In this study, an improved extension of the Poisson-Ailamujia distribution is introduced. The new distribution was derived using a binomial mixing approach, and the new model is named the “Binomial Poisson-Ailamujia (Bin-PA)” distribution. Some important statistical properties are derived, including mode, quantile function, moments and their associated measures, actuarial (risk) measures, and reliability features such as survival, hazard (failure) rate, and mean residual life function. The parameters of the proposed distribution are estimated using the maximum likelihood estimation method. A comprehensive simulation study is also carried out to access the behavior-derived maximum likelihood estimators. Furthermore, a new count-regression model was also introduced. Two datasets are utilized to demonstrate the applicability and usefulness of the new model. It is concluded that the Binomial Poisson-Ailamujia distribution is more flexible and efficiently analyzed both datasets as compared to competitive discrete distributions.
Dental panoramic radiography (DPR) and cone beam computed tomography (CBCT) are an imaging modalities in dentistry. However, these procedures involve exposure to ionising radiation, raising concerns about radiation-induced thyroid damage. To mitigate these risks, thyroid shields have been introduced. This study aimed to evaluate the effectiveness of the SABA thyroid shield in reducing thyroid radiation exposure during DPR and CBCT scans.
An experimental study to measure surface dose in the thyroid region during (DPR) and (CBCT) scans using an Anthropomorphic Phantom and a Solid-State Detector. The attenuation percentage was calculated between radiation surface doses with and without shielding. The significance of the Saba thyroid shield was calculated using a t-test.
For DPR scans, the average surface doses in the thyroid reduced significantly from 301.1 μGy (at 85 kVp) and 170.3 μGy (at 60 kVp) to 64.43 μGy and 12.94 μGy, respectively, when the Saba thyroid shield was used. For CBCT scans, the average surface doses in the thyroid decreased significantly from 814.43 μGy (for 11 × 13 cm2 field size) and 32.40 μGy (for 5 × 5 cm2 field size) to 62.91 μGy and 12.07 μGy, respectively, with the application of the Saba thyroid shield. The maximum attenuation percentages in the thyroid for the DPR and CBCT scans were 92.40% and 92.27%, respectively. Statistically significant differences between the surface dose reduction with the Saba shield and without it was observed in both DPR scans (p < 0.0000) and CBCT scans (p < 0.0003).
The study demonstrates that Saba thyroid shields effectively reduce doses in the thyroid region during dental DPR and CBCT scans. The dose reduction depends on tube voltage for DPR scans, field size, and its position for CBCT scans. Findings highlight the importance of using Saba thyroid shields to minimise radiation exposure and protect the thyroid gland during dental scans.
Incidental findings (IFs) are unintentional discoveries that are unrelated to the original imaging goal. Imaging exams of persons with suspected intracranial disorders may reveal IFs in the paranasal sinuses (PNS). The current investigation aims to determine the frequency and features of unexpected discoveries in the PNS in persons who have had brain computed tomography (CT) scans for suspected intracranial abnormalities.
Between December 2022 and February 2023, 100 patients who met the inclusion criteria for clinically suspected intracranial disorders had CT brain scans as part of this retrospective cross-sectional study. Two board-certified radiologists with at least three years of experience assessed the CT scans independently. The study investigated the incidence and proportion of paranasal sinuses incidental findings (PNS IFs) observed during brain CT scans to detect brain abnormalities.
The study discovered that 27% of patients had IFs, with the retired population (aged 61 and up) having the highest prevalence, particularly among men. Acute sinusitis was the most commonly diagnosed incidental finding (IF), accounting for 15% of total cases. In addition, we found polyps, retention cysts, chronic sinusitis, mucoceles, and fungal infections. Left-sided maxillary sinus abnormalities outnumbered right-sided ones. Bilateral involvement was unusual.
These findings emphasize the importance of addressing IFs in the PNS when treating patients, as they may necessitate further inspection or intervention. These results have the potential to help establish strategies for treating patients with incidental paranasal sinus findings, ultimately improving patient care.
The aim of this paper is to investigate the Burr-X competing risks model in the context of adaptive progressively Type-II censored samples. In this scenario, the removal pattern is assumed to be a random variable that follows the binomial distribution, which is a more realistic assumption compared to assuming a fixed removal pattern. In this study, we explore both classical and Bayesian estimation approaches to estimate the parameters of the Burr-X competing risks model, as well as the reliability parameter and the parameter of the binomial distribution. The interval ranges of different parameters are determined by utilizing the asymptotic normality of the maximum likelihood estimators. Furthermore, the Bayes credible intervals are calculated by sampling from the joint posterior distribution using the Markov Chain Monte Carlo procedure. To assess the efficiency of the acquired estimators, a comprehensive simulation study that considered various types of experimental designs is conducted. Finally, two applications are considered by analyzing data sets of electrodes and electronics.
Analysis of thermal transport in nanolubricants is an interesting and potential topic. Hence, the current research focuses on the study of ZnO-SAE50 by adding the major effects of variable thermal conductivity, combined convection, and thermal radiations. The physical set up is designed for 3D dimensional flow through a surface and then investigated the results via numerical scheme. From detailed analysis of the physical results, it is examined that ZnO concentration and suction effects cause reduction in the fluid movement while for stretching case these variations are quite rapid than shrinking case. Further, the combined convective effects greatly influenced the fluid motion over the surface. The velocity increases rapidly under increasing Grashof effects and maximum motion is observed for stretching case. The temperature of ZnO-SAE50 enhanced due to increasing thermal radiations and ZnO concentration. However, minimal changes are investigated under variable thermal conductivity number , shterching/shrinking and maximum drop in the temperature is examined due to stronger Grashof number effects.
The purpose of this study was to evaluate the usefulness of apparent diffusion coefficient (ADC) variation in irradiated parotid glands during and after 3D conformal radiotherapy (3DCRT).
This study enrolled 15 head and neck cancer (HNC) patients who were treated with 3DCRT underwent diffusion weighted imaging (DWI) at rest and after gustatory stimulation in three time points as follows: before, during (one day after receiving the mean dose 26 Gy) and 6 months after the end of radiotherapy (RT). Salivary Ejection Fraction (SEF) data was also obtained from salivary gland scintigraphy (SGS) at the same three time points as MRI. Mean ADC at rest and after stimulation (ADCr, ADCs) and SEF were extracted from parotid region for three time points. Finally, SEF and changes of ADC over time at rest and after stimulation and in each time point were compared.
Difference between mean ADCr before RT and during RT was not significant (p = 0.12). The ADCr values were significantly higher after RT than before RT (p = 0.003) and during RT (after dose 26 Gy) (p = 0.001). The difference between ADCr and ADCs in each time point showed that there is significant difference between ADCr and ADCs before RT (p = 0.005). Difference between these parameters after RT was also significant (p = 0.05). Non-significant difference (p = 0.21) between ADCr and ADCs during RT was observed. The result of SEF also was significantly lower after RT than during (p = 0.008) and before RT (p = 0.001).
Mean ADC values could be used as a surrogate marker to characterize the parotid function in different stages of RT.
Exposure to low radiation has been indispensable due to it is necessity in radiography and it is induced potential effects in patients and radiology staff. Hence the measurement and detection became inevitable for radiation practitioners and the patients. The study aimed to present the synthesis of polyvinyl alcohol gel doped with Thymol Blue dye (PVA/TB) as gel and films for low radiation dosimetry.
The polymer was prepared under controlled parameters (PVA 5%, temperature 80 °C, dopped with 0.01, 0.03, 0.05, 0.07, 0.09 mw of TB). The formed gel and films were irradiated with x-ray as: 0, 2, 4, 6, 8, 10 mGy.
Gel and films showed color quenching from yellow to light yellow as the radiation dose increased, as well as gradual significant (R2 = 0.97) reduction in optical density for all concentration (0.01, 0.02–0.09 mw of TB). The UV-spectroscope for (PVA/TB0.01mw) gel showed absorbance peaks at λ = 435, 326 and 279 nm, that decreased significantly (R2 = 0.97) as the radiation dose increases. And XRD for (PVA/TB0.01 mw) reveals the crystallinity of PVA at 2 thetas = 19.5° which increased to 40.5% at 10 mGy. FTIR revealed the chemical bonds with relevant intensities (CH (725.1–781 & 3030 cm−1), C – C (883.2 & 1170.6 cm−1), C – O (1063- 1096 cm−1), CH2 (1357.6 cm−1 wagging, 2879.2 cm−1 alkyl stretch), C = C (1623.8–1651.5 cm−1), C = O (1681.6–1683.6 cm−1), C – OH (3288 cm−1)) that decreased as the radiation dose increased and new radiogenic bonds at vibrational bands 1063–1096 cm−1 referring to C – O – C of Polysaccharide's pyranose and at 2125.1 cm−1 for C ≡ C alkenyl).
Both forms of PVA/TB films and gel showed feasibility and applicability for radiation detection and measurement.
Image fusion and deep learning are actively investigating fields of research. Their application domains include machine vision, clinical imaging, remote sensing, and other areas, all of which are used to obtain comprehensive information about a specific image. Image fusion is a process that integrates multiple imaging modalities to create a single image, for the sake of providing comprehensive information. Extensive literature shows that various methodologies, requirements, and network types are utilized for diverse modality fusion. This paper addresses the previously described issue by utilizing a unique Y-shaped Residual Convolution Autoencoder Neural Network to combine images from various modalities using the same network specifications and thereby eliminating the need for manual fusion. The combined convolved features are recreated in the decoder part using a symmetric nested residual approach with the encoder. By employing MS-SSIM as the loss function, the network is capable of generating images that are perceptually and pixel-wise indistinguishable from the target images. The fusion results are compared with five other current approaches, and the Y-shaped convolutional autoencoder result demonstrates superior achievement in both quantitative and qualitative aspects.
Chronic disease (CD) recognition involves identifying the existence or risk of CDs in individuals. CDs have chronic health illnesses categorized by slow progression and frequent reduction from intricate reasons. CDs comprise chronic respiratory diseases, heart disease, diabetes mellitus, and certain cancers. Earlier diagnosis is vital in handling CDs proficiently. Then, it permits lifestyle modifications, timely intervention, and medical services to avoid the progression of the disease and reduce its effect on their health. Recently, technical development, particularly in healthcare statistics and artificial intelligence (AI), has assisted in advancing sophisticated approaches and systems for CD recognition. These methodologies usually employ deep learning (DL) and machine learning (ML) models for investigating enormous databases, identifying patterns, and making predictions that rely on distinct health-related parameters. This study presents an accurate chronic disease detection and classification model using binary meta-heuristics with an ensemble deep learning (ACDDC-BMEDL) approach. The ACDDC-BMEDL methodology focuses on the procedure of average ensemble classifier with meta-heuristic-based feature selection (FS) and hyperparameter tuning processes. The ACDDC-BMEDL methodology uses a binary arithmetic optimization algorithm (BAOA) to choose better feature subsets. Additionally, the ACDDC-BMEDL methodology uses an average ensemble technique encompassing recurrent neural network (RNN), gated recurrent unit (GRU), and extreme learning machine (ELM) for classification procedure. The marine predator's algorithm (MPA) is employed for the hyperparameter tuning process. The experimental value of the ACDDC-BMEDL methodology was examined on 2 CD datasets. The performance validation of the ACDDC-BMEDL methodology portrays a superior value of 98.70% and 94.51% with recent methods concerning several metrics under Diabetes and HD datasets.
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