Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed fiber-optic pressure sensor based on Bourdon tubes metered by optical frequency-domain reflectometry

Abstract. We report a distributed fiber-optic pressure sensor based on Bourdon tubes using Rayleigh backscattering metered by optical frequency-domain reflectometry (OFDR). In the proposed sensor, a piece of single-mode fiber (SMF) is attached to the concave surfaces of Bourdon tubes using a thin layer of epoxy. The strain profiles along the concave surface of the Bourdon tube vary with applied pressure, and the strain variations are transferred to the attached SMF through the epoxy layer, resulting in spectral shifts in the local Rayleigh backscattering signals. By monitoring the local spectral shifts of the OFDR system, the pressure applied to the Bourdon tube can be determined. By cascading multiple Bourdon tubes and correspondingly attaching SMF sections (i.e., a series of SMF-modified Bourdon tubes), distributed pressure measurements can be realized. Three Bourdon tubes are employed to demonstrate the proposed spatially distributed sensing scheme. The experimental results showed that linear relationships between spectral shift and pressure were obtained in all three SMF-Bourdon tubes (i.e., at three spatial locations). It is expected that the proposed sensing device, the SMF-Bourdon tube, can be used in applications where distributed/multipoint pressure measurements are needed.
The Effect of Academic Stress, Burnout, and Class Engagement, on Cyberloafing Behavior in UNTAR Students

Nowadays, human life cannot be separated from technology, especially the internet. The internet helps human activities, so the internet is needed, but with the presence of the internet, humans abuse the internet provided. This phenomenon is known as cyberloafing. Cyberloafing is a form of internet abuse behavior during working hours. Cyberloafing behavior itself is influenced by several variables. The variables examined in this research that influence cyberloafing are academic stress, class engagement, and burnout. This research was conducted with the aim of finding out whether the variables of academic stress, class engagement, and burnout influence a person's cyberloafing behavior. In the process of collecting research data using the Simple Random Sampling (SRS) technique by submitting questionnaires online via WhatsApp, Line and Discord with Google Form. The sample chosen was students studying at Tarumanagara University, with 371 participants studying at Tarumanagara University. The results of this research show that the class engagement and academic stress variables have a positive and significant effect, but the burnout variable has a positive and insignificant effect on cyberloafing behavior. So from this research it can be concluded that academic stress and class engagement variables influence students to carry out cyberloafing
Quantum-Safe Puncturable Signatures With Their Application in Blockchain

Energy-efficient proof-of-stake (PoS) consensus protocols in blockchain have gained much attention from academia and industry recently. Despite their potential advantages, PoS protocols have not been extensively deployed in the existing digital currency market due to inherent security concerns, e.g., long-range attacks. Such attacks enable an adversary to rewrite the entire transaction history of a blockchain, severely compromising its immutability. The puncturable signature provides an efficient solution against long-range attacks due to secret key leakage. More specifically, a signer can update the secret key with chosen messages selectively, while the public key is unchanged. Unfortunately, the existing puncturable signature schemes suffer from either updating the public key repeatedly or large key sizes, which makes them unsuitable for PoS protocols. To resolve these drawbacks, we adopt a delegated approach to performing key puncture operations and propose a generic puncturable signature construction from delegated (key-policy) constrained signatures. We present a concrete puncturable signature scheme over lattices that is proven secure based on the short integer solution (SIS) assumption in the standard model.
The Inhibitory Effect of Geraniol on CCL4-induced Hepatorenal Toxicity in Pregnant Mice through the PI3K/AKT Signaling Pathway

Background: Hepatotoxicity caused by CCL4 is well known. Geraniol (GNL) has high antioxidant effect that can induces liver regeneration. However, the protective effect of GNL effect on CCL4-induced hepatorenal toxicity in pregnant mice has not yet been studied. Objective: To investigate whether GNL could protect against oxidative stress induced by CCL4 via the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway, which is regulated by phosphatidylinositol 3 kinase/protein kinase B (PI3K/AKT), and has been found to have protective effects on renal and hepatic tissues. Materials and Methods: Forty-eight female albino mice weighing 25–30 g were randomly allocated to 4 groups: Group I served as a control; Group II received a toxicity-inducing single dose of 15 μL of CCL4 on the 4th day after mating; Group III received 40 mg/kg GNL + CCL4 (with GNL from the 1st day of assimilation to delivery); and Group IV received GNL alone from the 1st day of assimilation to the end of the delivery period. GNL was evaluated for its protective effects on hepatotoxicity in CCL4-treated pregnant mice. Litter size, weight, survival rate, and resorption were recorded. In addition, H & E staining was done for liver and kidney pathology as well as biochemical markers and oxidative markers malondialdehyde, superoxide dismutase, and catalase were analyzed. Results: CCL4 significantly reduced survival rate and increased resorption after exposure. Alanine transaminase and aspartate aminotransferase concentrations in the serum, tissue MDA, blood urea nitrogen, and creatinine were increased after CCL4 exposure. GNL improved enzyme and antioxidant levels and prevented CCL4-induced hepatic injury in mice. Caspase-3 cleavage was decreased by GNL, which increased PI3K, phosphorylated AKT, Nrf2, and B-cell lymphoma 2. Conclusion: GNL demonstrates a protective effect against CCl4-induced hepatorenal toxicity, mediated through the activation of the PI3K/AKT signaling pathway and the upregulation of Nrf2. These findings highlight the potential therapeutic implications of GNL in mitigating oxidative stress and inflammation in liver and kidney tissues.
OPTIMIZATION NAÏVE BAYES ALGORITHM IN SENTIMENT ANALYSIS OF BUKALAPAK APP REVIEWS

Bukalapak application reviews on Google Play Store include useful information if processed correctly. The activity of analyzing application reviews is not enough to see the number of stars, it is necessary to see the entire contents of the review comments to be able to know the intent of the review. Sentiment analysis system is a system used to automatically analyze reviews. Review data is retrieved via the bukalapak application API and then classified using Naive Bayes Multinomial. A total of 1,000 reviews of bukalapak application users were collected to be used as dataset samples. The purpose of this research is to determine the accuracy level of sentiment analysis using the multinomial Naive Bayes algorithm. The stages of this research include, data collection, automatic labeling using python, pre-processing, sentiment classification, and evaluation. In the pre-processing stage there are 6 stages, namely Cleaning, Casefolding, Word Normalizer, Tokenizing, Stopword Removal and Stemming. TF-IDF (Term Frequency - Inverse Document Frequency) method is used for word weighting. The data will be grouped into two categories, namely negative and positive. The test results show an accuracy value of 90%, this result shows that the bukalapak application reviews tend to be negative. The research at this time only looks for accuracy values and provides an overview of the bulapak application to potential new users.
Ecosystem Productivity and Water Stress in Tropical East Africa: A Case Study of the 2010–2011 Drought

Characterizing the spatiotemporal patterns of ecosystem responses to drought is important in understanding the impact of water stress on tropical ecosystems and projecting future land cover transitions in the East African tropics. Through the analysis of satellite measurements of solar-induced chlorophyll fluorescence (SIF) and the normalized difference vegetation index (NDVI), soil moisture, rainfall, and reanalysis data, here we characterize the 2010–2011 drought in tropical East Africa. The 2010–2011 drought included the consecutive failure of rainy seasons in October–November–December 2010 and March–April–May 2011 and extended further east and south compared with previous regional droughts. During 2010–2011, SIF, a proxy of ecosystem productivity, showed a concomitant decline (~32% lower gross primary productivity, or GPP, based on an empirical SIF–GPP relationship, as compared to the long-term average) with water stress, expressed by lower precipitation and soil moisture. Both SIF and NDVI showed a negative response to drought, and SIF captured the response to soil moisture with a lag of 16 days, even if it had lower spatial resolution and much smaller energy compared with NDVI, suggesting that SIF can also serve as an early indicator of drought in the future. This work demonstrates the unique characteristics of the 2010–2011 East African drought and the ability of SIF and NDVI to track the levels of water stress during the drought.
Pineapple Quality Classification in a Multimodal Audio-Visual Dataset

In this paper, a multimodal classification model is constructed to predict the pineapple quality. We compile a pineapple dataset consisting of 500 pineapples with two modalities: one is tapping a pineapple to record the sounds, and the other is taking pictures by cameras. Three classification models, including audio model, visual model, and audio-visual model, are built based on the deep learning architectures for the corresponding feature representations. The experimental evaluation demonstrates that the audio-visual model, which combines both audio and visual representations, can yield the best accuracy than the other two unimodal models.
Terrestrial and aquatic palynomorphs in Holocene sediments from the Chukchi–Alaskan margin, western Arctic Ocean: Implications for the history of marine circulation and climatic environments

Two sediment cores from the Chukchi Sea margin north of Alaska were analyzed for palynological composition including terrestrial and aquatic palynomorphs. Based on 13 radiocarbon ages, the investigated sedimentary record represents most of the Holocene with a century to multidecadal age resolution. Three palynological zones were discriminated based on the abundance of major palynomorph groups (terrestrial and freshwater palynomorphs and dinoflagellate cysts) and composition of spore and pollen assemblages. They are interpreted in terms of depositional and paleoclimatic changes including predominance of redeposition by meltwater or sea ice in the early-Holocene, a strong input of contemporaneous material related to Pacific water advection culminating after ca. 6000 yr BP, and more subtle changes in the late-Holocene. It is concluded that depositional environments, such as current transportation and mixing, have an overall major control on palynomorph distribution. The climatic factors may have also played an important role in palynomorph abundance and composition, especially in the middle- to late-Holocene, when circulation changes were less dramatic than during the flooding of the Bering Strait and the shallow Chukchi Sea shelf. Comprehending these linkages requires a better knowledge of the Holocene vegetation history in the coastal areas of Alaska and Chukchi Peninsula.
Virginia Earth Science Collaborative: Developing Highly Qualified Earth Science Teachers

A collaborative of seven institutes of higher education and two non-profit organizations developed and implemented five earth science courses totaling eighteen credits that enabled secondary teachers to acquire an add-on earth science endorsement: Geology I: Physical Geology (4), Geology II: Geology of Virginia (4), Oceanography (4), Astronomy (Space Science for Teachers) (3), and Meteorology (3). These courses were collaboratively developed and included rigorous academic content, research-based instructional strategies, and intense field experiences. The thirty-three sections offered statewide served 499 participants. Three courses were offered to strengthen the skills of earth science teachers: Teaching Earth Science Topics to Special Education Students (3), Integrating New Technologies in the Earth Sciences (3), and GeoVirginia: Creating Virtual Field Trips (non-college credit). In these six sections, seventy-four people participated. Outcomes included an increased pool of endorsed earth science teachers and teachers with coursework in the earth sciences, a website with virtual field trips, and a
The Effect of Digital Literacy on Entrepreneurial Intention through Entrepreneurial Attitude

The lack of Vocational High School graduates who have an entrepreneurial spirit and entrepreneurial intentions and can create new businesses is where the role of digital literacy in entrepreneurship education is needed which is expected to provide a theoretical basis for the concept of entrepreneurship, forming the mindset, attitudes, and behavior of an entrepreneur. This study uses a quantitative approach with Partial Least Squares (PLS-SEM) structural equation modeling to predict and confirm the hypotheses given. This study uses Google Forms for data collection on private vocational students in Mojokerto Regency. Respondents in this study were 150 respondents from the three schools used as research objects. The results showed that Digital Literacy can explain Entrepreneurial Attitude and Entrepreneurial Intention.
Automatic Music Generation Using Machine Learning

The artificial intelligence domain is currently evolving at a prominent level. Imitating the human brain is one of the most challenging tasks for computer science engineers, especially for the automation engineers whose purpose is to find efficient solutions for contemporary problems. It is well known that music has a major influence in the everyday world since it is linked to communication, well-being, and education. It has effects on the mental state, improving efficiency in the workplace by raising the energy and motivation levels. Relevant studies and empirical investigations prove that music has essential functions in society. This paper's objectives consist in finding and analyzing ways in which we can use machine learning algorithms to generate music. The motivation part looks up to the creation of original music, based on other songs, as well as the actual composers often listen and combine different sequences from music scores. Music generated by AI algorithms could be sold as background music for video games or commercials, giving a variety of styles that can be generated.
Geospatial Enrichment of Urban Data for Advanced City Planning: a Pilot Study

Data enrichment facilitates the creation of rich, expressive, and high-quality datasets, enabling valuable analytics and enhanced decision-making. The accurate geolocation of residential addresses and travel routes is crucial for determining the most appropriate locations of critical social infrastructure, such as educational and medical centres. This paper introduces an interactive semantic enrichment approach that enhances urban data by integrating high-quality geospatial information. The approach is supported by a modular and extensible data enrichment framework, which leverages existing geolocation services, enabling seamless data integration. Human-in-the-loop revision is employed to enhance the quality of geocoding results. A real-world pilot study conducted in Sofia, Bulgaria, was used to validate this approach, demonstrating its promising potential in addressing pressing issues in parametric urban planning.
Analyzing vowel variation in Korean dialects using phone
 recognition*

This study aims to propose an automatic method of detecting vowel variation in the Korean dialects of Gyeong-sang and Jeol-la. The method is based on error patterns extracted using phone recognition. Canonical and recognized phone sequences are compared, and statistical analyses distinguish the vowels appearing in both dialects, the dialect-common vowels, and the vowels with high mismatch rates for each dialect. The dialect-common vowels show monophthongization of diphthongs. The vowels unique to the dialects are /we/ to [e] and /ʌ/ to [ɯ] for Gyeong-sang dialect, and /ɰi/ to [ɯ] in Jeol-la dialect. These results corroborate previous dialectology reports regarding phonetic realization of the Korean dialects. The current method provides a possibility of automatic explanation of the dialect patterns.
Detecting the Anomaly Attacks in Industrial IoT using Hybrid Feature Selection Based Modified Convolutional Neural Network

When it comes to digitally transforming classic sectors towards Industry 4.0, the Industrial Internet of Things (IIoT) is crucial. The IIoT improves the efficiency and productivity of businesses by connecting sensors, equipment used in industries to the Internet for the purposes of data collecting, data analysis, and automated control. Because of the intricate nature of the IIoT infrastructure, anomaly detection has emerged as a crucial method for guaranteeing the platform's viability. As IIoT is inherently suited to continuously growing networks, graph-level anomaly detection has proven to be an effective method for detecting and predicting abnormalities across a wide range of application domains, including transportation, energy, and the factory. This research work suggests an optimization strategy using a combination of the fuzzy technique for order of favorite by similarity to ideal solution (FTOPSIS). For network anomaly detection, the selected characteristics are fed into a modified Convolutional Neural Network (MConvNet). The approach is put through its paces using NBaIoT-balanced benign and malicious network traffic so that it can pick up on the differences between the two. When compared to state-of-the-art models, the findings show that the suggested model outperforms them thanks to the optimization approach's feature selection.
Fabrication of bagel-like graphene aerogels and its application in pressure sensors

In this study, a new bagel-like graphene aerogel composed of graphene sheets is reported. This bagel-like graphene aerogel was fabricated by a droplet containing graphene oxide sheets, which had an impact on the coagulation bath of the mixed ethanol and hydrochloric acid. During the impact process, the graphene oxide droplet locked the ripple of the coagulation bath while forming a special structure with the stagnating ripple, which was gradually transformed into a bagel-like graphene oxide hydrogel. Then, this graphene oxide hydrogel was processed by reduction and freeze drying to obtain a small and light-weight bagel-like graphene aerogel. As characterized by Raman spectrum, XRD and XPS, we confirmed that graphene oxide sheets were effectively reduced. The excellent electrical conductivity and stable mechanical properties of this bagel-like graphene aerogel were experimentally measured. We found that it could be applied as piezoresistive pressure sensors with high sensitivity at low pressure, wide measurement range and good stability. As confirmed by SEM, this outstanding sensing property was attributed to the aerogel’s special lamellar multi-layered microstructure, which played a key role in the piezoresistive working mechanism.
Interconnections between Clean Energy and Traditional Commodities: Analysis of Energy Fuels, S&P Global Clean Energy Index, and Ishares Global Clean Energy ETF Compared to Oil, Gold, and Natural Gas Prices

The study of the changes between the Energy Fuels Index, S&P Global Clean Energy Index, iShares Global Clean Energy ETF, iShares Global Energy (SWX) ETF, as well as the changes in the prices of crude oil (BRENT), gold (DJ), and natural gas (DG) was deemed extremely relevant given the im­portance and emergence of clean energies in the global landscape, as well as the need to develop more empirical studies, especially confirmative stud­ies on the financial dynamics in these markets. The daily returns under anal­ysis exhibit negative and leptokurtic asymmetry rather than a normal distri­bution. Comparatively, the pre-crisis linkages between the markets for dirty and clean energy are in favor of global portfolio diversification since those low levels of dependence are appropriate to reduce investor exposure to risk. The crude oil market already exhibited a significant effect on the clean ener­gy markets during the Stress subperiod, particularly on the Clean Energy Fuels Index, the iShares Global Clean Energy ETF, and the iShares Global Clean En­ergy (SWX) ETF. It should be highlighted that the clean energy markets have also increased their impact on the markets for gold and dirty energy (crude oil and natural gas). The findings point to an increase in comovements between the examined indices and the events of 2020 and 2022. These results decrease the possibility that clean energy markets will serve as a portfolio diversifica­tion substitute for the gold and dirty energy markets. For investors and finan­cial analysts who are interested in understanding how the various sectors of the energy market interact, these results may also have consequences. These results can enable a more precise forecast of energy market trends and more informed investment decisions by offering a more detailed knowledge of the link between clean and dirty energy prices.
Complete Traumatic Rupture of the Pancreas by a Horse Saddle: A Case Report

Pancreatic trauma is one of the least observed diagnoses in the emergency room, much less in pediatric emergencies. Any cause of direct abdominal blunt trauma can cause it. With only a few cases presented in the literature, horse accidents have been associated with this complication, but it has been never seen in literature as a case where the horse-riding saddle is the one causing the pancreatic trauma, until now. Emphasizing the importance of an early diagnosis is the key point, but more importantly, to highlight that the correct diagnostic approach will grant the opportunity for a lesion in the main pancreatic duct to be identified, which will allow a timely surgical approach, increasing overall survival rates and decreasing morbidity in these patients. Here lies the importance of not only utilizing a specific study, such as a computerized tomography (CT) scan to evaluate abdominal trauma but also using other image studies that are better suited for pediatric patients, such as magnetic resonance image (MRI) with cholangiopancreatography (MRCP).