bluuebunny/crossref_metadata_2025 huggingface dataset:
Synthesis and Characterization of Spherical Calcium Carbonate Nanoparticles Derived from Cockle Shells
Authors: Hussein Abbas Ibrahim , Ab-Ghani Zuryati , Che Mat Ahmad Nazeer , Ab Ghani Nur Atikah , Husein Adam , Ab. Rahman Ismail
Published: October 2020

Cockle shells are a natural reservoir of calcium carbonate (CaCO3), which is widely used in bone repair, tissue scaffolds, and the development of advanced drug delivery systems. Although many studies report on the preparation of CaCO3, the development of a nanosized spherical CaCO3 precursor for calcium oxide (CaO) that is suitable to be incorporated in dental material was scarce. Therefore, this study aimed to synthesize a nanosized spherical CaCO3 precursor for CaO derived from cockle shells using a sol–gel method. Cockle shells were crushed to powder form and mixed with hydrochloric acid, forming calcium chloride (CaCl2). Potassium carbonate (K2CO3) was then fed to the diluted CaCl2 to obtain CaCO3. The effect of experimental parameters on the morphology of CaCO3, such as volume of water, type of solvents, feeding rate of K2CO3, and drying method, were investigated using field-emission scanning electron microscopy, energy-dispersive X-ray spectroscopy, X-ray diffractometry (XRD), Brunauer–Emmett–Teller surface area analysis, and thermogravimetric analysis. Optimized CaCO3 was then calcined to form CaO. XRD analysis of CaCO3 nanoparticles was indicative of the formation of a calcite phase. The well-structured spherical shape of CaCO3 was obtained by the optimum condition of the addition of 50 mL of water into CaCl2 in ethanolic solution with a 1 h feeding rate of K2CO3. Less agglomeration of CaCO3 was obtained using a freeze-drying technique with the surface area of 26 m2/g and average particle size of 39 nm. Spherical shaped nanosized CaO (22–70 nm) was also synthesized. The reproducibility, low cost, and simplicity of the method suggest its potential applications in the large-scale synthesis of the nanoparticles, with spherical morphology in an industrial setting.
Design of a Reinforcement Learning-Based Lane Keeping Planning Agent for Automated Vehicles
Authors: Kővári Bálint , Hegedüs Ferenc , Bécsi Tamás
Published: October 2020

Reinforcement learning-based approaches are widely studied in the literature for solving different control tasks for Connected and Autonomous Vehicles, from which this paper deals with the problem of lateral control of a dynamic nonlinear vehicle model, performing the task of lane-keeping. In this area, the appropriate formulation of the goals and environment information is crucial, for which the research outlines the importance of lookahead information, enabling to accomplish maneuvers with complex trajectories. Another critical part is the real-time manner of the problem. On the one hand, optimization or search based methods, such as the presented Monte Carlo Tree Search method, can solve the problem with the trade-off of high numerical complexity. On the other hand, single Reinforcement Learning agents struggle to learn these tasks with high performance, though they have the advantage that after the training process, they can operate in a real-time manner. Two planning agent structures are proposed in the paper to resolve this duality, where the machine learning agents aid the tree search algorithm. As a result, the combined solution provides high performance and low computational needs.
Real-Time Multidepth Multiphoton Microscopy Using Pixel-to-Pixel Focus-Switching
Authors: Qin Yifan , Chen Deying , Xia Yuanqin
Published: October 2020

Multiphoton microscopy is a well-established technique for biomedical applications, but real-time multidepth multimodal multiphoton microscopy using non-imaging detection has barely been discussed. We demonstrate a novel label-free imaging system capable of generating multimodal multiphoton signals at different focal planes simultaneously. Two spatially overlapped and temporally interlaced beams are obtained by applying cost-effective electro-optic modulator (EOM)-based fast-switching light paths. The switching beams have different divergence properties, enabling imaging at different depths into samples. The EOM is synchronized to the pixel clock from the microscope, achieving pixel-to-pixel focus-switching. The capability of the imaging system is demonstrated by performing real-time multidepth two-photon fluorescence (TPF) and second-harmonic generation (SHG) imaging of freshly excised mouse lung lobes. TPF and SHG images are acquired at two wavelength ranges. One is between 415 and 455 nm, and the other is between 495 and 635 nm. The microenvironment of pulmonary alveoli is depicted by the distributions of both elastin fibers visualized by TPF and collagen fibers illustrated by SHG. Macrophages residing inside apparent alveolar lumens are also identified by TPF, which shows that the imaging system is capable of localizing biological objects in three dimensions and has the potential of monitoring in vivo cellular dynamics in the axial direction.
Analysis of Dynamic Characteristics of Deformed Concrete Slab Track on Transition Zone in High-Speed Train Line According to Train Speeds
Authors: Park Sungbum , Kim Ja Yeon , Kim Jongwon , Lee Sehee , Cho Kook-Hwan
Published: October 2020

A concrete track, such as Rheda 2000 is commonly used in high-speed railways in Korea, Germany, China, etc., to enhance the operational safety of trains and to reduce the maintenance costs. However, when settlement of embankment or a crack in the concrete slab track occurs, the durability of the concrete track deteriorates significantly. Transition zones are considered to be vulnerable and are often deformed in railway embankments. The characteristics of track support stiffness and stresses on the rail in a deformed section are different from those in an undeformed section. In this study, a field measurement and numerical analysis were carried out to identify the dynamic response on transition zones where the track is deformed. A numerical analysis model was built to simulate the deformed concrete slab track, and it was compared with measured data from field tests for verification. The field tests to measure the stresses on the rail were performed in a train speed range from 280 to 300 km/h. According to the numerical analysis, the dynamic characteristics of the track varied with train speeds, which were then compared with the allowable stresses of the concrete slab track.
Analysis of the Effect of Shape Factor on Cork–Rubber Composites under Small Strain Compression
Authors: Lopes Helena , Silva Susana , Machado José
Published: October 2020

Like other types of elastomers, different geometries of the same cork–rubber material present different mechanical behaviour when subject to compression between bonded plates. To validate the application of Hooke’s Law on cork–rubber materials, under compression at small strains, a set of experimental and numerical analyses has been conducted. Using finite element analysis, a methodology is described to relate frictionless and frictional compression between a cork–rubber sample and loading plates. Based on that, the performance of square cross-section blocks with other dimensions can be evaluated. The results obtained by this approach showed a good agreement with experimental compression tests and with outputs from other models available in the literature relating Young and apparent compression moduli.
Conversion between Logic and Algebraic Expressions of Boolean Control Networks
Authors: Wang Cailu , Tao Yuegang
Published: October 2020

The conversion between logic and algebraic expressions of Boolean control networks plays a worthy role in the analysis and design of digital circuits. In this paper, for a single Boolean function, a direct conversion between the minterm canonical form and the structure matrix is provided. For a Boolean control network consisting of systems of Boolean functions, two algorithms are developed to achieve the mutual conversion between the logic and algebraic expressions. The presented algorithms decrease exponentially the complexity of the semi-tensor product based method. Some numerical examples are given to demonstrate the algorithms and to compare our method with the existing ones.
Holographic Micromirror Array with Diffuse Areas for Accurate Calibration of 3D Light-Field Display
Authors: Jorissen Lode , Oi Ryutaro , Wakunami Koki , Ichihashi Yasuyuki , Lafruit Gauthier , Yamamoto Kenji , Bekaert Philippe , Jackin Boaz Jessie
Published: October 2020

Light field 3D displays require a precise alignment between the display source and the micromirror-array screen for error free 3D visualization. Hence, calibrating the system using an external camera becomes necessary, before displaying any 3D contents. The inter-dependency of the intrinsic and extrinsic parameters of display-source, calibration-camera, and micromirror-array screen, makes the calibration process very complex and error-prone. Thus, several assumptions are made with regard to the display setup, in order to simplify the calibration. A fully automatic calibration method based on several such assumptions was reported by us earlier. Here, in this paper, we report a method that uses no such assumptions, but yields a better calibration. The proposed method adapts an optical solution where the micromirror-array screen is fabricated as a computer generated hologram with a tiny diffuser engraved at one corner of each elemental micromirror in the array. The calibration algorithm uses these diffusing areas as markers to determine the relation between the pixels of display source and the mirrors in the micromirror-array screen. Calibration results show that virtually reconstructed 3D scenes align well with the real world contents, and are free from any distortion. This method also eliminates the position dependency of display source, calibration-camera, and mirror-array screen during calibration, which enables easy setup of the display system.
Beyond 5G Network Architecture Study: Fractal Properties of Access Network
Authors: Paramonov Alexander , Muthanna Ammar , Aboulola Omar I. , Elgendy Ibrahim A. , Alharbey Riad , Tonkikh Evgeny , Koucheryavy Andrey
Published: October 2020

Wireless networks connect various devices through radio waves in which the network connection may have different structures. Moreover, the network structure is determined based on the placement areas of the network elements, which can be affected by the building and their locations. However, the numerical characteristic which describe the features of the real environment and allow them to be related to the properties of the model are still a challenge that has not been well addressed. To this end, in this paper, we analyze the modeling problems related to the structure of user placement in the access network. Our proposed solution is based on a description of the user environment structure in which cities in the form of buildings and constructions are considered as a typical environment. We propose a new model for addressing the wireless network structure in an efficient manner in which the features of the environment are considered, which are numerically expressed in the form of the Hurst parameter or fractal dimension. In addition, the fractal dimension, geometric fractals, and the characteristics of the user’s distribution territory and urban development are efficiently utilized. Then, we analyze the influence of the fractal properties of the environment on the structure of promising communication networks; in particular, on the structure of the Internet of Things network. Finally, simulation results proved that the proposed model is considered as a beneficial solution for modeling mobile communication and wireless access networks, including fifth-generation networks.
Experimental Characterization Protocols for Wear Products from Disc Brake Materials
Authors: Sinha Ankur , Ischia Gloria , Menapace Cinzia , Gialanella Stefano
Published: October 2020

The increasing interest in the emission from the disc brake system poses new challenges for the characterization approaches used to investigate the particles emitted from the wearing out of the relevant tribological systems. This interest stems from different factors. In the first place, a thorough characterization of brake wear particles is important for a complete understanding of the active tribological mechanisms, under different testing and servicing conditions. This information is an important prerequisite not only for the general improvement of brake systems, but also to guide the development of new materials for discs and brake pads, responding better to the specific requirements, including not only performance, but also the emission behavior. In this review paper, the main material characterization protocols used for the analyses of the brake wear products, with particular regard for the airborne fraction, are presented. Reliable results require investigating the fine and ultrafine particles as concerns their composition together with their structural and microstructural aspects. For this reason, in general, multi-analytical protocols are very much recommended.
The Nuclear Envelope in Lipid Metabolism and Pathogenesis of NAFLD
Authors: Östlund Cecilia , Hernandez-Ono Antonio , Shin Ji-Yeon
Published: October 2020

Nonalcoholic fatty liver disease (NAFLD) is a burgeoning public health problem worldwide. Despite its tremendous significance for public health, we lack a comprehensive understanding of the pathogenic mechanisms of NAFLD and its more advanced stage, nonalcoholic steatohepatitis (NASH). Identification of novel pathways or cellular mechanisms that regulate liver lipid metabolism has profound implications for the understanding of the pathology of NAFLD and NASH. The nuclear envelope is topologically connected to the ER, where protein synthesis and lipid synthesis occurs. Emerging evidence points toward that the nuclear lamins and nuclear membrane-associated proteins are involved in lipid metabolism and homeostasis. We review published reports that link these nuclear envelope proteins to lipid metabolism. In particular, we focus on the recent work demonstrating the essential roles for the nuclear envelope-localized torsinA/lamina-associated polypeptide (LAP1) complex in hepatic steatosis, lipid secretion, and NASH development. We also discuss plausible pathogenic mechanisms by which the loss of either protein in hepatocytes leads to hepatic dyslipidemia and NASH development.
Body Fluid Biomarkers for Alzheimer’s Disease—An Up-To-Date Overview
Authors: Bălașa Adrian Florian , Chircov Cristina , Grumezescu Alexandru Mihai
Published: October 2020

Neurodegeneration is a highly complex process which is associated with a variety of molecular mechanisms related to ageing. Among neurodegenerative disorders, Alzheimer’s disease (AD) is the most common, affecting more than 45 million individuals. The underlying mechanisms involve amyloid plaques and neurofibrillary tangles (NFTs) deposition, which will subsequently lead to oxidative stress, chronic neuroinflammation, neuron dysfunction, and neurodegeneration. The current diagnosis methods are still limited in regard to the possibility of the accurate and early detection of the diseases. Therefore, research has shifted towards the identification of novel biomarkers and matrices as biomarker sources, beyond amyloid-β and tau protein levels within the cerebrospinal fluid (CSF), that could improve AD diagnosis. In this context, the aim of this paper is to provide an overview of both conventional and novel biomarkers for AD found within body fluids, including CSF, blood, saliva, urine, tears, and olfactory fluids.
The Androgen Receptor in Prostate Cancer: Effect of Structure, Ligands and Spliced Variants on Therapy
Authors: Messner Elisabeth A. , Steele Thomas M. , Tsamouri Maria Malvina , Hejazi Nazila , Gao Allen C. , Mudryj Maria , Ghosh Paramita M.
Published: October 2020

The androgen receptor (AR) plays a predominant role in prostate cancer (PCa) pathology. It consists of an N-terminal domain (NTD), a DNA-binding domain (DBD), a hinge region (HR), and a ligand-binding domain (LBD) that binds androgens, including testosterone (T) and dihydrotestosterone (DHT). Ligand binding at the LBD promotes AR dimerization and translocation to the nucleus where the DBD binds target DNA. In PCa, AR signaling is perturbed by excessive androgen synthesis, AR amplification, mutation, or the formation of AR alternatively spliced variants (AR-V) that lack the LBD. Current therapies for advanced PCa include androgen synthesis inhibitors that suppress T and/or DHT synthesis, and AR inhibitors that prevent ligand binding at the LBD. However, AR mutations and AR-Vs render LBD-specific therapeutics ineffective. The DBD and NTD are novel targets for inhibition as both perform necessary roles in AR transcriptional activity and are less susceptible to AR alternative splicing compared to the LBD. DBD and NTD inhibition can potentially extend patient survival, improve quality of life, and overcome predominant mechanisms of resistance to current therapies. This review discusses various small molecule and other inhibitors developed against the DBD and NTD—and the current state of the available compounds in clinical development.
Plasmonic Sensors for Monitoring Biological and Chemical Threat Agents
Authors: Saylan Yeşeren , Akgönüllü Semra , Denizli Adil
Published: October 2020

Sensors are excellent options owing to their ability to figure out a large number of problems and challenges in several areas, including homeland security, defense, medicine, pharmacology, industry, environment, agriculture, food safety, and so on. Plasmonic sensors are used as detection devices that have important properties, such as rapid recognition, real-time analysis, no need labels, sensitive and selective sensing, portability, and, more importantly, simplicity in identifying target analytes. This review summarizes the state-of-art molecular recognition of biological and chemical threat agents. For this purpose, the principle of the plasmonic sensor is briefly explained and then the use of plasmonic sensors in the monitoring of a broad range of biological and chemical threat agents is extensively discussed with different types of threats according to the latest literature. A conclusion and future perspectives are added at the end of the review.
The Photoluminescence and Biocompatibility of CuInS2-Based Ternary Quantum Dots and Their Biological Applications
Authors: Jose Varghese Rajendran , Oluwafemi Oluwatobi Samuel
Published: October 2020

Semiconductor quantum dots (QDs) have become a unique class of materials with great potential for applications in biomedical and optoelectronic devices. However, conventional QDs contains toxic heavy metals such as Pb, Cd and Hg. Hence, it is imperative to find an alternative material with similar optical properties and low cytotoxicity. Among these materials, CuInS2 (CIS) QDs have attracted a lot of interest due to their direct band gap in the infrared region, large optical absorption coefficient and low toxic composition. These factors make them a good material for biomedical application. This review starts with the origin and photophysical characteristics of CIS QDs. This is followed by various synthetic strategies, including synthesis in organic and aqueous solvents, and the tuning of their optical properties. Lastly, their significance in various biological applications is presented with their prospects in clinical applications.
Communicating with Youth about Pain: Developmental Considerations
Authors: Emerson Natacha D. , Bursch Brenda
Published: October 2020

Background: Pain experiences can negatively impact children and adolescents, leading to trauma symptoms and nonadherence to important health behaviors. Developmentally-tailored communication strategies may mitigate this risk. Methods: This article reviews cognitive and linguistic developmental factors, within the familial and cultural context, that are important to consider when communicating with youth about acute, procedural, and/or chronic pain. Results: Youth undergoing acute or procedural pain benefit from pain education, truthful information about the procedure, and advance preparation. The use of analogies may be particularly helpful for patient understanding of chronic pain development, maintenance, and treatment. Youth with developmental disabilities may express pain differently than their normative peers, requiring adaptation of communication strategies. Conclusion: Developmentally-tailored pain communication is an important tool for caregivers and healthcare providers that may foster adaptive functioning in youth who experience pain.
Structure, Formation, and Biological Interactions of Supported Lipid Bilayers (SLB) Incorporating Lipopolysaccharide
Authors: Sondhi Palak , Lingden Dhanbir , Stine Keith J.
Published: October 2020

Biomimetic membrane systems play a crucial role in the field of biosensor engineering. Over the years, significant progress has been achieved creating artificial membranes by various strategies from vesicle fusion to Langmuir transfer approaches to meet an ever-growing demand for supported lipid bilayers on various substrates such as glass, mica, gold, polymer cushions, and many more. This paper reviews the diversity seen in the preparation of biologically relevant model lipid membranes which includes monolayers and bilayers of phospholipid and other crucial components such as proteins, characterization techniques, changes in the physical properties of the membranes during molecular interactions and the dynamics of the lipid membrane with biologically active molecules with special emphasis on lipopolysaccharides (LPS).
Direct and Indirect Evidence of the Microbially Induced Pitting Corrosion of Steel Structures in Humid Environments
Authors: Zhou Jingzhong , Sun Kuoteng , Huang Songqiang , He Xuemin , Hu Zhaowei , Li Wenge
Published: October 2020

Corrosion is a severe problem for steel structures in humid environments. In particular, humidity usually triggers the surface adhesion of microorganisms, leading to microbiologically induced corrosion. This study aims to explore the effect of bacterial biofilm formation on the pitting corrosion of stainless steel. This research uses electrochemical methods to obtain indirect evidence of the pitting corrosion of steel. In addition, in order to obtain direct evidence of the pitting corrosion of stainless steel, field emission scanning electron microscopy (FESEM) and atomic force microscopy (AFM) were used to characterize the dimensional morphology of the stainless steel after pitting. It was shown that the bacterial adhesion increased with the pH and temperature, which significantly increased the surface roughness of the stainless steel. Electrochemical analysis revealed that the formation of biofilm greatly destroyed the oxide film of 304 SS and accelerated the corrosion of stainless steel by forming an oxygen concentration battery. SEM and AFM analyses showed cracks and dislocations on the surface of stainless steel underneath the attached bacteria, which suggested a direct role of biofilm in corrosion induction. The results presented here show that the bacterial biofilm formation on the steel surfaces significantly accelerated the corrosion and affected the pitting corrosion process of the steel structure.
Designing Metasurfaces with Canonical Unit Cells
Authors: Barbarić Dominik , Šipuš Zvonimir
Published: October 2020

Among different approaches to designing metasurfaces, surface sheet impedance is proving to be a straightforward path for many applications. Recent publications have proposed several methods for optimizing this design approach, enabling rapid metasurface development. Upon finding the requirements using the sheet impedance approach, design continues with the selection of unit cell geometry. This choice is usually based on approximate expressions that have been published throughout the years. We review the approximate expressions for metasurface unit cell design, with consideration of their applicability to certain applications, namely polarization-dependent beam-shaping metasurfaces. We evaluate the accuracy of the approximate expressions against simulation results from a full-wave electromagnetic solver, and propose an optimization approach to correct the proposed design for the observed error. The applicability of different unit cell types is discussed, especially considering the limitations of technological processes typically used in metasurface production. A prototype was developed to verify the validity of this design approach.
Can Functional Traits Explain Plant Coexistence? A Case Study with Tropical Lianas and Trees
Authors: Mello Felipe N. A. , Estrada-Villegas Sergio , DeFilippis David M. , Schnitzer Stefan A.
Published: October 2020

Organisms are adapted to their environment through a suite of anatomical, morphological, and physiological traits. These functional traits are commonly thought to determine an organism’s tolerance to environmental conditions. However, the differences in functional traits among co-occurring species, and whether trait differences mediate competition and coexistence is still poorly understood. Here we review studies comparing functional traits in two co-occurring tropical woody plant guilds, lianas and trees, to understand whether competing plant guilds differ in functional traits and how these differences may help to explain tropical woody plant coexistence. We examined 36 separate studies that compared a total of 140 different functional traits of co-occurring lianas and trees. We conducted a meta-analysis for ten of these functional traits, those that were present in at least five studies. We found that the mean trait value between lianas and trees differed significantly in four of the ten functional traits. Lianas differed from trees mainly in functional traits related to a faster resource acquisition life history strategy. However, the lack of difference in the remaining six functional traits indicates that lianas are not restricted to the fast end of the plant life–history continuum. Differences in functional traits between lianas and trees suggest these plant guilds may coexist in tropical forests by specializing in different life–history strategies, but there is still a significant overlap in the life–history strategies between these two competing guilds.
Implementing and Monitoring the Use of Artificial Canopy Bridges by Mammals and Birds in an Indonesian Agroforestry Environment
Authors: Nekaris K. A. I. , Handby Victoria , Campera Marco , Birot Hélène , Hedger Katherine , Eaton James , Imron Muhammad Ali
Published: October 2020

Deforestation is a major threat to biodiversity, particularly within tropical forest habitats. Some of the fastest diminishing tropical forest habitats in the world occur in Indonesia, where fragmentation is severely impacting biodiversity, including on the island of Java, which holds many endemic species. Extreme fragmentation on the western part of the island, especially due to small-scale agriculture, impacts animal movement and increases mortality risk for mainly arboreal taxa. To mitigate this risk in an agroforest environment in Garut District, West Java, we installed 10 canopy bridges and monitored them through camera trapping between 2017 and 2019. Five of the monitored bridges were made of waterlines and five of rubber hose. We recorded Javan palm civets using the waterline bridges 938 times, while Javan slow lorises used the waterlines 1079 times and the rubber bridges 358 times. At least 19 other species used the bridges for crossing or perching. Our results demonstrate that relatively simple and cost-effective materials can be used to mitigate the effects of habitat fragmentation. We also recommend the use of camera traps to monitor the effectiveness of these interventions.
(Hyper)graph Kernels over Simplicial Complexes
Authors: Martino Alessio , Rizzi Antonello
Published: October 2020

Graph kernels are one of the mainstream approaches when dealing with measuring similarity between graphs, especially for pattern recognition and machine learning tasks. In turn, graphs gained a lot of attention due to their modeling capabilities for several real-world phenomena ranging from bioinformatics to social network analysis. However, the attention has been recently moved towards hypergraphs, generalization of plain graphs where multi-way relations (other than pairwise relations) can be considered. In this paper, four (hyper)graph kernels are proposed and their efficiency and effectiveness are compared in a twofold fashion. First, by inferring the simplicial complexes on the top of underlying graphs and by performing a comparison among 18 benchmark datasets against state-of-the-art approaches; second, by facing a real-world case study (i.e., metabolic pathways classification) where input data are natively represented by hypergraphs. With this work, we aim at fostering the extension of graph kernels towards hypergraphs and, more in general, bridging the gap between structural pattern recognition and the domain of hypergraphs.
Perturbed and Unperturbed: Analyzing the Conservatively Perturbed Equilibrium (Linear Case)
Authors: Xi Yiming , Liu Xinquan , Constales Denis , Yablonsky Gregory S.
Published: October 2020

The “conservatively perturbed equilibrium” (CPE) technique for a complex chemical system is computationally analyzed in a batch reactor considering different linear mechanisms with three and four species. Contrary to traditional chemical relaxation procedures, in CPE experiments only some initial concentrations are modified; other conditions, including the total amount of chemical elements and temperature are kept unchanged. Generally, for “unperturbed” species with initial concentrations equal to their corresponding equilibrium concentrations, unavoidable extreme values are observed during relaxation to the equilibrium. If the unperturbed species is involved in one step only, this extremum is a momentary equilibrium of the step; if the unperturbed species is involved in more reactions, the extremum is not a momentary equilibrium. The acyclic mechanism with four species may exhibit two extrema and an inflection point, which corresponds to an extremum of the rate of the species change. These facts provide essential information about the detailed mechanism of the complex reaction.
A Labeling Method for Financial Time Series Prediction Based on Trends
Authors: Wu Dingming , Wang Xiaolong , Su Jingyong , Tang Buzhou , Wu Shaocong
Published: October 2020

Time series prediction has been widely applied to the finance industry in applications such as stock market price and commodity price forecasting. Machine learning methods have been widely used in financial time series prediction in recent years. How to label financial time series data to determine the prediction accuracy of machine learning models and subsequently determine final investment returns is a hot topic. Existing labeling methods of financial time series mainly label data by comparing the current data with those of a short time period in the future. However, financial time series data are typically non-linear with obvious short-term randomness. Therefore, these labeling methods have not captured the continuous trend features of financial time series data, leading to a difference between their labeling results and real market trends. In this paper, a new labeling method called “continuous trend labeling” is proposed to address the above problem. In the feature preprocessing stage, this paper proposed a new method that can avoid the problem of look-ahead bias in traditional data standardization or normalization processes. Then, a detailed logical explanation was given, the definition of continuous trend labeling was proposed and also an automatic labeling algorithm was given to extract the continuous trend features of financial time series data. Experiments on the Shanghai Composite Index and Shenzhen Component Index and some stocks of China showed that our labeling method is a much better state-of-the-art labeling method in terms of classification accuracy and some other classification evaluation metrics. The results of the paper also proved that deep learning models such as LSTM and GRU are more suitable for dealing with the prediction of financial time series data.
Decomposing Inequality in Household Consumption Expenditure in Malaysia
Authors: Ayyash Mohsen , Sek Siok Kun
Published: October 2020

This study aims to examine the sources and determinants of consumption expenditure inequality in Malaysia as well as to quantify their proportional contributions to the total explained inequality using the Household Expenditure Survey (HES) data for the year 2014 collected from the Malaysian Department of Statistics (DOSM). The study applies Field’s regression-based decomposition method to the log-linear regression model of per capita monthly consumption expenditure. It is found that the model explains about 55.2% of the variability in the logged monthly consumption expenditure per capita. The findings suggest that the size of households, education of household heads, and regional variations are the major contributing factors to consumption expenditure inequality in Malaysia, with household size being among the highest. Other household head characteristics, including ethnicity, strata, and citizenship, have small contributions to the total explained inequality. However, sex and age of household heads contributed negatively to inequality and have inequality decreasing effects, with a negative impact on inequality. A large percentage of unexplained inequality is not captured by these factors, which may be attributed to either unobserved household attributes or residuals. The results are important for policy implications and should be taken into account in formulating future policies, especially those aiming to reduce inequality among the population and thus improving living standards and well-being.
Research on Cross-Domain Study Curricula in Cyber-Physical Systems: A Case Study of Belarusian and Ukrainian Universities
Authors: Zabasta Anatolijs , Peuteman Joan , Kunicina Nadezda , Kazymyr Volodymyr , Hvesenya Sergey , Hnatov Andrii , Paliyeva Tatsiana , Ribickis Leonids
Published: October 2020

The increasing importance of continuingly complex Cyber-Physical Systems (CPS) challenges and encourages universities worldwide to organize up-to-date CPS oriented educational programs. The Erasmus+ project “CybPhys” aims to support CPS oriented educational programs in Belarus and Ukraine. We put forward a hypothesis that the educational situation, training opportunities, situation of the labor market, and general economic development are related to each other and can vary depending on the region. In order to validate our hypothesis and to provide input to curricula modernizations, an in-depth study in the form of surveys was conducted in the years 2019–2020. The results of the study showed that the differences between the perceptions of stakeholders in different regions of one country were more significant than the deviations between the global evaluation marks obtained from Belarusian and Ukrainian respondents. In order to increase the synergy of the CybPhys partners, the transdisciplinary and T-shape skills approaches are introduced to the education programs of the partner’s universities. An innovative ICT based teaching and learning environment and associated teaching methodologies will be developed. The research provides valuable input to the development of industry and research-oriented cross-domain study programs in Cyber-Physical Systems focused on the needs of Belarusian and Ukrainian industries.
Lex-Pos Feature-Based Grammar Error Detection System for the English Language
Authors: Agarwal Nancy , Wani Mudasir Ahmad , Bours Patrick
Published: October 2020

This work focuses on designing a grammar detection system that understands both structural and contextual information of sentences for validating whether the English sentences are grammatically correct. Most existing systems model a grammar detector by translating the sentences into sequences of either words appearing in the sentences or syntactic tags holding the grammar knowledge of the sentences. In this paper, we show that both these sequencing approaches have limitations. The former model is over specific, whereas the latter model is over generalized, which in turn affects the performance of the grammar classifier. Therefore, the paper proposes a new sequencing approach that contains both information, linguistic as well as syntactic, of a sentence. We call this sequence a Lex-Pos sequence. The main objective of the paper is to demonstrate that the proposed Lex-Pos sequence has the potential to imbibe the specific nature of the linguistic words (i.e., lexicals) and generic structural characteristics of a sentence via Part-Of-Speech (POS) tags, and so, can lead to a significant improvement in detecting grammar errors. Furthermore, the paper proposes a new vector representation technique, Word Embedding One-Hot Encoding (WEOE) to transform this Lex-Pos into mathematical values. The paper also introduces a new error induction technique to artificially generate the POS tag specific incorrect sentences for training. The classifier is trained using two corpora of incorrect sentences, one with general errors and another with POS tag specific errors. Long Short-Term Memory (LSTM) neural network architecture has been employed to build the grammar classifier. The study conducts nine experiments to validate the strength of the Lex-Pos sequences. The Lex-Pos -based models are observed as superior in two ways: (1) they give more accurate predictions; and (2) they are more stable as lesser accuracy drops have been recorded from training to testing. To further prove the potential of the proposed Lex-Pos -based model, we compare it with some well known existing studies.
LoS Theoretical and Experimental MIMO Study from 1–40 GHz in Indoor Environments
Authors: Pascual-García Juan , Martinez-Ingles Maria-Teresa , Gaillot Davy P. , Juan-Llácer Leandro , Molina-Garcia-Pardo Jose-Maria
Published: October 2020

In this work, line-of-sight multiple-input multiple-output (MIMO) measurements in the frequency range from 1 GHz to 40 GHz are presented for an indoor environment in the scope of new 5G bands. For the sake of comparison, the measured radio channels are reproduced with great accuracy using ray-tracing techniques by tuning all propagation mechanisms. The relative received power, root mean square of the delay spread (RMS DS) and K-factor provide an insight of how propagation behaves in indoor scenarios within a large and contiguous frequency band. Results show that a decay factor behaves nearly constant with frequency and the RMS DS is quite sensible to frequency. From these results, faithful one-slope 5G models are proposed. Finally, the contribution of the simulated propagation mechanisms to the radio channel is investigated which suggests that the simulation of the low-mmW radio channel can be simplified.
A Machine Learning Based Two-Stage Wi-Fi Network Intrusion Detection System
Authors: A. Reyes Abel , D. Vaca Francisco , Castro Aguayo Gabriel A. , Niyaz Quamar , Devabhaktuni Vijay
Published: October 2020

The growth of wireless networks has been remarkable in the last few years. One of the main reasons for this growth is the massive use of portable and stand-alone devices with wireless network connectivity. These devices have become essential on the daily basis in consumer electronics. As the dependency on wireless networks has increased, the attacks against them over time have increased as well. To detect these attacks, a network intrusion detection system (NIDS) with high accuracy and low detection time is needed. In this work, we propose a machine learning (ML) based wireless network intrusion detection system (WNIDS) for Wi-Fi networks to efficiently detect attacks against them. The proposed WNIDS consists of two stages that work together in a sequence. An ML model is developed for each stage to classify the network records into normal or one of the specific attack classes. We train and validate the ML model for WNIDS using the publicly available Aegean Wi-Fi Intrusion Dataset (AWID). Several feature selection techniques have been considered to identify the best features set for the WNIDS. Our two-stage WNIDS achieves an accuracy of 99.42% for multi-class classification with a reduced set of features. A module for eXplainable Artificial Intelligence (XAI) is implemented as well to understand the influence of features on each type of network traffic records.
A Systolic Accelerator for Neuromorphic Visual Recognition
Authors: Tian Shuo , Wang Lei , Xu Shi , Guo Shasha , Yang Zhijie , Zhang Jianfeng , Xu Weixia
Published: October 2020

Advances in neuroscience have encouraged researchers to focus on developing computational models that behave like the human brain. HMAX is one of the potential biologically inspired models that mimic the primate visual cortex’s functions and structures. HMAX has shown its effectiveness and versatility in multi-class object recognition with a simple computational structure. It is still a challenge to implement the HMAX model in embedded systems due to the heaviest computational S2 phase of HMAX. Previous implementations such as CoRe16 have used a reconfigurable two-dimensional processing element (PE) array to speed up the S2 layer for HMAX. However, the adder tree mechanism in CoRe16 used to produce output pixels by accumulating partial sums in different PEs increases the runtime for HMAX. To speed up the execution process of the S2 layer in HMAX, in this paper, we propose SAFA (systolic accelerator for HMAX), a systolic-array based architecture to compute and accelerate the S2 stage of HMAX. Using the output stationary (OS) dataflow, each PE in SAFA not only calculates the output pixel independently without additional accumulation of partial sums in multiple PEs, but also reduces the multiplexers applied in reconfigurable accelerators. Besides, data forwarding for the same input or weight data in OS reduces the memory bandwidth requirements. The simulation results show that the runtime of the heaviest computational S2 stage in HMAX model is decreased by 5.7%, and the bandwidth required for memory is reduced by 3.53 × on average by different kernel sizes (except for kernel = 12) compared with CoRe16. SAFA also obtains lower power and area costs than other reconfigurable accelerators from synthesis on ASIC.
From Conventional to State-of-the-Art IoT Access Control Models
Authors: Malik Ahmad Kamran , Emmanuel Naina , Zafar Sidra , Khattak Hasan Ali , Raza Basit , Khan Sarmadullah , Al-Bayatti Ali H. , Alassafi Madini O. , Alfakeeh Ahmed S. , Alqarni Mohammad A.
Published: October 2020

The advent in Online Social Networks (OSN) and Internet of Things (IoT) has created a new world of collaboration and communication between people and devices. The domain of internet of things uses billions of devices (ranging from tiny sensors to macro scale devices) that continuously produce and exchange huge amounts of data with people and applications. Similarly, more than a billion people are connected through social networking sites to collaborate and share their knowledge. The applications of IoT such as smart health, smart city, social networking, video surveillance and vehicular communication are quickly evolving people’s daily lives. These applications provide accurate, information-rich and personalized services to the users. However, providing personalized information comes at the cost of accessing private information of users such as their location, social relationship details, health information and daily activities. When the information is accessible online, there is always a chance that it can be used maliciously by unauthorized entities. Therefore, an effective access control mechanism must be employed to ensure the security and privacy of entities using OSN and IoT services. Access control refers to a process which can restrict user’s access to data and resources. It enforces access rules to grant authorized users an access to resources and prevent others. This survey examines the increasing literature on access control for traditional models in general, and for OSN and IoT in specific. Challenges and problems related to access control mechanisms are explored to facilitate the adoption of access control solutions in OSN and IoT scenarios. The survey provides a review of the requirements for access control enforcement, discusses several security issues in access control, and elaborates underlying principles and limitations of famous access control models. We evaluate the feasibility of current access control models for OSN and IoT and provide the future development direction of access control for the same.
Experimental Investigation of the Effects of Water and Polymer Flooding on Geometric and Multifractal Characteristics of Pore Structures
Authors: Zhang Xianguo , Lin Chengyan , Wu Yuqi , Zhang Tao , Wang Hongwei , Wang Hanwei , Wu Xiaoxiao , Huang Derong
Published: October 2020

During water and polymer flooding for enhanced oil recovery, pore structures may vary because of the fluid–rock interactions, which can lead to variations in petrophysical properties and affect oil field production. To investigate the effects of fluid flooding on pore structures, six samples were subjected to brine water, dual-system, and alkaline–surfactant–polymer (ASP) polymer displacement experiments. Before and after experiments, samples were scanned by X-ray CT. Thin sections, X-ray diffraction, and high pressure mercury injection tests were also carried out to characterize mineralogy and fractal dimension of pore systems before experiments. Experiment results show that water flooding with low injection pore volume ratio (IPVR) can improve reservoir quality since total porosity and connected porosity of samples rise after the flooding and the proportion of large pores also increases and heterogeneity of pore structure decreases. However, water flooding with high IPVR has reverse effects on pore structures. Polymer flooding reduces the total porosity, connected porosity, the percentage of small pores and enhances the heterogeneity of pore structures. It can be found that pore structures will change in fluid flooding and appropriate water injection can improve reservoir quality while excessive water injection may destroy the reservoir. Meanwhile, injected polymer may block throats and destroy reservoirs. The experimental results can be used as the basis for oil field development.
Comparative Analysis of the Mining Cribs Models Filled with Gangue
Authors: Skrzypkowski Krzysztof
Published: October 2020

In the article, comparative laboratory strength tests of three-point and four-point wooden cribs models are presented. In the case of cribs with a triangular cross-section, the notches made at an angle of 60 degrees were used for the first time. The individual beams of the three-point and four-point cribs were stacked horizontally and connected to each other by means of a quick-drying adhesive. The main aim of the research was to compare the empty models with cribs filled with a gangue. In order to better understand the mechanism of behavior of models under loads, load-displacement and pressure-compressibility characteristics are presented. It was found that filling the three-point and four-point crib with gangue increases its maximal load several times compared to the empty cribs.