The Impact of Urbanization on Farmland Productivity: Implications for China’s Requisition–Compensation Balance of Farmland Policy

The rapid growth of China’s economy since the reform in 1978 should be largely attributed to urbanization. Nonetheless, in terms of farmland productivity, urbanization may lead to perverse incentives and thus threaten food security. On the one hand, the requisition−compensation balance of farmland (RCBF) policy could reduce farmland productivity because of a “superior occupation and inferior compensation”; on the other hand, urbanization promotes the transfer of the younger labor force and thus reduces the productivity of the agricultural labor force. To investigate the undesirable effects, based on some stylized facts, this study selects 29,415 county-level samples in a Chinese county from 2000−2014 to construct an empirical model. With a new stochastic frontier analysis method that eliminates the classical econometric issues of endogeneity and heterogeneity, the empirical results show that there is a U-shaped relationship between the farmland use efficiency (productivity) and urbanization rate, indicating that only when the urbanization rate is relatively low would urbanization decrease the farmland use efficiency; in contrast, when the urbanization rate is relatively high, technical progress would obviously be accompanied by urbanization, and thus, the undesirable effects are fully offset. Furthermore, the U-shaped relationship is robust after considering the endogeneity of the urbanization rate and total-factor farmland use efficiency. With these findings, recommendations to implement sustainable management and conservation policies regarding farmland resources are made

Mechanism testing of the empowerment of green transformation and upgrading of industry by the digital economy in China

With the continuous advancement of industrialization, global environmental problems are becoming increasingly severe. Maintaining economic growth while improving the environment has been an important issue for many countries, especially developing countries. As industry is a major source of environmental pollution, industrial green transformation and upgrading have become particularly important. In the era of the digital economy (DE), there is a new path for industrial green transformation and upgrading. Based on provincial data on industry from 2008 to 2021, a difference-in-differences (DID) model was constructed to analyze the environmental and economic benefits. New pathways for trade-offs between environmental improvement and economic growth in China are presented. In addition, new ideas are concerning global environmental issues and economic issues in the DE are presented. The present study indicates that the DE has reduced the intensity of pollution emissions and elevated total factor productivity (TFP), which has helped to promote industrial green transformation and upgrading. Further mechanism testing showed that the DE has promoted industrial green transformation and upgrading by improving the utilization of energy and resources and promoting technological innovation. Considering the utilization of energy and resources, the DE has decreased China’s total industrial consumption of energy and coal, reduced industrial water consumption, and reduced the share of coal consumption and increased the share of clean energy consumption in China’s total industrial energy consumption; these effects have optimized the efficiency and structure of China’s energy utilization to contribute to the green transformation and upgrading of industry. Regarding technological innovation, the development of the DE has increased industrial innovation output and R&D input. Furthermore, it has promoted innovation with respect to green processes, accelerating technological innovation, and realized industrial green transformation and upgrading

DiGAN breakthrough: advancing diabetic data analysis with innovative GAN-based imbalance correction techniques

In the rapidly evolving field of medical diagnostics, the challenge of imbalanced datasets, particularly in diabetes classification, calls for innovative solutions. The study introduces DiGAN, a groundbreaking approach that leverages the power of Generative Adversarial Networks (GAN) to revolutionize diabetes data analysis. Marking a significant departure from traditional methods, DiGAN applies GANs, typically seen in image processing, to the realm of diabetes data. This novel application is complemented by integrating the unsupervised Laplacian Score for sophisticated feature selection. The pioneering approach not only surpasses the limitations of existing techniques but also sets a new benchmark in classification accuracy with a 90% weighted F1-score, achieving a remarkable improvement of over 20% compared to conventional methods. Additionally, DiGAN demonstrates superior performance over popular SMOTE-based methods in handling extremely imbalanced datasets. This research, focusing on the integrated use of Laplacian Score, GAN, and Random Forest, stands at the forefront of diabetic classification, offering a uniquely effective and innovative solution to the long-standing data imbalance issue in medical diagnostics

Fixed and mobile energy storage coordination optimization method for enhancing photovoltaic integration capacity considering voltage offset

Mobile energy storage has the characteristics of strong flexibility, wide application, etc., with fixed energy storage can effectively deal with the future large-scale photovoltaic as well as electric vehicles and other fluctuating load access to the grid resulting in the imbalance of supply and demand. To this end, this paper proposes a coordinated two-layer optimization strategy for fixed and mobile energy storage that takes into account voltage offsets, in the context of improving the demand for local PV consumption. Among them, the upper layer optimization model takes into account the minimum operating cost of fixed and mobile energy storage, and the lower layer optimization model minimizes the voltage offset through the 24-h optimal scheduling of fixed and mobile energy storage in order to improve the in-situ PV consumption capacity. In addition, considering the multidimensional nonlinear characteristics of the model, the interaction force of particles in the Universe is introduced, and the hybrid particle swarm-gravitational search algorithm (PSO-GSA) is proposed to solve the model, which is a combination of the individual optimization of the particle swarm algorithm and the local search capability of the gravitational search algorithm, which improves the algorithm’s optimization accuracy. Finally, the feasibility and effectiveness of the proposed model and method are verified by simulation analysis with IEEE 33 nodes

Probing electronic-vibrational dynamics of N2+ induced by strong-field ionization

The coupled electronic-vibrational dynamics of nitrogen ions induced by strong-field ionization is investigated theoretically to corroborate the recent transient X-ray K-edge absorption experiment [PRL 129, 123002 (2022)], where the population distribution of three electronic states in air lasing of N2+ was determined for the first time. By extending the ionization-coupling model to include the transient absorption, we successfully reproduce the time-resolved X-ray absorption spectra of nitrogen ions observed in the experiment. By identifying the contributions from different electronic states, the study provides different interpretation revealing the significant role of excited state A arising from the strong coupling between vibrational states in strong laser fields. It indicates that the electronic population inversion occurs at least for certain alignment of nitrogen molecules. The theory helps uncovering new features of absorption from forbidden transitions during ionization and confirming that the vibration coherence at each electronic channel induces the modulation of absorbance after strong field ionization. A new scheme is proposed to determine the population transfer at different probing geometry to avoid the spectral overlap. This work offers valuable insights into the intricate interplay between electronic and vibrational dynamics and helps to resolve the debate on nitrogen air lasing

Simulation and Analysis of Fluid-Solid Coupling of Wave Impact Sandcastle Based on COMSOL

It is a complex problem to study the interaction between sand castle and flowing water, which needs to consider the complexity of seawater flow and the stress of sand castle structure. The authors use the fluid-solid coupling model to establish the connection between the fluid field and the structural mechanical field, and use the finite element analysis to complete the simulation modeling of the transient process of wave impact and sandcastle foundation deformation. This paper analyzes the stress and the first principal strain of the sand castle foundation in the direction of flow velocity when the sand castle foundation is hit by waves, as a method to judge the strength of the sand castle.The best shape: the boundary value of sand castle collapse caused by strain have been determined, so as to obtain the maximum stress that a sand castle foundation can bear before collapse, which makes it possible to use the fatigue strength calculation theory of sand castle solid to carry out the quantitative calculation of sand castle durability. At the same time, the impact of waves is abstracted as wave motion equation. Finally, the finite element analysis technology is adopted to calculate the main strain of sandcastles of different shapes under the impact of the same wave, and through the comparison of the main strain, the authors get the sandcastle shape with the strongest anti-wave impact ability, which is the eccentric circular platform body.Affected by rain: the authors considered the effect of rainwater infiltration on the sandcastle's stress, and simplified the process of rain as a continuous and uniform infiltration of rain into the sandcastle's surface. The rain changes the gravity of the sand on the castle's surface. Simulation analysis is adopted to calculate the surface stress of sand castle with different degree of water seepage and different geometry. By comparison, it has been found that the smooth cone is more able to withstand the infiltration of rain without collapse.

Reinforcement learning based adaptive handover in ultra-dense cellular networks with small cells

The dense deployment of the small base station (BS) in fifth-generation commination system can satisfy the user demand on high data rate transmission. On the other hand, such a scenario also increases the complexity of mobility management. In this paper, we developed a Q-learning framework exploiting user radio condition, that is, reference signal receiving power (RSRP), signal to inference and noise ratio (SINR) and transmission distance to learn the optimal policy for handover triggering. The objective of the proposed approach is to increase the mobility robustness of user in ultra-dense networks (UDNs) by minimizing redundant handover and handover failure ratio. Simulation results show that our proposed triggering mechanism efficiency suppresses ping-pong handover effect while maintaining handover failure at an acceptable level. Besides, the proposed triggering mechanism can trigger the handover process directly without HOM and TTT. The respond speed of triggering mechanism can thus be increased