mdpi-ai/citelens huggingface dataset:
A review on the efficacy of artificial intelligence for managing anxiety disorders
Field: AI

Anxiety disorders are psychiatric conditions characterized by prolonged and generalized anxiety experienced by individuals in response to various events or situations. At present, anxiety disorders are regarded as the most widespread psychiatric disorders globally. Medication and different types of psychotherapies are employed as the primary therapeutic modalities in clinical practice for the treatment of anxiety disorders. However, combining these two approaches is known to yield more significant benefits than medication alone. Nevertheless, there is a lack of resources and a limited availability of psychotherapy options in underdeveloped areas. Psychotherapy methods encompass relaxation techniques, controlled breathing exercises, visualization exercises, controlled exposure exercises, and cognitive interventions such as challenging negative thoughts. These methods are vital in the treatment of anxiety disorders, but executing them proficiently can be demanding. Moreover, individuals with distinct anxiety disorders are prescribed medications that may cause withdrawal symptoms in some instances. Additionally, there is inadequate availability of face-to-face psychotherapy and a restricted capacity to predict and monitor the health, behavioral, and environmental aspects of individuals with anxiety disorders during the initial phases. In recent years, there has been notable progress in developing and utilizing artificial intelligence (AI) based applications and environments to improve the precision and sensitivity of diagnosing and treating various categories of anxiety disorders. As a result, this study aims to establish the efficacy of AI-enabled environments in addressing the existing challenges in managing anxiety disorders, reducing reliance on medication, and investigating the potential advantages, issues, and opportunities of integrating AI-assisted healthcare for anxiety disorders and enabling personalized therapy.
Synchronous online learning and career readiness in higher education: student perceptions, challenges, and solutions
Field: EDUCATION

Synchronous Online Learning (SOL) environments have rapidly transformed the educational landscape. However, there is limited research on their efficacy in equipping students with the necessary skills to succeed in the workforce, particularly in developing essential professional skills like digital literacy, interpersonal communication, and practical experience. This study explores how SOL impacts students’ readiness for the workforce and the development of these critical skills. The research employed a qualitative methodology involving in-depth interviews with 27 third- and fourth-year students from a South African university. Purposive sampling was used to capture diverse experiences regarding SOL and its influence on professional skill development. Thematic analysis was performed to identify critical patterns and insights from the interviews. Findings reveal that SOL environments effectively enhance students’ technical skills and digital adaptability, essential for navigating a digital workforce. However, SOL is inadequate in developing interpersonal skills and providing practical, hands-on experiences. Students reported a lack of networking opportunities and expressed concerns about their preparedness for the demands of real-world employment, particularly in fields requiring strong interpersonal skills and practical experience. The study highlights the need for educational innovations that combine the benefits of digital learning with comprehensive skill development strategies, particularly in soft skills and experiential learning.
Feasibility of a complex psychosocial intervention for families with parental cancer: acceptability, suitability, implementability, and perceived support
Field: ONCOLOGY

Purpose: This study aimed to assess the feasibility of a comprehensive psychosocial intervention for families coping with parental cancer. Methods: A quasi-experimental trial with intervention and control group, employing a mixed-methods approach, was conducted. A total of 472 families affected by parental cancer participated. The feasibility of the intervention was evaluated based on study monitoring measures (on-site visits, team supervision meeting observations, case conference observations, best practice workshops, coordinating information exchange between intervention sites, and reviewing intervention documentation), process evaluation (semi-structured interviews, focus group discussion) and survey data. Data analysis involved thematic coding and descriptive statistics. Results: The intervention was well-received by the participating families, with a high degree of acceptance observed. The feasibility of the intervention was found to be associated with specific dynamics within each family system and the motivation of the family members. The success of the intervention was described as dependent on the family-centered arrangement of the encounters, including factors such as frequency, duration, and mode, which greatly influenced its overall acceptability. Conclusion: The family-scout intervention demonstrates its feasibility as an effective intervention to reduce the burden experienced by families coping with parental cancer. Psychosocial oncology services should continue to develop and implement family-centered interventions to offer support to families during their cancer journey. Trial registration: ClinicalTrials.gov, NCT04186923. Retrospectively registered on 4 December 2019.
The impact of pedagogical beliefs on the adoption of generative AI in higher education: predictive model from UTAUT2
Field: AI

Artificial Intelligence in Education (AIEd) offers advanced tools that can personalize learning experiences and enhance teachers’ research capabilities. This paper explores the beliefs of 425 university teachers regarding the integration of generative AI in educational settings, utilizing the UTAUT2 model to predict their acceptance and usage patterns through the Partial Least Squares (PLS) method. The findings indicate that performance expectations, effort expectancy, social influence, facilitating conditions, and hedonic motivation all positively impact the intention and behavior related to the use of AIEd. Notably, the study reveals that teachers with constructivist pedagogical beliefs are more inclined to adopt AIEd, underscoring the significance of considering teachers’ attitudes and motivations for the effective integration of technology in education. This research provides valuable insights into the factors influencing teachers’ decisions to embrace AIEd, thereby contributing to a deeper understanding of technology integration in educational contexts. Moreover, the study’s results emphasize the critical role of teachers’ pedagogical orientations in their acceptance and utilization of AI technologies. Constructivist educators, who emphasize student-centered learning and active engagement, are shown to be more receptive to incorporating AIEd tools compared to their transmissive counterparts, who focus on direct instruction and information dissemination. This distinction highlights the need for tailored professional development programs that address the specific beliefs and needs of different teaching philosophies. Furthermore, the study’s comprehensive approach, considering various dimensions of the UTAUT2 model, offers a robust framework for analyzing technology acceptance in education.
Improving cognitive function in Chinese children with ADHD and/or RD through computerized working memory training
Field: PSYCHOLOGY

Prior research has found that children with attention-deficit hyperactivity disorder (ADHD) and reading difficulties (RD) are at an elevated risk of developing further cognitive deficits and developmental challenges [1]. ADHD and RD are characterized by a deficit in working memory, which negatively affects learning and behavior. The main aims of this study were to design a working memory training app and examine its effectiveness through a 5-week training program in Chinese children with ADHD and/or RD. There were three experimental groups, with 26 participants in the ADHD group, 38 participants in the RD group, and 24 participants in the ADHD + RD group. The typically developing (TD) control group had 32 participants. All participants completed the pretest and posttest assessments on executive function and reading performance. The findings indicate that the experimental groups improved performance in verbal and visual-spatial working memory as well as Chinese word reading. There was an overall reduction in functional impairment following the training, in contrast to the TD group. This study showed that working memory can be improved through computerized training in children with ADHD and/or RD. The implications of future research in working memory are discussed. Clinical Trials Identifier: NCT06567444 (retrospectively registered) on 20 August 2024.
Heuristic machine learning approaches for identifying phishing threats across web and email platforms
Field: AI

Life has become more comfortable in the era of advanced technology in this cutthroat competitive world. However, there are also emerging harmful technologies that pose a threat. Without a doubt, phishing is one of the rising concerns that leads to stealing vital information such as passwords, security codes, and personal data from any target node through communication hijacking techniques. In addition, phishing attacks include delivering false messages that originate from a trusted source. Moreover, a phishing attack aims to get the victim to run malicious programs and reveal confidential data, such as bank credentials, one-time passwords, and user login credentials. The sole intention is to collect personal information through malicious program-based attempts embedded in URLs, emails, and website-based attempts. Notably, this proposed technique detects URL, email, and website-based phishing attacks, which will be beneficial and secure us from scam attempts. Subsequently, the data are pre-processed to identify phishing attacks using data cleaning, attribute selection, and attacks detected using machine learning techniques. Furthermore, the proposed techniques use heuristic-based machine learning to identify phishing attacks. Admittedly, 56 features are used to analyze URL phishing findings, and experimental results show that the proposed technique has a better accuracy of 97.2%. Above all, the proposed techniques for email phishing detection obtain a higher accuracy of 97.4%. In addition, the proposed technique for website phishing detection has a better accuracy of 98.1%, and 48 features are used for analysis.
The limits of personal experience
Field: PSYCHOLOGY

This article examines how three types of experience—personal, related others, and unrelated others—influence decision-making. We present the complexities and nuances in using these experiential sources to suggest that personal experience is preferred to the other two sources. We discuss the implications of this preference for decision-making processes, especially in contexts involving transformative outcomes. To conclude, we discuss how people rely on other experiential sources when their preferred source is limited.
Machine Learning Models Informed by Connected Mixture Components for Short- and Medium-Term Time Series Forecasting
Field: AI

This paper presents a new approach in the field of probability-informed machine learning (ML). It implies improving the results of ML algorithms and neural networks (NNs) by using probability models as a source of additional features in situations where it is impossible to increase the training datasets for various reasons. We introduce connected mixture components as a source of additional information that can be extracted from a mathematical model. These components are formed using probability mixture models and a special algorithm for merging parameters in the sliding window mode. This approach has been proven effective when applied to real-world time series data for short- and medium-term forecasting. In all cases, the models informed by the connected mixture components showed better results than those that did not use them, although different informed models may be effective for various datasets. The fundamental novelty of the research lies both in a new mathematical approach to informing ML models and in the demonstrated increase in forecasting accuracy in various applications. For geophysical spatiotemporal data, the decrease in Root Mean Square Error (RMSE) was up to 27.7%, and the reduction in Mean Absolute Percentage Error (MAPE) was up to 45.7% compared with ML models without probability informing. The best metrics values were obtained by an informed ensemble architecture that fuses the results of a Long Short-Term Memory (LSTM) network and a transformer. The Mean Squared Error (MSE) for the electricity transformer oil temperature from the ETDataset had improved by up to 10.0% compared with vanilla methods. The best MSE value was obtained by informed random forest. The introduced probability-informed approach allows us to outperform the results of both transformer NN architectures and classical statistical and machine learning methods.
RETRACTED ARTICLE: Single-cell omics and machine learning integration to develop a polyamine metabolism-based risk score model in breast cancer patients
Field: ONCOLOGY

Background Breast cancer remains the leading malignant neoplasm among women globally, posing significant challenges in terms of treatment and prognostic evaluation. The metabolic pathway of polyamines is crucial in breast cancer progression, with a strong association to the increased capabilities of tumor cells for proliferation, invasion, and metastasis. Methods We used a multi-omics approach combining bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) to study polyamine metabolism. Data from The Cancer Genome Atlas, Gene Expression Omnibus, and Genotype-Tissue Expression identified 286 differentially expressed genes linked to polyamine pathways in breast cancer. These genes were analyzed using univariate COX and machine learning algorithms to develop a prognostic scoring algorithm. Single-cell RNA sequencing validated the model by examining gene expression heterogeneity at the cellular level. Results Our single-cell analyses revealed distinct subpopulations with different expressions of genes related to polyamine metabolism, highlighting the heterogeneity of the tumor microenvironment. The SuperPC model (a constructed risk score) demonstrated high accuracy when predicting patient outcomes. The immune profiling and functional enrichment analyses revealed that the genes identified play a crucial role in cell cycle control and immune modulation. Single-cell validation confirmed that polyamine metabolism genes were present in specific cell clusters. This highlights their potential as therapeutic targets. Conclusions This study integrates single cell omics with machine-learning to develop a robust scoring model for breast cancer based on polyamine metabolic pathways. Our findings offer new insights into tumor heterogeneity, and a novel framework to personalize prognosis. Single-cell technologies are being used in this context to enhance our understanding of the complex molecular terrain of breast cancer and support more effective clinical management.
Mindfulness and mental health: the importance of a clinical intervention to prevent the effects of a traumatic event. A pilot study
Field: PSYCHOLOGY

Numerous research studies show that mindfulness can mitigate the negative impact of trauma on mental health by reducing symptoms of anxiety, mediating the relationship between trauma exposure and mental health, and treating symptoms resulting from traumatic events. During the COVID-19 pandemic, which was considered a traumatic event, the wellbeing of adults and children was severely compromised. Although children seem less vulnerable to the physical effects of the virus, this does not seem to be true for the psychological effects. Indeed, a prolonged period of loss of family activities and routines can have a negative impact on the mental health of children and adolescents. To investigate how mindfulness can help preschool children cope with the effects of COVID-19, a study was conducted on 46 children aged 4–5 years. The programme, based on the work of Jon Kabat-Zinn and adapted to the age of the participants, consisted of eight weekly 45-min sessions. Qualitative and quantitative results showed positive feedback, indicating that mindfulness helps children make sense of their experiences and achieve functional post-traumatic growth. This approach is seen as a challenge to guide children toward the restoration of psychological wellbeing, which is essential for good psychological balance.
A scoping review of well-being measures: conceptualisation and scales for overall well-being
Field: PSYCHOLOGY

This study aims to identify the conceptualisation of overall well-being used for well-being assessment through a review of the characteristics and key components and/or dimensions of well-being scales as presented in current literature. Scopus and Web of Science were searched, and thematic analysis was conducted inductively to analyse the identified components within scales, as well as the types of well-being these scales measure. 107 peer-reviewed articles from 2003 to 2022 were included, and 69 well-being scales were identified covering nine areas of well-being. Four final themes were identified as the foundational dimensions of overall well-being: hedonic; eudaimonic; physical health; and generic happiness. Notably, these 69 scales are mainly validated and adopted in the Western context. ‘4 + N’ frameworks of overall well-being are recommended for assessing overall well-being. This review provides researchers with a synthesis of what types of well-being have been measured and which measures have been used to assess these types of well-being for which research participants. Non-Western-based well-being research is called for that incorporates a broader range of research participants and cultural contexts in contributing to a more inclusive understanding of well-being.
Impact of hypertension on coronary artery plaques and FFR-CT in type 2 diabetes mellitus patients: evaluation utilizing artificial intelligence processed coronary computed tomography angiography
Field: AI

Objective: This study utilized artificial intelligence (AI) to quantify coronary computed tomography angiography (CCTA) images, aiming to compare plaque characteristics and CT-derived fractional flow reserve (FFR-CT) in type 2 diabetes mellitus (T2DM) patients with or without hypertension (HTN).Methods: A retrospective analysis was conducted on 1,151 patients with suspected coronary artery disease who underwent CCTA at a single center. Patients were grouped into T2DM (n = 133), HTN (n = 442), T2DM (HTN+) (n = 256), and control (n = 320). AI assessed various CCTA parameters, including plaque components, high-risk plaques (HRPs), FFR-CT, severity of coronary stenosis using Coronary Artery Disease Reporting and Data System 2.0 (CAD-RADS 2.0), segment involvement score (SIS), and segment stenosis score (SSS). Statistical analysis compared these parameters among groups.Results: The T2DM (HTN+) group had the highest plaque volume and length, SIS, SSS, and CAD-RADS 2.0 classification. In the T2DM group, 54.0% of the plaque volume was noncalcified and 46.0% was calcified, while in the HTN group, these values were 24.0 and 76.0%, respectively. The T2DM (HTN+) group had more calcified plaques (35.7% noncalcified, 64.3% calcified) than the T2DM group. The average necrotic core volume was 4.25 mm3 in the T2DM group and 5.23 mm3 in the T2DM (HTN+) group, with no significant difference (p > 0.05). HRPs were more prevalent in both T2DM and T2DM (HTN+) compared to HTN and control groups (p < 0.05). The T2DM (HTN+) group had a higher likelihood (26.1%) of FFR-CT ≤0.75 compared to the T2DM group (13.8%). FFR-CT ≤0.75 correlated with CAD-RADS 2.0 (OR = 7.986, 95% CI = 5.466–11.667, cutoff = 3, p < 0.001) and noncalcified plaque volume (OR = 1.006, 95% CI = 1.003–1.009, cutoff = 29.65 mm3, p < 0.001). HRPs were associated with HbA1c levels (OR = 1.631, 95% CI = 1.387–1.918).Conclusion: AI analysis of CCTA identifies patterns in quantitative plaque characteristics and FFR-CT values. Comorbid HTN exacerbates partially calcified plaques, leading to more severe coronary artery stenosis in patients with T2DM. T2DM is associated with partially noncalcified plaques, whereas HTN is linked to partially calcified plaques.
The Role of Cognitive Learner Prerequisites for Cognitive Load and Learning Outcomes in AR-Supported Lab Work
Field: EDUCATION

Augmented Reality (AR) can enhance student-centered lab work by bridging the spatial and temporal split between virtual information and observed real-world phenomena. While the Cognitive Theory of Multimedia Learning and the Cognitive Load Theory suggest that AR can reduce extraneous cognitive load (ECL) and foster learning, the empirical results remain inconsistent. This re-analysis of three related studies with different target groups and AR devices explores whether learners’ spatial abilities and verbal working memory capacity moderate the effect of AR support in lab work settings on ECL and conceptual knowledge gains. Although these moderators could not be confirmed consistently, the results indicate that tablet-based AR holds the potential to support learners with low spatial abilities. Moreover, low verbal working memory learners were demonstrated to be particularly vulnerable to the spatial contiguity failure that can be caused by smartglasses AR. Moderation effects were only observed for ECL but not for conceptual knowledge gains. The findings highlight that the benefit of AR support can depend on learners’ cognitive prerequisites and additional contextual factors, such as the AR device used and the age of the target group. The design and implementation of AR-supported lab work environments should account for these factors to optimize the learning outcomes.
Tax credit for support of university-community partnerships in low-income urban school districts
Field: EDUCATION

Tying public school funding to property taxes has prevented low-income school districts in the United States from garnering adequate financial and social resources. As a result of this regressive funding system, millions of children find themselves trapped in underfunded schools and neighborhoods that perpetuate intergenerational trauma, tenuous employment, poor health, and poverty. However, in many underserved neighborhoods, including in cities like Philadelphia and Chicago, where poverty rates have been as high as 25 and 40%, respectively, many of the most under-resourced schools border or are adjacent to wealthy universities. Given this proximity of many universities and their wealth of resources spanning medical centers, community organizations, faculty, and students, the potential for mutual benefit, long-term structural change, and the ability to fulfill shared missions is significant, and partnerships that breakdown historical siloes must be encouraged. Therefore, this policy brief advocates for a tax credit at the federal level to incentivize and catalyze scaling of successful university-community partnership models that have been transformative in their respective communities.