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Chronocraft:unveiling enablers and overcoming barriers in Fintech-driven logistics and supply chain management in the digital era
Get PDFThis study examines the factors that promote and impede the adoption of financial technology in supply chain management. Previous research about Fintech primarily focuses on Fintech's functions, without delivering a complete examination of enablers and barriers. This study fills this gap and draws from 37 experienced supply chain managers in US and UK who currently use Fintech to uncover three main drivers of fintech adoption: promoting business expansion (through cost reduction, enhanced customer satisfaction, technology upgrades, and innovation), ease of doing business (via streamlined processes and information access), and achieving a competitive edge (through improved efficiency, transparency, and risk management). Conversely, Significant barriers were found in human resources (cultural resistance, skill gaps, and training challenges), financial (high initial investment and training costs), technology (compatibility and integration issues, and awareness challenges), and external factors (stakeholder resistance and regulatory hurdles). By demonstrating how these elements interact, this study offers an advanced view of the Fintech adoption process and useful advice for businesses looking to maximize Fintech integration in their supply chains. The findings deliver novel academic value by providing a comprehensive framework for assessing the dynamics of the Fintech-driven supply chain, assisting in strategic decision-making, and directing further research.</p
“Concomitant surgical ablation in atrial fibrillation patients undergoing cardiac surgery for isolated coronary and aortic valve disease: a multicentre study from The Netherlands Heart Registration”
Get PDFObjectives Concomitant surgical ablation (CSA) is recommended for atrial fibrillation (AF) patients undergoing cardiac surgery; however, its effects in non-mitral valve surgeries, specifically coronary artery bypass grafting (CABG) and aortic valve replacement (AVR), are less studied. This study aims to analyse outcomes and trends of CSA performance in the Netherlands. Methods This nationwide multicentre study utilized data from the Netherlands Heart Registration. AF patients undergoing CABG or AVR between 2013 and 2021 were included. Temporal trends in CSA performance were analysed and a multivariable regression model adjusted for confounders when comparing CSA and non-CSA. Results A total of 3260 patients were included, of which 1081 underwent CSA. CSA patients showed longer cardiopulmonary bypass (CPB) (111 vs 80, mean difference between groups: 31 min [95% CI, 27-34, P < 0.001]) and aortic cross clamping (AoX) times (67 vs 52, mean difference: 15 min [95% CI, 13-17, P < 0.001]). After correcting for confounders, CSA patients presented mean CPB and AoX times of 18 (95% CI, 16-21, P < 0.001) and 8 (95% CI, 6-10, P < 0.001) min longer. The CSA group showed higher survival rates (92.5% vs 86.4%, P = 0.039) and greater improvements in mental quality of life (QoL) (P = 0.047). CSA performance during CABG and AVR has increased significantly, from 29.7% in 2018 to 44.4% in 2021. Conclusions CSA resulted in slightly longer CPB and AoX times but no significant differences in major complications. Regression analysis showed better survival rates and improved mental QoL for CSA. CSA performance in CABG and AVR has increased in the Netherlands.</p
Training needs analysis of simulation-based training using Extracorporeal life support simulators
Get PDFWeak equals strong L<sup>2</sup> regularity for partial tangential traces on Lipschitz domains
Get PDFWe investigate the boundary trace operators that naturally correspond to H(curl,Ω), namely the tangential and twisted tangential trace, where Ω⊆R3. In particular we regard partial tangential traces, i.e., we look only on a subset Γ of the boundary ∂Ω. We assume both Ω and Γ to be strongly Lipschitz (possibly unbounded). We define the space of all H(curl,Ω) fields that possess a L2 tangential trace in a weak sense and show that the set of all smooth fields is dense in that space, which is a generalization of [1]. This is especially important for Maxwell's equation with mixed boundary condition as we answer the open problem by Weiss and Staffans in [10, Sec. 5] for strongly Lipschitz pairs.</p
Tales of boundary spanning for climate actions in cities: from theory to practice, and back
Get PDFEffective climate actions in cities require integrating knowledge into actionable policy and practice. This perspective article provides empirically grounded insights on different roles, practice challenges and impacts of boundary spanners in mediating the science, policy and practice knowledge universes, by drawing upon critical self-reflections of four boundary spanners. It offers important insights into existing challenges they face in practice, and highlights opportunities for supporting them in accelerating climate actions
Mapping mobile aource air pollutants:Comparison of binary and multiclass spatial classification using Hidden Markov Model
Get PDFAir pollution is a significant global issue affecting health, climate, and ecosystems, with urban areas especially impacted by emissions from mobile sources. A multitude of factors influences the intensity and spatial distribution of these emissions. Accurately estimating air pollutant emissions spatially is crucial for implementing effective reduction measures. However, previous studies did not focus on classifying emissions and fuel consumption (FC) from mobile sources independently while considering spatial trajectory characteristics. This approach is essential for accurately understanding environmental characteristics and their impacts on mobile source emissions in different locations, as well as for managing local air quality (AQ). The current research focuses on classifying FC and emissions of individual taxis. To achieve this, an approach utilizing Hidden Markov Models (HMM) is employed in binary and multiclass modes. The study area encompasses a segment of the Beijing metropolis in China. Results indicate that HMM performs better in binary classification compared to multi-class classification (three and four classes). For test data, the classification accuracy of HMM in binary mode for FC, nitrogen oxides (NOx), hydrocarbons (HC), and carbon monoxide (CO) stands at 0.80, 0.84, 0.88, and 0.84, respectively. Following model validation, a map detailing FC and emissions from mobile sources under scrutiny is generated. This map serves to identify areas with varying levels of FC and emissions, aiding in visualizing the risk posed by air pollutants from the tested mobile sources. By modeling individual mobile sources, this research sets a foundation for larger-scale studies, potentially offering valuable insights into combating air pollution more effectively.</p
Steerable Anatomical Shape Synthesis with Implicit Neural Representations
Get PDFGenerative modeling of anatomical structures plays a crucial role in virtual imaging trials, which allow researchers to perform studies without the costs and constraints inherent to in vivo and phantom studies. For clinical relevance, generative models should allow targeted control to simulate specific patient populations rather than relying on purely random sampling. In this work, we propose a steerable generative model based on implicit neural representations. Implicit neural representations naturally support topology changes, making them well-suited for anatomical structures with varying topology, such as the thyroid. Our model learns a disentangled latent representation, enabling fine-grained control over shape variations. Evaluation includes reconstruction accuracy and anatomical plausibility. Our results demonstrate that the proposed model achieves high-quality shape generation while enabling targeted anatomical modifications
A comparison of variable selection methods and predictive models for postoperative bowel surgery complications
Get PDFAccurate prediction of postoperative complications can support personalized perioperative care. However, in surgical settings, data collection is often constrained, and identifying which variables to prioritize remains an open question. We analyzed 767 elective bowel surgeries performed under an Enhanced Recovery After Surgery protocol at Medisch Spectrum Twente (Netherlands) between March 2020 and December 2023. Although hundreds of variables were available, most had substantial missingness or near-constant values and were therefore excluded. After data preprocessing, 34 perioperative predictors were selected for further analysis. Surgeries from 2020 to 2022 () formed the development set, and 2023 cases () provided temporal validation. We modeled two binary endpoints: any and serious postoperative complications (Clavien Dindo IIIa). We compared weighted logistic regression, stratified random forests, and Naive Bayes under class imbalance (serious complication rate 11\%; any complication rate 35\%). Probabilistic performance was assessed using class-specific Brier scores. We advocate reporting probabilistic risk estimates to guide monitoring based on uncertainty. Random forests yielded better calibration across outcomes. Variable selection modestly improved weighted logistic regression and Naive Bayes but had minimal effect on random forests. Despite single-center data, our findings underscore the value of careful preprocessing and ensemble methods in perioperative risk modeling
Physiological-Model-Based Neural Network for Heart Rate Estimation during Daily Physical Activities
Get PDFHeart failure (HF) poses a significant global health challenge, with early detection offering opportunities for improved outcomes. Abnormalities in heart rate (HR), particularly during daily activities, may serve as early indicators of HF risk. However, existing HR monitoring tools for HF detection are limited by their reliability on population-based averages. The estimation of individualized HR serves as a dynamic digital twin, enabling precise tracking of cardiac health biomarkers. Current HR estimation methods, categorized into physiologically-driven and purely data-driven models, struggle with efficiency and interpretability. This study introduces a novel physiological-model-based neural network (PMB-NN) framework for HR estimation based on oxygen uptake (VO2) data during daily physical activities. The framework was trained and tested on individual datasets from 12 participants engaged in activities including resting, cycling, and running. By embedding physiological constraints, which were derived from our proposed simplified human movement physiological model (PM), into the neural network training process, the PMB-NN model adheres to human physiological principles while achieving high estimation accuracy, with a median R score of 0.8 and an RMSE of 8.3 bpm. Comparative statistical analysis demonstrates that the PMB-NN achieves performance on par with the benchmark neural network model while significantly outperforming traditional physiological model (p=0.002). In addition, our PMB-NN is adept at identifying personalized parameters of the PM, enabling the PM to generate reasonable HR estimation. The proposed framework with a precise VO2 estimation system derived from body movements enables the future possibilities of personalized and real-time cardiac monitoring during daily life physical activities
The influence of anatomical shape variations of wrist bones on bone orientation values in CT scans
Get PDFIntroduction: Four-Dimensional Computed Tomography (4DCT) shows promise in diagnosing scapholunate ligament (SL) lesions. Wrist motion analysis requires local coordinate systems (LCS) for carpal bones, which might be affected by bone shape variations. These variations can affect the extraction of scapholunate angle (SLA) and capitolunate angle (CLA), indicative for SL lesions. This study characterizes the impact of anatomical shape variations on LCS determination and subsequent SLA and CLA estimates. Methods: A statistical shape model (SSM) was created for the scaphoid, lunate, and capitate using CT scans from a 4DCT dataset. The quality of the SSM was assessed using a leave-one-out approach by calculating the root mean squared error (RMSE). Subsequently, LCSs per bone were assigned to the SSM while 3000 shape variations were introduced. The rotational deviation of the LCSs was calculated by a combined rotation around the three axes. Finally, the SLAs and CLAs were calculated. Results: The SSM was created using 106 scans with an RMSE of 0.38 mm. The 95th percentile of the rotational deviations of the LCSs was below 5°. The resulting range due to anatomical variations in the calculation of the SLA and CLA was 5.7°and 6.8°, respectively. Conclusion: Bone shape variations have a minimal influence on the determination of LCSs and consequently on the resulting SLA and CLA estimates. Based on the naturally occurring variability of carpal angles found in literature, the ranges in the variation of the SLA and CLA are probably not clinically relevant but should be taken into account when performing inter-subject comparisons.</p