Embolization with Histoacryl Glue of an Anastomotic Pseudoaneurysm following Surgical Repair of Abdominal Aortic Aneurysm

We report a 62-year-old female who had surgical repair of abdominal aortic aneurysm with a bifurcated graft 2 years ago. She presented with a distal anastomotic pseudoaneurysm which was successfully embolized with histoacryl glue. Only one such similar case has been reported in the literature so far (Yamagami et al. (2006))

A bibliometric analysis of end-of-life vehicles related research:exploring a path to environmental sustainability

Considering rapid economic development and continuously increasing environmental concerns, end-of-life vehicles (ELVs) have significant socioeconomic value as a crucial waste stream. The research relating to ELVs has rapidly evolved over the last few years. However, existing review studies focus on specific research themes, and thus, fail to present a complete picture. Hence, this research intends to explain the current research scenario relating to ELVs by reviewing the critical published studies of the last 22 years. A total of 1405 research publications were extracted from the Scopus database covering the period from 2000 to 2021. Mainly employing bibliometric analysis techniques, this research analyzes the quantity of literature, researchers, institutions, countries, and research themes to understand the current status and future trends in ELV recycling and management. The results revealed a considerable rise in the number of articles published in the last five years. The key producers of influential ELV research are listed as the United States, China, and the United Kingdom. Globally, Chinese universities have the most ELV-related articles published. Similarly, Serbian researcher Vladimir Simic authored the most ELV-related articles during the research period. This article also identifies various research themes: management and recycling, resource recovery and components, life cycle evaluation, and socioeconomic effects. The results also reveal a strong association between distinct ELV research clusters

A One-Dimensional Convolutional Neural Network (1D-CNN) Based Deep Learning System for Network Intrusion Detection

The connectivity of devices through the internet plays a remarkable role in our daily lives. Many network-based applications are utilized in different domains, e.g., health care, smart environments, and businesses. These applications offer a wide range of services and provide services to large groups. Therefore, the safety of network-based applications has always been an area of research interest for academia and industry alike. The evolution of deep learning has enabled us to explore new areas of research. Hackers make use of the vulnerabilities in networks and attempt to gain access to confidential systems and information. This information and access to systems can be very harmful and portray losses beyond comprehension. Therefore, detection of these network intrusions is of the utmost importance. Deep learning based techniques require minimal inputs while exploring every possible feature set in the network. Thus, in this paper, we present a one-dimensional convolutional neural network-based deep learning architecture for the detection of network intrusions. In this research, we detect four different types of network intrusions, i.e., DoS Hulk, DDoS, and DoS Goldeneye which belong to the active attack category, and PortScan, which falls in the passive attack category. For this purpose, we used the benchmark CICIDS2017 dataset for conducting the experiments and achieved an accuracy of 98.96% as demonstrated in the experimental results

Deep Learning-Based Digital Image Forgery Detection System

The advancements of technology in every aspect of the current age are leading to the misuse of data. Researchers, therefore, face the challenging task of identifying these manipulated forms of data and distinguishing the real data from the manipulated. Splicing is one of the most common techniques used for digital image tampering; a selected area copied from the same or another image is pasted in an image. Image forgery detection is considered a reliable way to verify the authenticity of digital images. In this study, we proposed an approach based on the state-of-the-art deep learning architecture of ResNet50v2. The proposed model takes image batches as input and utilizes the weights of a YOLO convolutional neural network (CNN) by using the architecture of ResNet50v2. In this study, we used the CASIA_v1 and CASIA_v2 benchmark datasets, which contain two distinct categories, original and forgery, to detect image splicing. We used 80% of the data for the training and the remaining 20% for testing purposes. We also performed a comparative analysis between existing approaches and our proposed system. We evaluated the performance of our technique with the CASIA_v1 and CASIA_v2 datasets. Since the CASIA_v2 dataset is more comprehensive compared to the CASIA_v1 dataset, we obtained 99.3% accuracy for the fine-tuned model using transfer learning and 81% accuracy without transfer learning with the CASIA_v2 dataset. The results show the superiority of the proposed system

A One-Dimensional Convolutional Neural Network (1D-CNN) Based Deep Learning System for Network Intrusion Detection

The connectivity of devices through the internet plays a remarkable role in our daily lives. Many network-based applications are utilized in different domains, e.g., health care, smart environments, and businesses. These applications offer a wide range of services and provide services to large groups. Therefore, the safety of network-based applications has always been an area of research interest for academia and industry alike. The evolution of deep learning has enabled us to explore new areas of research. Hackers make use of the vulnerabilities in networks and attempt to gain access to confidential systems and information. This information and access to systems can be very harmful and portray losses beyond comprehension. Therefore, detection of these network intrusions is of the utmost importance. Deep learning based techniques require minimal inputs while exploring every possible feature set in the network. Thus, in this paper, we present a one-dimensional convolutional neural network-based deep learning architecture for the detection of network intrusions. In this research, we detect four different types of network intrusions, i.e., DoS Hulk, DDoS, and DoS Goldeneye which belong to the active attack category, and PortScan, which falls in the passive attack category. For this purpose, we used the benchmark CICIDS2017 dataset for conducting the experiments and achieved an accuracy of 98.96% as demonstrated in the experimental results

Review on variants of restricted boltzmann machines and autoencoders for cyber-physical systems

To understand the variants of Restricted Boltzmann Machines (RBMs) and Auto-encoders, we first need to outline what they are: RBMs are generative and unlike auto-encoders that are biased towards a limited set of data. RBMs are capable of generating new data with the joint set distribution. They are used to solve problems such as pattern recognition where there is a handwritten text that needs to be deciphered or a random pattern. It can also be used for recommendation engines where in collaboration with filtering techniques, recommendations are made to an end-user, and radar target recognition where it is used to detect intra-pulse with extremely low SNR and high noise. On the contrary, auto-encoders are not very commonly used in real-life applications; however, they are useful in reducing dimensionality and variational auto-encoders (VAE), where VAE learns the limitations of a probability distribution modeling the input data rather than learning the absolute function. RBMs and auto-encoders can be used for cyber-physical systems (CPS); RBMs are a dual-layer, two-part, erratic graphical model, allowing data to flow in two ways rather than one which forms the foundation of DBNs. CPS aims to use RBMs in making the model understand various functions, which will ultimately help to identify the hidden state and minimize the energy of the system. RBMs assign probabilities rather than definite values. Auto-encoders are unsupervised neural networks that use input vectors and try to match them to similar output vectors. These vectors are extremely skilled as they study compressed data encoding autonomously. This chapter will discuss in detail the breakthroughs with CPS and their findings. Previously CPS were only evaluated with techniques that did not differentiate the facts from the internal view, which ultimately resulted in a mismatch between the behavior of theoretical models and their real-life counterparts. This ultimately gave way to the question of how they could perform critical safety tasks. Another vital breakthrough related to CPS revolves around Intellectual Merit and Broader Impacts. They both revolve around the design and analysis of Artificial Intelligence as an integral part of CPS; the findings for these breakthroughs will be discussed in detail in the chapter. © 2024 selection and editorial matter, [Ali Ismail Awad, Atif Ahmad, Kim-Kwang Raymond Choo, Saqib Hakak]; individual chapters, the contributors

Preparation of microspheric Fe(III)-ion imprinted polymer for selective solid phase extraction

Abstract In this research work, an Fe(III)-IIP was prepared using methacrylic acid as monomer, divinylbenzene as cross-linker, azobisisobutyronitrile as initiator. The ion imprinted polymer was functionalized with Fe(III)8-hydroxy quinolone complex under thermal conditions by copolymerization with the monomer and the cross-linker. The prepared Fe(III)-ion imprinted polymer (IIP) and non-ion imprinted polymer (Non-IIP) were characterized with fourier transform-infrared spectroscopy, scanning electron microscopic analysis and thermal gravimetric analysis. The polymer showed a good stability to thermal analysis up to a temperature of 500 °C. The size of the polymer obtained was 1 µm, large enough to be filtered easily. At pH 2.5 more affinity was observed with ion imprinted polymer in comparison to non-ion imprinted polymer. For the kinetic study, the most linear and rhythmical relation were seen in pseudo second order. The maximum sorption capacity of Fe(III) ions on Fe(III)-IIP and non-IIP was 170 and 30.0 µmolg−1, respectively. The relative selectivity factor (αr) values of Fe(III)/Fe(II), Fe(III)/Al(III) and Fe(III)/Cr(III) were 151.0, 84.6 and 91.9, respectively. The preconcentration factor was found to be 240. The developed method was successfully applied to the determination of trace Fe in the drinking water

The Decision-Making Analysis on End-of-Life Vehicle Recycling and Remanufacturing under Extended Producer Responsibility Policy

This research develops a dual-cycle ELV recycling and remanufacturing system to better understand and improve the efficiency of the ELV recycling and remanufacturing businesses. For the flawless operation of this system, the researchers employed evolutionary game theory to establish a game model between original vehicle manufacturers (OVMs) and third-party recyclers with the government involved. This research presents evolutionary stable strategies (ESS) that could promote an ELV recycling and remanufacturing system. Results show that OVMs’ expected profit difference between choosing and not choosing authorization is crucial in their ESS. The licensing fee plays a part of OVMs’ expected profit difference. Based on the results, optimal ESS could be achieved when the OVMs’ expected profit difference between choosing authorization and not choosing authorization and the third-party recyclers’ profit when paying the licensing fee are both positive. Then, the two groups’ involvement in dual-cycle ELV recycling and the remanufacturing system can be ensured. This research implicates the government to devise appropriate reward and punishment strategy to encourage OVMs and third-party recyclers to collaborate for efficient recycling and remanufacturing systems. Particularly, the government is suggested to impose strict restrictions on OVMs to carry ELV recycling and provide support to promote recycling quantity standards. Hence, the ELV recycling and remanufacturing system would be strengthened, thus improving waste management which is crucial for both environmental and resource efficiency