Miracle-dz/UniFAR-FTUs huggingface dataset:
STEM Productive Learning of Lower Secondary School in Southern Zone, Malaysia

Sciences, Technology, Engineering, Mathematics Productive Learning (STEMPL) is an initiative of the Ministry of Education (MoE) to promote creative teaching and learning among STEM teachers, with the ultimate goal of producing students who can think creatively, systematically, and logically in problem-solving. Therefore, this study aims to identify the level of STEMPL practices among STEM teachers in lower secondary schools in the Southern Zone, Malaysia. The differences in STEMPL practices are also examined according to the subjects taught and the relationship of STEMPL practices based on the teaching experiences of the STEM teachers. A survey design was used in this study, applying a quantitative approach. A total of 556 STEM teachers who teach Science, Design and Technology, Computer Science, and Mathematics at the lower secondary level were selected using stratified random sampling techniques. Data were collected using a questionnaire developed by researchers, proven valid and highly reliable for measuring STEMPL practices among STEM teachers. SPSS 23 was used to analyse the data. The findings showed that the overall level of STEMPL practices among STEM teachers were moderate. The analysis also found significant differences in STEMPL practices based on the subjects taught and no significant relationship between STEMPL practices and teaching experience of the STEM teachers. These findings provide input to stakeholders that the STEMPL practices of STEM teachers need improvement in order to realise the education aspirations in Malaysia. Suggestions to improve existing practices are also provided to guide stakeholders in planning, implementing, and evaluating effective strategies.
LSTM-Based Multi-Task Method for Remaining Useful Life Prediction under Corrupted Sensor Data

Data-driven remaining useful life (RUL) prediction plays a vital role in modern industries. However, unpredictable corruption may occur in the collected sensor data due to various disturbances in the real industrial conditions. To achieve better RUL prediction performance under this situation, we propose a novel multi-task method for RUL prediction, which is named multi-task deep long short-term memory (MTD-LSTM). In MTD-LSTM, convolutional neural network (CNN) and long short-term memory (LSTM) are first employed for feature extraction and fusion. Then, the extracted features are fed into the multi-task learning module, which contains missing value imputation and RUL prediction module. The missing values imputation task and RUL prediction task are performed simultaneously. The purpose of the missing value imputation is to obtain integral degradation information by recovering the complete data; thus, the RUL prediction task performs better under corrupted sensor data. In addition, a novel loss term is proposed to smooth the RUL prediction results without any manual post-processing. The effectiveness of the proposed method is verified on the simulated dataset based on the C-MAPSS dataset.
Interaction between resonant scaling and fundamental frequency on codings in auditory system

It has been modeled that the auditory system encodes acoustic signals into two independent informations. One is tonotopic information reflecting the frequency response characteristics of the basilar membrane, and another is periodicity information reflecting the temporal patterns of phase-locked auditory nerve firing. Based on the previous study about size perception of sound source, a hypothesis can be proposed that the tonotopic and periodicity information are combined into an internal "two-dimensional representational plane". The current study tested that this hypothesis by conducting an experiment to recognize the transition pattern of vowel-like sounds on the plane. Listeners were able to recognize the transition patterns on the axes of tonotopic and periodicity information exclusively. However, they were unable to follow the trajectories of sounds in the two-dimensional representational plane. The vowel-like sounds were simulated by a computational model of auditory system, AIM. While the "excitatio.
The Influence of Overminus Lens Therapy on Control of Intermittent Exotropia: A Meta-Analysis of Randomized Clinical Trials

Background: Intermittent exotropia (IXT) is the most common type of strabismus, overminus lens (OML) therapy is frequently prescribed to treat IXT. Objectives: The purpose of this study was to compare the effectiveness of OML and observation in the treatment of IXT. Method: An exhaustive search of the literature in PubMed, Embase, Web of Science, and Cochrane Library databases was performed until July 2022. No language restrictions were used. The literature was rigorously screened according to eligibility criteria. Weighted mean differences and 95% confidence intervals (CIs) were calculated. Results: A total of 4 articles with 561 participants were included in this meta-analysis. Our pooled results showed that OML demonstrated superior outcomes compared with observation, with greater decreases in distance and near exodeviation control (MD = −1.08, 95% CI: −1.96 to −0.20, p = 0.02; MD, −0.64, 95% CI: −1.15 to −0.13, p < 0.001). Patients who received OML therapy had a greater decrease in the deviation at both distance and near (MD = −4.00, 95% CI: −7.03 to −0.98, p < 0.001; MD = −4.79, 95% CI: −6.29 to −3.30, p < 0.001). There was no statistical difference between the two groups in terms of post-treatment proximal stereopsis (MD, 0.00, 95% CI: −0.08 to 0.08, p = 1.00). Conclusions: The present meta-analysis indicated that OML therapy was effective in improving the control and decreasing exodeviation angle of IXT. However, it seemed not to be effective in improving the level of near stereopsis.
Explicit expressions for moments of Pareto order statistics

Pareto distributions are the first and most popular distributions used in economics and finance. The recent book by Kleiber and Kotz (2003) describes them as the pillar of statistical ‘size’ distributions. Suppose X1, X2, . ,Xn is an independent sample from a Pareto distribution and let X1:n5X2:n5 5Xn:n denote the corresponding order statistics. The problem of the moments of Xk:n is an important one. Three real examples in the context of finance are as follows.
System Analysis Based on Lipid-Metabolism-Related Genes Identifies AGT as a Novel Therapy Target for Gastric Cancer with Neoadjuvant Chemotherapy

Gastric cancer (GC) is one of the most common causes of cancer-related deaths worldwide, and chemotherapy is still a standard strategy for treating patients with advanced GC. Lipid metabolism has been reported to play an important role in the carcinogenesis and development of GC. However, the potential values of lipid-metabolism-related genes (LMRGs) concerning prognostic value and the prediction of chemotherapy responsiveness in GC remains unclear. A total of 714 stomach adenocarcinoma patients were enrolled from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Using univariate Cox and LASSO regression analyses, we developed a risk signature based on LMRGs that can distinguish high-GC-risk patients from low-risk patients with significant differences in overall survival. We further validated this signature prognostic value using the GEO database. The R package “pRRophetic” was applied to calculate the sensitivity of each sample from high- and low-risk groups to chemotherapy drugs. The expression of two LMRGs, AGT and ENPP7, can predict the prognosis and response to chemotherapy in GC. Furthermore, AGT significantly promoted GC growth and migration, and the downregulation of AGT enhanced the chemotherapy response of GC both in vitro and in vivo. Mechanistically, AGT induced significant levels of epithelial–mesenchymal transition (EMT) through the PI3K/AKT pathway. The PI3K/AKT pathway agonist 740 Y-P can restore the EMT of GC cells impaired by AGT knockdown and treatment with 5-fluorouracil. Our findings suggest that AGT plays a key role in the development of GC, and targeting AGT may help to improve the chemotherapy response of GC patients.
Book Review: Exploratory and Confirmatory Factor Analysis: Understanding Concepts and Applications

Exploratory and Confirmatory Factor Analysis: Understanding Concepts and Applications provides a concise (162 pages of text) and basic overview of two disciplines of assessing structure: exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). As Thompson explains, the book was written ‘‘to strike a happy medium between accuracy and completeness versus overwhelming technical complexity’’ (p. ix). Example data sets are used throughout the book to demonstrate important concepts. It must be noted, however, that this is not a cookbook that presents analysis steps along with software syntax and output. Instead, the focus is on the explanation of concepts and the logic of EFA and CFA. As the title implies, there is coverage of both EFA and CFA, but the majority of the book is dedicated to exploratory procedures. In addition, much of the discussion and demonstration of EFA focuses on the use of principal components (PC) analysis, which could be disappointing for those wanting a more detailed discussion of principal axis factor analysis (PAF). The book consists of 12 chapters. Chapter 1 provides a very brief introduction to both EFA and CFA. The purposes of factor-analytic methods are succinctly presented, and the importance of theory when employing CFA is emphasized. Chapter 2 introduces the basics of matrix algebra, major EFA concepts (e.g., pattern coefficients, structure coefficients, eigenvalues, number of factors), and the practical issue of sample size. A highlight of this chapter is the presentation of EFA concepts within the rubric of the general linear model and the emphasis on composite variables. By providing a quick introduction to matrix algebra concepts, Thompson hopes that readers ‘‘can have some understanding of what is being done in certain analyses’’ (p. 13). However, the coverage of matrix algebra concepts did not seem adequate to understand the formulas presented throughout the book. Given that the book was not meant to be overly technical, an appendix that more thoroughly introduces and explains the formulas by working through the examples would have been very useful. This would allow the reader uninterested in this level of detail to continue reading the body of the book, while allowing others to gain a better understanding of ‘‘what is being done in certain analyses’’ (p. 13). Currently, there is not enough detail for those unfamiliar with matrix algebra (which would seem to be a characteristic of the intended audience) to understand the formulas. Chapter 3 clearly introduces the major decisions the researcher must make when conducting EFA. The type of matrix to analyze, the number of factors to extract and the guidelines for making this decision, the extraction method (PC vs. PAF), and factor rotation are all briefly described. The chapter ends with a short description of factor scores. A discussion of the appropriateness of
HETEROSKEDASTICITY-AUTOCORRELATION ROBUST TESTING USING BANDWIDTH EQUAL TO SAMPLE SIZE

Asymptotic theory for heteroskedasticity autocorrelation consistent (HAC) covariance matrix estimators requires the truncation lag, or bandwidth, to increase more slowly than the sample size. This paper considers an alternative approach covering the case with the asymptotic covariance matrix estimated by kernel methods with truncation lag equal to sample size. Although such estimators are inconsistent, valid tests (asymptotically pivotal) for regression parameters can be constructed. The limiting distributions explicitly capture the truncation lag and choice of kernel. A local asymptotic power analysis shows that the Bartlett kernel delivers the highest power within a group of popular kernels. Finite sample simulations suggest that, regardless of the kernel chosen, the null asymptotic approximation of the new tests is often more accurate than that for conventional HAC estimators and asymptotics. Finite sample results on power show that the new approach is competitive.
Long‐term performance and reliability of silicon heterojunction solar modules

The high‐efficiency silicon heterojunction (SHJ) technology is now perceived mature enough to enter the Giga‐Watt manufacturing scale with several players around the globe. The growth of the SHJ technology requires confidence from manufacturers, investors, and system developers about its reliability and long‐term performance. In this work, we perform a literature survey collecting performance data (and performance loss rates [PLRs]) published for SHJ modules. Publications on this specific subject are still limited; however, enough available data exist to drive some preliminary conclusions. Despite a long list of caveats specific to this type of meta‐analysis, when considering all published datasets, we obtain for SHJ modules median and average PLR values of 0.56%/year and 0.70%/year, respectively. These numbers are in line with PLR generally reported for field‐deployed crystalline silicon (c‐Si modules). We then apply a filtering procedure to distinguish what we perceive to be high‐accuracy datasets from less accurate ones. This methodology is understandably arbitrary, but it helps increasing the robustness of the present analysis. Our refined analysis leads us to slightly higher PLR for SHJ modules: 0.80%/year and 0.83%/year for median and average values, respectively. These values fall between previously reported PLR of c‐Si and thin‐film modules. Additionally, we observe some mild correlations between the PLRs and the climatic conditions of the installation sites, even if we need to stress that for each climate, we find a large variability, including a PLR value as low as 0.29%/year. We complement the survey with information about the main failure modes reported in the literature for this technology and an analysis of the limits and caveats for this type of study. The most significant one is that the reported numbers refer—for the vast majority—to modules from just one manufacturer (i.e., the first company manufacturing and commercializing the SHJ technology). We finally point out that a deep understanding of the potential weaknesses of the technology—collected over the years—has led to several improvements in terms of reliability. A careful material choice and module design may in fact allow the SHJ technology targeting extended service lifetimes of 35+ years.
Cross-Layer-Design QoS Policy Management Framework for Wireless Heterogeneous Network

In future wireless and/or mobile networks, voice, highcapacity data, and multimedia contents might be converged onto a single wireless network platform, which leads to increased complexity and heterogeneity. To realize such advanced communications, it is necessary to consider users’ QoS requirements. In our related work, we proposed a novel QoS framework based on cross-layer design that is jointly optimized among all layers. In this paper, we propose a detailed protocol scheme, that is, a management scheme of users’ QoS requirements for wireless heterogeneous network. Moreover, we reveal the effectiveness of our framework.
Detecting Intrapartum Fetal Hypoxia from Cardiotocography Using Machine Learning

Intrapartum cardiotocography (CTG) can identify babies at risk of fetal hypoxia by detecting changes in fetal heart rate and uterine contractions during labour. However, variability in CTG interpretations affects intervention timings. Machine learning (ML) has been applied to this problem and has succeeded. We proposed to use a 5-minute Apgar score as the benchmark for hypoxia in our ML algorithms as it has shown a high correlation with abnormal CTG and better clinical support decision-making than pH umbilical cord blood. We used the CTU-UHB database containing 552 CTGs. We trained and compared five algorithms of decision tree (DT), random forest (RF), support vector machine (SVM), k-Nearest Neighbour (kNN) and artificial neural network (ANN). Performances were evaluated using precision, recall, F1 score and AUROC. The ANN with four deep layers had the highest recall (100%), while the RF classifier had the highest F1 (97%), AUROC (99.73%) and precision (97%) (Table 1). The longest deceleration length is the most important feature among the 21 features. Apgar scores can be used as a surrogate hypoxia marker for classifying CTG, making the model results more informative for clinical decision-making.
Load-Level Control Design for Demand Dispatch With Heterogeneous Flexible Loads

Over the past decade, there has been significant progress on the science of load control for the creation of virtual energy storage. This is an alternative to demand response, and it is termed demand dispatch. Distributed control is used to manage millions of flexible loads to modify the power consumption of the aggregation, which can be ramped up and down, just like discharging and charging a battery. A challenge with distributed control is heterogeneity of the population of loads, which complicates control at the aggregate level. It is shown in this article that additional control at each load in the population can result in a far aggregate model. The local control is designed to flatten resonances and produce approximately all-pass response. Analysis is based on mean-field control for the heterogeneous population; the mean-field model is only justified because of the additional local control introduced in this article. Theory and simulations indicate that the resulting input–output dynamics of the aggregate has a nearly flat input–output response: the behavior of an ideal, multi-GW battery system.
An Improved High-Gain Observer Design for Flexible Needle Steering

In the robot-assisted puncture surgery, the posture of the needle tip may not be available directly owing to the small dimension of the needle tip and the existence of measurement noise. On the other hand, the existing high-gain observers are robust against model uncertainty but sensitive to measurement noise. The peaking phenomenon is also a typical problem when applying those high-gain observers. Aiming at these issues, an improved high-gain observer is devised to observe the pose of the needle tip in this brief. To reduce the effect of the measurement noise and the peaking phenomenon, the power of observer gain is designed as 2 by extending the observer degree and a correction term of measurement estimation is added to improve the property of the observer against the measurement noise. Finally, the performance of the proposed observer is verified via a simulation example.
Prevalence of Antibiotic Residues in Pork in Kenya and the Potential of Using Gross Pathological Lesions as a Risk-Based Approach to Predict Residues in Meat

The human population is growing and urbanising. These factors are driving the demand for animal-sourced proteins. The rising demand is favouring livestock intensification, a process that frequently relies on antibiotics for growth promotion, treatment and prevention of diseases. Antibiotic use in livestock production requires strict adherence to the recommended withdrawal periods. In Kenya, the risk of residues in meat is particularly high due to lack of legislation requiring testing for antibiotic residues in meat destined for the local market. We examined pig carcasses for gross pathological lesions and collected pork samples for antibiotic residue testing. Our aim was to determine if a risk-based approach to residue surveillance may be adopted by looking for an association between lesions and presence of residues. In total, 387 pork samples were tested for antibiotic residues using the Premi®Test micro-inhibition kit. The prevalence of antibiotic residues was 41.26% (95% CI, 34.53–48.45%). A logistic regression model found no significant associations between gross pathological lesions and the presence of antibiotic residues. We recommend that the regulating authorities strongly consider routine testing of carcasses for antibiotic residues to protect meat consumers. Future studies should research on farming practices contributing to the high prevalence of residues.
Aerodynamic Forces on Multiple Unit Trains in Cross Winds

This paper describes the results of wind tunnel tests that were carried out to measure the aerodynamic characteristics of an electrical multiple unit (EMU) vehicle in a cross wind. The measurements were made on a 1/30 scale model of the Class 365 EMU in a simulation of the natural wind. The time histories of surface pressures were measured at a large number of points over the vehicle from which the aerodynamic characteristics and force coefficients were determined. This paper describes the complex fluctuating pressure field over the vehicle, through a consideration of the mean and fluctuating pressure coefficients and their spectra, and through a proper orthogonal decomposition analysis, which identifies the major modes of this distribution. The mean, fluctuating, and extreme aerodynamic side and lift forces are also discussed. It is shown that the flow pattern around the vehicle is dominated by large windward roof corner pressure fluctuations.
Exploring the perceptions of lecturers and final year students about the infusion of inclusion in initial teacher education programmes in South Africa

Introduction South Africa embraced the move to inclusive education after the political transformation in 1994 by partaking in and subscribing to the international Education for All (EFA) drive initiated in 1990 at the Jomtien World Conference on Education for All, which declared that all children, youth and adults should receive a basic education. Furthermore, the Salamanca Statement of 1994 the United Nations (UN) Convention on the Rights of Persons with Disabilities (CRPD) of 2006 and the Sustainable Development Goal 4 are internationally regarded as the most important influence on the transformation of education systems to become more inclusive and consequently continue to have an important influence on education policies and practices in South Africa. The key policy driving inclusive education in South Africa is Education White Paper 6 (EWP6). EWP6 affirms that teachers play a central role in implementing an inclusive education system. Therefore, training is emphasized as a key strategy to enable educators to become more inclusive in their teaching practices. The focus of this article is on Initial Teacher Education (ITE) for inclusion. Influenced by international developments to transform ITE programmes and the national endorsement of inclusive education the Department of Higher Education and Training (DHET) embarked on a project called the Teaching and Learning Capacity Development Improvement Project (TLCDIP). The project reported on in this article was one facet of the TLCDIP and focused specifically on teacher education for inclusion in the Foundation (Reception to Grade 3) and Intermediate Phases (Grade 4 to 6) of the Baccalaureus Educationis (B Ed) programme. Methods The primary research aim was: To explore the perceptions of final year students and their lecturers in ITE programmes regarding the preparation of pre-service teachers for teaching in inclusive and diverse learning environments. A qualitative research approach was employed to gain in-depth and rich data. Purposive sampling was used including final year students and their lecturers. Open questionnaires and group interviews were employed as data generation strategies. Results An inductive thematic analysis showed that the following themes were identified by the participants as critical to be considered in the development and implementation of ITE programmes: Understanding inclusive education, which is also linked to knowledge; the disconnect between theory and practice, the lack of knowledge and practical experience regarding inclusive teaching strategies and how inclusion is addressed in the B Ed curriculum.