Miracle-dz/UniFAR-FTUs huggingface dataset:
Cross-Country Data on Skills and the Quality of Schooling: A Selective Survey

Scores in standardized international student achievement tests and some recent adult literacy studies provide interesting data on the quality of educational outputs and on the skill level of the population that can be a useful complement to the data on the quantity of schooling which have been most commonly used in the growth literature. This paper describes the most recent available primary data on the subject, reviews different attempts to organize, standardize, and summarize them, and discusses the strengths and weaknesses of the existing indicators and their potential usefulness as explanatory variables in empirical analyses of the determinants of income and welfare levels and growth rates. A final section investigates the distribution of these indicators across a sample of 21 OECD countries.
Telomere Length and Arterial Stiffness Reflected by Brachial–Ankle Pulse Wave Velocity: A Population-Based Cross-Sectional Study

Telomere (TL) is a biomarker of biological aging, and its shortening is associated with major risk factors for cardiovascular diseases (CVD). This study aimed to identify whether TL is associated with arterial stiffness as reflected by brachial–ankle pulse wave velocity (baPWV). This population-based cross-sectional study involved 1065 individuals in the Iwaki area, Japan. Total TL length and TL G-tail length were measured by hybridization protection assay. The baPWV was measured on the right and left sides using a non-invasive vascular screening device. The associations between TL and baPWV were assessed by multivariate linear regression. Compared with the shortest total TL tertile, the longest total TL group showed a significant decrease in baPWV (lowest vs. highest tertile: adjusted beta: −41.24, 95% confidence interval (CI): −76.81, −5.68). The mean baPWV decreased with a longer TL (TL G-tail length: p trend < 0.001, total TL: p trend < 0.001). TL G-tail and total TL lengths were inversely associated with baPWV, implicating TL shortening in the development of CVD. This study provides evidence of the factors influencing CVD risks at a very early stage when individuals can still take necessary precautions before CVD gives rise to a symptomatic health outcome.
A Comprehensive Survey of Statistical Approaches for Differential Expression Analysis in Single-Cell RNA Sequencing Studies

Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput sequencing technique for studying gene expressions at the cell level. Differential Expression (DE) analysis is a major downstream analysis of scRNA-seq data. DE analysis the in presence of noises from different sources remains a key challenge in scRNA-seq. Earlier practices for addressing this involved borrowing methods from bulk RNA-seq, which are based on non-zero differences in average expressions of genes across cell populations. Later, several methods specifically designed for scRNA-seq were developed. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to comprehensively study the performance of DE analysis methods. Here, we provide a review and classification of different DE approaches adapted from bulk RNA-seq practice as well as those specifically designed for scRNA-seq. We also evaluate the performance of 19 widely used methods in terms of 13 performance metrics on 11 real scRNA-seq datasets. Our findings suggest that some bulk RNA-seq methods are quite competitive with the single-cell methods and their performance depends on the underlying models, DE test statistic(s), and data characteristics. Further, it is difficult to obtain the method which will be best-performing globally through individual performance criterion. However, the multi-criteria and combined-data analysis indicates that DECENT and EBSeq are the best options for DE analysis. The results also reveal the similarities among the tested methods in terms of detecting common DE genes. Our evaluation provides proper guidelines for selecting the proper tool which performs best under particular experimental settings in the context of the scRNA-seq.
A Multi-Load Capacitive Power Transfer System With Load-Independent Characteristic for Reefer Container Application

In order to supply power wirelessly to reefer containers without constructing an extra power tower, this article proposed a novel multiload capacitive power transfer (CPT) system with an SP-CL isolation compensation topology. Unlike the traditional multiload CPT system, where receivers function solely as power consumers, in the proposed system, each receiver not only supplies power to the connected load but transfers power wirelessly to the next receivers. The coupler model with the cross-coupling of metal containers is established for reefer container application. Then, the SP-CL isolation compensation topology analysis based on the single circuit is carried out to achieve the load-independent characteristics with constant output voltage. The output characteristics and efficiency of the proposed CPT system are analyzed, considering the impact of the parasitic resistance. Besides, the voltage stress of passive components is optimized to design the experiment system. A 1.8-kW prototype with three loads is built to validate the effectiveness of the proposed CPT system. The equal power distribution (600 W and 600 W and 600 W) is achieved, and the system efficiency can reach 83.7%.
Integrating Engineering Experiences within the Elementary Mathematics Curriculum

Engineering education for elementary school students is a new and increasingly important domain of research by mathematics, science, technology, and engineering educators. Recent research has raised questions about the context of engineering problems that are meaningful, engaging, and inspiring for young students. In the present study an environmental engineering activity was implemented in two classes of 11-year-old students in Cyprus. The problem required students to use the data to develop a procedure for selecting among alternative countries from which to buy water. Students created a range of models that adequately solved the problem although not all models took into account all of the data provided. The models varied in the number of problem factors taken into consideration and also in the different approaches adopted in dealing with the problem factors. At least two groups of students integrated into their models the environmental aspect of the problem (energy consumption, water pollution) and further refined their models. Results provide evidence that engineering model- eliciting activities can be successfully integrated in the elementary mathematics curriculum. These activities provide rich opportunities for students to deal with engineering contexts and to apply their learning in mathematics and science to solving real-world engineering problems.
A Review on BCI Emotions Classification for EEG Signals Using Deep Learning

Emotion awareness is one of the most important subjects in the field of affective computing. Using nonverbal behavioral methods such as recognition of facial expression, verbal behavioral method, recognition of speech emotion, or physiological signals-based methods such as recognition of emotions based on electroencephalogram (EEG) can predict human emotion. However, it is notable that data obtained from either nonverbal or verbal behaviors are indirect emotional signals suggesting brain activity. Unlike the nonverbal or verbal actions, EEG signals are reported directly from the human brain cortex and thus may be more effective in representing the inner emotional states of the brain. Consequently, when used to measure human emotion, the use of EEG data can be more accurate than data on behavior. For this reason, the identification of human emotion from EEG signals has become a very important research subject in current emotional brain-computer interfaces (BCIs) aimed at inferring human emotional states based on the EEG signals recorded. In this paper, a hybrid deep learning approach has proposed using CNN and a long short-term memory (LSTM) algorithm is investigated for the purpose of automatic classification of epileptic disease from EEG signals. The signals have been processed by CNN for feature extraction from runtime environment while LSTM has used for classification of entire data. Finally, system demonstrates each EEG data file as normal or epileptic disease. In this research to describes a state of art for effective epileptic disease detection prediction and classification using hybrid deep learning algorithms. This research demonstrates a collaboration of CNN and LSTM for entire classification of EEG signals in numerous existing systems.
High Expression of EIF4G2 Mediated by the TUG1/Hsa-miR-26a-5p Axis Is Associated with Poor Prognosis and Immune Infiltration of Gastric Cancer

Objective Eukaryotic translation initiation factor 4 gamma 2 (EIF4G2) is involved in the occurrence and development of various tumors. However, the effect of EIF4G2 in gastric cancer (GC) has not been fully explored. The purpose of this study was to explore the function and mechanism of EIF4G2 in GC. Methods The Tumor Immune Estimation Resource 2.0 database was used to analyze EIF4G2 expression in various cancers and the relationship between EIF4G2 expression and tumor-infiltrating immune cells. Gene Expression Profiling Interactive Analysis was utilized to assess the EIF4G2 expression level and its effect on survival in GC. UALCAN was conducted to analyze EIF4G2 expression in various subgroups of GC. The Kaplan–Meier plotter was employed for survival analysis. Receiver operator characteristic (ROC) curve analysis was applied to evaluate the diagnostic role of EIF4G2 in GC. LinkedOmics was used to identify the co-expressed genes and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. The Tumor-Immune System Interaction database was employed to analyze the correlation between EIF4G2 expression and tumor-infiltrating lymphocytes. The starBase web platform was used to predict the upstream microRNAs and long noncoding RNAs. Results EIF4G2 expression was upregulated in GC tissues compared to normal controls. High expression of EIF4G2 indicated poor prognosis in GC. ROC analysis revealed that EIF4G2 had good diagnostic ability to distinguish GC from normal tissues. Immune infiltration analysis indicated that EIF4G2 expression may be involved in the modulation of tumor immune infiltration in GC. Finally, we determined that the Taurine Upregulated 1 (TUG1)/hsa-miR-26a-5p/EIF4G2 axis was the most likely regulatory pathway involved in GC development. Conclusions EIF4G2 was upregulated in GC and elevated expression of EIF4G2 indicated unfavorable prognosis. Moreover, EIF4G2 expression may be involved in the regulation of tumor immune cell infiltration. The TUG1/hsa-miR-26a-5p axis is a likely upstream regulatory mechanism of EIF4G2 in GC. EIF4G2 may thus serve as a prognosis biomarker and present a new therapeutic target.
OSMAC Strategy Integrated with Molecular Networking for Accessing Griseofulvin Derivatives from Endophytic Fungi of Moquiniastrum polymorphum (Asteraceae)

Three endophytic fungi isolated from Moquiniastrum polymorphum (Less.) G. Sancho (Asteraceae) were cultivated using the one strain many compounds (OSMAC) strategy to evaluate the production of griseofulvin derivatives. Extracts obtained were analyzed by HPLC–MS/MS and the chromatographic and spectrometric data used to elaborate a feature-based molecular network (FBMN) through the GNPS platform. This approach allowed the observation of differences such as medium-specific and strain-specific production of griseofulvin derivatives and variations of cytotoxic activity in most extracts. To evaluate the efficiency of the OSMAC approach allied with FBMN analysis in the prospection of compounds of biotechnological interest, griseofulvin and 7-dechlorogriseofulvin were isolated, and the relative concentrations were estimated in all culture media using HPLC–UV, allowing for the inference of the best strain–medium combinations to maximize its production. Malt extract-peptone broth and Wickerham broth media produced the highest concentrations of both secondary metabolites.
ETHANOL–BIODIESEL–DIESEL BLENDS AS A DIESEL EXTENDER OPTION ON COMPRESSION IGNITION ENGINES

Abstract Increasing fuel demand, decreasing natural reserves and environmental consciousness have together led to testing and implementing new fuels and blending components of compression ignition engines. Biofuels are very commonly added to fossil fuels, mostly ethanol to gasoline and FAME to diesel. Harmonizing their properties with engines is a great challenge for automotive and oil industry. Increasing demand for diesel oil in Europe raised the question about the possibility of increasing the amount of bio extenders. There were and certainly there are a number of experiments aimed at substituting or blending diesel with other fuels. One group of such fuels makes bioethanol– biodiesel–diesel oil mixtures. The paper proposes a global overview on literature and presents the obtained results. The article explores the possibility of using bioethanol–biodiesel–diesel oil mixtures in vehicles and agricultural compression ignition engines. The main aspect of researches was to find blends substitutable for com.
Issues in High-ĸ Gate Stack Interfaces

We address current challenges in the fundamental understanding of physical and chemical processes that occur in the fabrication of the transistor gate stack structure. Critical areas include (1) the interface between bulk silicon and high-dielectric-constant (high- ĸ ) insulators, (2) the interface between high- ĸ insulators and advanced gate electrodes, and (3) the internal interfaces that form within dielectric stacks with nonuniform material and structure compositions. We approach this topic from a fundamental understanding of bonding and electronic structure at the interfaces, and of film-growth kinetics in comparison with thermodynamics predictions. Implications for the dielectric/electrode interface with metallic gates and issues with integration will also be presented.
The Correlation Analysis of Stock Price Towards Public Sentiment in Automotive Industry

Information from news and social media is an indispensable part of business and investment activity. Following the advancement of social media, the public's opinion has become abundant. Instagram, Facebook, and Twitter are social media platforms that have recently received much attention from the crowd. Furthermore, the sentiment of headline news reflects that stock price depends on the company's profit or loss. In the view of the electric car's issue, this study observes the public's opinion for car brands to examine the correlation between the stock price and the sentiment from social media and headline news by using Pearson correlation. The results show a reasonably strong correlation between the public sentiment in social media and headline news with the rise and fall in stock price. This study found a reasonably strong correlation between headline news, Twitter, Facebook, and automotive stock prices. On the other hand, Instagram has not shown a strong enough correlation because discussions on Instagram are not as extensive as Facebook and Twitter. The research benefit for investors is to understand mechanisms about public sentiment towards Volkswagen, General Motors, and Chrysler brands produced by the automotive industry and support them to make the right decisions in the capital market.
INDUSTRIAL ECOLOGY IN NORTHERN AREAS. PRACTICAL EXPERIENCE AND DEVELOPMENT

The possibilities to develop the industrial ecology in the northern regions of Europe are influenced by some common characteristics, e.g.: The regions are rich in natural resources and energy- and material intensive base industries. These industries cover se veral branches, e.g., Mining, Iron and Steel, Metal production, Pulp and Paper. Low pop ulation density and relatively long distances to the main customers are difficulties especially for transport and use of rest products. District heating with waste heat from the industries is an important part of the energy system that reduces the emission of greenhouse gases and improves the overall energy efficiency. The problems and possibilities connected to the industrial ecology are described for two examples, the energy system in Lulea and the Solander science park in Pitea Lulea example: The integrated steel plant in Lulea produced a surplus of fuel gas. SSAB (Svenskt Stal AB) and the Municipality of Lulea commissioned a CHP (Combined Heating and Power) plant, which used these energy gases to produce a mix of electric power + hot water for district heating. The plant is owned and administered by a separate company, Lulekraft AB, owned by the partners. The district -heating network covers almost all buildings in the city. The production of heating almost exactly matches the needs of the community during the cold part of the year. The production of electric po wer covers the needs of SSAB Tunnplat AB in Lulea and also gives a surplus to other units within SSAB. The system gives profit back to the owners but also to the population: Lulea has for many years had the lowest District heating prices in Sweden Solander Science Park: The region includes several Pulp and Paper mills. A development can be expected towards a production system where the biomass is used to pr oduce not only paper but also biofuel, synthetic diesel etc. It has been considered impo rtant to do this in an integrated production system. A network has been formed with the aim to develop the pulp mill biorefinery-concept. It includes the regional Pulp and Paper mills, Biorefinery actors, Regional universities and the foundation Energy Technology Centre in Pitea (ETC). (ETC is a research institute with pilot plant facilities). Several new technologies are or have been developed by the group or group members, e.g. Black li quor gasification, Synthetic diesel. For both cases other factors than the technological ones have shown to be of great importance, e.g. social parameters and stakeholder preferences etc. These parameters are relatively similar although the cases are from different branches and have different background. A European project with a widened scope of case types and backgrounds could be of interest.
Comparative analysis of laser ultrasonic propagation imaging system with capacitance and piezoelectric air-coupled transducers

Air-coupled nondestructive testing is an attractive technique that allows easy scanning inspection capability due to its noncontact nature with the specimen. A piezoelectric air-coupled transducer has been combined with a laser ultrasonic propagation imaging system as a fixed noncontact sensor. However, the large impedance mismatch between air and a specimen and its narrowband frequency range has prevented it from being efficiently used in real-world applications as a laser-air-coupled transducer ultrasonic propagation imaging system. Therefore, instead of combination with a piezoelectric air-coupled transducer, a combination between a laser and a capacitance air-coupled transducer is proposed through a comparative analysis between the capacitance and piezoelectric air-coupled transducers as the ultrasonic detector of the ultrasonic propagation imaging system. Under the same conditions, a series of experiments was conducted to examine the damage sensitivity in metal and composite specimens that have a hidden crack and barely visible impact damages, respectively. This noncontact system was further improved using repeat-scanning technique to enhance the signal-to-noise ratio and to overcome high attenuation problems, especially in composite specimens.
The optimal RLS parameter tracking algorithm for a power amplifier feedforward linearizer

Digital signal processing (DSP) techniques have been proposed in recent years to adaptively track the control parameters of a power amplifier (PA) feedforward linearizer. In most of the propositions, gradient-based searching algorithms are applied to the parameter tracking. In this paper, we propose an optimal recursive least square (RLS) parameter tracking algorithm, which significantly accelerates the convergence speed and eliminates the gradient noise. There exist two problems for the RLS algorithm. First, the least square solution is not the optimal solution because of the nonlinearity of the PA. Second, the vector modulator (VM) which introduces the control parameters into the linearizer circuit may not be accurate enough to provide a precise power gain and phase shift calculated by the DSP. We solve both problems, respectively; by rearranging the circuit components and by constraining the VM characteristics. We also present simulation results to verify the performance improvement of the proposed algorithm.
Performance analysis of frequency-domain block LMS adaptive digital filters

The performance of the frequency-domain block least-mean-square (FBLMS) adaptive digital filters, whose filter weights are updated efficiently using the fast Fourier transform, is investigated. In particular, the convergence of the unconstrained FBLMS algorithm with reduced complexity, which is obtained by removing the constraint that has been known to be required in adjusting the frequency-domain weights based on overlap-save sectioning, is analyzed. The performance of the self-orthogonalizing FBLMS algorithm with improved convergence speed, in which different convergence factors normalized by frequency-domain power estimates are used for different frequency components of the weights, is also studied. >
Instrumentation for noise measurements on CMOS transistors for fast detector preamplifiers

High-density, high-speed CMOS and BiCMOS technologies are today widely used for the design of readout integrated circuits for room temperature X and /spl gamma/-ray imaging detectors. This paper describes a laboratory instrument that was developed to characterize the noise performances of CMOS devices to be used for high speed analog signal processing. These instruments extend the noise measuring capabilities beyond 100 MHz to detect the white noise component beyond the 1/f noise corner frequency, which in shorter channel devices shifts to higher values as compared to long-channel transistors.
Testing approaches to visualize land cover/land use changes in time series with Web GIS microservice tools

Abstract. Web applications which are high functioning, efficient, and meet the performance demand of the client are essential in modern cartographic workflows. With more and more complex spatial data being integrated into web applications, such as time related features, it is essential to harmonize the means of data presentation so that the end product is aligned with the needs of the end-user. In this paper we present aWeb GIS application built as a microservice which displays various timeseries visualizations to the user to streamline intuitiveness and functionality. The prototype provides a solution which could help to understand various ways in which current web and spatial analysis methods can be combined to create visualizations that add value to existing spatial data for cartographic workflows.