Search Results (1,131)

This study presents and pilot-tests the Biomimetic Design Education Framework, a structured pedagogical model developed to systematize the translation of biological knowledge into furniture design within studio-based educational contexts. Positioned as a pilot implementation, the study introduces the Biomimetic Transfer Matrix as an accompanying analytical tool for assessing the depth of biological knowledge integration in student design work. It is based on 18 student projects developed during a furniture design course, assessed through qualitative content analysis. The projects were evaluated according to four types of biomimetic transfer: formal, structural, mechanical, and functional/behavioral. Results reveal that structural transfer was the most prevalent category (38.9%), followed by functional/behavioral transfer (33.3%), formal transfer (16.7%), and mechanical transfer (11.1%). This distribution indicates that structured pedagogical guidance can successfully direct students beyond surface-level morphological imitation toward deeper principle-based biological abstraction, while also identifying mechanical and system-based transfer as areas requiring targeted curricular development. On this basis, the study presents the Biomimetic Design Education Framework and introduces the Biomimetic Transfer Matrix as an analytical tool for examining different levels of biomimetic knowledge transfer in design. Results underline the importance of structured approaches to support deeper levels of biological abstraction in design education. The findings contribute to SDG 4 (Quality Education) by advancing evidence-based approaches to biomimetic design instruction. Full article

Cities face the need to implement urban planning solutions that support sustainable development; however, this is not fully possible due to inadequate legal regulations. This development can be understood as increasing the environmental and economic resilience of urban areas and improving the quality of life for city residents. A noticeable trend in urban development plans is the implementation of the “15 min city”, “20 min city”, or similar concepts, which aim to enhance walkability by ensuring access to basic urban services and functions within walking distance. The aim of this article is to evaluate accessibility to green areas and selected educational services in cities (named in the article as MSAS–Municipal Standards for Accessibility of Social Infrastructure), and then to compare the results with proposed legal regulations in Poland that set minimum distances between social infrastructure zones and residential areas. The study will be conducted using selected urban centers: in Poland as a case study and in Belgium as verification. The use of spatial analysis methods (GIS) and a method transferability test enables the assessment of accessibility zones, as well as the identification of potential discrepancies between legal standards and actual accessibility conditions. In this context, this article addresses the question of whether accessibility standards for elementary schools and public green spaces can affect the future directions of residential development and urban spatial policy. The conclusions indicate that, although MSAS are not perfect solutions for a variety of reasons, they represent a step toward sustainable development. Full article

Hepatocellular carcinoma is challenging to detect at an early stage, and its severity increases over time. Recently, the incidence of hepatocellular carcinoma has increased, partly due to lifestyle-related factors such as excessive alcohol intake, sedentary behavior, and diets high in fat, which contribute to the growing prevalence of fatty liver and hepatitis. Various therapeutic strategies are being explored for hepatocellular carcinoma, among which therapies targeting deubiquitinating enzymes (DUBs) have attracted growing attention. Ubiquitination acts as a crucial modulator in the regulation of intracellular signaling across many diseases. E3 ligase recognizes the target protein and transfers ubiquitin, received from the E2 enzyme, to the lysine residues of the substrate, thereby conferring specificity to the ubiquitination process. Once a ubiquitin chain is attached to a target protein by an E3 ligase, the protein is directed to the ubiquitin–proteasome system (UPS) for degradation. In this process, the 26S proteasome complex recognizes the ubiquitin chain and degrades the target protein, thereby serving as a major mechanism for maintaining protein homeostasis. Through this pathway, cells regulate signal transduction, eliminate abnormal proteins, and perform various essential functions. On the other hand, deubiquitinating enzymes (DUBs) recognize the ubiquitin chains on target proteins and remove them by hydrolyzing the isopeptide bonds of ubiquitin, thereby enabling the target proteins to evade degradation by the proteasome system. Furthermore, deubiquitinating enzymes independently remove ubiquitin from proteins and can serve as central regulators in signaling pathways related to hepatocellular carcinoma. Full article

Background: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is strongly associated with atrial structural remodeling driven by activated cardiac fibroblasts. Autophagy has been implicated in AF-related atrial remodeling; however, the non-coding RNA mechanisms that govern autophagic activation in atrial fibroblasts under rapid electrical stress remain poorly understood. Methods: Human cardiac fibroblasts from adult atria (HCF-aa) were subjected to rapid electrical stimulation (RES) at 0.5 V/cm and 10 Hz. Expression levels of exosomal metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), cytoplasmic miR-204-5p, and microtubule-associated protein light chain 3B (LC3B) were measured using quantitative real-time PCR and Western blot analyses. Luciferase reporter assays were performed to confirm direct molecular interactions. The functional roles of MALAT1 siRNA, miR-204-5p mimics/antagomirs, rapamycin, and 3-methyladenine (3-MA) on LC3B expression and autophagic activation were assessed by Western blot and immunofluorescence confocal microscopy for LC3B puncta formation. Results: RES significantly induced exosomal MALAT1 expression in a voltage- and time-dependent manner, peaking at 2 h post-stimulation, while cytoplasmic MALAT1 levels remained unchanged. Cytoplasmic miR-204-5p exhibited an initial transient rise followed by a significant decline at 2 h, inversely correlating with peak MALAT1 levels. LC3B mRNA and protein expression subsequently increased, peaking at 6 and 16 h, respectively. Luciferase reporter assays confirmed that miR-204-5p directly binds both the MALAT1 transcript and the 3′-UTR of LC3B mRNA. MALAT1 knockdown augmented miR-204-5p levels and suppressed LC3B expression, while miR-204-5p overexpression attenuated RES-induced LC3B upregulation and LC3B puncta accumulation. Conversely, miR-204-5p inhibition further enhanced autophagic activation, as evidenced by increased LC3B puncta density. Conclusions: In HCF-aa subjected to RES, MALAT1 functions intracellularly as a competing endogenous RNA to putatively sequester miR-204-5p, thereby de-repressing LC3B expression and promoting autophagic activation. Concurrent exosomal secretion of MALAT1 may additionally serve as a paracrine signal to neighboring cells, though this requires future conditioned-media transfer experiments to confirm. Full article

The subcellular localization of long noncoding RNAs (lncRNAs) is a central determinant of their function, yet its molecular determinants remain incompletely defined, and most existing predictors rely on the primary sequence. Because RNA-binding proteins (RBPs) are the proximal effectors of RNA compartmentalization, this study tested whether the composition of RBPs bound to a lncRNA is predictive of its nuclear or cytoplasmic localization. Enhanced crosslinking and immunoprecipitation (eCLIP) occupancy for 139 RBPs in K562 cells was integrated with the cytoplasmic–nuclear relative concentration indices (CN-RCIs) derived from matched subcellular fractionation, and localization was modeled under chromosome-grouped cross-validation with nested regularization. RBP-occupancy composition predicted localization beyond the transcript size and total binding amount (incremental cross-validated coefficient of determination, delta-R-squared = 0.17; receiver-operating-characteristic area under the curve, AUC = 0.73, a moderate-strength association; Freedman–Lane permutation, p = 0.005). This increment persisted (delta-R-squared = 0.12; p = 0.005) against an expanded baseline that additionally absorbed the transcript abundance, intron content and exon number, indicating predictive information that is not reducible to these transcript features, and the classifier was well calibrated (Brier score = 0.10; expected calibration error = 0.02). The signed coefficient profile separated RBP function systematically: factors acting in nuclear processes (splicing, 3′-end processing, and nuclear-matrix association) carried negative, nuclear-direction weights, whereas factors acting in cytoplasmic processes (translation and messenger RNA stability) carried positive, cytoplasmic-direction weights (Mann–Whitney p = 0.013). The profile generalized across cell lines: a K562-trained model predicted HepG2 localization (transfer AUC = 0.71 using 76 shared RBPs), and HepG2 reproduced the association independently (AUC = 0.77). The association is correlational and of moderate strength; it is presented as an interpretable, RBP-occupancy-based complement to sequence-based predictors of lncRNA localization. Full article

Pharmacogenomics AI offers significant potential for individualized drug therapy; however, its clinical benefits remain unevenly distributed. Models trained predominantly on European-ancestry data consistently underperform in non-European populations, with polygenic risk scores (PRS) showing an estimated 39–73% reduction in predictive accuracy in African-ancestry cohorts across complex traits. These disparities have driven increased interest in moving beyond single-layer genomic approaches. Multi-omics frameworks integrating genomic, transcriptomic, proteomic, and metabolomic data have emerged as a promising strategy to improve prediction across heterogeneous clinical populations, as each molecular layer provides distinct and complementary biological information. Among these layers, metabolomics may represent a particularly transferable component across populations. Metabolite profiles capture the downstream functional output of biological systems influenced by genetic, environmental, dietary, and microbiome-related factors, and may therefore be less reliant on ancestry-stratified allele frequency structures that underlie performance disparities in genomic models. This review synthesizes evidence regarding the mechanistic basis of genomic bias in pharmacogenomics AI, the emerging role of multi-omics integration, especially metabolomics, in improving predictive performance, and the current landscape of computational strategies for bias mitigation, including federated learning, transfer learning, domain adaptation, and synthetic data generation. Collectively, current evidence supports metabolomics-inclusive multi-omics frameworks as a biologically plausible, hypothesis-generating strategy to reduce reliance on ancestry-linked genomic features. However, direct evidence that such frameworks reduce ancestry-related bias in clinical AI outputs remains limited, underscoring the need for globally diverse datasets and prospective multi-population validation. Full article

High-resolution surface soil moisture (SM) is needed for local hydrological and agricultural applications, but reliable retrieval at 100 m remains challenging. Within this broader methodological context, radiometer-constrained SAR change detection remains a practical and interpretable option for high-resolution soil moisture retrieval. It uses SAR-derived temporal changes to describe fine-scale wetting and drying processes, while passive microwave observations provide volumetric moisture references. This study proposes an improved SMAP-anchored Sentinel-1 change-detection framework (ISSF) for 100 m SM mapping. ISSF addresses these limitations by fitting NDVI-binned upper-envelope samples with a nonlinear quadratic function to normalize the vegetation-dependent backscatter-change range and by using multi-year SMAP dry/wet quantiles to scale the normalized relative wetness into volumetric SM. ISSF was evaluated using in situ measurements, a near-concurrent airborne reference, SMAP-based products, and direct transfer to OzNet. In the Shandian River Basin, ISSF achieved R = 0.549 and ubRMSE = 0.062 m³ m⁻³ at the point scale. Relative to three benchmark change-detection methods, ISSF increased R by 11–53% and reduced ubRMSE by 7–15%. For the airborne-referenced event, ISSF showed R = 0.635 and ubRMSE = 0.027 m³ m⁻³. Under direct transfer to OzNet, ISSF achieved mean R = 0.55 and mean ubRMSE = 0.05 m³ m⁻³. These results indicate that ISSF provides a practical and interpretable approach for 100 m soil moisture mapping in semi-arid regions with sparse to moderate vegetation. Full article

Accurate prediction of the load–settlement response of axially loaded piles remains challenging because the pile–soil interface undergoes progressive elastoplastic shear deformation accompanied by stress-dependent volumetric changes. Conventional one-dimensional load-transfer models are computationally efficient but usually rely on empirical or hyperbolic fitting functions, making it difficult to explicitly describe the coupled evolution of interface shear hardening, volumetric hardening, and radial effective stress. Although three-dimensional elastoplastic models provide a more rigorous mechanical representation, their high computational cost limits routine engineering application. To address this gap, this study develops a double-hardening elastoplastic load-transfer model for axially loaded piles based on a physically interpretable pile–soil interface constitutive formulation. In the proposed model, the Hardening Soil model is used to characterize interface shear hardening, while the Modified Cam-clay model is introduced to describe volumetric hardening. These two mechanisms are coupled through a stress–dilatancy relationship. According to the loading direction and the position of the current stress point relative to the shear and volumetric yield surfaces, the p′–q stress plane is divided into elastic, shear-hardening, volumetric-hardening, and coupled double-hardening regions. The corresponding incremental constitutive equations are derived and embedded into a conventional load-transfer framework. The model is validated using interface direct shear tests and field-scale static pile load tests. The predicted shear stress–displacement curves and pile-head load–settlement responses agree well with the measured data. Quantitative evaluation shows that the MAPE values are lower than 5%, the maximum relative errors are below 7.6%, and the R² values exceed 0.96 for all validation cases. Full article

Accurate spot-centroid localization is fundamental for determining optical metrics such as modulation transfer function (MTF) and effective focal length (EFL). Conventional methods struggle under non-ideal conditions—asymmetric spots, high noise, and vibration—and mid-wave infrared (MWIR) vibration has received little attention. To address these gaps, we propose multi-scale Gaussian fitting with subpixel edge reconstruction (MSGF-SER), combining image pyramid fitting, Zernike-moment edge extraction, and adaptive eccentricity-weighted fusion. Validated on simulated spots with varying SNRs and experimental sequences (visible off-axis aberration, long-wave infrared (LWIR) high-noise, MWIR micro-vibration), MSGF-SER achieved a noise-free RMSE of 0.03 pixel and 0.84 pixel at 5 dB SNR. On real MWIR vibration sequences, the Y-direction standard deviation (STD) dropped to 0.098 pixel, and the trajectory displacement variance was more than an order of magnitude lower than that of conventional methods. MTF deviations remained within 0.01, and the deviation of the measured mean EFL from the nominal focal length was better than 0.05 mm, and the STD was below 0.02 mm. These results demonstrate that MSGF-SER substantially improves centroid localization accuracy, repeatability, and smoothness under challenging conditions, providing reliable support for high-precision optical system parameter measurement. Full article

The pyrolysis of polypropylene (PP) microplastics offers a potential route to convert plastic waste into fuel-range hydrocarbon mixtures and chemical feedstocks. However, the elementary radical pathways underlying the formation of medium-chain hydrocarbon fragments remain insufficiently resolved. In this study, a representative isotactic PP oligomer model (C₄₅H₉₂) was evaluated using a comparative density functional theory (DFT) framework. The main mechanistic analysis was based on M06-2X, ωB97X-D, and M11 calculations combined with the def2-TZVP basis set, whereas LANL2DZ was retained only as a lower-cost comparative level during reaction-pathway exploration. Thermochemical profiles were evaluated over a temperature range of 298–923 K. Three selected pathways involving mid-chain homolytic cleavage, intramolecular hydrogen transfer (backbiting), radical rearrangement, and β-scission were examined. Within the selected reaction set, Route 1 exhibited a comparatively more favorable thermochemical profile than Routes 2 and 3 and provided a mechanistically plausible sequence toward medium-chain hydrocarbon fragments. The −TΔS contribution strongly influenced the calculated Gibbs free-energy profiles because fragmentation increases the number of molecular species under the ideal-gas thermochemical approximation. Accordingly, the ΔG values were interpreted comparatively and were not treated as direct evidence of spontaneous fragmentation under condensed-phase pyrolysis conditions or as quantitative predictions of experimental product selectivity. Differences among the evaluated functionals further indicate that the relative description of radical intermediates and transition-state regions is method-dependent. These results provide a molecular-level framework for future studies integrating quantum-chemical calculations, microkinetic modeling, and experimental product characterization. Full article

The excessive emission of greenhouse gases (CO₂, CH₄, SF₆, and CF₄.) is a primary driver of global climate change, making the development of efficient adsorption and separation technologies critically important for achieving carbon reduction goals. Metal–organic frameworks (MOFs) have attracted considerable attention in this field due to their crystalline porous structures, ultrahigh surface areas, and tunable pore architectures. However, pristine MOFs face significant bottlenecks including poor water stability, high bed pressure drops caused by their powdered form, and limited mass transfer, which severely hinder their industrial application. The integration of MOFs with functional materials such as carbon materials, polymers, metal oxides, and porous SiO₂ offers a synergistic strategy to overcome these limitations. Carbon materials provide hydrophobic barriers and mesoporous transport channels, polymers enhance processability and mechanical strength, metal oxides introduce basic sites for enhanced chemisorption, and MOF-on-MOF heterostructures enable atomic-level interfacial integration and pore synergy. This review systematically summarizes recent advances in MOF composites for the separation of CO₂, CH₄, and fluorinated greenhouse gases (SF₆, CF₄.), with an emphasis on design strategies, structure–performance relationships, and synergistic mechanisms across different composite types. Finally, the current challenges including scalable synthesis, long-term stability, and separation performance under realistic conditions are discussed, and future directions toward rational design and functional synergy for industrial carbon capture and fluorinated gas emission reduction are envisioned. Full article

Biosensing performance in graphene-derived field-effect transistors (BioFETs) is widely attributed to surface chemistry, yet the role of the underlying charge transport mechanism remains poorly understood. This work establishes a direct correlation between disorder-driven transport and biosensing transduction in vertical graphene (VG) and nanocrystalline graphite (NCG) FET devices. Temperature-dependent electrical characterization (15–500 K) reveals a hybrid transport regime: three-dimensional Mott variable-range hopping below 240 K, transitioning to thermally activated Arrhenius-type conduction above 240 K. The extracted VRH parameters characteristic temperature T₀, localization length ξ, and density of states N(E_F) quantify fundamentally distinct disorder landscapes: VG operates in a strongly localized, edge-dominated regime, while NCG forms a continuous percolative network with greater transport stability. Surface functionalization via PASE and amine-terminated ssDNA probes, followed by DNA hybridization across four nucleobase systems, demonstrates that the sequence-dependent electrical response is mechanistically interpretable within the VRH–transconductance framework. NCG transduces biomolecular binding through direct charge transfer and hopping pathway perturbation, whereas VG responds through interfacial electrostatic reorganization. These results introduce a unified VRH–transconductance–sensing framework, providing a rational physical basis for next-generation graphene BioFET design. Full article

Introduction: Despite more than two decades of implementation, the European Water Framework Directive (WFD) still faces major challenges in achieving good ecological status across European water bodies. Key limitations persist in connectivity restoration, transboundary harmonization, monitoring network design, and biological assessment of complex systems such as large rivers, reducing the Directive’s capacity to provide consistent ecological diagnoses and support effective river basin management. Objective: This work had four objectives: (I) incorporate ecological status into connectivity assessments; (II) evaluate harmonization in Iberian transboundary basins; (III) optimize the national fish monitoring network through co-creation; (IV) develop a fish-based multimetric index for Portuguese large rivers. Methodology: The work combined four approaches: (1) graph-based connectivity analysis integrating the probability of achieving good ecological status to evaluate functional connectivity across European river networks; (2) cross-border comparison of ecological classifications between Portugal and Spain in shared Iberian basins; (3) optimization of the Portuguese fish monitoring network through a co-creation approach involving the national authority; (4) development of a fish-based multimetric index designed for Portuguese large rivers. Results: Integrating ecological status into connectivity analyses reduced estimated connectivity and highlighted the combined effects of fragmentation and degradation. Cross-border comparisons showed that formal harmonization does not ensure consistent ecological classification. The optimized monitoring networks improved ecological representativeness without increasing sampling effort, while co-creation ensured operational feasibility. The new fish index for large rivers captures spatial variation in ecological quality and responds to pressure gradients, addressing a recognized methodological gap. Conclusions: Improving WFD implementation requires progress across multiple complementary components rather than isolated advances. More effective river management depends on integrating ecological processes, comparable assessment outputs, representative monitoring networks, and system-specific tools. These approaches provide transferable pathways for strengthening freshwater assessment and supporting more coherent river restoration and management across Europe. Full article

Lateral femoral neck and peritrochanteric fractures are common and clinically challenging injuries, particularly in the elderly population, with significant implications for morbidity, mortality, and functional recovery. Traditional classification systems are widely used to guide treatment, yet their reproducibility and clinical applicability remain debated. Increasing attention has been directed toward trabecular architecture and its role in fracture behavior and reduction strategies. This review aims to summarize current evidence on classification systems, trabecular-based fracture patterns, pre-operative reduction techniques, and fixation strategies. A narrative review was conducted using PubMed/MEDLINE, Embase, and Scopus databases up to May 2026. Original studies, reviews, and biomechanical investigations focusing on proximal femur fracture classification, reliability, trabecular alignment, reduction techniques, and fixation methods were included. Data were qualitatively analyzed, with emphasis on interobserver reliability, biomechanical implications, and clinical outcomes. Conventional classification systems, including anatomical, Evans–Jensen, and AO/OTA frameworks, demonstrated variable and generally moderate reproducibility, with reported interobserver agreement ranging from approximately κ = 0.30 to 0.60. Emerging evidence highlights the importance of trabecular architecture, distinguishing intradigital fractures—confined within trabecular pathways and relatively stable—from extradigital fractures, which disrupt load-bearing structures and are associated with increased mechanical instability and higher failure rates. Biomechanical and clinical studies indicate that inadequate reduction with trabecular misalignment significantly increases the risk of varus collapse and implant cut-out. Reduction strategies tailored to fracture pattern, such as internal rotation for intradigital fractures and external or combined maneuvers for extradigital patterns, improve alignment and load transfer. In terms of fixation, dynamic hip screws remain effective in stable fractures, whereas cephalomedullary nails demonstrate superior performance in unstable patterns, with lower reoperation rates reported (approximately 5–8% vs. 10–15%). Management of lateral femoral neck and peritrochanteric fractures should extend beyond traditional classification systems to incorporate trabecular biomechanics. Restoration of trabecular alignment, alongside established parameters such as neck–shaft angle and tip–apex distance, is critical for optimizing outcomes. Further prospective studies are needed to validate trabecular-based classifications and standardize reduction strategies. Full article

Hydrogen is a promising clean-energy carrier, but its low ignition energy, high diffusivity, and wide flammability range demand reliable leak detection. Chemiresistive sensors based on n-type metal oxide semiconductors are attractive owing to their simple architecture, low cost, large resistance modulation, thermal robustness, and compatibility with miniaturized devices. This review focuses on n-type metal oxide semiconductor nanomaterials for hydrogen sensing, particularly ZnO, SnO₂, In₂O₃, WO₃, TiO₂, and related mixed oxides. The fundamental sensing mechanisms are examined, including oxygen chemisorption, electron-depletion-layer modulation, grain-boundary barrier control, catalytic hydrogen spillover, and hydrogen-induced surface reduction or metallization, together with the way these mechanisms compete and cooperate under different operating conditions. Recent performance-enhancement strategies are organized around morphology and porosity control, noble-metal sensitization, defect and dopant engineering, n–n heterojunctions, molecular sieving, and low-temperature activation. Density functional theory is discussed as a design tool for evaluating adsorption energetics, vacancy formation, work-function shifts, band alignment, and interfacial charge transfer, along with its current limitations for modeling humid surfaces. Finally, key challenges and future directions, including humidity tolerance, standardized reporting, device integration, and emerging materials, are summarized to guide the development of high-performance hydrogen sensors. Full article