The end of the political transition in Afghanistan in August 2021 altered radically the spheres of activities of International Non-Governmental Organizations (INGOs), compelling them to make an urgent change in priorities, making the development-focused programming change to emergency humanitarian response. This paper explores the combined effect of INGOs on the health and education sector in Afghanistan during the 2020-2025 timeframe, specifically how innovation and Information Systems (IS) have helped the service delivery to be sustained in the face of extreme institutional and political constraints. The paper uses a mixed-methodology approach to review secondary data concerning key health and education indicators, as well as a thematic analysis of the operational strategies of INGOs. The quantitative data show that the female secondary school enrollment (38.5 percent in 2020 and estimated 1.5 percent in 2025) fell precipitously, and the Maternal Mortality Ratio (MMR) increased, indicating the humanitarian crisis. On the other hand, the INGO health coverage became even more important with a manifestation of their urgency as the main service provider. Qualitative analysis revealed that the introduction of sustainable, localized information systems, particularly remote monitoring and resource tracking, was the key institutional innovation that enabled the INGO to achieve approximately 82 % resource allocation efficiency after the transition, compared to 65 % before the transition. The paper concludes that INGOs have played an essential role in ensuring that there is no complete breakdown of vital services, but their overall effect in the long term is limited due to the absence of a consistent institutional structure and policy limitations. Policy recommendations focus on the necessity to have long-term, adaptable funding, and investment in digital resilience and a localized approach to service provision to maneuver the humanitarian-development nexus in fragile contexts.

The longstanding blockade and intermittent Israeli attacks have severely exacerbated the existing health challenges in Gaza, and the recent escalation has introduced new health crises. These developments have had profoundly negative impacts on both physical and mental health. The already fragile healthcare system in Gaza has now reached the point of collapse. Women, children, and newborns are particularly affected, facing extreme difficulties in accessing essential maternal, neonatal, and child health services. The destruction of healthcare facilities, the breakdown of medical services, critical shortages of medication, forced displacement, as well as interruptions in water, electricity, and access to food, pose grave threats to public health. Despite these blatant violations of human rights, war crimes, and public and environmental health disasters, the scientific community has not shown adequate attention to the crisis. It is imperative that scientists, physicians, and public health professionals bring this issue to the forefront and work toward ending and reversing the long-standing human rights violations and their devastating impacts on public and environmental health in occupied Gaza and Palestine. Moreover, this situation must be recognized by all international health organizations as a global public health emergency. In this context, scientists, researchers, and academic journals bear significant responsibility.

This qualitative study examines the impact of the ongoing siege and blockade on parenting, family structures, and childhood in the Gaza Strip. Based on in-depth interviews with thirty displaced Palestinian mothers and fathers aged 23–58, living in shelters in Rafah and Jabalia, the research reveals the psychosocial effects of engineered starvation, systemic helplessness, and the breakdown of healthcare services. Findings show how traditional parental roles are disrupted, with fathers experiencing “thwarted masculinities” due to their inability to provide and protect, while mothers face guilt and despair amid the weaponization of motherhood. Children's suffering appears as direct evidence of structural violence, where hunger affects not only physical survival but emotional and developmental continuity. The study highlights the fragmentation of family bonds, the destruction of childhood, and the effects of global neglect. It calls for urgent recognition of siege policies as genocidal mechanisms that undermine Palestinian social fabric and demands a shift in humanitarian and political responses.

Companies in the digital age rely on Customer Relationship Management (CRM) to build loyalty and keep users engaged. Traditionally built around structured inputs such as transactional logs or survey results, CRM systems are now challenged by the overwhelming influx of unstructured, experience-driven feedback from digital forums, review platforms, and social media. This sudden shift introduces feedback that is both high in emotional expressiveness and rich in feature-level granularity, posing significant interpretation challenges for legacy CRM workflows. This problem is obvious in the gaming industry, where users actively and continuously respond to game updates, service breakdowns, or balance issues through real-time public discourse. The domain-oriented tools are needed to manage this type of feedback to parse multi-dimensional opinion data. The proposed framework is specifically designed for gaming-related CRM applications. The current framework considers various online user reviews to process them through a dual pipeline architecture, considering the Removal of Personally Identifiable Information (PII), linguistic normalization, and ontology-driven tagging. The rule-based approach was used in the product pipeline using feature proximity. A hybrid NLP approach was employed for the service path. The sentiment tags were initialized by zero-shot RoBERTa, and a high-confidence-based fine-tuning was carried out using Sentiment140. Final predictions were reassigned and evaluated against pseudo-gold standards. Various interactive dashboards were designed to visualize the insights obtained from the framework, that asists CRM teams to act on player sentiment with feature-level and time-sensitive clarity.

Customers' trust in Islamic banking post-cyberattack leads to digital service breakdowns in Indonesia

Older persons often stay in conflict zones, abandoned by younger generations and neglected by the government, putting them at risk of becoming victims of violence. This meta-ethnographic study aims to review and synthesise qualitative research on violence in contexts of war and armed conflicts as experienced by older persons and explore how violence in war and armed conflicts affects the health and well-being. Databases (CINAHL, PsychINFO, Web of Science, and Scopus) were searched for studies with a qualitative approach and participants aged ≥ 55 years. Twenty qualitative studies were included, describing experiences of persons from seven countries. Guarding the past and ensuring a future was established as an overarching metaphor in a lines-of-argument synthesis, accompanied by five themes: To endure a violent situation; Home - the heart of existence; To witness a fragile family line; Alienated and abandoned by society- adding insult to injury and Maintaining normality in an abnormal situation. Through interpretation, an understanding emerges of how separation from loved ones, the breakdown of healthcare services, and remaining in conflict areas can significantly increase vulnerability, while simultaneously demonstrating the resilience of older persons and their willingness to serve as resources within their communities.

Purpose This study aims to explore the impact of the severity of artificial intelligence (AI) service failure on consumer forgiveness willingness in the context of contactless hotel services. In addition, it examines the mediating role of perceived betrayal and the moderating effects of brand attachment and the cuteness of hotel robots on these relationships. Design/methodology/approach A 2 (severity of AI service failure: high vs low) × 2 (robot cuteness: high vs low) between-subjects experiment was conducted on the Credamo platform. A total of 840 participants completed the questionnaire, and after data screening, 735 valid responses were used for analysis. Findings This study finds that the severity of AI service failure significantly increases consumers’ perceived betrayal and decreases forgiveness willingness. Perceived betrayal mediates the relationship between AI service failure severity and forgiveness willingness. In addition, brand attachment moderates the effect of AI service failure severity on perceived betrayal. The moderating effect of hotel robot cuteness is not significant. Originality/value This study extends service failure research to AI-powered contactless contexts, clarifying the mediating role of perceived betrayal and the moderating role of brand attachment. It fills a theoretical gap and offers actionable insights for managing consumer forgiveness after AI service breakdowns.

This study examines the multifaceted impacts and challenges faced by Dupcheshwor Rural Municipality in the aftermath of the 2072 Gorkha Earthquake, with a focus on both immediate and long-term consequences for the local population. It adopts a descriptive research design and a mixed-methods approach, combining qualitative and quantitative data collected from 200 randomly selected households. Primary data were gathered through semi-structured interviews and focus group discussions, while secondary data were sourced from government reports, NGO assessments, and academic literature. The core findings reveal extensive destruction of homes, infrastructure, and agricultural assets, which significantly disrupted livelihoods and heightened food insecurity. Access to essential services such as healthcare, education, water, and sanitation was severely affected. Displacement and the breakdown of essential community services further complicated the recovery process. Beyond physical and economic hardships, the earthquake also had profound psychosocial impacts, particularly among women, children, and the elderly, who experienced elevated levels of trauma and emotional distress. Despite these challenges, local communities demonstrated notable resilience through mutual aid, informal networks, and self-initiated recovery efforts. However, institutional responses were hindered by inefficiencies, poor coordination, and limited reach, particularly in remote and marginalized areas. The study underscores the urgent need for comprehensive and inclusive disaster risk reduction strategies that prioritize community-based approaches, strengthen local institutional capacities, and address both structural vulnerabilities and psychosocial needs. The findings contribute to broader discussions on disaster governance by emphasizing the importance of resilience-building and equitable recovery planning in rural, disaster-prone settings.

BackgroundEven before the COVID-19 pandemic, caregivers of children with rare neurogenetic conditions (NGCs) experienced physical and mental health challenges. These challenges escalated during the COVID-19 pandemic due to crisis-level breakdowns in support services. Tele–mental health and parenting support services expanded rapidly in response to the COVID-19 pandemic and may be well suited to facilitate necessary support interventions for NGC caregivers. However, it remains unclear how to match these evidence-based interventions to individual NGC caregivers’ needs.ObjectiveProject WellCAST (Supporting Well-Being of Caregivers via Telehealth) is an early-phase clinical trial designed to prospectively test which evidence-based telehealth interventions best meet the needs of NGC caregivers.MethodsInterested and eligible NGC caregivers are enrolled in a 24-week program with 5 phases, including baseline (2 weeks), support program (12 weeks), and follow-up (2 weeks) periods; a 4-week gap separates the phases. Caregivers participate in 2 randomizations, namely support program assignment via a precision health algorithm versus quasi-random assignment and motivational coaching by another NGC caregiver and project staff member (“peer coaching”) versus standard check-ins by a staff member who is not an NGC caregiver (“staff coaching”). Virtual support programs include acceptance and commitment therapy, dialectical and behavioral therapy, culturally informed cognitive behavioral therapy, research units in behavioral intervention, naturalistic communication intervention, Durand sleep intervention, and self-guided resources. A subset of caregivers will participate as waitlist controls before engaging in support programs. We developed and optimized a personalized health decision tree algorithm that matches caregivers to telehealth support programs. We then proceeded to test the feasibility and efficacy of algorithm-assigned support programs across 4 waves of data collection, relative to quasi-random assignment and waitlist controls. During each wave, the personalized health algorithm relies on 2 weeks of baseline data collection using clinical tools and innovative smartphone-based ecological momentary assessments. Across waves, we also test the efficacy of a motivational peer-to-peer coaching protocol, deployed by trained NGC caregiver staff, in enhancing support program uptake and clinical outcomes.ResultsFour waves of data collection are scheduled for August 2023 to September 2025. Preregistered analyses will contrast feasibility, efficacy, and acceptability across algorithms and coaching assignments. Multiple waves of data collection will allow us to continually optimize the algorithm and test incremental improvements across project phases. Secondary analyses will probe the feasibility and efficacy of individual evidence-based support programs and peer coaching.ConclusionsProject WellCAST will test whether a digital personalized health decision tree algorithm and peer coaching protocol can prospectively enhance telehealth support program outcomes among NGC caregivers. This project is relevant to the specific population of NGC caregivers and may also inform how brief digital assessments, precision health tools, and community-academic partnerships can enhance the public health response to mental health crises across other high-need populations.Trial RegistrationClinicalTrials.gov NCT05999448; https://clinicaltrials.gov/study/NCT05999448 and OSF Registries 10.17605/OSF.IO/8WNDP; https://osf.io/8wndpInternational Registered Report Identifier (IRRID)DERR1-10.2196/64360

Quantum attacks on cryptographic systems remain hypothetical but are grounded in strong theoretical foundations. The emergence of quantum computing presents a significant challenge to national security, particularly in protecting critical infrastructures such as energy grids, financial systems, and healthcare networks. Quantum algorithms like Shor’s may soon be capable of breaking widely used cryptographic standards (RSA, ECC, AES), rendering current encryption obsolete and exposing essential services to disruption and data breaches. These vulnerabilities could threaten economic stability and public safety on a national scale. This paper analyzes the risks posed by quantum computing to classical cryptographic frameworks and evaluates quantum-resistant alternatives such as lattice-based, hash-based, and code-based cryptography. It assesses their theoretical soundness and suitability for securing national critical infrastructure. The analysis also explores the dangers of delayed implementation, where postponed adoption of post-quantum cryptography (PQC) could expose systems to future quantum-enabled cyberattacks. Additionally, the paper discusses the challenges of integrating PQC into existing systems, including regulatory compliance, interoperability, and operational readiness. Without coordinated strategies and accelerated transition plans, nations risk severe consequences, including financial disruption, healthcare service breakdowns, and energy supply chain failures. Finally, the study highlights the need for international cooperation, policy alignment, and robust testing to ensure the effective deployment of quantum-resistant solutions. Prompt action is essential to preserve the confidentiality, integrity, and availability of vital national systems in the face of the advancing quantum threat landscape.

The integration of artificial intelligence (AI) in fulfillment systems has revolutionized supply chain operations, yet the success of these systems heavily depends on human-centered design principles. This study examines how human factors, including customer expectations, employee adoption, and decision-making trade-offs, can be effectively integrated into AI-enabled fulfillment systems. Through a mixed-methods approach combining surveys (n=547), interviews (n=28), and case studies from 12 organizations, we developed a comprehensive framework for balancing automation with human oversight to prevent service breakdowns. Our findings reveal that successful AI implementation requires a 70:30 automation-to-human ratio for optimal performance, with key success factors including transparent decision-making processes, adaptive interfaces, and continuous feedback loops. The Human-Centered AI Fulfillment Framework (HCAIFF) developed in this study provides practical guidelines for organizations seeking to implement AI while maintaining human agency and service quality. Results indicate that human-centered approaches increase system adoption rates by 43% and reduce service breakdowns by 57% compared to purely automated systems.

Capacity problems in healthcare lead organizations to seek new and fluid ways of organizing care to safeguard access to services. Task reallocation, triage and stepped care models are increasingly foregrounded as promising interventions that enhance the capacity, efficiency, and resilience of medical services and through which access can be maintained for a growing client base. In this paper, we argue that interventions meant to enhance capacity and increase efficiency have their limits in a system that is already under strain. We draw on the cybernetics of Gregory Bateson and his concept of 'budgets of flexibility' to understand how stress accrues in systems and depletes their capacity for adaptive change. We analyze a case in which regional evening, weekend and night shifts (EWNs) were organized for nursing homes, which included the implementation of a new triage system and task reallocation between various professionals. We show how this initiative rerouted and reprocessed information pathways between professionals, and how this rewiring resulted in a buildup of stress and concomitant emotions of frustration, anxiety, fear and disempowerment through four different mechanisms: (1) fragmentation of information flows; (2) accumulation of information; (3) a loss of richness of information, and (4) slow-moving information flows. The accrual of stress depleted the overall capacity for adaptive change in the system and eventually culminated in a partial breakdown of the new medical service.

Branding has played a critical role in the management of negative events of marketing including product harm recall, brand transgressions and service breakdowns. The treatment of isolated branding facets notably brand equity as boundary condition in the extant literature on the subject of service failure however, has resulted in fragmented corpora and siloed theoretical frameworks. This has resulted in a lack of theoretical integration across event types, theoretical lenses and branding constructs. This integrative review agenda represents an attempt to reconcile these divergent strains in terms of theories and constructs that are explored within the gamut of branding focusing its impact on one negative event in marketing in particular that is service failure. The article contributes through the identification of six insights and recommendation of four propositions for future research. The insights encapsulate the modus operandi of brand equity as both a strategic resource and volatility amplifier in service failure and recovery literature based on contextual elements. The propositions help integrate the existing divergence in service failure literature vis a vis brand equity by identifying boundary conditions like relationship norms, self-brand relevance, failure attributions and norm violations and offering an alternative to the present conceptualization of the binary effects of branding through the contingency perspective.

This review article investigates and examines the obstacles and critical challenges to Yemen's development and economic security, focusing on the consequences of protracted conflict, hyperinflation, and inadequate government. The study employs a qualitative research methodology, synthesizing insights from scholarly literature, reports from international organizations, and case studies to explore the complex dynamics at play. Data was gathered by doing focused searches using keywords like “problems,” “development,” “economic security,” “stabilization,” and “Yemen” on websites including Google Scholar, ScienceDirect, and Scopus. This comprehensive analysis aims to provide a nuanced understanding of Yemen’s current economic landscape and the factors contributing to its decline. Results reveal that Yemen's protracted conflict has seriously harmed the country's economic framework, resulting in pervasive poverty, unstable food supplies, and a breakdown of social services. Corruption and inept governance have exacerbated these problems, impeding efforts to stabilize the economy and restore public trust. The impact of large international help is still limited because of continued violence and logistical obstacles in the delivery of relief. According to the study's findings, Yemen's recovery would necessitate a multimodal strategy that includes investments in education and career training, currency stabilization, economic diversification, and governance reforms. Effective governance and ongoing international support are also critical to achieving sustainable development and long-term stability.

This study conducted a systematic literature review to examine the trajectory of AI research over the past five years, from 2019 to 2023, focusing on emerging ethical and social concerns related to the deployment of AI technologies. The study also aimed at enhancing the understanding and promotion of robust AI ethics for societal benefit. The explosive rise of the internet, AI, and mobile technology has dramatically changed how we live, work, consume, learn, and communicate. AI is improving the quality of human life but poses dangers from unintended disastrous and undesirable outcomes, if unregulated. Cyberattacks on critical infrastructure networks pose grave threats, exponentially increasing risks of fatalities and service breakdowns. AI can instantly diagnose rare diseases, robots can perform precision surgeries and chatbots can write assignments for students. AI is also used for surveillance, monitoring financial activities and autonomous weapon systems in the military. Two hundred and twenty-five publications from Scopus database were selected to determine the central themes, the affordances and constraints of AI and principles that enhance public trust and accountability. Results show an upward trajectory in AI ethics research from 6.2% in 2019 to 40.3% in 2023. Furthermore, results revealed the emerging ethical and social concerns in major socioeconomic domains. Results also show that AI collects data about individuals and data breaches have catastrophic consequences. The growing complexity and opacity of AI systems make it hard to understand decision-making, hindering accountability for developers and deployers. AI algorithms may be biased against minorities; perpetuating prejudices. The study contributes to the ongoing discourse on the ethical and societal concerns surrounding unregulated AI adoption. The issues identified in this study may assist policymakers in developing frameworks and policies for AI usage.

The Syrian civil war has been one of the most devastating conflicts of our time, leaving in its wake a trail of destruction, displacement and loss, as well as a breakdown of basic services. It has turned the country into a stage where tragic events and actions are taking place. As the conflict unfolded, it became apparent that women and children were disproportionately affected, facing unique challenges and vulnerabilities amidst the chaos. This critical reflection aims to explore the intersection of feminism and the Syrian war, shedding light on the experiences of women and children and examining the broader implications of this crisis.

The spread of infectious diseases is significantly influenced by emergencies, particularly military conflicts, which disrupt healthcare systems and increase the risks of epidemics. The full-scale Russian invasion of Ukraine has exacerbated these challenges, causing environmental damage, mass displacement, and the breakdown of healthcare services, all of which contribute to the spread of infectious diseases. This study aims to develop a comprehensive methodology for assessing the impact of emergencies on the spread of infectious diseases, focusing on the full-scale invasion of Ukraine. The object of this study is to address epidemic threats posed by emergencies, particularly the increased spread of infectious diseases due to war-related disruptions. The subject of this study is methods and models of infectious disease transmission under conditions of emergencies, emphasizing the Russian full-scale invasion of Ukraine. The tasks of this study are to provide an analysis of the current state of research and develop a methodology for assessing the impact of emergencies on the spread of infectious diseases. The proposed methodology includes several key components. Comprehensive data from public health organizations includes infectious disease statistics, demographic shifts, healthcare disruptions, and environmental factors exacerbated by emergencies. Data preprocessing removes inconsistencies, standardization of formats, and normalization for population size differences. Machine learning models, including convolutional neural networks and recurrent neural networks, have been developed to simulate the spread of diseases based on demographic, environmental, and healthcare-related variables. Deep learning models analyze spatial and temporal patterns, whereas compartmental models such as SIR estimate changes in reproductive numbers (R₀ and Re). Additionally, models of excess mortality incorporate mixed effects to account for regional and time-based variations. The methodology incorporates real-time monitoring of epidemic threats using real-time data from multiple sources, enabling dynamic assessments of disease spread and facilitating predictive modeling. The models were trained on historical data and validated using cross-validation techniques to ensure robustness and reliability, with a specific focus on the pre- and post-invasion phases in Ukraine.Results: The study provides a comprehensive framework for collecting and processing data on infectious diseases and epidemic threats in emergencies. The proposed model introduces advanced machine learning and epidemiological models trained on pre- and post-invasion data to analyze disease transmission patterns and forecast future epidemic dynamics. Conclusion: The proposed methodology addresses current gaps in infectious disease during emergencies by integrating real-time data and machine learning techniques. This research improves decision-making in public health management and biosafety during crises, particularly in war-affected regions like Ukraine.

Abstract Introduction Patient navigators (PNs) are members of the community who help with the transition from inpatient to outpatient care. PNs have demonstrated efficacy in enhancing patient engagement and addressing care gaps in fields such as cancer and vulnerable populations, such as urban, low socioeconomic-status populations. Despite the long-term care needs of burn patients, the role of PNs in the aftercare of burn injury has not been studied. This research investigates the current use of PNs in burn care at a safety-net hospital and sheds light on potential areas of improvement. Methods This was a retrospective observational cohort study conducted from November 2022 to August 2023. Data regarding patient contacts, needs identification, and associated referral requisites were collected. The PN role was filled by a representative from a local nonprofit community center. The PN checked in with outpatients after their discharge and inpatients nearing discharge to identify their needs and facilitate appropriate referrals to the relevant community services. Results Over eight months, the PN liaised with 658 burn unit patients, comprising 406 outpatients and 252 inpatients. A focused examination of non-COVID services requested reveals that only 3.2% of outpatients requested referrals to community resources. This contrasts with inpatients, where a significantly larger fraction of 10.3% requested similar community service referrals. Figure 1 shows the breakdown of services requested. Conclusions While PNs are progressively integrated into various healthcare domains, the newly implemented program in burn care yielded low utilization rates, particularly among outpatients. The disparity between inpatient and outpatient engagement indicates the need to optimize the role of PNs in the transition process from the hospital to outpatient care in burn patients. Applicability of Research to Practice The aftercare for burn injuries is complex and extends beyond hospital discharge, requiring effective coordination between various healthcare and community resources. This study highlights the untapped potential of PNs in enhancing outpatient care in the setting of burn injury. Tailoring the PN training to meet the specialized needs of burn patients could enhance their role and improve patient outcomes, particularly for those who face barriers to engagement with the healthcare system.

Disabled persons' chairside dentistry is challenging. We aimed for a retrospective breakdown of dental services delivered to disabled patients by dental students and to discuss feasibility of a chairside approach. Consecutive patients, who received scheduled dental treatment by dental students from 2002 to 2021, were included. Demographic data, medical diagnoses, number of treatment sessions, performed treatments, and treatment break-offs were collected and analyzed with descriptive statistics. In total, 224 individuals with various disabilities (mean age 36.4 ± 14.6 years) received dental services in 2282 sessions altogether (10.3 ± 11. sessions per patient). Professional tooth cleaning was the most frequently provided treatment (55.8% of sessions). A total of 654 teeth were restored with fillings, 97 teeth were extracted, 56 teeth had endodontic treatment, and 25 removable dentures were fitted. Treatment break-off due to incompliance and referral to dental general anesthesia occurred in 74 patients (33%). Chairside treatment of disabled persons by dental students is feasible in many cases. Our study may serve as an incentive for clinicians/researchers to report on treatment modalities and outcomes of chairside dentistry in patients with special oral health care needs, preferably by the use of prospective study designs, to contribute data and strategies in the fight for control of oral health inadequacies.

In a process, to ensure increased reliability and better availability, it is very important to detect any anomalies that refer to any abnormality observed in the behaviour of a standard process. The breakdown of service(s) eventually leads to production loss, and at the same time, a system that is unreliable brings lots of challenges to the operations team. Anomaly detection plays a significant role to ensure that an application is reliable, secured and available for user requests. For the overall performance optimization of a cloud microservice based application without any disruption in service, and identification of possible security threat, it is much essential that the anomalies must be detected and responded to in time. In real life large microservice based production infrastructures environments, even though ample instance of normal activities is available, it is not possible to predict and create a dataset of anomalies. So these kind of data are not suitable for a supervised two-class classification. In this work, unsupervised one-class approaches such as Local Outlier Factor, Isolation Forest, and One Class SVM are used to find anomalies. On experimentation these models have obtained a high accuracy of 98% to 99%. On comparing the performance of the models, One-Class SVM is found to produce significantly higher number of False Positives in comparison to other two considered model