Development of a New Type of Alkali-Free Liquid Accelerator for Wet Shotcrete in Coal Mine and Its Engineering Application

In order to address issues such as large rebound rate, high dust concentration, and low compressive strength of shotcrete when adding liquid accelerator during wet spraying, the factors influencing the efficiency of liquid accelerator were experimentally analyzed. The single-admixture, combination, and orthogonal tests were conducted on the five fundamental raw materials required to develop the new liquid accelerator. The WT-1 type liquid accelerator, which had better adaptability to different kinds of cement, was developed with the mass concentration ratio of 55% aluminum sulfate octadecahydrate, 4% sodium fluoride, 2.5% triethanolamine, 0.5% polyacrylamide, 5% bentonite, and 33% water. Experimental investigation showed that the initial setting time of the reference cement with 6% mass content of this liquid accelerator was 2 minutes and 15 seconds, and the final setting time was 7 minutes and 5 seconds. The compressive strength after 1 day of curing was 13.6 MPa and the strength ratio after 28 days of curing was 94.8%, which met the first grade product requirements of the China National Standard. Compared with the conventional type liquid accelerator, the proposed type WT-1 accelerator is capable of effectively reducing the rebound rate and dust concentration while significantly increasing the compressive strength of the shotcrete

Role-based and agent-oriented teamwork modeling

Teamwork has become increasingly important in many disciplines. To support teamwork in dynamic and complex domains, a teamwork programming language and a teamwork architecture are important for specifying the knowledge of teamwork and for interpreting the knowledge of teamwork and then driving agents to interact with the domains. Psychological studies on teamwork have also shown that team members in an effective team often maintain shared mental models so that they can have mutual expectation on each other. However, existing agent/teamwork programming languages cannot explicitly express the mental states underlying teamwork, and existing representation of the shared mental models are inefficient and further become an obstacle to support effective teamwork. To address these issues, we have developed a teamwork programming language called Role-Based MALLET (RoB-MALLET) which has rich expressivity to explicitly specify the mental states underlying teamwork. By using roles and role variables, the knowledge of team processes is specified in terms of conceptual notions, instead of specific agents and agent variables, allowing joint intentions to be formed and this knowledge to be reused by different teams of agents. Further, based on roles and role variables, we have developed mechanisms of task decomposition and task delegation, by which the knowledge of a team process is decomposed into the knowledge of a team process for individuals and then delegate it to agents. We have also developed an efficient representation of shared mental models called Role-Based Shared Mental Model (RoB-SMM) by which agents only maintain individual processes complementary with others?? individual process and a low level of overlapping called team organizations. Based on RoB-SMMs, we have developed tworeasoning mechanisms to improve team performance, including Role-Based Proactive Information Exchange (RoB-PIE) and Role-Based Proactive Helping Behaivors (RoBPHB). Through RoB-PIE, agents can anticipate other agents?? information needs and proactively exchange information with them. Through RoB-PHB, agents can identify other agents?? help needs and proactively initialize actions to help them. Our experiments have shown that RoB-MALLET is flexible in specifying reusable plans, RoB-SMMs is efficient in supporting effective teamwork, and RoB-PHB improves team performance

Matched filtering for gravitational wave detection without template bank driven by deep learning template prediction model bank

The existing matched filtering method for gravitational wave (GW) search relies on a template bank. The computational efficiency of this method scales with the size of the templates within the bank. Higher-order modes and eccentricity will play an important role when third-generation detectors operate in the future. In this case, traditional GW search methods will hit computational limits. To speed up the computational efficiency of GW search, we propose the utilization of a deep learning (DL) model bank as a substitute for the template bank. This model bank predicts the latent templates embedded in the strain data. Combining an envelope extraction network and an astrophysical origin discrimination network, we realize a novel GW search framework. The framework can predict the GW signal's matched filtering signal-to-noise ratio (SNR). Unlike the end-to-end DL-based GW search method, our statistical SNR holds greater physical interpretability than the $p_{score}$ metric. Moreover, the intermediate results generated by our approach, including the predicted template, offer valuable assistance in subsequent GW data processing tasks such as parameter estimation and source localization. Compared to the traditional matched filtering method, the proposed method can realize real-time analysis. The minor improvements in the future, the proposed method may expand to other scopes of GW search, such as GW emitted by the supernova explosion.Comment: 20 pages,12 figure

(1H-1,2,3-Benzotriazol-1-yl)methyl benzoate

In the title compound, C14H11N3O2, the dihedral angle between the phenyl ring and the benzotriazole ring system is 76.80 (19)° and the mol­ecule has an L-shaped conformation. In the crystal, weak aromatic π–π stacking is observed, the closest centroid–centroid distance being 3.754 (2) Å

Bis[(1H-1,2,3-benzotriazol-1-yl)methyl 2,2-dimethyl­propano­ato-κN 3]dichlorido­copper(II)

In the title compound, [CuCl2(C12H15N3O2)2], the CuII ion is located on an inversion center and is four-coordinated in a distorted square-planar geometry by two chloride anions and two N atoms from two (1H-1,2,3-benzotriazol-1-yl)methyl 2,2-dimethyl­propano­ate ligands. The Cl—Cu—N angles of 90.55 (9) and 89.45 (9)° are close to ideal values. In the crystal, weak π–π stacking inter­actions are observed between inversion-related benzene rings [centroid–centroid distance = 4.0028 (6) Å]

Security and Privacy Issues in Wireless Mesh Networks: A Survey

This book chapter identifies various security threats in wireless mesh network (WMN). Keeping in mind the critical requirement of security and user privacy in WMNs, this chapter provides a comprehensive overview of various possible attacks on different layers of the communication protocol stack for WMNs and their corresponding defense mechanisms. First, it identifies the security vulnerabilities in the physical, link, network, transport, application layers. Furthermore, various possible attacks on the key management protocols, user authentication and access control protocols, and user privacy preservation protocols are presented. After enumerating various possible attacks, the chapter provides a detailed discussion on various existing security mechanisms and protocols to defend against and wherever possible prevent the possible attacks. Comparative analyses are also presented on the security schemes with regards to the cryptographic schemes used, key management strategies deployed, use of any trusted third party, computation and communication overhead involved etc. The chapter then presents a brief discussion on various trust management approaches for WMNs since trust and reputation-based schemes are increasingly becoming popular for enforcing security in wireless networks. A number of open problems in security and privacy issues for WMNs are subsequently discussed before the chapter is finally concluded.Comment: 62 pages, 12 figures, 6 tables. This chapter is an extension of the author's previous submission in arXiv submission: arXiv:1102.1226. There are some text overlaps with the previous submissio