A surface treatment method for improving the attachment of PDMS: acoustofluidics as a case study.

A method for a permanent surface modification of polydimethylsiloxane (PDMS) is presented. A case study on the attachment of PDMS and the lithium niobate (LiNbO3) wafer for acoustofluidics applications is presented as well. The method includes a protocol for chemically treating the surface of PDMS to strengthen its bond with the LiNbO3 surface. The PDMS surface is modified using the 3-(trimethoxysilyl) propyl methacrylate (TMSPMA) silane reagent. The effect of silane treatment on the hydrophilicity, morphology, adhesion strength to LiNbO3, and surface energy of PDMS is investigated. The results demonstrated that the silane treatment permanently increases the hydrophilicity of PDMS and significantly alters its morphology. The bonding strength between PDMS and LiNbO3increased with the duration of the silane treatment, reaching a maximum of approximately 500 kPa. To illustrate the effectiveness of this method, an acoustofluidic device was tested, and the device demonstrated very promising enhanced bonding and sealing capabilities with particle manipulation at a flow rate of up to 1 L/h by means of traveling surface acoustic waves (TSAW). The device was reused multiple times with no fluid leakage or detachment issues. The utility of the presented PDMS surface modification method is not limited to acoustofluidics applications; it has the potential to be further investigated for applications in various scientific fields in the future.

Numerical Modeling Using Immersed Boundary-Lattice Boltzmann Method and Experiments for Particle Manipulation under Standing Surface Acoustic Waves.

In this work, we employed the Immersed Boundary-Lattice Boltzmann Method (IB-LBM) to simulate the motion of a microparticle in a microchannel under the influence of a standing surface acoustic wave (SSAW). To capture the response of the target microparticle in a straight channel under the effect of the SSAW, in-house code was built in C language. The SSAW creates pressure nodes and anti-nodes inside the microchannel. Here, the target particle was forced to traverse toward the pressure node. A mapping mechanism was developed to accurately apply the physical acoustic force field in the numerical simulation. First, benchmarking studies were conducted to compare the numerical results in the IB-LBM with the available analytical, numerical, and experimental results. Next, several parametric studies were carried out in which the particle types, sizes, compressibility coefficients, and densities were varied. When the SSAW is applied, the microparticles (with a positive acoustic contrast factor) move toward the pressure node locations during their motion in the microchannel. Hence, their steady-state locations are controlled by adjusting the pressure nodes to the desired locations, such as the centerline or near the microchannel sidewalls. Moreover, the geometric parameters, such as radius, density, and compressibility of the particles affect their transient response, and the particles ultimately settle at the pressure nodes. To validate the numerical work, a microfluidic device was fabricated in-house in the cleanroom using lithographic techniques. Experiments were performed, and the target particle was moved either to the centerline or sidewalls of the channel, depending on the location of the pressure node. The steady-state placements obtained in the computational model and experiments exhibit excellent agreement and are reported.

Secure Bluetooth Communication in Smart Healthcare Systems: A Novel Community Dataset and Intrusion Detection System.

Smart health presents an ever-expanding attack surface due to the continuous adoption of a broad variety of Internet of Medical Things (IoMT) devices and applications. IoMT is a common approach to smart city solutions that deliver long-term benefits to critical infrastructures, such as smart healthcare. Many of the IoMT devices in smart cities use Bluetooth technology for short-range communication due to its flexibility, low resource consumption, and flexibility. As smart healthcare applications rely on distributed control optimization, artificial intelligence (AI) and deep learning (DL) offer effective approaches to mitigate cyber-attacks. This paper presents a decentralized, predictive, DL-based process to autonomously detect and block malicious traffic and provide an end-to-end defense against network attacks in IoMT devices. Furthermore, we provide the BlueTack dataset for Bluetooth-based attacks against IoMT networks. To the best of our knowledge, this is the first intrusion detection dataset for Bluetooth classic and Bluetooth low energy (BLE). Using the BlueTack dataset, we devised a multi-layer intrusion detection method that uses deep-learning techniques. We propose a decentralized architecture for deploying this intrusion detection system on the edge nodes of a smart healthcare system that may be deployed in a smart city. The presented multi-layer intrusion detection models achieve performances in the range of 97-99.5% based on the F1 scores.

A review of active and passive hybrid systems based on Dielectrophoresis for the manipulation of microparticles.

Particle separation is essential in a broad range of systems and has several biological applications. Microfluidics has emerged as a potentially transformational method for particle separation. The approach manipulates and separates particles at the micrometer scale by using well-defined microstructures and precisely managed force fields. Depending on the source of the principal manipulating forces, particle manipulation and separation in microfluidics may be classified as active or passive. Passive microfluidic devices depend on drag and inertial forces and microchannel structure, while active microfluidic systems rely on external force fields. Active microfluidics, in general, can properly control and place particles of interest in real time. Due to the low flow rate, the residual time required to apply an appropriate external manipulating force to the target particles is reduced, thereby limiting overall throughput. Passive microfluidics, on the other hand, has a simple architecture, robustness, and high throughput. Hybrid techniques, which combine active and passive processes, have been created to address the shortcomings of each while maximizing the benefits of each. Numerous hybrid techniques for particle separation have been developed. This study reviews the most recent developments in the field of hybrid devices based on dielectrophoresis. Dielectrophoresis-passive and dielectrophoresis-active hybrid approaches are described and evaluated. Dielectrophoresis-inertial, dielectrophoresis-hydrophoresis, dielectrophoresis- deterministic lateral displacement, and insulator-based dielectrophoresis are examples of dielectrophoresis-passive hybrid devices. Dielectrophoresis with acoustophoresis, magnetophoresis, and optophoresis are examples of dielectrophoresis-active devices. Each hybrid system will be assessed based on its operating principles, advantages, and disadvantages. Following that, a comprehensive explanation of dielectrophoresis physical concepts and operating procedures will be offered. As part of this review, the advantages and disadvantages of DEP-based separation devices will be examined. All these hybrid devices will be thoroughly examined and evaluated. Finally, a summary of present difficulties in the hybrid separation sector will be offered, as well as future suggestions and aspirations.

Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks.

Drones are becoming increasingly popular not only for recreational purposes but in day-to-day applications in engineering, medicine, logistics, security and others. In addition to their useful applications, an alarming concern in regard to the physical infrastructure security, safety and privacy has arisen due to the potential of their use in malicious activities. To address this problem, we propose a novel solution that automates the drone detection and identification processes using a drone's acoustic features with different deep learning algorithms. However, the lack of acoustic drone datasets hinders the ability to implement an effective solution. In this paper, we aim to fill this gap by introducing a hybrid drone acoustic dataset composed of recorded drone audio clips and artificially generated drone audio samples using a state-of-the-art deep learning technique known as the Generative Adversarial Network. Furthermore, we examine the effectiveness of using drone audio with different deep learning algorithms, namely, the Convolutional Neural Network, the Recurrent Neural Network and the Convolutional Recurrent Neural Network in drone detection and identification. Moreover, we investigate the impact of our proposed hybrid dataset in drone detection. Our findings prove the advantage of using deep learning techniques for drone detection and identification while confirming our hypothesis on the benefits of using the Generative Adversarial Networks to generate real-like drone audio clips with an aim of enhancing the detection of new and unfamiliar drones.

DroneRF dataset: A dataset of drones for RF-based detection, classification and identification.

Modern technology has pushed us into the information age, making it easier to generate and record vast quantities of new data. Datasets can help in analyzing the situation to give a better understanding, and more importantly, decision making. Consequently, datasets, and uses to which they can be put, have become increasingly valuable commodities. This article describes the DroneRF dataset: a radio frequency (RF) based dataset of drones functioning in different modes, including off, on and connected, hovering, flying, and video recording. The dataset contains recordings of RF activities, composed of 227 recorded segments collected from 3 different drones, as well as recordings of background RF activities with no drones. The data has been collected by RF receivers that intercepts the drone's communications with the flight control module. The receivers are connected to two laptops, via PCIe cables, that runs a program responsible for fetching, processing and storing the sensed RF data in a database. An example of how this dataset can be interpreted and used can be found in the related research article "RF-based drone detection and identification using deep learning approaches: an initiative towards a large open source drone database" (Al-Sa'd et al., 2019).

Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach.

Recent advances in mobile technology have created a shift towards using battery-driven devices in remote monitoring settings and smart homes. Clinicians are carrying out diagnostic and screening procedures based on the electrocardiogram (ECG) signals collected remotely for outpatients who need continuous monitoring. High-speed transmission and analysis of large recorded ECG signals are essential, especially with the increased use of battery-powered devices. Exploring low-power alternative compression methodologies that have high efficiency and that enable ECG signal collection, transmission, and analysis in a smart home or remote location is required. Compression algorithms based on adaptive linear predictors and decimation by a factor B / K are evaluated based on compression ratio (CR), percentage root-mean-square difference (PRD), and heartbeat detection accuracy of the reconstructed ECG signal. With two databases (153 subjects), the new algorithm demonstrates the highest compression performance ( CR = 6 and PRD = 1.88 ) and overall detection accuracy (99.90% sensitivity, 99.56% positive predictivity) over both databases. The proposed algorithm presents an advantage for the real-time transmission of ECG signals using a faster and more efficient method, which meets the growing demand for more efficient remote health monitoring.

Occurrence of aflatoxin in commodities imported into Qatar, 1997-2000.

The occurrence of aflatoxin in commodities imported into Qatar was investigated from 1999 to 2000. During the 4 years, 351 samples of susceptible commodities were analysed. Aflatoxin was detected in 71 (20%) samples in the range 0.1-20 microg kg(-1) and in 50 (14%) samples above the permitted level of 20 microg kg(-1). The highest incidence and levels of aflatoxin contamination were recorded in pistachio without shell followed by pistachio with shell. Aflatoxin levels >20 microg kg(-1) in the pistachio samples varied from 8.7 to 33%. The highest level of total aflatoxin found in pistachio without shell was 289 microg kg(-1). A few samples of corn and corn products (three of 54 analysed), peanut and peanut products (nine of 42 analysed) and other nuts like almond, walnut and cashew (one of 40 analysed) were found contaminated with low levels (0.1-20 microg kg(-1)) of aflatoxins. Only one sample of custard powder and one sample of roasted peanut were found with aflatoxin >20 microg kg(-1)