Aerial Robot Arms (ARAs) enable aerial drones to interact and influence objects in various environments. Traditional ARA controllers need the availability of a high-precision model to avoid high control chattering. Furthermore, in practical applications of aerial object manipulation, the payloads that ARAs can handle vary, depending on the nature of the task. The high uncertainties due to modeling errors and an unknown payload are inversely proportional to the stability of ARAs. To address the issue of stability, a new adaptive robust controller, based on the Radial Basis Function (RBF) neural network, is proposed. A three-tier approach is also followed. Firstly, a detailed new model for the ARA is derived using the Lagrange–d’A

Fifty one patients with serologically confirmed brucellosis and 70 healthy controls were phenotyped for HLA-A, -B, -DR and -DQ antigens by using standard microlympho-cytotoxicity method, and lymphocytes defined by their CD markers (CD3, CD4, CD8 and CD19). The results revealed a significant (Pc = 0.001) increased frequency of HLA-DR8 (41.18 vs. 10.0%) in the patients . A significant increased percentage of CD8+ lymphocytes was also increased in the patients (25.15 vs. 22.0%; P = 0.006), while CD3+ lymphocytes were significantly decreased (75.1 vs. 79.4%; P = 0.02).

One of the most opportunistic mycosis globally is the Candida ssp., which is considered as the most agent that cause nosocomial urinary tract infections (UTIs), oral candidiasis and genitourinary candidiasis. This study included 100 samples of Iraqi subjects suffering from urinary tract infections. Identification of Candida have been done by different methods such as; characteristic of colony on culture, gram stain, and microscopically. This study aimed to isolation and identification of Candida spp from urine sample of UTI patients and find the relevance of ages and blood group of patients with the infections rate, also determine the effect of age on ESR and CRP levels in the patients. The results showed the higher frequency of

This paper proposes a hybrid artificial intelligence (AI) model that combines an Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) to predict the Quality of Service (QoS) in 5G networks. The model utilizes radio channel indicators such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Received Signal Strength Indicator (RSSI), and Channel Quality Indicator (CQI) to forecast throughput and latency levels. These indicators are critical factors affecting network performance; however, the nonlinear relationship among them makes traditional analytical models inadequate for accurate QoS prediction. The importance of the current study lies in estimating QoS in 5G networks by combining

Conjugate heat transfer has significant implications on heat transfer characteristics, particularly in thick wall applications and small diameter pipes. In this study, a three-dimensional numerical investigation was carried out using commercial CFD software “ANSYS FLUENT” to study the influence of conjugate heat transfer of laminar flow in mini channels at constant heat flux wall conditions. Two parameters were studied and analyzed: the wall thickness and thermal conductivity and their effect on heat transfer characteristics such as temperature profile and Nusselt number. Thermal conductivity of (0.25, 10, 202, and 387) W/m²C and wall thickness of (1, 5, and 50) mm were used for a channel of (1*2) mm cross