Polycyclobutanes Constructed From Biomass-Based Building Blocks

A series of sustainable polycyclobutane (PCB) materials were prepared from biomass-based chemicals by [2 + 2] photocycloaddition reaction in sunlight/floodlight or conventional organic synthesis. At the beginning, a family of gemini monomers was synthesized from furfural, which can be obtained from corncobs. First, a photoreactive building block, 3-(2-furyl)acrylic acid also called (E)-3-(furan-2-yl)acrylic acid, was synthesized from furfural and malonic acid. Five photostable diols were used to connect two photoreactive building blocks to obtain the monomers. These synthesized monomers were characterized by NMR, UV-Vis and FT-IR spectroscopy, XRD, P-XRD, TGA, and DSC. The photoreaction was monitored by FT-IR spectroscopy and the results indicated that all of the monomers were photoreactive. XRD analysis suggested that these monomers can be used to synthesize polyladderanes and linear polyesters. Monomer (2E,2\u27E)-butane-1,4-diyl bis[3-(furan-2-yl)acrylate] formed a polyladderane. Two pieces of independent evidence were obtained, single-crystal-to-single-crystal (SCSC) and a dimer intermediate. Monomer (2E,2\u27E)-pentane-1,5-diyl bis[3-(furan-2-yl)acrylate] formed a linear polyester thermoplastic. Both the polyladderane and linear polyesters are amorphous. TGA and DSC showed they decomposed around 300 oC. Two-dimensional (2D) polymers were synthesized from cinnamic acid, which can be obtained from cinnamon and from a byproduct of biofuels manufacture. Four molecules of cinnamic acids were linked with a durene group through nuclear substitution reaction. This four-armed monomer provided four reacting centers, which was the key forming a 2D polymer. The obtained 2D polymer was insoluble in most common organic solvents. The thin layered 2D polymer was observed under TEM and SEM after exfoliation in DMF and H2O. The key hydrolysis product trans-2,4-diphenylcyclobutane-1,3-dicarboxylic acid or alpha-truxillic acid was captured, which proved the newly formed cyclobutane rings. The alpha-truxillic acid has two carboxylic groups on the opposite sides of the cyclobutane rings which, provided a semi-rigid structure. This diacid is one of the family members of cyclobutane-diacids (CBDA). This class of diacid can be used to synthesize thermoplastics through conventional synthetic method. Four polyesters, PEAT, PBAT, PPAT5 and PHAT, were successfully prepared from trans-2,4-diphenylcyclobutane-1,3-dicarboxylic acid (CBDA-1) by coupling reactions. TGA showed a high thermostability of these polymers. DSC results showed glass transition temperature decreased as the carbon chains increasing, which may be due to the flexibility of the polymers

Research on the Path of Teaching Staff Construction in Independent Colleges

Independent college is an important part of higher education in China, and it is also the guarantee of providing applied talents for China’s economic and social development. Therefore, the construction of teaching staff in independent colleges is particularly important, and its comprehensive strength of teachers directly affects the quality of personnel training in independent colleges. Compared with public colleges, the overall faculty of independent colleges is still relatively weak, which is not conducive to the overall promotion of the school-running level of independent colleges. In order to better promote the construction of teachers in independent colleges, Promote the improvement of the school-running strength and teachers’ level of independent colleges, Taking the construction of teaching staff in an independent college in Zhejiang Province as an example, On the basis of a profound analysis of the shortcomings faced by the construction of the teaching staff in this college, this paper puts forward some countermeasures to improve the construction of the teaching staff in this independent college, in order to make suggestions for the construction of the talent team in this independent college and provide case reference for the construction of the teaching staff in other independent colleges of the same type in Chin

A Principled Framework for Knowledge-enhanced Large Language Model

Large Language Models (LLMs) are versatile, yet they often falter in tasks requiring deep and reliable reasoning due to issues like hallucinations, limiting their applicability in critical scenarios. This paper introduces a rigorously designed framework for creating LLMs that effectively anchor knowledge and employ a closed-loop reasoning process, enhancing their capability for in-depth analysis. We dissect the framework to illustrate the contribution of each component to the LLMs' performance, offering a theoretical assurance of improved reasoning under well-defined assumptions.Comment: 10 page

Sample-Efficient Multi-Agent RL: An Optimization Perspective

We study multi-agent reinforcement learning (MARL) for the general-sum Markov Games (MGs) under the general function approximation. In order to find the minimum assumption for sample-efficient learning, we introduce a novel complexity measure called the Multi-Agent Decoupling Coefficient (MADC) for general-sum MGs. Using this measure, we propose the first unified algorithmic framework that ensures sample efficiency in learning Nash Equilibrium, Coarse Correlated Equilibrium, and Correlated Equilibrium for both model-based and model-free MARL problems with low MADC. We also show that our algorithm provides comparable sublinear regret to the existing works. Moreover, our algorithm combines an equilibrium-solving oracle with a single objective optimization subprocedure that solves for the regularized payoff of each deterministic joint policy, which avoids solving constrained optimization problems within data-dependent constraints (Jin et al. 2020; Wang et al. 2023) or executing sampling procedures with complex multi-objective optimization problems (Foster et al. 2023), thus being more amenable to empirical implementation

Application of LSTM and CONV1D LSTM Network in Stock Forecasting Model

Predicting the direction of the stock market has always been a huge challenge. Also, the way of forecasting the stock market reduces the risk in the financial market, thus ensuring that brokers can make normal returns. Despite the complexities of the stock market, the challenge has been increasingly addressed by experts in a variety of disciplines, including economics, statistics, and computer science. The introduction of machine learning, in-depth understanding of the prospects of the financial market, thus doing many experiments to predict the future so that the stock price trend has different degrees of success. In this paper, we propose a method to predict stocks from different industries and markets, as well as trend prediction using traditional machine learning algorithms such as linear regression, polynomial regression and learning techniques in time series prediction using two forms of special types of recursive neural networks: long and short time memory (LSTM) and spoken short-term memory