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At this moment, there is substantial anxiety surrounding the fire safety of huge reinforced concrete (RC) constructions. The limitations enforced by test facilities, technology, and high costs have significantly limited both full-scale and scaled-down structural fire experiments. The behavior of an individual structural component can have an impact on the entire structural system when it is connected to it. This paper addresses the development and testing of a self-straining preloading setup that is used to perform thermomechanical action in RC beams and slabs.

Thermomechanical action is a combination of both structural loads and a high-temperature effect. Buildings undergo thermomechanical action when it is exposed to fire. RC beams and slabs are one of the predominant structural members. The conventional method of testing the beams and slabs under high temperatures will be performed by heating the specimens separately under the desired temperature, and then mechanical loading will be performed. This gives the residual strength of the beams and slabs under high temperatures. This method does not show the real-time behavior of the element under fire. In real-time, a fire occurs simultaneously when the structure is subjected to desired loads and this condition is called thermomechanical action. To satisfy this condition, a unique self-training test setup was prepared. The setup is based on the concept of a prestressing condition where the load is applied through the bolts.

To validate the test setup, two RC beams and slabs were used. The test setup was tested in service load range and a temperature of 300 °C. One of the beams and slabs was tested conventionally with four-point bending and point loading on the slab, and another beam and slab were tested using the preloading setup. The results indicate the successful operation of the developed self-strain preloading setup under thermomechanical action.

Gaining insight into the unpredictable reaction of structural systems to fire is crucial for designing resilient structures that can withstand disasters. However, comprehending the instantaneous behavior might be a daunting undertaking as it necessitates extensive testing resources. Therefore, a thorough quantitative and qualitative numerical analysis could effectively evaluate the significance of this research.

The study was performed to validate the thermomechanical load setup for beams and slabs on a single-bay single-storey RC frame with and without slab under various fire possible scenarios. The thermomechanical load setup for RC members is found to be scarce.

In structural fire engineering, the design of columns is done either by prescriptive approaches or by empirical equations derived from experimental research. Performance-based design is the emerging methodology for designing structures under fire, which is case-specific. There is a need to develop design equations from first principles to design/find the residual strength of the column at elevated temperatures. The present study aims to develop equations from stress block parameters to find the residual strength of reinforced concrete (RC) columns subjected to elevated temperatures.

The stress-strain variation across the cross-section of the RC column is determined at elevated temperatures. Based on the updated stress distribution diagram, stress block parameters are derived for various depths of neutral axis (NA) and different temperatures. Using updated stress block parameters, Pu-Mu interaction curves are generated for elevated temperatures. The results are verified against conventional methods and experimental results.

The load-carrying capacity calculated from the proposed methodology is analogous to the experimental results. The methodology can be utilized to estimate the residual strength of RC columns subjected to elevated temperatures.

The work done here attempts to develop the equations to estimate the residual strength of the column. The work involves calculating the strength of columns subjected to fire curves.

This paper aims to review the literature on the relationship between the implementation and performance of 4.0 industrial revolution (IR) technologies and explores the extent to which the effects of several internal and external contingency factors on these relationships have been considered by the existing empirical studies.

To achieve its purposes, this study follows a systematic review of the literature and explores the published empirical research on implementation and performance links of 4.0 IR technologies and the effects of contingency factors on these links in mainly three main databases.

The findings of this study reveal that in general several contingency factors tend to have significant effects on the implementation and performance links of 4.0 IR in several contexts. This study also shows that the effects of these contingencies the effects of contingency factors on the implementation and performance links of 4.0 IR technologies are receiving growing attention from researchers and have been studied in different approaches but the moderation approach was the highest.

The review of the literature conducted in this study refers to those studies published mostly by three main databases (i.e. Scopus, Web of Sciences, and Science Direct), and only those papers published in English, and thus does not contain publications out of these restrictions.

This is one of the early literature review studies to explore and discuss the current state of research on the effects of contingency factors on the relationships between the implementation and performance of 4.0 IR technologies in the contexts of logistics and supply chain management.

This study aims to examine empirically the nature of supply chain disruptions caused by the COVID-19 pandemic, particularly on the Indian automobile sector.

The authors evaluate the stock market performance of individual company and its quantitative relationship to certain variables related to company’s supply chain.

The authors analysed the company’s operations considering several ratios like asset intensity, company size, labour intensity and inventory to revenue.

The results of analysis can help the companies to understand how disruptions in the supply chain can affect the company’s operations and how it is perceived by the investors in the stock market.

Also, investors are benefitted, as they can understand how different companies with different operational characteristics react to global disruptions in supply chains, which in turn would help them to find better investment opportunities.

Although there is some literature available on the qualitative as well as quantitative analysis, the authors go further to analyse the impact of supply chain disruption on the stocks of the automobile sector.

This study seeks to investigate the ability of blockchain technology (BCT) to increase circular economy (CE) practices in the electronics industry, emphasising India and Taiwan.

The study adopts a mixed-methods approach. Initial qualitative semi-structured interviews examined how BCT could inform CE practice. The qualitative aspects were followed by the Analytical Hierarchy Process (AHP), integrating qualitative and quantitative approaches, and fuzzy-set Qualitative Comparative Analysis (fsQCA), to analyse data from 391 industry experts surveyed.

The results show that the BCT has great potential to promote CE processes by improving the aspects of security, transparency and traceability. BCT adoption is driven by a conducive regulatory regime, stakeholder collaboration, and the availability of required technology.

By identifying key drivers and requisite requirements for successful BCT adoption in CE practices, this research offers critical guidance for policymakers, practitioners and researchers. It adds to the wider conversation about how emerging technologies can support sustainability and efficiency in industry.

This study contributes to the literature by providing a new lens to study BCT and CE practices intersectionality, particularly in the context of the emerging electronics industry and in countries such as India and Taiwan. Unlike previous research that studied either BCT or CE independently, this study uniquely illustrates how the principles underpinning these concepts, when implemented together, can positively impact sustainability outcomes within a resource-intensive industry notorious for generating highly significant waste streams.

This study aims to evaluate the potential of using the in-nozzle impregnation approach to reuse recycled PET (RPET) to develop continuous banana fiber (CBF) reinforced bio-composites. The mechanical properties and fracture morphology behavior are evaluated to establish the relationships between layer spacing–microstructural characteristics–mechanical properties of CBF/RPET composite.

This study uses RPET filament developed from post-consumer PET bottles and CBF extracted from agricultural waste banana sap. RPET serves as the matrix material, while CBF acts as the reinforcement. The test specimens were fabricated using a customized fused deposition modeling 3D printer. In this process, customized 3D printer heads were used, which have a unique capability to extrude and deposit print fibers consisting of a CBF core coated with an RPET matrix. The tensile and flexural samples were 3D printed at varying layer spacing.

The Young’s modulus (E), yield strength (sy) and ultimate tensile strength of the CBF/RPET sample fabricated with 0.7 mm layer spacing are 1.9 times, 1.25 times and 1.8 times greater than neat RPET, respectively. Similarly, the flexural test results showed that the flexural strength of the CBF/RPET sample fabricated at 0.6 mm layer spacing was 47.52 ± 2.00 MPa, which was far greater than the flexural strength of the neat RPET sample (25.12 ± 1.94 MPa).

This study holds significant social implications highlighting the growing environmental sustainability and plastic waste recycling concerns. The use of recycled PET material to develop 3D-printed sustainable structures may reduce resource consumption and encourages responsible production practices.

The key innovation lies in the concept of in-nozzle impregnation approach, where RPET is reinforced with CBF to develop a sustainable composite structure. CBF reinforcement has made RPET a superior, sustainable, environmentally friendly material that can reduce the reliance on virgin plastic material for 3D printing.

This study aims to investigate the fit of a promotion (prevention) focus with malicious (benign) envy and how this fit influences positive and negative behaviours, depending on the context.

Four empirical studies (two laboratory and two online experiments) were used to test key hypotheses. Study 1 manipulated regulatory focus and envy in a job application setting with university students. Study 2 engaged similar manipulations in a social media setting. Studies 3 and 4 extended the regulatory focus and envy manipulations to the general population in pay-what-you-want (PWYW) and pay-it-forward (PIF) restaurant contexts.

The findings showed that a promotion (prevention) focus fits with the emotion of malicious (benign) envy. In the social media context, promotion and prevention foci demonstrated negative behaviour, including unfollowing the envied person, when combined with malicious and benign envy. In the PWYW and PIF contexts, combining envy with a specific type of regulatory focus encouraged both positive and negative behaviours through influencing payments.

Future research could validate and extend this study’s findings with different product/service categories, cross-cultural samples and research methods such as field experiments.

The four studies’ findings will assist managers in formulating marketing strategies to enhance their positioning of target products/services, possibly leading to higher prices for PWYW and PIF businesses.

The conceptual model is novel as, to the best of the authors’ knowledge, no prior research has proposed and tested the fit between envy type and regulatory foci.

Three-dimensional (3D) printing is highly dependent on printing process parameters for achieving high mechanical strength. It is a time-consuming and expensive operation to experiment with different printing settings. The current study aims to propose a regression-based machine learning model to predict the mechanical behavior of ulna bone plates.

The bone plates were formed using fused deposition modeling (FDM) technique, with printing attributes being varied. The machine learning models such as linear regression, AdaBoost regression, gradient boosting regression (GBR), random forest, decision trees and k-nearest neighbors were trained for predicting tensile strength and flexural strength. Model performance was assessed using root mean square error (RMSE), coefficient of determination (R²) and mean absolute error (MAE).

Traditional experimentation with various settings is both time-consuming and expensive, emphasizing the need for alternative approaches. Among the models tested, GBR model demonstrated the best performance in predicting both tensile and flexural strength and achieved the lowest RMSE, highest R2 and lowest MAE, which are 1.4778 ± 0.4336 MPa, 0.9213 ± 0.0589 and 1.2555 ± 0.3799 MPa, respectively, and 3.0337 ± 0.3725 MPa, 0.9269 ± 0.0293 and 2.3815 ± 0.2915 MPa, respectively. The findings open up opportunities for doctors and surgeons to use GBR as a reliable tool for fabricating patient-specific bone plates, without the need for extensive trial experiments.

The current study is limited to the usage of a few models. Other machine learning-based models can be used for prediction-based study.

This study uses machine learning to predict the mechanical properties of FDM-based distal ulna bone plate, replacing traditional design of experiments methods with machine learning to streamline the production of orthopedic implants. It helps medical professionals, such as physicians and surgeons, make informed decisions when fabricating customized bone plates for their patients while reducing the need for time-consuming experimentation, thereby addressing a common limitation of 3D printing medical implants.