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This study aims to develop a machine learning model to detect structure fire fatalities using a dataset comprising 11,341 cases from 2011 to 2019.

Exploratory data analysis (EDA) was conducted prior to modelling, in which ten machine learning models were experimented with.

The main fatal structure fire risk factors were fires originating from bedrooms, living areas and the cooking/dining areas. The highest fatality rate (20.69%) was reported for fires ignited due to bedding (23.43%), despite a low fire incident rate (3.50%). Using 21 structure fire features, Random Forest (RF) yielded the best detection performance with 86% accuracy, followed by Decision Tree (DT) with bagging (accuracy = 84.7%).

Limitations of the study are pertaining to data quality and grouping of categories in the data pre-processing stage, which could affect the performance of the models.

The study is the first of its kind to manipulate risk factors to detect fatal structure classification, particularly focussing on structure fire fatalities. Most of the previous studies examined the importance of fire risk factors and their relationship to the fire risk level.

With the help of basic physics, the application of computer algorithms in the form of recent advances such as machine learning and neural networking in textile Industry has been discussed in this review. Scientists have linked the underlying structural or chemical science of textile materials and discovered several strategies for completing some of the most time-consuming tasks with ease and precision. Since the 1980s, computer algorithms and machine learning have been used to aid the majority of the textile testing process. With the rise in demand for automation, deep learning, and neural networks, these two now handle the majority of testing and quality control operations in the form of image processing.

The state-of-the-art of artificial intelligence (AI) applications in the textile sector is reviewed in this paper. Based on several research problems and AI-based methods, the current literature is evaluated. The research issues are categorized into three categories based on the operation processes of the textile industry, including yarn manufacturing, fabric manufacture and coloration.

AI-assisted automation has improved not only machine efficiency but also overall industry operations. AI's fundamental concepts have been examined for real-world challenges. Several scientists conducted the majority of the case studies, and they confirmed that image analysis, backpropagation and neural networking may be specifically used as testing techniques in textile material testing. AI can be used to automate processes in various circumstances.

This research conducts a thorough analysis of artificial neural network applications in the textile sector.

The purpose of this paper is to apply the concept of equivalent initial flaw size (EIFS) to the anisotropic nickel-based single crystal (SX) material, and to predict the fatigue life on this basis. The crack propagation law of SX material at different temperatures and the weak correlation of EIFS values verification under different loading conditions are also investigated.

A three-parameter time to crack initial (TTCI) method with multiple reference crack lengths under different loading conditions is established, which include the TTCI backstepping method and EIFS fitting method. Subsequently, the optimized EIFS distribution is obtained based on the random crack propagation rate and maximum likelihood estimation of median fatigue life. Then, an effective driving force based on anisotropic and mixed crack propagation mode is proposed to describe the crack propagation rate in the small crack stage. Finally, the fatigue life of three different temperature ESE(T) standard specimens is predicted based on the EIFS values under different survival rates.

The optimized EIFS distribution based on EIFS fitting - maximum likelihood estimation (MLE) method has the highest accuracy in predicting the total fatigue life, with the range of EIFS values being about [0.0028, 0.0875] (mm), and the mean value of EIFS being 0.0506 mm. The error between the predicted fatigue life based on the crack propagation rate and EIFS distribution for survival rates ranges from 5% to 95% and the experimental life is within two times dispersion band.

This paper systematically proposes a new anisotropic material EIFS prediction method, establishing a framework for predicting the fatigue life of SX material at different temperatures using fracture mechanics to avoid inaccurate anisotropic constitutive models and fatigue damage accumulation theory.

This paper establishes a quality model for automation assembly of range hood impeller based on generalized grey relational degree, it improves the debugging efficiency of the newly developed assembly workstation.

First, spot check the trial production impellers and obtain three indexes that reflect the assembly quality of the impellers. Then, analyze the parameters that affect the assembly quality of the impeller using grey relational analysis (GRA), establish a model for the assembly quality of the range hood impeller based on the generalized grey relational degree and identify the main parameters. After that, analyze the transmission structure of automation assembly workstation, identify the reasons that affect parameters and propose improvement plans. Finally, a trial production is conducted on the automation assembly workstation after adopting the improved plan to verify the quality model of impeller automation assembly.

The research shows that compared to manual assembly, the automation assembly quality of the impeller using GRA model has been improved, shortening the debugging cycle of the newly developed assembly workstation.

The newly developed automation equipment will have some problems in the trial production stage, which often rely on the experience of engineers for debugging. In this paper, the automation assembly quality model of range hood impeller based on GRA is established, which can not only ensure the quality of finished impeller but also shorten the debugging cycle of the equipment. In addition, GRA can be widely used in the commissioning of other automation equipment.

This study has developed a set of impeller automation assembly workstation. The debugging method in the trial production stage is beneficial to shorten the trial production time and improve the economic benefits.

This study aims to evaluate how the criteria of sustainability standards (SS) ensure the social dimension of corporate sustainability (CS) in rural entrepreneurships using the case of banana agribusinesses in Magdalena (Colombia).

The methodological design was quantitative, explanatory and cross-sectional, where a sample of banana producers from Magdalena (Colombia) was selected. A structural equation model (SEM) was developed to evaluate the hypotheses. The SEM goodness-of-fit and fit indices were all acceptable.

There is a strong and statistically significant correlation between SS criteria and the social dimension of CS. In particular, local development is the most influential factor in shaping CS in the context of banana agribusinesses in Magdalena, Colombia, operating as rural entrepreneurship. Meanwhile, working conditions and human rights show moderate effects, while labor rights do not have a perceptible impact.

Only the SS criteria that ensure the social dimension of CS in the banana agribusinesses of Magdalena (Colombia) were considered. It is important to note that other variables may be involved in ensuring CS. Future research to identify these possible variables is recommended.

This investigation explores an understudied issue within the CS sphere, explicitly focusing on rural entrepreneurship in developing countries, notably Colombia. The study scrutinizes the impact of SS on the social dimension of CS in rural environments, using banana cooperatives as a case study and highlighting the value of developing strategies to help improve the CS performance of this type of organization.

El objetivo de esta investigación es evaluar cómo los criterios de los Estándares de Sostenibilidad (SS) aseguran la dimensión social de la Sostenibilidad Corporativa (CS) en los emprendimientos rurales utilizando el caso de los agronegocios bananeros en Magdalena (Colombia).

El diseño metodológico fue cuantitativo, explicativo y transversal, donde se seleccionó una muestra de productores bananeros del Magdalena (Colombia). Se desarrolló un modelo de ecuaciones estructurales (SEM) para evaluar las hipótesis. Los índices de bondad de ajuste y ajuste del SEM fueron aceptables.

existe una correlación fuerte y estadísticamente significativa entre los criterios de SS y la dimensión social de la SC. En particular, el desarrollo local aparece como el factor más influyente en la conformación de la SC en el contexto de las agroempresas bananeras de Magdalena, Colombia, que operan como empresas rurales. Mientras tanto, las condiciones de trabajo y los derechos humanos muestran efectos moderados, mientras que los derechos laborales no parecen tener un impacto perceptible.

sólo se consideraron los criterios de SS que aseguran la dimensión social de la SC en los agronegocios bananeros de Magdalena (Colombia). Es importante señalar que otras variables pueden estar involucradas en el aseguramiento de la CS. Se recomiendan futuras investigaciones para identificar estas posibles variables.

Esta investigación explora un tema poco estudiado dentro de la esfera de la Sostenibilidad Corporativa (SC), centrándose explícitamente en el empresariado rural en los países en desarrollo, en particular Colombia. El estudio analiza el impacto de los Estándares de Sostenibilidad (SS) en la dimensión social de la SC en entornos rurales, utilizando las cooperativas bananeras como caso de estudio y resaltando el valor de desarrollar estrategias que ayuden a mejorar el desempeño en SC de este tipo de organizaciones.

O objetivo desta pesquisa é avaliar como os critérios dos Padrões de Sustentabilidade (SS) garantem a dimensão social da Sustentabilidade Corporativa (SC) em empreendimentos rurais usando o caso das agroindústrias de banana em Magdalena (Colômbia).

O desenho metodológico foi quantitativo, explicativo e transversal, onde foi selecionada uma amostra de produtores de banana de Magdalena (Colômbia). Foi desenvolvido um modelo de equação estrutural (SEM) para avaliar as hipóteses. Os índices de adequação e de ajuste do SEM foram todos aceitáveis.

existe uma correlação forte e estatisticamente significativa entre os critérios de SS e a dimensão social da SC. Em particular, o desenvolvimento local aparece como o fator mais influente na formação da SC no contexto dos agronegócios da banana em Magdalena, Colômbia, que operam como empresas rurais. Entretanto, as condições de trabalho e os direitos humanos apresentam efeitos moderados, enquanto os direitos laborais não parecem ter um impacto percetível.

apenas foram considerados os critérios de SS que garantem a dimensão social da SC nas agroindústrias da banana de Magdalena (Colômbia). É importante notar que outras variáveis podem estar envolvidas na garantia da SC. Recomenda-se a realização de investigação futura para identificar estas possíveis variáveis.

Esta investigação explora uma questão pouco estudada no âmbito da Sustentabilidade Empresarial (SC), focando explicitamente o empreendedorismo rural nos países em desenvolvimento, nomeadamente na Colômbia. O estudo examina o impacto dos Padrões de Sustentabilidade (SS) na dimensão social da SC em ambientes rurais, utilizando cooperativas de banana como estudo de caso e destacando o valor do desenvolvimento de estratégias para ajudar a melhorar o desempenho da SC deste tipo de organização.

The present study is intended to assess the risk factors associated with digital buying. Also aims to design and develop an instrument to assess the digital buyers risk factor score (DBRFS) in light of pandemic.

Present investigation uses a quantitative approach to achieve the stated objectives. The survey instrument for the purpose of assessing risk factors associated with digital buying was developed in two phases. The present study adopts theory of planned behaviour (TPB), built based on the theory of reasoned action (TRA). The data were collected and analysed considering 500 valid responses, sampling unit being digital buyers using social media platforms in tyre-II city of India. The data collection was undertaken between June 2021 and August 2021. The instrument is designed and validated using exploratory factor analysis (EFA) followed by confirmatory factor analysis (CFA).

The present research identified six perceived risk factors that are associated with digital buying; contractual risk, social risk, psychological risk, perceived quality risk, financial risk and time risk. The DBRFS of male is 3.7585, while female is 3.7137. Thus, risk taking by the male and female is at par. For the age group 15–30, DBRFS is 3.6761, while age group 31–45 noted as 3.7889 and for the 46–50 age groups it is measured as 3.9649.

The marketers are expected to have the knowledge about how people responds to the pandemic. The outcome of the research helps to understand consumer behaviour but disentangling consumer’s “black box” is challenging especially during global distress. The present study outcome helps the digital shopkeepers to respond positively to meet the needs of digital buying.

The scale development and to quantify the DBRFS. A deeper understanding of about digital consumers during pandemics will help digital shopkeepers to connect issues related digital buying.

The built environment is responsible for approximately 40% of the world’s energy consumption, 30% of raw material use, 25% of solid waste, 25% of water use, 12% of land use and 33% of greenhouse gas emissions. Thus, environmental improvement and decarbonization are becoming increasingly critical objectives for the construction industry. Sustainable construction can be achieved through several practices, including: considering life-cycle assessment, circular construction, resource efficiency and waste management and providing eco-efficient materials, reducing energy demands and consumption and incorporating low-carbon technologies and renewable energy sources. To achieve sustainable construction goals, it is critical to educate the future workforce about decarbonization, circular construction and how to overcome the challenges involved in transitioning to sustainable construction. This study aims to understand the gap in student knowledge related to decarbonization and circular construction and the importance of incorporating these topics in civil engineering and construction management curricula.

This study surveyed 120 undergraduate and graduate students at one of the largest minority-serving institutions in the USA to understand the gap in student knowledge related to decarbonization and circular construction as well as the importance of incorporating these topics in civil engineering and construction management curricula. The authors conducted several statistical measures to assess the consistency, reliability and adequacy of the sample size, including the Kaiser–Meyer–Olkin measure of sampling adequacy, the normality test to evaluate the appropriateness of using an ordered probit regression analysis and a multicollinearity test to observe the correlation between independent variables. The data was analyzed using ordered probit regression analysis to investigate the need for a curriculum that serves in educating students about decarbonization and circular construction.

The results of this research highlight the gaps in students’ knowledge pertaining to sustainable practices and the importance of providing future construction workforce with such knowledge to tackle global inevitable challenges.

The findings of this study contribute to sustainable construction bodies of knowledge by advocating for a reformed curriculum to prepare the future workforce and adopt less carbonized, more circular approaches within the engineering and construction industry.

In successfully meeting city and metropolitan growth, sustainable development is compulsory. Sustainability is a must-focus for any project, particularly for large and mega rail infrastructure. This paper aims to investigate to what degree social, environmental and economic factors influence the government when planning sustainable rail infrastructure projects. To respond to such a matter, this paper focuses on two Australian mega-rail projects: the South West Rail Link (SWRL) and the Mernda Rail Extension (MRE).

As the basis of an experimental evaluation framework strengths, weaknesses, opportunities and threats (SWOT) and factor analysis were used. These two methods were specifically selected as comparative tools for SWRL and SWRL projects, to measure their overall sustainability effect.

Using factor analysis, in the MRE, the factors of network capacity, accessibility, employment and urban planning were seen frequently throughout the case study. However, politics and economic growth had lower frequencies throughout this case study. This difference between the high-weighted factors is likely a key element that determined the SWRL to be more sustainable than the MRE. The SWOT analysis showed the strengths the MRE had over the SWRL such as resource use and waste management, and natural habitat preservation. These two analyses have shown that overall, calculating the sustainability levels of a project can be subjective, based on the conditions surrounding various analysis techniques.

This paper first introduces SWRL and MRE projects followed by a discussion about their overall sustainable development. Both projects go beyond the traditional megaprojects' goal of improving economic growth by developing and enhancing infrastructure. Globally, for such projects, sustainability measures are now considered alongside the goal of economic growth. Second, SWOT and factor analysis are undertaken to further evaluate the complexity of such projects. This includes their overall sustainable development vision alignment with environmental, economic and social factors.

This exploratory research aims to identify the effect of perceived stress level and self-efficacy on management quality and what practices and theories need to be enhanced to improve management quality under volatility business environments.

The study surveyed 291 working women, using the Perceived Stress Scale and the General Self-Efficacy Scale. Latent class analysis (LCA) for classifications of respondents, using categorical observed variables and MANCOVA, are applied to determine the relationship between stress and self-efficacy on the assigned classes.

The study suggests that in a highly volatile business environment, where stress is high, affecting management quality, managers as individuals fall into one of four classes that describe their techniques of coping with the stress, namely Uncommitted Experimenters, Try Anything, Intrinsically Motivated and Externally Motivated. Techniques of stress management classification are significantly related to the combined perceived stress and self-efficacy measures, with Externally Motivated respondents as the classification with a significant mean difference.

The main limitation of the study at hand refers to the sample size versus the number of potential factors of stress. This limitation highlights the need for further data gathering and research in this area, as stress is a critical factor of performance and often ignored in traditional management theories. Another limitation of this study is the lack of in-depth analysis of the use of meditation; its benefits and how to best use this practice in traditional work settings.

The outcome of the study could have significant implications for quality of management in business, private and social sectors by providing meditation as a tool for employees and stakeholders to handle stress in conflict zones.

Using stress management techniques might prove to be a low-cost tool for better quality management of human assets.

The authors study focuses on women in volatile economic turmoil, natural devastations, conflict areas and politically insecure environments. This socioeconomic segment was rarely scrutinized despite its direct effect on a large number of economies hosting a sizeable portion of the world’s population. Interesting potential results highlight the relationship between the respondents in the Intrinsically Motivated class and stress reduction for the benefit of management quality.