Electrolyte Imbalance

The subject of this paper is Electrolyte Imbalance. Unqualified, such a title may sound like an overambitious attempt to embrace within the span of forty-five minutes the whole gamut of the disordered physiology of the anions and cations. Mindful of the penalty of such vaulting ambition which would, I think, without doubt "fall on the other" its content will be limited largely to some of the more acute and dramatic examples of disturbances of body fluid that are commonly encountered in surgical patients. Surgery deals with the sort of patients of whom John Donne might well have been thinking when he said "this minute I was well and I am ill this minute " and consequently the pattern of electrolyte imbalance encountered by the surgeon is usually drawn in starker and bolder lines than is the more subtle, long drawn out problem with which the physician is often confronted. This makes life considerably easier for the surgeon who is not uncommonly a simple man of action rather than a profound thinker; an extrovert rather than an introvert, a Roman rather than a Greek. Therefore, this account is mainly from a surgeon's point of view with an occasional digression

Non-Invasive Driver Drowsiness Detection System.

Drowsiness when in command of a vehicle leads to a decline in cognitive performance that affects driver behavior, potentially causing accidents. Drowsiness-related road accidents lead to severe trauma, economic consequences, impact on others, physical injury and/or even death. Real-time and accurate driver drowsiness detection and warnings systems are necessary schemes to reduce tiredness-related driving accident rates. The research presented here aims at the classification of drowsy and non-drowsy driver states based on respiration rate detection by non-invasive, non-touch, impulsive radio ultra-wideband (IR-UWB) radar. Chest movements of 40 subjects were acquired for 5 m using a lab-placed IR-UWB radar system, and respiration per minute was extracted from the resulting signals. A structured dataset was obtained comprising respiration per minute, age and label (drowsy/non-drowsy). Different machine learning models, namely, Support Vector Machine, Decision Tree, Logistic regression, Gradient Boosting Machine, Extra Tree Classifier and Multilayer Perceptron were trained on the dataset, amongst which the Support Vector Machine shows the best accuracy of 87%. This research provides a ground truth for verification and assessment of UWB to be used effectively for driver drowsiness detection based on respiration

A simple and surprisingly accurate approach to the chemical bond obtained from dimensional scaling

We present a new dimensional scaling transformation of the Schrodinger equation for the two electron bond. This yields, for the first time, a good description of the two electron bond via D-scaling. There also emerges, in the large-D limit, an intuitively appealing semiclassical picture, akin to a molecular model proposed by Niels Bohr in 1913. In this limit, the electrons are confined to specific orbits in the scaled space, yet the uncertainty principle is maintained because the scaling leaves invariant the position-momentum commutator. A first-order perturbation correction, proportional to 1/D, substantially improves the agreement with the exact ground state potential energy curve. The present treatment is very simple mathematically, yet provides a strikingly accurate description of the potential energy curves for the lowest singlet, triplet and excited states of H_2. We find the modified D-scaling method also gives good results for other molecules. It can be combined advantageously with Hartree-Fock and other conventional methods.Comment: 4 pages, 5 figures, to appear in Phys. Rev. Letter

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

Ultra-Wide Band Radar Empowered Driver Drowsiness Detection with Convolutional Spatial Feature Engineering and Artificial Intelligence

Driving while drowsy poses significant risks, including reduced cognitive function and the potential for accidents, which can lead to severe consequences such as trauma, economic losses, injuries, or death. The use of artificial intelligence can enable effective detection of driver drowsiness, helping to prevent accidents and enhance driver performance. This research aims to address the crucial need for real-time and accurate drowsiness detection to mitigate the impact of fatigue-related accidents. Leveraging ultra-wideband radar data collected over five minutes, the dataset was segmented into one-minute chunks and transformed into grayscale images. Spatial features are retrieved from the images using a two-dimensional Convolutional Neural Network. Following that, these features were used to train and test multiple machine learning classifiers. The ensemble classifier RF-XGB-SVM, which combines Random Forest, XGBoost, and Support Vector Machine using a hard voting criterion, performed admirably with an accuracy of 96.6%. Additionally, the proposed approach was validated with a robust k-fold score of 97% and a standard deviation of 0.018, demonstrating significant results. The dataset is augmented using Generative Adversarial Networks, resulting in improved accuracies for all models. Among them, the RF-XGB-SVM model outperformed the rest with an accuracy score of 99.58%

Infrared 3-4 Micron Spectroscopic Investigations of a Large Sample of Nearby Ultraluminous Infrared Galaxies

We present infrared L-band (3-4 micron) nuclear spectra of a large sample of nearby ultraluminous infrared galaxies (ULIRGs).ULIRGs classified optically as non-Seyferts (LINERs, HII-regions, and unclassified) are our main targets. Using the 3.3 micron polycyclic aromatic hydrocarbon (PAH) emission and absorption features at 3.1 micron due to ice-covered dust and at 3.4 micron produced by bare carbonaceous dust, we search for signatures of powerful active galactic nuclei (AGNs) deeply buried along virtually all lines-of-sight. The 3.3 micron PAH emission, the signatures of starbursts, is detected in all but two non-Seyfert ULIRGs, but the estimated starburst magnitudes can account for only a small fraction of the infrared luminosities. Three LINER ULIRGs show spectra typical of almost pure buried AGNs, namely, strong absorption features with very small equivalent-width PAH emission. Besides these three sources, 14 LINER and 3 HII ULIRGs' nuclei show strong absorption features whose absolute optical depths suggest an energy source more centrally concentrated than the surrounding dust, such as a buried AGN. In total, 17 out of 27 (63%) LINER and 3 out of 13 (23%) HII ULIRGs' nuclei show some degree of evidence for powerful buried AGNs, suggesting that powerful buried AGNs may be more common in LINER ULIRGs than in HII ULIRGs. The evidence of AGNs is found in non-Seyfert ULIRGs with both warm and cool far-infrared colors. These spectra are compared with those of 15 ULIRGs' nuclei with optical Seyfert signatures taken for comparison.The overall spectral properties suggest that the total amount of dust around buried AGNs in non-Seyfert ULIRGs is systematically larger than that around AGNs in Seyfert 2 ULIRGs.Comment: 56 pages, 9 figures, accepted for publication in ApJ (20 January 2006, vol 637 issue

An Approach to Detect Chronic Obstructive Pulmonary Disease Using UWB Radar-Based Temporal and Spectral Features

Chronic obstructive pulmonary disease (COPD) is a severe and chronic ailment that is currently ranked as the third most common cause of mortality across the globe. COPD patients often experience debilitating symptoms such as chronic coughing, shortness of breath, and fatigue. Sadly, the disease frequently goes undiagnosed until it is too late, leaving patients without the care they desperately need. So, COPD detection at an early stage is crucial to prevent further damage to the lungs and improve quality of life. Traditional COPD detection methods often rely on physical examinations and tests such as spirometry, chest radiography, blood gas tests, and genetic tests. However, these methods may not always be accurate or accessible. One of the key vital signs for detecting COPD is the patient’s respiration rate. However, it is crucial to consider a patient’s medical and demographic characteristics simultaneously for better detection results. To address this issue, this study aims to detect COPD patients using artificial intelligence techniques. To achieve this goal, a novel framework is proposed that utilizes ultra-wideband (UWB) radar-based temporal and spectral features to build machine learning and deep learning models. This new set of temporal and spectral features is extracted from respiration data collected non-invasively from 1.5 m distance using UWB radar. Different machine learning and deep learning models are trained and tested on the collected dataset. The findings are promising, with a high accuracy score of 100% for COPD detection. This means that the proposed framework could potentially save lives by identifying COPD patients at an early stage. The k-fold cross-validation technique and performance comparison with the state-of-the-art studies are applied to validate its performance, ensuring that the results are robust and reliable. The high accuracy score achieved in the study implies that the proposed framework has the potential for the efficient detection of COPD at an early stage

Spectral Energy Distributions of Be and Other Massive Stars

We present spectrophotometric data from 0.4 to 4.2 microns for bright, northern sky, Be stars and several other types of massive stars. Our goal is to use these data with ongoing, high angular resolution, interferometric observations to model the density structure and sky orientation of the gas surrounding these stars. We also present a montage of the H-alpha and near-infrared emission lines that form in Be star disks. We find that a simplified measurement of the IR excess flux appears to be correlated with the strength of emission lines from high level transitions of hydrogen. This suggests that the near-IR continuum and upper level line fluxes both form in the inner part of the disk, close to the star.Comment: 2010, PASP, 122, 37

Phase waves in mode-locked superfluorescent lasers

We present results from both theoretical and experimental studies of the noise characteristics of mode-locked superfluorescent lasers. The results show that observed macroscopic broadband amplitude noise on the laser pulse train has its origin in quantum noise-initiated ''phase-wave'' fluctuations, and we find an associated phase transition in the noise characteristics as a function of laser cavity detuning