How to represent paintings: a painting classification using artistic comments

The goal of large-scale automatic paintings analysis is to classify and retrieve images using machine learning techniques. The traditional methods use computer vision techniques on paintings to enable computers to represent the art content. In this work, we propose using a graph convolutional network and artistic comments rather than the painting color to classify type, school, timeframe and author of the paintings by implementing natural language processing (NLP) techniques. First, we build a single artistic comment graph based on co-occurrence relations and document word relations and then train an art graph convolutional network (ArtGCN) on the entire corpus. The nodes, which include the words and documents in the topological graph are initialized using a one-hot representation; then, the embeddings are learned jointly for both words and documents, supervised by the known-class training labels of the paintings. Through extensive experiments on different classification tasks using different input sources, we demonstrate that the proposed methods achieve state-of-art performance. In addition, ArtGCN can learn word and painting embeddings, and we find that they have a major role in describing the labels and retrieval paintings, respectively

Soil salinity is the main factor influencing the soil bacterial community assembly process under long-term drip irrigation in Xinjiang, China

Identifying the potential factors associated with the impact of long-term drip irrigation (DI) on soil ecosystems is essential for responding to the environmental changes induced by extensive application of DI technology in arid regions. Herein, we examined the effects of the length of time that DI lasts in years (NDI) on soil bacterial diversity as well as the soil bacterial community assembly process and the factors influencing it. The results showed that long-term DI substantially reduced soil salinity and increased soil bacterial diversity while affecting the soil bacterial community structure distinctly. Null model results showed that the soil bacterial community assembly transitioned from stochastic processes to deterministic processes, as NDI increased. Homogeneous selection, a deterministic process, emerged as the dominant process when NDI exceeded 15 years. Both random forest and structural equation models showed that soil salinity was the primary factor affecting the bacterial community assembly process. In summary, this study suggested that soil bacteria respond differently to long-term DI and depends on the NDI, influencing the soil bacterial community assembly process under long-term DI

Transcriptome profiling of Pinus radiata juvenile wood with contrasting stiffness identifies putative candidate genes involved in microfibril orientation and cell wall mechanics

<p>Abstract</p> <p>Background</p> <p>The mechanical properties of wood are largely determined by the orientation of cellulose microfibrils in secondary cell walls. Several genes and their allelic variants have previously been found to affect microfibril angle (MFA) and wood stiffness; however, the molecular mechanisms controlling microfibril orientation and mechanical strength are largely uncharacterised. In the present study, cDNA microarrays were used to compare gene expression in developing xylem with contrasting stiffness and MFA in juvenile <it>Pinus radiata </it>trees in order to gain further insights into the molecular mechanisms underlying microfibril orientation and cell wall mechanics.</p> <p>Results</p> <p>Juvenile radiata pine trees with higher stiffness (HS) had lower MFA in the earlywood and latewood of each ring compared to low stiffness (LS) trees. Approximately 3.4 to 14.5% out of 3, 320 xylem unigenes on cDNA microarrays were differentially regulated in juvenile wood with contrasting stiffness and MFA. Greater variation in MFA and stiffness was observed in earlywood compared to latewood, suggesting earlywood contributes most to differences in stiffness; however, 3-4 times more genes were differentially regulated in latewood than in earlywood. A total of 108 xylem unigenes were differentially regulated in juvenile wood with HS and LS in at least two seasons, including 43 unigenes with unknown functions. Many genes involved in cytoskeleton development and secondary wall formation (cellulose and lignin biosynthesis) were preferentially transcribed in wood with HS and low MFA. In contrast, several genes involved in cell division and primary wall synthesis were more abundantly transcribed in LS wood with high MFA.</p> <p>Conclusions</p> <p>Microarray expression profiles in <it>Pinus radiata </it>juvenile wood with contrasting stiffness has shed more light on the transcriptional control of microfibril orientation and the mechanical properties of wood. The identified candidate genes provide an invaluable resource for further gene function and association genetics studies aimed at deepening our understanding of cell wall biomechanics with a view to improving the mechanical properties of wood.</p

A Variable Parameter Method Based on Linear Extended State Observer for Position Tracking

This paper presents a control strategy with a linear extended state observer (LESO) and Kalman filter to achieve a high performance of the motion control system. The moment of inertia of the system, which is variable with the robotic joint motion, is estimated in the established model. A LESO with variable gain is designed, which could estimate the states and the total disturbance of the plant without a precision mathematical model. The disturbance caused by variable load and unknown dynamics can be compensated based on the LESO, while the moment of inertia is variable. In order to restrain the process noise and measure the noise of the system, the Kalman filter was applied. Tracking differentiator was utilized to avoid the overshoot of the system for the step signal. The designed control strategy with the LESO and the Kalman filter could improve the tracking performance for the servo system with parametric uncertainties, unknown dynamics, and disturbances. The effectiveness of the proposed method is implemented and validated in the experiment of the robotic joint, for which desired servo tracking performance is achieved with the conditions of load variation and sudden disturbance

An Integrative Framework for Online Prognostic and Health Management Using Internet of Things and Convolutional Neural Network

With the development of the internet of things (IoTs), big data, smart sensing technology, and cloud technology, the industry has entered a new stage of revolution. Traditional manufacturing enterprises are transforming into service-oriented manufacturing based on prognostic and health management (PHM). However, there is a lack of a systematic and comprehensive framework of PHM to create more added value. In this paper, the authors proposed an integrative framework to systematically solve the problem from three levels: Strategic level of PHM to create added value, tactical level of PHM to make the implementation route, and operational level of PHM in a detailed application. At the strategic level, the authors provided the innovative business model to create added value through the big data. Moreover, to monitor the equipment status, the health index (HI) based on a condition-based maintenance (CBM) method was proposed. At the tactical level, the authors provided the implementation route in application integration, analysis service, and visual management to satisfy the different stakeholders&rsquo; functional requirements through a convolutional neural network (CNN). At the operational level, the authors constructed a self-sensing network based on anti-inference and self-organizing Zigbee to capture the real-time data from the equipment group. Finally, the authors verified the feasibility of the framework in a real case from China

A New Fuzzy Robust Control for Linear Parameter-Varying Systems

The linear parameter-varying (LPV) models have broad applications in advanced mathematics and modern control systems. This paper introduces a new method for controlling the LPV systems. This method includes the gain-scheduled state-feedback technique and a fuzzy system to calculate the state-feedback gain. The main goal of the control system is to stabilize the system and bring its states to equilibrium points. Linear matrix inequalities calculate feedback gains to stabilize the system. On the other hand, a fuzzy control system also produces a combined signal with the primary controller signal to speed up this operation. Lyapunov&rsquo;s theory is used to guarantee the control system&rsquo;s stability. Finally, to evaluate the performance of the proposed control system, the inverted pendulum has been investigated as a case study. The results show that the proposed method has good efficiency and performance

Differences in Injury Severities Between 2-Vehicle and 3-Vehicle Crashes

<p><b>Objective and Methods:</b> In traditional injury severity–related studies, 2-vehicle and 3-vehicle crashes are typically considered in a combinatory manner and thus the injury casual factors for these 2 crash types are implicitly assumed to be the same. This article attempts to investigate the potential discrepancy between 2- and 3-vehicle crash severities with the aid of a continuation ratio logit model with the property of partial proportional odds.</p> <p><b>Results:</b> The modeling results show that there are a number of significant differences between 2- and 3-vehicle crash injury severities in terms of the contributing factors, the magnitude of impact, and even the direction of effects.</p> <p><b>Conclusions:</b> The research illustrates that a series of environmental and crash factors (e.g., rear-end straight crashes, urban roadways, alcohol usage, and different driving cohorts) are statistically significant in interpreting the disparity of coefficients between 2- and 3-vehicle crash injury severity models. It raises awareness that the combined analysis of 2- and 3-vehicle crashes should be exercised with caution, particularly when safety research targets crashes with less severe injuries.</p

Differing Roles of Bacterial and Fungal Communities in Cotton Fields by Growth Stage

The global demand for cotton makes sustainable cotton production an important issue that can be improved by a better understanding of the influence of soil microbes on cotton growth. We collected cotton field soils at the seedling and flowering/boll-setting (FBS) stages in order to obtain soil properties and cotton growth indices. Bacterial and fungal community compositions were assessed by high-throughput sequencing of 16S rRNA and internal transcribed spacer genes, respectively, after which the differences in microbial functions and their influencing factors at different growth stages were analyzed. Both the diversity and composition of soil bacterial and fungal communities were found to be significantly different between the seedling and FBS stages. Microbes in the seedling stage had significantly higher richness and biomass than those in the FBS stage. Compared with the seedling stage, the stability of the soil bacterial communities was decreased. The cotton growth indices at both the seedling and FBS stages were associated with compositional shifts in the bacterial community and but not the fungal community. The abundance of specific soil microbial taxa (e.g., Pseudarthrobacter, Thiobacillus, Cephalotrichum, Chaetomium, and Fusarium) were correlated with cotton growth indices at the seedling stage, being mainly regulated by soil salinity and nitrate content. Our results highlight the importance of soil microbial communities in mediating cotton growth and will be useful in providing better strategies for the improvement of cotton agriculture