bluuebunny/crossref_metadata_2025 huggingface dataset:
INVESTIGATION OF REGULARITIES OF PLASTIC DEFORMATION OF HOLLOW STEEL PROFILES BY COMPRESSION
Authors: Gerasimova Alla Alexandrovna
Published: November 2019

The article studies the kinematics of elastic-plastic deformation of hollow steel profiles. The aim of the work was to improve the power characteristics of the processes of plastic deformation of energy-absorbing structural elements of the hollow profile of vehicles and to increase the passive safety and reliability of equipment. Finite element modeling of kinematics of processes with control of parameters of energy absorption of steel pipes of various cross sections was carried out. The results of virtual studies of bending and precipitation at the end of a number of hollow profiles in the form of pipes of different sections have shown ample opportunities to control the process of mechanical energy absorption by selecting the design of pipes, control the results of modeling the absorbed energy and the maximum effort on the indicator diagram of the deformation process.
H Tipi Plan Düzensizliği olan Betonarme Yapılarda Perde Yeri Seçiminin Yapısal Davranışa Etkisinin İrdelenmesi
Authors: ŞENTÜRK Eyüp , MERT Necati , KASAP Hüseyin
Published: November 2019

Due to various architectural and structural requirements, irregularities in the structures are inevitable. In order to determine the behavior of the structure under the effect of the earthquake and to find the relevant cross-sectional effects, the regular or irregularity of the structure of the structure of the structure is significantly effective. Irregularities present in the design cause further strain of the carrier elements. With the increase in the number of floors in earthquake risk regions, curtains are needed in order to ensure sufficient rigidity and strength in the frame systems and to limit the floor displacements. When the shear walls are used together with the frame system in a conveyor system, their stiffness is larger than the columns and they meet a significant part of their horizontal loads such as earthquake and wind. In this study, the effect of shear walls placement on structural behavior in reinforced concrete buildings with H-type irregularity was investigated. For this purpose, 7 models, each of which are 4,8,12 stories and one of which are reference models, have been examined on the irregularity and behavior of the structure in different shear walls settlements. The results obtained by using the İDECAD V7 program and DBYBHY-2007 principles ware analyzed by using 2018 TBDY for 4 storey models from the results found using Sap2000 program and the results were compared. The buildings were evaluated among themselves according to the reference model withhout plan disorder.
Numerical Investigation of Behavior of RC Columns Strengtheneg with RC Jacket
Authors: Navdar M. Burhan , Caglar Naci
Published: November 2019

Strengthening of structural elements which are insufficient in terms of earthquake performance is very important for the safety of existing structures. Nowadays, one of the most commonly used methods is strengthening such members with RC jacketing. Several experimental studies have been conducted in the literature to investigate behavior of structural elements reinforced with RC jacketing. Moreover, numerical studies are frequently preferred due to the high cost of experimental studies and the limited availability of laboratory facilities. In this study, a finite element model has been created with OpenSees program in order to investigate the behavior of strengthened RC columns with jacketing. Later, the accuracy of the model has been verified successful by the experimental results selected from the literature. By using that verified model, the effects of concrete strength and amount of spacing of transverse reinforcement used on the jacketing part on reinforced column behavior were investigated. As a result of the study, it is deduced that the change in the concrete strength and amount of transverse reinforcement results in a significant change in ductility, load capacity and rigidity of the reinforced concrete columns.
One-Step Preparation of Nickel Nanoparticle-Based Magnetic Poly(Vinyl Alcohol) Gels
Authors: Li Jun , Lee Kwang-Pill , Gopalan Anantha Iyengar
Published: November 2019

Magnetic nanoparticles (MNPs) are of great interest due to their unique properties, especially in biomedical applications. MNPs can be incorporated into other matrixes to prepare new functional nanomaterials. In this work, we described a facile, one-step strategy for the synthesis of magnetic poly(vinyl alcohol) (mPVA) gels. In the synthesis, nickel nanoparticles and cross-linked mPVA gels were simultaneously formed. Ni nanoparticles (NPs) were also incorporated into a stimuli-responsive polymer to result in multiresponsive gels. The size of and distribution of the Ni particles within the mPVA gels were controlled by experimental conditions. The mPVA gels were characterized by field emission scanning electron microscope, X-ray diffraction, magnetic measurements, and thermogravimetric analysis. The new mPVA gels are expected to have applications in drug delivery and biotechnology.
Dynamical Sampling with Langevin Normalization Flows
Authors: Gu Minghao , Sun Shiliang , Liu Yan
Published: November 2019

In Bayesian machine learning, sampling methods provide the asymptotically unbiased estimation for the inference of the complex probability distributions, where Markov chain Monte Carlo (MCMC) is one of the most popular sampling methods. However, MCMC can lead to high autocorrelation of samples or poor performances in some complex distributions. In this paper, we introduce Langevin diffusions to normalization flows to construct a brand-new dynamical sampling method. We propose the modified Kullback-Leibler divergence as the loss function to train the sampler, which ensures that the samples generated from the proposed method can converge to the target distribution. Since the gradient function of the target distribution is used during the process of calculating the modified Kullback-Leibler, which makes the integral of the modified Kullback-Leibler intractable. We utilize the Monte Carlo estimator to approximate this integral. We also discuss the situation when the target distribution is unnormalized. We illustrate the properties and performances of the proposed method on varieties of complex distributions and real datasets. The experiments indicate that the proposed method not only takes the advantage of the flexibility of neural networks but also utilizes the property of rapid convergence to the target distribution of the dynamics system and demonstrate superior performances competing with dynamics based MCMC samplers.
Quantum Games with Unawareness with Duopoly Problems in View
Authors: Frąckiewicz Piotr , Bilski Jakub
Published: November 2019

Playing the Cournot duopoly in the quantum domain can lead to the optimal strategy profile in the case of maximally correlated actions of the players. However, that result can be obtained if the fact that the players play the quantum game is common knowledge among the players. Our purpose is to determine reasonable game outcomes when players’ perceptions about what game is actually played are limited. To this end, we consider a collection consisting of the classical and quantum games that specifies how each player views the game and how each player views the other players’ perceptions of the game. We show that a slight change in how the players perceive the game may considerably affect the result of the game and, in the case of maximally correlated strategies, may vary from the inefficient Nash equilibrium outcome in the classical Cournot duopoly to the Pareto optimal outcome. We complete our work by investigating in the same way the Bertrand duopoly model.
Biorthogonal-Wavelet-Based Method for Numerical Solution of Volterra Integral Equations
Authors: Mohammad Mutaz
Published: November 2019

Framelets theory has been well studied in many applications in image processing, data recovery and computational analysis due to the key properties of framelets such as sparse representation and accuracy in coefficients recovery in the area of numerical and computational theory. This work is devoted to shedding some light on the benefits of using such framelets in the area of numerical computations of integral equations. We introduce a new numerical method for solving Volterra integral equations. It is based on pseudo-spline quasi-affine tight framelet systems generated via the oblique extension principles. The resulting system is converted into matrix equations via these generators. We present examples of the generated pseudo-splines quasi-affine tight framelet systems. Some numerical results to validate the proposed method are presented to illustrate the efficiency and accuracy of the method.
Evaluation of Energy Efficiency Potential for the Building Sector in the Arab Region
Authors: Krarti Moncef
Published: November 2019

The paper overviews the current energy demand trends in the building sector for the Arab region using reported historical energy consumption. Moreover, the paper describes the current energy efficiency policies and regulations for all the Arab countries specific to both residential and commercial buildings. Finally, the paper evaluates potential benefits for large-scale energy efficiency programs specific to new and existing building stocks within the Arab region using a bottom-up analysis approach. The analysis of the available energy consumption for all the Arab countries has shown that the Arab region presents a significant variation in energy consumption levels between its sub-regions and countries. Indeed, the Arab region includes oil-producing countries such as Saudi Arabia with the largest energy use per capita in the World with over 9000 kWh/person of electricity used annually in buildings. However, the same region has the least developed countries such as Sudan and Yemen with the lowest energy use per capita in the World with barely 100 kWh/person/year of electrical consumption. The review of the existing regulations has indicated that several Arab countries have not implemented any energy efficiency codes and standards for building envelope, lighting, heating and cooling equipment, and appliances. A cost-effectiveness analysis has indicated that the Arab region can incur significant benefits in upgrading the energy efficiency of its new and existing buildings especially its households. Specifically, the adoption and the enforcement of stringent energy efficiency codes for new residential and commercial buildings can result in a reduction of 12.7 TWh/year in final annual energy consumption for the Arab region. Moreover, retrofit programs targeting existing buildings can save up to 470 TWh or a third of the building sector final energy consumption per year after 2030. Combining comprehensive energy efficiency requirements for new buildings and extensive retrofit programs for existing buildings would reduce the total final energy consumption of the building sector in the Arab region by 600 TWh by 2030 and by 900 TWh by 2050 if all the energy programs start to be implemented by 2020.
Conceptual Design of IoT-Based AMR Systems Based on IEC 61850 Microgrid Communication Configuration Using Open-Source Hardware/Software IED
Authors: Mlakić Dragan , Baghaee Hamid , Nikolovski Srete , Vukobratović Marko , Balkić Zoran
Published: November 2019

This paper presents an intelligent electronic device (IED) utilized for automatic meter readings (AMR) scheme using “Open-Source” software. This IED is utilized to measure a low-voltage intelligent electronic device) system with a boundless number of sensors, and it is accessible on the Internet of Things (IoT). The utilized equipment for this task is Arduino UNO R3 motherboard and fringe sensors, which are used for measurement of the referenced information. The Arduino motherboard is used not only for sole tranquility of equipment but also for serving as wireless fidelity (Wi-Fi) switch for the sensors. The personal computer is utilized to gather information and perform client-side calculations. The server works based on an open-source program written in Java programming language. The underlying objective of the proposed scheme is to make the meter based on the “Do It Yourself” methodology which requires considerably fewer funds. Also, it is conceivable by keeping easy to understand interface, information legitimacy, precision of measured information and convenience for the conclusive client. The information is measured in just about 1 ms which is superb for custom-designed IED. Furthermore, the measured qualities are calculated based on their RMS values to be used for analyzing and further presentation of data.
Attenuation of Wave Energy Due to Mangrove Vegetation off Mumbai, India
Authors: S. V. Samiksha , Vethamony P. , Bhaskaran Prasad K. , Pednekar P. , Jishad M. , James R. Arthur
Published: November 2019

Coastal regions of India are prone to sea level rise, cyclones, storm surges, and human-induced activities, resulting in flood, erosion, and inundation, and some of these impacts could be attributed to climate change. Mangroves play a very protective role against some of these coastal hazards. The primary aim of the study was to estimate wave energy attenuation by mangrove vegetation using modeling, and to validate the model results with measurements conducted off Mumbai coast, where a mangrove forest is present. Wave measurements were carried out from 5–8 August 2015 at three locations in a transect normal to the coast using surface-mounted pressure-level sensors in spring tide conditions. The measured data presented wave height attenuation of the order of 52%. Model set-up and sensitivity analyses were conducted to understand the model performance with respect to vegetation parameters. It was observed that wave attenuation increases with an increase in drag coefficient, vegetation density, and stem diameter. For a typical set-up in the Mumbai coastal region having a vegetation density of 0.175 per m2, stem diameter of 0.3 m, and drag coefficient varying from 0.4 to 1.5, the model reproduced attenuation ranging from 49% to 55%, which matches reasonably well with the measured data. Spectral analysis performed for the cases with and without vegetation very clearly portrays energy dissipation in the vegetation area. This study also highlights the importance of climate change and mangrove vegetation.
Data Mining Applications in Understanding Electricity Consumers’ Behavior: A Case Study of Tulkarm District, Palestine
Authors: AbuBaker Maher
Published: November 2019

This paper presents a comprehensive data analysis and visualization of electricity consumers’ prepaid bills of Tulkarm district. We analyzed 250,000 electricity consumers’ prepaid bills covering the time period from June to December 2018. The application of data mining techniques for understanding electricity consumers’ behavior in electricity consumption and their behavior in charging their electricity meter’s smart cards in terms of quantities charged and charging frequencies in different time periods, areas and tariffs are used. Understanding consumers’ behavior will support planning and decision making at strategic, tactical and operational levels. This analysis is useful for predicting and forecasting future demand with a certain degree of accuracy. Monthly, weekly, daily and hourly time periods are covered in the analysis. Outliers detection using visualization tools such as box plot is applied. K-means unsupervised machine learning clustering algorithm is implemented. The support vector machine classification method is applied. As a result of this study, electricity consumers’ behavior in different areas, tariffs and timing periods is understood and presented by numbers and graphs and new electricity consumer segmentation is proposed.
Forecasting Oil Price Using Web-based Sentiment Analysis
Authors: Zhao Lu-Tao , Zeng Guan-Rong , Wang Wen-Jing , Zhang Zhi-Gang
Published: November 2019

International oil price forecasting is a complex and important issue in the research area of energy economy. In this paper, a new model based on web-based sentiment analysis is proposed. For the oil market, sentiment analysis is used to extract key information from web texts from the four perspectives of: compound, negative, neutral, and positive sentiment. These are constructed as feature and input into oil price forecasting models with oil price itself. Finally, we analyze the effect in various views and get some interesting discoveries. The results show that the root mean squared error can be reduced by about 0.2 and the error variance by 0.2, which means that the accuracy and stability are thereby improved. Furthermore, we find that different types of sentiments can all improve performance but by similar amounts. Last but not least, text with strong intensity can better support oil price forecasting than weaker text, for which the root mean squared error can be reduced by up to 0.5, and the number of the bad cases is reduced by 20%, indicating that text with strong intensity can correct the original oil price forecast. We believe that our research will play a strong supporting role in future research on using web information for oil price forecasting.
Research on Optimal D-PMU Placement Technology to Improve the Observability of Smart Distribution Networks
Authors: Kong Xiangyu , Yuan Xiaoxiao , Wang Yuting , Xu Yong , Yu Li
Published: November 2019

With the continuous development of smart distribution networks, their observable problems have become more serious. Research on the optimal placement of the distribution phasor measurement unit (D-PMU) is an important way to improve the measurability, observability and controllability of a smart distribution network. In this paper, the optimal D-PMU placement methods and implementation technology were studied to determine the optimal D-PMU placement scheme. Considering the bus vulnerability index and the different operating states of the system, the more practical one-time optimal placement methods to ensure complete system observability was proposed. On this basis, the system's measurement redundancy and unobservable depth were considered to realize the multistage optimal D-PMU placement. The corresponding mathematical model and solution flow were given. Then the implementation technology of the methods was studied and the optimal D-PMU placement assistant decision-making software for smart distribution network was developed. Thereby, the structure and requirements of different distribution networks can be satisfied. The application analysis, functional architecture and the overall design process were given. Finally, the methods and software were analyzed by using the IEEE 33 bus system and an actual project, the Guangzhou Nansha Yuan'an Substation. The verification results showed that the method and software mentioned in this paper can provide convenient and quick operation for optimal D-PMU placement, improve the efficiency of smart distribution network planning work, and promote the theoretical application level of smart distribution network planning results.
Sector Coupling in the North Sea Region—A Review on the Energy System Modelling Perspective
Authors: Maruf Md. Nasimul Islam
Published: November 2019

Sector coupling is one of the emerging topics in recent energy and climate change policy discussions. It can play a significant role in creating the pathway of a renewable-based energy system in the European energy sector. The North Sea region is very likely to play a key role in the transition to a sustainable energy system. Although different energy modelling approaches allow a versatile use, they lead to the problem of an unclear understanding of specific aspects of sector coupling, and the relevance of existing tools and techniques to model and analyze such a system. This paper is aimed at providing a comprehensive understanding of sector coupling and its incorporation in energy system models. Following a thorough literature review on sector coupling and energy system modelling, the paper outlines an approach to select an appropriate tool based on the specific rationales of the research. The paper also presents the open energy modelling framework, ‘Oemof’, as an open model tool to address the complex challenges of energy systems. The conclusions from the literature review provide a detailed understanding of the concept of sector coupling and indicate that it can be advantageous from the viewpoints of decarbonization, flexibility, network optimization, and system efficiency. To solve the coupling barriers, diversified techno-socio-economic circumstances should be taken into account through the use of model collaboration. It is also demonstrated how a list of appropriate tools for model collaboration can be picked up methodologically from an available wide range of models. Finally, ‘Oemof’ is hypothesized as a progressive tool to design a sector-coupled and renewable-based energy system in the North Sea region.
e4clim 1.0: The Energy for a Climate Integrated Model: Description and Application to Italy
Authors: Tantet Alexis , Stéfanon Marc , Drobinski Philippe , Badosa Jordi , Concettini Silvia , Cretì Anna , D’Ambrosio Claudia , Thomopulos Dimitri , Tankov Peter
Published: November 2019

We develop an open-source Python software integrating flexibility needs from Variable Renewable Energies (VREs) in the development of regional energy mixes. It provides a flexible and extensible tool to researchers/engineers, and for education/outreach. It aims at evaluating and optimizing energy deployment strategies with higher shares of VRE, assessing the impact of new technologies and of climate variability and conducting sensitivity studies. Specifically, to limit the algorithm’s complexity, we avoid solving a full-mix cost-minimization problem by taking the mean and variance of the renewable production–demand ratio as proxies to balance services. Second, observations of VRE technologies being typically too short or nonexistent, the hourly demand and production are estimated from climate time series and fitted to available observations. We illustrate e4clim’s potential with an optimal recommissioning-study of the 2015 Italian PV-wind mix testing different climate data sources and strategies and assessing the impact of climate variability and the robustness of the results.
Improving Energy Efficiency Fairness of Wireless Networks: A Deep Learning Approach
Authors: Lee Hoon , Jang Han Seung , Jung Bang Chul
Published: November 2019

Achieving energy efficiency (EE) fairness among heterogeneous mobile devices will become a crucial issue in future wireless networks. This paper investigates a deep learning (DL) approach for improving EE fairness performance in interference channels (IFCs) where multiple transmitters simultaneously convey data to their corresponding receivers. To improve the EE fairness, we aim to maximize the minimum EE among multiple transmitter–receiver pairs by optimizing the transmit power levels. Due to fractional and max-min formulation, the problem is shown to be non-convex, and, thus, it is difficult to identify the optimal power control policy. Although the EE fairness maximization problem has been recently addressed by the successive convex approximation framework, it requires intensive computations for iterative optimizations and suffers from the sub-optimality incurred by the non-convexity. To tackle these issues, we propose a deep neural network (DNN) where the procedure of optimal solution calculation, which is unknown in general, is accurately approximated by well-designed DNNs. The target of the DNN is to yield an efficient power control solution for the EE fairness maximization problem by accepting the channel state information as an input feature. An unsupervised training algorithm is presented where the DNN learns an effective mapping from the channel to the EE maximizing power control strategy by itself. Numerical results demonstrate that the proposed DNN-based power control method performs better than a conventional optimization approach with much-reduced execution time. This work opens a new possibility of using DL as an alternative optimization tool for the EE maximizing design of the next-generation wireless networks.
The Role of the N-Methyl-D-Aspartate Receptors in Social Behavior in Rodents
Authors: Zoicas Iulia , Kornhuber Johannes
Published: November 2019

The appropriate display of social behaviors is essential for the well-being, reproductive success and survival of an individual. Deficits in social behavior are associated with impaired N-methyl-D-aspartate (NMDA) receptor-mediated neurotransmission. In this review, we describe recent studies using genetically modified mice and pharmacological approaches which link the impaired functioning of the NMDA receptors, especially of the receptor subunits GluN1, GluN2A and GluN2B, to abnormal social behavior. This abnormal social behavior is expressed as impaired social interaction and communication, deficits in social memory, deficits in sexual and maternal behavior, as well as abnormal or heightened aggression. We also describe the positive effects of pharmacological stimulation of the NMDA receptors on these social deficits. Indeed, pharmacological stimulation of the glycine-binding site either by direct stimulation or by elevating the synaptic glycine levels represents a promising strategy for the normalization of genetically-induced, pharmacologically-induced or innate deficits in social behavior. We emphasize on the importance of future studies investigating the role of subunit-selective NMDA receptor ligands on different types of social behavior to provide a better understanding of the underlying mechanisms, which might support the development of selective tools for the optimized treatment of disorders associated with social deficits.
In Situ Synthesis of MIL-100(Fe) at the Surface of Fe3O4@AC as Highly Efficient Dye Adsorbing Nanocomposite
Authors: Hamedi Asma , Trotta Francesco , Borhani Zarandi Mahmood , Zanetti Marco , Caldera Fabrizio , Anceschi Anastasia , Nateghi Mohammad Reza
Published: November 2019

A new magnetic nanocomposite called MIL-100(Fe) @Fe3O4@AC was synthesized by the hydrothermal method as a stable adsorbent for the removal of Rhodamine B (RhB) dye from aqueous medium. In this work, in order to increase the carbon uptake capacity, magnetic carbon was first synthesized and then the Fe3O4 was used as the iron (III) supplier to synthesize MIL-100(Fe). The size of these nanocomposite is about 30–50 nm. Compared with activated charcoal (AC) and magnetic activated charcoal (Fe3O4@AC) nanoparticles, the surface area of MIL-100(Fe) @Fe3O4@AC were eminently increased while the magnetic property of this adsorbent was decreased. The surface area of AC, Fe3O4@AC, and MIL-100(Fe) @Fe3O4@AC was 121, 351, and 620 m2/g, respectively. The magnetic and thermal property, chemical structure, and morphology of the MIL-100(Fe) @Fe3O4@AC were considered by vibrating sample magnetometer (VSM), thermogravimetric analysis (TGA), zeta potential, X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), Brunner-Emmet-Teller (BET), and transmission electron microscopy (TEM) analyses. The relatively high adsorption capacity was obtained at about 769.23 mg/g compared to other adsorbents to eliminate RhB dye from the aqueous solution within 40 min. Studies of adsorption kinetics and isotherms showed that RhB adsorption conformed the Langmuir isotherm model and the pseudo second-order kinetic model. Thermodynamic amounts depicted that the RhB adsorption was spontaneous and exothermic process. In addition, the obtained nanocomposite exhibited good reusability after several cycles. All experimental results showed that MIL-100(Fe) @Fe3O4@AC could be a prospective sorbent for the treatment of dye wastewater.
APOBEC3-Mediated RNA Editing in Breast Cancer is Associated with Heightened Immune Activity and Improved Survival
Authors: Asaoka Mariko , Ishikawa Takashi , Takabe Kazuaki , Patnaik Santosh K.
Published: November 2019

APOBEC3 enzymes contribute significantly to DNA mutagenesis in cancer. These enzymes are also capable of converting C bases at specific positions of RNAs to U. However, the prevalence and significance of this C-to-U RNA editing in any cancer is currently unknown. We developed a bioinformatics workflow to determine RNA editing levels at known APOBEC3-mediated RNA editing sites using exome and mRNA sequencing data of 1040 breast cancer tumors. Although reliable editing determinations were limited due to sequencing depth, editing was observed in both tumor and adjacent normal tissues. For 440 sites (411 genes), editing was determinable for ≥5 tumors, with editing occurring in 0.6%–100% of tumors (mean 20%, SD 14%) at an average level of 0.6%–20% (mean 7%, SD 4%). Compared to tumors with low RNA editing, editing-high tumors had enriched expression of immune-related gene sets, and higher T cell and M1 macrophage infiltration, B and T cell receptor diversity, and immune cytolytic activity. Concordant with this, patients with increased RNA editing in tumors had better disease- and progression-free survivals (hazard ratio = 1.67–1.75, p < 0.05). Our study identifies that APOBEC3-mediated RNA editing occurs in breast cancer tumors and is positively associated with elevated immune activity and improved survival.
Using PAR4 Inhibition as an Anti-Thrombotic Approach: Why, How, and When?
Authors: Li Simeng , Tarlac Volga , Hamilton Justin R.
Published: November 2019

Protease-activated receptors (PARs) are a family of four GPCRs with a variety of cellular functions, yet the only advanced clinical endeavours to target these receptors for therapeutic gain to date relates to the impairment of platelet function for anti-thrombotic therapy. The only approved PAR antagonist is the PAR1 inhibitor, vorapaxar—the sole anti-platelet drug against a new target approved in the past 20 years. However, there are two PARs on human platelets, PAR1 and PAR4, and more recent efforts have focused on the development of the first PAR4 antagonists, with first-in-class agents recently beginning clinical trial. Here, we review the rationale for this approach, outline the various modes of PAR4 inhibition, and speculate on the specific therapeutic potential of targeting PAR4 for the prevention of thrombotic conditions.
Mechanisms of Neuronal Death in the Cerebral Cortex during Aging and Development of Alzheimer’s Disease-Like Pathology in Rats
Authors: Telegina Darya V. , Suvorov Gleb K. , Kozhevnikova Oyuna S. , Kolosova Nataliya G.
Published: November 2019

Alzheimer’s disease (AD) is the commonest type of late-life dementia and damages the cerebral cortex, a vulnerable brain region implicated in memory, emotion, cognition, and decision-making behavior. AD is characterized by progressive neuronal loss, but the mechanisms of cell death at different stages of the disease remain unknown. Here, by means of OXYS rats as an appropriate model of the most common (sporadic) AD form, we studied the main pathways of cell death during development of AD-like pathology, including the preclinical stage. We found that apoptosis is activated at the pre-symptomatic stage (age 20 days) correlating with the retardation of brain development in the OXYS strain early in life. Progression of the AD-like pathology was accompanied by activation of apoptosis and necroptosis resulting from a decline of autophagy-mediated proteostasis. Our results are consistent with the idea that the nature of changes in the pathways of apoptosis, autophagy, and necrosis depends on the stage of AD.
Novel Descriptors and Digital Signal Processing- Based Method for Protein Sequence Activity Relationship Study
Authors: Fontaine Nicolas , Cadet Xavier , Vetrivel Iyanar
Published: November 2019

The work aiming to unravel the correlation between protein sequence and function in the absence of structural information can be highly rewarding. We present a new way of considering descriptors from the amino acids index database for modeling and predicting the fitness value of a polypeptide chain. This approach includes the following steps: (i) Calculating Q elementary numerical sequences (Ele_SEQ) depending on the encoding of the amino acid residues, (ii) determining an extended numerical sequence (Ext_SEQ) by concatenating the Q elementary numerical sequences, wherein at least one elementary numerical sequence is a protein spectrum obtained by applying fast Fourier transformation (FFT), and (iii) predicting a value of fitness for polypeptide variants (train and/or validation set). These new descriptors were tested on four sets of proteins of different lengths (GLP-2, TNF alpha, cytochrome P450, and epoxide hydrolase) and activities (cAMP activation, binding affinity, thermostability and enantioselectivity). We show that the use of multiple physicochemical descriptors coupled with the implementation of the FFT, taking into account the interactions between residues of amino acids within the protein sequence, could lead to very significant improvement in the quality of models and predictions. The choice of the descriptor or of the combination of descriptors and/or FFT is dependent on the couple protein/fitness. This approach can provide potential users with value added to existing mutant libraries where screening efforts have so far been unsuccessful in finding improved polypeptide mutants for useful applications.
Insights into the Effects of Cancer Associated Mutations at the UPF2 and ATP-Binding Sites of NMD Master Regulator: UPF1
Authors: Kalathiya Umesh , Padariya Monikaben , Pawlicka Kamila , Verma Chandra S. , Houston Douglas , Hupp Ted R. , Alfaro Javier Antonio
Published: November 2019

Nonsense-mediated mRNA decay (NMD) is a quality control mechanism that recognizes post-transcriptionally abnormal transcripts and mediates their degradation. The master regulator of NMD is UPF1, an enzyme with intrinsic ATPase and helicase activities. The cancer genomic sequencing data has identified frequently mutated residues in the CH-domain and ATP-binding site of UPF1. In silico screening of UPF1 stability change as a function over 41 cancer mutations has identified five variants with significant effects: K164R, R253W, T499M, E637K, and E833K. To explore the effects of these mutations on the associated energy landscape of UPF1, molecular dynamics simulations (MDS) were performed. MDS identified stable H-bonds between residues S152, S203, S205, Q230/R703, and UPF2/AMPPNP, and suggest that phosphorylation of Serine residues may control UPF1-UPF2 binding. Moreover, the alleles K164R and R253W in the CH-domain improved UPF1-UPF2 binding. In addition, E637K and E833K alleles exhibited improved UPF1-AMPPNP binding compared to the T499M variant; the lower binding is predicted from hindrance caused by the side-chain of T499M to the docking of the tri-phosphate moiety (AMPPNP) into the substrate site. The dynamics of wild-type/mutant systems highlights the flexible nature of the ATP-binding region in UPF1. These insights can facilitate the development of drug discovery strategies for manipulating NMD signaling in cell systems using chemical tools.
Anti-Obesity Effect of Diphlorethohydroxycarmalol Isolated from Brown Alga Ishige okamurae in High-Fat Diet-Induced Obese Mice
Authors: Ding Yuling , Wang Lei , Im SeungTae , Hwang Ouibo , Kim Hyun-Soo , Kang Min-Cheol , Lee Seung-Hong
Published: November 2019

Diphlorethohydroxycarmalol (DPHC) is one of the most abundant bioactive compounds in Ishige okamurae. The previous study suggested that DPHC possesses strong in vitro anti-obesity activity in 3T3-L1 cells. However, the in vivo anti-obesity effect of DPHC has not been determined. The current study explored the effect of DPHC on high-fat diet (HFD)-induced obesity in C57BL/6J mice. The results indicated that oral administration of DPHC (25 and 50 mg/kg/day for six weeks) significantly and dose-dependently reduced HFD-induced adiposity and body weight gain. DPHC not only decreased the levels of triglyceride, low-density lipoprotein cholesterol, leptin, and aspartate transaminase but also increased the level of high-density lipoprotein cholesterol in the serum of HFD mice. In addition, DPHC significantly reduced hepatic lipid accumulation by reduction of expression levels of the critical enzymes for lipogenesis including SREBP-1c, FABP4, and FAS. Furthermore, DPHC remarkably reduced the adipocyte size, as well as decreased the expression levels of key adipogenic-specific proteins and lipogenic enzymes including PPARγ, C/EBPα, SREBP-1c, FABP4, and FAS, which regulate the lipid metabolism in the epididymal adipose tissue (EAT). Further studies demonstrated that DPHC significantly stimulated the phosphorylation of adenosine monophosphate-activated protein kinase (AMPK) and acetyl-CoA carboxylase (ACC) in both liver and EAT. These results demonstrated that DPHC effectively prevented HFD-induced obesity and suggested that DPHC could be used as a potential therapeutic agent for attenuating obesity and obesity-related diseases.
Visual Evoked Potentials Used to Evaluate a Commercially Available Superabsorbent Polymer as a Cheap and Efficient Material for Preparation-Free Electrodes for Recording Electrical Potentials of the Human Visual Cortex
Authors: Straßer Torsten , Kramer Susanne , Kempf Melanie , Peters Tobias , Kurtenbach Anne , Zrenner Eberhart
Published: November 2019

The aim of this study was to investigate the use of inexpensive and easy-to-use hydrogel “marble” electrodes for the recording of electrical potentials of the human visual cortex using visual evoked potentials (VEPs) as example. Top hat-shaped holders for the marble electrodes were developed with an electrode cap to acquire the signals. In 12 healthy volunteers, we compared the VEPs obtained with conventional gold-cup electrodes to those obtained with marble electrodes. Checkerboards of two check sizes—0.8° and 0.25°—were presented. Despite the higher impedance of the marble electrodes, the line noise could be completely removed by averaging 64 single traces, and VEPs could be recorded. Linear mixed-effect models using electrode type, stimulus, and recording duration revealed a statistically significant effect of the electrode type on only VEP N75 peak latency (mean ± SEM: 1.0 ± 1.2 ms) and amplitude (mean ± SEM: 0.8 ± 0.9 µV) The mean amplitudes of the delta, theta, alpha, beta, and gamma frequency bands of marble electrodes were statistically significantly different and, on average, 25% higher than those of gold-cup electrodes. However, the mean amplitudes showed a statistically significant strong correlation (Pearson’s r = 0.8). We therefore demonstrate the potential of the inexpensive and efficient hydrogel electrode to replace conventional gold-cup electrodes for the recording of VEPs and possibly other recordings from the human cortex.
A Semi-Automated Object-Based Gully Networks Detection Using Different Machine Learning Models: A Case Study of Bowen Catchment, Queensland, Australia
Authors: Shahabi Hejar , Jarihani Ben , Tavakkoli Piralilou Sepideh , Chittleborough David , Avand Mohammadtaghi , Ghorbanzadeh Omid
Published: November 2019

Gully erosion is a dominant source of sediment and particulates to the Great Barrier Reef (GBR) World Heritage area. We selected the Bowen catchment, a tributary of the Burdekin Basin, as our area of study; the region is associated with a high density of gully networks. We aimed to use a semi-automated object-based gully networks detection process using a combination of multi-source and multi-scale remote sensing and ground-based data. An advanced approach was employed by integrating geographic object-based image analysis (GEOBIA) with current machine learning (ML) models. These included artificial neural networks (ANN), support vector machines (SVM), and random forests (RF), and an ensemble ML model of stacking to deal with the spatial scaling problem in gully networks detection. Spectral indices such as the normalized difference vegetation index (NDVI) and topographic conditioning factors, such as elevation, slope, aspect, topographic wetness index (TWI), slope length (SL), and curvature, were generated from Sentinel 2A images and the ALOS 12-m digital elevation model (DEM), respectively. For image segmentation, the ESP2 tool was used to obtain three optimal scale factors. On using object pureness index (OPI), object matching index (OMI), and object fitness index (OFI), the accuracy of each scale in image segmentation was evaluated. The scale parameter of 45 with OFI of 0.94, which is a combination of OPI and OMI indices, proved to be the optimal scale parameter for image segmentation. Furthermore, segmented objects based on scale 45 were overlaid with 70% and 30% of a prepared gully inventory map to select the ML models’ training and testing objects, respectively. The quantitative accuracy assessment methods of Precision, Recall, and an F1 measure were used to evaluate the model’s performance. Integration of GEOBIA with the stacking model using a scale of 45 resulted in the highest accuracy in detection of gully networks with an F1 measure value of 0.89. Here, we conclude that the adoption of optimal scale object definition in the GEOBIA and application of the ensemble stacking of ML models resulted in higher accuracy in the detection of gully networks.
Low-Cost Real-Time PPP/INS Integration for Automated Land Vehicles
Authors: Elsheikh Mohamed , Abdelfatah Walid , Noureldin Aboelmagd , Iqbal Umar , Korenberg Michael
Published: November 2019

The last decade has witnessed a growing demand for precise positioning in many applications including car navigation. Navigating automated land vehicles requires at least sub-meter level positioning accuracy with the lowest possible cost. The Global Navigation Satellite System (GNSS) Single-Frequency Precise Point Positioning (SF-PPP) is capable of achieving sub-meter level accuracy in benign GNSS conditions using low-cost GNSS receivers. However, SF-PPP alone cannot be employed for land vehicles due to frequent signal degradation and blockage. In this paper, real-time SF-PPP is integrated with a low-cost consumer-grade Inertial Navigation System (INS) to provide a continuous and precise navigation solution. The PPP accuracy and the applied estimation algorithm contributed to reducing the effects of INS errors. The system was evaluated through two road tests which included open-sky, suburban, momentary outages, and complete GNSS outage conditions. The results showed that the developed PPP/INS system maintained horizontal sub-meter Root Mean Square (RMS) accuracy in open-sky and suburban environments. Moreover, the PPP/INS system could provide a continuous real-time positioning solution within the lane the vehicle is moving in. This lane-level accuracy was preserved even when passing under bridges and overpasses on the road. The developed PPP/INS system is expected to benefit low-cost precise land vehicle navigation applications including level 2 of vehicle automation which comprises services such as lane departure warning and lane-keeping assistance.
Similarity Graph-Based Camera Tracking for Effective 3D Geometry Reconstruction with Mobile RGB-D Camera
Authors: An Jaepung , Lee Sangbeom , Park Sanghun , Ihm Insung
Published: November 2019

In this paper, we present a novel approach for reconstructing 3D geometry from a stream of images captured by a consumer-grade mobile RGB-D sensor. In contrast to previous real-time online approaches that process each incoming image in acquisition order, we show that applying a carefully selected order of (possibly a subset of) frames for pose estimation enables the performance of robust 3D reconstruction while automatically filtering out error-prone images. Our algorithm first organizes the input frames into a weighted graph called the similarity graph. A maximum spanning tree is then found in the graph, and its traversal determines the frames and their processing order. The basic algorithm is then extended by locally repairing the original spanning tree and merging disconnected tree components, if they exist, as much as possible, enhancing the result of 3D reconstruction. The capability of our method to generate a less error-prone stream from an input RGB-D stream may also be effectively combined with more sophisticated state-of-the-art techniques, which further increases their effectiveness in 3D reconstruction.
Adaptive-Cognitive Kalman Filter and Neural Network for an Upgraded Nondispersive Thermopile Device to Detect and Analyze Fusarium Spores
Authors: Pham Son , Dinh Anh
Published: November 2019

Noises such as thermal noise, background noise or burst noise can reduce the reliability and confidence of measurement devices. In this work, a recursive and adaptive Kalman filter is proposed to detect and process burst noise or outliers and thermal noise, which are popular in electrical and electronic devices. The Kalman filter and neural network are used to preprocess data of three detectors of a nondispersive thermopile device, which is used to detect and quantify Fusarium spores. The detectors are broadband (1 µm to 20 µm), λ 1 (6.09 ± 0.06 µm) and λ 2 (9.49 ± 0.44 µm) thermopiles. Additionally, an artificial neural network (NN) is applied to process background noise effects. The adaptive and cognitive Kalman Filter helps to improve the training time of the neural network and the absolute error of the thermopile data. Without applying the Kalman filter for λ 1 thermopile, it took 12 min 09 s to train the NN and reach the absolute error of 2.7453 × 104 (n. u.). With the Kalman filter, it took 46 s to train the NN to reach the absolute error of 1.4374 × 104 (n. u.) for λ 1 thermopile. Similarly, to the λ 2 (9.49 ± 0.44 µm) thermopile, the training improved from 9 min 13 s to 1 min and the absolute error of 2.3999 × 105 (n. u.) to the absolute error of 1.76485 × 105 (n. u.) respectively. The three-thermopile system has proven that it can improve the reliability in detection of Fusarium spores by adding the broadband thermopile. The method developed in this work can be employed for devices that encounter similar noise problems.
Cadmium-Sensitive Measurement Using a Nano-Copper-Enhanced Carbon Fiber Electrode
Authors: Wu Jian , Xu Zhipeng , Wang Xian , Wang Li , Qiu Huadong , Lu Kechao , Zhang Wenhong , Feng Qing , Chen Jun , Yang Lei
Published: November 2019

Enrichment of cadmium ion (Cd2+) from the environment may lead to kidney disease and weakened immunity in the body. Current techniques are not convenient enough to measure Cd2+ concentration in the environment due to low sensitivity and poor linear range. In this paper, a new measurement technique is proposed using a new sensing electrode made of nano-copper-enhanced carbon fiber. Nano-copper was deposited onto the surface of carbon fiber to enhance the current concentration and mass transfer rate of Cd2+ during measurement, which improved the electrochemical detection sensitivity significantly (by up to 3.7 × 108 nA/nM) and broadened the linear range to 10~105 nM. This device provides a low-cost solution for measuring Cd2+ concentration in the environment.