bluuebunny/crossref_metadata_2025 huggingface dataset:
Exacerbation of AMD Phenotype in Lasered CNV Murine Model by Dysbiotic Oral Pathogens
Authors: Arjunan Pachiappan , Swaminathan Radhika , Yuan Jessie , Elashiry Mohamed , Tawfik Amany , Al-Shabrawey Mohamed , Martin Pamela M. , Muthusamy Thangaraju , Cutler Christopher W.
Published: February 2021

Emerging evidence underscores an association between age-related macular degeneration (AMD) and periodontal disease (PD), yet the biological basis of this linkage and the specific role of oral dysbiosis caused by PD in AMD pathophysiology remains unclear. Furthermore, a simple reproducible model that emulates characteristics of both AMD and PD has been lacking. Hence, we established a novel AMD+PD murine model to decipher the potential role of oral infection (ligature-enhanced) with the keystone periodontal pathogen Porphyromonas gingivalis, in the progression of neovasculogenesis in a laser-induced choroidal-neovascularization (Li-CNV) mouse retina. By a combination of fundus photography, optical coherence tomography, and fluorescein angiography, we documented inflammatory drusen-like lesions, reduced retinal thickness, and increased vascular leakage in AMD+PD mice retinae. H&E further confirmed a significant reduction of retinal thickness and subretinal drusen-like deposits. Immunofluorescence microscopy revealed significant induction of choroidal/retinal vasculogenesis in AMD+PD mice. qPCR identified increased expression of oxidative-stress, angiogenesis, pro-inflammatory mediators, whereas antioxidants and anti-inflammatory genes in AMD+PD mice retinae were notably decreased. Through qPCR, we detected Pg and its fimbrial 16s-RrNA gene expression in the AMD+PD mice retinae. To sum-up, this is the first in vivo study signifying a role of periodontal infection in augmentation of AMD phenotype, with the aid of a pioneering AMD+PD murine model established in our laboratory.
Baseline Subsoil CO2 Gas Measurements and Micrometeorological Monitoring: Above Canopy Turbulence Effects on the Subsoil CO2 Dynamics in Temperate Deciduous Forest
Authors: Adisaputro Didi , De Donato Philippe , Saint-Andre Laurent , Barres Odile , Galy Catherine , Nourrisson Gilles , Piedevache Médéric , Derrien Marion
Published: February 2021

Accurate and continuous measurement of the subsoil CO2 is critical to better understand the terrestrial and atmosphere gas transfer process. This work aims to develop and field test a specific flow system to continuously measure the soil gas concentration (χc) and understand its main physical drivers. Hourly data measured in situ were collected through two dedicated wells at 1 m and 6 m depth coupled with micrometeorological measurement. Our study shows that χc at -1 m was at the lowest in winter and highest in summer. Meanwhile, the seasonal variation of χc at -6m is somewhat unclear. While it is inevitable that temperature plays a significant role, this factor related to biological activity cannot fully explain the variation. The decrease in χc at both depths in summer coincides with an increase of friction velocity, especially during dry periods with R2 of 0.68, which shows strong empirical evidence that wind turbulence plays a significant role in driving the deep soil CO2. A monitoring strategy for gas measurement combining borehole and micrometeorological measurement offers excellent long-term monitoring possibilities to derive the vertical distribution of CO2 and better understand the main physical drivers of gas exchange.
Root System and Its Relations with Soil Physical and Chemical Attributes in Orange Culture
Authors: Barbosa Ronny Sobreira , de Souza Zigomar Menezes , Carneiro Marina Pedroso , Farhate Camila Viana Vieira
Published: February 2021

Citrus companies have sought and developed alternative systems of tillage or implanting orchards so as not to significantly alter the physical and chemical attributes of the soil and, consequently, the root development of plants. Therefore, the aim of this study was to identify the physical and chemical attributes of the soil that most influence the root volume of the orange crop in different tillage systems. The experiment was carried out in the region of Avaré, state of São Paulo, Brazil, in Utissol and Oxisol. For the planting of the orange crop, the following tillages were made: minimum tillage, subsoiler tillage and soil tillage using a triple tillage implement. The physical and chemical attributes evaluated were bulk density, macroporosity, microporosity, total porosity, soil moisture, soil mean weight-diameter, soil resistance to penetration, sum of bases, cation exchange capacity, base saturation, pH, exchangeable cations, potential acidity, available phosphorus, analysis of micronutrients such as copper, iron, manganese and zinc, and organic carbon content. The root system was evaluated using SIARCS® software. For data classification, data mining techniques were used such as attribute selection and decision tree induction. Regardless of the soil type, the use of the triple operation implement provided greater root volume for orange plants. For the Utisol area, the pH value of 4.2 was the main attribute that provided a high root volume. For the Oxisol, the presence of copper, in levels that did not generate toxicity for the plants, provided a high volume of root for the crop.
Molecular Mechanisms of Topography Sensing by Osteoblasts: An Update
Authors: Rougerie Pablo , Silva dos Santos Rafaela , Farina Marcos , Anselme Karine
Published: February 2021

Bone is a specialized tissue formed by different cell types and a multiscale, complex mineralized matrix. The architecture and the surface chemistry of this microenvironment can be factors of considerable influence on cell biology, and can affect cell proliferation, commitment to differentiation, gene expression, matrix production and/or composition. It has been shown that osteoblasts encounter natural motifs in vivo, with various topographies (shapes, sizes, organization), and that cell cultures on flat surfaces do not reflect the total potential of the tissue. Therefore, studies investigating the role of topographies on cell behavior are important in order to better understand the interaction between cells and surfaces, to improve osseointegration processes in vivo between tissues and biomaterials, and to find a better topographic surface to enhance bone repair. In this review, we evaluate the main available data about surface topographies, techniques for topographies’ production, mechanical signal transduction from surfaces to cells and the impact of cell–surface interactions on osteoblasts or preosteoblasts’ behavior.
A Novel Method for Network Design and Optimization of District Energy Systems: Considering Network Topology Planning and Pipe Diameter
Authors: Wu Jiazheng , Liu Hongyun , Ruan Yingjun , Wang Shanshan , Yuan Jiamin , Lu Huiming
Published: February 2021

This paper proposes a new network topology design method that considers all the road nodes, energy stations and load centers to ensure the distribution of pipes along the road. The traditional graph theory and Prim Minimum Spanning Tree (MST) are used to simplify the map and minimize the length of the pipeline. After analyzing the limitations of the traditional network topology model, Point-to-Point (PTP), we present a new model, Energy Station-to-Load Point (ESLP). The model is optimized by minimum cost, not the shortest path. Finally, Pipe Diameter Grading (PDG) is proposed based on ESLP by solving for the pipe diameter that gives the minimum cost under different load demands in the process of optimization. The network design method is effectively applied in a case, and the results show that the path of the optimized plan is 1.88% longer than that of the pre-optimized plan, but the cost is 2.38% lower. The sensitivity analysis shows that the cost of pipeline construction, project life and electricity price all have an impact on the optimization results, and the cost of pipeline construction is the most significant. The difference between the different classifications of pipelines affects whether PDG is effective or not.
Estimation of Aircraft-Dependent Bumpiness Severity in Turbulent Flight
Authors: Wang Haofeng , Gao Zhenxing , Gu Hongbin , Qi Kai
Published: February 2021

Atmospheric turbulence threatens flight safety of civil aviation aircraft by inducing aircraft bumpiness. A severity estimation method of aircraft bumpiness in turbulent flight is explored according to in-situ Eddy Dissipation Rate (EDR) indicator. With the turbulence intensity derived from EDR value, a time series of longitudinal and vertical turbulence was generated according to von Karman turbulence model. In order to obtain the vertical acceleration response of aircraft, the continuous change of aerodynamic force on the assembly of wing and horizontal tail was computed by Unsteady Vortex Lattice Method (UVLM). The computing accuracy was improved by using semi-circle division and assigning the vortex rings on the mean camber surface. Furthermore, the adverse effects of control surface deflections on bumpiness severity estimation can be effectively removed by separating turbulence-induced and aircraft maneuvers-induced aerodynamic force change. After that, the variance of vertical acceleration, as the severity indicator of aircraft bumpiness, was obtained by Welch spectrum estimation. With the refined grid level, the pitching moment change due to control surface deflections can be solved accurately by UVLM. The instantaneous acceleration change obtained by UVLM approximates recorded acceleration data with better accuracy than linear transfer function model. A further test with a set of flight data on the same airway shows that compared with in-situ EDR indicator, the proposed method gives an aircraft-dependent estimation of bumpiness severity, which can not only be used to estimate in-situ bumpiness but also be applied to forecast the bumpiness severity of other different aircrafts.
Application of Learning Analytics in Virtual Tutoring: Moving toward a Model Based on Interventions and Learning Performance Analysis
Authors: Oliva-Córdova Luis Magdiel , Garcia-Cabot Antonio , Amado-Salvatierra Héctor R.
Published: February 2021

The research area related to the use of Learning Analytics and the prediction of student performance is multidimensional; therefore, it can be explored and analyzed through different perspectives. This research addresses the relationship between pedagogical interventions based on Learning Analytics and student learning performance. The research problem of predicting student performance can be analyzed from various angles. This study presents an analysis based on the technique of Path Analysis (PA) and proposes a model based on the following variables: Mediation, Motivation, Communication, Learning Design, and Learning Performance. The study’s findings demonstrate the importance of the role of virtual tutors in carrying out pedagogical interventions thanks to the information retrieved from the Learning Analytics tools and its appropriate analysis.
Foreground Objects Detection by U-Net with Multiple Difference Images
Authors: Kim Jae-Yeul , Ha Jong-Eun
Published: February 2021

In video surveillance, robust detection of foreground objects is usually done by subtracting a background model from the current image. Most traditional approaches use a statistical method to model the background image. Recently, deep learning has also been widely used to detect foreground objects in video surveillance. It shows dramatic improvement compared to the traditional approaches. It is trained through supervised learning, which requires training samples with pixel-level assignment. It requires a huge amount of time and is high cost, while traditional algorithms operate unsupervised and do not require training samples. Additionally, deep learning-based algorithms lack generalization power. They operate well on scenes that are similar to the training conditions, but they do not operate well on scenes that deviate from the training conditions. In this paper, we present a new method to detect foreground objects in video surveillance using multiple difference images as the input of convolutional neural networks, which guarantees improved generalization power compared to current deep learning-based methods. First, we adjust U-Net to use multiple difference images as input. Second, we show that training using all scenes in the CDnet 2014 dataset can improve the generalization power. Hyper-parameters such as the number of difference images and the interval between images in difference image computation are chosen by analyzing experimental results. We demonstrate that the proposed algorithm achieves improved performance in scenes that are not used in training compared to state-of-the-art deep learning and traditional unsupervised algorithms. Diverse experiments using various open datasets and real images show the feasibility of the proposed method.
100% Renewable Energy for Austria’s Industry: Scenarios, Energy Carriers and Infrastructure Requirements
Authors: Geyer Roman , Knöttner Sophie , Diendorfer Christian , Drexler-Schmid Gerwin , Alton Verena
Published: February 2021

The need for decarbonization raises several questions. How can renewable energy supply for the industrial sector be realized in the long term? Furthermore, how must the existing energy system be transformed to achieve the ambitious climate targets in place? In Austria, the share of renewable energy supplying industrial energy demand currently accounts for only 45% of final energy consumption. This clearly shows that a conversion of industrial energy systems is necessary. Different ambitious perspectives for a renewable energy supply for the Austrian industrial sector are calculated for three defined scenarios (base, efficiency, transition) in this paper. In addition, corresponding requirements for the energy infrastructures are discussed. The scenario results show a range of industrial final energy consumption from 78 TWh (efficiency) to 105 TWh (transition) through decarbonizing the industrial energy supply (cf. 87 TWh in 2019). Decarbonization requires an increasing shift towards electrical energy, especially in the transition scenario, whereas in the base and efficiency scenarios, biogenic fuels play an important role. Comprehensive decarbonization and the associated substitution of energy carriers in industry pose significant challenges for the existing energy infrastructure, its expansion, and optimization.
Shake Table Testing of Standard Cold-Formed Steel Storage Rack
Authors: Firouzianhaji Ahmad , Usefi Nima , Samali Bijan , Mehrabi Peyman
Published: February 2021

Full-scale shake table investigations are strongly required to understand the actual performance of storage racks and to improve the rack design guidelines. This paper presents the results of full-scale shake table tests on New Zealand standard storage rack frames with two-bay and two-level to determine the dynamic characteristics of a standard rack structure and to measure the damping of the system. The experimental program was conducted in three phases. First, the identification parameters including the natural frequency and damping of the system were determined through a series of preliminary tests. Then, shake table tests were performed to capture the inelastic response of rack frames under low to medium intensities of El-Centro ground motion. Finally, the shake-table tests were repeated with scaling down the time domain and broader ranges of ground motion intensities to consider the performance of taller rack systems. In addition, a comprehensive discussion on the damping of the system is also provided based on the test results. The performance of the rack frame is described through an extensive set of measurements, including rack displacement, pallet sliding, the acceleration of a concrete block and rack frame and the damping of the system in the down-aisle direction. The results indicate that the standard rack frames are able to endure large inelastic deformations without loss of stability.
Loss of parla Function Results in Inactivity, Olfactory Impairment, and Dopamine Neuron Loss in Zebrafish
Authors: Merhi Rawan , Kalyn Michael , Zhu-Pawlowsky Amanda , Ekker Marc
Published: February 2021

The presenilin-associated rhomboid-like (PARL) gene was found to contribute to mitochondrial morphology and function and was linked to familial Parkinson’s disease (PD). The PARL gene product is a mitochondrial intramembrane cleaving protease that acts on a number of mitochondrial proteins involved in mitochondrial morphology, apoptosis, and mitophagy. To date, functional and genetic studies of PARL have been mainly performed in mammals. However, little is known about PARL function and its role in dopaminergic (DA) neuron development in vertebrates. The zebrafish genome comprises two PARL paralogs: parla and parlb. Here, we established a loss-of-function mutation in parla via CRISPR/Cas9-mediated mutagenesis. We examined DA neuron numbers in the adult brain and expression of genes associated with DA neuron function in larvae and adults. We show that loss of parla function results in loss of DA neurons, mainly in the olfactory bulb. Changes in the levels of tyrosine hydroxylase transcripts supported this neuronal loss. Expression of fis1, a gene involved in mitochondrial fission, was increased in parla mutants. Finally, we showed that loss of parla function translates into impaired olfaction and altered locomotion parameters. These results suggest a role for parla in the development and/or maintenance of DA neuron function in zebrafish.
Zoom in on Antibody Aggregates: A Potential Pitfall in the Search of Rare EV Populations
Authors: Rasmussen Rikke W. , Botha Jaco , Prip Frederik , Sanden Mathilde , Nielsen Morten H. , Handberg Aase
Published: February 2021

High-resolution flow cytometers (hFCM) are used for the detection of extracellular vesicles (EV) in various biological fluids. Due to the increased sensitivity of hFCM, new artifacts with the potential of interfering with data interpretation are introduced, such as detection of antibody aggregates. The aim of this study was to investigate the extent of aggregates in labels commonly used for the characterization of EVs by hFCM. Furthermore, we aimed to compare the efficacy of centrifugation and filtering treatments to remove aggregates, as well as to quantify the effect of the treatments in reducing aggregates. For this purpose, we labeled phosphate buffered saline (PBS) with fluorescently conjugated protein labels and antibodies after submitting them to 5, 10, or 30 min centrifugation, filtering or washed filtering. We investigated samples by hFCM and quantified the amount of aggregates found in PBS labeled with untreated and pre-treated labels. We found a varying amount of aggregates in all labels investigated, and further that filtering is most efficient in removing all but the smallest aggregates. Filtering protein labels can reduce the extent of aggregates; however, how much remains depends on the specific labels and their combination. Therefore, it is still necessary to include appropriate controls in a hFCM study of EVs.
Extracellular Vesicles from Thapsigargin-Treated Mesenchymal Stem Cells Ameliorated Experimental Colitis via Enhanced Immunomodulatory Properties
Authors: Joo Hansol , Oh Mi-Kyung , Kang Ji Yeon , Park Hyun Sung , Chae Dong-Hoon , Kim Jieun , Lee Jong-Hee , Yoo Hee Min , Choi Uimook , Kim Do-Kyun , Lee Hakmo , Kim Sungjoo , Yu Kyung-Rok
Published: February 2021

Therapeutic applications of extracellular vesicles (EVs) derived from mesenchymal stem cells (MSCs) have attracted considerable attention because of their immunomodulatory properties against immune-mediated, inflammatory diseases. Here, we demonstrated enhanced immunomodulatory properties of EVs secreted from endoplasmic reticulum (ER) stress inducer thapsigargin (TSG)-primed human Wharton’s jelly-derived MSCs (WJ-MSCs). EVs from TSG-primed WJ-MSCs (TSG-EV) showed increased yield and expression of immunomodulatory factors, such as transforming growth factor-β1 (TGFβ), cyclooxygenase-2 (COX2), and especially indoleamine 2,3-dioxygenase (IDO), compared to control EVs. TSG-EV showed a significantly enhanced immunosuppressive effect on human peripheral blood-derived T cell proliferation and Th1 and Th17 differentiation, whereas Treg and M2-type macrophage were enriched compared to a control EV-treated group. Furthermore, TSG-EV substantially mitigated mouse experimental colitis by reducing the inflammatory response and maintaining intestinal barrier integrity. A significant increase of Tregs and M2-type macrophages in colitic colons of a TSG-EV-treated mouse suggests an anti-inflammatory effect of TSG-EV in colitis model, possibly mediated by Treg and macrophage polarization. These data indicate that TSG treatment promoted immunomodulatory properties of EVs from WJ-MSCs, and TSG-EV may provide a new therapeutic approach for treatment of colitis.
Oocyte Biobanks: Old Assumptions and New Challenges
Authors: Tozzo Pamela
Published: February 2021

The preservation of fertility is a clinical issue that has been emerging considerably in recent decades, as the number of patients of childbearing age who risk becoming infertile for many reasons is increasing. The cryopreservation technique of oocytes has been developed for many years and nowadays constitutes a method of safe storage with impressive efficacy and high rates of successful thawing. The storage and use for research of oocytes taken for medical or non-medical can be carried out by both public and private structures, through egg sharing, voluntary egg donation and so-called “social freezing” for autologous use. This paper focuses on the oocyte bank as an emerging cryopreservation facility, in which a collaboration between public and private and the creation of a network of these biobanks can be useful in enhancing both their implementation and their functions. Good oocyte biobank practice would require that they be collected, stored, and used according to appropriate bioethical and bio-law criteria, collected and stored according to procedures that guarantee the best preservation of their structural components and a high level of safety, connected with appropriate procedures to protect the rights and privacy of the parties involved and associated with the results of the bio-molecular investigations that will be carried out gradually.
Pb(II) Determination in a Single Drop Using a Modified Screen-Printed Electrode
Authors: Žurga Nika , Majer David , Finšgar Matjaž
Published: February 2021

This work presents the development and validation of an electroanalytical method for Pb(II) determination in a single drop. The electrochemical sensors used were an unmodified screen-printed electrode (SPE) and a Bi-film SPE (BiFSPE). Anodic square wave stripping voltammetry (SWASV) was performed at an accumulation potential of −1.5 V vs. Ag/AgCl and an accumulation time of 60 s. Electroanalysis with an unmodified SPE did not yield satisfactory results, whereas the BiFSPE was a much better analysis method. The linear concentration using the BiFSPE was in the range of 138.8–162.5 µg/L. The accuracy and precision were evaluated for different spiked concentrations, but the method using the unmodified SPE was neither accurate nor precise. Using the BiFSPE, the method was found to be both accurate and precise for Pb(II) determination at a concentration of 140.0 μg/L, with recovery and relative standard deviation (RSD) of 106.6% and 12.1%, respectively. In addition, using the BiFSPE, LOD and LOQ values of 1.2 μg/L and 3.3 μg/L were obtained, respectively. The possible interference effect on Pb(II) stripping signal was checked in the presence of Cd(II), Zn(II), Cu(II), Sn(IV), Sb(III), Hg(II), Fe(III), As(V), K(I), I−, Ca(II), and NO3−. Electrochemical impedance spectroscopy measurements were also performed for the unmodified SPE and BiFSPE. The application of single drop Pb(II) analysis was tested by real water sample analysis.
A Cost Analysis of an Outreach School-Based Dental Program: Teeth on Wheels
Authors: Nguyen Tan Minh , Tonmukayakul Utsana , Calache Hanny
Published: February 2021

Background: This study evaluated an outreach mobile dental service called Teeth on Wheels (TOW). The dental program targeted Australian children from low household income, who are eligible for the Child Dental Benefits Scheme (CDBS) in Victoria, Australia. The program is complemented with a school-based oral health promotion element. Methods: A retrospective cohort study was performed with a convenience sample. Children must have had at least three dental examinations during the 2016–2019 calendar years to be included in the study. Comparisons were made between the 2016–17 and 2018–19 calendar years. It was hypothesised that the program would result in reduced costs and the number of restorations and extractions in the latter period. Results: A total of 414 children were included in the analysis. The total mean costs of the program per child reduced from AU$605.3 in 2016–17 to AU$531.1 in 2018–19. The results showed an overall mean reduction in all restorations and extractions performed, but only statistical significance was noted for reductions of restored deciduous teeth. Conclusions: This outreach program, which is focused on prevention and minimally invasive dentistry, can be a promising alternative model of delivery for dental services in young children.
Bio-Hydrogen Production from Wastewater: A Comparative Study of Low Energy Intensive Production Processes
Authors: Islam A K M Khabirul , Dunlop Patrick S. M. , Hewitt Neil J. , Lenihan Rose , Brandoni Caterina
Published: February 2021

Billions of litres of wastewater are produced daily from domestic and industrial areas, and whilst wastewater is often perceived as a problem, it has the potential to be viewed as a rich source for resources and energy. Wastewater contains between four and five times more energy than is required to treat it, and is a potential source of bio-hydrogen—a clean energy vector, a feedstock chemical and a fuel, widely recognised to have a role in the decarbonisation of the future energy system. This paper investigates sustainable, low-energy intensive routes for hydrogen production from wastewater, critically analysing five technologies, namely photo-fermentation, dark fermentation, photocatalysis, microbial photo electrochemical processes and microbial electrolysis cells (MECs). The paper compares key parameters influencing H2 production yield, such as pH, temperature and reactor design, summarises the state of the art in each area, and highlights the scale-up technical challenges. In addition to H2 production, these processes can be used for partial wastewater remediation, providing at least 45% reduction in chemical oxygen demand (COD), and are suitable for integration into existing wastewater treatment plants. Key advancements in lab-based research are included, highlighting the potential for each technology to contribute to the development of clean energy. Whilst there have been efforts to scale dark fermentation, electro and photo chemical technologies are still at the early stages of development (Technology Readiness Levels below 4); therefore, pilot plants and demonstrators sited at wastewater treatment facilities are needed to assess commercial viability. As such, a multidisciplinary approach is needed to overcome the current barriers to implementation, integrating expertise in engineering, chemistry and microbiology with the commercial experience of both water and energy sectors. The review concludes by highlighting MECs as a promising technology, due to excellent system modularity, good hydrogen yield (3.6–7.9 L/L/d from synthetic wastewater) and the potential to remove up to 80% COD from influent streams.
Diabetes Awareness, Treatment, and Control among Mexico City Residents
Authors: Barquera Simón , Hernández-Alcaraz César , Jáuregui Alejandra , Medina Catalina , Mendoza-Herrera Kenny , Pedroza-Tobias Andrea , Tolentino Mayo Lizbeth , Guillen Pineda Luz Elizabeth , López-Ridaura Ruy , Aguilar Salinas Carlos A.
Published: February 2021

Early diagnosis and control of diabetes can reduce premature mortality and disability. We described the diabetes prevalence, awareness, treatment, and control in Mexico City. Data came from the Mexico City Representative Diabetes Survey, conducted between May to June 2015. Participants (20–69 y, n = 1307) reported their diabetes condition, treatment, and outcomes. Fasting blood samples were collected and HbA1c, blood glucose, and blood lipids were determined. We used multivariate logistic regression to identify inequalities in diabetes prevalence and awareness. The overall prevalence of diabetes was 13.6% (95% CI: 11.7, 15.7). Of those living with diabetes, 70.5% were aware of their condition. Among those aware of their diabetes, around 10% to 65% received diabetes care according to international guidelines, and around 30% to 40% achieved HbA1c, LDL cholesterol, or blood pressure targets. Overall, only 4.1% of those aware of their diabetes achieved all treatment targets and 35.4% had never presented a diabetes complication. Diabetes prevalence was higher among older age groups and lower among the most educated. Having access to health care was associated with lower odds for diabetes and diabetes awareness. The low rates of diabetes awareness, treatment, and control highlight the urgent need of strengthening diabetes care in Mexico City.
Ensemble-Based Classification Using Neural Networks and Machine Learning Models for Windows PE Malware Detection
Authors: Damaševičius Robertas , Venčkauskas Algimantas , Toldinas Jevgenijus , Grigaliūnas Šarūnas
Published: February 2021

The security of information is among the greatest challenges facing organizations and institutions. Cybercrime has risen in frequency and magnitude in recent years, with new ways to steal, change and destroy information or disable information systems appearing every day. Among the types of penetration into the information systems where confidential information is processed is malware. An attacker injects malware into a computer system, after which he has full or partial access to critical information in the information system. This paper proposes an ensemble classification-based methodology for malware detection. The first-stage classification is performed by a stacked ensemble of dense (fully connected) and convolutional neural networks (CNN), while the final stage classification is performed by a meta-learner. For a meta-learner, we explore and compare 14 classifiers. For a baseline comparison, 13 machine learning methods are used: K-Nearest Neighbors, Linear Support Vector Machine (SVM), Radial basis function (RBF) SVM, Random Forest, AdaBoost, Decision Tree, ExtraTrees, Linear Discriminant Analysis, Logistic, Neural Net, Passive Classifier, Ridge Classifier and Stochastic Gradient Descent classifier. We present the results of experiments performed on the Classification of Malware with PE headers (ClaMP) dataset. The best performance is achieved by an ensemble of five dense and CNN neural networks, and the ExtraTrees classifier as a meta-learner.
Accuracy Improvement of Stator Inductance Identification Method Based on Low-Frequency Current Injection for Three-Level NPC Inverter-Fed IM Drives in Locked-Rotor Standstill Condition
Authors: Khojakhan Yerganat , Choo Kyoung-Min , Yi Junsin , Won Chung-Yuen
Published: February 2021

In this paper, a stator inductance identification process is proposed. The process is based on a three-level neutral-point-clamped (NPC) inverter-fed induction motor (IM) drive with a standstill condition. Previously, a low-speed alternating current (AC) injection test for stator inductance identification was proposed to overcome practical problems in conventional identification methods for three-level NPC inverter-based IM drives. However, the low-speed AC injection test-based identification method has some problems if a heavy load or mechanical brake is connected, as these can forcibly bring the rotor to a standstill during parameter identification. Since this low-speed testing-based identification assumes the motor torque is considerably lower in low-speed operations, some inaccuracy is inevitable in this kind of standstill condition. In this paper, the proposed current injection speed generator is based on the previously studied low-speed test-based stator inductance identification method, but the proposed approach gives more accurate estimates under the aforementioned standstill conditions. The proposed method regulates the speed for sinusoidal low-frequency AC injection on the basis of the instantaneous reactive and air-gap active power ratio. This proposed stator inductance identification method is more accurate than conventional fixed low-frequency AC signal injection identification method for three-level NPC inverter-fed IM drive systems with a locked-rotor standstill condition. The proposed method’s accuracy and reliability were verified by simulation and experiment using an 18.5 kW induction motor.
An Improved Variational Mode Decomposition and Its Application on Fault Feature Extraction of Rolling Element Bearing
Authors: An Guoping , Tong Qingbin , Zhang Yanan , Liu Ruifang , Li Weili , Cao Junci , Lin Yuyi
Published: February 2021

The fault diagnosis of rolling element bearing is of great significance to avoid serious accidents and huge economic losses. However, the characteristics of the nonlinear, non-stationary vibration signals make the fault feature extraction of signal become a challenging work. This paper proposes an improved variational mode decomposition (IVMD) algorithm for the fault feature extraction of rolling bearing, which has the advantages of extracting the optimal fault feature from the decomposed mode and overcoming the noise interference. The Shuffled Frog Leap Algorithm (SFLA) is employed in the optimal adaptive selection of mode number K and bandwidth control parameter α. A multi-objective evaluation function, which is based on the envelope entropy, kurtosis and correlation coefficients, is constructed to select the optimal mode component. The efficiency coefficient method (ECM) is utilized to transform the multi-objective optimization problem into a single-objective optimization problem. The envelope spectrum technique is used to analyze the signals reconstructed by the optimal mode components. The proposed IVMD method is evaluated by simulation and practical bearing vibration signals under different conditions. The results show that the proposed method can improve the decomposition accuracy of the signal and the adaptability of the influence parameters and realize the effective extraction of the bearing vibration signal.
Virtual Energy Storage in RES-Powered Smart Grids with Nonlinear Model Predictive Control
Authors: Trigkas Dimitrios , Ziogou Chrysovalantou , Voutetakis Spyros , Papadopoulou Simira
Published: February 2021

The integration of a variety of heterogeneous energy sources and different energy storage systems has led to complex infrastructures and made apparent the urgent need for efficient energy control and management. This work presents a non-linear model predictive controller (NMPC) that aims to coordinate the operation of interconnected multi-node microgrids with energy storage capabilities. This control strategy creates a superstructure of a smart-grid consisting of distributed interconnected microgrids, and has the ability to distribute energy among a pool of energy storage means in an optimal way, formulating a virtual central energy storage platform. The goal of this work is the optimal exploitation of energy produced and stored in multi-node microgrids, and the reduction of auxiliary energy sources. A small-scale multi-node microgrid was used as a basis for the mathematical modelling and real data were used for the model validation. A number of operation scenarios under different weather conditions and load requests, demonstrates the ability of the NMPC to supervise the multi-node microgrid resulting to optimal energy management and reduction of the auxiliary power devices operation.
Optimal Pricing of Vehicle-to-Grid Services Using Disaggregate Demand Models
Authors: Latinopoulos Charilaos , Sivakumar Aruna , Polak John W.
Published: February 2021

The recent revolution in electric mobility is both crucial and promising in the coordinated effort to reduce global emissions and tackle climate change. However, mass electrification brings up new technical problems that need to be solved. The increasing penetration rates of electric vehicles will add an unprecedented energy load to existing power grids. The stability and the quality of power systems, especially on a local distribution level, will be compromised by multiple vehicles that are simultaneously connected to the grid. In this paper, the authors propose a choice-based pricing algorithm to indirectly control the charging and V2G activities of electric vehicles in non-residential facilities. Two metaheuristic approaches were applied to solve the optimization problem, and a comparative analysis was performed to evaluate their performance. The proposed algorithm would result in a significant revenue increase for the parking operator, and at the same time, it could alleviate the overloading of local distribution transformers and postpone heavy infrastructure investments.
Trendline and Monthly Variations of Nitrate in Water Supply Wells in Upper Egypt
Authors: Shamrukh Mohamed , Akib Shatirah
Published: February 2021

Safe drinking water is among the most important environmental as well as health challenges facing Egypt. Groundwater from the aquifer of the Nile valley in Upper Egypt accounts for about 40% of the overall source of drinking water. Nearly, all rural residents rely on groundwater as the source of drinking water. Irrigation water is the main source of groundwater recharge in Nile Valley. Moreover, nitrate fertilizer utilization in Egypt has risen significantly due to the requirement for further agricultural production. Nitrate levels higher than the permissible limit (45 mg/L) for drinking water have been related to health issues. To determine the effect of agrochemical nitrate on the health of pumping water, municipal water wellfields established in Upper Egypt have been examined. Pumped water from those municipal wells (60 m depth and 2000 m3/day gross pumping rate) was evaluated from 2000 to 2018 to accomplish this objective. Monthly analysis of the quality of groundwater, in particular nitrate, has been conducted. Sulfate and nitrate concentrations as well as other consistency metrics were assessed. Water sampling findings show that the water already comes under the drinking-water limits. Nevertheless, increased nitrate and sulfate values are found beyond naturally existing levels in the aquifer. Consequently, to determine the possible future likelihood of nitrate contamination in such municipal wells, the trendline of nitrate level has been used. The observations suggest that the intensive utilization of nitrogen fertilizer in Upper Egypt in the last 30 years would endanger the quality of the groundwater supply. Resultantly, the usage of nitrogen fertilizers in Upper Egypt must be regulated and continuous groundwater monitoring must be implemented.
Microwave-Assisted Saponification Method Followed by Solid-Phase Extraction for the Characterization of Sterols and Dialkyl Ketones in Fats
Authors: Mascrez Steven , Danthine Sabine , Purcaro Giorgia
Published: February 2021

Unlike other fields, the methods routinely applied for fats and oils are still tied to traditional, time- and solvent-consuming procedures, such as saponification, column chromatography and thin-layer chromatography. In this paper, microwave-assisted saponification followed by a lab-made solid-phase extraction was optimized for the characterization of either dialkyl ketones (DAK) or sterols or both simultaneously. The instrumental determination was performed by gas chromatography- flame ionization detector (GC-FID) for quantification and gas chromatography-mass spectrometry (GC-MS) for confirmation purposes. The proposed method showed good recoveries (>80%) and limit of quantification (0.04–0.07 μg/g for the 4 DAK and of 0.07 μg/g for α-cholestanol). Repeatabilities (n = 3) were below 15% for DAKs and generally lower than 6% for sterols. Accuracy on the entire sterol profile was confirmed in comparison to the International Olive Council reference method. The method was finally applied to real-world samples before and after chemical interesterification.
Non-Destructive Spectroscopic and Imaging Techniques for the Detection of Processed Meat Fraud
Authors: Edwards Kiah , Manley Marena , Hoffman Louwrens C. , Williams Paul J.
Published: February 2021

In recent years, meat authenticity awareness has increased and, in the fight to combat meat fraud, various analytical methods have been proposed and subsequently evaluated. Although these methods have shown the potential to detect low levels of adulteration with high reliability, they are destructive, time-consuming, labour-intensive, and expensive. Therefore, rendering them inappropriate for rapid analysis and early detection, particularly under the fast-paced production and processing environment of the meat industry. However, modern analytical methods could improve this process as the food industry moves towards methods that are non-destructive, non-invasive, simple, and on-line. This review investigates the feasibility of different non-destructive techniques used for processed meat authentication which could provide the meat industry with reliable and accurate real-time monitoring, in the near future.
Fourier Transform Infrared Spectroscopy as a Tool to Study Milk Composition Changes in Dairy Cows Attributed to Housing Modifications to Improve Animal Welfare
Authors: Bahadi Mazen , Ismail Ashraf A. , Vasseur Elsa
Published: February 2021

Animal welfare status is assessed today through visual evaluations requiring an on-farm visit. A convenient alternative would be to detect cow welfare status directly in milk samples, already routinely collected for milk recording. The objective of this study was to propose a novel approach to demonstrate that Fourier transform infrared (FTIR) spectroscopy can detect changes in milk composition related to cows subjected to movement restriction at the tie stall with four tie-rail configurations varying in height and position (TR1, TR2, TR3 and TR4). Milk mid-infrared spectra were collected on weekly basis. Long-term average spectra were calculated for each cow using spectra collected in weeks 8–10 of treatment. Principal component analysis was applied to spectral averages and the scores of principal components (PCs) were tested for treatment effect by mixed modelling. PC7 revealed a significant treatment effect (p = 0.01), particularly for TR3 (configuration with restricted movement) vs. TR1 (recommended configuration) (p = 0.03). The loading spectrum of PC7 revealed high loadings at wavenumbers that could be assigned to biomarkers related to negative energy balance, such as β-hydroxybutyrate, citrate and acetone. This observation suggests that TR3 might have been restrictive for cows to access feed. Milk FTIR spectroscopy showed promising results in detecting welfare status and housing conditions in dairy cows.
Influence of Different Vinification Techniques on Volatile Compounds and the Aromatic Profile of Palomino Fino Wines
Authors: Roldán Ana M. , Sánchez-García Fini , Pérez-Rodríguez Luis , Palacios Víctor M.
Published: February 2021

The aim of this study was to evaluate the influence of vinification techniques on volatile compounds and sensory profiles in young Palomino fino white wines. Four winemaking techniques (pellicular maceration, supra-extraction and use of commercial yeast strains and of β-glycosidase enzymes) were implemented to enhance the aromatic quality of wines elaborated from this neutral variety of grape. Volatile compound content, aromatic profile (OAVs) and sensorial analysis were determined. The results showed that all the vinification techniques studied led to an increase in volatile compounds compared to the control wine. Likewise, an influence of the vineyard and must extraction method on these compounds was observed. However, the greatest changes in aroma activity and sensory profile were a result of the pellicular maceration and supra-extraction techniques. The latter was differentiated by the highest content of terpenes and, consequently, the highest odour activity values of floral series. In addition, the supra-extraction was a very selective technique since it extracted terpenes and aromatic precursors, but not the acids responsible for the fatty characteristic, such as octanoic acid. In terms of sensory profile, the supra-extraction technique improved the intensity of the Palomino fino white wine and its aromatic quality with a previously not-determined floral character.
Seasonal and Ephemeral Snowpacks of the Conterminous United States
Authors: Hatchett Benjamin J.
Published: February 2021

Snowpack seasonality in the conterminous United States (U.S.) is examined using a recently-released daily, 4 km spatial resolution gridded snow water equivalent and snow depth product developed by assimilating station-based observations and gridded temperature and precipitation estimates from PRISM. Seasonal snowpacks for the period spanning water years 1982–2017 were calculated using two established methods: (1) the classic Sturm approach that requires 60 days of snow cover with a peak depth >50 cm and (2) the snow seasonality metric (SSM) that only requires 60 days of continuous snow cover to define seasonal snow. The latter approach yields continuous values from −1 to +1, where −1 (+1) indicates an ephemeral (seasonal) snowpack. The SSM approach is novel in its ability to identify both seasonal and ephemeral snowpacks. Both approaches identify seasonal snowpacks in western U.S. mountains and the northern central and eastern U.S. The SSM approach identifies greater areas of seasonal snowpacks compared to the Sturm method, particularly in the Upper Midwest, New England, and the Intermountain West. This is a result of the relaxed depth constraint compared to the Sturm approach. Ephemeral snowpacks exist throughout lower elevation regions of the western U.S. and across a broad longitudinal swath centered near 35° N spanning the lee of the Rocky Mountains to the Atlantic coast. Because it lacks a depth constraint, the SSM approach may inform the location of shallow but long-duration snowpacks at risk of transitioning to ephemeral snowpacks with climatic change. A case study in Oregon during an extreme snow drought year (2014/2015) highlights seasonal to ephemeral snowpack transitions. Aggregating seasonal and ephemeral snowpacks to the HUC-8 watershed level in the western U.S. demonstrates the majority of watersheds are at risk of losing seasonal snow.
Another Weapon against Cancer and Metastasis: Physical-Activity-Dependent Effects on Adiposity and Adipokines
Authors: Perego Silvia , Sansoni Veronica , Ziemann Ewa , Lombardi Giovanni
Published: February 2021

Physically active behavior has been associated with a reduced risk of developing certain types of cancer and improved psychological conditions for patients by reducing anxiety and depression, in turn improving the quality of life of cancer patients. On the other hand, the correlations between inactivity, sedentary behavior, and overweight and obesity with the risk of development and progression of various cancers are well studied, mainly in middle-aged and elderly subjects. In this article, we have revised the evidence on the effects of physical activity on the expression and release of the adipose-tissue-derived mediators of low-grade chronic inflammation, i.e., adipokines, as well as the adipokine-mediated impacts of physical activity on tumor development, growth, and metastasis. Importantly, exercise training may be effective in mitigating the side effects related to anti-cancer treatment, thereby underlining the importance of encouraging cancer patients to engage in moderate-intensity activities. However, the strong need to customize and adapt exercises to a patient’s abilities is apparent. Besides the preventive effects of physically active behavior against the adipokine-stimulated cancer risk, it remains poorly understood how physical activity, through its actions as an adipokine, can actually influence the onset and development of metastases.