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The research about energy efficiency in buildings has exponentially increased during the last few years. Nevertheless, both research and practice still cannot rely on complete methodologies tailored for building portfolios as a whole, because the attention has always been drawn to individual premises. Yet, energy efficiency analyses need to go beyond the single building perspective and incorporate strategic district approaches to optimize the retrofit investment. For this purpose, several aspects should be considered simultaneously, and new methodologies should also be promoted. Therefore, this paper aims to discuss energy retrofit campaigns in building portfolios, drawing an exhaustive and updated review about the challenge of jumping from the single-building perspective to a stock-based analysis. This research discusses the publications available on the topic from five key aspects that are all essential steps in achieving a complete and reliable study of energy efficiency at a portfolio level. They are energy modelling and assessment, energy retrofit design, decision-making criteria assessment, optimal allocation of (financial) resources and risk valuation. This review, therefore, advocates for joint consideration of the problem as a basis on which to structure further disciplinary developments. Research gaps are highlighted, and new directions for future research are suggested.

Along the roadmap to a Sustainable Real Estate-Scape, energy retrofit campaigns on wide city compartments represent a pivotal task, where the importance of the collaboration between the public and private sectors is crucial. Energy retrofit programs on building assets are subject to multiple uncertainty factors (e.g., climate, energy-economy forecasts, etc.) that act as a primary barrier to investment in this field. This paper aims to discuss risk management techniques to understand better how to deal with this kind of uncertainty. The research specifically addresses the techniques of sensitivity analysis and Monte Carlo simulation, focusing first on the phase of variables selection and their probability definition, including climatic, environmental, energy, economic, financial, and stochastic parameters. In this article, it is suggested to include correlation coefficients in the input variables of risk analysis, preferring the two-dimension Monte Carlo simulation to its simple version, since the results are more reliable when separating aleatory from epistemic uncertainty; thus, the results are more reliable. Moreover, it is seen how a sensitivity analysis based on percentile variations of the inputs provides a more accurate representation of risk if compared to the most common sensitivity analysis based on percentage deviations of the inputs. Conducting a sensitivity analysis using percentile variations gives realistic and reliable results, reflecting the tailored definition of uncertainty around the inputs on the basis of specific market analyses or historical series.

Background Noonan syndrome (NS) and Noonan syndrome with multiple lentigines (NSML) are neurodevelopmental conditions caused by genetic variants leading to upregulated signaling in the RAS-MAPK pathway. While previous research has focused on genetic variability in cognitive and cardiac phenotypes, behavioral phenotypes, and their correlations across genetic variants and within the PTPN11 gene remain poorly characterized. Methods This study included 121 individuals with NS ( PTPN11 : 88, SOS1 : 18, RAF1 : 6, KRAS : 2, RIT1 : 3, NRAS : 2, LZTR1 : 2, SOS2 : 1) and seven individuals with NSML ( PTPN11 ), compared to age- and sex-matched typically developing (TD) (N = 71). Behavioral questionnaires assessed social responsiveness and ASD-related traits (using SRS-2), and emotional problems (using CBCL) to identify genetic variant-specific behavioral profiles. Biochemical profiling of SHP2 activity in PTPN11 -associated NS variants examined genotype–phenotype relationships. Results Compared to TD individuals, those with PTPN11 -associated NS, NSML, and SOS1 -associated NS exhibited clinically elevated scores, indicating increased ASD-related behaviors, poorer social functioning, and heightened emotional problems. Genetic variant comparisons revealed that individuals with PTPN11 -associated NS and NSML exhibited greater ASD-related challenges than those with RAF1 . Individuals with NSML exhibit elevated attention problems compared to all other genetic groups. Logistic regression results suggested each one-unit increase in SHP2 fold activation for PTPN11 -associated NS corresponded to a 64% higher likelihood of markedly elevated restricted and repetitive behaviors, suggesting genotype–phenotype links. Limitations Small sample sizes for rarer variants, leading to unequal group sizes across subgroups, with PTPN11 variants comprising most of the NS group. Future research should address these sampling constraints and conduct functional studies to clarify variant impacts. Longitudinal assessments could elucidate behavioral phenotype trajectories. Conclusions This study underscores the importance of genetic variant-specific research to understand unique behavioral phenotypes in NS and NSML. Our findings indicate a higher risk for ASD-related symptoms in PTPN11 -associated NS and NSML compared to other variants. Additionally, individuals with PTPN11 -associated NS and higher SHP2 fold activation exhibited greater impairments in restricted and repetitive behaviors, suggesting SHP2 activation variations may contribute to phenotypic variability. By linking ASD-related symptoms to biochemical predictors in PTPN11 -associated NS, this study may inform future targeted treatment approaches.

Nowadays, some of the most expensive real estate is not “real” at all. Several investors are purchasing land in the virtual world of the Metaverse. To be more accurate in the wording, they are buying “meta-estates”. This work is dedicated to opening a debate about this emerging research field within the real estate discipline. It begins by discussing market segmentation, ownership, and pricing by comparing the traditional real estate market with the virtual estate market. Furthermore, this study involved interviews with six seasoned Metaverse land investors who participated in two Analytic Hierarchy Processes (AHPs). The first AHP ranked 14 investment typologies, while the second focused on ranking and discussing the most important characteristics of meta-estates that influence the formation of prices. As a result, the most appealing investments identified were day-trading, virtual land trading (buying to resell), and virtual land development (transforming and reselling). The primary characteristics of meta-estates considered by investors include the platform (e.g., Earth 2, Sandbox), the location within the platform (proximity to famous neighbours), and the architectural design of the buildings (designed by renowned architects). It is evident that the Metaverse represents a new frontier for land investors, and the primary aim of this study was to encourage other researchers to explore and investigate this evolving field.

Regarding environmental sustainability and market pricing, the energy class is an increasingly more decisive characteristic in the real estate sector. For this reason, a great deal of attention is now devoted to exploring new technologies, energy consumption forecasting tools, intelligent platforms, site management devices, optimised procedures, software, and guidelines. New investments and smart possibilities are currently the object of different research in energy efficiency in building stocks to reach widespread ZEB standards as soon as possible. In this light, this work focuses on analysing 13 cities in Northern Italy to understand the impact of energy class on market values. An extensive data-mining process collects information about 13,093 properties in Lombardia, Piemonte, Emilia Romagna, Friuli Venezia-Giulia, Veneto, and Trentino alto Adige. Then, a feature importance analysis and a machine learning forecasting tool help understand the influence of energy class on market prices today.

In this research, the authors aim to detect the marginal appreciation of construction and neighbourhood characteristics of property prices at three different time points: before the COVID-19 pandemic, two years after the first COVID-19 alert but before the War in Ukraine, and one year after the outbreak of the War. The marginal appreciations of the building’s features are analysed for a pilot case study in Northern Italy using a Random Forest feature importance analysis and a Multivariate Regression. Several techniques are integrated into this study, such as computer programming in Python language, multi-parametric value assessment techniques, feature selection procedures, and spatial analysis. The results may represent an interesting ongoing monitoring of how these anomalous events affect the buyer’s willingness to pay for specific characteristics of the buildings, with particular attention to the location features of the neighbourhood and accessibility.

Building energy modeling (BEM) is used to support (nearly) zero-energy building (ZEB) projects, since this kind of software represents the only available option to forecast building energy consumption with high accuracy. BEM may also be used during preliminary analyses or feasibility studies, but simulation results are usually too detailed for this stage of the project. Aside from that, when optimization algorithms are used, the implied high number of energy simulations causes very long calculation times. Therefore, designers could be discouraged from the extensive use of BEM to conduct optimization analyses. Thus, they prefer to study and compare a very limited amount of acknowledged alternative designs. In relation to this problem, the scope of the present study is to obtain an easy-to-use tool to quickly forecast the energy consumption of a building with no direct use of BEM to support fast comparative analyses at the early stages of energy projects. In response, a set of automatic energy assessment tools was developed based on machine learning techniques. The forecasting tools are artificial neural networks (ANNs) that are able to estimate the energy consumption automatically for any building, based on a limited amount of descriptive data of the property. The ANNs are developed for the Po Valley area in Italy as a pilot case study. The ANNs may be very useful to assess the energy demand for even a considerable number of buildings by comparing different design options, and they may help optimization analyses.

This research aims to understand how market transparency and data reliability can influence valuation procedures and decision-making processes in the Italian real estate market. Through the analysis of three different real estate markets and the validation of the information collected, this paper’s goal is to understand whether and to what extent the use of asking prices instead of actual purchase and sale prices can lead to valuation errors, increase the uncertainty of valuation, and undermine investment decision-making processes. The research results highlight the primary sources of information opacity in the Italian real estate market, classifying them according to their impact on real estate value. The novelty of this research lies in the integrated use of machine learning techniques, computer programming and multi-parametric valuation procedures to understand and manage information opacity in the Italian real estate market, particularly regarding the estimation of the market value of properties belonging to the residential segment.

Purpose This paper aims to develop a forecasting tool for the automatic assessment of both environmental and economic benefits resulting from low-carbon investments in the real estate sector, especially when applied in large building stocks. A set of four artificial neural networks (NNs) is created to provide a fast and reliable estimate of the energy consumption in buildings due to heating, hot water, cooling and electricity, depending on some specific buildings’ characteristics, such as geometry, orientation, climate or technologies. Design/methodology/approach The assessment of the building’s energy demand is performed comparing the as-is status (pre-retrofit) against the design option (post-retrofit). The authors associate with the retrofit investment the energy saved per year, and the net monetary saving obtained over the whole cost after a predetermined timeframe. The authors used a NN approach, which is able to forecast the buildings’ energy demand due to heating, hot water, cooling and electricity, both in the as-is and in the design stages. The design stage is the result of a multiple attribute optimization process. Findings The approach here developed offers the opportunity to manage energy retrofit interventions on wide property portfolios, where it is necessary to handle simultaneously a large number of buildings without it being technically feasible to achieve a very detailed level of analysis for every property of a large portfolio. Originality/value Among the major accomplishments of this research, there is the creation of a methodology that is not excessively data demanding: the collection of data for building energy simulations is, in fact, extremely time-consuming and expensive, and this NN model may help in overcoming this problem. Another important result achieved in this study is the flexibility of the model developed. The case study the authors analysed was referred to one specific stock, but the results obtained have a more widespread importance because it ends up being only a matter of input-data entering, while the model is perfectly exportable in other contexts.

Abstract Background The hypothalamus is central to many hormonal and autonomous nervous system pathways. Emerging evidence indicates that these pathways may be disrupted in schizophrenia and bipolar disorder. Yet, few studies have examined the volumes of hypothalamic subunits in these patient groups. We compared hypothalamic subunit volumes in individuals with psychotic disorders to healthy controls. Study Design We included 344 patients with schizophrenia spectrum disorders (SCZ), 340 patients with bipolar disorders (BPD), and 684 age- and-sex-matched healthy controls (CTR). Total hypothalamus and five hypothalamic subunit volumes were extracted from T1-weighted magnetic resonance imaging (MRI) using an automated Bayesian segmentation method. Regression models, corrected for age, age2, sex, and segmentation-based intracranial volume (sbTIV), were used to examine diagnostic group differences, interactions with sex, and associations with clinical symptoms, antipsychotic medication, antidepressants and mood stabilizers. Study Results SCZ had larger volumes in the left inferior tubular subunit and smaller right anterior-inferior, right anterior-superior, and right posterior hypothalamic subunits compared to CTR. BPD did not differ significantly from CTR for any hypothalamic subunit volume, however, there was a significant sex-by-diagnosis interaction. Analyses stratified by sex showed smaller right hypothalamus and right posterior subunit volumes in male patients, but not female patients, relative to same-sex controls. There was a significant association between BPD currently taking antipsychotic medication and the left inferior tubular subunits volumes. Conclusions Our results show regional-specific alterations in hypothalamus subunit volumes in individuals with SCZ, with relevance to HPA-axis dysregulation, circadian rhythm disruption, and cognition impairment.