mdpi-ai/citelens huggingface dataset:
Persisting inhibition biases efficient rule inference under uncertainty
Field: PSYCHOLOGY

Introduction Task set inhibition supports optimal switching among tasks by actively suppressing the interference from recently executed competing task sets. It is typically studied in cued task-switching paradigms where there is no uncertainty about the task set or rule to prepare for on each trial. While inhibition has been shown to influence the speed and the accuracy of task execution, affecting task set retrieval, preparation, or implementation in conditions of task set switching, it remains uninvestigated whether it also affects rule selection under uncertainty. Methods We implemented an ad-hoc four-rule card sorting task and categorized the rules selected by participants after a rule shift according to the recency of their last usage. We included a measure of working memory capacity (WMC) to control for its involvement in the rule selection process. Results Participants exhibited a reduced preference for recently abandoned rules than less recently abandoned ones. Furthermore, we found that such a preference was not associated with WMC. Discussion The results suggest that decision-making processes underlying rule inference and selection may be influenced by task-set inhibition, configuring as a conflict adjustment mechanism to the sequential history of rules application.
Eye-movement reveals word order effects on comparative sentences in older adults using a verb-final language
Field: PSYCHOLOGY

Objectives This study aimed to examine age-related differences in the comprehension of Korean comparative sentences with varying word orders by employing both offline and online measures, and to investigate how variations in word order affect sentence processing across different age groups. Methods A total of 52 monolingual native Korean speakers, 26 young adults, and 26 older adults, completed a sentence-picture-matching task under two word order conditions: comparative-first and nominative-first. Offline measures included accuracy and response time, while an online method involved eye-tracking within the Visual World Paradigm. Data analyses were performed using linear and generalized linear mixed-effects models. Results Older adults demonstrated lower accuracy and longer response times compared to younger individuals. Distinctive fixation patterns were observed, particularly in the sentential-final phrase, across different age groups. Specifically, nominative-first sentences elicited greater target advantage scores among younger adults, whereas older adults showed higher scores in comparative-first sentences. Conclusion The study highlights the potential of comparative sentences in elucidating age-related changes in sentence comprehension. These differences were evident not only in offline tasks but also in real-time processing, as evidenced by eye-tracking data. The findings suggest distinct processing strategies employed by young and older adults and underscore the importance of considering both syntactic and semantic cues in sentence comprehension.
Measuring the menu, not the food: “psychometric” data may instead measure “lingometrics” (and miss its greatest potential)
Field: PSYCHOLOGY

This is a review of a range of empirical studies that use digital text algorithms to predict and model response patterns from humans to Likert-scale items, using texts only as inputs. The studies show that statistics used in construct validation is predictable on sample and individual levels, that this happens across languages and cultures, and that the relationship between variables are often semantic instead of empirical. That is, the relationships among variables are given a priori and evidently computable as such. We explain this by replacing the idea of “nomological networks” with “semantic networks” to designate computable relationships between abstract concepts. Understanding constructs as nodes in semantic networks makes it clear why psychological research has produced constant average explained variance at 42% since 1956. Together, these findings shed new light on the formidable capability of human minds to operate with fast and intersubjectively similar semantic processing. Our review identifies a categorical error present in much psychological research, measuring representations instead of the purportedly represented. We discuss how this has grave consequences for the empirical truth in research using traditional psychometric methods.
Integrative analysis with machine learning identifies diagnostic and prognostic signatures in neuroblastoma based on differentially DNA methylated enhancers between INSS stage 4 and 4S neuroblastoma
Field: ONCOLOGY

Introduction: Accumulating evidence demonstrates that aberrant methylation of enhancers is crucial in gene expression profiles across several cancers. However, the latent effect of differently expressed enhancers between INSS stage 4S and 4 neuroblastoma (NB) remains elusive. Methods: We utilized the transcriptome and methylation data of stage 4S and 4 NB patients to perform Enhancer Linking by Methylation/Expression Relationships (ELMER) analysis, discovering a differently expressed motif within 67 enhancers between stage 4S and 4 NB. Harnessing the 67 motif genes, we established the INSS stage related signature (ISRS) by amalgamating 12 and 10 distinct machine learning (ML) algorithms across 113 and 101 ML combinations to precisely diagnose stage 4 NB among all NB patients and to predict the prognosis of NB patients. Based on risk scores calculated by prognostic ISRS, patients were categorized into high and low-risk groups according to median risk score. We conducted comprehensive comparisons between two risk groups, in terms of clinical applications, immune microenvironment, somatic mutations, immunotherapy, chemotherapy and single-cell analysis. Ultimately, we empirically validated the differential expressions of two ISRS model genes, CAMTA2 and FOXD1, through immunochemistry staining. Results: Through leave-one-out cross-validation, in both feature selection and model construction, we selected the random forest algorithm to diagnose stage 4 NB, and Enet algorithm to develop prognostic ISRS, due to their highest average C-index across five NB cohorts. After validations, the ISRS demonstrated a stable predictive capability, outperforming the previously published NB signatures and several clinic variables. We stratified NB patients into high and low-risk group based on median risk score, which showed the low-risk group with a superior survival outcome, an abundant immune infiltration, a decreased mutation landscape, and an enhanced sensitivity to immunotherapy. Single-cell analysis between two risk groups reveals biologically cellular variations underlying ISRS. Finally, we verified the significantly higher protein levels of CAMTA2 and FOXD1 in stage 4S NB, as well as their protective prognosis value in NB. Conclusion: Based on multi-omics data and ML algorithms, we successfully developed the ISRS to enable accurate diagnosis and prognostic stratification in NB, which shed light on molecular mechanisms of spontaneous regression and clinical utilization of ISRS.
Socio-Economically Disadvantaged Male Students’ Hesitancy to Study Biology in Ireland: Factors Effecting Intent in the Transition to Upper Secondary School
Field: EDUCATION

While it is evident that Ireland has the ambition to widen access to higher education, there are challenges; especially regarding the decision-making process of socio-economically disadvantaged youth when selecting subjects for transition into higher education. This is of particular concern when there are abundant science-related courses in higher education, as well as careers, which are fundamental to the global economy, and a cohort of Ireland’s youth is disadvantaged in choosing this pathway. National statistics highlight the disproportionate participation rate across genders in upper secondary school science education. Extensive research has been invested in exploring supporting female access into male-dominated science fields (e.g., physics) but less so regarding male access into female-dominated science fields (e.g., biology) to achieve gender equity. Thus, this paper uses the Theory of Planned Behaviour as a theoretical framework to examine the possible psychosocial elements affecting the decision-making process of socio-economically disadvantaged male students attending DEIS schools in the Republic of Ireland and their intent to study biology as a subject at the upper secondary school level. Data collected from 344 secondary school-level students from 20 schools across nine Irish counties, and subsequently descriptively analysed, revealed that male students were considerably less likely than female students to choose biology at upper secondary level education. Many male students expressed anxiousness and hopelessness when evaluating the study of biology. Teachers were identified as lead influencers and self-efficacy was highlighted as a significant factor in male affinity to the subject. Finally, higher levels of female students’ intent to study biology at upper second level suggested the familial influence of parents and wider family contributes to their overall perspective on the relevance of science to their future prosperity. Considering the various factors influencing intent, the authors suggest that a targeted pedagogical intervention that includes the promotion of self-efficacy; male student mastery experiences through assessment; emphasis on scientific knowledge; and raising the awareness of the various career pathways that studying biology affords could ameliorate this trend amongst teenage males. Additionally, targeted messaging for parents and the wider family as well as continuous professional development for teachers should be integral to any work conducted in this area.
Prior experiences as students and instructors play a critical role in instructors’ decision to adopt evidence-based instructional practices
Field: EDUCATION

Background: There has been a growing interest in characterizing factors influencing teaching decisions of science, technology, engineering, and mathematics (STEM) instructors in order to address the slow uptake of evidence-based instructional practices (EBIPs). This growing body of research has identified contextual factors (e.g., classroom layout, departmental norms) as primary influencers of STEM instructors’ decision to implement EBIPs in their courses. However, models of influences on instructional practices indicate that context is only one type of factor to consider. Other factors fall at the individual level such as instructors’ past teaching experience and their views on learning. Few studies have been able to explore in depth the role of these individual factors on the adoption of EBIPs since it is challenging to control for contextual features when studying current instructors. Moreover, most studies exploring adoption of EBIPs do not take into account the distinctive features of each EBIP and the influence these features may have on the decision to adopt the EBIP. Rather, studies typically explore barriers and drivers to the implementation of EBIPs in general. In this study, we address these gaps in the literature by conducting an in-depth exploration of individual factors and EBIPs’ features that influence nine future STEM instructors’ decisions to incorporate a selected set of EBIPs in their teaching. Results: We had hypothesized that the future instructors would have different reasoning to support their decisions to adopt or not Peer Instruction and the 5E Model as the two EBIPs have distinctive features. However, our results demonstrate that instructors based their decisions on similar factors. In particular, we found that the main drivers of their decisions were (1) the compatibility of the EBIP with their past experiences as students and instructors as well as teaching values and (2) experiences provided in the pedagogical course they were enrolled in. Conclusions: This study demonstrates that when considering the adoption of EBIPs, there is a need to look beyond solely contextual influences on instructor’s decisions to innovate in their courses and explore individual factors. Moreover, professional development programs should leverage their participants past experiences as students and instructors and provide an opportunity for instructors to experience new EBIPs as learners and instructors.
Fault Detection, Classification and Localization Along the Power Grid Line Using Optimized Machine Learning Algorithms
Field: AI

Distributed energy generation increases the need for smart grid monitoring, protection, and control. Localization, classification, and fault detection are essential for addressing any problems immediately and resuming the smart grid as soon as possible. Simultaneously, the capacity to swiftly identify smart grid issues utilizing sensor data and easily accessible frequency and voltage data from PMU devices is a prerequisite of this task. Therefore, this paper proposes new methods using fuzzy logic and adaptive fuzzy neural networks as well as machine learning and meta-heuristic algorithms. First, line voltage is used by a fuzzy thresholding method to estimate when a transmission line defect would develop in less than 1.2 clock cycles. Next, features taken from frequency signals in the real-time interval are utilized to classify the type of error using machine learning systems (decision tree algorithm and random forest algorithm) optimized with wild horse meta-heuristic algorithm. To locate the precise problem location, we finally use a neural fuzzy inference system that is capable of adapting to new data. We employ a simulated power transmission system in MATLAB to test our proposed solutions. Mean square error (MSE) and confusion matrix are used to assess the efficiency of a classifier or detector. For the decision tree algorithm method, the detector attained an acceptable MSE of 2.34e−4 and accuracy of 98.1%, and for the random forest algorithm method, an acceptable MSE of 3.54e−6 and accuracy of 100%. Furthermore, the placement error was less than 153.6 m in any direction along the line.
Hybrid Strategies Based Seagull Optimization Algorithm for Solving Engineering Design Problems
Field: AI

The seagull optimization algorithm (SOA) is a meta-heuristic algorithm proposed in 2019. It has the advantages of structural simplicity, few parameters and easy implementation. However, it also has some defects including the three main drawbacks of slow convergence speed, simple search method and poor ability of balancing global exploration and local exploitation. Besides, most of the improved SOA algorithms in the literature have not considered the drawbacks of the SOA comprehensively enough. This paper proposes a hybrid strategies based algorithm (ISOA) to overcome the three main drawbacks of the SOA. Firstly, a hyperbolic tangent function is used to adjust the spiral radius. The spiral radius can change dynamically with the iteration of the algorithm, so that the algorithm can converge quickly. Secondly, an adaptive weight factor improves the position updating method by adjusting the proportion of the best individual to balance the global and local search abilities. Finally, to overcome the single search mode, an improved chaotic local search strategy is introduced for secondary search. A comprehensive comparison between the ISOA and other related algorithms is presented, considering twelve test functions and four engineering design problems. The comparison results indicate that the ISOA has an outstanding performance and a significant advantage in solving engineering problems, especially with an average improvement of 14.67% in solving welded beam design problem.
Exploring a practitioner-athlete relationship and facilitated learning throughout a psychological skills training program
Field: PSYCHOLOGY

Psychological skills training (PST) programs have been consistently reported as an important part of preparation for optimal performance in high performance sport. However, there is much less research about the quality and characteristics of the working relationship between a sport psychology practitioner (SPP) and an athlete and, importantly, how that relationship facilitates learning. Therefore, the purpose of the present paper was to explore the working relationship between a SPP and a volleyball player and how that working relationship facilitated the learning processes utilized by this player, as she prepared for the demands of her sport and life. An instrumental case study methodology with a qualitative description approach was employed to illustrate different aspects of the evolving relationship and the athlete’s experiences. The results of this case reflect an approach that combined features of both a directive approach in teaching specific psychological skills and a less directive and more collaborative approach, which, in turn, allowed an athlete to begin to learn how to guide their own learning.
Centering Educators’ Voices in the Development of Professional Learning for Data-Rich, Place-Based Science Instruction
Field: EDUCATION

This self-reflective case study describes our project team’s efforts to promote equity in science professional learning (PL) by centering the voices of educators in the PL design process and within the course itself. We believe that educators’ experiences, priorities, and expertise are essential to developing professional learning that meets the needs of teachers and their students. We have a particular interest in amplifying the voices of those in historically underrepresented communities. Toward that end, we engaged science educators who work with Indigenous students and recent immigrants as collaborators in developing PL to support data-rich, place-based Earth Science instruction. In this case study, we share and critique the practices and tools that we have employed to center educator voices, rather than those of the PL designers and researchers. Our strategies for developing more equitable science professional learning include the use of: (a) equity-focused research methods, such as asset-based needs-sensing questions and peer-to-peer interviews; (b) a humanistic stance toward data-rich science learning, which emphasizes the typically unnamed sociocultural inputs and outputs that permeate all aspects of data; (c) a participatory design process that centers educators’ voices; and (d) a model of professional learning that uses representations of educator and student experiences as objects for reflection.
The Moderating Effect of Body Appreciation on the Relationship between Self-Esteem and Life Satisfaction
Field: PSYCHOLOGY

Background: Although positive associations between life satisfaction, self-esteem, and body image have previously been established, differences in these variables by gender and age have yielded mixed results. Moreover, little is known about the interplay between self-esteem and body appreciation on life satisfaction. This study aims to investigate the moderating effect of body appreciation on the relationship between self-esteem and life satisfaction, considering disparities between females and males and also between emerging adults (before the age of thirty) and older adults. Methods: A cross-sectional online survey was performed in Poland with a sample of 449 adults aged between 18 and 75 (M = 30.41, SD = 12.72), including 68% of women. The survey included the Satisfaction With Life Scale (SWLS), Rosenberg Self-Esteem Scale (RSES), and Body Appreciation Scale (BAS-2). Results: Men scored higher than women in terms of life satisfaction and self-esteem, while older participants (age > 30) scored higher than younger individuals (age ≤ 30) in terms of life satisfaction, self-esteem, and body appreciation. The study confirmed positive and moderate correlations between life satisfaction, self-esteem, and body appreciation. The interactive effect of self-esteem and body appreciation on life satisfaction was also found by controlling for age and gender. Conclusions: Some intervention programs focused on increasing levels of self-esteem and body appreciation should be implemented, especially among women and emerging adults, to improve their well-being.
Decision regret of cancer patients after radiotherapy: results from a cross-sectional observational study at a large tertiary cancer center in Germany
Field: ONCOLOGY

Purpose: The decision-making process regarding cancer treatment is emotionally challenging for patients and families, harboring the risk of decision regret. We aimed to explore prevalence and determinants of decision regret following radiotherapy. Methods: This cross-sectional observational study was conducted at a tertiary cancer center to assess decision regret following radiotherapy. The study employed the German version of the Ottawa Decision Regret Scale (DRS) which was validated in the study population. Decision regret was categorized as absent (0 points), mild (1–25 points), and strong (> 25 points). Various psychosocial outcome measures were collected using validated questionnaires to identify factors that may be associated with decision regret. Results: Out of 320 eligible patients, 212 participated, with 207 completing the DRS. Median age at start of radiotherapy was 64 years [interquartile range (IQR), 56–72], genders were balanced (105 female, 102 male), and the most common cancer types were breast (n = 84; 41%), prostate (n = 57; 28%), and head-and-neck cancer (n = 19; 9%). Radiotherapy was applied with curative intention in 188 patients (91%). Median time between last radiotherapy fraction and questionnaire completion was 23 months (IQR, 1–38). DRS comprehensibility was rated as good or very good by 98% (196 of 201) of patients. Decision regret was reported by 43% (n = 90) as absent, 38% (n = 78) as mild, and 18% (n = 38) as strong. In the multiple regression analysis, poor Eastern Cooperative Oncology Group performance status, low social support, and dissatisfaction with care were independent risk factors for higher decision regret after radiotherapy. Conclusions: The German version of the DRS could be used to assess decision regret in a diverse cohort of cancer patients undergoing radiotherapy. Decision regret was prevalent in a considerable proportion of patients. Further studies are necessary to validate these findings and obtain causal factors associated with decision regret after radiotherapy.