Miracle-dz/UniFAR-FTUs huggingface dataset:
Calculation of heat transfer on shuttle type configurations including the effects of variable entropy at boundary layer edge

A relatively simple method is presented for including the effect of variable entropy at the boundary-layer edge in a heat transfer method developed previously. For each inviscid surface streamline an approximate shockwave shape is calculated using a modified form of Maslen's method for inviscid axisymmetric flows. The entropy for the streamline at the edge of the boundary layer is determined by equating the mass flux through the shock wave to that inside the boundary layer. Approximations used in this technique allow the heating rates along each inviscid surface streamline to be calculated independent of the other streamlines. The shock standoff distances computed by the present method are found to compare well with those computed by Maslen's asymmetric method. Heating rates are presented for blunted circular and elliptical cones and a typical space shuttle orbiter at angles of attack. Variable entropy effects are found to increase heating rates downstream of the nose significantly higher than those computed using normal-shock entropy, and turbulent heating rates increased more than laminar rates. Effects of Reynolds number and angles of attack are also shown.
Feature transfer learning by reinforcement learning for detecting software defect

Software defects, produced inevitably in software projects, seriously affect the efficiency of software testing and maintenance. An appealing solution is the software defect prediction (SDP) that has achieved good performance in many software projects. However, the difference between features and the difference of the same feature between training data and test data may degrade defect prediction performance if such differences violate the model's assumption. To address this issue, we propose a SDP method based on feature transfer learning (FTL), which performs a transformation sequence for each feature in order to map the original features to another feature space. Specifically, FTL first uses the reinforcement learning scheme that automatically learns a strategy for transferring the potential feature knowledge from the training data. Then, we use the learned feature knowledge to inspire the transformation of the test data. The classifier is trained by the transformed training data and predicts defects for transformed test data. We evaluate the validity of FTL on 43 projects from PROMISE and NASA MDP using three classifiers, logistic regression, random forest, and Naive Bayes (NB). Experimental results indicate that FTL is better than the original classifiers and has the best performance on the NB classifier. For PROMISE, after using FTL, the average results of F1‐score, AUC, MCC are 0.601, 0.757, and 0.350 respectively, which are 24.9%, 2.6%, and 16.7% higher than the original NB classifier results. The number of projects with improved performance accounts for 83.87%, 83.87%, and 64.52%. Similarly, FTL performs well on NASA MDP. Besides, compared with four feature engineering (FE) methods, FTL achieves an excellent improvement on most projects and the average performance is also better than or close to the FE methods.
Mechanical Steel Stitches: An Innovative Approach for Strengthening Shear Deficiency in Undamaged Reinforced Concrete Beams

In this study, reinforced concrete beams with insufficient shear capacity were strengthened on both sides of the beam along the shear openings by a novel approach: Mechanical Steel Stitches (MSS). This innovative method facilitates the application of strengthening the beams with a low-cost solution. In this concept, six specimens were experimentally investigated under vertical load. While one of the specimens was tested as a reference, the others were strengthened with MSS application at different ratios (ρMS), ranging from 0.2% to 1% at both the beams’ shear span. MSS were applied with the angle of 90° considering stirrup logic. The diameter, anchorage depth and mechanical properties of the MSSs were kept constant, and their effects on the strengthening of the beams in terms of ductility, strength, stiffness, and energy dissipation capacities were investigated by changing the spacing of the MSSs. The results revealed that increasing MSS ratio caused a dramatic positive change in the behavior in terms of both strength and energy dissipation capacity. MSSs to be made at appropriate intervals ((%1) MSS ratio or (d/5) MSS spacing) significantly improved the shear capacity. However, a 43% loss in stiffness occurred with the increase in ρMS since the MSSs are applied to the beams by drilling and anchoring from the outside.
LINEAR GROUP PRECODING FOR MASSIVE MIMO SYSTEMS UNDER EXPONENTIAL SPATIAL CORRELATION

In this paper, a low-complexity linear group precoding algorithm in the exponential correlation channel model is proposed for massive MIMO systems. The proposed precoder consists of two components: The first one minimizes the interferences among neighboring user groups; The second one improves the system performance by utilizing the ELR-SLB technique. Numerical and simulation results show that the proposed precoder has remarkably lower computational complexity than its LC-RBD-LR-ZF counterpart, while its bit error rate (BER) performance is asymptotic to that of the LC-RBD-LR-ZF precoder as the number of groups increases.
Ensemble Dilated Convolutional Neural Network and Its Application in Rotating Machinery Fault Diagnosis

Fault diagnosis of rotating machinery is an attractive yet challenging task. This paper presents a novel intelligent fault diagnosis scheme for rotating machinery based on ensemble dilated convolutional neural networks. The novel fault diagnosis framework employs a model training strategy based on early stopping optimization to ensemble several one-dimensional dilated convolutional neural networks (1D-DCNNs). By varying the dilation rate of the 1D-DCNN, different receptive fields can be obtained to extract different vibration signal features. The early stopping strategy is used as a model update threshold to prevent overfitting and save computational resources. Ensemble learning uses a weighted mechanism to combine the outputs of multiple 1D-DCNN subclassifiers with different dilation rates to obtain the final fault diagnosis. The proposed method outperforms existing state-of-the-art classical machine learning and deep learning methods in simulation studies and diagnostic experiments, demonstrating that it can thoroughly mine fault features in vibration signals. The classification results further show that the EDCNN model can effectively and accurately identify multiple faults and outperform existing fault detection techniques.
Potensi Narasi Visual Video Pertunjukan Wayang Dewa Ruci sebagai Media Pembelajaran Bahasa Jawa

The wayang story is one of the chapters or main material in Javanese language subjects which is considered difficult for high school students to learn. So that the results of student achievement at SMA N 2 Wonogiri on the material for wayang stories are the lowest among other materials in Javanese language subjects. The Javanese language teacher's effort to overcome this problem is to show a video of a shadow puppet show with Dewa Ruci's story to the students. The video has visual narrative elements that can convey information on wayang stories sequentially and effectively to students. The purpose of this study was to determine the potential of a video of a shadow puppet show that can be used as learning media for students to recognize and understand the Javanese language subjects. This research method uses a mixed-method (quantitative-qualitative). The results showed that learning material for wayang stories in Javanese language subjects through a video of a shadow puppet show with Dewa Ruci stories for high school students proved to be more effective to understand because it contained visual narrative elements. High school students can find out the characters, setting, plot, themes, and moral values and can rewrite Dewa Ruci's story into Javanese. As a result, through the storytelling of the dalang and the dialogue between the characters in the performances, the dalang becomes known and understood by students.
Voltammetry Peak Tracking for Longer-Lasting and Reference-Electrode-Free Electrochemical Biosensors

Electrochemical aptamer-based sensors offer reagent-free and continuous analyte measurement but often suffer from poor longevity and potential drift even with a robust 3-electrode system. Presented here is a simple, software-enabled approach that tracks the redox-reporter peak in an electrochemical aptamer-based sensor and uses the measurement of redox peak potential to reduce the scanning window to a partial measure of redox-peak-height vs. baseline (~10X reduction in voltage range). This same measurement further creates a virtual reference standard in buffered biofluids such as blood and interstitial fluid, thereby eliminating the effects of potential drift and the need for a reference electrode. The software intelligently tracks voltammogram peak potential via the inflection points of the rising and falling slopes of the measured redox peak. Peak-tracking-derived partial scanning was validated over several days and minimized electrochemically induced signal loss to <5%. Furthermore, the peak-tracking approach was shown to be robust against confounding effects such as fouling. From an applied perspective in creating wearable biosensors, the peak-tracking approach further enables use of a single implanted working electrode, while the counter/reference-electrode may utilize a simple gel-pad electrode on the surface of the skin, compared to implanting working, counter, and reference electrodes conventionally used for stability and reliability but is also costly and invasive. Cumulatively, peak-tracking provides multiple leaps forward required for practical molecular monitoring by extending sensor longevity, eliminating potential drift, simplifying biosensor device construction, and in vivo placement for any redox-mediated sensor that forms parabolic-like data.
On Legendre Multiplier Sequences

In this paper we give a complete characterization of linear, quadratic, and geometric Legendre multiplier sequences. We also prove that all Legendre multiplier sequences must be Hermite multiplier sequences, and describe the relationship between the Legendre and generalized Laguerre multiplier sequences. We conclude with a list of open questions for further research.
Outer measure preserving ergodic transformations generate the Carath\'eodory definition of measurable sets

It is known that there are specific examples of ergodic transformations on measure spaces for which the calculation of the outer measure of transformation invariant sets leads to a condition closely resembling Carath\'eodory's condition for sets to be measurable. It is then natural to ask what functions are capable of `generating', that is leading to, the Carath\'eodory definition in the same way. The present work answers this question by showing that the property of generating Carath\'eodory's definition holds for the general class of outer measure preserving ergodic transformations on measure spaces. We further show that the previously found specific examples of functions generating Carath\'eodory's definition fall into this family of transformations.
Characterizing the river migration process based on migration events of individual bends: a case study of the lower Yellow River, China

Abstract Frequent channel shifts in the Lower Yellow River easily leads to embankment collapse causing geomorphological processes necessary to characterize. This paper segmented the river centreline from its component unit-bends and established a migration event model to characterize the migration process of rivers. The inflection points on the river centreline can be used to segment individual bends. Three types of migration events, disappearance, lateral migration and decomposition, are defined for the first time to categorize river migration. Furthermore, these migration events can be rapidly detected using the Meander Migration Index. The four-tuple BMModel (Bend Migration Model), including events, spatial positions, bend types and migration attributes, systematically quantifies the river migration process. In addition, these results can be implemented with an integrated PyBend (Python-Bend migarition) package, providing promising technical support for the studies of river migration and evolution over long time series.
Large-eddy simulation of laminar-turbulent breakdown at high speeds with dynamic subgrid-scale modeling

The laminar-turbulent breakdown of a boundary-layer flow along a hollow cylinder at Mach 4.5 is investigated with large-eddy simulation. The subgrid scales are modeled dynamically, where the model coefficients are determined from the local resolved field. The behavior of the dynamic-model coefficients is investigated through both an a priori test with direct numerical simulation data for the same case and a complete large-eddy simulation. Both formulations proposed by Germano et al. and Lilly are used for the determination of unique coefficients for the dynamic model and their results are compared and assessed. The behavior and the energy cascade of the subgrid-scale field structure are investigated at various stages of the transition process. The investigations are able to duplicate a high-speed transition phenomenon observed in experiments and explained only recently by the direct numerical simulations of Pruett and Zang, which is the appearance of 'rope-like' waves. The nonlinear evolution and breakdown of the laminar boundary layer and the structure of the flow field during the transition process were also investigated.
Long range order in AlxGa1−xN films grown by molecular beam epitaxy

The first observation of atomic long range ordering in AlxGa1−xN thin films grown by electron cyclotron resonance assisted molecular beam epitaxy on sapphire and 6H-SiC substrates is reported. The phenomenon was investigated by studying the superlattice peaks (0001), (0003), and (0005) using x-ray diffraction. The relative intensity of these peaks was found to be largest for Al content in the 30%–50% range in qualitative agreement with expectations for an ordered structure of ideal Al0.5Ga0. 5N stoichiometry. The average size of the ordered domains in the films was found to be within a factor of 4 of the films’ thicknesses. The degree of ordering depends on the III/V flux ratio and exhibits a weaker dependence on Si doping.
Are Galaxy Clusters Suggesting an Accelerating Universe Independent of SNe Ia and Gravity Metric Theory

A kinematic method to access cosmic acceleration based exclusively on the Sunyaev-Zel'dovich effect (SZE) and X-ray surface brightness data from galaxy clusters is proposed. By using the SZE/X-ray data from 38 galaxy clusters [Bonament et al., Astrop. J. 647, 25 (2006)], we find that the present Universe is accelerating and that the transition from an earlier decelerating to a late time accelerating regime occurred relatively recent. Such results are fully independent on the validity of any metric gravity theory, the possible matter-energy contents filling the Universe, as well as on the SNe type Ia Hubble diagram from which the present acceleration was inferred. The ability of the ongoing Planck satellite mission to obtain tighter constraints on the expansion history through SZE/X-ray angular diameters is also discussed. Two simple simulations of future Planck data suggest that such technique will be competitive with supernova data besides being complementary to it.
False Event Message Detection Robust to Burst Attacks in Wireless Sensor Networks

A sensor device can be used to detect target events at low cost. Moreover, there is a significant risk of sensor nodes being compromised or captured within large wireless sensor networks (WSNs). The transmission of valid event messages to users by a WSN can be hindered by network congestion due to false event messages in a compromised node. Several methods are available for detecting false event messages. However, they rely on long bits of authentication codes and hence do not provide a fundamental solution to prevent network congestion. In the proposed method, various hashing vectors, which are spaceefficient data structures that can determine whether the given data are an element of a set, are created based on the correct combination of authentication codes and placed in each node in advance. An event message contains an XOR of the authentication codes, and each node verifies it based on its hashing vector. If a node is acquired illegally, the information of the hashing vector and the XOR information of the authentication codes assigned to the correct event message is compromised, so we propose an algorithm to update the information securely. Compared to existing research, the number of hops required to detect a false event message increases by only about one hop, but the amount of traffic that a malicious node can generate can be reduced by about 60% or more. In other words, the proposed method effectively reduces the amount of traffic an attacker can generate with false event messages, which also reduces the overall network congestion.
Functional Limitations among European Older Adults: Cross-Country Differences in Health Improvements and Social Environment Influence

This study explores differences on health status transitions based on functional health and dead records among older population in Europe. We also study the influence of residence pattern on health changes over time. Method: Data used came from EU-SILC registers on individuals aged 50 + from 19 countries. Fixed and Mixed effect Cox Proportional Hazards Models are used to ascertain any country differences on health outcomes and then by co-residence pattern. Results: There are remarkable country heterogeneity among older people to experience changes on their health status, specially, for health improvements and mortality risk. As expected, Baltic and Eastern European countries have a higher propensity for health deterioration and Southern European countries for health improvements on their functional capacities. However, there are exceptions as Bulgaria and Romania, which have a lower risk of deterioration and death transition than average. Overall, living with partner and adult children-rather than living only with a couple shown positive effects for older European to experience health improvements with notably differences in Southern countries as in Italy and Spain. Discussion: country framework differences play a fundamental role to understand changes on health status at older ages, in particular, how health care support toward older people is managed by health systems within European countries and the availability of close relatives among older adult population.
The correlation factors and prognostic significance of coagulation disorders after chimeric antigen receptor T cell therapy in hematological malignancies: a cohort study

Background Along with cytokine release syndrome (CRS) and neurotoxicity, coagulation disorder is a common early complication of chimeric antigen receptor (CAR)-T cell therapy. However, the mechanisms and prognostic significance of CAR-T-related coagulation disorders are not fully known. This study explored the possible correlation factors and prognostic significance of coagulation disorders after CAR-T cell infusion in patients with relapsed/refractory hematological malignancies. Methods This cohort study included 56 patients with relapsed/refractory hematological malignancies who were treated with CAR-T cells between April 2017 and February 2022. The median follow-up was 26.8 months. Coagulation disorders were defined as the abnormality in at least one coagulation parameters, including prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time (TT), fibrinogen, and D-dimer. The correlation factors of coagulation disorders were analyzed using Wilcoxon rank-sum test, Fisher’s exact test, paired t-test and Spearman correlation coefficient. The prognostic significance of coagulation disorders was analyzed using Kaplan-Meier method and stepwise multivariate Cox regression model. Results The incidence of coagulation disorders was 59% within 1 month of CAR-T cell infusion. PT prolongation, APTT prolongation, TT prolongation, and D-dimer increase peaked at a median of 6–9 days, and fibrinogen decreased to its lowest value at a median of 12 days. Coagulation disorders in patients with severe CRS were more significant (P<0.001). Abnormality of coagulation parameters was closely related to cytokines, CAR-T cells, liver function parameters, and von Willebrand Factor (VWF) in both peak level and peak time (P<0.05). Statistical analysis showed that coagulation disorders were associated with higher initial response rates (TT, P=0.006; D-dimer, P=0.010) and also longer progression‐free survival (PFS) (PT, P=0.017; APTT, P=0.018; TT, P=0.001; Fibrinogen, P=0.003; D-dimer, P<0.001) in CAR-T therapy, with TT prolongation (HR =0.279, 95% CI: 0.099–0.782, P=0.015) and D-dimer increase (HR =0.218, 95% CI: 0.087–0.548, P=0.001) independent predictors for PFS. Conclusions The protection of liver and endothelial cells may reduce CAR-T-related coagulation disorders. Further, coagulation disorders occurring within 1 month of CAR-T cell infusion can serve as a new predictor for prognosis in patients with hematological malignancies.
Reconstruction of Optical Image Time Series With Unequal Lengths SAR Based on Improved Sequence–Sequence Model

Optical remote sensing time series are optimal for understanding and monitoring biochemical changes of key phenological parameters, which is essential for the assessment of vegetation health. However, due to cloud contamination, optical images often lack several days’ to months’ worth of information. Therefore, reconstructing optical time series based on the synthetic aperture radar (SAR) is necessary, which has the advantage of the production of continuous images under all weather conditions. In this study, an improved sequence-to-sequence (Seq2Seq) model was proposed, which integrated teacher forcing (TF) with the attention mechanism to handle input and output time series with unequal lengths. The proposed model could be used to reconstruct optical full-band time series using SAR time series data and reduce sample requirements based on the modification of the loss calculation method. To explore the spatiotemporal scalability of the proposed model, the test samples were divided into three categories: same time but different spatial (STDS) domain, different time but same spatial (DTSS) domain, and both different temporal and spatial (DTDS) domains. The major findings can be summarized as follows: 1) 72.7% of the generated Landsat 8 sequence values had an absolute error (AE) of less than 0.05; 2) the mean AE (MAE) of all bands was less than 0.0812; 3) the mean squared errors (MSEs) of all samples were lower than 0.015 regardless of the type of the test sample; and 4) TF was introduced to the model to improve the accuracy of the generated Landsat 8 sequence, yielding an increase of more than 5.97%. We concluded that the proposed model had good robustness both temporally and spatially. It performed well in the reconstruction of optical image time series, provided a basis for the use of time series pairs with unequal lengths, and can be applied to cloud removal and vegetation index reconstruction.