Search Results (754)

Anti-corrosive coatings are among the most widely used methods for corrosion protection. Zero-dimensional (0D) carbon nanomaterials have attracted increasing attention due to their advantages, such as small size, high specific surface area, ease of surface functionalization, and strong interfacial regulation capability, which enable enhanced barrier properties, densification, and multifunctional protection of coatings. However, existing reviews have largely focused on the application of 2D carbon nanomaterials in anti-corrosive coatings, with a lack of systematic summaries on 0D carbon nanomaterials, particularly comprehensive reviews that combine quantitative bibliometric analysis with qualitative content analysis. To address this gap, this review employs a combined approach of bibliometric analysis and content analysis to systematically summarize the research progress of three typical types of 0D carbon nanomaterials, including nanodiamonds, fullerenes, and carbon dots, in the field of corrosion protective coatings. The quantitative analysis is conducted using CiteSpace 6.4 R.2 to reveal publication trends, research hotspots, and frontier evolution in this field, while the qualitative analysis selects representative studies to summarize application systems, performance characteristics, and underlying mechanisms. On this basis, the key challenges currently faced are identified, and future research directions are proposed. This review provides a systematic reference for the material design, mechanistic understanding, and engineering application of 0D carbon nanomaterial-based anti-corrosive coatings. Full article

Improving productivity under resource and environmental constraints is a key challenge for sustainability-oriented transformation in manufacturing. Using panel data from 30 Chinese provinces during the period 2010–2024, this study examines how regional digital innovation capability is associated with manufacturing total factor productivity at the provincial level. A multidimensional digital innovation index is constructed using the entropy-weighting method, while manufacturing total factor productivity (TFP) is measured using the DEA–Malmquist index. In this study, conventional manufacturing TFP is treated as a productivity-oriented proxy within a sustainability-oriented transformation context, rather than as a direct measure of environmental performance. The empirical framework applies a two-way fixed-effects model and is complemented by supplementary instrumental-variable estimation, mediation analysis, and threshold regression to examine transmission channels and nonlinear effects. The results indicate that digital innovation capability is positively associated with manufacturing TFP, with stronger associations observed in regions that have more developed digital and innovation foundations. Decomposition results show that the gains are mainly related to technological progress rather than short-term efficiency improvements, suggesting that digitalization is reflected primarily through innovation-led upgrading. Mechanism tests further show that improvements in R&D efficiency, data element allocation, and human capital structure play important mediating roles. A significant threshold effect is also observed: When the share of digital-skilled labor exceeds a critical level, the productivity return from digital innovation increases markedly. These findings underscore the role of digital innovation and digital maturity in supporting manufacturing productivity upgrading within a sustainability-oriented transformation context and imply that policy should prioritize coordinated investment in digital infrastructure, data governance, and digital skills development. Full article

In this paper, an intelligent localization framework based on deep learning is proposed to address the limitations of insufficient accuracy and robustness in defect identification and localization during the ultrasonic guided-wave non-destructive testing (NDT) of receiver tubes in tower-type molten salt Concentrated Solar Power (CSP) stations. In the proposed method, a 1D convolutional neural network (1D-CNN) initially processes raw time-series-guided wave signals, achieving coarse identification and preliminary localization of defective segments. Then, Mel spectrograms are employed to exploit multi-dimensional features in the time–frequency domain and transform 1D signals into 2D representations, thereby enriching feature diversity. A regression-based 2D-CNN was designed to predict the start and end points of defect segments, enabling precise interval localization. Furthermore, the Tree Seed Algorithm (TSA) was integrated to jointly optimize key hyperparameters, enhancing training efficiency and prediction accuracy. Experimental validation on a dataset of ultrasonic guided-wave signals from molten salt receiver tubes demonstrates that the TSA-optimized Mel+2D-CNN model achieves superior performance, with a Mean Absolute Error (MAE) of 75.11 sampling points and a Coefficient of Determination (R²) of 0.90. At an Intersection over Union (IoU) threshold of 0.3, the model achieves a hit rate of 89.21%, exhibiting significantly higher localization accuracy and stability compared to the 1D-CNN baseline model. These findings indicate that the proposed method effectively enhances the accuracy and robustness of guided wave-based defect localization in slender structures. While promising, the model’s generalization capability remains dependent on the data distribution and operating conditions; future work will focus on validating its engineering applicability across diverse, multi-scenario industrial environments. Full article

Pig weighing is an essential procedure for monitoring growth and animal health; however, conventional methods are often labor-intensive, costly, and potentially stressful. In this context, this study proposes a non-invasive approach for estimating the body weight of pigs during the growing stage based on computer vision and the YOLOv11 algorithm, enabling automatic segmentation and individual identification in multi-animal environments. The study used RGB images of 10 group-housed pigs captured throughout the growing phase, in which automatic dorsal segmentation was combined with individual identification through numerical markings. From the generated binary masks, the segmented dorsal area was extracted and used as a predictor variable in Linear Regression and a Multilayer Perceptron (MLP) Artificial Neural Network. The YOLOv11 model showed consistent performance in the segmentation task, achieving test-set metrics of Precision = 0.849, Recall = 0.886, mAP@0.50 = 0.936, and mAP@0.50–0.95 = 0.819, demonstrating good generalization capability in scenarios with intense animal interaction. In the weight prediction stage, Linear Regression and the MLP achieved high coefficients of determination (R² = 0.96 and 0.95, respectively) with low errors (RMSE = 1.52 kg and 1.63 kg; MAE = 1.20 kg and 1.25 kg), indicating a strong correlation between segmented dorsal area and actual body weight. Class-wise analysis revealed superior performance for classes 7 and 9, with R² values up to 0.98 and RMSE below 1.1 kg, whereas class 8 showed greater error dispersion, associated with higher morphological variability and a smaller number of available samples. These results demonstrate that the direct use of morphometric information extracted from segmented masks in 2D images constitutes a robust, accurate, and low-cost approach for automatic pig body-weight estimation. Moreover, this study is among the few addressing this task specifically during the growing stage, highlighting its potential for future deployment in embedded systems and intelligent monitoring platforms for precision pig farming, although further evaluation of computational efficiency and real-time performance is still required. Full article

This study addressed the issue of insufficient activity in CALB lipase during the catalytic synthesis of key chiral intermediates for moxifloxacin. A structure-guided protein engineering strategy was employed to systematically modify its functional domains. Through molecular dynamics simulations of CALB-I189K, multiple regions exhibiting high conformational flexibility were preliminarily identified. Subsequently, by integrating 3D structural alignment with active site pocket distance analysis, the functionally most critical region (143–146) was selected. A site-directed saturation mutation library was constructed specifically targeting this region. Building upon the previously reported CALB-I189K, a mutant I189K/L144R/A146K was ultimately obtained through high-throughput screening combined with chiral HPLC validation. This mutant maintains excellent stereoselectivity (E = 206.52) while enhancing catalytic efficiency (k_cat/Κ_m) to 273.73 min⁻¹·mM⁻¹, approximately 4.5-fold that of I189K. At a substrate concentration of 1 M, it achieves 50% conversion within 2.6 h, demonstrating kinetic resolution capabilities approaching industrial standards. Molecular simulation analysis indicates that the L144R and A146K mutations synergistically enhance catalytic performance primarily by optimizing spatial distances between catalytic residues. This study not only provides a high-performance catalyst for the efficient biosynthesis of moxifloxacin chiral intermediates but also offers new insights for enzyme rational design based on dynamic structural information. Full article

Organic Rankine Cycles (ORCs) are widely recognized as an effective solution for Waste heat recovery (WHR). However, the design and optimization of these systems must address the tradeoff between computational efficiency and the need to capture complex component behavior. This requires moving beyond purely energetic 0D modeling approaches to account for constructional, spatial, and operational constraints. This work presents a novel modeling framework with a specific focus on the expansion device. Radial inflow turbine stages are selected for their capability to achieve high pressure ratios while maintaining compactness and high efficiency. Heat exchangers follow a generic one-dimensional counterflow configuration, with a shell-and-tube geometry adopted for sizing purposes. The turbine stages are modeled by resolving several internal sections in order to capture local thermofluid dynamic conditions. The framework predicts turbine efficiency and incorporates a newly developed formulation for shock-induced losses, improving performance prediction under trans-sonic flow conditions. After validation against experimental data, the model is applied to a WHR system integrated with an internal combustion engine fueled by biofuels. The results highlight the existence of optimal operating conditions arising from competing physical mechanisms. The analysis also shows the transition from single-stage to two-stage turbine configurations at high pressure ratios and emphasizes the role of real gas effects in determining stage performance and optimal expansion distribution. The results of simulations carried out for three different working fluids (ethanol, toluene, and R1234ze(E)) highlight that the available mechanical power ranges from 10 to 22 kW for single-stage turbine configurations and from 24 to 36 kW for two-stage configurations, with total system volumes varying between approximately 600 and 9000 L. Among the working fluids considered here, ethanol provides the best overall performance for the present case study. Overall, the proposed approach provides a reliable and computationally efficient tool for the preliminary design and optimization of ORC-based WHR systems. Full article

This article presents the design and characterization process of a lightweight Vivaldi antenna for high-voltage ultra-wideband systems. The proposed antenna consists of two radiating arms with different exponential curves on their inner and outer edges fed with an insulated-coplanar-plates transmission line. Weight reduction is achieved by implementing the antenna with sheets composed of a polyester layer between two aluminum layers, with a polylactic acid insulator inserted between the arms. The reflection coefficient of the implemented antenna demonstrates an impedance bandwidth ranging from 0.61 GHz to 3.44 GHz. High-voltage operation of up to 12.4 kV is also experimentally demonstrated. In addition to satisfying the high-voltage and ultra-wideband operational requirements, the proposed antenna is shown to achieve, among antennas with comparable characteristics, the most effective combination of low minimum operating frequency and low weight. The transfer function between the voltage applied to the antenna, $V_s$, and the radiated electric field, $E_r$, is measured. Using this transfer function, the radiated electric field is calculated for an input voltage pulse with a rise time of 110 ps to confirm the antenna’s capability of producing radiated pulses with low distortion. The calculated radiated electric field pulse closely matches the results obtained with full-wave simulation. To assess the similarity between the radiated and applied pulses, the pulse width stretch ratio is calculated, yielding a variation of 3.86% for the direction of maximum gain and 9.36% for 30° in the H-plane of the antenna. This feature is desirable for EMC, EMI and sensing applications. The antenna is also characterized in the frequency domain, achieving a maximum gain of 10.09 dBi at 3.63 GHz and a 30° 3 dB beamwidth for ultra-wideband pulses. Full article

Monascus sp. NP1 is a significant filamentous fungus with valuable properties for food industries. Initially isolated from the fermented rice product ang-kak, this strain is known for its ability to produce natural pigments. In this study, we therefore sequenced its genome together with the 26S rRNA D1/D2 domain and ITS fragment for identifying species of Monascus sp. NP1, and further conducted functional annotations of its overall genes related to metabolic capability and growth adaptation using comparative genomics. As a result, promisingly, the NP1 strain was identified as Monascus purpureus with the genome sequences, which was shown to be 23.54 Mb with a GC content of 49.01%. Genome annotation predicted 8031 protein-encoding genes. Comparative genomics between NP1 and 11 other related strains revealed 6024 core groups, 2204 accessory groups, and 5 strain-specific groups. Metabolic pathway analysis promisingly showed carbohydrate metabolism as the most enriched category, particularly central carbon metabolism involving key precursors, e.g., acetyl-CoA and pyruvate that support energy generation and the biosynthesis of pigments, fatty acids, and lipids. These findings highlighted the metabolic versatility and adaptive growth potential of M. purpureus NP1. This study provides key genetic insights into the cellular functions of M. purpureus NP1, laying the groundwork for exploring metabolic properties. It offers a comprehensive understanding for developing targeted applications of M. purpureus NP1 as an alternative fungal cell factory in food and nutrition. Full article

Background/Objectives: Precise intracellular delivery of chemotherapeutics remains a major challenge in HER2-positive breast cancer, where intratumoral heterogeneity and limited tissue penetration constrain efficacy. A key contributor is the tumor-restricted epidermal growth factor receptor variant III (EGFRvIII), a constitutively active, ligand-independent mutant generated by deletion of exons 2–7. Although classically associated with glioblastoma, lung (NSCLC), head/neck, and prostate cancers, EGFRvIII is also present in subsets of HER2-positive breast cancers, where low-abundance subclones drive aggressive phenotypes and attenuate therapeutic responses. HER2–EGFRvIII co-expression amplifies oncogenic signaling, supported by frequent co-expression in ErbB2-positive primary tumors and metastases, and by sustained receptor phosphorylation in the absence of EGFR gene amplification, depicting EGFRvIII as a compelling therapeutic target. Methods: We evaluated the shark-derived single-domain antibody vNAR R426 as a modular theranostic platform for receptor-mediated cisplatin delivery. Conjugation to cisplatin and fluorescein enabled simultaneous intracellular drug transport and immunofluorescence-based detection in EGFRvIII-positive SKBR3 cells and 3D spheroids. The compact vNAR-based immunoconjugates support efficient receptor recognition, internalization, and intracellular trafficking, features rarely achieved by conventional IgG antibodies. Results: vNAR_CDDP elicited robust, receptor-mediated cytotoxicity, achieving an IC₅₀ of 2.68 µM—approximately 50-fold lower than that of free cisplatin—while unconjugated vNAR maintained scaffold biocompatibility. In three-dimensional spheroid models, the theranostic vNAR (vNAR_CDDP+FITC) exhibited deep and uniform penetration throughout tumor-like architectures, with immunofluorescence intensity closely correlating with regions of intracellular drug delivery and the initiation of cytotoxic responses. Notably, cisplatin conjugation did not impair tissue diffusion or receptor engagement, facilitating effective payload delivery to both peripheral and central cell populations. Conclusions: By integrating tumor-restricted targeting and efficient intracellular drug delivery within a modular single-domain scaffold, vNAR R426 represents a next-generation theranostic platform capable of addressing intratumoral heterogeneity. This approach combines potent cytotoxic activity with immunofluorescence-based detection, thereby advancing the rational design of precision therapeutics for HER2-positive breast cancer. Full article

Reliable temperature distribution measurement in ultra-low temperature (ULT) chest freezers is crucial for preserving biospecimen integrity in cryopreservation, but dense sensor arrays required for accuracy are often impractical due to space constraints and cost limitations. To address this critical challenge, this work presents a systematic data-driven framework for optimal sensor placement in large-scale (3 m³) ULT chest freezers under stable operating conditions. To our knowledge, it is the first realization of high-fidelity cryogenic temperature field reconstruction coupled with sparse sensor layout optimization tailored to large-volume ULT chest freezers. First, high-resolution reference temperature fields were constructed via universal kriging interpolation, validated with leave-one-out cross-validation (LOOCV) to achieve mean absolute error (MAE) $\le 0.67 °\mathrm{C}$ and coefficient of determination $R^2>0.92$. Principal component analysis (PCA) was then applied to training data to extract a tailored proper orthogonal decomposition (POD) basis. The first three principal components captured 99.8% of cumulative energy. Optimal sensor locations were determined via QR-column pivoting on the rank-3 POD basis, converging to a minimal configuration of 3 sensors (a 94% reduction from the 48-sensor full-scale setup). This sparse sensor network achieved exceptional reconstruction performance: grid-level MAE $\le 0.079 °\mathrm{C}$ and root mean squared error (RMSE) $\le 0.093 °\mathrm{C}$ against reference fields ($R^2\ge 0.999$), while point-level validation against experimental measurements yielded MAE $\le 0.502 °\mathrm{C}$ and RMSE $\le 0.842 °\mathrm{C}$ ($R^2\ge 0.971$). The results demonstrate that, for large-scale ULT chest freezers, the proposed data-driven approach is capable of automatically determining an optimal sparse sensor subset and enabling reliable 3D cryogenic temperature field reconstruction for efficient thermal monitoring. By resolving the trade-off between monitoring accuracy, space efficiency, and cost-effectiveness, this framework provides a scientifically rigorous alternative to empirical sensor deployment standards, offering practical scalability for cryogenic biobanking applications. Full article

This study examines the spatio-temporal evolution and restricting mechanisms of agricultural supply chain resilience in the Yangtze River Basin from a gradient perspective. An evaluation index system encompassing the dimensions of the supply side, demand side, circulation side, and support side was developed. The Entropy-Weighted TOPSIS method, kernel density estimation, and obstacle degree model were comprehensively applied to measure and dynamically analyze supply chain resilience across 11 provinces from 2013 to 2023. The findings reveal distinct spatio-temporal evolution patterns: while the overall resilience shows an upward trend, significant gradient disparities exist, with downstream areas exhibiting markedly higher resilience than the mid- and upstream regions. Regarding the restricting mechanisms, the circulation and support sides exhibit higher levels of obstacles, representing key constraints to resilience enhancement. Among these, express delivery volume, freight turnover, and local R&D personnel full-time equivalents are the core obstacle factors affecting resilience. Based on these findings, this study proposes targeted recommendations, including optimizing rural last-mile logistics, upgrading inter-provincial freight hubs, improving rail–water intermodal transport, and strengthening cold-chain infrastructure, as well as implementing differentiated regional strategies and establishing cross-regional coordination mechanisms. These recommendations aim to provide decision-making guidance for enhancing the risk-response capabilities of agricultural supply chains in the Yangtze River Basin and to promote balanced regional development. Full article

Soil salinization in arid oases constrains soil functioning and crop production, making spatially explicit monitoring important for land management. Multispectral optical remote sensing enables large-area salinity assessment, but in oasis environments such as the Keriya Oasis, its performance can be limited by spectral confusion between salt crusts and bright bare soils, sparse vegetation cover, and strong surface heterogeneity. Synthetic aperture radar (SAR), by contrast, provides all-weather imaging capability and sensitivity to surface scattering and dielectric-related conditions, but its salinity interpretation is often affected by surface complexity and environmental coupling. To address these, a spectral index–polarimetric scattering integration framework that combines RADARSAT-2 and Landsat-8 OLI features within a simple two-dimensional (2D) feature space was developed. Two groups of models were constructed from variables selected through a data-driven screening process: (1) polarimetric feature space models based on combinations such as VanZyl volume scattering with Pauli odd-bounce or Touzi alpha scattering; and (2) multi-source feature space models that integrate the optimal polarimetric component with key spectral indicators such as SI4 and MSAVI. Among all tested models, VanZyl_vol-SI4 achieved the best performance (fitting: R² = 0.749, RMSE = 5.798 dS m⁻¹, MAE = 4.086 dS m⁻¹; validation: R² = 0.716, RMSE = 5.566 dS m⁻¹, MAE = 4.528 dS m⁻¹). The results indicate that integrating PolSAR scattering information with optical indices can improve salinity mapping relative to single-source feature spaces in the Keriya Oasis. The proposed 2D framework provides a concise way to compare different feature combinations and supports regional identification of salt-affected soils. Full article

Soil health monitoring is critical for the sustainable management of the black soil region, a key resource for global food security. However, traditional field surveys are constrained by high operational costs, limited spatial coverage, and low temporal frequency, making them inadequate for high-resolution and time-sensitive soil monitoring. The recently launched ZY1-02E satellite, equipped with an advanced hyperspectral imager, offers a new potential data source, yet its capability for quantitative soil modelling requires rigorous cross-sensor validation. This study conducts a cross-sensor evaluation of ZY1-02E and its predecessor, ZY1-02D, for mapping soil organic matter (SOM) and soil texture (sand, silt, and clay) in Northeast China. Optimal spectral indices were constructed through exhaustive band combination and correlation screening, and quantitative inversion models were established using a hybrid framework integrating Random Frog feature selection with Gaussian Process Regression (GPR) and Boosting Trees, based on synchronous ground observations. Results demonstrate strong cross-sensor consistency, with spectral indices showing significant linear correlations (${\mathrm{R}}^2>0.65$) between ZY1-02E and ZY1-02D. Furthermore, the quantitative retrieval models applied to ZY1-02E imagery achieved robust performance, with cross-sensor retrieval consistency exceeding ${\mathrm{R}}^2=0.60$ for all parameters and SOM exhibiting the highest agreement (${\mathrm{R}}^2=0.74$). These findings confirm the radiometric stability and algorithm transferability of ZY1-02E, demonstrating its capability to generate soil parameter products comparable to ZY1-02D without extensive model recalibration. The validated interoperability of the twin-satellite constellation substantially enhances temporal observation capacity during the narrow bare-soil window, effectively mitigating cloud-induced data gaps in high-latitude agricultural regions. Importantly, the enhanced monitoring framework provides a scalable technical paradigm for high-frequency hyperspectral soil mapping, offering critical spatial decision support for precision fertilization, soil degradation mitigation, and conservation tillage management in the Mollisol belt. Full article

In this study, we investigate how corporate policies are influenced by the presence of rookie independent directors (RIDs). We hypothesize that RIDs, due to their inexperience, impact corporate policies in ways that may amplify agency problems. Specifically, firms with RIDs demonstrate higher investment in R&D and capital expenditure, increased leverage (both short- and long-term), enhanced liquidity (cash holdings and working capital), and elevated risk-taking, while their presence leads to a conservative payout policy. Using a sample of Chinese-listed firms from 2008 to 2022, our findings confirm these predictions. Additional analyses reveal that RIDs’ effects are more pronounced in high-CEO-power environments, where their limited governance capabilities may align with managerial interests, exacerbating financial risks. This study contributes to the corporate governance literature by integrating upper echelon and agency theories, shedding light on the dual-edged role of RIDs in shaping corporate outcomes. Full article

Firms’ attention to physical climate risks arising from extreme weather events and long-term climate change has become a crucial strategic orientation that shapes how firms perceive, interpret, and respond to climate-related uncertainties. However, despite growing scholarly interest in climate risk and corporate sustainability, limited research has systematically examined whether and how firms’ climate risk attention (CRA) translates into value chain upgrading (VCU). Using panel data on Chinese A-share listed companies from 2008 to 2024, this study investigates the relationship between CRA and VCU. The empirical results show that CRA significantly promotes firms’ VCU, and that this effect is more evident among firms in climate-sensitive industries. Mechanism analyses further reveal that CRA facilitates firms’ embedding into green R&D networks, green investor networks, and green governance networks, which in turn enhance VCU. Further analyses indicate that green governance capability, green subsidies, and green outcome transformation ability strengthen the positive effect of CRA on VCU. These findings deepen the understanding of how climate-related strategic attention shapes firms’ sustainable transformation and provide evidence that proactive attention to physical climate risks not only improves environmental governance, but also serves as an important catalyst for firms to move toward higher value-added segments of the value chain. Full article