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This paper aims to investigate the experimental behavior and the reliability of concrete columns repaired using fiber-reinforced polymers (FRPs) under axial compression loading. The expression of the ultimate axial resistance was assessed from the experimental data of damaged concrete cylinders repaired by externally bonded double-FRP spiral strips.

The tested columns bearing capacity mainly depends of the elasticity modulus of both damaged and undamaged concrete have been considered in addition to the applied load and the cylinder diameter as random variables in the expression of the failure criterion. The reliability indicators were assessed using first order second moment method.

The emphasized test results, statistically fitted show that the strength has been retrofitted for all repaired specimens whatever the degree of initial damage. However, the gain in axial strength is inversely proportional to the degree of damage.

The efficiency of a new FRP repair procedure using double-spiral strips was studied. This research provides a technical and economical solution for retrofitting existing concrete columns. Finally, the random character of the variables that govern the studied system shows the accuracy and safety of the proposed original design.

Research on the “black box” of cognitive capital remains limited in supply chain resilience (SCRES) literature. Drawing from an in-depth single case study of a major consumer electronics multinational facing the COVID-19 disruption, this paper aims to develop a clearer picture of cognitive capital’s elements while contextualizing how they interact with SCRES temporal capabilities to prepare, respond, recover and learn.

Consisting of 40 in-depth interviews collected during a four-month period, this single case revolves around the buyer’s view across 36 multiregional buyer–supplier dyads, spanning 17 product and service categories. Data were processed during the pandemic, while findings discuss pre- and intra-crisis events based on two scenarios: the impact of disruption on category demand, comparing sudden pandemic-driven product and service demand fluctuations (i.e. increase, decrease); and the geographical proximity of the supplier relative to the buying firm.

The case unveils different elements of cognitive capital (e.g. shared goals, assumptions, values, kinesics language, multilingualism, virtual negotiation, prior disruption experience, shared process capabilities) during a major global disruption, suggesting that different cognitive capital elements influence positively and differently SCRES’ temporal capabilities. Overall, buying firms are urged to build on cognitive capital to improve SCRES preparation, response, recovery and learning.

This paper extends the understanding of cognitive capital in buyer–supplier relationships by identifying its elements and offering a theoretical articulation of how they enable episodically the four SCRES temporal capabilities under contingencies of increased and decreased demands, and suppliers’ geographical proximity.

This paper investigates the impact of trade liberalization measured by trade openness (OPN) and tariffs on women empowerment measured by the gender gap index and gender development index, for two groups of Arab countries divided based on their income levels using annual data for the period 1995–2020. The study also considers other factors that may influence the gender gap, such as GDP growth and the female unemployment rate. The purpose of this paper is to address these issues and explorers whether the effects of trade liberalization differ based on the countries' income levels.

This study employs the fully modified ordinary least squares (FM-OLS) regression model for heterogeneous cointegrated panels to examine the impact of trade liberalization on women empowerment. The study constructs an empirical two regression model of women empowerment measured by the gender gap model and gender development model for the two groups of higher-income countries and lower and middle-income countries.

The authors’ findings reveal that the impact of OPN on the gender gap varies between the two groups of Arab countries where more OPN within the higher-income group may increase the gender disparity, while it may reduce disparity within the lower and middle-income countries. In addition, GDP growth may reduce the gender disparity, while female unemployment raises the gender disparity between the two groups of countries in the long run. Findings also reveal that more OPN, tariffs and female unemployment may reduce gender development within the two groups, but more GDP growth may support the gender development in the long run.

This paper not only assesses the impact of trade liberalization on women empowerment generally, but also assess the women empowerment via two indices that are the gender gap and gender development in Arab countries which is – to the knowledge of the researchers – not yet investigated; further it explores if the effects of trade liberalization differs based on the countries' income levels.

This research aims to investigate the influence of building design on the thermal comfort of occupants of naturally ventilated hospital (NVH) wards to identify the aspects with the most significant influence on the thermal comfort of hospital buildings during the hot-dry season in the hot-humid tropics of Southeast Nigeria.

Field measurements, physical observations and a questionnaire survey of 60 occupants of the wards of the Joint Presbyterian Hospital, Uburu in Ebonyi State, Nigeria were undertaken. The data were analysed using Humphreys' neutral temperature formula, descriptive statistics and multiple regression analysis.

The results revealed that the neutral temperature for the wards ranges from 26.2 °C to 29.9 °C, the thermal condition in the wards was not comfortable because it failed to meet the ASHRAE Standard 55 as only 65% of the occupants said the thermal condition was acceptable. The number and sizes of windows, building orientation, the presence of high-level windows and higher headroom significantly influenced the occupants' thermal comfort vote.

This research is valuable in estimating comfort temperature and identifying aspects that require attention in enhancing the capacity of NVH wards to effectively meet the thermal comfort needs of occupants in the hot-humid tropics of Southeast Nigeria and other regions that share similar climatic conditions.

To the best of the authors’ knowledge, this is the first study of this nature that provides valuable feedback for building design professionals on the performance of existing hospital buildings in meeting users' thermal comfort needs in the hot-dry season of the hot-humid tropics in Southeast Nigeria.

Synthetic materials have many drawbacks in high-performance garments because they absorb less moisture and cause allergies to sensitive individuals. Cotton materials cannot satisfy all the requirements and cannot provide the required high performance. This study aims to use eco-friendly materials with a common structure to analyse their suitability for high-performance garment application.

This study used two eco-friendly yarns (bamboo, modal and bamboo: modal 50:50) and yarns per needle (two- and four-ply yarns). with a single jersey knit construction and gauge of 7. The physical, mechanical, appearance, comfort, thermal and ultraviolet protection factor (UPF) protection characteristics were evaluated using 15 tests.

The produced knitted fabrics showed high performance for use as garments with physical, mechanical, appearance, comfort, thermal and UPF protection characteristics that were achieved, tested and analysed. The highest-achieved samples with a good UPF (<15) were made from bamboo material, which has other high-performance characteristics such as antibacterial characteristics, a soft surface, thermal insulation and others.

The single jersey structure was used for producing fabrics as it is the common structure in the garment. Also, only gauge 7 was used for its economics and ease of production.

Big data analytics (BDA) plays a crucial role in understanding customer behavior through Customer Relationship Management (CRM), especially in a rapidly changing business environment. This paper investigates the direct effect of BDA use on market performance, besides the mediating effect through Big Data-enabled CRM strategies adoption (e.g. customization and personalization). The paper also examines the moderating role of competitive intensity in these effects.

Drawing from a knowledge-based view (KBV) and Organizational Information Processing Theory (OIPT), the authors formulated the research model. Subsequently, the measurement model and hypotheses were tested through PLS-SEM on online survey data of 229 managers from 167 companies out of Egypt's top 500.

The results indicated that BDA use does not directly affect the market performance, but this effect was significant through customization and personalization strategies adoption. The results also revealed a positive association between BDA use and the adoption of these strategies. Furthermore, competitive intensity only moderates the relationship between BDA use and personalization strategy adoption.

Companies can use BDA to improve customer knowledge and experience through customization and personalization, leading to better market performance and moving towards becoming a Big Data-driven organization. This study is limited to companies in the Egyptian context, which restricts the generalizability of the results.

This study conceptually and empirically explores how BDA usage, customization and personalization strategies impact market performance under competitive intensity situations, especially in the context of emerging markets.

This study aims to investigate environmental efficiency based on energy change by using energy-related or nonenergy-related variables by reckoning with months and years as decision-making units (DMUs) for a hospital under radial and nonradial models.

The non-oriented slack-based measures (SBM)-data envelopment analysis (DEA) model considering desirable and undesirable outputs has been embraced in this study, where its obtained results were compared with the results of other DEA models are output-oriented SBM-DEA and Banker, Charnes, & Cooper-DEA. For this purpose, this research has used a data set covering the 2012–2018 period for a reference hospital, which includes energy-related and nonenergy-related variables.

The results demonstrate that environmental efficiency based on energy reached the highest level in the winter months, whereas the summer months have the lowest efficiency values arising from the increasing electricity consumption due to high cooling needs. According to results of the non-oriented SBM model, the month with the highest efficiency in all periods is January with a 0.936 average efficiency score, the lowest month is August with a 0.406 value.

This paper differs from other studies related to energy and environmental efficiencies in the literature with some aspects. First, to the best of the authors’ knowledge, this study is the first one that takes into account time periods (months and years) as (DMUs for a single organization. Second, this study investigates environmental nonefficiencies, which are derived from energy uses and factors affecting energy use.

The current paper proposes a prediction model for a cryptocurrency that encompasses three properties observed in the markets for cryptocurrencies—namely high volatility, illiquidity, and regime shifts. As far as the authors’ knowledge extends, this paper is the first attempt to introduce a stochastic differential equation (SDE) for pricing cryptocurrencies while explicitly integrating the mentioned three significant stylized facts.

Cryptocurrencies are increasingly utilized by investors and financial institutions worldwide as an alternative means of exchange. To the authors’ best knowledge, there is no SDE in the literature that can be used for representing and evaluating the data-generating process for the price of a cryptocurrency.

By using Ito calculus, the authors provide a solution for the suggested SDE along with mathematical proof. Numerical simulations are performed and compared to the real data, which seems to capture the dynamics of the price path of two main cryptocurrencies in the real markets.

The stochastic differential model that is introduced and solved in this article is expected to be useful for the pricing of cryptocurrencies in situations of high volatility combined with structural changes and illiquidity. These attributes are apparent in the real markets for cryptocurrencies; therefore, accounting explicitly for these underlying characteristics is a necessary condition for accurate evaluation of cryptocurrencies.

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.