An optimal Islamic investment decision in two-region economy: The case of Indonesia and Malaysia

In this work, the possibility of cross-border activities between two regions in the framework of the investment contract is viewed as optimal allocation problems. The problems of determining the optimal proportion of funds to be invested in liquidity and technology are analyzed in two different environments. In the first case, we consider a two-region and two-technology economy in which both regions possess the same productive technology or project, but a different stream of return. While in the second case, we examine an economy where two regions (i.e., Indonesia and Malaysia) hold different Islamic productive projects with identical returns. Allocation models are formulated in terms of investors’ expected utility maximization problem under budget constraints with respect to regional and sectoral shocks. It is revealed that optimal parameters for liquidity ratio, technological investment profile, and bank repayment are analytically characterized by the return of a more productive project and the proportion of impatient and patient investors in the region. Even though both cases employ different assumptions, they provide the same expressions of optimal parameters. The model suggests that cross-border Islamic investment activities between two regions might be realized, provided both regions hold productive projects with an identical stream of return. This paper also shows that by increasing the lower return of the project approaching the higher return, a room for inter-region investment can be created. An analytical framework of an investment contract in terms of optimal allocation model is provided.

Spectrum Allocation and Device Association in Federated Learning-Enabled Industrial IoT via Hypergraph Matching

In this paper, a joint spectrum allocation and device association problem is investigated for a federated learning aided hierarchical Industrial Internet of Things (IIoT) system for smart factory. To achieve the optimization jointly, we design a weighted learning utility maximization problem, which is a 0-1 integer linear programming problem. To solve this problem, we convert it into a weighted 3D hypergraph model by capturing the 3D mapping relation for IIoT device, subchannel, and edge server. A local search algorithm is then presented to find a 3D hypergraph matching with maximum total weights as the suboptimal solution. Simulation results demonstrate the superior performance of the proposed algorithm compared with the greedy algorithm in the system learning utility.

Spectrum Allocation and Device Association in Federated Learning-Enabled Industrial IoT via Hypergraph Matching

In this paper, a joint spectrum allocation and device association problem is investigated for a federated learning aided hierarchical Industrial Internet of Things (IIoT) system for smart factory. To achieve the optimization jointly, we design a weighted learning utility maximization problem, which is a 0-1 integer linear programming problem. To solve this problem, we convert it into a weighted 3D hypergraph model by capturing the 3D mapping relation for IIoT device, subchannel, and edge server. A local search algorithm is then presented to find a 3D hypergraph matching with maximum total weights as the suboptimal solution. Simulation results demonstrate the superior performance of the proposed algorithm compared with the greedy algorithm in the system learning utility.

Optimum insurance contract of flood damage index insurance for rice farmers in Myanmar

Climate change is an underlying cause of many extreme events such as enormous cyclones and erratic torrential rains. These phenomena threaten farmers in coastal regions of Myanmar. Self-insurance through means such as crop diversification is insufficient for sustainable farm management. Weather index insurance (WII) is receiving much attention because of its low management costs. An optimum WII contract for flood damage using rainfall as the index has been proposed for rice farmers in coastal regions. According to the model, an insurance payment demand function is derived by solving the expected utility maximization problem. The utility function of the contract is specified as constant relative risk aversion type. The production function is specified as Cobb–Douglas type. By substituting real average data of rice production of farmers into this function, the optimum insurance payment and premium are obtained. Changes in insurance compensation by payments according to an increase in the price of rice, rainfall, and degree of risk aversion are investigated. Results suggest that if an average farmer pays around 41.5 US dollars per year, then farm management will be optimally stabilized under the flood risk.

Life Insurance and Subsistence Consumption with an Exponential Utility

In this paper, we derive an explicit solution to the utility maximization problem of an individual with mortality risk and subsistence consumption constraint. We adopt an exponential utility for the individual’s consumption and the martingale and duality method is employed. From the explicit solution, we exhibit how the mortality intensity and subsistence consumption constraint affect, separately and together, portfolio, consumption and life insurance purchase.

Optimal Investment, Consumption and Leisure with an Option to File for Bankruptcy

This paper investigates the optimal personal bankruptcy decision of a debtor who participates in the labor market. This paper is based on a mathematical finance model that assumes a Black-Scholes financial market and describes a decision problem as an expected discounted utility maximization problem. Our optimization problem can be cast into a mixed optimal stopping and control problem, and has a symmetry feature with a voluntary retirement decision problem in characterizing the stopping times. To obtain value function and optimal strategies, we use dynamic programming method and transform the relevant nonlinear Bellman equation into a linear equation. Numerical illustrations from our explicit expressions for the optimal strategies reveal how an opportunity to file for bankruptcy affects debtor’s consumption, leisure, and portfolio decisions.

Dynamic Power Optimization for Secondary Wearable Biosensors in E-Healthcare Leveraging Cognitive WBSNs with Imperfect Spectrum Sensing

<div><div>Abstract The integration of cognitive radio with e-healthcare systems assisted by wireless body sensor networks (WBSNs) has been regarded as an enabling approach for a new generation of pervasive healthcare services, to provide differentiated quality of service requirements and avoid harmful electromagnetic interference to primary medical devices (PMDs) over the crowded radio spectrum. Due to the sharing spectrum bands with PMDs in e-healthcare scenario using cognitive WBSNs (CWBSNs), efficient transmit power control and optimization strategies for resource-constrained secondary wearable biosensors (SWBs) play a key role in controlling the inter-network interference and improving the energy efficiency. This paper investigates the problem of dynamic power optimization for SWBs in e-healthcare leveraging CWBSNs with practical limitations, e.g., imperfect spectrum sensing and quality of physiological data sampling. We develop a distributed optimization framework of dynamic power optimization via the theory of differential game, by jointly considering utility maximization and quality of physiological data sampling for every SWB, while satisfying the evolution law of energy consumption in SWB's battery. With the non-cooperation and cooperation relations for all SWBs in mind, we transform the differential game model into two subproblems, namely, utility maximization problem and total utility maximization problem. Utilizing Bellman's dynamic programming, we derive a non-cooperative optimal solution for power optimization as a Nash equilibrium point for the utility maximization problem posed by competitive scenario. By exploiting Pontryagin's maximum principle, a cooperative optimal solution is obtained for the total utility maximization problem, wherein all SWBs fully cooperate to obtain the highest total utilities. Building upon the analytical results, the actual utility distributed to each SWB is compared between the non-cooperative and cooperative schemes. Extensive simulations show that the proposed optimization framework is indeed an efficient and practical solution for power control compared with the benchmark algorithm.</div> </div>