广义正交码索引调制系统的性能分析

摘 要：为了提高码索引调制（code index modulation，CIM）系统的传输效率，提出了一种具有更低复杂度的单输入单输出（single input single output，SISO）的广义正交码索引调制（generalized orthogonal code index modulation，GQCIM）系统。CIM 系统使用扩频码和星座符号传输信息，但只能激活两个扩频码索引和一个调制符号。而 GQCIM 系统以一种新颖的方式克服了只激活一个调制符号的限制，同时充分利用了调制符号的正交性，增加扩频码索引以传输更多的额外信息位，提高了系统的传输效率。此外，分析了GQCIM系统的理论性能，推导了误码率性能的上界。通过蒙特卡罗仿真验证了GQCIM系统的性能，对比发现GQCIM系统的理论和仿真性能一致。而且在相同的传输效率下，结果显示GQCIM系统的性能优于同样具有正交性的调制系统，如广义码索引调制（generalized code index modulation，GCIM）系统、CIM系统、码索引调制-正交空间调制（code index modulation aided quadrature spatial modulation，CIM-QSM）系统、码索引调制-正交空间调制（code index modulation aided spatial modulation，CIM-SM）系统、脉冲索引调制（pulse index modulation，PIM）系统。     

Designing optical glasses by machine learning coupled with a genetic algorithm

Engineering new glass compositions have experienced a sturdy tendency to move forward from (educated) trial-and-error to data- and simulation-driven strategies. In this work, we developed a computer program that combines data-driven predictive models (in this case, neural networks) with a genetic algorithm to design glass compositions with desired combinations of properties. First, we induced predictive models for the glass transition temperature (T_g) using a dataset of 45,302 compositions with 39 different chemical elements, and for the refractive index (n_d) using a dataset of 41,225 compositions with 38 different chemical elements. Then, we searched for relevant glass compositions using a genetic algorithm informed by a design trend of glasses having high n_d (1.7 or more) and low T_g (500 °C or less). Two candidate compositions suggested by the combined algorithms were selected and produced in the laboratory. These compositions are significantly different from those in the datasets used to induce the predictive models, showing that the used method is indeed capable of exploration. Both glasses met the constraints of the work, which supports the proposed framework. Therefore, this new tool can be immediately used for accelerating the design of new glasses. These results are a stepping stone in the pathway of machine learning-guided design of novel glasses.     

我国水-能源-粮食耦合关系及影响因素

为缓解我国水、能源和粮食资源紧张问题，促进资源可持续利用，构建水-能源-粮食系统，利用耦合协调度模型对我国的30个省（自治区、直辖市）进行测算,并利用空间杜宾模型分析主要影响因素。结果表明：2003—2017年，我国能源、粮食评价[JP]指数高于水资源评价指数，系统综合评价指数逐年递增；大部分省份耦合协调度处于初级协调水平且呈现逐年上升的态势，个别省份耦合协调度濒临失调；耦合协调度空间自相关性较强，虽有明显波动，但是呈现逐年加强的态势；影响耦合协调度的主要因素有从业人口数、固定资产投资额、人均生产总值、人口总数、[JP]文盲人口占比、工业污染排放、城镇化。     

Designing amorphous formulations of polyphenols with naringin by spray-drying for enhanced solubility and permeability

Naringin (NAR), a major flavanone (FVA) glycoside, is a component of food mainly obtained from grapefruit. We used NAR as a food additive to improve the solubility and permeability of hydrophobic polyphenols used as supplements in the food industry. The spray-dried particles (SDPs) of NAR alone show an amorphous state with a glass transition temperature (T_g) at 93.2 °C. SDPs of hydrophobic polyphenols, such as flavone (FVO), quercetin (QCT), naringenin (NRG), and resveratrol (RVT) were prepared by adding varying amounts of NAR. All SDPs of hydrophobic polyphenols with added NAR were in an amorphous state with a single T_g, but SDPs of hydrophobic polyphenols without added NAR showed diffraction peaks derived from each crystal. The SDPs with NAR could keep an amorphous state after storage at a high humidity condition for one month, except for SDPs of RVT/NAR. SDPs with NAR enhanced the solubility of hydrophobic polyphenols, especially NRG solubility, which was enhanced more than 9 times compared to NRG crystal. The enhanced solubility resulted in the increased membrane permeability of NRG. The antioxidant effect of the hydrophobic NRG was also enhanced by the synergetic effect of NAR. The findings demonstrated that NAR could be used as a food additive to enhance the solubility and membrane permeability of hydrophobic polyphenols.     

Structure,stability and optical parameters of cobalt zinc borate glasses

In this study, the impact of cobalt oxide (CoO) on the structure, stability, linear and nonlinear optical parameters of B₂O₃–Na₂O–ZnO glasses was scrutinized. A series of glass system (ZnCoNaB-glasses) was successfully prepared through the melt quenching approach. Optical absorbance, reflectance, transmittance and FTIR spectroscopy were performed for all ZnCoNaB-glasses. The FTIR results showed that the BO₄ units are enhanced while nonbridging oxygens are decreased with further CoO addition. Furthermore, ZnO exists as four-coordinated [ZnO₄] units and these units decreased with further doping of CoO. These structural variations produce a decreasing impact in Urbach energy and nonlinear refractive index, meanwhile enhance the glass stability. Further, the metallization criterion (M) values indicate that our glass samples can be used for a new generation of nonlinear optical glasses. The preceding results can predict that the investigated ZnCoNaB-glasses will be utilized in versatile applications; especially optical switching and computing.     

Identifying catalyst layer compositions of proton exchange membrane fuel cells through machine-learning-based approach

Membrane electrode assembly (MEA) is considered a key component of a proton exchange membrane fuel cell (PEMFC). However, developing a new MEA to meet desired properties, such as operation under low-humidity conditions without a humidifier, is a time- and cost-consuming process. This study employs a machine-learning-based approach using K-nearest neighbor (KNN) and neural networks (NN) in the MEA development process by identifying a suitable catalyst layer (CL) recipe in MEA. Minimum redundancy maximum relevance and principal component analysis were implemented to specify the most important predictor and reduce the data dimension. The number of predictors was found to play an essential role in the accuracy of the KNN and NN models although the predictors have self-correlations. The KNN model with a K of 7 was found to minimize the model loss with a loss of 11.9%. The NN model constructed by three corresponding hidden layers with nine, eight, and nine nodes can achieve the lowest error of 0.1293 for the Pt catalyst and 0.031 for PVA as a good additive blending in the CL of the MEA. However, even if the error is low, the prediction of PVA seems to be inaccurate, regardless of the model structure. Therefore, the KNN model is more appropriate for CL recipe prediction.     

A Highly Sensitive,Reversible, and Bidirectional Humidity Actuator by Calcium Carbonate Ionic Oligomers Incorporated Poly(Vinylidene Fluoride)

Sensitivity and multi-directional motivation are major two factors for developing optimized humidity-response materials, which are promising for sensing, energy production, etc. Organic functional groups are commonly used as the water sensitive units through hydrogen bond interactions with water molecules in actuators. The multi-coordination ability of inorganic ions implies that the inorganic ionic compounds are potentially superior water sensitive units. However, the particle forms of inorganic ionic compounds produced by classical nucleation limit the number of exposed ions to interact with water. Recent progress on the inorganic ionic oligomers has broken through the limitation of classical nucleation, and realized the molecular-scaled incorporation of inorganic ionic compounds into an organic matrix. Here, the incorporation of hydrophilic calcium carbonate ionic oligomers into hydrophobic poly(vinylidene fluoride) (PVDF) is demonstrated. The ultra-small calcium carbonate oligomers within a PVDF film endow it with an ultra-sensitive, reversible, and bidirectional response. The motivation ability is superior to other bidirectional humidity-actuators at present, which realizes self-motivation on an ice surface, converting the chemical potential energy of the humidity gradient from ice to kinetic energy.     

High performance H2 sensor based on rGO-wrapped SnO2–Pd porous hollow spheres

Hydrogen (H₂) sensors based on metal oxide semiconductors (MOS) are promising for many applications such as a rocket propellant, industrial gas and the safety of storage. However, poor selectivity at low analyte concentrations, and independent response on high humidity limit the practical applications. Herein, we designed rGO-wrapped SnO₂–Pd porous hollow spheres composite (SnO₂–Pd@rGO) for high performance H₂ sensor. The porous hollow structure was from the carbon sphere template. The rGO wrapping was via self-assembly of GO on SnO₂-based spheres with subsequent thermal reduction in H₂ ambient. This sensor exhibited excellently selective H₂ sensing performances at 390 °C, linear response over a broad concentration range (0.1–1000 ppm) with recovery time of only 3 s, a high response of ～8 to 0.1 ppm H₂ in a minute, and acceptable stability under high humidity conditions (e. g. 80%). The calculated detection limit of 16.5 ppb opened up the possibility of trace H₂ monitoring. Furthermore, this sensor demonstrated certain response to H₂ at the minimum concentration of 50 ppm at 130 °C. These performances mainly benefited from the special hollow porous structure with abundant heterojunctions, the catalysis of the doped-PdO_x, the relative hydrophobic surface from rGO, and the deoxygenation after H₂ reduction.     

Crowding as an Agronomic Factor Determining the Development of Plants and the Productivity of Linseed (Linum usitatissimum L.)

ABSTRACT

Nine sowing densities of linseed were compared in a two-year experiment. Self-regulation of the density of a linseed stand resulted in a differentiated branching of the plants. The number of capsules on a linseed shoot, as well as seeds in the capsule, and the number of seeds from the shoot were highly varied from year to year, and there is even a stronger differentiation in the weight of 1000 seeds. Linear relationships were noted between the mass of seeds from the linseed plant and the number of capsules. The yield of linseed was affected by both factors. An increasing sowing rate reduced the seed yield from the shoot and the plant. At lower seeding rates, the lateral shoots contributed in the seeds yield of the plant to a higher degree compared to the main ones.     

综放面本煤层预抽瓦斯的治理技术

为了极大限度地提高本煤层预抽效果，以马兰煤矿为研究对象，针对目前马兰煤矿的瓦斯赋存情况和现阶段所采取的抽放措施，从抽放钻孔的孔径、孔深、布置方式、封孔质量和增加煤层透气性等方面进行探索。实践表明，以上方法的运用，能够有效地提高本煤层预抽抽放效果，保证工作面安全生产。