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Humanitarian action and disaster risk reduction are essential in addressing global vulnerability to disasters and crises. The Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR), adopted in 2015, has garnered significant attention for its role in fostering disaster risk reduction. The role the SFDRR plays vis-à-vis humanitarian action represents a crucial space where policies, practices, and priorities (could) converge and diverge. Understanding the dynamics of this SFDRR-humanitarian action relationship is essential for advancing both disaster risk reduction and humanitarian goals. This article comprehensively examines this relationship since the adoption of the SFDRR. Employing a multimethod approach, including a systematic literature review, mapping exercise, and expert interviews, the study identified key themes and challenges in integrating the SFDRR within humanitarian action. Findings indicate that while SFDRR references are prevalent in post-disaster discussions, their full integration into humanitarian strategies remains nascent. Notably, advancements in anticipatory humanitarian action represent primary arenas for SFDRR integration within humanitarianism. The role of the International Disaster Response Law in bridging SFDRR and humanitarianism also emerged as an important finding. The study also underscored blurred distinctions between humanitarianism and disaster-related actions, highlighting the limited systemic integration of the SFDRR by traditional humanitarian actors. Moving forward, the study advocates for improved collaboration between humanitarian and disaster management sectors to strengthen disaster prevention, response, and mitigation. By examining the relationship between SFDRR objectives and modern humanitarian practices, this research aims to enhance disaster preparedness, response, and recovery strategies, alongside other crisis management approaches.

This article implements a hybrid Machine Learning (ML) model to classify stoppage events in a copper-crushing equipment, more specifically, a conveyor belt. The model combines Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) with Principal Component Analysis (PCA) to identify the type of stoppage event when they occur in an industrial sector that is significant for the Chilean economy. This research addresses the critical need to optimise maintenance management in the mining industry, highlighting the technological relevance and motivation for using advanced ML techniques. This study focusses on combining and implementing three ML models trained with historical data composed of information from various sensors, real and virtual, as well from maintenance reports that report operational conditions and equipment failure characteristics. The main objective of this study is to improve the efficiency when identifying the nature of a stoppage serving as a basis for the subsequent development of a reliable failure prediction system. The results indicate that this approach significantly increases information reliability, addressing the persistent challenges in data management within the maintenance area. With a classification accuracy of 96.2% and a recall of 96.3%, the model validates and automates the classification of stoppage events, significantly reducing dependency on interdepartmental interactions. This advancement eliminates the need for reliance on external databases, which have previously been prone to errors, missing critical data, or containing outdated information. By implementing this methodology, a robust and reliable foundation is established for developing a failure prediction model, fostering both efficiency and reliability in the maintenance process. The application of ML in this context produces demonstrably positive outcomes in the classification of stoppage events, underscoring its significant impact on industry operations.

The considerable increase in the complexity associated with the formulation of maintenance plans has enabled the development of new techniques to bring maintenance scheduling optimization models to more realistic environments. In this sense, a previous optimization model was proposed considering the use of time windows for the formation of grouping schemes under an opportunistic strategy for maintenance activities considering non-negligible execution times, thus offering the possibility of analysing scenarios with limited resources. This article proposes a risk analysis based on the failure probability of each component involved in the maintenance scheduling optimization model, which has the particularity of enabling a greater number of combinations of grouped PM activities. Moreover, it seeks to identify the general behaviour of the optimization model against different scenarios of periodicities and execution times of each maintenance activity. The proposed optimization model is formulated under a mixed integer linear programming (MILP) paradigm and its objective function seeks to minimize the unavailability of the system associated with the execution times of the activities developed, generating different experimental cases, and varying the start time scheduling under a tolerance factor from 0% up to a maximum of 25% for advance or delay. Results show in contrast with the base optimization model, an 8% less unavailability when the tolerance factor is 10%. Finally, it was possible to quantify the risk present in each maintenance schedule, at the same time a behaviour towards advancing PM activities is evidenced by the optimization model proposed over the delay.

In high-conflict scenarios, humanitarian needs often surpass resources, and humanitarians are faced with ongoing challenges of whom to prioritise and where to work. This process is often referred to as ‘targeting’, but this article uses the concept of ‘triage’ to emphasise how prioritisation is a continuous and political process, rather than a one-off exercise to find the best match between needs and programme objectives. This study focused on South Sudan, exploring the formal and informal dynamics at the national, regional and local levels of humanitarian decisions. The article is based on semi-structured interviews and multiple meetings and observations of programmes over four months of fieldwork in 2017. This fieldwork was beset by many of the problems that humanitarians also encounter in their work, including complicated access, logistics difficulties and security challenges. Humanitarian action is meant to be flexibly deployed to respond to priority needs resulting from conflict or disasters, and agencies have multiple tools and policies to facilitate this. However, in reality, we find humanitarian action largely locked into path-dependent areas of intervention because agencies must rely on logistics, trust and local partners, all of which take years to develop, and because local actors’ commitment to see programmes continued.

PurposeThe notion that disasters are not natural is longstanding, leading to a growing number of campaigns aimed at countering the use of the term “natural disaster.” Whilst these efforts are crucial, critical perspectives regarding the potential risks associated with this process are lacking, particularly in places affected by violent conflict. This paper aims to present a critical analysis of these efforts, highlighting the need to approach them with care.Design/methodology/approachThe author draws upon insights and discussions accumulated over a decade of research into the relationship between disasters and conflict. The article includes a critical literature review on the disaster–conflict relationship and literature specifically addressing the idea that disasters are not natural. The analysis of field notes led to a second literature review covering topics such as (de) politicisation, instrumentalisation, disaster diplomacy, ethics, humanitarian principles, disaster risk reduction, peacebuilding and conflict sensitivity.FindingsThis analysis underscores the importance of advocating that disasters are not natural, especially in conflict-affected areas. However, an uncritical approach could lead to unintended consequences, such as exacerbating social conflicts or obstructing disaster-related actions. The article also presents alternatives to advance the understanding that disasters are not natural whilst mitigating risks, such as embracing a “do-no-harm” approach or conflict-sensitive analyses.Originality/valueThe author offers an innovative critical approach to advancing the understanding that disasters are not natural but socio-political. This perspective is advocated, especially in conflict-affected contexts, to address the root causes of both disasters and conflicts. The author also invites their peers and practitioners to prioritise reflective scholarship and practices, aiming to prevent the unintentional exacerbation of suffering whilst working towards its reduction.

Under Condition-Based Maintenance, the Proportional Hazards Model (PHM) uses Cox’s partial regression and vital signs as covariates to estimate risk for predictive management. However, maintenance faces challenges when dealing with a multi-covariate scenario due to the impact of the conditions’ heterogeneity on the intervention decisions, especially when the combined measurement lacks a physical interpretation. Therefore, we propose an advanced framework based on a PHM-machine learning formulation integrating four key areas: covariate prioritization, covariate weight estimation, state band definition, and the generation of an enhanced predictive intervention policy. The paper validates the framework’s effectiveness through a comparative analysis of reliability metrics in a case study using real condition monitoring data from an energy company. While the traditional log-likelihood minimization may fall short in covariate weight estimation, sensitivity analyses reveal that the proposed policy using IPOPT and a non-scaler transformation results in consistent prediction quality. Given the challenge of interpreting merged covariates, the scheme yields improved results compared to expert criteria. Finally, the advanced framework strengthens the PHM modeling by coherently integrating diverse covariate scenarios for predictive maintenance purposes.

Wastewater treatment plants (WWTPs) face two fundamental challenges: on the one hand, they must ensure an efficient application of preventive maintenance plans for their survival under competitive environments; and on the other hand, they must simultaneously comply with the requirements of reliability, maintainability, and safety of their operations, ensuring environmental care and the quality of their effluents for human consumption. In this sense, this article seeks to propose a cost-efficient alternative for the execution of preventive maintenance (PM) plans through the formulation and optimization of the opportunistic grouping strategy with time-window tolerances and non-negligible execution times. The proposed framework is applied to a PM plan for critical high-risk activities, addressing primary treatment and anaerobic sludge treatment process in a wastewater treatment plant. Results show a 26% system inefficiency reduction versus the initial maintenance plan, demonstrating the capacity of the framework to increase the availability of the assets and reduce maintenance interruptions of the WWTP under analysis.

The Proportional Hazards Model (PHM) under a Condition-Based Maintenance (CBM) policy is used by asset-intensive industries to predict failure rate, reliability function, and maintenance decisions based on vital covariates data. Cox’s partial likelihood optimization is a method to assess the weight of time and conditions into the hazard rate; however, parameter estimation with diverse covariates problem could have multiple and feasible solutions. Therefore, the boundary assessment and the initial value strategy are critical matters to consider. This paper analyzes innovative non/semi-parametric approaches to address this problem. Specifically, we incorporate IPCRidge for defining boundaries and use Gradient Boosting and Random Forest for estimating seed values for covariates weighting. When applied to a real case study, the integration of data scaling streamlines the handling of condition data with diverse orders of magnitude and units. This enhancement simplifies the modeling process and ensures a more comprehensive and accurate underlying data analysis. Finally, the proposed method shows an innovative path for assessing condition weights and Weibull parameters with data-driven approaches and advanced algorithms, increasing the robustness of non-convex log-likelihood optimization, and strengthening the PHM model with multiple covariates by easing its interpretation for predictive maintenance purposes.

The proportional hazards model (PHM) is a vital statistical procedure for condition-based maintenance that integrates age and covariates monitoring to estimate asset health and predict failure risks. However, when dealing with multi-covariate scenarios, the PHM faces interpretability challenges when it lacks coherent criteria for defining each covariate’s influence degree on the hazard rate. Hence, we proposed a comprehensive machine learning (ML) formulation with Interior Point Optimizer and gradient boosting to maximize and converge the logarithmic likelihood for estimating covariate weights, and a K-means and Gaussian mixture model (GMM) for condition state bands. Using real industrial data, this paper evaluates both clustering techniques to determine their suitability regarding reliability, remaining useful life, and asset intervention decision rules. By developing models differing in the selected covariates, the results show that although K-means and GMM produce comparable policies, GMM stands out for its robustness in cluster definition and intuitive interpretation in generating the state bands. Ultimately, as the evaluated models suggest similar policies, the novel PHM-ML demonstrates the robustness of its covariate weight estimation process, thereby strengthening the guidance for predictive maintenance decisions.

Humanitarian action and disaster risk reduction are essential in addressing global vulnerability to disasters and crises. The Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR), adopted in 2015, has garnered significant attention for its role in fostering disaster risk reduction. The role the SFDRR plays vis-à-vis humanitarian action represents a crucial space where policies, practices, and priorities (could) converge and diverge. Understanding the dynamics of this SFDRR-humanitarian action relationship is essential for advancing both disaster risk reduction and humanitarian goals. This article comprehensively examines this relationship since the adoption of the SFDRR. Employing a multimethod approach, including a systematic literature review, mapping exercise, and expert interviews, the study identified key themes and challenges in integrating the SFDRR within humanitarian action. Findings indicate that while SFDRR references are prevalent in post-disaster discussions, their full integration into humanitarian strategies remains nascent. Notably, advancements in anticipatory humanitarian action represent primary arenas for SFDRR integration within humanitarianism. The role of the International Disaster Response Law in bridging SFDRR and humanitarianism also emerged as an important finding. The study also underscored blurred distinctions between humanitarianism and disaster-related actions, highlighting the limited systemic integration of the SFDRR by traditional humanitarian actors. Moving forward, the study advocates for improved collaboration between humanitarian and disaster management sectors to strengthen disaster prevention, response, and mitigation. By examining the relationship between SFDRR objectives and modern humanitarian practices, this research aims to enhance disaster preparedness, response, and recovery strategies, alongside other crisis management approaches.