Search Results (2,219)

The white-rot fungus Coriolopsis trogii MUT3379 produces Lac3379-1 laccase at high yields due to the previous development of a robust fermentation process. Throughout the extended use of this strain, we observed the occurrence of substrate-specific guaiacol and ABTS (2,2’-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid)) oxidizing enzymes other than Lac3379-1 Since we did not succeed in producing these enzymes in significant amounts by conventional strain selection and fermentation tools, we developed an approach based on protoplast preparation and regeneration to isolate stable producers of these alternative oxidative enzymes from the complex multinucleate mycelium of C. trogii MUT3379. A cost-effective and efficient protocol for protoplast preparation was developed by using the enzymatic cocktail VinoTaste Pro by Novozymes. A total of 100 protoplast-derived clones were selected and screened to produce laccases and other oxidative enzymes. A variable spectrum of oxidative activity levels, including both high and low producers, was revealed. Notably, a subset of clones exhibited diverse guaiacol/ABTS positive enzymatic patterns. These findings suggest that it is possible to isolate different lineages from the mycelium of C. trogii MUT337, each producing a distinct pattern of oxidative enzymes. This highlights the potential of protoplast-mediated genome separation to uncover novel metabolic traits that would otherwise remain cryptic. These data hold outstanding significance for accessing and producing novel oxidative enzymes from native fungal populations. Full article

Persistence of seagrass meadows varies depending on community composition, substrate stability, environmental forcing, and water quality/clarity. Spatial trends in decadal scale persistence are difficult to assess at the meadow scale using in situ approaches and assessments using Earth Observation often lack temporal consistency. This study utilises a multi-decadal field monitoring dataset and high-resolution multispectral satellite imagery in a cloud-processing environment to assess species distribution, seagrass cover, and meadow persistence. In this work, we investigate long-term trends in overall meadow and species-specific persistence in the Eastern Banks, Moreton Bay, Australia, a shallow, semi-enclosed, subtropical embayment (∼200 km²). Here, we have identified an overall decline in seagrass cover (−15% of the total study area), between 2011 and 2025, through contraction of meadow extent, with most losses in colonising species (Halophila spinulosa and Halophila ovalis) across the deeper sections of the study area. We have also quantified the spatial extent of a previously identified broad-scale ecosystem shift from meadows dominated by Zostera muelleri to a prevalence of Oceana serrulata, and reduction in the sparse cover species H. spinulosa and H. ovalis. We have presented a semi-automated cloud-processing based pipeline to combine in situ seagrass observations, derived from an expertly trained machine learning model, with high resolution multispectral data to assess seagrass cover and persistence. The variability in decadal-scale persistence between the six key species found in this region has been assessed, with dense cover species (e.g., O. serrulata and Z. muelleri) exhibiting moderate persistence (>0.32) and sparse cover species (H. ovalis and H. spinulosa) with low persistence (∼0.15). Colonising/opportunistic growth patterns characterise the species examined in this study, indicating quick response to disturbance but a lack temporal consistency in meadow form, which has critical implications for resilience. Full article

In this study, a multiphysics coupling numerical model was developed to investigate the thermal-fluid dynamics and microstructure evolution during the laser metal deposition of AlCoCrFeNi high-entropy alloy (HEA) coatings on 430 stainless steel substrates. The model integrated laser-powder interactions, temperature-dependent material properties, and the coupled effects of buoyancy and Marangoni convection on melt pool dynamics. The simulation results were compared with experimental data to validate the model’s effectiveness. The simulations revealed a strong bidirectional coupling between temperature and flow fields in the molten pool: the temperature distribution governed surface tension gradients that drove Marangoni convection patterns, while the resulting fluid motion dominated heat redistribution and pool morphology. Initially, the Peclet number (Pe_T) remained below 5, indicating conduction-controlled heat transfer with a hemispherical melt pool. As the process progressed, Pe_T exceeded 50 at maximum flow velocities of 2.31 mm/s, transitioning the pool from a circular to an elliptical geometry with peak temperatures reaching 2850 K, where Marangoni convection became the primary heat transfer mechanism. Solidification parameter distributions (G and R) were computed and quantitatively correlated with scanning electron microscopy (SEM)-observed microstructures to elucidate the columnar-to-equiaxed transition (CET). X-ray diffraction (XRD) analysis identified body-centered cubic (BCC), face-centered cubic (FCC), and ordered B2 phases within the coating. The resulting hierarchical microstructure, transitioning from fine equiaxed surface grains to coarse columnar interfacial grains, synergistically enhanced surface properties and established robust metallurgical bonding with the substrate. Full article

This study investigates the morphometric and anthropogenic controls governing the occurrence and spatial distribution of tributary–junction fans (TJFs) along the Chaudière River, Québec, Canada. Using GIS-based morphometric analysis, field validation, and multivariate statistics (PCA, CART, LDA), 142 tributary watersheds were analyzed, of which 41 display fan-shaped depositional features. Basin relief, drainage density, contributing area, and slope–area coupling emerge as the dominant predictors of TJF development, delineating an intermediate energy domain where sediment supply and transport capacity become balanced enough to allow partial geomorphic coupling at confluence nodes. CART analysis identified approximate slope and area thresholds (slope < 9°, area > 20 km²; 66% accuracy), while LDA achieved 76%, indicating that morphometry provides useful but incomplete predictive power. These moderate performances reflect the additional influence of event-scale hydrological forcing and unquantified Quaternary substrate heterogeneity typical of postglacial terrain. Beyond morphometry, anthropogenic disturbance exerts a secondary but context-dependent influence, with moderately disturbed watersheds (10–50% altered) showing higher frequencies of fans than both highly engineered (>50%) and minimally disturbed (<10%). This pattern suggests that land-use modification can locally reinforce or offset morphometric predisposition by altering sediment-routing pathways. Overall, TJFs function as localized sediment-storage buffers that may be periodically reactivated during high-magnitude floods. The combined effects of basin geometry, land-use pressures, and hydroclimatic variability explain their spatial distribution. The study provides an indicative, process-informed framework for evaluating sediment connectivity and depositional thresholds in cold-region fluvial systems, with implications for geomorphic interpretation and hazard management. Full article

Altitudinal gradients offer powerful natural frameworks to investigate how environmental factors shape biodiversity, especially on young oceanic volcanic islands where short spatial distances encompass sharp climatic transitions. This study documents bryophyte diversity and examines how elevation, substrate, and environmental variables influence the structure of bryophyte communities on Flores Island (Azores). Across five sites and 385 microplots, 89 species from 37 families were recorded, with liverworts predominating (liverwort-to-moss ratio of 1.41). Species richness and abundance followed a unimodal pattern, peaking at mid-elevations (400–600 m a.s.l.), where humid and thermally stable conditions favor the coexistence of lowland and montane taxa. Even modest altitudinal shifts corresponded to pronounced turnover in community composition, revealing strong ecological filtering along the gradient. Substrate type further influenced diversity patterns, with liverworts dominating epiphytic and lignicolous habitats, while mosses were more diverse on terricolous and rupicolous substrates. The presence of several Azorean and Macaronesian endemics, including threatened taxa, highlights the conservation importance of mid-elevation habitats. Overall, these results show that fine-scale altitudinal variation generates substantial ecological differentiation, underscoring the role of montane forests as refugia for hygrophilous and endemic bryophytes and as sensitive indicators of environmental change in island ecosystems. Full article

In this study, simulations and analyses of arc characteristics in EP (positive polarity) and EN (negative polarity) stages (including the arc polarity transition stage) of variable polarity cold metal transition (VP-CMT) during arc additive manufacturing of aluminum alloys are carried out. Temperature field, potential field, and current density distribution of arc plasma at different stages are systematically investigated by establishing a numerical model of arc heat–force coupling in combination with single-layer single-pass additive manufacturing experiments. The results indicate that the arc’s high-temperature zone in EP stage shows the wider distribution range, with enhanced heat transfer efficiency, reaching a surface temperature of up to 11,555.8 K at 2 mm from the substrate. In contrast, the arc during the EN stage demonstrates a more concentrated high-temperature zone, attributed to a more pronounced electromagnetic contraction effect, resulting in reduced heat input and a lower peak substrate temperature in comparison with EP stage. As revealed by analysis of potential and current density distribution, the arc in EP stage shows the “bell-shaped” expansion pattern with widely distributed current density, whereas the EN stage arc displays a “wrapped” contraction pattern with a more concentrated current density. The transition from EN to EP stage exhibits greater arc stability than the reverse transition. Moreover, electrode spacing significantly influences arc characteristics; a reduction in spacing leads to a more focused high-temperature zone and a substantial increase in peak current density. This study elucidates the dynamic variations in heat transfer behavior between the EP and EN stages, offering a theoretical foundation for optimizing process parameters in aluminum alloy arc additive manufacturing. Full article

This article presents the design of a dual-mode resonant, dual-band textile microstrip patch antenna for bent sensing applications. The antenna has a simple, slit-perturbed circular sector patch configuration. Unlike traditional single-mode resonant bending sensor antennas, dual-mode resonance brings a unique dual-band sensing characteristic to textile antennas. It effectively covers 2.45 GHz and 5.8 GHz Industrial, Scientific and Medical (ISM) frequency bands. Experimental results demonstrate that the proposed antenna achieves −10 dB impedance bandwidths of 1.4% (2.43–2.465 GHz) and 2.4% (5.775–5.915 GHz), with maximum peak gains of 8.8 dBi and 9.1 dBi, respectively. As experimentally validated on flannel substrates, the antenna achieves maximum bent sensing sensitivities of 1.1 MHz/mm and 1.78 MHz/mm at 2.45 GHz and 5.8 GHz bands, respectively. Furthermore, the antenna is able to provide stable E-plane broadside radiation patterns in bending situations. It would be an ideal candidate for radio frequency identification (RFID), health monitoring systems, and flexible communication applications. Full article

A special class of complex adaptive systems—biological and social—thrive not by passively accumulating patterns, but by engineering coherence, i.e., the deliberate alignment of prior knowledge, real-time updates, and teleonomic purposes. By contrast, today’s AI stacks—Large Language Models (LLMs) wrapped in agentic toolchains—remain rooted in a Turing-paradigm architecture: statistical world models (opaque weights) bolted onto brittle, imperative workflows. They excel at pattern completion, but they externalize governance, memory, and purpose, thereby accumulating coherence debt—a structural fragility manifested as hallucinations, shallow and siloed memory, ad hoc guardrails, and costly human oversight. The shortcoming of current AI relative to human-like intelligence is therefore less about raw performance or scaling, and more about an architectural limitation: knowledge is treated as an after-the-fact annotation on computation, rather than as an organizing substrate that shapes computation. This paper introduces Mindful Machines, a computational paradigm that operationalizes coherence as an architectural property rather than an emergent afterthought. A Mindful Machine is specified by a Digital Genome (encoding purposes, constraints, and knowledge structures) and orchestrated by an Autopoietic and Meta-Cognitive Operating System (AMOS) that runs a continuous Discover–Reflect–Apply–Share (D-R-A-S) loop. Instead of a static model embedded in a one-shot ML pipeline or deep learning neural network, the architecture separates (1) a structural knowledge layer (Digital Genome and knowledge graphs), (2) an autopoietic control plane (health checks, rollback, and self-repair), and (3) meta-cognitive governance (critique-then-commit gates, audit trails, and policy enforcement). We validate this approach on the classic Credit Default Prediction problem by comparing a traditional, static Logistic Regression pipeline (monolithic training, fixed features, external scripting for deployment) with a distributed Mindful Machine implementation whose components can reconfigure logic, update rules, and migrate workloads at runtime. The Mindful Machine not only matches the predictive task, but also achieves autopoiesis (self-healing services and live schema evolution), explainability (causal, event-driven audit trails), and dynamic adaptation (real-time logic and threshold switching driven by knowledge constraints), thereby reducing the coherence debt that characterizes contemporary ML- and LLM-centric AI architectures. The case study demonstrates “a hybrid, runtime-switchable combination of machine learning and rule-based simulation, orchestrated by AMOS under knowledge and policy constraints”. Full article

This study focused on the measurement requirements of Digital Image Correlation (DIC) in high-temperature environments of aero-engines and systematically investigated the applicability and stability of high-temperature speckle materials. Five common coating materials (Ti, TiN, Ta, NiCr alloy, and SiC) were selected. Corresponding thin films were deposited on Al₂O₃ ceramic substrates using magnetron sputtering technology, and their surface color evolution from room temperature up to 1200 °C was examined. The film compositions were analyzed by Raman spectroscopy and X-ray photoelectron spectroscopy (XPS), revealing the mechanisms behind the color changes. The results indicate that Ti, TiN, Ta, and NiCr alloy exhibit significant color variations, which leads to insufficient color contrast for high-temperature speckle patterns. Further investigation shows that depositing an outer SiO₂ coating can improve surface scattering and reflection, while also inhibiting oxygen penetration, thereby enhancing oxidation resistance and improving speckle contrast. The SiC/SiO₂ composite structure demonstrates excellent thermal stability, making it an ideal speckle material for high-temperature DIC measurements. Full article

The integration of 2D materials with artificially textured substrates offers exceptional opportunities for engineering novel functional devices. A straightforward technological route towards such devices is a mechanical dry or wet transfer of 2D layer or heterostructure onto prepared patterned elements with subsequent van der Waals bonding. An issue of van der Waals bond stability is crucial for device operation but is almost unexplored. In our research, we address it by studying transport properties of hBN/graphene heterostructures transferred onto metallic island arrays and subjected to thermal cycling. We reveal that heating from cryogenic to room temperature and cooling back leads to irreversible changes in electronic transport properties: the contact between metal and graphene degrades, and signatures of suspended graphene regions transport disappear. These changes are accompanied by slight movement of the flakes and atomic-force-microscope-detected breakdown of van der Waals bonds between the flake and substrate near the metal electrodes. Interestingly, a hot pressing allows us to restore the metal-to-graphene contact. We relate the observed metastability to the thermal-expansion-driven flake delamination and argue that it is accompanied by redistribution of the interfacial water or organic residues. Our findings provide useful insights into the topic of interfacial stability in van der Waals heterostructures and establish constraints for low-temperature applications of transferred 2D devices. We also add up an additional control parameter for the experimentalists in the field of 2D materials—degree of quenched disorder. Full article

The chemical mechanical polishing (CMP) process in semiconductor fabrication faces challenges such as slurry agglomeration, scratches, and contamination, which degrade process reliability and device quality. To mitigate these challenges, this study investigated the application of hydrophobic surface coatings on CMP components. Polytetrafluorothylene (PTFE) was deposited onto stainless steel substrates, while diamond-like carbon (DLC) films were coated on PEEK-based retainer rings, with material selection guided by their surface energy characteristics and mechanical robustness. The hydrophobic performance of the coatings was systematically evaluated through contact angle measurements and surface roughness analysis (Ra, Rpk, Sa, Spk). Under oxide CMP conditions, 60 h reliability tests using non-patterned wafers demonstrated that PTFE-coated stainless-steel surfaces significantly reduced slurry-induced particle accumulation and suppressed scratches compared with uncoated substrates. In addition, PTFE provided stable hydrophobicity and effective scratch resistance, while DLC exhibited superhydrophobic behavior with contact angles exceeding 160°, offering potential for even greater protection against surface damage. The wettability of DLC coatings was further tunable via sp³/sp² carbon bonding ratios and surface roughness, consistent with the predictions of the Cassie–Baxter and Wenzel models. These findings establish a framework for surface modification of CMP hardware. The integration of PTFE and DLC coatings effectively enhances hydrophobicity, suppresses slurry contamination, and improves scratch reliability, thereby offering a practical route for designing hydrophobic CMP components that strengthen process stability and extend equipment lifetime in advanced semiconductor manufacturing. Full article

Environmental degradation from rapid urbanization significantly threatens ecological resilience (ER). Nevertheless, accurately evaluating ER remains a persistent challenge. Prior studies’ limited attention to resilience’s cross-scale complexity has hindered evidence-based management. This study, based on long-term time series remote sensing and multi-source data, developed a cross-scale spatiotemporal ER analysis framework integrating landscape ecology and panarchy perspectives. A local “resistance–adaptation–recovery” substrate resilience evaluation was combined with telecoupling-based global network resilience to quantify multi-scale ER from 2000 to 2020. Key drivers across time scales were identified using a hybrid XGBoost–SHAP and genetic algorithm (GA)–optimized dynamic Bayesian network (DBN), and spatial optimization scenarios were simulated with patch-generating land use simulation (PLUS) model. ER decreased slightly from 0.4856 in 2000 to 0.4503 in 2020, with dynamic fluctuations across periods. A clear spatial pattern emerged, with higher ER in the east and lower in the west. Forest land contributed strongly to ER, while construction and cropland reduced it. Spatial composition factors—especially the proportions of forest and construction land—were dominant drivers, outweighing structural factors such as landscape pattern. DBN backward inference revealed nonlinear threshold effects among socio–natural–spatial drivers. Scenario-based simulations confirmed that regulating spatial composition via our optimization pathway can enhance ER. This is particularly effective when expanding forestland in mountainous regions while restraining the growth of built-up areas. This study proposes an integrated framework of “resilience assessment—driver analysis—spatial optimization,” which not only advances the theoretical basis for nested ER assessment but also offers a transferable approach for optimizing spatial patterns and sustainable land management, thereby enhancing ecological resilience in rapidly urbanizing regions. Full article

Enzyme kinetics is fundamental across diverse fields—from enzymology and medicine to biocatalysis and metabolic engineering. Analyses of enzyme kinetics provide insights into catalytic rates, substrate affinities, inhibition patterns, productivities and mechanistic pathways, which are critical for areas such as drug development, industrial biocatalysis and mechanistic enzymology. However, each research field emphasizes different types of kinetic parameters, leading to challenges in establishing a common ground for understanding and interpreting enzyme properties. This review covers interpretation of enzyme kinetic parameters under three main categories—steady-state, transient-state and performance metrics—in a descriptive way and discusses their relevance with respect to different scientific and applied fields that investigate and utilize enzymes. By comparatively defining key kinetic and thermodynamic parameters, the review aims to help researchers interpret and report enzyme behavior more effectively, bridging gaps across interdisciplinary fields. Full article

The development of electronic textiles (e-textiles) has advanced significantly thanks to the integration of printing technologies such as flexography, which enables the efficient and reproducible production of conductive circuits on fabrics. This study evaluates the impact of different surface pretreatments (hydrophobic and oleophobic) on the electrical conductivity of flexographically printed circuits on a variety of polyester textile substrates. Key parameters such as grammage, fabric type and surface uniformity are analyzed using stereomicroscopy and profilometry techniques to characterize conductive ink distribution. The results demonstrate that oleophobic pretreatment is more effective at reducing the resulting electrical resistance, promoting better ink adhesion and distribution. Among the fabrics with the best results, those with a more regular and compact structure, such as 15 thread/cm and 666.7 dtex polyester taffeta, show homogeneous ink coverage and the lowest electrical resistance (∼0.5 Ω/cm) compared to more irregular fabrics with discontinuities and higher resistance. The results show that uniformity in ink distribution, assessed by profilometry and color analysis, directly correlates with low electrical resistance. It can be concluded that the combination of a regular and compact textile structure, an adequate surface pretreatment, and a printing direction of the circuit pattern aligned with the weft permits optimizing the conductivity and quality of e-textiles produced by flexography. Full article

KY₃F₁₀:Yb³⁺, Tm³⁺ upconversion microparticles (UCMPs) with varying Mn²⁺ doping concentrations were synthesized via a hydrothermal method. Under 980 nm laser excitation, the sample with 3 mol% Mn²⁺ doping demonstrated markedly enhanced luminescence performance, exhibiting a significant intensity increase compared to undoped samples. The as-synthesized UCMPs were successfully incorporated into an anti-counterfeiting ink. Target information was encrypted using a hash function to generate a QR code, which was then screen-printed onto substrate materials. Under 980 nm laser irradiation, the printed QR code exhibited visible blue fluorescence with high stability, confirming its anti-counterfeiting capability. Furthermore, an image recognition system for anti-counterfeiting, based on a hybrid Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) architecture, was developed on the Matlab platform. The system achieved 100% recognition accuracy for the luminescent QR code patterns, providing valuable insights for the development of deep learning-based image anti-counterfeiting technologies. Full article