Mangrove crab growers in the Philippines still rely on wild-caught late instar to early juvenile mangrove crablets, as supplies from hatcheries are limited. Any batch of crablets caught from the wild is a mix of the three native species under the genus Scylla. Scylla species have different growth rates. Since grow-out culture depends heavily on species' growth, growers should be able to distinguish the species as early as the juvenile stage, which is taxonomically difficult. This study was done to consolidate low-cost traditional identification techniques for juvenile Scylla from fishers of the Philippines for future validation. Focused group discussions were done in fishing communities from Bataan, Pangasinan, and Cagayan on the island of Luzon. The study was continued through online surveys, as travel was restricted due to the Covid-19 pandemic. Results indicate that 70.58% of respondents identify the species of crabs by looking at their claws and 55.88% observe the color of the crabs. Almost half, or 41.17% of respondents, consider the width and size of the carapace. Unique methods in certain Philippine regions include observation of the behavior patterns, carapace texture, rate of weight gain, and seasonality. Validation of the traditional practices identified in this study would result in a reliable "at-a-glance" method of identifying juvenile Scylla in the Philippines, which would shorten the culture period, improve production gains, and manage local populations.

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Wind power generation prediction plays an important role in the safety and economic operation of the power system. There are many parameters recorded in wind farm data, such as wind power, wind speed, wind direction, and so on. Traditional wind power prediction modeling methods lack the mining of these parameter data and fail to make good use of some potential physical information. To address this challenge, this paper proposes a multiview neural network learning framework to predict wind power. One is the data attribute view of wind power, which uses the historical data feature of the wind power itself to learn the future wind power feature. The other is the physical attribute view of wind power, which uses the physical attribute features associated with the wind power definition to learn the future features. Then all the learned features are jointly fused to predict the future wind power values. In addition, an uncertain factor is proposed and computed, which is inspired by the wind power formula and usually associated with internal and external environment perturbation. All time series features are input into the gated recurrent unit neural network to form a hybrid neural network framework for wind power prediction. Experimental results under the measured condition and the standard condition of wind farms demonstrate the effectiveness of the proposed method.