Microbial consortia-mediated rice residue decompositon for eco-friendly management.

Burning rice straw contribute to Atmospheric Pollution, which makes it unsustainable in the long-run, but are still opted by farmers due to faster removal of residue. Lignocellulose Degrading Microorganisms, facilitating sustainable management, may accelerate the breakdown of various crop residues. A study comprised of twenty-one treatments including fungal strains, bacterial strains and microbial consortia.

Trapped by the Arrows: Avoidant/Restrictive Food Intake Disorder and the Illusion of Control in Type 1 Diabetes Mellitus.

We present the case of a 25-year-old female patient with a 20-year history of type 1 diabetes mellitus (T1DM), who experienced recurrent episodes of shakiness, diaphoresis, and dizziness, despite normal blood glucose levels. These symptoms were attributed to changes in the direction of the arrows displayed on her continuous glucose monitoring (CGM) device. Further investigations, including capillary and venous blood glucose measurements, ruled out biochemical hypoglycemia.

Generalizing location-centric variations to enhance contactless human activity recognition.

Contactless Human Activity Recognition (HAR) has played a critical role in smart healthcare and elderly care homes to monitor patient behavior, detect falls or abnormal activities in real time. The effectiveness of non-invasive HAR is often hindered by location-centric variations in Channel State Information (CSI). These variations limit the ability of HAR models to generalize across new unseen cross-domain environments, for instance, a model trained in one location might not perform well in another physical location.

A novel multifunctional chiral metasurface with asymmetric transmission.

The multiband, multifunctional chiral metasurface with asymmetric transmission exhibits significant potential for diverse applications in modern communication systems, ranging from enhanced signal modulation and polarization control to advanced beam steering and compact antenna design. This research presents a versatile and advanced chiral metasurface operating at multiple bands with diverse functionalities, including asymmetric transmission. The proposed metasurface effectively transforms an incoming Linearly Polarized (LP) wave into a Circularly Polarized (CP) wave.

Wireless Channel Modelling for Identifying Six Types of Respiratory Patterns With SDR Sensing and Deep Multilayer Perceptron.

Contactless or non-invasive technology has a significant impact on healthcare applications such as the prediction of COVID-19 symptoms. Non-invasive methods are essential especially during the COVID-19 pandemic as they minimise the burden on healthcare personnel. One notable symptom of COVID-19 infection is a rapid respiratory rate, which requires constant real-time monitoring of respiratory patterns.

Public Perception of the Fifth Generation of Cellular Networks (5G) on Social Media.

With the advancement of social media networks, there are lots of unlabeled reviews available online, therefore it is necessarily to develop automatic tools to classify these types of reviews. To utilize these reviews for user perception, there is a need for automated tools that can process online user data. In this paper, a sentiment analysis framework has been proposed to identify people's perception towards mobile networks.

Malignancy rates in thyroid nodules classified as Bethesda categories III and IV; a subcontinent perspective.

Background: Bethesda category III and IV thyroid nodules fall in the indeterminate risk of malignancy category. These nodules have been a relatively elusive entity to manage as previous studies have shown a wide variation in malignancy rates in different regions and institutions across the world. However, data from the subcontinent with regards to this is scarce.

Hardware-Based Hopfield Neuromorphic Computing for Fall Detection.

With the popularity of smart wearable systems, sensor signal processing poses more challenges to machine learning in embedded scenarios. For example, traditional machine-learning methods for data classification, especially in real time, are computationally intensive. The deployment of Artificial Intelligence algorithms on embedded hardware for fast data classification and accurate fall detection poses a huge challenge in achieving power-efficient embedded systems.

Clinical Characteristics and Outcome of Patients With Pheochromocytoma: A Single Center Tertiary Care Experience.

Objective The aim of this study was to evaluate the demographic and clinical characteristics of patients with pheochromocytoma and determine the treatment outcome with overall survival rates of patients with pheochromocytoma. Methods A retrospective, cross-sectional study was performed on all the patients with histologically proven pheochromocytoma presenting to Shaukat Khanum Memorial Cancer Hospital and Research Center (SKMCH & RC) Lahore, between August 2006 and July 2018. Clinical, biochemical and radiological data were collected at presentation, post-surgery, at discharge and till the last follow-up; data was retrieved from hospital records.

An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare.

Human motion detection is getting considerable attention in the field of Artificial Intelligence (AI) driven healthcare systems. Human motion can be used to provide remote healthcare solutions for vulnerable people by identifying particular movements such as falls, gait and breathing disorders. This can allow people to live more independent lifestyles and still have the safety of being monitored if more direct care is needed.

A qualitative exploration of Pakistan's street children, as a consequence of the poverty-disease cycle.

Background: Street children are a global phenomenon, with an estimated population of around 150 million across the world. These children include those who work on the streets but retain their family contacts, and also those who practically live on the streets and have no or limited family contacts. In Pakistan, many children are forced to work on the streets due to health-related events occurring at home which require children to play a financially productive role from an early stage.