Search Results (88)

The background to this research paper examines why people with disabilities often have additional problems with authentication (i.e., logging in to online services). While the primary focus is on accessible authentication, we also explore its relevance to electronic identification and consider the post-authentication stage of authorization (allowing continued use of a particular service once logged in). While people without disabilities regularly log into websites and applications without too much thought for the process, with an end-goal or task in mind to be achieved with the service that they are accessing, extra barriers exist for people with disabilities. We discover how there is a societal gap in terms of ease-of-use, as previous studies show that people with disabilities can find this step difficult, frustrating, or virtually impossible. For people who have a disability, complications will arise in this process, and we examine the nature of these problems identified by this group. A series of interviews (n = 15) is analyzed using Constructivist Grounded Theory methods to identify patterns in participants’ responses and develop a theory explaining why Accessible Authentication is a problem. While aiming to follow a constructivist methodology, this paper categorizes common traits revealed by participants in interviews. The key findings reveal that most users with disabilities say that the ability to authenticate effectively is reduced by accessibility barriers; in other words, participants felt hindered when logging in because of their disability. This leads us to conclude, with some degree of confidence, that the data implies a lack of accessibility for users of traditional authentication systems. A further area of concern for the participants is that maintaining security alongside ease-of-use was important to them (albeit with no clear winner between usability and security preferences), so future work on improving accessibility should ensure that users with disabilities’ information is not left vulnerable, while maintaining a sufficient level of accessibility for people with disabilities. Further to this, suggestions for achieving an accessible solution are presented in a preliminary Theoretical Framework. Full article

Infected URLs are always regarded as a serious threat to cybersecurity, serving as pathways to phishing, maliciousness, and other offenses. Although transformer-based models have demonstrated good performance in malicious URL detection, their high computational cost and latency make them impractical for deployment in real-time or resource-constrained systems. Allocated on the basis of knowledge distillation (KD), lightweight models tend to be efficient but are commonly not sufficiently discriminative to distinguish between malicious and benign URLs with non-cataclysmic lexical overlaps, particularly when dealing with an imbalanced dataset. In order to address these issues, we propose Contra-KD, a lightweight transformer model that incorporates contrastive learning (CL) and KD. This proposed framework imposes structured embedding matching, allowing the student model to learn more meaningful and generalized depictions. Contra-KD uses a compact 6-layer student transformer architecture based on ELECTRA to scale parameters up and can achieve more than 90% computational fidelity with a high accuracy. In this scheme, CL improves the feature of discrimination by semantically clustering similar URLs and separating different URLs. This tendency serves to limit confusion, especially when a common lexical trait is held between two words and/or in the presence of adversarial obfuscation. Through a large-scale publicly available Kaggle dataset of 651,191 URLs in imbalanced scenarios, the proposed Contra-KD can achieve 99.05% accuracy, 99.96% ROC-AUC, and 98.18% MCC which are superior to their counterparts including lightweight models and transformer-based ones. To summarize, Contra-KD proposes an efficient transformer architecture that is both small and effective in computation while delivering stable detection performance. Full article

This study aims to construct a nattokinase (NK) high-yielding strain using the multiple-scale breeding method. First, an NK-producing strain Bacillus subtilis A-1 was isolated from fermented soybean, which produces 254 FU/mL of NK. Subsequently, ARTP mutagenesis was employed to screen high-yield mutants with resistance to rifampicin (i.e., strain R-F7), kanamycin (i.e., strain K-E11), and gentamicin (i.e., strain G-D5), and the resulted strains showed NK activity increases of 113.78%, 76.38%, and 62.99%, respectively. Moreover, a fusion strain C-D7 with resistant to the above three antibiotics (i.e., rifampicin, kanamycin, and gentamicin) was obtained by protoplast fusion, which produced 610 FU/mL of NK and represents a 140.16% higher that of strain A-1. The fermenting property of strain C-D7 was also done in a 5-L bioreactor, and results indicated that strain C-D7 produced 1020 ± 35 FU/mL of NK under a two-stage pH control strategy and a two-step feeding strategy. To elucidate the genetic basis for the high-yield phenotype of C-D7. comparative whole-genome analysis was performed between C-D7 and the parental strain A-1. The results revealed that C-D7 harbors specific mutations across multiple functional categories, primarily in genes related to transcription, translation, global regulation, as well as metabolism and secretion. The biological processes affected by these mutations show a strong correlation with the high-yield trait, suggesting their potential collective role in contributing to the observed increase in nattokinase production. Lastly, ituD and srfAC were knocked out to reduce foam during fermentation, thus reducing the use of antifoaming agents and mitigating the negative effects on cell growth. In a word, a genetically stable, high-yield, and low-foaming Bacillus subtilis strain C-D7-ΔDouble was constructed in this study, which provides a core microbial resource and process foundation for the low-cost industrial production of nattokinase. Full article

Cyberbullying demonstrates notable metaphorical and contextual traits, characterized by a high-dimensional sparse semantic space and dynamic evolution. Pre-trained models utilize extensive textual data for learning and employ transformer-based word vector generation techniques to accurately capture intricate semantics and nuanced syntax in text. However, although a single pre-trained model demonstrates strong performance in contextual modeling, it still faces challenges including inadequate feature representation and limited generalization capability in classifying cyberbullying texts. This study proposes a cyberbullying detection model employing BERT-BiGRU-CNN (BBGC) to address this issue. The BBGC model initially employs BERT to produce word embeddings, subsequently inputs them into a BiGRU layer to acquire sequence features, and finally utilizes a CNN for the extraction of local features. The features derived from BERT, BiGRU, and CNN are integrated, followed by the application of the softmax function to yield the final outcome of cyberbullying detection. Experimental findings indicate that the BBGC fusion model surpasses individual pre-trained models in the task of detecting cyberbullying text. Furthermore, in comparison to hybrid neural network models utilizing RoBERTa, ALBERT, DistilBERT and other pre-trained models, the BBGC model demonstrates considerable advantages. Full article

This study examined how narcissistic traits influence women’s cognitive processing of verbal violence. Using a lexical decision task, an emotional Stroop task, and event-related potentials, we analyzed neural responses to violent versus neutral words in 70 women. Behaviorally, while narcissism showed no significant impact on performance in the Lexical Decision Task, a specific interference effect emerged in the emotional Stroop task, where higher narcissistic vulnerability predicted reduced accuracy for violent words relative to neutral ones. Notably, ERP results revealed a consistent pattern across both tasks: higher PNI total scores significantly predicted reduced amplitudes of early components, specifically the N170 and P2. Furthermore, in the emotional Stroop task, the vulnerability dimension emerged as a significant predictor of reduced EPN and P2 amplitudes. These findings suggest that when exposed to verbal violence, narcissistic women exhibit attenuated early evaluation and attentional allocation. This reflects a preemptive cognitive avoidance strategy used to protect the self-concept, driven primarily by a general narcissistic defensive pattern that manifests most acutely in vulnerable traits under high-interference conditions. Full article

Background/Objectives: This pilot study employed a quasi-experimental, single-group, pre-post design to examine the acute effects of single music lessons on executive function and to explore whether affective changes are associated with cognitive improvement in trained musicians. Drawing on Fredrickson’s broaden-and-build theory and Eysenck’s processing efficiency theory, we hypothesized that changes in positive affect and state anxiety would be statistically associated with cognitive outcomes. Methods: Using purposive sampling, 60 musicians (34 female, 26 male; Mage = 26.0, SD = 9.8; range: 16–58 years) completed assessments before and after a 45–60 min instrumental lesson (guitar, n = 20; violin, n = 20; piano, n = 20). Executive function was measured using the Stroop Color-Word Test (Golden version, Greek-validated). Affective states were assessed using the Positive and Negative Affect Schedule (PANAS; 20 items) and State-Trait Anxiety Inventory-State (STAI-S; 20 items). Data were analyzed using paired t-tests, Pearson correlations, path analysis, and bootstrap mediation analysis (5000 resamples). Results: Music lessons were associated with improved executive function (Stroop interference: d = 0.59, p < 0.001), increased positive affect (d = 1.87, p < 0.001), and reduced negative affect (d = −2.34, p < 0.001) and state anxiety (d = −2.64, p < 0.001). Path analysis demonstrated excellent model fit (CFI = 1.00; RMSEA = 0.00), with affective changes associated with 61.3% of the total effect on cognitive improvement. Conclusions: Single music lessons were associated with both cognitive and affective benefits, with affective changes statistically linked to cognitive outcomes. As a pilot study, these exploratory findings require replication using controlled designs before generalization. Future research should incorporate neuroimaging methods and cross-cultural validation. Full article

The dissociation between emotion-label and emotion-laden words has been investigated in both behavioral and electrophysiological studies. However, how individual differences modulates the processing of emotional words has not been fully explored. Trait anxiety, as an important individual difference variable, plays a vital role in emotion processing, and may influence the processing of emotional words. To reveal the effects of trait anxiety and emotional word type on the processing of Chinese words, the present study adopted a lexical decision task (LDT) and event-related potential (ERP) technique to collect the behavioral and electrophysiological data from high-trait-anxious (HTA), medium-trait-anxious (MTA) and low-trait-anxious (LTA) individuals. Behaviorally, participants demonstrated longer reaction times (RTs) and lower accuracy (ACC) when processing emotion-laden words, as opposed to emotion-label words and neutral words. Electrophysiologically, both emotion-label and emotion-laden words induced enhanced N170 amplitudes relative to neutral ones. Compared with neutral words, emotion-laden words elicited larger early posterior negativity (EPN) amplitudes in the right hemisphere and increased late positive component (LPC) amplitudes, whereas emotion-label words elicited a stronger N400. EPN amplitudes were modulated by the interaction between trait anxiety and emotional word type. In HTA individuals, emotion-laden words evoked a larger EPN than emotion-label and neutral words, supporting the mediated emotion concept account, density hypothesis, and embodiment emotion account. During the late elaborative processing stage, LTA participants exhibited larger LPC amplitudes than HTA individuals, which aligns with the “vigilance-avoidance” pattern. Full article

Recommendation systems are popular information systems that help consumers manage information overload. Whilst personality has been recognised as an important factor influencing consumers’ choice, it has not yet been fully exploited in recommendation systems. This study proposes a restaurant recommendation approach that integrates customer personality traits, opinions and preferences, extracted either directly from online review platforms or derived from electronic word of mouth (eWOM) text using information extraction techniques. The proposed method leverages the concept of venue personality grounded in personality–brand congruence theory, which posits that customers are more satisfied with brands whose personalities align with their own. A novel model is introduced that combines fine-tuned BERT embeddings with linguistic features to infer users’ personality traits from the text of their reviews. Customers’ preferences are identified using a custom named-entity recogniser, while their opinions are extracted through structural topic modelling. The overall framework integrates neural collaborative filtering (NCF) features with both directly observed and derived information from eWOM to train an extreme gradient boosting (XGBoost) regression model. The proposed approach is compared to baseline collaborative filtering methods and state-of-the-art neural network techniques commonly used in industry. Results across multiple performance metrics demonstrate that incorporating personality, preferences and opinions significantly improves recommendation performance. Full article

This study reports findings from a nationwide replication and the second randomized controlled trial (RCT) of Strategic and Interactive Writing Instruction (SIWI), a linguistically responsive framework for teaching writing to deaf students. A total of 50 teachers and their 294 students in grades 3–6 were randomly assigned to either SIWI or business-as-usual (BAU) instruction. Writing outcomes were assessed with trait-based rubrics and the Structured Analysis of Written Language (SAWL) in two genres (recount and information report), along with the Woodcock–Johnson IV broad written language composite and genre-specific motivation surveys administered at the beginning and end of the school year. Students receiving SIWI outperformed peers in the BAU group on writing traits across both genres, with effect sizes ranging from moderately large (d = 0.70) for informational reports to very large (d = 1.11) for recounts. On the SAWL, SIWI students demonstrated significantly greater gains in grammatical clarity on recount writing, as measured by the word efficiency ratio, with a moderate effect size (d = 0.64), although this effect was not observed for information reports. Students in the treatment group also reported significantly higher motivation for both genres. Unlike the prior RCT, no statistically significant differences emerged on the broad written language measure (d = 0.27). This may reflect spurious findings in the previous study or limitations in this study caused by the COVID-19 pandemic. Nonetheless, the effect size observed suggests some practical importance that warrants further investigation. Findings provide robust evidence that SIWI positively impacts deaf elementary students’ writing development and motivation, particularly for recount genres, while underscoring the importance of replication for understanding the generalizability of intervention effects. Full article

Children with Down syndrome (DS) show considerable variability in social-communication and cognitive profiles, and a subset meet criteria for co-occurring autism. In the present study, we examined the associations between developmental domains and autistic trait severity in toddlers with DS. Participants included 38 toddlers (M = 4.19 years, SD = 0.99) who completed a home-based assessment, including measures of language, fine motor, and visual reception skills. Caregivers also completed standardized questionnaires on communication and executive functioning. Multiple regression analyses tested the degree of association between these developmental domains and autistic traits. Fewer words produced fewer gestures, and more impaired fine motor and visual reception scores were significantly associated with higher autism trait severity, whereas executive function domains were not significantly associated. Preliminary findings indicate that variability in language and nonverbal developmental skills contributes to the expression of autism traits in DS, underscoring the need for early, multidomain assessment approaches to support accurate identification and tailored intervention. Full article

The ability of large language models to recall human emotions provides a novel opportunity to investigate links among memory, affect, and mental health. This study explores whether the Emotional Recall Task (ERT), a free word-association paradigm, can reveal cognitive markers of distress in both humans and large language models (LLMs). Using spreading activation simulations grounded in cognitive network science, we examined how the recall of emotional concepts (e.g., stress, anxiety, and depression) relates to psychometric measures of well-being and personality. In Study 1, correlations were tested between activation dynamics and clinical scales (DASS-21, PANAS, and Life Satisfaction) in human participants (N = 1200) and artificial participants generated by GPT-4, Claude Haiku, and Anthropic Opus. For both human and LLM samples, spreading activation was modeled from participants’ ERT words within a human-derived semantic network, enabling a direct comparison of structural activation dynamics rather than psychological states. Humans with higher distress scores exhibited stronger, faster, and more persistent activation of negative concepts, supporting theories of rumination and memory bias. GPT-4 approximated human-like trajectories most closely, though with reduced variability. Study 2 linked recall dynamics with the Big Five traits, confirming that neuroticism predicted greater activation of negative concepts, while extraversion acted as a protective factor. While LLMs lack autobiographical memory, their semantic activation partially mirrored human associations. These findings demonstrate that network-based spreading activation analysis can reveal cognitive signatures of distress while also highlighting the limits of LLMs in modeling human affect. Full article

Electronic word of mouth (eWOM) is a powerful form of online communication that strongly influences consumer purchasing behaviour. However, what remains less clear is the combined influence of situational factors versus personality traits when assessed simultaneously. The aim of this paper is to address this gap by developing an integrative conceptual model to assess the comparative relevance of situational factors and personality traits in driving eWOM generation in the Romanian accommodation sector. To implement the research scope, an empirical, quantitative, questionnaire-based investigation was pursued, data being collected from 291 tourists who had previous experience with online accommodation platforms such as booking.com, Airbnb, Trivago, etc. Based on the proposed conceptual model, data were analysed by means of structural equation modelling via SmartPLS 4.0. The research extends previous knowledge based on the Theory of Reasoned Action (TRA) and the Theory of Cognitive Dissonance (TCD), showing the combined multiple effects of situational factors and personality traits on consumers’ behaviour in generating eWOM in the accommodation sector. The results show that acquisition regret strongly drives eWOM generation intention, this regret being significantly increased by the unpleasantness, unacceptability, and importance of the consumer’s situation. Consumer expressivity predicts eWOM generation and is positively influenced by perceived social support, a relationship newly validated in the literature. Full article

Background: With the global population rapidly aging, resilience has emerged as a critical determinant of healthy aging. While many factors are associated with resilience, a comprehensive synthesis is needed to inform targeted interventions and policy. Objectives: This systematic review aimed to identify and synthesize the conceptual models and key protective factors associated with resilience in older people. Methods: Following PRISMA guidelines, a systematic literature search was conducted in Web of Science, PubMed, PsycNet, and JSTOR for studies published between 2017 and 2025. Search terms included (including synonyms and closely related words) “resilience,” “older people,” and “models.” Studies were screened based on relevance to resilience models, measurement tools, and associated factors. Included studies underwent a formal risk of bias assessment. Results: From 7109 initial records, 54 studies met the inclusion criteria. Ten studies explored conceptual models, while 44 investigated contributing factors. Resilience was predominantly assessed using standardized psychometric tools. Findings were synthesized by mapping key determinants across Macro-Environmental, Meso-Social, Micro-Individual and Bio-Physiological domains. Conclusions: Resilience in later life is a dynamic and multifactorial process, not a fixed trait. The evidence suggests a range of modifiable factors at various levels that can be targeted to support wellbeing. An integrated, systems-based perspective is essential for guiding future research and developing effective interventions to promote resilience across the aging trajectory. Full article

Background: Previous research has found an association between attractiveness and personality traits, but the neural mechanisms are largely unknown. Method: We used a Stroop-like paradigm combined with EEG recordings to investigate how personality traits affect the perception of facial and vocal attractiveness. Twenty-three female participants classified the attractiveness of male faces and male voices paired with positive or negative personality trait words. Results: The behavioral results indicate that personality trait words that are semantically congruent with attractiveness levels facilitate the perception of attractiveness, whereas incongruent trait information produces the opposite effect. Event-related potentials revealed that the influence of personality trait words on facial attractiveness was primarily related to motivated attention as indicated by the late positive component. In the voice task, personality trait words impacted vocal attractiveness processing first during semantic integration (N400 component) and then modulated motivated attention. Conclusions: These results suggest that alleged personality traits modify attractiveness processing in faces and voices in relatively late and partially modality-specific stages. Full article

Morningness-eveningness is usually assessed as either a trait or a state using either a morning–evening preference scale or sleep timing reported for free days, respectively. These assessments were implemented in numerous studies exploring the associations between morningness-eveningness and health, mood, and sleep problems. Evening types almost always had more problems than morning types. We examined these associations in university students with conflicting results of trait and state assessments of morningness-eveningness and tried to confirm their chronotype using a multidimensional chronotyping approach that recognizes four types other than morning and evening (lethargic, vigilant, napping, and afternoon). The conflicting trait and state assessments of morningness-eveningness were found in 141 of 1582 students. Multidimensional chronotyping supported morningness of morning types with late weekend sleep timing, and the associations with health, mood, and sleep problems resembled the associations of other morning types (i.e., these associations persisted despite late sleep timing). In contrast, evening types with early weekend sleep timing were more likely classified as lethargic or napping types rather than evening types. They did not resemble evening types in their associations with health, mood, and sleep problems (i.e., early sleep timing did not change these associations). Model-based simulations of the sleep–wake cycles of students with conflicting trait and state assessments suggested that their bedtimes cannot be solely determined by their biological clocks. On weekdays or weekends, mind-bedtime procrastination can lead to missing the bedtime signal from their biological clocks (i.e., self-deprivation of sleep or, in other words, voluntary prolongation of the wake phase of the sleep–wake cycle). Full article