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While sixth-generation (6G) wireless systems are expected to bring about an explosion of accessible user information and novel technologies, along with new threats to terrestrial and non-terrestrial networks, major concerns associated with the development of 6G networks are privacy and security. Reconfigurable intelligent surfaces (RISs), which have recently emerged as promising candidates to support 6G physical platforms, have proved to be capable of boosting security of next-generation wireless systems. However, due to their easy reconfigurability and low cost, RISs are vulnerable to several security threats, and this vulnerability has not yet been thoroughly addressed in previous research. To fill this gap in the literature, in this review, we aim to thoroughly analyze the security challenges affecting RIS-empowered 6G wireless networks. To this end, we review the attributes of RISs that distinguish them from other relevant technologies, such as multiple-input-multiple-output (MIMO), conventional relaying, backscatter communication (BackCom), as well as outline security and privacy attacks in RIS-assisted 6G applications. Our specific focus is on security and privacy threats associated with the use of RISs with different vital 6G technologies, including millimeter wave (mmWave), terahertz (THz), device-to-device (D2D) communication, Internet of Things (IoT) networks, multi-access edge computing (MEC), integrated sensing and communication (ISAC), simultaneous wireless information and power transfer (SWIPT), and nonterrestrial network. The review concludes with an outline of open research challenges and promising future directions to further increase secrecy of RIS-assisted 6G applications. The results of this review contribute to previous research on 6G network security, in general, and RIS-based 6G network security, in particular.

A modern-day society demands resilient, reliable, and smart urban infrastructure for effective and in telligent operations and deployment. However, unexpected, high-impact, and low-probability events such as earthquakes, tsunamis, tornadoes, and hurricanes make the design of such robust infrastructure more complex. As a result of such events, a power system infrastructure can be severely affected, leading to unprecedented events, such as blackouts. Nevertheless, the integration of smart grids into the existing framework of smart cities adds to their resilience. Therefore, designing a resilient and reliable power system network is an inevitable requirement of modern smart city infras tructure. With the deployment of the Internet of Things (IoT), smart cities infrastructures have taken a transformational turn towards introducing technologies that do not only provide ease and comfort to the citizens but are also feasible in terms of sustainability and dependability. This paper presents a holistic view of a resilient and sustainable smart city architecture that utilizes IoT, big data analytics, unmanned aerial vehicles, and smart grids through intelligent integration of renew able energy resources. In addition, the impact of disasters on the power system infrastructure is investigated and different types of optimization techniques that can be used to sustain the power flow in the network during disturbances are compared and analyzed. Furthermore, a comparative review analysis of different data-driven machine learning techniques for sustainable smart cities is performed along with the discussion on open research issues and challenges.

Now a day food fortification using genetically modified organism was highly popular, secured and affordable for the current food demanded population. Many commendable uses of microbes were found in genetically modified Food. This review paper attempted to address the impact of microorganisms employed in genetically modified food. PubMed, Science Direct, Google Scholar, and other search engines were used to collect papers. The impact of microorganisms in Food Productions was briefly explored and illustrated in the table and figures. Climate resilience, high yield, environmental adaptability, and high protein, 40–50% and 20–40% produced by bacteria and alga respectively, were only a few advantages of foods that have been genetically modified foods with microbes. Additionally, it improves human health by reducing poverty, ensuring food security, and preventing disease. Therefore, genetically modified foods brought a positive impact for human health.

Unpasteurized milk consumption is common throughout the globe, despite the negative consequence for consumer health. This review paper identified public health risks resulting from unpasteurized milk. Several keywords were used to search online, including Google Scholar, PubMed, Science Direct, and academic publications. Significant data were rigorously extracted and reported as per the PRISMA statement guideline. Individuals' risk from consuming raw milk was examined in 15 studies. According to a laboratory investigation report, raw milk contained several parasites and harmful bacteria in unpasteurized milk. These include campylobacteriosis, brucellosis, Staphylococcus aureus, streptococcus, salmonellosis, E. coli 0157, and other hazardous poisons like Aflatoxin M1. People are exposed to pathogenic microorganisms, parasites, and other dangerous toxic agents while consuming raw milk. Therefore, ensuring the safety and quality of raw milk through implementing hazard-critical control points during production and distribution is mandatory in the dairy industry to safeguard general public health.

Metaverse is an evolving orchestrator of the next-generation Internet architecture that produces an immersive and self-adapting virtual world in which humans perform activities similar to those in the real world, such as playing sports, doing work, and socializing. It is becoming a reality and is driven by ever-evolving advanced technologies such as extended reality, artificial intelligence, and blockchain. In this context, Metaverse will play an essential role in developing smart cities, which becomes more evident in the post COVID 19 pandemic metropolitan setting. However, the new paradigm imposes new challenges, such as developing novel privacy and security threats that can emerge in the digital Metaverse ecosystem. Moreover, it requires the convergence of several media types with the capability to quickly process massive amounts of data to keep the residents safe and well-informed, which can raise issues related to scalability and interoperability. In light of this, this research study aims to review the literature on the state of the art of integrating the Metaverse architecture concepts in smart cities. First, this paper presents the theoretical architecture of Metaverse and discusses international companies interest in this emerging technology. It also examines the notion of Metaverse relevant to virtual reality, identifies the prevalent threats, and determines the importance of communication infrastructure in information gathering for efficient Metaverse operation. Next, the notion of blockchain technologies is discussed regarding privacy preservation and how it can provide tamper-proof content sharing among Metaverse users. Finally, the application of distributed Metaverse for social good is highlighted.

Minimally invasive surgery is highly operator dependant with a lengthy procedural time causing fatigue to surgeon and risks to patients such as injury to organs, infection, bleeding, and complications of anesthesia. To mitigate such risks, real-time systems are desired to be developed that can provide intra-operative guidance to surgeons. For example, an automated system for tool localization, tool (or tissue) tracking, and depth estimation can enable a clear understanding of surgical scenes preventing miscalculations during surgical procedures. In this work, we present a systematic review of recent machine learning-based approaches including surgical tool localization, segmentation, tracking, and 3D scene perception. Furthermore, we provide a detailed overview of publicly available benchmark datasets widely used for surgical navigation tasks. While recent deep learning architectures have shown promising results, there are still several open research problems such as a lack of annotated datasets, the presence of artifacts in surgical scenes, and non-textured surfaces that hinder 3D reconstruction of the anatomical structures. Based on our comprehensive review, we present a discussion on current gaps and needed steps to improve the adaptation of technology in surgery., Comment: This paper is to be submitted to International journal of computer vision

Surgical tool detection in minimally invasive surgery is an essential part of computer-assisted interventions. Current approaches are mostly based on supervised methods which require large fully labeled data to train supervised models and suffer from pseudo label bias because of class imbalance issues. However large image datasets with bounding box annotations are often scarcely available. Semi-supervised learning (SSL) has recently emerged as a means for training large models using only a modest amount of annotated data; apart from reducing the annotation cost. SSL has also shown promise to produce models that are more robust and generalizable. Therefore, in this paper we introduce a semi-supervised learning (SSL) framework in surgical tool detection paradigm which aims to mitigate the scarcity of training data and the data imbalance through a knowledge distillation approach. In the proposed work, we train a model with labeled data which initialises the Teacher-Student joint learning, where the Student is trained on Teacher-generated pseudo labels from unlabeled data. We propose a multi-class distance with a margin based classification loss function in the region-of-interest head of the detector to effectively segregate foreground classes from background region. Our results on m2cai16-tool-locations dataset indicate the superiority of our approach on different supervised data settings (1%, 2%, 5%, 10% of annotated data) where our model achieves overall improvements of 8%, 12% and 27% in mAP (on 1% labeled data) over the state-of-the-art SSL methods and a fully supervised baseline, respectively. The code is available at https://github.com/Mansoor-at/Semi-supervised-surgical-tool-det, Comment: Paper accepted at Augmented Reality, Augmented Environments for Computer Assisted Interventions (AE-CAI), Computer Assisted and Robotic Endoscopy (CARE) and Context-Aware Operating Theaters (OR 2.0) at MICCAI 2022

Recent advances in electronic devices and communication infrastructure have revolutionized the traditional healthcare system into a smart healthcare system by using IoMT devices. However, due to the centralized training approach of artificial intelligence (AI), the use of mobile and wearable IoMT devices raises privacy concerns with respect to the information that has been communicated between hospitals and end users. The information conveyed by the IoMT devices is highly confidential and can be exposed to adversaries. In this regard, federated learning (FL), a distributive AI paradigm has opened up new opportunities for privacy-preservation in IoMT without accessing the confidential data of the participants. Further, FL provides privacy to end users as only gradients are shared during training. For these specific properties of FL, in this paper we present privacy related issues in IoMT. Afterwards, we present the role of FL in IoMT networks for privacy preservation and introduce some advanced FL architectures incorporating deep reinforcement learning (DRL), digital twin, and generative adversarial networks (GANs) for detecting privacy threats. Subsequently, we present some practical opportunities of FL in smart healthcare systems. At the end, we conclude this survey by providing open research challenges for FL that can be used in future smart healthcare systems

New version of Novikov helical gear combining double circular arc profile with crowned involute profile has been proposed in this paper. Generation of this gear is carried out using the required equations for these two profiles which are programmed using SOLIDWORK. To compare the resulting contact and bending stresses, three models of gear pairs are generated and investigated using finite element software package (ANSYS). The profiles of these models are as following: double circular arc for first model, crowned involute for second model, while the third model formed by combining double circular arc with involute crowned profiles. The results of stress analysis show that the generated stresses are lower in the proposed (combined) gear especially when the contact in the circular arc side. Keywords: Generation of gears, versions of Novikov Gears, circular arc gear, crowned involute gear, stress analysis in gears.

Doctorat en Sciences, info:eu-repo/semantics/nonPublished

Doctorat en Sciences, info:eu-repo/semantics/nonPublished

Export pattern of Indian trade