bluuebunny/crossref_metadata_2025_split huggingface dataset:
End-to-End Delay Performance Analysis of the 5G-TSN Network Using Network Calculus
Authors: Zhang Xiaohuan , Lu Yiqin , Qin Jiancheng , Qian Kaili 
Published: December 2024

Abstract

The emerging Ultra-Reliable and Low-Latency Communication (URLLC) of the Fifth Generation Communication Network (5G) makes the integration of Time-Sensitive Networking (TSN) and 5G communication access possible. Unfortunately, there is a lack of a theoretical study for analyzing the end-to-end (E2E) delay upper bound of different characteristic traffic in the integrated 5G-TSN scenario. Furthermore, current scheduling solutions are not able to meet the scheduling demands of different hybrid traffic in the integrated 5G-TSN network. Therefore, in this paper, we deduce the probabilistic E2E delay bound of the 5G-TSN, considering its wired and wireless complex network structure, its different traffic characteristics and requirements, and the impact of the improved strict priority Deficit Round-Robin scheduling (SP-DRR) strategy. We employ Jake’s fading model for the 5G wireless channel service process analysis based on Meijer G-functions and obtain a tighter delay upper bound, compared with current works. The results show that the 5G wireless component is what limits the E2E delay of the 5G-TSN network. The wireless resource scheduling strategy is the key to the E2E service guarantee in the 5G-TSN scenarios. Moreover, related parameters such as weight, quantum, and the number of allocated resource blocks (RBs) should be appropriately allocated to improve E2E delay according to a match requirement of delay upper bound and reliability. It is expected that this theoretical study will provide resource allocation advice and guidelines for the following 5G-TSN delay optimization simulation experiments.
Best-subset instrumental variable selection method using mixed integer optimization with applications to health-related quality of life and education–wage analyses
Authors: Qasim Muhammad , Månsson Kristofer , Balakrishnan Narayanaswamy 
Published: December 2024

Abstract

The classical best-subset selection method has been demonstrated to be nondeterministic polynomial-time hard and thus presents computational challenges. This problem can now be solved via advanced mixed integer optimization (MIO) algorithms for linear regression. We extend this methodology to linear instrumental variable (IV) regression and propose the best-subset instrumental variable (BSIV) method incorporating the MIO procedure. Classical IV estimation methods assume that IVs must not directly impact the outcome variable and should remain uncorrelated with nonmeasured variables. However, in practice, IVs are likely to be invalid, and existing methods can lead to a large bias relative to standard errors in certain situations. The proposed BSIV estimator is robust in estimating causal effects in the presence of unknown IV validity. We demonstrate that the BSIV using MIO algorithms outperforms two-stage least squares, Lasso-type IVs, and two-sample analysis (median and mode estimators) through Monte Carlo simulations in terms of bias and relative efficiency. We analyze two datasets involving the health-related quality of life index and proximity and the education–wage relationship to demonstrate the utility of the proposed method.
JEL Classification: C13; C26; C36.
A Study on the Synergistic Effects of Carbon Reduction and Economic Growth in Green Finance: Evidence from Green Finance Reform Pilot Zones
Authors: Zhuodong Liu , Weihao Kong , Yuchen Liu , Zeng Yaxi , Qi Ruihan 
Published: December 2024

Abstract

Market-based environmental policy represented by green finance is a powerful tool for exploring a new development model that is a win-win situation for both economic development and environmental protection, and is also a necessary way to achieve the dual-carbon goal. However, existing studies have paid less attention to the practical path of green finance synergies. Based on this, this study selects China's municipal panel data from 2010 to 2021, and adopts a multi-temporal double-difference model to analyse the impacts of the green finance reform pilot zone policy on the carbon intensity and economic development level, and to explore the path of the synergistic effect of the pilot zone policy, and further explore the impacts of the pilot zone policy on the differences between different regions. The results of the study show that the implementation of the pilot zone policy significantly reduces the carbon emission intensity of the pilot area and improves the economic development level of the area. In terms of the path of action, the pilot zone policy mainly achieves synergistic effects through optimising industrial structure, promoting green technological innovation and enhancing resource allocation efficiency. The policy effects are different for key environmental protection cities, resource-based cities, and cities in the east, centre and west. Therefore, in the future, we should deepen and expand the work of the pilot zone, continue to promote the optimisation of industrial structure, increase the strength of technological innovation policies, and formulate green financial policies with regional characteristics according to the regional environment and level of economic development, so as to empower the sustainable development of the economy.
Differences in depression levels in patients who underwent coronary artery bypass grafting and stenting
Authors: Raskaliev R. A. , Kalyuta T. Yu. , Andriyanova E. A. , Fedonnikov A. S. 
Published: December 2024

Introduction. Cardiovascular diseases define the pathological landscape of the 21st century. Personal characteristics of patients with cardiovascular diseases who have undergone stenting and bypass surgery are among the main factors in the success of their rehabilitation or, on the contrary, in determining the deterioration of their health. Regarding prevention of postoperative complications, taking into account the personal characteristics of patients is of primary importance. The study compared groups of patients (based on the level of depression) with various forms of cardiovascular diseases: acute coronary syndrome and chronic ischemic heart disease; types of surgical intervention – stenting or bypass surgery.Aim. To describe and explain the differences in depression levels in patients with different forms of cardiovascular disease who have undergone stenting and bypass surgery.Materials and methods. The study included 123 patients of both sexes (34–85 years old). The patients were divided into 3 groups: group 1 – 26/123 (21.1%) patients with HIHD who underwent bypass grafting; group 2 – 45/123 (36.6%) patients with HIHD who underwent stenting; group 3 – 52/123 (42.3%) patients with ACS who underwent stenting. The average age of the patients was 61 years ± 6 years. The severity of depression in patients was assessed using the Zung Self-Rating Depression Scale. ANOVA analysis of variance (p < 0,05) was used to compare the mean values of quantitative data.Results. In Group 1, according to the Zung Depression Self-Rating Scale, the average depression score was 35.5 points; in Group 2, the average depression score was 39.3 points; in Group 3, the average depression score was 42.1 points. There was a statistically significant difference in the severity of the average depression scores between Groups 1 and 3 according to the Scheffe method (p < 0,05).Conclusion. Patients with ACS who underwent stenting are characterized by a greater tendency to develop depressive disorder than patients with HIHD who underwent bypass. The obtained result indicates greater anxiety in patients with ACS, most probably, due to the unexpected manifestation of symptoms of the disease and an emergency decision to perform surgery.
Research on water flow characteristics and stress around bridges during floods
Authors: Liu Yang 
Published: December 2024

ABSTRACT
Due to the frequent threat of floods to structures and engineering construction in rivers, in order to study the impact characteristics of river hydrodynamics on structures under excessive flow during floods, this study takes Jiefang Bridge as an example and uses the fluid volume two-phase flow model (VOF) in computational fluid dynamics (CFD), combined with advanced geometric reconstruction techniques such as iso Advector method, to simulate the dynamic changes of gas-liquid interface in the river. On the premise of guaranteeing the independence of the grid, the high precision structure grid is used for numerical simulation. The results indicate that when a flood contacts a bridge, the force on the bridge rapidly increases and fluctuates, reaching a significant peak of over 6,000,000 N. Bridge piers have a significant hindering effect on floods, and their impact gradually expands over time. Vorticity and turbulence energy analysis indicate the presence of strong flow disturbances around the bridge pier. In addition, the characteristics of the contact surface between water flow and bridges also significantly affect the stress and flow characteristics of bridges. Especially at a 45° angle of attack, the force on the bridge decreases, but the wake velocity increases.
Assessment of Pregnancy Status in Patients with Acquired Immunodeficiency Syndrome and their Partners
Authors: Cabalak Mehmet , Karapınar Oya Soylu , El Cigdem 
Published: December 2024

Background:
HIV is a globally prevalent infection for which there is currently no cure
or vaccine. As the number of individuals with HIV infection increases, so does the number of individuals
wishing to have children despite being infected. This situation has highlighted issues related
to couples where one partner is infected while the other is not (serodiscordant couples) and couples
where both partners are positive. Major issues include pregnancy complications, transmission
to the child, and potential side effects of the ART treatment on the health of the child. Women living
with HIV who become pregnant or contract the virus during pregnancy are at risk for both maternal
and perinatal morbidity and mortality, especially if the virus is not adequately controlled.
Additionally, there is a risk of vertical transmission through breastfeeding during pregnancy and
postpartum. To mitigate the consequences of HIV during pregnancy, it is ideal to start with prepregnancy
counseling and plan pregnancies during periods of minimal viral load using appropriate
methods.

Aims and Objective:
There are limited studies and shared experiences regarding fertility status
and issues of HIV/AIDS patients, especially in Turkey. Hence, this study aimed to investigate the
fertility status of HIV/AIDS patients and their partners followed up in our clinic, shed light on the
encountered issues, and share our experiences.

Methods:
This retrospective, observational, single-center cross-sectional study included
HIV/AIDS patients and their partners followed at Hatay Mustafa Kemal University (MKÜ) Medical
Faculty Hospital from January 2018 to December 2023. Demographic data of the patients,
their sexual orientations, HIV/AIDS status of their partners and children, treatments received,
CD4 cell counts, and viral load data were retrospectively obtained from the automation system of
our hospital and patient files.

Results:
Among the couples, there were 21 pairs where both partners were HIV positive, 10 pairs
where the woman was HIV positive and the man was seronegative (serodiscordant), and 5 pairs
where the man was HIV positive and the woman was seronegative. In our study, eight couples
with both partners HIV positive had nine children, and six couples with women who were HIV
positive (serodiscordant) had ten children. The five couples with men who were HIV positive
(serodiscordant) had six children. No vertical transmission was observed in our study, but three patients
did not attend regular follow-ups. During the follow-up period, no seroconversion was detected
in the partners of serodiscordant patients. The average gestational age was 38 weeks, and
the average birth weight was 2873 ± 349 grams.

Conclusion:
Our study found no vertical transmission and no seroconversion in partners of
serodiscordant couples. Issues related to pregnancy in HIV-positive couples can be managed with
pre-pregnancy counseling by specialists, protective methods, appropriate ART, and perinatal follow-
up.
Towards Explainable Detection of Alzheimer's Disease: A Fusion of Deep Convolutional Neural Network and Enhanced Weighted Fuzzy C-Mean
Authors: Vetrithangam D. , Arunadevi B. , Pegada Naresh Kumar , Mehta Anshu , Kumar Puneet , Neha  , Parihar Parul , Selvakumar Subramanian 
Published: December 2024

Background:
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, posing a significant challenge for
individuals and society. Early detection and treatment are essential for effective disease management.

Objective:
The objective of this research is to develop a novel and interpretable deep learning model for rapid and accurate Alzheimer's disease detection,
incorporating Explainable Artificial Intelligence (XAI) techniques. The model aims to ensure generalizability through cross-validation and data
augmentation, while enhancing interpretability and transparency by using Explainable Artificial Intelligence methods such as Grad-CAM, SHAP,
and LIME, alongside an Enhanced Fuzzy C-Means (FCM) algorithm to clarify feature categorization and improve understanding of the model's
decision-making process.

Methods:
The proposed model employs a multi-stage approach. Initially, MRI scans are transformed into feature vectors suitable for input into a Deep
Convolutional Neural Network (CNN). Subsequently, an Enhanced Fuzzy C-Mean (FCM) algorithm, incorporating spatial information, refines
these features to improve clustering precision. The model integrates Explainable Artificial Intelligence techniques, including Grad-CAM, SHAP,
and LIME, to elucidate critical features and regions influencing classification outcomes. The performance metrics such as Accuracy, Recall and
Specificity are used for assessing the performance of the model.

Results:
The XAI-DEF Alzheimer's disease detection model consistently demonstrated exceptional performance across both the ADNI and OASIS datasets.
On ADNI, the model achieved an accuracy of 99.39%, recall of 99.47%, and specificity of 99.3%. Similarly, on OASIS, the model attained an
accuracy of 99.36%, recall of 99.53%, and specificity of 99.15%. These results underscore the model's effectiveness in accurately classifying
Alzheimer's disease cases while minimizing false positives and negatives.

Conclusion:
Through the development of this model, we contribute to the advancement of dependable diagnostic tools tailored for the detection and
management of Alzheimer's disease. By prioritizing interpretability alongside accuracy, our approach provides valuable insights into the decisionmaking
process of the model, ultimately improving patient outcomes and facilitating further research in neurodegenerative disorders.
Multi-modal Medical Image Fusion Approach Utilizing Gradient Domain Guided Image Filtering
Authors: Sun Menghui , Zhu Xiaoliang , Niu Yunzhen , Li Yang , Wen Mengke 
Published: December 2024

Background:
Currently, most multimodal medical image fusion techniques focus solely on integrating the edge details of image features, often overlooking color
preservation from the source images. Hence, this paper proposes a multi-channel fusion algorithm based on gradient domain-guided image
filtering.

Purpose:
This study aims to enhance the color preservation of source images in multimodal medical image fusion algorithms.

Methods:
Utilizing gradient field-guided image filters for image smoothing, the process involves constructing different image layers, decomposing using
wavelet transforms, and downsampling. Various fusion rules are then applied before inverse wavelet transformation.

Results:
Regarding MSE, CCI, PSNR, SSIM, DD, SM, and other metrics, the proposed algorithm consistently ranks highest compared to alternative
methods.

Conclusion:
Through both subjective and objective analyses, experimental results substantiate the significant edge-preserving effects of the proposed fusion
algorithm while effectively maintaining image fidelity and spectral integrity.
PENGARUH LITERASI WAKAF TERHADAP MINAT MASYARAKAT MUSLIM DALAM WAKAF PRODUKTIF
Authors: Rahfi Alif Dihza 
Published: December 2024

This study aims to examine the influence of waqf literacy on the productive waqf interest of Muslim communities in Malang City, as well as to explore the role of control variables such as gender, age, and income in this relationship. A quantitative approach was employed using purposive sampling to select a sample of 100 respondents from the Muslim community in Malang City. Data were collected through questionnaires focusing on measuring waqf literacy and productive waqf interest. Data analysis involved simple regression to test the relationship between waqf literacy and productive waqf interest, and hypothesis testing to examine the significance of control variables. The results indicate that waqf literacy significantly influences productive waqf interest (β = 0.295, p < 0.001). Control variables, such as gender dan income also affect productive waqf interest, with males showing higher interest compared to females. Meanwhile, Age did not significantly influence productive waqf interest in this study. This study provides valuable insights into the factors influencing community participation in productive waqf, with practical implications for the development of inclusive education and socialization programs tailored to the needs of the Muslim community in Malang City.
Performance of GPT-3.5 and GPT-4 on the Korean Pharmacist Licensing Examination: Comparison Study
Authors: Jin Hye Kyung , Kim EunYoung 
Published: December 2024

Abstract

Background
ChatGPT, a recently developed artificial intelligence chatbot and a notable large language model, has demonstrated improved performance on medical field examinations. However, there is currently little research on its efficacy in languages other than English or in pharmacy-related examinations.

Objective
This study aimed to evaluate the performance of GPT models on the Korean Pharmacist Licensing Examination (KPLE).

Methods
We evaluated the percentage of correct answers provided by 2 different versions of ChatGPT (GPT-3.5 and GPT-4) for all multiple-choice single-answer KPLE questions, excluding image-based questions. In total, 320, 317, and 323 questions from the 2021, 2022, and 2023 KPLEs, respectively, were included in the final analysis, which consisted of 4 units: Biopharmacy, Industrial Pharmacy, Clinical and Practical Pharmacy, and Medical Health Legislation.

Results
The 3-year average percentage of correct answers was 86.5% (830/960) for GPT-4 and 60.7% (583/960) for GPT-3.5. GPT model accuracy was highest in Biopharmacy (GPT-3.5 77/96, 80.2% in 2022; GPT-4 87/90, 96.7% in 2021) and lowest in Medical Health Legislation (GPT-3.5 8/20, 40% in 2022; GPT-4 12/20, 60% in 2022). Additionally, when comparing the performance of artificial intelligence with that of human participants, pharmacy students outperformed GPT-3.5 but not GPT-4.

Conclusions
In the last 3 years, GPT models have performed very close to or exceeded the passing threshold for the KPLE. This study demonstrates the potential of large language models in the pharmacy domain; however, extensive research is needed to evaluate their reliability and ensure their secure application in pharmacy contexts due to several inherent challenges. Addressing these limitations could make GPT models more effective auxiliary tools for pharmacy education.
Projections of Climate Change Impact on Acute Heat Illnesses in Taiwan: Case-Crossover Study
Authors: Yang Hsiao-Yu , Wu Chang-Fu , Tsai Kun-Hsien 
Published: December 2024

Abstract

Background
With global warming, the number of days with extreme heat is expected to increase and may cause more acute heat illnesses. While decreasing emissions may mitigate the climate impacts, its effectiveness in reducing acute heat illnesses remains uncertain. Taiwan has established a real-time epidemic surveillance and early warning system to monitor acute heat illnesses since January 1, 2011. Predicting the number of acute heat illnesses requires forecasting temperature changes that are influenced by adaptation policies.

Objective
The aim of this study was to estimate the changes in the number of acute heat illnesses under different adaptation policies.

Methods
We obtained the numbers of acute heat illnesses in Taiwan from January 2011 to July 2023 using emergency department visit data from the real-time epidemic surveillance and early warning system. We used segmented linear regression to identify the join point as a nonoptimal temperature threshold. We projected the temperature distribution and excess acute heat illnesses through the end of the century when Taiwan adopts the “Sustainability (shared socioeconomic pathways 1‐2.6 [SSP1-2.6]),” “Middle of the road (SSP2-4.5),” “Regional rivalry (SSP3-7.0),” and “Fossil-fueled development (SSP5-8.5)” scenarios. Distributed lag nonlinear models were used to analyze the attributable number (AN) and attributable fraction (AF) of acute heat illnesses caused by nonoptimal temperature.

Results
We enrolled a total of 28,661 patients with a mean age of 44.5 (SD 15.3) years up to July 2023, of whom 21,619 (75.4%) were male patients. The nonoptimal temperature was 27 °C. The relative risk of acute heat illnesses with a 1-degree increase in mean temperature was 1.71 (95% CI 1.63-1.79). In the SSP5-8.5 worst-case scenario, the mean temperature was projected to rise by +5.8 °C (SD 0.26), with the AN and AF of acute heat illnesses above nonoptimal temperature being 19,021 (95% CI 2249‐35,792) and 89.9% (95% CI 89.3%‐90.5%) by 2090‐2099. However, if Taiwan adopts the Sustainability SSP1-2.6 scenario, the AN and AF of acute heat illnesses due to nonoptimal temperature will be reduced to 12,468 (95% CI 3233‐21,704) and 62.1% (95% CI 61.2‐63.1).

Conclusions
Adopting sustainable development policies can help mitigate the risk of acute heat illnesses caused by global warming.