Search

To effectively improve the absorption capacity of the integrated energy system (IES) to clean energy, reduce the carbon dioxide emissions of the system, and improve the economic benefits of the system, this paper constructs a comprehensive energy system model that includes both electricity to hydrogen and hydrogen to gas and introduces the carbon dioxide trading mechanism, the green certificate trading mechanism and the response mechanism to the load demand. Different scenarios are set for example analysis and the model is solved using Matlab software's CPLEX solver. The results show that the operation strategy of the IES considering demand response under the carbon dioxide–green certificate joint trading mechanism performs best, reducing the total cost of the system by 65.8% and the carbon dioxide emissions by 23.1%, which can provide a strong reference and guidance for the low carbon dioxide economy operation of the IES.

This paper considers distributed stochastic optimization, in which a number of agents cooperate to optimize a global objective function through local computations and information exchanges with neighbors over a network. Stochastic optimization problems are usually tackled by variants of projected stochastic gradient descent. However, projecting a point onto a feasible set is often expensive. The Frank-Wolfe (FW) method has well-documented merits in handling convex constraints, but existing stochastic FW algorithms are basically developed for centralized settings. In this context, the present work puts forth a distributed stochastic Frank-Wolfe solver, by judiciously combining Nesterov's momentum and gradient tracking techniques for stochastic convex and nonconvex optimization over networks. It is shown that the convergence rate of the proposed algorithm is $\mathcal{O}(k^{-\frac{1}{2}})$ for convex optimization, and $\mathcal{O}(1/\mathrm{log}_2(k))$ for nonconvex optimization. The efficacy of the algorithm is demonstrated by numerical simulations against a number of competing alternatives., Comment: 15 pages, 11 figures, 2 tables

To compare the short-term outcomes of a new gastrointestinal decompression tube combined with conservative treatment in patients with esophagojejunal anastomotic leakage (EJAL) after total gastrectomy. We retrospectively analyzed the data of 81 patients with EJAL who had undergone total gastrectomy and Roux-en-Y reconstruction at Fujian Medical University Union Hospital between January 2014 and December 2021. The patients were divided into experimental (12 patients with new gastrointestinal decompression tube plus conservative treatment) and control (69 patients with conservative treatment) groups, according to the different treatment methods they received. Anatomic defect size linearly correlated with time to clinical success, hospital stay, and hospital cost in the control group. The two groups showed no significant differences in anastomotic defect size, time of defect after surgery, hospitalization cost, and time of antibiotic use. However, the time to clinical success was significantly shorter in the experimental group than in the control group (16.0 ± 8.3 vs. 23.6 ± 17.8, P = 0.04), as was the length of hospital stay (30.1 ± 6.3 vs. 36.8 ± 16.7, P = 0.017). Furthermore, when the defect size was ≥ 4 mm, the time to clinical success, hospital stay, and hospital cost in the experimental group were lower than those in the control group (P 0.05). Placement of a new gastrointestinal decompression tube is a safe treatment. When the defect size is ≥ 4 mm, the time to clinical success, length of hospital stay, and hospital cost can be reduced.

Recently, the natural heterostructure of (PbSe)5(Bi2Se3)6 has been theoretically predicted and experimentally confirmed as a topological insulator. In this work, we induce superconductivity in (PbSe)5(Bi2Se3)6 by implementing high pressure. As increasing pressure up to 10 GPa, superconductivity with Tc ~ 4.6 K suddenly appears, followed by an abrupt decrease. Remarkably, upon further compression above 30 GPa, a new superconducting state arises, where pressure raises the Tc to an unsaturated 6.0 K within the limit of our research. Combining XRD and Raman spectroscopies, we suggest that the emergence of two distinct superconducting states occurs concurrently with the pressure-induced structural transition in this topological heterostructure (PbSe)5(Bi2Se3)6., Comment: 16 pages,5 figures, 1 table

Pre-trained models have proved to be powerful in enhancing task-oriented dialog systems. However, current pre-training methods mainly focus on enhancing dialog understanding and generation tasks while neglecting the exploitation of dialog policy. In this paper, we propose GALAXY, a novel pre-trained dialog model that explicitly learns dialog policy from limited labeled dialogs and large-scale unlabeled dialog corpora via semi-supervised learning. Specifically, we introduce a dialog act prediction task for policy optimization during pre-training and employ a consistency regularization term to refine the learned representation with the help of unlabeled dialogs. We also implement a gating mechanism to weigh suitable unlabeled dialog samples. Empirical results show that GALAXY substantially improves the performance of task-oriented dialog systems, and achieves new state-of-the-art results on benchmark datasets: In-Car, MultiWOZ2.0 and MultiWOZ2.1, improving their end-to-end combined scores by 2.5, 5.3 and 5.5 points, respectively. We also show that GALAXY has a stronger few-shot ability than existing models under various low-resource settings., 7 pages, 5 figures. Accepted by AAAI 2022

Background: Compared with other types of compressors, although the scroll compressor has a simple structure and low noise, due to the limitation of the structure, it has defects such as excessively high discharge temperature, and has extremely high requirements on the machining accuracy of parts. The oil-free scroll compressor is a new type of scroll machine, which does not contain oil during the working process; it can be applied to cases with low displacement requirements and a high-pressure ratio. Objective: Taking a scroll compressor with a rated displacement of 0.6 m3/min as the research object, the analysis and research of the working performance parameters of the scroll compressor in actual work provide a certain theoretical basis for the improvement and optimization of the test prototype. Methods: The thermodynamic model of the scroll compressor in the actual working process is established by the variable-mass system thermodynamics and the control volume method. Based on the CFD method, a three-dimensional unsteady-state numerical simulation of the flow characteristics of the working fluid in the scroll compressor's working chamber is carried out in order to verify the thermodynamic model. Considering the accuracy of numerical simulation, a test platform with air as the working fluid is set up. Results: Through the thermodynamic model and numerical simulation, the changes of temperature, pressure and velocity in the working chamber of the scroll compressor with the orbiting angle of the main shaft as well as the gas force and torque acting on the orbiting scroll tooth are obtained. Through experiments, the law of volume flow and shaft power of the scroll compressor with the speed of change, and the law of the change of discharge temperature with pressure at different speeds are obtained. Conclusion: At the same time, the thermodynamic model established by considering heat transfer and leakage is more in line with the actual working process of the compressor; the mass exchange between adjacent working chambers of the scroll compressor has a greater impact on the temperature and pressure in the working chamber. Due to internal leakage and irreversible loss, such as heat transmission, there is a deviation between the theoretical volume flow and the actual volume flow.

Lysine demethylase 5A (KDM5A) is a histone demethylase frequently involved in cancer progression. This research aimed to explore the function of KDM5A in prostate adenocarcinoma (PRAD) and the molecular mechanism. KDM5A was highly expressed in collected PRAD tissues and acquired PRAD cells. High KDM5A expression was correlated with reduced survival and poor prognosis of patients with PRAD. Knockdown of KDM5A suppressed the proliferation, colony formation, migration, and invasiveness of PRAD cells and reduced angiogenesis ability of endothelial cells. Downstream molecules implicated in KDM5A mediation were predicted using integrated bioinformatic analyses. KDM5A enhanced ETS proto-oncogene 1 (ETS1) expression through demethylation of H3K4me2 at its promoter. ETS1 suppressed the transcription activity of miR-330-3p, and either further ETS1 overexpression or miR-330-3p inhibition blocked the functions of KDM5A knockdown in PRAD. miR-330-3p targeted coatomer protein complex subunit β2 (COPB2) mRNA. Downregulation of miR-330-3p restored the expression of COPB2 and activated the PI3K/AKT pathway in PRAD. The results in vitro were reproduced in vivo where KDM5A downregulation suppressed the growth and metastasis of xenograft tumors in nude mice. In conclusion, this study demonstrated that KDM5A promoted PRAD by suppressing miR-330-3p and activating the COPB2/PI3K/AKT axis in an ETS1-dependent manner.

To evaluate the relationships among clones and open pollinated families from the same plus trees and to select elite breeding materials, growth, and wood characteristics of 33-year-old Pinus koraiensis clones and families were measured and analyzed. The results show that growth and wood characters varied significantly. The variation due to clonal effects was higher than that of family effects. The ratio of genetic to phenotypic coefficient of variation of clones in growth and wood traits was above 90%, and the repeatability of these characteristics was more than 0.8, whereas the ratio of genetic to phenotypic coefficient of variation of families was above 90%. The broad-sense heritability of all characteristics exceeded 0.4, and the narrow-sense family heritability of growth traits was less than 0.3. Growth characteristics were positively correlated with each other, but most wood properties were weakly correlated in both clones and families. Fiber length and width were positively correlated between clones and families. Using the membership function method, eleven clones and four families were selected as superior material for improved diameter growth and wood production, and two families from clonal and open-pollinated trees showed consistently better performance. Generally, selection of the best clones is an effective alternative to deployment of families as the repeatability estimates from clonal trees were higher than narrow-sense heritability estimates from open pollinated families. The results provide valuable insight for improving P. koraiensis breeding programs and subsequent genetic improvement.

BackgroundThe incidence of primary lung cancer (LC) in children and adolescence was rare. We analyzed data from a SEER database to better define the incidence, clinical characters, pathology, treatment, and outcomes of rare primary malignant pulmonary tumors in childhood and adolescence.MethodsPatients were chosen from the SEER database (SEER*Stat 8.4.0 software) from 2000 to 2019 and all patients were pathologically diagnosed with primary malignant tumors of the lung and bronchus. Demographic characteristics of patients (age, gender, race, primary site, laterality, location, differentiation grade, operation methods, histology, and history of radiotherapy and chemotherapy), as well as TNM stage and survival time, were collected.ResultsA total of 301 cases of children ≤19 years of age with a primary malignant pulmonary tumor were reported to the SEER database from 2000 to 2019. There were 143 men (47.5%) and 158 women (52.5%). Whites represented majority of patients (79.7%), followed by Black (13.6%) and others (6.7%). As for the primary site, the main site was the lower lobe (33.2%), followed by the upper lobe (26.9%). Most of the patients (80.4%) underwent surgery. Lobectomy (39.9%) is the main operation method. Only 28 (9.3%) patients received radiotherapy and 112 (37.2) patients received chemotherapy. Carcinoid tumor was the most common histology (29.6%), followed by pulmonary blastoma (PB) (22.3%), mucoepidermoid carcinoma (MEC) (12.3%), adenocarcinoma (10.3%), neuroendocrine tumor (NET) (5.7%), squamous cell carcinoma (SCC) (5.3%), atypical carcinoma (2.3%). The mean follow-up time was 100 months. For the entire group of children and adolescents, the 1-year OS was 89.1%, and the 3-year overall survival (OS) was 79.7%. the 5-year OS was 77.9%, the 10-year OS was 75.7%, and the 15-year OS was 73.9%. And 1-year lung cancer specificity survival (LCSS) was 89.8%, and the 3-year LCSS was 80.4%. the 5-year LCSS was 79.4%, the 10-year LCSS was 77.7%, and the 15-year LCSS was 75.9%. The OS of atypical carcinoma, carcinoid tumor, and MEC were in the top three.ConclusionsPrimary LC in children and adolescent were rare and histopathological diverse. Fortunately, children and adolescents with LC had an overall favorable outcome after treatment. Histology, differentiation grade, surgery, TNM stage, and therapeutic modalities have important influence on OS. The further treatment experience of each pathological type would make better evidence-based practice possible.

BackgroundToxoplasmosis caused by Toxoplasma gondii is a globally distributed zoonosis. Most infections appear asymptomatic in immunocompetent individuals, but toxoplasmosis can be fatal in fetuses and immunocompromised adults. There is an urgent need to research and develop effective and low-toxicity anti-T. gondii drugs because of some defects in current clinical anti-T. gondii drugs, such as limited efficacy, serious side effects and drug resistance.MethodsIn this study, 152 autophagy related compounds were evaluated as anti-T. gondii drugs. The activity of β-galactosidase assay based on luminescence was used to determine the inhibitory effect on parasite growth. At the same time, MTS assay was used to further detect the effects of compounds with over 60% inhibition rate on host cell viability. The invasion, intracellular proliferation, egress and gliding abilities of T. gondii were tested to assess the inhibitory effect of the chosen drugs on the distinct steps of the T. gondii lysis cycle.ResultsThe results showed that a total of 38 compounds inhibited parasite growth by more than 60%. After excluding the compounds affecting host cell activity, CGI-1746 and JH-II-127 were considered for drug reuse and further characterized. Both CGI-1746 and JH-II-127 inhibited tachyzoite growth by 60%, with IC50 values of 14.58 ± 1.52 and 5.88 ± 0.23 μM, respectively. TD50 values were 154.20 ± 20.15 and 76.39 ± 14.32 μM, respectively. Further research found that these two compounds significantly inhibited the intracellular proliferation of tachyzoites. Summarize the results, we demonstrated that CGI-1746 inhibited the invasion, egress and especially the gliding abilities of parasites, which is essential for the successful invasion of host cells, while JH-II-127 did not affect the invasion and gliding ability, but seriously damaged the morphology of mitochondria which may be related to the damage of mitochondrial electron transport chain.DiscussionTaken together, these findings suggest that both CGI-1746 and JH-II-127 could be potentially repurposed as anti-T. gondii drugs, lays the groundwork for future therapeutic strategies.

Polyvinylidene fluoride (PVDF) membranes are favored for their excellent properties. However, the inherent strong hydrophobicity of PVDF membranes limits their development in the field of water treatment. The objective of this study was to improve the performance of PVDF membranes using the self-polymerization, strong adhesion properties, and biocompatible effects of dopamine (DA). The PVDF/DA membrane modification conditions were simulated and optimized using response surface methodology (RSM), and the experimental design was used to investigate three main parameters. The results showed that the DA solution concentration of 1.65 g/L, the coating time of 4.5 h, the post-treatment temperature of 25°C, the contact angle decreased from 69° to 33.9°, and the pure water flux on the PVDF/DA membrane was higher than that on the original membrane. The absolute value of the relative error between the actual and predicted values is only 3.36 %. In the MBR parallel comparison test, compared with the PVDF/DA membrane, the total amount of extracellular polymers (EPS) of the PVDF membrane increased by 1.46 times and the polysaccharide increased by 1.56 times, which further showed that the PVDF/DA modified membrane had the excellent anti-pollution ability. Through Alpha diversity analysis, the biodiversity detected on PVDF/DA membranes was higher than that of PVDF membranes, which further proved its good bio-adhesion ability. These findings could offer a reference for the hydrophilicity, antifouling, and stability of PVDF/DA membranes, which would establish the foundation for the comprehensive applications in MBR.

Objective This study aimed to evaluate the condylar positional changes following mandibular reconstruction with preservation of the condylar head using Cone-Beam Computed Tomography (CBCT). Also, to assess joint space changes and the overall volumetric space compared to the preoperative status. Methodology This prospective study included 30 patients (60 joints) subjected to unilateral mandibular resection and reconstruction with preservation of the condylar head. The Helkimo index and preoperative (T1), two weeks postoperative (T2), and follow-up CBCTs (T3) after at least six months were gathered and processed to evaluate the condylar position and TMJ joint space using Anatomage Invivo 6. A student’s t-test and repeated-measures ANOVA statistics were used. A P value of less than 0.05 was considered statistically significant. Results Thirty patients (14 males, 16 females) with a mean age of 40.01 ± 12.7 years (a range of 18.1–62.9 years) were included. On the tumor side, there were significant variances in the vertical and mediolateral condylar positions between the three-time points (T1, T2, T3). Immediately after the operation, the condyles were significantly displaced in a downward direction at T2, which became larger after the last follow-up period (T3) (p = 0.007). The condylar positions at the anteroposterior direction were relatively stable without significant differences between the three times points (p = 0.915). On the non-tumor side, the condylar positions were relatively stable in the mediolateral and anteroposterior positions. In the tumor side, all of the TMJ spaces were significantly increased in size following the mandibular reconstructions (T2 and T3). However, on the non-tumor side, the anterior, posterior, and medial joint spaces were significantly changed postoperatively. Conclusion After mandibular reconstruction with condylar preservation, the condylar position and volumetric measurement immediately changed noticeably and continued to be a permanent change over time compared to relatively stable condyles on the non-tumor side. According to Helkimo index, patients become adapted to the postoperative changes without significant differences between the two sides.

BACKGROUND Pancreatic cancer is a highly heterogeneous disease, making prognosis prediction challenging. Altered energy metabolism to satisfy uncontrolled proliferation and metastasis has become one of the most important markers of tumors. However, the specific regulatory mechanism and its effect on prognosis have not been fully elucidated. AIM To construct a prognostic polygene signature of differentially expressed genes (DEGs) related to lipid metabolism. METHODS First, 9 tissue samples from patients with pancreatic cancer were collected and divided into a cancer group and a para-cancer group. All patient samples were subjected to metabolomics analysis based on liquid tandem chromatography quadrupole time of flight mass spectrometry. Then, mRNA expression profiles and corresponding clinical data of pancreatic cancer were downloaded from a public database. Least absolute shrinkage and selection operator Cox regression analysis was used to construct a multigene model for The Cancer Genome Atlas. RESULTS Principal component analysis and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) based on lipid metabolomics analysis showed a clear distribution in different regions. A Euclidean distance matrix was used to calculate the quantitative value of differential metabolites. The permutation test of the OPLS-DA model for tumor tissue and paracancerous tissue indicated that the established model was consistent with the actual condition based on sample data. A bar plot showed significantly higher levels of the lipid metabolites phosphatidylcholine (PC), phosphatidyl ethanolamine (PE), phosphatidylethanol(PEtOH), phosphatidylmethanol (PMeOH), phosphatidylserine (PS) and diacylglyceryl trimethylhomoserine (DGTS) in tumor tissues than in paracancerous tissues. According to bubble plots, PC, PE, PEtOH, PMeOH, PS and DGTS were significantly higher in tumor tissues than in paracancerous tissues. In total, 12.3% (25/197) of genes related to lipid metabolism were differentially expressed between tumor tissues and adjacent paracancerous tissues. Six DEGs correlated with overall survival in univariate Cox regression analysis (P < 0.05), and a 4-gene signature model was developed to divide patients into two risk groups, with patients in the high-risk group having significantly lower overall survival than those in the low-risk group (P < 0.05). ROC curve analysis confirmed the predictive power of the model. CONCLUSION This novel model comprising 4 lipid metabolism-related genes might assist clinicians in the prognostic evaluation of patients with pancreatic cancer.

The Chinese traditional medicinal plants Rheum palmatum L., Scutellaria baicalensis Georgi, and Houttuynia cordata Thunb in a ratio of 108:65:27 form a compound named Dahuang Qinyu San (DQS), which inhibits and kills Escherichia coli and Salmonella to a certain extent in fish and shrimp aquaculture environments. The active ingredients quercetin, emodin, baicalin, and aloe-emodin are obtained from the semi-biomimetic extract of DQS (SEDQS). However, the antibacterial mechanism of SEDQS against Salmonella is still unclear. This study used the microwell-plate method to determine the Minimum Inhibitory Concentration (MIC) of SEDQS against Salmonella enteritidis (S. enteritidis) isolated from geese. In addition, the effect of SEDQS on the growth curve, respiratory metabolic system, cell wall, soluble protein, and nucleic acid in bacterial liquid of S. enteritidis was detected by spectrophotometer and reagent kit. The effects of SEDQS on S. enteritidis DNA, binding gel blocking, virulence gene expression, and pathogenicity-related proteins were determined by gel electrophoresis, SDS–PAGE, and fluorescence quantitative PCR. The study found that a concentration of 1/4 MIC−2 MIC (2.27–18.2 mg/ml) SEDQS can significantly inhibit the normal growth of S. enteritidis, destroy the cell membrane structure of bacteria resulting in the leak of nucleic acid, protein, and other contents (P < 0.01). It also significantly inhibited the activities of succinate dehydrogenase (SDH) and malate dehydrogenase (MDH; P < 0.01) in a concentration-dependent manner. When the concentration of SEDQS was 1/2 MIC to 2 MIC (4.55–18.2 mg/ml), the expression levels of gyrB, fimA, filC, spvR, Hcp, and vgrG virulence genes (P < 0.01) all decreased by more than 31, 11, 18, 30, 34, and 21% respectively compared with the control group. SEDQS could significantly inhibit the expression of six virulence genes and play an important role in the pathogenicity of the S. enteritidis infected host. The SEDQS could exert antibacterial pharmacological effects by inhibiting the growth and metabolism of S. enteritidis and inhibiting the expression of major virulence factors. It has potential application value as an antibiotic alternative.

Supplementary Material 8

Supplementary Material 5

Additional file 1. Supplemental tables and figures.

Supplementary Material 6

Supplementary Material 4

Transformers, the de-facto standard for language modeling, have been recently applied for vision tasks. This paper introduces sparse queries for vision transformers to exploit the intrinsic spatial redundancy of natural images and save computational costs. Specifically, we propose a Dynamic Grained Encoder for vision transformers, which can adaptively assign a suitable number of queries to each spatial region. Thus it achieves a fine-grained representation in discriminative regions while keeping high efficiency. Besides, the dynamic grained encoder is compatible with most vision transformer frameworks. Without bells and whistles, our encoder allows the state-of-the-art vision transformers to reduce computational complexity by 40%-60% while maintaining comparable performance on image classification. Extensive experiments on object detection and segmentation further demonstrate the generalizability of our approach. Code is available at https://github.com/StevenGrove/vtpack., Comment: Accepted by NeurIPS2021

Modeling and forecasting multivariate time series not only facilitates the decision making of practitioners, but also deepens our scientific understanding of the underlying dynamical systems. Spatial-temporal graph neural networks (STGNNs) are emerged as powerful predictors and have become the de facto models for learning spatiotemporal representations in recent years. However, existing architectures of STGNNs tend to be complicated by stacking a series of fancy layers. The designed models could be either redundant or enigmatic, which pose great challenges on their complexity and scalability. Such concerns prompt us to re-examine the designs of modern STGNNs and identify core principles that contribute to a powerful and efficient neural predictor. Here we present a compact predictive model that is fully defined by a dense encoder-decoder and a message-passing layer, powered by node identifications, without any complex sequential modules, e.g., TCNs, RNNs, and Transformers. Empirical results demonstrate how a simple and elegant model with proper inductive basis can compare favorably w.r.t. the state of the art with elaborate designs, while being much more interpretable and computationally efficient for spatial-temporal forecasting problem. We hope our findings would open new horizons for future studies to revisit the design of more concise neural forecasting architectures.

There have been tumultuous political and social changes in Chinese society in recent years. In this context the present research assumes the widespread operation of psychological processes of affective adaptation from international games on national identity and patriotism. Affective adaptation involves the waxing and waning of emotional responses based on exposure to relevant events. Short-lived effects of affective adaptation are found. Dissipation duration of such effects from the Olympics cannot be determined. This is ascribed to the Asian Games sporting event following the Olympics. The study also discloses the joint effect on national identity between elapsed time following the Olympics regional origins of the public events. System justification processes are used to generate hypotheses about the positive effects of economic inequality on national identity. Contextual analyses test this perspective by disclosure of regional effects on individuals of select economic indicators. Two such indicators – Engels Coefficient and GDP Growth – predicted the fit of theoretically expected trajectories between Chinese regions. This research contributes to general theoretical knowledge as it relates to social identity activation.

Elemental materials provide clean and fundamental platforms for studying superconductivity. However, the highest superconducting critical temperature (Tc) yet observed in elements has not exceeded 30 K. Discovering elemental superconductors with a higher Tc is one of the most fundamental and challenging tasks in condensed matter physics. In this study, by applying high pressure up to approximately 260 GPa, we demonstrate that the superconducting transition temperature of elemental scandium (Sc) can be increased to 36 K, which is a record-high Tc for superconducting elements. The pressure dependence of Tc implies the occurrence of multiple phase transitions in Sc, which is in agreement with previous X-ray diffraction results. Optimization of Tc is achieved in the Sc-V phase, which can be attributed to the strong coupling between d electrons and moderate-frequency phonons, as suggested by our first-principles calculations. This study provides insights for exploring new high-Tc elemental metals., Comment: 12 pages, 5 figures

This paper develops a unified distributed method for solving two classes of constrained networked optimization problems, i.e., optimal consensus problem and resource allocation problem with non-identical set constraints. We first transform these two constrained networked optimization problems into a unified saddle-point problem framework with set constraints. Subsequently, two projection-based primal-dual algorithms via Optimistic Gradient Descent Ascent (OGDA) method and Extra-gradient (EG) method are developed for solving constrained saddle-point problems. It is shown that the developed algorithms achieve exact convergence to a saddle point with an ergodic convergence rate $O(1/k)$ for general convex-concave functions. Based on the proposed primal-dual algorithms via saddle-point dynamics, we develop unified distributed algorithm design and convergence analysis for these two networked optimization problems. Finally, two numerical examples are presented to demonstrate the theoretical results.

Traffic volume is an indispensable ingredient to provide fine-grained information for traffic management and control. However, due to limited deployment of traffic sensors, obtaining full-scale volume information is far from easy. Existing works on this topic primarily focus on improving the overall estimation accuracy of a particular method and ignore the underlying challenges of volume estimation, thereby having inferior performances on some critical tasks. This paper studies two key problems with regard to traffic volume estimation: (1) underdetermined traffic flows caused by undetected movements, and (2) non-equilibrium traffic flows arise from congestion propagation. Here we demonstrate a graph-based deep learning method that can offer a data-driven, model-free and correlation adaptive approach to tackle the above issues and perform accurate network-wide traffic volume estimation. Particularly, in order to quantify the dynamic and nonlinear relationships between traffic speed and volume for the estimation of underdetermined flows, a speed patternadaptive adjacent matrix based on graph attention is developed and integrated into the graph convolution process, to capture non-local correlations between sensors. To measure the impacts of non-equilibrium flows, a temporal masked and clipped attention combined with a gated temporal convolution layer is customized to capture time-asynchronous correlations between upstream and downstream sensors. We then evaluate our model on a real-world highway traffic volume dataset and compare it with several benchmark models. It is demonstrated that the proposed model achieves high estimation accuracy even under 20% sensor coverage rate and outperforms other baselines significantly, especially on underdetermined and non-equilibrium flow locations. Furthermore, comprehensive quantitative model analysis are also carried out to justify the model designs.

Supplementary Material 1

Supplementary file 1 (DOCX 558 KB)

Deep networks trained on the source domain show degraded performance when tested on unseen target domain data. To enhance the model's generalization ability, most existing domain generalization methods learn domain invariant features by suppressing domain sensitive features. Different from them, we propose a Domain Projection and Contrastive Learning (DPCL) approach for generalized semantic segmentation, which includes two modules: Self-supervised Source Domain Projection (SSDP) and Multi-level Contrastive Learning (MLCL). SSDP aims to reduce domain gap by projecting data to the source domain, while MLCL is a learning scheme to learn discriminative and generalizable features on the projected data. During test time, we first project the target data by SSDP to mitigate domain shift, then generate the segmentation results by the learned segmentation network based on MLCL. At test time, we can update the projected data by minimizing our proposed pixel-to-pixel contrastive loss to obtain better results. Extensive experiments for semantic segmentation demonstrate the favorable generalization capability of our method on benchmark datasets., Comment: 13 pages, 9 figures, accepted at AAAI 2023 (Oral Presentation)

This paper addresses the joint state estimation and control problems for unknown linear time-invariant systems subject to both process and measurement noise. The aim is to redesign the linear quadratic Gaussian (LQG) controller based solely on data. The LQG controller comprises a linear quadratic regulator (LQR) and a steady-state Kalman observer; while the data-based LQR design problem has been previously studied, constructing the Kalman gain and the LQG controller from noisy data presents a novel challenge. In this work, a data-based formulation for computing the steady-state Kalman gain is proposed based on semi-definite programming (SDP) using some noise-free input-state-output data. Additionally, a data-based LQG controller is developed, which is shown to be equivalent to the model-based LQG controller. For cases where offline data are corrupted by noise, a robust data-based observer gain is constructed by tackling a relaxed SDP. The proposed controllers are proven to achieve robust global exponential stability (RGES) for state estimation and input-to-state practical stability (ISpS) under standard conditions. Finally, numerical tests are conducted to validate the proposed controllers' correctness and effectiveness.

Continuous trajectory tracking control of quadrotors is complicated when considering noise from the environment. Due to the difficulty in modeling the environmental dynamics, tracking methodologies based on conventional control theory, such as model predictive control, have limitations on tracking accuracy and response time. We propose a Time-attenuating Twin Delayed DDPG, a model-free algorithm that is robust to noise, to better handle the trajectory tracking task. A deep reinforcement learning framework is constructed, where a time decay strategy is designed to avoid trapping into local optima. The experimental results show that the tracking error is significantly small, and the operation time is one-tenth of that of a traditional algorithm. The OpenAI Mujoco tool is used to verify the proposed algorithm, and the simulation results show that, the proposed method can significantly improve the training efficiency and effectively improve the accuracy and convergence stability.

Additional file 1: Methods. Supplementary figures and legends. Table S1: Basic information of enrolled patients. Table S2: Brief summary of all participants. Table S13: Performance of single mutation site in the diagnosis of HCC.

Picosecond laser-induced damage of HfO2-Al2O3 mixture-based mirror coatings in atmosphere and vacuum environments supplemental materials

Out-of-distribution (OOD) detection is essential for the reliable and safe deployment of machine learning systems in the real world. Great progress has been made over the past years. This paper presents the first review of recent advances in OOD detection with a particular focus on natural language processing approaches. First, we provide a formal definition of OOD detection and discuss several related fields. We then categorize recent algorithms into three classes according to the data they used: (1) OOD data available, (2) OOD data unavailable + in-distribution (ID) label available, and (3) OOD data unavailable + ID label unavailable. Third, we introduce datasets, applications, and metrics. Finally, we summarize existing work and present potential future research topics.

Supplementary Material 7

Objective: To perform a meta-analysis on the efficacy and dose-response relationship of blood flow restriction training on muscle strength reported worldwide.Methods: Thirty-four eligible articles with a total sample size of 549 participants were included in the meta-analysis. This study was performed using the method recommended by the Cochrane Handbook (https://training.cochrane.org/handbook), and the effect size was estimated using the standardized mean difference (SMD) and using RevMan 5.3 software (The Nordic Cochrane Centre, The Cochrane Collaboration, Copenhagen, 2014).Results: The meta-analysis showed that blood flow restriction training increased the lower limb extensor muscle strength [SMD = 0.72, 95%; confidence interval (CI): 0.43 to 1.00, p < 0.01], knee extensor isokinetic torque SMD = 0.48 [95% CI: 0.24 to 0.73, p < 0.01], knee flexor isokinetic torque SMD = 0.39 [95% CI: 0.11 to 0.67, p < 0.01], and squat one-repetition maximum [SMD = 0.28, 95% CI: 0.01 to 0.55, p < 0.01]. There was no publication bias. Evaluation of dose-response relationship showed that the training load, mode, frequency, duration, and maximum cuff pressure affected the muscle function.Conclusion: blood flow restriction training. 16 significantly improved lower limb muscle strength, and the optimal training conditions consisted of a weight load smaller or equal to 30% of one-repetition maximum, training duration longer than 4 weeks, frequency of more than 3 times/week, and maximum cuff pressure lower than 200 mmHg.Systematic Review Registration: website, identifier registration number.

Background: With the rapid consumption of non-renewable energy such as coal, oil and natural gas and the growing demand for environmental protection, the reutilization of low-grade waste heat energy has become an important approach to improve energy utilization efficiency. As a new technology, organic Rankine cycle (ORC) power generation technology can make full use of and convert waste heat. Objective: Both the suction and discharge pressures of the scroll expander have a certain influence on the output and motion characteristics of the orbiting scroll. By studying the position and arrangement of the suction and discharge ports of the expander, a theoretical basis can be provided for the design of these ports. Methods: For the scroll expander using working fluid R134a, by establishing the geometrical and three-dimensional models of the suction and discharge ports of the scroll expander with different positions and structures, based on the Computational Fluid Dynamics (CFD) method. Results/Discussion: Through comprehensive comparison, it was found that the structure of the original suction pipe outperformed any of the other structures; the fluid flow in the original discharge pipe was more complicated, and the simplified model of the commonly used scroll mechanical discharge pipe had the optimal performance. Conclusion: Compared with the original prototype SEI2, the suction port area is increased, and the suction port pulsation intensity coefficient and the suction pressure loss coefficient of the prototype SEI4 are reduced by 34.833% and 5.264% respectively, which can make the suction process of the expander more stable. Since the unilateral discharge ports Outlet3 and Outlet5 are located in the moving and static regions, respectively, there is a difference in the perturbation of the outlet fluid by the movable scroll, so that the gas pulsation intensity at Outlet3 is nearly double that of Outlet5.

Japonica rice (Oryza sativa L.) is an important staple food in high-latitude regions and is widely distributed in northern China, Japan, Korea, and Europe. However, the genetic diversity of japonica rice is relatively narrow and poorly adapted. Weedy rice (Oryza sativa f. spontanea) is a semi-domesticated rice. Its headings are earlier than the accompanied japonica rice, making it a potential new genetic resource, which can make up for the defects of wild rice that are difficult to be directly applied to japonica rice improvement caused by reproductive isolation. In this study, we applied a natural population consisting of weedy rice, japonica landrace, and japonica cultivar to conduct a genome-wide association study (GWAS) of the heading date and found four loci that could explain the natural variation of the heading date in this population. At the same time, we developed recombinant inbred lines (RILs) crossed by the early-heading weedy rice WR04-6 and its accompanied japonica cultivar ShenNong 265 (SN265) to carry out a QTL mapping analysis of the heading date and mapped four quantitative trait locus (QTLs) and three epistatic effect gene pairs. The major locus on chromosome 6 overlapped with the GWAS result. Further analysis found that two genes, Hd1 and OsCCT22, on chromosome 6 (Locus 2 and Locus 3) may be the key points of the early-heading character of weedy rice. As minor effect genes, Dth7 and Hd16 also have genetic contributions to the early heading of weedy rice. In the process of developing the RIL population, we introduced fragments of Locus 2 and Locus 3 from the weedy rice into super-high-yielding japonica rice, which successfully promoted its heading date by at least 10 days and expanded the rice suitable cultivation area northward by about 400 km. This study successfully revealed the genetic basis of the early heading of weedy rice and provided a new idea for the genetic improvement of cultivated rice by weedy rice.

In this paper, we present Duplex Conversation, a multi-turn, multimodal spoken dialogue system that enables telephone-based agents to interact with customers like a human. We use the concept of full-duplex in telecommunication to demonstrate what a human-like interactive experience should be and how to achieve smooth turn-taking through three subtasks: user state detection, backchannel selection, and barge-in detection. Besides, we propose semi-supervised learning with multimodal data augmentation to leverage unlabeled data to increase model generalization. Experimental results on three sub-tasks show that the proposed method achieves consistent improvements compared with baselines. We deploy the Duplex Conversation to Alibaba intelligent customer service and share lessons learned in production. Online A/B experiments show that the proposed system can significantly reduce response latency by 50%., Comment: Accepted by KDD 2022, ADS track

Aluminum, as the most abundant metallic elemental content in the Earth’s crust, usually exists in the form of alumina (Al2O3). However, the oxidation state of aluminum and the crystal structures of aluminum oxides in the pressure range of planetary interiors is not well established. Here, we predicted two aluminum suboxides (Al2O, AlO) and two superoxides (Al4O7, AlO3) with uncommon stoichiometries at high pressures using first-principles calculations and crystal structure prediction methods. We find that the P4/nmm Al2O becomes stable above ~765 GPa, and may survive in the deep mantles or cores of giant planets such as Neptune. Interestingly, the Al2O and AlO are metallic and have electride features, in which some electrons are localized in the interstitials between atoms. We find that Al2O has an electrical conductivity one order of magnitude higher than that of iron under the same pressure-temperature conditions, which may influence the total conductivity of giant planets. Our findings enrich the high-pressure phase diagram of aluminum oxides and improve our understanding of the interior structure of giant planets.

Based on our previous successful material-level verification of a shape-memory polymer composite (SMPC) on the SJ-17 geostationary satellite on a geostationary orbit since November 2016, a structur...

Since its emergence, blockchain technology has received great attention because of its advantages in terms of decentralization, transparency, traceability, and the ability to be tamper proof. These advantages help blockchain become a better option for fields, such as digital currency and information storage. Specifically, consortium blockchain is preferred by researchers because it provides a certain degree of access control and a supervisory mechanism. However, in real-world applications, blockchain platforms pervasively show bottlenecks, such as ultrahigh energy consumption, time inefficiency, low transaction throughput, vulnerability to targeted attacks, and poor fairness of user profits, which seriously influence the performance of this technology and thus hinder its development and adoption. In this article, we try to enhance the performance of blockchain platforms by optimizing the quality of its core module, known as the consensus algorithm. To do so, we introduce proof of assets and proof of reputation to design a voting-based decentralized consensus (VDC) algorithm for consortium blockchain. Combined with the verifiable random function (VRF), VDC realizes better fairness of user profits and time efficiency with acceptable energy consumption and without sacrificing security. The simulation results show that the proposed algorithm achieves a faster consensus process and better user fairness than existing algorithms while still maintaining a negligible energy cost and adequate security.

In recent years, the important role of sustainable development is highlighted in the literature; therefore, a company to make a sustainable business success needs to be well focused on social, environmental, economic, and technological characteristics of the business to address risks and capture value properly. The process of Sustainability Enterprise Risk Management (SERM) is typically implemented to integrate all social, economic, environmental, and technological aspects in a systematic way to properly manage emerging risks and other non-quantifiable risks for company survival. Therefore, in this study, an attempt has been carried out to develop a comprehensive framework to implement the ERM framework under the sustainability platform by considering 29 sub-criteria based on four main aspects, including social, environmental, technological, and economic. In addition, a new fuzzy decision-making approach using the VIKOR (VlseKriterijumska Optimizacija Kompromisno Resenje) approach under q-rung orthopair fuzzy set (q-ROFSs) called VIKOR-q-ROFSs approach is introduced to identify, rank, and evaluate the main criteria of SERM in the universal appeal for all industries such as Chinese manufacturing Small And Mid-Size Enterprises (SMEs) based on expert opinions and literature review. To determine the weights of SERM criteria by the decision experts, this study has introduced q-ROFNs. Moreover, to facilitate the q-ROFNs, a novel q-rung orthopair fuzzy weighted averaging operator (q-ROFWAO) is proposed. Furthermore, VIKOR is applied to rank and evaluate the manufacturing SMEs as the alternatives for this study. The results of this paper showed that technological suitability was the important risk factor followed by technological advance, occupational safety and health, product and services responsibility, (benefit) anti-corruption labor practices, and technological practicability. In addition, the findings of the analysis show that the proposed method was efficient and effective in evaluating risk assessment of SERM in the SMEs.