Effect of preparation methods and carbon black distribution on mechanical properties of short pineapple leaf fiber-carbon black reinforced natural rubber hybrid composites

The aim of this work is to improve the performance of natural rubber reinforced with a hybrid of pineapple leaf fiber with carbon black. When there are multiple components to be mixed into a rubber matrix, mixing can be carried out in more than one way. Thus, in this study, the effects of preparation method and the resulting carbon black distribution on the mechanical properties of the hybrid composite were evaluated. Pineapple leaf fiber (PALF) and carbon black contents were fixed at 10 parts (by weight) and 30 parts (by weight) per hundred parts of rubber (phr), respectively. In order to improve the dispersion, PALF with rubber was prepared as a masterbatch. Carbon black was added to the compound either as a single portion or as two separate portions, one in the PALF masterbatch and the other in the main mixing step. It was found that, despite using the same final compound formulation, the mixing scheme significantly affected the medium strain region of the vulcanizate stress-strain curve. No stress drop in this strain region was observed for the two-step mixing scheme. Models for composites with different preparation methods are proposed and discussed.     

Mass deacidification and reinforcement of papers and books VI - Study of aminopropylmethyldiethoxysilane treated papers

In libraries and archives some of the items which, upon ageing, have acidified considerably since their production are so brittle that they cannot be handled without risking loss of material. In contrast to current deacidification processes, aminoalkylakoxysilanes (AAAS) improve the mechanical properties of paper. A simple AAAS, aminopropylmethyldiethoxysilane (AMDES) was used as a model to better understand previous observations made on these systems (hydrolysis, condensation and possible reactions with the organic substrate). The evaluation of the mechanical properties of papers that were treated with AMDES showed that there was no formation of a polymer network on the fibres’ surface. However, treated papers not only exhibited a high alkaline reserve, but also a significant increase in both their tensile breaking length and their folding endurance. Treatment of hygrothermally aged papers with AMDES provided improved folding endurance as well as a good resistance to ageing. Various oxidation treatments of the model papers using sodium hypochlorite led to the conclusion that the nature of the oxidised groups formed on cellulose did not seem to play a significant role in the strengthening mechanism but rather that this effect of AMDES mainly arose from hydrogen bonding between the two molecules.     

三山岛金矿控矿断层F1的力学性质及补强措施

作者以正在开采中的三山岛金矿采场为研究对象，分析矿区开采过程中的岩体结构特征及重力方向矿柱、护顶矿柱与控矿断层F1断层泥及其上覆岩体之间的关系和力学机制。通过试验研究了断层泥的物质成分和力学性质，总结了力学特性与断层泥厚度及含水量之间的关系。在此基础上提出锚固大于中倾角的结构面，提高结构面的抗滑阻力；增加含有高、陡倾角结构面矿柱的刚度等对矿柱的补强措施。对护顶矿柱提出预先锚固；分阶段开采的补强方法。在裂隙密集带设置排水设施，改善断层泥的赋存条件，提高它的力学性能。     

国道108线K15+760~K15+980段路堤边坡稳定性研究及处治效果监测

对国道108线广南段K15+760~K15+980段高路堤边坡稳定性的分析与计算,分析了路堤产生变形的原因,并提出了相应的处治加固措施,同时进行了包括表面位移监测、路面沉降监测等监测,总结了边坡不稳定情况下路堤表面位移及路堤沉降变形的规律,从而掌握了路堤的变形动态,控制了处治施工活动带来的超量位移,进而确保了路堤及边坡的稳定。     

Prediction of the penetrated rust into the microcracks of concrete caused by reinforcement corrosion

Consider a steel-rust-concrete composite consisting of a circular cylindrical concrete cover and a coaxial uniformly corroding steel reinforcement. Prediction of the amount of rust penetrated into the microcracks of concrete cover from a set of data measured at the surface of the concrete is of particular interest. The steel is assumed to be linear isotropic and rust follows a power law stress–strain relation. For the concrete, anisotropic behavior and post-cracking softening model is employed. The formulations lead to a nonlinear boundary value problem which is solved analytically. A key parameter β, defined as the ratio of the volume of corrosion products inside the cracks to the volume of the cracks, is calculated. With some efforts, this parameter is also extracted from the available theoretical and experimental studies for the purpose of comparison. The effects of the mechanical properties of rust and concrete on β is addressed.     

Reinforcement learning versus heuristics for order acceptance on a single resource

Order Acceptance (OA) is one of the main functions in business control. Accepting an order when capacity is available could disable the system to accept more profitable orders in the future with opportunity losses as a consequence. Uncertain information is also an important issue here. We use Markov decision models and learning methods from Artificial Intelligence to find decision policies under uncertainty. Reinforcement Learning (RL) is quite a new approach in OA. It is shown here that RL works well compared with heuristics. It is demonstrated that employing an RL trained agent is a robust, flexible approach that in addition can be used to support the detection of good heuristics.     

Effect of a characteristic length on crack spacing in a reinforced concrete bar under tension

The paper presents quasi-static FE-simulations of the crack formation in a reinforced concrete bar without stirrups subject to tension. The material was modeled with a continuum smeared crack model using an elasto-plastic constitutive law. A linear Rankine criterion with isotropic softening and associated flow rule was adopted in a tensile regime. To ensure the mesh-independency, the softening parameter was enhanced by a characteristic length of micro-structure by means of a non-local theory. Attention was laid to the effect of a different characteristic length of micro-structure and initial bond-slip stiffness on the spacing of localized zones.     

生成模型在蛋白质序列设计中的应用

蛋白质是一切生命体的物质基础,是生命活动的主要承担者,参与各种生理功能的调节.设计具有特定功能的蛋白质在蛋白质工程、生物医药、材料科学等领域具有重要意义.蛋白质序列设计的目标是设计能够折叠成期望结构并具有相应功能的氨基酸序列,是所有理性蛋白质工程的核心问题,具有极其重要的研究和应用潜力.随着蛋白质序列数据的指数型增长和...     

Effect of filling mixtures of sepiolite and a surface modified fumed silica on the mechanical and swelling behavior of a styrene-butadiene rubber

A styrene-butadiene copolymer is filled with mixtures of pyrogenic silica combined with a silane coupling agent and fibers of organophilic sepiolite. The mechanical properties of the composites reveal that a mixture of double fillers impart to the elastomeric matrix a higher degree of reinforcement than that which would result from a simple addition of the two types of fillers. The swelling ratio of the composite containing the two types of fillers was found to highly decrease with regard to the pure polymer reflecting strong interactions with the matrix. The changes in the state of dispersion by adding the second filler were evaluated by transmission electron microscopy.     

A greedy-model-based reinforcement learning algorithm for Beyond-5G cooperative data collection

Data collection is an essential part of Beyond-5G and Internet of Things applications. In urban area, heterogeneous access points such as Wi-Fi routers and base stations can meet the required communication coverage and bandwidth in data collection processes. However, in remote area, without communication infrastructures, it is hard to guarantee the communication quality of a large-scale data aggregation network. An existing approach is to use an unmanned aerial vehicle (UAV) to act as a mobile sink to perform data collection and increase the coverage of intelligent wireless sensing and communications. The efficiency and the reliability of such a UAV-assisted data collection system can be significantly enhanced with an intelligent cooperative strategy for the sensors deployed in the field to communicate with the UAV. Furthermore, an energy-efficient trajectory planning algorithm is crucial to address the physical limitations of the UAV in this application. In this paper, a data collection process is modeled as a Markov decision process (MDP). The paper begins with proposing two heuristic greedy algorithms, namely distance-greedy (DG) algorithm and rate-greedy (RG) algorithm, which are designed based on prior knowledge of the system and can guarantee the completion of the data collection process in a remote area without the help of fixed communication infrastructures. Based on the outcomes, a multi-agent greedy-model-based reinforcement learning (MG-RL) algorithm is proposed, which specifically designs the environmental state and the reward scheme, and introduces multiple UAVs with different parameters to explore environments in parallel to accelerate the training. In conclusion, the two proposed greedy algorithms have lower complexity of implementation while the proposed MG-RL algorithm yields practical UAVs’ flight trajectories and shortens the time for completing a data collection task.