Search Results (266)

Disaster frequency is rising, and response-phase planners face a binding constraint: relief capacity is always smaller than need, so directing affected people to relief centres is a triage decision under time pressure. Published optimisation models speak operations-research vocabulary (cost, distance, equity); civil-protection planners speak supply-chain-resilience vocabulary (the 4R framework of Bruneau et al.). We close the gap with a four-objective humanitarian last-mile allocation model whose objectives map one-to-one onto the 4R components—Resourcefulness, Robustness, Rapidity, Redundancy—evaluated by Mamdani fuzzy inference systems and solved by NSGA-II, NRGA, and NSGA-III. Across 540 runs over three problem sizes, the four-objective formulation surfaces independent Robustness and Rapidity axes that the baseline three-objective formulation fuses into one, a visibility gain established by rank-correlation analysis; NSGA-III is faster than NSGA-II at every size (27–37%, mean 32%), with the hypervolume ranking depending on the reference-point scheme. Weight- and rule-perturbation sweeps confirm the recommendations are robust to expert-elicitation noise, flagging the three-rule Rapidity classifier as a sensitivity hot-spot. The framework is released as a production-grade, openly licensed decision-support tool—continuously tested, dependency-audited, and reproducible bit-for-bit on stock hardware—treating reproducibility as a first-class resilience criterion and positioning audit-ready operational tooling as the digital-technology innovation supply-chain-resilience practice needs. Full article

This study aims to assess the impact of the October 2023 Gaza War on water and sanitation systems and propose actionable solutions for effective management. The study adopted a mixed-methods approach (quantitative and qualitative). Primary data were collected through semi-structured interviews, targeted questionnaires, and direct field observations. Secondary data were gathered from reports by international organizations (UNRWA, OCHA, WHO), Palestinian authorities, academic research, in addition to satellite imagery and Geographic Information System (GIS) data for damage assessment. The results showed that the institutional level of emergency preparedness before the war varied significantly. Despite the existence of pre-developed emergency plans (mean score 3.26, relative weight 65.28%, p = 0.0355), there were significant shortcomings in practical staff training (mean score 2.64, relative weight 52.83%, p = 0.006), logistical readiness, and the activation of international partnerships. The study also revealed major challenges faced in providing water and sanitation services during the war, including fuel and resource shortages, safety risks for field teams, weak coordination with security agencies, lack of materials and equipment, insufficient emergency funding, coordination gaps, staff displacement, communication breakdowns, difficulty accessing water sources, area division and blockade, and loss of personnel. Specifically, the inability to access water sources was a critical challenge (mean score 4.30, relative weight 86.04%, p < 0.0001), as was indiscriminate shelling impacting field team safety (mean score 4.19, relative weight 83.77%, p < 0.0001) and lack of materials and equipment (mean score 4.15, relative weight 83.02%, p < 0.0001). Residents reported water availability only every 10–15 days, and over 70% of water and sanitation systems were damaged. While funding proposals were being prepared (mean score 3.26, relative weight 65.28%, p = 0.02), weaknesses in reporting systems and coordination of roles were observed (mean score 2.55, relative weight 50.94%, p < 0.0001). The research contributes to guiding local and international efforts towards reconstruction, enhancing resilience, and ensuring the provision of safe drinking water and adequate sanitation for the affected population, thereby reducing the risks of waterborne diseases and improving public health. Full article

Efficient and resilient humanitarian logistics is critical for smart cities facing large-scale disasters, where infrastructure disruptions, uncertain demand, and time-critical deliveries complicate operational planning. This study proposes a multi-echelon vehicle-routing framework that integrates trucks, electric unmanned aerial vehicles (UAVs), and micromobility systems under uncertainty. Demand and travel-time variability are modeled through scenario-based representations, while delivery flexibility is captured through triangular fuzzy time windows defined by earliest-acceptable, preferred, and latest-tolerable delivery times. The problem is formulated as a four-objective optimization model that minimizes total cost, response time, CO₂ emissions, and fuzzy lateness. To solve the resulting highly constrained multi-objective problem, NSGA-II and NSGA-III are adapted with problem-specific repair operators, including capacity-splitting, range-feasibility correction, and fuzzy time-shift adjustment mechanisms, and embedded in a simulation-based evaluation framework. The proposed approach is validated using a geo-referenced earthquake scenario in Istanbul, constructed from open-source GIS, traffic, and demographic data. The computational results show that evolutionary methods generate feasible solutions within minutes, whereas exact optimization approaches fail to converge for realistic instances. Compared with NSGA-II, NSGA-III achieves superior performance, including a 9% higher hypervolume and improved robustness under stress-test scenarios. Furthermore, the hybrid truck–UAV–micromobility strategy reduces average cost by up to 30%, delivery time by 43%, and CO₂ emissions by 65% relative to a truck-only baseline, while eliminating fuzzy lateness. These findings demonstrate that evolutionary multi-objective optimization provides an effective and scalable decision-support framework for uncertainty-aware and sustainable humanitarian logistics in smart cities. Full article

In forced displacement contexts, refugee education is often governed through hybrid arrangements that blend national education systems with humanitarian financing and bordering regimes. While scholarship typically focuses on student enrollment and learning outcomes, less attention has been paid to how education is governed through teachers’ work and compensation. This study integrates and compares data from three interrelated research studies, including a multi-country mixed-methods study, a global donor mapping, and a focused case study of South Sudan. Drawing on teacher interviews and focus groups, along with key informant interviews with donors, UN agencies, and stakeholders, we examine how compensation policies affect teachers’ lives. The findings reveal that differentiated systems produce profound inequities. Refugee teachers are largely excluded from national payrolls and are governed through humanitarian “incentive” schemes that normalize precarity and mask long-term state responsibility. We argue that teacher compensation operates as a mechanism of “policying and policing” within the broader architecture of refugee education governance. Full article

Background: Humanitarian logistics decisions extend beyond immediate relief delivery to include preparedness, recovery, service continuity and the development of durable local capabilities. Simulation can support these decisions under uncertainty, yet the evidence remains fragmented across logistics domains, modelling approaches and phases of the relief–development continuum. This review synthesises how simulation has been used in humanitarian logistics and identifies where the evidence is concentrated and where important gaps remain. Methods: A systematic scoping review was conducted in accordance with PRISMA-ScR and PRISMA-S, using multi-disciplinary and specialist database searches supplemented by backward and forward citation searching. Included studies were coded by logistics decision problem, continuum phase, decision level, performance outcome, simulation approach and operational grounding. Results: The literature is concentrated in preparedness and response, particularly around coordination, network design, inventory, allocation, transport and capacity. System dynamics, agent-based modelling and discrete-event simulation are well established, whereas hybrid simulation and digital twin applications remain limited. Early recovery, reconstruction, development-oriented transition and practice-embedded modelling are comparatively underdeveloped. Conclusions: Simulation-supported humanitarian logistics is strongest for structured preparedness and response problems. Future research should connect decisions across phases and strengthen beneficiary-sensitive, operationally grounded modelling of recovery, localisation, service continuity and longer-term logistics capability. Full article

Background: This study examines how humanitarian supply chain cooperation in Morocco may evolve toward 2030, focusing on the roles of donors, volunteers, and digital technologies in strengthening coordination, operational performance, and resilience in crisis environments. Methods: A mixed-methods approach was adopted, combining qualitative data collected through interviews and focus groups with 57 humanitarian actors and quantitative data obtained from 1183 logistics professionals. Regression analysis, analysis of variance (ANOVA), and qualitative foresight techniques were employed to explore current cooperation patterns and future humanitarian supply chain dynamics. Results: The findings indicate that donors are increasingly expected to support digital coordination, predictive planning, and capacity-building initiatives rather than acting solely as financial contributors, while volunteers continue to play a critical role in adaptive and decentralized operations, particularly in rural areas. The results further suggest that digital technologies may enhance visibility, responsiveness, and collaboration when integrated into human-centered governance systems. Conclusions: The study proposes an integrated socio-technical framework for understanding humanitarian supply chain cooperation toward 2030 and provides practical insights for developing resilient, collaborative, and future-ready humanitarian logistics systems in emerging economies. Full article

This study examines the impact of public attention on financial markets during the Israel–Hamas conflict, focusing on the TA35 stock index and the Israeli Shekel (ILS) exchange rate over the period October 2023 to April 2025. By distinguishing between global and domestic Google search activity, the analysis investigates whether the origin of attention differentially affects market performance and currency dynamics. Public attention is treated as a real-time proxy for investor sentiment and perceived risk. Methodologically, the study combines Google Trends data with EGARCH(1,1) models to capture both return effects and asymmetric volatility responses. To enhance robustness, Principal Component Analysis (PCA) is applied separately to global and domestic search datasets, generating latent indices that reflect conflict-related and humanitarian narratives. These indices are subsequently incorporated into the empirical models. The findings reveal that global search intensity related to conflict topics exerts a significant negative effect on stock returns and contributes to currency depreciation, reflecting heightened uncertainty and risk aversion. In contrast, domestic search activity is associated with stabilizing or positive effects, suggesting local resilience and confidence. PCA-based models improve explanatory power and confirm that the geographical origin of attention plays a crucial role in shaping financial outcomes. Additionally, the results indicate that attention-driven shocks influence volatility asymmetrically, amplifying downside risk during periods of intensified global concern. Overall, the study contributes to the literature by integrating behavioral indicators into financial risk modeling and providing a novel, real-time framework for assessing how digital attention transmits geopolitical risk into asset prices. Full article

In protracted humanitarian crises, solid waste management (SWM) becomes a major challenge due to limited resources, inadequate infrastructure, and competing response priorities. Waste generated in humanitarian settings typically consist of heterogeneous streams, where plastics, biodegradable fractions, and packaging materials represent the dominant components. Proper management of this waste is essential to reduce health risks and environmental impacts on local communities. Within this framework, sustainable bio-based alternatives and compostable solutions represent promising alternatives. The EU-funded Bio4HUMAN project promotes the integration of innovative bio-based solutions aligned with humanitarian and sustainability goals. An exploratory assessment focused on analyzing waste production, material composition, and handling practices in two case study locations in Sub-Saharan Africa (Democratic Republic of Congo (DRC) and South Sudan (SS)). The results indicate that humanitarian waste cannot be clearly distinguished from household or commercial waste, as streams are typically mixed. Waste composition is dominated by organic matter (43–65%), followed by plastics (15–33%), while other fractions such as paper, glass, metals, and textiles are less significant. Further insights into challenges and opportunities were obtained through a combination of quantitative surveys (n = 29), qualitative interviews with key informants (KIIs) (44) and group discussions sessions (FDG) (9), direct observations, and literature review. Subsequently, a scoping approach was applied to map and classify suitable sustainable solutions into two main categories: bio-based products (BBPs) and organic waste valorization technologies. These were assessed through life cycle assessment (LCA) in accordance with ISO 14040 and 14044, applying SimaPro v.10.2.0.3 software and the Ecoinvent 3.10 database, and compared against fossil-based alternatives. This study compares two case scenarios: a HDPE oil bottle versus PLA alternative (functional unit 6 L), and PE water container versus PLA alternative (functional unit 10 L). For the oil bottle, PLA shows a lower carbon footprint (1.33 kg CO₂-eq) than HDPE (2.37 kg CO₂-eq). In contrast, for the water container, PLA performs worse (2.22 kg CO₂-eq) compared to PE (1.59 kg CO₂-eq), due to higher material demand. The results suggest that benefits are context-dependent and most evident for lightweight products with high leakage risks, particularly when composting infrastructure is accessible. This study advances previous work on humanitarian SWM by integrating field-based waste flow characterization with context-specific screening and life cycle assessment of bio-based alternatives, providing quantitative evidence on the conditions under which these solutions can effectively reduce environmental burdens in protracted crisis settings. Full article

The ongoing Palestinian conflict, particularly the escalation in Gaza since October 2023, has raised pressing concerns regarding human rights and international justice. This study explores how university students in northern Spain perceive the situation in Palestine, analyzing their levels of knowledge, emotional responses, and critical positioning. Using a mixed-method approach based on an online questionnaire and the Grid Elaboration Method, data were gathered from 147 students enrolled in education-related programs. The findings reveal three core themes in students’ representations of the conflict: resistance as a form of national identity, humanitarian suffering of civilians, and structural injustice perpetuated by global power dynamics. Gender and academic background influenced discursive emphasis, with Social Education students showing more politicized perspectives and women focusing more on Palestinian dignity and resistance. These insights underscore the potential of higher education to foster critical thinking, empathy, and engagement with international conflicts, and highlight the role of universities in cultivating a culture of peace and human rights. Full article

Access to clean and safe drinking water for all remains a global challenge, mainly for rural populations and areas affected by natural disasters or humanitarian crises. The traditional water quality treatment technologies can work well in laboratory or controlled settings, but they are usually applied under conditions unavailable in these types of conditions. Traditional water quality treatment methods are limited by established infrastructure, expensive operating costs, energy requirements, and the ability to perform in-field water treatment. To improve the barriers of traditional water quality treatment technologies, recently developed scientific discoveries of nanozymes, a new class of nanomaterials with enzyme-like catalytic activity, have shown the ability to decentralise water purification. Nanozymes provide a mechanism for water treatment that does not require the infrastructure or the cost of traditional water quality treatment methods. Also, nanozymes possess extremely high catalytic activity, chemical stability, are inexpensive, and are suitable for a variety of contaminants. This review gives a systematic overview of the development of suitable nanozyme-based portable water purification systems. It shows their catalytic mechanisms, the class of nanozymes used, and the design characteristics related to their working use, also highlighting the developments that consider the specific needs of rural contexts, provide rapid responses to disaster areas, and offer drinking water with reliable, simple, and sustainable apparatus. Full article

Drought remains a persistent challenge affecting agricultural and pastoral livelihoods, particularly in dryland mixed crop–livestock systems. Water Monitoring and Early Warning Systems (WM-EWS) have increasingly been promoted as tools for delivering climate information services and supporting drought-related decision-making. However, empirical understanding of how users perceive and engage with such systems in pastoral contexts remains limited. This study explores stakeholder perceptions regarding the usefulness and operational relevance of a WM-EWS implemented in the Borana zone of Ethiopia. A mixed-methods approach was employed, combining survey data from 71 purposively selected mixed stakeholders with qualitative insights obtained through focus group discussions and key informant interviews. Findings indicate that respondents widely reported using WM-EWS information for water-related decision-making and perceived the system as useful in supporting drought preparedness and adaptive responses. Participants associated WM-EWS use with perceived changes in areas such as livestock management, access to water-related information, and coordination among stakeholders. Respondents also reported adopting multiple coping strategies, including early livestock sales, strategic herd mobility, and engagement with external support mechanisms. Respondents perceived fewer conflicts over water resources and greater engagement from humanitarian actors following WM-EWS implementation. Overall, the study provides exploratory insights into stakeholder experiences, perceived usefulness, and operational relevance of user-centered WM-EWS in drought-prone pastoral systems. The findings contribute to understanding how pastoral communities engage with climate information services while highlighting the need for future research using objective and longitudinal approaches to assess system effectiveness more rigorously. Full article

This paper investigates the settlement experiences of Hong Kong British National (Overseas) [BN(O)] migrants in the UK, with a particular focus on housing as a central mechanism shaping their wellbeing, security, and integration. Following the introduction of the BN(O) visa route in 2021, this study draws on qualitative interviews with migrants in the North of England to explore how housing mediates conditional settlement under a marketised migration regime. Findings reveal that housing functions as the primary infrastructure of settlement, influencing employment, education, and family life, while access is conditioned by migrants’ capacity to absorb market risks such as advance rent payments and landlord discretion. The study highlights significant intra-group stratification shaped by financial resources, family composition, and transnational support, with family responsibilities intensifying housing precarity and constraining choices. Moreover, a moralised ethos of self-reliance among migrants normalises hidden insecurity and limits formal support-seeking. This research contributes to migration and housing scholarship by demonstrating how ostensibly humanitarian migration pathways reproduce uneven security through housing systems, underscoring the need for policy interventions that address the cumulative effects of housing insecurity on settlement and wellbeing. Full article

The rise in large language models (LLMs) has sparked renewed interest in how firms, particularly multinational aid organizations, can enhance learning related to meta-dynamic capabilities (DCs), such as agility, sensing, and adaptation, in response to disasters and humanitarian crises. A key strategic priority is developing meta-rules that combine general engagement frameworks with locally tailored action plans, grounded in cultural and institutional contexts. LLMs offer potential in supporting this need, but premature deployment risks harmful or misleading outcomes. This underscores the critical importance of collaboration between artificial and human intelligence (AI-HI). While AI brings computational power, it lacks the tacit knowledge—encompassing cultural, contextual, and intuitive understanding—that is essential in high-stakes, unpredictable environments. Our experimental study provides two core insights: (1) AI alone cannot effectively handle tasks requiring tacit knowledge, and (2) AI-HI collaboration thrives when human input guides AI using deep awareness of local social and political dynamics. We contribute to the discourse on dynamic capabilities in multinational contexts during catastrophic situations by offering practical strategies to support successful AI-HI partnerships and a framework for organizations aiming to enhance meta-DCs through responsible, human-centered use of disruptive technologies. Our findings clarify how the international dimensions of these capabilities influence their effectiveness across diverse cultural and institutional environments. Full article

City Information Modelling (CIM) and Urban Digital Twins (UDT) are pivotal for advancing smart urban planning and city management, yet empirical evidence on their real-world implementation is scarce. Following a sequential mixed-methods design, this study addresses this gap through a global investigation analyzing 33 projects across diverse geographic contexts. Findings reveal that these technologies are predominantly applied in 3D visualization (60.6%) and urban planning (48.5%), with significant underutilization in climate adaptation (9.1%) and AI-driven robotics (3.0%). A pronounced physical–social data divide exists, with infrastructure data prioritized over human-centric inputs. Technology stacks converge on GIS, IoT, and BIM. However, an interoperability paradox persists, as internal integration outpaces cross-organizational connectivity. Governance is predominantly public-sector-led, but multi-actor ecosystems are also involved. The study concludes with actionable recommendations to rebalance implementation portfolios, integrate socio-economic data, and advance both technical and institutional interoperability, thereby harnessing CIM and UDT for transformative urban planning and city management. Full article

Social media posts by individuals affected by disasters and their relatives provide a significant source of data for identifying emergencies and needs, assessing the situation, and determining affected areas. These posts often contain not only text but also text embedded within images. Therefore, focusing solely on text data may compromise the integrity of the information and lead to incomplete or limited analyses. In this study, a topic modelling-based clustering approach is proposed that accounts for the complementary nature of text and image text in social media posts, as well as the limitations of manual annotation during disasters. In this context, data pre-processing was performed on text and text extracted from images. Text extracted from images via Optical Character Recognition (OCR) was corrected using the GPT-4.0-mini model. Then, both data types were clustered separately using BERTopic with k-means, and the resulting clusters were integrated. A dictionary-based analysis was conducted to identify humanitarian relief categories and locations within the clusters. The proposed framework was applied to the social media dataset related to the Kahramanmaraş earthquakes, one of the largest disasters in recent times. The findings show that text and image text data complement each other. The resulting clusters are meaningful, with average coherence scores of 0.710 for text and 0.687 for image text. LLM-based post-OCR correction also yielded a 62.81% reduction in average character error rate and a 56.91% decrease in average word error rate compared to the normalized ground truth image text. Furthermore, the proposed approach outperformed both keyword-based filtering with k-means and BERTopic with HDBSCAN. In summary, the results demonstrate that the proposed unsupervised learning approach is effective for extracting humanitarian needs and locations from social media in disaster response. Full article