Miracle-dz/UniFAR-FTUs huggingface dataset:
Eddy Current Probability of Detection for Mid-Bore and Corner Cracks in Bolt Holes of Service Material

Fatigue cracks are prone to develop around fasteners found in multilayer aluminum structures on aging aircraft. Probability of detection (POD) studies using eddy current techniques within the bolt holes contribute to risk assessments used in evaluating the serviceability of these aircraft. Signal response to corner and mid-bore cracks by eddy current testing using standard split-D differential probes has been examined for in-service material from the horizontal stabilizer of a Hercules aircraft. Results are compared to data obtained from laboratory samples with drilled holes. Based on a logistic regression analysis, the results show no significant difference for a90/95 (90% of the cracks of size a will be detected 95% of the time) for mid-bore crack length of 0.60 mm in this study compared to 0.62 mm in earlier work on drilled holes. However, there was a substantial loss of sensitivity for corner cracks. In this study on in-service material, the a90/95 for corner cracks was 0.82 mm compared to 0.31 mm obtained from drilled holes. Decrease in sensitivity was attributed to damage at the hole’s corners of the in-service material, including rounding, folded metal, and burrs. Hole damage also significantly increased the risk of a false call.
Nature of ideal MHD instabilities as described by multi-region relaxed MHD

In this work, the stepped pressure equilibrium code (SPEC) (Hudson et al 2012 Phys. Plasmas 19 112502), which computes the equilibria of the multi-region relaxed magnetohydrodynamic energy principle (MRxMHD), has been upgraded to determine the MRxMHD stability in toroidal geometry. A theoretical formalism for SPEC is obtained by relating the second variation of the MRxMHD energy functional to the Hessian matrix, enabling the prediction of magnetohydrodynamic (MHD) linear instabilities. Negative eigenvalues of this matrix imply instability. Further, we demonstrate our method on simplified test scenarios in both tokamak and stellarator magnetic topologies, with a systematic comparison study between the marginal stability prediction of the SPEC with the ideal MHD stability code packages CAS3D and MISHKA-1.
STUDENT SATISFACTION OF ONLINE COUNSELING - A DESCRIPTIVE ANALYSIS

This paper investigates students’ satisfaction in online off-campus counseling for admission to engineering courses in the state of Madhya Pradesh, India. A survey was conducted via online at www.onlinecounselingsurvey. in during counseling process. Respondents were selected by simple random sampling procedure. The researchers used SPSS as a statistical tool to furnish data input and analysis for assessing satisfaction level of students’ towards the counseling. The results show that students and their parents highly satisfied with the process due to saving of time, money and efforts.
Audit committee characteristics and debt choice: evidence from the S&P 500

ABSTRACT This study examines the impact of audit committee (AC) characteristics on firms’ debt choice. Specifically, we focus on AC independence, the frequency of meetings, financial experts, gender diversity, and the gender of the AC chair. The sample of this study is composed of 300 firms listed on the S&P 500. To examine the impact of the composition of the AC on debt choice, we use Ordinary Least Squares (OLS) regression and Tobit regression. The empirical findings reveal that the number of independent directors, the ratio of female directors, and a female AC chair are positively associated with public debt. This finding is in line with ‘the bank monitoring substitution hypothesis’ that the AC’s mentioned features substitute for the monitoring role of the bank. On the other hand, the frequency of AC meetings and the ratio of financial experts on the AC are negatively associated with public debt. The findings of this study offer some important insights for policy implications. For instance, firms can access the public debt market by increasing the number of independent directors, improving the gender diversity of the AC, and appointing a female AC chair.
ABIDE: A Novel Scheme for Ultrasonic Echo Estimation by Combining CEEMD-SSWT Method with EM Algorithm

Ultrasonic echo estimation has played an important role in industrial non-destructive testing and analysis. The ability to estimate parameters in the ultrasonic echo model is crucial to ensure the effectiveness of practical ultrasonic testing applications. In this paper, a scheme called ABIDE for identifying both multiple noises in the echo signal and the distribution of the denoised signal is proposed for ultrasonic echo signal parameter estimation. ABIDE integrates complementary ensemble empirical mode decomposition and the synchrosqueezed wavelet transform (CEEMD-SSWT) as well as the expectation maximization (EM) algorithm. The echo signal is split into a series of IMF components and a residual with the help of CEEMD, and then these IMFs are classified into the noise-dominant part and signal-dominant part by analyzing the correlation of each IMF and the echo signal using grey relational analysis. Considering the effect of noise in the signal-dominant part, SSWT is adopted to remove the noise in the signal-dominant part. Lastly, the signal output by the SSWT algorithm is used for reconstructing a denoised signal combined with the residual from CEEMD. Considering the distribution characteristic of the denoised signal, the EM algorithm is used to estimate parameters in the ultrasonic echo model. The relative performance of the proposed scheme was evaluated on synthetic data and real-world data and then compared with the state-of-the-art methods. Simulation results on synthetic data show that ABIDE outperforms the state-of-the-art methods in parameter estimation. Physical results on real-world data show that the proposed scheme has a greater PCC value in estimating echo model parameters. This paper also shows that ABIDE requires less convergence time than competitive methods.
Influence of briefing clarity on construction projects: a fuzzy hybrid simulation approach

Abstract Predicting brief clarity and assessing its impact on project performance at an early stage of the project delivery can aid in mitigating possible cost overruns and delays. However, this is extremely challenging because of two main issues: the complexity of multiple interrelationships between factors influenced by the briefing process, and the lack of data at the early stage of the project delivery. This study proposes a hybrid simulation approach that addresses these issues and models the influence of briefing clarity on project cost and time performance. This was achieved by developing a system dynamics (SD) model of the briefing process. The initial brief clarity is predicted using a fuzzy control system. The fuzzy outcomes were compiled into the SD model, and the obtained hybrid model was validated. Finally, the model was applied to refurbishment projects to evaluate its efficiency. The implication for the refurbishment projects shows that by clearly defining the brief, it significantly reduces the detailed design and construction cost and duration. The results confirm that the proposed hybrid method can be effective in evaluating the impact of briefing clarity on project performance, particularly at an early stage where there is a lack of project data.
Transpedal and Tibiopedal Retrograde Revascularization for Peripheral Vascular Disease

Peripheral vascular disease, or peripheral artery disease (PAD), is a chronic and debilitating disease that affects millions of people worldwide. PAD is associated with abnormal arterial narrowing, specifically outside of the heart and brain. PAD is primarily observed in the legs, but it can also affect the kidneys, arms, and neck. Patients with PAD often complain of acute leg pain that occurs when walking. However, the pain resolves with rest. The phenomenon of acute pain due to narrowed arteries is known as intermittent claudication. Common symptoms of PAD include abnormal hair and nail growth, bluish skin, skin ulcers, and cold skin. Untreated and unmanaged PAD can lead to serious complications such as tissue infection or necrosis, which in turn could lead to amputation. In rare cases, PAD may cause a stroke or coronary artery disease. Among all the management options available, the endovascular approach remains the recommended and the gold standard nowadays. In this paper, we examine and analyze the transpedal and tibiopedal retrograde revascularization in PAD patients in which the conventional antegrade approach is not successful intra-operatively with emphasis on the challenges and postoperative complications. It also correlates the different studies and its outcomes with an up-to-date worldwide results.
Using Group Predictive Voltage and Frequency Regulators of Distributed Generation Plants in Cyber-Physical Power Supply Systems

The widespread use of distributed generation (DG) plants in cyber-physical power supply systems (CPPSS) requires solving the complex problem of setting their regulators. The presented study aimed to determine the performance of the group predictive voltage and frequency regulators of DG plants in CPPSS. These studies were conducted in the MatLab environment on the CPPSS models with gas turbine units and with a small-scale hydroelectric power plant. The proposed method for tuning group predictive regulators makes it possible to improve the quality control indices. The research has established that with an additional load connected, the maximum voltage dip is reduced by a factor of 3.5 compared to conventional control regulators. In addition, the time of a transient process for the generator rotor speed is decreased by a factor of 3. In the case of a short-term short circuit, predictive regulators can reduce the time of the transient process by a factor of 1.5 and the overshoot by more than 2 times. The simulation results have confirmed the efficiency of group predictive regulators when used in DG plants, i.e., improvement of the quality of control processes in various operating modes.
Data Fitting with Rational Functions: Scaled Null Space Method with Applications of Fitting Large Scale Shocks on Economic Variables and S-Parameters

Curve fitting discrete data (x, y) with a smooth function is a complex problem when faced with sharply oscillating data or when the data are very large in size. We propose a straightforward method, one that is often overlooked, to fit discrete data (s, ys) with rational functions. This method serves as a solid data fitting choice that proves to be fast and highly accurate. Its novelty lies on scaling positive explanatory data to the interval [0, 1], before solving the associated linear problem Ax = 0. A rescaling is performed once the fitting function is derived. Each solution in the null space of A provides a rational fitting function. Amongst them, the best is chosen based on a pointwise error check. This avoids solving an overdetermined nonhomogeneous linear system Ax = b with a badly conditioned and scaled matrix A. With large data, the latter can lack accuracy and be computationally expensive. Furthermore, any linear combination of at least one solution in the basis of the null space produces a new fitting function, which gives the flexibility to choose the best rational function that fits the constraints of specific problems. We tested our method with many economic variables that experienced sharp oscillations owing to the effects of COVID-19-related shocks to the economy. Such data are intrinsically difficult to fit with a smooth function. Deriving such continuous model functions over a desired period is important in the analysis and prediction algorithms of such economic variables. The method can be expanded to model behaviors of interest in other applied sciences, such as electrical engineering, where the method was successfully fitted into network scattering parameter measurements with high accuracy.
Dynamical Analysis of Firing Patterns Exhibited by Neuron Exposed to Static Direct Current Electric Field

Nowadays,noninvasive stimulation of central nervous system is a promising therapeutic option to alleviate the symptoms of a number of neurological disorders.In order to explore the effects of static direct current(DC)electric field on neuronal activity,this paper investigated the dynamic behaviors of neuron under the effects of external static DC electric field using a two-dimensional minimal model.Through bifurcation and phase plane analysis,it can be found that different responses of three classes of neurons to the same stimulation are caused by different spike initiating dynamics.This paper illustrated the effects of static DC electric field on neuron activity from the point of the dynamics of action potential initiation.It can provide some theoretical guidance for the development and application of noninvasive DC brain modulation technology.
Application of Embedded RTOS in Automotive Electronics Based on OSEK

RTOS is necessary for the complex control system,it can dispatch multi-task according to each priority,carry out task synchronization by segment or event flag,and achieve task communication by message array or mailbox mechanism,deal with urgency event by interrupt mechanism.It has better real-time and reliable performance compared with traditional front-back system.OSEK plays a leading role in international automotive electronics field.The professional RTOS RX850 of NEC is introduced,the advantages and disadvantages of RTOS is explained,the RTOS software development platform based on RX850 is constructed,the tasks dispatching of engine functions and simulation by software are completed.It is very significant for the development of complex control system.
Intrusion Detection and Identification Using Tree-Based Machine Learning Algorithms on DCS Network in the Oil Refinery

Recently, Critical Infrastructures (CI) such as energy, power, transportation, and communication have come to be increasingly dependent on advanced information and communication technology (ICT). This change has increased the connection between the Industrial Control System (ICS) supporting the CI and the Internet, resulting in an increase in security threats and allowing a malicious attacker to manipulate and control the ICS arbitrarily. On the other hand, ICS operators are reluctant to install security systems for fear of adverse effects on normal operations due to system changes. Therefore, new research is needed to detect anomalies quickly and identify attack types while ensuring the high availability of ICS. This study proposes a host-based method to detect and identify abnormalities in an Oil Refinery’s Distributed Control System (DCS) network using DCS vendor-proprietary protocols using a proposed method based on the tree-based machine learning algorithm. The results demonstrate that the proposed method can effectively detect an abnormality with the eXtreme Gradient Boosting (XGB) classifier, with up to 99% accuracy. Taken together, the results of this study contribute to the accurate detection of abnormal events and identification of attack types on the network without disrupting the normal operation of the DCS in the Oil Refinery.
sgcocaller and comapr: personalised haplotype assembly and comparative crossover map analysis using single-gamete sequencing data

Profiling gametes of an individual enables the construction of personalised haplotypes and meiotic crossover landscapes, now achievable at larger scale than ever through the availability of high-throughput single-cell sequencing technologies. However, haplotyping single gametes from high-throughput single-cell DNA sequencing datasets using existing methods requires intensive processing. Here we introduce an efficient software toolset using modern programming languages for the common tasks of haplotyping haploid gamete genomes and calling crossovers (sgcocaller), and constructing and visualising individualised crossover landscapes (comapr) from single gametes. With additional data pre-possessing, the tools can also be applied to bulk sequenced samples.
Mixed Matrix Membranes for Efficient CO2 Separation Using an Engineered UiO-66 MOF in a Pebax Polymer

Mixed matrix membranes (MMMs) have attracted significant attention for overcoming the limitations of traditional polymeric membranes for gas separation through the improvement of both permeability and selectivity. However, the development of defect-free MMMs remains challenging due to the poor compatibility of the metal–organic framework (MOF) with the polymer matrix. Thus, we report a surface-modification strategy for a MOF through grafting of a polymer with intrinsic microporosity onto the surface of UiO-66-NH2. This method allows us to engineer the MOF–polymer interface in the MMMs using Pebax as a support. The insertion of a PIM structure onto the surface of UiO-66-NH2 provides additional molecular transport channels and enhances the CO2 transport by increasing the compatibility between the polymer and fillers for efficient gas separation. As a result, MMM with 1 wt% loading of PIM-grafted-MOF (PIM-g-MOF) exhibited very promising separation performance, with CO2 permeability of 247 Barrer and CO2/N2 selectivity of 56.1, which lies on the 2008 Robeson upper bound. Moreover, this MMM has excellent anti-aging properties for up to 240 days and improved mechanical properties (yield stress of 16.08 MPa, Young’s modulus of 1.61 GPa, and 596.5% elongation at break).
Progresses in Polymer and Related Chemical Flooding for EOR

The recent progresses in polymer and related chemical flooding for EOR in China and abroad are reviewed and discussed. Three topics are covered:①polymer flooding and polymers for EOR(HPAM and xanthane); ②surfactant polymer and alkaline surfactant polymer combination flooding; ③weak polymer gels(and CDG) for flooding and reservoir permeability adjustment.
Anti‐bloater class restructuring: An exploratory study

Proper software modularization still poses challenges to developers. One of the symptoms of inappropriate modularization is the large size of object‐oriented classes. In that case, a possible solution would be class restructuring with refactorings, such as Extract Class, Extract Super‐Class, or Move Method. However, class refactoring is challenging because of the possible side effects of improper changes. In this context, more effective decision support systems on which classes are worthwhile for restructuring to improve modularity are still lacking. This work focuses on exploring possible alternatives for supporting decision on class restructuring. A prospective study was performed on selected kinds of restructuring, aiming at determining what types of strategies are typically adopted to restructure bloated classes and which classes developers decided to restructure. Then, we proposed and evaluated a predictive model for indicating which classes to restructure, aiming at delivering a restructuring guide on those classes. Finally, we conducted a qualitative study to evaluate the perception of developers on such guides based on predictions for real software. The results have shown situations in which the proposed predictions could help the restructuring process but also elucidated possible improvements and limitations.
Perceived stress, sources of stress and coping strategies among undergraduate medical students of Nepal: a cross-sectional study

Background: Medical students are subjected to various stressors throughout their training, which has a considerable impact on their physical and mental health. Some students have positive ways of coping, while others take to maladaptive coping measures. This study aims to assess severity, sources of stress, and coping strategies among medical students of a non-Western low-income country from South Asia. Methods: A self-administered questionnaire-based cross-sectional study was carried out. Demographic variables were collected and stress level was assessed using PSS 14 (Perceived Stress Scale 14). The sources of stress were assessed using MSSQ (Medical Student Stressor Questionnaire) and coping strategies were evaluated using the Brief-Coping Orientation to Problems Experienced 28. Results: The response rate was 95%. The mean PSS score was 27.85. Overall, 55% of students were stressed (male 52%, female 60%), the difference among gender was not significant. Basic science students perceived higher levels of stress than clinical clerkship students. Academic related stressors caused higher stress, whereas other variables caused moderate stress. The major stressors were examinations, inadequate time to study, poor marks, extensive amount of learning content, and the need to performing well in the exam. The five most common coping strategies used were active coping, acceptance, planning, self-distraction, and instrumental support. The least common coping mechanism was substance use. All MSSQ domains positively correlated with the total PSS score. Students with higher PSS scores were likely to use behavioral disengagement, venting, and self-blame as the primary coping strategies. Conclusions: Stress level among the medical students is high and mainly in relation to academia. Inadequate guidance from teachers contributed significantly. Stressed students were likely to use maladaptive coping strategies. Strategies to enhance teacher-student communication and adaptive coping measures should be implemented. Further studies should be done to evaluate the effects of stress on the academic outcomes of students.
Technological Uncertainty and Standardization Strategies: A Coopetition Framework

Standards may be arrived at through various coordination mechanisms, including cooperation, coopetition, or competition. This article explores how technological uncertainty affects the coordination mechanism for standardization. The article is based on the Community Innovation Survey, a sizeable firm-level survey representative of the Luxembourgish economy. The econometric analysis finds evidence that firms facing technological uncertainty will choose for standardization through competition and coopetition.
A Novel Glycolysis and Hypoxia Combined Gene Signature Predicts the Prognosis and Affects Immune Infiltration of Patients with Colon Cancer

Purpose We aimed to characterize the expression patterns of glycolysis and hypoxia genes in colon cancers as well as their value in prognosis and immune microenvironment. Methods The expression profiles were acquired from the Cancer Genome Atlas database. Enrichment of hypoxia and glycolysis gene sets in colon cancer was identified by gene set enrichment analysis. Then, a prognostic signature was built up after Cox regression analyses, and overall survival analysis validated the predictive ability. Immune status and infiltration in cancer tissues were explored using the single sample gene set enrichment analysis and CIBERSORT algorithm. A nomogram model integrating clinical variables and the gene signature was established and assessed. Results Altogether, 378 cancer and 39 control cases were enrolled. Three glycolysis gene sets and two hypoxia gene sets were enriched in colon cancer (P < 0.05). Five independent genes (ENO3, GPC1, P4HA1, SPAG4, and STC2) were significantly correlated with prognosis of colon cancer patients. Patients with higher risks had significantly better prognosis than those with lower risks (P = 0.002 and AUC = 0.750), which was also observed in the elderly, female and stage I–II subgroups (P < 0.05). In high-risk cases, proportion of NK cells resting increased (P < 0.05) while that of dendritic cells activated (P < 0.05), dendritic cells resting (P < 0.01) and monocytes (P < 0.01) decreased. Besides, expressions of 22 checkpoint genes were found abnormal in groups with different risks (P < 0.05). The predictive nomogram presented satisfactory performance with C-index of 0.771 (0.712–0.830). The area under ROC curve was 0.796 and 0.803 for 3- and 5-year survival prediction, respectively. Conclusion A glycolysis and hypoxia combined gene signature was a promising method to evaluate the prognosis and immune infiltration of colon cancer patients, which may provide a new tool for cancer management.