Miracle-dz/UniFAR-FTUs huggingface dataset:
Disentangling motivation and engagement: Exploring the role of effort in promoting greater conceptual and methodological clarity

Conflation over motivation and engagement has historically impeded research and practice. One reason for this is because definition and measurement have often been too general or diffuse—especially in the case of engagement. Recently conceptual advances aimed at disentangling facets of engagement and motivation have highlighted a need for better psychometric precision—particularly in the case of engagement. To the extent that engagement is inadequately assessed, motivation research involving engagement continues to be hampered. The present study investigates multidimensional effort (a specific facet of engagement) and how it relates to motivation. In particular, we examine the associations between specific positive and negative motivation factors and dimensions of effort, thereby shedding further insight into how different types of motivation interplay with different types of engagement. Drawing on data from a sample of 946 Australian high school students in 59 mathematics classrooms at five schools, this study hypothesized a tripartite model of academic effort in terms of operative, cognitive, and social–emotional dimensions. A novel nine-item self-report Effort Scale measuring each of the three factors was developed and tested for internal and external validity—including its relationship with multidimensional motivation. Multilevel confirmatory factor analyses were conducted to test the factor structure and validity of multidimensional effort. Additionally, doubly-latent multilevel structural equation models were conducted to explore the hypothesized motivation → engagement (effort) process, and the role of student- and classroom-level background attributes as predictors of both motivation and effort. Results supported the hypothesized model of tripartite effort and its distinctiveness from motivation, and showed that key dimensions of motivation predicted effort at student-and classroom-levels. This study provides implications and suggestions for future motivation research and theorizing by (1) establishing evidence for the validity of a novel engagement framework (multidimensional effort), and (2) supporting future measurement and practice in academic engagement juxtaposed with multidimensional motivation—critical for better understanding engagement, and motivation itself.
Identification and validation of senescence-related genes in circulating endothelial cells of patients with acute myocardial infarction

Background Acute myocardial infarction (AMI) is the main clinical cause of death and cardiovascular disease and thus has high rates of morbidity and mortality. The increase in cardiovascular disease with aging is partly the result of vascular endothelial cell senescence and associated vascular dysfunction. This study was performed to identify potential key cellular senescence-related genes (SRGs) as biomarkers for the diagnosis of AMI using bioinformatics. Methods Using the CellAge database, we identified cellular SRGs. GSE66360 and GSE48060 for AMI patients and healthy controls and GSE19322 for mice were downloaded from the Gene Expression Omnibus (GEO) database. The GSE66360 dataset was divided into a training set and a validation set. The GSE48060 dataset was used as another validation set. The GSE19322 dataset was used to explore the evolution of the screened diagnostic markers in the dynamic process of AMI. Differentially expressed genes (DEGs) of AMI were identified from the GSE66360 training set. Differentially expressed senescence-related genes (DESRGs) selected from SRGs and DEGs were analyzed using Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein-protein interaction (PPI) networks. Hub genes in DESRGs were selected based on degree, and diagnostic genes were further screened by gene expression and receiver operating characteristic (ROC) curve. Finally, a miRNA-gene network of diagnostic genes was constructed and targeted drug prediction was performed. Results A total of 520 DEGs were screened from the GSE66360 training set, and 279 SRGs were identified from the CellAge database. The overlapping DEGs and SRGs constituted 14 DESRGs, including 4 senescence suppressor genes and 10 senescence inducible genes. The top 10 hub genes, including FOS, MMP9, CEBPB, CDKN1A, CXCL1, ETS2, BCL6, SGK1, ZFP36, and IGFBP3, were screened. Furthermore, three diagnostic genes were identified: MMP9, ETS2, and BCL6. The ROC analysis showed that the respective area under the curves (AUCs) of MMP9, ETS2, and BCL6 were 0.786, 0.848, and 0.852 in the GSE66360 validation set and 0.708, 0.791, and 0.727 in the GSE48060 dataset. In the GSE19322 dataset, MMP9 (AUC, 0.888) and ETS2 (AUC, 0.929) had very high diagnostic values in the early stage of AMI. Finally, based on these three diagnostic genes, we found that drugs such as acetylcysteine and genistein may be targeted for the treatment of age-related AMI. Conclusion The results of this study suggest that cellular SRGs might play an important role in AMI. MMP9, ETS2, and BCL6 have potential as specific biomarkers for the early diagnosis of AMI.
Distinct binding pattern of EZH2 and JARID2 on RNAs and DNAs in hepatocellular carcinoma development

Hepatocellular carcinoma (HCC) is one of the most malignant cancers worldwide, with high mortality. However, the molecular regulatory mechanisms of liver cancer, especially transcriptional and post-transcriptional mechanisms, should be further studied. Here we used chromatin and cross-linking immunoprecipitation with high throughput sequencing methods (ChIP-seq and CLIP-seq) to capture the global binding profiles on RNAs and DNAs of Enhancer of zeste homolog 2 (EZH2) and its partner Jumonji And AT-Rich Interaction Domain Containing 2 (JARID2) in liver carcinoma cell lines (HepG2) and normal liver cell line (THLE-2), respectively. We also integrated HCC transcriptome data from the TCGA to analyze the expression pattern of bound genes. We found that EZH2 and JARID2 both showed distinct binding profiles between HepG2 and THLE-2 cells. By binding to the primary RNAs, bound transcripts of EZH2 and JARID2 in HepG2 showed significantly increased transcriptional levels in HCC patients. By performing gene set enrichment analysis (GSEA), the bound transcripts were also highly related to HCC development. We also found EZH2 and JARID2 could specifically bind to several long noncoding RNAs (lncRNAs), including H19. By exploring the DNA binding profile, we detected a dramatically repressed DNA binding ability of EZH2 in HepG2 cells. We also found that the EZH2-bound genes showed slightly increased transcriptional levels in HepG2 cells. Integrating analysis of the RNA and DNA binding profiles suggests EZH2 and JARID2 shift their binding ability from DNA to RNA in HepG2 cells to promote cancer development in HCC. Our study provided a comprehensive and distinct binding profile on RNAs and DNAs of EZH2 and JARID2 in liver cancer cell lines, suggesting their potential novel functional manners to promote HCC development.
Do proton pump inhibitors affect the effectiveness of chemotherapy in colorectal cancer patients? A systematic review with meta-analysis

Proton pump inhibitors (PPI), one of the most commonly prescribed medications, carry a myriad of adverse events. For colorectal cancer (CRC) patients, it still remains unclear whether the concurrent use of proton pump inhibitors (PPI) would negatively affect chemotherapy. PubMed, Medline, Embase, and Cochrane Library were searched from inception to 10 June 2022, to identify relevant studies involving CRC patients receiving chemotherapy and reporting comparative survival outcomes between PPI users and non-users. Meta-analyses were performed using random-effects models. We identified 16 studies involving 8,188 patients (PPI = 1,789; non-PPI = 6,329) receiving either capecitabine-based or fluorouracil-based regimens. The overall survival (HR, 1.02; 95% CI, 0.91 to 1.15; I2 = 0%) and progression-free survival (HR, 1.15; 95% CI, 0.98 to 1.35; I2 = 29%) were similar between PPI users and non-users in patients taking capecitabine-based regimens, with low statis-tical heterogeneity. Although the subgroup analysis indicated that early-stage cancer patients taking capecitabine monotherapy with concurrent PPI had a significantly higher disease progression rate (HR, 1.96; 95% CI, 1.21 to 3.16; I2 = 0%) than those who did not use PPIs, both groups had comparable all-cause mortality (HR, 1.31; 95% CI, 0.75 to 2.29; I2 = 0%). On the other hand, there was little difference in both OS and PFS in both early- and end-stage patients taking capecitabine combination therapy between PPI users and non-users. Conversely, the use of concomitant PPI in patients taking fluorouracil-based regimens contributed to a marginally significant higher all-cause mortality (HR, 1.18; 95% CI, 1.00 to 1.40; I2 = 74%), but with high statistical heterogeneity. In conclusion, PPI has little survival influence on CRC patients treated with capecitabine-based regimens, especially in patients taking capecitabine combination therapy. Thus, it should be safe for clinicians to prescribe PPI in these patients. Although patients treated with fluorouracil-based regimens with concomitant PPI trended toward higher all-cause mortality, results were subject to considerable heterogeneity. Systematic Review Registration: identifier https://www.crd. york.ac. uk/prospero/display_record. php? ID=CRD42022338161
The exact linearization and LQR control of semiactive connected hydropneumatic suspension system

Based on differential geometry theory, the nonlinear system of connected hydropneumatic suspension was transformed to a linear one. What is more, it realized the decoupling and inverter between the control variables and system outputs. With LQR (Linear Quadratic Regulator) control theory, a semiactive system has been developed for connected hydropneumatic suspension in this paper. By AMESim/Simulink cosimulation, the results show that the semiactive connected hydropneumatic suspension decreases the vibration of upper vehicle quickly and reduces the impact acceleration strongly both in displacement and inroll angle. Moreover, the semiactive suspension could increase the suspension dynamic deflection, which would make the system reach balance quickly and keep small vibration amplitude under the effect of disturbance.
Unbiased decision feedback equalization

We consider the problem of decision feedback equalization (DFE) with the use of finite impulse response (FIR) filters. We show that, contrarily to what seems to be believed, unbiased finite-length DFE solutions exist for any FIR channel, provided that the feed-forward (FFF) and feedback (FBF) filter lengths are properly chosen. This allows for the derivation of the expressions for the optimal zero-forcing (ZF) and minimum-mean-square-error (MMSE) finite-length solutions. The result can be also used as a guideline for choosing the lengths of the FFF and FBF DFE filters.
On updating the singular value decomposition

In this paper, a new technique for updating the SVD is described. It starts from the fact that the SVD can be reduced to a corresponding symmetric eigenvalue problem and utilizes an efficient eigensystem solver, called the GMRQI-JKL, to update the SVD. Based on the updating technique, a parallel and recursive total least squares algorithm (PRTLS) for solving a time variant TLS problem is proposed. Some numerical examples are given to confirm the performance of the algorithms.
An architecture for monitoring public education policies based on big and open linked data

Open government data (OGD) is increasingly being deployed in many countries. However, the role of OGD in policy-making monitoring has been explored only marginally due to its complexity and inter-organizational boundaries. Big Open Linked Data (BOLD) is a recent technological approach where data from multiple institutions with different data governance policies must be integrated to achieve a common goal. In this work, we present a BOLD infrastructure to support the monitoring of an educational public policy using a linked data approach. The design science research methodology (DSR-M) was followed in this study. We chose the PNE (National Education Plan) as a case study since it sets out goals for national education at different levels and perspectives for ten years. We used its goals and indicators to elicit requirements and evaluate the architecture. As a result, we created an infrastructure based on virtual knowledge graphs at a municipality level and used it to generate detailed and transparent statistics on calculating the goals. Finally, we highlight some implications and future challenges of our approach.
Estimation of dissolved organic carbon from inland waters using remote sensing data and machine learning

This thesis presents the first attempt to estimate Dissolved Organic Carbon (DOC) in inland waters over a large-scale area using satellite data and machine learning (ML) methods. Four ML approaches, namely Random Forest Regression (RFR), Support Vector Regression (SVR), Gaussian Process Regression (GPR), and a Multilayer Backpropagation Neural Network (MBPNN) were tested to retrieve DOC using a filtered version of the recently published open source AquaSat dataset with more than 16 thousand samples across the continental US matched with satellite data from Landsat 5, 7 and 8 missions. In this work, the AquaSat dataset was extended with environmental data from the ERA5-Land product. Including environmental data considerably improved the prediction of DOC for all algorithms, with GPR showing the best and most robust performance results with moderate estimation errors (RMSE: 4.08 mg/L). Permutation feature importance analysis showed that from the Landsat bands, the wavelength in the visible green and for the ERA5-Land product, the monthly average air temperature were the most important variables for the machine learning approaches. The results demonstrate the predictive strength of advanced ML approaches faced with a complex learning task, such as GPR and MBPNN, and highlight the important role of considering environmental processes to explain DOC variations over large scales. While performance evaluation showed that DOC concentrations can be retrieved with adequate accuracy, algorithm development was challenged by the heterogenous nature of large-scale open source in situ data, issues related to atmospheric correction, and the low spatial and temporal resolution of the environmental predictors. Although locally tuned models are likely to outperform the developed model in terms of accuracy, the model can address key issues of inland water remote sensing as a promising approach to overcome the lack of in-situ measurements and to map large scale trends of inland DOC dynamically over long time periods and seasons. This research demonstrates how open source, large scale datasets like AquaSat in combination with ML and remote sensing can make research toward large scale estimations of inland water DOC more realistic while highlighting its remaining limitations and challenges.
Synthesis of Zinc Nanoparticles using Portulaca oleracea Plant Extract

Zinc oxide (ZnO) nanoparticles (NPs) were successfully synthesized using green synthesis techniques, which was the focus of this study by using Portulaca oleracea extract, a reducing, capping, and stabilizing agent alongside with zinc acetate dihydrate acts as a precursor. The functionalization of Zn nanoparticles is investigated through (UV-vis) spectroscopy, X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, field emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), thermal gravimetric-differential thermal analysis (TG-DTA), dynamic light scattering DLS and zeta potential, energy dispersive X-ray spectroscopy (EDX), and Brunauer-Emmett-Teller (BET) analysis. UV-Vis- analysis revealed that the indirect bandgap of the biosynthesized ZnO NPs is 3.54 eV. In addition, the XRD and EDX analysis found that the structure of the nanoparticles is Wurtzite hexagonal with an average crystalline size of 16.58 nm. Moreover, the FT-IR spectra revealed the functional groups and the presence of ZnO NPs. According to the FE-SEM study, the average diameter of the grains was 95.53 nm. TEM analysis showed that the shape of the biosynthesized ZnO NPs was quasi-spherical, and their diameter was around 21.13 nm. Furthermore, DLS analysis demonstrated that the particle size distribution of ZnO NPs ranged from 40–55 nm. This study has that the specific surface area to volume ratios of the biosynthesized ZnO NPs was equal to 5.8772 m 2 /g.
Effect of pH on Antioxidant and Phytochemical Activities of Mulhatti Roots ( Glycyrrhiza glabra L.)

: Glycyrrhiza glabra L. is the most widely used herb in the ancient history of Ayurvedic medicine, as a medicinal value as well as an aromatic herb, and it is commonly known as Mulhatti. Mulhatti roots are useful for medically and are also a good source of phytoproducts and secondary metabolites present in Mulhatti roots are triterpenoid saponin, glycosides, glycyrrhizin, prenylated biaurone, licoaagrone, 7-acetoxy-2-methylisoflavone, 4-methylcoumarin, liqcoumarin, glycyrrhetinic acid, quercetin, liquiritigenin, isoliquiritigenin, etc. This study was carried out to study the evaluation of phenolic compounds, 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical activity and general antioxidant capacity of water extracts of Mulhatti roots prepared at different pH values, namely 2, 4, 7 and 9. Data have shown great differences in terms of results. Most of the phenolic compounds are at pH 7 (19.25), followed by pH 9 (17.25), pH 2 (14.62) and pH 4 (8.89 mg GAE/g), respectively. Similarly, the flavonoid data also showed variations, the maximum has been present in pH 2 (5.39), then pH 7 (3.02), pH 9 (1.79) and pH 4 (1.40 mg CE/g), respectively. The value for DPPH IC 50 free radical scavenging activity was the lowest at pH 7 (82.22), followed by pH 2 (110.40), pH 4 (111.99) and pH 9 (146.24 µg/mL) and IC 50 reference standard (ascorbic acid) was 59.52 µg/mL in distilled water. The total capacity of the antioxidant was the highest at pH 2 (9.93) followed by pH 4 (5.54), pH 7 (5.34) and pH 9 (4.23 mg AAE/g). According to current research, pH 7 is the best for phytochemicals as well as antioxidants that catch harmful radicals.
Doherty PA Linearization by Injection of the 2nd order Digitally Processed Signals for 5G FBMC modulation

– In this paper, the analysis of the linearization effects of the 2nd order digitally processed signal for the linearization that modulates the 2nd harmonic of the fundamental carrier has been performed on the Doherty power amplifier for FBMC 5G modulation form. In the first method, linearization signals are injected at the input and output of the main Doherty amplifier, whereas in the second method, they are inserted at the outputs of the Doherty main and auxiliary amplifiers.
The Impact of Mobile Banking Service on Customer Satisfaction: A Case Study of Commercial Banks in China

Purpose – this report focus on this tends to present what is the relationship between mobile banking service in the financial industry in China and customer satisfaction and how transaction speed, accessibility, affordability, adaptability, ease of use, relative advantage, and perception of risk. Design/Methodology/Approach – This study mainly discusses the influencing factors of mobile banking service on customer satisfaction, and finally creates a new theoretical framework based on the analysis of the theoretical framework of the previous two works of literature. Findings – Through the research on the influence of mobile banking service on customer satisfaction, it is found that the speed of transactions of mobile banking, accessibility, affordability, adaptability, ease of use, and relative advantage have a significant impact on customer satisfaction. Research Limitations/Implications –The factors affecting customer satisfaction with mobile banking services mainly depend on the mobile banking services used. Different banks have different technologies and service standards, thus affecting customer satisfaction levels, so the factors of the survey are also limited. In addition, the scope of respondents in the questionnaire is small, and the data analysis is also limited to some extent. At the same time, especially before the COVID-19 pandemic, there is relatively little literature on the impact on mobile banking with customer satisfaction, and there are relatively few reference standards for research. Originality/value this study is about the important influencing variables that affect mobile banking service and customer satisfaction. Keywords— mobile banking; the speed of transactions; accessibility; affordability; adaptability; ease of use; relative advantage; perception of risk; customer satisfaction.
Evaluation of the effectiveness of stress management training on hardiness and anxiety of nurses of Noor Didehgan clinic in Karaj

Today, work and personal life are intertwined. It has created many problems. Nurses' jobs expose them to various stresses. The aim of this study was to investigate the effect of stress management training on the hardiness of nurses in Karaj . Method : The present study is a quasi-experimental study. The study population consisted of 30 nurses and was divided into two groups of 15 experimental and control using the available sampling method. The stress management training program based on cognitive-behavioral theory was performed on the experimental group for 16 hours. Research data using the scale of Kubasa (1). Hardiness Questionnaire. Also Beck Anxiety Questionnaire (2). Before and after the educational intervention, individuals from both groups were tested and collected. Findings : Data analysis was performed using descriptive statistical methods and analysis of covariance in SPSS software version 23 . The results showed that the stress management training program and communication skills significantly reduced the mean score of anxiety (p <0.05) and increased the score of hardiness (p <0.05) in the experimental group compared to the control group . Conclusion : A stress management training program, based on cognitive-behavioral theory, can improve nurses' health by reducing anxiety symptoms and increasing the hardiness level.
Denoising the open-loop step response using an encoder-decoder convolutional neural network

—Denoising signals is used as a preprocessing step for all signal processing. Encoder-decoder neural networks are often proposed as a method of denoising 1D and 2D signals, because of their ability to extract essential features from the signal and then recreate it without noise. In this paper we propose a simple architecture of a convolutional neural network for denoising step responses of systems with different open-loop transfer function. The network is trained on synthetic data with added noise of different distributions, then tested on a portion of synthetic data and real-life step responses.