Mauricio González-Palacio

Crushed brick waste (CBW) is a major fraction of construction and demolition waste, yet design-oriented shear strength data for its reuse in earthworks remain scarce. This study examines CBW–soil mixtures within the block-in-matrix soil (Bimsoil) framework and quantifies their shear strength at large scale. The block-to-matrix contrast was checked through elastic modulus, friction angle, and unconfined compressive strength ratios. Large-scale multistage direct shear tests with a double shear-plane configuration were performed on one specimen per mixture, with the volumetric block proportion (VBP) ranging from 0% to 67%, and the failure point of every stage was defined with a single strain-based criterion. The friction angle increased from 21.3° to 38.3° with VBP, following a significant linear trend (R² = 0.94), whereas the apparent cohesion dropped from 29.1 kPa for the natural soil to 10.6–18.5 kPa for the mixtures, without a significant trend among them. For VBP ≥ 48%, the envelopes intersect that of the natural soil at 46–52 kPa, within the tested range, so the natural soil governs at lower and the mixtures at higher normal stresses. In illustrative 6 m high gabion- and geogrid-reinforced soil walls, VBP of 67% reduced the gabion weight and geogrid area by 44.4% and 59.1% and by 28–45% and 19–61% over the examined wall heights and surcharges. These results provide a material-specific basis for selecting CBW content in embankments and retaining-wall backfills.

The agricultural and livestock sectors of Latin America produce a large number of residues that could be converted into energy through bioenergy production processes. However, the bioenergy sector still faces several limitations across the region, including fragmented logistics systems, weak coordination among institutions, and limited integration of environmental, digital, and compliance-related performance indicators. This review systematically analyses residue-based bioenergy value chains in Latin America between 2015 and 2025 using the PRISMA methodology to evaluate selected peer-reviewed studies and regional reports indexed in Scopus, ScienceDirect, SpringerLink, and IEEE Xplore, and institutional repositories. The final synthesis included 37 studies and institutional contributions, which were further disaggregated into 208 country–residue observations for the regional and feedstock distribution analysis. The review identified three main research gap categories: the limited integration of collection and logistics systems, the insufficient treatment of uncertainty, circularity, and traceability within optimization models, and the weak incorporation of governance and institutional coordination into bioenergy value-chain design. The analysis includes biogas, biomethane and related residue-based systems, with attention to supply-chain optimization, policy alignment, methane mitigation metrics, and traceability requirements. Results indicate that although technologies such as biomass pretreatment, process intensification, and upgrading processes continue to improve conversion performance, most studies still focus mainly on technical feasibility and biomass potential. Less attention is given to governance constraints, uncertainty analysis, and monitoring systems capable of supporting regulatory compliance. This research introduced the Sustainable Bioenergy Chain Management Framework (SBCMF) to respond to these limitations and bring together different aspects of bioenergy management within one analytical structure. The framework combines supply-chain optimization under spatial and temporal constraints, circular economy valorisation, methane-related climate performance, and digital traceability, while also linking techno-economic system design with governance and monitoring requirements. In this way, it can help support the development of more transparent and low-carbon bioenergy systems across Latin America.

Obstructive Sleep Apnea (OSA) is a prevalent respiratory disorder affecting millions worldwide, yet it remains underdiagnosed due to limitations in conventional diagnostic methods such as polysomnography. Recent advances in wearable technology and the integration of Internet of Things (IoT)-based systems have expanded options for OSA detection, offering real-time monitoring and improved accessibility. This paper presents a comprehensive survey of state-of-the-art diagnostic techniques, highlighting emerging wearable solutions, IoT-enabled data transmission, and machine learning algorithms for classification. While portable biomedical devices like HSAT and Morphea enhance patient comfort, challenges such as sensor displacement and measurement sensitivity persist. Furthermore, cloud-based analysis and smart patch antennas demonstrate feasibility in early studies but require rigorous validation and optimization to ensure accuracy and reliability. By synthesizing current advances, this study underscores the need for further research on sensor precision, algorithmic enhancements, and data integration to foster innovation toward effective and accessible OSA diagnostics.

La apnea obstructiva del sueño [...] es un trastorno respiratorio grave que afecta a aproximadamente mil millones de personas entre 30 y 69 años en todo el mundo. [...] Tradicionalmente, la polisomnografía (PSG) ha sido el estándar de oro para diagnosticar la OSA, puesto que mide variables fisiológicas como la actividad cerebral, los movimientos oculares, la actividad muscular, el ritmo cardíaco y la respiración. Sin embargo, la PSG tiene limitaciones en términos de capacidad de captura de datos, clasificación de eventos respiratorios y requiere de un entorno controlado para su ejecución. Este estudio se propone desarrollar un nuevo sistema de medición y diagnóstico para OSA utilizando un sensor de deformación torácica. Dicho sistema está basado en una antena parche vestible e integra un analizador de redes vectoriales para la adquisición de datos durante el sueño. El sistema registrará la deformación de la caja torácica durante la inhalación y exhalación, apoyado en estrategias de detección de patrones para clasificar índices de apnea. La precisión y confiabilidad del sistema se validarán comparando sus resultados con los obtenidos mediante PSG. [...] Dicho enfoque innovador podría revolucionar el diagnóstico de la OSA [...].

To achieve an adequate tradeoff between range and energy efficiency, LoRaWAN End Nodes (ENs) choose their transmission parameters using an Adaptive Data Rate (ADR) scheme based on the maximum value of previous Signal-to-Noise (SNR) values. However, the ADR only performs well in favorable channel conditions. In fact, if the SNR exhibits high variability, these parameters could be inefficiently set and may negatively affect the Packet Delivery Rate (PDR). Therefore, a link margin could be overestimated to improve the PDR by the ADR algorithm, which may, however, waste the EN’s energy. This paper proposes a novel ADR that does not rely on the past SNR values. Still, our proposed design directly predicts the current SNR and transmission parameters using Machine Learning. Specifically, the underlying Machine Learning models were trained using in-field measurements for six months in Medellín, Colombia, including different environmental variables and their effects on the SNR. Our ADR scheme improved energy consumption by 47.1% with a PDR of 99% and reduced collisions in dense networks up to 9.5% compared with the ADR scheme. Furthermore, we show that our proposed design outperforms some enhanced versions of the ADR scheme proposed in the literature in both energy consumption and collision rate. Finally, our proposed framework enables simple implementation since it runs directly in the ENs, improving the response time compared with the traditional ADR scheme.