Miracle-dz/UniFAR-FTUs huggingface dataset:
Grounding Theory in Digital Data: A Methodological Approach for a Reflective Procedural Framework

Instead of looking for new paradigms for Digital Humanities (DH), we present Grounded Theory Methodology (GTM) as a methodological approach to frame digital research practices more reflectively. By turning to the epistemological and practical implications of digital tools like Topic Modeling and digital data sources like YouTube comments, we highlight the theoretical assumptions that are already in the game—and call for more explicitness and methodical monitoring. To explain the procedures of GTM and the proposed worth for DH, we present an example of a qualitative research project using machine learning techniques to narrow down a large scale of data to human interpretable resample. The methodically monitored resampling process provided valuable means to validly minimize the amount of data without losing a qualitative trajectory of the process itself. Defining and tracing relevant content in our original data set enabled us to find related comments and textual conversations to be analyzed further. We discuss the example iteration in two ways: Our prototype and procedure show on the one hand, how qualitative research and computational methods can be better intertwined without compromising their epistemological foundations. On the other hand, we argue for an understanding of DH as research practice, that should follow an abductive research agenda in order to ground its theories in data.
A deep learning-based building defects detection tool for sustainability monitoring

To ensure sustainability of buildings, detection of building defects is crucial. Conventional practices of defects detection from building inspection data are mostly manual and error prone. With the advancements in computer vision, imaging technology and machine learning-based tools have been developed for real-time, accurate and efficient defects detection. Deep learning (DL), which is a branch of ML is more robust in automatically retrieving elements’ semantics to detect building defects. Although DL algorithms are robust in object detection, the computational complexities and configurations of these models are high. Therefore, this study presents a process of developing a computationally inexpensive and less complicated DL model using transfer learning and Google Colab virtual machine to improve automation in building defects detection. Cracks is one of the major building defects that constraint the safety and durability of buildings thus hindering building sustainability. Building cracks images were sourced from the Internet to train the model, which was built upon You Only Look Once (YOLO) DL algorithm. To test the DL model, inspection images of five (05) buildings collected by the Facilities Management department of a University in Sydney city were used. The DL model developed using this process offers a monitoring tool to ensure the sustainability of buildings with its’ ability of detecting cracks from building inspection data in real time accurately and efficiently. Although the current model is built to detect cracks, this process can be employed to automated detection of any building defect upon providing the training images of defects.
The Design of Weldment Model Based on Improved Delaunay Triangulation Algorithm

By using Delaunay triangulation algorithm, triangulating with split-merge algorithm and the incremental insertion algorithm, improving the judgment that if the new inserted point meets the Delaunay triangulation "empty circle" criterion in synthesis algorithm, meanwhile, with the corresponding improvement of the LOP optimization algorithm, the design of weldment model in the virtual welding system has achieved good result.
A Data Driven Approach for Motion Planning of Autonomous Driving Under Complex Scenario

—To guarantee the safe and efficient motion planning of autonomous driving under dynamic traffic environment, the autonomous vehicle should be equipped with not only the optimal but also a long-term efficient policy to deal with complex scenarios. The first challenge is that to acquire the optimal planning trajectory means to sacrifice the planning efficiency. The second challenge is that most search based planning method cannot find the desired trajectory in extreme scenario (e.g. lane change in crowd scenario). In this paper, we propose a data driven approach for motion planning to solve the above challenges. We transform the lane change mission into Mixed Integer Quadratic Problem (MIQP) with logical constraints, allowing the planning module to provide feasible, safe and comfortable actions in more complex scenario. Furthermore, we propose a hierarchical learning structure with ‘ classification layer ’ and ‘ action learning layer ’ to guarantee online, fast and more generalized motion planning. Our approach’s performance is demonstrated in the simulated lane change scenario and compared with related planning method.
Characteristics and predictors of short-term mortality in decompensated cirrhotic patients with acute-on-chronic liver failure

Aim of the study We aimed to investigate the characteristics of acute-on-chronic liver failure (ACLF) and factors associated with 28-day mortality in patients with ACLF. Material and methods This prospective study included ACLF patients based on the European Association for the Study of the Liver-Chronic Liver Failure (EASL-CLIF) Consortium criteria, admitted between March 2021 and February 2022. We examined variables associated with 28-day mortality using multivariate Cox regression analysis. Results Of 326 patients admitted with acute decompensation (AD) of cirrhosis, 109 (33.44%) patients were diagnosed with ACLF (mean age 63.61 ±11.15 years, 65.14% males). Of these, 26.61%, 35.78%, and 37.61% of patients were in ACLF grades 1, 2, and 3 respectively. HCV (80.73%) was the main aetiology of cirrhosis. Upper gastrointestinal bleeding (25.69%) was the most common trigger. Kidney failure (73.39%) was the most common organ failure. The 28-day mortality rate was 66.97%. Cox regression analysis revealed that the existence of 2 (HR = 6.99, 95% CI: 2.68-18.25, p < 0.0001) or ≥ 3 (HR = 9.34, 95% CI: 3.6-24.74, p < 0.0001) organ failures, hepatic encephalopathy (HR = 2.96, 95% CI: 1.27-6.94, p = 0.01), and elevated serum bilirubin (HR = 1.03, 95% CI: 1.00-1.06, p = 0.04) were independent predictors for 28-day mortality, while shifting blood pH to the normal range was associated with a decrease in the HR of ACLF mortality (HR = 0.03, 95% CI: 0.002-0.44, p = 0.01). Conclusions ACLF has a very high 28-day mortality, which is associated with the existence of 2 or more organ failures, hepatic encephalopathy, elevated serum bilirubin, and low blood pH.
Using R Package RAMpath for Tracing SEM Path Diagrams and Conducting Complex Longitudinal Data Analysis

In this article, we introduce and demonstrate the application of a newly developed R package RAMpath for tracing path diagrams and conducting structural longitudinal data analysis. RAMpath was developed to preserve the essential features of the classic DOS version of the RAMpath program (McArdle & Boker, 1990) and ease data analysis done through structural equation modeling (SEM). The applicability of RAMpath is demonstrated through a mediation model, a MIMIC model, several latent growth curve models, a univariate latent change score model, and a bivariate latent change score model. In addition to performing regular SEM analysis, RAMpath has unique features. First, it can generate path diagrams according to a given model. Second, it can display path tracing rules through path diagrams and decompose total effects into their respective direct and indirect effects as well as decompose variances and covariances into individual bridges. Furthermore, RAMpath can fit dynamic system models automatically based on latent change scores and generate vector field plots based on results obtained from a bivariate dynamic system. RAMpath is provided as an open-source R package.
Piezoelectric Micromachined Ultrasound Transducer Technology: Recent Advances and Applications

The objective of this article is to review the recent advancement in piezoelectric micromachined ultrasound transducer (PMUT) technology and the associated piezoelectric materials, device fabrication and characterization, as well as applications. PMUT has been an active research topic since the late 1990s because of the ultrasound application needs of low cost large 2D arrays, and the promising progresses on piezoelectric thin films, semiconductors, and micro/nano-electromechanical system technology. However, the industrial and medical applications of PMUTs have not been very significant until the recent success of PMUT based fingerprint sensing, which inspired growing interests in PMUT research and development. In this paper, recent advances of piezoelectric materials for PMUTs are reviewed first by analyzing the material properties and their suitability for PMUTs. PMUT structures and the associated micromachining processes are next reviewed with a focus on the complementary metal oxide semiconductor compatibility. PMUT prototypes and their applications over the last decade are then summarized to show the development trend of PMUTs. Finally, the prospective future of PMUTs is discussed as well as the challenges on piezoelectric materials, micro/nanofabrication and device integration.
Upregulation of miRNA-200c during Disease Progression in COVID-19 Patients

The COVID-19 pandemic has caused more than 6 million deaths worldwide since its first outbreak in December 2019 and continues to be a major health problem. Several studies have established that the infection by SARS-CoV-2 can be categorized in a viremic, acute and recovery or severe phase. Hyperinflammation during the acute pneumonia phase is a major cause of severe disease progression and death. Treatment of COVID-19 with directly acting antivirals is limited within a narrow window of time between first clinical symptoms and the hyperinflammatory response. Therefore, early initiation of treatment is crucial to assure optimal health care for patients. Molecular diagnostic biomarkers represent a potent tool to predict the course of disease and thus to assess the optimal treatment regimen and time point. Here, we investigated miRNA-200c as a potential marker for the prediction of the severity of COVID-19 to preventively initiate and personalize therapeutic interventions in the future. We found that miRNA-200c correlates with the severity of disease. With retrospective analysis, however, there is no correlation with prognosis at the time of hospitalization. Our study provides the basis for further evaluation of miRNA-200c as a predictive biomarker for the progress of COVID-19.
Social Media Influencer Promoted Sustainable Fashion: Effects of Sponsorship and Benefit Association

Abstract Increasing pro-sustainability concerns have motivated clothing and fashion brands to adopt sustainability-focused business practices and offer a sustainable product range. The influence of sponsored versus non-sponsored social media influencer (SMI) posts in promoting sustainable fashion brands was investigated in this study. Further, the moderation of the benefit association (self vs. other-oriented) of the SMI-generated sustainable fashion brand-related post was investigated in this research. Three studies were conducted. Study 1 was an exploratory social media field study, and Studies 2 and 3 were experimental. It was revealed in the results that non-sponsored SMI posts were more persuasive, leading to higher message authenticity and behavior intention. Additionally, a significant interaction effect of sponsorship status and benefit association on customer outcomes was found. The benefit association was other-oriented, leading to higher message authenticity and behavioral intention in sponsored posts. Valuable insights for sustainable fashion brands engaging in SMIs were offered by the findings. Authenticity may be stimulated by SMI posts containing other-oriented benefit associations, persuading the intention to purchase.
The Effects of Different Training Interventions on Soccer Players’ Sprints and Changes of Direction: A Network Meta-Analysis of Randomized Controlled Trials

Objective: To compare and rank the effects of different training interventions on the sprint and change of direction (COD) abilities of soccer players using a network meta-analysis. Methods: The PubMed, Cochrane, and Web of Science databases were searched for papers published up to June 2022. Inclusion criteria: (1) No distinction was made between nationality, region, or gender. No distinction was made between physical activity times or habits. Healthy and disease-free soccer players (age ≥ 18 years old) were eligible. (2) Different training methods and their combinations were used. (3) Groups of either no training or single training, or combined training were included. (4) Randomized controlled trials (RCTs) were included. (5) The outcome indicators included at least one of the following: sprint, agility, and change of direction (COD). Exclusion criteria: (1) studies in non-English were not included. (2) Individual studies, general public studies, literature review studies, qualitative studies, case studies, and studies with unclear data such as means and standard deviations were not included. (3) Studies using the same data were not included. (4) Interventions that could not be statistically analyzed because of insufficient numbers of studies were not included. RCTs that satisfied the inclusion criteria were included. Paired analyses and network meta-analyses were performed using random-effects models. The included studies were assessed using the Cochrane risk-of-bias assessment tool. The surface under the cumulative ranking curve (SUCRA) metric was used to rank the effectiveness of each treatment and identify the best treatment. Results: The network meta-analysis (NMA) included eleven RCTs with a total of two-hundred and seventy-seven participants and six interventions: plyometrics combined with sprint, agility, and resistance training (P+T+S+A), plyometrics combined with sprint and agility training (P+S+A), resisted sprinting combined with agility training (RS+A), plyometric training (P), resistance training (T), soccer skills, and a strategy training control group (C). P+T+S+A ranked highest in terms of improving soccer players’ 10 m sprint performance (SUCRA = 70.2%) and COD (SUCRA = 75.0%). P+S+A ranked highest in terms of improving soccer players’ 20 m sprint performance (SUCRA = 69.8%). Conclusions: Based on the network meta-analysis, for combined training, P+T+S+A was more effective at improving the sprinting and COD ability of soccer players. In the single training mode, plyometric training was the most effective. To improve sprint and COD ability, P+T+S+A should be chosen. Such improvements may be expected after P+T+S+A interventions for a duration of six or more weeks. However, gender, competitive level, and other factors will affect the assessment results. Given the limitations of the above analysis, these results should be interpreted with caution.
Role of big-five personality traits in predicting behavioral intention: A case of Indian corporate bond investors

Personality traits are qualities that make a person distinctive and describe stable behavior patterns. Therefore, understanding the influence of personality traits on behavioral intention will help predict investors’ investment decisions. This study aims to assess the impact of personality traits, i.e., openness to experience, neuroticism, conscientiousness, agreeableness, and extraversion, on investors’ behavioral intentions. Moreover, it assesses the mediating effect of attitude, subjective norms, and perceived behavioral control between investors’ personality traits and behavioral intention. The study employed a structured questionnaire on a sample of 413 retail investors. Further, obtained data were empirically examined on Smart-PLS 3.3 using the PLS-SEM method. The study found that perceived behavioral control, subjective norms, and attitude positively affected behavioral intention. However, the personality traits did not influence the intention directly. Further, mediation analysis revealed that attitude and subjective norm fully mediated the relationship between extraversion, neuroticism, openness, and intention. In contrast, attitude and subjective norms did not exhibit a mediating relationship between agreeableness, conscientiousness, and intention. Finally, perceived behavioral control fully mediated the relationship between personality traits and intention, except for conscientiousness. The study contributes by extending the applicability of the theory of planned behavior by examining the impact of big-five personality traits on behavioral intention and mediating the role of the theory of planned behavior’s dimension between personality traits and intention.
Creating institutions to protect the environment: the role of Chinese central environmental inspection

ABSTRACT The working mechanism of Chinese central environmental inspection (CEI) has been evolving. Through integrating insights from the notions of institutional pillars, institutional work and the level of institutionalisation, this study constructs a framework that is suited to unravel the working mechanisms of the new CEI and to assess its potential in transforming local environmental governance regime. Through two sets of empirical evidence, we found that CEI has employed six types of institutional work to change both formal and informal institutions regarding environmental protection at local level, and has led to an increased in local governments’ political attention on environmental planning. The findings from this study make important supplement to current research on CEI, which have been dominated by evaluating CEI’s impact on curbing end-of-pipe pollutants through econometric models. Based on our findings, it is suggested that successive CEIs are required in the near future to further deepening the level of institutionalisation of environmental protection and prevent the situation regressing.
NORMS OF RANDOM MATRICES AND WIDTHS OF FINITE-DIMENSIONAL SETS

Precise orders are given for the Kolmogorov and linear widths of the unit ball of the space in the metric of for . The determination of the upper estimates is based on approximation by random objects. This method goes back to Kashin (Math. USSR Izv. 11 (1977), 317-333). The corresponding lower estimates were obtained in a previous article of the author (Vestnik Leningrad. Univ. Math. 14 (1982), 163-170). Bibliography: 12 titles.
Metasurface-Inspired Flexible Wearable MIMO Antenna Array for Wireless Body Area Network Applications and Biomedical Telemetry Devices

This article presents a sub-6GHz ISM-band flexible wearable MIMO antenna array for wireless body area networks (WBANs) and biomedical telemetry devices. The array is based on metasurface inspired technology. The antenna array consists of $2\times 2$ matrix of triangular-shaped radiation elements that were realized on 0.8 mm thick Rogers RT/duroid 5880 substrate. Radiation characteristics of the array are enhanced by isolating the surface current interaction between the individual radiators in the array. This is achieved by inserting an electromagnetic bandgap (EBG) decoupling structure between the radiating elements. The radiating elements were transformed into a metasurface by etching sub-wavelength slots inside them. The periodic arrangement of slots acts like resonant scatterers that manipulate the electromagnetic response of the surface. Results confirm that by employing the decoupling structure and sub-wavelength slots the isolation between the radiators is significantly improved ( $>34.8$ dB). Moreover, there is an improvement in the array’s fractional bandwidth, gain and the radiation efficiency. The optimized array design for operation over 5.0-6.6 GHz has an average gain and efficiency of 10 dBi and 83%, respectively. Results show that the array’s performance is not greatly affected by a certain amount of bending. In fact, the antenna maintains a gain between 8.65-10.5 dBi and the efficiency between 77-83%. The proposed MIMO antenna array is relatively compact, can be easily fabricated on one side of a dielectric material, allows easy integration with RF circuitry, is robust, and maintains its characteristics with some bending. These features make it suitable for various wearable applications and biomedical telemetry devices.
Pressure‐induced phase transitions in silicon studied by neural network‐based metadynamics simulations

We present a combination of the metadynamics method for the investigation of pressure‐induced phase transitions in solids with a neural network representation of high‐dimensional density‐functional theory (DFT) potential‐energy surfaces. In a recent illustration of the method for the complex high‐pressure phase diagram of silicon [Behler et al., Phys. Rev. Lett. 100, 185501 (2008)] we have shown that the full sequence of phases can be reconstructed by a series of subsequent simulations. In the present paper we give a detailed account of the underlying methodology and discuss the scope and limitations of the approach, which promises to be a valuable tool for the investigation of a variety of inorganic materials. The method is several orders of magnitude faster than a direct coupling of metadynamics with electronic structure calculations, while the accuracy is essentially maintained, thus providing access to extended simulations of large systems. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
A gold/platinum thermocouple reference table

The thermoelectric voltage of the gold/platinum thermocouple has been measured over the range 0–962 °C by comparison with calibrated platinum resistance thermometers. From 0 to 500 °C, the measurements were carried out in stirred liquid baths; from 660 to 964 °C, they were carried out in a pressure‐controlled sodium‐filled heat pipe furnace that provided an isothermal intercomparison environment. Measurements were also made in the metal freezing points of indium, tin, zinc, antimony, and silver, at the melting point of gallium, and at the liquidus point of the silver‐copper eutectic. By fitting the measured thermoelectric voltages to a single eighth‐degree polynomial in temperature by the method of least squares, a reference function is obtained for the Au/Pt thermocouple that provides emf as a function of temperature (ITS‐90) to within ±10 mK from 0 to 962 °C. The Au/Pt thermocouple merits serious consideration for precise temperature measurements as its stability approaches that of the high‐temperature .
Understanding the Impact of Environmentally Specific Servant Leadership on Employees’ Pro-Environmental Behaviors in the Workplace: Based on the Proactive Motivation Model

The increasingly severe environmental situation has brought challenges to the world, and organizations are aware of the importance of environmental management and are committed to changing individuals’ environmental behavior patterns. Based on the proactive motivation model, this study constructs a moderated multiple mediation model to explore the impact mechanism and boundary conditions between environmentally specific servant leadership (ESSL) and employees’ workplace pro-environmental behaviors (WPB). This study examines 440 Chinese industrial sector employees, and the linear regression method test results show that: (1) ESSL significantly promotes employees’ WPB. (2) Green self-efficacy (GSE), green organizational identity (GOI), and environmental passion (EP) act as multiple mediators between ESSL and employees’ WPB. (3) Green shared vision (GSV) positively moderates the relationship between ESSL and employees’ WPB. This study provides a new theoretical perspective on how ESSL affects employees’ WPB, which is analyzed from three aspects: “can do”, “reason to”, and “energized to”. This new mechanism reveals that leaders should transform their leadership style to that of environmental service, and focus on enhancing the employees’ GSE, GOI, and EP. Moreover, ESSL should make copious use of green strategy tools, such as GSV, to realize the above mechanism.