bluuebunny/crossref_metadata_2025_split huggingface dataset:
Using Biophysical modelling and marine connectivity to assess the risk of natural dispersal of non-indigenous species to comply with the Ballast Water Management Convention
Authors: Hansen Flemming Thorbjørn , Pastor Ane , Christensen Asbjørn , Stuer-Lauridsen Frank 
Published: January 2024

Abstract

Marine Non-Indigenous Species (NIS) are considered a major threat to biodiversity as well as ecosystem functioning and services worldwide, and ballast water has been considered one of the main pathways for transferring NIS between regions. The Ballast Water Management Convention (BWMC) aims at reducing the risk of both primary and secondary introduction of NIS via take-up and discharge of ballast water by de facto requirements for ballast water on-board treatment. However, the BWMC allows for national authorities to grant exemptions from this requirement for shipping routes operating within a geographical area, known as a Same-Risk-Area (SRA). An SRA can be established in areas where a risk assessment (RA) concludes a low risk of NIS spreading via ballast water relative to their predicted natural dispersal. According to the BWMC this risk assessment should be based on modelling of the natural dispersal of NIS, but to date no standard procedures have been proposed. In this paper we present a methodology for how to apply biophysical modelling and analyses of marine connectivity of NIS as a basis for SRA RA and delineation. This work is intended to provide a standardised procedure and a benchmark for future SRA studies.
Predicting Hypoglycemia in Elderly Inpatients with Type 2 Diabetes: The ADOCHBIU Model
Authors: Zhang Rui-Ting , Liu Yu , Sun Chao , Wu Quan-Ying , Guo Hong , Wang Gong-Ming , Lin Ke-Ke , Wang Jing , Bai Xiao-Yan 
Published: January 2024

Abstract

Background Hypoglycemic episodes cause varying degrees of damage in the functional system of elderly inpatients with type 2 diabetes mellitus (T2DM). The purpose of the study is to construct a nomogram prediction model for the risk of hypoglycemia in elderly inpatients with T2DM and to evaluate the predictive performance of the model.Methods From August 2022 to April 2023, 546 elderly inpatients with T2DM were recruited in seven tertiary-level general hospitals in Beijing and Inner Mongolia province, China. Medical history and clinical data of the inpatients were collected with a self-designed questionnaire, with follow up on the occurrence of hypoglycemia within one week. Factors related to the occurrence of hypoglycemia were screened using regularized logistic analysis(r-LR), and a nomogram prediction visual model of hypoglycemia was constructed. AUROC, Hosmer-Lemeshow, and DCA were used to analyze the prediction performance of the model.Results The incidence of hypoglycemia of elderly inpatients with T2DM was 41.21% (225/546). The risk prediction model included 8 predictors as follows(named ADOCHBIU): duration of diabetes (OR = 2.276, 95%CI 2.097ཞ2.469), urinary microalbumin(OR = 0.864, 95%CI 0.798ཞ0.935), oral hypoglycemic agents (OR = 1.345, 95%CI 1.243ཞ1.452), cognitive impairment (OR = 1.226, 95%CI 1.178ཞ1.276), insulin usage (OR = 1.002, 95%CI 0.948ཞ1.060), hypertension (OR = 1.113, 95%CI 1.103ཞ1.124), blood glucose monitoring (OR = 1.909, 95%CI 1.791ཞ2.036), and abdominal circumference (OR = 2.998, 95%CI 2.972ཞ3.024). The AUROC of the prediction model was 0.871, with sensitivity of 0.889 and specificity of 0.737, which indicated that the nomogram model has good discrimination. The Hosmer-Lemeshow was χ2 = 2.147 (P = 0.75), which meant that the prediction model is well calibrated. DCA curve is consistently higher than all the positive line and all the negative line, which indicated that the nomogram prediction model has good clinical utility.Conclusions The nomogram hypoglycemia prediction model constructed in this study had good prediction effect. It is used for early detection of high-risk individuals with hypoglycemia in elderly inpatients with T2DM, so as to take targeted measures to prevent hypoglycemia.Trial registration ChiCTR2200062277. Registered on 31 July 2022.
Time-Varying Invariance and Factor Structure of the MASC: Analyzing Cognitive Vulnerability to Emotional Disorders in Adolescents and Comparisons with Normal Populations
Authors: Cheng JiaYi , Cao Yanjing , Xu Chenchen , Lu Shan , Xiao Jing , Li Qi 
Published: January 2024

Abstract
Background: The factor structure and invariance of the Multidimensional Anxiety Scale for Children (MASC) yield mixed results in extant research, underscoring the need for robust anxiety assessment tools. This need is critical for understanding the rising prevalence of anxiety among adolescents. Moreover, longitudinal studies examining factor invariance and research focusing on adolescents are scarce. Our study aims to assess the factor structure of MASC and its temporal and population invariance over a three-month period among adolescents.
Methods: A cohort of 924 adolescents (mean age = 16.27 years, including 428 boys) participated in this study. They completed the MASC and the Cognitive Style Questionnaire (CSQ) at the outset (Wave 1). The same participants completed these surveys again after three months (Wave 2). We employed Confirmatory Factor Analysis (CFA) to determine MASC's factor structure. Both cross-sectional (comparing mood disorder-affected, cognitively vulnerable individuals with the general adolescent population) and longitudinal CFAs (Wave 1 vs. Wave 2) were conducted to assess the scale's factor invariance.
Results: CFA identified a four-factor model within MASC, encompassing 'somatic complaints', 'depressive effects', and 'positive effects'. The study's findings affirm the factor invariance of this four-factor model across cognitively vulnerable individuals and the general adolescent population.
Limitations: The study's limitations include an underrepresentation of adolescents from diverse regions of Mainland China, particularly rural areas, and a relatively short follow-up duration of three months.
Conclusion: This pioneering study reveals that MASC exhibits four consistent dimensions among adolescents. These dimensions remain stable across individuals with cognitive vulnerability to emotional disorders and the general population, as well as over time.
RAGMCL: A Correlation-Feature-Incorporated Multichannel Network for Signal Modulation Recognition
Authors: Wang Yangyang , Cao Fei , Wang Xiaolong , Hou Shunhu 
Published: January 2024

Abstract

Signal modulation recognition (SMR) is crucial in wireless communication systems. In recent years, various modulation recognition algorithms based on deep learning have emerged. However, the problems of low recognition accuracy and a large number of network parameters have not been well solved. To overcome these challenges, this article presents an improved spatiotemporal multi-channel network (MCLDNN) called RAGMCL network by combining the signal correlation feature, attention mechanism module, and random Gaussian noise. The proposed approach introduces a four-channel convolutional network by augmenting the input of the original MCLDNN network with an additional channel. This channel utilizes the IQCLNet technique to extract relevant features from IQ signals, thereby enhancing the feature set used for network training. Moreover, an attention mechanism module is incorporated into the convolutional layer to assign training weights, emphasizing significant features during the learning process. Replacing the LSTM layer with BiGRU to exploit the IQ signal features fully. Additionally, introducing random Gaussian noise after the fully connected layer to discard irrelevant output features randomly, enhances the model's robustness and recognition accuracy. The RAGMCL network is evaluated using four publicly available datasets: RML2016.10a, RML2016.10b, RML2016.04c, and RML2018.01a. Experimental results demonstrate that the RAGMCL network achieves higher recognition accuracy across all four datasets while reducing the model parameters by 19.85%, combining the advantages of low number of parameters and high recognition accuracy. The RAGMCL network designed in this thesis holds potential for satellite communication applications.
Nomogram based on immune-inflammatory indicators and age-adjusted Charlson comorbidity index score to predict prognosis of postoperative parotid gland carcinoma patients
Authors: Cheng Hao , Xu Jin-Hong , He Jia-Qi , Wu Chen-Chen , Li Jia-Fan , Xu Xue-Lian 
Published: January 2024

Abstract
Background
Parotid gland carcinoma (PGC) is a rare malignant tumor. The purpose of this study was to investigate the role of immune-inflammatory-nutrition indicators and age-adjusted Charlson comorbidity index score (ACCI) of PGC and develop the nomogram model for predicting prognosis.
Method
All patients diagnosed with PGC in two tertiary hospitals, treated with surgical resection, from March 2012 to June 2018 (n = 344) were obtained. Potential prognostic factors were identified by univariate and multivariate Cox regression analyses. The nomogram model was established based on these identified independent prognostic factors. The performance of the developed prognostic model was estimated by related indexes and plots.
Result
The study population consisted of 344 patients with PGC who underwent surgical resection, 285 patients without smoking (82.8%), and 225 patients (65.4%) with mucoepidermoid carcinoma, with a median age of 50.0 years. American Joint Committee on Cancer (AJCC) stage (p < 0.001), pathology (p = 0.019), tumor location (p < 0.001), extranodal extension (ENE) (p < 0.001), systemic immune-inflammation index (SII) (p = 0.004), prognostic nutrition index (PNI) (p = 0.003), ACCI (p < 0.001), and Glasgow prognostic Score (GPS) (p = 0.001) were independent indicators for disease free survival (DFS). Additionally, the independent prognostic factors for overall survival (OS) including AJCC stage (p = 0.015), pathology (p = 0.004), tumor location (p < 0.001), perineural invasion (p = 0.009), ENE (p < 0.001), systemic immune-inflammation index (SII) (p = 0.001), PNI (p = 0.001), ACCI (p = 0.003), and GPS (p = 0.033). The nomogram models for predicting DFS and OS in PGC patients were generated based on these independent risk factors. All nomogram models show good discriminative capability with area under curves (AUCs) over 0.8 (DFS 0.802, and OS 0.825, respectively). Decision curve analysis (DCA), integrated discrimination improvement (IDI), and net reclassification index (NRI) show good clinical net benefit of the two nomograms in both training and validation cohorts. Kaplan-Meier survival analyses showed superior discrimination of DFS and OS in the new risk stratification system compared with the AJCC stage system. Finally, postoperative patients with PGC who underwent adjuvant radiotherapy had a better prognosis in the high-, and medium-risk subgroups (p < 0.05), but not for the low-risk subgroup.
Conclusion
The immune-inflammatory-nutrition indicators and ACCI played an important role in both DFS and OS of PGC patients. Adjuvant radiotherapy had no benefit in the low-risk subgroup for PGC patients who underwent surgical resection. The newly established nomogram models perform well and can provide an individualized prognostic reference, which may be helpful for patients and surgeons in proper follow-up strategies.
Geographic variation in patient continuity and discontinuity in general practice in Region of Southern Denmark - a retrospective registry-based study
Authors: Ahnfeldt-Mollerup Peder , Kristensen Troels 
Published: January 2024

Abstract
Background
It is well known that patients in general practice live in health care systems where geography may be their destiny because of different circumstances in different geographic locations. Therefore, it is important to explore the variation in continuity of care to understand this central characteristic of the health care system.
Aim
To describe the geographic variation in continuity and discontinuity of care for patients listed in general practices across municipalities and between urban and rural areas in the Region of Southern Denmark.
Methods
This was a retrospective cohort registry study of patients older than 12 years who were enrolled in general practice in 22 municipalities in the Region of Southern Denmark from 2007–2018. Continuity of care is here the length of enrollment with a general practice, and discontinuity of care is the number of times a patient changes general practice. The data are based on civil registration numbers for patients, general practice provider numbers, patient age at each change of their general practice, and data on the municipality where each patient has their address.
Results
The cohort included 982,644 patients in 22 municipalities. The average duration of enrollment per patient at a general practice was 7.9 years. A total of 27.1% of patients had been enrolled for 0–5 years, 28.7% for 5–10 years and 44.2% for more than ten years. Discontinuity in terms of the number of changes between practices revealed that 44.7% of patients had no discontinuity, 28.7% had experienced one change in general practice, 13.8% had two changes, 6.3% had three changes, and 7.1% had four or more changes. The average number of times a patient changes their general practice was 1.14. Compared to rural municipalities, there is a trend toward more discontinuity and shorter times of enrollment in general practice in urban municipalities.
Conclusions
There was a significant difference between urban and rural settings, where at least a portion of patients in urban municipalities had shorter enrollments in general practice and more frequent changes between practices. Given that many patients change their general practice, focusing on how to address discontinuity is recommended.
Climatic response of Juniperus monticola Martinez, a multi-century alpine shrub from the high mountains of central Mexico
Authors: Vázquez-Selem Lorenzo , Franco-Ramos Osvaldo , Villanueva-Diaz Jose , Cerano-Paredes Julian , Stahle David W. 
Published: January 2024

Abstract

The mountain juniper Juniperus monticola Martinez, a decumbent alpine shrub growing at elevations up to 4600 m a.s.l. on the high mountains of central Mexico, reaches an age of nearly a millennium. We conducted a dendrochronological study of this species at Pico de Orizaba volcano, the highest peak in central Mexico, to analyze its dendroclimatic potential and the influence of ocean-atmospheric forcing on the interannual variability of radial growth. A ring-width chronology was developed extending from 1178 to 2016 (839 years). Climate data from a local weather station and from CRU TS version 4.01 were used to determine the climatic response of J. monticola. The species is positively correlated to the average maximum temperature of May-June (r = 0.38, p < 0.05) and negatively to the March-April precipitation (r= -0.44, p < 0.05) of the local weather station. Gridded drought indices (PDSI, SPEI) were used to analyze the combined effect of rising temperatures and evapotranspiration on ring-width. A significant negative response (r = -0.354, p < 0.01) was found between the instrumental PDSI records (June, July, August) and the ring-width series; similarly, a negative correlation was obtained for the SPEI from December of the previous year to June of the current year (r = -0.4, p < 0.01). These negative correlations suggest that higher temperatures occurring in recent decades are favoring increases in radial growth. The ring-width chronology is significantly correlated, although weakly, with the El Niño/Southern Oscillation, the Pacific Decadal Oscillation, and the Atlantic Multi-decadal Oscillation, consistent with the influence of those phenomena on PDSI and SPEI over central Mexico.
Statistical Properties of K6
Authors: arvapally ruchitha , stern steven 
Published: January 2024

Abstract

We consider five different ways to estimate the effects of demographic and other variables on mental illness using data from the NSDUH. The first four methods use an aggregated K6 score, and the last uses the six individual K6 questions together. We identify many problems with using the K6 questions and their sum as a measure of mental illness. We think that the correlated ordered probit model are the best for fitting and understanding the data. But we also think that this method makes it clearer about the problems with K6 in general.
A Recruitment Maneuver after Apnea Testing Improves Oxygenation and Reduces Atelectasis in Organ Donors after Brain Death
Authors: Dhar Rajat , Braun Porche , Kumar Atul , Patel Jayesh , Lee Flavia , Arshi Baback 
Published: January 2024

Abstract
Background and Purpose
Hypoxemia is the main modifiable factor preventing lungs from being transplanted from organ donors after brain death (BD). One major contributor to impaired oxygenation in brain-injured patients is atelectasis. Apnea testing (AT), an integral component of BD declaration, promotes atelectasis and can worsen hypoxemia. In this study, we tested whether performing a recruitment maneuver (RM) after AT could mitigate hypoxemia and radiographic atelectasis.
Methods
During the study period, an RM (PEEP of 15 cm H2O for 15 seconds then 30 cm H2O for 30 seconds) was performed immediately after AT. We measured PaO2 before and after RM. The primary outcomes were oxygenation (PaO2:FiO2 ratio, PFR) and the severity of atelectasis (proportion of lung without aeration on CT scans after BD, quantified using an image analysis algorithm) in those who became organ donors. Outcomes in RM cases were compared to controls undergoing AT without RM in the two years prior.
Results
RM was performed in 54 cases after AT, with a median immediate increase in PaO2 of 63 mm Hg (IQR 0-109, p = 0.07). Eighteen RM resulted in hypotension but none were life-threatening. Of this cohort, 37 became organ donors, who were compared with 37 donors who had AT without RM. PFR was higher in the RM group (355 ± 129 vs. 288 ± 127, p = 0.03), and fewer had hypoxemia (PFR < 300: 22% vs. 57%, p = 0.04) at the start of donor management. The RM group showed less radiographic atelectasis (median 6% vs. 13%, p = 0.045). Although there was no difference in lungs transplanted (35% vs. 24%, p = 0.44), both better oxygenation and less atelectasis on arrival were associated with a higher likelihood of lungs being transplanted.
Conclusion
RM after AT results in immediate improvement in oxygenation and reduces hypoxemia and atelectasis in organ donors after BD. This effect may translate into more lungs being transplanted.
COVID-19 Impact, Medical Insurance System, and Health Equity
Authors: Qin Cheng , Zhu Yuchen , Lu Mengyao , Fang Xiangming 
Published: January 2024

Abstract
Background
The widespread outbreak of the COVID-19 pandemic poses a significant threat to human health. In the context of Healthy China, enhancing residents' health performance is an inevitable choice to promote the equalization of public medical and health services.
Methods
This article utilized online survey data, employs the O-probit model and Health Equity Gap Model to empirically analyze the relationship between COVID-19 infection, the medical insurance system, and health equity.
Results
Findings revealed that as of mid-January 2023, the nationwide infection rate of COVID-19 in China is 82.34%, with 24.86% of respondents reporting the loss of relatives or friends, and the average age of death being 71.30 years old. In terms of impact of COVID-19, Chinese residents are most concerned about the future impact of COVID-19 on their health and work. Compared to the Urban-Rural Resident Medical Insurance in China, the Urban Employee Medical Insurance is more effective in alleviating the negative impact of COVID-19 infection on residents' health, especially for middle-aged and elderly individuals, rural residents, those with chronic diseases, and in the eastern regions of China. Urban Employee Medical Insurance improves residents' health by influencing their drug purchase amount and outpatient visits following the impact of COVID-19. If Chinese residents receive the same treatment as Urban Employee Medical Insurance, their health would improve by 0.240 units, equivalent to 56.21% of the actual health gap between different medical insurance coverage groups.
Conclusions
Compared to the Urban-Rural Resident Medical Insurance, the Urban Employee Medical Insurance is more effective in mitigating the negative impact of COVID-19 infection on residents' health. Under the impact of the COVID-19 pandemic, different medical insurance systems have widened the health gap between urban employees and rural residents.
An Efficient Projection Screen Detection from Presentation Videos using CustomYOLOv7 Object Detection Method
Authors: Purushotham E. , Ramani Kasarapu , Bindu C. Shobha 
Published: January 2024

Abstract
Lecture videos are analyzed for the development of various applications that involves indexing, summarization, content extraction, search, and navigation. Lecture videos captured in classrooms and conference rooms has digital slides projected on to the screen on stage. Projection screen detection is a crucial task for the extraction of slide region in such presentation videos. In this paper, we present an interesting approach for detecting the location of slide region in video frames using the You Only Look Once (YOLO) object detection model. First we train the custom YOLOv7 model on a labelled dataset of frames from presentation videos showing projected slides and then the trained model is applied on new images that are not used in training to predict the location of projector screens. We collected and annotated over 2000 frames from various presentation videos and then various augmented techniques are applied to prepare a dataset of 5000 images. We evaluated this method on our custom dataset and the results are compared with other popular object detection methods. Our experiments demonstrated that our custom YOLOv7 model outperforms basic YOLOv7 and Retinanet with regards to accuracy and computational effectiveness. Our results suggest that custom YOLOv7 provides a promising solution for projector screen detection and has the potential to be applied in various practical applications
Human action Recognition with Transformer based on Convolutional Features
Authors: Shi Chengcheng , Liu Shuxin 
Published: January 2024

Abstract

As one of the key research directions in the field of computer vision, human action recognition has a wide range of practical application values and prospects. In the fields of video surveillance, human-computer interaction, sports analysis, and healthcare, human action recognition technology shows a broad application prospect and potential. However, the diversity and complexity of human actions bring many challenges, such as handling complex actions, distinguishing similar actions, coping with changes in viewing angle, and overcoming occlusion problems. To address the challenges, this paper proposes an innovative framework for human action recognition. The framework combines the latest pose estimation algorithms, pre-trained CNN models, and a Vision Transformer to build an efficient system. The first step involves utilizing the latest pose estimation algorithm to accurately extract human pose information from real RGB image frames. Then, a pre-trained CNN model is used to perform feature extraction on the extracted pose information. Finally, the Vision Transformer model is applied for fusion and classification operations on the extracted features. Experimental validation is conducted on two benchmark datasets, UCF 50 and UCF 101, to demonstrate the effectiveness and efficiency of the proposed framework. The applicability and limitations of the framework in different scenarios are further explored through quantitative and qualitative experiments, providing valuable insights and inspiration for future research.