Executives Political Connection and Over-investment in New Energy Companies：Empirical Evidence from China\u27s Capital Market

Establishing links between business and government is a common phenomenon in the world. Using data of new energy companies listed in Shenzhen and Shanghai Stock Exchange，the paper examines the relationship between political connection and firms’ over-investment. We find that executives political connection is a significant promotion of firms’ over-investment; the political connection is divided into the central- and local-level, and further tests find that political connections with different levels have no significant impact on firms’ over-investment. Our findings provide an empirical evidence for strengthening the Governance Reform of the government

Constructing the Lyapunov Function through Solving Positive Dimensional Polynomial System

We propose an approach for constructing Lyapunov function in quadratic form of a differential system. First, positive polynomial system is obtained via the local property of the Lyapunov function as well as its derivative. Then, the positive polynomial system is converted into an equation system by adding some variables. Finally, numerical technique is applied to solve the equation system. Some experiments show the efficiency of our new algorithm

Schiff base functionalized silica aerogels for enhanced removal of Pb (II) and Cu (II): Performances, DFT calculations and LCA analysis

Schiff base silica aerogels (SCA-X) were synthesized using amino-containing organosilanes and salicylic aldehyde as functional monomers with ethyl orthosilicate hydrolysis condensation as carrier. The influence of SCA-X on the adsorption of Pb (II) and Cu (II) under different adsorption conditions was evaluated, including the effect of solution pH, isotherm, kinetics, thermodynamics and adsorption mechanism. The batch adsorption experiments showed that SCA2 had the optimum adsorption capacity for Pb (II) (357.1 mg/g) and Cu (II) (243.9 mg/g), leading to adsorption equilibrium within 120 min and 360 min, respectively. After six adsorption–desorption cycles, SCA2 still possessed satisfactory adsorption for Pb (II) and Cu (II), demonstrating the reusability of the SCA2 adsorbent material. Kinetic studies indicated that the adsorption process could be described by a pseudo- second-order kinetic model, adsorption isotherms were in accordance with the Langmuir model, indicative of monomolecular layer adsorption. Thermodynamics evaluation revealed the nature of the adsorption process was an endothermic spontaneous process. XPS analysis combined with DFT calculations confirmed that the inter- action mechanism between SCA2 and Pb (II) occurred through the coordination between the nitrogen atom donor in the Schiff base and the oxygen atom donor in the benzene ring, while the interaction between SCA2 and Cu (II) occurred mainly through the coordination between the nitrogen atom in the Schiff base and Cu (II). Life Cycle Assessment (LCA) was introduced to analyze the environmental impact of the SCA2 fabrication process and eco-friendly approaches were provided, which eventually provided theoretical evidence for the application of as-prepared material in the handling of heavy metal effluents

Selective adsorption of Pb (II) and Cu (II) on mercapto-functionalized aerogels: Experiments, DFT studies and LCA analysis

Mercapto-functionalized aerogels (MA-X) were fabricated using γ-mercaptopropyltrimethoxysilane (MPTMS) as a modification reagent to eliminate Pb (II) and Cu (II) ions from wastewater. Mercapto-functionalized aerogel (MA2) with the MPTMS/TEOS molar ratio of 0.5 exhibited the maximum adsorption amounts of 163.99 mg/g for Pb (II) and 172.41 mg/g for Cu (II) in single ion system, respectively. In binary ion system, selective adsorption experiments revealed that the equilibrium adsorption capacity for Cu (II) (161.29 mg/g) was significantly greater than Pb (II) (90.42 mg/g), and the selectivity factor α showed greater selectivity for Cu (II), demonstrating that Cu (II) was more readily adsorbed on MA2. The results showed that adsorption was consistent with pseudo- second order model and Langmuir model. Thermodynamic results demonstrated that adsorption phenomenon was an exothermic reaction that occurred spontaneously. XPS analysis and density functional theory (DFT) simulations showed that the main mechanism for the adsorption of Pb (II) and Cu (II) on MA2 was through coordination chelation of the –SH groups with Pb (II) and Cu (II). DFT calculations showed a lower adsorption energy (Eads) of Cu (II) ( 2.72 eV) with respect to Pb (II) ( 0.80 eV), indicating that Cu (II) was more stably adsorbed on MA2 and more difficult to exchange by Pb (II). In order to determine the environmental impact of the MA2 preparation process, a life cycle assessment (LCA) was conducted and contribution of each material to MA2 production was analyzed. Finally, a strategy that is environmentally friendly and effective has been pro-posed in order to facilitate MA-X adsorbents production and to improve their application for the treatment of heavy metal polluted wastewater

Learning to Learn from APIs: Black-Box Data-Free Meta-Learning

Data-free meta-learning (DFML) aims to enable efficient learning of new tasks by meta-learning from a collection of pre-trained models without access to the training data. Existing DFML work can only meta-learn from (i) white-box and (ii) small-scale pre-trained models (iii) with the same architecture, neglecting the more practical setting where the users only have inference access to the APIs with arbitrary model architectures and model scale inside. To solve this issue, we propose a Bi-level Data-free Meta Knowledge Distillation (BiDf-MKD) framework to transfer more general meta knowledge from a collection of black-box APIs to one single meta model. Specifically, by just querying APIs, we inverse each API to recover its training data via a zero-order gradient estimator and then perform meta-learning via a novel bi-level meta knowledge distillation structure, in which we design a boundary query set recovery technique to recover a more informative query set near the decision boundary. In addition, to encourage better generalization within the setting of limited API budgets, we propose task memory replay to diversify the underlying task distribution by covering more interpolated tasks. Extensive experiments in various real-world scenarios show the superior performance of our BiDf-MKD framework

Task-Distributionally Robust Data-Free Meta-Learning

Data-Free Meta-Learning (DFML) aims to efficiently learn new tasks by leveraging multiple pre-trained models without requiring their original training data. Existing inversion-based DFML methods construct pseudo tasks from a learnable dataset, which is inversely generated from the pre-trained model pool. For the first time, we reveal two major challenges hindering their practical deployments: Task-Distribution Shift (TDS) and Task-Distribution Corruption (TDC). TDS leads to a biased meta-learner because of the skewed task distribution towards newly generated tasks. TDC occurs when untrusted models characterized by misleading labels or poor quality pollute the task distribution. To tackle these issues, we introduce a robust DFML framework that ensures task distributional robustness. We propose to meta-learn from a pseudo task distribution, diversified through task interpolation within a compact task-memory buffer. This approach reduces the meta-learner's overreliance on newly generated tasks by maintaining consistent performance across a broader range of interpolated memory tasks, thus ensuring its generalization for unseen tasks. Additionally, our framework seamlessly incorporates an automated model selection mechanism into the meta-training phase, parameterizing each model's reliability as a learnable weight. This is optimized with a policy gradient algorithm inspired by reinforcement learning, effectively addressing the non-differentiable challenge posed by model selection. Comprehensive experiments across various datasets demonstrate the framework's effectiveness in mitigating TDS and TDC, underscoring its potential to improve DFML in real-world scenarios

The ectonucleotidases CD39 and CD73 on T cells: The new pillar of hematological malignancy

Hematological malignancy develops and applies various mechanisms to induce immune escape, in part through an immunosuppressive microenvironment. Adenosine is an immunosuppressive metabolite produced at high levels within the tumor microenvironment (TME). Adenosine signaling through the A2A receptor expressed on immune cells, such as T cells, potently dampens immune responses. Extracellular adenosine generated by ectonucleoside triphosphate diphosphohydrolase-1 (CD39) and ecto-5’-nucleotidase (CD73) molecules is a newly recognized ‘immune checkpoint mediator’ and leads to the identification of immunosuppressive adenosine as an essential regulator in hematological malignancies. In this Review, we provide an overview of the detailed distribution and function of CD39 and CD73 ectoenzymes in the TME and the effects of CD39 and CD73 inhibition on preclinical hematological malignancy data, which provides insights into the potential clinical applications for immunotherapy