| 1. |
- Ademuyiwa, Adesoji O., et al.
(författare)
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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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Ingår i: BMJ Global Health. - : BMJ Publishing Group Ltd. - 2059-7908. ; 1:4
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Tidskriftsartikel (refereegranskat)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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| 2. |
- Thomas, HS, et al.
(författare)
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- 2019
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swepub:Mat__t
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| 3. |
- Micah, Angela E., et al.
(författare)
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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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Ingår i: The Lancet. - : Elsevier. - 0140-6736 .- 1474-547X. ; 398:10308, s. 1317-1343
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Forskningsöversikt (refereegranskat)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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| 4. |
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| 5. |
- Hashim, Ahmed, et al.
(författare)
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Pattern of novel psychoactive substance use among patients presented to the poison control centre of Ain Shams University Hospitals, Egypt : A cross-sectional study
- 2022
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Ingår i: Heliyon. - : Elsevier BV. - 2405-8440. ; 8:8
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Tidskriftsartikel (refereegranskat)abstract
- Background: Novel psychoactive substances (NPSs) are relatively new substances in the illicit drug market, notpreviously listed in the United Nations Office on Drugs and Crime (UNDOC). Strox and Voodoo are consideredsome of the most popular blends of NPS in the Egyptian drug market.Objectives: The current study was conducted to assess NPS's use pattern: Voodoo and Strox among acutelyintoxicated patients presented to the poison control center of Ain Shams University Hospitals (PCC- ASUH).Methods: A single center based cross-sectional study was carried out in the PCC-ASUH among acutely intoxicatedpatients presenting to the emergency department (ED) over four months (from January–April 2019. using apreviously adopted and validated Fahmy and El-Sherbini socioeconomic scale (SES). Data were presented asmean, median and range as appropriate. Both smoking and crowding indexes were calculated and presented aspreviously reported.Results: Fifty-one patients were presented to the ED of PCC-ASUH during the study period. A total of 96.1% (n ¼49) were males. The mean age was 25 7.5 years. The most common NPS used was Strox: 54.9% (n ¼ 28),followed by Voodoo: 27.4% (n ¼ 14). Neurological and gastrointestinal (GI) symptoms were the most frequentpresentations. The most common motive behind NPS use was the desire to give a trial of new psychoactivesubstances. The mean SES score was 35.1 13.17. Most patients have the preparatory as the highest education36.0% (n ¼ 18).Conclusions: NPS use is common among young males in preparatory education from different social classes,starting it most commonly as a means to experiencing a new high. Neurological and GI manifestations are themost common presenting symptoms of NPS intoxication.
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| 6. |
- Abdel-Hameed, Amal Mohamed, et al.
(författare)
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Estimation of Potato Water Footprint Using Machine Learning Algorithm Models in Arid Regions
- 2024
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Ingår i: Potato Research. - : Springer Nature. - 0014-3065 .- 1871-4528.
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Tidskriftsartikel (refereegranskat)abstract
- Precise assessment of water footprint to improve the water consumption and crop yield for irrigated agricultural efficiency is required in order to achieve water management sustainability. Although Penman-Monteith is more successful than other methods and it is the most frequently used technique to calculate water footprint, however, it requires a significant number of meteorological parameters at different spatio-temporal scales, which are sometimes inaccessible in many of the developing countries such as Egypt. Machine learning models are widely used to represent complicated phenomena because of their high performance in the non-linear relations of inputs and outputs. Therefore, the objectives of this research were to (1) develop and compare four machine learning models: support vector regression (SVR), random forest (RF), extreme gradient boost (XGB), and artificial neural network (ANN) over three potato governorates (Al-Gharbia, Al-Dakahlia, and Al-Beheira) in the Nile Delta of Egypt and (2) select the best model in the best combination of climate input variables. The available variables used for this study were maximum temperature (Tmax), minimum temperature (Tmin), average temperature (Tave), wind speed (WS), relative humidity (RH), precipitation (P), vapor pressure deficit (VPD), solar radiation (SR), sown area (SA), and crop coefficient (Kc) to predict the potato blue water footprint (BWF) during 1990–2016. Six scenarios (Sc1–Sc6) of input variables were used to test the weight of each variable in four applied models. The results demonstrated that Sc5 with the XGB and ANN model gave the most promising results to predict BWF in this arid region based on vapor pressure deficit, precipitation, solar radiation, crop coefficient data, followed by Sc1. The created models produced comparatively superior outcomes and can contribute to the decision-making process for water management and development planners.
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| 7. |
- Alsafadi, Karam, et al.
(författare)
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An evapotranspiration deficit-based drought index to detect variability of terrestrial carbon productivity in the Middle East
- 2022
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Ingår i: Environmental Research Letters. - UK : Institute of Physics Publishing (IOPP). - 1748-9326. ; 17:1
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Tidskriftsartikel (refereegranskat)abstract
- The primary driver of the land carbon sink is gross primary productivity (GPP), the gross absorption of carbon dioxide (CO2) by plant photosynthesis, which currently accounts for about one-quarter of anthropogenic CO2 emissions per year. This study aimed to detect the variability of carbon productivity using the standardized evapotranspiration deficit index (SEDI). Sixteen countries in the Middle East (ME) were selected to investigate drought. To this end, the yearly GPP dataset for the study area, spanning the 35 years (1982–2017) was used. Additionally, the Global Land Evaporation Amsterdam Model (GLEAM, version 3.3a), which estimates the various components of terrestrial evapotranspiration (annual actual and potential evaporation), was used for the same period. The main findings indicated that productivity in croplands and grasslands was more sensitive to the SEDI in Syria, Iraq, and Turkey by 34%, 30.5%, and 29.6% of cropland area respectively, and 25%, 31.5%, and 30.5% of grass land area. A significant positive correlation against the long-term data of the SEDI was recorded. Notably, the GPP recorded a decline of >60% during the 2008 extreme drought in the north of Iraq and the northeast of Syria, which concentrated within the agrarian ecosystem and reached a total vegetation deficit with 100% negative anomalies. The reductions of the annual GPP and anomalies from 2009 to 2012 might have resulted from the decrease in the annual SEDI at the peak 2008 extreme drought event. Ultimately, this led to a long delay in restoring the ecosystem in terms of its vegetation cover. Thus, the proposed study reported that the SEDI is more capable of capturing the GPP variability and closely linked to drought than commonly used indices. Therefore, understanding the response of ecosystem productivity to drought can facilitate the simulation of ecosystem changes under climate change projections.
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| 8. |
- Ba, Moussa Hamath, et al.
(författare)
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Mapping mafic dyke swarms, structural features, and hydrothermal alteration zones in Atar, Ahmeyim and Chami areas (Reguibat Shield, Northern Mauritania) using high-resolution aeromagnetic and gamma-ray spectrometry data
- 2020
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Ingår i: Journal of African Earth Sciences. - : Elsevier BV. - 1464-343X. ; 163
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Tidskriftsartikel (refereegranskat)abstract
- Analysis of an airborne geophysical data covering the Tasiast-Tijirit Terrane in the western part of the Reguibat Shield (including the 1:200,000 geological sheets of Chami, Ahmeyim and Atar), provided an improved mapping of mafic dyke swarms, structural features, and hydrothermal alteration zones. It also extended the mapping into extensive areas covered by sand. A low-altitude (100 m) airborne survey collected high-resolution magnetic and gamma-ray spectrometry data. The magnetic data were enhanced using techniques such as reduction to the pole, analytic signal and first vertical derivative, and revealed dyke swarms with trends of NNE-SSW to NE-SW, NW-SE to WNW-ESE and E-W. The use of the Euler deconvolution method provided constraints on the continuity and the depth of magnetic sources. Gamma-ray spectrometry which maps the three main radioactive elements, i.e. potassium, uranium and thorium helped discriminate lithological units of the Archean basement, notably felsic intrusions. The radiometric data also helped delineate potassic alteration zones, which could testify to hydrothermal activities of relevance to sulfide mineralisation.
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| 9. |
- Bouras, Rabah, et al.
(författare)
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Investigation of Thermoluminescence Properties of Li2B4O7 : Ag, Cu, Ni Nano-Composites
- 2023
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Ingår i: Journal of Cluster Science. - : Springer Science and Business Media LLC. - 1040-7278 .- 1572-8862. ; 34:5, s. 2347-2359
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Tidskriftsartikel (refereegranskat)abstract
- The present paper explores the TL characteristics of Ag, Cu and Ni doped and co-doped Li2B4O7 nano-composites (NCs). These NCs were synthesized via a Sol–gel process. The influence of doping agents on the optical and TL properties of Li2B4O7 NCs was investigated. Obtained nano-composites were calcined at 700 °C for 8 h. The incorporation of Ag, Cu and Ni impurities in Li2B4O7 lattice has been confirmed by the FT-IR spectra; this may be explained by the formation of Ag–O (513 cm−1), Cu–O (497 cm−1) and Ni–O (419 cm−1) bonds. XRD diffractograms have shown the only crystallographic phases tetragonal. The crystallite size was found to be in the range from 355 to 463 Å. The set of kinetic parameters have been calculated. TL glow curves showed a multiple TL peaks after various beta irradiation. The addition of Ag, Cu and Ni ions induced changes in the structure and the kinetic properties of the TL glow curves, modifying the radiative recombination efficiency. TL response results suggest that the Ag, Cu, Ni co-doped Li2B4O7 nano-composites phosphor present a good potential for beta irradiation dosimeter applications.
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| 10. |
- Ellabban, Mohamed A., et al.
(författare)
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Virtual planning of the anterolateral thigh free flap for heel reconstruction
- 2022
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Ingår i: Microsurgery. - : WILEY. - 0738-1085 .- 1098-2752. ; 42:5, s. 460-469
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Tidskriftsartikel (refereegranskat)abstract
- Purpose three dimensional (3-D) virtual planning is an example of computer assisted surgery that improved management of composite tissue defects. However, converting the 3-D construct into two dimensional format is challenging. The purpose of this study was to assess 3-D virtual planning of complex heel defects for better optimized reconstruction. Patients and methods a prospective analysis of 10 patients [9 male and 1 female; mean age = 27.9 years] with post-traumatic heel defects was performed. Heel defects comprised types II (three patients) or III (seven patients) according to Hidalgo and Shaw and were managed using anterolateral thigh (ALT) free flap adopting 3-D virtual planning of the actual defect which was converted into a silicone two dimensional mold. The mean definitive size of the defects was 63.4 cm(3). Functional, aesthetic, and sensory evaluations of both donor and recipient sites were performed 1 year after surgery. Results Six patients received thinned ALT (mean size = 139 cm(3)) while four patients received musculofasciocutaneous ALT flap (mean size = 199 cm(3)). One flap exhibited partial skin flap necrosis. Another flap was salvaged after re-exploration secondary to venous congestion. The mean follow-up was 20.2 months. The Maryland foot score showed 4 excellent, 5 good, and 1 fair cases. The mean American Orthopedic Foot and Ankle hind foot scoring was 76.3 (range: 69-86). All patients regained their walking capability. Conclusions 3-D virtual planning of complex heel defects facilitates covering non-elliptical defects while harvesting a conventional elliptical flap with providing satisfactory functional outcomes and near-normal contour, volume, and sensibility.
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| 11. |
- Khalifa, Shaden A. M., et al.
(författare)
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Overview of Bee Pollination and Its Economic Value for Crop Production
- 2021
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Ingår i: Insects. - : MDPI AG. - 2075-4450. ; 12:8
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Forskningsöversikt (refereegranskat)abstract
- Pollination plays a significant role in the agriculture sector and serves as a basic pillar for crop production. Plants depend on vectors to move pollen, which can include water, wind, and animal pollinators like bats, moths, hoverflies, birds, bees, butterflies, wasps, thrips, and beetles. Cultivated plants are typically pollinated by animals. Animal-based pollination contributes to 30% of global food production, and bee-pollinated crops contribute to approximately one-third of the total human dietary supply. Bees are considered significant pollinators due to their effectiveness and wide availability. Bee pollination provides excellent value to crop quality and quantity, improving global economic and dietary outcomes. This review highlights the role played by bee pollination, which influences the economy, and enlists the different types of bees and other insects associated with pollination.
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| 12. |
- Mokhtar, Ali, et al.
(författare)
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Estimation of SPEI Meteorological Drought using Machine Learning Algorithms
- 2021
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Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 9, s. 65503-65523
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Tidskriftsartikel (refereegranskat)abstract
- Accurate estimation of drought events is vital for the mitigation of their adverse consequences on water resources, agriculture and ecosystems. Machine learning algorithms are promising methods for drought prediction as they require less time, minimal inputs, and are relatively less complex than dynamic or physical models. In this study, a combination of machine learning with the Standardized Precipitation Evapotranspiration Index (SPEI) is proposed for analysis of drought within a representative case study in the Tibetan Plateau, China, for the period of 1980-2019. Two timescales of 3 months (SPEI-3) and 6 months (SPEI-6) aggregation were considered. Four machine learning models of Random Forest (RF), the Extreme Gradient Boost (XGB), the Convolutional neural network (CNN) and the Long-term short memory (LSTM) were developed for the estimation of the SPEIs. Seven scenarios of various combinations of climate variables as input were adopted to build the models. The best models were XGB with scenario 5 (precipitation, average temperature, minimum temperature, maximum temperature, wind speed and relative humidity) and RF with scenario 6 (precipitation, average temperature, minimum temperature, maximum temperature, wind speed, relative humidity and sunshine) for estimating SPEI-3. LSTM with scenario 4 (precipitation, average temperature, minimum temperature, maximum temperature, wind speed) was relatively better for SPEI-6 estimation. The best model for SPEI-6 was XGB with scenario 5 and RF with scenario 7 (all input climate variables, i.e., scenario 6 + solar radiation). Based on the NSE index, the performances of XGB and RF models are classified as good fits for scenarios 4 to 7 for both timescales. The developed models produced satisfactory results and they could be used as a rapid tool for decision making by water-managers.
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| 13. |
- Mokhtar, Ali, et al.
(författare)
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Prediction of irrigation water quality indices based on machine learning and regression models
- 2022
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Ingår i: Applied water science. - : Springer. - 2190-5487 .- 2190-5495. ; 12
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Tidskriftsartikel (refereegranskat)abstract
- Assessing irrigation water quality is one of the most critical challenges in improving water resource management strategies. The objective of this work was to predict the irrigation water quality index of the Bahr El-Baqr, Egypt, based on non-expensive approaches that requires simple parameters. To achieve this goal, three artificial intelligence (AI) models (Support vector machine, SVM; extreme gradient boosting, XGB; Random Forest, RF) and four multiple regression models (Stepwise Regression, SW; Principal Components Regression, PCR; Partial least squares regression, PLS; Ordinary least squares regression, OLS) were applied and validated for predicting six irrigation water quality criteria (soluble sodium percentage, SSP; sodium adsorption ratio, SAR; residual sodium carbonate, RSC; potential of salinity, PS; permeability index, PI; Kelly’s ratio, KR). Electrical conductivity (EC), sodium (Na+), calcium (Ca2+) and bicarbonate (HCO3−) were used as input exploratory variables for the models. The results indicated the water source is not suitable for irrigation without treatment. A good soil drainage system and salinity control measures are required to avoid salt accumulation within the soil. Based on the performance statistics of the root mean square error (RMSE) and the scatter index (SI), SW emerged as the best (0.21% and 0.03%) followed by PCR and PLS with RMSE 0.22% and 0.21% for SAR, respectively. Based on the classification of the SI, all models applied having values less than 0.1 indicate good prediction performance for all the indices except RSC. These results highlight potential of using multiple regressions and the developed machine learning methods in predicting the index of irrigation water quality, and can be rapid decision tools for modelling irrigation water quality.
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| 14. |
- 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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| 15. |
- Yaqoob, Ibrar, et al.
(författare)
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Big data : From beginning to future
- 2016
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Ingår i: International Journal of Information Management. - : Elsevier BV. - 0268-4012 .- 1873-4707. ; 36:6B, s. 1231-1247
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Tidskriftsartikel (refereegranskat)abstract
- Big data is a potential research area receiving considerable attention from academia and IT communities. In the digital world, the amounts of data generated and stored have expanded within a short period of time. Consequently, this fast growing rate of data has created many challenges. In this paper, we use structuralism and functionalism paradigms to analyze the origins of big data applications and its current trends. This paper presents a comprehensive discussion on state-of-the-art big data technologies based on batch and stream data processing. Moreover, strengths and weaknesses of these technologies are analyzed. This study also discusses big data analytics techniques, processing methods, some reported case studies from different vendors, several open research challenges, and the opportunities brought about by big data. The similarities and differences of these techniques and technologies based on important parameters are also investigated. Emerging technologies are recommended as a solution for big data problems.
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