Search Results (1,987)

Human trafficking is a complex global problem maintained by a web of interacting individual, community, national and international factors. Its complexity is not always robustly addressed in policy, practice and research, with issues commonly approached in silos. This commentary creates the case for more intentional application of systems thinking in the counter-trafficking field. We do this by describing some of the origins of a more systemic focus in counter-trafficking work including the emergence of public health approaches, survivor- or lived-experience leadership and developments in the research domain. We then provide three case examples of how a systems lens has been applied in research, in policy and in practice across three countries, the UK, Canada and the US. We conclude with reflections on the promise of and challenges for systems thinking and a call to action for more systems-informed counter-trafficking work. Full article

This study examines staff and student experiences of an online sustainability induction module at an Australian university, exploring how such a module can contribute to whole-institution sustainability culture within a higher education institution (HEI). Using Sterling’s transformational framework, we analyse participant perceptions and reflections across module content spanning diverse SDGs, including Indigenous land management (SDG 15), ethical consumption (SDG 12), modern slavery (SDG 8), governance (SDG 16) and community engagement (SDG 17). Findings suggest how staff and students may experience the parallels between working across SDGs and learning about sustainable actions within personal, organisational, and community contexts of HEIs. While some participants appreciated the interconnectedness of sustainability challenges, they also highlighted difficulties associated with the breadth and complexity of addressing multiple SDGs within a single induction experience. This research advances understanding of how transition-oriented learning spaces that are situated between individual and institutional development and those involving affective, cognitive, and intentional dimensions of change can support HEIs in considering how to progress the 2030 Agenda. At the same time, it highlights pedagogical challenges in designing induction modules that integrate multiple SDGs in practice. Full article

Background: Male nurses form a minority group in many healthcare institutions and can face gender-based stereotypes, patient refusal, gender role assignment, marginalization in the workplace, and questioning of their suitability for working in caring professions. This scoping review examines the literature on gender discrimination, psychological well-being, and adaptation in professional contexts among male nurses and male nursing students. Methods: This scoping review was performed according to the PRISMA-ScR guidelines. Searches were performed in Scopus and Web of Science databases and complemented with the use of pre-designed Google Scholar keyword searches. Included studies had a focus on male nurses, men in nursing, or male nursing students and were concerned with gender discrimination, stereotypes, discrimination, psychological well-being, emotional labor, professional identity, burnout, job satisfaction, adaptation, education, organizational support, or retention. Data were extracted and analyzed using narrative and thematic approaches. Results: Forty included studies were identified. Four major themes were revealed: (1) gender discrimination and stereotyping; (2) psychological and professional effects; (3) coping strategies and professional adaptation; and (4) organizational responses and support mechanisms. The included studies reported patient refusal, discomfort in intimate care, gendered role assignment, workplace marginalization, and masculinity-related stereotypes. These experiences were discussed in relation to emotional labor, identity conflict, reduced sense of belonging, burnout, job dissatisfaction, work engagement, and turnover intention. Conclusions: Gender discrimination against male nurses should be understood as both an interpersonal and organizational issue. Full article

Prolonged short-video engagement in the generative-AI era may be shaped by interface cues that encourage or interrupt repeated continuation decisions in algorithmic feeds. This study examines whether AI-generated content disclosure functions as interface-level digital friction for prolonged short-video engagement among late-adolescent and emerging-adult TikTok users. Prolonged watching intention is treated as a cognitive-behavioral proximal tendency relevant to problematic social media use (PSMU), rather than as a clinical diagnosis or an emotional-disturbance outcome. Drawing on the heuristic-systematic model, we tested a dual-pathway risk-trust model in which disclosure directly affects prolonged watching intention, while perceived risk and content trust operate as mediators and AI literacy operates as a person-level boundary condition. An online between-subjects experiment was conducted with 720 valid participants aged 18–24. Disclosure had a positive direct effect on prolonged watching intention, suggesting that AI labels can initially work as salient curiosity and novelty cues. At the same time, disclosure increased perceived risk and reduced content trust, generating negative indirect pathways that constrained prolonged watching intention. AI literacy strengthened both appraisal pathways. The findings reposition AI disclosure from a mere transparency notice to a behavioral cue that can simultaneously attract attention and activate protective appraisal. They contribute to developmental and media-psychological research on prolonged engagement and PSMU-relevant mechanisms without overstating clinical implications. Full article

This paper develops and evaluates a predictive analytics framework for influencer marketing return on investment (ROI), integrating hybrid deep learning architectures with trust-aware modelling to address the dual purpose of (a) developing a rigorous evaluation framework for influencer campaign performance and (b) examining the effectiveness of influencer marketing predictors. The concept of influencer marketing has quickly grown to be one of the most effective mediums within the contemporary digital advertising landscape. Due to the growing number of brands dedicating huge amounts of budgets to social media partnerships, the importance of data-driven approaches that can predict the outcomes of campaigns and, consequently, ensure the best possible return on investment (ROI) has become urgent. This paper introduces a machine learning system that can be used to forecast the sales of products promoted by influencer marketing campaigns based on campaign-level features, including type of platform, influencer type, type of campaign, time of the year, number of engagements, estimated reach, and campaign duration. A publicly available influencer marketing ROI dataset was trained and tested on an XGBoost regression model with a coefficient of determination ($R^2$) of 0.95 indicating high predictive power and generalization. The results show that engagement metrics and estimated reach are some of the most impactful factors in sales performance, and additional contextual factors like platform selection, type of campaign, and timing of the year also moderate results. In addition to predictive modelling, this paper explains how artificial intelligence (AI) can be strategically integrated throughout the influencer marketing lifecycle. With the inclusion of AI-based analytics, marketers will be able to leverage their intuitive decision-making processes with quantifiable and replicable measures and approaches that can lead to true consumer trust and lasting brand resonance. The framework proposed can provide practitioners and researchers with a scalable basis for implementing intelligent systems in the context of influencer marketing. Recent computer science research further demonstrates that AI-driven frameworks spanning generative content modelling, AI-powered CRM architectures for understanding consumer preferences on social media, and parasocial-trust models of influencer engagement provide strong methodological complements to the predictive approach developed here, while governance and project management considerations for deploying such systems are increasingly addressed in the literature. Concurrently, a growing body of influencer marketing research examines how platform affordances shape information-seeking and trust, how influencer attributes and social satisfaction mediate purchase intention, how influencer marketing drives sustainable consumption, and how social media measurably shapes health-related behaviours all of which motivate the predictive and trust-modelling objectives of this work. Full article

Background: Resource-constrained programs perform epilepsy surgery under limited access to advanced imaging and neuromodulation. We describe presurgical evaluation findings, early seizure outcomes, and safety from a mixed adult-pediatric cohort. Methods: Retrospective single-center series of 22 consecutive patients meeting surgical candidacy. We captured demographics, epilepsy classification/etiology, presurgical investigations (long-term Video-EEG, MRI, selective FDG-PET), procedure type, histopathology when available, and postoperative seizure outcome (ILAE). Complications were recorded using the ILAE adverse-event taxonomy. Results: Mean age at surgery was 21.2 ± 11.2 years; 15 (68%) were male. Epilepsy was focal in 15 (68%); structural etiologies predominated in 15 (68%). MRI was concordant with the presumed epileptogenic zone in 13 (59%). FDG-PET was obtained in 10 (45.5%) and was concordant in 7 (70%). Long-term Video-EEG (≥2 habitual seizures) was completed in 21. Mean delay to surgery was 10 years (IQR [8–15]); presurgical work-up averaged 10 months (IQR [6–15]). Procedures were resective 14 (64%), disconnective 6 (27%), and neuromodulatory 2 (9%). Histopathology was available in 16 cases, most commonly showing hippocampal sclerosis (n = 5) and focal cortical dysplasia (n = 5). At 14 months median follow-up (range 12–34), ILAE outcomes were: I 41% (9), II 14% (3), III 23% (5), IV 23% (5). Outcomes significantly differed by procedure: curative-intent (resection/disconnection) achieved ILAE I 60% (9) versus ILAE III/IV 7 after palliative-intent (corpus callosotomy/neuromodulation). No deaths or permanent deficits occurred; one corpus callosotomy case developed transient aseptic meningitis. Conclusions: In a resource-limited program, structured presurgical evaluation and careful selection yield resection/disconnection outcomes comparable to high-resource benchmarks, while corpus callosotomy/neuromodulation remain largely palliative. Practical, reproducible pathways may help shorten delays and improve access in similar settings. Full article

Background and Objectives: Hospitals increasingly use AI tools that give nurses on-screen alerts and recommendations (AI-supported clinical decision support, AI-DSS). When nurses override these alerts too often, useful guidance can be lost; when they trust them blindly, errors can slip through. We examined which work and wellbeing factors are associated with nurses’ self-reported intention to override AI alerts, rather than observed override behavior. Methods: We surveyed 239 registered nurses (76.6% female; mean age 33.7 years) at a large hospital in Timișoara, Romania, from January to March 2025. Questionnaires measured alert fatigue, moral distress, mental workload, sleep problems, resilience, team psychological safety, and how strongly nurses intended to override AI alerts. Results: Nurses fell into three groups: those who tended to over-trust AI (26.8%), those with balanced trust (41.0%), and those who resisted it (32.2%). The resistant group had the strongest intention to override alerts and the weakest sense of psychological safety. Alert fatigue was the factor most strongly associated with override intention, and this association was partly accounted for by moral distress. The indirect association was weaker among nurses reporting higher team psychological safety. An exploratory model using these factors distinguished nurses with high self-reported override intention with acceptable accuracy. Because all variables were measured at a single time point, findings are associative and hypothesis-generating rather than causal. Conclusions: How nurses respond to AI alerts depends less on the technology than on their workload, ethical strain, and team climate. Cutting unnecessary alerts, easing moral distress, and building psychological safety may help nurses use AI more safely. Full article

This work points out a stability bifurcation which appears at low mass flow rates when simulating the flow inside of a compressor rotor alone, using the Body Force Modeling (BFM) approach with the Hall–Thollet formulation. This phenomenon is observed for a small propulsive axial fan, with and without model calibration. It does not have any consequence, since it happens far beyond the surge limit of the fan stage and involves “virtual” operating points which cannot be captured with blade simulations. However, this bifurcation also exists with centrifugal impellers, for which a flow recirculation usually takes place at low mass flow rates, enabling extension of the stable operating range. Thus, this represents a serious limitation of this method. This numerical behavior has not yet been documented in the BFM literature. Given the complexity of this subject and the number of parameters, the intention is not to carry out an exhaustive study here. But since the Hall–Thollet formulation is now quite commonly used in turbomachinery CFD, the objective of this work is to briefly report and describe the dichotomy in the solutions obtained. Full article

This paper examines the association between two Financial Technology (FinTech)-related policy windows and the profitability of Qatari commercial banks over a twenty-year horizon (2005–2024). The analysis is anchored by two structural breaks: in 2017, the Qatar Central Bank (QCB) established its FinTech task force and lifted restrictions on the implementation of a regulatory sandbox and centralised electronic know your customer (e-KYC) framework; and during the digital-acceleration period in 2020 in response to the COVID-19 pandemic and the issuance of digital banking licences. FinTech adoption is not measured directly at the bank level; the two policy windows are used as intent-to-treat proxies. Using return on assets (ROA) and return on equity (ROE), bank performance is measured and influenced by bank size (log of total assets), bank age and type (Islamic and conventional). Multiple diagnostics of Hausman and Breusch–Pagan support the use of fixed-effects (FE) panel regressions with cluster-robust standard errors on an unbalanced panel of 125 bank–year observations. The results show a positive coefficient on the post-2017 dummy in the ROE model (β = 0.0306, p = 0.054, cluster-robust) and no detectable change in ROA (β = −0.00058, p = 0.868). For the post-2020 phase, both coefficients are positive but do not reach conventional significance (ROA: β = 0.00218, p = 0.539; ROE: β = 0.0224, p = 0.150). There is no systematic difference between Islamic and conventional banks that is offered by the interaction terms in either phase. Given the small sample (nine banks, eight effective clusters after the FE singleton drop; 125 observations) and the use of policy-window proxies rather than direct bank-level FinTech measures, the design cannot isolate the effect of the FinTech-related reforms from concurrent macroeconomic, sectorial or pandemic-related developments, and the cluster-robust p-values should be read as approximate. The results are therefore presented as preliminary and indicative. Read in light of these design constraints, the results are consistent with incremental rather than transformative change around the FinTech-related policy windows in Qatar, with results influenced more by timing, scale economies, and regulatory saturation than by bank type. The country-specific empirical findings can help restore context to the literature on the Gulf Cooperation Council (GCC) average, and provide measured guidance for bank managers and regulators working toward the Qatar National Vision 2030 digital aspiration. Full article

This paper examines the under-researched pivotal career moment of Shen Wei (沈伟, b.1968, last name Shen and first name Wei), one of the most renowned Chinese American dance artists in the West, when he traveled from China to the United States for the first time in 1995 and his unacclaimed early career in New York from 1995 to 2000. Based on primary sources, I compare the divergent conceptualizations of universalism in China and the United States, as embodied in Shen’s choreographies and as manifested in the contrasting receptions of his works by Chinese and American critics. Building on existing dance scholarship on universalism and situated at the intersection of Asian studies and Asian American studies, this paper explores how Shen’s choreography of universalism convinced Chinese critics but discouraged American critics from recognizing the significant value of his art. I question the United States as a neutral platform for artistic freedom and propose that Shen must perform according to the expectations of American society—the “dress code” for Asian artists—to gain acceptance and success. In so doing, I challenge the intent that primarily examines Shen based on his widely acclaimed works. The “failures” that Shen experienced in his career contain the same, if not more, research value as his “successes.” Full article

Smart Hospitals integrated within 6G edge networks aim to enhance hospital connectivity and operational efficiency by enabling intelligent and personalized e-health services and applications while optimizing resource utilization and maintaining a high degree of autonomy. Nevertheless, the interconnectivity and 6G integration, which comprise core components of Smart Hospitals, are susceptible to a wide range of security threats, posing significant risks to the confidentiality, integrity, and availability of hospital data and operations. Given that security is a critical concern for Smart Hospitals, there is an urgent need to develop novel security mechanisms to safeguard these environments within 6G edge networks. In particular, this work highlights how defining 6G characteristics, such as Ultra-Reliable Low-Latency Communications, massive IoMT connectivity, distributed edge intelligence, and AI-native network operation, not only enable next-generation hospital services but also reshape the security and privacy threat landscape and the requirements of mitigation mechanisms. In this context, the first essential step is to comprehensively understand both existing and emerging threats targeting Smart Hospitals in the 6G edge network ecosystem. Therefore, this article provides a categorization of security and privacy attacks based on their primary targets. Moreover, it presents a survey of mitigation techniques derived from recent literature, specifically designed to counter threats facing Smart Hospitals in 6G edge networks. The intent is to establish a foundation that supports ongoing research towards the development of effective, 6G-aware security countermeasures capable of protecting Smart Hospitals under the stringent latency, scalability, and reliability requirements of future healthcare environments. Full article

The contemporary concept of human interpersonal forgiveness presupposed by Catholic believers is challenged, both in academia and in popular culture. The core of the problem is the real possibility and coherence of its gratuitousness: forgiving seems to be always vitiated by the forgiver’s self-seeking motives and thus morally impaired. But Thomas Aquinas’s thought offers sufficient conceptual elements to dispel the charge. Although he addresses such forgiveness relatively rarely, his later works consider it a spiritual work of mercy, and therefore an act of the virtues of mercy and charity. In order to defend the notion’s gratuitousness (from Aquinas’s Latin term gratuitus—that is, the characteristic of the unselfish and undue gift), it is necessary to have a full understanding of his notion of the love of friendship and how this love shapes mercy and charity. This holds true on both the supernatural and natural levels, even if, following original sin, the natural level requires divine grace to fully escape the charge of lacking true gratuitousness. To resolve this question, this article demonstrates the coherence of gratuitous forgiveness regarding both the intention of the human agent and the essence of forgiveness itself. Its coherence depends on that of gratuitousness and, consequently, of charity. First, this study outlines the core of contemporary skepticism, drawing upon the thought of Jacques Derrida as its primary exemplar. Second, it proposes a solution by reconstructing Aquinas’s account, situating it within the love of friendship, mercy, and charity, and demonstrating why its intention and structure are coherently gratuitous. Several of the textual interpretations advanced here remain subject to scholarly debate. Finally, before concluding, this paper addresses a major objection to human gratuitousness: namely, that a gratuitous act, as described by Aquinas, could not be performed by a human person, insofar as human agents are finite and inherently profit from their own morally good actions. To resolve this, a metaphysical argument grounded in analogy is proposed. Full article

In recent decades, low birth rates, delayed parenthood, and a decline in the average number of children per couple have become prevalent issues, sparking growing scientific interest in understanding their causes. While cultural, social, and gender-related factors have been widely examined, work-related dimensions remain underexplored within work and organizational psychology. This qualitative study explores how individuals make sense of the interplay between perceived work-related factors (specifically, working conditions, organizational culture, the work-family interface, and career ambitions) and declared fertility intentions. Semi-structured interviews were conducted with 33 participants (22 women; 19 without children, 14 with one child; aged 24–45). A subtle realist positioning was adopted, and template analysis was employed to examine the data. The findings highlight how participants interpret multiple work-related dimensions as relevant to their fertility intentions, including high job demands, job insecurity, and family-unfriendly organizational cultures. Concerns about work-family conflict also emerged as central. At the personal level, career ambitions and self-efficacy were identified as key dimensions. Notably, the perception of career-parenthood interference is often associated with limited or postponed decisions about having children. This study contributes to a better understanding of the work-related dimensions that can contribute to shaping fertility intentions and offers practical recommendations for interventions. Full article

Background/Objectives: Although Hospital Information Systems (HIS) are essential to the provision of contemporary healthcare, clinical professionals’ use of HIS is still uneven. Robust healthcare decision-making is based on the systematic collection, storage, and analysis of health data, and it is crucial to comprehend the elements that promote or impede adoption. In a tertiary-care multi-specialty hospital in Chennai, India, this study sought to evaluate the role of the Unified Theory of Acceptance and Use of Technology (UTAUT) constructs—Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions—on the Behavioural Intention of healthcare professionals to adopt HIS. Methods: 140 medical professionals (physicians, nurses, and hospital technicians) from a 750-bed teaching hospital where HIS had been in use for at least 24 months were chosen by stratified random sampling to participate in a descriptive, cross-sectional study. The original UTAUT instrument was modified into a structured, self-administered questionnaire using a validated 5-point Likert scale. Expert review was used to demonstrate face validity, while Cronbach’s Alpha (α > 0.70) was carried out. Statistical analysis methods included Pearson correlation, multiple linear regression, one-way ANOVA with Tukey’s HSD post hoc analysis, and Structural Equation Modelling (SEM). Results: The majority of responders in the sample were female (51.5%), primarily nurses (47%), and had less than five years of work experience (36%). All four UTAUT constructs were found to be significantly correlated with Behavioural Intention by Pearson correlation, with Performance Expectancy showing the strongest association. The structural model explained a significant proportion of the variance in technology adoption. Multiple regression analysis indicated that Performance Expectancy (β = 0.480, p < 0.01) and Social Influence (β = 0.180, p < 0.05) were significant positive predictors of Behavioural Intention. Confirmatory Factor Analysis verified acceptable measurement boundaries (χ²/df = 1.42, RMSEA = 0.043, SRMR = 0.062, CFI = 0.94. An exploratory one-way ANOVA revealed that perceptions of Facilitating Conditions differed significantly by professional designation (F (2, 137) = 6.42, p = 0.002), with nurses scoring significantly lower than physicians (p = 0.002) and technicians (p = 0.011). Conclusions: Performance Expectancy is the main driver of healthcare professionals’ Behavioural Intention to adopt HIS. Compared to doctors and technical professionals, nurses reported considerably lower perceptions of Facilitating Conditions, indicating a substantial support gap. In order to close the clinical digital gap and enhance patient safety, these findings advocate for role-specific infrastructure investments and focused implementation techniques. Full article

Industrial digitalization is moving from Industry 4.0 toward Industry 5.0’s emphasis on resilience, human-centric operation, and sustainability. This shift is enabled by the convergence of Operational Technology and Information Technology, but this integration also broadens the exposure of industrial infrastructures to cyber threats targeting communication integrity and process continuity. Mitigating these risks requires network control that is both programmable and aware of each asset’s operational context. However, there is still a lack of operational interfaces that translate the semantics of industrial assets into programmable, runtime-enforceable network behavior. In this paper, following a Design Science Research methodology, we introduce an asset-aware, closed-loop network control abstraction in which the industrial network itself is modeled as a managed asset through Asset Administration Shells. Asset state, lifecycle phase, and operational intent are translated into network policies enforced at runtime on programmable data planes, while in-network telemetry is exposed at the asset level and correlated with operational metrics. We validate the abstraction on a hybrid testbed that combines virtualized components with industrial-grade hardware and virtualized 5G connectivity, through three security-oriented use cases: (i) asset-driven customization of forwarding policies; (ii) human-centric secure maintenance with controlled remote access over 5G; and (iii) anomaly detection and isolation based on cross-layer telemetry correlation. The results show that asset-level operations can drive programmable network enforcement and make network telemetry available at the asset layer. Finally, the work outlines a first step toward standardizing network-oriented asset submodels by separating control-plane operations from data-plane state and telemetry. Full article