benzoic acid (BZA) derivatives directly to the ZnO precursor solution, which are then present at the surface of the resulting ZnO film. We demonstrate this approach for three different BZA derivatives, namely benzoic acid, chlorobenzoic acid, and 4-hydrazinobenzoic acid. For all molecules, improved device performance and stability is demonstrated in solar cells using an active layer blend of PTQ10 (donor) and ITIC-Br (non-fullerene acceptor) compared to such cells prepared using untreated ZnO. Furthermore, similar or improved device performance and stability is demonstrated compared to conventional PEIE treatment of ZnO. The presence of the BZA derivatives at the surface after processing is established using X-ray photoelectron spectroscopy and near-edge X-ray absorption fine-structure spectroscopy. From atomic force microscopy analysis and X-ray diffraction studies, the addition of BZA derivatives appears to restrict ZnO grain growth; however, this does not negatively impact device performance. ZnO layers treated with BZA derivatives also exhibit higher water contact angle and lower work function compared to untreated ZnO. This approach enables simplification of device manufacture while still allowing optimization of the surface properties of metal oxide ETLs. Keywords: electron transport layers, zinc oxide, organic solar cells, surface modification, Comment: Manuscript: 25 pages, 8 figures, 5 tables. Supplementary Material: 36 pages, 22 figures, 13 tables. Submitted to Solar Energy Materials and Solar Cells

When the traffic stream is extremely congested and surrounding vehicles are not cooperative, the mandatory lane changing can be significantly difficult. In this work, we propose an interactive trajectory planner, which will firstly attempt to change lanes as long as safety is ensured. Based on receding horizon planning, the ego vehicle can abort or continue changing lanes according to surrounding vehicles' reactions. We demonstrate the performance of our planner in extensive simulations with eight surrounding vehicles, initial velocity ranging from 0.5 to 5 meters per second, and bumper to bumper gap ranging from 4 to 10 meters. The ego vehicle with our planner can change lanes safely and smoothly. The computation time of the planner at every step is within 10 milliseconds in most cases on a laptop with 1.8GHz Intel Core i7-10610U.