Miracle-dz/UniFAR-FTUs huggingface dataset:
Ensemble of fine-tuned convolutional neural networks for urine sediment microscopic image classification

In this study, an ensemble of fine-tuned convolutional neural networks (CNNs) is proposed. As CNN training requires large annotated data, which are lacking in the field of urine sediment microscopic image processing, the authors first pre-trained the CNNs, including ResNet50 and GoogLeNet, and developed AlexNet on an ImageNet dataset. Thereafter, some of the weights of the pre-trained CNNs were transferred to the urine sediment microscopic image dataset. To guide fine-tuning of the learning rate and cascading features, the hierarchical nature of features in different convolutional layers was investigated by visualising the CNN. Then, they combined three CNNs as an ensemble of CNNs to decrease the differences and impurity interference among features of urine sediment microscopic image. These fusion features were employed to train the fully connected neural network for classification. In this study, they improved the accuracy of each CNN by an average of 2.2% through fine-tuning of the learning rate and cascading features. Moreover, the better experimental results were achieved compared with other state-of-the-art methods and indicated that a 97% classification accuracy can be attained.
Fabrication and optical property of ZnO nanorod array by hydrothermal method

Abstract We synthesize a large area of ZnO nanorod array by using the RF magnetron sputtering and low-temperature hydrothermal method. The sample morphology, crystal structure and optical properties are characterized and analyzed by means of the SEM, XRD and fluorescence spectrophotometer. The results show that ZnO nanorod array has the advantages of large aspect ratio, ordered orientation, uniform and compact at a lower OH−/Zn2+ concentration. The XRD results show that the samples are all hexagonal wurtzite structures and the photoluminescence (PL) pattern shows that an obvious ultraviolet peak of the samples appears around 375 nm, and an emission band of strong yellow green light appears in the wavelength between 450–650 nm. With the increase of the concentration ratio of OH−/Zn2+, the ultraviolet peak and the emission band of strong yellow green light both show a blue shift. The above results indicate that the ZnO nanorod array is a type of potentially preferred materials for ultraviolet optoelectronic devices.
Inferring Network Structure and Estimating Dynamical Process From Binary-State Data via Logistic Regression

Inferring the structures and the dynamics of the complex networked systems based on time series data is a challenging problem. The existing reconstruction methods often rely on the knowledge of the dynamics on networks. In many cases, a prior knowledge of the dynamics is unknown, so it is natural to ask: is it possible to reconstruct network and estimate the dynamical processes on complex networks only rely on the observed data? In this article, we develop a framework to reconstruct the structures of networks with binary-state dynamics, in which the knowledge of the original dynamical processes is unknown. Within the reconstruction framework, the transition probabilities of binary dynamical processes are described by the Sigmoid function in logistic regression, we then apply the mean-field approximation to enable maximum likelihood estimation (MLE), which gives rise to that the network structure can be inferred by solving the linear system of equations. Meanwhile, the original dynamical processes can be simulated by estimating the parameters in the Sigmoid function. Our framework has been validated by a variety of binary dynamical processes on synthetic and empirical networks, indicating that our method can not only reveal the network structures but also estimate the dynamical processes. Moreover, the high accuracy of our method is highlighted by comparing it with the existing methods.
Thermal emission from magnetised neutron stars

X-ray dim isolated neutron stars and magnetars are neutron stars with strong magnetic fields, B ∼ 1013− 1015 G. Under these magnetic fields, the properties of the matter and the vacuum are expected to change dramatically. As such, these sources are (unique) natural laboratories for studying fundamental physics in the strong magnetic field regime. In this thesis, I study the polarised thermal emission from both X-ray dim isolated neutron stars and magnetars. First, I developed a ray tracing code that allows me to compute the polarisation properties of X-ray dim isolated neutron stars by considering different surface emission models and by accounting for QED vacuum birefringence effects. I show that combined optical and X-ray polarimetric observations can allow us to infer the state of the matter at the surface of X-ray dim isolated neutron stars. Then, these models are confronted with the optical VLT polarimetric observation of RX J1856.5-3754, the nearest and brightest X-ray dim isolated neutron star. I found that the observed polarisation fraction, 16±5%, can be explained if vacuum birefringence is present, the first ever evidence for this QED effect. In order to confirm this measurement by future missions of Xray polarimetry, I model the polarisation properties of magnetars, active sources with outburst activity in which the X-ray flux decay is explained in the context of an untwisting magnetosphere. I show that if both vacuum birefringence and an untwisting magnetosphere are present, then during the X-ray flux decay of the source, the polarisation angle can change up to 23◦ (for an untwisting of ∆Φ = 0.5 rad). Finally, I model the atmosphere of magnetars heated by a particle bombardment. I compute the thermal emission in the case of a grey atmosphere and discuss the potential spectrum of the source and polarisation signal.
Low‐profile circularly polarized planar antenna for GPS L1, L2, and L5 bands

In this paper, a circularly polarized (CP) planar antenna covering global positioning system (GPS) L1 (1575.42 MHz), L2 (1227.6 MHz), and L5 (1176.45 MHz) bands is presented. The antenna consists of an elliptical patch excited through the inset feed line. A pair of concentric ring slots is etched on the ground plane to achieve dual‐band response that covers L5/L2 and L1 bands. The circular polarization is achieved by integrating a quarter‐wavelength arc with the inset feed and loading a pair of slits on the elliptical patch. In addition, the quarter‐wavelength arc improves the impedance bandwidth of the lower resonant band to cover the GPS L5 band. The antenna shows right‐hand circular polarization (RHCP) in all the three bands. The antenna also satisfies the ±1 dBi gain flatness criteria of the GPS antenna. A prototype antenna is fabricated and experimental results are found in a good match with the simulated results.
THE SHOOTING METHOD FOR SOLVING SECOND ORDER FUZZY TWO-POINT BOUNDARY VALUE PROBLEMS

We consider the fuzzy two-point boundary value problem (FBVP) subject to some fuzzy boundary conditions on an interval [a, b]. Numerically, we start by transforming the two-point boundary value problem into a system of fuzzy initial value problems (FIVP). To solve the resulting system, we use an improved s−stage Runge-Kutta Nystrom 4th order method adopted to handle fuzzy problems. Numerical results will be presented to give the numerical details and to show the efficiency of the method. AMS Subject Classification: 65H10
Physicochemical Quality of Milk from Dairy Cows Supplemented with Liquid Brewer’s Yeast in Smallholder Dairy Farms

A study was conducted to evaluate physicochemical quality of raw milk from dairy cows supplemented with liquid brewer’s yeast (LBY) in smallholder dairy farms. The milk was delivered from different routes to Githunguri Dairy Farmers’ Cooperative Society in Kiambu County, Githunguri Sub-county, Kenya. The main objective was to ascertain suitability for use of LBY as alternative feed source for dairy cows without compromising on milk quality. Thirty farms (sampling units) were randomly selected from three milk delivery routes (sampling frame). A longitudinal survey was conducted where farms were nested within routes and equal number of farms selected per route based on supplementation of lactating cows with either LBY or commercial dairy meal (CDM). A repeated measure analysis was performed using the Linear Mixed Models methodology by PROC MIXED of SAS for milk quality and questionnaire data was summarized using descriptive statistics. Milk samples were analysed for physicochemical parameters such as butter fat (BF), protein, lactose, total ash, solid not fat (SNF), density and milk freezing point (MFP). The results indicated significantly (p 0.05) by the type of supplementation regime, although higher levels were observed on LBY supplemented diets than CDM diets. The study indicates that LBY can be used as feed supplement for dairy cows without compromising on physicochemical quality of milk. In view of this, the research recommends use of LBY as a cost effective alternative protein source for dairy cows.
Santa Fe Time Series Competition Data Set B

This is a multivariate data set recorded from a patient in the sleep laboratory of the Beth Israel Hospital (now the Beth Israel Deaconess Medical Center) in Boston, Massachusetts. This data set was extracted from record slp60 of the MIT-BIH Polysomnographic Database, and it was submitted to the Santa Fe Time Series Competition in 1991 by our group. The data are presented in text form and have been split into two sequential parts, b1.txt and b2.txt. Each line contains simultaneous samples of three parameters; the interval between samples in successive lines is 0.5 seconds. The first column is the heart rate, the second is the chest volume (respiration force), and the third is the blood oxygen concentration (measured by ear oximetry). The sampling frequency for each measurement is 2 Hz (i.e., the time interval between measurements in successive rows is 0.5 seconds).
Patterns of risky sexual behaviors among emerging adults in intimate sexual relationships in two tertiary institutions in southwestern Nigeria

ABSTRACT This study explored patterns of risky sexual practices and perceived vulnerability to sexually transmitted infections among 450 sexually active emerging adults (18–25 years) through a self-administered questionnaire and 43 interviews. Findings showed that 97.4% of men and 98.1% of women were in intimate sexual relationships, among which 74% of men and 46.5% of women had unprotected vaginal intercourse and anal sex (α = 1.74 for men and 1.09 for women) within the past month. Consequences of unprotected sexual practices notwithstanding, engagement in risky sexual practices prevail. Redesigning of current behavioral modification approaches targeted toward promoting healthy sexual practices in young adulthood is required.
Non‐circular signals for nested array: sum–difference co‐array and direction of arrival estimation algorithm

Nested array has aroused remarkable attention due to the capability to obtain both enhanced degrees of freedom (DOFs) and enlarged array aperture, whereas only difference co-array is employed. Here, the authors construct the sum co-array of nested array by utilising the non-circular (NC) characteristic and, subsequently, the sum-difference (SD) co-array is obtained by careful crafting of the sum co-array and the typical difference co-array, which doubles the resulting co-array aperture and, simultaneously, further increases the achievable DOF compared with the difference co-array of nested array. Moreover, to circumvent the time-consuming two-dimensional spectral search, the authors proposed the reduced-dimension multiple signals classification (MUSIC) algorithm with the SD (SD-RD-MUSIC) co-array of the nested array by discarding the NC phase. Resultantly, the proposed SD-RD-MUSIC co-array algorithm can resolve more signals and obtain more accurate direction of arrival estimates. In addition, the effectiveness and superiority of the proposed method are validated by numerical simulations.
On influence of clustering population on accuracy of population total estimation

ABSTRACT The paper compares the accuracy of using cluster samples and using stratified samples to estimate a population total. Several clustering algorithms are used to partition a finite population into strata or clusters. Several variants of stratified sampling designs and one-stage cluster sampling designs, including those dependent on various inclusion probabilities, are taken into account. The accuracies of the estimators are compared using simulation experiments. The results of this paper let us conclude that partitioning a population into clusters could significantly improve the accuracy of estimating the total using sampling dependent on inclusion probabilities proportional to the aggregated auxiliary variable. Moreover, the considered estimators based on cluster sampling designs could be easily used in practice.
Sequential Edge Clustering in Temporal Multigraphs.

Interaction graphs, such as those recording emails between individuals or transactions between institutions, tend to be sparse yet structured, and often grow in an unbounded manner. Such behavior can be well-captured by structured, nonparametric edge-exchangeable graphs. However, such exchangeable models necessarily ignore temporal dynamics in the network. We propose a dynamic nonparametric model for interaction graphs that combine the sparsity of the exchangeable models with dynamic clustering patterns that tend to reinforce recent behavioral patterns. We show that our method yields improved held-out likelihood over stationary variants, and impressive predictive performance against a range of state-of-the-art dynamic interaction graph models.
Biologically Plausible Fuzzy-Knowledge-Out and Its Induced Wide Learning of Interpretable TSK Fuzzy Classifiers

As an alternative to existing construction methods of Takagi–Sugeno–Kang (TSK) fuzzy classifiers, this paper presents a novel design methodology formulated by a new concept called fuzzy-knowledge-out and its induced wide learning way. Analogous to the “dropout” concept in deep learning, the concept of fuzzy-knowledge-out in TSK fuzzy classifiers is motivated by the firing pattern of knowledge in biological neural networks. Our theoretical analysis reveals that a fuzzy classifier built after fuzzy-knowledge-out from a complete set of highly interpretable fuzzy rules is distinctive in generalization and coadaption avoidance. As such, an ensemble called wide learning based TSK (WL-TSK), of highly interpretable zero-order TSK fuzzy subclassifiers constructed quickly by means of fuzzy-knowledge-out operations in a wide learning manner is proposed to achieve enhanced classification performance and high interpretability. With the use of the proposed halving or averaging operations, WL-TSK essentially behaves like only one zero-order TSK fuzzy classifier. Thus, the proposed method can be considered as a new design methodology of TSK fuzzy classifiers. Our experimental results on 15 datasets indicate the effectiveness of WL-TSK in terms of both enhanced classification performance and high interpretability.
Ad Campaigns Generation Using Deep Learning Approach

الحملة الإعلانية عبارة عن مجموعة من الرسائل الإعلانية التي تشارك نفس الفكرة والتي تحدد الرسالة الترويجية للعلامات التجارية والمنتجات والخدمات. الحملات عادة تستخدم عدة وسائط لترويج المنتجات والخدمات مثل النصوص أو الصور والفيديو. الحملات النصية تستخدم في محركات البحث مثل جوجل وبينج (Bing) , بينما حملات الصور والفيديو تستخدم في منصات الشبكات الاجتماعية واليوتيوب. يعد إنشاء إعلان نصي يدويا تحديا كبيرا ويحتاج إلى خبير لإنشاء هذا الإعلان الذي يجب أن يستهدف العملاء. وهذا يتطلب الكثير من الوقت والمال والجهد لأنه يتعين على الخبير إنشاء واختبار حملات نصية عديدة تتطابق مع اهتمام واحتياجات العميل. بالإضافة إلى ذلك، فإن بعض الشركات لديها فهم محدود لعملائها بسبب أن بيانات العملاء مبعثرة في أماكن متعددة مثل رسائل البريد الإلكتروني وأوامر الشراء، لذلك، تحتاج الشركات إلى فهم شامل لاتخاذ الإجراءات والقرارات في الوقت المناسب. في هذا البحث، قمنا بتطوير نظام قادر على توليد حملات إعلانية نصية لمحركات البحث مثل Google وBing استنادا إلى الكلمات الرئيسية التي يبحث عنها العميل. التقنية الخاصة بنا تتكون من أربع مراحل رئيسية. المرحلة الأولى هي جمع بيانات عالية الجودة، أما المرحلة الثانية تتضمن معالجة البيانات، والمرحلة الثالثة هي تطوير نظام إنشاء حملات الإعلانات باستخدام الشبكة العصبية المتكررة (RNN) والتعلم العميق. هذا العمل يختبر معماريات مختلفة مثل LSTM وGRU. المرحلة الأخيرة في هذا البحث هي التقييم. قمنا بإجراء العديد من التجارب لتحديد أفضل نموذج وذلك بالتحقق لأقل قيمة للخطأ أثناء عميلة التعلم والـ Perplexity (PPL) للنص الذي تم إنشاؤه. يتم تقييم النتائج باستخدام مقياسين وهما قابلية القراءة للنص المولد مدى علاقته بالكلمات المفتاحية. تم تقييم مقياس قابلية القراءة بواسطة مختصين بشريين ومكتبة برمجية، في حين تم تقييم علاقة النص المولد بالكلمات المفتاحية فقط بواسطة تقييم بشري. يظهر نتائج التقييم أن النصوص التي تم إنشاؤها بواسطة الشبكة العصبية العميقة (DNN) على الأغلب سهلة القراءة ولها علاقة بالكلمات المفتاحية.
Measuring Trapped Ion Qudits

Quantum information has typically focused on using 2-level qubits to perform simulation and computation. We propose to expand the number of levels for computation using qudits, where d > 2. Doing so could be a viable option for making trapped ion systems scalable for quantum computation. Our group in particular will use Barium ions because of some energy features and convenient laser wavelengths. This thesis presents much of the necessary background needed to work with Barium as a qudit for quantum computation. Energy structure, branching ratios, and saturation intensities are derived and presented. In addition, a method for selecting different isotopes of Barium for trapping is discussed. A method for measuring out the state of a Barium qudit is presented, with error rates estimated to be under 1% for up to 5-level qudits. Finally, various optics projects which were necessary for building up our first ion trap are outlined.
Linear and nonlinear fractional elliptic problems

This paper surveys recent analytical and numerical research on linear problems for the integral fractional Laplacian, fractional obstacle problems, and fractional minimal graphs. The emphasis is on the interplay between regularity, including boundary behavior, and approximability by piecewise linear finite element methods. We discuss several error estimates on graded meshes, and computational challenges associated to implementing and solving efficiently the ensuing integral equations, along with numerical experiments.
INFLUENCE OF MHD ON FREE CONVECTION OF NON-NEWTONIAN FLUIDS OVER A VERTICAL PERMEABLE PLATE IN POROUS MEDIA WITH INTERNAL HEAT GENERATION

The heat and mass transfer characteristics of the influence of uniform blowing/suction and MHD (magnetohydrodynamic) on the free convection of non-Newtonian fluids over a vertical plate in porous media with internal heat generation and Soret/Dufour effects are numerically analyzed. The surface of the vertical plate has a uniform wall temperature and uniform wall concentration (UWT/UWC). The numerical modeling of this problem attracts considerable attention, owing to its practical applications in biological sciences, electronic cooling, advanced nuclear systems, etc. The transformed governing equations are solved by Keller box method. Comparisons showed excellent agreement with the numerical data in previous works. Numerical data for the dimensionless temperature profile, the dimensionless concentration profile, the local Nusselt number and the local Sherwood number are presented for the main parameters: the magnetic field parameter M, the blowing/suction parameter  , the power-law index of the non-Newtonian fluid n and the internal heat generation A*. The physical aspects of the problem are discussed in details.
The Feynman Path Integral to Characterize and Anticipate Bacteria Chemotaxis in a Host Healthy Body

In this paper the idea of Feynman’s path integral is introduced inside a nano biological system such as bacteria population where due to their property of chemotaxis, a stochastic modeling might be drawn to describe their mobility due essentially to electrical interactions among them as a recurrent resource to protect themselves against antibacterial agents such as macrophages. Due to composition of K+, Cl− and Na+ exists there a net charge along the internal and external phospholipid membrane of bacteria. The model of path’s integration invented by Richard Feynman has been extensively used to tackle crucial problems in quantum mechanics for various decades essentially in atomic physics and nano physics. The idea of the path’s integral assumes a space-time pathway where the spacetime bacteria displacements are governed by physics interactions that gives rise to changes of position in the space-time plane in a fully accordance to biological and physics laws. We worked out the idea of the Feynmans path integral to describe space-time dynamics of aggregations of bacteria trying to host a healthy body. We assumed that the bacteria interactions is governed by electric fields and potentials. While the net charge is predominantly positive due to the high concentrations of Cl+, there are clearly external electric fields and potentials that might seriously affect the behavior of bacteria space-time dynamics. In this manner this phenomenology would fit the path integral theory. Therefore the change of the net charge in bacteria due to the presence of others charged nano organisms would affect their translational dynamics by being vulnerable to macrophages. Thus the knowledge of the pathway of these bacteria populations is seen as an advantage to tackle the beginning of diseases inside the framework of Internet of Bio-nano Things that targets to anticipate infections using electromagnetic pulses through advanced software-hardware interfaces. In order to assess possible advantages and disadvantages of this theory we use the Machine Learning algorithm.