The prognostic value of deep earlobe creases in patients with acute ischemic stroke

Background and purposeData on earlobe crease (ELC) among patients with acute ischemic stroke (AIS) are limited. Here, we determined the frequency and characteristics of ELC and the prognostic effect of ELC among AIS patients.MethodsA total of 936 patients with acute AIS were enrolled during the period between December 2018 and December 2019. The patients were divided into those without and with ELC, unilateral and bilateral ELC, and shallow and deep ELC, according to the photographs taken of the bilateral ears. Logistic regression models were used to estimate the effect of ELC, bilateral ELC, and deep ELC on poor functional outcomes at 90 days (a modified Rankin Scale score ≥2) in AIS patients.ResultsAmong the 936 AIS patients, there were 746 (79.7%) patients with ELC. Among patients with ELC, there were 156 (20.9%) patients with unilateral ELC and 590 (79.1%) with bilateral ELC and 476 (63.8%) patients with shallow ELC and 270 (36.2%) with deep ELC. After adjusting for age, sex, baseline NIHSS score, and other potential covariates, patients with deep ELC were associated with a 1.87-fold [odds ratio (OR) 1.87; 95% confidence interval (CI), 1.13–3.09] and 1.63-fold (OR 1.63; 95%CI, 1.14–2.34) increase in the risk of poor functional outcome at 90 days in comparison with those without ELC or shallow ELC.ConclusionELC was a common phenomenon, and eight out of ten AIS patients had ELC. Most patients had bilateral ELC, and more than one-third had deep ELC. Deep ELC was independently associated with an increased risk of poor functional outcome at 90 days

Link Prediction for Temporal Heterogeneous Networks Based on the Information Lifecycle

Link prediction for temporal heterogeneous networks is an important task in the field of network science, and it has a wide range of real-world applications. Traditional link prediction methods are mainly based on static homogeneous networks, which do not distinguish between different types of nodes in the real world and do not account for network structure evolution over time. To address these issues, in this paper, we study the link prediction problem in temporal heterogeneous networks and propose a link prediction method for temporal heterogeneous networks (LP-THN) based on the information lifecycle, which is an end-to-end encoder–decoder structure. The information lifecycle accounts for the active, decay and stable states of edges. Specifically, we first introduce the meta-path augmented residual information matrix to preserve the structure evolution mechanism and semantics in HINs, using it as input to the encoder to obtain a low-dimensional embedding representation of the nodes. Finally, the link prediction problem is considered a binary classification problem, and the decoder is utilized for link prediction. Our prediction process accounts for both network structure and semantic changes using meta-path augmented residual information matrix perturbations. Our experiments demonstrate that LP-THN outperforms other baselines in both prediction effectiveness and prediction efficiency

A Novel Link Prediction Method for Social Multiplex Networks Based on Deep Learning

Due to the great advances in information technology, an increasing number of social platforms have appeared. Friend recommendation is an important task in social media, but newly built social platforms have insufficient information to predict entity relationships. In this case, platforms with sufficient information can help newly built platforms. To address this challenge, a model of link prediction in social multiplex networks (LPSMN) is proposed in this work. Specifically, we first extract graph structure features, latent features and explicit features and then concatenate these features as link representations. Then, with the assistance of external information from a mature platform, an attention mechanism is employed to construct a multiplex and enhanced forecasting model. Additionally, we consider the problem of link prediction to be a binary classification problem. This method utilises three different kinds of features to improve link prediction performance. Finally, we use five synthetic networks with various degree distributions and two real-world social multiplex networks (Weibo–Douban and Facebook–Twitter) to build an experimental scenario for further assessment. The numerical results indicate that the proposed LPSMN model improves the prediction accuracy compared with several baseline methods. We also find that with the decline in network heterogeneity, the performance of LPSMN increases

Measuring Landscape Albedo Using Unmanned Aerial Vehicles

Surface albedo is a critical parameter in surface energy balance, and albedo change is an important driver of changes in local climate. In this study, we developed a workflow for landscape albedo estimation using images acquired with a consumer-grade camera on board unmanned aerial vehicles (UAVs). Flight experiments were conducted at two sites in Connecticut, USA and the UAV-derived albedo was compared with the albedo obtained from a Landsat image acquired at about the same time as the UAV experiments. We find that the UAV estimate of the visibleband albedo of an urban playground (0.037 ± 0.063, mean ± standard deviation of pixel values) under clear sky conditions agrees reasonably well with the estimates based on the Landsat image (0.047 ± 0.012). However, because the cameras could only measure reflectance in three visible bands (blue, green, and red), the agreement is poor for shortwave albedo. We suggest that the deployment of a camera that is capable of detecting reflectance at a near-infrared waveband should improve the accuracy of the shortwave albedo estimation

Experimental Study on the Impact of CO₂ Treatment on Different Lithofacies in Shale Oil Reservoirs

CO2 technology has been progressively used in the development of shale oil. After injection, CO2 can react with formation water to form carbonic acid, which then reacts with carbonate and silicate minerals, resulting in changes in porosity and permeability. However, there are some debates as to whether the effect of CO2 improves or damages porosity and permeability. So, in this paper, systematic experiments were carried out to clarify the interaction between CO2 and shale in different lithofacies and to draw a pertinent conclusion. The results showed that the shale in Qingshankou Formation could be divided into three main lithofacial types: foliaceous shale, laminated feisic shale and laminated diamictic shale. There were relatively more pores, some natural microfractures and small mineral particles in foliaceous shale, a few micropores and large mineral particles in laminated feisic shale, some biogenic calcium carbonate minerals and hardly any micropores in laminated diamictic shale. Due to the diversity of micromorphology and mineral composition, the effects of CO2 treatment had significant differences. For foliaceous shale, CO2 treatment had both improving and damaging effects on porosity and permeability; for laminated shale, both porosity and permeability improved significantly. So, it is necessary to identify the main lithofacies of target formation before the application of CO2 technology in shale oil reservoirs

Nitrate Absorption and Desorption by Biochar

Biochar is a potential solution for addressing environmental problems related to excessive nitrogen (N). However, there is still some debate about the absorption and desorption of nitrate nitrogen (NO3−-N). Therefore, this study investigated the NO3−-N adsorption and desorption performance onto biochar and biochar-soil mixture to address this gap. The results showed that the biochar produced from apple branches had the ability to absorb NO3−-N with an absorption capacity of 3.51 mg·g−1. The absorption data fitted well with the pseudo-second-order kinetic model and Langmuir model. The application of biochar significantly improved soil absorption capacity and slow release of NO3−-N. While higher NO3−-N concentrations had better NO3−-N supply capacity and poorer slow-release effect. Integrating nutrient supply and slow-release effect, it is recommended to control the application ratio of biochar to NO3−-N at 34–42.75 g·g−1. Although the unoptimized biochar application rate cannot be directly applied to the soil as a slow-release fertilizer carrier to meet commercial standards, biochar modification provides new possibilities for this purpose. Moreover, compared with traditional slow-release fertilizer, biochar had good stability and regeneration performance, alleviating the high cost due to the biochar price. In general, biochar still has potential and prospects as a slow-release material. This study provides support for biochar in mitigating environmental problems associated with excess N

Map Construction and Path Planning Method for a Mobile Robot Based on Multi-Sensor Information Fusion

In order to solve the path planning problem of an intelligent vehicle in an unknown environment, this paper proposes a map construction and path planning method for mobile robots based on multi-sensor information fusion. Firstly, the extended Kalman filter (EKF) is used to fuse the ambient information of LiDAR and a depth camera. The pose and acceleration information of the robot is obtained through the pose sensor. The SLAM algorithm based on a fusion of LiDAR, a depth camera, and the inertial measurement unit was built. Secondly, the improved ant colony algorithm was used to carry out global path planning. Meanwhile, the dynamic window method was used to realize local planning and local obstacle avoidance. Finally, experiments were carried out on a robot platform to verify the reliability of the proposed method. The experiment results showed that the map constructed by multi-sensor information fusion was closer to the real environment, and the accuracy and robustness of SLAM were effectively improved. The turning angle of the path was smoothed using the improved ant colony algorithm, and the real-time obstacle avoidance was able to be realized using the dynamic window method. The efficiency of path planning was improved, and the automatic feedback control of the intelligent vehicle was able to be realized

Independent Innovation or Secondary Innovation: The Moderating of Network Embedded Innovation

Based on the provincial data of China’s high-tech industries from 2009 to 2019, this paper constructs a stepwise regression to analyze the effect of innovation inputs, independent and secondary innovation, and innovation value, while being mediated and moderated by innovation ability and innovation network, respectively. We found that in general, innovation inputs had a significant positive direct effect on innovation valuation: a one unit increase of independent innovation increased 0.60 units of innovation valuation, and a one unit increase of secondary innovation input increased 0.78 units of innovation valuation. Innovation ability was found to be a partial mediator for independent innovation (0.74), and a complete mediator for secondary innovation (0.90). Finally, the innovation network showed significant moderating effects in both innovation input methods. Empirical research indicates that China is entering an era shifting from secondary innovation to independent innovation, and Chinese high-tech companies should focus on independent innovation

Map Construction and Path Planning Method for a Mobile Robot Based on Multi-Sensor Information Fusion

In order to solve the path planning problem of an intelligent vehicle in an unknown environment, this paper proposes a map construction and path planning method for mobile robots based on multi-sensor information fusion. Firstly, the extended Kalman filter (EKF) is used to fuse the ambient information of LiDAR and a depth camera. The pose and acceleration information of the robot is obtained through the pose sensor. The SLAM algorithm based on a fusion of LiDAR, a depth camera, and the inertial measurement unit was built. Secondly, the improved ant colony algorithm was used to carry out global path planning. Meanwhile, the dynamic window method was used to realize local planning and local obstacle avoidance. Finally, experiments were carried out on a robot platform to verify the reliability of the proposed method. The experiment results showed that the map constructed by multi-sensor information fusion was closer to the real environment, and the accuracy and robustness of SLAM were effectively improved. The turning angle of the path was smoothed using the improved ant colony algorithm, and the real-time obstacle avoidance was able to be realized using the dynamic window method. The efficiency of path planning was improved, and the automatic feedback control of the intelligent vehicle was able to be realized