mdpi-ai/citelens huggingface dataset:
Enhancing programming learning performance through a Jigsaw collaborative learning method in a metaverse virtual space
Field: EDUCATION

Computational thinking (CT) is crucial to fostering critical thinking and problem-solving skills. Many elementary schools have been cultivating students’ CT through block-based programming languages such as Scratch using traditional teacher-centered teaching methods. However, the approach excessively relies on teacher lectures, so the teacher’s teaching load is hefty, and most students cannot receive timely assistance from teachers, thus reducing students’ programming learning performance, interest, and confidence. Therefore, this study designs a Jigsaw collaborative learning method implemented in a metaverse virtual space (JCLM-MVS) for peer-to-peer Scratch programming learning to promote learners’ programming learning performance, CT, and learning attitudes. This study used a quasi-experimental research method, with 48 fifth-grade students from two classes in Taiwan’s elementary school as the research participants. One class of 24 students was randomly assigned to the experimental group using JCLM-MVS to conduct Scratch programming learning, whereas the other class of 24 students was assigned to the control group using the traditional teacher-centered teaching method. The study found that the experimental group of learners showed significantly better Scratch programming learning performance and attitude than the control group, and there was no statistically significant difference in CT between both groups, but both learning approaches effectively promoted CT. Additionally, the interview results showed that most interviewees stated that using JCLM-MVS for Scratch programming learning could receive timely assistance from peers, make collaborative learning more efficient and learning more enjoyable, and more intend to use JCLM-MVS for Scratch programming learning than using traditional teacher-centered teaching method due to the effective collaborative interaction mechanisms and realistic learning space provided in the metaverse virtual space. This study presents a novel and engaging learning approach by integrating a metaverse virtual space with the Jigsaw collaborative learning method, referred to as JCLM-MVS, which enhances the effectiveness of the Jigsaw collaborative learning method in promoting Scratch programming learning performance, CT, and attitudes.
Online Portfolio Selection of Fuzzy Mean Regression Strategy Considering Investor Sentiment Based on Text Data
Field: AI

Investors are often affected by emotion, cognition, and other psychological factors in stock trading when making decisions. At present, people can use machine learning and other technologies to obtain a massive amount of text data from the Internet to mine information related to investor behavior and sentiment. Building intelligent online portfolio trading strategies that consider investor sentiment has become an important topic and key challenge in the financial field. Therefore, this paper explores how to use text data to depict investor sentiment, fuzzifies historical stock price data, designs a new weight transfer equation, and finally obtains a novel fuzzy mean regression strategy that considers investor sentiment based on text data. We conduct empirical tests on this strategy by using the stock price data selected from CSI300 constituent stocks, as well as the text data of investors’ opinions on the internet. The results show that the strategy proposed in this study has a higher Calmar ratio than other mean regression strategies previously studied.
A Fairness Group Recommendation Algorithm Based On User Activity
Field: AI

In the process of group recommendation, due to the different preferences of group members, the recommendation results cannot meet the needs of all users. How to maximize the fairness of group recommendation is still a challenge. Therefore, this paper proposes a group recommendation algorithm based on user activity. Firstly, a group discovery algorithm based on item cluster preference was used to mine potential groups. Secondly, considering the dynamic change of activity, a sliding time window is designed to investigate the recent activity of each member in the group at the time of subgroup division, and the group is divided into active subgroup and inactive subgroup. Finally, the group recommendation list was generated by aggregating the subgroup preferences by average consensus. Experimental results on the public dataset show that compared with the AGREE algorithm, the recommendation accuracy and coverage of the proposed algorithm are improved by 2.1% and 2.9%, respectively. By focusing on the preference needs of inactive users, the proposed algorithm effectively improves the recommendation satisfaction and group fairness.
EFection: Effectiveness Detection Technique for Clustering Cloud Workload Traces
Field: AI

Clustering is widely used in cloud computing studies to extract vital information. These studies have ignored investigating the potential improvements in clustering quality from better selection of its dimensions and methods. Consequently, developing an automated technique to perform such a selection was not addressed thoroughly. Most of the recent attempts either relied on feature reduction or general non-automated techniques, which were deemed unreliable for sufficient selection. Therefore, we first conducted a comprehensive investigation to study the impact of selecting better clustering dimensions and methods. Our results indicate achieving significant improvement by 15–70% points through better selection. Then, we developed a novel technique (EFection) to detect the best selection in advance using a combination of internal validation metrics (Davies–Bouldin) and the Pearson correlation coefficient. We evaluate our technique’s accuracy by comparing the clustering quality of its suggestions with that of the optimal selection. We then compare EFection’s performance with recent attempts to measure its superiority. Finally, we validate its applicability when adopted in cloud clustering-based studies. The results show that EFection offers high accuracy, around 83%, and surpasses prior art by 11%.
Enhanced fingerprint classification through modified PCA with SVD and invariant moments
Field: AI

This research introduces a novel MOMENTS-SVD vector for fingerprint identification, combining invariant moments and SVD (Singular Value Decomposition), enhanced by a modified PCA (Principal Component Analysis). Our method extracts unique fingerprint features using SVD and invariant moments, followed by classification with Euclidean distance and neural networks. The MOMENTS-SVD vector reduces computational complexity by outperforming current models. Using the Equal Error Rate (EER) and ROC curve, a comparative study across databases (CASIA V5, FVC 2002, 2004, 2006) assesses our method against ResNet, VGG19, Neuro Fuzzy, DCT Features, and Invariant Moments, proving enhanced accuracy and robustness.
Advancements in triple-negative breast cancer sub-typing, diagnosis and treatment with assistance of artificial intelligence : a focused review
Field: ONCOLOGY

Triple negative breast cancer (TNBC) is most aggressive type of breast cancer with multiple invasive sub-types and leading cause of women’s death worldwide. Lack of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER-2) causes it to spread rapidly making its treatment challenging due to unresponsiveness towards anti-HER and endocrine therapy. Hence, needing advanced therapeutic treatments and strategies in order to get better recovery from TNBC. Artificial intelligence (AI) has been emerged by giving its high inputs in the automated diagnosis as well as treatment of several diseases, particularly TNBC. AI based TNBC molecular sub-typing, diagnosis as well as therapeutic treatment has become successful now days. Therefore, present review has reviewed recent advancements in the role and assistance of AI particularly focusing on molecular sub-typing, diagnosis as well as treatment of TNBC. Meanwhile, advantages, certain limitations and future implications of AI assistance in the TNBC diagnosis and treatment are also discussed in order to fully understand readers regarding this issue. Graphical Abstract
Stochastic heuristics for decisions under risk and uncertainty
Field: PSYCHOLOGY

Models of heuristics are often predicated on the desideratum that they should possess no free parameters. As a result, heuristic implementations are usually deterministic and do not allow for any choice errors, as the latter would require a parameter to regulate the magnitude of errors. We discuss the implications of this in light of research that highlights the evidence supporting stochastic choice and its dependence on preferential strength. We argue that, in principle, the existing models of deterministic heuristics should, and can, be quite easily modified to stochastic counterparts through the addition of an error mechanism. This requires a single free parameter in the error mechanism, whilst otherwise retaining the parameter-free cognitive processes in the deterministic component of existing heuristics. We present various types of error mechanisms applicable to heuristics and discuss their comparative virtues and drawbacks, paying particular attention to their impact on model comparisons between heuristics and parameter-rich models.
Rates of, and factors associated with, common mental disorders in homeworking UK Government response employees’ during COVID-19: a cross-sectional survey and secondary data analysis
Field: PSYCHOLOGY

Introduction: Working on the frontline during the COVID-19 pandemic has been associated with increased risk to mental health and wellbeing in multiple occupations and contexts. The current study aimed to provide an insight into the rate of probable mental health problems amongst United Kingdom (UK) Government employees who contributed to the COVID-19 response whilst working from home, and to ascertain what factors and constructs, if any, influence mental health and wellbeing in the sample population.Method: This paper reports on the findings from two studies completed by UK Government employees. Study 1: A cross-sectional online survey, containing standardised and validated measures of common mental health disorders of staff who actively contributed to the COVID-19 response from their own homes. Binary logistic regression was used to assess factors associated with mental health outcomes. Study 2: A secondary data analysis of cross-sectional survey data collected across three timepoints (May, June, and August) in 2020 focusing on the wellbeing of employees who worked from home during the COVID-19 pandemic.Results: Study 1: 17.9% of participants met the threshold criteria for a probable moderate anxiety disorder, moderate depression, or post-traumatic stress disorder. Younger, less resilient, less productive individuals, with lower personal wellbeing and less enjoyment of working from home, were more likely to present with poorer mental health. Study 2: Found lower wellbeing was consistently associated with having less opportunities to look after one’s physical and mental health, and having unsupportive line managers and colleagues.Conclusion: It is important to ensure UK Government employees’ psychological needs are met whilst working from home and responding to enhanced incidents. It is recommended that workplaces should be seeking to continually build and improve employee resilience (e.g., through opportunities to increase social ties and support networks), essentially ensuring employees have necessary resources and skills to support themselves and others.
ATG10-dependent autophagy is required for DDX10 to regulate cell proliferation, apoptosis and stemness in colorectal cancer
Field: ONCOLOGY

Colorectal cancer (CRC) remains a highly prevalent gastrointestinal neoplasm, presenting significant prevalence and lethality rate. DEAD/H box RNA helicase 10 (DDX10) has been proposed as a potential oncogene in CRC, the specific action mechanism by which DDX10 modulates the aggressive biological cellular events in CRC remains implicitly elucidated, however. During this study, DDX10 expression was detected via RT-qPCR and Western blotting. Cell proliferation was estimated via EDU staining. TUNEL staining and Western blotting appraised cell apoptosis. Cell stemness was evaluated by sphere formation assay, RT-qPCR, Western blotting as well as immunofluorescence staining. Relevant assay kit examined aldehyde dehydrogenase (ALDH) activity. Western blotting and immunofluorescence staining also detected autophagy. DDX10 was hyper-expressed in CRC cells. Down-regulation of DDX10 hampered cell proliferation, aggravated the apoptosis while eliminated the ability to form spheroid cells in CRC. In addition, DDX10 deletion improved ATG10 expression and therefore activated autophagy in CRC cells. Consequently, ATG10 depletion or treatment with autophagy inhibitor 3-Methyladenine (3-MA) partially compensated the influences of DDX10 silencing on the proliferation, apoptosis and stemness of CRC cells. Accordingly, DDX10 deficiency may aggravate autophagy mediated by ATG10 to impede cell proliferation, stemness and facilitate cell apoptosis, hence blocking the progression of CRC.
Exploring Teacher Awareness of Artificial Intelligence in Education: A Case Study from Northern Cyprus
Field: PSYCHOLOGY

This study investigates the level of awareness among teachers regarding the use of artificial intelligence (AI) in education, focusing on whether this awareness varies according to socio-demographic characteristics, access to technology, and specific knowledge and beliefs about AI. Conducted in Northern Cyprus during the 2023–2024 academic year, this study employed a survey model with purposive and snowball sampling methods, involving 164 teachers. Teachers at different levels, namely, primary school, secondary school, high school, and university, were included in this study. The “Artificial Intelligence Awareness Scale”, developed by Ferikoğlu and Akgün (2022), was used to measure AI awareness. Data normality was verified through skewness and kurtosis values, allowing for parametric statistical tests such as t-tests, one-way ANOVA, logistic regression, and chi-square analysis. This study explored the distribution of AI use across different school types and educational levels and assessed the impact of sub-dimensions of AI awareness on its application in teaching. Findings revealed no significant influence of teacher demographics (age, gender, education level, type of school, institution level, and monthly income) on AI awareness. However, usage patterns indicated that university lecturers were more likely to incorporate AI in their teaching, followed by primary and high school teachers, with secondary school teachers using it the least. A Multilayer Neural Network Analysis identified practical knowledge as the most critical factor influencing the use of AI in teaching (importance weight of 0.450), followed by beliefs and attitudes (0.298), relatability (0.148), and theoretical knowledge (0.104). These results highlight the importance of practical knowledge for fostering AI integration in educational practices, underscoring significant implications for teacher training and professional development programs.
Improving Teaching and Learning in Higher Education through Machine Learning: Proof of Concept’ of AI’s Ability to Assess the Use of Key Microskills
Field: EDUCATION

Advances in artificial intelligence (AI), including intelligent machines, are opening new possibilities to support teaching and learning in higher education. This research has found a ‘proof of concept’ in the application of machine learning in the assessment of educators’ use of four key microskills, drawn from an internationally established framework. The analysis of teaching videos where these microskills were demonstrated multiple times in front of a green screen or in a space formed the data set. Multiple videos of this nature were recorded to allow for increased analysis and deconstruction of the video components to enable the application of machine learning. The results showed how AI can be used to support the collaborative and reflective practice of educators in a time when online teaching has become the norm. Having achieved a ‘proof of concept’, this research has laid the groundwork to allow for the whole framework of ten microskills to be applied in this way thus adding a new dimension to its use. Providing such critical information that is not currently available in such a systematic and personalised way to educators in the higher education sector can also support the validity of formative assessment practices.
Deep Neural Network Integrated into Network-Based Stratification (D3NS): A Method to Uncover Cancer Subtypes from Somatic Mutations
Field: ONCOLOGY

(1) Background: The identification of tumor subtypes is fundamental in precision medicine for accurate diagnoses and personalized therapies. Cancer development is often driven by the accumulation of somatic mutations that can cause alterations in tissue functions and morphologies. In this work, a method based on a deep neural network integrated into a network-based stratification framework (D3NS) is proposed to stratify tumors according to somatic mutations. (2) Methods: This approach leverages the power of deep neural networks to detect hidden information in the data by combining the knowledge contained in a network of gene interactions, as typical of network-based stratification methods. D3NS was applied using real-world data from The Cancer Genome Atlas for bladder, ovarian, and kidney cancers. (3) Results: This technique allows for the identification of tumor subtypes characterized by different survival rates and significant associations with several clinical outcomes (tumor stage, grade or response to therapy). (4) Conclusion: D3NS can provide a base model in cancer research and could be considered as a useful tool for tumor stratification, offering potential support in clinical settings.
Psychometric properties of the newly developed self-report environmental determinants of health questionnaire (EDH-Q): development and validation
Field: PSYCHOLOGY

The environmental determinants of health (EDH) have a significant impact on people’s physical, mental, and social wellbeing. Everyone needs access to environmental resources of all types, including food, materials, and energy, to survive. Currently, no valid and reliable instrument exists for evaluating individuals’ perceived levels of EDH. Hence, the purpose of this study was to develop and validate the environmental determinants of health questionnaire (EDH-Q) among undergraduate students in Nigeria. We conducted a cross-sectional survey among undergraduate students in Nigeria to assess the psychometric properties of the newly developed Environmental Determinants of Health Questionnaire (EDH-Q). Respondents were selected using a convenience sampling approach to evaluate their perceptions of environmental determinants of health. The Content Validity Index (CVI) and Face Validity Index (FVI) were calculated to ascertain the scale’s content validity and response process validity, respectively. Additionally, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), composite reliability (CR), average variance extracted (AVE), Cronbach’s alpha, and intraclass correlation coefficient (ICC) were computed to assess the scale’s construct validity. The study involved 300 respondents in the EFA (males 55.7%, females 44.3%) and 430 respondents in the CFA (males 54.0%, females 46.0%). In the EFA, two constructs were identified (the natural environment and the built environment). The EFA model was able to explain 63.57% of the total cumulative variance, and the factor correlation was 0.671. The whole scale Cronbach’s alpha value was 0.947, while the two constructs’ Cronbach’s alpha values were 0.918 (natural environment) and 0.935 (built environment). In the CFA, six pairs of error covariances were included between items within the same construct to improve the fit indices of the initial proposed measurement model. The final re-specified measurement model showed that the EDH-Q, which has two constructs and 18 items, has adequate construct validity (CFI = 0.948, TLI = 0.938, SRMR = 0.046, RMSEA = 0.052, and RMSEA p-value = 0.344). The CRs were 0.845 (natural environment) and 0.854 (built environment). The ICCs were 0.976 (natural environment) and 0.970 (built environment). The results show that the newly created EDH-Q has sufficient construct validity and may be utilized to assess participants’ perceptions of their level of EDH. Researchers should examine this instrument in populations with different age ranges and other demographic characteristics, as the present study only applied it to undergraduate students who may share similar characteristics.
The Application of Big Data and Fuzzy Decision Support Systems in the Innovation of Personalized Music Teaching in Universities
Field: AI

Personalized music teaching in universities improves students’ learning and efficiency through adaptive guidance. This adaptability requires large study data and intelligent decisions based on the learner’s ability. This article introduces a Definitive Teaching Support System (DTSS) exclusive to music learning to augment this concept. This system is designed to increase the adaptability of music learning based on student interest and ability. The system is powered by a fuzzy decision system for identifying maximum teaching adaptability to personalized processes. Low-to-high-sorted personalization provides new endorsements for further music sessions in the fuzzy derivative process. Maximum adaptability is the target for new personalized sessions in the universities. This differs for various students from which a common adaptability level for monotonous recommendations is identified. The identified adaptability is set as a global maximum solution towards music learning personalization. The defuzzification reduces the chances of low adaptability by expelling the stationary adaptability outcomes.
Applying enhanced recovery after surgery protocols in a patient with a giant spleen: a case report
Field: ONCOLOGY

Although splenomegaly is a common finding in several diseases, massive splenomegaly is rare. Patients with massive splenomegaly often present with a complex clinical picture. This case report describes a 72-year-old female with a complex medical history. Fifteen years ago, she was diagnosed with primary myelofibrosis, which subsequently led to progressive abdominal enlargement and bloating over the past 5 years. Recently, she developed edema in her limbs, accompanied by dizziness, shortness of breath, and fatigue. A massive splenomegaly was discovered during her hospitalization. Additionally, the patient has a history of Crohn’s disease, gout, renal insufficiency, and hypertension. Laboratory results reveal severe anemia and thrombocytopenia. Abdominal CT scans confirm the enlarged spleen and show ascites. She was treated by a multidisciplinary team comprising several departments. Even after a period of comprehensive treatment, the symptoms of massive splenomegaly did not significantly improve. Thus, the patient underwent an open surgical excision of the giant spleen. The weight of the giant spleen was 5.0 kg. During the perioperative period, Enhanced Recovery After Surgery (ERAS) protocols were applied to facilitate recovery. Her recovery was uneventful, and she was able to resume her regular daily routine shortly after the procedure. This report presented a complex and rare case of massive splenomegaly, and underscored that a proper medical and nursing care is the key to better recovery.