- EC| ERA-MIN3
Vogt Duberg, Johan; Sakao, Tomohiko;Vogt Duberg, Johan
Harvested from ORCID Public Data FileVogt Duberg, Johan in OpenAIREIntroductionWith the much-needed transformation from linear to more circular resource flows, it is imperative for enterprises to understand their financial prospects. Transforming towards Product-as-a-Service (PaaS) with circular elements introduces new considerations that must be addressed to ensure profitability and sustainability. However, there is a lack of comprehensive financial assessments based on empirical cases to guide companies in assessing the lucrativeness of their transformations. This paper addresses this gap by proposing a simple-to-use and flexible financial assessment model for PaaS, helping practitioners identify the conditions necessary for financial viability from both provider and user perspectives.MethodsGrounded in transdisciplinary research, this study focuses on a construction machine manufacturer's transformation from a traditional one-off sales business model to PaaS. The transformation is analysed through the development of a life cycle costing financial assessment model that addresses relevant cost drivers.ResultsUsing discounting methods, the model can help practitioners generate scenarios to identify feasible solutions for profitable PaaS setups. Additionally, the paper presents an analytical procedure to identify conditions for a financially viable PaaS. The procedure includes scenario-based analysis that accounts for systemic changes often necessary for successfully realising PaaS.DiscussionBy following the proposed procedure, along with the financial assessment model, manufacturers can streamline their financial assessments to identify necessary changes. For the analysed case company, the modelling results indicate that it must redesign its products for PaaS to outperform its previous business model.
Access RoutesGreen gold 3 citations 3 popularity Top 10% influence Average impulse Average 
Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product. - Bradley Patrick White; Suellen Breakey; Margaret J. Brown; Jenny Rand Smith; Amanda Tarbet;
Patrice K. Nicholas; Ana M. Viamonte Ros;Patrice K. Nicholas
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesPatrice K. Nicholas in OpenAIREBackground: Climate change has been shown to be directly linked to multiple physiological sequelae and to impact health consequences. However, the impact of climate change on mental health globally, particularly among vulnerable populations, is less well understood. Objective: To explore the mental health impacts of climate change in vulnerable populations globally. Methods: We performed an integrative literature review to identify published articles that addressed the research question: What are the mental health impacts of climate change among vulnerable populations globally? The Vulnerable Populations Conceptual Model served as a theoretical model during the review process and data synthesis. Findings/Results: One hundred and four articles were selected for inclusion in this review after a comprehensive review of 1828 manuscripts. Articles were diverse in scope and populations addressed. Land-vulnerable persons (either due to occupation or geographic location), Indigenous persons, children, older adults, and climate migrants were among the vulnerable populations whose mental health was most impacted by climate change. The most prevalent mental health responses to climate change included solastalgia, suicidality, depression, anxiety/eco-anxiety, PTSD, substance use, insomnia, and behavioral disturbance. Conclusions: Mental health professionals including physicians, nurses, physician assistants and other healthcare providers have the opportunity to mitigate the mental health impacts of climate change among vulnerable populations through assessment, preventative education and care. An inclusive and trauma-informed response to climate-related disasters, use of validated measures of mental health, and a long-term therapeutic relationship that extends beyond the immediate consequences of climate change-related events are approaches to successful mental health care in a climate-changing world.
Access RoutesGreen gold 65 citations 65 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product. - EC| HIGREEW ,EC| CompBat ,EC| CUBER ,EC| SONARDieterle, M; Fischer, P; Pons, MN; Blume, N; +2 Authors
Fischer, P
Harvested from ORCID Public Data FileFischer, P in OpenAIREDieterle, M; Fischer, P; Pons, MN; Blume, N; Minke, C;Fischer, P
Harvested from ORCID Public Data FileFischer, P in OpenAIRE Bischi, A; A transition from fossil to renewable energy requires the development of sustainable electric energy storage systems capable to accommodate an increasing amount of energy, at larger power and for a longer time. Flow batteries are seen as one promising technology to face this challenge. As different innovations in this field of technology are still under development, reproducible, comparable and verifiable life cycle assessment studies are crucial to providing clear evidence on the sustainability of different flow battery systems. Based on a review of 20 relevant life cycle assessment studies for different flow battery systems, published between 1999 and 2021, this contribution explored relevant methodological choices regarding the sequence of phases defined in the ISO 14,040 series: goal and scope definition, inventory analysis, impact assessment and interpretation. Inspired by good practice examples, common gaps and weaknesses were identified and recommendations for comparative life cycle assessment studies were derived. This includes suggestions for an expanded functional unit definition, a provision of more detailed and transparent reporting of LCI data while using input/output tables. Outcomes of this study are also of relevance for the amendment of the Batteries Directive 2006/66/EC, where first drafts are under revision in the European Council, including the introduction of a battery passport, which should encourage battery producers to reduce their carbon footprint and avoid problematic materials.
Access RoutesGreen hybrid 9 citations 9 popularity Top 10% influence Average impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product. - Álvarez Dorado, Manuel;
Álvarez Dorado, Manuel
Harvested from ORCID Public Data FileÁlvarez Dorado, Manuel in OpenAIRE Ferrández Vega, Daniel; Ferrández Vega, Daniel
Harvested from ORCID Public Data FileFerrández Vega, Daniel in OpenAIRE Zaragoza Benzal, Alicia; Zaragoza Benzal, Alicia
Harvested from ORCID Public Data FileZaragoza Benzal, Alicia in OpenAIRE Colorado Pastor, Bryan; Colorado Pastor, Bryan
Harvested from ORCID Public Data FileColorado Pastor, Bryan in OpenAIREThe annual production of plastic waste worldwide has doubled in just two decades, with approximately 390 million tonnes of plastic waste now being generated. In this context, the construction industry must move towards the development of new, more sustainable materials made under circular economy criteria. In this work, a physico-mechanical characterisation of gypsum composites with the incorporation of high-density polyethylene (HDPE) waste, replacing 2–4–6–8–10% by volume of the original raw material, has been conducted. The results show how the incorporation of these plastic wastes improves the water resistance of the gypsum material without additions, as well as producing a decrease in thermal conductivity and greater resistance to impact. On the other hand, it has been found that, as the percentage of recycled raw material added increases, the mechanical resistance to bending and compression decreases, leading to fracture due to a lack of cohesion between the matrix and the waste. Nevertheless, in all the cases studied, mechanical strengths higher than those established by the EN 13279-2 standard were obtained. Thus, the results confirm the viability of these secondary raw materials to be used in the development of new products for sustainable building, especially in the design of prefabricated panels for false ceilings.
Access Routesgold 8 citations 8 popularity Top 10% influence Average impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product. - Mohamed H. Hassan;
Mohamed H. Hassan
Harvested from ORCID Public Data FileMohamed H. Hassan in OpenAIRE Salah Kamel; Salah Kamel
Harvested from ORCID Public Data FileSalah Kamel in OpenAIRE Mahmoud A. El-Dabah; Mahmoud A. El-Dabah
Harvested from ORCID Public Data FileMahmoud A. El-Dabah in OpenAIRE Tahir Khurshaid; +1 AuthorsTahir Khurshaid
Harvested from ORCID Public Data FileTahir Khurshaid in OpenAIRE Mohamed H. Hassan; Mohamed H. Hassan
Harvested from ORCID Public Data FileMohamed H. Hassan in OpenAIRE Salah Kamel; Salah Kamel
Harvested from ORCID Public Data FileSalah Kamel in OpenAIRE Mahmoud A. El-Dabah; Mahmoud A. El-Dabah
Harvested from ORCID Public Data FileMahmoud A. El-Dabah in OpenAIRE Tahir Khurshaid; Tahir Khurshaid
Harvested from ORCID Public Data FileTahir Khurshaid in OpenAIRE Jose Luis Dominguez-Garcia; Jose Luis Dominguez-Garcia
Harvested from ORCID Public Data FileJose Luis Dominguez-Garcia in OpenAIREThe appropriate control and management of reactive power is of great relevance in the electrical reliability, stability, and security of power grids. This issue is considered in order to increase system efficiency and to maintain voltage under the acceptable value range. In this regard, novel technologies as FACTS, renewable energies, among others, are varying conventional grid behavior leading to unexpected limit capacity reaching due to large reactive power flow. Thus, optimal planning of this must be considered. This paper proposes a new application for a simple and easy implementation optimization algorithm, called Rao-3, to solve the constrained non-linear optimal reactive power dispatch problem. Moreover, the integration of solar and wind energy as the most applied technologies in electric power systems are exploited. Due to the continuous variation and the natural intermittence of wind speed and solar irradiance as well as load demand fluctuation, the uncertainties which have a global concern are investigated and considered in this paper. The proposed single-objective and multi-objective deterministic/stochastic optimal reactive power dispatch algorithms are validated using three standard test power systems, namely IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus. The simulation results show that the proposed optimal reactive power dispatch algorithms are superior compared with two recent algorithms (Artificial electric field algorithm (AEFA) and artificial Jellyfish Search (JS) algorithm) and other optimization algorithms used for solving the same problem.
Access Routesgold 51 citations 51 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product. - EC| AMVA4NewPhysics ,EC| INSIGHTS ,EC| LHCTOPVLQSirunyan, A. M.; Tumasyan, A.; Adam, W.; Ambrogi, F.; Bergauer, T.; Dragicevic, M.; Ero, J.;
Dragicevic, M.
Harvested from ORCID Public Data FileDragicevic, M. in OpenAIRE Del Valle, A. Escalante; Fruhwirth, R.; Jeitler, M.; Krammer, N.; Lechner, L.; Liko, D.; Madlener, T.; Mikulec, I; Pitters, F. M.; Rad, N.;Del Valle, A. Escalante
Harvested from ORCID Public Data FileDel Valle, A. Escalante in OpenAIRE Schieck, J.; Schofbeck, R.; Spanring, M.; Templ, S.;Schieck, J.
Harvested from ORCID Public Data FileSchieck, J. in OpenAIRE Waltenberger, W.; Wulz, C-E; Zarucki, M.; Chekhovsky, V; Litomin, A.;Waltenberger, W.
Harvested from ORCID Public Data FileWaltenberger, W. in OpenAIRE Makarenko, V; Gonzalez, J. Suarez; Darwish, M. R.; De Wolf, E. A.;Makarenko, V
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesMakarenko, V in OpenAIRE Di Croce, D.; Janssen, X.; Kello, T.; Lelek, A.; Pieters, M.; Sfar, H. Rejeb; Van Haevermaet, H.; Van Mechelen, P.; Van Putte, S.;Di Croce, D.
Harvested from ORCID Public Data FileDi Croce, D. in OpenAIRE Van Remortel, N.; Van Remortel, N.
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesVan Remortel, N. in OpenAIRE Blekman, F.; Blekman, F.
Harvested from ORCID Public Data FileBlekman, F. in OpenAIRE Bols, E. S.; Chhibra, S. S.;Bols, E. S.
Harvested from ORCID Public Data FileBols, E. S. in OpenAIRE D'Hondt, J.; D'Hondt, J.
Harvested from ORCID Public Data FileD'Hondt, J. in OpenAIRE De Clercq, J.; Lontkovskyi, D.; Lowette, S.; Marchesini, I;De Clercq, J.
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesDe Clercq, J. in OpenAIRE Moortgat, S.; Morton, A.; Python, Q.;Moortgat, S.
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesMoortgat, S. in OpenAIRE Tavernier, S.; Van Doninck, W.; Van; Mulders, P.; Beghin, D.; Bilin, B.; Clerbaux, B.; De; Lentdecker, G.; Dorney, B.; Favart, L.; Grebenyuk, A.;Tavernier, S.
Harvested from ORCID Public Data FileTavernier, S. in OpenAIRE Kalsi, A. K.; Makarenko, I; Moureaux, L.; Petre, L.; Popov; A.; Postiau, N.;Kalsi, A. K.
Harvested from ORCID Public Data FileKalsi, A. K. in OpenAIRE Starling, E.; Thomas, L.; Vander Velde, C.; Vanlaer, P.; Vannerom, D.; Wezenbeek, L.;Starling, E.
Harvested from ORCID Public Data FileStarling, E. in OpenAIRE Cornelis, T.; Dobur, D.; Gruchala, M.; Khvastunov, I; Niedziela, M.; Roskas, C.;Cornelis, T.
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesCornelis, T. in OpenAIRE Skovpen, K.; Skovpen, K.
Harvested from ORCID Public Data FileSkovpen, K. in OpenAIRE Tytgat, M.; Verbeke, W.; Vermassen; B.; Vit, M.; Bruno, G.; Bury, F.;Tytgat, M.
Harvested from ORCID Public Data FileTytgat, M. in OpenAIRE Caputo, C.; Caputo, C.
Harvested from ORCID Public Data FileCaputo, C. in OpenAIRE David, P.; Delaere, C.; Delcourt, M.; Donertas, I. S.;Delaere, C.
Harvested from ORCID Public Data FileDelaere, C. in OpenAIRE Giammanco, A.; Lemaitre, V; Mondal, K.; Prisciandaro, J.; Taliercio, A.; Teklishyn, M.;Giammanco, A.
Harvested from ORCID Public Data FileGiammanco, A. in OpenAIRE Vischia, P.; Wuyckens, S.; Zobec, J.;Vischia, P.
Harvested from ORCID Public Data FileVischia, P. in OpenAIRE Alves, G. A.; Correia Silva, G.; Hensel, C.;Alves, G. A.
Harvested from ORCID Public Data FileAlves, G. A. in OpenAIRE Moraes, A.; Alda Junior, W. L.; Belchior Batista Das Chagas, E.; Brandao; Malbouisson, H.; Carvalho, W.; Chinellato, J.; Coelho, E.;Moraes, A.
Harvested from ORCID Public Data FileMoraes, A. in OpenAIRE Da Costa, E. M.; Da Costa, E. M.
Harvested from ORCID Public Data FileDa Costa, E. M. in OpenAIRE Da Silveira, G. G.; Da Silveira, G. G.
Harvested from ORCID Public Data FileDa Silveira, G. G. in OpenAIRE De Jesus Damiao, D.; Fonseca De Souza, S.; Martins, J.; Matos Figueiredo, D.; Medina; Jaime, M.; Melo De Almeida, M.;De Jesus Damiao, D.
Harvested from ORCID Public Data FileDe Jesus Damiao, D. in OpenAIRE Mora Herrera, C.; Mora Herrera, C.
Harvested from ORCID Public Data FileMora Herrera, C. in OpenAIRE Mundim, L.; Nogima, H.;Mundim, L.
Harvested from ORCID Public Data FileMundim, L. in OpenAIRE Rebello Teles, P.; Rebello Teles, P.
Harvested from ORCID Public Data FileRebello Teles, P. in OpenAIRE Sanchez Rosas, L. J.; Santoro, A.; Silva Do Amaral, S. M.;Sanchez Rosas, L. J.
Harvested from ORCID Public Data FileSanchez Rosas, L. J. in OpenAIRE Sznajder, A.; Thiel, M.;Sznajder, A.
Harvested from ORCID Public Data FileSznajder, A. in OpenAIRE Tonelli Manganote, E. J.; Tonelli Manganote, E. J.
Harvested from ORCID Public Data FileTonelli Manganote, E. J. in OpenAIRE Torres Da Silva De Araujo, F.; Torres Da Silva De Araujo, F.
Harvested from ORCID Public Data FileTorres Da Silva De Araujo, F. in OpenAIRE Vilela Pereira, A.; Vilela Pereira, A.
Harvested from ORCID Public Data FileVilela Pereira, A. in OpenAIRE Bernardes, C. A.; Bernardes, C. A.
Harvested from ORCID Public Data FileBernardes, C. A. in OpenAIRE Calligaris, L.; Fernandez;Calligaris, L.
Harvested from ORCID Public Data FileCalligaris, L. in OpenAIRE Perez Tomei, T. R.; Gregores, E. M.;Perez Tomei, T. R.
Harvested from ORCID Public Data FilePerez Tomei, T. R. in OpenAIRE Lemos, D. S.; Mercadante; P. G.;Lemos, D. S.
Harvested from ORCID Public Data FileLemos, D. S. in OpenAIRE Novaes, S. F.; Novaes, S. F.
Harvested from ORCID Public Data FileNovaes, S. F. in OpenAIRE Padula, Sandra S.; Aleksandrov, A.; Antchev, G.; Atanasov, I; Hadjiiska, R.; Iaydjiev, P.; Misheva, M.; Rodozov, M.;Padula, Sandra S.
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesPadula, Sandra S. in OpenAIRE Shopova, M.; Sultanov, G.; Bonchev, M.; Dimitrov, A.; Ivanov, T.;Shopova, M.
Harvested from ORCID Public Data FileShopova, M. in OpenAIRE Litov, L.; Pavlov, B.; Petkov, P.; Petrov, A.; Fang, W.; Guo, Q.; Wang, H.; Yuan, L.; Ahmad, M.; Hu, Z.; Wang, Y.;Petrov, A.
Harvested from ORCID Public Data FilePetrov, A. in OpenAIRE Chapon, E.; Chen; G. M.;Chapon, E.
Harvested from ORCID Public Data FileChapon, E. in OpenAIRE Chen, H. S.; Chen, M.; Kapoor, A.; Leggat, D.; Liao, H.;Chen, H. S.
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesChen, H. S. in OpenAIRE Liu, Z.; Liu, Z.
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesLiu, Z. in OpenAIRE Sharma, R.; Spiezia, A.;Sharma, R.
Harvested from ORCID Public Data FileSharma, R. in OpenAIRE Tao, J.; Thomas-wilsker, J.; Wang, J.; Zhang, H.; Zhang, S.; Zhao, J.; Agapitos, A.; Ban, Y.; Chen, C.; Huang, Q.; Levin, A.;Wang, J.
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesWang, J. in OpenAIRE Li, Q.; Lu, M.; Lyu, X.; Mao, Y.; Qian, S. J.; Wang; D.; Wang, Q.; Xiao, J.;Wang, Q.
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesWang, Q. in OpenAIRE10.1140/epjc/s10052-020-08817-8 , 10.48550/arxiv.2009.01186 , 10.3204/pubdb-2021-01404 , 10.5445/ir/1000133495 , 10.3204/pubdb-2020-03553 , 10.18154/rwth-2021-04402 , 10.18154/rwth-2021-04323
10486/704420 , 10651/61050 , 11588/981266 , 11368/2981217 , 20.500.12960/1096 , 10281/308797 , 10679/8214 , 10067/1775930151162165141 , 11449/210711 , 11492/4967 , 10831/111002 , 11503/974 , 11486/5265 , 11577/3400582 , 11573/1639263 , 11584/420886 , 11567/1050491 , 11568/1134020 , 11589/257781 , 11391/1507393 , 11384/101251 , 11585/853320 , 20.500.11769/526794 , 2158/1297765 , 1854/LU-8702116 , 2318/1841118 , 11579/135374 , 11563/159092 , 10044/1/87583 , 11586/374199 , 11571/1478316
10.1140/epjc/s10052-020-08817-8 , 10.48550/arxiv.2009.01186 , 10.3204/pubdb-2021-01404 , 10.5445/ir/1000133495 , 10.3204/pubdb-2020-03553 , 10.18154/rwth-2021-04402 , 10.18154/rwth-2021-04323
10486/704420 , 10651/61050 , 11588/981266 , 11368/2981217 , 20.500.12960/1096 , 10281/308797 , 10679/8214 , 10067/1775930151162165141 , 11449/210711 , 11492/4967 , 10831/111002 , 11503/974 , 11486/5265 , 11577/3400582 , 11573/1639263 , 11584/420886 , 11567/1050491 , 11568/1134020 , 11589/257781 , 11391/1507393 , 11384/101251 , 11585/853320 , 20.500.11769/526794 , 2158/1297765 , 1854/LU-8702116 , 2318/1841118 , 11579/135374 , 11563/159092 , 10044/1/87583 , 11586/374199 , 11571/1478316
AbstractThe production of Z boson pairs in proton–proton ($${\mathrm{p}} {\mathrm{p}} $$ p p ) collisions, $${{\mathrm{p}} {\mathrm{p}} \rightarrow ({\mathrm{Z}}/\gamma ^*)({\mathrm{Z}}/\gamma ^*) \rightarrow 2\ell 2\ell '}$$ p p → ( Z / γ ∗ ) ( Z / γ ∗ ) → 2 ℓ 2 ℓ ′ , where $${\ell ,\ell ' = {\mathrm{e}}}$$ ℓ , ℓ ′ = e or $${{\upmu }}$$ μ , is studied at a center-of-mass energy of 13$$\,\text {TeV}$$ TeV with the CMS detector at the CERN LHC. The data sample corresponds to an integrated luminosity of 137$$\,\text {fb}^{-1}$$ fb - 1 , collected during 2016–2018. The $${\mathrm{Z}} {\mathrm{Z}} $$ Z Z production cross section, $$\sigma _{\text {tot}} ({\mathrm{p}} {\mathrm{p}} \rightarrow {\mathrm{Z}} {\mathrm{Z}} ) = 17.4 \pm 0.3 \,\text {(stat)} \pm 0.5 \,\text {(syst)} \pm 0.4 \,\text {(theo)} \pm 0.3 \,\text {(lumi)} \text { pb} $$ σ tot ( p p → Z Z ) = 17.4 ± 0.3 (stat) ± 0.5 (syst) ± 0.4 (theo) ± 0.3 (lumi) pb , measured for events with two pairs of opposite-sign, same-flavor leptons produced in the mass region $${60< m_{\ell ^+\ell ^-} < 120\,\text {GeV}}$$ 60 < m ℓ + ℓ - < 120 GeV is consistent with standard model predictions. Differential cross sections are also measured and agree with theoretical predictions. The invariant mass distribution of the four-lepton system is used to set limits on anomalous $${\mathrm{Z}} {\mathrm{Z}} {\mathrm{Z}} $$ Z Z Z and $${{\mathrm{Z}} {\mathrm{Z}} \gamma }$$ Z Z γ couplings.
Access RoutesGreen gold add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product. - UKRI| Investigating the role of a kinesin gene in butterfly mimicryDavid As Smith; Jon J Bennie; Ian J Gordon; Simon Martin; +3 Authors
Simon Martin
Harvested from ORCID Public Data FileSimon Martin in OpenAIREDavid As Smith; Jon J Bennie; Ian J Gordon; Simon Martin; Piera Ireri;Simon Martin
Harvested from ORCID Public Data FileSimon Martin in OpenAIRE Kennedy S Omufwoko; Kennedy S Omufwoko
Harvested from ORCID Public Data FileKennedy S Omufwoko in OpenAIRE Richard H Ffrench-Constant; Richard H Ffrench-Constant
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesRichard H Ffrench-Constant in OpenAIREAbstract Heterosis, Haldane and Bateson-Dobzhansky-Muller effects have been widely documented amongst a range of plants and animals. However, typically these effects are shown by taking parents of known genotype into the laboratory and measuring components of the F1 progeny under laboratory conditions. This leaves in doubt the real significance of such effects in the field. Here we use the well-known colour pattern genotypes of the African monarch or queen (Danaus chrysippus), which also control wing length, to test these effects both in the laboratory and in a contact zone in the field. By measuring the wing lengths in animals of known colour pattern genotype we show clear evidence for all three hybrid effects at the A and BC colour patterning loci, and importantly, that these same effects persist in the same presumptive F1s when measured in hybrid populations in the field. This demonstrates the power of a system in which genotypes can be directly inferred in the field and highlights that all three hybrid effects can be seen in the East African contact zone of this fascinating butterfly.
Access RoutesGreen hybrid 2 citations 2 popularity Average influence Average impulse Average Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product. - EC| PAIDEIA Zanetti Giacomo; Carlotto A.; Lam Tran T. N.; Szczurek A.; +13 Authors
Zanetti Giacomo
Harvested from ORCID Public Data FileZanetti Giacomo in OpenAIRE Zanetti Giacomo; Carlotto A.; Lam Tran T. N.; Szczurek A.; Babiarczuk B.;Zanetti Giacomo
Harvested from ORCID Public Data FileZanetti Giacomo in OpenAIRE Sayginer O.; Varas S.; Krzak J.;Sayginer O.
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesSayginer O. in OpenAIRE Bursi O.; Bursi O.
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesBursi O. in OpenAIRE Zonta D.; Zonta D.
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesZonta D. in OpenAIRE Baldi G.; Bonomo M.; Bonomo M.
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesBonomo M. in OpenAIRE Galliano S.; Galliano S.
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesGalliano S. in OpenAIRE Barolo C.; Barolo C.
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesBarolo C. in OpenAIRE Bazzanella N.; Bazzanella N.
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesBazzanella N. in OpenAIRE Pietralunga S. M.; Pietralunga S. M.
Harvested from ORCID Public Data FilePietralunga S. M. in OpenAIRE Chiasera A.; Chiasera A.
Harvested from ORCID Public Data FileChiasera A. in OpenAIREWe present the results of the spectral transmittance and reflectance of a SiO2/TiO2 1D photonic crystal deposited by Radio Frequency sputtering on a flexible polymeric substrate, which shows a blue-shift in its transmittance stopband proportional to the incidence angle when bent. An adjustable sample holder was designed to regulate the bending and keep the sample in a bent condition. Different angles of incidence were also achieved through variable angle reflectance, where an increase in incidence angle led to the blue-shift and narrowing of the transmittance stopband. We addressed the sample's resistance against bending wear and tear by comparing the transmittance spectra acquired for the flat sample before and after the measurements made in bent configurations. An important stability has been observed.
Access RoutesGreen gold 1 citations 1 popularity Average influence Average impulse Average Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product. - Mazzeo D.; Leva S.; Matera N.; Kontoleon K. J.; +3 Authors
Leva S.
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesLeva S. in OpenAIRE Mazzeo D.; Leva S.; Matera N.; Kontoleon K. J.; Saboor S.;Leva S.
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesLeva S. in OpenAIRE Pirouz B.; Elkadeem M. R.; Elkadeem M. R.
Harvested from ORCID Public Data FileElkadeem M. R. in OpenAIREWhile traditional methods for modelling the thermal and electrical behaviour of photovoltaic (PV) modules rely on analytical and empirical techniques, machine learning is gaining interest as a way to reduce the time, expertise, and tools required by designers or experts while maintaining high accuracy and reliability. This research presents a data-driven machine learning tool based on artificial neural networks (ANNs) that can forecast yearly PV electricity directly at the optimal PV inclination angle without geographic restrictions and is valid for a wide range of electrical characteristics of PV modules. Additionally, empirical correlations were developed to easily determine the optimal PV inclination angle worldwide. The ANN algorithm, developed in Matlab, systematically and quantitatively summarizes the behaviour of eight PV modules in 48 worldwide climatic conditions. The algorithm’s applicability and robustness were proven by considering two different PV modules in the same 48 locations. Yearly climatic variables and electrical/thermal PV module parameters serve as input training data. The yearly PV electricity is derived using dynamic simulations in the TRNSYS environment, which is a simulation program primarily and extensively used in the fields of renewable energy engineering and building simulation for passive as well as active solar design. Multiple performance metrics validate that the ANN-based machine learning tool demonstrates high reliability and accuracy in the PV energy production forecasting for all weather conditions and PV module characteristics. In particular, by using 20 neurons, the highest value of R-square of 0.9797 and the lowest values of the root mean square error and coefficient of variance of 14.67 kWh and 3.8%, respectively, were obtained in the training phase. This high accuracy was confirmed in the ANN validation phase considering other PV modules. An R-square of 0.9218 and values of the root mean square error and coefficient of variance of 31.95 kWh and 7.8%, respectively, were obtained.The results demonstrate the algorithm’s vast potential to enhance the worldwide diffusion and economic growth of solar energy, aligned with the seventh sustainable development goal.
Access RoutesGreen gold 14 citations 14 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product. - ANR| BIOECO Guaygua-Amaguaña, Priscila;
Guaygua-Amaguaña, Priscila
Harvested from ORCID Public Data FileGuaygua-Amaguaña, Priscila in OpenAIRE Vaca-Medina, Guadalupe; Vaca-Medina, Guadalupe
Harvested from ORCID Public Data FileVaca-Medina, Guadalupe in OpenAIRE Vialle, Claire; Sablayrolles, Caroline; +1 AuthorsVialle, Claire
Harvested from ORCID Public Data FileVialle, Claire in OpenAIRE Guaygua-Amaguaña, Priscila; Guaygua-Amaguaña, Priscila
Harvested from ORCID Public Data FileGuaygua-Amaguaña, Priscila in OpenAIRE Vaca-Medina, Guadalupe; Vaca-Medina, Guadalupe
Harvested from ORCID Public Data FileVaca-Medina, Guadalupe in OpenAIRE Vialle, Claire; Sablayrolles, Caroline;Vialle, Claire
Harvested from ORCID Public Data FileVialle, Claire in OpenAIRE Evon, Philippe; Evon, Philippe
Harvested from ORCID Public Data FileEvon, Philippe in OpenAIREThe hot pressing process for 100% coriander-based fiberboards was optimized using an induction RocTool system, which offers rapid mold heating and cooling. The fiberboards were made with deoiled press cake as a proteic binder and extrusion-refined straw as reinforcement. A Doehlert’s experimental design evaluated the influence of pressure (10-50 MPa), molding time (60-300 s), and mold temperature (155-205°C) on fiberboard properties, energy consumption, cost, and environmental impact. The results showed that the RocTool device allows better temperature control during shaping, resulting in exacerbated adhesive properties for proteins. Using the isoresponse curves, optimal hot pressing conditions were 35 MPa, 300 s, and 205°C, corresponding to a 40.6 MPa flexural strength. However, it was observed that to achieve a MDF-like fiberboard with minimal production cost, much less restrictive molding conditions were sufficient, i.e., 32.5 MPa, 170 s, and 160°C. The study revealed that maximum thermopressing conditions emitted 3.87 kg of CO2 eq., while conditions leading to the MDF-like board reduced emissions to 1.45 kg CO2 eq., resulting in a more environmentally-friendly material.
Access RoutesGreen gold 1 citations 1 popularity Average influence Average impulse Average Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.
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