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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.

Abstract The hybrid pneumatic power system (HPPS) proposed in this research replaces the battery’s electric-chemical energy with flow work and optimizes the management and utilization of the energy. This power system is able to keep the internal-combustion engine working at its optimal condition and turn its waste energy into effective mechanical energy and so enhance the thermal efficiency of the whole system. Using computer simulation software ITI-SIM, this study simulates the overall dynamic characteristics of the system in accordance with the regulated running-vehicle test-mode ECE47, and, with experimental verification and analysis, proves that this system can meet the requirements of the standard running-car mode. As for recycling the waste energy, the experimental results show that this design could offset the shortcomings of the low-density of pneumatic power and so effectively enhance the efficiency of the whole system.

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.

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.

Abstract In this study, soil temperatures at different depths in Turkey's different regions were investigated theoretically. Soil temperature data are critical for different research interests such as ecology, biology, technique processes, forestry, agriculture, energy, food sector, ground heat exchanger applications, thermal energy storage applications, and so forth. This investigation gives information related to the prediction of soil temperature's dependence with depth and time especially for shallow geothermal applications. Soil temperature values depend on a great deal of varied parameters such as thermal conductivity, short term climatic conditions and moisture content. The main issue is that despite these temperatures are extremely important values, they can not be obtained in a short time. Due to this reason, we study a mathematical model related to the prediction of soil temperature. Within this context, 81 cities and their approximately 300.000 data, both, monthly air and soil temperatures between 1960 and 2015 were studied and finally seven regions in Turkey were investigated and final average soil temperature values were achieved. Measured data taken from the Izmir State Meteorological Station, and predicted soil temperatures at depths of 5 cm, 10 cm, 20 cm, 50 cm, and 100 cm were analyzed for each region in Turkey according to data obtained fifty years ago. Finally, at depths of 5 cm, 10 cm, 20 cm, 50 cm and 100 cm, the maximum average percentage errors in Turkey were 16%, 14.8%, 13.5%, 14.4%, 13.9% respectively. In conclusion, we evaluate the relationship between ambient air temperatures and soil temperatures in terms of depths from 5 to 3000 cm.

In this paper, we present the conjugate heat transfer analysis in a 167-kVA dry-type transformer using the parallel version of the computational fluid dynamics code Fluent 6.0. The renormalization group kappa-epsiv model is proposed to compute the turbulent aspect of the convective airflow inside the transformer metal tank for Air Natural/Air Natural cooling conditions. An experimental approach was used to assess Joule losses in the low-/high-voltage windings and eddy-current losses in the magnetic core. The resulting mathematical model was solved using 14 compute nodes on a distributed machine.

AbstractMononuclear trivalent lanthanide complexes with formula [Ln(L)(NO3)3] [in which L=4,4‐difluoro‐8‐(2′:2′′;6′′:2′′′‐terpyridin‐4′′‐yl)‐1,3,5,7‐tetramethyl‐2,6‐diethyl‐4‐bora‐3a,4a‐diaza‐s‐indacene (Boditerpy)] are reported for Ln=Yb, Nd, Er, La and Gd. According to the crystal structure of the Yb complex, the lanthanide ion is bound to the terdentate terpyridine and the inner coordination sphere of the nine‐coordinate lanthanide ion is completed by three bidentate nitrate anions. The coordination polyhedron can be described as a distorted tricapped antiprism. The terpyridine chelate is almost planar and tilted by nearly 60° from the indacene subunit. FT‐IR spectra confirm the bidentate binding mode of the nitrate anions for the other complexes. NMR and ES‐MS spectra (through characteristic isotopic patterns) confirm the chemical formulation. The complexes have high molar absorption coefficients in the visible spectral region (65 000 M−1 cm−1 at 529 nm) and display sizeable NIR luminescence (900 to 1600 nm, for Ln=Yb, Nd and Er), upon irradiation through the electronic state of the indacene moiety at 514 nm. Crystal‐field splitting was analysed at low temperature. The quantum yield of the Yb solution (10−4 M) in dichloromethane amounts to 0.31 %, corresponding to a sensitisation efficacy of the ligand of ca. 63 %.