The complexity and partially defined nature of jet grouting make it hard to predict the performance of grouted piles. So the trials of cement injection at a location with similar soil properties as the erecting site are necessary to assess the performance of the grouted piles. Nevertheless, instead of executing trial-injected piles at the pilot site, which wastes money, time, and effort, the laboratory cement injection devices are essential alternatives for evaluating soil injection ability. This study assesses the performance of a low-pressure laboratory grouting device by improving loose sandy soil injected using binders formed of Silica Fume (SF) as a chemical admixture (10% of Ordinary Portland Cement OPC mass) to di

        In this study the design and installation of evaporative air cooler was carried out using completely outdoor air (fresh air) according to two stage evaporative cooling principle. The laboratory equipment was installed by designing and manufacturing a cross flow plate heat exchanger, where aluminum plates used for this purpose with dimensions (50 × 30 × 40 cm). The surfaces of heat exchanger were covered by sawdust from wetted channels side, to increase the percentage of wetting these surfaces and hence improve the performance and efficiency of air cooler.

        An experimental study was carried out to estimate the performance of cooling system, where som

Titanium alloy surface properties have an essential role in the interaction of dental implants with bone, and alteration of the surface of the implant could improve osseointegration. This study was designed to investigate the effect of different heat treatment temperatures on titanium alloy surface properties for dental implants. The effect of different temperatures of heat treatment (750°C, 850°C, 950°C and 1050°C) were investigated on the surface topography, surface chemistry, titanium oxide layer thickness, blood contact angle, & blood drop diameter of titanium alloy. The disks were prepared from titanium alloy (Ti-6Al-4V) and the samples were divided into five groups depending on the different temperatures of heat treatment. The hea

Heat shock protein 70 (HSP70) is a crucial protein with vital biological tasks in cell continuation of life. The variation of HSP70 activation occurs as a consequence of stress that includes temperature states, toxicity, poisoning with heavy metals, and tumor-related conditions. One of the master jobs of the HSP family is the suppression of caspase-mediated apoptosis signals. A high level of the expression of HSP70 is accountable for tumorigenesis and resistance against chemotherapeutic drugs. For this reason, the detection of HSP70 may help to diagnose cancerous diseases. From the other side, targeting this chaperone might help in treatment by maintaining late caspase-dependent events. This study was conducted to detect the presenc

The present study aimed to shed light on the urine HSP70 concentration of patients with urinary bladder carcinoma UBC and control subjects as new urinary biomarker. The second aim was to associate this protein concentration with UBC stage and grade in patients with UBC. A direct ELISA was used to quantify urine HSP concentrations in 58 patients with urinary bladder carcinoma UBC with different grades (G) and stages (T) all malignant of them was transitional cell carcinoma (TCC) type , 15 from patients with urinary Bladder disorders other than cancer UBD and 15 healthy subjects(control) . Urine concentrations of HSP70 were elevated in patients with UBC compared to those without UBC (healthy and UBD, P< 0.5). There was a high signifi

This study proposes a hybrid predictive maintenance framework that integrates the Kolmogorov-Arnold Network (KAN) with Short-Time Fourier Transform (STFT) for intelligent fault diagnosis in industrial rotating machinery. The method is designed to address challenges posed by non-linear and non-stationary vibration signals under varying operational conditions. Experimental validation using the FALEX multispecimen test bench demonstrated a high classification accuracy of 97.5%, outperforming traditional models such as SVM, Random Forest, and XGBoost. The approach maintained robust performance across dynamic load scenarios and noisy environments, with precision and recall exceeding 95%. Key contributions include a hardware-accelerated K