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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.
Women’s safety remains an urgent challenge, particularly in moments when conventional panic button devices fail due to a victim’s inability to act or poor network coverage. To overcome these shortcomings, TRIAD-Lite is introduced as an IoT-enabled wearable framework that unites multimodal physiological sensing with lightweight deep learning for proactive distress identification. The system captures heart rate, blood pressure, galvanic skin response, and motion patterns, while incorporating a triple-tap gesture to confirm user intent, all processed locally on a Raspberry Pi for real-time inference. Unlike reactive mechanisms, this design anticipates danger by analyzing variations in physiological signals that often precede visible distress. Communication reliability is reinforced through a hybrid strategy: alerts are transmitted via GSM or Wi-Fi under normal conditions, but in the event of limited connectivity, a LoRa-based backup ensures long-range transmission. Experimental analysis using simulated datasets yielded an AUC of 1.000 with flawless precision and recall, highlighting the model’s reliability and calibration. Further field evaluation demonstrated that LoRa maintained connectivity across 5.7 kilometers with complete packet delivery, proving effective for both rural and urban environments. By combining predictive analytics, gesture-based confirmation, and dual communication layers, TRIAD-Lite offers a scalable, privacy-conscious, and highly resilient framework that strengthens women’s safety and extends protective technology into regions where conventional systems often fail.
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.
In precision agriculture, crop disease detection can be a highly valuable undertaking in which scalable and correct solutions may save considerable amounts of money and loss of yield. This paper introduces a comparative analysis of state-of-the-art deep learning models with special attention to EfficientNetB3 hybrids, which are trained on a balanced subsample of the PlantVillage dataset with 33 classes based on nine crops. To overcome the shortcomings of the previous studies, which used unbalanced sample, a leakage-free balancing approach was used, resulting in 13,200 training and 3,300 validation samples. Custom head transfer learning was used where it was tested using two strategies; FreezeUnfreeze fine-tuning, and Singlephase training. MobileNetV2, InceptionV3, DenseNet121, GhostNet, in addition to other baseline CNNs, were compared to baseline Convolutional Neural Networks (CNNs). The findings indicate that EfficientNetB3 hybrids are superior with an accuracy of ≥99.5% and 99.9% Area Under the Curve (AUC) and specificity than the previous CNN-based systems. The paper logically defines a performance ladder between model options and real-life deployment demands, such as lightweight mobile applications to precision agriculture systems, and points out future trends in the field-based validation.