This research explores the prospective implementations of artificial intelligence (AI) algorithms within the agrifood sector, focusing on the Spanish context. AI methodologies, encompassing machine learning, deep learning, and neural networks, are increasingly integrated into various agrifood sectors, including precision farming, crop yield forecasting, disease diagnosis, and resource management. Utilizing a comprehensive bibliometric analysis of scientific literature from 2020 to 2024, this research outlines the increasing incorporation of AI in Spain and identifies the prevailing trends and obstacles associated with it in the agrifood industry. The findings underscore the extensive application of AI in remote sensing, water management, and environmental sustainability. These areas are particularly pertinent to Spain’s diverse agricultural landscapes. Additionally, the study conducts a comparative analysis between Spain and global research outputs, highlighting its distinctive contributions and the unique challenges encountered within its agricultural sector. Despite the considerable opportunities presented by these technologies, the research identifies key limitations, including the need for enhanced digital infrastructure, improved data integration, and increased accessibility for smaller agricultural enterprises. The paper also outlines future research pathways aimed at facilitating the integration of AI in Spain’s agriculture. It addresses cost-effective solutions, data-sharing frameworks, and the ethical and societal implications inherent to AI deployment.

This review underscores the pivotal role of Cognitive Systems (CS) in enhancing energy efficiency within the industrial sector, exploring the application of sophisticated algorithms, data analytics, and machine learning techniques to the real-time optimization of energy consumption. This methodology has the potential to reduce operational expenses and further diminish environmental repercussions; however, it also leverages data-driven insights and predictive maintenance to foresee equipment malfunctions and modulate energy utilization accordingly. The viability of integrating renewable energy sources is emphasized, supporting a transition towards sustainability. Furthermore, this research includes a bibliometric literature analysis from the past decade on the deployment of CS and Artificial Intelligence in enhancing industrial energy efficiency.

Artificial intelligence (AI) has begun to permeate the strategies adopted for rural development and climate adaptation, especially in those regions where economic and infrastructural constraints are most acute. Rather than offering mere technological panaceas, the current landscape reveals a mosaic of partial successes and persistent obstacles, frequently shaped by the unique realities of small producers and community organizations. This study offers a critical examination of the concrete difficulties faced when deploying AI‐driven solutions in domains such as agriculture, water governance, and renewable energy management. Particular attention is paid to the sometimes underappreciated influence of regulatory environments and the subtle interplay of local incentives. Throughout the analysis, it becomes apparent that the true value of AI lies not only in algorithmic sophistication but in the ability to design energy‐ and computation‐conscious systems that remain sensitive to the diversity of local contexts. The findings suggest that progress in resource‐limited settings depends equally on participatory approaches, digital literacy, and the adaptation of methodologies—ranging from climate prediction to financial risk assessment—to specific community needs. As a result, the work proposes a set of actionable principles, in harmony with the Sustainable Development Goals, that may serve as a practical guide for those seeking to translate technological promise into tangible benefits at the local scale.

A longstanding ambiguity surrounds the operationalization of consciousness in artificial systems, complicated by the philosophical and cultural weight of subjective experience. This work examines whether cognitive architectures may be designed to support a functionally explicit form of artificial consciousness, focusing not on the replication of phenomenology, but rather on measurable, technically realizable introspective mechanisms. Drawing on a critical review of foundational and contemporary literature, this study articulates a conceptual and methodological shift: from investigating the experiential perspective of agents (“what it is like to be a bat”) to analyzing the informational, self-regulatory, and adaptive structures that enable purposive behavior. The approach combines theoretical analysis with a comparative review of major cognitive architectures, evaluating their capacity to implement access consciousness and internal monitoring. Findings indicate that several state-of-the-art systems already display core features associated with functional consciousness—such as self-explanation, context-sensitive adaptation, and performance evaluation—without invoking subjective states. These results support the thesis that cognitive engineering may progress more effectively by focusing on operational definitions of consciousness that are amenable to implementation and empirical validation. In conclusion, this perspective enables the development of artificial agents capable of autonomous reasoning and self-assessment, grounded in technical clarity rather than speculative constructs.

Industrial engineering research has been reshaped by the integration of artificial intelligence (AI) within the framework of Industry 4.0, characterized by the interplay between cyber-physical systems (CPS), advanced automation, and the Industrial Internet of Things (IIoT). While this integration opens new opportunities, it also introduces biases that undermine the reliability and robustness of scientific and industrial outcomes. This article presents a systematic literature review (SLR), supported by natural language processing techniques, aimed at identifying and classifying biases in AI-driven research within industrial contexts. Based on this meta-research approach, a taxonomy is proposed that maps biases across the stages of the scientific method as well as the operational layers of intelligent production systems. Statistical analysis confirms that biases are unevenly distributed, with a higher incidence in hypothesis formulation and results dissemination. The study also identifies emergent AI-related biases specific to industrial applications such as predictive maintenance, quality control, and digital twin management. Practical implications include stronger reliability in predictive analytics for manufacturers, improved accuracy in monitoring and rescue operations through transparent AI pipelines, and enhanced reproducibility for researchers across stages. Mitigation strategies are then discussed to safeguard research integrity and support trustworthy, bias-aware decision-making in Industry 4.0.

The alcoholic fermentation of wine is a complex, multivariable chemical process. This study proposes a cognitive system for the dynamic control of the industrial wine fermentation process based on acoustic emission signals. The core of the system uses machine learning algorithms to perform perception tasks and predict density as a relevant chemical parameter for control and decision-making during the process. A hydrophone submerged in the fermentation tank is used to monitor the process. At the TRL4 stage we are currently at, measurements were taken at a winery in the Rioja Designation of Origin and were acquired and stored during the alcoholic fermentation process to be used as input data. Manual measurements collected by the winemaker throughout the fermentation process were used to train and validate the results. The performance of the machine learning model was measured using statistical metrics. The results of the experiments show a high correlation between the density calculated using the model and the densities measured by the winemaker. The proposed system is a valid and innovative tool for controlling a process as multivariable as alcoholic fermentation. The anticipatory nature of the acoustic signal with regard to the evolution of temperature in the process is used as the starting point for the new proposal. Its application helps to ensure stable fermentation by reducing the thermal stress on yeasts caused by the thermal shocks of current temperature control systems, improving process control in wineries and serving as a cognitive system for control and decision-making.