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This study addresses the reliability of a composite fiber (carbon fibers/epoxy matrix) at microscopic level, with a specific focus on its behavior under compressive stresses. The primary goal is to investigate the factors that influence the reliability of the composite, specifically considering the effects of initial fiber deformation and fiber volume fraction.

The analysis involves a multi-step approach. Initially, micromechanics theory is employed to derive limit state equations that define the stress levels at which the fiber remains within an acceptable range of deformation. To assess the composite's structural reliability, a dedicated code is developed using the Monte Carlo method, incorporating random variables.

Results highlight the significance of initial fiber deformation and volume fraction on the composite's reliability. They indicate that the level of initial deformation of the fibers plays a crucial role in determining the composite reliability. A fiber with 0.5% initial deformation exhibits the ability to endure up to 28% additional stress compared to a fiber with 1% initial deformation. Conversely, a higher fiber volume fraction contributes positively to the composite's reliability. A composite with 60% fiber content and 0.5% initial deformation can support up to 40% additional stress compared to a composite containing 40% fibers with the same deformation.

The study's originality lies in its comprehensive exploration of the factors affecting the reliability of carbon fiber-epoxy matrix composites under compressive stresses. The integration of micromechanics theory and the Monte Carlo method for structural reliability analysis contributes to a thorough understanding of the composite's behavior. The findings shed light on the critical roles played by initial fiber deformation and fiber volume fraction in determining the overall reliability of the composite. Additionally, the study underscores the importance of careful fiber placement during the manufacturing process and emphasizes the role of volume fraction in ensuring the final product's reliability.

Barriers to adequate healthcare in rural areas remain a grand challenge for local healthcare systems. In addition to patients' travel burdens, lack of health insurance, and lower health literacy, rural healthcare systems also experience significant resource shortages, as well as issues with recruitment and retention of healthcare providers, particularly specialists. These factors combined result in complex change management-focused challenges for rural healthcare systems. Change management initiatives are often resource intensive, and in rural health organizations already strapped for resources, it may be particularly risky to embark on change initiatives. One way to address these change management concerns is by leveraging socio-technical simulation models to estimate techno-economic feasibility (e.g., is it technologically feasible, and is it economical?) as well as socio-utility feasibility (e.g., how will the changes be utilized?). We present a framework for how healthcare systems can integrate modeling and simulation techniques from systems engineering into a change management process. Modeling and simulation are particularly useful for investigating the amount of uncertainty about potential outcomes, guiding decision-making that considers different scenarios, and validating theories to determine if they accurately reflect real-life processes. The results of these simulations can be integrated into critical change management recommendations related to developing readiness for change and addressing resistance to change. As part of our integration, we present a case study showcasing how simulation modeling has been used to determine feasibility and potential resistance to change considerations for implementing a mobile radiation oncology unit. Recommendations and implications are discussed.

Ensuring the early detection of structural issues in aircraft is crucial for preserving human lives. One effective approach involves identifying cracks in composite structures. This paper employs experimental modal analysis and a multi-variable Gaussian process regression method to detect and locate cracks in glass fiber composite beams.

The present study proposes Gaussian process regression model trained by the first three natural frequencies determined experimentally using a roving impact hammer method with crystal four-channel analyzer, uniaxial accelerometer and experimental modal analysis software. The first three natural frequencies of the cracked composite beams obtained from experimental modal analysis are used to train a multi-variable Gaussian process regression model for crack localization. Radial basis function is used as a kernel function, and hyperparameters are optimized using the negative log marginal likelihood function. Bayesian conditional probability likelihood function is used to estimate the mean and variance for crack localization in composite structures.

The efficiency of Gaussian process regression is improved in the present work with the normalization of input data. The fitted Gaussian process regression model validates with experimental modal analysis for crack localization in composite structures. The discrepancy between predicted and measured values is 1.8%, indicating strong agreement between the experimental modal analysis and Gaussian process regression methods. Compared to other recent methods in the literature, this approach significantly improves efficiency and reduces error from 18.4% to 1.8%. Gaussian process regression is an efficient machine learning algorithm for crack localization in composite structures.

The experimental modal analysis results are first utilized for crack localization in cracked composite structures. Additionally, the input data are normalized and employed in a machine learning algorithm, such as the multi-variable Gaussian process regression method, to efficiently determine the crack location in these structures.

Bisphosphonate (BP) medications can be applied to prohibit the damage of bone density and the remedy of bone illnesses such as osteoporosis. As the metal chelating of phosphonate groups are nearby large with six O atoms possessing the high negative charge, these compounds are active toward producing the chelated complexes through drug design method. BP agents have attracted much attention for the clinical treatment of some skeletal diseases depicted by enhancing of osteoclast-mediated bone resorption.

In this work, it has been accomplished the CAM-B3LYP/6–311+G(d, p)/LANL2DZ to estimate the susceptibility of SWCNT for adsorbing alendronate, ibandronate, neridronate and pamidronate chelated to two metal cations of 2Mg²⁺, 2Ca²⁺, 2Sr²⁺ through nuclear magnetic resonance and thermodynamic parameters. Therefore, the data has explained that the feasibility of using SWCNT and BP agents becomes the norm in metal chelating of drug delivery system which has been selected through alendronate → 2X, ibandronate → 2X, neridronate → 2X and pamidronate → 2X (X = Mg²⁺/Ca²⁺/Sr²⁺) complexes.

The thermodynamic results have exhibited that the substitution of 2Ca²⁺ cation by 2Sr²⁺ cation in the structure of bioactive glasses can be efficient for treating vertebral complex fractures. However, it has been observed the most fluctuation in the Gibbs free energy for BPs → 2Sr²⁺ at 300 K. Furthermore, Monte Carlo simulation has resulted by increasing the dielectric constant in the aqueous medium can enhance the stability and efficiency of BP drugs for preventing the loss of bone density and treating the osteoporosis.

According to this research, by incorporation of chelated 2Mg²⁺, 2Ca²⁺ and 2Sr²⁺ cations to BP drugs adsorbed onto (5, 5) armchair SWCNT, the network compaction would increase owing to the larger atomic radius of Sr²⁺ cation rather than Ca²⁺ and Mg²⁺, respectively.

This study aims to propose a more precise method for robust design optimization of mechanical structures with black-box problems, while also considering the efficiency of uncertainty analysis.

The method first introduces a dual adaptive chaotic flower pollination algorithm (DACFPA) to overcome the shortcomings of the original flower pollination algorithm (FPA), such as its susceptibility to poor accuracy and convergence efficiency when dealing with complex optimization problems. Furthermore, a DACFPA-Kriging model is developed by optimizing the relevant parameter of Kriging model via DACFPA. Finally, the dual Kriging model is constructed to improve the efficiency of uncertainty analysis, and a robust design optimization method based on DACFPA-Dual-Kriging is proposed.

The DACFPA outperforms the FPA, particle swarm optimization and gray wolf optimization algorithms in terms of solution accuracy, convergence speed and capacity to avoid local optimal solutions. Additionally, the DACFPA-Kriging model exhibits superior prediction accuracy and robustness contrasted with the original Kriging and FPA-Kriging. The proposed method for robust design optimization based on DACFPA-Dual-Kriging is applied to the motor hanger of the electric multiple units as an engineering case study, and the results confirm a significant reduction in the fluctuation of the maximum equivalent stress.

This study represents the initial attempt to enhance the prediction accuracy of the Kriging model using the improved FPA and to combine the dual Kriging model for uncertainty analysis, providing an idea for the robust optimization design of mechanical structure with black-box problem.

This paper aims to accommodate the influence of both economic policy uncertainty and geopolitical risks in the relationship between oil price and exchange-rate returns in the Brazil, Russia, India, China and South Africa (BRICS) countries through an interaction term (EPGR).

The authors use continuous wavelet transform (CWT), wavelet coherence (WC) and partial wavelet coherence (PWC). First, the authors apply the CWT to examine the evolution of oil prices, EPGR and exchange rate returns. Second, the authors use WC to investigate the relationship between oil price and exchange rate returns (excluding EPGR). Third, the authors use PWC to account for EPGR’s impact on the oil exchange rate returns dynamics and explore partial correlations in the oil and exchange rate returns dynamics.

The empirical results generally show that EPGR is a key driver in the oil and exchange rate returns nexus.

The relevance of EPGR in influencing exchange rate volatility is confirmed by the findings. As a result, it is critical for government officials and foreign exchange investors to use EPGR as a leading indicator when establishing foreign exchange trading strategies and economic forecasts.

This study is the first, to the best of the authors’ knowledge, to apply a wavelet-based technique to account for EPGR in the relationship between oil and exchange rate returns in the BRICS countries.

This study investigates the nexus between the returns on oil prices (OP) and unemployment (UR) while taking into account the influences of two of the most representative measures of uncertainty, the Baker et al. (2016) and Caldara and Iacovello (2021) indexes of economic policy uncertainty (EP) and geopolitical risks (GP), in the relationship.

The authors use data on the US, Canada, France, Italy, Germany and Japan from January 2000 to February 2022 and the UK from January 2000 to December 2021. The authors then apply the continuous wavelet transform (CWT), wavelet coherence (WC), partial wavelet coherence (PWC) and multiple wavelet coherence (MWC) to examine the returns within a time and frequency framework.

The CWT tracks the movement and evolution of individual return series with evidence of high variances and heterogenous tendencies across frequencies that also align with critical events such as the GFC and COVID-19 pandemic. The WC reveals the presence of a bidirectional relationship between OP and UR across economies, showing that the two variables affect each other. The authors’ findings establish the predictive influence of oil price on unemployment in line with theory and also show that the variation in UR can impact the economy and alter the dynamics of OP. The authors employ the PWC and MWC to capture the impact of uncertainty indexes in the co-movement of oil price and unemployment in line with the theory of “investment under uncertainty”. Taking into account the common effects of EP and GP, PWC finds that uncertainty measures significantly drive the co-movement of oil prices and unemployment. This result is robust when the authors control for the influence of economic activity (proxied by the GDP) in the co-movement. Furthermore, the MWC reveals the combined intensity, strength and significance of both oil prices and the uncertainty measures in predicting unemployment across countries.

This study investigates the relationship between oil prices, uncertainty measures and unemployment under a time and frequency approach.

1. Wavelet approaches are used to examine the relationship between oil prices and unemployment in the G7.

2. We account for uncertainty measures in the dynamics of oil prices and unemployment.

3. We observe a bidirectional relationship between oil prices and unemployment.

4. Uncertainty measures significantly drive oil prices and unemployment co-movement.

5. Both oil prices and uncertainty measures significantly drive unemployment.

Wavelet approaches are used to examine the relationship between oil prices and unemployment in the G7.

We account for uncertainty measures in the dynamics of oil prices and unemployment.

We observe a bidirectional relationship between oil prices and unemployment.

Uncertainty measures significantly drive oil prices and unemployment co-movement.

Both oil prices and uncertainty measures significantly drive unemployment.

This study aims to investigate the role of interpersonal influence on consumer purchase decisions regarding foreign products, specifically by exploring consumers’ social reaction styles (acquisitive and protective) when confronted with normative pressures and their subsequent impact on consumers’ purchase behavior in the context of situational animosity.

Three studies were conducted in China to empirically examine the proposed research model. The US–China Chip War of 2022 was used as the research context for situational animosity, while the Japan–China relationship representing a stable animosity condition was used for contrast.

This study establishes the mediating role of perceived normative pressure in linking animosity attitudes to purchase avoidance in situational animosity. It also validates that consumers’ social reaction styles (acquisitive and protective) help predict distinct behavioral outcomes, holding significant implications for advancing research in the field of product and brand consumption.

This research provides a novel perspective by exploring consumers’ social reaction styles when dealing with normative pressure in situational animosity. The distinction between acquisitive and protective reaction styles adds depth and originality to the study. Moreover, this study examines consumer behavior in two distinct consumption contexts: switching intentions to local products and purchase intentions for products from offending countries in hidden consumption situations. This dual perspective offers a comprehensive exploration of consumers’ purchase behavior under normative pressure, contributing to the novelty of this research.

The purpose of the present work is to improve the industry performance by identifying and quantifying the risks faced by the Indian pharmaceutical industry (IPI). The risk values for the prominent risks and overall industry are determined based on the four risk parameters, which would help determine the most contributive risks for mitigation.

An extensive literature survey was done to identify the risks, which were also validated by industry experts. The finalized risks were then evaluated using the fuzzy synthetic evaluation (FSE) method, which is the most suitable approach for the risk assessment with parameters having a set of different risk levels.

The three most contributive sub-risks are counterfeit drugs, demand fluctuations and loss of customers due to partners' poor service performance, while the main risks obtained are demand, financial and logistics. Also, the overall risk value indicates that the industry faces medium to high risk.

The study identifies the critical risks which need to be mitigated for an efficient industry. The industry is most vulnerable to the demand risk category. Therefore, the managers should minimize this risk by mitigating its sub-risks, like demand fluctuations, bullwhip effect, etc. Another critical sub-risk, the counterfeit risk, should be managed by adopting advanced technologies like blockchain, artificial intelligence, etc.

There is insufficient literature focusing on risk quantification. Therefore, this work addresses this gap and obtains the industry's most critical risks. It also discusses suitable mitigation strategies for better industry performance.

This study aims to investigate the synergies between the economic environment and the smart living dimension embedded in the current smart city initiatives, focusing on the localization of female entrepreneurship in contemporary cities. This interaction is under-investigated and controversial as it includes cities' practices enabling users and citizens to develop their potential and build their own lives, affecting entrepreneurial and economic outcomes. Building upon the perspective of the innovation ecosystems, this study focuses on the impact of smart living dimensions and R&D investments on the localization of female entrepreneurial activities.

The study uses a Generalized Method of Moments (GMM) and a panel dataset that considers 30 Italian smart city projects for 12 years to demonstrate the relationship between smart living practices in cities and the localization of female entrepreneurship. The complementary effect of public R&D investment is also included as a driver in the “smart” city transition.

The study found that the advancement of smart living practices in cities drives the localization of female entrepreneurship. The study highlights the empirical results, the interaction over the years and a current overview through choropleth maps. The public R&D investment also affects this relationship.

This study advances the theoretical discussion on (1) female entrepreneurial intentions, (2) smart city advancement (as a context) and (3) smart living dimension (as a driver) and offers valuable insight for governance and policymakers.

This study offers empirical contributions to the preliminary academic debate on enterprise development and smart city trajectories at the intersection between human-based practices and female entrepreneurship.

This study offers empirical contributions to the preliminary academic debate on enterprise development and smart city trajectories at the intersection between human-based practices and female entrepreneurship. The findings provide valuable insights into the localization of female entrepreneurship in the context of smart cities.