Impact Of Disruption Research Articles

Analyzing Airline Fleet Resilience Using the Disruption Funnel Framework

Background: Defining the optimal fleet portfolio is a crucial process in airline planning. The published efforts in literature provide ways to anticipate the disruption effects on the passenger demand; however, the proposed solution in this paper provides visibility on the impact of sustainable disruption and the way an airline can resist it. Methods: This paper proposes a two-stage methodology to find the best portfolio for airline operational requirements under the impact of disruption. The first stage considers optimization for normal airline operations under a specific fleet portfolio using an Integer Linear Programming (ILP) model. The second stage of the analysis is a mapping for the scenario-based methodology to find a way out for an airline subjected to some given disruption in operations. Results: The result of the two-stage analysis shall define the best fleet portfolio to withstand sustained disruptions by mapping the results in a disruption funnel and showing the impact of the supply and demand gap on the airline’s sustainable profitability. Conclusions: This paper provides a novel, practical way of evaluating strategic decisions to choose the best fleet portfolio and make airlines rely on the mapping of the disruption funnel to modify their network while increasing supply chain resilience.

The EU’s Quest for Digital Sovereignty: A Matter of Quantum Innovation?

The EU increasingly seeks to assert its digital sovereignty by boosting innovation and norm-setting in, among other, quantum technologies. This objective is generally reflected in numerous policy documents and crystallised in the Digital Decade Policy Programme, which sets specific targets to achieve it. The EU policy documents recognise a world-changing potential of quantum technologies whilst remaining vigilant due to their potential disruptive impact. This white paper maps the way the ambition of digital sovereignty is interwoven with the development of quantum technologies in the EU digital policy and legislation. It documents empirical work, identifying thirty policy and legal documents which were produced during the past five years and bind digital sovereignty and quantum technologies together. The aim of this white paper is to bring attention to and invite further examination of the complex interrelation between digital sovereignty and quantum innovation. In this way, the white paper wishes to spark a broader conversation on the feasibility and desirability of emerging and future tech governance approaches.

Artificial intelligence enabled supply chain resilience: insights from FMCG industry

Purpose The purpose of this study is to investigate and develop capabilities to make supply chains resilient using qualitative analysis of fast-moving consumer goods (FMCG) industry located in India. In particular, authors aim to propose a framework to make supply chains resilient by infusing artificial intelligence (AI). Design/methodology/approach The authors acquired supportive data by conducting semi-structured interviews with 25 FMCG supply chain professionals during 2023. Using open, axial and selective coding approaches, the authors mapped and discovered the themes that constitute the essential elements of AI-enabled supply chain resilience. Findings The research findings reveal that supply chain capabilities are useful for mitigating the disruptions impact when infused with AI. The authors’ analysis underscore four principal domains in which AI is poised to enhance the resilience of supply chains. This study delves into four key capabilities of interest, namely: Routing Optimization, Efficiency, Periodic Monitoring and Demand Forecasting. The result of this study is the proposed framework which shows the impact of different AI-powered capabilities on supply chain which builds resilient supply chains. Research limitations/implications Infusing AI to different supply chain capabilities appears to be a successful way for making FMCG supply chains resilient. Only the supply chain capabilities cannot overcome the impact of disruptions, but the use of AI helps professionals and policymakers to better respond to disruptions. Originality/value Few studies demonstrate the impact of advanced technology in building resilient supply chains. To the best of the authors’ knowledge, no earlier researcher has attempted to infuse AI into supply chain capabilities to make them resilient with empirical studies with the theoretical framework of Dynamic Capability View (DCV).

Suprachiasmatic nucleus-wide estimation of oscillatory temporal dynamics.

The suprachiasmatic nucleus (SCN), locus of a circadian clock, is a small nucleus of approximately 20,000 neurons that oscillate with a period of about 24 hours. While individual neurons produce circadian oscillations even when dispersed in culture, the coherence and robustness of oscillation of the SCN as a whole is dependent on its circuitry. Surprisingly, the individual neurons of the intact SCN do not all oscillate in phase with each other. To understand the oscillatory dynamics across the intact nucleus, we develop a model of the relation of the phase of neurons to their PER2 expression at a particular subjective time (CT1900) using time series data from SCN slice preparations. Next, we use the model, which produces a surprisingly good fit in the SCN slice data, to estimate oscillator phase at a single time point (CT1900) in snapshot data from PER2 expression measurements in intact, unsliced SCN-wide tissue. To monitor temporal changes in phase in time series data, we use PER2::LUC imaging in an ex vivo SCN slice preparation. To study phase in the intact SCN at a fixed time point we use data generated by PER2 staining and a tissue clearing protocol. Because PER2 expression, as measured in the time series slices and the snapshot intact SCN are not directly comparable, the model estimated from time series slices to the snapshot intact SCN data requires a calibrating constant. The results indicate that our model provides a surprisingly good fit to the SCN slice data and is therefore a meaningful method for estimating phase in the intact SCN snapshot data, permitting the study of virtual interventions such as virtual tissue slicing. We next compare oscillation in circuits in the SCN-wide tissue to those that have been disrupted by virtual slicing using a Kuramoto model to simulate the dynamics. The results support prior evidence that the damage done by coronal slicing has the most disruptive impact on SCN oscillation, while horizontal slicing has the least damage. The results point to the importance of connectivity along the caudal-to-rostral axis and indicate that SCN circuit organization depends on the caudal-to-rostral flow of information. In summary, the construction of this model is a major finding of the paper. Our modeling allows us to perform the previously impossible analysis of oscillatory dynamics in static data in an intact SCN captured at a single time point.

Revisit bus network robustness from the perspective of service-physical dependency

ABSTRACT This study revisits bus network robustness by addressing the dependency of bus service networks on the underlying physical road network. A novel methodology integrates resistance distance theory with a multilayer physical-service network framework, accounting for disruptions at the road layer and detour mechanisms. Two robustness indices are introduced: the absolute resistance robustness index, which quantifies the overall impact of disruptions, and the relative resistance robustness index, which reflects network redundancy. Using data from 29 cities, the empirical analysis reveals that geographical and socioeconomic factors significantly influence bus network robustness, with higher GDP, flatter terrains, and concentrated urban areas associated with greater relative robustness. Sensitivity analysis identifies terrain compactness as the most influential factor. This methodology advances the theoretical understanding of transportation network resilience and provides practical tools for urban transportation planning. Future research could extend the approach to directional networks and incorporate travel demand data for more refined analyses.

INOVASI ATAU STAGNASI? PENGARUH DISRUPSI TEKNOLOGI INFORMASI TERHADAP EFISIENSI PENGELOLAAN KEUANGAN PUBLIK DI SULAWESI UTARA

The background of this research focuses on the rapid development of information technology that has transformed various sectors, including public financial management. In North Sulawesi, the implementation of information technology in financial management is expected to enhance transparency, accountability, and efficiency. However, challenges such as inadequate infrastructure and limited human resource capacity may hinder effective implementation. The objective of this study is to evaluate the impact of information technology disruption on the efficiency of public financial management in North Sulawesi. The method employed is a quantitative approach with descriptive-analytical analysis, involving data collection from technology-based systems such as e-budgeting, SIPKD, and cloud-based financial management systems. The results indicate that the implementation of information technology significantly improves efficiency, transparency, and accountability in public financial management. Nevertheless, challenges related to infrastructure and human resource capacity remain obstacles to achieving optimal results. The conclusion of this research is that information technology disruption has great potential to enhance public financial management, but greater attention is needed towards infrastructure development and human resource training to ensure successful implementation.

Systemic financial risks in an era of geopolitical tensions, climate change, and technological disruptions: Predictive analytics, stress testing and crisis response strategies

Systemic financial risks have become increasingly complex and unpredictable due to the interplay of geopolitical tensions, climate change, and rapid technological disruptions. Financial markets and institutions face heightened uncertainty, driven by shifting global power dynamics, trade conflicts, and regulatory changes that impact economic stability. Concurrently, climate-related risks, including extreme weather events, resource depletion, and the transition to sustainable energy, pose long-term threats to financial assets, investment portfolios, and economic resilience. Technological disruptions, particularly those stemming from artificial intelligence, cybersecurity vulnerabilities, and digital financial innovations, further complicate risk management by introducing new vulnerabilities and systemic contagion pathways. In this evolving risk landscape, predictive analytics and stress testing have emerged as essential tools for identifying vulnerabilities, quantifying financial shocks, and developing proactive mitigation strategies. By leveraging machine learning models, big data analysis, and scenario-based forecasting, financial institutions can assess the potential impact of geopolitical instability, environmental shocks, and technological disruptions on financial systems. Additionally, stress testing frameworks enable regulators and policymakers to evaluate the resilience of financial institutions under extreme conditions, ensuring adequate capital buffers and liquidity measures. This research explores how predictive analytics, stress testing, and crisis response strategies can be effectively integrated to enhance financial stability in an era of escalating global uncertainties. It examines case studies of past financial crises, emerging threats, and best practices for mitigating systemic risks. By providing a comprehensive approach to financial risk management, this study contributes to the development of adaptive strategies that safeguard economic stability amid an increasingly volatile global environment.

Understanding the Association Between Economic Abuse, Abuser-Initiated Workplace Disruptions and Mental Health Outcomes Among Nursing Professionals.

Participants were recruited from statewide nursing membership organizations and major healthcare providers (N = 174). Outcomes of anxiety, depression, and posttraumatic stress disorder (PTSD) were tested for association with two key predictors; economic abuse and abuser-initiated workplace disruptions. Utilizing multiple linear regression analyses, findings indicate economic abuse experienced among nurses increases anxiety and depression. However, abuser-initiated workplace disruption improved model fit when added, significantly increasing anxiety, depression, and PTSD even when controlling for demographics and economic abuse. The impact of abuser-initiated workplace disruptions could stretch beyond mental health problems and warrants future exploration.

Impact of the Covid-19 pandemic on odontogenic infections in maxillofacial surgery

BackgroundThis study investigates the impact of the COVID-19 pandemic on the distribution, characteristics, and treatment of odontogenic infections (OI) in a cranio-maxillofacial hospital, comparing data from pre-covid (PC) and intra-covid (IC) periods.Materials and methodsA retrospective analysis was conducted on 194 patients treated for OIs from February 2019 to January 2021. Patients were categorized into two cohorts: PC (02/2019 – 01/2020) and IC (02/2020 – 01/2021). Data collected included demographics, infection types, symptoms, pre-existing conditions, treatments, length of hospital stay, and time from symptom onset to treatment.ResultsThe IC cohort exhibited a significant increase in submandibular (13.9% to 26.7%) and floor of mouth abscesses (6.5% to 18.6%). Patients in the IC period were more likely to present with fever (69.8% vs. 36.1%, p < 0.001) and dyspnea (48.8% vs. 33.3%, p = 0.029). Additionally, there was an increase in multi-space infections (7.4% to 22.1%) and higher inflammatory markers, with leukocyte counts rising from 12.51 × 10^9/l to 15.41 × 10^9/l (p < 0.001). The mean length of stay in the hospital also increased significantly from 3.24 days to 8.01 days (p < 0.001).ConclusionThe COVID-19 pandemic has significantly altered the landscape of OIs in oral and maxillofacial surgery, leading to more severe presentations, prolonged treatment durations, and increased hospital admissions. These findings underscore the necessity for improved public health strategies to ensure timely access to dental care during health crises. Enhanced awareness and proactive management are essential to mitigate the long-term impact of the aforementioned disruptions on patient outcomes.

Labor Disruptions under the Ascendancy of Digital Technologies

Purpose: This study aimed to assess the impact of labor disruptions caused by the rise of digital technologies on workers and organizations, considering both opportunities and challenges. Methodology: A systematic review of literature was conducted, analyzing research from peer-reviewed journals, industry reports, and case studies. The review focused on key themes such as productivity, job displacement, skill development, and mental health challenges related to digital technologies. Findings: According to the study findings, digital technologies significantly boosted productivity and created new job opportunities in emerging fields, while also offering greater work flexibility. However, these advancements also led to job displacement, widened the skills gap, exacerbated income inequality, and contributed to mental health issues, especially among older workers. Unique Contribution to Theory, Practice and Policy: It is recommended that organizations implement upskilling programs to close the skills gap and provide mental health support. Institutions should develop policies that promote work-life balance. Governments are advised to introduce measures to mitigate income inequality and assist displaced workers in transitioning to new roles. This study provides a comprehensive review of the dual effects of digital technologies on labor markets, offering valuable insights for organizations, policymakers, and governments to manage the challenges while capitalizing on the opportunities presented by digital transformations.

The Business Value of IoT and Big Data Analytics

This study analyzes the disruptive impact of combining the Internet of Things (IoT) and Big Data Analytics on contemporary business models. This research investigates how these technologies use data-driven decision-making, better operational efficiency, and innovative business models across retail, healthcare, manufacturing, and logistics by integrating a review of current literature and evidenced case study examples across industries. Strategic issues to think about when developing a sustainable growth strategy with IoT and Big Data, building competitive advantage in value creation, transformation of market spaces, and return on investment are considered for companies. The findings indicate that the strategic deployment of IoT and Big Data provides room for new service-led business models and personalization in customer experience.

From Crisis to Recovery: Understanding the Impact of COVID-19 on Indonesian Employment Dynamics

This research analyzes the impact of the economic disruption caused by the COVID-19 pandemic on Indonesia’s labor market by utilizing data from SAKERNAS, or the National Labor Force Survey, for 2020 and 2021. A logistic regression model is used to analyze the impact of sociodemographic factors, job-related variables, and the Kartu Prakerja (a pre-employment program focused on training and skill development) on unemployment, labor force participation, and reduced working hours caused by the economic disruption of COVID-19. The findings underscore the enduring repercussions of the pandemic on employment landscapes, with pronounced impacts observed among older workers, married individuals, less educated workers, and urban residents. The manufacturing and service sectors experienced more significant implications than agriculture, revealing sector-specific challenges. Kartu Prakerja influenced working hour reductions in 2021, showing the effects of evolving government intervention. Increased Internet use was linked to reduced working hours, signaling the need for accelerated digital skill programs for jobs dependent on Internet use.

Disruptive Impacts of Central Bank Digital Currencies on the Traditional Financial System: A Regulatory Framework Perspective

Central bank digital currency (CBDC) is a groundbreaking innovation that provides a digital currency issued by the state to replace cash and commercial bank deposits, and is gradually changing the operating mode of the traditional financial system. This paper explores the disruptive impact of CBDC on financial institutions, monetary policy and payment systems through theoretical analysis and empirical data. The study pointed out that CBDC has the potential to improve financial inclusion, reduce transaction costs and enhance the efficiency of monetary policy transmission. However, the challenges it has caused, such as financial disintermediation, systemic risks and privacy issues, urgently need to establish a strong regulatory framework to address them. Through data-driven insights and case studies, this paper provides a comprehensive perspective on how to find a balance between innovation and financial stability.

Changes in healthcare engagement during the COVID-19 pandemic

BackgroundHealthcare engagement, defined as the self-efficacy to enact the behaviors needed to obtain optimal benefit from health services, is an important aspect of healthcare quality. Measuring changes to healthcare engagement is essential to informing current and ongoing adaptations to health service delivery. The objective of the present study was to explore the responsiveness of the recently developed PROMIS® Healthcare Engagement measure (PHE), a patient-reported outcome, through investigating the impact of COVID and COVID-related healthcare disruptions on healthcare engagement from pre- to peri-pandemic.MethodsBaseline data (2018–2019) were collected via a national mail survey of Veterans receiving VA care. For follow-up data, a subset of participants was randomly selected to be invited to a follow-up survey. Administrative data was used from the VA’s Corporate Data Warehouse (CDW). We used mixed effects linear modeling to compare changes in healthcare engagement from baseline to follow-up between Veterans who reported healthcare disruptions and Veterans who did not report healthcare disruptions, adjusting for covariates.ResultsFrom baseline to follow-up, healthcare engagement scores increased on average by 2.84 points. Compared to Veterans who reported no disruptions, Veterans who experienced COVID-related healthcare disruptions demonstrated greater decreases to healthcare engagement (difference scores ≥ − 1.98, ps ≤ 0.002) Further, Veterans with more healthcare disruptions showed greater decreases in healthcare engagement relative to those with fewer healthcare disruptions, such that Veterans with 2 healthcare disruptions (difference score = -4.20) significantly differed from Veterans reporting only 1 healthcare disruption, and Veterans reporting 3 or more disruptions (difference score = -3.75) significantly differed from those with 2 disruptions.ConclusionOur results provide preliminary evidence of the PHE’s responsiveness through demonstrating that environmental factors, such as pandemic-related factors, influence healthcare engagement. The COVID-19 pandemic had a complex effect on healthcare engagement, with healthcare engagement scores increasing overall during the pandemic but Veterans reporting COVID-related healthcare disruptions showing decreased changes in healthcare engagement. These findings support the utility of the PHE as a measure of healthcare engagement.

NEK2 Control of Esophageal Squamous Cell Carcinoma Growth Based on Circadian Oscillation.

Esophageal squamous cell carcinoma (ESCC) is a globally prevalent malignancy known for its aggressive nature and unfavorable outcomes. Identifying new biomarkers is crucial for the early detection and improved prognostication of ESCC. The circadian clock and NIMA-related kinase 2 (NEK2) are pivotal in cancer development. While the impact of circadian rhythm disruptions on ESCC progression is evident, the specific contribution of NEK2 to these changes is not well understood. Our study discovered NEK2 as a consistently differentially expressed gene across multiple datasets, with elevated expression in ESCC tissues. Notably, NEK2 overexpression was linked to increased ESCC cell proliferation, whereas its inhibition led to reduced cell growth and proliferation. Pathway analyses, including KEGG and Gene Set Enrichment Analysis (GSEA), indicated NEK2's association withestablished pathways like the cell cycle, and intriguingly, identified the circadian rhythm as a novel pathway influenced byNEK2. RNA sequencing data demonstrated NEK2's circadian rhythmic expression, and subsequent invitro experiments confirmed its oscillation in synchronized ESCC cells. Moreover, we found a positive correlation between the efficacy of theNEK2 inhibitor INH6 and NEK2 expression levels in ESCC. In conclusion, our findings position NEK2 as a time-dependent oncogene and a potential biomarker in ESCC, highlighting its role in both tumorigenesis and the circadian rhythm.

Cross-Dimensional Integration and Innovative Applications of Piano and Vocal Sound Assets in the AI Era

The rapid advancement of artificial intelligence (AI) technology is reshaping paradigms in musical creation, education, and performance. This paper focuses on piano and vocal sound assets as core research subjects, exploring how AI enables innovative applications through cross-dimensional integration (technology, art, education, and industry). Case studies on interactive smart piano pedagogy, ethical boundaries in vocal synthesis, and data-driven compositional models are analyzed to demonstrate AI’s multifaceted penetration into the music ecosystem and its disruptive impact on traditional practices. A fusion strategy balancing technological optimization and humanistic values is proposed. Findings indicate that AI enhances the efficient management and re-creation of sound assets, promoting the democratization, personalization, and globalization of music. However, systemic solutions are required to address challenges in copyright protection, emotional expression, and ethical governance.

The impact of COVID-related school disruption on kindergarten children’s development

ABSTRACT Prior research on COVID-related school disruptions focused mainly on primary or secondary, with limited studies on kindergarten children. Using a naturalistic quasi-experimental design, we examined whether school disruption during the first year of kindergarten (K1) affected early numeracy, literacy, and non-academic development. We also examined whether disruption during the first or second year of kindergarten (K2) was more detrimental to children's development. A cohort of K2 students from each of two consecutive years (N = 235, 4–5-year-old) were recruited from ten kindergartens in Hong Kong, which had long periods of COVID-related school disruptions. One cohort was assessed just before the onset of COVID-19; the other, one year later. K1 disruption did not affect performance at K2. In contrast, performance at K3 showed that K2 disruption had a more detrimental effect on physical performance than K1 disruption. Findings highlight the period and aspects of children's development that are particularly sensitive to schooling.

Transmission Control Protocol (TCP)-Based Delay Tolerant Networking for Space-Vehicle Communications in Cislunar Domain: An Experimental Approach.

The integrated heterogeneous 7G/8G networks may face multiple challenges for reliable data delivery such as link disruption, intermittent link availability, long latency and a highly lossy channel. Delay tolerant networking (DTN) was proposed as a highly reliable networking technology for space networks that will be part of future 7G/8G networks. In this paper, an experimental evaluation of transmission control protocol (TCP)-based DTN (i.e., running TCP at the transport layer of DTN) for space-vehicle communications in the cislunar domain is presented. The impact of link disruption is also considered. The evaluation was conducted using the DTN protocol suites over a realistic experimental testbed. The study results show that TCP-based DTN works effectively for space-vehicle communications in cislunar domain in the presence of a link disruption event. However, a roughly exponential goodput decrease is observed with a linear increase in link delay from 1250 ms to 5 s.

Virus-induced perturbations in the mouse microbiome are impacted by microbial experience.

The bacterial microbiome has a major impact on health and can shape metabolism, host tolerance, immune responses, and the outcome of future infections. The bacterial microbiome is highly variable between individuals. Specific pathogen-free animals have reduced microbiome diversity, making it difficult to evaluate the impact of infection-induced microbiome disruption that would be observed in free-living animals, including people. Mice are commonly used as a preclinical model but unfortunately often fail to predict translation success or failure, particularly for immune and infectious disease-targeting therapies. Here, we utilize pet store mouse cohoused "dirty" mice with diverse microbial experience to explore how host variability and infection may be interacting to drive unique microbiome changes. We found that cohoused animals had significantly increased bacterial diversity in the small intestine and cecum but not in the large intestine. There were differentially abundant taxa between clean and dirty animals in all three tissues. After infection with influenza A virus, samples clustered by both housing condition and infection status in the cecum and large intestine, while small intestine samples clustered predominantly by infection. Altogether, these results highlight the differential impact of housing, infection, and interaction between the two in dictating community composition across the gastrointestinal microbiome.IMPORTANCETraditionally housed pathogen-free mouse models do not fully capture the natural variability observed among human microbiomes, which may underlie their poor translationally predictive value. Understanding the difference between pathogen-induced shifts in the bacterial microbiome and natural microbiome variance is a major hurdle to determining bacterial biomarkers of disease. It is also critical to understand how diverse baseline microbiomes may be differentially impacted by infection and contribute to disease. Pet store cohoused "dirty" mice have diverse microbial experiences and microbiomes, allowing us to evaluate how baseline variation, infection, and interaction between the two impact the microbiome.

A Scoping Review of Planetary Health Education in Pharmacy Curricula.

A Scoping Review of Planetary Health Education in Pharmacy Curricula.