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In order to understand the tectonic control on shale oil migration and accumulation, samples of the Daanzhai Member of the Lower Jurassic Ziliujing Formation from the well core in a tectonically stable area and upright anticline outcrop were selected for total organic carbon (TOC) content analysis, rock pyrolysis, fluorescence scanning, and scanning electron microscopy. The results show the following: (1) In the tectonically stable area, the TOC of shale oil reservoirs is positively correlated with S1, and a high OSI interval usually occurs in high-TOC shales. The oil content of the limestone lamina decreases with an increasing distance from black shale. The vertical migration of shale oil into or across the lamina is not obvious and is mainly micro-scale. (2) The migration pathway includes a lamina interface, shell–clay interface, calcite cleavage, feldspar or calcite dissolution pores, and quartz or kaolinite intergranular pores. Large-scale shale oil migration time occurs at the peak of oil generation. (3) In the area of strong tectonic deformation, the formation of fractures in limestone further promotes the migration of oil from shale into the lamina. (4) The re-migration of shale oil during the uplift and deformation period involves three processes: upward migration in a clay matrix, then entry and migration along the limestone–shale interface from the lateral pinch-out points of the lamina, migration into the lamina joints, and then short diffusion into the limestone. (5) The migration of shale oil in the Daanzhai shale was controlled by the history of hydrocarbon generation and tectonic deformation and occurred in several stages. [ABSTRACT FROM AUTHOR]

Phospholipase D (PLD) plays a pivotal role in the biosynthesis of phosphatidylserine (PS), but its practical application is constrained by limitations in stability and reusability. In this study, we successfully fabricated the Fe3O4@SiO2–graphene oxide (GO) nanocomposite by chemical binding of Fe3O4@SiO2 and GO. Subsequently, PLD was immobilized onto the nanocomposite via physical adsorption, with the aim of enhancing catalytic stability, reducing mass transfer resistance, and improving reusability. Under optimal conditions, the immobilization efficiency reached 84.4%, with a PLD loading capacity of 111.4 mg/gsupport. The optimal pH for PS production by immobilized PLD shifted from 6.0 to 6.5, while the optimal temperature increased from 45 °C to 50 °C. Notably, the immobilized PLD demonstrated a shorter reaction time and a higher PS yield, achieving a 95.4% yield within 90 min, compared to the free PLD (78.1% yield within 150 min), representing a 1.04-fold improvement in production efficiency. Furthermore, the immobilized PLD exhibited outstanding storage stability and thermal stability, along with remarkable reusability. Even after being reused for 10 cycles, the PS yield still stays as high as 78.3%. These findings strongly suggest that the Fe3O4@SiO2–GO immobilized PLD has the potential for the efficient production of PS. [ABSTRACT FROM AUTHOR]

Digital technology has transformed agriculture by changing traditional production methods and resource allocation. This paper investigates how agricultural digitization affects land productivity, based on farm household data. Findings indicate that agricultural digitization significantly enhances land productivity, with results remaining robust under sensitivity and endogeneity tests. Heterogeneity analysis reveals that the positive effects are more pronounced for larger landholdings, lower levels of human capital, and older farming populations. Mechanism analysis indicates that agricultural digitization improves land productivity by optimizing resource allocation, reducing pollution, enhancing risk resilience, and facilitating the adoption of new technologies. These findings provide valuable insights for developing countries pursuing agricultural digital transformation. [ABSTRACT FROM AUTHOR]

Phosphatidylserine (PS) has significant applications in various sectors, such as the medical and food industries. However, its production relies heavily on phospholipase D (PLD), a crucial tool that is hindered by issues like poor stability and irrecoverability. Immobilization presents itself as an effective solution to overcome these limitations. In this study, magnetic graphene oxide (MGO) modified with an amino (NH2) group was synthesized and utilized for PLD immobilization. The activity of the immobilized PLD (MGO-PLD) reached 3062 U/gMGO, with a specific activity of 33.9 U/mgPLD, virtually identical to that of the free PLD. MGO-PLD was utilized to synthesize PS efficiently in a biphasic system. Under optimal conditions, the PS yield reached 18.66 g/L, with a conversion rate of 92.8% and a productivity of 3.11 g/L/h. Notably, MGO-PLD retained an impressive PS conversion rate of 77.4% even after seven repetitive usages. Moreover, MGO-PLD displayed enhanced thermal and pH resistance properties compared to free PLD, alongside augmented storage stability. After an 8-week preservation at 4 °C, its residual activity was maintained at 76.3%. This study provides a sustainable and highly efficient pathway for the biocatalytic synthesis of PS. [ABSTRACT FROM AUTHOR]

Phosphatidylserine (PS) has significant biological and nutritional effects and finds wide applications in the food, pharmaceutical, and chemical industries. To produce high-value PS efficiently, phospholipase D (PLD)-induced transphosphatidylation of low-value phosphatidylcholine (PC) with L-serine has been explored. In this research, we purified recombinant PLD from Streptomyces antibioticus SK-3 using ion exchange chromatography and gel filtration chromatography. Subsequently, we thoroughly characterized the purified enzyme and optimized the transphosphatidylation conditions to identify the most favorable settings for synthesizing PS in a biphasic system. The purified recombinant PLD displayed a robust transphosphatidylation function, facilitating efficient catalysis in the synthesis of PS. Under the optimal conditions (butyl acetate/enzyme solution 1:1, L-serine 160 mg/mL, soybean lecithin 2 mg/mL, and MgCl2 15 mM, at 50 °C for 2.5 h with shaking), we achieved a conversion rate of 91.35% and a productivity of 0.73 g/L/h. These results demonstrate the applicability of the process optimization strategy for using the candidate enzyme in the efficient synthesis of PS. Overall, this study presents a novel and scalable approach for the efficient large-scale synthesis of PS. [ABSTRACT FROM AUTHOR]

The cloud manufacturing system can provide consumers with on-demand manufacturing services, which significantly improve the utilization rate of distributed manufacturing resources and the response speed of personalized product needs. In the cloud manufacturing platform, the successful implementation of various industrial applications relies on the uploading and streaming of related field-level manufacturing data. For example, the realization of manufacturing service composition application should match the manufacturing tasks with distributed manufacturing resources according to their working state data and performance measurement data. Therefore, this paper proposes a data integration and analysis framework of a cloud manufacturing system based on cloud–edge collaboration and the Industrial Internet of Things (IIoT). A service-oriented information model is established to uniformly describe the related operational data and functional attributes of heterogeneous manufacturing resources. Secondly, a real-time transmission and integration method of high-volume operational field and sensor data based on message middleware is proposed to realize the remote monitoring of distributed manufacturing resources and efficient distribution of related data. Finally, a cloud–edge collaboration mechanism is put forward to train and update the parameters of various artificial intelligence models deployed at edge gateways. In the experiment, taking the computer numerical control (CNC) lathe as an example, the effectiveness of the proposed manufacturing resource access method is verified. Taking the fault diagnosis model of the CNC lathe as an example, the efficiency of the proposed cloud–edge collaboration mechanism for model updating is verified. [ABSTRACT FROM AUTHOR]

The study aimed to investigate the relationship between smoking, Sugar-Sweetened Beverage (SSB) consumption and tooth brushing among adolescents in China. A valid sample of 6084 middle school students from the Zhejiang province was included. Participants were questioned about smoking status, SSB consumption, tooth brushing, and oral health-related quality of life (OHRQoL). Among the participants, smoking prevalence was 1.9% and nearly half of the students consumed SSBs. The demographic factors associated with smoking were gender, place of residence, and parental level of education. There are co-variations between smoking status, SSB consumption, and tooth brushing. Logistic regression showed that smoking adolescents were more likely to brush their teeth less than once per day (OR = 1.74, p < 0.05), consume soft drinks once or more per day (OR = 2.18, p < 0.01) and have a higher score on the Child Oral Health Impact Profile (OR = 1.05, p < 0.05) after adjusting for demographic factors. The findings provide compelling evidence for governments and related stakeholders to intervene in the lifestyle of adolescents. Future studies are needed to understand the interaction effects of such behaviors, and should help to inform appropriate interventions. [ABSTRACT FROM AUTHOR]

Objectives: Deep learning methods have achieved impressive diagnostic performance in the field of radiology. The current study aimed to use deep learning methods to detect caries lesions, classify different radiographic extensions on panoramic films, and compare the classification results with those of expert dentists. Methods: A total of 1160 dental panoramic films were evaluated by three expert dentists. All caries lesions in the films were marked with circles, whose combination was defined as the reference dataset. A training and validation dataset (1071) and a test dataset (89) were then established from the reference dataset. A convolutional neural network, called nnU-Net, was applied to detect caries lesions, and DenseNet121 was applied to classify the lesions according to their depths (dentin lesions in the outer, middle, or inner third D1/2/3 of dentin). The performance of the test dataset in the trained nnU-Net and DenseNet121 models was compared with the results of six expert dentists in terms of the intersection over union (IoU), Dice coefficient, accuracy, precision, recall, negative predictive value (NPV), and F1-score metrics. Results: nnU-Net yielded caries lesion segmentation IoU and Dice coefficient values of 0.785 and 0.663, respectively, and the accuracy and recall rate of nnU-Net were 0.986 and 0.821, respectively. The results of the expert dentists and the neural network were shown to be no different in terms of accuracy, precision, recall, NPV, and F1-score. For caries depth classification, DenseNet121 showed an overall accuracy of 0.957 for D1 lesions, 0.832 for D2 lesions, and 0.863 for D3 lesions. The recall results of the D1/D2/D3 lesions were 0.765, 0.652, and 0.918, respectively. All metric values, including accuracy, precision, recall, NPV, and F1-score values, were proven to be no different from those of the experienced dentists. Conclusion: In detecting and classifying caries lesions on dental panoramic radiographs, the performance of deep learning methods was similar to that of expert dentists. The impact of applying these well-trained neural networks for disease diagnosis and treatment decision making should be explored. [ABSTRACT FROM AUTHOR]

This study aimed to develop a novel detection model for automatically assessing the real contact relationship between mandibular third molars (MM3s) and the inferior alveolar nerve (IAN) based on panoramic radiographs processed with deep learning networks, minimizing pseudo-contact interference and reducing the frequency of cone beam computed tomography (CBCT) use. A deep-learning network approach based on YOLOv4, named as MM3-IANnet, was applied to oral panoramic radiographs for the first time. The relationship between MM3s and the IAN in CBCT was considered the real contact relationship. Accuracy metrics were calculated to evaluate and compare the performance of the MM3–IANnet, dentists and a cooperative approach with dentists and the MM3–IANnet. Our results showed that in comparison with detection by dentists (AP = 76.45%) or the MM3–IANnet (AP = 83.02%), the cooperative dentist–MM3–IANnet approach yielded the highest average precision (AP = 88.06%). In conclusion, the MM3-IANnet detection model is an encouraging artificial intelligence approach that might assist dentists in detecting the real contact relationship between MM3s and IANs based on panoramic radiographs. [ABSTRACT FROM AUTHOR]

With increasingly stringent environmental regulations on emission standards, enterprises and investigators are looking for effective ways to decrease GHG emission from products. As an important method for reducing GHG emission of products, low-carbon product family design has attracted more and more attention. Existing research, related to low-carbon product family design, did not take into account remanufactured products. Nowadays, it is popular to launch remanufactured products for environmental benefit and meeting customer needs. On the one hand, the design of remanufactured products is influenced by product family design. On the other hand, the launch of remanufactured products may cannibalize the sale of new products. Thus, the design of remanufactured products should be considered together with the product family design for obtaining the maximum profit and reducing the GHG emission as soon as possible. The purpose of this paper is to present an optimization model to concurrently determine product family design, remanufactured products planning and remanufacturing parameters selection with consideration of the customer preference, the total profit of a company and the total GHG emission from production. A genetic algorithm is applied to solve the optimization problem. The proposed method can help decision-makers to simultaneously determine the design of a product family and remanufactured products with a better trade-off between profit and environmental impact. Finally, a case study is performed to demonstrate the effectiveness of the presented approach. [ABSTRACT FROM AUTHOR]