A New Photovoltaic Energy Sharing System between Homes in Standalone Areas

Today, global energy consumption is dominated by fossil fuels such as oil, coal and gas. The intensive consumption of these energy sources gives rise to greenhouse gas emissions and therefore an increase in CO2 emissions. Photovoltaic energy has persistently been considered as a green and pollution-free renewable energy source to overcome greenhouse effect and energy crisis. This paper describes a new method of photovoltaic energy sharing in standalone micro-grids using photovoltaic panels. This approach is based on automatic electrical energy sharing depending on the state of charge (SOC) of the electrical storage unit using by each home and on the electrical power consumption of each home.The monitoring system is connected to each home in micro-grid, it manage each home’s energy use, and assigns more energy to a large energy-consuming home. This architecture contributes to reducing total energy lost

Design and Implementation Intelligent Adaptive Front-lighting System of Automobile using Digital Technology on Arduino Board

The automatic light AFS (Adaptive Front - Lighting System) is added to the capabilities of modern vehicles that will improve the safety of vehicle drivers and passengers traveling at night. A new architecture of the AFS has proposed in this paper. This architecture is powerful and intelligent using the PWM technique on ARDUINO Board replaces the old mechanical system based on stepper motors

Prosthesis-patient mismatch after aortic valve replacement in the PARTNER 2 trial and registry

Objectives This study aimed to compare incidence and impact of measured prosthesis-patient mismatch (PPMM) versus predicted PPM (PPMP) after surgical aortic valve replacement (SAVR) and transcatheter aortic valve replacement (TAVR). Background TAVR studies have used measured effective orifice area indexed (EOAi) to body surface area (BSA) to define PPM, but most SAVR series have used predicted EOAi. This difference may contribute to discrepancies in incidence and outcomes of PPM between series. Methods The study analyzed SAVR patients from the PARTNER (Placement of Aortic Transcatheter Valves) 2A trial and TAVR patients from the PARTNER 2 SAPIEN 3 Intermediate Risk registry. PPM was classified as moderate if EOAi ≤0.85 cm2/m2 (≤0.70 if obese: body mass index ≥30 kg/m2) and severe if EOAi ≤0.65 cm2/m2 (≤0.55 if obese). PPMM was determined by the core lab–measured EOAi on 30-day echocardiogram. PPMP was determined by 2 methods: 1) using normal EOA reference values previously reported for each valve model and size (PPMP1; n = 929 SAVR, 1,069 TAVR) indexed to BSA; and 2) using normal reference EOA predicted from aortic annulus size measured by computed tomography (PPMP2; n = 864 TAVR only) indexed to BSA. Primary endpoint was the composite of 5-year all-cause death and rehospitalization. Results The incidence of moderate and severe PPMP was much lower than PPMM in both SAVR (PPMP1: 28.4% and 1.2% vs. PPMM: 31.0% and 23.6%) and TAVR (PPMP1: 21.0% and 0.1% and PPMP2: 17.0% and 0% vs. PPMM: 27.9% and 5.7%). The incidence of severe PPMM and severe PPMP1 was lower in TAVR versus SAVR (P < 0.001). The presence of PPM by any method was associated with higher transprosthetic gradient. Severe PPMP1 was independently associated with events in SAVR after adjustment for sex and Society of Thoracic Surgeons score (hazard ratio: 3.18;95% CI: 1.69-5.96; P < 0.001), whereas no association was observed between PPM by any method and outcomes in TAVR. Conclusions EOAi measured by echocardiography results in a higher incidence of PPM following SAVR or TAVR than PPM based on predicted EOAi. Severe PPMP is rare (<1.5%), but is associated with increased all-cause death and rehospitalization after SAVR, whereas it is absent following TAVR

Social Media Toxicity Classification Using Deep Learning: Real-World Application UK Brexit

Social media has become an essential facet of modern society, wherein people share their opinions on a wide variety of topics. Social media is quickly becoming indispensable for a majority of people, and many cases of social media addiction have been documented. Social media platforms such as Twitter have demonstrated over the years the value they provide, such as connecting people from all over the world with different backgrounds. However, they have also shown harmful side effects that can have serious consequences. One such harmful side effect of social media is the immense toxicity that can be found in various discussions. The word toxic has become synonymous with online hate speech, internet trolling, and sometimes outrage culture. In this study, we build an efficient model to detect and classify toxicity in social media from user-generated content using the Bidirectional Encoder Representations from Transformers (BERT). The BERT pre-trained model and three of its variants has been fine-tuned on a well-known labeled toxic comment dataset, Kaggle public dataset (Toxic Comment Classification Challenge). Moreover, we test the proposed models with two datasets collected from Twitter from two different periods to detect toxicity in user-generated content (tweets) using hashtages belonging to the UK Brexit. The results showed that the proposed model can efficiently classify and analyze toxic tweets

Boosted Nutcracker optimizer and Chaos Game Optimization with Cross Vision Transformer for medical image classification

This paper presents an alternative breast cancer classification method based on enhancing the efficiency of the Nutcracker optimizer (NO) algorithm using Chaos Game Optimization (CGO). In addition, we use the Cross Vision Transformer to extract features from breast images. After that, the relevant features are allocated using the modified version of NO based on CGO. This modification aims to enhance the exploration ability of the NO algorithm to discover the region of a feasible solution (an optimal subset of features). The performance of the developed model is validated by using twelve functions from the CEC2022 benchmark and comparing the results with traditional CGO and NO algorithms. In addition, to assess the applicability of the developed technique, a set of three datasets, and the results were compared with other techniques. The results illustrate the high ability of the developed method to enhance the detection of breast cancer and find the optimal solution of CEC2022 functions according to different performance measures

Nurse-led care for the management of rheumatoid arthritis: A review of the global literature and proposed strategies for implementation in Africa and the Middle East

Globally, increasing demand for rheumatology services has led to a greater reliance on non-physician healthcare professionals (HCPs), such as rheumatology nurse specialists, to deliver care as part of a multidisciplinary team. Across Africa and the Middle East (AfME), there remains a shortage of rheumatology HCPs, including rheumatology nurses, which presents a major challenge to the delivery of rheumatology services, and subsequently the treatment and management of conditions such as rheumatoid arthritis (RA). To further explore the importance of nurse-led care (NLC) for patients with RA and create a set of proposed strategies for the implementation of NLC in the AfME region, we used a modified Delphi technique. A review of the global literature was conducted using the PubMed search engine, with the most relevant publications selected. The findings were summarized and presented to the author group, which was composed of representatives from different countries and HCP disciplines. The authors also drew on their knowledge of the wider literature to provide context. Overall, results suggest that NLC is associated with improved patient perceptions of RA care, and equivalent or superior clinical and cost outcomes versus physician-led care in RA disease management. Expert commentary provided by the authors gives insights into the challenges of implementing nurse-led RA care. We further report practical proposed strategies for the development and implementation of NLC for patients with RA, specifically in the AfME region. These proposed strategies aim to act as a foundation for the introduction and development of NLC programs across the AfME region