Dynamic spatio-temporal graph neural networks for hot topic prediction in scientific literature

With information explosion occurring in past decades, the rapid growth of papers published results in the rapid change of hot topics, especially in the biomedical domain. It turns out very hard for researchers who are interested in biomedical domain to track hot topics over time, as well as to predict the trends of them in the near future. Based on the above demand, it is important to have a model which is able to follow and predict the trend of hot topics continuously. Deep learning has been proven to be an efficient method to extract information from texts and use the information to predict the future trends. Under the thriving background of Deep Learning, Graph Neural Network (GNN) is able to capture the information from graph structures. There are various applications using GNN models, such as traffic flow prediction, chemical structure discovering, etc. In this research project, a dynamic spatio-temporal graph neural network is presented to keep track of the selected hot keywords and topics in the biomedical domain and predict the possible frequencies in the near future. The input of the model is obtained by extracting the monthly frequency information of selected keywords and topics from paper abstracts in PubMed, the largest biomedical literature collection. After training with data over a decade, the model is able to predict trends of selected hot keywords and topics in next 5 months. Thus, the presented model can help follow the trend of hot topics in the biomedical domain.Includes bibliographical reference

Regularization Method for the Approximate Split Equality Problem in Infinite-Dimensional Hilbert Spaces

We studied the approximate split equality problem (ASEP) in the framework of infinite-dimensional Hilbert spaces. Let , , and   be infinite-dimensional real Hilbert spaces, let and   be two nonempty closed convex sets, and let and   be two bounded linear operators. The ASEP in infinite-dimensional Hilbert spaces is to minimize the function over and . Recently, Moudafi and Byrne had proposed several algorithms for solving the split equality problem and proved their convergence. Note that their algorithms have only weak convergence in infinite-dimensional Hilbert spaces. In this paper, we used the regularization method to establish a single-step iterative for solving the ASEP in infinite-dimensional Hilbert spaces and showed that the sequence generated by such algorithm strongly converges to the minimum-norm solution of the ASEP. Note that, by taking in the ASEP, we recover the approximate split feasibility problem (ASFP)

Deep Reinforcement Learning for Solving Management Problems: Towards A Large Management Mode

We introduce a deep reinforcement learning (DRL) approach for solving management problems including inventory management, dynamic pricing, and recommendation. This DRL approach has the potential to lead to a large management model based on certain transformer neural network structures, resulting in an artificial general intelligence paradigm for various management tasks. Traditional methods have limitations for solving complex real-world problems, and we demonstrate how DRL can surpass existing heuristic approaches for solving management tasks. We aim to solve the problems in a unified framework, considering the interconnections between different tasks. Central to our methodology is the development of a foundational decision model coordinating decisions across the different domains through generative decision-making. Our experimental results affirm the effectiveness of our DRL-based framework in complex and dynamic business environments. This work opens new pathways for the application of DRL in management problems, highlighting its potential to revolutionize traditional business management

Nonalcoholic Fatty Liver Disease and Associated Metabolic Risks of Hypertension in Type 2 Diabetes: A Cross-Sectional Community-Based Study

The mechanisms facilitating hypertension in diabetes still remain to be elucidated. Nonalcoholic fatty liver disease (NAFLD), which is a higher risk factor for insulin resistance, shares many predisposing factors with diabetes. However, little work has been performed on the pathogenesis of hypertension in type 2 diabetes (T2DM) with NAFLD. The aim of this study is to investigate the prevalence of hypertension in different glycemic statuses and to analyze relationships between NAFLD, metabolic risks, and hypertension within a large community-based population after informed written consent. A total of 9473 subjects aged over 45 years, including 1648 patients with T2DM, were enrolled in this cross-sectional study. Clinical and biochemical parameters of all participants were determined. The results suggested that the patients with prediabetes or T2DM were with higher risks to have hypertension. T2DM with NAFLD had significantly higher levels of blood pressure, triglyceride, uric acid, and HOMA-IR than those without NAFLD. Data analyses suggested that hypertriglyceridemia [OR = 1.773 (1.396, 2.251)], NAFLD [OR = 2.344 (1.736, 3.165)], hyperuricemia [OR = 1.474 (1.079, 2.012)], and insulin resistance [OR = 1.948 (1.540, 2.465)] were associated with the higher prevalence of hypertension independent of other metabolic risk factors in type 2 diabetes. Further studies are needed to focus on these associations

Thrombospondin-1 Contributes to Mortality in Murine Sepsis through Effects on Innate Immunity

BACKGROUND:Thrombospondin-1 (TSP-1) is involved in many biological processes, including immune and tissue injury response, but its role in sepsis is unknown. Cell surface expression of TSP-1 on platelets is increased in sepsis and could activate the anti-inflammatory cytokine transforming growth factor beta (TGFβ1) affecting outcome. Because of these observations we sought to determine the importance of TSP-1 in sepsis. METHODOLOGY/PRINCIPAL FINDINGS:We performed studies on TSP-1 null and wild type (WT) C57BL/6J mice to determine the importance of TSP-1 in sepsis. We utilized the cecal ligation puncture (CLP) and intraperitoneal E. coli injection (i.p. E. coli) models of peritoneal sepsis. Additionally, bone-marrow-derived macrophages (BMMs) were used to determine phagocytic activity. TSP-1-/- animals experienced lower mortality than WT mice after CLP. Tissue and peritoneal lavage TGFβ1 levels were unchanged between animals of each genotype. In addition, there is no difference between the levels of major innate cytokines between the two groups of animals. PLF from WT mice contained a greater bacterial load than TSP-1-/- mice after CLP. The survival advantage for TSP-1-/- animals persisted when i.p. E. coli injections were performed. TSP-1-/- BMMs had increased phagocytic capacity compared to WT. CONCLUSIONS:TSP-1 deficiency was protective in two murine models of peritoneal sepsis, independent of TGFβ1 activation. Our studies suggest TSP-1 expression is associated with decreased phagocytosis and possibly bacterial clearance, leading to increased peritoneal inflammation and mortality in WT mice. These data support the contention that TSP-1 should be more fully explored in the human condition

Measurement of enterprise management efficiency based upon information entropy and evidence theory

The measurement of enterprise management efficiency is a complex and important system involving many mutual-coupling and unknown or uncertain factors. Using the information entropy principle, the weight vector of these factors can be calculated objectively. Based on this, the evidences were given by the product vector of the factor weight vector and the factor sample vector and the basic probability assignment (BPA) was obtained using matrix theory. Furthermore, the measurement model of management efficiency was established by D-S evidence. Finally, a verification case of management efficiency was given using the data that is stored in the database of enterprise management information system and the information that is given by management experts. The case results indicated that both the above method and model could decouple these mutual-coupling factors and could measure the enterprise management efficiency objectively and accurately. The method and model are not only capable for measuring management efficiency of same and different enterprises, but also for extracting decision-making information to improve enterprise management efficiency.enterprise management efficiency; information entropy principle; Dempster-Shafer theory; D-S evidence theory; uncertain reasoning technology; management science; factor weight vectors; factor sample vectors; basic probability assignment; BPA; matrix theories; management information systems; MIS; decision making.

Regularization Method for the Approximate Split Equality Problem in Infinite-Dimensional Hilbert Spaces

We studied the approximate split equality problem (ASEP) in the framework of infinite-dimensional Hilbert spaces. Let 1 , 2 , and 3 be infinite-dimensional real Hilbert spaces, let ⊂ 1 and ⊂ 2 be two nonempty closed convex sets, and let : 1 → 3 and : 2 → 3 be two bounded linear operators. The ASEP in infinite-dimensional Hilbert spaces is to minimize the function ( , ) = (1/2)‖ − ‖ 2 2 over ∈ and ∈ . Recently, Moudafi and Byrne had proposed several algorithms for solving the split equality problem and proved their convergence. Note that their algorithms have only weak convergence in infinite-dimensional Hilbert spaces. In this paper, we used the regularization method to establish a single-step iterative for solving the ASEP in infinite-dimensional Hilbert spaces and showed that the sequence generated by such algorithm strongly converges to the minimum-norm solution of the ASEP. Note that, by taking = in the ASEP, we recover the approximate split feasibility problem (ASFP)

Resonant Coupling of Hermite-Gaussian Transverse Modes in the Triangular Cavity of a Cavity Ring-down Spectroscope

During resonance in resonant cavities, such as those used in laser or cavity ring-down spectroscopes (CRDS), resonant coupling between higher-order transverse modes and fundamental modes can seriously affect the quality of the beam and introduce measurement errors. Several coupling models, such as thermal deformation coupling and scattering coupling, have been established according to existing coupling theory and specific application scenarios; however, these coupling models have not been attributed to a unified theory. In this paper, we reveal that the same resonant coupling excitation factors exist under different types of environmental perturbation. The conditions and range of resonant coupling in a CRDS ring-down cavity are systematically analyzed, and a preferential coupling model of the middle-order modes is proposed. The time-domain characteristics of the CRDS are used in experiments to analyze the resonant coupling between the modes in a weak energy system. The order and coupling range of the middle-order modes involved in resonant coupling are verified using the modal filtering characteristics of the triangular cavity; this paper presents a unified explanation for various types of resonant coupling and also provides a new approach to resonant coupling experiments performed in high-finesse resonant cavities

Ecosystem Service Trade-Offs and Spatial Pattern Optimisation under Different Land Use Scenarios: A Case Study in Guanzhong Region, China

Understanding the complex interactions (i.e., trade-offs and synergies) among ecosystem services (ESs) and exploring land use optimisation are important to realize regional ecological governance and sustainable development. This study examined Guanzhong Region, Shaanxi Province, as the research object. We established 12 future land use scenarios and projected the future land use patterns under the future climate change scenarios and local development policies. Next, we assessed the four main ecosystem services—carbon sequestration (CS), habitat quality (HQ), soil conservation (SC), and food supply (FS) by using related formulas and the InVEST model. Furthermore, the production possibility frontier (PPF) was used to measure trade-offs and synergistic relationships among ESs, and extract the optimal ES group under the different target needs. The results are as follows: (1) In the future 12 land use scenarios of 2050 in Guanzhong Region, forested land increased evidently in the RCP2.6 ecological protection scenario (18,483.64 km). In the RCP6.0 rapid urban development scenario, construction land showed evident expansion in the central and northeastern areas (4764.52 km2). (2) Compared with the ESs under the future multiple scenarios, CS and HQ achieved the maximum value in the RCP8.5 ecological protection scenario. In the RCP2.6 ecological protection scenario, the amount of SC was the largest (3.81 × 106 t). FS in the RCP2.6 business as usual scenario got the maximum value (18.53 × 106 t). (3) By drawing the optimal PPF curve of multiple scenarios in 2050, trade-off relationships were found between FS and CS, HQ, and SC, and synergistic relationships were found between CS, HQ, and SC. Next, the optimal ES groups under the fitted curve were selected by comparing with the ESs of 2018, and adjusting the land use areas and spatial pattern to finally optimise the relationships between ES and achieve the best land use spatial pattern