Flexural fatigue and failure probability analysis of polypropylene-glass hybrid fibres reinforced epoxy composite laminates

Flexural fatigue tests have been conducted on polypropylene/epoxy composite and its hybrids with glass fibre. Specimens were fabricated in intraply and inter-intraply configurations using the hand lay-up technique. Fatigue tests were performed at a frequency of 25 Hz and zero mean stress. A 20% reduction of the flexural stiffness was taken as a failure criterion. The effects of fibre hybridisation, hybrid configuration and stacking sequence on S–N curves were investigated. The fractured specimens were examined using SEM. Weibull distribution function was used to obtain the failure probabilities and the scatter in the results. Results indicated that hybridising polypropylene fibre with glass improves the flexural fatigue resistance. The fabricated hybrids have economical and specific fatigue strength benefits. The stacking sequence has a great effect on the composite's fatigue resistance. The inter-intraply hybrid laminate with glass fibre layers skin and polypropylene fibre layers core presents the most favourable flexural fatigue behaviour.     

Multimodal temporal machine learning for Bipolar Disorder and Depression Recognition

Pattern Analysis and Applications - Mental disorder is a serious public health concern that affects the life of millions of people throughout the world. Early diagnosis is essential to ensure...     

Hybrid Sine Cosine and Stochastic Fractal Search for Hemoglobin Estimation

The sample's hemoglobin and glucose levels can be determined by obtaining a blood sample from the human body using a needle and analyzing it. Hemoglobin (HGB) is a critical component of the human body because it transports oxygen from the lungs to the body's tissues and returns carbon dioxide from the tissues to the lungs. Calculating the HGB level is a critical step in any blood analysis job. The HGB levels often indicate whether a person is anemic or polycythemia vera. Constructing ensemble models by combining two or more base machine learning (ML) models can help create a more improved model. The purpose of this work is to present a weighted average ensemble model for predicting hemoglobin levels. An optimization method is utilized to get the ensemble's optimum weights. The optimum weight for this work is determined using a sine cosine algorithm based on stochastic fractal search (SCSFS). The proposed SCSFS ensemble is compared to Decision Tree, Multilayer perceptron (MLP), Support Vector Regression (SVR) and Random Forest Regressors as model-based approaches and the average ensemble model. The SCSFS results indicate that the proposed model outperforms existing models and provides an almost accurate hemoglobin estimate.     

Security analysis on “Three-factor authentication protocol using physical unclonable function for IoV”

International Journal of Information Security - The advent of the Internet of Things has enriched the network field with new themes, among which we find the Internet of Vehicles (IoV). IoV improved...     

Synthesis and Anti-Breast Cancer Evaluation of Novel N-(Guanidinyl)benzenesulfonamides

A series of 4-(substituted)-N-(guanidinyl)benzenesulfonamides bearing biologically active pyrazole, pyrimidine and pyridine moieties were prepared and evaluated for their anticancer activity against human tumor breast cell line (MCF7). These sulfonamides showed promising activity with IC₅₀ values ranging from 49.5 to 70.2 μM. The structure-activity relationship of the synthesized compounds was studied. Interestingly, it was found that the most potent compounds in this study were the corresponding 2-cyanoacrylate 3, 3-oxobutanoate 4, pyrazole 6, pyridine 9 and pyrazole 13. Compounds 7 and 8 are nearly as active as Doxorubicin as reference drug with (IC₅₀ values = 70.2, 68.1 μM), while compounds 5, 10 and 11 exhibited a moderate activity.     

Deep learning based neural network application for automatic ultrasonic computed tomographic bone image segmentation

Deep-learning techniques have led to technological progress in the area of medical imaging segmentation especially in the ultrasound domain. In this paper, the main goal of this study is to optimize a deep-learning-based neural network architecture for automatic segmentation in Ultrasonic Computed Tomography (USCT) bone images in a short time process. The proposed method is based on an end to end neural network architecture. First, the novelty is shown by the improvement of Variable Structure Model of Neuron (VSMN), which is trained for both USCT noise removal and dataset augmentation. Second, a VGG-SegNet neural network architecture is trained and tested on new USCT images not seen before for automatic bone segmentation. Therefore, we offer a free USCT dataset. In addition, the proposed model is implemented on both the CPU and the GPU, hence overcoming previous works by a value of 97.38% and 96% for training and validation and achieving high segmentation accuracy for testing with a small error of 0.006, in a short time process. The suggested method demonstrates its ability to augment USCT data and then to automatically segment USCT bone structures achieving excellent accuracy outperforming the state of the art.

     

Improved hepatoprotective activity of silymarin via encapsulation in the novel vesicular nanosystem bilosomes

The main objective of the present work was to formulate, characterize, and evaluate silymarin (SM)-loaded bilosomes, compared to conventional liposomes, aiming at increasing the hepatoprotective activity of the drug. SM-loaded bilosomes were prepared by thin film hydration technique employing soybean phosphatidyl choline (SPC) and different bile salts. After being subjected to different methods of characterization, SM-loaded bilosomes were investigated for their hepatoprotective activity, in CCl₄ hepatointoxicated rat model. The developed SM dispersions exhibited an entrapment efficiency ranging from 21.80?±?2.01 to 84.54?±?2.51% and a particle size diameter in the nanometric dimensions (413?±?96.9 to 686.9?±?62.38?nm), with a negative zeta potential values (<–45?mV). In vitro release study revealed a lower cumulative amount of drug released from the developed formulae, compared to free drug. Ex vivo intestinal uptake study, performed using confocal laser scanning calorimetry, revealed the superiority of bilosomal uptake compared to that of liposomes. In vivo studies revealed an enhanced hepatoprotective effect of SM-loaded bilosomes/liposomes compared to free drug. These results were in good correlation with histopathological examination. These findings support the potential use of bilosomes for improving the hepatoprotective activity of SM via oral administration.     

High-level design flow and environment for FPGA-based dynamic partial reconfiguration

The main motivation of this paper is related to the lack of a high-level design flow for field-programmable gate array (FPGA) partial dynamic reconfiguration management. Our contribution consists in proposing a high-level add-on methodology to the Xilinx’s design flow for dynamic partial reconfiguration (DPR). The main objective is to give an abstract view of the developed application in order to facilitate the designer task. The suggested design flow offers an application-centric view on dynamic reconfiguration designs, which permits simplifying the optimisation and generation of such designs. A new formulation of the reconfigurable modules’ mapping process is put forward. This allows a design space exploration so as to find the convenient number of reconfigurable regions and their sizes as well as the reconfiguration sequence. A new tool was proposed to support our methodology by allowing creating and synthesising graphical models of the developed application. We introduce a new block diagram to represent this latter and a sequence model that can be used for the design optimisations. To validate the proposed DPR design environment, two application examples are given at the end of the paper. They demonstrate the usefulness of the suggested models and methods.     

Acoustic estimation of thermal distribution in the vicinity of femtosecond laser-induced optical breakdown

Laser-induced optical breakdown (LIOB), or photo-disruption, can generate individual microbubbles in tissues for biomedical applications. We have previously developed a co-localized high-frequency ultrasound system to detect and characterize these laser-induced microbubbles. Because ultrasound speed varies with temperature, this system can also be used to directly estimate thermal effects in the vicinity of photodisruption. In this study, individual bubbles (sizes 60-100 microm) were created at the bottom of a water tank using a 793-nm, 100-fs Ti:Sapphire laser pulsed at 250 kHz. During and after breakdown, pulse-echoes from the tank bottom in the region surrounding a bubble were recorded with a single-element 85-MHz ultrasonic transducer, and temperature-dependent pulse-echo displacements were calculated using phase-sensitive correlation tracking. These displacements were then fit to a finite-element heat transfer model to estimate the effective thermal distribution. Estimates were calculated for laser exposure times ranging from 6.25 to 312.5 ms (1600 to 78 000 laser pulses), at 1.5 and 4 J/cm2 fluences. Results suggest a minimal temperature increase (<1 degrees C) within 100 microm of a bubble created with <1600 laser pulses at 1.5 J/cm2 fluence. This implies that LIOB can be controlled to be thermally noninvasive in the bubble vicinity.     

Adrenomedullin Mitigates Doxorubicin-Induced Nephrotoxicity in Rats: Role of Oxidative Stress,Inflammation, Apoptosis,and Pyroptosis

Doxorubicin (DOX) is an anticancer antibiotic which has various effects in human cancers. It is one of the commonly known causes of drug-induced nephrotoxicity, which results in acute renal injury. Adrenomedullin (ADM), a vasodilator peptide, is widely distributed in many tissues and has potent protective effects. Therefore, the current study aimed to examine the protective potential mechanisms of ADM against DOX-induced nephrotoxicity. A total of 28 male Wistar rats were randomized into four groups: control group, doxorubicin group (15 mg/kg single intraperitoneal injection of DOX), adrenomedullin + doxorubicin group (12 μg/kg/day intraperitoneal injection of ADM) 3 days prior to DOX injection and continuing for 14 days after the model was established, and adrenomedullin group. Kidney function biomarkers, oxidative stress markers, and inflammatory mediators (TNF-α, NLRP3, IL-1β, and IL-18) were assessed. The expressions of gasdermin D and ASC were assessed by real-time PCR. Furthermore, the abundances of caspase-1 (p20), Bcl-2, and Bax immunoreactivity were evaluated. ADM administration improved the biochemical parameters of DOX-induced nephrotoxicity, significantly reduced oxidative damage markers and inflammatory mediators, and suppressed both apoptosis and pyroptosis. These results were confirmed by the histopathological findings and revealed that ADM’s antioxidant, anti-inflammatory, anti-apoptotic, and anti-pyroptotic properties may have prospective applications in the amelioration of DOX-induced nephrotoxicity.