bluuebunny/crossref_metadata_2025_split huggingface dataset:
Risk Analysis of Water Grid Systems Using Threat Modeling
Authors: Rahim Fiza Abdul , Jamil Norziana , Cob Zaihisma Che , Sidek Lariyah Mohd , Sharizan@Sharizal Nur Izz Insyirah 
Published: July 2022

Abstract
Critical infrastructure systems consist of physical and cyber assets that are essential to the operation of the economy and the government. As one of the most important critical infrastructures worldwide, the water sector has become vulnerable to new risks in the form of cyber threats that can severely impact public health, and are difficult to detect. A water grid system (WGS) plays an important role in guarding the business processes of the water sector against possible threats and risks. Threat modeling can be used to analyze threats to the WGS. It is applied to identify points of access to the assets and devices of the system, classify threats to them, assess the risks posed by them, and suggest mitigation measures. Each threat is classified based on its type according to the STRIDE methodology, and the results of the threat classification can be used to assess the level of risk by using the DREAD methodology. This yields a risk rating for each threat that can be used to devise mitigation measures to minimize the risk posed by it. Through the threat modeling stage, it is known that the high-risk threats on WGSs are tampering with a risk score of 14, denial of service threats with a risk score of 13, and repudiation threats with a risk score of 12. The results of the ranking are used to formulate recommendations in the form of mitigation controls against these threats.
Spectrum Sensing for Cognitive Radio Based on Feature Extraction and Deep Learning
Authors: Geng Yue , Huang Jingyi , Yang Jianxin , Zhang Sen 
Published: July 2022

Abstract
In cognitive radio, spectrum sensing is used to determine whether the primary user is using the spectrum based on the signal received on a specific frequency band, thereby determining whether the secondary user can use the spectrum. The main problem faced by spectrum sensing is how to identify the existence of the primary signal under the condition of low signal-to-noise ratio (SNR). Compared with traditional technologies, deep learning methods can identify the features of input data more efficiently and accurately. Based on convolutional neural network (CNN), This paper regard spectrum sensing as a binary classification problem. In the method we proposed, different features of received are extracted, and a dataset of feature matrices obtained under different SNRs is constructed for the training of the CNN network. Experiment results show that under the condition of low signal-to-noise ratio, the performance of our method is improved compared with the traditional method, and the combination of different features can improve the sensing accuracy.
Securing IEC60870-5-101 Communication Protocol using SCADA Cryptographic and Device Authentication Gateway
Authors: Qassim Qais Saif , Jamil Norziana , Mahdi Mohammed Najah , Ahmad Zawawi Md Nabil , Jamaludin Md Zaini , Jidin Razali , Rusli Mohd Ezanee 
Published: July 2022

AbstractSince the renowned Stuxnet attack on the Iranian refinery planet in 2010, there have been many attempts to harden the security of these systems. One of the most significant challenges impacting these systems’ security is the outdated and insecure communication protocols that have been used for many years in various sectors. This work presents a practical solution to protect the SCADA’s critical communications’ confidentiality, authenticity, and integrity. This work put forward a hardware-based general-purpose cryptographic device based on FPGA technology. The device contains a handful of cryptographic algorithms that can be customized to harden the security of devices operating on the IEC 60870-5-101 communication protocol. The experimental results show that the proposed solution enhances the secrecy and integrity of the IEC 60870-5-101 messages and ensures the source’s authenticity.
Classification of Overlapping Eggs Based on Image Processing
Authors: Purahong B , Krungseanmuang W , Chaowalittawin V , Pumee T , Kanjanasurat I , Lasakul A 
Published: July 2022

Abstract
This paper presents a method for classifying the overlapped eggs and counting the number of eggs on the conveyor belt using image processing techniques. The image was acquired by a webcam camera that connected to the computer and then rescaled. The image was then converted to grayscale and noise was reduced using a Gaussian blur filter. Otsu’s Binarization is used to convert the image to binary. The binary image is then subjected to morphological operations. Following that, using the Watershed Algorithm, separate the egg’s overlapped area. Finally, the prepared image is ready to be counted using the contour matrix method. This method independently classifies each egg segmentation and can count up to 18 eggs per frame with a processing time of less than 1 second.
The Application of GAP Software in Constructing the Non-Normal Subgroup Graphs of Alternating Groups
Authors: Rahin N F , Sarmin N H , Ilangovan S 
Published: July 2022

Abstract
A graph in group theory is constructed by using any elements of a group as a set of vertices. Some of the properties of a group are used to form the edges of the graph. A finite group can be represented in a graph by its subgroup structure. A subgroup H of a group G is a subset of G, where H itself is a group under the same operation as in G, whereas a subgroup H is said to be a normal subgroup if its left and right cosets coincide. The non-normal subgroup graph of a group G is defined as a directed graph with a vertex set G and two distinct elements x and y are adjacent if xy ∈ H. In this paper, the non-normal subgroups graph of alternating groups for order twelve is determined by using GAP software. The graphs are found to be a union of complete digraphs and directed graphs with the same pattern depending on the order of the non-normal subgroups.
Sanskruti Index of some Chemical Trees and Unicyclic Graphs
Authors: Al-Janabi H , Bacsó G 
Published: July 2022

Abstract
In chemical graph theory, topological indices are used to estimate the bio-activity of chemical compounds. The molecular graph models molecular compounds. A molecular graph represents the structural formula of a chemical compound relative to the graph. Its vertices represent the atoms of the compound, and its edges represent the chemical bonds. In 2017, Hosamani introduced the Sanskruti index S(G) and defined it as S(G) = Σuv∈E(G) ((δu ⋅ δv)/(δu + δv − 2))3, where δu is the sum of the degrees of all neighbors of the vertex u in G. The Sanskruti index displays a good connection with an entropy of octane isomers. In this paper, the Sanskruti index S(G) is computed for some chemical trees and unicyclic graphs.
Gradient-induced Model-free Variable Selection Based on Composite Quantile Regression in Reproducing Kernel Hilbert Space
Authors: Tan Xin , Kong Yan , Yan Mei 
Published: July 2022

AbstractVariable selection plays an important role to identify truly informative variables in high-dimensional data analysis. In his paper, we propose a variable selection method with composite quantile regression in reproducing kernel Hilbert space (RKHS), which has two main advantages. The first is that our method requires no special model structure assumption and no independence of error term. It is suitable for general non-parametric models and even heteroscedastic models. The second is that the calculation is simple and fast. So, it can also work in high-dimensional situations. Finally, the numerical experiments and real data analysis demonstrate its superior performance in variable selection.
Analysis of the Stability and Hopf Bifurcation of a Three-Dimensional System with Delays
Authors: Yao Kutorzi Edwin , Shi Yufeng 
Published: July 2022

Abstract
We propose a model of three-dimensional autonomous system with delays. We explore the dynamical behavior of the proposed autonomous system by examining bifurcation diagrams, Lyapunov exponents, equilibrium and stability, and the influence of time delay on Hopf bifurcation. A bifurcation theory is used to analyze and detail the problem. In addition, the explicit algorithm that determines the direction of Hopf bifurcation, along with the stability of bifurcating periodic, has been established. Also, there are specific operating conditions that must be met in order to achieve Hopf bifurcation. In the proposed autonomous system, we analyze the procedures for designing chaotic based systems including parameter selection, discretization of the results, as well as exploring the changing regularity of the bifurcation value. A series of numerical simulations is presented to illustrate the analytical results.
Accelerated failure time models in analyzing duration of employment
Authors: Basha L , Gjika E 
Published: July 2022

AbstractParametric accelerated failure time models (AFT), that assess the relationship between the event times and dependent variables constitute an essential class of regression models. In this research we have used the accelerated failure time models: in particular, we have taken into consideration exponential distribution, Weibull distribution, log-logistic model and lognormal distribution, to analyze the duration of employment for employees that have been in their current job or otherwise known as job tenure. A variety of commercial and public companies in Albania were surveyed for this study. Initially the goal of the research is to find the optimal probability distribution to suit the data. Then, the accelerated failure time model is used to assess the impact of the predictors, such as: the employee's age, wages, the employee's age when he has started the job, sex, profession, academic degree, marital status and years of experience prior to this position, in job termination. The log-normal AFT model was the most accurate model for predicting the outcomes of the study, and from this model we can conclude that the employee's age, the employee's age when he has started the job, wages, academic degree and position in the company can affect how long someone stays employed.
Prediction Method of Public Participation in Decision-making Based on Bayes Theorem
Authors: Shang Jin , Feng Qinyu 
Published: July 2022

Abstract
Public participation is an activity that citizens participate in the decision-making without government and experts. It could create better informed and more creative decision-making. However, it is difficult to invite all stakeholders to participate in the urban design and planning project. So most of projects could just receive a few decisions from the public. This article would introduce a model based on Bayes theorem. This algorithm model can predict the possibility of other stakeholders' decision-making on a proposal based on the feedback results of a small part of the surveyed participants and big data analysis. This article mainly studies how the algorithm model can be displayed in the actual project through a case of local residents' decision-making and prediction of the building height of the planned area in a planning project. Key words: Public participation, Decision-making prediction, Bayes theorem
A Machine Learning Model for Healthcare Stocks Forecasting in the US Stock Market during COVID-19 Period
Authors: Jariyapan Prapatchon , Singvejsakul Jittima , Chaiboonsri Chukiat 
Published: July 2022

Abstract
This paper study the nowcasting and forecasting for the healthcare stock price in the united states during the Covid-19 period including the google trend data information. The data is collected in monthly data from 2015 to 2020 which are five interested stock price indexes in the healthcare sector. Empirically, the finding reveals that the Bayesian structural time series analysis can be used to investigate the stock price indexes with the google trend data is becoming useful for the prediction in term of current movement. In term of the machine learning algorithms, the unsupervised learning k-Mean algorithm is employed to cluster the cycle regimes of the stock market which provided three regimes such as Bull market, Sideways and Bear market. There are twenty-nine months stand for bull market, thirty-seven months are predictively provided sideways market and five months are referred as the bear market. Additionally, the supervised learning algorithms by using the Linear Discriminant Analysis (LDA), k-Nearest Neighbors (kNN) and Support vector machine (SVM) are used to investigate the cycle regimes of healthcare stock in next five year. The results indicated that LDA is chosen by the highest coefficient validation which represented the the regimes of stock in the healcare sector of the unites states of America will stay on the sideways periods in the next five years. Thus, the finding in this paper can be the useful information for investor to manage their portfolio especially, in healthcare sector during the Covid-19 period.
Information Flow Between Crude Palm Oil and Crude Oil Futures
Authors: Jeong M , Kim S , Yi E 
Published: July 2022

AbstractThis study finds asymmetric information flow from the crude palm oil (CPO) futures to the West Texas Intermediate (WTI) crude oil futures market despite the CPO futures market’s low liquidity and small market capitalization. Our finding is robust regardless of the 2019 Coronavirus outbreak and the asymmetric information flow becomes even unilateral considering the exchange rate risk on the Malaysian Ringgit. Finally, we explain the asymmetric information flow from the CPO futures to WTI futures market given that the impact of speculation on market efficiency crowds out that of liquidity.
The Structural Design and Analysis of a Symmetrical Twin-tower Conjoined Building with Vibration Absorber
Authors: Deng Boyuan 
Published: July 2022

Abstract
Throughout the ages, earthquakes have caused numerous instability and destruction of buildings. With the increasing of the height and volume of modern buildings, the seismic force on the buildings gradually increases. To avoid serious damage and ensure the safety, seismic design has become a crucial element in the analysis and design of the structure. In traditional seismic design, increasing stiffness of the building is usually adopted to reduce the structural response during an earthquake. This approach is usually costly, and the effect of practical application is not obvious, which could not fundamentally enhance the bearing capacity of the structure. Basing on existing researches, both theoretical and physical model of symmetrical, conjoined twin tower is made, meanwhile, the reasonable design using damping vibration absorber with the function of energy dissipation, utilizes the movement of additional weight to reduce vibration amplitude of main body structure. The use of shaking table test on the analysis and verification of bearing capacity of the model of symmetrical, conjoined twin tower with vibration absorber, improves the seismic performance of the building. It helps propel innovative and practical design ideas in structural seismic design of symmetrical conjoined twin tower.
The Glauber Model And Flow Analysis with Pb-Pb Collisions at

SNN=2.76 TeV
Authors: Hu Ceran , He Wentao , Lyu Chengze , Yang Kangjie , Yao Lily , Zhao Ruichong 
Published: July 2022

Abstract
This work presents data analysis on Pb-Pb collisions with

S

N
N

=
2.76

TeV

in centrality 40%-50%. Introduction and Monte-Carlo simulation results on the Glauber model are presented, which shed light on the initial geometric configuration of heavy ion collisions. Three-dimensional correlation function is plotted, and Fourier decomposition is carried out in order to obtain elliptic flow. Based on the assumption that non-flow effect is less prominent in long-range area, we separate it from the second Fourier decomposition of two-particle correlation function by making polynomial curve fitting.
Moderate Radio Flux Density of Single and Group III Bursts from AR2887 Active Region on 26th October 2021
Authors: Hamidi Z S , Chen Chen L. , Nurlisman Z. K.D. 
Published: July 2022

Abstract
The radio flux density is one of the most important parameters for predicting solar activity, such as solar flares and Coronal Mass Ejections. Compared to other burst types, solar burst type III (SRBT III) occurs most frequently due to solar flare phenomena. In the meter and decimetre wavelength range, gyro synchrotrons dominate, as do localized disturbances in the plasma frequencies caused by SRBT III. In this paper, we examine the most recent solar event that took place on 26th October 2021 in this gradual phase of the 25th solar cycle and can be detected from our site. In order to compare our results to other regions of the electromagnetic spectrum, we also used the data from the Solar Monitor and Space Weather Live website, where the data from NOAA and SDO/AIA were made publicly available. A wavelength of 10.7 cm corresponds to 101 solar flux units. Approximately 4.7 nT of interplanetary magnetic field exists and 1.7 nT in the northward direction. In the past 6 hours and 24 hours, flares of class C2 and M1 have been detected due to active region AR2887 generated by a 'beta-gamma' magnetic field.
Effects of CME-induced Geomagnetic Storm on Geomagnetic Induced Current at High and Middle Latitudes
Authors: Mohamad Ansor N , Hamidi Z S 
Published: July 2022

Abstract
Geomagnetic storm has been one of greatest events in space weather studies. Apart from the formation of aurora, geomagnetic induced current (GIC) is also induced during the storm when the storm intensity gets severe. Coronal mass ejection (CME) is a massive eruption created by the Sun and is believed to be the driver of geomagnetic storm. This paper is aimed to examine the effects of CME-induced geomagnetic storm on geomagnetic induced current (GIC) at high and middle latitudes by studying four levels of storm: minor (Kp 5), moderate (Kp 6), strong (Kp 7) and severe (Kp 8). We applied qualitative analysis through descriptive approach to describe the relationship between the storm intensity and GIC activity. Since the exact value of GIC were inaccessible, we adopted horizontal component time derivative magnetic field (dH/dt) as the indicator for GIC activity. At the end of the study, we found that the GIC highly depends on the storm intensity, by which higher levels of storm triggers more actively induced current. Besides, high latitude ionosphere was showing strong reactions to the storm arrival compared to middle latitude ionosphere as the magnitudes of dH/dt recorded by Barrow station at high latitude fluctuated within wider and higher range.
A Survey of the Influence of Air Distribution on Indoor Environment and Building Energy Efficiency
Authors: Ma Longkai , Zeng Zhili 
Published: July 2022

Abstract
With society paying more and more attention to energy conservation, building energy conservation has become a problem that must be considered in the design phase. In addition, modern life makes people spend more time indoors every day. Reasonable indoor air distribution positively impacts a comfortable indoor thermal environment and reduces building energy consumption. This paper discusses the influence of air distribution on indoor thermal comfort and building energy saving. Reasonable air distribution in the air-conditioned room will create a comfortable indoor thermal environment, which is helpful for the physical and mental health of personnel and efficient work. Meanwhile, it can also effectively reduce the equipment's initial investment and operating energy consumption and realize building energy savings. The design of indoor air distribution cannot be a single, isolated project. A comprehensive analysis based on each station's environmental thermal comfort requirements in the work area must be carried out to reduce air-conditioning operation energy consumption and achieve building energy-saving goals. For existing buildings and large space buildings, the corresponding air distribution design scheme should be designed reasonably. This paper also suggests future research directions on indoor air distribution.
A New Risk-based Excavation Inspection Decision-making for Long-distance Pipelines
Authors: Lu Duhui , Cong Guangpei , Li Bing 
Published: July 2022

Abstract
With the number of long-distance pipelines increasing in China,the excavation inspection and maintenance is the most important means to avoid the leakage of pipeline. However, the current excavation and maintenance decision-making models are absent to make satisfying results based on the comprehensive data of pipeline. To address this problem, a new quantitative risk-based model is proposed to guide decision-making on excavation inspection and maintenance. Based on previous failure cases, the model includes data about the surrounding soils as well as about the pipeline’s protective layer, cathodic protection and thickness readings. Case of the proposed model on previous failure cases shows that the new model can correctly predict a rational excavation inspection and maintenance span for a long-distance pipeline during its whole service life.
Development of Particle-fluid Drag Model for Individual Wheat Straw Particle
Authors: Yang Yang , Zhu Hongguang 
Published: July 2022

Abstract
To deal with the sedimentation of wheat straw particles in the process of conveying and mixing, the drag model of the non-spherical particle is to be developed. In this work the settling velocity of sheared and the defibered particles are acquired by image analysis. The simulating settling velocity of particle is acquired by ANSYS Fluent. As a result, the KG-based drag models with shape factors of Π, AR, BR, and Φ are obtained respectively by fitting the relation between drag coefficient and particle Reynold for 24 sheared particles. The settling velocity of 24 particles are acquired by simulation. In addition, the six drag models are used to simulate settling velocity of 134 defibered particles. At last, the means value of relative error of velocity between the shear and the defibered particles are acquired, they are 20%, 23%, 19%, 59%, respectively, corresponding to the shape factors of Π, AR, BR and Ψ. Considering the pValue of curve fitting, the drag model with shape factor Π is best for simulating the wheat straw particles in particle-liquid flow.
Modelling of a Stilling Basins with Sloping Apron in IBER to Improve Efficiency in High-slope Rivers
Authors: Zabaleta Y , Espejo O K , Fernández J M , Santos S 
Published: July 2022

Abstract
This research shows the influence of stilling basin slopes on energy loss in rivers with a high gradient. This study takes as a case San Pedro water intake (Ayacucho, Peru). The main objective is to improve efficiency of stilling basins in rivers with high slope. Five dissipation pools of different slopes were modelled: 0%, 1.52%, 3.04%, 4.56% and 6.08% to propose the optimum pool among these, for the San Pedro intake. Results were validated by means of a Sensitivity Analysis, trough comparison with the results of previous investigation and results of modelling San Pedro river with HEC-RAS and IBER. It was obtained that the steeper the slope of the stilling basin, the higher the specific energy loss, the higher the output rate, the longer the stilling basin. It can be concluded that the 3.04% slope stilling basin is the most appropriate for the 6.08% slope river since the slope variation is not abrupt as in the case of the horizontal one, that is, 30% more energy loss with respect to the horizontal pool and velocity and Froude results similar to the modelling of the San Pedro river.