Miracle-dz/UniFAR-FTUs huggingface dataset:
State Feedback Design for Multi-machine Power System Using Genetic Algorithm

An optimal design of state feedback, as a power system stabilizer (PSSs) is presented based on genetic algorithm (GA) by using eigenvalue-based objective functions. The practical implementation of PSSs and The relative stability of low-frequency modes are all included in the constraints. The locally measured states are fed back at the AVR reference input of each machine after multiplication by suitable feedback gains. The proposed method is confirmed by eigenvalue analysis and simulation results for a multi-machine power system under different operating conditions.
A LolliMon Foundation for Distributed Trust Management

Distributed trust management supports resource protection in modern distributed computing environments. Trust management systems provide a framework to express security policies, and provide actors with a means to establish trust relations across machine boundaries. Trust management systems such as SPKI/SDSI [18] and RT [13] are especially suited to modern distributed computing environments, since they establish security in decentralized settings, where collaborations between actors are loose and dynamic. This paper focuses on logical foundations for trust management system specification and implementation, particularly within the RT framework.
Russian Generations: Sequencing the Transition to Adulthood

This paper demonstrates how Russian men and women from different generations undertake their first demographic and socio-economic events. For the first attempt at understanding how these events are ordered, we used the descriptive techniques of Sequence Analysis, including chronograms, parallel coordinate plots, the durations of statuses, and the frequencies of subsequences on tables. The study was performed on a panel of the Russian part of the “Generations and Gender Survey” (GGS: 2004, 2007, and 2011). The subsample consists of 4,595 respondents of 1935-1984 years of birth. Our analysis reveals the changes between sexes and generations. Men devote a significant part of their youth to achieving socio-economic events, while women much earlier and more actively initiate their demographic careers. Nevertheless, by the age of 35, there are more respondents among men who have children and relationships than among women. Young people, compared to older generations, much more actively enter into cohabitations and have children in them, but they delay the onset of all these events, especially childbearing, to later ages.
Use of Different Antibiotic Combinations against Fluoroquinolone-Resistant Salmonella enterica from Humans in Egypt

MERGENCE of antibiotic resistance by pathogenic Salmonella . spp. is a worldwide problem. Antibiotic combination was used as a clinical cure to solve the problem. Over one and a half years , one hundred gastroenteritis bacterial pathogens were collected from three Egyptian hospitals from fecal and blood sources. 58% Salmonella isolates were purified and identified using phenotyping and serotyping techniques. Out of the 58% Salmonella spp. , 41.4% represented S. Typhimurium, 27.6% S. Enteritidis, 5.2% S. Typhi, 3.4 % S. Paratyphi A, 3.4 % S. Paratyphi B and 19.0% other Salmonella. A total of 36 (62%) out of 58 Salmonella spp. were fluroquinolone resistant by disk diffusion method. Resistance of five Salmonella strains to the fluroquinolone group was confirmed by MICs values. The level of these values was from 32 >512 μg/ml, which is higher than those recommended by CLSI. Seventy-five combined microtitre checkerboards were performed on the five fluroquinolone multiresistant Salmonella strains to assess the potential for combination therapy. No antagonism was observed with any combination. Synergy and additivity were achieved with 41.4% and 58.6%, respectively. Time-kill synergy was more often seen at 24hr. There is 100%, 50% and 40% agreement between time-kill and checkerboard results for three Salmonella strains. Resistant Salmonella has increased in Egypt. Fluroquinolone combination with β-lactams (gentamycin, amikacin) and aminoglycosides (cefotaxime) were effective in the treatment of resistant Salmonella Typhimurium, Enteritidis and Typhi.
In Vitro Effect of Gallium-Doped Bioactive Glass on Enamel Anti-Demineralization and Bond Strength of Orthodontic Resins

White spot lesions (WSL) that occur on teeth after orthodontic appliances have been attached are caused by bacterial demineralization of the enamel surface. This study investigated the anti-demineralization effect of orthodontic resins containing mesoporous bioactive glass nanoparticles (MBN) doped with gallium, which has antibacterial activity, as well as MBN with increased calcium and phosphate contents as these ions can remineralize enamel. Resins (CF, CharmFill Flow, Dentkist, Seoul, South Korea) containing 1%, 3%, and 5% Ga-doped MBN (GaMBN) were characterized using scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDX), X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, and isothermal tests, and their physical properties were measured in terms of Vickers microhardness, bracket retention force, and adhesive remnant index (ARI). Cell viability in the resins was confirmed by testing human dental pulp stem cells (hDPSCs), and ion release tests were performed after 1, 7, and 14 days to determine whether the resins released Ga3+, Ca2+, and PO4. After 14 days, antibacterial activity was determined using Streptococcus mutans (S. mutans)—the bacteria that causes tooth decay—and the chemical remineralization effect was investigated using a cycle of acid–base solutions. The microhardness of the resins increased with GaMBN concentration whereas their bracket retention force, ARI, and cell viability remained unchanged. The bacterial activity of the 5%-GaMBN resin decreased after 24 and 48 h; however, the change in activity was not statistically significant. Anti-demineralization testing demonstrated that the degree of enamel demineralization decreased as the GaMBN concentration increased, which indicates that resins containing 5%-GaMBN may be viable orthodontic adhesives for preventing WSLs.
Web Page Classification using Text and Visual Features

The world of Internet grows up every day. There are a large number of web pages actives at this moment and more are released every day. It is impossible to perform the web page classification manually. It was already developed several approaches in this area. Most of them only use the text information contained in the web pages, ignoring the visual content of them. This work shows that the visual content can improve the accuracies of the classifications that only use the text. It was extracted the text features of the web pages using the term frequency inverse document frequency method. As well, it was also extracted two different types of visual features: the low-level features and the local SIFT ones. Since the amount of the SIFT features is extremely high, it was created a dictionary using the “Bag-of-Words” method. After this extraction the features were merged, using all the types of combinations of them. It was also used the Chi-Square method that selects the best features of a vector. In the classification it was used four different classifiers. It was implemented a multi-label classification, for which we gave unknown web pages to the classifiers, so they could predict the main topic of the web page. It was also implemented a binary classification, for which we used only visual features to verify if a web page was a blog or non-blog. It was obtained good results that shows that adding the visual content to the text the accuracies improve. The best classification it was obtained using only four different categories, where was achieved 98% of accuracy. Later it was developed a web application, where the user can find out the main topic of a web page only inserting the web page URL. It can be accessed in ”http://scrat.isr. uc.pt/uniprojection /wpc.html”.
Profiles of Long Non-Coding RNAs and mRNA Expression in Human Macrophages Regulated by Interleukin-27

Macrophages play an essential role in the immune system. Recent studies have shown that long non-coding RNAs (lncRNAs) can regulate genes encoding products involved in the immune response. Interleukin (IL)-27 is a member of the IL-6/IL-12 family of cytokines with broad anti-viral effects that inhibits human immunodeficiency virus (HIV) type-1 and herpes simplex virus (HSV). However, little is known about the role of lncRNAs in macrophages affected by IL-27. Therefore, we investigated the expression profiles of mRNA and lncRNA in human monocyte-derived macrophages (MDMs) regulated by IL-27. Monocytes were differentiated in the presence of macrophage-colony stimulatory factor (M-CSF)- or human AB serum with or without IL-27, and these cells were the subject for the profile analysis using RNA-Seq. We identified 146 lncRNAs (including 88 novel ones) and 434 coding genes were differentially regulated by IL-27 in both M-CSF- and AB serum-induced macrophages. Using weighted gene co-expression network analysis, we obtained four modules. The immune system, cell cycle, and regulation of complement cascade pathways were enriched in different modules. The network of mRNAs and lncRNAs in the pathways suggest that lncRNAs might regulate immune activity in macrophages. This study provides potential insight into the roles of lncRNA in macrophages regulated by IL-27.
A restaurant process with cocktail bar and relations to the three-parameter mittag–leffler distribution

Abstract In addition to the features of the two-parameter Chinese restaurant process (CRP), the restaurant under consideration has a cocktail bar and hence allows for a wider range of (bar and table) occupancy mechanisms. The model depends on three real parameters, $\alpha$ , $\theta_1$ , and $\theta_2$ , fulfilling certain conditions. Results known for the two-parameter CRP are carried over to this model. We study the number of customers at the cocktail bar, the number of customers at each table, and the number of occupied tables after n customers have entered the restaurant. For $\alpha>0$ the number of occupied tables, properly scaled, is asymptotically three-parameter Mittag–Leffler distributed as n tends to infinity. We provide representations for the two- and three-parameter Mittag–Leffler distribution leading to efficient random number generators for these distributions. The proofs draw heavily from methods known for exchangeable random partitions, martingale methods known for generalized Pólya urns, and results known for the two-parameter CRP.
Blackhole Detection in 6LoWPAN Based Internet of Things: An Anomaly Based Approach

The Internet of things networks is vulnerable to many DOS attacks. Among them, Blackhole attack is one of the severe attacks as it hampers communication among network devices. In general, the solutions presented in the literature for Blackhole detection are not efficient. In addition, the existing approaches do not factor-in, the consumption in resources viz. energy, bandwidth and network lifetime. Further, these approaches are also insensitive to the mechanism used for selecting a parent in on Blackhole formation. Needless to say, a blackhole node if selected as parent would lead to orchestration of this attack trivially and hence it is an important factor in selection of a parent. In this paper, we propose SIEWE (Strainer based Intrusion Detection of Blackhole in 6LoWPAN for the Internet of Things) - an Intrusion detection mechanism to identify Blackhole attack on Routing protocol RPL in IoT. In contrast to the Watchdog based approaches where every node in network runs in promiscuous mode, SIEWE filters out suspicious nodes first and then verifies the behavior of those nodes only. The results that we obtain, show that SIEWE improves the Packet Delivery Ratio (PDR) of the system by blacklisting malicious Blackhole nodes.
A greedy algorithm for sparse recovery using precise metrics

We explore a new approach to reconstruct sparse signals from a set of projected measurements. Unlike older methods that rely on the near orthogonality property of the s ampling matrixΦ for perfect reconstruction, our approach can be used to reconstruct signals where the columns of the sa mpling matrix need not be nearly orthogonal. We use the blockwise matrix inversion formula[13] to get a clos ed form expression for the increase or decrease in the L2 norm of the residue obtained by eliminating or adding(respe ctiv ly) to the assumed support of the unknown signalx. We use this formula to design a computationally tractable a lgorithm to obtain the non-zero components of the unknown signal x. Compared to popular existing sparsity seeking matching pu rsuit algorithms, each step of the proposed algorithm is locally optimal with respect to th e actual objective function. Experiments show that our algorithm is significantly better than conventional techni ques when the sparse coefficients are drawn from N (0, 1) or decays exponentially.
Micro-Cap on 2-Core-Fiber Facet Hybrid Interferometer for Dual-Parameter Sensing

In this article, we demonstrate a “Fabry-Perot + Michelson” hybrid interferometer based on a polymer micro-cap located on asymmetric 2-core fiber (2CF) facet. Because the polymer micro-cap comprises a flat reflector and a concave reflector, the light propagating in the centric core will be reflected twice forming Fabry-Perot interference. Due to the bias angle between the eccentric core and the concave reflector, the light in the eccentric core reflects only once on the flat reflector and would interfere with the other reflected light in the 2CF. The experimental results show that Fabry-Perot and Michelson interference occur and are superimposed on the directly detected spectrum. Independent interferometric spectrums can be separated by Discrete Fourier Transform to achieve humidity-temperature simultaneous measurement based on coefficient matrix demodulation. Moreover, this new optical fiber sensing platform is all-solid, high-integration, and flexible, so it has potential in lab-on-fiber applications.
An Intrusion Detection System Using Unsupervised Feature Selection

Intrusion Detection Systems (IDS) has become an indespensive tool to protect the network by detecting the attacks. As each attack is associated with a large number of attributes, it is challenging to select a good set of attributes for achieving better classification accuracy. This work proposes an effective unsupervised feature selection technique using Genetic algorithm (GA) for analyzing IDS data. The search capability of GA has been utilized for optimizing different unsupervised feature quality measures including Pearson correlation, mutual information, and entropy. Different combinations of these features are utilized as fitness functions of the proposed GA based framework. The algorithm is able to find out that subset of features which are uncorrelated and mutually exclusive to each other. Finally, the optimal feature subset obtained is utilized for developing classification systems using some popular machine learning models like decision trees, support vector machines, k-nearest neighbor classifier on the KDD-Cup 99 dataset. The experimental results show that decision tree produces better results than other classifiers. The result confirms 99.62% accuracy, 98.78% detection rate and 0.25% false alarm rate. The most attractive feature of the proposed scheme is that it does not require any labeled information during the feature selection process.
Randomized Selective Search for Locating Object Candidates

The effective search for localizing object candidates is a significant method to enhance computational efficiency of object detection and recognition. In this paper, randomized selective search is proposed to improve the hierarchical grouping of neighboring regions and overlap bounding boxes. The main idea is to generate as many potential grouping samples of localizing object candidates as possible with a random neighboring region of the highest similarity. In order to efficiently extract candidates, an output of bounding boxes is selected randomly and combines with its all nearby overlapping boxes. Mean Average Best Overlap (MABO) scores are used to measure the best performance out of all the object candidates. Also, the proposed algorithm is assessed by comparing with the existing method evaluation. Experimental results indicates that the proposed method outperforms the existing one in terms of the quality of object location performance and the quantity of bounding box windows. keywords: Object candidates, Hierarchical grouping, Random neighboring, Highest similarity, Bounding boxes student number: 2015-20938
A K To Ka Band Single-ended To Balanced Ultra-wideband LNA In 45nm CMOS SOI

In this paper, a K to Ka band single-ended to balanced ultra-wideband low noise amplifier (LNA) is presented. By employing shunt feedback network, the LNA circuit can achieve a better wideband performance. On the other hand, adding another transistor in the first-stage circuit to invert the signal, the LNA circuit can operate at a differential mode while maintaining a single-ended signal input. In addition, a capacitance cross coupling network is introduced to solve the problem of signal amplitude inconsistency. Using a standard 45nm SOI CMOS process, the proposed LNA exhibits 23 dB gain and a noise figure of 2.7 dB to 3.2 dB from 18 to 40 GHz, while the input and output return loss are less than 10 dB, respectively.
Simulation and Analysis of Ionospheric Electric Field and Current Based on the Electrodynamic Process

Research of ionospheric electric current density is generally based on some theoretical formulas and empirical models. The electrodynamic process of charged particles in plasma with a magnetic field can help analyze the formation mechanism of ionospheric electric field and current so that the ionospheric electric field intensity and current density can be simulated and studied. Furthermore, by establishing partial differential equations in magnetic dipole coordinates, electric current density in mid- and low-latitude ionosphere can be calculated by means of a series of more reasonable boundary conditions in this article. Besides, the wind drag and particles diffusion are taken into account to make the results more accurate. The electric field value fitted by the empirical model is used to verify the rationality of current density calculated indirectly. Through this theoretical model, the distribution of electric fields and currents in ionosphere has been carefully studied. In addition, several physical elements influencing the current value and direction are also studied, including external environmental factors, such as solar activity and geomagnetic index, as well as the internal factors, such as electrons and ions temperature. These works will be a great help in the future for the precise determination of ionospheric currents.
Multi-modal authentication system based on audio-visual data

Biometrics is an emerging technology in this era, which has been widely used in many application such as secured access to a computer and any other system, criminal identification, person authentication, etc. Face recognition is a biometric method of identifying a person by comparing the data with the stored information of that person. But the recognition of face can be affected by illumination variation, facial expression and other issues. Therefore, the authentication using only face image might be difficult for the system. To overcome this challenge, biometric authentications have to rely on more than one method. In this paper, we consider audio-video person authentication based on two sources of information. Single modality evidence has limitations in both security and robustness. Therefore, here, audio recognition is added with visual recognition. Facial features, as well as speech is extracted separately from audio-visual data and integrate both the modality for secure user authentication. Mel Frequency Cepstral Coefficients (MFCC) is used as the speech feature. Viola-Jones and Scale-Invariant Feature Transform (SIFT) algorithm are used for visual feature extraction. After the extraction of audio and visual features, the feature selection method is employed. The two-phase algorithm consisting of statistical analysis, Analysis of Variance (ANOVA) with Incremental Feature Selection (IFS) is proposed to select significant features from audio-visual data. The Audio and Video processing is done in two separate phases using machine learning algorithms. The results of both the modalities are then combined at the decision level based on majority voting. It has been observed that multiple modalities of both audio-visual information give immensely good results compared to a standalone single modality.
Angular MIMO for Underwater Wireless Optical Communications: Channel Modelling and Capacity

An alternative architecture for imaging multiple-input/multiple-output (MIMO) underwater wireless optical communications (UWOCs) is proposed which relies on the transmission in angle rather than directly in space. The angular MIMO (A-MIMO) system has advantages over conventional underwater MIMO systems (C-MIMO) in that the image magnification is invariant to link range, and angular transmission greatly relaxes the alignment requirements of such links. These features are especially important for underwater wireless sensor networks (UWSNs), where the maintaining alignment between underwater vehicles and floating sensors nodes may be difficult in such dynamic environments. In this paper, we describe the architecture of A-MIMO for underwater channels and numerically characterize it in clear and coastal seawater conditions. For comparison a C-MIMO underwater system with similar parameters is also characterized and the channel capacities for A-MIMO and a C-MIMO are contrasted against link distances and misalignment errors. The numerical results show that A-MIMO maintains the link capacity against the misalignment conditions far better than C-MIMO while using a smaller size transceiver.
An Assessment of Decisional Balance, Self Efficacy, and Condom Stages of Change Among White and Minority Women

Between 1992 and 1999, the number of persons living with AIDS increased in all groups of women in the United States. In terms of ethnicity, the number of African American and Hispanic American infected with HIV has increased tremendously. In order to address the HIV pandemic among ethnic women in the United States, systematic methods for studying condom use and HIV prevention are being developed and validated. The Transtheoretical Model (TTM) has been postulated as an integrative and comprehensive model of intentional behavior change that incorporates process oriented variables in an effort to predict how and when people adopt healthy behaviors. The purpose of this study is to assess the effectiveness of the decisional balance and selfefficacy constructs of the TTM in predicting condom stage of change for white and minority women. Much research has been done applying the TTM to white populations , but little has been done to apply this model to minority women. Because HIV/ AIDS is such a prevalent disease among these groups and because there are cultural and ethnic differences among these groups, assessing the effectiveness of this model between white and minority women is essential. In this study, it was hypothesized that most women would be in earlier condom stages of change or not ready to use condoms. Decisional balance and self-efficacy variables were also hypothesized to differ based on stage and ethnicity when predicting condom stage. Furthermore , Transtheoretical variables (decisional balance and selfefficacy) were hypothesized to predict over and above demographic characteristics. A subsample of 340 women at risk for HIV were selected from a pool of 816 sexually at-risk New England Community who had participated as part of a longitudinal