Jose Gabriel’s scientific contributions

In today's competitive business environment, small businesses face numerous challenges in crafting effective business plans that can drive growth and success. However, the integration of Information Architecture (IA), Enterprise Engineering (EE), and Artificial Intelligence (AI) offers a promising solution to these challenges. This paper explores how IA, EE, and AI can be leveraged to enhance small business planning processes in the United States. The paper begins by defining IA, EE, and AI and highlighting their individual contributions to business planning. It then examines the potential synergies between these disciplines and explores real-world examples of their integration in small business contexts. Through case studies and best practices, the paper illustrates the transformative impact of integrating IA, EE, and AI on small business planning, including improved decision-making, increased efficiency, and enhanced competitiveness. Furthermore, the paper discusses emerging trends and future opportunities in IA, EE, and AI that can further enhance small business planning processes. It concludes by emphasizing the importance of embracing technology as a strategic enabler and investing in digital transformation initiatives to drive sustainable growth and success for small businesses in the future.

This paper explores the transition from autonomy to accountability in artificial intelligence (AI) and the conceptualization of AI's legal personhood. It begins with an overview of AI's evolution, emphasizing its increasing autonomy and the emerging discourse on its legal status. Understanding AI autonomy and its implications on decision-making sets the stage for discussing legal personhood, a complex concept deeply rooted in legal history and contemporary debates. Arguments both for and against granting legal personhood to AI are examined, considering issues of accountability, rights, and societal integration. Various potential models for AI legal personhood, ranging from limited rights to hybrid human-AI decision-making structures, are presented and analyzed. Regulatory and ethical considerations, including the need for robust governance frameworks and ethical guidelines, are discussed in light of the challenges posed by AI autonomy. Case studies and global perspectives provide real-world insights into the implications of AI legal personhood. Finally, the paper concludes with reflections on the challenges and future directions in AI regulation and a call to action for further research and discussion on this critical topic.

Remote sensing image scene classification plays a crucial role in various applications such as environmental monitoring, urban planning, and disaster management. However, traditional methods for feature extraction often face limitations, especially when dealing with limited labeled data. In this study, we propose a novel approach that leverages open self-supervised feature extraction techniques to address these challenges. By learning meaningful representations from unlabeled remote sensing data, our approach aims to improve classification accuracy even when only very few labeled samples are available. We conduct experiments on diverse datasets and compare our approach with traditional methods and state-of-the-art techniques in remote sensing image classification. Results demonstrate the effectiveness of our approach in achieving accurate scene classification with minimal labeled data. Furthermore, we discuss the implications of our findings and suggest potential avenues for future research in this domain.

Microfluidics allows for the manipulation and analysis of minuscule amounts of liquid within a system that contains multiple channels, ports, and samples. Advanced microfluidic technology can incorporate numerous functional units onto a tiny chip made of glass, plastic or polymers. By combining microfluidic systems with artificial intelligence (AI) models, it is possible to optimize the design and testing processes, leading to increased automation and intelligence in experiments. The AI models are divided into four general categories of unsupervised learning, supervised learning, semi-supervised learning, and reinforcement learning. These models prove invaluable in discovering and optimizing chemical synthesis, which can be costly and time-consuming. Additionally, AI models aid in simulating the assembly of colloidal materials in microfluidics, speeding up the prediction of material characteristics necessary for designing novel materials with interesting physical or chemical properties. Similarly, AI algorithms can predict the behavior of multiphase fluids, assisting in the design of microfluidic chips for various applications. Another application of AI models in microfluidics involves the detection of cellular matter, including DNA, RNA, proteins, and other metabolites, using droplet-based biotechnology techniques. Machine learning techniques can be employed to segment and classify droplets in images for this purpose. In this review, we cover the known applications of AI algorithms in the design of microfluidic systems and flow techniques, including droplet microfluidics. In this review, we cover the known Journal of Sustainable Technologies and Infrastructure Planning applications of AI algorithms in the design of microfluidic systems and flow techniques, including droplet microfluidics.