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This study adopts the multi-step model developed by Avraham and Ketter (2008), for altering place images, based on past academic literature on destination marketing. The purpose of this study is to determine the state of Fukushima’s sake breweries before and after 2011, and its strategies for overcoming negative images and strengthening regional branding. Semi-structured interviews were conducted with seven sake breweries in Fukushima.

Fukushima Prefecture, located in northern Japan, is renowned for its hot springs, lakes, historical architecture, gastronomy, and particularly its sake (or Japanese rice wine). However, pre-existing problems such as the prefecture’s changing demographics and economic development, the effects of the 2011 Great East Japan Earthquake (GEJE) and fears of radioactive contamination have made consumers reluctant to consume products from the region or to visit the prefecture. This study illustrates how various sake brewery stakeholders have sought to reverse and alter negative images associated with the prefecture. To examine these initiatives, this study uses the multi-step destination marketing and counter-branding model to identify the strategies and techniques used by the stakeholders, with the aim of altering the way the prefecture is perceived and reversing the negative image people may have of the prefecture. To acquire data for this model, this study uses semi-structured interviews conducted in 2018 and 2020 with local sake breweries, tourism associations and the local government on how they sought to retore a positive image of the prefecture and rebrand it into a new type of tourism destination that focuses on the strengths of its breweries.

The results indicate that through a combination of collaboration between the breweries, local government and the local communities, the sake breweries were able to reverse many of the negative effects of the 2011 GEJE. The success of the sake industry has prompted the local government to focus more strongly on tourism marketing that places sake products and breweries at the center of its campaign to promote the region.

While this paper focuses on the recovery of breweries, it does not include the recovery of wineries in Fukushima, which have made similar progress in their recovery. In addition, the interviews focused primarily on the perspectives of the suppliers and not the consumers.

The results of this research can help guide other destinations undergoing prolonged association with negative images on the path toward image recovery. In particular, this paper highlights the importance of a coordinated strategy by all stakeholders, the local government, businesses and communities, to create a united image and response for addressing the causes of these image problems and to create new opportunities for all stakeholders.

This research contributes to the field of image restoration, which combines theories regarding destination marketing and crisis management. Also, the research highlights the importance of collective stakeholder mobilization when attempting to help communities that are facing economic and tourism crises.

Japan's decision to release nuclear wastewater into the Pacific Ocean in 2023 has sparked strong opposition at home and abroad. In this study, Graph Model for Conflict Resolution (GMCR) method is adopted to analyze the conflict problem, and reasonable equilibrium solutions are given to solve the conflict event.

In this study, GMCR is adopted to solve the conflict problem. First, identify the key decision-makers (DMs) on the issue of nuclear effluent and the relevant options they might adopt. Second, the options of each DM are arranged and combined to form a set of feasible states. Thirdly, the graph model is constructed according to the change of DM's options, and the relative preference of each DM is determined. Finally, the conflict problem is solved according to the definition of GMCR equilibrium.

Discharging nuclear wastewater into the ocean is not the right choice to solve the problem. Developing more space to store nuclear wastewater is more conducive to the protection of the ocean environment.

It is undesirable for the Japanese government to unilaterally discharge nuclear wastewater into the ocean. Objectively assessing the radioactivity of nuclear wastewater and the cooperation of relevant stakeholders can better solve this conflict.

The problem arising from Japan's releasing plan is complicated because of a lack of information and the existence of multiple stakeholders, while GMCR can help us with a better view of the current circumstance in the conflict.

This invited article explores current developments in supply chain risk management (SCRM) practices by revisiting the classical case of Ericsson (Norrman and Jansson, 2004) after 15 years, and updating its case description and analysis of its organizational structure, processes and tools for SCRM.

An exploratory case study is conducted with a longitudinal focus, aiming to understand both proactive and reactive SCRM practices using a holistic perspective of a real-life example.

The study demonstrates how Ericsson's SCRM practices have developed, indicating that improved functional capabilities are increasingly combined across silos and leveraged by formalized learning processes. Important enablers are IT capabilities, a fine-grained and cross-functional organization, and a focus on monitoring and compliance. Major developments in SCRM are often triggered by incidents, but also by requirements from external stakeholders and new corporate leaders actively focusing on SCRM and related activities.

Relevant areas for future research are proposed, thereby increasing the knowledge of how companies can develop SCRM practices and capabilities further.

Being one of few in-depth holistic case studies of SCRM, decision-makers can learn about many practices and tools. Of special interest is the detailed description of how Ericsson reactively responded to the Fukushima incident (2011), and how it proactively engaged in monitoring and assessment activities. It is also exemplified how SCRM practices could continuously be developed to make them “stick” to the organization, even in stable times.

This is one of the first case studies to delve deeper into the development of SCRM practices through taking a longitudinal approach.

This paper aims to suggest the structure of a platform for education and capacity building for Future Earth, which is an intensive program open to the eight stakeholders and which utilizes existing research programs/facilities associated with Future Earth. An intention of this paper is to facilitate a policy brief for projects associated with Future Earth.

This paper reviewed backgrounds and necessary items for education and capacity buildings in Future Earth projects by implementing three main priorities in Future Earth and current surrounding environments.

This paper then suggested a possible structure, competencies, contents and human resources for education and capacity building and education for Future Earth.

The suggestions can be implemented in capacity building and education programs associated with Future Earth.

Computer vision for automated analysis of cells and tissues usually include extracting features from images before analyzing such features via various machine learning and machine vision algorithms. The purpose of this work is to explore and demonstrate the ability of a Convolutional Neural Network (CNN) to classify cells pictured via brightfield microscopy without the need of any feature extraction, using a minimum of images, improving work-flows that involve cancer cell identification.

The methodology involved a quantitative measure of the performance of a Convolutional Neural Network in distinguishing between two cancer lines. In their approach, they trained, validated and tested their 6-layer CNN on 1,241 images of MDA-MB-468 and MCF7 breast cancer cell line in an end-to-end fashion, allowing the system to distinguish between the two different cancer cell types.

They obtained a 99% accuracy, providing a foundation for more comprehensive systems.

Value can be found in that systems based on this design can be used to assist cell identification in a variety of contexts, whereas a practical implication can be found that these systems can be deployed to assist biomedical workflows quickly and at low cost. In conclusion, this system demonstrates the potentials of end-to-end learning systems for faster and more accurate automated cell analysis.