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Nepal is exposed to multi-hazard risk and is highly prone to disasters such as earthquake, flood, landslide, and drought. The climate, topography, nature-based livelihood, and weak economy and institutional capability to deal with such disasters put the country at frontline of the highly vulnerable countries in the world. Majority of the population of Nepal live in rural areas which are characterized by poorly developed infrastructures. Several disasters in such areas induce temporarily or permanently displacements of the people. Vulnerable communities, households, and individuals lack the capacity to withstand shocks, while more resilient communities are able to reduce the associated risks. Migration during disasters is itself an adaptive response which is both the opportunity and challenge for developing a resilient community. This chapter presents the trends and patterns of disaster-induced displacements in Nepal and explores how disaster-induced displacements could be an opportunity to build back better to enhance the resilience of the vulnerable communities. This chapter also highlights the existing institutions and disaster management framework of the national policies and strategies at the federal, provincial, and local levels in the light of disaster-induced displacement, and recommend actions that need to be taken to manage the risk of disaster-induced displacements and enhance resilient livelihoods in Nepal.

This study investigates paradox-responding strategies and enabling mechanisms in humanitarian temporary supply networks (TSNs). Given the high stakes involved in life-saving supply networks, understanding how diverse, often under-resourced, organisations jointly tackle paradoxical tensions under time pressure is crucial.

A qualitative single case study approach is adopted and a TSN deployed to meet shelter needs following the 2015 Nepal earthquake is selected as the case. The authors use diverse secondary data sources to establish how the TSN responded to paradoxical tensions.

Results show that paradox-responding in humanitarian TSNs is ongoing, dynamic and a collective effort. Most strategies entail tackling the paradoxical tensions at the same time, using the same TSN structure, but there are differences in the treatment of the paradoxical elements. Additionally, the authors find that the execution of the responding strategies is enabled by the appropriate types of network-level mechanisms which can vary in novelty, complexity, depth and reach.

This study provides rich explanations of paradox-responding and develops insights into collective action within TSNs. However, further research is needed to extend and refine insights given the single-case setting design.

This study develops a framework of paradox-responding strategies and a corresponding mix of enabling mechanisms that can guide decision-makers in the humanitarian sector when deploying TSNs.

To the best of the authors' knowledge, this is the first study that investigates paradox-responding strategies in humanitarian TSNs in particular and enabling mechanisms in general.

The purpose of this paper is to investigate relationships of urban seismic resilience assessment indicators.

To achieve this aim, construction of the urban seismic resilience assessment indicators system was conducted and 20 indicators covering five dimensions, namely building and lifeline infrastructure, environment, society, economy and institution were identified. Following this, this study used evidence fusion theory and intuitionistic fuzzy sets to process the information from experts then developed the fuzzy total interpretive structure model.

A total of 20 urban seismic resilience assessment indicators are reconstructed into a hierarchical and visual system structure including five levels. Indicators in the bottom level including debris flow risk, landslide risk, earthquake experience and demographic characteristics are fundamental indicators that significantly impact other indicators. Indicators in the top level including open space, gas system and public security are direct indicators influenced more by other indicators. Other indicators are in middle levels. Results of MICMAC analysis visually categorize these indicators into independent indicators, linkage indicators, autonomous indicators and dependent indicators according to driving power and dependence.

This paper attempts to explore relationships of urban seismic resilience assessment indicators with the interpretive structural model method. Additionally, Fuzzy total interpretive structure model is developed combined with evidence fusion theory and intuitionistic fuzzy sets, which is the extension of total interpretive structure model. Research results can assist the analytic network process method in assessing urban seismic resilience in future research.

This short paper builds on and critiques work presenting potential non-disasters: disasters that did not seem to happen despite a major hazard. Previous work does not differentiate among different types of potential non-disasters. This short paper uses local information to propose three categories according to reasons for vulnerability being low or absent. These proposed categories are used to critique the construct of “potential non-disasters”.

This short paper uses a subjective approach to examples of potential non-disasters in 2022, focusing on local information that describes what happened. This information is applied and analysed for the three proposed categories using examples from Japan, Nepal, the Philippines and Vietnam. Such comparisons are useful for critiquing “potential non-disasters”, by understanding better local approaches and information available for reporting on situations that could be disasters.

Potential non-disasters remain relevant for exploring mechanisms, tools and actions for educating about vulnerability causes and vulnerability reduction to avert disasters. Limitations are evident by relying on media reports, even local ones with local authors. A suggestion is to implement a grant programme for collecting data immediately after a major hazard without an evident, major disaster. Additionally, an annual report and critique of each year's potential non-disasters, categorised and analysed, would help to evidence the presence and limits of the “potential non-disaster” construct.

This short paper contributes a much deeper theoretical dive into understanding potential non-disasters, both describing them and the drawbacks of the construct. To practitioners, the construct now offers more avenues for actions while illustrating their effectiveness in reducing vulnerabilities. Thus, this paper supports multiple, linked pathways towards more non-disasters.

This research is aimed at preparing landslide susceptibility using spatial analysis and soft computing machine learning techniques based on convolutional neural networks (CNNs), artificial neural networks (ANNs) and logistic regression (LR) models.

Using the Geographical Information System (GIS), a spatial database including topographic, hydrologic, geological and landuse data is created for the study area. The data are randomly divided between a training set (70%), a validation (10%) and a test set (20%).

The validation findings demonstrate that the CNN model (has an 89% success rate and an 84% prediction rate). The ANN model (with an 84% success rate and an 81% prediction rate) predicts landslides better than the LR model (with a success rate of 82% and a prediction rate of 79%). In comparison, the CNN proves to be more accurate than the logistic regression and is utilized for final susceptibility.

Land cover data and geological data are limited in largescale, making it challenging to develop accurate and comprehensive susceptibility maps.

It helps to identify areas with a higher likelihood of experiencing landslides. This information is crucial for assessing the risk posed to human lives, infrastructure and properties in these areas. It allows authorities and stakeholders to prioritize risk management efforts and allocate resources more effectively.

The social implications of a landslide susceptibility map are profound, as it provides vital information for disaster preparedness, risk mitigation and landuse planning. Communities can utilize these maps to identify vulnerable areas, implement zoning regulations and develop evacuation plans, ultimately safeguarding lives and property. Additionally, access to such information promotes public awareness and education about landslide risks, fostering a proactive approach to disaster management. However, reliance solely on these maps may also create a false sense of security, necessitating continuous updates and integration with other risk assessment measures to ensure effective disaster resilience strategies are in place.

Landslide susceptibility mapping provides a proactive approach to identifying areas at higher risk of landslides before any significant events occur. Researchers continually explore new data sources, modeling techniques and validation approaches, leading to a better understanding of landslide dynamics and susceptibility factors.

This study aims to help understand individual donors’ preferences over different designs of humanitarian–business partnerships in managing humanitarian operations and to help understand if donors’ preferences align with their actual donation behavior.

Choice-based conjoint analysis was used to understand donation preferences for partnership designs, and a donation experiment was performed using real money to understand the alignment of donors’ preferences with actual donation behavior.

The results show that partnering with the business sector can be a valuable asset for humanitarian organizations in attracting individual donors if these partnerships are managed well in terms of partnership strategy, partnership history and partnership report and disclosure. In particular, the study finds that the donation of services and products from businesses corporations to humanitarian organizations are preferable to individual donors, rather than cash. Furthermore, donors’ preferences are not necessarily aligned with actual donation behavior.

The results highlight the importance of presenting objective data on projects to individual donors. The results also show that donors value the provision of services and products by business corporations to humanitarian operations.

Partnerships between humanitarian organizations and business corporations are important for the success of humanitarian operations. However, little is known about which partnership designs are most preferable to individual donors and have the biggest chance of being supported financially.

Spam emails classification using data mining and machine learning approaches has enticed the researchers' attention duo to its obvious positive impact in protecting internet users. Several features can be used for creating data mining and machine learning based spam classification models. Yet, spammers know that the longer they will use the same set of features for tricking email users the more probably the anti-spam parties might develop tools for combating this kind of annoying email messages. Spammers, so, adapt by continuously reforming the group of features utilized for composing spam emails. For that reason, even though traditional classification methods possess sound classification results, they were ineffective for lifelong classification of spam emails duo to the fact that they might be prone to the so-called “Concept Drift”. In the current study, an enhanced model is proposed for ensuring lifelong spam classification model. For the evaluation purposes, the overall performance of the suggested model is contrasted against various other stream mining classification techniques. The results proved the success of the suggested model as a lifelong spam emails classification method.

The purpose of this systematic review is to assess if there is a relationship between literacy abilities and mental health outcomes as reported in global literature. Fourteen percent of the global population has little or no literacy. Literacy skills impact on daily functioning and have been shown to impact on social outcomes. Whilst there has been research examining the potential association between literacy and mental health outcomes in specific populations, there has been no systematic review of this literature to date.

A systematic review was carried out using Embase, PsycINFO and PubMed to identify relevant papers that measured both literacy and mental health. Data relating to the association between literacy and mental health were extracted. The papers included were assessed for quality using a bespoke quality rating tool. A narrative synthesis describes the findings.

Nineteen studies from across nine countries were included in the analysis. Seventeen studies showed a significant association between literacy and mental health, those with lower literacy had greater mental health difficulties. Some papers reported factors that interacted with this association, such as age, gender, poverty and years of education.

To the best of the authors’ knowledge, this is the first systematic review to look at the global picture of literacy and mental health. It suggests there is a relationship between literacy abilities and mental health outcomes, highlighting the importance of healthcare professionals and services including identification of literacy needs within routine mental health practice.

The purpose of this paper is to explore the impact of earthquakes on the labor market. The authors try to estimate the impact of two major earthquakes (Izmir and Elazig) in Turkey.

In order to analyze the effects of devastating earthquakes in the nearby regions of the province where the earthquake took place, on the labor market, monthly and annual data from the TUIK and ISKUR database will be used. For this purpose, the authors consider the earthquake a natural experiment and employ a Synthetic Control Method (SCM). In addition, the analysis will be carried out using seasonally adjusted data, taking into account the seasonal effects of the monthly data to be used in the study.

The results show that the impact varies based on the labor market structure of the regions. While the earthquake positively affects the labor market of agriculture-oriented regions, it harms the labor market of nonagricultural-oriented regions.

A major limitation of the study is that we cannot fully separate the impact of Covid-19 from our estimate. The authors believe that Covid-19 overestimates the negative impact of earthquakes on the labor market.

Earthquakes have adverse effects on the labor market. The estimation of the earthquake-related costs may provide a useful guide on policy planning and government incentives.

The originality of the study lies in the fact that this is the first study to evaluate how the dynamics of the labor market has changed as a result of the earthquakes that have taken place in Turkey, within the framework of causality.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-08-2022-0568