Miracle-dz/UniFAR-FTUs huggingface dataset:
TOWARDS LOW-COST FAULT DIAGNOSIS IN LARGE COMPONENT-BASED SYSTEMS 1

Abstract We address the problem of fault diagnosis in discrete-event systems. Our contribution is the development of a set of specialised diagnosers whose computation is much more realistic than that of the classical diagnoser. A specialised diagnoser is devoted to the diagnosis of one particular type of fault and is based on the observation of only a subpart of the system.
An improved technique of single-hidden layer feedforword networks for class-imbalanced data

The framework of Single-hidden Layer Feedforword neural Network with random weights (SLFN) has been shown simple in theory and fast in implementation. However, the decision boundary of SLFN classifier can be affected by the class imbalance of the data. In this work, to deal with the class-imbalanced problem on SLFN, a newly designed algorithm called SW-SLFN is proposed. SW-SLFN connects the synthetic minority oversampling technique (SMOTE) with Weighted-SLFN by an adjustable coefficient called balance factor. Experiments show that SW-SLFN obtains good generalization ability and achieve an improved performance when compared with several other SLFN-based classifiers in imbalance learning.
Efficient Computational Methods for Strongly Coupled Cardiac Electromechanics

Strongly coupled cardiac electromechanical models can further our understanding of the relative importance of feedback mechanisms in the heart, but computational challenges currently remain a major obstacle, which limit their widespread use. To address this issue, we present a set of efficient computational methods including an efficient adaptive cell model integration scheme and a solution method for the monodomain equations that maintains high conduction velocity for time steps greater than 0.1 ms. We also present a novel method for increasing the efficiency of simulating electromechanical coupling, which shows a significant reduction in computational cost of the mechanical component on a personalized left ventricular geometry with an active contraction cell model reparametrized for human cells.
Methane contamination of drinking water accompanying gas-well drilling and hydraulic fracturing

Directional drilling and hydraulic-fracturing technologies are dramatically increasing natural-gas extraction. In aquifers overlying the Marcellus and Utica shale formations of northeastern Pennsylvania and upstate New York, we document systematic evidence for methane contamination of drinking water associated with shale-gas extraction. In active gas-extraction areas (one or more gas wells within 1 km), average and maximum methane concentrations in drinking-water wells increased with proximity to the nearest gas well and were 19.2 and 64 mg CH4 L-1 (n = 26), a potential explosion hazard; in contrast, dissolved methane samples in neighboring nonextraction sites (no gas wells within 1 km) within similar geologic formations and hydrogeologic regimes averaged only 1.1 mg L-1 (P < 0.05; n = 34). Average δ13C-CH4 values of dissolved methane in shallow groundwater were significantly less negative for active than for nonactive sites (-37 ± 7‰ and -54 ± 11‰, respectively; P < 0.0001). These δ13C-CH4 data, coupled with the ratios of methane-to-higher-chain hydrocarbons, and δ2H-CH4 values, are consistent with deeper thermogenic methane sources such as the Marcellus and Utica shales at the active sites and matched gas geochemistry from gas wells nearby. In contrast, lower-concentration samples from shallow groundwater at nonactive sites had isotopic signatures reflecting a more biogenic or mixed biogenic/thermogenic methane source. We found no evidence for contamination of drinking-water samples with deep saline brines or fracturing fluids. We conclude that greater stewardship, data, and—possibly—regulation are needed to ensure the sustainable future of shale-gas extraction and to improve public confidence in its use.
Base extrusion is found at helical junctions between right- and left-handed forms of DNA and RNA

Base extrusion is a major structural feature at the junction between B- and Z-DNA (the B–Z junction) where a base pair is broken, and the two bases are extruded from the double helix. Despite the demonstration of base extrusion at the B–Z junction, it is not clear whether a similar base extrusion occurs at other types of junctions involving the left-handed Z conformation. Here, we investigate structural changes of bases at three Z-form junctions: DNA B–Z and Z–Z and RNA A–Z junctions. By monitoring fluorescently labeled duplex nucleic acids using 2-aminopurines at various positions relative to the junction point, we show that base extrusion occurs not only at the DNA B–Z junction, but also at the RNA A–Z and DNA Z–Z junctions. Our data suggest that base extrusion is a general feature of Z-form nucleic-acid junctions.
Single-Tone Continuous-Wave Interference Detection and Estimation Using Matrix Features

Radio-frequency communication system performance can be affected by interference signal. Although spread spectrum techniques can provide a level of robustness for secure communication, but they can be still affected by strong interferences. Therefore, early detection and estimation of interference signal presence can enable the user or system to make preemptive actions. In the current article, a new detection and estimation strategy utilizing flipped Toeplitz-covariance-matrix Frobenius-norm is proposed for single-tone continuous-wave interference. By bordering the autocorrelation matrix, the Frobenius norm is calculated in each step. Then the derivation is obtained and the Fourier transform of the result, provides the input signal frequency. This method could be utilized for early caution level in secure-communication direct-sequence system. The efficiency of the introduced algorithm is verified by real dataset simulation and comparison with some other methods which shows good performance.
Lung nodule malignancy prediction using multi-task convolutional neural network

In this paper, we investigated the problem of diagnostic lung nodule malignancy prediction using thoracic Computed Tomography (CT) screening. Unlike most existing studies classify the nodules into two types benign and malignancy, we interpreted the nodule malignancy prediction as a regression problem to predict continuous malignancy level. We proposed a joint multi-task learning algorithm using Convolutional Neural Network (CNN) to capture nodule heterogeneity by extracting discriminative features from alternatingly stacked layers. We trained a CNN regression model to predict the nodule malignancy, and designed a multi-task learning mechanism to simultaneously share knowledge among 9 different nodule characteristics (Subtlety, Calcification, Sphericity, Margin, Lobulation, Spiculation, Texture, Diameter and Malignancy), and improved the final prediction result. Each CNN would generate characteristic-specific feature representations, and then we applied multi-task learning on the features to predict the corresponding likelihood for that characteristic. We evaluated the proposed method on 2620 nodules CT scans from LIDC-IDRI dataset with the 5-fold cross validation strategy. The multitask CNN regression result for regression RMSE and mapped classification ACC were 0.830 and 83.03%, while the results for single task regression RMSE 0.894 and mapped classification ACC 74.9%. Experiments show that the proposed method could predict the lung nodule malignancy likelihood effectively and outperforms the state-of-the-art methods. The learning framework could easily be applied in other anomaly likelihood prediction problem, such as skin cancer and breast cancer. It demonstrated the possibility of our method facilitating the radiologists for nodule staging assessment and individual therapeutic planning.
Programas de entrenamiento neuromuscular para la prevención de lesiones en jóvenes deportistas: Revisión de la literatura

espanolLos objetivos de esta revision son: (1) describir los principales programas de prevencion de lesiones de la extremidad inferior en jovenes deportistas que cuentan con una eficacia demostrada, cuya prevencion ha estado dirigida a deportes colectivos de balon, no requieren de material adicional para su aplicacion y la duracion permite su implementacion como calentamiento previo a la practica deportiva; y (2) analizar las ventajas e inconvenientes de las medidas preventivas de acuerdo a su posibilidad de implantacion de manera regular en el deporte. Para ello, un total de 5 programas neuromuscularespara la prevencionde lesiones fueron seleccionados. Aunque todos los programas han demostradoefectos positivossobre la reduccion de la incidencia lesional, el analisis pormenorizado de las caracteristicas de las 5 medidas preventivas recomienda el uso de los programas FIFA 11+ y el Knakontroll por su mayor adaptabilidad al contexto de aplicaciony la facilidad de su implementacion de manera regular en el deporte. EnglishThe aims of this review are: (1) describing the principal lower extremity injury prevention programs in young athletes that have a demonstrated efficacy, whose prevention have been destined for ball team sports, do not require any additional material for their application and the duration allows for their implementation as previous warm-upprograms; and (2) analyzing the advantages and disadvantages of the prevention strategies according to their possibility of implantation in the regular sport practice. For this purpose, a total of 5 neuromuscular injury prevention programs were selected. Although all the programs have shownpositive effects in terms of the reduction of sport injury incidence, the detailed analysis of the characteristics of the prevention strategies recommend the utilization of the FIFA 11+ and Knakontroll programs for their greater adaptability to the application context and their facility for the implementation in the regular sport practice.
Ensemble-based evaluation for protein structure models

Motivation: Comparing protein tertiary structures is a fundamental procedure in structural biology and protein bioinformatics. Structure comparison is important particularly for evaluating computational protein structure models. Most of the model structure evaluation methods perform rigid body superimposition of a structure model to its crystal structure and measure the difference of the corresponding residue or atom positions between them. However, these methods neglect intrinsic flexibility of proteins by treating the native structure as a rigid molecule. Because different parts of proteins have different levels of flexibility, for example, exposed loop regions are usually more flexible than the core region of a protein structure, disagreement of a model to the native needs to be evaluated differently depending on the flexibility of residues in a protein. Results: We propose a score named FlexScore for comparing protein structures that consider flexibility of each residue in the native state of proteins. Flexibility information may be extracted from experiments such as NMR or molecular dynamics simulation. FlexScore considers an ensemble of conformations of a protein described as a multivariate Gaussian distribution of atomic displacements and compares a query computational model with the ensemble. We compare FlexScore with other commonly used structure similarity scores over various examples. FlexScore agrees with experts’ intuitive assessment of computational models and provides information of practical usefulness of models. Availability and implementation: https://bitbucket.org/mjamroz/flexscore Contact: dkihara@purdue.edu Supplementary information: Supplementary data are available at Bioinformatics online.
High gain, naturally clamped Dc to Dc converter for X-ray application

The paper presents a novel converter for a medical X-ray machine application. A resonant, current fed inverter is proposed. The high voltage transformer step up ratio is kept very low to reduce the effects of its leakage inductance and other non-idealities. Moreover this leakage inductance and the parasitic capacitance of HV transformer and switches are used as resonating components for attaining a soft switched conversion. The switches are soft switched with ZCS, which works over a wide load range. A set of Cockroft Walton multiplier is employed on the secondary side of the transformer accomplishes the final output. The converter operates at 100 kHz frequency with overlap time switching to achieve 100kV and 2.5 mA required by the X-ray machine. A very systematically elaborated analysis for selection of every discrete component in this particular topology is discussed in this paper.
Corrections to D-brane Action and Generalized Boundary State

In this paper, we generalize a boundary state to the one incorporating non-constant gauge field strength as an external background coupled to the boundary of a string worldsheet in bosonic string theory. This newly defined boundary state satisfies generalized nonlinear boundary conditions with non-constant gauge field strength, and is BRST invariant. The divergence immanent in this boundary state coincide with the one calculated in a string sigma model. We extract the relevant massless part of this generalized boundary state, and give a part of the D-brane action with the non-constant gauge field strength, that is, derivative corrections to the D-brane action.
Pengaruh Value Added of Capital Employed, Human Capital Structural Capital Value Added, Intelectual Coeffisients, Return on Assets terhadap Nilai Perusahaan

This study examines the effect of the value added of capital employed, human capital structural capital value added, intelectual coeffisients, return on assets on company value. This research takes as its object the companies that are members of the customers goods industry listed on the Indonesia Stock Exchange for the period 2018 - 2022 which have made consecutive profits. Based on the research results, value added of capital employed return on assets influence company value. On the other hand, there are human capital, structural capital value added, intelectual capital which have no effect on company value
A Novel Hybrid Deep Neural Network for Early Detection and Classification of Chicken Diseases

Detection and classification of chicken diseases has been a longstanding concern for farmers. Relying solely on expert opinions could be a costly option. Moreover, there are certain chicken diseases that spread quickly, and necessary procedures are immediately needed to carryon. On the other hand, the lack of research activity or low availability of data on this topic leads to many misunderstandings and challenges so as to correctly detect a certain type of chicken disease. The existing techniques show low accuracy on disease classification, which leads to a complex engineering problem and critical analysis is required for sustainable development. In this paper, we develop a novel hybrid deep neural network, namely ChickenNet21, which combines a Convolutional Neural Network (CNN) and a fine-tuned Visual Geometry Group (VGG) model to address this challenge. Using a large dataset of 6,812 farm-labeled fecal images, we assess the effectiveness of ChickenNet21 and it achieves 98.83% accuracy in identifying chicken diseases.
Review of Garcinia mangostana and its Xanthones in Metabolic Syndrome and Related Complications

Metabolic syndrome is coexistence of abdominal obesity, hyperglycemia, hyperlipidemia and hypertension that causes cardiovascular diseases, diabetes and their complications, low quality and short lifespan. Garcinia mangostana and its xanthones such as α‐mangostin have been shown desirable effects such as anti‐obesity, anti‐hyperglycemic, anti‐dyslipidemia, anti‐diabetic and antiinflammatory effects in experimental studies. Various databases such as PubMed, Scopus and Web of Science with keywords of ‘Garcinia mangostana’, ‘mangosteen’, ‘α‐mangostin’, ‘metabolic syndrome’, ‘hypoglycemic’, ‘antihyperglicemic’, ‘antidiabetic’, ‘hypotensive’, ‘antihypertensive’, ‘atherosclerosis’, ‘arteriosclerosis’ and ‘hyperlipidemia’ have been investigated in this search without publication time limitation. This study reviewed all pharmacological effects and molecular pathways of G. mangostana and its xanthones in the management of metabolic syndrome and its complications in in‐vitro and in‐vivo studies. Based on these studies, mangosteen and its xanthones have good potential to design human studies for controlling and modification of metabolic syndrome and its related disorders such as obesity, disrupted lipid profile, diabetes and its complications. Copyright © 2017 John Wiley & Sons, Ltd.
Comparative Study between Different Agents Used in Chemical Pleurodesis

Background : Pleurodesis is a medical technique used to eliminate the gap among the parietal and visceral pleura in order to avoid the reoccurrence of pleural effusion or pneumothorax. This method involves the introduction of a chemical substance into the pleural space, which leads to the scarring and adhesion of the membranes. Objective: The objective of this research was to evaluate the outcomes of medical pleurodesis utilizing three distinct agents and two delivery routes. Patients and methods: Our university hospital treated 135 individuals who had recurrent malignant and benign pleural effusions with pleurodesis by employing three distinct chemical agents: povidone-iodine solution, bleomycin ampoules, and fresh frozen plasma (FFP), administered by two different methods, namely chest tube and small-bore pigtail catheter. Results : All patients were dyspneic. Seventy were men; their ages varied from 45 to 76. Chest tubes were used for effusion drainage and sclerosing agent instillation in eighty-seven individuals (64.45 percent), whereas pigtail catheters were used in forty-eight individuals (35.55). With 83 cases of malignant effusion and 52 cases of non-malignant effusion, 60 cases were treated with bleomycin, 30 with fresh frozen plasma (FFP), and 45 with povidone-iodine. Sclerosing agent type was associated with a change in overall success rate. Conclusion : FFP is a good alternative sclerosing agent to bleomycin and it is safe, available and less costy.
The Network Theory: 21 Years Later

The network theory was proposed 21 years ago, attracting then much interest as applied to the regulation of (clonal) immune responses. The first 10 years of 'idiotypic network' research have thus addressed questions that were already appropriately solved by the clonal selection theory, leading to a justifiable loss of its impact. In contrast, 'second generation networks', concentrate on systemic properties that emerge from a network organization, thus providing a framework for several major questions that seem to supersede clonal solutions: the developmental 'learning' of self antigenic composition and the maintenance of the respective 'memory' (self-tolerance), repertoire selection and the homeostatic regulation of lymphocyte numbers, natural immune activities that are independent of external antigens, the physiology of autoreactivity. The immune network may well contribute solutions to autoimmune diseases, where clonal approaches, classical and modern alike, have failed.
On the User–Scheduler Dialogue: Studies of User-Provided Runtime Estimates and Utility Functions

Effective communication between user and scheduler is an important prerequisite to achieving a successful scheuling outcome from both parties' perspectives. In a grid or stand-alone high-performance computing (HPC) enviroment, this communication typically takes the form of a user-provided job script containing essential configuration information, including processors/resources required, a requested runtime, and a priority. Users' requested runtimes are notoriously inaccurate as a predictor of actual runimes. This study examines whether users can improve their runtime estimates if a tangible reward is provided for accuracy. We show that under these conditions, about half of users provide an improved estimate, but there is not a substantial improvement in the overall average accracy. Priority, as implemented in many production scheduers, is a very crude approximation of the value users may attach to timely job completion. We show users are capble of providing richer utility functions than most schedulers elicit. Thus we explore two elements of the user–scheuler dialogue to understand if accuracy and completeness of information conveyed could be improved.
The non-canonical functions of the heme oxygenases

Heme oxygenase (HO) isoforms catalyze the conversion of heme to carbon monoxide (CO) and biliverdin with a concurrent release of iron, which can drive the synthesis of ferritin for iron sequestration. Most of the studies so far were directed at evaluating the protective effect of these enzymes because of their ability to generate antioxidant and antiapoptotic molecules such as CO and bilirubin. Recent evidences are suggesting that HO may possess other important physiological functions, which are not related to its enzymatic activity and for which we would like to introduce for the first time the term “non canonical functions”. Recent evidence suggest that both HO isoforms may form protein-protein interactions (i.e. cytochrome P450, adiponectin, CD91) thus serving as chaperone-like protein. In addition, truncated HO-1 isoform was localized in the nuclear compartment under certain experimental conditions (i.e. excitotoxicity, hypoxia) regulating the activity of important nuclear transcription factors (i.e. Nrf2) and DNA repair. In the present review, we discuss three potential signaling mechanisms that we refer to as the non-canonical functions of the HO isoforms: protein-protein interaction, intracellular compartmentalization, and extracellular secretion. The aim of the present review is to describe each of this mechanism and all the aspects warranting additional studies in order to unravel all the functions of the HO system.
On Inference-Proof View Processing of XML Documents

This work aims at treating the inference problem in XML documents that are assumed to represent potentially incomplete information. The inference problem consists in providing a control mechanism for enforcing inference-usability confinement of XML documents. More formally, an inference-proof view of an XML document is required to be both indistinguishable from the actual XML document to the clients under their inference capabilities, and to neither contain nor imply any confidential information. We present an algorithm for generating an inference-proof view by weakening the actual XML document, i.e., eliminating confidential information and other information that could be used to infer confidential information. In order to avoid inferences based on the schema of the XML documents, the DTD of the actual XML document is modified according to the weakening operations as well, such that the modified DTD conforms with the generated inference-proof view.