mdpi-ai/citelens huggingface dataset:
The risks of unconcern: low sensitivity to threat can have unfortunate consequences
Field: PSYCHOLOGY

Each one of us is confronted with warnings of danger or threats to wellbeing in our everyday life, whether in the form of certain road signs, Public Service Announcements, ominous changes in bodily functioning, or cautionary tales heard from family or friends. There is great inter-individual variation in how people respond to such threats, with some people habitually tending to ignore or dismiss them, often to their peril. The first purpose of the present paper is to review several studies showing that individuals—most often men—who score very low on measures of trait anxiety are more likely to engage in behaviors that could jeopardize their physical wellbeing. The general hypothesis that is derived from that review is that when attention to everyday threats is chronically muted by way of a dispositional trait, the likelihood of proceeding down some dangerous path is increased. Those findings are then discussed within the broader context of personality theory to highlight the importance of recognizing the bipolarity of common traits. Here the case is made for replacing the term trait anxiety with the term threat sensitivity in order to capture the full breadth of this basic personality variable. A discussion of the neurobiological underpinnings of threat sensitivity is then presented with an emphasis on individual and sex differences in the workings of the defensive survival circuitry. Taken together, this paper has implications for two subfields within psychology. For the area of personality theory, this paper provides support for the adaptationist view with the argument that low threat sensitivity has both adaptive and maladaptive potential. For the area of health psychology, it is argued that some individuals who demonstrate a habitual tendency to neglect their physical wellbeing may be acting—at least in part—in accordance with their innate neurobiological constitution.
Effect of match location on the playing style of teams coached by ‘Pep’ Guardiola
Field: PSYCHOLOGY

Introduction: Analysis in football seeks to find the performance factors that bring teams closer to success.Methods: This study aims to analyze the playing styles of two teams managed by Pep Guardiola (F.C. Barcelona and Manchester City) based on match location (home or away). Two methods of analysis were used: descriptive statistics through chi-square tests to evaluate game characteristics and the polar coordinates technique to analyze the relationships between the different lines of each team (goalkeeper, defenders, midfielders, and forwards).Results: The results showed that F.C. Barcelona maintained a consistent playing style regardless of location, exhibiting significant differences only in actions that involved shots or header (p = 0.035), with better performance at home. In contrast, Manchester City displayed significantly different performance in action success (p < 0.001), level of play elaboration (p = 0.004), density (p = 0.033), duration (p = 0.036), and actions that included a shot (p = 0.001) depending on the location. Additionally, qualitative analyses revealed differences in the relationships among the team lines according to match location, with Manchester City displaying more variability in these interactions than F.C. Barcelona.Discussion: The study concludes that although Guardiola applies a consistent set of strategies, match location has a greater influence on Manchester City’s performance, suggesting that this team adjusts its playing style on the basis of contextual conditions. These findings highlight the importance of considering factors such as location when preparing tactics to increase the probability of success in elite football.
SkyMap: a generative graph model for GNN benchmarking
Field: AI

Graph Neural Networks (GNNs) have gained considerable attention in recent years. Despite the surge in innovative GNN architecture designs, research heavily relies on the same 5-10 benchmark datasets for validation. To address this limitation, several generative graph models like ALBTER or GenCAT have emerged, aiming to fix this problem with synthetic graph datasets. However, these models often struggle to mirror the GNN performance of the original graphs. In this work, we present SkyMap, a generative model for labeled attributed graphs with a fine-grained control over graph topology and feature distribution parameters. We show that our model is able to consistently replicate the learnability of graphs on graph convolutional, attention, and isomorphism networks better (64% lower Wasserstein distance) than ALBTER and GenCAT. Further, we prove that by randomly sampling the input parameters of SkyMap, graph dataset constellations can be created that cover a large parametric space, hence making a significant stride in crafting synthetic datasets tailored for GNN evaluation and benchmarking, as we illustrate through a performance comparison between a GNN and a multilayer perceptron.
Sequence labeling via reinforcement learning with aggregate labels
Field: AI

Sequence labeling is pervasive in natural language processing, encompassing tasks such as Named Entity Recognition, Question Answering, and Information Extraction. Traditionally, these tasks are addressed via supervised machine learning approaches. However, despite their success, these approaches are constrained by two key limitations: a common mismatch between the training and evaluation objective, and the resource-intensive acquisition of ground-truth token-level annotations. In this work, we introduce a novel reinforcement learning approach to sequence labeling that leverages aggregate annotations by counting entity mentions to generate feedback for training, thereby addressing the aforementioned limitations. We conduct experiments using various combinations of aggregate feedback and reward functions for comparison, focusing on Named Entity Recognition to validate our approach. The results suggest that sequence labeling can be learned from purely count-based labels, even at the sequence-level. Overall, this count-based method has the potential to significantly reduce annotation costs and variances, as counting entity mentions is more straightforward than determining exact boundaries.
The relationship between perceived social support and social anxiety in Chongqing rural secondary school students: the chain mediating effect of core self-evaluation and shyness
Field: PSYCHOLOGY

Adolescents in less economically developed areas are susceptible to social anxiety, so finding ways to effectively prevent and intervene in social anxiety could be a major step forward for poverty alleviation. However, little is known about the inner workings of social anxiety in this group. Exploring the risk and protective factors of social anxiety among adolescents in less developed rural areas is crucial for maintaining their mental health and improving their social adaptability. The purpose of this study is to explore the relationships among perceived social support, core self-evaluation, shyness and social anxiety among rural secondary school students and analyze the risk and protective factors of social anxiety. A total of 626 rural secondary school students are investigated with the Perceived Social Support Scale (PSSS), Core Self-Evaluation Scale (CSES), Shyness Scale (SS) and Social Avoidance and Distress Scale (SADS). Structural equation modeling is used to analyze the mediating effects of core self-evaluation and shyness. The results reveal that (1) the perceived social support and core self-evaluation of rural secondary school students are significantly negatively correlated with social anxiety, whereas their shyness is significantly positively correlated with social anxiety. There are significant gender differences in perceived social support, core self-evaluation, shyness and social anxiety. (2) There is a significant chain mediating effect of core self-evaluation and shyness between perceived social support and social anxiety, and the mediation model is cross-gender consistent. These results confirm that perceived social support and core self-evaluation are protective factors against social anxiety in rural secondary school students and that shyness is a risk factor for social anxiety. Moreover, perceived social support can indirectly affect social anxiety through core self-evaluation and shyness. Prevention and intervention of social anxiety can be carried out in three ways: improving the perceived ability of social support, enhancing positive self-evaluation, and reducing shyness and avoidance behaviors.
Controlling the narrative: the relationship between narrative ability and executive functioning in children with developmental language disorder
Field: PSYCHOLOGY

Children with developmental language disorder (DLD) experience problems in language comprehension and/or production. In particular, storytelling or narrative ability is often impaired, as this type of discourse involves all domains of language. These problems may lead to a lower quality of social interaction and mental health. Moreover, problems in oral narrative ability during early development have a negative effect on later literacy. However, telling a story involves more than language alone. Executive functioning is thought to play an important part in stimulating narrative ability, as linguistic utterances need to be planned in a temporal and causal order, and switching is needed between multiple characters and events in the story. Research has shown that children with DLD experience problems with executive functioning, independent of their language ability. Thus, the difficulties in storytelling may be caused by both impaired language and executive functioning, as both domains follow hierarchical developmental paths during the early childhood years. In this review, we discuss three components of narrative ability (comprehension, production of macrostructure and production of microstructure) and how they may be interconnected to the three core components of executive functioning (working memory, switching and inhibition) and attention. This review shows that updating and monitoring information in working memory plays an important part in all three components of narrative ability, across multiple studies. This result may give direction in the development of narrative assessment and intervention, and urge further research to disentangle the interplay between language and executive control in DLD.
From Language Models to Medical Diagnoses: Assessing the Potential of GPT-4 and GPT-3.5-Turbo in Digital Health
Field: AI

Background: Large language models (LLMs) like GPT-3.5-Turbo and GPT-4 show potential to transform medical diagnostics through their linguistic and analytical capabilities. This study evaluates their diagnostic proficiency using English and German medical examination datasets. Methods: We analyzed 452 English and 637 German medical examination questions using GPT models. Performance metrics included broad and exact accuracy rates for primary and three-model generated guesses, with an analysis of performance against varying question difficulties based on student accuracy rates. Results: GPT-4 demonstrated superior performance, achieving up to 95.4% accuracy when considering approximate similarity in English datasets. While GPT-3.5-Turbo showed better results in English, GPT-4 maintained consistent performance across both languages. Question difficulty was correlated with diagnostic accuracy, particularly in German datasets. Conclusions: The study demonstrates GPT-4’s significant diagnostic capabilities and cross-linguistic flexibility, suggesting potential for clinical applications. However, further validation and ethical consideration are necessary before widespread implementation.
Efficient Prediction of Judicial Case Decisions Based on State Space Modeling
Field: AI

With the rapid advancement of information technology and artificial intelligence, the digitization of legal texts has caused a swift increase in the volume of legal materials. Judges now face increased professional demands, larger information loads, and more complex case structures, which heightens their workload and demands. To enhance the quality and efficiency of judicial work and drive the modernization of the judicial system, the application of intelligent prediction models has become essential. This paper presents the MambaEffNet model, which integrates multiple modules such as Convolutional Neural Networks (CNN) and Multilayer Perceptrons (MLP). The core convolutional structure is improved using a state space model, and a multi-directional feature fusion structure is designed to enhance the performance of sequence feature extraction. Generative Adversarial Networks (GAN) are employed for data augmentation, to address the issue of missing features in judicial case predictions. The EfficientNetV2 architecture is used to optimize the kernel size and the expansion ratio of input and output channels. Experimental results demonstrate that the MambaEffNet model achieves a prediction accuracy of 92.05% on the Nigerian Supreme Court judgment dataset and performs excellently on other judicial datasets, significantly improving prediction accuracy and efficiency. Specifically, the MambaEffNet model increases the prediction accuracy for criminal and civil case judgments by 9.53% and 11.57%, respectively. Additionally, the model excels in handling long sequence data, effectively capturing key features and providing comprehensive decision support.
Channel2DTransformer: A Multi-level Features Self-attention Fusion Module for Semantic Segmentation
Field: AI

Semantic segmentation is a crucial technology for intelligent vehicles, enabling scene understanding in complex driving environments. However, complex real-world scenarios often contain diverse multi-scale objects, which bring challenges to the accurate semantic segmentation. To address this challenge, we propose a multi-level features self-attention fusion module called Channel2DTransformer. The module utilizes self-attention mechanisms to dynamically fuse multi-level features by computing self-attention weights between their channels, resulting in a consistent and comprehensive representation of scene features. We perform the module on the Cityscapes and NYUDepthV2 datasets, which contain a large number of multi-scale objects. The experimental results validate the positive contributions of the module in enhancing the semantic segmentation accuracy of multi-scale objects and improving the performance of semantic segmentation in complex scenes.
Studying the Impact of Changing Consumer Behavior During Crisis Periods Through Store Classification
Field: AI

Since customer behavior changes unpredictably during crisis periods such as pandemics, many sectors have been affected differently. The retail sector in particular has been one of the most affected sectors. Retail companies that could not determine the right strategies against customer behavior change were in a difficult situation, and some even had to close down. The inability of consumers to do physical shopping for reasons such as socializing, experiencing products and interacting during the pandemic process required an understanding of changing consumer needs. In this study, to determine the changes in customer purchasing behaviors during the pandemic period, using the sales data of a company operating in the women’s clothing sector and whose sales loss approached 50% during the pandemic period, separate stores were divided into clusters using machine learning methods for the pre-pandemic and pandemic period. The clusters formed were examined and the stores in different clusters were determined depending on customer purchasing behavior. The aim of the study is to ensure that the company segments its stores correctly to gain competitive advantage. Firms will be able to determine the right strategies against changing consumer behavior through a correct store segmentation. First, stores that do not belong to any classification group are clustered using unsupervised machine learning methods. No significant change was observed in the clusters formed before and during the pandemic. This indicated that the pandemic had a similar effect on all stores. Then, pre-pandemic, pandemic period and both periods data were analyzed using 7 different machine learning classification algorithms. The results obtained were compared. For all three analyses, the random forest algorithm gave the highest accuracy rate. The random forest algorithm with the highest accuracy was hybridized with 3 different classification algorithms. The hybrid model consisting of random forest and support vector machine gave the highest accuracy rate (90%) for the period including all data for store classification. Thanks to the hybrid model created with random forest and support vector machines, companies can be advantageous against other companies in the competitive environment by creating separate strategies for each store class.
Deep Learning Algorithm for Optimized Sensor Data Fusion in Fault Diagnosis and Tolerance
Field: AI

Environmental perception is one of the key technologies to realize autonomous vehicles. The fault diagnosis process involves identifying the fault that occurred or the cause of the out-of-control condition. Here, the major objective is to locate problems in detection by analysing previous data or sequential patterns of data that cause failure. This study evaluates the use of deep learning for improved sensor data fusion in fault identification and tolerance using the KITTI dataset. The input video from the dataset has been transformed to frames through median filtering. Next, feature extraction is applied to a preprocessed image, resulting in the fusion of sensor data. Data fusion is then carried out utilizing an enhanced RPN (region proposal network). The enhanced RPN also has a loss function (object detection loss, bounding box loss and target classification loss), an estimate of ROI and feature extraction network (FEN). Through the use of the COOT connected blue monkey optimization (CCBMO) model, the weight of the optimally enhanced RPN is established. Next, using global non-maximum suppression with both global and local confidence, fault identification and tolerance are carried out. From the analysis, it clearly shows that proposed method accomplished better results in terms of accuracy, precision and specificity of 97.78%, 93.76% and 93.43%, respectively, when compared with various conventional models with respect to diverse performance measures.
A decade of advancing development, diversity, engagement, and excellence in STEM education
Field: EDUCATION

From August 2014 to July 2024, the International Journal of STEM Education has completed ten full publication cycle years. In this editorial, I offer a brief reflection of the journal’s successful growth over the past decade. I also celebrate the collective achievements of all those involved and highlight the journal’s continued commitment to excellence, driven by the engagement of diverse researchers and readers from around the world.
Extending access for all chemistry students with extended reality
Field: EDUCATION

Equal access to instructor’s time and attention in chemistry classes and laboratories can be a barrier experienced by students from historically excluded groups. An instructor’s own biases will determine the nature of their interaction with students, and even well-meaning instructors can interact with students in slightly different ways, which might prevent certain students from having access to all the available instructional resources for the class. This is an additive problem, which may or may not be recognized in peer and student evaluations, and an issue that might escape self-reflection even in educators that are committed to diversity, inclusion, and justice. This issue conflates both actual and perceived biases, introducing a complex dynamic between instructor and student. Extended reality (XR) provides an avenue to generate materials that can be used to enhance or replace classroom instruction with a great degree of realism. In this paper we will discuss the implementation of a set of virtual reality (VR) organic chemistry labs. We will show that XR learning tools are by their very nature accessible and inclusive of a wide variety of students and will provide evidence from student reflections that shows that students from historically excluded groups find the XR content offered in our virtual reality labs more personal than in-person activities covering the same material.
The effect of AI on pink marketing: the case of women’s purchasing behavior using mobile applications
Field: AI

This research looks in detail at the dynamics of pink marketing and its effect on the purchase behavior of Saudi women through mobile applications, with an emphasis on Artificial Intelligence (AI) as a moderator. Furthermore, this study assesses the effects of customized pink marketing strategies – product, price, promotion, and place – on buying intentions and behaviors. A closed-ended questionnaire was adopted to measure constructs associated with women’s mobile app purchase behavior influenced by pink marketing and AI elements. Structural Equation Modeling (SEM) was the study tool used to examine how AI affects women’s consumer behavior and how it influences pink marketing. The results suggest that each component of the pink marketing mix significantly influences buying behavior, especially price and promotion. Additionally, AI has a significant moderating effect, improving the personalization and effectiveness of marketing activities. The results of this study highlight the essential role of AI in forming consumer engagement in the digital market, providing useful input for marketers who intend to target women in Saudi Arabia. This study complements the understanding of gender marketing in the digital era and provides a vision for the possibility of AI fundamentally changing traditional approaches.
Examining the effect of AI-powered virtual-human training on STEM majors’ self-regulated learning behavior
Field: EDUCATION

Introduction: Students pursuing science, technology, engineering, and math (STEM) majors often struggle with essential skills critical to their academic success and future careers. Traditional self-regulated learning (SRL) training programs, while effective, require significant time investments from both students and instructors, limiting their feasibility in large lecture-based STEM courses.Methods: This study investigates whether completion of three AI-powered virtual-human training modules—focused on planning, self-monitoring, and reflection—leads to increased use of corresponding MS Planner tools among STEM majors compared to a control group.Results: Results indicate that students who did not complete the first two training modules were less likely to use MS Planner features for planning and self-monitoring; however, the reflection module did not yield comparable results.Discussion: These findings highlight the potential of AI-powered virtual-human training as a scalable solution to enhance desirable learning behaviors among STEM majors, particularly in large and diverse classrooms. This research contributes to the understanding of effective interventions for fostering SRL behaviors in STEM education and suggests avenues for future refinement and implementation of digital training tools.