bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development of a three-dimensional variational data assimilation system for 137Cs based on WRF-Chem model and applied to the Fukushima nuclear accident
Authors: Hu Yiwen , Li Yi , Zang Zengliang , Wang Yijie , Fang Sheng , Zhuang Shuhan , Liu Lang , Liu Ning 
Published: September 2024

Abstract
Nuclear explosions and accidents release large amounts of radionuclides that harm human health and the environment. Accurate forecasting of nuclide pollutants and assessment of the ramifications of nuclear incidents are necessary for the emergency response and disaster assessment of nuclide pollution. In this study, we developed a three-dimensional variational (3Dvar) system to assimilate 137Cs based on the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) model. The distribution of 137Cs after the Fukushima nuclear accident in Japan on 15 March 2011 was analysed. The 137Cs background field at 06:00 UTC was assimilated using a 3Dvar system and surface observational data to optimise the 137Cs analysis field. Compared with the background field, the root mean square error (RMSE) and mean bias in the 137Cs analysis field decreased by 98% and 94%, respectively. The average fraction of predictions within factors of 2 (FAC2), 5 (FAC5), and 10 (FAC10) increased from 0.67, 0.72, and 0.72 to 0.90, 1.00, and 1.00, respectively. This substantial enhancement indicated the effectiveness of the 3DVar system in mitigating the uncertainty associated with the background field. Two 12 h forecast experiments were conducted to gauge the advancement in 137Cs forecasting facilitated by data assimilation (DA). The control experiment was conducted without DA, whereas the assimilation experiment was conducted with DA. Compared with the control experiment, the average FAC2, FAC5, and FAC10 in the assimilation experiment increased by 28%, 30%, and 29%, respectively. The average RMSE decreased by 33%. The mean bias and correlation coefficient increased by 41% and 36%, respectively. These results indicated that the 3Dvar method improves the forecast accuracy of 137Cs concentration.
Additively manufactured carbon fiber reinforced-polyamide component: a study based on statistical modeling and regression analysis
Authors: Veeman Dhinakaran , Dutta Hrishikesh , Vellaisamy Murugan 
Published: September 2024

Abstract
This paper presents the experimental assessment of the hardness characteristic of additively manufactured polyamide (PA 6) composite reinforced with carbon micro-fibers. The carbon fiber-reinforced polyamide (CFPA) components were manufactured using the additive manufacturing technique—fused deposition modeling (FDM). The experiments were conducted for testing the hardness of the samples using a Shore-D hardness tester. The novel contributions of the work towards the manufacturing fraternity include selecting a scantly researched material like CFPA, and the elaborative investigation of hardness variation with the alteration of the prime parameters pertaining to FDM. The effect of the print-related parameters, namely, layer height (LH), infill density (ID), and raster orientation (RO) on the hardness of the CFPA component was studied, and the results were analyzed using statistical analysis tool ‘analysis of variance (ANOVA)’. Moreover, a regression model was developed to predict the output response, i.e. hardness for different combinations of the input parameters. Considering an ID of 100% and an RO of 0°, the hardness value of 93.89 at 0.1 mm LH reduced to 88.44 at 0.3 mm LH, depicting a reduction of 5.81%. An increasing trend was observed for hardness with the increase in ID for all the levels of LH and RO. The highest value of hardness (93.89) was achieved at an ID of 100%, with the LH and RO values kept at 0.1 mm and 0°, respectively. The ANOVA suggested that the effect of all three parameters is significant in the study, ID being the most affecting parameter with an effect contribution of 37.88%. The fitness of the adopted model was well justified by the high R-sq value of 0.9618 and significantly low error values in the range of 0.002–0.08.
Physical model and Bayesian theory based hydraulic component fault diagnosis method
Authors: Qiu Zhiwei , Li Wanli , Wang Daozhi 
Published: September 2024

Abstract
Hydraulic components are integral parts of hydraulic systems, and their failures directly impact the normal operation of hydraulic systems. Due to the dynamic variation of working pressure in hydraulic components with load, it is challenging to diagnose faults of hydraulic components under dynamic pressure conditions using existing methods. This paper focuses on the research of internal leakage faults in hydraulic directional control valves and proposes a fault diagnosis method based on physical model and Bayesian theory for hydraulic components. Firstly, this method investigates the working fault mechanism of hydraulic directional control valves and constructs an internal leakage model. Then, correlation analysis is introduced to select fault feature signals, and principal component analysis is adopted to construct internal leakage features. Finally, based on Bayesian model, Markov chain Monte Carlo sampling, and internal leakage features, the parameters of the internal leakage model are estimated to achieve fault diagnosis of internal leakage in hydraulic directional control valves. Experimental results demonstrate that compared to several traditional fault diagnosis methods, the proposed method exhibits higher adaptability and accuracy under dynamic pressure conditions. This method, based on the internal leakage model and Bayesian theory, realizes fault diagnosis of hydraulic components under dynamic pressure conditions, avoiding the shortcomings of deep learning methods that rely on a large amount of fault data to train fault models.
LoGAN: local generative adversarial network for novel structure prediction
Authors: Kovács Péter , Heid Esther , Landsheere Jasper De , Madsen Georg K H 
Published: September 2024

Abstract
The efficient generation and filtering of candidate structures for new materials is becoming increasingly important as starting points for computational studies. In this work, we introduce an approach to Wasserstein generative adversarial networks for predicting unique crystal and molecular structures. Leveraging translation- and rotation-invariant atom-centered local descriptors addresses some of the major challenges faced by similar methods. Our models require only small sets of known structures as training data. Furthermore, the approach is able to generate both non-periodic and periodic structures based on local coordination. We showcase the data efficiency and versatility of the approach by recovering all stable C5H12O isomers using only 39 C4H10O and C6H14O training examples, as well as a few randomly selected known low-energy SiO2 crystal structures utilizing only 167 training examples of other SiO2 crystal structures. We also introduce a filtration technique to reduce the computational cost of subsequent characterization steps by selecting samples from unique basins on the potential energy surface, which allows to minimize the number of geometry relaxations needed after structure generation. The present method thus represents a new, versatile approach to generative modeling of crystal and molecular structures in the low-data regime, and is available as open-source.
WBC-KICNet: knowledge-infused convolutional neural network for white blood cell classification
Authors: P Jeneessha , Balasubramanian Vinoth Kumar , Murugappan M 
Published: September 2024

Abstract
White blood cells (WBCs) are useful for diagnosing infectious diseases and infections. Machine learning and deep learning have been used to classify WBCs from blood smear images. Despite advances in machine learning, there has been little research on applying medical domain knowledge to convolutional neural networks (CNNs) to improve WBC classification. The existing models are often inaccurate, rely on manual input, and fail to incorporate external medical knowledge into decision-making. This study used the blood cell count and detection dataset which contains images of monocytes, lymphocytes, neutrophils, and eosinophils for WBC classification. In this paper, we propose a CNN model for WBC classification called WBC-KICNet (knowledge-infused convolutional neural network). The present work uses two CNN models: the first model generates the knowledge vector from input images and the domain expert (hematologist); the second model extracts deep features from the input image. A feature fusion mechanism is then used to combine these two features to classify the WBCs. Several metrics have been used to evaluate the performance of the WBC-KICNet model. These measures yielded impressive results. Accuracy, precision, recall, specificity, and F1-score were rated 99.22%, 99.25%, 99%, 99.77%, and 99.25%, respectively. In each of the WBC classes, accuracy rates are: 98.7% for eosinophils, 99.83% for lymphocytes, 100% for monocytes, and 98.32% for neutrophils. As a result, the proposed WBC-KICNet classifies WBCs accurately and without much misclassification, and the results have been confirmed by a statistical hypothesis test (t-test).
Environmental savings from concrete reuse: examining the limitations and optimal practices for cutting thresholds of concrete building components for reuse
Authors: Xiong Shuyan , Frossard Mija , Lasvaux Sébastien , Zea Escamilla Edwin , Habert Guillaume 
Published: September 2024

Abstract
Concrete, a widely used construction material, presents both opportunities and challenges for promoting environmentally responsible practices. This research focuses on the concept of concrete reuse as a sustainable strategy, examining the limitations of cutting dimensions. To investigate the greenhouse gas (GHG) emissions associated with preparing concrete for reuse, a mathematical model was developed considering several factors, such as transportation emissions, energy used in cutting, and the effects of varying cutting performance and energy sources. By incorporating real-life data and connecting with industrial practices, this study establishes a screening standard to determine the minimum cutting size for concrete reuse. The results indicate that the ratio of transportation distances for the reuse case versus the new production of concrete case has an influence on the minimum cutting size for concrete reuse. Moreover, the cutting size itself significantly impacts the overall GHG emissions of the reuse strategy. We offer practical insights for industry stakeholders to optimize concrete reuse practices and effectively reduce GHG emissions. As further research explores cost-effectiveness and on-site case studies, the potential for large-scale implementation of concrete reuse has become increasingly promising. Thus, concrete reuse has emerged as a viable and effective approach to sustainable construction, provided that the minimum cutting dimensions are carefully considered.
Racial Differences in the Association Between Loneliness and Cognitive Impairment Among Older Black and White Men
Authors: Beech Bettina M , Bruce Marino A , Siddhanta Ankita , Marshall Gillian L , Whitfield Keith E , Thorpe Roland J 
Published: September 2024

Abstract

Background
Loneliness is a biopsychosocial stressor linked to poor health outcomes including dementia. Few studies have focused on this association among men and even fewer have examined racial disparities in loneliness and cognitive functioning among this group. The purpose of this study was to examine racial differences in the association between loneliness and cognitive functioning among men in the 2016 wave of the Health and Retirement Study.

Methods
This cross-sectional study included Black and White men who completed the core questionnaire and the Leave Behind Questionnaire (n = 2 227). Any cognitive impairment was the primary outcome and was measured by a dichotomous variable derived from a modified version of the Telephone Interview for Cognitive Status. Loneliness was the primary independent variable and was derived from the 3-item University of California, Los Angeles (UCLA) Loneliness Scale. Modified Poisson regression models with robust standard errors were estimated to generate prevalence ratios (PRs) and corresponding 95% confidence intervals (CIs).

Results
Black men comprised 18.4% of the study sample; however, the proportion of this group with scores indicating cognitive impairment (35.9%) doubled the corresponding percentage of white men (17.6%). Findings from race-stratified modified Poisson regression models indicated that loneliness was associated with a higher prevalence of any cognitive impairment for White men (PR = 1.24, CI: 1.05–1.47), but not for Black men (PR = 0.92, CI: 0.73–1.16).

Conclusions
Our results underscore the complexity of race when investigating the association between loneliness and cognitive impairment among older men. Additional studies are needed to further examine how loneliness may have racially distinct implications for cognitive outcomes among the population.
Leapfrogging Sycamore: Harnessing 1432 GPUs for 7× faster quantum random circuit sampling
Authors: Zhao Xian-He , Zhong Han-Sen , Pan Feng , Chen Zi-Han , Fu Rong , Su Zhongling , Xie Xiaotong , Zhao Chaoxing , Zhang Pan , Ouyang Wanli , Lu Chao-Yang , Pan Jian-Wei , Chen Ming-Cheng 
Published: September 2024

Abstract
Random quantum circuit sampling serves as a benchmark to demonstrate quantum computational advantage. Recent progress in classical algorithms, especially those based on tensor network methods, has significantly reduced the classical simulation time and challenged the claim of the first-generation quantum advantage experiments. However, in terms of generating uncorrelated samples, time-to-solution, and energy consumption, previous classical simulation experiments still underperform the Sycamore processor. Here we report an energy-efficient classical simulation algorithm, using 1432 GPUs to simulate quantum random circuit sampling which generates uncorrelated samples with higher linear cross entropy score and is 7 times faster than the Sycamore 53 qubits experiment. We propose a post-processing algorithm to reduce the overall complexity, and integrate state-of-the-art high-performance general-purpose GPU to achieve two orders of lower energy consumption compared to previous works. Our work provides the first unambiguous experimental evidence to refute Sycamore’s claim of quantum advantage, and redefines the boundary of quantum computational advantage using random circuit sampling.
Diversity and Inclusion Through Collaboration: Co-Producing a Simulation Curriculum to Address Discrimination Against Trainees
Authors: Torres Christian , Morales David , Whitley Amber , Porter Barbara , Greene Richard , Zabar Sondra 
Published: September 2024

Abstract
Discrimination toward trainees is a pervasive problem, with surveys showing it is often perpetrated by patients. For several years, residents and faculty in an internal medicine residency have participated in a workshop offering a framework for responding to discriminatory behavior by patients. As part of a larger effort to reinforce this teaching and promote an inclusive environment, the authors pursued a multipronged simulation curriculum that could be incorporated into graduate medical education programs across their institution. First, the authors conducted trainee and faculty focus groups to better understand their experiences. Qualitative data were collected, including recommendations for responding to discrimination, characteristics of the most common experiences, and trainees’ own ideas for worthwhile simulation scenarios. Trainees and faculty were then brought together in a collaborative process to co-develop simulation cases that were later implemented in curricula across multiple learner levels, specialties, and contexts. Participants in these simulations reported improved comfort in responding to discrimination in the moment and/or in the wake of such incidents. Through trainee-faculty collaboration, the project yielded authentic and impactful simulation experiences for learners, while also giving trainees an opportunity to turn previous trauma into constructive learning opportunities that promote an inclusive environment.
Progressive Loss of Sensitivity to Electrical Stimulation After Cochlear Implantation in X-Linked Incomplete Partition Type III Deafness
Authors: Eklöf Martin , Smeds Henrik , Karltorp Eva , Wales Jeremy 
Published: September 2024

Objectives:
Patients with X-linked incomplete partition type III (IP3) deafness treated with cochlear implants exhibit higher “Most Comfortable Loudness” (MCL) levels of stimulation and more electrode deactivation than patients with normal morphology. We endeavored to analyze the progression of the MCL levels and electrode deactivation over time and assess those factors that could have led to deactivation. Furthermore, we aimed to assess whether speech perception was affected by a progressive loss of neural contact.

Design:
All 13 patients with the IP3 malformation in our clinical database were analyzed retrospectively with regard to impedance, stimulation levels, deactivated electrodes, and speech perception. A control group of patients with normal anatomy was included.

Results:
MCL levels increased over time by 2.5 charge units (qu) per year, which was not seen in the control group. Electrode deactivation was more common in IP3 malformation, and it was estimated that 25% of electrodes would be deactivated by 15 years of age. Impedance was stable but higher in the study population. Speech perception was lower in IP3 malformation generally and was correlated to the number of deactivated electrodes.

Conclusions:
Patients diagnosed with IP3 malformation deafness may suffer a greater risk of cochlear implant discontinuation compared with those with normal anatomy. A progressive loss of sensitivity to electrical stimulation may indicate a form of neural degradation in the abnormal cochlea. With time, patients in this group, even with cochlear implant technology, may experience gradual deterioration of speech perception. This has clinical implications for the counseling of parents.
Care Bundle Approach for Oral Health Maintenance and Reduction of Ventilator-Associated Pneumonia
Authors: Sabrah Nagwa Yehya Ahmed , Pellegrino Jeffrey L. , Mansour Hend El-Sayed , Mostafa Marwa Fathallah , Kandeel Nahed Attia 
Published: September 2024

Caring for patients in the intensive care unit (ICU) creates competing priorities of interventions for nurses and other health care providers. Oral care might be prioritized lower; however, its neglect may lead to sequelae such as extended time in the ICU, nosocomial diseases most notably ventilator-associated pneumonia (VAP), or oral problems. Safe patient care depends on effective and efficient oral care. The aim of this study was to lower the incidence of VAP and maintain oral health through implementing an “oral care bundle” for mechanically ventilated (MV) patients. Using a quasi-experimental design, we divided 82 adult MV patients in the ICUs of a university-based hospital in Egypt into a control group (n = 41) that received the standard of care and a bundle group (n = 41) that received an “oral care bundle.” The results of the study reported a significantly lower incidence of VAP in the intervention group (P = .015). It can be concluded that there is a significant relationship between receiving an oral care bundle and improved oral health and a reduction in the VAP rate among MV patients. This highlights the need to incorporate the oral care bundle in the daily nursing care for MV patients.
A survey and analysis of peri-operative quality indicators promoted by National Societies of Anaesthesiologists in Europe
Authors: Wacker Johannes , Haller Guy , Hendrickx Jan F.A. , Ponschab Martin 
Published: September 2024

BACKGROUND
To capture preventable peri-operative patient harm and guide improvement initiatives, many quality indicators (QIs) have been developed. Several National Anaesthesiologists Societies (NAS) in Europe have implemented quality indicators. To date, the definitions, validity and dissemination of such quality indicators, and their comparability with validated published indicators are unknown.

OBJECTIVES
The aim of this study was to identify all quality indicators promoted by NAS in Europe, to assess their characteristics and to compare them with published validated quality indicators.

DESIGN
A cross-sectional study with mixed methods analysis. Using a survey questionnaire, representatives of 37 NAS were asked if their society provided quality indicators to their members and, if so, to provide the list, definitions and details of quality indicators. Characteristics of reported quality indicators were analysed.

SETTING
The 37 NAS affiliated with the European Society of Anaesthesiology and Intensive Care (ESAIC) at the time. Data collection, translations: March 2018 to February 2020.

PARTICIPANTS
Representatives of all 37 NAS completed the survey.

MAIN OUTCOME MEASURES
QIs reported by NAS.

RESULTS
Only 12 (32%) of the 37 NAS had made a set of quality indicators available to their members. Data collection was mandatory in six (16.2%) of the 37 countries. We identified 163 individual quality indicators, which were most commonly descriptive (60.1%), anaesthesia-specific (50.3%) and related to intra-operative care (21.5%). They often measured structures (41.7%) and aspects of safety (35.6%), appropriateness (20.9%) and prevention (16.6%). Patient-centred care (3.7%) was not well covered. Only 11.7% of QIs corresponded to published validated or well established quality indicator sets.

CONCLUSIONS
Few NAS in Europe promoted peri-operative quality indicators. Most of them differed from published sets of validated indicators and were often related to the structural dimension of quality. There is a need to establish a European-wide comprehensive core set of usable and validated quality indicators to monitor the quality of peri-operative care.

TRIAL REGISTRATION
No registration.
Biomechanical Evaluation of an Atraumatic Polymer-assisted Peripheral Nerve Repair System Compared with Conventional Neurorrhaphy Techniques
Authors: Wlodarczyk Alexie I. , Collin Estelle C. , Pereira Maria J.N. , Bindra Randy , Power Dominic M. 
Published: September 2024

Background:
Microsuturing, the gold standard for peripheral nerve repair, can create tension and damage at the repair site, potentially impacting regeneration and causing neuroma formation. A sutureless and atraumatic polymer-assisted system was developed to address this challenge and support peripheral nerve repair. The system is based on a biocompatible and biodegradable biosynthetic polymer and consists of a coaptation chamber and a light-activated polymer for securing to the nerve. In this study, we compare the system’s biomechanical performance and mechanism of action to microsutures and fibrin repairs.

Methods:
The system’s fixation force was compared with microsutures and fibrin glue, and evaluated across various nerve diameters through tensile testing. Tension and tissue morphology at the repair site were assessed using finite element modeling and scanning electron microscopy.

Results:
The fixation force of the polymer-assisted repair was equivalent to microsutures and superior to fibrin glue. This force increased linearly with nerve diameter, highlighting the correlation between polymer surface contact area and performance. Finite element modeling analysis showed stress concentration at the repair site for microsuture repairs, whereas the polymer-assisted repair dissipated stress along the nerve, away from the repair site. Morphological analysis revealed nerve alignment with no tissue trauma for the polymer-assisted repair, unlike microsutures.

Conclusions:
The mechanical performance of the polymer-assisted coaptation system is suitable for peripheral nerve repair. The achieved fixation forces are equivalent to those of microsutures and superior to fibrin glue, minimizing stress concentration at the repair site and avoiding trauma to the severed nerve ends.