Miracle-dz/UniFAR-FTUs huggingface dataset:
Structure–property relationships in nematic gold nanoparticles

Design rules for the investigation of nanoparticles covered with an organic monolayer are explored. The combination of small “spherical” particles in conjunction with nematogenic mesogens and hydrocarbon chains allows for the rational design of materials with nematic phase behaviour. The usefulness of the approach is demonstrated for two systems, where the particles have been characterised by NMR and TEM and mesomorphic properties have been identified using DSC and OPM.
Coastal Water Remote Sensing From Sentinel-2 Satellite Data Using Physical, Statistical, and Neural Network Retrieval Approach

Recent optical remote sensing satellite missions, such as Sentinel-2 with the MultiSpectral Imager (MSI) onboard, allow the estimation of coastal water key parameters with very high spatial resolutions (down to 10 m). In this article, multiple approaches are proposed for retrieving chlorophyll-a (Chl-a) and total suspended matter (TSM) along the Adriatic and Tyrrhenian coasts in Italy, using both empirical and model-based frameworks to design regressive and neural network (NN) estimation methods. The latter proves to be more accurate on a regional scale, where standard ocean color physical models exhibit high uncertainty in their local parameterization due to the complex spectral characteristics of the observed scene. Retrieval results are encouraging for Chl-a with a coefficient of determination <inline-formula> <tex-math notation="LaTeX">${R}^{2}$ </tex-math></inline-formula> up to 0.72 with a root-mean-square error (RMSE) of 0.33 mg <inline-formula> <tex-math notation="LaTeX">$\text{m}^{-3}$ </tex-math></inline-formula>, using an empirical NN. The TSM algorithms exhibit higher uncertainty, mainly due to scarcity of <italic>in situ</italic> measurements and model parameterizations, with <inline-formula> <tex-math notation="LaTeX">$R^{2}= 0.52$ </tex-math></inline-formula> and RMSE = 1.95 g/m<sup>3</sup> using NNs. The bio-optical model, used for the development of model-based algorithms, shows some inadequacies in representing the inherent and apparent optical properties for the case study areas, especially considering the different spectral features between the oligotrophic Tyrrhenian Sea and the eutrophic Adriatic Sea. This study confirms the potential of Sentinel-2 MSI products for coastal water monitoring, but it also highlights key issues to be further tackled such as the atmospheric correction impact, the need of reliable <italic>in situ</italic> measurements, and possible bathymetry effects near the shores.
Feature-based optimized deep residual network architecture for diabetic retinopathy detection

For the last few decades, the detection of diseases using deep learning architecture has gained huge popularity specifically for diabetic retinopathy. This disease progresses at different levels so early detection is crucial. The study proposes feature-based optimized deep learning neural network architecture for the detection of diabetic retinopathy. The features include a canny edge detector and histogram of oriented gradients. The proposed approach achieves an accuracy of 97.01% and 97.88% on publicly available, standard Messidor-2 and EyePACS datasets of Diabetic Retinopathy and outperforms existing state-of-the-art deep learning image classification Residual Network architectures of ResNet-50 and Inception-v3.
An Efficient Method to Discover Association Rules of Mode-Dependent Alarms Based on the FP-Growth Algorithm

State-based alarming is an advanced technique to reduce nuisance alarms and alarm floods by suppressing alarms associated with certain conditions or operating modes. To implement this technique, the key is to find the associations between operating modes and alarms. In practice, this is mainly done based on the experience of plant operators and expert knowledge of process engineers, and thus is very time consuming. Therefore, this paper proposes a data driven method to discover the association rules of mode-dependent alarms for both single and multiple operating modes from the historical Alarm and Event (A&E) logs in an efficient way based on a data mining approach named FP-Growth. The effectiveness of the proposed method is demonstrated by an industrial case study.
Residual Learning of Cycle-GAN for Seismic Data Denoising

Random noise attenuation has always been an indispensable step in the seismic exploration workflow. The quality of the results directly affects the results of subsequent inversion and migration imaging. This paper proposes a cycle-GAN denoising framework based on the data augmentation strategy. We introduced residual learning into the cycle-GAN to improve the training efficiency of the network. We proposed a method for generating labeled datasets directly from unlabeled real noisy data. Then we significantly improve the diversity of the training samples through an augmentation strategy. Through RCGAN, we can realize intelligent seismic data denoising work, which dramatically reduces the manual selection and intervention of denoising parameters. Finally, numerical experiments prove that our method has a remarkably good random noise suppression ability and a minimally damaging effect on useful seismic signals. The experiment tests on synthetic and real data also show the effectiveness and superiority of the proposed method RCGAN compared to the state-of-the-art denoising methods.
A Novel Message Dissemination Scheme Based on Pub/Sub Model and Vehicular Fog Computing

Modern vehicular communication systems are facing significant challenges since the rapid development of intelligent transportation systems and the ever-growing vehicular applications have activated intense information exchange among vehicles, infrastructures, and pedestrians. Without the support of powerful communication, many vehicular services will just stay in conceptual phase and cannot be put into practice. To provide low-latency services to vehicles on roads, vehicular fog computing (VFC) is recently introduced as a promising solution, which utilizes enormous vehicles on roads as communication and computation resources and extends cloud computing service to an edge network. On the other hand, the publish/subscribe (pub/sub) paradigm provides a loosely coupled and scalable communication which can facilitate flexible and dynamic vehicular network services. Motivated by the merits of these two research fields, in this paper, we propose a novel joint design of pub/sub communication model based on VFC architecture, which employs fog nodes as the data platform for messages aggregation. Specifically, we describe a method to measure vehicle mobility and construct a stable VFC on roads. Then, a message dissemination approach is designed based on our communication model. Finally, the experimental results confirm the efficiency of our proposed model in various urban scenarios.
The Impact of Cybersecurity on Siting Distributed Generation Units in AC Power Systems

The vulnerabilities of the smart grid to cyberattacks can pose dangerous threats to the assets connected to the power network. It is important to identify cyber vulnerabilities in power systems and implement effective defense mechanisms to prevent cyber threats on these assets. Existing research works have not shown effective techniques for the vulnerability assessment and countermeasures against cyberattacks for the AC power systems. In this paper, we emphasize the impact of cybersecurity on siting distributed generation (DG) units in the AC power grid. We present an AC-based false data injection (FDI) attack model to analyze the cyber vulnerabilities of the system and propose an optimal defense strategy to prevent cyber incidents on these units. We perform experiments on the SaskPower network of Saskatchewan, Canada, to demonstrate the optimal siting for DG units under the cybersecurity constraint. Our simulation results obtained for the SaskPower grid show the effectiveness of our proposed approaches to analyze the cyber vulnerabilities of the power grid and find secure sites for the DG units in Saskatchewan.
Time Domain Channel Estimation and Equalization of CP-OTFS Under Multiple Fractional Dopplers and Residual Synchronization Errors

In the last few years, orthogonal time frequency space (OTFS) modulation has received significant attention as an alternative to OFDM especially for high mobilty scenarios. In this work, we develop a delay-Doppler domain embedded pilot based time domain channel estimation for cyclic prefix (CP)-OTFS in the presence of residual frame timing offset, carrier frequency offset and fractional multiple Doppler. One of the reasons for time domain processing is that the time domain channel representation is relatively more sparse as compared to its delay Doppler domain representation in the presence of residual synchronization errors. We also describe a time domain low complexity linear minimum mean square error (MMSE) equalization and successive interference cancellation (SIC) receiver for LDPC (low density parity check) coded CP-OTFS in this work. We further show the impact of residual frame timing offset, carrier frequency offset and fractional multiple Doppler on OTFS symbols. It is seen from the extensive Monte Carlo simulation results that the estimation and compensation methods presented here provide necessary resilience properties to OTFS. We bring out the tolerance of OTFS to such residual synchronization errors. It is further observed that the SIC is able to improve the performance of the system such that it almost matches that of the ideal knowledge based MMSE equalization. We also show the performance of RCP (reduced CP)-OTFS when used with the developed channel estimation and equalization algorithms. A unified signal processing flow for OTFS and orthogonal frequency division multiplexing (OFDM) is also described in this work to motivate studies on coexistence between the two as well as to encourage investigations on a seamless transition between OFDM and OTFS based systems for future adaptive air interface design.
Plastic Waste Management: Global Facts, Challenges and Solutions

Plastic waste becomes one of the world’s growing concerns due to its increasing production and consumption by human. By 2050, the world might have plastics in the oceans much more than fish. Therefore, it is threatening the world’s environment, economy and human health. Based on latest global statistics, most common plastic waste is either landfilled, recycled or incinerated. Recycling is the least implemented method. Degradation of manufactured plastics can take between 100 to 600 years. They get fragmented in the terrestrial and aquatic environments into little particles called "microplastics", which may end in human body through food chain, derma products and drinking bottled water. This research paper highlighted some global facts and challenges of plastic waste management, to end with best proposed solutions to the world’s governments. 143 resources have been reviewed, 90 of which, included articles, books, and some international organizations websites were selected to cover the introduction, results and discussion parts. The research found out that plastic waste management is the responsibility of both, global governments, and individuals. Most parts of the world lack laws that address "plastic waste" in particular, not as "all waste". Moreover, laws have to be strictly enforced in countries where they are being violated. Individuals’ awareness should be raised through their engagement in new innovative strategies built by their governments. Further information is necessary to be provided by researchers to measure microplastics’ negative effects on the environment and public health.
Manipulating Etch Selectivities in XeF₂ Vapour Etching

The vapour etching of silicon sacrificial layers is often a critical process in the fabrication of micro/nanosystems. This method has a number of attractive features, in particular, high etch rates of sacrificial silicon layers and good selectivities associated with photoresist, SiO2, stoichiometric Si3N4 and a number of regularly used metal films. However, materials that are commonly inert to XeF2 are etched when located in the proximity of a silicon sacrificial layer. This proximity is a common situation in the fabrication of such systems and can become a critical issue affecting process control and device reliability. This work uses test structures that have been designed to be very sensitive, thereby delivering much lower selectivities then are typically reported in the literature. This sensitive quantification of the proximity effect is used to evaluate methods designed to improve the selectivity. This work suggests that a reduction in the processing temperature from 25°C to 10°C increases the Si: PECVD SiN selectivity by 68%. However, a more easily implemented modification is to flow hydrogen into the reaction chamber. This method improves the Si: PECVD SiN selectivity by an order of magnitude and the Si: LPCVD SiN selectivity between 200% and 600%. [2020-0346]
Remote Assessment of Parkinson’s Disease Symptom Severity Using the Simulated Cellular Mobile Telephone Network

Telemonitoring of Parkinson’s Disease (PD) has attracted considerable research interest because of its potential to make a lasting, positive impact on the life of patients and their carers. Purpose-built devices have been developed that record various signals which can be associated with average PD symptom severity, as quantified on standard clinical metrics such as the Unified Parkinson’s Disease Rating Scale (UPDRS). Speech signals are particularly promising in this regard, because they can be easily recorded without the use of expensive, dedicated hardware. Previous studies have demonstrated replication of UPDRS to within less than 2 points of a clinical raters’ assessment of symptom severity, using high-quality speech signals collected using dedicated telemonitoring hardware. Here, we investigate the potential of using the standard voice-over-GSM (2G) or UMTS (3G) cellular mobile telephone networks for PD telemonitoring, networks that, together, have greater than 5 billion subscribers worldwide. We test the robustness of this approach using a simulated noisy mobile communication network over which speech signals are transmitted, and approximately 6000 recordings from 42 PD subjects. We show that UPDRS can be estimated to within less than 3.5 points difference from the clinical raters’ assessment, which is clinically useful given that the inter-rater variability for UPDRS can be as high as 4–5 UPDRS points. This provides compelling evidence that the existing voice telephone network has potential towards facilitating inexpensive, mass-scale PD symptom telemonitoring applications.
Collaborative Text and Image based Information Retrieval Model using BiLSTM and Residual Networks

Information retrieval (IR) becomes a crucial process in different applications like digital libraries, medicine, entertainment, and so on. Presently, the deep learning (DL) techniques are found to be applicable in several computer vision areas like object tracking, image classification, and natural language processing. So, the DL models are mainly applied in this work for the retrieval of text and images effectively. This paper develops a collaborative text and image based information retrieval system using DL models. The presented model involves two distinct processes namely text retrieval and image retrieval. Primarily, the convolutional neural network (CNN) based residual network 50 (ResNet) model called CNN-ResNet50 with Manhattan distance based similarity measurement technique is applied for the effectual retrieval of images. On the other side, bi-directional long short term memory (Bi-LSTM) technique optimized by differential evolution (DE) is applied for text retrieval. The proposed system highlights that the accuracy can be gained by increasing the depth( intermediate layers ) in DL model and providing an optimal result when using differential evolution algorithm with Bi-LSTM. A detailed implementation analysis was carried out to highlight the proficient results of the presented model. The outcomes indicated that the DE-BiLSTM technique has resulted in effectual outcome with the precision of 99.08%, recall of 99.24%, and F-score of 99.21%. Similarly, the CNN-ResNet50 model has also proficient retrieved the images with the average precision of 0.96 and an average recall of 0.91.
Wireless NoC for Inter-FPGA Communication: Theoretical Case for Future Datacenters

Integration of FPGAs in datacenters might have different motivations from acceleration to energy efficiency, but the goal of better performance tops all. FPGAs are being utilized in a variety of ways today, tightly coupled with heterogenous computing resources, and as a standalone network of homogenous resources. Open source software stacks, propriety tool chain, and programming languages with advanced methodologies are hitting hard on the programmability wall of the FPGAs. The deployment of FPGAs in datacenters will neither be sustainable nor economical, without realizing the multi-tenancy in multiple FPGAs. Inter-FPGA communication among multiple FPGAs remained relatively less addressed problem this area. In this proposed study, we made a theoretical case as a potential solution for this bottleneck problem to realize multi-tenant multi-FPGAs in datacenters. The proposed scheme poses a challenge as well as an opportunity for the scientific community to contribute.
Comparative Analysis of DSRC and LTE-V2X PC5 Mode 4 with SAE Congestion Control

In vehicle-to-everything (V2X) communications, ensuring the scalability is one of the most important aspects of V2X safety applications. This paper analyzes and compares the scalability performance of dedicated short-range communication (DSRC) based on IEEE 802.11p and 3GPP Rel-14 LTE-V2X PC5 mode 4 (i.e., direct communication mode without the help of base stations) in transmitting the Basic Safety Messages using SAE congestion control mechanisms in freeway scenarios with up to 4000 vehicles over a 5-km road. Simulation results show that for V2V distances below 300 m, DSRC over 10-MHz bandwidth is comparable with LTE-V2X over 20-MHz bandwidth with HARQ retransmission. Also, we reveal that LTE-V2X has a consecutive packet loss issue due to its semi-persistent scheduling mechanism. The results show that the use of a wider bandwidth and packet retransmission in LTE-V2X can partly mitigate a consecutive packet loss issue.
A hybrid deep transfer learning based approach for COVID-19 classification in chest X-ray images

As a contagious disease originating from a novel coronavirus, COVID-19 leads to swollen air sacs in the lungs. It can be diagnosed using a chest X-ray (CXR) images, which is usually cheaper and less harmful than a CT scan and is always available in small or rural hospitals. X-ray machines, however, sometimes cannot diagnose COVID-19. Since the COVID-19 dataset is small and cannot be diagnosed from CXR, pre-trained neural networks can be employed for coronavirus diagnosis. This paper mainly aims to use pre-trained deep transfer learning (DTL) architectures and conventional machine learning (ML) models as an automated instrument to diagnose COVID-19 from CXRs. To overcome the lack of a large number of images, DTL is utilized to extract image features for better classification. Then, to optimize the decision-making level for infectious diseases similar to bacterial and viral pneumonia, the extracted features are selected and classified. Our proposed method was validated by creating a new CXR database from Vasei Hospital in Sabzevar, Iran. Our hybrid model achieved hit rates above 99% and outperformed for CXR of COVID-19 and similar pneumonia classification. Comparative analysis shows the superiority of the proposed COVID-19 classification model based on DTL over other competitive methods.
Intelligent Surface-Aided Transmitter Architectures for Millimeter-Wave Ultra Massive MIMO Systems

In this article, we study two novel massive multiple-input multiple-output (MIMO) transmitter architectures for millimeter wave (mmWave) communications which comprise few active antennas, each equipped with a dedicated radio frequency (RF) chain, that illuminate a nearby large intelligent reflecting/transmitting surface (IRS/ITS). The IRS (ITS) consists of a large number of low-cost and energy-efficient passive antenna elements which are able to reflect (transmit) a phase-shifted version of the incident electromagnetic field. Similar to lens array (LA) antennas, IRS/ITS-aided antenna architectures are energy efficient due to the almost lossless over-the-air connection between the active antennas and the intelligent surface. However, unlike for LA antennas, for which the number of active antennas has to linearly grow with the number of passive elements (i.e., the lens aperture) due to the non-reconfigurablility (i.e., non-intelligence) of the lens, for IRS/ITS-aided antennas, the reconfigurablility of the IRS/ITS facilitates scaling up the number of radiating passive elements without increasing the number of costly and bulky active antennas. We show that the constraints that the precoders for IRS/ITS-aided antennas have to meet differ from those of conventional MIMO architectures. Taking these constraints into account and exploiting the sparsity of mmWave channels, we design two efficient precoders; one based on maximizing the mutual information and one based on approximating the optimal unconstrained fully digital (FD) precoder via the orthogonal matching pursuit algorithm. Furthermore, we develop a power consumption model for IRS/ITS-aided antennas that takes into account the impacts of the IRS/ITS imperfections, namely the spillover loss, taper loss, aperture loss, and phase shifter loss. Moreover, we study the effect that the various system parameters have on the achievable rate and show that a proper positioning of the active antennas with respect to the IRS/ITS leads to a considerable performance improvement. Our simulation results reveal that unlike conventional MIMO architectures, IRS/ITS-aided antennas are both highly energy efficient and fully scalable in terms of the number of transmitting (passive) antennas. Therefore, IRS/ITS-aided antennas are promising candidates for realizing the potential of mmWave ultra massive MIMO communications in practice.
The Effect of Stem on The Knee Joint Prosthesis Flexion Considering Natural Gait Forces

The knee as a vital organ in the human body skeleton is one of the largest joints. In recent years, there have been a variety of therapies offered to improve the efficiency and performance of an implanted prosthetic knee joint. Knee replacement is considered as one of the most common and effective treatments which is technically known as knee arthroplasty. Various factors could affect the mechanical behavior of knee arthroplasty, including the patients’ sex, physical, and bone characteristics as well as prosthesis type, structure, and geometry. The very recent advances in arthroplasty have enabled the orthopedists to achieve more desirable results by following the standards offered for prosthesis considering the anatomical geometry and material structure. Knee prosthetic implants with and without stem are the two prevalent types that orthopedists utilize for the joint replacement. In this case study, a real knee joint geometry is reconstructed using the CT images and MIMICS software. The knee arthroplasty model was then imported to the ABAQUS to perform FE analysis on the two aforementioned types of prosthesis. According to the results, the prosthesis with stem causes more stress on the joint components in the vicinity of the implantation region but will allow the knee joint to have a greater angular displacement with lower stress.