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Design and Performance Analysis of a Tissue-Load Stable H-Slotted Patch Antenna for 2.45 GHz Breast Phantom Sensing Applications
(Centrepoint Journal (Science Edition), 2026) Salaudeen K. Olawale; Sani Saminu; Yahya A. Suleiman; Muniru O. Idris; Adisa Y. Adebayo
The detection of solid tumors, especially in the breast, is crucial to improving patient outcomes and minimizing the side effects of cancer treatment. This study presents a highly miniaturized rectangular H-slotted PIFA operating in the 2.45 GHz ISM band; the design incorporates an H-shaped slot on top of the radiating patch to reduce the antenna size and improve current-path meandering. Simulation results from CST Studio Suite indicate that the proposed design resonates at 2.45 GHz with a -10 dB impedance bandwidth of approximately 277 MHz (2.33 – 2.61 GHz), a record Return Loss (S11) of -51.65 dB, and a VSWR of 1.005 in free-space conditions, demonstrating a near-perfect impedance match. The antenna exhibits a realized gain of 3.5 dBi at 70% efficiency. and a directional radiation pattern with a directivity of 4.16 dBi, enabling effective electromagnetic energy penetration into breast tissues. The antenna was also shown to be stable in its resonant frequency while modeled within a three-layered breast phantom model (skin-fat-gland). The presence of a 5 mm malignant tumor produces a noticeable shift in the reflection coefficient from -51.65 dB to -39.20 dB, demonstrating the antenna’s sensitivity to dielectric-property variations between healthy and cancerous tissues. Finally, a maximum 1-g averaged specific absorption rate (SAR) of 0.103 W/kg at 10 mW input was achieved, which is substantially below the FCC safety limit of 1.6 W/kg. The results demonstrate that the proposed H-slotted antenna is a compact and stable sensor with the capability to detect early-stage breast tumors effectively
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Design and Performance Analysis of a Tissue-Load Stable H-Slotted Patch Antenna for 2.45 GHz Breast Phantom Sensing Applications
(University of Ilorin Library and Publications Committee, 2026) Salaudeen K. Olawale; Sani Saminu; Yahya A. Suleiman; Muniru O. Idris; Adisa Y. Adebayo
The detection of solid tumors, especially in the breast, is crucial to improving patient outcomes and minimizing the side effects of cancer treatment. This study presents a highly miniaturized rectangular H-slotted PIFA operating in the 2.45 GHz ISM band; the design incorporates an H-shaped slot on top of the radiating patch to reduce the antenna size and improve current-path meandering. Simulation results from CST Studio Suite indicate that the proposed design resonates at 2.45 GHz with a -10 dB impedance bandwidth of approximately 277 MHz (2.33 – 2.61 GHz), a record Return Loss (S11) of -51.65 dB, and a VSWR of 1.005 in free-space conditions, demonstrating a near-perfect impedance match. The antenna exhibits a realized gain of 3.5 dBi at 70% efficiency. and a directional radiation pattern with a directivity of 4.16 dBi, enabling effective electromagnetic energy penetration into breast tissues. The antenna was also shown to be stable in its resonant frequency while modeled within a three-layered breast phantom model (skin-fat-gland). The presence of a 5 mm malignant tumor produces a noticeable shift in the reflection coefficient from -51.65 dB to -39.20 dB, demonstrating the antenna’s sensitivity to dielectric-property variations between healthy and cancerous tissues. Finally, a maximum 1-g averaged specific absorption rate (SAR) of 0.103 W/kg at 10 mW input was achieved, which is substantially below the FCC safety limit of 1.6 W/kg. The results demonstrate that the proposed H-slotted antenna is a compact and stable sensor with the capability to detect early-stage breast tumors effectively
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Effects of pre-operative isolation on postoperative pulmonary complications after elective surgery: an international prospective cohort study
(Association of Anaesthestetics Great Britain and Ireland., 2021-07-14) Simoes, J.F.,; Li, E; Nepogodiev, D.,; Bhangu, A.,; Abdur-Rahman, LO., ...............; .......... Gwadabe Sadiya Musa........... et al
Weaimedtodeterminetheimpactofpre-operativeisolationonpostoperativepulmonarycomplicationsafter electivesurgeryduringtheglobalSARS-CoV-2pandemic.Weperformedaninternationalprospectivecohort study includingpatientsundergoingelectivesurgery inOctober2020. Isolationwasdefinedastheperiod beforesurgeryduringwhichpatientsdidnotleavetheirhouseorreceivevisitorsfromoutsidetheirhousehold. Theprimaryoutcomewaspostoperativepulmonary complications, adjusted inmultivariablemodels for measuredconfounders.Pre-definedsub-groupanalyseswereperformedfortheprimaryoutcome.Atotalof 96,454patientsfrom114countrieswereincludedandoverall,26,948(27.9%)patientsisolatedbeforesurgery. Postoperativepulmonarycomplicationswererecordedin1947(2.0%)patientsofwhich227(11.7%)were associatedwithSARS-CoV-2infection.Patientswhoisolatedpre-operativelywereolder,hadmorerespiratory comorbiditiesandweremorecommonlyfromareasofhighSARS-CoV-2incidenceandhigh-incomecountries. Althoughtheoverall ratesofpostoperativepulmonarycomplicationsweresimilar inthosethat isolatedand thosethatdidnot (2.1%vs2.0%, respectively), isolationwasassociatedwithhigher ratesofpostoperative pulmonarycomplicationsafteradjustment(adjustedOR1.20,95%CI1.05–1.36,p=0.005).Sensitivityanalyses revealednofurtherdifferenceswhenpatientswerecategorisedby:pre-operativetesting;useofCOVID-19-free pathways; or community SARS-CoV-2 prevalence. The rate of postoperative pulmonary complications increasedwithperiodsofisolationlongerthan3days,withanOR(95%CI)at4–7daysor≥8daysof1.25(1.04 1.48), p=0.015and1.31 (1.11–1.55), p=0.001, respectively. Isolationbeforeelectivesurgerymightbe associatedwithasmall but clinically important increasedriskof postoperativepulmonarycomplications. Longerperiodsofisolationshowednoreductionintheriskofpostoperativepulmonarycomplications.These findingshavesignificantimplicationsforglobalprovisionofelectivesurgicalcare.
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Site quality assessment and yield models for Tectona grandis (Linn. F.) stands in Ibadan metropolis
(Forestry Association of Nigeria (FAN), 2010-12-07) Adeyemi Adesoji Akinwumi; Adesoye Peter Oluremi
The study involved site quality assessment and development of yield models for Tectona grandis in Ibadan metropolis. Five stands of Tectona grandis selected for this study were located at University of Ibadan Second Gate (UISG), International School Ibadan (ISI), Agodi garden (AG), Oke Baba-agba (OKBA) and Obegimo (OBGM). Twenty seven temporary sample plots of size 25m×25m were proportionally allocated to the five stands with respect to their land areas; and the sample plots were randomly located within each stand. Ten plots in UISG, 2 plots in ISI, 5 plots each, in AG, OKBA and OBGM respectively. Measurement of tree growth variables (diameters at breast height, base, middle and top, crown diameter and length, total height, merchantable height and stem quality) of all the trees in each of the plots in the study area were taken. Ages of the five stands were noted. For the purpose of sight quality estimation, seven largest trees were chosen from each of the plots as the dominant trees per plots. The measured variables were processed and used to compute basal areas and stem volume of trees at individual tree and whole-stand levels. The dominant height-age data set were used to construct site index equation. This was used to estimate site index values for each of the plots. In addition, volume computed from all the trees within each sample plot was used to construct yield models at individual tree and whole-stand levels. The site index equation obtained for site quality assessment in this study was: SI = ln 7.48  .04 1    Exp H A d . The most suitable individual tree yield model found was Ln SV = 1.59 + 0.02A + 1.05InBA while the most suitable whole-stand yield model was InSV = 1.83 – 11.89A-1 + 0.73InBA + 0.07SI.
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You Only Look Once (YOLO) Real-time Object Detection Algorithms: A Systematic Literature Review
(2025-07) Asaju Christine Bukola,; Florence Funke Abiola
Real-time object detection is essential in various computer vision applications, such as autonomous driving, surveillance, and robotics. Among deep learning based object detection frameworks, the You Only Look Once (YOLO) algorithm family stands out for its impressive speed, accuracy, and efficiency. This paper provides a systematic review of YOLO-based object detection models, examining their development over the years, enhancements, and practical uses. It explores the evolution of YOLO from its initial version to the latest advancements, focusing on key architectural changes, training methodologies, and performance optimizations. Furthermore, YOLO is compared with other leading object detection models, which outline its strengths and limitations. The review also explores domain-specific applications of YOLO, including medical imaging, autonomous navigation, and industrial automation. By consolidating findings from recent research, this study sheds light on existing challenges and potential future directions in real-time object detection using YOLO.