bluuebunny/crossref_metadata_2025_split huggingface dataset:
Physical performance capacity after pediatric kidney transplant and clinical parameters associated with physical performance capacity
Authors: Mäenpää Heidi , Tainio Juuso , Arokoski Jari , Jahnukainen Timo 
Published: October 2022

Abstract
Background
History of chronic kidney disease and kidney transplantation is known to influence physical performance capacity. The aim of this study was to compare the physical performance of pediatric kidney transplant recipients to healthy controls and to find possible correlations between clinical parameters and physical performance capacity.

Methods
Twenty-four pediatric kidney transplant recipients (62.5% boys) were tested at a median age of 10.8 years. Physical performance capacity was tested with a test set including six different components assessing muscle endurance, strength, speed, and flexibility. The control group consisted of 273 healthy age-matched schoolchildren. Clinical parameters were collected as part of routine follow-up protocol. The majority of patients (62.5%) had congenital nephrotic syndrome of Finnish type (CNS) as primary diagnosis, and therefore, the results of CNS recipients were compared to the other disease groups.

Results
The physical performance capacity in pediatric kidney transplant recipients was lower compared to healthy controls. Surprisingly, no statistically significant correlation was found between graft function and physical performance capacity. The CNS patients scored worse than patients with other diagnoses in all test domains except for sit-and-reach and shuttle run, but the differences did not reach statistical significance.

Conclusion
The physical performance of pediatric kidney transplant recipients is reduced, especially in those with congenital nephrotic syndrome. Clinical parameters, including graft function, did not predict physical performance capacity, suggesting that the reduced physical performance seems to be of multivariable cause.

Graphical abstract
Predictors of successful discontinuation of continuous kidney replacement therapy in a pediatric cohort
Authors: Wei Elizabeth Y. , Vuong Kim T. , Lee Euyhyun , Liu Lin , Ingulli Elizabeth , Coufal Nicole G. 
Published: October 2022

Abstract

Background
Recognizing the optimal time to discontinue continuous kidney replacement therapy (CKRT) is necessary to advance patient recovery and mitigate complications. The aim of this study was to identify predictors of successful CKRT cessation in pediatric patients.

Methods
All patients requiring CKRT between January 2010 and March 2021 were evaluated. Patients on peritoneal or hemodialysis, who transferred between institutions, or who did not trial off CKRT were excluded. Successful discontinuation was defined as remaining off CKRT for at least 7 days. Demographics, admission diagnoses, PRISM III scores, and reasons for CKRT initiation were obtained. Clinical and biochemical variables were evaluated at CKRT initiation and discontinuation and in the 12-h period following discontinuation. Comparisons were conducted using Wilcoxon rank sum and Fisher’s exact tests for continuous and categorical variables, respectively. A logistic regression model was fitted to identify significant factors.

Results
Ninety-nine patients underwent a trial off CKRT. Admission and initiation characteristics of the success and failure groups were similar. Patients who required re-initiation (n = 26) had longer ICU lengths of stay (27.2 vs. 44.5 days, p = 0.046) and higher in-hospital mortality (15.1% vs. 46.2%, p = 0.002). Urine output greater than 0.5 mL/kg/h irrespective of diuretic administration in the 6-h period before CKRT discontinuation was a significant predictor (AUC 0.72, 95% CI 0.60–0.84, p = 0.0009).

Conclusions
Determining the predictors of sustained CKRT discontinuation is critical. Urine output greater than 0.5 mL/kg/h in this pediatric cohort predicted successful discontinuation. Future studies are needed to validate this threshold in disease- and age-specific cohorts and evaluate additional biomarkers of kidney injury.

Graphical abstract
Heuristic optimisation of multi-task dynamic architecture neural network (DAN2)
Authors: Zhang Sushen , Vassiliadis Vassilios S. , Hao Zhimian , Cao Liwei , Lapkin Alexei A. 
Published: October 2022

AbstractThis article proposes a novel method to optimise the Dynamic Architecture Neural Network (DAN2) adapted for a multi-task learning problem. The multi-task learning neural network adopts a multi-head and serial architecture with DAN2 layers acting as the basic subroutine. Adopting a dynamic architecture, the layers are added consecutively starting from a minimal initial structure. The optimisation method adopts an iterative heuristic scheme that sequentially optimises the shared layers and the task-specific layers until the solver converges to a small tolerance. Application of the method has demonstrated the applicability of the algorithm to simulated datasets. Comparable results to Artificial Neural Networks (ANNs) have been obtained in terms of accuracy and speed.
Formation of multiple aerial LED signs in multiple lanes formed with AIRR by use of two beam splitters
Authors: Sakane Shinya , Kudo Daiki , Mukojima Naoya , Yasugi Masaki , Suyama Shiro , Yamamoto Hirotsugu 
Published: October 2022

AbstractWe propose multiple aerial imaging system by use of two beam splitters in the optical system of aerial imaging by retro-reflection (AIRR). The AIRR optical system consists of a light source, a beam splitter, and a retro-reflector. Use of two-layered beam splitters enables us to show multiple aerial signs to drivers in multiple driving lanes. The purpose of this paper is to confirm feasibility that an aerial display by use of AIRR for a novel traffic information provision. First, we explain an optical configuration to show aerial images to multiple road lanes, a driving lane and an overtaking lane with AIRR by use of two beam splitters. Next, we have developed a prototype optical system by use of two large beam splitters and a large high-brightness LED sign that is designed to be used for actual road displays. The light source was an LED display with a height of 450 mm and a width of 390 mm, which shows a character. We observed aerial images by naked eyes as experiments. And we discuss the issues for practical application that the experiments have revealed.
Lipoprotein (a) is related to In-Stent neoatherosclerosis incidence rate and plaque vulnerability: Optical Coherence Tomography Study
Authors: Yuan Xiaohang , Han Yan , Hu Xin , Jiang Mengting , Feng Huanhuan , Fang Yan , Liu Miao , Chen Yundai , Gao Lei 
Published: October 2022

Abstract
Background
In-stent neoatherosclerosis (ISNA) is an important reason for stent failure. High lipoprotein (a) [Lp (a)] level is an independent predictor of in-stent restenosis (ISR). To date, the relationship between the level of serum Lp (a) and the incidence rate and vulnerability of ISNA has never been verified.

Methods
A total of 119 patients with 125 drug-eluting stent ISR lesions who underwent percutaneous coronary intervention guided by optical coherence tomography were enrolled in this study. According to their Lp (a) level, the patients were divided into two groups [high Lp (a) group ≥ 30 mg/dL, n = 47; or low Lp (a) group < 30 mg/dL, n = 72]. The clinical baseline, angiographic characteristics, and optical coherence tomography data of both groups were recorded and analyzed.

Results
No significant differences in clinical and angiographic characteristics were found between the two groups (P > 0.05). The incidence rate of ISNA in the high Lp (a) group was significantly higher than that in the low Lp (a) group (94.0% [n = 47] vs. 52.0% [n = 39], P < 0.001). The incidence rate of thin-cap fibroatheroma in ISR lesions was significantly higher in the high Lp (a) group than in the low Lp (a) group (42% [n = 21] vs. 5.3% [n = 4], P < 0.001).

Conclusion
A high Lp (a) level is associated with the high incidence rate and plaque vulnerability of ISNA.
Commentary: Engage or disengage? Interpretations from a resource conservation perspective
Authors: Gendolla Guido H. E. 
Published: October 2022

AbstractIn this commentary, I discuss the eight empirical contributions to the Motivation and Emotion special issue on goal disengagement from a resource conservation perspective. This process was not in the focus of the reported studies, but is central for understanding both engaging and disengaging. I will outline that many of the new findings on disengagement reported in this special issue are highly compatible with the predictions of and research on motivational intensity theory. Examples are the roles of commitment, subjective goal value, affective experiences, autonomy, self-awareness, and action planning. These variables have been found to be central for both engagement and disengagement and their consideration in a resource mobilization perspective should contribute to a more complete understanding of “letting go”.
School principals’ social support and teachers’ basic need satisfaction: The mediating role of job demands and job resources
Authors: Maas Jasper , Schoch Simone , Scholz Urte , Rackow Pamela , Schüler Julia , Wegner Mirko , Keller Roger 
Published: October 2022

AbstractMany teachers report high levels of occupational stress. Teachers’ basic need satisfaction is essential for teachers’ well-being at work. Social support from school principals is assumed to play an important role for teachers’ basic need satisfaction. However, the underlying mechanisms of the relationship between social support from school principals and teachers’ basic need satisfaction are mostly unknown. Previous research suggests that job demands and job resources may play an important mediating role. Therefore, we examine whether teachers’ perceived job demands and job resources serve as mediators between social support from the school principal and teachers’ basic need satisfaction. Using longitudinal data ofN = 1071 teachers over the course of one school year, we applied structural equation modelling to test the hypothesised mediation model. Results showed that the relationship between social support from the school principal and teachers’ basic need satisfaction was mediated by teachers’ perceived job demands and job resources. Particularly, the job demand ‘unclear organisational conditions’ and job resource ‘social support from colleagues’ indicated the strongest effects on teachers’ basic need satisfaction. These findings emphasise the responsibility of school principals to provide social support to their teachers and create a well-structured and supportive workplace. In doing so, school principals contribute to a work environment in which teachers can thrive.
Multilayer Fisher extreme learning machine for classification
Authors: Lai Jie , Wang Xiaodan , Xiang Qian , Wang Jian , Lei Lei 
Published: October 2022

AbstractAs a special deep learning algorithm, the multilayer extreme learning machine (ML-ELM) has been extensively studied to solve practical problems in recent years. The ML-ELM is constructed from the extreme learning machine autoencoder (ELM-AE), and its generalization performance is affected by the representation learning of the ELM-AE. However, given label information, the unsupervised learning of the ELM-AE is difficult to build the discriminative feature space for classification tasks. To address this problem, a novel Fisher extreme learning machine autoencoder (FELM-AE) is proposed and is used as the component for the multilayer Fisher extreme leaning machine (ML-FELM). The FELM-AE introduces the Fisher criterion into the ELM-AE by adding the Fisher regularization term to the objective function, aiming to maximize the between-class distance and minimize the within-class distance of abstract feature. Different from the ELM-AE, the FELM-AE requires class labels to calculate the Fisher regularization loss, so that the learned abstract feature contains sufficient category information to complete classification tasks. The ML-FELM stacks the FELM-AE to extract feature and adopts the extreme leaning machine (ELM) to classify samples. Experiments on benchmark datasets show that the abstract feature extracted by the FELM-AE is more discriminative than the ELM-AE, and the classification results of the ML-FELM are more competitive and robust in comparison with the ELM, one-dimensional convolutional neural network (1D-CNN), ML-ELM, denoising multilayer extreme learning machine (D-ML-ELM), multilayer generalized extreme learning machine (ML-GELM), and hierarchical extreme learning machine with L21‑norm loss and regularization (H-LR21-ELM).
Revision of TD1 and TD2 stratigraphic sequence of Gran Dolina cave (Sierra de Atapuerca, Spain)
Authors: Campaña I. , Benito-Calvo A. , Pérez-González A. , Álvaro-Gallo A. , Miguens-Rodríguez L. , Iglesias-Cibanal J. , de Castro J. M. Bermúdez , Carbonell E. 
Published: October 2022

AbstractGran Dolina is a cavity infilled by at least 25 m of Pleistocene sediments divided into 12 lithostratigraphic units and 19 sedimentary facies. These sedimentary facies have been divided into allochthonous facies, defined as sediment inputs from the outside, and autochthonous facies, defined as sediments generated within the karst; but this division has been challenged in recent works. In this study, TD1 and TD2 units of Gran Dolina have been detailed studied and the use of autochthonous facies has been assessed. For that purpose, we have studied the stratigraphic excavation profile, combining field observation with laboratory sedimentary analysis (sieving, laser diffraction, and XRD) to characterize the texture and structure of the sediments. Based on these studies, a total of 8 sedimentary facies have been identified. Consequently, TD1 unit has been separated into two sub-units and 13 layers, while the TD2 unit has been divided into three sub-units. The facies associations indicate a succession of phreatic and vadose phases that would define together epiphreatic conditions inside the cave, related to the transition between Arlanzón valley terraces T3 and T4. Interior facies (and entrance facies for allochthonous facies) is proposed to define Gran Dolina's sediments since the facies analyses indicate transport by underground flows.
How does technological system design affect value creation? A systematic literature review of digital co-production
Authors: Mu Rui , Wang Yuting , Song Haoqi 
Published: October 2022

AbstractThe existing studies on co-production display two research gaps. First, most studies focus on non-digital/offline co-production and value creation; little attention has been paid to value creation of digital/online co-production cases. Second, traditional co-production studies examine political, organizational, administrative, and personal factors that influence co-production. However, few studies investigate how technological factors will affect co-production in terms of value creation. To bridge the gaps, this article conducts a systematic literature review of 52 articles. The review results distill seven technological factors and five value categories from digital co-production cases. It further examines how these technological factors affect the creation of various value categories. Based on the review results, this article proposes a future research agenda on digital co-production.
ERP Staff versus AI recruitment with employment real-time big data
Authors: Strang Kenneth David , Sun Zhaohao 
Published: October 2022

AbstractThe purpose of this study was to evaluate the effectiveness of using natural language processing (NLP) artificial intelligence (AI) in enterprise resources planning (ERP) to identify specialized job candidates in real-time big data—globally across the internet. The central problem was that companies using traditional methods for recruiting remote specialists were missing good candidates because the skilled employees may not be looking for a job yet they may be receptive to an offer. The auxiliary problem was too much data on the internet for human resources management (HRM) staff to make sense of to find the best-fitting candidate. Thus, the research question was: could NLP AI identify good candidates for ERP remote specialist jobs using internet real-time big data? Job criteria were developed using machine learning to identify key skills from existing staff in a case study company. The skills were transformed into ERP remote specialists hiring criteria. The NLP AI software was activated to find the best candidate. The HRM staff at the case study company evaluated the effectiveness of the candidate selected by the NLP AI. The case study company set 70% as the acceptable mean evaluation score. ANOVA was used to determine if HRM staff agreed about their evaluation scores. A Z-test was used to determine if the NLP AI was faster than the mean time needed for HRM to select ERP candidates. The results were that the NLP AI outperformed the humans by a factor of almost 8 h. All HRM staff agreed that the NLP AI was effective in selecting a candidate to match the hiring criteria. The proposed approach might facilitate the research and development of big data, data analytics, NLP AI, and HRM process improvement.
Teacher, caregiver, and student acceptability of teachers delivering task-shifted mental health care to students in Darjeeling, India: a mixed methods pilot study
Authors: Cruz Christina M. , Dukpa Choden , Vanderburg Juliana L. , Rauniyar Abhishek K. , Giri Priscilla , Bhattarai Surekha , Thapa Arpana , Hampanda Karen , Gaynes Bradley N. , Lamb Molly M. , Matergia Michael 
Published: October 2022

Abstract
Background
The acceptability of teachers delivering task-shifted mental health care to their school-aged students is understudied. Here, we evaluate teachers’, students’, and caregivers’ acceptability of Tealeaf (Teachers Leading the Frontlines), an alternative system of care in which teachers are trained and supervised to deliver transdiagnostic, non-manualized task-shifted care to their students.

Methods
In a 2019 single-arm, mixed methods, pragmatic acceptability pilot study in Darjeeling, India, 13 teachers delivered task-shifted child mental health care to 26 students in need. Teachers delivered care through using a transdiagnostic, non-manualized therapy modality, “education as mental health therapy” (Ed-MH). Measured with validated scales, teachers’ and students’ acceptability were compared after teacher training (PRE) and at the end of intervention (POST) using paired t tests. Teachers (n = 7), students (n = 7), and caregivers (n = 7) completed semi-structured interviews POST.

Results
Teachers’ quantitative measures indicated moderate acceptability PRE and POST and did not change PRE to POST. Children’s measures showed acceptability PRE and POST but decreased PRE to POST. Teachers and caregivers universally expressed acceptability in interviews. Facilitators of acceptability included impact, trust of teachers, and teachers’ ability to make adaptations. Conditions required for acceptability included supervision and teachers emphasizing academics benefits over mental health benefits to caregivers. Barriers to acceptability included a lack of teacher time and stigma. Interviewed students universally were unaware of receiving care; teachers intentionally avoided singling them out.

Conclusion
Teachers, caregivers, and children found teacher delivering task-shifted care acceptable, a key factor in care adoption and sustainability, though interviewed children were unaware of receiving care.
Trial registration The trial was registered on January 01, 2018 with Clinical Trials Registry—India (CTRI), Reg. No. CTRI/2018/01/011471, Ref. No. REF/2017/11/015895. http://ctri.nic.in/Clinicaltrials/pdf_generate.php?trialid=21129&EncHid=&modid=&compid=%27,%2721129det%27
RecNet: A Resource-Constraint Aware Neural Network for Used Car Recommendation
Authors: Shi Haihua , Qian Jianjun , Zhu Nengjun , Zhang Tong , Cui Zhen , Wu Qianliang , Feng Shanshan 
Published: October 2022

AbstractResource constraints, e.g., limited product inventory or financial strength, may affect consumers’ choices or preferences in some recommendation tasks but are usually ignored in previous recommendation methods. In this paper, we aim to mine the cue of user preferences or intention in the case of resource-constraint recommendation tasks. For this purpose, we specifically build a largely used car transaction dataset possessing resource-constraint characteristics. Accordingly, we propose a resource-constraint-aware network to predict the user’s future interaction based on dynamic connections between users and items. To describe the user-item connection dynamically, mutually recursive recurrent neural networks (MRRNNs) are introduced to capture long-term interactive dependencies, and effective representations of users and items are obtained. To further consider the resource constraint, a resource-constraint branch is built to explore resource variation’s influence on user preferences. Finally, mutual information is introduced to measure the similarity between the future user action and fused historical behavior features to predict future interaction. The fused features come from both MRRNNs and resource-constraint branches. We test the performance on the built used car transaction dataset and the Tmall dataset, and the experimental results verify the effectiveness of our framework.
Optimal design for network mutual aid
Authors: Li Jingchao , Fang Zichen , Nie Ciyu , Chen Sizhe 
Published: October 2022

AbstractNetwork mutual aid platforms is one of the popular risk-sharing models in recent years, and they have almost 200 million members in China. However, current mutual aid platforms does not satisfy the actuarial rules in either the apportionment method or the pricing principle. Hence, a variety of mutual aid models which enable mutual aid members with different risks to exchange their risks in a transparent and actuarial fair way have been proposed in this paper. Besides, the decision-making frameworks for participants choosing between the mutual aid platform and similar insurance products, or choosing no risk sharing are constructed, respectively. Decisions are made based on the principle of maximizing expected utility. Moreover, the optimization problems of maximizing profit and minimizing risk are constructed, respectively. Through the principle of individual fairness and relative fairness, the problem of adverse selection of the platform can also be reduced. Finally, the actual mutual aid plan is compared with similar insurance products to discuss the advantages of the optimized plan.