Showing papers by "Thapar University published in 2022"

A comprehensive review on effects of mineral admixtures and fibers on engineering properties of ultra-high-performance concrete

Abstract: The unparalleled attributes of ultra-high-performance concrete (UHPC) reported over the last three decades sum up the development of this material in two phases: UHPC and ultra-high-performance fiber-reinforced concrete (UHP-FRC)/ultra-high-performance hybrid fiber-reinforced concrete (UHP-HFRC). The integration of scientific knowledge pertaining to material interactions and technical observations of developed UHPC has laid the foundation for further research and development. Microstructural research has revealed the contribution of finer sizes mineral admixtures along with lower water/binder ratios in the range of 0.16–0.24 to the development of high-density calcium silicate hydrate with a very low possibility of ettringite formation and alkali silica reactions in UHPC matrix. The addition of fibers is challenging when seeking to maintain the rheology of UHPC; therefore, technically sound concrete experts are required for the field applications. The challenges preventing the widespread use of UHPC are the higher initial cost, the requirement of special skilled labor for execution, and the lack of open discussions about various UHPC standards available worldwide to reach an agreement on minimum strength achievements and test standards. The hurdles hindering the use of UHPC in recent emerging 3D printing technology are also discussed in the present study. The vast literature compilation also reveals that less research has concentrated on UHP-HFRC as compared to UHPC and UHP-FRC. Therefore, a comprehensive review regarding the effects of mineral admixtures and fibers on the microstructural characteristics , fresh properties, and mechanical properties overall to address the upcoming challenges preventing the widespread use of UHPC and UHP-FRC/UHP-HFRC is essential.

A Switched-Capacitors-Based 13-Level Inverter

Abstract: The merit of switched-capacitors-based multilevel inverters (SCMLIs) is generally quantified in terms of a “cost function” (CF) that incorporates parameters such as voltage gain, component count, total standing voltage (TSV), and number of levels. In this article, a 13-level inverter is proposed with the aim of achieving a low value of CF. The proposed single-stage SCMLI uses one input source and three capacitors to attain a voltage gain of 3. It requires 13 power switches, of which the peak inverse voltage (PIV) of nine switches is restricted to the source voltage. The remaining four switches have PIV equal to twice the source voltage and they operate at low frequency. Thus, for all switches, the PIV is less than the amplitude of the output voltage. Moreover, the capacitors are self-balanced at all regions of modulation index values. The proposed inverter is validated through simulation and experimental results. A comparison of the proposed topology with other contemporary SCMLIs shows that it is highly competent in terms of CF, PIV and TSV requirements, waveform resolution, and capability to achieve voltage balancing of capacitors at low values of modulation index.

Effect of alum sludge ash on the high-temperature resistance of mortar

Abstract: The effect of high temperature on the mechanical, durability, and microstructural properties of mortar containing alum sludge ash (ASA) was investigated in this paper. The ASA was derived from grinding and calcinating alum sludge, a typical by-product of the drinking water treatment processes. Four different mortar mixtures with ASA content at weight percentages of 0%, 10%, 20%, and 30% (as cement replacement) were exposed to high temperatures of 300 °C, 550 °C, and 800 °C, respectively. The experimental results showed that mortar samples containing up to 20% of cement replaced with ASA exhibited superior high-temperature resistance to the reference ones (without ASA), especially after exposure to 800 °C. The thermal analysis determined the portlandite consumption because of ASA pozzolanic reaction, and the x-ray diffraction pattern showed that the ASA reaction might contribute to the formation of aluminum-bearing phases with excellent refractoriness in the binder matrix. In addition, crack examination conducted by backscattered electron images evidenced that the ASA addition mitigated the binder paste degradation.

Neutrality aggregation operators based on complex q-rung orthopair fuzzy sets and their applications in multiattribute decision-making problems

Abstract: Complex q‐rung orthopair fuzzy sets (CQROFSs) are proposed to convey vague material in decision‐making problems. The CQROFSs can enthusiastically modify the region of proof by altering the factor q≥1 for real and imaginary parts based on the variation degree and, therefore, favor further uncountable options. Consequently, this set reverses over the existing theories, such as complex intuitionistic fuzzy sets (CIFSs) and complex Pythagorean fuzzy sets (CPFS). In everyday life, there are repeated situations that can occur, which involve an impartial assertiveness of the decision‐makers. To determine the best decision to handle such situations, in this study, we propose modern operational laws by joining the characteristics of the truth factor sum and collaboration between the truth degrees into the analysis for CQROFSs. Based on these principles, we determined several weighted averaging neutral aggregation operators (AOs) to collect the CQROF knowledge. Subsequently, we established an original multiattribute decision‐making (MADM) procedure by using the demonstrated AOs based on CQROFS. To evaluate the effectiveness, in terms of reliability and consistency, of the proposed operators, they were applied to some numerical examples. A comparative analysis of the investigated operators and other existing operators was also performed to find the dominance and validity of the introduced MADM method.

Fractional-Order Notch Filter for Grid-Connected Solar PV System With Power Quality Improvement

Abstract: In this article, we deal with the development of a fractional-order notch filter (FONF) for a grid-connected solar photovoltaic (PV) system. The developed FONF control approach is used to estimate fundamental active constituents from the distorted load currents, and hence, gating pulses for operating voltage source converter (VSC) are used in the PV system. This control approach for the grid-connected solar PV system is designed to achieve several purposes, such as feeding active power demand of the load/grid and countercurrent-related power quality issues at the common connecting point. The power quality issues taken into consideration are harmonics distortion, reactive power burden on the system, and unbalancing of connected loads. The FONF-based control proposes a modified structure of an integer-order notch filter. The integer-order filters have a limitation due to the fixed integrator and differentiator terms. In FONF, the power of integrator used in a notch filter can be modified according to the application required for obtaining the accurate response of the system. A prototype of the grid-connected solar PV system is developed in the laboratory using IGBTs based VSC and dSPACE MicroLabBox (DS-1202) to demonstrate the behavior of the FONF-based control. Simulation and experimental results are obtained for steady-state and unbalanced loads with variation in the solar irradiance. The harmonic distortions in the system are observed as per the IEEE-519 standard.

The personal protective equipment fabricated via 3D printing technology during COVID-19

Abstract: COVID-19 has been spread in more than 220 countries and caused global health concerns. The supply chain disruptions have abruptly affected due to the second wave of COVID-19 in various countries and caused unavailability and shortage of medical devices and personal protective equipment for frontline healthcare workers. Three-dimensional (3D) printing has proven to be a boon and revolutionized technology to supply medical devices and tackle the situation caused by the COVID-19 pandemic. The diverse designs were produced and are currently used in hospitals by patients and frontline healthcare doctors. This review summarises the application of 3D printing during COVID-19. It collects the comprehensive information of recently designed and fabricated protective equipment like nasopharyngeal swabs, valves, face shields, facemasks and many more medical devices. The drawbacks and future challenges of 3D printed medical devices and protective equipment is discussed.

Multistep traffic speed prediction: A deep learning based approach using latent space mapping considering spatio-temporal dependencies

Abstract: Traffic management in a city has become a major problem due to the increasing number of vehicles on roads. Intelligent Transportation System (ITS) can help the city traffic managers to tackle the problem by providing accurate traffic forecasts. For this, ITS requires a reliable traffic prediction algorithm that can provide accurate traffic prediction at multiple time steps based on past and current traffic data. In recent years, a number of different methods for traffic prediction have been proposed which have proved their effectiveness in terms of accuracy. However, most of these methods have either considered spatial information or temporal information only and overlooked the effect of other. In this paper, to address the above problem a deep learning based approach has been developed using both the spatial and temporal dependencies. To consider spatio-temporal dependencies, nearby road sensors at a particular instant are selected based on the attributes like traffic similarity and distance. Two pre-trained deep auto-encoders were cross-connected using the concept of latent space mapping and the resultant model was trained using the traffic data from the selected nearby sensors as input. The proposed deep learning based approach was trained using the real-world traffic data collected from loop detector sensors installed on different highways of Los Angeles and Bay Area. The traffic data is freely available from the web portal of the California Department of Transportation Performance Measurement System (PeMS). The effectiveness of the proposed approach was verified by comparing it with a number of machine/deep learning approaches. It has been found that the proposed approach provides accurate traffic prediction results even for 60-min ahead prediction with least error than other techniques.

Linking stakeholder and competitive pressure to Industry 4.0 and performance: Mediating effect of environmental commitment and green process innovation

Abstract: In the recent few years, with an increase in focus on sustainability, firms have been actively pursuing different strategies to contribute towards sustainability. Industry 4.0 (I4) technologies can help organizations to achieve superior environmental as well as economic performance. Through the lens of stakeholder theory (ST) and Schumpeterian view of competition (SCV), this paper examines whether stakeholder and competitive pressures towards sustainability stimulate organizations to implement I4 technologies and commensurate performance outcomes. The study further tests the mediating role of environmental commitment and green process innovation (GPI) on these relationships. The proposed hypotheses are examined using the survey data from 173 manufacturing firms in India by partial least squares (PLS) approach. Findings show that environmental commitment mediates the effect of stakeholder and competitive pressures on I4 technologies. Further, results also show that GPI mediates between I4 technologies and performance. The findings provide insights for managers on how they can best respond to stakeholder and competitive pressures on sustainability and contribute towards sustainable development.

The opportunistic nature of gut commensal microbiota

Abstract: The abundance of gut commensals has historically been associated with health-promoting effects despite the fact that the definition of good or bad microbiota remains condition-specific. The beneficial or pathogenic nature of microbiota is generally dictated by the dimensions of host-microbiota and microbe-microbe interactions. With the increasing popularity of gut microbiota in human health and disease, emerging evidence suggests opportunistic infections promoted by those gut bacteria that are generally considered beneficial. Therefore, the current review deals with the opportunistic nature of the gut commensals and aims to summarise the concepts behind the occasional commensal-to-pathogenic transformation of the gut microbes. Specifically, relevant clinical and experimental studies have been discussed on the overgrowth and bacteraemia caused by commensals. Three key processes and their underlying mechanisms have been summarised to be responsible for the opportunistic nature of commensals, viz. improved colonisation fitness that is dictated by commensal-pathogen interactions and availability of preferred nutrients; pathoadaptive mutations that can trigger the commensal-to-pathogen transformation; and evasion of host immune response as a survival and proliferation strategy of the microbes. Collectively, this review provides an updated concept summary on the underlying mechanisms of disease causative events driven by gut commensal bacteria.

Rare earth added barium alumino borosilicate glass-ceramics as sealants in solid oxide fuel cells

Abstract: Glasses with composition 50BaO-(5-x) Al2O3-xR2O3–30B2O3–15SiO2 (x = 0,5 and R = Nd, Gd, La, Dy) have been synthesized by conventional melt quenching method. Controlled crystallization has been carried out to convert these glasses to corresponding glass-ceramics. X-ray diffraction (XRD) technique has been used to identify the crystalline phases formed in glass-ceramics. Archimede's method has been used to measure the density of glass-ceramics. The co-efficient of thermal expansion of all glass-ceramics lie within the range (11.60 – 12.81) x 10−6/°C, which matches with that of the other components of solid oxide fuel cell. The interaction study of glass-ceramics with Crofer22APU interface and Crofer22APU/Glass-ceramic/Crofer22APU showed no evidence of interfacial failure and cracks. Hence the prepared glass-ceramics can be considered as a suitable candidate for sealant materials in SOFC applications.

Performance evaluation of Non-Uniform circular antenna array using integrated harmony search with Differential Evolution based Naked Mole Rat algorithm

Abstract: In many wireless applications, circular antenna arrays are commonly used but managing the lower side lobe with high directivity is still challenging. Many typical methods could be used to synthesize the array in real time but lag behind to retain high directivity and the low sidelobe level. This study formulates an optimization problem to obtain the desired main lobe direction, suppress the subsidiary lobe, and maximizes the directivity. A modified optimization algorithm is proposed called Integrated Harmony Search with Differential Evolution based Naked Mole Rat Algorithm (IHDN) to rapidly and reliably refine control parameters. Simulations are performed, and results are compared to other traditional algorithms. The findings show that the proposed algorithm results in excellent side lobe minimization with good directivity. The size analysis indicates that good directivity values with low sidelobe levels can be obtained for a smaller number of antenna elements.

Decoding the protein–ligand interactions using parallel graph neural networks

Abstract: Protein-ligand interactions (PLIs) are essential for biochemical functionality and their identification is crucial for estimating biophysical properties for rational therapeutic design. Currently, experimental characterization of these properties is the most accurate method, however, this is very time-consuming and labor-intensive. A number of computational methods have been developed in this context but most of the existing PLI prediction heavily depends on 2D protein sequence data. Here, we present a novel parallel graph neural network (GNN) to integrate knowledge representation and reasoning for PLI prediction to perform deep learning guided by expert knowledge and informed by 3D structural data. We develop two distinct GNN architectures: [Formula: see text] is the base implementation that employs distinct featurization to enhance domain-awareness, while [Formula: see text] is a novel implementation that can predict with no prior knowledge of the intermolecular interactions. The comprehensive evaluation demonstrated that GNN can successfully capture the binary interactions between ligand and protein's 3D structure with 0.979 test accuracy for [Formula: see text] and 0.958 for [Formula: see text] for predicting activity of a protein-ligand complex. These models are further adapted for regression tasks to predict experimental binding affinities and [Formula: see text] crucial for compound's potency and efficacy. We achieve a Pearson correlation coefficient of 0.66 and 0.65 on experimental affinity and 0.50 and 0.51 on [Formula: see text] with [Formula: see text] and [Formula: see text], respectively, outperforming similar 2D sequence based models. Our method can serve as an interpretable and explainable artificial intelligence (AI) tool for predicted activity, potency, and biophysical properties of lead candidates. To this end, we show the utility of [Formula: see text] on SARS-Cov-2 protein targets by screening a large compound library and comparing the prediction with the experimentally measured data.