Bücher von Xin Yao

L'autoriparazione, come fenomeno naturale, è stata ampiamente osservata e studiata in diversi campi. Con l'autoriparazione, le specie viventi si auto-riparano le ferite per ripristinare le funzioni fisiologiche, mentre i materiali non biologici tornano al loro stato originale, ad esempio la crescita di un sottile strato superficiale si verifica nella rigenerazione di cristalli incompleti di KH2PO4. Qui abbiamo sviluppato due strategie di semina per creare forme cristallografiche incomplete (ad esempio, angoli concavi retti) di cristalli superconduttori YBa2Cu3O7-¿ (YBCO) con capacità di autoriparazione nella crescita per fusione top-seed. Una è la semina in situ dell'autoassemblaggio, grazie alla quale l'auto-riparabilità promuove la crescita di YBCO, mentre l'altra è la semina a connessione verticale, grazie alla quale l'auto-riparabilità innesca la nucleazione di YBCO. Di conseguenza, la cristallizzazione rapida ha avuto origine negli angoli concavi e ha generato rapidamente una morfologia di crescita iniziale che si avvicina all'equilibrio. Inoltre, queste regioni a crescita rapida, che includono il cristallo concavo o il seme, funzionano innanzitutto come regioni di semina efficaci, consentendo l'ampliamento del settore di crescita orientato a c e il miglioramento delle proprietà dei cristalli YBCO.

Self-repairing, as a natural phenomenon, has been vastly observed and investigated in a variety of fields. With self-reparability, living species self-heal their wounds to restore physiological functions while non-biological materials return to their original states, e.g., thin surface layer growth occurs in the regeneration of incomplete KH2PO4 crystals. Here, we developed two seeding strategies for creating incomplete crystallographic shapes (i.e., right-angled concave corners) of YBa2Cu3O7¿¿ (YBCO) superconducting crystals with self-repairing capability in top-seeded melt-growth. One is in situ self-assembly seeding, by which self-reparability promotes YBCO growth, while the other is vertically-connected seeding, by which self-reparability triggers YBCO nucleation. Consequently, rapid crystallization originated at concave corners and swiftly generated initial growth morphology approaching equilibrium. Furthermore, these rapid-growth regions including the concave crystal or seed innately functioned as sizable effective seeding regions, enabling the enlargement of c-oriented growth sector and the enhancement of properties for YBCO crystals.

This volume contains selected papers presented at the Second Asia-Paci c C- ference on Simulated Evolution and Learning (SEAL'98), from 24 to 27 Nov- ber 1998, in Canberra, Australia. SEAL'98 received a total of 92 submissions (67 papers for the regular sessions and 25 for the applications sessions). All papers were reviewed by three independent reviewers. After review, 62 papers were - cepted for oral presentation and 13 for poster presentation. Some of the accepted papers were selected for inclusion in this volume. SEAL'98 also featured a fully refereed special session on Evolutionary Computation in Power Engineering - ganised by Professor Kit Po Wong and Dr Loi Lei Lai. Two of the ve accepted papers are included in this volume. The papers included in these proceedings cover a wide range of topics in simulated evolution and learning, from self-adaptation to dynamic modelling, from reinforcement learning to agent systems, from evolutionary games to e- lutionary economics, and from novel theoretical results to successful applications, among others. SEAL'98 attracted 94 participants from 14 di erent countries, namely A- tralia, Belgium, Brazil, Germany, Iceland, India, Japan, South Korea, New Z- land, Portugal, Sweden, Taiwan, UK and the USA. It had three distinguished international scientists as keynote speakers, giving talks on natural computation (Hans-Paul Schwefel), reinforcement learning (Richard Sutton), and novel m- els in evolutionary design (John Gero). More information about SEAL'98 is still available at http://www.cs.adfa.edu.au/conference/seal98/.

This book constitutes the refereed proceedings of the 14th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2013, held in Hefei, China, in October 2013. The 76 revised full papers presented were carefully reviewed and selected from more than 130 submissions. These papers provided a valuable collection of latest research outcomes in data engineering and automated learning, from methodologies, frameworks and techniques to applications. In addition to various topics such as evolutionary algorithms, neural networks, probabilistic modelling, swarm intelligent, multi-objective optimisation, and practical applications in regression, classification, clustering, biological data processing, text processing, video analysis, including a number of special sessions on emerging topics such as adaptation and learning multi-agent systems, big data, swarm intelligence and data mining, and combining learning and optimisation in intelligent data engineering.