bluuebunny/crossref_metadata_2025 huggingface dataset:
Cost-Effective CNNs for Real-Time Micro-Expression Recognition
Authors: Belaiche Reda , Liu Yu , Migniot Cyrille , Ginhac Dominique , Yang Fan
Published: July 2020

Micro-Expression (ME) recognition is a hot topic in computer vision as it presents a gateway to capture and understand daily human emotions. It is nonetheless a challenging problem due to ME typically being transient (lasting less than 200 ms) and subtle. Recent advances in machine learning enable new and effective methods to be adopted for solving diverse computer vision tasks. In particular, the use of deep learning techniques on large datasets outperforms classical approaches based on classical machine learning which rely on hand-crafted features. Even though available datasets for spontaneous ME are scarce and much smaller, using off-the-shelf Convolutional Neural Networks (CNNs) still demonstrates satisfactory classification results. However, these networks are intense in terms of memory consumption and computational resources. This poses great challenges when deploying CNN-based solutions in many applications, such as driver monitoring and comprehension recognition in virtual classrooms, which demand fast and accurate recognition. As these networks were initially designed for tasks of different domains, they are over-parameterized and need to be optimized for ME recognition. In this paper, we propose a new network based on the well-known ResNet18 which we optimized for ME classification in two ways. Firstly, we reduced the depth of the network by removing residual layers. Secondly, we introduced a more compact representation of optical flow used as input to the network. We present extensive experiments and demonstrate that the proposed network obtains accuracies comparable to the state-of-the-art methods while significantly reducing the necessary memory space. Our best classification accuracy was 60.17% on the challenging composite dataset containing five objectives classes. Our method takes only 24.6 ms for classifying a ME video clip (less than the occurrence time of the shortest ME which lasts 40 ms). Our CNN design is suitable for real-time embedded applications with limited memory and computing resources.
Model Updating Using Frequency Response Functions Based on Sherman–Morrison Formula
Authors: Wu Yimeng , Zhu Rui , Cao Zhifu , Liu Ying , Jiang Dong
Published: July 2020

Model updating plays an important role in dynamics modeling with high accuracy, which is widely used in mechanical engineering. In this paper, a model updating method using frequency response function (FRF) is proposed based on Sherman–Morrison formula, in which only the initial FRFs and parameter perturbations are employed to calculate the sensitivity avoiding repeated finite element (FE) analyses and improving the computational efficiency. Firstly, the sensitivity of FRFs to the design parameters is calculated by Sherman–Morrison formula based on the QR decomposition of the system dynamic stiffness matrix variation after parameter perturbations, then the influence of damping on the amplitude of FRFs is considered to select an appropriate frequency range, and finally conduct the model updating according to the sensitivity of the FRFs. By employing simulation examples of a truss and a solar wing and the experiment of an aluminum frame, the updating error is still within ±1.00% in the condition of 5% random white noise, which shows the proposed method has high accuracy and a certain anti-noise capability. When only a few numbers of frequency points are selected near the resonance peak of the FRFs, the result shows that selecting the appropriate frequency range and points can reduce the computational cost. The results of the experiment study show that the proposed method can effectively identify the structural parameters. The above results verify the feasibility and effectiveness of proposed model updating method using FRFs.
Playful Machines and Heritage: How to Prepare Future Cultural Histories?
Authors: Filiciak Mirosław
Published: July 2020

How are we to tackle digital heritage? The fact that its code can be copied, combined with a strong reliance on user interaction, is a distinguishing characteristic of digital art, one which also complicates framing it with the traditional categories of art history. Therefore, in my search for the new ways to preserve heritage, appropriate for digital objects, I will use a case study where technical and social elements play an important role and where we can already speak of a partly institutionalized network aimed at preservation, even if its identification within the field of art, or heritage, is not exactly obvious. I propose an analysis based on the research of the Polish community of pinball machine collectors. My case study will also address the question of the category of locality with regard to projects featuring seemingly universal digital elements. Reflecting on the strategies that the pinball community uses to preserve its artifacts and to animate social activity centered upon those artifacts, can help facilitate modeling at least some practices needed to preserve digital art, practices more inclusive than the traditional approaches, and uniting, even if imperfectly, rather than dividing various social groups.
Effect of Edge-to-Edge Mitral Valve Repair on Chordal Strain: Fluid-Structure Interaction Simulations
Authors: Toma Milan , Einstein Daniel R. , Kohli Keshav , Caroll Sheridan L. , Bloodworth Charles H. , Cochran Richard P. , Kunzelman Karyn S. , Yoganathan Ajit P.
Published: July 2020

Edge-to-edge repair for mitral valve regurgitation is being increasingly performed in high-surgical risk patients using minimally invasive mitral clipping devices. Known procedural complications include chordal rupture and mitral leaflet perforation. Hence, it is important to quantitatively evaluate the effect of edge-to-edge repair on chordal integrity. in this study, we employ a computational mitral valve model to simulate functional mitral regurgitation (FMR) by creating papillary muscle displacement. Edge-to-edge repair is then modeled by simulated coaptation of the mid portion of the mitral leaflets. in the setting of simulated FMR, edge-to-edge repair was shown to sustain low regurgitant orifice area, until a two fold increase in the inter-papillary muscle distance as compared to the normal mitral valve. Strain in the chordae was evaluated near the papillary muscles and the leaflets. Following edge-to-edge repair, strain near the papillary muscles did not significantly change relative to the unrepaired valve, while strain near the leaflets increased significantly relative to the unrepaired valve. These data demonstrate the potential for computational simulations to aid in the pre-procedural evaluation of possible complications such as chordal rupture and leaflet perforation following percutaneous edge-to-edge repair.
Subcritical Methanol Extraction of the Stone of Japanese Apricot Prunus mume Sieb. et Zucc.
Authors: Kawabata Tomoaki , Tanaka Yoshie , Horinishi Asako , Mori Megumi , Hosoda Asao , Yamamoto Nami , Mitani Takahiko
Published: July 2020

The pits of Japanese apricot, Prunus mume Sieb. et Zucc., which are composed of stones, husks, kernels, and seeds, are unused by-products of the processing industry in Japan. The processing of Japanese apricot fruits generates huge amounts of waste pits, which are disposed of in landfills or, to a lesser extent, burned to form charcoal. Mume stones mainly consist of cellulose, hemicellulose, and lignin. Herein, we attempted to solubilize the wood-like carapace (stone) encasing the pit by subcritical fluid extraction with the aim of extracting useful chemicals. The characteristics of the main phenolic constituents were elucidated by liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) analyses. The degrees of solubility for various treatments (190 °C; 3 h) were determined as follows: subcritical water (54.9%), subcritical 50% methanol (65.5%), subcritical 90% methanol (37.6%), subcritical methanol (23.6%), and subcritical isopropyl alcohol (14.4%). Syringaldehyde, sinapyl alcohol, coniferyl alcohol methyl ether, sinapyl alcohol methyl ether, 5-(hydroxymethyl)-2-furfural, and furfural were present in the subcritical 90% methanol extract. Coniferyl and sinapyl alcohols (monolignols) are source materials for the biosynthesis of lignin, and syringaldehyde occur in trace amounts in wood. Our current findings provide a solubilization method that allows the main phenolic constituents of the pits to be extracted under mild conditions. This technique for obtaining subcritical extracts shows great potential for further applications.
Glutamate-Gated NMDA Receptors:
Insights into the Function
and Signaling in the Kidney
Authors: Valdivielso José M. , Eritja Àuria , Caus Maite , Bozic Milica
Published: July 2020

N-Methyl-d-aspartate receptor (NMDAR) is a glutamate-gated ionotropic receptor that intervenes in most of the excitatory synaptic transmission within the central nervous system (CNS). Aside from being broadly distributed in the CNS and having indispensable functions in the brain, NMDAR has predominant roles in many physiological and pathological processes in a wide range of non-neuronal cells and tissues. The present review outlines current knowledge and understanding of the physiological and pathophysiological functions of NMDAR in the kidney, an essential excretory and endocrine organ responsible for the whole-body homeostasis. The review also explores the recent findings regarding signaling pathways involved in NMDAR-mediated responses in the kidney. As established from diverse lines of research reviewed here, basal levels of receptor activation within the kidney are essential for the maintenance of healthy tubular and glomerular function, while a disproportionate activation can lead to a disruption of NMDAR’s downstream signaling pathways and a myriad of pathophysiological consequences.
Erratum: Treatment of Dystrophic mdx Mice with an ADAMTS-5 Specific Monoclonal Antibody Increases the Ex Vivo Strength of Isolated Fast Twitch Hindlimb Muscles. Biomolecules 2020, 10, 416
Authors: Addinsall Alex B. , Forgan Leonard G. , McRae Natasha L. , Kelly Rhys W. , McDonald Penny L. , McNeill Bryony , McCulloch Daniel R. , Stupka Nicole
Published: July 2020

The authors wish to make a change to this published paper [...]
Biochemical Composition of Cumin Seeds, and Biorefining Study
Authors: Merah Othmane , Sayed-Ahmad Bouchra , Talou Thierry , Saad Zeinab , Cerny Muriel , Grivot Sarah , Evon Philippe , Hijazi Akram
Published: July 2020

A new biorefinery approach has been developed in the present study, and applied on cumin (Cuminum cyminum) seeds as a potential source of phytochemicals of interest. Cumin is a popular spice used widely for its distinctive aroma. It is a rich reserve of both vegetable and essential oils. The biorefinery approach here focused on the evaluation of the influence of four different geographical origins (i.e., Lebanon, France, Algeria and Syria) on oil yield and quality in cumin seed, and on the valorization of remaining by-products by investigating their nutritional content and biological activity for the first time. Vegetable and essential oils were extracted, and their compositions were determined. Nutritional traits were also assessed. The delipidated and hydrodistillated cakes just as aromatic water were characterized for their fiber, sugar, protein, phenol and flavonoid contents. Antibacterial and antioxidant activities were also determined. Cumin seeds showed high contents in both vegetable and essential oils, proteins and sugars regardless their origin. Moreover, this Apiaceae species exhibited high levels of petroselinic fatty acid (an isomer of oleic acid) and sterols. Cakes and aromatic water also presented high levels of proteins, fibers, sugars and phenols. These residues revealed interesting antioxidant and antibacterial activities. These results emphasized the potential use of cumin in a biorefinery concept, with a multi-purpose industrial process. In addition, large differences were observed between the four geographical origins for phytochemical contents and compositions. These findings highlight the perspectives for developing selection programs for nutritional traits and industrial interests. All obtained results validate the health promoting effect of cumin composition as well as its industrial importance along with the residues.
Over-Expression of Allograft Inflammatory Factor-1 (AIF-1) in Patients with Rheumatoid Arthritis
Authors: Piotrowska Katarzyna , Słuczanowska-Głabowska Sylwia , Kurzawski Mateusz , Dziedziejko Violetta , Kopytko Patrycja , Paczkowska Edyta , Rogińska Dorota , Safranow Krzysztof , Machaliński Bogusław , Pawlik Andrzej
Published: July 2020

Allograft inflammatory factor-1 (AIF-1) is a cytoplasmic protein that is encoded by the AIF1 gene. The main functions of AIF-1 are the activation of macrophages and enhancing the production of pro-inflammatory cytokines. To date, three different AIF-1 isoforms have been identified. In this study, we examined the expression of AIF-1 isoforms on the level of mRNA, and we compared the percentage of AIF-1-positive white blood cells (WBCs) in blood and AIF-1/CD68 cells in the synovial membranes in patients with rheumatoid arthritis (RA) and osteoarthritis (OA). We examined 15 patients with RA and 15 patients with OA who had previously undergone knee arthroplasty. Peripheral blood and synovial membranes (SMs) were collected from these patients during knee arthroplasty. We identified three AIF-1 mRNA expression variants in peripheral mononuclear cells (PBMCs) and SMs from patients in both groups. Spearman’s rank correlation coefficient tests showed strong, positive, and significant correlations between the three AIF-1 mRNA expression variants in PBMCs and/or SMs in patients with RA and OA. There were no statistically significant correlations for any of the AIF-1 mRNA expression variants between PBMCs and SMs in patients with RA and OA. We observed a statistically significant increased percentage of AIF-1-positive cells in patients with RA in comparison to patients with OA. The percentage of AIF-1-positive cells in the blood of patients with RA and OA was 1.35 ± 0.81% and 0.71 ± 0.25% (p < 0.01), respectively, whereas the percentage of AIF-1/CD68-positive WBC cells in the SMs was 24.05 ± 7.17% and 4.78 ± 1.52% (p < 0.001), respectively. In conclusion, three AIF-1 mRNA expression variants occurred in PBMCs and SM cells in patients with RA and OA. The AIF-1 mRNA expression levels of the variants correlated with each other in PBMCs and SM cells, but there were no statistically significant correlations for AIF-1 mRNA expression variants between PBMCs and SM cells in patients with RA and OA. Both in the blood and SMs, we observed an increased percentage of AIF-1-positive cells in patients with RA in comparison to patients with OA. The above results suggested that AIF-1 was the cytokine involved in the pathogenesis of RA. The precise knowledge of the role of AIF-1 in RA pathogenesis and the development of inflammatory response requires further investigations.
The Multifaceted Pyruvate Metabolism: Role of the Mitochondrial Pyruvate Carrier
Authors: Zangari Joséphine , Petrelli Francesco , Maillot Benoît , Martinou Jean-Claude
Published: July 2020

Pyruvate, the end product of glycolysis, plays a major role in cell metabolism. Produced in the cytosol, it is oxidized in the mitochondria where it fuels the citric acid cycle and boosts oxidative phosphorylation. Its sole entry point into mitochondria is through the recently identified mitochondrial pyruvate carrier (MPC). In this review, we report the latest findings on the physiology of the MPC and we discuss how a dysfunctional MPC can lead to diverse pathologies, including neurodegenerative diseases, metabolic disorders, and cancer.
The Droserasin 1 PSI: A Membrane-Interacting Antimicrobial Peptide from the Carnivorous Plant Drosera capensis
Authors: Sprague-Piercy Marc A. , Bierma Jan C. , Crosby Marquise G. , Carpenter Brooke P. , Takahashi Gemma R. , Paulino Joana , Hung Ivan , Zhang Rongfu , Kelly John E. , Kozlyuk Natalia , Chen Xi , Butts Carter T. , Martin Rachel W.
Published: July 2020

The Droserasins, aspartic proteases from the carnivorous plant Drosera capensis, contain a 100-residue plant-specific insert (PSI) that is post-translationally cleaved and independently acts as an antimicrobial peptide. PSIs are of interest not only for their inhibition of microbial growth, but also because they modify the size of lipid vesicles and strongly interact with biological membranes. PSIs may therefore be useful for modulating lipid systems in NMR studies of membrane proteins. Here we present the expression and biophysical characterization of the Droserasin 1 PSI (D1 PSI.) This peptide is monomeric in solution and maintains its primarily α -helical secondary structure over a wide range of temperatures and pH values, even under conditions where its three disulfide bonds are reduced. Vesicle fusion assays indicate that the D1 PSI strongly interacts with bacterial and fungal lipids at pH 5 and lower, consistent with the physiological pH of D. capensis mucilage. It binds lipids with a variety of head groups, highlighting its versatility as a potential stabilizer for lipid nanodiscs. Solid-state NMR spectra collected at a field strength of 36 T, using a unique series-connected hybrid magnet, indicate that the peptide is folded and strongly bound to the membrane. Molecular dynamics simulations indicate that the peptide is stable as either a monomer or a dimer in a lipid bilayer. Both the monomer and the dimer allow the passage of water through the membrane, albeit at different rates.
Critical Roles of N6-Methyladenosine (m6A) in Cancer and Virus Infection
Authors: Asada Ken , Bolatkan Amina , Takasawa Ken , Komatsu Masaaki , Kaneko Syuzo , Hamamoto Ryuji
Published: July 2020

Studies have shown that epigenetic abnormalities are involved in various diseases, including cancer. In particular, in order to realize precision medicine, the integrated analysis of genetics and epigenetics is considered to be important; detailed epigenetic analysis in the medical field has been becoming increasingly important. In the epigenetics analysis, DNA methylation and histone modification analyses have been actively studied for a long time, and many important findings were accumulated. On the other hand, recently, attention has also been focused on RNA modification in the field of epigenetics; now it is known that RNA modification is associated with various biological functions, such as regulation of gene expression. Among RNA modifications, functional analysis of N6-methyladenosine (m6A), the most abundant RNA modification found from humans to plants is actively progressing, and it has also been known that m6A abnormality is involved in cancer and other diseases. Importantly, recent studies have shown that m6A is related to viral infections. Considering the current world situation under threat of viral infections, it is important to deepen knowledge of RNA modification from the viewpoint of viral diseases. Hence, in this review, we have summarized the recent findings regarding the roles of RNA modifications in biological functions, cancer biology, and virus infection, particularly focusing on m6A in mRNA.
POSCAbilities: The Application of the Prion Organotypic Slice Culture Assay to Neurodegenerative Disease Research
Authors: Pineau Hailey , Sim Valerie
Published: July 2020

Prion diseases are fatal, transmissible neurodegenerative disorders whose pathogenesis is driven by the misfolding, self-templating and cell-to-cell spread of the prion protein. Other neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis and Huntington’s disease, share some of these prion-like features, with different aggregation-prone proteins. Consequently, researchers have begun to apply prion-specific techniques, like the prion organotypic slice culture assay (POSCA), to these disorders. In this review we explore the ways in which the prion phenomenon has been used in organotypic cultures to study neurodegenerative diseases from the perspective of protein aggregation and spreading, strain propagation, the role of glia in pathogenesis, and efficacy of drug treatments. We also present an overview of the advantages and disadvantages of this culture system compared to in vivo and in vitro models and provide suggestions for new directions.
Synthesis of Biodegradable Polymers: A Review on the Use of Schiff-Base Metal Complexes as Catalysts for the Ring Opening Polymerization (ROP) of Cyclic Esters
Authors: Santoro Orlando , Zhang Xin , Redshaw Carl
Published: July 2020

This review describes the recent advances (from 2008 onwards) in the use of Schiff-base metal complexes as catalysts for the ring opening polymerization (ROP) of cyclic esters. The synthesis and structure of the metal complexes, as well as all aspects concerning the polymerization process and the characteristics of the polymers formed, will be discussed.
Synthesis, Characterization, and Anti-Algal Activity of Molybdenum-Doped Metal Oxides
Authors: Nandanwar Sondavid , Lee Myung Won , Borkar Shweta , Cho Jeong Hyung , Tarte Naresh H. , Kim Hak Jun
Published: July 2020

In this study, we attempted to synthesize visible light active nano-sized photocatalysts using metal oxides such as zinc oxide, zirconium oxide, tungsten oxide, and strontium titanium oxide with (MoCl5)2 as a dopant by the simple ball-milling method. Fourier-transform infrared spectroscopy data confirmed the presence of M-O-Mo linkage (M = Zn, Zr, W, and SrTi) in all the molybdenum-doped metal oxides (MoMOs), but only MoZnO inhibited the growth of the bloom-forming Microcystis aeruginosa under visible light in a concentration-dependent manner up to 10 mg/L. Further, structural characterization of MoZnO using FESEM and XRD exhibited the formation of typical hexagonal wurtzite nanocrystals of approximately 4 nm. Hydroxyl radical (·OH), reactive oxygen species (ROS), and lipid peroxidation assays revealed ·OH generated by MoZnO under the visible light seemed to cause peroxidation of the lipid membrane of M. aeruginosa, which led to an upsurge of intracellular ROS and consequently introduced the agglomeration of cyanobacteria. These results demonstrated that nano-sized MoZnO photocatalyst can be easily synthesized in a cost-effective ball-mill method and utilized for biological applications such as the reduction of harmful algal blooms. Further, our study implies that a simple ball-milling method can provide an easy, green, and scalable route for the synthesis of visible light active doped metal oxides.
An Eco-Friendly Fluidizable FexOy/CaO-γ-Al2O3 Catalyst for Tar Cracking during Biomass Gasification
Authors: Torres Cindy , Rostom Samira , de Lasa Hugo
Published: July 2020

The present study deals with the development, characterization, and performance evaluation of an eco-friendly catalyst, using 2-methoxy-4-methylphenol (2M4MP) as a surrogate tar. The 2M4MP was selected due to its chemical functionalities and the fact that it is a good model compound to represent the tar formed during biomass low temperature gasification. The eco-friendly catalyst was prepared using the typical Fe and Ca minerals which are present in ash. These ash components were added to a fluidizable γ-Al2O3 support using a multistep incipient impregnation, yielding Fe oxides as an active phase and CaO as the promoter. The prepared catalyst displayed a 120 m2/g BET specific surface area, with few γ-Al2O3 bulk phase changes, as observed with XRD. TPD-NH3 and pyridine FTIR allowed us to show the significant influence of CaO reduced support acidity. A TPR analysis provided evidence of catalyst stability during consecutive reduction–oxidation cycles. Furthermore, catalyst evaluation vis-à-vis catalytic steam 2M4MP gasification was performed using the fluidized CREC riser simulator. The obtained results confirm the high performance of the developed catalyst, with 2M4MP conversion being close to 100% and with selectivities of up to 98.6% for C1-C2 carbon-containing species, at 500 °C, with a 7.5 s reaction time and 1.5 g steam/g 2M4MP. These high tar conversions are promising efficiency indicators for alumina catalysts doped with Fe and Ca. In addition, the used catalyst particles could be blended with biochar to provide an integrated solid supplement that could return valuable mineral supplements to the soil.
Direct Decomposition of NO over Co-Mn-Al Mixed Oxides: Effect of Ce and/or K Promoters
Authors: Jirátová Květa , Pacultová Kateřina , Karásková Kateřina , Balabánová Jana , Koštejn Martin , Obalová Lucie
Published: July 2020

Co-Mn-Al mixed oxides promoted by potassium are known as active catalysts for the direct decomposition of nitric oxide (NO). In this study, the answer to the following question has been considered: does the presence of cerium in K-promoted Co-Mn-Al catalysts substantially affect the physical-chemical properties, activity, and stability in direct NO decomposition? The Co-Mn-Al, Co-Mn-Al-Ce, and Co-Mn-Al-Ce-K mixed oxide catalysts were prepared by the precipitation of corresponding metal nitrates with a solution of Na2CO3/NaOH, followed by the washing of the precipitate and calcination. Two other catalysts were prepared by impregnation of the Ce-containing catalysts with Co and Co+K nitrates. After calcination, the solids were characterized by chemical analysis, XRD, N2 physisorption, FTIR, temperature-programmed reduction, CO2 and O2 desorption (H2-TPR, CO2-TPD, O2-TPD), and X-ray photoelectron spectrometry (XPS). Cerium and especially potassium occurring in the catalysts affected the basicity, reducibility, and surface concentration of active components. Adding cerium itself did not contribute to the increase in catalytic activity, whereas the addition of cerium and potassium did. Catalytic activity in direct NO decomposition depended on combinations of both reducibility and the amount of stronger basic sites determined in the catalysts. Therefore, the increase in cobalt concentration itself in the Co-Mn-Al mixed oxide catalyst does not determine the achievement of high catalytic activity in direct NO decomposition.
On Directional Dendritic Growth and Primary Spacing—A Review
Authors: Strickland Joel , Nenchev Bogdan , Dong Hongbiao
Published: July 2020

The primary spacing is intrinsically linked with the mechanical behavior of directionally solidified materials. Because of this relationship, a significant amount of solidification work is reported in the literature, which relates the primary spacing to the process variables. This review provides a comprehensive chronological narrative on the development of the directional dendritic growth problem over the past 85 years. A key focus within this review is detailing the relationship between key solidification parameters, the operating point of the dendrite tip, and the primary spacing. This review critiques the current state of directional dendritic growth and primary spacing modelling, briefly discusses dendritic growth computational and experimental research, and suggests areas for future investigation.
Impact of Breast Density Awareness on Knowledge about Breast Cancer Risk Factors and the Self-Perceived Risk of Breast Cancer
Authors: Bojanic Kristina , Vukadin Sonja , Sarcevic Filip , Malenica Luka , Grgic Kaja , Smolic Robert , Kralik Kristina , Bilic Curcic Ines , Ivanac Gordana , Wu George Y. , Smolic Martina
Published: July 2020

Breast density (BD) reduces sensitivity of mammography, and is a strong risk factor for breast cancer (BC). Data about women’s awareness and knowledge of BD are limited. Our aim is to examine whether the BD information disclosure and BD awareness among women without BC are related to their knowledge about BC risk factors. We examined self-reported BC risk perception and its association to BD awareness and level of health literacy. A cross-sectional, single site study included 263 Croatian women without BC who had mammographic examination. Data were collected by interviews using questionnaires and a validated survey. Of the total, 77.1% had never heard of BD, and 22.9% were aware of their BD. Most participants who knew their BD (88.2%, p < 0.001) had higher levels of education. Majority of subjects (66.8%) had non-dense breasts and 33.2% had dense breasts. Subjects aware of their BD knew that post-menopausal hormone replacement therapy (p = 0.04) and higher BD (p = 0.03) are BC risk factors. They could more easily access information about health promotion (p = 0.03). High-BD informed women assessed their lifetime BC risk as significantly higher than all others (p = 0.03). Comprehension of BD awareness and knowledge is crucial for reinforcement of educational strategies and development of amendatory BC screening decisions.
Corneal In Vivo Laser-Scanning Confocal Microscopy Findings in Dry Eye Patients with Sjögren’s Syndrome
Authors: Matsumoto Yukihiro , Ibrahim Osama M. A. , Kojima Takashi , Dogru Murat , Shimazaki Jun , Tsubota Kazuo
Published: July 2020

Purpose: To evaluate the changes in cornea in Sjögren’s syndrome (SS) with a novel confocal microscopy device. Methods: Twenty-three right eyes of patients with SS (23 women; mean age, 65.4 ± 11.4 years) and 13 right eyes of 13 age- and sex-matched control subjects (13 women; mean age, 68.8 ± 9.8 years) were studied. Furthermore, eight right eyes of patients with SS (8 women; mean age, 66.9 ± 9.6 years) were studied to evaluate the corneal microscopic alterations after the treatment with topical 3% diquafosol sodium eye drops. All cases had tear quantity, tear breakup time (BUT), ocular surface staining measurements, and corneal in vivo laser-scanning confocal microscopy examinations. The density and area of corneal epithelial cells (superficial, wing, and basal), density of corneal stromal cells (anterior, intermediate, and posterior), density and area of corneal endothelial cells, density and morphology of corneal sub-basal nerve plexus, density of corneal sub-basal inflammatory cells were also assessed. Results: The tear quantity, stability, and vital staining scores were significantly worse in patients with SS than in control subjects (p < 0.0001). Corneal superficial epithelial cell density was significantly lower in SS compared with control subjects (p < 0.0001). Corneal superficial epithelial cell area was significantly larger in SS compared with control subjects (p = 0.007). Corneal sub-basal nerve fiber density was lower in SS compared with control subjects (p < 0.0001). Morphological abnormality of nerve fibers was observed in SS patients. Corneal sub-basal inflammatory cell density was significantly higher in SS patients compared with control subjects (p < 0.0001). Furthermore, the mean corneal superficial epithelial cell density and area, inflammatory cell density, corneal sub-basal nerve fiber density, and morphological abnormality of nerve fibers, were improved with topical 3% diquafosol sodium treatment in the dry eye patients with SS (p < 0.05). Conclusions: The diagnostic modality using in vivo laser-scanning confocal microscopy was a useful method for the evaluation of the corneal cell density and area, nerve fiber density and morphology, and inflammatory cell density in patients with SS and also a useful tool in the assessment of treatment effect with topical 3% diquafosol sodium in the SS patients.
Quantum Correlation Dynamics in Controlled Two-Coupled-Qubit Systems
Authors: Ghiu Iulia , Grimaudo Roberto , Mihaescu Tatiana , Isar Aurelian , Messina Antonino
Published: July 2020

We study and compare the time evolutions of concurrence and quantum discord in a driven system of two interacting qubits prepared in a generic Werner state. The corresponding quantum dynamics is exactly treated and manifests the appearance and disappearance of entanglement. Our analytical treatment transparently unveils the physical reasons for the occurrence of such a phenomenon, relating it to the dynamical invariance of the X structure of the initial state. The quantum correlations which asymptotically emerge in the system are investigated in detail in terms of the time evolution of the fidelity of the initial Werner state.
Active Learning: Subtypes, Intra-Exam Comparison, and Student Survey in an Undergraduate Biology Course
Authors: McGreevy Kristen M. , Church Frank C.
Published: July 2020

Active learning improves undergraduate STEM course comprehension; however, student comprehension using different active learning methods and student perception of active learning have not been fully explored. We analyze ten semesters (six years) of an undergraduate biology course (honors and non-honors sections) to understand student comprehension and student satisfaction using a variety of active learning methods. First, we describe and introduce active learning subtypes. Second, we explore the efficacy of active learning subtypes. Third, we compare student comprehension between course material taught with active learning or lecturing within a course. Finally, we determine student satisfaction with active learning using a survey. We divide active learning into five subtypes based on established learning taxonomies and student engagement. We explore subtype comprehension efficacy (median % correct) compared to lecture learning (median 92% correct): Recognition (100%), Reflective (100%), Exchanging (94.1%), Constructive (93.8%), and Analytical (93.3%). A bivariate random intercept model adjusted by honors shows improved exam performance in subsequent exams and better course material comprehension when taught using active learning compared to lecture learning (2.2% versus 1.2%). The student survey reveals a positive trend over six years of teaching in the Perceived Individual Utility component of active learning (tau = 0.21, p = 0.014), but not for the other components (General Theoretical Utility, and Team Situation). We apply our findings to the COVID-19 pandemic and suggest active learning adaptations for newly modified online courses. Overall, our results suggest active learning subtypes may be useful for differentiating student comprehension, provide additional evidence that active learning is more beneficial to student comprehension, and show that student perceptions of active learning are positively changing.
Combining K-Means and XGBoost Models for Anomaly Detection Using Log Datasets
Authors: Henriques João , Caldeira Filipe , Cruz Tiago , Simões Paulo
Published: July 2020

Computing and networking systems traditionally record their activity in log files, which have been used for multiple purposes, such as troubleshooting, accounting, post-incident analysis of security breaches, capacity planning and anomaly detection. In earlier systems those log files were processed manually by system administrators, or with the support of basic applications for filtering, compiling and pre-processing the logs for specific purposes. However, as the volume of these log files continues to grow (more logs per system, more systems per domain), it is becoming increasingly difficult to process those logs using traditional tools, especially for less straightforward purposes such as anomaly detection. On the other hand, as systems continue to become more complex, the potential of using large datasets built of logs from heterogeneous sources for detecting anomalies without prior domain knowledge becomes higher. Anomaly detection tools for such scenarios face two challenges. First, devising appropriate data analysis solutions for effectively detecting anomalies from large data sources, possibly without prior domain knowledge. Second, adopting data processing platforms able to cope with the large datasets and complex data analysis algorithms required for such purposes. In this paper we address those challenges by proposing an integrated scalable framework that aims at efficiently detecting anomalous events on large amounts of unlabeled data logs. Detection is supported by clustering and classification methods that take advantage of parallel computing environments. We validate our approach using the the well known NASA Hypertext Transfer Protocol (HTTP) logs datasets. Fourteen features were extracted in order to train a k-means model for separating anomalous and normal events in highly coherent clusters. A second model, making use of the XGBoost system implementing a gradient tree boosting algorithm, uses the previous binary clustered data for producing a set of simple interpretable rules. These rules represent the rationale for generalizing its application over a massive number of unseen events in a distributed computing environment. The classified anomaly events produced by our framework can be used, for instance, as candidates for further forensic and compliance auditing analysis in security management.
A Dynamic Path Planning Method for Social Robots in the Home Environment
Authors: Zhang Chen , Zhou Lelai , Li Yibin , Fan Yong
Published: July 2020

The home environment is a typical dynamic environment with moveable obstacles. The social robots working in home need to search for feasible paths in this complex dynamic environment. In this work, we propose an improved RRT algorithm to plan feasible path in home environment. The algorithm pre-builds a tree that covers the whole map and maintains the effectiveness of all nodes with branch pruning, reconnection, and regrowth process. The method forms a path by searching the nearest node in the tree and then quickly accessing the nodes near the destination. Due to the effectiveness-maintaining process, the proposed method can effectively deal with the complex dynamic environment where the destination and multiple moving obstacles change simultaneously. In addition, our method can be extended to the path-planning problem in 3D space. The simulation experiments verify the effectiveness of the algorithm.
An Embedded Platform for Positioning and Obstacle Detection for Small Unmanned Aerial Vehicles
Authors: Ponte Salvatore , Ariante Gennaro , Papa Umberto , Del Core Giuseppe
Published: July 2020

Unmanned Aerial Vehicles (UAV) with on-board augmentation systems (UAS, Unmanned Aircraft System) have penetrated into civil and general-purpose applications, due to advances in battery technology, control components, avionics and rapidly falling prices. This paper describes the conceptual design and the validation campaigns performed for an embedded precision Positioning, field mapping, Obstacle Detection and Avoiding (PODA) platform, which uses commercial-off-the-shelf sensors, i.e., a 10-Degrees-of-Freedom Inertial Measurement Unit (10-DoF IMU) and a Light Detection and Ranging (LiDAR), managed by an Arduino Mega 2560 microcontroller with Wi-Fi capabilities. The PODA system, designed and tested for a commercial small quadcopter (Parrot Drones SAS Ar.Drone 2.0, Paris, France), estimates position, attitude and distance of the rotorcraft from an obstacle or a landing area, sending data to a PC-based ground station. The main design issues are presented, such as the necessary corrections of the IMU data (i.e., biases and measurement noise), and Kalman filtering techniques for attitude estimation, data fusion and position estimation from accelerometer data. The real-time multiple-sensor optimal state estimation algorithm, developed for the PODA platform and implemented on the Arduino, has been tested in typical aerospace application scenarios, such as General Visual Inspection (GVI), automatic landing and obstacle detection. Experimental results and simulations of various missions show the effectiveness of the approach.
A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions
Authors: Asharf Javed , Moustafa Nour , Khurshid Hasnat , Debie Essam , Haider Waqas , Wahab Abdul
Published: July 2020

The Internet of Things (IoT) is poised to impact several aspects of our lives with its fast proliferation in many areas such as wearable devices, smart sensors and home appliances. IoT devices are characterized by their connectivity, pervasiveness and limited processing capability. The number of IoT devices in the world is increasing rapidly and it is expected that there will be 50 billion devices connected to the Internet by the end of the year 2020. This explosion of IoT devices, which can be easily increased compared to desktop computers, has led to a spike in IoT-based cyber-attack incidents. To alleviate this challenge, there is a requirement to develop new techniques for detecting attacks initiated from compromised IoT devices. Machine and deep learning techniques are in this context the most appropriate detective control approach against attacks generated from IoT devices. This study aims to present a comprehensive review of IoT systems-related technologies, protocols, architecture and threats emerging from compromised IoT devices along with providing an overview of intrusion detection models. This work also covers the analysis of various machine learning and deep learning-based techniques suitable to detect IoT systems related to cyber-attacks.
Experimental Evaluation of a New Approach for a Two-Stage Hydrothermal Biomass Liquefaction Process
Authors: Klemm Marco , Kröger Michael , Görsch Kati , Lange Rüdiger , Hilpmann Gerd , Lali Farzad , Haase Stefan , Krusche Michael , Ullrich Frank , Chen Zihao , Wilde Nicole , Al-Naji Majd , Gläser Roger
Published: July 2020

A new approach for biomass liquefaction was developed and evaluated in a joint research project. Focus of the project, called FEBio@H2O, lies on a two-step hydrothermal conversion. Within step 1, the input biomass is converted employing a hydrothermal degradation without added catalyst or by homogeneous catalysis. Within step 2, the hydrogen accepting products of step 1, e.g., levulinic acid (LA) are upgraded by a heterogeneously catalyzed hydrogenation with hydrogen donor substances, e.g., formic acid (FA). As a result, components with an even lower oxygen content in comparison to step 1 products are formed; as an example, γ-valerolactone (GVL) can be named. Therefore, the products are more stable and contained less oxygen as requested for a possible application as liquid fuel. As a hydrothermal process, FEBio@H2O is especially suitable for highly water-containing feedstock. The evaluation involves hydrothermal conversion tests with model substances, degradation of real biomasses, transfer hydrogenation or hydrogenation with hydrogen donor of model substances and real products of step 1, catalyst selection and further development, investigation of the influence of reactor design, the experimental test of the whole process chain, and process assessment.
Forecasts of Value-at-Risk and Expected Shortfall in the Crude Oil Market: A Wavelet-Based Semiparametric Approach
Authors: Yang Lu , Hamori Shigeyuki
Published: July 2020

We propose the use of wavelet-based semiparametric models for forecasting the value-at-risk (VaR) and expected shortfall (ES) in the crude oil market. We compared the forecast outcomes across different time scales for three semiparametric models, three nonparametric, distribution-based, generalized, autoregressive, conditional, heteroskedasticity (GARCH) models, and three rolling-window models. We found that the GARCH model estimated by the Fissler and Ziegel (FZ) zero loss minimization (GARCH-FZ) model performs the best at forecasting the VaR and ES in the short term, whereas the hybrid model performs the best for mid- and long-term time scales. Thus, long-term investors should consider the hybrid model and short-term investors should employ the GARCH-FZ model in their risk management processes. Overall, our proposed wavelet-based semiparametric models outperform the other models tested for all time scales and market conditions. As such, we suggest that these models are considered for the management of crude oil price risk and in the development of energy policy.
Fluidised Bed Gasification of Diverse Biomass Feedstocks and Blends—An Overall Performance Study
Authors: Valin Sylvie , Ravel Serge , Pons de Vincent Philippe , Thiery Sébastien , Miller Hélène , Defoort Françoise , Grateau Maguelone
Published: July 2020

The aim of this work is to investigate the fluidised bed gasification of several pure and blended feedstock prepared in the form of pellets: oak bark, two bark/wheat straw blends (85/15 and 50/50 wt%) and lignin residue remaining from bioethanol production. Gasification conditions were defined to be representative of dual fluidised bed ones (steam gasification at 850 °C, followed by air combustion of the char). The cold gas efficiency (77–81%), gas composition and tar content (0.9–2.3 g/kgdaf) are close for the gasification of bark and the two bark/wheat straw blends. For lignin residue, the cold gas efficiency is lower (71%), and the tar content is 9.1 g/kgdaf. The agglomeration propensity is much higher for lignin residue than for the other feedstock. This was put into evidence with in-bed temperature measurements at different levels, and confirmed with post-test size screening of the bed material particles. The 50/50 wt% bark/wheat straw blend seems to undergo defluidisation in combustion, however followed by refluidisation of the bed. These findings were also well correlated with a predictive model for defluidisation.
A Three-Level DC-Link Quasi-Switch Boost T-Type Inverter with Voltage Stress Reduction
Authors: Tran Vinh-Thanh , Do Duc-Tri , Do Van-Dung , Nguyen Minh-Khai
Published: July 2020

In recent years, the three-level T-Type inverter has been considered the best choice for many low and medium power applications. Nevertheless, this topology is known as a buck converter. Therefore, in this paper, a new topology incorporating the dc-link type quasi-switched boost network with the traditional three-level T-type inverter is proposed to overcome the limit of traditional three-level T-Type inverter. The space vector pulse width modulation scheme is considered to control this topology, which provides some benefits such as enhancing modulation index and reducing the magnitude of common-mode voltage. For this scheme, the zero, medium, and large vectors are utilized to generate the output voltage. The shoot-through state which is adopted by turning on all power switches of inverter leg is inserted into zero vector to boost the dc-link voltage. As a result, there is no distortion at the output waveform. The control signal of intermediate network power switches is also detailed to improve the boost factor and voltage gain. As a result, the voltage stress on power devices like capacitors, diodes, and switches is decreased significantly. To demonstrate the outstanding of proposed structure and its control strategy, some comparisons between the proposed method and other ones are performed. Simulation and experimental prototype results are conducted to verify the accuracy of the theory and effectiveness of the inverter.