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Building trust and living interpersonal trust are crucial corporate executive virtues that are needed today. Once you have developed and solidified a high level of genuine interpersonal trust with all your stakeholders, especially customers, suppliers, and employees, then you are on the right path of managing and transforming your company. A high level of interpersonal trust between all stakeholders and corporates in a business situation will break down communication barriers, foster serious conversation and sharing of ideas, and will eliminate corporate transactional anxieties of fear, mistrust, guilt, rigidity, blame, and resentment. When stakeholders trust you and you trust them, then you speak freely, they speak freely, and your mutual sustained transparency is a gateway to survival, revival, and sustained corporate recovery and transformation, and steady growth and prosperity. Conversely, when there is low trust, high mistrust, and high distrust among stakeholders in a business situation, communications and conversations are stressed and fragmented, teamwork and team spirit are very low, and the company is heading toward its ruin and extermination. Such is the crucial role of interpersonal trust in business. This chapter explores the crucial phenomenon of corporate interpersonal trust. We review various cases, models, concepts, definitions, and theories of trust from the management literature in general, and from the marketing field in particular, to derive psychological, behavioral, ethical, and moral principles of corporate trust, trusting relations, and trusting strategies.

A novel framework for expedited antenna optimization with an iterative prediction-correction scheme is proposed. The methodology is comprehensively validated using three real-world antenna structures: narrow-band, dual-band and wideband, optimized under various design scenarios.

The keystone of the proposed approach is to reuse designs pre-optimized for various sets of performance specifications and to encode them into metamodels that render good initial designs, as well as an initial estimate of the antenna response sensitivities. Subsequent design refinement is realized using an iterative prediction-correction loop accommodating the discrepancies between the actual and target design specifications.

The presented framework is capable of yielding optimized antenna designs at the cost of just a few full-wave electromagnetic simulations. The practical importance of the iterative correction procedure has been corroborated by benchmarking against gradient-only refinement. It has been found that the incorporation of problem-specific knowledge into the optimization framework greatly facilitates parameter adjustment and improves its reliability.

The proposed approach can be a viable tool for antenna optimization whenever a certain number of previously obtained designs are available or the designer finds the initial effort of their gathering justifiable by intended re-use of the procedure. The future work will incorporate response features technology for improving the accuracy of the initial approximation of antenna response sensitivities.

The proposed optimization framework has been proved to be a viable tool for cost-efficient and reliable antenna optimization. To the knowledge, this approach to antenna optimization goes beyond the capabilities of available methods, especially in terms of efficient utilization of the existing knowledge, thus enabling reliable parameter tuning over broad ranges of both operating conditions and material parameters of the structure of interest.