Cu (In, Ga) Se2 (CIGS) nano ink were synthesized from molecular precursors of CuCl, In Cl3, GaCl3 and Se metal heated to 240 °C for 1 hour in N2-atmosphere to form CIGS nanocrystal ink, Thin films were deposited onto Au/soda-lime glass (SLG) substrates. This work focused on CIGS nanocrystals, including their synthesis and application as the active light absorber layer in photovoltaic devices (PVs). This approach, using spin-coating deposition of the CIGS light absorber layers (75 mg/ml and 150 nm thickness), without high temperature selenization, has enabled up to 1.398 % power conversion efficiency under AM 1.5 solar illumination. X-ray diffraction (XRD) studies show that the structural formation of CIGS chalcopyrite structure. The mo

We present a reliable algorithm for solving, homogeneous or inhomogeneous, nonlinear ordinary delay differential equations with initial conditions. The form of the solution is calculated as a series with easily computable components. Four examples are considered for the numerical illustrations of this method. The results reveal that the semi analytic iterative method (SAIM) is very effective, simple and very close to the exact solution demonstrate reliability and efficiency of this method for such problems.

Charge transfer in styryl dyes STQ-1, STQ-2,and STQ-3 with organic media system has been studied theoretically depending on the Franck- Condon rule and continuum dielectric model . The reorientation energies (eV) were evaluated theoretically depending on dipole momentum, dielectric constant , and refrective index n. The rate constant of charge transfer has been calculated depending on the reorientation energy (eV) ,effective free energy , potential height barrier , and coupling coefficient . A matlap program has been written to calculated the rate constant of charge transfer and other parameter. The results of calculations show that STQ-2 dye is more reaction for charge transfer compare with STQ-1 and STQ-3 dyes

Nanocomposites of polymer material based on CdS as filler
material and poly methyl methacrylate (PMMA) as host matrix have
been fabricated by chemical spray pyrolysis method on glass
substrate. CdS particles synthesized by co-precipitation route using
cadimium chloride and thioacetamide as starting materials and
ammonium hydroxide as precipitating agent. The structure is
examined by X-ray diffraction (XRD), the resultant film has
amorphous structure. The optical energy gap is found to be (4.5,
4.06) eV before and after CdS addition, respectively. Electrical
activation energy for CdS/PMMA has two regions with values of
0.079 and 0.433 eV.

GH and IGF-2 were examined histologically in the present study on adult hens to learn more about the organs’ responses to GH and IGF-2. Cardiac protein synthesis is stimulated by GH and IGF-2, according to microscopic examination. The recent research found a considerable amount of adipose tissue in the cardiac muscle bundles, which is linked to the metabolic process. In addition, GH and IGF-2 were shown to promote protein synthesis and mitosis in liver and gizzard tissues, according to the research. In addition, the apoptosis, regeneration, and secretory activity of gizzard glands are increased by the aforementioned hormones.

Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system

Emotion recognition has important applications in human-computer interaction. Various sources such as facial expressions and speech have been considered for interpreting human emotions. The aim of this paper is to develop an emotion recognition system from facial expressions and speech using a hybrid of machine-learning algorithms in order to enhance the overall performance of human computer communication. For facial emotion recognition, a deep convolutional neural network is used for feature extraction and classification, whereas for speech emotion recognition, the zero-crossing rate, mean, standard deviation and mel frequency cepstral coefficient features are extracted. The extracted features are then fed to a random forest classifier. In