Miracle-dz/UniFAR-FTUs huggingface dataset:
In Vitro Determination of the Effect of Indoleglycerol Phosphate on the Interaction of Purified TrpI Protein with Its DNA-Binding Sites

Expression of the trpBA gene pair of Pseudomonas aeruginosa is regulated by the endogenous level of indoleglycerol phosphate (InG? ) and the trpI gene product. The TrpI protein binds to the -77 to -32 region of the trpBA promoter. This region is divisible into two sites: site I, which is protected by TrpI in the presence and absence of InGP; and site II, which is protected by TrpI only in the presence of InGP. Recently, the trpl gene was subcloned into an expression vector and the protein was overproduced in Escherichia coli. The TrpI protein was purified to 80 to 95% purity. The molecular weight ofnative TrpI protein is estimated to be 129,000 by gel exclusion chromatography, and therefore it is likely a tetramer composed of 31,000-dalton monomers. Gel retardation assays with the purified TrpI protein demonstrated that InGP increases the affmnity of TrpI for sites I and II approximately 17- and 14-fold, respectively. Binding of TrpI to site I is site II independent. However, the protein has low intrinsic affinity for site II and its binding to site H is site I dependent. Therefore, binding of TrpI to site II probably requires its interaction with a second TrpI molecule at site I. Tryptophan synthase catalyzes the last step in the tryptophan synthetic pathway, the conversion of indoleglycerol phosphate (InGP) plus serine to tryptophan plus glyceralde-hyde-3-phosphate. Tryptophan synthase is composed of two a subunits, encoded by trpA, and a 2 subunit, encoded by
General Regression Neural Network Approach for Image Transformation Based Hybrid Graphical Password Authentication System

In this digital generation, computer, and information security plays a prominent role for both individuals and business organizations. In this interconnected business environment, information is the most valuable asset and it is of utmost importance to both individuals and organizations. The task of protecting information can be achieved through authentication. Today, textual password authentication is with username and password combination commonly used for many web applications. But textual passwords are the weakest form of authentication and it is easily guessed by the attacker by applying the various techniques such as brute force, dictionary attack, etc. To provide security from vulnerable attacks, graphical passwords are another alternative authentication mechanism for replacing the textual passwords. This paper proposes image transformation based hybrid graphical password authentication model utilizes general regression neural network model and feature extraction methods for user identification. Three types of image transformations such as normal image, mirror image and shift image are considered to enhance security. In this paper, three types of feature extraction techniques such as SURF, LBP and HOG are considered for extracting image features. The performance of the proposed model is analysed, in terms of usability, security and storage space analysis and the results proved that the proposed system is resistant against various attacks like brute force, dictionary attack, shoulder surfing etc.
Decision Making by a Fuzzy Regression Model with Modiﬁed Kernel

—Regression model is a popular and powerful model for ﬁnding a rule from large amount of collected data. It is widely used in various areas for predicting the value derived from observable values. Especially in multivariate numerical analysis, several types of regression models, not only linear but also polynomial or exponential, are established. In case of non-numerical data, although fuzzy regression models are proposed and investigated by some researchers, most of them are linear models. In order to construct a non-linear regression model with fuzzy type data set, new type of devices are needed since fuzzy numbers have a complicated behavior in multiplication and division. In this paper, we try to extend a linear fuzzy regression model to non-linear model by adapting a modiﬁed kernel method.
D ATA PROCESSING PIPELINE FOR DECAMETER PULSAR/TRANSIENT SURVEY

The study of the low-frequency radio sky at short time scales provides the insights into the nature of transient radio sources as well as gives a sensitive tool for interstellar medium sensing. The decameter survey of the northern sky is aimed to discover new sources of transient and repetitive emission within more than 100 Tb of the high time resolution data. The efficient pipeline of data cleaning and processing is developed and successfully tested.
EVALUATION THE EFFICIENCY OF SOME INSECTICIDES AND MINERAL OILS AGAINST THE WHEAT APHID ON TURFGRASS

The field experiment was carried out in a turfgrass farm located at Menofia Governorate during 2006 year. Six insecticides and three mineral oils were evaluated for their efficacy as aphicides on wheat aphid insect, Schizaphis graminum (Rondani) infesting Seashore paspalum, cv Sea Dwarf turfgrass. All treatments gave satisfactory results, whereas Malathion 57% EC, Reldan 40% EC, Deltan 40% EC and Sumithion 50% EC gave highly superior effect. The residual toxicity of these insecticides were 97.7, 97.7, 97.4 and 92.4%, respectively. Folk oil 2% EC gave the least aphicidal effect with a residual toxicity of 63.68%.
Machine learning classification techniques for heart disease prediction: a review

The most crucial task in the healthcare field is disease diagnosis. If a disease is diagnosed early, many lives can be saved. Machine learning classification techniques can significantly benefit the medical field by providing an accurate and quick diagnosis of diseases. Hence, save time for both doctors and patients. As heart disease is the number one killer in the world today, it becomes one of the most difficult diseases to diagnose. In this paper, we provide a survey of the machine learning classification techniques that have been proposed to help healthcare professionals in diagnosing heart disease. We start by overviewing the machine learning and de-scribing brief definitions of the most commonly used classification techniques to diagnose heart disease. Then, we review represent-able research works on using machine learning classification techniques in this field. Also, a detailed tabular comparison of the sur-veyed papers is presented. Basis Function (RBF), which is a type of ANN. The algorithms were applied to a patient dataset in India consisting of 214 records and 19 attributes and predicting whether a person has heart disease or not. The performance of the algorithms was evaluated using the overall average through training and testing the dataset, 5-fold cross-validation, and 10-fold cross-validation. The overall average performance yielded 86.42% and 80.81% accuracy for SVM and RBF respectively. Their results showed that SVM provided a better accuracy.
Outdoor Scene Image Segmentation for background recognition using Perceptual organization model

— In this paper we recognize the background objects like sky, tree, ground. In processing we improve the quality of input image (auto contrast) by thresholding & then bottom up segmentation applied on it for clustering of similar patch. After that Edge detection principle is used for the identification of any type of boundaries. Finally Perceptual Organization Model which mainly include list of Gestalt’s laws & capture structural object. Our proposed method outperformed on Berkeley image data set & natural outdoor images.
Fine Grained Topic Modeling Approach for Efficient Opinion Feature Extractions

— extual content on the planet can be for the most part arranged into two fundamental classifications, realities and suppositions. Realities are target articulations about elements and occasions on the planet. Suppositions are subjective explanations that mirror individuals' slants or discernments about the substances and occasions. A significant part of the current exploration on content data preparing has been (only) focused on recovery and mining of accurate data, e.g., Web look, data recovery, and numerous other regular dialect handling errors and content mining. However, assessments are significant to the point that at whatever point one needs to settle on a choice one needs to hear others' conclusions. The principle point of the proposed work is to apply fine grained theme displaying approach for recognizing feeling highlights. Further, this framework likewise extends the subject demonstrating way to deal with discover the non thing highlights, occasional components lastly understood elements. By discovering every one of these components the exactness of feeling highlight choice may increment.
99Analysis of Degradation Mechanisms in Donor-Acceptor Copolymer Based Organic Photovoltaic Devices Using Impedance Spectroscopy

The stability of organic photovoltaic (OPV) devices in ambient conditions has been a serious issue which needs to be addressed and resolved timely. In order to probe the degradation mechanism in a donor-acceptor block copolymer PDPP-TNT:PC71BM bulk heterojunction based OPV devices, we have studied current-voltage (J-V) behaviour and impedance spectroscopy (IS) of fresh and aged devices. The current-voltage characteristic of optimized fresh devices exhibit a short circuit current ( J sc ) of 8.9 mA/cm 2 , open circuit voltage ( V oc ) of 0.79 V, fill factor ( FF ) of 54.6 %, and power conversion efficiency ( PCE ) of 3.8 %. For aged devices, J sc , V oc , FF, and PCE was reduced to 42 %, 10 %, 56% and 76% of its initial value, respectively. The impedance spectra measured under illumination for these devices was successfully fitted using a CPE based circuit model. For aged devices, the low frequency response in impedance spectra suggests an accumulation of the photo-generated charge carriers at the interfaces which impedes the charge extraction at the electrodes and leads to a significant lowering in fill factor. Such degradation in device performance is attributed to the incorporation of oxygen and water molecules in devices. An increase in the recombination resistance associated with the bulk heterojunction active layer indicates a deterioration of free charge carrier generation and conduction in devices. Such observations from impedance measurement appear as an electrical signature of the probable physical mechanisms responsible for degradation of OPV devices.
An Active Learning Approach for Teaching Web Development-Action Research Study

— This paper presents an Action Research study for incorporating new learning and teaching methods into a Web Development module at a U.K. University, addressing the problems of varied experiences amongst the students with programming languages and computational thinking. Active learning concepts were introduced using three experiments involving Learning by Teaching, Jigsaw Puzzle, and Observations from Videos. These experiments were aimed at enhancing the students learning capabilities and computational thinking. All three experiments have achieved positive results among the students who were evaluated using formative assessments during their practical sessions. The study has found that Active Learning concepts can enhance student learning capabilities and give them more control over their own learning.
Harmonic forcing of a laminar bluff body wake with rear pitching flaps

Abstract A numerical study on the response of a two-dimensional bluff body wake subjected to harmonic forcing imposed by two rear pitching flaps is performed. The wake is generated by a rectangle at a height-based Reynolds number $Re=100$, characterised by laminar vortex shedding. Two forcing strategies are examined corresponding to in-phase ‘snaking’ and out-of-phase ‘clapping. ’ The effects of the bluff body aspect ratio ($AR=1,2,4$), flapping frequency, flapping amplitude, flap length and Reynolds number are investigated. For the snaking motion, a strong fundamental resonance of the root mean square (r.m.s.) drag is observed when the wake is forced near the vortex shedding frequency. For the clapping motion, a weak subharmonic resonance is observed when the forcing is applied near twice the vortex shedding frequency resulting in an increase of the lift r.m.s. whereas the drag r.m.s. remains unaffected. Both resonances intensify the vortex shedding and a concomitant mean drag increase is observed for the snaking motion. Forcing away from the resonant regimes, both motions result in considerable drag reduction through wake symmetrisation and propulsion mechanisms. The formation of two vortex dipoles per oscillation period due to the flapping motion, which weaken the natural vortex shedding, has been identified as the main symmetrisation mechanism. A single scaling parameter is proposed to collapse the mean drag reduction of the forced flow for both motions over a wide range of flapping frequencies, amplitudes and flap lengths. Finally, the assessment of the performance of the forcing strategies has revealed that clapping is more effective than snaking.
General Graphs are Easier than Bipartite Graphs: Tight Bounds for Secretary Matching

Online algorithms for secretary matching in bipartite weighted graphs have been studied extensively in recent years. We generalize this study to secretary matching in general weighted graphs, for both vertex and edge arrival models. Under vertex arrival, vertices arrive online in a uniformly random order; upon the arrival of a vertex v, the weights of edges from v to all previously arriving vertices are revealed, and the algorithm decides which of these edges, if any, to include in the matching. We provide a tight 5/12-competitive algorithm for this setting. Interestingly, it outperforms the best possible algorithm for secretary matching in bipartite graphs with 1-sided arrival, which cannot be better than 1/e-competitive. Under edge arrival, edges arrive online in a uniformly random order; upon the arrival of an edge e, its weight is revealed, and the algorithm decides whether to include it in the matching or not. For this setting we provide a 1/4-competitive algorithm, which improves upon the state of the art bound.
Binaural Sound Source Localization based on Neural Networks in Mismatched HRTF Condition

Binaural sound source localization is a field with wide applications, such as virtual sound localization in VR or speech enhancement, which has drawn many researchers’ attention. However, the mismatched HRTF condition is a severe problem, which has been ignored in most of the previous researches. In this paper, an experiment is firstly conducted to prove the negative influence of HRTF individualization on binaural localization performance. In face with this problem, an improved localization method is proposed in this paper. Both DNN and CNN are used in this method and the performance is compared. Then, another experiment is also operated to prove the efficiency of this method, providing ideas for subsequent iterations.
Region Segmentation of Retina OCT Image Layer Based on MS-UNet

The accurate segmentation of the retinal layer is a key link in the analysis of retinal OCT images. If the various tissue layers of the retina can be accurately segmented from the OCT image, it will help doctors make a more accurate and rapid diagnosis. Aiming at the high complexity of the features of each layer of the retinal OCT image and the low accuracy of the existing segmentation methods, this paper designs an improved U-Net model, MS-UNet (Multiscale Skip-UNet), which realizes the precise segmentation of seven retinal layers. To prevent problems such as over-fitting and gradient dispersion, this method uses the residual structure of deep over-parameterized convolution for feature extraction firstly. Then uses the multiscale skip structure to introduce global context information, and performs edge contour maintenance operations on the feature tensor. Finally, through a series of upsampling operations, the effective segmentation of each layer region is achieved. The experimental results proved that the method proposed in this paper improves MIoU by 1.39% compared with the method based on the original U-Net model. Compared with other semantic segmentation methods, this method effectively avoids the problems of layer edge blur and loss of details and has better segmentation performance.
Brain Tumor Grading Prediction and Brain Health Management Based on Multi-Physiological Data Fusion

Prediction of brain tumor grade is the only way to obtain brain tumor grade without physiological biopsy. It is a reliable way to prolong the life of patients. The author proposes an effective PTGP model, which can predict and grade the brain tumor grade of the segmented MRI image. It is another research path of medical image recognition. Through the segmented RGB color image and integrating the patient's physiological data (age), the classification method is used to divide it into high-grade and low-grade gliomas, and the prediction success rate is 98%, Finally, the least square method is used to predict the survival cycle of patients after operation, and a reasonable brain health management method is proposed.
Multi-stage Floor Plan Recognition and 3D Reconstruction

The floor plan recognition problem is a sophistical and time-consuming task that requires architects to craft with their professional skills and tools. To solve Raster to vector (R2V) issues, this paper presents a deep-learning-based framework mainly consisting of YOLOv5, deep residual network (DRN), and OCR extraction algorithms. Our work can be interpreted in five steps: 1. Apply YOLOv5 to identify and separate a two-dimensional floor plan main body from the entire figure. Remove the annotation arrows by means of YOLOv5; 3. Extract the main structure data of the floor plan in the light of DRN;4. Apply OCR technology to read the annotation information; 5. Revise the floor plan to a real scale by means of the structured data from DRN recognition. The comparison experiment between our model and the original DRN model is carried out on the classical dataset RJFM (Recognition and 3D Reconstruction of Japan Residential Floor plan Model) and evaluated by a total of four loss functions, i.e. cross-entropy loss, near-neighbor field loss, endpoint regression loss, and multi-task loss. The results show that our model outperforms other methods in 2D floor plan recognition and further isometric 3D reconstruction.
An Homage to the Beautiful Mathematical EWDs

Edsger W. Dijkstra was a giant figure in the area of programming theory and prac­ tice. He was one of the pioneers of “structured programming” that permeates current programming practices. Later in his career, Dijkstra studied the applica­ tion of structuring in mathematics. He believed that the ideas of structuring, so successful in programming, would apply in mathematics as well. He experimented with constructing and describing proofs in succinct and elegant styles, which he called “streamlining mathematical arguments. ” He had a range of interest in mathematics, from Euclidean geometry to num­ ber theory, combinatorics, graph theory, numerical analysis, modern algebra, and logic. In all cases, he wrote down his ideas in the most elegant way he could as a series of notes known as the EWDs. In this chapter, I discuss a few of his notes. I hope the reader will appreciate his treatments of the chosen problems, the abstractions of the problems, the nota­ tions chosen or avoided, the heuristics employed to arrive at a solution, and the orderly presentation of the arguments. In each case, his originality in approaching a problem is a thing of beauty. It will take a volume to explicate his various contributions in streamlining mathematics. I have chosen to discuss just a few representative EWDs.1 An Homage to the Beautiful Mathematical EWDs
Hierarchical Portfolios in Recommender Ecosystems: Multi-level aggregations of heterogeneous ensembles integrating long-term and short-term motivations

This paper focuses on the recommendation problem from the perspective of hierarchical heterogeneous portfolios. These portfolios consist of several base recommenders. Each of them processes different subset of available data and achieves the best performance under different circumstances. By running base recommenders in parallel and employing a suitable aggregation of their results (i.e. ensemble approach) considerable performance gains can be achieved. The main contribution of this paper is the proposal of a hierarchical ensemble approach to the recommendation problem and its utilization in the case of repeated recommendations. We extend flat portfolios to hierarchical ones with two levels of aggregation. For the aggregation of base recommenders, we experimented with Thompson sampling multi-armed bandits algorithm and a modified version of D’Hondt’s mandates allocation algorithm. As for the hierarchical aggregation, we implemented a modified version of D21-Janecek mandate allocation algorithm, which allows us to incorporate implicit negative feedback as well. Experiments were performed on real-world data from the domain of e-commerce. Hierarchical portfolios outperformed both flat portfolios as well as individual base recommenders w.r. t. click-through rate.
A System-Level Analysis of Conference Peer Review

We undertake a system-level analysis of the conference peer review process. The process involves three constituencies with different objectives: authors want their papers accepted at prestigious venues (and quickly), conferences want to present a program with many high-quality and few low-quality papers, and reviewers want to avoid being overburdened by reviews. These objectives are far from aligned; the key obstacle is that the evaluation of the merits of a submission (both by the authors and the reviewers) is inherently noisy. Over the years, conferences have experimented with numerous policies and innovations to navigate the tradeoffs. These experiments include setting various bars for acceptance, varying the number of reviews per submission, requiring prior reviews to be included with resubmissions, and others. The purpose of the present work is to investigate, both analytically and using agent-based simulations, how well various policies work, and more importantly, why they do or do not work. We model the conference-author interactions as a Stackelberg game in which a prestigious conference commits to a threshold acceptance policy which will be applied to the (noisy) reviews of each submitted paper; the authors best-respond by submitting or not submitting to the conference, the alternative being a "sure accept" (such as arXiv or a lightly refereed venue). Our findings include: observing that the conference should typically set a higher acceptance threshold than the actual desired quality, which we call the resubmission gap and quantify in terms of various parameters; observing that the reviewing load is heavily driven by resubmissions of borderline papers --- therefore, a judicious choice of acceptance threshold may lead to fewer reviews while incurring an acceptable loss in quality; observing that depending on the paper quality distribution, stricter reviewing may lead to higher or lower acceptance rates --- the former is the result of self selection by the authors. As a rule of thumb, a relatively small number of reviews per paper, coupled with a strict acceptance policy, tends to do well in trading off these two objectives; finding that a relatively small increase in review quality or in self assessment by the authors is much more effective for conference quality control (without a large increase in review burden) than increases in the quantity of reviews per paper. ; showing that keeping track of past reviews of papers can help reduce the review burden without a decrease in conference quality. For robustness, we consider different models of paper quality and learn some of the parameters from real data.