Miracle-dz/UniFAR-FTUs huggingface dataset:
Antimicrobial Resistance in Class 1 Integron-Positive Shiga Toxin-Producing Escherichia coli Isolated from Cattle, Pigs, Food and Farm Environment

The aim of this study was to investigate the presence of class 1 integrons in a collection of Shiga toxin-producing Escherichia coli (STEC) from different origins and to characterize pheno- and genotypically the antimicrobial resistance associated to them. A collection of 649 isolates were screened for the class 1 integrase gene (intI1) by Polymerase chain reaction The variable region of class 1 integrons was amplified and sequenced. Positive strains were evaluated for the presence of antimicrobial resistance genes with microarray and for antimicrobial susceptibility by the disk diffusion method. Seven out of 649 STEC strains some to serogroups, O26, O103 and O130 isolated from cattle, chicken burger, farm environment and pigs were identified as positive for intl1. Different arrangements of gene cassettes were detected in the variable region of class 1 integron: dfrA16, aadA23 and dfrA1-aadA1. In almost all strains, phenotypic resistance to streptomycin, tetracycline, trimethoprim/sulfamethoxazole, and sulfisoxazole was observed. Microarray analyses showed that most of the isolates carried four or more antimicrobial resistance markers and STEC strains were categorized as Multridrug-resistant. Although antimicrobials are not usually used in the treatment of STEC infections, the presence of Multridrug-resistant in isolates collected from farm and food represents a risk for animal and human health.
Image Forensic using Block-based Copy-move Forgery Detection

Copy-move forgery is the image tampering procedure where a region or more regions of the image is copied and pasted into one or more areas of the same image. Detection of image copy-move forgery is essential because, images are used as the primary element of communication and security in many places. In place of law and order, used images must be original or authentic and it is necessary to ensure its authenticity. Therefore, the detection of copy-move forgery has become a prominent and active research area. This paper highlight the different block-based approaches used in copy-move forgery detection with their important results. This paper also critically discusses the advantages and limitations of each approach and enlists the different image datasets used for image forgery detection, different unsolved issues and challenges in the field of forgery detection.
An Effective Algorithm for Hemorrhages Detection in Retinal Images

Periodic screening for Diabetic retinopathy (DR) reduces the progress of disease and also prevent loss of visual capability of a person at early stage. This can be achieved using automatic detection of abnormality in DR. To achieve this two methods are proposed for hemorrhages detection in retinal images. Firstly, detect the optic disc for easier segmentation of hemorrhages. After removal of optic disc H-maxima Transform and thresholding approaches are proposed for Hemorrhages(HAs) detection. The proposed algorithms are applied on the DIARETDB1 retinal image database. Receiving operating curve (ROC) and weighted error rate (WER) are used to evaluate the results of proposed algorithm. The quantitative analysis of these parameters shows better performance for thresholding approach to detect HAs.
Sleep Quality Analysis with Cardiopulmonary Coupling

Sleep quality is commonly assessed with subject self-reporting, interviews and psychological variables. However, more precise methods comprise estimation of physiological signals where polysomnography is considered to be the gold standard and can be performed to produce qualitative or quantitative measurements regarding the subjects sleep. However, polysomnography is known to be a complex and expensive process that is inaccessible to a large group of the population. The main objective of this work is to produce an algorithm that is capable to ascertain sleep quality, using only data from a single-lead electrocardiogram, that can be implemented in a small digital signal processor or a small field-programmable gate array. Therefore, providing a tool that can be used for identifying the need of a polysomnography analysis or as a complementary method to improve the detection capability of other pathologies. The employed method first determined both normal-to-normal sinus interbeat interval series and electrocardiogram derived respiration signal, producing the cardiopulmonary coupling with the cross-spectral coherence method. This information was fed to a classifier to determine the non-rapid eye movement (non-REM) and cyclic alternating pattern (CAP) periods. Cyclic alternating pattern rate was then computed to provide a qualitative measure of sleep quality, considering the age-related cyclic alternating pattern rate percentages in healthy subjects as reference threshold. The employed method provided a reasonable non-rapid eye movement sleep classification (70% accuracy) but a poor cyclic alternating pattern detection capability (62% accuracy). From the ten tested classifiers deep stacked autoencoder produced the best results and the average absolute difference of predicted and true cyclic alternating pattern rate was 15%, classifying correctly 67% of the subjects, regarding their sleep quality (either good or bad).
Multipurpose Color Image Watermarking in Wavelet Domain using Multiple Decomposition Techniques

A multipurpose color image watermarking method is presented to provide © protection and ownership verification of the multimedia information. For robust color image watermarking, color watermark is utilized to bring universality and immense applicability to the proposed scheme. The cover information is first converted to Red, Green and Blue components image. Each component is transformed in wavelet domain using DWT (Discrete Wavelet Transform) and then decomposition techniques like Singular Value Decomposition (SVD), QR and Schur decomposition are applied. Multiple watermark embedding provides the watermarking scheme free from error (false positive). The watermark is modified by scrambling it using Arnold transform. In the proposed watermarking scheme, robustness and quality is tested with metrics like Peak Signal to Noise Ratio (PSNR) and Normalized Correlation Coefficient (NCC). Further, the proposed scheme is compared with related watermarking schemes.
A 9-bit Low-Power Fully Differential SAR ADC Using Adaptive Supply and Reference Voltages

This paper presents a 9-bit fully differential 200KS/s low-power successive-approximation-register (SAR) analog-to-digital converter (ADC). Adaptive comparator supply voltage and DAC reference voltage are exploited to decrease the power consumption of these blocks in specific bit conversion steps. The proposed circuit was simulated using a $\pmb{0.18\mu \text{m}}$ standard CMOS process. The proposed SAR ADC achieves 51.9dB SNDR (8.3 ENOB) and 56.2dB SFDR with 200KS/sec. The total power consumption is $\pmb{6.98\ \mu \text{W}}$ from both 1.8V and 0.9V supplies.
Fall Detection System Using Artificial Neural Network

Falls are a major concern in health care nowadays. The senior citizens and patients suffering from genetic disorders like muscular dystrophy are more likely to be prone to falls as their muscles weaken over the period of time. Apart from the fall itself, most significant consequences are generally the long lie periods associated with falls. This can cause severe side effects and can be dangerous especially if the person is alone or becomes unconscious after falling. So there is a need for a robust fall detector which is able to accurately detect fall and alert the concerned people for assistance so that the adverse effects of long lie period can be mitigated. This project aims in building an efficient fall detection system using neural network. The key to any fall detection system is the algorithm used. This paper proposes the use of machine learning technique specifically, neural network for the classification of an activity as a fall or non fall event.
Methodology for Text Classification using Manually Created Corpora-Based Sentiment Dictionary

This paper presented the methodology of Textual Content Classification, which is based on a combination of algorithms: preliminary forming a contextual framework for the texts in particular problem area; manual creation of the Hierarchical Sentiment Dictionary (HSD) on the basis of a topically-oriented Corpus; tonality texts recognition via using HSD for analysing the documents as a collection of topically completed fragments (paragraphs). For verification of the proposed methodology a case study of Polish-language film reviews Corpora was used. The main scientific contributions of this research are: the writing style of analysed text determines the possibility of adaptation of the Texts Classification algorithms; the Hierarchically-oriented Structure of the HSD allows customizing the classification process to qualitative recognize the text tonality in the context of the individual paragraphs topics; the text of Persuasive style most often initially empowered authors with a certain tonality. The tone, expressed in the author's opinion, effect on the qualitative indicators of sentiment recognition. Negative emotions of the author usually reduce the level of vocabulary variability as well as the variety of topics raised in the document, but simultaneously increase the level of unpredictability of words contextually use with both positive and negative emotional colouring.
Low-power Time-to-Digital Converter Based on Vernier Gated-Ring-Oscillator

This paper introduces a time-to-digital converter (TDC) based on Vernier gated ring oscillator (GRO) with high resolution and very low power consumption for biomedical imaging applications. The effective blocks in the power consumption of this converter include two ring oscillators, a single-transition end-of-conversion detection array block, and finally an Arbiter array block. This paper has greatly improved the power by modifying the Arbiter block structure. This converter has been simulated in the $0.18\mu \mathbf{m}$ CMOS technology with a supply voltage of 1.8 V. The proposed 8-bit Vernier GRO has a resolution of 14.3 ps. By using a dynamic D-type flip-flop for implementing the arbiter block and applying a few modification to ring-oscillators control circuit, the power consumption is reduced by 65% compared to the original design. The Monte Carlo analysis in 400 runs, including both process and mismatch variations, shows the standard deviation of 3-code in the output code for the proposed structure.
Estrogen Modulates Glycerol Permeability in Sertoli Cells through Downregulation of Aquaporin-9

High 17β-Estradiol (E2) levels are known to cause alterations of spermatogenesis and environments throughout the male reproductive tract. Sertoli cells (SCs) ensure an adequate environment inside the seminiferous tubule. Glycerol stands as essential for the maintenance of blood–testis barrier created by SCs, however, the role of E2 in this process is not known. Herein, we hypothesized that the effect of E2 on glycerol permeability in mouse SCs (mSCs) could be mediated by aquaglyceroporins. The expression of aquaglyceroporins was assessed by RT-PCR and qRT-PCR. Glycerol permeability was evaluated by stopped-flow light scattering. We were able to identify the expression of AQP3 and AQP9 in mSCs where AQP9 is more abundant than AQP3. Our results show that high E2 levels decrease AQP9 mRNA abundance with no influence on AQP3 in mSCs. Interestingly, high E2 levels decreased mSCs’ permeability to glycerol, while downregulating AQP9 expression, thus suggesting a novel mechanism by which E2 modulates fluid secretion in the testis. In conclusion, E2 is an important regulator of mSCs physiology and secretion through changes in AQP9 expression and function. Thus, alterations in glycerol permeability induced by E2 may be the cause for male infertility in cases associated with the presence of high E2 levels.
Assessing Similarity Value between Two Ontologies

The aim of this paper is to present an appraoch for assessing similarity between ontolgies. This approach is based on set theory, edges-based semantic similarity and features based similarity. We first determine the set of concepts that is shared by two ontologies and the sets of concepts that are different from them. Then we extend ontologies by using the set of concepts shared by the two ontologies. Then we redetermine set of concepts shared by the two extended ontologies. finally, we end with the assessment of the similarity between ontologies by using the average values of similarity of the sets of specific concepts to each not extended ontologies, and the set of concepts shared by extended ontologies.
Testing the conformity of GPS and IRNSS in terms of ionospheric delay and position errors

With the establishment of Indian Regional Navigation Satellite System (IRNSS), India envisages to have an independent navigation system apart from GNSS. This system also brings great opportunity to the space science community to compare and study its performance with existing satellite-based navigation systems. The present study is focused on the variations of the IRNSS signals at different frequencies as well as with GPS, based on the parameters of ionospheric delay and receiver position.
A Graph Partitioning Algorithm with Application in Synthesizing Single Flux Quantum Logic Circuits

In this paper, a new graph partitioning problem is introduced. The depth of each part is constrained, i.e., the node count in the longest path of the corresponding sub-graph is no more than a predetermined positive integer value p. An additional constraint is enforced such that each part contains only nodes selected from consecutive levels in the graph. The problem is therefore transformed into a Depth-bounded Levelized Graph Partitioning (DLGP) problem, which is solved optimally using a dynamic programming algorithm. As an example application, we have shown that DLGP can effectively generate timing-correct circuit solutions for Single Flux Quantum (SFQ) logic, which is a magnetic-pulse-based, gate-level pipelined superconductive computing fabric. Experimental results confirm that DLGP generates circuits with considerably lower path balancing overheads compared with a baseline full-path-balancing approach. For example, the balancing overhead (a critical measure of quality metric) for the SFQ circuit realization in terms of D-Flip-Flop count is reduced by 3.61 times on average for 10 benchmark circuit, given p=5.
Interference-Dependent Performance of Multi-User Detection in High Throughput Satellite Systems

System availability is a crucial design issue for a satellite system and depends on various factors. The design of high throughput satellites (HTS) is complicated because of multiple spot beams and increased intra-system interference levels. This fact demands for suitable interference mitigation techniques in order to cope with this situation enabling the benefit from the increased system capacity provided by HTS. This work analyzes the system throughput and availability of multi-user detection (MUD) as advanced interference mitigation technique applied in the user downlink of the satellite forward link and investigates its sensitivity w.r. t. different interference levels. Expected through-puts, achievable with MUD considering realistic interference levels within a single spot beam, are presented as well.
Dynamic Pricing Control for Open Queueing Networks

Pricing control is an important problem in service systems and it aims to control customer behaviors through an economic way, instead of administrative commands. In this paper, we study a dynamic pricing and service rate control problem in an open Jackson network with limited capacity. The goal is to determine the optimal admission prices and the optimal service rates at every state such that the long-run average social welfare is maximized. The original problem is decomposed into a rate-setting problem plus a price-setting problem. To solve the rate-setting problem, we derive a difference formula based on the sensitivity-based optimization theory. When the cost rate function is convex in service rates and the value rate function is concave in arrival rates, we decompose the rate-setting problem into a series of convex optimization subproblems. When the rate functions have linear structure, these subproblems are even simpler and a bang–bang control is optimal. For the price-setting problem, we determine the state-dependent prices so as to induce the optimal arrival rates obtained by the rate-setting problem. We propose a recursive algorithm to numerically compute the conditional expected delays at every state. Finally, we conduct numerical experiments to explore the optimality properties and some useful insights for this dynamic pricing control problem.
Interdisciplinary research framework for identifying research needs

A loss of biological diversity continues in spite of the existing, and in some respects, rather elaborate and heavy attempts at management and protection. It has been argued that one of the reasons for the lack of success is the unmet and challenging knowledge needs. Meeting the needs requires integration of various sciences and expertise, since attempts to manage biodiversity gives rise also to many emerging, complex and political questions. Integration of the disciplines needs practices that are able to overcome practical, institutional and cultural obstacles. ALTERNet, a European network for research on biological diversity under the 6th framework programme, has aimed to undertake further interdisciplinary research that will feed into the addressing of societal needs.This book presents a framework which creates possibilities for communication between research and decisionmaking. By using the framework, relevant topics for discussion are brought up, emerging research needs are reacted to for elaboration into concrete research projects, and finally research findings are conveyed to support decision-making. To achieve this requires organised and open collaboration between research
Motion Estimation and Compensation in Frequency Domain

In this paper, a novel method for motion estimation and compensation in frequency domain for the purpose of video compression is proposed. The proposed scheme is based on the coefficients of frequency domain obtained from the image blocks after the application of Orthogonal Polynomials Transformation (OPT). It uses the three-step search algorithm to estimate the search area for finding the best match in the subsequent frame with respect to each block in the current frame. The quality of match found will be contingent upon the value of Mean Absolute Difference, more commonly known as MAD, between the blocks. The motion vectors produced during motion estimation are utilized in the motion compensation process in order to produce the predicted frame for subsequent compression. The performance of the proposed scheme is experimented with good number of standard video and measured with Mean Square Error (MSE). The results are encouraging.
Near-Field Microwave Imaging Using Focused Near-Field Beams: An Approach to Mitigate Undesired Scattering Effects

We discuss the use of focused near-field (NF) distributions to irradiate the object of interest in NF microwave imaging. This is pursued with the aim of suppressing the effect of undesired scatterers, which could then lead to the use of simpler inversion algorithms dealing with fewer unknowns. To this end, we investigate the use of an existing NF plate, which is capable of focusing the NF distribution to a subwavelength line (one-dimensional) focus. Similarly, we investigate the same concept, i.e., suppressing the effect of undesired scatterers, using an existing Bessel beam launcher to create a confined NF distribution in free space.
When and How to Apply Statistics, Machine Learning and Deep Learning Techniques

Machine Learning has become 'commodity' in engineering and experimental sciences, as calculus and statistics did before. After the hype produced during the 00's, machine learning (statistical learning, neural networks, etc.) has become a solid and reliable set of techniques available to the general researcher population to be included in their common procedures, far from the mysticism surrounding this field when only ML experts could solve modeling and prediction problems using such novel algorithms. But while knowledge on this field has settled among professionals, novice ML users still have trouble to decide when determined techniques could and should be applied to solve a given problem, sometimes ending with over-complicated solutions for simplistic problems, or complex problems partially solved by simplistic methods. This tutorial wants to introduce the most common techniques on statistical learning and neural networks, towards showing the proper techniques for each given scenario.
Investigating the Use of Semantic Relatedness at Document and Passage Level for Query Augmentation

This paper documents an approach that i) uses graphs to capture the semantic relatedness between terms in text and ii) augmenting queries with those terms deemed to be semantically related to the query terms. In building the graphs we use a relatively straightforward approach based on term locations; we investigate approaches that aid query improvement by capturing the semantic relatedness that is extracted at passage level as well as the complete document level. Semantic relatedness between is represented on a graph, where the terms are stored as nodes and the strength of their connection is recorded as an edge weight. In this fashion, we recorded the degree of connection between terms and use this to suggest possible additional words for improving the precision of a query. We compare the results of both approaches to a traditional approach and present a number of experiments at passage and document level. Our findings are that the approaches investigated achieve a competitive standard against a well known baseline.