环境适应型压电振动发电微电源研究新进展

针对环境振动源的多样性,对环境适应型压电振动发电微电源的发展进行了回顾,对各种类型压电振动发电微电源的性能进行了分析和总结。在现有环境适应型压电振动发电微电源的基础上,提出了一种新颖的环境适应型压电振动发电微电源结构,该结构能够敏感多个方向振源的振动且能够在较宽的频率范围内收集到较多的能量。指出了环境适应型压电振动发电微电源在进一步发展中需要解决的问题,压电振动发电微电源作为一种绿色新能源装置,随着研究的不断深入,必将带来良好的应用前景。

基于动态决策实时修正的家庭能量管理系统环境适应型调度方案研究

在智能电网发展的背景下,旨在满足用户节电要求及配合电网需求响应的家庭能量管理系统(home energymanage system, HEMS)应运而生。针对基于整体优化方法的传统HEMS静态调度无法灵活地适应外部环境动态变化的问题,建立HEMS基本结构,提出基于动态决策实时修正的家庭能量管理系统环境适应型调度方案。在日前整体优化调度计划的基础上,根据实时采集的电价、气温、光伏出力等信息判别时段环境状态,利用动态决策模型对调度计划进行实时修正,给出符合实际需求的环境适应型调度方案。经试验仿真,该方案可在动态调度中做出符合用户需求的最佳决策,既继承了传统HEMS调度的优越经济性,又能适应动态变化较大的实际环境。

构建环境适应型英语学习评价模型

评价作为一种价值判断过程，势必涉及环境因素，包括社会环境、学校环境和学习者环境。环境在评价中的作用要求英语学习评价必须采用环境适应型评价模型，根据价值主体的需求设置评价标准。设计评价过程；根据环境的具体需求调整评价结构、选择评价方式；遵循学习规律，适应学习者的多元需求，采用真实性评价方式。同时，为了保证评价的效度，必须对评价的结构、过程、结果和反馈进行元评价。

环保型无溶剂环氧涂料耐候性的探索研究

研制出一种新型的环保型无溶剂耐候性环氧涂料。对无溶剂环氧涂料的基本配方进行了筛选试验 ,研究了各种光稳定剂。

论仿生机器人未来的几种可能发展

自然界在长期的演化中孕育出了各种各样的生物,而这些生物都具有神奇的结构和功能,能够在复杂多变的环境中生存下去,因此,通过研究,学习,模仿来复制和再造某些生物特性和功能将极大的提高人类对自然的适应和改造能力。从20世纪60年代开始仿生学诞生,到现在短短的几十年时间,在这方面的研究成果已经非常可观,大到军事小到日常生活,我们已经可以处处见其身影了。那么未来的仿生机器人又会往什么方向发展呢?该文将对未来仿生机器人的几种可能的发展趋势,包含群体型机器人,多环境适应型机器人以及学习型机器人进行分析。

Safflower Evaluation under Contrasted Environment Conditions and Selection of Promising Genotypes

Development of stable crops cultivars adapted to environmental constraints is very important for food security. Safflower, an oilseed crop which tolerates environmental abiotic stresses, is suitable for marginal lands relatively dry and deprived from fertilizer inputs or irrigation. A set of Moroccan and introduced cultivars as well as international accessions were conducted at Oujda （Eastern of Morocco） during 2009-2010 for late and conventional sowing under two water regimes, in a field experiment using a completely randomized design, with three replications. The objective was to evaluate the effect of genotype and contrasting environment on safflower behavior and to select genotypes with large adaptation to the contrasted environmental conditions. Morphological, physiological and agronomic traits, as well as the stress susceptibility index （SSI）, were recorded in this study. Results showed significant effect of genotype, year （sowing time）, water regime and their interaction on most of the studied parameters. Late sowing and drought affected negatively all the parameters except seed oil which lightly increased under drought stress. Number of heads per plant （NHP） had the strongest association with seed yield under both drought and non-drought conditions, and hence could be taken as selection criterion for safflower seed yield improvement. Five accessions showed the highest overall mean seed yield （~ 1,000 kg/ha） and four accessions exhibited the highest overall mean seed oil content （〉 310 g/kg）. For late sowing, the accessions P1262421 and PI537604 produced the highest seed yield （〉 800 kg/ha） and the highest seed oil content （〉 290 g/kg）. For conventional sowing, the accessions PI250076 and PI250523 were the most performant, with a seed yield 〉 1,300 kg/ha and a seed oil content 〉 330 g/kg. Based on their mean productivity across environments, their SSI and their MDA, P1271073 and P1250076 could be selected and used as promising germplasm in safflower breeding program in Morocco as well as other dry areas throughout the world.

Sensorless Predictive Algorithm for Permanent Magnet Brushless DC Drives

A high resolution speed and position identification algorithm, suitable for brushless DC drives, is presented in this paper. In particular, the algorithm is proposed for BLDC （brushless DC） machines that are characterized by an un-ideal trapezoidal emfs shape. The algorithm, which is developed basing upon the MRAS technique （model reference adaptive system） and the Popov＇s hyperstability criterion, guarantees the convergence of the estimated rotor speed and position signals to their corresponding actual values. The identification procedure can be performed starting from the knowledge of low resolution rotor position signals, phase currents and the BLDC emfs shape. The identification algorithm is properly tested on a BLDC drive controlled by a predictive algorithm, by performing a simulation study in the Matlab-Simulink environment. The corresponding results have highlighted the effectiveness of the proposed sensorless predictive control system, at both low and high speed operation.

Effects of environment and genotype-by-environment interaction on phenotype of Rorippa elata(Brassicaceae),an endemic alpine plant in the Hengduan mountains

Global climate change poses a severe threat to mountain biodiversity.Phenotypic plasticity and local adaptation are two common strategies for alpine plant to cope with such change.They may facilitate organismal adaptation to contrasting environments,depending on the influences of the environment or genotype or their interacted effects.In this study,we use an endemic alpine plant(Rorippa elata)in the Hengduan mountains(HDM)to unravel its phenotypic basis of adaptation strategy and evaluate the relative contributions of environment and genotype to its phenotype.We transplanted 37 genotypes of R.elata into two common gardens across low and high elevations(2800 vs.3800 m)during 2021-2022.Nine fitness-related traits were measured,including flowering probability and glucosinolates(GS)content.We estimated the environmental or genotypic contributions to the phenotype and identified the main environmental components.Our results revealed that both environment and genotype-by-environment interactions contributed to the phenotypes of R.elata.Latitudinal heterogeneity was identified as a key factor that explained 24%of the total phenotypic variation.In particular,genotypes of the northern HDM showed significantly higher plasticity in flowering probability than those of the southern HDM.Furthermore,within the southern HDM,GS content indicated local adaptation to herbivory stresses for R.elata genotypes along elevations.In conclusion,our results suggest that R.elata may have adapted to the alpine environment through species-level plasticity or regional-level local adaptation.These processes were shaped by either complex topography or interactions between genotype and mountain environments.Our study provides empirical evidence on the adaptation of alpine plants.

An enhanced hybrid and adaptive meta-model based global optimization algorithm for engineering optimization problems

Due to the good balance between high efficiency and accuracy, meta-model based optimization algorithm is an important global optimization category and has been widely applied. To better solve the highly nonlinear and computation intensive en- gineering optimization problems, an enhanced hybrid and adaptive meta-model based global optimization （E-HAM） is first proposed in this work. Important region update method （IRU） and different sampling size strategies are proposed in the opti- mization method to enhance the performance. By applying self-moving and scaling strategy, the important region will be up- dated adaptively according to the search results to improve the resulting precision and convergence rate. Rough sampling strategy and intensive sampling strategy are applied at different stages of the optimization to improve the search efficiently and avoid results prematurely gathering in a small design space. The effectiveness of the new optimization algorithm is verified by comparing to six optimization methods with different variables bench mark optimization problems. The E-HAM optimization method is then applied to optimize the design parameters of the practical negative Poisson＇s ratio （NPR） crash box in this work. The results indicate that the proposed E-HAM has high accuracy and efficiency in optimizing the computation intensive prob- lems and can be widely used in engineering industry.