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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.

This study delineates the effect of cover thickness on reinforced concrete (RC) columns and beams under an elevated fire scenario. Columns and beams are important load-carrying structural members of buildings. Under all circumstances, the columns and beams were set to be free from damage to avoid structural failure. Under the high-temperature scenario, the RC element may fail because of the material deterioration that occurs owing to the thermal effect. This study attempts to determine the optimum cover thickness for beams and columns under extreme loads and fire conditions.

Cover thicknesses of 30, 40, 45, 50, 60 and 70 mm for the columns and 10, 20, 25, 30, 35, 40, 50, 60 and 70 mm for the beams were adopted in this study. Both steady-state and transient-state conditions under thermomechanical analysis were performed using the finite element method to determine the heat transfer through the RC section and to determine the effect of thermal stresses.

The results show that the RC elements have a greater influence on the additional cover thickness at extreme temperatures and higher load ratios than at the service stages. The safe limits of the structural members were obtained under the combined effects of elevated temperatures and structural loads. The results also indicate that the compression members have a better thermal performance than the flexural members.

Numerical investigations concerning the high-temperature behavior of structural elements are useful. The lack of an experimental setup encourages researchers to perform numerical investigations. In this study, the finite element models were validated with existing finite element models and experimental results.

The obtained safe limit for the structural members could help to understand their resistance to fire in a real-time scenario. From the safe limit, a suitable design can be preferred while designing the structural members. This could probably save the structure from collapse.

There is a lack of both numerical and experimental research works. In numerical modeling, the research works found in the literature had difficulties in developing a numerical model that satisfactorily represents the structural members under fire, not being able to adequately understand their behavior at high temperatures. None of them considered the influence of the cover thickness under extreme fire and loading conditions. In this paper, this influence was evaluated and discussed.

This paper delineates a literature review on fire-induced progressive collapse on structures and the effect of high temperature on structures and elements. After the occurrences of fire in the World Trade Center in the USA, the researchers started concentrating on the progressive collapse that happens due to high temperature. Currently, most of the researchers are working on fire-induced progressive collapse on structures using high-temperature behavior on materials which are used for construction. The researchers have been doing an intensive study to find a better strategy to prevent the building from structural fire damage or collapse with available codes and guidelines throughout the world. This paper aims to provide a better understanding and analytical solutions on the basis of the recent works done by researchers in fire-induced progressive collapse and methods adopted to find the collapse mechanism.

This paper is written by studying different literature papers of 109 related to progressive collapse on structures and fire-induced progressive collapse.

The behavior of structures due to high temperature and collapse conditions due to fire in different scenarios is identified.

This paper fulfills an identified need to study how the structure can withstand high-temperature conditions in our day-to-day lives.

This paper presents the performance analysis of the automatic ticket vending machine (ATVM) through the functioning of its different hardware and software failures.

Frequent failures in the working of ATVM have been observed; therefore, the authors of the paper intend to analyze the performance measures of the same. Authors have developed a mathematical model based on different hardware and software failures/repairs, which may occur during the operation, with the help of the Markov process. The developed model has been solved for two kinds of failure/repair rates namely variable failures (very much similar to real-time failure) and constant failures. Lagrange's method and Laplace transformation are used for the solution of the developed model.

Reliability and mean time to failure of the ATVM are determined. Sensitivity analysis for ATVM is also carried out in the paper. Critical components of the ATVM, which affect the performance of the same, in terms of reliability and MTTF are also identified.

A mathematical model based on different hardware and software failures/repairs of ATVM has been developed to analyze its performance, which has not been done in the past.

The purpose of this study is to present a systematic literature review of academic peer-reviewed articles in English published between 2005 and 2021. The articles were reviewed based on the following features: research topic, conceptual and theoretical characterization, artificial intelligence (AI) methods and techniques.

This study examines the extent to which AI features within academic research in retail industry and aims to consolidate existing knowledge, analyse the development on this topic, clarify key trends and highlight gaps in the scientific literature concerning the role of AI in retail.

The findings of this study indicate an increase in AI literature within the field of retailing in the past five years. However, this research field is fairly fragmented in scope and limited in methodologies, and it has several gaps. On the basis of a structured topic allocation, a total of eight priority topics were identified and highlighted that (1) optimizing the retail value chain and (2) improving customer expectations with the help of AI are key topics in published research in this field.

This study is based on academic peer-reviewed articles published before July 2021; hence, scientific outputs published after the moment of writing have not been included.

This study contributes to the in-depth and systematic exploration of the extent to which retail scholars are aware of and working on AI. To the best of the author’s knowledge, this study is the first systematic literature review within retailing research dealing with AI technology.

This study aims to evaluate the financial performance of the textile industry in Haryana located in the northern part of India.

Input-oriented Cooper, Charnes and Rhodes (CCR) and Banker, Charnes and Cooper (BCC) techniques of data envelopment analysis, as well as the return to scale (RTS) technique, were used to conduct the analysis.

The findings show that textile units in Haryana have hugely underperformed financially with a consolidated technical efficiency score of only 0.35. Both private and public limited textile companies with respective scores of 0.46 and 0.24 are technically efficient. Public limited textile companies are more efficient than private limited companies. Private limited textile companies need to increase their input scale because they are operating at an increasing return to scale while public limited textile companies have to lower their input scale because most companies are operating at a decreasing return to scale to enhance their efficiency.

The study can assist in decision-making to all key stakeholders (Shareholders, management, government, tax authorities, debtors and creditors, among others) by identifying efficient and inefficient companies. Appropriate policies can be framed based on that knowledge.

The aim of this study was to investigate the themes related to the achievement of Sustainable Development Goal (SDG) 12 in relation to tourism, and specifically to explore how the emotional psyche affects tourists’ environmentally responsible behaviors.

Based on the value-belief-norm theory, a research framework was developed to examine the serial mediation effects of environmental emotions in predicting tourists’ environmentally responsible behaviors. A total of 741 responses was collected from an online survey. Data were analyzed by the partial least squares structural equation modeling.

Environmental concern does not directly predict tourists’ environmentally responsible behaviors. Instead, environmental awe and environmental worry serially mediate the relationship between environmental concern and tourists’ environmentally responsible behaviors.

This study extends the value-belief-norm theory by integrating environmental emotions and empirically tests the effect of multiple psyches on responsible consumption, contributing to the achievement of SDG 12 in UN Agenda 2030.

本研究的目的是探究与旅游相关的可持续发展目标12的实现, 特别是探讨环境情感如何影响旅游者的环境责任行为。

基于价值信念-规范理论, 构建了环境情感预测旅游者环境责任行为的链式中介模型。研究共收集741份有效样本, 并采用偏最小二乘结构方程模型进行分析。

环境关心并不能直接预测旅游者的环境责任行为。但是, 环境敬畏和环境忧虑在环境关心与环境责任行为之间起链式中介作用。

本研究将环境情感扩展到价值信念规范理论中, 并实证检验了环境敬畏和环境忧虑两种环境情感对旅游者的负责任消费行为的影响, 呼应了联合国2030年议程中的可持续发展目标12。

el objetivo de este estudio fue investigar los temas relacionados con el logro del Objetivo de Desarrollo Sostenible 12 en relación con el turismo, y específicamente explorar cómo la psique emocional afecta los comportamientos ambientalmente responsables de los turistas.

Basado en la teoría del valor-creencia-norma, se desarrolló un marco de investigación para examinar los efectos de mediación en serie de las emociones ambientales en la predicción de los comportamientos ambientalmente responsables de los turistas. Se recopiló un total de 741 respuestas de una encuesta en línea. Los datos se analizaron mediante el modelo de ecuaciones estructurales de mínimos cuadrados parciales.

la preocupación ambiental no predice directamente los comportamientos ambientalmente responsables de los turistas. En cambio, el temor ambiental y la preocupación ambiental median en serie la relación entre la preocupación ambiental y los comportamientos ambientalmente responsables de los turistas.

este estudio amplía la teoría del valor-creencia-norma al integrar las emociones ambientales y prueba empíricamente el efecto de múltiples psiques en el consumo responsable, contribuyendo al logro del ODS 12 en la Agenda 2030 de la ONU.

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 research work focuses on implementing this methodology in reducing the rejection rate of the turbocharger component problem that occurs during the manufacturing process. Using design, measure, analyze, improve and control (DMAIC) processes, it has been identified that clamping pressure on the component is one factor that affects quality. The impact of clamping pressure is studied to arrive at the ideal clamping pressure in which the rejection rate is the least.

Quality is the keyword in manufacturing where the production of a defect-free component is the most sought out objective. The definition of quality keeps getting refined throughout the years, from making products with no defects to minimizing rejection and scrap in the manufacturing process. Production facilities, to achieve this purpose, have adopted various methods and use of the DMAIC of Six Sigma methodology is one among them.

The study identified the fault causing the defect and suggested the methods to correct the fault. The suggestions would result in reducing the losses arising due to this and similar rejection causes.

With the adoption of DMAIC, it is found that misalignment of top and side clamp pressure is zero. When the side clamp pressure is at 75 PSI, and top clamp pressure is changed from 90 PSI to 95 PSI, the mean of responses is greater than the side clamp pressure of 80 PSI. Therefore, from the three-combination top clamp pressure of 100 PSI and the side clamp pressure of 75 PSI is the optimal condition.

This research aims to outline the key factors responsible for industry 4.0 (I4.0) application in industries and establish a factor stratification model.

This article identifies the factor pool responsible for I4.0 from the extant literature. It aims to identify the set of key factors for the I4.0 application in the manufacturing industry and validate, classify factor pool using appropriate statistical tools, for example, factor analysis, principal component analysis and item analysis.

This study would shed light on critical factors and subfactors for implementing I4.0 in manufacturing industries from the factor pool. This study would shed light on critical factors and subfactors for implementing I4.0 in manufacturing industries. Strategy, leadership and culture are found key elements of transformation in the journey of I4.0. Additionally, design and development in the digital twin, virtual testing and simulations were also important factors to consider by manufacturing firms.

The proposed I4.0 factor stratification model will act as a starting point while designing strategy, adopting readiness index for I4.0 and creating a roadmap for I4.0 application in manufacturing. The I4.0 factors identified and validated in this paper will act as a guide for policymakers, researchers, academicians and practitioners working on the implementation of Industry 4.0. This work establishes a solid groundwork for developing an I4.0 maturity model for manufacturing industries.

The existing I4.0 literature is critically examined for creating a factor pool that further presented to experts to ensure sufficient rigor and comprehensiveness, particularly checking the relevance of subfactors for the manufacturing sector. This work is an attempt to identify and validate major I4.0 factors that can impact its mass adoption that is further empirically tested for factor stratification.