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Recently, wind speed forecasting as an effective computing technique plays an important role in advancing industry informatics, while dealing with these issues of control and operation for renewable power systems. However, it is facing some increasing difficulties to handle the large-scale dataset generated in these forecasting applications, with the purpose of ensuring stable computing performance. In response to such limitation, this paper proposes a more practical approach through the combination of extreme-learning machine (ELM) method and deep-learning model. ELM is a novel computing paradigm that enables the neural network (NN) based learning to be achieved with fast training speed and good generalization performance. The stacked ELM (SELM) is an advanced ELM algorithm under deep-learning framework, which works efficiently on memory consumption decrease. In this paper, an enhanced SELM is accordingly developed via replacing the Euclidean norm of the mean square error (MSE) criterion in ELM with the generalized correntropy criterion to further improve the forecasting performance. The advantage of the enhanced SELM with generalized correntropy to achieve better forecasting performance mainly relies on the following aspect. Generalized correntropy is a stable and robust nonlinear similarity measure while employing machine learning method to forecast wind speed, where the outliers may exist in some industrially measured values. Specifically, the experimental results of short-term and ultra-shortterm forecasting on real wind speed data show that the proposed approach can achieve better computing performance compared with other traditional and more recent methods.

This paper develops a methodology on sampled-data-based event-triggered active disturbance rejection control (ET-ADRC) for disturbed systems in networked environment when only using measurable outputs. By using disturbance/uncertainty estimation and attenuation technique, an event-based sampled-data composite controller is proposed together with a discrete-time extended state observer. Under the presented new framework, the newest state and disturbance estimates as well as the control signals are not transmitted via the common sensor-controller network, but instead communicated and calculated until a discrete-time event-triggering condition is violated. Compared with the periodic updates in the traditional time-triggered active disturbance rejection control, the proposed ET-ADRC scheme can remarkably reduce the communication frequency while maintaining a satisfactory closed-loop system performance. The proposed discrete-time control scheme provides the engineers with a manner of direct and easier implementation via networked digital computers. It is shown that the bounded stability of the closed-loop system can be guaranteed. Finally, an application design example of a dc-dc buck converter with experimental results is conducted to illustrate the efficiency of the proposed control scheme.

How to dynamically replenish inventory from two supply sources or shipping modes with general lead times. The fast source is more expensive than the slow source. (2) Academic/Practical Relevance: Dual sourcing provides supply chain flexibility to mitigate demand and supply risk. Despite its relevance in practice, characterizing the optimal dual sourcing policy is extremely challenging, and the optimal stochastic policy for non-consecutive lead times has been unknown for over 50 years. (3) Methodology: We present and solve a robust rolling horizon model for periodically reviewed dual sourcing inventory systems that minimizes the total cost of purchasing, inventory holding and backlogging. (4) Results: We prove that the optimal robust policy is a dual index, dual base-stock policy that constrains or "caps" the slow order and provide closed-form expressions for the three control parameters. The optimality result is established for general lead times and can accommodate non-stationary demand. Our numerical study shows that as the lead time difference grows, the capped dual index policy increasingly smooths slow orders and, for stationary demand, converges to the tailored base surge policy, which places a constant slow order and has been shown to be asymptotically optimal. (5) Managerial Implications: Our capped dual index policy is easy to understand, explain and implement in practice. In an extensive simulation study, the capped dual index policy performs as well as, and can even outperform, the best heuristics presented in the stochastic inventory literature.

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The effects of chronic exposure to ovalbumin (OA) aerosol were studied in Brown Norway rats following intraperitoneal injections with OA and AI(OH)3 and exposure to OA or saline aerosols, once or every third day for 3 to 8 wk. Measurements of airway responsiveness to acetylcholine (ACh) aerosol at 18 to 24 h after allergen exposure showed a significant increase in -logPC150, the concentration of ACh needed to cause a 150% increase in baseline lung resistance, in animals single-exposed or chronic OA-exposed for 3 wk, compared with saline-exposed control animals. The group receiving 8 wk of OA exposure demonstrated no difference from the control animals with -logPC150 lower than that of the two previous groups (p < 0.001). In all three groups, BAL fluid showed a significant increase in neutrophils, but a significant increase in eosinophils (p < 0.01) was only observed in the single-exposed group when compared with saline-exposed control animals. In the 8-wk exposed rats, there was a higher recovery of macrophages and lymphocytes (p < 0.01) compared with control animals and the other two groups. AHR, present after single or 3-wk repeated exposure, disappears by 8 wk of continuous allergen exposure. Both the enhancement and suppression of AHR may be linked to OA-induced immune and inflammatory mechanisms.

Conventional optimization algorithms that prescribe order packing instructions (which items to pack in which sequence in which box) focus on box volume utilization yet tend to overlook human behavioral deviations. We observe that packing workers at the warehouses of the Alibaba Group deviate from algorithmic prescriptions for 5.8% of packages, and these deviations increase packing time and reduce operational efficiency. We posit two mechanisms and demonstrate that they result in two types of deviations: (1) information deviations stem from workers having more information and in turn better solutions than the algorithm; and (2) complexity deviations result from workers’ aversion, inability, or discretion to precisely implement algorithmic prescriptions. We propose a new “human-centric bin packing algorithm” that anticipates and incorporates human deviations to reduce deviations and improve performance. It predicts when workers are more likely to switch to larger boxes using machine learning techniques and then proactively adjusts the algorithmic prescriptions of those “targeted packages.” We conducted a large-scale randomized field experiment with the Alibaba Group. Orders were randomly assigned to either the new algorithm (treatment group) or Alibaba’s original algorithm (control group). Our field experiment results show that our new algorithm lowers the rate of switching to larger boxes from 29.5% to 23.8% for targeted packages and reduces the average packing time of targeted packages by 4.5%. This idea of incorporating human deviations to improve optimization algorithms could also be generalized to other processes in logistics and operations. This paper was accepted by Charles Corbett, operations management.