The accurate diagnosis of localized defects in cylindrical roller bearings is crucial for ensuring the reliability and longevity of rotating machinery. However, fault detection is often hindered by harmonic interference, noise contamination, and complex signal components, making feature extraction challenging and reducing classification accuracy. To address these issues, this study proposes an enhanced Aquila optimizer (EAO)-based variational modal decomposition (VMD) framework for intelligent bearing fault diagnosis. The EAO algorithm, incorporating chaotic inverse learning, a sinusoidal search strategy, and an adaptive variation mechanism, enhances the optimization of VMD parameters, thereby improving the decomposition of vibration signals and preserving critical fault-related features. Experimental validation is conducted using a dual rotor-bearing test rig, where vibration signals from healthy and defective bearings with varying fault sizes are analyzed. The extracted fault features are classified using support vector machines, extreme learning machines, and deep extreme learning machines. The results demonstrate that the improved Aquila optimizer-variational modal decomposition framework achieves a diagnostic accuracy of 99.57%, significantly outperforming conventional methods. This research underscores the effectiveness of the proposed method for real-time condition monitoring and predictive maintenance, offering a reliable and robust approach for early fault detection in industrial rotating machinery.
Parkinson’s disease (PD) is a progressive neurological disorder that significantly impacts quality of life. Over the past 25 years, deep brain stimulation (DBS) has emerged as an effective treatment option for individuals with advanced PD.. However, in India, DBS remains underutilized primarily due to financial constraints and a general lack of awareness, compounded by biases towards certain treatment centers. Additionally, the absence of definitive guidelines for implementing DBS in India further hampers its accessibility and adoption. Based on expert consensus, we propose a stepwise, five-point approach to optimize clinical outcomes for deep brain stimulation (DBS). This approach focuses on key areas including patient selection, indications for DBS in cases of medically refractory levodopa-induced motor complications or resistant tremor, and precise target selection. We emphasize the necessity of ensuring psychiatric stability and highlight the importance of a multidisciplinary approach, a comprehensive preoperative evaluation by multidisciplinary team of specialists including movement disorder experts, functional neurosurgeons etc critical for better, long-term outcomes. Furthermore, we recommend that DBS procedures be performed at specialized centers to ensure the highest standards of care and expertise.
Neonatal screening (NBS) enables early detection of treatable congenital disorders, but physiological immaturity in preterm and critically ill neonates may reduce screening accuracy. We conducted a prospective observational study of 8231 neonates screened over three years at a tertiary care centre in Western India for congenital hypothyroidism (CH), congenital adrenal hyperplasia (CAH), G6PD deficiency, sickle cell disease, and selected amino acidopathy. A total of 70 were confirmed with a screened disorder, yielding an incidence of 8.5 per 1000 live births; G6PD deficiency (1:175) and CH (1:588) were most common.
