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Impact of Frontline Employees’ Perceived Benefits of Artificial Intelligence on AI Adoption and Job Engagement: A Meta-analysis
Full text linkThis study explores how frontline employees’ perceived benefits of artificial intelligence (AI) influence two behavioural outcomes: AI adoption and job engagement. Using the Social Shaping of Technology (SST) framework, which highlights the role of social, organizational, and contextual factors over technical features, the meta-analysis synthesises 71 studies involving 23,051 employees across 37 countries. Perceived AI benefits (e.g., increased efficiency, reduced monotony, decision-making support) positively relate to both adoption and engagement, though risks like job displacement anxiety can weaken these effects. To identify when and for whom these benefits matter most, the study analyses 13 moderators (user-level: gender, education, age, tenure, role, position; organizational-level: industry, control, AI task and knowledge; macrolevel:year, country). Industry type, control over AI, and work level/position were most significant. The findings underscore the need for tailored AI strategies aligned with workforce characteristics and socio-technical contexts, advancing employee-centred AI research on contextual variation and moderating effects.Full Tex
Conservation of India's freshwater megafauna: Publication patterns and trends over time
No full textFreshwater megafauna are critical components of aquatic ecosystems, yet they remain understudied compared to their terrestrial counterparts. This study provides the first comprehensive assessment of India's freshwater megafauna (IFM), analyzing taxonomic and thematic patterns in the research landscape. By examining the published studies, we assessed focal points in the scientific discourse on the IFM from 1960 to 2024. Despite strong legal safeguards under India's Wild Life (Protection) Act (WLPA) for mega-mammals and mega-reptiles, our findings reveal critical gaps in their ecological, life history, and physiological research, leaving clear room for more scientific effort to better inform conservation strategies. Conversely, mega-fishes dominate the literature but lack proportionate legal protection, highlighting misaligned research and policy priorities. Furthermore, critical conservation topics such as climate change and hydrological modifications are conspicuously absent, despite their implications for species recovery. These findings provide a comprehensive overview of the evolving focus in IFM research, highlighting both well-established areas and emerging trends. Our results underscore the need for enhanced focus on critically endangered and endangered species and call for a recalibration of research and policy frameworks to address these gaps. By aligning national legislation with global conservation priorities and leveraging serendipitous policy windows, this work advocates for a robust, interdisciplinary approach to secure the future of these keystone species and the ecosystems they sustain.No Full Tex
Taiwanese parental perspectives on RSV: knowledge, risks, and acceptance toward immunization
No full textTaiwanese parents' understanding of respiratory syncytial virus (RSV) remains limited even as new prevention tools, such as long-acting monoclonal antibodies for infants, become available. We surveyed 300 expectant mothers and parents of infants (0–12 months) in Taiwan in late 2023 to assess knowledge of RSV vs. other common pediatric infections, perceptions of disease risk and severity, and assess their willingness to immunize infants with RSV monoclonal antibody (mAb) by asking how likely they were to have their infant receive an immunization. Just 38 % of respondents reported even basic knowledge of RSV, compared with over 90 % familiar with influenza or enterovirus; first-time pregnant women were among the least aware. Risk and severity of RSV were viewed as moderate but not high, and lower than that of illnesses like pneumonia. After a description of the infant RSV immunization information, the mean willingness score was 6/10 and about 30 % of participants were very willing (score ≥ 8). Prior experience with self-paid (non-mandatory) vaccines strongly predicted RSV knowledge; full-time employment was associated with lower knowledge, but higher willingness once respondents were informed. These findings suggest that while openness to infant RSV immunization exists, awareness gaps may undermine uptake. Efforts to educate parents, especially those expecting their first child, and to clarify what “infant RSV immunization” entails could facilitate acceptance.No Full Tex
FedDA-HSI: Federated Class-Aware Framework for Hyperspectral Image Classification with Diffusion Augmentation
Full text linkHyperspectral images (HSIs) have demonstrated remarkable potential in remote sensing applications due to their rich spectral and spatial characteristics. However, the scarcity of labeled HSI data from individual sources significantly hinders the deployment of deep learning models in this domain. Leveraging multi-source HSI can mitigate single source data limitations, but direct data sharing poses serious privacy challenges. Federated learning (FL) provides a compelling framework for privacy-preserving collaborative learning, yet practical challenges remain, particularly data scarcity and the non-independent and identically distributed (non-IID) nature of client data. In this work, we introduce a diffusion-based data augmentation framework, namely FedDA-HSI, tailored for federated HSI classification. FedDA-HSI addresses two core issues in real-world federated HSI (FedHSI) deployments. First, we propose a federated diffusion model capable of generating high-fidelity synthetic hyperspectral data locally, enabling data augmentation without compromising privacy, while allowing the aggregated global model to capture shared knowledge across clients. Second, we incorporate a class-aware augmentation strategy to alleviate data imbalance by injecting synthetic samples of missing or underrepresented classes, effectively improving inter-client data diversity and mitigating non-IID effects. Extensive experiments on benchmark HSI datasets validate that our approach significantly improves classification performance under heterogeneous conditions.Full Tex
Scalable and effective negative sample generation for hyperedge prediction
No full textHypergraphs have demonstrated their superiority in modeling complex systems compared to traditional graphs by directly capturing the interactions among multiple entities. Hyperedge prediction, which aims to predict unobserved potential hyperedges, is a fundamental task in hypergraph analysis. A critical component in hyperedge prediction is the sampling of informative negative hyperedges from significantly larger candidate negative sets, compared to traditional graphs, to enhance model training efficacy. Most existing methods utilize predefined heuristics to sample negative hyperedges, resulting in limited generalizability due to their reliance on these predefined rules. The new state-of-the-art in this field is generation-based methods, which treat negative sampling as a generative task. Nevertheless, current generation-based approaches are not scalable to large hypergraphs. Additionally, diffusion models have demonstrated superior performance in numerous generative tasks, yet their potential application in the generation of negative hyperedges remains unexplored. However, the adaptation of diffusion models to this specific task presents challenges due to: (1) diffusion models are inherently designed to generate high-quality positive samples, which are well-defined, as opposed to negative samples; (2) diffusion models are traditionally employed in continuous space, whereas negative sampling for hyperedge prediction operates in discrete space.To address these complexities, we introduce SEHP (Scalable and Effective Negative Sample Generation for Hyperedge Prediction), which employs a conditional diffusion model to iteratively generate and refine negative hyperedges, thereby advancing them towards the decision boundary to improve model performance. SEHP further enhances scalability by effectively sampling sub-hypergraphs, integrating global structural information into the diffusion model for batch training. Extensive experiments conducted on real-world datasets demonstrate that SEHP surpasses existing state-of-the-art methods in both prediction accuracy and scalability. The code of our paper is available at https://github.com/SLQu/SEHPFull Tex
Designing Versatile p-d Dual-Atom Catalysts via Frontier Orbital Engineering for Efficient Photocatalytic Urea Production
No full textConstructing multimetal centers on carbon-based substrates is a promising strategy to enhance C-N coupling for efficient urea synthesis, while the underlying design principles, particularly how metal-metal and metal-substrate interactions govern reactant activation and reaction pathways, remain intangible. To address this gap, we developed a frontier orbital interaction-guided C-N coupling selectivity map based on the p-d asymmetric dual-atom models (DACs) through the synergistic integration of DFT calculations and machine learning classification. Specifically, efficient NOx reduction was found to require a narrow energy gap (ΔE1 1.39 eV) between the LUMO of p-block metals and the HOMO of the d@substrate, signifying weaker p-d interactions. Moreover, such an asymmetric dual-atom structure enables a tunable bandgap while simultaneously optimizing the visible-light absorption range. As a result, the AlPd@PCN and GaPt@PCN systems stand out as exceptional candidates, exhibiting fully thermodynamically favorable energy profiles throughout the photocatalytic cycle. These insights not only extend frontier orbital theory to DACs systems but also establish a robust, generalizable framework for designing high-performance dual-atom urea synthesis catalysts.Full Tex
A unified spatial-spectral-temporal network for hyperspectral object tracking
No full textHyperspectral object tracking offers superior performance over conventional color-based tracking by leveraging rich spectral information to enhance material discrimination ability. Due to the limited availability of hyperspectral video datasets, many hyperspectral trackers rely on spectral correlation modeling to bridge hyperspectral images (HSIs) and color images. They are often combined with pre-trained deep feature extractors for robust representation. However, these methods face two key limitations: 1) they ignore the intrinsic relationship between the object template in the initial frame and the search image in the current frame during spectral correlation modeling. This limits the ability to distinguish spectral differences between objects and backgrounds; and 2) they insufficiently utilize temporal information, which prevents the construction of a robust spatial-spectral-temporal representation and thereby limits the improvement of tracking performance. To overcome these two issues, we propose CSSTrack, a novel unified network for end-to-end hyperspectral object tracking. First and foremost, we propose a spectral-aware representation enhancement (SaRE) module that employs physical models of spectral self-expression to perform cross-frame spectral correlations between the template and search images. Different from previous works, our method enhances the discrimination of foreground-background spectral differences, thereby facilitating the extraction of discriminative spatial-spectral features. Moreover, we design a spatial-spectral-temporal modeling (S2TM) module, which utilizes a sequence of autoregressive temporal embeddings to capture motion dynamics across spectral bands and integrates static and dynamic features through a fusion network. Extensive experiments on the HOT2020 and IMEC25 datasets demonstrate the effectiveness of our proposed CSSTrack, which achieves state-of-the-art tracking performance. The source code is available at https://github.com/hscv/CSSTrack.No Full Tex
Procedural Support for Neurodivergent Children During Medical Procedures: A Scoping Review
Full text linkNeurodivergent children face unique challenges during medical procedures due to distinct sensory processing patterns and communication difficulties. Evidence-based interventions for procedural pain/distress may inadequately address their specific needs, leading to undertreated distress and negative healthcare experiences.Following Joanna Briggs Institute methodology, we conducted comprehensive searches across six databases on January 10, 2025, for studies published 2014-2025 focusing on neurodivergent children (0-21 years) undergoing medical procedures. Two independent reviewers screened studies and extracted data, with results presented as a narrative synthesis with evidence mapping.From 14,393 initial records, 144 studies met the inclusion criteria. Most studies (n = 121, 84.0%) focused on autism spectrum disorder, with limited representation of other neurodivergent diagnoses. Hospital outpatient settings (n = 93, 64.6%) and dental specialty (n = 50, 34.7%) were most commonly studied. The most frequently used support strategies were visit preparation and support (n = 61, 42.4%), pharmacological agents (n = 48, 33.3%), and patient care plans/pathways (n = 43, 29.9%). Few studies used validated pain and distress assessment tools, with only 4.2% (n = 6) reporting child-reported pain measures. While 74.3% (n = 107) of studies reported distress outcomes, these were primarily observational rather than validated measures.Findings highlight significant gaps in procedural support strategies for neurodivergent children, particularly for conditions beyond autism. There is a critical need for research using validated pain and distress measures, especially those capturing the child's perspective. Future studies should prioritise diverse neurodivergent populations, incorporate structured assessment tools, and evaluate tailored interventions across wider clinical settings.Full Tex
Acoustic detection of Eastern Ground Parrot Pezoporus wallicus – temporal variation and monitoring design
No full textParrots across the globe are at risk from habitat removal and modification. The Vulnerable Eastern Ground Parrot Pezoporus wallicus of Australia is subject to anthropogenic influences and monitoring is paramount for its protection. Pezoporus wallicus is shy and rarely seen, but it calls predictably and distinctively. Autonomous Recording Units were used to record the diel patterns of calling behaviour and investigate the detectability of P. wallicus during a breeding season, to derive optimal sampling methods. Recorders were deployed within heathlands in south-east Queensland during 2020, recording for 60 minutes around civil dawn and dusk. Call rates across recording periods were generally higher in the evening (27.8 calls per site) than morning (7.4 calls per site). Most calling occurred over half hour periods, with peaks at civil dawn and dusk. Daily calling was variable within and across sites. Occupancy modelling indicated a minimum survey effort of four evening acoustic survey sessions per site over four days if manually searching for calls on spectrograms or 3 days, if call recognisers could be utilised, would maximise the likelihood of a 95% probability of detecting P. wallicus at an occupied site. Efficient surveys using ARU technology will be of great benefit to monitoring this and other declining bird species around the world.No Full Tex
Treatment persistence and efficacy of subcutaneous infliximab monotherapy in patients with inflammatory bowel disease: a real-world setting
No full textBackground and aims Subcutaneous infliximab (SC-IFX) offers an alternative to intravenous therapy with potential advantages in treatment persistence, medication burden and patient adherence. This study evaluated 12-month treatment persistence along with clinical, biochemical and endoscopic outcomes among patients with inflammatory bowel disease (IBD) established on SC-IFX monotherapy. Methods A retrospective observational cohort study was conducted at a tertiary centre. Baseline clinical (Crohn’s Disease Activity Index or partial Mayo score), biochemical (C reactive protein (CRP), albumin, haemoglobin, faecal calprotectin, IFX trough levels) and endoscopic data were collected at SC-IFX commencement and on follow-up at 12months. Logistic regression analysis was used to identify predictors of treatment discontinuation. Results 101 adult patients were on SC-IFX monotherapy, 57 (56.4%) had Crohn’s disease and 44 (43.6%) had ulcerative colitis. Treatment persistence at 12 months was 91.1% (92/101), with sustained or improved clinical (100/101, 99.0%), biochemical (CRP (87/101, 88.1%), albumin (97/101, 96.0%), haemoglobin (100/101, 99.0%), faecal calprotectin (87/95, 91.6%), infliximab trough levels (86/94, 91.5%)) and endoscopic (90/93, 96.8%) outcomes at 12 months. Multivariate regression analysis only identified prior ileal resection as an independent predictor of treatment discontinuation at 12months. Conclusion SC-IFX monotherapy demonstrated high treatment persistence along with maintenance or improvement of clinical, biochemical and endoscopic outcomes at 12 months. These findings provide encouraging real-world evidence supporting SC-IFX monotherapy as an effective IBD treatment option. Larger studies with longer follow-up are required to identify predictors of treatment discontinuation.No Full Tex