Synthesis of bimetallic oxides (SrO-CoO) nanoparticles decorated polyacrylamide hydrogels for controlled drug release and wound healing applications.

Hydrogels are polymeric structures characterized by their three-dimensional nature, insolubility in aqueous media, and remarkable ability to absorb significant amounts of water. Owing to their exceptional biocompatibility with living tissues, hydrogels find extensive use in various biomedical applications. Guggul gum grafted polyacrylamide hydrogels (SG) were prepared and green synthesized SrO, CoO and SrO-CoO nanoparticles (NPs) were incorporated with hydrogels (SrG, CoG, Sr-CoG) respectively. The fabricated hydrogels were characterized by various analytical techniques such as FTIR, XRD and SEM. XRD results confirmed the presence of Sr and Co metal nanoparticles in the fabricated hydrogels matrix, SrG pattern showed diffraction peaks at 2θ = 30°, 36.59°, 44.11°, 50.22° and 62.20° while CoG peaks appeared at 2θ = 36.59°, 42.32°, 61.18°, 74.05° and 77.08°. SG, SrG, CoG and Sr-CoG hydrogels showed 11%, 32%, 23% and 45% radical scavenging activity respectively as compared to standard BHT (Butylated hydroxyl toluene). In vitro drug release tests results showed that SG, SrG, CoG and Sr-CoG exhibited 21%, 16%, 13% and 10% sustained release of naproxen respectively. The results revealed that SrO and CoO nanoparticles dopped hydrogels possessed good wound healing potential as compared to conventional hydrogels, which provides great potential in clinical treatment for wounds.

Deformable registration of magnetic resonance images using unsupervised deep learning in neuro-/radiation oncology.

PURPOSE: Accurate deformable registration of magnetic resonance imaging (MRI) scans containing pathologies is challenging due to changes in tissue appearance. In this paper, we developed a novel automated three-dimensional (3D) convolutional U-Net based deformable image registration (ConvUNet-DIR) method using unsupervised learning to establish correspondence between baseline pre-operative and follow-up MRI scans of patients with brain glioma. METHODS: This study involved multi-parametric brain MRI scans (T1, T1-contrast enhanced, T2, FLAIR) acquired at pre-operative and follow-up time for 160 patients diagnosed with glioma, representing the BraTS-Reg 2022 challenge dataset. ConvUNet-DIR, a deep learning-based deformable registration workflow using 3D U-Net style architecture as a core, was developed to establish correspondence between the MRI scans. The workflow consists of three components: (1) the U-Net learns features from pairs of MRI scans and estimates a mapping between them, (2) the grid generator computes the sampling grid based on the derived transformation parameters, and (3) the spatial transformation layer generates a warped image by applying the sampling operation using interpolation. A similarity measure was used as a loss function for the network with a regularization parameter limiting the deformation. The model was trained via unsupervised learning using pairs of MRI scans on a training data set (n = 102) and validated on a validation data set (n = 26) to assess its generalizability. Its performance was evaluated on a test set (n = 32) by computing the Dice score and structural similarity index (SSIM) quantitative metrics. The model's performance also was compared with the baseline state-of-the-art VoxelMorph (VM1 and VM2) learning-based algorithms. RESULTS: The ConvUNet-DIR model showed promising competency in performing accurate 3D deformable registration. It achieved a mean Dice score of 0.975 ± 0.003 and SSIM of 0.908 ± 0.011 on the test set (n = 32). Experimental results also demonstrated that ConvUNet-DIR outperformed the VoxelMorph algorithms concerning Dice (VM1: 0.969 ± 0.006 and VM2: 0.957 ± 0.008) and SSIM (VM1: 0.893 ± 0.012 and VM2: 0.857 ± 0.017) metrics. The time required to perform a registration for a pair of MRI scans is about 1 s on the CPU. CONCLUSIONS: The developed deep learning-based model can perform an end-to-end deformable registration of a pair of 3D MRI scans for glioma patients without human intervention. The model could provide accurate, efficient, and robust deformable registration without needing pre-alignment and labeling. It outperformed the state-of-the-art VoxelMorph learning-based deformable registration algorithms and other supervised/unsupervised deep learning-based methods reported in the literature.

A pioneering study of the radiological mapping in the world's largest mangrove forest (the Sundarbans) and implications for the public and environment.

Coastal Mangroves are facing growing threats due to the harmful consequences of human activities. This first-ever detailed study of natural radioactivity in soil samples collected from seven tourist destinations within the Sundarbans, the world's largest mangrove forest, was conducted using HPGe gamma-ray spectrometry. Although the activity levels of 226Ra (11 ± 1-44 ± 4 Bq/kg) and 232Th (13 ± 1-68 ± 6 Bq/kg) generally align with global averages, the concentration of 40K (250 ± 20-630 ± 55 Bq/kg) was observed to surpass the worldwide average primarily due to factors like salinity intrusion, fertilizer application, agricultural runoff, which suggests the potential existence of potassium-rich mineral resources near the study sites. The assessment of the hazard parameters indicates that the majority of these parameters are within the recommended limits. The soil samples do not pose a significant radiological risk to the nearby population. The results of this study can establish important radiological baseline data before the Rooppur Nuclear Power Plant begins operating in Bangladesh.

Bentonite SDBS-loaded composite for methylene blue removal from wastewater: An experimental and theoretical investigation.

This study addresses the urgent need for practical solutions to industrial water contamination. Utilizing Algerian Bentonite as an adsorbent due to its regional prevalence, we focused on the efficiency of the Bentonite/Sodium dodecylbenzene sulfonate (SDBS) matrix in Methylene Blue (MB) removal. The zero-charge point and IR spectroscopy characterized the adsorbent. Acidic pH facilitated SDBS adsorption on Bentonite, achieving equilibrium in 30 min with a pseudo-second-order model. The UPAC and Freundlich model indicated a qmax of 25.97 mg/g. SDBS adsorption was exothermic at elevated temperatures. The loaded Bentonite exhibited excellent MB adsorption (pH 3-9) with PSOM kinetics. Maximum adsorption capacity using IUPAC and GILES-recommended isotherms was qmax = 23.54 mg/g. The loaded Bentonite's specific surface area was 70.01 m2/g, and the Sips model correlated well with experimental data (R2 = 0.98). This study highlights adsorption, mainly Bentonite/SDBS matrices, as a promising approach for remediating polluted areas by efficiently capturing and removing surfactants and dyes, contributing valuable insights to address industrial water contamination challenges.

Hybrid deep learning model based smart IOT based monitoring system for Covid-19.

Recently, COVID-19 becomes a hot topic and explicitly made people follow social distancing and quarantine practices all over the world. Meanwhile, it is arduous to visit medical professionals intermittently by the patients for fear of spreading the disease. This IoT-based healthcare monitoring system is utilized by many professionals, can be accessed remotely, and provides treatment accordingly. In context with this, we designed an IoT-based healthcare monitoring system that sophisticatedly measures and monitors the parameters of patients such as oxygen level, blood pressure, temperature, and heart rate. This system can be widely used in rural areas that are linked to the nearest city hospitals to monitor the patients. The collected data from the monitoring system are stored in the cloud-based data storage and for the classification our approach proposes an innovative Recurrent Convolutional Neural Network (RCNN) based Puzzle optimization algorithm (PO). Based on the outcome further treatments are made with the assistance of physicians. Experimental analyses are made and analyzed the performance with state-of-art works. The availability of more data storage capacity in the cloud can make physicians access the previous data effortlessly.

Quantification of radiological hazards associated with natural radionuclides in soil, granite and charnockite rocks at selected fields in Ekiti State, Nigeria.

Assessment of activity levels of radionuclides that exist in soil, granite, and charnockite rock samples is very crucial because it exhibits an enhanced elemental concentration of uranium (U) and thorium (Th) contributing higher natural background activity than usual in the environment and it may cause health risk to human health through the external and internal exposure. This study determined the radioactivity levels of 238U, 232Th, and 40K radionuclides in soil, granite, and charnockite rock samples collected from selected fields in Ekiti State, Nigeria using Caesium iodide CsI(Tl) scintillation gamma spectrometer. It also evaluated indices of the radiological parameters consisting of radium equivalent activity (Raeq), absorbed dose rate (DR), annual effective dose equivalent (AEDE), internal hazard index (Hin), and excess lifetime cancer risk (ELCR). The calculated average activity concentrations of 238U, 232Th, and 40K are 30.40 ± 0.71 Bq kg-1, 3.31 ± 0.05 Bq kg-1, and 222.25 ± 14.72 Bq kg-1, respectively, which were lower than their respective world average values. Comparatively, potassium concentrations in these collected samples have a higher value than concentrations of uranium and thorium (40K > 238U > 232Th). All the evaluated values of the radiological parameters (except DR) of the appraised radionuclides were below the global permissible limits. The granite rocks, charnockite rocks, and soils from Ekiti State in Nigeria do not pose any hazardous risk to humans, but continued monitoring is necessary when these materials are used as building materials, which cause long-term radiation exposure.

Assessment of patient and occupational exposure and radiation risk from cath-lab procedure.

Due to the extended localized fluoroscopy, many radiographic exposures, and multiple procedures that might result in tissue reaction, patients and personnel received a significant radiation dose during interventional cardiology (IR) procedures. This study aims to calculate the radiation risk and assess patient and staff effective doses during IC procedures. Thirty-two patients underwent a Cath lab treatment in total. Ten Cath lab personnel, including six nurses, two cardiologists, and two X-ray technologists. Optical stimulating-luminescent dosimeters (OSL) (Al2O3:C) calibrated for this purpose were used to monitor both occupational and ambient doses. Using an automated OSL reader, these badges were scanned. The Air Kerma (mGy) and Kerma Area Products (KAP, mGy.cm2) have a mean and standard deviation (SD) of 371 ± 132 and 26052, respectively. The average personal dose equivalent (mSv) and its range for cardiologists, nurses and X ray technologists were 1.11 ± 0.21 (0.96-1.26), 0.84 ± 0.11 (0.68-1.16), and 0.68 ± 0.014 (0.12-0.13), respectively. The current study findings showed that the annual effective dose for cardiologists, nurses, and X-ray technologists was lesser than the yearly occupational dose limit of 20 mSv recommended by national and international guidelines. The patients' doses are comparable with some previously published studies and below the tissue reaction limits.

Exploring Radiographers' Engagement in Research: Motivation and Barriers in Five Arab Countries.

(1) Background: This study aims to comprehensively understand the motivations driving radiographers in five Arab countries to engage in research. (2) Methods: A cross-sectional study employing an anonymous online survey was conducted for 12 weeks from May to July 2023. The study sample consisted of 250 radiographers, with equal representation from Iraq, the Kingdom of Saudi Arabia, Palestine, Sudan, and the United Arab Emirates. (3) Results: Overall, the participants showed limited involvement in research-related activities in all five countries, particularly in presenting at conferences and publishing in peer-reviewed journals. Most participants believed research positively impacts their professional development (34.8%) and patient care and outcomes (40%). The participants perceived professional development (36.4%) as a key motivator for research engagement. A significant majority (81.6%) expressed motivation to start research in clinical practice. A total of 66.8% found research opportunities available during clinical practice. Barriers included time constraints (56%), limited resources (47.2%), and lack of support and skills (33.2% and 32%, respectively). (4) Conclusion: This study emphasises the need for targeted strategies to enhance research engagement among radiographers in the Arab region. Addressing barriers, such as time constraints and resource limitations, while leveraging intrinsic motivators, such as professional development, is crucial for fostering a culture of research-driven excellence in radiography.

Cyclodextrin nanoparticles in targeted cancer theranostics.

The field of cancer nanotheranostics is rapidly evolving, with cyclodextrin (CD)-based nanoparticles emerging as a promising tool. CDs, serving as nanocarriers, have higher adaptability and demonstrate immense potential in delivering powerful anti-cancer drugs, leading to promising and specific therapeutic outcomes for combating various types of cancer. The unique characteristics of CDs, combined with innovative nanocomplex creation techniques such as encapsulation, enable the development of potential theranostic treatments. The review here focuses mainly on the different techniques administered for effective nanotheranostics applications of CD-associated complex compounds in the domain of cancer treatments. The experimentations on various loaded drugs and their complex conjugates with CDs prove effective in in vivo results. Various cancers can have potential nanotheranostics cures using CDs as nanoparticles along with a highly efficient process of nanocomplex development and a drug delivery system. In conclusion, nanotheranostics holds immense potential for targeted drug delivery and improved therapeutic outcomes, offering a promising avenue for revolutionizing cancer treatments through continuous research and innovative approaches.

Contrast-enhanced MRI synthesis using dense-dilated residual convolutions based 3D network toward elimination of gadolinium in neuro-oncology.

Recent studies have raised broad safety and health concerns about using of gadolinium contrast agents during magnetic resonance imaging (MRI) to enhance identification of active tumors. In this paper, we developed a deep learning-based method for three-dimensional (3D) contrast-enhanced T1-weighted (T1) image synthesis from contrast-free image(s). The MR images of 1251 patients with glioma from the RSNA-ASNR-MICCAI BraTS Challenge 2021 dataset were used in this study. A 3D dense-dilated residual U-Net (DD-Res U-Net) was developed for contrast-enhanced T1 image synthesis from contrast-free image(s). The model was trained on a randomly split training set (n = 800) using a customized loss function and validated on a validation set (n = 200) to improve its generalizability. The generated images were quantitatively assessed against the ground-truth on a test set (n = 251) using the mean absolute error (MAE), mean-squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity (SSIM), normalized mutual information (NMI), and Hausdorff distance (HDD) metrics. We also performed a qualitative visual similarity assessment between the synthetic and ground-truth images. The effectiveness of the proposed model was compared with a 3D U-Net baseline model and existing deep learning-based methods in the literature. Our proposed DD-Res U-Net model achieved promising performance for contrast-enhanced T1 synthesis in both quantitative metrics and perceptual evaluation on the test set (n = 251). Analysis of results on the whole brain region showed a PSNR (in dB) of 29.882 ± 5.924, a SSIM of 0.901 ± 0.071, a MAE of 0.018 ± 0.013, a MSE of 0.002 ± 0.002, a HDD of 2.329 ± 9.623, and a NMI of 1.352 ± 0.091 when using only T1 as input; and a PSNR (in dB) of 30.284 ± 4.934, a SSIM of 0.915 ± 0.063, a MAE of 0.017 ± 0.013, a MSE of 0.001 ± 0.002, a HDD of 1.323 ± 3.551, and a NMI of 1.364 ± 0.089 when combining T1 with other MRI sequences. Compared to the U-Net baseline model, our model revealed superior performance. Our model demonstrated excellent capability in generating synthetic contrast-enhanced T1 images from contrast-free MR image(s) of the whole brain region when using multiple contrast-free images as input. Without incorporating tumor mask information during network training, its performance was inferior in the tumor regions compared to the whole brain which requires further improvements to replace the gadolinium administration in neuro-oncology.

Predicting compressive strength of eco-friendly plastic sand paver blocks using gene expression and artificial intelligence programming.

Plastic sand paver blocks provide a sustainable alternative by using plastic waste and reducing the need for cement. This innovative approach leads to a more sustainable construction sector by promoting environmental preservation. No model or Equation has been devised that can predict the compressive strength of these blocks. This study utilized gene expression programming (GEP) and multi-expression programming (MEP) to develop empirical models to forecast the compressive strength of plastic sand paver blocks (PSPB) comprised of plastic, sand, and fibre in an effort to advance the field. The database contains 135 results for compressive strength with seven input parameters. The R2 values of 0.87 for GEP and 0.91 for MEP for compressive strength reveal a relatively significant relationship between predicted and actual values. MEP outperformed GEP by displaying a higher R2 and lower values for statistical evaluations. In addition, a sensitivity analysis was conducted, which revealed that the sand grain size and percentage of fibres play an essential part in compressive strength. It was estimated that they contributed almost 50% of the total. The outcomes of this research have the potential to promote the reuse of PSPB in the building of green environments, hence boosting environmental protection and economic advantage.

A comparative study of deep learning-based knowledge-based planning methods for 3D dose distribution prediction of head and neck.

PURPOSE: In this paper, we compare four novel knowledge-based planning (KBP) algorithms using deep learning to predict three-dimensional (3D) dose distributions of head and neck plans using the same patients' dataset and quantitative assessment metrics. METHODS: A dataset of 340 oropharyngeal cancer patients treated with intensity-modulated radiation therapy was used in this study, which represents the AAPM OpenKBP - 2020 Grand Challenge dataset. Four 3D convolutional neural network architectures were built. The models were trained on 64% of the data set and validated on 16% for voxel-wise dose predictions: U-Net, attention U-Net, residual U-Net (Res U-Net), and attention Res U-Net. The trained models were then evaluated for their performance on a test data set (20% of the data) by comparing the predicted dose distributions against the ground-truth using dose statistics and dose-volume indices. RESULTS: The four KBP dose prediction models exhibited promising performance with an averaged mean absolute dose error within the body contour <3 Gy on 68 plans in the test set. The average difference in predicting the D99 index for all targets was 0.92 Gy (p = 0.51) for attention Res U-Net, 0.94 Gy (p = 0.40) for Res U-Net, 2.94 Gy (p = 0.09) for attention U-Net, and 3.51 Gy (p = 0.08) for U-Net. For the OARs, the values for the D m a x ${D_{max}}$ and D m e a n ${D_{mean}}$ indices were 2.72 Gy (p < 0.01) for attention Res U-Net, 2.94 Gy (p < 0.01) for Res U-Net, 1.10 Gy (p < 0.01) for attention U-Net, 0.84 Gy (p < 0.29) for U-Net. CONCLUSION: All models demonstrated almost comparable performance for voxel-wise dose prediction. KBP models that employ 3D U-Net architecture as a base could be deployed for clinical use to improve cancer patient treatment by creating plans with consistent quality and making the radiotherapy workflow more efficient.

Carotenoids: Role in Neurodegenerative Diseases Remediation.

Numerous factors can contribute to the development of neurodegenerative disorders (NDs), such as Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, Huntington's disease, and multiple sclerosis. Oxidative stress (OS), a fairly common ND symptom, can be caused by more reactive oxygen species being made. In addition, the pathological state of NDs, which includes a high number of protein aggregates, could make chronic inflammation worse by activating microglia. Carotenoids, often known as "CTs", are pigments that exist naturally and play a vital role in the prevention of several brain illnesses. CTs are organic pigments with major significance in ND prevention. More than 600 CTs have been discovered in nature, and they may be found in a wide variety of creatures. Different forms of CTs are responsible for the red, yellow, and orange pigments seen in many animals and plants. Because of their unique structure, CTs exhibit a wide range of bioactive effects, such as anti-inflammatory and antioxidant effects. The preventive effects of CTs have led researchers to find a strong correlation between CT levels in the body and the avoidance and treatment of several ailments, including NDs. To further understand the connection between OS, neuroinflammation, and NDs, a literature review has been compiled. In addition, we have focused on the anti-inflammatory and antioxidant properties of CTs for the treatment and management of NDs.

Assessment of patients radiation doses associated with computed tomography coronary angiography.

Computed tomography coronary angiography (CTCA) has generated tremendous interest over the past 20 years by using multidetector computed tomography (MDCT) because of its high diagnostic accuracy and efficacy in assessing patients with coronary artery disease. This technique is related to high radiation doses, which has raised serious concerns in the literature. Effective dose (E, mSv) may be a single parameter meant to reflect the relative risk from radiation exposure. Therefore, it is necessary to calculate this quantity to point to relative radiation risk. The objectives of this study are to evaluate patients' exposure during diagnostic CCTA procedures and to estimate the risks. Seven hundred ninety patients were estimated during three successive years. The patient's exposure was estimated based on a CT device's delivered radiation dose (Siemens Somatom Sensation 64 (64-MDCT)). The participating physicians obtained the parameters relevant to the radiation dose from the scan protocol generated by the CT system after each CCTA study. The parameters included the volume CT dose index (CTDIvol, mGy) and dose length product (DLP, mGy × cm). The mean and range of CTDIvol (mGy) and DLP (mGy × cm) for three respective year was (2018):10.8 (1.14-77.7) and 2369.8 ± 1231.4 (290.4-6188.9), (2019): 13.82 (1.13-348.5), and 2180.5 (501.8-9534.5) and (2020) 10.9 (0.7-52.9) and 1877.3 (149.4-5011.1), respectively. Patients' effective doses were higher compared to previous studies. Therefore, the CT acquisition parameter optimization is vital to reduce the dose to its minimal value.

A comprehensive computational approach for the identification of structure-based potential pharmacological candidates as selective AKR1B1 and AKR1B10 inhibitors: repurposing of purine alkaloids for the treatment of cancer.

Significant metabolic pathways have been linked to AKR1B1 and AKR1B10. These enzymes are crucial biological targets in the therapy of colon cancer. In the past several decades, drug repurposing has gained appeal as a time and cost-efficient strategy for providing new indications for existing drugs. The structural properties of the plant-based alkaloidal drugs theobromine and theophylline were examined using density functional theory (DFT) computations, where the B3LYP/SVP method was used to quantify the dipole moment, polarizability, and optimization energy. Optimized structures obtained through DFT studies were docked inside the active pocket of target proteins to evaluate their inhibitory potential. Moreover, molecular dynamic simulation provides significant insight into a dynamic view of molecular interactions. The findings of current revealed theobromine and theophylline as strong AKR1B1 and AKR1B10 inhibitors, respectively. In addition, the anti-cancer potential of theophylline and theobromine was validated by targeting various tumor proteins, i.e. NF-κB, cellular tumor antigen P53 and caspase-3 using a molecular docking approach. Theobromine was found to be strongly interacted with NF-κB and caspase-3, whereas theophylline potentially inhibited cellular tumor antigen P53. In addition, the ADMET characteristics of theobromine and theophylline were identified, confirming their drug-like capabilities. These results should open the way for further experimental validation and structure-based drug design/repurposing of AKR1B1/AKR1B10 inhibitors for the treatment of colon cancer and associated malignancies.Communicated by Ramaswamy H. Sarma.

Assessment of breast dose and cancer risk for young females during CT chest and abdomen examinations.

CT scanning deliver much higher radiation doses than planar radiological procedures, which puts patients to high risks. This study measures and evaluates patient doses during chest and abdomen computed tomography procedures. Particular attention is given to measuring the dose to the equivalent breast (mSv) and to estimate the associated risks of breast cancer to young female patients (15-35 years). Data was obtained from standard examinations from three hospitals. The measured values of CT dose indexes, CTDI (mGy) as well as exposure-related parameters were used for assessment. Breast and effective doses were extrapolated using a software. The results showed remarkable variations of the mean organ equivalent doses for similar CT examinations in the studied hospitals. This could be attributed to the variation in CT scanning imaging technique, and clinical indications. The average effective dose to the chest was 7.9 mSv (2.3-47.0 mSv) and for the abdomen the mean dose was 6.6 mSv, ranging from (3.3-27 mSv). The breast received equivalent doses from chest and abdomen procedures as follows: 10.2 (1.6-33 mSv) and 10.1(2.3-19 mS) Sv respectively. Each procedure yielded high risks of breast cancer for young females. Implementation of accurate referral criteria is recommended to avoid unnecessary breast radiation exposure.

Roles of Inorganic Oxide Based HTMs towards Highly Efficient and Long-Term Stable PSC-A Review.

In just a few years, the efficiency of perovskite-based solar cells (PSCs) has risen to 25.8%, making them competitive with current commercial technology. Due to the inherent advantage of perovskite thin films that can be fabricated using simple solution techniques at low temperatures, PSCs are regarded as one of the most important low-cost and mass-production prospects. The lack of stability, on the other hand, is one of the major barriers to PSC commercialization. The goal of this review is to highlight the most important aspects of recent improvements in PSCs, such as structural modification and fabrication procedures, which have resulted in increased device stability. The role of different types of hole transport layers (HTL) and the evolution of inorganic HTL including their fabrication techniques have been reviewed in detail in this review. We eloquently emphasized the variables that are critical for the successful commercialization of perovskite devices in the final section. To enhance perovskite solar cell commercialization, we also aimed to obtain insight into the operational stability of PSCs, as well as practical information on how to increase their stability through rational materials and device fabrication.

Cancerous and Non-Cancerous Brain MRI Classification Method Based on Convolutional Neural Network and Log-Polar Transformation.

Magnetic resonance imaging (MRI) offers visual representations of the interior of a body for clinical analysis and medical intervention. The MRI process is subjected to a variety of image processing and machine learning approaches to identify, diagnose, and classify brain diseases as well as detect abnormalities. In this paper, we propose an improved classification method for distinguishing cancerous and noncancerous tumors from brain MRI images by using Log Polar Transformation (LPT) and convolutional neural networks (CNN). The LPT has been applied for feature extraction of rotation and scaling of distorted images, while the integration of CNN introduces a machine learning approach for the tumor classification of distorted images. The dataset was formed with images of seven different brain diseases, and the training set was formed by applying CNN with the extracted features. The proposed method is then evaluated in comparison to state-of-the-art algorithms, showing a definite improvement of the former. The obtained results show that the machine learning approach offers better classification with a success rate of about 96% in both plain brain MR images and rotation- and scale-invariant brain MR images. This work also successfully classified T-1 and T-2 weighted images of neoplastic and degenerative brain diseases. The obtained accuracy is perfected by several kernel procedures, while the combined performance of the two wavelet transformations and a strong dataset make our method robust and efficient. Since no earlier study on machine learning approaches with rotated and scaled brain MRI has come to our attention, it is expected that our proposed method introduces a new paradigm in this research field.

Body-Centered Double-Square Split-Ring Enclosed Nested Meander-Line-Shaped Metamaterial-Loaded Microstrip-Based Resonator for Sensing Applications.

The strong localization of the electric and magnetic fields in metamaterial-based structures has attracted a new era of radiation fields in the microwave range. In this research work, we represent a double split ring enclosed nested meander-line-shaped metamaterial resonator with a high effective medium ratio layered on a dielectric substrate to enhance the sensitivity for the material characterization. Tailoring a metallic design and periodical arrangement of the split ring resonator in a subwavelength range introduced field enhancement and strong localization of the electromagnetic field. The design methodology is carried out through the optimization technique with different geometric configurations to increase the compactness of the design. The CST microwave studio is utilized for the extraction of the scattering computational value within the defined boundary condition. The effective parameters from the reflection and transmission coefficient are taken into account to observe the radiation characteristics for the interaction with the applied electromagnetic spectrum. The proposed metamaterial-based sensor exhibits high sensitivity for different dielectric materials with low permittivity values. The numerical data of the frequency deviation for the different dielectric constants have shown good agreement using the linear regression analysis where the sensitivity is R2 = 0.9894 and the figure of merit is R2 = 0.9978.