Search

Drowning is among the most common reasons for children’s death aged one to fourteen around the globe, ranking as the third leading cause of unintentional injury death. With rising populations and the growing popularity of swimming pools in hotels and villas, the incidence of drowning has accelerated. Accordingly, the development of systems for detecting and preventing drowning has become increasingly critical to provide safe swimming settings. In this paper, we propose a comprehensive review of recent existing advancements in automated drowning detection and prevention systems. The existing approaches can be broadly categorized according to their objectives into two main groups: detection-based systems, which alert lifeguards or parents to perform manual rescues, and detection and rescue-based systems, which integrate detection with automatic rescue mechanisms. Automatic drowning detection approaches could be further categorized into computer vision-based approaches, where camera-captured images are analyzed by machine learning algorithms to detect instances of drowning, and sensing-based approaches, where sensing instruments are attached to swimmers to monitor their physical parameters. We explore the advantages and limitations of each approach. Additionally, we highlight technical challenges and unresolved issues related to this domain, such as data imbalance, accuracy, privacy concerns, and integration with rescue systems. We also identify future research opportunities, emphasizing the need for more advanced AI models, uniform datasets, and better integration of detection with autonomous rescue mechanisms. This study aims to provide a critical resource for researchers and practitioners, facilitating the development of more effective systems to enhance water safety and minimize drowning incidents.

Advancements in wireless communication and automation have revolutionized mobility systems, notably through autonomous vehicles and unmanned aerial vehicles (UAVs). UAV spatial coordinates, determined via Global Positioning System (GPS) signals, are susceptible to cyberattacks due to unencrypted and unauthenticated transmissions with GPS spoofing being a significant threat. To mitigate these vulnerabilities, intrusion detection systems (IDSs) for UAVs have been developed and enhanced using machine learning (ML) algorithms. However, Adversarial Machine Learning (AML) has introduced new risks by exploiting ML models. This study presents a UAV-IDS employing AML methodology to enhance the detection and classification of GPS spoofing attacks. The key contribution is the development of an AML detection model that significantly improves UAV system robustness and security. Our findings indicate that the model achieves a detection accuracy of 98%, demonstrating its effectiveness in managing large-scale datasets and complex tasks. This study emphasizes the importance of physical layer security for enhancing IDSs in UAVs by introducing a novel detection model centered on an adversarial training defense method and advanced deep learning techniques.

Wireless physical layer authentication has emerged as a promising approach to wireless security. The topic of wireless node classification and recognition has experienced significant advancements due to the rapid development of deep learning techniques. The potential of using deep learning to address wireless security issues should not be overlooked due to its considerable capabilities. Nevertheless, the utilization of this approach in the classification of wireless nodes is impeded by the lack of available datasets. In this study, we provide two models based on a data-driven approach. First, we used generative adversarial networks to design an automated model for data augmentation. Second, we applied a convolutional neural network to classify wireless nodes for a wireless physical layer authentication model. To verify the effectiveness of the proposed model, we assessed our results using an original dataset as a baseline and a generated synthetic dataset. The findings indicate an improvement of approximately 19% in classification accuracy rate.

Printed energy storage components attracted attention for being incorporated into bendable electronics. In this research, a homogeneous and stable ink based on vanadium dioxide (VO2) is hydrothermally synthesized with a non-toxic solvent. The structural and morphological properties of the synthesized material are determined to be well-crystalline monoclinic-phase nanoparticles. The charge storage mechanisms and evaluations are specified for VO2 electrodes, gold (Au) electrodes, and VO2/Au electrodes using cyclic voltammetry, galvanostatic charge–discharge, and electrochemical impedance spectroscopy. The VO2 electrode shows an electrical double layer and a redox reaction in the positive and negative voltage ranges with a slightly higher areal capacitance of 9 mF cm−2. The VO2/Au electrode exhibits an areal capacitance of 16 mF cm−2, which is double that of the VO2 electrode. Due to the excellent electrical conductivity of gold, the areal capacitance 18 mF cm−2 of the Au electrode is the highest among them. Based on that, Au positive electrodes and VO2 negative electrodes are used to build an asymmetric supercapacitor. The device delivers an areal energy density of 0.45 μWh cm−2 at an areal power density of 70 μW cm−2 at 1.4 V in the aqueous electrolyte of potassium hydroxide. We provide a promising electrode candidate for cost-effective, lightweight, environmentally friendly printed supercapacitors.

Inorganic nitrate (NO3−) has emerged as a potential ergogenic aid over the last couple of decades. While recent systematic reviews and meta-analyses have suggested some small positive effects of NO3− supplementation on performance across a range of exercise tasks, the effect of NO3− supplementation on performance during single and repeated bouts of short-duration, high-intensity exercise is unclear. This review was conducted following PRISMA guidelines. MEDLINE and SPORTDiscus were searched from inception to January 2023. A paired analysis model for cross-over trials was incorporated to perform a random effects meta-analysis for each performance outcome and to generate standardized mean differences (SMD) between the NO3− and placebo supplementation conditions. The systematic review and meta-analysis included 27 and 23 studies, respectively. Time to reach peak power (SMD: 0.75, p = 0.02), mean power output (SMD: 0.20, p = 0.02), and total distance covered in the Yo-Yo intermittent recovery level 1 test (SMD: 0.17, p < 0.0001) were all improved after NO3− supplementation. Dietary NO3− supplementation had small positive effects on some performance outcomes during single and repeated bouts of high-intensity exercise. Therefore, athletes competing in sports requiring single or repeated bouts of high-intensity exercise may benefit from NO3− supplementation.

The physical layer security of wireless networks is becoming increasingly important because of the rapid development of wireless communications and the increasing security threats. In addition, because of the open nature of the wireless channel, authentication is a critical issue in wireless communications. Physical layer authentication (PLA) is based on distinctive features to provide information-theory security and low complexity. However, although many researchers are interested in the PLA and how it might be used to improve wireless security, there is surprisingly little literature on the subject, with no systematic overview of the current state-of-the-art PLA and the main foundations involved. Therefore, this paper aims to determine and systematically compare existing studies in the physical layer authentication. This study showed whether machine learning approaches in physical layer authentication models increased wireless network security performance and demonstrated the latest techniques used in PLA. Moreover, it identified issues and suggested directions for future research. This study is valuable for researchers and security model developers interested in using machine learning (ML) and deep learning (DL) approaches for PLA in wireless communication systems in future research and designs.

Drowning is one of the top five causes of death for children aged 1–14 worldwide. According to data from the World Health Organization (WHO), drowning is the third most common reason for unintentional fatalities. Designing a drowning detection system is becoming increasingly necessary in order to ensure the safety of swimmers, particularly children. This paper presents a computer vision and deep learning-based early drowning detection approach. We utilized five convolutional neural network models and trained them on our data. These models are SqueezeNet, GoogleNet, AlexNet, ShuffleNet, and ResNet50. ResNet50 showed the best performance, as it achieved 100% prediction accuracy with a reasonable training time. When compared to other approaches, the proposed approach performed exceptionally well in terms of prediction accuracy and computational cost.

In this work, we introduce an environmental and sustainable approach to grow free standing heterogeneous Cu2O-Cu(OH)2 nanocomposites on a Cu mesh using spinach leaf extract and glycerol. Structural characterizations for samples annealed at 200 °C revealed that there is more Cu(OH)2 than Cu2O on the mesh surface. The photocatalytic activity of the green synthesized catalyst was studied for degradation of a cationic dye methylene blue (MB), an anionic dye methyl orange (MO) and a mixture of both dyes. The effect of changing the dye’s initial pH value on the photodegradation process was explored. After 40 min of irradiation under sunlight, with a maximum intensity of 5 mW/cm2, a basic MB dye (pH-11) showed about 80% color removal with an average kinetic rate of 94.5 m·min−1. In contrast, 93% of the acidified MO dye (pH-2) was degraded with an average kinetic rate of 126.5 m·min−1. Moreover, the versatility of the Cu2O-Cu(OH)2@Cu mesh was evaluated using a remarkable selective separability for a mixture of MB and MO at pH = 2, in the dark and under normal sunlight. Such promising outcomes indicate the potential of our green composites to degrade dyes as both photocatalysts under daylight and as Fenton-like catalysts in darkness.

Although numerous methods have been widely used to prepare nickel hydroxide materials, there is still a demand for lowering the required heating time, temperature, and cost with maintaining a high-quality nanomaterial for electrochemical energy storage. In this research, we study the relationship between microwave-assisted heating parameters and material properties of nickel hydroxide nanoflakes and evaluate their effect on electrochemical performance. X-ray diffraction spectra show that the samples prepared at the highest temperature of 220 °C have crystallized in the beta phase of nickel hydroxide crystal. While the sample synthesized at 150 °C in 30 min contains both beta and alpha phases. Interestingly, we obtained the pure alpha phase at 150 °C in just 10 min. A scanning electron microscope shows that increasing the temperature and heating time leads to enlarging the diameter of the macro-porous flower-like clusters of interconnected nanoflakes. Electrochemical measurements in potassium hydroxide electrolytes demonstrate that the alpha phase’s electrodes have much higher capacities than samples containing only the beta phase. The maximum areal capacity of 17.7 µAh/cm2 and gravimetric capacity of 35.4 mAh/g are achieved, respectively, at 0.2 mA/cm2 and 0.4 A/g, with a small equivalent series resistance value of 0.887 ohms on flexible stainless-steel mesh as a current collector. These improved nickel hydroxide electrodes can be ascribed to utilizing the diffusion-controlled redox reactions that are detected up to the high scan of 100 mV/s. Such fast charge-discharge processes expand the range of potential applications. Our nickel hydroxide electrode, with its rapid preparation at medium temperature, can be a cost-effective candidate for flexible supercapacitors and batteries.

The demand for supercapacitors has been high during the integration of renewable energy devices into the electrical grid. Although activated carbon materials have been widely utilized as supercapacitor electrodes, the need for economic and sustainable processes to extract and activate carbon nanomaterials is still crucial. In this work, the biomass waste of date palm fronds is converted to a hierarchical porous nanostructure of activated carbon using simple ball-milling and sonication methods. Chemical and physical activation agents of NaOH and CO2, receptively, were applied on two samples separately. Compared with the specific surface area of 603.5 m2/g for the CO2-activated carbon, the NaOH-activated carbon shows a higher specific surface area of 1011 m2/g with a finer nanostructure. Their structural and electrochemical properties are functionalized to enhance electrode–electrolyte contact, ion diffusion, charge accumulation, and redox reactions. Consequently, when used as electrodes in an H2SO4 electrolyte for supercapacitors, the NaOH-activated carbon exhibits an almost two-fold higher specific capacitance (125.9 vs. 56.8 F/g) than that of the CO2-activated carbon at the same current density of 1 A/g. Moreover, using carbon cloth as a current collector provides mechanical flexibility to our electrodes. Our practical approach produces cost-effective, eco-friendly, and flexible activated carbon electrodes with a hierarchical porous nanostructure for supercapacitor applications.

This systematic review examined the effects of whole protein and commonly consumed amino acid supplements on markers of exercise-induced inflammation and oxidative stress and was reported according to the PRISMA guidelines. MEDLINE and SPORTDiscus were searched from inception until June 2021. The inclusion criteria were randomized clinical trials in humans, healthy adult participants (≥18 years), dietary protein/amino acid interventions, and measurements of oxidative stress/the redox status or inflammation post-exercise. The Cochrane Collaboration risk of bias 2 tool was used to critically appraise the studies. Data extracted from thirty-four studies were included in the systematic review (totaling 757 participants with only 10 females; age range 19–40 years). The included trials examined five types of whole protein and seven different amino acids supplements; most studies (n = 20) failed to identify statistically significant effects on markers of inflammation or oxidative stress after exercise; some (n = 14) showed either anti-inflammatory or antioxidant effects on some, but not all, markers. In conclusion, we found weak and inconsistent evidence that dietary protein/amino acid interventions can modify exercise-induced changes in oxidative stress and inflammation. However, given that these were not the primary outcomes in many of the included studies and many had design limitations, further research is warranted (Open Science Framework registration number: 10.17605/OSF.IO/AGUR2).

Supercapacitors have been considered as one of the main energy storage devices. Recently, electrospun nanofibers have served as promising supercapacitor electrodes because of their high surface area, high porosity, flexibility, and resistance to aggregation. Here, we investigate the effects of electrospinning parameters and nickel precursors on the nanostructure of electrospun nickel oxide (NiO), as well as on their electrochemical performance as supercapacitor electrodes. In contrast to the case of using nickel nitrate, increasing the nickel acetate molar concentration maintains the flexible fibrous sheet morphology of the as-spun sample during the polycondensation and calcination of NiO. As a result, our flexible electrode of NiO nanofibers derived from nickel acetate (NiO-A) exhibits much better electrochemical performance values than that of nickel nitrate-derived NiO. To further improve the electrochemical storage performance, we combined NiO-A nanofibers with single-walled carbon nanotubes (CNTs) as a hybrid electrode. In both half-cell and full-cell configurations, the hybrid electrode displayed a higher and steadier areal capacitance than the NiO-A nanofibers because of the synergetic effect between the NiO-A nanofibers and CNTs. Altogether, this work demonstrates the potency of the hybrid electrodes combined with the electrospun NiO-A nanofibers and CNTs for supercapacitor applications.