An Evolutionary Field Theorem: Evolutionary Field Optimization in Training of Power-Weighted Multiplicative Neurons for Nitrogen Oxides-Sensitive Electronic Nose Applications.

Neuroevolutionary machine learning is an emerging topic in the evolutionary computation field and enables practical modeling solutions for data-driven engineering applications. Contributions of this study to the neuroevolutionary machine learning area are twofold: firstly, this study presents an evolutionary field theorem of search agents and suggests an algorithm for Evolutionary Field Optimization with Geometric Strategies (EFO-GS) on the basis of the evolutionary field theorem. The proposed EFO-GS algorithm benefits from a field-adapted differential crossover mechanism, a field-aware metamutation process to improve the evolutionary search quality.

Multilayered Mesoporous Composite Nanostructures for Highly Sensitive Label-Free Quantification of Cardiac Troponin-I.

Cardiac troponin-I (cTnI) is a well-known biomarker for the diagnosis and control of acute myocardial infarction in clinical practice. To improve the accuracy and reliability of cTnI electrochemical immunosensors, we propose a multilayer nanostructure consisting of FeO-COOH labeled anti-cTnI monoclonal antibody (FeO-COOH-Ab) and anti-cTnI polyclonal antibody (Ab) conjugated on Au-Ag nanoparticles (NPs) decorated on a metal-organic framework (Au-Ag@ZIF-67-Ab). In this design, FeO-COOH was used for separation of cTnI in specimens and signal amplification, hierarchical porous ZIF-67 extremely enhanced the specific surface area, and Au-Ag NPs synergically promoted the conductivity and sensitivity.

Urban and sub-urban disparities in health outcomes among patients with COVID-19; a cross-sectional study of 234 418 patients in Iran.

Background: It remains crucial to understand socio-demographic determinants of COVID-19 infection to improve access to care and recovery rates from the disease. This study aimed to investigate the urban and sub-urban disparities associated with COVID-19 in patients visiting healthcare facilities in the province of Tehran, Iran.

Methods: Data from 234 418 patients who were diagnosed with COVID-19 infection from March 2020 to March 2021 in the province of Tehran were used in this analysis.

Potentially inappropriate brain CT-scan requesting in the emergency department: A retrospective study in patients with neurologic complaints.

Background: Potentially inappropriate brain CT scan requesting in the emergency department imposes extra charges to the healthcare system and patients. Besides, the unnecessary radiation exposure may cause irreparable damage to the patient. In this study we investigated the percentage of potentially inappropriately conducted brain CT scan for different chief complaints in non-traumatic patients presented to our emergency department.

A template-free approach for waveform extraction of gravitational wave events.

We develop a general data-driven and template-free method for the extraction of event waveforms in the presence of background noise. Recent gravitational-wave observations provide one of the significant scientific areas requiring data analysis and waveform extraction capability. We use our method to find the waveforms for the reported events from the first, second, and third LIGO observation runs (O1, O2, and O3).