Vitreous microparticles contain apoptotic signals suggesting a diabetic vitreopathy

AIM: To evaluate differences in microparticle profiles in vitreous samples between diabetic and non-diabetic eyes undergoing vitrectomy. METHODS: Un-masked cross-sectional series of 34 eyes undergoing vitrectomy. Vitreous specimens were collected and processed to evaluate for membrane integrity (DAPI), apoptosis (Annexin-V), and endothelial-cell origin (V-Cadherin). A BD LSR II flow cytometer was used for analysis and standardized sub-micron-sized beads were used for size comparison. RESULTS: Thirty-four specimens underwent analysis. Greater levels of Annexin-V were found on microparticles from specimens in which blood had entered the vitreous (n=12) compared to those without blood (n=22; 52.3%±30.7% vs 19.6%±27.2%, P=0.002). Patients with diabetes having surgery with hemorrhage (n=7) had greater expression of Annexin-V than those without hemorrhage (n=8; 62.1%±31.7% vs 18.9%±20.9%, P=0.009). However, in patients with non-diabetic vitreous hemorrhage, the level of Annexin-V expression was not significantly different compared to other disease processes (38.6%±25.7%, n=5 vs 20.0%±30.9%, n=14, P=0.087). CONCLUSION: Increased expression of the apoptotic marker, Annexin-V is detected on vitreous microparticles in diabetes-related vitreous hemorrhage. When evaluating vitreous hemorrhage in patients without diabetes, the apoptotic signal is not significantly different. Vitrectomy in patients with diabetes, and improvement in visual outcomes, may be related to the removal of a serum-derived, pro-apoptotic vitreous. Further investigation is warranted in order to identify the molecular characteristics of microparticles that regulate disease.

Nonlinear Robust Control on Yaw Motion of a Variable-Speed Unmanned Aerial Helicopter under Multi-Source Disturbances

This paper studies the multi-source disturbances attenuation problem on the yaw motion of unmanned aerial helicopter with a variable-speed rotor. The yaw motion subsystem dominated by an electrically-driven tail rotor is firstly introduced, and its trajectory accuracy requires particularly close attention. To this end, we establish a fourth-order yaw error dynamic equation; subsequently, a nonlinear robust control scheme based on optimal H∞ principle is developed, consisting of laws of virtual functions, parameter estimation and a compensation signal. The novelty of this scheme lies in unifying the techniques to deal with the uncertain parameters, noise perturbations, actuator output fault and external airflow turbulence into a simple framework. Stability analysis guarantees that the yaw closed-loop system has the predefined performance of disturbance suppression in the sense of a finite L2-gain. Comparison results with the extended state observer based backstepping controller verify the effectiveness and superior performance of proposed scheme in an aircraft prototype.

An Advanced Galerkin Approach to Solve the Nonlinear \\[6pt]Reaction-Diffusion Equations With Different Boundary Conditions

This study proposed a scheme originated from the Galerkin finite element method (GFEM) for solving nonlinear parabolic partial differential equations (PDEs) numerically with initial and different types of boundary conditions. The scheme is applied generally handling the nonlinear terms in a simple way and throwing over restrictive assumptions. The convergence and stability analysis of the method are derived. The error of the method is estimated. In the series, eminent problems are solved, such as  Fisher's equation, Newell-Whitehead-Segel equation, Burger's equation, and  Burgers-Huxley equation to demonstrate the validity, efficiency, accuracy, simplicity and applicability of this scheme. In each example, the comparison results are presented both numerically and graphically

A Quantifying Approach to Soil Salinity Based on a Radar Feature Space Model Using ALOS PALSAR-2 Data

In arid and semi-arid areas, timely and effective monitoring and mapping of salt-affected areas is essential to prevent land degradation and to achieve sustainable soil management. The main objective of this study is to make full use of synthetic aperture radar (SAR) polarization technology to improve soil salinity mapping in the Keriya Oasis, Xinjiang, China. In this study, 25 polarization features are extracted from ALOS PALSAR-2 images, of which four features are selected. In addition, three soil salinity inversion models, named the RSDI1, RSDI2, and RSDI3, are proposed. The analysis and comparison results of inversion accuracy show that the overall correlation values of the RSDI1, RSDI2, and RSDI3 models are 0.63, 0.61, and 0.62, respectively. This result indicates that the radar feature space models have the potential to extract information on soil salinization in the Keriya Oasis.

Automatic freezing-tolerant rapeseed material recognition using UAV images and deep learning

Background Freezing injury is a devastating yet common damage that occurs to winter rapeseed during the overwintering period which directly reduces the yield and causes heavy economic loss. Thus, it is an important and urgent task for crop breeders to find the freezing-tolerant rapeseed materials in the process of breeding. Existing large-scale freezing-tolerant rapeseed material recognition methods mainly rely on the field investigation conducted by the agricultural experts using some professional equipments. These methods are time-consuming, inefficient and laborious. In addition, the accuracy of these traditional methods depends heavily on the knowledge and experience of the experts. Methods To solve these problems of existing methods, we propose a low-cost freezing-tolerant rapeseed material recognition approach using deep learning and unmanned aerial vehicle (UAV) images captured by a consumer UAV. We formulate the problem of freezing-tolerant material recognition as a binary classification problem, which can be solved well using deep learning. The proposed method can automatically and efficiently recognize the freezing-tolerant rapeseed materials from a large number of crop candidates. To train the deep learning network, we first manually construct the real dataset using the UAV images of rapeseed materials captured by the DJI Phantom 4 Pro V2.0. Then, five classic deep learning networks (AlexNet, VGGNet16, ResNet18, ResNet50 and GoogLeNet) are selected to perform the freezing-tolerant rapeseed material recognition. Result and conclusion The accuracy of the five deep learning networks used in our work is all over 92%. Especially, ResNet50 provides the best accuracy (93.33$$\%$$ % ) in this task. In addition, we also compare deep learning networks with traditional machine learning methods. The comparison results show that the deep learning-based methods significantly outperform the traditional machine learning-based methods in our task. The experimental results show that it is feasible to recognize the freezing-tolerant rapeseed using UAV images and deep learning.

Online Learning and Optimization for Revenue Management Problems with Add-on Discounts

We study in this paper a revenue-management problem with add-on discounts. The problem is motivated by the practice in the video game industry by which a retailer offers discounts on selected supportive products (e.g., video games) to customers who have also purchased the core products (e.g., video game consoles). We formulate this problem as an optimization problem to determine the prices of different products and the selection of products for add-on discounts. In the base model, we focus on an independent demand structure. To overcome the computational challenge of this optimization problem, we propose an efficient fully polynomial-time approximation scheme (FPTAS) algorithm that solves the problem approximately to any desired accuracy. Moreover, we consider the problem in the setting in which the retailer has no prior knowledge of the demand functions of different products. To solve this joint learning and optimization problem, we propose an upper confidence bound–based learning algorithm that uses the FPTAS optimization algorithm as a subroutine. We show that our learning algorithm can converge to the optimal algorithm that has access to the true demand functions, and the convergence rate is tight up to a certain logarithmic term. We further show that these results for the independent demand model can be extended to multinomial logit choice models. In addition, we conduct numerical experiments with the real-world transaction data we collect from a popular video gaming brand’s online store on Tmall.com. The experiment results illustrate our learning algorithm’s robust performance and fast convergence in various scenarios. We also compare our algorithm with the optimal policy that does not use any add-on discount. The comparison results show the advantages of using the add-on discount strategy in practice. This paper was accepted by J. George Shanthikumar, big data analytics.

A comparison of 3 different methods for assessment of skeletal age when treating leg-length discrepancies: an inter- and intra-observer study

Background and purpose — Skeletal maturity is a crucial parameter when calculating remaining growth in children. We compared 3 different methods, 2 manual and 1 automated, in the radiological assessment of bone age with respect to precision and systematic difference. Material and methods — 66 simultaneous examinations of the left hand and left elbow from children treated for leg-length discrepancies were randomly selected for skeletal age assessment. The radiographs were anonymized and assessed twice with at least 3 weeks’ interval according to the Greulich and Pyle (GP) and Sauvegrain (SG) methods by 5 radiologists with different levels of experience. The hand radiographs were also assessed for GP bone age by use of the automated BoneXpert (BX) method for comparison. Results — The inter-observer intraclass correlation coefficient (ICC) was 0.96 for the GP and 0.98 for the SG method. The inter- and intra-observer standard error of the measurement (SEm) was 0.41 and 0.32 years for the GP method and 0.27 and 0.21 years for the SG method with a significant difference (p < 0.001) between the methods and between the experienced and the less experienced radiologists for both methods (p = 0.003 and p < 0.001). In 25% of the assessments the discrepancy between the GP and the SG methodwas > 1 year. There was no systematic difference comparing either manual method with the automatic BX method. Interpretation — With respect to the precision of skeletal age determination, we recommend using the SG method or preferably the automated BX method based on GP assessments in the calculation of remaining growth.

Flooding depths and burial effects on seedling emergence of five California weedy rice (Oryza sativa spontanea) accessions

Weedy rice (Oryza sativa f. spontanea Roshev.) has recently become a significant botanical pest in California rice (Oryza sativa L.) production systems. The conspecificity of this pest with cultivated rice, Oryza sativa (L.), negates the use of selective herbicides, rendering the development of non-chemical methods a necessary component of creating management strategies for this weed. Experiments were conducted to determine the emergence and early growth responses of O. sativa spontanea to flooding soil and burial conditions. Treatment combinations of four flooding depths (0, 5, 10, and 15 cm) and four burial depths (1.3, 2.5, 5, and 10 cm) were applied to test the emergence of five O. sativa spontanea accessions as well as ‘M-206’, a commonly used rice cultivar in California, for comparison. Results revealed that burial depth had a significant effect on seedling emergence. There was a 43-91% decrease in emergence between seedlings buried at 1.3 and 2.5 cm depending on the flooding depth and accession, and an absence of emergence from seedlings buried at or below 5 cm. Flooding depth did not affect emergence, but there was a significant interaction between burial and flooding treatments. There was no significant difference between total O. sativa spontanea emergence from the soil and water surfaces regardless of burial or flooding depths, implying that once the various accessions have emerged from the soil they will also emerge from the floodwater. Most accessions had similar total emergence compared to M-206 cultivated rice, but produced more dry weight than M-206 when planted at 1.3 cm in the soil. The results of this experiment can be used to inform stakeholders of the flooding conditions necessary as well as soil burial depths that will promote or inhibit the emergence of California O. sativa spontanea accessions from the weed seedbank.

Stability of Generalized Proportional Caputo Fractional Differential Equations by Lyapunov Functions

In this paper, nonlinear nonautonomous equations with the generalized proportional Caputo fractional derivative (GPFD) are considered. Some stability properties are studied by the help of the Lyapunov functions and their GPFDs. A scalar nonlinear fractional differential equation with the GPFD is considered as a comparison equation, and some comparison results are proven. Sufficient conditions for stability and asymptotic stability were obtained. Examples illustrating the results and ideas in this paper are also provided.

Model for evaluation of technical and economic indicators of offshore wind farms

THE PURPOSE. An urgent problem in the development of offshore wind energy is the high cost of generating electricity, which is due to large capital investments. The solution to this problem is possible by increasing efficiency while reducing costs as much as possible, which requires optimal design of offshore wind farms.GOAL. Development of model for the technical and economic indicators of offshore wind farms based on configuration data, taking into account the factors of climatic conditions and the topography of the seabed at the site of the planned wind farm location.METHODS. Mathematical modeling using Matlab software environment.RESULTS. The model evaluates the impact of wake and electrical losses in the main components of the electrical system on the operation of an offshore wind farm, and also allows to take into account the influence of the seabed relief on the economic characteristics of wind turbine foundations. The model was tested on the example of calculating two existing offshore wind farms «Horns Rev 1» and «Horn Rev 2» by comparing the calculated indicators of the average annual electricity generation, capacity factor, capital expenditures and normalized cost of electricity with the actual indicators obtained during their operation. The comparison results show slight deviations within 5% of the actual values.CONCLUSION. The model for assessing the technical and economic indicators of offshore wind farms was developed and tested on the basis of data on the wind farm configuration and layout, as well as factors of climatic conditions and terrain. Evaluation of the computational speed showed a sufficiently high efficiency of the algorithm, which allows the model to be applied to optimize large offshore wind farms.