bluuebunny/crossref_metadata_2025_split huggingface dataset:
Alters- und Geschlechterunterschiede
Authors: Julmi Christian , Eifert Anna , Dammert Jakob E. , Wittwer Sebastian 
Published: August 2024

ZusammenfassungWir analysieren Unterschiede in der Bedeutung der Merkmale und deren Zusammenhänge über die Altersgruppen und Geschlechter hinweg. Auffällig ist, dass für Frauen die Arbeitsatmosphäre der wichtigste Faktor ist und bei einer anhaltend schlechten Atmosphäre ihre Bereitschaft zu kündigen höher ist als die von Männern. Zudem sind sie unzufriedener mit der Arbeitsatmosphäre. Es zeigen sich erhebliche Unterschiede in Bezug auf die Zufriedenheit mit der Arbeitsatmosphäre über die Generationen hinweg.
Typen von Atmosphären
Authors: Julmi Christian , Eifert Anna , Dammert Jakob E. , Wittwer Sebastian 
Published: August 2024

ZusammenfassungIm Fokus dieses Kapitels stehen verschiedene Typen von Atmosphären, die wir tendenziell in gut, neutral oder schlecht unterscheiden und deren Verbreitung wir untersuchen. Außerdem identifizieren wir Schlüsselfaktoren der Typen.
Management von Atmosphären
Authors: Julmi Christian , Eifert Anna , Dammert Jakob E. , Wittwer Sebastian 
Published: August 2024

ZusammenfassungDie Studie zeigt eindeutige Indizien, dass der Handlungsbedarf zum Management von Atmosphären groß ist. Wir identifizieren Anhaltspunkte, die für die gezielte Gestaltung von Atmosphären von hoher Relevanz sind.
Recurrent neural networks as virtual cavity pressure and temperature sensors in high-pressure die casting
Authors: Rudack Maximilian , Rom Michael , Bruckmeier Lukas , Moser Mario , Pustal Björn , Bührig-Polaczek Andreas 
Published: August 2024

AbstractHigh-pressure die casting (HPDC) is a permanent mold-based production technology that facilitates the casting of near net shape components from nonferrous alloys. The pressure and temperature conditions within the cavity impact the cast product quality during and after the conclusion of the die filling process. Die surface cavity sensors can deliver information describing the conditions at the die-casting interface. They are associated with high costs and limited service lifetimes below the achievable total cycle count of the die inserts and therefore ill-suited for industrial use cases. In this work, the suitability of long short-term memory (LSTM) recurrent neural networks (RNN) for substituting physical cavity temperature and pressure sensors virtually after the production ramp-up or at the end of the sensor service life is investigated. Training LSTMs with data of 233 casting cycles with different process parameters provides networks which are then applied to 99 further cycles. The prediction accuracy is investigated for different time interval lengths in the solidification and cooling phase. For longer time intervals, the cavity pressure prediction deteriorates, potentially due to a highly individual and hardly ascertainable buildup of casting distortion and internal stresses. Overall, however, the accuracy of the developed LSTMs is excellent for the cavity temperatures and good for the cavity pressures.
Chien-physics-informed neural networks for solving singularly perturbed boundary-layer problems
Authors: Wang Long , Zhang Lei , He Guowei 
Published: August 2024

AbstractA physics-informed neural network (PINN) is a powerful tool for solving differential equations in solid and fluid mechanics. However, it suffers from singularly perturbed boundary-layer problems in which there exist sharp changes caused by a small perturbation parameter multiplying the highest-order derivatives. In this paper, we introduce Chien’s composite expansion method into PINNs, and propose a novel architecture for the PINNs, namely, the Chien-PINN (C-PINN) method. This novel PINN method is validated by singularly perturbed differential equations, and successfully solves the well-known thin plate bending problems. In particular, no cumbersome matching conditions are needed for the C-PINN method, compared with the previous studies based on matched asymptotic expansions.
Is there an association between mastitis and breast cancer? a retrospective cohort study from Germany
Authors: Krishnan Vedanth D. , Kostev Karel , Kalder Matthias 
Published: August 2024

Abstract
Purpose
The aim of the study was to explore the association between mastitis and subsequent breast cancer.

Methods
This retrospective cohort study included women aged ≥ 18 years with an initial mastitis diagnosis from 315 office-based gynecologists in Germany between January 2005 and December 2021. Women without mastitis were matched to women with mastitis using propensity score matching based on age, index year, average yearly consultation frequency during the follow-up period, and coexisting diseases such as obesity, benign mammary dysplasia, hypertrophy of the breast, unspecified lump of breast, and other disorders of the breast.
The 10-year cumulative incidence of breast cancer for the mastitis-cohort and non-mastitis-cohort was studied with Kaplan–Meier curves using the log-rank test. The association between mastitis and breast cancer was studied separately for four age groups with univariable Cox regression analyses.

Results
In the follow-up period of 7 months to 10 years after the index date, 2.9% of mastitis patients and 2.4% of matched non-mastitis patients were diagnosed with breast cancer. A Cox regression analysis revealed a significant association between mastitis and subsequent breast cancer (HR: 1.37; 95% CI: 1.11–1.70). According to the age-stratified analyses, a strong and significant association was only observed in the age group > 50 years (HR: 1.73; 95% 1.25–2.40).

Conclusion
The findings of our retrospective cohort study support an association between mastitis and subsequent breast cancer diagnoses in women aged > 50 years. The pathophysiological basis and possibility of confounders however requires further investigation.
Model-agnostic variable importance for predictive uncertainty: an entropy-based approach
Authors: Wood Danny , Papamarkou Theodore , Benatan Matt , Allmendinger Richard 
Published: August 2024

AbstractIn order to trust the predictions of a machine learning algorithm, it is necessary to understand the factors that contribute to those predictions. In the case of probabilistic and uncertainty-aware models, it is necessary to understand not only the reasons for the predictions themselves, but also the reasons for the model’s level of confidence in those predictions. In this paper, we show how existing methods in explainability can be extended to uncertainty-aware models and how such extensions can be used to understand the sources of uncertainty in a model’s predictive distribution. In particular, by adapting permutation feature importance, partial dependence plots, and individual conditional expectation plots, we demonstrate that novel insights into model behaviour may be obtained and that these methods can be used to measure the impact of features on both the entropy of the predictive distribution and the log-likelihood of the ground truth labels under that distribution. With experiments using both synthetic and real-world data, we demonstrate the utility of these approaches to understand both the sources of uncertainty and their impact on model performance.
The Camino de Santiago as a ‘Spiritual Journey’: A Contemporary Challenge to Religion?
Authors: Roszak Piotr , Mróz Franciszek 
Published: August 2024

AbstractAccording to many surveys, the pilgrimage along the Way of St James (Camino de. Santiago) can lead to spiritual benefits, but there is some disagreement about this because these benefits can be associated with the pilgrim's motivation. This article presents a conceptual framework for understanding the phenomenon of pilgrimages to Compostela and their impact on human spiritual well-being. Many diaries mention various positive psychological effects from these trips, but they are presented in religious/spiritual dialectical tension. The article presents the classical concept of spirituality as related to the ability to transcend, and then classifies what is spiritual in the writings of some Polish pilgrims. In this way, conceptual precision will be offered, which is important for understanding the positive impact of pilgrimages on well-being and empowerment.
Green agro storage and electric vehicle integrated nano grid for rural livelihood improvement: a detailed review and case analysis
Authors: Nadimuthu Lalith Pankaj Raj , Victor Kirubakaran , Karthikeyan P. N. , Premkumar I. J. Isaac , Naavaneethakrishnan G. , Palanisamy R. , Saleel C. Ahamed , Ağbulut Ümit 
Published: August 2024

Abstract
Livelihood improvement in the rural areas is the key parameters to achieve the Sustainable Development Goals. This paper attempts the livelihood improvement in rural areas through green energy technologies. The manuscript comprises a detailed review of electric vehicles with unique features of micro cold storage and vehicle-to-grid technologies. A critical analysis of the intrinsic properties of thermoelectric cooler-based micro cold storage for better material selection, performance, and optimization techniques for effective electric vehicle integration is reported. The manuscript encapsulates the thermoelectric intrinsic parameters like Seebeck coefficient (S), electrical conductivity (σ), thermal conductivity (K), and figure of merit (ZT) parameters with coefficient of performance and cooling capacity (Q
C) for different types of thermoelectric modules. The review narrows down into suitable parameters for effective combined system design, such as optimal operating voltage (V
opt) and current (I
opt). The manuscript further reviewed and presented the V2G-enabled nanogrid, control, and grid integration techniques for better-integrated operation. This paper reported an experimental investigation on the designed and developed green agro storage integrated V2G enabled nano grid for a rural village in India. The case analysis was carried out by short distance agro produce transportation, decentralized DC–DC control, and phase-locked loop grid synchronization technique. The electrical, thermal and dynamic system characteristics study was carried out and reported. Also, the manuscript highlights the potential strengths, challenges, opportunities and research gaps for the stakeholders to build a sustainable future. The proposed combined system design will pave a sustainable pathway for achieving sustainable development goals.
Recommendations on the surveillance and supplementation of vitamins and minerals for upper gastrointestinal cancer survivors: a scoping review
Authors: Tan Sim Yee , Tsoukalas Tiffany , Javier Kirsten , Fazon Tiffany , Singh Sheena , Vardy Janette 
Published: August 2024

Abstract
Background
Early-stage upper gastrointestinal (UGI) cancer patients, after surgery, have altered gastrointestinal functions, compromising their nutritional status and health outcomes. Nutritional care provision to UGI survivors rarely focuses on long-term survivorship. Here, we explore recommendations for surveillance of micronutrient deficiency and supplementation for UGI cancer survivors after surgery.

Methods
A scoping review, based on the Joanna Briggs Institute methodology for scoping reviews. Six databases (Medline, Embase, CINAHL, Cochrane, Scopus, and PsycINFO) and 21 cancer-related organisation websites were searched. Publications between 2010 and March 2024 with recommendations aimed at adult UGI cancer (oesophageal, gastric, pancreatic, small bowel, and biliary tract) survivors were included.

Results
Twenty-six publications met the selection criteria: 11 reviews (8 narrative reviews, 2 systematic, 1 meta-analysis), 7 expert opinions, 6 guidelines, and 2 consensus papers. Twenty-two publications recommended monitoring of micronutrient deficiencies, and 23 suggested supplementation, with 8 lacking details. Most were targeted at patients with gastric cancer (n = 19), followed by pancreatic cancer (n = 7) and oesophageal cancer (n = 3) with none for biliary tract and small bowel cancers. Vitamin B12 and iron were the most consistently recommended micronutrients across the three tumour groups.

Conclusion
Limited publications recommend surveillance of micronutrient status in UGI cancer survivors during the survivorship phase, especially for oesophageal and pancreatic cancer survivors; most were narrative reviews. These recommendations lacked details, and information was inconsistent.

Implications for cancer survivors
Long-term UGI cancer survivors are at risk of micronutrient deficiency after surgery. A standardised approach to prevent, monitor, and treat micronutrient deficiencies is needed.
Understanding self-esteem in the consumer journey: A model of purchase self-esteem antecedents
Authors: Razmus Wiktor , Laguna Mariola 
Published: August 2024

AbstractWhile the significance of self-esteem in various consumer behaviors has been well recognized, there is still a noticeable research gap in understanding its specific determinants, especially in the context of the purchase process. The purpose of our study is to fill this gap by elucidating the factors shaping consumers’ self-esteem throughout the purchase journey utilizing our model of purchase self-esteem antecedents. Based on social exchange theory and affect theory of social exchange our model proposes that consumer brand engagement (CBE) and feelings of authentic pride play an important role in predicting self-esteem. We tested this model with longitudinal data collected at two measurement times: 1 week before a purchase (n = 111) and around 1 week after a purchase (n = 99). The longitudinal structural equation modeling results revealed that CBE elicited feelings of authentic pride, and feelings of authentic pride led to higher self-esteem. Both relationships were statistically significant within and between measurement times. The results confirmed that authentic pride mediated the relationship between CBE and self-esteem. These findings provide strong support for our model of purchase self-esteem antecedents.
BCAM (basal cell adhesion molecule) protein expression in different tumor populations
Authors: Burela Sneha , He Mengni , Trontzas Ioannis P. , Gavrielatou Niki , Schalper Kurt A. , Langermann Solomon , Flies Dallas B. , Rimm David L. , Aung Thazin N. 
Published: August 2024

AbstractBasal Cell Adhesion Molecule (BCAM), a receptor for laminin subunit α5, plays a crucial role in the pathogenesis of various malignancies. Notably, evidence of hypermethylation at multiple immune checkpoints in patients with low BCAM expression suggests these individuals may respond favorably to immunotherapy using ICIs (immune checkpoint inhibitors). This finding lays the foundation for the hypothesis that BCAM may serve as an important biomarker in cancer patients. To investigate this potential, we evaluated BCAM expression patterns in 3114 patients from both discovery and validation cohorts, spanning seven cancer types, using quantitative immunofluorescence (QIF). We also explored the correlation between BCAM and PD-L1 expressions within these cohorts, aiming to establish its potential predictive value for immunotherapy response. Our findings indicate that BCAM was highly expressed in ovarian (79.2%) and lung (78.5%) tumors, with lower yet significant expression in breast (37.7%), head and neck (31.3%), and bladder-urothelial tumors (27.6%). Notably, high BCAM expression was associated with better OS in NSCLC. More importantly, BCAM expression did not correlate with PD-L1 protein expression in any of these tumors, highlighting its independent predictive potential. The widespread expression of BCAM across multiple tumor types, coupled with its lack of correlation with PD-L1 expression, highlights its potential as a predictive novel biomarker across various cancer types.
Bayesian Approaches to Proxy Uncertainty Quantification in Paleoecology: A Mathematical Justification and Practical Integration
Authors: Aquino-López Marco A. , Anderson Lysanna , Sanchez-Cabeza Joan-Albert , Ruiz-Fernández Ana Carolina , Christen J. Andrés 
Published: August 2024

AbstractPaleoenvironmental data are essential for reconstructing environmental conditions in the distant past, and these reconstructions strongly depend on proxies and age–depth models. Proxies are indirect measurements that substitute for variables that cannot be directly measured, such as past precipitation. Conversely, an age–depth model is a tool that correlates the observed proxy with a specific moment in time. Bayesian age–depth modelling has proved to be a powerful method for estimating sediment ages and their associated uncertainties. However, there remains considerable potential for further integration into proxy analysis. In this paper, we explore a mathematical justification and a computational approach that integrates uncertainty at the age–depth level and propagates it to the proxy scale in the form of a posterior predictive distribution. This method mitigates potential biases and errors by removing the need to assign a single age to a given proxy measurement. It allows for quantifying the likelihood that proxy data values correspond to modelled ages, thus enabling the quantification of uncertainty in both the temporal and proxy value domains. The use of Bayesian statistics in proxy analysis represents a relatively recent advancement. We aim to mathematically justify incorporating the Markov chain Monte Carlo output from age–depth models into proxy analysis and to present a novel methodology for constructing environmental reconstructions using this approach.
The use of match-based exact movement activities to classify elite rugby league players into positional groups
Authors: Adeyemo Victor Elijah , Palczewska Anna , Jones Ben , Weaving Dan 
Published: August 2024

AbstractThe cluster analysis of elite rugby league players identified groups of distinct playing positions that can be referred to as broad positional groups. However, the identified positional groups were based on traditional indicators (physical and technical–tactical) that provided no information about the exact match-based movement activities that led to such similarity grouping and the classification of elite rugby league players into these broad positional groups remains unexplored. Hence, this study finds the best model to classify elite rugby league players into positional groups, using data characterised by movement patterns to uncover the similar movement activities of distinct playing positions within a positional group. Key movement patterns for the positional group classification and differences between the groups were also investigated. A total of 18,173 unique movement patterns were derived from 422 players’ GPS data across the 2019 and 2020 seasons, where only 36 were identified as key patterns. The highest classification accuracy of 77.58% using all unique patterns and 74.5% accuracy using the key patterns was achieved, outperforming studies that used traditional indicators. Further analyses based on key patterns revealed differences between forwards and backs. These findings establish movement patterns as viable indicators to classify rugby league players into positional groups, enabling coaches and trainers to develop position-specific training programmes that cater to the unique physical demands of each position, leading to better player development and team performance. Movement patterns are therefore recommended as an alternative approach to quantifying players’ external loads and obtaining granular information.
Sleep alterations in treatment-resistant depression patients undergoing ketamine treatment
Authors: Kwaśny Aleksander , Cubała Wiesław Jerzy , Włodarczyk Adam , Pastuszak Krzysztof 
Published: August 2024

Abstract
Background
This study examines self-reported sleep alterations in treatment-resistant depression (TRD) inpatients following intravenous ketamine administration.

Methods
This is a post-hoc analysis of a naturalistic observational study, which enrolled 28 inpatients with treatment-resistant major depressive disorder and analyzed self-reported sleep changes (items 1–4; ‘insomnia’, ‘nighttime restlessness’, ‘early morning waking’, ‘hypersomnia’) in Inventory of Depressive Symptomatology 30-item (IDS SR-30) in responders and non-responders stratified per Montgomery-Åsberg Depression Rating Scale (MADRS) during short-term ketamine treatment.

Results
Responders, as well as non-responders, did not experience significant changes in IDS SR-30 sleep items (‘insomnia’, ‘nighttime restlessness’, ‘early morning waking’, ‘hypersomnia’) (p’s > 0.05) at 7-day follow-up after eight intravenous ketamine infusions as compared to baseline.

Conclusion
Neither responders, nor non-responders reported any significant alterations in sleep patterns during ketamine infusions. These findings are not in line with current literature, as so far modest improvements in sleep during ketamine treatment have been reported. Results should be interpreted with caution, primarily due to the small sample size.
A q-SUPERCONGRUENCE ARISING FROM ANDREWS’ IDENTITY
Authors: LIU JI-CAI , LIU JING 
Published: August 2024

Abstract
We establish a q-analogue of a supercongruence related to a supercongruence of Rodriguez-Villegas, which extends a q-congruence of Guo and Zeng [‘Some q-analogues of supercongruences of Rodriguez-Villegas’, J. Number Theory145 (2014), 301–316]. The important ingredients in the proof include Andrews’

$_4\phi _3$

terminating identity.
Sensitivity Analysis in the Presence of Intrinsic Stochasticity for Discrete Fracture Network Simulations
Authors: Murph A. C. , Strait J. D. , Moran K. R. , Hyman J. D. , Viswanathan H. S. , Stauffer P. H. 
Published: August 2024

AbstractLarge‐scale discrete fracture network (DFN) simulators are standard fare for studies involving the sub‐surface transport of particles since direct observation of real world underground fracture networks is generally infeasible. While these simulators have successfully been used in several engineering applications, estimates of output quantities of interest (QoI) — such as breakthrough time of particles reaching the edge of the system — suffer from two distinct types of uncertainty. A run of a DFN simulator requires several parameters to be set that dictate the placement and size of fractures, the density of fractures, and the overall permeability of the system; uncertainty on the proper parameters will lead to uncertainty in the QoI, called epistemic uncertainty. Furthermore, since these input settings to DFN simulators control the stochastic processes which place fractures and govern flow, understanding how this randomness affects the QoI requires several runs of the simulator at distinct random seeds. The uncertainty in the QoI attributed to different realizations (i.e., different seeds) of the same random process (i.e., identical input parameters) leads to a second type of uncertainty, called aleatoric uncertainty. In this paper, we perform a Sensitivity Analysis, which directly attributes the uncertainty observed in the QoI to the epistemic uncertainty from each input parameter and to the aleatoric uncertainty. Beyond the specific takeaways on which input variables influence uncertainty in the QoI the most, a major contribution of this paper is the introduction of a statistically rigorous workflow for characterizing the uncertainty in DFN flow simulations that exhibit heteroskedasticity.