Miracle-dz/UniFAR-FTUs huggingface dataset:
Incorporation of Multiwalled Carbon Nanotubes and Graphene Nanoplatelets on the Morphology and Properties of Polyethylene Terephthalate Nanocomposites

In this work, the interaction effect between polyethylene terephthalate (PET) and multiwalled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) on the morphology and thermal properties of the nanocomposites have been investigated. PET nanocomposites with the incorporation of 0.1 wt% and 0.5 wt% of MWCNTs and GNPs were prepared by the melt compounding and injection moulding method. The presence of MWCNTs and GNPs in the PET matrix was confirmed by the X-ray diffraction (XRD) technique. MWCNTs and GNPs acted as a nucleating agent which enhanced the crystallization of PET/MWCNT/GNP nanocomposites at both weight percentages. The result obtained from thermogravimetric analysis (TGA) showed that the incorporation of MWCNTs and GNPs into pure PET improved the thermal stability of the nanocomposites. The nanofillers served as efficient heat sinks which prevent thermal degradation of PET. From the fractured cross-section morphology in field emission scanning electron microscope (FESEM), the nanofillers displayed good dispersion in the PET matrix. Better dispersion distribution found in 0.1 wt% PET/MWCNTs/GNPs nanocomposites compared to 0.5 wt% PET/MWCNTs/GNPs nanocomposites which favor less mechanical and physical failures like crack, delamination, and agglomeration.
Modeling of Ionization and Conformations of Starlike Weak Polyelectrolytes

The target of this work is to study conformational properties of starlike polyelectrolytes with pH-sensitive (annealed) dissociation in salt-free solutions. We confront hybrid Monte Carlo (HMC) simulations with computationally less expensive approximate numerical self-consistent field (SCF) calculations and with analytical theories. We demonstrate when the mean-field results are reliable and their advantage over MC in terms of efficiency can be exploited and when not. In the interior of the star, where inter-arm interactions dominate over intra-arm ones, the mean-field approximation works well and SCF agrees with the MC results. Intra-arm interactions dominate at star periphery, and their role is underestimated by the mean field. Here, conformations and dissociation resemble those of linear polyelectrolytes. Consequently, the dissociation profile along the chain contour is qualitatively different between MC and SCF. Comparison of the two methods and a distinction between intra-arm and inter-arm contributi.
Social media use and anti-immigrant attitudes: evidence from a survey and automated linguistic analysis of Facebook posts

ABSTRACT Social media has a role to play in shaping the dynamic relations between immigrants and citizens. This study examines the effects of threat perceptions, consumptive and expressive use of social media, and political trust on attitudes against immigrants in Singapore. Study 1, based on a survey analysis (N = 310), suggests that symbolic but not realistic threat perception, is positively associated with anti-immigrant attitudes. The consumptive use of social media and political trust is negatively related to anti-immigrant attitudes. Moderation analyses suggest that consumptive social media use has negative consequences for individuals with increased symbolic threat perception and high political trust. But is there a correspondence between consumptive and expressive use of social media in terms of predicting prejudicial attitudes? Study 2 benchmarks the survey findings against participants’ opinion expression via Facebook posts (N = 146,332) discussing immigrants. Automated linguistic analyses reveal that self-reported survey measures correlate with the expressive use of social media for discussing immigrants. Higher anti-immigrant attitudes are associated with higher negative sentiment, anger, and swear words in discussing immigrants. The findings highlight the need to pay attention to the combined influence of social media use and individual political beliefs when analyzing intergroup relations.
Towards Sustainable Digital Innovation of SMEs from the Developing Countries in the Context of the Digital Economy and Frugal Environment

This study aims to explore the direct impact of the digital orientation, Internet of Things (IoT) and digital platforms on the sustainable digital innovation in the context of the digital economy and frugal environment. This study also investigated the mediating role of the digital platforms in these relations. The study was based on the quantitative research design and data were collected from the 397 CEOs and managing directors of Small and Medium Enterprises in Pakistan. Correlation and structural equation modeling approaches were applied for the analysis and testing of the hypotheses. Results revealed that the digital orientation, IoT and digital platform are major antecedents of the sustainable digital innovation. Results also show that the digital platforms mediate between both digital orientation-sustainable digital innovation link and IoT-sustainable digital innovation link. The rapid pace of change in the technology has forced the business organizations to think out of box and align their operational mechanism accordingly. The need for the sustainable digital innovation is a major need of the current decade for meeting the increasing demands of the society in a sustainable way. Organizations, especially SMEs, should be able to deal with these challenges and rapid technological transformations through cost effective frugal business models. The frugal innovation is an important element of sustainable digital innovation enables SMEs to reduce resources usage and waste and to enhance sustainable economic activities. In this way, they can develop and gain advantages in this highly competitive digital environment. This is the first study showing the bright harmony of the digital orientation, IoT and digital platforms for achieving the sustainable digital innovation in the rapid evolving digital economy.
Association of Maternal Dietary Patterns With Birth Weight and the Mediation of Gestational Weight Gain: A Prospective Birth Cohort

The associations among maternal diet, birth weight, and gestational weight gain are still inconclusive. This study aimed to investigate the associations between maternal dietary patterns and birth weight, and further explore whether GWG mediates these associations. A total of 3,334 pregnant women who completed a validated semi-quantitative food frequency questionnaire from the Tongji Maternal and Child Health Cohort were included. Dietary patterns were extracted by using principal component analysis. Regression models and mediation analyses were performed to explore the associations between dietary patterns and birth weight and the effects of GWG on these associations. Five dietary patterns were identified: “Beans-vegetables,” “Fish-meat-eggs,” “Nuts-whole grains,” “Organ-poultry-seafood” and “Rice-wheat-fruits. ” Only women following the “Beans-vegetables” pattern had heavier newborns (β = 47.39; 95% CI: 12.25, 82.54). Women following the “Beans-vegetables” pattern had significantly lower GWG (β = −0.7; 95% CI: −1.15, −0.25) and had a 16% lower risk of excessive GWG and 11% higher odd of adequate GWG. The association between the “Beans-vegetables” pattern and birth weight was negatively mediated by GWG. A dietary pattern enriched in beans and vegetables is beneficial for effectively controlling GWG and increasing birth weight. GWG serves. Clinical Trial Registry: This trial was registered at ClinicalTrials.gov (NCT03099837).
Introducing Machine Learning Models to Response Surface Methodologies

Traditional response surface methodology (RSM) has utilized the ordinary least squared (OLS) technique to numerically estimate the coefficients for multiple influence factors to achieve the values of the responsive factor while considering the intersection and quadratic terms of the influencers if any. With the emergence and popularization of machine learning (ML), more competitive methods has been developed which can be adopted to complement or replace the tradition RSM method, i.e. the OLS with or without the polynomial terms. In this chapter, several commonly used regression models in the ML including the improved linear models (the least absolute shrinkage and selection operator model and the generalized linear model), the decision trees family (decision trees, random forests and gradient boosting trees), the model of the neural nets, (the multi-layer perceptrons) and the support vector machine will be introduced. Those ML models will provide a more flexible way to estimate the response surface function that is difficult to be represented by a polynomial as deployed in the traditional RSM. The advantage of the ML models in predicting precise response factor values is then demonstrated by implementation on an engineering case study. The case study has shown that the various choices of the ML models can reach a more satisfactory estimation for the responsive surface function in comparison to the RSM. The GDBT has exhibited to outperform the RSM with an accuracy improvement for 50% on unseen experimental data.
Interaction between bark extract of Anadenanthera colubrina var. cebil (Griseb.) Altschul with antibiotics against methicillin-resistant Staphylococcus aureus (MRSA)

Anadenanthera colubrina var. cebil (Griseb.) Altschul, a plant often found in areas of the Caatinga in northeastern Brazil, is widely used in unconventional medicine for the treatment of infections and inflammations. Thus, the aim of the present study was to evaluate the antibacterial activity of A. colubrina bark extracts against methicillin-resistant Staphylococcus aureus (MRSA) clinical isolates, to investigate if intact and regenerated bark extracts have the same effect against MRSA and to determine the interaction between these extracts and antibiotics. The antibacterial activity was performed by the determination of the minimum inhibitory concentration (MIC) according to the Clinical and Laboratory Standards Institute (CLSI) and the interaction assay was performed by the checkerboard method. A. colubrina extracts showed bacteriostatic activity (MIC = 8-32 mg/L) against MRSA clinical strains and no difference was found in antibacterial activity between intact and regenerated barks, suggesting that even after regeneration, the barks of this species have the same antibacterial activity. Moreover, the in vitro interaction of A. colubrina extracts with ciprofloxacin or erythromycin was additive (FICI = 0.52). Thus, the bark extracts of Anadenanthera colubrina exhibit antibacterial activity and can be used alone or in combination with antibiotics against MRSA clinical isolates.
A Note on Estimating Unreported Sample Statistics for Meta-Analysis

A major challenge confronting meta-analysts seeking to synthesize existing empirical research on a given topic is the frequent failure of primary studies to fully report their sample statistics. Because such research cannot be included in a meta-analysis unless the unreported statistics can somehow be recovered, a number of methods have been devised to estimate the sample mean and standard deviation from other quantities. This note compares several recently proposed sets of estimators that rely on extrema and/or quartiles to estimate unreported statistics for any given sample. The simplest method relies on an underlying model of normality, while the more complex methods are explicitly designed to accommodate non-normality. Our empirical comparison uses a previously developed data set containing 58 samples, ranging in size from 48 to 2,528 observations, from a standard depression screening instrument, the nine-item Patient Health Questionnaire (PHQ-9). When only the median and extrema are known, we find that the estimation method based on normality yields the most accurate estimates of both the mean and standard deviation, despite the existence of asymmetry throughout the data set; and when other information is given, the normality-based estimators have accuracy comparable to that of the other estimators reviewed here. Additionally, if the sample size is unknown, the method based on normality is the only feasible approach. The simplicity of the normalitybased approach provides an added convenience for practitioners.
Mobile Genetic Elements Drive the Antibiotic Resistome Alteration in Freshwater Shrimp Aquaculture

Shrimp aquaculture environments are a natural reservoir of multiple antibiotic resistance genes (ARGs) due to the overuse of antibiotics. Nowadays, the prevalence of these kinds of emerging contaminants in shrimp aquaculture environments is still unclear. In this study, high-throughput sequencing techniques were used to analyze the distribution of ARGs and mobile genetic elements (MGEs), bacterial communities, and their correlations in water and sediment samples in two types of typical shrimp (Procambarus clarkii and Macrobrachium rosenbergii) freshwater aquaculture environments. A total of 318 ARG subtypes within 19 ARG types were detected in all the samples. The biodiversity and relative abundance of ARGs in sediment samples showed much higher levels compared to water samples from all ponds in the study area. Bacitracin (17.44–82.82%) and multidrug (8.57–49.70%) were dominant ARG types in P. clarkii ponds, while sulfonamide (26.33–39.59%) and bacitracin (12.75–37.11%) were dominant ARG types in M. rosenbergii ponds. Network analysis underlined the complex co-occurrence patterns between bacterial communities and ARGs. Proteobacteria, Cyanobacteria, and Actinobacteria exhibited a high abundance in all samples, in which C39 (OTU25355) and Hydrogenophaga (OTU162961) played important roles in the dissemination of and variation in ARGs based on their strong connections between ARGs and bacterial communities. Furthermore, pathogens (e.g., Aeromonadaceae (OTU195200) and Microbacteriaceae (OTU16033)), which were potential hosts for various ARGs, may accelerate the propagation of ARGs and be harmful to human health via horizontal gene transfer mediated by MGEs. Variation partitioning analysis further confirmed that MGEs were the most crucial contributor (74.76%) driving the resistome alteration. This study may help us to understand the non-ignorable correlations among ARGs, bacterial diversity, and MGEs in the shrimp freshwater aquaculture environments.
Prioritizing Autism Risk Genes Using Personalized Graphical Models Estimated From Single-Cell RNA-seq Data

Abstract Hundreds of autism risk genes have been reported recently, mainly based on genetic studies where these risk genes have more de novo mutations in autism subjects than healthy controls. However, as a complex disease, autism is likely associated with more risk genes and many of them may not be identifiable through de novo mutations. We hypothesize that more autism risk genes can be identified through their connections with known autism risk genes in personalized gene–gene interaction graphs. We estimate such personalized graphs using single-cell RNA sequencing (scRNA-seq) while appropriately modeling the cell dependence and possible zero-inflation in the scRNA-seq data. The sample size, which is the number of cells per individual, ranges from 891 to 1241 in our case study using scRNA-seq data in autism subjects and controls. We consider 1500 genes in our analysis. Since the number of genes is larger or comparable to the sample size, we perform penalized estimation. We score each gene’s relevance by applying a simple graph kernel smoothing method to each personalized graph. The molecular functions of the top-scored genes are related to autism diseases. For example, a candidate gene RYR2 that encodes protein ryanodine receptor 2 is involved in neurotransmission, a process that is impaired in ASD patients. While our method provides a systemic and unbiased approach to prioritize autism risk genes, the relevance of these genes needs to be further validated in functional studies. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
Electric Field Multifractal Features in the High-Latitude Ionosphere: CSES-01 Observations

In the polar ionosphere, the electric field is characterized by broadband and power law spectral densities at small/short spatio-temporal scales, which support a possible turbulent nature of the electric field fluctuations. Here, we investigate the multifractal character of the full three-dimensional electric field in the polar ionosphere as recorded on board the first Chinese Seismo-Electromagnetic Satellite (CSES-01). The results of our analysis prove a clear different degree of multifractality of the electric field fluctuations approaching either the polar cap trailing edge or the auroral region. The observed differences in the multifractal character are interpreted in terms of the different natures of the particle precipitation in the polar cap and in the auroral region. A possible link between the multifractal nature of electric field fluctuations, parallel to the geomagnetic field, and filamentation of field aligned currents (FACs) is established.
Assessing the Spatiotemporal Variability of Leaf Functional Traits and Their Drivers Across Multiple Amazon Evergreen Forest Sites: A Stochastic Parameterization Approach With Land‐Surface Modeling

Most earth system models fail to capture the seasonality of carbon fluxes in radiation‐limited tropical evergreen forests (TEF) in the Amazon. Kim et al. (2012, https://doi.org/10.1111/j.1365-2486.2011.02629.x) first statistically incorporated a light‐controlled phenology module into an ecosystem model to improve carbon flux simulations at one TEF site. However, it is not clear how their approach can be extended to other TEF sites with different climatic conditions. Here we evaluated temporal variability in plant functional traits at three different TEF sites using a data‐conditioned stochastic parameterization method. We showed that previously studied links—between seasonal photosynthetically active radiation (PAR) and the traits Vcmax25 and leaf longevity—occur across sites. We further determined that seasonal PAR could similarly drive variations in the stomatal conductance slope parameter. Differences found in temporal trait estimates among sites indicate that dynamic trait parameters cannot be applied uniformly over space, but it may be possible to extrapolate them based on climatic factors. Motivated by recent observations that physiological capacity develops as leaves mature, we built new regression models for predicting traits that not only include PAR but also an autoregressive lag term to capture observed physiological delays behind PAR‐driven phenology shifts. With our stochastic parameterization, we predicted the three sites to be carbon neutral or carbon sinks under the RCP 8.5 future climate scenario. In contrast, projections using standard static trait parameters show most of the Amazonian TEF region becoming a carbon source. We further approximated that variable traits may allow at least a third of the radiation‐limited TEF region in the Amazon to serve as a future net carbon sink.
Flux Linkage-Based Direct Model Predictive Current Control for Synchronous Machines

This article presents a flux linkage-based direct model predictive current control approach that achieves favorable performance both during steady-state and transient operation. The former is achieved by computing the optimal time instants at which a new switch position is applied to the converter. To this end, the future current behavior is not computed based on the machine inductances or inductance look-up tables; instead, flux linkage maps are utilized to predict the trajectory of the magnetic flux linkage, and subsequently of the current. This is advantageous for electric drives with noticeable magnetic nonlinearity in terms of saturation and/or cross-coupling effects. Hence, by using flux linkage maps in the prediction process, the evolution of the stator current can be calculated more accurately, enabling the controller to make better switching decisions. Moreover, the discussed predictive controller exhibits excellent dynamic performance owing to its direct control nature, i.e., the control and modulation tasks are performed in one computational stage rendering a dedicated modulation stage redundant. Three different drive systems based on permanent magnet synchronous motors are examined to demonstrate the effectiveness of the presented control approach.
Clinicopathological features and outcomes in different histologic subtypes of HPV-independent endocervical adenocarcinomas

Background: We aim to analyze the clinicopathological features and outcomes among different histologic subtypes of HPV-independent endocervical adenocarcinomas（HPVI ECAs). Methods: Forty-five HPVI ECAs, including 16 minimal deviation adenocarcinoma (MDA), 17 non-MDA type of gastric adenocarcinoma (GAS), and 12 non-GAS HPVI ECAs were studied. Data of clinical features, pathological characteristics, treatment, and outcomes were evaluated. Results: The median age of patients with GAS was 46 years old (IQR: 41.5, 59.5), with no significant difference compared to patients with non-GAS HPVI ECA (48-year-old, IQR: 40.5, 60.5) (p=0.92). Compared with non-GAS HPVI ECAs, GAS hade more common complains of vaginal watery discharge (p=0.047). GAS cases were also associated with higher clinical stage at diagnosis (P=0.016), deeper cervical stromal invasion (p=0.01), and worse 5-year progression free survival (PFS) (p=0.032). Compared with non-MDA GAS, MDA had similar clinical and pathological features and prognosis. Of note, cytology results showed a lower positivity rate for HPVI ECAs (65.2% for GAS and 60% for non-GAS HPVI ECA), and MDA had a lower positivity rate than that for non-MDA (40.0% vs 84.6%, p=0.026). Serum CA19-9 levels were significantly higher in MDA than those in non-MDA (184.5 U/ml vs 22.4U/ml, p=0.045) and non-GAS cases (184.5U/ml vs 10.6U/ml, p=0.006). Conclusions: GAS HPVI ECA had different clinical presentation with genital watery discharge compared with non-GAS HPVI ECA cases. Comparison with those of non-GAS HPVI ECAs, GAS cases were more likely to have high risk pathological factors and poorer PFS. Serum CA19-9 may be helpful for diagnosis and screening in patients with GAS, especially those with MDA.