Search Results (661)

The historical pipe organ, an instrument of vast scale and complex construction, has a significant connection to liturgical celebration and to the history of European churches. It is also one of the few musical instruments considered to be a work of architecture. The evolution of organ building, especially in the seventeenth to nineteenth centuries, required deep knowledge of musical culture and technology. The significance of this relationship is illustrated by the example of the former and present organs of the church of St. Mary Magdalene in Wroclaw (Breslau). The first church organs appeared here in the Middle Ages, and as will be shown, in subsequent eras, their location, form, and décor were changed according to evolving cultural and liturgical mandates as well as changes to the structure of the church architecture. The history of the specific organs of the church of St. Mary Magdalene is the product of a rich history of monumental construction, reconstruction, conservation, and restoration, and it is poised to offer a continuation of this tradition in the present and future of the parish and in music history with proposed restorations and renovations of their historic space and instruments. Full article

Microcontroller units (MCUs) serve as the core components of embedded systems. In the era of smart IoT, embedded devices are increasingly deployed on mobile platforms, leading to a growing demand for low-power consumption. As a result, low-power technology for MCUs has become increasingly critical. This paper systematically reviews the development history and current technical challenges of MCU low-power technology. It then focuses on analyzing system-level low-power optimization pathways for integrating MCUs with artificial intelligence (AI) technology, including lightweight AI algorithm design, model pruning, AI acceleration hardware (NPU, GPU), and heterogeneous computing architectures. It further elaborates on how AI technology empowers MCUs to achieve comprehensive low power consumption from four dimensions: task scheduling, power management, inference engine optimization, and communication and data processing. Through practical application cases in multiple fields such as smart home, healthcare, industrial automation, and smart agriculture, it verifies the significant advantages of MCUs combined with AI in performance improvement and power consumption optimization. Finally, this paper focuses on the key challenges that still need to be addressed in the intelligent upgrade of future MCU low power consumption and proposes in-depth research directions in areas such as the balance between lightweight model accuracy and robustness, the consistency and stability of edge-side collaborative computing, and the reliability and power consumption control of the sensor-storage-computing integrated architecture, providing clear guidance and prospects for future research. Full article

Spiking Neural Networks (SNNs), with their event-driven and energy-efficient characteristics, have shown great promise in processing data from neuromorphic sensors. However, the sparse and non-stationary nature of event-based data poses significant challenges to optimization, particularly when using conventional algorithms such as AdamW, which assume smooth gradient dynamics. To address this limitation, we propose MemristiveAdamW, a novel algorithm that integrates memristor-inspired dynamic adjustment mechanisms into the AdamW framework. This optimization algorithm introduces three biologically motivated modules: (1) a direction-aware modulation mechanism that adapts the update direction based on gradient change trends; (2) a memristive perturbation model that encodes history-sensitive adjustment inspired by the physical characteristics of memristors; and (3) a memory decay strategy that ensures stable convergence by attenuating perturbations over time. Extensive experiments are conducted on two representative event-based datasets, Prophesee NCARS and GEN1, across three SNN architectures: Spiking VGG-11, Spiking MobileNet-64, and Spiking DenseNet-121. Results demonstrate that MemristiveAdamW consistently improves convergence speed, classification accuracy, and training stability compared to standard AdamW, with the most significant gains observed in shallow or lightweight SNNs. These findings suggest that memristor-inspired optimization offers a biologically plausible and computationally effective paradigm for training SNNs on event-driven data. Full article

Keratoconus (KC) is a progressive, multifactorial corneal ectatic disorder characterized by localized stromal thinning and irregular astigmatism, with incidence and prevalence varying markedly among populations. These differences are influenced by environmental exposures, behavioral factors, and genetic predisposition. A positive family history is a well-established high-risk factor, and KC has also been documented in association with syndromic disorders such as Down syndrome, connective tissue disorders, and certain metabolic diseases. Over the past decades, numerous candidate genes have been investigated, encompassing those involved in extracellular matrix (ECM) assembly, collagen synthesis and cross-linking, oxidative stress defense, wound healing, and transcriptional regulation. Modern genomic approaches, including genome-wide association studies (GWAS), linkage analyses, and next-generation sequencing, have identified multiple loci and variants with potential pathogenic roles. Nonetheless, several genes have also been systematically tested and found to show no association in specific populations, highlighting the genetic variability of KC and the potential influence of population-specific factors. This dual landscape of positive and negative genetic findings underscores the complexity of KC pathogenesis and the necessity for ethnically diverse cohorts. In this review, we synthesize current evidence on genes implicated in KC, integrating confirmed pathogenic variants, associations, and negative findings across diverse populations, to provide a comprehensive overview of the genetic architecture of KC and to outline priorities for future research aimed at improving diagnosis, risk stratification, and therapeutic development. Full article

Background and Clinical Significance: Mucinous tubular and spindle cell carcinoma (MTSCC) is an uncommon subtype of renal cell carcinoma, representing 1–4% of epithelial renal tumors. It usually shows a low-grade morphology and indolent behavior, although sarcomatoid variants with an aggressive course have been described. Because of its overlap with papillary renal cell carcinoma (papRCC), sarcomatoid RCC, mesenchymal tumors, and oncocytic neoplasms, diagnosis requires the integration of imaging, histopathology, and immunohistochemistry. Case Presentation: We report a 71-year-old female who presented with a three-month history of right-sided lumbar pain and intermittent hematuria. Her laboratory tests were unremarkable. Contrast-enhanced CT revealed a well-circumscribed nodular lesion in the mid-portion of the right kidney, measuring 50 × 47 × 52 mm. The patient underwent right nephrectomy. Macroscopic findings revealed an encapsulated, yellowish-gray nodule (5.2 × 5 × 4 cm) without renal pelvis invasion. Microscopically, the tumor consisted of cuboidal- to spindle-shaped cells arranged in cords and tubular structures within a mucinous stroma, with focal necrosis and foamy macrophages. Immunohistochemistry showed positivity for CK19, CK7, EMA, PAX8, and AMACR, with a Ki-67 index <10%, while CD117, RCC, CD10, and chromogranin were negative. Together, the low Ki-67 proliferation index, absence of invasion, and low-grade histological architecture confirmed MTSCC of low malignant potential. At a five-year follow-up, the patient remained disease-free. Conclusions: MTSCC is a rare renal neoplasm that can be diagnosed by integrating clinico-radiological, histopathological, and immunophenotypic features. Molecular profiling may further distinguish MTSCC from papRCC and identify aggressive variants. Surgical excision remains the cornerstone of management, supported by vigilant long-term follow-up. Full article

Mascagnia (Malpighiaceae) is the lianescent genus with the most convoluted taxonomic history and polyphyly of Malpighiaceae, formerly comprising eight unrelated accepted genera and over 300 species. This genus currently encompasses 48 accepted Neotropical species, of which 20 are reported for Brazil in the taxonomic treatment presented herein. This synopsis was based on the morphological study of specimens deposited in over 80 herbaria using a stereomicroscope, the specialized literature, and consulting type specimens for all accepted species. A total of 20 species of Mascagnia are found in all biomes of Brazil. A taxonomic synopsis is presented for these species, including an identification key, notes on distribution, conservation and taxonomy, and photographic plates for 12 species. Two main morphological groups were identified based on the inflorescence architecture and petal color, and four species were identified as under a conservation threat. Full article

Dolpo is a culturally Tibetan region in the inaccessible parts of the Himalayas, where Buddhist and Bon traditions coexist to this day. Due to their religious significance, the temples and monasteries of Dolpo reflect the historical connection to the religious centres of the Tibetan area. Accordingly, a holistic documentation of the preserved buildings can provide new insights for the development of typologies of Buddhist architecture in the Western Himalayas. To shed light on the mainly undocumented Buddhist architecture of this region, we launched our first research project in 2018. As a result, the architectural documentation of eighteen Buddhist sites in Dolpo is now available for the first time, based on the on-site assessment and survey during four field missions between 2018 and 2023. It provides an overview of the location, descriptions and references, photographs, sets of plans and 3D models of the surveyed buildings, enabling an inventory of the region’s cultural heritage to be initiated. The results enable us to establish a comprehensive typology of Buddhist architecture in Dolpo that allows for comparative analyses with buildings already documented in previous projects. Last but not least, the documentation provides the basis for much-needed restoration work. Full article

As artificial intelligence (AI) systems continue to outperform humans in an increasing range of specialised tasks, a fundamental question emerges at the intersection of philosophy, cognitive science, and engineering: should we aim to build AIs that think like humans, or should we embrace non-human-like architectures that may be more efficient or powerful, even if they diverge radically from biological intelligence? This paper draws on a compelling analogy from the history of aviation: the fact that aeroplanes, while inspired by birds, do not fly like birds. Instead of flapping wings or mimicking avian anatomy, engineers developed fixed-wing aircraft governed by aerodynamic principles that enabled superior performance. This decoupling of function from the biological form invites us to ask whether intelligence, like flight, can be achieved without replicating the mechanisms of the human brain. We explore this analogy through three main lenses. First, we consider the philosophical implications: What does it mean for an entity to be intelligent if it does not share our cognitive processes? Can we meaningfully compare different forms of intelligence across radically different substrates? Second, we examine engineering trade-offs in building AIs modelled on human cognition (e.g., through neural–symbolic systems or cognitive architectures) versus those designed for performance alone (e.g., deep learning models). Finally, we explore the ethical consequences of diverging from human-like thinking in AI systems. If AIs do not think like us, how can we ensure alignment, predictability, and shared moral frameworks? By critically evaluating these questions, this paper advocates for a pragmatic and pluralistic approach to AI design: one that values human-like understanding where it is useful (e.g., for interpretability or human–AI interaction) but also recognises the potential of novel architectures unconstrained by biological precedent. Intelligence may ultimately be a broader concept than the human example suggests, and embracing this plurality may be key to building robust and beneficial AI systems. Full article

Documenting and preserving cultural heritage assets is increasingly important, with threats from natural disasters, conflicts, climate change, and neglect, and some sites are both contested and physically difficult to access or document, posing the issue of “challenging heritage”. A range of innovative digital methods have emerged, offering practical, low-cost, efficient techniques for the 3D documentation of threatened heritage, including smart phone-based mobile light detection and ranging (LiDAR) and photogrammetry. Such techniques offer quick, accessible, and cost-effective alternatives to terrestrial laser scanners, albeit with reduced accuracy and detail, offering practical solutions in cases with restricted funding, limited time for access, complex architectural geometries, or the unavailability of high-end equipment on site. This paper presents a real-world case study integrating iPhone LiDAR with aerial photogrammetry for the rapid documentation of Sourp Magar Monastery, a Medieval site located in a forested slopes of the Kyrenia Range, Cyprus. Due to its poor state of preservation and years of abandonment, as well as its remote nature and location, the monastery is considered a “challenging heritage” monument. In the context of a recent international restoration initiative, a preliminary digital survey was undertaken to both document the current condition of Sourp Magar and contribute to a better understanding of its construction history. This paper outlines the workflow integrating the use of smartphone LiDAR and aerial photogrammetry, evaluates its efficacy in challenging heritage sites, and discusses its potential implications for rapid, low-cost documentation. Finally, the present paper aims to show the multifaceted benefit of easy-to-use, low-cost technologies in the preliminary study of sites and monuments. Full article

Architectural education programs are rapidly expanding the use of immersive technologies worldwide. An increasing number of architecture schools have incorporated 360-degree videos as one of the accessible and cost-effective immersive tools. Despite their availability and ease of use, research on their effectiveness as a learning tool in architectural pedagogy remains limited and mostly focused on architectural design education. Few studies have discussed their application in theoretical courses and their potential to support cognitive understanding of architecture. Learning cultural heritage is considered a foundation of architectural theory. This study examines how the utilization of 360-degree videos, compared to conventional 2D videos, supports spatial cognition, learning performance and experience in cultural heritage education among undergraduate architecture students. An educational experiment was conducted with 89 students in their second year of the architecture degree at the Applied Science Private University, Jordan. Both 360-degree videos and conventional 2D videos were inserted as learning tools within the curriculum of History of Architecture 1 and 2 courses. A mixed-research-method framework, including observation and a post-test survey, was carried out. Using SPSS and Excel programs, the data were analyzed through a set of statistical analyses such as paired-sample t-tests, AHP, and basic descriptive analysis. The findings demonstrate that students were highly immersed and motivated when using 360-degree videos. Compared to conventional 2D videos, 360-degree videos enhanced students’ spatial cognition, performance, engagement, and participation levels in both face-to-face and online courses. These results suggest that 360-degree videos can serve as a sufficient, low-cost, and equipment-free learning tool, responding to the urgent need to utilize technologies in both theoretical and practical architectural pedagogy. Full article

Knee osteoarthritis (KOA) is a most prevalent chronic muscoloskeletal disorder causing pain and functional impairment. Accurate predictions of KOA evolution are important for early interventions and preventive treatment planning. In this paper, we propose a novel dynamic hypergraph-based risk model (DyHRM) which integrates the encoder–decoder (ED) architecture with hypergraph convolutional neural networks (HGCNs). The risk model is used to generate longitudinal forecasts of KOA incidence and progression based on the knee evolution at a historical stage. DyHRM comprises two main parts, namely the dynamic hypergraph gated recurrent unit (DyHGRU) and the multi-view HGCN (MHGCN) networks. The ED-based DyHGRU follows the sequence-to-sequence learning approach. The encoder first transforms a knee sequence at the historical stage into a sequence of hidden states in a latent space. The Attention-based Context Transformer (ACT) is designed to identify important temporal trends in the encoder’s state sequence, while the decoder is used to generate sequences of KOA progression, at the prediction stage. MHGCN conducts multi-view spatial HGCN convolutions of the original knee data at each step of the historic stage. The aim is to acquire more comprehensive feature representations of nodes by exploiting different hyperedges (views), including the global shape descriptors of the cartilage volume, the injury history, and the demographic risk factors. In addition to DyHRM, we also propose the HyGraphSMOTE method to confront the inherent class imbalance problem in KOA datasets, between the knee progressors (minority) and non-progressors (majority). Embedded in MHGCN, the HyGraphSMOTE algorithm tackles data balancing in a systematic way, by generating new synthetic node sequences of the minority class via interpolation. Extensive experiments are conducted using the Osteoarthritis Initiative (OAI) cohort to validate the accuracy of longitudinal predictions acquired by DyHRM under different definition criteria of KOA incidence and progression. The basic finding of the experiments is that the larger the historic depth, the higher the accuracy of the obtained forecasts ahead. Comparative results demonstrate the efficacy of DyHRM against other state-of-the-art methods in this field. Full article

Gerronema lapidescens (Lei Wan), a valued medicinal basidiomycete traditionally employed for antiparasitic and digestive ailments, faces severe conservation threats due to unsustainable wild harvesting and the absence of reliable cultivation protocols. To address this crisis and unlock its pharmacotherapeutic potential, we present the first chromosome-scale genome assembly and comprehensive methylome profile for the wild strain G. lapidescens QL01, domesticated from the Qinling Mountains. A multi-platform sequencing strategy (Illumina and PacBio HiFi) yielded a high-quality 82.23 Mb assembly anchored to 11 chromosomes, exhibiting high completeness (98.4% BUSCO) and 46.03% GC content. Annotation predicted 15,847 protein-coding genes, with 81.12% functionally assigned. Genome-wide analysis identified 8.46 million high-confidence single-nucleotide polymorphisms (SNPs). Notably, methylation profiling revealed 3.25 million methylation events, with elevated densities on chromosomes 4, 9, and 10, suggesting roles in gene silencing and environmental adaptation. Phylogenomic analyses clarified the evolutionary status of G. lapidescens, whilst gene family evolution indicated moderate dynamics reflecting niche adaptation. Carbohydrate-Active enzymes (CAZymes) analysis identified 521 enzymes, including 211 Glycoside Hydrolases (GHs), consistent with organic matter degradation. Additionally, 3279 SSRs were catalogued as molecular markers. This foundational resource elucidates G. lapidescens’s genetic architecture, epigenetic regulation, evolutionary history, and enzymatic toolkit, underpinning future research into medicinal compound biosynthesis, environmental adaptation, germplasm conservation, and sustainable cultivation. Full article

Laminated glass (LG) composite systems are increasingly being utilized in architectural and security applications due to their enhanced strength and safety features. Understanding the structural response of LG systems is crucial for optimizing their performance under blast loads. This paper presents a comprehensive study of an analytical model for predicting the static and dynamic resistance functions of various LG systems used in blast-resistant designs to advance engineering analysis and design methods. The proposed analytical model integrates the strain-rate-dependent interlayer behavior with the glass dynamic increase factors to generate a physically consistent post-fracture membrane resistance, offering a unified framework for deriving the static and dynamic resistance functions directly applicable to single-degree-of-freedom (SDOF) analyses across different LG layups. The developed models were validated statistically using full-scale water chamber results and dynamically against experimental blast field data and the results from shock tube testing. We validated the model’s accuracy for various LG layup configurations, including variations in the glass and interlayer sizes, types, and thicknesses. The established dynamic resistance model was developed by incorporating a strain-rate-dependent interlayer material model. The energy absorption of LG panels, influenced by factors like interlayer thickness and type, is critical for blast design, as it determines the panels’ ability to withstand and dissipate energy, thereby reducing the transmitted forces and deformations to a building’s structure. The dynamic model closely matched the dynamic deflection time histories, with a maximum difference of 6% for all the blast experiments. The static resistance validations across the various LG configurations consistently demonstrated reliable prediction results. The energy absorption comparisons between the analytical and quasi-static LG panel responses ranged from 1% to 17%. These advancements provide higher-fidelity SDOF predictions and clear guidance for selecting the interlayer type and thickness to optimize energy absorption. This will result in enhanced blast resistance and contribute to more effective blast mitigation in glazing system design. Full article

Cricula trifenestrata Helfer was recently documented on cinnamon (Cinnamomum spp.), a novel host plant in Thailand. We compared life history and behavior under natural field, semi-natural caged, and laboratory conditions on cinnamon, plus opportunistic collection from hog plum (Spondias pinnata). Laboratory rearing significantly extended development (62.30 ± 3.68 days) versus field conditions (56.30 ± 1.83 days, p < 0.001) through delayed egg and pupal stages, indicating life history plasticity. We discovered density-dependent oviposition plasticity (linear arrangements in field: 155.6 ± 84.9 eggs/batch; clustered in laboratory) and novel gregarious cocooning behavior, where 85.1% of individuals (n = 47 sites) aggregated to form communal clusters with unique reticulated architecture. Female cocoon shell ratio (CSR) was significantly higher in laboratory (5.02 ± 0.72%) and hog plum cohorts (5.30 ± 0.30%) than field conditions (3.92 ± 0.51%, p = 0.002). Opportunistic rearing yielded >2 kg fresh cocoons, with clusters reaching 0.220 kg. These findings establish biological baselines for C. trifenestrata Helfer on cinnamon, reveal sophisticated social behaviors expanding lepidopteran sociality concepts, and demonstrate commercial potential for sustainable sericulture integrated with pest management. Full article

As digital infrastructures become increasingly dynamic and complex, traditional static access control mechanisms are no longer sufficient to counter advanced and persistent cyber threats. In response, Zero Trust Architecture ($ZTA$) emphasizes continuous verification and context-aware access decisions. To realize these principles in practice, this study introduces a Trust Score ($TS$)-based access control model as a systematic alternative to legacy, rule-driven approaches that lack adaptability in real-time environments. The proposed $TS$ model quantifies the trustworthiness of users or devices based on four core factors—User Behavior (B), Network Environment (N), Device Status (D), and Threat History (T)—each derived from measurable operational attributes. These factors were carefully structured to reflect real-world Zero Trust environments, and a total of 20 detailed sub-metrics were developed to support their evaluation. This design enables accurate and granular trust assessment using live operational data, allowing for fine-tuned access control decisions aligned with Zero Trust principles. A comprehensive sensitivity analysis was conducted to evaluate the relative impact of each factor under different weight configurations and operational conditions. The results revealed that B and N are most influential in real-time evaluation scenarios, while B and T play a decisive role in triggering adaptive policy responses. This analysis provides a practical basis for designing and optimizing context-aware access control strategies. Empirical evaluations using the UNSW-NB15 dataset confirmed the $TS$ model’s computational efficiency and scalability. Compared to legacy access control approaches, the $TS$ model achieved significantly lower latency and higher throughput with minimal memory usage, validating its suitability for deployment in real-time, resource-constrained Zero Trust environments. Full article