| 1. |
- Micah, Angela E., et al.
(author)
-
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
-
In: The Lancet. - : Elsevier. - 0140-6736 .- 1474-547X. ; 398:10308, s. 1317-1343
-
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.
|
|
| 2. |
|
|
| 3. |
- Minhaj, Syed Usama, et al.
(author)
-
How SIC-enabled LoRa Fares under Imperfect Orthogonality?
- 2021
-
In: IWCMC 2021. - : IEEE. - 9781728186160 ; , s. 729-734
-
Conference paper (peer-reviewed)abstract
- With the increase of connected Internet-of-things (IoT) devices, the need for low-power wide-area networks (LP-WANs) is imminent, and LoRaWAN is one such technology that offers an elegant solution to the problem of long-range communication and battery consumption. A parameter of special interest in LoRaWAN is the spreading factor (SF), and it is often assumed that communication between different SFs is independent of each other. However, this claim has been practically debunked by many works, proving that SFs have imperfect orthogonality. To maximize connectivity and throughput, several techniques have been introduced, such as non-orthogonal-multiple-access (NOMA) and dynamic resource allocation. NOMA is getting a lot of attention recently, especially for IoT networks, because it embraces interference and tries to obtain desired information packets from corrupted ones. Furthermore, NOMA can be easily implemented on the base-station side by using the principle of successive interference cancellation (SIC). In this paper, we investigate how SIC, under the assumption of imperfect orthogonality of SF channels, can be used to increase the performance of the system. We find the expressions for success and coverage probability considering various SF allocation schemes and found the most efficient scheme for different scenarios.
|
|
| 4. |
- Minhaj, Syed Usama, et al.
(author)
-
Intelligent Resource Allocation in LoRaWAN Using Machine Learning Techniques
- 2023
-
In: IEEE Access. - 2169-3536. ; 11, s. 10092-10106
-
Journal article (peer-reviewed)abstract
- With the ubiquitous growth of Internet-of-things (IoT) devices, current low-power wide-area network (LPWAN) technologies will inevitably face performance degradation due to congestion and interference. The rule-based approaches to assign and adapt the device parameters are insufficient in dynamic massive IoT scenarios. For example, the adaptive data rate (ADR) algorithm in LoRaWAN has been proven inefficient and outdated for large-scale IoT networks. Meanwhile, new solutions involving machine learning (ML) and reinforcement learning (RL) techniques are shown to be very effective in solving resource allocation in dense IoT networks. In this article, we propose a new concept of using two independent learning approaches for allocating spreading factor (SF) and transmission power to the devices using a combination of a decentralized and centralized approach. SF is allocated to the devices using RL for contextual bandit problem, while transmission power is assigned centrally by treating it as a supervised ML problem. We compare our approach with existing state-of-the-art algorithms, showing a significant improvement in both network level goodput and energy consumption, especially for large and highly congested networks.
|
|
| 5. |
|
|
| 6. |
- Kazmi, Bilal, et al.
(author)
-
Thermodynamic and economic assessment of cyano functionalized anion based ionic liquid for CO2 removal from natural gas integrated with, single mixed refrigerant liquefaction process for clean energy
- 2022
-
In: Energy. - : Pergamon Press. - 0360-5442 .- 1873-6785. ; 239
-
Journal article (peer-reviewed)abstract
- The study proposes a novel integrated process in which ionic liquid is utilized to control carbon dioxide (CO2) emissions from the natural gas combined with a single mixed refrigerant-based liquefaction process to assist safe transportation over long distances providing a sustainable and cleaner energy. Commercially amines are utilized for CO2 sequestration, but amines entail energy-intensive regeneration with elevated process costs. The present study offers a solvent screening mechanism based on important parameters such as heat of dissolution, viscosity, selectivity, working capacity, vapor pressure, corrosivity, and toxicity. The selected solvents' performance is computed by sensitivity analysis suggesting imidazolium-based cation 1-hexyl-3-methylimidazolium[Hmim] functionalized with tricyanomethanide(tcm) as anion a potential natural gas sweetening solvent in comparison with commercially used solvent monoethanoloamine(MEA), conventional ILs 1-butyl-3-methylimidazolium hexa-fluorophosphate [Bmim][Pf(6)] and 1-butyl-3-methylimidazolium methyl sulfate [Bmim][MeSO4]. The obtained sweet gas is liquefied using a single mixed refrigerant-based process providing 0.99 mol fraction of liquefied CH4 with less overall specific compression power requirement of 0.41 kW/kg of natural gas. Moreover, an exergy analysis demonstrates that the [Hmim][tcm] based process has lower total exergy destruction of 7.49 x 10(3) kW and is found to utilize less overall specific energy consumption 0.49 kWh/kg of NG in contrast to other studied solvents. Furthermore, a detailed economic analysis establishes [Hmim][tcm]-based CO2 integrated with liquefaction technology offers 50.7%, 74.4%, and 85.8% of total annualized cost (TAC) savings compared with the MEA-amim][Pf(6)]-, and [Bmim][MeSO4], respectively. Hence, [Hmim][tcm] for CO2 removal and integration with liquefaction process will incur unit cost based on the total annualized cost to be $2.2 x 10(4)/kmol of purified NG.
|
|
| 7. |
|
|
