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BackgroundThe majority of quantitative genetic models used to map complex traits assume that alleles have similar effects across all individuals. Significant evidence suggests, however, that epistatic interactions modulate the impact of many alleles. Nevertheless, identifying epistatic interactions remains computationally and statistically challenging. In this work, we address some of these challenges by developing a statistical test for polygenic epistasis that determines whether the effect of an allele is altered by the global genetic ancestry proportion from distinct progenitors.ResultsWe applied our method to data from mice and yeast. For the mice, we observed 49 significant genotype-by-ancestry interaction associations across 14 phenotypes as well as over 1,400 Bonferroni-corrected genotype-by-ancestry interaction associations for mouse gene expression data. For the yeast, we observed 92 significant genotype-by-ancestry interactions across 38 phenotypes. Given this evidence of epistasis, we test for and observe evidence of rapid selection pressure on ancestry specific polymorphisms within one of the cohorts, consistent with epistatic selection.ConclusionsUnlike our prior work in human populations, we observe widespread evidence of ancestry-modified SNP effects, perhaps reflecting the greater divergence present in crosses using mice and yeast.

In supporting the phase-out of the fossil fuels, Roof Top Photovoltaic (RTPV) deployment has been adopted worldwide as an important step of a bottom-up driving pathway of citizens’ transformation to become net energy producers within the community of their localized building environment. However, the diverse bioclimatic conditions of this environment may affect the best RTPV implementation. This is facilitated by climate-related characterization and regional adaptation. Hence, the built environment globally as a function of the global horizontal irradiation (GHI), the local environmental parameters of the different climatic zones and the associated technological developments are surveyed.In this work, we have critically assessed the RTPV effect on the building's overall energy performance and found beneficial over a diverse range of moderate and warm climates. By applying adequate insulation beneath the RTPVs, the increased heating needs in winter in cold climates or higher nighttime cooling needs in summertime can be avoided. To design low-energy buildings, we propose an analytical framework based on the space energy coverage by RTPV and the global horizontal irradiation. Moreover, RTPV cooling at elevated temperatures improves the efficiency up to 20 % and increases the generated electricity up to 15 %. Increasing the RTPV efficiency with emerging technologies could extend the decarbonization of high-rise buildings with energy efficiency and RTPV measures. To accelerate the clean energy transition, rooftop PVs should be widely adopted for sustainable solar building applications. Combined with electrical storage, this will allow renewable energy resources to cover a large fraction of future building energy needs worldwide.

To achieve the objectives of COP28 for transitioning away from fossil fuels and phasing these out, both natural and technological solutions are essential, necessitating a step-change in how we implement social innovation. Given the significant CO _2 emissions produced by the building sector, there is an urgent need for a transformative shift towards a net-zero building stock by mid-century. This transition to zero-energy and zero-emission buildings is difficult due to complex processes and substantial costs. Building integrated photovoltaics (BIPV) offers a promising solution due to the benefits of enhanced energy efficiency and electricity production. The availability of roof and façade space in offices and other types of buildings, especially in large cities, permits photovoltaic integration in both opaque and transparent surfaces. This study investigates the synergistic relationship between solar conversion technologies and nature-based components. Through a meta-analysis of peer-reviewed literature and critical assessment, effective BIPVs with greenery (BIPVGREEN) combinations suitable for various climatic zones are identified. The results highlight the multi-faceted benefits of this integration across a range of techno-economic and social criteria and underscore the feasibility of up-scaling these solutions for broader deployment. Applying a SWOT analysis approach, the internal strengths and weaknesses, as well as the external opportunities and threats for BIPVGREEN deployment, are investigated. The analysis reveals key drivers of synergistic effects and multi-benefits, while also addressing the challenges associated with optimizing performance and reducing investment costs. The strengths of BIPVGREEN in terms of energy efficiency and sustainable decarbonization, along with its potential to mitigate urban and climate temperature increases, enhance its relevance to the built environment, especially for informal settlements. The significance of prioritizing this BIPVGREEN climate mitigation action in low-income vulnerable regions and informal settlements is crucial through the minimum tax financing worldwide and citizen’s engagement in architectural BIPVGREEN co-integration.

This data article refers to the paper ''Optimizing photovoltaic electric generation and roof insulation in existing residential buildings” [1].The reported data deal with roof retrofit in different types of existing residential buildings (single-family, multi-family and apartment complex) located in Milan (Northern Italy). The study focus on the optimization of envelope insulation and photovoltaic (PV) energy production associated with different building geometries, initial insulation level, roof constructions, and materials.The data linked within this article relate to the modelled building energy consumption, renewable production, potential energy savings, and costs. Data refer to two main scenarios: refurbishment (roof in need of replacement and insulation) and re-roofing (energy intervention for roof improvement). Data allow to visualize energy consumption before and after the optimization, selected insulation level and material, costs and PV renewable production (with and without energy storage). The reduction of energy consumption can be visualized for each building type and scenario. Further data is available on CO2 emissions, envelope, materials, and systems.

The building sector has a strategic role in the clean energy transition towards a fully decarbonized stock by mid-century. This data article investigates the use of different weather datasets in building energy simulations across Europe. It focuses on a standard performing building optimized to a nearly-zero level accounting for climate projections towards 2060. The provided data quantify the building energy performance in the current and future scenarios. The article investigates how heating and cooling loads change depending on the location and climate scenario. Hourly weather datasets frequently used in building energy simulations are analyzed to investigate how climatic conditions have changed over recent decades. The data give insight into the implications of the use of weather datasets on buildings in terms of energy consumption, efficiency measures (envelope, appliances, systems), costs, and renewable production. Due to the ongoing changing climate, basing building energy simulations and design optimization on obsolete weather data may produce inaccurate results and related building designs with an increased energy consumption in the coming decades. Energy efficiency will become more crucial in the future when cooling and overheating will have to be controlled with appropriate measures used in combination with renewable energy sources.

This data article refers to the research paper A model for the cost-optimal design of Nearly Zero Energy Buildings (NZEBs) in representative climates across Europe [1]. The reported data deal with the design optimization of a residential building prototype located in representative European locations. The study focus on the research of cost-optimal choices and efficiency measures in new buildings depending on the climate. The data linked within this article relate to the modelled building energy consumption, renewable production, potential energy savings, and costs. Data allow to visualize energy consumption before and after the optimization, selected efficiency measures, costs and renewable production. The reduction of electricity and natural gas consumption towards the NZEB target can be visualized together with incremental and cumulative costs in each location. Further data is available about building geometry, costs, CO2 emissions, envelope, materials, lighting, appliances and systems.

The choice of the most appropriate technologies in buildings is often a challenge at the design stage, especially when many different criteria are taken into account. Consequently, the decision process relies often on one criterion only, such as costs or energy savings. We propose a multi-criteria approach based on multi-attribute utility theory to assess alternative energy efficiency measures, explicitly considering both environmental and economic criteria. We apply it to the design of a new residential building in Milan (Italy), with the aim to maximize CO2 emission savings related to electricity and gas consumption, and to minimize embodied energy and investment costs. After modelling the building prototype, alternative energy efficiency measures are assessed and ranked according to the selected criteria.The building optimized through the implementation of the best performing measures showed an overall 90% reduction in operational primary energy compared to the baseline building. The inclusion of the embodied energy altered the energy performance calculations resulting in 55–67% reduction in total energy over a 10-year period, and 77–82% over a 30-year period. Results point to the importance of a comprehensive implementation of measures, such as thermal improvements, high efficiency equipment, appliances, and renewable energy generation. The paper demonstrates the feasibility of this framework to support the decision process from a multi-criteria perspective, proposing a flexible method that can be adapted to other building types, environmental conditions, materials and technologies. It also highlights the importance of considering both environmental and economic criteria when designing a new building. It stresses how the embodied energy should be a criterion for technology selection, as current strategies to reduce operational energy often increase the amount of energy embodied into buildings with environmental consequences. Keywords: Multi-criteria decision making, Energy efficiency measures, Embodied energy, Multi-attribute utility theory (MAUT), Building modelling and simulation, CO2 emission savings

Cup anemometers are frequently employed in the wind power industry for wind resource assessment at prospective wind farm sites. In this paper, we demonstrate a method for identifying faulty three cup anemometers. This method is applicable to cases where data is available from two or more anemometers at equal height and cases where data is available from anemometers at different heights. It is based on examining the Weibull parameters of the distribution generated from the difference between the anemometer’s reported measurements and utilizing a discriminant function technique to separate out the data corresponding to bad cup anemometers. For anemometers at different heights, only data from the same height pair combinations are compared. In addition, various preprocessing techniques are discussed to improve performance of the algorithm. These include removing data that corresponds to poor wind directions for comparing the anemometers and removing data that corresponds to frozen anemometers. These methods are employed on the data from the PHM 2011 Data Competition with results presented.

Pertussis is highly contagious; thus, prompt identification of cases is essential to control outbreaks. Clinicians experienced with the disease can easily identify classic cases, where patients have bursts of rapid coughing followed by gasps, and a characteristic whooping sound. However, many clinicians have never seen a case, and thus may miss initial cases during an outbreak. The purpose of this project was to use voice-recognition software to distinguish pertussis coughs from croup and other coughs.We collected a series of recordings representing pertussis, croup and miscellaneous coughing by children. We manually categorized coughs as either pertussis or non-pertussis, and extracted features for each category. We used Mel-frequency cepstral coefficients (MFCC), a sampling rate of 16 KHz, a frame Duration of 25 msec, and a frame rate of 10 msec. The coughs were filtered. Each cough was divided into 3 sections of proportion 3-4-3. The average of the 13 MFCCs for each section was computed and made into a 39-element feature vector used for the classification. We used the following machine learning algorithms: Neural Networks, K-Nearest Neighbor (KNN), and a 200 tree Random Forest (RF). Data were reserved for cross-validation of the KNN and RF. The Neural Network was trained 100 times, and the averaged results are presented.After categorization, we had 16 examples of non-pertussis coughs and 31 examples of pertussis coughs. Over 90% of all pertussis coughs were properly classified as pertussis. The error rates were: Type I errors of 7%, 12%, and 25% and Type II errors of 8%, 0%, and 0%, using the Neural Network, Random Forest, and KNN, respectively.Our results suggest that we can build a robust classifier to assist clinicians and the public to help identify pertussis cases in children presenting with typical symptoms.