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Biosensors are used to detect and quantify chemicals produced in industrial microbiology with high specificity, sensitivity, and portability. Most biosensors, however, are limited by the need for transcription factors engineered to recognize specific molecules. In this study, we overcome the limitations typically associated with traditional biosensors by engineering Pseudomonas putida for whole-cell sensing of a variety of chemicals. Our approach integrates fluorescent reporters with synthetic auxotrophies within central metabolism that can be complemented by target analytes in growth-coupled setups. This platform enables the detection of a wide array of structurally diverse chemicals under various conditions, including co-cultures of producer cell factories and sensor strains. We also demonstrate the applicability of this versatile biosensor platform for monitoring complex biochemical processes, including plastic degradation by either purified hydrolytic enzymes or engineered bacteria. This microbial system provides a rapid, sensitive, and readily adaptable tool for monitoring cell factory performance and for environmental analyzes. Biosensors are powerful tools for quantification of a wide range of molecules but require extensive engineering for each analyte. Here, the authors engineered a robust environmental bacterium for sensing a diverse set of chemicals, such as lactate and PET degradation products, via growth-coupling

Cellular membrane fusion is thought to proceed through intermediates including docking of apposed lipid bilayers, merging of proximal leaflets to form a hemifusion diaphragm, and fusion pore opening. A membrane-bridging four-helix complex of soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNAREs) mediates fusion. However, how assembly of the SNARE complex generates docking and other fusion intermediates is unknown. Using a cell-free reaction, we identified intermediates visually and then arrested the SNARE fusion machinery when fusion was about to begin. Partial and directional assembly of SNAREs tightly docked bilayers, but efficient fusion and an extended form of hemifusion required assembly beyond the core complex to the membrane-connecting linkers. We propose that straining of lipids at the edges of an extended docking zone initiates fusion.

This research presents a solution to the problem of controlling the energy demand and carbon footprint of old buildings, with the focus being on a (heated) building located in Madrid, Spain. A framework that incorporates AI and advanced hybrid ensemble approaches to make very accurate energy consumption predictions was developed and tested using the MATLAB environment. At first, the study evaluated six individual AI models (ANN, RF, XGBoost, RBF, Autoencoder, and Decision Tree) using a dataset of 100 points that were collected from the building’s sensors. Their performance was evaluated with high-quality data, which were ensured to be free of missing values or outliers, and they were prepared using L1/L2 normalization to guarantee optimal model performance. Later, higher accuracy was achieved through combining the models by means of hybrid ensemble techniques (voting, stacking, and blending). The main contribution is the application of a Long Short-Term Memory (LSTM) model for predicting the energy consumption of the building and, very importantly, its carbon footprint over a 30-year period until 2050. Additionally, the proposed methodology provides a structured pathway for existing buildings to progress toward nearly Zero-Energy Building (nZEB) performance by enabling more effective control of their energy demand and operational emissions. The comprehensive assessment of predictive models definitively concludes that the blended ensemble method is the most powerful and accurate forecasting tool, achieving 97% accuracy. A scenario where building heating energy use jumps to 135 by 2050 (a 35% increase above 2020 levels) represents an alarming complete failure to achieve energy efficiency and decarbonization goals, which would fundamentally jeopardize climate targets, energy security, and consumer expenditure.

The decarbonization of the building sector is essential to mitigate climate change, aligning with the EU’s Energy Performance of Buildings Directive (EPBD) and the transition from near-Zero-Energy Buildings (nZEBs) to Zero-Emission Buildings (ZEBs). This study introduces a novel and streamlined Life Cycle Assessment (LCA) methodology, in accordance with EN 15978, to holistically evaluate the Global Warming Potential (GWP) of buildings. Our approach integrates a calibrated dynamic simulation of operational energy use, performed with DesignBuilder, to determine precise operational CO2 emissions. This is combined with a comprehensive assessment of embodied emissions, encompassing construction materials and transportation phases, using detailed Environmental Product Declarations (EPDs). Applied to the IndUVa nZEB case study, the findings reveal that embodied emissions dominate the life cycle GWP, accounting for 69%, while operational emissions contribute just 31% over 50 years. The building’s use of 63.8% recycled materials highlights the transformative role of circular economy strategies in reducing embodied impacts. A comparative analysis of three energy-efficiency scenarios demonstrates the IndUVa building’s exceptional performance, achieving energy demand reductions of 78.4% and 85.6% compared to the ASHRAE and CTE benchmarks, respectively. This study underscores the growing significance of embodied emissions as operational energy demand declines. Achieving ZEBs requires prioritizing embodied carbon reduction through sustainable material selection, recycling, and reuse, targeting a minimum of 70% recycled content. By advancing the LCA framework, this study presents a pathway for achieving ZEBs, driving a substantial reduction in global energy consumption and carbon emissions, and contributing to climate change mitigation.

This study presents the evaluation of the performance and acceptability of thermal comfort by students in the classrooms of a university building with minisplit-type air-conditioning systems, in a tropical climate. To carry out the study, temperature and humidity measurements were recorded, both outside and inside the selected classrooms, while the students were asked to complete thermal surveys on site. The survey model is based on the template proposed by Fanger and it was applied to a total number of 584 students. In each classroom, the Predicted Mean Vote (PMV) and the Predicted Percentage Dissatisfied (PPD) were estimated according to Fanger’s methodology, as well as the Thermal Sensation Vote (TSV) and the Actual Percentage Dissatisfied (APD), which were obtained from the measurements and the surveys. The results of this study showed that the PMV values, although they may vary with the insulation of the clothing, do not affect the TSV. Furthermore, comparing PMV vs. TSV scores, a 2 °C to 3 °C difference in operating temperature was found, whereby the thermal sensitivity for TSV was colder, so it could be assumed that the PMV model overestimates the thermal sensitivity of students in low-temperature conditions. In addition, an acceptability by 90% with thermal preferences between 23 °C and 24 °C were also found. These results indicate that it is possible to increase the temperature set point in minisplit-type air-conditioning system from 4 °C to 7 °C with respect to the currently set temperatures, without affecting the acceptability of the thermal environment to the students in the building.

Industry 5.0 is transforming energy demand by integrating sustainability into energy planning, ensuring market stability while minimizing environmental impact for future generations. There are several patterns for calculating energy consumption depending on whether it is measured daily, monthly, or annually through the integration of artificial intelligence approaches, particularly Artificial Neural Networks (ANNs) and Random Forests (RFs), and within the framework of Industry 5.0. This study employs machine learning techniques to analyze energy consumption data from two distinct buildings in Spain: the LUCIA facility in Valladolid and the FUHEM Building in Madrid. The implementation was conducted using custom MATLAB code developed in-house. Our approach systematically evaluates and compares the predictive performance of Artificial Neural Networks (ANNs) and Random Forests (RFs) for energy demand forecasting, leveraging each algorithm’s unique characteristics to assess their suitability for this application. The performances of both models are calculated using the Root Mean Square Percentage Error (RMSPE), Root Mean Square Relative Percentage Error (RMSRPE), Mean Absolute Percentage Error (MAPE), Mean Absolute Relative Percentage Error (MARPE), Kling–Gupta Efficiency (KGE), and also the coefficient of determination, R2. Training times are validated using ANN and RF models. Lucia RF took 2.8 s, while Lucia ANN took 40 s; FUHEM RF took 0.3 s, compared to FUHEM ANN, which took 1.1 s. The performances of the two models are described in detail to show the effectiveness of each of them.

Understanding and forecasting energy consumption patterns is crucial for improving energy efficiency and human well-being, especially in diverse infrastructures like Spain. This research addresses a significant gap in energy demand forecasting across three building types by comparing six machine learning algorithms: Artificial Neural Networks, Random Forest, XGBoost, Radial Basis Function Network, Autoencoder, and Decision Trees. The primary aim is to identify the most effective model for predicting energy consumption based on historical data, contributing to the relationship between energy systems and urban well-being. The study emphasizes challenges in energy use and advocates for sustainable management practices. By forecasting energy demand over the next three years using linear regression, it provides actionable insights for energy providers, enhancing resilience in urban environments impacted by climate change. The findings deepen our understanding of energy dynamics across various building types and promote a sustainable energy future. Stakeholders will receive targeted recommendations for aligning energy production with consumption trends while meeting environmental responsibilities. Model performance is rigorously evaluated using metrics like Squared Mean Root Percentage Error (RMSPE) and Coefficient of Determination (R2), ensuring robust analysis. Training times for models in the LUCIA building ranged from 2 to 19 s, with the Decision Tree model showing the shortest times, highlighting the need to balance computational efficiency with model performance.

This paper develops an energy analysis for an existing near Zero Energy (nZEB) and Zero Carbon Emissions building called LUCIA, located at the university campus in Valladolid (Spain). It is designed to supply electricity, cooling and heating needs through solar energy (Photovoltaic Systems, PV), biomass and an Earth–Air Heat Exchanger (EAHE), besides a Combined Heat Power (CHP). It is currently among the top three buildings with the highest LEED certification in the World. The building model is simulated with DesignBuilder version 5. The results of the energy analysis illustrate the heating, cooling and lighting consumptions expected, besides other demands and energy uses. From this data, we carried out an energy balance of the nZEB, which will help to plan preventive actions when compared to the actual energy consumptions, improving the management and control of both the building and its systems. The primary energy indicator obtained is 67 kWh/m2 a year, and 121 kWh/m2 a year for renewable energy generation, with respect to 55 kWh/m2 and 45 kWh/m2 set as reference in Europe. The Renewable Energy Ratio (RER) is 0.66. These indicators become a useful tool for the energy analysis of the nZEB according to the requirements in the European regulations and for its comparison with further nZEB.

The soluble N-ethylmaleimide–sensitive factor attachment protein receptor (SNARE) complex drives the majority of intracellular and exocytic membrane fusion events. Whether and how SNAREs cooperate to mediate fusion has been a subject of intense study, with estimates ranging from a single SNARE complex to 15. Here we show that there is no universally conserved number of SNARE complexes involved as revealed by our observation that this varies greatly depending on membrane curvature. When docking rates of small (∼40 nm) and large (∼100 nm) liposomes reconstituted with different synaptobrevin (the SNARE present in synaptic vesicles) densities are taken into account, the lipid mixing efficiency was maximal with small liposomes with only one synaptobrevin, whereas 23–30 synaptobrevins were necessary for efficient lipid mixing in large liposomes. Our results can be rationalized in terms of strong and weak cooperative coupling of SNARE complex assembly where each mode implicates different intermediate states of fusion that have been recently identified by electron microscopy. We predict that even higher variability in cooperativity is present in different physiological scenarios of fusion, and we further hypothesize that plasticity of SNAREs to engage in different coupling modes is an important feature of the biologically ubiquitous SNARE-mediated fusion reactions.

This study proposes a comprehensive artificial intelligence (AI)-based framework to predict, disaggregate, and optimize energy consumption and associated CO2 emissions across a multi-building university campus. Leveraging real-world data from 27 buildings at the University of Valladolid (Spain), six AI models—artificial neural networks (ANN), radial basis function (RBF), autoencoders, random forest (RF), XGBoost, and decision trees—were trained on heat exchanger performance metrics and contextual building parameters. The models were validated using an extensive set of key performance indicators (MAPE, RMSE, R2, KGE, NSE) to ensure both predictive accuracy and generalizability. The ANN, RBF, and autoencoder models exhibited the highest correlation with actual data (R > 0.99) and lowest error rates, indicating strong suitability for operational deployment. A detailed analysis at building level revealed heterogeneity in energy demand patterns and model sensitivities, emphasizing the need for tailored forecasting approaches. Forecasts for a 5-year horizon further demonstrated that, without intervention, energy consumption and CO2 emissions are projected to increase significantly, underscoring the relevance of predictive control strategies. This research establishes a robust and scalable methodology for campus-wide energy planning and offers a data-driven pathway for CO2 mitigation aligned with European climate targets.