A multiband SSr diode RF rectifier with an improved frequency ratio for biomedical wireless applications.

This paper described a four-band implantable RF rectifier with simplified circuit complexity. Each RF-rectifier cell is sequentially matched to the four operational frequencies to accomplish the proposed design. The proposed RF rectifier can harvest RF signals at 1.830, 2.100, and white space Wi-Fi bands between 2.38 to 2.68 GHz, respectively. At 2.100 GHz, the proposed RF harvester achieved a maximum (radio frequency direct current) RF-to-DC power conversion efficiency (PCE) of 73.00% and an output DC voltage [Formula: see text] of 1.61 V for an RF power of 2 dBm. The outdoor performance of the rectenna shows a [Formula: see text] of 0.440 V and drives a low-power bq25504-674 evaluation module (EVM) at 1.362 V. The dimension of the RF-rectifier on the FR-4 PCB board is 0.27[Formula: see text] [Formula: see text] 0.29[Formula: see text]. The RF-rectifier demonstrates the capacity to effectively utilize the frequency domain by employing multi-band operation and exhibiting a good impedance bandwidth through a sequential matching technique. Thus, by effectively controlling the rectenna's ambient performance, the proposed design holds the potential for powering a range of low-power biomedical implantable devices. (BIDs).

Optimal Deep-Learning-Enabled Intelligent Decision Support System for SARS-CoV-2 Classification.

Intelligent decision support systems (IDSS) for complex healthcare applications aim to examine a large quantity of complex healthcare data to assist doctors, researchers, pathologists, and other healthcare professionals. A decision support system (DSS) is an intelligent system that provides improved assistance in various stages of health-related disease diagnosis. At the same time, the SARS-CoV-2 infection that causes COVID-19 disease has spread globally from the beginning of 2020. Several research works reported that the imaging pattern based on computed tomography (CT) can be utilized to detect SARS-CoV-2. Earlier identification and detection of the diseases is essential to offer adequate treatment and avoid the severity of the disease. With this motivation, this study develops an efficient deep-learning-based fusion model with swarm intelligence (EDLFM-SI) for SARS-CoV-2 identification. The proposed EDLFM-SI technique aims to detect and classify the SARS-CoV-2 infection or not. Also, the EDLFM-SI technique comprises various processes, namely, data augmentation, preprocessing, feature extraction, and classification. Moreover, a fusion of capsule network (CapsNet) and MobileNet based feature extractors are employed. Besides, a water strider algorithm (WSA) is applied to fine-tune the hyperparameters involved in the DL models. Finally, a cascaded neural network (CNN) classifier is applied for detecting the existence of SARS-CoV-2. In order to showcase the improved performance of the EDLFM-SI technique, a wide range of simulations take place on the COVID-19 CT data set and the SARS-CoV-2 CT scan data set. The simulation outcomes highlighted the supremacy of the EDLFM-SI technique over the recent approaches.

Satisfaction of patients with health care services in tertiary care facilities of Riyadh, Saudi Arabia: A cross-sectional approach.

As part of Saudi Vision 2030, the country's healthcare system is undergoing a significant makeover, with accessibility and effectiveness serving as the benchmarks for measuring patient care quality. This study's goal was to ascertain the degree of patient satisfaction with the medical care and services received in Riyadh's tertiary care facilities. The PSQ-18 (Patient Satisfaction Questionnaire-18), a standardized validated questionnaire including areas of "overall satisfaction," "technical quality," "interpersonal aspect," "communication," "financial aspect," "time spent with the doctor," and "accessibility and convenience," was used in this cross-sectional study on 384 patients of two tertiary care facilities in Riyadh, Saudi Arabia, over a 6-month period. The degree to which sociodemographic characteristics and components of patient satisfaction are correlated was assessed using binary and multiple regression analysis. When the P-value was < 0.05, the results were considered significant and were presented as adjusted odds ratios (AOR). To ascertain how each PSQ-18 subscale affected other subscales, a Pearson Correlation analysis was conducted. The overall degree of satisfaction with all 18 items was 73.77%. The financial component received a rating of 81% compared to 77% for general satisfaction. Technical quality (75%) was followed by accessibility and convenience (73.5%), communication (73%), and interpersonal elements (72%). At 68%, the time spent in the doctor's domain received the lowest rating. The odds of satisfaction were increased by 3.87 times, 3.45 times, and 3.36 times among those who are employed, qualified by university education, and married compared to unemployed (P-value = 0.018), less qualified (P-value = 0.015) and singles (P-value = 0.026), respectively. The younger age group also made 1.78 times more of a difference in higher satisfaction ratings. The general satisfaction domain showed a positive association with other areas. Participants who were satisfied with the communication and accessibility and convenience domains of healthcare providers were the only ones who were typically satisfied with the domain of doctor time spent. The study's findings could act as a benchmark for Saudi Arabia's healthcare services as well as a starting point for quality assurance procedures.