Miracle-dz/UniFAR-FTUs huggingface dataset:
The iterative solution of a sequence of linear systems arising from nonlinear finite element analysis

Research on failure mechanisms of engineering components often focuses on modeling complex, nonlinear response. The analysis by finite element methods requires large-scale, very refined 3D solid models. Domain decomposition methods are frequently employed. Finite element methods for quasi-static and transient responses over longer time scales generally adopt an implicit formulation. Together with a Newton scheme for the nonlinear equations, such implicit formulations require the solution of large linear systems, thousands of times, to accomplish a realistic analysis. This represents an enormous computational burden. Robust, domain based iterative solvers are essential to increase scalability in parallel simulation codes. We consider improvements to solver technology to reduce the overall solution time for a sequence of linear systems. Linear solvers that retain a subspace determined while solving previous systems can use that subspace to reduce the cost of solving the next system in the sequence. We call this process “Krylov subspace recycling”. We develop two recycling solvers, and demonstrate on several model problems that we can reduce the iteration count required to solve a linear system by up to factor of two. We analyze the convergence of one of the new solvers, which recycles nearly invariant subspaces, and establish residual bounds that suggest a convergence rate similar to one obtained by removing select eigenvector components from the initial residual vector. Experimental and theoretical results show that while recycling invariant subspaces can be beneficial, better choices exist. Improved preconditioners form one avenue to bolster the performance of iterative solvers for this problem class. Domain decomposition preconditioners based on substructuring have been applied successfully to many engineering problems. For a domain decomposition method to exhibit satisfactory scalability, it must employ a “coarse-space” preconditioner. We consider the finite element tearing and interconnecting (FETI) method, as it is a popular domain decomposition method showing both numerical and parallel scalability. Application of the one-level FETI method produces a KKT (Karush-Kuhn-Tucker) linear system. We develop new connections between KKT and FETI solvers and preconditioners and show potential improvements to the FETI method, including the use of a less expensive approximate Schur complement in the FETI coarse problem.
Combination of White Blood Cell Count to Mean Platelet Volume Ratio and Neutrophil-to-Platelet Ratio Predicts Long-Term Adverse Events in Patients with MINOCA

Background White blood cell count to mean platelet volume ratio (WMR) and neutrophil-to-platelet ratio (NPR) have been demonstrated as prognostic inflammatory biomarkers of the acute coronary syndrome. We aimed to evaluate the prognostic value of WMR and NPR among myocardial infarction with nonobstructive coronary arteries (MINOCA) patients. Method A total of 274 MINOCA patients were enrolled. Baseline clinical data, blood cell panel, and biochemical parameters were evaluated. The patients were classified according to the medians of WMR and NPR. The primary endpoint of the present study was major adverse cardiovascular events (MACE). Multivariable Cox regression analysis was used to assess the effect of independent variables of WMR and NPR on the dependent variable (MACE). Result The median values of WMR and NPR were 701 and 0.03, respectively. During the median follow-up of 28 months, a total of 58 incidences of MACE occurred. The MACE rate was more frequent in high WMR and high NPR patients. In Kaplan-Meier analysis, the incidence of MACE was higher in WMR>701 and NPR>0.03 (long-rank P = 0.004 and P = 0.002, respectively). The combined high WMR and high NPR showed a significantly higher rate of MACE (long-rank P = 0.001). Cox regression analysis showed that the combined high WMR and high NPR were independent predictors of long-term MACE with the highest hazard ratio (HR, 2.511; 95% CI, 1.271 to 4.960; P = 0.008). Conclusion High WMR and NPR separately or in combination were correlated with increased risk of MACE among MINOCA patients, suggesting WMR and NPR may assist as a reliable inflammatory marker in risk prediction of MINOCA patients.
Recaptured Screen Image Demoiréing in Raw Domain

Capturing screen content by smart-phone cameras has become a daily routine to record or share instant information from display screens for convenience. However, these recaptured screen images are often degraded by moiré patterns and usually present color cast against the original screen source. We observe that performing demoiréing in raw domain before feeding into the image signal processor (ISP) is more effective than demoiréing in the sRGB domain as done in recent demoiréing works. In this paper, we investigate the demoiréing of raw images through a class-specific learning approach. To this end, we build the first well-aligned raw moiré image dataset by pixel-wise alignment between the recaptured images and source ones. Noting that document images occupy a large portion of screen contents and have different properties from generic images, we propose a class-specific learning strategy for textual images and natural color images. In addition, to deal with moiré patterns with various scales, a multi-scale encoder with multi-level feature fusion is proposed. The shared encoder enables us to extract rich representations for the two kinds of contents and the class-specific decoders benefit the specific content reconstruction by focusing on targeted representations. Experiment results demonstrate that our method achieves state-of-the-art demoiréing performance. We have released the code and dataset in https://github.com/tju-chengyijia/RDNet
Accelerating NoC Verification Using a Complete Model and Active Window

This work presents formal modeling of Network-on-Chip (NoC) considering detailed functional units of NoC. The intricate modeling of NoC router components like buffer, switch, and arbiter is accomplished using Finite State Machine (FSM). As in the case of a real NoC, parallel execution of these functional units is carried out by maintaining the synchronization between these functional units within a router and between each of the adjacent routers in the presented formal model. Important properties for the correctness of the proposed model are verified using a model checker. Implementing a detailed and a complete NoC model is a memory extensive operation while verifying with a model checker. We have introduced a concept of active windows to verify each router and its communication with the adjacent routers. The correctness of the model is checked by verifying the synchronization between NoC functional units and NoC routers, verifying progress in functional units, and verifying the successful transfer of packets. Verification of starvation freedom in an NoC router is also performed for round-robin arbiter and fixed-priority arbiter. Parallel threads are used in the experiments to reduce the verification time.
Estimation of the distribution of change-points with appliction to fMRI data

Functional magnetic resonance imaging (fMRI) is now a well-established technique for studying the brain. However, in many situations, such as when data are acquired in a resting state, it is difficult to know whether the data are truly stationary or if level shifts have occurred. To this end, change-point detection in sequences of functional data is examined where the functional observations are dependent and where the distributions of change-points from multiple subjects are required. Of particular interest is the case where the change-point is an epidemic change—a change occurs and then the observations return to baseline at a later time. The case where the covariance can be decomposed as a tensor product is considered with particular attention to the power analysis for detection. This is of interest in the application to fMRI, where the estimation of a full covariance structure for the three-dimensional image is not computationally feasible. Using the developed methods, a large study of resting state fMRI data is conducted to determine whether the subjects undertaking the resting scan have nonstationarities present in their time courses. It is found that a sizeable proportion of the subjects studied are not stationary. The change-point distribution for those subjects is empirically determined, as well as its theoretical properties examined.
Convolution Neural Network With Coordinate Attention for the Automatic Detection of Pulmonary Tuberculosis Images on Chest X-Rays

Tuberculosis is a chronic respiratory infectious disease that seriously endangers human health. Diagnosis of pulmonary tuberculosis usually depend on the analysis of chest X-rays by radiologists. However, there is a certain misdiagnosis rate with time consuming. Therefore, the purpose of this study is to propose a low-cost and automatic detection method of pulmonary tuberculosis images on chest X-rays to help primary radiologists. A pulmonary tuberculosis classification algorithm based on convolution neural network is proposed, which uses deep learning to classify chest X-ray images. Our method introduces coordinate attention mechanism into convolutional neural network (VGG16), so that the algorithm can capture not only cross-channel information, but also direction sensing and position sensing information, in order to better identify and classify pulmonary tuberculosis images. During the training process, we use the method of transfer learning and freeze network to make the model fit faster. The performance of our method is evaluated on the public dataset of tuberculosis classification of Shenzhen Third Hospital, China. We take the average data through 5-fold cross validation: accuracy=92.73%, AUC=97.71%, precision=92.73%, recall=92.83%, F1 score =92.82%. Compared with the existing end-to-end method based on convolutional neural network (CNN), our method is superior to ConvNet, FPN + Faster RCNN and other methods. The comparison results with other methods show that our method has better accuracy, which can help radiologists make auxiliary diagnosis.
The Improvement of Sumatran Elephant (Elephas maximus sumatranus) Dung Particleboard Characteristics Using Bamboo Layering

The use of natural fibers or particles as alternative raw materials for particleboard production is essential due to the shrinking forest area. Currently, dung waste from the Sumatran elephant (Elephas maximus sumatranus) is being used as a raw material for particleboard due to its high fiber content. Although the product still has inferior mechanical and physical characteristics, it can be improved by layering bamboo. Therefore, this study aimed to enhance the mechanical and physical qualities of elephant dung particleboard by adding layers of bamboo. The particleboard constructed had three layers; namely, the face and back in the form of a bamboo layers, as well as the core, which was in the form of elephant dung. The elephant dung was evenly mixed with isocyanate adhesive using a spray gun, and the bamboo layers were coated with adhesive on one side of the surface. The sample was subjected to a hot press at a temperature of 150 °C and 30 kg/cm2 pressure for 10 min. Generally, JIS A 5908-2003 is the specification used to test the physical and mechanical properties of particleboard. During the experiment, the characteristics examined include density, moisture content, water absorption, thickness swelling, modulus of elasticity, modulus of rupture, and internal bonding, which were enhanced by using layers of bamboo. The results showed that the physical properties of the particleboard with bamboo layers were a density of 0.62–0.69 g/cm3, a moisture content of 7.87–10.35%, water absorption of 38.27–68.58%, and a thickness swelling of 10.87–30.00%, which met the minimum standards of JIS A 5908-2003. The mechanical characteristics had values for the modulus of elasticity of 1952–7282 MPa, the modulus of rupture of 20.44–68.27 MPa, and the internal bonding of 0.16–0.38 MPa, which met the JIS A 5908-2003 standard. Based on these results, the particleboard with Belangke bamboo layers was the best in this study.
Effects of Short-Term Transportation and Lairage Time on Meat Quality, CAST, CAPN1, CAPN3, HSPA1A Gene Expression Levels and Myofibrillary Structure of M. longissimus dorsi of Kıvırcık Breed Sheep

The objective of this study was to examine the effect of short term transportation and lairage time on meat quality, myofibrillary structure and calpastatin (CAST), µ-calpain (CAPN1), calpain 3 (CAPN3) and heat shock protein 70 (HSPA1A) expression levels of longissimus dorsi of Kıvırcık breed sheep (n=40). Meat quality was evaluated by muscle glycogen amounts, pH, temperature, cooking loss, water holding capacity, shear force, instrumental color, sarcomere length and organoleptic properties. Cellular changes in the muscle during the ageing process was scanned by electron microscopy and CAST, CAPN1, CAPN3, HSPA1A expression levels were measured to reveal the association on meat tenderness. As a result, positive effect of long lairage time groups was observed in the pH, glycogen, SF values and organoleptic evaluation. CAST, CAPN1, CAPN3 expression showed no difference, however, HSPA1A showed significant difference in aging process. In conclusion, genotypic differences, their effect on gene expression and protein level on meat quality should be further investigated.
Low Tucker rank tensor completion using a symmetric block coordinate descent method

Low Tucker rank tensor completion has wide applications in science and engineering. Many existing approaches dealt with the Tucker rank by unfolding matrix rank. However, unfolding a tensor to a matrix would destroy the data's original multi‐way structure, resulting in vital information loss and degraded performance. In this article, we establish a relationship between the Tucker ranks and the ranks of the factor matrices in Tucker decomposition. Then, we reformulate the low Tucker rank tensor completion problem as a multilinear low rank matrix completion problem. For the reformulated problem, a symmetric block coordinate descent method is customized. For each matrix rank minimization subproblem, the classical truncated nuclear norm minimization is adopted. Furthermore, temporal characteristics in image and video data are introduced to such a model, which benefits the performance of the method. Numerical simulations illustrate the efficiency of our proposed models and methods.
A large-scale trust-region approach to the regularization of discrete ill-posed problems

We consider the problem of computing the solution of large-scale discrete ill-posed problems when there is noise in the data. These problems arise in important areas such as seismic inversion, medical imaging and signal processing. We pose the problem as a quadratically constrained least squares problem and develop a method for the solution of such problem. Our method does not require factorization of the coefficient matrix, it has very low storage requirements and handles the high degree of singularities arising in discrete ill-posed problems. We present numerical results on test problems and an application of the method to a practical problem with real data.
Study on Antioxidant Property of Syzygium aromaticum (Clove)

This paper is aimed at evaluating the antioxidant properties of Eugenia Caryophyllata (Syzygium aromaticum), 100% aqueous and 20:80% (water and alcohol) for hydroethanolic and hydromethanolic extracts were prepared as working solutions for the studies. About 5 different antioxidant examinations of Syzygium aromaticum (Clove) extracts were carried out which include total phenolic content, antioxidant assay, free radical’s DPPH scavenging activity, scavenging hydrogen peroxide and reducing power assay, which was estimated spectrophotometrically by the use of butylated hydroxyanisole (BHA) as standard, and tocopherol used as the standard for reducing power test. Hydromethanolic extract exhibited higher free radical scavenging activity (62.12%), at the highest concentration of 1000 µg/mL followed by hydroethanolic and aqueous (58.80% and 48.32% respectively), less reducing power properties were observed with high absorbance value in the highest concentration of hydromethanolic extract (0.198 ± 0.001 A) and also hydromethanolic extract shows highest scavenging hydrogen peroxide (76.99 ± 0.09).
Channel Propagation Model for Train to Vehicle Alert System at 5.9 GHz using Dedicated Short Range Communication

The most common railroad accidents today involve collisions between trains and passenger vehicles at railroad grade crossings [1][2]. Due to the size and speed of a train, these collisions generally result in significant damage and serious injury. Despite recent efforts by projects such as Operation Lifesaver to install safety features at grade crossings, up to 80% of the United States railroad grade crossings are classified as “unprotected” with no lights, warnings, or crossing gates [2]. Further, from January to September 2012, nearly 10% of all reported vehicle accidents were a result of train-to-vehicle collisions. These collisions also accounted for nearly 95% of all reported fatalities from vehicular accidents [2]. To help provide a more rapidly deployable safety system, advanced dedicated short range communication (DSRC) systems are being developed. DSRC is an emerging technology that is currently being explored by the automotive safety industry for vehicle-to-vehicle (V2V) and vehicle-toinfrastructure (V2I) communications to provide intelligent transportation services (ITS). DSRC uses WAVE protocols and the IEEE 1609 standards. Among the many features of DSRC systems is the ability to sense and then provide an early warning of a potential collision [6]. One potential adaption for this technology is for use as a train-to-vehicle collision warning system for unprotected grade crossings. These new protocols pose an interesting opportunity for enhancing cybersecurity since terrorists will undoubtedly eventually identify these types of mass disasters as targets of opportunity. To provide a thorough channel model of the train to vehicle communication environment that is proposed above, large-scale path loss and small scale fading will both be analyzed to characterize the propagation environment. Measurements were collected at TTCI in Pueblo Colorado to measure the received signal strength in a train to vehicle communication environment. From the received signal strength, different channel models can be developed to characterize the communication environment. Documented metrics include large scale path loss, Rician small scale fading, Delay spread, and Doppler spread. An analysis of the DSRC performance based on Packet Error Rate is also included. Channel Propagation Model for Train to Vehicle Alert System at 5.9 GHz using Dedicated Short Range Communication
On the Finite Increment Calculus method for stabilizing advection‐diffusion equations, analysis and computation of the stabilization parameter

In this work we are concerned with the finite increment calculus (FIC) method. The method has been developed for efficient approximation of advection‐diffusion equations with high Péclet numbers. Since the natural application of FIC is within the framework of the FEM, we consider the BVP in a weak sense on finite dimensional spaces. Here we provide a result on existence and uniqueness of the solution as well as an error analysis. Also we propose a choice of the stabilization parameter. We test the method on some troublesome 2D problems. Copyright © 2011 John Wiley & Sons, Ltd.
EVOLUTION OF THE ACTIVATED SLUDGE COMMUNITY OF A WASTEWATER TREATMENT PLANT WITH INDUSTRIAL DISCHARGES

Stability and high microbial activity of the biological processes in wastewater treatment plants (WWTPs) based on activated sludge processes, is a key factor for the correct treatment of wastewater prior to its discharge into the environment. This can be a challenge, as WWTPs are obliged to treat all the wastewater they receive, including possible uncontrolled discharges, some containing toxic substances or poorly balanced loads to the microorganisms in charge of carrying out the wastewater treatment. In fact, the effects of these pollutants on biological treatment by activated sludge include inhibition of bacteria in the removal of organic compounds and nutrients, reduction of the efficiency of solids separation and modification of the compaction properties of the sludge, effects that can have a negative impact on the treatment processes and on the quality of WWTP effluents. The aim of this study is to describe the composition and changes of the bacterial community in a Spanish full-scale activated sludge WWTP which suffers seasonal discharges from the vegetable canning industry during a year.
No Significant Change in MRI Abnormalities or Back Pain Prevalence in the Thoraco-Lumbar Spine of Young Elite Skiers Over a 2-Year Follow-Up

Background Young athletes are at increasing risk for spinal column injuries due to overloading the spine with excessive sports activities, with potential development of complications later in life. Purpose The purpose of this 2-year follow-up study of young elite skiers and non-athletes was to investigate any potential change in the thoraco-lumbar findings on MRI and to outline any change in back pain prevalence with continuing sporting activity and age. Study Design Longitudinal cross-sectional study. Methods MRI of the thoraco-lumbar spine was performed on 30 skiers (mean age 20 years, female 43%) and 16 non-athletes (mean age 19, female 75%), available for the 2-year follow-up. The intervertebral discs were evaluated for signal, height, bulge/herniation, and additionally according to Pfirrmann classification, and the endplates were graded according to endplate defect score. Any of the following disc findings was defined as disc degenerative change: reduced signal, reduced height, bulge, or herniation. All participants answered a specific back pain questionnaire. Results No significant difference in spinal column abnormalities, nor back pain, was found between baseline and 2-year follow-up in neither skiers nor controls. There was significantly higher prevalence of disc degenerative changes in skiers (73%) than in non-athletes (44%, p=0.05). Skiers (63%) had significantly more Pfirrmann grade ≥3 discs compared to non-athletes (25%) (p=0.03). There was no significant difference in number of endplates with score ≥4 between skiers and non-athletes (50% vs 38%, p=0.40) nor in lifetime prevalence of back pain between skiers (46%) and non-athletes (40%). Conclusion There was no significant change over time of the spinal column MRI abnormalities, nor back pain prevalence, during a 2-year follow-up of skiers and non-athletes. Young skiers had significantly higher prevalence of spine abnormalities compared with non-athletes. There was no significant difference of the back pain lifetime prevalence in skiers compared with non-athletes.