The growing demand for renewable energy positions it as a cornerstone for climate change mitigation and greenhouse gas emissions reduction. Although renewable energy sources generate around 30% of global electricity, their production and deployment involve significant environmental challenges. This review analyzes renewable energy projects from a life cycle perspective, focusing on environmental impacts throughout the supply chain. Particular emphasis is placed on the energy-intensive nature of manufacturing phases, which account for 60% to 80% of total emissions. The extraction of critical raw materials such as neodymium, dysprosium, indium, tellurium, and silicon is associated with emission levels ranging from 0.02 to 0.09 kg of carbon dioxide equivalent per kilowatt-hour for rare earth elements, along with an estimated average land degradation of 0.2 hectares per megawatt installed. Furthermore, the production of solar-grade silicon for photovoltaic panels consumes approximately 293 kilowatt-hours of electricity per kilogram, significantly contributing to the overall environmental footprint. Through a comprehensive review of the existing literature, this study integrates life cycle assessment and sustainable supply chain management approaches to identify environmental hotspots, quantify emissions, and propose strategic improvements. The analysis provides a structured, systematized, and data-driven evaluation, highlighting the relevance of circular economy principles, advanced recycling technologies, and digital innovations to enhance sustainability, traceability, and resilience in renewable energy supply chains. This work offers actionable insights for decision-makers and policymakers to guide the low-carbon transition.

Electromyographic signals have been used with low-degree-of-freedom prostheses, and recently with multifunctional prostheses. Currently, they are also being used as inputs in the human–computer interface that controls interaction through hand gestures. Although there is a gap between academic publications on the control of an upper-limb prosthesis developed in laboratories and its service in the natural environment, there are attempts to achieve easier control using multiple muscle signals. This work contributes to this, using a database and biomechanical simulation software, both open access, to seek simplicity in the classifiers, anticipating their implementation in microcontrollers and their execution in real time. Fifteen predefined finger movements of the hand were identified using classic classifiers such as Bayes, linear and quadratic discriminant analysis. The idealized movements of the database were modeled with Opensim for visualization. Combinations of two preprocessing methods—the forward sequential selection method and the feature normalization method—were evaluated to increase the efficiency of these classifiers. The statistical methods of cross-validation, analysis of variance (ANOVA) and Duncan were used to validate the results. Furthermore, the classifier with the best recognition result was redesigned into a new feature space using the sparse matrix algorithm to improve it, and to determine which features can be eliminated without degrading the classification. The classifiers yielded promising results—the quadratic discriminant being the best, achieving an average recognition rate for each individual considered of 96.16%, and with 78.36% for the total sample group of the eight subjects, in an independent test dataset. The study ends with the visual analysis under Opensim of the classified movements, in which the usefulness of this simulation tool is appreciated by revealing the muscular participation, which can be useful during the design of a multifunctional prosthesis.

Autonomous electronic systems are becoming increasingly important in people’s lives, as a result of advances in efficient energy storage systems, devices that can be permanently implanted in humans, and the trend towards compact devices that function as an extension of the human body. In addition, strategies continue to be found for the integration of energy harvesting in a constant and efficient manner. Covering the numerous advances made in biomedical devices can be quite overwhelming. This work presents a review of the latest strategies developed to produce energy from human body activity, the energy collectors for devices, and the strategies to create ultra-low-power wearable devices. The review focuses on the potential energy sufficiency required to power devices that can be implanted or worn, while also providing information about the patient’s condition. A comparison between the energies produced by different instruments and the improvements in the development of low-consumption devices is presented, with a focus on the type of medical devices. A new approach is established to classify and locate the most recent advances in autonomous systems in biomedicine based on their complexity/function.

En la última década el desarrollo de modelos de clasificación a través de aprendizaje automático para control de dispositivos protésicos multifuncionales ha ido en aumento. La electromiografía (EMG) son registros producidos por las fibras musculares de forma natural al realizar movimientos, de modelarse podrían tener un papel de forma más activa en este tipo de control. Estas señales son utilizadas para control de dispositivos/aplicaciones, el problema con estos modelos es la naturaleza estocástica de la señal, la variabilidad entre sujetos y la comunicación cruzada inherente que los vuelve inexactos ante un número alto de movimientos. La naturaleza estocástica y la variabilidad de la señal ya son ampliamente estudiadas, sin embargo, no existen aún resultados definitivos que describan modelos de clasificación de movimientos generalizables. Aquí se estudian dos bases de datos disponibles en la red CapgMyo y the Ninapro project, se evalúan las características de estas, teniendo como objetivo investigar la variabilidad de la señal muscular entre sujetos, los factores que la modifican y como afecta el uso de análisis de componentes principales (PCA) y el análisis de componentes independientes (ICA) a la información del EMG en modelos de clasificación. Se realizó una comparación entre los resultados en términos de porcentajes de reconocimiento de métodos clásicos de aprendizaje automático como el análisis discriminante lineal (LDA) y el cuadrático (QDA) utilizando técnicas de trasformación a nuevos espacios introduciendo la posibilidad de realizar una reducción de la dimensionalidad con PCA e ICA, algoritmos usualmente utilizados para resolver problemas como la separación ciega de fuentes (BSS) que es aplicable al fenómeno presentado en señales musculares y su adquisición a través de electrodos superficiales. Los resultados pueden evaluarse a través del porcentaje de reconocimiento de los modelos de clasificación creados, estos muestran que para señales crudas de EMG los métodos de PCA e ICA son útiles para realizar una reducción de la dimensionalidad de los datos sin aportar un aumento significativo en los porcentajes de reconocimiento. Se demostró que los porcentajes de reconocimiento en la clasificación de los movimientos para la base de datos Capgmyo fueron superiores gracias a las características que la definen, se obtuvo un mayor porcentaje de reconocimiento que va del 72.5% al 87.9% con QDA, y del 82.8 al 90% para QDA con PCA. La aportación principal es la evaluación de la eficacia de algoritmos como PCA e ICA en tareas de aprendizaje automático con datos crudos de EMG. Como trabajo futuro esta ir plasmando las bases para reducir los efectos de la comunicación cruzada en los registros de EMG.

A classifier is commonly generated for multifunctional prostheses control or also as input devices in human–computer interfaces. The complementary use of the open-access biomechanical simulation software, OpenSim, is demonstrated for the hand-movement classification performance visualization. A classifier was created from a previously captured database, which has 15 finger movements that were acquired during synchronized hand-movement repetitions with an 8-electrode sensor array placed on the forearm; a 92.89% recognition based on a complete movement was obtained. The OpenSim’s upper limb wrist model is employed, with movement in each of the joints of the hand–fingers. Several hand-motion visualizations were then generated, for the ideal hand movements, and for the best and the worst (53.03%) reproduction, to perceive the classification error in a specific task movement. This demonstrates the usefulness of this simulation tool before applying the classifier to a multifunctional prosthesis.

Buildings and construction account for nearly one-third of global energy demand, making the thermal performance of building envelopes an important factor in reducing heating and cooling loads. Conventional steady-state indicators are useful for comparison, but they do not always describe how complete assemblies respond to changing outdoor conditions. This study presents a dynamic experimental method for evaluating construction assemblies using energy compensation under controlled indoor conditions. A highly insulated test module was used to maintain a stable indoor temperature while measuring the electrical heating energy required to compensate for heat losses through interchangeable assemblies exposed to outdoor temperature variations. Three systems were tested: double gypsum board, hollow concrete block, and externally insulated hollow concrete block. To compare the assemblies under different outdoor conditions, corrected heating energy was normalized by test area and heating degree-hours (HDHs), resulting in a Dynamic Thermal Performance Index (DTPI). The DTPI decreased from 52.4 Wh/(m²·°C·h) for the double gypsum board assembly to 21.5 Wh/(m²·°C·h) for the externally insulated hollow concrete block, while the mean corrected heating energy decreased from 2.23 to 0.91 kWh/day. The gypsum board assembly showed the fastest thermal response and the highest energy demand, whereas the externally insulated block provided the most stable indoor-side behavior by reducing the effect of outdoor fluctuations on the concrete mass. The method links transient thermal behavior with measured energy consumption and offers a practical complement to conventional steady-state metrics for comparing building envelope systems.