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Percorrer por autor "Liu, Chao"

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  • Early career ocean professionals' declaration on ocean negative carbon emissions for our ocean and future.
    Publication . Li, Shenghui; Addey, Charles I.; Roman, Raphaël; Hayashida, Hakase; Jiang, Chunhua; Hu, Chen; Coronado-Álvarez, Luz de Lourdes Aurora; Lim, Hyung-Gyu; Akmal, Surya Gentha; Orji, Chukwuka Moses; Arora, Parth; Li, Ruiqi; Pm, Sohan; Adesina, Rasheed B.; Lindemann, Christian; Ma, Deqiang; Sarkar, Saydul; Mascioni, Martina; Monteiro, Thiago; Liu, Chao; Ojwala, Renis Auma; Tabilog, Matthew Vincent; Roeroe, Kakaskasen Andreas; Oladejo, Hafeez O.; Daramola, Samuel O.; Da Costa, Delio; Guo, Ting; Chicaiza-Ortiz, Cristhian; Adebiyi, Abiola A.; Ahmed, Md Rasel; Baloch, Aidah; Andueza, Santiago Thomé; Ansong, Joseph Kofi; Appalanaidu, Sura; Asif, Furqan; Awa, Andrew Taylor; Baguya, Elnalee; Batista, Matheus; Benedict, Okeke Ebuka; Bobby, Fulton; Busumprah, Peter Teye; Cardoso, Marta; de Oliveira Carvalho, Andréa da Consolação; Crea, Terrence Daniel; Channimol, Ky; Cheah, Wee; Chinwendu, Igbodiegwu Gloria; Dinoi, Alessia; Egbe, King-James I; Eshun, Joseph; Gaitan Espitia, Juan Diego; Essel, Dorcas Akua; Fox, Natalie; Fraser, Kate; Gaglioti, Martina; Gerbrand, Koren; Gusatu, Laura; Hernández Contreras, Diego Alexander; Iradukunda, Theddy-Michel; Khalfan, Zahor Mwalim; Khatib, Laura; Kim, Minkyoung; Koch, Marta; Liu, Jihua; Mandal, Shailendra K; Manivong, Soukphansa; McAteer, Benedict; Mgbechidinma, Chiamaka Linda; Ngo, Thuy Hao; Nirmale, Manasi Suhas; Noonan Birch, Ronnie; Oginni, Tolulope E; Olalekan, Isa Elegbede; Offei-Darko, Lord; Puigcorbé, Viena; Gandhi, Rishi Rajendra; Rozaimi, Mohammad; Sanganyado, Edmond; Sengupta, Debarati; Singh, Priyatma; Sridhar, Dumpala; Sunanda, N.; Tailor, Falguni; Tintoré, Beatriz; Ugochukwu, Okoli Moses; Uthaipan, Khanittha; Vargas-Fonseca, O Alejandra; Verma, Anmol; Vives, Clara R.; Wallschuss, Sina; Wang, Lin; Wang, Yuhao; Wang, Yuntao; Meng, Yabing; Schoenbeck, María; Yan, Wei; Yen, Hanna; Luo, Tingwei
    This paper highlights the urgent need to accelerate research and action on ocean carbon sinks through human intervention, known as the Global Ocean Negative Carbon Emissions (Global-ONCE) Programme, as a vital strategy in global efforts to mitigate climate change. Achieving "net zero" by 2050 cannot rely on emission reductions alone, emphasizing the necessity of complementary approaches. Global-ONCE's mission extends beyond scientific exploration. It embodies a profound commitment to protecting and restoring blue carbon ecosystems, as well as implementing ocean-based solutions that are sustainable, equitable, and inclusive. Early career ocean professionals (ECOPs) are at the heart of these efforts, and their innovative approaches, technical expertise, and passion make them indispensable leaders in advancing ONCE initiatives. ECOPs bridge the gap between science and society, playing a relevant role in integrating cutting-edge research, technological advancements, and community-driven action to address climate threats. By bringing together diverse perspectives and leveraging their interdisciplinary expertise, ECOPs ensure that ONCE strategies are grounded in scientific rigor and practical feasibility. Through advocacy, education, and collaboration, ECOPs not only spearhead research and innovation but also inspire collective action to safeguard our oceans. This paper amplifies the critical role of ECOPs as agents of change and calls for a unified global commitment to harness the ocean's potential for a climate-resilient future.
    2025-09-08Artigo científico Desconhecido Ver mais
  • Regression modeling for enterprise electricity consumption: A comparison of recurrent neural network and its variants
    Publication . Bai, Yun; Xie, Jingjing; Liu, Chao; Tao, Ying; Zeng, Bo; Li, Chuan
    Effective electricity consumption forecasting is extremely significant for enterprises' electricity planning which can provide data support for production decision, thus improving the level of enterprises' clean production. In recent years, recurrent neural network (RNN) and its variants have led to extensive research for time series forecasting. However, the performance and selection of these models in enterprise electricity forecasting have not been reported. With this study, we attempted to back some of these solutions with experimental results. This paper focused on a comparison for daily enterprise electricity consumption forecasting using different RNN models, i.e, standard RNN, long short-term memory-based RNN (LSTM), and gated recurrent unit-based RNN (GRU). To test their regression performance, three Chinese enterprises with different scales of electricity consumption are investigated. The comparison results show that the LSTM and the GRU models are slightly better than that of the RNN in terms of normalized root-mean-square error, mean absolute percentage error and threshold statistic. Moreover, the GRU model with the simplest structure is significantly different from the RNN, but not from LSTM in terms of Friedman testing. Hence the GRU model can be regarded as the first candidate for the enterprise electricity consumption forecasting in the future work.
    2021-03Artigo científico Acesso restrito Ver mais