Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling and Simulation of a Magnetic Reciprocating Engine

This research analyses the dynamic response of a single-cylinder magnetic reciprocating engine (MRE) and the mathematical model formulation with experimental validation. In efforts to reduce the pollution generated by internal combustion engines (ICE), the MREs have emerged as an alternative to this problem. An MRE uses the ICE structure and replaces fossil fuels with magnetic fields to work with the principle of magnetic repulsion between an electromagnet and a permanent magnet. The work focuses on determining the correct angles of when to energize the electromagnet based on the angular position of the crankshaft concerning the top dead center (TDC) of the engine to generate the magnetic repulsion and obtain the highest possible speed in the engine as in the ICE. The experimental results indicate that the energization angle should be between 45° and 55° before the TDC for generating the highest engine velocity. Using the energization angle of 55° before the TDC allows the engine’s velocities to be between 836 RPM and 840 RPM, and the mathematical model shows a goodness of fit (FIT) of 81.21% throughout its dynamics with the experimental comparison. The present work demonstrates the mathematical model’s ability to represent the MRE’s experimental dynamics and how the magnetic force is formulated and integrated with the mechanical analysis of the engine.
ANALYSIS OF STUDENTS' MATHEMATICAL CREATIVE THINKING ABILITY IN THE MATH ESSENTIALS AT AN ISLAMIC MIDDLE SCHOOL IN BANDUNG

Creativity is an important ability for students to have in the 21st century. The ability to think creatively is very important for innovation, developing original ideas, and finding new ways of thinking for students. In the context of mathematics, the mathematical creativity thinking ability to solve mathematical problems related to everyday life is important. However, the results of previous studies revealed that students' mathematical creative thinking abilities were still relatively low. This study aims to analyze the level of students' mathematical creativity thinking ability at an Islamic Middle School in Bandung. The research method used is descriptive-quantitative. The sampling technique used is purposive sampling. The subjects in this study were 17 eighth-grade students at an Islamic Middle School in Bandung. while the data collection techniques used are tests, questionnaires, and interviews. The test consists of four questions on math essentials based on indicators of mathematical creative thinking ability: fluency, flexibility, originality, and elaboration. The questionnaire consists of 15 closed statements based on 5 attitudes related to mathematical creative thinking abilities, namely: curiosity, imaginativeness, liking challenges, courage, and respect. Unstructured interviews were conducted as a validation of the results of the questionnaire. The results showed that the level of students' mathematical creative thinking ability was divided into 29% of students at level 3, 24% of students at level 2, 18% of students at level 1, and 29% of students at level 0.
Historical land use and land cover for assessing the northern Colorado Front Range urban landscape

ABSTRACT We describe historical land-use and land-cover (LULC) maps for the northern Colorado urban Front Range. The Front Range urban landscape is diverse and interspersed with highly productive agriculture, as well as natural land cover types including evergreen forest in the Rocky Mountain foothills and Great Plains grassland. To understand the dynamics of urban growth, raster maps were created at a 1-meter resolution for each of four time steps, nominally 1937, 1957, 1977, and 1997. In total, 38 detailed LULC classes were identified using manual interpretation techniques, aerial photographs, historical maps, and other available information. The maps provide high-resolution spatial data for understanding the historical progression of urbanization and will allow further analysis of the effects of urban growth on social and ecological systems.
PENGARUH DEBT TO EQUITY RATIO (DER), RETURN ON EQUITY (ROE), DAN NET PROFIT MARGIN (NPM) TERHADAP HARGA SAHAM PERUSAHAAN SEKTOR PERTAMBANGAN YANG TERDAFTAR DI BURSA EFEK INDONESIA PERIODE 2011-2015

Penelitian ini bertujuan untuk mengetahui: Pengaruh Debt to Equity Ratio (DER) , Return on Equity (ROE) , dan Net Profit Margin (NPM) baik secara parsial maupun simultan terhadap Harga Saham perusahaan pertambangan yang terdaftar di BEI periode 2011-2015. Data penelitian diambil dari website BEI. Uji prasyarat analisis meliputi uji normalitas, multikolinearitas, heteroskedastisitas, dan autokorelasi. Teknik analisis data yang digunakan adalah analisis regresi sederhana dan analisis regresi berganda. Hasil penelitian menunjukkan bahwa: (1) Debt to Equity Ratio (DER) berpengaruh negatif dan signifikan terhadap Harga Saham perusahaan pertambangan yang terdaftar di BEI periode 2011-2015 dengan nilai t hitung -5,326 > t tabel 2,003 dan nilai sig 0,000 t tabel 2,003 dan nilai sig 0,000 t tabel 2,003 dan nilai sig 0,000 F tabel 2,79 dan nilai sig 0,000 < 0,05. Kata Kunci: Harga Saham, Debt to Equity Ratio, Return On Equity, Net Profit Margin
Deep Learning in Dental Radiographic Imaging

Deep learning algorithms are becoming more prevalent in dental research because they are utilized in everyday activities. However, dental researchers and clinicians find it challenging to interpret deep learning studies. This review aimed to provide an overview of the general concept of deep learning and current deep learning research in dental radiographic image analysis. In addition, the process of implementing deep learning research is described. Deep-learning-based algorithmic models perform well in classification, object detection, and segmentation tasks, making it possible to automatically diagnose oral lesions and anatomical structures. The deep learning model can enhance the decision-making process for researchers and clinicians. This review may be useful to dental researchers who are currently evaluating and assessing deep learning studies in the field of dentistry.
BCH Based U-UV Codes and Its SCL Decoding

U-UV codes are constructed by a number of component codes in the (U<inline-formula><tex-math notation="LaTeX">$ | $</tex-math></inline-formula>U<inline-formula><tex-math notation="LaTeX">$ + $</tex-math></inline-formula>V) recursive structure, where the U codes and V codes are component codes. This construction is known as the Plotkin construction and the U-UV codes are also known as the generalized concatenated codes with inner polar codes. This paper proposes U-UV codes with primitive BCH component codes as a pursuit of designing competent short-to-medium length codes for future ultra low-latency communications. The U-UV code design considers both the finite length rate of the subchannels and the equal error probability rule, yielding a good performing U-UV code that is designed for a targeted transmission rate. The successive cancellation list (SCL) decoding and its complexity reduction variant are proposed to maximize the code's performance. Their decoding complexity and latency are analyzed. Decoding performance of the U-UV codes is further studied, showing that SCL decoding of the U-UV codes can approach its approximated maximum likelihood (ML) decoding bound. They can outperform other competent short-to-medium length codes, including polar codes, BCH codes and tail-biting convolutional (TBC) codes.
Unsupervised 3-D Seismic Erratic Noise Attenuation With Robust Tensor Deep Learning

Due to the non-Gaussian distribution of erratic noise, conventional Gaussian denoising methods often encounter substantial challenges and pressures when suppressing this kind of noise. To overcome this challenge, several state-of-the-art (SOTA) schemes, for instance, robust low-rank approximation (LRA) and deep-learning (DL) methods, have been designed and achieved promising results in the treatment of erratic noise. However, these SOTA denoising methods focus mainly on matrix-based modeling representations and fail to fully reflect the correlations associated with erratic noise and valid signals in the spatial dimension and thus may display suboptimal performance. As an alternative, a robust tensor DL (RTDL) denoising method for unsupervised 3-D seismic erratic noise suppression that involves the use of a reasonable combination of tensor sparse representation (SR) and a tensor neural network (tNN) is proposed in this study. The key to RTDL is to introduce a robust tensor sparse norm for erratic noise to exhaustively exploit the spatial tubular sparse distribution properties in 3-D space; notably, adding a tensor sparse norm to the tNN model yields a new data-driven model with 3-D erratic noise reduction capabilities. To find the optimized parameters of the new model, an efficiency tensor optimization method is established on the basis of alternating minimization (Alt), the aim of which is to alternately solve two subproblems involving tensor SR and a tNN. This paper presents well-designed experiments and satisfactory results compared with those of SOTA methods based on both synthetic and real field datasets.
Evaluating the Successful Factors of Arknights as a Mobile Gacha Game with GameFlow

GameFlow is a widely used model of player enjoyment, with numerous applications to game designation and evaluation. Derived from a general set of heuristics for creating enjoyable player experiences, GameFlow has already become a general model of player enjoyment,suitable for all kinds of game genres and platforms. However, research so far has been focused on traditional mobile games while gacha game has not received enough attention. Arknights is a famous gacha games released in 2020 worldwide. This study evaluates how Arknights grows into one of the most successful mobile gacha games and IP in recent years,using the GameFlow model as a conceptual foundation. One reason is that designers of Arknights are good at dealing with the balance between game challenges and increasing players’ skills. In other words, the complexity management of Arknights is pretty good. Another one is that players can get high concentration and immersion due to Arknights’ excellent auditory and visual experience. The two advantages behind the success of Arknights are worth studying for global mobile game industry.
An analysis of classification skills of the 6th grade students on fractions

The purpose of this study is to examine how the categorization skills of the 6th grade students in fractions are distributed and to determine the classification errors made by the students, along with the procedural errors of the students in fractions. The descriptive survey model, one of the quantitative research methods, was used in the study. In this study, 292 6th grade students from four middle schools in two districts of a province in Turkey participated. Developed by the researcher, the fraction operation skill test and classification skills identification test were used. The results showed that students had difficulty in distinguishing examples and characteristics of fractions and fraction types, and even though they partially succeeded in operations, they made procedural errors. The researchers recommend combining procedural knowledge with conceptual knowledge and explaining the basic characteristics of the concept by comparing examples with non-examples.
Crimean-Congo Hemorrhagic Fever Geographic and Environmental Risk Assessment in the Balkan and Anatolian Peninsulas

ABSTRACT Crimean-Congo hemorrhagic fever (CCHF) is a severe, life-threatening viral zoonotic pathogen transmitted to humans primarily by ticks of the genus Hyalomma. The virus is geographically diverse and found throughout a number of countries in central and southwest Asia, Africa, and Eastern Europe. Throughout the Balkan and Anatolian Peninsulas, sporadic outbreak events have been reported since the 1950s. Human infection is found in high-risk occupations ranging from agricultural and livestock workers to health care practitioners and veterinarians. The virus is highly pathogenic and recognized as the most medically important tick-borne disease affecting people. This study incorporated both maximum entropy modeling (Maxent) and spatial scan statistics (SaTScan) to map potential disease transmission risk, and to further explore the spatiotemporal distribution of confirmed human cases from 1954 to 2013. These findings provide evidence of the preferred environmental geography of CCHF and highlight the importance of integrating dynamic disease surveillance systems with geospatial technologies.
Evaluation of binding mechanism of dietary phytochemical, capsaicin, with human transferrin: targeting neurodegenerative diseases therapeutics

Human transferrin (htf) plays a crucial role in regulating the balance of iron within brain cells; any disruption directly contributes to the development of Neurodegenerative Diseases (NDs) and other related pathologies, especially Alzheimer’s Disease (AD). In recent times, a transition towards natural compounds is evident to treat diseases and this shift is mainly attributed to their broad therapeutic potential along with minimal side effects. Capsaicin, a natural compound abundantly found in red and chili peppers, possess neuroprotective potential. The current work targets to decipher the interaction mechanism of capsaicin with htf using experimental and computational approaches. Molecular docking analysis revealed that capsaicin occupies the iron binding pocket of htf, with good binding affinity. Further, the binding mechanism was investigated atomistically using Molecular dynamic (MD) simulation approach. The results revealed no significant alterations in the structure of htf implying the stability of the complex. In silico observations were validated by fluorescence binding assay. Capsaicin binds to htf with a binding constant (K) of 3.99 × 106 M−1, implying the stability of the htf-capsaicin complex. This study lays a platform for potential applications of capsaicin in treatment of NDs in terms of iron homeostasis.
The Acute Effect of Dynamic vs. Proprioceptive Neuromuscular Facilitation Stretching on Sprint and Jump Performance

Participating in sports has been shown to promote overall wellness and, at the same time, reduce health risks. As more people are participating in sports, competitions have increased, and every aspect of the game has been focused by coaches and athletes in order to improve performance. One of these aspects is the warm-up session. The purpose of this study was to investigate the acute effect of a dynamic warm-up versus a proprioceptive neuromuscular facilitation (PNF) warm-up on the sprint and jump performance of recreationally active men. Thirty (n = 30) males were randomly assigned to undergo three sessions of different warm-up types, 72 h apart, involving either proprioceptive neuromuscular facilitation (PNF), dynamic stretching (DS), or no stretching session (control). The PNF and dynamic modes of stretching improved vertical jump performance, F (2.58) = 5.49, p = 0.046, to a certain extent (mean + 3.32% vs. control, p = 0.002 for dynamic and mean + 1.53% vs. control, p = 0.048 for PNF stretching). Dynamic stretching is best used to get a better vertical jump height. Sprint performance was also increased to a greater extent following the stretching session, F (2.58) = 5.60, p = 0.01. Sprint time was +1.05% faster vs. the control, with a value of p = 0.002 after dynamic stretching, while PNF stretching demonstrated a sprint time of +0.35% vs. the control, with a value of p = 0.049. Dynamic stretching showed a better sprint performance and also vertical jump height performance in this study. PNF and dynamic stretching prove to be equally efficacious in flexibility conditioning depending on the type of movement involved. This type of stretching should be utilized to help preserve or improve the performance output of physical activity, especially in sprinting and jumping events.
The effectiveness of artificial intelligence (AI) on daily educational activities of undergraduates in a modern and diversified university environment

This study assessed the effectiveness of AI and AI-related technologies in the daily educational activities of undergraduate students in a modern and diversified university environment. The participants were 13 undergraduate Indonesian students participating in a mobility program for two weeks in Malaysia. A survey questionnaire designed with the help of the existing literature and ChatGPT, which includes ten (10) structured items and seven (7) open-ended questions, was used to collect the data. The relevant SPSS tests were used to analyze the data. Based on the results, of all 13 participants, 12 (92.3%) of them already experienced AI in their daily educational activities, and there were strong positive correlations between the attitudes toward AI and AI experiences, and attitudes toward AI and the effects of AI on education attributes, with correlation scores of . 663 and . 833 respectively. Based on the participant's answers to the qualitative questions, most of them believed that AI and AI technologies, such as ChatGPT, are helpful in daily educational activities and help them gain information regardless of time and space limitations and do their university-related assignments quickly. Based on the results, AI and AI-related technologies could transform different aspects of modern education.
Clinical application of machine learning‐based pathomics signature of gastric atrophy

Background The diagnosis of gastric atrophy is highly subjective, and we aimed to establish a model of gastric atrophy based on pathological features to improve diagnostic consistency. Methods We retrospectively collected the HE-stained pathological slides of gastric biopsies and used CellProfiler software for image segmentation and feature extraction of ten representative images for each sample. Subsequently, we employed the Least absolute shrinkage and selection operator (LASSO) to select features and different machine learning (ML) algorithms to construct the diagnostic models for gastric atrophy. Results We selected 289 gastric biopsy specimens for training, testing, and external validation. We extracted 464 pathological features and screened ten features by LASSO to establish the diagnostic model for moderate-to-severe atrophy. The range of area under the curve (AUC) for various machine learning algorithms was 0.835-1.000 in the training set, 0.786-0.949 in the testing set, and 0.689-0.818 in the external validation set. LR model had the highest AUC value, with 0.900 (95% CI: 0.852-0.947) in the training set, 0.901 (95% CI: 0.807-0.996) in the testing set, and 0.818 (95% CI: 0.714-0.923) in the external validation set. The atrophy pathological score based on the LR model was associated with endoscopic atrophy grading (Z=-2.478, P=0.013) and gastric cancer (GC) (OR=5.70, 95% CI: 2.63-12.33, P<0.001). Conclusion The ML model based on pathological features could improve the diagnostic consistency of gastric atrophy, which is also associated with endoscopic atrophy grading and GC.
UCST-Type Soluble Immobilized Cellulase: A New Strategy for the Efficient Degradation and Improved Recycling Performance of Wastepaper Cellulose

This paper reports an innovative study that aims to address key issues in the efficient recycling of wastepaper cellulose. The research team utilized the temperature-responsive upper critical solution temperature (UCST) polymer P(NAGA-b-DMA) in combination with the LytA label’s affinity for choline analogs. This innovative approach enabled them to successfully develop a novel soluble immobilized enzyme, P(NAGA-b-DMA)-cellulase. This new enzyme has proven highly effective, significantly enhancing the degradation of wastepaper cellulose while demonstrating exceptional stability. Compared with the traditional insoluble immobilized cellulase, the enzyme showed a significant improvement in the pH, temperature stability, recycling ability, and storage stability. A kinetic parameter calculation showed that the enzymatic effectiveness of the soluble immobilized enzyme was much better than that of the traditional insoluble immobilized cellulase. After the immobilization reaction, the Michaelis constant of the immobilized enzyme was only increased by 11.5%. In the actual wastepaper degradation experiment, the immobilized enzyme was effectively used, and it was found that the degradation efficiency of wastepaper cellulose reached 80% of that observed in laboratory conditions. This novel, thermosensitive soluble immobilized cellulase can efficiently catalyze the conversion of wastepaper cellulose into glucose under suitable conditions, so as to further ferment into environmentally friendly biofuel ethanol, which provides a solution to solve the shortage of raw materials and environmental protection problems in the paper products industry.
Enhancing Energy Efficiency of Cumene Production Through Reactor Output Recycling Modification in a Heat Exchanger

Cumene production generally involves irreversible and exothermic reactions that leads to an increase in product temperature and needs to be cooled for further processes. The high temperature of reactor product can be utilized as a heat transfer fluid in heat exchanger. This modification process is essential for improving energy efficiency by recycling the reactor output with high temperature as a heat transfer fluid, so that the implementation of replacing the heater with a heat exchanger can be carried out for the process of increasing the initial feed temperature. In an effort to enhance energy efficiency in the cumene production process, simulations were conducted using HYSYS and the comparison of energy efficiency for both basic and modified processes can be evidenced by comparing the total amount of energy required during the process. The results shows that the total amount of energy required for the modified process is 39,814,003.7 kJ/h, while for the basic process is 40,588,937.4 kJ/h, with a difference of 774,933.7299 kJ/h. Since the total amount of energy required in the modified process is smaller than the basic process, then the modification process will increase the energy efficiency of cumene production. Copyright © 2024 by Authors, Published by Universitas Diponegoro and BCREC Publishing Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).
A Versatile Board for Event-Driven Data Acquisition

Event-driven data acquisition is used to capture information from fast transient phenomena typically requiring a high sampling speed. This is an important requirement in the ITER Neutral Beam Test Facility for the development of one of the heating systems of the ITER nuclear fusion experiment. The Red Pitaya board has been chosen for this project because of its versatility and low cost. Versatility is provided by the hosted Zynq System on Chip (SoC), which allows full configuration of the module architecture and the OpenSource architecture of Red Pitaya. Price is an important factor, because the boards are installed in a hostile environment where devices can be damaged by EMI and radiation. A flexible solution for event-driven data acquisition has been developed in the Zynq SoC and interfaced to the Linux-based embedded ARM processor. It has been successfully adopted in a variety of data acquisition applications in the test facility.