Analysis of rule-based and shallow statistical models for COVID-19 cough detection for a preliminary diagnosis

Coronavirus pandemic that has spread all over the world, is one of its kind in the recent past, that has mobilized researchers in areas such as (not limited to) pre-screening solutions, contact tracing, vaccine developments, and crowd estimation. Pre-screening using symptoms identification, cough classification, and contact tracing mobile applications gained significant popularity during the initial outbreak of the pandemic. Audio recordings of coughing individuals are one of the sources that can help in the pre-screening of COVID-19 patients. This research focuses on quantitative analysis of covid cough classification using audio recordings of coughing individuals. For analysis, we used three different publicly available datasets i.e., COUGHVID, NoCoCoDa, and a self-collected dataset through a web application. We observed that wet cough has more correlation with covid cough as opposed to dry cough. However, the classification model trained with wet and dry coughs, both, has similar test performance as that of the model trained with wet cough samples only. We conclude that audio-signal recordings of coughing individuals have the potential as a pre-screening test for COVID-19

Learning fruit class from short wave near infrared spectral features, an AI approach towards determining fruit type

This paper analyzes the potential of using shortwave NIRS (near-infrared spectroscopy) for fruit classification problems. The research focuses on O-H and C-H overtone features of fruit and its correlation with NIRS and therefore opens a new dimension of fruit classification problems using NIRS. Eleven fruits, which include apple, cherry, hass, kiwi, grapes, mango, melon, orange, loquat, plum, and apricot, were used in this study to cover physical characteristics such as peel thinness, pulp, seed thickness, and size. NIR spectral data is collected using the industry-standard F-750 fruit quality meter (wavelength range 300-1100nm) for all fruit mentioned above. Different shallow machine learning architectures were trained to classify fruits using spectral feature vectors. At first, using 83 features vectors within the range of 725-975nm (3nm-resolution) and then using only four features of wavelength 770nm, 840nm, 910nm, and 960nm (corresponding to O-H and C-H overtone features). For the 83 spectral features range as an input, the QDA classifier achieved a cross-validation accuracy of 100% and a test data accuracy of 93.02%. For the four features vector as an input, the QDA classifier achieved a cross-validation accuracy of 97.1% and test data accuracy of 90.38%. The results demonstrate that fruit classification is mainly a function of absorptivity of short wave NIR radiation primarily with respect to O-H and C-H overtones features. An LED-based device mainly having 770nm, 840nm, 910nm, and 960nm range LEDs can be used in applications where automation in fruit classification is required

Dual-polarized chipless humidity sensor tag

In this letter, a miniaturized, flexible and high data dense dual-polarized chipless radio frequency identification (RFID) tag is presented. The tag is designed within a minuscule footprint of 29 × 29 mm2 and has the ability to encode 38-bit data. The tag is analyzed for flexible substrates including Kapton® HN DuPont™ and HP photopaper. The humidity sensing phenomenon is demonstrated by mapping the tag design, using silver nano-particle based conductive ink on HP photopaper substrate. It is observed that with the increasing moisture, the humidity sensing behavior is exhibited in RF range of 4.1–17.76 GHz. The low-cost, bendable and directly printable humidity sensor tag can be deployed in a number of intelligent tracking applications

Side effects of Sinopharm Vaccine experienced by healthcare professionals of Holy Family Hospital, Rawalpindi, Pakistan

Objectives: To determine the gender and age based disparities in side effects among healthcare workers in response to COVID-19 (Sinopharm) vaccination Subjects & Methods: Total 216 healthcare workers were vaccinated against COVID-19 by administering Sinopharm vaccine during February and March 2021 at Infectious Diseases Department of Holy Family Hospital Rawalpindi were enrolled in the study through consecutive sampling. Data for this cross-sectional descriptive study was gathered pertinent to age, gender and side effects of Sinopharm vaccination. The information regarding vaccination side effects was inquired through telephonic calls. Data was analyzed by means of SPSS version 25.0. Results: Mean age of healthcare workers in our study was 35.7 ± 9.5 years. Most (54.6%) of them were females. About 79.2% of health professionals were 21-40 years old. Side effects after first dose of Sinopharm vaccine were experienced by 46.3% males and 42.4% females. About 45.2% and 42.3% males and females respectively overlooked the second jab adversity. Greater proportion (43.6%) complained of vaccine related side effects after the second dose than 37.5% subjects who noted side effects after the first dose of vaccine. Bodyaches, injection site pain, headache and fever were established as the commonest post-vaccination side effects. Conclusion: Side effects resulting from Sinopharm vaccine among our healthcare personnel were minimal. Fortunately none of them complained of serious aftereffects. Despite the COVID vaccination, our healthcare workers should strictly adhere to COVID SOPs amidst pandemic in order to avoid catastrophe in future

New FxLMAT-Based Algorithms for Active Control of Impulsive Noise

In the presence of non-Gaussian impulsive noise (IN) with a heavy tail, active noise control (ANC) algorithms often encounter stability problems. While adaptive filters based on the higher-order error power principle have shown improved filtering capability compared to the least mean square family algorithms for IN, however, the performance of the filtered-x least mean absolute third (FxLMAT) algorithm tends to degrade under high impulses. To address this issue, this paper proposes three modifications to enhance the performance of the FxLMAT algorithm for IN. To improve stability, the first alteration i.e. variable step size FxLMAT (VSSFxLMAT)algorithm is suggested that incorporates the energy of input and error signal but has slow convergence. To improve its convergence, the second modification i.e. filtered x robust normalized least mean absolute third (FxRNLMAT) algorithm is presented but still lacks robustness. Therefore, a third modification i.e. modified filtered-x RNLMAT (MFxRNLMAT) is devised, which is relatively stable when encountered with high impulsive noise. With comparable computational complexity, the proposed MFxRNLMAT algorithm gives better robustness and convergence speed than all variants of the filtered-x least cos hyperbolic algorithm, and filtered-x least mean square algorithm

Knowledge of Safe Swaddling Practices among Mothers of Neonates Visiting a Tertiary-Care Hospital in a Developing Country

OBJECTIVES Swaddling of new-borne and infants remains common in the developing world, but little is known about maternal knowledge of swaddling. Therefore, this study aimed to determine the level of knowledge of safe swaddling practices among mothers of neonates visiting a Tertiary-Care Hospital.METHODOLOGY This cross-sectional study was conducted in the paediatric unit of tertiary care hospital in Peshawar city, Pakistan, between July and December 2018. A total number of 370 mothers of neonates who volunteered their participation were selected using a non-probability consecutive sampling technique. The study was based on a questionnaire comprised of socio-demographic and other questions related to the knowledge of safe swaddling practices.RESULTSA total number of 370 mothers of neonates knowledge were assessed. The study participants ranged between 17 - 49 years, with a mean age of 27.14 (SD ± 5.46). Of the total, 365 (98.6%) mothers were swaddling their babies, while only 5 (1.3%) reported not practising swaddling. Most mothers (51.1%) had good knowledge, while 44.3% had adequate knowledge, and only 4.6% had insufficient knowledge regarding safe swaddling. Knowledge of safe swaddling increased with age and parity. Most mothers (90%) correctly identified that "cotton cloth or light blanket should be used to swaddle baby".CONCLUSIONIt is concluded from this study that most mothers have adequate knowledge about safe swaddling, and the level of knowledge increases with age and parity. Safe swaddling techniques and information should be given to mothers at the beginning of antenatal care to benefit from its positive outcomes and, at the same time to avoid its drawbacks

Polarization Insensitive Compact Chipless RFID Tag

This research article proposes a highly dense, inexpensive, flexible and compact 29 x 29 mm(2) chipless radio frequency identification (RFID) tag. The tag has a 38-bit data capacity, which indicates that it has the ability to label 238 number of different objects. The proposed RFID tag has a bar-shape slot/resonator based structure, which is energized by dual-polarized electromagnetic (EM) waves. Thus, portraying polarization insensitive nature of the tag. The radar cross-section (RCS) response of the proposed tag design is analyzed using different substrates, i.e., Rogers RT/duroid (R)/5880, Taconic (TLX-0), and Kapton (R) HN (DuPont (TM)). A comparative analysis is done, which reveal the changes observed in the RCS curve, as a result of using different substrates and radiators. Moreover, the effect on the RCS response of the tag is also examined, by bending the tag at different bent radii. The compactness and flexible nature of the tag makes it the best choice for Internet of things (IoT) based smart monitoring applications

Towards Sweetness Classification of Orange Cultivars Using Short‑Wave NIR Spectroscopy

The global orange industry constantly faces new technical challenges to meet consumer demands for quality fruits. Instead of traditional subjective fruit quality assessment methods, the interest in the horticulture industry has increased in objective, quantitative, and non-destructive assessment methods. Oranges have a thick peel which makes their non-destructive quality assessment challenging. This paper evaluates the potential of short-wave NIR spectroscopy and direct sweetness classification approach for Pakistani cultivars of orange, i.e., Red-Blood, Mosambi, and Succari. The correlation between quality indices, i.e., Brix, titratable acidity (TA), Brix: TA and BrimA (Brix minus acids), sensory assessment of the fruit, and short-wave NIR spectra, is analysed. Mix cultivar oranges are classified as sweet, mixed, and acidic based on short-wave NIR spectra. Short-wave NIR spectral data were obtained using the industry standard F-750 fruit quality meter (310–1100 nm). Reference Brix and TA measurements were taken using standard destructive testing methods. Reference taste labels i.e., sweet, mix, and acidic, were acquired through sensory evaluation of samples. For indirect fruit classification, partial least squares regression models were developed for Brix, TA, Brix: TA, and BrimA estimation with a correlation coefficient of 0.57, 0.73, 0.66, and 0.55, respectively, on independent test data. The ensemble classifier achieved 81.03% accuracy for three classes (sweet, mixed, and acidic) classification on independent test data for direct fruit classification. A good correlation between NIR spectra and sensory assessment is observed as compared to quality indices. A direct classification approach is more suitable for a machine-learning-based orange sweetness classification using NIR spectroscopy than the estimation of quality indices

STAPHYLOCOCCUS CASSETTE CHROMOSOME MEC TYPING OF METHICILLIN RESISTANT STAPHYLOCOCCUS AUREUS STRAINS PREVAILING IN HAYATABAD MEDICAL COMPLEX, PESHAWAR

Introduction: Methicillin resistance Staphylococcus aureus is a very potential human pathogen, and its significant antibiotic resistance further complicates the management of this pathogen. Methicillin resistance in S. aureus is conferred by the presence of SCCmec elements but there are different types of SCCmec in MRSA which results in the need of typing of SCCmec elements. Material & Methods: This cross-sectional study was conducted to determine the current antibiotic resistance pattern and prevalence of different types of SCCmec elements in the circulating MRSA at Hayatabad Medical Complex, Peshawar. A total of 60 non repetitive MRSA isolates collected from pus aspirate and wound swab were enrolled in the study. All the MRSA isolates were tested by disc diffusion method against the ten antibiotics and further subjected to the SCCmec typing through two multiplex PCR reactions. Results: Out of the total tested MRSA isolates 80% were resistant to Ciprofloxacin, 63.4% to Erythromycin, 58.4% to Gentamicin, 55.0% to Cotrimoxazole, 51.6% to Tetracycline, 48.4% Fusidic acid, 46.6% to Clindamycin, 35.0% to Doxycycline, while Quinupristin/Dalfopristin and Linezolid kill 100% strains of the MRSA included in the study. SCCmec typing of MRSA isolates showed that prevalence of SCCmec type-III was 3.3% (3/60), types-IV was 58.3% (35/60), and type-V was 38.3% (23/60). Conclusion: The studied MRSA showed worrisome resistance, but Quinupristin/Dalfopristin and Linezolid kill all the strains of MRSA. The prevalence of SCCmec types IV and V is very high which indicates that the circulating MRSA clone are community associated, because they harbour SCCmec type IV and V