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The current limited efficiency and reliability of tidal current turbines (TCTs) have posed significant challenges in effectively harnessing tidal stream energy. To address this issue, this paper undertakes both numerical and experimental studies to explore the advantages of swept blades over conventional straight blades in terms of energy capture efficiency and cavitation resistance. It is found that both the sweep length and sweep angle of the blade can influence the power generation efficiency of the TCT. For the particular swept blade investigated in this study, the highest power coefficient is achieved when the sweep length is 0.544 m and the sweep angle is 28.88°. The research also demonstrated that the swept-blade TCT shows a higher power generation efficiency than the straight-blade TCT across a broad range of rotor speeds. To be precise, with the swept blades, the power coefficient of the TCT can be improved by 5–17%, depending on the tip speed ratio. Additionally, swept blades exhibit a superior cavitation resistance. This is evidenced by their higher cavitation numbers across all tip speed ratios in comparison to conventional straight blades.

As the main demand side of rural financial services, farmers’ cooperatives are an important part of China’s rural finance. However, due to the lack of effective collateral, farmers’ cooperatives have problems such as difficulty in obtaining loans or expensive loans, which not only hinder the high-quality development of farmers’ cooperatives, but also limit the development of regional rural finance. Digital credit as a new financing model can effectively alleviate the problems of difficult and expensive loans and has received wide attention from the government and academia. Based on this, this paper analyzes the digital credit behavior of farmers’ cooperatives in detail by applying the “6C” family model to the grounded theory, and constructs a theoretical analysis model of farmers’ cooperatives’ digital credit behavior. The findings are as follows: The motivation for the digital credit of farmers’ cooperatives is that the credit procedures are simple, the loan period is short, and the loan interest rate is low; the condition is the farmers’ cooperative reputation advantage and government policy support,; the main form is the participation of cooperatives in short- and long-cycle digital credit; and the consequence is reflected in increasing the income of cooperative members, improving the availability of cooperative loans, promoting cooperative credit building, and achieving sustainable agricultural development. Different participation motivations have different effects on the form of credit. When motivated by simple credit procedures and short loan periods, farmers’ cooperatives choose “Huinong e-loan”; when motivated by simple procedures and low loan interest rates, farmers’ cooperatives choose “Funong Loan”. Different forms of credit will produce different performances. Farmers’ cooperatives choosing “Huinong e-loan” will produce economic performance; farmers’ cooperatives choosing “Funong Loan” will produce economic performance and social performance. In order to deal with the problem of digital credit of farmers’ cooperatives, the government needs to improve the relevant policies and regulations, reduce credit risks, and establish a sound credit system to provide credit guarantees for cooperatives and farmers. Financial institutions need to improve their financial services and innovate financial products and services to meet the multi-level credit needs of cooperatives.

Nicotinamide mononucleotide (NMN) is an essential precursor of nicotinamide adenine dinucleotide (NAD+), which is widely applied in the pharmaceutical and biotech industries. The biosynthesis of NMN is currently attracting much attention because it has non-toxic reaction conditions and low amounts of isomers, whereas chemical synthesis has low yields and is not environmentally friendly. This review systematically describes the two biosynthetic pathways of NMN in detail for the first time and introduces the latest studies on NMN production through different pathways using metabolic engineering strategies. NMN accumulation can be improved by optimizing the activity of key enzymes, enhancing the supply of precursors and co-factors, inhibiting the synthesis of byproducts, and promoting product export. Finally, we also discuss the current challenges of producing NMN and possible solutions for the future.

Heme b, which is characterized by a ferrous ion and a porphyrin macrocycle, acts as a prosthetic group for many enzymes and contributes to various physiological processes. Consequently, it has wide applications in medicine, food, chemical production, and other burgeoning fields. Due to the shortcomings of chemical syntheses and bio-extraction techniques, alternative biotechnological methods have drawn increasing attention. In this review, we provide the first systematic summary of the progress in the microbial synthesis of heme b. Three different pathways are described in detail, and the metabolic engineering strategies for the biosynthesis of heme b via the protoporphyrin-dependent and coproporphyrin-dependent pathways are highlighted. The UV spectrophotometric detection of heme b is gradually being replaced by newly developed detection methods, such as HPLC and biosensors, and for the first time, this review summarizes the methods used in recent years. Finally, we discuss the future prospects, with an emphasis on the potential strategies for improving the biosynthesis of heme b and understanding the regulatory mechanisms for building efficient microbial cell factories.

Dihydromyricetin (DHY), the main flavonoid component in Ampelopsis grossedentata, has important benefits for health. The present study aimed to investigate the exact effects and possible mechanisms of DHY on diabetic cardiomyopathy (DCM). Male C57BL/6 mice and sirtuin 3 (SIRT3) knockout (SIRT3-KO) mice were injected with streptozotocin (STZ) to induce a diabetic model. Two weeks later, DHY (250 mg/kg) or carboxymethylcellulose (CMC) were administrated once daily by gavage for twelve weeks. We found that DHY alleviated fasting blood glucose (FBG) and triglyceride (TG) as well as glycosylated hemoglobin (HbA1c) levels; increased fasting insulin (FINS); improved cardiac dysfunction; ameliorated myocardial hypertrophy, fibrosis and injury; suppressed oxidative stress, inflammasome and necroptosis; but improved SIRT3 expression in STZ-induced mice. Neonatal rat cardiomyocytes were pre-treated with DHY (80 μM) with or without high glucose (HG) stimulation. The results showed that DHY attenuated cell damage but improved SIRT3 expression and inhibited oxidative stress, inflammasome and necroptosis in cardiomyocytes with high glucose stimulation. Moreover, the above protective effects of DHY on DCM were unavailable in SIRT3-KO mice, implying a promising medical potential of DHY for DCM treatment. In sum, DHY improved cardiac dysfunction; ameliorated myocardial hypertrophy, fibrosis and injury; and suppressed oxidative stress, inflammation and necroptosis via SIRT3 activation in STZ-induced diabetic mice, suggesting DHY may serve as a candidate for an agent to attenuate diabetic cardiomyopathy.

Algorithms and computing power have consistently been the two driving forces behind the development of artificial intelligence. The computational power of a platform has a significant impact on the implementation cost, performance, power consumption, and flexibility of an algorithm. Currently, AI algorithmic models are mainly trained using high-performance GPU platforms, and their inferencing can be implemented using GPU, CPU, and FPGA. On the one hand, due to its high-power consumption and extreme cost, GPU is not suitable for power and cost-sensitive application scenarios. On the other hand, because the training and inference of the neural network use different computing power platforms, the data of the neural network model needs to be transmitted on platforms with varying computing power, which affects the data processing capability of the network and affects the real-time performance and flexibility of the neural network. This paper focuses on the high computing power implementation method of the integration of convolutional neural network (CNN) training and inference in artificial intelligence and proposes to implement the process of CNN training and inference by using high-performance heterogeneous architecture (HA) devices with field programmable gate array (FPGA) as the core. Numerous repeated multiplication and accumulation operations in the process of CNN training and inference have been implemented by programmable logic (PL), which significantly improves the speed of CNN training and inference and reduces the overall power consumption, thus providing a modern implementation method for neural networks in an application field that is sensitive to power, cost, and footprint. First, based on the data stream containing the training and inference process of the CNN, this study investigates methods to merge the training and inference data streams. Secondly, high-level language was used to describe the merged data stream structure, and a high-level description was converted to a hardware register transfer level (RTL) description by the high-level synthesis tool (HLS), and the intellectual property (IP) core was generated. The PS was used for overall control, data preprocessing, and result analysis, and it was then connected to the IP core via an on-chip AXI bus interface in the HA device. Finally, the integrated implementation method was tested and validated with the Xilinx HA device, and the MNIST handwritten digit validation set was used in the tests. According to the test results, compared with using a GPU, the model trained in the HA device PL achieves the same convergence rate with only 78.04 percent training time. With a processing time of only 3.31 ms and 0.65 ms for a single frame image, an average recognition accuracy of 95.697%, and an overall power consumption of only 3.22 W @ 100 MHz, the two convolutional neural networks mentioned in this paper are suitable for deployment in lightweight domains with limited power consumption.

The improvement of grain processing capacity is crucial to the realization of grain security. Enterprises are important grain processing bodies and their productivity directly determines grain processing capacity. Chinese grain processing enterprises still have difficulties, and how to further improve grain processing capacity and the total factor productivity of grain processing enterprises may be an important aspect. We used the meta-frontier Malmquist index to measure the total factor productivity of grain enterprises as well as judge the change trend and regional gap, applying the classical regression model to test the convergence of China’s overall and regional grain enterprises’ total factor productivity. This research finds that the total factor productivity of grain processing enterprises increased by 1.18% annually during the sample period, and that of the central region rose more quickly than the other areas of China. Technical progress contributes more to enterprises’ total factor productivity, but technical efficiency may become a key factor in determining it. The difference in the growth rate of the grain processing enterprises’ total factor productivity among different ones in the eastern and western regions is gradually narrowing, while that of the central region is gradually expanding; there is an obvious technological catch-up effect between and within the regions, especially in the central area of China.

Reducing food waste is a priority for all sectors of society as it threatens national food security and the sustainability of global agriculture. Many studies on food waste have focused on a single subject, and the psychological factors of consumer waste are often overlooked. Based on evolutionary game theory, this paper introduces consumers’ normative illusion, constructs an evolutionary game model in which the government, caterers and consumers collaborate to reduce food waste, and simulates and analyses the behavioural strategies of the three stakeholders. The results show that: Firstly, food waste can be reduced under certain conditions by incentive-guided and punishment-inhibited policies. Moreover, incentive-guided policies can reduce government expenditures more than punishment-inhibited ones. Secondly, implementation of prior intervention, the resultant intervention and reducing the probability of consumers’ aversion to the intervention of caterers can optimise the government’s punishment-inhibited policy. Finally, under the punishment-inhibited policy, caterers can bear 60% of the prior intervention costs for food waste management. When caterers invest 40–60% of the prior intervention costs, both caterers and consumers can achieve the ideal state of cooperation; caterers can accept 40% of the resultant intervention cost for food waste management, and when the resultant intervention cost is less than 40%, consumers choose not to waste. Both caterers and consumers are involved in reducing food waste when the probability of consumer dissatisfaction with a caterer’s intervention is reduced to less than 40%.

The phenomenon of “separation of people and land” between urbanized farmers and rural land hinders the optimal allocation of land resources and is not conducive to the development of agricultural modernization and the implementation of rural revitalization strategies. Although the “separation of three rights” in agricultural land partially solves this problem, it also causes social inequity in the phenomenon of urbanized wealthy farmers collecting rent from poor farmers who depend on the land for a living. The Chinese government carried out a pilot reform aimed at the withdrawal of urbanized farmers from contracted land, and proposed a paid withdrawal policy, but the reform results were unsatisfactory. Based on evolutionary game theory and prospect theory, this paper constructed a two-party evolutionary game model between the government and farmers and simulated the behavioral strategies of the government and farmers in the contracted land withdrawal problem. The results show that first, the initial probability of government policy choice will affect the decision-making behavior of the government and farmers. Second, when the government’s economic compensation for farmers is higher than the farmers’ ideal expectation for land withdrawal compensation, the implementation of individualized withdrawal policy has a positive effect on farmers’ willingness to withdraw from contracted land. Third, farmers’ emotional needs for land, farmers’ ideal economic compensation, and farmers’ risk aversion all impede farmers’ withdrawal from contracted land. The government’s implementation of individualized withdrawal policy can improve farmers’ willingness to withdraw from contracted land by reducing farmers’ concerns about unstable land rights, improving the government’s security compensation, and reducing farmers’ sensitivity to profit and loss.

In recent years, the continuous development of artificial intelligence has largely been driven by algorithms and computing power. This paper mainly discusses the training and inference methods of artificial intelligence from the perspective of computing power. To address the issue of computing power, it is necessary to consider performance, cost, power consumption, flexibility, and robustness comprehensively. At present, the training of artificial intelligence models mostly are based on GPU platforms. Although GPUs offer high computing performance, their power consumption and cost are relatively high. It is not suitable to use GPUs as the implementation platform in certain application scenarios with demanding power consumption and cost. The emergence of high-performance heterogeneous architecture devices provides a new path for the integration of artificial intelligence training and inference. Typically, in Xilinx and Intel’s multi-core heterogeneous architecture, multiple high-performance processors and FPGAs are integrated into a single chip. When compared with the current separate training and inference method, heterogeneous architectures leverage a single chip to realize the integration of AI training and inference, providing a good balance of training and inference of different targets, further reducing the cost of training and implementation of AI inference and power consumption, so as to achieve the lightweight goals of computation, and to improve the flexibility and robustness of the system. In this paper, based on the LeNet-5 network structure, we first introduced the process of network training using a multi-core CPU in Xilinx’s latest multi-core heterogeneous architecture device, MPSoC. Then, the method of converting the network model into hardware logic implementation was studied, and the model parameters were transferred from the processing system of the device to the hardware accelerator structure, composed of programmable logic through the bus interface AXI provided on the chip. Finally, the integrated implementation method was tested and verified in Xilinx MPSoC. According to the test results, the recognition accuracy of this lightweight deployment scheme on MNIST dataset and CIFAR-10 dataset reached 99.5 and 75.4% respectively, while the average processing time of the single frame was only 2.2 ms. In addition, the power consumption of the network within the SoC hardware accelerator is only 1.363 W at 100 MHz.

Increasing grain production and ensuring food security are always major issues in China, which are related to the sustainable development of the nation. The sudden outbreak of COVID-19 in 2020 has brought new challenges to global food security, which highlights the “ballast stone” and “stabilizer” role of food. China’s food security must rely on domestic production. As an important production factor, the Internet is also an important channel for farmers to obtain agricultural information, which can effectively reduce the information search cost and information asymmetry. Existing studies have mainly focused on the impact of Internet use on agricultural inputs, agricultural prices, and agricultural efficiency; there are few studies on the impact of Internet use on grain production. To fill this gap, based on the micro survey data of 1242 maize farmers in 13 provinces in China, this paper employs linear regression models and PSM methods to deeply explore the impact of Internet use on farmers’ grain production. The results show that Internet use has a significant positive impact on the grain production of farmers. Compared with the farmers who do not use the Internet, Internet use increases the maize yield of farmers by 14.25%. The heterogeneity analysis further shows that the impact of Internet use on the grain production of different farmers varies significantly: the maize yield per ha after using the Internet for farmers of younger, low education level, large-scale, and living in well-developed villages had increased by 13.65%, 15.38%, 23.94%, and 10.64%, respectively, compared with the counterfactual scenario of farmers who do not use the Internet. The results of this paper have passed the robustness test.