Graphene and related materials can lead to disruptive advances in next-generation photonics and optoelectronics. The challenge is to devise growth, transfer and fabrication protocols providing high...

pdf/wafer-scale-integration-of-graphene-based-photonic-devices-30li3xvort.pdf

Wafer-Scale Integration of Graphene-Based Photonic Devices.

Secure and efficient data collaboration with hierarchical attribute-based encryption in cloud computing

This paper presents a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack: PHY, MAC and network. First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning to help non-machine learning experts understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-of-service (QoS) and quality-of-experience (QoE). We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed.

https://www.mdpi.com/2079-9292/10/3/318/pdf

A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer

Cybersecurity has seen an increasing frequency and impact of cyberattacks and exposure of Protected Health Information (PHI). The uptake of an Electronic Medical Record (EMR), the exponential adoption of Internet of Things (IoT) devices, and the impact of the COVID-19 pandemic has increased the threat surface presented for cyberattack by the healthcare sector. Within healthcare generally and, more specifically, within anaesthesia and Intensive Care, there has been an explosion in wired and wireless devices used daily in the care of almost every patient-the Internet of Medical Things (IoMT); ventilators, anaesthetic machines, infusion pumps, pacing devices, organ support and a plethora of monitoring modalities. All of these devices, once connected to a hospital network, present another opportunity for a malevolent party to access the hospital systems, either to gain PHI for financial, political or other gain or to attack the systems directly to cause erroneous monitoring, altered settings of any device and even to access the EMR via this IoMT window. This exponential increase in the IoMT and the increasing wireless connectivity of anaesthesia and ICU devices as well as implantable devices presents a real and present danger to patient safety. There has, at the same time, been a chronic underfunding of cybersecurity in healthcare. This lack of cybersecurity investment has left the sector exposed, and with the monetisation of PHI, the introduction of technically unsecure IoT devices for monitoring and direct patient care, the healthcare sector is presenting itself for further devastating cyberattacks or breaches of PHI. Coupled with the immense strain that the COVID-19 pandemic has placed on healthcare and the changes in working patterns of many caregivers, this has further amplified the exposure of the sector to cyberattacks. 

https://link.springer.com/content/pdf/10.1007/s10877-023-01013-5.pdf

The elephant in the room: cybersecurity in healthcare

Conventional machine learning approaches applied for the security intrusion detection degrades in case of big data input ( $$10^6$$ and more samples in a dataset). Model training and computing by traditional machine learning executed on big data at a common computing environment may produce accurate outputs but take a long time, or produce poor accuracy by quick training, both disparate to malicious activity. The paper observes the quantum machine learning (QML) methods overcoming the barriers of big data and the computing abilities of common hardware for the purpose of high performance intrusion detection. Quantum support vector machine (QSVM) and quantum convolution neural network (QCNN) as concurrent methods are discussed and evaluated comparing to the conventional intrusion detectors running on the traditional computer. The QML-based intrusion detection utilizes our own dataset that implements the grouping of the network packets into the input streams eatable for the QML. We have developed the software solution that encodes the network traffic streams ready to the quantum computing. Experimental results show the ability of the QML-based intrusion detection for processing big data inputs with high accuracy (98%) providing a twice faster speed comparing to the conventional machine learning algorithms utilized for the same task. 

/pdf/security-intrusion-detection-using-quantum-machine-learning-geluk3d3.pdf

Security intrusion detection using quantum machine learning techniques

This book picks Cisco as an example to propose a framework of ambidextrous integration of innovation and operation 

Cisco


 Hydrogen energy is characterized by its environmental friendliness, high efficiency, lack of carbon emissions and wide range of applications. However, its transportation and storage are challenges that limit further development of the hydrogen-energy industry. Ammonia is a carbon-free hydrogen-rich carrier. The storage of hydrogen in ammonia has unique advantages of high energy density, easy storage and transportation, reliable safety, a mature industrial foundation and no tail-end carbon emissions. However, industrial ammonia synthesis still heavily relies on the Haber–Bosch process, which accounts for significant energy consumption and greenhouse gas emissions. Therefore, the development of green and sustainable ammonia-synthesis methods is extremely important and urgent. Recently, ammonia-synthesis technologies such as electrocatalysis, photocatalysis, photoelectrocatalysis and biocatalysis have successfully produced ammonia from nitrogen and water, resulting in lower costs. The nitrogen-reduction-reaction conditions of these methods are mild and can be carried out under ambient temperatures and atmospheric pressure with low energy consumptions. Meanwhile, these methods bypass the traditional hydrogen-production section and their routes are simpler. Therefore, these technologies can be used to flexibly integrate renewable energy, including intermittent renewable energy, to achieve distributed ammonia synthesis. These benefits contribute to both global energy and environmental sustainability goals. In this study, the mechanisms of ammonia synthesis under ambient conditions are reviewed and the technical difficulties of various catalysts for ammonia synthesis are summarized. Based on the optimization strategies reported for various catalysts, the high-performing catalysts reported for ammonia synthesis are reviewed and the developmental trend of this field has been forecasted.

Research progress in green synthesis of ammonia as hydrogen-storage carrier under ‘hydrogen 2.0 economy’

The denitrification catalyst without metal active component has excellent anti-poisoning performance and stability.

An anti-poisoning defective catalyst without metal active sites for NH3-SCR via in situ stabilization

Pt@TiO /TiO2 as a highly effective and stable recipe for C3H8 and C3H6 combustion

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Cisco

Research progress in green synthesis of ammonia as hydrogen-storage carrier under ‘hydrogen 2.0 economy’

An anti-poisoning defective catalyst without metal active sites for NH3-SCR via in situ stabilization

Pt@TiO /TiO2 as a highly effective and stable recipe for C3H8 and C3H6 combustion