Search Results (29,734)

Non-invasive, real-time monitoring of plant water status is critical for precision agriculture and plant physiology. However, existing methods often lack continuous in situ measurement capability or are limited by temporal resolution. This paper proposes a novel non-invasive method based on xylem electrical conductivity, inspired by industrial non-contact fluid measurement. As a ground-based complement to remote sensing, this approach demonstrates the feasibility of online, in situ, and non-invasive monitoring of water stress in grapevine stems under controlled laboratory conditions. The industrial C⁴D sensing system is adaptively modified into a specialized Plant-C⁴D sensor with an array-based design for batch signal acquisition. To validate the electrical response to water loss, a gravimetric natural dehydration experiment was conducted, demonstrating a clear correlation between electrical signals and water content changes in detached stem samples. Full-day dynamic experiments are conducted under three conditions: normal water supply, varying water stress, and plant inactivation. Sensitive characteristic parameters are extracted through signal analysis, and a pattern recognition framework is established to eliminate environmental interference and suppress individual differences. Experimental results on 24 plant samples (Shine Muscat) show that the method accurately discriminates viable from inactivated plants with an accuracy of 91.67% (22/24 correct). Furthermore, the Fuzzy C-Means (FCM) clustering algorithm successfully quantifies the severity of water stress in viable plants, yielding results consistent with actual water supply conditions. While these findings demonstrate the capability of Plant-C⁴D sensor to capture stem water status-related information, the current results do not establish full physiological validation, warranting further exploration with in vivo experiments. Full article

The aim of this research article is to explore the framework of tvs-cone b-metric spaces and establish some generalized common fixed point theorems for self mappings under generalized contractive conditions. To extend this theory, we introduce the notion of a generalized Hausdorff distance function in the framework of tvs-cone b-metric spaces and prove some novel fixed point results for multi-valued mappings. Our findings naturally encompass and generalize several known results in tvs-cone metric spaces, cone b-metric spaces, and cone metric spaces. To illustrate the applicability of the developed framework, we apply the main results to Fredholm integral equations. Full article

Carbon dioxide replacement represents a promising hydrate development strategy that effectively balances production efficiency and environmental considerations. However, its production efficiency is lower than that of the depressurization strategy. This limitation can be effectively alleviated by coupling carbon dioxide replacement with inhibitor injection. The design and optimization of the temperature–pressure operating window constrain its effective implementation. In the present work, the phase equilibrium conditions of carbon dioxide hydrate and methane hydrate were experimentally investigated. It was found that the experimental values obtained in this study are in excellent agreement with those calculated by the CSMHyd program. The average absolute relative deviations (AARD) for the experimental versus calculated results are 5.77% for methane hydrate and 2.66% for carbon dioxide hydrate. Then, the methodology for determining the recommended temperature–pressure combinations used in the carbon dioxide replacement strategy was proposed, and the size of region in which these combinations occur was quantified. The investigation results found that there are significant differences in the size of the recommended region for different sea areas, and inhibitor injection reduces the size of this recommended region. Injection of 3.0 wt% NaCl solution reduces the size of recommended region from 10.968 K·MPa to 8.366 K·MPa for pure methane hydrate, and a similar trend is also observed for natural gas hydrates. Based on the experimental results, the carbon sequestration potential of natural gas hydrate development using the carbon dioxide replacement strategy on core size was analyzed. The final simulation results show that 9.05 mol of carbon dioxide hydrate was obtained in the reaction vessel, which achieves effective CO₂ sequestration. The investigation in this work provides theoretical support for dual goals of carbon sequestration and efficient gas production from gas hydrates. Full article

Spatiotemporal differentiation and coupled driving mechanisms of urban ecological resilience in transboundary composite economic belts remain an understudied niche within human–land coupling system research. Taking 29 prefecture-level and county-level units of the Huaihe River Eco-Economic Belt from 2014 to 2023 as research samples, this study constructs an ecological resilience evaluation framework tailored to the pollution disturbance characteristics of the Huaihe River Basin under a three-dimensional theoretical framework encompassing resistance, adaptability, and recoverability. The entropy weight method is adopted to calculate comprehensive ecological resilience values, while the geographically and temporally weighted regression (GTWR) model is applied to identify spatiotemporal heterogeneous correlations among multiple influencing factors. This paper further characterizes the spatiotemporal evolutionary patterns of urban ecological resilience across the study area and unpacks the coupled associative effects of natural, economic, and social driving factors. The empirical results reveal three key findings: (1) Temporally, the overall comprehensive ecological resilience of the study region rose from 0.318 to 0.416, with a total growth rate of 30.91%. Its evolutionary trajectory follows three successive phases: rapid growth, steady improvement, and slow saturation. Adaptability, which is predominantly boosted by anthropogenic environmental governance, constitutes the primary contributor to resilience growth. The range of urban resilience values narrowed by 9.97%, indicating continuous advancement in balanced regional development. (2) Spatially, ecological resilience presents a prominent core-periphery pattern, with high-resilience zones concentrated in mountainous southwestern areas and low-resilience zones distributed across northeastern plains. All low-resilience county-level units were eliminated by 2023. (3) In terms of driving associations, topographic relief and environmental governance investment maintain persistent positive correlations with ecological resilience, while per capita GDP acts as the core economic supportive factor. The proportion of secondary industry and population density exhibit significant negative correlations with resilience. The normalized difference vegetation index (NDVI) shifts from a negative correlation to a weak positive correlation alongside progressive ecological restoration, whereas river network variables exert negligible long-term associative impacts. Collectively, the spatiotemporally heterogeneous coupling of natural endowments, industrial-economic conditions, and social governance factors shapes the evolutionary patterns of regional ecological resilience. This study fills the research gap regarding long-timescale resilience driving mechanisms for transprovincial composite river basins covering five provinces. It identifies novel human–land coupling mechanisms, including the temporal reversal of vegetation’s ecological benefits and the dual stress imposed by industrial agglomeration and dense human settlements in plain regions. The quantitative outputs of this research can provide data-based references for differentiated coordinated ecological governance across the Huaihe Ecological Economic Belt. Full article

Orthoflaviviruses comprise genetically diverse mosquito-borne viruses responsible for a broad spectrum of human diseases. Although naturally circulating clinical isolates exhibit biological variability, the extent to which they generate distinct early epithelial innate immune responses remains incompletely understood. Here, we characterized five clinical orthoflavivirus isolates obtained in Oaxaca, Mexico, using human HaCaT keratinocytes as an in vitro model of early infection. Productive infection was assessed by immunofluorescence microscopy, immunostained focus appearance under isolate-optimized assay conditions, and infectious virus production, whereas host responses were evaluated by transcriptional profiling and quantitative whole-slide single-cell immunofluorescence. All isolates established productive infection and exhibited different viral replication profiles. Temporal transcriptional analyses revealed variable expression of antiviral (IFNβ, Mx1, OAS1, PKR, IFITM3, Viperin, and RANTES) and inflammatory (TNF-α, IL-8, and MCP-1) genes. Quantitative whole-slide analysis provided complementary evidence of variable STAT1 and NF-κB signaling activation across the analyzed isolates. Within this limited panel, viral replication was not consistently aligned with the selected transcriptional and signaling readouts, although the exploratory nature of these comparisons precludes establishing independence between these variables. Together, the virological, transcriptional, and imaging analyses revealed distinct multidimensional functional profiles across the isolate panel. Overall, these findings demonstrate functional heterogeneity among the analyzed clinical orthoflavivirus isolates and highlight integrated functional phenotyping as a useful framework for examining virus–host interactions beyond viral replication alone. Full article

Due to the rapid development of the textile industry and increased consumption of various textiles composed of both synthetic and natural fibers, large amounts of textile waste are produced, leading to environmental and economic problems on a global scale. Turning textile waste into carbon materials that can be used in a broad range of applications has become a viable solution to address this challenge in terms of sustainability and value generation. Natural and synthetic textile fibers have distinctive molecular structures with relatively high carbon content and variable chemical functionality; therefore, they have been identified as highly promising precursors for fabricating carbon materials with various electrochemical and environmental applications. At the same time, the properties of carbonized and activated textile fibers are strongly dependent on the molecular transformations taking place during thermal treatment and functionalization of textile fibers. This review will provide a comprehensive overview of the molecular evolution of natural and synthetic textile fibers during carbonization and activation processes in terms of dehydration, depolymerization, aromatization, heteroatom preservation, and graphitization mechanisms. The effect of precursor chemical composition, pyrolysis conditions, activation process, and heteroatom incorporation on the structure of carbonized and activated textile fibers and their physical and electrochemical properties will be analyzed. Particular emphasis is placed on electrochemical applications, including capacitive deionization, supercapacitors, electrocatalysis, and emerging smart electrochemical textile systems, highlighting how molecular transformation, pore engineering, and surface chemistry govern charge storage, ion adsorption, and catalytic behavior. In addition, major characterization techniques such as Raman spectroscopy, X-ray diffraction, X-ray photoelectron spectroscopy, and Brunauer–Emmett–Teller surface area analysis will be reviewed and discussed in relation to understanding the interdependence between molecular structure and material properties. Finally, recent issues related to feedstock heterogeneity, scalability, energy efficiency, and sustainability of processing are highlighted, and future perspectives on multifunctional carbon structures and circular utilization of textile waste are discussed. Full article

High-g accelerometers are in high demand across sectors such as aerospace, defense, and industrial inspection. This paper presents a MEMS accelerometer based on graphene piezoresistors, designed for precise acceleration measurement under sudden impacts, intense vibrations, and extreme conditions, such as engine fault diagnosis and weapon impact testing. A step-by-step structural optimization and simulation analysis were conducted using finite-element simulation. Taking the peak strain at the beam root, the first-order natural frequency, and the maximum equivalent stress as optimization objectives, progressive parametric optimization was sequentially performed on four progressive architectures: a simple beam, a beam mass, a beam mass with stress concentration grooves, and a beam mass with stress concentration grooves and symmetric masses. The results indicate that the introduction of a central mass enhances the peak strain by more than 15 times compared to the simple beam. The addition of stress concentration grooves further increases the strain by approximately 30%. Finally, the incorporation of symmetric masses yields a further 9% strain enhancement while reducing cross-axis sensitivity by 5.6%, effectively suppressing off-axis interference. The final structure achieves maximized strain while maintaining a first-order natural frequency above 200 kHz, with the maximum equivalent stress staying within the allowable limit. This optimal comprehensive performance provides essential technical support for high-performance graphene-based accelerometers. In addition to the mechanical structural optimization, the graphene piezoresistors were treated as surface sensing regions at the beam-root locations, and the area-averaged longitudinal strain was extracted as the input of a piezoresistive transduction model. The simulated strain was converted to resistance variation and bridge output voltage using a graphene gauge-factor-based readout model incorporating contact-resistance effects, thereby providing a sensor-level electromechanical performance estimation for the proposed high-g accelerometer. Full article

Accurate measurement of the 6-DOF motion responses of marine floating structures is essential for structural safety assessment and operational decision-making. To address the critical issues of integration drift in inertial navigation systems, susceptibility of GNSS to sea-surface multipath effects, and deployment complexity of binocular vision systems, this paper proposed a 6-DOF motion measurement method for floating structures based on monocular vision and natural feature point tracking. This method eliminates the reliance on artificial cooperative targets and auxiliary sensors, instead utilizing the inherent surface textures of the floating structures as feature sources. Stable feature point tracking is achieved through multi-strategy cascaded detection and the pyramidal KLT optical flow algorithm. RANSAC geometric consistency verification is introduced to eliminate outlier matches, retaining only identical physical points between two consecutive frames for motion estimation. In-plane translations and $RZ$ angle are extracted from the similarity transformation, while $RX$ and $RY$ angles are estimated using principal component analysis of the covariance matrix of the feature point set. The depth-direction displacement is linearly mapped from variations in the scale factor. Subsequently, two series of physical model tests under different conditions were conducted to validate the measurement accuracy and robustness of the proposed method on different floating structures. The results demonstrate that the proposed method can accurately capture the motion attitudes of floating structures, maintaining a consistently high inlier ratio exceeding 80% in regular waves and averaging 85.2% in irregular waves, with a reprojection error of less than 0.05 pixels. The NRMSE for the primary motion directions are all below 10%, and the dominant frequency errors are essentially zero. It offers advantages such as low cost, easy deployment, and strong robustness, thereby providing valuable technical support for field monitoring of marine floating structures. Full article

This study evaluated natural halloysite from the Dunino deposit as a sorbent for Pb²⁺, Cd²⁺, Cu²⁺, Zn²⁺, Hg²⁺, and Cr³⁺ ions from aqueous solutions. Batch experiments were conducted to examine the effects of solution pH, initial metal concentration, contact time, and multicomponent conditions. Equilibrium data were evaluated using the Langmuir and Freundlich models, whereas adsorption kinetics were analyzed using pseudo-first-order, pseudo-second-order, and Weber–Morris intraparticle diffusion models. Metal uptake increased markedly between pH 3 and 5, while changes between pH 5 and 7 were smaller. Nonlinear Langmuir fitting yielded maximum adsorption capacities ranging from 47.68 to 63.87 mg g⁻¹, with the highest values obtained for Hg²⁺ and Pb²⁺. The pseudo-first-order model provided a closer empirical description of the kinetic data than the pseudo-second-order model, and the adsorption profiles approached a plateau after approximately 480–720 min. SEM, FTIR, and nitrogen adsorption measurements revealed changes in surface morphology, the chemical environment of surface functional groups, and nitrogen-accessible surface area after metal loading. These observations are consistent with the involvement of surface sites but do not identify a unique molecular adsorption mechanism. Acidic desorption using 0.1 M HCl released 75.0–92.5% of the retained metals, compared with 25.0–47.5% under alkaline conditions. The results support further evaluation of natural halloysite as a mineral sorbent, including matched competition experiments and repeated adsorption–desorption cycles. Full article

The current paper deals with the containment control issue of fractional-order complex networks (FCNs) under communication uncertainties occurring with both multiplicative and additive noises and denial-of-service attacks. The dynamics of the followers are incorporated through the use of Caputo fractional derivatives, which are able to capture the nature of memory and hereditary dynamics of the complex systems. In order to reduce stochastic noise caused by the noisy communication medium, a new distributed containment protocol is proposed that takes both the multiplicative and additive noise effects in the interactions between the leader and the followers. Using the Mittag–Leffler stability theory, stochastic Lyapunov analysis, Itô calculus, the derivation of necessary conditions to ensure that the followers converge to the convex hull of the leaders was done. The explicit stability conditions are stipulated based on system parameters and control gains as well as intensities of noise. In addition, the robustness of the protocol suggested for use against intermittent DoS attacks is critically examined. The theoretical findings are substantiated by simulation experiments that demonstrate that the suggested methodology guarantees containment and resilience to the fluctuations in the fractional order and communication breakdowns. The findings offer an inclusive framework in the development of robust distributed controllers of fractional-order MASs operating under adversarial and uncertain networked frameworks. Full article

An excavation-induced clayey landslide in Jianshui County, Yunnan Province, China, threatens a national refined oil pipeline near the rear slope. Field investigation, borehole logging, laboratory testing, and three-dimensional finite-element analyses were integrated to investigate the excavation–rainstorm instability mechanism and evaluate circular anti-slide piles with toe backfilling. Under natural excavation, the reported factor of safety was 1.39, and the maximum displacement was 2.35 mm. Under a 60 mm/day rainstorm, increased pore-water pressure and saturation in the shallow sliding mass and strongly weathered claystone, together with saturated-state strength parameters, reduced the shear-strength reserve. The deformation and stability analyses yielded a maximum computed displacement of 1.36 m and a factor of safety of 0.95, respectively, indicating pronounced pre-failure deformation and loss of stability. After mitigation, the factor of safety increased to 1.41, while the maximum slope and pile-head displacements were both approximately 6.90 mm. The pile row redistributed nonuniform landslide thrust and reduced deformation transfer toward the pipeline. The results are site-specific engineering estimates for the investigated rainfall and parameter conditions. Full article

Brazilian hydropower plants have been facing increasing challenges during periods of hydrological instability associated with climate change, making adaptation imperative. One promising approach to achieving adaptation is to learn from nature, given its inherent capacity for adaptability and resilience to environmental change. Accordingly, this study proposes an integrated framework that combines Animal Decision-Making (ADM) theory with a successful example of natural adaptation, namely Amazon rainforest ant colonies. The proposed methodology comprises the following stages: (1) identification; (2) definition; (3) alternative generation; (4) solution selection; and (5) implementation and testing. A simulated case study and a real-world case study involving the Ponte de Pedra Hydropower Plant, located in the state of Mato Grosso, Brazil, were investigated. Human operators and ant colonies exhibited similar probabilities of shifting towards adaptation (human operators, p_R = 0.7088; ant colonies, p_R = 0.7091). However, the adaptation strategy adopted by the ant colonies proved to be more focused, concentrating on a smaller number of performance areas. Human operators identified Operation and Maintenance (O&M), Finance, and Plant as the most affected performance areas, with similar levels of importance (12%, 11%, and 11%, respectively). In contrast, the ant colonies prioritised O&M, Plant, and Environmental Impact, with corresponding importance levels of 18%, 13%, and 10%, respectively. Furthermore, the ant colonies demonstrated a dynamic and integrated decision-making strategy that prioritised different activities according to local environmental conditions. Future research should focus on evaluating, calibrating, and validating the proposed framework using historical data. Full article

Geranylgeranyl pyrophosphate (GGPP) is a key precursor for carotenoids, chlorophyll derivatives, and numerous high-value isoprenoids used in food, nutraceutical, and pharmaceutical industries. Microbial and photosynthetic production approaches offer promising alternatives to conventional plant extraction and chemical synthesis; however, further optimization and comprehensive sustainability assessments are required for industrial implementation. In this study, a photosynthetic production platform was developed by redirecting carbon flux in Synechocystis sp. PCC 6803 to enhance GGPP production via activation of the 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway. Two engineered mutants, ΔP (deficient in poly(3-hydroxybutyrate) synthesis) and ΔGP (deficient in both glycogen and poly(3-hydroxybutyrate) synthesis), were evaluated alongside the wild-type (WT) strain under multiple physiological conditions, including carbon supplementation and UV radiation. Redirecting cellular carbon metabolism significantly enhanced pigment accumulation and overall isoprenoid biosynthesis, with ΔGP strain showing the greatest improvement. Elevated intracellular GGPP levels were observed under all tested conditions, with the highest concentrations obtained under glucose supplementation and UV treatment. The best-performing strain exhibited approximately twelvefold increase in GGPP content relative to the control. These findings demonstrate that rational metabolic engineering of cyanobacteria can markedly improve precursor supply for isoprenoid synthesis, establishing an efficient photosynthetic platform for producing natural pigments, flavor compounds, and other value-added bioproducts. Full article

Environmental stressors like low temperature, low relative humidity (RH), and particulate matter (PM2.5), promote tear film instability and dry eye disease. This study investigates how these conditions alter the interfacial behavior of meibomian gland secretion (MGS) films in vitro and evaluates the capacity of mucomimetic polymers (0.5% hyaluronic acid [HA], polyvinylpyrrolidone [PVP], and chondroitin sulfate [CHS]) to suppress these impacts. MGS films over polymer-containing aqueous subphases were analyzed using a Langmuir trough and Brewster angle microscopy under adverse conditions (20 °C subphase, 20% RH, PM2.5 exposure). A sophisticated analytical framework was developed to evaluate MGS duplex multilayers: (i) a Volmer equation-based 2D-VES model to probe interfacial molecular properties (limiting area, compressibility, cohesion pressure) and (ii) a combined Maxwell viscoelastic and diffusion-relaxation model to quantify the dilatational relaxation modulus. Results indicate that despite their distinct nature, environmental stressors similarly disrupt the multilayer structure, reorganization, and rheological properties of MGS layers during blink-like deformations. Polymer supplementation moderated these adverse effects, yielding partial recovery of film structure and isothermal reversibility. Distinct mechanisms of action for HA, PVP, and CHS at the film/aqueous interface are elucidated. Full article

Accurate land cover mapping is a fundamental tool for environmental management and ecosystem monitoring. This study presents a comparative evaluation of four deep learning architectures, namely a Convolutional Neural Network (CNN), UNet++, ConvNeXt and Vision Transformer (ViT), for land cover classification into five primary classes: water, cropland, forest, low/natural vegetation and built-up. The broader Lake Kerkini basin was selected as the primary training and evaluation area. The multispectral input data were generated through Google Earth Engine and consisted of multi-seasonal Sentinel-2 composite mosaics for the 2021 mapping year, covering winter, spring, summer and autumn. To obtain a more reliable performance estimate and mitigate the effects of spatial autocorrelation, a four-fold spatial cross-validation approach was implemented. Under this spatial validation framework, the convolution-based architectures achieved the strongest performance. CNN obtained the highest numerical fold-mean performance, with an overall accuracy of 81.53% and a macro-averaged F1 score (Macro-F1) of 80.09%, followed closely by UNet++ and ConvNeXt. Non-parametric repeated-measures statistical testing indicated a significant overall architecture effect, with CNN, UNet++ and ConvNeXt showing broadly comparable fold-level Macro-F1 performance, while the tested ViT configuration trained from scratch ranked last across all spatial folds. Regional transferability was further evaluated in the nearby independent Lake Doirani region, where the convolutional architectures, particularly CNN and UNet++, showed strong agreement with the WorldCover-derived reference labels without fine-tuning. Finally, feature-importance analysis indicated that specific spectral-seasonal channels, especially the Blue band (B2) in winter and the Short-Wave Infrared band (B12) in summer, were consistently influential in the models’ predictions. Overall, under the tested 2021 Mediterranean case-study conditions, the results highlight the importance of spatially rigorous validation and show that the evaluated convolution-based configurations achieved stronger performance than the tested ViT configuration trained from scratch. Full article