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In this final chapter, we explore the ever-evolving 21st century landscape where ethics drive community development toward resilience and progress. Drawing inspiration from the subheadings mapping our journey, we traverse international case studies spanning Canada, Brazil, Sweden, Kenya, China, Australia, Antarctica, and India. Through these global insights, we uncover the impacts of dynamic forces on communities worldwide, navigating ethical dilemmas and opportunities. We present strategies tailored to diverse continent-specific needs, explore inclusive governance models, and highlight the transformative power of ethical engagement. This journey underscores the vital role of resilience and concludes with a global call to embrace ethical approaches for inclusive community development and a sustainable future.

The purpose of this paper is to make the case that ethical guardrails in emerging technology businesses are inadequate and to develop solutions to strengthen these guardrails.

Based on literature and first principles reasoning, the paper develops theoretical arguments about the fundamental purpose of ethical guardrails and how they evolve and then uses this along with the characteristics that distinguish emerging technology businesses to identify inadequacies in the ethical guardrails for emerging technology businesses and develop solutions to strengthen the guardrails.

The paper shows that the ethical guardrails for emerging technology businesses are inadequate and that the reasons for this are systematic. The paper also develops actionable recommendations to strengthen these guardrails.

The paper develops the novel argument that reasons for the inadequate ethical guardrails in emerging technology businesses are systematic and stem from the inadequacy of laws and regulations, inadequacy of boards and the focus of business executives.

Gamma-aminobutyric acid (GABA) is a four-carbon and nonprotein amino acid, made by various microorganisms, especially lactic acid bacteria (LAB) and probiotics. GABA has various physiological roles, for instance, insomnia, depression, hypotensive activity as well as diuretic effects. The production of GABA-based foods is a favorable result from GABA’s biological and functional properties. The purpose of this study is to investigate different methods of improving GABA production in probiotics and LAB to select the superlative method and bacterial strain.

In this review paper, all articles from five electronic databases containing Google Scholar, Web of Science, PubMed, Scopus and Science Direct were considered from 2000 to January 11, 2023, with keywords “Optimization” OR “Enhance” OR “Increase” AND “Gamma-aminobutyric acid” OR “GABA” AND “Probiotics” OR “Lactic acid bacteria” and selected according to the purpose of the study.

It seems that among all the investigated methods, an effective fermentation procedure with optimal conditions including fermentation medium, 5 L; glutamic acid, 295 g/L; incubation temperature, 32°C; inoculum, 10% (v/v); and agitation, 100 rpm, after 48 h of fermentation led to producing 205,800 mg/L of GABA from the effective bacterial strain Lactobacillus brevis NCL912.

With a simple but optimized fermentation, L. brevis NCL912 can be used for the efficient GABA production in the pharmacy and food factories.

Metaheuristic algorithms based on biology, evolutionary theory and physical principles, have been widely developed for complex global optimization. This paper aims to present a new hybrid optimization algorithm that combines the characteristics of biogeography-based optimization (BBO), invasive weed optimization (IWO) and genetic algorithms (GAs).

The significant difference between the new algorithm and original optimizers is a periodic selection scheme for offspring. The selection criterion is a function of cyclic discharge and the fitness of populations. It differs from traditional optimization methods where the elite always gains advantages. With this method, fitter populations may still be rejected, while poorer ones might be likely retained. The selection scheme is applied to help escape from local optima and maintain solution diversity.

The efficiency of the proposed method is tested on 13 high-dimensional, nonlinear benchmark functions and a homogenous slope stability problem. The results of the benchmark function show that the new method performs well in terms of accuracy and solution diversity. The algorithm converges with a magnitude of 10-4, compared to 102 in BBO and 10-2 in IWO. In the slope stability problem, the safety factor acquired by the analogy of slope erosion (ASE) is closer to the recommended value.

This paper introduces a periodic selection strategy and constructs a hybrid optimizer, which enhances the global exploration capacity of metaheuristic algorithms.