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Abstract Seasonal variability could have an impact on the incidence and outcome of stroke. However, little is known about the correlation between seasonal variability and location of acute cerebral infarction. This study aimed to explore the relationship between onset season and the lesions distribution of acute ischemic stroke (AIS). We retrospectively analysis data from 1488 AIS patients admitted to the Second Hospital of Tianjin Medical University from 2018 to 2022. All subjects completed head magnetic resonance imaging examination (MRI) and were divided into four groups according to the onset seasons. The lesions distribution of AIS was evaluated for anterior/posterior/double circulation infarction (DCI), unilateral/bilateral infarctions, and single/multiple cerebral infarctions based on MRI. Logistic regression models were employed to assess the association of season with lesions distribution of AIS. Subgroup analysis was performed in different stroke subtypes. Of 1488 patients, 387 (26.0%) AIS occurred in spring, 425 (28.6%) in summer, 331 (22.2%) in autumn and 345 (23.2%) in winter. Multivariate logistic regression demonstrated that the winter group had 2.15 times (95% CI:1.44–3.21) risk of multiple infarctions, 2.69 times (95% CI:1.80–4.02) of bilateral infarctions and 1.54 times (95% CI:1.05–2.26) of DCI compared with summer group, respectively. Subgroup analysis showed an increased risk of multiple (p

In searching for the optimal control strategy of sub-module capacitance voltage equalization of Modular Multilevel Converter (MMC), the most important issue is how to address the significant increase in computing time of the controller, switching frequency of the sub-module, device switching loss and MMC fault rate led by the increase in the number of sub-modules of the upper/lower bridge arm of the MMC. Therefore, this paper proposes a control strategy based on the retaining factor method + improved quick sort, namely, an improved quick sort algorithm is used to sort the sub-module capacitance voltages to reduce the amount of computation of the MMC controller; at the same time, a retaining factor is introduced to participate in the improved quick sort to significantly reduce the switching frequency of the sub-module. MMC of large ship grid-connected photovoltaic power system is taken as a research object in this paper, a 31-level MMC model is built in Matlab/Simulink. Simulation results verify that the improved quick sort has a good rapidity compared to the algorithms of bubble sort, quick sort, bidirectional bubble sort, and improved merge sort, that the retention factor method has a good frequency reduction effect, and that the proposed retention factor + improved quick sort can provide a good performance compared to improved quick sort and retention factor + improved merge sort under conditions of the normal operation and introducing disturbance.

Multi-motor synchronous drive system is increasingly widely used in industry and manufacturing, where its control structure and control strategy affect the quality and efficiency of production. In order to solve the contradiction between fastness and overshoot, and the difficulty in determining the compensation law in the conventional PID, cross-coupling control, and master-slave control strategies used in multi-motor control, this paper proposes a self-coupling PID control strategy based on ring adjacent compensation to reduce the complexity of the control structure. Furthermore, this paper analyzes the self-coupling PID parameter tuning rules and establishes the control structure of the ring coupling strategy, and proves its validity mathematically. The simulation results verify that the proposed strategy provides a fast response speed, high control precision, good disturbance rejection, and synchronization performance.

Abstract With rapid industrialization and urbanization in China, inadequate preservation of traditional architecture coupled with natural deterioration have led to the fragmentation of architectural landscapes. Drawing from ecological fragmentation research in landscape ecology, we consider the cultural landscape as our research object, viewing buildings as landscape patches, and determine a system for measuring architectural landscape fragmentation in traditional villages. The study shows the degree of landscape fragmentation can reveal the characteristics of traditional villages and the process of regional modernization. The results are as follows: (1) From the perspective of landscape diversity, the study area was rich in landscape types in all dimensions, and the relative evenness index was high, signifying evident or severe fragmentation. (2) The index of landscape heterogeneity in the dimensions of building quality, height, and landscape appearance is low in the study area, with mild levels of landscape fragmentation caused by heterogeneity in the aforementioned dimensions. (3) Mild fragmentation suggests the integrity and homogeneity of architectural landscape types, reflecting a lagging level of economic development, whereas high fragmentation signifies rapid economic development, leading to a substantial deterioration in the integrity and homogeneity of architectural landscape types. Therefore, efforts to preserve and develop traditional villages should not solely aim for low fragmentation as it could potentially constrain sustainable development.

Abstract Combining information from multispectral images into a fused image is informative and beneficial for human or machine perception. Currently, multiple photodetectors with different response bands are used, which require complicated algorithms and systems to solve the pixel and position mismatch problem. An ideal solution would be pixel-level multispectral image fusion, which involves multispectral image using the same photodetector and circumventing the mismatch problem. Here we presented the potential of pixel-level multispectral image fusion utilizing colloidal quantum dots photodiode array, with a broadband response range from X-ray to near infrared and excellent tolerance for bending and X-ray irradiation. The colloidal quantum dots photodiode array showed a specific detectivity exceeding 1012 Jones in visible and near infrared range and a favorable volume sensitivity of approximately 2 × 105 μC Gy−1 cm−3 for X-ray irradiation. To showcase the advantages of pixel-level multispectral image fusion, we imaged a capsule enfolding an iron wire and soft plastic, successfully revealing internal information through an X-ray to near infrared fused image.

Abstract Lead sulfide (PbS) colloidal quantum dot (CQD) photodiodes integrated with silicon‐based readout integrated circuits (ROICs) offer a promising solution for the next‐generation short‐wave infrared (SWIR) imaging technology. Despite their potential, large‐size CQD photodiodes pose a challenge due to high dark currents resulting from surface states on non‐passivated (100) facets and trap states generated by CQD fusion. In this work, we present a novel approach to address this issue by introducing double‐ended ligands that supplementally passivate (100) facets of halide‐capped large‐size CQDs, leading to suppressed bandtail states and reduced defect concentration. Our results demonstrate that the dark current density is highly suppressed by about an order of magnitude to 9.6 nA cm−2 at −10 mV, which is among the lowest reported for PbS CQD photodiodes. Furthermore, the performance of the photodiodes is exemplary, yielding an external quantum efficiency of 50.8% (which corresponds to a responsivity of 0.532 A W−1) and a specific detectivity of 2.5 × 1012 Jones at 1300 nm. By integrating CQD photodiodes with CMOS ROICs, the CQD imager provides high‐resolution (640 × 512) SWIR imaging for infrared penetration and material discrimination.

Traditional Chinese buildings serve as a carrier for the inheritance of traditional culture and national characteristics. In the context of rural revitalization, achieving the 3D reconstruction of traditional village buildings is a crucial technical approach to promoting rural planning, improving living environments, and establishing digital villages. However, traditional algorithms primarily target urban buildings, exhibiting limited adaptability and less ideal feature extraction performance for traditional residential buildings. As a result, guaranteeing the accuracy and reliability of 3D models for different types of traditional buildings remains challenging. In this paper, taking Jingping Village in Western Hunan as an example, we propose a method that combines multiple algorithms based on the slope segmentation of the roof to extract feature lines. Firstly, the VDVI and CSF algorithms are used to extract the building and roof point clouds based on the MVS point cloud. Secondly, according to roof features, village buildings are classified, and a 3D roof point cloud is projected into 2D regular grid data. Finally, the roof slope is segmented via slope direction, and internal and external feature lines are obtained after refinement through Canny edge detection and Hough straight line detection. The results indicate that the CSF algorithm can effectively extract the roofs of I-shaped, L-shaped, and U-shaped traditional buildings. The accuracy of roof surface segmentation based on slope exceeds 99.6%, which is significantly better than the RANSAC algorithm and the region segmentation algorithm. This method is capable of efficiently extracting the characteristic lines of roofs in low-rise buildings within traditional villages. It provides a reference method for achieving the high-precision modeling of traditional village architecture at a low cost and with high efficiency.

Background: Breakthrough infections have been widely reported in vaccinated individuals. However, the clinical characteristics, outcomes, and risk factors of SARS-CoV-2 breakthrough infections among fully vaccinated (BBIBP-CorV) hospitalized patients have not yet been fully elucidated. Methods: In the single-center cohort study conducted at Xiangya Hospital of Central South University, we enrolled the hospitalized COVID-19 patients who had received full (2 doses) vaccination with the BBIBP-CorV vaccine between December 5, 2022, and January 31, 2023. We collected and analyzed information related to clinical characteristics, laboratory results, treatments, outcomes and prognostic data. Univariate and multivariable Cox regression were performed to assess the impact of clinical characteristics and laboratory results on the composite outcome (including the initiation of endotracheal intubation, non-invasive respiratory support, intensive care unit admission, and all-cause death). Results: A total of 572 COVID-19 hospitalized patients with fully vaccinated (BBIBP-CorV) were included. The median age of the patients was 66 years (IQR 53, 74). The most common symptoms included fever (347 [60.7 %]), dry cough (401 [70.1 %]), and expectoration (333 [58.2 %]). Among those with pre-existing chronic comorbidities, 44.2 % had hypertension and 20.5 % had diabetes. Laboratory tests revealed that the majority of patients (425/549 [77.4 %]) had normal white blood cell counts. Composite outcome occurred in 11.9 % of patients, with 96.7 % of patients discharged and 3.3 % of patients died. Multivariate Cox regression analyses suggested that the NLR >4 (adjusted HR, 5.50 [95%CI: 1.56–19.47]; P = 0.008), D-dimer >0.5 mg/ml (adjusted HR, 2.17 [95%CI: 1.03–4.59]; P = 0.042) and procalcitonin >0.1 ng/ml (adjusted HR, 3.22 [95%CI: 1.38–7.52]; P = 0.007) were independently associated with the composite outcome. Conclusion: Breakthrough infection after being fully vaccinated (BBIBP-CorV) is more likely to occur in older patients and patients with pre-existing chronic comorbidities. NLR >4, D-dimer >0.5 mg/ml and procalcitonin >0.1 ng/ml were independent risk factors for composite outcome.

BackgroundThe pks island and its production of the bacterial secondary metabolite genotoxin, colibactin, have attracted increasing attention. However, genomic articles focusing on pks islands in Klebsiella pneumoniae, as well as comparative genomic studies of mobile genetic elements, such as prophages, plasmids, and insertion sequences, are lacking. In this study, a large-scale analysis was conducted to understand the prevalence and evolution of pks islands, differences in mobile genetic elements between pks-negative and pks-positive K. pneumoniae, and clinical characteristics of infection caused by pks-positive K. pneumoniae.MethodsThe genomes of 2,709 K. pneumoniae were downloaded from public databases, among which, 1,422 were from NCBI and 1,287 were from the China National GeneBank DataBase (CNGBdb). Screening for virulence and resistance genes, phylogenetic tree construction, and pan-genome analysis were performed. Differences in mobile genetic elements between pks-positive and pks-negative strains were compared. The clinical characteristics of 157 pks-positive and 157 pks-negative K. pneumoniae infected patients were investigated.ResultsOf 2,709 K. pneumoniae genomes, 245 pks-positive genomes were screened. The four siderophores, type VI secretion system, and nutritional factor genes were present in at least 77.9% (191/245), 66.9% (164/245), and 63.3% (155/245) of pks-positive strains, respectively. The number and fragment length of prophage were lower in pks-positive strains than in pks-negative strains (p

Summary: Objective: Spinal cord injury is a common occurrence during spinal surgery. In this study, we proposed a zoning laminectomy, which could reduce the incidence of nerve injury. We also discussed the safety and clinical efficacy of the zoning laminectomy for thoracic ossification of the ligamentum flavum (TOLF). Methods: Forty-five patients with TOLF who underwent zoning laminectomy from October 2016 to February 2020 were included in the retrospective analysis. The Japan Orthopedic Association (JOA) score was used to evaluate clinical outcomes. Meanwhile, the occurrence of complications was recorded. Results: All 45 patients underwent the operation successfully, and the mean follow-up period was 25.3 months, the mean operation time was 160.2 min, the average blood loss was 474.2 ml, and the average hospital time was 8.0 days. At the final evaluation, the JOA score was significantly higher than the preoperative JOA score (P

Sufficient and stable positioning torque is necessary for the parking of an electric vehicle driven by switched reluctance motor (SRM). Considering the highly nonlinear torque characteristic of SRM, the parking control with high stability and power consumption economy is an important issue. This paper presents an optimized parking control method for an electric vehicle driven by SRM to achieve optimal parking stability and power consumption economy simultaneously. The static torque-angle characteristic curve of a four-phase 16/12 SRM is analyzed, based on which the optimized region of parking position (presented by rotor angle) is obtained considering the uncertainty of parking conditions. To ensure the vehicle is properly parked in the optimized position region, active disturbance rejection control (ADRC) is utilized to achieve the smooth switch from zero-speed control to parking position control. Simulation and experimental results verify that the proposed method can better stabilize the parking on the ramp and reduce power consumption in all conditions.

Traffic sign detection plays an important role in traffic safety and traffic management. In view of the complex and changeable environment and detection accuracy of traffic sign detection, this paper proposes UCN-YOLOv5 model based on the framework of YOLOv5.This model first replaces a new backbone network, which uses the core module RSU of U2Net to enhance the feature extraction of the network. Then, ConvNeXt-V2 is integrated, and the C3 module of its Block and YOLOv5 network is used to construct the C3_CN2 structure. The utilization of the proposed lightweight receptive field attention module LPFAConv in the Head Section represents a potential enhancement for the extraction of receptive field features. Finally, for small targets in traffic signs, Normalized Wasserstein Distance (NWD), which is insensitive to targets of different scales, is added to calculate the position loss function to replace the IoU metric to a certain extent, which further improves the detection ability of our model for traffic signs. Experiments on the TT100K dataset show that UCNYOLOv5 has excellent detection performance. Compared with the baseline model (Y0Lov5s, YOLOV5m, YOLOV5l), it improves the Map.5 index by 5.9 %, 4.9 % and 4.6 %; in the Map.5:.95 index, it is 4.4 %, 3.5 % and 2.8 % better. Moreover, the enhanced algorithm demonstrated favorable performance on the LISA and CCTSDB2021 traffic sign datasets. This research has important value for the accurate detection of traffic sign detection, and has guiding significance for in-depth research in related fields.

PurposeThe type VI system (T6SS) has the potential to be a new virulence factor for hypervirulent Klebsiella pneumoniae (hvKp) strains. This study aimed to characterize the molecular and clinical features of T6SS-positive and T6SS-negative K. pneumoniae isolates that cause abscesses.Patients and methodsA total of 169 non-duplicate K. pneumoniae strains were isolated from patients with abscesses in a tertiary hospital in China from January 2018 to June 2022, and clinical data were collected. For all isolates, capsular serotypes, T6SS genes, virulence, and drug resistance genes, antimicrobial susceptibility testing, and biofilm formation assays were assessed. Multilocus sequence typing was used to analyze the genotypes of hvKp. T6SS-positive hvKp, T6SS-negative hvKp, T6SS-positive cKP, and T6SS-negative cKP (n = 4 strains for each group) were chosen for the in vivo Galleria mellonella infection model and in vitro competition experiments to further explore the microbiological characteristics of T6SS-positive K. pneumoniae isolates.ResultsThe positive detection rate for T6SS was 36.1%. The rates of hvKp, seven virulence genes, K1 capsular serotype, and ST23 in T6SS-positive strains were all higher than those in T6SS-negative strains (p

Background and purposeBlood pressure (BP) variability (BPV) increases the risk of cerebral disease in both hemorrhagic and ischemic strokes. However, whether BPV is associated with different types of ischemic stroke remains unclear. In this study, we explored the relationship between BPV and ischemic stroke subtypes.MethodsWe enrolled consecutive patients aged 47–95 years with ischemic stroke in the subacute stage. We categorized them into four groups based on their artery atherosclerosis severity, brain magnetic resonance imaging markers, and disease history: large-artery atherosclerosis, branch atheromatous disease, small-vessel disease, and cardioembolic stroke. Twenty-four-hour ambulatory blood pressure monitoring was performed, and the mean systolic blood pressure/diastolic blood pressure, standard deviation, and coefficient of variation were calculated. A multiple logistic regression model and random forest were used to test the relationship between BP and BPV in the different types of ischemic stroke.ResultsA total of 286 patients, including 150 men (73.0 ± 12.3 years) and 136 women (77.8 ± 9.6 years) were included in the study. Of these, 86 (30.1%) patients had large-artery atherosclerosis, 76 (26.6%) had branch atheromatous disease, 82 (28.7%) had small-vessel disease, and 42 (14.7%) had cardioembolic stroke. There were statistically significant differences in BPV between subtypes of ischemic stroke in 24-h ambulatory blood pressure monitoring. The random forest model showed that BP and BPV were important features associated with ischemic stroke. Multinomial logistic regression analysis demonstrated that systolic blood pressure levels; systolic blood pressure variability at 24 h, daytime and nighttime; and nighttime diastolic blood pressure were independent risk factors for large-artery atherosclerosis after adjustment for confounders. When compared to branch atheromatous disease and small-vessel disease, nighttime diastolic blood pressure and standard deviation of diastolic blood pressure were significantly associated with patients in the cardioembolic stroke group. However, a similar statistical difference was not seen in patients with large-artery atherosclerosis.ConclusionThe results of this study indicate a discrepancy in blood pressure variability among different ischemic stroke subtypes during the subacute stage. Higher systolic blood pressure and systolic blood pressure variability during the 24 h, daytime, and nighttime, and nighttime diastolic blood pressure were independent predictors for large-artery atherosclerosis stroke. Increased nighttime diastolic BPV was an independent risk factor for cardioembolic stroke.

BackgroundEpidemiological evidence suggests a correlation between ambient temperature and ischemic stroke. However, evidence on the impact of daily temperature variability on the onset of ischemic stroke is lacking and limited.ObjectiveWe aimed to investigate the short-term association between temperature variability and ischemic stroke occurrence in Tianjin.MethodsWe performed a 10-year analysis of ischemic stroke patients hospitalized in two affiliated hospitals of Tianjin Medical University from 2011 to 2020. Daily meteorological data were collected from the Tianjin Meteorological Bureau. Temperature variability was calculated from the standard deviation (SD) of daily minimum and maximum temperatures over exposure days. A quasi-Poisson generalized linear regression combined with distributed lag non-linear model (DLNM) was used to estimate the effect of temperature variability on daily stroke onset, while controlling for daily mean temperature, relative humidity, long-term trend and seasonality, public holiday, and day of the week.ResultsTemperature variability was positively associated with ischemic stroke. A 1°C increase in temperature variability at 0–1 days (TV0–1) was associated with a 4.1% (1.9–6.3%) increase of ischemic stroke onset. In a stratified analysis, men, people aged ≤65 years, and individuals with pre-existing hypertension, hyperlipidemia, hyperhomocysteinemia were more susceptible to temperature variability. Furthermore, the influence pattern of temperature variability on ischemic stroke was different in the cold season (November–April) and the warm season (May–October).ConclusionOur findings suggested that short-term temperature variability exposure could increase the risk of ischemic stroke, which may provide new insights into the impact of climate change on health.

Autonomous driving systems rely on a comprehensive understanding of the surrounding environment, and trees, as important roadside features, have a significant impact on vehicle positioning and safety analysis. Existing methods use mobile LiDAR systems (MLS) to collect environmental information and automatically generate tree inventories based on dense point clouds, providing accurate geometric parameters. However, the use of MLS systems requires expensive survey-grade laser scanners and high-precision GNSS/IMU systems, which limits their large-scale deployment and results in poor real-time performance. Although LiDAR-based simultaneous localization and mapping (SLAM) techniques have been widely applied in the navigation field, to the best of my knowledge, there has been no research conducted on simultaneous real-time localization and roadside tree inventory. This paper proposes an innovative approach that uses LiDAR technology to achieve vehicle positioning and a roadside tree inventory. Firstly, a front-end odometry based on an error-state Kalman filter (ESKF) and a back-end optimization framework based on factor graphs are employed. The updated poses from the back-end are used for establishing point-to-plane residual constraints for the front-end in the local map. Secondly, a two-stage approach is adopted to minimize global mapping errors, refining accumulated mapping errors through GNSS-assisted registration to enhance system robustness. Additionally, a method is proposed for creating a tree inventory that extracts line features from real-time LiDAR point cloud data and projects them onto a global map, providing an initial estimation of possible tree locations for further tree detection. This method uses shared feature extraction results and data pre-processing results from SLAM to reduce the computational load of simultaneous vehicle positioning and roadside tree inventory. Compared to methods that directly search for trees in the global map, this approach benefits from fast perception of the initial tree position, meeting real-time requirements. Finally, our system is extensively evaluated on real datasets covering various road scenarios, including urban and suburban areas. The evaluation metrics are divided into two parts: the positioning accuracy of the vehicle during operation and the detection accuracy of trees. The results demonstrate centimeter-level positioning accuracy and real-time automatic creation of a roadside tree inventory.

Recently, energy efficiency and the sharing economy have become significant factors for sustainable economic development (SED), which require the attention of researchers and policymakers. Thus, the present research investigates the impact of energy efficiency and the sharing economy on SED in China. Secondary data is used covering the period from 1991 to 2020. The study runs the augmented Dickey-Fuller (ADF) test to check the unit root and the quantile autoregressive distributed lag (QARDL) model to investigate the linkages amongst the variables. The results indicate that efficient energy use, sharing economy users, sharing economy values, population growth, urbanization and industrialization have positive linkages with SED in China. This study guides policymakers developing policies related to the achievement of SED using efficient energy and a valuable sharing economy.

In this paper, a six-phase $\text{N}+2$ power converter for switched reluctance motor is studied. An open-circuit fault diagnosis and faulty devices location scheme based on midpoint current spectrum analysis is proposed. In order to realize fault diagnosis, five fault cases are distinguished, including single-phase fault, multiphase fault and midpoint switch fault. Taking the phase current frequency as the fundamental frequency, the DC component, the fundamental frequency component, and the second harmonic component have different change rules in the five fault cases. The third harmonic component is used as the denominator, three spectrum coefficient could be obtained and be used as the fault characteristic. Through this scheme, using a single current sensor to detect the midpoint current can ensure open-circuit faults accurately. After the fault case is confirmed, the amplitude of the harmonic component is used to define a fault location threshold, and the threshold is numerically compared with the midpoint current to realize the faulty bridge arm location. Finally, the complete process of fault diagnosis and location is described, then the effectiveness of the proposed diagnosis method is validated by the simulation and experiment.

Spiking neural networks (SNNs) have attracted considerable attention as third-generation artificial neural networks, known for their powerful, intelligent features and energy-efficiency advantages. These characteristics render them ideally suited for edge computing scenarios. Nevertheless, the current mapping schemes for deploying SNNs onto neuromorphic hardware face limitations such as extended execution times, low throughput, and insufficient consideration of energy consumption and connectivity, which undermine their suitability for edge computing applications. To address these challenges, we introduce EdgeMap, an optimized mapping toolchain specifically designed for deploying SNNs onto edge devices without compromising performance. EdgeMap consists of two main stages. The first stage involves partitioning the SNN graph into small neuron clusters based on the streaming graph partition algorithm, with the sizes of neuron clusters limited by the physical neuron cores. In the subsequent mapping stage, we adopt a multi-objective optimization algorithm specifically geared towards mitigating energy costs and communication costs for efficient deployment. EdgeMap—evaluated across four typical SNN applications—substantially outperforms other state-of-the-art mapping schemes. The performance improvements include a reduction in average latency by up to 19.8%, energy consumption by 57%, and communication cost by 58%. Moreover, EdgeMap exhibits an impressive enhancement in execution time by a factor of 1225.44×, alongside a throughput increase of up to 4.02×. These results highlight EdgeMap’s efficiency and effectiveness, emphasizing its utility for deploying SNN applications in edge computing scenarios.

PurposeIt is difficult to contour nerve structures with the naked eye due to poor differentiation between the nerve structures with other soft tissues on CT images. Magnetic resonance neurography (MRN) has the advantage in nerve visualization. The purpose of this study is to identify one MRN sequence to better assist the delineation of the lumbosacral plexus (LSP) nerves to assess the radiation dose to the LSP using the magnetic resonance (MR)/CT deformable coregistration technique.MethodsA total of 18 cases of patients with prostate cancer and one volunteer with radiation-induced lumbosacral plexopathy (RILSP) were enrolled. The data of simulation CT images and original treatment plans were collected. Two MRN sequences (Lr_NerveVIEW sequence and Cs_NerveVIEW sequence) were optimized from a published MRN sequence (3D NerveVIEW sequence). The nerve visualization ability of the Lr_NerveVIEW sequence and the Cs_NerveVIEW sequence was evaluated via a four-point nerve visualization score (NVS) scale in the first 10 patients enrolled to determine the better MRN sequence for assisting nerve contouring. Deformable registration was applied to the selected MRN sequence and simulation CT images to get fused MR/CT images, on which the LSP was delineated. The contouring of the LSP did not alter treatment planning. The dosimetric data of the LSP nerve were collected from the dose–volume histogram in the original treatment plans. The data of the maximal dose (Dmax) and the location of the maximal radiation point received by the LSP structures were collected.ResultsThe Cs_NerveVIEW sequence gained lower NVS scores than the Lr_NerveVIEW sequence (Z=-2.887, p=0.004). The LSP structures were successfully created in 18 patients and one volunteer with MRN (Lr_NerveVIEW)/CT deformable registration techniques, and the LSP structures conformed with the anatomic distribution. In the patient cohort, the percentage of the LSP receiving doses exceeding 50, 55, and 60 Gy was 68% (12/18), 33% (6/18), and 17% (3/18), respectively. For the volunteer with RILSP, the maximum irradiation dose to his LSP nerves was 69 Gy.ConclusionThe Lr_NerveVIEW MRN sequence performed better than the Cs_NerveVIEW sequence in nerve visualization. The dose in the LSP needs to be measured to understand the potential impact on treatment-induced neuropathy.

PurposeTo investigate the potential clinical benefit of utilizing intensity-modulated proton therapy (IMPT) to reduce acute hematologic toxicity for locally advanced non-small cell lung cancer (LA-NSCLC) patients and explore the feasibility of a model-based patient selection approach via the normal tissue complication probability (NTCP).MethodsTwenty patients with LA-NSCLC were retrospectively selected. Volumetric modulated arc photon therapy (VMAT) and IMPT plans were generated with a prescription dose of 60 Gy in 30 fractions. A wide range of cases with varied tumor size, location, stations of metastatic lymph nodes were selected to represent the general cancer group. Contouring and treatment planning followed RTOG-1308 protocol. Doses to thoracic vertebral bodies (TVB) and other organ at risks were compared. Risk of grade ≥ 3 acute hematologic toxicity (HT3+) were calculated based on the NTCP model, and patients with a reduction on NTCP of HT3+ from VMAT to IMPT (△NTCP_HT3+) ≥ 10% were considered to ‘significantly benefit from proton therapy.’ResultsCompared to VMAT, IMPT significantly reduced the dose to the TVB, the lung, the heart, the esophagus and the spinal cord. Tumor distance to TVB was significantly associated with △NTCP _HT3+ ≥ 10%. For the patients with tumor distance ≤ 0.7 cm to TVB, the absolute reduction of dose (mean, V30 and V40) to TVB was significantly lower than that in patients with tumor distance > 0.7 cm.ConclusionIMPT decreased the probability of HT3+ compared to VMAT by reducing the dose to the TVB in LA-NSCLC patients. Patients with tumor distance to TVB less than 0.7 cm are likely to benefit most from proton over photon therapy.

Precise pedestrian positioning based on smartphone-grade sensors has been a research hotspot for several years. Due to the poor performance of the mass-market Micro-Electro-Mechanical Systems (MEMS) Magnetic, Angular Rate, and Gravity (MARG) sensors, the standalone pedestrian dead reckoning (PDR) module cannot avoid long-time heading drift, which leads to the failure of the entire positioning system. In outdoor scenes, the Global Navigation Satellite System (GNSS) is one of the most popular positioning systems, and smartphone users can use it to acquire absolute coordinates. However, the smartphone’s ultra-low-cost GNSS module is limited by some components such as the antenna, and so it is susceptible to serious interference from the multipath effect, which is a main error source of smartphone-based GNSS positioning. In this paper, we propose a multi-phase GNSS/PDR fusion framework to overcome the limitations of standalone modules. The first phase is to build a pseudorange double-difference based on smartphone and reference stations, the second phase proposes a novel multipath mitigation method based on multipath partial parameters estimation (MPPE) and a Double-Difference Code-Minus-Carrier (DDCMC) filter, and the third phase is to propose the joint stride lengths and heading estimations of the two standalone modules, to reduce the long-time drift and noise. The experimental results demonstrate that the proposed multipath error estimation can effectively suppress the double-difference multipath error exceeding 4 m, and compared to other methods, our fusion method achieves a minimum error RMSE of 1.63 m in positioning accuracy, and a minimum error RMSE of 4.71 m in long-time robustness for 20 min of continuous walking.

As the demand for embedded-vision grows, solving large optimization problems in real-time with energy and cost budget is a challenge. We present BAX, a hardware accelerator of bundle adjustment (BA), which solves the least-squares problem of state estimation in visual odometry (VO). BAX consists of a frontend and a backend for control and computation, respectively. The frontend generates instructions on-the-fly executed at the backend to perform the BA algorithm. The backend adopts decoupled access/execute (DAE) architecture, which separates the memory access unit (MAU) from the pipeline. The MAU can prefetch vectors and matrices ahead of computations. To further reduce the latency of data reorganization, three transpose-free dataflows are proposed for matrix multiplication operations on the vector processing unit (VPU). Besides, a unified architecture for both forward and backward substitution is designed for matrix decomposition in the linear solver. All the data are stored in 442kB on-chip memory, and the local map is maintained efficiently by the hierarchical graph memory. Compared with the baseline architecture, the processing time is reduced by 53.9% through the above techniques. BAX is implemented in 32-bit floating-point precision with data normalization on FPGA. It completes a full BA in about 63.44ms at 200MHz, consuming 1.12W power. BAX is $1.73\times $ and $22.38\times $ faster than the desktop and embedded CPUs, respectively, and achieves 90% performance of the GPU at much less power consumption.

PurposeTo investigate the potential clinical benefits of using stereotactic body radiation therapy (SBRT) with simultaneous integrated boost (SIB) technique for locally advanced pancreatic cancer (LAPC) among different treatment modalities and planning strategies, including photon and proton.MethodA total of 19 patients were retrospectively selected in this study: 13 cases with the tumor located in the head of the pancreas and 6 cases with the tumor in the body of the pancreas. SBRT-SIB plans were generated using volumetric modulated arc therapy (VMAT), two-field Intensity Modulated Proton Therapy (IMPT), and three-field IMPT. The IMPT used the robust optimization parameters of ± 3.5% range and 5-mm setup uncertainties. Root-mean-square deviation dose (RMSD) volume histograms were used to evaluate the target coverage robustness quantitatively. Dosimetric metrics based on the dose-volume histogram (DVH), homogeneity index (HI), and normal tissue complication probability (NTCP) were analyzed to evaluate the potential clinical benefits among different planning groups.ResultsWith a similar CTV and SIB coverage, two-field IMPT provided a lower maximum dose for the stomach (median: 18.6GyE, p

Based on the landscape architecture of traditional settlements in southern China, this study takes water as a vital element through field investigation and model analysis to explore the water management strategies of two traditional villages in Xiangjiang River Basin, Hunan Province. We have found that traditional settlements are located between rivers and mountains. The community of the settlement has a strong interaction with the water environment. The water management system consists of two parts: the rainwater collection and storage system of a single building and the settlement’ s water collection and drainage system. Through calculation, we found that the amounts of water collected (per year) between the two villages are different: ZhangGuYing (Z village) = 5.73 million L, ShangGanTang (S village) = 1.784 million L, in spite of the fact that water management strategies of the two settlements are similar. Further analysis shows that the difference is related to the adaption of the precipitation and topography of the surrounding areas. The above-mentioned systematic management strategy of water resources has been used until currently, with adaptability, low cost, and sustainability. It has outstanding significance for the current demand for sustainable development from both resource management and cultural aspects.

The classification of architectural style for Chinese traditional settlements (CTSs) has become a crucial task for developing and preserving settlements. Traditionally, the classification of CTSs primarily relies on manual work, which is inefficient and time consuming. Inspired by the tremendous success of deep learning (DL), some recent studies attempted to apply DL networks such as convolution neural networks (CNNs) to achieve automated classification of the architecture styles. However, these studies suffer overfitting problems of the CNNs, leading to inferior classification performance. Moreover, most of the studies apply the CNNs as a black box providing limited interpretability. To address these limitations, a new DL classification framework is proposed in this study to overcome the overfitting problem by transfer learning and learning-based data augmentation technique (i.e., AutoAugment). Furthermore, we also employ class activation map (CAM) visualization technique to help understand how the CNN classifiers work to abstract patterns from the input. Specifically, due to a lack of architectural style datasets for the CTSs, a new annotated dataset is first established with six representative classes. Second, several representative CNNs are leveraged to benchmark the new dataset. Third, to address the overfitting problem of the CNNs, a new DL framework is proposed which combines transfer learning and AutoAugment to improve the classification performance. Extensive experiments are conducted on the new dataset to demonstrate the effectiveness of our framework. The proposed framework achieves much better performance than baselines, greatly mitigating the overfitting problem. Additionally, the CAM visualization technique is harnessed to explain what and how the CNN classifiers implicitly learn for recognizing a specified architectural style.

The robot control technology combined with a machine vision system provides a feasible method for the autonomous operation of moving target. However, designing an effective visual servo control system is a great challenge. For the autonomous operation of the objects moving on the pipeline, this article is dedicated to developing a capture and placement control system for the six degrees of freedom (6-DOF) manipulator equipped with an eye-in-hand camera. Firstly, a path planning strategy of online capture and offline placement is proposed for real-time capture and efficient placement. Subsequently, to achieve the fast, stable, and robust capture for a moving target, a position-based visual servo (PBVS) controller is developed by combining estimated velocity feedforward and refined PID control. Feedforward control is designed using the estimated velocity by a proposed motion estimation method for high response speed. PID control is refined by dead zone constraint to reduce the manipulator’s jitter caused by the frequent adjustment of manipulator control system. Besides, the proportional, integral, and differential coefficients of PID controller are adaptively tuned by fuzzy control to reject the noise, disturbance, and dynamic variation in the capture process. Finally, validation experiments are performed on the constructed ROS–Gazebo simulation platform, demonstrating the effectiveness of the developed control system.

Non-invasive biomarkers to identify patients with bladder outlet obstruction (BOO)-related dysfunction are still needed to guide clinical practice. The current study aims to investigate molecular alterations and biomarkers associated with partial BOO (PBOO) in rats. Sprague–Dawley rats were used to establish the BOO model. Serum samples from 60 patients with benign prostatic hyperplasia (BPH) were used for enzyme-linked immunosorbent assay analysis. RNA sequencing and TMT-labeling proteomic analyses were conducted to identify molecular alterations. Masson’s trichrome, H&E, and immunohistochemical staining and western blotting were conducted by using conventional methods following the manufacturer’s instructions. Rats with PBOO experienced hypertrophy of smooth muscle cells and hyperplasia of interstitial cells during the first 4 weeks after the initiation of obstruction. Four weeks later, rats with PBOO showed activation of the adaptive immune response, cell death and apoptosis. The levels of brain-derived neurotrophic factor (BDNF) and fibroblast growth factor 2 (FGF2) in the serum gradually increased in the first 4 weeks and gradually decreased after week 4. FGF2 levels slightly correlated with prostate volume (R = 0.156, P = 0.0028) but not with age or BMI in BPH patients. No correlations were found between BDNF levels and prostate volume, age or BMI. BOO induces a change from bladder compensation to decompensation at week 4. FGF2 is involved in the development of hypertrophy in the PBOO bladder and shows a positive correlation with prostate volume in BPH patients.

Human hedgehog-interacting protein (HHIP), a negative regulator of hedgehog (HH) signaling pathway, has been reported to be dysregulated in many types of cancer, including gastric cancer. However, the inhibitory role of HHIP as well as the underlying molecular mechanism of HHIP regulation in gastric cancer haven’t been fully elucidated yet. In this study, we demonstrated that HHIP overexpression significantly suppressed the proliferation and invasion of AGS cells evaluated by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and transwell assays, respectively. Interestingly, methylation-specific polymerase chain reaction (MS-PCR, MSP) showed that HHIP overexpression dramatically decreased its de novo promoter methylation levels in AGS cells. Furthermore, HHIP expression was higher in adjacent non-cancerous tissue compared to matched gastric cancer tissue. High HHIP level was negatively correlated with metastasis (p = 0.035) but not local recurrence (p = 0.58). Taken together, our study suggested that HHIP can modulate gastric cancer progression and metastasis via regulation of its de novo promoter methylation levels in a feedback manner. Lower HHIP levels is positively associated with gastric cancer metastasis, which not only indicates HHIP could be served as a protective marker for gastric cancer, but also suggests restoring HHIP expression might be a potential therapeutic strategy for clinical treatment.

Sectoral coverage that plays a critical role in operationalizing the emission trading scheme (ETS), has gained substantive attention. Despite the insightful views on sectoral coverage from the emission reduction potential or carbon leakage, previous studies overlook the cost-effectiveness of ETS in the sense that the varying marginal contributions of each sector to reducing emission abatement costs (EACs) (which is defined as marginal cost savings, MCSs) remain underexplored. To fill this gap, this paper proposes a costs-oriented approach for sectoral coverage (COASCO), which ranks the sectors by estimating and comparing their MCSs. Taking China’s climate targets by 2030 as an example, we conduct an empirical study that implements the COASCO method to explore the impacts of sectoral coverage on China’s EACs. Our analysis demonstrates that, while coverage extension generally reduces China’s EACs, a small sectoral coverage can already lead to a substantial decline in the national EACs. The results underpin the Pareto principle that covering six sectors (i.e. Electricity production, Metallurgy, Transport and storage, Petroleum and gas, Nonmetal mining ) out of 29 can reduce China’s EACs by over 80% compared to covering Electricity production only. Although coverage extension may reduce the differences in EACs between sectors and improve market activation, extending the sectoral coverage probably gives rise to the number of big carbon traders, which thenincreases the risks of market manipulation. As a result, covering those six sectors can reach a balance between ETS market activation and risks. By providing a generalized and systematic framework for determining the sectoral coverage, this study makes it possible to minimize the total EACs associated with any sectoral coverages, thus assisting policymakers in fulfilling China’s latest ambitious goals of reaching carbon peaking by 2030 and carbon neutrality by 2060 in a cost-effective manner.

Upon interactions with its specific ligand hepatocyte growth factor (HGF), the c-Met signal is relayed to series of downstream pathways, exerting essential biological roles. Dysregulation of the HGF-c-Met signaling pathway has been implicated in the onset, progression and metastasis of various cancers, making the HGF-c-Met axis a promising therapeutic target. Both c-Met and HGF undergo glycosylation, which appears to be biologically relevant to their function and structural integrity. Different types of glycoconjugates in the local cellular environment can also regulate HGF/c-Met signaling by distinct mechanisms. However, detailed knowledge pertaining to the glycosylation machinery of the HGF-c-Met axis as well as its potential applications in oncology research is yet to be established. This mini review highlights the significance of the HGF-c-Met signaling pathway in physiological and pathological context, and discusses the molecular mechanisms by which affect the glycosylation of the HGF-c-Met axis. Owing to the crucial role played by glycosylation in the regulation of HGF/c-Met activity, better understanding of this less exploited field may contribute to the development of novel therapeutics targeting glycoepitopes.

This paper addresses the robust beamforming problem for speech recognition using a novel time-frequency mask estimator. The beamformer first estimates the time-frequency mask using a deep neural network (DNN) based on which the covariance matrices of the target speech and noise are computed. Then, the beamformer coefficients are directly obtained via generalized eigenvector decomposition. To achieve accurate covariance matrix estimation for robust beamforming, we propose a DNN-based mask estimator which can exploit the spatial features of the multi-channel microphone signals. The proposed mask estimator leverages the spatial information of the microphone array by using multi-channel signals to estimate a speech-aware mask and a noise-aware mask simultaneously. Using the target-specified masks, accurate covariance matrices of the target speech and noise can be obtained from the observation independently. Experiments on CHiME4 data sets demonstrate that, compared with the baseline toolkit (BeamformIt) and the winner in the CHiME3 challenge, the proposed method achieves better results both in terms of perceptual speech quality and speech recognition error rate.

In the past decade, sparse and low-rank recovery has drawn much attention in many areas such as signal/image processing, statistics, bioinformatics, and machine learning. To achieve sparsity and/or low-rankness inducing, the ℓ1 norm and nuclear norm are of the most popular regularization penalties due to their convexity. While the ℓ1 and nuclear norm are convenient as the related convex optimization problems are usually tractable, it has been shown in many applications that a nonconvex penalty can yield significantly better performance. In recent, nonconvex regularization-based sparse and low-rank recovery is of considerable interest and it in fact is a main driver of the recent progress in nonconvex and nonsmooth optimization. This paper gives an overview of this topic in various fields in signal processing, statistics, and machine learning, including compressive sensing, sparse regression and variable selection, sparse signals separation, sparse principal component analysis (PCA), large covariance and inverse covariance matrices estimation, matrix completion, and robust PCA. We present recent developments of nonconvex regularization based sparse and low-rank recovery in these fields, addressing the issues of penalty selection, applications and the convergence of nonconvex algorithms. Code is available at https://github.com/FWen/ncreg.git.

Abstract Background More than a decade after the outbreak of human coronaviruses (HCoVs) SARS in Guangdong province and Hong Kong SAR of China in 2002, there is still no reoccurrence, but the evolution and recombination of the coronaviruses in this region are still unknown. Therefore, surveillance on the prevalence and the virus variation of HCoVs circulation in this region is conducted. Methods A total of 3298 nasopharyngeal swabs samples were collected from cross-border children ( 37.5 °C), from 2014 May to 2015 Dec. Viral nucleic acids were analyzed and sequenced to study the prevalence and genetic diversity of the four human coronaviruses. The statistical significance of the data was evaluated with Fisher chi-square test. Results 78 (2.37%; 95%CI 1.8-2.8%) out of 3298 nasopharyngeal swabs specimens were found to be positive for OC43 (36;1.09%), HKU1 (34; 1.03%), NL63 (6; 0.18%) and 229E (2;0.01%). None of SARS or MERS was detected. The HCoVs predominant circulating season was in transition of winter to spring, especially January and February and NL63 detected only in summer and fall. Complex population with an abundant genetic diversity of coronaviruses was circulating and they shared homology with the published strains (99-100%). Besides, phylogenetic evolutionary analysis indicated that OC43 coronaviruses were clustered into three clades (B,D,E), HKU1 clustered into two clades(A,B) and NL63 clustered into two clades(A,B). Moreover, several novel mutations including nucleotides substitution and the insertion of spike of the glycoprotein on the viral surface were discovered. Conclusions The detection rate and epidemic trend of coronaviruses were stable and no obvious fluctuations were found. The detected coronaviruses shared a conserved gene sequences in S and RdRp. However, mutants of the epidemic strains were detected, suggesting continuous monitoring of the human coronaviruses is in need among cross-border children, who are more likely to get infected and transmit the viruses across the border easily, in addition to the general public.

This paper addresses the recovery and demixing problem of signals that are sparse in some general dictionary. Involved applications include source separation, image inpainting, super-resolution, and restoration of signals corrupted by clipping, saturation, impulsive noise, or narrowband interference. We employ the ℓq-norm (0 ≤ q q-minimization formulation for the recovery and demixing problem. This nonconvex formulation is approximately solved by two efficient first-order algorithms based on proximal coordinate descent and alternative direction method of multipliers (ADMM), respectively. The new algorithms are convergent in the nonconvex case under some mild conditions and scale well for high-dimensional problems. A convergence condition of the new ADMM algorithm has been derived. Furthermore, the extension of the two algorithms for multichannels joint recovery has been presented, which can further exploit the joint sparsity pattern among multichannel signals. Various numerical experiments showed that the new algorithms can achieve considerable performance gain over the ℓ1-regularized algorithms.

Background/Aims: Alzheimer's disease (AD) is known to be related to alterations in neuronal intracellular calcium activity ([Ca2+]i). The present study revealed the distinct role of leptin in Na+/Ca2+-exchanger activity. Methods: [Ca2+]i was determined utilizing Fura-2 fluorescence. The activity of NCX was measured by removal of extracellular Na+ in the presence of external Ca2+. Na+/Ca2+-exchanger activity was further quantified from whole cell currents following removal of extracellular Na+. Na+/Ca2+-exchanger isoform NCX1 transcript levels and protein abundance were quantified by RT-PCR and Western blotting, respectively. Results: Exposure of PC12 cells to 30 µM amyloid (Aβ42) increased [Ca2+]i, an effect significantly blunted by 6 hours incubation with leptin before Aβ42 treatment. Moreover, leptin treatment significantly increased Na+/Ca2+-exchanger mediated Ca+ transport and current, NCX1 transcript level as well as NCX1 membrane protein abundance. Conclusion: We show that leptin blunts Aβ42-evoked [Ca2+]i increase by increasing expression and activity of Na+/Ca2+-exchanger NCX1.

Background/Aims: Cilostazol has been previously demonstrated to inhibit IL-23 production in human synovial macrophages via a RhoA/ROCK-dependent pathway. However, whether cilostazol affects IL-23 production in human dendritic cells remains largely unknown. The present study was designed to investigate this question and elucidate the possible underlying mechanisms. Methods: Human monocyte-derived dendritic cells (mo-DCs) were pretreated with or without cilostazol and then incubated with zymosan. Enzyme-linked immunosorbent assay (ELISA) and real time PCR analyses were used to measure IL-23 protein expression and RNA levels, respectively, whereas Western blotting was used to measure the expression and phosphorylation level of AMPK. Results: Our results demonstrated that cilostazol suppressed zymosan-induced IL-23 protein production in a concentration dependent manner without affecting dendritic cell viability. In addition, it was found that cilostazol suppressed the expression of the p19 and p40 subunits of IL-23. Moreover, cilostazol mimicked the effect of the AMPK agonist A-769662, as demonstrated by the fact that IL-23 production was also inhibited by A-769662, and the effect of cilostazol on IL-23 production was blocked by the AMPK antagonist Compound C. More importantly, Western blotting demonstrated that cilostazol led to an increased phosphorylation of AMPK. Conclusion: Collectively, our data suggest that cilostazol inhibits the production of IL-23 in human mo-DCs, potentially via the activation of AMPK. This suggests that cilostazol could be an effective anti-inflammatory agent in IL-23- and dendritic cell-related diseases.

Acute kidney injury (AKI) is an extremely dangerous clinical syndrome with high morbidity and mortality. Stem cell-based therapies have shown great promise for AKI treatment. Urine-derived stem cells (USCs) are a novel cell source in tissue engineering and cell therapy which provide advantages of simple, noninvasive, and low-cost harvest methods, efficient proliferation, and multi-differentiation potential. Here, we described the therapeutic effects of USCs in a rat model of cisplatin-induced AKI as a novel therapy. In vivo, the intravenous administration of USCs alleviated the renal functional damage in AKI rats, for the levels of blood urea nitrogen (BUN) and serum creatinine (SCr) were significantly decreased. The USCs-treated group also exhibited improved histological and ultrastructural changes, promoted proliferation, and inhibited apoptosis in renal tissues. After the USC therapy, the expression levels of proinflammatory cytokines (TNF-α and IL-6) and apoptosis-related proteins (BAX and cleaved caspase-3) were downregulated. In addition, the presence of a few GFP-labeled USCs was confirmed in rat renal tissues. In vitro, rat tubular epithelial (NRK-52E) cells were incubated with cisplatin to induce cell damage and then cocultured with USCs. After coculture with USCs, the cisplatin-induced NRK-52E cells showed higher cell viability and a lower apoptosis ratio than those of the control group, and cell cycle arrest was improved. In conclusion, our results demonstrated that USC therapy significantly improved the renal function and histological damage, inhibited the inflammation and apoptosis processes in the kidney, and promoted tubular epithelial proliferation. Our study exhibited the potential of USCs in the treatment of AKI, representing a new clinical therapeutic strategy.

In production environments, failure data of a complex system are difficult to obtain due to the high cost of experiments; furthermore, using a single model to analyze risk, reliability, availability and uncertainty is a big challenge. Based on the fault tree, fuzzy comprehensive evaluation and Markov method, this paper proposed a fuzzy Markov method that takes the full advantages of the three methods and makes the analysis of risk, reliability, availability and uncertainty all in one. This method uses the fault tree and fuzzy theory to preprocess the input failure data to improve the reliability of the input failure data, and then input the preprocessed failure data into the Markov model; after that iterate and adjust the model when uncertainty events occur, until the data of all events have been processed by the model and the updated model obtained, which best reflects the system state. The wellhead connector of a subsea production system was used as a case study to demonstrate the above method. The obtained reliability index (mean time to failure) of the connector is basically consistent with the failure statistical data from the offshore and onshore reliability database, which verified the accuracy of the proposed method.

This paper addresses the issue of large covariance matrix estimation in a high-dimensional statistical analysis. Recently, improved iterative algorithms with positive-definite guarantee have been developed. However, these algorithms cannot be directly extended to use a nonconvex penalty for sparsity inducing. In general, a nonconvex penalty has the capability of ameliorating the bias problem of the popular convex lasso penalty, and thus is more advantageous. In this paper, we propose a class of positive-definite covariance estimators using generalized nonconvex penalties. We develop a first-order algorithm based on the alternating direction method framework to solve the nonconvex optimization problem efficiently. The convergence of this algorithm has been proved. Furthermore, the statistical properties of the new estimators have been analyzed for generalized nonconvex penalties. Moreover, extension of this algorithm to covariance estimation from sketched measurements has been considered. The performances of the new estimators have been demonstrated by both a simulation study and a gene clustering example for tumor tissues. Code for the proposed estimators is available at https://github.com/FWen/Nonconvex-PDLCE.git.

This experiment was designed to determine the effects of variations in dietary energy intake on reproductive performance and gene expression of luteal and endometrium tissues in Large White (LW) and Meishan (MS) gilts during early and middle pregnancy. After insemination, 32 LW gilts were assigned to high and low (HEL and LEL, 14.23 and 12.56 MJ DE/kg, respectively) diet treatment groups, while 32 MS gilts were allocated to HEM and LEM (12.56 and 10.88 MJ DE/kg) groups. Gilts were slaughtered on days 35, 55 and 90 of gestation. The fetal survival and luteal progesterone (P4) concentration in the HEL group were higher on day 35 but lower on day 90 of gestation compared with the LEL group (P < 0.05) for LW gilts. However, fetal survival and luteal P4 concentration on day 35 of gestation were greater (P < 0.05) in the LEM group than in the HEM group for MS gilts, but no significant difference in mid-gestation was showed. The fetal weights of both breeds were higher for the high energy diets compared with the respective control group on day 90 of gestation (P < 0.05). In addition, the mRNA levels of P4 synthesis-related proteins had correlated with luteal P4 concentration in both breeds. Further, endometrial levels of uteroferrin (ACP5), retinol-binding protein 4 (RBP4) and secreted phosphoprotein 1 (SPP1) mRNA were upregulated in the HEL group on day 35 of gestation but ACP5 and SPP1 were downregulated on day 55 of gestation compared with the LEL group (P < 0.05) for LW gilts. In MS gilts, diet only affected the expression of SPP1 (P < 0.05). Our results revealed the differential sensitivity of LW and MS breeds to variations in dietary energy intake. For LW gilts, the HEL group improved fetal survival on day 35 but a sustained high energy diet decreased fetal survival on day 90 of gestation. The differences in dietary energy intake did not influence fetal survival on day 90 of gestation but the higher energy diet did increase fetal weight in the MS breed compared with the lower energy intake diet. These results may be due to differential luteal secretion activity and endometrium gene expression in these two breeds. Keywords: Energy level, Fetal survival, Corpus luteum, Endometrium, pig

By means of compressive sampling (CS), a sparse signal can be efficiently recovered from its far fewer samples than that required by the Nyquist–Shannon sampling theorem. However, recovering a speech signal from its CS samples is a challenging problem, as it is not sparse enough on any existing canonical basis. To solve this problem, we propose a method which combines the approximate message passing (AMP) and Markov chain that exploits the dependence between the modified discrete cosine transform (MDCT) coefficients of a speech signal. To reconstruct the speech signal from CS samples, a turbo framework, which alternately iterates AMP and belief propagation along the Markov chain, is utilized. In addtion, a constrain is set to the turbo iteration to prevent the new method from divergence. Extensive experiments show that, compared to other traditional CS methods, the new method achieves a higher signal-to-noise ratio, and a higher perceptual evaluation of speech quality (PESQ) score. At the same time, it maintaines a better similarity of the energy distribution to the original speech spectrogram. The new method also achieves a comparable speech enhancement effect to the state-of-the-art method.

Indoor pedestrian localization measurement is a hot topic and is widely used in indoor navigation and unmanned devices. PDR (Pedestrian Dead Reckoning) is a low-cost and independent indoor localization method, estimating position of pedestrians independently and continuously. PDR fuses the accelerometer, gyroscope and magnetometer to calculate relative distance from starting point, which is mainly composed of three modules: step detection, stride length estimation and heading calculation. However, PDR is affected by cumulative error and can only work in two-dimensional planes, which makes it limited in practical applications. In this paper, a novel localization method V-PDR is presented, which combines VPR (Visual Place Recognition) and PDR in a loosely coupled way. When there is error between the localization result of PDR and VPR, the algorithm will correct the localization of PDR, which significantly reduces the cumulative error. In addition, VPR recognizes scenes on different floors to correct floor localization due to vertical movement, which extends application scene of PDR from two-dimensional planes to three-dimensional spaces. Extensive experiments were conducted in our laboratory building to verify the performance of the proposed method. The results demonstrate that the proposed method outperforms general PDR method in accuracy and can work in three-dimensional space.

The paper presents a hybrid indoor positioning solution based on a pedestrian dead reckoning (PDR) approach using built-in sensors on a smartphone. To address the challenges of flexible and complex contexts of carrying a phone while walking, a robust step detection algorithm based on motion-awareness has been proposed. Given the fact that step length is influenced by different motion states, an adaptive step length estimation algorithm based on motion recognition is developed. Heading estimation is carried out by an attitude acquisition algorithm, which contains a two-phase filter to mitigate the distortion of magnetic anomalies. In order to estimate the heading for an unconstrained smartphone, principal component analysis (PCA) of acceleration is applied to determine the offset between the orientation of smartphone and the actual heading of a pedestrian. Moreover, a particle filter with vector graph assisted particle weighting is introduced to correct the deviation in step length and heading estimation. Extensive field tests, including four contexts of carrying a phone, have been conducted in an office building to verify the performance of the proposed algorithm. Test results show that the proposed algorithm can achieve sub-meter mean error in all contexts.

In the design phase of sensor arrays during array signal processing, the estimation performance and system cost are largely determined by array aperture size. In this article, we address the problem of joint direction-of-arrival (DOA) estimation with distributed sparse linear arrays (SLAs) and propose an off-grid synchronous approach based on distributed compressed sensing to obtain larger array aperture. We focus on the complex source distribution in the practical applications and classify the sources into common and innovation parts according to whether a signal of source can impinge on all the SLAs or a specific one. For each SLA, we construct a corresponding virtual uniform linear array (ULA) to create the relationship of random linear map between the signals respectively observed by these two arrays. The signal ensembles including the common/innovation sources for different SLAs are abstracted as a joint spatial sparsity model. And we use the minimization of concatenated atomic norm via semidefinite programming to solve the problem of joint DOA estimation. Joint calculation of the signals observed by all the SLAs exploits their redundancy caused by the common sources and decreases the requirement of array size. The numerical results illustrate the advantages of the proposed approach.

Consistent state estimation is a vital requirement in numerous real life applications from localization to multi-source information fusion. The Kalman filter and its variants have been successfully used for solving state estimation problems. Kalman filtering-based estimators are dependent upon system model assumptions. A deviation from defined assumptions may lead to divergence or failure of the system. In this work, we propose a Kalman filtering-based robust state estimation model using statistical estimation theory. Its primary intention is for multiple source information fusion, although it is applicable to most non-linear systems. First, we propose a robust state prediction model to maintain state constancy over time. Secondly, we derive an error covariance estimation model to accept deviations in the system error assumptions. Afterward, an optimal state is attained in an iterative process using system observations. A modified robust MM estimation model is executed within every iteration to minimize the impact of outlying observation and approximation errors by reducing their weights. For systems having a large number of observations, a subsampling process is introduced to intensify the optimized solution redundancy. Performance is evaluated for numerical simulation and real multi sensor data. Results show high precision and robustness of proposed scheme in state estimation.

State-of-the-art human detection methods focus on deep network architectures to achieve higher recognition performance, at the expense of huge computation. However, computational efficiency and real-time performance are also important evaluation indicators. This paper presents a fast real-time human detection and flow estimation method using depth images captured by a top-view TOF camera. The proposed algorithm mainly consists of head detection based on local pooling and searching, classification refinement based on human morphological features, and tracking assignment filter based on dynamic multi-dimensional feature. A depth image dataset record with more than 10k entries and departure events with detailed human location annotations is established. Taking full advantage of the distance information implied in the depth image, we achieve high-accuracy human detection and people counting with accuracy of 97.73% and significantly reduce the running time. Experiments demonstrate that our algorithm can run at 23.10 ms per frame on a CPU platform. In addition, the proposed robust approach is effective in complex situations such as fast walking, occlusion, crowded scenes, etc.