We study sourcing and pricing decisions of a firm with correlated suppliers and a price‐dependent demand. With two suppliers, the insight—cost is the order qualifier while reliability is the order winner—derived in the literature for the case of exogenously determined price and independent suppliers, continues to hold when the suppliers' capacities are correlated. Moreover, a firm orders only from one supplier if the effective purchase cost from him, which includes the imputed cost of his unreliability, is lower than the wholesale price charged by his rival. Otherwise, the firm orders from both. Furthermore, the firm's diversification decision does not depend on the correlation between the two suppliers' random capacities. However, its order quantities do depend on the capacity correlation, and, if the firm's objective function is unimodal, the total order quantity decreases as the capacity correlation increases in the sense of the supermodular order. With more than two suppliers, the insight no longer holds. That is, when ordering from two or more suppliers, one is the lowest‐cost supplier and the others are not selected on the basis of their costs. We conclude the paper by developing a solution algorithm for the firm's optimal diversification problem.
Botanical air filtration is a promising technology for reducing indoor air contaminants, but the underlying mechanisms need better understanding. Here, we made a set of chamber fumigation experiments of up to 16 weeks of duration, to study the filtration efficiencies for seven volatile organic compounds (VOCs; decane, toluene, 2-ethylhexanol, α-pinene, octane, benzene, and xylene) and to monitor microbial dynamics in simulated green wall systems. Biofiltration functioned on sub-ppm VOC levels without concentration-dependence. Airflow through the growth medium was needed for efficient removal of chemically diverse VOCs, and the use of optimized commercial growth medium further improved the efficiency compared with soil and Leca granules. Experimental green wall simulations using these components were immediately effective, indicating that initial VOC removal was largely abiotic. Golden pothos plants had a small additional positive impact on VOC filtration and bacterial diversity in the green wall system. Proteobacteria dominated the microbiota of rhizosphere and irrigation water. Airborne VOCs shaped the microbial communities, enriching potential VOC-utilizing bacteria (especially Nevskiaceae and Patulibacteraceae) in the irrigation water, where much of the VOC degradation capacity of the biofiltration systems resided. These results clearly show the benefits of active air circulation and optimized growth media in modern green wall systems.
Interferometric Synthetic Aperture Radar (InSAR) is one of the most sufficient technologies to provide global digital elevation modeling (DEM). It unwraps the interferometric phase to provide observations for phase-to-height conversion. However, the phase gradient has great influence on the phase unwrapping quality, thereby affecting the topographic mapping accuracy. Multi-look processing can improve the reliability of phase unwrapping by reducing the noise phase gradient. Nevertheless, it reduces the spatial resolution while increasing the height phase gradient, thus lowering the reliability of height values. In this paper, we propose a multi-look iteration algorithm to suppress the noise and maintain the reliability of height values. First, we use a large number of looks (NL) to suppress the noise phase gradient and obtain a coarse DEM. Then taking this coarse DEM as a reference, we remove the topographic phase from the interferogram with a small NL, which will reduce height phase gradient and ensure the accuracy of phase unwrapping. Finally, we obtain DEM products with high precision and fine resolution. We validate the proposed algorithm using both simulated and real data, and obtain DEM products in a greatly undulated region using TanDEM-X data. Results show that the proposed method is capable of providing DEM with resolution of 4 m and accuracy of 1.73 m.
Precise ionospheric total electron content (TEC) is critical for many aerospace applications, and forecasting ionospheric TEC is of great significance to it. Besides, short-term prediction of TEC values fills the gap between the TEC product latency and the precision. The machine learning-based approaches are promising in solving the non-linear prediction issues, particularly suitable for short-term GPS TEC forecasting due to its complex temporal and spatial variation. In this paper, four different machine learning models, i.e., Artificial Neural Network (ANN), Long Short-Term Memory (LSTM) networks, Adaptive Neuro-Fuzzy Inference System based on Subtractive Clustering (ANFIS-SC), and Gradient Boosting Decision Tree (GBDT) are applied for forecasting ionospheric TEC in three IGS GNSS monitoring stations at the low-latitude region (16°S to 10°S). The performance of these approaches in extreme conditions is investigated, including the high solar activity and magnetic storm, which are the most challenging scenario for TEC prediction. The results show that the machine learning algorithms outperform the Global Ionospheric Map (GIM) prediction model. The prediction accuracy during the high solar activity period was improved from 37.93% to 49.28%. During the magnetic storm period, the prediction accuracy was improved from 28.16% to 67.39% . Among the machine learning algorithms, the GBDT model outperforms the rest three algorithms in ionosphere prediction scenarios, which improves the prediction accuracy by 5.6% and 12.7% than the rest three approaches on average during high solar activity (2012-2015) and magnetic storm periods respectively.
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