The development of effective treatments and the improvement of patient survival rates depend heavily on the timely and precise diagnosis of brain tumors. Nevertheless, the current deep-learning methods have trouble generalizing, are prone to overfitting with small datasets, and are imprecise in identifying the subtle boundaries of tumors, all of which could limit their application in clinical settings. To tackle these concerns, this paper has introduced an innovative BrainMorphNet (BMN) structure which can automatically detect and categorize brain tumors on MRI images completely, hence overcoming these problems. The first step of the approach is the application of data-specific preprocessing to clear the images of artifacts and improve their quality. Afterwards, the multilevel feature extraction generates the global and local structures of the tumor regions simultaneously. Lastly, the framework applies adaptive attention mechanisms to spatial and contextual features and thus improve them. There is a very strong multiclass separation between gliomas, meningiomas, pituitary tumors, and nontumor cases, and the whole setup is such that cancers can be diagnosed at a particular place. Our rigorous testing on the Brain Tumor MRI and Figshare datasets gave a model accuracy of 0.993, precision of 0.985, and F1 score of 0.988. The system is a very effective and time-saving tool for brain tumor analysis automation, and it is a significant step towards more intelligent medical imaging and diagnostic systems. These impressive numbers show that BMN has good generalization and clinical reliability.
This study designs, simulates, and optimizes an automated robotic vehicle for a smart cell fabrication plant. AISI 316 stainless steel was chosen for its strength and durability. Vehicle performance was evaluated across three layouts—dynamic path, grid-based, and free navigation—using Technomatix Plant Simulation. Structural and dynamic analyses were performed in ANSYS. Response surface methodology (RSM) optimized key performance indicators including collision rate, system responsiveness, handling error rate, and downtime from navigation errors. The RSM models showed high accuracy (R2 = 0.9732–0.9817). Optimization achieved a collision rate of 0.7936 collisions/h, 77.10
Soft sensing technology has recently attracted significant attention in industrial processes, particularly chemical processes, as it can ensure process safety and lay the foundation for optimizing operation by predicting product quality in real time. In the acetic acid synthesis process via acetaldehyde oxidation, real-time estimation of the composition of crude acetic acid, the product, is crucial for improving process safety and product quality. In this paper, we propose a distributed adaptive neuro fuzzy inference system (ANFIS) based on principal component analysis (PCA) and fuzzy C means (FCM) clustering, and apply it to the soft sensing of the acetic acid synthesis process. Simulation and experimental results demonstrate the effectiveness of the proposed soft sensing method using the distributed ANFIS.
The continuous need for increased data speeds and reduced latency in contemporary wireless communication systems, especially 5G and the upcoming 6G, requires the advancement of antennas with improved performance. Microstrip patch antennas (MPAs) are excellent choices because of their slim design, adaptability, and affordability. Nonetheless, traditional MPAs face fundamental limitations such as restricted bandwidth, minimal gain, and limited power handling capability. This paper introduces an innovative design of a high-efficiency MPA functioning in the 28 GHz 5G mm-wave spectrum. The suggested antenna features a Sierpiński carpet fractal structure for size reduction and multiband functionality, combined with a hexagonal split-ring resonator (H-SRR) metamaterial layer to markedly improve gain and bandwidth. A thorough evaluation utilizing a finite-difference time-domain (FDTD) approach, executed in Python, is presented to confirm the design. The simulated outcomes indicate a –10 dB impedance bandwidth of 4.2 GHz (15
Aiming at the problems of large trajectory offset, low trajectory tracking control accuracy and long control time in the traditional trajectory tracking control algorithm of robot bionic manipulator, a trajectory tracking control algorithm of robot bionic manipulator based on RBF neural network is proposed. Through the position description and orientation description of the robot bionic manipulator in space, the spatial coordinate transformation of the manipulator is carried out. Using the Lagrange method, the dynamic model is constructed through the kinetic energy and potential energy of the robot bionic manipulator. On this basis, the trajectory of the manipulator is tracked and controlled through the designed RBF neural network controller. The simulation results show that the trajectory of the robot bionic manipulator controlled by the proposed algorithm is consistent with the theoretical control trajectory, and the trajectory tracking control accuracy is high and the control time is short.
