The unprecedented rise in the development of industries has led to detection of new classes of pollutants referred to as emerging pollutants which have recently been deemed as an ecological threat. The current work discusses the potential application of enzymes to treat these emerging pollutants, among which Peroxidases namely Dye De-colorizing Peroxidases were utilized after their expression in bacterial system. To investigate their ability to treat and potentially remove emerging pollutants, 31 emerging pollutants were subjected to enzymatic treatment by bacterial Dye De-colorizing Peroxidases using LCMSMS method. Our data showed complete removal of 2-Mercapto-benzothiazole (MBT), and significant degradation of other emerging pollutants. These data provide the first instance of the potential effect of Dye De-colorizing Peroxidases in degrading emerging pollutants.
The water and sanitation technologies to be implemented in a community need to be accessible by all people living in the area. Based on a study done by the Rural Water Supply Network, 15% to 30% of water and sanitation (watsan) installed infrastructures in developing countries are not operating due to inappropriate selection of watsan technologies. This paper presents a decision framework for the selection of appropriate watsan technologies. This decision model is made up of three modules. The first component is used to assess the community's capacity to manage local watsan services, the second component is a database of available watsan technologies, classified by their capacity requirement level (CRL), and the third component is a matching model between the two first components. This paper focuses on the second component, the database of watsan technologies, and more specifically on technologies' classification. The classification method proposed is based on Machine Learning.
Early prediction of Alzheimer's disease, a neuro- logical disease with irreversible damage to the brain cells, is a vast area of research dedicated to reconciling the growing need to assess, study, and treat affected subjects. Deep learning and image processing techniques have been used to facilitate the creation of Alzheimer's disease diagnosis and prediction systems. In this paper, we propose an early prediction system for Alzheimer's Disease that relies on textural analysis, regional ensemble learning, and deep learning, while utilizing structural MRI scans and clinical data.
The security of the World Wide Web is based on Transport Layer Security (TLS) and certificates issued to web sites by Certification Authorities (CAs). This allows users to securely "talk to" web sites over HTTPS. Recent developments have enabled CAs to automatically issue Domain Validated certificates to web sites that can prove ownership of their domain names e.g. mycompany.com. However, anyone can register any domain name, so this does not guarantee that the domain name is actually owned by a genuine business and not by a fraudster wishing to cheat users. Our project will allow CAs to automatically issue Extended Validation (EV) certificates to nationally registered organizations that can prove their identities using newly standardized Verifiable Credentials. This will help to assure users that the web sites are run by genuine businesses and not by fraudsters.
The use of Unmanned Aerial Vehicles (UAVs) has been popular area of research and more in demand especially with the emergence of new technologies that support it, such as the 5G networks. However, with the increasing numbers of UAV in the air as well as their different types of services. It gets more difficult to control and secure their network. In this paper, we discuss the up-to-date research that has been done where the blockchain technology has been used to support multiple applications of the UAV network. We also conclude that a single blockchain ledger is still limited in providing full services to the UAV network. Thus, we address the open issues found in use of a single blockchain scheme and introduce a new scheme and research field that uses the technology of cross-blockchain to support the application of UAV networks as well as the challenges that arise with it.
Despite the growing interest in the field of security, there is yet to be developed like a system that can inspect insider intrusion intent before it happens. However, several systems have been developed to protect critical places from outsider intrusion attacks by applying various authentication mechanisms or restrictive technologies to detect any attack attempts. All this great focus led to overlook the knowledge or access privileges that an employee has which in turn can maliciously abuse their role to harm the place or damage the system. Hence, this research paper aims to produce a reliable and accurate system to recognize any intention to cause damage or investigate any previous incidents. It be achieved through detecting concealed information stored in the brain by measuring Event related-potential signals, analyzing them using continues wavelets transform, and training convolutional neural network.
Nowadays, smartphones and smart devices have become essential for everyday activities including business, communication and entertainment. The spread of these devices along with the rich set of sensors they are equipped with has led to the emergence of Mobile Crowdsensing. Although a large number of participants is essential to make the sensing effective, some obstacles may still prevent the task requester from obtaining reliable information such as having malicious workers who either try to sabotage a task or try to attain multiple tasks completion for increased monetary rewards. This work tackles the problem of malicious nodes by profiling workers over time using data obtained from their continuous interactions with their mobile devices. We can leverage such information to uniquely distinguish each user's interaction from the rest of the users.
A promising solution to the current spectrum crunch is the proposal of visible light communications (VLC), which explores the unregulated visible light spectrum to enable high-speed short range communications, in addition to providing efficient lighting. Although VLC is inherently secure and able to overcome the shortcomings of current RF wireless systems, it suffers from several limitations, including the limited modulation bandwidth of light-emitting diodes. In this respect, several interesting solutions have been proposed in the recent literature to overcome this limitation. In this article, we consider the integration of the newly emerged multiple access scheme rate splitting multiple access (RSMA) with VLC systems. Our results illustrate the flexibility of RSMA in generalizing other multiple access techniques, namely NOMA and SDMA, as well as its superiority in terms of weighted sum rate.
In this paper, an adaptive bit loading algorithm for multicarrier non-orthogonal multiple access (NOMA) systems is proposed. The obtained results show that the bit loading provides NOMA with an additional degree of freedom that allows non uniform spectrum sharing among the users. It is shown that NOMA can outperform orthogonal multiple access (OMA) by 100% in terms of spectral efficiency, for the two-user scenario.
Future wireless networks are evolving towards enabling reliable communications for miniature-sized and resource constrained Internet of things (IoT) devices, imposing stringent requirements on the future sixth-generation (6G) mobile networks. These requirements include low cost, ultra-low latency, improved spectral and energy efficiencies, higher reliability, and significantly enhanced data rate. Emphasizing on the fact that these devices have limited capabilities and might be in inaccessible places, which make battery replacement or recharging a challenging task, energy-efficient solutions should be developed to ensure uninterrupted and seamless wireless communications for power-limited IoT devices. In this paper, we propose a framework for Long Range (LoRa)-enhanced backscatter communications (BackCom) and present the error-rate performance analysis of the system in Additive white Gaussian Noise (AWGN) channels.
