Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance of the Stator Winding Fault Diagnosis in Sensorless Induction Motor Drive

This paper deals with the diagnosis of stator winding inter-turn faults for an induction motor drive operating without a speed sensor in a speed-sensorless mode. The rotor direct field oriented control structure (DFOC) was applied, its reference current and voltage component values were analyzed, and their selected harmonics were applied as effective fault indicators. To ensure robust speed estimation, a sliding mode model reference adaptive system (SM-MRAS) estimator was selected. The influence of load torque, reference speed, proportional-integral (PI) controller parameters, and short-circuit current on fault diagnosis and speed estimation performance was verified. Experimental test results obtained for a 3 kW induction motor drive are included.
SVM-RFE With MRMR Filter for Gene Selection

We enhance the support vector machine recursive feature elimination (SVM-RFE) method for gene selection by incorporating a minimum-redundancy maximum-relevancy (MRMR) filter. The relevancy of a set of genes are measured by the mutual information among genes and class labels, and the redundancy is given by the mutual information among the genes. The method improved identification of cancer tissues from benign tissues on several benchmark datasets, as it takes into account the redundancy among the genes during their selection. The method selected a less number of genes compared to MRMR or SVM-RFE on most datasets. Gene ontology analyses revealed that the method selected genes that are relevant for distinguishing cancerous samples and have similar functional properties. The method provides a framework for combining filter methods and wrapper methods of gene selection, as illustrated with MRMR and SVM-RFE methods.
Improved predictive functional control for ethylene cracking furnace

The objective of this paper is to show the design and application of pass temperature balance control system using an improved predictive functional control method in eight 800 tone/year USC ethylene cracking furnaces. The advanced pass temperature balance controller is developed using the proposed method and implemented in proprietary APC-ISYS software, which is connected to Yokogawa distributed control system via an OPC server. The advantage of it lies in the fact that the dynamics of pass temperature with nonlinearity and time delay are described by Takagi–Sugeno model and transformed into time-varying extended state space model, and thus, the proposed controller can regulate pass temperature based on the extended state space formulation. In addition, the control law with a linear iterative form, easily applied to industrial process, is derived. The robust analysis for the set point, input disturbance and output disturbance to the output verifies the ability of tracking and disturbance rejection of the proposed method. Application results from an industrial furnace are shown to be markedly better in terms of lower variability in the outlet temperature of both the passes compared to the current proportional–integral–derivative control scheme.
Nitric oxide impairs mitochondrial movement in cortical neurons during hypoxia

Cortical nitric oxide (NO) production increases during hypoxia/ischemia in the immature brain and is associated with both neurotoxicity and mitochondrial dysfunction. Mitochondrial redistribution within the cell is critical to normal neuronal function, however, the effects of hypoxia on mitochondrial dynamics are not known. This study tested the hypothesis that hypoxia impairs mitochondrial movement via NO‐mediated pathways. Fluorescently labeled mitochondria were studied using time‐lapse digital video microscopy in cultured cortical neurons exposed either to hypoxia/re‐oxygenation or to diethyleneamine/nitric oxide adduct, DETA‐NO (100–500 µm). Two NO synthase inhibitors, were used to determine NO specificity. Mitochondrial mean velocity, the percentage of movement (i.e. the time spent moving) and mitochondrial morphology were analyzed. Exposure to hypoxia reduced mitochondrial movement to 10.4 ± 1.3% at 0 h and 7.4 ± 1.7% at 1 h of re‐oxygenation, versus 25.6 ± 1.4% in controls (p < 0.05). Mean mitochondrial velocity (µm s−1) decreased from 0.374 ± 0.01 in controls to 0.146 ± 0.01 at 0 h and 0.177 ± 0.02 at 1 h of re‐oxygenation (p < 0.001). Exposure to DETA‐NO resulted in a significant decrease in mean mitochondrial velocity at all tested time points. Treatment with NG‐nitro‐l‐arginine methyl ester (l‐NAME) prevented the hypoxia‐induced decrease in mitochondrial movement at 0 h (30.1 ± 1.6%) and at 1 h (26.1 ± 9%) of re‐oxygenation. Exposure to either hypoxia/re‐oxygenation or NO also resulted in the rapid decrease in mitochondrial size. Both hypoxia and NO exposure result in impaired mitochondrial movement and morphology in cultured cortical neurons. As the effect of hypoxia on mitochondrial movement and morphology can be partially prevented by a nitric oxide synthase (NOS) inhibitor, these data suggest that an NO‐mediated pathway is at least partially involved.
Multimodal Comprehension of Language and Graphics: Graphs with and without annotations

An experimental investigation into interaction between language and information graphics in multimodal documents served as the basis for this study. More specifically, our purpose was to investigate the role of linguistic annotations in graph-text documents. Participants were presented with three newspaper articles in the following conditions: one text-only, one text plus non-annotated graph, and one text plus annotated graph. Results of the experiment showed that, on one hand, annotations play a bridging role for integration of information contributed by different representational modalities. On the other hand, linguistic annotations have negative effects on recall, possibly due to attention divided by the different parts of a document.
Validation of the German Version of the Social Functioning Scale (SFS) for Schizophrenia

Deficits in social functioning are a core symptom of schizophrenia and an important criterion for evaluating the success of treatment. However, there is little agreement regarding its measurement. A common, often cited instrument for assessing self-reported social functioning is the Social Functioning Scale (SFS). The study aimed to investigate the reliability and validity of the German translation. 101 patients suffering from schizophrenia (SZ) and 101 matched controls (C) (60 male / 41 female, 35.8 years in both groups) completed the German version. In addition, demographic, clinical, and functional data were collected. Internal consistency was investigated calculating Cronbach’s alpha for SFS full scale (α: .81) and all subscales (α: .59-.88). Significant bivariate correlation coefficients were found between all subscales as well as between all subscales and full scale (p <.01). For the total sample, principal component analysis gave evidence to prefer a single-factor solution (eigenvalue ≥ 1) accounting for 48.5 % of the variance. For the subsamples, a two-component solution (SZ; 57.0 %) and a three-component solution (C; 65.6 %) fitted best, respectively. For SZ and C, significant associations were found between SFS and external criteria. The main factor “group” emerged as being significant. C showed higher values on both subscales and full scale. The sensitivity of the SFS was examined using discriminant analysis. 86.5% of the participants could be categorized correctly to their actual group. The German translation of the SFS turned out to be a reliable and valid questionnaire comparable to the original English version. This is in line with Spanish and Norwegian translations of the SFS. Concluding, the German version of the SFS is well suited to become a useful and practicable instrument for the assessment of social functioning in both clinical practice and research. It accomplishes commonly used external assessment scales.
Learning Dynamic Stream Weights for Linear Dynamical Systems Using Natural Evolution Strategies

Multimodal data fusion is an important aspect of many object localization and tracking frameworks that rely on sensory observations from different sources. A prominent example is audiovisual speaker localization, where the incorporation of visual information has shown to benefit overall performance, especially in adverse acoustic conditions. Recently, the notion of dynamic stream weights as an efficient data fusion technique has been introduced into this field. Originally proposed in the context of audiovisual automatic speech recognition, dynamic stream weights allow for effective sensory-level data fusion on a per-frame basis, if reliability measures for the individual sensory streams are available. This study proposes a learning framework for dynamic stream weights based on natural evolution strategies, which does not require the explicit computation of oracle information. An experimental evaluation based on recorded audiovisual sequences shows that the proposed approach outperforms conventional methods based on supervised training in terms of localization performance.
The intracellular sRNA transcriptome of Listeria monocytogenes during growth in macrophages

Small non-coding RNAs (sRNAs) are widespread effectors of post-transcriptional gene regulation in bacteria. Currently extensive information exists on the sRNAs of Listeria monocytogenes expressed during growth in extracellular environments. We used deep sequencing of cDNAs obtained from fractioned RNA (<500 nt) isolated from extracellularly growing bacteria and from L. monocytogenes infected macrophages to catalog the sRNA repertoire during intracellular bacterial growth. Here, we report on the discovery of 150 putative regulatory RNAs of which 71 have not been previously described. A total of 29 regulatory RNAs, including small non-coding antisense RNAs, are specifically expressed intracellularly. We validated highly expressed sRNAs by northern blotting and demonstrated by the construction and characterization of isogenic mutants of rli31, rli33-1 and rli50* for intracellular expressed sRNA candidates, that their expression is required for efficient growth of bacteria in macrophages. All three mutants were attenuated when assessed for growth in mouse and insect models of infection. Comparative genomic analysis revealed the presence of lineage specific sRNA candidates and the absence of sRNA loci in genomes of naturally occurring infection-attenuated bacteria, with additional loss in non-pathogenic listerial genomes. Our analyses reveal extensive sRNA expression as an important feature of bacterial regulation during intracellular growth.
A Novel Semantic Discovery Method Using Transaction Requirements in Database Reverse Engineering

Existing database reverse engineering approaches mainly analyze the database schema, especially the key constraints, to discover the relationships between relations and attributes. However these methods are only effective for direct semantics discovery, while large part of inferred semantics is still missing. This paper proposes a transaction-oriented semantic discovery method which analyzes the transaction requirements supported by a database to explore hidden semantics. Our method relaxes the constraints on normalization of database design to suit for wide range applications, and is effective for discovering transitive relationships, general-specific relationships and master-detail relationships.
Dismantle a network efficiently during the entire process by a compound algorithm

The dismantling network problem only asks the minimal vertex set of a graph after removing which the remaining graph will break into connected components of sub-extensive size, but we should also consider the efficiency of intermediate states during the entire dismantling process, which is measured by the general performance R in this paper. In order to improve the general performance of the belief-propagation decimation (BPD) algorithm, we introduce a compound algorithm (CA) mixing the BPD and the node explosive percolation (NEP) algorithm. In this CA, the NEP algorithm will rearrange and optimize the head part of a dismantling sequence given by the BPD. Two ancestor algorithms are connected at the joint point where the general performance can be optimized. It dismantles a graph to small pieces as quickly as the BPD, and it is with the efficiency of the NEP during the entire dismantling process. We find that a wise joint point is where the BPD breaks the original graph to subgraphs no longer larger than the 1% of the original one. We refer the CA with this settled joint point as the fast CA and the fast CA is in the same complexity class with the BPD algorithm. The computation on some real-world instances also exhibits that using the fast CA to optimize the intermediate process of a dismantling algorithm is an effective approach.
Cell-Graph Mining for Breast Tissue Modeling and Classification

We consider the problem of automated cancer diagnosis in the context of breast tissues. We present graph theoretical techniques that identify and compute quantitative metrics for tissue characterization and classification. We segment digital images of histopathological tissue samples using k-means algorithm. For each segmented image we generate different cell-graphs using positional coordinates of cells and surrounding matrix components. These cell-graphs have 500-2000 cells(nodes) with 1000-10000 links depending on the tissue and the type of cell-graph being used. We calculate a set of global metrics from cell-graphs and use them as the feature set for learning. We compare our technique, hierarchical cell graphs, with other techniques based on intensity values of images, Delaunay triangulation of the cells, the previous technique we proposed for brain tissue images and with the hybrid approach that we introduce in this paper. Among the compared techniques, hierarchical-graph approach gives 81.8% accuracy whereas we obtain 61.0%, 54.1% and 75.9% accuracy with intensity-based features, Delaunay triangulation and our previous technique, respectively.
Should tetracycline be contraindicated for therapy of presumed Rocky Mountain spotted fever in children less than 9 years of age?

Antibiotic treatment for suspected Rocky Mountain spotted fever usually must be started prior to confirmation of the diagnosis because of the following: (1) currently available laboratory diagnostic methods usually do not allow definitive diagnosis of Rocky Mountain spotted fever until the second week of illness, and (2) delays in the initiation of antimicrobial therapy until confirmation of diagnosis may lead to significant morbidity and mortality, the latter being as high as approximately 25% in untreated cases.1,2 In areas of high incidence, such as North Carolina, a heightened index of suspicion, especially during the season of peak incidence (May through September) means that a large number of patients with compatible signs and symptoms will be treated annually for presumed Rocky Mountain spotted fever.3
A latent shared-component generative model for real-time disease surveillance using Twitter data

Exploiting the large amount of available data for addressing relevant social problems has been one of the key challenges in data mining. Such efforts have been recently named "data science for social good" and attracted the attention of several researchers and institutions. We give a contribution in this objective in this paper considering a difficult public health problem, the timely monitoring of dengue epidemics in small geographical areas. We develop a generative simple yet effective model to connect the fluctuations of disease cases and disease-related Twitter posts. We considered a hidden Markov process driving both, the fluctuations in dengue reported cases and the tweets issued in each region. We add a stable but random source of tweets to represent the posts when no disease cases are recorded. The model is learned through a Markov chain Monte Carlo algorithm that produces the posterior distribution of the relevant parameters. Using data from a significant number of large Brazilian towns, we demonstrate empirically that our model is able to predict well the next weeks of the disease counts using the tweets and disease cases jointly.
Generation of Monoclonal Autoantibodies from Babesia rodhaini-Infected Mice

ABSTRACT The presence of anti-erythrocyte autoantibodies in animals infected with various Babesia species is well reported. However, the pathogenesis of autoantibodies in babesiosis is poorly understood. Here, we demonstrated that anti-erythrocyte immunoglobulin (Ig) M and IgG were present in B. rodhaini-infected mice at 6 and 8 days after infection, respectively. Furthermore, we generated monoclonal antibodies against erythrocyte antigen from B. rodhaini-infected mice. Five clones were generated. By Western blotting analysis using whole erythrocyte antigens, one clone reacted with a broad band around 90–150 kDa, and the 2 clones reacted with a band larger than 150 kDa. B. rodhaini-infected mice and/or autoreactive monoclonal antibodies established in this study might be a powerful tool for in vivo pathogenesis studies of autoantibody development in infectious diseases.
Shannon Meets Nyquist: Capacity of Sampled Gaussian Channels

We explore two fundamental questions at the intersection of sampling theory and information theory: how channel capacity is affected by sampling below the channel's Nyquist rate, and what sub-Nyquist sampling strategy should be employed to maximize capacity. In particular, we derive the capacity of sampled analog channels for three prevalent sampling strategies: sampling with filtering, sampling with filter banks, and sampling with modulation and filter banks. These sampling mechanisms subsume most nonuniform sampling techniques applied in practice. Our analyses illuminate interesting connections between undersampled channels and multiple-input multiple-output channels. The optimal sampling structures are shown to extract out the frequencies with the highest SNR from each aliased frequency set, while suppressing aliasing and out-of-band noise. We also highlight connections between undersampled channel capacity and minimum mean-squared error (MSE) estimation from sampled data. In particular, we show that the filters maximizing capacity and the ones minimizing MSE are equivalent under both filtering and filter-bank sampling strategies. These results demonstrate the effect upon channel capacity of sub-Nyquist sampling techniques, and characterize the tradeoff between information rate and sampling rate.
Differences in Bacterial Diversity, Composition and Function due to Long-Term Agriculture in Soils in the Eastern Free State of South Africa

Land-use change from natural to managed agricultural ecosystems significantly impacts soil bacterial diversity and function. The Eastern Free State (EFS) is one of the most productive agricultural regions in South Africa. However, no studies aiming to understand the changes in bacterial diversity, composition and function due to land-use change in this area have been conducted. This study investigated, using high-throughput 16S rRNA gene amplicon sequencing, the effects of long-term agriculture on bacterial diversity, composition and putative function in the EFS by comparing microbiomes from lands that have been under agronomic activity for over 50 years to those from uncultivated land. Results indicate that agriculture increased bacterial diversity. Soil chemical analysis showed that land-use shifted soils from being oligotrophic to copiotrophic, which changed bacterial communities from being Actinobacteria dominated to Proteobacteria dominated. Predictive functional analysis using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) suggested that agricultural soil was abundant in genes associated with plant fitness and plant growth promotion, while non-agricultural soil was abundant in genes related to organic matter degradation. Together, these results suggest that edaphic factors induced by long-term agriculture resulted in shifts in bacterial diversity and putative function in the EFS.
Applicative-layer multipath for low-latency adaptive live streaming

End-users in online video services are sensitive to the overall quality of the video at screen, but also, and more importantly, to other factors, including the latency between the video generation and the playback for live videos. If the improvement of the quality of the perceived video has been well investigated by the multimedia community, the impact of delay, latency, and re-buffering has not received a significant attention. Adaptive streaming technologies, which have been widely adopted in the recent years, contribute to this lack of consideration since vendors usually recommend the introduction of significant extra-latency. In this paper, we address the problem of low-latency live video streaming. We propose an implementation of multi-path video delivery at the applicative level, which exploits the information provided in the latest version of the video delivery standards. We present an implementation of a video player, which takes advantage of multi-path networking to enable video playback using TCP with a latency below 100 ms. By initiating the multi-path delivery from the client side, our mechanism is compatible with current network equipments and does not require any change neither at the server nor in the middleboxes.
Well-being in schools: a conceptual model.

Health and well-being have mostly been separated from other aspects of school life. Health services and health education have been available for school-aged children in Western societies for a long time. Recently, more comprehensive school health programmes have been developed, e.g. the WHO 'health promoting school' and 'coordinated school health programme' in the USA. They focus on how to implement health promotion and health education in school. However, a theoretically grounded model based on the sociological concept of well-being is needed for planning and evaluation of school development programmes. The School Well-being Model is based on Allardt's sociological theory of welfare and assesses well-being as an entity in school setting. Well-being is connected with teaching and education, and with learning and achievements. Indicators of well-being are divided into four categories: school conditions (having), social relationships (loving), means for self-fulfilment (being) and health status. 'Means for self-fulfilment' encompasses possibilities for each pupil to study according to his/her own resources and capabilities. 'Health status' is seen through pupils' symptoms, diseases and illnesses. Each well-being category contains several aspects of pupils' life in school. The model takes into account the important impact of pupils' homes and the surrounding community. Compared with others, The School Well-being Model's main differences are the use of the well-being concept, the definition of health and the subcategory means for self-fulfilment. Making the outline of the well-being concept facilitates the development of theoretically grounded subjective and objective well-being indicators.