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The required data was provided by Energex. The study received partial funding from the Ministry of Science and Innovation, Spain (Project ID: PID2020-115454GB-C21). Partial support of this work was through the LATENTIA project PID2022-140786NB-C31 of the Spanish Ministry of Science, Innovation and Universities (MICINNU) . This work presents a Temporal Convolution Network (TCN) model for half-hourly, three-hourly and daily-time step to predict electricity demand ( ) with associated uncertainties for sites in Southeast Queensland Australia. In addition to multi-step predictions, the TCN model is applied for probabilistic predictions of where the aleatoric and epistemic uncertainties are quantified using maximum likelihood and Monte Carlo Dropout methodologies. The benchmarks of TCN model include an attention-based, bi-directional, gated recurrent unit, seq2seq, encoder–decoder, recurrent neural networks and natural gradient boosting models. The testing results show that the proposed TCN model attains the lowest relative root mean square error of 5.336-7.547% compared with significantly larger errors for all benchmark models. In respect to the 95% confidence interval using the Diebold–Mariano test statistic and key performance metrics, the proposed TCN model is better than benchmark models, capturing a lower value of total uncertainty, as well as the aleatoric and epistemic uncertainty. The root mean square error and total uncertainty registered for all of the forecast horizons shows that the benchmark models registered relatively larger errors arising from the epistemic uncertainty in predicted electricity demand. The results obtained for TCN, measured by the quality of prediction intervals representing an interval with upper and lower bound errors, registered a greater reliability factor as this model was likely to produce prediction interval that were higher than benchmark models at all prediction intervals. These results demonstrate the effectiveness of TCN approach in electricity demand modelling, and therefore advocates its usefulness in now-casting and forecasting systems.

This study aims to analyze the effects of venture capital (VC) financing schemes on the financial and environmental performance of their VC-backed companies. This research leverages a dataset including 325 U.S. firms between 2002 and 2022 and examines two issues of interest: independent venture capital (IVC) and corporate venture capital (CVC) funding. The results show that IVC-backed companies have significantly better environmental, social, and governance (ESG) ratings and emit fewer greenhouse gases (GHG) emissions when compared to companies backed by CVC. This highlights that the function of IVC is to improve the environmental sustainability of businesses. Together this helps provide a valuable perspective about which VC models (CVC, IVC) does have an impact on how businesses pursue sustainability practices alongside financial performance. This paper contributes to the sustainable entrepreneurship literature by focusing on the importance of funding types with performing sustainable practices. info:eu-repo/semantics/publishedVersion

A new energy-consistent discretization of the viscous dissipation function in incompressible flows is proposed. It is implied by choosing a discretization of the diffusive terms and a discretization of the local kinetic energy equation and by requiring that continuous identities like the product rule are mimicked discretely. The proposed viscous dissipation function has a quadratic, strictly dissipative form, for both simplified (constant viscosity) stress tensors and general stress tensors. The proposed expression is not only useful in evaluating energy budgets in turbulent flows, but also in natural convection flows, where it appears in the internal energy equation and is responsible for viscous heating. The viscous dissipation function is such that a consistent total energy balance is obtained: the 'implied' presence as sink in the kinetic energy equation is exactly balanced by explicitly adding it as source term in the internal energy equation. Numerical experiments of Rayleigh-Bénard convection (RBC) and Rayleigh-Taylor instabilities confirm that with the proposed dissipation function, the energy exchange between kinetic and internal energy is exactly preserved. The experiments show furthermore that viscous dissipation does not affect the critical Rayleigh number at which instabilities form, but it does significantly impact the development of instabilities once they occur. Consequently, the value of the Nusselt number on the cold plate becomes larger than on the hot plate, with the difference increasing with increasing Gebhart number. Finally, 3D simulations of turbulent RBC show that energy balances are exactly satisfied even for very coarse grids; therefore, we consider that the proposed discretization forms an excellent starting point for testing sub-grid scale models.

# databases used for statistical analyses in manuscript WLB-2024-01345 [https://doi.org/10.5061/dryad.tht76hf7v](https://doi.org/10.5061/dryad.tht76hf7v) ## Description of the data and file structure **List of excel files used for GLMMs and other analyses in manuscript WLB-2024-01345.R1 – “Precipitation and female experience are major determinants of the breeding performance of Canarian houbara bustards”** ### Files and variables #### File: GLM2b2NestInitiatDate.xlsx **Description:** ** database for GLMM Nest Initiation Date (NIDF, see Supplementary Table S3)** ##### Variables * definitions as in other excel files #### File: GLM4aNestAttemptSuccess.xlsx **Description:** **database for GLMM Nest Attempt Success (see Supplementary Table S3)** ##### Variables * clutchOrder: order of the clutch (1 first, 2 second –replacement-, 3 third –replacement-clutch), chicksSurvived: chicks survived until productivity control (1= yes/0= no, see Methods), MeanTemp: during 23 days incubation + 2 months in nestings that have chicks on the control date, AvMaxTemp: average maximum temperature during 23 days incubation + 2 months in nestings that have chicks on the control date, AvMinTemp: average minimum temperature during 23 days incubation + 2 months in nestings that have chicks on the control date, pp: precipitation during 23 days incubation + 2 months in nestings that have chicks on the control date, other variables as defined in other excel files #### File: GLM4bFledSuccess.xlsx **Description:** **database for GLMM Fledging Success (see Supplementary Table S3)** ##### Variables * Variables:** **pp: precipitation during** **23 days since nesting start + 2 months in nestings that have chicks on the control date; 23+1 month, in nestings that do not have chicks on the control date, other variables as defined in other excel files #### File: GLM5ReClutchProb.xlsx **Description:** **database for GLMM Re-clutching Probability (see Supplementary Table S3)** ##### Variables * Variables: Reclutch: 1= has a replacement clutch/0= does not have a replacement clutch, DurationIncubation: duration of the incubation period (days), MeanTemp, AvMaxTemp, AvMinTemp, pp: measured over the incubation period, other variables as defined in other excel files #### File: Weighted\_precipitations.xlsx **Description:** **databases to calculate weighted precipitation amounts, periods of precipitation and nestings (see Methods for details)** ##### Variables * definitions as in other excel files #### File: GLM6a3FemaleProductivity.xlsx **Description:** **database for GLMM Productivity (see Supplementary Table S3)** ##### Variables * nClutches: number of clutches (1,2,3), NchicksSurvived (1,2 up to fledging), pp2: precipitation measured from one month before the first laying to the laying date of the last clutch, other variables as defined in other excel files #### File: GLM6bFemaleProductivity.xlsx **Description:** **database for GLMM Productivity (as GLM6a, but measuring precipitation over the same period for all years: from 1 September to 13 March [mean hatching start date of the latest year, which was 2022]; see Supplementary Table S3)** ##### Variables * as in GLM6a3FemaleProductivity.xlsx, but measuring precipitation over the same period for all years: from 1 September to 13 March [mean hatching start date of the latest year, which was 2022; other variables as defined in other excel files #### File: GLM7aLengthBreedSeason.xlsx **Description:** **database for GLMM Length of the Breeding Season (see Supplementary Table S3)** ##### Variables * daysBreeding: duration of the breeding season in days (see definition in Methods), temperature and precipitation (PP) measured from 1 month before the first day of incubation of that year in any female until the date of independence of the last chick (see Azar et al 2018: Total rainfall during the nesting period (the period between the first and last nest found each year). other variables as defined in other excel files #### File: GLM7bLengthBreedSeason.xlsx **Description:** **database for GLMM Length of the Breeding Season, same as GLM7aLengthBreedSeason.xlsx, but precipitation and temperature measured over an equal period for all years: from 1 September to 13 March (= average hatching starting date of the latest year, 2022) (see Supplementary Table S3)** ##### Variables * as in GLM7aLengthBreedSeason.xlsx, but precipitation and temperature measured over an equal period for all years: from 1 September to 13 March #### File: WeightedPrecipitationPeriods.xlsx **Description:** **database to calculate weighted precipitation periods and nestings (see Methods for details)** ##### Variables * as in other excel files #### File: GLM2cNestInitiatDate.xlsx **Description:** **database for GLMM Nest Initiation Date of First Clutches (NIDF2, see Supplementary Table S3)** ##### Variables * definitions as in other excel files #### File: GLM1bNestingRate.xlsx **Description:** **database for GLMM Nesting Rate (see Supplementary Table S3)** ##### Variables * indiv= individual female, year, femaleNests: 1=Yes/0=No, startNest = date when nesting started, pp30days: precipitation on the 30 days before (in mm), pp60ays: precipitation on the 60 days before (in mm), pp90days: precipitation on the 90 days before (in mm), TempMean30days: mean temperature on the 30 days before (in oC), TempMax30days: maximum temperature on the 30 days before (in oC), TempMin30days: minimum temperature on the 30 days before (in oC), TempMean60days: mean temperature on the 60 days before (in oC), TempMax60days: maximum temperature on the 60 days before (in oC), TempMean60days: mean temperature on the 60 days before (in oC), Weight: weight of the female (g), PC1p: Principal Component 1 of the PCA including weight, PC1sinP: Principal Component 1 of the PCA excluding weight, Breedingexperience: breeding experience of the female, as defined in Methods. #### File: GLM1cNestingRate2.xlsx **Description:** ##### Variables * indiv= individual female, year, femaleNests: 1=Yes/0=No, startNest = date when nesting started, pp30days: precipitation on the 30 days before (in mm), pp60ays: precipitation on the 60 days before (in mm), pp90days: precipitation on the 90 days before (in mm), TempMean30days: mean temperature on the 30 days before (in oC), TempMax30days: maximum temperature on the 30 days before (in oC), TempMin30days: minimum temperature on the 30 days before (in oC), TempMean60days: mean temperature on the 60 days before (in oC), TempMax60days: maximum temperature on the 60 days before (in oC), TempMean60days: mean temperature on the 60 days before (in oC), Weight: weight of the female (g), PC1p: Principal Component 1 of the PCA including weight, PC1sinP: Principal Component 1 of the PCA excluding weight, Breedingexperience: breeding experience of the female, as defined in Methods, pp_1sep_13mar: precipitation measured between 1st September and 13th March, T_1sep_13mar: temperature measured between 1st September and 13th March #### File: GLM2a2NestInitiatDate.xlsx **Description:** **database for GLMM Nest Initiation Date (NIDF, see Supplementary Table S3)** ##### Variables * ordinalDate: Ordinal date as defined in Methods, rest of variables: definitions as in other excel files #### File: GLM3HatchSuccess.xlsx **Description:** **database for GLMM Hatching Success (see Supplementary Table S3)** ##### Variables * : endNest: date when incubation finished, ppIncub: precipitation during incubation (23 days since start incubation), AvMaxTempIncub: average maximum temperature during incubation, AvMaxTempIncub: average maximum temperature during incubation, ppIncub: mean temperature during incubation, hatchSuccess: 1= incubation until hatching date is successful/ 0= incubation until hatching date is not successful, rest of variables: definitions as in other excel files ## Code/software data can be viewed using EXCEL; other files from the process of statistical analysis were obtained using package “lme4” (Bates et al. 2015) in R v.2.15.1 (R Development Core Team, 2015) Precipitation is one of the main triggers of reproduction in desert-breeding birds. The unpredictability of rainfall patterns in arid environments has led species to adapt their breeding effort to episodes of abundant food after rainfall. The response is not the same for all individuals in a population, and may vary especially with the age and experience of each female. Here we investigate the effects of precipitation, temperature, body size and breeding experience, among other variables, on reproductive parameters of 20 females of Canarian houbara bustard (Chlamydotis undulata fuertaventurae), an endangered desert bird endemic of the eastern Canary Islands. Precipitation and breeding experience were the main determinants of female breeding performance. Higher rainfall determined an increase in nesting rate, and earlier autumn rains caused an advancement of nesting to October, allowing the breeding season to be extended to eight months. This favoured an extraordinary increase in productivity in more rainy breeding seasons, with 15 times more females nesting in the two most rainy winters than in dry years. In addition, females with more breeding experience showed a higher tendency to breed, higher nest attempt and fledging success, and longer breeding season, which allowed them to rear more chicks. A female even double brooded successfully in the same season, which, considering that chicks remain with the mother for up to six months, indicates a great capacity to optimise reproductive investment, by adapting to highly variable rainfall regimes. In recent decades, the eastern Canary Islands have undergone a process of aridification, and climate models predict a medium-term increase in the frequency and duration of drought periods. Thus, Canarian houbaras are particularly vulnerable to climate change, so measures are urgently needed to reduce their mortality and improve the quality of their habitat, in order to favour their reproduction and prevent their extinction.  We used 5-year breeding phenology and breeding success data from 20 female houbara bustards captured in Lanzarote and equipped with backpack-mounted GSM/GPRS data loggers. The influence of predictor variables on breeding parameters was modeled by means of generalized linear mixed models (GLMMs) using package “lme4” (Bates et al. 2015) in R v.2.15.1 (R Development Core Team, 2015).

A significant body of research suggests that traditional masculine beliefs act as a barrier to reducing meat consumption and transitioning to a more ethical and sustainable food system. Here, we report a pre-registered experiment examining whether men who eat meat are more open to adopting plant-based diets when these diets are associated with traditional models of masculinity. A total of 1069 men who eat meat were randomly assigned to one of four conditions: a social media post with a plant-based meal featuring a male entrepreneur or a male bodybuilder (two experimental conditions), a social media post with a plant-based meal without a masculine model (social media post control condition), or a condition without any stimuli (no-information control condition). Both the entrepreneur and the bodybuilder were perceived as highly masculine, but these experimental conditions did not significantly affect participants’ perceived fit between plant-based eating and masculinity, nor did they affect tendencies to justify eating meat as necessary, attitudes towards plant-based diets, or willingness to adopt a plant-based diet. Nevertheless, the results supported previous research findings indicating that men who strongly identify as meat-eaters and those who consume more meat tend to perceive themselves as more masculine, feel more pressure from societal expectations to eat meat, justify meat-eating more strongly, view plant-based diets as less masculine, and are more negative about and less willing to adopt plant-based diets. Our findings raise questions about the “masculinization” of plant-based diets as a strategy for promoting dietary change among men. info:eu-repo/semantics/publishedVersion

While the operating cost of electricity grids based on thermal generation was largely driven by the cost of fuel, as renewable penetration increases, ancillary services represent an increasingly large proportion of the running costs. Electric frequency is an important magnitude in highly renewable grids, as it becomes more volatile and therefore the cost related to maintaining it within safe bounds has significantly increased. So far, costs for frequency-containment ancillary services have been socialised in most countries, but it has become relevant to rethink this regulatory arrangement. In this paper, we discuss the issue of cost allocation for these services, highlighting the need to evolve towards a causation-based regulatory framework. We argue that parties responsible for creating the need for ancillary services should bear these costs. However, this would imply an important change in electricity market policy, therefore it is necessary to understand the impact on current and future investments on generation, as well as on electricity tariffs. Here we provide a mostly qualitative analysis of this issue, defining guidelines for practical implementation and further study. Published in journal Energy Policy