| 1. |
- Glasbey, JC, et al.
(author)
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- 2021
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swepub:Mat__t
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| 2. |
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| 3. |
- Bravo, L, et al.
(author)
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- 2021
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swepub:Mat__t
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| 4. |
- Tabiri, S, et al.
(author)
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- 2021
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swepub:Mat__t
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| 5. |
- Thomas, HS, et al.
(author)
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- 2019
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| 9. |
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| 10. |
- Drake, TM, et al.
(author)
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Surgical site infection after gastrointestinal surgery in children: an international, multicentre, prospective cohort study
- 2020
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In: BMJ global health. - : BMJ. - 2059-7908. ; 5:12
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Journal article (peer-reviewed)abstract
- Surgical site infection (SSI) is one of the most common healthcare-associated infections (HAIs). However, there is a lack of data available about SSI in children worldwide, especially from low-income and middle-income countries. This study aimed to estimate the incidence of SSI in children and associations between SSI and morbidity across human development settings.MethodsA multicentre, international, prospective, validated cohort study of children aged under 16 years undergoing clean-contaminated, contaminated or dirty gastrointestinal surgery. Any hospital in the world providing paediatric surgery was eligible to contribute data between January and July 2016. The primary outcome was the incidence of SSI by 30 days. Relationships between explanatory variables and SSI were examined using multilevel logistic regression. Countries were stratified into high development, middle development and low development groups using the United Nations Human Development Index (HDI).ResultsOf 1159 children across 181 hospitals in 51 countries, 523 (45·1%) children were from high HDI, 397 (34·2%) from middle HDI and 239 (20·6%) from low HDI countries. The 30-day SSI rate was 6.3% (33/523) in high HDI, 12·8% (51/397) in middle HDI and 24·7% (59/239) in low HDI countries. SSI was associated with higher incidence of 30-day mortality, intervention, organ-space infection and other HAIs, with the highest rates seen in low HDI countries. Median length of stay in patients who had an SSI was longer (7.0 days), compared with 3.0 days in patients who did not have an SSI. Use of laparoscopy was associated with significantly lower SSI rates, even after accounting for HDI.ConclusionThe odds of SSI in children is nearly four times greater in low HDI compared with high HDI countries. Policies to reduce SSI should be prioritised as part of the wider global agenda.
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| 11. |
- Abbafati, Cristiana, et al.
(author)
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- 2020
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Journal article (peer-reviewed)
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| 12. |
- Ademuyiwa, Adesoji O., et al.
(author)
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Determinants of morbidity and mortality following emergency abdominal surgery in children in low-income and middle-income countries
- 2016
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In: BMJ Global Health. - : BMJ Publishing Group Ltd. - 2059-7908. ; 1:4
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Journal article (peer-reviewed)abstract
- Background: Child health is a key priority on the global health agenda, yet the provision of essential and emergency surgery in children is patchy in resource-poor regions. This study was aimed to determine the mortality risk for emergency abdominal paediatric surgery in low-income countries globally.Methods: Multicentre, international, prospective, cohort study. Self-selected surgical units performing emergency abdominal surgery submitted prespecified data for consecutive children aged <16 years during a 2-week period between July and December 2014. The United Nation's Human Development Index (HDI) was used to stratify countries. The main outcome measure was 30-day postoperative mortality, analysed by multilevel logistic regression.Results: This study included 1409 patients from 253 centres in 43 countries; 282 children were under 2 years of age. Among them, 265 (18.8%) were from low-HDI, 450 (31.9%) from middle-HDI and 694 (49.3%) from high-HDI countries. The most common operations performed were appendectomy, small bowel resection, pyloromyotomy and correction of intussusception. After adjustment for patient and hospital risk factors, child mortality at 30 days was significantly higher in low-HDI (adjusted OR 7.14 (95% CI 2.52 to 20.23), p<0.001) and middle-HDI (4.42 (1.44 to 13.56), p=0.009) countries compared with high-HDI countries, translating to 40 excess deaths per 1000 procedures performed.Conclusions: Adjusted mortality in children following emergency abdominal surgery may be as high as 7 times greater in low-HDI and middle-HDI countries compared with high-HDI countries. Effective provision of emergency essential surgery should be a key priority for global child health agendas.
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| 13. |
- Feigin, Valery L., et al.
(author)
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Global, regional, and national burden of stroke and its risk factors, 1990-2019 : a systematic analysis for the Global Burden of Disease Study 2019
- 2021
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In: Lancet Neurology. - : Elsevier. - 1474-4422 .- 1474-4465. ; 20:10, s. 795-820
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Journal article (peer-reviewed)abstract
- Background Regularly updated data on stroke and its pathological types, including data on their incidence, prevalence, mortality, disability, risk factors, and epidemiological trends, are important for evidence-based stroke care planning and resource allocation. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) aims to provide a standardised and comprehensive measurement of these metrics at global, regional, and national levels. Methods We applied GBD 2019 analytical tools to calculate stroke incidence, prevalence, mortality, disability-adjusted life-years (DALYs), and the population attributable fraction (PAF) of DALYs (with corresponding 95% uncertainty intervals [UIs]) associated with 19 risk factors, for 204 countries and territories from 1990 to 2019. These estimates were provided for ischaemic stroke, intracerebral haemorrhage, subarachnoid haemorrhage, and all strokes combined, and stratified by sex, age group, and World Bank country income level. Findings In 2019, there were 12.2 million (95% UI 11.0-13.6) incident cases of stroke, 101 million (93.2-111) prevalent cases of stroke, 143 million (133-153) DALYs due to stroke, and 6.55 million (6.00-7.02) deaths from stroke. Globally, stroke remained the second-leading cause of death (11.6% [10.8-12.2] of total deaths) and the third-leading cause of death and disability combined (5.7% [5.1-6.2] of total DALYs) in 2019. From 1990 to 2019, the absolute number of incident strokes increased by 70.0% (67.0-73.0), prevalent strokes increased by 85.0% (83.0-88.0), deaths from stroke increased by 43.0% (31.0-55.0), and DALYs due to stroke increased by 32.0% (22.0-42.0). During the same period, age-standardised rates of stroke incidence decreased by 17.0% (15.0-18.0), mortality decreased by 36.0% (31.0-42.0), prevalence decreased by 6.0% (5.0-7.0), and DALYs decreased by 36.0% (31.0-42.0). However, among people younger than 70 years, prevalence rates increased by 22.0% (21.0-24.0) and incidence rates increased by 15.0% (12.0-18.0). In 2019, the age-standardised stroke-related mortality rate was 3.6 (3.5-3.8) times higher in the World Bank low-income group than in the World Bank high-income group, and the age-standardised stroke-related DALY rate was 3.7 (3.5-3.9) times higher in the low-income group than the high-income group. Ischaemic stroke constituted 62.4% of all incident strokes in 2019 (7.63 million [6.57-8.96]), while intracerebral haemorrhage constituted 27.9% (3.41 million [2.97-3.91]) and subarachnoid haemorrhage constituted 9.7% (1.18 million [1.01-1.39]). In 2019, the five leading risk factors for stroke were high systolic blood pressure (contributing to 79.6 million [67.7-90.8] DALYs or 55.5% [48.2-62.0] of total stroke DALYs), high body-mass index (34.9 million [22.3-48.6] DALYs or 24.3% [15.7-33.2]), high fasting plasma glucose (28.9 million [19.8-41.5] DALYs or 20.2% [13.8-29.1]), ambient particulate matter pollution (28.7 million [23.4-33.4] DALYs or 20.1% [16.6-23.0]), and smoking (25.3 million [22.6-28.2] DALYs or 17.6% [16.4-19.0]). Interpretation The annual number of strokes and deaths due to stroke increased substantially from 1990 to 2019, despite substantial reductions in age-standardised rates, particularly among people older than 70 years. The highest age-standardised stroke-related mortality and DALY rates were in the World Bank low-income group. The fastest-growing risk factor for stroke between 1990 and 2019 was high body-mass index. Without urgent implementation of effective primary prevention strategies, the stroke burden will probably continue to grow across the world, particularly in low-income countries.
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| 14. |
- Micah, Angela E., et al.
(author)
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Tracking development assistance for health and for COVID-19 : a review of development assistance, government, out-of-pocket, and other private spending on health for 204 countries and territories, 1990-2050
- 2021
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In: The Lancet. - : Elsevier. - 0140-6736 .- 1474-547X. ; 398:10308, s. 1317-1343
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Research review (peer-reviewed)abstract
- Background The rapid spread of COVID-19 renewed the focus on how health systems across the globe are financed, especially during public health emergencies. Development assistance is an important source of health financing in many low-income countries, yet little is known about how much of this funding was disbursed for COVID-19. We aimed to put development assistance for health for COVID-19 in the context of broader trends in global health financing, and to estimate total health spending from 1995 to 2050 and development assistance for COVID-19 in 2020. Methods We estimated domestic health spending and development assistance for health to generate total health-sector spending estimates for 204 countries and territories. We leveraged data from the WHO Global Health Expenditure Database to produce estimates of domestic health spending. To generate estimates for development assistance for health, we relied on project-level disbursement data from the major international development agencies' online databases and annual financial statements and reports for information on income sources. To adjust our estimates for 2020 to include disbursements related to COVID-19, we extracted project data on commitments and disbursements from a broader set of databases (because not all of the data sources used to estimate the historical series extend to 2020), including the UN Office of Humanitarian Assistance Financial Tracking Service and the International Aid Transparency Initiative. We reported all the historic and future spending estimates in inflation-adjusted 2020 US$, 2020 US$ per capita, purchasing-power parity-adjusted US$ per capita, and as a proportion of gross domestic product. We used various models to generate future health spending to 2050. Findings In 2019, health spending globally reached $8. 8 trillion (95% uncertainty interval [UI] 8.7-8.8) or $1132 (1119-1143) per person. Spending on health varied within and across income groups and geographical regions. Of this total, $40.4 billion (0.5%, 95% UI 0.5-0.5) was development assistance for health provided to low-income and middle-income countries, which made up 24.6% (UI 24.0-25.1) of total spending in low-income countries. We estimate that $54.8 billion in development assistance for health was disbursed in 2020. Of this, $13.7 billion was targeted toward the COVID-19 health response. $12.3 billion was newly committed and $1.4 billion was repurposed from existing health projects. $3.1 billion (22.4%) of the funds focused on country-level coordination and $2.4 billion (17.9%) was for supply chain and logistics. Only $714.4 million (7.7%) of COVID-19 development assistance for health went to Latin America, despite this region reporting 34.3% of total recorded COVID-19 deaths in low-income or middle-income countries in 2020. Spending on health is expected to rise to $1519 (1448-1591) per person in 2050, although spending across countries is expected to remain varied. Interpretation Global health spending is expected to continue to grow, but remain unequally distributed between countries. We estimate that development organisations substantially increased the amount of development assistance for health provided in 2020. Continued efforts are needed to raise sufficient resources to mitigate the pandemic for the most vulnerable, and to help curtail the pandemic for all. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd.
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| 15. |
- Salah, Heba, et al.
(author)
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Muscle-specific differences in expression and phosphorylation of the Janus kinase 2/Signal Transducer and Activator of Transcription 3 following long-term mechanical ventilation and immobilization in rats
- 2018
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In: Acta Physiologica. - : WILEY. - 1748-1708 .- 1748-1716. ; 222:3
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Journal article (peer-reviewed)abstract
- Aim: Muscle wasting is one of the factors most strongly predicting mortality and morbidity in critically ill intensive care unit (ICU). This muscle wasting affects both limb and respiratory muscles, but the understanding of underlying mechanisms and muscle-specific differences remains incomplete. This study aimed at investigating the temporal expression and phosphorylation of the Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway in muscle wasting associated with the ICU condition to characterize the JAK/STAT proteins and the related changes leading or responding to their activation during exposure to the ICU condition.Methods: A novel experimental ICU model allowing long-term exposure to the ICU condition, immobilization and mechanical ventilation, was used in this study. Rats were pharmacologically paralysed by post-synaptic neuromuscular blockade and mechanically ventilated for durations varying between 6hours and 14days to study muscle-specific differences in the temporal activation of the JAK/STAT pathway in plantaris, intercostal and diaphragm muscles.Results: The JAK2/STAT3 pathway was significantly activated irrespective of muscle, but muscle-specific differences were observed in the temporal activation pattern between plantaris, intercostal and diaphragm muscles.Conclusion: The JAK2/STAT3 pathway was differentially activated in plantaris, intercostal and diaphragm muscles in response to the ICU condition. Thus, JAK2/STAT3 inhibitors may provide an attractive pharmacological intervention strategy in immobilized ICU patients, but further experimental studies are required in the study of muscle-specific effects on muscle mass and function in response to both short- and long-term exposure to the ICU condition prior to the translation into clinical research and practice.
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| 16. |
- Wiedorn, Max O., et al.
(author)
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Megahertz serial crystallography
- 2018
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In: Nature Communications. - : Nature Publishing Group. - 2041-1723. ; 9
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Journal article (peer-reviewed)abstract
- The new European X-ray Free-Electron Laser is the first X-ray free-electron laser capable of delivering X-ray pulses with a megahertz inter-pulse spacing, more than four orders of magnitude higher than previously possible. However, to date, it has been unclear whether it would indeed be possible to measure high-quality diffraction data at megahertz pulse repetition rates. Here, we show that high-quality structures can indeed be obtained using currently available operating conditions at the European XFEL. We present two complete data sets, one from the well-known model system lysozyme and the other from a so far unknown complex of a beta-lactamase from K. pneumoniae involved in antibiotic resistance. This result opens up megahertz serial femtosecond crystallography (SFX) as a tool for reliable structure determination, substrate screening and the efficient measurement of the evolution and dynamics of molecular structures using megahertz repetition rate pulses available at this new class of X-ray laser source.
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| 17. |
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| 18. |
- Alawsi, Mustafa A., et al.
(author)
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Drought Forecasting: A Review and Assessment of the Hybrid Techniques and Data Pre-Processing
- 2022
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In: Hydrology. - : MDPI. - 2306-5338. ; 9:7
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Research review (peer-reviewed)abstract
- Drought is a prolonged period of low precipitation that negatively impacts agriculture, animals, and people. Over the last decades, gradual changes in drought indices have been observed. Therefore, understanding and forecasting drought is essential to avoid its economic impacts and appropriate water resource planning and management. This paper presents a recent literature review, including a brief description of data pre-processing, data-driven modelling strategies (i.e., univariate or multivariate), machine learning algorithms (i.e., advantages and disadvantages), hybrid models, and performance metrics. Combining various prediction methods to create efficient hybrid models has become the most popular use in recent years. Accordingly, hybrid models have been increasingly used for predicting drought. As such, these models will be extensively reviewed, including preprocessing-based hybrid models, parameter optimisation-based hybrid models, and hybridisation of components combination-based with preprocessing-based hybrid models. In addition, using statistical criteria, such as RMSE, MAE, NSE, MPE, SI, BIC, AIC, and AAD, is essential to evaluate the performance of the models.
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| 19. |
- Alawsi, Mustafa A., et al.
(author)
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Tuning ANN Hyperparameters by CPSOCGSA, MPA, and SMA for Short-Term SPI Drought Forecasting
- 2022
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In: Atmosphere. - : MDPI. - 2073-4433. ; 13:19
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Journal article (peer-reviewed)abstract
- Modelling drought is vital to water resources management, particularly in arid areas, to reduce its effects. Drought severity and frequency are significantly influenced by climate change. In this study, a novel hybrid methodology was built, data preprocessing and artificial neural network (ANN) combined with the constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA), to forecast standard precipitation index (SPI) based on climatic factors. Additionally, the marine predators algorithm (MPA) and the slime mould algorithm (SMA) were used to validate the performance of the CPSOCGSA algorithm. Climatic factors data from 1990 to 2020 were employed to create and evaluate the SPI 1, SPI 3, and SPI 6 models for Al-Kut City, Iraq. The results indicated that data preprocessing methods improve data quality and find the best predictors scenario. The performance of CPSOCGSA-ANN is better than MPA-ANN and SMA-ANN algorithms based on various statistical criteria (i.e., R2, MAE, and RMSE). The proposed methodology yield R2 = 0.93, 0.93, and 0.88 for SPI 1, SPI 3, and SPI 6, respectively.
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| 20. |
- Corpeno Kalamgi, Rebeca, et al.
(author)
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Mechano-signalling pathways in an experimental intensive critical illness myopathy model.
- 2016
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In: Journal of Physiology. - 0022-3751 .- 1469-7793. ; 594:15, s. 4371-88
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Journal article (peer-reviewed)abstract
- KEY POINTS: Using an experimental rat intensive care unit (ICU) model, not limited by early mortality, we have previously shown that passive mechanical loading attenuates the loss of muscle mass and force-generation capacity associated with the ICU intervention. Mitochondrial dynamics have recently been shown to play a more important role in muscle atrophy than previously recognized. In this study we demonstrate that mitochondrial dynamics, as well as mitophagy, is affected by mechanosensing at the transcriptional level, and muscle changes induced by unloading are counteracted by passive mechanical loading. The recently discovered ubiquitin ligases Fbxo31 and SMART are induced by mechanical silencing, an induction that similarly is prevented by passive mechanical loading.ABSTRACT: The complete loss of mechanical stimuli of skeletal muscles, i.e. loss of external strain related to weight bearing and internal strain related to activation of contractile proteins, in mechanically ventilated, deeply sedated and/or pharmacologically paralysed intensive care unit (ICU) patients is an important factor triggering the critical illness myopathy (CIM). Using a unique experimental ICU rat model, mimicking basic ICU conditions, we have recently shown that mechanical silencing is a dominant factor triggering the preferential loss of myosin, muscle atrophy and decreased specific force in fast- and slow-twitch muscles and muscle fibres. The aim of this study is to gain improved understanding of the gene signature and molecular pathways regulating the process of mechanical activation of skeletal muscle that are affected by the ICU condition. We have focused on pathways controlling myofibrillar protein synthesis and degradation, mitochondrial homeostasis and apoptosis. We demonstrate that genes regulating mitochondrial dynamics, as well as mitophagy are induced by mechanical silencing and that these effects are counteracted by passive mechanical loading. In addition, the recently identified ubiquitin ligases Fbxo31 and SMART are induced by mechanical silencing, an induction that is reversed by passive mechanical loading. Thus, mechano-cell signalling events are identified which may play an important role for the improved clinical outcomes reported in response to the early mobilization and physical therapy in immobilized ICU patients.
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| 21. |
- Ethaib, Saleem, et al.
(author)
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Evaluation water scarcity based on GIS estimation and climate-change effects: A case study of Thi-Qar Governorate, Iraq
- 2022
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In: Cogent Engineering. - : Taylor & Francis. - 2331-1916. ; 9:1
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Journal article (peer-reviewed)abstract
- This work aims to evaluate water scarcity in Thi-Qar governorate, Iraq, based on GIS estimation, environmental data, climate-change effects, and detection of the changes in marshes over the last three decades (1991–2021). The methodology process included collecting and analysing the related data sets such as water quality indicators, surface water quantity, climatic data, and Landsat’s images. GIS-based data and spatial data were acquired from the USGS website. Arc GIS 10.4.1 software was used to create a hydrological analysis. The results showed that generally, in Iraq, the annual volume of water available per person is 1,390.95 m3/cap/year, which is lower than the threshold for water scarcity (1700 m3/cap/year). The average daily potable water per person in Thi-Qar governorate was 284 L/cap/day, lower than the general average daily potable water per person of Iraq (340 L/cap/day). Meanwhile, 6% of the months along 1998–2018 did not meet the water demands. Water quality tests exhibited some high amounts of pollutants in drinking water, e.g., biological pollution was recorded in 55% of the total number of annual samples. Landsat’s images illustrated a high variation in water areas of marshes over the selected period, whereas the highest marshes area was 1548.21 km2 in 1991 compared to the lowest area, 65.45 km2 found in 1999. To sum up, the research outcomes revealed that the study area faced a serious water scarcity, which had a negative impact on the local people. Also, this research offered a scientific view for the decision-makers to mitigate and manage the water scarcity problem.
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| 22. |
- Ethaib, Saleem, et al.
(author)
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Function of Nanomaterials in Removing Heavy Metals for Water and Wastewater Remediation: A Review
- 2022
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In: Environments. - : MDPI. - 2076-3298. ; 9:10
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Research review (peer-reviewed)abstract
- Although heavy metals are typically found in trace levels in natural waterways, most of them are hazardous to human health and the environment, even at extremely low concentrations. Nanotechnology and nanomaterials have gained great attention among researchers as a sustainable route to addressing water pollution. Researchers focus on developing novel nanomaterials that are cost-effective for use in water/wastewater remediation. A wide range of adsorbed nanomaterials have been fabricated based on different forms of natural materials, such as carbonaceous nanomaterials, zeolite, natural polymers, magnetic materials, metal oxides, metallic materials, and silica. Hence, this review set out to address the ability of various synthesized nanoadsorbent materials to remove different heavy metal ions from water and wastewater and to investigate the influence of the functionalization of nanomaterials on their adsorption capacity and separation process. Additionally, the effect of experimental variables, such as pH, initial ion concentration, adsorbent dose, contact time, temperature, and ionic strength, on the removal of metal ions has been discussed.
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| 23. |
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| 24. |
- Hess, Timo, et al.
(author)
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Dissecting the genetic heterogeneity of gastric cancer
- 2023
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In: EBioMedicine. - : Elsevier. - 2352-3964. ; 92
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Journal article (peer-reviewed)abstract
- Background: Gastric cancer (GC) is clinically heterogenous according to location (cardia/non-cardia) and histopathology (diffuse/intestinal). We aimed to characterize the genetic risk architecture of GC according to its subtypes. Another aim was to examine whether cardia GC and oesophageal adenocarcinoma (OAC) and its precursor lesion Barrett's oesophagus (BO), which are all located at the gastro-oesophageal junction (GOJ), share polygenic risk architecture.Methods: We did a meta-analysis of ten European genome-wide association studies (GWAS) of GC and its subtypes. All patients had a histopathologically confirmed diagnosis of gastric adenocarcinoma. For the identification of risk genes among GWAS loci we did a transcriptome-wide association study (TWAS) and expression quantitative trait locus (eQTL) study from gastric corpus and antrum mucosa. To test whether cardia GC and OAC/BO share genetic aetiology we also used a European GWAS sample with OAC/BO.Findings: Our GWAS consisting of 5816 patients and 10,999 controls highlights the genetic heterogeneity of GC according to its subtypes. We newly identified two and replicated five GC risk loci, all of them with subtype-specific association. The gastric transcriptome data consisting of 361 corpus and 342 antrum mucosa samples revealed that an upregulated expression of MUC1, ANKRD50, PTGER4, and PSCA are plausible GC-pathomechanisms at four GWAS loci. At another risk locus, we found that the blood-group 0 exerts protective effects for non-cardia and diffuse GC, while blood-group A increases risk for both GC subtypes. Furthermore, our GWAS on cardia GC and OAC/BO (10,279 patients, 16,527 controls) showed that both cancer entities share genetic aetiology at the polygenic level and identified two new risk loci on the single-marker level.Interpretation: Our findings show that the pathophysiology of GC is genetically heterogenous according to location and histopathology. Moreover, our findings point to common molecular mechanisms underlying cardia GC and OAC/BO.
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| 25. |
- Kareem, Baydaa Abdul, et al.
(author)
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Applicability of ANN Model and CPSOCGSA Algorithm forMulti-Time Step Ahead River Streamflow Forecasting
- 2022
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In: Hydrology. - : MDPI. - 2306-5338. ; 9:10
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Journal article (peer-reviewed)abstract
- Accurate streamflow prediction is significant when developing water resource management and planning, forecasting floods, and mitigating flood damage. This research developed a novel methodology that involves data pre-processing and an artificial neural network (ANN) optimised with the coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA-ANN) to forecast the monthly water streamflow. The monthly streamflow data of the Tigris River at Amarah City, Iraq, from 2010 to 2020, were used to build and evaluate the suggested methodology. The performance of CPSOCGSA was compared with the slim mold algorithm (SMA) and marine predator algorithm (MPA). The principal findings of this research are that data pre-processing effectively improves the data quality and determines the optimum predictor scenario. The hybrid CPSOCGSA-ANN outperformed both the SMA-ANN and MPA-ANN algorithms. The suggested methodology offered accurate results with a coefficient of determination of 0.91, and 100% of the data were scattered between the agreement limits of the Bland–Altman diagram. The research results represent a further step toward developing hybrid models in hydrology applications.
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| 26. |
- Kareem, Baydaa Abdul, et al.
(author)
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Review of Recent Trends in the Hybridisation of Preprocessing-Based and Parameter Optimisation-Based Hybrid Models to Forecast Univariate Streamflow
- 2024
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In: CMES - Computer Modeling in Engineering & Sciences. - : Tech Science Press. - 1526-1492 .- 1526-1506. ; 138:1, s. 1-41
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Research review (peer-reviewed)abstract
- Forecasting river flow is crucial for optimal planning, management, and sustainability using freshwater resources. Many machine learning (ML) approaches have been enhanced to improve streamflow prediction. Hybrid techniques have been viewed as a viable method for enhancing the accuracy of univariate streamflow estimation when compared to standalone approaches. Current researchers have also emphasised using hybrid models to improve forecast accuracy. Accordingly, this paper conducts an updated literature review of applications of hybrid models in estimating streamflow over the last five years, summarising data preprocessing, univariate machine learning modelling strategy, advantages and disadvantages of standalone ML techniques, hybrid models, and performance metrics. This study focuses on two types of hybrid models: parameter optimisation-based hybrid models (OBH) and hybridisation of parameter optimisation-based and preprocessing-based hybrid models (HOPH). Overall, this research supports the idea that meta-heuristic approaches precisely improve ML techniques. It's also one of the first efforts to comprehensively examine the efficiency of various meta-heuristic approaches (classified into four primary classes) hybridised with ML techniques. This study revealed that previous research applied swarm, evolutionary, physics, and hybrid metaheuristics with 77%, 61%, 12%, and 12%, respectively. Finally, there is still room for improving OBH and HOPH models by examining different data pre-processing techniques and metaheuristic algorithms.
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| 27. |
- Khairan, Hadeel E., et al.
(author)
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Examination of Single- and Hybrid-Based Metaheuristic Algorithms in ANN Reference Evapotranspiration Estimating
- 2023
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In: Sustainability. - : MDPI. - 2071-1050. ; 15:19
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Journal article (peer-reviewed)abstract
- Hydrological resource management, including crop watering and irrigation scheduling, relies on reliable estimates of reference evapotranspiration (ETo). However, previous studies of forecasting ETo have not dealt with comparing single and hybrid metaheuristic algorithms in much detail. This study aims to assess the efficiency of a novel methodology to simulate univariate monthly ETo estimates using an artificial neural network (ANN) integrated with the hybrid particle swarm optimisation–grey wolf optimiser algorithm (PSOGWO). Several state-of-the-art algorithms, including constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithms (CPSOCGSA), the slime mould algorithm (SMA), the marine predators algorithm (MPA) and the modified PSO algorithm were used to evaluate PSOGWO’s prediction accuracy. Monthly meteorological data were collected in Al-Kut City (1990 to 2020) and used for model training, testing and validation. The results indicate that pre-processing techniques can improve raw data quality and may also suggest the best predictors scenario. That said, all models can be considered efficient with acceptable simulation levels. However, the PSOGWO-ANN model slightly outperformed the other techniques based on several statistical tests (e.g., a coefficient of determination of 0.99). The findings can contribute to better management of water resources in Al-Kut City, an agricultural region that produces wheat in Iraq and is under the stress of climate change.
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| 28. |
- Khairan, Hadeel E., et al.
(author)
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Parameter Optimisation-Based Hybrid Reference Evapotranspiration Prediction Models: A Systematic Review of Current Implementations and Future Research Directions
- 2023
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In: Atmosphere. - : MDPI. - 2073-4433. ; 14:1
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Research review (peer-reviewed)abstract
- A hybrid machine learning (ML) model is becoming a common trend in predicting reference evapotranspiration (ETo) research. This study aims to systematically review ML models that are integrated with meta-heuristic algorithms (i.e., parameter optimisation-based hybrid models, OBH) for predicting ETo data. Over five years, from 2018–2022, the articles published in three reliable databases, including Web of Science, ScienceDirect, and IEEE Xplore, were considered. According to the protocol search, 1485 papers were selected. After three filters were applied, the final set contained 33 papers related to the nominated topic. The final set of papers was categorised into five groups. The first group, swarm intelligence-based algorithms, had the highest proportion of papers, (23/33) and was superior to all other algorithms. The second group (evolution computation-based algorithms), third group (physics-based algorithms), fourth group (hybrid-based algorithms), and fifth group (reviews and surveys) had (4/33), (1/33), (2/33), and (3/33), respectively. However, researchers have not treated OBH models in much detail, and there is still room for improvement by investigating both newly single and hybrid meta-heuristic algorithms. Finally, this study hopes to assist researchers in understanding the options and gaps in this line of research.
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| 29. |
- Khudhair, Zahraa S., et al.
(author)
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A CPSOCGSA-tuned neural processor for forecasting river water salinity: Euphrates river, Iraq
- 2022
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In: Cogent Engineering. - : Taylor & Francis Group. - 2331-1916. ; 9:1
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Journal article (peer-reviewed)abstract
- Salinity is a classic problem in water quality management since it is directly associated with low water quality indices. Debate continues about selecting the best model for water quality forecasting, it remains a major challenge and causes much uncertainty. Accordingly, identifying the optimal modelling that can capture the salinity behaviour is becoming a common trend in recent water quality research. This study applies novel combined techniques, including data pre-processing and artificial neural network (ANN) optimised with constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA) to forecast monthly salinity data. Historical monthly total dissolved solids (TDS) and electrical conductivity (EC) data of the Euphrates River at Al-Musayyab, Babylon, and climatic factors from 2010 to 2019 were used to build and validate the methodology. Additionally, for more validation, the CPSOCGSA-ANN was compared with the slime mould algorithm (SMA-ANN), particle swarm optimisation (PSO-ANN) and multi-verse optimiser (MVO-ANN). The results reveal that the pre-processing data approaches improved data quality and selected the best predictors’ scenario. The CPSOCGSA-ANN algorithm is the best based on several statistical criteria. The proposed methodology accurately simulated the TDS and EC time series based on R2 = 0.99 and 0.97, respectively, and SI = 0.003 for both parameters.
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| 30. |
- Khudhair, Zahraa S., et al.
(author)
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A Review of Hybrid Soft Computing and Data Pre-Processing Techniques to Forecast Freshwater Quality’s Parameters: Current Trends and Future Directions
- 2022
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In: Environments. - : MDPI. - 2076-3298. ; 9:7
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Journal article (peer-reviewed)abstract
- Water quality has a significant influence on human health. As a result, water quality parameter modelling is one of the most challenging problems in the water sector. Therefore, the major factor in choosing an appropriate prediction model is accuracy. This research aims to analyse hybrid techniques and pre-processing data methods in freshwater quality modelling and forecasting. Hybrid approaches have generally been seen as a potential way of improving the accuracy of water quality modelling and forecasting compared with individual models. Consequently, recent studies have focused on using hybrid models to enhance forecasting accuracy. The modelling of dissolved oxygen is receiving more attention. From a review of relevant articles, it is clear that hybrid techniques are viable and precise methods for water quality prediction. Additionally, this paper presents future research directions to help researchers predict freshwater quality variables.
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| 31. |
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| 32. |
- Mohammed, Sarah J., et al.
(author)
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Application of hybrid machine learning models and data pre-processing to predict water level of watersheds: Recent trends and future perspective
- 2022
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In: Cogent Engineering. - : Taylor & Francis Group. - 2331-1916. ; 9:1
-
Research review (peer-reviewed)abstract
- The community’s well-being and economic livelihoods are heavily influenced by the water level of watersheds. The changes in water levels directly affect the circulation processes of lakes and rivers that control water mixing and bottom sediment resuspension, further affecting water quality and aquatic ecosystems. Thus, these considerations have made the water level monitoring process essential to save the environment. Machine learning hybrid models are emerging robust tools that are successfully applied for water level monitoring. Various models have been developed, and selecting the optimal model would be a lengthy procedure. A timely, detailed, and instructive overview of the models’ concepts and historical uses would be beneficial in preventing researchers from overlooking models’ potential selection and saving significant time on the problem. Thus, recent research on water level prediction using hybrid machines is reviewed in this article to present the “state of the art” on the subject and provide some suggestions on research methodologies and models. This comprehensive study classifies hybrid models into four types algorithm parameter optimisation-based hybrid models (OBH), pre-processing-based hybrid models (PBH), the components combination-based hybrid models (CBH), and hybridisation of parameter optimisation-based with preprocessing-based hybrid models (HOPH); furthermore, it explains the pre-processing of data in detail. Finally, the most popular optimisation methods and future perspectives and conclusions have been discussed.
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| 33. |
- Mohammed, Sarah J., et al.
(author)
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Application of Metaheuristic Algorithms and ANN Model for Univariate Water Level Forecasting
- 2023
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In: Advances in Civil Engineering / Hindawi. - : Hindawi Publishing Corporation. - 1687-8086 .- 1687-8094.
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Journal article (peer-reviewed)abstract
- With the rapid development of machine learning (ML) models, the artificial neural network (ANN) is being increasingly applied for forecasting hydrological processes. However, researchers have not treated hybrid ML models in much detail. To address these issues, this study herein suggests a novel methodology to forecast the monthly water level (WL) based on multiple lags of the Tigris River in Al-Kut, Iraq, over ten years. The methodology includes preprocessing data methods, and the ANN model optimises with a marine predator algorithm (MPA). In the optimisation procedure, to decrease uncertainty and expand the predicting range, the slime mould algorithm (SMA-ANN), constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithms (CPSOCGSA-ANN), and particle swarm optimisation (PSO-ANN) are applied to compare and validate the MPA-ANN model performance. Analysis of results revealed that the data pretreatment methods improved the original data quality and selected the ideal predictors’ scenario by singular spectrum analysis and mutual information methods, respectively. For example, the correlation coefficient of the first lag improved from 0.648 to 0.938. Depending on various evaluation metrics, MPA-ANN tends to forecast WL better than SMA-ANN, PSO-ANN, and CPSOCGSA-ANN algorithms with coefficients of determination of 0.94, 0.81, 0.85, and 0.90, respectively. Evidence shows that the proposed methodology yields excellent results, with a scatter index equal to 0.002. The research outcomes represent an additional step towards evolving various hybrid ML techniques, which are valuable to practitioners wishing to forecast WL data and the management of water resources in light of environmental shifts.
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| 34. |
- Mohammed, Sarah J., et al.
(author)
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Hybrid Technique to Improve the River Water Level Forecasting Using Artificial Neural Network-Based Marine Predators Algorithm
- 2022
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In: Advances in Civil Engineering / Hindawi. - : Hindawi Publishing Corporation. - 1687-8086 .- 1687-8094. ; 2022
-
Journal article (peer-reviewed)abstract
- Water level (WL) forecasting has become a difficult undertaking due to spatiotemporal fluctuations in climatic factors and complex physical processes. This paper proposes a novel hybrid machine learning model based on an artificial neural network (ANN) and the Marine Predators algorithm (MPA) for modeling monthly water levels of the Tigris River in Al-Kut, Iraq. Data preprocessing techniques are employed to enhance data quality and determine the optimal input model. Historical data for water level and climatic factors data are utilized from 2011 to 2020 to build and assess the model. MPA-ANN algorithm’s performance is compared with recent constriction coefficient-based particle swarm optimization and chaotic gravitational search algorithm (CPSOCGSA-ANN) and slime mold algorithm (SMA-ANN) to reduce uncertainty and raise the prediction range. The finding demonstrated that singular spectrum analysis is a highly effective method to denoise time series. MPA-ANN outperformed CPSOCGSA-ANN and SMA-ANN algorithms based on different statistical criteria. The suggested novel methodology offers good results with scatter index (SI) = 0.0009 and coefficient of determination (R2 = 0.98).
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| 35. |
- Potapov, Anton M., et al.
(author)
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Global fine-resolution data on springtail abundance and community structure
- 2024
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In: Scientific Data. - : Nature Publishing Group. - 2052-4463. ; 11:1
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Journal article (peer-reviewed)abstract
- Springtails (Collembola) inhabit soils from the Arctic to the Antarctic and comprise an estimated ~32% of all terrestrial arthropods on Earth. Here, we present a global, spatially-explicit database on springtail communities that includes 249,912 occurrences from 44,999 samples and 2,990 sites. These data are mainly raw sample-level records at the species level collected predominantly from private archives of the authors that were quality-controlled and taxonomically-standardised. Despite covering all continents, most of the sample-level data come from the European continent (82.5% of all samples) and represent four habitats: woodlands (57.4%), grasslands (14.0%), agrosystems (13.7%) and scrublands (9.0%). We included sampling by soil layers, and across seasons and years, representing temporal and spatial within-site variation in springtail communities. We also provided data use and sharing guidelines and R code to facilitate the use of the database by other researchers. This data paper describes a static version of the database at the publication date, but the database will be further expanded to include underrepresented regions and linked with trait data.
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| 36. |
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| 37. |
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| 38. |
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| 39. |
- Zubaidi, Salah L., et al.
(author)
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Assessing the Benefits of Nature-Inspired Algorithms for the Parameterization of ANN in the Prediction of Water Demand
- 2023
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In: Journal of water resources planning and management. - : American Society of Civil Engineers (ASCE). - 0733-9496 .- 1943-5452. ; 149:1
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Journal article (peer-reviewed)abstract
- Accurate forecasting techniques for a stochastic pattern of water demand are essential for any city that faces high variability in climate factors and a shortage of water resources. This study was the first research to assess the impact of climatic factors on urban water demand in Iraq, which is one of the hottest countries in the world. We developed a novel forecasting methodology that includes data preprocessing and an artificial neural network (ANN) model, which we integrated with a recent nature-inspired metaheuristic algorithm [marine predators algorithm (MPA)]. The MPA-ANN algorithm was compared with four nature-inspired metaheuristic algorithms. Nine climatic factors were examined with different scenarios to simulate the monthly stochastic urban water demand over 11 years for Baghdad City, Iraq. The results revealed that (1) precipitation, solar radiation, and dew point temperature are the most relevant factors; (2) the ANN model becomes more accurate when it is used in combination with the MPA; and (3) this methodology can accurately forecast water demand considering the variability in climatic factors. These findings are of considerable significance to water utilities in planning, reviewing, and comparing the availability of freshwater resources and increasing water requests (i.e., adaptation variability of climatic factors).
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