Complex Landscape Research Articles

Forest cover is more important than its integrity or landscape configuration in determining habitat use by mammals in a human-modified landscape in Colombia

Human activities shape the structure of landscapes in different ways and hence modify animal communities depending on the type and intensity of these activities. The Montes de Maria subregion of Colombia has experienced a heavy transformation of most areas despite covering one of the last remnants of dry forest, a critically endangered ecosystem. However, the effects of this transformation have been little explored. Here, we used a multispecies occupancy model (MSOM) to understand the relative influence of three components of land-use change – deforestation (remaining forest amount), degradation of forest integrity (forest quality) and fragmentation (landscape configuration) on mammalian habitat use across a mosaic of tropical dry forest in Colombia. Our data suggest that the percentage of forest cover was substantially important for herbivores, and consistently showed a moderate effect on the entire community and some individual species. High variability in species-specific responses to the examined variables hindered broad taxonomic generalizations, nevertheless, we detected a moderate positive effect of forest cover in both diet specialists and generalists species, as well as in species with small home ranges. Although omnivores responses, tended to use less complex landscapes (mosaics of land uses), there was high uncertainty in this response. The lack of substantial effects on most species, and the absence of threatened species across this anthropogenic landscape, suggests that the current community is composed of species tolerant to habitat modifications, but not only diet generalist species. This is most likely the result of a long filtering process caused by land use transformation and hunting which could have caused non-sensitive species to distribute relatively homogenously across this landscape. Our results suggest that conservation strategies in the study area should focus on conserving and expanding as much forest as possible rather than only improving the quality of already existing forest patches.

Innovative approaches in complex data forensics: error rate assessment and its impact on cybersecurity

In the realm of cybersecurity, the integrity of forensic investigations is paramount, especially as the volume and complexity of data continue to escalate. This paper explores innovative approaches to complex data forensics, focusing on the methodologies used to assess error rates in data retrieval and analysis. High error rates in forensic processes can compromise the reliability of findings, leading to erroneous conclusions that may impact security measures and legal proceedings. This research examines various techniques for error rate assessment, including statistical methods and data validation protocols, which serve to quantify the accuracy of forensic analysis. Furthermore, the paper discusses the profound implications that high error rates can have on the integrity of forensic findings, emphasizing the need for meticulous attention to detail in data handling and processing. To counter these challenges, we present strategies aimed at enhancing data reliability, such as implementing rigorous quality assurance processes, leveraging machine learning algorithms for anomaly detection, and utilizing advanced encryption methods to protect data integrity throughout the forensic lifecycle. By addressing the critical role of error rate assessment in data forensics, this research contributes to the broader discourse on cybersecurity and underscores the necessity of adopting robust methodologies to ensure accurate and reliable forensic outcomes in an increasingly complex digital landscape.

Conceptual framework for enhancing decision-making in higher education through data-driven governance

In an increasingly complex and competitive landscape, higher education institutions must leverage data-driven governance to enhance decision-making processes. This paper presents a conceptual framework aimed at integrating data analytics into the governance structures of higher education, facilitating informed decision-making, and improving institutional effectiveness. The framework emphasizes the importance of collecting, analyzing, and utilizing data across various dimensions, including academic performance, student engagement, financial management, and operational efficiency. By adopting a data-driven approach, institutions can identify trends, forecast outcomes, and make strategic decisions that align with their mission and goals. The study outlines key components of the framework, including data governance, analytics capabilities, stakeholder engagement, and institutional culture. Data governance ensures that data quality, integrity, and security are maintained, while analytics capabilities empower decision-makers with actionable insights. Engaging stakeholders—faculty, administrators, students, and external partners—fosters a collaborative environment where data-driven decisions are embraced and supported. Furthermore, the framework addresses the challenges institutions face when implementing data-driven governance, such as resistance to change, data silos, and inadequate technological infrastructure. Strategies for overcoming these challenges include investing in professional development, enhancing data literacy, and promoting a culture of continuous improvement. The framework also highlights the significance of ethical considerations and transparency in data usage, ensuring that decision-making processes are equitable and accountable. The findings indicate that by embracing data-driven governance, higher education institutions can enhance their responsiveness to changing demands, optimize resource allocation, and improve overall institutional performance. Ultimately, the conceptual framework serves as a guide for higher education leaders seeking to harness the power of data in their decision-making processes, fostering a proactive and adaptive institutional environment.

Leveraging cryptocurrencies and digital assets for Small and Medium-Sized Enterprises (SMEs): Opportunities and challenges

This review paper explores the potential opportunities and challenges associated with leveraging cryptocurrencies and digital assets for small and medium-sized enterprises (SMEs). The paper highlights the benefits of adopting digital currencies, including financial inclusion, reduced transaction costs, access to new markets, and enhanced transaction transparency and security. However, it also addresses SMEs' challenges, such as regulatory and legal uncertainties, volatility, and cybersecurity risks. Strategic approaches for SMEs are discussed to mitigate these challenges, including risk management, using stablecoins, and adopting best practices for integrating cryptocurrencies into business operations. The paper offers recommendations for SMEs considering digital assets. The future outlook for cryptocurrencies in the SME sector is considered, emphasizing the potential for further growth as regulatory clarity improves and technological innovations advance. This paper serves as a guide for SMEs looking to navigate the complex landscape of cryptocurrency adoption while maximizing the benefits and minimizing the risks.

Modeling of fluence-dependent hole-burned spectra and hole-growth kinetics using multiple two-level system model.

Numerical formalism is presented that perfectly describes resonant low-temperature hole-burned spectra (including zero-phonon holes, ZPHs) and spectral hole-growth dynamics of Al-phthalocyanine tetrasulphonate embedded in hyperquenched glassy water films over more than seven orders of fluence magnitude (0.4 µJ/cm2-5.9 J/cm2). Frequency changes during spectral hole-burning (HB) are traditionally explained with the help of a single extrinsic two-level-system (TLSext) associated with impurity molecules. The new multiple two-level system (n-TLSext) models and data analysis presented in this work show that each chromophore in an amorphous medium can couple with multiple independent TLSext, which maintain perfect photo-memory, allowing a full return of the photoproduct to the initial ("preburn") state. Modeling reveals that the experimentally observed narrow photoproduct peak at higher energies, in close vicinity of the zero-phonon hole (ZPH), reflects a dynamical feature of the HB process populating so-called "terminal" states (states that do not interact with laser excitation). Within the n-TLSext model, each chromophore possesses multiple possibilities to create a photoproduct when in interaction with the burning laser, i.e., chromophores can interact with burning laser-light multiple times until reaching the terminal states. Due to phonon-assisted absorption, terminal states are typically at higher energies than the ZPH, in agreement with the hole burned spectra reported for many molecules embedded in various amorphous solids. However, many HB systems reveal both blue- (high-energy) and red-shifted (low-energy) antiholes (i.e., photoproducts). We suggest that future modeling of resonant holes in various proteins using our n-TLSext model will provide more insight on the complexity of the protein energy landscape.

Leveraging digital adherence technologies to enhance public health: insights from TB care in Ukraine

Abstract Background Effective tuberculosis (TB) care is vital for public health, especially in war-affected Ukraine. In response to the complex healthcare delivery landscape, where traditional directly observation treatment methods became impractical, digital adherence technologies (DATs) emerged as a promising solution, offering more autonomy for people, and remote monitoring capabilities for healthcare providers. Intervention The PATH-led USAID-funded Support TB Control Efforts in Ukraine (STBCEU) project implemented smart pill boxes in eleven Ukrainian regions. These devices equipped with built-in reminders helped people adhere to their medication regimen, and signaled healthcare providers when the box was opened. Results Since April 2022, STBCEU has supported 2,022 people with TB who used smart pill boxes during their treatment, including 489 (24%) with drug-resistant TB (DR-TB). A total of 482 people continue utilizing boxes. Of 1,540 people with TB who completed the use of boxes, 1,207 people (78%) successfully completed TB treatment with an average of 84% treatment adherence rate digitally reported by the technology. Conclusions The integration of smart pill boxes in TB management has enhanced people-centered care, ensuring treatment continuity even in challenging circumstances and providing healthcare providers with real-time adherence data. The innovation contributed to TB control efforts and health equity through expanding opportunities for people to choose their treatment model based on their needs, available resources and community support programs. Leveraging digital solutions proved to be essential in strengthening healthcare systems. In the realm of TB management, DATs enhance differentiated care, embodying a harmonious integration of technology automation and provider interventions. Key messages • Integration of smart pill boxes enhanced patient-centered care, providing real-time adherence data and empowering individuals to choose personalized treatment models. • Digital technologies like smart pill boxes transformed TB care, offering tailored support for patients and remote monitoring capabilities for healthcare providers even in challenging circumstances.

Research-Based Practices to Engage Youth in a Vibrant Catholic Church: The Case for Implementing Sacred Listening Practices

This article explores the theological and practical implications of Sacred Listening, a concept developed in response to the Synod on Synodality initiated by Pope Francis. The Synod emphasizes the church’s call to a more relational and participatory ecclesial life, rooted in listening, dialogue, and accompaniment. Sacred Listening is proposed as a theologically grounded practice designed to rebuild trust and foster deeper connections within the faith community, particularly in a low-trust environment. Drawing on the theological principles of Imago Dei, as well as contemporary social science research, the article presents Sacred Listening as a means to move beyond traditional modes of institutional authority toward relational trust and authentic engagement. The article further introduces practical tools that operationalize these principles, providing concrete strategies for religious leaders to engage with their communities more effectively. This work contributes to the ongoing theological discourse on synodality and offers a framework for creating a more inclusive and responsive church in today’s complex social landscape.

Unravelling misinformation: investigating the associations between health literacy and resilience

Abstract Background Resilience, the capacity to adapt to adversity, encompasses navigating challenges in health information. Despite the profound impact of health misinformation on behavior and trust in healthcare systems, effective tools to enhance resilience against misinformation remain scarce. Health literacy (HL) is pivotal for adeptly navigating information landscapes. While HL principles facilitate critical evaluation of health information, their link to resilience against misinformation has received inadequate attention. This study delves into the associations between HL and resilience to misinformation. Methods Within the BeE-school project, a randomized controlled trial in primary schools serving vulnerable populations, we developed a questionnaire to assess parental resilience to misinformation. Two dimensions were examined: self-regulation regarding misinformation and stress resistance to misinformation, stratified into low and high resilience categories. Parental HL was evaluated using the HLS-Q12 tool. General HL and three specific domains-health promotion, disease prevention, and healthcare management-were assessed. HL scores ranged from 0 to 100. Binary regression analyses examined the association between HL and resilience to misinformation. Results Parents with higher HL levels demonstrated significantly greater resilience to misinformation, with odds ratios ranging from 1.039 to 1.049 across different HL dimensions. These associations persisted after adjusting for potential confounders, including parental education and respondent sex. Conclusions This study highlights the potential role of health literacy in enhancing individuals’ resilience to misinformation. By promoting HL, individuals can develop critical thinking abilities necessary for navigating complex information landscapes effectively. Empowering communities to critically assess health information fosters resilience to misinformation and informed decision-making, thereby advancing public health.

Navigating the Complex Landscape of Shock Filter Cahn–Hilliard Equation: From Regularized to Entropy Solutions

Navigating the Complex Landscape of Shock Filter Cahn–Hilliard Equation: From Regularized to Entropy Solutions

Beyond chromosomal rearrangements: The expanding landscape of gene fusions and chimeric RNAs

BCR–ABL, the pioneering gene fusion resulting from chromosomal translocation, has marked a major milestone in understanding genetic alterations in cancer. Initially, gene fusions were linked solely to chromosomal rearrangements, serving as diagnostic markers and cancer drivers. However, advancements in high-throughput sequencing and bioinformatics have revealed additional mechanisms underlying gene fusion. The term “gene fusion” primarily refers to fusion events at the DNA level, whereas “chimeric RNA” encompasses a wide range of transcripts containing exons from different parental genes, including gene fusion transcripts. Recent developments have identified numerous chimeric RNAs in various cancer types, extending even to non-cancerous tissues. Chimeric RNAs, originating from events such as trans-splicing, read-through, and intergenic splicing, form a complex landscape with varied functions. While some chimeric RNAs have defined roles and therapeutic potential, a comprehensive understanding of their diverse functions remains a priority. Exploring the full spectrum of chimeric RNA activities is crucial for revealing their clinical and therapeutic implications. In addition, chimeric RNAs are key players in tumorigenesis, affecting cellular processes, and driving cancer progression. Understanding their intricate interactions with cellular pathways is essential for developing targeted therapies and precision medicine approaches. The dynamic nature of chimeric RNAs highlights the need for ongoing research to fully harness their diagnostic and therapeutic potential.

10.E. Scientific session: Confronting the hidden risks: unveiling the disease burden of chemicals in Europe

Abstract With increasing industrialization and globalization, Europe faces a complex landscape of chemical hazards, ranging from persistent organic pollutants like PFAS to foodborne chemicals and beyond. Understanding the extent of these exposures and their associated population health risks is crucial for environmental and health policy making across Europe. The burden of disease methodology, particularly through metrics such as Disability-Adjusted Life Years (DALYs), combined with economic costs, plays a crucial role in assessing and addressing the impacts of chemicals on population health. DALYs provide a comprehensive measure that quantifies both the years of life lost due to premature death and the years lived with disability, offering a holistic understanding of the disease burden caused by chemical exposures. Additionally, incorporating economic costs into the analysis enables the evaluation of the financial burden imposed by chemical-related illnesses on healthcare systems, productivity, and societal well-being. By leveraging the burden of disease methodology, stakeholders can make evidence-based decisions to protect health of populations and allocate resources effectively, ultimately promoting healthier environments and communities. Several global and European initiatives provide platforms for evidence generation, knowledge-sharing, and action to address the disease burden of chemicals. At the global level, the Foodborne Disease Burden Epidemiology Reference Group of the World Health Organization (WHO/FERG) has estimated the global burden of foodborne diseases, including those caused by chemical contaminants in foods. At European level, the health impact of PFAS and other chemicals of high concern is being spearheaded by the European Environment Agency’s European Topic Centre on Human Health and the Environment, and the European Partnership on the Assessment of Risks from Chemicals (PARC). Furthermore, the Organisation for Economic Cooperation and Development has established a project of an unprecedented scale to measure the economic value of the effects of chemicals on human health. Complementary to these international initiatives, various countries have started to unravel the burden of chemicals based on local data and contextual insights. In this workshop, we will present the state-of-the-art of the disease burden of chemicals, by bringing together insights from key international and national burden of disease initiatives, emphasizing the imperative of addressing the cost of policy inaction in safeguarding public health. We will also show how burden of disease estimates for chemicals can help to monitor the trends towards relevant environment and health goals such as those described in the EU’s zero pollution ambition. Through collaborative discussions and knowledge-sharing, we aspire to identify actionable strategies for policymakers, researchers, and stakeholders to effectively address these challenges and promote a healthier future for all. Key messages • Understanding the vital importance of chemical risks for European health, the utilization of DALY and economic costs reveals their true burden, guiding evidence-based policies. • Global, regional and national initiatives are examining the impacts of chemicals worldwide, contributing to evidence gathering and policy shaping.

Identifying and Managing Risks in High-Tech Supply Networks using Explainable Artificial Intelligence

Supply chains have become vital in enabling the smooth movement of material and information in the swiftly changing high-tech industries. By utilising technological innovations like automation and AI, supply chains may improve both safety and effectiveness at every level of the process, from manufacturing to sustainability. The necessity to adjust operations, give the adoption of technology top priority for resilience, and improve supply chain management efficiency. Transformation of the Supply Chain is becoming a choice of subject, when it comes to maintain the ideal inventory levels and Just in Time service deliveries. Both AI and ML are the utmost importance for the maintenance of the inventory or service through the technology. It not only helps in reducing the loss of opportunity costs, but also it resolves the issue of overstocks of any inventory and the enhanced level of service delivery. This ultimately results in the optimum utilization of the resources through the help of technology. Moreover, it is the win-win situation for the customer and provider. Even while technology has only recently begun to be incorporated into supply chains, the manufacturing and distribution sectors have adopted it more quickly, emphasising the need for effective risk identification and mitigation techniques. Notwithstanding the possible advantages of transforming the supply chain, there are obstacles and limitations that make an entire technological shift complex. The chapter also explores the concept explainable AI which can be understood as the set of techniques that makes the decision-making processes of AI systems comprehensible to humans. Acknowledging the inherent complexities and relationships within these chains, the goal of this chapter is to explore risk identification and management in high-tech supply networks. A competent and nimble stakeholder base can manage the complex landscape of the high-tech world through the incorporation of innovative technologies, proactive actions, and constructive alliances.

Mastering the Digital Regulatory Maze: Strategies for Marketing Success in a Complex Landscape

Mastering the Digital Regulatory Maze: Strategies for Marketing Success in a Complex Landscape

Comparative proteomic analysis of male and female androgenetic alopecia: elucidating gender-specific molecular patterns.

This study presents a comprehensive comparative proteomic analysis aimed at elucidating the molecular mechanisms underlying male androgenetic alopecia (AGA) and female AGA. Scalp samples from both male AGA and female AGA patients, along with their respective normal controls, were subjected to proteomic analysis, followed by bioinformatics investigations. Our findings revealed distinct proteomic profiles between male AGA and female AGA, with a total of 68 differentially expressed proteins identified in male AGA and 84 in female AGA. Among these, specific proteins were altered in male AGA and female AGA, highlighting the sex-specific molecular pathways involved in the pathogenesis of pattern hair loss. Protein-protein interaction network analyses further delineated the most impacted biological processes, including cytoskeleton organization, stress response, and metabolic pathways, with particular emphasis on the differing altered stress responses and metabolic states associated with hair loss between sexes. Our study not only uncovered the complex molecular landscape of male AGA and female AGA but also identified potential biomarkers and therapeutic targets, offering new insights into the sex-specific pathogenesis of pattern hair loss.

DNA methylation dysregulation patterns in the 1p36 region instability.

In the monosomy 1p36 deletion syndrome, the role of DNA methylation in the genomic stability of the 1p36 region remains elusive. We hypothesize that changes in the methylation pattern at the 1p36 breakpoint hotspot region influenced the chromosomal breakage leading to terminal deletions. From the monosomy 1p36 material collection, four cases with 4.0 to 5.5Mb terminal deletions and their parents were investigated. DNA samples were assessed by targeted bisulfite sequencing (NimbleGen SeqCap Epi) to examine DNA methylation status in the 1p36 hotspot region at single-base resolution as compared to the chromosomal hotspot regions, 9p22, 18q21.1, and 22q11.2. Additionally, in in silico assessment, the mean GC content of various classes of repeats in the genome and especially in the breakpoint regions was evaluated. A complex landscape of DNA methylation in the 1p36 breakpoint hotspot region was found. Changes in DNA methylation level in the vicinity of the breakpoint in the child's DNA when compared to parents' and control DNA were observed, with a shift from 15.1 to 70.8% spanning the breakpoint region. In the main classes of evaluated repeats, higher mean GC contents in the 1p36 breakpoint region (47.06%), 22q11.2 (48.47%), and 18q21.1 (44.21%) were found, compared to the rest of the genome (40.78%). The 9p22 region showed a lower GC content (39.42%) compared to the rest of the genome. Both dysregulation of DNA methylation and high GC content were found to be specific for the 1p36 breakpoint hotspot region suggesting that methylation abnormalities could contribute to aberrations at 1p36.

A Comparison of the Efficacy of Fuzzy Overlay and Random Forest Classification for Mapping and Shaping Perceptions of the Post-Mining Landscape of Gauteng, South Africa

Post-mining landscapes are multifaceted, comprising multiple characteristics, more so in big metropolitan regions such as Gauteng, South Africa. This paper evaluates the efficacy of Fuzzy overlay and Random Forest classification for integrating and representing post-mining landscapes and how this influences the perception of these landscapes. To this end, this paper uses GISs, MCDA, Fuzzy overlay, and Random Forest classification models to integrate post-mining landscape characteristics derived from the literature. It assesses the results using an accuracy assessment, area statistics, and correlation analysis. The findings from this study indicate that both Fuzzy overlay and Random Forest classification are applicable for integrating multiple landscape characteristics at varying degrees. The resultant maps show some similarity in highlighting mine waste cutting across the province. However, the Fuzzy overlay map has higher accuracy and extends over a larger footprint owing to the model’s use of a range of 0 to 1. This shows both areas of low and high memberships, as well as partial membership as intermediate values. This model also demonstrates strong relationships with regions characterised by landscape transformation and waste and weak relationships with areas of economic decline and inaccessibility. In contrast, the Random Forrest classification model, though also useful for classification purposes, presents a lower accuracy score and smaller footprint. Moreover, it uses discrete values and does not highlight some areas of interaction between landscape characteristics. The Fuzzy overlay model was found to be more favourable for integrating post-mining landscape characteristics in this study as it captures the nuances in the composition of this landscape. These findings highlight the importance of mapping methods such as Fuzzy overlay for an integrated representation and shaping the perception and understanding of the locality and extent of complex landscapes such as post-mining landscapes. Methods such as Fuzzy overlay can support research, planning, and decision-making by providing a nuanced representation of how multiple landscape characteristics are integrated and interact in space and how this influences public perception and policy outcomes.

Efficient sampling using macrocanonical Monte Carlo and density of states mapping

In the context of Monte Carlo sampling for lattice models, the complexity of the energy landscape often leads to Markov chains being trapped in local optima, thereby increasing the correlation between samples and reducing sampling efficiency. This study proposes a Monte Carlo algorithm that effectively addresses the irregularities of the energy landscape through the introduction of the estimated density of states. This algorithm enhances the accuracy in the study of phase transitions and is not model specific. Although our algorithm is primarily demonstrated on the two-dimensional square lattice model, the method is also applicable to a broader range of lattice and higher-dimensional models. Furthermore, the study develops a method for estimating the density of states of large systems based on that of smaller systems, enabling high-precision density of states estimation within specific energy intervals in large systems without additional sampling. For regions of lower precision, a reweighting strategy is employed to adjust the density of states to enhance the precision further. This algorithm is not only significant within the field of lattice model sampling but may also inspire applications of the Monte Carlo method in other domains. Published by the American Physical Society 2024

Germline Variants in Proto-Oncogenes and Tumor Suppressor Genes in Women with Cervical Cancer

Background/Objectives: Cervical cancer (CC) remains a significant global health challenge, characterized by genetic heterogeneity and a complex molecular landscape, both of which contribute to its pathogenesis. This study aimed to investigate germline variants in proto-oncogenes and tumor suppressor genes in cervical cancer patients, with the objective of clarifying their potential role in disease development. Methods: We utilized a custom next-generation sequencing (NGS) panel targeting 48 genes implicated in oncogenesis. Germline DNA samples from cervical cancer patients were analyzed in order to identify nucleotide sequence alterations. Variants were classified according to pathogenicity and clinical relevance, based on established guidelines. Results: A total of 148 nucleotide variants were detected within the cohort. Of these, 35 variants (23.6%) were classified as benign. In contrast, 105 variants (70.9%) were identified as variants of uncertain significance (VUSs). Moreover, seven pathogenic or likely pathogenic mutations were discovered, along with the polymorphic variant rs1042522 in the TP53 gene, which has been associated with an increased risk of cervical cancer. Conclusions: Our findings contribute to expanding our understanding of the molecular genetic landscape of cervical cancer. They emphasize the potential contribution of rare germline mutations to its development and progression. These results highlight the importance of comprehensive genetic screening in order to improve diagnostic and therapeutic approaches for cervical cancer patients.

Use of Artificial Intelligence tools in supporting decision-making in hospital management

BackgroundThe use of Artificial Intelligence (AI) tools in hospital management holds potential for enhancing decision-making processes. This study investigates the current state of decision-making in hospital management, explores the potential benefits of AI integration, and examines hospital managers’ perceptions of AI as a decision-support tool.MethodsA descriptive and exploratory study was conducted using a qualitative approach. Data were collected through semi-structured interviews with 15 hospital managers from various departments and institutions. The interviews were transcribed, anonymized, and analyzed using thematic coding to identify key themes and patterns in the responses.ResultsHospital managers highlighted the current inefficiencies in decision-making processes, often characterized by poor communication, isolated decision-making, and limited data access. The use of traditional tools like spreadsheet applications and business intelligence systems remains prevalent, but there is a clear need for more advanced, integrated solutions. Managers expressed both optimism and skepticism about AI, acknowledging its potential to improve efficiency and decision-making while raising concerns about data privacy, ethical issues, and the loss of human empathy. The study identified key challenges, including the variability in technical skills, data fragmentation, and resistance to change. Managers emphasized the importance of robust data infrastructure and adequate training to ensure successful AI integration.ConclusionsThe study reveals a complex landscape where the potential benefits of AI in hospital management are balanced with significant challenges and concerns. Effective integration of AI requires addressing technical, ethical, and cultural issues, with a focus on maintaining human elements in decision-making. AI is seen as a powerful tool to support, not replace, human judgment in hospital management, promising improvements in efficiency, data accessibility, and analytical capacity. Preparing healthcare institutions with the necessary infrastructure and providing specialized training for managers are crucial for maximizing the benefits of AI while mitigating associated risks.

“No punishment is enough for monsters”: vindictiveness as an important political emotion in dealing with Belgian (potential) returnee men

ABSTRACT Building on the call to further integrate emotions in critical security and terrorism studies, we explore the ways emotions figure in media discourses and shape responses and policies on the returnee question. Through critical discourse analysis and thick description, we show how Belgian (potential) returnee men were subject to varying securitising discourses and emotional portrayals in media coverage over the past decade. Our argument contends that media discourse(s) on the returnee question exhibit a complex emotional landscape. We discern dominant, alternative and rising emotions that undergird the media coverage. We will illustrate how the “dominant” emotion of fear oscillated with an “alternative” emotion of vulnerability and, over the course of a decade, was accompanied by a “rising” emotion of vindictiveness. We argue that the cultivation of vindictiveness contributes to securitising narratives that portray Belgian (potential) returnee men as irredeemable monsters deserving of extreme punishments. This rising discourse undermines democratic principles and the rule of law. We speak of political emotions because emotions are deeply intertwined with power dynamics, societal structures, and political processes, which shape collective attitudes, behaviours, and policies towards (potential) returnees.