bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dynamic Service Provisioning in Heterogenous Fog Computing Architecture Using Deep Reinforcement Learning
Authors: Govarchinghaleh Yaghoub Alizadeh , Sabaei Masoud 
Published: February 2024

Abstract

The exponential growth of IoT devices and the surge in the data volume, coupled with the rise of latency-intensive applications, has led to a heightened interest in Fog computing to meet user demands. In this context, the service provisioning problem consists of dynamically selecting desirable fog computing nodes and routing user traffic to these nodes. Given that the fog computing layer is composed of heterogeneous nodes, which vary in resource capacity, availability, and power sources, the service provisioning problem becomes challenging. Existing solutions, often using classical optimization approaches or heuristics algorithms due to the NP-hardness of the problem, have struggled to address the issue effectively, particularly in accounting for the heterogeneity of fog nodes and uncertainty of the ad hoc fog nodes. These techniques show exponential computation times and deal only with small network scales. To overcome these issues, we are motivated to replace these approaches with Deep Reinforcement Learning (DRL) techniques, specifically employing the Proximal Policy Optimization (PPO) algorithm to understand the dynamic behavior of the environment. The main objective of the proposed DRL-based dynamic service provisioning (DDSP) algorithm is minimizing service provisioning costs while considering service delay constraints, the uncertainty of ad hoc fog nodes, and the heterogeneity of both ad hoc and dedicated fog nodes. Extensive simulations demonstrate that our approach provides a near-optimal solution with high efficiency. Notably, our proposed algorithm selects more stable fog nodes for service provisioning and successfully minimizes cost even with uncertainty regarding ad hoc fog nodes, compared to heuristic algorithms.
Deep Learnıng Based Enhanced Secure Emergency Vıdeo Streamıng Approach by Leveraging Blockchain Technology for Vehicular AdHoc 5G Networks
Authors: Awais Muhammad , Ali Abid , saeed Yousaf , Jabbar Sohail , Ahmad Awais , Alkhrijah Yazeed , Raza Umar , Saleem Yasir 
Published: February 2024

Abstract

VANET is a category of MANET that aims to provide wireless communication. It increases the safety of roads and passengers. Millions of people lose their precious lives in accidents yearly, millions are injured, and others incur disability daily. Emergency vehicles need clear roads to reach their destination faster to save lives. Video streaming can be more effective as compared to textual messages and warnings. To address this issue, we proposed a methodology to use visual sensors, cameras, and OBU to record emergency videos. Initially, the frames are detected. After re-recording, the frames detection algorithm detects the specific event from the video frames. Blockchain encrypts an emergency or specific event using hashing algorithms in the second layer of our proposed framework. In the third layer of the proposed methodology, encrypted video is broadcast with the help of 5G wireless technology to the connected nodes in the VANET. The dataset used in this research comprises up to 72 video sequences averaging about 120 seconds per video. All videos have different traffic conditions and vehicles. The ResNet-50 model is used for the feature extraction process of extracted frames. The model is trained using Tensorflow and Keras deep learning models. The Elbow method finds the optimal K number for the K Means model. This data is split into training and testing. 70% is reserved for training the support vector machine (SVM) model and test datasets, while 30%. 98% accuracy is achieved with 98% precision and 99% recall as results for the proposed methodology.
Fine-Scale Spatiotemporal Analysis of Urban Heat Island Dynamics in the Central Yunnan City Cluster
Authors: Fang Qingping , Liu Chang , Ren Zhibin , Fu Yao , Fan Huapeng , Wang Yongshu 
Published: February 2024

Abstract
With the acceleration of urbanization, the construction and population density of cities have continuously increased, leading to a more pronounced urban heat island (UHI) effect. This phenomenon has significant impacts on urban ecological environments and human health, including elevated temperatures, reduced water resources, decreased visibility, and deterioration of ecological conditions. It is imperative to mitigate Urban Heat Island effects (UHIs) and improve ecological conditions. Previous studies on UHIs have primarily focused on large cities, and it remains unclear whether UHIs exist in small-scale cities and how their spatiotemporal patterns evolve. This article conducts a detailed study of the spatiotemporal variations of Surface Urban Heat Island Intensity (SUHI) in the Central Yunnan City Cluster from 2000 to 2021 using Landsat satellite data, completing the time series of Urban Heat Island Intensity index (SUHII) for 44 cities at the county level. The study quantitatively estimates the overall SUHI trend and explores driving factors, yielding the following conclusions: Small cities also exhibit UHIs, irrespective of city size. From 2000 to 2021, the probability of UHIs occurrence in the Central Yunnan City Cluster was 75.4%, resulting in an overall reduction of 1.21°C in surface urban heat island (SUHI) intensity; The severity of Urban Heat Island effects increases with larger defined suburban areas, accompanied by a faster rate of change; The method of fitting SUHII trends for all cities allows for a robust quantitative estimation of the overall SUHII trend in the Central Yunnan City Cluster, achieving a high coefficient of determination (R²) value of 0.95; Human activities and land use changes influence the formation of decreasing SUHII trends; Urban expansion areas experience the highest temperature growth rate, while the city centers show the slowest growth rate, contributing to the formation of decreasing SUHII trends. This research highlights the presence of UHIs even in small cities, providing insights for enhancing ecological conditions.
Investigating the Causal Association Between Characteristic Genes and Osteoarthritis: A Mendelian Randomization and Bioinformatics Approach
Authors: Zhang Chongyang , Zhang Fan , Min Shengwei , Liu Dun , Qin Xin , Wang Zhemin , Zhang Mingyong 
Published: February 2024

Abstract

Objective
This study investigates the causal association of characteristic genes in osteoarthritis (OA) by integrating Mendelian Randomization (MR) with bioinformatics analyses, focusing on the interplay between these genes and immune cell infiltration.
Methods
We analyzed gene expression data from the Gene Expression Omnibus, employing differential expression analysis, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, alongside protein-protein interaction networks, to identify characteristic genes. These genes were further examined for their diagnostic value in a validation dataset. CIBERSORT was utilized to assess immune cell infiltration and its association with the characteristic genes. MR provided insights into the causal association between characteristic genes and OA.
Results
The study identified 452 differentially expressed genes, highlighting CDKN1A, FOSB, MYC, ATF3, CXCL2 and CXCL8 as central to OA's pathogenesis. Immune infiltration analysis revealed significant variations in specific immune cell types within OA synovial tissue. MR analysis suggested a protective role for CDKN1A against OA development.
Conclusion
Our findings reveal associations between OA-related characteristic genes and immune cell infiltration, emphasizing CDKN1A's potential protective effect against OA. This work lays a foundation for further exploration into the molecular mechanisms of OA and its targeted treatment.
Differences between Patients with Heartburn Refractory to Vonoprazan and Those Refractory to Proton Pump Inhibitors
Authors: Matsumura Tomoaki , Sonoda Michiko , Okimoto¹ Kenichiro , Dao Hang Viet , Takahashi¹ Satsuki , Akizue Naoki , Horio¹ Ryosuke , Goto¹ Chihiro , Kurosugi Akane , Kaneko Tatsuya , Ohta Yuki , Taida Takashi , Kikuchi Atsuko , Fujie Mai , Kato Jun , Kato Naoya 
Published: February 2024

Abstract

Background Vonoprazan, a potassium-competitive acid blocker, demonstrates more potent acid inhibition than proton pump inhibitors (PPIs). This study aimed to evaluate the effect of vonoprazan in patients with unproven gastroesophageal reflux disease (GERD) by comparing patients with vonoprazan-refractory heartburn with those with PPI-refractory heartburn.Methods This study included 104 consecutive patients with vonoprazan- or PPI-refractory heartburn (52 patients each), no erosive esophagitis on endoscopy and who underwent combined multichannel intraluminal impedance–pH (MII-pH) testing with vonoprazan/PPI discontinuation. Patients’ backgrounds, symptom scores from four questionnaires, MII-pH results and high-resolution manometry results were compared between the two groups.Results The vonoprazan group demonstrated significantly higher GERD symptoms and scores of abdominal pain and diarrhea on the Gastrointestinal Symptom Rating Scale questionnaire. MII-pH results revealed that the vonoprazan group demonstrated 40.4%, 17.3% and 42.3% and the PPIs group exhibited 26.9%, 17.3% and 55.8% of abnormal acid reflux (true non-erosive reflux disease [NERD]), reflux hypersensitivity and functional heartburn, respectively. The vonoprazan group demonstrated higher true NERD rates but with no significant difference. Among the vonoprazan group, eight patients with true NERD underwent another MII-pH test on vonoprazan, and all cases demonstrated normal acid exposure times (0.0% [0.0–0.3]).Conclusions Patients with unproven GERD with vonoprazan-refractory heartburn demonstrated more symptoms, including not only GERD symptoms but also functional dyspepsia and irritable bowel syndrome symptoms, than those with PPI-refractory heartburn.
Experience of breast cancer patients participating in a virtual reality psychological rehabilitation: a qualitative study
Authors: Chen Junyi , Wu Jialing , Xie Xinxin , Wu Shanshan , Yang Jie , Bi Zhuofei , Qiu Yihong , Chen Jie 
Published: February 2024

Abstract

Objective
Breast cancer is the most common type of cancer in China and worldwide. Apart from cancer, a majority of breast cancer patients suffer from various psychological disorders concurrently. The purpose of this study is to understand the actual experience of breast cancer patients participating in Virtual Reality (VR) for psychological intervention, and to provide a theoretical basis for the development of VR psychological rehabilitation in China.
Methods
Twenty-three breast cancer patients who met the inclusion criteria were selected for qualitative study by purposive sampling method. Semi-structured interviews were conducted with patients involved in VR psychological rehabilitation. Data were analyzed, summarized, and generalized through Colaizzi analysis.
Results
The results of the interviews were consistent with the PAC structural analysis. Three themes and six sub-themes captured how patients felt about adopting VR for their psychological rehabilitation, including: parent ego state (the feeling of loss when expectations are not met, improvement of sleep), adult ego state (relief of psychological stress, optimization and recommendations for VR therapy), and child ego state (novel and fun immersive experience, contagiousness of variable emotions).
Conclusion
Breast cancer patients participating in the virtual reality psychological rehabilitation had a favorable experience. To maximize the benefit of VR psychological rehabilitation, healthcare professionals should comprehensively assess the psychological status of breast cancer, optimize VR technology, and enable patients to enter the adult ego state. The new model of VR psychological rehabilitation needs to be further explored.
Measuring deep fascia parameters - Interrater reliability
Authors: Štěpánková Tereza , Quittková Adéla , Čech Zdeněk , Machač Stanislav 
Published: February 2024

Abstract

Purpose: The deep fascia has recently been a current topic in many medical fields, including rehabilitation. Some research has already focused on assessing deep fascia, however results of individual authors differ in certain aspects. This study focuses on the inter-rater reliability of ultrasound (US) measurement of the thickness of deep fascia and loose connective tissue (LCT). The aim was to define the causes of any discrepancies in measurement that could contribute to the unification of management of evaluating fascia.
Methods: An observational study was performed including 20 healthy individuals in whom fascia lata of the anterior thigh was examined by US imaging and then measured in Image J software. Three raters participated in this study: the first with 6 years of US imaging experience, other two were newly trained. The measurement of fascial parameters was conducted in two phases with special consultation between them resulting in an agreement of the research team on the more precise way of measurement.
Results: Results revealed the value of inter-rater reliability ICC3,1 = 0.454 for deep fascia thickness and ICC3,1 = 0.265 for LCT thickness in the first phase and any significant difference in the second phase. This poor inter-rater reliability led to a search for possible causes of discrepancies, which authors subsequently highlighted.
Conclusion: The findings of the study show the main pitfalls of deep fascia measurement that should contribute to the unification of evaluation.
Causal relationships between immune cells and iron deficiency anemia: a bidirectional Mendelian randomization study
Authors: xu xuanxuan , li zhixia , Liu Huimin , Huang Zhiping , xiong tao , Tang Yuanyan 
Published: February 2024

Abstract
Background
Observational studies have shown a potential link between immune factors and the risk of iron deficiency anemia (IDA), yet the causal relationship between immune cells and IDA remains enigmatic. Herein, we used Mendelian randomization (MR) to assess whether this association is causal.
Methods
We selected IDA genetic variants, including 8376 samples and 9810691 single nucleotide polymorphisms, and immune cells from a large open genome-wide association study (GWAS) for a bidirectional MR study. The primary method was inverse variance weighting (IVW), and auxiliary analyses were MR-Egger, weighted median, simple mode and weighted mode. The reliability of the results was subsequently verified by heterogeneity and sensitivity analysis.
Results
IVW method showed that 19 types of immune cells may be the risk factors of IDA, whereas 15 types of immune cells are the protective factors of IDA. Reverse MR analysis suggested that immune cells from upstream etiology of IDA are not involved in follow-up immune activities. Next, we selected 731 immune cell types as the results. The research revealed that IDA may result in a rise in 23 kinds of immune cells and a reduction in 12 kinds of immune cells. In addition, sensitivity analysis demonstrated no evidence of heterogeneity or horizontal pleiotropy.
Conclusions
From a genetic standpoint, our study suggests that specific immune cells are involved in the occurrence of IDA. Inversely, IDA may also contribute to immune dysfunction, thus guiding future clinical investigations.
DEThresh: Enhancing Cloud Computing Performance with Differential Evolution-Driven Datacenter Selection and Threshold-based Load Balancing Optimization
Authors: Katangur Ajay , Chowdhury Shusmoy 
Published: February 2024

Abstract
The advent of cloud computing has introduced a novel paradigm in user services, enabling on-demand access to information technology services, irrespective of time and location, with a pay-per-use model. Meeting user expectations necessitates cloud service providers to furnish an infrastructure that is efficient, reliable, and resilient to faults. Addressing the substantial volume of user requests, selecting cloud datacenters and load balancing strategies become pivotal for delivering real-time services economically and selecting appropriate datacenters and virtual machines. This research introduces DEThresh, combining a smart differential evolution-based datacenter selection method with a threshold-based load balancing mechanism aimed at enhancing resource management by focusing on cost and performance considerations. The differential evolution-based datacenter selection employs evolutionary operations to identify the most suitable DC for the userbase. Simultaneously, threshold-based load balancing ensures the reasonable distribution of tasks and optimal resource utilization, preventing task overload on virtual machines and underutilization due to task scarcity. Simulation results demonstrate that the proposed DEThresh algorithm exhibits superior response and data processing time compared to well-established popular algorithms currently in use, especially under varying userbase and datacenter conditions. Impressively, DEThresh achieves this even with fewer datacenters, ultimately reducing cloud service costs.
Effects of kaempherol-3-rhamnoside on metabolic enzymes and AMPK in the Liver Tissue of STZ-Induced Diabetes in Mice
Authors: Aodah Alhussain H. , Alkholifi Faisal K , Devi Sushma , Foudah Ahmed I. , Yusufoglu Hasan S , Alam Aftab 
Published: February 2024

Abstract
The current study aimed to investigate the potential antidiabetic properties of kaempherol-3-rhamnoside (Afzelin), both alone and in combination with insulin. To accomplish this, different groups of animals received kaempherol-3-rhamnoside doses and combinations of Humalog insulin and kaempherol-3-rhamnoside for 28 days. The objective was to evaluate the role of kaempherol-3-rhamnoside in glycolytic, gluconeogenic and NADP-linked lipogenic enzymes in liver tissues from STZ-induced diabetic mice while examining pharmacological modulations within the AMPK pathway. These could further regulate metabolic enzymes. The results indicated that in diabetic mice, glycolytic enzyme activities were significantly lower while gluconeogenic ones were higher; however, lipid-based enzyme activity decreased. It was observed that kaempherol-3-rhamnoside had a therapeutic role in the treatment of diabetes by normalising enzyme activities involved in glucose and lipid metabolism. Furthermore, kaempherol-3-rhamnoside treatment activated AMPK activity within liver tissues in diabetic mice by increasing the p-AMPK/AMPK ratio. The inhibited AMPK activity observed in these mice was overcome with this treatment. Additionally, the biochemical analysis indicated that kaempherol-3-rhamnoside has the potential to restore cellular function at the molecular level.
QSPR model for bond energy of Y-junction nanotubes through M, NM-polynomials based on reverse, reduced reverse degree and neighborhood degree based topological indices
Authors: Huilgol Medha Itagi , Shobha P H , Udupa H. Jayakrishna , Cangul Ismail Naci 
Published: February 2024

Abstract

M polynomials and NM polynomials are integral concepts in polynomial graph theory. M polynomials, like the matching polynomial, provide insights into matching structures in graphs, while NM polynomials extend this to non-matching edges. These tools are crucial in understanding graph properties and are applied in diverse fields such as network design and chemistry. Often topological indices are derived from these polynomials, which are used in Quantitative Structure Activity Relations (QSAR)/ Quantitative Structure Property Relations (QSPR) studies that have applications in protein structure analysis, network communication optimization, drug design, drug discovery, pharmacokinetics, etc. In this paper, we have defined M, NM polynomials for reverse, reduced reverse, neighborhood reverse and neighborhood reduced reverse topological indices. And we have derived closed form expressions for topological indices from these polynomials for Y junction nanotubes. Also as an application we have developed a QSPR model for an important thermodynamic property, viz., bond energy, of Y-junction nanotubes using indices derived from the defined polynomials Mr, Mrr, NMr, NMrr. The QSPR model developed here is statistically robust with an R2 of 0.999, with marginal error and high F value, hence serves as a justification to our new definitions for more polynomials, topological indices to the ever increasing set.
A Comprehensive Numerical Approach to Coil Placement in Cerebral Aneurysms: Mathematical Modeling and In Silico Occlusion Classification
Authors: Holzberger Fabian , Muhr Markus , Wohlmuth Barbara 
Published: February 2024

Abstract

Endovascular coil embolization is one of the primary treatment techniques for cerebral aneurysms. Although it is a well established and minimally invasive method, it bears the risk of sub-optimal coil placement which can lead to incomplete occlusion of the aneurysm possibly causing recurrence. One of the key features of coils is that they have an imprinted natural shape supporting the fixation within the aneurysm. For the spatial discretization our mathematical coil model is based on the Discrete Elastic Rod model which results in a dimension-reduced 1D system of differential equations. We include bending and twisting responses to account for the coils natural curvature. Collisions between coil segments and the aneurysm-wall are handled by an efficient contact algorithm that relies on an octree based collision detection. The numerical solution of the model is obtained by a symplectic semi-implicit Euler time stepping method. Our model can be easily incorporated into blood flow simulations of embolized aneurysms.In order to differentiate optimal from sub-optimal placements, we employ a suitable in silico Raymond-Roy type occlusion classification and measure the local packing density in the aneurysm at its neck, wall-region and core. We investigate the impact of uncertainties in the coil parameters and embolization procedure. To this end, we vary the position and the angle of insertion of the microcatheter, and approximate the local packing density distributions by evaluating sample statistics.
Excitation Signal Optimization for Minimizing Fluctuations in Knock Out Slow Extraction
Authors: Niedermayer Philipp , Singh Rahul 
Published: February 2024

Abstract

The synchrotron is a circular particle accelerator used for high energy physics experiments, material and life science, as well as hadron cancer therapy. After acceleration to the desired energies, particle beams are commonly extracted from the synchrotron using the method of resonant slow extraction. The goal is to deliver a steady particle flux – referred to as spill – to experiments and treatment facilities over the course of seconds while slowly emptying the storage ring. Any uncontrolled intensity fluctuations in the spill are detrimental to the efficiency of beam usage, as they lead to detector pileups or detector interlocks, hindering experiments and cancer treatment. Among the most widely used extraction scheme in medical facilities is the Radio Frequency Knock Out (RF-KO) driven resonant slow extraction, where the stored beam is transversely excited with a radio frequency (RF) field and the spill intensity is controlled by the excitation signal strength. This article presents particle dynamics simulations of the RF-KO system with the focus on finding effective mechanism for minimizing the intensity fluctuations while maintaining a good extraction efficiency and other advantages of KO extraction. An improved beam excitation signal which optimizes these main objectives is found, and is rigorously compared experimentally with other commonly applied techniques.