Artificial Photosynthesis Would Unify the Electricity-Carbohydrate-Hydrogen Cycle for Sustainability

Sustainable development requires balanced integration of four basic human needs – air (O2/CO2), water, food, and energy. To solve key challenges, such as CO2 fixation, electricity storage, food production, transportation fuel production, water conservation or maintaining an ecosystem for space travel, we wish to suggest the electricity-carbohydrate-hydrogen (ECHo) cycle, where electricity is a universal energy carrier, hydrogen is a clean electricity carrier, and carbohydrate is a high-energy density hydrogen (14.8 H2 mass% or 11-14 MJ electricity output/kg)carrier plus a food and feed source. Each element of this cycle can be converted to the other reversibly & efficiently depending on resource availability, needs, and costs. In order to implement such cycle, here we propose to fix carbon dioxide by electricity or hydrogen to carbohydrate (starch) plus ethanol by cell-free synthetic biology approaches. According to knowledge in the literature, the proposed artificial photosynthesis must be operative. Therefore, collaborations are urgently needed to solve several technological bottlenecks before large-scale implementation

Holographic Algorithm with Matchgates Is Universal for Planar #\#CSP Over Boolean Domain

We prove a complexity classification theorem that classifies all counting constraint satisfaction problems ($\#$CSP) over Boolean variables into exactly three categories: (1) Polynomial-time tractable; (2) $\#$P-hard for general instances, but solvable in polynomial-time over planar graphs; and (3) $\#$P-hard over planar graphs. The classification applies to all sets of local, not necessarily symmetric, constraint functions on Boolean variables that take complex values. It is shown that Valiant's holographic algorithm with matchgates is a universal strategy for all problems in category (2).Comment: 94 page

Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel Removal

Deep Convolutional Neural Networks (CNNs) are widely employed in modern computer vision algorithms, where the input image is convolved iteratively by many kernels to extract the knowledge behind it. However, with the depth of convolutional layers getting deeper and deeper in recent years, the enormous computational complexity makes it difficult to be deployed on embedded systems with limited hardware resources. In this paper, we propose two computation-performance optimization methods to reduce the redundant convolution kernels of a CNN with performance and architecture constraints, and apply it to a network for super resolution (SR). Using PSNR drop compared to the original network as the performance criterion, our method can get the optimal PSNR under a certain computation budget constraint. On the other hand, our method is also capable of minimizing the computation required under a given PSNR drop.Comment: This paper was accepted by 2018 The International Symposium on Circuits and Systems (ISCAS

Screening for Soybean Varieties Resistant to Soybean Aphid

One of the basic measures to control insect pests uses insect resistance found in crop varieties. We carried out screening work on cultivars of soybeans resistant to soybean aphids (Aphis glycines Matsumura) in order to obtain resistant sources and study the mechanism of resistance. The objectives of screening are to identify soybean cultivars that are not only resistant to aphids, but also to virus diseases. We adopted simple and convenient screening methods due limitations of human resources and time. During the peak of the soybean aphid occurrence in 1983, we selected 181 accessions of soybean materials from 902 grown in our institute's nursery of cultivar resources, after investigatng the occurrence damage caused by aphids. After planting these 181 soybean accessions in plots in 1984 and carrying out two investigations on June 13 and September 5, 42 further accessions of soybean materials were selected. Of the 42 accessions, 19 accessions were selected after two investigations carried out July 2 and October 5. The 19 accessions were planted again in 1986 in plots, and two investigations were carried out June 27 and July 12, respectively. Plot-setting methods: every cultivar was planted in one plot, and no replicates were made. Each plot was 2 meters long and 3 rows were planted. Scoring standard in investigations: the scoring standard of resistance varies according to the different dates of investigation.Originating text in Chinese.Citation: Fan, Yi-Heng. (1988). Screening for Soybean Varieties Resistant to Soybean Aphid. Soybean Science, 7(2), 167-169