Search Results (6)

Network functions virtualization (NFV) has become the platform for decomposing the sequence of virtual network functions (VNFs), which can be grouped as a forwarding graph of service function chaining (SFC) to serve multi-service slice requirements. NFV-enabled SFC consists of several challenges in reaching the reliability and efficiency of key performance indicators (KPIs) in management and orchestration (MANO) decision-making control. The problem of SFC fault tolerance is one of the most critical challenges for provisioning service requests, and it needs resource availability. In this article, we proposed graph neural network (GNN)-based deep reinforcement learning (DRL) to enhance SFC fault tolerance (GRL-SFT), which targets the chain graph representation, long-term approximation, and self-organizing service orchestration for future massive Internet of Everything applications. We formulate the problem as the Markov decision process (MDP). DRL seeks to maximize the cumulative rewards by maximizing the service request acceptance ratios and minimizing the average completion delays. The proposed model solves the VNF management problem in a short time and configures the node allocation reliably for real-time restoration. Our simulation result demonstrates the effectiveness of the proposed scheme and indicates better performance in terms of total rewards, delays, acceptances, failures, and restoration ratios in different network topologies compared to reference schemes. Full article

In ground-based astronomical observations or artificial space target detections, images obtained from a ground-based telescope are severely distorted due to atmospheric turbulence. The distortion can be partially compensated by employing adaptive optics (pre-detection compensation), image restoration techniques (post-detection compensation), or a combination of both (hybrid compensation). This paper focuses on the improvement of the most commonly used practical post-processing techniques, Richardson–Lucy (R–L) iteration blind deconvolution, which is studied in detail and improved as follows: First, the total variation (TV) norm is redefined using the Gaussian gradient magnitude and a set scheme for regularization parameter selection is proposed. Second, the Gaussian TV constraint is proposed to impose to the R–L algorithm. Last, the Gaussian TV R–L (GRL) iterative blind deconvolution method is finally presented, in which the restoration precision is visually increased and the convergence property is considerably improved. The performance of the proposed GRL method is tested by both simulation experiments and observed field data. Full article

The production of photovoltaic modules is increasing to reduce greenhouse gas emissions. However, this results in a significant amount of waste at the end of their lifespan. Therefore, recycling these solar panels is important for environmental and economic reasons. However, collecting and separating crystalline silicon, cadmium telluride, and copper–indium–gallium–selenide panels can be challenging, especially in underdeveloped countries. The innovation in this work is the development of a process to recycle all solar panel waste. The dissolution of all metals through the leaching process is studied as the main step of the flowchart. In the first step of leaching, 98% of silver can be recovered by 0.5 M nitric acid. Then, the second and third step involves the use of glycine for base metal dissolution, followed by the leaching of valuable metals with hydrochloric acid. The effect of parameters such as the initial pH, acid concentration, solid/liquid ratio, and hydrogen peroxide concentration is studied. The results show that up to 100% of Cu, Pb, Sn, Zn, Cd, In, Ga, and Se can be recovered under optimal conditions. The optimal conditions for the dissolution of Cu, Zn, and Cd were a glycine concentration of 0.5 M, a temperature of 25 °C, a solid/liquid ratio of 10 gr/L, and 1% of hydrogen peroxide. The optimized glycine concentration for the leaching of lead and tin was 1.5 M. Indium and gallium were recovered at 100% by the use of 5 M hydrochloric acid, S/L ratio = 10 gr/L, and T = 45 °C. Separation of selenium and tellurium occurred using 0.5 M HCl at a temperature of 60 °C. Additionally, for the first time, a general outlook for the recycling of various end-of-life solar panels is suggested. Full article

Localized cutaneous leishmaniasis caused by Leishmania braziliensis can either respond well or poorly to the treatment or heal spontaneously; It seems to be dependent on the parasite and/or host factors, but the mechanisms are not fully understood. We evaluated the in situ immune response in eighty-two active lesions from fifty-eight patients prior to treatment classified as early spontaneous regression (SRL-n = 14); treatment responders (GRL-n = 20); and non-responders (before first treatment/relapse, PRL1/PRL2-n = 24 each). Immunohistochemistry was used to identify cell/functional markers which were correlated with the clinical characteristics. PRL showed significant differences in lesion number/size, clinical evolution, and positive parasitological examinations when compared with the other groups. SRL presented a more efficient immune response than GRL and PRL, with higher IFN-γ/NOS2 and a lower percentage of macrophages, neutrophils, NK, B cells, and Ki-67+ cells. Compared to SRL, PRL had fewer CD4+ Tcells and more CD163+ macrophages. PRL1 had more CD68+ macrophages and Ki-67+ cells but less IFN-γ than GRL. PRL present a less efficient immune profile, which could explain the poor treatment response, while SRL had a more balanced immune response profile for lesion healing. Altogether, these evaluations suggest a differentiated profile of the organization of the inflammatory process for lesions of different tegumentary leishmaniasis evolution. Full article

Semi-formal software techniques have been very successful in industry, government institutions and other areas such as academia. Arguably, they owe a large part of their success to their graphical notation, which is more human-oriented than their counterpart text-based and formal notation techniques. However, ensuring the consistency between two or more models is one of the known challenges of these techniques. This work looks closely at the specific case of the User Requirements Notation (URN) technique. Although the abstract model of URN provides for link elements to ensure the consistency between its two main components, namely, Goal-Oriented Requirement Language (GRL) and Use Case Maps (UCM), the effective implementation of such links is yet to be fully addressed. This paper performs a detailed analysis of the existing URN models construction process and proposes an improved process with some guidelines to ensure, by construction, the correctness and consistency of the GRL and UCM models. A case study is used throughout the paper to illustrate the suggested solution. Full article

This paper’s novel focus is predicting the leaf nitrogen content of rice during growing and maturing. A multispectral image processing-based prediction model of the Radial Basis Function Neural Network (RBFNN) model was proposed. Moreover, this paper depicted three primary points as the following: First, collect images of rice leaves (RL) from a controlled condition experimental laboratory and new shoot leaves in different stages in the visible light spectrum, and apply digital image processing technology to extract the color characteristics of RL and the morphological characteristics of the new shoot leaves. Secondly, the RBFNN model, the General Regression Model (GRL), and the General Regression Method (GRM) model were constructed based on the extracted image feature parameters and the nitrogen content of rice leaves. Third, the RBFNN is optimized by and Partial Least-Squares Regression (RBFNN-PLSR) model. Finally, the validation results show that the nitrogen content prediction models at growing and mature stages that the mean absolute error (MAE), the Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) of the RFBNN model during the rice-growing stage and the mature stage are 0.6418 (%), 0.5399 (%), 0.0652 (%), and 0.3540 (%), 0.1566 (%), 0.0214 (%) respectively, the predicted value of the model fits well with the actual value. Finally, the model may be used to give the best foundation for achieving exact fertilization control by continuously monitoring the nitrogen nutrition status of rice. In addition, at the growing stage, the RBFNN model shows better results compared to both GRL and GRM, in which MAE is reduced by 0.2233% and 0.2785%, respectively. Full article