Miracle-dz/UniFAR-FTUs huggingface dataset:
Perceptions of control and satisfaction with hospital birth experiences for first-time mothers

Satisfaction with childbirth is associated with women's future emotional wellbeing. This study examined whether firsttime mother's antenatal expectations, postnatal evaluations of control during labour and delivery, and the discrepancy between expectations and evaluations wc;;re significant predictors of women's satisfaction with their childbirth experiences. The effect of medical interventions (e.g., obstetrical interventions and pain relief medication) on women's perceptions of control and satisfaction was also examined. The purposive sample of eighty first-time mothers, mean age 26 years (excluding women who had caesareans) delivered at the public hospital where they attended antenatal classes. During their fourth antenatal class women completed a questionnaire, which included a 12 item scale, devised for the study. The P.ntenatal Expectation Scale (AES) asked women about their expectations regarding labour and delivery in four main areas: partner support, medical support, use of interventions, and personal control. These sub-scales headings were derived from the Childbirth Expectation Scale, (CEQ) a 36 item Likert Scale developed by Beaton, Bramadat, Gupton and Sloan (1991). Eighty women were re-contacted 4-6 weeks after childbirth and completed a post-natal questionnaire, presented as a phone interview. This questionnaire included a 12 item Perceptions of Hospital Births iii Postnatal Evaluation Scale (PES), which matched the items presented in the AES, a 25 item affective measure of control during labour and delivery, the Labour Agency Scale, (LAS) devised by Hodnett and Simmons-Tropea ( ! 991) and a six item Satisfaction Scale that reflected the four subscales of the CEQ. A sub-set (N ~22) of the sample also completed the CEQ in the postpartum to enable a comparison with the PES. The study replicated Bramadat and Driedger's (1993) results. Expectations, and the discrepancy between expectations and perception of childbirth did not significantly predict women's satisfaction with childbirth. The LAS was the strongest predictor of satisfaction, accounting for 63% of the variance, comparable to 59% of the variance in satisfaction reported by Bramadat and Driedger ( 1993). Furthermore, a significant difference was found for medical interventions on women's satisfaction 1 (78) = 2.78, 1. <. 001 with women (N=40) in a high intervention group being less satisfied (M~33.25, SD~ 5.43) than women (N~40) in a low intervention group (M ~36.72, SD~5.76).
Implementation of ORB and Object Classification using KNN and SVM Classifiers

— Object identification and classification has been topic of interest for researchers in computer vision due to its numerous applications in various domains since decades. But object detection and classification faces certain issues and challenges like scaling variations, rotational variations, occlusion, noise etc. Hence, there is need to design descriptors which are robust, compact and efficient. The extraction of features and the classification process should be done with minimal compromises in the performances. This paper proposes an orientation and rotation invariant feature descriptor named as ORB (Oriented FAST and Rotated BRIEF). This feature vector computes scale, rotation and translation invariant features for the test and trainee images. For matching the computed feature sets we used supervised classification method i.e. K-Nearest Neighbors Algorithm (K-NN) and Support Vector Machine (SVM) for the classification of various object categories in the dataset. Comparative experimental results based on analysis of the SVM and KNN classifiers on the basis of recognition accuracy and execution time is given. Results show that SVM gives better matching score whereas KNN is time efficient in comparison to SVM.
Dispraxia verbal: características clínicas y tratamiento logopédico

Introduccion. La dispraxia verbal es un trastorno especifico del desarrollo del lenguaje que se caracteriza por un deficit en varios niveles del procesamiento del habla: deficit en la planificacion fonologica, en la planificacion fonetica y en la implementacion del programa motor de habla. Desarrollo. En este trabajo se revisan los criterios de diagnostico diferencial, las pruebas que resultan utiles para ello y las posibles lineas de tratamiento logopedico.
A Novel Outlier Detection Applied to an Adaptive K-Means

—Data clustering is an important task for data management because it groups similar data into clusters and acquires significant knowledge. K-means is one of the popular clustering algorithms; however, there are several weaknesses such as cluster quality often depended on initial centers and too sensitive to an outlier. To address the problems, this study proposed a new method of initial centers selection based on data density and a novel approach of outlier detection based on data distance. I conducted some experiments to evaluate the methods. For the new method of initial centers selection, I compared the number of iterations and the Silhouette scores from this method and the traditional K-means. For the outlier detection system, I measured the system performance by using a confusion matrix. As the results, the system of the study outperformed the traditional K-means because of higher speed and great accuracy acquired.
A blockchain‐based computation offloading method for edge computing in 5G networks

Edge computing (EC) emerges as a novel computing paradigm to offload computing tasks from user equipments (UEs) to edge notes (ENs) in fifth‐generation networks, which definitely breaks the resource limitation of UEs to a certain degree. However, it is troublesome to guarantee the overall operating performance of ENs due to the uneven distributed resource demands of UEs, the resulting transmission delay and the data loss for computation offloading between the covered EN and the deployed destination EN. In view of this challenge, a blockchain‐based computation offloading method, named BCO, is proposed in this paper. Technically, since blockchain is a promising technique for the decentralized system, a blockchain‐based EC framework is designed to degrade the data loss possibility by integrating blockchain and EC. Then, the nondominated sorting genetic algorithm, the third version (NSGA‐III), is leveraged to acquire the balanced offloading strategies. Furthermore, by taking advantage of Simple Additive Weighting and Multiple Criteria Decision Making, the optimal offloading strategy is identified. Finally, systematic experiments and analyses on the comparative experiment are conducted to verify the efficiency of our proposed method BCO.
Tetanus: a review.

: Tetanus is caused by the organism Clostridium tetani, which produces tetanospasmin, a neurotoxin responsible for the clinical manifestations of muscle rigidity and reflex spasms. The majority of cases follow an anaerobic wound infection associated with trauma. Incubation period is usually 3 days to 3 weeks. 75% of patients present with trismus. Reflex spasms are seen in 70% of patients and characterize the severity of the disease. Treatment involves removal of the offending organism, neutralization of free neurotoxin, controlling rigidity and reflex spasm, and minimizing complications. Diazepam may be used alone in mild cases. Severe cases require the addition of nondepolarizing neuromuscular blocking agents and mechanical ventilation. Respiratory complications occur early and require aggressive airway management. A serious, late complication is the syndrome of sympathetic nervous system overactivity that is treated with alpha and beta blockade. High mortality rates seen in the United States may be due to delays in diagnosis and lack of familiarity with treatment. The disease is preventable with adequate immunization.
Predicting Daily Returns of Global Stocks Indices: Neural Networks vs Support Vector Machines

Uniqueness in economies and stock markets has given rise to an interesting domain of exploring data mining techniques across global indices. Previously, very few studies have attempted to compare the performance of data mining techniques in diverse markets. The current study adds to the understanding regarding the variations in performance of data mining techniques across the global stock indices. We compared the performance of Neural Networks and Support Vector Machines using accuracy measures Mean Absolute Error (MAE) and R­­­­oot Mean Square Error (RMSE) across seven major stock markets. For prediction purpose, technical analysis has been employed on selected indicators based on daily values of indices spanning a period of 12 years. We created 196 data sets spanning different time periods for model building such as 1 year, 2 years, 3 years, 4 years, 6 years and 12 years for selected seven stock indices. Based on prediction models built using Neural Networks and Support Vector Machines, the findings of the study indicate there is a significant difference, both for MAE and RMSE, across the selected global indices. Also, Mean Absolute Error and Root Mean Square Error of models built using NN were greater than Mean Absolute Error and Root Mean Square Error of models built using SVM.
Segmentation of WBCs using HSV Saturation and Blob Analysis Technique

: Now we are in the 21st century technology makes an great impact on our life. so we decided to do our project in technology field. technology gives us knowledge of techniques and process which will help us to reduce our efforts.In medical fields most of applications are based on engineering field, so by using image processing techniques we decided to do work on project which will combine engineering and medical fields together so we did our project in biomedical field.So we are using matlab software to count the WBC’s which will help to reduce cost required for counting and also reduces time which is required for counting.
Unsupervised Raw Waveform Representation Learning for ASR

In this paper, we propose a deep representation learning approach using the raw speech waveform in an unsupervised learning paradigm. The first layer of the proposed deep model performs acoustic filtering while the subsequent layer performs modulation filtering. The acoustic filterbank is implemented using cosine-modulated Gaussian filters whose parameters are learned. The modulation filtering is performed on log transformed outputs of the first layer and this is achieved using a skip connection based architecture. The outputs from this two layer filtering are fed to the variational autoencoder model. All the model parameters including the filtering layers are learned using the VAE cost function. We employ the learned representations (second layer outputs) in a speech recognition task. Experiments are conducted on Aurora-4 (additive noise with channel artifact) and CHiME-3 (additive noise with reverberation) databases. In these experiments, the learned representations from the proposed framework provide significant improvements in ASR results over the baseline filterbank features and other robust front-ends (average relative improvements of 16% and 6% in word error rate over baseline features on clean and multicondition training, respectively on Aurora-4 dataset, and 21% over the baseline features on CHiME-3 database).
Análise da eficiência de corte de instrumentos de NiTi em movimento recíproco

A habilidade de corte de instrumentos endodonticos e complexa e esta relacionada a parâmetros como geometria da secao transversal, ângulos de corte, comprimento de pitch, ângulo helicoidal, propriedades metalurgicas e tratamento de superficie dos instrumentos. Este trabalho tem como objetivo, avaliar a eficiencia de corte, bem como as caracteristicas dimensionais e geometricas de instrumentos endodonticos, por meio dos parâmetros diâmetro a cada milimetro da ponta, comprimento de pitch, ângulo helicoidal, geometria e area da secao transversal e ângulo de corte dos instrumentos Reciproc - RC (VDW, Munich, Germany) e WaveOne - WO (Dentsply Maillefer, Ballaigues, Switzerland) de calibre de ponta 25 e conicidade . 08. Dez instrumentos de cada sistema foram fotografados de forma padronizada utilizando uma câmera digital Canon EOS 20D e a analise das imagens foi realizada utilizando o software ImagePro Plus 6.0. Os ensaios de eficiencia de corte foram realizados em dispositivo de bancada, consistindo na peca de mao de um motor X-SmartR Plus , presa a uma base de acrilico fixado na base da maquina de teste NA8032. Blocos de acrilico pre-fabricados, foram previamente explorados.Cada bloco foi fixado em outra base menor, tambem acrilica, presa a maquina de testes, que se movimentava no sentido vertical a uma velocidade constante de 0,09 mm/seg. Para simular a condicao clinica, a formatacao do canal foi realizada em dois momentos: os instrumentos prepararam inicialmente 13,5 mm do canal, e em seguida, os 3mm finais.Foi observado que o diâmetro aumentou progressivamente ao longo da parte ativa nos dois sistemas analisados e que o diâmetro a 3mm da ponta (D3) nao apresentou diferenca estatisticamente significativa entre os instrumentos (p = 0,521). Os valores medios dos comprimentos de pitch nos instrumentos RC foram superiores aqueles nos WO, embora a variacao nao tenha apresentado diferenca significativa (p = 0,489). Os instrumentos WO apresentaram maiores valores medios de ângulos helicoidais na parte ativa que os RC, mas tambem sem diferenca significativa entre eles (p = 0,135). A geometria da secao transversal, bem como a area a 3mm da ponta (A3) variou entre os instrumentos analisados. Os instrumentos RC apresentaram A3 ligeiramente maior que os WO, porem sem diferenca significativa entre si (p = 0,521). Os valores medios dos dois ângulos de corte em RC sao semelhantes, em contrapartida, os cinco ângulos de corte do WO foram todos significativamente diferentes entre si. Maiores valores de forca axial foram requeridos pelos instrumentos WO na primeira parte do estudo e por RC na segunda etapa. Os instrumentos Reciproc e WaveOne analisados apresentaram caracteristicas dimensionais semelhantes, o que pode explicar o comportamento apresentado pelos instrumentos durante o ensio de eficiencia de corte. Caracteristicas geometricas dos instrumentos podem explicar este comportamento.
Analytical description of nanowires. I. Regular cross sections for zincblende and diamond structures.

Semiconductor nanowires (NWires) experience stress and charge transfer from their environment and impurity atoms. In response, the environment of a NWire experiences a NWire stress response which may lead to propagated strain and a change in the shape and size of the NWire cross section. Here, geometric number series are deduced for zincblende- (zb-) and diamond-structured NWires of diameter dWire to obtain the numbers of NWire atoms NWire(dWire[i]), bonds between NWire atoms Nbnd(dWire[i]) and interface bonds NIF(dWire[i]) for six high-symmetry zb NWires with the low-index faceting that occurs frequently in both bottom-up and top-down approaches of NWire processing. Along with these primary parameters, the specific lengths of interface facets, the cross-sectional widths and heights and the cross-sectional areas are presented. The fundamental insights into NWire structures revealed here offer a universal gauge and thus could enable major advancements in data interpretation and understanding of all zb- and diamond-structure-based NWires. This statement is underpinned with results from the literature on cross-section images from III-V core-shell NWire growth and on Si NWires undergoing self-limiting oxidation and etching. The massive breakdown of impurity doping due to self-purification is shown to occur for both Si NWires and Si nanocrystals (NCs) for a ratio of Nbnd/NWire = Nbnd/NNC = 1.94 ± 0.01 using published experimental data.
Rethinking the Combined and Individual Orders of Derivative of States for Differential Recurrent Neural Networks

Due to their special gating schemes, Long Short-Term Memory (LSTM) has shown greater potential to process complex sequential information than the traditional Recurrent Neural Network (RNN). The conventional LSTM, however, fails to take into consideration the impact of salient spatio-temporal dynamics present in the sequential input data. This problem was first addressed by the differential Recurrent Neural Network (dRNN), which uses a differential gating scheme known as Derivative of States (DoS). DoS uses higher orders of internal state derivatives to analyze the change in information gain originated from the salient motions between the successive frames. The weighted combination of several orders of DoS is then used to modulate the gates in dRNN. While each individual order of DoS is good at modeling a certain level of salient spatio-temporal sequences, the sum of all the orders of DoS could distort the detected motion patterns. To address this problem, we propose to control the LSTM gates via individual orders of DoS. To fully utilize the different orders of DoS, we further propose to stack multiple levels of LSTM cells in an increasing order of state derivatives. The proposed model progressively builds up the ability of the LSTM gates to detect salient dynamical patterns in deeper stacked layers modeling higher orders of DoS; thus, the proposed LSTM model is termed deep differential Recurrent Neural Network (d2RNN). The effectiveness of the proposed model is demonstrated on three publicly available human activity datasets: NUS-HGA, Violent-Flows, and UCF101. The proposed model outperforms both LSTM and non-LSTM based state-of-the-art algorithms.
Measuring Long Wire Leakage with Ring Oscillators in Cloud FPGAs

Recent investigations into FPGA routing resources have shown that long wires in FPGAs leak information about their state in a way which can be measured using ring oscillators. Although in many cases this leakage does not pose a security threat, the possibility of multi-tenant use of FPGA resources invites potential side-and covert-channel attacks exploiting long wire leakage. However, prior work has ignored the realities of cloud environments, which may pose restrictions on the generated bitstreams, such as disallowing combinatorial loops. In this paper, we first demonstrate that the long wire leakage phenomenon persists even in the high-end Virtex UltraScale+ FPGA family. We then evaluate two ring oscillator designs that overcome combinatorial loop restrictions employed by cloud FPGA providers. We experimentally measure the long wire leakage of Virtex UltraScale+ FPGAs in the lab as well as in the Amazon and Huawei FPGA clouds. We show that the two new ring oscillator designs provide almost-identical estimates for the strength of the leakage as traditional ring oscillators, allowing us to measure femtosecond-scale changes in the delays of the long wires. We finally present a set of defense mechanisms that can prevent the new ring oscillator designs from being instantiated in the cloud and the long wire leakage from being exploited.
Protective functions of Lycium barbarum polysaccharides in H2O2-injured vascular endothelial cells through anti-oxidation and anti-apoptosis effects

Cell injury in the cardiovascular endothelia caused by oxidative stress is among the major inducers of endothelium dysfunction and serves an important role in initiating cardiovascular diseases (CVDs). Therefore, protecting and improving the normal function of endothelial cells are considered key measures against CVDs. As a traditional Chinese medicinal component, Lycium barbarum is regarded to have high medicinal value. The present study aimed to investigate the potential anti-apoptosis and anti-oxidation effects of Lycium barbarum polysaccharides (LBPs) on injured rat artery endothelial cells, to demonstrate the experimental and medicinal values of LBPs. In the present study, the aortic endothelial cells of rats were cultivated and randomly divided into five groups: A control group, H2O2-injured group (H2O2 group), H2O2+LBPs (110 µg/ml) group (low-dose group, LT), H2O2+LBPs (220 µg/ml) group (medium-dose group, MT) and H2O2+LBPs (440 µg/ml) group (high-dose group, HT). Among these, the activity of superoxide dismutase (SOD), and the levels of malondialdehyde (MDA) and nitric oxide (NO) were detected by colorimetry. Additionally, the expression of B-cell lymphoma-2 (Bcl-2) and Bcl-2-associated X protein (Bax) were detected by western blotting. It was observed that SOD activity and NO content decreased while MDA content increased significantly in the H2O2 group (P<0.05 vs. control); that SOD activity in the MT and HT group, and NO content in all three LBP groups were increased, while MDA content in the three LBP groups was decreased, compared with the H2O2 group (all P<0.05); that Bcl-2 expression decreased significantly in the H2O2 group while the expression of Bax increased significantly compared with the control group (both P<0.05); and that Bcl-2 expression in all three LBP groups increased, while Bax expression in the MT and HT groups decreased compared with the H2O2 group (all P<0.05), with these altered Bax levels being statistically similar to those in the control group (P>0.05). On light microscopy, the cells in the control group exhibited spindle-shaped morphology, consistent sizes, defined boundaries, and distinct nuclei of equivalent sizes with round or oval morphology. Additionally, the chromatin in the nuclei was evenly distributed, and all cells were adhered in a paving-stone arrangement. Notably, only few cells died. Conversely, the cells in the H2O2 group exhibited signs of damage and enlarged gaps, and focal cells died. In the HT group, the cells once again appeared adherent and exhibited similar morphological status to the normal cells. Overall, these results indicate that LBPs serve a protective role in oxidative-injured vascular endothelial cells through anti-apoptosis and anti-oxidation effects.