Alternative estimators have been derived for estimating the variance components according to Iterative Almost Unbiased Estimation (IAUE). As a result two modified IAUEs are introduced. The relative performances of the proposed estimators and other estimators are studied by simulating their bias, Mean Square Error and the probability of getting negative estimates under unbalanced nested-factorial model with two fixed crossed factorial and one nested random factor. Finally the Empirical Quantile Dispersion Graph (EQDG), which provides a comprehensive picture of the quality of estimation, is depicted corresponding to all the studied methods.

No abstract

No abstract

Additional information is available at the end of the chapter http://dx.doi.org/10.5772/55715 . IntroductionDespite recent increases in recycling, composting, and incineration, the sanitary landfill remains the predominant and most economical municipal solid waste MSW management alternative. Modern MSW landfills strive to optimize the design, construction, and operation processes in order to mitigate many of the potentially negative impacts, and improve the profitability. The bioreactor landfill "L is considered one of the promising developments that have recently gained significant attention. This waste-to-energy technology requires specific management activities and operational procedures that enhance the microbial decomposition processes inside the landfill resulting in higher production of landfill gas [ ]. The recirculation of leachate, which is conducted by recycling the water passing through and collected from the landfill, is considered the main operational characteristic in the "L to increase moisture, and consequently stimulate the biodegradation process Figure . The potential benefits of the "L include increased waste settlement rates and airspace utilization, decreased costs for leachate treatment, more rapid gas production which improves the economics of gas recovery , and more rapid waste stabilization which may reduce the post-closure maintenance period . These potential benefits have led to many full-scale "L applications in the last decade, mostly in the United States, resulting in the generation of design and operation data. In , the Solid Waste "ssociation of North "merica conducted an inventory that identified over "Ls in North "merica [ ]. Many of these experiences revealed scale-up issues and technical limitations that merit further research and development [ -].One of the most critical, yet little studied, issues in the operation of "Ls is process control. In field applications, unsupervised operational procedures can disturb the dynamics of the landfill biological processes causing serious consequences on the overall evolution of the ecosystem, i.e., unstable and sometimes unsuccessful transition from one operational phase to

<div>Deep Neural Networks (DDNs) have achieved tremendous success in handling various Machine Learning (ML) tasks, such as speech recognition, Natural Language Processing, and image classification. However, they have shown vulnerability to well-designed inputs called adversarial examples. Researchers in industry and academia have proposed many adversarial example defense techniques. However, none can provide complete robustness. The cutting-edge defense techniques offer partial reliability. Thus, complementing them with another layer of protection is a must, especially for mission-critical applications. This paper proposes a novel Online Selection and Relabeling Algorithm (OSRA) that opportunistically utilizes a limited number of crowdsourced workers (budget-constraint crowdsourcing) to maximize the ML system’s robustness. OSRA strives to use crowdsourced workers effectively by selecting the most suspicious inputs (the potential adversarial examples) and moving them to the crowdsourced workers to be validated and corrected (relabeled). As a result, the impact of adversarial examples gets reduced, and accordingly, the ML system becomes more robust. We also proposed a heuristic threshold selection method that contributes to enhancing the prediction system’s reliability. We empirically validated our proposed algorithm and found that it can efficiently and optimally utilize the allocated budget for crowdsourcing. It is also effectively integrated with a state-ofthe- art black-box (transfer-based) defense technique, resulting in a more robust system. Simulation results show that OSRA can outperform a random selection algorithm by 60% and achieve comparable performance to an optimal offline selection benchmark. They also show that OSRA’s performance has a positive correlation with system robustness.<br></div>