Guidelines for model adaptation: a study of the transferability of a general seagrass ecosystem DBN model

Ecological models are extensively and increasingly used in support of environmental policy and decision making. Dynamic Bayesian Networks (DBN) as a tool for conservation have been demonstrated to be a valuable tool for providing a systematic and intuitive approach to integrating data and other critical information to help guide the decision-making process. However, data for a new ecosystem are often sparse. In this case, a general DBN developed for similar ecosystems could be applicable, but this may require the adaptation of key elements of the network. The research presented in this paper focused on a case study to identify and implement guidelines for model adaptation. We adapted a general DBN of a seagrass ecosystem to a new location where nodes were similar, but the conditional probability tables varied. We focused on two species of seagrass (Zostera noltei and Z. marina) located in Arcachon Bay, France. Expert knowledge was used to complement peer-reviewed literature to identify which components needed adjustment including parameterisation and quantification of the model and desired outcomes. We adopted both linguistic labels and scenario-based elicitation to elicit from experts the conditional probabilities used to quantify the DBN. Following the proposed guidelines, the model structure of the DBN was retained, but the conditional probability tables were adapted for nodes that characterised the growth dynamics in Zostera spp. population located in Arcachon Bay, as well as the seasonal variation on their reproduction. Particular attention was paid to the light variable as it is a crucial driver of growth and physiology for seagrasses. Our guidelines provide a way to adapt a general DBN to specific ecosystems to maximise model reuse and minimise re-development effort. Especially important from a transferability perspective are guidelines for ecosystems with limited data, and how simulation and prior predictive approaches can be used in these contexts.

Comparison of Static Model, Adaptation Study, and CFD Simulation in Evaluating Thermal Comfort Based on Köppen Climate Classification System in Churches in Indonesia

This research examined thermal comfort in church buildings in Indonesia by making a comparison between three different Indonesian climatic regions using three different research models. A static model, an adaptation study model and a CFD simulation were used to find the similarities and differences between the results generated from determining thermal comfort in church buildings in the three regions. The comparison revealed that church buildings had different PMV scores at each measuring point that were inversely proportional to the subjects’ response on thermal comfort inside the buildings, i.e. points adjoining with openings affect a low PMV score and a high perceived thermal sensation, and vice versa. The CFD simulation showed that changing the conditions of the openings affects air velocity and flow into the building, which influences the subjects’ thermal comfort response inside the churches.

Handling concept drifts and limited label problems using semi-supervised combine-merge Gaussian mixture model

When predicting data streams, changes in data distribution may decrease model accuracy over time, thereby making the model obsolete. This phenomenon is known as concept drift. Detecting concept drifts and then adapting to them are critical operations to maintain model performance. However, model adaptation can only be made if labeled data is available. Labeling data is both costly and time-consuming because it has to be done by humans. Only part of the data can be labeled in the data stream because the data size is massive and appears at high speed. To solve these problems simultaneously, we apply a technique to update the model by employing both labeled and unlabeled instances to do so. The experiment results show that our proposed method can adapt to the concept drift with pseudo-labels and maintain its accuracy even though label availability is drastically reduced from 95% to 5%. The proposed method also has the highest overall accuracy and outperforms other methods in 5 of 10 datasets.

Zen and the art of model adaptation: Low-utility-cost attack mitigations in collaborative machine learning

In this study, we aim to bridge the gap between the theoretical understanding of attacks against collaborative machine learning workflows and their practical ramifications by considering the effects of model architecture, learning setting and hyperparameters on the resilience against attacks. We refer to such mitigations as model adaptation. Through extensive experimentation on both, benchmark and real-life datasets, we establish a more practical threat model for collaborative learning scenarios. In particular, we evaluate the impact of model adaptation by implementing a range of attacks belonging to the broader categories of model inversion and membership inference. Our experiments yield two noteworthy outcomes: they demonstrate the difficulty of actually conducting successful attacks under realistic settings when model adaptation is employed and they highlight the challenge inherent in successfully combining model adaptation and formal privacy-preserving techniques to retain the optimal balance between model utility and attack resilience.

Business Model Adaptation of Small and Medium-Sized Information Technology Firms

Small and medium-sized information technology firms operating in high-velocity business environments have to continuously adapt their business models. Prior research on business model adaptation, however, remains under-developed. In this study, we address the gap by drawing on the dynamic capability perspective. Based on the qualitative data collected from 35 interviews with ten companies in China, we develop a processual model and unveil how these companies employ dynamic capabilities (i.e. sensing, seizing and transforming), complemented by ordinary capabilities, to enact, manage and implement business model adaptation. This study provides novel insights into a theoretical issue of business model adaptation for information technology firms and managerial implications while using an adaptive business model innovation strategy.

Business Model Adaptation of Small and Medium-Sized Information Technology Firms

Small and medium-sized information technology firms operating in high-velocity business environments have to continuously adapt their business models. Prior research on business model adaptation, however, remains under-developed. In this study, we address the gap by drawing on the dynamic capability perspective. Based on the qualitative data collected from 35 interviews with ten companies in China, we develop a processual model and unveil how these companies employ dynamic capabilities (i.e. sensing, seizing and transforming), complemented by ordinary capabilities, to enact, manage and implement business model adaptation. This study provides novel insights into a theoretical issue of business model adaptation for information technology firms and managerial implications while using an adaptive business model innovation strategy.

Business model adaptation as a strategic response to crises: navigating the COVID-19 pandemic

Purpose This study aims to explore how hotels adapt their business models as a strategic response to crisis situations. It sheds light on the processes and methods of business model adaptation during severe crisis situations, such as the COVID-19 outbreak. Design/methodology/approach A single-case study was conducted. Data were collected from the owner/manager of a boutique hotel chain in Chiang Mai, Thailand through an extensive interviewing process. The authors also examined corporate documents. The authors then re-organized the material as a coherent narrative about how the company navigated the COVID-19 crisis. Findings The findings show that the hotels in the study adapted their business models by cutting costs through stopping non-essential operations, increasing non-room revenues and adding new revenue channels, bringing in cash from advance bookings, securing financial support from creditors, leveraging government support and training staff for the “new normal.” Originality/value Few previous studies have focused on business model adaptation during the COVID-19 crisis. The investigation of this largely neglected area provides two main contributions. First, it extends the literature on crisis management in hospitality firms by examining business model adaptation patterns and processes during unprecedented crisis conditions. Second, it provides managerial insights and a business model adjustment framework to help practitioners in urban settings in their efforts toward recovery from the COVID crisis.