bluuebunny/crossref_metadata_2025_split huggingface dataset:
Experimental research on characteristics of the ultrasonic p-wave velocity of coal-based paste filling materials
Authors: An Baifu , Song Jie , Li Junmeng , Cui Chenghao , Wang Jiale , Ren Jinfang , Bai Wenting 
Published: November 2023

Abstract
The research aims to explore changes in the ultrasonic p-wave velocity of coal-based paste filling materials, which is used for recovering standing pillars in the closed/closing coal mines, under influences of multiple material factors. To this end, orthogonal tests were designed and coal grains were taken as main materials to evaluate the influences of four factors (particle size of coal grains (A), high-water material content (B), cement content (C), and water content (D)) on ultrasonic p-wave velocities of samples. Test results show that (1) Ultrasonic p-wave velocity of coal-based paste filling materials is in the range of 1.596 to 2.357 km/s and these are listed (in descending order) as D, B, C, then A according to their influences on the ultrasonic p-wave velocity. (2) Ultrasonic p-wave velocity is positively correlated with the compressive strength and shear strength, with correlation coefficients separately of 0.82 and 0.69. (3) Formulae for changes in the ultrasonic p-wave velocity under influences of different factors were fitted. The ultrasonic p-wave velocity of coal-based paste filling[1] materials has a quadratic polynomial relationship with factor B while having exponential relationships with factors A, C, and D. Based on fitting equations for changes in single factors and the ultrasonic p-wave velocity, a predictive model for comprehensive parameters of characteristics of ultrasonic p-wave velocity of coal-based paste filling materials under joint action of the four factors was established, and the predictive model was verified.
Rethinking AI Code Generation: A One-shot Correction Approach Based on User Feedback
Authors: Le Kim Tuyen , Andrzejak Artur 
Published: November 2023

Abstract
Code generation has become an integral feature of modern IDEs, gathering significant attention. Notable approaches like GitHub Copilot and TabNine have been proposed to tackle this task. However, these tools may shift code writing tasks towards code reviewing, which involves modification from users. Despite the advantages of user feedback, their responses remain transient and lack persistence across interaction sessions. This is attributed to the inherent characteristics of generative AI models, which require explicit re-training for new data integration. Additionally, the non-deterministic and unpredictable nature of AI-powered models limits thorough examination of their unforeseen behaviors.
We propose a methodology named One-shot Correction to mitigate these issues in natural language to code translation models with no additional training. We utilize decomposition techniques to break down code translation into sub-problems. The final code is constructed using code snippets of each query chunk, extracted from user feedback or selectively generated from a generative model. Our evaluation indicates comparable or improved performance compared to other models. Moreover, the methodology offers straightforward and interpretable approaches, which enable in-depth examination of unexpected results and facilitate insights for potential enhancements. We also illustrate that user feedback can substantially improve code translation models without re-training. Ultimately, we develop a preliminary GUI application to demonstrate the utility of our methodology in simplifying customization and assessment of suggested code for users.
The role of miR-34a-5p, PRR11 and SURf4 as potential biomarkers in B-acute lymphoblastic leukemia cells
Authors: Ruiz-Ciancio Dario , Cotignola Javier , González-Conejero Rocío , Martínez Constantino 
Published: November 2023

Abstract
Despite advancements in B-cell acute lymphoblastic leukemia (B-ALL) therapy, a significant number of patients still experience treatment resistance, leading to relapse and poor prognosis. Recent studies have revealed the importance of non-genetic mechanisms in mediating resistance to cancer therapies. MicroRNAs (miRNAs) have emerged among non-genetic mechanisms as crucial regulators of tumor development, progression, and resistance to anticancer therapies. In particular, miR-34a has been implicated in cell invasion, migration, apoptosis, and abnormal response to chemotherapy in various tissues. However, the role of miR-34a-5p in B-ALL cells remains unexplored. Our results discovered that miR-34a-5p was downregulated in B-ALL cells, while its target SIRT1 was upregulated. Although the restoration of miR-34a-5p levels did not affect SIRT1 levels in B-ALL cells, restoring miR-34a-5p sensitized the cells to doxorubicin treatment. Additionally, to explain these results, we performed an extensive bioinformatic analysis in human B-ALL samples downloaded from online repositories to study miR-34a-5p as a potential biomarker for predicting response to B-ALL treatment. Notably, miR-34a-5p was observed to be downregulated in B-ALL cells from relapsed patients. We also identified four genes targeted by miR-34a-5p in these patient cells, which had not been previously associated with B-ALL. Finally, miR-34a-5p, PRR11, and SURF4 were identified as independent predictive markers for increased risk of death in B-ALL patients. Overall, these findings shed light on the significance of miR-34a-5p in B-ALL cells, and suggest that the combination of miR-34a-5p, PRR11, and SURF4 hold promise as potential markers for estimating the survival outcomes of B-ALL patients.
ALGAN: Time Series Anomaly Detection with Adjusted-LSTM GAN
Authors: Bashar Md Abul , Nayak Richi 
Published: November 2023

Abstract

Anomaly detection in time series data, to identify points that deviate from normal behaviour, is a common problem in various domains such as manufacturing, medical imaging, and cybersecurity. Recently, Generative Adversarial Networks (GANs) are shown to be effective in detecting anomalies in time series data. The neural network architecture of GANs (i.e. Generator and Discriminator) can significantly improve anomaly detection accuracy. In this paper, we propose a new GAN model, named Adjusted-LSTM GAN (ALGAN), which adjusts the output of an LSTM network for improved anomaly detection in both univariate and multivariate time series data in an unsupervised setting. We evaluate the performance of ALGAN on 46 real-world univariate time series datasets and a large multivariate dataset that spans multiple domains. Our experiments demonstrate that ALGAN outperforms traditional, neural network-based, and other GAN-based methods for anomaly detection in time series data.
Designing Multifunctional MXene Nanosheets Hair Dye
Authors: Kim Hwan , Park Hyeongtaek , Park Young Ho , Kim Soojin , Murali G. , Hwang Nathaniel , In Insik 
Published: November 2023

Abstract
The increasing demand for natural and safer alternatives to traditional hair dyes has led to the investigation of nanomaterials as potential candidates for hair coloring applications. MXene nanosheets have emerged as a promising alternative in this context due to their unique optical and electronic properties. In this study, we aimed to evaluate the potential of MXene nanosheets as a hair dye. We comprehensively reviewed the current research on MXene nanosheets for hair coloring applications, including their synthesis, characterization, and performance. We found that MXene nanosheets have the potential to offer safe and effective alternatives to traditional hair dyes, but further research and development are needed to determine the optimal formulation and evaluate the safety of these nanosheets for use in hair coloring products. Overall, our study highlights the potential of MXene nanosheets as a promising alternative for hair dye applications.
On the operational optimization of pump storage systems in water supply systems using PATs and time-differentiated energy prices
Authors: Lourenço Flávio , Reis Ana Luísa , Andrade-Campos António 
Published: November 2023

Abstract
Power generation from fossil fuels has long had a negative impact on the environment. Nowadays, a paradigm shift in power generation is being witnessed, with increasing investment in renewable energy sources. Despite this progress, efficient energy storage is still limited. Given this challenge, pumped storage technology can be one of the viable solutions. This involves storing gravitational energy by pumping water into a reservoir at a higher altitude, which is later converted into electrical energy using a turbine. This paper studies a pump hydro storage system (PHS) operation in water supply systems (WSSs), with the aim of minimizing operating costs and evaluating its effectiveness. Replacing conventional pumps with pump-as-turbines (PATs) provides a flexible and cost-effective approach. The proposed methodology aims to optimize the operation of these PATs considering dynamic energy prices. The developed computational model was applied to different operational scenarios and analyzed in terms of cost-effectiveness. The results show that the lower the average ratio between time-differentiated purchase and fixed sell energy tariffs, the greater the optimization potential of using PAT. In the WSS case study analyzed, energy cost reductions of 43.4–68.1% were achieved, demonstrating the effectiveness of PHS in WSS particularly for energy tariffs with large variations.
Effects of reaction conditions on the wet oxidation of excess sludge from the caprolactam wastewater treatment process
Authors: Shulin Qin , Zhongquan Wang , Weicheng Zheng , Yingxi Zhang , Xu Zeng 
Published: November 2023

Abstract

In this study, wet oxidation of excess sludge from the caprolactam wastewater treatment process was investigated. The effects of reaction conditions, including sludge concentration, the amount of sludge, reaction temperature and time and the oxygen supply amount, were discussed. The highest removal rates of chemical oxygen demand (COD) and volatile suspended solid (VSS) that can be attained at 78.6 and 89.3% were acquired separately under 260 °C for 60 min with an initial oxygen gas pressure of 1.3 MPa. The sludge was hydrolyzed and oxidized under hydrothermal conditions, producing small molecule organic acids, such as acetic, formic and oxalic acids, as the main products, which could be used as carbon sources for wastewater treatment. These results indicated that wet oxidation provides a favourable and feasible alternative method for the treatment of excess sludge from the coal chemical industry.
Adoption of a COVID-19 Contact Tracing App by Czech Youth: Cross-Cultural Replication Study
Authors: Dolezel Michal , Smutny Zdenek 
Published: November 2023

Background
During the worldwide COVID-19 pandemic crisis, the role of digital contact tracing (DCT) intensified. However, the uptake of this technology expectedly differed among age cohorts and national cultures. Various conceptual tools were introduced to strengthen DCT research from a theoretical perspective. However, little has been done to compare theory-supported findings across different cultural contexts and age cohorts.

Objective
Building on the original study conducted in Belgium in April 2020 and theoretically underpinned by the Health Belief Model (HBM), this study attempted to confirm the predictors of DCT adoption in a cultural environment different from the original setting, that is, the Czech Republic. In addition, by using brief qualitative evidence, it aimed to shed light on the possible limitations of the HBM in the examined context and to propose certain extensions of the HBM.

Methods
A Czech version of the original instrument was administered to a convenience sample of young (aged 18-29 y) Czech adults in November 2020. After filtering, 519 valid responses were obtained and included in the quantitative data analysis, which used structural equation modeling and followed the proposed structure of the relationships among the HBM constructs. Furthermore, a qualitative thematic analysis of the free-text answers was conducted to provide additional insights about the model’s validity in the given context.

Results
The proposed measurement model exhibited less optimal fit (root mean square error of approximation=0.065, 90% CI 0.060-0.070) than in the original study (root mean square error of approximation=0.036, 90% CI 0.033-0.039). Nevertheless, perceived benefits and perceived barriers were confirmed as the main, statistically significant predictors of DCT uptake, consistent with the original study (β=.60, P<.001 and β=−.39; P<.001, respectively). Differently from the original study, self-efficacy was not a significant predictor in the strict statistical sense (β=.12; P=.003). In addition, qualitative analysis demonstrated that in the given cohort, perceived barriers was the most frequent theme (166/354, 46.9% of total codes). Under this category, psychological fears and concerns was a subtheme, notably diverging from the original operationalization of the perceived barriers construct. In a similar sense, a role for social influence in DCT uptake processes was suggested by some respondents (12/354, 1.7% of total codes). In summary, the quantitative and qualitative results indicated that the proposed quantitative model seemed to be of limited value in the examined context.

Conclusions
Future studies should focus on reconceptualizing the 2 underperforming constructs (ie, perceived severity and cues to action) by considering the qualitative findings. This study also provided actionable insights for policy makers and app developers to mitigate DCT adoption issues in the event of a future pandemic caused by unknown viral agents.
Fostering Patient-Clinician Communication to Promote Rapid HIV, Hepatitis B Virus, and Hepatitis C Virus Diagnostic Testing: Conceptual Development of a Multilingual App
Authors: Brown Carter , Roucoux Guillaume , Rousset-Torrente Olivia , Ali Saleh , Yombo-Kokule Lisa , Chaplin John , Chassany Olivier , Duracinsky Martin 
Published: November 2023

Background
Migrants are disproportionately affected by HIV, hepatitis B virus (HBV), and hepatitis C virus (HCV). Clinicians, at times, fail to offer rapid diagnostic testing (RDT) for these viruses when a language barrier exists in the patient-clinician relationship, therefore creating missed testing opportunities. Although their effectiveness has been demonstrated elsewhere, conventional, in-person interpreters are costly and underused in practice. Furthermore, clinicians often call upon ad hoc interpreters, which introduces complexities in the clinical relationship. Digital solutions exist to diminish the burden of language barriers; however, the challenges of developing a multilingual and multicultural app have yet to be documented with respect to RDT in the nonfrancophone migrant population in France.

Objective
Our goal was to design a multilingual app to overcome language barriers, health literacy barriers, and fears related to being tested to promote RDT of HIV, HBV, and HCV in the nonfrancophone migrant population in France.

Methods
A combination of qualitative methods, agile development, and user-centered design was used. We conducted 2 focus groups (FGs) with 12 participants, including physicians, nurses, and social workers conducting RDT, as well as 1 modified Delphi survey with 68 participants including physicians and nurses. FGs explored the content (risk factors and medical history), functions (cultural adaptation and instant translation), and interface ergonomics (graphics and font) needed in the app. The Delphi presented 95 content items that the researchers sought to include in the app.

Results
Using FGs to inform the Delphi survey, we scientifically determined the app’s content consisting of 95 items using expert consensus, developed a mock-up, and conducted initial user testing. We created an app that contains both migrant and clinician interfaces and includes a sociodemographic, risk assessment, health literacy, and testing barrier questionnaires available in 11 languages. Educational content is related to HIV, HBV, and HCV, along with the ability to understand whether the migrant agrees to be tested.

Conclusions
This study allowed us to conceptualize a multilingual app that aims to increase the acceptance of RDT for HIV, HBV, and HCV. The specific features of the Assistant intelligent au dépistage des allophones app were designed to overcome the testing barriers in the nonfrancophone migrant population. The next phase will be an implementation study, as we intend to validate our app.
Unraveling the Interplay of Language and Culture in BIPA Learning for International Students
Authors: Febria Feby , Tiawati Refa Lina , Rahmat Wahyudi 
Published: November 2023

This research aims to describe the role of language and culture in the success of international students in BIPA learning at AndalasUniversity. The method used in this research is descriptive. The data used is data obtained from observations, interviews and documentation on the role of language and culture with BIPA students at Andalas University. Data collection techniques use observation, interviews and documentation techniques. The data source in this research is interviews with Andalas University students. According to the results of the analysis, it was found that 24 language roles and 30 cultural roles were known to Andalas University international students.
La forme de vie et le motif : comment penser la genealogie des tableaux ?
Authors: Colas-Blaise Marion 
Published: November 2023

Dans cet article, nous nous demandons comment on peut associer une forme de vie, dûment théorisée (BASSO FOSSALI ; COLAS-BLAISE ; FONTANILLE ; WITTGENSTEIN), à une ou plusieurs images, en étudiant la forme de vie d’un tableau ainsi que la ou les formes de vie (« jeux culturels ») associées à un tableau. Il s’agit, plus particulièrement, d’approcher la question des filiations entre tableaux – de la généalogie de tableaux – sous l’angle des migrations et mutations du motif (figuratif, plastique, figural). L’objectif est non seulement de dégager des modes de constitution des formes de vie (par élargissements concentriques, par ressaisie paradigmatique…), mais encore de montrer que la forme de vie d’un tableau peut accueillir des formes picturales en devenir (genèse). Les hypothèses sont mises à l’épreuve de peintures choisies du peintre limougeaud Georges Laurent.
Analisis Kebutuhan Guru dan Peserta Didik pada Pengembangan Media Counting Box Kelas 1 Sekolah Dasar
Authors: Ibrohim M. , Arsita Cindy 
Published: November 2023

Abstrak
Penelitian ini bertujuan untuk mendeskripsikan Analisis Kebutuhan Guru dan Peserta Didik pada Pengembangan Media Counting Box kelas 1 sekolah dasar. Tujuan utama penelitian ini adalah untuk menilai respons guru dan peserta didik terhadap kebutuhan pengembangan media pembelajaran Counting Box. Penelitian ini merupakan penelitian deskriptif kualitatif yang menggunakan metode survei untuk mengidentifikasi kebutuhan guru dan peserta didik pada pengembangan media Counting Box kelas 1 sekolah dasar. Dalam penelitian ini anallisis kebutuhan mengacu pada pencapaian hasil belajar peserta didik. Subjek penelitian ini terdiri dari 26 peserta didik kelas 1 dan 1 guru wali kelas SD Negeri 103/II Tanjung Agung. Data diperoleh melalui proses wawancara langsung dan kemudian dianalisis untuk disimpulkan berdasarkan interpretasi hasil analisis. Hasil penelitian menunjukkan bahwa: 1) Sebanyak 42,30% nilai peserta didik pada pembelajaran matemtika masih di bawah KKM, 2) Diperlukan pengembangan media Counting Box, untuk mendukung pembelajaran dalam mata pelajaran matematika.
Assessing Event Volunteer Motivational Factors and Quality of Life among University Students
Authors: Mohd Rashid Norhidayah , Ma’amor Hairunnisa , Zamzuri Norol Hamiza , Ramli Nurakmal , Ghazali Ayu Rohaidah , Saridan Shazril 
Published: November 2023

Volunteerism is a selfless and altruistic act of individuals dedicating their time, skills, and resources to support various causes and organizations without financial compensation. Hence, this paper aims to propose a conceptual framework for event volunteer motivation among university students and its impact on quality of life. Motivational factors measured by the volunteer motivational scale were proposed as independent variables, and quality of life as the dependent variable. This variable will regress to identify the relationship between quality of life among university students. The motivation to engage in volunteer work significantly impacts an individual's quality of life. However, few studies have been conducted on the impact of volunteering activities on quality of life. Therefore, this study aims to develop a quality of life framework by considering event volunteer’s motivation as independent variables. This study will take place at Universiti Teknologi MARA (UiTM), Puncak Alam Campus whereby students from this university who already experience becoming volunteers will be involved as respondents. It is hoped that the contingency model of sports event volunteer motivation that is developed through this study will contribute to a better understanding of volunteerism and quality of life among students in higher institutions. Encouraging volunteering activities will educate youths to become responsible citizens by practicing a positive quality of life.
Evaluation of the effectiveness of training and the use of neural networks to predict biological age.
Authors: Berezina Tatiana Nikolaevna , Balan Aleksandr , Zimina Al'bina Aleksandrovna 
Published: November 2023

Neural network training is widely used in various educational fields: staff training, mastering the curriculum at school and university, forming recommendations for private use by respondents, for teaching pensioners health-saving techniques. It is relevant to analyze the learning process of neural networks and evaluate their effectiveness on various models. For the study, a model was chosen for predicting the index of biological aging of a person based on the characteristics of his personality. To train neural networks, a data matrix of 1,632 people aged 35 to 70 years was compiled. Output variable: biological aging index; input variables: gender, age, family and professional status, place of residence, body type, type of functional asymmetry, style of relationships with people, as well as personal resources. Four neural networks were trained: for men and women, for working professionals and for pensioners. Results: 1) trained neural networks give different recommendations for men and women of pre-retirement and post-retirement age. 2) The effectiveness of predicting the biological aging index using neural networks turned out to be significantly high for all trained programs. 3) Neural networks can be used to model various social situations and identify what changes this will lead to for output variables. Situations were modeled: a) if all single adults get married, b) if all family adults break up, c) if everyone receives the recommendations of psychologists on the selection of personal resources and will use them. The neural network has issued a forecast: it is better for adult women not to change their family status/it is better for adult men to change their status. The use of personal resources selected by psychologists is effective for everyone.