Time Epochs Research Articles

Classification of Defective Fabrics Using Capsule Networks

Fabric quality has an important role in the textile sector. Fabric defect, which is a highly important factor that influences the fabric quality, has become a concept that researchers are trying to minimize. Due to the limited capacity of human resources, human-based defect detection results in low performance and significant loss of time. To overcome human-based limited capacity, computer vision-based methods have emerged. Thanks to new additions to these methods over time, fabric defect detection methods have begun to show almost one hundred percent performance. Convolutional Neural Networks (CNNs) play a leading role in this high-performance success. However, Convolutional Neural Networks cause information loss in the pooling process. Capsule Networks is a useful technique for minimizing information loss. This paper proposes Capsule Networks, a new generation method that represents an alternative to Convolutional Neural Networks for deep learning tasks. TILDA dataset as source data for training and testing phases are employed. The model is trained for 100, 200, and 270 epoch times. Model performance is evaluated based on accuracy, recall, and precision performance metrics. Compared to mainstream deep learning algorithms, this method offers improved performance in terms of accuracy. This method has been performed under different circumstances and has achieved a performance value of 98.7%. The main contributions of this study are to use Capsule Networks in the fabric defect detection domain and to obtain a significant performance result.

Bi-level bayesian control scheme for fault detection under partial observations

Due to limitations of sensor techniques and system structures, the internal states of most complex devices are unobservable. Only partial observations can be monitored at discrete time epochs, which poses great challenges for fault detection and prediction. To facilitate the development of advanced approaches, this paper proposes a bi-level Bayesian control scheme for detecting faults of complex devices under partial observations. The deterioration of an operational system is modeled as a hidden three-state multivariate Markov process, in which the parameter estimates are obtained by an expectation maximization (EM) algorithm. Based on the multivariate Markov process, a bi-level Bayesian control scheme is presented to monitor the Bayesian posterior probability of the system in a warning state and uses two levels of sampling frequency to dynamically detect impending failures. The decision variables of the bi-level fault detection scheme are optimized and solved in a semi-Markov decision process (SMDP) framework. The particularity of this work is that two phases of system deterioration are considered into detection scheme design to avoid unnecessary false alarms. Using partially observed multivariate data from discrete monitoring, the proposed approach is validated to be capable of detecting the early fault occurrences. By comparing with other advanced methods, the superiority of the proposed model is demonstrated.

Evaluation of early postoperative day 1 discharge after endoscopic endonasal pituitary adenoma resection.

While multiple studies have evaluated the length of stay after endonasal transsphenoidal surgery (ETS) for pituitary adenoma, the potential for early discharge on postoperative day 1 (POD 1) remains unclear. The authors compared patients discharged on POD 1 with patients discharged on POD > 1 to better characterize factors that facilitate early discharge after ETS. A retrospective chart review was performed for patients undergoing ETS for pituitary adenoma at a single tertiary care academic center from February 2005 to February 2020. Discharge on POD 1 was defined as a discharge within 24 hours of surgery. A total of 726 patients (mean age 55 years, 52% male) were identified, of whom 178 (24.5%) patients were discharged on POD 1. These patients were more likely to have pituitary incidentaloma (p = 0.001), require dural substitutes and DuraSeal (p = 0.0001), have fewer intraoperative CSF leaks (p = 0.02), and have lower postoperative complication rates (p = 0.006) compared with patients discharged on POD > 1. POD 1 patients also showed higher rates of macroadenomas (96.1% vs 91.4%, p = 0.03) and lower rates of functional tumors (p = 0.02). POD > 1 patients were more likely to have readmission within 30 days (p = 0.002), readmission after 30 days (p = 0.0001), nasal synechiae on follow-up (p = 0.003), diabetes insipidus (DI; 1.7% vs 9.8%, p = 0.0001), postoperative hypocortisolism (21.8% vs 12.1%, p = 0.01), and postoperative steroid usage (44.6% vs 59.7%, p = 0.003). The number of patients discharged on POD 1 significantly increased during each subsequent time epoch: 2005-2010, 2011-2015, and 2016-2020 (p = 0.0001). On multivariate analysis, DI (OR 7.02, 95% CI 2.01-24.57; p = 0.002) and intraoperative leak (OR 2.02, 95% CI 1.25-3.28; p = 0.004) were associated with increased risk for POD > 1 discharge, while operation epoch (OR 0.46, 95% CI 0.3-0.71; p = 0.0001) was associated with POD 1 discharge. This study demonstrates that discharge on POD 1 after ETS for pituitary adenomas was safe and feasible and without increased risk of 30-day readmission. On multivariate analysis, surgical epoch was associated with decreased risk of prolonged length of stay, while factors associated with increased risk of prolonged length of stay included DI and intraoperative CSF leak. These findings may help in selecting patients who are deemed reasonable for safe, early discharge after pituitary adenoma resection.

Hemorrhage Enlargement Is More Frequent in the First 2 Hours: A Prehospital Mobile Stroke Unit Study.

Hematoma enlargement (HE) after intracerebral hemorrhage (ICH) is a therapeutic target for improving outcomes. Hemostatic therapies to prevent HE may be more effective the earlier they are attempted. An understanding of HE in first 1 to 2 hours specifically in the prehospital setting would help guide future treatment interventions in this time frame and setting. Patients with spontaneous ICH within 4 hours of symptom onset were prospectively evaluated between May 2014 and April 2020 as a prespecified substudy within a multicenter trial of prehospital mobile stroke unit versus standard management. Baseline computed tomography scans obtained <1, 1 to 2, and 2 to 4 hours postsymptom onset on the mobile stroke unit in the prehospital setting were compared with computed tomography scans repeated 1 hour later and at 24 hours in the hospital. HE was defined as >6 mL if baseline ICH volume was <20 mL and 33% increase if baseline volume >20 mL. The association between time from symptom onset to baseline computed tomography (hours) and HE was investigated using Wilcoxon rank-sum test when time was treated as a continuous variable and using Fisher exact test when time was categorized. Kruskal-Wallis and Wilcoxon rank-sum tests evaluated differences in baseline volumes and HE. Univariable and multivariable logistic regression analyses were conducted to identify factors associated with HE and variable selection was performed using cross-validated L1-regularized (Lasso regression). This study adhered to STROBE guidelines (Strengthening the Reporting of Observational Studies in Epidemiology) for cohort studies. One hundred thirty-nine patients were included. There was no difference between baseline ICH volumes obtained <1 hour (n=43) versus 1 to 2 hour (n=51) versus >2 hours (n=45) from symptom onset (median [interquartile range], 13 mL [6-24] versus 14 mL [6-30] versus 12 mL [4-19]; P=0.65). However, within the same 3 time epochs, initial hematoma growth (volume/time from onset) was greater with earlier baseline scanning (median [interquartile range], 17 mL/hour [9-35] versus 9 mL/hour [5-23]) versus 4 mL/hour [2-7]; P<0.001). Forty-nine patients had repeat scans 1 hour after baseline imaging (median, 2.3 hours [interquartile range. 1.9-3.1] after symptom onset). Eight patients (16%) had HE during that 1-hour interval; all of these occurred in patients with baseline imaging within 2 hours of onset (5/18=28% with baseline imaging within 1 hour, 3/18=17% within 1-2 hour, 0/13=0% >2 hours; P=0.02). HE did not occur between the scans repeated at 1 hour and 24 hours. No association between baseline variables and HE was detected in multivariable analyses. HE in the next hour occurs in 28% of ICH patients with baseline imaging within the first hour after symptom onset, and in 17% of those with baseline imaging between 1 and 2 hours. These patients would be a target for ultraearly hemostatic intervention.

Right-Wing, Populist, Controlled by Foreign Powers? Topic Diversification and Partisanship in the Content Structures of German-Language Alternative Media

This study investigates the overall topic compositions and traces of right-wing populist partisanship in the content structures of German-language digital alternative news outlets. We argue that a topically heterogeneous landscape of right-wing populist alternative news outlets could maximize the reach of right-wing populist messages. Using LDA topic modeling, we explore the topical structures of nine different outlets (n = 66,965 articles) and empirically identify meaningful groups of outlets. Our analyses suggest the existence of two topically heterogeneous clusters of alternative news media: (1) core right-wing populist outlets, with around 45–50% of articles coming from topics that are related to right-wing or populist politics; and (2) topically diverse outlets. The latter group's topic selection also reveals right-wing populist partisanship, but less prominently so, while putting more emphasis on general interest news topics, such as sports, economy, or international relations. This group also includes Russian state-owned RT deutsch and Falun Gong-affiliated Epoch Times whose institutional roots, however, are only partially reflected in their topic selection. Additionally, longitudinal analyses reveal an increase of populism-related topics in both groups around the election date of the 2017 German federal election.

Evaluation of a deep-learning model for multispectral remote sensing of land use and crop classification

High-resolution deep-learning-based remote-sensing imagery analysis has been widely used in land-use and crop-classification mapping. However, the influence of composite feature bands, including complex feature indices arising from different sensors on the backbone, patch size, and predictions in transferable deep models require further testing. The experiments were conducted in six sites in Henan province from 2019 to 2021. This study sought to enable the transfer of classification models across regions and years for Sentinel-2A (10-m resolution) and Gaofen PMS (2-m resolution) imagery. With feature selection and up-sampling of small samples, the performance of UNet++ architecture on five backbones and four patch sizes was examined. Joint loss, mean Intersection over Union (mIoU), and epoch time were analyzed, and the optimal backbone and patch size for both sensors were Timm-RegNetY-320 and 256 × 256, respectively. The overall accuracy and F1 scores of the Sentinel-2A predictions ranged from 96.86% to 97.72% and 71.29% to 80.75%, respectively, compared to 75.34%–97.72% and 54.89%–73.25% for the Gaofen predictions. The accuracies of each site indicated that patch size exerted a greater influence on model performance than the backbone. The feature-selection-based predictions with UNet++ architecture and up-sampling of minor classes demonstrated the capabilities of deep-learning generalization for classifying complex ground objects, offering improved performance compared to the UNet, Deeplab V3+, Random Forest, and Object-Oriented Classification models. In addition to the overall accuracy, confusion matrices, precision, recall, and F1 scores should be evaluated for minor land-cover types. This study contributes to large-scale, dynamic, and near-real-time land-use and crop mapping by integrating deep learning and multi-source remote-sensing imagery

Post maximum light and late time optical imaging polarimetry of type I superluminous supernova 2020znr

ABSTRACTOptical imaging polarimetry was conducted on the hydrogen poor superluminous supernova 2020znr during three phases after maximum light (≈ +34 d, +288 d, and +289 d). After instrumental and interstellar polarization correction, all measurements are consistent with null-polarization detection. Modelling the light curve with a magnetar spin-down model shows that SN2020znr has similar magnetar and ejecta parameters to other SLSNe. A comparison of the best-fitting values discussed in the literature on SN 2017egm and SN 2015bn, two hydrogen poor SLSNe showing an increase of polarization after maximum light, suggests that SN 2020znr has higher mass ejecta that may prevent access to the geometry of the inner ejecta with optical polarimetry. The combined information provided by spectroscopy and light-curve analysis of type I SLSNe may be an interesting avenue to categorize the polarization properties of this class of transients. This approach would require to expand the sample of SLSNe polarimetry data currently available with early and late time epochs new measurements.

Evaluation of the Isometric and Dynamic Rates of Force Development in Multi-Joint Muscle Actions

The rate of force development (RFD) in the isometric leg press (ILP), the countermovement jump (CMJ) and the squat jump (SJ) were examined in twenty-three (9 females) team sport athletes aged 21-24 years. Peak force (Fpeak), peak rate of force development (RFDpeak) and RFD at different time epochs (0-50, 50-100, 100-150, 150-200) were calculated from the force-time curve, as well as CMJ and SJ height and the center of mass displacement. RFDpeak, RFD at 0-50, 50-100 and 100-150 ms and Fpeak were similar between the CMJ and the SJ (p = 0.26 to 0.99). Furthermore, RFD0-50 ms was similar between the ILP, the CMJ, and the SJ (p = 0.99 to 0.57). Higher values were observed in the ILP compared with the CMJ and the SJ in RFD50-100 ms and RFD100-150 ms (p < 0.001) and these differences were maintained even when RFD was scaled to body mass. The higher RFD normalized to Fpeak was attained at the 50-100 ms time interval with no differences between the ILP, CMJ and SJ and between males and females. These results suggest that the ability to exert rapid force is similar between the CMJ and the SJ, irrespective of the type of muscle action. Furthermore, RFD normalized to Fpeak is a muscle-force independent index of explosive force production, facilitating comparisons between individuals with different levels of muscle strength. The similarities between the CMJ and the SJ in RFDpeak, Fpeak, and RFD at different time epochs may imply that these types of jumps could be used interchangeably to assess explosive lower limb performance.

On the second-order excess wealth order and its properties

AbstractIn the literature, some stochastic orders have been extended to the higher orders in different scenarios. In this paper, inspired by interesting properties of the excess wealth order and its wide range application particularly in comparing the tail variability of risks, we consider the second-order excess wealth order and study its main properties. We obtain two results characterizing the proposed order. We also investigate its relationship with other well-known variability orders and criteria to compare risks. An application of the results in comparing the epoch times of two nonhomogeneous poisson processes is also given.

Abstract TP75: LVO Screening Saves Lives

Background: Since publication of the landmark endovascular thrombectomy trials for large vessel occlusion (LVO) ischemic stroke patients, many clinical tools to identify these patients have been developed. In the real world, resource constraints may limit these early activation strategies. Definitive guidelines by the American Heart Association recommending LVO screening tools await studies proving their impact on outcome. We therefore aimed to evaluate the implementation of our LVO screening tool in our hospital on both time and clinical metrics. Methods: Our previously validated “SNO” scale (expressive aphasia/speech, neglect, ocular deviation), has acceptable balance of user ease, sensitivity and specificity. October 2020 we began an LVO alert for patients presenting with at least 1 of the 3 above signs, and collected their data including baseline National Institutes of Health Stroke scale scores (NIHSSS), time metrics, and mortality rates and compared them between LVO activation and non activation groups using t-test and chi square. Results: Since 10/2020, 93 (22 endovascularly treated) LVO alerts were called compared with 55 (23 treated) patients in the same time epoch who fulfilled criteria but not LVO activated (1219 stroke alerts called in 2020). Mean age was 71 vs 74 years and NIHSSS were 21 vs 18. There was a trend towards earlier minutes from door to puncture (94 vs 110), device placement (113 vs 129), and recanalization (146 vs 152) between them. The most significant difference was in door to thrombolysis times (33 vs 49 minutes, p&lt;0.03). Both groups had over 90% rate of successful recanalization. Discharge NIHSSS were not significantly different: 11 vs 9. There were more than twice the number of mortalities among the latter group (2 vs 5, p&gt;0.05). Discussion: Although LVO activation using SNO criteria has shortened times to intervention in our hospital, clinical improvements may be dampened by ongoing issues limiting full adoption. But, those treatment modalities that require no additional resources, regardless of LVO (IV TPA) were expedited. Conclusion: LVO activation based on SNO criteria has shortened time to treatment. NIHSSS at discharge did not notably improve among the LVO activation group, but in house mortality was halved.

Impaired one-legged landing balance in young female athletes with previous ankle sprain: a cross-sectional study.

Ankle sprain is the most common type of sports injury, especially in team sports. Standing and dynamic landing balance, as an indicator of ankle instability, were investigated using varying experimental approaches. In the present cross-sectional study, 81 adolescent female elite handball and football players were divided into two groups based on previous ankle sprain injury (PI) or not (C). At time of test, all players were fully returned to elite-level sport. Subjects were tested during a one-legged landing (OLL) and in a one-legged static standing balance test (OLBT). In the OLL CoP trajectory displacement was calculated in 200 ms time epochs for evaluation of the initial stages of dynamic landing balance. OLBT was evaluated by calculating total displacement of the CoP trajectory. CoP displacement was greater in PI than C during the first 200 milliseconds epoch after landing (P=0.001, 252 mm [44], vs. 223 mm [28]), respectively) and in the subsequent 200 ms epoch (P=0.021, 72 mm [20], vs. 61 mm [16], respectively). No significant differences between PI and C were observed in time epochs from 400 to 1000 milliseconds or in OLBT. Adolescent elite athletes with a history of previous ankle sprain demonstrate impaired OLL balance in the first 400 milliseconds following jump landing compared to non-injured controls. Consequently, although athletes with previous ankle sprain may return to sport, dynamic postural control may not be fully restored. Future prospective studies are needed to decide, if the OLL test could be considered a relevant criterion tool for safe return-to-sport.

Deep Learning Based Convolutional Neural Network Structured New Image Classification Approach for Eye Disease Identification

A deep learning-based convolutional artificial neural networks structured a new image classification method approach was implemented in the study. Sample application was carried out with Diabetic Retinopathy disease. Obtaining information about the blood vessels and any abnormal patterns from the rest of the phonoscopic image and assessing the degree of retinopathy is the problem itself. To solve this problem developed methodology and algorithmic structure of this new approach is presented in the study. An approach called care model was used in this study different from the classical CNN structure. The care approach is based on the idea that the best solution will be taken from the new data obtained by rescale the available data according to total number of pixels before the average data pool is created and then CNN processes will continue. In the care model approach, all data is multiplied by the number of elements by the number of epoch time eight tensors. The purposed care model include VGG19 image classification model and developed mathematical model presented. Pre-trained model and all image dataset taken from kaggle and keras for implementation of case study. The purposed model provide train accuracy 87%, test accuracy 88%, precision 93% and recall 83%.

Assessment of Peak Physical Demands in Elite Women Soccer Players: Can Contextual Variables Play a Role?

ABSTRACT Aim: To describe and compare the peak physical demands through the worst-case scenario method (WCS), according to different rolling average (RA) time epochs (i.e. 1 min, 3 min, and 5 min) and contextual variables in women soccer players. Methods: Using an observational-comparative study design, an elite women soccer team from the Spanish 1st league division was monitored during 27 matches. Nine WCS-dependent variables were assessed, including total distance (TD), high-speed running distance (HSR), sprint distance (SP), acceleration, and deceleration distance at different intensities by players position (i.e., central defenders [CD], wide defenders [WD], central midfielders [CM], wide midfielders [WM], forwards [F]), match half, location, and match outcome. Results: The 1-min RA showed the lowest variability (CV = 9.8–65.8%) for all nine dependent variables. The WD presented the highest TD (168 ± 15.71 m/min). Differences between positions were observed for: CM<F (−10.19 m/min), CM<WM (−11.20 m/min), and CM<WD (−12.70 m/min). For SP: CM<F (−10.03 m/min), CM<WM (−11.91 m/min), CM<WD (−10.03 m/min), and CM<CD (−5.31 m/min). The WCS-dependent variables were also affected by match half and match outcome. Conclusions: 1-min RA time epochs allow greater accuracy and reliability to identify nine key WCS outcomes in elite women soccer players. Different contextual variables, particularly player’s position, influenced the peak periods.

Neighbor-Aware Non-Orthogonal Multiple Access Scheme for Energy Harvesting Internet of Things.

In a non-orthogonal multiple access (NOMA) environment, an Internet of Things (IoT) device achieves a high data rate by increasing its transmission power. However, excessively high transmission power can cause an energy outage of an IoT device and have a detrimental effect on the signal-to-interference-plus-noise ratio of neighbor IoT devices. In this paper, we propose a neighbor-aware NOMA scheme (NA-NOMA) where each IoT device determines whether to transmit data to the base station and the transmission power at each time epoch in a distributed manner with the consideration of its energy level and other devices’ transmission powers. To maximize the aggregated data rate of IoT devices while keeping an acceptable average energy outage probability, a constrained stochastic game model is formulated, and the solution of the model is obtained using a best response dynamics-based algorithm. Evaluation results show that NA-NOMA can increase the average data rate up to compared with a probability-based scheme while providing a sufficiently low energy outage probability (e.g., ).

J-shape relation of blood pressure reduction and outcome in acute intracerebral hemorrhage: A pooled analysis of INTERACT2 and ATACH-II individual participant data.

The aim of this study was to better define the shape of association between the degree ("magnitude") of early (< 1 h) reduction in systolic blood pressure (SBP) and outcomes in patients with acute intracerebral hemorrhage (ICH) through pooled analysis of the second Intensive Blood Pressure Reduction in Acute Cerebral Hemorrhage Trial (INTERACT2) and second Antihypertensive Treatment of Acute Cerebral Hemorrhage (ATACH-II) datasets. Association of the continuous magnitude of SBP reduction described using cubic splines and an ordinal measure of the functional outcome on the modified Rankin scale (mRS) scores at 90 days were analyzed in generalized linear mixed models. Models were adjusted for achieved (mean) and variability (standard deviation, SD) of SBP between 1 and 24 h, various baseline covariates, and trial as a random effect. Among 3796 patients (mean age 63.1 (SD = 13.0) years; female 37.4%), with a mean magnitude (< 1 h) of SBP reduction of 28.5 (22.8) mmHg, those with larger magnitude were more often non-Asian and female, had higher baseline SBP, received multiple blood pressure (BP) lowering agents, and achieved lower SBP levels in 1-24 h. Compared to those patients with no SBP reduction within 1 h (reference), the adjusted odds of unfavorable functional outcome, according to a shift in mRS scores, were lower for SBP reductions up to 60 mmHg with an inflection point between 32 and 46 mmHg, but significantly higher for SBP reductions > 70 mmHg. Similar J-shape associations were evident across various time epochs across 24 h and consistent according to baseline hematoma volume and SBP and history of hypertension. A moderate degree of rapid SBP lowering is associated with improved functional outcome after ICH, but large SBP reductions over 1 h (e.g. from > 200 to target < 140 mmHg) were associated with reduction, or reversal, of any such benefit.

Emotion Recognition and EEG Analysis Using ADMM-Based Sparse Group Lasso

This study presents an efficient sparse learning-based pattern recognition framework to recognize the discrete states of three emotions—happy, angry, and neutral emotion—using electroencephalogram (EEG) signals. In affective computing with massive spatiotemporal brainwave signals, a large number of features can be extracted to capture various information from multivariate brain data. However, it is often a challenge to model high-dimensional features efficiently in consideration of the intrinsic structure, such as channel location, feature group, time epoch, etc. In this study, features were extensively extracted from EEG signals and were applied on a structured sparse learning model to perform feature selection and classification simultaneously. An efficient ADMM-based algorithm with a closed-form solution was developed to solve the sparse group model. Experimental results show that the proposed method is capable of selecting a small number of important neural features to discriminate the three emotion states with high classification accuracy. With greatly enhanced interpretability and efficiency to learn neural signatures of brain activity from high-dimensional-feature, low-sample-size brain imaging data, the presented computational framework is promising for handling emotion recognition problems with high-dimensional brain imaging data.

Ocena pomeranja inženjerskih struktura u programskom paketu PANDA

The paper describes the assessment of the significance of the displacement of the points of the deformation structure (object) by the geodetic method of deformation analysis using the PANDA software package. According to the pre-assumed displacement and the measurement plan, the data of length measurements in two different time epochs were simulated. and the intensity and direction of deformation were assessed using the PANDA program. Within the deformation assessment, different assessment strategies were analyzed, through a one - and two - step procedure. In both cases, the expected agreement of the displacement estimate with the pre-assumed value was reached.

A Theoretical Analysis of Granulometry-Based Roughness Measures on Cartosat DEMs

The study of water bodies such as rivers is an important problem in the remote sensing community. A meaningful set of quantitative features reflecting the geophysical properties help us better understand the formation and evolution of rivers. Typically, river sub-basins are analyzed using Cartosat Digital Elevation Models (DEMs), obtained at regular time epochs. One of the useful geophysical features of a river sub-basin is that of a roughness measure on DEMs. However, to the best of our knowledge, there is not much literature available on theoretical analysis of roughness measures. In this article, we revisit the roughness measure on DEM data adapted from multiscale granulometries in mathematical morphology, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multiscale directional granulometric index (MDGI)</i> . This measure was classically used to obtain shape-size analysis in grayscale images. In earlier works, MDGIs were introduced to capture the characteristic surficial roughness of a river sub-basin along specific directions. Also, MDGIs can be efficiently computed and are known to be useful features for classification of river sub-basins. In this article, we ask the question <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">when does a MDGI fail to classify distinct DEMs?</i> We provide a theoretical analysis of a MDGI to answer this question. In particular, we identify nonrivial sufficient conditions on the structure of DEMs under which MDGIs cannot distinguish between distinct DEMs. These properties are illustrated with some fictitious DEMs. We also provide connections to a discrete derivative of volume of a DEM. Based on these connections, we provide intuition as to why a MDGI is considered a roughness measure. We empirically verify that MGDIs capture the topographical characteristics using the Lower-Indus, Wardha, and Barmer river sub-basins. We show that a simple decision tree based on MDGI features alone can successfully distinguish between sub-basins with a high accuracy. We obtain upto 94% accuracy while the baseline of random classifier is around 33.34%.

Judgement Modality In The Editorials Of The Epoch Times Newspaper

The purpose of this study was to determine the type of epistemic modality most frequently used and also to determine the speaker's attitude towards his editorial. The level of editorial confidence in the written editorial can be seen by looking at the use of verb modals which is epistemic modality. The writer assesses modality based on qualitatif method to describe the speaker’s attitude and quantitative to determine the type of modality that is the most often used. The data collection procedure was sourced from newspaper and used 43 editorial texts as the research sample. The results of this study found that the type of epistemic modality most often used was the assumptive type which reached 61,8%. The second is the speculative type with a percentage of 26,4%. The third is deductive type with a percentage of 8,9%. And the fourth is concessive type with a percentage of 2,9%. Apart from that, this study also found that the speaker's attitude was doubtful about his proposition when based only on possible conclusions. The speaker's attitude is confident in his proposition when it is based on a sound conclusion. The speaker's attitude of being confident and believing what is necessity to do is based on the only possible conclusion. And the speaker predicts something to be true when the speaker has evidence that contrasts with the proposition.

«Часть речи» / “A Part of Speech” И. Бродского: двуязычие как творческий метод в поэзии

This article is an attempt of textological conceptualization of an interlingual transfer (doubling) of a literary text. This transfer, in case it has been done by the author him/herself, is traditionally called self-translation, yet it can also by qualified as an adaptation of the “old” text to the perceptive abilities of the “new” recipient, i.e. a collective reader representing a different national and cultural medium and bearing its values as a prevalent aesthetic waymark. Special importance in the above process is attached to such a significant feature of any literary text of high quality as its elasticity. The textological approach to the scientific understanding of the differentlanguage text binary is often complemented by the bilinguological categorization of the subject, i.e. a text of poetry and, at the same time, a poetic cycle. From this standpoint, the mode of primary/secondary character of the two interconnected texts might lose its substantial preciseness. Further on, the article scrutinizes a landmark (for the Russian culture) literary text and its self-translation: the poetic cycle “A Part of Speech” by Joseph Brodsky. Four conceptual dominants of the cycle, with regard to their axiological hierarchy, are distinguished: 1) space/distance – location – oblivion; 2) time (epoch) – history – event – oblivion; 3) love – breakup – oblivion; 4) speech – art – extension – immortality. Further, a comparative analysis of their different-language verbalization is carried out. It is stated that Brodsky’s creation of this bilingual cycle reflects his striving for a cultural adaptation of each version to the background knowledge, historical experience, principles, traditions, aesthetic attitudes and, generally, sentiments of different groups of readers. In some cases this is done by means of the national language only, in other cases by the general complication of the poems’ figurative fabric.