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Agricultural machinery users face a high risk of incidents and injuries, making the correct interpretation of safety warnings on machinery critical. However, limited evidence is available on how well the operators comprehend safety pictograms, especially in Turkey, where farmers generally have insufficient knowledge of occupational health and safety, and workers receive minimal training in this area. A questionnaire, adapted from previous studies, was administered to 230 agricultural machinery operators to investigate how age, education, work experience, and familiarity with safety symbols affect comprehension of 12 ISO 11684:2023 pictograms related to common machinery hazards. The study's results revealed different levels of comprehension among participants, with five pictograms achieving a comprehension rate equal to or greater than the minimum rate (75%) required by the ISO reference standard. Education, prior experience with agricultural machinery, and familiarity with pictograms significantly increased participants' ability to correctly interpret the safety symbols. When compared with previous studies conducted in other countries, pictogram #4, referring to the risk of injury from rotating knives, and pictogram #11, depicting tractor rollover risk, were investigated in seven out of eight and in all previous studies, respectively, and yielded high levels of comprehension across countries. The findings emphasized the need to increase the familiarity of potential users with safety symbols through targeted training programs. Furthermore, the visibility and placement of pictograms should be systematically considered in safety communication strategies. Standardization bodies should also consider improving pictograms design through a user-centered approach to enhance their clarity and effectiveness in communicating crucial safety information in different national and operational contexts.

Social connection is a core determinant of mental and physical health, and connection-oriented services (e.g. peer programmes, groups, and social prescribing) are increasingly implemented. Yet social connection is culturally patterned. When participants' interaction styles align with a service's implicit organisational norms, belonging may be easier to achieve; when they do not, cultural mismatch may reduce recognition, constrain participation, and intensify loneliness, leaving some participants feeling more disconnected. This paper operationalises organisational norms around participation and help-seeking using three sociological elements (symbols, values, and norms), and proposes a brief reflective audit that can be embedded in routine quality improvement. The audit is intended to help services make implicit rules of engagement more visible (e.g. expectations around disclosure, turn-taking, help-seeking, disagreement, and what counts as "good participation"), and identify which interaction styles are more readily recognised versus under-supported. Two possible outputs are proposed: (1) a participant-facing, plain-language participation brief that may improve clarity about how participation works; and (2) an internal action plan intended to widen routes to connection (e.g. structured turn-taking, opt-in written or one-to-one routes, small-group formats, and facilitator prompts validating quiet participation). Making organisational norms more explicit may offer a practical reflective approach to strengthen inclusivity by design in connection-focused services.

Icons, as simplified visual symbols, play a key role in visual communication, yet little neuroimaging research has addressed how icons are represented in the brain. We investigated how concreteness and attractiveness modulate the spatiotemporal dynamics of icon processing in a 2 × 2 factorial design in 35 adults using magnetoencephalography. Source-level event-related field (ERF) analysis and representational similarity analysis (RSA) were used to characterize neural responses, with partial RSA isolating each feature's unique contribution after controlling for low-level visual similarity. ERF results showed that concreteness exerted a robust and sustained influence on neural dynamics, with concrete icons eliciting stronger responses than abstract ones from 90 to 1,000 ms, emerging in bilateral occipital cortices and extending to occipitotemporal, temporal, and parietal regions. RSA confirmed concreteness as a representational dimension across processing stages. Attractiveness showed an early but transient effect in occipital and ventral occipitotemporal regions (80 to 130 ms), though this did not survive RSA after controlling for low-level visual property models. A concreteness × attractiveness interaction modulated early occipital and later parietal processing (100 to 185 ms), indicating these features do not operate independently. Our results reveal that semantic content outweighs esthetic appeal in shaping the neural representation of icons.

Decision-making is a critical component of basketball performance. Within the framework of Reinforcement Learning (RL), understanding the environment and task structure can facilitate model-based (MB) behavior. This study tested whether domain-specific contexts promote MB decision strategies by using the two-stage task in a 2 (Group: basketball players vs. novices) × 2 (Condition: abstract symbols vs. basketball tactical diagrams) mixed design. Thirty-five national basketball players and twenty-seven novices completed both conditions. A hierarchical one-trial-back logistic regression estimated intercept, reward, transition, and reward × transition coefficients; two-factor repeated-measures ANOVAs assessed coefficients, reaction times, and subjective evaluations. Compared to novices, basketball players exhibited a more MB decision-making strategy across all condition, accompanied by higher subjective understanding. Novices exhibited higher reward coefficients indicated a more model-free (MF) tendency. For basketball players, MB strategies were associated with longer RTs, yet longer RTs in novices did not necessarily indicate MB strategies use. These findings show that expertise facilitates the integration of outcome feedback with the task's structure, thereby leading to MB decision-making. Incorporating sport-specific displays into training may enhance decision-making performance without relying on explicit structural instruction.

We present the Poincaré-Section Reservoir (PSR), a deterministic reservoir computer whose recurrent graph is learned directly from data. A single trajectory of the target system is sliced by a transverse hyperplane; the resulting sequence of crossing points is coarse-grained into symbols, and empirical transition frequencies yield a row-stochastic matrix T. After spectral rescaling, T becomes the reservoir adjacency W, so each neuron corresponds to a concrete region of the attractor and each edge weight reproduces an observed return probability. We prove that, as the partition is refined, W converges in operator norm to a scaled Perron-Frobenius operator of the true Poincaré map, providing a formal consistency guarantee absent from classical Echo-State Networks. Coupled with a once-trained quadratic read-out, a 300-node PSR extends valid prediction time by up to 1.9 ×  over the best existing reservoirs on the Lorenz, Rössler, Chen-Ueta and Chua chaotic benchmarks-without gradients, equation knowledge or hyper-parameter tuning. By fusing Ulam's operator discretisation with reservoir computing, PSR offers a lean, interpretable and fully data-driven paradigm for long-horizon forecasting of strongly chaotic dynamics.

Humans can combine symbols to generate new meanings. Here, we studied the regional neural mechanisms that might make this possible. We asked participants to combine two discrete, symbolic features (a shape and a color) to make a novel spatial inference. Blood-oxygen-level-dependent (BOLD) data suggested that the hippocampus encoded elementary visual attributes in a high-dimensional, parallel format that permitted flexible individuation. In the ventromedial prefrontal cortex (vmPFC), posterior parietal cortex (PPC), and primary visual cortex (V1), neural patterns for novel stimuli (composites) could be predicted as a linear combination of signals for familiar stimuli (elements). In the vmPFC, this composition occurred in a high-dimensional format, but in PPC and V1, it took place in a low-dimensional, spatial frame of reference that was aligned with the response space. These data offer new insights into the neural circuits underlying compositional generalization.

In this letter, by an approach that employs Weyl symbols for operators, a semiclassical theory is developed for the off-diagonal function in the eigenstate thermalization hypothesis, which is for off-diagonal elements 〈E_{i}|O|E_{j}〉 of an observable O on the energy basis. It is shown analytically that the matrix of O has a banded structure, possessing a bandwidth w_{b} that scales linearly with ℏ, a phase-space gradient of the classical Hamiltonian 〈|∇H_{cl}|〉 and an O-dependent property. This predicts that the thermalization timescale of a quantum system may be inversely proportional to the phase-space gradient of the Hamiltonian, aligning with intuitions in classical thermalization. This approach also elucidates the origin of a ρ_{dos}^{-1/2} scaling of the off-diagonal function. The analytical predictions are checked numerically in the Lipkin-Meshkov-Glick model.

Depression and anxiety rank among the leading causes of global disability, yet traditional treatments reach only a minority of affected individuals. The COVID-19 pandemic further exacerbated this crisis, triggering a ~ 25% worldwide surge in anxiety and depression prevalence. In parallel, immersive digital environments (the "metaverse") are maturing as platforms for creative expression and social connection. This review proposes that immersive metaverse art - interactive art experiences in VR/AR - can act as a multilevel psychobehavioral modulator. We integrate recent evidence showing that such experiences enhance engagement (flow, presence), enable identity exploration (customizable avatars, cultural narratives), and engage neurocognitive systems (reward, attention, regulation). Empirical studies of VR art interventions report acute mood improvements, stress reduction, and greater social connectedness. We synthesize these findings into a conceptual model linking core components (immersion, creative agency, social avatar, cultural symbolism) to mediating processes (flow, meaning-making, belonging) and outcomes (symptom relief, emotional regulation, behavioral activation). Rather than examining these domains as separate interdisciplinary themes, the review integrates them within a unified clinical framework focused on transdiagnostic mechanisms relevant to depression and anxiety. We compare immersive art therapy with traditional art therapy, noting unique advantages (scalability, personalization) and novel risks (overdependence, identity diffusion). Finally, we outline translational pathways - e.g. integrating VR art with cognitive therapies and highlight the need for rigorous trials and cross-cultural validation. Overall, immersive metaverse art emerges as a promising, if nascent, approach to mental health intervention, warranting further empirical and ethical scrutiny.

Multiple sclerosis (MS) is an immune-mediated inflammatory and neurodegenerative disease of the central nervous system (CNS) that leads to demyelination, axonal injury, and progressive disability. Glucagon-like peptide 1 receptor agonists (GLP-1RAs) have been proposed as adjunctive therapy with potential neuroprotective properties. We evaluated the effects of GLP-1RA treatment on metabolic parameters, functional performance, and biomarkers of neurodegeneration in relapsing-remitting MS (RRMS). In this prospective, randomized, open-label, single-center proof-of-concept study, 28 patients with RRMS receiving stable natalizumab treatment were randomized to adjunctive GLP-1RA (dulaglutide; 0.75 mg subcutaneously once weekly for 12 months; n = 15) or to a control group without adjunctive therapy (n = 13). Assessments were performed at baseline and 12 months. Primary outcomes included plasma neurofilament light chain (NfL) and brain magnetic resonance imaging (MRI) volumetry. Secondary outcomes included resting metabolic rate, functional performance (Timed 25-Foot Walk [T25FW] and 9-Hole Peg Test [9HPT]), cognitive performance (Symbol Digit Modalities Test [SDMT]), anthropometric and metabolic measures, and oral glucose tolerance test (OGTT)-derived insulin sensitivity indices. Dulaglutide significantly reduced weight, body mass index (BMI), body fat, and visceral fat, and improved glucose tolerance (reduced glucose area under the curve [AUC]). No adverse events were recorded in either group; no discontinuations occurred. Exploratory analyses indicated trends toward improved walking speed (T25FW, group × time interaction: F1,26 = 9.4, p = 0.005) and non-dominant hand manual dexterity (9HPT, F1,26 = 4.7, p = 0.039) in the dulaglutide group. However, no significant between-group differences were observed in plasma NfL, brain MRI volumetric measures, or cognitive performance. Adjunctive dulaglutide 0.75 mg weekly for 12 months improved metabolic parameters and showed exploratory signals of functional benefit, but did not modify plasma NfL or MRI volumetric measures in natalizumab-treated RRMS. Larger, adequately powered studies with higher-dose GLP-1RA regimens and longer follow-up are warranted. The trial was registered in the EU Clinical Trials Register (EudraCT 2019-003001-94).

Patient involvement is gaining more and more importance in clinical research, with funding bodies increasingly requiring participatory study designs. However, these approaches entail additional efforts and require specific competencies that are not yet systematically taught or institutionalised. This study aimed to explore the perspectives and support needs of clinical researchers with regard to active patient involvement in order to derive tailored support strategies. Between July and November 2022, semi-structured interviews were conducted with seven clinical researchers and three research coordinators at Jena University Hospital. Audio-based interviews were documented using a manual, non-verbatim rapid transcription approach, and subsequently analysed using qualitative content analysis. Participants reported support needs in the areas of knowledge, communication, coordination, and structural conditions. They emphasised the need for practice-oriented counselling services, compact training formats, and central platforms for patient recruitment. The barriers identified included the late integration of patients, limited resources, and the risk of merely symbolic or formalised involvement. Participants also expressed a need for clearer guidance from funding bodies. Findings highlight a tension between the normative demand for patient involvement and its practical implementation. Without institutional support and targeted qualification, there is a risk that participation remains symbolic. Clear conceptual distinctions and supportive funding structures are essential for effective participatory research. Sustainable implementation of patient involvement in clinical research requires low-threshold, practice-oriented support structures. The findings offer starting points for tailored training, coordination, and funding strategies.

Snakebite envenoming is a major public health problem in the Amazon, disproportionately affecting Indigenous populations with high incidence and mortality rates. Efforts to decentralize antivenom treatment to remote areas require not only logistical adaptations, but also a deeper understanding of Indigenous medical systems to enable culturally appropriate care. This study aimed to construct an explanatory model of snakebites from the perspective of the Munduruku people, an Indigenous group in the Central Brazilian Amazon. We conducted a qualitative study based on in-depth interviews with nineteen traditional healers. Our methodological orientation follows the Amerindian perspectivism theory. Data was sorted into five relevant categories: 1) Participants' identities; 2) Snakes and snakebites; 3) Course of sickness; and 4) Therapeutic resources in the Munduruku medicine. Munduruku healers interpret snakebites as events involving both natural and supernatural dimensions, integrating bodily, social, and spiritual factors. Snakes are perceived as intentional beings, and envenomation may result not only from physical encounters but also from sorcery or transgression of social norms; perceived severity is shaped by the type of snake, adherence to dietary and sexual restrictions, and spiritual causality. Therapeutic practices predominantly involve topical preparations, rituals, and symbolic interventions embedded within broader relational and cosmological frameworks. Despite these distinct explanatory models, most participants recognized the importance of biomedical care, particularly for severe cases, and did not oppose referral to hospital-based treatment, while Indigenous healing practices remain central throughout the therapeutic itinerary. Improving snakebite outcomes in the Amazon requires intercultural health strategies that integrate biomedical and Indigenous systems, with symmetrical partnerships with Indigenous healers being essential to ensure timely access to antivenom while respecting local knowledge and practices.

In the past decade, many successful networks are on novel architectures, which almost exclusively use the same type of neurons. Recently, more and more deep learning studies have been inspired by the idea of NeuroAI and the neuronal diversity observed in human brains, leading to the proposal of novel artificial neuron designs. Designing well-performing neurons represents a new dimension relative to designing well-performing neural architectures. Biologically, the brain does not rely on a single type of neuron that universally functions in all aspects. Instead, in our brain, neurons are often task-based. In this study, we address the following question: since the human brain is a task-based neuron user, can the artificial network design go from the task-based architecture design to the task-based neuron design? Since methodologically there are no one-size-fits-all neurons, given the same structure, task-based neurons can enhance the feature representation ability relative to the existing universal neurons due to the intrinsic inductive bias for the task. Specifically, we propose a two-step framework for prototyping task-based neurons. First, symbolic regression is used to identify optimal formulas that fit input data by utilizing base functions such as polynomials. We introduce VSR that stacks all variables in a vector and regularizes each input variable to perform the same computation, which can increase the regression speed, facilitate efficacy in high dimensions, and enable parallel computation. Second, we parameterize the acquired elementary formula to make parameters learnable, which serves as the aggregation function of the neuron. The activation functions such as ReLU and the sigmoidal functions remain the same because they have proven to be good. As the initial step, we evaluate the proposed framework using polynomials as base functions. Empirically, systematic experimental results on synthetic data, classic benchmarks, and real-world applications show that the proposed task-based neuron design is not only feasible but also delivers competitive performance over other state-of-the-art models. We have shared our code in https://github.com/NewT123-WM/Task_based_neurons.

Hamiltonian systems describe a broad class of dynamical systems governed by Hamiltonian functions, which encode the total energy and dictate the evolution of the system. Data-driven approaches, such as symbolic regression and neural network-based methods, provide a means to learn the governing equations of dynamical systems directly from observational data of Hamiltonian systems. However, these methods often struggle to accurately capture complex Hamiltonian functions while preserving energy conservation. To overcome this limitation, we propose the Finite Expression Method for learning Hamiltonian Systems (H-FEX), a symbolic learning method that introduces novel interaction nodes designed to capture intricate interaction terms effectively. Our experiments, including those on highly stiff dynamical systems, demonstrate that H-FEX can recover Hamiltonian functions of complex systems that accurately capture system dynamics and preserve energy over long time horizons. These findings highlight the potential of H-FEX as a powerful framework for discovering closed-form expressions of complex dynamical systems.

To investigate how Perceived Cultural Embeddedness (PCE) influences Purchase Intention (PI) and to uncover the underlying psychological mechanisms shaping consumer responses to culturally embedded products, this study adopts a dual-stage analytical framework grounded in Symbolic Consumption and Social Identity Theories based on survey data (N = 1376). Structural Equation Modeling is employed to examine the hypothesized linear relationships, while an Artificial Neural Network is applied to capture potential nonlinear patterns and enhance predictive validity. The results indicate that PCE significantly predicts PI through the parallel mediation of Collective Self-Esteem (CSE) and Cultural Belongingness (CB), while Cultural Identity (CI) moderates the effects of PCE on both mediators. The ANN analysis further demonstrates superior predictive performance compared to the baseline regression model and reveals nonlinear relationships among variables, with sensitivity analysis identifying PCE and CI as the most influential predictors. These findings contribute to the literature by integrating cultural psychology and consumer behavior perspectives, offering both explanatory and predictive insights into the psychological mechanisms underlying cultural consumption.

This study investigated key-press responses to five spoken Japanese vowels: [ɑ], [i], [ɯ], [e], and [o]. In the first experiment, 30 participants pushed only a single key when they listened to spoken Japanese vowels (response task). In the second experiment, another 29 participants matched the heard vowels with represented vowels by pushing one of five corresponding keys (matching task). The results of the first experiment showed no difference in the response times to the five vowels. In the second experiment, the response times to [i] and [e] were faster than those to [ɯ] and [o]. These results suggest that front vowels promote faster key-press responses than back vowels when the keys corresponding to the heard vowels are consciously selected.

Artificial hydration (AH) in terminally ill cancer patients remains ethically and clinically controversial. Although evidence suggests limited benefit near the end of life, AH is frequently administered, particularly in East Asian settings where it may carry symbolic meaning. This study conceptualized physician decision-making as a clinical spectrum and examined demographic, clinical, and ethical factors associated with AH decision orientations. A nationwide cross-sectional survey of palliative care-trained physicians in Taiwan assessed clinical practices and ethical domains (autonomy, beneficence, non-maleficence, justice, cultural, and emotional factors). Scenario-based scores were used to derive continuation, withdrawal, and variability indices. Two-step cluster analysis identified decision-orientation profiles. Group differences were analyzed using ANOVA, chi-square tests, and multinomial logistic regression. Among 377 respondents, four decision-orientation clusters emerged: contextual/proportional, selective continuation, conservative/continuation-leaning balancing, and consistent withdrawal. Several demographic and professional characteristics differed across clusters. Ethical domains also differed significantly. Multinomial regression showed that cultural and emotional factors were associated with contextual/proportional orientation, whereas beneficence independently predicted selective continuation orientation. Hydration volume and consideration of life expectancy differed across clusters, supporting behavioral distinctions among decision orientations. AH decision-making reflects multiple context-sensitive physician orientations shaped by both ethical considerations and professional characteristics, rather than a binary continuation-withdrawal model.

Understanding how conscious cognition remains stable under uncertainty, conflict, and perturbation requires a framework that links neural dynamics to the geometry of evolving representational states. Here we develop Recursive Informational Curvature (RIC), a neurogeometric framework in which conscious access is modeled as a stability regime of trajectories on a stratified informational manifold. In this framework, recursive gain, symbolic entropy dispersion, and loop-level timing coherence jointly determine whether neural activity remains within closure-supporting regimes or approaches collapse. We formalize this balance through an effective curvature index, [Formula: see text], defined relative to a declared critical boundary [Formula: see text], and through circulation-based timing statistics that quantify phase-organized loop stability. The theory integrates three coupled geometric layers: a Fisher layer for precision-weighted discriminability, a Finsler layer for direction-dependent transition cost, and a Hermitian layer for phase-coded recursive coordination. We further propose mechanistic hypotheses linking identifiable cortical neuronal classes, including mirror circuits, von Economo neuron-rich salience territories, TPJ mentalizing ensembles, and prefrontal phase-modulating hubs, to class-specific curvature control. To connect the framework to data, we specify measurement-facing estimators for gain, symbolic entropy structure, loop instability, and effective curvature, and we provide a reduced EEG-based empirical analysis showing that a geometry-sensitive neural state-space proxy is related to moral judgment bias, while broader socially mediated outcomes are not captured by this reduced measure alone. RIC therefore offers a formal and operational framework for studying stability, collapse, and recovery in conscious dynamics across theoretical, empirical, and translational settings.

Multiple sclerosis (MS) affects both Caucasian and African Americans in the U.S., with evidence of faster disability progression among African Americans. We compared physical and cognitive performance between groups and examined within-group associations between cognitive and motor outcomes (i.e., cognitive-motor coupling). Participants completed remote Zoom-based assessments, including the 30-Second Sit-to-Stand (30STS), Symbol Digit Modalities Test (SDMT), and the California Verbal Learning Test, Second Edition (CVLT-II). Data were analyzed using independent t-tests, analysis of covariance (ANCOVA), and Pearson correlations. The sample included 310 adults with MS (mean age=49.8&#xb1;12.3 years) who self-identified as Caucasian (n=206) or African American (n=104). Caucasian participants demonstrated higher 30STS (d=0.25, P<0.05) and CVLT-II (d=0.35, P<0.01) scores than African Americans participants, and differences remained after adjustment for age, disease duration, education, and income. Small-to-moderate correlations were observed between 30STS and both CVLT-II and SDMT for Caucasian (r=0.236, r=0.294) and African American (r=0.237, r=0.356) participants. African American participants with MS demonstrated lower verbal and physical performance than Caucasian participants. Cognitive-motor coupling was evident in both groups, indicating interconnected cognitive and physical function. We support the feasibility of remote assessment and highlight opportunities for interventions targeting both domains to improve MS outcomes.

We review the use of optimality in the investigation of the large-scale geometry of the DNA-protein complex of chromatin using a generalized information principle. Considering systems level optimality requires that not only should all sub-parts of a natural process be optimal, but this also be true for the unfolding of higher recursive levels that include symbolic abstractions. An information-theoretic geometry of the genetic code data, together with the principle of maximum entropy, explains the variation in the codon groupings that map into different amino acids and explains its underlying self-similar structure. This analysis is reviewed for the investigation of the fundamental geometry underlying physical and biological space, which has a dimensionality of e as it gets reflected in aggregates associated with genomic DNA. The analysis is consistent with the fractal dimension of chromatin, but other non-optimal structures with lower dimensionality also exist.