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The blistering progress of convolutional neural networks (CNNs) in numerous applications of the real-world usually obstruct by a surge in network volume and computational cost. Recently, researchers concentrate on eliminating these issues by compressing the CNN models, such as pruning filters and weights. In comparison with the technique of pruning weights, the technique of pruning filters doesn’t effect in sparse connectivity patterns. In this article, we have proposed a fresh new technique to estimate the significance of filters. More precisely, we combined L1-norm with capped L1-norm to represent the amount of information extracted by the filter and control regularization. In the process of pruning, the insignificant filters remove directly without any loss in the test accuracy, providing much slimmer and compact models with comparable accuracy and this process is iterated a few times. To validate the effectiveness of our algorithm. We experimentally determine the usefulness of our approach with several advanced CNN models on numerous standard data sets. Particularly, data sets CIFAR-10 is used on VGG-16 and prunes 92.7% parameters with float-point-operations (FLOPs) reduction of 75.8% without loss of accuracy and has achieved advancement in state-of-art.

A bstract We constructed all possible kinematically allowed three-point interactions of two massless Dirac spinors with massive higher-spin bosons. In any D spacetime, the interactions have been constructed using the projections of the higher spin irreducible representations of Spin( D − 1) over the product of two irreducible spinor representations of Spin( D − 2). Based on this analysis, we have further classified the space of theories involving two massless Dirac spinors and a single (or multiple) massive higher spin(s) based on the discrete symmetries: C , R , and T . We found that in any D = 2 m + 1/2 m , the interacting theories of a single massive higher spin have a “ m ” mod 2 (or D mod 4) classification.

Although it is known that strain-hardening helps enhance the wear resistance of metallic materials, puzzles or inconsistent phenomena still exist regarding the effect of strain-hardening on the wear resistance. It was reported that strain-hardening showed little or limited benefits to the wear resistance of some carbon steels. Besides, if the strain-hardening works, its benefit to the wear resistance may not be as large as expected. In this study, effects of strain-hardening (cold work) on dry sliding wear of Cu and Mg were investigated. The dry sliding wear tests were performed under different normal loads of 2 N, 5 N, 8 N, and 12 N, respectively, at the room temperature. It was demonstrated that the strain-hardening decreased Young’s moduli of Cu and Mg samples due to deteriorated crystalline integrity, which reduced the benefit of strain-hardening to their wear resistances. The strain-hardening benefited the FCC Cu more than the HCP Mg, and the effectiveness of strain-hardening decreased with the increase in the contact load. Relevant mechanisms are elucidated from the viewpoint of wearing energy consumption.

Interface diffusion along a metal/ceramic interface present in numerous energy and electronic devices can critically affect their performance and stability. Hole formation in a polycrystalline Ni film on an α -Al 2 O 3 substrate coupled with a continuum diffusion analysis demonstrates that Ni diffusion along the Ni/ α -Al 2 O 3 interface is surprisingly fast. Ab initio calculations demonstrate that both Ni vacancy formation and migration energies at the coherent Ni/ α -Al 2 O 3 interface are much smaller than in bulk Ni, suggesting that the activation energy for diffusion along coherent Ni/ α -Al 2 O 3 interfaces is comparable to that along (incoherent/high angle) grain boundaries. Based on these results, we develop a simple model for diffusion along metal/ceramic interfaces, apply it to a wide range of metal/ceramic systems and validate it with several ab initio calculations. These results suggest that fast metal diffusion along metal/ceramic interfaces should be common, but is not universal. Little is known about diffusion along metal/ceramic interfaces even though it controls the physical behavior and lifetimes of many devices (including batteries, microelectronics, and jet engines). Here, the authors show that diffusion along a nickel/sapphire interface is abnormally fast due to nickel vacancies and generalise their findings to a wide-range of metal/ceramic systems.

Agriculture is the backbone of the country’s economy. Sugarcane is a globally important crop and a major source of sugar, ethanol and jaggery. One of the problems faced by the sugar cane industry is the diseases that attack the crops. If these diseases are not identified early, they may result in exterminating the whole crops surrounding them. Manually checking each and every corner of a large farm is physically impossible. Machine learning is the contemporary solution to the problem, which can be resolved using the Convolution Neural Network (CNN) techniques. The drone images of all corners of the farm can be fed into the trained model for distinguishing the health status. For training the model the secondary data is taken from Kaggle, which includes both healthy and unhealthy sugarcane plant images, with various diseases in the unhealthy class. Machine learning models effectively identify early-stage crop diseases. This helps the farmer to treat that part of the affected farm quickly in order to avoid the spread of the disease to the remaining parts of the farm. This study deals with sugarcane disease prediction using the CNN model. Two different layered CNN models (VGG-16 and VGG-19) that were tested. The models were trained based on the images of 2165 containing both healthy and unhealthy leaves. The whole dataset is divided into three parts, validating data, testing data, and training data. The selected model – VGG-19 performed better and was found to be analysing the image up to an accuracy of 92% with 90% precision.

Image segmentation is an essential research field in image processing that has developed from traditional processing techniques to modern deep learning methods. In medical image processing, the primary goal of the segmentation process is to segment organs, lesions or tumors. Segmentation of tumors in the brain is a difficult task due to the vast variations in the intensity and size of gliomas. Clinical segmentation typically requires a high-quality image with relevant features and domain experts for the best results. Due to this, automatic segmentation is a necessity in modern society since gliomas are considered highly malignant. Encoder-decoder-based structures, as popular as they are, have some areas where the research is still in progress, like reducing the number of false positives and false negatives. Sometimes these models also struggled to capture the finest boundaries, producing jagged or inaccurate boundaries after segmentation. This research article introduces a novel and efficient method for segmenting out the tumorous region in brain images to overcome the research gap of the recent state-of-the-art deep learning-based segmentation approaches. The proposed 4-staged 2D-VNET + + is an efficient deep learning tumor segmentation network that introduces a context-boosting framework and a custom loss function to accomplish the task. The results show that the proposed model gives a Dice score of 99.287, Jaccard similarity index of 99.642 and a Tversky index of 99.743, all of which outperform the recent state-of-the-art techniques like 2D-VNet, Attention ResUNet with Guided Decoder (ARU-GD), MultiResUNet, 2D UNet, Link Net, TransUNet and 3D-UNet.

Background Diabetes mellitus (DM) is a group of metabolic disorders that have an increased risk of macro and micro-vascular complications due to lipid dysfunction. The present drug treatments for the management of DM either have numerous side effects or do not have long-lasting therapeutic effects. So it is essential to find a newer class of drug for DM treatment. Method Broad information has been researched regarding Tyrosine kinase Inhibitors (TKIs) and their mechanism of action. They are proven for the management of various kinds of cancers. TKIs produce anti-hyperglycemic effects by acting on multiple targets such as c-Abl, Platelet-Derived Growth Factor Receptor (PDGFR), Vascular Endothelial Growth Factor Receptor (VEGFR), Epidermal Growth Factor Receptor (EGFR), and c-Kit. Result This family of drugs blocks numerous tyrosine kinases by acting as a partial agonist of PPAR-γ receptors and results in an anti-diabetic effect by improving insulin sensitivity and glucose disposal rate. Conclusion Therefore, it is said that TKI drugs will be great potential for the treatment of Diabetes. This review summarizes the possible targets of TKIs and TKIs being a potential drug class in the management of Diabetes mellitus.

BRASH (bradycardia, renal failure, atrioventricular [AV] nodal blocking medications, shock, hyperkalemia) syndrome describes the phenomenon of profound bradycardia from a combination of hyperkalemia and use of AV nodal blocking medication with underlying renal injury. We present a case of BRASH syndrome in a patient on chronic beta-blocker therapy for his coronary artery disease who presented with life-threatening hyperkalemia and acute renal failure. Due to failure in early recognition and superimposed effect with further beta-blocker dosing, the patient developed profound bradycardia and later went into pulseless electrical activity cardiac arrest requiring cardiopulmonary resuscitation. Metabolic derangements and bradycardia later resolved with medical management, but unfortunately, the patient developed diffuse anoxic brain injury after the cardiac arrest and was declared brain dead.

In complex environments with dense pedestrian traffic, mobile robots often experience errors and instability during trajectory tracking and dynamic obstacle avoidance tasks. This paper presents a scene perception and decision-making strategy combined with deep reinforcement learning. Temporal sequences of LiDAR data and sub-goal were used as input, and action output is generated via an end-to-end network. We designed an adaptive heading reward that guides the robot to proactively avoid pedestrians while efficiently moving toward its target. Through continuous interaction with a dynamic environment, the robot learns an optimal decision-making strategy by maximizing cumulative rewards. A series of simulation experiments and real-world validations demonstrate that the proposed strategy achieves an effective balance between collision avoidance and real-time performance in robotic navigation. Furthermore, extensive results confirm that the method remains robust in unfamiliar environments and in varying crowd densities. Finally, tests on a hardware platform indicate that the strategy offers strong stability and adaptability in practical applications, effectively meeting obstacle avoidance requirements and validating its reliability in complex dynamic settings.

Background: Metformin plays a major part in the treatment of polycystic ovarian syndrome .Trials are being conducted to compare the effectiveness of combination of metformin with cabergoline in the treatment of hyperprolactinemia and polycystic ovarian syndrome. Objectives: The purpose of this study is to compare the effectiveness of metformin monotherapy and combination therapy with cabergoline versus metformin for the management of polycystic ovarian syndrome with hyperprolactinemia. Methodology: An extensive search up until 31 May 2024 of electronic databases (PubMed, Registry of Controlled Clinical Trials, Web of Sciences, SCOPUS) to find pertinent studies. An analysis was conducted with both observational data and randomized clinical trials . To compute the standard mean difference, weighted mean difference, odds ratio, and 95% confidence interval, RevMan (v5.3) was utilized. Primary outcomes that were assessed included body-mass index, regular menstruation, weight change, prolactin, testosterone, and dehydroepiandrosterone-sulfate levels. Results: Three randomized controlled trials and 1 observational study, taking a total patient population of n = 535, were part of our final analysis. Prolactin (SMD = −3.23 95% CI: (−4.90, −1.55)) and dehydroepiandrosterone-sulfate levels (SMD = −0.27 95% CI: (−0.52, −0.01)) were significantly lower in the metformin and cabergoline combination therapy group; monthly regularity was also significantly higher (OR = 3.07 95% CI: (2.09, 4.51)). Statistically, there was no significant difference in weight, body-mass index, or testosterone levels. Conclusions: In the treatment of polycystic ovarian syndrome, the combination of metformin and cabergoline significantly lowers prolactin levels and encourages regular menstrual cycles. Although metformin has the potential to suppress testosterone levels, more investigation is required to determine how combination therapy affect dehydroepiandrosterone-sulfate and testosterone levels. It’s interesting to note that while neither intervention had a substantial impact on weight or body-mass index, metformin and cabergoline combination therapy outperformed metformin monotherapy in terms of supporting regular menstrual cycles. Customized therapy approaches are essential, and large-scale trials involving a variety of groups are required to comprehend the safety and effectiveness of treatments. Plain language summary: Efficacy of metformin compared to metformin and cabergoline combination In this study, 2 therapies for women with high prolactin levels—a hormone associated with PCOS—were examined. Their goal was to determine which combination of metformin and cabergoline produced the best results.Observational data and randomized clinical trials were included while searching through several databases for pertinent studies. Researchers discovered that the combination of metformin and cabergoline was superior to using metformin alone in reducing prolactin and another hormone called DHEAS. The menstrual periods of women receiving the combined therapy were also more regular. However, there wasn’t much difference in weight, body mass index (BMI), or testosterone levels between the 2 groups. In summary, it appears that the combination of cabergoline and metformin is a more effective way to treat the symptoms of PCOS, which include irregular periods and elevated prolactin levels. To find out how it impacts other hormones and whether it’s long-term safe and effective, further research is still required.