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The purpose of this study is to investigate the influence of light intensity and sources of carbon and nitrogen on the cultivation of Spirulina maxima.

Cultures were carried out in a modified Zarrouk medium using urea, sodium acetate and glycerol. A Taguchi experimental design was used to evaluate the effect on the production of biocompounds: productivities in biomass, carbohydrates, phycocyanin and biochar were analyzed.

Statistical data analysis revealed that light intensity and sodium acetate concentration were the most important factors, being significant in three of the four response variables studied. The highest productivities in biomass (46.94 mg.L⁻¹.d⁻¹), carbohydrates (6.11 mg.L⁻¹.d⁻¹), phycocyanin (3.62 mg.L⁻¹.d⁻¹) and biochar (22, 48 mg.L⁻¹.d⁻¹) were achieved in experiment 4 of the Taguchi matrix, highlighting as the ideal condition for the production of biomass, carbohydrates and phycocyanin.

Sodium acetate and urea can be considered, respectively, as potential sources of carbon and nitrogen to increase Spirulina maxima productivity. From the results, an optimized cultivation condition for the sustainable production of bioproducts was obtained.

This work focuses on the study of the influence of light intensity and the use of alternative sources of nitrogen and carbon on the growth of Spirulina maxima, as well as on the influence on the productivity of biomass and biocompounds. There are few studies in the literature focused on the phycocyanin production from microalgae, justifying the need to deepen the subject.

In this study, the central objective is to foresee stock market signals with the use of a proper structure to achieve the highest accuracy possible. For this purpose, three hybrid models have been developed for the stock markets which are a combination of support vector machine (SVM) with meta-heuristic algorithms of particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).All the analyses are technical and are based on the Japanese candlestick model.

Further as per the results achieved, the most suitable algorithm is chosen to anticipate sell and buy signals. Moreover, the authors have compared the results of the designed model validations in this study with basic models in three articles conducted in the past years. Therefore, SVM is examined by PSO. It is used as a classification agent to search the problem-solving space precisely and at a faster pace. With regards to the second model, SVM and ICA are tested to stock market timing, in a way that ICA is used as an optimization agent for the SVM parameters. At last, in the third model, SVM and GA are studied, where GA acts as an optimizer and feature selection agent.

As per the results, it is observed that all new models can predict accurately for only 6 days; however, in comparison with the confusion matrix results, it is observed that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the data for stock market of the years 2013–2021 were analyzed; the long length of timeframe makes the input data analysis challenging as they must be moderated with respect to the conditions where they have been changed.

In this study, two methods have been developed in a candlestick model; they are raw-based and signal-based approaches in which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

A learning-focused culture promotes creativity, innovativeness and the acquisition of novel insights and competencies. The study aims to explore the relationship between human resource development (HRD) practice and employee competencies using organizational learning culture as a mediating variable.

Data were collected from 828 employees of 37 health care institutions comprising 24 (internationally-owned) and 13 (indigenously-owned). Construct reliability and validity was established through a confirmatory factor analysis. The proposed model and hypotheses were evaluated using structural equation modeling.

Data supported the hypothesized relationships. The results show that training and development and employee competencies were significantly related. Career development and employee competencies were significantly related. Organizational learning culture mediates the relationship between training and development and employee competencies. However, organizational learning culture did not mediate the relationship between career development and employee competencies.

The generalizability of the findings will be constrained due to the research’s health care focus and cross-sectional data.

The study’s findings will serve as valuable pointers to policy makers and stakeholders of health care institutions in developing system-level capacities that promote continuous learning and adaptive learning cultures to ensure sustainability and competitive advantage.

By evidencing empirically that organizational learning culture mediates the relationship between HRD practices and employee competencies the study extends the literature.