Search Results (1,515)

This research aims to analyze audiovisual productions related to the development of tourism at the Armero ruins and the visitor experience in the area. The methodology used is qualitative and was carried out in two phases. This research began in August 2023 with interviews with visitors to Armero and a content analysis of YouTube videos that recount the Armero tragedy. The impact on collective memory and the sense of belonging among visitors is highlighted. The visitors’ personal productions show that the experience in Armero becomes an emotional journey, where history is tangled with the hope of rebuilding the social fabric and honoring the memory of the thousands who lost their lives. This research reveals a range of emotions: from awe at the natural beauty to respect and sadness when remembering the tragedy that buried this prosperous Colombian city in 1985. In conclusion, the importance of preserving historical memory is evident, so that tragedies like Armero’s are not forgotten and can promote reflection on natural risk management and community resilience. Full article

This article discusses advanced artificial intelligence (AI)-based strategies for the design and personalization of three-dimensionally (3D) fabricated hand exoskeletons, with a focus on adaptive, data-driven methodologies. It highlights the crucial role of intelligent personalization in improving user comfort, functional performance, and rehabilitation outcomes, particularly in medical and care settings. The proposed approach integrates biomechanical modeling, high-resolution 3D scanning, and machine learning (ML) algorithms to create exoskeleton systems tailored to the unique anatomical and motor characteristics of individual users. This article presents both a theoretical framework and practical implementation of AI-based adaptation, addressing key challenges such as precise anatomical fit, ergonomic optimization, and real-time responsiveness. Specific emphasis is placed on AI-based feedback mechanisms that enable continuous, dynamic adjustment of control parameters during device operation. Case studies illustrate the effectiveness of these techniques in improving performance and rehabilitation progress for individual users. By combining intelligent modeling, adaptive control, and additive manufacturing, this research advances the field of wearable robotics and points the way to more accessible, efficient, and fully personalized assistive technologies. Full article

Mycelium-bound composites (MBCs) have emerged as a promising class of biofabricated materials that integrate fungal hyphal networks with lignocellulosic substrates to form lightweight, biodegradable structures without synthetic adhesives. Despite rapid growth in the field, the current literature remains fragmented, with inconsistent methodologies and widely varying reported material properties. This review advances the field by moving beyond descriptive synthesis toward a quantitative and conceptual integration of existing studies. We systematically analyze how key fabrication variables—including fungal species, substrate composition, growth conditions, and post-processing parameters—govern density, porosity, and mechanical performance. A process–structure–property (PSP) framework is proposed to combine these relationships and explain discrepancies across studies. We highlight the dominant role of densification and moisture conditioning in determining compressive strength, often outweighing species-level effects. A comparative synthesis of reported data reveals significant variability in compressive strength (0.05–1.2 MPa) and elastic modulus, attributable to inconsistencies in sample preparation, testing protocols, and environmental conditioning. To address this, we identify critical gaps in standardization and propose actionable testing protocols and reporting guidelines for reproducibility. Furthermore, we assess the technology readiness level (TRL) of MBC systems and distinguish between laboratory-scale innovations and commercially viable processes. While hybridization strategies and biofunctional applications offer promising avenues, their maturity varies widely. This work provides a decision-oriented framework for MBC design and a roadmap for transitioning these materials from experimental systems to scalable, standardized, and application-ready biomaterials. Full article

While the effects of urbanization are widely studied, the effects of soil sealing, particularly in the case of Hungary, have only received limited attention in recent years. Our study aimed at understanding the underutilized capacity of urban soils at the national level. We have applied a 20 m resolution, spatially explicit daily water balance-based methodology to calculate the potential water dynamics for the top 75 cm of the soils currently covered by urban fabric in Hungary, for the time period of 1971–2024. We aimed to utilize primarily publicly available data and open-source software to support further use and development. Our results indicated that these (currently sealed) soil surfaces could allow between 0.14 and 0.29 km³ of water to infiltrate into the soil, equaling about 7% of the estimated annual water withdrawal in Hungary. The on-site evaporation from these surfaces would produce about 400 PJ of total cooling service annually, corresponding to an average of 145 MJ/m². Our findings highlighted the water storage potential of soils in Hungary, particularly in urban areas, supporting the future application of nature-based solutions and blue-green infrastructure. Full article

Fused Deposition Modeling (FDM) has emerged as one of the most widely adopted additive manufacturing (AM) technologies, valued for its simplicity, cost-effectiveness, and versatility in fabricating complex geometries. The geometry of the extrusion nozzle plays a critical role in determining melt flow behavior, extrusion stability, and final print quality of thermoplastic materials. When utilizing natural fiber-reinforced thermoplastic composites (NFRCs), understanding and optimizing nozzle geometry becomes increasingly important due to the complex rheological behavior of fiber-filled melts, including challenges such as increased viscosity, shear-thinning effects, and susceptibility to nozzle clogging. The reviewed literature shows that optimized nozzle geometry, supported by computational and statistical tools, can improve the printability and mechanical performance of natural fiber composites, although further advancements are needed to address material variability and complex fiber–matrix interactions. This review paper presents a comprehensive overview of the effects of nozzle geometry on melt flow behavior in FDM, covering computational modeling approaches, experimental characterization studies, and optimization methodologies for enhancing the performance of natural fiber-reinforced composites in additive manufacturing applications. The integration of sustainable materials into FDM processes represents a significant advancement toward environmentally responsible manufacturing while maintaining mechanical performance requirements. Full article

Introduction: Heavy metal contamination in water represents a major environmental and public health challenge because toxic ions frequently occur as complex multi-species mixtures rather than isolated pollutants. This study presents a numerical design and optimization of a surface plasmon resonance (SPR) sensor based on a LaF/Au/BaTiO₃/WS₂ heterostructure for monitoring refractive-index changes associated with mixed heavy metal ions in aqueous media. Methodology: The optical response of the multilayer sensor was evaluated using the transfer matrix method under TM-polarized illumination at 633 nm. Systematic optimization was performed for the prism substrate, Au thickness, dielectric oxide layer, and 2D nanomaterial interface. The final configuration consisted of a LaF prism, 50 nm Au film, 2.0 nm BaTiO₃ spacer, and 0.80 nm WS₂ monolayer. Sensor performance was assessed using resonance-angle shift, sensitivity, detection accuracy, quality factor, figure of merit, FWHM, attenuation, and estimated limit of detection. Results and Discussion: The optimized LaF/Au/BaTiO₃/WS₂ configuration produced stable simulated SPR responses across single, binary, quaternary, and five-ion heavy metal matrices. The WS₂ monolayer provided the highest angular displacement among the evaluated 2D materials, while BaTiO₃ improved field confinement and limited optical damping in the numerical model. The configuration maintained attenuation near 1.6%, FWHM values around 7.9°, detection accuracy between 0.030 and 0.032 deg⁻¹, and model-based refractometric LoD values down to 3.49 × 10⁻⁵ RIU under the assumed angular-resolution criterion. Conclusions: The proposed LaF/Au/BaTiO₃/WS₂ SPR configuration provides a numerical framework for label-free monitoring of refractive-index changes associated with complex heavy-metal-ion mixtures in contaminated water. Experimental fabrication and testing are required to validate the simulated performance. Full article

Surface-enhanced Raman spectroscopy (SERS) has emerged as a highly promising analytical technique for disease diagnostics due to its exceptional sensitivity, molecular specificity, and ability to detect a broad range of biomarkers in complex biological matrices. This review provides a comprehensive overview of gold- and silver-nanoparticle-based SERS platforms for plasma disease diagnostics, covering advances in plasmonic nanostructures, biological sample analysis, biomarker detection, and AI-driven spectral data processing. Particular emphasis is placed on the application of SERS to clinically relevant biofluids, especially plasma, where the technique has demonstrated considerable potential for detecting diseases such as cancer, inflammatory disorders, and neurological conditions. The review also critically examines the major challenges currently limiting the clinical translation of SERS technologies. These include variability associated with substrate fabrication, matrix-induced signal fluctuations, limited interlaboratory reproducibility, and the lack of standardized protocols for spectral preprocessing and data analysis. Strategies proposed to address these issues are discussed, including comprehensive post-synthesis substrate characterization, optimization of biological sample preparation, advanced spectral preprocessing workflows, and the integration of machine learning and artificial intelligence algorithms to improve diagnostic robustness and reproducibility. Collectively, the advances summarized in this review indicate that SERS-based diagnostic technologies are rapidly progressing beyond proof-of-concept studies toward clinically applicable systems. Continued interdisciplinary collaboration and standardization efforts will be essential to bridge the remaining gap between experimental SERS methodologies and routine clinical implementation. Full article

Fresh fruits are prone to spoilage due to microbial contamination and moisture loss, highlighting the need for effective packaging materials with strong barrier and antimicrobial functions. In this work, a bilayer composite film with antimicrobial and preservative properties was fabricated using the solution casting method, consisting of an inner chitosan (CS) layer and an outer complex layer of chitosan-2-pyridinecarboxaldehyde-Ag(I) (CS-PCA-Ag(I)). Preparation conditions were optimized via single-factor experiments combined with response surface methodology. The resulting composite film showed significantly enhanced mechanical properties, with tensile strength of 38.52 ± 2.07 MPa and elongation at break of 67.32 ± 1.47%, as well as a low water vapor permeability of 2.81 × 10⁻⁷ g·m⁻¹·h⁻¹·Pa⁻¹. It also exhibited strong antibacterial activity against Staphylococcus aureus and Escherichia coli, with inhibition zone diameters of 19.8 ± 0.3 mm and 15.6 ± 0.2 mm, respectively. Strawberry preservation tests demonstrated that the CS/CS-PCA-Ag(I) film effectively suppressed microbial growth and reduced fruit weight loss (8.27 ± 0.42% after 5 days), thereby extending the shelf life of strawberries. Cytotoxicity evaluation and silver ion migration analysis further confirmed the film’s good biocompatibility and safety. Collectively, the CS/CS-PCA-Ag(I) composite film holds considerable promise for fresh food preservation applications. Full article

The exponential growth of AI-driven data traffic necessitates the evolution of Data Center Networks toward high bandwidths and sub-microsecond latency. While co-packaged optics (CPO) offer a pathway to reduced energy consumption and increased capacity, they introduce significant challenges in optical chip coupling and packaging complexity. This study explores monolithically integrated metasurfaces as an alternative for optical interfaces, potentially reducing the need for bulky external microlens arrays or extremely precise mechanical alignment. We design an amorphous silicon (a-Si) metasurface on a Silicon-On-Insulator (SOI) platform operating at 1310 nm. By spatially mapping nanopillar radii to satisfy a spherical phase profile, we achieved near-vertical beam emission with an emission angle of 0.88° focused at a focal length of 98.99 μm. Broadband characterization across a 20 nm band confirms stable focusing and a confined spot size with moderate roll-off toward the band edges. The sensitivity of the emission profile of the device to fabrication imperfections in pillar radius, height, and sidewall taper is quantified. The coupling to a polymer-based optical redistribution layer (ORDL) is also studied, and the corresponding modal analysis demonstrates a maximum coupling efficiency of 68.2% into an SU-8 polymer waveguide. Tolerance analysis results reveal deterioration of 0.9 dB and 0.4 dB for $\pm 0.6 \mathsf{\mu }\mathrm{m}$ horizontal and $\pm 1.5 \mathsf{\mu }\mathrm{m}$ vertical misalignment respectively, making the interface compatible with relaxed alignment assembly assumptions, although experimental packaging validation remains required. The methodology is further validated at 1550 nm, demonstrating its applicability across telecom bands. These results suggest that integrated metasurfaces may simplify the packaging stack and enhance density for next-generation CPO links by providing precise, on-chip wavefront manipulation. Full article

Ceramic additive manufacturing offers strong potential for fabricating geometrically complex and application-specific components, yet achieving reliable dimensional fidelity remains challenging because dimensional deviation is governed by highly coupled material, process, thermal, and environmental factors. To address this problem, this study proposes an uncertainty-aware computational framework for dimensional error prediction in ceramic 3D printing under variable material and process conditions. The contribution is positioned as a system-level integration of established learning, uncertainty estimation, calibration, and reliability-interpretation components within a ceramic additive manufacturing dimensional-error prediction workflow, rather than as a fundamental methodological breakthrough. The validation is conducted using the publicly available Ceramic 3D Printing Process Control Dataset, a 1000-sample tabular dataset, and the resulting findings are therefore interpreted as dataset-specific computational evidence rather than direct proof of industrial deployment readiness. The methodology begins with a structured data-driven preprocessing pipeline that transforms the Ceramic 3D Printing Process Control Dataset into a multi-condition feature space through data cleaning, one-hot material encoding, min–max normalization, and engineered descriptors capturing extrusion–speed balance, thermal gradients, cooling intensity, deposition density, and material-conditioned interactions. A multi-branch deep computational architecture is then developed to encode material, process, thermal-environmental, and engineered-feature streams separately, followed by adaptive cross-condition fusion to learn nonlinear dependencies across ceramic printing regimes. To improve reliability beyond deterministic regression, the framework jointly models aleatoric and epistemic uncertainty and incorporates calibration refinement to align predictive confidence with observed error behavior, thereby enabling preliminary reliability-oriented interpretation of stable and high-risk operating conditions. Experimental results demonstrate that the full model achieves the best overall within-dataset performance, with a test MAE of 0.0118, RMSE of 0.0172, $R^2=0.999$, MAPE of 1.74%, calibration error of 0.003, PICP of 0.996, reliability score of 0.992, and a stable prediction rate of 98.7%. Although these values indicate strong predictive behavior under the current structured dataset, the exceptionally high $R^2$ should be interpreted cautiously because external experimental validation, larger measured datasets, and cross-machine ceramic printing trials are still required. These findings show that the proposed framework provides an effective system-level computational strategy for dataset-specific reliability-aware dimensional quality prediction in ceramic additive manufacturing and offers a preliminary data-driven foundation for uncertainty-aware intelligent process optimization. Full article

Advances in imaging and fabrication technologies offer new opportunities to develop tools that support the visualization and understanding of complex biological materials. This contribution presents a comprehensive methodological framework for generating anatomically representative, species-specific 3D models of wood microstructure, intended to enhance student comprehension in wood science and related fields. The workflow integrates micro-X-ray computed tomography (micro-CT) scanning, image segmentation, STL model preparation, and additive manufacturing. Using micro-CT, we captured high-resolution, non-destructive 3D datasets of four wood species—European beech (Fagus sylvatica), oak (Quercus robur L.), Norway spruce (Picea abies), and Scots pine (Pinus sylvestris). The resulting volumetric data were processed with dedicated software to isolate and reconstruct key anatomical features, which were subsequently converted into printable STL models. These models were fabricated at a 1:400 scale using filaments composed of 40% wood particles and 60% biodegradable polylactic acid (PLA), underscoring the relevance of sustainable materials in educational tool development. The primary aim of this work is to document and justify each stage of the technological process, thereby providing a replicable pathway for producing detailed, pedagogically useful representations of wood microstructure. The resulting models are publicly available on the Sketchfab platform as part of the “3D Wood Micro Structure Collection.” Full article

Analog in-memory computing (AIMC) has emerged as a promising approach to mitigate the Von Neumann bottleneck in matrix operations, which are common in deep learning applications. However, the practical implementation of resistive crossbar arrays is limited by challenges in signed weight representation, conductance quantization, and device nonlinearity. This paper presents a differential mixed-signal architecture for accurate signed matrix–vector multiplication (MVM), integrated with a RISC-V microcontroller for edge inference applications. A structured digital-to-analog mapping framework encodes quantized neural network weights into programmable conductance values while preserving arithmetic correctness. The design employs voltage-mode input encoding, differential current summation, and transimpedance-based readout followed by analog-to-digital conversion, enabling single-cycle signed accumulation without duplicating crossbar resources. A 32 × 16 dual-layer prototype crossbar was fabricated and experimentally characterized. Measurements demonstrate a mean absolute percentage error (MAPE) below 1% within the linear operating region and below 4% over the full-scale conductance range. These results validate the robustness of the proposed mapping methodology and confirm the feasibility of hybrid analog–digital acceleration for edge AI systems. Consequently, this discrete prototype serves as a physical verification platform for the AIMC approach, providing valuable insights for more efficient mixed-signal computing integrated circuit (IC) designs. Full article

Field-Programmable Gate Array (FPGA) technology allows for the creation of unique hardware implementations based on mass-produced chips. The process of project prototyping for such systems using Hardware Description Languages (HDLs) remains complex, even with modern tools. The comparison of HDL coding styles, for example, a behavioral case statement against a structural binary-tree decomposition, shows that the choice is capable of affecting post-implementation timing and area. The performed study, using the open-source yosys/nextpnr toolchain, shows that the validity of such a comparison is decided by the fabric domain. Logic that falls through to generic Look-Up Table (LUT) mapping is governed by the mapper’s heuristic fixed point rather than by source intent: on the crossbar, the behavioral and structural netlists become identical in cell composition; on the priority encoder, the verdict reverses; and on the barrel shifter, the LUT area collapses, so the comparison does not isolate the coding-style variable. It was measured that the keep_hierarchy attribute restores a meaningful comparison at ~17% LUT cost (N = 8) and provides a structural invariant to the ABC mapper variant, but the behavioral result is mapper-sensitive and the N = 4 verdict reverses under the legacy -noabc9 mapper (Cohen’s d from +2.4 to −1.6). By contrast, logic that involves a dedicated primitive before LUT mapping—an adder bound to the carry chain or a multiplier bound to a DSP block—yields source-meaningful verdicts that do not reverse with a mapper. Replication on a second fabric (Lattice iCE40) confirms that this behavior is fabric- rather than vendor-specific. The main contribution of this work is the proposed first fabric-domain characterization of synthesis canonicalization as a methodological hazard for cross-HDL FPGA studies on open-source toolchains, which identifies the two-phase synthesis mechanism that delimits it and supplies a decision rule (inspect post-synthesis composition) to identify whether a given comparison is susceptible. Full article

This article addresses the role of geometry in architecture throughout history as a language that supports and connects the domains of design and aesthetic expression. This study focuses on the analysis of contemporary curvilinear glass architecture, in which geometry becomes a fundamental tool for shaping both form and its visual perception. We define and investigate panelization strategies for freeform surfaces, adopting surface continuity as the primary criterion for their classification. Research is conducted through the confrontation of two complementary approaches: a descriptive one, based on case study analysis, and a generative one, employing parametric modeling of curvilinear surfaces. In the descriptive approach, selected architectural realizations are analyzed, in which panelization strategies and their impact on the aesthetic expression of glass façades are identified. In the generative approach, a digital surface analysis is conducted, enabling the assessment of relationships between geometry, panel typology, and visual continuity. The results provide a basis for developing theoretical and methodological frameworks for the analysis and design of curvilinear glass architecture. This study identifies interdependencies between geometry, material, and fabrication processes. The main contribution of this study is a method for analyzing curved surfaces based on digital analysis, enabling a systematic evaluation of the relationships between geometry, panelization typology, and surface continuity in the context of freeform architectural design. This method may support informed and conscious design decision-making. Full article

Façades play a decisive role in shaping the visual and symbolic character of historic urban environments. Recent European funding schemes promoting energy-efficient retrofitting have accelerated interventions on building envelopes. Although aligned with decarbonization objectives, these processes are generating significant chromatic and material transformations that risk eroding the visual coherence and cultural sustainability of consolidated urban areas. In the historic Ensanches of San Sebastián, the replacement of traditional envelope systems with new cladding solutions is leading to the loss of the architectural style of some facades and altering their materials, textures, and colors. A progressive “contagion effect” has been identified, whereby dissonant chromatic schemes—often associated with the proliferation of so-called “zebra blocks”, residential buildings with façades clad in alternating black and white stripes that have proliferated in recent urban developments—are replicated across adjacent buildings, gradually weakening spatial continuity and the genius loci of the neighborhood. In response to this phenomenon, this research develops a systematic methodology to analyze, quantify, and anticipate chromatic transformation in consolidated urban fabrics. The study combines historical morphological analysis, classification of architectural periods, and chromatic mapping of recent façade interventions. Based on this framework, a CARI, Chromatic Alteration Risk Index is proposed to evaluate the potential impact of façade alterations on urban chromatic coherence. Drawing on an epidemiological framework, the methodology enables the identification of critical transformation clusters, the assessment of contagion dynamics, and the definition of regulatory thresholds for color and material interventions. By integrating perceptual criteria, urban morphology, and spatial distribution patterns, the study moves beyond descriptive diagnosis and offers a transferable tool for municipal planning. The proposed approach supports the proactive regulation of façade rehabilitation processes, balancing energy efficiency objectives with the preservation of collective memory, material identity, and urban sensory quality. This study proposes a quantitative model of “urban chromatic contagion” to assess how façade color interventions propagate within a neighborhood. We define the Chromatic Integration Percentage (CIP) and the Chromatic Alteration Risk Index (CARI) of the analyzed area. Results indicate that poorly regulated façades show higher chromatic dissonance (low CIP) and act as contagion hotspots, while a clear risk gradient emerges: highly protected buildings present lower risk, whereas mixed typologies and recent rehabilitations concentrate higher CARI values. The model supports preventive urban color management by identifying areas at risk before visible alteration. Full article