Effects of Digging Substrate on Growth and Fur in Blue Versus Shadow Type of Alopex Lagopus

. Our study sought to establish the extent to which digging substrate in the cage affects growth performance and fur properties in farmed foxes (Alopex lagopus) of the shadow white and blue colour types. The plates were on either the wall or the floor; the sandbox was always on the floor. A standard cage without any digging substrate was used as a control. There were 20 foxes in each group (one male and one female per cage). The cage setups were as follows: 1) a standard cage (105 cm long × 115 cm wide × 70 cm high) without digging substrates, which housed the control group; 2) a standard cage (105 cm long × 115 cm wide × 70 cm high) with a solid metal plate (210 × 297 mm) on the wall for digging and scratching; 3) a standard cage (105 cm long × 115 cm wide × 70 cm high) with a solid metal plate (210 × 297 mm) on the floor for digging and scratching; and 4) a standard cage (105 cm long × 115 cm wide × 70 cm high) with a metal sandbox for digging and scratching (80 × 40 × 14 cm, L×W×H). The sandbox had a 10 cm layer of sand (ca. 25 kg, particle size 0-18 mm) on the bottom. All animals grew well and reached normal body weights. No significant growth differences were found between blue and shadow types within the groups. Furthermore, skin length did not differ between colour types or between groups. Skin weight, on the other hand, was heavier in the blue than in the shadow type in the plate floor groups. No differences were recorded in the other groups. Fur quality was poorest in the blue type of the standard group and best in the shadow type of the plate wall group. Cover and mass were also best in the shadow type of the plate wall group. Furs were dirtiest in the sandbox groups, irrespective of colour type. Our findings tempt us to conclude that body growth is highly affected by digging substrate and that a sandbox in the cage causes the dirtiest fur and may, therefore, be avoided in farming practice

Identifying hybrid heating systems in the residential sector from smart meter data

In this paper, we identify hybrid heating systems on a single residential customer’s premises using smart meter data. A comprehensive methodology is developed at a generic level for residential sector buildings to identify the type of primary and support heating systems. The methodology includes the use of unsupervised and supervised learning algorithms both separately and combined. It is applied to two datasets that vary in size, quality of data, and availability and reliability of background information. The datasets contain hourly electricity consumption profiles of residential customers together with the outdoor temperature. The validation metrics for the developed algorithms are elaborated to provide a probabilistic evaluation of the model. The results show that it is possible to identify the types of both primary and support heating systems in the form of probability of having electric- or non-electric type of heating. The results obtained help estimate the flexibility domain of the residential building sector and thereby generate a high value for the energy system as a whole

Whole blood transcriptome analysis reveals footprints of cattle adaptation to sub‐arctic conditions

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Private and social benefits of a pumped hydro energy storage with increasing amount of wind power

Abstract In this paper, we calculate the long-term profitability of a pumped hydro energy storage (PHES) plant that is planned to be built in an old mine. We model the optimal PHES operation for several scenarios with different wind power penetration levels. Our modelling approach first involves estimating wholesale electricity prices for the day-ahead, intraday and balancing market as a function of wind power penetration. The estimated price profiles are implemented in a dynamic programming model, where the PHES plant maximises its balancing market revenue given the optimal commitment in the day-ahead market. We show that increasing the wind penetration changes the optimal PHES operation and increases the PHES profits. Additionally, we quantify how the costs of wind power balancing are affected by the PHES investment. Policy implications are drawn based on the estimated private and social benefits from the investment

WelFur - foxes: the inter-observer reliability of the WelFur health measures, and the prevalence of health disorders on fox farms during the growth period

v2012o

Identifying hybrid heating systems in the residential sector from smart meter data

Abstract In this paper, we identify hybrid heating systems on a single residential customer’s premises using smart meter data. A comprehensive methodology is developed at a generic level for residential sector buildings to identify the type of primary and support heating systems. The methodology includes the use of unsupervised and supervised learning algorithms both separately and combined. It is applied to two datasets that vary in size, quality of data, and availability and reliability of background information. The datasets contain hourly electricity consumption profiles of residential customers together with the outdoor temperature. The validation metrics for the developed algorithms are elaborated to provide a probabilistic evaluation of the model. The results show that it is possible to identify the types of both primary and support heating systems in the form of probability of having electric- or non-electric type of heating. The results obtained help estimate the flexibility domain of the residential building sector and thereby generate a high value for the energy system as a whole

Violence detection from ECG signals:a preliminary study

Abstract This research studied violence detection from less than 6-second ECG signals. Features were calculated based on the Bivariate Empirical Mode Decomposition (BEMD) and the Recurrence Quantification Analysis (RQA) applied to ECG signals from violence simulation in a primary school, involving 12 pupils from two grades. The feature sets were fed to a kNN classifier and tested using 10-fold cross validation and leave-one-subject-out (LOSO) validation in subject-dependent and subject-independent training models respectively. Features from BEMD outperformed the ones from RQA in both 10-fold cross validation, i.e. 88% vs. 73% (2nd grade pupils) and 87% vs. 81% (5th grade pupils), and LOSO validation, i.e. 77% vs. 75% (2nd grade pupils) and 80% vs. 76% (5th grade pupils), but have larger variation than the ones from RQA in both validations. Average performances for subject-specific system in 10-fold cross validation were 100% vs. 93% (2nd grade pupils) and 100% vs. 97% (5th grade pupils) for features from the BEMD and the RQA respectively. The results indicate that ECG signals as short as 6 seconds can be used successfully to detect violent events using subject-specific classifiers

Crush: mapping historical, material and affective force relations in young children's hetero-sexual playground play

Drawing on ethnographic multi-modal data of the gendered and sexual dynamics of pre-school play (age 6) in a rapidly declining fishing and farming community in North Finland, this paper offers a glimpse into our sense-making of a short video-recorded episode in which three boys repeatedly pile up on and demand a kiss from one of their girl classmates. Our analyses resonate with a wider community of feminist and queer scholars who are bringing affective methodologies and posthuman approaches to re-invigorate how we might understand the complexities of gender and sexual power relations in the early years. Inspired by the writings of Guattari and his concept of ‘existential refrains’, we create three ‘crush’ assemblages to map the more-than-human territorialising and de-territorialising force relations at play. Each assemblage offers a thinking Otherwise about gender, sexuality, violence and consent in which place, space, objects, affect and history entangle in predictable and unpredictable ways