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Atomic diffusion in solids is an important process in various phenomena. However, atomistic simulations of diffusion processes are confronted with the timescale problem: the accessible simulation time is usually far shorter than that of experimental interests. In this work, we developed a long-timescale method using reinforcement learning that simulates diffusion processes. As a testbed, we simulate hydrogen diffusion in pure metals and a medium entropy alloy, CrCoNi, getting hydrogen diffusivity reasonably consistent with previous experiments. We also demonstrate that our method can accelerate the sampling of low-energy configurations compared to the Metropolis-Hastings algorithm using hydrogen migration to copper (111) surface sites as an example.

The Weihe Basin is a crucial part of the Fenwei Graben System and is a typical example of an extensional fault basin. Over time, the region has been developed around 212 ground fissures, with the majority of them being associated with heavy rainfall. These water-eroded ground fissures have caused serious damage to farmland, houses, and roads, with the Liangjiacun ground fissures, Meiyuancun ground fissures, and Dizhentai ground fissures being the most known. To understand the developmental characteristics and formation mechanisms of water-eroded ground fissures in the Weihe Basin, we utilized a variety of methods, including ground surveys, mapping, trenching, drilling, and geophysical exploration. These three ground fissures are distributed along corresponding fault zones, with their orientations being closely linked to local topographic changes. Additionally, the trenching profiles showed that all three typical ground fissure zones have distinct hidden holes above the paleosol. The formation of these ground fissures is closely associated with tectonic activity and heavy rainfall, with soil ruptures resulting from tectonic activity becoming the dominant channels for surface water infiltration. Furthermore, the erosion caused by heavy rainfall accelerates the rupture and expansion of ground fissures in the shallow subsurface. This paper is a valuable resource for disaster prevention and mitigation strategies aimed at mitigating the damage caused by ground fissures in the Weihe Basin.

Consistent joint penetration is essential to mechanical properties of weldments, yet difficult to realize in gas tungsten arc welding due to heat accumulation, varying welding conditions, and uncertain disturbance, especially for manufacturing large-scale structural components. The solution is to monitor welding penetration in real-time and implement closed-loop control. In this paper a directional light-assisted vision sensing scheme is proposed to measure the transient profile of the backside weld pool/bead for monitoring and further controlling the joint penetration state. The developing zone and the developed zone of the backside weld bead are recognized, helping determining its final width. And the relationship between the transient backside bead width and the corresponding final width is established. Furthermore, the backside bead profile is employed as a feedback signal and the closed-loop control of weld formation is implemented to achieve full penetration and a constant backside bead width. A multiple-input single-output model predictive controller is utilized in the control system, creatively combined with an anti-windup integrator eliminating the steady-state error caused by nonlinearity in welding. Experimental results have proved that the developed control system is effective in keeping a consistent backside bead width with a rapid response, a small overshoot and robustness against varying heat transfer conditions.

Alkali metal ions such as sodium and potassium cations play fundamental roles in biology. Developing highly sensitive and selective methods to both detect and quantify these ions is of considerable importance for medical diagnostics and bioimaging. Fluorescent nanoparticles have emerged as powerful tools for nanoscale imaging, but their optical properties need to be supplemented with specificity to particular chemical and biological signals in order to provide further information about biological processes. Nitrogen-vacancy (NV) centers in diamond are particularly attractive as fluorescence markers, thanks to their optical stability, biocompatibility and further ability to serve as highly sensitive quantum sensors of temperature, magnetic and electric fields in ambient conditions. In this work, by covalently grafting crown ether structures on the surface of nanodiamonds (NDs), we build sensors that are capable of detecting specific alkali ions such as sodium cations. We will show that the presence of these metal ions modifies the charge state of NV centers inside the ND, which can then be read out by measuring their photoluminescence spectrum. Our work paves the way for designing selective biosensors based on NV centers in diamond., 8 pages, 4 figures

Solid-state spin defects are attractive candidates for developing quantum sensors and simulators. The spin and charge degrees of freedom in large defect ensembles are a promising platform to explore complex many-body dynamics and the emergence of quantum hydrodynamics. However, many interesting properties can be revealed only upon changes in the density of defects, which instead is usually fixed in material systems. Increasing the interaction strength by creating denser defect ensembles also brings more decoherence. Ideally one would like to control the spin concentration at will, while keeping fixed decoherence effects. Here we show that by exploiting charge transport, we can take some first steps in this direction, while at the same time characterizing charge transport and its capture by defects. By exploiting the cycling process of ionization and recombination of NV centers in diamonds, we pump electrons from the valence band to the conduction band. These charges are then transported to modulate the spin concentration by changing the charge state of material defects. By developing a wide-field imaging setup integrated with a fast single photon detector array, we achieve a direct and efficient characterization of the charge redistribution process by measuring the complete spectrum of the spin bath with micrometer-scale spatial resolution. We demonstrate the concentration increase of the dominant spin defects by a factor of 2 while keeping the $T_2$ of the NV center, which also provides a potential experimental demonstration of the suppression of spin flip-flops via hyperfine interactions. Our work paves the way to studying many-body dynamics with temporally and spatially tunable interaction strengths in hybrid charge-spin systems.

Aiming to link natural language descriptions to specific regions in a 3D scene represented as 3D point clouds, 3D visual grounding is a very fundamental task for human-robot interaction. The recognition errors can significantly impact the overall accuracy and then degrade the operation of AI systems. Despite their effectiveness, existing methods suffer from the difficulty of low recognition accuracy in cases of multiple adjacent objects with similar appearances.To address this issue, this work intuitively introduces the human-robot interaction as a cue to facilitate the development of 3D visual grounding. Specifically, a new task termed Embodied Reference Understanding (ERU) is first designed for this concern. Then a new dataset called ScanERU is constructed to evaluate the effectiveness of this idea. Different from existing datasets, our ScanERU is the first to cover semi-synthetic scene integration with textual, real-world visual, and synthetic gestural information. Additionally, this paper formulates a heuristic framework based on attention mechanisms and human body movements to enlighten the research of ERU. Experimental results demonstrate the superiority of the proposed method, especially in the recognition of multiple identical objects. Our codes and dataset are ready to be available publicly.

Low-light image enhancement (LLIE) investigates how to improve illumination and produce normal-light images. The majority of existing methods improve low-light images via a global and uniform manner, without taking into account the semantic information of different regions. Without semantic priors, a network may easily deviate from a region's original color. To address this issue, we propose a novel semantic-aware knowledge-guided framework (SKF) that can assist a low-light enhancement model in learning rich and diverse priors encapsulated in a semantic segmentation model. We concentrate on incorporating semantic knowledge from three key aspects: a semantic-aware embedding module that wisely integrates semantic priors in feature representation space, a semantic-guided color histogram loss that preserves color consistency of various instances, and a semantic-guided adversarial loss that produces more natural textures by semantic priors. Our SKF is appealing in acting as a general framework in LLIE task. Extensive experiments show that models equipped with the SKF significantly outperform the baselines on multiple datasets and our SKF generalizes to different models and scenes well. The code is available at Semantic-Aware-Low-Light-Image-Enhancement., Comment: Accepted to CVPR 2023

Video Moment Retrieval (VMR) aims at retrieving the most relevant events from an untrimmed video with natural language queries. Existing VMR methods suffer from two defects: (1) massive expensive temporal annotations are required to obtain satisfying performance; (2) complicated cross-modal interaction modules are deployed, which lead to high computational cost and low efficiency for the retrieval process. To address these issues, we propose a novel method termed Cheaper and Faster Moment Retrieval (CFMR), which well balances the retrieval accuracy, efficiency, and annotation cost for VMR. Specifically, our proposed CFMR method learns from point-level supervision where each annotation is a single frame randomly located within the target moment. It is 6 times cheaper than the conventional annotations of event boundaries. Furthermore, we also design a concept-based multimodal alignment mechanism to bypass the usage of cross-modal interaction modules during the inference process, remarkably improving retrieval efficiency. The experimental results on three widely used VMR benchmarks demonstrate the proposed CFMR method establishes new state-of-the-art with point-level supervision. Moreover, it significantly accelerates the retrieval speed with more than 100 times FLOPs compared to existing approaches with point-level supervision., Comment: 10 pages, 7 figures

Achieving fast, sensitive, and parallel measurement of a large number of quantum particles is an essential task in building large-scale quantum platforms for different quantum information processing applications such as sensing, computation, simulation, and communication. Current quantum platforms in experimental atomic and optical physics based on CMOS sensors and CCD cameras are limited by either low sensitivity or slow operational speed. Here we integrate an array of single-photon avalanche diodes with solid-state spin defects in diamond to build a fast wide-field quantum sensor, achieving a frame rate up to 100~kHz. We present the design of the experimental setup to perform spatially resolved imaging of quantum systems. A few exemplary applications, including sensing DC and AC magnetic fields, temperature, strain, local spin density, and charge dynamics, are experimentally demonstrated using an NV ensemble diamond sample. The developed photon detection array is broadly applicable to other platforms such as atom arrays trapped in optical tweezers, optical lattices, donors in silicon, and rare earth ions in solids.

We introduce and experimentally demonstrate a quantum sensing protocol to sample and reconstruct the auto-correlation of a noise process using a single-qubit sensor under digital control modulation. This Walsh noise spectroscopy method exploits simple sequences of spin-flip pulses to generate a complete basis of digital filters that directly sample the power spectrum of the target noise in the sequency domain -- from which the auto-correlation function in the time domain, as well as the power spectrum in the frequency domain, can be reconstructed using linear transformations. Our method, which can also be seen as an implementation of frame-based noise spectroscopy, solves the fundamental difficulty in sampling continuous functions with digital filters by introducing a transformation that relates the arithmetic and logical time domains. In comparison to standard, frequency-based dynamical-decoupling noise spectroscopy protocols, the accuracy of our method is only limited by the sampling and discretization in the time space and can be easily improved, even under limited evolution time due to decoherence and hardware limitations. Finally, we experimentally reconstruct the auto-correlation function of the effective magnetic field produced by the nuclear-spin bath on the electronic spin of a single nitrogen-vacancy center in diamond, discuss practical limitations of the method, and avenues for further improving the reconstruction accuracy.

In recent years, neuroimaging has undergone a paradigm shift, moving away from the traditional brain mapping approach toward developing integrated, multivariate brain models that can predict categories of mental events. However, large interindividual differences in brain anatomy and functional localization after standard anatomical alignment remain a major limitation in performing this analysis, as it leads to feature misalignment across subjects in subsequent predictive models.

The processes of soil freezing-thawing lead to soil water and heat movement in cold regions, which significantly influences the hydrological and energy cycles in the soil-plant-atmosphere system. This study presents a soil water content coupled with heat transfer model based on physical processes of water and heat movement in frozen soil. The model was calibrated and validated using the measured data of soil temperature and frost and thaw depth at 19 stations in and around the Three-River Source Region of China. The results show that the frozen soil model could capture the processes of soil freezing-thawing processes well at this region. The relationship between model parameters and climate and vegetation factors was analyzed using the observation data and remote sensing data obtained from MODIS, and results showed that the parameter c which represents the soil properties has a good correlation with longitude and vegetation coverage. A multi-regression model was established to estimate the model parameters in regions without observation data and its determination coefficient R2 was 0.82. The mean relative error between calibration and inversion parameters of 19 stations is 6.29%. Thus, the proposed method can be applied to cold regions without observation data to obtain the parameters and simulated the soil freezing-thawing processes.

Glioblastoma (GBM) is the most common and malignant primary brain tumor in adults. Currently, the standard treatment of glioblastoma includes surgery, radiotherapy, and chemotherapy. Despite aggressive treatment, the median survival is only 15 months. GBM progression and therapeutic resistance are the results of the complex interactions between tumor cells and tumor microenvironment (TME). TME consists of several different cell types, such as stromal cells, endothelial cells and immune cells. Although GBM has the immunologically “cold” characteristic with very little lymphocyte infiltration, the TME of GBM can contain more than 30% of tumor-associated microglia and macrophages (TAMs). TAMs can release cytokines and growth factors to promote tumor proliferation, survival and metastasis progression as well as inhibit the function of immune cells. Thus, TAMs are logical therapeutic targets for GBM. In this review, we discussed the characteristics and functions of the TAMs and evaluated the state of the art of TAMs-targeting strategies in GBM. This review helps to understand how TAMs promote GBM progression and summarizes the present therapeutic interventions to target TAMs. It will possibly pave the way for new immune therapeutic avenues for GBM patients.

Background: Synechococcus sp. WH8102 is one of the most abundant photosynthetic organisms in many ocean regions. Objective: The aim of this study is to identify genomic islands (GIs) in Synechococcus sp. WH8102 with integrated methods. Methods: We have applied genomic barcode to identify the GIs in Synechococcus sp. WH8102, which could make genomic regions of different origins visually apparent. The gene expression data of the predicted GIs was analyzed through microarray data which was collected for functional analysis of the relevant genes. Results: Seven GIs were identified in Synechococcus sp. WH8102. Most of them are involved in cell surface modification, photosynthesis and drug resistance. In addition, our analysis also revealed the functions of these GIs, which could be used for in-depth study on the evolution of this strain. Conclusion: Genomic barcodes provide us with a comprehensive and intuitive view of the target genome. We can use it to understand the intrinsic characteristics of the whole genome and identify GIs or other similar elements.

Asteroid anchoring is the premise for the spacecraft to conduct in-situ scientific exploration on the surface of a weak gravitational asteroid. There is not any precedent for successful anchoring on the asteroid surface all over the world. Ultrasonic drill actuated by the piezoelectric ceramic is especially suitable for drilling on the surface of a weak gravitational asteroid, showing outstanding advantages such as low power consumption, low required drilling pressure and wide temperature range. In order to solve the technical problem of anchoring, a novel anchoring device based on multiple ultrasonic drill cross-drilling geometric force closure is proposed. Firstly, the structure and working principle of the anchoring device are introduced. Then the EDEM software based on the Discrete Element Method is used to simulate the influencing factors of the anchoring force. Finally, the effectiveness of the anchoring device is proved by experiments. The results show that the ultrasonic drill is effective for the anchoring device; the anchoring device can provide an anchoring force of 60–250 N for the spacecraft; its structure is simple, compact and adaptable.

Stress and deformation are important factors that affect the safe operation of pipelines. Plastic deformation in the pipeline indicates a state of critical damage. Based on micro-magnetization and stress equivalent magnetic field theories, the change mechanism of ferromagnetic materials under external stress is analyzed. The 3D simulation model of the iron–carbon alloy system is established by the Material Studied software, and the relationship between the system magnetism and stress is obtained by calculating the electronic spin state distribution changes under different stress applications. The pipeline suppression experiment is designed to study the change characteristics of the magnetic induction intensity on the pipeline surface during the repeated suppressions until yield. Results show that the weak magnetic signal continuously varies as the pressure on the pipeline increases. The greater the pressure, the lower the magnetic induction intensity on the pipe surface. The pipeline exhibits different weak magnetic signal characteristics in the elastic and plastic deformation stages. The magnetic signal variation curve has apparent inflection points. The deformation and damage states of the pipeline can be assessed by the change characteristics of the weak magnetic signal. Repeated suppression causes the pipeline magnetism to weaken and the initial magnetization of the material to decrease. The experimental results are consistent with the theoretical analysis.

Let $R$ be a finite commutative unitary ring. An idempotent in $R$ is an element $e\in R$ with $e^2=e$. Let $\Psi$ be a subgroup of the group ${\rm Aut}(R)$ of all automorphisms of $R$. The $\Psi-$weighted Erd\H{o}s-Burgess constant ${\rm I}_{\Psi}(R)$ is defined as the smallest positive integer $\ell$ such that every sequence over $R$ of length at least $\ell$ must contain a nonempty subsequence $a_1,\ldots, a_{r}$ such that $\prod\limits_{i=1}^r \psi_i(a_i)$ is one idempotent of $R$ where $\psi_1,\ldots,\psi_r\in \Psi$. In this paper, for the finite quotient ring of a Dedekind domain $R$, a connection is established between the $\Psi-$weighted-Erd\H{o}s-Burgess constant of $R$ and the $\Psi-$weighted Davenport constant of its group of units by all the prime ideals of $R$., Comment: 18 pages

BackgroundIn non-small cell lung cancer (NSCLC) patients treated by immune checkpoint inhibitors (ICIs), tumor mutation burden (TMB) has been found to have predictive potential for survival. When compared to TMB detection in tissue (tTMB), detecting TMB in the blood (bTMB) has practical advantages; yet, the results of various studies are conflicting. The question of whether bTMB can be utilized as a predictive biomarker is becoming increasingly contentious. To confirm the predictive efficacy of bTMB, researchers did a systematic review and meta-analysis to look into the relationship between ICIs and bTMB.MethodFrom the inception to March 2021, Cochrane Library, PubMed, EMBASE and other databases were systematically searched. The predictive value of bTMB in ICIs, or the efficacy of ICIs against chemotherapy, was studied. The results were presented as pooled ratio rate (RR) and hazard ratio (HR) with 95% confidence intervals for the Objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). Subgroup analysis, heterogeneity analyses, and sensitivity analysis were also performed.ResultsA total of 2,610 NSCLC patients were studied in seven trials. There were no significant differences in OS (HR = 1.09; 95% CI: 0.62–1.91, P = 0.774) or PFS (HR = 0.73; 95% CI: 0.20–2.65, P = 0.629) between high and low bTMB groups in the ICIs cohort. When ICIs were compared to chemotherapy, ICIs were found to enhance OS (HR = 0.74; 95% CI: 0.59–0.92, P = 0.006), but the improvement in PFS and ORR was only a numerical trend (PFS: HR = 0.83; 95% CI: 0.63–1.09, P = 0.173; ORR: RR = 0.92, 95% CI: 0.77–1.10, P = 0.372). NSCLC patients treated with ICIs in the high bTMB group had better survival benefits than chemotherapy patients in terms of OS (HR = 0.63; 95% CI: 0.51–0.76, P <0.001), PFS (HR = 0.63; 95% CI: 0.52–0.76, P <0.001), and ORR (RR = 1.86; 95% CI: 1.32–2.62, P <0.001), while in the low TMB group, the results were no different or even reversed (OS: HR = 0.89; 95% CI: 0.64–1.24, P = 0.485; PFS: HR = 1.21, 95% CI: 0.93–1.58, P = 0.154; ORR: RR = 0.68, 95% CI: 0.54–0.85, P = 0.001).ConclusionsTMB could predict the enhanced survival benefit of NSCLC patients treated with ICIs; however the role of bTMB is limited at this stage. For NSCLC patients with high TMB, ICIc may be a better option than chemotherapy.

Quantum sensing of low-frequency magnetic fields using nitrogen-vacancy (NV) center ensembles has been demonstrated in multiple experiments with sensitivities as low as $\sim$1 pT/$\sqrt{\text{Hz}}$. To date, however, demonstrations of high-frequency magnetometry in the GHz regime with NV diamond are orders of magnitude less sensitive, above the nT/$\sqrt{\text{Hz}}$ level. Here we adapt for microwave frequencies techniques that have enabled high-performance, low-frequency quantum sensors. Using a custom-grown NV-enriched diamond combined with a noise cancellation scheme designed for high-frequency sensing, we demonstrate a Rabi-sequence-based magnetometer able to detect microwave fields near 2.87 GHz with a record sensitivity of 3.4 pT/$\sqrt{\textrm{Hz}}$. We demonstrate both amplitude and phase sensing and project tunability over a 300 MHz frequency range. This result increases the viability of NV ensembles to serve as microwave circuitry imagers and near-field probes of antennas., Comment: 7 pages, 4 figures

Objective. This meta-analysis was performed to evaluate the anatomical efficacy and functional improvement of the conventional inverted internal limiting membrane (ILM), flap covering technique, and ILM flap filling technique for patients with idiopathic macular hole (MH). Methods. Literature from Pubmed, Embase, Cochrane Central Register of Controlled Trials, and Web of Science were comprehensively retrieved. The primary outcomes included the MH closure rate and postoperative best-corrected visual acuity (BCVA). The secondary outcomes were the proportion of external limiting membrane (ELM) and ellipsoid zone (EZ) defect recovery. Pooled odds ratios (ORs), weighted mean differences (WMDs), and 95% confidence intervals (CIs) were calculated using STATA 17.0 software. Results. 7 studies that contained 139 eyes in the inverted ILM flap covering group and 121 eyes in the ILM flap filling group were selected. Pooled data suggested that the surgical treatment resulted in an overall MH closure rate of up to 97.12% (135/139 eyes) in the inverted ILM flap covering group and 99.17% (120/121 eyes) in the filling group, with no significant difference between the 2 groups (OR = 1.98, 95% CI: 0.55 to 7.09, and P=0.29). Similarly, the 2 techniques demonstrated equal effectiveness on the anatomical closure in MH with the average diameter smaller than 650 μm (OR = 2.17, 95% CI: 0.48 to 9.77, and P=0.31) and larger than 650 μm (OR = 1.58, 95% CI: 0.14 to 17.37, and P=0.71). However, compared with the filling technique, the inverted ILM flap covering technique was superior in postoperative BCVA (WMD = 0.11, 95% CI: 0.04 to 0.18, and P=0.0017) and presented a significantly higher proportion of reconstitution of ELM (OR = 0.02, 95% CI: 0.00 to 0.08, and P

To achieve promising results on removing noise from real-world images, most of existing denoising networks are formulated with complex network structure, making them impractical for deployment. Some attempts focused on reducing the number of filters and feature channels but suffered from large performance loss, and a more practical and lightweight denoising network with fast inference speed is of high demand. To this end, a \textbf{Thu}mb\textbf{n}ail based \textbf{D}\textbf{e}noising Netwo\textbf{r}k dubbed Thunder, is proposed and implemented as a lightweight structure for fast restoration without comprising the denoising capabilities. Specifically, the Thunder model contains two newly-established modules: (1) a wavelet-based Thumbnail Subspace Encoder (TSE) which can leverage sub-bands correlation to provide an approximate thumbnail based on the low-frequent feature; (2) a Subspace Projection based Refine Module (SPR) which can restore the details for thumbnail progressively based on the subspace projection approach. Extensive experiments have been carried out on two real-world denoising benchmarks, demonstrating that the proposed Thunder outperforms the existing lightweight models and achieves competitive performance on PSNR and SSIM when compared with the complex designs.

Solid-state spin defects, especially nuclear spins with potentially achievable long coherence times, are compelling candidates for quantum memories and sensors. However, their current performances are still limited by the decoherence due to the variation of their intrinsic quadrupole and hyperfine interactions. We propose an \textit{unbalanced echo} to overcome this challenge by using a second spin to refocus the variation of these interactions, which preserves the quantum information stored in the free evolution. The unbalanced echo can be used to probe the temperature and strain distribution in materials. Experimentally, we demonstrate a 20-fold $T_2^*$ coherence time increase in an ensemble of $\sim10^{10}$ nuclear spins in diamond. Theoretically, we develop first-principles methods to predict these interaction variations and reveal their correlation in large temperature and strain ranges. We numerically show that our method can refocus stronger noise variations than our current experiments and achieves a 400-fold coherence improvement for a 25~K temperature inhomogeneity.

Let $G$ be an abelian group, let $\mathcal F (G)$ be the free commutative monoid with basis $G$. For $Ω\subset \mathcal F (G)$, define ${\mathsf d}_Ω(G)$ to be the smallest integer $\ell$ such that every sequence $T$ over $G$ of length $\ell$ has a subsequence in $Ω$. The invariant ${\mathsf d}_Ω(G)$ unifies many classical zero-sum invariants. Let $\mathcal B (G)$ be the submonoid of $\mathcal F (G)$ consisting of all zero-sum sequences over $G$. A set $Ω\subset \mathcal B (G)$ is called {\bf minimal} with respect to the Davenport constant provided that ${\mathsf d}_Ω(G)={\rm D}(G)$ and ${\mathsf d}_{Ω'}(G)\neq{\rm D}(G)$ for any proper subset $Ω'$ of $Ω$. We determine the intersection of all minimal sets $Ω$ with respect to the Davenport constant ${\rm D}(C_n)$ for the cyclic group $C_n$. The universal zero-sum invariant ${\mathsf d}_{Ω; Ψ}(G)$ with weights set $Ψ$ of homomorphisms of groups was introduced for all abelian groups. It is shown that given any positive integer $t$ and any set $A\subset \mathcal F (G)$ such that ${\mathsf d}_{A; Ψ}(G)=t$, there exists a minimal set $Ω$ contained in $A$ with the equality ${\mathsf d}_{Ω; Ψ}(G)=t$. The weighted Davenport constant ${\rm D}_Ψ(G)$ (being an special form of the universal invariant with weights) was also investigated for {\sl infinite} abelian groups. Among other results, we obtain the necessary and sufficient conditions such that ${\rm D}_Ψ(G), 26 pages

Let $R$ be a finite commutative unitary ring. An idempotent in $R$ is an element $e\in R$ with $e^2=e$. Let $��$ be a subgroup of the group ${\rm Aut}(R)$ of all automorphisms of $R$. The $��-$weighted Erd��s-Burgess constant ${\rm I}_��(R)$ is defined as the smallest positive integer $\ell$ such that every sequence over $R$ of length at least $\ell$ must contain a nonempty subsequence $a_1,\ldots, a_{r}$ such that $\prod\limits_{i=1}^r ��_i(a_i)$ is one idempotent of $R$ where $��_1,\ldots,��_r\in ��$. In this paper, for the finite quotient ring of a Dedekind domain $R$, a connection is established between the $��-$weighted-Erd��s-Burgess constant of $R$ and the $��-$weighted Davenport constant of its group of units by all the prime ideals of $R$., 18 pages

Documenting year-to-year variations in carbon-sequestration potential in terrestrial ecosystems is crucial for the determination of carbon dioxide (CO2) emissions. However, the magnitude, pattern and inner biomass partitioning of carbon-sequestration potential, and the effect of the changes in climate and CO2 on inner carbon stocks, remain poorly quantified. Herein, we use a spatially explicit individual based-dynamic global vegetation model to investigate the influences of the changes in climate and CO2 on the enhanced carbon-sequestration potential of vegetation. The modelling included a series of factorial simulations using the CRU dataset from 1916 to 2015. The results show that CO2 predominantly leads to a persistent and widespread increase in above-ground vegetation biomass carbon-stocks (AVBC) and below-ground vegetation biomass carbon-stocks (BVBC). Climate change appears to play a secondary role in carbon-sequestration potential. Importantly, with the mitigation of water stress, the magnitude of the above- and below-ground responses in vegetation carbon-stocks gradually increases, and the ratio between AVBC and BVBC increases to capture CO2 and sunlight. Changes in the pattern of vegetation carbon storage was linked to regional limitations in water, which directly weakens and indirectly regulates the response of potential vegetation carbon-stocks to a changing environment. Our findings differ from previous modelling evaluations of vegetation that ignored inner carbon dynamics and demonstrates that the long-term trend in increased vegetation biomass carbon-stocks is driven by CO2 fertilization and temperature effects that are controlled by water limitations.

Background: Autistic Spectrum Disorder (ASD) is a disorder associated with genetic and neurological components leading to difficulties in social interaction and communication. According to statistics of WHO, the number of patients diagnosed with ASD is gradually increasing. Most of the current studies focus on clinical diagnosis, data collection and brain images analysis, but do not focus on the diagnosis of ASD based on machine learning. Objective: This study aims to classify ASD data to provide a quick, accessible and easy way to support early diagnosis of ASD. Methods: Three ASD datasets are used for children, adolescences and adults. To classify the ASD data, we used the k-Nearest Neighbours method (kNN), the Support Vector Machine method (SVM) and the Random Forests method (RF). In our experiments, the data was randomly split into training and test sets. The parts of the data were randomly selected 100 times to test the classification methods. Results: The final results were assessed by the average values. It is shown that SVM and RF are effective methods for ASD classification. In particular, the RF method classified the data with an accuracy of 100% for all above datasets. Conclusion: The early diagnosis of ASD is critical. If the number of data samples is large enough, we can achieve a high accuracy for machine learning-based ASD diagnosis. Among three classification methods, RF achieves the best performance for ASD data classification.

To meet the radar data requirements of target detection technology research and address the lack of publicly available sea-detecting radar data, a data-sharing program for sea-detecting radar is proposed herein. The aim of the proposed data-sharing program is to conduct sea detection experiments using an X-band solidstate phase-coherent radar and other multi-type radars to obtain the target and sea clutter data under different sea conditions, resolutions, and grazing angles. Moreover, the marine meteorological and hydrological data, target position, and track data are simultaneously obtained using the proposed data-sharing program to help achieve the standardized management of radar-measured data. The proposed data-sharing program aims to promote the open sharing of data sets, serve as the basis for research on sea clutter characteristics, and facilitate the research on sea clutter suppression and target detection technology.

In this work, we used density functional theory calculations to comprehensively study the influence of Au on adsorption property, electronic structure and catalytic performance of trimetallic PdAgAu/Pd(111) surface. The results indicated that Au can not only isolate the contiguous Pd atoms, but also bind with acetylene. Furthermore, due to the high electronegativity of Au, the interaction between the surface and ethylene is weakened. Thus, through the geometric and electronic effects, Au addition endows the surface with strong and weak bonding to acetylene and ethylene, respectively, and also enhanced reactivity and selectivity in acetylene selective hydrogenation reaction.

The large-scale application of new technologies such as cloud computing in power supply companies makes the mode of traditional power security zones cannot efficiently support the existing business. A simplified interaction protocol of crossing security zones is proposed based on the TCP, which encapsulate the file transfer protocol in the base of isolation devices. It unifies the exchange interface between the production & control zone and the management & information zone, and accord with the transmission modes of request/reply and subscription/publication. Multi-channel data transmission in isolation devices is implemented by multi-thread technology. It established data confirmation mechanism by priority control in crossing security zones. It implemented high reliability of transmission between forward & reverse isolation devices. Finally, it gives process and results of verification through comparing. The method helps to improve the quality of data sharing for application systems crossing different security zones.

Hemorrhagic fever with renal syndrome (HFRS) is an acute natural focal viral disease caused by viruses of the genus hantavirus, characterized by damage to small blood vessels, kidneys, lungs and other organs of a person. MicroRNAs (miRNAs) are 18-22 nucleotide endogenously expressed RNA molecules that inhibit gene expression at the post-transcriptional level by binding to the 3-untranslated region of the target mRNA. It has been proven that miRNAs play a significant role in various biological processes, including the cell cycle, apoptosis, cell proliferation and differentiation. It has been proven that miRNAs may be involved in the pathogenesis of infectious diseases, including HFRS. Hantavirus infection predominantly affects endothelial cells and causes dysfunction of the endothelium of capillaries and small vessels. It is known that the immune response induced by Hantavirus infection plays an important role in disrupting the endothelial barrier. In a few studies, both in vitro and in vivo, it has been shown that endothelial dysfunction and the immune response after infection with Hantavirus can be partially regulated by miRNAs by acting on certain genes. Most of the miRNAs is expressed within the cells themselves. However, in some biological fluids of the human body, for example, plasma or blood serum, numerous miRNAs, called circulating miRNAs, have been found. Circulating miRNAs can be secreted by cells into human biological fluids as part of extracellular vesicles as exosomes or be part of an RNA-bound protein complex as miRNA-Argonaute 2 (Ago2). These miRNAs are resistant to nucleases, which makes them attractive as potential biomarkers in various human diseases. There is no specific antiviral therapy for HFRS, and the determination of laboratory parameters that are used to diagnose, assess the severity, and predict the course of the disease remains a challenge due to the peculiarities of the pathophysiology and clinical course of the disease. Studying the role of miRNAs in HFRS seems to be expedient for the development of specific and effective therapy, as well as for use as diagnostic and prognostic biomarkers (in relation to circulating miRNAs).

Background. Nonsmall cell lung cancer (NSCLC) is the most common type of lung cancer, and the majority of NSCLC patients are diagnosed at the advanced stage. Chemotherapy is still the main treatment at present, and the overall prognosis is poor. In recent years, immunotherapy has developed rapidly. Immune checkpoint inhibitors (ICIs) as the representative have been extensively applied for treating various types of cancers. Tumor mutation burden (TMB) as a potential biomarker is used to screen appropriate patients for treatment of ICIs. To verify the predictive efficacy of TMB, a systematic review and meta-analysis were conducted to explore the association between TMB and ICIs. Method. PubMed, EMBASE, Cochrane Library, and son on were systematically searched from inception to April 2020. Objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) were estimated. Results. A total of 11 studies consisting of 1525 nonsmall cell lung cancer (NSCLC) patients were included. Comparison of high and low TMB: pooled HRs for OS, 0.57 (95% CI 0.32 to 0.99; P = 0.046 ); PFS, 0.48 (95% CI 0.33 to 0.69; P < 0.001 ); ORR, 3.15 (95% CI 2.29 to 4.33; P < 0.001 ). Subgroup analysis values: pooled HRs for OS, 0.75 (95% CI 0.29 to 1.92, P = 0.548 ) for blood TMB (bTMB), 0.44 (95% CI 0.26 to 0.75, P = 0.003 ) for tissue TMB (tTMB); for PFS, 0.54 (95% CI 0.29 to 0.98, P = 0.044 ) and 0.43 (95% CI 0.26 to 0.71, P = 0.001 ), respectively. Conclusions. These findings imply that NSCLC patients with high TMB possess significant clinical benefits from ICIs compared to those with low TMB. As opposed to bTMB, tTMB was thought more appropriate for stratifying NSCLC patients for ICI treatment.

Functional magnetic resonance imaging (fMRI) has provided invaluable insight into our understanding of human behavior. However, large inter-individual differences in both brain anatomy and functional localization after anatomical alignment remain a major limitation in conducting group analyses and performing population-level inference. This paper addresses this problem by developing and validating a new computational technique for reducing misalignment across individuals in functional brain systems by spatially transforming each subject's functional data to a common reference map. Our proposed Bayesian functional registration approach allows us to assess differences in brain function across subjects and individual differences in activation topology. It combines intensity-based and feature-based information into an integrated framework and allows inference to be performed on the transformation via the posterior samples. We evaluate the method in a simulation study and apply it to data from a study of thermal pain. We find that the proposed approach provides increased sensitivity for group-level inference.

Removing rainy artefacts (e.g., rainstreaks and raindrops) can benefit advanced computer vision systems, such as intelligent surveillance systems and self-driving cars. Data-driven models produce more promising results than model-based approaches. However, by evaluating representative deep-learning based deraining models on benchmark datasets, two unfavorable results are obtained: (1) they cannot handle various rainy conditions (i.e., rains of various directions and densities) equally well, and (2) the models trained on synthetic images cannot generalize well to process real rainy images. This Ph.D. thesis has made progress towards solving both issues. A deraining network dissection tool (DNDT) was firstly designed to analyze the behavior of filters in deraining models. Task-specific issues are then identified and solved by formulating a series of novel deraining models: (1) it was found that the imbalanced distribution (between rains and backgrounds) results in biased representation, and then a context-enhanced representation learning and deraining network (CERLD-Net) was proposed to learn an explainable and controlled representation; (2) it was found that the representations learned in existing models suffer from the loss of features depicting the background, and then an entangled representation learning network (ERL-Net) was proposed to learn more complete features; (3) it was found that some negative factors are learned in the latent space of ERL-Net, and then a disentangled representation learning and enhancement network (DRLE-Net) was proposed to embed a rainy image into two separated spaces such that only the positive factors are selected for deraining; and (4) by dissecting DRLE-Net, the significance of using rain distribution map (RDM) as prior is verified, motivating the formulation of a novel cascaded attention guidance network (CAG-Net) for better exploiting RDM in a hierarchical manner. The issues of the coarse-to-fine deraining models are also identified and solved by proposing a novel attentive feature refinement network (AFR-Net) to remove heavy rainy artefacts. In summary, this thesis investigates and solves the problems of existing deraining models by proposing novel networks, which are designed towards learning unbiased and explainable representations for reconstructing clean images without over-/under- deraining artefacts. Extensive experiments demonstrate that the proposed methods outperform the state-of-the-arts by large margins.

Optical Phase Array - OPA is a fast, flexible and non-mechanical beam pointing technology, which can be regarded as the extension of microwave phased array to the Optical wave frequency band. It has a wide application prospect and research value in target detection, imaging recognition, tracking scanning, laser interference and other fields. Optical phased array system is composed of a number of array elements, the basic idea is through electric scanning technology and modular design method. The beam can be transmitted, received and detected with high precision by using multiple small aperture beam oriental arrays. Based on acoustic-optic and elector-optic devices, the propagation direction of the beam is changed by controlling the initial phase of each element in the optical aperture, thus completed the laser beam array scan. In this paper, the development background and working principle of optical phased array technology are introduced. Based on the typical application of optical phased array technology, that is, laser phased array radar system, this paper summarizes the research progress and key technology breakthrough of this system in domestic and abroad. Finally, by analyzing the outstanding advantages of optical phased array technology, for one hand the research direction and application prospect of this technology in industrial and military fields are put forward, for the other hand the conceptual design and engineering research of this technology are summarized and prospected.

It introduces configuration and implementation method of integrated technology between transmission network model and distribution network model for Chinese power grid based on IEC 61970 series standards in this paper. It contains the transmission network model of CIM/E format is extracted from the business system in security zone of production & control, the distribution network model of CIM/XML format is extracted from the business system in security zone of management & information. It gives a method to translate the model format from CIM/E to CIM/XML, and then finish the technology verification. It achieves integrated and completed grid model from high voltage to low voltage at last. All of the above, it provides a technology program to get completed map of power grid.

Let $\mathcal{S}$ be a finite cyclic semigroup written additively. An element $e$ of $\mathcal{S}$ is said to be idempotent if $e+e=e$. A sequence $T$ over $\mathcal{S}$ is called {\sl idempotent-sum free} provided that no idempotent of $\mathcal{S}$ can be represented as a sum of one or more terms from $T$. We prove that an idempotent-sum free sequence over $\mathcal{S}$ of length over approximately a half of the size of $\mathcal{S}$ is well-structured. This result generalizes the Savchev-Chen Structure Theorem for zero-sum free sequences over finite cyclic groups., 16 pages

Let $R$ be a commutative unitary ring. An idempotent in $R$ is an element $e\in R$ with $e^2=e$. The Erd\H{o}s-Burgess constant associated with the ring $R$ is the smallest positive integer $\ell$ (if exists) such that for any given $\ell$ elements (not necessarily distinct) of $R$, say $a_1,\ldots,a_{\ell}\in R$, there must exist a nonempty subset $J\subset \{1,2,\ldots,\ell\}$ with $\prod\limits_{j\in J} a_j$ being an idempotent. In this paper, we prove that except for an infinite commutative ring with a very special form, the Erd\H{o}s-Burgess constant of the ring $R$ exists if and only if $R$ is finite., Comment: 8 pages. arXiv admin note: text overlap with arXiv:2002.11489

Global reanalysis precipitation products could provide valuable meteorological information for flow forecasting in poorly gauged areas, helping to overcome a long-standing challenge in the field. But not all data sources are suitable for all regions or perform the same way in hydrological modeling, so it is essential to test the suitability of precipitation products before applying them. In this study, five widely used global high-resolution precipitation products—Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), National Centers for Environmental Prediction Climate Forecast System Reanalysis (NCEP-CFSR), Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS), China Gauge-based Daily Precipitation Analysis developed by China Meteorological Administration (CMA) and Agricultural Model Intercomparison and Improvement Project based on the NASA Modern-Era Retrospective Analysis for Research and Applications (AgMERRA)—were evaluated using statistical methods and a hydrological approach for their suitability for the Lancang River Basin. The results indicated that APHRODITE, CMA, AgMERRA and CHIRPS were more accurate precipitation indicators than NCEP-CFSR in terms of the multiyear average and seasonal spatial distribution pattern, all of the CHIRPS, AgMERRA and APHRODITE perform better than CMA and NCEP-CFSR at the small, medium and high precipitation intensities ranges in subbasin11 and sunbabsin46. All five products performed better in subbasin46 (a low-altitude region) than in subbasin11 (a high-altitude region) on the daily and monthly scales. In addition to NCEP-CFSR, the other four products all presented encouraging potential for streamflow simulation at daily (Yunjinghong) and monthly (Yunjinghong, Jiuzhou and Gajiu) scale. Hydrological simulations forced with APHRODITE were the best of the five for the Yunjinghong station in capturing daily and monthly measured streamflow. Except for NCEP-CFSR, all products were very good for hydrological simulations for the Gajiu and Jiuzhou stations.

The nanocomposite of TiO2 and MgAl-layered double hydroxide (TiO2@LDH) possessing high photocatalytic activity was synthesized by dispersing TiO2 nanoparticles on the surface of LDHs with a distinct heterojunction. The structure and property of resulted materials were carefully characterized via XRD, SEM, EDS, (HR)TEM, XPS, FT-IR, TGA, BET and UV–vis spectroscopy. Pseudo in situ measurement in ultraviolet and visible domains (Pseudo in situ means that the toluene will be detected in the cuvette during the photodegradation progress) strategy was chosen for the photodegradation of volatile organic compounds. Gaseous toluene as a model pollutant was selected to estimate the photocatalyst efficiency of TiO2@LDH. Results showed that the as-prepared TiO2@LDH nanocomposites have extremely high photocatalytic activity towards the degradation of toluene under simulated sunlight and true sunlight, with a removal rate of 85.9% and 91.7%, respectively. According to the kinetic data, the reaction rate constant of 30 mg TiO2@LDH photocatalyst for the degradation of a toluene solution 171.6 mg m−3 is 0.0100 min−1. The high photocatalytic activity of TiO2@LDH nanocomposite is due to the positive charges on the surface of LDHs and reactive radicals (HO and O2−) generated at the TiO2@LDH interface. Moreover, the TiO2@LDH catalyst exhibits high stability during the toluene degradation even after four continuous cycles.

To evaluate the clinical efficacy and safety of Heyan Kuntai capsule (HYKT) in treating women with infertility caused by diminished ovarian reserve (DOR).One hundred eight eligible patients from three Chinese hospitals were randomly divided into an HYKT treatment group (n = 55) or a dehydroepiandrosterone (DHEA) treatment group (n = 53). Patients in the HYKT group were treated orally with four 0.5 g HYKT three times a day; patients in the DHEA group were treated with one 25.0 mg DHEA capsule three times a day. All patients were treated for 3 months and followed up over a 3-month period.Of 108 patients, 12 dropped out: six from the HYKT group, and six from the DHEA group. Eleven patients got pregnant during the treatment. Serum anti-Müllerian hormone levels and antral follicle counts increased significantly in both groups after treatment (P0.05) especially in the HYKT group (P0.05). Serum follicle stimulating hormone (FSH) levels and FSH/luteinizing hormone ratios decreased (P0.05) with no significant difference between the two groups. Estradiol levels in the HYKT group and DHEA-sulfate levels in the DHEA group both increased (P0.05). The spontaneous pregnancy rates were 12% and 11% in the HYKT and DHEA groups, respectively (not significant). During the follow-up period, 16 patients in the HYKT group underwent in vitro fertilization-embryo transfer (IVF-ET) and the number of retrieved oocytes was (5.1 c1.8). In DHEA group, 20 patients underwent IVF-ET and the number of retrieved oocyte was (4.2 ± 1.9) (not significant); clinical pregnancy rates were 38% in the HYKT group and 20% in DHEA group (not significant). No significant adverse reactions were observed.HYKT can improve the ovarian reserve and hormone levels in patients with infertility caused by DOR. Pregnancy rates after HYKT treatment were similar to those of DHEA treatment. HYKT might be an alternative to the treatment of infertility caused by DOR.

In this paper, we propose a dynamical model to describe the transmission of COVID-19, which is spreading in China and many other countries. To avoid a larger outbreak in the worldwide, Chinese government carried out a series of strong strategies to prevent the situation from deteriorating. Home quarantine is the most important one to prevent the spread of COVID-19. In order to estimate the effect of population quarantine, we divide the population into seven categories for simulation. Based on a Least-Squares procedure and officially published data, the estimation of parameters for the proposed model is given. Numerical simulations show that the proposed model can describe the transmission of COVID-19 accurately, the corresponding prediction of the trend of the disease is given. The home quarantine strategy plays an important role in controlling the disease spread and speeding up the decline of COVID-19. The control reproduction number of most provinces in China are analyzed and discussed adequately. We should pay attention to that, though the epidemic is in decline in China, the disease still has high risk of human-to-human transmission continuously. Once the control strategy is removed, COVID-19 may become a normal epidemic disease just like flu. Further control for the disease is still necessary, we focus on the relationship between the spread rate of the virus and the meteorological conditions. A comprehensive meteorological index is introduced to represent the impact of meteorological factors on both high and low migration groups. As the progress on the new vaccine, we design detail vaccination strategies for COVID-19 in different control phases and show the effectiveness of efficient vaccination. Once the vaccine comes into use, the numerical simulation provide a promptly prospective research.

A mathematical mechanism of the n -pentane pyrolysis process based on free radical reaction model was presented. The kinetic parameters of n -pentane pyrolysis are obtained by quantum chemistry and the reaction network is established. The solution of the stiff ordinary differential equations in the n -pentane pyrolysis model is completed by semi implicit Eular algorithm. Then the pyrolysis mechanism based on free radical reaction model is built, and the computational efficiency increases 10 times by algorithm optimization. The validity of this model and its solution method is confirmed by the experimental results of n -pentane pyrolysis.