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Urea is the most common nitrogen (N) fertilizer in agriculture, due to its cheaper price and high N content. Although the reciprocal influence between NO3- and NH4+ nutrition are well known, urea (U) interactions with these N-inorganic forms are poorly studied. Here, the responses of two tomato genotypes to ammonium nitrate (AN), U alone or in combination were investigated. Significant differences in root and shoot biomass between genotypes were observed. Under AN+U supply, Linosa showed higher biomass compared to UC82, exhibiting also higher values for many root architectural traits. Linosa showed higher Nitrogen Uptake (NUpE) and Utilization Efficiency (NUtE) compared to UC82, under AN+U nutrition. Interestingly, Linosa exhibited also a significantly higher DUR3 transcript abundance. These results underline the beneficial effect of AN+U nutrition, highlighting new molecular and physiological strategies for selecting crops that can be used for more sustainable agriculture. The data suggest that translocation and utilization (NUtE) might be a more important component of NUE than uptake (NUpE) in tomato. Genetic variation could be a source for useful NUE traits in tomato; further experiments are needed to dissect the NUtE components that confer a higher ability to utilize N in Linosa.

Abstract Electricity dispatch is a difficult optimisation problem that aims at minimising total fuel cost while satisfying system power demand and certain thermal unit constraints. Modelling valve point effects, multiple fuel options or transmission losses brings non-convexity and non-smoothness into the mathematical models. This paper proposes mathematical programming-based models for valve point effects, multiple fuel options and transmission loss problems respectively. Mathematical programming leads to robust and rigorous optimisation methods for these problems. The applicability of the proposed methods is demonstrated through a number of case studies. For all the case studies, the corresponding methods identify power schedules at least as good as or better than the best known in literature.

Abstract In the context of climate change, global industrialised nations are grappling with transforming energy networks to support a low carbon future. Using an energy justice framework this work aims to understand holistic outcomes of one low-carbon energy network intervention: demand-side response enacted on domestic heat pumps. By exploring participants’ lived experience of a pilot project, from recruitment to installation and use, this work reveals how injustices were reduced, introduced and amplified. Choice, consent, cost, comfort, disruption, and control are highlighted as key aspects of interest when considering the distributive, procedural, and recognition implications of this domestic innovation. For a net reduction of energy injustices to be realised, we highlight the need for project designers to work in partnership with end users to optimise the benefits for the household and the electricity system. Whilst this is a UK study, the themes and findings are internationally applicable for interventions that aim to harness the flexibility of heating, the largest global energy end-use.

Energy management is an enabling technique to guarantee the reliability and economy of hybrid electric systems. This paper proposes a novel machine learning-based energy management strategy for a hybrid electric bus (HEB), with an emphasized consciousness of both thermal safety and degradation of the onboard lithium-ion battery (LIB) system. Firstly, the deep deterministic policy gradient (DDPG) algorithm is combined with an expert-assistance system, for the first time, to enhance the “cold start” performance and optimize the power allocation of HEB. Secondly, in the framework of the proposed algorithm, the penalties to over-temperature and LIB degradation are embedded to improve the management quality in terms of the thermal safety enforcement and overall driving cost reduction. The proposed strategy is tested under different road missions to validate its superiority over state-of-the-art techniques in terms of training efficiency and optimization performance.

AbstractThe co‐occurrence of chronic pain and alcohol use disorders (AUDs) involves complex interactions between genetic and neurophysiological aspects, and the research has reported mixed findings when they both co‐occur. There is also an indication of a gender‐dependent effect; males are more likely to use alcohol to cope with chronic pain problems than females. Recently, a new conceptualization has emerged, proposing that the negative affective component of pain drives and maintains alcohol‐related behaviors. We studied in a longitudinal fashion alterations in alcohol drinking patterns and pain thresholds in a mouse model of chronic neuropathic pain in a sex‐dependent manner. Following partial denervation (spared nerve injury [SNI]), stimulus‐evoked pain responses were measured before chronic alcohol consumption, during drinking, during a deprivation phase, and following an episode of excessive drinking. During the course of alcohol drinking, we observed pronounced sex differences in pain thresholds. Male mice showed a strong increase in pain thresholds, suggesting an analgesic effect induced by alcohol over time, an effect that was not observed in female mice. SNI mice did not differ from sham‐operated controls in baseline alcohol consumption. However, following a deprivation phase and the reintroduction of ethanol, male SNI mice but not female mice showed more pronounced excessive drinking than controls. Finally, we observed decreased central ethanol sensitivity in male SNI mice but not in females. Together with our finding, that ethanol is able to decrease a pain‐induced negative affective memory we come to following conclusion. We propose that a lower sensitivity to the intoxicating effects of alcohol together with the ability of alcohol to reduce the negative affective component of pain may explain the higher co‐occurrence of AUD in male chronic pain patients.

Berlin (Germany) has one of the longest climate records in the world (Cubasch and Kadow, 2011). In the 17th century mainly the family of the astronomer Kirch started measuring the temperature and wrote down general weather patterns. In the beginning of the 18th century the measurements became more regular including multiple measures per day. Over time, more inner city stations appeared but also disappeared. Therefore, this Berlin Climate Record is a moving station, but representable for the inner city of Berlin. With these datasets we reactivate the long inner city climate record in several variables. In this study, we digitized, analyzed, corrected, reconstructed, and provide the datasets on the very rare daily time frequency.

Qualitative data set, containing formal and informal interviews field notes from non-participant observations archival documents from a case study of sustainability implementation (spec. living wages, BSCI, DETOX) along a footwear value chain between Germany and China. Data was collected with four parties of the value chain between 2015 and 2020. Languages: German, English, Mandarin (interpreted) Collected and analyzed for the dissertation (doc. rer. pol.) by Stephanie Schrage at Universität Hamburg (defended in October 2020)

Abstract Alternative fuel vehicles (AFV) may help to reduce global CO2 emissions from the transport sector. One obstacle for AFV market diffusion is the lack of refueling infrastructure which prevents potential users from buying AFVs. Meanwhile infrastructure suppliers await market developments before investing in an extensive infrastructure roll-out. This mutual interaction has been a field of research for several years and a variety of modeling approaches have been applied. This paper aims at reviewing models of combined AFV and refueling infrastructure market diffusion and pointing out research gaps. We retrieve stylized facts which models should account for from empirical studies on natural gas vehicles, user acceptance analyses on AFVs and technical restraints of plug-in electric vehicles (PEVs) and fuel cell electric vehicles (FCEVs). Ten interaction models are evaluated with respect to the identified aspects. We find that simulation is the most common approach for an interaction model. Besides, the majority of the examined stylized facts is covered by the models. However, models for FCEVs could be improved by reflecting the transport of fuel to refueling infrastructure while new models on PEVs should include charging duration, frequency and charging station ownership.

Abstract Membrane separation of absorption solutions has potential application in refrigeration technology. Operating osmotic pressure is a basic parameter governing the design and evaluation of a membrane separation absorption system. This study presents a thermodynamic approach to calculating the osmotic pressure of an absorption solution using its density and vapour pressure. A correlation between osmotic pressure, density and vapour pressure has been deduced and a calculation example is given. Furthermore, combination of this correlation with the Clausius–Clapeyron equation has produced a simple and interesting result. For a membrane separation absorption cycle, the operating osmotic pressure required is related to the specific latent heat of refrigerant, operating temperatures and density of absorption solution.