Search

As a new type of aerial robotics, drones are easy to use and inexpensive, which has facilitated their acquisition by individuals and organizations. This unequivocal and widespread presence of amateur drones may cause many dangers, such as privacy breaches by reaching sensitive locations of authorities and individuals. In this paper, we summarize the performance-affecting factors and major obstacles to drone use and provide a brief background of deep learning. Then, we summarize the types of UAVs and the related unethical behaviors, safety, privacy, and cybersecurity concerns. Then, we present a comprehensive literature review of current drone detection methods based on deep learning. This area of research has arisen in the last two decades because of the rapid advancement of commercial and recreational drones and their combined risk to the safety of airspace. Various deep learning algorithms and their frameworks with respect to the techniques used to detect drones and their areas of applications are also discussed. Drone detection techniques are classified into four categories: visual, radar, acoustics, and radio frequency-based approaches. The findings of this study prove that deep learning-based detection and classification of drones looks promising despite several challenges. Finally, we provide some recommendations to meet future expectations. [ABSTRACT FROM AUTHOR]

Quantum dot (QD) sensitized TiO 2 is considered as a highly promising photoanode material for photoelectrochemical (PEC) solar hydrogen production. However, due to its limited stability, the photoanode suffers from degradation of its long-term PEC performance. Here, we report the design and characterization of a high-efficiency and long-term stable Pt-free PEC cell. The photoanode is composed of a mesoporous TiO 2 nanoparticle film sensitized with “giant” core@shell QDs for PEC solar hydrogen generation. The thick shell enhances light absorption in the visible range, increases the stability of the QDs and does not inhibit charge separation, injection and transport, needed for proper operation of the device. We prepared thin films of Cu 2 S nanoflakes through a simple and reproducible procedure, and used them as counter-electrodes replacing the standard Pt film, resulting in equivalent performances of the PEC cell. We obtained an unprecedented photocurrent density (~10 mA/cm 2 ) for “giant” QDs based PEC devices (and corresponding H 2 generation) and a very promising stability, indicating that the proposed cell architecture is a good candidate for long-term stable QD-based PEC solar hydrogen generation. [ABSTRACT FROM AUTHOR]

Mobile cloud computing (MCC) plays a significant role in obtaining virtualized data stored on cloud servers without additional waiting time. However, the limited battery power of mobile devices limits access to ample data. To address this problem, job scheduling algorithms based on completion time constraints are introduced to reduce energy consumption. The time complexity of scheduling algorithms increases for applications not based on completion time constraints. This paper proposes a hybrid energy-efficient (HEE) paradigm to support the Internet of Things (IoT). The proposed HEE algorithm leverages the features from known pheromone termite (PT) and cuckoo search (CS) algorithms, supporting IoT-enabled users and devices. The integration of PT and CS helps determine the optimal path that leads to improved throughput and reduced energy consumption. The performance of the HEE algorithm is considerably higher than that of the PT and CS algorithms when the number of tasks increases. The proposed paradigm involves several modules (e.g., physical machine (PM) module, controller module, blockchain-enabled authentication module, and data mining-enabled mobile cloud user (MCU) authentication). The PM module helps to attain a higher energy efficiency, regardless of whether the cloud's level of utilization is at its maximum efficiency. The controller module schedules the PMs efficiently and thus effectively provides resources to MCUs. Securing the identity of MCUs is of paramount significance. Safeguarding the identity of the MCU, the blockchain-enabled authentication module is employed, which authenticates the MCU to control the malicious role of the adversary. Furthermore, the data-mining blockchain-enabled authentication module authenticates the insider and outsider MCUs based on the data contents. The legitimate MCUs are allowed, and illegitimate MCUs are restricted. The proposed HEE paradigm is tested on the CloudSim simulator. Finally, the performance of the HEE paradigm is compared with that of known algorithms in terms of energy, security, throughput, and latency. • Proposes a hybrid energy-efficient (HEE) paradigm to support the IoT. • Leverages the features from known pheromone termite (PT) and cuckoo search (CS). • The proposed algorithm has the support of a data oriented detection algorithm. • The Blockchain enabled detection module is incorporated to authenticate the MCU to control malicious adversaries. [ABSTRACT FROM AUTHOR]

Accurate detection of highly toxic aromatic nitro compounds in the water medium is urgent for the protection of human health and eco system. In the present study, we report the graphene oxide/ magnesium oxide nanocomposite (GO/MgO-NC) has been proposed for electrochemical analysis of nitrobenzene (NBZ) in water samples. The GO/MgO-NC was characterized by X-ray diffraction, Raman spectroscopy, scanning electron microscope, transmission electron microscopy, and energy dispersive X-ray analysis. Likewise, the electrochemical behaviour of NBZ was investigated through cyclic voltammetry and differential pulse voltammetry. The GO/MgO-NC electrode shows an excellent NBZ sensing properties compared to other electrodes. Under optimized conditions, the GO/MgO-NC/GCE can well measure the concentrations of NBZ in the range of 0.1–38.9 µM and 58.5–2333.5 µM with a low limit of detection of 0.01 µM. Besides, the GO/MgO-NC/GCE exhibits excellent selectivity, repeatability, reproducibility and stability. More importantly, the proposed GO/MgO-NC/GCE was further expanded to quantify NBZ in water samples with recoveries between 99.35–99.80%, providing an effective approach for the trace of NBZ in environmental medium. [Display omitted] • A hydrothermal method was used to prepare the magnesium oxide (MgO) decorated on graphene oxide (GO). • GO/MgO owing electrochemical activity towards the detection of nitrobenzene (NBZ). • The designed sensor has a wide working range and with low detection (LOD) limit. • A multi-purpose and efficient electrocatalyst for the electrochemical determination of NBZ in water species. • The nanocomposite provides rapid electron transport and shorten diffusion pathway. [ABSTRACT FROM AUTHOR]

Polymer matrix composites are excellent materials for a variety of industrial applications. They have superior mechanical, thermal and electrical properties, making them preferable to traditional materials such as metal. To make polymer matrix composite materials, thermosetting, elastomers and thermoplastic polymers are mainly the three types of polymers that can be utilized as matrices. In comparison to thermosetting and elastomers polymers, carbon fiber reinforced thermoplastic (CFRTP), is the subject of this research, are gaining popularity in many industrial sectors due to its recyclability, simplicity of processing, good characteristics, flexibility, and less production time. This review covers conventional and state-of-the-art manufacturing techniques of CFRTP. Moreover, the potential and existing of CFRTP's application as well as the techniques of carbon fiber recovery and recycling methods of such materials were also examined. Overall, this study considers the research and development on manufacturing CFRTP and recycling techniques of polymer composites to recover carbon fiber materials. [Display omitted] • Conventional and state-of-the-art manufacturing techniques of carbon fiber reinforced thermoplastic were investigated. • The potential and existing of carbon fiber reinforced thermoplastic application were reviewed. • Techniques of carbon fiber recovery and recycling methods of polymer composites materials were examined. [ABSTRACT FROM AUTHOR]

A solid oxide fuel cell (SOFC) is an energy converter device that directly converts the fuel's chemical energy into thermal and electrical energies. However, the performance of a SOFC is sensitive to the operating conditions, which indicates the need to control the operating parameters such as the operating temperature and inlet gas physical and chemical properties. In this paper, a three-dimensional (3- D) computational fluid dynamics (CFD) model of a button SOFC has been developed based on fluid flow, mass and heat transfer, and current and potential transport to study the performance of the button SOFC at different operating temperatures and flow rates. The results of all the investigated operating conditions have been validated with an in-house button SOFC. The results showed that the current density of the button SOFC increased significantly, to about 108.5% at average cell voltage 0.6 V and at oxygen flow rate of 1.8 L min−1 and hydrogen flow rate of 0.6 L min−1, when the operating temperature increased from 973 to 1023 K. However, less sensitivity has been found at the same average voltage of 0.6 V and an operating temperature of 973 K, the increase in the current density was about 9.7% when the flow rate of the oxygen increased from 0.4 to 0.6 L min−1, and the hydrogen flow rate increased from 1.2 to 1.8 L min−1, respectively; this was in good agreement with the experimental results. [ABSTRACT FROM AUTHOR]