This article offers a theoretical and applied discussion of how restitution may be re-interpreted for Ottoman provincial mosques by focusing on the Hacı Kaya Mosque in Balıkesir, a structure that still stands today but whose original, first-phase configuration cannot be definitively identified. Due to the absence of surviving evidence for its initial plan, structure, and ornamentation, conventional document-based restitution approaches prove inadequate. Instead, the study proposes a multi-layered, representation-oriented model grounded in ethical, typological, and contextual analysis. Restitution is thus reframed not merely as a physical reconstruction, but as an epistemic practice involving architectural representation, cultural continuity, and spatial memory. The research follows a three-phase methodology. First, it discusses the conceptual and ethical limits of restitution as defined in international charters and academic literature. Second, the historical continuity of Hacı Kaya Mosque is examined using tahrir registers, cadastral data, and oral histories, while spatial clues related to the early phase are traced through the morphological evolution and naming consistency of its neighborhood. Third, a comparative typological analysis is conducted with four early Ottoman mosques in Bursa—Şehabettin Paşa, Şeker Hoca, İsa Bey, and Alaattin Bey—to generate plausible restitution scenarios for the plan layout, roofing type, and spatial organization. The article concludes that restitution should not aim for absolute certainty, but rather operate as an ethical research activity that makes architectural memory visible through alternative representations. The interpretive restitution model developed here provides a flexible and replicable framework for other provincial mosques lacking material or documentary evidence.

Abstract. The integration of digital technologies aimed at the knowledge of the existing built heritage is now a well-established practice in the scientific field. The objective remains the optimization of methodologies intended to coordinate different needs, ranging from the integration of technical and documentary aspects to the enhancement of communication and dissemination of knowledge of heritage assets. While Heritage Building Information Modelling (HBIM) represents the most widespread methodology for Facility Management (FM), enabling control over the entire life cycle of buildings, the challenge lies in integrating Extended Reality (XR) tools, which allow operators to consult, update, and interact immersively with digital models, expanding their potential both for technical management and for heritage valorization. In this direction, the contribution proposes a workflow that integrates Augmented Reality (AR) into Heritage Building Information Modelling (HBIM) processes, enabling real-time updating of information through on-site observations. The case study concerns the complex of Santa Maria del Rifugio in Naples (Italy), characterized by historical stratifications from the 15th to the 20th century and by complex structural and conservation conditions. Starting from the digital survey, the information model was enriched with dimensional and analytical metadata, synchronized in AR through the structuring of a Visual Programming Language (VPL) algorithm that promotes the overlap between real and virtual through Image Targets, interaction with data stored in the BIM Common Data Environment (CDE), and their real-time updating. The result is an interactive system that enhances understanding, management, and valorization of cultural heritage, offering an engaging cognitive and immersive representation of architecture.

Animal models have been key for disease mechanism research for years, but ethical concerns partly fuelled by the realisation that not all diseases can be recapitulated in animal systems have led to regulatory changes that are driving a shift towards animal-free alternatives. As regulations continue to evolve, the transition to animal-free models is becoming increasingly crucial for laboratories aiming to comply with new standards without compromising on scientific progress. The past decade has seen a boost in the development of animal-free three-dimensional models including explants, co-cultures, spheroids, organoids, and organ-on-chip systems, creating a varied landscape that has significantly transformed disease research. These models incorporate advancements in stem cell technology, bioengineering, and microfluidics to provide more physiologically relevant systems that bridge the gap between traditional two-dimensional cell culture and in-vivo studies. Whilst traditional two-dimensional cultures offer a cost-effective method with replicable results, they fail to accurately represent the natural structure of tissues and cell-cell interactions. By contrast, animal-free culture systems provide a more appropriate representation of human physiology and tissue architecture with relevance to in-vivo conditions. Therefore, these models enable more translatable research outcomes and have the potential to provide data for the reduction of the high failure rates currently marring clinical trials. This review explores the evolution, advantages, and applications of animal-free models in advancing human disease research and refining preclinical studies with an emphasis on cancer research.

Single-cell RNA sequencing (scRNA-seq) allows transcriptomic profiling at single-cell resolution, providing valuable insights into cellular diversity across tissues, developmental stages, and diseases. However, accurately identifying cell types remains challenging due to the high dimensionality, sparsity, and noise inherent in scRNA-seq data. To address these challenges in cell type identification in scRNA-seq data, we introduce scAURA (single cell Alignment- and Uniformity-based Graph Debiased Contrastive Representation Architecture), a unified framework that integrates graph debiased contrastive learning with self-supervised clustering. We evaluated scAURA on 18 real single-cell datasets collected from six sequencing platforms spanning diverse tissue and cell types in human and mouse. scAURA outperformed all state-of-the-art (SOTA) methods in nine and eight datasets in Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI), respectively. On average, scAURA obtained average ranks of 2.28 (ARI) and 2.39 (NMI) across all 13 SOTA methods, demonstrating its consistent superiority across datasets. scAURA also exhibited strong robustness to dropout noise by maintaining stable clustering performance even under increasing sparsity levels. Furthermore, in an external single-cell Alzheimer’s disease dataset, scAURA accurately clustered different cell types, identified novel cell type–specific marker genes, and inferred their potential transcriptional regulators. The source code and datasets are available at https://github.com/bozdaglab/scAURA.Contact:Serdar.Bozdag@unt.edu

Designing effective neural architectures remains a central challenge in deep learning, and Neural Architecture Search (NAS) has become a popular tool for automating this process. However, many existing NAS approaches depend on hand-crafted architecture descriptors or shallow performance predictors, which fail to capture the structural complexity of candidate networks and often lead to unreliable search guidance. We introduce Graph Embedding Comparator with Isomorphic Multi-Comparison (GEC-IMC), an evolutionary NAS framework that learns architecture representations directly from their graph structure. A graph convolutional network encodes architectures into embeddings, while a contrastive learning strategy ensures that architectures with similar accuracy are mapped closer in the embedding space. On top of these embeddings, a comparator estimates the relative performance between two architectures, enabling more precise pairwise assessments during search. To further increase robustness, GEC-IMC incorporates an isomorphic multi-comparison mechanism, which evaluates multiple structurally equivalent variants of each architecture and aggregates their pairwise outcomes into a global score. This ranking score provides consistent feedback for evolutionary selection. Experiments on standard NAS benchmarks demonstrate that GEC-IMC achieves state-of-the-art performance with improved robustness over existing predictors. Ablation studies confirm the complementary roles of embedding learning and multi-comparison in enhancing search efficiency.

Modern warfare has changed the understanding of strategy, tactics, and the use of new types of equipment, both in terms of operational-tactical actions and new technologies in military systems. The emergence of UAVs of various types has enabled their use not only on the battlefield but also deep behind enemy lines. However, to achieve parity with the adversary in military operations, it is necessary to ensure the rapid implementation of innovations in the development of new military equipment, with the ability to scale and mass-produce the required volume of weaponry. Therefore, research on establishing a new set of design activities that will reduce the development the life cycle (LC) of military equipment development is relevant. The subject of the study is the development of analytical and simulation models that make it possible to analyze and plan the necessary project activities to reduce the LC of high-tech products (UAVs). The purpose of the study is to create a set of mathematical and simulation models that enable the formation of a new product architecture, the optimization of key project indicators, and defining project activities aimed at LC reduction under limited capabilities. The tasks addressed include: systematically analyzing the LC of developing a high-tech product with a focus on its reduction; creating a new method for forming the product architecture using a component-based approach; establishing parallel design processes that will ensure LC reduction; optimizing the time, cost, and risks of project activities under resource constraints; modelling of the proposed activities for LC reduction in the development of a high-tech product (UAV). The research utilizes a range of mathematical methods and models, including: system analysis for establishing the sequence of project activities that ensure LC reduction; a model of component-based representation of the new product architecture; a method for selecting a rational design variant using lexicographic ordering; a model for optimizing time and cost under acceptable project risks; agent-based simulation of the sequence of project activities to ensure LC reduction for new equipment development. The following results were achieved: a system representation of project activities for LC reduction was proposed; a component architecture of the new product was substantiated; the basic architecture was adapted to the requirements of developing new equipment; a set of possible design variants for creating new equipment was generated, with the selection of a rational one; time and cost optimization was conducted within acceptable levels of project risk; and a simulation of the sequence of project activities was performed. Conclusions: the proposed approach makes it possible to justify project activities for reducing the development LC of new equipment, enabling the rapid production of relevant systems under the special conditions of the country. The scientific novelty of the approach lies in the scientific justification of project activities for LC reduction using a component-based method and the parallel execution of design processes, which ensures the rapid development of military equipment and enables the mass production of relevant systems for the frontline.

Reconnecting building occupants with nature while encouraging sustainability has become more important due to ongoing urbanization and growing environmental concerns. In order to enhance both the natural environment and the well-being of residents. This study investigated the role of biophilic design in sustainable housing, aiming to develop a framework that guides architectural decisions, fosters restorative living environments, and informs future designs that strengthen the human-nature connection. This research addresses a research gap, as the existing body of knowledge is limited in its investigation of how biophilic design principles can be effectively integrated into residential architecture, particularly sustainable housing, to enhance occupant well-being and environmental performance. With the aid of a qualitative approach, the research analyzed biophilic design principles adopted in housing design and their impacts on sustainable housing delivery. A review of thirty-one articles published between 2004 and 2024 was conducted through internet-based searches in academic databases, including Scopus, Google Scholar, ScienceDirect, ResearchGate, and the Covenant University Repository. The collected data were subjected to content analysis. The findings informed the development of a biophilic design framework and a corresponding architectural proposal. The results were presented descriptively, supported by visual architectural representations to enhance clarity and comprehension. The study proposed a framework for integrating biophilic design into sustainable housing, exemplified by a design constructed from upcycled metal shipping containers. Biophilic architecture offers innovative solutions in residential projects, fostering hope for the future of sustainable housing. The study highlighted the alignment of biophilic principles with the Sustainable Development Goals (SDGs), promoting well-being (SDG 3), urban sustainability (SDG 11), climate change mitigation (SDG 13), and biodiversity conservation in urban ecosystems (SDG 15). The research adds to the discourse on biophilic architecture in housing projects, showcasing its opportunities and applications for sustainable housing schemes.

ABSTRACT Integrated sensing and communication (ISAC) is a key technology for next‐generation cognitive radio (CR) systems that allows for the smooth coexistence of several wireless services and effective spectrum utilization. A comprehensive review of current developments in ISAC antenna configurations specifically designed for CR applications is provided in this article. The significant representations of CR architecture are dielectric resonator antenna (DRA)‐based CR systems and microstrip patch antenna (MPA)‐based CR systems. A communication and sensing antenna enables CR antennas to perform multiple purposes. The communication antenna, which is combined with a reconfigurable filter, offers narrowband operation for dependable data transmission, while the sensing antenna, which is usually constructed as an ultrawideband (UWB) structure, allows spectrum awareness by collecting a wide range of frequencies. The study covers key design approaches, reconfigurability strategies, and trade‐offs related to ISAC antennas in CR systems, focusing on their use in adaptive wireless communication and dynamic spectrum access. The review paper also addresses the design, challenges, and performance parameters of CR.

Three-dimensional, self-organizing structures derived from stem cells, known as organoids, represent a groundbreaking advancement in preclinical drug development. Organoid-based platforms advance preclinical testing by providing an accurate representation of human tissue architecture and genetics, surpassing traditional two-dimensional cultures and animal models in testing both drug safety and efficacy. Researchers are shifting toward organoid-based systems as primary components of new approach methodologies, as global regulatory bodies increasingly acknowledge animal testing limitations. This review delivers an exhaustive examination of organoid technologies and their applications in drug testing. Our study explores current methods used to model toxic responses in different organs-such as the liver, kidney, and heart-while highlighting how personalized and disease-specific organoids can enhance the accuracy of efficacy testing. Our investigation also examines regulatory frameworks and outlines the path toward organoid platform standardization and validation before their integration into drug development processes. Complex neural organoids show great promise but continue to face significant challenges, including biological variability, a lack of universal standards, and ethical concerns. The combination of organoid technology with microengineering techniques, artificial intelligence-based analysis, and high-throughput screening methods represents a transformative change in translational medicine. Organoid-based systems represent both scientific breakthroughs and ethical necessities, as they provide human-specific data while reducing dependence on animal testing. If organoid development progresses with regulatory approval, it could fundamentally transform drug discovery and safety evaluation methods.

Architectural representation is a fundamental part of architecture. Architectural representation is not merely a tool used to convey architectural ideas; it is also a multi-layered phenomenon that can be approached in terms of the cultural, technological, and social dimensions of architecture. Therefore, revealing the trends in academic production in this field is of critical importance for future research. This study aims to identify trends and potential research gaps in the field by examining international publications on architectural representation in the Web of Science (WoS) database using bibliometric analysis methods. Bibliometric analyses in the study were performed using the Bibliometrix program and included the distribution of publications in the field of architectural representation by year, the distribution of publications by country, sources’ production over time, trend topics, thematic map, and co-occurrence network analysis. The findings reveal that four main topics stand out in architectural representation studies: the design process, design education, digital technologies, and cultural heritage. However, a lack of studies focusing on the relationship between architectural representation and spatial experience was identified. By identifying current trends and potential research gaps related to architectural representation, the study provides a guiding framework for future academic research.

This study explores an alternative design methodology for architectural image generation using generative AI, addressing the challenge of how AI-generated imagery can preserve formative tendencies while enabling creative variation and user agency. Departing from conventional prompt-based approaches, the process utilizes only a minimal initial image set and proceeds by reintroducing solely the synthesized outcomes during the blending and iterative synthesis stages. The central research question asks whether AI can sustain and transform architectural tendencies through iterative synthesis despite limited input data, and how such tendencies might accumulate into consistent typological patterns. The research examines how formative tendencies are preserved and transformed, based on four aesthetic elements: layer, scale, density, and assembly. These four elements reflect diverse architectural ideas in spatial, proportional, volumetric, and tectonic characteristics commonly observed in architectural representations. Observing how these tendencies evolve across iterations allows the study to evaluate how AI negotiates between structural preservation and creative deviation, revealing the generative patterns underlying emerging AI typologies. The study employs SSIM, LPIPS, and CLIP similarity metrics as supplementary indicators to contextualize these tendencies. The results demonstrate that iterative blending enables the deconstruction and recomposition of archetypal formal languages, generating new visual variations while preserving identifiable structural and semantic tendencies. These outputs do not converge into generalized imagery but instead retain identifiable tendencies. Furthermore, the study positions user selection and intervention as a crucial mechanism for mediating between accidental transformation and intentional direction, proposing AI not as a passive generator but as a dialogical tool. Finally, the study conceptualizes such consistent formal languages as “AI Typologies” and presents the potential for a systematic design methodology founded upon them as a complementary alternative to prompt-based workflows.

With the rising ubiquity of digital touch devices and sketch-based interfaces, freehand sketching has become an essential mode of visual communication. Nevertheless, interpreting these often ambiguous and sparse sketches poses challenges for computers. This paper presents Sketchformer++, a hierarchical transformer architecture for the neural representation of vector sketches. It treats a vector sketch as a three-level structure, at sketch level, stroke level, and segment level. Three self-attention modules are adopted in the network architecture, corresponding to the sketch hierarchy. The semantics of sketches are aggregated from local to global levels, resulting in neural representations of sketches. Extensive experiments show that Sketchformer++ helps to achieve superior performance in various downstream tasks, including sketch reconstruction, sketch recog-nition, sketch semantic segmentation, and sketch retrieval, demonstrating its robustness and effectiveness as a means of sketch representation. Code is available at https://github.com/BHR7/SketchformerPlus.

The cognitive neuropsychology of action semantics: A review.

This study delves into the intricate relationship between cinema and architecture by analyzing how architectural space is transformed into cinematic space, particularly within the framework of historical reality. Both cinema and architecture are mediums through which social and cultural meanings are conveyed. In architecture, spaces are shaped by historical processes and carry layers of social- cultural significance, while in cinema, these spaces are depicted as structures that encapsulate symbolic and cultural signs. Using Roland Barthes' semiotic theory with a focus on the concepts of meaning, connotation and myth, this research focuses on Bodies, an 8-part British miniseries adapted by Paul Tomalin from Si Spencer's graphic novel. The series spans four distinct time periods—1890, 1941, 2023, and 2053—and is examined for its representation of historical reality through the lens of architectural elements, technological advancements, and everyday practices of these eras. The architectural styles explored in this study include Victorian architecture, particularly Gothic revival and Queen Anne styles, as well as modernism, post-modernism, and dystopian futuristic designs that depict the evolution of space over time. This study explores how cinema constructs and reimagines historical reality through architectural representation, emphasizing the polysemic nature of cinematic images and the role of audience interpretation. Drawing on Roland Barthes’ semiotic concepts of denotation, connotation, and myth as articulated in Mythologies, The Death of the Author, and The Pleasure of the Text, the research examines how filmic representations of space render historically constructed meanings as natural and inevitable. In this context, the research presents a semiotic framework for understanding the relationship between cinema and space in social, temporal and cultural contexts and the relationship between cinematic space and historical periods. The study draws attention to the importance of spatial design in visual media and emphasizes how architectural spaces are not only a backstage plan but also function as a historical and ideological narrative tool. The findings contribute to interdisciplinary debates by demonstrating how cinematic space becomes a tool of historical ideology, offering a new semiotic lens to architectural and visual analysis.

To develop a self-supervised scan-specific deep learning framework for reconstructing accelerated multiparametric quantitative MRI (qMRI). We propose REFINE-MORE (REference-Free Implicit NEural representation with MOdel REinforcement), combining an implicit neural representation (INR) architecture with a model reinforcement module that incorporates MR physics constraints. The INR component enables informative learning of spatiotemporal correlations to initialize multiparametric quantitative maps, which are then further refined through an unrolled optimization scheme enforcing data consistency. To improve computational efficiency, REFINE-MORE integrates a low-rank adaptation strategy that promotes rapid model convergence. We evaluated REFINE-MORE on accelerated multiparametric quantitative magnetization transfer imaging for simultaneous estimation of free water spin-lattice relaxation, tissue macromolecular proton fraction, and magnetization exchange rate, using both phantom and in vivo brain data. Under 4× and 5× accelerations on in vivo data, REFINE-MORE achieved superior reconstruction quality, demonstrating the lowest normalized root-mean-square error and highest structural similarity index compared to baseline methods and other state-of-the-art model-based and deep learning approaches. Phantom experiments further showed strong agreement with reference values, underscoring the robustness and generalizability of the proposed framework. Additionally, the model adaptation strategy improved reconstruction efficiency by approximately fivefold. REFINE-MORE enables accurate and efficient scan-specific multiparametric qMRI reconstruction, providing a flexible solution for high-dimensional, accelerated qMRI applications.

This study investigates the factors influencing residential preferences for detached houses in North-Central Algeria, as well as the associated social and architectural representations, in order to forecast future housing developments and impose structure on the disorder arising from the proliferation of this model nationwide. The research also examines the conceptual representations and idealized frameworks of this architectural production through a qualitative methodology utilizing a semi-structured questionnaire survey conducted with its creators. NVivo text analysis software was employed to reconstruct recurring themes in residents’ discourse, and word clouds were produced. The analysis results reveal that residential decisions are shaped by both rational and emotional factors – most notably by economic status – and by a “residential imaginary” characterized by frustration and perseverance. These findings highlight the complex interplay between socioeconomic realities and subjective aspirations in shaping housing preferences in North-Central Algeria.

The tendency in the visualization of architecture is better observed in the transformation of the architectural visualization as inert image to the multisensory and active digital mediation. Audio-responsive visualization is one of these new directions, providing a contentious conceptual base on which to reconsider the process of corporate translation of spatial perception, affect and environmental awareness into real-time architectural imagery. In this study, the researcher presents the concept of a hybrid conceptual-technical model that can be used to connect musical feature extraction and adaptive rendering rules so that the visual outputs can evolve continuously depending on the sonic features. At the crossroads of computational design, architectural representation, interactive media, and phenomenology, the paper outlines the theoretical basis as well as the system process by which sound-based visualization can be accomplished. The conceptual part investigates the role of rhythm, timbre, frequency, and amplitude in the formulation of spatial interpretation of the musical qualities that could be brought forth as generative stimulants in the architectural imaging logics. The technical part presents a prototype workflow described with short time fourier transforms (STFT), spectral centroid tracking, rhythmic onset detection and energy mapping to affect color dynamics, geometry deformation, particle behavior and environmental lighting. The hybrid paradigm is then understood through the representational agency, digital materiality and human perceptual agency. These findings imply that audio-responsive visualization extends the architectural visualization beyond traditional photorealism and still composition to produce images that are immersive, time-based and full of affect, and appeal to modern representational cultures. The research paper then provides a conclusion on the implications of the study to real-time rendering practice, design pedagogy, public installations, and future interdisciplinary research at the convergence of architecture, sound studies, and computational media.

Background: Examining the influence of phonological neighborhoods on the early stages of visual word recognition provides insights into the architecture and dynamics of lexical representation and processing. Methods: Using event-related potentials (ERPs), this investigation explored how phonological neighborhood density (PND; large vs. small) and type (PNT; tone-edit vs. constituent-edit neighbors) influence the recognition of monosyllabic words in Mandarin Chinese. Participants engaged in a priming paradigm combined with a visual lexical decision task. Results: Behavioral data demonstrated the main effect of PNT: words with tone-edit neighbors produced greater processing inhibition compared to those with constituent-edit neighbors. ERP results revealed that large PND enhanced the P200 amplitude, a frontal-mediated effect that was particularly pronounced for tone-edit neighbors. This early differentiation subsequently propelled a stronger N400 response to tone-edit neighbors, culminating in a significant interaction between PND and PNT during the N400 window. Conclusions: These findings support a cascaded competition model: early PND assessment (P200), enhanced for tone neighbors, amplifies their later N400 conflict. This neural mechanism elucidates the hierarchical organization of phonological processing in Chinese monosyllabic words, thereby clarifying a core component which underpins the recognition of more complex words in Mandarin.

Abstract This paper critically examines the ethics of attributing national identity to architecture, focusing on how built form becomes a medium of cultural representation. While architecture often reflects collective memory and tradition, assigning it an exclusive ethnic or national label risks obscuring its pluralistic origins and shared authorship. Through theoretical reflection and firsthand experience, the study explores how monuments, motifs, and civic spaces can be appropriated into political narratives, often undermining inclusivity and nuanced interpretation. Drawing on global examples and nationalist framings in the Balkans, the paper highlights how identity becomes embedded in form, reinforcing symbolic boundaries. Particular attention is given to Skanderbeg Square in Skopje, where its transformation from an inclusive civic platform to an ethnonational site illustrates the fragile balance between cultural specificity and public function. This case reflects wider trends in which public space is instrumentalized to serve dominant narratives. The paper argues for reflective design practices that engage with history and culture without collapsing into exclusivity. It calls for recognizing the layered and transnational character of architectural meaning as essential in pluralistic societies, especially in contexts marked by ethnic or political division. Methodologically, the paper adopts a qualitative, theoretical-analytical approach combining critical literature review with reflexive case-based analysis drawn from the author’s professional involvement, offering an original ethical perspective on architectural representation in multicultural contexts.

A controllable generative design framework for residential communities with multi-scale architectural representations