Persistent Topological Laplacian Analysis of SARS-CoV-2 Variants

Topological data analysis (TDA) is an emerging field in mathematics and data science. Its central technique, persistent homology, has had tremendous success in many science and engineering disciplines. However, persistent homology has limitations, including its inability to handle heterogeneous information, such as multiple types of geometric objects;being qualitative rather than quantitative, e.g., counting a 5-member ring the same as a 6-member ring, and a failure to describe nontopological changes, such as homotopic changes in protein–protein binding. Persistent topological Laplacians (PTLs), such as persistent Laplacian and persistent sheaf Laplacian, were proposed to overcome the limitations of persistent homology. In this work, we examine the modeling and analysis power of PTLs in the study of the protein structures of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike receptor binding domain (RBD). First, we employ PTLs to study how the RBD mutation-induced structural changes of RBD-angiotensin-converting enzyme 2 (ACE2) binding complexes are captured in the changes of spectra of the PTLs among SARS-CoV-2 variants. Additionally, we use PTLs to analyze the binding of RBD and ACE2-induced structural changes of various SARS-CoV-2 variants. Finally, we explore the impacts of computationally generated RBD structures on a topological deep learning paradigm and predictions of deep mutational scanning datasets for the SARS-CoV-2 Omicron BA.2 variant. Our results indicate that PTLs have advantages over persistent homology in analyzing protein structural changes and provide a powerful new TDA tool for data science. [ FROM AUTHOR] Copyright of Journal of Computational Biophysics & Chemistry is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Real-time vibration monitoring and analysis of agricultural tractor drivers using an IoT-based system

Agricultural tractor drivers experience a high amplitude of vibration, especially during soil tillage operations. In the past, most research studied vibration exposure with more focus on the vertical (z) axis than on the fore-and-aft (x) and lateral (y) axes. This study examines how rotary soil tillage affects the vibration acceleration and frequency, and the power spectral densities (PSDs) at the seat pan and head along three translational axes in a real-field multiaxis vibration context. Moreover, this study aimed to identify the characteristics of the seat-to-head transmissibility (STHT) response to identifying the most salient resonant frequencies along the x-, y-, and z-axes. Nine (9) male tractor drivers operated the tractor with a mounted rotary tiller throughout the soil tillage process. In the event of a COVID-19 pandemic, and to respect social distancing, this study developed an Internet of Things (IoT) module with the potential to integrate with existing data loggers for online data transmission and to make the experimentation process more effective by removing potential sources of experimenter errors. The raw acceleration data retrieved at the seat pan and the head were utilized to obtain daily exposure (A(8)), PSDs, and STHT along the x-, y-, and z-axes. The vibration energy was found to be dominant along the z-axis than the x- and y-axes. A(8) response among tractor drivers exceeds the exposure action value explicitly stated by Directive 2002/44/EU. PSDs along the x-, y-, and z-axes depicted the low-frequency vibration induced by rotary soil tillage operation. The STHT response exhibited a higher degree of transmissibility along the y- and z-axes when compared with that along the x-axis. The frequency range of 4-7 Hz may plausibly be associated with cognitive impairment in tractor drivers during rotary soil tillage.

Digitalisation and modern imaging technologies in dermatology

Teledermatology is one of the most important developments of digitalisation in dermatology. It has helped to ensure continuity of care during the COVID-19 pandemic. The combination of teledermatology with artificial intelligence can significantly improve medical decision-making. Among imaging modalities, dermoscopy is the most widely used, and its effectiveness can be significantly enhanced when combined with artificial intelligence. Novel techniques that have emerged in recent years include high-frequency ultrasound, optical coherence tomography or multispectral imaging. These are currently used in dermatological research but are expected to gradually become part of daily patient care. The knowledge of digital technologies and new imaging techniques is essential for the modern dermatologist. In the future, it is expected to be an essential part of modern and optimised patient care.

Preface

The title of the ConferenceXXII Conference of PhD Students and Young Scientists "Interdisciplinary topics in mining and geology”The location and the date of the conferencevirtual event – online conference, June 29th to July 1st, 2022 in Wrocław, PolandXXIInd Conference of PhD Students and Young Scientists "Interdisciplinary topics in mining and geology” continues a series of events that started in 2000 at Wrocław University of Science and Technology. Scientific programme of the Conference focuses on four thematic panels:1. Mining Engineering: sustainable development, digitalisation in mining, problems of securing, protecting and using remnants of old mining works, underground mining, opencast mining, mineral processing, waste management, mining machinery, mine transport, economics in mining, mining aeronautics, ventilation and air conditioning in mines,2. Earth and Space Sciences: geology, hydrogeology, environmental protection, extraterrestrial resources, groundwater and medicinal waters, engineering and environmental protection, geotourism,3. Geoengineering: environmental protection, applied geotechnics, rock and soil mechanics, geohazards,4. Geoinformation: mining geodesy, GIS, photogrammetry and remote sensing, geodata modeling and analysis.The XXII Conference of PhD Students and Young Scientists was held as a virtual event, that is as a virtual, online conference in real-time. The reason why the Organizing Committee decided to change the traditional formula of the event to online formula was related to the concern for the health of the participants due to the COVID-19 epidemic.The XXII Conference of PhD Students and Young Scientists took place from June 29th to July 1st, 2022 in Wroclaw, Poland. That is the organizers worked and managed the event from the Wrocław University of Science and Technology Geocentre building. Because the conference focused on four thematic panels, four different special opening lectures were delivered by wellknown scientists- Professor Jan Zalasiewicz (University of Leicester, England)- Associate Professor Artur Krawczyk (AGH University of Science and Technology, Poland)- Professor Biljana Kovacević-Zelić (University of Zagreb, Croatia)- Assistant Professor Eduard Kan (Tashkent Institute of Irrigation and Agricultural Mechanizations Engineers, Uzbekistan).The Conference was divided into 8 oral sessions (with 33 presentations) and 1 poster session (with 33 posters). The amount of time provided to one presentation was 15 minutes, after presentation there was 5 minutes available for discussion. The poster session was available throughout the event, and the posters were available for online viewing on the Conference's website with the possibility of make discussion and ask questions in real time via zoom meeting application as well. Every day of the Conference one "virtual coffee break” was devoted for discussion between participants and question and answer session for the Organizers.There were 96 registered participants from 13 countries. The online XXII Conference of PhD Students and Young Scientists was conducted using the Zoom meeting platform with commemorative screen shots taken. By tradition two competitions, for the best oral presentation and for the best poster were held. The award for the best oral presentation was given ex aequo to Julia Tiganj (TH Georg Agricola University of Applied Sciences, Germany) for the presentation entitled Post-mining goes international: hurdles to climate neutrality using the example of China and Oksana Khomiak, Jörg Benndorf (TU Bergakademie Freiberg, Germany) for the presentation entitled Spectral analysis of ore hyperspectral images at different stages of the mining value chain, whereas the best poster was awarded to Adam Wróblewski, Jacek Wodecki, Paweł Trybała, Radosław Zimroz (Wrocław University of Science and technology, Poland) for the poster entitled Large underground structures geometry evaluation based on point cloud data analysis.List of Scientific Committee, Organizing Committee, Editorial Team are available i this pdf.

Application of Localized Surface Plasmon Resonance of Conjugated Gold Nanoparticles in Spectral Diagnosis of SARS-CoV-2: A Numerical Study.

Severe respiratory syndrome COVID-19 (SARS-CoV-2) outbreak has became the most important global health issue, and simultaneous efforts to fast and low-cost diagnosis of this virus were performed by researchers. One of the most usual tests was colorimetric methods based on the change of color of gold nanoparticles in the presence of viral antibodies, antigens, and other biological agents. This spectral change can be due to the aggregation of the particles or the shift of localized surface plasmon resonance due to the electrical interactions of surface agents. It is known that surface agents could easily shift the absorption peak of metallic nanocolloids which is attributed to the localized surface plasmon resonance. Experimental diagnosis assays for colorimetric detection of SARS-CoV-2 using Au NPs were reviewed, and the shift of absorption peak was studied from the viewpoint of numerical analysis. Using the numerical method, the refractive index and real and imaginary parts of the effective relative permittivity of the viral biological shell around Au NPs were obtained. This model gives a quantitative description of colorimetric assays of the detection of SARS-CoV-2 using Au NPs.

Method for Determining the Dominant Type of Human Breathing Using Motion Capture and Machine Learning

Since the COVID-19 pandemic, the demand for respiratory rehabilitation has significantly increased. This makes developing home (remote) rehabilitation methods using modern technology essential. New techniques and tools, including wireless sensors and motion capture systems, have been developed to implement remote respiratory rehabilitation. Significant attention during respiratory rehabilitation is paid to the type of human breathing. Remote rehabilitation requires the development of automated methods of breath analysis. Most currently developed methods for analyzing breathing do not work with different types of breathing. These methods are either designed for one type (for example, diaphragmatic) or simply analyze the lungs' condition. Developing methods of determining the types of human breathing is necessary for conducting remote respiratory rehabilitation efficiently. We propose a method of determining the type of breathing using wireless sensors with the motion capture system. To develop that method, spectral analysis and machine learning methods were used to detect the prevailing spectrum, the marker coordinates, and the prevailing frequency for different types of breathing. An algorithm for determining the type of human breathing is described. It is based on approximating the shape of graphs of distances between markers using sinusoidal waves. Based on the features of the resulting waves, we trained machine learning models to determine the types of breathing. After the first stage of training, we found that the maximum accuracy of machine learning models was below 0.63, which was too low to be reliably used in respiratory rehabilitation. Based on the analysis of the obtained accuracy, the training and running time of the models, and the error function, we choose the strategy of achieving higher accuracy by increasing the training and running time of the model and using a two-stage method, composed of two machine learning models, trained separately. The first model determines whether the breath is of the mixed type;if it does not predict the mixed type of breathing, the second model determines whether breathing is thoracic or abdominal. The highest accuracy achieved by the composite model was 0.81, which surpasses single models and is high enough for use in respiratory rehabilitation. Therefore, using three wireless sensors placed on the patient's body and a two-stage algorithm using machine learning models, it was possible to determine the type of human breathing with high enough precision to conduct remote respiratory rehabilitation. The developed algorithm can be used in building rehabilitation applications.

Novel oxazolidinone zinc(II) complexes as antibacterial and anti-SARS-CoV-2 agents: synthesis, characterization, DFT calculations, ADMET, in silico molecular docking and biological activities

A series of Zn(II) complexes with oxazolidinone derivatives has been synthesized and characterized using spectroscopic techniques: IR, H-1 NMR, UV-Vis spectroscopy, and TGA/DTG thermal investigation. Theoretical computations were carried out using B3LYP/6-31G(d) and B3LYP/LanL2DZ to analyze the vibrational properties, NBO charges, global chemical reactivity indices and to illustrate the FOMs. TD-DFT calculations using WB97XD functional were realized with 6-31 G(d) and LAN2DZ basis set on oxazolidinone ligands and their zinc complexes. The pharmacokinetic properties and toxicity of the investigated compounds were predicted using in silico ADMET studies. Moreover, the S. aureus, E. coli, S. pneumoniae, ribosome 50S subunit, SARS-Cov-2 spike protein and ACE2 human receptor were selected for molecular docking study. The docking study shows that HL4 and ZnL4 bind better to the spike protein and hACE2 receptor. The redox properties were also studied for ligands and their corresponding complexes using cyclic voltammetry. Finally, antioxidant activity studies using DPPH radical scavenging showed efficiency for HL2 and [Zn(L-2)(2)] with low values of IC50 compared to ascorbic acid. The antimicrobial activity against B. subtilis (ATCC 9372), E. faecalis (ATCC 29212), S. aureus (ATCC 6538), E. coli (ATCC 4157), bacteria strains, C. albicans (ATCC 24433) and A. niger fungi strains were evaluated.

How Australia's economy gained momentum because of Covid-19

The objective of the paper is to assess the resilience of the economy of Australia following the Covid-19 pandemic that hit the global economy in Q4 2019, in years 2020, 2021 and 2022. Quarterly growth rates (annualised) of the Real GDP of Australia and Canada are forecasted between Q2 2022 and Q4 2050. Two sets of forecasts are generated: forecasts using historical data including the pandemic (from Q1 1961 to Q1 2022) and excluding the pandemic (from Q1 1961 to Q3 2019). The computation of the difference of their averages is an indicator of the resilience of the economies during the pandemic, the greater the difference the greater the resilience. Used as a benchmark, Canada's economy shows a slightly lower resilience to the Covid-19 pandemic (+0.37%) than Australia's economy (+0.39%) based on Q2 2022-2050 forecasts. However, driven by stronger growth than Canada, the average estimate of the Q2 2022-Q4 2050 quarterly (annualised) growth rate forecasts of Australia is expected to be +2.09% with the Q1 1961-Q1 2022 historical data while it should be +1.61% for Canada. Supported by higher growth, Australia's Real GDP is expected to overtake Canada's in Q1 2040.

Estimation of Inactivation time for the SARS-CoV-2 virus from the UV biometer in South Korea.

The coronavirus disease 2019 (COVID-19) is a result of the infection by "severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and has caused various social and economic effects over the globe. As the SARS-CoV-2 is effectively inactivated by the exposure to the UV-B radiation (shorter than 315 nm), the exposure time for inactivation of the SARS-CoV-2 was estimated using the broadband UV observation instrument over 11 observation sites in South Korea. For the limitation of the UV biometer, which has limited spectral information, the coefficient for conversion from the erythemal UV (EUV) to the radiation for virus inactivation was adopted before estimating the inactivation time. The inactivation time of SARS-CoV-2 is significantly dependent on seasonal and diurnal variations due to the temporal variations of surface incident UV irradiance. The inactivation times in summer and winter were around 10 and 50 min, respectively. The inactivation time was unidentified during winter afternoons due to the weak spectral UV solar radiation in winter. As the estimation of inactivation time using broadband observation includes the uncertainty due to the conversion coefficient and the error due to the solar irradiance, the sensitivity analysis of the inactivation time estimation was also conducted by changing the UV irradiance.

Evaluating the quality consistency of antiviral oral liquid by high-performance liquid chromatography five-wavelength matched average fusion fingerprint combined with electrochemical fingerprint and ultraviolet spectral quantum fingerprint.

The antiviral oral liquid (AOL) was an antiviral drug currently in clinical trials against coronavirus disease 2019. This study aimed to improve its quality consistency evaluation method using fingerprint techniques from several aspects. First, the five-wavelength matched average fusion fingerprint (FMAFFP) for HPLC, electrochemical fingerprint (ECFP), and ultraviolet spectral quantum fingerprint (UVFP) was established for 22 samples, respectively. Their quality was then assessed using the average linear quantitative fingerprint method, and 22 samples were classified into eight quality grades. OPLS and PCA were then used further to explore the characteristic parameters of these three fingerprints. Five compounds were quantified simultaneously for the first time, and then the relationship between the average linear quantitative similarity (PL) and the sum of the five quantitative components (P5c) was investigated. A linear correlation (r ≥ 0.9735) between PL and P5c suggested that PL may be used to predict chemical content. Finally, to investigate the antioxidant potential of the AOL, correlation analyses were performed for FMAFFP peaks-PEC and UVFP peaks-PEC, respectively, where the PEC value was defined as the quantitative similarity of ECFP. The Pearson correlation coefficient and gray correlation analysis were consistent, allowing us to initially explore the antioxidant capacity of the unidentified components of the samples. This study researched AOL using multidimensional fingerprints to provide a comprehensive and reliable method for quality consistency control of herbal compound preparations.

Jaya Honey Badger optimization-based deep neuro-fuzzy network structure for detection of (SARS-CoV) Covid-19 disease by using respiratory sound signals

PurposeThe Covid-19 prediction process is more indispensable to handle the spread and death occurred rate because of Covid-19. However early and precise prediction of Covid-19 is more difficult because of different sizes and resolutions of input image. Thus these challenges and problems experienced by traditional Covid-19 detection methods are considered as major motivation to develop JHBO-based DNFN.Design/methodology/approachThe major contribution of this research is to design an effectual Covid-19 detection model using devised JHBO-based DNFN. Here, the audio signal is considered as input for detecting Covid-19. The Gaussian filter is applied to input signal for removing the noises and then feature extraction is performed. The substantial features, like spectral roll-off, spectral bandwidth, Mel-frequency cepstral coefficients (MFCC), spectral flatness, zero crossing rate, spectral centroid, mean square energy and spectral contract are extracted for further processing. Finally, DNFN is applied for detecting Covid-19 and the deep leaning model is trained by designed JHBO algorithm. Accordingly, the developed JHBO method is newly designed by incorporating Honey Badger optimization Algorithm (HBA) and Jaya algorithm.FindingsThe performance of proposed hybrid optimization-based deep learning algorithm is estimated by means of two performance metrics, namely testing accuracy, sensitivity and specificity of 0.9176, 0.9218 and 0.9219.Research limitations/implicationsThe JHBO-based DNFN approach is developed for Covid-19 detection. The developed approach can be extended by including other hybrid optimization algorithms as well as other features can be extracted for further improving the detection performance.Practical implicationsThe proposed Covid-19 detection method is useful in various applications, like medical and so on.Originality/valueDeveloped JHBO-enabled DNFN for Covid-19 detection: An effective Covid-19 detection technique is introduced based on hybrid optimization–driven deep learning model. The DNFN is used for detecting Covid-19, which classifies the feature vector as Covid-19 or non-Covid-19. Moreover, the DNFN is trained by devised JHBO approach, which is introduced by combining HBA and Jaya algorithm.

A Traceable Spectral Radiation Model of Radiation Thermometry

Featured ApplicationRadiation thermometry of real objects under real conditions.Despite great technical capabilities, the theory of non-contact temperature measurement is usually not fully applicable to the use of measuring instruments in practice. While black body calibrations and black body radiation thermometry (BBRT) are in practice well established and easy to accomplish, this calibration protocol is never fully applicable to measurements of real objects under real conditions. Currently, the best approximation to real-world radiation thermometry is grey body radiation thermometry (GBRT), which is supported by most measuring instruments to date. Nevertheless, the metrological requirements necessitate traceability;therefore, real body radiation thermometry (RBRT) method is required for temperature measurements of real bodies. This article documents the current state of temperature calculation algorithms for radiation thermometers and the creation of a traceable model for radiation thermometry of real bodies that uses an inverse model of the system of measurement to compensate for the loss of data caused by spectral integration, which occurs when thermal radiation is absorbed on the active surface of the sensor. To solve this problem, a hybrid model is proposed in which the spectral input parameters are converted to scalar inputs of a traditional scalar inverse model for GBRT. The method for calculating effective parameters, which corresponds to a system of measurement, is proposed and verified with the theoretical simulation model of non-contact thermometry. The sum of effective instrumental parameters is presented for different temperatures to show that the rule of GBRT, according to which the sum of instrumental emissivity and instrumental reflectivity is equal to 1, does not apply to RBRT. Using the derived models of radiation thermometry, the uncertainty of radiation thermometry due to the uncertainty of spectral emissivity was analysed by simulated worst-case measurements through temperature ranges of various radiation thermometers. This newly developed model for RBRT with known uncertainty of measurement enables traceable measurements using radiation thermometry under any conditions.

Numerical Simulation for COVID-19 Model Using a Multidomain Spectral Relaxation Technique

The major objective of this work is to evaluate and study the model of coronavirus illness by providing an efficient numerical solution for this important model. The model under investigation is composed of five differential equations. In this study, the multidomain spectral relaxation method (MSRM) is used to numerically solve the suggested model. The proposed approach is based on the hypothesis that the domain of the problem can be split into a finite number of subintervals, each of which can have a solution. The procedure also converts the proposed model into a system of algebraic equations. Some theoretical studies are provided to discuss the convergence analysis of the suggested scheme and deduce an upper bound of the error. A numerical simulation is used to evaluate the approach's accuracy and utility, and it is presented in symmetric forms.

The euro to dollar exchange rate in the Covid‐19 era: Evidence from spectral causality and Markov‐switching estimation

In this paper, we analyse how the Covid‐19 pandemic changed the dynamics of the euro to dollar exchange rate. To do so, we make use of spectral non‐causality tests to uncover the determinants of the euro to dollar exchange rate, using data that cover the pre‐Covid‐19 and the actual Covid‐19 era, by considering the exchange rate movements of other currencies, the stock market index of S&P 500, and the price of oil and gold, as well as their realized volatilities. Based on our findings, the Covid‐19 pandemic has indeed significantly changed the determinants of the euro to dollar exchange rate. Also, to investigate the potential shifts in the regimes of the euro to dollar exchange rate, we formulate a Markov‐switching model with two regimes, based on the determinants that have been found in the previous step. Based on our findings, the duration of the high volatility state in the Covid‐19 era has doubled, from almost 3 to approximately 6 days, compared to the pre‐Covid‐19 era, whereas the high volatility state in the Covid‐19 era is characterized by a statistically significant higher range of volatility compared to the pre‐Covid‐19 era.

Synthesis, DFT and In Silico Anti-COVID Evaluation of Novel Tetrazole Analogues

A new series of 3-aryl/heteroaryl-2-(1H-tetrazol-5-yl) acrylamides have been synthesized through catalyst-free, one-pot cascade reactions, utilizing click chemistry approach and evaluated for their anti-COVID activities against two proteins in silico. The structural properties of the synthesized molecules were evaluated based on DFT calculations. Total energy of the synthesized tetrazole compounds were obtained through computational analysis which indicate the high stability of the synthesized compounds. The Frontier Molecular Orbitals (FMO) and associated energies and molecular electrostatic potential (MEP) surfaces were generated for the compounds. Spectral analysis by DFT gave additional evidence to the structural properties of the synthesized molecules. All tetrazole analogues come under good ADMET data as they followed the standard value for ADMET parameters. Docking studies offered evidence of the molecules displaying excellent biological properties as an anti-Covid drug. Compound 4 g exhibited excellent anti-COVID-19 properties with four hydrogen binding interactions with amino acids GLN 2.486 Å, GLN 2.436 Å, THR 2.186 Å and HSD 2.468 Å with good full-fitness score (–1189.12) and DeltaG (–7.19). Similarly, compound 4d shown potent activity against anti-COVID-19 mutant protein (PDB: 3K7H) with three hydrogen binding interactions, i.e., SER 2.274 Å, GLU 1.758 Å and GLU 1.853 Å with full-fitness score of –786.60) and DeltaG (–6.85). The result of these studies revealed that the compounds have the potential to become lead molecules in the drug discovery process.

Direct Multi-Material Reconstruction via Iterative Proximal Adaptive Descent for Spectral CT Imaging.

Spectral computed tomography (spectral CT) is a promising medical imaging technology because of its ability to provide information on material characterization and quantification. However, with an increasing number of basis materials, the nonlinearity of measurements causes difficulty in decomposition. In addition, noise amplification and beam hardening further reduce image quality. Thus, improving the accuracy of material decomposition while suppressing noise is pivotal for spectral CT imaging. This paper proposes a one-step multi-material reconstruction model as well as an iterative proximal adaptive decent method. In this approach, a proximal step and a descent step with adaptive step size are designed under the forward-backward splitting framework. The convergence analysis of the algorithm is further discussed according to the convexity of the optimization objective function. For simulation experiments with different noise levels, the peak signal-to-noise ratio (PSNR) obtained by the proposed method increases approximately 23 dB, 14 dB, and 4 dB compared to those of other algorithms. Magnified areas of thorax data further demonstrated that the proposed method has a better ability to preserve details in tissues, bones, and lungs. Numerical experiments verify that the proposed method efficiently reconstructed the material maps, and reduced noise and beam hardening artifacts compared with the state-of-the-art methods.

Diagnosis of COVID-19 Infection via Association Rules of Cough Encoding

COVID-19 has roused the scientific community, prompting calls for immediate solutions to avoid the infection or at least reduce the virus's spread. Despite the availability of several licensed vaccinations to boost human immunity against the disease, various mutated strains of the virus continue to emerge, posing a danger to the vaccine's efficacy against new mutations. As a result, the importance of the early detection of COVID-19 infection becomes evident. Cough is a prevalent symptom in all COVID-19 mutations. Unfortunately, coughing can be a symptom of various of diseases, including pneumonia and infiuenza. Thus, identifying the coughing behavior might help clinicians diagnose the COVID-19 infection earlier and distinguish coronavirus-induced from non-coronavirus-induced coughs. From this perspective, this research proposes a novel approach for diagnosing COVID-19 infection based on cough sound. The main contributions of this study are the encoding of cough behavior, the investigation of its unique characteristics, and the representation of these traits as association rules. These rules are generated and distinguished with the help of data mining and machine learning techniques. Experiments on the Virufy COVID-19 open cough dataset reveal that cough encoding can provide the desired accuracy (100%). © 2023, ECTI Association. All rights reserved.

Ambient Intelligence Telework System to Enhance Concentration for Each Individual: Easy to Concentrate Based on Neuroticism

In recent years, telework has enormously increased because of the COVID-19 pandemic. However, some people believe that teleworkers can concentrate the same as in-person office work, while others do not. Previous studies showed that the work environment necessary for concentration tends to differ depending on the worker's individual attributes. The goal of this study is to propose an Ambient Intelligence (AMI) telework system suited for each individual to enhance concentration. In this paper, we used two videos with different levels of diligence on a task to investigate what kind of environment makes it easier to concentrate, depending on the degree of neuroticism. Concentration was estimated using the auditory steady-state response (ASSR), which is an oscillatory brain signal elicited by repetitive auditory stimulation and used as a hearing test for infants and children. The changes in the power spectral density of the ASSR with concentration were increased by comparing EEG at rest and during concentration. The effect of the two types of videos on concentration was investigated by evaluating the relationship between neuroticism scores and the power spectral density of the ASSR. The results showed that there was a significant difference in concentration influenced by the two types of videos between the high and low neuroticism score groups. In addition, a negative correlation was found between the neuroticism score and the concentration influenced by the two types of videos. We found that people with lower neuroticism tended to easy to concentrate on their work after seeing someone working hard whereas people with higher neuroticism tended to easy to concentrate on their work after seeing someone working lazily. The experimental results suggest the possibility of constructing an AMI system suited to each individual that enhances concentration. © 2023 IEEE.

Detection of COVID-19 in X-Ray Images Using Constrained Multi-view Spectral Clustering

Machine learning, and specifically classification algorithms, has been widely used for the diagnosis of COVID-19 cases. However, these methods require knowing the labels of the datasets, and use a single view of the dataset. Due to the widespread of the COVID-19 cases, and the presence of the huge amount of patient datasets without knowing their labels, we emphasize in this paper to study, for the first time, the diagnosis of COVID-19 cases in an unsupervised manner. Thus, we can benefit from the abundance of datasets with missing labels. Nowadays, multi-view clustering attracts many interests. Spectral clustering techniques have attracted more attention thanks to a well-developed and solid theoretical framework. One of the major drawbacks of spectral clustering approaches is that they only provide a nonlinear projection of the data, which requires an additional clustering step. Since this post-processing step depends on numerous factors such as the initialization procedure or outliers, this can affect the quality of the final clustering. This paper provides an improved version of a recent method called Multiview Spectral Clustering via integrating Nonnegative Embedding and Spectral Embedding. In addition to keeping the benefits of this method, our proposed model incorporates two types of constraints: (i) a consistent smoothness of the nonnegative embedding across all views, and (ii) an orthogonality constraint over the nonnegative embedding matrix columns. Its advantages are demonstrated using COVIDx datasets. Besides, we test it with other image datasets to prove the right choice of this method in this study. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Comparison of photographic photonic assessment and conventional clinical assessment in diagnosis of contact allergy with patch testing: a pilot study

The SARS-CoV-2 (COVID-19) pandemic has led to the rapid implementation of virtual clinics across the healthcare sector. Alternatives to the conventional face-to-face patient assessment have been sought and piloted within dermatology departments. Cutaneous patch testing is traditionally assessed on days 2 and 4, and often delayed readings are required. Strategies to minimize physical attendance and the potential risk of COVID-19 transmission were required in order to maintain access to services. Photographic assessment of patch testing was introduced in our department. In addition, we employed photographic phototonics to augment the patch-test result image. Phototonics is the technology of generating, detecting and manipulating physical light, whose quantum unit is the photon. Photonics can be used to assess levels of blood flow in a clinical photograph of skin acting as a surrogate marker for cutaneous inflammation. Our aim was to assess if clinical photography and photonic image analysis can improve the detection of positive reactions in the virtual interpretation of patchtest results. Consecutive patients attending for patch testing were recruited and written consent was obtained. Photographs of patch-test results were taken using a 40-megapixel colour camera, on day 5, contemporaneous to patch-test assessment by the study investigators. The photographs were then analysed using spectral imaging technology software (HyperCube). The analysis employed principal component analysis, a technique used to reduce the dimensionality of datasets. The phototonic images were then examined to determine a combination of variables or colour patterns (red-green-blue) that would indicate a positive result and a surrogate marker for cutaneous inflammation. Thirty patients were recruited from September to November 2020. Two blinded investigators determined whether the results were positive, ?positive, irritant or other. Phototonic, photographic and clinical results were then compared. Photonic evaluation captured 59% of positive patch-test readings, while photographic assessment captured 50%. Interpretation of the results was almost identical between both investigators. This pilot study outlines the potential application of phototonic technology in the interpretation of virtual patch-test results. It is evident that physical attendance for patch-test reading is superior to both photographic phototonic assessment and photographic assessment. However, there may be role for the use of phototonics in order to augment the evaluation of virtual patch-test results. Interpretation of phototonics can be difficult and is generally modelled to validated results. Analysis using a multispectral camera to include specific wavelengths to monitor increased blood flow may have a role.