Artifact Detection in Lung Ultrasound: An Analytical Approach

Lung ultrasound is used to detect various artifacts in the lungs that support the diagnosis of different conditions. There is ongoing research to support the automatic detection of such artifacts using machine learning. We propose a solution that uses analytical computer vision methods to detect two types of lung artifacts, namely A- and B-lines. We evaluate the proposed approach on the POCUS dataset and data acquired from a hospital. We show that by using the Fourier transform, we can analyze lung ultrasound images in real-time and classify videos with an accuracy above 70%. We also evaluate the method's applicability for segmentation, showcasing its high success rate for B-lines (89% accuracy) and its shortcomings for A-line detection. We then propose a hybrid solution that uses a combination of neural networks and analytical methods to increase accuracy in horizontal line detection, emphasizing the pleura.

Influence of Nitrogen-Doped Carbon Dot and Silver Nanoparticle Modified Carbon Paste Electrodes on the Potentiometric Determination of Tobramycin Sulfate: A Comparative Study

Two inexpensive and simple methods for synthesis of carbon nanodots were applied and compared to each other, namely a hydrothermal and microwave-assisted method. The synthesized carbon nanodots were characterized using transmission electron microscopy (TEM), ultraviolet-visible (UV-Vis), photoluminescence (PL), Fourier transform-infrared spectroscopy (FTIR), and X-ray diffraction (XRD). The synthesized microwave carbon nanodots had smaller particle size and were thus chosen for better electrochemical performance. Therefore, they were used for our modification process. The proposed electrodes performance characteristics were evaluated according to the IUPAC guidelines, showing linear response in the concentration range 10−6–10−2, 10−7–10−2, and 10−8–10−2 M of tobramycin with a Nernstian slope of 52.60, 58.34, and 57.32 mV/decade for the bare, silver nanoparticle and carbon nanodots modified carbon paste electrodes, respectively. This developed potentiometric method was used for quantification of tobramycin in its co-formulated dosage form and spiked human plasma with good recovery percentages and without interference of the co-formulated drug loteprednol etabonate and excipients.

Thai Covid-19 Wave Breaks Analysis Based on Fourier Transform

Around the beginning of April 2021, the Thai Covid-19 Alpha-Delta wave began to spread. After approximately 37 weeks, the Omicron wave emerged in mid-December 2021. This paper proposes a Fourier transform approach for examining the exact wave breaks and reveals the hidden information in the power spectrum of the pandemic by converting the number of infected cases, death cases, and recovered cases caused by Covid-19 in Thailand from the time domain to the frequency domain. Analyses are conducted on wave intensity, pattern, and cycle duration. Two validation procedures are proposed to ensure the correct identification of wave breaks. The first strategy employs cross-correlation, whereas the second technique utilizes matching peak frequencies. Our results mathematically determined the Alpha-Delta wave's end date and the Omicron wave's beginning date. The matched peak frequency between the waves was discovered at frequency 5.286, corresponding to a cycle length of 7.027 days. The outcome enables policymakers to comprehend the pandemic's trend and determine if the policy should be strengthened or relaxed. © 2022 IEEE.

A Lightweight Method for Fast Deep Learning Inference in Medical Image Classification Based on Fourier Transform and Signal Processing

Medical image classification often relies on Convolutional Neural Network (CNN) for its powerful ability to obtain accurate predictions. However, considering novel diseases such as COVID variants and complications, the medical and clinical field desires diagnosis that is both fast and accurate. This paper proposes a lightweight method that conducts deep learning-based classification in the Fourier domain without convolution operation and reduces the computational cost. The paper focuses specifically on pneumonia, which is a lung infection and a typical COVID complication. To achieve a decent accuracy that is comparable to the CNN performance, signal processing techniques, namely Fourier transform is utilized to extract features from the frequency domain. The proposed method uses Discrete Cosine Transform (DCT) to find the frequency domain values as well as other useful parameters. As part of the methodology, a fundamental Artificial Neural Network (ANN) is built to perform the classification task. In the meanwhile, two pre-trained CNN architectures, ResNet50V2 and VGG19, are implemented under the same environment as standards for comparison. With the same hyperparameters and training epochs, the ANN obtained a validation accuracy that is 2.35% lower than the CNNs but 15 times faster in training. The experimental result demonstrates the advantage of the proposed method in inference speed and model size, indicating that the overall objective is attained. The findings also open the possibility of generalizing such an approach for other medical diagnosis in the future. © 2022 IEEE.

Structural characterization and anti-inflammatory activity of fucoidan isolated from Ecklonia maxima stipe

Enzyme-assisted hydrolysis is frequendy used as a cost-effective and efficient method to obtain functional ingredients from bioresources. This study involved die enzyme-assisted hydrolyzation and purification of fucoidan from Ecklonia maxima stipe and die investigation of its anti-inflammatory activity in lipopolysaccharide (LPS)-induced RAW 264.7 cells. Fucoidans of Viscozyme-assisted hydrolysate from E. maxima (EMSFs) harvested in Jeju, Korea. Structural and chemical characterizations were performed using fourier transform infrared spectroscopy, scanning electron microscope, and monosaccharide analysis. Among fucoidans, EMSF6 was rich in fucose and sulfate and had a similar structural character to commercial fucoidan. EMSF6 showed a strong inhibitory effect on nitric oxide generation in LPS-induced RAW 264.7 cells and significantly decreased die production of LPS-induced pro-inflammatory cytokines, including interleukin-6, interleukin-1 p, and tumor necrosis factor a. The anti-inflammatory potential of EMSF6 was mediated through the down-regulation of inducible nitric oxide synthase and cyclooxygenase-2 expression. Thus, fucoidans from&temppound;. maxima stipe are promising candidates for functional food products.

Efficient Clustering of Genetic Entities

The paper proposes a novel application of a highly efficient method for comparing symbol sequences based on convolution. The technique utilizes Fast Fourier Transform (FFT) to compare long symbol sequences achieving practical results using commodity PC hardware. While the main focus is on bioinformatics, the proposed approach is general and can work beyond genetic sequences. One of the main advantages of the proposed method is the robustness to insertion/deletion. Also, unlike standard alignment algorithms, the proposed method is parameter-free. The paper shows that the FFT-based comparison allows for efficient clustering of long sequences in bioinformatics as a practical application. Exploration of coronaviruses offers an illustration of the proposed clustering techniques. © 2022 IEEE.

Wireless Communication System and Its Application in Big Data Remote Monitoring and Decision-Making

The development of computer technology has promoted the widespread application of unmanned technology. Remote monitoring of wireless devices is an application of unmanned technology. To improve the remote monitoring of wireless devices, this study establishes a remote monitoring and decision-making framework based on wireless communication systems. With the wireless communication system, signals that characterize the operating status of devices can be obtained in real-time. Based on the collected signals, the remote monitoring system can identify the current health status of wireless devices, thereby providing auxiliary decision-making for device operation. In the case study, the main engine of an unmanned surface vehicle is used as the study object. The results show that most of the relative errors corresponding to the state identification results of the established remote monitoring framework are within 5%. Moreover, the results present that the linear correlation coefficients between the predicted and real results are greater than 0.95. Therefore, the established remote monitoring framework based on the wireless communication system has good reliability in the state identification of wireless devices.

Antimicrobial Activity of a Titanium Dioxide Additivated Thermoset

The transmission of pathogens via surfaces poses a major health problem, particularly in hospital environments. Antimicrobial surfaces can interrupt the path of spread, while photocatalytically active titanium dioxide (TiO2) nanoparticles have emerged as an additive for creating antimicrobial materials. Irradiation of such particles with ultraviolet (UV) light leads to the formation of reactive oxygen species that can inactivate bacteria. The aim of this research was to incorporate TiO2 nanoparticles into a cellulose-reinforced melamine-formaldehyde resin (MF) to obtain a photocatalytic antimicrobial thermoset, to be used, for example, for device enclosures or tableware. To this end, composites of MF with 5, 10, 15, and 20 wt% TiO2 were produced by ultrasonication and hot pressing. The incorporation of TiO2 resulted in a small decrease in tensile strength and little to no decrease in Shore D hardness, but a statistically significant decrease in the water contact angle. After 48 h of UV irradiation, a statistically significant decrease in tensile strength for samples with 0 and 10 wt% TiO2 was measured but with no statistically significant differences in Shore D hardness, although a statistically significant increase in surface hydrophilicity was measured. Accelerated methylene blue (MB) degradation was measured during a further 2.5 h of UV irradiation and MB concentrations of 12% or less could be achieved. Samples containing 0, 10, and 20 wt% TiO2 were investigated for long-term UV stability and antimicrobial activity. Fourier-transform infrared spectroscopy revealed no changes in the chemical structure of the polymer, due to the incorporation of TiO2, but changes were detected after 500 h of irradiation, indicating material degradation. Specimens pre-irradiated with UV for 48 h showed a total reduction in Escherichia coli when exposed to UV irradiation.

The Variation of Rice Quality and Relevant Starch Structure during Long-Term Storage

The main substances of rice are starches, which vary their metabolism during storage. We conducted a series of tests including rice physicochemical properties, edible quality, starch content and chain length distribution along with starch structure variation to disclose the shift of rice quality by observing the changes of rice during storage. The results showed that: (1) the rice deterioration occurred as time passed, and the germination rate decreased from 70.8% to 29.4% during the storage;(2) fatty acid values increased significantly during long-term storage;(3) electrical conductivity increased as time passed;and (4) the two-year-storage rice showed significantly decreased viscosity and edible quality after sensory evaluation, decreased hardness and damaged surface area of starch granules as storage time passed. Additionally, the damaged surface area of starch granules increased with storage time. Fourier transform infrared spectroscopy (FTIR) showed that the short-range order and spiral degree of rice starch first decreased in the first year and then increased over the storage time. Furthermore, X-ray diffraction showed that the main starch of rice was A-type crystalline. Meanwhile, apparent amylose content increased from 31.00% to 33.85%, then decreased to 31.75%. The peak viscosity reduced from 2735.00 mPa·s to 2163.67 mPa·s and the disintegration value was brought down from 1377.67 mPa·s to 850.33 mPa·s. Based on the results, rice should not be stored for more than 2 years under suitable granary conditions to maintain it at a good quality.

Off-the-Shelf Solution for Measurement and Calculation of Respiration and Heart Rates for COVID-19 Diagnosis and Monitoring

In this paper, we present hardware-software methodology to measure and calculate heart and respiratory rates by using single, low-cost microcontroller chip of limited computation and memory performances, as ATTINY85. Sensors and analog front-end are very simple, directly interfaced to microcontroller pins. Implemented time and frequency domain signal processing algorithms are optimized for low-bits, low-memory architectures and allow fast reading of rates, provide satisfactory accuracy, noise immunity and low power consumption. The same methodology can be used in similar applications to determine dominant spectral frequency of slow signal, from a smaller number of points. © 2022 IEEE.

Predict Students’ Attention in Online Learning Using EEG Data

In education, it is critical to monitor students’ attention and measure the extents to which students participate and the differences in their levels and abilities. The overall goal of this study was to increase the quality of distance education. In particular, in order to craft an approach that will effectively augment online learning using objective measures of brain activity, we propose a brain–computer interface (BCI) system that aims to use electroencephalography (EEG) signals for the detection of student’s attention during online classes. This system will aid teachers to objectively assess student attention and engagement. To this end, experiments were conducted on a public dataset;we extracted power spectral density (PSD) features using used a fast Fourier transform. Different attention indexes were calculated. Then, we built three different classification algorithms: k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF). Our proposed random forest classifier achieved a higher accuracy (96%) than KNN and SVM. Moreover, our results compared to state-of-the-art attention-detection systems with respect to the same dataset. Our findings revealed that the proposed RF approach can be used to effectively distinguish the attention state of a user.

XCO2 and XCH4 Reconstruction Using GOSAT Satellite Data Based on EOF-Algorithm

The Greenhouse Gases Observing Satellite (GOSAT) can help to ascertain the global distribution of carbon dioxide (CO2) and methane (CH4), and how the sources and sinks of these gases vary by season, year, and location. However, the data provided by the GOSAT level 2 and 3 products have certain limitations due to their lack of spatial and temporal information;even with the application of the kriging geostatistical method on the level 2 products, the processing algorithms still need further upgrades. In this study, we apply an empirical orthogonal function (EOF)-based method on the GOSAT L3 products (137 images, from January 2010 to May 2021) to estimate the column average of carbon dioxide and methane (XCO2–XCH4) within the entire Earth. The reconstructed results are validated against the Total Carbon Column Observing Network (i.e., TCCON), with 31 in situ stations, and GOSAT L4B column-averaged data, using 107 layers. The results show an excellent agreement with the TCCON data and exhibit an R-squared coefficient of 0.95 regarding the CO2 measurements and 0.86 regarding the CH4 measurements. Therefore, this methodology can be incorporated into the processing steps used to map global greenhouse gases.

Analysis of Environmental and Pathogenic Bacteria Attached to Aerosol Particles Size-Separated with a Metal Mesh Device

Metal mesh devices (MMDs) are novel materials that enable the precise separation of particles by size. Structurally, MMDs consist of a periodic arrangement of square apertures of characteristic shapes and sizes on a thin nickel membrane. The present study describes the separation of aerosol particles using palm-top-size collection devices equipped with three types of MMDs differing in pore size. Aerosols were collected at a farm located in the suburbs of Nairobi, Kenya;aerosol particles were isolated, and pathogenic bacteria were identified in this microflora by next-generation sequencing analysis. The composition of the microflora in aerosol particles was found to depend on particle size. Gene fragments were obtained from the collected aerosols by PCR using primers specific for the genus Mycobacterium. This analysis showed that Mycobacterium obuense, a non-tuberculous species of mycobacteria that causes lung diseases, was present in these aerosols. These findings showed that application of this MMD analytical protocol to aerosol particles can facilitate the investigation of airborne pathogenic bacteria.

Change and Periodic Events: Relevance to the Pandemic

With developments of technology to connecting everything and spreading information via different mediums, data is analyzed in a variety of ways. We dive into two concepts deeply and observe how they resonate with every hidden information in the data, specially with time series that is most commonly generated by Internet of Things (IoT). We focus on change owing to the dynamic nature and periodic events owing to the constant oscillation between states in nature. Multiple theories stumble upon the idea of frequency to solve a problem, and we translate the meaning of frequency from one theory to another. We discuss the theories, such as Simple Harmonic Motion (SHM) to model and visualize periodic events, string theory to understand how frequency is used to define the nature of fundamental particles, and differential equations to model change. Lastly, we describe how the Fourier transform allows us to convert any signal into a sum of sinusoidal signals. We further analyze the rise of COVID-19 in India, Brazil, and the USA. We develop this chapter consisting of a road map of problems that require more than just basic statistics to manoeuvre and understand. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Branching Random Walks with Two Types of Particles on Multidimensional Lattices

We consider a continuous-time branching random walk on a multidimensional lattice with two types of particles and an infinite number of initial particles. The main results are devoted to the study of the generating function and the limiting behavior of the moments of subpopulations generated by a single particle of each type. We assume that particle types differ from each other not only by the laws of branching, as in multi-type branching processes, but also by the laws of walking. For a critical branching process at each lattice point and recurrent random walk of particles, the effect of limit spatial clustering of particles over the lattice is studied. A model illustrating epidemic propagation is also considered. In this model, we consider two types of particles: infected and immunity generated. Initially, there is an infected particle that can infect others. Here, for the local number of particles of each type at a lattice point, we study the moments and their limiting behavior. Additionally, the effect of intermittency of the infected particles is studied for a supercritical branching process at each lattice point. Simulations are presented to demonstrate the effect of limit clustering for the epidemiological model.

Non-invasive Machine Learning approach for classifying Blood Pressure using PPG Signals in COVID situation

Blood pressure is one of the possible factors that cause cardiovascular diseases. It is one of the useful parameters for early detection, using which we can diagnose and treat cardiac diseases. Continuous monitoring of blood pressure can help us to maintain good health and to have a longer life span. At present, BP estimation is principally based on cuff-based techniques[1] which can cause inconvenience or discomfort to patients. ECG is one of the cuff-based methods to estimate or classify Blood Pressure. Nowadays, Studies are taking place on non-invasive and cuff-less-based methods and one of them is PPG signals (photoplethysmography). PPG is a non-invasive optical method for estimating the blood volume changes per pulse[21]. We can also say that the PPG signal indicates the mechanical activity of the heart[8]. In this paper, we proposed a non-invasive method using a whole-based approach that uses raw values from PPG signals to classify blood pressure. Using Machine learning algorithms to classify blood pressure is a feasible way for the analysis and predicting the results. In this paper, we applied various machine learning models(Random forest, Gradient boost, and XGBoost). In order to avoid overfitting, we used Repeated-stratified k-fold cross-validation and obtained enough accuracy in classifying the BP. when compared to the parameter-based method, our method(whole based method) is independent of the PPG waveform of a signal. © 2021 IEEE.

Using the Short-Time Fourier Transform and ResNet to Diagnose Depression from Speech Data

Depression is a common illness that is affecting many people nowadays, this is especially true now with the advent of the COVID-19 pandemic. It often arises when a person is having difficulty coping with stressful life events. It can occur throughout the lifespan of a person, and it pervades all aspects of our lives. Currently, depression diagnoses rely on patient interviews and self-report questionnaires, which depend heavily on the patient honesty and the subjective experience of the clinician. In this paper, we will begin with investigating the viability of using the Short-Time Fourier Transform (STFT) as a feature descriptor to objectively diagnose depression from speech data. The dataset used in this research is the Audio-Visual Emotion Challenging 2017 (AVEC2017). The model is based on a modified ResNet18 model architecture to perform a binary classification (i.e., depressed or non-depressed). The STFT is computed from the speech signal to generate a mel-spectrogram for training and testing the model. The experiment shows that relying solely on STFT as an input feature resulted in an F1 score of 74.71% in classifying depression. © 2021 IEEE.

Microplastic Extraction from the Sediment Using Potassium Formate Water Solution (H2O/KCOOH)

Microplastics (MPs) are considered an important stratigraphic indicator, or ‘technofossils’, of the Anthropocene. Research on MP abundance in the environment has gained much attention but the lack of a standardized procedure has hindered the comparability of the results. The development of an effective and efficient method of MP extraction from the matrix is crucial for the proper identification and quantifying analysis of MPs in environmental samples. The procedures of density separation used currently have various limitations: high cost of reagents, limited solution density range, hazardous reagents, or a combination of the above. In this research, a procedure based on density separation with the use of potassium formate water solution (H2O/KCOOH) in controlled conditions was performed. Experimental sediment mixtures, spiked with polyethylene (PE), polystyrene (PS), polyurethane (PUR) and polyethylene terephthalate (PET) particles were prepared and an extraction procedure was tested in the context of a weight-based quantitative analysis of MPs. This article discusses the effectiveness and safety of the method. It additionally provides new information on the interactions between MP particles and the mineral matter of the sediment. Results were acquired with the use of instrumental methods, namely thermogravimetry (TG), Fourier Transform Infrared (FTIR) spectroscopy, Field Emission Scanning Electron microscopy and Energy Dispersive spectrometry (SEM/EDS), as well as X-ray fluorescence (XRF) analysis.

Amphiphilic Janus Microspheres Prepared by Caged Photoactivatable Alkoxysilane

A simple photolysis route was proposed to prepare Amphiphilic Janus Particles (AJP) based on SiO2 microspheres. The surface of SiO2 microspheres were modified by photoactive alkoxysilane, which was synthesized by dealcoholization condensation of 6-nitroveratroyloxycarbonyl and isocyanatopropyl-triethoxysilane. UV irradiation caused eater-breaking allowed for the precise control of hydrophilic modification of the hemispherical exposed particles surfaces. The component and morphology of the obtained particles were characterized by fourier transform infrared spectroscopy and ultraviolet-visible spectroscopy, and the Janus feature was evaluated by scanning electron microscopy, transmission electron microscopy, and dispersity in the oil–water dual-phases. The following results were obtained. The AJP with 450 nm size processes the hydrophilic amino groups on one side and the hydrophobic 6-nitroveratryloxycarbonyl moieties on the other. Additionally, the AJP were located at the phase boundary between water and n-hexane, and the negative charged gold nanoparticles with 25 nm size were adsorbed only onto the side with the positive charged amino groups. The AJP have interfacial adsorption energies that can be as much as three times larger than that of homogeneous particles and thus exhibit excellent surface activities.

The price fluctuation in Chinese carbon emission trading market: New evidence from adaptive Fourier decomposition

This paper studies the carbon price fluctuation in China through the adaptive Fourier decomposition (AFD). Apart from the transient time-frequency distribution of the original AFD model, we also reconstruct the mono-components of this model to obtain the components in different time-frequency scales. Our empirical results based on the carbon price in Hubei Province demonstrate that there are three periods when the price fluctuates dramatically, mainly affected by the governmental policies about carbon emission and the development of clean energies, as well as the outbreak of COVID-19. Furthermore, the fluctuations of the price in the three identified periods are reflected in different scales. The comparison of the decomposition results and those of EMD and VMD shows that the AFD performs best in absorbing the price's useful information extracted through all these methods. © 2021 The Authors. Published by Elsevier B.V.