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The causal eating habits study alternative energy, energy-efficiency, house protection under the law, and also As well as emissions in civilized world: Any map for ecological durability.
The addition of kraft microfibre to primary pulp produced the highest tensile and tear strength but compromised light scattering. Additionally, all microfibre composites showed improved elongation, as opposed to no notable change in elongation with conventional LC refining. This investigation proposes an alternative, cost-effective approach for developing high bulk, high strength mechanical pulp by limiting the extent of second stage refining and using LC refined microfibres for pulp reinforcement. The high tear-high bulk open construction of the composite paper is likely to benefit boxboard and packaging applications. © The Author(s) 2020.Much environmental enrichment for laboratory animals is intended to enhance animal welfare and normalcy by providing stimulation to reduce 'boredom'. Behavioural manifestations of boredom include restless sensation-seeking behaviours combined with indicators of sub-optimal arousal. Here we explored whether these signs could be reduced by extra daily play opportunity in laboratory ferrets. Specifically, we hypothesised that playtime would reduce restlessness, aggression, sensation-seeking and awake drowsiness, even 24h later in the homecage. Female ferrets (n = 14) were group housed in enriched multi-level cages. Playtime involved exploring a room containing a ball pool, paper bags, balls containing bells, and a familiar interactive human for 1h. This was repeated on three consecutive mornings, and on the fourth morning, homecage behaviour was compared between ferrets who had experienced the playtime treatment versus control cagemates who had not. Their investigation of stimuli (positive = mouse odour or ball; ambiguous = empty bottle or tea-strainer; and negative = peppermint or bitter apple odour) was also recorded. We then swapped treatments, creating a paired experimental design. Ferrets under control conditions lay awake with their eyes open and screeched significantly more, but slept and sat/stood less, than following playtime. They also contacted negative and ambiguous stimuli significantly more under control conditions than they did following playtime; contact with positive stimuli showed no effects. Attempts to blind the observer to treatments were unsuccessful, so replication is required, but the findings suggest that playtime may have reduced both sub-optimal arousal and restless sensation seeking behaviour, consistent with reducing boredom.Background Portion sizes in the food environment may communicate information about what constitutes a 'normal' amount of food to eat. Here we examined whether mere visual exposure to a smaller vs. larger portion size of snack food affects perceptions of how much a 'normal' sized portion is and how much people choose to eat of that food in future. Methods Under the guise of a study on taste preference and personality, 104 female participants were randomly allocated to be exposed to either a smaller or larger portion size of snack food. Sonrotoclax Twenty-four hours later participants freely selected a portion of the snack food to consume and reported on their perception of what constituted a normal sized portion of the snack food. Results Participants that were exposed to a smaller, as opposed to larger portion size subsequently believed that a normal portion of the snack food was smaller in size. Exposure to the smaller as opposed to the larger portion size also resulted in participants consuming less snack food the next day. Conclusions Environmental exposure to smaller, as opposed to larger portion sizes of food may change perceptions of what constitutes a normal amount of food to eat and affect the amount of food people choose to eat in future. © 2019 The Authors.There is an urgent need for high-quality and high-spatial-resolution hourly precipitation products around the globe, including the UK. Although hourly precipitation products exist for the UK, these either contain large errors, or are insufficient in spatial resolution. An efficient way to solve this is to develop a merged precipitation product that combines the information and benefits from multiple data sources, improving both the spatial resolution and accuracy of hourly precipitation estimates over the UK. In this study, we develop a UK high-resolution gauge-radar-satellite merged hourly precipitation analysis the UKGrsHP. It covers the UK from 12.5° W to  3.5° E, 49° N-60° N, with a spatial resolution of 0.01° × 0.01° in latitude/longitude (equivalent to 1 km resolution in the mid-latitudes). An optimal interpolation (OI)-based multi-source merging scheme with compound strategy is developed and tested for producing the UKGrsHP. Three input data sources are used gauge analysis data interpolated from 1903 quality-controlled hourly observations, the UK Nimrod radar precipitation analysis and the GSMaP global satellite precipitation analysis. Using independent tests against ~ 220 independent gauge observations on 1 year's experimental UKGrsHP, covering the period from January to December 2014, we find that the final merged data performs better than three individual precipitation analyses used as inputs. A full version of the UKGrsHP starting in April 2004 is now under production, which will have wide applications in climate services and scientific research across multiple disciplines. © The Author(s) 2020.The non-stationary nature of electroencephalography (EEG) signals makes an EEG-based brain-computer interface (BCI) a dynamic system, thus improving its performance is a challenging task. In addition, it is well-known that due to non-stationarity based covariate shifts, the input data distributions of EEG-based BCI systems change during inter- and intra-session transitions, which poses great difficulty for developments of online adaptive data-driven systems. Ensemble learning approaches have been used previously to tackle this challenge. However, passive scheme based implementation leads to poor efficiency while increasing high computational cost. This paper presents a novel integration of covariate shift estimation and unsupervised adaptive ensemble learning (CSE-UAEL) to tackle non-stationarity in motor-imagery (MI) related EEG classification. The proposed method first employs an exponentially weighted moving average model to detect the covariate shifts in the common spatial pattern features extracted from MI related brain responses.
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