En el presente trabajo se analizan las nuevas teorías y conceptos relacionados con lagestión del diseño, que centran su atención en las experiencias de las personas y en lascaracterísticas particulares de cada una de ellas. Específicamente, con un enfoque desdeel Diseño Industrial, se estudiarán los alcances y las relaciones entre estas definicionesconceptuales —ahora visibilizadas— que siempre pertenecieron al campo proyectualde la disciplina y se intentará identificar cómo influyen en la innovación y el desarrollode productos. Finalmente, se concluirá acerca de su relevancia estratégica para lasorganizaciones públicas y privadas, y la incorporación al conjunto de las actividadesprofesionales.

The costs of agricultural inputs added to those of labor represent almost a third of the total cost of Brazilian sugarcane production. This study analyzes the behavior of the price per ton of sugarcane in Brazil, relating it to the main production costs of this cultivation. Twelve price indicators from January 2015 to December 2020 were evaluated. First, the data were adjusted to a multiple linear regression model to identify the significant variables on variation in the price per ton of sugarcane. Then, the Monte Carlo simulation was used to measure the level of certainty of occurrence of these variables, and forecasts were obtained from the adjustment of ARIMA models. The results showed the influence of the costs of diesel oil, two agricultural pesticides, and daily laborers on the price of sugarcane, besides an increasing trend of its, providing relevant short-term projections for decision-making about investments in the agribusiness sector.

Purpose: The research explored empirical evidence to assess the impact of cyber security and supply chain risk on digital operations in the UAE pharmaceutical industry. Methodology/Design/Approach: Based on responses from 243 personnel working at 14 pharmaceutical manufacturing companies in Dubai, data were examined for normality, instrument validity and regression analysis. Cyber security and SC risk on digital operations were explored by applying convenient sampling and descriptive and analytical research design. Findings: The findings validated the significant positive association between cyber security and supply chain risk with digital operations. Research implications and Limitations: The research model was developed with three variables evaluated only on the pharmaceutical industry in Dubai. Future research should focus on multiple manufacturing industries by covering a larger geographic area. Practical implications: When suppliers are highly concentrated, customer-focused organisations with uncertain levels of digital transformation could improve their ability to manage supply chain risk by diversifying their clients. Pharmaceutical firms may require a greater focus on technology-based manufacturing firms to build and maintain customer trust. Originality Value: To illustrate how the pharmaceutical industry has explored the relationship between cyber security, supply chain risk and digital operations, the research highlights the significant and convoluted consequences of the relationship model that have not been previously considered.

Healthcare professionals decide wisely about personalized medicine, treatment plans, and resource allocation by utilizing big data analytics and machine learning. To guarantee that algorithmic recommendations are impartial and fair, however, ethical issues relating to prejudice and data privacy must be taken into account. Big data analytics and machine learning have a great potential to disrupt healthcare, and as these technologies continue to evolve, new opportunities to reform healthcare and enhance patient outcomes may arise. In order to investigate the patient’s outcomes with empirical evidence, this research was conducted using an online survey to incorporate healthcare professionals, patient’s reviews, and clinical staff. The data were analyzed using SmartPLS 4.0 to predict the structural model. The findings revealed a direct impact as positive influence of using machine learning on healthcare performance and patient outcomes through big data analytics. Moreover, it is evident that this can lead to personalized treatment plans, early interventions, and improved patient outcomes. Additionally, big data analytics can help healthcare providers optimize resource allocation, improve operational efficiency, and reduce costs. The impact of big data analytics on patient outcome and healthcare performance is expected to continue to grow, making it an important area for investment and research