Abstract This paper presents a multilayer model for analyzing the identity and transformation of symbols within writing systems (hereafter referred to as scripts). The model comprises five interconnected layers: topology, visual identity, phonetic, semantic, and style. The topology layer defines the geometric and structural attributes of the glyphs of each symbol. The visual identity layer captures canonical features shared across glyph variants of a symbol. The phonetic layer links symbols to sound values, where applicable. The semantic layer situates symbols within their linguistic and cultural contexts. The style layer accounts for graphical variations introduced by instruments, scribal practices, and aesthetic conventions. Together, these layers constitute a general symbol model that can be applied across diverse scripts. As demonstrated through selected case studies, the model supports computational paleography, cross-script comparison, and the analysis of undeciphered inscriptions, advancing the formal modeling of script evolution and facilitating computational comparison and analysis of manuscripts.

Arabic sentence construction is intrinsically built upon two pivotal elements: the predicate and the subject. These elements manifest as the nominal subject and predicate in nominal sentences, and as the verb alongside the actor or its equivalent in verbal sentences. Typically, a predefined sequence governs these components; however, this sequence can be modified for enhanced rhetorical effect. Such modifications, detailed by Abdul Qahir al-Jurjani, involve various adjustments like advancements and delays. These are not merely stylistic choices but are carefully implemented to preserve clarity and structural integrity, avoiding the need for rote memorization of sentence structure. By deviating from the conventional order, speakers can unveil deeper semantic layers and achieve specific lexical effects. This paper will explore these rhetorical strategies through a contemporary linguistic lens.

Digital twin (DT) platforms are increasingly used for PHM, yet most systems still lack real-time, bi-directional control between physical assets and virtual models, and a unified semantic layer that grounds natural-language commands in plant constraints. To address this gap, this research present Twinverse, an interactive metaverse environment that integrates a ROS/Kafka-based bi-directional DT, a knowledge-graph (KG) semantic backbone, and an LLM-powered agent. The KG encodes structural/operational constraints (e.g., kinematics, limits) and is serialized into a vector store to support RAG-based intent interpretation, while a constraint-aware execution pipeline verifies workspace, joint limits, and speed bounds prior to dispatch. Implemented on an industrial robot cell in Unity, the system provides real-time synchronization and multi-user operation within a single immersive interface. In evaluation, the platform maintained tight virtual–physical tracking and stable latency under increasing user load, and it enabled PHM-oriented functions such as anomaly interrogation and explainable, context-aware action generation. Our contribution is a cohesive DT–KG–LLM architecture that (1) grounds language-to-action in machine-readable plant constraints, (2) closes the loop from natural-language intent to verified execution, and (3) operationalizes PHM analytics inside an immersive DT environment. This work demonstrates a practical path toward interactive, explainable, and real-time PHM decision support.

Stop writing repetitive code! Scaffolding a semantic data access layer to abstract developers from semantic technologies

The article attempts to view parenthood as one of the dimensions of family biography, which determines the dynamics of the family system, both in the developmental and semantic layers. By systemic and biographical perspective author indicated a new, in pedagogical science, category of family biography, which is understood as multiplied (being more than the sum of individual biographies), subject to systemic rules of circularity and equipotentiality, endless reconstruction resulting from the tension between the course of family life and the story about the history of life. Recognition of the family as a dynamic and learning system enables the use of qualitative methods to study selected areas and topics of family life and diverse family structures. In this approach, parenthood is a dimension that constitutes the biography of the family system and is subject to research. Parenting is understood, on the one hand, as a biographical solution for a system that is subject to a dynamic equilibrium of tensions, and on the other hand, as a functional dimension of the family (along with cohesion, flexibility and communication). The author indicates the possibilities of qualitative research on the family system with the use of narrative and biographical methods.

Sintering processes play a critical role in materials manufacturing; however, their optimization remains highly dependent on empirical knowledge, fragmented datasets, and costly experimental trials. Existing modeling and machine learning approaches often lack a unified structure for representing complex relationships among processing parameters, microstructural evolution, and final material properties. This perspective article argues that knowledge graphs can serve as a missing semantic layer for organizing sintering-related data, enabling structured representation of process–property relationships across heterogeneous databases. Furthermore, the integration of autonomous AI agents equipped with memory-augmented learning models is proposed as a promising direction for continuously constructing, updating, and reasoning over such knowledge graphs. By combining structured knowledge representation with adaptive learning and agent-based optimization, this framework has the potential to transform sintering research into a self-improving, data-driven ecosystem. This perspective highlights future research directions toward intelligent, explainable, and autonomous sintering systems for advanced materials engineering.

This study addresses a significant scholarly gap concerning a lack of a comprehensive framework for systematically examining the connotative dimensions of fundamental Qurʾānic terms related to thinking (tafakkur, taʿaqqul, tadabbur, tafaqquh) and knowledge (ʿilm, maʿrifah). While the presence and importance of these terms in Qurʾān are widely acknowledged, existing scholarship has tended to prioritise their denotative or literal meanings, often focusing on a restricted subset of these terms, particularly those associated with Qurʾānic epistemological discussions. As a result, insufficient attention has been given to the contextual and connotative significance of these terms and to how their deeper semantic layers articulate the Qurʾān’s holistic worldview of thinking and knowledge. Consequently, this study proposes an innovative connotative analytical framework that categorises Qurʾānic thinking terms into four distinct semantic dimensions. It employs a qualitative method of textual analysis, drawing upon classical and contemporary Qurʾānic tafsīr literature, alongside studies on Qur’anic terminology (muṣṭalaḥāt Qurʾāniyyah), as its primary sources of data. The study demonstrates that a connotative and contextual interpretation of these terms reveals the dynamic, multi-layered, and holistic system of thought that characterises the Qurʾānic approach to human cognition and epistemology. Overall, it provides a comprehensive overview of these terms as foundational building blocks of an Islamic holistic epistemological framework of thinking and knowledge.

Early detection of Acute Lymphocytic Leukemia (ALL) as well as Multiple Myeloma (MM) is critical for reducing mortality rates. One promising new approach for the early detection of these blood malignancies is the Deep Learning (DL) model. However, in order to provide high-quality microscope images for highly accurate blood cancer detection, certain models do not include data diversity enhancement. In order to generate high-quality microscopic images for the prediction of ALL and MM, DeepBCDnet was developed utilizing Resolution Enhanced and Noise Suppression Generative Adversarial Network (RENS-GAN). However, a segmentation approach is necessary for this model to enhance its accuracy. Its scale-invariant structure ignores spatial variations across receptive fields, leading to misclassification of object edge pixels when using Mask Regional Convolutional Neural Network (Mask R-CNN) for image segmentation tasks. This research proposes R-Mask R-CNN, a Refined Mask R-CNN that fuses deep semantic and shallow high-resolution features in the Region Proposal Network (RPN) as well as Region of Interest (RoI) layers using an attention mechanism and a bottom-up structure. At the pixel level, this model successfully identifies and segments micrographs. By incorporating the bottom-up structure into Mask R-Feature Pyramid Network's (FPN) CNN, the path between the lower and top layers is shortened, leading to better usage of features from the lower layers. To fine-tune pixel-level focus, channel-wise and spatial attention methods apply weights to feature maps. A new semantic segmentation layer takes the place of the earlier fully connected (FC) layer; this layer allows for feature fusion through the construction of an FPN and the summing of backward and forward transmissions of feature maps of identical resolution. This layout enhances the data propagation between layers, which in turn improves the accuracy of detection and segmentation. In order to aid classification during segmentation, the network takes into account receptive fields of varying sizes all at once by combining input from multi-scale feature maps. Mask head structure optimizes feature fusion by adjusting the input image scale. Lastly, the forms of blood cancer (ALL and MM) are classified using Dense Convolutional Neural Networks (DCNNs). Deep Blood Cancer Segmentation and Detection network (DeepBCSDnet) is the entire name of the model. The DeepBCSDnet models outperform the state-of-the-art models in terms of accuracy, with 94.71% and 95.57% correspondingly achieved on the SN-AM Dataset, MiMM_SBILab, and C-NMC datasets, respectively.

Smart contracts are vulnerable to critical, design-level Business Logic Flaws (BLFs) that conventional analysis tools often fail to detect. To address this semantic gap, this study introduces a novel ontological framework that formally models the link between high-level architectural intent and low-level Sui Move code. The methodology employs a rigorous Linked Open Terms (LOT) approach to construct a comprehensive ontology, integrated with a library of secure design patterns and process-aware Object-Centric Dynamic Condition Response (OC-DCR) graphs. Qualitative validation was conducted on four canonical security patterns (Access Control, Circuit Breaker, Time Incentivization, Escapability) drawn from the official Sui Framework, confirming the framework’s representational adequacy and logical consistency. Ultimately, this work contributes the first machine-readable semantic layer for Sui Move, decoupling reasoning from raw code availability, and providing the essential semantic foundation for the future development of pattern-aware auditing tools.

In sensor-based monitoring systems, the rapid and accurate recognition of alarm semantic levels is essential for maintaining operational reliability. Traditional static visualizations often fail to communicate these distinctions effectively under time pressure, whereas dynamic stacked bar charts (DSBCs) integrate multiple semantic layers into a compact, dynamic display. This study systematically investigated how color cues applied to auxiliary visual elements (background, foreground, labels, and scale lines) and chart orientation (horizontal vs. vertical) affect users’ alarm recognition performance. Thirty-two participants completed a semantic alarm recognition task involving DSBCs with various combinations of color-coded elements and orientations. Reaction time (RT) and accuracy (ACC) were analyzed using mixed-effects regression models. The results revealed that color cues in foreground and labels significantly enhanced both RT and ACC, whereas background and scale line color cues produced negligible effects. Orientation exerted a significant main effect on RT but not on ACC. Participants responded faster to horizontally oriented charts, indicating improved scanning efficiency. Moreover, increasing the number of color cues yielded higher ACC and shorter RTs, supporting a redundancy gain effect. However, no interaction was found between color cues and orientation, suggesting that these factors influence performance through distinct cognitive pathways. The findings align with theories of attentional guidance, redundancy gain, and spatial compatibility, and offer practical recommendations for alarm visualization design. Consequently, designers are advised to prioritize color coding of perceptually dominant elements, employ horizontal layouts in time-critical contexts, and implement redundant but non-overwhelming cues to enhance alarm recognition in complex sensor-based monitoring environments.

Continuous and personalized monitoring are beneficial for patients suffering from neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease and multiple sclerosis. However, such levels of monitoring are seldom ensured by traditional models of care. This paper presents NeuroPredict, a secure edge–cloud Internet of Medical Things (IoMT) platform that addresses this problem by integrating commercial wearables and in-house sensors with cognitive and behavioral evaluations. The NeuroPredict platform links high-frequency physiological signals with periodic cognitive tests through the use of a modular architecture with lightweight device connectivity, a semantic integration layer for timestamp alignment and feature harmonization across heterogeneous streams, and multi-timescale data fusion. Its use of encrypted transport and storage, role-based access control, token-based authentication, identifier separation, and GDPR-aligned governance addresses security and privacy concerns. Moreover, the platform’s user interface was built by considering human-centered design principles and includes role-specific dashboards, alerts, and patient-facing summaries that are meant to encourage engagement and decision-making for patients and healthcare providers. Experimental evaluation demonstrated the NeuroPredict platform’s data acquisition reliability, coherence in multimodal synchronization, and correctness in role-based personalization and reporting. The NeuroPredict platform provides a smart system infrastructure for eHealth and remote monitoring in neurodegenerative care, aligned with priorities on wearables/IoMT integration, data security and privacy, interoperability, and human-centered design.

This article analyzes the linguocultural features of the female image in Korean and Uzbek folk proverbs. It compares the values, gender stereotypes, views on family life, and attitudes toward women found in the proverbs of both nations. During the study, the semantic, pragmatic, and cultural layers of paremiological units related to women are interpreted, and their similarities and differences are identified. The findings show that although both Korean and Uzbek proverbs recognize women as an essential pillar of society, there are noticeable distinctions in the intensity of patriarchal attitudes and their cultural foundations.

Detecting fine, weak-textured defects with discontinuous boundaries on complex industrial surfaces is challenging due to interference from background textures and characters, as well as the scarcity of labeled data. To address this issue, we propose YOLO-SR, an engineering modification of YOLO11 tailored to defect segmentation on smart-card surfaces. Rather than introducing a new detection architecture, YOLO-SR reuses the backbone–neck–head design of YOLO11 and only adjusts a few modules to better capture elongated, low-contrast defects. The approach comprises two key components: first, embedding Strip Pooling (SP) within the C3K2 module to form C3K2_SP; second, a Rectangular Self-Calibration Module (RCM) is interposed after the top-level semantic layer. RCM generates rectangular gates to spatially recalibrate local responses, suppressing interference from complex textures and characters. To mitigate data scarcity and distributional bias, a texture-adaptive procedural defect synthesis strategy was developed. This strategy generates defect samples that conform to the background texture statistics of high-quality backgrounds. Experiments on the integrated circuit chip (ICChip) and signature plate (SignPlate) datasets show that YOLO-SR outperforms the YOLO11 baseline. Results indicate that SP and RCM complement each other by integrating directional priors from mid-to-high layers with top-level shape self-calibration. This enhances the visibility and localization stability of elongated defects while maintaining efficient inference.

The quality of metadata plays a crucial role in many data FAIRification processes. So much so, in fact, that all the four main principles of data FAIRification prescribe the use of high-quality metadata. One of the main data management paradigms where metadata is a first-class citizen is Ontology-Based Data Management (OBDM). The goal of OBDM is to provide users with a reconciled view of a set of heterogeneous data sources by means of a semantic metadata layer comprising an ontology and a mapping. The former is a high-level, declarative representation of the domain of interest written in terms of a logical theory, and the latter is a formal description of the relation between the symbols in the ontology and the data at the sources. In this article, we introduce a novel data quality framework based on OBDM and specifically tailored for metadata analysis. The target of this framework is one of the most common forms of metadata currently in circulation, i.e., the integrity constraints defined by a database schema. Specifically, we will focus on the data quality dimension known as Consistency, i.e., the property of data that is free of contradictions and incoherence. In this context, our techniques provide a set of tools to compare the integrity constraints defined by a database schema against the knowledge encoded in an ontology and check whether these constraints are strict enough (i.e., protect) and are not too strict (i.e., are faithful to) for such knowledge. The contribution of the article is the presentation of the framework and the study of the related computational problems. We will present a detailed computational complexity analysis of such problems and show that they are decidable for classes of OBDM specifications and integrity constraints that are very popular in practice.

The article examines the numerological code and numerical symbolism in the Ukrainian poetic tradition of the military discourse within the period of the full-scale invasion. The research focuses on the ways numbers operate as semiotic, mnemonic, and existential categories, mediating between trauma, language, and memory. The purpose of study is to identify the functional potential of numerical markers in shaping the artistic and semantic dominants, as well as the ontological dimensions of modern war poetry, based on the collection Poems from the Embrasure by Maksym Kryvtsov and the anthology Love 2.0: Love and War. The main objectives include detecting and systematizing the types of numerical symbolism, tracing the transformation of archetypal numerical images under traumatic experiences, establishing correlations between numerological codes and the existential states of the lyrical subject, and substantiating the significance of the numerological code as a means of artistic representation of collective trauma and postmemory. The methodological framework integrates structural-semiotic, hermeneutic, comparative, and contextual approaches, as well as motif and symbolic analysis. The study draws on the Western tradition of numerical criticism (W.F. Hopper, A. Fowler, C. Butler) and adapts its conceptual tools to the Ukrainian literary context, where numbers function as a hybrid of linguistic signs, mythopoetic archetypes, and ethical categories. Enkvist’s distinction between statistical and symbolic numbers, Waterfield’s archetypal interpretations of numerical triads, and Pythagorean integrity, as well as Godwin’s analysis of completeness in sacred numerology, have been incorporated. The study employs Batts’s approach to numerical structures in literary texts along with Medieval transcendent significance, while adopting Fisher’s methodology for detecting the “noble numbers” within the poetic architecture and kabbalistic semantic layers specific to individual poetics. Hopper’s cultural-contextual framework for numerical symbolism and Major’s analysis of symbolic systems across traditions inform the cross-cultural dimension of this investigation, complemented by Moretti’s quantitative methodologies for analyzing numerical distribution patterns. Focusing specifically on lyrical poetry, this research examines how numerical codes operate within the poetic systems at both structural-compositional and symbolic-archetypal levels, studying how contemporary lyrical texts may employ deliberate semantic ambiguity in their numerical architecture, wherein meaning becomes fluid and participatory, transforming numerical language into an existential poetic gesture that embodies postmodern interpretive multiplicity. Thus, the approach enables the interpretation of numerical symbolism not merely as an element of poetic form, but as a dynamic mode of cognitive and emotional processing of wartime experiences. The results demonstrate differentiated numerical paradigms that fluctuate according to the speaker’s discursive position. The combatant discourse reveals somatic numerology (50×50 cm, 120 kg), military- technical codification (200, 300, b/k, MARCH), and the metrics of liminal endurance (500 meters under fire, counting to one hundred before the attack), expressing the bodily and procedural nature of survival. Conversely, the rear discourse features blurred numeration (endless days, countless strings) and symbolic temporal dilation (50 days as half a century), articulating the psychological stretching of time and the instability of perception under prolonged uncertainty. It has been established that the numerological code performs several core functions: depersonalizing (numbers instead of names, like 234, 457, 451), sacralizing (biblical and archetypal numbers, including 3, 5, 14, 33), temporal-traumatic (dates 2014, 24.02.2022 as mnemonic nodes of collective experience), and existential-meditative, where counting becomes a ritual of resistance and a means of preserving mental integrity. Numbers thus transcend their quantitative nature, transforming into ontological markers of war, mediating between speech and silence, presence and absence, memory and oblivion. The study concludes that the numerological code in Ukrainian war poetry after 2022 constructs a distinctive semiotic and philosophical model through which poets articulate the ineffable dimensions of trauma and convert loss into a form of symbolic creation. The number emerges as a vehicle of apophatic expression, a language of the unspeakable that preserves the sacred memory of war within the evolving cultural space of Ukrainian resistance.

This article presents a novel middleware architecture designed to address critical information exchange challenges in disaster response ecosystems. The middleware framework facilitates seamless integration between siloed humanitarian organizations through standardized APIs, secure data exchange protocols, and IoT integration capabilities optimized for austere environments. The socio-technical systems theory provides the theoretical foundation, highlighting how effective disaster response emerges from the intersection of technological infrastructure and organizational practices. By examining current interoperability challenges and existing integration approaches, the article identifies a substantial gap in standardized middleware protocols for emergency coordination. The proposed open-source, cloud-native architecture incorporates semantic interoperability layers, privacy-preserving data exchange mechanisms, and resilient communication capabilities designed to function across diverse connectivity conditions. A detailed case study from Indian healthcare networks demonstrates how middleware implementation transformed emergency response coordination, reducing resource mobilization delays while enabling complex cross-organizational resource sharing. Mixed-methods assessment reveals both technical performance improvements and the critical organizational factors that influence adoption patterns across different institutional contexts. The cost-benefit analysis establishes the economic viability of middleware implementation compared to traditional coordination methods, while highlighting how network effects enhance value as additional organizations join the system.

This article proposes a critical framework for understanding the intersection of three dynamic fields: Digital Humanities (DH), studies of transnational mobility, and environmental narrative. Moving beyond the traditional confines of textual analysis, it argues that the methodological toolkit of the Digital Humanities-including geospatial mapping, network analysis, and large-scale text mining-provides the essential means to trace, visualize, and analyze the complex relationships between human movement and environmental imagination across national and cultural boundaries. The article first establishes the theoretical underpinnings of this convergence, drawing from ecocriticism, postcolonial theory, and mobility studies. It then presents a conceptual "Three-Dimensional Analysis Framework" that integrates spatial, relational, and semantic layers of inquiry. Through illustrative case studies, the article demonstrates how DH methods can map the "eco-geographies" in transnational novels, uncover the networked agencies in climate change discourse, and track the semantic shifts in environmental rhetoric across different cultural contexts. The analysis reveals that this triple convergence not only expands the scale and precision of literary and cultural analysis but also fosters a more nuanced, systemic understanding of the global ecological crisis as a narratively constructed and materially consequential phenomenon. The article concludes by addressing the ethical and methodological challenges of this approach and posits its potential for fostering a more planetary, interdisciplinary mode of humanistic inquiry.

The article examines the phenomenon of text-centrism in Russian street art and analyzes the continuity between the conceptual art of the late Soviet period and contemporary street art. The relevance of the study is due to the growing interest in the use of text in paintings and works in public spaces, which has become a characteristic feature of contemporary Russian art. Using the example of the works of Erik Bulatov, one of the founders of Russian conceptualism, and Yekaterinburg street artist Timofey Rad, the use of text as a meaning-forming element in street art is considered. The study applies the methods of formal-stylistic, iconographic and historical analysis of works of art, which allow us to identify the features of the artistic language and semantic layers of the works. Particular attention is paid to the Yekaterinburg art scene as a unique regional phenomenon with a tolerant cultural environment for the development of street art, where special conditions for experimenting with text in the urban space have been formed. The study revealed a stable tradition of working with text in Russian art, originating in Soviet posters, propaganda banners and slogans of the early 20th century, which formed a certain approach to creating font compositions and a way of communicating with the viewer in a dialogue format. The key role of institutional support in the development of regional artistic practices and the formation of a national language of text-centric street art is determined. The results of the study show that text-centrism in Russian street art is characterized by a desire for direct dialogue with the viewer and an understanding of the word as a powerful tool for transforming public space and changing its semantic content. Contemporary Russian street art demonstrates clear continuity with the conceptual practices of the late Soviet period in the field of working with text, forming a special approach to creating font compositions and communicating with the viewer, based on cultural memory and current social contexts.

Cloud migration and information integration have historically required deep manual intervention at each stage of dependency mapping, schema alignment, hazard assessment, and workload orchestration, often resulting in huge operational bottlenecks that scale poorly with elevated architectural complexity. Most traditional migration frameworks rely on static blueprints and reactive decision-making approaches, which are unable to support a dynamic multi-cloud environment that is driven by ever-changing regulatory requirements, sustainability mandates, and heterogeneous platform constraints. Fortunately, the advent of artificial intelligence abilities brings with it unprecedented possibilities for enhancing architectural decision-making through intelligent automation. The article introduces the Cognitive Cloud Architect paradigm, where large language models enable semantic interpretation of system metadata across syntactically divergent platforms, reinforcement learning agents optimise migration flows through continuous policy refinement based on multi-objective performance landscapes, and generative AI systems synthesise executable infrastructure blueprints from natural language intent specifications. This cognitive architecture model repositions human architects from tactical configuration specialists toward strategic orchestrators of intelligent automation, allowing them to preserve governance authority while delegating computational optimisation burdens to AI agents. Implementation patterns are shown to illustrate approaches to integrating these technologies into existing toolchains by adding semantic pre-processing layers, sidecar learning services, and API-mediated blueprint generation. Governance frameworks that address AI decision transparency, accountability mechanisms, and validation checkpoints ensure that automated intelligence enhances and does not circumvent human oversight, thereby maintaining organizational control of consequential migration decisions in the enterprise.

In the AI era, high-value targeted injection attacks and defences based on the semantic layer of Large Language Models will become the main battlefield for security confrontations. Ultimately, any form of artificial information warfare boils down to a battle at the semantic level. This involves using information technology to attack the semantic layer and, consequently, the human brain. Specifically, the goal is to launch targeted attacks on the brains of specific decision-making groups within society, thereby undermining human social decision-making mechanisms. The ultimate goal is to maximize value output in the fields of political economy, religion, and ideology, including wealth and power, with minimal investment in information technology. This paper uses the pyramid model perspective to unify the information security confrontation protocol stack, including biological intelligence, human intelligence, and artificial intelligence. It begins by analysing the characteristics and explainable of AI models, and feasible means of their multi-dimensions offensive and defensive mechanisms, proposing an open engineering practice strategy that leverages semantic layer gaming between LLMs. This strategy involves targeted training set contamination at the semantic layer and penetration induction through social networks. At the end of this article, expands the contamination of training set data sources to the swarm oscillating environment in human-machine sociology and ethical confrontation, then discusses attacks targeting the information cocoon of individuals or communities and extends the interaction mechanism between humans and LLMs and GPTs above the semantic layer to the evolution dynamics of a Fractal Pyramid Model.