Miracle-dz/UniFAR-FTUs huggingface dataset:
Information Sharing on Social Media by Multicultural Individuals: Experiential, Motivational, and Network Factors

Drawing upon social cognitive and social network theories, this study examines individual-level and network-level factors which potentially affect multicultural individuals’ information sharing behavior via social media. The data has been collected from the foreigners (multicultural individuals) who visited visa centers in three different cities in China. The proposed model tests a combined effect of past sharing experience (PE), trust (TR), perceived benefit (PB), perceived richness (RI), and information sharing attitude (ATT) on information sharing behavior (Beh). The data has been analyzed through structural equation modeling (SEM). The results illustrate PE, TR, and PB positively affects ATT which further influences Beh. Since scant studies focus on information sharing behavior of multicultural individuals, this research contributes to the IS literature, particularly to the information sharing via social media in a multicultural perspective. The results are likely to be useful guidance for practitioners and scholars intending to evaluate social media information sharing behavior.
Identification and Validation of an Immune-related Prognostic Signature for Hepatocellular Carcinoma

Background and Aims The immune system plays vital roles in hepatocellular carcinoma (HCC) initiation and progression. The present study aimed to construct an immune-gene related prognostic signature (IRPS) for predicting the prognosis of HCC patients. Methods Gene expression data were retrieved from The Cancer Genome Atlas database. The IRPS was established via least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. The prognostic values of the IRPS were further validated using the International Cancer Genome Consortium (ICGC) dataset. Results A total of 62 genes were identified as candidate immune-related prognostic genes. According to the results of Lasso and multivariate Cox regression analysis, we established an IRPS and confirmed its stability and reliability in the ICGC dataset. The IRPS was significantly associated with advanced clinicopathological characteristics. Both Cox regression analyses revealed that the IRPS could be independent risk factors influencing prognosis of HCC patients. The relationships between the IRPS and infiltration of immune cells demonstrated that the IRPS was associated with immune cell infiltration. Furthermore, a nomogram was constructed to estimate the survival probability of HCC patients. Conclusions The IRPS was effective for predicting prognosis of HCC patients, which might serve as novel prognostic and therapeutic biomarkers for HCC.
Do the Personality Traits of Teacher Candidates Predict the Perception of Self-Efficacy and Alienation from School?

Certain personality traits have been associated with effective teacher behavior for many years. In addition, in recent years, there has been a growing interest in the discussion of the evaluation of personality traits as criteria in the selection of teacher candidates. However, research-based evidence was needed on how personality traits affect teacher candidates. This study examined the relationship between teacher candidates' personality traits and their self-efficacy perceptions and their alienation from school. The research was designed in the correlational research design. The participants of the study consisted of 516 teacher candidates studying at two universities, one is in the West and the other is in the East of Turkey. Descriptive statistics and multiple linear regression analysis were used to analyze the data. The results of the research showed that the personality traits of the teacher candidates significantly predicted their self-efficacy perceptions and their alienation from the school when gender and department selection decision were controlled. Extraversion, openness, conscientiousness, and agreeableness positively predicted teachers' self-efficacy, whereas neuroticism predicted negatively. Unexpectedly, extraversion positively predicted the normlessness dimension of alienation from school. The results were discussed in the context of the evaluation of personality traits in the selection of teacher candidates and the teacher training process.
Hydroxyapatite nano-rods for the fast removal of congo red dye from aqueous solution

Adsorption rate is one of the key indicators to evaluate the adsorption performance of a nanomaterial. Herein, nanosized rod-like HAP particles were prepared via a facile hydrothermal process by using guanidine phosphate as the phosphorus source. HAP nanoparticles show a rough surface and a high BET surface area (82.94 m2 g−1), and display an excellent adsorption rate for the removal of congo red (CR) dye in aqueous solution with a maximum adsorption capacity of 337.330 mg g−1. Thus, this kind of HAP nanoparticles can be used as an efficient, fast and low-cost adsorbent for the treatment of industrial effluents contaminated with similar anionic dyes.
Building Complete Heterogeneous Systems-on-Chip in C: From Hardware Accelerators to CPUs

High-Level Synthesis (HLS) dramatically accelerates the design and verification of individual components within larger VLSI systems. With most complex Integrated Circuits (ICs) being now heterogeneous Systems-on-Chip (SoCs), HLS has been traditionally used to design the dedicated hardware accelerators such as encryption cores and Digital Signal Processing (DSP) image processing accelerators. Unfortunately, HLS is a single process (component) synthesis method. Thus, the integration of these accelerators has to be performed at the RT level (Verilog or VHDL). This implies that the system-level verification needs to be performed at lower levels of abstraction, which significantly diminishes the benefits of using HLS. To address this, this work presents a methodology to generate entire heterogeneous SoCs in C. This work introduces two main contributions that enable this: first, an automatic bus generator that generates a synthesizable behavioral description of standard on-chip buses and, second, a library of synthesizable bus interfaces that allow any component in the system to send or receive data through the bus. Moreover, this work investigates the generation of processors and interfaces (peripherals) at the behavioral level as these are important parts of any SoCs, but have long been thought not to be efficiently synthesizable using HLS. Generating complete SoCs in C has significant advantages over traditional approaches. First, it enables the generation of fast cycle-accurate simulation models of the entire SoC, making the verification faster and easier. Second, it allows completely isolating the bus implementation details from the developers’ view, allowing the change between bus protocols with only minor changes in the designers’ code. Thirdly, it allows generating different SoC variants quickly by only changing the HLS synthesis options. Experimental results highlight these benefits.
Structural basis for branched substrate selectivity in a ketoreductase from Ascaris suum

: Ketoreductases are a prominent member of the oxidoreductase family with important applications in biotechnology and metabolic engineering, providing a general method for reversible and stereoselective conversion of C  O and C − OH functional groups. As such, developing a deeper understanding of their substrate selectivity would expand our ability to engineer the enzymatic or microbial production of small-molecule targets. Here, we report the crystal structure and biochemical characterization of a mitochondrial ketoreductase AsHadh2 from Ascaris suum with preference for an α -methyl branched substrate, ( S )-3-oxo-2-methylbutyryl-CoA (OMB-CoA), compared to its linear analog, 3-oxo-butyryl-CoA (OB-CoA). From docking studies, we found that the α -methyl group appears to be stabilized by a hydrophobic pocket formed by four residues, I155, A156, I199, and M258. Using a combination of saturation mutagenesis at 14 positions surrounding the acyl-CoA substrate and in vivo screening, we identi ﬁ ed a set of mutants that alter the α -methyl group selectivity. Mutation of I155 to the smaller A or T increased the OMB-CoA:OB-CoA selectivity by four- to ﬁ ve-fold over wild-type AsHadh2. In contrast, the R213S mutation within the substrate lid lowered α -methyl group selectivity by 2.7-fold. Further characterization shows that for the most part, changes in selectivity are related to tuning the kinetic parameters for the linear OB-CoA substrate and may be related to changes in dynamics in the active site. Taken together, these studies yield insights into the recognition of alkyl substituents and the factors that impact substrate selectivity in enzymatic systems.
Compound F779-0434 causes synthetic lethality in BRCA2-deficient cancer cells by disrupting RAD52–ssDNA association

Maintenance of genomic integrity is essential for the survival of all organisms. Homologous recombination (HR) is the major pathway for high-fidelity repair of DNA double-stranded breaks (DSBs). In addition to the classic BRCA–RAD51 pathway, another secondary HR sub-pathway dependent on RAD52 has been suggested to be functioning in mammalian cells. Importantly, RAD52 has been shown to be synthetically lethal to BRCA1/2-deficient cells, rendering RAD52 to be a desirable target in cancer therapy. In the current study, we performed a structure-based virtual screening of 47 737 drug-like compounds to identify RAD52-specific inhibitors. The top ranked virtual screening hits were further characterized using molecular dynamics simulation and biochemical and cell-based assays. We found that one compound, namely, F779-0434 specifically suppressed the growth of BRCA2-deficient cells and disrupted RAD52–ssDNA interaction in vitro. This RAD52-specific inhibitor identified in the current study is a promising compound for targeted cancer therapy, and it can also be used as a probe to study the mechanisms of DNA repair in human cells.
Geometric Deep Lean Learning: Evaluation Using a Twitter Social Network

The goal of this work is to evaluate a deep learning algorithm that has been designed to predict the topological evolution of dynamic complex non-Euclidean graphs in discrete–time in which links are labeled with communicative messages. This type of graph can represent, for example, social networks or complex organisations such as the networks associated with Industry 4.0. In this paper, we first introduce the formal geometric deep lean learning algorithm in its essential form. We then propose a methodology to systematically mine the data generated in social media Twitter, which resembles these complex topologies. Finally, we present the evaluation of a geometric deep lean learning algorithm that allows for link prediction within such databases. The evaluation results show that this algorithm can provide high accuracy in the link prediction of a retweet social network.
Implementation of technologies in the construction industry: a systematic review

PurposeWith interest in modern construction methods and new technologies on the rise, construction companies globally are increasingly looking at how to embrace new ideas and engage with new approaches to do things better. A significant amount of work has been carried out investigating the use of individual technologies in the construction sector. However, there is no holistic understanding of the new and emerging technologies that have had proven benefits for construction projects. To fill this gap, this paper aims to provide a landscape of technologies that have been implemented in the construction industry and the benefits associated with their implementation.Design/methodology/approachA systematic review approach and PRISMA guidelines were used. A total of 175 articles published between 2001 and 2020 were identified and thoroughly reviewed.FindingsThe results show that a total of 26 technologies were identified from the literature, and these can be categorised into five groups in terms of their functionality in construction process, namely: (1) data acquisition, (2) analytics, (3) visualisation, (4) communication and (5) design and construction automation. Digital technologies, especially for data acquisition and visualisation, generally appear to underpin and enable innovation in many aspects of construction. Improvements in work efficiency, health and safety, productivity, quality and sustainability have been cited as being the primary benefits of using these technologies. Of these, building information modelling (BIM) appears to be the single most commonly used technology thus far. With the development of computer technology, BIM has constantly been used in combination with other technologies/tools such as unmanned aerial vehicles/systems (unmanned aerial vehicle (UAV)/UAS), geographic information systems (GIS), light detection and ranging (LiDAR) and multidimensional modelling to realise a specifically defined benefit.Practical implicationsThe findings from this review would help construction practitioners identify the types of technologies that can be implemented in different stages of construction projects to achieve desired outcomes, and thus, make appropriate decisions on technology investment and adoption. This review also suggests that to reap the full potential that these technologies offer, aside from construction companies changing their culture and business models, corresponding changes in the construction sector’s operating systems related to building regulation, education and training, as well as contracting and procurement are required.Originality/valueThis paper undertakes a comprehensive systematic review of studies on technology implementation in the construction sector published between 2001 and 2020. It is the first attempt internationally to provide a holistic picture of technologies that have been studied and implemented in construction projects.
Synthesis and characterization of starch/Na-MMT nanocomposites

Nowadays, nanocomposites have attached great attention to many applications as packaging material, flocculating agents and adsorbents due to its improved mechanical properties, selective adsorption, environmental friendly and low cost. The objective of this article is to synthesize a nanocomposite based on starch and sodium montmorillonite (Starch/Na-MMT) by solution intercalation technique with different biopolymer to nanoclay ratios (5:1, 10:1 and 10:3, w/w). The resulting nanomaterial was characterized by XRD, SEM, and FTIR analysis in order to observe intercalation of starch into nanoclay, morphological structure and interaction of MMT with the starch matrix, respectively. The interaction of MMT into starch matrix was confirmed by FTIR due to the presence of alumina silicate bonds in starch/Na-MMT spectrum such as Si-O, Al-Al-OH, and Si-O-Al bending. The XRD patterns indicated an exfoliation process for starch/Na-MMT (10:1 and 5:1 w/w) nanocomposites and an increase in interlayer spacing for starch/Na-MMT (10:3 w/w) nanocomposite. SEM analysis revealed a round shape and agglomerated flakes for Starch/Na-MMT.
Links between Climate Change Knowledge, Perception and Action: Impacts on Personal Carbon Footprint

The current understanding of determinants of climate action and mitigation behaviour is largely based on measures of climate change including concerns, attitudes and beliefs. However, few studies have shown the actual effects of external and internal drivers on citizens’ lifestyles related to climate change, particularly in terms of their carbon footprint (CF). A questionnaire (N = 845) assessing the impact of potential explanation factors for personal CF was carried out in Spain. The study showed the importance of better understanding the factors affecting citizen’s consumption and climate change mitigation policies. Internal factors were not very explicative. Knowledge was linked to clothing and perceived commitment to food, with both sectors being more directly linked to personal choices than other CF sections. Both accounted for 40% of personal emissions. Frequency of action was not shown to be significantly related to any CF section. External factors, such as income, level of studies, age and type of work, were found to be more important than internal drivers in explaining personal CF, particularly type of work, age and income, which were linked to all CF sectors but household energy. Sex was highly associated to clothing, but also significant for transport. Political orientation was not found to be linked to any section of personal CF.
Disinfection Performance against Salmonella Typhi in Water by Radio Frequency Inductive Couple Plasma System

The disinfection performance of the radio frequency inductively coupled plasma (RFICP) system against Salmonella Typhi in water was examined using continuous flow experiments. The evaluation was based on disinfection efficiency, death rate constant, disinfection yield, and energy consumption. For all experiments the electromagnetic flux generated in the plasma reactor varied from 4 to 6 W/cm 2 . The disinfection efficiency and death rate constant of Salmonella Typhi decreased with the increase of the initial number of Salmonella Typhi bacteria. The disinfection yield increased from 784 to 1889 CFU/KWh and the energy consumption decreased from 0.28 to 0.07 KWh/L with the flowrate increasing from 5 to 20 mL/minute. The flowrate is an important parameter in predicting disinfection performance against pathogenic bacteria in water to design drinking water treatment plants.
Stress and Oral Hygiene Practices among South Kashmiri Residents during COVID-19 Pandemic - A Cross-Sectional Study

Background and Objectives: Covid-19 is highly contagious disease and has today become a major public health concern in the developing countries worldwide. This study attempted to assess the knowledge, attitude, anxiety experience, perceived mental healthcare, and oral health among adult Kashmiri population during the COVID-19 pandemic. Methods: An anonymous online questionnaire was developed using Google forms to avoid person-to-person contact. The structured questionnaire consisted of questions covering several areas: sociodemographic data, knowledge, awareness, attitude about the COVID-19 , the anxiety level, oral hygiene habits during confinement, care and disinfection control behaviours in the dental environment. Results: Due to continuing conflict in Kashmir during the last 18 years there has been a phenomenal increase in psychiatric morbidity. The results reveal that the prevalence of depression was high 46.3% among Kashmiri residents who were anxious with the thought of COVID-19 virus. In our study it was also found that 66.2% of the responders had a moderate level of knowledge about being getting quarantined and 97.7% of the participants had an adequate knowledge about its preventive aspects. The anxiety levels identified in the study were high i.e. more than 70% of the participants were preoccupied with the COVID-19 pandemic over the past months. Approximately, 42.6% of the participants have been angered and distressed due to being worried about the ongoing pandemic. Oral hygiene and last visit to the dentist were found highly significant in our study i.e. approximately 36.6% of the participants had visited a dentist among which 35.2% had visited only due to consultation advise. Conclusion: Population of Kashmir had a better mean knowledge score regarding this virus but yet mental health is an integral part of overall health and quality of life. Substantial and sustainable improvements can be achieved only when a comprehensive strategy for mental health which incorporates both prevention and care elements is adopted. There is a need to intensify the awareness and address the mental health issues, and importance of oral hygiene of people during this COVID-19 pandemic.
Smoking Cessation System for Preemptive Smoking Detection

Smoking cessation is a significant challenge for many people addicted to cigarettes and tobacco. Mobile health-related research into smoking cessation is primarily focused on mobile phone data collection either using self-reporting or sensor monitoring techniques. In the past five years with the increased popularity of smartwatch devices, research has been conducted to predict smoking movements associated with smoking behaviors based on accelerometer data analyzed from the internal sensors in a user’s smartwatch. Previous smoking detection methods focused on classifying current user smoking behavior. For many users who are trying to quit smoking, this form of detection may be insufficient as the user has already relapsed. In this article, we present a smoking cessation system utilizing a smartwatch and finger sensor that is capable of detecting presmoking activities to discourage users from future smoking behavior. Presmoking activities include grabbing a pack of cigarettes or lighting a cigarette and these activities are often immediately succeeded by smoking. Therefore, through accurate detection of presmoking activities, we can alert the user before they have relapsed. Our smoking cessation system combines data from a smartwatch for gross accelerometer and gyroscope information and a wearable finger sensor for detailed finger bend-angle information. We compare the results of a smartwatch-only system with a combined smartwatch and finger sensor system to illustrate the accuracy of each system. The combined smartwatch and finger sensor system performed at an 80.6% accuracy for the classification of presmoking activities compared to 47.0% accuracy of the smartwatch-only system.
First report of an aegagropilous form of Cladophora prolifera (Cladophorales, Chlorophyta) from the lagoon of Strunjan (Gulf of Trieste, northern Adriatic)

The occurrence of unattached spherical (aegagropilous) populations of the green alga Cladophora prolifera in the marine lagoon of Strunjan (Gulf of Trieste) is reported. Species identification was based on molecular and morphological data, and the distribution, ecology and morphological features of the aegagropilous populations are described. The ball-shaped form of this species differs from the typical upright attached form found on open shores by the radial arrangement of the branches and the absence of cells and rhizoids with clear annular constrictions. The formation of the aegagropilous form of C. prolifera likely results from specific hydrodynamic conditions in the human-mediated environment of the marine lagoon, in combination with morphological development of the species. The Strunjan marine lagoon appears to be the only location in the Mediterranean Sea, or even worldwide, where aegagropilous forms of C. prolifera occur in large abundance. Based on the high associated biodiversity, we propose the inclusion of these populations to be considered in conservation management plans in the area.
Structure of Electrospray Printed Deposits for Short Spray Times

In electrospray printing, a plume of highly charged droplets is created from a conductive ink. Printing occurs by positioning a target substrate (TS) in the path of the emitted material. Here, the ink used is a colloidal dispersion consisting of nanoparticles suspended in a volatile solvent. The selection of a volatile solvent allows for rapid evaporation of the droplets in-flight to produce dry nanoparticles. A net electric charge is imparted on the emitted particles during electrospray. The interaction of this charge with the global electric field and with other charged particles/droplets governs the particles’ trajectory and determines the microstructure of the printed deposit. In this study, we characterized the structure of nanoparticle deposits printed using electrospray for deposits with low particle count. During printing, the TS was: (i) held stationary and (ii) translated with various short spray times and substrate velocities, respectively. Examination of both a static and translating TS provided fundamental insights into the printing process. Electrospray printing is capable of exerting much finer control over microstructure compared to other printing techniques. This has significant implications for the manufacturing of thin-films. [DOI: 10.1115/1.4037695]