Search Results (1,796)

As artificial intelligence (AI) rapidly reshapes the production and labor landscape of agricultural SMEs, this study, based on survey data from 418 practitioners in Northwest China, employs a hybrid approach of SEM and NCA to conduct an in-depth investigation. The empirical results reveal a key theoretical finding: professional identity threat and technological uncertainty have a significant negative inhibitory effect on employees’ crisis management capabilities (CMCs), while lack of transparency increases reliance on adaptive crisis responses. Furthermore, CMC indirectly influences performance through employee resilience, which exhibits the strongest positive effect on performance. The study also demonstrates that perceived organizational support not only strengthens resilience and performance, but also significantly moderates the effect of CMC on performance. The NCA results further contribute by showing that organizational transparency and resilience are necessary preconditions for achieving high performance, while CMC functions as a sufficient but non-essential driver. Grounded in risk perception theory and crisis management theory, this study contributes to theoretical advancement by developing a dual-level framework that explains how employees perceive AI-related risks, mobilize crisis management capabilities, and translate psychological adaptation into performance outcomes. This paper contributes to theoretical literature by expanding the interaction between psychological and organizational mechanisms in the context of AI-driven transformations. It offers actionable implications for building organizational capabilities and fostering supportive governance in agricultural SMEs. Full article

The use of adsorbents based on naturally occurring materials to eliminate antibiotics from industrial effluents has attracted remarkable interest owing to the abundance of raw materials and the sustainability of this method. The ciprofloxacin (CIP) removal capacity of a previously synthesized antimicrobial hydrogel based on carboxymethyl chitosan (CMCs)/xanthan gum (XG) was investigated for the first time in this study. CMCs and XG were blended in an equivalent-weight ratio and crosslinked using trimellitic anhydride isothiocyanate (TAI) to synthesize an eco-friendly, low-cost hydrogel, which was characterized using FTIR, SEM, and XRD analyses. The pseudo-second-order model fitted the experimental data well: the experimental q_e (49.59 mg g⁻¹) was close to the theoretical value (51.81 mg g⁻¹). The Langmuir isotherm best fitted the adsorption results (R² = 0.999), with a maximum adsorption capacity of 147.06 mg g⁻¹. The thermodynamic results indicate that adsorption is spontaneous, favorable, and exothermic in nature. The percentages of desorption obtained were 95.72, 94.34, 89.52, 88, and 86.28% after five consecutive cycles. Thus, this hydrogel possesses potential for further testing and application in wastewater remediation. Full article

Central nervous system disorders drive disability, yet many neuroactive candidates fail because the brain is a hard compartment to dose. Plant-derived molecules spanning polyphenols, alkaloids, terpenoids, and cannabinoids are attractive because their pleiotropic actions can engage oxidative stress, neuroinflammation, and circuit dysfunction. In practice, the blood–brain barrier (BBB) restricts most native phytochemicals through tight-junction selectivity, rapid metabolism, low solubility, and transporter-mediated efflux. Key gaps include poor standardization of exposure metrics, limited human-relevant BBB models, and few head-to-head studies that compare delivery platforms on the same payload and outcome. This review tackles the mismatch between mechanistic promise and reliable brain exposure that stalls translation. The objectives are to link phytochemical liabilities to enabling strategies in nanomedicine, alternative routes, and transporter-targeted prodrugs, and to propose decision-grade endpoints for translation. We synthesize evidence on BBB transport logic, nanocarrier families, targeting ligands, intranasal delivery, focused ultrasound-mediated opening, and prodrug approaches that hijack influx transporters, while foregrounding safety and chemistry, manufacturing, and controls (CMC) constraints. Here we highlight that effective neurotherapeutics emerge when chemistry, carrier, route, and measurement are co-designed rather than optimized in isolation. This framework can guide platform selection, de-risk first in-human studies, and sharpen trial endpoints. More broadly, it offers a transferable playbook for barrier-limited drug development across neurology, psychiatry, and oncology. Full article

The design and fabrication of metal matrix materials (MMCs), as well as the densification and joining of ceramic matrix composites (CMC), are still very challenging. For SiC- and C-based composites, liquid-assisted processing routes, such as the spontaneous infiltration process, emerge among the most cost-effective processes. To succeed in Ir-Si/SiC refractory composite fabrication by spontaneous infiltration, the wetting characteristics of the Ir-Si/SiC system, the surface and transport properties (surface tension and viscosity) of liquid Ir-Si alloys, and microstructural evolution at the interfaces formed between solid SiC (or C) with Ir-Si melt, have been carefully examined. Specifically, the wettability and interaction phenomena occurring at the Si-Ir eutectics/SiC interface as a function of temperature were investigated in the temperature range of T = 1350–1400 °C by the sessile drop method under an inert atmosphere with reduced oxygen content, and the results are presented and discussed in this paper. Taking into account the thermodynamics of the Si-C-Ir system, the interfacial phenomena and subsequent microstructural evolution are well-related to the process parameters, and the properties and characteristics of the as-produced interfaces may be predicted accordingly. The experimental conditions and results of wetting experiments, together with thermodynamic-based models predicting thermophysical property values of liquid Ir-Si alloys, are valuable key input data that are now available for the numerical study of infiltration processes. Full article

Regrettably, glioblastoma multiforme (GBM) remains the deadliest form of brain cancer, with a very unfavorable prognosis for life expectancy for the patient. We report, for the first time, the green colloidal synthesis of cobalt-doped magnetic iron oxide nanoparticles (Co-MNPs) as aqueous ferrofluids, using two anionic polysaccharide biopolymers, hyaluronic acid (HA) and carboxymethyl cellulose (CMC), as surfactants. These ferrofluids based on magnetite nanoparticles (HA@Co-MNP and CMC@Co-MNP) demonstrated superparamagnetic properties and magnetic-to-thermal conversion upon exposure to an alternating magnetic field (AMF), with the extent of conversion dependent on surfactant type. In addition, the ferrophase acted as a nanozyme, mimicking peroxidase-like activity in response to hydrogen peroxide, which is present at higher levels in tumor cells. The coupling of magnetic-heat capabilities with biocatalytic behavior enhances glioblastoma cell elimination and suppresses 3D neurospheroid growth. The results also showed that active targeting based on the HA biopolymer shell, due to its affinity for CD44 membrane receptors overexpressed in GBM, outperformed CMC-coated ferrofluid analogs. These magnetocatalytic-responsive nanoplatforms offer a broad avenue for the diagnosis and therapy of numerous cancers, potentially improving patients’ quality of life and prognoses. Full article

Background: Microgravity exposure causes muscle atrophy and bone density loss in astronauts. Traditional motion analysis provides estimations of external kinematics and muscle activation, but cannot resolve internal load. OpenSim closes this gap by applying musculoskeletal modeling to estimate internal joint mechanics. Methods: In this study, we aimed to develop an OpenSim workflow to estimate joint angles and moments using datasets from two publicly available gait studies: the Politecnico di Milano study (Dataset 1), which includes level-floor walking, walking on heels, walking on toes, and step-down-from-stairs tasks, and Maclean et al.’s walking study in reduced gravities (Dataset 2), which includes four simulated gravity levels (1.0 G, 0.76 G, 0.54 G, and 0.31 G). Marker and ground reaction force (GRF) data, along with participants’ mass, were used to prepare the first three steps of OpenSim’s workflow, including scaling, inverse kinematics (IK), and inverse dynamics (ID). Scripts using MATLAB R2025a (The MathWorks, Inc., Natick, MA, USA) were created to store, normalize, and compare OpenSim outputs with reference data on the right leg. Pearson’s correlation coefficient (PCC) was used to quantify agreement between OpenSim-derived joint angles and moments and the reference data, and root mean square error (RMSE) was used to characterize accuracy. Results: Hip and knee angles showed excellent correlation across both datasets (PCC > 0.974). Ankle angles were more variable, particularly in Dataset 1 (PCC = 0.833; RMSE = 19.797°) compared to Dataset 2 (PCC = 0.995; RMSE = 8.73°). Joint moment correlations were strong for hip and knee (PCC > 0.85), though ankle moments in Dataset 1 exhibited lower correlation (PCC = 0.677) and higher error (0.30 Nm/kg) compared to the high accuracy observed across all joints in Dataset 2. Discussion: We speculate that the lower PCC values and higher RMSE observed for ankle dorsi/plantar flexion angle and moment in Dataset 1 are mainly attributable to differences in shank segment frame definitions between the OpenSim model and the human body model used in Dataset 1. Higher ankle angle RMSEs in Dataset 2 may be due to lower weights assigned to ankle markers in the scaling and IK setup files, resulting in different ankle joint center definitions. Conclusion: In the future, we plan to improve this OpenSim workflow by including additional participants and datasets collected in simulated reduced-gravity environments and by implementing a residual reduction algorithm (RRA) and computed muscle control (CMC) to enable muscle activation estimation. Full article

Ceramic Matrix Composites (CMC) are widely used in critical applications such as leading edges of aircraft wings and thermal insulation layers of thermal protection systems due to their advantages of being lightweight, high-temperature resistant, and impact-resistant. However, influenced by manufacturing processes and service environments, internal defects such as pores and delamination are prone to occur, significantly compromising the mechanical properties and service reliability of the material. This paper primarily evaluates the feasibility and applicability of using Terahertz Frequency Modulated Continuous Wave (FMCW) technology for the non-contact detection of CMC. First, the measurement principle of FMCW is introduced, and the structure of the detection system, including a two-dimensional mechanical scanning platform, optical lenses, a control platform, and a data acquisition unit, is outlined. Subsequently, scanning imaging was performed on CMC specimens and their bonded thermal protection structure (TPS) specimens, demonstrating the feasibility of Terahertz FMCW technology as an advanced non-destructive testing tool for CMC inspection. The issues of diffraction and the Rayleigh limit inherent in real-aperture terahertz imaging were analyzed and discussed. A multi-scale fusion defect detection method incorporating background estimation is proposed to enable precise delineation of defect regions. Experimental results show that, after processing with the proposed algorithm, the minimum detectable pore diameter at the focal plane is 1 mm, with a regional error of approximately 3%. The detection error for pores and debonding areas in CMC is maintained within 6.44%. Analysis indicates that combining terahertz imaging technology with image processing algorithms enables the quantitative analysis of internal defects in composite materials, offering a new technical approach for defect detection in composite materials. Full article

The growing environmental impact of petroleum-based plastics has intensified research into sustainable, biodegradable alternatives for food packaging. Among bio-derived polymers, carboxymethyl cellulose (CMC) has attracted increasing attention due to its abundance, non-toxicity, biodegradability, and excellent film-forming ability. Nevertheless, the intrinsic hydrophilicity and limited mechanical strength of neat CMC restrict its direct application in packaging systems. This review provides a comprehensive and critical overview of recent strategies developed between 2015 and 2025 to enhance the performance of CMC-based films for food packaging applications. Emphasis is placed on physical and chemical modification routes, including polymer blending, polyelectrolyte complex formation, incorporation of functional fillers and nanomaterials, and ionic or covalent crosslinking approaches. The influence of these strategies on key functional properties, such as mechanical behavior, water barrier performance, antimicrobial and antioxidant activity, is systematically discussed. Particular attention is given to CMC-rich systems, enabling meaningful comparison across studies. By highlighting structure–property relationships and identifying current limitations, this review aims to provide guidance for the rational design of advanced CMC-based materials as viable, eco-friendly alternatives to conventional plastic packaging. Full article

The rapid development of remote sensing satellite technology has enabled remote sensing images to be widely used in agriculture, meteorology, environmental monitoring and other fields. However, the presence of clouds in these images can lead to blurred and incomplete observations of the Earth’s surface, limiting the quality and applicability of the data. Current cloud detection networks usually adopt a single encoder–decoder structure that uniformly processes all spectral features without distinguishing between various spectral bands. To overcome this limitation, this paper proposes an Optical characteristics-guided Asymmetric Dual Encoder Feature Fusion cloud detection algorithm (OADEF²). The algorithm adopts an asymmetric dual encoder framework to divide the spectral bands of Sentinel-2A into two groups: RGB visible light bands and infrared/atmospheric correction bands, which are subsequently input into two different encoder branches. This method utilizes the unique physical characteristics of different spectral bands to improve the accuracy of cloud detection. In order to direct the focus of the network to cloud-related optical characteristics, an Optical characteristics-guided Multi-Scale cloud feature module (OCGMSCFM) based on Dynamic HOT Index and Full-Band Cloud Index is introduced. This module effectively solves the problem of insufficient representation of cloud features. In order to improve the efficiency of feature fusion, a Feature Aggregation and Filtering module (FAFM) is proposed. This module uses aggregation and techniques to filter basic features, thereby improving the accuracy of cloud detection. In order to overcome the limitations of feature modeling, a dual attention module that fuses Multi-interaction Local Spatial Attention mixed Channel Attention (MILSAMCAM) is added to the decoder. The experimental results validated the effectiveness of this algorithm in cloud detection tasks, achieving an F1-score of 97.30% on the S2-CMC dataset. Full article

This study addresses the pressing issue of high-ammonia nitrogen wastewater, such as landfill leachate, by developing immobilized microbial beads that combine high mechanical strength with efficient denitrification performance. The beads were prepared using a composite of sodium alginate (SA), carboxymethyl cellulose (CMC), and diatomite (DE), with a dual-ion (Ca²⁺-Al³⁺) stepwise cross-linking technique to encapsulate Alcaligenes faecalis. The material ratios were systematically optimized through single-factor and response surface methodology (RSM), identifying the optimal conditions as: SA 2.0%, CMC 1.5%, DE 1.0%, CaCl₂ 2.25%, and Al₂(SO₄)₃ 2.0%. Under these conditions, the beads achieved a mechanical strength of 3.20 N and exhibited an ammonia nitrogen removal rate of 93.10% after 96 h of treating actual landfill leachate (NH₃-N ≈ 1000 mg/L). In conclusion, the SA-CMC-DE dual-ion cross-linked beads demonstrate structural stability and efficient mass transfer, offering an economically viable and novel solution for the treatment of high-ammonia nitrogen wastewater. Full article

The aim of this study was to investigate the substitution of egg yolk in mayonnaise-type sauces with alternative protein components and to optimize the hydrocolloid composition for improved stability and rheological properties. Mustard powder (1%), soybean flour (1%), casein (2%), and cream powder (1%) blends were employed as emulsifiers. The influence of the ratio of potato starch, carboxymethylcellulose (CMC), pectin, and xanthan gum (0–1% each) on the properties of low-fat mayonnaise formulations with 30% oil content was examined. Sedimentation and thermal stability tests revealed high resistance of all samples (98–99%) after 24 h of storage. Optical microscopy confirmed a homogeneous structure with individual dispersed particles of 100–150 μm corresponding to plant protein inclusions. The particle size distribution D [3,4] exhibited a bimodal profile with peaks at 0.1–1 μm and 2–8 μm, indicating efficient homogenization. Storage experiments demonstrated an increase in particle size by 1.4–1.6 times and a decrease in viscosity, likely due to flocculation and aggregation of polysaccharide clusters into larger agglomerates. Among the tested formulations, the sample containing 0.3% CMC, 0.3% xanthan gum, and 0.4% pectin showed the most favorable physicochemical and sensory properties, highlighting the synergistic effect of hydrocolloid blends in stabilizing reduced-fat mayonnaise-type emulsions. Full article

Rhizoma coptidis (RC), known as Huang Lian, is widely used for treating diabetes in Traditional Chinese Medicine. G protein-coupled bile acid receptor 1 (TGR5) is a potential therapeutic target for glucose-lipid metabolic disorders due to its capacity to stimulate glucagon-like peptide-1 (GLP-1) secretion. However, whether RC contains ingredients targeting TGR5 remains unclear. In this study, we screened 330 secondary metabolites of RC via molecular docking and identified obacunone as a top candidate. We confirmed binding using Cell Membrane Chromatography (CMC) and quantified affinity via Surface Plasmon Resonance (SPR), determining a KD of 101 μM. cAMP assays identified obacunone as an activator of TGR5. Subsequently, concentration-dependent cAMP assays characterized it as a partial agonist with an EC50 of 9.6 μM. Finally, transcriptomic profiling revealed a stress adaptation response, identifying Heme Oxygenase 1 (HMOX1) as the most significantly upregulated gene (padj = 9.99 × 10⁻⁷², log₂FoldChange = 1.65). These findings contribute to understanding the pharmacological profile of RC and provide a screening example for identifying components with GPCR activity in RC. Full article

A simple and highly sensitive surface-enhanced Raman spectroscopy (SERS) method has been developed for the detection of thiram residues in fruit juices. Carboxymethyl cellulose (CMC) @ gold nanoparticles (Au NPs) hydrogel beads as SERS substrates were prepared through ionic crosslinking. The obtained porous CMC @ Au NPs hydrogel bead substrates showed excellent sensitivity for the detection of thiram in apple, grape, and orange juices, with detection limits of 0.001, 0.002, and 0.002 mg/L, respectively. The impact of primary non-target components in juices on SERS detection of thiram was investigated, revealing that the presence of sugars and acids caused varying degrees of interference in SERS measurements. This innovative, practical, and affordable CMC @ Au NPs porous hydrogel bead for thiram detection might be readily expanded to analyze a broad spectrum of other compounds found in food goods at trace amounts. Full article

The aim of this study was to evaluate the effects of protein-based (zein) and polysaccharide-based (carboxymethylcellulose, CMC) edible coatings and citric acid (CA) applied prior to freeze-drying on the quality parameters of Jerusalem artichoke (Helianthus tuberosus L.) slices from ‘Albik’ and ‘Rubik’ cultivars. Freeze-drying increased inulin extraction efficiency (57–61 g 100 g⁻¹ vs. 44–45 g 100 g⁻¹ in fresh samples). In the ‘Albik’ cv., CMC and CA coatings significantly minimized L-ascorbic acid losses, with a 10–20% reduction vs. control. For the same cultivar, enhanced polyphenol retention was observed (up to 13%) when CA coating was applied, while the use of zein reduced vitamin C content in both cultivars. Sensory analysis (PCA, 92.4% variance) revealed that CMC improved appearance, texture, and overall acceptability, while zein imparted an off-taste, odor, and fragility. Image texture analysis showed elevated parameters (e.g., HMean) post freeze-drying, with CA inducing the greatest structural changes and zein yielding samples most similar to raw material. Machine learning classification (quadratic/linear SVM, 10-fold CV) achieved 91.5% (‘Albik’) and 81.9% (‘Rubik’) accuracy, perfectly distinguishing raw slices (100%). These findings demonstrate that CMC and CA coatings optimize bioactive retention, sensory quality, and textural differentiation in freeze-dried Jerusalem artichoke, supporting their application in functional food production. Full article

The critical micelle concentration (CMC) is a fundamental physicochemical property of surfactants with significant implications across multiple industries. This paper presents an uncertainty-aware graph neural network (GNN) that integrates molecular structure and temperature to simultaneously predict CMC values and prediction uncertainties. Trained on a curated dataset of 2133 CMC values with temperature annotations, our GNN achieves comparatively similar performance on two external test sets from similar works. The model provides adequately calibrated uncertainty estimates that reliably quantify prediction confidence. This dual-output approach enables reliable CMC prediction with quantifiable confidence intervals, addressing a practical need for safety-critical applications where underestimation of uncertainty could have serious consequences. Full article