Miracle-dz/UniFAR-FTUs huggingface dataset:
RETRACTED ARTICLE: Clustering Optimization Algorithm for Blockchain Systems Based on Big Data Analysis

We, the Editor, Institution and Publisher of the journal IETE Journal of Research, have retracted the following article, which is part of the Special Issue titled “Federated Learning for Blockchain Systems and Industrial Internet of Things”: Yanjing Lu (2022) Clustering Optimization Algorithm for Blockchain Systems Based on Big Data Analysis, IETE Journal of Research, DOI:10.1080/03772063.2022. 2038287 Following publication, concerns were raised about the peer review and decision-making processes for this special issue. After an investigation by the Taylor & Francis Publishing Ethics & Integrity team, in full cooperation with the Editor-in-Chief and the Institution, it was confirmed that the articles included in this special issue were not reviewed appropriately, in line with the Journal’s peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special Issue. The journal has not confirmed if the authors were aware of this compromised peer review process. The journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’.
Triplet-Classifier GAN for Finger-Vein Verification

In finger-vein-based biometric verification, it is essential to robustly extract vein features with strong discrimination ability. Recently, deep learning methods have achieved remarkable performance in the field of finger-vein verification. However, the establishment of an effective deep learning model requires large-scale databases to prevent overfitting during the training process, while currently used finger-vein databases are not large enough. In our article, a new generative adversarial network (GAN), named triplet-classifier GAN, is designed for finger-vein verification. Unlike the traditional GAN-based method, the proposed triplet-classifier GAN uses the generated “fake” data to improve the learning ability of the triplet loss-based convolutional neural network (CNN) classifier. The combination of GAN and the triplet loss-based CNN classifier expands the training data and improves the discriminant ability of CNN. Experiments prove that the proposed triplet-classifier GAN has superior performance in finger-vein verification and has good prospects in finger-vein-based biometric verification.
Understanding Factors to COVID-19 Vaccine Adoption in Gujarat, India

The COVID-19 pandemic has posed threats to human life across the globe, including India. Vaccinating is an effective means of addressing the pandemic threat. The government of India has implemented a massive vaccination drive to save its citizens from the deadly virus. However, the effort has faced multiple challenges, including vaccine hesitancy. This research understands respondents’ perspectives on factors contributing to the lower vaccination uptake in Gujarat, India. Forty-four semi-structured interviews were conducted through convenience sampling representing different demographic backgrounds. Factors influencing vaccine adoption included religious leadership, political leadership and the government, and fear of side effects, especially among children and those with co-morbidities, resulting primarily from fake news and misinformation circulated through social media. Compared with nine countries from across the world, the study found similarities to vaccine hesitancy from misinformation and the fear of side effects among children. In contrast, the role of government and the influence of religious and political leaders was considered positive. The study recommends strategies to overcome people’s apprehensions about the adoption of vaccination. These include offering incentives, providing positive peer-to-peer communication, recruiting influencers such as religious and community leaders and early adopters such as the elderly population to endorse vaccination, targeting youth through social media, and reaching rural sections by involving NGOs and social service groups.
Camera, LiDAR, and Radar Sensor Fusion Based on Bayesian Neural Network (CLR-BNN)

Perception in automated vehicles (AV) is the main factor in achieving safe driving. In this perception task, multi-object detection (MOD) in diverse driving situations is the main challenge. Our recent survey [Ravindran et al. (2021)] shows the limitations of deep neural networks (DNN) in predicting the uncertainties of object detection in MOD. This research proposed a camera, LiDAR and RADAR sensor fusion Bayesian neural network (CLR-BNN) to improve detection accuracy and reduce uncertainties in diverse driving situations using these three primary sensing devices. The experiments were performed using the nuScence dataset with incorporation of various noises. The CLR-BNN performed better than its deterministic sensor fusion model (CLR-DNN) in terms of mAP. The CLR-BNN also showed improvement in categorical and bounding box location uncertainty using sensor fusion in diverse driving conditions. The uncertainty predictions of the CLR-BNN were validated using the calibration curve and other performance metrics.
An investigation study for risk calculation of security vulnerabilities on android applications

Applications within mobile devices, although useful and entertaining, come with security risks to private information stored within the device such as name, address, and date of birth. Standards, frameworks, models, and metrics have been proposed and implemented to combat these security vulnerabilities, but they remain to persist today. In this review, we discuss the risk calculation of android applications which is used to determine the overall security of an application. Besides, we also present and discuss the permission-based access control models that can be used to evaluate application access to user data. The study also focuses on examining the predictive analysis of security risks using machine learning. We conduct a comprehensive review of the leading studies accomplished on investigating the vulnerabilities of the applications for the Android mobile platform. The review examines various well-known vulnerabilities prediction models and highlights the sources of the vulnerabilities, prediction technique, applications and the performance of these models. Some models and frameworks prove to be promising but there is still much more research needed to be done regarding security for Android applications.
Automatic Detection of Timed-up and Go Tests with IMU Sensor Data

: The evaluation of the current state and examination of geriatric patients is a time consuming process for the medical staff and the patients. The independence of the geriatric patients is further reduced through the interruptions in their daily life caused by these examinations. One of these evaluation techniques is the so called Timed-Up and Go test (TUG). The test uses a simple sequence of motions to assess the fall risk of a person. Advances in the technology of wearable sensors and machine learning make it possible to automate these evaluation methods with a compact system. This paper continues the research that was already done in the ﬁelds of human activity recognition and automatic TUG detection and proposes a novel method for the automatic detection of the Timed-Up and Go without interrupting the daily life of the patients.
Molecular Identification, Characterization and Improvement of A Chitinase Producing Bacillus strain Showing Significant Control against Some Dermatophytic Fungi

Bacillus bacteria are advantageous antagonistic organisms that can be used as bio-control agents. This study is aimed at screening the antagonistic activity of different strains of isolated Bacillus bacteria and molecular identification of the superior chitinase producer strain against dermatophytes fungi. Soil samples were collected from different places of Kotoor city, Gharbia Governorate, Egypt and Al Madina Al Munawwarah, Kingdom of Saudi Arabia. A collection of Bacillus isolated from soil was tested in vitro against the dermatophytes: Microsporum sp. and Trichophyton sp. The bacterial strains Kh-B1 and Kh-B2 showed the highest antagonistic activity against dermatophytes pathogenic fungi. The highest amount of chitinase productivity (13.6 units/ml) was obtained from the original Bacillus strain (Kh-B1) at 3 days of incubation. BLAST analysis of the amplified 16S ribosomal RNA gene sequence identified the Bacillus strain (Kh-B1) as Paenibacillus macerans. Upon the mutation induction by UV light, the highest chitinase-producing mutant was Kh-UVB-4 as it showed 305.88 percent production higher than the wild-type strain. While, upon the mutation induction by EMS, the highest amount of chitinase produced was 54.8 units/ml by mutant Kh-ESB-20, and it has produced 402.94% more than the original untreated strain. The application of RAPD-PCR protocol using three 15-mer random primers was used to determine the genetic effects of mutagenic treatments on the wild type strain (Kh-B1) as well as to demonstrate the genetic variability between the five most chitinase producing mutants and the wild type (Paenibacillus macerans).
A Pipeline Case Study for Ice Scour Design

The Arbitrary Lagrangian Eulerian (ALE) Finite Element (FE) ice scour model developed in the previous years is used to study the importance of some of the pipe, seabed soil, and scour parameters. The effect of some of these parameters on the pipeline response is investigated. The paper reveals that the influence of these parameters on the pipeline response is very different than predicted by the Winkler models and also very beneficial for the pipeline design. A pipe-in-pipe (PIP) system is analyzed using the same model. It is shown that a carrier pipe installed in a PIP system would experience stresses less than those of the same size single pipe installed in the same trench by about one order of magnitude. © 2007 ASME
Multi-channel P-ISAR grating lobes cancellation

The coherent combination of multiple channels for ISAR imaging purposes in passive radar systems is required in order to achieve a good range resolution. Passive ISAR may provides a suitable means to implement automatic target classification (ATC) and automatic target recognition (ATR). The range resolution is related to the transmitted signal bandwidth, therefore adjacent and non adjacent multi-channel signals should be exploited to obtain a wide band signal. This paper addresses the grating lobes issue that rises when a wide band signal with bandwidth gaps is obtained by adjoining multiple adjacent DVB-T channels. A cancellation technique based on the spatially variant apodization (SVA) technique is proposed. Results both on simulated and real data are provided. (5 pages)
A New TPACK Training Model for Tackling the Ongoing Challenges of COVID-19

This study investigated the effects of integrating the “CloudClassRoom” (CCR) and the DEmo-CO-design/teach-feedback-DEbriefing (DECODE) model to improve pre-service teachers’ online technological pedagogical and content knowledge (TPACK). The DECODE model includes four stages: Teacher’s DEmonstrations, Students CO-train in using CloudClassRoom, Students CO-design a CloudClassRoom-integrated course, Students CO-teach, and finally DE-brief what they have learned through the stages mentioned above. This model integrates teacher-student experiences, teaching-learning processes, and technology-embedded systems to promote collaborative and active learning, information and resources sharing, and creative communication. A self-evaluating questionnaire with open-ended questions evaluated participants’ technological pedagogical and content knowledge outcomes. CloudClassRoom significantly increases technology-related knowledge considering the current social distancing measures provoked by COVID-19. The findings show that DECODE with CloudClassRoom provides an integrated process for improving pre-service teachers’ technological pedagogical and content knowledge, assisting pre-service teachers in designing educational technology-integrated courses.
A Survey on the Application of Control and Mechanical Systems for Human Motor Learning and Recovery

Rehabilitation engineering is a strong emerging field in control and mechanical systems that provides technological solutions to disabled individuals. One branch of rehabilitation engineering is the application of control systems and robotic technology for human motor recovery. This paper outlines the recent developments in rehabilitation robotic research and related control systems. The available techniques are discussed in the context of upper limb rehabilitation and lower limb / gait rehabilitation. The control systems technology utilized in rehabilitation robotics is discussed in three versions of complexity. The potential benefits of each version are outlined with the different improvements seen in the progression of technology. The conclusion of this paper identifies the outcomes and challenges faced in this area of research.
Evaluation of the quasi-steady-state overheating indicator for passive houses

Only recently energy efficiency and reduced cooling energy consumption have become a primary concern for many European countries. Consequently, the corresponding cooling technologies and calculation methods have not yet received the necessary attention in national energy performance regulations. For example, only few countries impose some kind of summer comfort evaluation. In response, several parties launched research projects to update the calculation methods. The co-workers of one such project, called EPIcool, improved the quasi-steady-state calculation of the overheating indicator and updated/developed methods to assess the energy performance of passive cooling technologies and mechanical cooling systems. However, they did not evaluate the sensitivity of the predicted performances. Therefore, the authors of underlying paper investigated the influence of several design parameters on the revised overheating indicator of a generic passive house. They performed a one-at-a-time analysis as well as a factorial design on (maximum) sixteen parameters. The results showed that especially primordial design choices, like a summer bypass or not, sun shading or not, influence the overheating indicator. Yet, choosing a favourable value for some parameter does not always improve the overheating indicator to the same extent. Combinations of generally important parameters show a large spread of predicted performances. However, the spread diminishes as the case has a better summer comfort.
Emotional Situation of Children and Adolescents during the COVID-19 Pandemic in Germany: Results from the COVID-19 Snapshot Monitoring Study (COSMO)

The COVID-19 pandemic led to numerous restrictions in daily life that had a significant impact on the well-being and mental health of the population. Among others, children and adolescents were particularly affected, being a vulnerable group at risk. The aim of this study was to assess the emotional situation of children and adolescents during different phases of the pandemic and to identify modifying factors. Data from the serial cross-sectional COVID-19 Snapshot Monitoring (COSMO) survey in Germany were used for this study. The survey waves 12 (19th/20th May 2020) and 21 (15th/16th September 2020) were investigated as examples of two different pandemic phases. The psychosocial and emotional situation and well-being of children were measured with the emotional subscale of the Strengths and Difficulties Questionnaire (SDQ) assessed by parents. Descriptive analyses and logistic regressions were calculated. In total, a third of the participating parents in wave 12 and in wave 21 reported having children and adolescents with emotional symptoms. Especially children with younger parents seemed to be more affected by emotional symptoms. Sociodemographic aspects, such as household language, showed a significant association with reported emotional symptoms in children (Wave 12: OR = 2.22; 95% CI: 1.20–4.09). Reported prevalences of emotional symptoms in children did not differ between the pandemic phases. In conclusion, the pandemic had negative influences on the emotional symptoms of children and adolescents in COVID-19 pandemic waves in 2020, indicating a forecasted reoccurrence and need for preventive measures for upcoming waves and other pandemics in the future.
Computation of higher-order hydrodynamic forces on ships and offshore structures in waves

This thesis concentrates on the computation of hydrodynamic forces on ships and offshore structures. The specific topic of this thesis is the higher-order forces and moment. This thesis consists of two parts. The aim of the first part is the development of a finite-depth unified theory for ship motions and its extension to the computation of the second-order mean-drift forces and moment. The mathematical background of the finite-depth unified theory is introduced, and an associated computer program is developed to verify this theory. The accuracy is checked from the comparison with the result of a three-dimensional panel code, and the nice agreement is shown. The theory is extended to the computation of the second-order quantities. The mean forces and moment are computed using the far-field formulae, and the wave drift damping matrix is obtained by Aranha's formula. Based on the present study, slender-body theory is shown to be a useful design tool for a floating ship, like an FPSO. The second part develops a model and a corresponding computational program for the prediction of nonlinear wave effects. The primary interest is the second-order highfrequency effect which induces flexural body responses. The Rankine panel method using the bi-quadratic B-spline basis function is adopted as a method of solution. The theoretical aspects of the numerical scheme are described to verify the consistency and stability of the method, and a thorough parametric study is carried out. The computational result includes linear and nonlinear run-up and wave loads up to the second order. Based in this numerical method, the sum-frequency wave loads are obtained for a monochromatic wave and multi-frequency waves. Good agreement is shown with existing numerical and experimental data. Thesis Supervisor: Paul D. Sclavounos Title: Professor of Naval Architecture
A neural network-informed self-aware deployable structure with application to phased array antennas

State of the art radio frequency (RF) arrays are growing larger in pursuit of increased signal-to-noise ratio. In support of this goal, elaborate forms of metrology are being developed to support the increased footprints. This work provides a unique solution to fulfill the metrology requirements of large-scale deployable RF antennas through the implementation of neural network demodulation of fiber optic strain sensors. The fiber optics are patterned with fiber Bragg gratings (FBGs) to encode strain on to back-reflected shifts in the wavelength of incident light. Experiments show the neural network can predict the deformation of a test structure within single millimeters for small amplitude motions. Therefore, the current technique meets the required λ/20 precision needed for large scale deployable RF arrays operating at S-band or longer wavelengths.
Untangling Computer-Aided Diagnostic System for Screening Diabetic Retinopathy Based on Deep Learning Techniques

Diabetic Retinopathy (DR) is a predominant cause of visual impairment and loss. Approximately 285 million worldwide population is affected with diabetes, and one-third of these patients have symptoms of DR. Specifically, it tends to affect the patients with 20 years or more with diabetes, but it can be reduced by early detection and proper treatment. Diagnosis of DR by using manual methods is a time-consuming and expensive task which involves trained ophthalmologists to observe and evaluate DR using digital fundus images of the retina. This study aims to systematically find and analyze high-quality research work for the diagnosis of DR using deep learning approaches. This research comprehends the DR grading, staging protocols and also presents the DR taxonomy. Furthermore, identifies, compares, and investigates the deep learning-based algorithms, techniques, and, methods for classifying DR stages. Various publicly available dataset used for deep learning have also been analyzed and dispensed for descriptive and empirical understanding for real-time DR applications. Our in-depth study shows that in the last few years there has been an increasing inclination towards deep learning approaches. 35% of the studies have used Convolutional Neural Networks (CNNs), 26% implemented the Ensemble CNN (ECNN) and, 13% Deep Neural Networks (DNN) are amongst the most used algorithms for the DR classification. Thus using the deep learning algorithms for DR diagnostics have future research potential for DR early detection and prevention based solution.
Exploring the Impacts of Shrub‐Overwash Feedbacks in Coastal Barrier Systems With an Ecological‐Morphological Model

Shrubs are common – and presently expanding – across coastal barrier interiors (the land between the foredune system and back‐barrier bay), and have the potential to influence barrier morphodynamics by obstructing cross‐shore overwash flow. The ecological and geomorphological consequences of ecomorphodynamic couplings of the barrier interior, however, remain largely unexplored. In this contribution, we add an ecological module of shrub expansion and mortality to a spatially‐explicit exploratory model of barrier evolution (Barrier3D) to explore the effects of shrub‐barrier feedbacks. In our model simulations, we find that the presence of shrubs significantly alters barrier morphology and behavior. Over timescales of decades to centuries, barriers with shrubs (relative to those without) tend to be narrower, migrate landward more slowly, and have a greater proportion of subaerial volume distributed toward the ocean‐side of the barrier. Shrubs also tend to increase the likelihood of discontinuous barrier retreat, a behavior in which a barrier oscillates between periods of transgression and relative immobility, because shrubs induce prolonged periods of barrier immobility by obstructing overwash flow. However, shrubs can increase barrier vulnerability to drowning by preventing periods of transgression needed to maintain barrier elevation relative to rising sea levels. Additionally, physical barrier processes influence shrub expansion in our simulations; we find that greater dune erosion and overwash disturbance tends to slow the rate of shrub expansion across the barrier interior. Complementing recent observational studies of barrier islands in Virginia, USA, our results suggest that interior ecology can be a key component of barrier evolution on annual to centurial timescales.