bluuebunny/crossref_metadata_2025_split huggingface dataset:
MOISTURE/SORPTION CHARACTERISTICS OF STARCH-FILLED POLY (STYRENE-CO-BUTYL ACRYLATE) LATEX BASED COMPOSITES REINFORCED WITH POLYESTER NONWOVEN FABRIC
Authors: Satheesh Kumar M. N. , Siddaramaiah  
Published: December 2023

Abstract
Poly (styrene-co-butyl acrylate) latex was prepared with a 70:30 weight ratio of styrene to butyl acrylate. The prepared latex was incorporated with various weight percentages of cornstarch. The cornstarch-filled copolymer latex was used as a matrix to fabricate the polyester nonwoven fabric reinforced composites. The pickup ratio of latex to fabric was maintained at 3.2:1. The moisture sorption characteristics of composites were examined at 270C for water activity (aw) from 0.1 to 0.9. The sorption data was used to fit six different sorption isotherm models proposed in the literature. The model constants were determined by linear fitting of the sorption equations. The ranges of applicability of water activity for the isotherm models reported in the article lies between 0.1 and 0.4 (monomolecular layer) for the Braunauer-Emmet-Teller (BET) model, and between 0.3 and 0.9 (multimolecular and capillary condensation layers) for other models. The value of the coefficient of determination (R2 = 0.97±0.02) confirms the linear fitting of the equations studied.
LIGHT TRANSMISSION THROUGH DECORATIVE KNITTED FABRICS IN CORRELATION WITH THEIR FABRIC COVER
Authors: Szmyt Joanna , Mikolajczyk Zbigniew 
Published: December 2023

Abstract
The cover factor of Jacquard decorative knitted fabrics determines the quantity of light going through the knitted material. By changing the fabric cover of the knitted material it is possible to influence both the aesthetic and functional features of the product. The intensity of light transmission, determined on the basis of an instrumental method of measuring the light-barrier properties of knitted decorative products, was correlated with values of the fabric cover determined on the basis of both theoretical analysis and experiments.
The Role of Registration in Perfecting Non-Possessory Secured Transactions over Movables: What is the Extent of the Third-Party Effectiveness
Authors: Gürsel Yaman , Süzel Ece Baş 
Published: December 2023

31Abstract
The World Bank uses getting credit as a measurement of a country’s economic development, and a common approach to bolstering economic activity is to carry out necessary reforms on secured transactions laws.1 Amongst many legal systems, a non-possessory security interest over movable assets subject to registration is prominent.2 While the number of security instruments has drastically increased within the last century, one of the most sophisticated ones was invented more than 150 years ago: floating charge.3 Many of its features have been modified in line with the evolving needs of the market, yet its constitutive specialties stayed the same to provide sufficient flexibility for borrowers and creditors. In this paper, we analyze the role of registration and its effects, while examining the functioning of the debtor’s (borrower’s) right to dispose of encumbered assets by identifying the limits imposed on their withdrawal from the ambit of creditors’ security interests, exploring diverging implementations given the different restrictions deployed by various legislators.
Appropriateness and clinical outcomes of short sustained low-efficiency dialysis: A national experience
Authors: Rahhal Alaa , Najim Mostafa , Mahfouz Ahmed , Habib Mhd Baraa , Hassen Sara Seife , Al-Shekh Isra’a , Ahmed Ashraf Omer , Toba Haneen , Abbarh Shahem , El Hassan Mawahib , Al Yafei Sumaya , Badr Amr , Mahmoud Khaled Mohamed 
Published: December 2023

Abstract
Sustained low-efficiency dialysis (SLED) is usually performed over 6–12 h among hemodynamically unstable patients. Conduction of 4-h SLED may spare time and manpower during hospitalization. Therefore, we conducted a retrospective observational study to explore the appropriateness and clinical outcomes of 4-h SLED among critically ill patients admitted to our center from 1/06/2016 to 1/06/2020. Renal parameters including blood urea nitrogen, serum creatinine, sodium, phosphorus, potassium, and bicarbonate were determined on the day of dialysis before SLED and within 24 h after SLED, and clinical outcomes including, acute kidney injury (AKI) recovery, in-hospital mortality, 30-day mortality, 180-day mortality, and re-admission with AKI, were evaluated. Of the 304 patients included, 69.4% were male. The majority of patients were from the Middle East (65.8%), followed by 28.6% from Asia. Four-hour SLED resulted in a significant improvement in the renal parameters. Recovery from AKI was observed in 25.4%, in-hospital mortality rate was 48.7%, while the 30- and 180-day mortality outcomes were 3.2 and 9.6%, respectively, and re-admission with AKI was observed in 16.9%. Our findings suggest that 4-h SLED significantly improved renal parameters and was associated with favorable clinical outcomes in terms of survival and AKI recovery, suggesting possible utilization of SLED shorter than 6 h in the acute settings to preserve time and manpower for procedures.
Hashtags and heritage: the use of #italianamerican on Instagram
Authors: Longo Stephanie A. , Towers Scott Andrea 
Published: December 2023

Abstract

Purpose
Using both framing theory and Collier’s cultural identity theory, this paper seeks to study the use of #italianamerican on Instagram by first attempting to determine if it is used exclusively by Italian Americans as a way to demonstrate their own cultural identity. The paper will then examine whether the message valence of posts using #italianamerican demonstrates users’ personal perceptions of Italian American culture. Finally, the paper will examine if there is a relationship between #italianamerican posters’ ethnicity and the message valence of their posts. This paper does not argue that Italian Americans are the only Americans of European ancestry taking part in the hashtag phenomenon; rather, this paper is meant to examine how one ethnic group attempts to define itself by its use of digital and social media.

Design/methodology
This simple exploratory study examined the 50 most recent Instagram posts using #italianamerican as they appeared on November 1, 2022. The posts were sent to two independent coders who analyzed their visual content.

Findings
This study provides insight into how ethnicity and cultural familiarity are associated. Results show that message valence and ethnicity are not linked, as anyone can admire and respect a culture and post a positive message about it on social media, even if it is not their culture of origin. Members of a specific ethnic group tend to have a stronger familiarity with their culture of origin, as demonstrated by the posts selected for this study.

Implications
This study showed that Italian Americans use social media to educate others about their heritage, as well as to celebrate their culture of origin.

Value
The methods used in this study could be applied to other European American ethnic groups and their social media usage, including Irish Americans, Polish Americans, and German Americans, among others.
Anticorrosion studies of 5-acetyl-4-(3-methoxyphenyl)-6-methyl-1-phenyl-3,4-dihydropyrimidin-2(1H)-one: approach from experimental, DFT studies, and MD simulation
Authors: Farzana Arifa B. , Ahamed Mashood F. M. , Sundaravadivelu Ambika , Gber Terkumbur E. , Odey Samuel E. , Odey Michael O. , Owen Aniekan E. , Louis Hitler 
Published: December 2023

Abstract
The effectiveness of 5-acetyl-4-(3-methoxyphenyl)-6-methyl-1-phenyl-3,4-dihydropyrimidin-2(1H)-one as a corrosion inhibitor for mild steel in acidic conditions was investigated herein through the experimental and theoretical approach. Experimental results demonstrated that this compound acts as a reliable corrosion inhibitor (η %) for mild steel in acidic environments, with its inhibition efficiency increasing as the inhibitor concentration rises. Adsorption behavior on the mild steel surface followed Langmuir and Temkin adsorption isotherms. Electrochemical polarization tests indicated that the compound exhibited a mixed corrosion type, and impedance spectroscopy revealed an increase in charge transfer resistance with higher inhibitor concentrations. Examination of the mild steel surface using SEM and Atomic Force Microscopy (AFM) confirmed the formation of a protective film. Wettability characteristics were assessed using the contact angle method. Frontier molecular orbital analysis revealed the HOMO and LUMO values for both the neutral and protonated forms of the compound. At 289 °C, the interaction energy for adsorption was found to be approximately −146.3006 kJ/mol for the neutral system and −135.8122 kJ/mol for the protonated system, while at 318 °C, the corresponding values were −140.6106 kJ/mol and −147.6022 kJ/mol. These findings collectively suggest the potential industrial utility of the investigated inhibitor as an effective corrosion inhibitor.
Memórias discentes sobre a cultura material escolar e a formação de professores: elos de um percurso formativo
Authors: Michel Caroline Braga , Amaral Alessandra , Vicente Magda de Abreu 
Published: December 2023

Este trabalho teve como intuito inventariar artefatos culturais presentes nas memórias escolares de estudantes de cursos de licenciaturas por compreender que por meio delas é possível problematizar a história das disciplinas escolares, as práticas e propostas pedagógicas relacionadas ao ensino e a formação de professores. Para realizar a coleta e a análise dos dados foi utilizada a perspectiva teórica da história cultural tendo como metodologia as rodas de conversas. Os dados apresentados neste texto foram produzidos no período de 2019 a 2020 em duas instituições públicas de Ensino Superior do Rio Grande do Sul, e correspondem à participação de 86 estudantes oriundos de três cursos distintos: Licenciatura em Matemática, Licenciatura em Ciências Exatas e Licenciatura em Educação Física. Assim, as memórias elencadas permitiram a identificação de um conjunto de artefatos que denotam similaridades nas materialidades e seus usos, bem como mudanças e/ou dissonâncias em suas compreensões.
A problematização do uso de comentários e descrições em posts da internet na pesquisa em história da educação
Authors: Martiarena Maria Augusta , Santos Bruna Luiz dos 
Published: December 2023

O presente artigo faz parte do projeto denominado “História da Educação, Educação Profissional e das relações Trabalho e Educação no Litoral Norte gaúcho (séculos XIX, XX e XXI)”. Assim, através da investigação em acervos digitais no que tange o referido projeto, surgiu a problemática de analisar o uso dos comentários e descrições feitos em posts na internet, seja em sites, blogs ou redes sociais. Pretende-se, portanto, discorrer sobre o assunto, seus obstáculos e contribuições, tendo como foco o âmbito da pesquisa em História da Educação. Ainda, vale salientar que, neste trabalho, abrange-se tanto a descrição do próprio autor em determinado post quanto, também, as colaborações de outras pessoas no mesmo post em forma de comentários. Por fim, acredita-se que, apesar das dificuldades encontradas, as descrições e comentários contribuem para que um novo olhar seja construído na pesquisa histórica.
ОСОБЛИВОСТІ ПРОФЕСІЙНО-ЕТИЧНОЇ ПІДГОТОВКИ МАЙБУТНІХ ФАХІВЦІВ СОЦІАЛЬНОЇ СФЕРИ
Authors: ZOZULIAK-SLUCHYK ROKSOLIANA 
Published: December 2023

У статті проаналізовано процес професійно-етичної підготовки майбутніх фахівців соціальної сфери в закладах вищої освіти.
Закцентовано увагу на ролі впливу змісту професійно-орієнтованих дисциплін та практичної підготовки на ефективність професійно-етичної підготовки студентів у закладах вищої освіти. Авторкою встановлено рівень розвиненості професіоналізму викладачів, котрі здійснюють професійно-етичну підготовку майбутніх фахівців соціальної сфери.
Simulating 2,4,6-trinitrotoluene (TNT) elimination in a pond inhabited by freshwater algae of the Rhizoclonium genus
Authors: Khromykh N. O. , Marenkov O. M. , Sharamok T. S. , Anishchenko A. O. , Yesipova N. B. , Nesterenko O. S. , Kurchenko V. O. , Mylostyvyi R. V. 
Published: December 2023

Military operations over large areas of Ukraine lead to release of explosives and their derivatives into the environment with subsequent accumulation in natural and artificial water bodies, which unwittingly serve as reservoirs for collecting pollutants from the catchment area. The need to restore aquatic ecosystems dictates the search for efficient, cost-effective and environmentally friendly methods for the elimination of explosives, which corresponds to the processes of biological treatment. In this work, we examined the ability of common freshwater algae of the genus Rhizoclonium to detoxify 2,4,6-trinitrotoluene (TNT) under model conditions of water pollution (at a TNT concentration of 100 mg/L). The exposure time of the algae to TNT was 48 hours, during which the content of TNT and nitrites in the aqueous medium was monitored, as well as the content of chlorophyll and the activity of glutathione S-transferase in plant tissues. 2,4,6-trinitrotoluene was extracted from the aqueous medium with toluene, followed by separation in a separatory funnel, removal of residual water with sodium sulfate, and reduction of the extract volume using a rotary evaporator. The decrease in the concentration of TNT, established by GC-MS technique, was 66.4% by the end of the experiment, while the content of nitrites increased almost 15-fold. In the algae cells, a threefold increase in the enzymatic activity was observed already in the second hour of exposure, followed by a gradual decrease and maintenance at a level of 50% of the control until the end of the experiment. The total chlorophyll content increased significantly from the sixth hour of exposure to the end of the experiment due to an increase in the content of chlorophyll b. The results obtained indicate the efficient biodegradation process and prospects of using algae of the genus Rhizoclonium for cleaning water bodies contaminated with TNT.
The past, present, and future of nuclear fuel
Authors: Andersson David A. , Stanek Christopher R. , Matthews Christopher , Uberuaga Blas P. 
Published: December 2023

AbstractNew reactor concepts have motivated study of a variety of nuclear fuel types. Most nuclear fuels have their origins dating back to the very beginnings of nuclear materials. We survey the most prevalent types of nuclear fuels and their properties and give some historical context as to their development. We end with our perspective on what the next 50 years of nuclear fuel research might lead to. In our opinion, while optimized microstructures and chemistries are certainly on the horizon, the biggest developments will be the continued integration of modeling and simulation with experiments to extract the greatest amount of energy possible from existing fuel candidates in a safe and economical way.
Graphical abstract
Hast Mudra: Hand Sign Gesture Recognition Using LSTM
Authors: Riddhi Mirajkar Et al. 
Published: December 2023

Even though using the most natural way of communication is sign language, deaf and mute people find it challenging to socialize. A language barrier is erected between regular people and D&M individuals due to the structure of sign language, which is distinct from text. They converse by using vision-based communication as a result. The gestures can be easily understood by others if there is a standard interface that transforms sign language to visible text. As a result, R&D has been done on a vision-based interface system that will allow D&M persons to communicate without understanding one another's languages. In this project, first gathered and acquired data and created a dataset, after which extracting useful data from the images. Keywords After verification and trained data and model using the (LSTM) algorithm, TensorFlow, and Keras technology, classified the gestures according to alphabet. Using our own dataset, this system achieved an accuracy of around 86.75% in an experimental test. system uses the (LSTM) algorithm to process images and data.
On the Development of Machine Learning Based Real-Time Stress Monitoring : A Pilot Study
Authors: V. H. Ashwin Et al. 
Published: December 2023

During specific environmental changes, the human body regulates itself through emotional, physical or mental responses. One such response is stress. The psychological and physical stability of an individual may be affected by recurrent occurrences of acute stress. This often leads to anxiety disorder, other psychological illnesses, hypertension, and other physiological disorders. The work performance of the individual is also negatively affected due to long-term stress. Across various age groups, the global population is primarily influenced by anxiety, depression and psychological stress. The long-term adverse effects of stress can be mitigated by effectively monitoring and managing stress through a cost-efficient and reliable stress detection system. This paper mainly focuses on stress detection using a machine-learning approach. Wearable sensor data from electroencephalogram (EEG) and electrocardiogram (ECG) are considered during exposure to stress and the level of stress undergone by the participant is further analyzed. This approach helps in stress detection, analysis and mitigation, which in turn improves the quality life of people. Machining Learning technique k-means clustering algorithm is used after removal of artifacts to obtain case-specific clusters that segregate features pointing to non-stress and stress periods. The results of the proposed K-means clustering algorithm are compared to state-of-the-art techniques such as Artificial Neural Network (ANN), Decision Tree (DT), Random Forest (RF) and Support Vector Machine (SVM). From the results, it was concluded that the proposed algorithm outperformed the other with an accuracy of 96% in the overall analysis.
Employee Attrition Prediction based on Grey Wolf Optimization and Deep Neural Networks
Authors: G Pratibha Et al. 
Published: December 2023

Despite the constructive application of promising technologies such as Neural Networks, their potential for predicting human resource management outcomes still needs to be explored. Therefore, the primary aim of this paper is to utilize neural networks and meta-heuristic technologies to predict employee attrition, thereby enhancing prediction model performance. The conventional Grey Wolf optimization optimization (GWO) has gained substantial attention notice because of its attributes of robust convergence, minimal parameters, and simple implementaton. However, it encounter problems with slow convergence rates and susceptibility to local optima in practical optimization scenarios. To address these problems, this paper introduces an enhanced Grey Wolf Optimization algorithm incorporating the utilization of Cauchy-Gaussian mutation, which contributes to enhancing diversity within the leader wolf population and enhances the algorithm's global search capabilities. Additionally, this work preserves exceptional grey wolf individuals through a greedy selection of 2 mechanisms to ensure accelerated convergence. Moreover, an enhanced exploration strategy is suggested to expand the optimization possibilities of the algorithm and improve its convergence speed. The results shows that the proposed model achieved the accuarcy of 97.85%, precision of 98.45%, recall of 98.14%, and f1-score of 97.11%. Nevertheless, this paper extends its scope beyond merely predicting employee attrition probability and activities to enhance the precision of such predictions by constructing an improved model employing a Deep Neural Network (DNN)..
An Innovative Deep Learning Method to Diagnose Mosquito-Borne Illnesses in Blood Image Analysis
Authors: Shaimaa Saadoon Mahmood ALrfae Et al. 
Published: December 2023

Introduction: Malaria, an infectious illness carried by the bite of infected mosquitoes, is a significant public health concern, especially in Africa. The management of mosquito-human contact is crucial to mitigate its transmission. Artificial intelligence, including machine learning and deep learning techniques, is being utilized to enhance the diagnosis and identification of mosquito species. This advancement aims to facilitate the development of more efficient control measures.
Aims and Objective: To analyze the efficiency of three deep learning models in identifying blood-borne diseases by evaluating the macro and micro picture of blood samples.
Method: In this retrospective investigation, three deep learning algorithms, namely Convolutional Neural Networks (CNN), MobileNetV2, and ResNet50, were used to identify mosquito-borne illnesses, focusing on malaria. The research used a dataset of 120 blood samples gathered over one year from the hospital's pathology department. The CNN model streamlines preprocessing with multilayer perceptrons, simplifying malaria component extraction. MobileNetV2, a lightweight network, outperforms others with fewer parameters. Its compact blocks in Dense-MobileNet models minimize constraints and computation expenses. ResNet50 resolves degradation issues with a residual structure, preventing overfitting as hidden layers increase.
Results: The study evaluated three deep learning models (CNN, MobileNetV2, and ResNet50) for medical classification. The study also demonstrated improved True Positive Rates as False Positive Rates increased, indicating better accurate identification while controlling false positives. ResNet50 consistently outperformed the other models, showcasing its superior performance. The study revealed high precision scores for all models, classifying "Uninfected" and "Infected" cases. ResNet50 exhibited slightly higher precision, indicating its precision-based superiority. Overall, all models demonstrated vital accuracy, and ResNet50 showed exceptional performance. The study found that ResNet50 performs better in True Positive and False Positive Rates.
Conclusion: The study has concluded that ResNet50 has shown the best performance in detecting blood-borne diseases.
Dimensionality Reduction using Feature Selection Techniques on EDM for Student Academic Performance Prediction
Authors: Kajal Mahawar Et al. 
Published: December 2023

The aim of this study is to perform dimensionality reduction on student datasets using different feature selection strategies in order to better forecast students' academic progress and minimize high-dimensionality concerns. The effectiveness of models built utilizing classification algorithms and pertinent features chosen using different feature selection strategies has been experimentally evaluated in this study. Nine feature selection methods, including Chi-Square, Pearson Correlation, Variance Threshold, Feature Importance, Recursive Feature Elimination (RFE), Lasso, Ridge, Random Forest, and XGBoost, as well as one classification method, logistic regression, were applied to the student dataset to determine the results of the analysis. In this experiment, dimensionality reduction techniques were used to reduce the number of features from 70 to the optimal 21 feature subsets for analysis of student academic performance prediction. Five measures were used to assess the effectiveness of the techniques: r2, precision, recall, f-score, and accuracy. With the logistic regression classifier, the feature subset chosen by the chi-square had the highest accuracy of 72.4%, precision of 71.4%, recall of 72.4%, f-score of 70%, and r-square of -0.221%.