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This study uses an incipient wet impregnation method to look into the structural and dielectric properties of Bentonite that have been changed by adding different amounts of Ce and Zn. X-ray diffraction (XRD) analysis showed that the montmorillonite phase was the most common in Ce- and Zn-doped bentonite. Silicon dioxide (SiO 2 ) and quartz (Q) were also found. Scanning electron microscopy (SEM) demonstrated enhanced platelet aggregation and porous structures in doped compounds. Energy-dispersive X-ray spectroscopy (EDX) confirmed the presence of Si, Al, Na, Ce, Zn, Ca, Mg, Cl, Fe, and K elements. Dielectric analysis, focusing on frequency and temperature dependence, unveiled an inverse relationship between frequency and the dielectric constant. Additionally, a strong correlation between temperature and dielectric properties was established through regression equations, indicating an increase in dielectric constant with temperature. Electric modulus analysis showed dispersion at high frequencies, suggesting charge carrier mobility. The AC conductivity analysis showed that the conductivity consistently decreased as the temperature rose. There were clear frequency-independent plateaus and intrinsic charge carrier relaxation that were seen. A fitting analysis showed that charge carriers hopped and the dielectric relaxed, and adding Ce and Zn made the material less conductive. The novelty of this paper lies in its focus on examining the dielectric characteristics of doped materials based on bentonite, particularly concerning the incorporation of Zn and Ce. Graphical Abstract Highlights Investigation of structural and dielectric properties of Bentonite modified with Ce and Zn. Utilization of incipient wet impregnation technique for modification. Enhanced platelet aggregation and porous structures observed in doped compounds via SEM. Dispersion at high frequencies indicated by electric modulus analysis. Decrease in conductivity with rising temperature observed in AC conductivity analysis. Charge carrier hopping, and dielectric relaxation mechanisms identified through fitting analysis. Incorporation of Ce and Zn resulted in reduced conductivity in the materials.

Data on the association between social isolation, loneliness, and risk of incident coronary heart disease (CVD) are conflicting. The objective of this study is to determine the relationship between social isolation and loneliness, and the risk of developing cardiovascular disease (CVD) in middle age and elderly using meta-analysis. The purpose of the bibliometric analysis is to systematically evaluate the existing literature on the relationship between social isolation, loneliness, and the risk of developing cardiovascular disease (CVD) in middle-aged and elderly individuals. A comprehensive search through four electronic databases (MEDLINE, Google Scholar, Scopus, and Web of Science) was conducted for published articles that determined the association between social isolation and/or loneliness and the risk of developing coronary heart disease from June 2015 to May 2023. Two independent reviewers reviewed the titles and abstracts of the records. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline to conduct the systematic review and meta-analysis. Data for the bibliometric analysis was obtained from the Scopus database and analyzed using VOSviewer and Bibliometrix applications. Six studies involving 104,511 patients were included in the final qualitative review and meta-analysis after screening the records. The prevalence of loneliness ranged from 5 to 65.3%, and social isolation ranged from 2 to 56.5%. A total of 5073 cardiovascular events were recorded after follow-up, ranging between 4 and 13 years. Poor social relationships were associated with a 16% increase in the risk of incident CVD (Hazard Ratio of new CVD when comparing high versus low loneliness or social isolation was 1.16 (95% Confidence Interval (CI) 1.10–1.22). The bibliometric analysis shows a rapidly growing field (9.77% annual growth) with common collaboration (6.37 co-authors/document, 26.53% international). The US leads research output, followed by the UK and Australia. Top institutions include University College London, Inserm, and the University of Glasgow. Research focuses on \"elderly,\" \"cardiovascular disease,\" and \"psychosocial stress,\" with recent trends in \"mental health,\" \"social determinants,\" and \"COVID-19\". Social isolation and loneliness increase the risk of and worsen outcomes in incident cardiovascular diseases. However, the observed effect estimate is small, and this may be attributable to residual confounding from incomplete measurement of potentially confounding or mediating factors. The results of the bibliometric analysis highlight the multidimensional nature of CVD research, covering factors such as social, psychological, and environmental determinants, as well as their interplay with various demographic and health-related variables.

Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer and one of the most lethal human cancers. Inflammation is a critical component in PDAC initiation and progression. Inflammation also contributes to the aggressiveness of PDAC indirectly via induction of epithelial-mesenchymal transition (EMT), altogether leading to enhanced resistance to chemotherapy and poor survival rates. This review gives an overview of the key pro-inflammatory signaling pathways involved in PDAC pathogenesis and discusses the role of inflammation in induction of EMT and development of chemoresistance in patients with PDAC.

There is substantial demand for high network traffic due to the emergence of new highly demanding services and applications such as the internet of things (IoT), big data, blockchains, and next-generation networks like 5G and beyond. Therefore, network resource planning and forecasting play a vital role in better resource optimization. Accordingly, forecasting accuracy has become essential for network operation and planning to maintain the minimum quality of service (QoS) for real-time applications. In this paper, a hybrid network- bandwidth slice forecasting model that combines long-short term memory (LSTM) neural network and various local smoothing techniques to enhance the network forecasting model's accuracy was proposed and analyzed. The results show that the proposed hybrid forecasting model can effectively improve the forecasting accuracy with minimal data loss.