Search Results (138)

In the Hexaflex model of Acceptance and Commitment Therapy (ACT), psychological flexibility refers to the ability to openly embrace difficult thoughts and emotions while acting in alignment with personal values. In contrast, psychological inflexibility involves rigid avoidance and control strategies that hinder adaptive functioning. Although previously studied, more culturally relevant evidence is needed to inform interventions that promote well-being and mental health among Latin American students. This study explored manifestations of psychological flexibility and inflexibility in 15 undergraduate students from the University of Magdalena in Colombia (mean age = 20.13 years; 53.33% female) through semi-structured, face-to-face interviews (~45 min each). Data were analyzed using Interpretative Phenomenological Analysis (IPA), focusing on how participants described and made sense of their experiences. A total of 25 emergent themes were identified and grouped into 12 subordinate themes, mapped onto the 6 core ACT processes. The participants reported efforts to control or avoid distressing internal experiences, often resulting in difficulty acting in accordance with their values. The findings highlight a recurring ambivalence between avoidance and acceptance, and barriers to committed action, underscoring the dynamic interplay between flexibility and inflexibility. These results support the relevance of ACT-based interventions, such as structured group sessions that foster acceptance, mindfulness, and values-based behavior. Integrating this training into counseling and academic support services could enhance students’ well-being and performance. Future research should examine these dynamics longitudinally and across diverse contexts. Full article

Despite notable advancements in smart home technologies, residential energy management continues to face critical challenges. These include the complex integration of intermittent renewable energy sources, issues related to data latency, interoperability, and standardization across diverse systems, the inflexibility of centralized control architectures in dynamic environments, and the difficulty of accurately modeling and influencing occupant behavior. To address these challenges, this study proposes an intelligent multi-agent system designed to accurately estimate and control energy consumption in residential buildings, with the overarching objective of optimizing energy usage while maintaining occupant comfort and satisfaction. The methodological approach employed is a hybrid framework, integrating multi-agent system architecture with system dynamics modeling and agent-based modeling. This integration enables decentralized and intelligent control while simultaneously simulating physical processes such as heat exchange, insulation performance, and energy consumption, alongside behavioral interactions and real-time adaptive responses. The system is tested under varying conditions, including changes in building insulation quality and external temperature profiles, to assess its capability for accurate control and estimation of energy use. The proposed tool offers significant added value by supporting real-time responsiveness, behavioral adaptability, and decentralized coordination. It serves as a risk-free simulation platform to test energy-saving strategies, evaluate cost-effective insulation configurations, and fine-tune thermostat settings without incurring additional cost or real-world disruption. The high fidelity and predictive accuracy of the system have important implications for policymakers, building designers, and homeowners, offering a practical foundation for informed decision making and the promotion of sustainable residential energy practices. Full article

Background/Objectives: Chronic Pelvic Pain (CPP) is a multifactorial condition that affects in many ways the daily life of patients suffering from it. Different psychological factors demonstrated to be associated with the genesis and maintenance of CPP. Less is known about the role of the Psychological Flexibility (PF) model. Thus, the aim of this study is to explore the relationship between the PF domains, psychological distress, pain, and quality of life in patients with chronic pelvic pain. Methods: A total of 114 women with a diagnosis of chronic pelvic pain were included in this study. Participants completed online self-report measures to assess psychological distress (anxiety, depression, stress), Psychological Flexibility, Pain interference, and Quality of life. Results: Psychological distress and Psychological Flexibility showed significant association with pain interference. Other PF dimensions related to pain interference were as follows: self as context, defusion, and values. Physical Quality of life showed significant association with Experiential avoidance and Lack of values clarity, while Mental Quality of life was associated with Psychological Inflexibility and Self as content. Conclusions: Psychological distress and Psychological Flexibility have a role in pain perception and its interference with a patient’s daily life, affecting also physical and mental quality of life of CPP patients. Full article

The rapid spread of artificial intelligence (AI) in education means that students need to master both AI literacy and personal agency. This study situates a sample of 425 Slovenian secondary technical students within a three-tier framework that maps psychological empowerment onto AI literacy outcomes within a cultural–historical activity system. The agency competence assessments yielded four profiles of student agency, ranging from fully empowered to largely disempowered. The cluster membership explained significant additional variance in AI literacy scores, supporting the additive empowerment model in an AI-rich vocational education and training context. The predictive modeling revealed that while self-efficacy, mastery-oriented motivations, and metacognitive self-regulation contributed uniquely—though small—to improving AI literacy, an unexpectedly negative relationship was identified for internal locus of control and for behavioral self-regulation focused narrowly on routines, with no significant impact observed for grit-like perseverance. These findings underscore the importance of fostering reflective, mastery-based, and self-evaluative learning dispositions over inflexible or solely routine-driven strategies in the development of AI literacy. Addressing these nuanced determinants may also be vital in narrowing AI literacy gaps observed between diverse disciplinary cohorts, as supported by recent multi-dimensional literacy frameworks and disciplinary pathway analyses. Embedding autonomy-supportive, mastery-oriented, student-centered projects and explicit metacognitive training into AI curricula could shift control inward and benefit students with low skills, helping to forge an agency-driven pathway to higher levels of AI literacy among high school students. The most striking and unexpected finding of this study is that students with a strong sense of competence—manifested as high self-efficacy—can achieve foundational AI literacy levels equivalent to those possessing broader, more holistic agentic profiles, suggesting that competence alone may be sufficient for acquiring essential AI knowledge. This challenges prevailing models that emphasize a multidimensional approach to agency and has significant implications for designing targeted interventions and curricula to rapidly build AI literacy in diverse learner populations. Full article

Background/Objectives: The COVID-19 pandemic has placed intensive care unit (ICU) nurses under intense psychological pressure, increasing emotional and psychological stress. Two constructs—difficulties in emotion regulation and psychological inflexibility (i.e., low contact with the present moment and a lack of committed action based on personal values)—have been associated with increased perceived stress levels but remain underexplored in this population. Aims: This study investigated whether psychological inflexibility mediates the relationship between emotion regulation difficulties and perceived stress in ICU nurses. It also examined whether years of ICU work experience moderate the direct relationship between emotion regulation difficulties and perceived stress. Methods: A cross-sectional study was conducted with 210 ICU nurses (65.2% women; 34.8% men; mean age = 40.25 years ± 11.36) from Italian public hospitals. The participants completed the Difficulties in Emotion Regulation Scale, the Acceptance and Action Questionnaire-II, and the Perceived Stress Scale. A moderated mediation model was tested to examine whether psychological inflexibility mediates the relationship between emotion regulation difficulties and perceived stress and whether years of ICU work experience moderate the path between these variables. Results: Higher difficulties in emotion regulation predicted greater psychological inflexibility, which, in turn, predicted higher perceived stress. Psychological inflexibility fully mediated the relationship between emotion regulation difficulties and perceived stress. Additionally, years of ICU work experience significantly moderated the direct link between emotion regulation difficulties and perceived stress. This relationship was strongest for nurses with 1–15 years of ICU experience. The model explained 33% of the variance in perceived stress. Conclusions: This study highlights the importance of the novel construct of psychological inflexibility in the context of healthcare professionals and its role in shaping perceived stress. Addressing psychological inflexibility through targeted interventions may help mitigate stress and promote well-being among ICU nurses. Full article

Probability models are instrumental in a wide range of applications by being able to accurately model real-world data. Over time, numerous probability models have been developed and applied in practical scenarios. This study introduces the AAP-X family of distributions—a novel, flexible framework for continuous data analysis named after authors Aadil Ajaz and Parvaiz. The proposed family effectively accommodates both symmetric and asymmetric characteristics through its shape-controlling parameter, an essential feature for capturing diverse data patterns. A specific subclass of this family, termed the “AAP Exponential” (AAPEx) model is designed to address the inflexibility of classical exponential distributions by accommodating versatile hazard rate patterns, including increasing, decreasing and bathtub-shaped patterns. Several fundamental mathematical characteristics of the introduced family are derived. The model parameters are estimated using six frequentist estimation approaches, including maximum likelihood, Cramer–von Mises, maximum product of spacing, ordinary least squares, weighted least squares and Anderson–Darling estimation. Monte Carlo simulations demonstrate the finite-sample performance of these estimators, revealing that maximum likelihood estimation and maximum product of spacing estimation exhibit superior accuracy, with bias and mean squared error decreasing systematically as the sample sizes increases. The practical utility and symmetric–asymmetric adaptability of the AAPEx model are validated through five real-world applications, with special emphasis on cancer survival times, COVID-19 mortality rates and reliability data. The findings indicate that the AAPEx model outperforms established competitors based on goodness-of-fit metrics such as the Akaike Information Criteria (AIC), Schwartz Information Criteria (SIC), Akaike Information Criteria Corrected (AICC), Hannan–Quinn Information Criteria (HQIC), Anderson–Darling (A*) test statistic, Cramer–von Mises (W*) test statistic and the Kolmogorov–Smirnov (KS) test statistic and its associated p-value. These results highlight the relevance of symmetry in real-life data modeling and establish the AAPEx family as a powerful tool for analyzing complex data structures in public health, engineering and epidemiology. Full article

Patient-reported health data, especially patient-reported outcomes measures, are vital for improving clinical care but are often limited by memory bias, cognitive load, and inflexible questionnaires. Patients prefer conversational symptom reporting, highlighting the need for robust methods in symptom extraction and conversational intelligence. This study presents a weakly-supervised pipeline for training and evaluating medical Named Entity Recognition (NER) models across eight languages, with a focus on low-resource settings. A merged English medical corpus, annotated using the Stanza i2b2 model, was translated into German, Greek, Spanish, Italian, Portuguese, Polish, and Slovenian, preserving the entity annotations medical problems, diagnostic tests, and treatments. Data augmentation addressed the class imbalance, and the fine-tuned BERT-based models outperformed baselines consistently. The English model achieved the highest F1 score (80.07%), followed by German (78.70%), Spanish (77.61%), Portuguese (77.21%), Slovenian (75.72%), Italian (75.60%), Polish (75.56%), and Greek (69.10%). Compared to the existing baselines, our models demonstrated notable performance gains, particularly in English, Spanish, and Italian. This research underscores the feasibility and effectiveness of weakly-supervised multilingual approaches for medical entity extraction, contributing to improved information access in clinical narratives—especially in under-resourced languages. Full article

Frozen gait (FG) is an increasingly prevalent concern in individuals with Parkinson’s disease (PD) that limits mobility and increases the risk of falls. Traditional FG detection and monitoring methods using clinical observations and wearable sensors face limitations, such as inflexibility, lack of portability, inaccessibility to individuals, and the inability to provide continuous monitoring in real-life environments. To address these challenges, this experimental study presents the development of a software-defined radio (SDR)-based radio frequency (RF) sensing platform for continuous FG monitoring. Data were collected through multiple experiments involving various physical activities, including FG episodes. The acquired data were processed using advanced signal-processing (ASP) techniques to extract relevant wireless channel state information (WCSI) patterns. The physical activities were classified using machine learning and deep learning models developed on the dataset prepared from the SDR-based RF sensing system. The results demonstrated that the deep learning models outperformed the machine learning models. The bidirectional gated recurrent unit (BiGRU) achieved the highest accuracy of 99.7%. This indicates that the developed system has the potential for accurate, real-time monitoring of FG and other PD symptoms. The proposed RF sensing platform using SDR technology and artificial intelligence (AI) offers an intelligent and continuous monitoring solution, addressing the limitations of traditional methods. This system provides portable, continuous detection of FG events, potentially improving patient care, safety, and early intervention. Full article

Building HVAC systems face significant challenges in energy optimization due to changing building characteristics and the need to balance multiple efficiency objectives. Current approaches are limited: physics-based models are expensive and inflexible, while data-driven methods require extensive data collection and ongoing maintenance. This paper introduces a systematic relearning framework for HVAC supervisory control that improves adaptability while reducing operational costs. Our approach features a Reinforcement Learning controller with self-monitoring and adaptation capabilities that responds effectively to changes in building operations and environmental conditions. We simplify the complex hyperparameter optimization process through a structured decomposition method and implement a relearning strategy to handle operational changes over time. We demonstrate our framework’s effectiveness through comprehensive testing on a building testbed, comparing performance against established control methods. Full article

Targeting the concern that nearby inflexible buildings may be at risk for safety issues due to the surface deformation caused by foundation pit excavation disruptions, this paper took the large-scale foundation pit in the Hongshaquan second mine stope in Xinjiang as the research backdrop. To examine the deformation mechanism, generic numerical simulation models were built with varying excavation depths. The unloading effect of foundation pit excavation was addressed using the Fourier integral approach, which is based on elastic theory. An elastic theoretical analytical approach for the surrounding deformation during disturbances due to the excavation of foundation pits was derived by superimposing the unloading impact of the surrounding soil and including pertinent boundary conditions. By contrasting the outcomes of the numerical simulation with the theoretical analysis and the real on-site monitoring data, the accuracy of this approach was confirmed. The findings indicated that the deformation of the surrounding ground surface rises as the excavation depth grows during the foundation pit excavation process in open-pit mines. The deformation decreases with increasing distance from the slope crest to the monitoring location. The deformation of the surrounding ground surface reduces as the rock and soil mass’s elastic modulus and Poisson’s ratio rise. However, the deformation of the surrounding ground surface increases as the excavation depth and slope angle rise. This study offers fresh ideas and approaches for examining how the surrounding ground surface deforms while a foundation hole is excavated. Full article

Validating measures of psychological flexibility (PF) and psychological inflexibility (PI) has occurred in multiple adult samples, but little research has validated PF and PI measures with adolescents. This manuscript describes two studies exploring the validity of responses to the Multidimensional Psychological Flexibility Inventory (MPFI) with two samples of adolescents. The first study used exploratory factor analyses on responses to the MPFI with a sample of 16–17-year-olds (N = 249). The results yielded a reduced and simplified measurement model that consisted of two general factors: one for PF and the other for PI. These exploratory findings were further investigated with confirmatory factor analyses in the second study, with a larger sample of 14–17-year-olds (N = 503). The results from the second study generally confirmed the factor model from the first study. Findings from both studies showed that scores derived from the reduced MPFI measurement model evidenced convergent and divergent validity with a variety of mental health criterion measures. Moreover, findings from the second study showed that PF and PI scores had differential predictive power on different concurrent mental health outcomes. This discussion highlights the implications of measuring PF and PI in adolescents, considers limitations of the present studies, and recommends next steps for research. Full article

Task offloading in satellite networks, which involves distributing computational tasks among heterogeneous satellite nodes, is crucial for optimizing resource utilization and minimizing system latency. However, existing approaches such as static offloading strategies and heuristic-based offloading methods neglect dynamic topologies and uncertain conditions that hinder adaptability to sudden changes. Furthermore, current collaborative computing strategies inadequately address satellite platform heterogeneity and often overlook resource fluctuations, resulting in inefficient resource sharing and inflexible task scheduling. To address these issues, we propose a dynamic gradient descent-based task offloading method. This method proposes a collaborative optimization framework based on dynamic programming. By constructing delay optimization and resource efficiency models and integrating dynamic programming with value iteration techniques, the framework achieves real-time updates of system states and decision variables. Then, a distributed gradient descent algorithm combined with Gradient Surgery techniques is employed to optimize task offloading decisions and resource allocation schemes, ensuring a precise balance between delay minimization and resource utilization maximization in dynamic network environments. Experimental results demonstrate that the proposed method enhances the global optimizing result by at least 1.97%, enhances resource utilization rates by at least 3.91%, and also reduces the solution time by at least 191.91% in large-scale networks. Full article

The fuel cell electric vehicle (FCEV) is a promising transportation technology for resolving the air pollution and climate change issues in the United States. However, a large-scale penetration of FCEVs would require a sustained supply of hydrogen which does not exist now. Water electrolysis can produce hydrogen reliably and sustainably if the electricity grid is clean, but the impacts of FCEVs on the electricity grid are unknown. In this paper, we develop a comprehensive framework to model FCEV-driving and -refueling behaviors, the water electrolysis process, and electricity grid operation. We chose the Western Electricity Coordinating Council (WECC) region for this case study. We modeled the existing WECC electricity grids and accounted for the additional electricity loads from FCEVs using a Production Cost Model (PCM). Additionally, the hydrogen need for five million FCEVs leads to a 3% increase in electricity load for WECC. Our results show that an inflexible hydrogen-producing process leads to a 1.55% increase to the average cost of electricity, while a flexible scenario leads to only a 0.9% increase. On the other hand, oversized electrolyzers could take advantage of cheaper electricity generation opportunities, thus lowering total system costs. Full article

Background: In recent years, the conceptual framework of psychological flexibility/inflexibility has been of global interest in the field of behavioral health. Nevertheless, studies and evidence of psychological flexibility/inflexibility remain limited for underrepresented groups of individuals, including people of color in the United States (U.S.). Among these groups of individuals are Black Americans in the U.S. In response to this empirical gap, the present cross-sectional study investigated whether psychological inflexibility and mindful awareness were uniquely related to general psychological distress, somatization, depression, and anxiety in Black American adults in the United States. Methods: A convenience sample of 359 Black American college students completed self-report measures of interest online. Results: As predicted, correlational analyses showed that psychological inflexibility was positively associated with general psychological distress, somatization, depression, and anxiety, and that mindful awareness was negatively associated with these four distress variables. A path analysis model revealed that psychological inflexibility, but not mindful awareness, was uniquely associated with these distress variables. Conclusions: The present study extended previous findings with a convenience sample of Black American college students, suggesting that psychological inflexibility may be a useful construct for understanding psychological distress, more so than mindful awareness, among Black American adults in the U.S. Full article

Currently, small object detection in complex remote sensing environments faces significant challenges. The detectors designed for this scenario have limitations, such as insufficient extraction of spatial local information, inflexible feature fusion, and limited global feature acquisition capability. In addition, there is a need to balance performance and complexity when improving the model. To address these issues, this paper proposes an efficient and lightweight SCM-YOLO detector improved from YOLOv5 with spatial local information enhancement, multi-scale feature adaptive fusion, and global sensing capabilities. The SCM-YOLO detector consists of three innovative and lightweight modules: the Space Interleaving in Depth (SPID) module, the Cross Block and Channel Reweight Concat (CBCC) module, and the Mixed Local Channel Attention Global Integration (MAGI) module. These three modules effectively improve the performance of the detector from three aspects: feature extraction, feature fusion, and feature perception. The ability of SCM-YOLO to detect small objects in complex remote sensing environments has been significantly improved while maintaining its lightweight characteristics. The effectiveness and lightweight characteristics of SCM-YOLO are verified through comparison experiments with AI-TOD and SIMD public remote sensing small object detection datasets. In addition, we validate the effectiveness of the three modules, SPID, CBCC, and MAGI, through ablation experiments. The comparison experiments on the AI-TOD dataset show that the mAP50 and mAP50-95 metrics of SCM-YOLO reach 64.053% and 27.283%, respectively, which are significantly better than other models with the same parameter size. Full article