Francisco José de Caldas District University

Polyhydroxyalkanoates (PHAs) are biodegradable biobased polymers with the potential to replace conventional plastics. To reduce production costs while contributing to wastewater treatment, mixed microbial cultures (MMCs) have been proposed as a sustainable platform for PHA production. This process comprises three stages: (1) acidogenic fermentation of wastewater streams to generate volatile fatty acids (VFAs), (2) selection and enrichment of MMCs under feast–famine conditions, and (3) PHA accumulation using the selected biomass. The aim of this study was to evaluate the selection and enrichment of MMCs for PHA production within a circular economy framework. VFAs obtained by sludge acidogenic fermentation were used as the carbon source, and two operational factors organic load (500–1500 mg COD L-1) and cycle length (3 or 6 days) were tested in a 3 × 2 factorial design. The condition of 500 mg COD L-1 with a 3-day cycle achieved the highest performance, with statistically significant effects (p < 0.05) for both main factors and their interaction. Pilot-scale validation in a 16-L working volume reactor confirmed these results, yielding 83.3 mg PHA L-1 (reported as PHB-equivalents following acid hydrolysis to crotonic acid and HPLC quantification) and a yield of 0.28 g COD g COD-1 ( Y_PHA/VFA , expressed as COD-based PHA produced per COD of VFA consumed). Molecular analysis revealed species classically associated with PHA dynamics (Paracoccus sp., Alcaligenes sp.) and the presence of Diaphorobacter limosus, suggesting complementary roles under selective pressure. These findings highlight wastewater valorization into bioplastics as a viable route within the circular economy framework.

Centralised architecture models are often adopted in the development of smart cities to take advantage of the technologies provided by dominant companies such as Google, Meta, and Amazon. This approach often comes with significant drawbacks, such as (i) application developers becoming dependent on the policies and rules imposed by large companies, which are subject to change, and (ii) users having their data collected and used haphazardly, and often without authorisation, which can undermine the trust that users place in the solution. Infrastructure for smart cities should, therefore, incorporate decentralised technologies that satisfy or reinforce important quality attributes such as trustworthiness, privacy, and security. The main objective of this article is to survey and discuss how the services of a smart city can be implemented using decentralised architectures; we explain the advantages and disadvantages of decentralised architectures and emerging technologies. We review the specialised literature and compile a set of emerging technologies that are essential to conceive a decentralised architectural solution for smart cities. The current state-of-the-art in decentralisation shows that this model can be implemented with current technologies and help to satisfy the quality attributes mentioned above, and potentially reinforce the degree of trustworthiness of the solution. We also propose a four-dimension guideline that functions as a conceptual driver for structuring actions for researchers, system architects, policymakers, and city authorities in the design, evaluation, and long-term maintenance of decentralised smart cities, thereby enhancing their capacity to address escalating urban challenges. We conclude that the set of technologies discussed in this article has the potential to be reused and replicated across smart cities.

Tropical ecosystems play a critical role in global climate regulation and host the highest biodiversity on Earth, yet their long-term responses to climate change remain poorly understood. The Miocene has garnered significant attention in recent years as an interval with potential analogs for future climate change, with pCO(2) levels and continental configurations like the modern. However, most Miocene paleoclimate reconstructions rely on marine or high-latitude records, leaving tropical terrestrial environments underrepresented, which limits our ability to test climate model predictions for lower latitudes and understand the relationship between climate and biosphere during this time. We integrate pedogenic carbonate clumped isotope paleothermometry and phytolith analyses to investigate Neotropical vegetation and climate change between the middle and late Miocene comprising the La Venta Konzentrat-Lagerstatte and overlying deposits, Colombia. Our record suggests a substantial similar to 18 degrees C decline in soil temperature, accompanied by declining delta C-13 values in soil-respired CO2. Phytolith assemblages from the late Miocene indicate closed-canopy forests that do not fully match any single modern analog, consistent with vegetation shaped by Miocene climatic and biogeographic conditions. Together, these results point to increasingly cool and densely vegetated landscapes in northern South America during the middle to late Miocene, in contrast with regional and global interpretations of widespread aridification. However, the magnitude of reconstructed cooling exceeds global expectations, and Quaternary soil-carbonate temperatures at the site are anomalously warm relative to modern climate, indicating that soil processes specific to tropical settings may influence temperatures inferred from carbonate clumped isotope paleothermometry. These findings underscore the need for expanded terrestrial proxy records and improved calibration of carbonate formation in tropical soils to refine paleotemperature reconstructions in the Neotropics.

This paper introduces a distributed sensitivity-conditioning approach for bilevel optimization in networked microgrids. The proposed method enhances the coordination between subsystems by embedding sensitivity-based predictive terms into the dynamic updates, thereby improving convergence stability without requiring strict time-scale separation. Unlike conventional singular perturbation techniques, the sensitivity-conditioning formulation enables faster and more robust convergence of the distributed dynamics under heterogeneous subsystem speeds. The approach is applied to a networked microgrid scenario where local agents perform decentralized optimization considering both internal generation and energy exchange with neighboring microgrids. Simulation results demonstrate that the proposed algorithm achieves efficient coordination, reduces convergence time, and maintains stability under diverse operating conditions. The results highlight the method’s potential as a scalable and computationally efficient alternative for real-time distributed energy management and bilevel control in power network applications.

Exposure to Particulate Matter (PM) is linked to respiratory and cardiovascular diseases, certain types of cancer, and accounts for approximately seven million premature deaths globally. While governments and organizations have implemented various strategies for Air Quality (AQ) such as the deployment of Air Quality Monitoring Networks (AQMN), these networks often suffer from limited spatial coverage and involve high installation and maintenance costs. Consequently, the implementation of networks based on Low-Cost Sensors (LCS) has emerged as a viable alternative. Nevertheless, LCS systems have certain drawbacks, such as lower reading precision, which can be mitigated through specific calibration models and methods. This paper presents the results and conclusions derived from simultaneous PM10 and PM2.5 monitoring comparisons between LCS nodes and a T640X reference sensor. Additionally, Relative Humidity (RH), temperature, and absorption flow measurements were collected via an Automet meteorological station. The monitoring equipment was installed at the Faculty of Environment of the Universidad Distrital in Bogotá. The LCS calibration process began with data preprocessing, which involved filtering, segmentation, and the application of FastDTW. Subsequently, calibration was performed using a variety of models, including two statistical approaches, three Machine Learning algorithms, and one Deep Learning model. The findings highlight the critical importance of applying FastDTW during preprocessing and the necessity of incorporating RH, temperature, and absorption flow factors to enhance accuracy. Furthermore, the study concludes that Random Forest and XGBoost offered the highest performance among the methods evaluated. While satellites map city-wide patterns and MAX-DOAS enables hourly source attribution, our calibrated LCS network supplies continuous, street-scale data at low CAPEX/OPEX—forming a practical backbone for sustained micro-scale monitoring in Bogotá.