Kadi Sarva Vishwavidyalaya

This study investigates the optimization of Shielded Metal Arc Welding (SMAW) parameters for IS 2062 E250 structural steel using the Taguchi L16 orthogonal array. Four critical process parameters as welding current (90–120 A), root face (1–4 mm), root gap (2–3.5 mm), and groove angle (30°–60°) were evaluated for their influence on tensile strength, impact strength, hardness, and angular distortion. Experimental results show that tensile strength varied from 231.56 to 447.12 MPa, with the maximum value 447.12 MPa obtained at 90 A welding current, 1 mm root face, 2 mm root gap, and 30° groove angle. The impact strength ranged from 23.3 to 264.5 J, with the highest value at 120 A current, 1 mm root face, 3.5 mm root gap, and 40° groove angle. This being said that the material gets stronger when it gets hotter and the joint shape gets bigger. The hardness levels were between 22.2 and 37.2 HRB. Higher hardness was associated with narrower groove angles and reduced welding currents. Moreover, the angular distortion ranged from 0.4° to 2.1°, with balanced joint shape and low current levels leading to the least distortion. According to the results of ANOVA, the welding current has an imperative effect on tensile strength, hardness, and angular distortions. Moreover, the groove directly affects the impact strength. Overall, the present work suggests direct relevant with the structural and industrial welding implications.

Detecting brain tumors early is still hard to get right, and it matters a lot because it feeds directly into how a patient gets treated. MRI is the modality most people trust for this because it shows soft tissue clearly, but drawing tumor boundaries by hand takes forever, and two radiologists rarely agree exactly. That disagreement problem is a big part of why automated segmentation has become such an active research area recently. In this paper, I look at where current deep learning methods for MRI tumor segmentation are still weak: they usually depend on just one MRI sequence, false positives don't go away easily, tumor boundaries are often imprecise (HD95 numbers make this obvious), and diffusion-based models in particular cost too much compute to run quickly. I implemented two baseline pipelines on BraTS 2020 to check where things actually stand - a GGMM-enhanced U-Net and a diffusion probabilistic model - and used what came out of that to shape a hybrid framework. The framework has four parts: it fuses T1, T2, FLAIR, and T1CE features instead of using just one sequence; it uses a Dice-BCE loss so class imbalance doesn't dominate training; it applies a threshold that adjusts itself based on the mean and standard deviation of each prediction rather than sticking to a fixed 0.5 cutoff; and it removes tiny, implausible regions in a post-processing step. The GGMM baseline landed around a 0.81-0.82 Dice score; the diffusion model got closer to 0.89 but took far longer per case to run. Based on that gap, four hypotheses are proposed for testing once the full model is trained: Dice of 0.90 or higher, IoU above 0.85, at least 15% fewer false positives than a plain U-Net, and no more than a 5% performance drop under noisy input. The hope is that this ends up being a reasonable middle ground - accuracy near what diffusion models get, minus the runtime problem that keeps them out of actual clinics.

In today’s world to identifying crowd unusual activities stands as an essential requirement to protect both smart cities and operate successful events together with operating transportation systems. When utilized for big datasets and variable crowd patterns traditional recognition approaches show difficulty in operation. The deep learning system uses Convolutional Neural Networks and Long Short-Term Memory networks to detect problems within heavily populated settings. The methodology processed ShanghaiTech along with UCF-Crime datasets and achieved 94.2

Background: Regulatory expectations for topical and nasal products differ across Gulf Cooperation Council (GCC) and Middle East–North Africa (MENA) markets. Clear understanding of dossier formats, stability, labeling and site requirements is essential for predictable approvals. Objectives: To compare prevailing frameworks and outline practical strategies that shorten review cycles while ensuring compliance for topical and nasal dosage forms. Methods: A structured desk review of primary guidance from GCC-DR, SFDA (Saudi Arabia), MOHAP (UAE), EDA (Egypt), and JFDA (Jordan), alongside WHO/ICH quality references, was synthesized into an actionable comparison. Results: GCC countries show higher alignment via centralized and national eCTD pathways, while MENA markets are heterogeneous in format and evidence expectations (e.g., device performance for nasal sprays). Conclusion: Regionaware sequencing, robust stability justifications (Zone IVB), and early alignment on country-specific documentation materially reduce risk of delay for topical and nasal submissions.