The electrical properties of CdO/porous Si/c-Si heterojunction prepared by deposition of CdO layer on porous silicon synthesized by electrochemical etching were studied. The structural, optical, and electrical properties of CdO (50:50) thin film prepared by rapid thermal oxidation were examined. X-ray diffraction (XRD) results confirmed formation of nanostructured silicon layer the full width half maximum (FWHM) was increased after etching. The dark J-V characteristics of the heterojunction showed strong dependence on etching current density and etching time. The ideality factor and saturation current of the heterojunction were calculated from J-V under forward bias. C-V measurements confirmed that the prepared heterojunctions are abrupt

Silicon nanowire arrays (SiNWs) are created utilizing the metal-assisted chemical etching method with an Ag metal as a catalyst and different etching time of 15, 30, and 60 minutes using n-Si (100). Physical properties such as structural, surface morphology, and optical properties of the prepared SiNWs are studied. The diameter of prepared SiNWs ranged from 20 to 280 nm, and the reflectance in the visible part of the wavelength spectrum was less than 1% for all prepared samples. The obtained energy gap of prepared SiNWs was around 2 eV, which is higher than the energy gap of bulk silicon. X-ray diffraction (XRD) has diffraction peaks at 68.70^o for all prepared samples. The heterojunction solar cell was fabricated based on the

In recent years, the number of applications utilizing mobile wireless sensor networks (WSNs) has increased, with the intent of localization for the purposes of monitoring and obtaining data from hazardous areas. Location of the event is very critical in WSN, as sensing data is almost meaningless without the location information. In this paper, two Monte Carlo based localization schemes termed MCL and MSL* are studied. MCL obtains its location through anchor nodes whereas MSL* uses both anchor nodes and normal nodes. The use of normal nodes would increase accuracy and reduce dependency on anchor nodes, but increases communication costs. For this reason, we introduce a new approach called low communication cost schemes to reduce communication

The main objective of this work is to propose a new routing protocol for wireless sensor network employed to serve IoT systems. The routing protocol has to adapt with different requirements in order to enhance the performance of IoT applications. The link quality, node depth and energy are used as metrics to make routing decisions. Comparison with other protocols is essential to show the improvements achieved by this work, thus protocols designed to serve the same purpose such as AODV, REL and LABILE are chosen to compare the proposed routing protocol with. To add integrative and holistic, some of important features are added and tested such as actuating and mobility. These features are greatly required by some of IoT applications and im

60 patients diagnosed as having urticaria were included in the study ; 30 patients were effected with acute urticaria and 30 patients were affected with chronic urticaria. In addition, 30 healthy adult volunteers were selected as control group .The patients and control groups sera were examined with enzyme linked immunosorbent assay ( ELISA) to detect total level IgE and radial immunodiffusion (RID) to detect levels of IgG , IgA and IgM . The total level of IgE in acute urticaria ( 1.45±0.13) IU/mL and chronic urticaria (2.12 ± 0.10) IU/mL patients were significantly higher than the control groups ( 0.85 ± 0.10)IU/mL (p<0.05). The level of IgG in acute urticaria ( 12.5± 0.42) g/L and chronic (13.16±0.40) g/L patients , IgA in acute (2.

In this paper, new brain tumour detection method is discovered whereby the normal slices are disassembled from the abnormal ones. Three main phases are deployed including the extraction of the cerebral tissue, the detection of abnormal block and the mechanism of fine-tuning and finally the detection of abnormal slice according to the detected abnormal blocks. Through experimental tests, progress made by the suggested means is assessed and verified. As a result, in terms of qualitative assessment, it is found that the performance of proposed method is satisfactory and may contribute to the development of reliable MRI brain tumour diagnosis and treatments.

Transformers are a specific category of neural network design. Transformers often depend on extensive pre-training on a large scale and exhibit a notable degree of computational complexity. The disadvantage of using this method is a significant increase in computational complexity, which necessitates a significant commitment of time and computing resources in order to successfully work with these models. Transformer networks possess the desirable benefit of extracting distant characteristics effectively via their self-attention mechanism. In this paper, the Global Self-Attention Transformer module is applied to tackle these issues. The model is based on a segmentation problem called Brain-GS that works as a mechanism and encompasses