Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Vehicle Gateway Automatic Test System Based on CANoe

As the center of network information interaction, gateway is very important for the accuracy and real-time of its information routing function. Aiming at the shortcomings of low efficiency and poor compatibility of a vehicle’s current vehicle gateway test system, based on the existing vehicle gateway test technology, a set of CANoe-based vehicle gateway automatic test system is designed. The CAPL language is used to design and write test scripts, mainly from the aspects of message routing, signal routing and diagnosis. Finally, by building the test environment, the actual vehicle gateway is used to verify the function and performance of the test system. Compared with the existing test system, the test efficiency and quality are improved. At the same time, through the manual test, the fault analysis and troubleshooting of the failed items in the automatic test are complemented and complement each other, and a good test result is obtained.
Disembodied AI and the limits to machine understanding of students' embodied interactions

The embodiment turn in the Learning Sciences has fueled growth of multimodal learning analytics to understand embodied interactions and make consequential educational decisions about students more rapidly, more accurately, and more personalized than ever before. Managing demands of complexity and speed is leading to growing reliance by education systems on disembodied artificial intelligence (dAI) programs, which, ironically, are inherently incapable of interpreting students' embodied interactions. This is fueling a potential crisis of complexity. Augmented intelligence systems offer promising avenues for managing this crisis by integrating the strengths of omnipresent dAI to detect complex patterns of student behavior from multimodal datastreams, with the strengths of humans to meaningfully interpret embodied interactions in service of consequential decision making to achieve a balance between complexity, interpretability, and accountability for allocating education resources to children.
Neuroprotective effect of Hibiscus sabdariffa extract against high glucose-induced toxicity in Neuro-2a cells and Caenorhabditis elegans

BACKGROUND: Chronic hyperglycemic conditions can activate aberrant metabolic pathways causing neurotoxicity. OBJECTIVE: This study aimed to explore the effect of ethanol extract of Hibiscus sabdariffa calyxes (HS) against high glucose-induced neurotoxicity in Neuro-2a cells and Caenorhabditis elegans. METHODS: To ascertain the neuroprotective effect, Neuro-2a cells were pre-treated with HS followed by high glucose and assessed for cell viability, reactive oxygen species (ROS) generation, alterations in mitochondrial membrane potential (ΔΨm) using confocal microscopy, Real-Time PCR, Western blot and in silico approaches for the compounds identified through LC-MS/MS analysis. Further, C. elegans were treated with HS extract in the presence of glucose and analyzed for the neuroprotective effect. RESULTS: High glucose exhibited toxicity in Neuro-2a cells by ROS generation, disrupting ΔΨm, modulating stress response and lipid metabolism genes, altering signaling proteins (AKT, JNK), and apoptosis (P53, Caspase-3). However, pre-treatment with HS extract reversed the effect and exhibited neuroprotection. Compounds including allo-Aromadendrene, and N-Feruloyltyramine were identified through LC-MS/MS analysis. Docking studies against candidate protein targets indicated that the compounds of HS extract exhibit higher docking scores and can inhibit/activate the targets. Further, HS extended the lifespan of C. elegans (CL2006) from high glucose toxicity through the downregulation of A β. CONCLUSION: Our results propose that HS with its active constituents can be considered a promising therapeutic agent to treat hyperglycemia associated neurodegenerative diseases.
Git Recognition with Incomplete GEI Based on Random Forests

Abstract. This paper proposes an incomplete GEI gait recognition method based on Random Forests. There are numerous methods exist for git recognition,but they all lead to high dimensional feature spaces. To address the problem of high dimensional feature space, we propose the use of the Random Forest algorithm to rank features' importance . In order to efficiently search throughout subspaces, we apply a backward feature elimination search strategy.This demonstrate static areas of a GEI also contain useful information.Then, we project the selected feature to a low-dimensional feature subspace via the newly proposed two-dimensional locality preserving projections (2DLPP) method.Asa sequence,we further improve the discriminative power of the extracted features. Experimental results on the CASIA gait database demonstrate the effectiveness of the proposed method.
Coded modulation based on rings of integers modulo-q. 2. Convolutional codes

The authors presents a coded modulation method based on multilevel convolutional codes defined over finite rings of integers. A previously developed theory is applied to multilevel convolutional codes. Some tables of convolutional codes defined over rings of integers modulo-q are obtained. Curves of soft- and hard-decision decoding performance are also shown.
The Role of Proteomics and Phosphoproteomics in the Discovery of Therapeutic Targets and Biomarkers in Acquired EGFR-TKI-Resistant Non-Small Cell Lung Cancer

The discovery of potent EGFR-tyrosine kinase inhibitors (EGFR-TKIs) has revolutionized the treatment of EGFR-mutated lung cancer. Despite the fact that EGFR-TKIs have yielded several significant benefits for lung cancer patients, the emergence of resistance to EGFR-TKIs has been a substantial impediment to improving treatment outcomes. Understanding the molecular mechanisms underlying resistance is crucial for the development of new treatments and biomarkers for disease progression. Together with the advancement in proteome and phosphoproteome analysis, a diverse set of key signaling pathways have been successfully identified that provide insight for the discovery of possible therapeutically targeted proteins. In this review, we highlight the proteome and phosphoproteomic analyses of non-small cell lung cancer (NSCLC) as well as the proteome analysis of biofluid specimens that associate with acquired resistance in response to different generations of EGFR-TKI. Furthermore, we present an overview of the targeted proteins and potential drugs that have been tested in clinical studies and discuss the challenges of implementing this discovery in future NSCLC treatment.
Propagation of partially incoherent light in nonlinear media via the Wigner transform method

The propagation of partially incoherent light in nonlinear media is analyzed using the Wigner transform method. The power and versatility of this approach is illustrated by several examples which clearly demonstrate how partial incoherence tends to suppress coherent instabilities by weakening the nonlinearity. In particular, it is found that the effect of partial incoherence on modulational instabilities can be described in terms of a Landau-like damping effect, which counteracts the coherent growth rate of the instability. Similarly, in the case of the self-focusing collapse instability, the nonlinear focusing effect becomes successively smaller as the coherence length of the light decreases and eventually no collapse phenomenon occurs.
Accelerated SPIRiT Parallel MR Image Reconstruction Based on Joint Sparsity and Sparsifying Transform Learning

Iterative self-consistent parallel imaging reconstruction (SPIRiT) was an autocalibrating model for parallel magnetic resonance imaging reconstruction, which is often formulated as a SPIRiT reconstruction problem with some regularization terms. Some methods based on the operator splitting and alternating direction method of multipliers (ADMM) have been employed to solve the formulated regularized SPIRiT problem. In this paper, we propose to combine the sparsifying transform learning and joint sparsity with Cartesian SPIRiT parallel magnetic resonance imaging, and solve the resulting reconstruction problem by using the variable splitting and ADMM techniques. Simulation experiments on four in vivo data sets demonstrate that the proposed algorithm achieves a better image reconstruction quality than that of other competing methods. In addition, the proposed algorithm is very suitable for graphics processing unit (GPU) parallel computing, and its accelerated version, achieved by using a GPU, is very fast, requiring only 6.7 s to reconstruct a 200 × 200 pixel image with 8 channels.
How Does Tobin’s Q Respond to Merger and Acquisition Announcements: Evidence of Listed Indian Firms

Purpose: The aim of this study is to examine the linkage between mergers and acquisition announcements and firm’s valuation taking into consideration Tobin’s q. Theoretical framework: The Q theory of investment propounded by James Tobin (1969) is suitable for the present study. According to this theory, there is a significant correlation between a company's market value and its rate of investment. Following this, indicators of the relative potential profitability of investments are the valuations that are placed in financial markets on the securities of companies as ratios to the costs of replacing their assets, which is denoted by q. Design/methodology/approach: The current investigation examines Tobin's q distribution for 140 listed firms on the Bombay Stock Exchange from 2011 to 2022. Tobin’s q is calculated for the financial service sector of the Indian economy. Findings: The results study suggests that Stock markets react significantly to the valuation of Indian firms due to M&A announcements in the category of non-banking financial companies. More specifically, 40 companies have a high Tobin’s q ratio in the full sample of firms. According to the findings, the majority of M&A announcements had no effect on the Tobin's q of the sampling companies. This indicates that the stocks of companies operating in the financial services sector are not affected by news of mergers and acquisitions. It demonstrates that the Indian capital market responded normally to information on mergers and acquisitions. Research, Practical & Social implications: The study helps financial analysts, top-level management, and stakeholders of a company to correctly evaluate the impact of mergers and acquisitions announcements on Tobin’s q of firms as a performance measurement metric. Originality/value: The value of the study can be evaluated from the fact that it investigates the relationship between mergers and acquisitions announcements and Tobin's q as a crucial parameter for corporate valuation, which is the first of its kind for emerging economies.
Detection of fretting damage in aerospace materials by thermoelectric means

Fretting is a wear phenomenon that occurs when cyclic loading causes two surfaces in intimate contact to undergo small oscillatory motions with respect to each other. During fretting, high points or asperities of the mating surfaces adhere to each other and small particles are pulled out, leaving minute, shallow pits and powdery debris. Sometimes these surface conditions are neglected, but they are important in some application such as the aerospace industry. In this research work, non-contacting and contacting thermoelectric power techniques are performed in fretted 7075-T6 and Ti-6Al-4V samples. It has been found that the contacting and non-contacting thermoelectric power measurements are associated directly with the subtle material variations such as work hardening and residual stresses due to plastic deformation produced in the fretting zone but surface topography. Therefore, both techniques could be used for a global characterization of the most relevant fretting induced effects. Potential of these techniques to monitor subsurface changes in other severe surface plastic deformation processes are clearly envisaged.
Topological and parametric optimization of gear trains

A method for automating the design of gear trains comprised of simple, compound, bevel and worm is described. The search process combines topological changes, discrete variable choices and continuous variable optimization. By combing best-first search, implicit enumeration, automated optimization invocation and gradient-based optimization, a near guarantee of the optimal solution can be made. While the combination of methods is specific to gear trains, there are aspects of the work that make it amenable to other engineering design problems. In addition, the topological and discrete modifications to the candidate solutions are specific to gear trains, but the graph grammar methodology that is adopted has been tailored to other problems. This article presents details on the rules that generate feasible gear trains, the evaluation routines used in determining the objective functions and constraints, and the interaction among the three search methods. Resulting gear trains are presented for a variety of gear problems.
Multilevel model analysis of energy extraction from a KrF laser medium by short pulses

Amplification of short pulse KrF lasers is theoretically studied by using both coherent and incoherent interaction models, which consist of five vibrational levels in the upper laser state and a single repulsive lower laser state. The multilevel models predict a significant decrease in the saturation energies for short applied laser pulses. When the laser pulse width is extremely short (≤1 ps), the higher vibrational levels in the upper KrF laser state are repumped from the lower laser state instead of being extracted by the laser pulse. Since the small‐signal gain also decreases as the applied pulse width decreases, due to mismatch between the laser pulse and the KrF gain spectrum, the energy extraction efficiencies for subpicosecond laser pulses deteriorate significantly. For longer pulse widths (≥10 ps), population repumping from the KrF(C) state increases the effective saturation energy of the KrF(B) state. A steady‐state population of ≊28% relative to the B‐state population is estimated for the C state from recent experimental results of KrF gain recovery.
Time Independent Fluids.

This class of fluids is characterized by the fact that, provided the temperature of the fluid remains constant, the shear rate depends only on the shear stress and is a single valued function of it. Newtonian fluids which have a viscosity that is independent of the shear rate are described. This behaviour is exhibited by all gases and, in general, liquids, and solutions of materials of low molecular weight. Fluids whose viscosity is a single valued function of shear rate are termed non-Newtonian fluids. Solutions or melts of high molecular weight, particular high polymers and suspensions of solids in liquids fall into this sub-group. This third subgroup described consists of fluids with Bingham plastic and plastic behaviour, shear-thinning behaviour and shear-thickening behaviour.
Risk factors associated with an outbreak of equine coronavirus at a large farm in North Carolina

Background Equine coronavirus (ECoV) leads to outbreaks with variable morbidity and mortality. Few previous reports of risk factors for infection are available in the literature. Objectives To describe unique clinical findings and risk factors for infection and development of clinical disease. Animals 135 horses on a farm affected by ECoV outbreak. Methods Retrospective cohort study. Data obtained included age, breed, gender, activity level, housing, and feed at the onset of the outbreak. Factors were evaluated for assessment of risk of infection using simple logistic regression or Fisher's exact test. Significance was set at p ≤ 0.05. Results and findings Forty-three of 54 (79.6%) horses tested on the farm were positive on fecal PCR for ECoV, and 17 horses (12.6%) developed clinical signs consistent with ECoV. Out of 17 horses in which the presence or absence of signs of colic was noted, 6 of 17 (35.3%) showed signs of colic. Three of these horses had small colon impactions, 2 of which required surgical intervention. Significant risk factors for having positive PCR results included being primarily stalled (OR 167.1, 95% CI 26.4–1719), housing next to a positive horse (OR 7.5, 95% CI 3.1–19.0), being in work (OR 26.9, 95% CI 4.6–281.9), being fed rationed hay vs. ad libitum (OR 1,558, 95% CI 130.8–15,593), and being fed alfalfa hay (OR 1,558, 95% CI 130.8–15,593). Conclusions and clinical importance This report describes risk factors for ECoV infection many of which were associated with intensive management of show horses. Clinicians should be aware that clinical signs vary and can include severe colic.
A Bayesian Approach to Graphical Record Linkage and Deduplication

ABSTRACT We propose an unsupervised approach for linking records across arbitrarily many files, while simultaneously detecting duplicate records within files. Our key innovation involves the representation of the pattern of links between records as a bipartite graph, in which records are directly linked to latent true individuals, and only indirectly linked to other records. This flexible representation of the linkage structure naturally allows us to estimate the attributes of the unique observable people in the population, calculate transitive linkage probabilities across records (and represent this visually), and propagate the uncertainty of record linkage into later analyses. Our method makes it particularly easy to integrate record linkage with post-processing procedures such as logistic regression, capture–recapture, etc. Our linkage structure lends itself to an efficient, linear-time, hybrid Markov chain Monte Carlo algorithm, which overcomes many obstacles encountered by previously record linkage approaches, despite the high-dimensional parameter space. We illustrate our method using longitudinal data from the National Long Term Care Survey and with data from the Italian Survey on Household and Wealth, where we assess the accuracy of our method and show it to be better in terms of error rates and empirical scalability than other approaches in the literature. Supplementary materials for this article are available online.
Automated Quantification of Pneumonia Infected Volume in Lung CT Images: A Comparison with Subjective Assessment of Radiologists

Objective: To help improve radiologists’ efficacy of disease diagnosis in reading computed tomography (CT) images, this study aims to investigate the feasibility of applying a modified deep learning (DL) method as a new strategy to automatically segment disease-infected regions and predict disease severity. Methods: We employed a public dataset acquired from 20 COVID-19 patients, which includes manually annotated lung and infections masks, to train a new ensembled DL model that combines five customized residual attention U-Net models to segment disease infected regions followed by a Feature Pyramid Network model to predict disease severity stage. To test the potential clinical utility of the new DL model, we conducted an observer comparison study. First, we collected another set of CT images acquired from 80 COVID-19 patients and process images using the new DL model. Second, we asked two chest radiologists to read images of each CT scan and report the estimated percentage of the disease-infected lung volume and disease severity level. Third, we also asked radiologists to rate acceptance of DL model-generated segmentation results using a 5-scale rating method. Results: Data analysis results show that agreement of disease severity classification between the DL model and radiologists is >90% in 45 testing cases. Furthermore, >73% of cases received a high rating score (≥4) from two radiologists. Conclusion: This study demonstrates the feasibility of developing a new DL model to automatically segment disease-infected regions and quantitatively predict disease severity, which may help avoid tedious effort and inter-reader variability in subjective assessment of disease severity in future clinical practice.
The search for signatures of cosmic bullets

Since its launch in 1999, the European X-ray observatory XMM-Newton has suffered, 4 times, possible micro-meteoroid impacts believed to occur in the mirror shells, scattering debris toward the focal plane. The latest event, on 2005 March 09th, caused the loss of EPIC MOS1 CCD6, as well as small damage to MOS1 CCD1. This latter defect is leaking into the whole column that passes a few pixels from the nominal target position on CCD1 and affects a significant fraction of the on-axis source PSF (though the effects can be mitigated by suitable on-board offsetting of the signal from that column). We report on our investigations looking for possible pinholes in the XMM-Newton EPIC MOS1 filter created by micro-meteoroid generated debris. New simulation code allows modelling of pinhole patterns in real data sets. We will show the comparison between simulations and specially performed in-orbit detection measurements. These allow us to define the limiting size above which pinholes can be detected and to show if XMM-Newton's filters have suffered significantly from the micro-meteoroids.
Exploring safety culture research in the construction industry

BACKGROUND: Despite numerous regulatory initiatives to improve health and safety in the construction industry, it still ranks as one of the most accident-prone industries worldwide. A dedicated focus on safety culture has been suggested as a complement to laws, regulations and management systems. OBJECTIVE: This article explores safety culture research conducted in the construction industry, with the aim to provide insight into the specific themes that tend to be in focus as well as what theoretical and methodological approaches that tend to be favored. METHODS: Searches in scientific databases were conducted twice. In a first attempt, searches resulted in 54 hits but only two articles fit the scope of the study. A revision of the search phrase resulted in 124 hits. Ultimately, 17 articles fit the scope of the study and were included. The content of the articles was analyzed and sorted thematically. RESULTS: The results show that four themes are prevalent in the existing literature: 1) unique challenges entail a need for situated applications, 2) models developed to operationalize safety culture, 3) measuring safety culture, and 4) safety management and leadership as key factors. CONCLUSION: Although research focusing on the construction industry has come to favor certain study designs and definitions of safety culture, further research may be enriched by broadening the theoretical and methodological perspectives. Specifically, researchers should conduct more in-depth qualitative studies that take the complexity of the industry into account, including the interpersonal relations between the actors involved.
Observational constraints on the early dark energy model

Dark energy can be studied by its influence on the expansion of the Universe. We investigate current constraints on early dark energy (EDE) achievable by the combined observational data from type Ia supernovae (557), baryon acoustic oscillations, the current cosmic microwave background and the observed Hubble parameter. We find that combining these data sets provides powerful constraints on early dark energy and the best fit values of the parameters in 68% and 95% confidence-level regions are: Omega(m0) = 0.2897(-0.0138-0.0194)(+0.0149+0.0207) Omega(e) = 0.0125(-0.0129-0.0129)(+0.0272+0.0381), omega(0) = -1.0415(-0.109-0.1604)(+0.0891+0.1182), and h = 0.6988(-0.0058-0.0081)(+0.0059+0.0083).