| 1. |
- Sumaila, U. Rashid, et al.
(författare)
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WTO must ban harmful fisheries subsidies
- 2021
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Ingår i: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 374:6567, s. 544-544
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 2. |
- Ethaib, Saleem, et al.
(författare)
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Function of Nanomaterials in Removing Heavy Metals for Water and Wastewater Remediation: A Review
- 2022
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Ingår i: Environments. - : MDPI. - 2076-3298. ; 9:10
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Forskningsöversikt (refereegranskat)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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| 3. |
- Mohammed, Sarah J., et al.
(författare)
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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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Ingår i: Cogent Engineering. - : Taylor & Francis Group. - 2331-1916. ; 9:1
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Forskningsöversikt (refereegranskat)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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| 4. |
- Mohammed, Sarah J., et al.
(författare)
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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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Ingår i: Advances in Civil Engineering / Hindawi. - : Hindawi Publishing Corporation. - 1687-8086 .- 1687-8094. ; 2022
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Tidskriftsartikel (refereegranskat)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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| 5. |
- Sundar Tikmani, Shiyam, et al.
(författare)
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Diagnostic accuracy of foot length measurement for identification of preterm newborn in rural Sindh, Pakistan
- 2024
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Ingår i: BMJ Paediatrics Open. - : BMJ Publishing Group Ltd. - 2399-9772. ; 8:1
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Tidskriftsartikel (refereegranskat)abstract
- Introduction Assessing gestational age accurately is crucial for saving preterm newborns. In low and middle-income countries, such as Pakistan, where access to antenatal ultrasonography (A-USG) is limited, alternative methods are needed. This study evaluated the diagnostic accuracy of foot length (FL) measurement for identifying preterm newborns in rural Pakistan using A-USG as the reference standard.Methods A test validation study was conducted between January and June 2023 in rural Sindh, Pakistan, within the catchment area of the Global Network for Maternal Newborn Health Registry, Thatta. Singleton newborns whose mothers had an A-USG before 20 weeks of gestation were enrolled. A research assistant measured FL three times using a rigid transparent plastic ruler within 48 hours of birth and the average FL was reported. Sensitivity, specificity, positive and negative predictive values (PPV, NPV) and likelihood ratios were calculated. The optimal FL cut-off for the identification of preterm newborns was determined using the Youden Index.Results A total of 336 newborns were included in the final analysis, of whom 75 (22.3%) were born before 37 weeks of gestation. The median gestational age of the newborns was 38.2 weeks, and the median FL was 7.9 cm. The area under the curve was 97.6%. The optimal FL cut-off for identifying preterm newborns was considered as ≤7.6 cm with a sensitivity of 90.8%, specificity of 96.0%, PPV of 86.7% and NPV of 97.3%. A lower cut-off of ≤7.5 cm had a sensitivity of 95.4%, specificity of 84.0%, PPV of 63.1% and NPV of 98.5%.Conclusion In conclusion, this study highlights the utility of FL measurement for identifying preterm newborns in rural settings where A-USG is unavailable before 20 weeks of gestation. Optimal cut-offs of ≤7.6 and ≤7.5 cm provide a simple, cost-effective and reliable tool for clinicians and frontline healthcare providers in rural areas, respectively.
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| 6. |
- Sunder Tikmani, Shiyam, et al.
(författare)
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Exploring gestational age, and birth weight assessment in Thatta district, Sindh, Pakistan : Healthcare providers' knowledge, practices, perceived barriers, and the potential of a mobile app for identifying preterm and low birth weight
- 2024
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 19:4
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Tidskriftsartikel (refereegranskat)abstract
- IntroductionReliable methods for identifying prematurity and low birth weight (LBW) are crucial to ending preventable deaths in newborns. This study explored healthcare providers’ (HCPs) knowledge, practice, perceived barriers in assessing gestational age and birth weight and their referral methods for preterm and LBW infants. The study additionally assessed the potential of using a mobile app for the identification and referral decision of preterm and LBW.MethodsThis qualitative descriptive study was conducted in Thatta District, Sindh, Pakistan. Participants, including doctors, nurses, lady health visitors, and midwives, were purposefully selected from a district headquarter hospital, and private providers in the catchment area of Global Network’s Maternal and Newborn Health Registry (MNHR). Interviews were conducted using an interview guide after obtaining written informed consent. Audio recordings of the interviews were transcribed and analyzed using NVIVO® software with an inductive approach.ResultsThe HCPs had extensive knowledge about antenatal and postnatal methods for assessing gestational age. They expressed a preference for antenatal ultrasound due to the perceived accuracy, though accept practical barriers including workload, machine malfunctions, and cost. Postnatal assessment using the Ballard score was only undertaken sparingly due to insufficient training and subjectivity. All HCPs preferred electronic weighing scales for birth weight Barriers encountered included weighing scale calibration and battery issues. There was variation in the definition of prematurity and LBW, leading to delays in referral. Limited resources, inadequate education, and negative parent past experiences were barriers to referral. Foot length measurements were not currently being used. While mobile apps are felt to have potential, unreliable electricity supply and internet connectivity are barriers.ConclusionThe HCPs in this study were knowledgeable in terms of potential tools, but acknowledged the logistical and parental barriers to implementation.
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| 7. |
- Tabiri, S, et al.
(författare)
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- 2021
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swepub:Mat__t
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