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The estimation of Unmanned Aerial Vehicle (UAV) poses using visual information is essential in Global Navigation Satellite System (GNSS)-denied environments. In this paper, we propose a UAV visual navigation algorithm based on visual-geography Bundle Adjustment (BA) to address the challenge of missing geolocation information in monocular visual navigation. This algorithm presents an effective approach to UAV navigation and positioning. Initially, Visual Odometry (VO) was employed for tracking the UAV’s motion and extracting keyframes. Subsequently, a geolocation method based on heterogeneous image matching was utilized to calculate the geographic pose of the UAV. Additionally, we introduce a tightly coupled information fusion method based on visual-geography optimization, which provides a geographic initializer and enables real-time estimation of the UAV’s geographical pose. Finally, the algorithm dynamically adjusts the weight of geographic information to improve optimization accuracy. The proposed method is extensively evaluated in both simulated and real-world environments, and the results demonstrate that our proposed approach can accurately and in real-time estimate the geographic pose of the UAV in a GNSS-denied environment. Specifically, our proposed approach achieves a root-mean-square error (RMSE) and mean positioning accuracy of less than 13 m.

In modern smart cities, road networks are becoming more and more complicated, resulting in more complex format of graphs. This brings many challenges to the forecasting of traffic flow in road graphs. Most of traditional traffic flow forecasting methods ignored many implicit relationships inside road graphs. And this cannot be well suitable for modern road networks in smart cities. Besides, the operation of smart cities is accompanied with real-time big data stream. The running efficiency of forecasting methods is another important concern. To handle this issue, this paper proposes a graph deep learning-based fast traffic flow forecasting method in urban road networks. Firstly, the theory about graph convolution operations is deduced and can be used as the basis of a graph convolution network (GCN). Then, the whole road network is viewed as a complex road graph, and the GCN is introduced to establish a novel forecasting method for graph-level traffic flow. With roads being regarded as nodes and their relations being regarded as edges, graph-level forecasting can be realized with the use of the proposed method. Experiments are carried out on a standard real dataset to evaluate the proposal. The experimental results show a proper performance of the proposal.

Li-ion battery state of health (SOH) is a key parameter for characterizing actual battery life. SOH cannot be measured directly. In order to further improve the accuracy of Li-ion battery SOH estimation, a combined model based on health feature parameters combined with EMD-ICA-GRU is proposed to predict the SOH of Li-ion batteries. The capacity regeneration phenomenon and data noise are decomposed by empirical mode decomposition (EMD), and then the SOH-related health indicators are deeply mined using incremental capacity analysis (ICA), and the peaks of IC curves and their corresponding voltages are extracted as the input of the model. Then, gated recurrent units (GRUs) are formed into a combined SOH estimation model by adaptive weighting factors. Finally, it is validated against the NASA lithium battery dataset. Experimental results show that the mean squared error (MSE) of the proposed combined model can reach about 0.3%, and it has stronger generalization and prediction accuracy than other algorithms driven by independent estimation data.

Abstract A growing body of evidence supports the presence of a population of cells in glioblastoma (GBM) with a stem cell-like phenotype which shares certain biological markers with adult neural stem cells, including expression of SOX2, CD133 (PROM1), and NES (nestin). This study was designed to determine the relationship between the expression of these stem cell markers and the clinical outcome in GBM patients. We quantified the intensity of expression of the proteins CD133 and SOX2 by immunohistochemistry (IHC) in a cohort of 86 patients with IDH-wildtype GBM, and evaluated patient outcomes using Kaplan–Meier and Cox proportional hazards analysis. In our patients, MGMT promoter methylation status and age were predictors of overall survival and progression free survival. The levels of SOX2 and CD133 were not associated with outcome in univariate analysis; however, stratification of tumors based on low or high levels of CD133 or SOX2 expression revealed that MGMT methylation was a predictor of progression-free survival and overall survival only for tumors with high levels of expression of CD133 or SOX2. Tumors with low levels of expression of CD133 or SOX2 did not show any relationship between MGMT methylation and survival. This relationship between MGMT and stem cell markers was confirmed in a second patient cohort, the TCGA dataset. Our results show that stratification of GBM by the level of expression of CD133 and SOX2 improved the prognostic power of MGMT promoter methylation status, identifying a low-expressing group in which the clinical outcome is not associated with MGMT promoter methylation status, and a high-expressing group in which the outcome was strongly associated with MGMT promoter methylation status. These findings support the concept that the presence of a high stem cell phenotype in GBM, as marked by expression of SOX2 or CD133, may be associated with the clinical response to treatment.

At present, lithium-ion batteries (LIBs) play an irreplaceable role in various fields of production and life as an efficient energy storage element. The state of health (SOH) for LIB is critical to the safe operation of energy storage system. In fact, it is currently difficult to estimate SOH of LIB quickly and accurately. This paper proposes a method for SOH estimation that combines bidirectional long short-term memory (BiLSTM) neural network and attention mechanism. We extract three features from the incremental capacity (IC) curve as inputs to the model. The correlation rates between the proposed features and battery capacity are more than 0.98. Finally, the NASA dataset is introduced for experimental verification. The verification results demonstrate that the proposed method achieves accurate estimation of the SOH for LIBs. In the experimental results, the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the proposed method can be as low as 0.0051 and 0.34%, respectively.

Abstract The estimation of state of health (SOH) of a lithium-ion battery (LIB) is of great significance to system safety and economic development. This paper proposes a SOH estimation method based on the SSA-Elman model for the first time. To improve the correlation rates between features and battery capacity, a method combining median absolute deviation filtering and Savitzky–Golay filtering is proposed to process the data. Based on the aging characteristics of the LIB, five features with correlation rates above 0.99 after data processing are then proposed. Addressing the defects of the Elman model, the sparrow search algorithm (SSA) is used to optimize the network parameters. In addition, a data incremental update mechanism is added to improve the generalization of the SSA-Elman model. Finally, the performance of the proposed model is verified based on NASA dataset, and the outputs of the Elman, LSTM and SSA-Elman models are compared. The results show that the proposed method can accurately estimate the SOH, with the root mean square error (RMSE) being as low as 0.0024 and the mean absolute percentage error (MAPE) being as low as 0.25%. In addition, RMSE does not exceed 0.0224 and MAPE does not exceed 2.21% in high temperature and low temperature verifications.

Estimating the geographic positions in GPS-denied environments is of great significance to the safe flight of unmanned aerial vehicles (UAVs). In this paper, we propose a novel geographic position estimation method for UAVs after road network alignment. We discuss the generally overlooked issue, namely, how to estimate the geographic position of the UAV after successful road network alignment, and propose a precise robust solution. In our method, the optimal initial solution of the geographic position of the UAV is first estimated from the road network alignment result, which is typically presented as a homography transformation between the observed road map and the reference one. The geographic position estimation is then modeled as an optimization problem to align the observed road with the reference one to improve the estimation accuracy further. Experiments on synthetic and real flight aerial image datasets show that the proposed algorithm can estimate more accurate geographic position of the UAV in real time and is robust to the errors from homography transformation estimation compared to the currently commonly-used method.

Abstract Background Gastric cancer is a heterogeneous disease with poorly understood genetic and microenvironmental factors. Mutations in collagen genes are associated with genetic diseases that compromise tissue integrity, but their role in tumor progression has not been extensively reported. Aberrant collagen expression has been long associated with malignant tumor growth, invasion, chemoresistance, and patient outcomes. We hypothesized that somatic mutations in collagens could functionally alter the tumor extracellular matrix. Methods We used publicly available datasets including The Tumor Cancer Genome Atlas (TCGA) to interrogate somatic mutations in collagens in stomach adenocarcinomas. To demonstrate that collagens were significantly mutated above background mutation rates, we used a moderated Kolmogorov-Smirnov test along with combination analysis with a bootstrap approach to define the background accounting for mutation rates. Association between mutations and clinicopathological features was evaluated by Fisher or chi-squared tests. Association with overall survival was assessed by Kaplan-Meier and the Cox-Proportional Hazards Model. Gene Set Enrichment Analysis was used to interrogate pathways. Immunohistochemistry and in situ hybridization tested expression of COL7A1 in stomach tumors. Results In stomach adenocarcinomas, we identified individual collagen genes and sets of collagen genes harboring somatic mutations at a high frequency compared to background in both microsatellite stable, and microsatellite instable tumors in TCGA. Many of the missense mutations resemble the same types of loss of function mutations in collagenopathies that disrupt tissue formation and destabilize cells providing guidance to interpret the somatic mutations. We identified combinations of somatic mutations in collagens associated with overall survival, with a distinctive tumor microenvironment marked by lower matrisome expression and immune cell signatures. Truncation mutations were strongly associated with improved outcomes suggesting that loss of expression of secreted collagens impact tumor progression and treatment response. Germline collagenopathy variants guided interpretation of impactful somatic mutations on tumors. Conclusions These observations highlight that many collagens, expressed in non-physiologically relevant conditions in tumors, harbor impactful somatic mutations in tumors, suggesting new approaches for classification and therapy development in stomach cancer. In sum, these findings demonstrate how classification of tumors by collagen mutations identified strong links between specific genotypes and the tumor environment.

Most current research in the field of autonomous vehicle control assumes that all vehicles will follow the same patterns of automated driving behavior, resulting in systems with “conservative” or “average” driving styles. These systems may not be acceptable to drivers who prefer a more aggressive style of driving, however, while extremely cautious drivers may consider the standard outputs to be too aggressive. To address this problem, in this paper, we introduce Risk Sensitive Control (RSC), an inverse optimal control algorithm that estimates risk-sensitive driving features and incorporates them into a receding-horizon controller. RSC uses a meta-learning algorithm to update the parameters of the cost function, continuously improving the controller online as more and more driving data is gathered from the user and subjective risk feedback. An estimator takes into account individual differences in subjective risk analysis, in terms of driving features and surrounding vehicle locations, by adjusting the cost function and its constraints. We test this approach using five lane change scenarios, some safe and some risky, with thirty real drivers in a CARLA simulation environment. Our quantitative and qualitative evaluations demonstrate that the proposed framework is able to generate a user’s preferred driving maneuvers during lane changes, i.e., control commands the user associates with lower subjective risk, outperforming conventional, model-based predictive control methods in terms of replicating the user’s own driving behavior.

Intelligent matching of heterogeneous remote sensing images is a common basic problem in the field of intelligent remote sensing image processing. Aiming at the difficulty of matching satellite-aerial remote sensing images, this article proposes an intelligent matching method for heterogeneous remote sensing images based on style transfer. First, based on the idea of image style transfer of a generative adversarial networks, this method improves the conversion effect of the model on heterogeneous images by constructing a new generative network loss function and converts satellite images into aerial images. Then, the advanced deep learning-based matching algorithms D2-Net and LoFTR are used to achieve matching between the generated aerial image and the original aerial image. Finally, this transformation relationship is mapped to the corresponding satellite–aerial image pair to obtain the final matching result. The image style transfer experiments and the matching experiments we carry out under different test datasets show that the smooth cycle-consistent generative adversarial networks proposed in this article can effectively reduce the complexity of the algorithm and improve the quality of image generation. In addition, combining it with deep learning-based feature-matching methods can effectively improve the accuracy and robustness of the matching algorithm. Our code and data can be found at: https://gitee.com/AZQZ/intelligent-matching.

ObjectiveTo investigate the clinical efficacy and imaging outcomes of unilateral biportal endoscopy (UBE) with unilateral laminotomy for bilateral decompression (ULBD) in the treatment of severe lumbar spinal stenosis (SLSS).MethodsWe retrospectively analyzed 50 patients with SLSS treated with UBE-ULBD from October 2018 to March 2021. Visual analog scale (VAS) for back and legs pain, Oswestry disability index (ODI), modified Macnab criteria, complications, hospital stay, preoperative and postoperative dural sac cross-sectional area (DSCA) and Schizas grade, mean angle of facetectomy and osseous lateral recess decompression rate were examined.ResultsThe mean follow-up period was 10.7 months. The mean hospital stay was 2.76 ± 1.02 days. At the final follow-up, VAS for back pain and legs pain decreased from 7.22 ± 0.95 to 1.26 ± 0.44 and from 7.88 ± 0.69 to 1.18 ± 0.39, respectively; ODI decreased from 69.88 ± 6.32% to 14.96 ± 2.75%. According to the modified Macnab criteria, the results were excellent in 24 (48%), good in 22 (44%), and fair in 4 (8%). Excellent or good results (a satisfactory outcome) were obtained in 92% of the patients. There were 2 cases of complications of dural sac tear. The postoperative DSCA was significantly enlarged compared with that before surgery, from 44.74 ± 9.85 to 126.86 ± 14.81 mm2. According to Schizas grade, the stenosis grade changes from preoperative grade C in 16 cases, grade D in 34 cases, to postoperative grade A in 40 cases, and grade B in 10 cases. The mean angle of facetectomy of the ipsilateral facet joint was 70.87 ± 5.68∘, contralateral was 65.07 ± 4.98∘. The decompression rate was 70.81 ± 4.43% (ipsilateral side) and 71.22 ± 3.68% (contralateral).ConclusionsUBE-ULBD has a good clinical effect in the treatment of SLSS, and has achieved satisfactory results in spinal canal enlargement, undercutting of facet joints, and decompression effect. It is a safe and effective surgical for SLSS.

Electrochemical supercapacitors are a promising type of energy storage device with broad application prospects. Developing an accurate model to reflect their actual working characteristics is of great research significance for rational utilization, performance optimization, and system simulation of supercapacitors. This paper presents the fundamental working principle and applications of supercapacitors, analyzes their aging mechanism, summarizes existing supercapacitor models, and evaluates the characteristics and application scope of each model. By examining the current state and limitations of supercapacitor modeling research, this paper identifies future development trends and research focuses in this area.

As an important energy storage device, lithium-ion batteries (LIBs) have been widely used in various fields due to their remarkable advantages. The high level of precision in estimating the battery’s state of health greatly enhances the safety and dependability of the application process. In contrast to traditional model-based prediction methods that are complex and have limited accuracy, data-driven prediction methods, which are considered mainstream, rely on direct data analysis and offer higher accuracy. Therefore, this paper reviews how to use the latest data-driven algorithms to predict the SOH of LIBs, and proposes a general prediction process, including the acquisition of datasets for the charging and discharging process of LIBs, the processing of data and features, and the selection of algorithms. The advantages and limitations of various processing methods and cutting-edge data-driven algorithms are summarized and compared, and methods with potential applications are proposed. Effort was also made to point out their application methods and application scenarios, providing guidance for researchers in this area.

Due to the unique features of metro central air conditioning systems’ architectural design and application scenarios, systems demand a greater degree of energy-savings than standard buildings. The central air conditioning system is the major energy user in metro stations, with the cooling source system accounting for a substantial portion. As a consequence, enhancing the energy efficiency of the cold source system is critical for optimizing the energy efficiency of the central air conditioning system. After analyzing the potential for energy-savings, we propose an energy-saving control technique for cold source systems based on the PCA-ANN data model. Firstly, an operating condition simulation was performed using operational data and cold source system equipment specifications. The effective operating data in the operational data-base was then filtered using the simulation data. Additionally, principal component analysis was used to examine the chosen dates. Finally, the fitted and calibrated data model was utilized to optimize the functioning of the cold source system. August’s revised approach resulted in a 10.5 percent decrease in system energy consumption. In comparison to using non-optimized energy parameters, the suggested technique provides a variety of energy efficiency advantages.

Land cover segmentation is an important and challenging task in the field of remote sensing. Even though convolutional neural networks (CNNs) provide great support for semantic segmentation, standard models are still difficult to capture global information and long-range dependencies in remote sensing images. To overcome these limitations, we proposed an attention guided encoder-decoder network with multi-scale context aggregation to achieve more accurate segmentation of land cover. Based on the structure of the encoder-decoder network, we introduce a multi-scale feature fusion module with two attention modules to the top of the encoder. The multi-scale feature fusion module is employed to aggregate multi-scale features and capture global correlations. The attention modules are used to exploit the long-range dependencies and the interdependence between channels from the perspective of space and channel respectively. The experimental results on the GF-2 images show that our proposed method achieves state-of-the-art performance, with an OA of 84.1% and the mIoU of 62.3%. Compared with the baseline network, our method improves the OA by 3.3% and the mIoU by 4.4%. The comparative experiments also demonstrate that the proposed approach can significantly improve the accuracy of land cover segmentation than other compared methods.

The state of health (SOH) is critical to the efficient and reliable use of lithium-ion batteries (LIBs). Recently, the SOH estimation method based on electrochemical impedance spectroscopy (EIS) has been proven effective. In response to different practical applications, two models for SOH estimation are proposed in this paper. Aiming at based on the equivalent circuit model (ECM) method, a variety of ECMs are proposed. Used EIS to predict the ECM, the results show that the improved method ensures the correctness of the ECM and improves the estimation results of SOH. Aiming at a data-driven algorithm, proposes a convolution neural network (CNN) to process EIS data which can not only extract the key points but also simplifies the complexity of manual feature extraction. The bidirectional long short-term memory (BiLSTM) model was used for serial regression prediction. Moreover, the improved Particle Swarm Optimization (IPSO) algorithm is proposed to optimize the model. Comparing the improved model (IPSO-CNN-BiLSTM) with the traditional PSO-CNN-BiLSTM, CNN-BiLSTM and LSTM models, the prediction results are improved by 13.6%, 93.75% and 94.8%, respectively. Besides that, the two proposed methods are 27% and 35% better than the existing gaussion process regression (GPR) model, which indicates that the proposed improved methods are more flexible for SOH estimation with higher precision.

Anterior cervical discectomy and fusion (ACDF) is a commonly used surgical method for the treatment of cervical spondylosis. As ACDF surgery is widely used in clinics, identifying suitable materials to design and prepare cervical interbody fusion cages is a hot research topic. Here, we describe a new three-dimensional (3D) printing approach to create stretchable and tough silk fibroin/nano-hydroxyapatite (SF/nHAp) composites with tunable mechanical properties. The compressive strength of the novel composites with biomimetic structure could reach more than 128 MPa. More importantly, the composites were prepared using 30% silk fibroin and 70% hydroxyapatite, a composition similar to the human bone tissue. Finite element analysis results indicate that the stress distribution of SF/nHAp composite cervical interbody fusion cages in vivo is more uniform than that of commercial Ti alloy cages. This study evaluates the effectiveness of SF/nHAp composites for application in cervical interbody fusion cages and in the field of bone tissue engineering.

Abstract Background The breast cancer microenvironment contributes to tumor progression and response to chemotherapy. Previously, we reported that increased stromal Type X collagen α1 (ColXα1) and low TILs correlated with poor pathologic response to neoadjuvant therapy in estrogen receptor and HER2-positive (ER+/HER2+) breast cancer. Here, we investigate the relationship of ColXα1 and long-term outcome of ER+/HER2+ breast cancer patients in an adjuvant setting. Methods A total of 164 cases with at least 5-year follow-up were included. Immunohistochemistry for ColXα1 was performed on whole tumor sections. Associations between ColXα1expression, clinical pathological features, and outcomes were analyzed. Results ColXα1 expression was directly proportional to the amount of tumor associated stroma (p = 0.024) and inversely proportional to TILs. Increased ColXα1 was significantly associated with shorter disease free survival and overall survival by univariate analysis. In multivariate analysis, OS was lower in ColXα1 expressing (HR = 2.1; 95% CI = 1.2–3.9) tumors of older patients (> = 58 years) (HR = 5.3; 95% CI = 1.7–17) with higher stage (HR = 2.6; 95% CI = 1.3–5.2). Similarly, DFS was lower in ColXα1 expressing (HR = 1.8; 95% CI = 1.6–5.7) tumors of older patients (HR = 3.2; 95% CI = 1.3–7.8) with higher stage (HR = 2.7; 95% CI = 1.6–5.7) and low TILs. In low PR+ tumors, higher ColXα1 expression was associated with poorer prognosis. Conclusion ColXα1 expression is associated with poor disease free survival and overall survival in ER+/HER2+ breast cancer. This study provides further support for the prognostic utility of ColXα1 as a breast cancer associated stromal factor that predicts response to chemotherapy.

The compilation of Compendium of Materia Medica by Li Shizhen is the product of both his great dream of writing and years of diligent work and it was also all round related to the historical times when he lived. At that time, the appreciation from the Emperor, competitive publication of medical works by the state kings and the rapid developments in medicine in the Ming dynasty all contributed to the establishment of a solid foundation for the success of Compendium of Materia Medica. This paper explored the contributing factors of the great work from a perspective of the historical environment.

Abstract Background The G protein-coupled bile acid receptor (TGR5) is a cell surface receptor which induces the production of intracellular cAMP and promotes epithelial-mesenchymal transition in gastric cancer cell lines. TGR5 is found in a wide variety of tissues including the kidney. However, the patterns of TGR5 expression have not been well characterized in physiologic kidney or renal neoplasms. We explore the expression of TGR5 in benign renal tissue and renal neoplasms and assess its utility as a diagnostic marker. Methods Sixty-one renal cortical neoplasms from 2000 to 2014 were retrieved. TGR5 protein expression was examined by immunohistochemistry. TGR5 mRNA was also measured by real-time PCR. Results In normal renal tissue, TGR5 was strongly positive in collecting ducts, distal convoluted tubules and thin loop of Henle. Proximal convoluted tubules showed absent or focal weak staining. In clear cell renal cell carcinomas (RCCs), 25 of 27 cases (92%) were negative for TGR5 (p

Social distancing (SD) is an effective measure to prevent the spread of the infectious Coronavirus Disease 2019 (COVID-19). However, a lack of spatial awareness may cause unintentional violations of this new measure. Against this backdrop, we propose an active surveillance system to slow the spread of COVID-19 by warning individuals in a region-of-interest. Our contribution is twofold. First, we introduce a vision-based real-time system that can detect SD violations and send non-intrusive audio-visual cues using state-of-the-art deep-learning models. Second, we define a novel critical social density value and show that the chance of SD violation occurrence can be held near zero if the pedestrian density is kept under this value. The proposed system is also ethically fair: it does not record data nor target individuals, and no human supervisor is present during the operation. The proposed system was evaluated across real-world datasets.

Detecting objects on unmanned aerial vehicles is a hard task, due to the long visual distance and the subsequent small size and lack of view. Besides, the traditional ground observation manners based on visible light camera are sensitive to brightness. This article aims to improve the target detection accuracy in various weather conditions, by using both visible light camera and infrared camera simultaneously. In this article, an association network of multimodal feature maps on the same scene is used to design an object detection algorithm, which is the so-called feature association learning method. In addition, this article collects a new cross-modal detection data set and proposes a cross-modal object detection algorithm based on visible light and infrared observations. The experimental results show that the algorithm improves the detection accuracy of small objects in the air-to-ground view. The multimodal joint detection network can overcome the influence of illumination in different weather conditions, which provides a new detection means and ideas for the space-based unmanned platform to the small object detection task.

Road extraction from remote sensing images is of great significance to urban planning, navigation, disaster assessment, and other applications. Although deep neural networks have shown a strong ability in road extraction, it remains a challenging task due to complex circumstances and factors such as occlusion. To improve the accuracy and connectivity of road extraction, we propose an inner convolution integrated encoder-decoder network with the post-processing of directional conditional random fields. Firstly, we design an inner convolutional network which can propagate information slice-by-slice within feature maps, thus enhancing the learning of road topology and linear features. Additionally, we present the directional conditional random fields to improve the quality of the extracted road by adding the direction of roads to the energy function of the conditional random fields. The experimental results on the Massachusetts road dataset show that the proposed approach achieves high-quality segmentation results, with the F1-score of 84.6%, which outperforms other comparable “state-of-the-art” approaches. The visualization results prove that the proposed approach is able to effectively extract roads from remote sensing images and can solve the road connectivity problem produced by occlusions to some extent.

We address the problem of aerial image geolocalization over an area as large as a whole city through road network matching, which is modeled as a 2D point set registration problem under the 2D projective transformation and solved in a two-stage manner. In the first stage, all the potential transformations aligning the query road point set to the reference road point set are found by local point feature matching. A local geometric feature, called the Projective-Invariant Contour Feature (PICF), which consists of a road intersection and the closest points to it in each direction, is specifically designed. We prove that the proposed PICF is equivariant under the 2D projective transformation group. We then encode the PICF with a projective-invariant descriptor to enable the fast search of potential correspondences. The bad correspondences are then removed by a geometric consistency check with the graph-cut algorithm effectively. In the second stage, a flexible strategy is developed to recover the homography transformation with all the PICF correspondences with the Random Sample Consensus (RANSAC) method or to recover the transformation with only one correspondence and then refine it with the local-to-global Iterative Closest Point (ICP) algorithm when only a few correspondences exist. The strategy makes our method efficient to deal with both scenes where roads are sparse and scenes where roads are dense. The refined transformations are then verified with alignment accuracy to determine whether they are accepted as correct. Experimental results show that our method runs faster and greatly improves the recall compared with the state-of-the-art methods.

Sparse representation has been widely researched for image-based classification. However, sparse representation classification directly treats training samples as a dictionary, so it needs a large training set and is time consuming, especially for a large training set. To derive a small dictionary, many dictionary learning algorithms are researched. Thus, object recognition problem is transformed to optimize the sparse representation errors on the compact dictionary. The sparse representation optimization is constraint by l 0 -norm, which is NP-hard problem. Though we can use l 1 -norm minimization instead to work effectively, it is still time consuming for optimization. To make the algorithm discriminative and simultaneously decrease the computational burden, we proposed a fast discriminative collaborative representation–based classification algorithm. The new algorithm incorporated the within-class scatter and the linear classification error terms into the objective function to derive a more discriminative dictionary and simultaneously added collaborative representation mechanism to cut off the time consuming. At the end of this article, we designed two experiments to validate our method using near-infrared and AR visible databases for multimodal face recognition. The results showed that our algorithm outperformance sparse representation–based, collaborative representation–based, and discriminative-KSVD classification algorithms.

Due to the essential limitation of optical sensors, its integration with traditional Inertial Navigation System (INS) tends to be the focus in indoor navigation applications. In a low-cost INS/vision integrated navigation system, the relative position and gestures between slave systems are important coefficients. In order to solve the initial bias estimation problems involved in INS/vision integrated system, this article proposes a novel alignment approach based on the time-domain constraints. At first, on the basis of traditional initial alignment model, the time-domain constrained model is deduced, in which the time-related states and measurements are all modeled. In order to verify the advantage of state evaluability, both the traditional alignment model and the corresponding time-domain constrained model are analyzed via the nonlinear observability analysis method. At the end of this article, two groups of numerical simulations are implemented, and the corresponding results validate the effectiveness of the proposed time-domain constrained model.

The technology used for road extraction from remote sensing images plays an important role in urban planning, traffic management, navigation, and other geographic applications. Although deep learning methods have greatly enhanced the development of road extractions in recent years, this technology is still in its infancy. Because the characteristics of road targets are complex, the accuracy of road extractions is still limited. In addition, the ambiguous prediction of semantic segmentation methods also makes the road extraction result blurry. In this study, we improved the performance of the road extraction network by integrating atrous spatial pyramid pooling (ASPP) with an Encoder-Decoder network. The proposed approach takes advantage of ASPP’s ability to extract multiscale features and the Encoder-Decoder network’s ability to extract detailed features. Therefore, it can achieve accurate and detailed road extraction results. For the first time, we utilized the structural similarity (SSIM) as a loss function for road extraction. Therefore, the ambiguous predictions in the extraction results can be removed, and the image quality of the extracted roads can be improved. The experimental results using the Massachusetts Road dataset show that our method achieves an F1-score of 83.5% and an SSIM of 0.893. Compared with the normal U-net, our method improves the F1-score by 2.6% and the SSIM by 0.18. Therefore, it is demonstrated that the proposed approach can extract roads from remote sensing images more effectively and clearly than the other compared methods.

Z-Guggulsterone is a major ingredient in the Indian traditional hypolipidemic remedy guggul. A study in mice has established that its hypolipidemic effect involves the farnesoid X receptor (FXR), presumably by acting as an antagonist of this receptor. It is generally assumed that the antagonism leads to induction of cytochrome P450 7A1 (CYP7A1), the rate-limiting enzyme converting free cholesterol to bile acids. In this study, we tested whether Z-guggulsterone indeed induces human CYP7A1. In addition, the expression of cholesteryl ester hydrolase CES1 and bile salt export pump (BSEP) was monitored. Contrary to the general assumption, Z-guggulsterone did not induce CYP7A1. Instead, this phytosterol significantly induced CES1 and BSEP through transactivation. Z-Guggulsterone underwent metabolism by CYP3A4, and the metabolites greatly increased the induction potency on BSEP but not on CES1. BSEP induction favors cholesterol elimination, whereas CES1 involves both elimination and retention (probably when excessively induced). Interestingly, clinical trials reported the hypolipidemic response rates from 18% to 80% and showed that higher dosages actually increased VLDL cholesterol. Our findings predict that better hypolipidemic outcomes likely occur in individuals who have a relatively higher capacity of metabolizing Z-guggulsterone with moderate CES1 induction, a scenario possibly achieved by lowering the dosing regimens.

In this work, a ZnO-layered double hydroxide@graphitic carbon nitride composite (ZnO-LDH@C3N4) was synthesized via co-precipitation method with solvothermal treatment. The structure and morphology of ZnO-LDH@C3N4 composite were characterized using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopes/transmission electron microscopes (SEM/TEM), N2 adsorption/desorption, ultraviolet visible diffuse reflectance spectroscopy (UV-Vis-DRS), photoluminescence spectrometer (PL) and electrochemical impedance spectroscopy (EIS). The adsorption and photocatalytic properties of ZnO-LDH@C3N4 composite towards the organic dyes: Orange II sodium salt (OrgII, an anionic azo dye) and methylene blue (MB, a cationic azo dye) were investigated. Compared to ZnO-LDH and g-C3N4, the ZnO-LDH@C3N4 composite displayed an excellent performance in both adsorption and photocatalytic degradation of the organic dyes. Moreover, a combination of ZnO-LDH and g-C3N4 significantly improved the photocatalytic performance of ZnO-LDH and g-C3N4 under visible-light irradiation. The adsorption and photocatalytic mechanism were also investigated.