Fundamentals and Knowledge Relevant to the Drag Reduction Through Air Cavitation of Ship's Hulls

Numerous experiments with ship drag reduction by air bottom cavitation in diverse countries have exhibited very different achievements. Therefore, a paper clarifying mechanics of this drag reduction and describing the proven design algorithms is appropriate. Solutions of an ideal fluid problem existing in diverse ranges of Froude number are compared and the solutions suitable for ship drag reduction are considered in more detail. It is emphasized in this paper that a cavity locker at the trailing edge of the bottom niche (recess) assigned for the cavity is necessary to reduce the necessary air supply to the cavity and to mitigate the cavity tail pulsation resulting in a drag penalty. It is also pointed out that the bottom niche depth must allow for cavity withstanding under impact of waves in seaways. Bottom cavitation may even reduce wave-induced loads on the hull. With taking into account the above-mentioned design aspects, the energy spent on the air supply can be minimized. An algorithm of bottom design based on ideal fluid theory is also explained in the paper. Comparisons with several model test results are provided to illustrate the algorithm employment.

Redesigning the Eterna100 for the Vienna 2 folding engine

The rational design of RNA is becoming important for rapidly developing technologies in medicine and biochemistry. Recent work has led to the development of several RNA secondary structure design algorithms and corresponding benchmarks to evaluate their performance. However, the performance of these algorithms is linked to the nature of the underlying algorithms for predicting secondary structure from sequences. Here, we show that an online community of RNA design experts is capable of modifying an existing RNA secondary structure design benchmark (Eterna100) with minimal alterations to address changes in the folding engine used (Vienna 1.8 updated to Vienna 2.4). We tested this new Eterna100-V2 benchmark with five RNA design algorithms, and found that neural network-based methods exhibited reduced performance in the folding engine they were evaluated on in their respective papers. We investigated this discrepancy, and determined that structural features, previously classified as difficult, may be dependent on parameters inherent to the RNA energy function itself. These findings suggest that for optimal performance, future algorithms should focus on finding strategies capable of solving RNA secondary structure design benchmarks independently of the free energy benchmark used. Eterna100-V1 and Eterna100-V2 benchmarks and example solutions are freely available at https://github.com/eternagame/eterna100-benchmarking.

Improving Welfare in One-Sided Matchings using Simple Threshold Queries

We study one-sided matching problems where each agent must be assigned at most one object. In this classic problem it is often assumed that agents specify only ordinal preferences over objects and the goal is to return a matching that satisfies some desirable property such as Pareto optimality or rank-maximality. However, agents may have cardinal utilities describing their preference intensities and ignoring this can result in welfare loss. We investigate how to elicit additional cardinal information from agents using simple threshold queries and use it in turn to design algorithms that return a matching satisfying a desirable matching property, while also achieving a good approximation to the optimal welfare among all matchings satisfying that property. Overall, our results show how one can improve welfare by even non-adaptively asking agents for just one bit of extra information per object.

A REVIEW OF MACHINE LEARNING AND ITS APPLICATIONS

Today, huge amounts of data are available everywhere. Therefore, analyzing this data is very important to derive useful information from it and develop an algorithm based on this analysis. This can be achieved through data mining and machine learning. Machine learning is an essential part of artificial intelligence used to design algorithms based on data trends and past relationships between data. Machine learning is used in a variety of areas such as bioinformatics, intrusion detection, information retrieval, games, marketing, malware detection, and image decoding. This paper shows the work of various authors in the field of machine learning in various application areas.

Design support of structural balance (inertial symmetry) of disk-shaped rotors

A quantitative criterion is proposed for differentiating products (rotors) as objects of balancing into disk-shaped and cylindrical ones with the subdivision of the first ones into types depending on the ratio of their length to diameter. For each of these types, the specifics of modeling the characteristics of imbalance from the specified values of the mass eccentricity and the skew of the main central axis of inertia relative to the rotation axis of the product are established, as well as the features of correcting these characteristics during product design. Algorithms for design ensure of dynamic balance (inertial symmetry) of structures of disc-shaped rotors for all types are developed. Keywords: disc-shaped rotor, inertial asymmetry, modeling, dynamic imbalance, correcting imbalances and masses, algorithms [email protected]