Search Results (254)

The indigenous community of Quisquinchir, in Saraguro (Loja, Ecuador), is facing a process of transformation of the rural Andean landscape associated with internal and external migration, as well as the influence of foreign architectural models. The new buildings symbolize, in the collective imagination, modernity and progress; however, they are alien to the natural environment characterized by the practice of agricultural and livestock activities. Although previous studies have described the loss of Andean vernacular architecture, its recent evolution in clear typologies has not been systematized. The objective of this study is to assess the current state of traditional dwellings and understand how migration reconfigures the landscape, collective memory, building traditions, and cultural identity of their inhabitants. Based on direct observation, photographic and stratigraphic analysis, and secondary sources, five typologies were identified: traditional one-story, traditional two-story, hybrid one-story, hybrid two-story, and eclectic. This classification indicates the replacement of earthen walls with cement blocks in 37% of the dwellings and of tile roofs with zinc roofs in 29%. However, 35% of the houses retain their traditional morphology and materials. These results and their classification are fundamental contributions to the design of local public policies that generate adequate interventions respectful of the environment. Full article

Nucleic acid strand displacement reactions (SDRs) are fundamental building blocks of dynamic DNA nanotechnology. A detailed understanding of their kinetics is crucial for designing efficient sequences and regulating reaction networks with applications in biosensing, synthetic biology, biocomputing, and medical diagnostics. Since the development of toehold-mediated strand displacement, researchers have devised many strategies to adjust reaction kinetics. These efforts have expanded the available tools in DNA nanotechnology. This review summarizes the basic principles and recent advances in activation strategies, emphasizing the role of strand proximity as a central driving force. Proximity-based approaches include toehold docking, associative toeholds, remote toeholds, and allosteric designs, as well as strategies that operate without explicit toehold motifs. These methods enable flexible and scalable construction of DNA reaction networks. We further discuss how combining different activation and kinetic control approaches gives rise to dynamic networks with complex and dissipative behaviors, providing new directions for DNA-based nanotechnology. Full article

Hyperspectral image classification (HSIC) involves analyzing high-dimensional data that contain substantial spectral redundancy and spatial noise, which increases the entropy and uncertainty of feature representations. Reducing such redundancy while retaining informative content in spectral–spatial interactions remains a fundamental challenge for building efficient and accurate HSIC models. Traditional deep learning methods often rely on redundant modules or lack sufficient spectral–spatial coupling, limiting their ability to fully exploit the information content of hyperspectral data. To address these challenges, we propose SGFNet, which is a spectral-guided fusion network designed from an information–theoretic perspective to reduce feature redundancy and uncertainty. First, we designed a Spectral-Aware Filtering Module (SAFM) that suppresses noisy spectral components and reduces redundant entropy, encoding the raw pixel-wise spectrum into a compact spectral representation accessible to all encoder blocks. Second, we introduced a Spectral–Spatial Adaptive Fusion (SSAF) module, which strengthens spectral–spatial interactions and enhances the discriminative information in the fused features. Finally, we developed a Spectral Guidance Gated CNN (SGGC), which is a lightweight gated convolutional module that uses spectral guidance to more effectively extract spatial representations while avoiding unnecessary sequence modeling overhead. We conducted extensive experiments on four widely used hyperspectral benchmarks and compared SGFNet with eight state-of-the-art models. The results demonstrate that SGFNet consistently achieves superior performance across multiple metrics. From an information–theoretic perspective, SGFNet implicitly balances redundancy reduction and information preservation, providing an efficient and effective solution for HSIC. Full article

Accurate and timely 3D vegetation structure information is essential for ecological modeling and land management. However, these needs often cannot be met with existing airborne LiDAR surveys, whose broad-area coverage comes with trade-offs in point density and update frequency. To address these limitations, this study introduces a deep learning framework built on attention mechanisms, the fundamental building block of modern large language models. The framework upsamples sparse (<22 pt/m²) airborne LiDAR point clouds by fusing them with stacks of multi-temporal optical (NAIP) and L-band quad-polarized Synthetic Aperture Radar (UAVSAR) imagery. Utilizing a novel Local–Global Point Attention Block (LG-PAB), our model directly enhances 3D point-cloud density and accuracy in vegetated landscapes by learning structure directly from the point cloud itself. Results in fire-prone Southern California foothill and montane ecosystems demonstrate that fusing both optical and radar imagery reduces reconstruction error (measured by Chamfer distance) compared to using LiDAR alone or with a single image modality. Notably, the fused model substantially mitigates errors arising from vegetation changes over time, particularly in areas of canopy loss, thereby increasing the utility of historical LiDAR archives. This research presents a novel approach for direct 3D point-cloud enhancement, moving beyond traditional raster-based methods and offering a pathway to more accurate and up-to-date vegetation structure assessments. Full article

Silicon photonics has emerged as a critical enabling technology for a diverse range of applications, from high-speed data communication and computing to advanced sensing and quantum information processing. This paper provides a comprehensive review of recent progress in the foundational passive devices that underpin this technological revolution. We survey the state of the art in fundamental building blocks, including strip, rib, and silicon nitride waveguides, with a focus on achieving ultra-low propagation loss. The review details essential components for light coupling and splitting, such as grating couplers, edge couplers, multimode interference couplers, and directional couplers, citing their typical performance metrics. Key wavelength filtering and routing components, including high-Q ring resonators, Mach–Zehnder interferometers, and arrayed waveguide gratings, are analyzed. Furthermore, we provide a comparative overview of the capabilities of major photonic foundries operating on a multi-project wafer model. The paper concludes by discussing persistent challenges in packaging and polarization management, and explores future trends driven by co-packaged optics, inverse design methodologies, and the expansion of silicon photonics into new application domains. Full article

Phase-Locked Loops (PLLs) are fundamental building blocks in modern electronic systems, enabling precise frequency synthesis, signal synchronization, and clock generation. This paper provides a comprehensive system-level analysis of PLLs, covering their fundamental operation, key architectures, performance considerations, and applications in emerging technologies. Additionally, this paper discusses current challenges and future trends in PLL design, including low-power optimization, noise reduction, and integration with machine learning techniques. Full article

A systematic literature review is fundamental to building a robust research foundation, informing experimental methodology, and ensuring the quality of future scientific output. However, manual extraction of targeted information from scientific publications is often laborious and prone to error, especially when researchers require rapid access to relevant findings without specialized hardware. This paper introduces an automated workflow for information extraction from scientific publications in the engineering domain. The proposed workflow consists of two primary stages: data preparation and information extraction. During data preparation, PDF files are converted to plain text and segmented into logical sections using a rule-based block detection and classification algorithm for keeping semantics. Information extraction is then performed by applying regular expressions both on keys and values in the same sentence to identify and extract relevant process and material data from the segmented text. The approach was evaluated on a dataset of 18 open-access scientific publications from various journals and conference proceedings in the AM domain. The results of the automated extraction were compared with manual extraction and with a modern large language model (LLM)-based approach. The findings demonstrate that the proposed workflow can accurately and efficiently extract relevant process and material data, achieving competitive performance relative to the LLM-based method. The workflow offers a significant reduction in time and potential errors associated with manual extraction, with automated processing averaging 15 s per document compared to one hour for manual extraction, and achieving a 76% match rate. This efficiency enables researchers to rapidly and effectively extract data. The methodology is readily transferable to other scientific fields where systematic literature reviews and structured data extraction are required. Full article

This review considers fundamental issues related to the genomics of birds (Aves), including the special organization and evolution of their chromosomes. In particular, we address the capabilities of molecular genetic/genomic approaches to clarify aspects of their evolutionary history, including how they have adapted to multiple habitats. We contemplate general genomic organization, including the small size and typical number of micro/macrochromosomes. We discuss recent genome sequencing efforts and how this relates to cytogenomic studies. We consider the emergence of this unique organization ~245 million years ago, examining examples where the “norm” is not followed. We address the functional role of synteny disruptions, centromere repositioning, repetitive elements, evolutionary breakpoints, synteny blocks and the role of the unique ZW sex chromosome system. By analyzing the cytogenetic maps and chromosomal rearrangements of eight species, the possibility of successfully applying modern genomic methods/technologies to identify general and specific features of genomic organization and an in-depth understanding of the fundamental patterns of the evolution of avian genomes are demonstrated. An interpretation of the observed genomic “variadicity” and specific chromosomal rearrangements is subsequently proposed. We also present a mathematical assessment of cross-species bacterial artificial chromosome (BAC) hybridization during genomic mapping in the white-throated sparrow, a species considered a key model of avian behavior. Building on model species (e.g., chicken), avian cytogenomics now encompasses hundreds of genomes across nearly all families, revealing remarkable genomic conservation with many dynamic aspects. Combining classical cytogenetics, high-throughput sequencing and emerging technologies provides increasingly detailed insights into the structure, function and evolutionary organization of these remarkable genomes. Full article

The precise mapping of rock joint traces is fundamental to the design and safety assessment of foundations, retaining structures, and underground cavities in building and civil engineering. Existing deep learning approaches either impose prohibitive computational demands for on-site deployment or disrupt the topological continuity of subpixel lineaments that govern rock mass behavior. This study presents BATNet-Lite, a lightweight encoder–decoder architecture optimized for joint segmentation on resource-constrained devices. The encoder introduces a Boundary-Aware Token-Mixing (BATM) block that separates feature maps into patch tokens and directionally pooled stripe tokens, and a bidirectional attention mechanism subsequently transfers global context to local descriptors while refining stripe features, thereby capturing long-range connectivity with negligible overhead. A complementary Multi-Scale Line Enhancement (MLE) module combines depth-wise dilated and deformable convolutions to yield scale-invariant responses to joints of varying apertures. In the decoder, a Skeletal-Contrastive Decoder (SCD) employs dual heads to predict segmentation and skeleton maps simultaneously, while an InfoNCE-based contrastive loss enforces their topological consistency without requiring explicit skeleton labels. Training leverages a composite focal Tversky and edge IoU loss under a curriculum-thinning schedule, improving edge adherence and continuity. Ablation experiments confirm that BATM, MLE, and SCD each contribute substantial gains in boundary accuracy and connectivity preservation. By delivering topology-preserving joint maps with small parameters, BATNet-Lite facilitates rapid geological data acquisition for tunnel face mapping, slope inspection, and subsurface digital twin development, thereby supporting safer and more efficient building and underground engineering practice. Full article

Various gels are integral for the food industry, providing unique textural and mechanical properties essential for the quality and functions of products. These properties are fundamentally governed by the gels’ nanostructural organization. This review investigates the mechanisms of nanostructure formation in food gels, the methods for their characterization and control, and how precise tuning of these nanostructures enables targeted food applications. We examine the role of various building blocks, including biopolymers, lipids, and particles, and the gelation mechanisms leading to specific nanostructures. Advanced techniques (e.g., microscopy, scattering, spectroscopy, and rheology) are discussed for their insights into nano-/microstructures. Strategies for tuning nanostructures through chemical composition adjustments (e.g., concentration, pH, ionic strength) and physical processing controls (e.g., temperature, shear, ultrasound) are presented. Incorporating nanostructures like nanoparticles and nanofibers to enhance gel properties is also explored. The review links these nanostructures to key functional properties, including mechanical strength, water-holding capacity, optical characteristics, and bioactive delivery. By manipulating nanostructures, products can achieve tailored textures, improved stability, and controlled nutrient release. Applications enabled by nanostructure tuning include tailored sensory experiences, fat reduction, innovative food structures, and smart packaging solutions. Although significant progress has been made, precise structural control and a comprehensive understanding of complex nanoscale interactions in food gels remain challenging. This review underscores the importance of nanostructure tuning in food gels, highlighting its potential to drive future research that unlocks innovative, functional food products. Full article

SA-ConvLSTM is a recently proposed spatiotemporal model for total electron content (TEC) prediction, which effectively catches long-term temporal evolution and global-scale spatial correlations in TEC. However, its reliance on standard convolution limits spatial feature extraction to fixed regular regions, reducing the flexibility for irregular TEC variations. To address this limitation, we enhance SA-ConvLSTM by incorporating deformable convolution, proposing SA-DConvLSTM. This achieves adaptive spatial feature extraction through learnable offsets in convolutional kernels. Building on this improvement, we design ED-SA-DConvLSTM, a TEC spatiotemporal prediction model based on an encoder–decoder architecture with SA-DConvLSTM as its fundamental block. Firstly, the effectiveness of the model improvement was verified through an ablation experiment. Subsequently, a comprehensive quantitative comparison was conducted between ED-SA-DConvLSTM and baseline models (C1PG, ConvLSTM, and ConvGRU) in the region of 12.5° S–87.5° N and 25° E–180° E. The experimental results showed that the ED-SA-DConvLSTM exhibited superior performance compared to C1PG, ConvGRU, and ConvLSTM, with prediction accuracy improvements of 10.27%, 7.65%, and 7.16% during high solar activity and 11.46%, 4.75%, and 4.06% during low solar activity, respectively. To further evaluate model performance under extreme conditions, we tested the ED-SA-DConvLSTM during four geomagnetic storms. The results showed that the proportion of its superiority over the baseline models exceeded 58%. Full article

This study investigated the nuanced influence of urban morphology on the thermal performance of nine mass housing blocks (21–26, 28–30) in New Belgrade’s Central Zone. These blocks, showcasing diverse structures, provided a robust basis for evaluating the design parameters. ENVI-met simulations were used to assess two scenarios: an “asphalt-only” environment, isolating the urban structure’s impact, and a “real-world” scenario, including green infrastructure (GI). Overall, the findings emphasize that while GI offers mitigation, the inherent urban built structure fundamentally determines thermal outcomes. An urban block’s thermal performance, it turns out, is a complex interplay between morphological factors and local climate. Crucially, simple metrics like Green Area Percentage (GAP) and Building Coverage Ratio (BCR) proved unreliable predictors of thermal performance. This highlights the critical need for urban planning regulations to evolve beyond basic surface indicators and embrace sophisticated, context-sensitive design principles for effective heat mitigation. Optimal performance arises from morphologies that actively manage heat accumulation and facilitate its dissipation, a characteristic exemplified by Block 22’s integrated design. However, even the best-performing Block 22 remains warmer compared to denser central areas, suggesting that urban densification can be a strategy for heat mitigation. Given New Belgrade’s blocks are protected heritage, targeted GI reinforcements remain the only viable approach for improving the outdoor thermal comfort. Full article

In advanced Complementary Metal-Oxide-Semiconductor (CMOS) technologies, where diminished feature sizes amplify radiation-induced soft errors, the optimization of fault-tolerant circuit designs requires detailed transistor-level analysis of reliability–performance trade-offs. As a fundamental building block in digital systems and critical data paths, the 2:1 multiplexer, widely used in data-path routing, clock networks, and reconfigurable systems, provides a critical benchmark for assessing radiation-hardened design methodologies. In this context, this work aims to analyze the power consumption, area overhead, and delay of 2:1 multiplexer designs under transient fault conditions, employing the CMOS and Differential Cascode Voltage Switch Logic (DCVSL) logic styles and mitigation strategies. Electrical simulations were conducted using 32 nm high-performance predictive technology, evaluating both the original circuit versions and modified variants incorporating three mitigation strategies: transistor sizing, D-Cells, and C-Elements. Key metrics, including power consumption, delay, area, and radiation robustness, were analyzed. The C-Element and transistor sizing techniques ensure satisfactory robustness for all the circuits analyzed, with a significant impact on delay, power consumption, and area. Although the D-Cell technique alone provides significant improvements, it is not enough to achieve adequate levels of robustness. Full article

This paper presents a high-fidelity multi-physics dynamic model for electric vehicles, serving as a fundamental building block for intelligent vehicle-to-grid (V2G) integration systems. The model accurately captures complex vehicle dynamics of the powertrain, battery, and regenerative braking, enabling precise energy consumption evaluation, including in AI-driven V2G scenarios. Validated using real-world data from a Citroën Ami operating on urban routes in Naples, Italy, it achieved exceptional accuracy with a root mean square error (RMSE) of 1.28% for dynamic state of charge prediction. This robust framework provides an essential foundation for AI-driven digital twin technologies in V2G applications, significantly advancing sustainable transportation and smart grid integration through predictive simulation. Its versatility supports diverse fleet applications, from residential energy management and coordinated charging optimization to commercial car sharing operations, leveraging backup power during peak demand or grid outages, so to maximize distributed battery storage utilization. Full article

Historical districts are the mark of the continuity of urban history and are non-renewable. Typhoon disasters rank among the most serious and frequent natural threats to China’s coastal regions. Improving the wind resilience of China’s coastal historical districts is of great significance for their protection and inheritance. Accurately analyzing the different characteristics of the influencing factors of wind resilience in China’s coastal historical districts can provide a theoretical basis for alleviating the damage caused by typhoons and formulating disaster prevention measures. This paper accurately identifies the main influencing factors of wind resilience in China’s coastal historical districts and constructs an influencing factor system from four aspects: block level, building level, typhoon characteristics, and emergency management. An IIM model for the systematic analysis of influencing factors of wind resilience in China’s coastal historical districts based on the Improved Decision Making Trial and Evaluation Laboratory (IDEMATEL), Interpretive Structural Modeling (ISM), and Matrices Impacts Croises-Multiplication Appliance Classement (MICMAC) methods is established. This allows us to explore the mechanism of action of internal influencing factors of typhoon disasters and construct an influencing factor system, in order to propose prevention measures from the perspective of typhoon disaster characteristics and the overall perspective of China’s coastal historical districts. The results show that the driving force of a building’s windproof design in China’s coastal historical districts is low, but its dependence is strong; the driving forces of block morphology, typhoon level, and emergency plan are strong, but their dependence is low. A building’s windproof design is a direct influencing factor of the wind resilience of China’s coastal historical districts; block morphology, typhoon level, and emergency plan are the most fundamental and key influencing factors of the wind resilience of China’s coastal historical districts. Full article