Miracle-dz/UniFAR-FTUs huggingface dataset:
Text Mining untuk Pengelompokan Skripsi di Prodi Pendidikan Informatika Universitas Trunojoyo Madura

Skripsi merupakan karya tulis ilmiah mahasiswa sebagai syarat kelulusan atau perolehan gelar sarjana. Meskipun demikian, dosen juga berperan aktif dalam mengarahkan penentuan topik skripsi sebagai pembimbing. Skripsi yang ideal mengacu pada topik – topik yang up to date. Hendaknya skripsi juga mendukung keberhasilan rencana induk penelitian (RIP) Universitas bahkan rencana induk riset nasional (RIRN). Selain itu, topik skripsi harus selaras dengan bidang minat sesuai kurikulum program studi. Untuk itu, perlu adanya penyelarasan antara kondisi universitas, kebutuhan masyarakat, dan tujuan nasional. Sehingga, analisis data skripsi diperlukan untuk tujuan tersebut. Selama ini data dokumen skripsi di Program Studi Pendidikan Informatika Universitas Trunojoyo Madura belum terorganisir dengan baik di lingkup Program Studi. Sedangkan, jumlah data terus meningkat. Hal itu menjadi tantangan dalam pencarian dan penentuan topik skripsi sebagai bahan referensi selanjutnya. Selain itu, hingga saat ini belum dilakukan analisis terkait berkembangan skripsi yang sudah ada. Belum dilakukan juga pemetaan atau pengelompokan skripsi. Sehingga, dapat memberikan peluang adanya kemiripan skripsi. Oleh karena itu, dikembangkan sistem pengelompokan skripsi menggunakan text mining (studi kasus Program Studi Pendidikan Informatika Universitas Trunojoyo Madura). Dengan adanya sistem tersebut, diharapkan dapat membantu manajemen pengelolahan data skripsi bagi bidang skripsi di Program Studi. Sehingga dapat membantu dalam penentuan kebijakan dosen pembimbing dan meminimalisir peluang adanya kemiripan topic skripsi. Hasil penelitian ini menunjukkan bahwa metode clustering menggunakan K-Means dapat melakukan pengelompokan topic skripsi secara optimal dengan nilai akurasi sebesar 0,972972973, nilai presisi sebesar 916666667, nilai recall sebesar 0,9849199722, dan F-Measure sebesar 0,949199722 dalam skala 0 – 1.
Differences in Gender Perceptions of Higher Education Institutions (HEI) Students and Teachers on Science Technology Engineering Agriculture Mathematics (STEAM) Education

The study compared the gender differences perceived by teachers and students on the current Science Technology Engineering Agriculture Mathematics (STEAM) practices in Philippine Higher Education Institutions (PHEIs). It is assumed in this study that STEAM fields are often male-dominated, and STEAM courses are less attractive to female students. A descriptive survey was used in this study. Data were collected from a validated questionnaire. Teachers of STEAM disciplines (n=1,016), and students (n=24,172) were selected using multi-stage random sampling. Purposive sampling was used in selecting CHED higher education institutions centers of excellence in selected regions. Means and t-tests were computed in analyzing the data using a statistical tool. Findings revealed that male and female students are significantly different in the perception of the program objectives and program outcomes, faculty, program curricula, instructional materials, procedures and techniques, assessment and evaluation, and administrative support and policies. Male and female teachers similarly perceived the factors except for some in specific STEAM fields. The research suggests that gender disparity could be minimized in STEAM degrees by implementing gender policies.
A Genetic Simulated Annealing Algorithm for Real-Time Track Reallocation in Busy Complex Railway Station

Track failure at a railway station is a common disruption in the station area caused by abnormal weather or frequent use. This paper focuses on the real-time track reallocation problem to recover the affected track utilization plan and minimize the total train delays and passenger inconveniences. Train platforming operations in busy complex passenger stations are generally conducted according to fixed track utilization rules. In this paper, we presented a mixed-integer linear programming model for train platforming problems with constraints relevant to fixed track utilization rule and objectives of balanced usage of tracks. Furthermore, we proposed an improved genetic simulated annealing algorithm based on improved crossover and selection methods without breaking the fixed track utilization rule constraint. An experiment of Guanzhou East Station with fixed track utilization rules shows the effectiveness of the proposed model and algorithm. The model and algorithm provide efficient approaches for track reallocation problems based on fixed track utilization rules in busy complex passenger stations.
Relation of Physical Fitness Index with Body Mass Index: Study Among Female Students of Balwant College Vita, Dist. Sangli, (M.S.), India

The present study was undertaken to find out the correlation between Physical Fitness Index (PFI) and Body Mass Index (BMI). Physical Fitness Index was measured using the Modified Harvard step test. BMI was calculated by the Quetelet Index. In the present study total 85 female students having the age group between 18 to 25 years were selected from Balwant College, Vita. The present study showed that 64.70 % students were having low average PFI. 28.23 % students were having poor average PFI. 5.88% students were having high average PFI.1. 75 % Students were having good PFI. This study showed that there was no correlation between BMI and Physical Fitness.
Agronomic Biofortification of Wheat Through Proper Fertilizer Management to Alleviate Zinc Malnutrition: A Review

ABSTRACT A deficiency of zinc (Zn) is ubiquitous causing threat of Zn malnutrition worldwide, especially, due to reliance on Zn-poor cereal-based diets. As a principal staple grain, wheat (Triticum aestivum L.) is consumed by 40% of global population. The wide gap between the available Zn concentration in wheat grain (20–35 mg kg−1) and that required Zn for human health (45 mg kg−1), urges the need for biofortification. Agronomic biofortification is a feasible and economic intervention strategy for mitigation of Zn deficiency or malnutrition by increasing Zn concentration and bioavailability in edible parts of cereals with increased yield; though there are bottlenecks at the root-shoot barrier and in grain filling. This review explores the reasons to enhance grain Zn bioavailability, and the role of fertilizer management on agronomic biofortification of wheat with Zn. Foliar, or soil + foliar application of Zn salts can increase the Zn concentration ≤60 mg kg−1 in whole wheat grain. The Zn-nanocarrier (Zn-Chitosan Nanoparticles) has higher Zn-use efficiency, than zinc sulfate. Grain Zn accumulation is regulated by Zn remobilization from shoot and continuous uptake during the grain filling of wheat. Foliar Zn application early during grain filling improves Zn transport in the endosperm, which is the main consumable grain fraction. Relevantly, newer-released cultivars exhibit Zn-Fe antagonism, may challenge a combined loading of Zn and Fe in cereal grains, but biofortification does not intend a trade-off between two. Consequently, the efficacy of Zn-biofortification needs to be studied using distinct biomarkers.
Robot Bionic Vision Technologies: A Review

The visual organ is important for animals to obtain information and understand the outside world; however, robots cannot do so without a visual system. At present, the vision technology of artificial intelligence has achieved automation and relatively simple intelligence; however, bionic vision equipment is not as dexterous and intelligent as the human eye. At present, robots can function as smartly as human beings; however, existing reviews of robot bionic vision are still limited. Robot bionic vision has been explored in view of humans and animals’ visual principles and motion characteristics. In this study, the development history of robot bionic vision equipment and related technologies are discussed, the most representative binocular bionic and multi-eye compound eye bionic vision technologies are selected, and the existing technologies are reviewed; their prospects are discussed from the perspective of visual bionic control. This comprehensive study will serve as the most up-to-date source of information regarding developments in the field of robot bionic vision technology.
A Novel Semiparametric Structural Model for Electricity Forward Curves

We propose a novel semi-parametric structural model to estimate the electricity forward curves based on elementary forward prices. The proposed model (i) explores the non-arbitrage relations between contracts with overlapping delivery periods, (ii) considers a parametric structure for price seasonality and exogenous variables, and (iii) uses non-parametric techniques to extract the remaining inter-temporal and cross-maturity information from data. Thus, our model allows users to estimate and complete the historical prices of any swap contract. We address the multi-objective estimation problem by hierarchical optimization. First, arbitrage levels are minimized. Then, the parametric part of the model is estimated. Finally, smoothness in the maturity and trading date dimensions are jointly considered in the estimation of the non-parametric part of the model. Based on a controlled study with real data from the Nordic power market, we show that our model outperforms benchmarks in terms of estimation error for missing data. We also isolate the effect of accounting for overlaps and smoothing in the trading dates dimension. Results show that these two key features of our model are crucial for improving the model accuracy. Finally, we apply our method to estimate the Brazilian forward curve and reconstruct the historical data.
A Multiple-Stimuli-Responsive Amphiphilic Copolymer for Antifouling and Antibacterial Functionality via a “Resistance–Kill–Release” Mechanism

In recent years, polymers with stimuli-responsive properties have been increasingly reported on due to their diverse applications. However, most of the studies have only focused on the performance of polymers under specific scenarios. The laws of changes in the properties in response to various external stimuli have been less systematically and quantitatively studied. In this paper, we prepared an amphiphilic polymer (PadaMX and PAdaM3QA−X) with temperature-, pH-, ion-, and β-cyclodextrin (β-CD)-responsive properties. According to the cloud point tested by the UV-Vis method, the lower critical soluble temperature (LCST) of PAdaM3QA−10% was more sensitive to a change in pH and less sensitive to a change in ions compared with PadaM3 due to quaternized side chains with a stronger intramolecular mutual repulsion. We then fabricated the coatings with responsive properties by immobilizing the adamantyl groups on β-CD-modified surfaces. The hydrophilicity of the coatings was improved after quaternization, as proven by the water contact angle (WCA) measurement. The antifouling and antibacterial performance was further evaluated via the fluorescence intensity of bovine serum albumin (BSA) adsorbed on the surfaces and the spread plate method. A 78.4% BSA desorption rate and a 96.8% sterilization rate were achieved by the PAdaM3QA−10% coating. In summary, this work prepared a multiple-stimuli-responsive amphiphilic copolymer for antifouling and antibacterial functionality via a “resistance–kill–release” mechanism.
Effect of Monkeypox Virus Preparation on the Lethality of the Intravenous Cynomolgus Macaque Model

For over two decades, researchers have sought to improve smallpox vaccines and also develop therapies to ensure protection against smallpox or smallpox-like disease. The 2022 human monkeypox pandemic is a reminder that these efforts should persist. Advancing such therapies have involved animal models primarily using surrogate viruses such as monkeypox virus. The intravenous monkeypox model in macaques produces a disease that is clinically similar to the lesional phase of fulminant human monkeypox or smallpox. Two criticisms of the model have been the unnatural route of virus administration and the high dose required to induce severe disease. Here, we purified monkeypox virus with the goal of lowering the challenge dose by removing cellular and viral contaminants within the inoculum. We found that there are advantages to using unpurified material for intravenous exposures.
Chemical Characterization of Essential Oils With a Biocide Base for Conservation and Restoration

Essential oils (EOs) are acclaimed for their antimicrobial properties, leading to their multiple applications in various fields. In this work, four aromatic plants were used, namely thyme (Thymus mastichina L.), everlasting (Helychrysum stoechas Moench), European pennyroyal (Mentha pulegium L.) and fennel (Foeniculum vulgare Mill.). Hydrodistillation was the extraction method used, and the obtained extracts were composed of a variety of volatile molecules, mainly terpenoids and phenylpropenoids. The EO yields were determined, and the chemical composition of these natural products was obtained by gas chromatography-mass spectrometry (GC-MS) analysis. The yields varied greatly in the range of 0.99 to 4.27% (v/w). The two major chemical constituents of the EOs analysed by GC-MS were as follows: thyme – 1,8-cineole and champhor; everlasting – α-pinene and limonene; European pennyroyal – pulegone and isomenthone; fennel – trans-anethole and limonene. This preliminary study is an important contribution to the understanding of EO bioactive compounds that are under investigation to establish their ability to control the biocolonization of cultural heritage. Keywords: aromatic plants, chemical characterization, essential oils, green biocides
Association of Eating Behavior, Nutritional Risk, and Frailty with Sarcopenia in Taiwanese Rural Community-Dwelling Elders: A Cross-Sectional Study

This cross-sectional study assessed the association of eating behavior, nutritional risk, and frailty with sarcopenia in 208 community-dwelling individuals aged ≥65 years who were recruited from random rural community care centers in Chiayi County, Taiwan. The participants’ eating behavior was categorized into six categories. The gait speed (GS), grip strength, and appendicular skeletal muscle mass (ASM) were assessed based on these three parameters, which revealed that 50.9% of the participants had sarcopenia. In an adjusted model, water intake (odds ratio (OR) = 0.99, p = 0.044), dairy product intake (OR = 0.42, p = 0.049), body mass index (BMI) (OR = 0.77, p = 0.019), and marital status with widowed (OR = 0.31, p = 0.005) were significantly associated with sarcopenia. After eight steps of eliminating the least significant independent variable, age (p = 0.002), sex (p = 0.000), marital status with widowed (p = 0.001), water intake (p < 0.018), dairy product intake (p < 0.019), and BMI (p = 0.005) were found to be indispensable predictors of sarcopenia. The logistic regression model with these six indispensable variables had a predictive value of 75.8%. Longitudinal analyses are warranted to examine whether eating behavior is a risk factor for sarcopenia onset.
Predicting Mandarin Fruit Acceptability: From High-Field to Benchtop NMR Spectroscopy

Recent advances in nuclear magnetic resonance (NMR) have led to the development of low-field benchtop NMR systems with improved sensitivity and resolution suitable for use in research and quality-control laboratories. Compared to their high-resolution counterparts, their lower purchase and running costs make them a good alternative for routine use. In this article, we show the adaptation of a method for predicting the consumer acceptability of mandarins, originally reported using a high-field 400 MHz NMR spectrometer, to benchtop 60 MHz NMR systems. Our findings reveal that both instruments yield comparable results regarding sugar and citric acid levels, leading to the development of virtually identical predictive linear models. However, the lower cost of benchtop NMR systems would allow cultivators to implement this chemometric-based method as an additional tool for the selection of new cultivars.
The limited influence of climate norms on leisure air travel

Abstract This paper adds to our understanding of how people’s climate change concern and norms influence their leisure air travel. It does so by examining the roles of Norwegians’ beliefs about climate change and emissions from air travel, their felt responsibility to limit emissions (personal norm), and expectations and behaviors of friends and family (social norms) in such travel. A representative sample of Norwegians was surveyed in 2019 and 2020 (N = 2842), based on a framework combining institutional and social-psychological perspectives. Structural equation modeling of the data reveals that leisure air travel is habituated and part of a lifestyle, supported by social norms, self-enhancement values and urban residency. Personal norms for reducing greenhouse gas emissions from flying are beginning to emerge in Norway, but their effect on leisure air travel is small and only indirect. Our findings suggest the need for interventions to complement and potentially amplify the emerging moralization process and support an evolution of new habits and travel lifestyle to limit the emissions from leisure air travel substantially. The disruption of travel habits by the COVID-19 pandemic might ease such a process.
COSMIC-RAY STREAMING FROM SUPERNOVA REMNANTS AND GAMMA-RAY EMISSION FROM NEARBY MOLECULAR CLOUDS

High-energy gamma-ray emission has been detected recently from supernova remnants (SNRs) and their surroundings. The existence of molecular clouds near some of the SNRs suggests that the gamma rays originate predominantly from p–p interactions with cosmic rays (CRs) accelerated at a closeby SNR shock wave. Here we investigate the acceleration of CRs and the gamma-ray production in the cloud self-consistently by taking into account the interactions of the streaming instability and the background turbulence both at the shock front and in the ensuing propagation to the clouds. We focus on the later evolution of SNRs, when the conventional treatment of the streaming instability is valid but the magnetic field is enhanced due to Bell's current instability and/or the dynamo generation of magnetic field in the precursor region. We calculate the time dependence of the maximum energy of the accelerated particles. This result is then used to determine the diffusive flux of the runaway particles escaping the shock region, from which we obtain the gamma spectrum consistent with observations. Finally, we check the self-consistency of our results by comparing the required level of diffusion with the level of the streaming instability attainable in the presence of turbulence damping. The energy range of CRs subject to the streaming instability is able to produce the observed energy spectrum of gamma rays.
Organ Donation in Romanian Online Media

Mass media represents the primary source of information about organ donation for the general public. The present study explored the content of Romanian online media concerning organ donation. We conducted a content analysis of 313 media materials published online between October 2012 and October 2019 in Romanian. The coding categories were year of publication, media outlet, type of material, type of evidence, valence, topic, and proximity of the story. A semestral web-search was conducted between February 2016 and November 2019. The analysis revealed that the Romanian online coverage of organ donation was modest across time. Regarding the valence, results indicated that 57.2% of the materials presented organ donation in a positive way, 14.1% were neutral, 12.5% were negative, whereas 16.3% of the materials were mixed. Moreover, the valence of the materials varied across media outlets, proximity of the story and publication year. Promotion, information about organ donation and system were the topics that occurred most frequently, followed by events, policy, and legal categories. Findings provide insight into how media may shape people's opinions about organ donation. The study also contributes to understanding the Romanian organ donation macro-social environment and provides valuable information for practice.
Intelligent and Interactive Healthcare System (I2HS) Using Machine Learning

There has been a gigantic stir in the world’s healthcare sector for the past couple of years with the advent of the Covid-19 pandemic. The healthcare system has suffered a major setback and, with the lack of doctors, nurses, and healthcare facilities the need for an intelligent healthcare system has come to the fore more than ever before. Smart healthcare technologies and AI/ML algorithms provide encouraging and favorable solutions to the healthcare sector’s challenges. An Intelligent Human-Machine Interactive system is the need of the hour. This paper proposes a novel architecture for an Intelligent and Interactive Healthcare System that incorporates edge/fog/cloud computing techniques and focuses on Speech Recognition and its extensive application in an interactive system. The focal reason for using speech in the healthcare sector is that it is easily available and can easily predict any physical or psychological discomfort. Simply put, human speech is the most natural form of communication. The Hidden Markov Model is applied to process the proposed approach as using the probabilistic approach is more realistic for prediction purposes. Ongoing projects and directions for future work along with challenges/issues are also addressed.