Environmental and economic analysis of solar systems in Madeira,Portugal

The solar market started growing in Madeira from 2008 to profit from great solar exposition. Initially, there was a strong investment, but the removal of the Feed-in Tariffs, and self-consumption regulations caused stagnation in new solar systems. This policy change is relevant to check the current lack of investments and an analysis of the environmental and economic effects over a decade is necessary to help defining the solar policy for the next decades. Results show savings of 100 thousand tons of CO2 and 38 million euros supporting the need of policy reversal to address a change in the net-metering regime.     

基于APD的紫外光语音通信系统设计

设计了一套基于雪崩光电二极管(APD)的紫外光语音通信系统,系统分为硬件电路、软件和光学系统三个部分.采用先进多带激励(AMBE)编码和开关键控(OOK)调制技术,使语音通信速率降低到2.5kb/s.实验表明,该系统在光功率仅为纳瓦量级时仍能完成实时语音通信,系统的研制对日盲APD的制造和应用具有现实的指导意义.     

On the selective electrogeneration of 1-aryl-4,4-dichlorobut-3-en-1-ones from 2,2,2-trichloroethylideneacetophenones: A combined experimental and HF and B3LYP computational study

Electrochemical reductions of 2,2,2-trichloroethylideneacetophenones carried out in the presence of acetic acid lead to 1-aryl-4,4-dichlorobut-3-en-1-ones in near quantitative yields with total exclusion of 2,2-dichloroethylideneacetophenones isomers. The origin of the selectivity towards the formation of unconjugated -unsaturated ketones without competitive generation of -unsaturated isomers has been studied on the basis of chemical, voltammetric and HF and B3LYP density functional computational methods. It has been concluded that dienol intermediates are involved in the operating reaction mechanism but dienolate anions are not its precursors. The participation of acetic acid as a catalyst promoting the ketonization of dienols through C- protonation with concomitant deprotonation of the hydroxyl group is proposed.     

Land use changes, greenhouse gas emissions and fossil fuel substitution of biofuels compared to bioelectricity production for electric cars in Austria

Bioenergy is one way of achieving the indicative target of 10% renewable energy in the transportation sector outlined in the EU Directive 2009/28/EC. This article assesses the consequences of increasing the use of bioenergy for road transportation on land use, greenhouse gas (GHG) emissions, and fossil fuel substitution. Different technologies, including first and second generation fuels and electric cars fuelled by bioelectricity are assessed in relation to existing bioenergy uses for heat and power production. The article applies a spatially explicit energy system model that is coupled with a land use optimization model to allow assessing impacts of increased biomass utilization for energy production on land use in agriculture and forest wood harvests. Uncertainty is explicitly assessed with Monte-Carlo simulations of model parameters. Results indicate that electric mobility could save GHG emissions without causing a significant increase in domestic land use for energy crop production. Costs of electric cars are still prohibitive. Second generation biofuels are more effective in producing fuels than first generation ethanol. However, competition with power and heat production from ligno-cellulosic feedstock causes an increase in GHG emissions when introducing second generation fuels in comparison to a baseline scenario.     

2010—2020年山西电网主结构规划探讨

探讨了山西电网主结构规划,认为经过10a急速发展期之后,2010～2020年山西电网将迎来平稳发展期。2010—2020年山西电网主网架以500kV南北纵向通道为主,横向通道适当联络。500kV电网主网架形成并不断加强的同时,全省电网逐步解开500／220kV电磁环网,实现分层分区运行。     

The Irish dairy industry – Recent history and strategy,current state and future challenges

This study explores drivers and resulting changes in the structure and technical efficiency of Irish dairy farms from 2005 to 2018 (covering pre‐ and post‐milk quotas) during which milk production increased by 54%. Over this period, farm structure changed dramatically (fourfold increase in farmers milking >100 cows) and farmers improved technical efficiency and profitability and reduced the greenhouse gas emission intensity of milk produced. The impact of the adoption of key technologies at farm level and the contribution of strategic direction nationally (the Irish Government's Food Harvest 2020 Strategy) influencing this development are explored as are future sector challenges.     

TransformerG:基于层级图结构与文本注意力机制的法律文本多跳阅读理解

该文针对Cail2020法律多跳机器阅读理解数据集进行研究,提出了TransformerG,一个基于不同层级的实体图结构与文本信息的注意力机制融合的多跳阅读理解模型。该模型有效地结合了段落中问题节点、问题的实体节点、句子节点、句中的实体节点的特征与文本信息的特征,从而预测答案片段。此外,该文提出了一种句子级滑动窗口的方法,有效解决在预训练模型中文本过长导致的截断问题。利用TransformerG模型参加中国中文信息学会计算语言学专委会(CIPS-CL)和最高人民法院信息中心举办的“中国法研杯”司法人工智能挑战赛机器阅读理解赛道,取得了第2名的成绩。     

Denitrification of Wastewater: Operating Experiences in the US and Pilot-Plant Studies in the UK

Due to the EC urban wastewater treatment Directive, considerable attention has been focused on nitrogen removal, particularly from biological filter nitrified effluents. The paper presents a reliable and proven full-scale fixed-film denitrification system installed in the US and evaluated on pilot scale by Severn Trent Water. Methanol has been used as a carbon source dosed prior to the deep-bed sand filter. The filter removes considerable suspended solids as well as providing the hydraulic retention and surface area for the growth of the biological denitrifying bacteria. Flow sheets of typical US installations are given and operational data with costs are presented for the pilot-plant evaluation.     

A Hybrid Attention-Based Residual Unet for Semantic Segmentation of Brain Tumor

Segmenting brain tumors in Magnetic Resonance Imaging (MRI) volumes is challenging due to their diffuse and irregular shapes. Recently, 2D and 3D deep neural networks have become famous for medical image segmentation because of the availability of labelled datasets. However, 3D networks can be computationally expensive and require significant training resources. This research proposes a 3D deep learning model for brain tumor segmentation that uses lightweight feature extraction modules to improve performance without compromising contextual information or accuracy. The proposed model, called Hybrid Attention-Based Residual Unet (HA-RUnet), is based on the Unet architecture and utilizes residual blocks to extract low- and high-level features from MRI volumes. Attention and Squeeze-Excitation (SE) modules are also integrated at different levels to learn attention-aware features adaptively within local and global receptive fields. The proposed model was trained on the BraTS-2020 dataset and achieved a dice score of 0.867, 0.813, and 0.787, as well as a sensitivity of 0.93, 0.88, and 0.83 for Whole Tumor, Tumor Core, and Enhancing Tumor, on test dataset respectively. Experimental results show that the proposed HA-RUnet model outperforms the ResUnet and AResUnet base models while having a smaller number of parameters than other state-of-the-art models. Overall, the proposed HA-RUnet model can improve brain tumor segmentation accuracy and facilitate appropriate diagnosis and treatment planning for medical practitioners.     

僵尸网络(BOTNET)监控技术研究

僵尸网络(BOTNET)是互联网网络的重大安全威胁之一,本文对僵尸网络的蔓延、通信和攻击模式进行了介绍,对僵尸网络发现、监测和控制方法进行了研究.针对目前最主要的基于IRC协议僵尸网络,设计并实现一个自动识别系统,可以有效的帮助网络安全事件处理人员对僵尸网络进行分析和处置.