The Effects of Environmental Regulation, Cooperation and Green Innovation on Regional Green Growth

Over past twenty years, green growth has been practiced by a lot of countries. Questions of factors driving green growth have become hot topic. Although some studies discuss determinants of green growth, a few studies integrate them in a methodological framework. In addition, innovation cooperation is considered as an effective method to improve green growth, but there is few significant attempt to investigate the relationship in quantity. As a result, this paper proposes an integrated model to explore determinants of green growth, including environmental regulation, innovation cooperation, and green innovation. Structural equation model is used to test the proposed model with research data of 30 Chinese provinces. In turn, we have several valuable findings. Firstly, new empirical relationship between innovation collaboration, green innovation and green growth development is examined. Our empirical results show that innovation collaboration significantly positively influences green innovation and green growth performance. Secondly, the findings display that environmental regulation is a significant positive determinant of innovation cooperation, green innovation and green growth performance respectively. Thirdly, the theoretical model is powerful and robust, which can make us advance the understanding of green growth performance in environmental regulation context. Finally, several implications are discussed while some limitations are also showed. Keywords: Environmental regulation; innovation collaboration; green innovation; green growth; structural equation model

The Effects of Environmental Regulation, Cooperation and Green Innovation on Regional Green Growth

Over past twenty years, green growth has been practiced by a lot of countries. Questions of factors driving green growth have become hot topic. Although some studies discuss determinants of green growth, a few studies integrate them in a methodological framework. In addition, innovation cooperation is considered as an effective method to improve green growth, but there is few significant attempt to investigate the relationship in quantity. As a result, this paper proposes an integrated model to explore determinants of green growth, including environmental regulation, innovation cooperation, and green innovation. Structural equation model is used to test the proposed model with research data of 30 Chinese provinces. In turn, we have several valuable findings. Firstly, new empirical relationship between innovation collaboration, green innovation and green growth development is examined. Our empirical results show that innovation collaboration significantly positively influences green innovation and green growth performance. Secondly, the findings display that environmental regulation is a significant positive determinant of innovation cooperation, green innovation and green growth performance respectively. Thirdly, the theoretical model is powerful and robust, which can make us advance the understanding of green growth performance in environmental regulation context. Finally, several implications are discussed while some limitations are also showed. Keywords: Environmental regulation; innovation collaboration; green innovation; green growth; structural equation mode

The Policy Process Research of Family Doctor System in China: from the Perspective of the Multiple-Streams Theory

Under context of aging population and chronic disease with high occurrence as well as difficulty and expensive costs in medical treatment, the establishment about system of family doctors has been brought about in China, but there lacks domestic research for process of policy evolution endowed with such significance. This paper tries to analyze process of institutional establishment of family doctors in view of multiple-streams theory; the evolution process about institutional construction of family doctors should have systematic classification with analysis of problem stream, policy stream and political stream respectively as well as facts based on fundamental challenges of grassroots public health; explanation should be made for features and inspiration about institutional construction of family doctors to further verify feasibility of multiple streams theory in China. The fact has found that social background and actor will influence agenda about institutional construction of family doctors, some agenda of basic public health service policy will not be promoted by accidental focus events; active public participation and attention will exert influence for formation of political stream. Keywords: family doctor, multiple-streams theory, policy process, agenda setting

SpecLLM: Exploring Generation and Review of VLSI Design Specification with Large Language Model

The development of architecture specifications is an initial and fundamental stage of the integrated circuit (IC) design process. Traditionally, architecture specifications are crafted by experienced chip architects, a process that is not only time-consuming but also error-prone. Mistakes in these specifications may significantly affect subsequent stages of chip design. Despite the presence of advanced electronic design automation (EDA) tools, effective solutions to these specification-related challenges remain scarce. Since writing architecture specifications is naturally a natural language processing (NLP) task, this paper pioneers the automation of architecture specification development with the advanced capabilities of large language models (LLMs). Leveraging our definition and dataset, we explore the application of LLMs in two key aspects of architecture specification development: (1) Generating architecture specifications, which includes both writing specifications from scratch and converting RTL code into detailed specifications. (2) Reviewing existing architecture specifications. We got promising results indicating that LLMs may revolutionize how these critical specification documents are developed in IC design nowadays. By reducing the effort required, LLMs open up new possibilities for efficiency and accuracy in this crucial aspect of chip design

Public Acceptability of Personal Carbon Trading in China: an Empirical Research

The global warming that is caused by a large number of greenhouse gases emissions has been a giant challenge of human society and sustainable development. China is the biggest carbon emissions country in the world. In order to solve this problem, implementation of an innovative policy is necessary. Personal carbon trading (PCT) is latest idea on the subject of new policy to control the carbon emission. But whether a new policy can be implemented, it depends on its public acceptability. In this article we discussed public acceptability of PCT by exploring the influencing factors and its level of acceptance in China. We designed a questionnaire with five aspects to collect data from three main cities of China. We applied ordinal logistic regression model to investigate the factors which influence public acceptability. The pre dominant results show that acceptability is affected by eight factors such as education, income, perceived threat to humans and the environment in the municipality, perceived level of personal carbon emissions, perceived fair, anticipated behavior to save carbon quotas for traveling, infringement of freedom and anticipated behavior to save carbon quotas for selling. Mostly have positive impact on public acceptability except last two factors. Moreover our results do not support the highly acceptance of PCT in China. There is a considerable validation to conduct this study, because empirical evidence—in developing countries—is limited. It is obvious that this dimension of understanding is necessary for promotion of new idea as new effective policy implementation to control the carbon emission. Keywords: Personal carbon trading, personal carbon allowances, carbon emissions, public acceptability, ordered logistic regression, climate change

RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Model

Inspired by the recent success of large language models (LLMs) like ChatGPT, researchers start to explore the adoption of LLMs for agile hardware design, such as generating design RTL based on natural-language instructions. However, in existing works, their target designs are all relatively simple and in a small scale, and proposed by the authors themselves, making a fair comparison among different LLM solutions challenging. In addition, many prior works only focus on the design correctness, without evaluating the design qualities of generated design RTL. In this work, we propose an open-source benchmark named RTLLM, for generating design RTL with natural language instructions. To systematically evaluate the auto-generated design RTL, we summarized three progressive goals, named syntax goal, functionality goal, and design quality goal. This benchmark can automatically provide a quantitative evaluation of any given LLM-based solution. Furthermore, we propose an easy-to-use yet surprisingly effective prompt engineering technique named self-planning, which proves to significantly boost the performance of GPT-3.5 in our proposed benchmark

RTLCoder: Outperforming GPT-3.5 in Design RTL Generation with Our Open-Source Dataset and Lightweight Solution

The automatic generation of RTL code (e.g., Verilog) using natural language instructions and large language models (LLMs) has attracted significant research interest recently. However, most existing approaches heavily rely on commercial LLMs such as ChatGPT, while open-source LLMs tailored for this specific design generation task exhibit notably inferior performance. The absence of high-quality open-source solutions restricts the flexibility and data privacy of this emerging technique. In this study, we present a new customized LLM solution with a modest parameter count of only 7B, achieving better performance than GPT-3.5 on two representative benchmarks for RTL code generation. This remarkable balance between accuracy and efficiency is made possible by leveraging our new RTL code dataset and a customized LLM algorithm, both of which will be made fully open-source. Furthermore, we have successfully quantized our LLM to 4-bit with a total size of 4GB, enabling it to function on a single laptop with only slight performance degradation. This efficiency allows the RTL generator to serve as a local assistant for engineers, ensuring all design privacy concerns are addressed

MasterRTL: A Pre-Synthesis PPA Estimation Framework for Any RTL Design

In modern VLSI design flow, the register-transfer level (RTL) stage is a critical point, where designers define precise design behavior with hardware description languages (HDLs) like Verilog. Since the RTL design is in the format of HDL code, the standard way to evaluate its quality requires time-consuming subsequent synthesis steps with EDA tools. This time-consuming process significantly impedes design optimization at the early RTL stage. Despite the emergence of some recent ML-based solutions, they fail to maintain high accuracy for any given RTL design. In this work, we propose an innovative pre-synthesis PPA estimation framework named MasterRTL. It first converts the HDL code to a new bit-level design representation named the simple operator graph (SOG). By only adopting single-bit simple operators, this SOG proves to be a general representation that unifies different design types and styles. The SOG is also more similar to the target gate-level netlist, reducing the gap between RTL representation and netlist. In addition to the new SOG representation, MasterRTL proposes new ML methods for the RTL-stage modeling of timing, power, and area separately. Compared with state-of-the-art solutions, the experiment on a comprehensive dataset with 90 different designs shows accuracy improvement by 0.33, 0.22, and 0.15 in correlation for total negative slack (TNS), worst negative slack (WNS), and power, respectively.Comment: To be published in the Proceedings of 42nd IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 202

Graphene quantum dots induce cascadic apoptosis via interaction with proteins associated with anti-oxidation after endocytosis by Trypanosoma brucei

Trypanosoma brucei, the pathogen causing African sleeping sickness (trypanosomiasis) in humans, causes debilitating diseases in many regions of the world, but mainly in African countries with tropical and subtropical climates. Enormous efforts have been devoted to controlling trypanosomiasis, including expanding vector control programs, searching for novel anti-trypanosomial agents, and developing vaccines, but with limited success. In this study, we systematically investigated the effect of graphene quantum dots (GQDs) on trypanosomal parasites and their underlying mechanisms. Ultrasmall-sized GQDs can be efficiently endocytosed by T. brucei and with no toxicity to mammalian-derived cells, triggering a cascade of apoptotic reactions, including mitochondrial disorder, intracellular reactive oxygen species (ROS) elevation, Ca2+ accumulation, DNA fragmentation, adenosine triphosphate (ATP) synthesis impairment, and cell cycle arrest. All of these were caused by the direct interaction between GQDs and the proteins associated with cell apoptosis and anti-oxidation responses, such as trypanothione reductase (TryR), a key protein in anti-oxidation. GQDs specifically inhibited the enzymatic activity of TryR, leading to a reduction in the antioxidant capacity and, ultimately, parasite apoptotic death. These data, for the first time, provide a basis for the exploration of GQDs in the development of anti-trypanosomials