Miracle-dz/UniFAR-FTUs huggingface dataset:
EMD and VMD in Pre-Movement EEG Signal Analysis: A Hybrid Mode Selection to Classify Upper Limb Complex Movements Using Statistical Features

Electroencephalogram (EEG) signals, inherently non-stationary and non-linear, present significant challenges in their processing and interpretation. This paper presents a hybrid mode selection approach using two advanced decomposition methods: Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD), to analyze these signals, targeting their application in the classification of upper limb complex movements for enhanced prosthetic limb control and rehabilitation therapy assessment. Using optimized statistical features extracted from selected modes, Intrinsic mode functions (IMFs) via EMD and modes via VMD, we seek to better distinguish neural activities in pre-movement EEG signal. Our methodology involves the following two strategies: straightforward extraction of statistical features from modes yielded by EMD and VMD; a genetic algorithm (GA) feature selection technique to select the most optimal set from these statistical features. These derived features train machine learning (ML) classifiers to differentiate limb movements. The results, derived from proprietary dataset from Aalborg University, Denmark, comprising five distinct upper limb movements, demonstrate the effectiveness of our hybrid approach. The usage of EMD and VMD significantly enhanced the discriminatory power of the extracted features, leading to improved classification performance. Furthermore, our hybrid approach yielded classification accuracies of 93.1 % and 95.6% with EMD and VMD respectively when the K-NN classifier was deployed with a 10-fold cross-validation. K-NN classifier outperformed traditional ML classifiers in terms of computational time, highlighting its potential as lightweight yet robust algorithm for classification of complex movements. The primary goal is to present and validate a hybrid mode (IMFs/modes) selection approach through EMD and VMD to analyze EEG signals associated with upper limb complex movements.
Frequency and Factors Associated with Early Repolarization Changes in ECG in Patients Presenting With Chest Pain

Objectives: The “early repolarization (ER)” pattern, previously regarded as benign, has recently shown associations with adverse outcomes, including all-cause, arrhythmic and cardiac mortality. This study aimed to assess the prevalence and factors linked to ER changes in the ECG among chest pain patients at a tertiary cardiac center. Methodology: We enrolled 271 patients aged 18-80 with chest pain complaints. Baseline 12-lead ECGs were used to assess the ER pattern. Multivariable binary logistic regression was conducted, and “odds ratios (OR)” with 95% “confidence intervals (CI)” were reported. Results: Of the 271 patients, 162 (59.8%) were male, with a mean age of 55.3 ± 10 years, and 40 (14.8%) were ≤45 years old. The ER pattern was present in 92 (33.9%) patients. The ER pattern was associated with low “body mass index (BMI)” (OR=0.85 [95% CI: 0.77 - 0.94; p=0.002]), shorter T-wave duration (OR=0.99 [95% CI: 0.98 - 1.00; p=0.008]), and lower heart rate (OR=0.94 [95% CI: 0.90 - 0.98; p=0.007]). Additionally, the odds of ER pattern were lower in patients with “ST-elevation myocardial infarction (STEMI)” (OR=0.23 [95% CI: 0.07 - 0.72; p=0.012]) and non-STEMI (OR=0.21 [95% CI: 0.07 - 0.63; p=0.006]) compared to non-cardiac chest pain. Conclusion: Early repolarization is a common ECG pattern in one-third of chest pain patients. Associated factors include low BMI, shorter T-wave duration, and lower heart rate, and it is less frequent in patients with STEMI and non-STEMI.
Can the Inflammatory Cell Ratio NLR and PLR be Used as a Reliable Marker in Colon Cancer? A Prospective Study

Microabstract In recent years, the neutrophil–lymphocyte ratio (NLR) and platelet–lymphocyte ratio (PLR) have been used in diagnosis, staging, and determination of long-term prognosis of various cancers. The study involved 90 colon cancer patients diagnosed between July 2021 and December 2022. The relationship between NLR/PLR and tumor features was investigated. In predicting colon cancer tumor depth/invasion, PLR was found to be significantly more precise than NLR.
Knowledge and Practices on Periconceptional use of Folic Acid for the Prevention of Neural Tube Defects Among Women in Childbearing Age in Boralesgamuwa Medical Officer of Health Area, Sri Lanka

Introduction: Neural Tube Defects (NTD) remain a major public health burden globally. It is widely acknowledged that folic acid plays a vital role to reduce the risk of developing NTDs. The study aimed to determine knowledge, and practice regarding periconceptional use of folic acid for the prevention of NTD among women in childbearing age at Boralesgamuwa Medical Officer of Health (MOH) area, Sri Lanka.Methods: A descriptive cross-sectional study was carried out among randomly selected 422 women in childbearing age in the said MOH area. Data were collected through a pre-tested interviewer administered questionnaire. The overall knowledge level was classified as adequate (>75%), moderately adequate (50-75%) and inadequate (<50%). Results: Out of 422 participants, the majority was Sinhalese (95.7%), married (66.8%) and unemployed (62.1%). Mean age was 32.02 ±8.625 years. Nearly 56 % had heard about NTDs and 95.5% had heard about folic acid. A majority (279, 66.1%) had ‘inadequate’ level of knowledge, 120 (28.4%) had ‘moderately adequate’ knowledge and only 5.5% (n=23) had ‘adequate’ knowledge. Approximately 60% had taken folic acid and out of them only 42.4% had started folic acid before pregnancy. Age (p<0.001), marital status (p<0.001), parity (p<0.001), educational qualification of participants (p<0.001), monthly household income (p=0.004) and where health services sought from (p <0.001) were associated with the level of knowledge.Conclusions: Majority of participants had inadequate knowledge regarding periconceptional use of folic acid for the prevention of NTDs. However, out of those who have used FA, majority had started to take it before pregnancy. The study findings warrant more educational programmes on the topic bridging the gap of knowledge among women in childbearing age in the study setting.
Automatic estimation of the ejection fraction from diastole and systole ultrasound images by a simplified end-to-end U-Net neural network

Left ventricular ejection fraction (LVEF) is the most useful cardiac index to assess the cardiac function of patients from echocardiograms (ECHO). Cardiologists manually delineate the left ventricular (LV) contour at the end of the diastolic (EDV) and systolic (ESV) times to calculate LVEF. This paper presents a novel end-to-end deep-learning model to directly estimate LVEF from only a pairwise sample (EDV and ESV) of ECHO images, which consists of two stages. Firstly, a simplified U-Net is constructed by reducing the number of convolutional filters per layer and removing the deepest layer. Secondly, the encoding part of the U-Net is used to extract features from the input ECHO images and their differences are concatenated into a vector entered into interconnected convolutional layers that reduce the dimensionality and produce an output that has a simple linear regressor applied to it for obtaining the ejection fraction value. The experimental results show a mean square error (MAE) of 5.61 and a coefficient of determination R2 of 0.76 of automatic estimation of LVEF. This suggests that a simplified end-to-end deep-learning approach is able to both directly estimate LVEF and auto-delineate the LV from only a pairwise sample (EDV and ESV) from ECHO, which could be more viable and efficient for clinical practice routine.
EnvClus*: Extracting Common Pathways for Effective Vessel Trajectory Forecasting

The task of accurately forecasting the trajectory of a vessel, and in general a moving object operating in free space until its destination remains an open challenge. This paper addresses this problem by describing an unsupervised data-driven framework for short and extended horizon forecasts, from the perspectives of data mining and machine learning. We propose a data-driven algorithmic approach named “EnvClus*’ that models efficiently historical vessel trajectories at a global scale, forming a mobility graph that depicts the most likely movements among two ports. EnvClus* is able to make tailored route forecasts considering the characteristics of the vessels (i.e. length, draught) along with information regarding the executed trip. The proposed method is able to forecast the most likely realistic and smooth trajectory from a given query position of a vessel (entire route or underway forecasting) towards its destination port. We illustrate the accuracy and effectiveness of our method through a series of scenarios for long and short term forecasting using real world data from around the globe. These experiments indicate an overall improvement of 33% over state-of-the-art and baseline methods; with the benefits of our approach being more apparent when dealing with longer trips from container vessels.
Wavelength and polarization state synchronization measurement based on MMF scattering pattern

We presented a system for the measurement of wavelength and polarization state synchronization based on multimode fiber speckle. By employing convolutional neural network, we successfully established the mapping relationship between speckle patterns and wavelengths, as well as between speckle patterns and Stokes parameters. Consequently, the system accomplishes synchronized measurement of both wavelengths and polarization states. The system achieves a wavelength resolution of 0.5 nm and can distinguish two SOPs on the Poincaré sphere with a vector difference of 0.365.
Text-to-Speech for Low-Resource Agglutinative Language With Morphology-Aware Language Model Pre-Training

Text-to-Speech (TTS) aims to convert the input text to a human-like voice. With the development of deep learning, encoder-decoder based TTS models perform superior performance, in terms of naturalness, in mainstream languages such as Chinese, English, etc. Note that the linguistic information learning capability of the text encoder is the key. However, for TTS of low-resource agglutinative languages, the scale of the $< $text, speech$>$ paired data is limited. Therefore, how to extract rich linguistic information from small-scale text data to enhance the naturalness of the synthesized speech, is an urgent issue that needs to be addressed. In this paper, we first collect a large unsupervised text data for BERT-like language model pre-training, and then adopt the trained language model to extract deep linguistic information for the input text of the TTS model to improve the naturalness of the final synthesized speech. It should be emphasized that in order to fully exploit the prosody-related linguistic information in agglutinative languages, we incorporated morphological information into the language model training and constructed a morphology-aware masking based BERT model (MAM-BERT). Experimental results based on various advanced TTS models validate the effectiveness of our approach. Further comparison of the various data scales also validates the effectiveness of our approach in low-resource scenarios.
A Masked Autoencoder -Based Approach for Defect Classification in Semiconductor Manufacturing

In semiconductor manufacturing, automatic defect classification is of paramount importance. Even the slightest defects can compromise chip performance or lead to complete failure, subsequently impacting chip yield rates. Currently, defect classification still heavily relies on manual processes, often leading to a significant number of misclassifications. In this paper, we propose a method based on MAE (Masked Autoencoder) for automatic defect classification in chip manufacturing. The core concept of MAE involves applying a high-proportion random mask to images, creating a challenging image reconstruction task. Using the unmasked image patches, the model predicts the masked patches for self-supervised pretraining. When applied to downstream tasks, this methodology enhances the model's generalization and feature representation capabilities. In a task-agnostic way, we conduct self-supervised pretraining on a large number of SEM (Scanning Electron Microscope) images without the necessity of any labels. In a task-specific way, we fine-tune the network using a limited amount of highly reliable labels. Experimental results suggest that our method is capable of accurately classifying defects with minimal labeled data, greatly reducing labor costs.
Analysis of the Effect Current Ratio on Return on Assets at PT Astra Otoparts TBK.

This study makes an important contribution in understanding the relationship between current ratio and return on assets at PT Astra Otoparts Tbk. Through the use of a simple linear regression test on financial statement data for the period 2015-2022, this study intends to explain the extent to which the current ratio affects the company's return on assets. Current ratio, as the independent variable, is used as an indicator of the company's ability to meet its short-term obligations, while return on assets, as the dependent variable, reflects the company's level of profitability. The results show that, although the current ratio has a positive influence on return on assets, the influence is not statistically significant. In other words, the increase in current ratio does not contribute significantly to the increase in return on assets at PT Astra Otoparts Tbk. The findings can provide valuable insights for company management in designing financial strategies, especially in terms of managing short-term assets and liabilities. In the context of the automotive and spare parts industry in Indonesia, where PT Astra Otoparts Tbk operates, an in-depth understanding of the factors that influence return on assets is crucial. The results of this study can serve as a basis for similar companies to evaluate and improve their financial performance, optimize asset allocation, and manage short-term liabilities more effectively. Further research and the integration of other factors in financial analysis can help shape a more holistic and sustainable business strategy amidst changing market dynamics.
Wilderness visitor experiences: A selective review of 50 years of research

Two of the foremost conclusions from 50 years of research on wilderness visitors are that experiences are highly idiosyncratic and visitors are highly adaptable. The reasons people visit wilderness, their experiential aspirations, and their experiences in wilderness vary greatly among people and within people from visit to visit. Along with people's adaptability to the conditions they find in wilderness, this diversity challenges managers in their efforts to provide high-quality wilderness experiences. Despite the existence of extensive research literature, managers seeking to steward wilderness experiences still must make difficult decisions about who and what they are managing for.
Applied Arabic Textbook Readability Level for University Students at Al-Balqa Applied University

Readability is considered one of the main indicators of students' ability to understand reading texts. The purpose of this study was to define the level of Applied Arabic textbook readability for university students at Al-Balqa Applied University in Jordan. In order to do so, Cloze Test was administrated on four texts that students didn't read before taken from the Applied Arabic textbook. A sample of (54) university students participated in the study. The results showed that the four texts were within the frustration level, as the students' results on the four texts ranged between (0.46-0.66). There were no statistically significant gender differences on the readability level; indicating that both male and female students are facing difficulty in reading and understanding the texts of books. The study suggested the need for experimentation of textbooks on students to determine their readability level before implementing them to students to be appropriate to students’ reading levels in order to address the problems students face while reading a text.
Coherent Interannual–Decadal Potential Temperature Variability in the Tropical–North Pacific Ocean and Deep South China Sea

Climate variability over the Tropical and North Pacific Ocean (TPO and NPO, respectively) modulates marginal sea variability. The South China Sea (SCS), the largest marginal sea in the western NPO, is an outstanding example of a region that responds quickly to climate change. However, there is considerable uncertainty regarding the response of the deep SCS to large‐scale climate variability. Multivariate empirical orthogonal function analysis revealed three prominent modes of interconnected temperature anomaly fluctuations within the TPO and NPO. These coherent modes highlight the interactive dynamics among climate variations and reveal their modulation mechanisms for previously less explored potential temperature variabilities in the deep SCS. On the atmospheric bridge, external forces modify the upper‐layer Luzon Strait Transport (LST) by adjusting the Ekman transport and Kuroshio intrusion. For the oceanic pathway, climate variations disturb the deep‐layer LST by adjusting the barotropic flows in the upper layer.
Evaluating Economic Outcomes: Single-Use Aspirin vs. Aspirin-Clopidogrel in Ischemic Stroke Patients Based on Barthel Index Scores

Ischemic stroke is a catastrophic disease that causes large costs in Indonesia. The high prevalence of stroke results in increased morbidity and mortality rates, which will impact the socioeconomic status of stroke patients. Cost Effectiveness Analysis (CEA) is a method in pharmaco-economics that aims to make decisions regarding the rational choice of therapy and evaluate the economic impact on ischemic stroke patients. This study aims to determine the Incremental Cost Effectiveness Ratio (ICER) value of antiplatelet therapy in the aspirin-clopidogrel combination group and the single aspirin group in ischemic stroke patients treated at the Regional General Hospital. Dr. M. Djamil Padang. The clinical parameter observed was the Barthel Index (BI) value. This research is a descriptive study with retrospective data collection using medical record data and the Hospital Management Information System (SIMRS) at RSUD Dr. M.Djamil Padang. The cost data shows the total direct medical costs incurred in treating ischemic stroke. Based on the results obtained, the Incremental Cost Effectiveness Ratio (ICER) value is IDR (-) 401,302. 71 per 1% increase. In this study it can be concluded that the aspirin-clopidogrel combination has better cost effectiveness than aspirin alone.
The Importance of Early Detection of Spinal Tumors Through Magnetic Resonance Imaging in Chiropractic Practices

Primary spinal tumors such as schwannomas are uncommon causes of back pain that can be easily missed during the initial workup. Delayed diagnosis is associated with further neurological impairment. A 46-year-old man presented with a six-month history of progressive lower back pain and left leg radiculopathy. Previous treatments failed, including medications, physical therapy, acupuncture, and chiropractic manipulations. Examination revealed weakness (4/5) in left knee extension and ankle dorsiflexion. Magnetic resonance imaging (MRI) revealed a 2-cm intraspinal schwannoma at the L4 level with nerve root compression. The patient underwent laminectomy and gross total resection without any complications. The patient had near-complete symptomatic resolution six weeks postoperatively and returned to normal functioning. After four months of postoperative rehabilitation, the patient remained asymptomatic. This case reinforces the urgent need for early MRI in the presence of neurological deficits and other symptoms, despite normal radiographs. An increased suspicion of spinal tumors can prevent delays in diagnosis and minimize adverse outcomes. Multidisciplinary care optimizes the treatment of complex cases.
Pengembangan LKPD Berbasis Android dengan Pendekatan STEM untuk Meningkatkan Kemampuan Berpikir Kritis

Berpikir kritis adalah keterampilan dasar yang dibutuhkan di abad ke-21. Penelitian ini bertujuan untuk mengetahui kelayakan, efektivitas, pengaruh dan respon siswa LKPD berbasis Android dengan Pendekatan STEM untuk meningkatkan kemampuan berpikir kritis siswa. Jenis penelitian ini adalah R&D dengan tipe ADDIE. Sampel penelitian ini menggunakan teknik purposive sampling pada uji coba skala kecil dan skala besar di SMP Negeri 2 Demak. Aspek kemampuan berpikir kritis yang diteliti adalah aspek menjelaskan, analisis, evaluasi, menyimpulkan, interpretasi, sintesis, inferensi. Hasil penelitian menunjukkan bahwa, 1) LKPD berbasis Android dengan pendekatan STEM layak digunakan dalam pembelajaran IPA materi alat optik; 2) LKPD berbasis Android dengan pendekatan STEM mampu untuk meningkatkan kemampuan berpikir kritis dengan nilai N-Gain sebesar 70,82% dengan kategori tinggi; 3) LKPD berbasis Android dengan pendekatan STEM berpengaruh terhadap kemampuan berpikir kritis dengan thitung sebesar 24,484; 4) Respon siswa terhadap LKPD berbasis Android dengan pendekatan STEM diperoleh presentase 85% dengan kategori sangat setuju. Berdasarkan hasil penelitian ini, dapat disimpulkan bahwa LKPD berbasis Android dengan pendekatan STEM layak digunakan untuk meningkatkan kemampuan berpikir kritis.
Optimization of cultural parameters for lipase production by indigenous stenotrophomonas sp. And pseudomonas aeruginosa isolated from lipid-rich environments

The present study aims to identify prospective lipase-producing bacteria and optimize different culture parameters to produce maximum lipase. A total of 37 lipase-positive bacteria were isolated from lipid-rich environments. Among them, strains S1N-2 and S10P-1 were found to have potential and were molecularly identified as Stenotrophomonas species ICMM10 and Pseudomonas aeruginosa BA7823, respectively. For maximum lipase production, the optimum pH was 7 and the temperature was 37ºC. Using these conditions, the isolates were grown on media with additional carbon and nitrogen sources along with different inoculum concentrations. The maximum amount of lipase produced by Stenotrophomonas sp. ICMM10 was 136.47 ± 1.63 U/ml, which is 5-fold higher than before. Pseudomonas aeruginosa BA7823 produced a maximum lipase of 96.33 ± 0.21 U/ml, which was about 9-fold higher than before. These results would provide useful information about lipase production by indigenous isolates, which may be employed as a potential bacterial lipase source of biotechnological interest. Bangladesh J. Bot. 52(4): 979-988, 2023 (December)
Enhancing Writing Quality and Self-Efficacy Beliefs through Process-oriented Task-based Instruction and Multiple Feedback Forms

Abstract: This article presents the research findings of a quasi-experimental study seeking to enhance the writing quality and self-efficacy beliefs of Algerian undergraduate learners. The study employed task-based writing instruction within the process approach framework, and various forms of formative feedback (self-, peer-, and teacher feedback). The sample comprised 24 English as a foreign language (EFL) undergraduate learners with an intermediate level in academic writing. Data were collected through pre- and post-intervention writing tests to assess the participants’ writing quality, as well as pre- and post-intervention surveys, along with post-hoc interviews, to measure their writing self-efficacy levels. The analysis of the findings revealed that, in comparison to their pre-intervention performance, the participants exhibited varying degrees of progress in writing quality and a moderate increase in their self-reported writing self-efficacy levels. These results suggest that adopting a process-oriented task-based approach to teaching academic writing, along with diverse forms of formative feedback, positively enhances EFL learners’ writing skills and fosters more positive beliefs in their English writing abilities.