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Abstract Understanding water saturation levels in tight gas carbonate reservoirs is vital for optimizing hydrocarbon production and mitigating challenges such as reduced permeability due to water saturation (Sw) and pore throat blockages, given its critical role in managing capillary pressure in water drive mechanisms reservoirs. Traditional sediment characterization methods such as core analysis, are often costly, invasive, and lack comprehensive spatial information. In recent years, several classical machine learning models have been developed to address these shortcomings. Traditional machine learning methods utilized in reservoir characterization encounter various challenges, including the ability to capture intricate relationships, potential overfitting, and handling extensive, multi-dimensional datasets. Moreover, these methods often face difficulties in dealing with temporal dependencies and subtle patterns within geological formations, particularly evident in heterogeneous carbonate reservoirs. Consequently, despite technological advancements, enhancing the reliability, interpretability, and applicability of predictive models remains imperative for effectively characterizing tight gas carbonate reservoirs. This study employs a novel data-driven strategy to prediction of water saturation in tight gas reservoir powered by three recurrent neural network type deep/shallow learning algorithms—Gated Recurrent Unit (GRU), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Support Vector Machine (SVM), K-nearest neighbor (KNN) and Decision tree (DT)—customized to accurately forecast sequential sedimentary structure data. These models, optimized using Adam's optimizer algorithm, demonstrated impressive performance in predicting water saturation levels using conventional petrophysical data. Particularly, the GRU model stood out, achieving remarkable accuracy (an R-squared value of 0.9973) with minimal errors (RMSE of 0.0198) compared to LSTM, RNN, SVM, KNN and, DT algorithms, thus showcasing its proficiency in processing extensive datasets and effectively identifying patterns. By achieving unprecedented accuracy levels, this study not only enhances the understanding of sediment properties and fluid saturation dynamics but also offers practical implications for reservoir management and hydrocarbon exploration in complex geological settings. These insights pave the way for more reliable and efficient decision-making processes, thereby advancing the forefront of reservoir engineering and petroleum geoscience.

A nearshore terminal fan is a special water system formed in arid environments. The characterisation of its thin-channel sand bodies has long been a challenge restricting oil and gas exploration. This study takes the Suning area of the Raoyang Sag as an example and uses the principles of seismic sedimentology to conduct seismic sedimentary research on the nearshore terminal fan of the first member of the Palaeogene Shahejie Formation (Es1) based on three-dimensional seismic, logging, and core analysis. Seven fourth-order sequences (SQV7) were identified within Es1, deposited by a fluvial river system terminating at the contracting bank of a lake. Prograding terminal fan sedimentary facies on a gentle slope zone were observed in the root mean square seismic attributes after spectral decomposition. We have successfully resolved the sandstone within the studied terminal fan system using a 90° phase conversion of the seismic data and red-green-blue (RGB) fusion of the various seismic attributes. The upper subsegment of the Shahejie Formation developed extensive nearshore terminal fan sedimentation, and the seismic sedimentological response characteristics were mainly channel-like and strip-shaped geomorphic systems deposited on gentle slope zones, indicating distributary channels and distal basin sedimentation. This study enriches our understanding of nearshore fans and provides ideas for predicting favourable sand bodies in this type of sedimentary facies.

Due to the recent implementation of carbon limits and certain government delays in promulgating these limits, some firms are faced with incomplete information regarding their carbon limits, which hinders their ability to make environmental investments based on accurate information. This study aims to investigate firms’ optimal decisions regarding carbon limit estimations and environmental investment amounts when faced with incomplete information on carbon limits. In addition, it explores the impact of such uncertain regulations on firms’ environmental investments. These issues are progressively addressed through the establishment and resolution of a two-stage model. The research indicates that it is not always appropriate for firms to estimate carbon limits as the minimum value. While this strategy helps them better adapt to strict emission regulations, it involves substantial financial costs. The level of environmental investment is negatively influenced by estimated carbon limits, owing to the escalating marginal cost nature associated with reducing carbon emissions. However, the impact of carbon limit uncertainty on environmental investment is not constant. It can be positive, negative, or neutral depending on the firm’s trade-off between decision robustness and profit objectives. These findings provide insights into how firms in countries that have recently implemented carbon limits and delayed the disclosure of carbon limits can make environmental investments.

The paper investigates the issue of post-impact fatigue damage of the 2060 aluminium–lithium alloy, a representative material of third-generation aluminium–lithium alloys extensively employed in the fuselage of C919 aircraft due to its notable attributes of high specific stiffness and strength. Initial impact damage is identified utilizing a residual stress–strain field obtained from a quasi-static simulation. Then, the continuum damage mechanics approach is applied to predict the fatigue life of the impacted 2060 aluminium–lithium alloy plates accounting for the combined effects of residual stress, plastic damage, and fatigue loading. A comparative analysis between calculated and experimental results is conducted to validate the efficacy of the proposed methodology.

The introduction of the concepts of shear elastic strain (ε−) and tensile elastic strain (ε+) is a catalyst for new horizons of research into supply chain elasticity. Functional formulas encompassing the metrics of ε− and ε+, their critical point, maximum strain value, and similar parameters are established through rigorous mathematical derivations. The supply chain elasticity of agricultural commodities, including grains, apples, and wheat, are assessed by utilizing the derived formulas. The results show that the metrics of supply chain elastic strain serve as direct metrics of measuring the supply chain’s anti-interference capability, and they also facilitate an objective assessment of the supply chain’s safety and stability. The formula is succinctly derived, and it yields objective outcomes with general applicability, particularly suited for research and application for supply chain elasticity.

Abstract Treating severe COVID-19 patients and controlling the spread of SARS-CoV-2 are concurrently important in mitigating the pandemic. Classically, antiviral drugs are primarily developed for treating hospitalized COVID-19 patients with severe diseases to reduce morbidity and/or mortality, which have limited effects on limiting pandemic spread. In this study, we simulated the expanded applications of oral antiviral drugs such as molnupiravir to mitigate the pandemic by treating nonhospitalized COVID-19 cases. We developed a compartmental mathematical model to simulate the effects of molnupiravir treatment assuming various scenarios in the Omicron variant dominated settings in Denmark, the United Kingdom and Germany. We found that treating nonhospitalized cases can limit Omicron spread. This indirectly reduces the burden of hospitalization and patient death. The effectiveness of this approach depends on the intrinsic nature of the antiviral drug and the strategies of implementation. Hypothetically, if resuming pre-pandemic social contact pattern, extensive application of molnupiravir treatment would dramatically (but not completely) mitigate the COVID-19 burden, and thus there remains lifetime cost of living with the virus.

Presently, the environmental pollution problem, especially that of the small and medium-sized enterprises, has become a bottleneck restricting the high-quality development of China’s economy. It is imperative to accelerate the construction of a collaborative governance system with multiple subject participation. The behavior modes of Environmental Non-governmental Organizations (ENGOs), a key player in environmental care, in collaborating with core enterprises to control pollution from small and medium-sized suppliers should be explored. Therefore, we constructed a collaborative governance system consisting of ENGOs, focal firms and small and medium-sized suppliers. Then a two-stage game model was established to analyze the strategies of the governance system. Specifically, we studied the collaborative green cooperation strategy and antagonistic pressure supervision strategy between ENGOs and focal firms, analyzed factors influencing the strategic choices of each subject, and gave the advantages of the cooperation strategies through further comparisons. The results showed that: Under the cooperation strategy, ENGOs provided knowledge of environmental protection to the focal firms, then both the audit efforts of focal firms and the environmental protection efforts of small and medium-sized suppliers were effectively improved. Since ENGOs could not fully obtain the pollution information of small and medium-sized suppliers or accurately trace it from their downstream focal firms, it was difficult to drive the supply chain’s endogenous governance by this external monitoring of ENGOs, making the effect of pressure supervision strategy limited. The effectiveness of green cooperation strategy was positively correlated with the knowledge absorption capacity of focal firms, the unit product revenue, and the focal firms' violation penalties for small and medium-sized suppliers. When ENGOs' violation penalties for small and medium-sized suppliers were higher, or the reputation loss of focal firms was higher, more unfavorable conditions of green cooperation strategy could be achieved. Accordingly, ENGOs should choose to cooperate with focal firms with strong knowledge absorption ability and high profit per product; focal firms should learn environmental protection knowledge to improve the screening standards and review capabilities, and promote the achievement of green cooperation strategy conditions by strengthening active information disclosure; the government should promote the collaboration between ENGOs and focal firms by issuing environmental guidelines.

Recently, core enterprises are compelled to invest in the environmental responsibility of upstream suppliers, since their brand image and market share will be significantly impacted when suppliers fail to comply with environmental regulations. However, the effectiveness of the core enterprises’ investment efficiency is limited by insufficient environmental knowledge. Although cooperating with the knowledge-advantaged environmental non-governmental organization (ENGO) may be an effective solution, we observe from the recent examples that not all core enterprises are willing to cooperate in a competitive market. Within this context, we develop a theoretical model to investigate whether competitive core enterprises can benefit from cooperating with the knowledge-advantaged ENGO to motivate the supplier’s environmental responsibility. Our results show that cooperation incentivizes core enterprises’ investments in the supplier’s environmental responsibility, which in turn motivates better environmental responsibility of the supplier. However, we illustrate that cooperation is not necessarily the optimal strategy for core enterprises in the duopoly scenario. Specifically, as the knowledge absorption ability increases, a core enterprise may free-ride on another’s investment, shifting its strategic preference from cooperating to not cooperating. In addition, competition may stimulate the core enterprises’ investments and cooperation motivation, thus improving the total environmental effort and supply chain members’ profits. Our findings provide insights into the competitive core enterprises’ strategic choice regarding suppliers’ environmental responsibility management.

Compared with conventional reservoirs, shale gas reservoirs usually have no natural productivity or lower productivity, and the rate of production decline is high in the later stage. The production of shale gas can be effectively improved by designing reasonably or fracturing. Therefore, it is critical for shale gas reservoir to study how to design proper parameters to make it effectively developed. Based on data of block-A region of the Zhejiang gas field, considering the contribution of rock compression to the production, the productivity formula of horizontal well at different seepage stages is deduced. Data from block-A are verified by orthogonal experiment, including gas reservoir parameters and engineering parameters. The results show that the order of reservoir parameters that affect the development of shale gas is as follows: Langmuir pressure, diffusion coefficient, cross flow coefficient, and Langmuir volume; the order of engineering parameters that affect the development of shale gas is as follows: number of fractures, horizontal section length, production pressure, fractures length, row spacing, and well spacing. The research results have been applied to the Zhejiang gas field. The initial rate of decline after adjustment is reduced by 26.08% and production increases by 17.06% after stabilization compared to wells without adjustment parameters. The research has important reference significance for the efficient development of similar gas reservoirs.

Background and purpose: Diagnosis of dementia with Lewy bodies (DLB) is highly challenging, primarily due to a lack of valid and reliable diagnostic tools. To date, there is no report of qualitative signature for the diagnosis of DLB. We aimed to develop a blood-based qualitative signature for differentiating DLB patients from healthy controls.Methods: The GSE120584 dataset was downloaded from the public database Gene Expression Omnibus (GEO). We combined multiple methods to select features based on the within-sample relative expression orderings (REOs) of microRNA (miRNA) pairs. Specifically, we first quickly selected miRNA pairs related to DLB by identifying reversal stable miRNA pairs. Then, an optimal miRNA pair subset was extracted by random forest (RF) and support vector machine-recursive feature elimination (SVM-RFE) methods. Furthermore, we applied logistic regression (LR) and SVM to build several prediction models. The model performance was assessed using the receiver operating characteristic curve (ROC) analysis. Lastly, we conducted bioinformatics analyses to explore the molecular mechanisms of the discovered miRNAs.Results: A qualitative signature consisted of 17 miRNA pairs and two clinical factors was identified for discriminating DLB patients from healthy controls. The signature is robust against experimental batch effects and applicable at the individual levels. The accuracies of the-signature-based models on the test set are 82.61 and 79.35%, respectively, indicating that the signature has acceptable discrimination performance. Moreover, bioinformatics analyses revealed that predicted target genes were enriched in 11 Go terms and 2 KEGG pathways. Moreover, five potential hub genes were found for DLB, including SRF, MAPK1, YWHAE, RPS6KA3, and KDM7A.Conclusion: This study provided a blood-based qualitative signature with the potential to be used as an effective tool to improve the accuracy of DLB diagnosis.

Whether third-degree price discrimination improves or damages social welfare has always been a hot topic for scholars of economics. At present, research studies on the impact of third-degree price discrimination on welfare have not been carried out under asymmetric price competition. To this end, we studied this problem. In the research process, we divided consumers into two market segments by setting different travel costs based on the Hotelling model; at the same time, we considered three scenarios in which both firms engage in uniform pricing, both engage in price discrimination, and price discrimination vs. uniform pricing, and some intriguing findings and conclusions that differ from the previous studies were obtained through game analysis: (1) compared with two symmetric price games, the total output effect of each firm is unchanged, but the total social welfare is reduced, and as the size of the strong market increases, the reduction effect of total social welfare increases first and then decreases; (2) from local social welfare analysis, although the output of the firm adopting price discrimination remains unchanged, it can produce more producer surplus, consumer surplus and social welfare third-degree; (3) while the firm that uses uniform pricing is at a disadvantage in competition, the local social welfare created by it is decreased, and the reduction effect of social welfare will increase first and then decrease as the increase of the size of the strong market occurs. These conclusions reveal in an oligopoly market why enterprises always choose price discrimination and the government acquiesces in the existence of price discrimination.

Under “the Belt and Road” initiative, China promoted cooperation between domestic enterprises and international ports vigorously, which brought back fruitful results, while the rational selection of strategic pivots ports and the optimization of the layout of the port network are important guarantees to a further promotion to the economic development of “the Belt and Road” ports and give full play to the driving and radiation role of strategic pivots ports. On the basis of constructing a network of 155 ports in the world, according to the number of ports crossed by the shortest path and betweenness centrality in the network, this paper uses K-Medoids clustering algorithm to train the strategic importance of ports and verifies the reliability of the analysis results. On this basis, the joint coverage analysis of strategically important ports is carried out, 17 ports are identified as strategic pivots ports of the global port network, and, finally, based on the two attributes of “the Belt and Road” and “Chinese enterprise participation” of strategic pivots ports, the leading role of strategic pivots ports in geographical location, path coverage, development potential, cooperation stability, and control is analyzed, and instructive suggestions are put forward.

With the growth of online commerce, companies have created virtual communities (VCs) where users can create posts and reply to posts about the company’s products. VCs can be represented as networks, with users as nodes and relationships between users as edges. Information propagates through edges. In VC studies, it is important to know how the number of topics concerning the product grows over time and what network features make a user more influential than others in the information-spreading process. The existing literature has not provided a quantitative method with which to determine key points during the topic emergence process. Also, few researchers have considered the link between multilayer physical features and the nodes’ spreading influence. In this paper, we present two new ideas to enrich network theory as applied to VCs: a novel application of an adjusted coefficient of determination to topic growth and an adjustment to the Jaccard coefficient to measure the connection between two users. A two-layer network model was first used to study the spread of topics through a VC. A random forest method was then applied to rank various factors that might determine an individual user’s importance in topic spreading through a VC. Our research provides insightful ways for enterprises to mine information from VCs.

Substantial changes took place in the role of consumers in the supply chain with the development of practices. They became creators from consumers of product values. More and more consumers express their consumption experiences by posting in network community. Consumer community network is an important place for feedback of product experiences and facilitating product innovation in future. Manufacturers can promote improvement and innovation of products by exploring effective information on the consumer community network, thus improving the experience level of consumers. Therefore, how to explore information in topics (posts) and their relationships becomes very important. Is it possible to describe the structure of consumer community network by complex network and explore information about products and consumers? There is important and positive significance to study the collaborative innovation in the supply chain in which consumers participate. In this paper, the consumer community network was constructed by Boolean retrieve programming and discussed in the methodology and empirical way based on the community data of Huawei P10/P10 Plus. In methodology, interaction difference and uniformity within consumer community were explored by the density of isolated nodes and generalized variance of degree of network. In empirical studies, community network users were divided into ordinary user group, intermediary user group, and enterprise user group according to empirical data, and corresponding interaction networks were constructed. A contrastive analysis on the interaction of these three groups was carried out by combining the existing properties and innovative properties. Topics in each network were put in the order according to significance. Research conclusions have important significance to enrich the network analysis methods, explore the effective information in consumer community network, facilitate product improvement and innovations, and improve the experience level of consumers.