bluuebunny/crossref_metadata_2025_split huggingface dataset:
Micromolar fluoride contamination arising from glass NMR tubes and a simple solution for biomolecular applications
Authors: Matwani Khushboo , Cornish Jasmine , DeBenedictis Erika Alden , Heller Gabriella T. 
Published: July 2024

AbstractFluorine (19F) NMR is emerging as an invaluable analytical technique in chemistry, biochemistry, structural biology, material science, drug discovery, and medicine, especially due to the inherent rarity of naturally occurring fluorine in biological, organic, and inorganic compounds. Here, we revisit the under-reported problem of fluoride leaching from new and unused glass NMR tubes. We characterised the leaching of free fluoride from various types of new and unused glass NMR tubes over the course of several hours and quantify this contaminant to be at micromolar concentrations for typical NMR sample volumes across multiple glass types and brands. We find that this artefact is undetectable for samples prepared in quartz NMR tubes within the timeframes of our experiments. We also observed that pre-soaking new glass NMR tubes combined with rinsing removes this contamination below micromolar levels. Given the increasing popularity of 19F NMR across a wide range of fields, increasing popularity of single-use screening tubes, the long collection times required for relaxation studies and samples of low concentrations, and the importance of avoiding contamination in all NMR experiments, we anticipate that our simple solution will be useful to biomolecular NMR spectroscopists.
Developing a Research Agenda with Victim-Survivors to Improve Health Sector Responses to Domestic, Family and Sexual Violence in Australia
Authors: Lamb Katie , McKay Heather , Baloch Surriya , Hegarty Kelsey 
Published: July 2024

Abstract
Purpose
The World Health Organisation acknowledges the health sector as playing a key role in responding to those who experience and use domestic, family and sexual violence. Limited resources need to be well spent to ensure the best outcomes for victim-survivors and their families. This study was undertaken to explore: What do victim-survivors, researchers and practitioners see as the key research priorities for improving health sector responses to domestic, family and sexual violence in Australia?

Methods
A Delphi methodology was adopted, with input from those with lived experience. Victim-survivors, researchers, practitioners and policy makers (n = 35) were engaged in a workshop and two rounds of Delphi surveys (n = 72 and 69).

Results
Participants highly rated four themes of research priorities 1) embedding the voices victim-survivors of domestic and family violence in health sector reform; 2) improving outcomes for children, young people and young families: 3) strengthening response to victim-survivors and perpetrators; and 4) better meeting the needs of victim-survivors from diverse backgrounds (including those with a disability, mental health issues, or from culturally diverse or Indigenous backgrounds).

Conclusions
This study identifies a solid research agenda for organisations and individuals interested in conducting or funding research to improve health sector responses to victim-survivors in Australia. It also demonstrates one way in which victim-survivors can be engaged in a meaningful way to inform future research about domestic, family and sexual violence.
A Multi-Dimensional Scale for Measuring Undergraduates Interest in STEM Disciplines
Authors: Ahmad Zubair , Sultana Almaas , Siby Nitha , Ammar Mohammad , Al-Thani Noora J. 
Published: July 2024

AbstractOne of the major challenges faced by the education system today is the lack of interest exhibited by undergraduates in Science, Technology, Engineering, and Mathematics (STEM) disciplines. This is particularly concerning as the world is becoming increasingly technology-driven, and a skilled workforce is necessary to meet future demands. Therefore, fostering interest among undergraduates in STEM fields is imperative to improve their engagement and persistence in these disciplines. Numerous initiatives have been undertaken to enhance students' STEM interests in formal, non-formal, and informal learning settings. However, assessing the effectiveness of these initiatives requires a robust and validated measurement tool grounded in theoretical and empirical evidence. Without a valid and reliable instrument, it would be challenging to determine the effectiveness of these initiatives in promoting STEM education and interest among undergraduates.This study presents a STEM Interest Scale (SIS) that captures diverse psychological factors that affect students’ interest in STEM. To develop this scale, the study employs a cross-sectional quantitative design. The data collection process involved two phases for developing and validating the scale. In phase I, 280 participants were surveyed to perform exploratory factor analysis (EFA) to identify the underlying dimensions of STEM interest. In phase II, a sample of 266 students was used to assess the psychometric characteristics of the ‘STEM Interest Scale (SIS) using confirmatory factor analysis (CFA) and nomological validity. The outcome of the EFA revealed that the STEM Interest scale is comprised of four psychological factors: intrinsic motivation, self-efficacy, self-concept, and employment aspiration, while the CFA and nomological network confirm the validity of these factors. The present research is a pioneering effort to holistically capture STEM interest, thereby paving the way for informed interventions in STEM education.
Supporting SME companies in mapping out AI potential: a Finnish AI development case
Authors: Jafarzadeh Pouya , Vähämäki Tanja , Nevalainen Paavo , Tuomisto Antti , Heikkonen Jukka 
Published: July 2024

AbstractProducts and services relying upon Artificial Intelligence (AI) have moved from mere concepts to reality. However, challenges still exist in applying AI technologies to traditional industrial and service enterprises. Two central problems are a proper understanding of the opportunities AI could bring to the business processes and making the business logic and data sources transparent to AI experts. As small and medium-sized enterprises (SMEs) are considered the economic backbone of many countries, this paper studies how to support SMEs in understanding the potential of AI in their business and how to prepare their data and requirements for a possible AI project. For this purpose, we first proposed the Cross-Industry Standard Process for Data Mining (CRISP-DM) an industry-proven way to apply AI solutions. The weight was in early business and data understanding. Then, we performed data visualization and developed some machine learning methods for 11 SMEs in South-western Finland as case studies to get more ideas for improving their business using AI. Two surveys probed the possible changes in AI practises of companies.
Effect of different strain rates on mechanical behavior and structure of Inconel 718 produced by powder bed fusion
Authors: Kolomy Stepan , Benc Marek , Harant Martin , Sedlak Josef , Jopek Miroslav 
Published: July 2024

Abstract
The paper aims to examine the effect of different strain rates on a mechanical behavior and structure of additively manufactured Inconel 718. The material was prepared by the powder bed fusion method, which is commonly employed for high-performance components subjected to both high static and dynamic loading. To analyze the material’s behavior at various strain rates, a conventional hydraulic testing machine and a split hopkinson pressure bar apparatus were utilized. Additionally, the effect of these conditions on mechanical properties and microstructure was investigated. Results of compressive tests revealed a positive strain rate sensitivity of the material. Furthermore, the microhardness exhibited an increase by 33.9% in the horizontal direction after deformation caused by 2·10–2 strain rate and 35.8% in the vertical direction, respectively. Additionally, the average grain size decreased by 43.3%, and the high-angle grain boundaries decreased by 5.4% in the horizontal direction after the excessive plastic deformation at the strain rate of 1.8·103 s-1. Scanning electron microscopy images showed that the as-built structure predominantly consisted of Laves phases in a long strip shape, while the structure after dynamic testing featured a granular shape. Transmission electron microscopy analysis of a sample tested at strain rate of 0.002 s-1 revealed finely developed grains within the structure, many of which contained a dislocation substructure. This study’s novelty and robustness lie in its significant contribution to practical industrial energy applications, in which parts are exposed to dynamic load such as gas turbines.
Calderón-Zygmund Decomposition, Hardy Spaces Associated with Operators and Weak Type Estimates
Authors: Bui The Anh , Duong Xuan Thinh 
Published: July 2024

AbstractLet $$(X, d, \mu )$$

(
X
,
d
,
μ
)

be a metric space with a metric d and a doubling measure $$\mu $$
μ
. Assume that the operator L generates a bounded holomorphic semigroup $$e^{-tL}$$

e

-
t
L

on $$L^2(X)$$

L
2

(
X
)

whose semigroup kernel satisfies the Gaussian upper bound. Also assume that L has a bounded holomorphic functional calculus on $$L^2(X)$$

L
2

(
X
)

. Then the Hardy spaces $$H^p_L(X)$$

H
L
p

(
X
)

associated with the operator L can be defined for $$0 < p \le 1$$

0
<
p
≤
1

. In this paper, we revisit the Calderón-Zygmund decomposition and show that a function $$f \in L^1(X)\cap L^2(X)$$

f
∈

L
1

(
X
)

∩

L
2

(
X
)

can be decomposed into a good part which is an $$L^{\infty }$$

L
∞

function and a bad part which is in $$H^p_L(X)$$

H
L
p

(
X
)

for some $$0< p <1$$

0
<
p
<
1

. An important result of our variants of Calderón-Zygmund decompositions is that if a sub-linear operator T is bounded from $$L^r(X)$$

L
r

(
X
)

to $$L^r(X)$$

L
r

(
X
)

for some $$r > 1$$

r
>
1

and also bounded from $$H^p_L(X)$$

H
L
p

(
X
)

to $$L^p(X)$$

L
p

(
X
)

for some $$0< p < 1$$

0
<
p
<
1

, then T is of weak type (1, 1) and bounded from $$L^q(X)$$

L
q

(
X
)

to $$L^q(X)$$

L
q

(
X
)

for all $$1< q <r$$

1
<
q
<
r

.
Acceptability of Four Intervention Components Supporting Medication Adherence in Women with Breast Cancer: a Process Evaluation of a Fractional Factorial Pilot Optimization Trial
Authors: Green Sophie M. C. , Rousseau Nikki , Hall Louise H. , French David P. , Graham Christopher D. , Lloyd Kelly E. , Collinson Michelle , Ow Pei Loo , Taylor Christopher , Howdon Daniel , Foy Robbie , Walwyn Rebecca , Clark Jane , Parbutt Catherine , Waller Jo , Buxton Jacqueline , Moore Sally J. L. , Velikova Galina , Farrin Amanda , Smith Samuel G. 
Published: July 2024

AbstractAdjuvant endocrine therapy (AET) reduces mortality in early-stage breast cancer, but adherence is low. We developed a multicomponent intervention to support AET adherence comprising: text messages, information leaflet, acceptance and commitment therapy (ACT), and side-effect website. Guided by the multiphase optimization strategy, the intervention components were tested in the ROSETA pilot optimization trial. Our mixed-methods process evaluation investigated component acceptability. The pilot optimization trial used a 24–1 fractional factorial design. Fifty-two women prescribed AET were randomized to one of eight experimental conditions, containing unique component combinations. An acceptability questionnaire was administered 4 months post-randomization, and semi-structured interviews with 20 participants further explored acceptability. Assessments were guided by four constructs of the theoretical framework of acceptability: affective attitude, burden, perceived effectiveness, and coherence. Quantitative and qualitative findings were triangulated to identify agreements/disagreements. There were high overall acceptability scores (median = 14–15/20, range = 11–20). There was agreement between the qualitative and quantitative findings when triangulated. Most participants “liked” or “strongly liked” all components and reported they required low effort to engage in. Between 50% (leaflet) and 65% (SMS) “agreed” or “strongly agreed,” it was clear how each component would help adherence. Perceived effectiveness was mixed, with 35.0% (text messages) to 55.6% (ACT) of participants “agreeing” or “strongly agreeing” that each component would improve their adherence. Interview data provided suggestions for improvements. The four components were acceptable to women with breast cancer and will be refined. Mixed-methods and triangulation were useful methodological approaches and could be applied in other optimization trial process evaluations.
Adaptive minimax estimation of service time distribution in the $$M_t/G/\infty $$ queue from departure data
Authors: Li Wenwen , Goldenshluger Alexander 
Published: July 2024

AbstractThis article deals with the problem of estimating the service time distribution of the $$M_t/G/\infty $$

M
t

/
G
/
∞

queue from observation of the departure epochs. We develop minimax optimal estimators of G and study behavior of the minimax pointwise risk over a suitable family of service time distribution functions. In addition, we address the problem of adaptive estimation and propose a data–driven estimation procedure that adapts to unknown smoothness of the service time distribution function G. Lastly, a numerical study is presented to illustrate practical performance of the developed adaptive procedure.
Acquired hypoprolactinemia in men, possible phenotype
Authors: Corona Giovanni , Rastrelli Giulia , Sparano Clotilde , Vignozzi Linda , Maggi Mario 
Published: July 2024

AbstractThe physiological role of prolactin (PRL) in men is still not well defined. The pathological increase is characterized by sexual function impairment along with possible negative consequences in body composition and metabolic profile. Conversely, the clinical significance of reduced PRL levels was only partially investigated or mainly neglected. The present paper aims to summarize and critically discuss possible phenotypes characterizing male subjects with reduced PRL levels. When possible, meta-analytic results were provided. Available data derived from patients seeking medical care for sexual dysfunction as well as from cross-sectional and longitudinal studies showed that low PRL in males is associated with a worse metabolic phenotype (including diabetes mellitus), mood disturbances (including anxiety and depression), and sexual dysfunctions (including psychogenic erectile and ejaculatory dysfunctions). Whether or not these features are direct consequences of reduced PRL levels or whether the latter reflect other pathway impairments such as serotoninergic failure cannot be clarified. The present data, however, emphasize that a deficiency of PRL should be taken into account and need further investigations.
Validation of the hind feet position score and its association with heel height difference in dairy cows
Authors: Hund Alexandra , Reiter Anna , Huber Johann , Kofler Johann 
Published: July 2024

AbstractHind feet position scoring (HFPS) categorizes the outward rotation of the hind feet from the line parallel to the midline of the body in standing dairy cows. It has previously been used as an indication of both differences in heel height between the lateral and medial hind claw (to determine the need of hoof trimming) and the presence of claw lesions in sound cattle. In this observational study, the agreement of HFPS with other types of hoof angle measurements, as well as its association with the heel height difference (HHD) between the claws were investigated.A total of 51 dairy cows were assessed independently by three observers in two measuring rounds on two different measuring days each. On each occasion, they scored the HFPS, and measured the angle of outward rotation of both hind feet using a digital protractor (DIG) and a compass app (COMP). Heel height difference was measured only during the second occasion. Intra- and interobserver agreement were calculated using weighted kappa statistics (HFPS) and intraclass correlation (DIG, COMP and HHD). Associations between HFPS and DIG, COMP and HHD were analyzed using linear mixed models.Intra- and interobserver reliability were poor to good for HFPS, DIG, COMP and HHD. HFPS was significantly associated with DIG and COMP but not with HHD. Using the median value of repeated HFPS scores could increase the robustness of the HFPS assessment, as our data indicate that the cows frequently shift the position of their hind claws. Overall, there was a poor correlation between HHD and HFPS, so HFPS may not be determined by HHD alone; future research should consider other reasons for outward rotation of the hind feet.
ESceme: Vision-and-Language Navigation with Episodic Scene Memory
Authors: Zheng Qi , Liu Daqing , Wang Chaoyue , Zhang Jing , Wang Dadong , Tao Dacheng 
Published: July 2024

AbstractVision-and-language navigation (VLN) simulates a visual agent that follows natural-language navigation instructions in real-world scenes. Existing approaches have made enormous progress in navigation in new environments, such as beam search, pre-exploration, and dynamic or hierarchical history encoding. To balance generalization and efficiency, we resort to memorizing visited scenarios apart from the ongoing route while navigating. In this work, we introduce a mechanism of Episodic Scene memory (ESceme) for VLN that wakes an agent’s memories of past visits when it enters the current scene. The episodic scene memory allows the agent to envision a bigger picture of the next prediction. This way, the agent learns to utilize dynamically updated information instead of merely adapting to the current observations. We provide a simple yet effective implementation of ESceme by enhancing the accessible views at each location and progressively completing the memory while navigating. We verify the superiority of ESceme on short-horizon (R2R), long-horizon (R4R), and vision-and-dialog (CVDN) VLN tasks. Our ESceme also wins first place on the CVDN leaderboard. Code is available: https://github.com/qizhust/esceme.
Learning Rate Curriculum
Authors: Croitoru Florinel-Alin , Ristea Nicolae-Cătălin , Ionescu Radu Tudor , Sebe Nicu 
Published: July 2024

AbstractMost curriculum learning methods require an approach to sort the data samples by difficulty, which is often cumbersome to perform. In this work, we propose a novel curriculum learning approach termed Learning Rate Curriculum (LeRaC), which leverages the use of a different learning rate for each layer of a neural network to create a data-agnostic curriculum during the initial training epochs. More specifically, LeRaC assigns higher learning rates to neural layers closer to the input, gradually decreasing the learning rates as the layers are placed farther away from the input. The learning rates increase at various paces during the first training iterations, until they all reach the same value. From this point on, the neural model is trained as usual. This creates a model-level curriculum learning strategy that does not require sorting the examples by difficulty and is compatible with any neural network, generating higher performance levels regardless of the architecture. We conduct comprehensive experiments on 12 data sets from the computer vision (CIFAR-10, CIFAR-100, Tiny ImageNet, ImageNet-1K, Food-101, UTKFace, PASCAL VOC), language (BoolQ, QNLI, RTE) and audio (ESC-50, CREMA-D) domains, considering various convolutional (ResNet-18, Wide-ResNet-50, DenseNet-121, YOLOv5), recurrent (LSTM) and transformer (CvT, BERT, SepTr) architectures. We compare our approach with the conventional training regime, as well as with Curriculum by Smoothing (CBS), a state-of-the-art data-agnostic curriculum learning approach. Unlike CBS, our performance improvements over the standard training regime are consistent across all data sets and models. Furthermore, we significantly surpass CBS in terms of training time (there is no additional cost over the standard training regime for LeRaC). Our code is freely available at: https://github.com/CroitoruAlin/LeRaC.
Knowledge Distillation Meets Open-Set Semi-supervised Learning
Authors: Yang Jing , Zhu Xiatian , Bulat Adrian , Martinez Brais , Tzimiropoulos Georgios 
Published: July 2024

AbstractExisting knowledge distillation methods mostly focus on distillation of teacher’s prediction and intermediate activation. However, the structured representation, which arguably is one of the most critical ingredients of deep models, is largely overlooked. In this work, we propose a novel semantic representational distillation (SRD) method dedicated for distilling representational knowledge semantically from a pretrained teacher to a target student. The key idea is that we leverage the teacher’s classifier as a semantic critic for evaluating the representations of both teacher and student and distilling the semantic knowledge with high-order structured information over all feature dimensions. This is accomplished by introducing a notion of cross-network logit computed through passing student’s representation into teacher’s classifier. Further, considering the set of seen classes as a basis for the semantic space in a combinatorial perspective, we scale SRD to unseen classes for enabling effective exploitation of largely available, arbitrary unlabeled training data. At the problem level, this establishes an interesting connection between knowledge distillation with open-set semi-supervised learning (SSL). Extensive experiments show that our SRD outperforms significantly previous state-of-the-art knowledge distillation methods on both coarse object classification and fine face recognition tasks, as well as less studied yet practically crucial binary network distillation. Under more realistic open-set SSL settings we introduce, we reveal that knowledge distillation is generally more effective than existing out-of-distribution sample detection, and our proposed SRD is superior over both previous distillation and SSL competitors. The source code is available at https://github.com/jingyang2017/SRD_ossl.
Auditors and client investment efficiency: a quasi-replication and further insights from a regulatory change
Authors: Bleibtreu Christopher , Erinc Mert , Orozco Luciana , Shi Zhenyang 
Published: July 2024

AbstractThis study is a quasi-replication and extension of Bae et al. (Account Rev 92(2):19–40, 2017), which examines the relationship between auditors’ characteristics and their audit clients’ investment efficiency. Whereas Bae et al. (Account Rev 92(2):19–40, 2017) use U.S. public firm data, we draw a more general picture by using both public and private firm data from Norway. Overall, the results for Norwegian public and private firms are in line with those Bae et al. (Account Rev 92(2):19–40, 2017) find for public U.S. firms. That is, audit clients invest more efficiently if their auditors have more knowledge and resources, measured by auditor market shares or whether a Big N audit firm performs the audit. Further, an auditor’s influence on its client’s investment efficiency is more pronounced when clients have a higher demand for information, proxied by client complexity. Finally, exploiting a regulatory change in 2011 that allowed small private Norwegian firms to opt out of previously mandatory auditing, we extend the study by Bae et al. (Account Rev 92(2):19–40, 2017). We find that audits can increase investment efficiency for small private firms. Specifically, firms that dismiss their auditors tend to overinvest more than similar firms that are not eligible to opt out of auditing. Further, firms that voluntarily keep their auditor have an overall higher investment efficiency than similar firms that are not audited.