A PV installation framework concerning electricity variable rates

This paper assesses building integrated photovoltaic (BIPV) installation parameters based on the profit generated by a photovoltaic system. It takes into consideration a home building case study and it investigates its monthly energy demand based on a specific location and a typical occupancy. The capability of a photovoltaic (PV) system to generate more profit occurs when solar intensity is maximum while the electric energy price is at its highest rate. The paper traces a framework that encompasses different aspects such as energy demand, energy price, and solar intensity. This framework identifies profit alternatives according to different installation parameters. A tool that predicts a PV installation hourly electric energy production is developed. The profit generated is simulated for home buildings located in Beirut (Lebanon) and Xihua (China), both at 33.8° latitude north. The paper highlights a new approach for BIPV installations, taking into account weather conditions, energy demand, and electric energy utility rates.     

综放高瓦斯矿井煤层瓦斯涌出量综合预测

为了成功预测竹林山煤矿综放高瓦斯矿井大采高工作面煤层瓦斯涌出量,以主采3号煤层为主要研究对象,针对3号煤层以往开采情况,通过布设测点测量其煤层瓦斯含量和了解相邻矿井瓦斯含量,采用分源预测法、回归法及统计法等预测方法得到了3号煤层瓦斯含量的分布规律,并绘制了3号煤层的瓦斯含量等值线图。对矿井不同生产时期的瓦斯含量进行预测,得到了生产前期、中期及后期采区的最大绝对瓦斯涌出量和最大相对瓦斯涌出量,说明了竹林山煤矿各个时期均属于高瓦斯矿井。     

Graphite Oxide and Aromatic Amines: Size Matters

Experimental and theoretical studies are performed in order to illuminate, for first time, the intercalation mechanism of polycyclic aromatic molecules into graphite oxide. Two representative molecules of this family, aniline and naphthalene amine are investigated. After intercalation, aniline molecules prefer to covalently connect to the graphene oxide matrix via chemical grafting, while napthalene amine molecules bind with the graphene oxide surface through π–π interactions. The presence of intercalated aromatic molecules between the graphene oxide layers is demonstrated by X‐ray diffraction, while the type of interaction between graphene oxide and polycyclic organic molecules is elucidated by X‐ray photoelectron spectroscopy. Combined quantum mechanical and molecular mechanical calculations describe the intercalation mechanism and the aniline grafting, rationalizing the experimental data. The present work opens new perspectives for the interaction of various aromatic molecules with graphite oxide and the so‐called “intercalation chemistry”.     

A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price

Creating an intelligent system that can accurately predict stock price in a robust way has always been a subject of great interest for many investors and financial analysts. Predicting future trends of financial markets is more remarkable these days especially after the recent global financial crisis. So traders who access to a powerful engine for extracting helpful information throw raw data can meet the success. In this paper we propose a new intelligent model in a multi-agent framework called bat-neural network multi-agent system (BNNMAS) to predict stock price. The model performs in a four layer multi-agent framework to predict eight years of DAX stock price in quarterly periods. The capability of BNNMAS is evaluated by applying both on fundamental and technical DAX stock price data and comparing the outcomes with the results of other methods such as genetic algorithm neural network (GANN) and some standard models like generalized regression neural network (GRNN), etc. The model tested for predicting DAX stock price a period of time that global financial crisis was faced to economics. The results show that BNNMAS significantly performs accurate and reliable, so it can be considered as a suitable tool for predicting stock price specially in a long term periods.     

Optical properties and conductivity of PEDOT:PSS films treated by polyethylenimine solution for organic solar cells

We report on conductivity and optical property of three different types of poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) films [pristine PH1000 film (PH1000-p), with 5 wt.% ethylene glycol additive (PH1000-EG) and with sulfuric acid post-treatment (PH1000-SA)] before and after polyethylenimine (PEI) treatment. The PEI is found to decrease the conductivity of all the PEDOT:PSS films. The processing solvent of 2-methoxyethanol is found to significantly enhance the conductivity of PH1000-p from 1.1 up to 744 S/cm while the processing solvent of isopropanol or water does not change the conductivity of PH1000-p much. As for the optical properties, the PEI treatment slightly changes the transmittance and reflectance of PH1000-p and PH1000-EG films, while the PEI leads to an substantial increase of the absorptance in the spectral region of 400–1100 nm of the PH1000-SA films. Though the optical property and conductivity of the three different types of PEDOT:PSS films vary with the PEI treatment, the treated PEDOT:PSS films exhibit similar low work function. We demonstrate solar cells with a simple device structure of glass/low-WF PEDOT:PSS/P3HT:ICBA/high-WF PEDOT:PSS cells that exhibit good performance with open-circuit voltage of 0.82 V and fill factor up to 0.62 under 100 mW/cm² white light illumination.     

Human mobility synthesis using matrix and tensor factorizations

Human mobility prediction is of great advantage in route planning and schedule management. However, mobility data is a high-dimensional dataset in which multi-context prediction is difficult in a single model. Mobility data can usually be expressed as a home event, a work event, a shopping event and a traveling event. Previous works have only been able to learn and predict one type of mobility event and then integrate them. As the tensor model has a strong ability to describe high-dimensional information, we propose an algorithm to predict human mobility in tensors of location context data. Using the tensor decomposition method, we extract human mobility patterns with multiple expressions and then synthesize the future mobility event based on mobility patterns. The experiment is based on real-world location data and the results show that the tensor decomposition method has the highest accuracy in terms of prediction error among the three methods. The results also prove the feasibility of our multi-context prediction model.     

Influence of Bridgman solidification on microstructures and magnetic behaviors of a non-equiatomic FeCoNiAlSi high-entropy alloy

The non-equiatomic FeCoNiAlSi alloy is prepared by the Bridgman solidification (BS) technique at different withdrawal velocities (V = 30, 100, and 200 μm/s). Various characterization techniques have been used to study the microstructure and crystal orientation. The morphological evolutions accompanying the crystal growth of the alloy prepared at different withdrawal velocities are nearly the same, from equiaxed grains to columnar crystals. The transition of coercivity is closely related to the local microstructure, while the saturation magnetization changes little at different sites. The coercivity can be significantly reduced from the equiaxed grain area to the columnar crystal area when the applied magnetic field direction is parallel to the crystal growth direction, no matter what is the withdrawal velocity. In addition, the alloy possesses magnetic anisotropy when the applied magnetic field is in different directions.     

Impact sensitivities of energetic materials: Exploring the limitations of a model based only on structural formulas

Using a comprehensive set of drop weight impact test data (h₅₀) newly compiled from literature for 308 materials, a recent approach to predict impact sensitivities of nitro compounds is generalized to most explosive substances of interest. Compared to previous ones, this procedure is more thoroughly validated and exhibits a good predictive value. Furthermore, it yields new insight into the physical mechanisms involved, explaining for instance the unexpected desensitization of some oxygen-deficient triazoles upon nitration.     

灵芝菌丝体多糖的分离纯化及其单糖组成分析与分子量测定

前期杂交优化后赤芝菌种经液体深层发酵后,提取灵芝菌丝体多糖,并过DEAE-Sepharose Fast Flow柱分离纯化,利用高效体积排阻色谱(HPSEC)检测多糖级分的纯度,采用完全酸水解PMP柱前衍生化RP—HPLC测定多糖级分的单糖组成,多角度光散射仪联用装置(SEC—MALLS)测定其绝对重均分子量(Mw),并且根据分子旋转半径与分子摩尔数的关系曲线斜率初步推断其空间构象。结果显示:分离纯化得到3个多糖级分GLMP1、GLMP2和GLMP3,HPSEC检测其峰面积百分比分别为93.58%,97.64%,99.19%,单糖组成分析结果表明GLMP1、GLMP2和GLMP3均含有甘露糖、鼠李糖、半乳糖醛酸、葡萄糖、半乳糖、木糖、阿拉伯糖和岩藻糖,但单糖摩尔比各异。SEC—MALLS测试GLMP1、GLMP2和GLMP3的Mw分别为4.526×105,4.603×104,3.760×103 g/mol,3个多糖级分构象可能均为高度紧缩且具有分支结构的聚合物。     

基于注意力机制与文本信息的用户关系抽取

随着社交媒体的发展,用户之间的关系网络对于社交媒体的分析有很大的帮助。因此,该文主要研究用户好友关系检测。以往的关于用户好友关系抽取的研究主要基于社交媒体上的结构化信息,比如其他好友关系,用户的不同属性等。但是,很多时候用户本身并没有大量的好友信息存在,同时也不一定有很多确定的属性。因此,我们希望基于用户发表的文本信息来对用户关系进行预测。不同于以往的潜在好友推荐算法,该文提出了一种基于注意力机制以及长短时记忆网络(long short-term memory,LSTM)的好友关系预测模型,将好友之间的评论分开处理,通过分析用户之间的评论来判断是否具备一定的好友关系。该模型将好友双方信息拼接后的结果作为输入,并将注意力机制应用于LSTM的输出。实验表明,用户之间的评论对于好友关系预测确实有较大的实际意义,该文提出的模型较之于多个基准系统的效果,取得了明显的提升。在不加入任何其它非文本特征的情况下,实验结果的准确率达到了77%。