Miracle-dz/UniFAR-FTUs huggingface dataset:
Aries: A Semiformal Technique for Fine-Grained Bug Localization in Hardware Designs

Effective bug localization during verification is a challenging step in the development cycle of complex hardware designs. While meeting different coverage goals is possible in the verification process, yet bug localization cannot be directly related to such goals. We propose a two-step methodology to achieve fine-grained design bug localization. First, we obtain multiple error traces based on a failing property. Starting from an initial error trace, we employ model checking to generate supportive error traces that are utilized to mine important assertions. In the second step, we utilize these assertions for fine-grained design bug localization. The mapping of the assertions leads to specific regions in register transfer level descriptions that are highly probable to be the root cause of the design bug. Specifically, we devise a binning methodology to categorize multiple suspects in different bins that need to be investigated by the design engineer for arriving at the correction for corresponding bugs. Experiments on multiple designs illustrate the efficacy of the proposed methodology in comparison to previous work and state-of-the-art industrial tool.
A new nonlocal low-rank regularization method with applications to magnetic resonance image denoising

Magnetic resonance (MR) images are frequently corrupted by Rician noise during image acquisition and transmission. And it is very challenging to restore MR data because Rician noise is signal-dependent. By exploring the nonlocal self-similarity of natural images and further using the low-rank prior of the matrices formed by nonlocal similar patches for 2D data or cubes for 3D data, we propose in this paper a new nonlocal low-rank regularization (NLRR) method including an optimization model and an efficient iterative algorithm to remove Rician noise. The proposed mathematical model consists of a data fidelity term derived from a maximum a posteriori estimation and a NLRR term using the log-det function. The resulting model in terms of approximated patch/cube matrices is non-convex and non-smooth. To solve this model, we propose an alternating reweighted minimization (ARM) algorithm using the Lipschitz-continuity of the gradient of the fidelity term and the concavity of the logarithmic function in the log-det function. The subproblems of the ARM algorithm have closed-form solutions and its limit points are first-order critical points of the problem. The ARM algorithm is further integrated with a two-stage scheme to enhance the denoising performance of the proposed NLRR method. Experimental results tested on 2D and 3D MR data, including simulated and real data, show that the NLRR method outperforms existing state-of-the-art methods for removing Rician noise.
Early Emergence Phase of SARS-CoV-2 Delta Variant in Florida, US

SARS-CoV-2, the causative agent of COVID-19, emerged in late 2019. The highly contagious B.1. 617.2 (Delta) variant of concern (VOC) was first identified in October 2020 in India and subsequently disseminated worldwide, later becoming the dominant lineage in the US. Understanding the local transmission dynamics of early SARS-CoV-2 introductions may inform actionable mitigation efforts during subsequent pandemic waves. Yet, despite considerable genomic analysis of SARS-CoV-2 in the US, several gaps remain. Here, we explore the early emergence of the Delta variant in Florida, US using phylogenetic analysis of representative Florida and globally sampled genomes. We find multiple independent introductions into Florida primarily from North America and Europe, with a minority originating from Asia. These introductions led to three distinct clades that demonstrated varying relative rates of transmission and possessed five distinct substitutions that were 3–21 times more prevalent in the Florida sample as compared to the global sample. Our results underscore the benefits of routine viral genomic surveillance to monitor epidemic spread and support the need for more comprehensive genomic epidemiology studies of emerging variants. In addition, we provide a model of epidemic spread of newly emerging VOCs that can inform future public health responses.
Long-range BOTDA denoising with multi-threshold 2D discrete wavelet

Multi-threshold 2D discrete wavelet based on BayesShrink method is utilized to enhance the SNR of BOTDA. Then a 154.42 BOTDA is experimentally demonstrated with 40ns pump pulses, and the measurement uncertainty is reduced from ±1.52MHz down to ±1.03MHz.
Water Sport Tourists’ Hesitation in Decision-Making during the COVID-19 Pandemic: The Moderating Effect of Destination Image

Understanding the effect of risk factors on tourists’ hesitation in choosing a destination is vital for successful management in tourism, especially in the pandemic era. Based on protection motivation theory, this study investigated the impact of sport tourists’ perceived risk associated with COVID-19 on their hesitation to choose a tourist destination in the context of water sport tourism. Further, we examined whether the relationship changes according to the levels of tourists’ destination image. Data from a survey of water sport tourists (n = 312) were analyzed using structural equation modeling and Hayes PROCESS macro with bootstrapping procedures. Findings of this research empirically demonstrated that risk factors influence hesitation in choosing a destination. Furthermore, the moderating effect of destination image on the influences of perceived risks on hesitation was identified. The study’s theoretical and practical contributions to the sport tourism literature are also discussed.
Creating a Community of Support for National Guard and Reserve Military Families

v I Personal Introduction 1 II Literature Review 4 III Methods 17 Participants 17 Procedures 18 Instruments 19 Demographic Questionnaire 19 Deployment History 19 Social Support Questionnaire 20 Qualitative Questionnaire 21 IV Results 22 V Discussion & Conclusion 33 VI Appendices 39 VII References 57
A FACE & EYE DETECTION MODEL FOR DRIVING AN AUTOMOBILE

This paper proposes an inexpensive vision-based system to exactly identify Eyes Off target (EOR). The device has three primary components: 1) robust facial feature monitoring 2) mind pose and gaze estimation and 3) 3-D geometric reasoning to recognize EOR. Within the video stream from the camera put on the controls column, our physiques tracks facial features within the driver’s face. While using the supervised landmarks plus a 3-D face model, the device computes mind pose and gaze direction. Distracted driving is probably the primary causes of vehicle collisions within the United States. States. Passively monitoring a driver’s activities comprises the building blocks from the automobile safety system that could potentially reduce the quantity of accidents by estimating the driver’s focus of attention. Your brain pose estimation formula is robust to no rigid face deformations due to modifications in expressions. Finally, employing a 3-D geometric analysis, the device reliably detects EOR. The recommended system does not need any driver-dependent calibration or manual initialization and works instantly (25 FPS), during the day and night. Our physiques accomplished above 90% EOR precision for individuals examined situations. To validate the performance in the system in the real vehicle atmosphere, we transported out a comprehensive experimental evaluation under all types illumination conditions, facial expressions, and individuals.
Long‐term ecological research and the COVID‐19 anthropause: A window to understanding social–ecological disturbance

Abstract The period of disrupted human activity caused by the COVID‐19 pandemic, coined the “anthropause,” altered the nature of interactions between humans and ecosystems. It is uncertain how the anthropause has changed ecosystem states, functions, and feedback to human systems through shifts in ecosystem services. Here, we used an existing disturbance framework to propose new investigation pathways for coordinated studies of distributed, long‐term social‐ecological research to capture effects of the anthropause. Although it is still too early to comprehensively evaluate effects due to pandemic‐related delays in data availability and ecological response lags, we detail three case studies that show how long‐term data can be used to document and interpret changes in air and water quality and wildlife populations and behavior coinciding with the anthropause. These early findings may guide interpretations of effects of the anthropause as it interacts with other ongoing environmental changes in the future, particularly highlighting the importance of long‐term data in separating disturbance impacts from natural variation and long‐term trends. Effects of this global disturbance have local to global effects on ecosystems with feedback to social systems that may be detectable at spatial scales captured by nationally to globally distributed research networks.
Multi-Modal Domain Adaptation Variational Autoencoder for EEG-Based Emotion Recognition

Traditional electroencephalograph (EEG)-based emotion recognition requires a large number of calibration samples to build a model for a specific subject, which restricts the application of the affective brain computer interface (BCI) in practice. We attempt to use the multi-modal data from the past session to realize emotion recognition in the case of a small amount of calibration samples. To solve this problem, we propose a multi-modal domain adaptive variational autoencoder (MMDA-VAE) method, which learns shared cross-domain latent representations of the multi-modal data. Our method builds a multi-modal variational autoencoder (MVAE) to project the data of multiple modalities into a common space. Through adversarial learning and cycle-consistency regularization, our method can reduce the distribution difference of each domain on the shared latent representation layer and realize the transfer of knowledge. Extensive experiments are conducted on two public datasets, SEED and SEED-IV, and the results show the superiority of our proposed method. Our work can effectively improve the performance of emotion recognition with a small amount of labelled multi-modal data.
Secondary Metabolism Rearrangements in Linum usitatissimum L. after Biostimulation of Roots with COS Oligosaccharides from Fungal Cell Wall

In vitro culture of flax (Linum usitatissimum L.) was exposed to chitosan oligosaccharides (COS) in order to investigate the effects on the growth and secondary metabolites content in roots and shoots. COS are fragments of chitosan released from the fungal cell wall during plant–pathogen interactions. They can be perceived by the plant as pathogen-associated signals, mediating local and systemic innate immune responses. In the present study, we report a novel COS oligosaccharide fraction with a degree of polymerization (DP) range of 2–10, which was produced from fungal chitosan by a thermal degradation method and purified by an alcohol-precipitation process. COS was dissolved in hydroponic medium at two different concentrations (250 and 500 mg/L) and applied to the roots of growing flax seedlings. Our observations indicated that the growth of roots and shoots decreased markedly in COS-treated flax seedlings compared to the control. In addition, the results of a metabolomics analysis showed that COS treatment induced the accumulation of (neo)lignans locally at roots, flavones luteolin C-glycosides, and chlorogenic acid in systemic responses in the shoots of flax seedlings. These phenolic compounds have been previously reported to exhibit a strong antioxidant and antimicrobial activities. COS oligosaccharides, under the conditions applied in this study (high dose treatment with a much longer exposure time), can be used to indirectly trigger metabolic response modifications in planta, especially secondary metabolism, because during fungal pathogen attack, COS oligosaccharides are among the signals exchanged between the pathogen and host plant.
Point Cloud Upsampling Algorithm: A Systematic Review

Point cloud upsampling algorithms can improve the resolution of point clouds and generate dense and uniform point clouds, and are an important image processing technology. Significant progress has been made in point cloud upsampling research in recent years. This paper provides a comprehensive survey of point cloud upsampling algorithms. We classify existing point cloud upsampling algorithms into optimization-based methods and deep learning-based methods, and analyze the advantages and limitations of different algorithms from a modular perspective. In addition, we cover some other important issues such as public datasets and performance evaluation metrics. Finally, we conclude this survey by highlighting several future research directions and open issues that should be further addressed.
Fractional order fuzzy sliding mode controller for the quarter car with driver model and dual actuators

The ride quality and travel comfort of the passenger is based on the type of the suspension system used in the vehicle. The active suspension system is one of the good choices to reduce the vibration and enhance the travel comfort. In this study, a quarter car with integrated seat suspension and driver model (QCSD) is considered for analysis. The controllers are designed for both single actuator (SA) and dual actuator (DA). To reduce the vibration and increase the travel comfort, different types of sliding mode controllers (SMCs) such as fuzzy SMC (FSMC), fractional order SMC and fractional order FSMC (FrFSMC) are designed and simulated in the active suspension system of the QCSD. Three types of road disturbances are used to stimulate the vibration in the system. The responses of the controllers with the QCSD are compared with the passive system and existing state feedback controller. The result shows that the FrFSMC performs better than the other controllers for DA as well as SA. While comparing the DA and SA, DA performs better than SA.
Recent Progress and Potential Biomedical Applications of Electrospun Nanofibers in Regeneration of Tissues and Organs

Electrospun techniques are promising and flexible technologies to fabricate ultrafine fiber/nanofiber materials from diverse materials with unique characteristics under optimum conditions. These fabricated fibers/nanofibers via electrospinning can be easily assembled into several shapes of three-dimensional (3D) structures and can be combined with other nanomaterials. Therefore, electrospun nanofibers, with their structural and functional advantages, have gained considerable attention from scientific communities as suitable candidates in biomedical fields, such as the regeneration of tissues and organs, where they can mimic the network structure of collagen fiber in its natural extracellular matrix(es). Due to these special features, electrospinning has been revolutionized as a successful technique to fabricate such nanomaterials from polymer media. Therefore, this review reports on recent progress in electrospun nanofibers and their applications in various biomedical fields, such as bone cell proliferation, nerve regeneration, and vascular tissue, and skin tissue, engineering. The functionalization of the fabricated electrospun nanofibers with different materials furnishes them with promising properties to enhance their employment in various fields of biomedical applications. Finally, we highlight the challenges and outlooks to improve and enhance the application of electrospun nanofibers in these applications.
Integrative framework for sustainable development of the cost estimating profession

The main purpose of this study is to deve lop an integrative learning framework for the estimating profession that will help compan ies in the construction industry adapt healthier environment and management practices to r e ain experienced estimators’ knowledge and skills. This multi-dimensional framew ork considers the effect of learning environment and motivation (intrinsic and extrinsic ) practices on the retention practices and development of the estimating competencies. To develop effective framework, two phases methodology was adapted. In the first phase, 23 core estimating competencies w re identified and classified into skills, knowledge, and personal attributes and also quantified the degree by which new estimators lack each competency. A criticality matr ix was developed to prioritize estimating competencies based on the combined effec ts of the level of importance of each competency and its associated gap between the ideal and actual level of competency. The factor analysis method was applied to investigate t h correlation effects of the 23 estimating competencies and resulted in seven core estimating competency factors representing the core estimating competencies. The second phase assessed the effectiveness of ten traditional and advanced capture and transfer methods in retaining and developing each o f the seven core estimating competency factors. Also, this phase assessed the c urr nt level of development of the learning environment, and intrinsic and extrinsic m otivations practices and their effects on the improvement of estimators’ capabilities. The Structural Equation Modeling (SEM) method was employed to assess the effectiveness of each of the capture and transfer methods, and evaluates the effect of the leaning en vironment and motivations on the development of the estimators’ capabilities by anal yzing the embedded correlations between variables. As a result, capture and transfe r models for the cost estimating profession was developed showing the effective meth ods to retain each of the estimating competency factors. Furthermore, the integrative fr amework result emphasized the significant effects of the learning environment and motivation practices on sustaining the development and retention of the cost estimating co mpetencies. The findings of this study can help companies in th e construction industry assess their estimators’ capabilities and design appropriate tra ining programs for their estimators based on their specific needs. Also, the findings w ill significantly improve companies’ practices in retaining estimators’ knowledge and sk ills, and help companies adapting a healthy learning environment that facilitate learni ng and sustain knowledge transfer.
A Modified Mean Stress Criterion for Considering Size Effects on Mode I Fracture Estimation of Rounded-Tip V-Notched Polymeric Specimens

The aim of this paper is to assess the size and geometry effects on the mode I notch fracture toughness of polymeric samples containing rounded-tip V-shaped (RV) notches (V-notch with a finite radius at the notch tip). First, using a large number of fracture tests on an RV-notched Brazilian disk and semi-circular bending polymeric samples with four different sizes, the size-dependent values of the notch fracture toughness are obtained. Then, the mean stress criterion is modified for characterizing the size-dependency of notch fracture toughness in polymeric samples. The resulting modified mean stress criterion considers higher order terms of the stress field when calculating the fracture process zone length around the tip of the defect. Additionally, the critical distance rc is assumed to be associated with the specimen size and a formula containing fitting parameters is utilized for considering this trend of rc. The comparison between the values of notch fracture toughness obtained from experiments and those predicted by the modified mean stress criterion shows that the suggested approach can provide accurate estimations of size-dependent values of notch fracture toughness in polymeric specimens containing RV notches.
Effects of Extraction Methods on Phenolic Content in the Young Bamboo Culm Extracts of Bambusa beecheyana Munro

Nowadays, many studies focus on the potential of bamboo as a source of bioactive compounds and natural antioxidants for nutraceutical, pharmaceutical, and food sources. This study is a pioneering effort to determine the total phenolic content, total flavonoid content and free radical scavenging activity, as well as the phenolic identification and quantification of Bambusa beecheyana. The study was conducted by using ethanol, methanol, and water for solvent extraction by applying cold maceration, Soxhlet, and ultrasonic-assisted extraction techniques. The results showed that Soxhlet and ultrasonic-assisted Bambusa beecheyana culm extracts had an increase in the extract’s dry yield (1.13–8.81%) but a constant p-coumaric acid (4) content (0.00035 mg/g) as compared to the extracts from the cold maceration. The ultrasonic-assisted extraction method required only a small amount (250 mL) of solvent to extract the bamboo culms. A significant amount of total phenolics (107.65 ± 0.01 mg GAE/g) and flavonoids (43.89 ± 0.05 mg QE/g) were found in the Soxhlet methanol culm extract. The extract also possessed the most potent antioxidant activity with an IC50 value of 40.43 µg/mL as compared to the positive control, ascorbic acid. The UHPLC–ESI–MS/MS analysis was carried out on the Soxhlet methanol extract, ultrasonic-assisted extract at 40 min, and cold methanol extract. The analysis resulted in the putative identification of a total of five phenolics containing cinnamic acid derivatives. The two cinnamic acid derivatives, p-coumaric acid (4) and 4-methoxycinnamic acid (5), were then used as markers to quantify the concentration of both compounds in all the extracts. Both compounds were not found in the water extracts. These results revealed that the extract from Soxhlet methanol of Bambusa beecheyana could be a potential botanical source of natural antioxidants. This study provides an important chemical composition database for further preclinical research on Bambusa beecheyana.
Targeting Oxidative Stress and Endothelial Dysfunction Using Tanshinone IIA for the Treatment of Tissue Inflammation and Fibrosis

Salvia miltiorrhiza Burge (Danshen), a member of the Lamiaceae family, has been used in traditional Chinese medicine for many centuries as a valuable medicinal herb with antioxidative, anti-inflammatory, and antifibrotic potential. Several evidence-based reports have suggested that Salvia miltiorrhiza and its components prevent vascular diseases, including myocardial infarction, myocardial ischemia/reperfusion injury, arrhythmia, cardiac hypertrophy, and cardiac fibrosis. Tanshinone IIA (TanIIA), a lipophilic component of Salvia miltiorrhiza, has gained attention because of its possible preventive and curative activity against cardiovascular disorders. TanIIA, which possesses antioxidative, anti-inflammatory, and antifibrotic properties, could be a key component in the therapeutic potential of Salvia miltiorrhiza. Vascular diseases are often initiated by endothelial dysfunction, which is accompanied by vascular inflammation and fibrosis. In this review, we summarize how TanIIA suppresses tissue inflammation and fibrosis through signaling pathways such as PI3K/Akt/mTOR/eNOS, TGF-β1/Smad2/3, NF-κB, JNK/SAPK (stress-activated protein kinase)/MAPK, and ERK/Nrf2 pathways. In brief, this review illustrates the therapeutic value of TanIIA in the alleviation of oxidative stress, inflammation, and fibrosis, which are critical components of cardiovascular disorders.