bluuebunny/crossref_metadata_2025_split huggingface dataset:
Gamified and Non-Gamified AI Tools in Enhancing EFL Listening Comprehension: An Analysis of Duolingo and Replika’s Impact on Engagement, Motivation, and Learning Outcomes
Authors: Tajik Aliakbar 
Published: February 2025

Abstract

Listening comprehension is a critical component of English language acquisition and a core foundation for effective communication and language development. This study investigates the impact of gamified and non-gamified AI-driven learning systems, Duolingo and Replika, on the improvement of English as a foreign language (EFL) listening comprehension. A concurrent mixed methods design was used to collect data from 53 Iranian high school students (aged 14–15) residing in Tehran Province. Quantitative data were collected through researcher-developed pre- and post-intervention listening comprehension assessments and a perception questionnaire, while qualitative data were derived from classroom observation checklists and semi-structured interviews (n = 15). Students were randomly allocated to two groups: Duolingo (n = 27) and Replika (n = 26), who participated in a 12-week intervention consisting of 24 sessions. Duolingo uses gamified elements such as rewards and points to increase engagement, while Replika uses conversation-based interactions. Quantitative analysis revealed significant improvements in listening comprehension for both groups, with the Duolingo group showing higher engagement metrics. Qualitative findings showed that Duolingo's adaptive algorithms and gamification elements fostered a more engaging and personalized learning experience. The study highlights how these AI-driven systems address previously overlooked aspects of EFL instruction through personalized, data-driven pathways, effectively improving adolescents' listening comprehension skills. The research reviewed here highlights how these systems address previously overlooked aspects of EFL instruction, particularly in improving adolescents' listening comprehension skills. By focusing on personalized, data-driven pathways, the study demonstrates how intelligent learning platforms can fill gaps in traditional teaching and effectively improve listening comprehension - a critical yet often underemphasized skill in language teaching.
Exploring Phylogenetic Diversity and Floristic Shifts Along an Elevational Gradient in a Northeast Brazilian Alpine Ecosystem
Authors: Santos-Neto Amadeu dos , Ribeiro Adauto de Souza 
Published: February 2025

Abstract

This study examines plant diversity along an elevational gradient in the Serra de Itabaiana National Park, northeastern Brazil, focusing on species richness, phylogenetic diversity, and beta diversity. We identified 108 plant species across five elevation bands, with species richness declining linearly with increasing elevation. Precipitation and temperature positively influenced richness, but no significant relationship was observed for phylogenetic diversity metrics. Notably, phylogenetic diversity peaked at mid-elevations, exhibiting a hump-shaped pattern, while higher elevations hosted phylogenetically clustered communities, reflecting environmental filtering. Beta diversity analysis revealed turnover as the dominant driver of species and phylogenetic dissimilarity between elevation belts, emphasizing the role of species replacement over nestedness. These findings underscore the influence of climatic variables and environmental constraints on community composition, with distinct patterns of species adaptation across elevations. Contrary to expectations, phanerophytes dominated across the gradient, challenging the predicted prevalence of hemicryptophytes at higher elevations. Our study highlights the importance of integrating phylogenetic data to understand biodiversity dynamics in understudied regions like northeastern Brazil. Conservation efforts in Serra de Itabaiana should prioritize maintaining beta diversity to capture species turnover and ensure ecosystem resilience. These findings contribute valuable insights into the interplay of ecological and evolutionary processes shaping plant communities in low-elevation mountain systems.
Enhancing Quantile Function Estimation with Beta-Kernel Smoothing
Authors: Li Juan , Yu Ping , Shi Jianhong , Song Weixing 
Published: February 2025

Abstract

This paper introduces a class of nonparametric quantile function estimators based on Beta kernel smoothing. We conduct a rigorous investigation into their large-sample properties, including asymptotic normality and mean squared equivalence to existing methods. Through extensive simulation studies, we demonstrate that the proposed Beta kernel estimators perform comparably to or outperform traditional empirical and symmetric location-scale kernel-based quantile estimators. Additionally, we provide two real-world applications to illustrate the practical effectiveness of our approach. The results suggest that Beta kernel smoothing offers a flexible and efficient alternative for quantile function estimation, particularly in cases where classical methods exhibit inefficiencies.
Psychosocial Impact of Cancer on Omani Children and Adolescents: A Qualitative Study of Parents’ Perspectives
Authors: Al-Saadi Laila , Sumri Hana AL , Chan Moon Fai , Sabahi Amal Al , Kendi Jalila Al , Al-Mashaikhi Nawal , Al-Azri Mohammed 
Published: February 2025

Abstract

Background: Cancer diagnosis and treatment poses considerable psychosocial and physical challenges for children and adolescents. This study explores the experiences of Omani parents regarding the impact of cancer on their children and adolescents.
Methods: Semi-structured individual interviews were conducted between December 2022 and June 2023 with parents of children and adolescents diagnosed with cancer, recruited from two major oncology centers in Oman. Data were analyzed using the framework analysis approach.
Results: Five main themes emerged: three related to the psychosocial impact of cancer on children and two concerning the availability of supportive care and coping resources. Parents reported that cancer affected their children emotionally (e.g., aggression, emotional suppression, fear, worry, jealousy/inferiority, and clinginess), physically (e.g., fluctuations in energy and appetite, persistent pain, body image concerns, and low immunity), and socially (e.g., bullying, loss of normal routines and daily activities, and self-imposed isolation). The two coping-related themes highlighted the role of healthcare professionals, including oncologists and nurses, and the support provided by extended family members, school communities, and volunteers from non-governmental organizations.
Conclusions: Cancer significantly impacts the emotional, physical, and social well-being of children and adolescents. However, various supportive networks, including healthcare teams, volunteers, family, friends, and school communities, are instrumental in helping them to adapt to their illness. There is a need for specialized psychological services to address these challenges and provide comprehensive care for children and adolescents with cancer in Oman.
The impact of worldviews on U.S. military affiliated behavioral health providers’ descriptions of moral injury: A qualitative exploratory multiple case study
Authors: Lane Mark C. 
Published: February 2025

Abstract

Humans are holistic
beings. Moral Injury (MI) creates holistic distress. There is
limited standardization in MI constructs, assessments, and
treatments. Current care for MI often limits spiritual integration.
The purpose of this qualitative exploratory multiple case study was
to explore how military behavioral health providers’ worldview
influenced their description of the construct of MI, the morbidity
of MI, and how these providers chose to treat clients they
perceived to be suffering through the effects of MI. A total of
eight U.S. Department of Defense or Veterans Affairs affiliated
behavioral health providers completed a worldview assessment,
prompted journal responses, and semi-structured interviews to
understand how their worldview impacts their descriptions of MI
constructs, morbidity, and treatment decisions. I analyzed the data
through postpositivist beliefs utilizing Codebook Template
Analysis. Participants’ worldviews influenced how they described MI
as a construct, what they assessed as MI morbidity, and how they
chose to treat it. More studies are needed to explore MI’s
spiritual dimensions and effects. Assessment and treatment should
include all stakeholders’ methodologies. Treatment for MI should
include care from providers for the body (medical doctors), soul
(chaplains), and mind (behavioral health providers).
Optimal mobile energy transportation: A Co-Optimization approach
Authors: Singh Praveen Prakash , Mamun Shamim Al , Mishra Sambeet , Bordin Chiara , Øyvang Thomas , Palu Ivo 
Published: February 2025

Abstract

This research paper develops a co-optimization framework using dynamic programming to optimize MESS by integrating sector coupling between the transportation and electricity networks. To capture market uncertainties, the framework incorporates traffic-aware, terrain-sensitive routing, empirical battery degradation models, and LMP. The framework enhances MESS performance across various scenarios by co-optimizing travel routes, traffic constraints, battery degradation, and charging costs. Comparative analyses establish cost trade-offs and scalability implications. Results demonstrate that cost-optimized routing reduces degradation costs while maintaining efficiency. The proposed approach improves MESS sustainability and economic viability in sector-coupled electricity markets.
Fault Detection and Prediction in Models: Optimizing Resource Usage in Cloud Infrastructure
Authors: Wu Wei 
Published: February 2025

Abstract

Cloud infrastructure management faces significant challenges in ensuring efficient resource usage while maintaining high availability. This paper presents a framework for Fault Detection and Prediction in Models, aiming to optimize resource allocation and minimize downtime by deploying proactive monitoring techniques. The framework leverages machine learning algorithms to analyze historical performance data, enabling the prediction of potential faults before they occur. This predictive capability initiates timely interventions that enhance operational reliability. Additionally, a feedback loop is integrated to continuously improve model performance based on real-time insights from cloud operations. Experiments across various cloud environments demonstrate substantial improvements in fault detection rates and resource utilization efficiency over traditional approaches, underscoring the value of predictive analytics for robust cloud infrastructure management.
Optimizing Image Classification Models for Cloud Infrastructure with Elastic Scaling
Authors: Wu Wei 
Published: February 2025

Abstract

Advances in image classification have led to significant improvements in various applications, yet challenges remain in optimizing these models for cloud infrastructures. Fluctuating workloads and the need for efficient resource allocation complicate the deployment of high-performing models. We propose a novel framework that enhances image classification performance specifically within cloud environments through Elastic Scaling. This approach leverages a modular architecture, facilitating real-time adjustments to computing resources based on demand and complexity of the models being utilized. Our methodology incorporates diverse data preprocessing techniques and evaluates multiple model architectures to strike a balance between accuracy and operational efficiency. Extensive experiments conducted across various datasets demonstrate the effectiveness of our solution, showcasing considerable enhancements in classification performance and substantial reductions in operational costs tied to cloud infrastructure. The findings highlight improvements in response times and resource utilization, creating an adaptive framework that effectively addresses differing image processing requirements. This work offers a comprehensive solution aimed at optimizing cloud-based image classification tasks while maximizing performance and efficiency.
Exploring the Diet-diverticular Disease Link: Insights from a Comprehensive Mendelian Randomization Study
Authors: Zhou Jie , Xu Yixin , Wang Haitao , Wang Kun , Chen Chao 
Published: February 2025

Background:
The interplay between dietary habits and the development of Diverticular
Disease (DD) has long been a subject of vibrant debate.

Objective:
Utilizing Mendelian Randomization (MR), this study aims to meticulously examine
the causal dynamics at play.

Methods:
The foundation for the Genome-Wide Association Studies (GWAS) on DD
was established using a dataset from the FinnGen consortium, encompassing 33,619 patients
and 329,381 control participants. Data on 18 dietary habits and DD for the validation
cohort were procured from the UK Biobank. An MR analysis was executed to delve
into the causal relationship between dietary habits and DD, adhering to a rigorous Bonferroni
correction threshold of 3.00E-03. Our main analysis method was the Inverse
Variance Weighted (IVW) approach. To improve the accuracy and reliability of our
study, we also conducted heterogeneity analysis, tests for horizontal pleiotropy, outlier
identification, and “leave-one-out” sensitivity analysis.

Results:
Our analysis unearthed a potential causal association between the consumption
of dried fruits and a lower risk of developing DD (IVW: odds ratio (OR) 0.372, 95%
confidence interval (CI) 0.272 - 0.509, p = 5.79E-10), a finding that was corroborated in
the validation cohort (IVW: OR 0.975, 95% CI 0.961 - 0.990, p = 1.04E-03). Conversely,
our results do not substantiate a causal link between the consumption of alcohol, dietary
fiber, and red meat and the risk of DD.

Conclusion:
Our detailed MR analyses show that eating dried fruit lowers the risk of
DD, providing strong support for prevention and treatment approaches for DD.
Gene Therapy and Gene Editing: Current Trends and Future Prospects of Molecular Medicine
Authors: Sen Aniruddha , Singh Vijay , Dwivedi Shailendra , Agrawal Ruchika , Bansal Akash , Shekhar Shashank , Kushwaha Sudhir Shyam 
Published: February 2025

Abstract:
Gene therapy and genome editing have emerged as transformative approaches in the
management of a diverse range of genetic and acquired diseases. This evaluation offers a thorough
examination of the present state and prospects of these innovative technologies. Gene therapy is a
prospective approach to the treatment and prevention of a variety of conditions, including complex
cancers and inherited genetic disorders, which entail the introduction, removal, or modification
of genetic material within a patient's cells. Genome editing, particularly through techniques
such as CRISPR-Cas9, enables targeted corrections of genetic defects and opens new possibilities
for personalized medicine by allowing for precise modifications at the DNA level. The review addresses
the ethical implications, clinical applications, and significant advancements of these technologies.
This article endeavors to underscore the substantial influence of gene therapy and
genome editing on contemporary medicine by assessing the most recent research and clinical
trials, thereby emphasizing their potential to revolutionize disease treatment and management.
Serotonergic Dynamics in Autism Spectrum Disorder: Unraveling the Intricate Connection
Authors: Chander Abhishek , Sharma Jeetesh , Mahajan Shushank , Dhankhar Sanchit , Chauhan Samrat , Saini Monika , Mehta Sanjana 
Published: February 2025

Abstract:
Autism Spectrum Disorder (ASD) constitutes a group of neurodevelopmental disorders
characterized by impairments in verbal and nonverbal communication skills, social interactions, and
stereotypes of behavior, with an estimated frequency of 1.2% of children throughout the world. The
lack of specific treatments or molecular biomarkers underscores the complexities of ASD as a nonunified
clinical entity. Comorbid medical conditions are particularly associated with gastrointestinal
issues that may suggest potential interactions between the brain and gut. This review suggests that
serotonin plays a significant role in the enteric and central nervous systems in relation to ASD. The
modulatory role of serotonin in the enteric nervous system is examined in relation to the pathophysiology
of ASD in order to shed light on prospective biomarkers and therapeutic targets that could
increase the precision of diagnosis and treatment.
Machine Learning for Chromatin Regulators in Coronary Artery Disease Diagnosis
Authors: Zhao Mei , Li Wanying , Peng Simin , Ma Xiaocong , Wang Ding , Zheng Jinghui 
Published: February 2025

Objective:
This study aims to investigate the mechanisms underlying the role of
chromatin regulator-related genes (CRRGs) in coronary artery disease (CAD) and develop a diagnostic
model for CAD.

Methods:
We downloaded CAD datasets from the GEO database and utilized R software for
machine learning, modeling, and classification of CAD based on CRRGs.

Results:
The random forest model was found to be the best approach, identifying USP44,
MOCS1, SSRP1, ZNF516, and SCML1 as the top contributing genes for CAD diagnosis and
prevention. Differentially expressed CRRGs were associated with aberrant immune cell infiltration
in CAD patients. CAD patients were classified into two subtypes based on the expression of
differentially expressed CRRGs. The differential expression analysis identified MMP9, LCE1D,
LOC92659, SYNGR4, EN2, CACNA1E, GPR78, and LOC92249 as differentially expressed
genes distinguishing the two subtypes of CAD. Functional analyses revealed that the differentially
expressed genes are enriched in biological processes related to cellular functions, such as responses
to metal ions and inorganic substances. The enriched pathways included inflammation
and hormone-related pathways, such as IL-17 signaling, endocrine resistance, TNF signaling,
and estrogen signaling pathways.

Conclusion:
CAD is associated with CRRGs, which may represent a new direction for CAD
treatment.
FAT COALITION AND CROWN-PRESIDENTIALISM: A CRITICAL REVIEW OF INDONESIA’S CONCURRENT ELECTIONS
Authors: Susanto Mei , Harijanti Susi Dwi , Manan Firman , Dramanda Wicaksana , Hernawan Muhammad Yoppy Adhi 
Published: February 2025

Concurrent elections in Indonesia, combining the executive (President) and legislative elections (DPR, DPD, DPRD), were introduced to strengthen the presidential system by fostering a stable and effective government. Supported by the Constitutional Court's Decision No. 14/PUU-XI/2013 and Decision No. 55/PUU-XVII/2019, this design aims to leverage the coattail effect, where a president’s electoral success boosts legislative support for their party. However, the 2019 and 2024 elections reveal a pseudo-coattail effect, where the president's party failed to secure a majority in the legislature. Consequently, oversized coalitions were formed, such as President Joko Widodo’s inclusion of opposing parties like Gerindra in 2019, and President Prabowo Subianto's coalition expansion in 2024, including parties that previously supported rival candidates. These developments weaken opposition, promote transactional politics, and foster crown-presidentialism, where presidential power becomes overly dominant, undermining checks and balances and democratic accountability. This article critiques the concurrent election system, examining how it exacerbates legislative fragmentation, weakens local election integrity, and entrenches excessive executive power. It proposes alternative models, such as separating national and local elections and introducing midterm elections for the legislature, to address these issues. These reforms aim to strengthen Indonesia’s democratic presidentialism and prevent excessive consolidation of power.
Strengthening Cloud Computing Security: A Malware Prevention and Detection Framework at the Hypervisor Level
Authors: Agarwal Anamika , Verma Satya Bhushan , Gupta Bineet Kumar , Singh Suyogita 
Published: February 2025

Abstract
The introduction outlines the significance of data storage and management in businesses and highlights the cost-effectiveness of data outsourcing, particularly in the context of cloud computing. It emphasizes the benefits and challenges of cloud computing, with a focus on security concerns related to malware threats. The proposed malware prevention and detection (MP&D) framework aims to address these challenges by providing a comprehensive set of processes and tools to secure virtual machines in cloud computing environments. The framework’s objectives include large-scale system management, identifying various attacks, early detection, rapid and accurate detection, scalability, and resistance to compromise. The section also mentions the prevalence of encrypted and modern malware, which poses additional challenges. The background procedures and tools for the MP&D framework are briefly outlined, along with a high-level algorithmic overview of its key components. The expected results of implementing the framework are discussed, highlighting improvements in security posture, reduced malware infections, early threat detection, faster incident response, and compliance benefits. The conclusion underscores the importance of robust security measures in cloud computing, particularly at the hypervisor level, and acknowledges the evolving nature of malware threats, indicating the need for ongoing research and development in this field.
Assessment of the Environmental Impacts of the Engenheiro Sérgio Motta HPP Plant Using the Leopold Matrix Methodology
Authors: Franco Lorena Cruz , Botan Maria Claudia Costa de Oliveira , Boschi Letícia Sabo , Zanette Andréia Fátima , Azevedo Claudia Gonçalves de 
Published: February 2025

Objective: The objective of this study is to investigate the main environmental impacts present in the Engenheiro Sérgio Motta Hydroelectric Power Plant, located in the far west of the state of São Paulo.

Theoretical Framework: The environmental impacts of hydroelectric power plants are widely known and discussed in the literature, but the texts are often exhaustive and difficult to understand, which prevents most of the population from understanding them.

Method: The methodology adopted for this study consists of assembling a Leopold Matrix, whose main characteristic is to summarize the environmental impacts and present them in the form of a table. In this way, understanding by the readers becomes simpler and more effective.

Results and Discussion: The results obtained show that the negative impacts are numerically greater than the positive impacts. Many of them occur directly, have a regional scope, are permanent, and of great magnitude. This leads to the question of the sustainability of energy sources labeled as clean.

Research Implications: This research is investigative and diagnostic in nature, and aims to discuss the sustainability of energy sources considered clean. After all, even though they are renewable, there is no energy conversion method that does not have environmental consequences, and in the case of hydroelectric plants, permanent and large-scale consequences.

Originality/Value: This study contributes to the literature due to the method used and the presentation of the results, making them visually appealing, since pages of information are compiled in a single table, called the Leopold Matrix.