Study on Multi-Scale Tensile Strength and Tensile Strain of Calcium Silicate Hydrate Layered Nanocomposites Under External Physical Field

Calcium silicate hydrate (C-S-H) is the mainly strength source of cement-based materials, but there is little basic research. In this paper, molecular dynamics method is applied to analyze the multi-scale tensile strength and tensile strain of C-S-H layered materials under the condition of external physical fields (temperature and strain rate). The results show that the tensile strength and strain of C-S-H model decrease with temperature raises. The temperature (from 1 K to 600 K) has obvious influence on the tensile strain and strength of C-S-H layered materials. In addition, at (0.00025 ps-1-0.001 ps-1), the tensile strain and strength of C-S-H layered materials are less sensitive to strain rate. The whole model is closer to a 3-dimensional deformation. However, at (0.001 ps-1-0.005 ps-1), the dynamic load effect begins to increase, and the work done by the load per unit time increased. The tensile strain and strength of C-S-H layered materials indicates intensified by the change of strain rate. The energies are randomly distributed in the system, not concentrated in a certain area

Enterprises Public Service Platform of China

Small and medium-sized enterprises public service platform(SMEPSP for short in the following paper) is a comprehensive service platform to provide services such as technology innovation, financing guarantee, business supporting and management consulting to small and medium-sized enterprise(SME for short in the following paper). This article is based on questionnaire surveys and interviews to those service departments who have participated the construction and operation of SMEPSP. After the statistics and analysis, we have firstly summarized the main features of SMEPSP of our country; Then we have analyzed the main existing problems of SMEPSP in construction and operation, such as repeated construction of hardware and software, weak coordination and integration of resources, and so on; At last, we have come up with a countermeasure that by establishing a national level total SMEPSP, adopt unified planning and top-level designing, to perfect the system construction of SMEPSP of our country

USING CLUSTERING ALGORITHM AND FOG COMPUTING FOR EN-ROUTE FILTERING IN LOW POWER AND LOSS NETWORKS

Due to trustless link quality in Wireless Mesh Networks (WMNs), Power Outage Notification (PON) and Power Restoration Notification (PRN) messages are often dropped or delayed en-route, which may fail to satisfy customer requirements in practice. Therefore, proposed herein are techniques that use machine learning and Fog computing to efficiently deduce missing PON/PRN messages

DISASTER MANAGEMENT SYSTEM USING NETWORK SNAPSHOT IN LOW POWER AND LOSSY NETWORKS (LLNS)

Presented herein are techniques to introduce snapshot technology into wireless networks. In particular, the remote side of the network (e.g., edge devices, border routers, cloud, etc.) collect status information from all nodes of a network, such as a personal area network (PAN), while at the same time collecting and storing one or more snapshot(s) of the network. If the wireless network crashes, the remote side is configured to restore the entire network from the existing saved snapshot(s)

Land-leasing behavior, local officials’ promotions, and Chinese cities’ debt risks

This study first analyzes how local governments’ land-leasing behaviors affect Chinese cities’ debt risk then examines the impact of officials’ promotion mechanisms on debt risk in China’s urban land bank system. The land-leasing behavior is reflected through three indicators, namely, land-leasing revenue, land-leasing scale, and land financial dependence level. Two new indicators are constructed to measure the local government’ debt risk from the perspective of debt scale and debt repayment: the debt scale risk and debt burden risk. Empirical analyses are based on the data of 281 prefecture-level cities from 2006–2015. The main findings are twofold. First, the debt scale risk is positively affected by the land-leasing revenue, and officials’ promotion pressure. The debt burden risk is positively affected by the land financial dependence and officials’ promotion pressure. Second, the officials’ promotion pressure significantly enhances the positive effect of land-leasing revenue on the debt scale risk. Local officials, who are under promotion pressure, are inclined to expand the size of urban investment bonds, which increases debt scale risk

Evolution and Efficiency in Neural Architecture Search: Bridging the Gap Between Expert Design and Automated Optimization

The paper provides a comprehensive overview of Neural Architecture Search (NAS), emphasizing its evolution from manual design to automated, computationally-driven approaches. It covers the inception and growth of NAS, highlighting its application across various domains, including medical imaging and natural language processing. The document details the shift from expert-driven design to algorithm-driven processes, exploring initial methodologies like reinforcement learning and evolutionary algorithms. It also discusses the challenges of computational demands and the emergence of efficient NAS methodologies, such as Differentiable Architecture Search and hardware-aware NAS. The paper further elaborates on NAS's application in computer vision, NLP, and beyond, demonstrating its versatility and potential for optimizing neural network architectures across different tasks. Future directions and challenges, including computational efficiency and the integration with emerging AI domains, are addressed, showcasing NAS's dynamic nature and its continued evolution towards more sophisticated and efficient architecture search methods.Comment: 7 Pages, Double Colum

Synthesizing mixed-integer linear programming models from natural language descriptions

Numerous real-world decision-making problems can be formulated and solved using Mixed-Integer Linear Programming (MILP) models. However, the transformation of these problems into MILP models heavily relies on expertise in operations research and mathematical optimization, which restricts non-experts' accessibility to MILP. To address this challenge, we propose a framework for automatically formulating MILP models from unstructured natural language descriptions of decision problems, which integrates Large Language Models (LLMs) and mathematical modeling techniques. This framework consists of three phases: i) identification of decision variables, ii) classification of objective and constraints, and iii) finally, generation of MILP models. In this study, we present a constraint classification scheme and a set of constraint templates that can guide the LLMs in synthesizing a complete MILP model. After fine-tuning LLMs, our approach can identify and synthesize logic constraints in addition to classic demand and resource constraints. The logic constraints have not been studied in existing work. To evaluate the performance of the proposed framework, we extend the NL4Opt dataset with more problem descriptions and constraint types, and with the new dataset, we compare our framework with one-step model generation methods offered by LLMs. The experimental results reveal that with respect to the accuracies of generating the correct model, objective, and constraints, our method which integrates constraint classification and templates with LLMs significantly outperforms the others. The prototype system that we developed has a great potential to capture more constraints for more complex MILPs. It opens up opportunities for developing training tools for operations research practitioners and has the potential to be a powerful tool for automatic decision problem modeling and solving in practice