bluuebunny/crossref_metadata_2025_split huggingface dataset:
Validity evidence for endoscopic ultrasound competency assessment tools: Systematic review
Authors: Walsh Catharine M , Grover Samir C , Ceccacci Alessandra , Hothi Harneet , Khan Rishad , Gimpaya Nikko , Chan Brian P.H. , Forbes Nauzer , James Paul , Low Daniel Jeffry , Mosko Jeffrey , Yeung Elaine T. 
Published: November 2024

Abstract
Background and study aims Competent endoscopic ultrasound (EUS) performance requires a combination of technical, cognitive, and non-technical skills. Direct observation assessment tools can be employed to enhance learning and ascertain clinical competence; however, there is a need to systematically evaluate validity evidence supporting their use. We aimed to evaluate the validity evidence of competency assessment tools for EUS and examine their educational utility.
Methods We systematically searched five databases and gray literature for studies investigating EUS competency assessment tools from inception to May 2023. Data on validity evidence across five domains (content, response process, internal structure, relations to other variables, and consequences) were extracted and graded (maximum score 15). We evaluated educational utility using the Accreditation Council for Graduate Medical Education framework and methodological quality using the Medical Education Research Quality Instrument (MERSQI).
Results From 2081 records, we identified five EUS assessment tools from 10 studies. All tools are formative assessments intended to guide learning, with four employed in clinical settings. Validity evidence scores ranged from 3 to 12. The EUS and ERCP Skills Assessment Tool (TEESAT), Global Assessment of Performance and Skills in EUS (GAPS-EUS), and the EUS Assessment Tool (EUSAT) had the strongest validity evidence with scores of 12, 10, and 10, respectively. Overall educational utility was high given ease of tool use. MERSQI scores ranged from 9.5 to 12 (maximum score 13.5).
Conclusions The TEESAT, GAPS-EUS, and EUSAT demonstrate strong validity evidence for formative assessment of EUS and are easily implemented in educational settings to monitor progress and support learning.
Snoring and its Associated Comorbidities
Authors: Kasmaoui Fatima Ezzahra , Benksim Abdelhafid , El harsi El Mahjoub , Amine Mohamed 
Published: November 2024

Abstract
Objective Snoring is often perceived as a simple social nuisance, whereas it can be a telltale sign of serious respiratory diseases. The present study aimed to determine the prevalence of self-reported habitual snoring and to identify its associated factors.
Materials and Methods This cross-sectional study surveyed 815 individuals about their medical history, anthropometric characteristics, and lifestyle using a questionnaire.
Results The prevalence of self-reported habitual snoring in our sample was 29.2%. Variables independently associated with snoring were advanced age (p = 0.008), asthma (p = 0.003), sleepiness (p < 0.001), hyperthyroidism (p = 0.006), smoking (p < 0.001), diabetes (p = 0.010), and abdominal obesity (p = 0.007).
Conclusion This survey has highlighted the importance of snoring in an Arab-African context, to bring more awareness to this respiratory disorder and to understand that a simple snore can be a silent cry of an organism in need of help.
Production losses caused by red leather leaf in hay and milling oats
Authors: Dadu Rama Harinath Reddy , McLean Mark S. , Hollaway Grant J. 
Published: November 2024

Context Red leather leaf (RLL), caused by Neospermospora avenae, is a common foliar disease of oats in south-eastern Australia but its impact on hay and grain yield and quality is unknown. Aim We aimed to determine the effect of RLL on grain yield and quality of commercial milling oat varieties, and biomass and nutrition of hay oat varieties, with a range of host plant resistance responses by comparing fungicide and disease treatments. Methods A total of eight field experiments were conducted in 2019 and 2020; four experiments each investigated the effects of RLL on hay or milling oats. In each experiment, six or seven oat varieties were sown with a disease or fungicide imposed. RLL severity was estimated visually for each experiment. Biomass yield and quality traits at early milk development stage were measured for hay oat experiments; and grain yield and quality for the milling oat experiments. Key results RLL severity (% leaf area affected: 0–44% in hay oats and 2–61% in milling oats) varied with the season, varietal resistance, and treatments. RLL caused significant losses in most experiments, with reductions in biomass of up to 3.5 t/ha (22%) in hay oats and grain yield of up to 1.1 t/ha (21%) in milling oats. Conclusions RLL can be a severe foliar disease of oats in south-eastern Australia and cause significant losses in both hay and milling oat crops. Implications Growing varieties with resistance can effectively reduce potential loss due to RLL and growers are required to adapt fungicide strategies reflecting seasonal conditions and product type (grain or hay).
Latitudinal and Solar Cycle Distribution of Extreme (≥X5) Flares During 1976–2018
Authors: Li Qi , Zhang Xi-Wen , Le Gui-Ming 
Published: November 2024

Abstract
We studied the latitudinal and solar cycle distribution of extreme (≥X5) solar flares spanning 1976–2018. We found that all such flares were confined within the latitudinal range of [S30, N35]. Nonetheless, the majority of these flares during different solar cycles were confined in different latitudinal scopes. Statistical results showed that the southeast quadrant experienced the highest activity of extreme flares. 47.5% of the extreme flares occurred within the latitudes ≤15° of the two hemispheres, with 26.2%, 31.1%, and 42.6% in the latitudinal bands [5°, 10°], >20° and [11°, 20°] of both hemispheres, respectively. Significant N–S asymmetries were observed in the ascending phase of SC 21, the descending phase of SC 23, and both phases of SC 24. Other phases showed asymmetries primarily in latitudinal distribution. The proportion of extreme flares in the ascending phases of SCs 21–24 was 22.2%, 33.3%, 38.9%, and 50%, respectively. Stronger flares (≥X10) were more likely to occur in the descending phase, with 39% of X5–X9 flares and 20% of (≥X10) flares occurring in the ascending phase. On average, 83.6% of extreme flares occurred within a period extending from two years prior to three years following the solar peak, according to our statistical analysis, with specific percentages for each cycle being 88.9%, 100%, 61.1%, and 75%.
A systematic review on feature extraction methods and deep learning models for detection of cancerous lung nodules at an early stage –the recent trends and challenges
Authors: Palani Mathumetha , Rajagopal Sivakumar , Chintanpalli Anantha Krishna 
Published: November 2024

Abstract
Lung cancer is one of the most common life-threatening worldwide cancers affecting both the male and the female populations. The appearance of nodules in the scan image is an early indication of the development of cancer cells in the lung. The Low Dose Computed Tomography screening technique is used for the early detection of cancer nodules. Therefore, with more Computed Tomography (CT) lung profiles, an automated lung nodule analysis system can be utilized through image processing techniques and neural network algorithms. A CT image of the lung consists of many elements such as blood vessels, ribs, nodules, sternum, bronchi and nodules. These nodules can be both benign and malignant, where the latter leads to lung cancer. Detecting them at an earlier stage can increase life expectancy by up to 5 to 10 years. To analyse only the nodules from the profile, the respected features are extracted using image processing techniques. Based on the review, textural features were the promising ones in medical image analysis and for solving computer vision problems. The importance of uncovering the hidden features allows Deep Learning algorithms (DL) to function better, especially in medical imaging, where accuracy has improved. The earlier detection of cancerous lung nodules is possible through the combination of multi-featured extraction and classification techniques using image data. This technique can be a breakthrough in the deep learning area by providing the appropriate features. One of the greatest challenges is the incorrect identification of malignant nodules results in a higher false positive rate during the prediction. The suitable features make the system more precise in prognosis. In this paper, the overview of lung cancer along with the publicly available datasets is discussed for the research purposes. They are mainly focused on the recent research that combines feature extraction and deep learning algorithms used to reduce the false positive rate in the automated detection of lung nodules. The primary objective of the paper is to provide the importance of textural features when combined with different deep-learning models. It gives insights into their advantages, disadvantages and limitations regarding possible research gaps. These papers compare the recent studies of deep learning models with and without feature extraction and conclude that DL models that include feature extraction are better than the others.
Near-optimal quantum kernel principal component analysis
Authors: Wang Youle 
Published: November 2024

Abstract
Kernel principal component analysis (kernel PCA) is a nonlinear dimensionality reduction technique that employs kernel functions to map data into a high-dimensional feature space, thereby extending the applicability of linear PCA to nonlinear data and facilitating the extraction of informative principal components. However, kernel PCA necessitates the manipulation of large-scale matrices, leading to high computational complexity and posing challenges for efficient implementation in big data environments. Quantum computing has recently been integrated with kernel methods in machine learning, enabling effective analysis of input data within intractable feature spaces. Although existing quantum kernel PCA proposals promise exponential speedups, they impose stringent requirements on quantum hardware that are challenging to fulfill. In this work, we propose a quantum algorithm for kernel PCA by establishing a connection between quantum kernel methods and block encoding, thereby diagonalizing the centralized kernel matrix on a quantum computer. The query complexity is logarithmic with respect to the size of the data vector, D, and linear with respect to the size of the dataset. An exponential speedup could be achieved when the dataset consists of a few high-dimensional vectors, wherein the dataset size is polynomial in

log
⁡
(
D
)

, with D being significantly large. In contrast to existing work, our algorithm enhances the efficiency of quantum kernel PCA and reduces the requirements for quantum hardware. Furthermore, we have also demonstrated that the algorithm based on block encoding matches the lower bound of query complexity, indicating that our algorithm is nearly optimal. Our research has laid down new pathways for developing quantum machine learning algorithms aimed at addressing tangible real-world problems and demonstrating quantum advantages within machine learning.
Digital–analog quantum learning on Rydberg atom arrays
Authors: Lu Jonathan Z , Jiao Lucy , Wolinski Kristina , Kornjača Milan , Hu Hong-Ye , Cantu Sergio , Liu Fangli , Yelin Susanne F , Wang Sheng-Tao 
Published: November 2024

Abstract
We propose hybrid digital–analog (DA) learning algorithms on Rydberg atom arrays, combining the potentially practical utility and near-term realizability of quantum learning with the rapidly scaling architectures of neutral atoms. Our construction requires only single-qubit operations in the digital setting and global driving according to the Rydberg Hamiltonian in the analog setting. We perform a comprehensive numerical study of our algorithm on both classical and quantum data, given respectively by handwritten digit classification and unsupervised quantum phase boundary learning. We show in the two representative problems that DA learning is not only feasible in the near term, but also requires shorter circuit depths and is more robust to realistic error models as compared to digital learning schemes. Our results suggest that DA learning opens a promising path towards improved variational quantum learning experiments in the near term.
Impact of coil envelope on the coupling coefficient in magnetic induction wireless power transmission systems
Authors: Bidjagare Akiza , Adanlete Adjanoh Assiongbon , Afenyiveh Dzo Mawuefa Serge 
Published: November 2024

Abstract
Research in the field of wireless energy transmission by magnetic induction has increased in recent years, due to the diversity of applications, such as medicine, electronics, and transport, that require this technology. Despite its maturity to date, this technology has struggled itself due to the short transmission distance and low coupling coefficient between the transmitting and receiving coils. These coils are usually protected by an envelope. However, the impact of the nature of the envelope on the coupling coefficient has not been studied. This work focuses on the impact of the nature of the coil protection envelope on the coupling coefficient. Three-dimensional flat spiral coils and the protective envelope are modeled and subjected to parametric analysis with variable air gaps and frequencies using ANSYS-Electronics Maxwell 2022 R1 software with the finite element method (FEM). Simulation results show that a coil protected by an insulating envelope has a higher coupling coefficient than a coil protected by a conducting material. Simulation in this work have also shown that the coupling coefficient decreases as operating frequencies increase. The ohmic losses in coils protected by an insulating envelope are greater than those in coils protected by a conductive material.
The effects of multimodal versus opioid-only analgesia on pain control and opioid exposure for inpatient acute pancreatitis: A retrospective cohort study
Authors: Hanners Emily K , Thambi Mathew , Soni Hailey P , Jun Julie , Datta Avisek , Bhaumik Dulal , M. Stranges Paul 
Published: November 2024

Abstract

Purpose
Acute pancreatitis (AP) is a common gastrointestinal condition that carries a significant financial and physical burden for patients due to the painful presentation and frequent need for hospitalization. Treatment varies in the type and route of analgesic used. Unmanaged pain can lead to worsening health outcomes and prolonged admissions. Further research is needed to understand current practices for managing AP.

Methods
This study was a retrospective, exploratory, single-center cohort study. Patients admitted to an internal medicine unit with AP from September 1, 2020, to December 31, 2021, were included. Our primary outcome was the change in pain scores during the first 24-hours after administration of the first pain-relieving medication. Secondary outcomes included change in pain scores within the first 12-hours, time to oral tolerance, characterization of analgesic orders, comparison of median pain scores by home analgesic usage, and specific inpatient opioid use.

Results
One hundred sixty-nine patients were screened, and 94 were included in the study. Forty-four patients were assigned to the multimodal cohort and 50 patients to the opioid-only cohort. Changes in pain scores per unit of time, assessed by a mixed-effects model, within the first 24 hours were –0.080 (SE, 0.018) in the multimodal cohort and –0.090 (SE, 0.014) in the opioid-only cohort (P = 0.780). Morphine milligram equivalents (MME) administered in the multimodal and opioid-only cohorts were 24.50 (interquartile range [IQR], 51.75) and 52.5 (IQR, 68.5) (P = 0.001), respectively.

Conclusion
There was no significant difference in the change in pain scores between patients receiving multimodal therapy and those receiving opioids only. Significantly fewer MME were administered in the multimodal group, suggesting that multimodal therapy can be opioid-sparing in AP.
What’s The Point of Constitutional Monarchy?
Authors: Barber N W 
Published: November 2024

Abstract
The article examines the point of constitutional monarchy as a form of leadership. It considers five functions of leadership and examines the extent to which constitutional monarchy fits these functions. It concludes the best argument for monarchy is found in the role of leader as exemplar, the capacity to act as prototypical group member. As such, the monarch exemplifies the values of state membership, acting as a point of reference and reassurance. Moreover, as the monarch can embody a range of differing conceptions of prototypicality, she can possess different meanings for different state members, providing a shared point of identification for those who would otherwise disagree. However, the mode of selection of the monarch is problematic, making it improbable officeholders will invariably possess the capacity to fulfil the role and bringing moral costs. In consequence, monarchy is a time-limited constitutional form, a transitional mode of leadership.
Effect of Facial Acupuncture Stimulation: MRI-Based Masseter Muscle Volume Analysis and Questionnaire Evaluation
Authors: Ogino Mieko , Iijima Megumi , Okada Yukinori , Okuda Itsuko 
Published: November 2024

Abstract

Background
Cosmetic acupuncture may reduce wrinkles, swelling, sagging, and facial asymmetry and may lift facial contours.

Objectives
To investigate the impact of cosmetic acupuncture on masseter muscle volume and its implications for facial aesthetics.

Methods
The authors included 10 apparently healthy adult females (average age, 50.3 ± 6.45 years) and performed acupuncture once a week for 8 weeks. MRI was conducted before acupuncture stimulation and 3 days after the treatment. The collected image data were analyzed using ZioCube software (ZioSoft Co., Ltd, Tokyo). Before and after the intervention, 2 evaluators measured the masseter muscle volume 3 times each and calculated the average value.

Results
The average total volume of the left and right masseter muscles decreased from 40.73 ± 8.2 to 37.81 ± 8.57 cm3 after cosmetic acupuncture, a significant reduction of 2.92 ± 2.48 cm3 (7.37%, P &lt; .05). All 10 subjective evaluation items showed significant decreases, particularly in facial sagging, contour, and asymmetry, confirming aesthetic improvements and psychological satisfaction among participants. Cosmetic acupuncture reduced masseter muscle volume, leading to noticeable aesthetic benefits.

Conclusions
Acupuncture stimulation at acupoints around the masseter muscle can reduce the muscle volume because of muscle relaxation. The improvement in subjective evaluation and changes and decreases in masseter muscle shape enable the attainment of facial contour aesthetics, contributing to the evidence in support of cosmetic acupuncture.

Level of Evidence: 4 (Therapeutic)
Lipid metabolism and food ingredients from the perspective of thermogenic adipocytes
Authors: Takada Mai , Kawarasaki Satoko , Kwon Jungin , Ni Zheng , Takahashi Haruya , Inoue Kazuo , Goto Tsuyoshi 
Published: November 2024

Abstract
The high heat-producing capacity of brown and beige adipocytes, collectively known as thermogenic adipocytes, contributes to whole-body energy expenditure and is an attractive target for the management of obesity. It has been revealed that the functions of thermogenic adipocytes are important for the regulation of whole-body carbohydrate and lipid metabolism, and the activation of thermogenic adipocytes seems to have beneficial effects for the management of obesity-related metabolic disorders, such as dyslipidemia. Recent studies have showed that specific food ingredients have the potential to activate thermogenic adipocytes via various mechanisms. Some of these are effective not only in rodents, but also in humans, and effective prevention of obesity using these food ingredients is expected. In this review, we introduce the recent findings on the regulatory mechanisms of lipid metabolism by thermogenic adipocytes and food ingredients, demonstrating the potential to activate thermogenic adipocytes and their underlying mechanisms.
Managing emotions and occupational demands: A phenomenological exploration of the experiences of child exploitation support workers
Authors: Mason Sarah-Jane , Childs Carrie 
Published: November 2024

Abstract
Protecting children from criminal and sexual exploitation is a priority for safeguarding teams across the globe. Supporting children who are at risk of exploitation is a demanding role with high staff turnover. Experiences of third-sector child exploitation support workers, commissioned to deliver interventions to safeguard children from exploitation, have been neglected. This study presents an Interpretative Phenomenological Analysis of semi-structured interviews of eight child exploitation support workers employed by a service in England. Three experiential group themes were developed that captured the emotional and occupational complexities of their experiences performing their role. ‘Costs and Rewards of Emotional Labour’ depicted how emotions were managed to meet the requirements of the role. ‘Navigating Autonomy: The Double-Edged Sword of Empowerment and Loneliness’ highlighted unique perceptions of autonomous working. ‘“We are in really vulnerable situations at times”: Exploring Perceptions of Risk’ demonstrated how, collectively, participants felt vulnerable to risks when carrying out their role. The study provides insight that these third-sector employees face similar challenges as the regulated workforce and that it is crucial that policymakers and senior managers develop safety and system improvements to increase employees’ perceived and actual safety and well-being.
Factors associated with the school exclusion gap for children with social work involvement: A retrospective cohort study using national administrative data
Authors: Hood Rick , Goldacre Allie , Jones Edward , Martin Emma , Clements Keith , Webb Calum 
Published: November 2024

Abstract
Children who receive social care services are known to have higher exclusion rates than those without social care involvement. However, there is limited evidence on which groups of children are most at risk of this exclusion gap. This article reports on a retrospective analysis of administrative data from the National Pupil Database in England, covering two age-based cohorts of children (n = 1.2 million) who took standardized tests in 2019. Children receiving social care services within the previous five years were compared with those with no social care involvement, focusing on rates of exclusion within a 12 month period. Regression models were estimated to examine how the relative likelihood of exclusion was associated with a range of covariates, including demographic characteristics, special educational needs and disabilities, categories of social care need, and various aspects of provision. Results showed that the gap in exclusions was wider for children with multiple episodes of social care involvement, some—but not all—types of special educational needs and disabilities, and for certain categories of social care needs. Implications for policy and practice are discussed, particularly with respect to children with behavioural difficulties.