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This research paper presents a data-driven framework for design optimization of renewable energy communities (RECs) in the residential sector, considering both techno-economic challenges and environmental impact. The study's focus is to determine suitable sizes for photovoltaic systems, wind turbines, and battery electrical energy systems by evaluating energy, economic, and environmental criteria. To achieve this, we develop a data-driven model that incorporates Homer Pro and an in-house tool developed in Python programming language that integrates a machine learning algorithm, life cycle cost (LCC), life cycle assessment (LCA) calculations of the REC model. Furthermore, a multi-objective optimization model is established to minimize the LCC and LCA parameters while maximizing green energy use. Moreover, a multi-criteria decision-making approach based on Weighted Sum Model (WSM) is proposed to help the stakeholders to see beyond the selection criteria based on LCC and LCA to choose the most appropriate scenario optimal solution for the desired energy community and interpret the effect of various economic parameters on the sustainable performance of REC. The framework application is illustrated through a case study for the optimal design of REC for a residential community in Tarragona, Spain, consisting of 100 buildings. The results revealed a substantial improvement in economic , environmental benefits for designing REC, the optimal minimum cost solution with a levelized cost of energy (LCOE = 0.044 $/kWh) and a payback period of 7.1 years with an LCOE reduction of 85.04% compared to the base case. The minimum impact with an LCOE = 0.220 $/kWh and a payback period of 12.5 years with a reduction in environmental impact of 54.59% compared to the base case. Overall, the de

People care about their own well-being and about the well-being of their families. It is currently, however, unknown how much people tend to value their own versus their family’s well-being. A recent study documented that people value family happiness over personal happiness across four cultures. In this study, we sought to replicate this finding across a larger sample size (N = 12,819) and a greater number of countries (N = 49). We found that the strength of the idealization of family over personal happiness preference was small (average Cohen’s ds =.20, range −.02 to.48), but present in 98% of the studied countries, with statistical significance in 73% to 75%, and variance across countries <2%. We also found that the size of this effect did vary somewhat across cultural contexts. In Latin American cultures highest on relational mobility, the idealization of family over personal happiness was very small (average Cohen’s ds for Latin America =.15 and.18), while in Confucian Asia cultures lowest on relational mobility, this effect was closer to medium (ds >.40 and.30). Importantly, we did not find strong support for traditional theories in cross-cultural psychology that associate collectivism with greater prioritization of the family versus the individual; country-level individualism–collectivism was not associated with variation in the idealization of family versus individual happiness. Our findings indicate that no matter how much various populists abuse the argument of “protecting family life” to disrupt emancipation, family happiness seems to be a pan-culturally phenomenon. Family well-being is a key ingredient of social fabric across the world, and should be acknowledged by psychology and well-being researchers and by progressive movements too. © The Author(s) 2023.

How can one conclude that well-being is higher in country A than country B, when well-being is being measured according to the way people in country A think about well-being? We address this issue by proposing a new culturally sensitive method to comparing societal levels of well-being. We support our reasoning with data on life satisfaction and interdependent happiness focusing on individual and family, collected mostly from students, across forty-nine countries. We demonstrate that the relative idealization of the two types of well-being varies across cultural contexts and are associated with culturally different models of selfhood. Furthermore, we show that rankings of societal well-being based on life satisfaction tend to underestimate the contribution from interdependent happiness. We introduce a new culturally sensitive method for calculating societal well-being, and examine its construct validity by testing for associations with the experience of emotions and with individualism-collectivism. This new culturally sensitive approach represents a slight, yet important improvement in measuring well-being. © 2022, The Author(s).

We explore to what extent previously observed pan-cultural association between dimensions of self-construal and personal life satisfaction (PLS) may be moderated by three national-contextual variables: national wealth, economic inequality, and religious heritage. The results showed that MSelf-reliance (vs. dependence on others) predicted PLSpositively in poorer countries but negatively in richer countries. Connectedness to others (vs. self-containment) predicted PLS more strongly in Protestant-heritage countries. Self-expression (vs. harmony) predicted PLS more weakly (and non-significantly) in Muslim-heritage countries. In contrast, previously reported associations of self-direction (vs. reception-to-influence), consistency (vs. variability), and decontextualized (vs. contextualized) self-understanding with personal life satisfaction were not significantly moderated by these aspects of societal context. These results show the importance of considering the impact of national religious and economic context. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Item does not contain fulltext, OBJECTIVE: To study the impact of a standardized postoperative anesthesia visit on 30-day mortality in medium to high-risk elective surgical patients. BACKGROUND: Postoperative complications are the leading cause of perioperative morbidity and mortality. Although modified early warning scores (MEWS) were instituted to monitor vital functions and improve postoperative outcome, we hypothesized that complementary anesthesia expertise is needed to adequately identify early deterioration. METHODS: In a prospective, multicenter, stepped-wedge cluster randomized interventional study in 9 academic and nonacademic hospitals in the Netherlands, we studied the impact of adding standardized postoperative anesthesia visits on day 1 and 3 to routine use of MEWS in 5473 patients undergoing elective noncardiac surgery. Primary outcome was 30-day mortality. Secondary outcomes included: incidence of postoperative complications, length of hospital stay, and intensive care unit admission. RESULTS: Patients were enrolled between October 2016 and August 2018. Informed consent was obtained from 5473 patients of which 5190 were eligible for statistical analyses, 2490 in the control and 2700 in the intervention group. Thirty-day mortality was 0.56% (n = 14) in the control and 0.44% (n = 12) in the intervention group (odds ratio 0.74, 95% Confidence interval 0.34-1.62). Incidence of postoperative complications did not differ between groups except for renal complications which was higher in the control group (1.7% (n = 41) vs 1.0% (n = 27), P = 0.014). Median length of hospital stay did not differ significantly between groups. During the postanesthesia visits, for 16% (n = 437) and 11% (n = 293) of patients recommendations were given on day 1 and 3, respectively, of which 67% (n = 293) and 69% (n = 202) were followed up. CONCLUSIONS: The combination of MEWS and a postoperative anesthesia visit did not reduce 30-day mortality. Whether a postoperative anesthesia visit with strong adherence to t

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In response to the European Commission's renewable energy targets for 2030, this study presents a comprehensive, data-driven evaluation of the potential for electricity self-consumption in the Spanish residential sector based on rooftop PV systems. Utilizing real-time hourly electricity demand data and various surplus compensation policies, the research highlights the significance of data granularity, indicating that annual data-based PV estimations can lead to rooftop PV self-consumption capacity overestimations when compared to hourly data assessments. Furthermore, geographical variations reveal distinct rooftop PV self-consumption capacities between urban and rural areas, driven mainly by the prevalent building typologies. This discrepancy suggests that while rural regions, with their predominance of single-family dwellings, offer higher PV generation potential due to more available rooftop space, urban areas, dominated by multi-story buildings, face significant constraints in rooftop surface availability. Economically, the current surplus compensation policy in Spain reduces the profitability of PV installations, underscoring the necessity for enhanced policies to fully utilize all the available rooftop areas in the residential sector. The study's findings, particularly the revelation that residential rooftops in Spain may not be enough to meet the current electricity demand, emphasize the need for adaptive, region-specific policies and the potential role of energy communities. Policymakers and industry stakeholders are urged to prioritize rooftop utilization, ensuring the deployment of PV systems is both promoted and economically viable, steering Spain closer to its renewable energy and sustainability aspirations.

Construction projects demand a higher amount of energy predominantly for heating, ventilation, and illumination purposes. Modular construction has come into the limelight in recent years as a construction method that uses sustainable building materials and optimizes energy efficiency. Ventilation openings in buildings are designed to facilitate air circulation by naturally driven ventilation and could aid in reducing energy consumption in construction projects. However, a knowledge gap makes it difficult to propose the best dimensions of ventilation openings in buildings. Hence, the aim of this work is to empower the decision-making process in terms of proposing the best ventilation opening dimensions toward sustainable energy use and management in buildings. A novel framework is presented herein to evaluate the impact and propose the best dimensions of ventilation openings for metal frame modular construction in Brazil, using building information modeling. The ventilation openings were constructed and their dimensions evaluated in eight Brazilian cities, based on the bioclimatic zone (BioZ) classification indicated in ABNT NBR 15220: Curitiba (1st BioZ); Rio Negro (2nd BioZ); São Paulo (3rd BioZ); Brasília (4th BioZ); Campos (5th BioZ); Paranaíbe (6th BioZ); Goiás (7th BioZ); and Rio de Janeiro (8th BioZ). The study results show that the energy consumption of the same building model would vary based on the dimensions of ventilation openings for each BioZ in Brazil. For instance, modeling the same modular construction unit in the city of Rio Negro could consume around 50% of the energy compared to the same unit constructed in the city of Rio de Janeiro, using the small opening sizes based on the smallest dimensions of the ventilation openings. Similarly, modeling the co

The planning of energy-intensive processes is intrinsically uncertain due to their dependence on the volatile energy market, with scheduling having a vast impact on the final production cost of these plants. Traditional stochastic methods are mathematically very complex, which translates into a significant computational effort that might prevent a timely response to varying electricity prices. To encounter this uncertainty, we develop a reliable hybrid simulation-optimization approach for optimizing the production plant scheduling, combining scenario analysis with risk analysis. The proposed methodology is demonstrated with real data from a cryogenic air separation plant in Tarragona (Spain). This approach also informs decision-makers about risk or expected shortfall associated with the implied scenario. The generic methodology used here can be easily adapted to schedule facilities in other energy-intensive sectors such as cement, metallurgy or pulp and paper.

There is a limited comprehensive analysis of the effectiveness of adopted carbon mitigation strategies for buildings over their life cycle, that are concerned with temporal perspectives of emissions. Accordingly, this paper explores a life cycle assessment (LCA) to address the concerns regarding mitigating the carbon footprint of a UK timber-frame low-energy dwelling. In particular, it aims to investigate the potential greenhouse gas (GHG) emission reduction in terms of three different heating and ventilation options, and to analyze the influence of decarbonization of electricity production as well as the technological progress of the waste treatment of timber on the building's environmental performance. Thus, the whole life‑carbon of the building case studies was evaluated for a total of eight investigated prospective scenarios, and they were compared to the LCA results of the baseline scenario, where the existing technology and context remained constant over time. Results show that using a compact heat pump would lead to a significant whole life-cycle emission reduction of the dwelling, by 19 %; while GHG emission savings can be reinforced if the assessed systems are employed simultaneously with grid decarbonization, exhibiting a 25 %–60 % reduction compared to the baseline scenario. Moreover, technological changes in the waste treatments of timber products could substantially reduce the buildings' embodied emissions, representing 3 %–23 %. From these emission-saving measures, the contribution of material efficiency strategies to achieve more embodied carbon savings should be highlighted in future construction practices.

Traditional methods for designing concrete mixtures provide good results; however, they do not guarantee the optimum composition. Consequently, applying operational research techniques is motivated by an increasing need for designers to proportion the concrete's raw materials that satisfy the concrete performance requirements such as mechanical properties, chemical properties, workability, sustainability, and cost. For this reason, many authors have been looking for mathematical programming and machine learning solutions to predict concrete mix properties and optimise concrete mixtures. Therefore, a comprehensive review of operational research techniques concerning the design and proportioning of concrete mixtures and a classification framework are presented herein.