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The purpose of this paper is to understand the dark side of blockchain technology (BCT) adoption in small and mid-size enterprises. The focus of the authors is to decode the intricate relationship among the selected variables missing in the existing literature.

A focused group approach is initiated by the authors to identify the barriers. Total interpretive structural modeling, Matrice d'impacts croisés multiplication appliquée á un classment, that is, matrix multiplication applied to classification and decision-making trial and evaluation laboratory are used to analyze the complex relationships among identified barriers.

This study finds that implementation of BCT reduces maintenance cost by withdrawing manual effort, as BCT has better capability to quantify the internal status of the system (observability characteristic). The observability characteristic of BCT provides high compatibility to the system. This study also finds that the compatibility of BCT with the organization reduces implementation cost and facilitates project management. The findings of this study recommend analyzing maintenance cost and compatibility of BCT before implementing it. Small and mid-size enterprises can select complex BCT depending on the sophistication level of IT usage and IT project management capabilities.

This study comes with various limitations, where the model developed by the authors may not be conclusive, as it is based exclusively on expert opinion. The samples collected may not help in validating the model statistically. Though the model has its limitations, it can still be considered as a nascent initiative for further investigation using structural equation modeling.

The outcomes of the theoretical and managerial contributions of the study can be categorized into three levels. This study can be used both by the industrialists and researchers to understand the barriers and the recovery methods thereafter. Suggestions that serve as future directives are also discussed by the authors.

In the era of multiple global disruptions, firms are finding it to continue their business. MSMEs are impacted more as they have constrained resources. Organizational flexibility has emerged as an organizational and management principle that would help firms stay competitive even in volatile markets. This study aims to present a set of guidelines and insights for MSME managers to implement organizational flexibility in their organizations.

This study uses total interpretive structural modelling to study how the various factors contributing to organizational flexibility behave together. Behavioural theory is used to explain why organizations need to incorporate flexibility, and systems theory of organization is used to explain why an organization needs to have open boundaries.

Organizational flexibility is a principle that may be supported by the systems theory of organization. The study has shown that it is important for MSMEs to have supply chain collaborations to be more flexible. The study also shows pressure from competitors as the key driver that would make a firm more flexible, and that adequate support from management and technological skills are required to drive flexibility in an organization.

Single respondent bias may have occurred in this study. This can be eliminated by interviewing multiple people from the same organization. Further research around the reasoning for linkages can be explored with theory-driven grounded studies.

This study attempts to use a multi-criteria decision-making technique to present insights to managers to help them make their organizations flexible.

Applied a hybrid approach using genetic algorithms (GAs) for a case-based retrieval process in order to increase the overall improved cost accuracy for a case-based library. The paper aims to discuss this issue.

A weight optimization approach using case-based reasoning (CBR) with proposed GAs for developing the CBR model. GAs are used to investigate optimized weight generation with an application to real project cases.

The proposed CBR model can reduce errors consistently, and be potentially useful in the early financial planning stage. The authors suggest the developed CBR model can provide decision-makers with accurate cost information for assessing and comparing multiple alternatives in order to obtain the optimal solution while controlling cost.

The system can operate with more accuracy or less cost, and CBR can be used to better understand the effects of factor interaction and variation during the developed system’s process.

Excessive tool wear is responsible for damage or breakage of the tool, workpiece, or machining center. Thus, it is crucial to examine tool conditions during the machining process to improve its useful functional life and the surface quality of the final product. AI-based tool wear prediction techniques have proven to be effective in estimating the Remaining Useful Life (RUL) of the cutting tool. However, the model prediction needs improvement in terms of accuracy.

This paper represents a methodology of fusing a feature selection technique along with state-of-the-art deep learning models. The authors have used NASA milling data sets along with vibration signals for tool wear prediction and performance analysis in 15 different fault scenarios. Multiple steps are used for the feature selection and ranking. Different Long Short-Term Memory (LSTM) approaches are used to improve the overall prediction accuracy of the model for tool wear prediction. LSTM models' performance is evaluated using R-square, Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) parameters.

The R-square accuracy of the hybrid model is consistently high and has low MAE, MAPE and RMSE values. The average R-square score values for LSTM, Bidirection, Encoder–Decoder and Hybrid LSTM are 80.43, 84.74, 94.20 and 97.85%, respectively, and corresponding average MAPE values are 23.46, 22.200, 9.5739 and 6.2124%. The hybrid model shows high accuracy as compared to the remaining LSTM models.

The low variance, Spearman Correlation Coefficient and Random Forest Regression methods are used to select the most significant feature vectors for training the miscellaneous LSTM model versions and highlight the best approach. The selected features pass to different LSTM models like Bidirectional, Encoder–Decoder and Hybrid LSTM for tool wear prediction. The Hybrid LSTM approach shows a significant improvement in tool wear prediction.

Working capital efficiency (WCE) is crucial for the sustainability of both large and small firms. This study aims to use the sample of micro, small and medium-sized enterprises (MSMEs) in India and tries to understand the critical determinants of WCE.

Using a fixed effect panel data model on a sample of 578 MSMEs (59 micro, 226 medium and 296 small firms), this study explores the relationship between the predictors of WCE. Additionally, the study adopted two metrics for measuring WCE among each type of firm (micro, small and medium).

Several firm-specific variables, including leverage (lever), firm age (AGE), firm size (Fsiz), profitability (Prof), extended payment terms (EPT), human capital (HCap), asset turnover ratio (ATR), reverse factoring (RF) and firm growth (FG), have a significant effect on working capital management efficiency (WCE). In contrast, tangibility (Tangib) and salary expenses (Sal) had an insignificant effect on working capital management efficiency.

The study is based on secondary data. Future studies may incorporate some primary data, which will facilitate qualitative analysis.

The studies explore the relationship between WCE and expenses in HCap, EPT, RF and Sal as the predictors for WCE, which was not studied earlier in MSMEs scenario, especially in case of developing nation.

Inefficient waste disposal technique and cement production methodology caused significant environmental impacts, leading to global warming. The purpose of the research was to invent an effective, sustainable technology to use the wastes and alternate for cement in concrete. Geopolymer technology could be the most desirable solution to use the wastes into an effective product.

The wood waste ash derived from nearby tea shops was used as an alternate binder for fly ash. The replacement of WWA with FA was varied from 0 to 100% at 10% intervals. In this research, setting and mechanical features of Geopolymer Concrete (GPC) along with Waste wood ash (WWA) was carried out. The influence of wood waste ash in the microstructure of the GPC was also assessed using scanning electron microscope and X-ray diffraction analysis.

The findings revealed that 30% replacement of wood waste ash was performed higher in all measured features. Besides, the formation of different phases was also observed with the inclusion of wood waste ash.

The demand for fly ash was increased in recent years, and the fly-based GPC has required more alkaline solution and temperature curing. Hence, there was a research gap on finding an alternative binder for fly ash.

The research novelty was to use the wood waste ash, which has inbuilt alkaline compounds on the production of sustainable geopolymer. The finding showed that the wood waste ash could be alternate fly ash that eliminates the environmental impacts and economic thrust.

Demand for Geopolymer concrete (GPC) has increased recently because of its many benefits, including being environmentally sustainable, extremely tolerant to high temperature and chemical attacks in more dangerous environments. Like standard concrete, GPC also has low tensile strength and deformation capacity. This paper aims to analyse the utilization of incinerated bio-medical waste ash (IBWA) combined with ground granulated blast furnace slag (GGBS) in reinforced GPC beams and columns. Medical waste was produced in the health-care industry, specifically in hospitals and diagnostic laboratories. GGBS is a form of industrial waste generated by steel factories. The best option to address global warming is to reduce the consumption of Portland cement production and promote other types of cement that were not a pollutant to the environment. Therefore, the replacement in ordinary Portland cement construction with GPC is a promising way of reducing carbon dioxide emissions. GPC was produced due to an alkali-activated polymeric reaction between alumina-silicate source materials and unreacted aggregates and other materials. Industrial pollutants such as fly ash and slag were used as raw materials.

Laboratory experiments were performed on three different proportions (reinforced cement concrete [RCC], 100% GGBS as an aluminosilicate source material in reinforced geopolymer concrete [GRGPC] and 30% replacement of IBWA as an aluminosilicate source material for GGBS in reinforced geopolymer concrete [IGRGPC]). The cubes and cylinders for these proportions were tested to find their compressive strength and split tensile strength. In addition, beams (deflection factor, ductility factor, flexural strength, degradation of stiffness and toughness index) and columns (load-carrying ability, stress-strain behaviour and load-deflection behaviours) of reinforced geopolymer concrete (RGPC) were studied.

As shown by the results, compared to Reinforced Cement Concrete (RCC) and 100% GGBS based Reinforced Geopolymer Concrete (GRGPC), 30% IBWA and 70% GGBS based Reinforced Geopolymer Concrete (IGRGPC) (30% IBWA–70% GGBS reinforced geo-polymer concrete) cubes, cylinders, beams and columns exhibit high compressive strength, tensile strength, flexural strength, load-carrying ability, ultimate strength, stiffness, ductility and deformation capacity.

All the results were based on the experiments done in this research. All the result values obtained in this research are higher than the theoretical values.

Material extrusion (MEX) 3D printers suffer from an intrinsic limitation of small size of the prints due to its restricted bed dimension. On the other hand, friction stir spot welding (FSSW) is gaining wide interest from automobile, airplane, off-road equipment manufacturers and even consumer electronics. This paper aims to explore the possibility of FSSW on Acrylonitrile Butadiene Styrene/Polylactic acid 3D-printed components to overcome the bed size limitation of MEX 3D printers.

Four different tool geometries (tapered cylindrical pin with/without concavity, pinless with/without concavity) were used to produce the joints. Three critical process parameters related to FSSW (tool rotational speed, plunge depth and dwell time) and two related to 3D printing (material combination and infill percentages) were investigated and optimized using the Taguchi L27 design of experiments. The influence of each welding parameter on the shear strength was evaluated by analysis of variance.

Results revealed that the infill percentage, a 3D printing parameter, had the maximum effect on the joint strength. The joints displayed pull nugget, cross nugget and substrate failure morphologies. The outcome resulted in the joint efficiency reaching up to 100.3%, better than that obtained by other competitive processes for 3D-printed thermoplastics. The results, when applied to weld a UAV wing, showed good strength and integrity. Further, grafting the joints with nylon micro-particles was also investigated, resulting in a detrimental effect on the strength.

To the best of the authors’ knowledge, this is the first study to demonstrate that the welding of dissimilar 3D-printed thermoplastics with/without microparticles is possible by FSSW, whilst the process parameters have a considerable consequence on the bond strength.

The use of flexible connections throughout the steel structures provides a high level of stiffness compared to that of fully welded connections. Flexible connections allow for rotation to an extent, which make them perform better during earthquake than welded connections. In hanger connections, the applied load produces tension in the bolts and bolts are designed for tensile forces. When the deformation of the flange plate is equal to that of the bolts, a plastic hinge is formed in the flange plate at the weld line and the bolts are pulled to failure. If the attached plate is allowed to deform, additional tensile forces called prying forces are developed in the bolts. The paper aims to discuss these issues.

This paper includes the results of investigation on prying force in T-stub connection fabricated with normal grade bolts and high strength friction grip (HSFG) bolts. Finite element analysis has been carried out by creating models and analyzing the effect of external tensile force and bolt force. For different grades of bolt (4.6, 8.8, 10.9, 12.9), the prying force is calculated.

It is found that prying force is increasing with the change in grade of bolt used from normal to HSFG. The results obtained from analysis using IS 800:2007 codal provision are also included. It is observed that HSFG bolts do not allow for any slip between the elements connected and hence rigidity is increased.

The prying force mainly depends on geometrical parameter of the connection. In this research work, the variation of prying force was studied based on the variation in dimensions of T-stub angle section and bolt grade (4.6, 8.8, 10.9, 12.9). The method of obtaining prying force from bolt load and applied load is a unique approach. The results of FE analysis is validated with the analytical calculation as per IS 800:2007 code provisions, which shows the originality of the research.