Cost-Effectiveness of Public Health Measures to Control COVID-19 in China: A Microsimulation Modeling Study

This study aimed to assess the cost-effectiveness of various public health measures in dealing with coronavirus disease 2019 (COVID-19) in China. A stochastic agent-based model was used to simulate the progress of the COVID-19 outbreak in scenario I (imported one case) and scenario II (imported four cases) with a series of public health measures. The main outcomes included the avoided infections and incremental cost-effectiveness ratios (ICERs). Sensitivity analyses were performed to assess uncertainty. The results indicated that isolation-and-quarantine averted the COVID-19 outbreak at the lowest ICERs. The joint strategy of personal protection and isolation-and-quarantine averted one more case than only isolation-and-quarantine with additional costs. The effectiveness of isolation-and-quarantine decreased with lowering quarantine probability and increasing delay time. The strategy that included community containment would be cost-effective when the number of imported cases was >65, or the delay time of the quarantine was more than 5 days, or the quarantine probability was below 25%, based on current assumptions. In conclusion, isolation-and-quarantine was the most cost-effective intervention. However, personal protection combined with isolation-and-quarantine was the optimal strategy for averting more cases. The community containment could be more cost-effective as the efficiency of isolation-and-quarantine drops and the imported cases increases.

Jamming Strategy Optimization through Dual Q-Learning Model against Adaptive Radar

Modern adaptive radars can switch work modes to perform various missions and simultaneously use pulse parameter agility in each mode to improve survivability, which leads to a multiplicative increase in the decision-making complexity and declining performance of the existing jamming methods. In this paper, a two-level jamming decision-making framework is developed, based on which a dual Q-learning (DQL) model is proposed to optimize the jamming strategy and a dynamic method for jamming effectiveness evaluation is designed to update the model. Specifically, the jamming procedure is modeled as a finite Markov decision process. On this basis, the high-dimensional jamming action space is disassembled into two low-dimensional subspaces containing jamming mode and pulse parameters respectively, then two specialized Q-learning models with interaction are built to obtain the optimal solution. Moreover, the jamming effectiveness is evaluated through indicator vector distance measuring to acquire the feedback for the DQL model, where indicators are dynamically weighted to adapt to the environment. The experiments demonstrate the advantage of the proposed method in learning radar joint strategy of mode switching and parameter agility, shown as improving the average jamming-to-signal radio (JSR) by 4.05% while reducing the convergence time by 34.94% compared with the normal Q-learning method.

An Optimized System to Reduce Procurement Risks and Stock-Outs: A Simulation Case Study for a Component Manufacturer

In the current global system; supply chains are at risk due to increasing procurement shortages, supply disruptions, and the reliability of on-time deliveries with the original order quantities. As a result, an anticipated management model is of vital importance to provide companies with the productive flexibility necessary to adapt quickly to supply changes, in order to ensure the quality and delivery time through efficient management of stocks and supply costs. In this context, this research aims to develop a system to complement classical procurement planning based on inventory management methods and MRP (material requirements planning) systems by considering suppliers’ behavior regarding procurement risks. For this purpose, a system is developed that seeks to simulate the impacts of procurement shortages of different natures. Moreover, the research investigates the development of a system that performs procurement planning of a component manufacturer to determine the supply orders necessary to meet the master production schedule. The system is analyzed based on a set of indicators in the event that the supplier of a material needed for production does not supply on time or has short-term problems. Several scenarios are simulated, and the results are quantified by changing the procurement order quantities, which may or may not follow the economic order quantity (EOQ) model, and the potential procurement disruptions or shortages. The results show how the simulation and anticipation of potential suppliers’ procurement behavior concerning potential shortages and their probability are key for successful procurement within a joint strategy with classical procurement methods.

A Joint Model Provisioning and Request Dispatch Solution for Low-Latency Inference Services on Edge

With the advancement of machine learning, a growing number of mobile users rely on machine learning inference for making time-sensitive and safety-critical decisions. Therefore, the demand for high-quality and low-latency inference services at the network edge has become the key to modern intelligent society. This paper proposes a novel solution that jointly provisions machine learning models and dispatches inference requests to reduce inference latency on edge nodes. Existing solutions either direct inference requests to the nearest edge node to save network latency or balance edge nodes’ workload by reducing queuing and computing time. The proposed solution provisions each edge node with the optimal number and type of inference instances under a holistic consideration of networking, computing, and memory resources. Mobile users can thus be directed to utilize inference services on the edge nodes that offer minimal serving latency. The proposed solution has been implemented using TensorFlow Serving and Kubernetes on an edge cluster. Through simulation and testbed experiments under various system settings, the evaluation results showed that the joint strategy could consistently achieve lower latency than simply searching for the best edge node to serve inference requests.

COVID-19 and heterogeneous vulnerabilities in the Peruvian labor market: implications for social inequalities and for gender gaps

Using quarterly data from the 2020 Peruvian National Household Survey (ENAHO), this paper estimates the differentiated impacts of the COVID-19 pandemic on a set of labor market indicators, such as labor participation, occupational categories, informality, and number of hours worked. The impacts are calculated from an individual perspective (effects on the activities of the heads of household and their spouses, distinguishing them according to sex) and also from a joint strategy perspective among the partners. The results indicate that the intersectionalities of vulnerability considered (rural/urban area, and those contained in the type of households and in the situation of single-parenting or two-parenting of household heads and their spouses) determine that women, who live in rural areas, have children and do not have a partner were the most affected by the global health crisis.

Joint decision of condition-based maintenance and production scheduling for multi-component systems

In this study, system maintenance and production scheduling are jointly decided to solve the problems of resource idleness and time cost increase due to system maintenance in the processing of production scheduling. Based on the previous research on the integration of scheduling and maintenance of single machine and multi machine, considering the deterioration and economic dependence of each component in the system, a joint strategy of multi component condition-based maintenance combined with production scheduling is formulated. All the maintenance combinations and probability calculation formulas in the production scheduling process are developed using the deterioration state space partition method, and the probability density function and its numerical solution method are derived. A joint decision model is established to minimize the total weighted expected completion time. Finally, taking the KS5 adjustable multi axis tapping machine as an example, numerical experiments were conducted to verify the correctness of the proposed strategy and the established model.

Implementasi ASEAN Tourism Strategic Plan (ATSP) dalam Pengembangan Pariwisata Sumatra Barat

Tourism in the ASEAN context is considered substantial in supporting the economic growth acceleration in each ASEAN country. Therefore, ASEAN has formed a joint strategy to support tourism development in each ASEAN member country through the formation of the ATSP (ASEAN Tourism Strategic Plan). Indonesia is one of the ASEAN member countries also has focus on the tourism sector. One of the provinces with good tourism opportunities in Indonesia is West Sumatra. This study will analyze how West Sumatra maximizes tourism potential through the implementation of ATSP. The method chosen to explain and analyze the problem in this research is a qualitative method with a descriptive-analytic type of research. Through a qualitative approach allows researchers to be able to produce a detailed description of the policies taken by the Government of West Sumatra within the framework of the ATSP for regional tourism development. The findings of this study indicate that the government of West Sumatra has not yet maximized the potential of the region in the tourism sector and has not implemented ATSP thoroughly.