Real-Time Machine Learning Enabled Low-Cost Magnetometer System

Geomagnetically Induced Currents (GICs) are one of the most hazardous effects of space weather. The rate of change in ground horizontal magnetic component dBH/dt is used as a proxy measure for GIC. In order to monitor and predict dBH/dt, ground-based fluxgate magnetometers are used. However, baseline correction is crucial before such magnetometer data can be utilized. In this paper, a low-cost Machine Learning (ML) enabled magnetometer system has been implemented to perform realtime baseline correction of magnetometer data. The predicted geomagnetic components are then used to derive a forecast for dBH/dt. Two different ML models were deployed, and their real-time and offline prediction accuracy were examined. The localized peaks of the predicted dBH/dt are further validated using binary event analysis.Comment: Selected in IEEE Sensors Conference 202

Physics-Enhanced TinyML for Real-Time Detection of Ground Magnetic Anomalies

Space weather phenomena like geomagnetic disturbances (GMDs) and geomagnetically induced currents (GICs) pose significant risks to critical technological infrastructure. While traditional predictive models, grounded in simulation, hold theoretical robustness, they grapple with challenges, notably the assimilation of imprecise data and extensive computational complexities. In recent years, Tiny Machine Learning (TinyML) has been adopted to develop Machine Learning (ML)-enabled magnetometer systems for predicting real-time terrestrial magnetic perturbations as a proxy measure for GIC. While TinyML offers efficient, real-time data processing, its intrinsic limitations prevent the utilization of robust methods with high computational needs. This paper developed a physics-guided TinyML framework to address the above challenges. This framework integrates physics-based regularization at the stages of model training and compression, thereby augmenting the reliability of predictions. The developed pruning scheme within the framework harnesses the inherent physical characteristics of the domain, striking a balance between model size and robustness. The study presents empirical results, drawing a comprehensive comparison between the accuracy and reliability of the developed framework and its traditional counterpart. Such a comparative analysis underscores the prospective applicability of the developed framework in conceptualizing robust, ML-enabled magnetometer systems for real-time space weather forecasting.Comment: 13 pages, 7 figure

SURVIVAL ASSESSMENT OF PATHOGENIC BACTERIA WITH ANTIBIOTIC RESISTANCE TRAITS FROM FRESH SUMMER ROYAL GRAPE : IN VITRO MICROBIAL CHALLENGE TEST

Table grape act as a popular and mouthwatering fruit supplement over the world. Unhygienic handling of these fruits can possess a diverse threat to public health safety. Moreover, the fruit juice can serve as a nutrient for bacterial growth as well if kept in an unsanitary condition. For this purpose, five imported grape samples (Red Globe, Perlette, Calmeria, Princess and Summer Royal) were tested to assess the presence of pathogenic bacteria and drug-resistant pattern of the isolates. The total bacterial count (TBC) and total fungal count (TFC) showed the presence large amount of bacterial (ranged from 4.4 x 10(4) to 8.1 x 10(7)) and fungal population on the grapes samples, especially on the skin part of the fruits. Several pathogenic isolates, Escherichia coli, Staphylococcus spp., and Pseudomonas spp. were isolated from tested grape samples. However, the presence of Salmonella spp., Shigella spp., Vibrio spp. and Klebsiella spp. were not found from any of the samples. The concerning part of the present study was drug-resistant bacterial strains isolated from supplied Fresh Summer Royal Grape. The most of the isolated bacterial spp. were resistant to all most all of the commonly used antibiotics such as ampicillin (10 mu g), trimethoprim/sulfamethoxazole (25 mu g), tetracycline (30 mu g), ceftriaxone (30 mu g), imipenem (10 mu g), chloramphenicol (30 mu g), azithromycin (15 mu g), novobiocin (30 mu g), vancomycin (30 mu g), penicillin G (10 mu g), ciprofloxacin (5 mu g), neomycin (30 mu g), levofloxacin (5 mu g), kanamycin (30 mu g) and cefiximycin (5 mu g). No grape samples showed any kind of antimicrobial activity against both pathogenic and non-pathogenic bacteria. The survival pattern of inoculated bacterial samples in the grape sample showed a slight decrease in growth in the course of time.Peer reviewe

A Multi Constrained Transformer-BiLSTM Guided Network for Automated Sleep Stage Classification from Single-Channel EEG

Sleep stage classification from electroencephalogram (EEG) is significant for the rapid evaluation of sleeping patterns and quality. A novel deep learning architecture, ``DenseRTSleep-II'', is proposed for automatic sleep scoring from single-channel EEG signals. The architecture utilizes the advantages of Convolutional Neural Network (CNN), transformer network, and Bidirectional Long Short Term Memory (BiLSTM) for effective sleep scoring. Moreover, with the addition of a weighted multi-loss scheme, this model is trained more implicitly for vigorous decision-making tasks. Thus, the model generates the most efficient result in the SleepEDFx dataset and outperforms different state-of-the-art (IIT-Net, DeepSleepNet) techniques by a large margin in terms of accuracy, precision, and F1-score

Parenting Practices and Aggression in Childhood Behaviour Disorders

Objective: To determine the association between parenting practices and aggression in children with behaviour disorders. Study Design: Cross-sectional study. Place and Duration of Study: Children Hospital and Institute of Child Health, Lahore Pakistan, from Nov 2020 to Jan 2021. Methodology: Parents of children and eighty-five children between the ages of 3 to 12 years with the diagnosis of behaviour disorders (autism spectrum disorder, attention deficit hyperactive disorder, social communication disorder) and showing aggressive behaviour were enrolled. The multidimensional assessment of parenting scale was used to assess positive(proactive parenting, positive reinforcement, warmth and supportiveness) and negative (hostility, lax control, physical control) parenting practices. The modified overt aggression scale was used to assess aggression, including verbal aggression,aggression against property, auto-aggression (towards self), and physical aggression (towards others). Correlation and multiple regression analysis were done. Results: Among negative practices, it was found that leniency and inconsistent parenting (lax control) was significant positive predictor of aggression (p<0.01) in children with behaviour disorders. Supportiveness was a significant negative predictor of verbal aggression (p<0.01), while physical control positively predicted total aggression (p<0.05). Among positive parenting practices, positive reinforcement decreased auto aggression (p<0.05) in children with behaviour disorders. Lower maternal education correlated with more aggressive behaviours (p<0.05) in these children. Conclusion: Negative parenting practices may lead to aggression in children with behaviour disorders. While positive reinforcement by parents may encourage less self-harm in these children and help improve their behaviour problems

National registry of interstitial lung disease from Pakistan

Introduction: Interstitial lung disease (ILD) is a heterogeneous group of over 200 parenchymal lung diseases with a myriad of etiologies. Interstitial lung disease registries from around the world show varying prevalence and incidence of these diseases. The aim of this study was to determine the epidemiology and characteristics of ILD in Pakistan.Methods: This web-based registry, which is the first multicenter registry of ILD from Pakistan, recruited patients from 10 centers of five major cities between January 2016 and March 2019.Results: A total of 744 patients were enrolled in the registry. The five most frequent ILDs were idiopathic pulmonary fibrosis (IPF) 34.4%, hypersensitivity pneumonitis (HP) - 17.7%, idiopathic nonspecific interstitial pneumonitis (iNSIP) - 16.8%, connective tissue disease-associated ILD (CTD-ILD) - 16.3%, and sarcoidosis - 9.1%.Conclusion: Idiopathic pulmonary fibrosis is the most prevalent ILD in Pakistan, followed by HP and iNSIP. An ongoing prospective registry with longitudinal follow-up will help us further elaborate on the clinical characteristics, treatment, and survival outcome of patients with ILD

Multidrug Resistance in Cancer: Understanding Molecular Mechanisms, Immunoprevention and Therapeutic Approaches

Cancer is one of the leading causes of death worldwide. Several treatments are available for cancer treatment, but many treatment methods are ineffective against multidrug-resistant cancer. Multidrug resistance (MDR) represents a major obstacle to effective therapeutic interventions against cancer. This review describes the known MDR mechanisms in cancer cells and discusses ongoing laboratory approaches and novel therapeutic strategies that aim to inhibit, circumvent, or reverse MDR development in various cancer types. In this review, we discuss both intrinsic and acquired drug resistance, in addition to highlighting hypoxia- and autophagy-mediated drug resistance mechanisms. Several factors, including individual genetic differences, such as mutations, altered epigenetics, enhanced drug efflux, cell death inhibition, and various other molecular and cellular mechanisms, are responsible for the development of resistance against anticancer agents. Drug resistance can also depend on cellular autophagic and hypoxic status. The expression of drug-resistant genes and the regulatory mechanisms that determine drug resistance are also discussed. Methods to circumvent MDR, including immunoprevention, the use of microparticles and nanomedicine might result in better strategies for fighting cancer

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P &lt; 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely