Respiration-Based COPD Detection Using UWB Radar Incorporation with Machine Learning

COPD is a progressive disease that may lead to death if not diagnosed and treated at an early stage. The examination of vital signs such as respiration rate is a promising approach for the detection of COPD. However, simultaneous consideration of the demographic and medical characteristics of patients is very important for better results. The objective of this research is to investigate the capability of UWB radar as a non-invasive approach to discriminate COPD patients from healthy subjects. The non-invasive approach is beneficial in pandemics such as the ongoing COVID-19 pandemic, where a safe distance between people needs to be maintained. The raw data are collected in a real environment (a hospital) non-invasively from a distance of 1.5 m. Respiration data are then extracted from the collected raw data using signal processing techniques. It was observed that the respiration rate of COPD patients alone is not enough for COPD patient detection. However, incorporating additional features such as age, gender, and smoking history with the respiration rate lead to robust performance. Different machine-learning classifiers, including Naïve Bayes, support vector machine, random forest, k nearest neighbor (KNN), Adaboost, and two deep-learning models—a convolutional neural network and a long short-term memory (LSTM) network—were utilized for COPD detection. Experimental results indicate that LSTM outperforms all employed models and obtained 93% accuracy. Performance comparison with existing studies corroborates the superior performance of the proposed approach

The future of artificial intelligence in neurosurgery: a narrative review

Background: Artificial intelligence (AI) and machine learning (ML) algorithms are on the tremendous rise for being incorporated into the field of neurosurgery. AI and ML algorithms are different from other technological advances as giving the capability for the computer to learn, reason, and problem-solving skills that a human inherits. This review summarizes the current use of AI in neurosurgery, the challenges that need to be addressed, and what the future holds. Methods: A literature review was carried out with a focus on the use of AI in the field of neurosurgery and its future implication in neurosurgical research. Results: The online literature on the use of AI in the field of neurosurgery shows the diversity of topics in terms of its current and future implications. The main areas that are being studied are diagnostic, outcomes, and treatment models. Conclusion: Wonders of AI in the field of medicine and neurosurgery hold true, yet there are a lot of challenges that need to be addressed before its implications can be seen in the field of neurosurgery from patient privacy, to access to high-quality data and overreliance on surgeons on AI. The future of AI in neurosurgery is pointed toward a patient-centric approach, managing clinical tasks, and helping in diagnosing and preoperative assessment of the patients

Toward Holistic Energy Management by Electricity Load and Price Forecasting: A Comprehensive Survey

Electricity load and price data pose formidable challenges for forecasting due to their intricate characteristics, marked by high volatility and non-linearity. Machine learning (ML) and deep learning (DL) models have emerged as valuable tools for effectively predicting data exhibiting high volatility, frequent fluctuations, mean-reversion tendencies, and non-stationary behavior. Therefore, this review article is dedicated to providing a comprehensive exploration of the application of machine learning and deep learning techniques in the context of electricity load and price prediction. In contrast to existing literature, our study distinguishes itself in several key ways. We systematically examine ML and DL approaches employed for the prediction of electricity load and price, offering a meticulous analysis of their methodologies and performance. Furthermore, we furnish readers with a detailed compendium of the datasets utilized by these forecasting methods, elucidating the sources and specific characteristics underpinning these datasets. Then, we rigorously conduct a performance comparison across various performance metrics, facilitating a comprehensive assessment of the efficacy of different predictive models. Notably, this comparison is carried out using the same datasets that underlie the diverse methodologies reviewed within this study, ensuring a fair and consistent evaluation. Moreover, we provide an in-depth examination of the diverse performance measures and statistical tools employed in the studies considered, providing valuable insights into the analytical frameworks used to gauge forecasting accuracy and model robustness. Lastly, we devote significant attention to the identification and analysis of prevailing challenges within the realm of electricity load and price prediction. Additionally, we delve into prospective directions for future research, thereby contributing to the advancement of this critical field

Nanobiochar Application in Combination with Mulching Improves Metabolites and Curd Quality Traits in Cauliflower

Sustainable nutrient management approaches could improve production and quality without affecting soil health. We conducted a field experiment to investigate the interactive effects of mulching (no mulch, plastic mulch, and straw mulch) and nanobiochar (NBC) foliar application on biomass, nutrient contents, and metabolites in the leaf and curd of cauliflower (Brassica oleracea var. botrytis). After 20 days of transplantation, NBC (0.1% w/v) was applied as a foliar spray for four consecutive weeks (one spray per week). At the curd initiation stage, changes in chlorophyll, carotenoids, and metabolite concentrations in leaves and curd were determined. The application of nanobiochar significantly enhanced the curd weight and improved curd morphology. Yield traits including curd weight, curd diameter, basal diameter, and stalk length were increased by 30, 13, 16, and 20% by NBC application compared to control. Plastic mulching also increased the aboveground biomass by 32% when compared to no mulching. Moreover, plastic mulching and nanobiochar prominently enhanced root dry weight, curd weight, rough solidity index, total soluble sugar in leaf and curd, calcium in curd, and potassium in leaf and curd of cauliflower. Overall, this study revealed the potential of the foliar application of NBC in promoting the biomass and nutritional properties of cauliflower

Simulation and Analysis of T-Junction Microchannel

Microelectromechanical (MEMS) bring the great revolution in microfluidics. Particularly microchannels are extensively studied in last few years. This paper presents the microfluidic analysis of T-junction type microchannel. ANSYS has been used for microfluidic analysis. The length of microchannel is 1000 µm and length of T-junction is 500 µm. The diameter of microchannel is 100 µm. The velocity and fluid flow variation through the T-junction have been analyzed in computational fluid dynamic (CFX) environment at applied pressure of 100 kPa, 200 kPa and 300 kPa. The maximum velocities vector at the T-Junction walls 3.910e1, 5.701e1 and 7.734e1 have been achieved. The flow rates of 341, 572 and 673 mL/min have been observed through the microchannel with diameters of 100 µm

Impact of Food Literacy on Consumer's Food Purchasing Habits and Dietary Intake - A Systematic Review

Introduction: A well-nourished population represents the sustainable development of a nation. Poor alignment between food intake and dietary recommendations results in consumption of high calorie, low nutrient dense foods. Aims & Objectives: The main goal of this study is to find the relation between food literacy and dietary intake. Secondly, to assess the influence of food literacy on food purchasing habits. Place and duration of study: For this review, collection of studies from PubMed and Cochrane databases was started in May 2020 and was finalized by June 2020. Material & Methods: The eligibility criteria were based on two factors; that the study be written in English and published through a peer reviewed journal. Through the database search, total 673 studies were identified. After checking studies thoroughly at various steps, only 26 were included in this review. Results: 11 studies claimed the link between food label reading and intake of nutrients, while there were 10 studies that measured the consumer's purchase and food choices by their awareness level about food labels. Conclusion: This systematic review demonstrates nutrition education to be directly correlated with the food-related habits of people. Further research is required to get a clear vision about knowledge of nutritional labels and its effect on real life dietary choices

Respiration-Based COPD Detection Using UWB Radar Incorporation with Machine Learning

COPD is a progressive disease that may lead to death if not diagnosed and treated at an early stage. The examination of vital signs such as respiration rate is a promising approach for the detection of COPD. However, simultaneous consideration of the demographic and medical characteristics of patients is very important for better results. The objective of this research is to investigate the capability of UWB radar as a non-invasive approach to discriminate COPD patients from healthy subjects. The non-invasive approach is beneficial in pandemics such as the ongoing COVID-19 pandemic, where a safe distance between people needs to be maintained. The raw data are collected in a real environment (a hospital) non-invasively from a distance of 1.5 m. Respiration data are then extracted from the collected raw data using signal processing techniques. It was observed that the respiration rate of COPD patients alone is not enough for COPD patient detection. However, incorporating additional features such as age, gender, and smoking history with the respiration rate lead to robust performance. Different machine-learning classifiers, including Naïve Bayes, support vector machine, random forest, k nearest neighbor (KNN), Adaboost, and two deep-learning models—a convolutional neural network and a long short-term memory (LSTM) network—were utilized for COPD detection. Experimental results indicate that LSTM outperforms all employed models and obtained 93% accuracy. Performance comparison with existing studies corroborates the superior performance of the proposed approach

Advances in the Optimization of Fe Nanoparticles: Unlocking Antifungal Properties for Biomedical Applications

In recent years, nanotechnology has achieved a remarkable status in shaping the future of biological applications, especially in combating fungal diseases. Owing to excellence in nanotechnology, iron nanoparticles (Fe NPs) have gained enormous attention in recent years. In this review, we have provided a comprehensive overview of Fe NPs covering key synthesis approaches and underlying working principles, the factors that influence their properties, essential characterization techniques, and the optimization of their antifungal potential. In addition, the diverse kinds of Fe NP delivery platforms that command highly effective release, with fewer toxic effects on patients, are of great significance in the medical field. The issues of biocompatibility, toxicity profiles, and applications of optimized Fe NPs in the field of biomedicine have also been described because these are the most significant factors determining their inclusion in clinical use. Besides this, the difficulties and regulations that exist in the transition from laboratory to experimental clinical studies (toxicity, specific standards, and safety concerns) of Fe NPs-based antifungal agents have been also summarized

Effect of preterm birth on blood pressure in later life: A systematic review and meta-analysis

Introduction: Preterm birth is linked to various complications in both infancy and adulthood. We assessed the association between preterm birth and hypertension in adulthood. Materials and Methods: PubMed, EMBASE, and Cochrane CENTRAL Register were searched for randomized controlled trials (RCT) comparing systolic and diastolic blood pressures in individuals born preterm and those born full-term, from inception till April 11th, 2022. Data were extracted, pooled, and analyzed. Forest plots were created for a visual demonstration. Results: Twenty-eight studies were included in our meta-analysis. SBP and DBP across all categories (Mean, Ambulatory, Daytime, and Nighttime) were higher in the preterm group compared to the term group. Mean SBP, mean ambulatory SBP, mean daytime SBP and mean nighttime SBP were 4.26 mmHg [95% CI: 3.09–5.43; P < 0.00001], 4.53 mmHg [95% CI: 1.82–7.24; P = 0.001], 4.51 mmHg [95% CI: 2.56–6.74; P < 0.00001], and 3.06 mmHg [95% CI: 1.32–4.80; P = 0.0006] higher in the preterm group, respectively. Mean DBP, mean ambulatory DBP, mean daytime DBP, and mean nighttime DBP were 2.32 mmHg [95% CI: 1.35–3.29; P < 0.00001], 1.54 mmHg [95% CI 0.68–2.39; P = 0.0004], 1.74 mmHg [95% CI: 0.92–2.56; P < 0.0001], and 1.58 mmHg [95% CI: 0.34–2.81; P = 0.01] higher in the preterm group, respectively. Conclusion: Our observations suggest that individuals who were born preterm may have higher blood pressures as compared to those who were born full-term