Search Results (4,511)

Contemporary architecture is undergoing a transformation from the modernist techno-functional paradigm towards practices that integrate technology with humanistic, cultural, and environmental values. Biophilia—understood as the innate human need for contact with nature—is becoming an important design category that supports health, well-being, and ecological awareness, yet it can also convey additional narratives. In this context, immersion plays a significant role: it is a process of deep engagement of the user with space, involving the senses, emotions, and imagination, while simultaneously fostering relationships between humans and their surroundings. The concept of immersiveness, originating in art theory and digital media studies, is now applied in architecture as a tool for creating spatial narratives and cultural experiences. Biophilic architecture employs immersive strategies to transform buildings into environments that support sensory, behavioural, and social practices. This article analyses selected examples of such projects (including the Rooftop Garden—Warsaw University Library, Musée du quai Branly, and apartment buildings Bosco Verticale) and proposes a Multi-criteria Method for Assessing Architectural Immersiveness (MMAAI). The findings indicate that the integration of nature, technology, and spatial narrative enables architecture to act as a mediator between humans and the environment, generating new qualities of spatial experience in the Anthropocene epoch. Full article

The rapid growth of technology has introduced robots into daily life, necessitating navigation frameworks that enable safe, human-friendly movement while accounting for social aspects. Such methods must also scale to situations with multiple humans and robots moving simultaneously. Recent advances in Deep Reinforcement Learning (DRL) have enabled policies that incorporate these norms into navigation. This work presents a socially aware navigation framework for mobile robots operating in environments shared with humans and other robots. The approach, based on single-agent DRL, models all interaction types between the ego robot, humans, and other robots. Training uses a reward function balancing task completion, collision avoidance, and maintaining comfortable distances from humans. An attention mechanism enables the framework to extract knowledge about the relative importance of surrounding agents, guiding safer and more efficient navigation. Our approach is tested in both dynamic and static obstacle environments. To improve training efficiency and promote socially appropriate behaviors, Imitation Learning is employed. Comparative evaluations with state-of-the-art methods highlight the advantages of our approach, especially in enhancing safety by reducing collisions and preserving comfort distances. Results confirm the effectiveness of our learned policy and its ability to extract socially relevant knowledge in human–robot environments where social compliance is essential for deployment. Full article

Theological empiricism is the view that knowledge of God must ultimately rest on human experience. This puts it in opposition to theologies that rely exclusively on conceptual analysis and biblical revelation, or some combination of the two. Theological empiricism is not new. It has forerunners in the natural theology of the 18th century, and the appeal to feeling and intuition characteristic of some 19th-century theologians. What is new is the concept of ‘experimental theology’ and the suggestion that in seeking to secure an empirical basis for knowledge of God, theology should turn to the methods characteristic of the natural sciences. This paper argues that empiricism in theology is more plausible if it resists this suggestion. It gives special attention to the faculty of imagination in both science and art, and seeks to articulate the ways in which literature, painting, music and architecture can be said to embody empirical knowledge of a broadly theological kind. Full article

Understanding and accurately estimating cognitive workload is crucial for the development of adaptive, user-centered interactive systems across a variety of domains including augmented reality, automotive driving assistance, and intelligent tutoring systems. Cognitive workload assessment enables dynamic system adaptation to improve user experience and safety. In this work, we introduce a novel framework that leverages smooth pursuit eye movements as a non-invasive and temporally precise indicator of mental effort. A key innovation of our approach is the development of trajectory-independent algorithms that address a significant limitation of existing methods, which generally rely on a predefined or known stimulus trajectory. Our framework leverages two solutions to provide accurate cognitive load estimation, without requiring knowledge of the exact target path, based on Kalman filter and B-spline heuristic classifiers. This enables the application of our methods in more naturalistic and unconstrained environments where stimulus trajectories may be unknown. We evaluated these algorithms against classical supervised machine learning models on a publicly available benchmark dataset featuring diverse pursuit trajectories and varying cognitive workload conditions. The results demonstrate competitive performance along with robustness across different task complexities and trajectory types. Moreover, our framework supports real-time inference, making it viable for continuous cognitive workload monitoring. To further enhance deployment feasibility, we propose a federated learning architecture, allowing privacy-preserving adaptation of models across heterogeneous devices without the need to share raw gaze data. This scalable approach mitigates privacy concerns and facilitates collaborative model improvement in distributed real-world scenarios. Experimental findings confirm that metrics derived from smooth pursuit eye movements reliably reflect fluctuations in cognitive states induced by working memory load tasks, substantiating their use for real-time, continuous workload estimation. By integrating trajectory independence, robust classification techniques, and federated privacy-aware learning, our work advances the state of the art in adaptive human–computer interaction. This framework offers a scientifically grounded, privacy-conscious, and practically deployable solution for cognitive workload estimation that can be adapted to diverse application contexts. Full article

Background: The combination of multi-omics data, including genomics, transcriptomics, and epigenomics, with medical imaging modalities (PET, CT, MRI, histopathology) has emerged in recent years as a promising direction for the advancement of precision oncology. Many researchers have contributed to this domain, exploring the multi-modality aspect of using both multi-omics and image data for better cancer identification, subtype classifications, cancer prognosis, etc. Methods: We present an umbrella review summarizing the state of the art in fusing imaging modalities with omics and artificial intelligence, focusing on existing reviews and meta-analyses. The analysis highlights early, late, and hybrid fusion strategies and their advantages and disadvantages, mainly in tumor classification, prognosis, and treatment prediction. We searched review articles until 25 May 2025 across multiple databases following PRISMA guidelines, with registration on PROSPERO (CRD420251062147). Results: After identifying 56 articles from different databases (i.e., PubMed, Scopus, Web of Science and Dimensions.ai), 35 articles were screened out based on the inclusion and exclusion criteria, keeping 21 studies for the umbrella review. Discussion: We investigated prominent fusion techniques in various contexts of cancer types and the role of machine learning in model performance enhancement. We address the problems of model generalizability versus interpretability within the clinical context and argue how these multi-modal issues can facilitate translating research into actual clinical scenarios. Conclusions: Lastly, we recommend future work to define clearer and more reliable validation criteria, address the need for integration of human clinicians with the AI system, and describe the trust issue with AI in cancer care, which requires more standardized approaches. Full article

Generative models can generate art within a single modality with high fidelity. However, translating a work of art from one domain to another (e.g., painting to music or poem to painting) in a meaningful way remains a longstanding, interdisciplinary challenge. We propose a novel approach combining a multi-agent system (MAS) architecture with an ontology-guided semantic representation to achieve cross-domain art translation while preserving the original artwork’s meaning and emotional impact. In our concept, specialized agents decompose the task: a Perception Agent extracts symbolic descriptors from the source artwork, a Translation Agent maps these descriptors using shared knowledge base, a Generator Agent creates the target-modality artwork, and a Curator Agent evaluates and refines the output for coherence and style alignment. This modular design, inspired by human creative workflows, allows complex artistic concepts (themes, moods, motifs) to carry over across modalities in a consistent and interpretable way. We implemented a prototype supporting translations between painting and poetry, leveraging state-of-the-art generative models. Preliminary results indicate that our ontology-driven MAS produces cross-domain translations that preserve key semantic elements and affective tone of the input, offering a new path toward explainable and controllable creative AI. Finally, we discuss a case study and potential applications from educational tools to synesthetic VR experiences and outline future research directions for enhancing the realm of intelligent agents. Full article

Bimodal emotion recognition based on audio and text is widely adopted in video-constrained real-world applications such as call centers and voice assistants. However, existing systems suffer from limited cross-domain generalization and monolingual bias. To address these limitations, a cross-lingual bimodal emotion recognition method is proposed, integrating Mamba-based temporal encoders for audio (Wav2Vec2.0) and text (Jina-v3) with a Transformer-based cross-modal fusion architecture (BiFormer). Three corpus-adaptive augmentation strategies are introduced: (1) Stacked Data Sampling, in which short utterances are concatenated to stabilize sequence length; (2) Label Smoothing Generation based on Large Language Model, where the Qwen3-4B model is prompted to detect subtle emotional cues missed by annotators, producing soft labels that reflect latent emotional co-occurrences; and (3) Text-to-Utterance Generation, in which emotionally labeled utterances are generated by ChatGPT-5 and synthesized into speech using the DIA-TTS model, enabling controlled creation of affective audio–text pairs without human annotation. BiFormer is trained jointly on the English Multimodal EmotionLines Dataset and the Russian Emotional Speech Dialogs corpus, enabling cross-lingual transfer without parallel data. Experimental results show that the optimal data augmentation strategy is corpus-dependent: Stacked Data Sampling achieves the best performance on short, noisy English utterances, while Label Smoothing Generation based on Large Language Model better captures nuanced emotional expressions in longer Russian utterances. Text-to-Utterance Generation does not yield a measurable gain due to current limitations in expressive speech synthesis. When combined, the two best performing strategies produce complementary improvements, establishing new state-of-the-art performance in both monolingual and cross-lingual settings. Full article

White matter, comprising 60% of the human brain, is formed by axonal fibers supported by oligodendrocytes. It is essential for brain communication, yet damage can accumulate silently leading to severe neurological problems. Current diagnostics detect changes only after symptoms appear. To enable earlier detection damage, we developed a blood test monitoring changes in oligodendrocyte-derived extracellular vesicles (ODEs) released from the brain into circulation. After validating the assay, we have shown that ODE levels vary from different individuals. However, ODE levels remain stable under mild head impacts in soccer heading practice (n = 15) and boxing/mixed martial arts (n = 10), whereas change markedly following neurological insults such as hemorrhagic (n = 7) and ischemic stroke (n = 14), or gynecological cancer after chemotherapy (n = 11). ODE measurement can potentially provide a minimally invasive window into white matter health and support early diagnosis, personalized assessment, and new insights into human brain biology. Full article

Sperm cryopreservation is a key technique in assisted reproductive technologies (ART), livestock breeding, fertility preservation, and wildlife conservation. However, the freeze–thaw process induces significant oxidative stress through the production of reactive oxygen species (ROS) by mitochondria, which can lead to impaired sperm motility, membrane damage, DNA fragmentation, and reduced fertilization potential. MitoQ is a mitochondria-targeted antioxidant consisting of a ubiquinone moiety conjugated to triphenylphosphonium (TPP⁺). MitoQ selectively accumulates in the mitochondrial matrix, where it efficiently scavenges reactive oxygen species (ROS) at their point of origin. This targeted action helps preserve mitochondrial function, sustain ATP production, and inhibit apoptotic signaling. Extensive experimental evidence across diverse species, including bulls, rams, boars, humans, dogs, and goats, shows that MitoQ supplementation during cryopreservation enhances post-thaw sperm viability, motility, membrane integrity, and DNA stability. Optimal dosing between 50 and 150 nM achieves these benefits without cytotoxicity, although higher doses may paradoxically increase oxidative damage. Compared to conventional antioxidants, MitoQ offers superior mitochondrial protection and enhanced preservation of sperm bioenergetics. Future directions involve exploring synergistic combinations with other cryoprotectants, advanced delivery systems such as nanoparticles and hydrogels, and detailed mechanistic studies on long-term effects. Overall, MitoQ represents a promising adjunct for improving sperm cryopreservation outcomes across clinical, agricultural, and conservation settings. Full article

Human Immunodeficiency Virus (HIV) infection is associated with a wide spectrum of urological manifestations, reflecting both the direct effects of viral infection and the indirect consequences of immunosuppression, opportunistic infections, malignancies and long-term combined antiretroviral therapy (cART). This narrative review provides a contemporary, multifaceted overview of the clinical and pathological presentations of urological conditions in people living with HIV (PLWHIV), based on articles published between 1989 and 2025. Conditions discussed include HIV-associated nephropathy (HIVAN), opportunistic genitourinary infections, malignancies such as Kaposi sarcoma and lymphoma, as well as non-infectious complications such as HIV-associated nephropathy and erectile dysfunction (ED). The review highlights the evolving epidemiology of these conditions in the cART era, with a noted decline in opportunistic infections but a rising burden of chronic kidney disease and malignancies, largely due to improved survival and ageing of the HIV-positive population. Pathological insights are explored and discussed, including mechanisms of HIV-associated renal injury, such as direct viral infection of renal epithelial cells and genetic predispositions linked to Apolipoprotein L1 (APOL1) variants. In addition, psychosocial factors, including anxiety, stress, stigma, and alcohol use, are discussed, as they may contribute to late presentation to clinical urology services. The review also considers the challenges faced in low and middle-income countries, the impact of HIV on urological services, and the important role of palliative care in advanced disease. Ultimately, this review underscores the need for early recognition, comprehensive diagnostic and surgical evaluation, and integrated social, psychological, and palliative management strategies tailored to the unique needs of PLWHIV. A deeper understanding of the interplay between HIV, cART, psychosocial determinants, and urological health is essential for improving patient outcomes and guiding future research in this evolving field. Full article

Autonomous systems, such as self-driving cars, surgical robots, and space rovers, require efficient and collision-free navigation in dynamic environments. Geometric optimal navigation and path planning have become critical research areas, combining geometry, optimization, and machine learning to address these challenges. This paper systematically reviews state-of-the-art methodologies in geometric navigation and path planning, with a focus on integrating advanced geometric principles, optimization techniques, and machine learning algorithms. It examines recent advancements in continuous optimization, real-time adaptability, and learning-based strategies, which enable robots to navigate dynamic environments, avoid moving obstacles, and optimize trajectories under complex constraints. The study identifies several unresolved challenges in the field, including scalability in high-dimensional spaces, real-time computation for dynamic environments, and the integration of perception systems for accurate environment modeling. Additionally, ethical and safety concerns in human–robot interactions are highlighted as critical issues for real-world deployment. The paper provides a comprehensive framework for addressing these challenges, bridging the gap between classical algorithms and modern techniques. By emphasizing recent advancements and unresolved challenges, this work contributes to the broader understanding of geometric optimal navigation and path planning. The insights presented here aim to inspire future research and foster the development of more robust, efficient, and intelligent navigation systems. This survey not only highlights the novelty of integrating geometry, optimization, and machine learning but also provides a roadmap for addressing critical issues in the field, paving the way for the next generation of autonomous systems. Full article

Natural disasters have various adverse effects on human lives, making it challenging for authorities to manage post-disaster situations with limited resources. Due to the extreme extent of the damage, the huge amount of resources needed to restore life to normality makes such a situation challenging. For this purpose, different methodologies have been proposed to effectively handle these types of situations. All these methodologies consider different aspects of the post-earthquake context, taking into account core parameters such as the time and cost required for reconstruction, as well as the people directly affected by the earthquake. In this paper, we conduct a Systematic Literature Review (SLR) of various state-of-the-art techniques proposed for different phases of post-earthquake situations, specifically for reconstruction planning with sustainability considerations. All these proposed solutions are differentiated on the basis of input data, parameters, and type of solutions (data sciences, civil engineering, socio-economics, and modelling). The time range chosen to filter out relevant studies is between 2000 and 2025. Eventually, we reviewed 55 related articles out of 47,539 analysed from seven different digital libraries. The findings of this SLR reveal that optimization and simulation-based approaches dominate the current research landscape, with a growing trend toward data-driven and AI-assisted reconstruction planning. However, only a few studies focus on integrating socio-economic, environmental, and physical infrastructure aspects, which represents a major research gap. These findings provide insights that can guide future researchers in designing more comprehensive frameworks to improve post-earthquake reconstruction in a sustainable manner by prioritising economic, social, and environmental infrastructures, as well as facilities for affected individuals, thereby utilising available resources more effectively. Full article

Consisting of phospholipids, sperm membranes surround the head and tail, playing essential roles in maintaining cellular structural integrity and functions. Their characteristics directly influence sperm fertility and cryopreservation outcomes. This minireview provides a summary of how sperm fertility and freezability are affected by the characteristics of its cell membranes. The primary emphasis is on the molecular and cellular anatomy as well as the physiology of sperm membranes and their attributes associated with fertility determinants or biomarkers for fertility and freezability. It also explores how this knowledge can guide the development of extenders to improve sperm freezability and enhance reproductive technologies in mammals. By providing integrity, fluidity, and selective permeability, the membranes play vitally important roles in sperm motility, which is required for successful fertilization. Cryopreservation, which involves freezing and thawing of sperm for storage or ART, alters the integrity and functionality of the sperm membranes. Sperm freezability, its viability following freezing and thawing, is influenced by several properties of the sperm cell membranes, such as lipid composition, cholesterol content, and structures and functions of the membrane proteins. This review provides concise information about the nature of sperm membranes. It highlights the importance of understanding specific biophysical and biochemical features, including lipid composition, protein distribution, and membrane phase behavior. Particular attention is given to parameters such as the cholesterol–phospholipid ratio and membrane phase transition temperature (Tm). A deeper understanding of these factors can contribute to the identification of reliable fertility biomarkers and the optimization of cryopreservation techniques used in ART and animal breeding programs. Furthermore, this review underscores the need for comprehensive investigations into the molecular and cellular architecture of sperm cells. Such studies are essential for advancing both fundamental and applied aspects of reproductive biology in food-producing animals, endangered species, and humans. Full article

Background: The binding of glycosaminoglycans (GAG) to Wnt signaling components plays a key regulatory role in bone formation and regeneration. We previously reported de novo designed chemically modified hyaluronan derivatives, named _REGAG (Rationally Engineered GAG), which demonstrated bone-regenerative properties in a mouse calvaria defect model. To gain initial insights into the pharmacological profile of two _REGAG currently under preclinical investigation in mice, we performed a comprehensive in silico investigation of their binding to human and murine serum albumin (HSA and MSA), as it might influence their ADME properties. Furthermore, we evaluated whether _REGAG binding might impact the recognition of well-characterized HSA-binding drugs. Methods: State-of-the-art in silico ADMET tools, docking and molecular dynamics simulations were used to predict and characterize the interaction of _REGAG with HSA and MSA, and to investigate the molecular mechanisms involved at the atomic level. Results: The investigated _REGAG molecules show a consistent binding preference for the FA1 site in both proteins, and an additional preference for the FA7 site in HSA. Their recognition might induce protein conformational changes and alter the functional state. Furthermore, _REGAG’s conformational adaptability is predicted to influence their binding to the FA5/6 and FA8/9 sites of HSA, and to the FA3/4 and FA7 sites of MSA. Conclusions: Our investigations predict the binding of two hyaluronan derivatives to HSA and MSA. The mechanistic insights gained into the molecular recognition of these two _REGAG molecules offer valuable information for their potential clinical application and serve as a rational basis for future molecular design aimed at improving pharmacokinetic properties. Full article

Deaf and hearing-impaired individuals rely on sign language, a visual communication system using hand shapes, facial expressions, and body gestures. Sign languages vary by region. For example, Arabic Sign Language (ArSL) is notably different from American Sign Language (ASL). This project focuses on creating an Arabic Sign Language Recognition (ArSLR) System tailored for healthcare, aiming to bridge communication gaps resulting from a lack of sign-proficient professionals and limited region-specific technological solutions. Our research addresses limitations in sign language recognition systems by introducing a novel framework centered on ResNet50ViT, a hybrid architecture that synergistically combines ResNet50’s robust local feature extraction with the global contextual modeling of Vision Transformers (ViT). We also explored a tailored Vision Transformer variant (SignViT) for Arabic Sign Language as a comparative model. Our main contribution is the ResNet50ViT model, which significantly outperforms existing approaches, specifically targeting the challenges of capturing sequential hand movements, which traditional CNN-based methods struggle with. We utilized an extensive dataset incorporating both static (36 signs) and dynamic (92 signs) medical signs. Through targeted preprocessing techniques and optimization strategies, we achieved significant performance improvements over conventional approaches. In our experiments, the proposed ResNet50-ViT achieved a remarkable 99.86% accuracy on the ArSL dataset, setting a new state-of-the-art, demonstrating the effectiveness of integrating ResNet50’s hierarchical local feature extraction with Vision Transformer’s global contextual modeling. For comparison, a fine-tuned Vision Transformer (SignViT) attained 98.03% accuracy, confirming the strength of transformer-based approaches but underscoring the clear performance gain enabled by our hybrid architecture. We expect that RAFID will help deaf patients communicate better with healthcare providers without needing human interpreters. Full article