bluuebunny/crossref_metadata_2025 huggingface dataset:
NTD Diagnostics for Disease Elimination: A Review
Authors: Taylor Emma Michelle
Published: June 2020

Neglected Tropical Diseases (NTDs) marked out for disease elimination provide a lens through which to explore the changing status of diagnosis in global health. This paper reports on the findings of a scoping review, which set out to explore the main debates around diagnosis for the elimination of NTDs, including the multiple roles diagnostic technologies are being ascribed and the ideal characteristics of tests. It also attempts to summarise the state of diagnosis for three NTDs with elimination goals. The review places special emphasis on point-of-care testing in acknowledgement of the remote and underserved areas where NTDs proliferate. Early NTD campaigns were largely focused on attack phase planning, whereby a similar set of interventions could be transplanted anywhere. Now, with elimination goals in sight, strategies must be tailored to local settings if they are to attain and sustain success. Diagnostic data helps with local adaptation and is increasingly used for programmatic decision-making. The review finds that elimination goals reframe whom diagnosis is for and the myriad roles diagnostics can play. The exigencies of elimination also serve to highlight deficiencies in the current diagnostic arsenal and development pipeline for many NTDs. Moving forward, a guiding framework is needed to drive research and stimulate investment in diagnosis to support NTD goals.
Optimized Breath-Hold Compressed-Sensing 3D MR Cholangiopancreatography at 3T: Image Quality Analysis and Clinical Feasibility Assessment
Authors: Song Ji Soo , Kim Seung Hun , Kuehn Bernd , Paek Mun Young
Published: June 2020

Magnetic resonance cholangiopancreatography (MRCP) has been widely used in clinical practice, and recently developed compressed-sensing accelerated MRCP (CS-MRCP) has shown great potential in shortening the acquisition time. The purpose of this prospective study was to evaluate the clinical feasibility and image quality of optimized breath-hold CS-MRCP (BH-CS-MRCP) and conventional navigator-triggered MRCP. Data from 124 consecutive patients with suspected pancreaticobiliary diseases were analyzed by two radiologists using a five-point Likert-type scale. Communication between a cyst and the pancreatic duct (PD) was analyzed. Signal-to-noise ratio (SNR) of the common bile duct (CBD), contrast ratio between the CBD and periductal tissue, and contrast-to-noise ratio (CNR) of the CBD and liver were measured. Optimized BH-CS-MRCP showed significantly fewer artifacts with better background suppression and overall image quality. Optimized BH-CS-MRCP demonstrated communication between a cyst and the PD better than conventional MRCP (96.7% vs. 76.7%, p = 0.048). SNR, contrast ratio, and CNR were significantly higher with optimized BH-CS-MRCP (p < 0.001). Optimized BH-CS-MRCP showed comparable or even better image quality than conventional MRCP, with improved visualization of communication between a cyst and the PD.
False-Positive Malignant Diagnosis of Nodule Mimicking Lesions by Computer-Aided Thyroid Nodule Analysis in Clinical Ultrasonography Practice
Authors: Molnár Krisztián , Kálmán Endre , Hári Zsófia , Giyab Omar , Gáspár Tamás , Rucz Károly , Bogner Péter , Tóth Arnold
Published: June 2020

This study aims to test computer-aided diagnosis (CAD) for thyroid nodules in clinical ultrasonography (US) practice with a focus towards identifying thyroid entities associated with CAD system misdiagnoses. Two-hundred patients referred to thyroid US were prospectively enrolled. An experienced radiologist evaluated the thyroid nodules and saved axial images for further offline blinded analysis using a commercially available CAD system. To represent clinical practice, not only true nodules, but mimicking lesions were also included. Fine needle aspiration biopsy (FNAB) was performed according to present guidelines. US features and thyroid entities significantly associated with CAD system misdiagnosis were identified along with the diagnostic accuracy of the radiologist and the CAD system. Diagnostic specificity regarding the radiologist was significantly (p < 0.05) higher than when compared with the CAD system (88.1% vs. 40.5%) while no significant difference was found in the sensitivity (88.6% vs. 80%). Focal inhomogeneities and true nodules in thyroiditis, nodules with coarse calcification and inspissated colloid cystic nodules were significantly (p < 0.05) associated with CAD system misdiagnosis as false-positives. The commercially available CAD system is promising when used to exclude thyroid malignancies, however, it currently may not be able to reduce unnecessary FNABs, mainly due to the false-positive diagnoses of nodule mimicking lesions.
Shedding Light on Bladder Cancer Diagnosis in Urine
Authors: Chan Kit Man , Gleadle Jonathan , Li Jordan , Vasilev Krasimir , MacGregor Melanie
Published: June 2020

Blue light cystoscopy (BLC) is the most recent clinical approach in the detection and diagnosis of bladder cancer, a common type of cancer with a high rate of recurrence. Representing a significant advance over previous approaches, this photodynamic diagnostic technique uses a photosensitiser prodrug as an adjunct to white light cystoscopy to enhance the in vivo detection of malignant tissues in the bladder based on their distinctive fluorescence. Whilst it does improve detection rates, BLC remains an invasive and costly procedure. Meanwhile, a variety of noninvasive urine detection methods and related microdevices have been developed, none of which have yet entered routine clinical use due to unsatisfactory sensitivity. Following a brief description of the current approaches and their limitations, we provide here a systematic review of a newer niche research aiming to develop a noninvasive adaptation of photodynamic diagnosis. The research to date surrounding the ex situ use of photosensitiser prodrugs for urinary diagnosis of bladder cancer is also discussed.
Dural Metastases of Advanced Prostate Cancer Detected by 18F-Fluorocholine
Authors: Morassi Mauro , Bonacina Mattia , Bnà Claudio , Zaniboni Alberto , Savelli Giordano
Published: June 2020

Prostate cancer with extensive dural metastases is very rare, with only few cases described in the literature. We report one such case of a 74-year-old man with advanced prostate cancer, and in relatively good clinical condition. The patient returned with complaints of headache and diplopia. Fluorocholine (18F) chloride (18F-FCH) is an analog of choline in which a hydrogen atom has been replaced by fluorine (18F). After crossing the cell membrane by a carrier-mediated mechanism, choline is phosphorylated by choline kinase to produce phosphorylcholine. 18F-FCH positron emission tomography–computed tomography (PET/CT) is widely used to stage and restage patients affected by prostate cancer with good sensitivity. 18F-FCH PET/CT showed disease progression with the onset of multiple skull lesions. Numerous suspicious dural hypermetabolic lesions indicating neoplastic involvement were detected along the fronto-parietal convexities, in the left fronto-orbital region and right lateral wall of the orbit, concerning for metastases in these regions. A contrast-enhanced computed tomography (CECT) scan was performed which showed corresponding enhancing tissue which correlated with the PET findings. The final imaging diagnosis was osteo-dural metastases from prostate cancer associated with poor outcome. Awareness of this pattern of metastases may be of clinical relevance in order to avoid unnecessary invasive diagnostic procedures in groups of patients with a dismal prognosis.
Prognostic Value of Ki67 Percentage, WT-1 Expression and p16/CDKN2A Deletion in Diffuse Malignant Peritoneal Mesothelioma: A Single-Centre Cohort Study
Authors: Pezzuto Federica , Serio Gabriella , Fortarezza Francesco , Scattone Anna , Caporusso Concetta , Punzi Alessandra , Cavone Domenica , Pennella Antonio , Marzullo Andrea , Vimercati Luigi
Published: June 2020

Diffuse malignant peritoneal mesothelioma (DMPM) is a rare malignant neoplasm with a poor survival. Although some advances in knowledge have been obtained for the pleural form, much less is known about DMPM. Advantages in terms of prognosis are still limited and strong efforts need to be made. The aim of our study was to correlate several histological and molecular factors with survival in a large cohort of 45 DMPMs. We evaluated histotype, nuclear grade, mitotic count, necrosis, inflammation, desmoplastic reaction, Ki67 percentage, WT-1 expression, p16 protein by immunohistochemistry and CDKN2A deletion by FISH. Our results showed that epithelioid histotype, nuclear grade 2, mitotic count ≤5 x mm2, absence of desmoplasia and p16/CDKN2A deletion, low Ki67 value, and high WT-1 expression were correlated with the most prolonged survival (p = 0.0001). Moreover, p16 loss in immunohistochemistry reflected CDKN2A deletion detected with FISH, and both were correlated with the worst survival (p = 0.0001). At multivariate analysis, Ki67 value, WT-1 expression and p16/CDKN2A deletion emerged as independent prognostic factors (p = 0.01, p = 0.0001 and p = 0.01, respectively). These parameters are easy to analyse at the time of DMPM diagnosis and may support better patient stratification, prediction of treatment effectiveness and therapeutic optimization.
Fold-Over Oversampling Effects in the Measurements of Cerebral Cerebrospinal Fluid and Blood Flows with 2D Cine Phase-Contrast MRI
Authors: Saadani-Makki Fadoua , Metanbou Serge , Arbeaumont-Trocme Garance , Van Gysel Julien , Makki Malek I.
Published: June 2020

This prospective study investigated the effects of fold-over oversampling on phase-offset background errors with 2D-Cine phase contrast (Cine-PC) magnetic resonance imaging (MRI). It was performed on brain MRI and compared to conventional Full-field of view FOV coverage and it was tested with two different velocity encoding (Venc) values. We chose Venc = 100 mm/s to encode cerebrospinal fluid (CSF) flows in the aqueduct and 600 mm/s to encode blood flow in the carotid artery. Cine-PC was carried out on 10 healthy adult volunteers followed simultaneously by an acquisition on static agar-gel phantom to measure the phase-offset background errors. Pixel-wise correction of both the CSF and the blood flows was calculated through 32 points of the cardiac-cycle. We compared the velocity-to-noise ratio, the section area, the absolute and the corrected velocity (peak; mean and minimum), the net flow, and the stroke volume before and after correction. We performed the statistical T-test to compare Full-FOV and fold-over and Bland–Altman plots to analyze their differences. Our results showed that following phase-offset error correction, the blood stroke-volume was significantly higher with Full-FOV compared to fold-over. We observed a significantly higher CSF mean velocity and net flow values in the fold-over option. Compared to Full-FOV, fold-over provides a significantly larger section area and significantly lower peak velocity-offset in the aqueduct. No significant difference between the two coverages was reported before and after phase-offset in blood flow measurements. In conclusion, fold-over oversampling can be chosen as an alternative to increase spatial resolution and accurate cerebral flow quantification in Cine-PC.
Effects of Physical Activity on Brain Energy Biomarkers in Alzheimer’s Diseases
Authors: Ebrahimi Khadijeh , Jourkesh Morteza , Sadigh-Eteghad Saeed , Stannard Stephen R , Earnest Conrad P. , Ramsbottom Roger , Antonio Jose , Navin Khan H.
Published: June 2020

The prevalence of dementia has substantially increased worldwide. Currently, there is no cure for dementia or Alzheimer’s disease (AD), and care for affected patients is financially and psychologically costly. Of late, more attention has been given to preventive interventions—in particular, physical activity/exercise. In this review, examine the risk factors associated with AD and the effects physical activity may play in the prevention of the degenerative process of this disease, loss of memory and cognitive performance in the elderly. To date, research has shown that physical activity, especially aerobic exercise, has a protective effect on cognitive function and memory in the elderly and Alzheimer’s patients. In comparison with aerobic exercise, several strength training studies have also shown positive effects, and the rare studies that compare the two different modalities show no difference.
Multi-Stage Meta-Learning for Few-Shot with Lie Group Network Constraint
Authors: Dong Fang , Liu Li , Li Fanzhang
Published: June 2020

Deep learning has achieved many successes in different fields but can sometimes encounter an overfitting problem when there are insufficient amounts of labeled samples. In solving the problem of learning with limited training data, meta-learning is proposed to remember some common knowledge by leveraging a large number of similar few-shot tasks and learning how to adapt a base-learner to a new task for which only a few labeled samples are available. Current meta-learning approaches typically uses Shallow Neural Networks (SNNs) to avoid overfitting, thus wasting much information in adapting to a new task. Moreover, the Euclidean space-based gradient descent in existing meta-learning approaches always lead to an inaccurate update of meta-learners, which poses a challenge to meta-learning models in extracting features from samples and updating network parameters. In this paper, we propose a novel meta-learning model called Multi-Stage Meta-Learning (MSML) to post the bottleneck during the adapting process. The proposed method constrains a network to Stiefel manifold so that a meta-learner could perform a more stable gradient descent in limited steps so that the adapting process can be accelerated. An experiment on the mini-ImageNet demonstrates that the proposed method reached a better accuracy under 5-way 1-shot and 5-way 5-shot conditions.
Information Theory in Computational Biology: Where We Stand Today
Authors: Chanda Pritam , Costa Eduardo , Hu Jie , Sukumar Shravan , Van Hemert John , Walia Rasna
Published: June 2020

“A Mathematical Theory of Communication” was published in 1948 by Claude Shannon to address the problems in the field of data compression and communication over (noisy) communication channels. Since then, the concepts and ideas developed in Shannon’s work have formed the basis of information theory, a cornerstone of statistical learning and inference, and has been playing a key role in disciplines such as physics and thermodynamics, probability and statistics, computational sciences and biological sciences. In this article we review the basic information theory based concepts and describe their key applications in multiple major areas of research in computational biology—gene expression and transcriptomics, alignment-free sequence comparison, sequencing and error correction, genome-wide disease-gene association mapping, metabolic networks and metabolomics, and protein sequence, structure and interaction analysis.
Review and Modification of Entropy Modeling for Steric Effects in the Poisson-Boltzmann Equation
Authors: Horng Tzyy-Leng
Published: June 2020

The classical Poisson-Boltzmann model can only work when ion concentrations are very dilute, which often does not match the experimental conditions. Researchers have been working on the modification of the model to include the steric effect of ions, which is non-negligible when the ion concentrations are not dilute. Generally the steric effect was modeled to correct the Helmholtz free energy either through its internal energy or entropy, and an overview is given here. The Bikerman model, based on adding solvent entropy to the free energy through the concept of volume exclusion, is a rather popular steric-effect model nowadays. However, ion sizes are treated as identical in the Bikerman model, making an extension of the Bikerman model to include specific ion sizes desirable. Directly replacing the ions of non-specific size by specific ones in the model seems natural and has been accepted by many researchers in this field. However, this straightforward modification does not have a free energy formula to support it. Here modifications of the Bikerman model to include specific ion sizes have been developed iteratively, and such a model is achieved with a guarantee that: (1) it can approach Boltzmann distribution at diluteness; (2) it can reach saturation limit as the reciprocal of specific ion size under extreme electrostatic conditions; (3) its entropy can be derived by mean-field lattice gas model.
Maximum-Likelihood Estimation in a Special Integer Autoregressive Model
Authors: Jung Robert C. , Tremayne Andrew R.
Published: June 2020

The paper is concerned with estimation and application of a special stationary integer autoregressive model where multiple binomial thinnings are not independent of one another. Parameter estimation in such models has hitherto been accomplished using method of moments, or nonlinear least squares, but not maximum likelihood. We obtain the conditional distribution needed to implement maximum likelihood. The sampling performance of the new estimator is compared to extant ones by reporting the results of some simulation experiments. An application to a stock-type data set of financial counts is provided and the conditional distribution is used to compare two competing models and in forecasting.
Quadruple Helix Models for Sustainable Regional Innovation: Engaging and Facilitating Civil Society Participation
Authors: Roman Mona , Varga Henry , Cvijanovic Vladimir , Reid Alasdair
Published: June 2020

Prior research has emphasized the importance of bringing together quadruple helix (QH) actors (academia, industry, government and civil society) to strengthen regional innovation. The QH model forms an integral part of European innovation policy, which aims to create sustainable and inclusive growth in Europe. As part of this policy, European Union (EU) regions are to design and implement research and innovation strategies for smart specialization (RIS3) through the participatory entrepreneurial discovery process (EDP). Despite the strong emphasis on the QH model, the model is still far from a well-established concept in innovation research and policy, and civil society participation in RIS3 has remained low. Our paper aims to support regional governments to engage with and facilitate the participation of civil society in a territorial EDP based on two case studies from Finland and Sweden. It contributes to the literature on regional innovation systems through identifying mechanisms to foster the QH model and suggests lessons learnt for the operationalization of the QH model as part of RIS3.
Model Selection Procedures in Bounds Test of Cointegration: Theoretical Comparison and Empirical Evidence
Authors: Badshah Waqar , Bulut Mehmet
Published: June 2020

Only unstructured single-path model selection techniques, i.e., Information Criteria, are used by Bounds test of cointegration for model selection. The aim of this paper was twofold; one was to evaluate the performance of these five routinely used information criteria {Akaike Information Criterion (AIC), Akaike Information Criterion Corrected (AICC), Schwarz/Bayesian Information Criterion (SIC/BIC), Schwarz/Bayesian Information Criterion Corrected (SICC/BICC), and Hannan and Quinn Information Criterion (HQC)} and three structured approaches (Forward Selection, Backward Elimination, and Stepwise) by assessing their size and power properties at different sample sizes based on Monte Carlo simulations, and second was the assessment of the same based on real economic data. The second aim was achieved by the evaluation of the long-run relationship between three pairs of macroeconomic variables, i.e., Energy Consumption and GDP, Oil Price and GDP, and Broad Money and GDP for BRICS (Brazil, Russia, India, China and South Africa) countries using Bounds cointegration test. It was found that information criteria and structured procedures have the same powers for a sample size of 50 or greater. However, BICC and Stepwise are better at small sample sizes. In the light of simulation and real data results, a modified Bounds test with Stepwise model selection procedure may be used as it is strongly theoretically supported and avoids noise in the model selection process.
Electrolyte Additives in Lithium Ion EV Batteries and the Relationship of the SEI Composition to Cell Resistance and Lifetime
Authors: Lain Micheal J. , Rubio Lopez Irene , Kendrick Emma
Published: June 2020

Sulphur, boron and phosphorous containing electrolyte additives were evaluated in cells containing pristine electrodes from a commercial EV lithium ion cell against a standard baseline electrolyte. Following formation and a full cell ageing step, cycling performance and impedance spectroscopy were used to elucidate the most effective additives. The additive tris trimethyl silyl phosphite (TTSPi) showed the most promise; with improved cell capacities and reduced impedances observed after formation. X-ray photoelectron spectroscopy (XPS) measurements on anode elemental surface profiles were correlated with the electrochemical performance. It was observed that increased lithium fluoride content on the surface of the anodes typically produced cells with lower impedance. Sulphur containing additives also showed improved cell behaviours; and the decomposition and chemical reactions of these compounds at the anode surface is discussed in detail. The main influence of TTSPi was to reduce the amount of oxygen (C=O) and sulphur in the electrolyte interphase (SEI) layer; to be replaced with hydrocarbons.
A Photovoltaic-Based DC Microgrid System: Analysis, Design and Experimental Results
Authors: Xiong Xiaoling , Yang Yuchen
Published: June 2020

Due to the exhaustion of fossil energy, the utilization of renewable energy resources is developing quickly. Due to the intermittent nature of the renewable energy resources, the energy storage devices are usually adopted in renewable power generation system to enhance the system reliability. In this paper, the photovoltaic-based DC microgrid (PVDCM) system is designed, which is composed of a solar power system and a battery connected to the common bus via a boost converter and a bidirectional buck/boost converter, respectively. As the photovoltaic (PV) panels might operate in a maximum power point tracking (MPPT) mode or constant voltage mode, meanwhile, the power can flow between the battery and the load bidirectionally. Therefore, for the sake of optimizing power utilization in the PVDCM system, a control strategy making the system able to switch from one operating mode to another smoothly and automatically is proposed in this paper. Moreover, the small-signal modeling method based on averaged state-space is no more applicable in this study, thus the nonlinear analysis method with discrete-time mapping model is adopted for stability analysis. Based on the stability analysis, the closed-loop parameters are designed to make sure the whole system can operate properly in all operating modes. The control strategy and stability analysis based on the nonlinear analysis method in the closed-loop design are verified by experiment results.
A Spherical Near-to-far-Field Transformation Using a Non-Redundant Voltage Representation Optimized for Non-Centered Mounted Quasi-Planar Antennas
Authors: D’Agostino Francesco , Ferrara Flaminio , Gennarelli Claudio , Guerriero Rocco , Migliozzi Massimo
Published: June 2020

This research falls in the antenna measurements related topic, and deals with the problem occurring in the classical spherical near-to-far-field (NTFF) transformation, when it becomes unpractical to mount the antenna under test (AUT) with its center at the center of the scanning sphere. This issue reflects in a growth of the number of near-field (NF) samples to be acquired, since this number depends on the radius of the minimum sphere, which contains the antenna, and is centered at the scanning sphere center. The non-redundant sampling representations of the electromagnetic field are conveniently exploited, to develop an effective spherical NTFF transformation for non-centered AUTs with quasi-planar geometry, requiring a minimum amount of NF samples, and nearly the same as that for a centered mounting of the AUT. Then, the NF data needed to perform the classical NTFF transformation are determined in efficient way from the acquired non-redundant NF samples by employing an accurate 2-D sampling interpolation scheme. Thus, it is possible to significantly save measurement time. Some simulation and laboratory results are reported to show the effectiveness of the developed technique, which takes into account a non-centered AUT mounting.
Combining Machine Learning Analysis and Incentive-Based Genetic Algorithms to Optimise Energy District Renewable Self-Consumption in Demand-Response Programs
Authors: Croce Vincenzo , Raveduto Giuseppe , Verber Matteo , Ziu Denisa
Published: June 2020

The recent rise of renewable energy sources connected to the distribution networks and the high peak consumptions requested by electric vehicle-charging bring new challenges for network operators. To operate smart electricity grids, cooperation between grid-owned and third-party assets becomes crucial. In this paper, we propose a methodology that combines machine learning with multi-objective optimization to accurately plan the exploitation of the energy district’s flexibility with the objective of reducing peak consumption and avoiding reverse power flow. Using historical data, acquired by the smart meters deployed on the pilot district, the district’s power profile can be predicted daily and analyzed to identify potentially critical issues on the network. District’s resources, such as electric vehicles, charging stations, photovoltaic panels, buildings energy management systems, and energy storage systems, have been modeled by taking into account their operational constraints and the multi-objective optimization has been adopted to identify the usage pattern that better suits the distribution operator’s (DSO) needs. The district is subject to incentives and penalties based on its ability to respond to the DSO request. Analysis of the results shows that this methodology can lead to a substantial reduction of both the reverse power flow and peak consumption.
Interleaved Buck Converter for Inductive Wireless Power Transfer in DC–DC Converters
Authors: Carbajal-Retana Marco , Hernandez-Gonzalez Leobardo , Ramirez-Hernandez Jazmin , Avalos-Ochoa Juan Gerardo , Guevara-Lopez Pedro , Loboda Igor , Sotres-Jara Luis Antonio
Published: June 2020

The use of Inductive Wireless Power Transfer (IWPT) varies from low-power applications such as mobile phones and tablets chargers to high-power electric vehicles chargers. DC–DC converters are used in IWPT systems, and their design needs to consider the demand of high efficiency in the power transfer. In this paper, a DC–DC power converter for IWPT is proposed. Its topology uses a DC–AC converter in the transmitter circuit and an AC–DC converter in the receptor. The transmitter has an interleaved coupled-Buck converter that integrates two Buck converters connected to a half inverter bridge and a parallel resonant load. The control strategy implemented for the semiconductor switching devices allows two operating modes to obtain a sinusoidal output voltage with a low distortion that makes it suitable in high-efficiency power transfer systems. To obtain a DC output voltage, a full wave bridge rectifier is used in the receptor circuit. The proposed topology and the control strategy are validated with simulation and experimental results for a 15 W prototype.
Individual Violin Recognition Method Combining Tonal and Nontonal Features
Authors: Wang Qi , Bao Changchun
Published: June 2020

Individual recognition among instruments of the same type is a challenging problem and it has been rarely investigated. In this study, the individual recognition of violins is explored. Based on the source–filter model, the spectrum can be divided into tonal content and nontonal content, which reflects the timbre from complementary aspects. The tonal/nontonal gammatone frequency cepstral coefficients (GFCC) are combined to describe the corresponding spectrum contents in this study. In the recognition system, Gaussian mixture models–universal background model (GMM–UBM) is employed to parameterize the distribution of the combined features. In order to evaluate the recognition task of violin individuals, a solo dataset including 86 violins is developed in this study. Compared with other features, the combined features show a better performance in both individual violin recognition and violin grade classification. Experimental results also show the GMM–UBM outperforms the CNN, especially when the training data are limited. Finally, the effect of players on the individual violin recognition is investigated.
Comparative Analysis of Rhino-Cytological Specimens with Image Analysis and Deep Learning Techniques
Authors: Dimauro Giovanni , Bevilacqua Vitoantonio , Fina Pio , Buongiorno Domenico , Brunetti Antonio , Latrofa Sergio , Cassano Michele , Gelardi Matteo
Published: June 2020

Cytological study of the nasal mucosa (also known as rhino-cytology) represents an important diagnostic aid that allows highlighting of the presence of some types of rhinitis through the analysis of cellular features visible under a microscope. Nowadays, the automated detection and classification of cells benefit from the capacity of deep learning techniques in processing digital images of the cytological preparation. Even though the results of such automatic systems need to be validated by a specialized rhino-cytologist, this technology represents a valid support that aims at increasing the accuracy of the analysis while reducing the required time and effort. The quality of the rhino-cytological preparation, which is clearly important for the microscope observation phase, is also fundamental for the automatic classification process. In fact, the slide-preparing technique turns out to be a crucial factor among the multiple ones that may modify the morphological and chromatic characteristics of the cells. This paper aims to investigate the possible differences between direct smear (SM) and cytological centrifugation (CYT) slide-preparation techniques, in order to preserve image quality during the observation and cell classification phases in rhino-cytology. Firstly, a comparative study based on image analysis techniques has been put forward. The extraction of densitometric and morphometric features has made it possible to quantify and describe the spatial distribution of the cells in the field images observed under the microscope. Statistical analysis of the distribution of these features has been used to evaluate the degree of similarity between images acquired from SM and CYT slides. The results prove an important difference in the observation process of the cells prepared with the above-mentioned techniques, with reference to cell density and spatial distribution: the analysis of CYT slides has been more difficult than of the SM ones due to the spatial distribution of the cells, which results in a lower cell density than the SM slides. As a marginal part of this study, a performance assessment of the computer-aided diagnosis (CAD) system called Rhino-cyt has also been carried out on both groups of image slide types.
A Two-Branch Network for Weakly Supervised Object Localization
Authors: Sun Chang , Ai Yibo , Wang Sheng , Zhang Weidong
Published: June 2020

Weakly supervised object localization (WSOL) has attracted intense interest in computer vision for instance level annotations. As a hot research topic, a number of existing works concentrated on utilizing convolutional neural network (CNN)-based methods, which are powerful in extracting and representing features. The main challenge in CNN-based WSOL methods is to obtain features covering the entire target objects, not only the most discriminative object parts. To overcome this challenge and to improve the detection performance of feature extracting related WSOL methods, a CNN-based two-branch model was presented in this paper to locate objects using supervised learning. Our method contained two branches, including a detection branch and a self-attention branch. During the training process, the two branches interacted with each other by regarding the segmentation mask from the other branch as the pseudo ground truth labels of itself. Our model was able to focus on capturing the information of all the object parts due to the self-attention mechanism. Additionally, we embedded multi-scale detection into our two-branch method to output two-scale features. We evaluated our two-branch network on the CUB-200-2011 and VOC2007 datasets. The pointing localization, intersection over union (IoU) localization, and correct localization precision (CorLoc) results demonstrated competitive performance with other state-of-the-art methods in WSOL.
Performance Comparison of Weak Filtering in HEVC and VVC
Authors: Lee Junghyun , Jeong Jechang
Published: June 2020

This study describes the need to improve the weak filtering method for the in-loop filter process used identically in versatile video coding (VVC) and high efficiency video coding (HEVC). The weak filtering process used by VVC has been adopted and maintained since Draft Four during H.265/advanced video coding (AVC) standardization. Because the encoding process in the video codec utilizes block structural units, deblocking filters are essential. However, as many of the deblocking filters require a complex calculation process, it is necessary to ensure that they have a reasonable effect. This study evaluated the performance of the weak filtering portion of the VVC and confirmed that it is not functioning effectively, unlike its performance in the HEVC. The method of excluding the whole of weak filtering from VVC, which is a non-weak filtering method, should be considered in VVC standardization. In experimental result in this study, the non-weak filtering method brings 0.40 Y-Bjontegaard-Delta Bit-Rate (BDBR) gain over VVC Test Model (VTM) 6.0.
Simplified Modal-Cancellation Approach for Substrate-Integrated-Waveguide Narrow-Band Filter Design
Authors: Celis Sebastian , Farhat Mohamed , Almansouri Abdullah S. , Bagci Hakan , Salama Khaled N.
Published: June 2020

Current substrate-integrated-waveguide (SIW) filter design methodologies can be extremely computational and time-inefficient when a narrow-band filter is required. A new approach to designing compact, highly selective narrow-band filters based on smartly positioned obstacles is thus presented here. The proposed modal-cancellation approach is achieved by translating or eliminating undesired modes within the frequency of interest. This is performed by introducing smartly located obstacles in the maxima and nulls of the modes of interest. This approach is different from the traditional inverter technique, where a periodic number of inductive irises are coupled in a ladder configuration to implement the desired response of an nth-order filter, and significantly reduces the complexity of the resulting filter structure. Indeed, the proposed method may be used to design different filters for several frequency bands and various applications. The methodology was experimentally verified through fabricated prototypes.
Performance Assessment of a Building Integrated Photovoltaic Thermal System in Mediterranean Climate—A Numerical Simulation Approach
Authors: Bot Karol , Aelenei Laura , Gomes Maria da Glória , Santos Silva Carlos
Published: June 2020

This study addresses the thermal and energy performance assessment of a Building Integrated Photovoltaic Thermal (BIPVT) system installed on the façade of a test room in Solar XXI, a Net Zero Energy Building (NZEB) located in Lisbon, Portugal. A numerical analysis using the dynamic simulation tool EnergyPlus was carried out for assessing the performance of the test room with the BIPVT integrated on its façade through a parametric analysis of 14 scenarios in two conditions: a) receiving direct solar gains on the glazing surface and b) avoiding direct solar gains on the glazing surface. Additionally, a computational fluid dynamics (CFD) analysis of the BIPVT system was performed using ANSYS Fluent. The findings of this work demonstrate that the BIPVT has a good potential to improve the sustainability of the building by reducing the nominal energy needs to achieve thermal comfort, reducing up to 48% the total energy needs for heating and cooling compared to the base case. The operation mode must be adjusted to the other strategies already implemented in the room (e.g., the presence of windows and blinds to control direct solar gains), and the automatic operation mode has proven to have a better performance in the scope of this work.
Hydrothermal Carbonization as a Strategy for Sewage Sludge Management: Influence of Process Withdrawal Point on Hydrochar Properties
Authors: Merzari Fabio , Goldfarb Jillian , Andreottola Gianni , Mimmo Tanja , Volpe Maurizio , Fiori Luca
Published: June 2020

Conventional activated sludge systems, still widely used to treat wastewater, produce large amounts of solid waste that is commonly landfilled or incinerated. This study addresses the potential use of Hydrothermal Carbonization (HTC) to valorize sewage sludge residues examining the properties of hydrochars depending on HTC process conditions and sewage sludge withdrawal point. With increasing HTC severity (process residence time and temperature), solid yield, total Chemical Oxygen Demand (COD) and solid pH decrease while ash content increases. Hydrochars produced from primary (thickened) and secondary (digested and dewatered) sludge show peculiar distinct properties. Hydrochars produced from thickened sludge show good fuel properties in terms of Higher Heating Value (HHV) and reduced ash content. However, relatively high volatile matter and O:C and H:C ratios result in thermal reactivity significantly higher than typical coals. Both series of carbonized secondary sludges show neutral pH, low COD, enhanced phosphorous content and low heavy metals concentration: as a whole, they show properties compatible with their use as soil amendments.
Causality between CO2 Emissions and Stock Markets
Authors: Chang Chia-Lin , Ilomäki Jukka , Laurila Hannu , McAleer Michael
Published: June 2020

It is generally accepted in the scientific community that carbon dioxide (CO2) emissions, which lead to global warming, arise from using fossil fuels, namely coal, oil and gas, as energy sources. Consequently, alleviating the effects of global warming and climate change necessitates substantial reductions in the use of fossil fuel energy. This paper uses a financial market-based approach to investigate whether positive stock returns cause changes in CO2 emissions, or vice-versa, based on the Granger causality test to determine cause and effect, or leader and follower. If Granger causality can be determined in any direction, this will enable a clear directional statement regarding temporal predictability between stock returns and CO2 emissions. The empirical data include annual CO2 emissions from fuel combustion of the three main fossil energy sources, namely coal, oil and gas, based on 18 countries with sophisticated financial markets that are in the Morgan Stanley Capital International (MSCI) World Index from 1971 to 2017. The empirical results show clearly that all the statistically significant causality findings are unidirectional from the stock market returns to CO2 emissions from coal, oil and gas, but not the reverse. More importantly, the regression results suggest that when stock returns rise by 1%, CO2 emissions from coal combustion decrease by 9% among the countries that are included in MSCI World Index. Furthermore, when stock returns rise 1%, CO2 emissions from oil combustion increase by 2%, but stock returns have no significant effect on CO2 emissions from gas combustion.
Axisymmetric Natural Convection of Liquid Metal in an Annular Enclosure under the Influence of Azimuthal Magnetic Field
Authors: Masuda Takuya , Tagawa Toshio
Published: June 2020

Natural convection of liquid metal in an annular enclosure under the influence of azimuthal static magnetic field was numerically studied. The liquid metal in the enclosure whose cross-sectional area is square was heated from an inner vertical wall and cooled from an outer vertical wall both isothermally whereas the other two horizontal walls were assumed to be adiabatic. The static azimuthal magnetic field was imposed by a long straight electric coil that was located at the central axis of the annular enclosure. The computations were carried out for the Prandtl number 0.025, the Rayleigh number 104, 5 × 105 and 107, and the Hartmann number 0–100,000 by using an in-house code. It was found that the contour map of the electric potential was similar to that of the Stokes stream function of the velocity regardless of the Hartmann number. Likewise, the contour map of the pressure was similar to the Stokes stream function of the electric current density in the case of the high Hartmann number. The average Nusselt number was decreased in proportion to the square of the Hartmann number in the high Hartmann number regime.
Simplification of Thermal Networks for Magnetic Components in Space Power Electronics
Authors: de la Hoz David , Salinas Guillermo , Šviković Vladimir , Alou Pedro
Published: June 2020

The volume of magnetic components for space applications, directly related to the launch cost, and their performance are critically influenced by their capability to dissipate the internal electromagnetic power losses. This is the reason why accurate thermal models are required. Furthermore, these models need to be simple and versatile to allow a fast analysis of many different designs. In this study, a specific methodology to analyze inductors and transformers by thermal networks and a simplification based on the Thevenin’s theorem leading to a simple equation are proposed. The specific details to address thermal modelling for space magnetic components are discussed. A generic 50 W Flyback transformer for space applications is analyzed in this study and experimental validation is provided, showing that the proposed method leads to a deviation in the temperature estimation between 1 °C and 5 °C, which is considered quite a good result from the literature review carried out.
Thermal Comfort and Energy Use with Local Heaters
Authors: Kaczmarczyk Jan , Ferdyn-Grygierek Joanna
Published: June 2020

This paper presents the investigation of a local heating system consisting of three heating mats incorporated within a sedentary workplace. Thermal comfort and individual power settings for the heating mats were studied in experiments with 41 human subjects. Experiments were performed in a climate chamber at two operative temperatures: 16 and 18 °C. Results showed that local heating systems improved occupants’ comfort and were able to create acceptable thermal conditions under both temperatures. The power settings identified were used to study the applicability of heating mats in an industrial hall. Multi-variant analysis of energy consumption for heating was carried out using an ESP-r (Environmental Systems Performance–Research) simulation program. The analysis results demonstrate that in some cases, the installation of local heating mats in industrial halls may reduce energy usage compared to having to heat the entire hall. However, the benefits depend on numerous parameters. Local heating is most advantageous when installed in halls with a small number of workstations, small internal heat gains, and low-efficiency central heating systems. In reality for the satisfactory implementation of local heating systems for a particular application, it is advised to perform analysis for the specific local conditions. In order to improve the effectiveness of local heating systems, an advanced heating control strategy should be considered.
Assessing the Impact of Investments in Cross-Border Pipelines on the Security of Gas Supply in the EU
Authors: Rqiq Yassine , Beyza Jesus , Yusta Jose M. , Bolado-Lavin Ricardo
Published: June 2020

The European Union (EU) is highly dependent on external natural gas supplies and has experienced severe gas cuts in the past, mainly driven by the technical complexity of the high-pressure natural gas system and political instability in some of the supplier countries. Declining indigenous natural gas production and growing demand for gas in the EU has encouraged investments in cross-border transmission capacity to increase the sharing of resources between the member states, particularly in the aftermath of the Russia–Ukraine gas crisis in January 2009. This article models the EU interconnected natural gas system to assess the impact of investments in the gas transmission network by comparing the performance of the system for scenarios of 2009 and 2017, using a mathematical optimization approach. The model uses the technical data of the infrastructures, such as production, storage, regasification, and exchange capacity through cross-border pipelines, and proposes an optimal collaborative strategy which ensures the best possible coverage of overall demand. The actual peak demand situations of the extreme cases of 2009 and 2017 are analyzed under hypothetical supply crises caused by geopolitical or commercial disputes. The application of the proposed methodology leads to results which show that the investments made in this system do not decongest the cross-border pipeline network but improve the demand coverage. Countries such as Spain and Italy experience a lower impact on gas supply due to the variety of mechanisms available to cover their demand. Furthermore, the findings prove that cooperation facilitates the supply of demand in crisis situations.
Permanent-Magnet SLM Drive System Using AMRRSPNNB Control System with DGWO
Authors: Chen Der-Fa , Shih Yi-Cheng , Li Shih-Cheng , Chen Chin-Tung , Ting Jung-Chu
Published: June 2020

Because permanent-magnet synchronous linear motors (SLM) still exhibit nonlinear friction, ending effects and time-varying dynamic uncertainties, better control performances cannot be achieved by using common linear controllers. We propose a backstepping approach with three adaptive laws and a beating function to control the motion of permanent-magnet SLM drive systems that enhance the robustness of the system. In order to reduce greater vibration in situations with uncertainty actions in the aforementioned control systems, we propose an adaptive modified recurrent Rogers–Szego polynomials neural network backstepping (AMRRSPNNB) control system with three adaptive laws and reimbursed controller with decorated gray wolf optimization (DGWO), in order to handle external bunched force uncertainty, including nonlinear friction, ending effects and time-varying dynamic uncertainties, as well as to reimburse the minimal rebuild error of the reckoned law. In accordance with the Lyapunov stability, online parameter training method of the modified recurrent Rogers–Szego polynomials neural network (MRRSPNN) can be derived by utilizing an adaptive law. Furthermore, to help reduce error and better obtain learning fulfillment, the DGWO algorithm was used to change the two learning rates in the weights of the MRRSPNN. Finally, the usefulness of the proposed control system is validated by tested results.