Miracle-dz/UniFAR-FTUs huggingface dataset:
A Systematic Literature Review on Performance Prediction in Learning Programming Using Educational Data Mining

Programming education has become an essential skill for the digital generation. However, it presents a unique set of challenges that can be difficult for beginners. Educational data mining (EDM) has been increasingly utilized in programming education to enhance learning outcomes and understand students' learning behavior. By collecting and analyzing data from various sources, such as students' learning activities, interactions with learning resources, and assessment results, EDM can provide valuable insights into students' learning performance and potential areas for improvement. This paper presents a systematic literature review of recent literature (last five years) and reports on state of the art and trends in using EDM for student performance prediction in programming courses. It provides a comprehensive analysis of the input data used in previous work, exploring the different types of datasets used and the features that affect student performance. In addition, it addresses the predictive objectives and target variables for performance prediction in programming courses. On the other hand, it explores the most common prediction approaches, data pre-processing procedures, cross-validation methods, and evaluation metrics used to describe the performance of prediction algorithms. In addition, we discuss the limitations and challenges of various prediction approaches and provide valuable insights and directions for future research.
So Much Time, So Little Reward: Structural Factors that Impact Student Experiences in a Computer Architecture Course

Previous electrical and computer engineering (ECE) studies show lower graduation rates than other engineering disciplines. ECE has many difficult-to-understand concepts that cannot be observed with the naked eye. Lab work is typically employed to help students experientially learn such concepts. However, other complex courses taken simultaneously, inefficient study techniques, and compounding content throughout the lab course can all cascade together to affect a student's academic satisfaction and well-being negatively, further undermining their ability to succeed in ECE courses. This study aimed to explore ECE students' experiences and satisfaction with an upper-level computer architecture course at Purdue University. Our survey focused on the number of hours students spent on the course, whether they received the grade they expected, and how their mental health fared throughout the course. This paper shares the survey results and offers recommendations for instructors teaching similar courses.
Brain imaging studies of emotional well-being: a scoping review

This scoping review provides an overview of previous empirical studies that used brain imaging techniques to investigate the neural correlates of emotional well-being (EWB). We compiled evidence on this topic into one accessible and usable document as a foundation for future research into the relationship between EWB and the brain. PRISMA 2020 guidelines were followed. We located relevant articles by searching five electronic databases with 95 studies meeting our inclusion criteria. We explored EWB measures, brain imaging modalities, research designs, populations studied, and approaches that are currently in use to characterize and understand EWB across the literature. Of the key concepts related to EWB, the vast majority of studies investigated positive affect and life satisfaction, followed by sense of meaning, goal pursuit, and quality of life. The majority of studies used functional MRI, followed by EEG and event-related potential-based EEG to study the neural basis of EWB (predominantly experienced affect, affective perception, reward, and emotion regulation). It is notable that positive affect and life satisfaction have been studied significantly more often than the other three aspects of EWB (i.e., sense of meaning, goal pursuit, and quality of life). Our findings suggest that future studies should investigate EWB in more diverse samples, especially in children, individuals with clinical disorders, and individuals from various geographic locations. Future directions and theoretical implications are discussed, including the need for more longitudinal studies with ecologically valid measures that incorporate multi-level approaches allowing researchers to better investigate and evaluate the relationships among behavioral, environmental, and neural factors. Systematic review registration https://osf.io/t9cf6/.
A personalized prediction model for urinary tract infections in type 2 diabetes mellitus using machine learning

Patients with type 2 diabetes mellitus (T2DM) are at higher risk for urinary tract infections (UTIs), which greatly impacts their quality of life. Developing a risk prediction model to identify high-risk patients for UTIs in those with T2DM and assisting clinical decision-making can help reduce the incidence of UTIs in T2DM patients. To construct the predictive model, potential relevant variables were first selected from the reference literature, and then data was extracted from the Hospital Information System (HIS) of the Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital for analysis. The data set was split into a training set and a test set in an 8:2 ratio. To handle the data and establish risk warning models, four imputation methods, four balancing methods, three feature screening methods, and eighteen machine learning algorithms were employed. A 10-fold cross-validation technique was applied to internally validate the training set, while the bootstrap method was used for external validation in the test set. The area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to evaluate the performance of the models. The contributions of features were interpreted using the SHapley Additive ExPlanation (SHAP) approach. And a web-based prediction platform for UTIs in T2DM was constructed by Flask framework. Finally, 106 variables were identified for analysis from a total of 119 literature sources, and 1340 patients were included in the study. After comprehensive data preprocessing, a total of 48 datasets were generated, and 864 risk warning models were constructed based on various balancing methods, feature selection techniques, and a range of machine learning algorithms. The receiver operating characteristic (ROC) curves were used to assess the performances of these models, and the best model achieved an impressive AUC of 0.9789 upon external validation. Notably, the most critical factors contributing to UTIs in T2DM patients were found to be UTIs-related inflammatory markers, medication use, mainly SGLT2 inhibitors, severity of comorbidities, blood routine indicators, as well as other factors such as length of hospital stay and estimated glomerular filtration rate (eGFR). Furthermore, the SHAP method was utilized to interpret the contribution of each feature to the model. And based on the optimal predictive model a user-friendly prediction platform for UTIs in T2DM was built to assist clinicians in making clinical decisions. The machine learning model-based prediction system developed in this study exhibited favorable predictive ability and promising clinical utility. The web-based prediction platform, combined with the professional judgment of clinicians, can assist to make better clinical decisions.
A Low-Power Analog Integrated Gaussian-based Neural Network Classifier with Application to Hepatitis Disease Recognition

Hepatitis is a medical condition characterized by inflammation of the liver. It can be triggered by various factors, including viral infections, excessive alcohol consumption, specific medications, or autoimmune disorders. Recognizing hepatitis early is crucial in reducing its symptoms, as it allows for timely treatment. In this study, a low-power (4.31$\mu$W) and low-voltage (0.6V) analog artificial neural network classifier is introduced, utilizing a Gaussian-based activation function. The architecture comprises a hidden layer: with a Gaussian activation function circuit and tanh approximation, an output layer with a softmax function circuit, and an argmax operator. A comparative analysis is performed to evaluate the performance of this methodology against commonly used analog classifiers. To conduct this assessment, a real-world hepatitis dataset is employed. The models are trained and results processed using the Python programming language. The hardware design and result processing are executed using Cadence IC Suite, utilizing the TSMC 90nm CMOS process technology, demonstrating the practical applicability and effectiveness of this methodology.
Application of Data Mining in Failure Estimation of Cold Forging Machines: An Industrial Research

: The industrial companies are now reaching out for solutions that would enable them to reduce the number of manufacturing defects in production so that they may be able to compete and maintain their sustainability in the market. All production processes need to be uninterruptible. This study utilizes data mining algorithms to turn the data created by machines into information. These data mining algorithms are effective tools for reducing the cold forging machine downtime. Furthermore, the selected data mining methodology, the J48 model, generates meaningful results for a large real-life data set and predicts the error according to a behavioral model. The J48 model successfully detected 28 failures from this data set which suggests that it can be a promising method for reducing the periods of downtime of the cold machine.
1-O-Alkylglycerol Ether Lipids in Two Holothurian Species: Cucumaria japonica and C. okhotensis

1-O-Alkylglycerol ether lipids were investigated in two holothurian species, Cucumaria japonica and C. okhotensis. Species specific differences in the content of 1-O-alkylglycerol ether lipids were found. Some alkyl radicals of the 1-O-alkylglycerol ether lipid molecules contained branched hydrocarbon chains. The ratio between the content of linear and branched saturated 1-O-alkylglycerol ether lipids was 1.53 in C. okhotensis and 0.55 in C. japonica.
Saliency and Power Aware Contrast Enhancement for Low OLED Power Consumption

The mass adoption of display devices calls for advanced power-reduction techniques. Power-constrained contrast enhancement (PCCE) gains many improvements in recent years, yet several important issues were neglected. Computation complexity increases significantly when processing high-resolution content, which is commonplace nowadays. Moreover, many works focus on improving a finite set of quality metrics while abandoning the salient information of image content. We propose to develop an efficient, saliency aware, on-demand PCCE method that is end-to-end optimized on image quality and saliency criterion. Our method starts by extracting multilevel features from a low-resolution luminance input using an efficient feature encoder. A lightweight power-attention mechanism realizes the on-demand power reduction via input image statistics. The last stage mitigates the artifacts introduced by the low-resolution saliency information using fast-guided filtering (GF) and local enhancement (LEN) to restore the high-frequency component. To bridge the unsupervised PCCE and supervised saliency task, we develop a local quality measure that captures a quality ratio given a desired power level. Experiments on multiple datasets with up to 4K resolution demonstrate the effectiveness of our method to produce high-quality and saliency scores. With a 20% power reduction on RAISE dataset, our method achieves structural similarity (SSIM) of 0.99 with backbone network computation of fewer than 0.1 giga multiply-accumulate operations per second (GMACs). Measurement on an organic light-emitting diode (OLED) panel indicates that our method can achieve 0.83 SSIM with a 61% reduction rate. The implementation is available at https://github.com/kuntoro-adi/SPACE.
Research on Electromagnetic Transient Simulation of 500kV Unified Power Flow Controller

As the most versatile flexible AC transmission system to date, the unified power flow controller(UPFC) not only controls the AC line power flow, but also provides reactive power compensation to the system. At present, UPFC has been developed to 500 kV voltage level, and the converters’ structure is modular multilevel converter(MMC). The special structure of UPFC’s series side transformer makes the control strategy and fault characteristics of UPFC different from the existing MMC-HVDC project, and electromagnetic transient simulation research is urgently needed. Based on the published data of 500 kV UPFC project in Suzhou Southern Power Grid, this paper conducts electromagnetic transient simulation research on 500 kV MMCUPFC in PSCAD, which can provide support for the control strategy, protection scheme and the overvoltage and insulation cooperation of UPFC.
DLG3 variants caused X-linked epilepsy with/without neurodevelopmental disorders and the genotype-phenotype correlation

Background The DLG3 gene encodes disks large membrane-associated guanylate kinase scaffold protein 3, which plays essential roles in the clustering of N-methyl-D-aspartate receptors (NMDARs) at excitatory synapses. Previously, DLG3 has been identified as the causative gene of X-linked intellectual developmental disorder—90 (XLID-90; OMIM# 300850). This study aims to explore the phenotypic spectrum of DLG3 and the genotype-phenotype correlation. Methods Trios-based whole-exome sequencing was performed in patients with epilepsy of unknown causes. To analyze the genotype-phenotype correlations, previously reported DLG3 variants were systematically reviewed. Results DLG3 variants were identified in seven unrelated cases with epilepsy. These variants had no hemizygous frequencies in controls. All variants were predicted to be damaging by silico tools and alter the hydrogen bonds with surrounding residues and/or protein stability. Four cases mainly presented with generalized seizures, including generalized tonic-clonic and myoclonic seizures, and the other three cases exhibited secondary generalized tonic-clonic seizures and focal seizures. Multifocal discharges were recorded in all cases during electroencephalography monitoring, including the four cases with generalized discharges initially but multifocal discharges after drug treating. Protein-protein interaction network analysis revealed that DLG3 interacts with 52 genes with high confidence, in which the majority of disease-causing genes were associated with a wide spectrum of neurodevelopmental disorder (NDD) and epilepsy. Three patients with variants locating outside functional domains all achieved seizure-free, while the four patients with variants locating in functional domains presented poor control of seizures. Analysis of previously reported cases revealed that patients with non-null variants presented higher percentages of epilepsy than those with null variants, suggesting a genotype-phenotype correlation. Significance This study suggested that DLG3 variants were associated with epilepsy with/without NDD, expanding the phenotypic spectrum of DLG3. The observed genotype-phenotype correlation potentially contributes to the understanding of the underlying mechanisms driving phenotypic variation.
Thermal Gradient Driven Variable Optical Attenuator With On-Chip CNT Absorber

A thermal gradient driven variable optical attenuator (VOA) has been designed, fabricated and characterized. This extremely simple device consists only one straight waveguide and one heater electrode. The narrow bar waveguide design causes the light field to expand in the cladding and become susceptible to temperature gradient generated by the top heater. To solve the problem with stray light, a slot filled with carbon nanotubes (CNTs) is integrated on the chip parallel to the waveguide. The selected mixture of CNTs can provide over 99.9% absorption of light from 1500 nm to 1630 nm regardless of polarization. The final device has an insertion loss lower than 0.88 dB including fiber coupling loss at both facets, and an attenuation level larger than 37 dB at 52.5 mW heater power over the entire wavelength window for both polarizations. This work demonstrates a practical VOA device to be readily connected with other polymer waveguide components, but also proves the feasibility to apply CNTs as effective stray light absorber in photonics integrated circuits.
Evaluation Model of Supervision of Service Quality of The Final Paper in The Management Study Program, Faculty of Economics and Business, Ahmad Dahlan University

This research aimed to test influence between service quality, student satisfaction also interaction between service quality and student satisfaction in the building word of mouth communication. Research conducted by Department of Management, Faculty of Economics and Business, University Ahmad Dahlan. Sample categorized with sampling convenience method and purposive sampling. Data collection efforted by giving list of question to the respondent that is contains about service quality, student satisfaction and word of mouth communication. This research use Moderator Regression Analysis. In the other side, T test and F test are used to test asked hypothesis. The result of partially regression coefficient test with T test shows that service quality, student satisfaction, interaction between them influence significantly to word of mouth communication. In the other side coefficient regression test’s result shows that variables of service quality student’s satisfaction also interaction between them both influence to word of mouth communication. Variables of service quality and student’s satisfaction are able to explain word of mouth communication variable up to 78,2% and the rest is 21,8% caused by another variable that is not included in the research model
On bilevel minimum and bottleneck spanning tree problems

We study a class of bilevel spanning tree (BST) problems that involve two independent decision‐makers (DMs), the leader and the follower with different objectives, who jointly construct a spanning tree in a graph. The leader, who acts first, selects an initial subset of edges that do not contain a cycle, from the set under her control. The follower then selects the remaining edges to complete the construction of a spanning tree, but optimizes his own objective function. If there exist multiple optimal solutions for the follower that result in different objective function values for the leader, then the follower may choose either the one that is the most (optimistic version) or least (pessimistic version) favorable to the leader. We study BST problems with the sum‐ and bottleneck‐type objective functions for the DMs under both the optimistic and pessimistic settings. The polynomial‐time algorithms are then proposed in both optimistic and pessimistic settings for BST problems in which at least one of the DMs has the bottleneck‐type objective function. For BST problem with the sum‐type objective functions for both the leader and the follower, we provide an equivalent single‐level linear mixed‐integer programming formulation. A computational study is then presented to explore the efficacy of our reformulation.
The Effects of Uncertainty Disclosures on REIT Returns

We examine the relationship between uncertainty-related disclosures in annual filings and market returns of REITs. We search annual filings (10-K) of Real Estate Investment Trusts (REITs) with the Securities and Exchange Commission (SEC) for uncertainty words. We then form rolling portfolios of REITs based on occurrences of uncertainty words in annual filing and track the returns. Our main finding is that there are no statically significant abnormal returns to these REIT portfolios. This supports the notion that the stock market is efficient in quickly incorporating any new information revealed in annual reports when the information becomes available.
Service System Innovation and Competitiveness In an Emerging Market: The Moderating Role of Managerial Competencies

This study empirically examines the correlation between service system innovation and competitiveness in Nigeria, an emerging market, by focusing on the country’s mobile telecommunications sector. The moderating effect of managerial competencies was also tested. Due to the contribution of the telecommunications sector to the economic well-being of Nigeria and the world’s economy at large, the sector witnesses a high level of competition among its players, resulting in unsustainable price wars, negatively impacting the telecommunication firms and their ability to invest in service improvements. Thus, there is a need for a study into the factors affecting competitiveness in the telecommunications sector. A cross sectional survey was used because the study explored the relationships between service system innovation, managerial competencies and competitiveness. The study used a survey instrument, which was sent to 450 respondents using Google Forms, out of which 230 responded. The 230 returned copies represented a 51.1 percent return rate, which satisfied the minimum required return rate for a cross sectional study. The study’s hypotheses were tested using the partial least squares structural equation model (PLS-SEM). The finding revealed that service system innovation (idea development, service development and commercialization) enhances the competitiveness of mobile telecommunication firms. Thus, an increase in idea development, service development and commercialization is essential for a competitive advantage. Also, managerial competencies were found to significantly moderate the relationship between service system innovation and competitiveness. The study provides a new insight into how service system innovation affects the competitiveness of telecommunications companies, particularly in emerging markets, with Nigeria as the focal point. It also shows the role managerial competencies play in the telecommunications sector. This study offers proof of the traits of service system innovation, managerial competencies, and competitiveness in the telecommunications sector. It also developed and tested a scale for measuring these variables, which future studies could adopt.