Diagnosis of COVID-19 from the X-Ray images using BAT Algorithm with Deep Convolutional Neural Network

The COVID-19 widespread has posed a chief contest to the scientific community around the world. For patients with COVID-19 illness, the international community is working to uncover, implement, or invent new approaches for diagnosis and action. A opposite transcription-polymerase chain reaction is currently a reliable tactic for diagnosing infected people. This is a time- and money-consuming procedure. Consequently, the development of new methods is critical. Using X-ray images of the lungs, this research article developed three stages for detecting and diagnosing COVID-19 patients. The median filtering is used to remove the unwanted noised during pre-processing stage. Then, Otsu thresholding technique is used for segmenting the affected regions, where Spider Monkey Optimization (SMO) is used to select the optimal threshold. Finally, the optimized Deep Convolutional Neural Network (DCNN) is used for final classification. The benchmark COVID dataset and balanced COVIDcxr dataset are used to test projected model's performance in this study. Classification of the results shows that the optimized DCNN architecture outperforms the other pre-trained techniques with an accuracy of 95.69% and a specificity of 96.24% and sensitivity of 94.76%. To identify infected lung tissue in images, here SMO-Otsu thresholding technique is used during the segmentation stage and achieved 95.60% of sensitivity and 95.8% of specificity. © 2023 IEEE.

Online or In-Person Course? Meaningful Discussion of Learning Effectiveness and Satisfaction of Microwave Filter Design Course in Graduate Students

Contribution: A research on applying blended teaching in microwave filter design in graduate students. Background: The Covid-19 epidemic has caused many universities worldwide to switch to online courses. Taiwan did not have a large-scale local infection in 2020, so the school has implemented a blended teaching plan, combining online and in-person courses. Intended Outcomes: Discuss the effectiveness and satisfaction of the Microwave Filter Design Course in Graduate Students for two classes, Online or In-person course. Application Design: This study uses a quasi-experiment to teach microwave filter courses in the two classes. The teacher integrated into the Flipped Classroom and Interactive Response System (IRS). Students must use the APP to complete the preclass preview and prepare materials. Class A [Formula Omitted] uses in-person classrooms for the whole course;Class B uses blended teaching. The first eight weeks are synchronized online, then mid-term exams, and in-person courses are used for the next ten weeks. Students in two classes in the last week filled out the course satisfaction questionnaire. Findings: Class B achieved better results in the eighth midterm exam week, showing better learning results. Although students in both classes are highly satisfied with the course, Class A is more satisfied than Class B. For graduate students participating in the microwave filter design course, in-person classrooms and blended teaching can achieve good learning results and satisfaction. However, teachers must pay attention to students' reception and understanding of flipped classrooms when using online teaching. And timely and in-depth guidance on the accuracy of APP use.

Soft Ionization: Improving Indoor Air Quality

In this paper we report two applications of a subcategory of air cleaning devices based on soft ionization that do not cause molecular fragmentation. A system that includes two unipolar ionizing modules has been used to simultaneously produce positive and negative ions in the air. In one set of experiments a large chamber (28 m3) was used to study the effect of ions on reducing PM1.0 particles produced by a research grade calibrated cigarette. The data presented in this paper were obtained using a carbon-brush-based bipolar ionizer and a MERV 10 filter with electret media in a recirculating HVAC system. Significant improvement in removal rate of fine and ultrafine particles was achieved when using the bipolar ionizer in conjunction with the MERV 10 filter. The second set of experiments were conducted using a 36 m3 chamber, following BSL-3 standards, to study the effect of ions on aerosolized SARS-CoV-2. Results of these investigations reveal the inactivation rate of SARS-CoV-2 are enhanced when ions are introduced in the air;inactivation rates were increased by more than 60%and 90%for ion densities of 10,000/cc and 18,000/cc. IEEE

Mitigation of Respirable Aerosol Particles from Speech and Language Therapy Exercises.

INTRODUCTION: Phonation and speech are known sources of respirable aerosol in humans. Voice assessment and treatment manipulate all the subsystems of voice production, and previous work (Saccente-Kennedy et al., 2022) has demonstrated such activities can generate >10 times more aerosol than conversational speech and 30 times more aerosol than breathing. Aspects of voice therapy may therefore be considered aerosol generating procedures and pose a greater risk of potential airborne pathogen (eg, SARS-CoV-2) transmission than typical speech. Effective mitigation measures may be required to ensure safe service delivery for therapist and patient. OBJECTIVE: To assess the effectiveness of mitigation measures in reducing detectable respirable aerosol produced by voice assessment/therapy. METHODS: We recruited 15 healthy participants (8 cis-males, 7 cis-females), 9 of whom were voice-specialist speech-language pathologists. Optical Particle Sizers (OPS) (Model 3330, TSI) were used to measure the number concentration of respirable aerosol particles (0.3 µm-10 µm) generated during a selection of voice assessment/therapy tasks, both with and without mitigation measures in place. Measurements were performed in a laminar flow operating theatre, with near-zero background aerosol concentration, allowing us to quantify the number concentration of respiratory aerosol particles produced. Mitigation measures included the wearing of Type IIR fluid resistant surgical masks, wrapping the same masks around the end of straws, and the use of heat and moisture exchange microbiological filters (HMEFs) for a water resistance therapy (WRT) task. RESULTS: All unmitigated therapy tasks produced more aerosol than unmasked breathing or speaking. Mitigation strategies reduced detectable aerosol from all tasks to a level significantly below, or no different to, that of unmasked breathing. Pooled filtration efficiencies determined that Type IIR surgical masks reduced detectable aerosol by 90%. Surgical masks wrapped around straws reduced detectable aerosol by 96%. HMEF filters were 100% effective in mitigating the aerosol from WRT, the exercise that generated more aerosol than any other task in the unmitigated condition. CONCLUSIONS: Voice therapy and assessment causes the release of significant quantities of respirable aerosol. However, simple mitigation strategies can reduce emitted aerosol concentrations to levels comparable to unmasked breathing.

Template for Extended Abstract contributions to INDOOR AIR 2022

This investigation presents results of Computational Fluid Dynamics (CFD) modelling of aerosol behaviour within an arbitrary 'realistic' 100m2 office environment, with dynamic and variable respiratory droplet release profile applied based on published findings (Morawska et al., 2009). A multitude of ventilation strategies and configurations have been applied to the base model to compare the effectiveness of reducing the concentration of suspended aerosols over time. A key finding of the investigation indicates a relatively low sensitivity to increasing outside air percentage, and that the benefit from this strategy is heavily dependent on the in-duct droplet decay factor. The application of local recirculating air filtration systems with MERV-13 filters mounted on occupant desks proved significantly more effectiveness than increasing outside air concentration from 25% to 100% in reducing the quantity of suspended aerosols. This highlights that the ventilation industry should perhaps focus on opportunities to integrate filtration systems into furniture, partitions, cabinetry etc., and that an appliance-based solution may be more beneficial for reducing COVID-19 transmission in buildings (and likely more straightforward) than modifications to central ventilation systems, particularly in the application of refurbishments and retrofits. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Life Cycle Assessment of Nanofibrous Air Filtering Materials and Respirators

The current spread of COVID-19 pandemics resulted in a surge of a need of respiratory protection devices, including medical facemasks and facepiece respirators. Large amounts of products based on nonwoven filtration material from non-renewable petroleum based plastics (polyethylene) has raised global concerns about excessive environmental impacts of these products. Unfortunately, the replacement of polypropylene nonwoven microfibre based single use masks by the multiple use products did not appear as an effective strategy due to a lower filtration performance, although potentially lower environmental impacts. Nanofibre based filtration devices introduce themselves as potentially more environmentally friendly ones due to a lower overall usage of raw polymer compared to microfibrous ones. We present the LCA modelling of environmental impacts of respiratory protective devices with nanofibrous filter materials and compare those against traditional micro fibrous materials (FFP1 and FFP2 respirator) and medical facemask. Generally, due to a lower mass of nanofibre, these products emerge as a better environmental option, providing similar protection level. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

New methodology to qualify office microbial air quality with air-handling-unit-filters

The Covid-19 pandemic reminded us the importance of maintaining adequate indoor air quality to reduce the risk of propagation of viral particles. The aim of this study is to use air handling unit (AHU) filters to develop a methodology to identify microbial contaminants present in office indoor air. The methodology involves discs of filter media collected periodically from the extraction filters and analysed by cultural and molecular methods. Results obtained from the 10 months study indicate in particular that the concentration of cultivable microorganisms on the filters display small variations of 37% from average value for the 5-culture media tested (e.g., 3.9×102 CFU/cm2 for LB medium). The genera Aspergillus, Cladosporium and Penicillium are the most represented among the cultivable microorganisms collected on the extraction filter. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

LSTM-based Forecasting using Policy Stringency and Time-varying Parameters of the SIR Model for COVID-19

Accurate forecasting of the number of infections is an important task that can allow health care decision makers to allocate medical resources efficiently during a pandemic. Two approaches have been combined, a stochastic model by Vega et al. for modelling infectious disease and Long Short-Term Memory using COVID-19 data and government's policies. In the proposed model, LSTM functions as a nonlinear adaptive filter to modify the outputs of the SIR model for more accurate forecasts one to four weeks in the future. Our model outperforms most models among the CDC models using the United States data. We also applied the model on the Canadian data from two provinces, Saskatchewan and Ontario where it performs with a low mean absolute percentage error. © 2023 IEEE.

Prophylactic Inferior Vena Cava Filters for Venous Thromboembolism in Adults With Trauma: An Updated Systematic Review and Meta-Analysis.

Background: Trauma is an independent risk factor for venous thromboembolism (VTE). Due to contraindications or delay in starting pharmacological prophylaxis among trauma patients with a high risk of bleeding, the inferior vena cava (IVC) filter has been utilized as alternative prevention for pulmonary embolism (PE). Albeit, its clinical efficacy has remained uncertain. Therefore, we performed an updated systematic review and meta-analysis on the effectiveness and safety of prophylactic IVC filters in severely injured patients. Methods: Three databases (MEDLINE, EMBASE, and Cochrane) were searched from August 1, 2012, to October 27, 2021. Independent reviewers performed data extraction and quality assessment. Relative risk (RR) at 95% confidence interval (CI) pooled in a randomized meta-analysis. A parallel clinical practice guideline committee assessed the certainty of evidence using the GRADE approach. The outcomes of interest included VTE, PE, deep venous thrombosis, mortality, and IVC filter complications. Results: We included 10 controlled studies (47 140 patients), of which 3 studies (310 patients) were randomized controlled trials (RCTs) and 7 were observational studies (46 830 patients). IVC filters demonstrated no significant reduction in PE and fatal PE (RR, 0.27; 95% CI, 0.06-1.28 and RR, 0.32; 95% CI, 0.01-7.84, respectively) by pooling RCTs with low certainty. However, it demonstrated a significant reduction in the risk of PE and fatal PE (RR, 0.25; 95% CI, 0.12-0.55 and RR, 0.09; 95% CI, 0.011-0.81, respectively) by pooling observational studies with very low certainty. IVC filter did not improve mortality in both RCTs and observational studies (RR, 1.44; 95% CI, 0.86-2.43 and RR, 0.63; 95% CI, 0.3-1.31, respectively). Conclusion: In trauma patients, moderate risk reduction of PE and fatal PE was demonstrated among observational data but not RCTs. The desirable effect is not robust to outweigh the undesirable effects associated with IVC filter complications. Current evidence suggests against routinely using prophylactic IVC filters.

PSImple: Practical Multiparty Maliciously-Secure Private Set Intersection

Private set intersection (PSI) protocols allow a set of mutually distrustful parties, each holding a private set of items, to compute the intersection over all their sets, such that no other information is revealed. PSI has a wide variety of applications including online advertising (e.g., efficacy computation), security (e.g., botnet detection, intrusion detection), proximity testing (e.g., COVID-19 contact tracing), and more. Private set intersection is a rapidly developing area and there exist many highly efficient protocols. However, almost all of these protocols are for the case of two parties or for semi-honest security. In particular, despite the high interest in this problem, prior to our work there has been no concretely efficient, maliciously secure multiparty PSI protocol. We present PSImple, the first concretely efficient maliciously-secure multiparty PSI protocol. Our construction is based on oblivious transfer and garbled Bloom filters, and has a round-optimal online phase. To demonstrate the practicality of PSImple, we implemented it and ran experiments with up to 32 parties and 220 inputs. We show that PSImple is competitive even with the state-of-the-art concretely efficient semi-honest multiparty PSI protocols. Additionally, we revisit the garbled Bloom filter parameters used in the 2-party PSI protocol of Rindal and Rosulek (Eurocrypt 2017). Using a more careful analysis, we show that the size of the garbled Bloom filters and the number of oblivious transfers required for malicious security can be significantly reduced, often by more than 20%. These improved parameters also imply a better security guarantee, and can be used both in the 2-party PSI protocol of Rindal and Rosulek and in PSImple.

Indoor Airflow Dynamics in Compartmentalized Pneumology Units Equipped with Variable-Thickness MERV-13 Filters

Infection containment in the post-pandemic scenario became a top priority for healthcare engineering control staffers, especially in pneumology sectors, where the treatment of airborne infectious diseases is frequent. In Brazil, where COVID-19 left a long record of casualties, there is a lack of information on the influence of filtration systems on the maintenance of regulated operational conditions for indoor comfort in hospital environments. This paper has the following objectives: to study arrangements of filtering systems in hospital acclimatization ducts;to verify how filtering characteristics could compromise safety regulations for airflow in hospital environments;and to identify airflow stagnation points that might favor suspended viral concentrations and increase contamination risks. We used the computational fluid dynamics STAR-CCM+© software to perform numerical simulations of different cases of indoor airflow in a model corresponding to a sector of the Lauro Wanderley University Hospital (João Pessoa city, Brazil). We concluded that standards for maximum velocity are reachable despite thinner or thicker filters affecting the spread of the air. In this way, acclimatization systems are limited by a tradeoff between regulation and protection. Our findings are relevant to future technological development, interventions, safety strategies amidst contamination scenarios, and new filtration arrangements in hospital environments.

Preparation of green-solvent-based polyamide nanofiber membrane and its air filtration performance

Objective The epidemic of COVID-19 and its variants is endangering human health. Wearing protective masks can effectively reduce the infection risk by resisting the inhalation of the polluted air containing the coronavirus. Electrospun polyamide nanofibers can be used as the core layer of protective masks and have lately received growing attention because of their high filtration performance and robust mechanical properties. However, existing electrospun polyamide nanofiber filters are usually prepared from toxic solvents which could cause severe environmental pollution and endanger workers' health, hence, their practical application should be restricted. Therefore, it is imperative to seek and develop green-solvent-based polyamide nanofiber filters. Method Innovative polyamide nanofiber filters were developed by direct electrospinning technique based on green solvents (Fig. 1). Ethanol as the solvent and water as the nonsolvent were adopted to prepare the green-solvent-based polyamide (GSPA) nanofibers by designing spinning solutions with different ethanol/water mass ratios (i.e., 10: 0, 9: 1, 8: 2, 7: 3, and 6: 4) . During electrospinning process, the working voltage, tip-to-collector distance, and solution extrusion speed were set as 30 kV, 15 cm and 1 mL/h, respectively. The nanofibers prepared with the different ethanol/water ratios were denoted as GSPA - 0, GSPA - 1, GSPA - 2, GSPA-3, and GSPA-4, respectively. Results It was found that water content had a great influence on the morphological structures of polyamide nanofibers (Fig. 2) - After introducing a small amount of water, the obtained GSPA - 1 nanofibers featuring thinner diameter of 332 nm were compared to the GSPA-0 nanofibers (499 nm). The enhanced conductivity (10. 5 μS/cm) of waterborne spinning solutions (Fig. 3) stimulated more charges on spinning jets and led to larger electrostatic force, thus greatly elongating the jets and thinning the fiber diameter. However, with the further increment of water concentrations from 20% to 40%, the obtained fibers exhibited an increased average diameter ranging from 443 to 1 553 nm, which was mainly attributed to the larger viscosity of spinning solutions. Although water cannot dissolve polyamide, homogenous waterborne polyamide/ethanol solutions can still be obtained with different ethanol/water mass ratios within a broad area in the stable region (Fig. 3) - The average pore size of GSPA -1 membranes decreased by 55% compared with that of GSPA-0 membranes, contributing to high filtration efficiency. Moreover, with different concentrations (10%, 20%, 30%) of water, the fluffy structure of GSPA nanofibers were achieved with a high porosity (> 80%), which would offer more passageways to transmit air rapidly. As the water concentration increased, the breaking strength of membranes increased at first and then decreased (Fig. 5), and the GSPA- 1 membranes exhibited the highest breaking strength of 5. 6 MPa, which was believed to be related to the enhanced entanglements and contacts among the adjacent fibers because of the small fiber diameter. The GSPA -1 membranes displayed the highest filtration efficiency (99. 02%) for the most penetration particles (PM0.3) by virtue of the small fiber diameter but suffered from poor permeability with a pressure drop of 158 Pa. Moreover, the GSPA- 1 membranes possessed the highest quality factor of 0. 029 3 Pa, suggesting the optimal filtration performance among different GSPA membranes. A high PM0.3 removal efficiency (>95%) was achieved for GSPA-1 filters under various airflow velocities ranging from 10 to 90 L/min (Fig. 7). Compared with conventional melt-blown fibers, the GSPA nanofibers featured a smaller diameter and higher Knudsen number (Fig. 8), and PM0.3 were captured mainly on the surfaces of green polyamide nanofibers (Fig. 9), demonstrating the higher adsorption ability benefiting from the larger specific surface area. Conclusion A cleaner production of polyamide nanofibers for air filtration was proposed by direct electrospinning based on green and sustaina le binary solvents of water and ethanol. For the first time, the structure including fiber diameter, porosity, and pore size of electrospun polyamide nanofibers were precisely tailored by manipulating water concentration in spinning solutions. The prepared environmentally friendly polyamide nanofiber filters feature the interconnected porous structure with the nanoscale ID building blocks (332 nm), mean pore size (0.7 μm), and porosity (84%), thus achieving efficient PM0.3 capture performance with the filtration efficiency of 99. 02% and pressure drop of 158 Pa, which could be comparable to previous toxic-solvent-processed nanofibers. Moreover, the GSPA nanofibers exhibit robust mechanical properties with an impressive breaking strength (5 . 6 MPa) and elongation (163. 9%), contributing to withstanding the external forces and deformation in the practical assembly and usage of resultant filters. It is envisaged that the green-solvent-based polyamide nanofibers could be used as promising candidates for next-generation air filters, and the proposed waterborne spinning strategy can provide valuable insights for cleaner production of advanced polyamide textiles. © 2023 China Textile Engineering Society. All rights reserved.

Efficient, Breathable and Biodegradable Filter Media for Face Masks

The global outbreak of COVID-19 results in the surge of disposable sanitary supplies, especially personal protective face masks. However, the charge dissipation of the electret meltblown nonwovens, which predominate in the commercial face mask filters, confines the durability and safety of commercial face masks. Furthermore, most of the face masks are made from nondegradable materials (such as PP) or part of their degradation products are toxic and contaminative to the environment. Herein, a type of face mask with biodegradable and highly effective PLA bi-layer complex fibrous membrane as filter core is reported. The prepared PLA complex membrane possesses a high-filtration efficiency of 99.1% for PM0.3 while providing a favorable pressure drop of 93.2 Pa. With the PLA complex membrane as the filter core, our face mask exhibits comparable or even higher wearability to commercial face masks, which further manifests our designed PLA complex membrane a promising filter media for face masks. © 2023, The Author(s), under exclusive licence to the Korean Fiber Society.

How effective are face coverings in reducing transmission of COVID-19?

In the COVID–19 pandemic, billions are wearing face masks, in both health care settings and in public. Which type of mask we should wear in what situation, is therefore important. There are three basic types: cotton, surgical, and respirators (e.g., FFP2, N95 and similar). All are essentially air filters worn on the face. Air filtration is relatively well understood, however, we have almost no direct evidence on the relative role played by aerosol particles of differing sizes in disease transmission. But if the virus concentration is assumed independent of aerosol particle size, then most virus will be in particles µm. We develop a model that predicts surgical masks are effective at reducing the risk of airborne transmission because the filtering material most surgical masks use is highly effective at filtering particles with diameters µm. However, surgical masks are significantly less effective than masks of FFP2, N95 and similar standards, mostly due to the poor fit of surgical masks. Earlier work found that of the air bypasses a surgical mask and is not filtered. This highlights the fact that standards for surgical masks do not specify how well the mask should fit, and so are not adequate for protection against COVID-19.Copyright © 2022 American Association for Aerosol Research

Utilizing CNN-LSTM techniques for the enhancement of medical systems

COVID-19 is one of the most chronic and serious infections of recent years due to its worldwide spread. Determining who was genuinely affected when the disease spreads more widely is challenging. More than 60% of affected individuals report having a dry cough. In many recent studies, diagnostic models were developed using coughing and other breathing sounds. With the development of technology, body sounds are now collected using digital techniques for respiratory and cardiovascular tests. Early research on identifying COVID-19 utilizing speech and diagnosing signs yielded encouraging findings. The gathering of extensive, multi-group, airborne acoustical sound data is used in the developed framework to conduct an efficient assessment to test for COVID-19. An effective classification model is created to assess COVID-19 utilizing deep learning methods. The MIT-Covid-19 dataset is used as the input, and the Weiner filter is used for pre-processing. Following feature extraction done by Mel-frequency cepstral coefficients, the classification is performed using the CNN-LSTM approach. The study compared the performance of the developed framework with other techniques such as CNN, GRU, and LSTM. Study results revealed that CNN-LSTM outperformed other existing approaches by 97.7%. © 2023 Faculty of Engineering, Alexandria University

A nanofiber Murray membrane with antibacterial properties for high efficiency oily particulate filtration

Particulate air pollution represented by PM2.5 is one of the biggest environmental challenges in the 21st century. Especially in 2020, the global outbreak of COVID-19 has brought new challenges to melt-blown filter materials, such as the attenuation of filtration efficiency with breathing, even no filtration effect for viruses as their smaller diameter, the sharp decline of filter efficiency after oily filtration cycle, and its limit in some explosive occasions. Here, using the diameter difference of polystyrene (PS), polyvinylidene fluoride (PVDF) and nylon 6(PA6) fibers, we report a multistage structure nanofiber membrane (PS/PVDF/PA6&Ag MSNMs) with high efficiency, low resistance and antibacterial effect by constructing gradient pore structure and introducing silver nanoparticles (Ag NPs), overcoming the above defects. The average filtration efficiency of PS/PVDF/PA6&Ag MSNMs for diisooctyl sebacate (DEHS) monodisperse particles from 0.2 μm to 4.9 μm was 99.88%, and the pressure drop was only 128 Pa. After repeated circulation for 100 times, the filtration efficiency and pressure drop remained stable. Above all, the antibacterial nanofiber membrane with high efficiency and low resistance has been preliminarily constructed, the future research will further focus on the performance after circulation. © 2023 Elsevier Ltd

Conceptual Design of a UVC-LED Air Purifier to Reduce Airborne Pathogen Transmission—A Feasibility Study

Existing indoor closed ultraviolet-C (UVC) air purifiers (UVC in a box) have faced technological challenges during the COVID-19 breakout, owing to demands of low energy consumption, high flow rates, and high kill rates at the same time. A new conceptual design of a novel UVC-LED (light-emitting diode) air purifier for a low-cost solution to mitigate airborne diseases is proposed. The concept focuses on performance and robustness. It contains a dust-filter assembly, an innovative UVC chamber, and a fan. The low-cost dust filter aims to suppress dust accumulation in the UVC chamber to ensure durability and is conceptually shown to be easily replaced while mitigating any possible contamination. The chamber includes novel turbulence-generating grids and a novel LED arrangement. The turbulent generator promotes air mixing, while the LEDs inactivate the pathogens at a high flow rate and sufficient kill rate. The conceptual design is portable and can fit into ventilation ducts. Computational fluid dynamics and UVC ray methods were used for analysis. The design produces a kill rate above 97% for COVID and tuberculosis and above 92% for influenza A at a flow rate of 100 L/s and power consumption of less than 300 W. An analysis of the dust-filter performance yields the irradiation and flow fields.

Low-Dose Chest CT Protocols for Imaging COVID-19 Pneumonia: Technique Parameters and Radiation Dose.

Chest computed tomography (CT) plays a vital role in the early diagnosis, treatment, and follow-up of COVID-19 pneumonia during the pandemic. However, this raises concerns about excessive exposure to ionizing radiation. This study aimed to survey radiation doses in low-dose chest CT (LDCT) and ultra-low-dose chest CT (ULD) protocols used for imaging COVID-19 pneumonia relative to standard CT (STD) protocols so that the best possible practice and dose reduction techniques could be recommended. A total of 564 articles were identified by searching major scientific databases, including ISI Web of Science, Scopus, and PubMed. After evaluating the content and applying the inclusion criteria to technical factors and radiation dose metrics relevant to the LDCT protocols used for imaging COVID-19 patients, data from ten articles were extracted and analyzed. Technique factors that affect the application of LDCT and ULD are discussed, including tube current (mA), peak tube voltage (kVp), pitch factor, and iterative reconstruction (IR) algorithms. The CTDIvol values for the STD, LDCT, and ULD chest CT protocols ranged from 2.79-13.2 mGy, 0.90-4.40 mGy, and 0.20-0.28 mGy, respectively. The effective dose (ED) values for STD, LDCT, and ULD chest CT protocols ranged from 1.66-6.60 mSv, 0.50-0.80 mGy, and 0.39-0.64 mSv, respectively. Compared with the standard (STD), LDCT reduced the dose reduction by a factor of 2-4, whereas ULD reduced the dose reduction by a factor of 8-13. These dose reductions were achieved by applying scan parameters and techniques such as iterative reconstructions, ultra-long pitches, and fast spectral shaping with a tin filter. Using LDCT, the cumulative radiation dose of serial CT examinations during the acute period of COVID-19 may have been inferior or equivalent to that of conventional CT.

A Fast Initial Response Approach to Sequential Financial Surveillance

Consider the problem of financial surveillance of a heavy-tailed time series modeled as a geometric random walk with log-Student's t increments assuming a constant volatility. Our proposed sequential testing method is based on applying the recently developed taut string (TS) univariate process monitoring scheme to the gaussianized log-differenced process data. With the signal process given by a properly scaled total variation norm of the nonparametric taut string estimator applied to the gaussianized log-differences, the change point detection procedure is constructed to have a desired in-control (IC) average run length (ARL) assuming no change in the process drift. If a change in the process drift is imminent, the proposed approach offers an effective fast initial response (FIR) instrument for rapid yet reliable change point detection. This framework may be particularly advantageous for protection against imminent upsets in financial time series in a turbulent socioeconomic and/or political environment. We illustrate how the proposed approach can be applied to sequential surveillance of real-world financial data originating from Meta Platforms, Inc. (FB) stock prices and compare the performance of the TS chart to that of the more prominent CUSUM and CUSUM FIR charts at flagging the COVID-19 related crash of February 2020. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Automated diagnosis of COVID stages using texture-based Gabor features in variational mode decomposition from CT images

COVID-19 is a deadly and fast-spreading disease that makes early death by affecting human organs, primarily the lungs. The detection of COVID in the early stages is crucial as it may help restrict the spread of the progress. The traditional and trending tools are manual, time-inefficient, and less accurate. Hence, an automated diagnosis of COVID is needed to detect COVID in the early stages. Recently, several methods for exploiting computed tomography (CT) scan pictures to detect COVID have been developed;however, none are effective in detecting COVID at the preliminary phase. We propose a method based on two-dimensional variational mode decomposition in this work. This proposed approach decomposes pre-processed CT scan pictures into sub-bands. The texture-based Gabor filter bank extracts the relevant features, and the student's t-value is used to recognize robust traits. After that, linear discriminative analysis (LDA) reduces the dimensionality of features and provides ranks for robust features. Only the first 14 LDA features are qualified for classification. Finally, the least square- support vector machine (SVM) (radial basis function) classifier distinguishes between COVID and non-COVID CT lung images. The results of the trial showed that our model outperformed cutting-edge methods for COVID classification. Using tenfold cross-validation, this model achieved an improved classification accuracy of 93.96%, a specificity of 95.59%, and an F1 score of 93%. To validate our proposed methodology, we conducted different relative experiments with deep learning and traditional machine learning-based models like random forest, K-nearest neighbor, SVM, convolutional neural network, and recurrent neural network. The proposed model is ready to help radiologists identify diseases daily. © 2023 Wiley Periodicals LLC.