Micro Electromechanical Systems (MEMS) based microfluidic devices have gained popularity in biomedicine field over the last few years. In this paper, a comprehensive overview of microfluidic devices such as micropumps and microneedles has been presented for biomedical applications. The aim of this paper is to present the major features and issues related to micropumps and microneedles, e.g., working principles, actuation methods, fabrication techniques, construction, performance parameters, failure analysis, testing, safety issues, applications, commercialization issues and future prospects. Based on the actuation mechanisms, the micropumps are classified into two main types, i.e., mechanical and non-mechanical micropumps. Microneedles can be categorized according to their structure, fabrication process, material, overall shape, tip shape, size, array density and application. The presented literature review on micropumps and microneedles will provide comprehensive information for researchers working on design and development of microfluidic devices for biomedical applications.

In this paper, we present design, fabrication and coupled multifield analysis of hollow out-of-plane silicon microneedles with piezoelectrically actuated microfluidic device for transdermal drug delivery (TDD) system for treatment of cardiovascular or hemodynamic disorders such as hypertension. The mask layout design and fabrication process of silicon microneedles and reservoir involving deep reactive ion etching (DRIE) is first presented. This is followed by actual fabrication of silicon hollow microneedles by a series of combined isotropic and anisotropic etching processes using inductively coupled plasma (ICP) etching technology. Then coupled multifield analysis of a MEMS based piezoelectrically actuated device with integrated silicon microneedles is presented. The coupledfield analysis of hollow silicon microneedle array integrated with piezoelectric micropump has involved structural and fluid field couplings in a sequential structural-fluid analysis on a three-dimensional model of the microfluidic device. The effect of voltage and frequency on silicon membrane deflection and flow rate through the microneedle is investigated in the coupled field analysis using multiple code coupling method. The results of the present study provide valuable benchmark and prediction data to fabricate optimized designs of the silicon hollow microneedle based microfluidic devices for transdermal drug delivery applications.

Bioengineered veins can benefit humans needing bypass surgery, dialysis, and now, in the treatment of varicose veins. The implant of this vein in varicose veins has significant advantages over the conventional treatment methods. Deep vein thrombosis (DVT), vein patch repair, pulmonary embolus, and tissue-damaging problems can be solved with this implant. Here, the authors have proposed biomedical microdevices as an alternative for varicose veins. MATLAB and ANSYS Fluent have been used for simulations of blood flow for bioengineered veins. The silver based microchannel has been fabricated by using a micromachining process. The dimensions of the silver substrates are 51 mm, 25 mm, and 1.1 mm, in length, width, and depth respectively. The dimensions of microchannels grooved in the substrates are 0.9 mm in width and depth. The boundary conditions for pressure and velocity were considered, from 1.0 kPa to 1.50 kPa, and 0.02 m/s to 0.07 m/s, respectively. These are the actual values of pressure and velocity in varicose veins. The flow rate of 5.843 (0.1 nL/s) and velocity of 5.843 cm/s were determined at Reynolds number 164.88 in experimental testing. The graphs and results from simulations and experiments are in close agreement. These microchannels can be inserted into varicose veins as a replacement to maintain the excellent blood flow in human legs.

No abstract

In recent past years, Deep Learning presented an excellent performance in different areas like image recognition, pattern matching, and even in cybersecurity. The Deep Learning has numerous advantages including fast solving complex problems, huge automation, maximum application of unstructured data, ability to give high quality of results, reduction of high costs, no need for data labeling, and identification of complex interactions, but it also has limitations like opaqueness, computationally intensive, need for abundant data, and more complex algorithms. In our daily life, we used many applications that use Deep Learning models to make decisions based on predictions, and if Deep Learning models became the cause of misprediction due to internal/external malicious effects, it may create difficulties in our real life. Furthermore, the Deep Learning training models often have sensitive information of the users and those models should not be vulnerable and expose security and privacy. The algorithms of Deep Learning and machine learning are still vulnerable to different types of security threats and risks. Therefore, it is necessary to call the attention of the industry in respect of security threats and related countermeasures techniques for Deep Learning, which motivated the authors to perform a comprehensive survey of Deep Learning security and privacy security challenges and countermeasures in this paper. We also discussed the open challenges and current issues.