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A novel grafted temperature-responsive ReO₄⁻ Imprinted composite membranes (Re-ICMs) was successfully prepared by using polyvinylidene fluoride (PVDF) resin membranes as substrates, this study aimed to separate and purify ReO effectively.

Re-ICMs were synthesized by PVDF resin membranes as the substrate, acrylic acid (AA), acrylamide (AM), ethylene glycol dimethacrylate (EGDMA) were functional monomers. The morphology and structure of Re-ICMs were characterized by scanning electron microscope and Fourier transform infrared spectroscopy.

The maximum adsorption capacity toward ReO₄⁻ was 0.1,163 mmol/g and the separation decree had relation to MnO₄⁻ was 19.3. The optimal operation conditions were studied detailedly and the results as follows: the molar ratios of AA, AM, EGDMA, ascorbic acid, NH4ReO₄, were 0.8, 0.96, 0.02, 0.003 and 0.006. The optimal time and temperature were 20 h and 40°C, respectively. The Langmuir and pseudo-second-order models were fit these adsorption characteristics well.

Rhenium (Re) is mainly used to chemical petroleum and make superalloys for jet engine parts. This study was representing a technology in separate and purify of Re, which provided a method for the development of the petroleum and aviation industry.

This contribution provided a novel method to separate ReO₄⁻ from MnO₄⁻. The maximum adsorption capacity was 0.1163 mmol/g at 35°C and the adsorption equilibrium time was within 2 h. Meanwhile, the adsorption selectivity rate ReO₄⁻/MnO₄⁻ was 19.3 and the desorption rate was 78.3%. Controlling the adsorption experiment at 35°C and desorption experiment at 25°C in aqueous solution, it could remain 61.3% of the initial adsorption capacity with the adsorption selectivity rate of 13.3 by 10 adsorption/desorption cycles, a slight decrease, varied from 78.3% to 65.3%, in desorption rate was observed.

The application of thermal barrier coatings (TBCs) allows aero-engine blades to operate at higher temperatures with higher efficiency. The preparation of the TBCs increases the surface roughness of the blade, which impacts the thermal cycle life and thermal insulation performance of the coating. To reduce the surface roughness of blades, particularly the blades with small size and complex curvature, this paper aims to propose a method for industrial robot polishing trajectory planning based on on-site measuring point cloud.

The authors propose an integrated robotic polishing trajectory planning method using point cloud processing technical. At first, the acquired point cloud is preprocessed, which includes filtering and plane segmentation algorithm, to extract the blade body point cloud. Then, the point cloud slicing algorithm and the intersection method are used to create a preliminary contact point set. Finally, the Douglas–Peucker algorithm and pose frame estimation are applied to extract the tool-tip positions and optimize the tool contact posture, respectively. The resultant trajectory is evaluated by simulation and experiment implementation.

The target points of trajectory are not evenly distributed on the blade surface but rather fluctuate with surface curvature. The simulated linear and orientation speeds of the robot end could be relatively steady over 98% of the total time within 20% reduction of the rest time. After polishing experiments, the coating roughness on the blade surface is reduced dramatically from Ra 7–8 µm to below Ra 1.0 µm. The removal of the TBCs is less than 100 mg, which is significantly less than the weight of the prepared coatings. The blade surface becomes smoothed to a mirror-like state.

The research on robotic polishing of aero-engine turbine blade TBCs is worthwhile. The real-time trajectory planning based on measuring point cloud can address the problem that there is no standard computer-aided drawing model and the geometry and size of the workpiece to be processed differ. The extraction and optimization of tool contact points based on point cloud features can enhance the smoothness of the robot movement, stability of the polishing speed and performance of the blade surface after polishing.

Customer expectations and preferences may evolve as they experience more. This study aims to analyze the dynamics of customer requirements (CRs) as customer experience familiarity increases, offering insights for enhancing product-service quality.

This study categorizes dynamic requirements into two conversion stages: from new to repeat customers and from repeat to frequent customers. First, crawl online reviews (ORs) and determine each review’s conversion stage. Second, identify product-service attributes from reviews and conduct aspect-level sentiment analysis. Then, examine each attribute’s trend direction and magnitudes in the two stages. Finally, a dynamic-trend importance-performance analysis (DTIPA) model is developed to analyze the dynamic requirements and provide strategies for optimizing product services.

This study identifies eight attributes of buffet restaurants with varying requirement change trends. In particular, customer attention to “waiting time,” “variety of dishes,” “cost performance” and “taste” decreases in the first stage. In the second stage, “environment” and “freshness” increase differently from the first. Satisfaction with “cost performance” increases in the first stage but decreases in the perception of frequent customers. Improvement strategies are also provided based on these trends.

Research on dynamic requirement trends based on ORs and customer experience familiarity is scarce, particularly in the context of buffet restaurants. Moreover, existing methods have their limitations. This study proposes a novel approach for the progressive exploration and extraction of evolving CRs from ORs. By incorporating the trend direction and magnitude of attributes’ importance and satisfaction, DTIPA is developed to form strategies for optimizing buffet restaurant product services.

This article intends to explore how organizational learning affects innovation in public library services, and the role of public librarians in innovation in library services.

This study collected 375 valid questionnaires from 19 public libraries in Shanghai and Zhejiang based on organizational learning, organizational innovation and employee psychological empowerment theory. Additionally, SPSS and HLM are used to analyze the relationship among the three processes of organizational learning: knowledge acquisition, knowledge sharing and knowledge application, and public library service innovation.

Results show that organizational learning has a significant positive effect on the service innovation of public libraries. Knowledge acquisition and knowledge application in the process of organizational learning have a significant positive influence on the service innovation of public libraries, but the impact of knowledge sharing on service innovation is weak. Employee psychological empowerment has a negative regulating influence on knowledge sharing–public library service innovation, but no significant influence on knowledge application–public library service innovation and knowledge acquisition–public library service innovation.

This research explores the effectiveness of the theory of organizational learning in the field of public libraries and also confirms the role of librarians in the work of public libraries. Together, they promote the innovation of public libraries.

This paper examines the impact of the COVID-19 pandemic on low-carbon consumption of dairy products through informational interventions. The empirical findings seek to enlighten developing countries' efforts in coping with climate change and potential dietary transitions.

A randomized controlled trial was designed to examine the effects of purpose-differentiated information interventions on individual dairy consumption. The experiment recruited and randomly assigned 1,002 college students into four groups to receive (or not) environmental or/and health information interventions.

The empirical analysis finds that health and combined information interventions have a positive impact on dairy consumption, while environmental information interventions' effect on dairy consumption is insignificant. In the context of the pandemic, health information interventions positively affected participants' perceptions and preferences for dairy products by delivering knowledge about their role in boosting immunity. However, environmental information interventions failed to do the same things as their insignificant effects on both perception and preference.

Macro-external shocks, such as public health events, may offset the impact of universal information interventions promoting pro-environmental behaviors. For a smooth dietary transition to achieve long-term environmental sustainability, diverse stakeholders must be included in more individualized interventions to guide daily consumption, especially in developing countries with large populations.

During the process of the robotic grinding and polishing operations on aero-engine blades, the key problem of calibration error lies in fixture error and uneven margin. To solve this problem, this paper aims to propose a novel method to achieve rapid online calibration of the workpiece coordinate system through laser-based measurement techniques.

The authors propose a calibration strategy based on point cloud registration algorithm. The main principle is presented as follows: aero blade mounted on clamping end-effector is hold by industry robot, the whole device is then scanned by a 3D laser scanner to obtain its surface point cloud, and a fast segmentation method is used to acquire the point cloud of the workpiece. Combining Super4PCS algorithm with trimmed iterative closest point, we can align the key points of the scanned point cloud and the sampled points of the blade model, thus obtaining the translation and rotation matrix for calculating the workpiece coordinate and machining allowance. The proposed calibration strategy is experimentally validated, and the positioning error, as well as the margin distribution, is finally analyzed.

The experimental results show that the algorithm can well accomplish the task of cross-source, partial data and similar local features of blade point cloud registration with high precision. The total time spent on point cloud alignment of 100,000 order of magnitude blade is about 4.2 s, and meanwhile, the average point cloud alignment error is reduced to below 0.05 mm.

An improved point cloud registration method is proposed and introduced into the calibration process of a robotic system. The online calibration technique improves the accuracy and efficiency of the calibration process and enhances the automation of the robotic grinding and polishing system.