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The study aims to cope with the problems confronted in the skin lesion datasets with less training data toward the classification of melanoma. The vital, challenging issue is the insufficiency of training data that occurred while classifying the lesions as melanoma and non-melanoma.

In this work, a transfer learning (TL) framework Transfer Constituent Support Vector Machine (TrCSVM) is designed for melanoma classification based on feature-based domain adaptation (FBDA) leveraging the support vector machine (SVM) and Transfer AdaBoost (TrAdaBoost). The working of the framework is twofold: at first, SVM is utilized for domain adaptation for learning much transferrable representation between source and target domain. In the first phase, for homogeneous domain adaptation, it augments features by transforming the data from source and target (different but related) domains in a shared-subspace. In the second phase, for heterogeneous domain adaptation, it leverages knowledge by augmenting features from source to target (different and not related) domains to a shared-subspace. Second, TrAdaBoost is utilized to adjust the weights of wrongly classified data in the newly generated source and target datasets.

The experimental results empirically prove the superiority of TrCSVM than the state-of-the-art TL methods on less-sized datasets with an accuracy of 98.82%.

Experiments are conducted on six skin lesion datasets and performance is compared based on accuracy, precision, sensitivity, and specificity. The effectiveness of TrCSVM is evaluated on ten other datasets towards testing its generalizing behavior. Its performance is also compared with two existing TL frameworks (TrResampling, TrAdaBoost) for the classification of melanoma.

This viewpoint engages with Jem Bendell’s deep adaptation framework which was developed as a response to the threat of collapse. Proponents of deep adaptation argue that societal collapse is either likely, inevitable or already underway. The deep adaptation framework is employed as a tool to contemplate the necessary adaptation of tourism development and planning in a context of polycrisis leading to collapse.

This is a conceptual viewpoint article that is built on deductive analysis of recent events, reports and scientific findings. It employs the deep adaptation framework to analyse possible alternative tourism futures in the face of the threat of collapse.

Bendell’s framework included four aspects of response to the recognition of the threat of collapse: resilience, relinquishment, restoration and reconciliation. In this work, the deep adaptation framework is employed to analyse what a deep adaptation approach to tourism might offer for efforts in securing optimal social and ecological outcomes. Findings highlight damaging activities that we should relinquish, more resilient approaches that communities could encourage and restorative practices such as rewilding and pluriversal economies as protective measures. This work recommends a precautionary approach to transform tourism education, research and practice in order to secure better tourism futures.

This work is novel in engaging with the threat of future collapse and in using the deep adaptation framework to consider alternative tourism futures.

This study provides a comprehensive framework of adaptation in triadic business relationship settings in the service sector. The framework is based on the industrial network approach (see, e.g., Axelsson & Easton, 1992; Håkansson & Snehota, 1995a). The study describes how adaptations initiate, how they progress, and what the outcomes of these adaptations are. Furthermore, the framework takes into account how adaptations spread in triadic relationship settings. The empirical context is corporate travel management, which is a chain of activities where an industrial enterprise, and its preferred travel agency and service supplier partners combine their resources. The scientific philosophy, on which the knowledge creation is based, is realist ontology. Epistemologically, the study relies on constructionist processes and interpretation. Case studies with in-depth interviews are the main source of data.

This paper aims to explore the deep Web as a solution for displacement and replacement challenges in libraries, addressing the challenges, benefits, strategies and case studies.

The paper synthesizes existing literature on deep Web integration in libraries, providing a comprehensive analysis of insights from scholarly articles, case studies and expert opinions.

The deep Web grants libraries access to unique content, improving information access, fostering collaboration and enabling personalized content. However, security, privacy, ethics and data protection must be considered.

This paper contributes to the literature by providing a comprehensive examination of deep Web integration in libraries, offering valuable recommendations for navigating the changing landscape and leveraging the deep Web’s potential.

Knowledge sharing for evidence-based climate change adaptation is key to reducing vulnerabilities. The disastrous effects of the recent tropical cyclone Idai in Zimbabwe’s eastern highlands, whereby lives and livelihoods were destroyed, calls for fundamental rethinking on how knowledge sharing can be useful in reducing vulnerabilities. This study aims to examine how knowledge sharing practices among selected climate action organisations can support positive results in evidence-based climate change adaptation in Zimbabwe and recommends a strategy to enhance knowledge sharing for evidence-based climate change adaptation.

The researcher opted for a qualitative research methodology, and interviews were conducted with research participants to get in-depth information. The target population for the research is drawn from three climate change organisations in Zimbabwe. These participants were chosen because of their direct involvement in climate change adaptation and mitigation. Purposive sampling was used because it was considered an easy and informal method to reach the target population. The responses were coded and readied for analysis. Categories for analysis were also determined and described. The next section focuses on the presentation and interpretation of the responses.

The study found out that even though there is evidence of knowledge sharing in the selected organisations, there was a need for a strategy that integrates the efforts of all climate action organisation to realise successful climate change adaptation. Successful climate change adaptation requires an integrative force in the form of a strategy that supports knowledge sharing. The strategy breaks the silo mode of operation by bringing together all stakeholders through knowledge sharing.

The research study was unique to the Zimbabwean climate change and adaptation context and only a purposive sampling of participants representing three climate change action was feasible.

The proposed model will enhance knowledge sharing among climate change and adaptation organisations in Zimbabwe. It will transform knowledge management practices among climate change organisations in Zimbabwe.

The study recommends a knowledge sharing strategy that can be useful in supporting climate change adaptation. The model will support decision-making by providing access to shareable information on climate change. The proposed knowledge sharing strategy integrates the efforts of all stakeholders, including communities, government and academia, among many. The study proposes an original model for climate change adaptation for Zimbabwe.

‘Planned relocation’ has emerged in the international climate policy arena as an ‘adaptation’ solution with the potential to enhance resilience, address underdevelopment and debunk age-old narratives around migration as a risk to peace and security. In 2018, Fiji became one of the first countries to develop Planned Relocation Guidelines, with upwards of 80 villages thought to require relocation over the coming years due to the impact of climate change. Through interviews carried out with representatives from organisations involved in planning for community relocations in Fiji, this chapter explores the creation of planned relocation as a form of climate change adaptation and development. Looking specifically at the value-based challenges of implementation in Fiji, this research provides insight into what happens when dominant international policy narratives play out in practice. Through the presentation of culturally nuanced ways of understanding the problem of climate-induced migration, this chapter invites policymakers to seek out these voices when devising displacement solutions.

Supply chain resilience and knowledge management (KM) processes have received increasing attention from researchers and practitioners. Nevertheless, previous studies often treat the two streams of literature independently. Drawing on the knowledge-based theory, this study aims to reconcile these two different streams of literature and examine how and when KM processes influence supply chain resilience.

This research develops a conceptual model to test a sample of data from 203 Chinese manufacturing firms using a structural equation modeling method. Specifically, the current study empirically examines how KM processes affect different forms of supply chain resilience (supply chain readiness, responsiveness and recovery) and examines the moderating effect of blockchain technology adaptation and organizational inertia on the relationship between KM processes and supply chain resilience.

The findings show that KM processes positively affect three dimensions of supply chain resilience, i.e., supply chain readiness, responsiveness and recovery. Besides, the study reveals that blockchain technology adoption positively moderates the relationships between KM processes and supply chain resilience, whereas organizational inertia negatively moderates these above relationships.

This research linked the two research areas of supply chain resilience and KM processes, further bridging the gap in the research exploration of KM in the supply chain field. Next, this study contributes to supply chain resilience research by investigating how KM systems positively impact supply chain readiness, responsiveness and recovery. In addition, this study found a moderating effect of blockchain technology adaption and organizational inertia on the relationship between KM processes and supply chain resilience. These findings provide a reference for Chinese manufacturing firms to strengthen supply chain resilience, achieve secure supply chain operations and gain a competitive advantage in the supply chain. This studys’findings advance the understanding of supply chain resilience and provide practical implications for supply chain managers.

The purpose of this paper was to assess the effects of quality coffee production on climate change adaptation using household surveys and interview data gathered from coffee farmers in Yirgacheffe, southern Ethiopia.

A sample of 352 households, stratified into conventional coffee farmers 232 (66%) and specialty coffee producers 120(34%), was used. The propensity score model for participating in quality coffee production was estimated using 14 covariates, and the impact of quality coffee production on adaptation to climate change adaptation was examined. The results are augmented with qualitative data collected through focus group discussions and key informant interviews held with randomly selected smallholder farmers. A telecoupling theoretical perspective was used to understand the link between coffee farmers’ adaptation practices and the demand for quality coffee, as coffee is a global commodity.

The PSM analysis reveals that quality coffee production positively influences climate change adaptation. This implies that conventional coffee producers would have performed better in adaptation to climate change if they had participated in quality coffee production. The results of group discussions also confirm the positive effects of quality coffee production on adaptation to climate change, which also suggests a positive spillover effects for sustainable coffee farm management.

This study suggests enhancing quality coffee production is essential if a more sustainable and climate change resilient coffee livelihood is envisioned.

Though many studies are available on adaptation to climate change in general, this study is one of the few studies focusing on the effects of quality coffee production on climate change adaptation by smallholders in one of the least developed countries, Ethiopia. This study provides a better understanding of the importance of adaptation strategies specific to coffee production, which in turn help develop a more resilient coffee sector, as coffee production is one of the most sensitive activity to climate change.