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The Ahe Formation in the Dibei Area is a key natural gas exploration formation in the northern structural belt. Based on geological data such as formation tests and physical properties, the tight reservoirs were determined using the empirical statistics method, distribution function method, oil-bearing occurrence method, and bound fluid saturation method reasonably. The lower limit of the oil-bearing physical properties of the layer was further analyzed for the differences between the methods. The pore-throat structure of tight sandstone was characterized by high-pressure mercury intrusion data, and the classification and evaluation standard of tight sandstone in the Ahe Formation in the Dibei Area was established combining fractal theory and physical property data. The results show that the lower physical limit porosity of the tight reservoir of the Ahe Formation in the Dibei Area is 2.4%, and the lower permeability limit is 0.021 × 10−3 μm2. According to the fractal dimension characteristics of the mercury injection curve, the different structural characteristics of four types of pores (fracture, macropore, mesopore, and small pore) can be divided with the boundary values are 3,000, 1,000, and 100 nm; the tight reservoir of the Ahe Formation in Dibei Area can be classified into four categories: (a) type I reservoir (Ф > 7%), mainly composed of fracture and macropore; (b) type II reservoir (4% < Ф < 7%), mainly made up of macropore and mesopore; (c) type III reservoir (2% < Ф < 4%), mainly formed by mesopore; and (d) type IV reservoir (Ф < 2%) dominated by small pore, followed by mesopore.

The transformation of production mode leads to the need to strictly ensure the order in-time delivery. The global control ability of manufacturing workshops has become a necessary capability for enterprises. The real-time accurate Order Remaining Completion Time (ORCT) prediction allows managers to master the production schedule fluctuation, which provides criteria for workshop management. Due to the widespread deployment of Internet of Things (IoT) devices in the workshop, the difficulty of systematic processing and analysis of multi-dimensional heterogeneous data has been the main pain point of ORCT prediction. To tackle these problems, a Manufacturing Big Data (MBD)-driven ORCT prediction method based on Salp Swarm Algorithm (SSA) and Bidirectional Long Short-Term Memory (BiLSTM) is proposed. An attribute selection algorithm combined with max-relevance and min-redundancy and regularisation is used to mine key data from MBD. An SSA-BiLSTM model is proposed to achieve efficient and accurate ORCT prediction. The prediction model is supported by BiLSTM, and the hyperparameters are optimised by SSA. Finally, a case study about ORCT prediction in the IoT-enabled manufacturing workshop is presented. The result verifies that the proposed ORCT prediction method has obvious advantages over the other three traditional methods in accuracy and efficiency. [ABSTRACT FROM AUTHOR]

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]

Many organizations are producing or collecting private data in fields such as medical research and government regulation. Due to current privacy protection, laws and regulations, commercial competition, and other issues, these institutions cannot directly share their data. Collaborative analysis of private data from multiple institutions will benefit each institution and create profits together. Therefore, we propose a Yannakakis-based multiparty outsourcing collaboration analysis scheme. It enables organizations to collaboratively analyze private data from multiple organizations according to their needs while ensuring that private data are not leaked to each other. Our scheme is based on the improved Yannakakis algorithm to build a series of query components, such as Semi-join, Join, Order-by, etc. We also optimized the join operation. By confusing the input tuples and protecting their authenticity through annotations, the join operation can be directly joined through the hash value without disclosing the join results. Through this series of configurations, we can execute a query with O (IN + OUT) runtime and communication, where IN is the total number of tuples in the input relationship, and OUT is the output size. We have carried out a series of comparative experiments, and the results show that our system is 1.3X–7.4X faster than the baseline. [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]

In make-to-order manufacturing workshops, accurate prediction value of production progress (PP) is a significant reference index for dynamic optimisation of production process and on-time delivery of production orders. The implementation of big data and Industrial Internet of Things (IIoT) in manufacturing workshops makes it possible to obtain large amounts of production data which can affect PP. However, the particularities of massive historical order data are not fully excavated and the amount of target order data is insufficient to support the training of high-precision prediction model, which will result in bad training approximation and generalisation. To overcome these shortcomings, a PP prediction approach consisting of two models with transfer learning (TL) is proposed. TL can avoid the training of PP prediction model from scratch every time. Consequently, computational efficiency can be greatly improved. A convolutional neural network (CNN) model with TL is devised to excavate the comprehensive features from historical and current orders. Additionally, a long short-term memory network (LSTM) model with TL is constructed to fit the nonlinear relation of the features provided by CNN-TL model for PP prediction. In order to validate the performance of the proposed PP prediction approach, comparative experiments of eight algorithms are conducted in an IIoT-enabled manufacturing workshop. [ABSTRACT FROM AUTHOR]