Physiological analysis of streamline topologies and their bifurcations for a peristaltic flow of nano fluid

In the present article, the techniques of dynamical systems are utilized to investigate the streamline patterns along their bifurcations for peristaltic flow under mixed convection effect. Here the peristaltic flow is discussed in a vertical channel. The flow is considered in a two-dimensional symmetric channel as well as an axisymmetric tube. We have evaluated the peristaltic flow of blood base nanofluid. The momentum equations are reduced by employing approximation of low Reynolds number and long wavelength. For the discourse of the path of particle in the wave frame, an arrangement of nonlinear independent differential equations are built up and the strategies for dynamical frameworks are utilized to examine the local bifurcations and their topological changes. Critical points classifications were made by scrutiny of the eigenvalues of the Jacobian matrix. This principle are utilized to divulge the local bifurcation of the critical points encountered for different flow situations. Flow situations marked as: backward flow, trapping or augmented flow. The analysis is disclosed that the number and size of trapped bolus increases in planner channel and decline in axisymmetric channel by increasing Grashof number. Moreover, the decreasing behaviour of temperature is depicted, which clarify the nanofluid as a cooling agent. Graphically, a wide range of topological changes of bifurcations are examined. At long last, to outline the bifurcation the diagram of global bifurcation is utilized.

     

Benzoselenadiazole-core asymmetric D-A-A small molecule for solution processed bulk heterojunction organic solar cells

The present work is accounted on the designing of a new and efficient asymmetric organic chromophore, named 4-(3,5-bis [trifluoromethyl] phenyl)-7-(5′-hexyl-[2,2′-bithiophen]-5-yl)-benzo[c [1, 2, 5]selenadiazole, (RT-BSe-F), based on benzoselenadiazole central acceptor building blocks. The acceptor unit of 3,5-bis (trifluoromethyl) benzene and donor unit of alkyl bithiophene attached with benzoselenadiazole central unit showed large impacts on the optical and electrochemical properties. The reasonable optical band gap of ~2.02 eV and HOMO of −5.33 eV were obtained by RT-BSe-F chromophore due to a strong electron accepting nature of fluorine based compound. With 3,5-bis(trifluoromethyl) benzene unit, the absorption of RT-BSe-F chromophore was considerably increased to higher wavelength which might enhance the crystallinity of thin film with high hole mobility. RT-BSe-F chromophore was effectively applied to fabricate the solution-processed bulk-heterojunction organic solar cells (BHJ-OSCs) and attained a high power conversion efficiency (PCE) of ~3.75% accompanying high J_SC of ~12.56 mA cm⁻², FF of ~0.42 and V_OC of ~0.71 V. The obtained high PCE might be associated to a high surface energy of TiO₂ layer as buffer and the use of high mobility organic RT-BSe-F chromophore.     

miRNAs in Regulation of Tumor Microenvironment,Chemotherapy Resistance,Immunotherapy Modulation and miRNA Therapeutics in Cancer

miRNAs are 20–22 long nucleotide non-coding ribonucleic acid molecules critical to the modulation of molecular pathways. Immune evasion and the establishment of a suitable tumor microenvironment are two major contributors that support tumor invasion and metastasis. Tumorigenic miRNAs support these two hallmarks by desensitizing important tumor-sensitive regulatory cells such as dendritic cells, M1 macrophages, and T helper cells towards tumors while supporting infiltration and proliferation of immune cells like Treg cells, tumor-associated M2 macrophages that promote self-tolerance and chronic inflammation. miRNAs have a significant role in enhancing the efficacies of immunotherapy treatments like checkpoint blockade therapy, adoptive T cell therapy, and oncolytic virotherapy in cancer. A clear understanding of the role of miRNA can help scientists to formulate better-targeted treatment modalities. miRNA therapeutics have emerged as diverse class of nucleic acid-based molecules that can suppress oncogenic miRNAs and promote the expression of tumor suppressor miRNAs.     

Towards Collaborative Robotics in Top View Surveillance: A Framework for Multiple Object Tracking by Detection Using Deep Learning

Collaborative Robotics is one of the high-interest research topics in the area of academia and industry. It has been progressively utilized in numerous applications, particularly in intelligent surveillance systems. It allows the deployment of smart cameras or optical sensors with computer vision techniques, which may serve in several object detection and tracking tasks. These tasks have been considered challenging and high-level perceptual problems, frequently dominated by relative information about the environment, where main concerns such as occlusion, illumination, background, object deformation, and object class variations are commonplace. In order to show the importance of top view surveillance, a collaborative robotics framework has been presented. It can assist in the detection and tracking of multiple objects in top view surveillance. The framework consists of a smart robotic camera embedded with the visual processing unit. The existing pre-trained deep learning models named SSD and YOLO has been adopted for object detection and localization. The detection models are further combined with different tracking algorithms, including GOTURN, MEDIANFLOW, TLD, KCF, MIL, and BOOSTING. These algorithms, along with detection models, help to track and predict the trajectories of detected objects. The pre-trained models are employed; therefore, the generalization performance is also investigated through testing the models on various sequences of top view data set. The detection models achieved maximum True Detection Rate 93% to 90% with a maximum 0.6% False Detection Rate. The tracking results of different algorithms are nearly identical, with tracking accuracy ranging from 90% to 94%. Furthermore, a discussion has been carried out on output results along with future guidelines.      

Bayesian Multidimensional Scaling for Location Awareness in Hybrid-Internet of Underwater Things

Localization of sensor nodes in the internet of underwater things(IoUT)is of considerable significance due to its various applications,such as navigation,data tagging,and detection of underwater objects.Therefore,in this paper,we propose a hybrid Bayesian multidimensional scaling(BMDS)based localization technique that can work on a fully hybrid IoUT network where the nodes can communicate using either optical,magnetic induction,and acoustic technologies.These communication technologies are already used for communication in the underwater environment;however,lacking localization solutions.Optical and magnetic induction communication achieves higher data rates for short communication.On the contrary,acoustic waves provide a low data rate for long-range underwater communication.The proposed method collectively uses optical,magnetic induction,and acoustic communication-based ranging to estimate the underwater sensor nodes’final locations.Moreover,we also analyze the proposed scheme by deriving the hybrid Cramer-Rao lower bound(H-CRLB).Simulation results provide a complete comparative analysis of the proposed method with the literature.     

Automated disease diagnosis and precaution recommender system using supervised machine learning

Similar to many other professions, the medical field has undergone immense automation during the past decade. The complexity and rise of healthcare data led to a surge in artificial intelligence applications. Despite increased automation, such applications lack the desired accuracy and efficiency for healthcare problems. To address the aforementioned issue, this study presents an automatic health care system that can effectively substitute a doctor at an initial stage of diagnosis and help save time by recommending the necessary precautions. The proposed approach comprises two modules where Modul-1 aims at training the machine learning models using the disease symptoms dataset and their corresponding symptoms and precautions. Preprocessing and feature extraction are done as prerequisite steps. In Module-1 several algorithms are applied to the disease dataset such as support vector machine, random forest, extra trees classifier, logistic regression, multinomial naive Bayes, and decision tree. Module-2 interacts with the user (patient) through which the patient can describe the illness symptoms using a microphone. The voice data are transformed into text using the Google speech recognizer. The transformed data is later used with the trained model for disease prediction, as well as, recommending the precautions. The proposed approach achieves an accuracy of 99.9% during the real-time evaluation.

     

A Comprehensive Utility Function for Resource Allocation in Mobile Edge Computing

In mobile edge computing (MEC), one of the important challenges is how much resources of which mobile edge server (MES) should be allocated to which user equipment (UE). The existing resource allocation schemes only consider CPU as the requested resource and assume utility for MESs as either a random variable or dependent on the requested CPU only. This paper presents a novel comprehensive utility function for resource allocation in MEC. The utility function considers the heterogeneous nature of applications that a UE offloads to MES. The proposed utility function considers all important parameters, including CPU, RAM, hard disk space, required time, and distance, to calculate a more realistic utility value for MESs. Moreover, we improve upon some general algorithms, used for resource allocation in MEC and cloud computing, by considering our proposed utility function. We name the improved versions of these resource allocation schemes as comprehensive resource allocation schemes. The UE requests are modeled to represent the amount of resources requested by the UE as well as the time for which the UE has requested these resources. The utility function depends upon the UE requests and the distance between UEs and MES, and serves as a realistic means of comparison between different types of UE requests. Choosing (or selecting) an optimal MES with the optimal amount of resources to be allocated to each UE request is a challenging task. We show that MES resource allocation is sub-optimal if CPU is the only resource considered. By taking into account the other resources, i.e., RAM, disk space, request time, and distance in the utility function, we demonstrate improvement in the resource allocation algorithms in terms of service rate, utility, and MES energy consumption.     

Green and eco-friendly synthesis of cobalt-oxide nanoparticle: Characterization and photo-catalytic activity

Cobalt-oxide nanoparticles (NPs) were fabricated using Punica granatum peel extract from cobalt nitrate hexahydrate at low temperature. The synthesized cobalt-oxide NPs were characterized using X-ray powder diffraction, scanning electron microscopy, energy-dispersive X-ray, atomic force microscopy, fourier transform infrared spectroscopy and UV-visible techniques. The cobalt-oxide NPs were in highly uniform shape and size was in the size of 40–80 nm. Photo-catalytic activity (PCA) of the synthesized NPs was evaluated by degrading Remazol Brilliant Orange 3R (RBO 3R) dye and a degradation of 78.45% was achieved (dye conc. 150 mg/L) using 0.5 g cobalt-oxide NPs for 50 min irradiation time. In view of eco-benign and cost-effective nature, the present investigation revealed that P. granatum could be used for the synthesis of cobalt-oxide NPs for photo-catalytic applications.