Implementing standard antenatal care interventions: health system cost at primary health facilities in Tanzania

Background Since 2002, Tanzania has been implementing the focused Antenatal Care (ANC) model that recommended four antenatal care visits. In 2016, the World Health Organization (WHO) reintroduced the standard ANC model with more interventions including a minimum of eight contacts. However, cost-implications of these changes to the health system are unknown, particularly in countries like Tanzania, that failed to optimally implement the simpler focused ANC model. We compared the health system cost of providing ANC under the focused and the standard models at primary health facilities in Tanzania. Methods We used a micro-costing approach to identify and quantify resources used to implement the focused ANC model at six primary health facilities in Tanzania from July 2018 to June 2019. We also used the standard ANC implementation manual to identify and quantify additional resources required. We used basic salary and allowances to value personnel time while the Medical Store Department price catalogue and local market prices were used for other resources. Costs were collected in Tanzanian shillings and converted to 2018 US$. Results The health system cost of providing ANC services at six facilities (2 health centres and 4 dispensaries) was US$185,282 under the focused model. We estimated that the cost would increase by about 90% at health centres and 97% at dispensaries to US$358,290 by introducing the standard model. Personnel cost accounted for more than one third of the total cost, and more than two additional nurses are required per facility for the standard model. The costs per pregnancy increased from about US$33 to US$63 at health centres and from about US$37 to US$72 at dispensaries. Conclusion Introduction of a standard ANC model at primary health facilities in Tanzania may double resources requirement compared to current practice. Resources availability has been one of the challenges to effective implementation of the current focused ANC model. More research is required, to consider whether the additional costs are reasonable compared to the additional value for maternal and child health.

Deep Reinforcement Learning for Collaborative Computation Offloading on Internet of Vehicles

With the increase of Internet of vehicles (IoVs) traffic, the contradiction between a large number of computing tasks and limited computing resources has become increasingly prominent. Although many existing studies have been proposed to solve this problem, their main consideration is to achieve different optimization goals in the case of edge offloading in static scenarios. Since realistic scenarios are complicated and generally time-varying, these studies in static scenes are imperfect. In this paper, we consider a collaborative computation offloading in a time-varying edge-cloud network, and we formulate an optimization problem with considering both delay constraints and resource constraints, aiming to minimize the long-term system cost. Since the set of feasible solutions to the problem is nonconvex, and the complexity of the problem is very large, we propose a Q-learning-based approach to solve the optimization problem. In addition, due to the dimensional catastrophes, we further propose a DQN-based approach to solve the optimization problem. Finally, by comparing our two proposed algorithms with typical algorithms, the simulation results show that our proposed approaches achieve better performance. Under the same conditions, by comparing our two proposed algorithms with typical algorithms, the simulation results show that our proposed Q-learning-based method reduces the system cost by about 49% and 42% compared to typical algorithms. And in the same case, compared with the classical two schemes, our proposed DQN-based scheme reduces the system cost by 62% and 57%.

Joint Load Balancing and Offloading Optimization in Multiple Parked Vehicle-Assisted Edge Computing

The introduction of mobile edge computing (MEC) in vehicular network has been a promising paradigm to improve vehicular services by offloading computation-intensive tasks to the MEC server. To avoid the overload phenomenon in MEC server, the vast idle resources of parked vehicles can be utilized to effectively relieve the computational burden on the server. Furthermore, unbalanced load allocation may cause larger latency and energy consumption. To solve the problem, the reported works preferred to allocate workload between MEC server and single parked vehicle. In this paper, a multiple parked vehicle-assisted edge computing (MPVEC) paradigm is first introduced. A joint load balancing and offloading optimization problem is formulated to minimize the system cost under delay constraint. In order to accomplish the offloading tasks, a multiple offloading node selection algorithm is proposed to select several appropriate PVs to collaborate with the MEC server in computing tasks. Furthermore, a workload allocation strategy based on dynamic game is presented to optimize the system performance with jointly considering the workload balance among computing nodes. Numerical results indicate that the offloading strategy in MPVEC scheme can significantly reduce the system cost and load balancing of the system can be achieved.

System cost minimisation in hybrid energy storage systems connected to microgrids: A comparative approach

Electricity is the most critical facility for humans. All traditional energy supplies are rapidly depleting. As a result, the energy resources are moved from traditional to non-conventional. In this research, mixture of two energy tools, namely wind and solar energy are used. Using a Hybrid Energy Storage System (HESS), continuous power can be provided. Electricity can be produced at a cost that is affordable. The integration of solar and wind in a hybrid system cause an increase in the system’s stability, which is the key benefit of this research. The system’s power transmission efficiency and reliability can be greatly enhanced by integrating these two intermittent sources. When one of the energy source is unavailable or inadequate to meet load demands, the other energy source will supply the power. The major contribution in this research is that, the proposed bidirectional single-inductor multiple-port (BSIMP) converter significantly lowers the component count, smaller circuit size and lower cost, allowing HESS to be integrated into DC microgrid. Minimum number of components are used for the same number of ESs in HESS in the proposed BSIMP converter. The hybridization of battery and supercapacitor (SC) for storage purpose is more cost effective, as compared to the battery energy storage system, thus improving the battery stress and hence used for large scale grid energy storage. SC’s are accepted as backup and found very useful in delivering high power, not possible with batteries. The use of SC in addition to batteries can be one solution for achieving the low life cycle economy. The Single Objective Adaptive Firefly Algorithm (SOAFA) is introduced for optimising the Proportional-Integral (PI) controller parameters. The system cost is reduced by about 32%, with the constraints on wind turbine swept area, PV area, total battery and SC capacity with the proposed optimisation algorithm.