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This paper investigates the effect of incorporating a single-walled carbon nanotubes (SWCNTs) layer into the perovskite solar cell (PSC) structure as an effective technique to boost energy harvesting. The proposed PSC structure utilizes the SWCNTs layer underneath the CH3NH3PbI3 perovskite layer as an absorbing layer forming a novel design of (ITO/SnO2/CH3NH3PbI3/SWCNTs/NiOx/C) half tandem PSC structure. The suggested PSC structure is numerically analyzed using finite element method (FEM). The effects of varying thickness and doping concentration of the added layer and the material of rear electrode are investigated to maximize the power conversion efficiency (PCE) of the suggested PSC. Results of the 3D opto-electrical study and the energy bandgap analysis confirm that utilizing a 680 nm SWCNTs layer with doping concentration of 1.1 × 1020 cm-3 beneath a 150 nm perovskite layer enhances the PCE and short circuit current density (JSC) to 25.6 % and 31.201 m A / cm 2 , respectively due to the improving of the PSC absorption by 27.65 % . Higher performance of the proposed PSC has been achieved by using gold (Au) electrode instead of carbon (C) one, causing total enhancement of PCE and JSC of 6.185 % and 9.18 m A / c m 2 , respectively over their values for (ITO/SnO2/CH3NH3PbI3/P3HT/NiOx/C) structure reported in previously published literature. The proposed design can be considered as an efficient alternative to the conventional PSC owing to its higher performance and reduced toxicity. [ABSTRACT FROM AUTHOR]

The rapid advancement of computational infrastructure has led to unprecedented growth in machine learning, deep learning, and computer vision, fundamentally transforming the analysis of retinal images. By utilizing a wide array of visual cues extracted from retinal fundus images, sophisticated artificial intelligence models have been developed to diagnose various retinal disorders. This paper concentrates on the detection of Age-Related Macular Degeneration (AMD), a significant retinal condition, by offering an exhaustive examination of recent machine learning and deep learning methodologies. Additionally, it discusses potential obstacles and constraints associated with implementing this technology in the field of ophthalmology. Through a systematic review, this research aims to assess the efficacy of machine learning and deep learning techniques in discerning AMD from different modalities as they have shown promise in the field of AMD and retinal disorders diagnosis. Organized around prevalent datasets and imaging techniques, the paper initially outlines assessment criteria, image preprocessing methodologies, and learning frameworks before conducting a thorough investigation of diverse approaches for AMD detection. Drawing insights from the analysis of more than 30 selected studies, the conclusion underscores current research trajectories, major challenges, and future prospects in AMD diagnosis, providing a valuable resource for both scholars and practitioners in the domain. [ABSTRACT FROM AUTHOR]

Double-layer capacitor electrode including activated carbon with multi-walled carbon nanotube as a conductive additive (AC/MWCNTs) and pseudocapacitor electrode with nano-structural porous manganese oxide (NP-MnO2) were prepared and used to improve the capacitive performance of the negative (N−ve) and positive (P+ve) electrodes for asymmetric hybrid supercapacitor. The investigated asymmetric NP-MnO2//AC/MWCNTs hybrid supercapacitor device was assembled with positive and negative electrodes using tissue paper as a separator. Galvanostatic charge–discharge (GCD), cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS) techniques were employed for measuring the electrochemical characterization of the individual electrodes and supercapacitor cell. The specific capacitance of the NP-MnO2 positive electrode and the AC/MWCNTs negative electrode was 333 F g−1 at 5 A g−1 and 250 F g−1 at 0.5 A g−1, respectively. Furthermore, after 1000 CV cycles and 2000 GCD cycles, the positive and negative electrodes demonstrated superior stability of cycles with capacitance retention of 100%. The asymmetric NP-MnO2//AC/MWCNTs hybrid supercapacitor device manifests a wide working potential window (0–1.8 V) and highest specific energy of 50 Wh kg−1 with a specific power of 1085 W kg−1 at 1 A g−1. After 10,000 cycles at 12 A g−1, an excellent cycling stability behavior with 95% capacitance retention was obtained. [ABSTRACT FROM AUTHOR]

Perovskite solar cells (PSCs) are introduced to photovoltaic field as a promising alternative to conventional silicon solar cells because of having low fabrication cost, flexible structure and thin thickness, yet with some environmental concerns of having metal cations. In this paper, we discuss the effect of using SiO2 nanospheres front surface grating as one of the light trapping techniques on the performance of the PSC and how this trapping method enhances the power conversion efficiency without further need of increasing perovskite absorber layer thickness. A 3D finite element method solver is employed to simulate the proposed solar cell structure and to obtain its optical and electrical properties, and then, we optimize both of the size and the periodicity of the nanospheres grating. Results show that maximum efficiency of the proposed model of PSC with frontal surface grating is 26.06 % , with short circuit current density of 31.6 m A / cm 2 , while the efficiency of flat surface PSC is 21.9 % , meaning that the power conversion efficiency is improved by 4.16 % in case of surface grated cell. The maximum value of output power observed in grated surface PSC is 26.058 m W / cm 2 , about 4.158 m W / cm 2 higher than its value for the flat surface one mentioned in the previously published relevant literature. [ABSTRACT FROM AUTHOR]

The increase in eye disorders among older individuals has raised concerns, necessitating early detection through regular eye examinations. Age-related macular degeneration (AMD), a prevalent condition in individuals over 45, is a leading cause of vision impairment in the elderly. This paper presents a comprehensive computer-aided diagnosis (CAD) framework to categorize fundus images into geographic atrophy (GA), intermediate AMD, normal, and wet AMD categories. This is crucial for early detection and precise diagnosis of age-related macular degeneration (AMD), enabling timely intervention and personalized treatment strategies. We have developed a novel system that extracts both local and global appearance markers from fundus images. These markers are obtained from the entire retina and iso-regions aligned with the optical disc. Applying weighted majority voting on the best classifiers improves performance, resulting in an accuracy of 96.85%, sensitivity of 93.72%, specificity of 97.89%, precision of 93.86%, F1 of 93.72%, ROC of 95.85%, balanced accuracy of 95.81%, and weighted sum of 95.38%. This system not only achieves high accuracy but also provides a detailed assessment of the severity of each retinal region. This approach ensures that the final diagnosis aligns with the physician's understanding of AMD, aiding them in ongoing treatment and follow-up for AMD patients., (© 2024. The Author(s).)