Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of Regional-CNN and SSD Machine Learning Object Detection Architectures for the Real Time Analysis of Blood Borne Pathogens in Dark Field Microscopy

The emerging use of visualization techniques in pathology and microbiol- ogy has been accelerated by machine learning (ML) approaches towards image preprocessing, classification, and feature extraction in an increasingly complex series of datasets. Modern Convolutional Neural Network (CNN) architectures have developed into an umbrella of vast image reinforcement and recognition methods, including a combined classification-localization of single/multi-object featured images. As a subtype neural network, CNN cre- ates a rapid order of complexity by initially detecting borderlines, edges, and colours in images for dataset construction, eventually capable in mapping intricate objects and conformities. This paper investigates the disparities between Tensorflow object detection APIs, exclusively, Single Shot Detector (SSD) Mobilenet V1 and the Faster RCNN Inception V2 model, to sample computational drawbacks in accuracy-precision vs. real time visualization capabilities. The situation of rapid ML medical image analysis is theoretically framed in regions with limited access to pathology and disease prevention departments (e.g. 3rd world and impoverished countries). Dark field mi- croscopy datasets of an initial 62 XML-JPG annotated training files were processed under Malaria and Syphilis classes. Model trainings were halted as soon as loss values were regularized and converged.
Impact of Fungicide Timing on the Composition of the Fusarium Head Blight Disease Complex and the Presence of Deoxynivalenol (DON) in Wheat

Kris Audenaert1,2, Sofie Landschoot1,2, Adriaan Vanheule1,2, Willem Waegeman3, Bernard De Baets3 and Geert Haesaert1,2 1Associated Faculty of Applied Bioscience Engineering, Ghent University College, Ghent 2Laboratory of Phytopathology, Faculty of Bioscience Engineering, Ghent University, Ghent 3KERMIT, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Ghent Belgium
Mining Movie Reviews using Machine Learning Techniques

Sentiment analysis has been observed as an important subject in data mining because of the wide range of direct applications such as analysis of products, customer profiles, and political trends and so on. It is the process of identifying people’s attitude and emotional state from language to language. In Natural Language Processing, sentiment analysis is an automated task where machine learning is used to rapidly determine the sentiment of large amounts of text or speech.In this research work a comparative study of effectiveness in which some of the Machine learning techniques like naive bayes and support vector machine. The results observed and noted that naive bayes performs better in terms of accuracy, precision, recall and F-Measure for movie review. General Terms Data mining, Classification, Data sets, Social network and Algorithms
Distribution-Aware Crowdsourced Entity Collection

The problem of crowdsourced entity collection solicits people (a.k.a. workers) to complete missing data in a database and has witnessed many applications in knowledge base completion and enterprise data collection. Although previous studies have attempted to address the “open world” challenge of crowdsourced entity collection, they do not pay much attention to the “distribution” of the collected entities. Evidently, in many real applications, users may have distribution requirements on the collected entities, e.g., even spatial distribution when collecting points-of-interest. In this paper, we study a new research problem, distribution-aware crowdsourced entity collection (CrowdDEC): Given an expected distribution w.r. t. an attribute (e.g., region or year), it aims to collect a set of entities via crowdsourcing and minimize the difference of the entity distribution from the expected distribution. Due to the openness of crowdsourcing, the CrowdDEC problem calls for effective crowdsourcing quality control. We propose an adaptive worker selection approach to address this problem. The approach estimates underlying entity distribution of workers on-the-fly based on the collected entities. Then, it adaptively selects the best set of workers that minimizes the difference from the expected distribution. Once workers submit their answers, it adjusts the estimation of workers’ underlying distributions for subsequent adaptive worker selections. We prove the hardness of the problem, and develop effective estimation techniques as well as efficient worker selection algorithms to support this approach. We deployed the proposed approach on Amazon Mechanical Turk and the experimental results on two real datasets show that the approach achieves superiority on both effectiveness and efficiency.
A High-Gain CMOS LNA for 2.4/5.2-GHz WLAN Applications

This paper describes a high-gain CMOS low-noise amplifier (LNA) for 2.4/5.2-GHz WLAN applications. The cascode LNA uses an inductor at the common-gate transistor to increase its transconductance equivalently, and therefore it enhances the gain effectively with no additional power consumption. The LNA is matched concurrently at the two frequency bands, and the input/output matching networks are designed with two notch frequencies to shape the frequency response. The dual-band LNA with the common-gate inductor is designed, implemented, and verified in a standard 0.18-μm CMOS process. The fabricated LNA which consumes 7.2mW features gains of 14.2 dB and 14.6 dB, and noise figures of 4.4 dB and 3.7 dB at the 2.4-GHz and 5.2-GHz frequency bands, respectively. The proposed LNA demonstrates a 4.9–7.8 dB gain enhancement compared to conventional cascode LNAs, and the chip size is 1.06mm× 0.79 mm including all testing pads.
Expanding the Impact of Practical Scientific Concepts for Low-literate Learners through an Inclusive and Participatory Virtual Knowledge Ecosystem

Extension materials for low-literate learners are often developed as part of specialized projects for specific regions or groups. In some cases these materials are inaccessible to other development organizations during the project’s life or are lost at the end of the project. More recently, such development materials are typically placed on individual websites for the purpose of preserving the content beyond the scope of any given project. However, such materials are (i) often challenging to find and (ii) those individuals that are able to find and adapt these materials for local use cannot return these local adaptations to the World Wide Web without creating new, often separate, websites. We propose an Internet-based virtual knowledge sharing system for development materials for low-literate learners that allows users to easily access the materials and to contribute their local adaptations and local solutions. The site we envision is similar to a “wikipedia” in terms of ease of use but contains materials that will be peer-reviewed before publication. The site also will allow for post-publication online reviews and updates as mechanisms to assist in the improvement of content and to facilitate the localized adaptation of content.
FINITE VOLUME APPROXIMATION OF ONE-DIMENSIONAL STIFF REACTION-CONVECTION EQUATIONS

In this work, we present a novel method to approximate stiff problems using a finite volume (FV) discretization. The stiffness is caused by the existence of a small parameter in the equation which introduces a boundary layer. The semi-analytic method consists in adding in the finite volume space the boundary layer corrector which encompasses the singularities of the problem. We verify the stability and convergence of our finite volume schemes which take into account the boundary layer structures. A major feature of the proposed scheme is that it produces an efficient stable second order scheme to be compared with the usual stable upwind schemes of order one or the usual costly second order schemes demanding fine meshes.
A TES Bolometer for THz FT-Spectroscopy

We recently reported on a Transition Edge Sensor (TES) composite bolometer for Fourier Transform spectroscopy which was designed, built and characterized. The design is based on a superconducting thermistor with SQUID readout, SiN membrane technology and a thin film metal mesh absorber which is optimized for the spectral range from 0.1 THz to 3 THz. A noise equivalent power (NEP) of 3.8×10-13 W/Hz0.5 was achieved with a linear dynamic range of at least four orders of magnitude.
The effect of Mediterranean thyme ( Thymbra spicata L. var. spicata) essential oil on fattening performance and ruminal parameters in lamb

Erciyes University, Faculty of Veterinary Medicine 1 Department of Animal Nutrition and Nutritional Diseases, 2 Department of Biostatistics, 38280, Kayseri, Turkey 3 Yüzüncü Yıl University, Faculty of Veterinary Medicine, Department of Animal Nutrition and Nutritional Diseases, 65080, Van, Turkey 4 Süleyman Demirel University, Faculty of Agriculture, Department of Animal Science, 32260, Isparta, Turkey 5 Harran University, Faculty of Veterinary Medicine, Department of Animal Nutrition and Nutritional Diseases, 63000, Şanlıurfa, Turkey 6 Sütçü İmam University, Faculty of Agriculture, Department of Animal Science, 46100, Kahramanmaraş, Turkey Mustafa Kemal University, 7 Faculty of Agriculture, Department of Field Crops, 8 Vocational School of Samandağ, Department of Plant and Animal Production, 31060, Hatay, Turkey
PEMS: MONITORING NOx EMISSIONS FROM GAS TURBINES

Predictive Emission Monitoring System (PEMS) was developed in 1990 to provide continuous monitoring of NOx emissions from stationary gas turbines with minimum maintenance. This system will meet the Enhanced Monitoring requirements under Title V of the Clean Air Act Amendments of 1990 when these requirements are finalized. The PEMS has been well received by various United States federal, state and local environmental agencies. It has been certified in the state of Colorado, and accepted in Pennsylvania and Texas.This paper reviews the Enhanced Monitoring requirements for gas turbine NOx emissions monitoring and discusses the technical background of the PEMS. The PEMS design is described, including inputs, outputs and operator interface. Experiences with some of the installed systems are presented.The PEMS predicts NOx emissions from turbine control system inputs and measurements of ambient air conditions. The prediction algorithms are based upon a time tested NOx emission model for gas turbines. This model has successfully predicted all measured NOx emission phenomena from gas turbines since 1974. The PEMS has been proven to be accurate within the 20% relative accuracy required for certification.The PEMS operates unattended, with extremely low maintenance and high reliability. Record keeping and report generation are automated. The PEMS is typically integrated into the turbine control and condition monitoring system. The PEMS meets regulatory requirements with a much lower cost than a conventional Continuous Emission Monitoring System (CEMS). Copyright © 1995 by ASME
Do We Really Like Robots that Match our Personality? The Case of Big-Five Traits, Godspeed Scores and Robotic Gestures

This work investigates the role of the attraction paradigm - the tendency to associate similarity and attraction in interpersonal relations - in Human-Robot Interaction. The experiment presented here involved 30 human observers who watched and rated 45 robotic gestures in terms of Big-Five personality traits and Godspeed scores. The results show that, for 24 of the 30 observers, there was a statistically significant correlation between the Godspeed scores and the perceived similarity between the robot's personality and their own. However, the association was positive for 15 subjects - meaning that for these there is a similarity-attraction effect - and negative for the other 9 - meaning that for these there is a complementarity-attraction effect. Furthermore, the strength of the effect depends on the particular trait under examination.
Applying Machine Learning Techniques to Find Important Attributes for Heart Failure Severity Assessment

The diagnosis of heart disease depends mostly on the combination of clinical and pathological data. It leads to the quality of medical care provided for the patient. In this paper, three machine learning (ML) techniques −Classification and Regression tree (CART), Neural Networks (NN), and Support vector machine (SVM)− are utilized to find the best attributes for estimating the severity of heart failure. The data is collected from three different resources, then each input attribute used for assessing the severity of heart failure is analyzed individually after implementing the machine learning techniques. Finally, the most important supportive attributes are presented in this paper by which medical staffs can identify heart failure severity fast and more accurately. In fact, by screening important attributes, clinicians can make better decision about right treatment procedures or preventive actions that reduce risk of heart attacks.
Cavity optimization of optically pumped broad-area microcavity lasers

We demonstrate a cavity optimization method for efficient optical pumping of a semiconductor microcavity. An enhanced pumping efficiency makes it possible to pump broad-area microcavities for, e.g., high-power lasers or parallel optical processing of information. This is especially important in the near-infrared where a pump not absorbed into the active material is absorbed into the substrate and converted into heat. We apply the method to optical pumping of a 80μm diameter laser with a spatially uniform pump profile. This method could also prove useful for the design of vertical external cavity surface emitting lasers.
Enhanced antibacterial activity of transition metal doped ZnO nanorods on thin films

Transition metal (Mn, Co and Cu) doped ZnO nanorods were prepared on thin film by hydrothermal method. The structural, morphological, optical and antibacterial properties of thin films were studied by using X- ray diffraction (XRD), Field emission scanning e lectron microscopy (FE-SEM) with energy dispersive X- ray spectroscopy (EDAX) and UV-Vis spectroscopy. Formation of hexagonal wurtzite phase of ZnO was confirmed by XRD analysis. The band gap of TM doped ZnO was estimated from the optical transmission spectra recorded in the wavelength range, 300-800 n m. FE-SEM images showed the well-defined hexagonal shape ZnO nanorods and it was confirmed that TM doping decreases the aspect ratio of ZnO nanorods. The bactericidal efficiency of TM doped ZnO nanorods were investigated against a Gram negative (Escherichi a coli) and a Gram positive (Staphylococcus aureus) b acteria. The result showed that TM doped ZnO nanorods enhances the antibacterial activity of pure ZnO nan orods.
Response Surface Methodology for Performance Analysis and Modeling of MANET Routing Protocols

Numerous studies have analyzed the performances of routing protocols in mobile Ad-hoc networks (MANETs); most of these studies vary at most one or two parameters in experiments and do not study the interactions among these parameters. Furthermore, efficient mathematical modeling of the performances has not been investigated; such models can be useful for performance analysis, optimization, and prediction. This study aims to show the effectiveness of the response surface methodology (RSM) on the performance analysis of routing protocols in MANETs and establish a relationship between the influential parameters and these performances through mathematical modeling. Given that routing performances usually do not follow a linear pattern according to the parameters; mathematical models of factorial designs are not suitable for establishing a valid and reliable relationship between performances and parameters. Therefore, a Box–Behnken design, which is an RSM technique and provides quadratic mathematical models, is used in this study to establish a relationship. The obtained models are statistically analyzed; the models show that the studied performances accurately follow a quadratic evolution. These models provide invaluable information and can be useful in analyzing, optimizing, and predicting performances for mobile Ad-hoc routing protocols.
The Double-Edged Effect of Contracts on Alliance Performance

Despite substantial scholarly interest in the role of contracts in alliances, few studies have analyzed the mechanisms and conditions relevant to their influence on alliance performance. In this paper, we build on the information-processing view of the firm to study contracts as framing devices. We suggest that the effects of contracts depend on the types of provisions included and differentiate between the consequences of control and coordination provisions. Specifically, control provisions will increase the level of conflict between alliance partners, whereas coordination provisions will decrease such conflict. Conflict, in turn, reduces alliance performance, suggesting a mediated relationship between alliance contracts and performance. We also contribute to a better understanding of contextual influences on the consequences of contracts and investigate the interactions of each contractual function with both internal and external uncertainties. Key informant survey data on 171 alliances largely support our conceptual model.
Antimicrobial activity of leaf and flower extracts of Lippia Nodiflora L. (Verbenacea)

Antimicrobial activities of the methanolic extracts from the leaves and flowers of Lippia nodiflora L. (Verbenaceae), were stud- ied by the disk diffusion method. The extracts showed antimicrobial impact on bacteria such as Bacillus subtilis, B. cereus, Micrococcus luteus, Staphylococcus aureus, Pseudomonas aeruginosa, Klebsiella pneumonia, K. oxytoca and Esherichia coli as well as fungi such Aspergillus niger and Candida albicanse. The results showed that increasing concentrations of extracts increased the antimicrobial activities in all of the microorganisms. Bacteria were more sensitive than fungi, and gram positive bacteria were more sensitive than gram negative ones.
Probing Quark-Gluon-Plasma properties with a Bayesian model-to-data comparison

Relativistic heavy-ion collisions are believed to be able to create in laboratories QuarkGluon Plasma (QGP), a state of matter which the early universe was once in. Given the theoretical difficulty to deduce properties of QGP from first principle calculations, computational models are thus of utmost importance for the advance of our knowledge of QGP. In this research, we study and compare two physically different models for the evolution of QGP. In particular, we quantitatively examine one relativistic hydrodynamic (Pure-Hydro) model based on Bayesian statistics and a multi-parameter model-to-data comparison. After calibrating the model to total charged multiplicity, transverse momentum and flow data from 2.76 TeV Pb-Pb collisions at ALICE, we perform an additional model run for the set of most-probable parameters. We then compare both its posterior distributions and the fits from its most-probable-parameters run with the results from the previously-analyzed relativistic fluid dynamic + hybrid microscopic transport (Hybrid) model. We develop a set of criteria to assess the quality of the two models with respect to their ability to describe current experimental data. We find that overall the Hybrid model gives better fit to the data than the Pure-Hydro model, with the Bayes’ Factor of Hybrid over Pure-Hydro being 1.549. We therefore interpret our result to be a positive indication for the preference of Hybrid model by the current experiments.
Microscopy Image Analysis: Blob Segmentation using Geodesic Active Contours

An Insight Toolkit (ITK) processing framework for blob segmentation with applications to microscopy images is presented in this paper. Our algorithm is a reﬁnement of the work of Mosaliganti et al. [3] for splitting cell clusters. The basic idea is to incorporate as many cues as possible into developing a suitable level-set speed function so that an evolving contour exactly segments a cell/nuclei blob. We use image gradients, distance maps, multiple channel information and a shape model to drive the evolution. The framework consists of a linear pipeline of 6 new ITK ﬁlters which are applied in succession to generate the segmentation. The ﬁlters extract the cell foreground, construct the speed image, ﬁnd seed points for evolution and collect cell statistics from the segmentation. We include 2D/3D example code, parameter settings and show the results generated on confocal images of the zebraﬁsh embryo.
A Novel Routing Algorithm for Mobile ad-hoc Networks Based on Q-learning and its Generalization to FSR Routing Protocol

: This study proposes a novel routing algorithm using Q-learning. Q-learning is a machine learning (artificial intelligence) algorithm using the reinforcement learning policy which can be used to solve problems for which there are different ways to reach their goal. The proposed algorithm, the Modified Q-learning routing algorithm (MQRA), has eliminated the episodes of Q-learning required to gradually learn in different stages and this has made it a rapid routing algorithm. MQRA can be used in various types of networks. This study uses MQRA in mobile ad-hoc networks, its generalization to fisheye state routing (FSR) (a routing algorithm) and its performance results are compared with the standard FSR. Experimental results confirm the applicability and potential of the proposed algorithm.
Introduction to inequality-constrained optimization: The logarithmic barrier method

where f : R ! R and h : R ! R. The reader should notice that h(x) 0 is to be interpreted component-wise; that is, h(x) 0 if and only if hi(x) 0 for each i = 1; 2; : : : ; p. Inequality constraints are common in optimization problems; indeed, almost every optimization problems does or could have inequality constraints, although it is sometimes safe to ignore them. The most common type of inequality constraints are simple bounds that the variables must satisfy. For example, if every variable must satisfy both lower and upper bound, then the constraints could be written as a x b; where a; b 2 R with a b. To incorporate these constraints into the standard form (2), one would write them as
Using Blended Learning to Address Instructional Challenges in a Freshman Engineering Course

Tareq Daher earned his Bachelors in Computer Science from Mutah University in Jordan. He pursued a Master’s of Instructional Technology at the University of Nebraska –Lincoln while working as the coordinator for the Student Technology Program on the UNL campus. Currently, Dr. Daher works as an Instructional Design Technology Coordinator for the Office of Online and Distance Education at the University of Nebraska – Lincoln leading the instructional design team at the College of Engineering. Dr. Daher collaborates with engineering faculty to document and research the integration of innovative instructional strategies and technologies in his classroom. His latest collaborative submitted publication discusses Using the Flipped approach in a water resources course.
ON PROBLEMS OF DANZER AND GOWERS AND DYNAMICS ON THE SPACE OF CLOSED SUBSETS OF R

Considering the space of closed subsets of R, endowed with the Chabauty-Fell topology, and the affine action of SLd(R)⋉R , we prove that the only minimal subsystems are the fixed points {∅} and {R}. As a consequence we resolve a question of Gowers concerning the existence of certain Danzer sets: there is no set Y ⊂ R such that for every convex set C ⊂ R of volume one, the cardinality of C ∩ Y is bounded above and below by nonzero contants independent of C. We also provide a short independent proof of this fact and deduce a quantitative consequence: for every ε-net N for convex sets in [0, 1] there is a convex set of volume ε containing at least Ω(log log(1/ε)) points of N .