Electronic and excitonic processes in multilayer organic light emitting devices incorporating PbSe quantum dots

In this work we investigate the operation mechanism of hybrid organic/inorganic quantum dot light emitting devices (QD-LEDs). We employ a numerical method previously established to describe current-voltage characteristics, spatial distributions of charge, electric field, and recombination rate in organic light emitting devices (OLEDs). The numerical solution of the continuity and Poisson equations have been extended to treat internal organic/Quantum Dot (QD) interfaces, recombination processes in the polymer matrix and in the QDs, and the charge acquired by the QDs. The contact boundary condition is taken to be Schottky contact boundary condition. Also, we consider the exciton formation and diffusion processes. The simulation results trend and experimental data are in good agreement.     

Early Skin Disease Identification Using eep Neural Network

Skin lesions detection and classification is a prominent issue and difficult even for extremely skilled dermatologists and pathologists. Skin disease is the most common disorder triggered by fungus, viruses, bacteria, allergies, etc. Skin diseases are most dangerous and may be the cause of serious damage. Therefore, it requires to diagnose it at an earlier stage, but the diagnosis therapy itself is complex and needs advanced laser and photonic therapy. This advance therapy involves financial burden and some other ill effects. Therefore, it must use artificial intelligence techniques to detect and diagnose it accurately at an earlier stage. Several techniques have been proposed to detect skin disease at an earlier stage but fail to get accuracy. Therefore, the primary goal of this paper is to classify, detect and provide accurate information about skin diseases. This paper deals with the same issue by proposing a high-performance Convolution neural network (CNN) to classify and detect skin disease at an earlier stage. The complete methodology is explained in different folds: firstly, the skin diseases images are pre-processed with processing techniques, and secondly, the important feature of the skin images are extracted. Thirdly, the pre-processed images are analyzed at different stages using a Deep Convolution Neural Network (DCNN). The approach proposed in this paper is simple, fast, and shows accurate results up to 98% and used to detect six different disease types.