Bis(μ-4-nitro­phthalato)bis­[diaqua­(1,10-phenanthroline)manganese(II)]

In the title compound, [Mn2(C8H3NO6)2(C12H8N2)2(H2O)4], the MnII atom in the centrosymmetric binuclear unit has a distorted octa­hedral geometry and is coordinated by a chelating 1,10-phenanthroline ligand, two monodentate carboxyl­ate anions from two 4-nitro­phthalates and two coordinated water mol­ecules. The two MnII ions in the mol­ecule are bridged by two 4-nitro­phthalate anions, both in a bis-monodentate mode, which finally leads to the formation of the binuclear unit. Intra­molecular O—H⋯O hydrogen bonds between the coordinated and uncoordinated O atoms of one monodentate carboxyl­ate group and the corresponding coordinated water mol­ecules result in an eight-membered and two six-membered rings. In the crystal structure, inter­molecular O—H⋯O hydrogen bonds link the dinuclear mol­ecules into supra­molecular chains propagating parallel to [100]

Optimization for Variable Height Wind Farm Layout Model

The optimization of wind farm layouts is very important for the effective utilization of wind resources. A fixed wind turbine hub height in the layout of wind farms leads to a low wind energy utilization and a higher LCOE (levelized cost of electricity). WOMH (Wind Farm Layout Optimization Model Considering Multiple Hub Heights) is proposed in this paper to tackle the above problem. This model is different from the traditional fixed hub height model, as it uses a variable height wind turbine. In WOMH, the Jensen wake and Weibull distribution are used to describe the wake effect on the wind turbines and wind speed distribution, respectively. An algorithm called DEGM (differential evolution and greedy method with multiple strategies) is proposed to solve WOMH, which is NP hard. In the DEGM, seven strategies are designed to adjust the distribution coordinates of wind turbines so that the height of the wind turbines will be arranged from low to high in the wind direction. This layout reduces the Jensen wake effect, thus reducing the value of the LCOE. The experimental results show that in the DEGM, when the number of wind turbines is 5, 10, 20, 30 and 50, the WOMH reduces the LCOE by 13.96%, 12.54%, 8.22%, 6.14% and 7.77% compared with the fixed hub height model, respectively. In addition, the quality of the solution of the DEGM is more satisfactory than that of the three-dimensional greedy algorithm and the DEEM (differential evolution with a new encoding mechanism) algorithm. In the case of five different numbers of wind turbines, the LCOE of DEGM is at least 3.67% lower than that of DEEM, and an average of 6.83% lower than that of three-dimensional greedy. The model and algorithm in this paper provide an effective solution for the field of wind farm layout optimization

Masked Spatial-Spectral Autoencoders Are Excellent Hyperspectral Defenders

Deep learning methodology contributes a lot to the development of hyperspectral image (HSI) analysis community. However, it also makes HSI analysis systems vulnerable to adversarial attacks. To this end, we propose a masked spatial-spectral autoencoder (MSSA) in this paper under self-supervised learning theory, for enhancing the robustness of HSI analysis systems. First, a masked sequence attention learning module is conducted to promote the inherent robustness of HSI analysis systems along spectral channel. Then, we develop a graph convolutional network with learnable graph structure to establish global pixel-wise combinations.In this way, the attack effect would be dispersed by all the related pixels among each combination, and a better defense performance is achievable in spatial aspect.Finally, to improve the defense transferability and address the problem of limited labelled samples, MSSA employs spectra reconstruction as a pretext task and fits the datasets in a self-supervised manner.Comprehensive experiments over three benchmarks verify the effectiveness of MSSA in comparison with the state-of-the-art hyperspectral classification methods and representative adversarial defense strategies.Comment: 14 pages, 9 figure

The ADH1B Arg47His polymorphism in East Asian populations and expansion of rice domestication in history

<p>Abstract</p> <p>Background</p> <p>The emergence of agriculture about 10,000 years ago marks a dramatic change in human evolutionary history. The diet shift in agriculture societies might have a great impact on the genetic makeup of Neolithic human populations. The regionally restricted enrichment of the class I alcohol dehydrogenase sequence polymorphism (ADH1BArg47His) in southern China and the adjacent areas suggests Darwinian positive selection on this genetic locus during Neolithic time though the driving force is yet to be disclosed.</p> <p>Results</p> <p>We studied a total of 38 populations (2,275 individuals) including Han Chinese, Tibetan and other ethnic populations across China. The geographic distribution of the ADH1B*47His allele in these populations indicates a clear east-to-west cline, and it is dominant in south-eastern populations but rare in Tibetan populations. The molecular dating suggests that the emergence of the ADH1B*47His allele occurred about 10,000~7,000 years ago.</p> <p>Conclusion</p> <p>We present genetic evidence of selection on the ADH1BArg47His polymorphism caused by the emergence and expansion of rice domestication in East Asia. The geographic distribution of the ADH1B*47His allele in East Asia is consistent with the unearthed culture relic sites of rice domestication in China. The estimated origin time of ADH1B*47His allele in those populations coincides with the time of origin and expansion of Neolithic agriculture in southern China.</p