Fuzzy symbolic convergent cross mapping: A causal coupling measure for EEG signals in disorders of consciousness patients.

Set theory functions as a foundational structure in mathematics, underpinning logical reasoning and the interpretation of relationships across diverse mathematical contexts. Nevertheless, research in mathematics education indicates that students often experience difficulty transferring abstract set-theoretic concepts into effective strategies for solving contextual problems. This challenge reflects a critical pedagogical gap: the lack of a systematic instructional framework that explicitly links the logical structure of set theory to students’ problem-solving processes. To address this gap, the present study proposes a novel pedagogical construct, termed the Bridge Model of the Set Theory Framework, which is designed to mediate between conceptual understanding and applied problem-solving competence. The primary aim of this study is to develop and explicate the Bridge Model and to examine how students employ it to operationalize set-theoretic concepts when engaging with contextual mathematical problems. A qualitative research design using a case study methodology was adopted. Data were collected through classroom observations, semi-structured interviews with mathematics teachers and purposively selected students, and analysis of students’ written solutions. Participants were selected based on their demonstrated engagement with set concepts. Data analysis was conducted inductively using narrative and grounded theory approaches to identify patterns in students’ cognitive and representational practices. The findings reveal recurrent difficulties in students’ translation of contextual information into formal mathematical representations and result in a three-phase Bridge Model, namely problem decontextualization, symbolic mapping of sets, and logical solution validation. Theoretically, this study contributes to mathematics education literature by articulating a structured mechanism that connects abstract set theory with mathematical reasoning in context. Practically, the model offers a principled instructional guide for teaching set theory as a core logical tool, supporting students’ analytical reasoning and systematic problem-solving abilities.

Diffuse optical imaging (DOI) uses scattered light to non-invasively sense and image highly diffuse media, including biological tissues such as the breast and brain. Despite its clinical potential, widespread adoption remains limited because physical constraints, limited available datasets, and conventional reconstruction algorithms struggle with the strongly nonlinear, ill-posed inverse problem posed by multiple photon scattering. We introduce Diffuse optical Imaging using Genetic Programming (DI-GP), a physics-guided and fully interpretable genetic programming framework for DOI. Grounded in the diffusion equation, DI-GP evolves closed-form symbolic mappings that enable fast and accurate 2-D reconstructions in strongly scattering media. Unlike deep neural networks, Genetic Programming (GP) naturally produces symbolic expressions, explicit rules, and transparent computational pipelines—an increasingly important capability as regulatory and high-stakes domains (e.g., FDA/EMA, medical imaging regulation) demand explainable and auditable AI systems, and where training data are often scarce. DI-GP delivers substantially faster inference and improved qualitative and quantitative reconstruction performance compared to analytical baselines. We validate the approach in both simulations and tabletop experiments, recovering targets without prior knowledge of shape or location at depths exceeding ~25 transport mean-free paths. Additional experiments demonstrate centimeter-scale imaging in tissue-like media, highlighting the promise of DI-GP for non-invasive deep-tissue imaging and its potential as a foundation for practical DOI systems.

On a map apart from position, expressed by X and Y coordinatesposition, the basic qualitative visual variables by Bertin (1967) are form-shape, orientation and hue (Forrest 2015).A symbol map of nominal points can be used to represent the distribution of points of interest in a city by using signs with a complex graphic structure (Lambert & Zanin 2020).Such pictorial signs are quickly and intuitively interpreted because they are associated by graphical elements, e.g. a cup represents a coffeehouse.In these considerations, we will focus on visual variables for pictorial signs in immersive geovisualization in the threedimensional space of the city.Immersive geovisualization associated with the virtual reality system is here dedicated to direct observation of the streets and buildings in the city.It is in such a three-dimensional space that box symbols with an internal graphic structure are fitted.It is interesting what visual variables are appropriate for distinguishing the location of objects at a distance from the observer (Figure 1). Figure 1.Juxtaposition of visual variables in city immersive geovisualization: Aposition, Bposition and size, Cposition and transparency, Dposition and sharpness-blur, Eposition, size and transparency, and Fposition, sharpness-blur and transparency (Fragment Pozna city from

ABSTRACT Systems theory provides an alternative lens for understanding the self as a multi‐layered, recursive and autopoietic system. While the self can be experienced as a coherent unity, it can equally be understood as the dynamic interplay of subsystems and supersystems. Henriques' Unified Theory of Knowledge (UTOK) describes human evolution through four nested domains: matter, life, mind and culture, each domain transcending and including the previous one. Building on this framework, the present paper employs the story of The Wizard of Oz as a guiding allegory for systemic transformation. Dorothy's journey is interpreted as a symbolic mapping of identity formation, with her companions representing dimensions of the psyche: the Scarecrow as cognition, the Tin Man as emotion, the Lion as courage, Toto as intuition and the Wicked Witch as shadow. The revelation of the Wizard as a fraud underscores the fragility of socially constructed identity, while Dorothy's ongoing journey underscores the recursive and adaptive nature of selfhood. By integrating systems theory, narrative analysis and symbolic mapping, this paper offers a diagnostic framework for facilitators and theorists seeking to navigate rupture, repair and renewal in complex adaptive systems.

ABSTRACT Generative artificial intelligence (AI) holds significant promise for cartography, but its application is hindered by a fundamental tension between creative freedom and rule-based design. This study addresses this challenge by proposing and evaluating a model-agnostic “template-render” framework that decouples conceptual design (“template”) from visual synthesis (“render”), creating a more controllable workflow. We implement this with a two-step approach: a Large Language Model (LLM) generates a structured symbol description, which a Text-to-Image (T2I) model then renders. Our baseline evaluation demonstrates that while the unguided approach is technically feasible, its outputs are often cartographically unsuitable. We then show that by introducing a knowledge-guided prompt to the template stage, the quality, clarity, and fitness of the symbols are significantly improved. We further present the Map Symbol Agent (MSA), a prototype that automates this pipeline. Our work validates the effectiveness of this framework, while also systematically identifying critical future challenges, such as ensuring stylistic consistency and mitigating model biases. This study serves as a crucial exploratory step, charting a promising path and defining a research agenda for developing more controllable generative systems in specialized domains.

ABSTRACT In cartographic generalization, vector buildings often experience spatial overlay conflicts because of the application of generalization algorithms and the addition of map symbols, which affect the effect of map expression. To address this issue, this paper proposes a method based on deep reinforcement learning for solving spatial overlay conflicts of vector buildings (DRL‐SOCVB). The method involves constructing a conflict detection model using constrained Delaunay triangulation, taking the detected conflicting buildings as mobile agents, grouping them by area size, and establishing multiple constraints and a reward function. Through the interaction between and optimization of agents and the environment, it resolves spatial overlay conflicts while maintaining a stable spatial distribution. The results show that this method can effectively handle conflicts of different scales with high efficiency and is superior to traditional methods in terms of feasibility and timeliness, providing support for the efficient resolution of spatial overlay conflicts.

Evoking flavor memories through real food sounds: A preliminary study on crossmodal compensation in taste and smell disorders.

Abstract. The paper examines the evolution of symbology in mineral resource maps within Czech school atlases from 1952 to 2025. By analyzing changes in map content and symbology, the research aims to identify trends and variations in the depiction of mineral resources. The methodology includes statistical analysis of symbology, and user testing among students aged 12 to 15. The study selected atlases that represent all editions using unified symbology for mineral resource maps, focusing on eight different symbologies across 19 maps from 9 atlases. Findings reveal significant differences in symbology detail, scope, and consistency among the atlases. User testing indicated that students found chemical symbols challenging and preferred two symbologies for their comprehensibility. The research highlights the importance of systematic incorporation of map symbols into the curriculum and the crucial role of teachers in facilitating students' understanding of maps. Recommendations include improving the readability and comprehensibility of map symbols, adopting interdisciplinary approaches, and supporting inclusive education for students with learning disabilities. This study provides valuable insights into the development of map symbology in educational materials and offers practical suggestions for enhancing the effectiveness of school atlases. The findings contribute to a deeper understanding of how different approaches to map symbols influence their perception and comprehension, emphasizing the need for standardization and intuitive design in cartographic representation.

BackgroundMigrant workers in the Republic of Korea face substantial barriers to healthcare due to linguistic, legal, and financial constraints, particularly among low-skilled labourers in high-risk occupations. Since 2008, the Korea Foundation for International Healthcare has operated a mobile clinic programme to deliver essential medical services to migrant workers. Despite its critical role, no systematic evaluation has been conducted to date. We examined the design and implementation of the 2024 programme, with a focus on regional disparities and health equity.MethodsWe conducted a cross-sectional descriptive analysis of mobile clinic operations from May 2024 to March 2025 across 22 city- and district-level sites. We calculated coverage proportion as the number of clinic beneficiaries divided by the registered E-type visa holder population. We visualised spatial disparities using graduated symbol maps and analysed regional differences using the Kruskal-Wallis test, followed by Dunn’s test with Bonferroni correction.ResultsA total of 2336 migrant workers received services through 50 deployments, most of whom were male from Southeast and South Asia. Internal medicine (47.9%), general services (21.1%), and traditional Korean medicine (9.1%) were the most delivered services. Coverage ranged from 0.5% to 46.9% across regions. Higher coverage was observed in Seoul districts, while industrial hubs such as Hwaseong and Pyeongtaek showed lower outreach. There were no statistically significant differences in coverage proportions across the five regional clusters (H = 9.470; df = 4; P = 0.0503; 95% confidence interval = 4.37–17.51). However, post-hoc analysis identified a statistically significant difference between Seoul and Seoul Metropolitan Area excluding Seoul (z = 2.46; P = 0.0410; 95% confidence interval = 0.91–4.83). Civil society organisations played a central role in programme implementation.ConclusionsMobile clinics provide a scalable, equity-oriented model to enhance the health access of migrant workers. However, addressing geographic disparities requires data-driven site selection and institutionalised collaboration with civil society. Locally adapted mobile clinic strategies are crucial for ensuring equitable access and the sustainable integration of migrant workers into healthcare systems.RegistrationOpen Science Framework (https://osf.io/wx7nz/).

Integrated sensing and communication (ISAC) is a transformative technology for sixth-generation (6G) wireless networks. In this paper, we investigate end-to-end constellation mapping and demapping in ISAC systems, leveraging OFDM-based waveforms and an adaptive DNN architecture for pulse-based transmission. Specifically, we propose an end-to-end autoencoder framework that optimizes the constellation through adaptive symbol distribution shaping via deep learning, enhancing communication reliability with symbol mapping and boosting sensing capabilities with an improved peak-to-sidelobe ratio (PSLR). The autoencoder consists of an autoencoder mapper (AE-Mapper) and an autoencoder demapper (AE-Demapper), jointly trained using a composite loss function to optimize constellation points and achieve flexible performance balance in communication and sensing. Simulation results demonstrate that the proposed DNN-based end-to-end design achieves dynamic balance between PSLR of the autocorrelation function (ACF) and bit error rate (BER).

In this work, we present an unique implementation of delay line-free reservoir computing based on state-of-the-art photonic technologies, which exploits chaotic optical frequency comb formation in an optical microresonator as the nonlinear reservoir. Our solution leverages the high resonator quality (Q)-factor both for memory and for enhancing high-dimensional nonlinear mapping of input symbols. We numerically demonstrate the accurate prediction of about one thousand symbols in chaotic time series without the need of dedicated optimization for specific tasks. Our results will enable design of optical neuromorphic computing architectures combining on-chip integrability, low footprint, high speed, and low power consumption.

The impact of sports experience on map symbol representation in orienteering athletes and its neural correlates: Evidence from eye-tracking and fNIRS.

The Inner Biographical Map (IBM) is a visual-narrative model designed to help individuals build and understand their identity through lived experiences. By utilizing symbolic maps with colors, shapes, and spatial sequencing, IBM facilitates recognition of emotional turning points and the reinterpretation of past events. The study explores IBM as a framework for identity transformation, promoting self-reflection, coherence, and psychological integration. Participants engaged in interviews and created their own maps, which were analyzed using Interpretative Phenomenological Analysis (IPA) to uncover themes and emotional trajectories. The findings highlight a four-stage identity shift—disruption, meaning-making, emotional release, and integration—demonstrating a transition towards psychological coherence. IBM serves as both a research tool and a transformative framework that enhances self-awareness and integration of identity.

Abstract Focusing on the early history of the tea industry in Tamsui, Taiwan, this study uses land used data extracted from maps of the 1900s and 1920s to explore the regional characteristics and changes in the distribution of tea plantations. Map symbol detection modeling was performed using Artificial Intelligence deep learning techniques, which have been growing in the field of map research in recent years. Through the constructed symbol detection model, the land use annotation data of historical maps can be automatically retrieved for GIS-based spatiotemporal analysis. Thus, the study presents the impact of global economic panic and the failure of tea exportation in the 1920s on the local tea industry and reflects the tea plantation landscape in response strategies.

Representational Theories of Mind have long dominated Cognitive Translation Studies, typically assuming that translation involves the manipulation of internal representations (symbols) that stand in for external states of affairs. In recent years, classical representationalism has given way to more nuanced, inferential, interpretive, context-sensitive, and modern representational models, some of which align naturally with probabilistic and predictive approaches. While these frameworks remain broadly compatible with one another, radical enactivism offers a more disruptive alternative: it denies representational content altogether, viewing translation instead as an affectively grounded, context-sensitive, self-evidencing activity shaped by the translator’s embodied engagement with text, context, and sociocultural norms. From an enactivist standpoint, translation emerges not from static symbolic mappings, but from situated, embodied, and affectively modulated inference processes that dynamically negotiate meaning across languages. The paper provides a theoretical synthesis, arguing that the Free Energy Principle under Predictive Processing and Active Inference provides a suitable mathematical framework amenable to representational and enactive accounts.

Abstract Optical orthogonal time frequency space (O-OTFS) modulation provides resilient performance in doubly dispersive and high-mobility wireless channels by symbol mapping in the delay-Doppler plane. Like conventional multicarrier systems, OTFS has the drawback of high peak-to-average power ratio (PAPR), thus degrading power amplifier efficiency as well as causing nonlinear distortions. In this paper, a hybrid reduction scheme of PAPR is introduced by integrating the partial transmit sequence (PTS) method with a differential evolutionary (DE) optimization algorithm. The PTS approach partitions the OTFS time-frequency signal into subblocks and performs phase rotations, while DE minimizes the PAPR by optimizing the phase vector. Simulation results show that for 64, 256, and 512 subcarriers, the DE-PTS approach achieves PAPR reductions of about 4.9 dB, 5.8 dB, and 6.4 dB, respectively, at a CCDF of 10−3. Further, BER analysis indicates that DE-PTS achieves a reduced SNR to attain a BER of 10−3 from 19.2 dB (original OTFS) to 15.1 dB with a gain of 4.1 dB. These findings identify DE-PTS not only reduces PAPR dramatically but also improves BER performance without compromising system fidelity. The proposed methodology is computationally efficient and scalable and hence can be applied to real-time OTFS communication in future high-mobility wireless networks like 6G and vehicular communication.

Abstract Building on our previous work (Hiranouchi in Ann. K-Theory 9(3):499–518, 2024), we investigate an analogue of the differential symbol map used in the Bloch–Gabber–Kato theorem. Within this framework, for an appropriate variety over a field, the higher Chow group corresponds to the 0-th Kato homology group. Inspired by Akhtar’s theorem (Commun Algebra 32(1):279–294, 2004) on higher Chow groups, we investigate the structure of the 0-th Kato homology groups for varieties over arithmetic fields, including finite fields, local fields, and global fields of positive characteristic. We also express our results in terms of reciprocity sheaves.

We propose a probabilistic shaping (PS) scheme based on a single-layer lookup table (LUT) that employs only one LUT for symbol mapping while achieving favorable system performance. This scheme reduces the average power of the signal by adjusting the symbol distribution using a specialized LUT architecture and a flexible shaping proportion. The simulation results indicate that the proposed PS scheme delivers performance comparable to that of the conventional constant-composition distribution-matching-based probabilistic shaping (CCDM-PS) algorithm. Specifically, it reduces the bit error rate (BER) from 1.2376 ×10−4 to 6.3256 ×10−5, corresponding to a 48.89% improvement. The radial basis function neural network (RBFNN) effectively compensates for nonlinear distortions and further enhances transmission performance due to its simple architecture and strong capacity for nonlinear learning. In this work, we combine lookup-table-based probabilistic shaping (LUT-PS) with RBFNN-based nonlinear equalization for the first time, completing the transmission of 16-QAM OFDM signals over a photonic terahertz-over-fiber system operating at 400 GHz. Simulation results show that the proposed approach reduces the BER by 81.45% and achieves a maximum Q-factor improvement of up to 23 dB.

Clients in high-acuity psychological states, marked by developmental neglect, trauma, or existential dislocation, often retreat into internally constructed psychic worlds. This paper explores the phenomenon of “Inner Architecture” as a self-protective and narrative-engineering strategy, drawing from autoethnographic accounts and composite client narratives. It proposes a model of sovereign inner governance, detailing how rites, roles, and symbolic maps are constructed by those for whom the external world offered no safe harbour.© The Author(s) 2025. Published by RITHA Publishing. This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited maintaining attribution to the author(s) and the title of the work, journal citation and DOI.