Improving the accuracy of load-deformation behavior, failure mode, and ultimate load capacity for reinforced concrete members subjected to in-plane loadings such as corbels, wall to foundation connections and panels need shear strength behavior to be included. Shear design in reinforced concrete structures depends on crack width, crack slippage and roughness of the surface of cracks.

This paper illustrates results of an experimental investigation conducted to investigate the direct shear strength of fiber normal strength concrete (NSC) and reactive powder concrete (RPC). The tests were performed along a pre-selected shear plane in concrete members named push-off specimens. The effectiveness of concrete compressiv

This work has been done with using of epoxy resin mixed with Granite powder were weighted by percent volume (5,10,15, and 20)%and then mixed with epoxy polymer to compose polymer composite. Hand lay-up technique is used in fabrication of the composite samples. Hardness test was carried out for the proper samples in both normal condition and after immersion in HCL (1 M and 2 M) solutions for periods ranging up to 10 weeks. After comparing the results between the polymer and their composite, the hardness increased with increasing Granite weight percent, it was found that Hardness were greater for the composites before immersion compared with their values after immersion.

In this paper, three types of epoxy-based coatings (Polyamide, pure Polyamine, and Polyamine reinforced by glass-flake) used as a lining for potable water tanks were studied using experimental and finite element methods. Tensile, impact, and fatigue tests were conducted on uncoated and coated AISI 316 stainless steel. The test results show that the applied epoxy based coating improves the mechanical properties, increases of fatigue crack resistance, and enhance the dynamic fracture toughness. The fatigue crack propagation is influenced by the compositions of epoxy coating, and the glass-flake improves the coating resistance to fatigue crack propagation compared to other types.

(Sb₂S₃)_1-xSn_x thin films with different concentrations (0, 0.05 and
0.15) and thicknesses (300,500 and 700nm) have been deposited by
single source vacuum thermal evaporation onto glass substrates at
ambient temperature to study the effect of tin content, thickness and
on its structural morphology, and electrical properties. AFM study
revealed that microstructure parameters such as crystallite size, and
roughness found to depend upon deposition conditions. The DC
conductivity of the vacuum evaporated (Sb₂S₃)_1-x Sn_x thin films was
measured in the temperature range (293-473)K and was found to
increase on order of magnitude with

<p> Traditionally, wireless networks and optical fiber Networks are independent of each other. Wireless networks are designed to meet specific service requirements, while dealing with weak physical transmission, and maximize system resources to ensure cost effectiveness and satisfaction for the end user. In optical fiber networks, on the other hand, search efforts instead concentrated on simple low-cost, future-proofness against inheritance and high services and applications through optical transparency. The ultimate goal of providing access to information when needed, was considered significantly. Whatever form it is required, not only increases the requirement sees technology convergence of wireless and optical networks but

Phase change materials (PCMs) such as paraffin wax can be used to store or release large amount of energy at certain temperature at which their solid-liquid phase changes occurs. Paraffin wax that used in latent heat thermal energy storage (LHTES) has low thermal conductivity. In this study, the thermal conductivity of paraffin wax has been enhanced by adding different mass concentration (1wt.%, 3wt.%, 5wt.%) of (TiO₂) nano-particles with about (10nm) diameter. It is found that the phase change temperature varies with adding (TiO₂) nanoparticles in to the paraffin wax. The thermal conductivity of the composites is found to decrease with increasing temperature. The increase in thermal conductivity ha

The Internet of Things (IoT) has significantly transformed modern systems through extensive connectivity but has also concurrently introduced considerable cybersecurity risks. Traditional rule-based methods are becoming increasingly insufficient in the face of evolving cyber threats.  This study proposes an enhanced methodology utilizing a hybrid machine-learning framework for IoT cyber-attack detection. The framework integrates a Grey Wolf Optimizer (GWO) for optimal feature selection, a customized synthetic minority oversampling technique (SMOTE) for data balancing, and a systematic approach to hyperparameter tuning of ensemble algorithms: Random Forest (RF), XGBoost, and CatBoost. Evaluations on the RT-IoT2022 dataset demonstrat