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Infiltrative non-GBM gliomas are common primary intracranial malignancies, and postoperative adjuvant radiotherapy is recommended for most adult patients diagnosed with this disease to enhance local control and prolong intracranial progression-free survival (PFS). However, RT-related neurocognitive function (NCF) consequences should not be ignored. Early neurocognitive decline principally includes episodic memory, associated significantly with functions of the hippocampus. This prospective study aims to investigate the impact of adjuvant brain irradiation on neurocognitive performances and relevant oncological outcomes. Twenty-five patients with intracranial infiltrative non-GBM gliomas were enrolled when postoperative adjuvant RT was recommended. All recruited patients should receive baseline brain magnetic resonance imaging, and neuropsychological assessments before and 4 months after the RT course. A battery of neuropsychological measures, mainly including executive functions, memory, psychomotor speed and visuoconstructive ability, was used to evaluate NCFs of interest. Analyzing the delta values between post-irradiation and baseline NCF scores, we observed a robust trend reflecting cognitive stabilization rather than deterioration in almost all NCF. Both verbal and visual memory functions exhibited significant differences in the corresponding scaled scores (Z = −2.722, p =.006, regarding verbal memory; Z = −2.246, p =.025, concerning non-verbal memory). Moreover, patients' neuropsychological performances associated with psychomotor speed and executive functions also disclosed a tendency toward stabilization/improvement. This prospective study demonstrated that patients with infiltrative non-GBM exhibited a marked tendency toward neurocognitive stabilization after receiving postoperative adjuvant RT. Clinical trial registration: Trial Registration with ClinicalTrials.gov identifier: NCT03534050 [ABSTRACT FROM AUTHOR]

Mobile technology is developing significantly. Mobile phone technologies have been integrated into the healthcare industry to help medical practitioners. Typically, computer vision models focus on image detection and classification issues. MobileNetV2 is a computer vision model that performs well on mobile devices, but it requires cloud services to process biometric image information and provide predictions to users. This leads to increased latency. Processing biometrics image datasets on mobile devices will make the prediction faster, but mobiles are resource-restricted devices in terms of storage, power, and computational speed. Hence, a model that is small in size, efficient, and has good prediction quality for biometrics image classification problems is required. Quantizing pre-trained CNN (PCNN) MobileNetV2 architecture combined with a Support Vector Machine (SVM) compacts the model representation and reduces the computational cost and memory requirement. This proposed novel approach combines quantized pre-trained CNN (PCNN) MobileNetV2 architecture with a Support Vector Machine (SVM) to represent models efficiently with low computational cost and memory. Our contributions include evaluating three CNN models for ocular disease identification in transfer learning and deep feature plus SVM approaches, showing the superiority of deep features from MobileNetV2 and SVM classification models, comparing traditional methods, exploring six ocular diseases and normal classification with 20,111 images post-data augmentation, and reducing the number of trainable models. The model is trained on ocular disorder retinal fundus image datasets according to the severity of six age-related macular degeneration (AMD), one of the most common eye illnesses, Cataract, Diabetes, Glaucoma, Hypertension, and Myopia with one class Normal. From the experiment outcomes, it is observed that the suggested MobileNetV2-SVM model size is compressed. The testing accuracy for MobileNetV2-SVM, InceptionV3, and MobileNetV2 is 90.11%, 86.88%, and 89.76% respectively while MobileNetV2-SVM, InceptionV3, and MobileNetV2 accuracy are observed to be 92.59%, 83.38%, and 90.16%, respectively. The proposed novel technique can be used to classify all biometric medical image datasets on mobile devices. [ABSTRACT FROM AUTHOR]

Due to its significant influence on numerous areas of mathematics and practical sciences, the theory of integral inequality has attracted a lot of interest. Convexity has undergone several improvements, generalizations, and extensions over time in an effort to produce more accurate variations of known findings. This article's main goal is to introduce a new class of convexity as well as to prove several Hermite–Hadamard type interval-valued integral inequalities in the fractional domain. First, we put forth the new notion of generalized convexity mappings, which is defined as U D - Ԓ -convexity on coordinates with regard to fuzzy-number-valued mappings and the up and down ( U D ) fuzzy relation. The generic qualities of this class make it novel. By taking into account different values for Ԓ , we produce several known classes of convexity. Additionally, we create some new fractional variations of the Hermite–Hadamard ( H H ) and Pachpatte types of inequalities using the concepts of coordinated U D - Ԓ -convexity and double Riemann–Liouville fractional operators. The results attained here are the most cohesive versions of previous findings. To demonstrate the importance of the key findings, we offer a number of concrete examples. [ABSTRACT FROM AUTHOR]

Benchmarking different optimization algorithms is tasky, particularly for network-based cellular communication systems. The design and management process of these systems involves many stochastic variables and complex design parameters that demand an unbiased estimation and analysis. Though several optimization algorithms exist for different parametric modeling and tuning, an in-depth evaluation of their functional performance has not been adequately addressed, especially for cellular communication systems. Firstly, in this paper, nine key numerical and global optimization algorithms, comprising Gauss–Newton (GN), gradient descent (GD), Genetic Algorithm (GA), Levenberg–Marguardt (LM), Quasi-Newton (QN), Trust-Region–Dog-Leg (TR), pattern search (PAS), Simulated Annealing (SA), and particle swam (PS), have been benchmarked against measured data. The experimental data were taken from different radio signal propagation terrains around four eNodeB cells. In order to assist the radio frequency (RF) engineer in selecting the most suitable optimization method for the parametric model tuning, three-fold benchmarking criteria comprising the Accuracy Profile Benchmark (APB), Function Evaluation Benchmark (FEB), and Execution Speed Benchmark (ESB) were employed. The APB and FEB were quantitatively compared against the measured data for fair benchmarking. By leveraging the APB performance criteria, the QN achieved the best results with the preferred values of 98.34, 97.31, 97.44, and 96.65% in locations 1–4. The GD attained the worst performance with the lowest APE values of 98.25, 95.45, 96.10, and 95.70 in the tested locations. In terms of objective function values and their evaluation count, the QN algorithm shows the fewest function counts of 44, 44, 56, and 44, and the lowest objective values of 80.85, 37.77, 54.69, and 41.24, thus attaining the best optimization algorithm results across the study locations. The worst performance was attained by the GD with objective values of 86.45, 39.58, 76.66, and 54.27, respectively. Though the objective values achieved with global optimization methods, PAS, GA, PS, and SA, are relatively small compared to the QN, their function evaluation counts are high. The PAS, GA, PS, and SA recorded 1367, 2550, 3450, and 2818 function evaluation counts, which are relatively high. Overall, the QN algorithm achieves the best optimization, and it can serve as a reference for RF engineers in selecting suitable optimization methods for propagation modeling and parametric tuning. [ABSTRACT FROM AUTHOR]