Search Results (1,238)

We equip a topological space $(X,\tau )$ with a function $\mathfrak{a}:X\to \tau$ satisfying the single axiom $x\in \mathfrak{a}\left(x\right)$. The resulting triple $(X,\tau ,\mathfrak{a})$, which we call an aura topological space, provides a point-to-open-set assignment that differs from all existing auxiliary structures in topology—ideals, filters, grills, primals, and the various non-classical frameworks based on fuzzy, soft, or neutrosophic sets. The aura-closure operator ${\mathrm{cl}}_{\mathfrak{a}}\left(A\right)=\{x\in X:\mathfrak{a}\left(x\right)\cap A\ne ⌀\}$ is shown to be an additive Čech closure operator; it satisfies extensivity, monotonicity, and finite additivity, but idempotency fails in general. Iterating ${\mathrm{cl}}_{\mathfrak{a}}$ transfinitely yields a Kuratowski closure whose topology ${\tau }_{\mathfrak{a}}^{\infty }$ satisfies ${\tau }_{\mathfrak{a}}^{\infty }={\tau }_{\mathfrak{a}}\subseteq \tau$, where ${\tau }_{\mathfrak{a}}$ is the collection of all $\mathfrak{a}$-open sets. We introduce $\mathfrak{a}$-semi-open, $\mathfrak{a}$-pre-open, $\mathfrak{a}$-$\alpha$-open, and $\mathfrak{a}$-$\beta$-open sets, determine the complete hierarchy among these classes and their classical counterparts, and separate all non-coinciding classes by counterexamples on finite spaces as well as on the real line. The notions of $\mathfrak{a}$-convergence of sequences and the corresponding continuity notions and their decompositions are studied. Separation axioms $\mathfrak{a}$-$T_i$$(i = 0, 1, 2)$ are introduced, and it is proved that $\mathfrak{a}$-$T_1$ and $\mathfrak{a}$-$T_2$ are equivalent. A detailed comparison with ideals, filters, grills, and primals highlights the distinctive features of the aura framework. Full article

This paper presents a systematic application of the Homotopy Perturbation Method (HPM) to the nonlinear static analysis of cantilever beams subjected simultaneously to three coplanar follower loads: an axial force H, a transverse force V, and a bending moment M₁. The studied configuration introduces complex mathematical self-coupling, as the bending moment depends on the solution of the differential equation even in its boundary conditions (γ₁), transforming the problem into a nonlinear one that is resistant to standard analytical methods. The primary methodological contribution of this work is the successful extension of the HPM framework to treat, within a unified mathematical formalism, this complete loading case, which has practical applications in compliant mechanisms, micro-electromechanical systems (MEMSs), and auxetic structures. The paper provides a complete mathematical formulation and explicit derivation of the HPM solution terms up to the third order and a rigorous demonstration of the method’s convergence, with quantitative error estimates and the establishment of a practical domain of validity, γ₁ < 30°, for an accuracy below 0.5%. As a direct consequence of this analytical advancement, we derive a series of practical engineering tools: nomograms, simplified empirical formulas, interaction diagrams, and a systematic six-step design procedure, which includes an adaptive algorithm for selecting the auxiliary parameter η to optimize convergence. The solution’s structure also lends itself to AI-based optimization frameworks, demonstrating how HPM solutions can serve as a foundation for machine learning surrogates and automated multi-objective optimizations. HPM proves to be a robust and efficient alternative, providing semi-analytical solutions in the form of convergent series without requiring an explicitly small physical parameter. This enables a direct parametric understanding of the structural response and offers rapid tools for the conceptual and preliminary sizing phases, thereby complementing the intensive numerical methods used in the final design stages. Full article

Burnout among long-term care workers is a public health concern, yet mixed-methods evidence from China is scarce. To examine multilevel correlates of burnout, a convergent mixed-methods study using a Social–Ecological Framework was conducted. In the quantitative strand, 494 workers were surveyed using two-stage cluster sampling, and probability-weighted multivariable linear regression examined factors associated with emotional exhaustion, depersonalization, and reduced personal accomplishment. In the qualitative strand, 15 participants completed semi-structured interviews; transcripts were managed in MAXQDA 2025 and analyzed thematically. Burnout was common (30.77% mild, 33.00% moderate, 17.00% severe). Quantitative findings showed that burnout dimensions were associated with gender, age, marital status, employment arrangement, institution type, training intensity, caregiver burden, and recognition of the long-term care insurance policy (p < 0.05). Qualitative findings highlighted cognitive adaptation, emotional reciprocity with older adults, organizational training and support, and policy recognition as potential buffering resources. These findings suggest that burnout is shaped by influences across multiple levels. Coordinated efforts may help alleviate burnout by strengthening training systems, reducing caregiving burden, enhancing recognition of long-term care policies, and elevating the societal value of care work. Future research should validate these potential courses of action through longitudinal or intervention studies. Full article

Background: Chronic non-cancer pain (CNCP) is an increasing global health concern and a multidimensional condition shaped by biological, psychological, social, and cultural factors, with impacts on functioning, quality of life, and healthcare. However, evidence remains fragmented, limiting integrated understanding and care. Objective: This study aimed to synthesize and critically analyze existing evidence on the biological, psychological, social, and cultural dimensions characterizing individuals with CNCP, and their impact on functionality, quality of life, and healthcare. Methodology: An integrative review was conducted following the Whittemore and Knafl framework. Searches were performed in Medline, Cumulative Index of Nursing and Allied Literature Complete (CINAHL), PsycINFO, Scopus, Web of Science, and grey literature in English and Spanish, without time restrictions. Studies were screened using predefined eligibility criteria and appraised with Joanna Briggs Institute tools. Data were systematically extracted and synthesized using thematic analysis to identify key attributes of people living with CNCP. Quantitative findings were summarized descriptively and mapped to thematic domains, while qualitative data were analyzed interpretively. Both evidence streams were integrated through convergent thematic synthesis. Results: Forty-four studies were included, predominantly cross-sectional and observational. Five themes emerged: biological aspects; functioning and quality of life; psychological and mental factors; social support and peer relationships; and social and gender determinants. CNCP was consistently associated with multimorbidity, sleep disturbance, psychological distress, and maladaptive coping, contributing to reduced functional capacity, greater disability, poorer quality of life, and increased healthcare utilization. Socioeconomic disadvantages and environmental constraints were linked to higher pain burden, whereas resilience and social support emerged as protective factors mitigating functional and psychosocial impact. Conclusions: Evidence largely concentrates on biomedical, functional, and psychological dimensions, whereas social determinants and healthcare quality remain comparatively underexplored. Broadening these perspectives is essential to inform public health strategies and support multidisciplinary, equitable care for individuals living with CNCP. Full article

We introduce cumulative tic-tac-toe, a novel variant of the classic $3\times 3$ tic-tac-toe game in which play continues until the board is completely filled. Each player’s final score is determined by the total number of three-in-a-row sequences they form. Using combinatorial game theory (CGT), we establish that under optimal play, the game is a draw, and we characterize its theoretical properties. To empirically validate and optimize practical play, we develop a reinforcement learning (RL) framework based on temporal-difference (TD) learning, which is enhanced with a domain-informed evaluation function to accelerate convergence. The experimental results show that our triplet-coverage difference (TCD) evaluation function reduces the average number of training episodes by approximately 23.1% compared with a random-initialization baseline, a statistically significant improvement at the 5% significance level. These results demonstrate the efficiency of our CGT–RL approach for cumulative tic-tac-toe and suggest that similar methods may be useful for analyzing related combinatorial games. We also discuss potential analogies in domains such as competitive resource allocation and coalition formation, illustrating how cumulative-scoring games connect abstract game-theoretic ideas to practical sequential decision problems. Full article

With the increasing demands for autonomy and coverage efficiency in tasks such as security patrol and post-disaster exploration using mobile robots, achieving random, efficient, and complete coverage path planning has become a critical challenge. Traditional chaotic path planning methods, while capable of generating unpredictable trajectories, still have limitations in terms of randomness strength, traversal uniformity, and convergence coverage. To address this, this study proposes a complete-coverage random path planning method based on a novel four-dimensional fractal-fractional multi-scroll chaotic system. The main contributions of this research are as follows: First, by introducing additional state variables and fractal-fractional operators into the classical Chen system, a fractal-fractional chaotic system with a multi-scroll attractor structure is constructed. The output of this system is then mapped into robot angular velocity commands to achieve area coverage in unknown environments. Key findings include: the novel chaotic system possesses two positive Lyapunov exponents; Spectral Entropy (SE) and Complexity (CO) analyses indicate that when parameter B is fixed and the fractional order α increases, the dynamic complexity of the system significantly rises; in a 50 × 50 grid environment, the robot driven by this system achieved a coverage rate of 98.88% within 10,000 iterations, outperforming methods based on Lorenz, Chua systems, and random walks; ablation experiments further demonstrate that the combined effects of the fractal order β, fractional order α, and multi-scroll nonlinear terms are key to enhancing system complexity and coverage performance. The significance of this study lies in that it not only provides new ideas for constructing complex chaotic systems but also offers a reliable theoretical foundation and practical solution for mobile robots to perform efficient, random, and high-coverage autonomous inspection tasks in unknown regions. Full article

Research suggests that the quality of mother–infant bonding (MIB) is a critical factor for long-term infant development. This study aimed to culturally adapt and psychometrically validate the Mother–to-Infant Bonding Scale (MIBS) for use among Greek mothers. Methods: A total of 750 mothers (Mage = 33.6 ± 4.6) with infants aged 0–12 months completed the MIBS and the Edinburgh Postnatal Depression Scale. The sample was randomly split to conduct exploratory and confirmatory factor analyses (EFA/CFA). Results: Analyses supported a unidimensional structure after removal of the ‘protective’ item. The MIBS demonstrated good reliability and convergent validity against the Edinburgh Postnatal Depression Scale (EPDS). Discussion: MIBS is a reliable and valid tool to assess bonding in a general population of Greek mothers up to one year postpartum. Future studies should examine the structure of MIBS in different timepoints during the postpartum period. The MIBS appears to be a reliable screening instrument for early identification of bonding difficulties. Full article

Structural aging and environmental changes associated with long-term operation can substantially modify the thermal behavior of machine tools, diminishing the accuracy of existing thermal compensation models. Traditional neural network approaches typically necessitate time-consuming and inefficient retraining from scratch for practical adaptation. To address this limitation, this study proposes a parameter-based transfer learning technique to enhance model adaptability under evolving machine tool operating conditions. The method establishes a composite fine-tuning architecture by adding hidden layers and selectively freezing neural network parameters, enabling the rapid adaptation of the pretrained model to new thermal characteristics using limited data. A full-factorial experimental design identified the optimal configuration, comprising (i) structural expansion via an LSTM layer inserted after the hidden layers; (ii) a strategy freezing parameters in all layers; and (iii) training under the selected optimal condition (C9), which reflects machine tool characteristics and environmental temperature variations. The baseline model achieved an RMSE of 3.88 µm. Traditional retraining using the complete dataset and retraining only on C9 yielded RMSE values of 3.21 and 3.84 µm, respectively. In contrast, the optimized transfer learning model trained on C9 achieved an RMSE of 3.47 µm. Experimental results demonstrate that the proposed strategy converges with limited data, reducing the number of datasets from 18 to nine and significantly shortening training time from 18 h 20 min to 30 s. This approach offers an effective solution for sustainable model maintenance and expedited industrial deployment. Full article

Zero-shot generalization to out-of-distribution (OOD) teammates and opponents in multi-agent systems (MASs) remains a fundamental challenge for general-purpose AI, especially in open-ended interaction scenarios. Existing multi-agent reinforcement learning (MARL) paradigms, such as self-play and population-based training, often collapse to a limited subset of Nash equilibria, leaving agents brittle when faced with semantically diverse, unseen behaviors. Recent approaches that invoke Large Language Models (LLMs) at run time can improve adaptability but introduce substantial latency and can become less reliable as task horizons grow; in contrast, LLM-assisted reward-shaping methods remain constrained by the inefficiency of the inner reinforcement-learning loop. To address these limitations, we propose LLM-TOC (LLM-Driven Theory-of-Mind Adversarial Curriculum), which casts generalization as a bi-level Stackelberg game: in the inner loop, a MARL agent (the follower) minimizes regret against a fixed population, while in the outer loop, an LLM serves as a semantic oracle that generates executable adversarial or cooperative strategies in a Turing-complete code space to maximize the agent’s regret. To cope with the absence of gradients in discrete code generation, we introduce Gradient Saliency Feedback, which transforms pixel-level value fluctuations into semantically meaningful causal cues to steer the LLM toward targeted strategy synthesis. We further provide motivating theoretical analysis via the PAC-Bayes framework, showing that LLM-TOC converges at rate $O(1/\sqrt{K})$ and yields a tighter generalization error bound than parameter-space exploration under reasonable preconditions. Experiments on the Melting Pot benchmark demonstrate that, with expected cumulative collective return as the core zero-shot generalization metric, LLM-TOC consistently outperforms self-play baselines (IPPO and MAPPO) and the LLM-inference method Hypothetical Minds across all held-out test scenarios, reaching 75% to 85% of the upper-bound performance of Oracle PPO. Meanwhile, with the number of RL environment interaction steps to reach the target relative performance as the core efficiency metric, our framework reduces the total training computational cost by more than 60% compared with mainstream baselines. Full article

We show exactly when the topology of convergence in measure in Banach ideal spaces is linear (equivalently, coarser than the norm topology). Next, we present the relationship between the Kadets–Klee and suitable monotonicity properties with respect to global convergence in measure. Applying these results, we characterize the Kadets–Klee property with respect to the global convergence in measure in infinite direct sums. We also prove the criteria of some related monotonicity properties in infinite direct sums. Furthermore, we solve the fundamental lifting (inheritance) problem completely for all these properties. We finish the paper with concrete examples showing how our general results can be applied. Full article

This paper proposes an advanced sensorless control strategy for Permanent Magnet Synchronous Motors (PMSMs) aimed at enhancing dynamic performance, robustness, and reliability while eliminating the need for mechanical sensors. The core contribution lies in a novel hybrid speed regulation framework that combines a terminal sliding mode control scheme with a high-order super-twisting algorithm (HOT-STA-SMC), ensuring finite-time convergence, effective chattering suppression, and strong disturbance rejection under varying operating conditions. For the inner current loop, an Exponential Reaching Law Sliding Mode Controller (ERL-SMC) is implemented to guarantee fast current response and precise current tracking, even in the presence of parameter uncertainties. Furthermore, the conventional Model Reference Adaptive System (MRAS) observer is embedded within the proposed control architecture, resulting in more accurate speed estimation and enhanced stability during load fluctuations. The complete control system is rigorously modeled and tested in MATLAB R2024b/Simulink, capturing the full interaction between machine dynamics, control loops, and observer mechanisms. The simulation results verify that the proposed design achieves superior torque smoothness, minimal current ripples, and fast transient response compared to conventional sensorless methods. By integrating high-order sliding modes with advanced adaptive observation, this work offers a robust and cost-effective solution for high-performance PMSM drives, suitable for demanding applications such as electric vehicles, renewable energy conversion, and industrial motion control. Full article

To tackle the practical engineering challenge that construction robots are required to track the reference trajectory completely and precisely, this study puts forward a control scheme based on the extended reference trajectory and develops a novel super-twisting sliding mode controller with predefined-time convergence capability. First, the influence mechanism of fluid materials on construction robots and their trajectory tracking control features are explored, and the design approach for the extended reference trajectory is elaborated. Subsequently, a nonsingular sliding surface with predefined-time convergence is constructed, and a RBF neural network with convergent weight vectors is established to approximate the composite disturbances existing in the robot system. On the basis of the proposed predefined-time convergent super-twisting control theory, a super-twisting sliding mode controller tailored for construction robots is devised, and the predefined-time convergence performance of the closed-loop system is theoretically validated. Numerical simulation results indicate that the proposed algorithm can guarantee that the construction robot’s angles move accurately along the actual reference trajectory, with the angular tracking error achieving a precision of 3 × 10⁻⁶ rad, thereby confirming the feasibility and effectiveness of the proposed method. Full article

Off-policy reinforcement learning is usually used to train the grasping task model of the manipulator. However, in the training process, it is difficult to collect enough successful experience data and rewards for learning and training; that is, there is a problem of sparse rewards. Hindsight experience replay (HER) allows the agent to relabel the completed states. However, not all failed experiences have the same effect on learning and training. Facing the many transitions generated by the environment during operation, adopting a random uniform sampling method from the experience replay buffer will result in low data utilization and slow convergence. This paper proposes using a prioritized sampling method to sample the relabelled transitions, and then combines various off-policy reinforcement learning algorithms with it for training in simulated environments. This paper uses the prioritized sampling method, which allows the agent to access more important transitions earlier and accelerate the convergence of training. The results demonstrate that hindsight experience replay with prioritization (PHER) exhibits significantly faster convergence compared to other methods. Full article

This study investigated how perceptual workload in driving situations is captured by subjective ratings versus eye-tracking metrics. Fifty participants completed low- and high-complexity conditions while fixation behavior, blinks, and pupil diameter were recorded, and workload was assessed using the DALI scale. High-load scenes elicited longer fixations, fewer fixations per minute, reduced blinking, and increased pupil dilation, indicating elevated attentional demand. DALI scores increased with scene complexity and were negatively associated with fixation duration, demonstrating that participants’ subjective ratings were driven primarily by perceptual strain rather than cognitive effort. Eye-tracking patterns supported this interpretation: fixation-based indicators tent to reflect the cognitive component of demand, whereas DALI selectively tracked perceptual overload. Together, these results show that DALI is highly sensitive to visual density, and that eye-movement measures provide converging evidence for its specificity as a perceptual load instrument. Full article

Background: Extreme weather events, such as storms, may evoke intense fear in individuals and impair their daily functioning, resulting in significant distress. In Brazil, recent climate-related disasters have highlighted the need to assess storm fear in the population. Objective: This study aimed to translate, adapt, and validate the Storm Fear Questionnaire (SFQ) for the Brazilian context. Methods: Translation and adaptation were conducted, followed by back-translation, review by an expert panel, and acceptability assessment by the target population. For the psychometric evaluation, a sample of 268 postpartum women exposed to a flood in southern Brazil completed the SFQ and the following questionnaires: the Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5), Beck Depression Inventory II (BDI-II), and the Pregnancy Experience Scale—Brief Version (PES-Brief). Results: The instrument showed excellent acceptability in the target population and good content validity. Regarding criterion validity, Pearson correlations indicated high convergence between the SFQ and PCL-5 and moderate convergence with the BDI-II. Regarding construct validity, SFQ scores were significantly higher among postpartum women who had to leave their homes due to the flood or had their houses affected by floodwaters. The first factor generated in the factor analysis explained 35.2% of the variance, with 14 out of 15 items presenting loadings greater than 0.40. Internal consistency was high (α = 0.88). Conclusions: The Brazilian version of the SFQ is a valid and reliable instrument for assessing fear of storms. Future studies are needed to evaluate the instrument’s applicability in diverse populations across the country. Full article