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Go to Editorial ManagerLightweight foamed concrete (LWFC) is characterized as a light in self-weight, self-compacting, self-levelling, and thermal and sound isolation. But it has low strength and low ductility which leads that the application of (LWFC) in the building construction is limited. The flowability of the fresh mix of (LWFC) was evaluated by flow test. While the hardened properties of (LWFC) include, compressive6 strength, tensile6 splitting6 strength, flexural6 strength, and 6modulus of 6elasticity. This6 study6 focuses6 on the effect of the adding of silica fume and steel fibre on the mechanical properties of (LWFC). Silica fume was added as (5%) and (10%) by the weight of cement and steel fiber (0.2%) and (0.4%) of the total volume of the mix. The density of lightweight foamed concrete was 1800±50kg/ , and cement to sand ratio was (1:1) with water cement ratio (0.28). The results indicated that adding of silica fume6 and steel6 fiber6 have great effect on the mechanical properties and improve them. The addition (10%) of silica fume and (0.4%) by volume of steel fiber was the best ratio that improves the mechanical properties of the lightweight foamed concrete (LWFC). The pozzolanic index of the (5%) and (10%) silica fume was (21.9%) and (74.76%), respectively.
In this work chopped carbon fibers are used to improve tensile strength of Porcelanite lightweight aggregate concrete. Silica fume was added in order to improve the mixes compressive strength. Silica fume increase water demand and using fibers reduce workability, to improve workability and decrease water demand high rang super plasticizers are used. The results showed that compressive strength, splitting tensile strength, modulus of elasticity of carbon fibers Porcelanite lightweight aggregate concrete increase with increasing of carbon fiber up to 2% compared to reference Porcelanite lightweight aggregate concrete without fibers. The percentages of increasing were 14.40%, 68.00%, and 10.66% for compressive strength, splitting tensile strength, and modulus of elasticity, respectively. Flexural Strength continues in increase with increase of fibers. The dry unite weight of mixes with chopped fiber decrease with increase of fiber percentage. Besides the chopped carbon improved the ductility of Porcelanite lightweight aggregate concrete and that clear from stress-strain relationship.
Regaining the activities of daily living after stroke and spinal cord injury requires repetitive and intensive tasks, meaning that rehabilitation therapy should be treated with a long duration. Thus, the need for rehabilitation devices based home is of most importance to increase the rehabilitation process and provide more comfortability for patients. This paper focuses on implementing and construction of a three degree of freedom (DOF) (flexion/extension, adduction/abduction, and pronation/supination), low cost, lightweight, and portable robotic exoskeleton for wrist-forearm rehabilitation. SolidWorks software program and 3D printer technology are used to model and construct the proposed robotic exoskeleton structure. In addition, the anthropometric parameters of the normal human lower arm are considered for this exoskeleton to provide a range of motion (ROM) and velocity for the links, joints, which matches with the anatomical structure of human and also to avoid the excesses motions over the normal range. The exoskeleton is constructed by a 3D printer utilizing polylactic acid (PLA) plastic material. The proposed implementing structure of the robotic exoskeleton shows comfortable, lightweight, simple and economic as well.
Lung cancer is the most common dangerous disease that, if treated late, can lead to death. It is more likely to be treated if successfully discovered at an early stage before it worsens. Distinguishing the size, shape, and location of lymphatic nodes can identify the spread of the disease around these nodes. Thus, identifying lung cancer at the early stage is remarkably helpful for doctors. Lung cancer can be diagnosed successfully by expert doctors; however, their limited experience may lead to misdiagnosis and cause medical issues in patients. In the line of computer-assisted systems, many methods and strategies can be used to predict the cancer malignancy level that plays a significant role to provide precise abnormality detection. In this paper, the use of modern learning machine-based approaches was explored. More than 70 state-of-the-art articles (from 2019 to 2024) were extensively explored to highlight the different machine learning and deep learning (DL) techniques of different models used for the detection, classification, and prediction of cancerous lung tumors. The efficient model of Tiny DL must be built to assist physicians who are working in rural medical centers for swift and rapid diagnosis of lung cancer. The combination of lightweight Convolutional Neural Networks and limited resources could produce a portable model with low computational cost that has the ability to substitute the skill and experience of doctors needed in urgent cases.