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Para conmemorar el 500º aniversario del descubrimiento del Nuevo Mundo por parte de Colón, la Expo 92 de Sevilla organizó una exposición titulada "Arte y cultura en torno a 1492", que presentaba obras de arte procedentes de todo el mundo del siglo XV. Este ensayo analizará los antecedentes de la exposición, sus desequilibrios en su composición, los tipos artísticos ignorados, combinando métodos estadísticos y de análisis de imágenes, para investigar la ausencia de una imagen del arte chino Ming generada por el discurso de la exposición. Al comparar la bibliografía sobre la historia del arte chino Ming con las obras de la exposición, queda claro que la imagen del arte chino construida por la exposición carece en realidad de estilos artísticos que logran mostrar luces y sombras, colores magníficos o perspectiva lineal, como, la pintura yuanti, la pintura mural y la pintura de xilografía. Estas pinturas, sin embargo, ocuparon realmente un lugar importante en el panorama artístico de la dinastía Ming. Se espera que este estudio ayude a establecer y corregir las ausencias y deficiencias en la identidad del arte chino Ming construidas por la exposición en el contexto español.

The full neutron spectrum code for advanced reactor simulation named FSAR has recently been developed at Nuclear Power Institute of China in order to meet the requirements of advanced reactor with large neutron energy range. Based on the two-step calculation scheme, FSAR consists of two-dimensional lattice calculation code and core calculation code. In two-dimensional lattice calculation, the subgroup method with ultrafine energy groups was implemented in the two-dimensional resonance self-shielding by incorporating the MOC to get the accurate self-shielded cross-section. For better consideration of the strong space coupling in different geometric sizes due to different mean free path of neutrons, the super-homogenization method and the leakage model were applied. In the core calculation, the discrete-ordinate method and micro burnup calculation method were used to simulate the core neutron transport and depletion. Preliminary calculation results showed that for the problems with wide spectrum, the self-shielded cross-sections have a good agreement with the Monte Carlo solution. The results shown in this paper indicate that FSAR has good performances of the cross-section generation in full neutron spectrum problem simulation.

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With the rapid evolution of large language models (LLMs), there is a growing concern that they may pose risks or have negative social impacts. Therefore, evaluation of human values alignment is becoming increasingly important. Previous work mainly focuses on assessing the performance of LLMs on certain knowledge and reasoning abilities, while neglecting the alignment to human values, especially in a Chinese context. In this paper, we present CValues, the first Chinese human values evaluation benchmark to measure the alignment ability of LLMs in terms of both safety and responsibility criteria. As a result, we have manually collected adversarial safety prompts across 10 scenarios and induced responsibility prompts from 8 domains by professional experts. To provide a comprehensive values evaluation of Chinese LLMs, we not only conduct human evaluation for reliable comparison, but also construct multi-choice prompts for automatic evaluation. Our findings suggest that while most Chinese LLMs perform well in terms of safety, there is considerable room for improvement in terms of responsibility. Moreover, both the automatic and human evaluation are important for assessing the human values alignment in different aspects. The benchmark and code is available on ModelScope and Github., Comment: Working in Process

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SSSSLEEPING Summer School Study on Sleep & Learning Entailing Every Participant In Nijmegen, Gelderland

The disruptive effects of anxious emotional states on the execution of sensorimotor skills has been well documented (Cappuccio et al., 2019; Hill et al., 2010; Payne et al., 2018; Roberts et al., 2017). However, we know less about the cognitive appraisals that lead to the state of anxiety in the first place. In this work we sought to test predictions of Attentional Control Theory Sport (Eysenck & Wilson, 2016) that momentary errors and cognitive appraisals of the probability and costs of failure will lead to anxiety.

Soil microorganisms play an indispensable role in plant growth and are widely used to promote plant growth. However, poor microbial strains are homogeneous. The heavy application of chemical fertilizers and pesticides to agricultural soil has adversely affected the soil flora, necessitating the regulation of the soil flora to maintain soil health. In this study, X-45, a highly efficient and phosphorus-dissolving strain of the lysogenic bacterium Serratia marcescens N1.14 was isolated from bare rock slope soil samples from Yueyang Avenue, Hunan Province, China. We observed that microbial strain X-45 could release P from the rocks into solution when the sample rocks were used as the only phosphorus source. Furthermore, we observed that the P content in media increased by 3.08 X compared to the control. After applying X-45 as a bacterial fertilizer, the growth of potted Indigofera pseudotinctoria plants significantly increased, the soil physicochemical properties were significantly improved, and the relative abundance of Bradyrhizobium in the soil increased significantly from 1 to 42%. Besides, Bradyrhizobium became the most dominant genus in the soil. The indirect promotion of another beneficial microorganism by X-45 further revealed the intrinsic mechanism by which X-45 exerted its effect on plant promotion and soil improvement. Using this bacteria, the hypothesis of the superposition effect of legume plant promotion was also confirmed.

All slides and associated shared materials for the SIOP 2022 symposium, "Machine Learning Utility: Examiniation and Improvement for Selection". ABSTRACT Machine learning (ML) is increasingly being considered and adopted in personnel selection procedures. This symposium integrates and presents five papers with each examining aspects of the utility of ML for selection purposes, with regard to evidence of validities, efficiencies, and fairness. These papers cover situations and cases when ML is used to conduct personality assessment, predict performance, balance diversity considerations in HR, and scoring multidimensional psychometric tests. Importantly, the papers also offer multiple perspectives on how to improve the utility of ML in selection. CITATIONS Sun, T. (Chair). (2022). Machine Learning Utility: Examination and Improvement for Selection [Symposium]. Society for Industrial and Organizational Psychology Annual Conference, Seattle, WA, United States. Sun, T., & Drasgow, F. (2022). A meta-analytic review on the psychometric validity evidence of language-based machine learning personality assessment. Liu, J., Zhao, T., Sun, T., Zhang, B., & Fan, J. (2022). Latent structure equivalence and prediction comparisons between traditionally-assessed and machine-inferred personality scores. Lee, V., Cavanaugh, K. J., & Landers, R. (2022). Predicting job performance with social media information in a small sample. Hickman, L., Kuruzovich, J., Ng, V., Arhin, K., & Wilson, D. (2022). Oversampling minority success during machine learning model training reduces adverse impact. Marin, S. (2022). On the utility of scoring multidimensional assessments using variational autoencoders.

SSSSLEEPING Summer School Study on Sleep & Learning Entailing Every Participant In Nijmegen, Gelderland

Periosteum is expected for bone repairing due to excellent regenerative potential. PDCs are the main source of cells for promoting bone repair. However, PDCs from different sites have been confirmed to be site specific due to their distinct embryonic origin and the methods of bone formation. Hippo-YAP pathway is proved to play a critical role in fate decision of mesenchymal stem cells. The effect of Hippo-YAP on PDCs has not been reported so far. Hence, we aim to explore the differences of PDCs from mandible and femur along with their possible responses to YAP signaling. mPDCs and fPDCs were obtained and tested through flow cytometry for identification. Follow-up results illustrated mPDCs was cubic shape and with better proliferation while fPDCs preferred slender cell shape with worse cell viability compared with mPDCs. mPDCs was superior to fPDCs in ALP activity, related mRNA expression and calcium deposits in late stage. Interestingly, downregulation of YAP promoted the ALP activity, related mRNA expression and calcium deposits of fPDCs while hindered that of mPDCs in vitro. Moreover, implant animal model in mandible and femur were constructed for evaluation in vivo. Histological results were similar to the results in vitro. We speculate this may result from their different embryonic origin and the way of bone formation. Taken together, results available suggested that mPDCs may serve as more optimal seed cells for tissue engineering compared with fPDCs; however, considering their different response to YAP signaling, to ensure sufficient YAP expression in mPDCs and appropriate declining YAP expression in fPDCs may establish better osteogenesis.

The complex bone regeneration microenvironment puts high demands on orthopedic implants. The implants are not only desired to satisfy the meeting of the biomechanical properties, but should also possess capabilities to promote osseointegration and bone immune regulation. PEEK is a promising polymeric material for bone implants due to its suitable mechanical properties that well match natural cortical bone tissue. However, its inert biological properties limit its application. As a newly-developed bioactive polysaccharides complex, strontium Eucommia ulmoides polysaccharides (EUP-Sr) has been proved to have capabilities including promoting osteogenesis and regulating bone immunity. In this study, in order to improve the bioactivities of PEEK, we introduce EUP-Sr to the PEEK surface via polydopamine-based coating and form a bioactive PEEK named DPEEK@EUP-Sr. The as-fabricated DPEEK@EUP-Sr was systematically investigated through scanning electron microscopy, X-ray photoelectron spectroscopy, water contact angle analysis to verify the successful introduction of EUP-Sr to PEEK, and in vitro biological experiments including cell proliferation and RT-PCR analysis suggested that the DPEEK@EUP-Sr can effectively promote the proliferation of preosteoblast MC3T3-E1, and exhibit significant anti-inflammation and osteogenesis effects, and so should be beneficial for osteointegration between bone and implants.

Additional file 2: Table S1. Key parameters of the AFADESI-MSI setting.

Supplemental material, sj-docx-1-cat-10.1177_10760296221121297 for Association of Prolonged Disease Duration and TG/HDL-C Ratio in Accelerating Atherosclerosis in Patients with Takayasu's Arteritis by Juan Du, Yanlong Ren, Jiayi Liu, Taotao Li, Yaxin Zhang, Shiyu Yang, Tieduo Kang, Shangqiu Ning, Liying Chen, Xi Guo, Wenxian Liu and Lili Pan in Clinical and Applied Thrombosis/Hemostasis

Movie genre classification has been widely studied in recent years due to its various applications in video editing, summarization, and recommendation. Prior work has typically addressed this task by predicting genres based solely on the visual content. As a result, predictions from these methods often perform poorly for genres such as documentary or musical, since non-visual modalities like audio or language play an important role in correctly classifying these genres. In addition, the analysis of long videos at frame level is always associated with high computational cost and makes the prediction less efficient. To address these two issues, we propose a Multi-Modal approach leveraging shot information, MMShot, to classify video genres in an efficient and effective way. We evaluate our method on MovieNet and Condensed Movies for genre classification, achieving 17% ~ 21% improvement on mean Average Precision (mAP) over the state-of-the-art. Extensive experiments are conducted to demonstrate the ability of MMShot for long video analysis and uncover the correlations between genres and multiple movie elements. We also demonstrate our approach's ability to generalize by evaluating the scene boundary detection task, achieving 1.1% improvement on Average Precision (AP) over the state-of-the-art.

Supplemental Material, sj-pdf-1-lup-10.1177_09612033211069853 for Elevated serum free IL-18 in neuropsychiatric systemic lupus erythematosus patients with seizure disorders by Renge Liang, Lijuan Zheng, Tingting Ji, Jiaman Zheng, Jiayi Liu, Chao Yuan, Qin Huang, and Min Yang in Lupus

Additional file 1: Figure S1. Total metabolite distributions in the HC, LC and HCC tissue samples. a HE and MSI diagrams of HC, LC and HCC whole samples under the negative ionization mode. b PLS-DA comparison of AFADESI-MSI data under the negative ionization mode. c, d Alterations of metabolites detected by AFADESI-MSI method in the HC, LC and HCC samples based on positive (c) and negative (d) ionization modes. e, f K-means diagrams of the HC, LC and HCC tissue samples based on positive (e) and negative (f) ionization modes. Figure S2. The alterations of metabolites’ spatial distributions in HCC sub-regions under negative ionization mode. a HE and MSI images of different sub-regions of HCC. b PLS-DA analysis of different HCC sub-regions. c Number of metabolites detected from different HCC sub-regions. d Heatmap of significantly differentiated metabolites based on VIP > 1. e KEGG analysis of key altered metabolic pathways in the sub-regions of HCC. Figure S3. Metabolites’ spatial distributions in the non-cancerous regions (CV) in the HC, LC and HCC samples under negative ionization mode. a HE and MSI images of the CV regions in the HC, LC and HCC samples. b PLS-DA analysis for the CV regions. c Heatmap of significantly differentiated metabolites under positive ionization mode in the CV regions based on VIP > 1. d KEGG analysis of key altered metabolic pathways in the CV regions. e Heatmap of significantly differentiated metabolites based on VIP > 1 under negative ionization mode. f Sample time series analysis of the key metabolite expressions in the CV regions. Figure S4. Metabolites’ spatial distributions of the cancer-related regions in the HC, LC and HCC samples under negative ionization mode. a HE and MSI images (negative mode) of the cancer-related regions in the HC, LC and HCC samples. b PLS-DA analysis of the cancer-related regions. c Heatmap of significantly differentiated metabolites in the cancer-related regions based on variable VIP > 1 under positive ionization mode. d Heatmap of significantly differentiated metabolites in the cancer-related regions based on variable VIP > 1 under negative ionization mode. e KEGG analysis of key altered metabolic pathways in cancer-related regions. Figure S5. Metabolic changes in different tissues and its predictability of disease progression. a Sample time series analysis of key metabolite expressions (negative mode) in the cancer-related regions. b Examples of key metabolites’ spatial expressions in cancer-related regions under negative ion mode. c ROC analyses of key metabolites at distinguishing between diseased tissues and between different subregions (anabasine was detected under positive ionization mode, docosahexaenoic acid was detected under negative ionization mode).

Main subjects usually exist in the images or videos, as they are the objects that the photographer wants to highlight. Human viewers can easily identify them but algorithms often confuse them with other objects. Detecting the main subjects is an important technique to help machines understand the content of images and videos. We present a new dataset with the goal of training models to understand the layout of the objects and the context of the image then to find the main subjects among them. This is achieved in three aspects. By gathering images from movie shots created by directors with professional shooting skills, we collect the dataset with strong diversity, specifically, it contains 107\,700 images from 21\,540 movie shots. We labeled them with the bounding box labels for two classes: subject and non-subject foreground object. We present a detailed analysis of the dataset and compare the task with saliency detection and object detection. ImageSubject is the first dataset that tries to localize the subject in an image that the photographer wants to highlight. Moreover, we find the transformer-based detection model offers the best result among other popular model architectures. Finally, we discuss the potential applications and conclude with the importance of the dataset.

We have investigated the tidal forces and geodesic deviation motion in the spacetime of a black hole in the galaxy with dark matter halo. Our results show that the tidal force and geodesic deviation motion depend on the dark matter halo mass and the typical lengthscale of galaxy. The effect of the typical lengthscale of galaxy on tidal force is opposite to that of dark matter mass. For the radial tidal force, with the increasing mass of dark matter, it increases in the region far from the black hole, but decreases in the region near black hole. For the angular tidal force, its absolute value of angular tidal force monotonously increases with the dark matter halo mass. Especially, the angular tidal force also depends on the particle's energy and the effects of dark matter become more distinct for the test particle with high energy, which is different from those in the usual static black hole spacetimes. We also present the change of geodesic deviation vector with the dark matter halo mass and the typical lengthscale of galaxy under two kinds of initial conditions., Comment: 14 pages, 6 figures, Accepted by Chin. Phys. C

Objectives: To determine the value of cardiac magnetic resonance (CMR) quantitative tissue markers in the diagnosis of acute myocarditis, compared with the traditional Lake-Louise criteria (LLC). Methods: Totally 35 cases of patients diagnosed as acute myocarditis in Beijing Anzhen Hospital and 35 healthy volunteers were enrolled in our study, from May 2014 to August 2016. CMR protocol included quantitative assessment of T1 relaxation times using modified Look-Locker inversion recovery (MOLLI), extracellular volume fraction (ECV), T2 relaxation times. Established Lake-Louise criteria (LLC) consisting of T2-weighted signal intensity ratio (T2-ratio), early gadolinium enhancement ratio (EGEr), and late gadolinium enhancement (LGE) were assessed. Receiver operating characteristics analysis was performed to compare diagnostic performance. Results: T2-ratio (1.85±0.21 vs. 1.58±0.15; P = 0.002) and EGEr (3.87±1.62 vs. 2.15±1.36; P =0.024) were significantly higher in myocarditis subjects than in control subjects. Non-ischemic LGE was found in 27/35 (77.1%) of all myocarditis patients. Regional myocardial edema was found in 23/35 (65.7%) of all myocarditis patients. Myocardial T1 and T2 relaxation times and ECV were significantly prolonged in the myocarditis group compared with the control group (T1 native relaxation time: 1310±62 vs. 1247±27 ms, T2 native relaxation time: 65.46±8.49 vs. 55.17±3.59 ms; ECV: 34.47±8.52 vs. 27.68±5.82, P < 0.001, respectively). Areas under the curve of native T1 (0.94) and T2 relaxation times (0.91) were higher compared with those of the other CMR parameters (T2-ratio: 0.73, EGEr: 0.72, LGE: 0.88, LLC: 0.90, ECV: 0.79). Combined with LGE, each native mapping technique outperformed the diagnostic performance of LLC (P < 0.01, respectively). A combination of native parameters (T1, T2 relaxation times) significantly increased the diagnostic performance of CMR compared with LLC without need of contrast media application (0.99 vs. 0.90; P < 0.05). Conclusion: CMR quantitative tissue markers has good diagnostic efficiency for acute myocarditis, it may be potential to replace the Lake-Louise criteria in the future in patients with contraindications for the use of gadolinium-based contrast agents.

As a link between the internal and external basin, the deep derived fluids play a key role during the processes of hydrocarbon (HC) formation and accumulation in the form of organic-inorganic interaction. Two questions remain to be answered: How do deep-derived fluids affect HC generation in source rocks by carrying a large amount of matter and energy, especially in post-mature source rocks with weak HC generation capability? Can hydrogen and catalysts from deep sources significantly increase the HC generation potential of the source rock? In this study, we selected the post-mature kerogen samples of the early Cambrian Yurtus Formation in the Tarim Basin of China. Under the catalytic environment of ZnCl2 and MoS2, closed system gold tube thermal simulation experiments were conducted to quantitatively verify the contribution of catalytic hydrogenation to "HC promotion" by adding H2. The catalytic hydrogenation increased the kerogen HC generation capacity by 1.4–2.1 times. The catalytic hydrogenation intensity reaction increased with temperature. The drying coefficient of the generated gas decreased significantly as the increasing yield of heavy HC gas. In the simulation experiment, alkane δ13C becomes lighter after the catalytic hydrogenation experiment, while δ13CCO2 becomes heavier. In the process of catalytic hydrogenation, the number of gaseous products catalyzed by ZnCl2 is higher than that catalyzed by MoS2 under the same conditions, indicating that ZnCl2 is a better catalyst for the generation of gaseous yield. Meanwhile, Fischer-Tropsch synthesis (FFT) reaction was happened in the catalytic hydrogenation process. The simulation experiment demonstrates that hydrogen-rich components and metal elements in deep-derived fluids have significant catalytic hydrogenation effects on organic-rich matter, which improved the HC generation efficiency of post-mature source rocks.

Additional file 6: Table S2. The orthologous AAT genes in foxtail millet, wheat, sorghum and Arabidopsis.

Additional file 5: Table S1. The detail information and sequence characterization of 94 putative AAT genes in foxtail millet.

Recent advances in generative models and adversarial training have enabled artificially generating artworks in various artistic styles. It is highly desirable to gain more control over the generated style in practice. However, artistic styles are unlike object categories -- there are a continuous spectrum of styles distinguished by subtle differences. Few works have been explored to capture the continuous spectrum of styles and apply it to a style generation task. In this paper, we propose to achieve this by embedding original artwork examples into a continuous style space. The style vectors are fed to the generator and discriminator to achieve fine-grained control. Our method can be used with common generative adversarial networks (such as StyleGAN). Experiments show that our method not only precisely controls the fine-grained artistic style but also improves image quality over vanilla StyleGAN as measured by FID.

Additional file 7: Table S3. The primers used for the spatiotemporal expression analysis with Quantitative Real-Time PCR (qRT-PCR).

In this paper, we propose a malware detection method based on feature library and machine learning. By using a combination of static and dynamic feature extraction method, we select 8 types of static features to build a feature library. In addition, for potentially unknown malwares, we use 9 groups of dynamic features to train a support vector machine model, and give interpretable detection results based on the influence of different features. To verify the performance of our method, we conducted various experiments on a total of 129,013 malware samples and compared the results with other schemes, demonstrating the effectiveness of our method.

With the development of the Internet, the amount of information on the Internet is increasing rapidly, which makes it difficult for people to select the information they really want. A recommendation system is an effective way to solve this problem. Fake users can be injected by criminals to attack the recommendation system; therefore, accurate identification of fake users is a necessary feature of the recommendation system. Existing fake user detection algorithms focus on designing recognition methods for different types of attacks and have limited detection capabilities against unknown or hybrid attacks. The use of deep learning models can automate the extraction of false user scoring features, but neural network models are not applicable to discrete user scoring data. In this paper, random walking is used to rearrange the otherwise discrete user rating data into a rating feature matrix with spatial continuity. The rating data and the text data have some similarity in the distribution mode. By effective analogy, the TextCNN model originally used in NLP domain can be improved and applied to the classification task of rating feature matrix. Combining the ideas of random walking and word vector processing, this paper proposes a TextCNN detection model for user rating data. To verify the validity of the proposed model, the model is tested on MoiveLens dataset against 7 different attack detection algorithms, and exhibits better performance when compared with 4 attack detection algorithms. Especially for the Aop attack, the proposed model has nearly 100% detection performance with F1 – value as the evaluation index.

Identity-Based Cryptograph (IBC) takes the user's identity (such as email address, mobile phone number, personal name, etc.) as the public key, which reduces the process of exchanging digital certificates and public keys, effectively reduces the complexity of key and certificate management in the public-key system, and makes the security system easy to deploy and manage. It is very suitable for end-to-end offline secure communication, cloud data encryption, attribute-based encryption, policy-based encryption of various occasions. In order to improve the safety level of end-to-end off-line safety communication in electric vehicle pile sharing application and reduce the system complexity, this paper proposes an identity authentication system solution based on identification algorithm.

The element vision in the graphic design further extends the meaning of the dimension, and introduces the imagination of the person into the graphic design element, so that the dimension in the graphic design element is greatly expanded, not only is limited to the real existing dimension in the nature and the environment, It is also an active collection and integration of social phenomena. A computer-aided multi-dimensional representation of graphic design visual elements is proposed. A computer-aided multi-dimensional representation of graphic design visual elements is presented. through the plane template characteristic matching method, the multi-dimensional characteristic equalization processing of the graphic design visual element is carried out, the graphic design visual image is subjected to the multi-dimensional information enhancement processing in the communication area, the spatial expansion and expression ability of the graphic design is improved, And the multi-dimensional expression of the graphic design visual element is carried out. The simulation results show that the method can improve the multi-dimensional expression of visual elements in graphic design, and improve the multi-dimensional expression of visual elements in graphic design.

With the general trend of increasing Convolutional Neural Network (CNN) model sizes, model compression and acceleration techniques have become critical for the deployment of these models on edge devices. In this paper, we provide a comprehensive survey on Pruning, a major compression strategy that removes non-critical or redundant neurons from a CNN model. The survey covers the overarching motivation for pruning, different strategies and criteria, their advantages and drawbacks, along with a compilation of major pruning techniques. We conclude the survey with a discussion on alternatives to pruning and current challenges for the model compression community.

Remanufacturing is the rebuilding of a product to specifications of the original manufactured product using a combination of directly reused, repaired and new parts. Disassembly, the first and arguably most important process in remanufacturing, tends to be labour-intensive due to complexities in the conditions of end-of-life products returned for remanufacture. Robotic disassembly is an attractive alternative to manual disassembly but robotic systems cannot plan disassembly sequences automatically and manual planning is still necessary. Planning requires machines to interpret physical space using a suitable representation to reflect physical contacts and constraints as well as rules for deciding the sequences of disassembly operations. This paper proposes a representation to describe physical contacts and constraints, and a new approach allowing machines to plan disassembly using the representation. The approach involves employing an assembly matrix and simple logic gates to generate a contact matrix, a space interference matrix and a relation matrix. Rules and algorithms are discussed to explain the calculation of sequences through manipulating the three matrices. A key benefit is that the proposed method can deal with interlocked mechanical structures which cannot be handled using conventional methods. The proposed method is also flexible and is suitable for either selective or complete disassembly.

Recent studies have shown that some inflammatory markers are associated with the prognosis of solid tumors. This study aims to evaluate the prognosis of glioma patients with or without adjuvant treatment using the systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), and platelet-lymphocyte ratio (PLR). All patients who were diagnosed with gliomas at the first and second affiliated hospital of Guangxi Medical University between 2011 and 2020 were included in this study. The optimal cutoff value of SII, NLR, and PLR was determined by X-tile software program. We stratified patients into several groups and evaluated the progression-free survival (PFS) and overall survival (OS) of SII, NLR, and PLR during the period of pre-surgical, con-chemoradiotherapy, and post-treatments. Multivariate Cox regression analyses were performed to detect the relationships between OS, PFS, and prognostic variables. A total of 67 gliomas patients were enrolled in the study. The cutoff values of SII, NLR, and PLR were 781.5 × 109/L, 2.9 × 109/L, and 123.2 × 109/L, respectively. Patients who are pre-SII

The national audit is produced and developed to meet the objective requirements of national governance, and is a basic institutional arrangement within the governance system. In terms of its institutional attributes, legal status and functions, the national audit is the cornerstone of national governance and an important guarantee driving the modernization of governance. In this new historical era, the national audit needs to be appropriate to the new situation and new requirements of comprehensively deepening reform and enhancing the rule of law. We must accelerate the improvement of the audit system, enhance the breadth of coverage of audit supervision, comprehensively increase audit capacity, give greater effect to the functional role of audit and boost the modernization of the national governance system and governance capability.

The predominant paradigm for using machine learning models on a device is to train a model in the cloud and perform inference using the trained model on the device. However, with increasing number of smart devices and improved hardware, there is interest in performing model training on the device. Given this surge in interest, a comprehensive survey of the field from a device-agnostic perspective sets the stage for both understanding the state-of-the-art and for identifying open challenges and future avenues of research. However, on-device learning is an expansive field with connections to a large number of related topics in AI and machine learning (including online learning, model adaptation, one/few-shot learning, etc.). Hence, covering such a large number of topics in a single survey is impractical. This survey finds a middle ground by reformulating the problem of on-device learning as resource constrained learning where the resources are compute and memory. This reformulation allows tools, techniques, and algorithms from a wide variety of research areas to be compared equitably. In addition to summarizing the state-of-the-art, the survey also identifies a number of challenges and next steps for both the algorithmic and theoretical aspects of on-device learning., Comment: Edge Learning, TinyML, Resource Constrained Machine Learning, Deep learning on device, Statistical Learning Theory, 45 pages survey

NoSQL data-stores are commonly used to provide flexibility and availability for big data handling. However, there is a lack of comprehensive studies about which NoSQL data-store performs the best from the two scalability aspects, (scale-up, and scale-out), in a distributed and parallel processing environment. This paper compares the popular NoSQL data-stores (Cassandra, HBase, and MongoDB) and analyzes the resulting performance. Our experiments measure throughput, latency, and run-time of the evaluated data-stores on a big data set that consist of standard benchmarking workloads. Our results provide that the performance of each NoSQL data-store varies according to two main factors, (a) the type of executed operation, (read, scan, update, write, and insert), and (b) the level of distribution.

In order to solve the problem of slow transmission efficiency, small amount of information, short coverage distance, easy to be disturbed, and easy packet loss in the transmission process of traditional narrowband modules,based on HPLC, this paper designed and applied the intelligent technical scheme of smart grid measurement operation.

To solve the problem that users cannot use network resources efficiently, based on mobile edge computer network architecture,in a resource-constrained system, an energy-efficient offload strategy and a wireless resource joint optimization algorithm (EEJS) are proposed to achieve a balance between user diversity and MS diversity.

Magnetic field in magnetic bearings is the physical medium to realize magnetic levitation, the distribution of the magnetic field determines the operating performance of magnetic bearings. In this paper, a thin-slice Fiber Bragg Grating-Giant Magnetostrictive Material magnetic sensor used for the air gap of magnetic bearings was proposed and tested in the condition of dynamic magnetic field. The static property of the sensor was calibrated and a polynomial curve was fitted to describe the performance of the sensor. Measurement of dynamic magnetic field with different frequencies in magnetic bearings was implemented. Comparing with the finite element simulations, the results showed the DC component of the magnetic field was detected by the sensor and error was less than 5.87%.

Objective: The aim of this study was to investigate the effect of vitamin D on the metabolic characteristics of polycystic ovary syndrome(PCOS) and its mechanism. Methods and Results: 155 cases of PCOS patients and 74 non-PCOS women withmatched age and BMI were enrolled in this study.Data were collected from May 2016 to November 2017. Plasma 25-hydroxyvitamin D[25(OH)D], metabolic markers, and sex hormones were measured. Homeostasis model assessment(HOMA-IR), triglyceride(TG), testosterone(T), free androgen index(FAI) and uric acid(UA) were significantly increased(P Conclusion: Serum level of vitamin D was lower in women with PCOS than controls. Vitamin D in overweight/obese women with PCOS was lower than normal-weight women with PCOS. Vitamin D is associated with insulin resistance and cardiovascular disease risk in overweight/obese women with PCOS. Disclosure S. Shanmei: None. L. Jiayi: None. L. Yikun: None. Q. Chengcheng: None. Y. Jiamiao: None. Y. Bi: None. D. Zhu: None.