Search Results (12,852)

Biogenic silver nanoparticles (AgNPs) were synthesized via a green chemistry strategy using wheat extract and subsequently functionalized with a carboxymethyl chitosan–cinnamaldehyde (CMC=CIN) conjugate through covalent imine bonding. The resulting nanohybrid (AgNP–CMC=CIN) was extensively characterized to confirm successful biofunctionalization: UV–Vis spectroscopy revealed characteristic cinnamaldehyde absorption peaks; ATR-FTIR spectra confirmed polymer–terpene bonding; and TEM analysis evidenced uniform nanoparticle morphology. Dynamic light scattering (DLS) measurements indicated an increase in hydrodynamic size upon coating (from 59.46 ± 12.63 nm to 110.17 ± 4.74 nm), while maintaining low polydispersity (PDI: 0.29 to 0.27) and stable surface charge (zeta potential ~ −30 mV), suggesting colloidal stability and homogeneous polymer encapsulation. Antifungal activity was evaluated against Fusarium oxysporum, Penicillium citrinum, Aspergillus niger, and Aspergillus brasiliensis. The minimum inhibitory concentration (MIC) against F. oxysporum was significantly reduced to 83 μg/mL with AgNP–CMC=CIN, compared to 708 μg/mL for uncoated AgNPs, and was comparable to the reference fungicide tebuconazole (52 μg/mL). Seed priming with AgNP–CMC=CIN led to improved germination (85%) and markedly reduced fungal colonization, while maintaining a favorable phytotoxicity profile. These findings highlight the potential of polysaccharide-terpene-functionalized biogenic AgNPs as a sustainable alternative to conventional fungicides, supporting their application in precision agriculture and integrated crop protection strategies. Full article

Understanding future changes in crop phenology and climate suitability is essential for sustaining winter wheat production in the Huang-Huai-Hai (3H) Plain under climate change. This study integrates bias-corrected CMIP6 climate projections, the DSSAT CERES-Wheat crop model, and Random Forest analysis to assess spatiotemporal shifts in winter wheat phenology and climate suitability. The assessment focuses on the mid- (2041–2060) and late 21st century (2081–2100) under the SSP2-4.5 and SSP5-8.5 scenarios. The results indicate that the vegetative and whole growing periods (VGP and WGP) will be extended in the mid-century but shorten by the late century. In contrast, the reproductive growing period (RGP) will be slightly reduced in the mid-century and extended under high emissions in the late century. Temperature suitability is projected to increase during the VGP and WGP but decline during the RGP. Precipitation suitability generally improves, except for a decrease during the reproductive period south of 32° N. Solar radiation suitability is expected to decline across all stages. Temperature is identified as the primary driver of phenological changes, with solar radiation and precipitation playing increasingly important roles in the mid- and late 21st century, respectively. Adaptive strategies, including the adoption of heat-tolerant varieties, longer reproductive periods, and earlier sowing, are recommended to enhance yield stability under future climate conditions. Full article

The structural health monitoring of historic buildings represents one of the most significant challenges in contemporary structural analysis, particularly for large-scale structures with accumulated damage. Obtaining reliable diagnostics is crucial yet complex due to the inherent uncertainties in both geometric definition and material properties of historic constructions, especially when structural stability may be compromised. This study presents a comprehensive structural assessment of the Bullring of the Real Maestranza de Caballería de Ronda (Spain), an emblematic 18th-century structure, through an innovative multi-technique approach aimed at evaluating its structural stability. The methodology integrates various non-destructive techniques: 3D laser scanning for precise geometric documentation, operational modal analysis (OMA) for global dynamic characterisation, experimental modal analysis (EMA) for local assessment of critical structural elements, and sonic tests (ST) to determine the elastic moduli of the principal materials that define the historic construction. The research particularly focuses on the inner ring of sandstone columns, identified as the most vulnerable structural component through initial dynamic testing. A detailed finite-element (FE) model was developed based on high-precision laser-scanning data and calibrated using experimental dynamic properties. The model’s reliability was validated through the correlation between numerical predictions and experimental observations, enabling a thorough stability analysis of the structure. Results reveal concerning stability issues in specific columns of the inner ring, identifying elements at significant risk of collapse. This finding demonstrates the effectiveness of the proposed methodology in detecting critical structural vulnerabilities in historic buildings, providing crucial information for preservation strategies. Full article

A chili pepper variety and origin detection system that integrates a field-programmable gate array (FPGA) with an electronic nose (e-nose) is proposed in this paper to address the issues of variety confusion and origin ambiguity in the chili pepper market. The system uses the AIRSENSE PEN3 e-nose from Germany to collect gas data from thirteen different varieties of chili peppers and two specific varieties of chili peppers originating from seven different regions. Model training is conducted via the proposed lightweight convolutional neural network ChiliPCNN. By combining the strengths of a convolutional neural network (CNN) and a multilayer perceptron (MLP), the ChiliPCNN model achieves an efficient and accurate classification process, requiring only 268 parameters for chili pepper variety identification and 244 parameters for origin tracing, with 364 floating-point operations (FLOPs) and 340 FLOPs, respectively. The experimental results demonstrate that, compared with other advanced deep learning methods, the ChiliPCNN has superior classification performance and good stability. Specifically, ChiliPCNN achieves accuracy rates of 94.62% in chili pepper variety identification and 93.41% in origin tracing tasks involving Jiaoyang No. 6, with accuracy rates reaching as high as 99.07% for Xianjiao No. 301. These results fully validate the effectiveness of the model. To further increase the detection speed of the ChiliPCNN, its acceleration circuit is designed on the Xilinx Zynq7020 FPGA from the United States and optimized via fixed-point arithmetic and loop unrolling strategies. The optimized circuit reduces the latency to 5600 ns and consumes only 1.755 W of power, significantly improving the resource utilization rate and processing speed of the model. This system not only achieves rapid and accurate chili pepper variety and origin detection but also provides an efficient and reliable intelligent agricultural management solution, which is highly important for promoting the development of agricultural automation and intelligence. Full article

The development of functional endothelial monolayers on synthetic vascular grafts remains challenging, particularly for small-diameter vessels (<6 mm) prone to thrombosis. Here, we present a pharmacological strategy combining 8-(4-chlorophenylthio) adenosine 3′,5′-cyclic monophosphate sodium salt (pCPT-cAMP, a tight junction promoter) with nitric oxide/cGMP pathway agonists 3-morpholinosydnonimine (SIN-1), captopril, and sildenafil) to enhance endothelialization. In human umbilical vein endothelial cells (HUVECs), this four-agent cocktail induced a flat, extended phenotype with a 3-fold increased cell area and 57.5% fewer cells required for surface coverage compared to controls. Immunofluorescence analysis revealed enhanced ZO-1 expression and continuous tight junction formation, while sustained nitric oxide (NO) production (3.9-fold increase) and restored prostacyclin (PGI₂) secretion demonstrated preserved endothelial functionality. Anticoagulation assays confirmed a significant reduction in thrombus formation (p < 0.01) via dual inhibition of platelet activation and thrombin binding. These findings establish a synergistic drug combination that promotes rapid endothelialization while maintaining antithrombogenic activity, offering a promising solution for small-diameter vascular grafts. Further studies should validate long-term stability and translational potential in preclinical models. Full article

AISI 347H stainless steel is widely used in high-temperature environments due to its excellent creep strength and oxidation resistance; however, its corrosion performance remains highly sensitive to thermal oxidation, and the effects of thermal history on its passive film stability are not yet fully understood. This study addresses this knowledge gap by systematically investigating the influence of solution treatment on the corrosion and oxidation resistance of AISI 347H stainless steel. The specimens were subjected to solution heat treatment at 1050 °C, followed by air cooling, and then evaluated through electrochemical testing, high-temperature oxidation experiments at 550 °C, and multiscale surface characterization techniques. The solution treatment refined the austenitic microstructure by dissolving coarse Nb-rich precipitates, as confirmed by SEM and EBSD, and improved passive film integrity. The stabilizing effect of Nb also played a critical role in suppressing sensitization, thereby enhancing resistance to intergranular attack. Electrochemical measurements and EIS analysis revealed a lower corrosion current density and higher charge transfer resistance in the treated samples, indicating enhanced passivation behavior. ToF-SIMS depth profiling and oxide thickness analysis confirmed a slower parabolic oxide growth rate and reduced oxidation rate constant in the solution-treated condition. At 550 °C, oxidation was suppressed by the formation of compact, Cr-rich scales with dual-distributed Nb oxides, effectively limiting diffusion pathways and stabilizing the protective layer. These findings demonstrate that solution treatment is an effective strategy to improve the long-term corrosion and oxidation performance of AISI 347H stainless steel in harsh service environments. Full article

To address the challenges posed by the instability of renewable energy output and load fluctuations on grid operations and to support the low-carbon sustainable development of the energy system, this paper integrates artificial intelligence technology to establish an economic stability dispatch framework for distributed resource aggregators. A phased multi-contract collaborative scheduling model oriented toward sustainable development is proposed. Through intelligent algorithms, the model dynamically optimises decisions across the day-ahead and intraday phases: During the day-ahead scheduling phase, intelligent algorithms predict load demand and energy output, and combine with elastic performance-based response contracts to construct a user-side electricity consumption behaviour intelligent control model. Under the premise of ensuring user comfort, the model generates a 24 h scheduling plan with the objectives of minimising operational costs and efficiently integrating renewable energy. In the intraday scheduling phase, a rolling optimisation mechanism is used to activate energy storage capacity contracts and dynamic frequency stability contracts in real time based on day-ahead prediction deviations. This efficiently coordinates the intelligent frequency regulation strategies of energy storage devices and electric vehicle aggregators to quickly mitigate power fluctuations and achieve coordinated control of primary and secondary frequency regulation. Case study results indicate that the intelligent optimisation-driven multi-contract scheduling model significantly improves system operational efficiency and stability, reduces system operational costs by 30.49%, and decreases power purchase fluctuations by 12.41%, providing a feasible path for constructing a low-carbon, resilient grid under high renewable energy penetration. Full article

This study developed a full solid waste-based cementitious material (ISWs-CM) using steel slag (SS), ground granulated blast furnace slag (GGBFS), phosphorus slag (PS), carbide slag (CS), and desulfurized gypsum (DG) to completely replace cement. A two-layer optimization strategy, combining three chemical moduli and simplex lattice experiments, was employed to determine the proportion and to investigate the impact of proportions on the uniaxial compressive strength of mortar. As an application case, the ISWs-CM with the optimal proportion was employed to stabilize aeolian sand, and its effectiveness as a cement substitute and the underlying mechanisms were investigated. The results indicated that the ISW proportion that maximized the strength of the mortar was SS:GGBFS:PS:CS = 5:20:20:40. The strength of the mortar was enhanced when the proportion of GGBFS exhibiting the highest reactivity was increased and also increased initially and then decreased with an increase in CS when the dosage of GGBFS was fixed. The aeolian sand stabilized by ISW-CM exhibited higher strength than that stabilized with cement. The greater number and variety of hydration products resulted in denser connections and encapsulation of sand particles, which highlights the synergistic effect of ISWs and the potential of ISW-CM as a cement replacement across diverse applications including aeolian sand stabilization. Full article

As lithium-ion batteries (LIBs) gain widespread use, detecting electrolyte–vapor emissions during early thermal runaway (TR) remains critical to ensuring battery safety; yet, it remains understudied. Gas sensors integrating oxide nanostructures offer a promising solution as they possess high sensitivity and fast response, enabling rapid detection of various gas-phase indicators of battery failure. Utilizing this approach, 3D ordered tin oxide (SnO₂) nanostructures were synthesized using polystyrene sphere (PS) templates of varied diameters (200–1500 nm) and precursor concentrations (0.2–0.6 mol/L) to detect key electrolyte–vapors, especially ethyl methyl carbonate (EMC), released in the early stages of TR. The 3D ordered SnO₂ nanostructures with ring- and nanonet-like morphologies, formed after PS template removal, were characterized, and the effects of template size and precursor concentration on their structure and sensing performance were investigated. Among various nanostructures of SnO₂, nanonets achieved by a 1000 nm PS template and 0.4 mol/L precursor showed higher mesoporosity (~28 nm) and optimal EMC detection. At 210 °C, it detected 10 ppm EMC with a response of ~7.95 and response/recovery times of 14/17 s, achieving a 500 ppb detection limit alongside excellent reproducibility/stability. This study demonstrates that precise structural control of SnO₂ nanostructures using templates enables sensitive EMC detection, providing an effective sensor-based strategy to enhance LIB safety. Full article

Grid-forming (GFM) energy storage can be utilized as a backup power source for the power grid to ensure the security of the power grid. GFM energy storage can also enhance the strength of the power grid and improve its stability. However, the GFM energy storage inherits the characteristics of the synchronous generator. Low-frequency oscillations may occur in GFM energy storage, which affect the stable operation of the power system. This paper proposed an additional damping control strategy for GFM energy storage to address the low-frequency oscillation. Firstly, this paper builds the state-space small-signal mathematical model of the GFM energy storage grid-connected system to analyze the participation factors of the low-frequency oscillation mode and clarify the key control parameters affecting the GFM energy storage grid-connected system the low-frequency oscillation. Then, this paper proposed an additional damping control strategy to increase the damping ratio of the low-frequency oscillation mode and improve the stability of the GFM energy storage grid-connected system. Finally, semi-physical experiments verified the effectiveness of the proposed additional damping control strategy. Full article

The need for renewable and eco-friendly materials is driving the increasing demand for biobased polymers for food applications, with cellulose emerging as a promising option due to its degradability and environmental sustainability. Therefore, in the present study, a strategy to obtain cellulose-based materials with antimicrobial properties was explored by using a selected antimicrobial peptide named RKT1, which was stably and efficiently tethered to cellulose films via physical adsorption, harnessing the high number of functional groups on the polymeric surface. Firstly, the peptide, identified among the previous or new projected compounds, was structurally and functionally characterized, evidencing high conformational stability under a wide range of environmental conditions and efficient antibacterial activity against the foodborne pathogens Escherichia coli, Salmonella Typhimurium, and Listeria monocytogenes and the spoilage bacteria Enterococcus and Pseudomonas koreensis, all isolated from meat products. Moreover, in an extended application, the RKT1-activated cellulose films were tested in vivo on beef carpaccio. The results supported their effectiveness in increasing the shelf life of carpaccio by least two days without affecting its organoleptic properties. Therefore, RKT1, physically adsorbed on cellulose, still retains its activity, and the newly generated biopolymers show potential for use as a green food packaging material. Full article

The large-scale integration of wind power, photovoltaic systems, and energy storage systems (ESSs) into power grids has increasingly influenced the transient stability of power systems due to their dynamic response characteristics. Considering the commercial confidentiality of core control parameters from equipment manufacturers, parameter identification has become a crucial approach for analyzing ESS dynamic behaviors during high-voltage ride-through (HVRT) and low-voltage ride-through (LVRT) and for optimizing control strategies. In this study, we present a multidimensional feature-integrated parameter identification framework for ESSs, combining a multi-scenario voltage disturbance testing environment built on a real-time laboratory platform with field-measured data and enhanced optimization algorithms. Focusing on the control characteristics of energy storage converters, a non-intrusive identification method for grid-connected control parameters is proposed based on dynamic trajectory feature extraction and a hybrid optimization algorithm that integrates an improved particle swarm optimization (PSO) algorithm with gradient-based coordination. The results demonstrate that the proposed approach effectively captures the dynamic coupling mechanisms of ESSs under dual-mode operation (charging and discharging) and voltage fluctuations. By relying on measured data for parameter inversion, the method circumvents the limitations posed by commercial confidentiality, providing a novel technical pathway to enhance the fault ride-through (FRT) performance of energy storage systems (ESSs). In addition, the developed simulation verification framework serves as a valuable tool for security analysis in power systems with high renewable energy penetration. Full article

Plants of the Annona genus have garnered increasing scientific interest due to their rich phytochemical profile and broad spectrum of biological activities, which include antimicrobial, antiproliferative, and cytotoxic effects. Among the most studied compounds are acetogenins and Annonacins, which exhibit potent bioactivity and have been identified as key agents in the green synthesis and stabilization of nanomaterials. In recent years, the integration of Annona plant extracts—particularly from leaves—into nanotechnology platforms has opened new avenues in the development of eco-friendly and biocompatible nanostructures for biomedical applications. This review provides a comprehensive overview of the current knowledge regarding the antimicrobial properties of nanomaterials synthesized using extracts from Annona species. This review encompasses 74 indexed articles published between 2012 and 2023, focusing on the synthesis of nanomaterials using extracts from this genus that exhibit antimicrobial and biomedical properties. The search was conducted in databases such as Google Scholar, Web of Science, and Scopus. Emphasis is placed on their antibacterial, antifungal, and anthelmintic effects, as well as additional therapeutic potentials, such as antidiabetic, antihypertensive, antiproliferative, and cytotoxic activities. The analysis of the recent literature highlights how Annona-derived phytochemicals contribute significantly to the functionalization and enhanced biological performance of these nanomaterials. This work aims to support future research focused on the rational design of Annona-based nanostructures as promising candidates in antimicrobial and therapeutic strategies. Full article

To achieve more precise and effective microseismic magnitude estimation, a classification model based on transfer learning with an improved deep residual network is proposed for predicting microseismic magnitudes. Initially, microseismic waveform images are preprocessed through cropping and blurring before being used as inputs to the model. Subsequently, the microseismic waveform image dataset is divided into training, testing, and validation sets. By leveraging the pretrained ResNet18 model weights from ImageNet, a transfer learning strategy is implemented, involving the retraining of all layers from scratch. Following this, the CBAM is introduced for model optimization, resulting in a new network model. Finally, this model is utilized in seismic magnitude classification research to enable microseismic magnitude prediction. The model is validated and compared with other commonly used neural network models. The experiment uses microseismic waveform data and images of magnitudes 0–3 from the Stanford Earthquake Dataset (STEAD) as training samples. The results indicate that the model achieves an accuracy of 87% within an error range of ±0.2 and 94.7% within an error range of ±0.3. This model demonstrates enhanced stability and reliability, effectively addressing the issue of missing data labels. It validates that using ResNet transfer learning combined with an attention mechanism yields higher accuracy in microseismic magnitude prediction, as well as confirming the effectiveness of the CBAM. Full article

Background/Objectives: As an essential polyunsaturated fatty acid, linoleic acid (LA) plays an important role in maintaining the integrity of cellular membranes, modulating inflammatory responses, and mediating intracellular signaling. This review explores the structure, properties, and nutritional significance of LA and its bioactive derivatives, with particular attention to sustainable production methods and their potential applications. Methods: A comprehensive review of the recent literature was conducted, emphasizing the use of green synthesis techniques, such as enzyme-catalyzed biocatalysis and microbiological transformations, in order to obtain LA-derived nutraceuticals. Analyses were conducted on the key aspects related to food industry applications, regulatory frameworks, and emerging market trends. Results: Through green synthesis strategies, LA derivatives with antioxidant, anti-inflammatory, and antimicrobial properties have been developed. There is potential for these compounds to be incorporated into health-oriented food products. In spite of this, challenges remain regarding their stability and bioavailability. Furthermore, there are inconsistencies in international regulatory standards which prevent these compounds from being widely adopted. Conclusions: The development of functional and sustainable food products based on linoleic acid derivatives obtained using ecological methods offers significant potential. Research is required to optimize production processes, enhance compound stability, and clinically validate health effects. The integration of the market and the safety of consumers will be supported by addressing regulatory harmonization. Full article