bluuebunny/crossref_metadata_2025 huggingface dataset:
Artificial Intelligence Applications in Accounting and Financial Reporting Systems
Authors: Almagtome Akeel Hamza
Published: September 2020

In light of globalization and the competitive economy, it became necessary for investors being ready to evaluate the accounting data necessary to make economic decisions that contribute to the development of their businesses. Nevertheless, many consumers of accounting information are still unable to make effective use of the accounting data presented in the financial statements. The chapter will therefore discuss innovative ideas that can allow investors and other stakeholders to understand and use accounting data in the light of best AI applications. It provides an overview of the impact of artificial intelligence on accounting and reporting practices. The chapter also discusses the latest developments in the field of artificial intelligence (AI) technology and their implementation in the context of global accounting practices, including accounting systems, financial reporting systems, and auditing.
A Research on Hedonic and Utilitarian Consumption Behavior of Young Consumers on Big Discount Days
Authors: Avcı İbrahim , Yıldız Salih
Published: September 2020

Recent changes and developments in social life affect both the marketing strategies of brands and the consumer behaviors. The big discount days were first implemented in the USA under the name “Black Friday,” and it was implemented in Turkey under the “Legend Friday, Legend Days, Super Friday, 11.11.” The big discount days campaigns also changed the consumer's sense of consumption. Together with the changing consumption concept, consumers are looking for psychological and social benefits in addition to the physical benefits in the products they purchase. The aim of this study is to determine the effect of hedonic and utilitarian consumption motives on consumers purchase intentions on big discount days. The questionnaire form was applied face to face to 621 students in a private university in Istanbul between 05.05.2019 and 15.05.2019. The obtained data were analyzed with SPSS 21 and AMOS 24 programs, and as a result of the analysis, it was determined that the hedonic and utilitarian consumption motives had a significant effect on consumer purchase intention on the big discount days.
Credit Scoring
Authors: Lappas Pantelis Z. , Yannacopoulos Athanasios N.
Published: September 2020

The main objective of this chapter is to propose a hybrid evolutionary feature selection approach for solving credit scoring problems subject to constraints. A hybrid scheme combining filter and wrapper-based approaches is proposed to develop an accurate credit scoring model with a high predictive performance. Initially, the minimum redundancy maximum relevance algorithm is applied to find an optimal set of features that is mutually and maximally dissimilar and can represent the response variable effectively, allowing for an ordering of features by their importance. Subsequently, an iterative procedure, where supervised machine learning algorithms such as the logistic regression and the linear-discriminant analysis are combined with an evolutionary optimization algorithm like the genetic algorithm, is applied to choose the feature subset that maximizes an appropriate classification measure according to the predefined features and subject to the predefined constraints. The performance of the proposed method is illustrated using standard credit scoring datasets.
Botnet Threats to E-Commerce Web Applications and Their Detection
Authors: Rahman Rizwan Ur , Tomar Deepak Singh
Published: September 2020

Security issues in e-commerce web applications are still exploratory, and in spite of an increase in e-commerce application research and development, lots of security challenges remain unanswered. Botnets are the most malicious threats to web applications, especially the e-commerce applications. Botnet is a network of BOTs. It executes automated scripts to launch different types of attack on web applications. Botnets are typically controlled by one or more hackers known as Bot masters and are exploited for different types of attacks including Dos (denial of service), DDos (distributed denial of service), phishing, spreading of malware, adware, Spyware, identity fraud, and logic bombs. The aim of this chapter is to scrutinize to what degree botnets can cause a threat to e-commerce security. In the first section, an adequate overview of botnets in the context of e-commerce security is presented in order to provide the reader with an understanding of the background for the remaining sections.
Advanced Network Data Analytics for Large-Scale DDoS Attack Detection
Authors: Xylogiannopoulos Konstantinos F. , Karampelas Panagiotis , Alhajj Reda
Published: September 2020

Internet-enabled devices or Internet of Things as it has been prevailed are increasing exponentially every day. The lack of security standards in the manufacturing of these devices along with the haste of the manufacturers to increase their market share in this area has created a very large network of vulnerable devices that can be easily recruited as bot members and used to initiate very large volumetric Distributed Denial of Service (DDoS) attacks. The significance of the problem can be easily acknowledged due to the large number of cases regarding attacks on institutions, enterprises and even countries which have been recently revealed. In the current paper a novel method is introduced, which is based on a data mining technique that can analyze incoming IP traffic details and early warn the network administrator about a potentially developing DDoS attack. The method can scale depending on the availability of the infrastructure from a conventional laptop computer to a complex cloud infrastructure. Based on the hardware configuration as it is proved with the experiments the method can easily monitor and detect abnormal network traffic of several Gbps in real time using the minimum hardware equipment.
A Survey of Methodologies for Protecting Privacy of User Data Within Enterprise Information Infrastructure
Authors: Manna Asmita , Sengupta Anirban , Mazumdar Chandan
Published: September 2020

During recent years, especially after the introduction of data protection regulations by different countries, protecting the privacy of user data has become an important research topic. Different threads of information privacy research have emerged, particularly in the areas of eliciting privacy requirements, incorporating privacy by design, performing privacy impact assessments, developing privacy enhancement techniques, implementing those techniques for privacy compliance, verifying privacy compliance, etc. Privacy engineering is integral to all phases of data lifecycle, right from privacy requirement elicitation to compliance verification. This chapter covers a brief survey of notable privacy researches and models proposed for different phases of privacy engineering (i.e., privacy requirement engineering, privacy by design, privacy impact assessment, privacy compliance, and modeling approaches for privacy in enterprise infrastructure). Models and frameworks have been chosen based on the relevance of their applicability in modeling and implementing privacy within an enterprise.
Contextual Anomaly Detection Methods for Addressing Intrusion Detection
Authors: Gottwalt Florian , Chang Elizabeth J. , Dillon Tharam S.
Published: September 2020

One promising method to detect cyber-crime is anomaly detection, which enables one to detect new, unseen attacks. Despite this ability, anomaly detection methods only have limited utilization in practice, due to the high number of false alarms generated. Recent research has shown that the number of false alarms can be reduced drastically by considering the context in which these alarms occur. However, important questions include, What does context mean in the realm of anomaly detection? and How can it be incorporated to identify potential cyber-crime? To address these questions, this chapter provides novel definitions of context and contextual anomaly detection methods. Based on these, a new taxonomy is proposed for contextual anomaly detection methods, which organizes the methods by the specific problems they address. Further, the chapter highlights the potential of contextual anomaly detection for the reduction of false alarms, particularly for network anomaly detection and provides an introduction and holistic overview of the field for professionals and researchers.
Cyberbullying From a Research Viewpoint
Authors: Fonseca Josélia Mafalda Ribeiro da , Borges-Tiago Maria Teresa
Published: September 2020

In less than two decades, social media has become a part of life for many people, increasing the positive and negative effects of social relationships. In a sense, the traditional offline behaviors moved into a ubiquitous environment, enhancing the social phenomenon of cyberbullying. Thus, it attracted much attention from different fields within academia. To understand how research has been conducted in the last 20 years and the topics addressed, this study applied a bibliometric analysis to academic literature from 2000 to 2020 related to cyberbullying in social media, using techniques such as citation analysis, co-citation analysis, and content analysis. The growing interest of the field is confirmed, and some research gaps are unveiled. Since this is one of the first studies to explore cyberbullying that occurs in social media networks, it is hoped that this chapter will stimulate further research on this topic reinforcing the gaps found.
Detection and Prediction of Spam Emails Using Machine Learning Models
Authors: P. Z Salma , Mohan Maya
Published: September 2020

One of today's important means of communication is email. The extensive use of email for communication has led to many problems. Spam emails being the most crucial among them. It is one the major issues in today's internet world. Spam emails contain mostly advertisements and offensive content, which are often sent without the recipient's request and are generally annoying, time consuming, and wasting space on the communication media's resources. It creates inconveniences and financial loss to the recipients. Hence, there is always the need to filter the spam emails and separate them from the legitimate emails. There are a lot of content-based machine learning techniques that have proven to be effective in detecting and filtering spam emails. Due to a large increase in email spamming, the emails are studied and classified as spam or not spam. In this chapter, three machine learning models, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BLSTM), are used classify the emails as spam and benign.
POSMASWEB
Authors: Mateus-Coelho Nuno Ricardo , Fonseca Benjamim Ribeiro , Castro António Vieira
Published: September 2020

It has recently been discovered that large companies and nations “observe” their customers and citizens, disregarding any remaining moral and technological lines, being able to listen to telephone conversations and monitoring communications through powerful monitoring and surveillance programs. Elsewhere on the planet, nations in turmoil or wrapped in a cloak of censorship persecute their citizens, controlling them by denying them access to the free web without the threat of repercussions that threatens their dignity. To support the present research, an analysis of platforms that allow anonymous and secure browsing and a study of technologies and programs with potential privacy breach and computer intrusion were performed. The main objective of this work was to analyse the computer monitoring and surveillance technologies, identifying the available tools, trying to find potential solutions, developing and providing a methodology that enhances any desktop, server, or mobile operating system, with characteristics that combat the exposed in this summary.
Efficient Regularization Framework for Histopathological Image Classification Using Convolutional Neural Networks.
Authors: Dif Nassima , Elberrichi Zakaria
Published: September 2020

Deep learning methods are characterized by their capacity to learn data representation compared to the traditional machine learning algorithms. However, these methods are prone to overfitting on small volumes of data. The objective of this research is to overcome this limitation by improving the generalization in the proposed deep learning framework based on various techniques: data augmentation, small models, optimizer selection, and ensemble learning. For ensembling, the authors used selected models from different checkpoints and both voting and unweighted average methods for combination. The experimental study on the lymphomas histopathological dataset highlights the efficiency of the MobileNet2 network combined with the stochastic gradient descent (SGD) optimizer in terms of generalization. The best results have been achieved by the combination of the best three checkpoint models (98.67% of accuracy). These findings provide important insights into the efficiency of the checkpoint ensemble learning method for histopathological image classification.
Fuzzy Crow Search Algorithm-Based Deep LSTM for Bitcoin Prediction
Authors: Ravi Chandrasekar
Published: September 2020

Prediction of stock market trends is considered as an important task and is of great attention as predicting stock prices successfully may lead to attractive profits by making proper decisions. Stock market prediction is a major challenge owing to non-stationary, blaring, and chaotic data and thus, the prediction becomes challenging among the investors to invest the money for making profits. Initially, the blockchain network is fed to the blockchain network bridge from which the bitcoin data is acquired that is followed with the bitcoin prediction. Bitcoin prediction is performed using the proposed FuzzyCSA-based Deep Long short-term memory (LSTM). At first, the flow strength indicators are extracted based on Double exponential moving average (DEMA), Rate of Change (ROCR), Average True Range (ATR), Simple Moving Average (SMA), and Moving Average Convergence Divergence (MACD) from the blockchain data. Based on the extracted features, the prediction is done using FuzzyCSA-based Deep LSTM, which is the combination of FuzzyCSA with Deep LSTM. Then, the CSA is modified using the fuzzy operator for determining the optimal weights in Deep LSTM. The experimentation of the proposed method is performed from the openly available dataset. The analysis of the method in terms of Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) reveals that the proposed FuzzyCSA-based Deep LSTM acquired a minimal MAE of 0.4811, and the minimal RMSE of 0.3905, respectively.
Fault-Tolerant Strategies in the Tree-Based Fog Computing Model
Authors: Oma Ryuji , Nakamura Shigenari , Enokido Tomoya , Takizawa Makoto
Published: September 2020

In the Fog Comput$ing (FC) model of the Internet of Things (IoT), application processes to handle sensor data are distributed to fog nodes and servers. In the Tree-based FC (TBFC) model proposed by the authors, fog nodes are hierarchically structured. In this article, the authors propose a TBFC for a General Process (TBFCG) model to recover from the faults of fog nodes. If a node gets faulty, the child nodes are disconnected. The authors propose Minimum Energy in the TBFCG tree (MET) and selecting Multiple Parents for recovery in the TBFCG tree (MPT) algorithms to select a new parent node for the disconnected nodes. A new parent node has to process data from not only the disconnected nodes, but also its own child nodes. In the evaluation, the energy consumption and execution time of a new parent node can be reduced by the proposed algorithms.
Opportunistic Two Virtual Machines Placements in Distributed Cloud Environment
Authors: Kumar Kamal , Thaman Jyoti
Published: September 2020

Cloud computing is a potentially tremendous platform and its presence is experienced in day to day life. Most infrastructure and technology enterprises have migrated to a cloud-based infrastructure and storage. With so much dependence on the cloud as a distributed and reliable platform, but a few issues remain as a challenge and provide food for the ever-active research entity. Considering a very basic aspect of VM migration followed by VM placement, one VM at a time is a prominent approach. This article presents a novel idea of placing two VMs at a time. This proposal is a draft of solution for the Two VM Placement problem. The experimental validation was done against a well-known placement algorithm, the power aware best fit decreasing (PABFD). PABFD and TVMP were applied on a given context and results were obtained for three important parameters, which include the number of VM migrations, reallocation means, and energy efficiency. Improvements on these parameters may prove beneficial.
Computational Performance Analysis of Neural Network and Regression Models in Forecasting the Societal Demand for Agricultural Food Harvests
Authors: Balaji Prabhu B. V.  , Dakshayini M.
Published: September 2020

Demand forecasting plays an important role in the field of agriculture, where a farmer can plan for the crop production according to the demand in future and make a profitable crop business. There exist a various statistical and machine learning methods for forecasting the demand, selecting the best forecasting model is desirable. In this work, a multiple linear regression (MLR) and an artificial neural network (ANN) model have been implemented for forecasting an optimum societal demand for various food crops that are commonly used in day to day life. The models are implemented using R toll, linear model and neuralnet packages for training and optimization of the MLR and ANN models. Then, the results obtained by the ANN were compared with the results obtained with MLR models. The results obtained indicated that the designed models are useful, reliable, and quite an effective tool for optimizing the effects of demand prediction in controlling the supply of food harvests to match the societal needs satisfactorily.
Beyond RoboDebt
Authors: D'Rosario Michael , D'Rosario Carlene
Published: September 2020

Automated decision support systems with high stake decision processes are frequently controversial. The Online Compliance Intervention (herewith “OCI” or “RoboDebt”) is a system of compliance implemented with the intention to facilitate automatic issuance of statutory debt notices to individuals, taking a receipt of welfare payments and exceeding their entitlement. The system appears to employ rudimentary data scraping and expert systems to determine whether notices should be validly issued. However, many individuals that take receipt of debt notices assert that they were issued in error. The commentary on the system has resulted in a lot of conflation of the system with other system types and caused many to question the role of decision of support systems in public administration given the potentially deleterious impacts of such systems for the most vulnerable. The authors employ a taxonomy of Robotic Process Automation (RPA) issues, to review the OCI and RPA more generally. This paper identifies potential problems of bias, inconsistency, procedural fairness, and overall systematic error. This research also considers a series of RoboDebt specific issues regarding contractor arrangements and the potential impact of the system for Australia's Indigenous population. The authors offer a set of recommendations based on the observed challenges, emphasizing the importance of moderation, independent algorithmic audits, and ongoing reviews. Most notably, this paper emphasizes the need for greater transparency and a broadening of criteria to determine vulnerability that encompasses, temporal, geographic, and technological considerations.
Influence of Input Factors on Material Removal Rate in PMEDM Cylindrical Shaped Parts with Silicon Carbide Powder Suspended Dielectric
Authors: Cuong Nguyen Manh , Tung Luu Anh , Danh Bui Thanh , Cuong Nguyen Van , Hong Tran Thi , Linh Nguyen Hong , Quy Le Thu , Pi Vu Ngoc
Published: September 2020

This work is done to determine the effects of the input factors of powder mixed electrical discharge machining (PMEDM) process on the material removal rate (MRR). In the study, the workpiece is cylindrical parts made from 90CrSi alloy steel. Also, five factors containing the pulse on time Ton, the powder concentration Cp, the pulse off time Toff, the pulse current IP, and the server voltage SV were discovered to find their impact on MRR. In addition, the Taguchi method and ANOVA analysis were used to design experiment and analyze the results. In addition, an optimal model of the MRR was introduced. Also, the model has been well verified by comparison with testing, and so it can be used for further studies in the PMEDM process.
Effect of Input Parameters on Electrode Wear in PMEDM Cylindrical Shaped Parts
Authors: Ky Le Hong , Danh Bui Thanh , Cuong Nguyen Van , Hong Tran Thi , Vinh Nguyen Thai , Nguyen Thi Quoc Dung , Le Thi Phuong Thao , Cuong Nguyen Manh
Published: September 2020

This paper deals with the effect of input parameters of Powder Mixed Electric Discharge Machining (PMEDM) process on the surface roughness when processing cylindrical shaped parts. In this work, the workpiece material is 90CrSi alloy tool steel and the nanopowder is silicon carbide. Also, five input parameters including the pulse on time, the pulse off time, the powder concentration, the current and the server voltage were selected to investigate their influence on the surface roughness. Taguchi method and ANOVA analysis were used and the effect of input parameters on the surface roughness was presented. Moreover, optimum input parameters for minimum surface roughness was suggested.
Study the Influence of Processing Oil on the Physical Mechanical and Adhesion Properties of Ethylene Propylene Diene Monomer/Chlorobutyl Blend to Polyester Fabric
Authors: Liem Nguyen Thanh , Linh Nguyen Pham Duy , Tung Nguyen Huy , Phuc Bach Trong , Chuong Bui
Published: September 2020

In this paper, the influence of paraffinic oil on the physical mechanical, thermal and adhesion properties of three blends of EPDM Buna EP T.6465, Keltan 5260Q and Keltan 6160 D and CIIR have been investigated. The results showed that the tensile strength values and elongation at break of keltan 5260Q/CIIR and keltan 6160 D/CIIR with 10 wt.% paraffinic oil represent the improvement of 57.8%, 57.6% and 71% to 81% respectively, compared with one without oil. The mean peel force of EPDM keltan 6260D with 10% oil loaded is about 36% and 32.5% higher than that of EPDM keltan 5260Q and EP.T 6465 blends respectively. With suitable oil content, in this case is 10% wt., the thermal resistance of keltan 5260 Q and 6160D seems to be higher than that of without processing oil and suitable for thermal resistance rubber application.
Development of Natural Rubber Latex Foam for Hand Exercising Application
Authors: Kajon Surasit , Somsongkul Voranuch , Suwanna Pimsiree
Published: September 2020

This study has developed an alternative compounding formulation and process for natural rubber latex (NRL) foams for hand exercising application. Deprotenized natural rubber latex was used as raw material and a combination of DIXP and TBzTD, which were fugitive and high molecular weight accelerators, respectively, was used to lower allergenic potential and harmful nitrosamines. The NRL foams were prepared to various densities, characterized by Scanning Electron Microscopy (SEM) and tested for hardness index, accelerated ageing and compression set in comparison to the foams with conventional compounding formulation. With the same processes, the foams with these two different formulations were found to have similar densities and hardness indices. However, the foams using DIXP and TBzTD accelerators performed better in compression set and accelerated ageing tests.
Influence of Layer Thickness and Continuous Carbon Fiber on the Mechanical Property of 3D Printed Polyamide
Authors: Lan Tian , Dong Li Chao , Lu Zhongyuan , Guo Shi Feng , Zhang Hao , Pei Yu Chen
Published: September 2020

3D printed carbon fiber reinforced composites (CFRP) have shown great potential in lightweight application. Here, we report a prepreg carbon fiber reinforced polyamide composite by fused filament fabrication 3D printing process. The influence of layer thickness and carbon fiber layers on mechanical properties of 3D printed parts was well studied. With the incorporation of prepreg carbon fibers, the value of tension and flexural strengths of 3D printed CFRP parts could achieve 2.7 and 13.6 times compared to neat polyamide, respectively. Result illustrates that with the prepreg process the carbon fiber have good interface bonding strength with neat polyimide. This work could also be used for more 3D printing composite systems.
Effect of Forming Technology on the Properties of Quartz Sands Porous Materials via Dry Pressing Method
Authors: Qin Yue Ning , Xu Hao , Yang Zhou , Yang Shou Xia , Yang Xue Qing , Song Jie Guang , Wang Fang , Du Da Ming
Published: September 2020

The effect of forming technology on the properties of quartz sand porous materials were researched with the low grade quartz sand along the Yangtze River via the pressureless sintering method in this paper, which lay technology foundation for researching new heat insulating materials. The wear of ball milling media is investigated through adjusting the ball milling parameter via the weigh methods. The results are shown that the porosity of quartz sand porous materials are decreased with increasing a forming pressure. The porosities of Φ30 and Φ50 materials are decreased with increasing a holding pressure time, however, the porosity decreasing rate of Φ30 is slower than that of Φ50. The porosity of Φ50 is higher than that of Φ30 under same forming conditions. Through optimized the forming conditions, the porosity of sintered Φ50 quartz matrix porous materials reaches 54.52% under the forming pressure for 5MPa and the holding pressure time for 5min.
Influence of Synthetic Concentration on Surface Properties of SnO2 Nanofilm by Hydrothermal Method
Authors: Yang Chao , Wang Fang , Wang Rui Hua , Xu Min Han , Ma Bo Feng , Li Shi Bin , Song Jie Guang , Luo Hui Hui , Guan Zhi Qiang , Chen Lin
Published: September 2020

Dye-sensitized solar cells have better development prospects than silicon cells, and their main structural composition of nanoporous semiconductor films is particularly important. It is the tin dioxide film, and the effect of preparing tin oxide film on dye-sensitized solar cells under different conditions is studied. In this paper, the SnO2 powder was prepared by hydrothermal method, and the experiment was studied by controlled variable method. The properties of the obtained tin dioxide powder were characterized by SEM and three-dimensional ultra-depth microscope and XRD. The XRD diffraction peak is shown as tetragonal phase rutile type SnO2, With the increase of the concentration of tin tetrachloride solution, the coarser the particle size of the tin oxide crystal in the film, the more complicated the surface morphology, so that the specific surface area of the film is larger. At 0.1 mol/L, the surface of the film is a porous structure in the form of a loose sheet is presented. After analyzing the surface microstructure and flatness of the tin dioxide film, it can be concluded that the effect of salt concentration on the specific surface area of the film is extremely large. When the salt concentration is higher or lower, the obtained two the surface of the tin oxide film is relatively flat and has poor performance. When the salt concentration is 0.1mol/L, the surface of the obtained film has a large undulation and a large specific surface area, and the ability to adsorb the dye molecules can be predicted to be the greatest, and the photoelectric conversion efficiency is optimal in the photocatalytic process.
Progress in Preparation of Electron Transport Layer in Perovskite Solar Cell
Authors: Han Li , Gao Yan , Guo Ying Kai , Gao Xing , He Wen
Published: September 2020

Electron transport layer (ETL) plays an important role in improving the performance and stability of perovskite solar cells (PSCs). SnO2 is a good semiconductor material with high electromigration and wide band gap. TiO2 has the advantages of superior position of conducting band (CB), long electronic life and low preparation cost, so SnO2 and TiO2 are often used in ETL of PSCs. In this paper, the preparation progress of SnO2, TiO2 and SnO2/TiO2 composite ETL is reviewed.
Hydrothermal Synthesis of Copper (II) Oxide Microparticle
Authors: Pookmanee Pusit , Somsri Piyarat , Phanichphant Sukon , Thanachayanont Chanchana
Published: September 2020

CuO microparticle was syntheszied by hydrothermal method. The starting precursors were used as copper (II) nitrate trihydrate (Cu (NO3)2·3H2O), nitric acid (HNO3) and sodium hydroxide (NaOH). The final pH value of the mixed solution was used 2M NaOH to adjust the pH was 8 and treated at 100-200 oC for 4-6 h in a hydrothermal vessel. The black fine powder was obtained after dried at 100 oC for 5 h. The phase and structure of CuO microparticle were characterized by X-ray diffraction (XRD). A single phase monoclinic structure synthezied by hydrothermal method at 200 oC for 4 and 6 h was obtained without calcination steps. The morphology CuO microparticle was investigated by scanning electron microscopy (SEM). It was likely grain in shape and the particle size in range of 2.94-4.06 μm. The element composition of CuO microparticle was indicated by energy dispersive X-ray spectrometry (EDX). The chemical compositions showed the characteristic X-ray energy of copper (Kα = 0.98 keV) and oxygen (Kα = 0.53 keV), respectively. The functional group of CuO microparticle was indentified by Fourier transform spectrophotometry (FTIR). The wavenumber at 690, 514 and 437 cm-1 was corresponded to vibration of Cu-O stretching.
Effects of Abrasive Particles on Lubricating Property of Oil-in-Water (O/W) Emulsions for Cold-Rolled Strip
Authors: Li Yan , Xia Lei , Liu Ren Dong , Sun Rong Sheng , Zhang Jian Jun , Cai Shun Da , Jin Xiao Long
Published: September 2020

The abrasive particles in the working emulsion were characterized by transmission electron microscopy (TEM) and X-ray diffraction (XRD). The wettability and lubricating properties of different emulsion samples were tested by MRS-10E four-ball friction and wear testing machine. The results show that the average particle size of the abrasive particles in the working emulsion is 2.72 μm, the smallest particle size of the abrasive particles is 0.36μm, and the largest size of the abrasive particles is 6.57μm. Furthermore, the abrasive particles can increase the internal friction of emulsions, which leads to the increasing wetting Angle. The abrasive particles cause the lubrication performance of working emulsion to decrease, which eventually leads to larger wear scar diameter and the worse morphology of wear scar on the tested ball.
Research on Tribological Properties of Environment-Friendly Nano-MoS2 Water-Based Rolling Liquid
Authors: Li Yan , Liu Ren Dong , Xia Lei , Sun Rong Sheng , Zhang Jian Jun , Cai Shun Da , Jin Xiao Long
Published: September 2020

In this research, the four-ball tribometer was examined to evaluate the tribological behavior of environment-friendly nano-MoS2 water-based rolling liquid. This experimental method is used to measure the maximum non-seizure load (PB) and the coefficient of friction (μ). The newly introduced parameter ω can be combined with PB and μ to evaluate the tribological performances of the rolling liquid comprehensively. The rolling lubrication performance of nano-MoS2 water-base lubricant was investigated using a single stand 4-high reversing rolling mill. The surface morphologies and rolling textures of the samples were measured by laser scanning confocal microscope after cold rolling. The results show that when the concentration of nano-MoS2 is 0.4wt.%, the water-based rolling liquid has excellent tribological and lubricating properties. Meanwhile, compared with the surface quality of the rolled strip under other lubrication conditions, the surface quality of the cold-rolled strip using 0.4wt% nano-MoS2 rolling liquid as the lubrication condition is smooth without visible surface defects.
Research on Microstructure and Wear Property of 45 Steel Quenched with Different Laser Power
Authors: Liu Yu , Xu Tian Hao , Liu Ying , Zhang Hai Cheng , Li Xing Xing , Chen Guang Bin
Published: September 2020

The surface of 45 steel is quenched by CO2 laser with scanning speed 1000 mm/min and different laser power 1000W, 1200W, 1400W, 1600W and 1800W. Experiments are carried out to analyze microstructure, friction and wear properties of quenched 45 steel. The results show that the quenching layer thickness increases gradually with the increase of laser power,and the maximum value of quenching layer hardness increases first and then decreases. When the laser power is 1600W, the maximum hardness value is 883HV0.5. But when the laser power is 1800W, the hardness of quenching layer becomes to decrease. The reason is the surface of 45 steel becomes to melt. The wear volume increases first and then decreases too. When laser power is 1600W, the minimum wear volume is 0.08mm3, which is 6.4% to the wear volume of 45 steel matrix without laser quenching. Therefore, better microstructure and properties of 45 steel can be obtained when laser scanning speed is 1000mm/min and laser power is 1600W.
Study on Preparation and Characterization of La2O3 Derived from Thai Monazite Ore Processing Supported Coal Fly Ash Catalyst
Authors: Nuchdang Sasikarn , Kingkam Wilasinee , Leelanupat Orapun , Rattanaphra Dussadee
Published: September 2020

This research aims to study the preparation and characterization of La2O3 supported coal fly ash catalyst. Studied La2O3 and coal fly ash (CFA) were obtained from Thai monazite ore processing and local supplier, respectively. The catalyst was prepared by wet impregnation method. The influences of La2O3 loading and impregnation temperature on the chemical composition, crystalline phase and surface morphology of the catalyst were examined by varying the amount of La2O3 (5, 10 and 20 wt%) and the impregnation temperature (room temperature, 100, 150 and 200 °C). Characterizations such as WDXRF, XRD and SEM were carried out. The XRD results demonstrated that the La2O3 was highly dispersed on the CFA support. A high La2O3 loading resulted in an increase free CaO dissolvation during the impregnation which inhibited the interaction between SiO2 and La2O3. The impregnation temperature had no significant effect on the chemical and physical properties of the catalyst. The coexist of Fe3O4 in the CFA support might impact to hinder the incorporation of La2O3 into SiO2 matrix.
Shale Stability Evaluation and Leakage Control of High Density Clay Free Brine Drilling Fluid
Authors: Chang Cong Bing , Zhang Yi Hang , Song Xu Hui
Published: September 2020

In view of the particularity of deep-water thick rock reservoir, in order to deal with the problem of poor reservoir stability and serious loss of drilling fluid in the drilling fluid design of M oilfield, this paper has carried out the reservoir inhibitor optimization evaluation test for the salt resistant high-density drilling fluid which has been designed and optimized, and also carried out the appropriate selection of plugging materials and plugging effect test for the loss of salt gypsum layer. The results show that the stability of saturated salt water drilling fluid in the salt gypsum layer with shale can be effectively ensured by the combination of CPI and uhib inhibitors within 2%. At the same time, FloSeal, a new plugging agent for 1-2mm fractures, is selected, and its plugging loss capacity is stably controlled below 30ml.
Research on Effect of Equivalent Diameter of Voids on Sound Absorption Performance of Porous Asphalt Concrete Based on Grey Entropy Method
Authors: Liu Ming Xi , Xie Jian Guang , Wang Zhan Qi , Liu Yan Ping
Published: September 2020

The sound absorption performance of porous asphalt concrete (PAC) is inseparable from the sizes of voids, as different sizes of voids have different absorption effects on noise in different frequency bands. However, the relationship between the two is not clear. In this study, the equivalent diameter of voids was obtained by the proposed image segmentation algorithm based on the square area, then grey entropy method was used to analyze the effect of different equivalent diameter of voids on the sound absorption performance of PAC in the frequency range of traffic noise. The results show that with the increase of air voids, the peak and average sound absorption coefficient of PAC increase, the sound absorption performance of PAC is improved; and the sound absorption performance of PAC is mainly affected by the equivalent diameter of voids of 3-4mm.
Effect of Raw Materials Formula on Performance of Steel Slag Cement
Authors: Li Liang Song , Zhu Min , Li Xiang , Wang Qiong , Xiong Yao , Pang Cai Liang , Song Jie Guang , Wang Fang , Liu Rong Jin
Published: September 2020

At present, the commonly used cement has low tensile strength and low flexural strength, which is a fatal defect of cement materials. It is extremely urgent to conduct research on this shortcoming of cement. In addition, the steel industry steel mill produces a large amount of waste steel slag. The chemical composition of steel slag is similar to that of cement. This experiment makes full use of waste steel slag as raw material to prepare cement to improve the performance of cement materials. Using limestone, sandstone, shale, steel slag and river sand as the main raw materials, through the powder preparation, mixing, calcination, grinding, molding process and performance analysis of raw materials. After the calcination, the cement clinker in the form of a block becomes higher in hardness as the steel slag is incorporated. The higher the calcination temperature, the smaller the steel slag incorporation maximum. When the calcined at 1150 °C is mixed with 17.5% steel slag, a cement with better performance can be obtained.
Consolidation Behavior of Clay Supported by Soil-Cement Column
Authors: Uchaipichat Anuchit
Published: September 2020

This paper presents the compression and consolidation behaviors of clay supported by soil-cement column. A series of consolidation tests was performed on kaolin samples supported by soil-cement column with the ratio between diameters of column and soil sample () ranging from 0 to 0.4. All samples with soil-cement column were cured for 28 days under the vertical pressure of 25 kPa. The sample preparation techniques simulating dry deep soil mixing method was developed. The test results showed the elastic region expanded with increasing value of . However, there was no effect of on deformation behavior in the elasto-plastic region. Moreover, the value of was considered constant over the range of testing stress level, for the value of of 0 and 0.2. For the value of of 0.3 and 0.4, the values of in the elastic region was greater than that in elasto-plastic region.
Effect of Moisture Content on Ultrasonic Infrared Detection of Concrete Structures
Authors: Luo Ming Kai , Zhao Xin Ming , Lei Tang , He Zhi Wen
Published: September 2020

Moisture content is an important parameter of concrete material, and ultrasonic excitation infrared thermal image detection technology is a new nondestructive detection technology. In this paper, the relationship between concrete moisture content and ultrasonic excitation infrared thermal image detection technology is studied by experiment and numerical simulation. The moisture content of concrete was changed by immersion method, and the time of immersion was controlled to control the moisture content. It is found that with the increase of water content, the maximum temperature rise of the defect area decreases gradually, that is, the detection effect is weakened. The results of numerical simulation are in good agreement with the experimental results, and the simulation is effective to some extent.
Numerical Study of Radiative Heat Flux Emitted by Stainless Wire-Net Porous Media
Authors: Ketchat Niwat , Krittacom Bundit
Published: September 2020

Numerical model of the convective-radiative heat transfer of porous media was proposed. A stainless wire-net was used as porous media. The physical properties, consisting of porosity (φ) and optical thickness (τ0), of porous media were independent variables. The air velocity was reported in the form of Reynolds number (Re). Two equations of the conservative energy with local thermal non-equilibrium were analyzed. The gas (θf) and solid (θs) phases of conservative energy equation inside porous media were investigated. The radiative heat flux (ψ) at down-stream of solid phase emitted into outside was dealt by the P1 approximation. From the study, it was found that the level of θf and θs decreased as Re increased because the effect of convection heat transfer. Inversely, the level of ψ increased as increasing Re. The level of θf, θs and ψ were decreased as φ increased owing to a lower volume of material depended on the increasing level of φ resulting to the heat transfer rate became lower. The level of θf, θs and ψ gave increased with τ0 becaues a wider distance in absorping energy leading to a higher emission energy from the porous media was achieved.
Effect of Secondary Aging Process on the Structure and Properties of 7050 Aluminum Alloy Forging
Authors: He Wei Wei , Hao Min , Li Hui Qu , Wang Liang , Chen Jun Zhou
Published: September 2020

The effect of the second-stage aging process on the tensile properties, fracture toughness and electrical conductivity of 7050 aluminum alloy die forgings was studied, and the mechanism of strengthening and toughening was analyzed by transmission electron microscope and scanning electron microscope. The results show that with the extension of the second-stage aging time, the morphology of the precipitation phase remains unchanged, but the average radius of the precipitation phase and the distance between each other gradually increase. The fracture modes at this aging temperature are mixed fracture mechanisms of dimple fracture and intergranular fracture, and the number of dimple fractures increases with time. With the extension of the second-stage aging time, the strength of the alloy decreases, and the fracture toughness and stress corrosion resistance increase. The alloys heat-treated at 120°C×6 h +177°C×6~8 h two-stage aging process have excellent comprehensive properties.
The Study on the Weldability of 1MnCrMoNi Alloy Steel and Q235 Carbon Steel
Authors: Hu Min
Published: September 2020

In this paper, the weldability of 1mncrmoni alloy steel and Q235 carbon steel is studied. The microhardness, tensile strength, impact and other mechanical properties of the welded joint are tested by manual arc welding. The variation trend of microhardness, tensile strength and impact toughness of the welded joint under different welding methods is studied, which provides theoretical support for practical engineering application.
Influence of Selective Laser Melting Process Parameters on Densification Behavior, Surface Quality and Hardness of 18Ni300 Steel
Authors: Li Gan , Guo Cheng , Guo Wen Feng , Lu Hong Xing , Wen Lin Ju , Hu Xiao Gang , Zhu Qiang
Published: September 2020

This study investigated the effect of laser power (P), scan speed (v) and hatch space (h) on densification behavior, surface quality and hardness of 18Ni300 maraging steel fabricated by selective laser melting (SLM). The results indicated that the relative density of the SLMed samples has a shape increase from 73% to 97% with the laser energy density increasing from 0.5 to 2.2 J/mm2. The relative density ≥ 99% was achieved at the energy density in the range of 2.2~5.9 J/mm2. The optimum process parameters were found to be laser power of 150~200 W, scan speed of 600mm/s and hatch space of 0.105mm. In addition, it was found that the hardness increased initially with the increasing relative density up to relative density of 90% and then little relationship, but finally increase again significantly. This work provides reference for determining process parameters for SLMed maraging steel and the development of 3D printing of die steels.
An Optimization Case Study on Single Point Incremental Forming
Authors: Minh Pham Son , Hoang Van Vinh , Le Minh Tai
Published: September 2020

Single point incremental forming is a major process in a number of different industrial applications. Blank are easily found around us from furniture and household equipment to industrial machinery and equipment. To ensure high-quality sheet metal, it is vital to consider the influence of parameters. Accordingly, in this paper, the parameters affecting product quality are analyzed. Through the testing process, we obtain the parameters that help the product achieve high quality in terms of mechanics, which is an important first step for the process of developing the technology.
Combining Three-Dimensional Modeling with Artificial Intelligence to Increase Specificity and Precision in Peptide–MHC Binding Predictions
Authors: Aranha Michelle P. , Jewel Yead S. M. , Beckman Robert A. , Weiner Louis M. , Mitchell Julie C. , Parks Jerry M. , Smith Jeremy C.
Published: September 2020

The reliable prediction of the affinity of candidate peptides for the MHC is important for predicting their potential antigenicity and thus influences medical applications, such as decisions on their inclusion in T cell–based vaccines. In this study, we present a rapid, predictive computational approach that combines a popular, sequence-based artificial neural network method, NetMHCpan 4.0, with three-dimensional structural modeling. We find that the ensembles of bound peptide conformations generated by the programs MODELLER and Rosetta FlexPepDock are less variable in geometry for strong binders than for low-affinity peptides. In tests on 1271 peptide sequences for which the experimental dissociation constants of binding to the well-characterized murine MHC allele H-2Db are known, by applying thresholds for geometric fluctuations the structure-based approach in a standalone manner drastically improves the statistical specificity, reducing the number of false positives. Furthermore, filtering candidates generated with NetMHCpan 4.0 with the structure-based predictor led to an increase in the positive predictive value (PPV) of the peptides correctly predicted to bind very strongly (i.e., Kd < 100 nM) from 40 to 52% (p = 0.027). The combined method also significantly improved the PPV when tested on five human alleles, including some with limited data for training. Overall, an average increase of 10% in the PPV was found over the standalone sequence-based method. The combined method should be useful in the rapid design of effective T cell–based vaccines.
Presbyphagia and dysphagia in old age
Authors: Cavallero Stefano , Dominguez Ligia J. , Vernuccio Laura , Barbagallo Mario
Published: September 2020

Although presbyphagia is a clinically relevant problem among elderly population, possibly leading to dysphagia in old age, it remains a still underestimated health condition. The present review analyzes swallowing related anatomical and functional changes during aging, both in healthy people and in those affected by dysphagia. First of all, dysphagia in old people must not be confused with presbyphagia. To distinguish these two different conditions, a correct definition of both should be considered. Subsequently, a comprehensive evaluation including instrumental analysis should be carried out. The aim of this narrative review is to analyze the current knowledge of this clinical condition and to provide the state of art to clinicians. A systematic PubMed research on dysphagia in the elderly was conducted and most relevant and most recent references were manually screened and selected. The aim of a correct diagnosis is to enable the choice of a correct intervention in order to prevent and treat complications of dysphagia, such as ab ingestis pneumonia and malnutrition. Moreover, the assessment, diagnosis and therapy of dysphagia/ presbyphagia should include the intervention of different specialists.