Cold goods manufacturers and logistics service providers are two essential groups of players in the goods safety issue in cold chains under the administration or inspection of governments and various stakeholders, including customers and final consumers. In this research, we applied the evolutionary game theory to examine the behavioral strategies of manufacturers and logistics service providers, while we formulated the governments and various other stakeholders’ impacts by contracted subsidy and penalty. First, we developed an evolutionary game theory model of the interaction between manufacturers and logistics service providers. Then, we examined the evolutionary stable strategy (ESS) of the manufacturers and logistics service providers under various constraints. Finally, we used simulation to demonstrate the impact of combinations of various parameters on the ESS and evolutionary paths. The results showed that the behavior strategies of the manufacturers and logistics service providers are interleaved and affected by the parameters in the developed model. We analyzed the ESSs and evolutionary paths by considering profits of the cold goods, the cold chain logistics costs, mainly the additional profits and costs of sharing information, and the subsidy and penalty regulated by contracts and governments. By tuning the parameters for numerical studies, we can find that the subsidy and penalty are essential for the cold chain manufactures and logistics service providers to adopt the information-sharing strategy, while the cost of the strategy and the profit of them constrains the positivity. Although, besides instant costs and profits, the information-sharing strategy can add values to cold chains in the long run, the administrators must consider the two populations of players and advocate them to adopt the information-sharing strategy consistently by using optimal policies.

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In a waist-worn Pedestrian Navigation System (PNS) based on Dead-Reckoning (DR), heading drift caused by Micro-Electro-Mechanical System (MEMS) gyro bias is an essential factor affecting DR accuracy. Considering the characteristics of pedestrian navigation and the poor bias repeatability of MEMS gyros, this paper presents a standing calibration method for MEMS gyro bias. The current gyro biases for each boot can be calibrated accurately in the initial stage before walking. Since the attitude angles calculated by the output data from magnetic sensor and accelerometers do not drift, this paper applies the reverse algorithm of attitude updating to calculate the angular velocities of human motion. Then the gyro biases at each moment can be acquired by subtraction operation between the measured angular velocities from gyros and the calculated angular velocities of human motion. Finally, in order to restrain the random error caused by sensor noise, the calculated biases in the initial stage are smoothed, and consequently the optimal estimate of current gyro biases after each boot can be obtained. Still and dynamic turntable experiments and a walking experiment are performed to compare and analyse the proposed method and the Zero Angular Rate Update (ZARU) method. Experimental results show that the proposed method can also calibrate the gyro bias accurately in the case of body swaying.