Have government environmental auditing contributed to the green transformation of Chinese cities?

Faced with growing ecological problems, governments around the world are increasingly focusing on improving ecology and the environment. The topic of urban green transformation has attracted a great deal of research. However, not much of it has focused on the effectiveness of government environmental auditing, especially from the perspective of its role in sustainable governance. This article takes 285 cities in China from 2009 to 2020 as the research scale. It innovatively measures government environmental audits with dual indicators and uses System Gaussian Mixture Model (SGMM) to estimate that government environmental audits significantly promote urban green transformation, and the impact of ''whether to implement government environmental auditing'' is greater than the ''intensity of government environmental auditing''. The results show that government environmental audit intensity has a stronger impact on urban green transformation in eastern cities. In contrast, environmental audit coverage has a stronger impact in western cities. Moreover, the effect of government environmental auditing on green transformation is more significant in small and medium-sized cities and key environmental protection cities than in large cities and non-key environmental protection cities, respectively. Government environmental auditing could facilitate urban green transformation by restraining local government behavior, forcing green technology innovation, and promoting industrial structural upgrading. In addition, the intensity of government environmental auditing can better act on green transformation through the fore-mentioned mechanisms. It can play a crucial role in green technology innovation

Dietary Supplementation with Rutin Alters Meat Quality, Fatty Acid Profile, Antioxidant Capacity, and Expression Levels of Genes Associated with Lipid Metabolism in Breast Muscle of Qingyuan Partridge Chickens

Consumer demand for tasty and quality meat has been quickly increasing. This study investigated how dietary supplemented rutin affects meat quality, muscle fatty acid profile, and antioxidant capacity in the Chinese indigenous Qingyuan partridge chicken. A cohort of 180 healthy 119-day-old chickens was subjected to a randomized assignment into three groups, identified as the control, R200, and R400 groups, with respective supplementation of 0, 200, and 400 mg/kg of rutin. The results revealed insignificance in growth performance, namely, average daily gain, average daily feed intake, and feed-to-gain ratio, across the various treatment groups (p > 0.05). Nevertheless, dietary rutin supplementation increased (p p p p p n-3), total polyunsaturated fatty acids (PUFAs), n-3 PUFAs, decanoic acid (C10:0), the activity of Δ5 + Δ6 (22:6 (n − 3)/18:3 (n − 3)), and the ratio of PUFA/SFA in breast muscle but decreased (p n-6/n-3 PUFAs, and the activity of Δ9 (16:1 (n − 7)/16:0). Rutin treatment also reduced (p p AMPKα and upregulated the expression of PPARG, FADS1, FAS, ELOVL7, NRF2, and CAT in breast muscle (p n-3 PUFAs, and the antioxidant capacity of Qingyuan partridge chickens

Accelerated Computation of Free Energy Profile at Ab Initio QM/MM Accuracy via a Semi-Empirical Reference-Potential. III. Gaussian Smoothing on Density-of-States

Calculations of free energy profile, aka potential of mean force (PMF), along a chosen collective variable (CV) are now routinely applied to the studies of chemical processes, such as enzymatic reactions and chemical reactions in condensed phases. However, if the ab initio QM/MM level of accuracy is required for the PMF, it can be formidably expensive even with the most advanced enhanced sampling methods, such as umbrella sampling. To ameliorate this difficulty, we developed a novel method for the computation of free energy profile based on the reference-potential method recently, in which a low-level reference Hamiltonian is employed for phase space sampling and the free energy profile can be corrected to the level of interest (the target Hamiltonian) by energy reweighting in a nonparametric way. However, when the reference Hamiltonian is very different from the target Hamiltonian, the calculated ensemble averages, including the PMF, often suffer from numerical instability, which mainly comes from the overestimation of the density-of-states (DoS) in the low-energy region. Stochastic samplings of these low-energy configurations are rare events. If a low-energy configuration has been sampled with a small sample size N, the probability of visiting this energy region is ~ 1/N (shall be exactly 1/N for a single ensemble), which can be orders-of-magnitude larger than the actual DoS. In this work, an assumption of Gaussian distribution is applied to the DoS in each CV bin, and the weight of each configuration is rescaled according to the accumulated DoS. The results show that this smoothing process can remarkably reduce the ruggedness of the PMF and increase the reliability of the reference-potential method

Affordable Ab Initio Path Integral for Thermodynamic Properties via Molecular Dynamics Simulations Using Semiempirical Reference Potential

Path integral molecular dynamics (PIMD) is becoming a routinely applied method for the incorporation of the nuclear quantum effect in computer simulations. However, direct PIMD simulations at an ab initio level of theory are formidably expensive. Using the protonated 1,8-bis(dimethylamino)naphthalene molecule as an example, we show in this work that the computational expense for the intra-molecular proton transfer between the two nitrogen atoms can be remarkably reduced by implementing the idea of reference-potential methods. The simulation time can be easily extended to a scale of nanosecond while maintaining the accuracy on an ab initio level of theory for thermodynamic properties. In addition, the post-processing can be carried out in parallel on massive computer nodes. A 545-fold reduction in the total CPU time can be achieved in this way as compared to a direct PIMD simulation at the same ab initio level of theory

The Training of Machine Learning Potentials for Reactive Systems: A Colab Tutorial on Basic Models

In the last several years, there has been a surge in the development of machine learning potential (MLP) models for describing molecular systems. We are interested in a particular area of this field — the training of system-specific MLPs for reactive systems — with the goal of using these MLPs to accelerate free energy simulations of chemical and enzyme reactions. To help new members in our labs become familiar with the basic techniques, we have put together a self-guided Colab tutorial (https://cc-ats.github.io/mlp_tutorial/), which we expect to be also useful to other young researchers in the community. Our tutorial begins with the introduction of simple fitting neural network (FNN) and kernel-based (using Gaussian Process Regression, GPR) models by fitting the two-dimensional Müller-Brown potential. Subsequently, two simple descriptors are presented for extracting features of molecular systems: symmetry functions (including the ANI variant) and embedding neural networks (such as DeepPot-SE). Lastly, these features will be fed into FNN and GPR models to reproduce the energy/force of molecular configurations of the Claisen rearrangement