Search

Fibrosis and cancer is described by some epidemiological studies as chronic stages of different disease conditions typically characterized by uncontrolled accumulation of extra-cellular matrix (ECM), thereby leading to inflammation of tissues and organ (lungs, heart, liver and kidney) dysfunction. It is highly prevalent, and contributes to increased mortality rate worldwide. Currently, the therapeutical approaches involving selected medications (bemcentinib, pirfenidone and nintedanib) obtained synthetically, and used in clinical practices for fibrosis and cancer management and treatment has shown to be unsatisfactorily, especially during progressive stages of the disease. With regards to finding a more potent, effective, and promising curative for fibrosis and cancer, there is need for continuous experimental studies universally. However, phytochemical constituents’ particularly phenolic compounds [Chlorogenic acid (CGA)] obtained from coffee, and coffee beans have been predominantly utilized in experimental studies, due to its multiple pharmacological properties against various disease forms. Considering its natural source alongside minimal toxicity level, CGA, a major precursor of coffee have gained considerable attention nowadays from researchers worldwide, owing to its wide, efficacious and beneficial action against fibrosis and cancer. Interestingly, the safety of CGA has been proven. Furthermore, numerous experimental studies have also deduced massive remarkable outcomes in the use of CGA clinically, as a potential drug candidate against treatment of fibrosis and cancer. In the course of this review article, we systematically discussed the beneficial contributions of CGA with regards to its source, absorption, metabolism, mechanistic effects, and molecular mechanisms against different fibrosis and cancer categorization, which might be a prospective remedy in the future. Moreover, we also highlighted CGA (in vitro and in vivo analytical studies) defensive effects against various disorders., Graphical abstract Fibrosis and cancer is described by some epidemiological studies as chronic stages of different disease conditions typically characterized by uncontrolled accumulation of extra-cellular matrix (ECM), thereby leading to inflammation of tissues and organ (lungs, heart, liver and kidney) dysfunction. It is highly prevalent, and contributes to increased mortality rate worldwide. With regards to finding a more potent, effective, and promising curative for fibrosis and cancer, there is need for continuous experimental studies universally. However, numerous experimental studies have deduced massive remarkable outcomes in the use of CGA clinically, as a potential drug candidate against treatment of fibrosis and cancer. In the course of this review article, we systematically discussed the beneficial contributions of CGA with regards to its source, absorption, metabolism, mechanistic effects, and molecular mechanisms against different fibrosis and cancer categorization, which might be a prospective remedy in the future. Moreover, we also highlighted CGA (in vitro and in vivo analytical studies) defensive effects against various disorders.Image, graphical abstract

Funding Information: This work was supported by the National Natural Science Foundation of China under grant 11875210, the Science and Technology Planning Project of Guangdong Province under grant 2018A050506082, China Postdoctoral Science Foundation under grant 2019M652687, and by the grant RFBR No.19-05-00554 in the part of the development of advanced approach to analysis of geochemical objects. In the present study Ar+ cluster ions accelerated by voltages in the range of 5-10 kV are used to irradiate single crystal ZnO substrates and nanorods to fabricate self-assembled surface nanoripple arrays. The ripple formation is observed when the incidence angle of the cluster beam is in the range of 30-70°. The influence of incidence angle, accelerating voltage, and fluence on the ripple formation is studied. Wavelength and height of the nanoripples increase with increasing accelerating voltage and fluence for both targets. The nanoripples formed on the flat substrates remind of aeolian sand ripples. The ripples formed at high ion fluences on the nanorod facets resemble well-ordered parallel steps or ribs. The more ordered ripple formation on nanorods can be associated with the confinement of the nanorod facets in comparison with the quasi-infinite surface of the flat substrates. publishersversion published

Support vector machine (SVM) is one of the effective classifiers in the field of network intrusion detection; however, some important information related to classification might be lost in the reprocessing. In this paper, we propose a granular classifier based on entropy clustering method and support vector machine to overcome this limitation. The overall design of classifier is realized with the aid of if-then rules that consists of a premise part and conclusion part. The premise part realized by the entropy clustering method is used here to address the problem of a possible curse of dimensionality, while the conclusion part realized by support vector machines is utilized to build local models. In contrast to the conventional SVM, the proposed entropy clustering-based granular classifiers (ECGC) can be regarded as an entropy-based support function machine. Moreover, an opposition-based genetic algorithm is proposed to optimize the design parameters of the granular classifiers. Experimental results show the effectiveness of the ECGC when compared with some classical models reported in the literatures.

The goal of management for bronchial asthma (referred to as asthma below) is to achieve overall control of the condition, including control of current symptoms and reduction on future risks. Asthma exacerbation as one of critical component of the future risks (1,2), may induce a variety of harms to the host, necessitate additional use of medical resources, impose serious socioeconomic burdens, and represent a significant cause of asthma-related disability and mortality (3). As such, prevention and reduction of the asthma exacerbation are of important relevance in improving the overall control of asthma (2-5).

Objective: To characterize predictors of patient satisfaction in outpatient radiology, we examined whether patient satisfaction differs across radiology modalities and demographic groups. Methods: A random sampling of Press-Ganey outpatient services surveys for radiology and non-radiology visits from September 2008 to September 2017 were retrospectively analyzed. Composite scores averaged across all Likert items were analyzed as both a continuous variable and a dichotomous variable of dissatisfaction (defined as ≤3 on the 5-point scale). Results: Among 9983 radiology surveys, mammography had higher composite scores than MRI, CT, radiography, US, and NM/PET (p

Idiopathic pulmonary fibrosis (IPF), a tumor-like disease, is a serious and fatal pulmonary inflammatory condition usually characterized by irreversible destruction of the lung parenchyma, excessive matrix accumulation, and decline in lung function. IPF still remains a great burden to the universe. At the moment, the available therapeutic regimens utilized for IPF such as non-pharmacological therapies (lung transplantation) and pharmacological therapies (drugs, nintedanib, pirfenidone, etc.) are normally accompanied by significant limitations, such as adverse reactions, low bioavailability, poor selectivity, low-tissue distribution, in vivo instability, systemic toxicity, inconveniency and unsafe usage. There is a need for the exploration and discovery of new novel remedies by researchers and scientists globally. Recent numerous preliminary studies have laid significant emphasis and demonstrated the antifibrotic importance, good curative actions (little or no adverse reactions), and multiple target sites of the active components from traditional herbal medicine (THM) against IPF, which could serve as a modern, alternative and potential therapeutics or drug candidates in treating IPF. This paper extensively summarizes the pharmacological actions and signaling pathways or mechanisms of active components obtained from THM for treating IPF. Moreover, the sources and modernization, markets, relevant FDA and CFDA studies (the USA and China), preclinical analysis, and various compositions of THM currently under clinical trials are also highlighted. Additionally, this present analytical data would be instrumental towards further drug progression or advancement of active components from THM for the potential therapeutics of IPF in the future.

As the abnormal conditions of manufacturing process seriously affect the machining performance of machine tool, the vibration signal of spindle is selected to monitor the manufacturing process of machine center. The vibration signals are decomposed by empirical mode decomposition (EMD) method, and the first five intrinsic module functions components are picked out to calculate the power spectrums. Then, the local tangential space arrangement (LTSA) method is developed for dimension reduction, and the one-dimensional feature vector indicating the vibration state is obtained. A support vector machine model is used to classify vibration states based on one-dimensional critical features of three different manufacturing processes. The classification result indicates that the EMD-LTSA method is an efficient feature extraction method for vibration status monitoring of machine tools.

The abnormal manufacturing state recognition of machine center is of great significance to reduce downtime and ensure quality of productions. A revised wavelet packet transform and principal component analysis method has been developed to extract features from vibration signals of machine center. The revised wavelet packet transform employs local discriminant bases method to select optimal wavelet packet nodes. The principal component analysis is conducted for features dimensionality reduction to obtain the final features. A BP neural network is used to classify manufacturing states based on final features of three different manufacturing processes. The comparison result indicates that the revised WPT and PCA method is an efficient feature extraction method for manufacturing stage recognition of machine tools.

In order to improve the automation and intelligence level of tobacco packet distribution and to automatically complete the stacking flow operation of tobacco packet, a set of automatic stacking robot systems based on machine vision were designed. This system includes the visual system and sorting palletizing system. In the visual system shape area, k-means clustering is adopted to establish the standard template library. A fast matching algorithm based on the shape of template was put forward, which could identify smoke a few shapes and cigarette package fast and accurately. Then the upper machine data information was sent to the palletizing system to finish the tobacco package, cigarette package group pile, and pallet cigarette packet scheduling algorithm of automatic palletizing homework. The field test results showed that the shape recognition rate of cigarette packs was over 99.90%, the shape matching time of cigarette packs was less than 80ms, the stacking efficiency was 5.04-5.14s per packet, the stacking tilt value was less than 3cm, the packing void rate was less than 5%, and the stacking error rate was less than 0.10%, which met the requirements of cigarette packing stacking. The system can provide technical support for improving tobacco logistics automation.

It is difficult to evaluate the degradation performance and the degradation state of a rolling bearing in noisy environment. A new method is proposed to solve the problem based on singular value decomposition (SVD)-sliding window linear regression and ResNeXt–multi-attention mechanism’s deep neural network (RMADNN). Firstly, the root mean square (rms) gradient value is calculated on the basis of rms based on SVD and sliding window linear regression, which is used as the rolling bearing performance degradation indicator in noisy environment. Secondly, the degradation state of the rolling bearing is divided by the rms gradient. Thirdly, for the deep learning network model, the soft attention mechanism is introduced into the bidirectional long short-term memory network to extract more important and deep fault features. At the same time, the ResNeXt layer is added into the convolutional neural network to extract more fault features and merge them through multi-scale grouped convolution. The hybrid domain attention mechanism (HDAM) is introduced after the ResNeXt layer. The HDAM can screen out more important features from the output features of the ResNeXt in the two dimensions of channel and spatial which demonstrates the improved deep learning network of the RMADNN. Finally, the labeled data set is input into the improved model for training, and the Softmax classifier is used to identify the life degradation state of the rolling bearing. Through the verification of multiple test data sets, the results show that the method proposed in this research is very effective on the rolling bearing performance degradation assessment.

This paper implements the construction of three-dimensional point cloud map based on RGB-D SLAM, and takes the three-dimensional point cloud information output by SLAM system as the input of Octomap, generates octree map and performs three-dimensional projection transformation, which converts point cloud map into two-dimensional raster map for path planning research. In order to solve the problem of secondary obstacle avoidance and the problem of emergency stop caused by no local optimal solution in local path planning, a new path planning algorithm for mobile robot based on dynamic object trajectory prediction is presented, and the best path is selected by combining the reinforcement learning algorithm, Sarsa, to avoid dynamic obstacles effectively. On the basis of using RGB-D camera to locate pedestrians in real time, Kalman filter algorithm is used to predict the global coordinates of pedestrians in the next moment. Then a new reward and punishment mechanism is designed to realize the dynamic obstacle avoidance based on the improved Sarsa algorithm, so that the mobile robot can leave the radiation circle of pedestrian prediction coordinates as soon as possible and avoid the pedestrian walking path.

This paper proposes to use nonlinear factors to recover odometry information and use it in the optimization of global consistent map construction. In the front-end, the pixel matching is carried out by the direct method assisted with IMU information. Then the reprojection error and IMU error are minimized to obtain the initial pose estimation of robot. In the back-end, we use a fix-size optimization window to optimize mapping. When new frames are added, we marginalize the old state. We use a set of nonlinear factors to approximate the marginal distribution, and combine it with loop-closing constraints to construct a globally consistent map. Finally, the performance of the system is verified on the open dataset EuRoC, and conduct experiments in a real environment. The results show that the method improves the accuracy and robustness of mapping.

To solve the problem that the navigation accuracy of logistics robot decreases with displacement, a combined Odom and Data Matrix (DM) landmark navigation method based on SLAM technology is proposed. Firstly, the odometer motion model was established to predict the particle position, and the DM code data information was used to correct its parameters. Then, the DM landmark observation model was established to update the particle importance weight. Then, during the improved particle resampling process, a fixed number of random particles were added according to the observed likelihood of the camera, so as to obtain more accurate particle distribution and effectively improve the positioning accuracy. At the same time, the particle degradation of Monte Carlo method (MCL) algorithm was weakened, and the improved algorithm solved the problem of position drift and hijacking of the robot. Finally, the position and pose of DM landmarks are constantly corrected through trajectory correction to improve the global navigation accuracy of the robot. Experiments based on the integrated navigation robot platform show that the accuracy of the integrated navigation method is 35.6% higher than that of the traditional laser SLAM navigation method, which verifies the effectiveness of the scheme.

In view of the low robustness and accuracy of traditional visual odometry in dynamic environment, a dynamic feature point detection method based on semantic-optical flow fusion based on dynamic threshold is used to solve the problem that single optical flow method is sensitive to noise. In this paper, the Mask R-CNN Network is used to segment the image semantics, and the LK (Lucas Kanade) optical flow is used to track the feature points in the image, and the optical flow motion vector is calculated. On the basis of optical flow motion vector, a dynamic point judgment method based on the depth information of feature points and camera motion information is designed, and the dynamic feature points are eliminated to adapt to the dynamic environment. The experimental results on the tum data set show that the absolute trajectory error of the proposed system is improved by more than 90% compared with ORB-SLAM2, while the absolute trajectory error is improved by more than 13% compared with the same type of DS-SLAM, which improves the robustness of the visual odometry.

With the rapid development of high-precision road network maps, low-precision road network maps (basic data unrelated to hardware) will need to be directly produced for traditional navigation software from high-precision maps. To do so, large amounts of vector data representing road networks must be simplified and spatial directional similarity in road networks must be maintained while reducing precision. In this study, an elite strategy genetic algorithm based on the grid model is applied to spatial directional adjustment in road networks for producing road network maps for traditional navigation. Firstly, semantic features and critical vertices are extracted from the road network with high precision. Secondly, some high-precision vertices are eliminated under constraints of the digital navigation map. During this process, the local shape maintenance of the road is considered, and the destruction of the spatial topological relationships is avoided. Thirdly, a genetic algorithm for minimizing the total changes in road azimuths at nodes of road networks is developed to maintain spatial directional relationships while reducing precision. Experimental results and visualization effects on the test data of different cities show that this method is suitable for generating road network maps for traditional navigation software from high-precision ones.

Machinery fault diagnosis is critical to the reliability and safety of modern industrial systems. In order to better diagnose the faults of rotating machinery, this paper presents a fault diagnosis model based on a weighted twodimensional (2D) time-frequency spectrum and multi-level information fusion. Firstly, multiple time-frequency analysis methods are performed on the collected vibration signals to get 2D time-frequency spectrum diagram. Secondly, a feature weighting strategy is proposed to weight the 2D time-frequency features to further enhance the fault information and reduce the disturbing of noise. Then, a 2D dimensionality reduction method is applied to reduce the dimensionality of the weighted 2D timefrequency features and extract the primary fault information contained in the 2D fault features. Finally, the extracted fault features are inputted to a support vector machine (SVM) for fault diagnosis model training. The proposed multi-level information fusion strategy based on Dempster-Shafer (D-S) evidence theory is also applied to resolve the contradiction of fault diagnosis results between different features and different sensors.

Background At present, serum Aspergillus IgG and IgM antibody detection is mainly used in the diagnosis of chronic pulmonary aspergillosis (CPA), but its value in the diagnosis of invasive pulmonary aspergillosis (IPA) in non-agranulocytic patients is still unclear. IgM can be used as a marker of acute infection to help diagnose acute infection-related diseases. IgG is a marker of long-term infection and is used to assist in the diagnosis of pre-existing or chronic infection-related diseases. The aim of this study was to investigate and compare the value of serum Aspergillus IgG and IgM antibody detection in the diagnosis of IPA and CPA in non-agranulocytic patients. Methods Fifty-eight cases of pulmonary aspergillosis (37 IPA and 21 CPA cases), 15 cases of community-acquired bacterial pneumonia and 50 cases in the healthy control group were collected. The serum (1,3)-β-D-glucan test (G test) was performed with a chromogenic method, and the galactomannan test (GM test) and Aspergillus IgG and IgM antibody detection were performed by commercial enzyme-linked immunosorbent assay (ELISA) in all patients. The sensitivity and specificity, cut-off value and area under the curve (AUC) of Aspergillus IgG and IgM antibodies were further obtained by receiver operating characteristic (ROC) curves. Results The positive rate of the G test, Aspergillus IgG antibody detection and the GM test also showed notable differences among the IPA, CPA, community-acquired bacterial pneumonia and healthy groups (P = 0.006, P P = 0.217, respectively). Only the positive rate of the GM test showed a significant difference between the IPA and CPA groups (P = 0.04). ROC curves indicated that Aspergillus IgG antibody detection had a higher specificity in the IPA group than in the CPA group (0.952). The detection of Aspergillus IgG antibody can preferably distinguish IPA from community-acquired bacterial pneumonia and healthy controls (sensitivity = 0.923, specificity = 0.459, cut-off value = 134.46, AUC = 0.727). It can also distinguish CPA from community-acquired bacterial pneumonia and healthy controls (sensitivity = 0.952, specificity = 0.692, cut-off value = 75.46, AUC = 0.873). Conclusions Serum Aspergillus IgG antibody detection may have certain clinical value in the diagnosis of IPA and CPA in non-agranulocytic patients.

Background: At present, serum Aspergillus IgG and IgM antibody detection is mainly used in the diagnosis of chronic pulmonary aspergillosis (CPA), but its value in the diagnosis of invasive pulmonary aspergillosis (IPA) in non-agranulocytic patients is still unclear. IgG is a marker of long-term infection and is used to assist in the diagnosis of pre-existing or chronic infection-related diseases. The aim of this study was to investigate and compare the value of serum Aspergillus IgG and IgM antibody detection in the diagnosis of IPA and CPA in non-agranulocytic patients. Methods: Fifty-eight cases of pulmonary aspergillosis (37 IPA and 21 CPA cases), 15 cases of community-acquired bacterial pneumonia and 50 cases in the healthy control group were collected. The serum (1,3)-β-D-glucan test (G test) was performed with a chromogenic method, and the galactomannan test (GM test) and Aspergillus IgG and IgM antibody detection were performed by commercial enzyme-linked immunosorbent assay (ELISA) in all patients. The sensitivity and specificity, cut-off value and area under the curve (AUC) of Aspergillus IgG and IgM antibodies were further obtained by receiver operating characteristic (ROC) curves. Results: The positive rate of the G test, Aspergillus IgG antibody detection and the GM test also showed notable differences among the IPA, CPA, community-acquired bacterial pneumonia and healthy groups ( P = 0.006, P < 0.001 and P = 0.217, respectively). Only the positive rate of the GM test showed a significant difference between the IPA and CPA groups ( P = 0.04). ROC curves indicated that Aspergillus IgG antibody detection had a higher specificity in the IPA group than in the CPA group (0.952). The detection of Aspergillus IgG antibody can preferably distinguish IPA from community-acquired bacterial pneumonia and healthy controls (sensitivity = 0.923, specificity = 0.459, cut-off value = 134.46, AUC = 0.727). It can also distinguish CPA from community-acquired bacterial pneumonia and healthy controls (sensitivity = 0.952, specificity = 0.692, cut-off value = 75.46, AUC = 0.873). Conclusions: Serum Aspergillus IgG antibody detection may have certain clinical value in the diagnosis of IPA and CPA in non-agranulocytic patients.

Purpose Details of patients hospitalized for asthma exacerbation in mainland China are lacking. To improve disease control and reduce economic burden, a large sample survey among this patient population is indispensable. This study aimed to investigate the clinical characteristics and outcomes of such patients. Methods A retrospective study was conducted on patients hospitalized for asthma exacerbation in 29 hospitals of 29 regions in mainland China during the period 2013 to 2014. Demographic features, pre-admission conditions, exacerbation details, and outcomes were summarized. Risk factors for exacerbation severity were analyzed. Results There were 3,240 asthmatic patients included in this study (57.7% females, 42.3% males). Only 28.0% used daily controller medications; 1,287 (39.7%) patients were not currently on inhaled corticosteroids. Acute upper airway infection was the most common trigger of exacerbation (42.3%). Patients with severe to life-threatening exacerbation tended to have a longer disease course, a smoking history, and had comorbidities such as hypertension, chronic obstructive pulmonary disease (COPD), and food allergy. The multivariate analysis showed that smoking history, comorbidities of hypertension, COPD, and food allergy were independent risk factors for more severe exacerbation. The number of patients hospitalized for asthma exacerbation varied with seasons, peaking in March and September. Eight patients died during the study period (mortality 0.25%). Conclusions Despite enhanced education on asthma self-management in China during recent years, few patients were using daily controller medications before the onset of their exacerbation, indicating that more educational efforts and considerations are needed. The findings of this study may improve our understanding of hospital admission for asthma exacerbation in mainland China and provide evidence for decision-making.

Vibrational spectroscopy is an effective non-destructive technique, and it has been successfully applied in characteristics identification for agro-food samples. However, owing to the high dimensionality of spectral dataset, it is difficult to distinguish samples of different characteristics from observing the raw spectral. In this study, t-Distributed Stochastic Neighbor Embedding (t-SNE), an state-of-art method, was applied for visulization on the five vibrational spectroscopy data sets. The performances of t-SNE and the other reference methods (PCA and Isomap) were illustrated both from the differentiation ability in the 2-dimensional space and the accuracy of sequential classification model. For the former, t-SNE showed more satisfied visual discrimination results in 2-dimensional space and obtained better scores of clustering metrics, Silhouette Coefficient (0.59 average score compared to 0.24 achieved by PCA and 0.59 by Isomap) and Davies-Bouldin Index (1.51 average score compared to 2.58 achieved by PCA and 1.52 by Isomap). For the latter, two supervised classification models, k-nearest neighbor (KNN) and support vector machine (SVM), were constructed based on the new representations in 2-dimensional space, in both cases, the representations given by t-SNE outperformed the other methods in terms of accuracy (for KNN, 96% average accuracy compared to the 85% achieved by PCA and 92% by Isomap; for SVM, 96% average accuracy compared to the 86% achieved by PCA and 92% by Isomap). The results showed great potential of t-SNE for recognizing minute spectral differences between classes, and proved that t-SNE is an effective dimensionality reduction and visualization method, especially when complex and highly overlapping vibrational spectra are used for analysis.

Counterfeiting products and abusing labels lead to less credibility for traceability system in China recently. Authentication and supervision agencies driven by government departments play an important role for ensuring the quality safety in the case of lacking the willingness and credit of enterprises. A complete authentication and supervision flow framework was constructed based on an identification code (IdC) for authenticated origin base, which linked two actors of the agencies and the enterprises, and three subsystems of On-line Authentication Subsystem (OAS), Safety Production Management Client (SMC) and Mobile Supervision Application (MSA). IdC consisted of longitude and latitude of origin base as position code, production code and authentication type code. With a relative position partition method on 6 zones every 27° for China map and a coordination transformation algorithm, an absolute longitude and latitude value was converted into a relative position value and a zone mark value. IdC and packaging date code formed initial traceability code (TC). 8 digits packaging date code was reconstructed into 3 digits relative time value and 1 digit period mark according to a relative time period partition method with a period of 999d as time intervals and four periods form a cycle. Validation code was generated integrating the zone mark value, period mark value and authentication type code. Therefore, transformed 20 digits TC with the characters of shorter code length and stronger encryption was formed with IdC, relative time value and validation code. Three subsystems for different actors which provide the main function such as origin base registration, agency authentication, QR code generation, data uploading and product verification, were developed. The system has been used in Tianjin city from 2012. 213 enterprises were audited through OAS and used SMC. Through investigating 8 supervision agency staffs, 30 origin base owners, and 50 customers, it is shown that the positive effects are approved by most of the investigators and two negative effects for enhancing the costs and doubting the authentication reliability are laid by 17 enterprises and 12 customers. Furthermore, 4 typical cases for counterfeiting and abusing the labels were exampled and can be solved to a certain extent with the system. However, except for the technology itself, a management measures fitting the supervision flow and system need to draft in order to improve the system application well in the future.

Apple yield estimation using a smartphone with image processing technology offers advantages such as low cost, quick access and simple operation. This article proposes distribution framework consisting of the acquisition of fruit tree images, yield prediction in smarphone client, data processing and model calculation in server client for estimating the potential fruit yield. An image processing method was designed including the core steps of image segmentation with R/B value combined with V value and circle-fitting using curvature analysis. This method enabled four parameters to be obtained, namely, total identified pixel area (TP), fitting circle amount (FC), average radius of the fitting circle (RC) and small polygon pixel area (SP). A individual tree yield estimation model on an ANN (Artificial Neural Network) was developed with three layers, four input parameters, 14 hidden neurons, and one output parameter. The system was used on an experimental Fuji apple (Malus domestica Borkh. cv. Red Fuji) orchard. Twenty-six tree samples were selected from a total of 80 trees according to the multiples of the number three for the establishment model, whereby 21 groups of data were trained and 5 groups o data were validated. The R2 value for the training datasets was 0.996 and the relative root mean squared error (RRMSE) value 0.063. The RRMSE value for the validation dataset was 0.284 Furthermore, a yield map with 80 apple trees was generated, and the space distribution o the yield was identified. It provided appreciable decision support for site-specific management.

As an endogenous neuromodulator, hydrogen sulfide (H2S) exerts multiple biological effects in the brain. Previous studies have shown that H2S is involved in the regulation of neural synaptic plasticity and cognition in healthy rodents. It is well known that there is a progressive decline of cognitive function that occurs with increased age. The purpose of this study was to investigate the role of H2S in aging-associated amygdalar synaptic plasticity and cued fear memory deficits as well as to explore the underlying mechanisms. We found that H2S levels in the amygdala were significantly lower in aged rats when compared with healthy adult rates, which displayed significant deficits in long-term potentiation (LTP) in the thalamo-lateral amygdala (LA) pathway and amygdala-dependent cued fear memory. Bath application of an H2S donor, sodium hydrogen sulfide (NaHS), significantly reversed the impaired LTP in brain slices from aged rats, and intra-LA infusion of NaHS restored the cued fear memory in aged rats. Mechanismly, we found that H2S treatment significantly enhanced NMDAR-mediated synaptic responses in the thalamo-LA pathway of aged rats. Notably, GluN2B-containing NMDARs, but not GluN2A-containing NMDARs, contributed to the effects of H2S on aging-associated impairments of amygdalar LTP and fear memory, because applying GluN2B antagonist could abolish the beneficial effects of NaHS treatment on amygdalar LTP and cognitive performance in aged rats. Collectively, these results show that H2S can reverse aging-associated amygdalar synaptic plasticity and fear memory deficits by restoring the function of GluN2B-containing NMDARs, suggesting that supplement of H2S might be a therapeutic approach for aging-related cognitive disorders.

A bidirectional acquisition system on smart phones.Farm information collection flow on tree identification with QR code.Sensors search rule on tree position and multi-point environment value model. An orchard precision management system plays an important role in improvement at the management level and the enhancement of decision abilities. A single orchard tree or an orchard tree microcommunity is the basic management unit, and bidirectional information on the environment and plants is the important content for precision management. A type of RFID label was applied with a UHF chip in the core and a QR code in the surface for single tree identification. A bidirectional acquisition system for orchard production, which included farming information collection for the forward direction and environmental information acquisition for the backward direction, was designed with smart phones. In the farming information collection part, information collection flow that included QR code image acquisition, image preprocessing, barcode decoding and farming information collection was established. An improved local threshold method was adopted to improve the QR code identification rate in the smart phone platform. In the environment information acquisition part, a sensor search rule on the single tree position and a multi-point environment value model were designed. The orchard information bidirectional acquisition system was developed on an Android platform with the Java language, which has the function of QR decoding, farm record information collection, environment information acquisition, data uploading and statistical analysis. The system was tested in an apple orchard. A total of 144 trees were chosen to decode the QR codes in the tree label. The success rate was approximately 96.52%. The identification time of 85% of the trees was less than 4s for the 20 chosen trees. In taking the temperature, for example, the difference between the computed temperature value and the measured temperature value around each tree was small. The system could decrease the cost of the professional equipment, such as portable RFID readers and writers, which was a low-cost and high-efficiency solution for orchard production information collection.

In this work, to improve the rate at which cool air ventilates the tail of a refrigerated compartment and thereby reduces the temperature difference between the front and rear of the cargo area, we increase the length of an air conduit in the direction of the air outlet of the refrigerating unit. In addition, minimizing the cooling time during transportation reduces energy consumption and improves the economics of cold-chain transport. To address this problem, we created a three-dimensional computational fluid dynamics model of a transport compartment and used potatoes as test cargo. The unsteady shear stress transport κ-ω model was adopted to simulate air flow and heat transfer inside the compartment. The results show that the additional air conduit inside the compartment should significantly improve the spatial uniformity of the temperature inside the cargo area. The results of the simulation are consistent with experiments. Practical Applications Although cold-chain transport must ensure a constant low-temperature environment for perishable foods, more important is the requirement to maintain temperature uniformity in the cargo area. This is the basis by which the quality, safety and shelf life of perishable food are ensured. In this study, we have investigated how to minimize the cooling time during transportation in order to reduce energy consumption and improve the rate at which cool air ventilates the tail of a refrigerated compartment. This research provides reliable theoretical arguments for enhancing temperature uniformity in the cargo enclosure of refrigeration trucks and for reducing unnecessary energy consumption during transportation.

In recent years, the system parameter identification attracts attention of scholars and engineers, as the system control precision can be improved with it. In this paper, we take advantage of Hopfield neural network to identify the system parameters. Firstly, a kind of self balancing vehicle dynamic model is proposed. Secondly, the subsystem of the self balancing vehicle is analyzed in the form of Hopfield neural network. Finally, taking the actual parameter into the dynamic model, the simulation of the system parameter identification is presented. From the simulation result, there are errors between the actual value deviation and the system parameter identification result achieved in the paper, but it is within the acceptable range, so the identification results are acceptable.

Infrastructure as a Service cloud provides elasticity and scalable virtual machines VMs as computing service to multiple tenants, but the tenants lose the full control of their data. Measuring the integrity of critical files of the VMs and providing the integrity attestation to the tenants on the basis of TCG trusted computing techniques is an effective way to alleviate their anxiety. This paper considers how to measure the integrity of the processes run in guest VMs and files opened in guest VMs. We propose an out-of-the-box integrity measurement approach to measure the integrity of critical files through system call syscall interception without any modification of the guest VMs. Out-of-the-box integrity measurement approach can not only measure the integrity of all files that have been considered by existing approaches but also measure the integrity of the system configuration files, program loaders, and script interpreters, which affect the system behaviors and integrity. The ability of supporting both system and manual measurement policies makes our approach flexible. We implement this approach in Xen hypervisor with little modification of the existing syscall interception method, and this approach can be ported to other virtualization platform easily. Copyright © 2014 John Wiley & Sons, Ltd.

When the semiconductor technology enters the 28 nm and below, defectivity becomes increasingly difficult to detect and remove. In this paper, we studied one kind of water-spot like defect which was first suspected to originate from the immersion lithography process. After immersion lithography, a step-by-step defect tracing had been performed among several reactive ion etch (RIE) steps and after chemical mechanical planarization (CMP). We found that the relatively long delay time between the wet process after hard mask (HM) etch and organic dielectric layer (ODL) coating process could be one possible origin for this water-spot like defect. Our analysis also showed that the defect can be caused by the bubbles generated during the ODL coating process, whose round shape resembles very much the relatively speaking more familiar water spot defect. Based on the above analysis, we have optimized our coating process to reduce the defect density and our results will be presented

As 193 nm immersion lithography moves toward sub 20 nm half pitch, a new type of mask known as opaque MoSi on glass (OMOG) has been introduced to overcome the shortcomings of the well-established phase shift mask (PSM). The OMOG gives smaller Mask Enhanced Error Factor (MEEF), better Mask CDU and registration but it also suffers some problems such as poor Line Width Roughness (LWR), lower image contrast. In this paper, we gave a series of experiments to compare the OMOG mask with PSM mask. We checked the thru pitch FEM result for both types of mask. And we have tried some methods to improve contrast and process window. The results showed very promising. The disadvantages for OMOG mask would cause some serious problems. PSM mask can give better LWR and 2D pattern profile but is also suffered higher MEEF and CD control issue.

The resonator micro optic gyro (MOG) that utilizes a resonance frequency change due to the Sagnac effect is a promising candidate for the next generation inertial rotation sensor. In this paper, an open-loop R-MOG is set up using laser frequency modulation spectroscopy technique. First, the demodulation curve is obtained using a lock-in amplifier. From the demodulation signal, a gyro dynamic range of ±2.0 × 103 rad/s is obtained. And the output of MOG is investigated by the equivalent gyro rotate. The slope of the linear fit is about 0.330 mV/(°/s) based on the −900 to 900 kHz equivalent frequency.

Nowadays, many scholars attach importance to how sieving parameters of vibration screen influence sieving efficiency. The traditional research of screening theory and technology depended on the screening test that consumed a lot of human labour, material resources and money.DEM software EDEM was used to establish the simulation model of screening in this paper. Five simulation particle shapes were built base on real shapes of stone particle. After completing 20 groups simulation, sieving efficiency were calculated based on ratio of undersize in feeding and undersize. The formula of sieving parameters and sieving efficiency was fitted with the method of multielement non-linear regression by Matlab based on simulation data. This paper provide a new method for vibrating screen designing and research of screening theory.

Sliding mode controller of a two-wheeled robot is designed in this paper. To simplify the design of the controller, the dynamic model is divided into two independent subsystems: balance subsystem and direction subsystem. Cascade sliding mode controller is designed for the balance subsystem, its control purpose is to track the ideal pitch angle and angular velocity and make the angular velocities of the two wheels stable. RBF network self-adaption controller is designed for the direction subsystem, its control purpose is to track the ideal yaw angle and angular velocity. Simulations of the controllers are taken, the results verify that the controllers are effective and robust.

A kind of front-wheel drive bicycle robot is researched in this paper. Firstly, the coordinate system is established by the analysis of the robot structure. Secondly, the vector velocity of each components of the robot is expressed. And the dynamic model of the bicycle robot is obtained based on Appell Equation. Meanwhile, the motion characteristics of the bicycle on circular motion are analyzed. Thirdly, robust controller of a front drive bicycle is designed and simulated for the circular motion. The simulation results prove that the controller based on the H ∞ robust control theory can meet the control requirements and has good robustness.

A dynamic model and a sliding mode controller are researched based on a new type variable structure two-wheeled robot in this paper. Firstly, the structure of the robot is designed according to the movement characteristics. Secondly, the vector velocity of each components of the robot is expressed. And the dynamic model of the robot is obtained based on Appell Equation. Thirdly, a sliding mode controller for the bicycle mode of the robot is designed and simulated for the track-stand motion. The simulation results prove that the controller based on the sliding mode control theory can meet the control requirements and has good robustness.

In this paper, we present a study on the overlay (OVL) shift issue in contact (CT) layer aligned to poly-silicon (short as poly) layer (prior layer) in an advanced technology node [1, 2]. We have showed the wafer level OVL AEI-ADI shift (AEI: After Etch Inspection; ADI: After Developing Inspection; AEI-ADI: AEI minus ADI). Within the shot level map, there exists a center-edge difference. The OVL focus subtraction map can well match the OVL AEI-ADI shift map. Investigation into this interesting correlation finally leads to the conclusion of PR tilt. The film stress of the thick hard mask is responsible for the PR tilt. The method of OVL focus subtraction can therefore be a powerful and convenient tool to represent the OVL mark profile. It is also important to take into account the film deposition when investigating OVL AEI-ADI shift.

A prevailing neuroinflammation hypothesis is that increased production of proinflammatory cytokines contributes to progressive neuropathology, secondary to the primary damage caused by a traumatic brain injury (TBI). In support of the hypothesis, post-injury interventions that inhibit the proinflammatory cytokine surge can attenuate the progressive pathology. However, other post-injury neuroinflammatory responses are key to endogenous recovery responses. Therefore, it is critical that pharmacological attenuation of detrimental or dysregulated neuroinflammatory processes avoid pan-suppression of inflammation. MW151 is a CNS-penetrant, small molecule experimental therapeutic that restores injury- or disease-induced overproduction of proinflammatory cytokines towards homeostasis without immunosuppression. Post-injury administration of MW151 in a closed head injury model of mild TBI suppressed acute cytokine up-regulation and downstream cognitive impairment. Here, we report results from a diffuse brain injury model in mice using midline fluid percussion. Low dose (0.5-5.0 mg/kg) administration of MW151 suppresses interleukin-1 beta (IL-1β) levels in the cortex while sparing reactive microglia and astrocyte responses. To probe molecular mechanisms, we used live cell imaging of the BV-2 microglia cell line to demonstrate that MW151 does not affect proliferation, migration, or phagocytosis of the cells. Our results provide insight into the roles of glial responses to brain injury and indicate the feasibility of using appropriate dosing for selective therapeutic modulation of injurious IL-1β increases while sparing other glial responses to injury.

The numerical thermal mechanical simulation of radial forging process of steel H13 stepped shaft with GFM (Gesellschaft fur Fertigungstechnik und Maschinenbau) forging machine was carried out by three-dimensional finite element code DEFORM 3D. According to the effective plastic strain, the mean stress and the mean plastic strain distribution of the radial forging, the forging penetration efficiency (FPE) was studied throughout each operation. The results show that the effective plastic strain in the center of the forging is always greater than zero for the desirable larger axial drawing velocity. The mean stress in the center of the workpiece is proposed to describe hydrostatic pressure in this paper. There is compressive strain layer beneath the surface of the workpiece to be found, while there is tensile strain core in the center of the workpiece. These results could be a valuable reference for designing the similar forging operations.

Trusted Computing technique can be applied to alleviate the concerns for the security of virtual machines (VMs), accompanying the increase in the use of virtualization technology. This paper aims to provide for guest para-virtualized machine (GPVM) applications a complete trusted chain from the physical TPM in a flexible way. We propose a robust and fast method for measuring the integrity of the system booting files of a GPVM. This method integrated with the existing integrity measure software such as Trusted Grub and Integrity Measurement Architecture (IMA) can provide the complete trusted chain. Compared to the existing method of measuring the GPVM integrity, our method is very flexible and fast.

When designing the cylindrical helical compression spring, many designers use high- strength materials or various strengthening technology approaches in order to improve the reliability of spring. These approaches increase the costs and reduce economy. With the help of MATLAB software, this paper analyzes how the chosen design parameters of the cylindrical helical compression spring influence its reliability based on stress-strength interference model with the way of controlling variables method when the fatigue life of spring is longer than one million cycles. It is shown that changing the spring design parameters, instead of using valuable or rare materials, can achieve high reliable level especially when the reliability of spring can't reach the requirement and hence can get a better economic benefit.

Objective To investigate the pregnancy complications, perinatal outcomes, and congenital abnormalities (CAs) that occurred in Beijing, China, when pregnant women became infected with the 2009 pandemic influenza A (H1N1) (H1N1pdm). Methods Pregnancy complications, perinatal outcomes, and CAs were compared among 3 groups of pregnant women. The 23 women in group 1 were confirmed to harbor viral RNA; the 23 in group 2 had serum levels of virus-specific antibodies against H1N1pdm, meaning that they were suspected of being infected with the virus; and the 93 in group 3 had no detectable virus-specific antibodies. Results Perinatal outcomes and pregnancy complications were not significantly different in groups 1 and 3. Higher percentages of stillbirths (12.0%) and placental disorders (13.0%) were observed in group 2 than in group 3. Many women in group 2 (62.5%) experienced symptoms of having a cold during pregnancy and most took no medication. Two cases of CA occurred in group 1, in the offspring of women infected in the second trimester. Conclusion When left untreated, infection with the 2009 H1N1pdm virus during pregnancy appears to have increased fetal mortality and morbidity. Because CAs are traumatic for all concerned, their possible association with the virus should be further evaluated.

Aiming at the fatal flaws of the traditional diagnosis methods for the large-scale photoelectric tracking devices, such as poor stability and adaptive capacity, lack of inspiration and narrow domain knowledge of expert system, etc, more importantly, fundamentally improve the diagnostic efficiency and universality, in this paper, an intelligent mixed inference diagnosis expert system based on multiple knowledge representation and BP neural network is put forward. Firstly, some related key basic concepts and principles of intelligent fault diagnosis technology and several major applied diagnosis knowledge representation methods such as diagnosis fault tree, frame representation production rule and so on, were elaborated. Secondly, in view of high concurrency and relevancy of the system faults, a mixed reasoning mechanism combining BPNN and ES was researched. Finally, some interrelated essential implementation techniques, such as system architecture and VR technology, were also presented. Actual applications and experiments demonstrate that the proposed approach is robust and effective.