Search Results (492)

Irony and sarcasm are forms of expression that emphasize the inconsistency between what is said and what is meant. Correctly classifying such expressions is an important text mining problem, especially on user-centered platforms such as social media. Due to the increasing prevalence of implicit expressions, this topic has become a significant area of research in Natural Language Processing (NLP). However, the simultaneous detection of ironic and sarcastic expressions is highly challenging, as both types of implicit sentiments often convey closely related meanings. To address the detection of irony and sarcasm, this study compares the performance of transformer-based models and an ensemble learning method on Turkish texts, using five textual datasets—monogram, bigram, trigram, quadrigram, and omnigram—that share the same textual content but differ in context length. To improve classification performance, an ensemble learning approach based on the Artificial Rabbit Optimization (ARO) algorithm was implemented, combining the outputs of the models to produce final predictions. The experimental results indicate that as the context width of the datasets increases, the models achieve better predictions, leading to improvements across all performance metrics. The ensemble learning method outperformed individual models in all metrics, with performance increasing as the context expanded, achieving the highest success in the omnigram dataset with 76.71% accuracy, 74.64% precision, 73.29% sensitivity, and 73.96% F-Score. This study demonstrates that both model architecture and data structure are decisive factors in text classification performance, showing that community methods can make significant contributions to the effectiveness of deep learning solutions in low-resource languages. Full article

This paper introduces a hybrid two-stage implicit scheme for efficiently solving fractional differential equations, with particular emphasis on fractional initial value problems formulated using the Caputo derivative. Classical numerical approaches to fractional differential equations often encounter challenges related to stability, convergence rate, and memory efficiency. To overcome these limitations, we propose a new discretization framework that directly embeds nonlinear source terms into the time-stepping process, thereby enhancing both stability and accuracy. Our method embeds nonlinear source terms directly into the time-stepping process, enhancing stability and accuracy. Nonlinear systems are efficiently solved using a parallel iterative algorithm with adaptive convergence control, yielding up to 35–50% faster convergence compared with conventional solvers. A rigorous theoretical analysis establishes the scheme’s convergence, stability, and consistency, extending earlier proofs to a broader class of fractional systems. Extensive numerical experiments on benchmark fractional problems confirm that the hybrid approach achieves markedly lower local and global errors, broader stability regions, and substantial reductions in computational time and memory usage compared with existing implicit methods. The results demonstrate that the proposed framework offers a robust, accurate, and scalable solution for nonlinear fractional simulations. Full article

Background/Objectives: Implicit theories of thoughts, emotions, and behavior (TEB) describe beliefs that these attributes are either changeable (growth mindset) or unchangeable (fixed mindset). While the impact of mindsets on negative mental health indicators, such as psychopathological symptoms, is well-documented, their relations with positive indicators such as life satisfaction, particularly in the context of stress, remain underexplored. This study aimed to address this gap by testing whether the association between adolescents’ implicit theories of TEB and life satisfaction is mediated by cognitive reappraisal and whether stressful life events moderated two paths within the mediation model. Methods: Participants were 620 high school students (49.5% female, 43.5% male, 5.8% gender-nonconforming, 1.1% undisclosed) aged 14 to 19 years (M = 17.51, SD = 1.23), who completed an online survey in Spring 2022, while the COVID-19 pandemic still significantly affected daily life. Mediation and moderated mediation models were tested using PROCESS macro in SPSS. Results: Mediation analysis revealed that growth mindset positively influenced life satisfaction both directly and indirectly through cognitive reappraisal. Stressful life events significantly moderated the direct effect of growth mindset on life satisfaction, with the positive direct effect diminishing as stress increased. Conclusions: The positive link between growth mindset and life satisfaction was strongest under lower stress and transmitted through cognitive reappraisal across stress levels. Given the cross-sectional design, findings should be interpreted as correlational, not causal. Future longitudinal research should clarify temporal directionality and reciprocal links among mindset, coping, and well-being to inform interventions that strengthen adaptive beliefs and regulation skills. Full article

When the Colebrook equation is used in its original implicit form, the unknown pipe flow friction factor can only be obtained through time-consuming and computationally demanding iterative calculations. The empirical Colebrook equation relates the unknown Darcy friction factor to a known Reynolds number and a known relative roughness of a pipe’s inner surface. It is widely used in engineering. To simplify computations, a variety of explicit approximations have been developed, the accuracy of which must be carefully evaluated. For this purpose, this Data Descriptor gives a sufficient number of pipe flow friction factor values that are computed using a highly accurate iterative algorithm to solve the implicit Colebrook equation. These values serve as reference data, spanning the range relevant to engineering applications, and provide benchmarks for evaluating the accuracy of the approximations. The sampling points within the datasets are distributed in a way that minimizes gaps in the data. In this study, a Python Version v1 script was used to generate quasi-random samples, including Halton, Hammersley, Sobol, and deterministic lattice-based Korobov samples, which produce smaller gaps than purely random samples generated for comparison purposes. Using these sequences, a total of 2²⁰ = 1,048,576 data points were generated, and the corresponding datasets are provided in in the zenodo repositoryWhen a smaller subset of points is needed, the required number of initial points from these sequences can be used directly. Full article

This study addresses the joint optimization of full-length and short-turning operations for the Nanchang Metro Airport Line, aiming to balance operational efficiency and passenger service quality. A novel mathematical model is proposed, which integrates train schedule design, capacity allocation, and passenger flow assignment into a linear programming framework. The model features three key innovations: (1) precise calculation of passenger waiting times under strict capacity constraints by incorporating dynamic passenger flow distribution and train occupancy thresholds; (2) implicit treatment of train numbers as decision variables, enabling flexible adjustments to service frequency based on time-varying demand patterns; and (3) a linear formulation for direct optimal solution computation, avoiding the complexity of nonlinear constraints through variable substitution and constraint relaxation. The model is validated through a case study of the Nanchang Metro Line 1 (Airport Line), where passenger demand is derived from historical data and flight schedules. Numerical experiments demonstrate that the optimized strategy reduces the number of full-length trains by 53%, achieves a 22% power cost saving, and decreases the waiting time for all passengers by 3.4%. The relevant findings and recommendations can offer valuable guidance to metro companies in making operational decisions related to the full-length and short-turning service plans and schedules. Full article

Background: Immersive Virtual Reality (IVR) is gaining increasing relevance in the field of mental health as a tool for therapeutic simulation and embodied experience. However, most existing VR applications are grounded in cognitive–behavioral frameworks, leaving unexplored the integration of symbolic, intersubjective, and unconscious dimensions. Psychoanalysis—particularly its constructs of setting, rêverie, and transference—offers a unique epistemological basis for designing therapeutic environments that engage implicit emotional processes. Aim: This paper aims to develop a conceptual framework for modeling IVR-based therapeutic settings inspired by psychoanalytic theory and enhanced through IoT-enabled biosensing technologies. Methods/Approach: We propose a three-layer architecture: (1) a somatic layer involving IoT-based real-time physiological monitoring (e.g., heart rate variability, galvanic skin response, eye-tracking, EEG); (2) a symbolic-narrative layer where the VR environment dynamically adapts to the user’s affective state through immersive visual and auditory stimuli; and (3) a relational layer where AI-driven avatars simulate transferential dynamics. The model is theoretically grounded in psychoanalytic literature and informed by current advances in affective computing and bioengineering. Conclusions: By bridging psychoanalytic metapsychology and bioengineering design, this framework proposes a novel approach to therapeutic IVR systems that move beyond explicit cognition to engage the embodied unconscious. The integration of IoT biosignals enables the mapping and modulation of internal states within a structured symbolic space, opening new pathways for the clinical application of digital psychoanalysis. Full article

The natural frequencies and damping characterisation of a new aerospace grade composite material were investigated using a modified impulse method combined with the half power bandwidth method, which is applicable to the structures with a low damping. The composite material of interest was unidirectional carbon fibre reinforced plastic. The tests were carried out with three identical square 4.6 mm thick plates consisting of 24 plies. The composite plates were clamped along one edge in a SignalForce shaker, which applied a sinusoidal signal generated by the signal conditioner exiting the bending modes of the plates. Laser vibrometer measurements were taken at three points on the free end so that different vibrational modes could be obtained: one measurement was taken on the longitudinal symmetry plane with the other two 35 mm on either side of the symmetry plane. The acceleration of the clamp was also recorded and integrated twice to calculate its displacement, which was then subtracted from the free end displacement. Two material orientations were tested, and the first four natural frequencies were obtained in the test. Damping was determined by the half-power bandwidth method. A linear relationship between the loss factors and frequency was observed for the first two modes but not for the other two modes, which may be related to the coupling of the modes of the plate and the shaker. The experiment was also modelled by using the Finite Element Method (FEM) and implicit solver of LS Dyna, where the simulation results for the first two modes were within 15% of the experimental results. The novelty of this paper lies in the presentation of new experimental data for the natural frequencies and damping coefficients of a newly developed composite material intended for the vibration analysis of rotating components. Full article

The present study aims to contribute to a better understanding of the attitude–behavior link in the sphere of environmental issues by taking into account the role of moral disengagement. Pro-environmental attitudes, at both the implicit and explicit levels, were considered under the hypothesis that they may have direct and indirect effects on pro-environmental behaviors (PEBs) through moral disengagement. The hypothesized relationships specified in the mediation model were tested by administering a cross-sectional online survey to a convenience sample of adult students enrolled in a digital university (N = 176; M_age = 40.54, SD_age = 14) via Millisecond Inquisit Web. The assessment included instruments measuring environmental moral disengagement and explicit attitudes toward the adoption of PEBs, together with an ad hoc Implicit Association Test designed to capture implicit attitudes toward sustainability, and the use of a pro-environmental behavior rating scale. While the sensitivity to model misfit was limited given the achieved sample size, the results from the path analysis show that implicit attitudes do not have a direct effect on PEBs, while explicit attitudes directly influence them. Moreover, as positive explicit and implicit pro-environmental and sustainability attitudes increase, moral disengagement decreases, which in turn negatively affects PEBs. Overall, the present findings confirm that moral disengagement plays a mediating role, and that attitudes can be targets for potential interventions aimed at promoting pro-environmental behaviors and addressing justificatory mechanisms that hinder their adoption. Full article

This paper explores the implicit thought of the “body-network” in Maurice Merleau-Ponty’s phenomenology of the body, drawing from both his earlier and later works. It demonstrates that, for Merleau-Ponty, the phenomenal body is inherently interconnected with the world through motor intentionality. Meanwhile, in his later concept of “flesh,” this interconnectedness deepens into a relationship of mutual reflection and chiasmic intertwining, where bodies and the world continuously mirror and permeate each other. The paper then introduces the Huayan Buddhist metaphor of Indra’s Net, along with Fazang’s interpretation of it. A detailed comparative analysis is conducted between Merleau-Pontian body-network and Fazang’s understanding of Indra’s Net. The paper argues for a profound resonance between the primordial characteristics of the Merleau-Pontian body-network—namely, relationality and reflectivity—and Fazang’s key concepts, such as “mutual identity” (相即), “mutual inclusion” (相入), and the contemplative idea that “the images of many bodies are reflected in one mirror” (多身入一鏡像觀). Despite their distinct cultural and philosophical vocabularies, both thinkers construct a relational ontology aimed at deconstructing entrenched dualisms. Through this in-depth comparative study using the Internet of Bodies (IoB) as a case study, this paper demonstrates that the IoB technology exhibits only superficial resemblances to the Merleau-Pontian body-network and Fazang’s interpretation of Indra’s Net. To address the ethical challenges posed by the IoB, it is imperative to integrate the shared philosophical insights of Merleau-Ponty and Fazang in constructing a critical techno-ethics capable of interrogating the ontological reduction and power asymmetries inherent in contemporary technological networks. Merleau-Ponty’s concept of reversible flesh inspires an ethics of contextual sensitivity and user agency, resisting the reduction of lived experience to data points. Meanwhile, Fazang’s Huayan Buddhism, with its principles of mutual identity and mutual inclusion, reveals the relational nature of data, challenging its treatment as neutral or absolute. Together, these philosophies advocate for a decentralized, reciprocal techno-ethics that prioritizes embodied meaning over surveillance and control. Full article

Stance detection identifies the attitude or stance toward specific targets and has a wide range of applications across various domains. Implicit mention of the target makes it difficult to establish connections between the target and the text. Existing approaches focus on integrating external knowledge but overlook the complex associations and stance information within it, which is crucial for maintaining reasoning consistency. To solve this problem, we propose a logical stance detection framework based on the principle of stance transitivity. Our framework achieves stance reasoning by leveraging symbolic and natural language reasoning. Specifically, we extract a list of targets related to the specific target from unlabeled data and use LLMs to construct a target-stance graph. This allows us to examine complicated interactions between targets and integrate stance information across related targets. We conducted massive experiments to validate the effectiveness of our proposed method. The experimental results indicate that our framework significantly improves the performance of stance detection tasks, offering a robust solution to the challenges posed by implicit targets. Full article

Objectives: To investigate family-related factors influencing alcohol use from the perspective of nursing students. Methods: A qualitative approach grounded in the Oral History method was employed. Data were collected through interviews with nursing students from a public higher education institution located in the Amazon. Thematic analysis was conducted, supported by a theoretical framework relevant to alcohol consumption. Results: Four thematic categories emerged: (1) family inhibition toward alcohol use, (2) implicit prohibition of alcohol within the household, (3) financial dependence on family, and (4) responsibilities associated with adulthood. Conclusions: The study highlights the protective role of family structure in shaping young adults’ attitudes toward alcohol. These findings can inform university-level interventions, including: Awareness and education campaigns, Prevention programs, Psychological and psychiatric support services, and Partnerships with local communities. Full article

Aspect-based sentiment quadruple prediction has important application value in the current information age. There are often implicit expressions and multi-level semantic relationships in sentences, making accurate prediction for existing methods still a complex and challenging task. To address the above problems, this paper proposes the Progressive Prompt-Driven Generative Graph Convolutional Network for Aspect-Based Sentiment Quadruple Prediction (ProPGCN). Firstly, a progressive prompt module is proposed. The module uses progressive prompt templates to generate paradigm expressions of corresponding orders and introduces third-order element prompt templates to associate high-order semantics in sentences, providing a bridge for modeling the final global semantics. Secondly, a graph convolutional relation-enhanced reasoning module is designed, which can make full use of contextual dependency information to enhance the recognition of implicit aspects and implicit opinions. In addition, a graph convolutional aggregation strategy is constructed. The strategy uses graph convolutional networks to aggregate adjacent node information and correct conflicting implicit logical relationships. Finally, experimental results show that the ProPGCN model can achieve state-of-the-art performance. Specifically, our ProPGCN model achieves overall F1 scores of 65.04% and 47.89% on the Restaurant and Laptop datasets, respectively, which represent improvements of +0.83% and +0.61% over the previous strongest generative baseline. Full article

This study examined how age and gender shape perceptions of suitability for educational roles. Participants were clustered into three groups based on their age. They completed an online study comprising five tasks: (1) Role Suitability Ratings with 7 point Likert scales for images of individuals varying in age and gender; (2) Role Preparation Ratings, indicating perceived preparation for each educational role measured on a 7 point scale; (3) the Gender Role Beliefs Questionnaire and (4) the Image of Aging Questionnaire, assessing explicit age and gender related biases; and (5) Face Age Estimation, assessing perceived age of the depicted individuals, i.e., the stimuli employed. Results showed that younger women are considered to be more suitable for Early Years and Primary school teaching roles, whereas older individuals and men were seen as more suitable for Secondary and University roles. Older adults, particularly women, were rated less favourably across all roles. Explicit gender beliefs aligned with implicit biases, manifesting in the undervaluation of young women for higher education roles. Female participants also showed bias against women, suggesting internalised stereotypes. These findings highlight the persistence of age- and gender-related stereotypes in perceptions of suitability for educational roles in the UK. Full article

A simplified kinetic model for the quantitative analysis of a potentiometric, pH-based urea biosensor is presented. The device was an electrolyte–insulator–semiconductor capacitor (EISCAP) with a pH-sensitive Ta₂O₅ gate functionalized by a polyallylamine hydrochloride (PAH)/urease bilayer. Within the steady-state approximation, the kinetic equations yielded an implicit algebraic relation linking the bulk urea concentration to the local pH at the sensor surface. Numerical solution of this equation, combined with a fitting routine, provides the apparent Michaelis–Menten constant ($K_M$) and the normalized maximum reaction rate (${\overline{k}}_V$). Validation against the literature data confirmed the reliability of the approach. Experimental results were then analyzed in both phosphate buffer (PBS) and artificial urine (AU), covering urea concentrations of 0.1–50 mM. The fitted parameters showed comparable $K_M$ values of 10.9 mM (PBS) and 32.4 mM (AU), but strongly different ${\overline{k}}_V$ values: $2.2\times {10}^{-4}$ (PBS) versus $8.6\times {10}^{-7}$ (AU). The three-order reduction in AU was attributed to the inhibitory effects inherent to complex biological fluids. These findings highlight the importance of the model-based quantitative analysis of EISCAP biosensors, enabling the accurate characterization of immobilized enzyme layers and guiding optimization for applications in realistic sample matrices. Full article

This study investigates the public health consequences of media exposure to terrorism by examining individuals’ emotional responses to photographs from the October 7th terror attack, assessing how such imagery interacts with personality traits to influence emotional states. The research aims to explore how these reactions are moderated by personality traits—specifically the Big Five. A diverse sample comprising Israeli Jews (final sample N = 826) viewed media-sourced images categorized as explicit negative (n = 279; e.g., photos of bodies or deceased individuals), implicit negative (n = 269; images depicting destruction and devastation without explicit death symbols), and positive (n = 278; images of reconstruction and renewal). Participants’ affective states and specific emotions were assessed both before and after exposure to capture potential shifts. Results revealed a significant increase in negative emotions and a corresponding decrease in positive emotions following exposure to negative images. Personality traits moderated these emotional responses in nuanced ways. Neuroticism exacerbated negative emotional reactions, particularly among men exposed to implicit negative imagery, likely reflecting heightened sensitivity to ambiguous threats. Similarly, agreeableness was associated with heightened anger responses—specifically among men exposed to implicit negative imagery and women exposed to explicit negative images—although this effect was limited to anger and did not extend to other negative emotions. In contrast, openness was linked to decreased anger but only for men exposed to implicit negative imagery. Together, these findings underscore the complex interplay between media exposure, personality traits, and emotional responses to terror-related content. From a public health perspective, the results highlight the need for the following: (a) targeted mental health interventions that account for personality-based vulnerabilities, (b) responsible media reporting practices that minimize unnecessary harm, and (c) media literacy initiatives that empower individuals to manage exposure to distressing content. By linking personality, media imagery, and emotional outcomes, this study provides actionable insights for strengthening resilience, guiding ethical media practices, and promoting psychological well-being in communities affected by terrorism. Full article