Search Results (15,041)

Understanding the spatiotemporal dynamics of coastal intertidal meta-ecosystems in response to sea-level rise (SLR) is essential for understanding the interactions between terrestrial and aquatic meta-ecosystems. However, given that annual SLR changes are typically measured in millimeters, ecosystems may take decades to exhibit noticeable shifts. As a result, the extent of lateral responses at a single point is constrained by the fragmented temporal and spatial scales. We integrated the tidal inundation gradient of a coastal meta-ecosystem—comprising a high-elevation flat (H), low-elevation flat (L), and mudflat—to quantify the potential application of inferring the spatiotemporal impact of environmental features, using China’s Yangtze Estuary, which is one of the largest and most dynamic estuaries in the world. We employed both flood ratio data and tidal elevation modeling, underscoring the utility of spatial modeling of the role of SLR. Our results show that along the tidal inundation gradient, SLR alters hydrological dynamics, leading to environmental changes such as reduced aboveground biomass, increased plant diversity, decreased total soil, carbon, and nitrogen, and a lower leaf area index (LAI). Furthermore, composite indices combining the enhanced vegetation index (EVI) and the land surface water index (LSWI) were used to characterize the rapid responses of vegetation and soil between sites to predict future ecosystem shifts in environmental properties over time due to SLR. To effectively capture both vegetation characteristics and the soil surface water content, we propose the use of the ratio and difference between the EVI and LSWI as a composite indicator (ELR), which effectively reflects vegetation responses to SLR, with high-elevation sites driven by tides and high ELRs. The EVI-LSWI difference (ELD) was also found to be effective for detecting flood dynamics and vegetation along the tidal inundation gradient. Our findings offer a heuristic scenario of the response of coastal intertidal meta-ecosystems in the Yangtze Estuary to SLR and provide valuable insights for conservation strategies in the context of climate change. Full article

The complexity of global food supply chains challenges public health, requiring advanced detection technologies beyond traditional lab methods. Fluorescent sensing, known for its sensitivity and quick response, is promising for food safety but hindered by inefficient probe design and difficulties in analyzing complex signals in food. Deep Learning (DL) offers solutions with its nonlinear modeling and pattern recognition capabilities. This review explores recent advancements in DL applications for fluorescent sensing. We explore deep learning methods for predicting fluorescent probe properties and generating fluorescent molecule structures, highlighting their role in accelerating high-performance probe development. We then offer a detailed discussion on the pivotal technologies of deep learning in the intelligent analysis of complex fluorescent signals. On this basis, we engage in a thorough reflection on the core challenges presently confronting the field and propose a forward-looking perspective on the future developmental trajectories of fluorescent sensing technology, offering a comprehensive and insightful roadmap for future research in this interdisciplinary domain. Full article

Sea grape kombucha has been known to exhibit high antioxidant activity due to its elevated total polyphenol content. This study aims to identify and characterize the active microbial community involved in the fermentation of kombucha using sea grapes (C. racemosa) as the primary substrate. Furthermore, it evaluates the effects of different Symbiotic Culture of Bacteria and Yeast (SCOBY) starter concentrations on the physicochemical properties and antioxidant activity of sea grape kombucha. Our results showed that the pH of the kombucha was higher after 7 days of fermentation compared to later time points. The microbial community was composed of 97.08% bacteria and 2.92% eukaryotes, divided into 10 phyla and 69 genera. The dominant genus in all samples was Komagataeibacter. Functional profiling based on 16S rRNA data revealed that metabolic functions accounted for 77.04% of predicted microbial activities during fermentation. The most enriched functional categories were carbohydrate metabolism (15.70%), cofactor and vitamin metabolism (15.54%), and amino acid metabolism (14.24%). At KEGG Level 3, amino acid-associated pathways, particularly alanine, aspartate, and glutamate metabolism (4.24%), were predominant. The fermentation process in sea grape kombucha is primarily driven by carbohydrate and amino acid metabolism, supported by energy-generating and cofactor biosynthesis pathways. Our findings indicate that different metabolic pathways lead to variations in kombucha components, and distinct fermentation stages result in different metabolic reactions. For instance, early fermentation stages (Day 7) are dominated by amino acid metabolism, whereas the late stages (Day 21) show increased activity in carbohydrate and sulfur metabolism. Metabolomic analysis revealed that increasing the SCOBY starter concentration significantly influenced pH, soluble solid content, vitamin C, tannin, and flavonoid content. These variations suggest that fermentation duration and microbial composition significantly influence the spectrum of bioactive metabolites, which synergistically provide functional benefits such as antimicrobial, antioxidant, and metabolic health-promoting activities. For example, sample K1 produced more fatty acids and simple sugar alcohols, sample K2 enriched complex lipid compounds and phytosterols, while sample K3 dominated the production of polyols and terpenoid compounds. Full article

Exploiting inherent symmetries in data and models is crucial for accurate renewable energy forecasting. To address limited accuracy improvements under complex temporal dependencies, this study proposes a hybrid Bi-xLSTM-Informer model that incorporates temporal symmetry via bidirectional processing of time-flipped sequences. First, key features are screened using the Boruta algorithm, followed by PCA dimensionality reduction to construct an optimal feature subset with orthogonal transformation properties. Second, a Bi-xLSTM-Informer hybrid forecasting model is constructed. In the xLSTM model, the mLSTM is modified into a bidirectional network structure to capture short-term fluctuation patterns via forward and time-reversed propagation; Informer then analyzes global dependencies via ProbSparse attention. Validated on data from the photovoltaic (PV) Power Plant AI Competition, the experimental results demonstrate that the Bi-xLSTM-Informer model achieves the best prediction performance and the lowest error among all compared models, with an R² of 98.76% and an RMSE of 0.3776. This work proves that explicitly modeling temporal symmetry and feature orthogonality significantly enhances PV forecasting, providing an effective solution for renewable energy utilization. Full article

Ivermectin (IVM), a broad-spectrum anthelmintic agent, has anti-inflammatory properties, and affects cellular and humoral immune responses. We recently showed that multiple pro-inflammatory cytokines (e.g., FGF2, CCL5, CD40L) bind to the allosteric site (site 2) of integrins and activate them. 25-Hydroxycholesterol, a pro-inflammatory lipid mediator, is known to bind to site 2 and induce integrin activation and inflammatory signals (e.g., IL-6 and TNF secretion), suggesting that site 2 is critically involved in inflammation. We showed that two anti-inflammatory cytokines (FGF1 and NRG1) bind to site 2 and inhibit integrin activation by inflammatory cytokines. We hypothesized that ivermectin binds to site 2 and inhibits inflammatory signaling by pro-inflammatory cytokines. A docking simulation predicts that ivermectin binds to site 2. Ivermectin inhibits the integrin activation induced by inflammatory cytokines, suggesting that ivermectin is a site 2 antagonist. We showed that TNF, a major pro-inflammatory cytokine, binds to integrin site 2 and induces allosteric integrin activation like other pro-inflammatory cytokines, suggesting that site 2 binding and integrin activation is a potential mechanism of the pro-inflammatory action of these cytokines. Ivermectin suppressed the activation of soluble β3 integrins by TNF and other pro-inflammatory cytokines in a dose-dependent manner in cell-free conditions. Binding to site 2 and the inhibition of binding of inflammatory cytokines may be a potential mechanism of anti-inflammatory action of ivermectin. Full article

Machine learning models and web-based tools have been developed for predicting key properties of imidazolium-based ionic liquids. Two high-quality datasets containing experimental density and viscosity values at 298 K were curated from the ILThermo database: one containing 434 systems for density and another with 293 systems for viscosity. Molecular structures were optimized using the GOAT procedure at the GFN-FF level to ensure chemically realistic geometries, and a diverse set of molecular descriptors, including electronic, topological, geometric, and thermodynamic properties, was calculated. Three support vector regression models were built: two for density (IonIL-IM-D1 and IonIL-IM-D2) and one for viscosity (IonIL-IM-V). IonIL-IM-D1 uses three simple descriptors, IonIL-IM-D2 improves accuracy with seven, and IonIL-IM-V employs nine descriptors, including DFT-based features. These models, designed to predict the mentioned properties at room temperature (298 K), are implemented as interactive applications on the atomistica.online platform, enabling property prediction without coding or retraining. The platform also includes a structure generator and searchable databases of optimized structures and descriptors. All tools and datasets are freely available for academic use via the official web site of the atomistica.online platform, supporting open science and data-driven research in molecular design. Full article

3D bioprinting has emerged as a key tool in tissue engineering by facilitating the creation of customized scaffolds with properties tailored to specific needs. Among the design parameters, porosity stands out as a determining factor, as it directly influences critical mechanical and biological properties such as nutrient diffusion, cell adhesion and structural integrity. This review comprehensively analyses the state of the art in scaffold design, emphasizing how porosity-related parameters such as pore size, geometry, distribution and interconnectivity affect cellular behavior and mechanical performance. It also addresses advances in manufacturing methods, such as additive manufacturing and computer-aided design (CAD), which allow the development of scaffolds with hierarchical structures and controlled porosity. In addition, the use of computational modelling, in particular finite element analysis (FEA), as an essential predictive tool to optimize the design of scaffolds under physiological conditions is highlighted. This narrative review analyzed 112 core articles retrieved primarily from Scopus (2014–2025) to provide a comprehensive and up-to-date synthesis. Despite recent progress, significant challenges persist, including the lack of standardized methodologies for characterizing and comparing porosity parameters across different studies. This review identifies these gaps and suggests future research directions, such as the development of unified characterization and classification systems and the enhancement of nanoscale resolution in bioprinting technologies. By integrating structural design with biological functionality, this review underscores the transformative potential of porosity research applied to 3D bioprinting, positioning it as a key strategy to meet current clinical needs in tissue engineering. Full article

Local variations in mechanical properties are commonly observed in engineering structures, driven by complex manufacturing histories and harsh service environments. The evaluation of mechanical properties accurately constitutes a fundamental requirement for structural integrity assessment. The Instrumented Indentation Technique (IIT) can play a critical role in the in-site testing of local properties. However, it could be often a challenge to correlate indentation characteristics with uniaxial stress–strain relationships. In this study, we investigated quantitatively the connection between the indentation responses of commonly used metals and their plastic properties using the finite element inversion method. Materials typically exhibit plastic deformation mechanisms characterized by either linear or power-law hardening behaviors. Consequently, conventional prediction methods based on a single constitutive model may no longer be universally applicable. Hence, this study developed methods for acquiring mechanical properties suitable for either the power-law and linear hardening model, or combined, respectively, based on analyses of microstructures of materials exhibiting different hardening behaviors. We proposed a novel integrated IIT incorporating microstructures and material-specific constitutive models. Moreover, the inter-dependency between microstructural evolution and hardening behaviors was systematically investigated. The proposed method was validated on representative engineering steels, including austenitic stainless steel, structural steel, and low-alloy steel. The predicted deviations in yield strength and strain hardening exponent are broadly within 10%, with the maximum error at 12%. This study is expected to provide a fundamental framework for the advancement of IIT and structural integrity assessment. Full article

Autonomous surgery involves surgical tasks performed by a robot with minimal or no human involvement. Thanks to its precise automation, surgical robotics offers significant benefits in enhancing the consistency, safety, and quality of procedures, driving its growing popularity. However, ensuring the safety of autonomous surgical robotic systems remains a significant challenge. To address this, we propose a simulation-based validation method to detect potential safety issues in the software of surgical robotic systems, complemented by a digital twin to estimate the gap between simulation and reality. The validation framework consists of a test case generator and a monitor for validating properties and evaluating the performance of the robotic system during test execution. Using a robotic arm for needle insertion as a case study, we present a systematic test case generation method that ensures effective coverage measurement for a three-dimensional, irregular model. Since no simulation can perfectly replicate reality due to differences in sensing and actuation, the digital twin bridges the gap between simulation and the physical robotic arm. This integration enables us to assess the discrepancy between virtual simulations and real-world operations by verifying whether the data from the simulation accurately predicts real-world outcomes. Through extensive experimentation, we identified several flaws in the robotic software. Co-simulation within the digital twin framework has highlighted these discrepancies that should be considered. Full article

Kidney diseases represent an increasingly significant global public health challenge, with an estimated prevalence of around 10% among adults and a rising trend influenced by factors such as population aging and exposure to nephrotoxic agents. Given the limitations of conventional treatments, which often only slow disease progression and may cause adverse effects, there is growing interest in exploring alternative therapies based on natural compounds. Sansevieria trifasciata, commonly known for its ornamental use, has been widely used in traditional medicine in Mexico and other tropical regions due to its antioxidant, anti-inflammatory, and regenerative properties. Recently, its phytochemical profile has drawn scientific attention, particularly due to its high content of hydroxylated aromatic compounds such as flavonoids, terpenes, and phenolic acids, which may offer protective effects on kidney function. For this review, searches were conducted in specialized databases such as PubMed, Scopus, and Google Scholar, as well as platforms like ChEMBL and SWISS, selecting articles published between 2008 and 2025. This work aims to compile and critically analyze the available scientific literature on the nephroprotective potential of the phytochemicals found in S. trifasciata, and includes a preliminary exploration of their possible mechanisms of action using pharmacokinetic and pharmacodynamic prediction tools. Full article

In extended-reach wells, cuttings bed formation in high-deviation sections presents a major challenge for hole cleaning and borehole stability. This study analyzes the morphological and mechanical behavior of cuttings beds, focusing on particle size distribution and repose angle as key indicators of accumulation behavior. The modeling approach considers dominant interparticle forces, including buoyancy and cohesion, while neglecting secondary microscale forces for clarity. A theoretical model is developed to predict repose angles under both rolling and sliding regimes and is calibrated through laboratory-scale experiments using simulated drilling fluid with field-representative rheological properties. Results show that cohesive effects are negligible when cuttings are of similar size but exhibit higher densities. Laboratory measurements reveal that the repose angle of cuttings beds varies between 23.9° and 31.7°, with increasing polyacrylamide (PAM) concentration and particle size contributing to steeper repose angles. Additionally, the rolling repose angle is found to be relatively stable, ranging from 25° to 30°, regardless of fluid or particle property variations. These findings provide a predictive framework and practical guidelines for optimizing hole cleaning strategies and designing more effective models in extended-reach drilling. Full article

Oil refineries depend greatly on the estimation of crude oil properties in order to understand the oil’s behaviour and the product fractions expected from the refining process. In yield estimation, the crude oil source and variant can cause variability in prediction and lead to the need for repeatable analysis. The necessity for fast, accurate, and high-generalization yield estimation initiates the framework of this review. This paper aims to comprehensively review the available techniques for estimating the yield of petroleum products in the petroleum refining industry. The review provides a brief overview of petroleum refining processes and high-value products, followed by a description of the traditional method, which utilizes laboratory analysis to offer detailed findings, but requires a tedious methodology. The improvement of yield estimation leads to process simulation, modelling, and machine learning, enabling a fast response and better prediction with higher accuracy. Thorough case studies related to simulation software, models, and algorithms are presented to discover the process and model development, applications, advantages, and drawbacks. Enhancing petroleum product yield estimation provides reliable techniques for oil refiners that enable them to achieve optimized production aligned with sustainability and modernization goals. Full article

Keratinocytes are the primary cells of the epidermis layer in our skin. They play a crucial role in maintaining skin health, responding to injuries, and counteracting disease progression. Understanding their behaviour is essential for advancing wound healing therapies, improving outcomes in regenerative medicine, and developing numerical models that accurately mimic skin deformation. To create physically representative models, it is essential to evaluate the nuanced ways in which keratinocytes deform, interact, and respond to mechanical and biochemical signals. This has prompted researchers to investigate various computational methods that capture these dynamics effectively. This review summarises the main mathematical and biomechanical modelling techniques (with particular focus on the literature published since 2010). It includes reaction–diffusion frameworks, finite element analysis, viscoelastic models, stochastic simulations, and agent-based approaches. We also highlight how machine learning is being integrated to accelerate model calibration, improve image-based analyses, and enhance predictive simulations. While these models have significantly improved our understanding of keratinocyte function, many approaches rely on idealised assumptions. These may be two-dimensional unicellular analysis, simplistic material properties, or uncoupled analyses between mechanical and biochemical factors. We discuss the need for multiscale, integrative modelling frameworks that bridge these computational and experimental approaches. A more holistic representation of keratinocyte behaviour could enhance the development of personalised therapies, improve disease modelling, and refine bioengineered skin substitutes for clinical applications. Full article

Given the multi-basin raw material base for coking that has been formed at most industry enterprises, there is an urgent need to optimize the component composition and improve the basic technological methods of coal raw material preparation, taking into account the petrographic characteristics of coal batches. A comprehensive study of the components included in a coke chemical enterprise’s coking raw material base was carried out. The work used standardized methods for studying coal and coal batches’ technological and plastic–viscous properties. The qualitative characteristics of coke were determined using physical–mechanical and thermochemical methods of studying standardized indicators: crushability (M₂₅), abrasion (M₁₀), reactivity (CRI), post-reaction strength (CSR), and specific electrical resistance (ρ). The results were analyzed using the licensed Microsoft Excel computer program. Based on the results of proximate, plastometric, and petrographic analyses of the studied coal samples and data from experimental industrial coking, proposals were made to optimize the component composition, properties of the coal batch, and technology for its preparation for coking. The established inverse dependence of Gibbs free energy (ΔG_f,total) on the reaction capacity of coke CRI and its direct reliance on its post-reaction strength CSR confirmed the feasibility of using ΔG_f,total as a thermodynamic predictive parameter for optimizing and compiling coal batches that produce less reactive, stronger coke. This made it possible to improve the quality indicators of metallurgical coke. Thus, according to the M₂₅ crushability index, the mechanical strength increased by 0.6%, and the M₁₀ abrasion decreased by 0.4%. Significant improvements in thermochemical properties and an increase in the orderliness of the carbon structure were recorded: the CRI reactivity decreased by 3.1%, the CSR post-reaction strength increased by 8.3%, and the specific resistance decreased by 8.4%. Full article

This study illustrates the complex interaction between environmental parameters and carbonate distribution in marine sediments along the Tarfaya–Boujdour coastline (26–28° N) of Northwest Africa. Analysis of 21 surface sediment samples and their associated bottom water properties (salinity, temperature, dissolved oxygen, nutrients) reveals CaCO₃ content ranging from 16.8 wt.% to 60.5 wt.%, with concentrations above 45 wt.% occurring in multiple stations, especially in nearshore deposits. Mineralogy indicates a general decrease in quartz, with an arithmetic mean and standard deviation of 52.5 wt.% ± 19.8 towards the open sea, and an increase in carbonate minerals (calcite ≤ 24%, aragonite ≤ 10%) with depth. Sediments are predominantly composed of fine sand (78–99%), poorly classified, with gravel content reaching 6.7% in energetic coastal stations. An inverse relationship between organic carbon (0.63–3.23 wt.%) and carbonates is observed in upwelling zones, correlated with nitrate concentrations exceeding 19 μmol/L. Hydrological gradients show temperatures from 12.41 °C (offshore) to 21.62 °C (inshore), salinity from 35.64 to 36.81 psu and dissolved oxygen from 2.06 to 4.21 mL/L. The weak correlation between carbonates and depth (r = 0.10) reflects the balance between three processes: biogenic production stimulated by upwelling, dilution by Saharan terrigenous inputs, and hydrodynamic sorting redistributing bioclasts. These results underline the need for models integrating hydrology, mineralogy and hydrodynamics to predict carbonate dynamics in desert margins under upwelling. Full article