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Ultra low permeability rocks such as shales exhibit complex fracture networks which must be discretely characterized in our reservoir models to evaluate stimulation designs and completion strategies properly. The pressure (Darcy’s law) and composition driven (Fick’s law) flow mechanisms when combined result in composition, pressure and saturationdependent slippage factor. The approach used in this study is to utilize pressure-dependent transmissibility multipliers to incorporate apparent gas-permeability changes resulting from multi-mechanism flows in commercial simulators. This work further expounds on the effectiveness of the theory by presenting a descriptive analysis between two commercially utilized numerical simulators. The applicability of dynamic slippage as an effective flow mechanism governing gas flow mechanisms within the computational environment of two different simulators is attempted in this analysis. Results indicate that slippage-governed flow in modelling shale reservoirs should not be ignored.

During pre-vaccine era, pharmaceutical supplies [self-care essentials (SCEs)] have been proved to be a major deflector, protector and safety guard against novel coronavirus disease (COVID-19). Hence, the objective of the study is to provide a comprehensive socio-technological decision-making framework based on multiple criteria for selecting the suppliers of pharmaceuticals, such as SCEs, by multi-brand enterprises (distributors) in the pandemic environment.

A hybrid methodology of Bayesian best worst method (BWM) and multi-attributive border approximation area comparison (MABAC) method has been applied for carrying out the study. Bayesian BWM has been applied for computing the importance of criteria identified for the selection of SCEs' suppliers during pandemic environment and MABAC method evaluated the suppliers of the SCEs.

In the study, the authors have identified eight criteria such as disinfection and sanitization of vehicles, social conscience of suppliers, brand (Technological recognition) of SCEs and logistics and distribution network, among others, which are critical to the selection of a supplier for the supply of SCEs. The application of the proposed hybrid model revealed that lead time and quality of SCEs are of utmost concern for pharmacies in a pandemic environment. Among the ten suppliers, results showed that Suppliers 2, 4 and 5 have been ranked first for supplying hand wash, hand sanitizer and face mask, respectively.

The proposed model has helped the multi-brand distributors of pharmaceuticals in selecting suppliers during the ongoing crisis of COVID-19. In addition to that, in future the outcomes of the study would be helpful for multi-brand distributors as well as pharmacies and hospitals in selecting the best suppliers. Policy makers will be able to make and revise the policies immediately with the help of the proposed decision-making framework.

The paper makes a novel contribution towards theory with the criteria identified for selecting best suppliers during the pandemic COVID-19. Additionally, the proposed hybrid model helps multi-brand distributors of pharmaceuticals in making decisions that lead to a huge social and economic success in pandemic time.

It is known that the iterative roots of continuous functions are not necessarily unique, if it exist. In this note, by introducing the set of points of coincidence, we study the iterative roots of order preserving homeomorphisms. In particular, we prove a characterization of identical iterative roots of an order preserving homeomorphism using the points of coincidence of functions.

This study aims to include the diagnosis of an old concrete deck steel truss rural road bridge in the damaged and retrofitted state through vibration response signals.

The analysis of the vibration response signals is performed in time and time-frequency domains using statistical features-root mean square, impulse factor, crest factor, kurtosis, peak2peak and Stockwell transform. The proposed methodology uses the Hilbert transform in combination with spectral kurtosis and bandpass filtering technique for obtaining robust outcomes of modal frequencies.

The absence or low amplitude of considered mode shape frequencies is observed both before and after retrofitting of bridge indicates the deficient nodes. The kurtosis feature among all statistical approaches is able to reflect significant variation in the amplitude of different nodes of the bridge. The Stockwell transform showed better resolution of present modal frequencies but due to the yield of additional frequency peaks in the vicinity of the first three analytical modal frequencies no decisive conclusions are achieved. The methodology shows promising outcomes in eliminating noise and visualizing distinct modal frequencies of a steel truss bridge.

The findings of the present study help in analyzing noisy vibration signals obtained from various structures (civil or mechanical) and determine vulnerable locations of the structure using mode shape frequencies.

The literature review gave an insight into few experimental investigations related to the combined application of Hilbert transform with spectral kurtosis and bandpass filtering technique in determining mode frequencies of a steel truss bridge.

The purpose of this study is two-fold. First, to identify and encapsulate the enablers that can facilitate technology integration in higher education and second, to understand and analyze the interplay between technology agility enablers.

The study used the Total Interpretive Structural Modeling (TISM) approach to construct a theoretical model of the technology agility enablers in higher education and MICMAC analysis for ranking and segregating the enablers based on their dependence power into four categories: Autonomous, Dependent, Linkage and Independent.

The study helped identify eight technology agility enablers, with the Covid-19 pandemic as the most significant enabler. The Covid-19 pandemic has catalyzed the diffusion of technology across the education sector in India, including tertiary higher education. The study revealed government initiatives and institutional commitment as other enablers that can promote technology agility in higher education.

The results of this study would assist the policymakers and management of universities and colleges in understanding the important enablers that can facilitate technology integration in higher education.

Research in the past on technology adoption in higher education has looked into each enabler in isolation. This research provides a comprehensive view of the enablers and has attempted to establish a multidirectional interplay between the enablers.

This study aims to perform the experimental work on a laboratory-constructed steel truss bridge model on which hammer blows are applied for excitation. The vibration response signals of the bridge structure are collected using sensors placed at different nodes. The different damaged states such as no damage, single damage, double damage and triple damage are introduced by cutting members of the bridge. The masked noise with recorded vibration responses generates challenge to properly analyze the health of bridge structure.

The analytical modal properties are obtained from finite element model (FEM) developed using SAP2000 software. The response signals are analyzed in frequency domain by power spectrum and in time-frequency domain using spectrogram and Stockwell transform. Various low pass signal-filtering techniques such as variational filter, lowpass sparse banded (AB) filter and Savitzky–Golay (SG) differentiator filter are also applied to refine vibration signals. The proposed methodology further comprises application of Hilbert transform in combination with MUSIC and ESPRIT techniques.

The outcomes of SG filter provided the denoised signals using appropriate polynomial degree with proper selected window length. However, certain unwanted frequency peaks still appeared in the outcomes of SG filter. The SG-filtered signals are further analyzed using fused methodology of Hilbert transform-ESPRIT, which shows high accuracy in identifying modal frequencies at different states of the steel truss bridge.

The sequence of proposed methodology for denoising vibration response signals using SG filter with Hilbert transform-ESPRIT is a novel approach. The outcomes of proposed methodology are much refined and take less computational time.

Micro-finance self-help groups empower caregivers to indulge in productive activities based on the local availability of resources to reduce their financial burden. The purpose of this paper is to assess the need for and feasibility of initiating micro-finance groups for the caregivers of persons with mental disability in a rural socio-economically backward community of Karnataka, India.

The design of the study was a cross-sectional survey, with mixed methodology design out of the eight localities where the Mental Health Public Health Centres (PHC) were running successfully in Konandur area, Thirthahalli Taluk, Karnataka, one PHC was selected using simple Random Sampling Design and a 5 kms radius from Konandur town was selected as the area of the survey (190 households). During door-to-door survey, if the family indicated that a particular member is mentally unwell, the GHQ-5 and Symptoms and Others checklist were administered on him/her and the women caregiver was interviewed using qualitative needs assessment schedule and Perceived Social Support Scale.

Ten persons/households with mental illness (5.26 per cent) were identified in the community. Themes of financial needs, capacity of the caregiver, community resources, need for the microfinance self-help groups, informational needs, social support, burn out, and stigma elicited in the interview were depicted in the form of a conceptual framework to understand the inter-connectedness between the various themes.

This study is the first initiative in the field of micro-finance self-help groups for the persons with mental illness and families. The design of the study was a cross-sectional survey, which is found globally to be the most suited in conducting prevalence studies, as it provides accurate results for future studies as well as it is the first step to obtain accurate baseline values to later plan a prospective follow up study. The study used mixed methodology design. Though the sample size was small, the information collected from the participants in qualitative and quantitative method was triangulated and conceptual frameworks were developed. As this study is one of the first of its kind in the country, the results of this study from the stated sample can be considered as an important pilot for future longitudinal and cross-sectional studies to be planned in the community.

There is hardly any scientific literature which talks about the need for Micro-finance self-help groups for Persons with Disability, especially with person with mental disability. In order to initiate any Micro-finance SHG activities, it is essential to first undertake the need for and feasibility of initiating such micro-finance group activities in any given area. This study will be an important milestone in initiating any self-help group activity for caregivers of persons with mental disability, as it would help us understand the financial needs of the community, based on which a draft proposal to initiate micro-finance self-help group activities can be drawn up.

Nowadays, the applications of metal matrix composites are tremendously increasing in engineering fields. Consequently, the demand for precise machining of composites has also grown enormously. The purpose of this paper is to reduce production cost and simultaneously improve desired product quality through optimal parameter setting using WASPAS and MOORA.

Metal matrix composites were fabricated using stir casting process, with aluminum 6063 as matrix and titanium carbide as reinforcement. Fabricated composite samples were machined on medium duty lathe using cemented carbide tool. All the experiments were carried out based on Box–Behnken design. Comparison of multi objective optimization based on ratio analysis and weighted aggregated sum product assessment in optimizing four parameters, namely, “cutting speed,” “feed rate,” “depth of cut” and “reinforcement weight percent of composite samples”; evaluating their influence on material removal rate, cutting force and surface roughness were carried out.

The output achieved by both MOORA and WASPAS are in similar MCDM) techniques in the selection of machining parameters.

The results obtained in the present paper will be helpful for decision makers in manufacturing industries, who work in metal cutting area, to select the suitable levels for the parameters by implementing the MCDM techniques.

The novelty of this paper is making an attempt to select better MCDM technique based on the comparison of results obtained for the individual technique.

Deep learning (DL) technologies assist manufacturers to manage their business operations. This research aims to present state-of-the-art insights on the trends and ways forward for DL applications in manufacturing operations.

Using bibliometric analysis and the SPAR-4-SLR protocol, this research conducts a systematic literature review to present a scientific mapping of top-tier research on DL applications in manufacturing operations.

This research discovers and delivers key insights on six knowledge clusters pertaining to DL applications in manufacturing operations: automated system modelling, intelligent fault diagnosis, forecasting, sustainable manufacturing, environmental management, and intelligent scheduling.

This research establishes the important roles of DL in manufacturing operations. However, these insights were derived from top-tier journals only. Therefore, this research does not discount the possibility of the availability of additional insights in alternative outlets, such as conference proceedings, where teasers into emerging and developing concepts may be published.

This research contributes seminal insights into DL applications in manufacturing operations. In this regard, this research is valuable to readers (academic scholars and industry practitioners) interested to gain an understanding of the important roles of DL in manufacturing operations as well as the future of its applications for Industry 4.0, such as Maintenance 4.0, Quality 4.0, Logistics 4.0, Manufacturing 4.0, Sustainability 4.0, and Supply Chain 4.0.

This paper investigates the causality among gold prices, crude oil prices, bitcoin and stock prices by using daily data from January 2014 to December 2021. The study also examines the data during the COVID-19 outbreak from January 2020 to December 2021.

To estimate the long- and short-run causality, this study considers the nonlinear autoregressive distributed lag (NARDL) cointegration test.

The analysis found the existence of an asymmetric long-run cointegration among selected assets. Findings indicate that positive changes in bitcoin do not affect stock market in the long term. Changes in crude oil prices have a significant impact on stock prices. Moreover, it is observed that variations in the stock prices trigger a negative impact on gold prices. During the COVID-19 period, the study notices the presence of an asymmetric long-term cointegration between selected assets except bitcoin. Besides, findings revealed that negative price adjustments in gold lead to significant positive shocks in stock market.

These results provide critical information for policy performers and researchers to develop new strategies. Policy regulators can also consider the potential effects of the COVID-19 outbreak while developing strategies for investment decisions.