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  1. Mistri RK, Mahto SK, Singh AK, Sinha R, Al-Gburi AJA, Alghamdi TAH, et al.
    Sensors (Basel), 2023 Oct 18;23(20).
    PMID: 37896656 DOI: 10.3390/s23208563
    This article presents a quad-element MIMO antenna designed for multiband operation. The prototype of the design is fabricated and utilizes a vector network analyzer (VNA-AV3672D) to measure the S-parameters. The proposed antenna is capable of operating across three broad frequency bands: 3-15.5 GHz, encompassing the C band (4-8 GHz), X band (8-12.4 GHz), and a significant portion of the Ku band (12.4-15.5 GHz). Additionally, it covers two mm-wave bands, specifically 26.4-34.3 GHz and 36.1-48.9 GHz, which corresponds to 86% of the Ka-band (27-40 GHz). To enhance its performance, the design incorporates a partial ground plane and a top patch featuring a dual-sided reverse 3-stage stair and a straight stick symmetrically placed at the bottom. The introduction of a defected ground structure (DGS) on the ground plane serves to provide a wideband response. The DGS on the ground plane plays a crucial role in improving the electromagnetic interaction between the grounding surface and the top patch, contributing to the wideband characteristics of the antenna. The dimensions of the proposed MIMO antenna are 31.7 mm × 31.7 mm × 1.6 mm. Furthermore, the article delves into the assessment of various performance metrics related to antenna diversity, such as ECC, DG, TARC, MEG, CCL, and channel capacity, with corresponding values of 0.11, 8.87 dB, -6.6 dB, ±3 dB, 0.32 bits/sec/Hz, and 18.44 bits/sec/Hz, respectively. Additionally, the equivalent circuit analysis of the MIMO system is explored in the article. It's worth noting that the measured results exhibit a strong level of agreement with the simulated results, indicating the reliability of the proposed design. The MIMO antenna's ability to exhibit multiband response, good diversity performance, and consistent channel capacity across various frequency bands renders it highly suitable for integration into multi-band wireless devices. The developed MIMO system should be applicable on n77/n78/n79 5G NR (3.3-5 GHz); WLAN (4.9-5.725 GHz); Wi-Fi (5.15-5.85 GHz); LTE5537.5 (5.15-5.925 GHz); WiMAX (5.25-5.85 GHz); WLAN (5.725-5.875 GHz); long-distance radio telecommunication (4-8 GHz; C-band); satellite, radar, space communications and terrestrial broadband (8-12 GHz; X-band); and various satellite communications (27-40 GHz; Ka-band).
  2. Uddin I, Awan HH, Khalid M, Khan S, Akbar S, Sarker MR, et al.
    Sci Rep, 2024 Sep 06;14(1):20819.
    PMID: 39242695 DOI: 10.1038/s41598-024-71568-z
    RNA modifications play an important role in actively controlling recently created formation in cellular regulation mechanisms, which link them to gene expression and protein. The RNA modifications have numerous alterations, presenting broad glimpses of RNA's operations and character. The modification process by the TET enzyme oxidation is the crucial change associated with cytosine hydroxymethylation. The effect of CR is an alteration in specific biochemical ways of the organism, such as gene expression and epigenetic alterations. Traditional laboratory systems that identify 5-hydroxymethylcytosine (5hmC) samples are expensive and time-consuming compared to other methods. To address this challenge, the paper proposed XGB5hmC, a machine learning algorithm based on a robust gradient boosting algorithm (XGBoost), with different residue based formulation methods to identify 5hmC samples. Their results were amalgamated, and six different frequency residue based encoding features were fused to form a hybrid vector in order to enhance model discrimination capabilities. In addition, the proposed model incorporates SHAP (Shapley Additive Explanations) based feature selection to demonstrate model interpretability by highlighting the high contributory features. Among the applied machine learning algorithms, the XGBoost ensemble model using the tenfold cross-validation test achieved improved results than existing state-of-the-art models. Our model reported an accuracy of 89.97%, sensitivity of 87.78%, specificity of 94.45%, F1-score of 0.8934%, and MCC of 0.8764%. This study highlights the potential to provide valuable insights for enhancing medical assessment and treatment protocols, representing a significant advancement in RNA modification analysis.
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