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Aerobic Granular Sludge (AGS) has advantages over Activated sludge (AS) but faces challenges with long granulation periods. In this study, a novel grey-box model is devised to optimize the cultivation of AGS to shorten the formation time. This model is based on an existing white-box model. The modeling process starts with the application of four sensitivity analysis methods to assess the 12 model metrics selected. Subsequently, 12 prediction models were constructed by combining the six Machine learning (ML) algorithms and integrated algorithms, with the best performance selected (R 2  = 0.98). Finally, an AGS selection pressure planning model was designed in conjunction with a simulated annealing (SA) algorithm to guide AGS training. The results demonstrate that AGS formation could be achieved within four days under the model's optimal control. Therefore, the establishment of this model provides a new technique for the cultivation of AGS., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The growing need for in-depth analysis of predictive models leads to a series of new methods for explaining their local and global properties. Which of these methods is the best? It turns out that this is an ill-posed question. One cannot sufficiently explain a black-box machine learning model using a single method that gives only one perspective. Isolated explanations are prone to misunderstanding, leading to wrong or simplistic reasoning. This problem is known as the Rashomon effect and refers to diverse, even contradictory, interpretations of the same phenomenon. Surprisingly, most methods developed for explainable and responsible machine learning focus on a single-aspect of the model behavior. In contrast, we showcase the problem of explainability as an interactive and sequential analysis of a model. This paper proposes how different Explanatory Model Analysis (EMA) methods complement each other and discusses why it is essential to juxtapose them. The introduced process of Interactive EMA (IEMA) derives from the algorithmic side of explainable machine learning and aims to embrace ideas developed in cognitive sciences. We formalize the grammar of IEMA to describe human-model interaction. It is implemented in a widely used human-centered open-source software framework that adopts interactivity, customizability and automation as its main traits. We conduct a user study to evaluate the usefulness of IEMA, which indicates that an interactive sequential analysis of a model may increase the accuracy and confidence of human decision making., Competing Interests: Conflict of interestThe authors declare that they have no conflict of interest., (© The Author(s) 2023.)

It is important to work out precise and easy-to-use mathematical models to predict the indoor temperature in buildings for human residence. Such models can support model-based/predictive controls to efficiently maintain the temperature at a comfortable level. Accordingly, as main contribution, the paper proposes a new, easy-to-use, black-box type, multiple linear regression (MLR) based model for the indoor temperature of mobile (office) containers. The model, having low computational demand, could be easily generalized for different types of residence places (in the future). A discretized, physically-based model version of the classical, widely used heat transfer theory (based on energy balance) is recalled for comparison with the MLR-based model. Both models have the same (exogenous) inputs: global solar irradiance, environment temperature and wind speed. Both models are validated based on measured data. The MLR-based model is more precise, its modelling error is 7.1%, which means that it can be used well for general engineering aims. Moreover, the model is detailed in time (it gives an output value per half minute), so it could be properly used for real-time prediction and control purposes. Another contribution of the paper is that the MLR-based model is used to estimate the application potential of solar collectors, installed on the top of the container, for space heating. Based on the results, two solar collectors could extend the time with comfortable indoor temperature by more than 5 h within a three-day period in spring (in Hungary). Finally, conclusions and possible topics for future researches are provided., Competing Interests: The authors declare no competing interests., (© 2022 The Author(s).)

Unsupervised domain adaptation (UDA) is an emerging technique that enables the transfer of domain knowledge learned from a labeled source domain to unlabeled target domains, providing a way of coping with the difficulty of labeling in new domains. The majority of prior work has relied on both source and target domain data for adaptation. However, because of privacy concerns about potential leaks in sensitive information contained in patient data, it is often challenging to share the data and labels in the source domain and trained model parameters in cross-center collaborations. To address this issue, we propose a practical framework for UDA with a black-box segmentation model trained in the source domain only, without relying on source data or a white-box source model in which the network parameters are accessible. In particular, we propose a knowledge distillation scheme to gradually learn target-specific representations. Additionally, we regularize the confidence of the labels in the target domain via unsupervised entropy minimization, leading to performance gain over UDA without entropy minimization. We extensively validated our framework on a few datasets and deep learning backbones, demonstrating the potential for our framework to be applied in challenging yet realistic clinical settings., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Liu, Yoo, Xing, Kuo, El Fakhri, Kang and Woo.)

The stability of dry anaerobic digestion (AD) of food waste (FW) as well as the resulting methane gas generation was investigated from the perspective of system dynamics. Various organic loading rates were applied to the system by modifying the water content in the FW feed and solid retention time (SRT). The excessive organic loading due to the accumulation of volatile fatty acids (VFAs) from the feed with 80% water content during the short SRT (15 and 20 d) caused system failure. In contrast, more intermediate materials, such as VFAs, was easily converted into methane at higher water contents. In addition, the biogas production rate of dry AD was effectively predicted based on SRT, soluble chemical oxygen demand, total VFA, total ammonia, and free ammonia using a recurrent neural network-the so-called "black-box" model. This implies the feasibility of applying this data-based black-box model for controlling and optimizing complex biological processes., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Models of runoff water quality at the scale of an urban catchment usually rely on build-up/wash-off formulations obtained through small-scale experiments. Often, the physical interpretation of the model parameters, valid at the small-scale, is transposed to large-scale applications. Testing different levels of spatial variability, the parameter distributions of a water quality model are obtained in this paper through a Monte Carlo Markov Chain algorithm and analyzed. The simulated variable is the total suspended solid concentration at the outlet of a periurban catchment in the Paris region (2.3 km 2 ), for which high-frequency turbidity measurements are available. This application suggests that build-up/wash-off models applied at the catchment-scale do not maintain their physical meaning, but should be considered as "black-box" models., (Copyright © 2016 Elsevier Ltd. All rights reserved.)