Mechanical characteristics of oil palm fiber reinforced thermoplastics as filament for fused deposition modeling (FDM)

Fibers are increasingly in demand for a wide range of polymer composite materials. This study's purpose was the development of oil palm fiber (OPF) mixed with the thermoplastic material acrylonitrile butadiene styrene (ABS) as a composite filament for fused deposition modeling (FDM). The mechanical properties of this composite filament were then analyzed. OPF is a fiber extracted from empty fruit bunches, which has proved to be an excellent raw material for biocomposites. The cellulose content of OPF is 43%-65%, and the lignin content is 13%-25%. The composite filament consists of OPF (5%, mass fraction) in the ABS matrix. The fabrication procedure included alkalinizing, drying, and crushing the OPF to develop the composite. The OPF/ABS materials were prepared and completely blended to acquire a mix of 250 g of the material for the composition. Next, the FLD25 filament extrusion machine was used to form the OPF/ABS composite into a wire. This composite filament then was used in an FDM-based 3D printer to print the specimens. Finally, the printed specimens were tested for mechanical properties such as tensile and flexural strength. The results show that the presence of OPF had increased the tensile strength and modulus elasticity by approximately 1.9% and 1.05%, respectively. However, the flexural strength of the OPF/ABS composite had decreased by 90.6% compared with the virgin ABS. Lastly, the most significant outcome of the OPF/ABS composite was its suitability for printing using the FDM method.The full text can be downloaded at https://link.springer.com/content/pdf/10.1007%2Fs40436-019-00287-w.pdf     

Reduced graphene oxide/metal oxide nanoparticles composite membranes for highly efficient molecular separation

Graphene oxide(GO) membranes play an important role in various nanofiltration applications including desalination, water purification, gas separation, and pervaporation. However, it is still very challenging to achieve both high separation efficiency and good water permeance at the same time. Here, we synthesized two kinds of GO-based composite membranes i.e. reduced GO(rGO)@MoO_2 and rGO@WO_3 by in-situ growth of metal nanoparticles on the surface of GO sheets. They show a high separation efficiency of ～100% for various organic dyes such as rhodamine B, methylene blue and evans blue, along with a water permeance over 125 Lm~(-2) h~(-1) bar~(-1). The high water permeance and rejection efficiency open up the possibility for the real applications of our GO composite membranes in water purification and wastewater treatment. Furthermore, this composite strategy can be readily extended to the fabrication of other ultrathin molecular sieving membranes for a wide range of molecular separation applications.     

Marker-Based and Marker-Less Motion Capturing Video Data: Person and Activity Identification Comparison Based on Machine Learning Approaches

Biomechanics is the study of physiological properties of data and the measurement of human behavior. In normal conditions, behavioural properties in stable form are created using various inputs of subconscious/conscious human activities such as speech style, body movements in walking patterns, writing style and voice tunes. One cannot perform any change in these inputs that make results reliable and increase the accuracy. The aim of our study is to perform a comparative analysis between the marker-based motion capturing system (MBMCS) and the marker-less motion capturing system (MLMCS) using the lower body joint angles of human gait patterns. In both the MLMCS and MBMCS, we collected trajectories of all the participants and performed joint angle computation to identify a person and recognize an activity (walk and running). Using five state of the art machine learning algorithms, we obtained 44.6% and 64.3% accuracy in person identification using MBMCS and MLMCS respectively with an ensemble algorithm (two angles as features). In the second set of experiments, we used six machine learning algorithms to obtain 65.9% accuracy with the k-nearest neighbor (KNN) algorithm (two angles as features) and 74.6% accuracy with an ensemble algorithm. Also, by increasing features (6 angles), we obtained higher accuracy of 99.3% in MBMCS for person recognition and 98.1% accuracy in MBMCS for activity recognition using the KNN algorithm. MBMCS is computationally expensive and if we re-design the model of OpenPose with more body joint points and employ more features, MLMCS (low-cost system) can be an effective approach for video data analysis in a person identification and activity recognition process.     

Enhance Intrusion Detection in Computer Networks Based on Deep Extreme Learning Machine

Networks provide a significant function in everyday life, and cybersecurity therefore developed a critical field of study. The Intrusion detection system (IDS) becoming an essential information protection strategy that tracks the situation of the software and hardware operating on the network. Notwithstanding advancements of growth, current intrusion detection systems also experience dif- ficulties in enhancing detection precision, growing false alarm levels and identifying suspicious activities. In order to address above mentioned issues, several researchers concentrated on designing intrusion detection systems that rely on machine learning approaches. Machine learning models will accurately identify the underlying variations among regular information and irregular information with incredible efficiency. Artificial intelligence, particularly machine learning methods can be used to develop an intelligent intrusion detection framework. There in this article in order to achieve this objective, we propose an intrusion detection system focused on a Deep extreme learning machine (DELM) which first establishes the assessment of safety features that lead to their prominence and then constructs an adaptive intrusion detection system focusing on the important features. In the moment, we researched the viability of our suggested DELMbased intrusion detection system by conducting dataset assessments and evaluating the performance factors to validate the system reliability. The experimental results illustrate that the suggested framework outclasses traditional algorithms. In fact, the suggested framework is not only of interest to scientific research but also of functional importance.     

Deep Learning Approach for Analysis and Characterization of COVID-19

Early diagnosis of a pandemic disease like COVID-19 can help deal with a dire situation and help radiologists and other experts manage human resources more effectively. In a recent pandemic, laboratories perform diagnostics manually, which requires a lot of time and expertise of the laboratorial technicians to yield accurate results. Moreover, the cost of kits is high, and well-equipped labs are needed to perform this test. Therefore, other means of diagnosis is highly desirable. Radiography is one of the existing methods that finds its use in the diagnosis of COVID-19. The radiography observes change in Computed Tomography (CT) chest images of patients, developing a deep learning-based method to extract graphical features which are used for automated diagnosis of the disease ahead of laboratory-based testing. The proposed work suggests an Artificial Intelligence (AI) based technique for rapid diagnosis of COVID-19 from given volumetric chest CT images of patients by extracting its visual features and then using these features in the deep learning module. The proposed convolutional neural network aims to classify the infectious and non-infectious SARS-COV2 subjects. The proposed network utilizes 746 chests scanned CT images of 349 images belonging to COVID-19 positive cases, while 397 belong to negative cases of COVID-19. Our experiment resulted in an accuracy of 98.4%, sensitivity of 98.5%, specificity of 98.3%, precision of 97.1%, and F1-score of 97.8%. The additional parameters of classification error, mean absolute error (MAE), root-mean-square error (RMSE), and Matthew’s correlation coefficient (MCC) are used to evaluate our proposed work. The obtained result shows the outstanding performance for the classification of infectious and non-infectious for COVID-19 cases.     

Local-Tetra-Patterns for Face Recognition Encoded on Spatial Pyramid Matching

Face recognition is a big challenge in the research field with a lot of problems like misalignment, illumination changes, pose variations, occlusion, and expressions. Providing a single solution to solve all these problems at a time is a challenging task. We have put some effort to provide a solution to solving all these issues by introducing a face recognition model based on local tetra patterns and spatial pyramid matching. The technique is based on a procedure where the input image is passed through an algorithm that extracts local features by using spatial pyramid matching and max-pooling. Finally, the input image is recognized using a robust kernel representation method using extracted features. The qualitative and quantitative analysis of the proposed method is carried on benchmark image datasets. Experimental results showed that the proposed method performs better in terms of standard performance evaluation parameters as compared to state-of-the-art methods on AR, ORL, LFW, and FERET face recognition datasets.     

Co-delivery of gemcitabine and simvastatin through PLGA polymeric nanoparticles for the treatment of pancreatic cancer: in-vitro characterization,cellular uptake,and pharmacokinetic studies

Despite the ongoing extensive research, cancer therapeutics still remains an area with unmet needs which is hampered by shortfall in the development of newer medicines. The present study discusses a nano-based combinational approach for treating solid tumor. Dual-loaded nanoparticles encapsulating gemcitabine HCl (GM) and simvastatin (SV) were fabricated by double emulsion solvent evaporation method and optimized. Optimized nanoparticles showed a particle size of 258?±?2.4?nm, polydispersity index of 0.32?±?0.052, and zeta potential of ?12.5?mV. The size and the morphology of the particles wee further confirmed by transmission electron microscopy (TEM) and scanning electron microscopy, respectively of the particles. The entrapment efficiency of GM and SV in the nanoparticles was 38.5?±?4.5% and 72.2?±?5.6%, respectively. The in vitro release profile was studied for 60?h and showed Higuchi release pattern. The cell toxicity was done using MTT assay and lower IC₅₀ was obtained with the nanoparticles as compared to the pure drug. The bioavailability of GM and SV in PLGA nanoparticles was enhanced by 1.4-fold and 1.3-fold respectively, compared to drug solution. The results revealed that co-delivery of GM and SV could be used for its oral delivery for the effective treatment of pancreatic cancer.     

Influence of gamma irradiation on structural,optical, and electrical characterization of Bi2S3 thin films

The induced effects of the gamma rays on properties of bismuth sulfide (Bi₂S₃) thin films synthesized using successive ionic layer adsorption and reaction (SILAR) have been investigated in details in this work. The Bi₂S₃ thin films are prepared on glass substrate and then exposed with low gamma radiation dose in the range of 0–1000 Gy. X-ray diffraction (XRD) confirmed the orthorhombic structural phase. Also, it was noticed in the XRD result that the crystallite size decreased from 115.29 to 73.63 nm with increasing gamma rays doses. For surface properties as well as stoichiometry of the prepared and irradiated thin film have been studied by field emission scanning electron microscope (FESEM). The optical transmission of irradiated samples increased and the energy band gap (E) decreased from 2.78 to 2.52 eV with gamma dose. Photoluminescence (PL) spectra revealed the improvement in the emission characteristics of Bi₂S₃ thin films with irradiation in the range of 250–1000 Gy. Impedance spectroscopy investigation exhibited that the resistance due to grain boundaries meaningfully contributed to the electrical characteristics of the Bi₂S₃ thin films. The achieved results suggested that Bi₂S₃ thin films are a good tool for further study of dosimetry and radiation sensing application.