An FPGA Kalman-MPPT implementation adapted in SST-based dual active bridge converters for DC microgrids systems

The design of digital hardware controllers for the integration of renewable energy sources in DC microgrids is a research topic of interest. In this paper, a Kalman filter-based maximum power point tracking algorithm is implemented in an FPGA and adapted in a dual active bridge (DAB) converter topology for DC microgrids. This approach uses the Hardware/Software (HW/SW) co-design paradigm in combination with a pipelined piecewise polynomial approximation design of the Kalman-maximum power point tracking (MPPT) algorithm instead of traditional lookup table (LUT)-based methods. Experimental results reveal a good integration of the Kalman-MPPT design with the DAB-based converter, particularly during irradiation and temperature variations due to changes in weather conditions, as well as a good balanced hardware design in complexity and area-time performance compared to other state-of-art FPGA designs

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

The mutation profile of JAK2, MPL and CALR in Mexican patients with Philadelphia chromosome-negative myeloproliferative neoplasms

Context and objective: By using molecular markers, it is possible to gain information on both the classification and etiopathogenesis of chronic myeloproliferative neoplasias (MPN). Methods: In a group of 27 Mexican mestizo patients with MPNs, we studied seven molecular markers: the BCR/ABL1 fusion gene, the JAK2 V617F mutation, the JAK2 exon 12 mutations, the MPL W515L mutation, the MPL W515K mutation, and the calreticulin (CALR) exon 9 deletion or insertion. Patients with the BCR/ABL1 fusion gene were excluded. We studied 14 patients with essential thrombocythemia (ET), eight with polycythemia vera (PV), four with primary myelofibrosis (MF), and one with undifferentiated MPN. Results: We found twelve individuals with the JAK2 V617F mutation; five of them had been clinically classified as PV, five as ET, and one as MF. One patient with the MPL W515L was identified with a clinical picture of ET. Five patients with the CALR mutation were identified, four ET and one MF. No individuals with either the MPL W515K mutation or the JAK2 exon 12 mutations were identified. The most consistent relationship was that between PV and the JAK2 V617F mutation (p = .01). Conclusions: Despite its small size, the study shows much less prevalence of JAK2 mutation in PV, ET and MF, which does not match international data. Keywords: Myeloproliferative neoplasms, Molecular markers, JAK2 V617F mutation, JAK2 exon 12 mutation, MPL W515L mutation, MPL W515K mutation, CAL-R mutation

Plant Microbial Fuel Cells–Based Energy Harvester System for Self-powered IoT Applications

The emergence of modern technologies, such as Wireless Sensor Networks (WSNs), the Internet-of-Things (IoT), and Machine-to-Machine (M2M) communications, involves the use of batteries, which pose a serious environmental risk, with billions of batteries disposed of every year. However, the combination of sensors and wireless communication devices is extremely power-hungry. Energy Harvesting (EH) is fundamental in enabling the use of low-power electronic devices that derive their energy from external sources, such as Microbial Fuel Cells (MFC), solar power, thermal and kinetic energy, among others. Plant Microbial Fuel Cell (PMFC) is a prominent clean energy source and a step towards the development of self-powered systems in indoor and outdoor environments. One of the main challenges with PMFCs is the dynamic power supply, dynamic charging rates and low-energy supply. In this paper, a PMFC-based energy harvester system is proposed for the implementation of autonomous self-powered sensor nodes with IoT and cloud-based service communication protocols. The PMFC design is specifically adapted with the proposed EH circuit for the implementation of IoT-WSN based applications. The PMFC-EH system has a maximum power point at 0.71 V, a current density of 5 mA cm − 2 , and a power density of 3.5 mW cm − 2 with a single plant. Considering a sensor node with a current consumption of 0.35 mA, the PMFC-EH green energy system allows a power autonomy for real-time data processing of IoT-based low-power WSN systems

Multimodal Power Management Based on Decision Tree for Internet of Wearable Things Systems

Precision medicine is now evolving to include internet-of-wearable-things (IoWT) applications. This trend requires the development of novel systems and digital signal processing algorithms to process large amounts of data in real time. However, performing continuous measurements and complex computational algorithms in IoWT systems demands more power consumption. A novel solution to this problem consists in developing energy-aware techniques based on low-power machine learning (ML) algorithms to efficiently manage energy consumption. This paper proposes a multimodal dynamic power management strategy (DPMS) based on the ML-decision tree algorithm to implement an autonomous IoWT system. The multimodal approach analyzes the supercapacitor storage level and the incoming biosignal statistics to efficiently manage the energy of the wearable device. A photoplethysmography (PPG) sensing prototype was developed to evaluate the proposed ML-DPMS programmed in a Nordic nRF52840 processor. The experimental results demonstrate an IoWT system’s low consumption of 25.74 J, and a photovoltaic solar power generation capacity of 380 mW. The proposed ML-DPMS demonstrates a battery life extension of 3.87×, i.e., 99.72 J of energy harvested, which represents the possibility to achieve at least 2.4× more data transmissions, in comparison with the widely used uniform power management approach. In addition, when the supercapacitor’s energy is compromised, the decision tree technique achieves a good energy conservation balance consuming in the same period of time 39.6% less energy than the uniform power approach

Risk of COVID-19 after natural infection or vaccinationResearch in context

Summary: Background: While vaccines have established utility against COVID-19, phase 3 efficacy studies have generally not comprehensively evaluated protection provided by previous infection or hybrid immunity (previous infection plus vaccination). Individual patient data from US government-supported harmonized vaccine trials provide an unprecedented sample population to address this issue. We characterized the protective efficacy of previous SARS-CoV-2 infection and hybrid immunity against COVID-19 early in the pandemic over three-to six-month follow-up and compared with vaccine-associated protection. Methods: In this post-hoc cross-protocol analysis of the Moderna, AstraZeneca, Janssen, and Novavax COVID-19 vaccine clinical trials, we allocated participants into four groups based on previous-infection status at enrolment and treatment: no previous infection/placebo; previous infection/placebo; no previous infection/vaccine; and previous infection/vaccine. The main outcome was RT-PCR-confirmed COVID-19 >7–15 days (per original protocols) after final study injection. We calculated crude and adjusted efficacy measures. Findings: Previous infection/placebo participants had a 92% decreased risk of future COVID-19 compared to no previous infection/placebo participants (overall hazard ratio [HR] ratio: 0.08; 95% CI: 0.05–0.13). Among single-dose Janssen participants, hybrid immunity conferred greater protection than vaccine alone (HR: 0.03; 95% CI: 0.01–0.10). Too few infections were observed to draw statistical inferences comparing hybrid immunity to vaccine alone for other trials. Vaccination, previous infection, and hybrid immunity all provided near-complete protection against severe disease. Interpretation: Previous infection, any hybrid immunity, and two-dose vaccination all provided substantial protection against symptomatic and severe COVID-19 through the early Delta period. Thus, as a surrogate for natural infection, vaccination remains the safest approach to protection. Funding: National Institutes of Health