Recent years include the world’s hottest year, while they have been marked mainly, besides the COVID-19 pandemic, by climate-related disasters, based on data collected by the Emergency Events Database (EM-DAT). Besides the human losses, disasters cause significant and often catastrophic socioeconomic impacts, including economic losses. Recent developments in artificial intelligence (AI) and especially in machine learning (ML) and deep learning (DL) have been used to better cope with the severe and often catastrophic impacts of disasters. This paper aims to provide an overview of the research studies, presented since 2017, focusing on ML and DL developed methods for disaster management. In particular, focus has been given on studies in the areas of disaster and hazard prediction, risk and vulnerability assessment, disaster detection, early warning systems, disaster monitoring, damage assessment and post-disaster response as well as cases studies. Furthermore, some recently developed ML and DL applications for disaster management have been analyzed. A discussion of the findings is provided as well as directions for further research.

Purpose: Developments in Industry 4.0 technologies and Artificial Intelligence (AI) have enabled data-driven manufacturing. Predictive maintenance (PdM) has therefore become the prominent approach for fault detection and diagnosis (FD/D) of induction motors (IMs). The maintenance and early FD/D of IMs are critical processes, considering that they constitute the main power source in the industrial production environment. Machine learning (ML) methods have enhanced the performance and reliability of PdM. Various deep learning (DL) based FD/D methods have emerged in recent years, providing automatic feature engineering and learning and thereby alleviating drawbacks of traditional ML based methods. This paper presents a comprehensive survey of ML and DL based FD/D methods of IMs that have emerged since 2015. An overview of the main DL architectures used for this purpose is also presented. A discussion of the recent trends is given as well as future directions for research.Design/methodology/approach: A comprehensive survey has been carried out through all available publication databases using related keywords. Classification of the reviewed works has been done according to the main ML and DL techniques and algorithmsFindings: DL based PdM methods have been mainly introduced and implemented for IM fault diagnosis in recent years. Novel DL FD/D methods are based on single DL techniques as well as hybrid techniques. DL methods have also been used for signal preprocessing and moreover, have been combined with traditional ML algorithms to enhance the FD/D performance in feature engineering. Publicly available datasets have been mostly used to test the performance of the developed methods, however industrial datasets should become available as well. Multi-agent system (MAS) based PdM employing ML classifiers has been explored. Several methods have investigated multiple IM faults, however, the presence of multiple faults occurring simultaneously has rarely been investigated.Originality/value: The paper presents a comprehensive review of the recent advances in PdM of IMs based on ML and DL methods that have emerged since 2015.

Supply chain planning and control approaches need to include a wide range of factors in order to optimize production. Supply chain simulation modeling has been identified as a potential methodology toward increasing the efficiency of current systems to this end. The purpose of this paper is to evaluate the impact of inventory management decisions on supply chain performance using a Colored Petri Net based simulation modeling method. The presented method uses hierarchical timed Colored Petri Nets to model inventory management in a multi-stage serial supply chain, under normal operating conditions, and under the presence of disruptions, for both traditional and information sharing configurations. Disruptions are introduced as canceled orders and canceled deliveries, in a time period. Supply chain performance has been evaluated, in the context of order variance amplification and stockout amplification. Validation of the method is done by comparing results obtained for the bullwhip effect with published literature results, as well as by state space analysis results.

In this research, we explore the use of Large Language Models (LLMs) and clustering techniques to automate the structuring and labeling of disaster-related social media content. With a gathered dataset comprising millions of tweets related to various disasters, our approach aims to transform unstructured and unlabeled data into a structured and labeled format that can be readily used for training machine learning algorithms and enhancing disaster response efforts. We leverage LLMs to preprocess and understand the semantic content of the tweets, applying several semantic properties to the data. Subsequently, we apply clustering techniques to identify emerging themes and patterns that may not be captured by predefined categories, with these patterns surfaced through topic extraction of the clusters. We proceed with manual labeling and evaluation of 10,000 examples to evaluate the LLMs’ ability to understand tweet features. Our methodology is applied to real-world data for disaster events, with results directly applicable to actual crisis situations.

The purpose of this paper is to describe in-depth a community-based social partnership, emerged in response to the financial crisis in Greece, with members from the private, public and civic sectors, using a case example of a grass-root self-organised national network. Formal and informal interviews as well as written communication with members of the partnership mainly formed the basis for the analysis. Topics covered formation and implementation activities, outcomes, relationship issues, such as trust and links to social capital. A shared community risk and a national media campaign to increase public awareness of the issue were catalysts for individuals’ sensitisation and participation in the partnership. The shared risk was the loss of community’s social cohesion, through poverty aggravated by the financial crisis. Self-organisation led to innovative relationships, whereas trust, collective action and collaboration show social capital attributes in the partnership enabling resilience development. The research contributes in the fields of community-based partnerships and engagement in building community and crisis resilience. The findings are based on a case example. More evidence is needed in order to derive generalised statements about the partnership’s contribution to crisis resilience. The partnership has shown impact on community engagement, health and well-being. This paper presents a partnership type for building community and crisis resilience with the case example of one such partnership in Greece, formed to alleviate community distress caused by the crisis.

In this research, we explore the use of Large Language Models (LLMs) and clustering techniques to automate the structuring and labeling of disaster-related social media con-tent. With a gathered dataset comprising millions of tweets related to various disasters, our approach aims to transform unstructured and unlabeled data into a structured and labeled format that can be readily used for training machine learning algorithms and en-hancing disaster response efforts. We leverage LLMs to preprocess and understand the semantic content of the tweets, applying several semantic properties to the data, followed by the application of clustering techniques to identify emerging themes and patterns that may not be captured by predefined categories and are surfaced through topic extraction of the clusters. We proceed with manual labeling and evaluation of 10,000 examples to evaluate the LLMs' ability to understand tweet features. Our methodology is applied to re-al-world data for disaster events, with results directly applicable to actual crisis situations.