bluuebunny/crossref_metadata_2025 huggingface dataset:
Factors Associated with the Dual Use of Electronic Cigarettes and Cigarettes among Thai Undergraduate Students Who Smoked Cigarettes
Authors: Chulasai Phantara , Vientong Purida , Chinwong Surarong , Hall John J. , Chinwong Dujrudee
Published: December 2021

This study proposed to identify factors associated with the dual use of electronic cigarettes (e-cigarettes) and cigarettes among undergraduate students who smoked cigarettes. This cross-sectional study employed a self-administered, anonymous online questionnaires to collect information from undergraduate smokers in northern Thailand. Of the 494 participants, 82.8% were dual users of e-cigarettes and cigarettes. The two main reasons for using e-cigarettes were an absence of cigarette smoke odor (76.8%) and availability of flavors (70.7%). Undergraduate smokers who initiated smoking at ≥18 years old were more likely to be dual users than those who initiated smoking at younger age (adjusted odds ratio [aOR]: 2.79, 95% confidence intervals [CI]: 1.32–5.89, p = 0.007). Undergraduate smokers who smoked ≥11 cigarettes daily were more likely to be dual users than those who smoked less (aOR: 2.64, 95% CI: 1.52–4.61, p = 0.001). Conversely, undergraduate smokers who had attempted to quit during the past year were less likely to be dual users (aOR: 0.26, 95% CI: 0.12–0.56, p = 0.001). In conclusion, dual use of e-cigarettes and cigarettes among undergraduate smokers was associated with older age at cigarette smoking initiation, a higher number of cigarettes smoked daily, and having no past year’s cigarette quit attempts.
The Effect of the ‘Touch Screen-Based Cognitive Training’ for Children with Severe Cognitive Impairment in Special Education
Authors: Sung In Young , Yuk Jin Sook , Jang Dae-Hyun , Yun Gijeong , Kim Chunye , Ko Eun Jae
Published: December 2021

Traditional education in special schools have some limitations. We aimed to investigate if the ‘touch screen-based cognitive training’ is feasible and effective for children with severe cognitive impairment (developmental age 18–36 months) in special education. In this case, 29 children were randomly allocated to intervention (n = 17, ‘touch screen-based cognitive training’, 30 min/session, 3 times/week, 12 weeks) and control (n = 12, traditional education) groups. Psychoeducational Profile-Revised (PEP-R), Early Childhood Behavior Questionnaire (ECBQ), Sequenced Language Scale for Infants (SELSI), Pediatric Evaluation of Disability Inventory (PEDI), and Goal Attainment Scale (GAS) were measured before and after 12 weeks of education. The ‘touch screen-based cognitive training’ was applicable in special education. When repeated measures analysis of variance (ANOVA) was used, significant groupⅹtime effect was found for GAS, and significant group effect was found for ECBQ (attentional shifting) and GAS. When adjusting for pre-education measurements, the intervention had a significant effect on the post-education measurements of ECBQ (attentional shifting) and GAS (p < 0.05). No relationship existed between the degree of improvements and the severeness of developmental delay in the measurements. ‘Touch screen-based cognitive training’ in special school was feasible and it improved cognition in children with severe cognitive impairment (developmental age 18–36 months), irrespective of the severeness of the developmental delay.
Case Report: Genetic Alterations Associated with the Progression of Carotid Paraganglioma
Authors: Pavlov Vladislav , Snezhkina Anastasiya , Kalinin Dmitry , Golovyuk Alexander , Kobelyatskaya Anastasiya , Bakhtogarimov Ildar , Volchenko Nadezhda , Krasnov George , Kudryavtseva Anna
Published: December 2021

Paragangliomas (PGLs) are rare neuroendocrine tumors that can develop from any paraganglion across the body. The carotid body is the most often location of PGLs in the head and neck region. Carotid PGLs (CPGLs) are characterized by predominantly non-aggressive behavior; however, all tumors have the potential to metastasize. To date, molecular mechanisms of paraganglioma progression remain elusive. We report a case of a 38-year-old woman with metastatic CPGL manifesting as a recurrent tumor with lymph node metastasis. The tumor was fast-growing and had a high Ki-67 proliferation index. Immunohistochemical (IHC) examination and whole-exome sequencing were performed for both recurrent tumor and metastasis. A germline pathogenic splice acceptor variant in the SDHB gene was found in the patient. Immunoreactivity of the SDHB subunit was weak diffuse in both samples, indicating deficiency of the succinate dehydrogenase. Moreover, the recurrent tumor exhibited loss of heterozygosity (LOH) at the SDHB locus, that is according to Knudson’s "two-hit" hypothesis of cancer causation. We also identified a rare somatic promotor mutation in the TERT gene associated with the tumor progression. Obtained results confirmed the indicative role of the germline SDHB mutation for metastatic CPGLs, as well as the potential prognostic value of the TERT promoter mutation.
Assessing the Capability of Computational Fluid Dynamics Models in Replicating Wind Tunnel Test Results for the Rose Fitzgerald Kennedy Bridge
Authors: Zhang Yuxiang , Cardiff Philip , Cahill Fergal , Keenahan Jennifer
Published: December 2021

Despite its wide acceptance in various industries, CFD is considered a secondary option to wind tunnel tests in bridge engineering due to a lack of confidence. To increase confidence and to advance the quality of simulations in bridge aerodynamic studies, this study performed three-dimensional RANS simulations and DESs to assess the bridge deck aerodynamics of the Rose Fitzgerald Kennedy Bridge and demonstrated detailed procedures of the verification and validation of the applied CFD model. The CFD simulations were developed in OpenFOAM, the results of which are compared to prior wind tunnel test results, where general agreements were achieved though differences were also found and analyzed. The CFD model was also applied to study the effect of fascia beams and handrails on the bridge deck aerodynamics, which were neglected in most research to-date. These secondary structures were found to increase drag coefficients and reduce lift and moment coefficients by up to 32%, 94.3%, and 52.2%, respectively, which emphasized the necessity of including these structures in evaluations of the aerodynamic performance of bridges in service. Details of the verification and validation in this study illustrate that CFD simulations can determine close results compared to wind tunnel tests.
Surf and Swash Dynamics on Low Tide Terrace Beaches
Authors: Mingo Ivana M. , Almar Rafael , Lacaze Laurent
Published: December 2021

Low tide terrace (LLT) beaches are characterised by a moderately steep beach face and a flat shallow terrace influencing the local hydro-morphodynamics during low tide. The upper beachface slope (β) and the terrace width (Lt) are the main morphological parameters that define the shape of LTT cross-shore beach profiles. This work aims at better understanding the behaviour of β and Lt and their link with the incoming wave forcing. For this purpose, our results are based on 3.5 years times series of daily beach profiles and wave conditions surveys at two different microtidal LTT beaches with similar sediments size but different wave climate, one at Nha Trang (Vietnam) and the other one at Grand Popo (Benin). While they look similar, two contrasting behaviour were linked to two sub-types of LTT regimes: the first one is surf regulated beaches (SRB) where the swash zone is highly regulated by the surf zone wave energy dissipation on the terrace, and the second is swash regulated beaches (SwRB) acting in more reflective regime where the terrace is not active and the energy dissipation is mainly produced in the swash zone, the terrace becomes a consequences of the high dynamics in the swash zone. Finally, extending the common view of an equilibrium beach profile as a power law of the cross-shore distance, the ability of a simple parametrized cubic function model with the Dean number as unique control parameters is proposed and discussed. This simple model can be used for the understanding of LLT environments but it can not be extended to the whole beach spectrum.
The Scent of Himantoglossum Species Found in Basilicata (Southern Italy)
Authors: Mecca Marisabel , Racioppi Rocco , Romano Vito Antonio , Viggiani Licia , Lorenz Richard , D’Auria Maurizio
Published: December 2021

The SPME (Solid Phase Microextraction) analysis of the scent of H. hircinum showed the presence of elemicin in the presence of a relevant amount of eugenol. The scent of the sample of H. adriaticum collected in Abruzzo showed the presence 4-amino-5-(4-morpholinylmethyl)-2-oxazolidinone, β-ocimene, decyl decanoate, and 9-tricosene as main components. The sample of H. adriaticum collected at Marsico Nuovo has an aroma where the main component was pentadecyl hexanoate, 9-tricosene, methyleugenol, tetradecane, pentadecane, and elemicin. The samples of H. adriaticum collected at Viggianello showed some similarities in the scent: the main components were 9-tricosene and methyleugenol.
Numerical Analysis of a Novel Twin-Impeller Centrifugal Compressor
Authors: Nguyen Van Thang , Danlos Amélie , Ravelet Florent , Deligant Michael , Solis Moises , Khelladi Sofiane , Bakir Farid
Published: December 2021

Centrifugal compressors are widely used in many industrial fields such as automotive, aviation, aerospace. However, these turbomachines suffer instability phenomenon when the flow rate is too high or too low, called rotating stall and surge. These phenomena cause the operation failure, pressure fluctuations and vibrations of the thorough system. Numerous mechanical solutions have been presented to minimize these instabilities and expand the operating range towards low-flow rates like active control of the flow path, variable inlet guide vane and casing treatment. Currently, our team has developed a novel compressor composed of a twin-impeller powered by autonomous systems. We notice the performance improvement and instabilities suppression of this compressor experimentally. In this paper, an active control method is introduced by controlling the speed and rotation direction of the impellers to expand the operating range. A CFD study is then conducted to analysis flow morphology and thermodynamic characteristics based on the experimental observations at three special points. Numerical results and experimental measurements of compressor maps are consistent.
A Computational Analysis for Active Flow and Pressure Control Using Moving Roller Peristalsis
Authors: Moulinos Iosif , Manopoulos Christos , Tsangaris Sokrates
Published: December 2021

Peristaltic motion arises in many physiological, medical, pharmaceutical and industrial processes. Control of the fluid volume rate and pressure is crucial for pumping applications, such as the infusion of intravenous liquid drugs, blood transportation, etc. In this study, a simulation of peristaltic flow is presented in which occlusion is imposed by pairs of circular rollers that squeeze a deformable channel connected to a reservoir with constant fluid pressure. Naturally, this kind of flow is laminar; hence, the computation occurred in this context. The effect of the number and speed of the pairs of rollers, as well as that of the intrapair roller gap, is investigated. Non-Newtonian fluids are considered, and the effect of the shear-thinning behavior degree is examined. The volumetric flow rate is found to increase with an increase in the number of rollers or in the relative occlusion. A reduction in the Bird–Carreau power index resulted in a small reduction in transport efficiency. The characteristic of the pumping was computed, i.e., the induced pressure as a function of the fluid volume rate. A strong positive correlation exists between relative occlusion and induced pressure. Shear-thinning behavior significantly decreases the developed pressure compared to Newtonian fluids. The immersed boundary method on curvilinear coordinates is adapted and validated for non-Newtonian fluids.
Evaluation of an Antigen Detection Rapid Diagnostic Test for Detection of SARS-CoV-2 in Clinical Samples
Authors: Lam Athene Hoi-Ying , Leung Ka-Yi , Zhang Ruiqi , Liu Danlei , Fan Yujing , Tam Anthony Raymond , Yip Cyril Chik-Yan , Cheng Vincent Chi-Chung , Yuen Kwok-Yung , Hung Ivan Fan-Ngai , Chan Kwok-Hung
Published: December 2021

Antigen detection rapid diagnostic tests have been developed for first-line large-scale screening given their rapidity, simplicity, and accuracy. This study evaluates the diagnostic performance of an antigen detection rapid diagnostic test (BLOK BioScience, London, UK) detecting SARS-CoV-2 nucleocapsid protein. Serially diluted SARS-CoV-2 isolate and 110 NPS from COVID-19 patients were tested to determine the test’s sensitivity, and other viral isolates and 20 NPS from non-infected individuals were, for specificity, also tested. Ten clinical samples from COVID-19 patients with SARS-CoV-2 variants, including alpha, beta, gamma, delta, and eta variants, were collected to evaluate the test’s potential application in detecting emerging variants. Overall sensitivity was 92%, and stratifying into viral loads yielded 100% for Ct < 25 samples including SARS-CoV-2 variants, but 11.11% for Ct ≥ 30 samples. The analytical sensitivity of log10 TCID50/mL 2.0 was identified for SARS-CoV-2. Ninety-seven percent specificity with only SARS-CoV cross-reactivity lead to the Youden index of 0.89. The rapid diagnostic test has a high sensitivity for detecting SARS-CoV-2 in high viral load samples, possibly including emerging SARS-CoV-2 variants, but reduced sensitivity in low viral load samples suggests its optimized usage as a complementary testing method to other tests, including RT-PCR or a point-of-care test for large-scale screening, particularly for pandemic areas or airport border infection control.
Diagnostic Performance of a Rapid Antigen Test Compared with the Reverse Transcription Polymerase Chain Reaction for SARS-CoV-2 Detection in Asymptomatic Individuals Referring to a Drive-in Testing Facility
Authors: Lombardo Fabio , Triolo Gianluca , Yang Biao , Liu Zhonghua , Maiuri Paolo , Orsini Emanuele , Jin Wei , Zacchigna Serena , Marcello Alessandro
Published: December 2021

Quick and reliable identification of severe acute respiratory syndrome coronavirus SARS-CoV-2 in the population is required to manage the COVID-19 pandemic. This is a prospective observational study of diagnostic accuracy. Paired swab samples from 317 asymptomatic individuals referring to a drive-in testing facility were tested in parallel by means of the rapid antigen test developed by Jiangsu Bioperfectus Technologies and routine nucleic acid detection. Overall specificity was 100% and sensitivity was 49% but reached 87% at higher viral loads (Ct < 25). In this study, the antigen detection test showed high specificity and good sensitivity in asymptomatic individuals carrying higher viral loads. The assay performance worsened with lower viral loads, making it useful when a rapidly deployable test is essential and to assess a potential risk of immediate transmission in the community, but not recommended for testing asymptomatic individuals.
Understanding the Attitudes and Beliefs of Oncologists Regarding the Transitioning and Sharing of Survivorship Care
Authors: Coschi Courtney H. , Bainbridge Daryl , Sussman Jonathan
Published: December 2021

Transitioning survivorship care from oncologists to primary care physicians (PCPs) is a reasonable alternative to oncologist-led care. This study assessed oncologists’ attitudes and beliefs regarding sharing/transitioning survivorship care. A prospective survey of oncologists within a regional cancer program assessing self-reported barriers and facilitators to sharing/transitioning survivorship care was disseminated. In total, 63% (n = 39) of surveyed oncologists responded. Patient preference (89%) and anxiety (84%) are key to transition of care decisions; reduced remuneration (95%) and fewer longitudinal relationships (63%) do not contribute. Oncologists agreed that more patients could be shared/transitioned. Barriers include treatment-related toxicities (82% agree), tumor-specific factors (60–90% agree) and perception of PCP willingness to participate in survivorship care (47% agree). Oncologists appear willing to share/transition more survivors to PCPs, though barriers exist that warrant further study. Understanding these issues is critical to developing policies supporting comprehensive survivorship care models that address both cancer and non-cancer health needs. The demonstrated feasibility of this project warrants a larger-scale survey of oncologists with respect to the transition of survivorship care to PCPs, to further inform effective interventions to support high-quality survivorship care.
Assessment of Sesamia nonagrioides (Lepidoptera: Noctuidae) EcR and USP Genes as Targets for Exogenous Non-Persistent RNAi
Authors: Kontogiannatos Dimitrios , Swevers Luc , Kourti Anna
Published: December 2021

RNA interference (RNAi) is a transforming technology with high potential for practical applications in biology, including specific and safe insect pest control. For developing RNAi-based pest-control products no general recommendations exist and the best strategy needs to be determined for each insect pest separately on a case-by-case basis. In this research, the potential of silencing the genes encoding the subunits of the ecdysone receptor complex, EcR and Ultraspiracle (USP) by RNAi was evaluated in the corn borer, Sesamia nonagrioides, using different delivery approaches and targeting different developmental stages. In conjunction with our previous research it is demonstrated that prepupae are sensitive to RNAi triggered by dsRNA injection and that feeding of dsRNA-expressing bacteria throughout S. nonagrioides’ larval life can lead to limited developmental malformations with no potent insecticidal results. Our results, consistent with previous studies, indicated a great fluctuation of exogenous RNAi effectiveness in the Lepidopteran species, suggesting that further factors should be taken into consideration in order to expand this very promising field into the ‘’RNAi-resistant’’ insect species.
The Influence of Climate Change on Three Dominant Alpine Species under Different Scenarios on the Qinghai–Tibetan Plateau
Authors: Hu Huawei , Wei Yanqiang , Wang Wenying , Wang Chunya
Published: December 2021

The Qinghai–Tibetan Plateau (QTP) with high altitude and low temperature is one of the most sensitive areas to climate change and has recently experienced continuous warming. The species distribution on the QTP has undergone significant changes especially an upward shift with global warming in the past decades. In this study, two dominant trees (Picea crassifolia Kom and Sabina przewalskii Kom) and one dominant shrub (Potentilla parvifolia Fisch) were selected and their potential distributions using the MaxEnt model during three periods (current, the 2050s and the 2070s) were predicted. The predictions were based on four shared socio-economic pathway (SSPs) scenarios, namely, SSP2.6, SSP4.5, SSP7.0, SSP8.5. The predicted current potential distribution of three species was basically located in the northeastern of QTP, and the distribution of three species was most impacted by aspect, elevation, temperature seasonality, annual precipitation, precipitation of driest month, Subsoil CEC (clay), Subsoil bulk density and Subsoil CEC (soil). There were significant differences in the potential distribution of three species under four climate scenarios in the 2050s and 2070s including expanding, shifting, and shrinking. The total suitable habitat for Picea crassifolia shrank under SSP2.6, SSP4.5, SSP7.0 and enlarged under SSP8.5 in the 2070s. On the contrary, the total suitable habitat for Sabina przewalskii enlarged under SSP2.6, SSP4.5, SSP7.0 and shrank under SSP8.5 in the 2070s. The total suitable habitat for Potentilla parvifolia continued to increase with SSP2.6 to SSP8.5 in the 2070s. The average elevation in potentially suitable habitat for Potentilla parvifolia all increased except under SSP8.5 in the 2050s. Our study provides an important reference for the conservation of Picea crassifolia, Sabina przewalskii, Potentilla parvifolia and other dominant plant species on the QTP under future climate change.
Influence of an Educational Innovation Program and Digitally Supported Tasks on Psychological Aspects, Motivational Climate, and Academic Performance
Authors: Vaquero-Cristóbal Raquel , Abenza-Cano Lucía , Albaladejo-Saura Mario , Meroño Lourdes , Marcos-Pardo Pablo J. , Esparza-Ros Francisco , González-Gálvez Noelia
Published: December 2021

Background: In university education, there is a need to provide students with the ability to use knowledge, and it has been shown that the cooperative model, with respect to information and communication technology (ICT), is effective. The aim of this study was to analyze the influence of an educational innovation program, based on the jigsaw technique and digitally supported tasks, on the psychological aspects, motivational climate, and academic performance of university students. Methods: A quasi-experimental study was conducted with an experimental group consisting of 100 university students (mean age: 21.84 ± 1.50 years). The motivational climate and the basic psychological needs in education, intrinsic motivation, academic self-concept, and academic performance were measured. Results: Significant increases were found in all variables after the intervention (p < 0.006–0.001), except for the variable, ego-motivational climate. The covariate perception of prior competences was significant for the model (p < 0.001). The students who had chosen a specific topic to develop with the jigsaw technique obtained a better grade than the rest of their classmates when the student’s academic performance was included as a covariate (p < 0.001). Conclusions: The psychological aspects, motivational climates, and academic performances of university students improved after the implementation of an educational innovation program, based on the cooperative learning model with the jigsaw technique, and the use of digitally supported tasks.
An Analysis on the Architecture and the Size of Quantized Hardware Neural Networks Based on Memristors
Authors: Romero-Zaliz Rocio , Cantudo Antonio , Perez Eduardo , Jimenez-Molinos Francisco , Wenger Christian , Roldan Juan Bautista
Published: December 2021

We have performed different simulation experiments in relation to hardware neural networks (NN) to analyze the role of the number of synapses for different NN architectures in the network accuracy, considering different datasets. A technology that stands upon 4-kbit 1T1R ReRAM arrays, where resistive switching devices based on HfO2 dielectrics are employed, is taken as a reference. In our study, fully dense (FdNN) and convolutional neural networks (CNN) were considered, where the NN size in terms of the number of synapses and of hidden layer neurons were varied. CNNs work better when the number of synapses to be used is limited. If quantized synaptic weights are included, we observed that NN accuracy decreases significantly as the number of synapses is reduced; in this respect, a trade-off between the number of synapses and the NN accuracy has to be achieved. Consequently, the CNN architecture must be carefully designed; in particular, it was noticed that different datasets need specific architectures according to their complexity to achieve good results. It was shown that due to the number of variables that can be changed in the optimization of a NN hardware implementation, a specific solution has to be worked in each case in terms of synaptic weight levels, NN architecture, etc.
Power Equipment Defects Prediction Based on the Joint Solution of Classification and Regression Problems Using Machine Learning Methods
Authors: Shcherbatov Ivan , Lisin Evgeny , Rogalev Andrey , Tsurikov Grigory , Dvořák Marek , Strielkowski Wadim
Published: December 2021

Our paper proposes a method for constructing a system for predicting defects and failures of power equipment and the time of their occurrence based on the joint solution of regression and classification problems using machine learning methods. A distinctive feature of this method is the use of the equipment’s technical condition index as an informative parameter. The results of calculating and visualizing the technical condition index in relation to the electro-hydraulic automatic control system of hydropower turbine when predicting the defect “clogging of drainage channels” showed that its determination both for an equipment and for a group of its functional units allows one to quickly and with the required accuracy assess the arising technological disturbances in the operation of power equipment. In order to predict the behavior of the technical condition index of the automatic control system of the turbine, the optimal tuning of the LSTM model of the recurrent neural network was developed and carried out. The result of the application of the model was the forecast of the technical condition index achievement and the limiting characteristic according to the current time data on its values. The developed model accurately predicted the behavior of the technical condition index at time intervals of 3 and 10 h, which made it possible to draw a conclusion about its applicability for early identification of the investigated defect in the automatic control system of the turbine. Thus, we can conclude that the joint solution of regression and classification problems using an information parameter in the form of a technical condition index allows one to develop systems for predicting defects, one significant advantage of which is the ability to early determine the development of degradation phenomena in power equipment.
Recent Progress on 3D NAND Flash Technologies
Authors: Goda Akira
Published: December 2021

Since 3D NAND was introduced to the industry with 24 layers, the areal density has been successfully increased more than ten times, and has exceeded 10 Gb/mm2 with 176 layers. The physical scaling of XYZ dimensions including layer stacking and footprint scaling enabled the density scaling. Logical scaling has been successfully realized, too. TLC (triple-level cell, 3 bits per cell) is now the mainstream in 3D NAND, while QLC (quad-level cell, 4 bits per cell) is increasing the presence. Several attempts and partial demonstrations were made for PLC (penta-level cell, 5 bits per cell). CMOS under array (CuA) enabled the die size reduction and performance improvements. Program and erase schemes to address the technology challenges such as short-term data retention of the charge-trap cell and the large block size are being investigated.
Deep Learning and Machine Learning Techniques of Diagnosis Dermoscopy Images for Early Detection of Skin Diseases
Authors: Abunadi Ibrahim , Senan Ebrahim Mohammed
Published: December 2021

With the increasing incidence of severe skin diseases, such as skin cancer, endoscopic medical imaging has become urgent for revealing the internal and hidden tissues under the skin. Diagnostic information to help doctors make an accurate diagnosis is provided by endoscopy devices. Nonetheless, most skin diseases have similar features, which make it challenging for dermatologists to diagnose patients accurately. Therefore, machine and deep learning techniques can have a critical role in diagnosing dermatoscopy images and in the accurate early detection of skin diseases. In this study, systems for the early detection of skin lesions were developed. The performance of the machine learning and deep learning was evaluated on two datasets (e.g., the International Skin Imaging Collaboration (ISIC 2018) and Pedro Hispano (PH2)). First, the proposed system was based on hybrid features that were extracted by three algorithms: local binary pattern (LBP), gray level co-occurrence matrix (GLCM), and wavelet transform (DWT). Such features were then integrated into a feature vector and classified using artificial neural network (ANN) and feedforward neural network (FFNN) classifiers. The FFNN and ANN classifiers achieved superior results compared to the other methods. Accuracy rates of 95.24% for diagnosing the ISIC 2018 dataset and 97.91% for diagnosing the PH2 dataset were achieved using the FFNN algorithm. Second, convolutional neural networks (CNNs) (e.g., ResNet-50 and AlexNet models) were applied to diagnose skin diseases using the transfer learning method. It was found that the ResNet-50 model fared better than AlexNet. Accuracy rates of 90% for diagnosing the ISIC 2018 dataset and 95.8% for the PH2 dataset were reached using the ResNet-50 model.
Deep and Transfer Learning Approaches for Pedestrian Identification and Classification in Autonomous Vehicles
Authors: Mounsey Alex , Khan Asiya , Sharma Sanjay
Published: December 2021

Pedestrian detection is at the core of autonomous road vehicle navigation systems as they allow a vehicle to understand where potential hazards lie in the surrounding area and enable it to act in such a way that avoids traffic-accidents, which may result in individuals being harmed. In this work, a review of the convolutional neural networks (CNN) to tackle pedestrian detection is presented. We further present models based on CNN and transfer learning. The CNN model with the VGG-16 architecture is further optimised using the transfer learning approach. This paper demonstrates that the use of image augmentation on training data can yield varying results. In addition, a pre-processing system that can be used to prepare 3D spatial data obtained via LiDAR sensors is proposed. This pre-processing system is able to identify candidate regions that can be put forward for classification, whether that be 3D classification or a combination of 2D and 3D classifications via sensor fusion. We proposed a number of models based on transfer learning and convolutional neural networks and achieved over 98% accuracy with the adaptive transfer learning model.
Analysis of Single Event Effects on Embedded Processor
Authors: Azimi Sarah , De Sio Corrado , Rizzieri Daniele , Sterpone Luca
Published: December 2021

The continuous scaling of electronic components has led to the development of high-performance microprocessors which are even suitable for safety-critical applications where radiation-induced errors, such as single event effects (SEEs), are one of the most important reliability issues. This work focuses on the development of a fault injection environment capable of analyzing the impact of errors on the functionality of an ARM Cortex-A9 microprocessor embedded within a Zynq-7000 AP-SoC, considering different fault models affecting both the system memory and register resources of the embedded processor. We developed a novel Python-based fault injection platform for the emulation of radiation-induced faults within the AP-SoC hardware resources during the execution of software applications. The fault injection approach is not intrusive, and it does not require modifying the software application under evaluation. The experimental analyses have been performed on a subset of the MiBench benchmark software suite. Fault injection results demonstrate the capability of the developed method and the possibility of evaluating various sets of fault models.
A Novel Approach for Securing Nodes Using Two-Ray Model and Shadow Effects in Flying Ad-Hoc Network
Authors: Kaur Manjit , Prashar Deepak , Rashid Mamoon , Alshamrani Sultan S. , AlGhamdi Ahmed Saeed
Published: December 2021

In the last decades, flying ad-hoc networks (FANET) have provided unique features in the field of unmanned aerial vehicles (UAVs). This work intends to propose an efficient algorithm for secure load balancing in FANET. It is performed with the combination of the firefly algorithm and radio propagation model. To provide the optimal path and to improve the data communication of different nodes, two-ray and shadow fading models are used, which secured the multiple UAVs in some high-level applications. The performance analysis of the proposed efficient optimization technique is compared in terms of packet loss, throughput, end-to-end delay, and routing overhead. Simulation results showed that the secure firefly algorithm and radio propagation models demonstrated the least packet loss, maximum throughput, least delay, and least overhead compared with other existing techniques and models.
A Hybrid Imputation Method for Multi-Pattern Missing Data: A Case Study on Type II Diabetes Diagnosis
Authors: Nadimi-Shahraki Mohammad H. , Mohammadi Saeed , Zamani Hoda , Gandomi Mostafa , Gandomi Amir H.
Published: December 2021

Real medical datasets usually consist of missing data with different patterns which decrease the performance of classifiers used in intelligent healthcare and disease diagnosis systems. Many methods have been proposed to impute missing data, however, they do not fulfill the need for data quality especially in real datasets with different missing data patterns. In this paper, a four-layer model is introduced, and then a hybrid imputation (HIMP) method using this model is proposed to impute multi-pattern missing data including non-random, random, and completely random patterns. In HIMP, first, non-random missing data patterns are imputed, and then the obtained dataset is decomposed into two datasets containing random and completely random missing data patterns. Then, concerning the missing data patterns in each dataset, different single or multiple imputation methods are used. Finally, the best-imputed datasets gained from random and completely random patterns are merged to form the final dataset. The experimental evaluation was conducted by a real dataset named IRDia including all three missing data patterns. The proposed method and comparative methods were compared using different classifiers in terms of accuracy, precision, recall, and F1-score. The classifiers’ performances show that the HIMP can impute multi-pattern missing values more effectively than other comparative methods.
Adaptive Noise-Resistant Low-Power ASK Demodulator Design in UHF RFID Chips
Authors: Xu Yao-Hua , Yang Shuai , Li Hang , Lv Ji-Ming , Bai Na
Published: December 2021

This paper presents a new signal demodulator for ultra-high frequency (UHF) radio frequency identification (RFID) tag chips. The demodulator is used to demodulate amplitude shift keying (ASK) modulated signals with the advantages of high noise immunity, large input range and low power consumption. The demodulator consists of a charge pump, an envelope detector, and a comparator. In particular, the demodulator provides a hysteresis input signal to the comparator through two envelope detectors, resulting in better noise immunity. The demodulator is based on a standard 0.13 µm CMOS process. The demodulator is suitable for demodulating high frequency signals at 900 MHz with a data rate of 128 Kbps and can operate up to 78 °C. The input signal has a peak of 1.2 V and consumes as little as 113.6 nW. The demodulator also has a noise immunity threshold of approximately 3.729 V.
Memory-Based LT Codes for Efficient 5G Networks and Beyond
Authors: Hayajneh Khaled F.
Published: December 2021

The next-generation networks (5G and beyond) require robust channel codes to support their high specifications, such as low latency, low complexity, significant coding gain, and flexibility. In this paper, we propose using a fountain code as a promising solution to 5G and 6G networks, and then we propose using a modified version of the fountain codes (Luby transform codes) over a network topology (Y-network) that is relevant in the context of the 5G networks. In such a network, the user can be connected to two different cells at the same time. In addition, the paper presents the necessary techniques for analyzing the system and shows that the proposed scheme enhances the system performance in terms of decoding success probability, error probability, and code rate (or overhead). Furthermore, the analyses in this paper allow us to quantify the trade-off between overhead, on the one hand, and the decoding success probability and error probability, on the other hand. Finally, based on the analytical approach and numerical results, our simulation results demonstrate that the proposed scheme achieves better performance than the regular LT codes and the other schemes in the literature.
Design and Development of Smart Parking System Based on Fog Computing and Internet of Things
Authors: Balfaqih Mohammed , Jabbar Waheb , Khayyat Mashael , Hassan Rosilah
Published: December 2021

Current parking systems employ a single gateway-centered solution (i.e., cloud) for data processing which leads to the possibility of a single point of failure, data loss, and high delays. Moreover, the parking-spot selection process considers criteria that do not maximize parking utilization and revenue. The pricing strategy does not achieve high revenue because a fixed pricing rate is utilized. To address these issues, this paper proposes a smart parking system based on the Internet of Things (IoT) that provides useful information to drivers and parking administrators about available parking spots and related services such as parking navigation, reservation, and availability estimation. A multi-layer architecture is developed that consists of multiple sensor nodes, and fog and cloud computing layers. The acquired parking data are processed through fog computing nodes to facilitate obtaining the required real-time parking data. A novel algorithm to obtain the optimal parking spot with the minimum arrival time is also presented. Proof-of-concept implementation and simulation evaluations are conducted to validate the system performance. The findings show that the system reduces the parking arrival time by 16–46% compared to current parking systems. In addition, the revenue is increased for the parking authority by 10–15%.
The SOC Based Dynamic Charging Coordination of EVs in the PV-Penetrated Distribution Network Using Real-World Data
Authors: Akil Murat , Dokur Emrah , Bayindir Ramazan
Published: December 2021

A successful distribution network can continue to operate despite the uncertainties at the charging station, with appropriate equipment retrofits and upgrades. However, these new investments in the grid can become complex in terms of time and space. In this paper, we propose a dynamic charge coordination (DCC) method based on the battery state of charge (SOC) of electric vehicles (EVs) in line with this purpose. The collective uncoordinated charging profiles of EVs charged at maximum power were investigated based on statistical data for distances of EVs and a real dataset for charging characteristics in the existing grid infrastructure. The proposed strategy was investigated using the modified Roy Billinton Test System (RBTS) performed by DIgSILENT Powerfactory simulation software for a total 50 EVs in 30 different models. Then, the load balancing situations were analyzed with the integration of the photovoltaic (PV) generation and battery energy storage system (BESS) into the bus bars where the EVs were fed into the grid. According to the simulation results, the proposed method dramatically reduces the effects on the grid compared to the uncoordinated charging method. Furthermore, the integration of PV and BESS system, load balancing for EVs was successfully achieved with the proposed approach.
Intelligent Scheduling of Smart Home Appliances Based on Demand Response Considering the Cost and Peak-to-Average Ratio in Residential Homes
Authors: Tutkun Nedim , Burgio Alessandro , Jasinski Michal , Leonowicz Zbigniew , Jasinska Elzbieta
Published: December 2021

With recent developments, smart grids assured for residential customers the opportunity to schedule smart home appliances’ operation times to simultaneously reduce both the electricity bill and the PAR based on demand response, as well as increasing user comfort. It is clear that the multi-objective combinatorial optimization problem involves constraints and the consumer’s preferences, and the solution to the problem is a difficult task. There have been a limited number of investigations carried out so far to solve the indicated problems using metaheuristic techniques like particle swarm optimization, mixed-integer linear programming, and the grey wolf and crow search optimization algorithms, etc. Due to the on/off control of smart home appliances, binary-coded genetic algorithms seem to be a well-fitted approach to obtain an optimal solution. It can be said that the novelty of this work is to represent the on/off state of the smart home appliance with a binary string which undergoes crossover and mutation operations during the genetic process. Because special binary numbers represent interruptible and uninterruptible smart home appliances, new types of crossover and mutation were developed to find the most convenient solutions to the problem. Although there are a few works which were carried out using the genetic algorithms, the proposed approach is rather distinct from those employed in their work. The designed genetic software runs at least ten times, and the most fitting result is taken as the optimal solution to the indicated problem; in order to ensure the optimal result, the fitness against the generation is plotted in each run, whether it is converged or not. The simulation results are significantly encouraging and meaningful to residential customers and utilities for the achievement of the goal, and they are feasible for a wide-range applications of home energy management systems.
Techno-Economic Evaluation of Co-Removal of NOx and SOx Species from Flue Gases via Enhanced Oxidation of NO by ClO2—Case Studies of Implementation at a Pulp and Paper Mill, Waste-to-Heat Plant and a Cruise Ship
Authors: Johansson Jakob , Normann Fredrik , Andersson Klas
Published: December 2021

Co-absorption of NO2 and SO2 from flue gases, in combination with the enhanced oxidation of NO by ClO2(g), is studied for three different flue gas sources: a medium sized waste-to-heat plant; the kraft recovery boiler of a pulp and paper mill; and a cruise ship. Process modeling results are used to present the technical potential for each site together with cost estimation and optimization using a bottom-up approach. A process set-up is proposed for each site together with equipment sizing and resulting flows of process fluids. The simulation results, supported by experimental results, show that removal rates equal to or greater than current best available technologies are achievable with more than 90% of NOx and 99% of SO2 removed from the flue gas. The resulting cost of removing both NOx and SO2 from the flue gases is 2100 €/ton for the waste-to-heat plant, 800 €/ton for the cruise ship and 3900 €/ton for the recovery boiler. The cost estimation show that the consumption and cost of chemical additives will play a decisive role in the economic feasibility of the investigated concept, between 50% and 90% of the total cost per ton acid gas removed.
Carbon-Neutrality in Wastewater Treatment Plants: Advanced Technologies for Efficient Operation and Energy/Resource Recovery
Authors: Bae Sungjun , Kim Young Mo
Published: December 2021

Recently, “carbon neutrality”, the state of net-zero carbon dioxide emission, has been one of the most frequently used terms in the sustainable development of environmental and energy industries [...]
A Novel Mean Field Game-Based Strategy for Charging Electric Vehicles in Solar Powered Parking Lots
Authors: Muhindo Samuel M. , Malhamé Roland P. , Joos Geza
Published: December 2021

We develop a strategy, with concepts from Mean Field Games (MFG), to coordinate the charging of a large population of battery electric vehicles (BEVs) in a parking lot powered by solar energy and managed by an aggregator. A yearly parking fee is charged for each BEV irrespective of the amount of energy extracted. The goal is to share the energy available so as to minimize the standard deviation (STD) of the state of charge (SOC) of batteries when the BEVs are leaving the parking lot, while maintaining some fairness and decentralization criteria. The MFG charging laws correspond to the Nash equilibrium induced by quadratic cost functions based on an inverse Nash equilibrium concept and designed to favor the batteries with the lower SOCs upon arrival. While the MFG charging laws are strictly decentralized, they guarantee that a mean of instantaneous charging powers to the BEVs follows a trajectory based on the solar energy forecast for the day. That day ahead forecast is broadcasted to the BEVs which then gauge the necessary SOC upon leaving their home. We illustrate the advantages of the MFG strategy for the case of a typical sunny day and a typical cloudy day when compared to more straightforward strategies: first come first full/serve and equal sharing. The behavior of the charging strategies is contrasted under conditions of random arrivals and random departures of the BEVs in the parking lot.
Mapping Bio-CO2 and Wind Resources for Decarbonized Steel, E-Methanol and District Heat Production in the Bothnian Bay
Authors: Karjunen Hannu , Inkeri Eero , Tynjälä Tero
Published: December 2021

Hydrogen is a versatile feedstock for various chemical and industrial processes, as well as an energy carrier. Dedicated hydrogen infrastructure is envisioned to conceptualize in hydrogen valleys, which link together the suppliers and consumers of hydrogen, heat, oxygen, and electricity. One potential hydrogen valley is the Bay of Bothnia, located in the northern part of the Baltic Sea between Finland and Sweden. The region is characterized as having excellent wind power potential, a strong forest cluster with numerous pulp and paper mills, and significant iron ore and steel production. The study investigates the hydrogen-related opportunities in the region, focusing on infrastructural requirements, flexibility, and co-operation of different sectors. The study found that local wind power capacity is rapidly increasing and will eventually enable the decarbonization of the steel sector in the area, along with moderate Power-to-X implementation. In such case, the heat obtained as a by-product from the electrolysis of hydrogen would greatly exceed the combined district heat demand of the major cities in the area. To completely fulfil its district heat demand, the city of Oulu was simulated to require 0.5–1.2 GW of electrolyser capacity, supported by heat pumps and optionally with heat storages.
LQR and Fuzzy Logic Control for the Three-Area Power System
Authors: Sibilska-Mroziewicz Anna , Ordys Andrzej , Możaryn Jakub , Alinaghi Hosseinabadi Pooyan , Soltani Sharif Abadi Ali , Pota Hemanshu
Published: December 2021

The three-area power system is widely considered a suitable example to test load frequency control of the distributed generation system. In this article, for such a system, for the power stabilization task, we introduce two controllers: Linear Quadratic Regulator (LQR), which is model-based, and Fuzzy Logic Controller (FLC), which is data-based. The purpose is to compare the two approaches from the point of view of (i) ease of implementation and tuning, and (ii) robustness to changes in the model. The model, together with controls strategies, has been implemented in the MATLAB software. Then, it has been tested for different simulation scenarios, taking into account the disturbances and faulty tie-lines between areas. Various quality measures allow to compare the performance of each control strategy. The comparison in terms of parameter change and load disturbances prompt us to propose suitable metrics and advice notes on the application of each controller.