scholarly journals On the temporal granularity of joint energy-reserve markets in a high-RES system

2021 ◽  
Vol 297 ◽  
pp. 117172
Author(s):  
Mathias Hermans ◽  
Kenneth Bruninx ◽  
Kenneth Van den Bergh ◽  
Kris Poncelet ◽  
Erik Delarue
Author(s):  
Lvquan Zhao ◽  
Wei Wang ◽  
Ying Qiu ◽  
Alex S. Torson

Abstract The accumulation of nutrients during diapause preparation is crucial because any lack of nutrition will reduce the likelihood of insects completing diapause, thereby decreasing their chances of survival and reproduction. The fall webworm, Hyphantria cunea, diapause as overwintering pupae and their diapause incidence and diapause intensity are regulated by the photoperiod. In this study, we test the hypothesis that photoperiod influences energy reserve accumulation during diapause preparation in fall webworm. We found that the body size and mass, lipid and carbohydrate content of pupae with a short photoperiod during the diapause induction phase were significantly greater than those of pupae with a relatively short photoperiod, and the efficiency of converting digested food and ingested food into body matter was greater in the short-photoperiod diapause-destined larvae than the relatively short-photoperiod diapause-destined larvae. We also observed higher lipase and amylase activities in short-photoperiod diapause-destined larvae relative to the counterparts. However, no obvious difference was found in protein and protease in the pupae with a short photoperiod during the diapause induction phase and short-photoperiod diapause-destined larvae compared with the counterparts. Therefore, we conclude that the energy reserve patterns of diapausing fall webworm pupae are plastic and that short-photoperiod diapause-destined larvae increase their energy reserves by improving their feeding efficiency and increase their lipid and carbohydrate stores by increasing the lipase and amylase activities in the midgut.


2018 ◽  
Vol 148 ◽  
pp. 684-692 ◽  
Author(s):  
Jerusa Maria Oliveira ◽  
Nicole Fontes Losano ◽  
Suellen Silva Condessa ◽  
Renata Maria Pereira de Freitas ◽  
Silvia Almeida Cardoso ◽  
...  

2018 ◽  
Vol 210 ◽  
pp. 896-913 ◽  
Author(s):  
Eduardo A. Martínez Ceseña ◽  
Nicholas Good ◽  
Angeliki L.A. Syrri ◽  
Pierluigi Mancarella

2018 ◽  
pp. 3970-3975
Author(s):  
Claudio Bettini ◽  
X. Sean Wang ◽  
Sushil Jajodia
Keyword(s):  

Demography ◽  
2021 ◽  
Vol 58 (1) ◽  
pp. 51-74
Author(s):  
Lee Fiorio ◽  
Emilio Zagheni ◽  
Guy Abel ◽  
Johnathan Hill ◽  
Gabriel Pestre ◽  
...  

Abstract Georeferenced digital trace data offer unprecedented flexibility in migration estimation. Because of their high temporal granularity, many migration estimates can be generated from the same data set by changing the definition parameters. Yet despite the growing application of digital trace data to migration research, strategies for taking advantage of their temporal granularity remain largely underdeveloped. In this paper, we provide a general framework for converting digital trace data into estimates of migration transitions and for systematically analyzing their variation along a quasi-continuous time scale, analogous to a survival function. From migration theory, we develop two simple hypotheses regarding how we expect our estimated migration transition functions to behave. We then test our hypotheses on simulated data and empirical data from three platforms in two internal migration contexts: geotagged Tweets and Gowalla check-ins in the United States, and cell-phone call detail records in Senegal. Our results demonstrate the need for evaluating the internal consistency of migration estimates derived from digital trace data before using them in substantive research. At the same time, however, common patterns across our three empirical data sets point to an emergent research agenda using digital trace data to study the specific functional relationship between estimates of migration and time and how this relationship varies by geography and population characteristics.


2021 ◽  
Author(s):  
Jiantao Shi ◽  
Ye Guo ◽  
Lang Tong ◽  
Wenchuan Wu ◽  
Hongbin Sun

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