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The realization of coupling coordination between digitalization and traditional industrial upgrading in the Yellow River Basin holds significant practical value for promoting high-quality industrial development in the region. In order to assess this coupling coordination, we utilized inter-provincial panel data from nine provinces in the Yellow River Basin, covering the period from 2011 to 2020. Through the application of a coupling coordination degree model, we calculated the degree of coupling coordination and relative development between digitalization and traditional industrial upgrading. Additionally, we conducted a spatial–temporal analysis to identify the characteristics and trends of digitalization and traditional industrial upgrading. Furthermore, we constructed a panel VAR model to examine the interactive relationship between these two factors. The findings are as follows: (1) overall, over the study period, the degree of coupling coordination between digitalization and traditional industrial upgrading in the Yellow River Basin transitioned from a disordered state to a run-in stage. The corresponding development type changed from a low steady state to a co-existence of low and medium steady states. Notably, the levels of digitalization, traditional industrial upgrading, and coupling coordination all exhibited a gradual increase, while the relative development degree declined. (2) The coupling coordination degree between digitalization and traditional industrial upgrading in the Yellow River Basin demonstrated significant regional variation. Provinces displaying a “high–high” agglomeration distribution and “low–low” agglomeration distribution were concentrated in the middle and lower reaches, as well as the upper reaches, of the Yellow River. Furthermore, there was a positive spatial autocorrelation between these regions. (3) Both digitalization and traditional industrial upgrading exhibit self-reinforcing mechanisms, and a long-term dynamic correlation exists between them.

Traditional path planning is mainly utilized for path planning in discrete action space, which results in incomplete ship navigation power propulsion strategies during the path search process. Moreover, reinforcement learning experiences low success rates due to its unbalanced sample collection and unreasonable design of reward function. In this paper, an environment framework is designed, which is constructed using the Box2D physics engine and employs a reward function, with the distance between the agent and arrival point as the main, and the potential field superimposed by boundary control, obstacles, and arrival point as the supplement. We also employ the state-of-the-art PPO (Proximal Policy Optimization) algorithm as a baseline for global path planning to address the issue of incomplete ship navigation power propulsion strategy. Additionally, a Beta policy-based distributed sample collection PPO algorithm is proposed to overcome the problem of unbalanced sample collection in path planning by dividing sub-regions to achieve distributed sample collection. The experimental results show the following: (1) The distributed sample collection training policy exhibits stronger robustness in the PPO algorithm; (2) The introduced Beta policy for action sampling results in a higher path planning success rate and reward accumulation than the Gaussian policy at the same training time; (3) When planning a path of the same length, the proposed Beta policy-based distributed sample collection PPO algorithm generates a smoother path than traditional path planning algorithms, such as A*, IDA*, and Dijkstra.

Due to promote manufacturing enterprises to carry out green technology innovation practice smoothly, achieve the goal of energy conservation and emission reduction, and win green competitive advantage, this paper first divides the green technology innovation path into two types, namely internal independent R&D of green technology (IIGT) and external green technology introduction (EGTI), and analyzes the operation mechanism of these two types of paths. Secondly, a two-agent game model of different types of environmental regulation tools on the choice of green technology innovation path of manufacturing enterprises is constructed. To be sure, the manufacturing enterprises include the leader enterprise A and the follower enterprise B. It is assumed that the two groups of manufacturing enterprises produce the same products or provide the same services in the natural state without considering other influencing factors. Finally, stability analysis and numerical simulation are employed to compare and analyze the heterogeneous effects of different environmental regulation tools on the path selection of green technology innovation in manufacturing enterprises.The simulation shows that when the government adopts or does not adopt environmental regulation means, the system, leader enterprise A and follower enterprise B will eventually choose the path of IIGT or EGTI respectively after a long-term evolution process. However, the effects of subsidy for green technology innovation and carbon tax rate on the path selection of green technology innovation are different among the three parties. Specifically, when the government adopts the means of subsidy for green technology innovation, the leader enterprise A will actively choose the the path of IIGT earlier than the follower enterprise B. On the contrary, when the government adopts the means of carbon tax, the leader enterprise A will actively choose the the path of IIGT later than the follower enterprise B. The research of this paper is helpful to explore the green and sustainable development mode of China’s manufacturing industry under the dual constraints of environment and resources, and provides decision support for the relevant national departments to make relevant policies.

A novel colorimetric and ratiometric fluorescence sensor has been established based on boron carbon oxynitride quantum dots (BCNO QDs) and Ca 2+ for the detection of doxycycline (DOX). BCNO QDs were synthesized by microwave-assisted method with boric acid and ethylenediamine. The fluorescence of BCNO QDs at 425 nm was quenched due to the electrostatic interaction and inner filter effect with doxycycline. Meanwhile, doxycycline was combined with Ca 2+ to form a fluorescence complex, which generated a new fluorescence peak at 520 nm. The fluorescence intensity ratio (F 520 /F 425 ) has a good linear relationship with doxycycline concentration, and the detection limit is 25 nM. Moreover, the fluorescence of the reaction solution showed a concentration-dependent visual color change from blue to green. In order to facilitate further application, a portable fluorescent test paper which is easy to store was prepared. The RGB values of the reaction solution and corresponding test paper were identified by smartphone, and the visual detection of doxycycline was performed by digital image colorimetric analysis. The application of smartphone and fluorescent test paper can effectively shorten the detection time and simplified the operation, providing an effective scheme for quantitative detection of doxycycline in actual samples. Overall, this work provides a method for the detection of doxycycline and shows that the BCNO QDs have great potential application in food safety. Graphical abstract

The coronavirus disease 2019 pandemic has impacted and changed consumer behavior because of a prolonged quarantine and lockdown. This study proposed a theoretical framework to explore and define the influencing factors of online consumer purchasing behavior (OCPB) based on electronic word-of-mouth (e-WOM) data mining and analysis. Data pertaining to e-WOM were crawled from smartphone product reviews from the two most popular online shopping platforms in China, Jingdong.com and Taobao.com . Data processing aimed to filter noise and translate unstructured data from complex text reviews into structured data. The machine learning based K-means clustering method was utilized to cluster the influencing factors of OCPB. Comparing the clustering results and Kotler’s five products level, the influencing factors of OCPB were clustered around four categories: perceived emergency context, product, innovation, and function attributes. This study contributes to OCPB research by data mining and analysis that can adequately identify the influencing factors based on e-WOM. The definition and explanation of these categories may have important implications for both OCPB and e-commerce.

A ratio fluorescence nanoprobe was constructed by simple mixing BCNO QDs with 8-hydroxyquinoline-5-sulfonic acid (HQSA), which had an obvious fluorescence peak at 420 nm and a weak fluorescence peak at 500 nm, corresponding to the BCNO QDs and HQSA, respectively. This fluorescence probe takes stable fluorescence of BCNO QDs as an internal standard, based on HQSA chelating enhanced fluorescence and specificity of phosphate in the presence of Mg 2+ , which realizes a rapid and sensitive detection of phosphate with good linearity in the range 0.3–50 μM and 50–100 μM and a detection limit of 0.073 μM. The recovery is between 94.1 and 111% and the relative standard deviations (RSDs) below 10%. At the same time, we took color photos of the reaction solution under 310-nm UV lamp with smartphones for visual detection through RGB data image analysis, which make the detection easier and faster. The proposed method provides a new strategy for the intelligent online detection of other targets in complex environment samples. Graphical abstract

Basalt Fiber Reinforced Polymer (BFRP) bolts offer a high mechanical performance, yet their non-destructive evaluation in anchorage systems remains scarcely investigated. This work examines guided wave propagation in BFRP bolt anchorage structures through a combined experimental and numerical analysis. Optimal excitation within 35–100 kHz was determined experimentally, revealing 40 kHz as the most stable mode, with a pronounced bottom reflection and a peak-to-peak amplitude of 0.31 V. Numerical simulations explored the influence of anchorage medium properties, bolt characteristics, and de-bonding defect locations and lengths on dispersion, attenuation, velocity, radial energy distribution, and echo response. The results indicate that denser anchorage media reduce velocity and attenuation but enhance radial nonuniformity, whereas a higher elastic modulus decreases amplitude and increases attenuation; a larger Poisson’s ratio elevates both velocity and attenuation. For the bolt, a higher density lowers velocity and attenuation, while a greater modulus amplifies both; Poisson’s ratio exerts a minor positive effect. Defect echo time varies linearly with defect position, and increasing the defect length elevates velocity yet diminishes amplitude. These findings elucidate the interplay between material parameters, defect geometry, and guided wave behavior, offering a basis for the optimized non-destructive testing (NDT) of BFRP bolts and facilitating their deployment in engineering applications.

A graphitic carbon nitride (g-C3N4/Fe3O4)–based magnetic solid-phase extraction (MSPE) approach was established for fast and simple analysis of estrogens in milk powders. The composites were characterized by X-ray diffractometer, scanning electron microscope, and Brunauer-Emmett-Teller surface area and pore size distribution analyzer. Compared with the bulk g-C3N4, g-C3N4/Fe3O4 gave a narrower distribution of mesopores and provided an enhanced surface area from 77.1 to 113.7 m2/g. Polar analytes of estrogens were selected as model compounds and the extraction of four estrogens was achieved in n-hexane using 15 mg of adsorbent within only 2 min. Possible extraction mechanism of g-C3N4/Fe3O4 for these estrogens was explored in terms of the polarity of the analytes and the adsorption performance of the adsorbent. The hydrophobicity and the hydrogen-bond interaction between the estrogens and g-C3N4 were responsible for the efficient adsorption. Combined with HPLC, MSPE with the prepared adsorbent gave the enhancement factors of 20 to 24 and the linear ranges of 2–200 μg/kg for 17β-estradiol and 17α-ethinylestradiol, 1.5–150 μg/kg for estrone, and 3–300 μg/kg for hexestrol. The detection limits and quantification limits for the estrogens in milk powders were 0.5–0.9 μg/kg and 1.5–3.0 μg/kg, respectively. The recoveries varied from 75.1 to 97.2%, with the intra-day and inter-day precisions ≤ 14.2%. Furthermore, the enrichment of the analytes and the clean-up of fat and protein interferences were achieved simultaneously with one-step g-C3N4-based MSPE. The present method was convenient, fast, and sensitive, and therefore could be successfully applied for the determination of estrogens in milk powders.

Corrosion of rock bolts in engineering exhibits random spatial distribution characteristics. To elucidate the influence mechanism of stochastic corrosion on the surface of rock bolts on the propagation behavior of ultrasonic guided waves, this study establishes a finite element model of rock bolts that incorporates stochastic corrosion characteristics. The coupled effects of corrosion depth and area ratio on guided wave propagation characteristics, time-domain response, energy distribution, and wave velocity variation are systematically investigated. Results indicate that corrosion depth and area ratio synergistically deteriorate guided wave morphology, transforming the stress field from symmetric and uniform to asymmetric and spiral. Reflections, scattering, and mode conversion induced by defects lead to a significant increase in the attenuation rate of pulse amplitude, with the two parameters governing the vertical interaction intensity and horizontal interference scope, respectively. Analysis of the Hilbert curve reveals that corrosion characteristics disrupt energy concentration. Under constant corrosion depth, an increase in area ratio disperses energy toward delayed scattered waves, while under constant area ratio, greater corrosion depth reduces the peak amplitude of the envelope curve. Overall, the energy integral exhibits an increasing trend with the degree of corrosion, whereas the peak-to-peak wave velocity shows a declining trend. The established multivariate nonlinear model accurately describes the coupled influence of corrosion parameters on wave velocity. This stochastic corrosion model overcomes the limitations of traditional simplified models and provides critical theoretical support for parameter calibration and engineering application of ultrasonic guided wave technology in the quantitative assessment of rock bolt corrosion.

Magnetic carbon nitride composites were synthesized via a solvothermal reaction and developed as an effective adsorbent for magnetic solid-phase extraction of trace hydroxylated polycyclic aromatic hydrocarbons (OH-PAHs) from urine samples prior to their determination by HPLC. The sorbent was characterized by Fourier transform infrared spectrometry, X-ray diffractometry, scanning electron microscopy, vibrating sample magnetometry and solvent stability experiments. The adsorption of hydroxy-PAHs is better by a factor or 20 to 49 compared to bare Fe 3 O 4 and comparable that of a commercial C 18 sorbent. The adsorbent amount, adsorption time and eluting solvent and volume were optimized. The complete extraction for the OH-PAHs at a level of 40 ng·mL −1 and by using 4 mg sorbent is completed within 3 min. With an enzymatic hydrolyzed urine sample loading volume of 2 mL, enhancement factors in the range of 9–10, and a limit of detection (at S / N  = 3) of 0.08 ng·mL −1 were achieved. The method showed a linear response in the 0.25–250 ng·mL −1 hydroxy-PAH concentration range, and intra-day and inter-day precisions are 1.5–7.7% and 2.2–8.7%, respectively. The recovery from spiked urine samples ranged from 90.1% to 102%. The sorbent was stable over 10 successive cycles of extraction/desorption of urine sample without significant loss of extraction efficiency. The method was successfully applied for the determination of OH-PAHs in urine samples of smoking volunteers. Graphical abstract Schematic presentation of the preparation of graphitic carbon nitride (g-C 3 N 4 )/magnetite (Fe 3 O 4 ) using a solvothermal reaction, and application for magnetic solid-phase extraction of three trace hydroxy polycyclic aromatic hydrocarbons (OH-PAHs) in urine samples of smoking volunteers.