The emerging novel variants and re-merging old variants of SARS-CoV-2 make it critical to study the transmission probability in mixed-mode ventilated office environments. Artificial neural network (ANN) and curve fitting (CF) models were created to forecast the R-Event. The R-Event is defined as the anticipated number of new infections that develop in particular events occurring over the course of

The widespread use of nanomaterials has raised the threat of nanoparticles (NPs) infection of soils and groundwater resources. This research aims to investigate three parameters including flow velocity, ionic strength (IS), and initial particle concentration effects on transport behavior and retention mechanism of functionalization form of graphene oxide with polyvinylpyrrolidone (GO-PVP). The tra

Context. Transmission spectroscopy is one of the most powerful techniques used to characterize transiting exoplanets, since it allows for the abundance of the atomic and molecular species in the planetary atmosphere to be measured. However, stellar lines may bias the determination of such abundances if their center-To-limb variations (CLVs) are not properly accounted for. Aims. This paper aims to

Machine learning models can analyze physiological data, such as electroencephalography (EEG), for various classification tasks. One such task is Auditory Attention Decoding (AAD), aimed at identifying the sound a person is actively attending to, offering significant benefits for users of hearing aids. However, EEG data often exhibits a low signal-to-noise ratio, and its collection is often expensi

In normal colon tissue, oestrogen receptor alpha (ERα) is expressed at low levels, while oestrogen receptor beta (ERβ) is considered the dominant subtype. However, in colon carcinomas, the ERα/β ratio is often increased, an observation that prompted us to further investigate ERα’s role in colorectal cancer (CRC). Here, we assessed ERα nuclear expression in 351 CRC patients. Among them, 119 exhibit

Fat-soluble vitamin D is an essential bioactive compound important for human health. Insufficient vitamin D levels can result not only in bone disease but also in other disorders, such as cancer, metabolic disorders, and diseases related to poor immune function. The current methods commonly used for vitamin D analysis are often applied to determine the levels of the most abundant metabolite in pla

In this paper, curve-fitting and an artificial neural network (ANN) model were developed to predict R-Event. Expected number of new infections that arise in any event occurring over a total time in any space is termed as R-Event. Real-time data for the office environment was gathered in the spring of 2022 in a naturally ventilated office room in Roorkee, India, under composite climatic conditions.

Hydropower is a renewable, reliable, and highly predictable source of energy. It has been used for centuries. The tariff of energy generation is divided into two parts: fixed charges and variable charges. Fixed charges are based on the availability of machinery (i.e., plant availability factor) and variable charges are based on the actual energy generation. The energy generation targets are decide

Quest for harnessing clean and affordable electricity has increased renewable energy installations, especially the rooftop solar photovoltaic (PV) systems in many residential homes; simultaneously, such homes are connected to the utility grid for reliable energy service, creating a hybrid power supply system (HPSS). However, the most stressful challenge with the HPSS is the confounding condition o

Extraction of vitamin D, including its hydroxylated and esterified metabolites, from soft tissues such as the liver is challenging due to the lipophilic character of matrix and analytes that are expected in very low concentration levels. In this study, we aimed at the optimization of two-step extraction using solid–liquid extraction as the first step, followed by solid-phase extraction. Various so

Despite tremendous progress seen in the computational fluid dynamics community for the past few decades, numerical tools are still too slow for the simulation of practical flow problems, consuming thousands or even millions of computational core-hours. To enable feasible multi-disciplinary analysis and design, the numerical techniques need to be accelerated by orders of magnitude. Reduced-order mo

A data-driven reduced basis (RB) method for parametrized time-dependent problems is proposed. This method requires the offline preparation of a database comprising the time history of the full-order solutions at parameter locations. Based on the full-order data, a reduced basis is constructed by the proper orthogonal decomposition (POD), and the maps between the time/parameter values and the proje

In solid mechanics, linear structures often exhibit (local) nonlinear behavior when close to failure. For instance, the elastic deformation of a structure becomes plastic after being deformed beyond recovery. To properly assess such problems in a real-life application, we need fast and multi-query evaluations of coupled linear and nonlinear structural systems, whose approximations are not straight

Purpose: The aim was to investigate the use of multiple small VOIs for kidney dosimetry in [177Lu]Lu-DOTA-TATE therapy. Method: The study was based on patient and simulated SPECT images in anthropomorphic geometries. Images were reconstructed using two reconstruction programs (local LundaDose and commercial Hermia) using OS-EM with and without resolution recovery (RR). Five small VOIs were placed

Air pollution is increasing profusely in Indian cities as well as throughout the world, and it poses a major threat to climate as well as the health of all living things. Air pollution is the reason behind degraded indoor air quality (IAQ) in urban buildings. Carbon dioxide (CO2) is the main contributor to indoor pollution as humans themselves are one of the generating sources of this pollutant. T

Sustainable concrete is the demand of the present era to reduce carbon emissions. Fly-ash-based geopolymer (FLAG) concrete has been used in the construction industry for more than one and a half decades. The compressive strength (CS) of concrete plays a crucial role in the mechanical properties of concrete. Laboratory experiments take a huge amount of time and cost to estimate the CS of concrete.

The bond strength between concrete and corroded steel reinforcement bar is one of the main responsible factors that affect the ultimate load-carrying capacity of reinforced concrete (RC) structures. Therefore, the prediction of accurate bond strength has become an important parameter for the safety measurements of RC structures. However, the analytical models are not enough to estimate the bond st

The development of physics-informed deep learning is radically changing compu-tational science and engineering, allowing for an effective integration ofphysics-based and datadriven modeling. Deep learning provides a powerful tool forthe discovery of governing dynamics underneath data and enables nonlinear model-reduction. A Bayesian viewpoint of deep learning is discussed in this chapter towards t

Due to the corrosion problem in reinforced concrete structures, the use of fiber-reinforced polymer (FRP) bars may be preferred in place of traditional reinforcing steel. FRP bars are used in concrete constructions to boost the strength of structural elements and retain their longevity. In this study, the axial load carrying capacity (ALCC) of the FRP-reinforced concrete columns has been evaluated

The world is experiencing deep climate changes caused by increased population and rapid urbanization. Hydropower is one of the renewable energy sources that can be used to meet energy demands, but most of the hydropower plants suffer from silt erosion and cavitation problems. Therefore, it is important to decide which parts to be repair or replace, as it affects the Operation and Maintenance (O&am