高地压严重突出区域下向穿层钻孔预抽消突技术

利用工作面高抽巷施工掩护顺槽掘进工作面下向穿层条带预抽钻孔,通过对钻场底板深、浅孔注浆封堵围岩裂隙解决了孔内返水不畅难题;采取了"两堵一注"封孔方式确保封孔严密不漏气;孔内全程下吹水管,实现了单孔、分组、定时吹水工作目标,提高了下向消突钻孔抽采效果,形成了一套高效、快速的下向穿层钻孔消突管理模式,实现了高地压严重突出区域煤层高效消突以及技术经济一体化目标,安全经济效益显著。     

基于信息熵抑制的大数据特征隐匿性加密仿真

针对传统大数据特征加密方法中大数据精度较差、加密耗时较长的问题,提出基于信息熵抑制的大数据特征隐匿性加密方法。分析信息熵抑制相关理论,采用信息熵映射函数对大数据分类,将信息熵抑制过程转化为计算2个文件相似性的过程,并根据余弦相似度公式以及Hamming距离值计算数据相似度,完成数据消冗。在完成数据消冗的基础上,分析分组密码方法和ECC加密方法,结合两种方法得到大数据特征隐匿性加密处理计算公式,实现基于信息熵抑制的大数据特征隐匿性加密。实验研究结果表明,所提方法能够有效去除冗余数据,提升大数据精度,加快数据加密的处理速度,高效完成大数据加密过程。     

高等级生物安全实验室通风空调系统冗余设计原则探讨

对比分析了国内外标准有关高等级生物安全实验室通风空调系统冗余设计的要求。结合我国已有高等级生物安全实验室,重点分析了送风机、排风机、排风高效过滤器冗余设计的具体实施形式和存在的问题,给出了冗余设计原则建议。     

不同含量低污染水对人工湿地中细菌的影响研究

探究水平流人工湿地(HFCW)系统处理低污染水过程中,相同COD/ρ(TN)下不同碳氮含量对细菌群落结构的影响。结果表明,进水为较高碳氮含量的HFCW(HF1)和进水为较低碳氮含量的HFCW(HF2)对COD和TN的去除效率具有一定的差异,HF1和HF2对COD的去除效率分别为48.26%和28.89%,对TN的去除率分别为79.06%和81.87%。HF1中细菌的丰富度和多样性均高于HF2,HF1中富集的优势细菌为Chloroflexaceae、Comamonadaceae和Rhodocyclaceae,均具有异养反硝化功能,HF2中富集的优势细菌为Xanthomonadaceae和Rhodocyclaceae,其中Xanthomonadaceae具有自养反硝化功能。COD、NH4^+-N和NO3^--N对HF1中细菌群落的影响大于对HF2中细菌群落的影响,HF1中COD对细菌群落的影响大于NH4^+-N和NO3^--N。     

Chemical Recycling of Carbon Fiber Reinforced Epoxy Composites Using Mild Conditions

The increased use of carbon fiber reinforced thermosets generates more waste and end-of-life products. However, an efficient recycling method for the expensive carbon fibers has not yet been developed. The selective decomposition of amine-cured epoxy resin under mild conditions is presented. A two-step method was investigated to decompose the epoxy resin. The optimum parameters were initially determined using a model compound. By analysis of the reaction products, a cleavage of the C–N bond according to the Cope elimination could be proven. Therefore, the Cope elimination is suggested as the main step of the decomposition of amine-cured epoxy resins in presence of hydrogen peroxide. By dissolving the resin, it is possible to recover resin-free fibers with unimpaired mechanical properties.     

The three Rs of river ecosystem resilience: Resources,recruitment, and refugia

Resilience in river ecosystems requires that organisms must persist in the face of highly dynamic hydrological and geomorphological variations. Disturbance events such as floods and droughts are postulated to shape life history traits that support resilience, but river management and conservation would benefit from greater understanding of the emergent effects in communities of river organisms. We unify current knowledge of taxonomic‐, phylogenetic‐, and trait‐based aspects of river communities that might aid the identification and quantification of resilience mechanisms. Temporal variations in river productivity, physical connectivity, and environmental heterogeneity resulting from floods and droughts are highlighted as key characteristics that promote resilience in these dynamic ecosystems. Three community‐wide mechanisms that underlie resilience are (a) partitioning (competition/facilitation) of dynamically varying resources, (b) dispersal, recolonization, and recruitment promoted by connectivity, and (c) functional redundancy in communities promoted by resource heterogeneity and refugia. Along with taxonomic and phylogenetic identity, biological traits related to feeding specialization, dispersal ability, and habitat specialization mediate organism responses to disturbance. Measures of these factors might also enable assessment of the relative contributions of different mechanisms to community resilience. Interactions between abiotic drivers and biotic aspects of resource use, dispersal, and persistence have clear implications for river conservation and management. To support these management needs, we propose a set of taxonomic, phylogenetic, and life‐history trait metrics that might be used to measure resilience mechanisms. By identifying such indicators, our proposed framework can enable targeted management strategies to adapt river ecosystems to global change.     

Evidence for alternative exhaled elimination profiles of disinfection by-products and potential markers of airway responses to swimming in a chlorinated pool environment

Chlorine-based disinfectants protect pool water from pathogen contamination but produce potentially harmful halogenated disinfection by-products (DBPs). This study characterized the bioaccumulation and elimination of exhaled DBPs post-swimming and investigated changes in exhaled breath profiles associated with chlorinated pool exposure. Nineteen participants provided alveolar-enriched breath samples prior to and 5, 90, 300, 510, and 600 minutes post-swimming. Known DBPs associated with chlorinated water were quantitated by thermal desorption-gas chromatography-mass spectrometry. Two distinct exhaled DBP elimination profiles were observed. Most participants (84%) reported peak concentrations immediately post-swimming that reduced exponentially. A sub-group exhibited a previously unobserved and delayed washout profile with peak levels at 90 minutes post-exposure. Metabolomic investigations tentatively identified two candidate biomarkers associated with swimming pool exposure, demonstrating an upregulation in the hours after exposure. These data demonstrated a hitherto undescribed exhaled DBP elimination profile in a small number of participants which contrasts previous findings of uniform accumulation and exponential elimination. This sub-group which exhibited delayed peak-exhaled concentrations suggests the uptake, processing, and immediate elimination of DBPs are not ubiquitous across individuals as previously understood. Additionally, non-targeted metabolomics highlighted extended buildup of compounds tentatively associated with swimming in a chlorinated pool environment that may indicate airway responses to DBP exposure.     

Optimal-correlation-based reconstruction for distributed compressed video sensing

Distributed compressed video sensing (DCVS) is a framework that integrates both compressed sensing and distributed video coding characteristics to achieve a low-complexity video coding. However, how to design an efficient joint reconstruction by leveraging more realistic signal models is still an open challenge. In this paper, we present a novel optimal-correlation-based reconstruction method for compressively sampled videos from multiple measurement vectors. In our method, the sparsity is mainly exploited through inter-signal correlations rather than the traditional frequency transform, wherein the optimization is not only over the signal space to satisfy data consistency but also over all possible linear correlation models to achieve minimum-l₁-norm correlation noise. Additionally, a two-phase Bregman iterative based algorithm is outlined for solving the optimization problem. Simulation results show that our proposal can achieve an improved reconstruction performance in comparison to the conventional approaches, and especially, offer a 0.7–9.9 dB gain in the average PSNR for DCVS.     

Dynamic modeling and redundant force optimization of a 2-DOF parallel kinematic machine with kinematic redundancy

High precision is still one of the challenges when parallel kinematic machines are applied to advanced equipment. In this paper, a novel planar 2-DOF parallel kinematic machine with kinematic redundancy is proposed and a method for redundant force optimization is presented to improve the precision of the machine. The inverse kinematics is derived, and the dynamic model is modeled with the Newton–Euler method. The deformations of the kinematic chains are calculated and their relationship with kinematic error of the machine is established. Then the size and direction of the redundant force acting on the platform are optimized to minimize the position error of the machine. The dynamic performance of the kinematically redundant machine is simulated and compared with its two corresponding counterparts, one is redundantly actuated and the other is non-redundant. The proposed kinematically redundant machine possesses the highest position precision during the motion process and is applied to develop a precision planar mobile platform as an application example. The method is general and suitable for the dynamic modeling and redundant force optimization of other redundant parallel kinematic machines.