Smart Detecting and Versatile Wearable Electrical Sensing Mediums for Healthcare

Optimized multi-frequency nonlinear broadband piezoelectric energy harvester designs

Many electrical devices can be powered and operated by harvesting the wasted energy of the surroundings. This research aims to overcome the challenges of output power with a sharp peak, small bandwidth, and the huge dimensions of the piezoelectric energy harvesters relative to the output power. The aforementioned challenges motivated us to investigate the effect of nonlinearity in the shape (tapered and straight cross-section area) as well as the fixation method (the number of fastened ends) to determine the optimal design with high output power and wide working frequency. This research proposes a novel piezoelectric energy harvester array, where each beam is made up of three fixed beams that are joined together by a center mass. The proposed design produces an output power of 35 mW between 25 and 40 Hz. The output power of the proposed design is 3.24 times more than the conventional designs. The recommended approach is simulated utilizing finite element analysis FEA. Analytical and experimental methods validate the proposed FEA, which exhibits excellent agreement.

Simple and cost-effective pH and T sensors from top to bottom: New chemical probes based on sonogel-carbon transducers for plasma analyses

The present work introduces two novel approaches to fabricate simple and cost-effective pH and temperature probes. Sinusoidal voltage methodologies were employed to electrodeposit polyaniline (PANI) at different growth times (10-20 min) on the surface of an affordable Sonogel-Carbon electrode to conform a robust pH sensor. The presence of PANI and its phases were corroborated by electrochemical means. The sensibility, reversibility and selectivity of the produced sensor were very adequate to apply it in physiological samples. In this regard, the proposed sensor was evaluated in artificial blood serum as well as untreated plasma samples obtaining outstanding results in comparison with a gold reference technique (error <2 %). In addition, a new composite sonogel material, intrinsically modified with multiwalled carbon nanotubes, was attached on top of an electrode couple to one-step fabricate a new temperature probe, relating resistance of the probe with the surroundings temperature. In this case, an optical microscopy characterization was performed to study the sturdiness of the layer. Remarkably, suitable results in terms of sensitivity and selectivity were obtained. The probes were assessed in artificial and untreated plasma samples as well, with the corresponding validation step (error <1 %) by using a commercial temperature probe.

Clinical Trials and Therapeutic Approaches for Healthcare Challenges in Pakistan

Pakistan faces tremendous challenges in providing healthcare due to a lack of consistent policymaking, increasing expenditure and exponential growth in population since its inception in 1947. These challenges are not just driven by politics, policy and allocation of resources but also by healthcare, environment and characteristics of the population biology. Clinical trials provide the best way to find population-specific, cost-effective treatments that do not merely mimic those used in wealthier nations. This article analyzes all clinical studies conducted with at least one site in Pakistan listed on ClinicalTrials.gov, combined with a short overview that considers new therapeutic approaches that can be investigated in future clinical trials. Therapies using repurposed medicines are of particular interest as they use affordable drugs that are already widely available.

Long-Short-Term-Memory-Based Deep Stacked Sequence-to-Sequence Autoencoder for Health Prediction of Industrial Workers in Closed Environments Based on Wearable Devices

To reduce the risks and challenges faced by frontline workers in confined workspaces, accurate real-time health monitoring of their vital signs is essential for improving safety and productivity and preventing accidents. Machine-learning-based data-driven methods have shown promise in extracting valuable information from complex monitoring data. However, practical industrial settings still struggle with the data collection difficulties and low prediction accuracy of machine learning models due to the complex work environment. To tackle these challenges, a novel approach called a long short-term memory (LSTM)-based deep stacked sequence-to-sequence autoencoder is proposed for predicting the health status of workers in confined spaces. The first step involves implementing a wireless data acquisition system using edge-cloud platforms. Smart wearable devices are used to collect data from multiple sources, like temperature, heart rate, and pressure. These comprehensive data provide insights into the workers' health status within the closed space of a manufacturing factory. Next, a hybrid model combining deep learning and support vector machine (SVM) is constructed for anomaly detection. The LSTM-based deep stacked sequence-to-sequence autoencoder is specifically designed to learn deep discriminative features from the time-series data by reconstructing the input data and thus generating fused deep features. These features are then fed into a one-class SVM, enabling accurate recognition of workers' health status. The effectiveness and superiority of the proposed approach are demonstrated through comparisons with other existing approaches.