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Emerging from the convergence of digital twin technology and the metaverse, consumer health (MCH) is witnessing a transformative shift. The amalgamation of bioinformatics with healthcare Big Data has ushered in a new era of disease prediction models that harness comprehensive medical data, enabling the anticipation of illnesses even before the onset of symptoms. In this model, deep neural networks stand out because they improve accuracy remarkably by increasing network depth and making weight changes using gradient descent. Nonetheless, traditional methods face their own set of challenges, including the issues of gradient instability and slow training. In this case, the Broad Learning System (BLS) stands out as a good alternative. It gets around the problems with gradient descent and lets you quickly rebuild a model through incremental learning. One problem with BLS is that it has trouble extracting complex features from complex medical data. This makes it less useful in a wide range of healthcare situations. In response to these challenges, we introduce DAE-BLS, a novel hybrid model that marries Denoising AutoEncoder (DAE) noise reduction with the efficiency of BLS. This hybrid approach excels in robust feature extraction, particularly within the intricate and multifaceted world of medical data. Validation using diverse datasets yields impressive results, with accuracies reaching as high as 98.50%. DAE-BLS's ability to rapidly adapt through incremental learning holds great promise for accurate and agile disease prediction, especially within the complex and dynamic healthcare scenarios of today. [ABSTRACT FROM AUTHOR]

Presents a study of the drug consumption pattern of elderly patients in nursing homes to identify risks for drug-induced weight loss and specific nutrient deficiencies. Higher risk of malnutrition in elderly patients who live in nursing homes; Responsibilities of health care institutions to diagnose at an early stage and prevent drug-induced malnutrition; Continuing education questionnaire.

• Dual battery energy storage system. • Fuzzy Logic controller-based energy management system. • Hybrid electric vehicle power system. • Energy management for Vehicular application. The advancement of energy vehicles has gained support among automotive firms as original equipment manufacturers have recently concentrated their efforts on creating greener propulsion options to satisfy social and ecological demands for clean transportation. In order to fulfil the constantly rising demands of vehicle applications, efficient hybrid energy storage system (HESS) looks to be extremely promising. Understanding how electrical energy storage solutions function is essential. A HESS made up of an active supercapacitor and battery has been proposed to enhance the performance of the electric vehicle power supply. These techniques enable increasing the HESS size or reducing the strains placed on the battery.To improve overall performance and efficiency, a fuzzy logic controller-based energy management method is comprehensively addressed. The suggested control and management technique makes sure that the battery meets the majority of the power demand on average while effectively managing the Supercapacitors' energy levels. To reallocate the necessary power, the SOC variation of the supercapacitor and battery creates imprecise rules. An assessment of the potential to more effectively coordinate the flow of energy in real-time while verifying the suggested energy management. [Display omitted] [ABSTRACT FROM AUTHOR]