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An electronic fingerprint device based on spiral patterned tactile pixel array for augmented human-machine interactions
Revealing bond level origin of stability in disordered solids from marginal stability to ultrastability
Molecular Engineering of the Nano‐Bio Interface for Programmable and Precision Protein Delivery
ABSTRACT Protein therapeutics offer unparalleled specificity and immediate bioactivity for addressing complex pathologies; however, their vast therapeutic potential is fundamentally constrained by intrinsic structural vulnerability and the formidable barrier of cellular membrane impermeability. This review delineates the transformative paradigm shift in nanoparticle‐mediated protein delivery, where advanced molecular engineering enables the precise orchestration of the nano‐bio interface and exceptional spatiotemporal resolution. We systematically summarize a diverse repertoire of cutting‐edge nanoplatforms—including lipid nanoparticles, polymeric assemblies, porous nanomaterials, and supramolecular architectures—highlighting how their chemical compositions and structural motifs are tailored to navigate multi‐scale physiological barriers. A central emphasis is placed on strategic innovations in high‐efficiency protein encapsulation, organ‐selective targeting, and the critical transition from endosomal sequestration to cytosolic liberation, all of which are pivotal for maximizing therapeutic potency. Furthermore, we critically address the current challenges in tissue‐specific delivery and envision future frontiers, such as artificial intelligence (AI)‐driven nanocarrier discovery, long‐term biosafety, the path toward manufacture and clinical translation. By bridging materials chemistry with the complex cell biology of proteins, this review provides a visionary roadmap for the next generation of delivery systems, aiming to redefine the landscape of protein‐based molecular medicine.
Dynamic analysis of GWO-Fuzzy MPPT technique applied to a SEPIC converter under partial shading conditions
Freezing depth prediction of surrounding rock in seasonally frozen tunnels based on bayes-optimized XGBoost
Rapid autofluorescence based 3D optical imaging of the pancreatic cancer milieu at mesoscopic scale – stain-free volumetric segmentation
Abstract Although major advances have been made in the field of mesoscopic imaging and associated tissue clearing protocols, these applications are greatly challenged when applied to imaging of pancreatic ductal adenocarcinoma (PDAC) tissue. Most importantly, penetration of labelling agents, typically antibodies, can be drastically reduced from the characteristically dense PDAC stroma. To circumvent this issue, we present a method by which machine learning assisted segmentation is applied to resolve the 3D PDAC microarchitecture from autofluorescence (AF) based light-sheet fluorescence microscopy (LSFM) scans. Hereby, PDAC tissue features could be studied in 3D space without the need for labelling or sectioning. In this proof of principle study, we applied this imaging pipeline on surgical specimens from five PDAC patients and normal pancreatic tissue, generating mosaics of cm 3 -sized tissue discs at micrometre resolution, each on scanning depths corresponding to thousands of standard pathological 2D tissue sections. Using this method, we generated 3D volumes for quantification of blood vasculature, neoplastic epithelium, islets of Langerhans and stromal components. We further showcase the potential for downstream 2D histochemical and immunohistochemical analysis of scanned specimen. As such, the method may facilitate studies of metastatic routes, vessel microarchitecture, islet phenotypes, and spatial relationships in the PDAC tumour microenvironment.
Preclinical Toxicological evaluation of the HIV entry inhibitor peptide 2P23 as a candidate rectovaginal microbicide
Effects on secondary outcomes following a three-month personalized app-based lifestyle intervention among working adults: a three-armed randomized controlled trial
Abstract Lifestyle improvements can bring individual and societal benefits. Most interventions are standardized; however, personalized interventions may be more effective. We examined the effect of a personalized app‑based lifestyle intervention, with or without monthly coaching, on physical activity, diet, alcohol, sleep, stress, and smoking, in a three‑month randomized controlled trial. Office workers and bus drivers ( n = 209) were randomized 1:1:1 to app, app-coach, or control group. Differences in change from baseline to follow‑up were analysed using robust linear regression, with the control group as the reference. Participants were on average 48.2 years and 42% were female. Both intervention groups increased minutes of exercise (app group β = 15.7 min/week, 95% CI: 3.5, 27.9; app-coach group β = 13.8 min/week, 95% CI: 1.4, 26.1). Compared to the control group, participants in the app-coach group increased their vegetable intake (β = 51.8 g/day, 95% CI: 2.5, 101.1) and fruit and vegetable intake (β = 87.7 g/day 95% CI: 8.9, 166.6). Participants in the app-coach group also increased time in bed (β = 0.43 h/night, 95% CI: 0.05, 0.81) and sleep duration (β = 0.40 h/night, 95% CI: 0.01, 0.78). No other outcomes differed between groups. While the effects seen on physical activity, diet and sleep were limited, even small behavioural changes can benefit health. Though, the effect of coaching needs further evaluation. Future personalized app-based interventions have potential to improve lifestyle behaviours.
Solanum incanum-mediated green synthesis and characterization of silver oxide, zinc oxide, and copper oxide nanoparticles for the control of maize weevils (Sitophilus zeamais) and postharvest microbial contamination
A secure encryption-steganography method for public images using symmetric keys without prior synchronization
Ground-station and ERA5-Land evaluation for heat-induced labour loss assessment across developed-economy regions in Pacific Asia
Hierarchy and ranking in fencing and tennis
Abstract Ranking athletes by their performance in competitions and tournaments is common in every popular sport and has significant benefits that contribute to both the organization and strategic aspects of competitions. Although rankings are perhaps the most concise and most straightforward representation of the relative strength among the competitors, beyond this one-dimensional characterization, it is also possible to capture the relationships between athletes in greater detail. Following this approach, our study examines the networks between athletes in individual sports such as fencing and tennis, where the nodes are associated with the contestants and the edges are directed from the winner to the loser. We demonstrate that the connections formed through matches arrange themselves into a time-evolving hierarchy, with the top players positioned at its apex. The structure of the resulting networks exhibits detectable differences depending on whether they are constructed purely from round-robin data or from purely elimination-style tournaments. We find that although elimination tournaments lead to networks with strong hierarchical networks due to their organisational concept (structural hierarchy), the dominance differences between players (competing hierarchy) are less effective in this format, which is manifested in the increased probability of circular win-loss situations (cycles). The position within the hierarchy, along with other network metrics, can be used to predict match outcomes. In the systems studied, these methods provide predictions with an accuracy comparable to that of forecasts based on official sports ranking points or the Elo rating system. A deeper understanding of the delicate aspects of pairwise contest networks enhances our ability to model, predict, and optimise the behaviour of many complex systems, whether in sports tournaments, social interactions, or other competitive environments.
Spatiotemporal trends in temperature and rainfall within the coffea arabica landscapes of Gimbo and Decha districts in the Kafa Zone, Southwest Ethiopia
Abstract Climate change poses escalating risks to rain-fed agricultural systems, particularly for Coffea arabica, a perennial crop with narrow bioclimatic requirements that supports the livelihoods and ecological heritage of Ethiopia. This study evaluates spatiotemporal trends in temperature and rainfall across the Gimbo and Decha districts from 1990 to 2022, with the specific objectives of characterizing shifts in thermal regimes, assessing rainfall distribution and concentration, and mapping the spatial heterogeneity of climatic trends. Historical records from ten meteorological stations were integrated with CHIRPS and ERA5-Land datasets, and analyzed using the Mann-Kendall test, Sen’s slope estimator, Innovative Trend Analysis, Precipitation Concentration Index, and regression kriging for spatial interpolation. Results reveal a coherent warming signal with annual mean temperatures increasing by +0.02°C year⁻ 1 (p < 0.001). Maximum temperature rise significantly during the dry months (January-March), while minimum temperature increased notably during the main rainy season (June-July), suggesting a gradual compression of the diurnal temperature range. Annual rainfall exhibited a modest upward trend (+2.69 mm year⁻ 1 , p = 0.016), yet seasonal precipitation is highly concentrated (PCI > 20 in 75.8% of Kiremt and 61.8% of Belg) and marked by noticeable interannual irregularity, including sharp oscillations between extreme dry and wet periods. Spatial interpolation highlighted topographically mediated microclimatic gradients, with warmer southern parts contrasting with cooler northern and eastern zones. Collectively, these patterns indicate a shifting environmental baseline characterized by escalating thermal stress and hydro-climatic unpredictability, which may progressively challenge coffee phenological synchronization and yield stability. The study forms a spatially explicit, ground-validated climatic baseline data that can inform targeted, landscape-level adaptation strategies, and serve as a foundational dataset for integrating process-based crop models and climate-resilient planning in this highly significant coffee-producing region.
Synergistic effects of alkalines, salts, and silica nanoparticles on in-situ surfactant generation from different crude oils in relation to enhanced oil recovery
Abstract The objective of this study was to evaluate the potential of three selected Iranian crude oils for in-situ surfactant generation in relation to alkaline-assisted enhanced oil recovery. The individual and synergistic effects of different alkaline agents, monovalent and divalent salts, and silica nanoparticles (SiO₂) were investigated using interfacial tension and contact angle measurements. In addition, zeta potential (ZP), elemental analysis (EDXA), X-ray Fluorescence (XRF), Hydrogen Nuclear Magnetic Resonance (H-NMR), and Fourier transform infrared Spectroscopy (FTIR) were conducted to assess these effects comprehensively. The results showed that the simultaneous use of two alkalines had a positive effect on interfacial phenomena. The surfactants produced using the heaviest oil in this study (Crude Oil C) have shown the best effects. The interfacial tension was decreased from 35.5 mN/m to 1 mN/m. Furthermore, incorporating silica nanoparticles into the optimal formulation yielded the greatest impact, reducing the interfacial tension to 0.69 mN/m. Additionally, the combination of silica nanoparticles (SiO₂), sodium sulfate, and alkaline agents altered the wettability of mineral samples from oil-wet to water-wet conditions. After 14 days aging times, the contact angle for glass changed from 114 ° to 47 °, and for calcite from 108° to 49°. The results of this study should be considered as an initial screening of oil types for the combined use of alkaline and nanoparticle in enhanced oil recovery operations.
Integrating ontology and knowledge graphs for intelligent assessment and feedback in E-learning systems
Dominant factors and prediction model of toppling collapse for steep cliffs in strong earthquake zones
Abstract Earthquake-induced collapses of steep cliffs with hazardous rock masses are a prevalent geological hazard in mountainous regions. Current methods for assessing the stability of grouped rock failures under seismic loading exhibit significant limitations. This study introduces an integrated multi-scale methodology combining scaled shaking table tests with 3DEC discrete element numerical modeling to systematically investigate the failure modes, critical collapse thresholds, and risk factors for hazardous rock formations along the China-Pakistan Highway. The approach uniquely bridges physical experimentation, numerical mechanistic analysis, and data-driven prediction to decipher complex failure mechanisms, with a focused analysis on toppling collapse. Key findings include: (1) a displacement angle threshold of ≈ 15° serves as a robust collapse indicator, outperforming conventional metrics; (2) the degree of rock weathering (fragmentation) exerts a dominant control on stability compared to joint inclination, height, and strength; (3) a developed BP neural network model effectively identifies toppling and sliding as the two predominant failure modes, utilizing joint inclination as a key discriminant; (4) collapse initiation shows a nonlinear dependence on rock geometry, where failure is accelerated by increased joint inclination or decreased rock size. Furthermore, a critical vibration velocity is established as a practical criterion for predicting toppling collapse. Factor importance analysis ranks the influencing parameters in the order: rock size > joint inclination > shape > layering configuration. The proposed thresholds, predictive criterion, and neural network model provide directly applicable tools for early warning and risk assessment, offering a refined theoretical and practical framework for mitigating seismic rockfall hazards in earthquake-prone regions.