Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal operation efficiency and control of water pumps in multiple water reservoir system: case study in Korea

Pumping regimes for water distribution systems have been established to reduce pump operation costs. Optimization of water reservoir networks has been studied extensively for operating pumps efficiently. According to research, optimization reduces pump operation costs by approximately 10–20%. However, comparing economic benefits per country would be too complex because the effects of optimization differ depending on the power cost estimation system of each country and the existing operations. This study presents an optimization algorithm to minimize the operating costs of an entire system via the application of dynamic programming. Case studies were conducted to apply the algorithm to real values in four scenarios. By setting cost minimization as an objective, an optimum water pump operation plan was derived that encompasses operating conditions, variations in water demand, and energy costs. Results showed that operating a standby pump in an existing system can increase the system9s efficiency. In addition, even if hourly water consumption remains constant, each case requires its own operating method, because each case has different operational costs. By considering various conditions such as those mentioned in this study, future pump operation schedules can be optimized and stabilized to maintain economic water supply.
Discovering Relationships between Skin Type and Life Style Using Data Mining Techniques: A Case Study of Korea

With the growing interest in skincare and maintenance, there are increasing numbers of studies on the classification of skin type and the factors influencing each type. This study presents a novel methodology by using data mining, for the determination of the relationships between skin type, lifestyle, and patterns of cosmetic utilization. Eight skin-specific factors, which are moisture, sebum in U-zone (both cheeks), sebum in T-zone (forehead, nose, and chin), pore, melanin, wrinkle, acne, hemoglobin, were measured in 1,246 subjects living in South Korea, in conjunction with a questionnaire survey analyzing their lifestyles and pattern of cosmetic utilization. Using various multivariate statistical methods and data mining techniques, we classified the skin types based on the skin-specific values, determined the relationship between skin type and lifestyle, and accordingly sorted the subjects into clusters. Logistic regression analysis revealed gender-related differences in the skin; therefore, separate analyses were performed for males and females. Using the Gaussian Mixture Modeling (GMM) technique, we classified the subjects based on skin type (two male and four female). Using the ANOVA and decision tree techniques, we attempted to characterize the relationship between each skin type and the lifestyles of the subjects. Menstruation, eating habits, stress, and smoking were identified as the major factors affecting the skin.
Covering points with axis parallel lines

In this thesis, we study the problem of covering points with axis parallel lines. We named it as the point cover problem. Given a set of the n points in d dimensional space, the goal is to cover the points with minimum number of axis parallel lines. We use the iterative rounding approach which gives an integral solution to the problem. We also propose a combinatorial algorithm by using the branch and bound technique which gives an optimal integral solution to the point cover problem. Later we implemented another approximation algorithm based on primal-dual and iterative rounding approaches. Finally we show the comparative analysis between the two iterative rounding algorithms.
An Efficient Network Intrusion Detection and Classification System

Intrusion detection in computer networks is of great importance because of its effects on the different communication and security domains. The detection of network intrusion is a challenge. Moreover, network intrusion detection remains a challenging task as a massive amount of data is required to train the state-of-the-art machine learning models to detect network intrusion threats. Many approaches have already been proposed recently on network intrusion detection. However, they face critical challenges owing to the continuous increase in new threats that current systems do not understand. This paper compares multiple techniques to develop a network intrusion detection system. Optimum features are selected from the dataset based on the correlation between the features. Furthermore, we propose an AdaBoost-based approach for network intrusion detection based on these selected features and present its detailed functionality and performance. Unlike most previous studies, which employ the KDD99 dataset, we used a recent and comprehensive UNSW-NB 15 dataset for network anomaly detection. This dataset is a collection of network packets exchanged between hosts. It comprises 49 attributes, including nine types of threats such as DoS, Fuzzers, Exploit, Worm, shellcode, reconnaissance, generic, and analysis Backdoor. In this study, we employ SVM and MLP for comparison. Finally, we propose AdaBoost based on the decision tree classifier to classify normal activity and possible threats. We monitored the network traffic and classified it into either threats or non-threats. The experimental findings showed that our proposed method effectively detects different forms of network intrusions on computer networks and achieves an accuracy of 99.3% on the UNSW-NB15 dataset. The proposed system will be helpful in network security applications and research domains.
Blind Channel Estimation based on Hadamard Matrix Interstream Transmission for Multi-Cell MIMO Networks

In this paper, we introduce a Hadamard matrix interstream transmission based blind channel estimation for multi-cells multiple-input and multiple-output (MIMO) networks. The proposed scheme is based on a network with mobile stations (MS) which are deployed with multi cells. We assume that the MS have the signals from both cells. The signal from near cell are considered as desired signal and the signals from the other cells are interference signal. Since the channel is blind, so that we transmit Hadamard matrix pattern pilot stream to estimate the channel; that gives easier and fast channel estimation for large scale MIMO channel. The computation of Hadamard based system takes only complex additions, and thus the complexity of which is much lower than the scheme with Fourier transform since complex multiplications are not needed. The numerical analysis will give perfection of proposed channel estimation.
Dynamics applications in the vehicle and two-wheeler accidents

Conflicts between the parties have been increased due to multiple accidents between vehicles and two-wheelers. The main problem lies to the responsibility identification in the accident. Speed collision by applying to scientific and accurate accident reconstruction is an important part for the responsibility identification in an accident. Thus, the different forms of accident analysis is of great significance for improving the handling level in accidents. In the research on analysis methods in the accident between vehicles and bicycles, the braking traces, the throw distance models of the rider and the bike, as well as the throw distance fallout will be analyzed.
Exploration of the Potential Transcriptional Regulatory Mechanisms of DNA Methyltransferases and MBD Genes in Petunia Anther Development and Multi-Stress Responses

Cytosine-5 DNA methyltransferases (C5-MTases) and methyl-CpG-binding-domain (MBD) genes can be co-expressed. They directly control target gene expression by enhancing their DNA methylation levels in humans; however, the presence of this kind of cooperative relationship in plants has not been determined. A popular garden plant worldwide, petunia (Petunia hybrida) is also a model plant in molecular biology. In this study, 9 PhC5-MTase and 11 PhMBD proteins were identified in petunia, and they were categorized into four and six subgroups, respectively, on the basis of phylogenetic analyses. An expression correlation analysis was performed to explore the co-expression relationships between PhC5-MTases and PhMBDs using RNA-seq data, and 11 PhC5-MTase/PhMBD pairs preferentially expressed in anthers were identified as having the most significant correlations (Pearson’s correlation coefficients > 0.9). Remarkably, the stability levels of the PhC5-MTase and PhMBD pairs significantly decreased in different tissues and organs compared with that in anthers, and most of the selected PhC5-MTases and PhMBDs responded to the abiotic and hormonal stresses. However, highly correlated expression relationships between most pairs were not observed under different stress conditions, indicating that anther developmental processes are preferentially influenced by the co-expression of PhC5-MTases and PhMBDs. Interestingly, the nuclear localization genes PhDRM2 and PhMBD2 still had higher correlations under GA treatment conditions, implying that they play important roles in the GA-mediated development of petunia. Collectively, our study suggests a regulatory role for DNA methylation by C5-MTase and MBD genes in petunia anther maturation processes and multi-stress responses, and it provides a framework for the functional characterization of C5-MTases and MBDs in the future.
Technologies for the integration of Through Silicon Vias in MEMS packages

Ein vertikales Stapeln von Si-Chips stellt eine neue Moglichkeit zur Erhohung der Bauelemente-Integrationsdichte in Gehausen dar. Chips werden dafur aufeinander platziert, fixiert und untereinander durch vertiakle Durchfuhrungen (Through Silion Vias) verbunden. In dieser Arbeit wird ein neuer Ansatz zur Integration von Through Silicon Vias in 3D MEMS - Aufbauten diskutiert.
Assessment of a Web-Enhanced Course in Numerical Methods*

Effectiveness of web-based modules developed for a course in numerical methods was measured via three mixed assessment instruments—student satisfaction survey, student performance on a multiple-choice examination based on Bloom’s taxonomy, and summative rating of the modules based on content, learning, usability and technology. The web-based modules are holistic and are customized based on a student’s engineering major and choice of computational system. Statistical analysis of the assessment data indicates that web-based modules improved both student satisfaction and performance. BACKGROUND AND PEDAGOGY AT UNIVERSITY of South Florida (USF), we have developed web-based modules for faculty teaching and for students enrolled in a juniorlevel course in numerical methods. The features of the web-based modules are discussed only summarily in this article since the complete details are already available [1, 2]. Our main objective here is to report the implementation of the webbased modules and the assessment data to measure the effectiveness of these modules. The authors emphasize that this paper is also being written to describe the assessment instruments (Appendices A, B, and C) so that educators who are creating webbased modules for other engineering courses can use them.
Security Issues in Cloud and Mobile cloud: A Comprehensive Survey

ABSTRACT Cloud computing is one of the rapidly growing technologies in computing. It includes many benefits such as improved reliability, enormous scalability, decreased costs, portability, enhanced geographic coverage with fastest time, less infrastructure investment though it has challenges such as data security, insufficient resources, skill, etc. For past few years, cloud computing has grown considerably in information technology. Safety of information is a great concern as enormous information of individuals and companies was being stored in cloud. Many software giants such as Microsoft joined hands to build cloud services. The growth of cloud computing had been affected by data security issues, which leads to complexity with data privacy and protection. The objective of this work is to study and analyze the security issues in cloud computing. The security issues pertaining to network, data, application, storage, and information in cloud domain with the algorithms and tools deployed were addressed and reviewed elaborately.
Effectiveness of Scientific Methods in Detecting Phishing Websites in Jordan

In this study, we measure and evaluate the effective ness of the scientific methods used by forensic laboratory, detective and preventive police in Amman (Jordan) for detecting phishing websites. A new method that uses the URL to strengthen anti-phishing in Jordan is introduced. Experimental results of the proposed method are compared with the currently used one. The quantitative data obtained from forensic laboratories investigators, detective and preventive police is analyzed using descriptive statistics (SPSS), including percentages, frequencies, means, and standard deviations for each study variable. A P- value of less than 0.05 will considered statistically significant. The loss and damage caused by phishing attacks via Internet; whether it is social, financial or moral is a serious issue to consider. This result could affect the Internet downsizing from users. Beside the weak technology used by users or organizations that encourage phishers to attack them is that attackers are updating their frequently and very fast. So organizations must in turn update their technologies and methods of protection frequently to protect their users. Descriptive, exploratory cross-sectional design is used in this study. A total of questionnaire in the three public security directorates are used in Amman-Jordan during December, 2012. The use of cross-sectional design may facilitates the identification of inter relationships among the variables in a short period.
A Mathematical Study of COVID-19 Spread by Vaccination Status in Virginia

We introduce a novel n-stage vaccination model and corresponding system of differential equations that stratify a population according to their vaccination status. The model is an extension of the classical SIR-type models commonly used for time-course simulations of infectious disease spread and allows for the mitigation effects of vaccination to be uncoupled from other factors, such as changes in social behavior and the prevalence of virus variants. We fit the model to the Virginia Department of Health data on new COVID-19 cases, hospitalizations, and deaths broken down by vaccination status. The model suggests that, from 23 January through 11 September, fully vaccinated individuals were 89.8% less likely to become infected with COVID-19 and that the B.1. 617.2 (Delta) variant is 2.08 times more transmissible than previously circulating strains of COVID-19. We project the model trajectories into the future to predict the impact of booster shots.
Construction Control of Long Span Bridges

The structural types and functions of modern long span bridges tend to become more and more complicated.To ensure the smooth proceeding of construction of the bridges and to achieve the anticipated goal of designed completion status of the bridges,the corresponding construction control measures based on the detailed theoretic analysis and the tracking measurement to be implementde in the construction process are necessitated.The construction control of the bridges generally include the geometry control,stress control,stability control and influential factor analysis and the methods for the construction control may fall into the post-control,precontrol, self-adaptability control and maximum tolerance control.The authors herewith present the necessity,task,principal contents,control methods,structural calculation and analysis methods of the construction control of the bridges and the factors having influence on the control. By way of examples of three bridges of different structural types,i.e.,the auxiliary bridge(continuous rigid-frame structure) of Sutong Bridge,Zhongxian Changjiang River Bridge(cablestayed bridge) and Wuhan Yangluo Changjiang River Bridge(suspension bridge),the authors also describe in considerable detail the principal contents,methods and the results of the construction control of long span bridges.
A Novel Deep Learning Model for Detection of Severity Level of the Disease in Citrus Fruits

Citrus fruit diseases have an egregious impact on both the quality and quantity of the citrus fruit production and market. Automatic detection of severity is essential for the high-quality production of fruit. In the current work, a citrus fruit dataset is preprocessed by rescaling and establishing bounding boxes with labeled image software. Then, a selective search, which combines the capabilities of both an extensive search and graph-based segmentation, is applied. The proposed deep neural network (DNN) model is trained to detect targeted areas of the disease with its severity level using citrus fruits that have been labeled with the help of a domain expert with four severity levels (high, medium, low and healthy) as ground truth. Transfer learning using VGGNet is applied to implement a multi-classification framework for each class of severity. The model predicts the low severity level with 99% accuracy, and the high severity level with 98% accuracy. The model demonstrates 96% accuracy in detecting healthy conditions and 97% accuracy in detecting medium severity levels. The result of the work shows that the proposed approach is valid, and it is efficient for detecting citrus fruit disease at four levels of severity.
A comparative study of bacterial diversity based on effects of three different shade shed types in the rhizosphere of Panax quiquefolium L.

Background Shading is an important factor affecting the cultivation of American ginseng, as it influences crop quality and yield. Rhizosphere microorganisms are also crucial for normal plant growth and development. However, whether different shade types significantly change American ginseng rhizosphere microorganisms is unknown. Methods This study evaluated the rhizosphere soils of American ginseng under traditional, high flag and high arch shade sheds. High-throughput 16S rRNA gene sequencing determined the change of rhizosphere bacterial communities. Results The microbial diversity in rhizosphere soils of American ginseng significantly changed under different shading conditions. The bacteria diversity was more abundant in the high arch shade than flat and traditional shades. Different bacterial genera, including Bradyrhizobium, Rhizobium, Sphingomonas, Streptomyces and Nitrospira, showed significantly different abundances. Different shading conditions changed the microbial metabolic function in the American ginseng rhizosphere soils. The three types of shade sheds had specific enriched functional groups. The abundance of ATP-binding cassette (ABC) transporters consistently increased in the bacterial microbiota. These results help understand the influence of shading systems on the rhizosphere microecology of American ginseng, and contribute to the American ginseng cultivation.
Digitally Moving an Electric Guitar Pickup

This paper describes a technique to transform the sound of an arbitrarily selected magnetic pickup into another pickup selection on the same electric guitar. This is a first step towards replicating an arbitrary electric guitar timbre in an audio recording using the signal from another guitar as input. We record 1458 individual notes from the pickups of a single guitar, varying the string, fret, plucking position, and dynamics of the tones in order to create a controlled dataset for training and testing our approach. Given an input signal and a target signal, a least squares estimator is used to obtain the coefficients of a finite impulse response (FIR) filter to match the desired magnetic pickup position. We use spectral difference to measure the error of the emulation, and test the effects of independent variables fret, dynamics, plucking position and repetition on the accuracy. A small reduction in accuracy was observed for different repetitions; moderate errors arose when the playing style (plucking position and dynamics) were varied; and there were large differences between output and target when the training and test data comprised different notes (fret positions). We explain results in terms of the acoustics of the vibrating strings.
Cell-Free Massive MIMO: Distributed Signal Processing and Energy Efficiency

In this era of rapid wireless technological advancements, wireless connectivity between humans, humans with machines, and machines with machines is gradually becoming an absolute necessity. The initial motivation for wireless connectivity was to enable voice communication between humans over a geographical area. Thanks to cellular communications advancements in the past decade, cellular wireless connectivity has become a global success, starting from 1G to the present generation 5G. However, the needs of humans often evolve with time, and now the world is witnessing an ever-growing demand for the internet with high data rates besides reliable voice communication. Current cellular networks suffer from non-uniform data rates across a cell, i.e., users at the cell center and the cell edges experience significant variations in signal-to-noise ratio, making the cellular technology less reliable to meet the future data demands. Moreover, cellular networks operating as cells, i.e., an access point (AP, the term we would use instead of base station) serving the users within its geographical location, cannot leverage the network’s total capacity without cooperation among APs of the neighboring cells. One potential solution is moving away from the cell to cell-free networks wherein all the APs will serve all the users within the geographical coverage area. Thus, there is a need for a paradigm shift in how cellular networks operate. Towards the goal mentioned above to fully leverage the network capacity, the Cell-Free Massive multiple-input-multiple-output (MIMO) technology is expected to be the next potential technology beyond 5G combining the benefits of Massive MIMO and cell-free distributed architectures. Distributed architectures require distributed signal processing algorithms, and also energy consumption of the network is crucial. Keeping in view the practical ease in deployment, we consider a sequentially connected CellFree Massive MIMO network called a “radio stripe”. In the first part of the thesis, we focus on developing an optimal sequential algorithm in the sense of mean-square-error (MSE) which has the same performance as that of centralized Cell-Free Massive MIMO implementation with the