mdpi-ai/citelens huggingface dataset:
One size doesn’t fit all: how different types of learning motivations influence engineering undergraduate students’ success outcomes
Field: EDUCATION

Motivation is the inherent belief to guide students learning goals and behaviors to make continuous efforts and strengthen learning outcomes. Previous research reported the positive impacts of learning motivation on student success, but there have been limited efforts in systematically and structurally studying different types of motivations and their impacts on students’ success in engineering education. The current study contributes to the literature by systematically examining two important types of motivations and their influences on undergraduate engineering students in a theoretically grounded manner while using an advanced analytical approach. The current study conducted a cross-sectional survey with undergraduate engineering students (n = 514) from 18 different schools across nine U.S. states. The survey assessed students’ self-report scores on six types of motivations to study developed based on formative research and the current literature and then collected students’ self-reported learning outcomes, current GPA, university satisfaction, engineering program satisfaction, and individual demographic factors. The data were then analyzed using structural equation modeling. The results showed that motivations related to family, personality, and academic expectations were consistently positively associated with all measured students’ success outcomes; motivations related to educators were associated with all four outcomes but student GPA; motivations related to course contents were associated with learning outcomes and student GPA; and motivations related to peers did not predict any of the four measured students’ success outcomes. We explain some of the unexpected results with further literature that examines engineering culture and ecology. We also make recommendations related to cognitive training, tailored engineering education, peer culture interventions, and family orientation programs.
Fall risk prediction using temporal gait features and machine learning approaches
Field: AI

Introduction: Falls have been acknowledged as a major public health issue around the world. Early detection of fall risk is pivotal for preventive measures. Traditional clinical assessments, although reliable, are resource-intensive and may not always be feasible.Methods: This study explores the efficacy of artificial intelligence (AI) in predicting fall risk, leveraging gait analysis through computer vision and machine learning techniques. Data was collected using the Timed Up and Go (TUG) test and JHFRAT assessment from MMU collaborators and augmented with a public dataset from Mendeley involving older adults. The study introduces a robust approach for extracting and analyzing gait features, such as stride time, step time, cadence, and stance time, to distinguish between fallers and non-fallers.Results: Two experimental setups were investigated: one considering separate gait features for each foot and another analyzing averaged features for both feet. Ultimately, the proposed solutions produce promising outcomes, greatly enhancing the model’s ability to achieve high levels of accuracy. In particular, the LightGBM demonstrates a superior accuracy of 96% in the prediction task.Discussion: The findings demonstrate that simple machine learning models can successfully identify individuals at higher fall risk based on gait characteristics, with promising results that could potentially streamline fall risk assessment processes. However, several limitations were discovered throughout the experiment, including an insufficient dataset and data variation, limiting the model’s generalizability. These issues are raised for future work consideration. Overall, this research contributes to the growing body of knowledge on fall risk prediction and underscores the potential of AI in enhancing public health strategies through the early identification of at-risk individuals.
Where Are the Costs? Using an Economic Analysis of Educational Interventions Approach to Improve the Evaluation of a Regional School Improvement Programme
Field: EDUCATION

Education systems are moving to a more evidence-informed paradigm to improve outcomes for learners. To help this journey to evidence, robust qualitative and quantitative research can help decisionmakers identify more promising approaches that provide value for money. In the context of the utilisation of scarce resources, an important source of evidence commonly used in health and social care research is an understanding of the economic impact of intervention choices. However, there are currently very few examples where these methodologies have been used to improve the evaluation of education interventions. In this paper we describe the novel use of an economic analysis of educational interventions (EAEI) approach to understand both the impact and the cost of activities in the evaluation of a formative assessment implementation project (FAIP) designed to improve teachers’ understanding and use of formative assessment strategies. In addition to utilising a mixed method quasi-experimental design to explore the impact on learner wellbeing, health utility and attainment, we describe the use of cost-consequence analysis (CCA) to help decisionmakers understand the outcomes in the context of the resource costs that are a crucial element of robust evaluations. We also discuss the challenges of evaluating large-scale, universal educational interventions, including consideration of the economic tools needed to improve the quality and robustness of these evaluations. Finally, we discuss the importance of triangulating economic findings alongside other quantitative and qualitative information to help decisionmakers identify more promising approaches based on a wider range of useful information. We conclude with recommendations for more routinely including economic costs in education research, including the need for further work to improve the utility of economic methods.
Effective Hybrid Structure Health Monitoring through Parametric Study of GoogLeNet
Field: AI

This paper presents an innovative approach that utilizes infused images from vibration signals and visual inspections to enhance the efficiency and accuracy of structure health monitoring through GoogLeNet. Scrutiny of the structure of GoogLeNet identified four key parameters, and thus, the optimization of GoogLeNet was completed through manipulation of the four key parameters. First, the impact of the number of inception modules on the performance of GoogLeNet revealed that employing eight inception layers achieves remarkable 100% accuracy while requiring less computational time compared to nine layers. Second, the choice of activation function was studied, with the Rectified Linear Unit (ReLU) emerging as the most effective option. Types of optimizers were then researched, which identified Stochastic Gradient Descent with Momentum (SGDM) as the most efficient optimizer. Finally, the influence of learning rate was compared, which found that a rate of 0.001 produces the best outcomes. By amalgamating these findings, a comprehensive optimized GoogLeNet model was found to identify damage cases effectively and accurately through infused images from vibrations and visual inspections.
Predictors of Clinical Hematological Toxicities under Radiotherapy in Patients with Cervical Cancer—A Risk Analysis
Field: ONCOLOGY

Background: Cervical cancer ranks third in frequency among female cancers globally and causes high mortality worldwide. Concurrent chemoradiotherapy improves the overall survival in cervical cancer patients by 6% but it can cause significant acute and late toxicities affecting patient quality of life. Whole pelvis radiotherapy doses of 10–20 Gy can lead to myelosuppression and to subsequent hematological toxicities since pelvic bones contain half of bone marrow tissue. Methods: A total of 69 patients with IB-IVB-staged cervical cancer have been included in this retrospective cohort study. We analyzed clinical adverse events and changes in blood cell counts (hemoglobin, neutrophils, leukocytes, and platelets) during radiation or chemoradiotherapy received at the Oncological Institute of Bucharest from 2018 to 2021. Results: Decreases in hemoglobin levels of over 2.30 g/dL during treatment were associated with BMI > 23.2 kg/m2 (OR = 8.68, 95%CI = [1.01, 75.01]), age over 53 years (OR = 4.60 95%CI = [1.10, 19.22]), with conformational 3D irradiation (OR = 4.78, 95%CI = [1.31, 17.40]) and with total EQD2 of over 66.1 Gy (OR = 3.67, 95%CI = [1.02, 13.14]). The hemoglobin decrease rate of 0.07 g/dL/day was related to 95% isodose volume (OR = 18.00). Neutropenia is associated frequently with gastrointestinal side effects and with the bowel and rectal V45 isodoses (OR = 16.5 and OR = 18.0, respectively). Associations of total external and internal radiation dose with the time durations calculated from the initiation of treatment to the onset of hematological adverse reactions were also obtained. The maximum drop in leukocytes was observed before day 35 from the RT initiation in patients who underwent treatment with 3D conformal radiotherapy (OR = 4.44, 95%CI = [1.25, 15.82]). Neutrophil levels under 2.2 × 103/μL and thrombocyte levels under 131 × 103/μL during the follow-up period were associated with a total planned dose of 54 Gy to the pelvic region volume (OR = 6.82 and OR = 6.67, respectively). Conclusions: This study shows the existence of clinical and blood predictors of hematological adverse reactions in cervical cancer patients. Thus, patients who are in a precarious clinical situation, with low hematological values (but not yet abnormal), should be monitored during days 29–35 after the initiation of RT, especially if they are obese or over 53 years of age.
Academic social comparison: a promising new target to reduce fear of negative evaluation in large-enrollment college science courses
Field: EDUCATION

Fear of negative evaluation, defined as a sense of dread associated with being unfavorably evaluated in a social situation, is the primary factor underlying student anxiety in college science courses and is disproportionately experienced by students who are underserved in science. Yet, it is unknown why fear of negative evaluation disproportionately affects these students and what can be done to reduce student fear of negative evaluation. Academic social comparison describes how students perceive themselves compared to their peers with regard to desirability as a groupmate, the extent they fit in among others in their major, and academic talent. We hypothesize that academic social comparison mediates the relationship between student identities and fear of negative evaluation, where individuals with underserved identities in science may perceive themselves as “less than” their peers, contributing to their fear of negative evaluation. We surveyed 909 undergraduate science majors across 15 research-intensive institutions in the United States (U.S.) to assess: (1) To what extent do student identities predict fear of negative evaluation among science undergraduates? and (2) For identities that significantly predict fear of negative evaluation, to what extent does academic social comparison mediate the relationship? We used regression, single-mediator models, and multi-mediator models to address our research questions. Women/non-binary and LGBTQ + science majors reported disproportionately high fear of negative evaluation compared to men and non-LGBTQ + science majors. Women/non-binary and LGBTQ + students also expressed lower academic social comparison relative to their respective counterparts, meaning they perceive themselves as less than their peers with regard to their desirability as a groupmate, the extent to which they fit in among others in their major, and their academic talent. Academic social comparison partially mediated the relationship between fear of negative evaluation and both gender and LGBTQ + status. Major fit, defined as the extent to which students perceive they fit in among others in their major, was found to be the primary mediating subconstruct of academic social comparison for both gender and LGBTQ + identities. Women/non-binary and LGBTQ + science majors perceive themselves as less than their peers to a greater extent than men and non-LGBTQ + science majors, contributing to their higher fear of negative evaluation in college science course. Major fit, defined as the extent to which students feel they fit in with others in their major, is the subconstruct of academic social comparison that had the strongest influence on fear of negative evaluation in our sample. Academic social comparison is a promising target for future efforts aimed at decreasing fear of negative evaluation in active learning college science courses.
MAICC model: development of complex thinking through citizen science project evaluation
Field: EDUCATION

As traditional education systems struggle to keep up with technological advances, incorporating open science into Education 5.0 is critical to addressing student skills gaps. In this study, the MAICC model is introduced, a tool designed to foster complex thinking in higher education students through the evaluation of citizen science projects. It integrates research-based learning and service learning, and helps develop critical and reflective skills by applying them to real-life settings. To assess student engagement and skills development, a mixed methods approach combining qualitative and quantitative analysis was used. Findings indicate that the MAICC model promotes complex thinking, enhances critical thinking through citizen science project evaluation, and features an emphasis on citizen science and educational technology. Discussion highlights citizen science’s important role in education and suggests future research exploring its wider application across disciplines and contexts to enhance 21st century skills.
A Justice-Oriented Conceptual and Analytical Framework for Decolonising and Desecularising the Field of Educational Technology
Field: EDUCATION

Education systems globally are increasingly being shaped by the logics, assumptions and pedagogical underpinnings of educational technology (EdTech) products, services, programmes, policies, and systems. These often promote rationalistic, secular, universal, objectivist, (post)modernist, written, behaviourist, and individualistic ways of being, marginalising religious, spiritual, oral, subjective, critical, and communitarian ways of being. Given that technological ways of being have been propagated globally, these logics are no longer predominantly promoted by those in the Global North, but by techno-solutionists globally, although the core-to-periphery flows of ideology and funding are still prominent. This article develops a conceptual and analytical framework for decolonising and desecularising the field of EdTech. Concepts are drawn from various discourses: the desecularisation of knowledge to set the ontological framing; embodied cognition to set the epistemological framing; and social justice and decolonial discourses to set the axiological framing. From this, the article develops the Dimensions of Human Injustice Analytical Framework—covering material, ontological and epistemic, and (geo)political injustices—to assist policymakers, educators, EdTech developers, and international development practitioners in identifying and confronting coloniality in their EdTech. Acknowledging the complexity and contentions within decolonial thought, this article does not claim a unified stance on achieving justice but aims to offer a tool for deconstructing and questioning injustices.
New insights into acinic cell carcinoma of the breast: clinicopathology, origin of histology, molecular features, prognosis, and treatment
Field: ONCOLOGY

Acinic cell carcinoma (AciCC) of the breast is a rare malignant epithelial neoplasm, with approximately 60 cases reported in the literature. It predominantly affects women and exhibits significant histological heterogeneity. The diagnosis of breast AciCC is primarily based on the presence of eosinophilic and/or basophilic granular cytoplasm and markers of serous acinar differentiation. Despite being considered a low-grade variant of conventional triple-negative breast cancer (TNBC), over 25% of patients with breast AciCC have adverse clinical outcomes. Additionally, in early research, microglandular adenosis (MGA) and atypical MGA were considered potential precursors for various breast cancers, including intraductal carcinoma, invasive ductal carcinoma, adenoid cystic carcinoma, metaplastic carcinoma, and AciCC. Similarly, some studies have proposed that breast AciCC should be considered a type of carcinoma developing in MGA with acinic cell differentiation rather than a distinct entity. Therefore, the pathogenesis of breast AciCC has not yet been clarified. Moreover, to the best of our knowledge, the literature has not summarized the latest prognosis and treatment of breast AciCC. In this review, we synthesized the current literature and the latest developments, aiming at exploring the clinicopathology, histological origin, molecular features, prognosis, and treatment of breast AciCC from a novel perspective.
An Improved Adaptive Neuro-fuzzy Inference Framework for Lung Cancer Detection and Prediction on Internet of Medical Things Platform
Field: AI

It has become increasingly difficult for medical practitioners to recognize illness in recent years due to the emergence of new diseases from their myriad causes on a daily basis. Due in large part to inadequate diagnostic and monitoring infrastructure, a substantial amount of illness and death are associated with lung cancer (LC). The aim of the paper is to find lung cancer early and help patients receive curative treatment. Quitting smoking or never starting is the best way to mitigate the potential for disease-related death. As a result, cutting-edge detection and monitoring technologies must be developed to enable rapid, accurate, and timely diagnosis. Fuzzy logic (FL) is one of the best approaches to modeling complex and uncertain systems; therefore, it helps us deal with these challenges. Fuzzy expert system for lung cancer [FES-LC] detection and prediction on Internet of medical things (IoMT) is employed to overcome the challenges. Hence, an enhanced adaptive neuro-fuzzy inference framework [ANF-IF] is proposed in the current research. The cloud-based application of an adaptive neuro-fuzzy inference system yields four risk categories: not at risk, slightly at risk, moderately at risk, and severely at risk. New methods and theoretical frameworks have made it possible to diagnose LC in its earliest stages with the help of magnetic nanoparticles (MNPs), which allow researchers to overcome the limitations of conventionally slow diagnostic efficiency. The proposed system exhibits a precision of 93.4%, accuracy of 95.1%, specificity of 90.6%, sensitivity of 92.8%, false positive rate of 0.22%, false negative ratio of 0.18%, and classification accuracy of 98.2%. The proposed method outperforms all methods and provides better lung cancer detection accuracy than others.
AI integration in nephrology: evaluating ChatGPT for accurate ICD-10 documentation and coding
Field: AI

Background: Accurate ICD-10 coding is crucial for healthcare reimbursement, patient care, and research. AI implementation, like ChatGPT, could improve coding accuracy and reduce physician burden. This study assessed ChatGPT’s performance in identifying ICD-10 codes for nephrology conditions through case scenarios for pre-visit testing.Methods: Two nephrologists created 100 simulated nephrology cases. ChatGPT versions 3.5 and 4.0 were evaluated by comparing AI-generated ICD-10 codes against predetermined correct codes. Assessments were conducted in two rounds, 2 weeks apart, in April 2024.Results: In the first round, the accuracy of ChatGPT for assigning correct diagnosis codes was 91 and 99% for version 3.5 and 4.0, respectively. In the second round, the accuracy of ChatGPT for assigning the correct diagnosis code was 87% for version 3.5 and 99% for version 4.0. ChatGPT 4.0 had higher accuracy than ChatGPT 3.5 (p = 0.02 and 0.002 for the first and second round respectively). The accuracy did not significantly differ between the two rounds (p > 0.05).Conclusion: ChatGPT 4.0 can significantly improve ICD-10 coding accuracy in nephrology through case scenarios for pre-visit testing, potentially reducing healthcare professionals’ workload. However, the small error percentage underscores the need for ongoing review and improvement of AI systems to ensure accurate reimbursement, optimal patient care, and reliable research data.
Prediction of Remaining Useful Life of Aero-engines Based on CNN-LSTM-Attention
Field: AI

Accurately predicting the remaining useful life (RUL) of aircraft engines is crucial for maintaining financial stability and aviation safety. To further enhance the prediction accuracy of aircraft engine RUL, a deep learning-based RUL prediction method is proposed. This method possesses the potential to strengthen the recognition of data features, thereby improving the prediction accuracy of the model. First, the input features are normalized and the CMAPSS (Commercial Modular Aero-Propulsion System Simulation) dataset is utilized to calculate the RUL for aircraft engines. After extracting attributes from the input data using a convolutional neural network (CNN), the extracted data are input into a long short-term memory (LSTM) network model, with the addition of attention mechanisms to predict the RUL of aircraft engines. Finally, the proposed aircraft engine model is evaluated and compared through ablation studies and comparative model experiments. The results indicate that the CNN-LSTM-Attention model exhibits superior prediction performance for datasets FD001, FD002, FD003, and FD004, with RMSEs of 15.977, 14.452, 13.907, and 16.637, respectively. Compared with CNN, LSTM, and CNN-LSTM models, the CNN-LSTM model demonstrates better prediction performance across datasets. In comparison with other models, this model achieves the highest prediction accuracy on the CMAPSS dataset, showcasing strong reliability and accuracy.
Generative Models Utilizing Padding Can Efficiently Integrate and Generate Multi-Omics Data
Field: AI

Technological advances in information-processing capacity have enabled integrated analyses (multi-omics) of different omics data types, improving target discovery and clinical diagnosis. This study proposes novel artificial intelligence (AI) learning strategies for incomplete datasets, common in omics research. The model comprises (1) a multi-omics generative model based on a variational auto-encoder that learns tumor genetic patterns based on different omics data types and (2) an expanded classification model that predicts cancer phenotypes. Padding was applied to replace missing data with virtual data. The embedding data generated by the model accurately classified cancer phenotypes, addressing the class imbalance issue (weighted F1 score: cancer type > 0.95, primary site > 0.92, sample type > 0.97). The classification performance was maintained in the absence of omics data, and the virtual data resembled actual omics data (cosine similarity mRNA gene expression > 0.96, mRNA isoform expression > 0.95, DNA methylation > 0.96). Meanwhile, in the presence of omics data, high-quality, non-existent omics data were generated (cosine similarity mRNA gene expression: 0.9702, mRNA isoform expression: 0.9546, DNA methylation: 0.9687). This model can effectively classify cancer phenotypes based on incomplete omics data with data sparsity robustness, generating omics data through deep learning and enabling precision medicine.