A topological transition-induced giant transverse thermoelectric effect in polycrystalline Dirac semimetal Mg3Bi2

To achieve thermoelectric energy conversion, a large transverse thermoelectric effect in topological materials is crucial. However, the general relationship between topological electronic structures and transverse thermoelectric effect remains unclear, restricting the rational design of novel transverse thermoelectric materials. Herein, we demonstrate a topological transition-induced giant transverse thermoelectric effect in polycrystalline Mn-doped Mg3+{\delta}Bi2 material, which has a competitively large transverse thermopower (617 uV/K), power factor (20393 uWm-1K-2), magnetoresistance (16600%), and electronic mobility (35280cm2V-1S-1). The high performance is triggered by the modulation of chemical pressure and disorder effects in the presence of Mn doping, which induces the transition from a topological insulator to a Dirac semimetal. The high-performance polycrystalline Mn-doped Mg3+{\delta} Bi2 described in this work robustly boosts transverse thermoelectric effect through topological phase transition, paving a new avenue for the material design of transverse thermoelectricity

Thymoquinone Attenuates Myocardial Ischemia/Reperfusion Injury Through Activation of SIRT1 Signaling

Background/Aims: Myocardial ischemia/reperfusion (MI/R) injury is a leading factor responsible for damage in myocardial infarction, resulting in additional injury to cardiac tissues involved in oxidative stress, inflammation, and apoptosis. Thymoquinone (TQ), the main constituent of Nigella sativa L. seeds, has been reported to possess various biological activities. However, few reports regarding myocardial protection are available at present. Therefore, this study was conducted aiming to investigate the protective effect of TQ against MI/R injury and to clarify its potential mechanism. Methods: MI/R injury models of isolated rat hearts and neonatal rat cardiomyocytes were established. The Langendorff isolated perfused heart system, triphenyltetrazolium chloride staining, gene transfection, TransLaser scanning confocal microscopy, and western blotting were employed to evaluate the cardioprotection effect of TQ against MI/R injury. Results: Compared with the MI/R group, TQ treatment could remarkably improve left ventricular function, decrease myocardial infarct size and production of lactate dehydrogenase (LDH), and attenuate mitochondrial oxidative damage by elevating superoxide dismutase (SOD) activity and reducing production of hydrogen peroxide (H2O2) and malonaldehyde (MDA). Moreover, the cardioprotective effect of TQ was accompanied by up-regulated expression of SIRT1 and inhibition of p53 acetylation. Additionally, TQ treatment could also enhance mitochondrial function and reduce the number of apoptotic cardiomyocytes. Nonetheless, the cardioprotective effect of TQ could be mitigated by SIRT1 inhibitor sirtinol and SIRT1 siRNA, respectively, which was achieved through inhibition of the SIRT1 signaling pathway. Conclusions: The findings in this study demonstrate that TQ is efficient in attenuating MI/R injury through activation of the SIRT1 signaling pathway, which can thus reduce mitochondrial oxidative stress damage and cardiomyocyte apoptosis

Application of LightGBM hybrid model based on TPE algorithm optimization in sleep apnea detection

IntroductionSleep apnoea syndrome (SAS) is a serious sleep disorder and early detection of sleep apnoea not only reduces treatment costs but also saves lives. Conventional polysomnography (PSG) is widely regarded as the gold standard diagnostic tool for sleep apnoea. However, this method is expensive, time-consuming and inherently disruptive to sleep. Recent studies have pointed out that ECG analysis is a simple and effective diagnostic method for sleep apnea, which can effectively provide physicians with an aid to diagnosis and reduce patients’ suffering.MethodsTo this end, in this paper proposes a LightGBM hybrid model based on ECG signals for efficient detection of sleep apnea. Firstly, the improved Isolated Forest algorithm is introduced to remove abnormal data and solve the data sample imbalance problem. Secondly, the parameters of LightGBM algorithm are optimised by the improved TPE (Tree-structured Parzen Estimator) algorithm to determine the best parameter configuration of the model. Finally, the fusion model TPE_OptGBM is used to detect sleep apnoea. In the experimental phase, we validated the model based on the sleep apnoea ECG database provided by Phillips-University of Marburg, Germany.ResultsThe experimental results show that the model proposed in this paper achieves an accuracy of 95.08%, a precision of 94.80%, a recall of 97.51%, and an F1 value of 96.14%.DiscussionAll of these evaluation indicators are better than the current mainstream models, which is expected to assist the doctor’s diagnostic process and provide a better medical experience for patients

Two-dimensional spin systems in PECVD-grown diamond with tunable density and long coherence for enhanced quantum sensing and simulation

Systems of spins engineered with tunable density and reduced dimensionality enable a number of advancements in quantum sensing and simulation. Defects in diamond, such as nitrogen-vacancy (NV) centers and substitutional nitrogen (P1 centers), are particularly promising solid-state platforms to explore. However, the ability to controllably create coherent, two-dimensional spin systems and characterize their properties, such as density, depth confinement, and coherence is an outstanding materials challenge. We present a refined approach to engineer dense ($\gtrsim$1 ppm$\cdot$nm), 2D nitrogen and NV layers in diamond using delta-doping during plasma-enhanced chemical vapor deposition (PECVD) epitaxial growth. We employ both traditional materials techniques, e.g. secondary ion mass spectrometry (SIMS), alongside NV spin decoherence-based measurements to characterize the density and dimensionality of the P1 and NV layers. We find P1 densities of 5-10 ppm$\cdot$nm, NV densities between 1 and 3.5 ppm$\cdot$nm tuned via electron irradiation dosage, and depth confinement of the spin layer down to 1.6 nm. We also observe high (up to 42$\%$) conversion of P1 to NV centers and reproducibly long NV coherence times, dominated by dipolar interactions with the engineered P1 and NV spin baths

Taking the pulse of COVID-19: A spatiotemporal perspective

The sudden outbreak of the Coronavirus disease (COVID-19) swept across the world in early 2020, triggering the lockdowns of several billion people across many countries, including China, Spain, India, the U.K., Italy, France, Germany, and most states of the U.S. The transmission of the virus accelerated rapidly with the most confirmed cases in the U.S., and New York City became an epicenter of the pandemic by the end of March. In response to this national and global emergency, the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implemented strategies to rapidly respond to this crisis, for supporting research, saving lives, and protecting the health of global citizens. This perspective paper presents our collective view on the global health emergency and our effort in collecting, analyzing, and sharing relevant data on global policy and government responses, geospatial indicators of the outbreak and evolving forecasts; in developing research capabilities and mitigation measures with global scientists, promoting collaborative research on outbreak dynamics, and reflecting on the dynamic responses from human societies.Comment: 27 pages, 18 figures. International Journal of Digital Earth (2020

The shortest path approximation algorithm for large scale road network

Node importance has significant influence on the calculation of shortest path of large-scale road network. A shortest path estimation method based on node importance is proposed in this paper that is suitable for large-scale network. This method integrates the criteria importance though intercrieria correlation (CRITIC) method with complex network theory, with a view to evaluate nodes importance. By combining the restriction strategy to realize network division, the effective simplification of large-scale road network and shortest path estimation are realized through the construction of hierarchical network. The results show that this method can be used to distribute the center nodes evenly, and make little difference in the size of the subnetwork. As the constraint parameter increases, the numbers of nodes and edges reduced gradually, and the query accuracy reached 1.026. Compared with single index and unlimited parameters methods, this paper significantly reduces the size of the network and obtains a high accuracy on the approximate calculation of the shortest path. These will provide a new way of thinking for approximate analysis of large-scale complex networks

Error-bounded and Number-bounded Approximate Spatial Query for Interactive Visualization

In the big data era, an enormous amount of spatial and spatiotemporal data are generated every day. However, spatial query result sets that satisfy a query condition are very large, sometimes over hundreds or thousands of terabytes. Interactive visualization of big geospatial data calls for continuous query requests, and large query results prevent visual efficiency. Furthermore, traditional methods based on random sampling or line simplification are not suitable for spatial data visualization with bounded errors and bound vertex numbers. In this paper, we propose a vertex sampling method—the Balanced Douglas Peucker (B-DP) algorithm—to build hierarchical structures, where the order and weights of vertices are preserved in binary trees. Then, we develop query processing algorithms with bounded errors and bounded numbers, where the vertices are retrieved by binary trees’ breadth-first-searching (BFS) with a maximum-error-first (MEF) queue. Finally, we conduct an experimental study with OpenStreetMap (OSM) data to determine the effectiveness of our query method in interactive visualization. The results show that the proposed approach can markedly reduce the query results’ size and maintain high accuracy, and its performance is robust against the data volume

Exploiting Two-Dimensional Geographical and Synthetic Social Influences for Location Recommendation

With the rapid development of location-based social networks (LBSNs), because human behaviors exhibit specific distribution patterns, personalized geo-social recommendation has played a significant role for LBSNs. In addition to user preference and social influence, geographical influence has also been widely researched in location recommendation. Kernel density estimation (KDE) is a key method in modeling geographical influence. However, most current studies based on KDE do not consider the problems of influence range and outliers on users’ check-in behaviors. In this paper, we propose a method to exploit geographical and synthetic social influences (GeSSo) on location recommendation. GeSSo uses a kernel estimation approach with a quartic kernel function to model geographical influences, and two kinds of weighted distance are adopted to calculate bandwidth. Furthermore, we consider the social closeness and connections between friends, and a synthetic friend-based recommendation method is introduced to model social influences. Finally, we adopt a sum framework which combines user’s preferences on a location with geographical and social influences. Extensive experiments are conducted on three real-life datasets. The results show that our method achieves superior performance compared to other advanced geo-social recommendation techniques