mdpi-ai/citelens huggingface dataset:
Developing Doctoral Theses in Education: The Role of Systematic Reviews in the Spanish Context
Field: EDUCATION

The production and development of doctoral theses have grown exponentially with the advent of the Internet and the democratization of access to information and education. In the field of education, this production is no stranger to this trend, so it is interesting to analyze the implications, causes and scientific–academic contributions of this increase. To this end, a systematic literature review (SLR) was carried out using the PRISMA 2020 protocol, with the Dialnet database as the documentary source, based on a previous study that justified its use and the availability of documents. Thus, a set of pre-established criteria are defined to identify doctoral theses carried out in Spanish universities and related to the education area that have been published in the last 17 years, finding a total of (n = 120) publications whose analysis answered the researchers’ questions focused on identifying patterns and strategies in the publication and methodological design of this type of document and what is the role of systematic reviews of the literature in them. In this sense, this research process aimed to analyze this kind of production and facilitate the process of designing new theses and research projects in the field of education. In this sense, this research process aimed to analyze this sort of output and facilitate the process of designing new theses and research projects in the field of education. The results make it possible to identify the increased importance of SLR in the development of doctoral theses and reveal the predominant models related to this type of production. Additionally, other aspects, such as the most common universities or research fields, the quantity and nature of subsequent studies, etc., concerning doctoral theses that incorporate an SLR were determined. Thus, conducting an SLR represents a solid and structured approach to initiate and build up the research process of doctoral theses, being essential to address students’ potential training needs in these regards.
A Systematic Review of the Use of Surgical Checklists in Transurethral Resection of Bladder Tumour
Field: ONCOLOGY

Context: Surgical checklists have previously been shown to improve surgical quality and patient outcomes. However, their use in transurethral resection of bladder tumour (TURBT), one of the most commonly performed urological procedures, has yet to be explored in depth. Objective: To evaluate the effect of surgical checklist implementation in TURBT on documentation quality, specimen quality, and oncological outcomes according to the existing literature. We then hope to develop an optimised TURBT checklist by identifying the most pertinent parameters for inclusion. Evidence acquisition: A literature search using PubMed was performed to identify literature pertaining to the use of surgical checklists in the context of TURBT. A systematic review was then performed on the 41 identified studies, of which six were included in the final analysis. Evidence synthesis: We explored three primary outcomes that arose from the literature, namely: (1) comprehensiveness of documentation; (2) resection quality; and (3) recurrence rates and recurrence-free survival (RFS). We found agreement in the literature that surgical checklist implementation does lead to an overall improvement in documentation. The effect of surgical checklists on resection quality and recurrence rates, however, was mixed in the literature, with some studies showing statistically significant improvements and others showing no significant change. Conclusions: There are multiple benefits to surgical checklist implementation in TURBT procedures. We propose an optimised 14-item surgical checklist that should be implemented in every TURBT report to ensure sufficient information documentation for risk stratification and post-operative management.
Leveraging Explainable Artificial Intelligence (XAI) for Expert Interpretability in Predicting Rapid Kidney Enlargement Risks in Autosomal Dominant Polycystic Kidney Disease (ADPKD)
Field: AI

Autosomal dominant polycystic kidney disease (ADPKD) is the predominant hereditary factor leading to end-stage renal disease (ESRD) worldwide, affecting individuals across all races with a prevalence of 1 in 400 to 1 in 1000. The disease presents significant challenges in management, particularly with limited options for slowing cyst progression, as well as the use of tolvaptan being restricted to high-risk patients due to potential liver injury. However, determining high-risk status typically requires magnetic resonance imaging (MRI) to calculate total kidney volume (TKV), a time-consuming process demanding specialized expertise. Motivated by these challenges, this study proposes alternative methods for high-risk categorization that do not rely on TKV data. Utilizing historical patient data, we aim to predict rapid kidney enlargement in ADPKD patients to support clinical decision-making. We applied seven machine learning algorithms—Random Forest, Logistic Regression, Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), Gradient Boosting Tree, XGBoost, and Deep Neural Network (DNN)—to data from the Polycystic Kidney Disease Outcomes Consortium (PKDOC) database. The XGBoost model, combined with the Synthetic Minority Oversampling Technique (SMOTE), yielded the best performance. We also leveraged explainable artificial intelligence (XAI) techniques, specifically Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP), to visualize and clarify the model’s predictions. Furthermore, we generated text summaries to enhance interpretability. To evaluate the effectiveness of our approach, we proposed new metrics to assess explainability and conducted a survey with 27 doctors to compare models with and without XAI techniques. The results indicated that incorporating XAI and textual summaries significantly improved expert explainability and increased confidence in the model’s ability to support treatment decisions for ADPKD patients.
Deep Learning in Finance: A Survey of Applications and Techniques
Field: AI

Machine learning (ML) has transformed the financial industry by enabling advanced applications such as credit scoring, fraud detection, and market forecasting. At the core of this transformation is deep learning (DL), a subset of ML that is robust in processing and analyzing complex and large datasets. This paper provides a comprehensive overview of key deep learning models, including Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), Deep Belief Networks (DBNs), Transformers, Generative Adversarial Networks (GANs), and Deep Reinforcement Learning (Deep RL). Beyond summarizing their mathematical foundations and learning processes, this study offers new insights into how these models are applied in real-world financial contexts, highlighting their specific advantages and limitations in tasks such as algorithmic trading, risk management, and portfolio optimization. It also examines recent advances and emerging trends in the financial industry alongside critical challenges such as data quality, model interpretability, and computational complexity. These insights can guide future research directions toward developing more efficient, robust, and explainable financial models that address the evolving needs of the financial sector.
Airfoil Shape Generation and Feature Extraction Using the Conditional VAE-WGAN-gp
Field: AI

A machine learning method was applied to solve an inverse airfoil design problem. A conditional VAE-WGAN-gp model, which couples the conditional variational autoencoder (VAE) and Wasserstein generative adversarial network with gradient penalty (WGAN-gp), is proposed for an airfoil generation method, and then, it is compared with the WGAN-gp and VAE models. The VAEGAN model couples the VAE and GAN models, which enables feature extraction in the GAN models. In airfoil generation tasks, to generate airfoil shapes that satisfy lift coefficient requirements, it is known that VAE outperforms WGAN-gp with respect to the accuracy of the reproduction of the lift coefficient, whereas GAN outperforms VAE with respect to the smoothness and variations of generated shapes. In this study, VAE-WGAN-gp demonstrated a good performance in all three aspects. Latent distribution was also studied to compare the feature extraction ability of the proposed method.
A Systematic Review of Digital Competence Evaluation in Higher Education
Field: EDUCATION

University students’ digital skills depend significantly on educators’ proficiency, necessitating regular assessments. Tools like DigComp and the TPACK model are provided in this technological context. A systematic review, following PRISMA criteria, aims to evaluate digital competencies through globally used tools. DigCompEdu is prominent, with Spain leading the research, while unvalidated instruments from Asia highlight global disparities. This review will identify key tools and expose geographical and validation gaps, stressing the need for standardized assessments. Understanding the predominance of DigCompEdu and seeing the variation that is generated in Asia highlights the poor ability to transmit self-perceived competencies to learners.
Dysregulation of SIGLEC1 in non-small cell lung cancer: prognostic implications and immunomodulatory role-a multicenter cohort study
Field: ONCOLOGY

Purpose To investigate the clinical significance and functional role of SIGLEC1-positive cells in non-small cell lungcancer (NSCLC) patients, focusing on their prognostic impact and therapeutic response. Methods A multicenter retrospective cohort analysis was conducted, integrating data from multiple sources. Weanalyzed SIGLEC1 expression in NSCLC tissues, clinicopathological features, overall survival outcomes,chemotherapy responsiveness, and sensitivity to targeted therapies. We also developed a prognostic model basedon SIGLEC1 expression and clinical variables. Results SIGLEC1 expression was significantly downregulated in NSCLC tissues, and the density of SIGLEC1-positivecells was inversely correlated with various clinicopathological features. Notably, patients with high infiltration ofSIGLEC1-positive cells exhibited significantly better overall survival outcomes. Furthermore, elevated SIGLEC1expression was associated with improved responsiveness to chemotherapy and demonstrated distinct patterns ofsensitivity to targeted therapies. A robust prognostic model was developed by integrating SIGLEC1 expression andclinical variables. Conclusions This study highlighted the downregulation of SIGLEC1 in NSCLC tissues and its significant associationwith patient prognosis and therapeutic response. The findings suggested that SIGLEC1 played a critical role inmodulating the tumor immune microenvironment and has potential as both a prognostic biomarker and therapeutictarget in NSCLC.
Fuzzy Association Rule Mining for Personalized Chinese Language and Literature Teaching from Higher Education
Field: AI

Due to rapid information technology growth, teaching Chinese in higher education has changed, and Chinese literary majors have vigorously evolved. The key teaching difficulties are scalability, individualized teaching, and a lack of resources and methodologies. Research shows individualized education improves topic comprehension, cultural engagement, and learner interest. Fuzzy association rule mining uses fuzzy linguistic values and membership functions to provide more realistic results. Hence, an algorithm, EF-PCL2T, has been proposed to improve personalized Chinese language and literature teaching (PCL2T) using enhanced fuzzy (EF) Apriori association rule mining integrated with the genetic algorithm. Fuzzy Apriori association rule mining identified frequent itemsets with relevant learning patterns and produced applicable association rules from datasets with fuzzy or unclear information, capturing fluctuating itemset importance and providing a flexible representation of relationships to determine student preferences. From fuzzy-related data, a genetic algorithm optimizes skill sets and creates individualized lesson plans considering each student’s competency and preferences for adjusting to personalized teaching tactics. Testing shows that fuzzy enhancement association rule mining for the PCL2T model improves student retention, PET (personalized teaching efficiency), minimal support and confidence update with fuzzy rules, and student involvement compared to other state-of-the-art methods. Students agree that tailored Chinese language and literary instruction is possible. The improvement results show fuzzy rules with minimum confidence levels of 50% to 100%, highly correlated in this model, student retention ratio of 96%, improved assessment grade of various language skills by 40 marks, PTE analysis of 93%, and student involvement ratio of 97%.
Federated Learning Enhanced MLP–LSTM Modeling in an Integrated Deep Learning Pipeline for Stock Market Prediction
Field: AI

In this study, the research presents the Federated Learning Enhanced Multi-Layer Perceptron (Fed-MLP) Long Short-Term Memory that is suggested by the research. The research intends to use the LSTM networks extensively that are proficient in spatial dependence capturing and integrate them with the collaborative learning framework of Federated Learning in an endeavor to augment the predictive competency. In the first step, we gather stock market indices from various financial organizations, using CAC40 stocks as the index for the French stock market. To guarantee data consistency and quality, pre-processing methods including linear interpolation and Z-score normalization are used. There are two types of models for each of the three basic elements within the Fed-MLP–LSTM, namely, MLP for feature extraction and LSTM for sequence modeling. Institutionally, each refining institution trains a local MLP–LSTM on the corpus specific to their institution, with only the model parameters being transferred to a central server through Federated Learning. A global model is created and updated through repeated training and totaling of parameters while preserving privacy of the data going to each node. In the performance evaluation, quantitative measures like Root-Mean-Square Error (RMSE), and accuracy are seven used. Hypothesis testing shows that we have good evidence to support that the proposed Fed-MLP–LSTM outperforms the other methods with the lowest RMSE of 0. 0108 and 98.3% of accuracy with reference to their respective cocaine molecule target. The proposed method is implemented in python. This suggests that using Federated Learning along with MLP and LSTM as the components of this vector enhanced the function increasing its capacity and reliability in predicting the trends of stocks. In conclusion, the present study suggests a sound solution for effective and secure stock market forecasting in collaboration environments that can find its use in the financial domains and securities businesses.
Enhancing English as a Foreign Language (EFL) learning in Saudi Arabia: the academic contribution of YouTube in EFL learning and cultural awareness
Field: EDUCATION

The incorporation of technology in English as a Foreign Language (EFL) classroom has drastically transformed conventional learning models by offering innovative ways to boost students’ engagement and improve learning outcomes. This study investigates the impact of using YouTube as an educational tool to enhance EFL instruction in Saudi Arabia. A total of 200 EFL students were divided into two groups: An experimental group which used You Tube as a source of learning and another group which used normal curriculum. A mixed-methods approach was employed, combining quantitative data from a paired-sample t-test with qualitative feedback from students. The results for paired-sample t-test computations revealed a statistically significant difference (p = 0. 003) across the proficiency level indicating improvement in students’ speaking and listening skills at higher proficiency levels. Of the qualitative answers, motivational and participation enhancement were overemphasized. The research points that the integration of YouTube in EFL classrooms can lead to a remarkable increase in language learning especially in the learning area of listening and speaking abilities among the students. These results offer practical implications for educators seeking to integrate multimedia tools into their teaching strategies to promote more interactive and effective learning experiences.
From cognitive coach to social architect: shifts in learning assistants’ valued practices
Field: EDUCATION

Learning assistants (LAs) are undergraduate students who serve as instructional assistants in STEM classrooms. In addition to engaging in active practice, they take a pedagogy seminar and regularly meet with a content instructor. While aspects of LAs’ pedagogical beliefs and actions have been investigated, there remains a gap in understanding how LAs make sense of their new instructional roles and how they negotiate between their experiences as students and their responsibilities as instructors. This study uses a sequential, exploratory mixed-methods approach, which includes constant comparative open-coding, thematic analysis, and epistemic network analysis, to analyze 178 reflections written by 89 LAs across five terms at two institutions. Here, we identify each LA’s expressed goals and intended actions at the start and the end of their first term as an LA. Using a community of practice framework, we seek to explicate the shifts in these LAs’ values as they become more central members of the LA community. LAs’ expressed roles shift from being cognitive coaches, where they focus on student thinking, sense-making, and understanding of disciplinary concepts, to being social architects, where their focus shifts to attending to the aspects of the environment that can support productive interactions for learning. A social architect prioritizes goals related to mutual trust, respect, & approachability, understanding and learning about students, and creating a sense of belonging. Similarly, their intended actions emphasize compassion, understanding, and facilitating group discussion. While all LAs studied exhibited this shift, it manifested in different ways and to different extents, as illustrated in detail by four selected cases. These cases illustrate how the shifts coupled to a change in language around teaching, becoming more specific and contextual. LAs express a shift in their valued practices over their first term as LAs related to their instructional role. The goal of student-centered instructional practice is often framed as becoming a better cognitive coach; however, this orientation does not foreground ideas around teaching practice that aim to foster engagement, belonging, and student agency. Implications for both the LA model and, more generally, for postsecondary STEM instructors are discussed.
Preoperative subjective impairments in language and memory in brain tumor patients
Field: ONCOLOGY

Background: Subjective reports can reveal relevant information regarding the nature of the impairment of brain tumor patients, unveiling potential gaps in current assessment practices. The co-occurrence of language and memory impairments has been previously reported, albeit scarcely. The aim of this study is therefore to understand the co-occurrence of subjective language and memory complaints in the preoperative state of brain tumor patients and its impact on Quality of Life (QoL).Methods: 31 brain tumor patients (12 LGG, 19 HGG) underwent a semi-structured interview to assess subjective complaints of language deficits, co-occurrences between language and memory dysfunction, and changes in QoL. Group and subgroup analyses were conducted to provide general and tumor grade specific data.Results: 48.4% of patients mentioned co-occurrence of language and memory impairments in reading, writing, and conversation. The HGG group reported co-occurrences in all three of these (reading: 31.6%; writing: 21.1%; conversation: 26.3%), while the LGG only described co-occurrences in reading (25%) and conversation (8.3%), although these were not statistically significant. All patients with co-occurring language and memory deficits reported these to be linked to reduced QoL (48.4%). In patients with an HGG, this number was slightly higher (52.6%) than in patients with an LGG (41.7%).Conclusion: Language impairments co-occur with memory dysfunction as perceived in patients’ daily life. Patients see these impairments as affecting their quality of life. Further attention to dedicated language and memory tasks seems necessary.
How schoolchildren use digital media in class and outside of school over several weeks: a quantitative case study with media diaries
Field: EDUCATION

Introduction: Digital media play a central role in the lives of today’s schoolchildren, immersed in an increasingly digital world. Modern technologies blur the lines between formal school settings and informal settings outside of school. Although formats like bring-your-own-device align the use in the formal setting with informal usage, a disjunction exists between children’s interactions with digital technologies in their home environments and those within the educational setting. For bridging the gap between school learning and children’s lives outside of school, it is essential to explore the differences and similarities in media usage in both settings.Methods: In our case study, we examined schoolchildren’s motives and evaluations of digital media usage in both settings, addressing individual needs. Additionally, we explored several dimensions of digital literacy through self-assessment, identified associated learning opportunities within and outside the school environment, and captured self-reported learning gains. We collected this data over the course of several weeks in a longitudinal design with media diaries, aiming to estimate the extent of the fluctuation.Results: Eighty-four German schoolchildren aged between 10 and 16 years participated over a six-week period. We found differences but also similarities between media usage outside of school and in class. Digital media were less frequently used in class for entertainment, communication, and learning compared to outside of school, but no differences were reported regarding information search. Schoolchildren expressed above-average satisfaction with their media usage in both settings, but they perceived the usage of digital media outside of school as significantly more important than in class. Regarding their digital competencies, the schoolchildren displayed high self-confidence in most areas. Only in the areas of algorithms and programming, schoolchildren rated themselves as below average. While learning opportunities were identified in class and outside of school, the frequency of these opportunities varied across different digital skills. The self-reported learning gain in digital media usage remained consistently low in both settings. Across all analyses, there was no substantial temporal fluctuation in media usage over the study period.Discussion: The findings raise crucial considerations regarding the integration of digital media in the classroom, fostering a discussion on their implications for both research and educational practices.