Miracle-dz/UniFAR-FTUs huggingface dataset:
FOG: Função de Objetivo Genérico para Roteamento e Alocação de Comprimentos de Onda em Redes Ópticas com Tráfego de Demanda Estática

This doctoral thesis proposes a mathematical formulation to simulate routing and wavelength assignment in optical networks, without the inclusion of constraints that are not inherent to the basic problem and in order to be applicable to any type of optical network with a static traffic demand. The establishment of a route followed by wavelength assignment is one of the keys to the smooth operation of an optical network, as this influences on how the resources of the network will be managed. Thus, the process of routing and wavelength assignment (RWA) in optical networks, needs a solution that seeks its optimization. However, despite numerous studies aiming to optimize RWA, it is observed that there is no solution, a priori, that could lead to a standardization of RWA process. Whereas consolidating standardization is desirable in the use of any technology, the thesis described in this work is a Generic Objective Function (GOF) that deals with the process of routing and wavelength assignment to establish a basis from which we can develop a standard or several standards for optical networks. The GOF was tested by simulation in the process of wavelength assignment (WA) and the RWA process as a whole. In both cases, tests were performed considering opaque networks, bringing surprising results, considering the simplicity of solution for a non-trivial problem.
Reinforcement Learning for Sepsis Treatment: Baselines and Analysis

In this work, we consider the task of learning effective medical treatment policies for sepsis, a dangerous health condition, from observational data. We examine the performance of various reinforcement learning methodologies on this problem, varying the state representation used. We develop careful baselines for the performance of these methods when using different state representations, standardising the reward function formulation and evaluation methdology. Our results illustrate that simple, tabular Q-learning and Deep Q-Learning both lead to the most effective medical treatment strategies, and that temporal encoding in the state representation aids in discovering improved policies.
A Novel ARED Protocol for Detection of Clone Attacks in Wireless Sensor Networks

Objectives: To develop a new technique that avoids the clone attack without affecting the performance of the wireless network. Methods: An Authentic, Randomized, Efficient, Distributed (ARED) protocol is introduced for detect the clone attacks from wireless sensor networks. This method does not require high memory and energy like other methods but provides high authentic context. The attractive feature of this method is that it detects the clones even before it is introduced into the network by the adversary. It provides continuous communication to the end users while detecting the clone attacks. Thus the system can be highly secured and can be used for the real time data acquisition systems. Findings: The proposed method achieves high performance in terms of end to end delay, packet delivery ratio and Throughput. Application/Improvements: The proposed detection mechanism is done by using Authentic, Randomized, Efficient, Distributed (ARED) protocol. It achieves high detection performance compared to the existing system.
Combining patient centred processes and anaesthesiologic optimisation for the preoperative assessment clinic : discovering patient arrivals and facilitating timely screening at preoperative assessment clinic in Amsterdam Medical Centre

Challenged by aging population and the rise of comorbidities and polypharmacy, the anaesthesiologists at the Academic Medical Centre in Amsterdam use the implementation of a new hospital and patient information system as a start to initiate a project to optimise preoperative care and redesign the process around the patient. This research assesses the quantification of the patient arrivals to the preoperative assessment clinic and designs a schedule for the clinic to facilitate timely screening at the clinic for patients in the ideal time window to prepare patients for surgery.
An Interactive Framework for Multi‐Person, Multiobjective Decisions

Group decision making in the presence of multiple conflicting objectives is complex and difficult. This paper describes and evaluates an iterative technique to facilitate multiple objective decision making by multiple decision makers. The proposed method augments an interactive multiobjective optimization procedure with a preference ranking tool and a consensus ranking heuristic. Two multiple objective linear programming (MOLP) solution approaches, the SIMOLP method of Reeves and Franz [39] and the interactive weighted Tchebycheff procedure of Steuer and Choo [49], are recommended optimization strategies to be used independently or in concert. Computational experience suggests that the proposed framework is an effective decision-making tool. The procedure quickly located excellent compromise solutions in a series of test problems with hypothetical decision makers. In addition, human decision makers gave positive evaluations of the procedure and the production plans the procedure provided for a resource allocation case problem.
FPGA Implementation for Image Processing Algorithms Using Xilinx System Generator

The paper presents information about FPGA implementation for various Image Processing Algorithms using the most efficient tool called Xilinx System Generator (XSG) for Matlab. System Generator is a DSP design tool from Xilinx that enables the use of the Math Works model-based Simulink design environment for FPGA design. In this paper various morphological and intensity image processing algorithms for negatives, image enhancement, threshold,, contrast stretching, Edge detection, boundary extraction for grayscale and color images are explored. Use of Xilinx system generator for image processing effectively reduces intricacy in structural design also provides additional feature for hardware co-simulation
Modified Map Reduce Model using Fuzzy Neural Network

The MapReduce paradigm is now the de facto standard for processing and generation large scale datasets. Generally in MapReduce paradigm, user specifies a map function that processes a key/value pair to generate a set of intermediate key-value pairs & reduce function which merges all intermediate values associated with the same intermediate key. This paradigm is enough to solve many real world problems. Here fuzzy neural network is applied in the MapReduce paradigm to improve its efficiency. It is also referred as Neuro-fuzzy which is combination of artificial neural network and fuzzy logic. Now, here the key/value generated pair via map function is feed as input in neuro fuzzy system or model to improve efficiency of current map reduce paradigm. The proposed Fuzzy Neural Network model aims at training the fuzzy Neural Network model in MapReduce programming model. Since the MapReduce programming model has the ability to rapidly process large amount of data in parallel and can use the outcome of training process for the prediction purpose. This can produce better prediction accuracy. In order to train large data in distributed mode it can be effective in reducing the processing time.
IL-33 in murine abdominal aortic aneurysm: a novel inflammatory mediator awaiting clinical translation.

Abdominal aortic aneurysm (AAA) affects approximately 5% of the high-risk general population consisting of men between the ages of 60–75 (1). Histopathologically, AAA are characterized by degradation of the extracellular matrix (particularly elastin), medial smooth muscle cell (SMC) depletion or apoptosis, and inflammation (2,3). Very little is known about risk factors for AAA expansion, once an aneurysm has been detected.
Evaluation of the diagnostic utility of case definitions to detect influenza virus infection in Vojvodina, Serbia

Introduction/Objective. A case definition recommended by the World Health Organization is commonly used for influenza surveillance worldwide. The aim of this study was to evaluate prognostic values of proposed case definitions of Influenza Like Illness (ILI), Severe Acute Respiratory Illness (SARI) and Acute Respiratory Distress Syndrome (ARDS) for laboratory confirmed-influenza and to compare the age distribution of influenza patients across virus types and subtypes in Vojvodina. Methods. We conducted a descriptive epidemiological study using surveillance reports and laboratory data from October 1, 2010 to May 20, 2017 (seven surveillance seasons). Results. We included 2,937 participants, 48.6% of whom were laboratory-confirmed influenza cases, and most of the confirmed cases (30.1%) were detected in February. In the 15? 29 years age group, the type A influenza (H3N2) was more frequent among patients with ILI (54.9% vs. 34.2%, p = 0.040), and less frequent in patients with SARI (39.4% vs. 65.8%, p = 0.009) compared with influenza type B. In patients aged 30? 64 years with ARDS, influenza type B was more common than influenza type A (H3N2) (13.4% vs. 6.2%, p = 0.032), but less common in compared to influenza type A (H1N1) pdm09 (13.4% vs. 25.7%, p = 0.017). The SARI case definition of influenza was associated with an increased likelihood of laboratoryconfirmed influenza for all age groups (p < 0.05). During the epidemic period, it was observed that the ILI case definition had the highest diagnostic value for influenza in the age group 5? 14 (AUC = 0.733; 95% CI: 0.704? 0.764), while the SARI and ARDS case definitions were the best predictors of influenza for patients 15? 29 years of age (AUC = 0.565; 95% CI: 0.504? 0.615 and AUC = 0.708; 95% CI: 0.489? 0.708, respectively). The case definition of ARDS had the maximum sensitivity (100%) among patients 15? 29 years of age. Conclusion. The proposed case definitions of influenza appeared to be good predictors of influenza and therefore can be useful for influenza surveillance, especially in the countries with limited laboratory capacities.
Heterosis: Reappraisal of Theory and Practice

1 Biometrical Genetics of Heterosis. - 2 Heterosis in Maize: Theory and Practice. - 3 Heterosis and Hybrid Seed Production in Barley. - 4 Hybrid Wheat. - 5 Heterosis and Hybrid Seed Production in Fodder Grass. - 6 Heterosis in Vegetable Crops. - 7 Heterosis in the Tomato. - 8 Heterosis and Hybrid Cultivars in Onions. - 9 Heterosis in Ornamentals. - 10 Heterosis and Intergenomic Complementation: Mitochondria, Chloroplast, and Nucleus.
Clinicopathological Risk Factors Affecting Sexual Function after Radiotherapy for Cervical Cancer Patients

Aim: To specify the clinicopathological risk factors that influence sexual function after radiotherapy for cervical cancer.Methods: This observational and cross-sectional study includes a population of 60 adult women diagnosed with stage I to III cervical cancer who underwent radiotherapy. Sexual function was assessed using a self-reported standardized questionnaire, the Female Sexual Function Index (FSFI). Age, clinical stage, tumor diameter, type of radiotherapy, use of hormone replacement therapy, and period of time elapsed since the completion of radiotherapy at the time of data collection were documented using participants’ medical records. Multivariate logistic regression was used to identify independent risk factors for decreased sexual function.Results: The median age of participants was 53 years (Interquartile range, IQR, 45-60 years). Using the FSFI total score compared by variable, sexual dysfunction was significantly more prevalent among women in FIGO stage III than those in stage I /II (P = 0.038), and at ≥ 12 months after the completion of radiotherapy than at < 12 months after completion (P = 0.008). Multivariate analysis revealed that sexual morbidity was significantly more likely in stage III women (OR 4.60, 95% CI, 1.07, 24.39, P = 0.040), or where radiotherapy had occurred more than 12 months before (OR 4.78, 95% CI, 1.17, 25.20, P = 0.028). Conclusion: FIGO stage III and a period of ≥ 12 months after radiotherapy are associated with reduced sexual function. In medical consultations with women in these categories, adequate treatment should be provided where appropriate.
Automatic Localization of Liver Tumors

Localization of tumors is a frequent task of a radiologist. For example it can be a necessary step of preoperative examination which’s result gives the radiologist information what part of liver parenchyma should be removed and if the remaining liver volume is sufficient for survival of a patient. In presented work an automatic method for tumors localization from CT image data is presented. Described approach is based on a n-dimensional segmentation method called Grow-cut, which was fully automated. By this method both hyperdense and hypodense tumors are localized, the later ones are then tested on presence of hyperdense capsule. Keywords-image segmentation; Grow-cut; histogram analysis; liver tumor
Real-Time Isothermal Detection of Shiga Toxin–Producing Escherichia coli Using Recombinase Polymerase Amplification

Abstract Shiga toxin–producing Escherichia coli (STEC) are a major family of foodborne pathogens of public health, zoonotic, and economic significance in the United States and worldwide. To date, there are no published reports on use of recombinase polymerase amplification (RPA) for STEC detection. The primary goal of this study was to assess the potential application of RPA in detection of STEC. This study focused on designing and evaluating RPA primers and fluorescent probes for isothermal (39°C) detection of STEC. Compatible sets of candidate primers and probes were designed for detection of Shiga toxin 1 and 2 (Stx1 and 2), respectively. The sets were evaluated for specificity and sensitivity against STEC (n=12) of various stx genotypes (stx1/stx2, stx1, or stx2, respectively), including non-Stx-producing E. coli (n=28) and other genera (n=7). The primers and probes that were designed targeted amplification of the subunit A moiety of stx1 and stx2. The assay detected STEC in real time (within 5–10 min.
Fast Personalized Electrophysiological Models from CT Images for Ventricular Tachycardia Ablation Planning

Aims Clinical application of patient-specific cardiac computer models requires fast and robust processing pipelines that can be seamlessly integrated into clinical workflows. We aim at building such a pipeline from computed tomography (CT) images to personalised cardiac electrophysiology (EP) model. The simulation output could be useful in the context of post-infarct ventricular tachycardia (VT) radio-frequency ablation (RFA) planning for pre-operative targets prediction.
Diagnosis and management of eosinophilic granuloma of the jaw: a case report

ABSTRACT The eosinophilic granuloma is the most common form of the Langherans’ cell histiocytosis. In the majority of cases it is represented by a unilocular osteolytic lesion which can occur in adults and children. It is an intraosseous destructive lesion characterized by the presence of vast numbers of eosinophils and histiocytes. It can be localized or multifocal. The tumor is more prevalent in the first two decades of life and tends to affect mandibular body and angle. Most frequent signs and symptoms are pain, swelling, ulceration, gingival necrosis, alveolar bone destruction with mobility and loss of teeth. Therapeutic interventions for this pathology are surgical curettage, local radiotherapy and chemotherapy. This article presents the case of a 9-year old patient who came in for treatment with facial swelling, pain, tooth mobility and intraoral ulcer in the vestibular sulcus of the right mandibular angle region. Imaging and incisional biopsy were obtained. After the histopathological examination confirmed the diagnosis of eosinophilic granuloma, the lesion was removed by curettage and a reconstruction titanium plate was inserted to avoid a pathological fracture. After 14 years of follow-up no recurrence was found and mandibular growth was not impaired. A discussion about this type of tumor is presented, based on the current literature.
Implementation of a Face Recognition System Based on Matlab and LabVIEW

A face recognition system based on Matlab and LabVIEW is designed in this paper, which is implemented by mixed programming of LabVIEW and Matlab. This system is composed of image acquisition module, image preprocessing module and face recognition module. Face acquisition module is mainly consisted of several subVIs of NI vision image processing module in LabVIEW. Face image preprocessing is to normalize the face image. Face recognition adopts the eigenface method to recognize faces. By extracting low dimensional part of the face with K-L transformation feature subspace of the face is generated. In recognition, the collected image will be projected into this space, and a group of projection coefficients are obtained. Then they will be compared with every face image in training base. At last, the system will find the smallest gap and the identification is completed. The collected face image, the most similar face found in the training base and the face image number will be shown as the final result. Experiments show that the recognition system can recognize persons' identity accurately.
Transaction-level models for AMBA bus architecture using SystemC 2.0

The concept of a SOC platform architecture introduces the concept of a communication infrastructure. In the transaction-level a finite set of architecture components (memories, arithmetic units, address generators, caches, etc) communicate among each other over shared resources (buses). Until recently, modeling architectures required pin-level hardware descriptions, typically coded in RTL. Great effort is required to design and verify the models, and simulation at this level of detail is tediously slow. Transaction level modeling is the solution. Transaction level models (TLMs) effectively create an executable platform model that simulates orders of magnitude faster than a RTL model. In this paper, we present a SystemC 2.0 TLM of the AMBA architecture developed by ARM, oriented to SOC platform architectures.
Spectral mapping for voice conversion

Voice conversion is the process to modify a speech signal of one speaker (source) to sound like an intended speaker (target) without changing the language content. This technology has several applications, such as personalized speech synthesis and speech to singing, and a possible malicious application to fool a voice biometric system, also called a speaker verification system. This thesis focuses on techniques to improve voice conversion performance and the use of voice conversion technology to attack speaker verification systems. The study is important as the robustness of the conversion function will affect the performance of voice conversion directly. To this end, the thesis first proposes a method to improve the voice conversion function by benefiting from nonparallel data of background speakers. The strategy is to decompose a speech spectral vector into a phonetic component and a speaker-specific component, which are modeled by a factor analysis model. The nonparallel data are used to estimate the phonetic component and the factor loadings. The speaker-specific component can then be represented by a low-dimensional set of variables via factor loadings, and hence allow for more robust modeling under sparse training data condition. The experimental results show that the proposed method outperforms the conventional Gaussian mixture model (GMM) based method considerably when there are limited parallel training data. The second contribution of the thesis focuses on implementing the conversion function by directly modelling the high-dimensional spectral features using exemplars found in the training data. In this approach, each speech segment is reconstructed as a weighted linear combination of a set of basis exemplars with residual compensation. An exemplar is defined as a speech segment spanning multiple frames extracted from training data. The value of the linear combination weights is constrained to be nonnegative, and most of them are restricted to have a value close to zero. Experiments are
Recent scenario of Opportunistic infection in HIV-infected individuals at a tertiary care hospital in Bilaspur (C.G.)

Introduction: Opportunistic Infections are now more frequent and more severe because of immunosuppression in HIV infected person. Its spectrum varies from continent to continent. The aim of present study was to document the spectrum of opportunistic infections in HIV infected patients in a tertiary care hospital of Bilaspur (C.G.) Method: A cross sectional study design was conducted among 689 clinically suspected HIV patients in one year duration in Chhattisgarh institute of medical sciences, a teaching hospital, Bilaspur (C.G.), to evaluate different opportunistic infections in individual in which HIV positivity was confirmed by three rapid tests as recommended by NACO guidelines. Result: Out of 689 clinically suspected HIV patients, 161 were HIV seropositive (23.4%). Heterosexual contact was the major route of transmission (95%). Majority of them were in the age group of (20-39 years) i.e. 62%. Majority of HIV patients were male (77%). Tuberculosis was most common opportunistic infection (31.4%) followed by diarrhea caused by Cryptosporidium parvum (15.7%). Conclusion: Tuberculosis is the commonest opportunistic infection seen in HIV patients. Early diagnosis of opportunistic infection and appropriate antimicrobial prophylaxis can reduce the morbidity and mortality in HIV patients.
Converting the parallel treebank ParTUT in Universal StanfordDependencies

English. Assuming the increased need of language resources encoded with shared representation formats, the paper describes a project for the conversion of the multilingual parallel treebank ParTUT in the de facto standard of the Stanford Dependencies (SD) representation. More specifically, it reports the conversion process, currently implemented as a prototype, into the Universal SD format, more oriented to a cross-linguistic perspective and, therefore, more suitable for the purpose of our resource. Italiano. Considerando la crescente necessita di risorse linguistiche codificate in formati ampiamente condivisi, l’articolo presenta un progetto per la conversione di una risorsa multilingue annotata a livello sintattico nel formato, considerato uno standard de facto, delle Stanford Dependencies (SD). Piu precisamente l’articolo descrive il processo di conversione, di cui e attualmente sviluppato un prototipo, nelle Universal Stanford Dependencies, una versione delle SD maggiormente orientata a una prospettiva inter-linguistica e, per questo, particolarmente adatta agli scopi della nostra risorsa.
Complex Adaptive WSNs for Polluted Environment Monitoring

The development of low cost, low power, multi-functional sensor nodes created a new form of networking applicable to a variety of fields. The goal is to create low cost, low power consumption and web accessed wireless sensor nodes in order to be used for environment monitoring. A wireless sensor network consists of large number of sensor nodes. One of these nodes is used as a gateway to be connected to the internet; this node is called a base station or a sink node. The capabilities of the gateway node will be larger than the other nodes in the system. However it will be directly connected to the internet via Wi-Fi or Ethernet adapter. Moreover a web server may be installed on this node; this will enable the system to be directly accessed any time from any location. The low power consumption is strongly needed to be used in A wireless sensor networks; this because the wireless sensor nodes almost powered by batteries. In order to achieve this goal an intelligent hardware and intelligent software should be implemented carefully to adapt the environment changes and do some adjustments and calibrations on the system. Keywords—WSN, Multi Agents, Complexity, Co-evolution