A Web Integration Framework for Cheap Flight Fares

This chapter presents the Distributed Adaptive Control (DAC) theory of the mind and brain of living machines. DAC provides an explanatory framework for biological brains and an integration framework for synthetic ones. DAC builds on several themes presented in the handbook: it integrates different perspectives on mind and brain, exemplifies the synthetic method in understanding living machines, answers well-defined constraints faced by living machines, and provides a route for the convergent validation of anatomy, physiology, and behavior in our explanation of biological living machines. DAC addresses the fundamental question of how a living machine can obtain, retain, and express valid knowledge of its world. We look at the core components of DAC, specific benchmarks derived from the engagement with the physical and the social world (the H4W and the H5W problems) in foraging and human–robot interaction tasks. Lastly we address how DAC targets the UTEM benchmark and the relation with contemporary developments in AI.

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Q-Learnheuristics: Towards Data-Driven Balanced Metaheuristics

Mathematics ◽

10.3390/math9161839 ◽

2021 ◽

Vol 9 (16) ◽

pp. 1839

Author(s):

Broderick Crawford ◽

Ricardo Soto ◽

José Lemus-Romani ◽

Marcelo Becerra-Rozas ◽

José M. Lanza-Gutiérrez ◽

...

Keyword(s):

Combinatorial Problems ◽

Set Covering ◽

Covering Problem ◽

Integration Framework ◽

Q Learning ◽

Whale Optimization ◽

Sine Cosine Algorithm ◽

Exploration Exploitation ◽

Selection Of

One of the central issues that must be resolved for a metaheuristic optimization process to work well is the dilemma of the balance between exploration and exploitation. The metaheuristics (MH) that achieved this balance can be called balanced MH, where a Q-Learning (QL) integration framework was proposed for the selection of metaheuristic operators conducive to this balance, particularly the selection of binarization schemes when a continuous metaheuristic solves binary combinatorial problems. In this work the use of this framework is extended to other recent metaheuristics, demonstrating that the integration of QL in the selection of operators improves the exploration-exploitation balance. Specifically, the Whale Optimization Algorithm and the Sine-Cosine Algorithm are tested by solving the Set Covering Problem, showing statistical improvements in this balance and in the quality of the solutions.

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A BIM and Machine Learning Integration Framework for Automated Property Valuation

Journal of Building Engineering ◽

10.1016/j.jobe.2021.102636 ◽

2021 ◽

pp. 102636

Author(s):

Tengxiang Su ◽

Haijiang Li ◽

Yi An

Keyword(s):

Machine Learning ◽

Integration Framework ◽

Property Valuation

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Developing a postgraduate professional education framework for emergency nursing: a co-design approach

BMC Nursing ◽

10.1186/s12912-021-00560-z ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Karen A. Theobald ◽

Fiona Maree Coyer ◽

Amanda Jane Henderson ◽

Robyn Fox ◽

Bernadette F. Thomson ◽

...

Keyword(s):

Professional Education ◽

Service Providers ◽

Postgraduate Education ◽

Evidence Based ◽

Emergency Nursing ◽

Integration Framework ◽

Graduate Certificate ◽

University Settings ◽

Assessment Strategies ◽

University Industry

Abstract Background Hospital and university service providers invest significant but separate resources into preparing registered nurses to work in the emergency department setting. This results in the duplication of both curricula and resource investment in the health and higher education sectors. This paper describes an evidence-based co-designed study with clinical-academic stakeholders from hospital and university settings. Methods The study was informed by evidence-based co-design, using emergency nursing as an exemplar. Eighteen hours of co-design workshops were completed with 21 key clinical-academic stakeholders from hospital and university settings. Results Outcomes were matrices synchronising professional and regulatory imperatives of postgraduate nursing coursework; mutually-shaped curriculum content, teaching approaches and assessment strategies relevant for postgraduate education; a new University-Industry Academic Integration Framework; five agreed guiding principles of postgraduate curriculum development for university-industry curriculum co-design; and a Graduate Certificate of Emergency Nursing curriculum exemplar. Conclusion Industry-academic service provider co-design can increase the relevance of postgraduate specialist courses in nursing, strengthening the nexus between both entities to advance learning and employability. The study developed strategies and exemplars for future use in any mutually determined academic-industry education partnership.

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