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1. Investigation of Wind Speed to Generate Energy Using Machine Learning Algorithms Approach Over Selected Nigerian Stations

Author

Francis Aweda, Segun Adebayo, Adetunji Adeniji, Timothy Samson, and Jacob Akinpelu

Subjects
meteorological data, machine learning, neural network, statistical models, Energy industries. Energy policy. Fuel trade, HD9502-9502.5
Abstract

Wind energy has been identified as a critical component in the growth of all countries throughout the world. Nigeria has been identified as having energy issues as a result of poor maintenance of hydro and thermal energy generating stations. As a result, the current study uses some machine learning approaches over wind speed data for energy generation in the country. Machine learning models were employed for wind speed using selected meteorological parameters. Little research was done using some meteorological data and machine learning to investigate wind speed across Nigerian sub-stations, resulting in the need for further research. This research, on the other hand, focuses on a neural network for forecasting, a Long Short-Term Memory (LSTM) network model based on several fire-work algorithms (FWA). The data for this study came from the archive of the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) Web service, which was modeled. The LSTM predicts the wind speed model based on the FWA, which used hyper-parameter optimization and was based on a real-time prediction model that was dependent on the change and dependence of the neural network. The study data was split into two categories: test and training. According to the validation technique, the sample data was reviewed, and the first 80 % of the data was utilized for training, as revealed by the (LSTM) network model. The remaining 20 % of the data was used as forecast data to ensure that the model was accurate. The normalization of the data for the wind speed range of 0 to 1 which illustrates the process data, the high peak in 1985 (a = 0.12 m/s, b = 0.11 m/s, c = 0.13 m/s, d = 0.08 m/s, e = 0.06 m/s, f = 0.10 m/s) was discovered. However, the summary result of the performances of different 11 Machine Learning algorithms of regression type for each of the seven locations in Nigeria has different values. As a result, it is recommended that this study will facilitate the prediction of wind speed for energy generation in Nigeria.

Published
2023
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4. Effect of early tranexamic acid administration on mortality, hysterectomy, and other morbidities in women with post-partum haemorrhage (WOMAN): an international, randomised, double-blind, placebo-controlled trial

Author

Haleema Shakur, Ian Roberts, Bukola Fawole, Rizwana Chaudhri, Mohamed El-Sheikh, Adesina Akintan, Zahida Qureshi, Hussein Kidanto, Bellington Vwalika, Abdulfetah Abdulkadir, Saturday Etuk, Shehla Noor, Etienne Asonganyi, Zarko Alfirevic, Danielle Beaumont, Carine Ronsmans, Sabaratnam Arulkumaran, Adrian Grant, Kaosar Afsana, Metin Gülmezoglu, Beverley Hunt, Oladapo Olayemi, Iain Chalmers, Pisake Lumbiganon, Gilda Piaggio, Tony Brady, Diana Elbourne, Eni Balogun, Tracey Pepple, Danielle Prowse, Nigel Quashi, Lin Barneston, Collette Barrow, Lisa Cook, Lauren Frimley, Daniel Gilbert, Catherine Gilliam, Rob Jackson, Taemi Kawahara, Hakim Miah, Sergey Kostrov, Maria Ramos, Phil Edwards, Tom Godec, Sumaya Huque, Olujide Okunade, Olusade Adetayo, Aasia Kayani, Kiran Javaid, Chrstine Biryabarema, Robert Tchounzou, Mohan Regmi, Kastriot Dallaku, Mateus Sahani, Sayeba Akhter, Nicolas Meda, Anthony Kwame Dah, Olufemi Odekunle, Oluwabusola Monehin, Austin Ojo, Grace Akinbinu, Ifeoma Offiah, Ubong Akpan, Uduak Udofia, Useneno Okon, Ezukwa Omoronyia, Okpe James, Nike Bello, Blessed Adeyemi, Chris Aimakhu, Olufemi Akinsanya, Bamidele Adeleye, Oluwaseun Adeyemi, Kayode Oluwatosin, Abiodun Aboyeji, Abiodun Adeniran, Adebayo Adewale, Noah Olaomo, Lawrence Omo-Aghoja, Emmanuel Okpako, Lucky Oyeye, Francis Alu, John Ogudu, Ezekiel Ladan, Ibrahim Habib, Babasola Okusanya, Olatunde Onafowokan, David Isah, Abalaka Aye, Felix Okogbo, Egbaname Aigere, Mark Ogbiti, Temitope Onile, Olaide Salau, Yinka Amode, Kamil Shoretire, Adebola Owodunni, Kehinde Ologunde, Akintunde Ayinde, Moses Alao, Olalekan Awonuga, Babatunde Awolaja, Omololu Adegbola, Fatimah Habeebu-Adeyemi, Adeyemi Okunowo, Hadiza Idris, Ola Okike, Nneka Madueke, Josiah Mutihir, Nankat Joseph, Babatunde Adebudo, Adeniyi Fasanu, Olugbenga Akintunde, Olufemi Abidoye, Owigho Opreh, Sophia Udonwa, Gladys Dibia, Simeon Bazuaye, Arafat Ifemeje, Aniefiok Umoiyoho, Emmanuel Inyang-Etoh, Sununu Yusuf, Kayode Olayinka, Babalola Adeyemi, Olusegun Ajenifuja, Umar Ibrahim, Yusuf Baffah Adamu, Oluwarotimi Akinola, Grace Adekola-Oni, Paul Kua, Roseline Iheagwam, Audu Idrisa, Ado Geidam, Andrea Jogo, Joseph Agulebe, Joseph Ikechebelu, Onyebuchi Udegbunam, Jacob Awoleke, Oluseyi Adelekan, Hajaratu Sulayman, Nkeiruka Ameh, Nurudeen Onaolapo, Affiss Adelodun, William Golit, Dachollom Audu, Adetunji Adeniji, Folasade Oyelade, Lamaran Dattijo, Palmer Henry, Olabisi Loto, Odidika Umeora, Abraham Onwe, Emily Nzeribe, Bartthy Okorochukwu, Augustine Adeniyi, Emmanuel Gbejegbe, Akpojaro Ikpen, Ikemefuna Nwosu, Abdulrasaq Sambo, Olubunmi Ladipo, Sola Abubakar, Ola Nene Okike, Enyinnaya Chikwendu Nduka, Eziamaka Pauline Ezenkwele, Daniel Onwusulu, Theresa Azonima Irinyenikan, Swati Singh, Amaitari Bariweni, Hadiza Galadanci, Peter Achara, Osagie Osayande, Mohammed Gana, Kiran Jabeen, Ayesha Mobeen, Sadaf Mufti, Maliha Zafar, Basharat Ahmad, Maimoona Munawar, Jeharat Gul, Naseema Usman, Fehmida Shaheen, Mariam Tariq, Nadia Sadiq, Rabia Batool, Habiba Sharaf Ali, Manahil Jaffer, Asma Baloch, Noonari Mukhtiar, Tasneem Ashraf, Raheela Asmat, Salma Khudaidad, Ghazala Taj, Roshan Qazi, Saira Dars, Faryal Sardar, Sanobar Ashfaq, Saeeda Majeed, Sadaqat Jabeen, Rukhsana Karim, Farzana Burki, Syeda Rabia Bukhari, Fouzia Gul, Musarrat Jabeen, Akhtar Sherin, Qurratul Ain, Shahid Rao, Uzma Shaheen, Samina Manzoor, Shabween Masood, Shabana Rizvi, Anita Ali, Abida Sajid, Aisha Iftikhar, Shazia Batool, Lubna Dar, Shahenzad Sohail, Shazia Rasul, Shamsa Humayun, Rashida Sultana, Sofia Manzoor, Syeda Mazhar, Afshan Batool, Asia Nazir, Nasira Tasnim, Hajira Masood, Razia Khero, Neelam Surhio, Samana Aleem, Naila Israr, Saba Javed, Lubna Bashir, Samina Iqbal, Faiza Aleem, Rubina Sohail, Saima Iqbal, Samina Dojki, Alia Bano, Naseem Saba, Maimoona Hafeez, Nishat Akram, Riffat Shaheen, Haleema Hashmi, Sharmeen Arshad, Rubina Hussain, Sadia Khan, Nighat Shaheen, Safia Khalil, Pushpa Sachdev, Gulfareen Arain, Amtullah Zarreen, Sara Saeed, Shamayela Hanif, Nabia Tariq, Mahwish Jamil, Shama Chaudhry, Hina Rajani, Tayyiba Wasim, Summera Aslam, Nilofar Mustafa, Huma Quddusi, Sajila Karim, Shazia Sultana, Misbah Harim, Mohd Chohan, Nabila Salman, Fareesa Waqar, Shamsunnisa Sadia, Lubna Kahloon, Shehla Manzoor, Samar Amin, Umbreen Akram, Ambreen Ikram, Samina Kausar, Tahira Batool, Brigadier Naila, Tahir Kyani, Christine Biryabarema, Ruth Bulime, Regina Akello, Bernadette Nakawooya Lwasa, Joselyn Ayikoru, Christine Namulwasira, Patrick Komagum, Isabirye Rebecca, Nayiga Annet, Nakirigya Nuulu, Elizabeth Nionzima, Rose Bwotya, Margret Nankya, Sarah Babirye, Joseph Ngonzi, Cesar Sanchez, Nkonwa Innocent, Kusasira Anitah, Ayiko Jackson, Elizabeth Ndagire, Christine Nanyongo, Dominic Drametu, Grace Meregurwa, Francis Banya, Rita Atim, Emmanuel Byaruhanga, Lema Felix, Hussein Iman, Vincent Oyiengo, Peninah Waigi, Rose Wangui, Faiza Nassir, Musimbi Soita, Rophina Msengeti, Zeinab Zubier, Hillary Mabeya, Antony Wanjala, Henry Mwangi, Brian Liyayi, Evelyn Muthoka, Alfred Osoti, Amos Otara, Veronicah Ongwae, Victor Wanjohi, Bonface Musila, Kubasu Wekesa, Alex Nyakundi Bosire, Alice Ntem, Angeline Njoache, Alice Ashu, André Simo, Dorothy Keka, Kenfack Bruno, Amadou Ndouoya, Martin Saadio, Mesack Tchana, Odel Gwan, Pauline Assomo, Venantius Mutsu, Nji Eric, Pascal Foumane, Philemon Nsem, Jeanne Fouedjio, Ymele Fouelifack, Pierre Marie Tebeu, Georges Nko'ayissi, Eta Ngole Mbong, Wisal Nabag, Riham Desougi, Hadia Mustafa, Huida Eltaib, Taha Umbeli, Khalid Elfadl, Murwan Ibrahim, Abdalla Mohammed, Awadia Ali, Somia Abdelrahiem, Mohammed Musa, Khidir Awadalla, Samirra Ahmed, Mahdi Bushra, Omer Babiker, Hala Abdullahi, Mohamed Ahmed, Elhassan Safa, Huida Almardi, Duria Rayis, Saeed Abdelrahman Abdelgabar, Gillian Houghton, Andrew Sharpe, Jim Thornton, Nick Grace, Carys Smith, Kim Hinshaw, Dawn Edmundson, Paul Ayuk, Alison Bates, George Bugg, Joanne Wilkins, Clare Tower, Alysha Allibone, Eugene Oteng-Ntim, Ahmad Kazumari, Anna Danford, Matilda Ngarina, Muzdalifat Abeid, Khadija Mayumba, Magreth Zacharia, George Mtove, Leonard Madame, Anthony Massinde, Berno Mwambe, Rwakyendela Onesmo, Sebastian Kitengile Ganyaka, Shyam Gupta, Rabindra Bhatt, Ajay Agrawal, Pramila Pradhan, Nikita Dhakal, Punita Yadav, Gyanendra Karki, Bhola Ram Shrestha, Mwansa Lubeya, Jane Mumba, Willies Silwimba, Isaiah Hansingo, Noojiri Bopili, Ziche Makukula, Alexander Kawimbe, Mwansa Ketty Lubeya, Willard Mtambo, Mathew Ng'ambi, Saimir Cenameri, Ilir Tasha, Aferdita Kruja, Besnik Brahimaj, Armida Tola, Leon Kaza, Desire Tshombe, Elizabeth Buligho, Roger Paluku-Hamuli, Charles Kacha, Kato Faida, Badibanga Musau, Herman Kalyana, Phanny Simisi, Serge Mulyumba, Nzanzu Kikuhe Jason, Jean Robert Lubamba, Willis Missumba, Ferdousi Islam, Nazneen Begum, Ferdousi Chowdhury, Rokeya Begum, Farjana Basher, Nazlima Nargis, Abu Kholdun, Shahela Jesmin, Shrodha Paul, Hailemariam Segni, Getachew Ayana, William Haleke, Hassen Hussien, Fikre Geremew, Moussa Bambara, Adolphe Somé, Amadou Ly, Roamba Pabakba, Horace Fletcher, Leslie Samuels, Henry Opare-Addo, Roderick Larsen-Reindorf, Kwadwo Nyarko-Jectey, Glen Mola, Malts Wai, Magdy El Rahman, Wafaa Basta, Hussein Khamis, Maria Fernanda Escobar, Liliana Vallecilla, and Gabriel Essetchi Faye

Subjects
medicine.medical_specialty, Hysterectomy, business.industry, medicine.medical_treatment, Placebo-controlled study, General Medicine, 030204 cardiovascular system & hematology, Placebo, medicine.disease, 3. Good health, 03 medical and health sciences, 0302 clinical medicine, Internal medicine, Relative risk, Anesthesia, Clinical endpoint, medicine, Caesarean section, Maternal death, 030212 general & internal medicine, business, Tranexamic acid, medicine.drug
Abstract

Background\ud Post-partum haemorrhage is the leading cause of maternal death worldwide. Early administration of tranexamic acid reduces deaths due to bleeding in trauma patients. We aimed to assess the effects of early administration of tranexamic acid on death, hysterectomy, and other relevant outcomes in women with post-partum haemorrhage.\ud \ud Methods\ud In this randomised, double-blind, placebo-controlled trial, we recruited women aged 16 years and older with a clinical diagnosis of post-partum haemorrhage after a vaginal birth or caesarean section from 193 hospitals in 21 countries. We randomly assigned women to receive either 1 g intravenous tranexamic acid or matching placebo in addition to usual care. If bleeding continued after 30 min, or stopped and restarted within 24 h of the first dose, a second dose of 1 g of tranexamic acid or placebo could be given. Patients were assigned by selection of a numbered treatment pack from a box containing eight numbered packs that were identical apart from the pack number. Participants, care givers, and those assessing outcomes were masked to allocation. We originally planned to enrol 15 000 women with a composite primary endpoint of death from all-causes or hysterectomy within 42 days of giving birth. However, during the trial it became apparent that the decision to conduct a hysterectomy was often made at the same time as randomisation. Although tranexamic acid could influence the risk of death in these cases, it could not affect the risk of hysterectomy. We therefore increased the sample size from 15 000 to 20 000 women in order to estimate the effect of tranexamic acid on the risk of death from post-partum haemorrhage. All analyses were done on an intention-to-treat basis. This trial is registered with ISRCTN76912190 (Dec 8, 2008); ClinicalTrials.gov, number NCT00872469; and PACTR201007000192283.\ud \ud Findings\ud Between March, 2010, and April, 2016, 20 060 women were enrolled and randomly assigned to receive tranexamic acid (n=10 051) or placebo (n=10 009), of whom 10 036 and 9985, respectively, were included in the analysis. Death due to bleeding was significantly reduced in women given tranexamic acid (155 [1·5%] of 10 036 patients vs 191 [1·9%] of 9985 in the placebo group, risk ratio [RR] 0·81, 95% CI 0·65–1·00; p=0·045), especially in women given treatment within 3 h of giving birth (89 [1·2%] in the tranexamic acid group vs 127 [1·7%] in the placebo group, RR 0·69, 95% CI 0·52–0·91; p=0·008). All other causes of death did not differ significantly by group. Hysterectomy was not reduced with tranexamic acid (358 [3·6%] patients in the tranexamic acid group vs 351 [3·5%] in the placebo group, RR 1·02, 95% CI 0·88–1·07; p=0·84). The composite primary endpoint of death from all causes or hysterectomy was not reduced with tranexamic acid (534 [5·3%] deaths or hysterectomies in the tranexamic acid group vs 546 [5·5%] in the placebo group, RR 0·97, 95% CI 0·87-1·09; p=0·65). Adverse events (including thromboembolic events) did not differ significantly in the tranexamic acid versus placebo group.\ud \ud Interpretation\ud Tranexamic acid reduces death due to bleeding in women with post-partum haemorrhage with no adverse effects. When used as a treatment for postpartum haemorrhage, tranexamic acid should be given as soon as possible after bleeding onset.\ud \ud Funding\ud London School of Hygiene & Tropical Medicine, Pfizer, UK Department of Health, Wellcome Trust, and Bill & Melinda Gates Foundation.

Published
2017
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