This paper presents a comprehensive review of loss functions and performance metrics in deep learning, highlighting key developments and practical insights across diverse application areas. We begin by outlining fundamental considerations in classic tasks such as regression and classification, then extend our analysis to specialized domains like computer vision and natural language processing including retrieval-augmented generation. In each setting, we systematically examine how different loss functions and evaluation metrics can be paired to address task-specific challenges such as class imbalance, outliers, and sequence-level optimization. Key contributions of this work include: (1) a unified framework for understanding how losses and metrics align with different learning objectives, (2) an in-depth discussion of multi-loss setups that balance competing goals, and (3) new insights into specialized metrics used to evaluate modern applications like retrieval-augmented generation, where faithfulness and context relevance are pivotal. Along the way, we highlight best practices for selecting or combining losses and metrics based on empirical behaviors and domain constraints. Finally, we identify open problems and promising directions, including the automation of loss-function search and the development of robust, interpretable evaluation measures for increasingly complex deep learning tasks. Our review aims to equip researchers and practitioners with clearer guidance in designing effective training pipelines and reliable model assessments for a wide spectrum of real-world applications.

Digital transformation is reshaping the healthcare field by streamlining diagnostic workflows and improving disease management. Within this transformation, Digital Twins (DTs), which are virtual representations of physical systems continuously updated by real-world data, stand out for their ability to capture the complexity of human physiology and behavior. When coupled with Artificial Intelligence (AI), DTs enable data-driven experimentation, precise diagnostic support, and predictive modeling without posing direct risks to patients. However, their integration into healthcare requires careful consideration of ethical, regulatory, and safety constraints in light of the sensitivity and nonlinear nature of human data. In this review, we examine recent progress in DTs over the past seven years and explore broader trends in AI-augmented DTs, focusing particularly on movement rehabilitation. Our goal is to provide a comprehensive understanding of how DTs bolstered by AI can transform healthcare delivery, medical research, and personalized care. We discuss implementation challenges such as data privacy, clinical validation, and scalability along with opportunities for more efficient, safe, and patient-centered healthcare systems. By addressing these issues, this review highlights key insights and directions for future research to guide the proactive and ethical adoption of DTs in healthcare.

An experimental study of a solar-concentrating system based on thermoelectric generators (TEGs) was performed. The system included an electrical generating unit with 6 serially connected TEGs using a traditional semiconductor material, Bi₂Te₃, which was illuminated by concentrated solar radiation on one side and cooled by running water on the other side. A sun-tracking concentrator with a mosaic set of mirrors was used; its orientation towards the sun was achieved with two pairs of radiation sensors, a differential amplifier, and two servomotors. The hot side of the TEGs at midday has a temperature of around 200°C, and the cold side is approximately 50°C. The thermosiphon cooling system was designed to absorb the heat passing through the TEGs and provide optimal working conditions. The system generates 20 W of electrical energy and 200 W of thermal energy stored in water with a temperature of around 50°C. The hybrid system studied can be considered as an alternative to photovoltaic/thermal systems, especially in countries with abundant solar radiation, such as Mexico, China, and India.

In the fast-growing automotive technology sector, it has become increasingly clear that there is a need for cars with smarter and more interactive systems. This article presents the Intelligent Driving Assistance System (IDAS), an artificial intelligence system that enables the driver to use voice commands to access various features of a car. The primary component of IDAS is a Large Language Model (LLM), which, through retrieval augmented generation (RAG), can efficiently read and understand the car manual for immediate context-based aid. In addition, this system incorporates speech recognition and speech synthesis capabilities, it can understand commands given in multiple languages, improving user experiences among diverse driver communities. Our results show a minimum response time of one second for the pipeline using GPT-4o-mini and Mistral Nemo.

This article presents a systematic review of optimization methods applied to enhance the performance of photovoltaic (PV) systems, with a focus on critical challenges such as system design and spatial layout, maximum power point tracking (MPPT), energy forecasting, fault diagnosis, and energy management. The emphasis is on the integration of classical and algorithmic approaches. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PRISMA) methodology, 314 relevant publications from 2020 to 2025 were analyzed to identify current trends, methodological advances, and practical applications in the optimization of PV performance. The principal novelty of this review lies in its integrative critical analysis, which systematically contrasts the applicability, performance, and limitations of deterministic classical methods with emerging stochastic metaheuristic and data-driven artificial intelligence (AI) techniques, highlighting the growing dominance of hybrid models that synergize their strengths. Traditional techniques such as analytical modeling, numerical simulation, linear and dynamic programming, and gradient-based methods are examined in terms of their efficiency and scope. In parallel, the study evaluates the growing adoption of metaheuristic algorithms, including particle swarm optimization, genetic algorithms, and ant colony optimization, as well as machine learning (ML) and deep learning (DL) models applied to tasks such as MPPT, spatial layout optimization, energy forecasting, and fault diagnosis. A key contribution of this review is the identification of hybrid methodologies that combine metaheuristics with ML/DL models, demonstrating superior results in energy yield, robustness, and adaptability under dynamic conditions. The analysis highlights both the strengths and limitations of each paradigm, emphasizing challenges related to data availability, computational cost, and model interpretability. Finally, the study proposes future research directions focused on explainable AI, real-time control via edge computing, and the development of standardized benchmarks for performance evaluation. The findings contribute to a deeper understanding of current capabilities and opportunities in PV system optimization, offering a strategic framework for advancing intelligent and sustainable solar energy technologies.