Hormones, their receptors, and the associated signaling pathways make compelling drug targets because of their wide-ranging biological significance to study the role of asprosin in obese male patients with diabetic mellitus type II. ELISA method was used to assay asprosin and insulin. Blood was taken with drawn sample from 30 obese normal patients with age range (40-60) years, 30 diabetic patients with age range (40-60) years at duration of disease (1-5) years and 30 normal healthy patients. The mean difference between T2DM according to insulin % (23.8±0.6) was increased than the mean of IFG (17.7±1.0) (P 0.000). The mean difference between T2DM according to asprosin (122.1±21.8) was increased than the mean of IFG (51.4±2.7) (P 0.000).the mean differences between DM2 and IFG cases in different weight groups (Ob., Ow. and Nw) according to insulin was studied, the results showed that, there were significant differences in DM and IFG obese groups (G1 and G2) according to insulin (24.18±1.13, 15.56±0.66) P (0.00), however, there were significant differences between DM and IFG in Normal weight groups (G5 and G6) according to insulin (19.98±0.93, 11.12) P (0.00), while no significant differences between DM and IFG in Over weight groups (G3 and G4) according to insulin (27.22±0.34,28.56±1.59) P (0.42).The mean differences between diabetic mellitus type 2 and impaired fasting glucose cases in different weight groups (obese, over weight and normal weight) according to Asprosin were shown in Table (3), Figure (). The results showed that, there were significant differences between DM and IFG in obese groups (G1 and G2) according to Asprosin (307.42±8.4, 66.3±2.2) P (0.00), However, there were significant differences between DM and IFG in overweight groups (G3 and G4) according to Asprosin (28.3±0.5, 51.7±3.2) P (0.00) In addition to that, there were significant differences between DM and IFG in normal weight groups (G5 and G6) according to Asprosin (30.5±1.7, 21.2±1.6)
When optimizing the performance of neural network-based chatbots, determining the optimizer is one of the most important aspects. Optimizers primarily control the adjustment of model parameters such as weight and bias to minimize a loss function during training. Adaptive optimizers such as ADAM have become a standard choice and are widely used for their invariant parameter updates' magnitudes concerning gradient scale variations, but often pose generalization problems. Alternatively, Stochastic Gradient Descent (SGD) with Momentum and the extension of ADAM, the ADAMW, offers several advantages. This study aims to compare and examine the effects of these optimizers on the chatbot CST dataset. The effectiveness of each optimizer is evaluat
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Co+2, Ni+2, Cu+2 as well Zn+2 compounds mixed ligand from 8-hydroxyquinoline(8-HQ) also tributylphosphine (PBu3) have been attended at aquatic ethyl alcohol for (1:2:2) (M:8-HQ:PBu3). Produced complexes have been identified by utilizing atomic absorption flame, FT-IR as well UV-Vis spectrum manners also magnetic susceptibility as well as conductivity methods. At addendum antibacterial efficiency from the ligands as well complexes oboist three species about bacteria have been as well examined. Ligands and their complexes show good bacterial efficiencies. Of the gained datum the octahedral geometry was proposed into whole prepared complexes
The current research aims to identify the time-management skills based on the post-test of the experimental group as well as to examine the effect of a training program on developing the skills of managing time among the study sample. To achieve the research objectives, the researcher designed a scale of time management skill included (30) paragraphs. The research reached that the training program is significantly effective in managing and organizing time. There are statistically significant differences in pre-posttest between the experimental and control groups.