Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Tigecycline, Linezolid and Vancomycin on Biofilms of Viridans Streptococci Isolates from Patients with Endocarditis

Background Endocarditis, and prosthetic valve endocarditis in particular, is a serious disease with high morbidity and mortality. We investigate the effects of tigecycline, linezolid and vancomycin on biofilms of viridans group streptococci (VGS) isolated from patients with definite native or prosthetic valve endocarditis. Methods and Results Ten of 20 VGS blood stream isolates from patients with endocarditis formed biofilms in the microtiter plate biofilm model. The minimal inhibitory concentrations (MIC) for tigecycline, linezolid and vancomycin were determined using the microdilution broth method. Biofilms were grown for 24 hours and were incubated with tigecycline, linezolid and vancomycin at increasing concentrations from 1-128x MIC of the isolate being tested. Biofilm thickness was quantified by measuring the optical density (OD) after dyeing it with crystal violet. The incubation of the biofilms with tigecycline, linezolid or vancomycin resulted in a significant reduction of OD compared to the control biofilm without antibiotic (p<0.05). The optical density ratio (Odr) decreased significantly at 2x MIC for tigecycline, and at 8x MIC for linezolid and vancomycin (p<0.05). Although biofilms persisted even at the highest antibiotic concentrations of 128x MIC, bacterial growth was eradicated starting at concentrations of 16x MIC for vancomycin and of 32x MIC for linezolid, but not for tigecycline, up to a concentration of 128x MIC. Conclusions In the present study on viridans streptococci isolated from patients with endocarditis, tigecycline and linezolid reduced the density of the biofilms as effectively as vancomycin. However, linezolid and vancomycin were bactericidal at higher concentrations. Linezolid and vancomycin at very high doses may be useful in the treatment of biofilm-associated diseases caused by VGS infections.
Brief Survey of K-Means Clustering Algorithms

K-means is one of the most widely used algorithms for clustering. Ease of implementation, efficiency, simplicity, and empirical success are the main reasons for its popularity. In actual application, there are some defects in traditional k-means, for example, the value of K need to be specified ahead, initial clustering center is a random choice and so on; this influences the performance of the K-means. In order to overcome these obstacles, many variants of K-means algorithm have appeared. We provide a brief overview of k-means, point out existing problems; summarize major improvements in the determination of clusters number, the initialization of the cluster, the similarity measurement, the sensitivity of noise and outliers and so on. Further study directions of K-means are pointed at last.
A Heuristic Mining Algorithm Using Web Hyperlink Structure

Web mining aims to discover useful information or knowledge from the Web hyperlink structure, page content and usage log. Based on the primary kind of data used in the mining process, Web mining tasks are categorized into three main types: Web structure mining, Web content mining and Web usage mining. Following is what they do on Web Data Mining. This paper proposed a heuristic mining algorithm.
Incremental learning in automatic target recognition

ATR in two dimensional images is valuable for precision guidance, battlefield awareness and surveillance applications. Current ATR methods are largely data-driven and as a result, their recognition accuracy relies on the quality of training dataset. These methods fail to reliably recognize new target types and targets in new backgrounds and/or atmospheric conditions. Thus, there is a need for an ATR solution that can constantly update itself with information from new data samples (samples may belong to existing classes, background clutter or new target classes). In the paper, this problem is addressed in two steps: 1) Incremental learning with Fully Adaptive Approximate Nearest Neighbor Classifier (FAAN) - A novel data structure is designed to allow incremental learning in approximate nearest neighbor classifier. New data samples are assimilated at reduced complexity and memory without retraining on existing data samples, 2) Data Categorization using Data Effectiveness Measure (DEM) - DEM of a data sample is a degree to which each sample belongs to a local cluster of samples. During incremental learning, DEM is used to filter out redundant samples and outliers, thereby reducing computational complexity and avoiding data imbalance issues. The performance of FAAN is compared with proprietary Bagging-based Incremental Decision Tree (ABAFOR) implementation. Tests performed on Army ATR database with over 37,000 samples shows that while classification accuracy of FAAN is comparable to ABAFOR (both close to 95%), the process of incremental learning is significantly quicker.
STUDIES OF A LATENT-CLASS SIGNAL-DETECTION MODEL FOR CONSTRUCTED-RESPONSE SCORING

Rater behavior in essay grading can be viewed as a signal-detection task, in that raters attempt to discriminate between latent classes of essays, with the latent classes being defined by a scoring rubric. The present report examines basic aspects of an approach to constructed-response (CR) scoring via a latent-class signal-detection model. The model provides a psychological framework for CR scoring and includes rater parameters with a clear cognitive basis. Simulations are used to examine how well rater parameters and latent-class sizes are recovered as well as the accuracy of classification. The relation of rater parameters to agreement statistics and classification accuracy is examined. The effects of using a balanced, incomplete block design are compared to those for a fully crossed design. The model is applied to several ETS datasets.
Block Compressed Sensing Based on Image Complexity

Compressed sensing (CS) is a new Compressed sensing (CS) is a new technique for simultaneous data sampling and compression. Inspired by recent theoretical advances in compressive sensing, we propose a new CS algorithm which takes the image complexity into consideration. Image will be divided into small blocks, and then acquisition is conducted in a block-by-block manner. Each block has independent measurement and recovery process. The extraordinary thought proposed is that we sufficiently take advantage of image characteristics in measurement process, which make our measurement more effective and efficient. Experimental results tell that our algorithm has better recovery performance than traditional method, and its calculation amount has greatly reduced.
Cyclin D1 G870A Polymorphism is Associated with an Increased Risk of Simple Endometrial Hyperplasia in Egyptian Women

Endometrial hyperplasia (EH) is a common diagnosis in women presenting with abnormal uterine bleeding, leading to cancer if untreated. Cyclin D1 A870G polymorphism was associated with increased risk of endometrial carcinoma, but no reported study has evaluated the association between the cyclin D1 A870G polymorphism and the risk of EH. We aimed to study the association of cyclin D1 A870G polymorphism with the risk of simple endometrial hyperplasia (SEH) in Egyptian women. Results showed that A allele was associated with a significantly elevated OR of 3.2 (95% CI =2.15 -5.01, P = 0.0001) in SEH cases, and was found to be associated with a significantly elevated OR of 4.03 (95% CI = 2.3 -7.1, P = 0.000) in premenopausal cases, as well as postmenopausal cases (OR of 2.3, 95% CI = 1.18-4.4, P = 0.01). Using the GG genotype as the reference genotype, the AA genotype was associated with a significantly elevated OR of 8.9 (95% CI = 3.7-21.5, P = 0.0001) in SEH cases, and was found to be associated with a significantly elevated OR of 16.4 (95% CI = 4.8-55.5, P = 0.000) in premenopausal cases as well as post-menopausal cases (OR of 4.6 (95% CI = 1.18-18.1, P = 0.02). In conclusion, the common G to A polymorphism in the CCND1 gene is associated with an increased risk of simple endometrial hyperplasia. Biochemistry & Physiology: Open Access B i o c h e m is try & Phyogy: Open Ac c e s s ISSN: 2168-9652 Citation: Abdel Aziz AF, El-RefaeeyAA, Elsaeid AM, Refaat M (2014) Cyclin D1 G870A Polymorphism is Associated with an Increased Risk of Simple Endometrial Hyperplasia in Egyptian Women. Biochem Physiol 3: 123. doi: 10.4172/2168-9652.1000123
Information system development by business process modeling based on adaptability to specification changes

Business modeling has been attracting attention in the development of information systems to be used for analyzing and designing the working procedures of the system under development. In this paper, we propose a method of information system development by business process modeling based on adaptability to specification changes. The proposed method builds a business process model from the requirement specifications, translates the model into UML, identifies the elements bridging the translation from Business Process Modeling to UML, that is, the analytical stage and the design stage, and seeks to perform both processes continuously while finalizing the design. The proposed method was applied to the prototyping of a bus booking and management system, comparing the results using only UML for the configuration with those obtained by using the proposed method. The proposed method proved effective in the early detection and removal of bottlenecks, in accurate modeling, and in adjusting to changes in the specifications. © 2010 Wiley Periodicals, Inc. Electr Eng Jpn, 174(2): 54–64, 2011; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/eej.21045
Self-Adjustment Time Quantum in Round Robin Algorithm Depending on Burst Time of the Now Running Processes

Problem statement: The performance and efficiency of multitasking operating systems mainly depends on the used CPU scheduling algorithm where the CPU is one of the primary computer resources and as round robin scheduling algorithm is considered most widely used scheduling algorithms in this research a new proposed variant of this algorithm presented, discussed in detail, tested and verified. Approach: The new proposed algorithm called Self-Adjustment-Round-Robin (SARR) based on a new approach called dynamic-time-quantum; the idea of this approach is to make the time quantum repeatedly adjusted according to the burst time of the now-running processes. Results: Based on the experiments and calculations that I have made the new modified algorithm radically solves the fixed time quantum problem which is considered a challenge for round robin algorithm. Conclusion: The use of dynamic scheduling algorithm increased the performance and stability of the operating system and support building of an self-adaptation operating system, which means that the system is who will adapt itself to the requirements of the user and not vice versa.
Probabilistic analysis for optimal power flow under uncertainty

This study presents a probabilistic analysis to consider the impact of uncertain system parameters on optimal power flow (OPF). A general OPF under uncertainty is formulated as a chance-constrained programming model and its stochastic features are investigated. The impact of the uncertain variables is addressed by the probabilistic analysis with the method of Monte-Carlo simulation (MCS) combined with a deterministic optimisation. In this way, relations between the optimality and reliability in power systems under uncertainty can be achieved. The information derived from the statistical results can be applied in the system risk assessment and operation performance evaluation, which is helpful for making decisions in power system optimal dispatch under uncertainty. Case studies of a 5-bus system and the IEEE 30-bus system are presented to illustrate the probabilistic analysis.
Antennas and humans in personal communications: an engineering approach to the interaction evaluation

A novel and powerful computational technique, based on hybridization of the Method of Moments and the Eigenfunction Expansion Method, has been developed to investigate electromagnetic interactions between a multilayered spherical head model and antennas used in personal communications. This technique efficiently utilizes the Method of Moments to determine current distribution on an antenna in the presence of a multilayered spherical head. Once the antenna current is determined, accurate near and far field distributions are obtained through eigenfunction expansion. The technique is very flexible and suitable for systematic and parametric studies for EM interaction evaluation. In the paper, the concept and the mathematical formulation of the technique, and examples of EM interaction calculations are presented.
The noise figure of a sampling mixer: theory and measurement

The concept of noise figure as applied to a sampling mixer, based on a track and hold circuit, has been investigated for a case where the mixer is resistively terminated at its input. A noise model accounting for the sampled thermal noise and the time continuous noise of the hold amplifier is developed. Measurements are made on a sampling mixer implemented in a 0.6 /spl mu/m CMOS process to verify the model. The model and the measurements are found to show good correspondence. A significant conclusion is that the noise of the hold amplifier may give a significant noise contribution and prevent the lowering of the mixer noise figure expected from a higher sampling frequency.
Realistic world of limited sample neural network applications: how to proceed on a firm methodological foundation with small-n

Improving evaluation, especially with small sample, or small-n, applications, may be highly dependent on incorporating expanding knowledge about methodological pitfalls to avoid. It is the intent of the current paper to provide an informational guide to key evaluation issues with small-n. Although the present paper is focused on supervised learning classification paradigms typified by the back-propagation network, the principles hold true in various degrees for other artificial neural networks.
Waves4U: Waveform Availability Through AutoDRM's

It's time to revisit one of the more successful and popular seismic data access systems available on the Internet. The Automatic Data Request Manager (AutoDRM) is approaching ten years old but is going strong, providing access to ever-increasing amounts of seismic data for more and more users. A fairly complete description of the development of the AutoDRM was one of the first guest columns of the Electronic Seismologist back in the summer of 1996. The system has undergone relatively few changes and enhancements since then, attesting to its original good design and thoughtful execution. If anything, its success is also one of its current problems; there are so many data available via the AutoDRM mechanism that it is hard to keep up with what is available when and from where. To help with this problem the father of AutoDRM is back at it, having now produced a system for summarizing data availability from the myriad of AutoDRM's and providing this summary via a simple set of Web pages and, yes, via an AutoDRM e-mail request method as well. Urs Kradolfer is again our guest columnist to explain this new service. Seismograms are the key data for many seismological researchers, and since seismic waveforms are now widely recorded and archived in digital form, global communications facilities, such as the Internet, potentially allow the seismological community to access lots of data. An important step toward making seismic signals available for research was the foundation of international data centers such as those at IRIS and ORFEUS. These centers collect and provide waveforms for significant earthquakes originating from many stations worldwide. Mainly in order to allow access to near-real-time waveform data, but also to facilitate international data exchange, many observatories have implemented an AutoDRM, which is an e-mail-based data exchange system (Kradolfer 1993, …
Ubichip Virtual Machine and visualization of Spiking Neural Network Parameters

Since ages people have been wondering the inner workings of brain. There is more and more information about processes involving particular brain regions, yet synthesizing neural-networks in a large scale has always been a problem. Despite the fact that the popular desktop computer is becoming more powerful, simulating massively parallel computations aways proved to be a challenge. Due to its genericness, in very specialized work, such as neural networks, it is far better to design and use dedicated array of processors. Such approach has been used in development of the Ubichip – a device designed specifically for such purposes. The design goal was to simplify the execution elements to suit the needs of efficient neuron model algorithms emulation. As with every development cycle of a complex tool there are many tasks that may be carried out in parallel. This, of course, effects in allowing the development team to shorten the cycle and provide the solution faster. This however, requires a set of tools that will allow to prototype and verify work progress against some set of basic rules. The Ubichip was already supported by a toolkit named SpiNDeK, that allowed to create networks and with the use of ModelSim simulate the code. This solution for proof-checking the algorithms however proved to be cumbersome and due to many levels of indirection – slow. To increase efficiency when working with code, the programmer should not have to wait endlessly watching the progress bars. To remedy this, improve efficiency and encourage more precise tuning and development of new neuron-model algorithms the Ubichip Virtual Machine was born. At first it was just meant to be a simple visualization tool, but as it turned out there was a missing link in the chain of tools available for the Ubichip, which had to be filled. Thus, in this work a virtual machine for the Ubichip has been developed, as well as a visualization tool that enables a convenient display of the evolution of spiking neurons in a network. i
An Extend SIR Opinion Dynamic Model Base on Empirical Signed Network Mining

Relations between users on online social media sites often reflect a mixture of positive and negative interactions. The network composed by those positive and negative relations is called signed social network. We design a web crawler to collect the data base on a special web event of battle between Fang Zhouzi and Han Han. And we construct a signed social network with sentiment weighted relationships base on this empirical data. Under this empirical spread web structure, we construct an extended SIR spread model in such a signed social network with sentiment weighted relationships, and we study influence with the network factors of signed, directed and weighted on opinion spreading. Under this model, we could know the proportion of signed edges is most important factor to the spread result.
Multi-Level Recommendation for Sketch Based 3D Toy Modeling

Models recommendation is always a key part in part-composition system based on sketch. In this paper, proper models are suggested via three steps. Firstly, the possible type of model parts is suggested according to the topological features. Then toy parts that have similar profile types with the inputted sketches are clustered and recommended by lineage clustering method. Finally, based on the promoted $1 recognizer, the suggested parts are sorted according to the shape similarity with the inputted sketch. Experimental results show that the models can be recommended quickly and properly via our method.
Loot boxes : gambling in disguise ? A qualitative study on the motivations behind purchasing loot boxes

Background: In the last two decades, the rapid technological advancement in digital solutions had paved way for a transition of traditional gambling activities to internet-based platforms. Online casinos with video-game-like features have become a common platform for gambling. Consequently, gambling-like features is increasingly being adopted by mobileand computer games. The latest example of these features called loot boxes, are getting a lot of attention from gamers and regulators alike. Problem: Game publishers are reporting hundreds of millions of dollars of revenue from loot boxes and governmental agencies are struggling with determining whether to classify loot boxes as a form of gambling, therefore regulating it. The main reason for this conflict is the lack of empirical studies in the subject. Purpose: This thesis aims to shed light upon the phenomenon. More specifically, it will do so from the gamers’ perspectives and reveal the underlying motivations for loot boxes activities, as well as their views on loot boxes. Method: A qualitative approach with semi-structured interviews with twelve participants has been conducted. These findings have later been compared to existing literature regarding gambling. Results: The findings showed that there are distinct similarities between gambling and loot boxing. In terms of motivations, the same nature is applied for socialization and amusement. Two new motivations were discovered, value-based motive and collecting purpose which are video-game specific. Additional components that influenced both gambling and loot boxing were found to be impulsivity and distorted beliefs. Other than that, the participants see loot boxes as a form of gambling based on the uncanny likeness of the mechanism and the emotional effects. Despite having a negative view on loot boxes, they do not wish the feature to be removed.
Cloud Computing Effectiveness Assessment

We present a comprehensive model for cloud computing effectiveness assessment. The model consists of technical, organizational, economical and external dimensions and addresses providers of various levels of cloud computing service as well as users. Independent and dependent variables of effectiveness are identified. Generic measures of each of the four dimensions of the model are presented in the form of footprint diagrams. In the research that led to the final model, after a detailed literature review, a large number of experts were interviewed and questionnaires were applied to construct the initial model. By applying factor and cluster analysis to the results of a cloud survey, the model was updated. Then, four qualitative exploratory case studies were carried out to finalize and validate the model.
Modeling duration adjustment with dynamic time warping

Dynamic time warping (DTW) is used to time-align frames in a naturally spoken monosyllabic word-a long-duration syllabic-with the corresponding frames in the same syllable excised from a polysyllabic word-a shorter-duration rendition of the syllable. The DTW path, referenced to the monosyllable, indicates which portions of the monosyllable are shortened (and by how much) to match the shorter version of the syllable in a polysyllabic word. An experiment is presented which indicates that most shortening occurs in the latter portion of the syllable and that DTW path slopes are correlated with a measure of the amount of formant movement in the syllable. For speech synthesis by rule, a formant-movement time function computed for each template in an inventory in combination with additional information like phonemic vowel identity and consonant position within a syllable can define a time-compression function for the template/. <<ETX>>