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The efficiency of some 2‐heterocarboxaldehyde‐2′‐pyridyl‐hydrazones as inhibitors for the acid dissolution of Al was studied by mass loss, hydrogen evolution and polarization measurement techniques. The corrosion rate was measured in 2M HCI at different temperature and concentrations; maximum percentage protection (85%) was obtained at 103M for the compound containing oxygen atom. The activation energies were calculated for all the additives used. The rate constant decreases as the inhibition efficiency increases. The results show that the studied materials influencing both the cathodic and anodic process. The mechanism of inhibition was proposed on the basis of the formation of a protective monolayer by the inhibitors molecules on the metal surface according to Langmuir isotherm.

Corrosion inhibitors are widely used in industry, although in many cases their surface chemistry is not well understood. Several nitrogen‐containing organic compounds have been used as corrosion inhibitors. Corrosion inhibition is a surface process which involves the specific adsorption of inhibitors on the metal surface. The extent of inhibition of metallic corrosion may depend on the nature of the metal surface and extent of adsorption of the inhibitor. The type of interaction of the inhibitor on the metal surface during corrosion has been deduced from its adsorption characteristics.

Sustainable manufacturing is one of the most important and most challenging issues in present industrial scenario. With the intention of diminish negative effects associated with cutting fluids, the machining industries are continuously developing technologies and systems for cooling/lubricating of the cutting zone while maintaining machining efficiency. In the present study, three regression based machine learning techniques, namely, polynomial regression (PR), support vector regression (SVR) and Gaussian process regression (GPR) were developed to predict machining force, cutting power and cutting pressure in the turning of AISI 1045. In the development of predictive models, machining parameters of cutting speed, depth of cut and feed rate were considered as control factors. Since cooling/lubricating techniques significantly affects the machining performance, prediction model development of quality characteristics was performed under minimum quantity lubrication (MQL) and high-pressure coolant (HPC) cutting conditions. The prediction accuracy of developed models was evaluated by statistical error analyzing methods. Results of regressions based machine learning techniques were also compared with probably one of the most frequently used machine learning method, namely artificial neural networks (ANN). Finally, a metaheuristic approach based on a neural network algorithm was utilized to perform an efficient multi-objective optimization of process parameters for both cutting environment.

The purpose of this study is to investigate the influence of external enablers (EEs) – factors that are exogenous and independent of the actors – on the rate of new venture creation (NVC) in digital entrepreneurship.

The authors established a unique data panel comprising all formal digital entrepreneurial firms and startups licensed to operate in the digital space from Iran E-Commerce Development Center, from 2012 to 2022, totaling 124,751 businesses classified in 16 sectors. The authors used Poisson regression with robust standard errors to analyze the relationship between external factors and NVC. Additionally, the authors segmented the data set along demographic, spatial and sectoral subsets to compare them using the average treatment effect model.

This analysis confirms that technology readiness and technology intensity, as EEs, significantly boost digital entrepreneurship in a developing country. The authors also explored the impact of the COVID-19 pandemic as a natural environmental enabler and explored its facilitating effects and the enduring impacts post-pandemic. Moreover, the authors assessed the heterogeneous impact of the EEs across different groups and derived implications for each.

While this study is comprehensive, it focuses exclusively on formal digital entrepreneurship within a single developing country, namely, Iran. Although the authors explored various EEs, they did not address all possible types, highlighting the potential for further research in other entrepreneurial sectors for a comparative analysis of EEs.

The initial findings affirm that most NVC in Iran stems from external factors. These findings have been widely used by policymakers to define the goals and objectives of EEs concerning their nature and impact. This research reveals that EEs have heterogeneous effects on NVC, varying across different characteristics such as gender, geography and industry sectors. The results of this study suggest that governments should reassess the role of “macro” policies to support vulnerable groups. EEs disproportionately benefit male founders, which exacerbates existing gender gaps and necessitates ongoing attention and intervention. Additionally, EEs have diverse impacts across industries, potentially favoring some while disadvantaging others because of varying social, cultural and economic factors.

NVC, central to entrepreneurship in developing nations, encounters numerous challenges, yet the role of EEs remains underexplored in this context. EEs are opportunities that are not limited to a subset of entrepreneurial actors; they act as levers influencing a wide range of entrepreneurial activities. This research distinctively investigates the empirical impact of EEs in a Middle Eastern developing country context, contributing to the growing literature on the external enablers framework.

This research aims to shed light on the utilization of open spaces at Effat University through a gender lens. By eliminating gender segregation, the study seeks to examine how these spaces are equitably shared between genders, while also delving into the realm of gender sensitive urban design (GSUD). Furthermore, the research strives to gain valuable insights into the experiences of female students in open spaces to propose a framework for the design of fair shared open spaces.

To achieve these objectives, a multi-faceted approach was adopted. First, the research started by reviewing the literature about gender sensitive urban design and inclusive urban design to conclude the indicators examine how open spaces are shared fairly between both genders; second, an online questionnaire was administered to gather comprehensive data. Third, meticulous observations were conducted, involving the study, analysis and mapping of students' activities using maps and photos. Lastly, a walk shop was organized, with a focus group comprising of female participants. The collected data was then analyzed using SPSS descriptive statistics and the Pearson product-moment correlation coefficient, enabling the identification of key patterns and evaluating female students' satisfaction levels.

The findings unearth a distinctive perspective on the perception and needs of females within a unique subset of open spaces, referred to as “campus open spaces,” in the context of a gender sensitive urban design. This exploration provides valuable insights into the intricacies of gender dynamics within public spaces, contributing to a more inclusive and equitable environment for all individuals at Effat University, framework shows some of significant indicators that effect on other indicators strongly as sense of safety and security and the diversity of activities that increase visiting rates, sense of belonging.

This research presents a framework aimed at improving the concept of equitable and inclusive, open spaces, particularly on campuses within significant communities like KSA. The focus is on creating fair shared places that facilitate interaction and cater to the social and cultural needs of students, with a particular emphasis on supporting female students. By enhancing their sense of belonging and addressing their unique requirements, this framework aims to positively impact their academic performance while fostering an inclusive and engaging campus environment.

This paper aims to identify the role of internet of things (IoT) in water supply chain management and helps to understand its future path from the junction of computer science and resource management.

The current research was studied through bibliometric review and content analysis, and various contributors and linkages were found. Also, the possible directions and implications of the field were analyzed.

The paper’s key findings include the role of modern computer science in water resource management through sensor technology, big data analytics, IoT, machine learning and cloud computing. This, in turn, helps in understanding future implications of IoT resource management.

A more extensive database can add up to more combinations of linkages and ideas about the future direction. The implications and understanding gained by the research can be used by governments and firms dealing with water management of smart cities. It can also help find ways for optimizing water resources using IoT and modern-day computer science.

This study is one of the very few investigations that highlighted IoT’s role in water supply management. Thus, this study helps to assess the scope and the trend of the case area.

The purpose of this paper is to determine whether the original and the revised versions of the existing prediction models are the best tools for assessing the going concern assumption of a firm in the creditor-oriented regime.

The analysis begins from estimating the classification accuracy of the original versions of the bankruptcy, going concern and liquidation prediction models. At the second step, the revised versions of the aforesaid existing prediction models are developed. At the third step, the accounting-based going concern prediction model is proposed by using multiple discriminant analysis for the creditor-oriented regime. The sample contains the financial ratios of manufacturing firms for the period 1997–2014.

The finding indicates that the five discriminatory variables, which belong to “income statement” and “statement of financial position,” of the proposed model are not only useful for evaluating the going concern assumption of a firm, but also give aid for evaluating the financial fraud risk of a firm as compared to the original and revised versions of the prediction models that are developed for the debtor-oriented regime.

The external validity of the proposed prediction model can be tested on the large data sets of the countries where the liquidation provisions are a part of their local corporate law.

The proposed accounting prediction model will be helpful for the internal and external auditors in order to determine the going concern assumption at planning, performing and evaluation stages.

The proposed accounting-based going concern prediction model is based on liquidated firms.

The purpose of the this study is to discover the impact of practices of knowledge management, intellectual property protection and management innovation on entrepreneurial leadership, which in turn leads to sustainable growth in small and medium-sized enterprises.

The data was collected from 292 small and medium-sized businesses (SMEs) in the USA using a cross-sectional survey. To evaluate the study hypotheses and analyse the data, structural equation modelling was used. SMART-PLS software was used for both confirmatory factor analysis and structural analysis.

The work has significantly contributed in revealing that knowledge management practices, management innovation and intellectual property protection have a substantial and constructive impact on entrepreneurial leadership, which in turn leads to sustainable growth in SMEs.

The study findings recommend that SMEs must focus on knowledge management practices, intellectual property protection and management innovation to nurture entrepreneurial leadership, which can lead to sustainable growth. SMEs can benefit from investing in knowledge management practices, protecting their intellectual property and innovating their management practices to achieve sustainable growth. Also, the absorptive capacity of an SME can help it to aggravate the impact of the above factors and lead them to sustainable growth faster.

The current work studies the association between knowledge management practices, intellectual property protection, management innovation, entrepreneurial leadership and sustainable growth in SMEs, thus contributing to the literature. The study provides insights into the factors that can nurture entrepreneurial leadership and contribute to sustainable growth in SMEs, which can inform policy and practice in the field of entrepreneurship.

This research aims to analyse the perceptions of practitioners in three regions regarding the challenges faced by their firms during the pandemic, considered a black-swan event. It examines the strategies implemented to mitigate and recover from risks, evaluates the effectiveness of these strategies and assesses the difficulties encountered in their implementation.

In the summer of 2022, an online survey was conducted among supply chain (SC) practitioners in France, Poland and the St. Louis, Missouri region of the USA. The survey aimed to understand the impact of COVID-19 on their firms and the SC strategies employed to sustain operations. These regions were selected due to their varying levels of SC development, including infrastructure, economic resources and expertise. Moreover, they exhibited different responses in safeguarding the well-being of their citizens during the pandemic.

The study reveals consistent perceptions among practitioners from the three regions regarding the impact of COVID-19 on SCs. Their actions to enhance SC resilience primarily relied on strengthening collaborative efforts within their firms and SCs, thus validating the tenets of the relational view.

COVID-19 is (hopefully) our black-swan pandemic occurrence during our lifetime. Nevertheless, the lessons learned from it can inform future SC risk management practices, particularly in dealing with rare crises. During times of crisis, leveraging existing SC structures may prove more effective and efficient than developing new ones. These findings underscore the significance of relationships in ensuring SC resilience.

Autistic Spectrum Disorder is a developmental disorder estimated to inflict 1 in every 150 children, regardless of socio-cultural aspects, with a four to one prevalence in males over females, (ADDM, 2007). It involves a complex sensory perceptual model, variant from that of the typical individual. It manifests itself in the form of repetitive behaviour, lack of social skills and communication delays and challenges. Being a life-long infliction, individuals with autism require a comprehensive range of specialized support services, including residential, from childhood to adulthood.

Housing services for special needs, in general, conventionally deal with issues of physical access. The provisions required for developmentally challenged individuals, such as those with autism are rarely considered.

This paper aims to provide a precedent to help guide the adaptation process in the case of group residential accommodation for autistic adults in mass housing projects. After a brief examination of the available literature in the field, a case study will be presented, illustrating design criteria developed for adapting housing for autistic use.