Miracle-dz/UniFAR-FTUs huggingface dataset:
A brief review on the spin valve magnetic tunnel junction composed of 2D materials

Two-dimensional (2D) materials including graphene, hexagonal boron nitride, and transition metal dichalcogenides have revolutionized electronic, optoelectronic and spintronic devices. Recent progress has been made in the knowledge of spin injection, detection, and manipulation utilizing spintronic devices based on 2D materials. However, some bottlenecks still need to be addressed to employ spintronic devices for logical applications. Here, we review the major advances and progress in vertical magnetic tunnel junctions (MTJs) made of various 2D materials as spacer layers between distinct ferromagnetic electrodes. Spin transportation characteristics depending on the magnetic field are investigated by considering the magnetoresistance (MR) and tunneling MR ratio in vertically stacked structures. This review examines the important features of spin transfer through the various spacer 2D materials in MTJs by carefully analyzing the temperature-dependent phenomena. The underlying physics, reliance of spin signals on temperature, quality of junction, and various other parameters are discussed in detail. Furthermore, newly discovered 2D ferromagnets introduce an entirely new type of van der Waals junction enabling effective dynamic control and spin transport across such heterojunctions. Finally, the challenges and prospects of 2D materials-based spin-valve MTJs for improving spintronic devices are discussed in detail.
A Qualitative Exploration of the Functional, Social, and Emotional Impacts of the COVID-19 Pandemic on People Who Use Drugs

Since 2020, people who use drugs (PWUD) experienced heightened risks related to drug supply disruptions, contamination, overdose, social isolation, and increased stress. This study explored how the lives of PWUD changed in Philadelphia over a one-year period. Using semi-structured interviews with 20 participants in a Housing First, low-barrier medication for opioid use (MOUD) program in Philadelphia, the effects of the first year of the COVID-19 pandemic on the daily lives, resources, functioning, substance use, and treatment of PWUD were explored. Interviews were analyzed using a combination of directed and conventional content analysis. Six overarching themes emerged during data analysis: (1) response to the pandemic; (2) access to MOUD and support services; (3) substance use; (4) impacts on mental health, physical health, and daily functioning; (5) social network impacts; and (6) fulfillment of basic needs. Participants reported disruptions in every domain of life, challenges meeting their basic needs, and elevated risk for adverse events. MOUD service providers offset some risks and provided material supports, treatment, social interaction, and emotional support. These results highlight how there were significant disruptions to the lives of PWUD during the first year of the COVID-19 pandemic and identified critical areas for future intervention and policies.
Development of Novel Residual-Dense-Attention (RDA) U-Net Network Architecture for Hepatocellular Carcinoma Segmentation

The research was based on the image recognition technology of artificial intelligence, which is expected to assist physicians in making correct decisions through deep learning. The liver dataset used in this study was derived from the open source website (LiTS) and the data provided by the Kaohsiung Chang Gung Memorial Hospital. CT images were used for organ recognition and lesion segmentation; the proposed Residual-Dense-Attention (RDA) U-Net can achieve high accuracy without the use of contrast. In this study, U-Net neural network was used to combine ResBlock in ResNet with Dense Block in DenseNet in the coder part, allowing the training to maintain the parameters while reducing the overall recognition computation time. The decoder was equipped with Attention Gates to suppress the irrelevant areas of the image while focusing on the significant features. The RDA model was used to identify and segment liver organs and lesions from CT images of the abdominal cavity, and excellent segmentation was achieved for the liver located on the left side, right side, near the heart, and near the lower abdomen with other organs. Better recognition was also achieved for large, small, and single and multiple lesions. The study was able to reduce the overall computation time by about 28% compared to other convolutions, and the accuracy of liver and lesion segmentation reached 96% and 94.8%, with IoU values of 89.5% and 87%, and AVGDIST of 0.28 and 0.80, respectively.
Global longitudinal strain and echocardiographic parameters of left ventricular geometry and systolic function in healthy adult Angolans: Effect of age and gender

Introduction: Studies on the normal echocardiographic reference values in Africans are limited. Objectives: This study aims to establish the normal left ventricular echocardiographic parameters for adult Angolans, stratified by gender and age. Methods: A cross-sectional study was performed involving healthy adults attending a diagnostic center in Luanda, Angola. The two-dimensional transthoracic echocardiography was performed according to the Guidelines of the American Society of Echocardiography and the European Association of Cardiovascular Imaging. Results: A total of 103 men (47.5%) (mean age: 39.5 ± 10,8) and 114 women (52.5%) (mean age: 43.0 ± 12,5 years) were included. Compared to men, women were older (p = 0.025) and had a significantly smaller body surface area (BSA) (p < 0.001). Left ventricular wall thickness, left ventricular end-diastolic dimension and volume (LVEDV), left ventricular mass (LVM) and LVM indexed to BSA (LVMi) were significantly lower in women (p < 0.005). LVEDD indexed to BSA and left ventricular ejection fraction (LVEF) were significantly higher in women than in men (p = 0.007), (p = 0.01), respectively. Mitral annular plane systolic excursion, LVEF by strain, and global longitudinal strain showed no gender differences. Posterior wall thickness showed a statistically significant increase in the older groups (p = 0.043). The VST, relative wall thickness, LVM, and LVMi showed no significant differences between age categories. In turn, the shortening fraction and the ejection fraction increase with age. Conclusion: For a more sensitive morphological and functional assessment of the left ventricle, it is necessary to take into account the gender and age of the individual.
Sex, Neuropsychiatric Profiles, and Caregiver Burden in Alzheimer’s Disease Dementia: A Latent Class Analysis

Background: Neuropsychiatric symptoms (NPS) in Alzheimer’s disease (AD) can be disruptive for patients and their families. Objective: We aimed to classify patients based on NPS and to explore the relationship of these classes with sex and with caregiver burden. Methods: The study cohort comprised individuals with AD dementia diagnosed at Ace Alzheimer Center in Barcelona, Spain, between 2011–2020. NPS were ascertained by using the Neuropsychiatric Inventory-Questionnaire. Latent class analysis was used to identify clusters of individuals sharing a similar NPS profile. We evaluated the caregiver burden using the Zarit Burden Interview. Multivariable regression models were used to obtain adjusted estimates of the association between sex, NPS classes, and caregiver burden. Results: A total of 1,065 patients with AD dementia and their primary caregivers were included. We classified patients into five different classes according to their NPS profile: “Affective”, “High-behavioral-disturbance”, “Negative-affect”, “Affective/deliriant”, and “Apathy”. We found that age, sex, and type of AD diagnosis differed greatly across classes. We found that patients from the “High-behavioral-disturbance” (OR = 2.56, 95% CI: 1.00–6.56), “Negative-affect” (OR = 2.72, 95% CI: 1.26–3.64), and “Affective/deliriant” (OR = 2.14, 95% CI: 1.26–3.64) classes were over two times more likely to have a female caregiver than those in “Apathy” class. These three classes were also the ones associated to the greatest caregiver burden in the adjusted analyses, which seems to explain the increased burden observed among female caregivers. Conclusion: Caregiver burden is highly dependent on the patient’s NPS profiles. Female caregivers provide care to patients that pose a greater burden, which makes them more susceptible to become overwhelmed.
Introducing Improved Transformer to Land Cover Classification Using Multispectral LiDAR Point Clouds

The use of Transformer-based networks has been proposed for the processing of general point clouds. However, there has been little research related to multispectral LiDAR point clouds that contain both spatial coordinate information and multi-wavelength intensity information. In this paper, we propose networks for multispectral LiDAR point cloud point-by-point classification based on an improved Transformer. Specifically, considering the sparseness of different regions of multispectral LiDAR point clouds, we add a bias to the Transformer to improve its ability to capture local information and construct an easy-to-implement multispectral LiDAR point cloud Transformer (MPT) classification network. The MPT network achieves 78.49% mIoU, 94.55% OA, 84.46% F1, and 0.92 Kappa on the multispectral LiDAR point cloud testing dataset. To further extract the topological relationships between points, we present a standardization set abstraction (SSA) module, which includes the global point information while considering the relationships among the local points. Based on the SSA module, we propose an advanced version called MPT+ for the point-by-point classification of multispectral LiDAR point clouds. The MPT+ network achieves 82.94% mIoU, 95.62% OA, 88.42% F1, and 0.94 Kappa on the same testing dataset. Compared with seven point-based deep learning algorithms, our proposed MPT+ achieves state-of-the-art results for several evaluation metrics.
Building Analyses from Syntactic Inference in Local Languages: An HPSG Grammar Inference System

We present a grammar inference system that leverages linguistic knowledge recorded in the form of annotations in interlinear glossed text (IGT) and in a meta-grammar engineering system (the LinGO Grammar Matrix customization system) to automatically produce machine-readable HPSG grammars. Building on prior work to handle the inference of lexical classes, stems, affixes and position classes, and preliminary work on inferring case systems and word order, we introduce an integrated grammar inference system that covers a wide range of fundamental linguistic phenomena. System development was guided by 27 geneologically and geographically diverse languages, and we test the system’s cross-linguistic generalizability on an additional 5 held-out languages, using datasets provided by field linguists. Our system out-performs three baseline systems in increasing coverage while limiting ambiguity and producing richer semantic representations, while also producing richer representations than previous work in grammar inference.
Research on the Impact of Entrepreneurship Education on Employee Creativity from the Perspective of Career Environment Based on an Intermediary Model

Based on the human capital theory and creativity component theory, this study empirically examines the direct effect of entrepreneurship education on employees' environment protection creativity in the workplace and the dual mediating effect of boundary-free mental model and organizational mobility preference based on 266 valid sample data. The results show that entrepreneurship and environmental protection education received in colleges and universities can significantly promote the improvement of employees' environment protection creativity. Borderless mental model and organizational mobility preference play an intermediary role between them. The impact of entrepreneurship education on creativity is expanded from college students to employees through the bridge of borderless career attitude, which effectively verifies the lag effect of entrepreneurship education in colleges and universities and the dual intermediary effect of borderless mental model and organizational mobility preference. It further expands the research on the impact of entrepreneurship education in colleges and universities and has certain theoretical value.
Consumption of Omega-3 and Maintenance and Incidence of Depressive Episodes: The ELSA-Brasil Study

Depression affects 264 million persons in the world, accounting for some 4.3% of the global burden of disease. Current studies indicate that the decrease in the consumption of omega-3 food sources is associated with the increasing incidence of depression. The study aims to assess the association between the consumption of omega-3 and the maintenance and incidence of depressive episodes in adults (39–64 years) and elderly adults (>65 years). This was a longitudinal study using data from the baseline and first follow-up wave of the Longitudinal Study of Adult Health (ELSA-Brasil). Depressive episodes were obtained with the Clinical Interview Schedule Revised (CIS-R), and food consumption was measured with the Food Frequency Questionnaire (FFQ). Logistic regression was used to analyze associations between the consumption of omega-3 and depressive episodes. Fatty acids from the omega-3 family showed a protective effect against the maintenance of depressive episodes. In relation to incidence, the estimates suggest that the higher the consumption of omega-3 acids, the lower the risk of developing depressive episodes, and significant associations were found between the consumption of omega-3 and alpha-linolenic acid. Dietary consumption of omega-3, DHA, EPA, DPA, and alpha linolenic fatty acids may have a protective effect against the maintenance and incidence of depressive episodes.
Monte Carlo simulation for estimating rare event probabilities and parameters in Markov process models

Markov processes models appear in many different fields, for example in queueing theory, epidemic models, stochastic kinetics of chemical reactions or financial engineering. Often, simple algorithms exist to generate sample paths of this processes. This makes analysis based on simulation, so called Monte Carlo methods, very attractive. We study two specific estimation problems in this context. First we consider a simulation technique, called importance splitting, to estimate very small entrance probabilities for Markov processes by splitting sample paths at various level surfaces of the so called importance function before reaching the set of interest. This can be done in many ways, yielding different variants of the method. In this context, we propose a new one, called fixed number of successes. We prove unbiasedness for the new and some known variants, because in many papers, the proof is based on an incorrect argument. Further, we analyze its behavior in a simplified setting, which is appropriate if the importance function is well chosen, in terms of efficiency and asymptotics in comparison to the standard variant. The main difference is that the new variant controls the precision of the estimator rather than the computational effort. Our analysis and simulation examples show that it is rather robust in terms of parameter choice and we present a two-stage procedure which also yields confidence intervals. The choice of the importance function which governs the placement
Visionary leadership, organizational trust, organizational pride, and organizational citizenship behaviour: a sequential mediation model

ABSTRACT This paper investigates the relationship between visionary leadership (VL) and organizational citizenship behaviour (OCB) in Lebanon. We particularly examined how this relationship is mediated by organizational trust (OGT) and organizational pride (OP). Data were collected from 144 Lebanese workers who completed measures of VL, OCB, OGT, and OP. Data were analysed using sequential mediation analysis. Significant relationships were found between VL and OCB, while we identified a sequential mediation of OGT and OP between VL and OCB. Thus, organizations wishing to foster OCB need to develop a clear business vision, strengthen trust, and develop practical measures of pride to ensure a high level of OCB that in turn positively impacts organizational outcomes. Through the lens of the social exchange theory (SET), this research makes an original contribution towards the role of VL in supporting OCB through developing trust and pride at the organizational level. We also provide insightful organizational recommendations related to the results.
A Comprehensive Model for the Auto-Ignition Prediction in Spark Ignition Engines Fueled With Mixtures of Gasoline and Methane-Based Fuel

The introduction of natural gas (NG) in the road transport market is proceeding through bifuel vehicles, which, endowed of a double-injection system, can run either with gasoline or with NG. A third possibility is the simultaneous combustion of NG and gasoline, called double-fuel (DF) combustion: the addition of methane to gasoline allows to run the engine with stoichiometric air even at full load, without knocking phenomena, increasing engine efficiency of about 26% and cutting pollutant emissions by 90%. The introduction of DF combustion into series production vehicles requires, however, proper engine calibration (i.e., determination of DF injection and spark timing maps), a process which is drastically shortened by the use of computer simulations (with a 0D two zone approach for in-cylinder processes). An original knock onset prediction model is here proposed to be employed in zero-dimensional simulations for knock-safe performances optimization of engines fueled by gasoline-NG mixtures or gasoline-methane mixtures. The model takes into account the negative temperature coefficient (NTC) behavior of fuels and has been calibrated using a considerable amount of knocking in-cylinder pressure cycles acquired on a Cooperative Fuel Research (CFR) engine widely varying compression ratio (CR), inlet temperature, spark advance (SA), and fuel mixture composition, thus giving the model a general validity for the simulation of naturally aspirated or supercharged engines. As a result, the auto-ignition onset is predicted with maximum and mean error of 4.5 and 1.4 crank angle degrees (CAD), respectively, which is a negligible quantity from an engine control standpoint.
Sufficient Condition-Based Stability Analysis of a Power Converter Applied Switching Transient Waveform Modification Using Kharitonov’s Theorem

The rapid switching action of power metal-oxide-semiconductor field-effect transistor (MOSFET) causes high-level electromagnetic interference (EMI) in power converters. The switching transient waveform modification method realized by closed-loop gate drive has been recognized as an effective high-frequency EMI reduction approach. However, feedback control of power MOSFET in the saturation region would introduce stability problems. This paper presents a sufficient condition-based stability analysis of all the operating points during turn-off using Kharitonov’s theorem. Firstly, a small-signal MOSFET model during turn-off was used to derive the closed-loop system transfer function. The nonlinear capacitances and the rest constant parameters of the small-signal model were determined based on the device characteristics and the expected outcome of the drain-source voltage. Then we split the turn-off switching transient into several subintervals, during which the system characteristic equation became an interval polynomial due to the nonlinear capacitances. Finally, Kharitonov’s theorem was applied in each subinterval to evaluate the stability, thereby achieving the overall system stability analysis during turn-off. Experiments were conducted to investigate the system’s stability and the results confirmed the validity of the proposed analysis. This work presents an implementable design guideline for the applied switching transient waveform modification of power converters via closed-loop gate drive.
Generation of three-dimensional body-fitted grids by solving hyperbolic and parabolic partial differential equations

Hyperbolic grid generation procedures are described which have been used in external flow simulations about complex configurations. For many practical applications a single well-ordered (i.e., structured) grid can be used to mesh an entire configuration, in other problems, composite or unstructured grid procedures are needed. Although the hyperbolic partial differential equation grid generation procedure has mainly been utilized to generate structured grids, extension of the procedure to semiunstructured grids is briefly described. Extensions of the methodology are also described using two-dimensional equations.
Alternative Versions of the Reset Test for Binary Response Index Models: A Comparative Study

Binary response index models may be affected by several forms of misspecification, which range from pure functional form problems (e.g. incorrect specification of the link function, neglected heterogeneity, heteroskedasticity) to various types of sampling issues (e.g. covariate measurement error, response misclassification, endogenous stratification, missing data). In this article we examine the ability of several versions of the RESET test to detect such misspecifications in an extensive Monte Carlo simulation study. We find that: (i) the best variants of the RESET test are clearly those based on one or two fitted powers of the response index; and (ii) the loss of power resulting from using the RESET instead of a test directed against a specific type of misspecification is very small in many cases.
A predictive control based approach to networked Wiener systems

A predictive control based approach is proposed to deal with a Wiener type system which is closed through a network. In this approach, an output feedback predictive controller is designed using delayed sensing data with a specially designed state observer. The network constraints, i.e., the network-induced delay and data packet dropout, are compensated in both the forward and backward channels by taking advantage of the characteristics of both the predictive controller and the network transmission. Stability of the closed-loop system is derived by using the separation principle and switched system theory. Simulations illustrate the validity of the proposed approach.