Miracle-dz/UniFAR-FTUs huggingface dataset:
The Common European Framework of Reference for Languages Literacy of Teachers Working in Turkish Teaching Centers

The Common European Framework of Reference for Languages, which will be briefly referred to as CEFR in this article, is accepted as a reference by academics working for the Council of Europe Committee on Languages. It sets the criteria for the teaching of any language as a foreign language. It aims to assess and evaluate foreign language acquisition, foreign language teaching, and foreign language levels in the world within certain criteria. In an attempt to determine the prevalence of the literacy of the Common European Framework of Reference for Languages among 88 randomly selected teachers teaching Turkish to foreigners, this study aims to explore, based on the concepts of information literacy and academic literacy, how well the framework is known and received by the teachers. The data revealing the perceptions of the teachers towards the concept of “CEFR” were analyzed and interpreted with the content analysis method. As it can be concluded from the results of the current study, more research is needed on CEFR literacy. In addition, teachers of Turkish as a foreign language should be encouraged to develop professionally and improve their CEFR literacy. This is indeed a key step for the future of teaching Turkish as a foreign language.
Optimized Machine Learning-Based Intrusion Detection System for Fog and Edge Computing Environment

As a new paradigm, fog computing (FC) has several characteristics that set it apart from the cloud computing (CC) environment. Fog nodes and edge computing (EC) hosts have limited resources, exposing them to cyberattacks while processing large streams and sending them directly to the cloud. Intrusion detection systems (IDS) can be used to protect against cyberattacks in FC and EC environments, while the large-dimensional features in networking data make processing the massive amount of data difficult, causing lower intrusion detection efficiency. Feature selection is typically used to alleviate the curse of dimensionality and has no discernible effect on classification outcomes. This is the first study to present an Effective Seeker Optimization model in conjunction with a Machine Learning-Enabled Intrusion Detection System (ESOML-IDS) model for the FC and EC environments. The ESOML-IDS model primarily designs a new ESO-based feature selection (FS) approach to choose an optimal subset of features to identify the occurrence of intrusions in the FC and EC environment. We also applied a comprehensive learning particle swarm optimization (CLPSO) with Denoising Autoencoder (DAE) for the detection of intrusions. The development of the ESO algorithm for feature subset selection and the DAE algorithm for parameter optimization results in improved detection efficiency and effectiveness. The experimental results demonstrated the improved outcomes of the ESOML-IDS model over recent approaches.
Jasmonic Acid Boosts Physio-Biochemical Activities in Grewia asiatica L. under Drought Stress

It has been shown that jasmonic acid (JA) can alleviate drought stress. Nevertheless, there are still many questions regarding the JA-induced physiological and biochemical mechanisms that underlie the adaptation of plants to drought stress. Hence, the aim of this study was to investigate whether JA application was beneficial for the antioxidant activity, plant performance, and growth of Grewia asiatica L. Therefore, a study was conducted on G. asiatica plants aged six months, exposing them to 100% and 60% of their field capacity. A JA application was only made when the plants were experiencing moderate drought stress (average stem water potential of 1.0 MPa, considered moderate drought stress), and physiological and biochemical measures were monitored throughout the 14-day period. In contrast to untreated plants, the JA-treated plants displayed an improvement in plant growth by 15.5% and increased CO2 assimilation (AN) by 43.9% as well as stomatal conductance (GS) by 42.7% on day 3. The ascorbate peroxidase (APX), glutathione peroxidase (GPX), and superoxide dismutase (SOD) activities of drought-stressed JA-treated plants increased by 87%, 78%, and 60%, respectively, on day 3. In addition, G. asiatica plants stressed by drought accumulated 34% more phenolics and 63% more antioxidants when exposed to JA. This study aimed to understand the mechanism by which G. asiatica survives in drought conditions by utilizing the JA system.
Differentially Methylated DNA Regions and Left Ventricular Hypertrophy in African Americans: A HyperGEN Study

Left ventricular (LV) hypertrophy (LVH) is an independent risk factor for cardiovascular disease, and African Americans experience a disparate high risk of LVH. Genetic studies have identified potential candidate genes and variants related to the condition. Epigenetic modifications may continue to help unravel disease mechanisms. We used methylation and echocardiography data from 636 African Americans selected from the Hypertension Genetic Epidemiology Network (HyperGEN) to identify differentially methylated regions (DMRs) associated with LVH. DNA extracted from whole blood was assayed on Illumina Methyl450 arrays. We fit linear mixed models to examine associations between co-methylated regions and LV traits, and we then conducted single CpG analyses within significant DMRs. We identified associations between DMRs and ejection fraction (XKR6), LV internal diastolic dimension (TRAK1), LV mass index (GSE1, RPS15 A, PSMD7), and relative wall thickness (DNHD1). In single CpG analysis, CpG sites annotated to TRAK1 and DNHD1 were significant. These CpGs were not associated with LV traits in replication cohorts but the direction of effect for DNHD1 was consistent across cohorts. Of note, DNHD1, GSE1, and PSMD7 may contribute to cardiac structural function. Future studies should evaluate relationships between regional DNA methylation patterns and the development of LVH.
Comparative Profiling of Hot and Cold Brew Coffee Flavor Using Chromatographic and Sensory Approaches

Coffee brewing is a complex process from roasted coffee bean to beverage, playing an important role in coffee flavor quality. In this study, the effects of hot and cold brewing on the flavor profile of coffee were comprehensively investigated on the basis of chromatographic and sensory approaches. By applying gas chromatography–mass spectrometry and odor activity value calculation, most pyrazines showed higher contribution to the aroma profile of cold brew coffee over hot brew coffee. Using liquid chromatography, 18 differential non-volatiles were identified, most of which possessed lower levels in cold brew coffee than hot brew coffee. The sensory evaluation found higher fruitiness and lower bitterness and astringent notes in cold brew coffee than hot brew coffee, which was attributed by linalool, furfural acetate, and quercetin-3-O-(6″-O-p-coumaroyl) galactoside. This work suggested coffee brewing significantly affected its flavor profile and sensory properties.
Improved gibbs sampling for detecting mosaic structures in DNA sequence alignments

A recently proposed method for detecting mosaic structures in DNA sequence alignments is based on the combination of hidden Markov models (HMMs) with phylo- genetic trees. Inference is done in a Bayesian way by sampling the model parameters and hidden state sequences from the posterior distribution with Markov chain Monte Carlo (MCMC). In an earlier method, proposed in [1], this was effected with a nested Gibbs-within-Gibbs scheme. The present article discusses a modification of the MCMC sampling method, based on a modification of the standard forward-backward algorithm and an unnested Gibbs sampling procedure. We have tested the modified algorithm on various synthetic and real-world DNA sequence alignments, where we have observed a significant improvement in the mixing and convergence of the Markov chain. As a practical consequence, the computational costs are substantially reduced, and the predictions become more reliable.
Plasma pressure generated auroral current system: A case study

An anti‐sunward directed azimuthal pressure gradient drives meridional currents perpendicular to the magnetic field in the dusk and dawn closed field line regions. These currents are closed through the auroral ionosphere consisting of a pair of upward and downward sheet field aligned currents cross connected in the ionosphere. Using a conjunction of FAST and Polar the intensity of the up‐down sheet FAC pair was measured in the dusk sector in typical auroral conditions. The T96 field model was used to define the geometry and it predicted that field lines at the satellites were stretched to 15 RE. The steady state pressure gradient current was calculated through a surface in the magnetosphere separating the upward and downward going FAC. With the field stretching predicted by the model a typical quiet time azimuthal pressure gradient of 0.01 nPa/Re generated sufficient current to be the source of the observed auroral current loops.
Application of Microfluidic Chips in the Detection of Airborne Microorganisms

The spread of microorganisms in the air, especially pathogenic microorganisms, seriously affects people’s normal life. Therefore, the analysis and detection of airborne microorganisms is of great importance in environmental detection, disease prevention and biosafety. As an emerging technology with the advantages of integration, miniaturization and high efficiency, microfluidic chips are widely used in the detection of microorganisms in the environment, bringing development vitality to the detection of airborne microorganisms, and they have become a research highlight in the prevention and control of infectious diseases. Microfluidic chips can be used for the detection and analysis of bacteria, viruses and fungi in the air, mainly for the detection of Escherichia coli, Staphylococcus aureus, H1N1 virus, SARS-CoV-2 virus, Aspergillus niger, etc. The high sensitivity has great potential in practical detection. Here, we summarize the advances in the collection and detection of airborne microorganisms by microfluidic chips. The challenges and trends for the detection of airborne microorganisms by microfluidic chips was also discussed. These will support the role of microfluidic chips in the prevention and control of air pollution and major outbreaks.
Approximating Solutions of Optimization Problems via Fixed Point Techniques in Geodesic Spaces

The principal objective of this paper is to find the solution to a constrained minimization problem and the zero of the monotone operator in geodesic spaces. The basic tool in our study is a nonexpansive mapping. Further, we employ the general Picard–Mann iterative method to approximate fixed points of nonexpansive mappings under various conditions. We obtain certain theorems concerning Δ and strong convergence.
Identification of the Barriers and Key Success Factors for Renewable Energy Public-Private Partnership Projects: A Continental Analysis

The global energy demand has been increasing and posing multiple challenges across the globe, including global warming, environmental pollution, and energy-sustainability issues. Thus, multiple countries have been adopting renewable-energy (RE) sources to provide clean, reliable, affordable, and sustainable energy. Previously, a number of renewable energy projects has been delivered in the form of a public–private partnership (PPP) to take advantage of the private sector’s investment, technology advancements, and expertise. In general, renewable-energy projects are considered large-scale universal projects that involve expertise from different countries and require a clear understanding of the barriers and key success factors (KSFs) across the globe. Thus, this paper focuses on providing a comprehensive understanding of the main barriers and success factors of renewable-energy projects across the globe. For that aim, a comprehensive literature review was first carried out to identify and report on the barriers and KSFs of renewable-energy projects. This was followed by a questionnaire survey wherein the opinions of 60 experts with wide experience in RE PPPs in multiple countries were collected and analyzed. The analysis shows that political and regulatory barriers are the main risks globally. Additionally, well-prepared contract documentations and skilled and efficient parties are the KSFs. However, these factors change from one continent to another. Additionally, this paper sheds light on the difference between the public and private sectors’ perceptions on the severity of the risks and the importance of the KSFs to each sector.
Multisymplectic Geometry Method for Maxwell's Equations and Multisymplectic Scheme ∗

In this paper we discussed the self-adjointness of the Maxwell’s equations with variable coefficients" andµ. Three different Lagrangian are attained. By the Legendre transformation, a multisymplectic Bridge’s (Hamilton) form is obtained. Based on the multisymplectic structure, the multisymplectic conservation law of the system is derived and a nine-point Preissman multisymplectic scheme which preserve the multisymplectic conservation law is given for the Maxwell’s equations in an inhomogeneous, isotropic and lossless medium. At last a numerical example is illustrated.
Aerial image metrology (AIMS) based mask-model accuracy improvement for computational lithography

In this paper, we study the feasibility of direct aerial image measurements with the ZEISS AIMS® EUV tool for quantification of mask effects that impact EPE budget and OPC model accuracy. We demonstrate the application of aerial image metrology for OPC model calibration, pattern shift detection, quantitative mask metrology and for Optical process window characterization.
Amyloid-Tau-Neurodegeneration Profiles and Longitudinal Cognition in Sporadic Young-Onset Dementia

We examined amyloid-tau-neurodegeneration biomarker effects on cognition in a Southeast-Asian cohort of 84 sporadic young-onset dementia (YOD; age-at-onset <65 years) patients. They were stratified into A+N+, A– N+, and A– N– profiles via cerebrospinal fluid amyloid-β1–42 (A), phosphorylated-tau (T), MRI medial temporal atrophy (neurodegeneration– N), and confluent white matter hyperintensities cerebrovascular disease (CVD). A, T, and CVD effects on longitudinal Mini-Mental State Examination (MMSE) were evaluated. A+N+ patients demonstrated steeper MMSE decline than A– N+ (β = 1.53; p = 0.036; CI 0.15:2.92) and A– N– (β = 4.68; p = 0.001; CI 1.98:7.38) over a mean follow-up of 1.24 years. Within A– N+, T– CVD+ patients showed greater MMSE decline compared to T+CVD– patients (β = – 2.37; p = 0.030; CI – 4.41:– 0.39). A+ results in significant cognitive decline, while CVD influences longitudinal cognition in the A– sub-group.
Treatment Discontinuation Prediction in Patients With Diabetes Using a Ranking Model: Machine Learning Model Development

Treatment discontinuation (TD) is one of the major prognostic issues in diabetes care, and several models have been proposed to predict a missed appointment that may lead to TD in patients with diabetes by using binary classification models for the early detection of TD and for providing intervention support for patients. However, as binary classification models output the probability of a missed appointment occurring within a predetermined period, they are limited in their ability to estimate the magnitude of TD risk in patients with inconsistent intervals between appointments, making it difficult to prioritize patients for whom intervention support should be provided. This study aimed to develop a machine-learned prediction model that can output a TD risk score defined by the length of time until TD and prioritize patients for intervention according to their TD risk. This model included patients with diagnostic codes indicative of diabetes at the University of Tokyo Hospital between September 3, 2012, and May 17, 2014. The model was internally validated with patients from the same hospital from May 18, 2014, to January 29, 2016. The data used in this study included 7551 patients who visited the hospital after January 1, 2004, and had diagnostic codes indicative of diabetes. In particular, data that were recorded in the electronic medical records between September 3, 2012, and January 29, 2016, were used. The main outcome was the TD of a patient, which was defined as missing a scheduled clinical appointment and having no hospital visits within 3 times the average number of days between the visits of the patient and within 60 days. The TD risk score was calculated by using the parameters derived from the machine-learned ranking model. The prediction capacity was evaluated by using test data with the C-index for the performance of ranking patients, area under the receiver operating characteristic curve, and area under the precision-recall curve for discrimination, in addition to a calibration plot. The means (95% confidence limits) of the C-index, area under the receiver operating characteristic curve, and area under the precision-recall curve for the TD risk score were 0.749 (0.655, 0.823), 0.758 (0.649, 0.857), and 0.713 (0.554, 0.841), respectively. The observed and predicted probabilities were correlated with the calibration plots. A TD risk score was developed for patients with diabetes by combining a machine-learned method with electronic medical records. The score calculation can be integrated into medical records to identify patients at high risk of TD, which would be useful in supporting diabetes care and preventing TD.
Leaf Adaptation of Eurya japonica Thunb. (Pentaphylacaceae) in Coastal Area

To clarify the process of plant adaptation to coastal areas, we conducted morphological and anatomical analyses of Eurya japonica Thunb. (Pentaphylacaceae). There was no significant difference in leaf shape between the inland and coastal populations, although the leaves in coastal populations tended to be thicker. However, our anatomical analysis revealed significant differences in stomatal size and adaxial and abaxial epidermal cell sizes. The smaller stomata of the coastal population of this species were effective in reducing transpiration during gas exchange. Furthermore, the expansion of epidermal cells could be an adaptive strategy to retain water in leaves in coastal environments.
An Ultralow Power LixTiO2‐Based Synaptic Transistor for Scalable Neuromorphic Computing

Artificial synapses based on electrochemical synaptic transistors (SynTs) have attracted tremendous attention toward massive parallel computing operations. However, most SynTs still suffer from downscaling limitations and high energy consumption. To overcome such drawbacks, a complementary metal–oxide–semiconductor (CMOS) back‐end‐of‐line compatible solid‐state SynT is presented, which includes an ultrathin (10 nm thick) quasiamorphous LixTiO2 channel. A nonvolatile conductance modulation (<75 nS) is achieved through reversible lithium intercalation into the channel, and synaptic functions, such as long‐term potentiation/depression involve ultralow switching energy of 2 fJ µm−2. Moreover, this SynT shows excellent endurance (>105 weight updates) and recognition accuracy (>95% on the MNIST data test using crossbar simulations). Furthermore, a comprehensive electrochemical study allows deeper insight into the specific pseudocapacitive mechanism at the origin of conductance modulation. These results underline the high potential of LixTiO2‐based SynTs for energy‐efficient neuromorphic applications.
Clinical and Metabolic Characteristics of Hyperuricemia with Risk of Liver Fibrosis: A Cross-Sectional Study

The role of serum uric acid (SUA) in the role of advanced fibrosis is not fully explored. The study assesses the risk of advanced fibrosis according to SUA in an Asian population with a total of 3612 subjects enrolled in one health management center between 2006 and 2008. The fibrosis-4 score was used for the prediction of the high risk of advanced fibrosis. SUA scores higher than 7.6 mg/dL in men and 6.6 mg/dL in women were defined as hyperuricemia. A proportional odds model was used to assess cumulative risks of advanced fibrosis. The prevalence of high risk of advanced fibrosis was 2.5% in the hyperuricemia group and 0.6% in the normal SUA group (p < 0.001). After adjustment for confounding factors, the odds ratios (OR) for more severe advanced fibrosis were 1.37 (95% confidence interval [CI]: 1.07–1.78) in the hyperuricemia group. Hyperuricemia only increased the risk of advanced fibrosis in the non-T2DM group (OR, 1.29; 95% CI, 1.04 to 1.74) instead of T2DM group (OR, 1.85; 95% CI, 0.97 to 3.53). SUA is a risk factor for a higher risk of advanced fibrosis, with the disease likely progressing from a steatotic to a fibrotic picture. The focus should be more emphasized in non-T2DM groups.
Synthesis of Axiom Systems for the Three-Valued Predicate Logic by Means of the Special Four-Valued Logic,

Abstract : In this paper, M. Wajsberg's axiom system is improved to the complete system by means of the special 4-valued logic. Similarly Kleene's 3-valued logic and Bochvar's one are also axiomatized to the complete systems. Finally those three systems are extended to the predicate logic systems. Such method is applicable to other multiple-valued logic systems. (Author)
Multi-Omics Analysis of GNL3L Expression, Prognosis, and Immune Value in Pan-Cancer

Simple Summary Guanine nucleotide-binding protein-like 3-like (GNL3L) is a novel GTP-binding nucleolar protein. In this study, we analyzed the expression, prognosis, and immune roles of GNL3L in pan-cancer from multiple omics analyses. The final results showed that GNL3L is differentially expressed in a variety of cancers, plays a prognostic role, and has good immune value. Moreover, GNL3L may affect the occurrence of cancer through processes such as ribonucleoprotein, ribosomal RNA processing, and cell proliferation. At the same time, we established an esophageal cancer (ESCA) prediction model with strong predictive ability and proved that GNL3L can significantly affect the proliferation ability of esophageal cancer cells through clone formation assays. In conclusion, GNL3L is an important biomarker. Abstract Guanine nucleotide-binding protein-like 3-like protein (GNL3L) is a novel, evolutionarily conserved, GTP-binding nucleolar protein. This study aimed to investigate the expression, prognosis, and immune value of GNL3L in pan-cancer from multiple omics analyses. Firstly, the expression and prognostic value of GNL3L in pan-cancer were discussed using the TIMER2 database, the GEPIA database, the cBioportal database, COX regression analysis, and enrichment analysis. The association of GNL3L with tumor mutational burden (TMB), tumor microsatellite instability (MSI), mismatch repair (MMR) genes, and immune cells was then analyzed. Finally, an esophageal cancer (ESCA) prediction model was established, and GNL3L clone formation assays were performed. The final results showed that GNL3L is differentially expressed in the vast majority of cancers, is associated with the prognosis of various cancers, and may affect cancer occurrence through processes such as ribonucleoprotein, ribosomal RNA processing, and cell proliferation. At the same time, it was found that the correlation between GNL3L and TMB, MSI, MMR, and various immune cells is significant. The established ESCA prediction model had a strong predictive ability, and GNL3L could significantly affect the proliferation of esophageal cancer cells. In conclusion, GNL3L may serve as an important prognostic biomarker and play an immunomodulatory role in tumors.