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Previous research indicates that vitamin D and omega-3 co-supplementation may benefit overall health, but current evidence regarding its effects on lipid profile remains unclear. The present systematic review and meta-analysis aimed to examine the effects of vitamin D and omega-3 co-supplementation on lipid profile (total cholesterol [TC], low-density lipoprotein [LDL], triglyceride [TG] and high-density lipoprotein [HDL]) in adults.

In this systematic review and meta-analysis, relevant studies were obtained by searching the PubMed, Scopus and Web of Science databases (from inception to January 2022). Weighted mean differences and 95% confidence intervals were estimated via a random-effects model. Heterogeneity, sensitivity analysis and publication bias were reported using standard methods.

Pooled analysis of six randomized controlled trials (RCTs) revealed that vitamin D and omega-3 co-supplementation yielded significant reductions in TG (p = 0.631). A pooled analysis of five trials indicated a significant association between omega-3 and vitamin D treatment and reductions in TC (p = 0.001) and LDL (p = 0.001). Although, pooled analyses of omega-3 and vitamin D did not significantly affect HDL.

The findings suggest that vitamin D and omega-3 co-supplementation lowers TG, TC and LDL in adults. Future, large-scale, RCTs on various populations are needed to elucidate further beneficial effects of vitamin D and omega-3 co-supplementation on lipid profile and establish guidelines for clinical practice.

Untimely responses to emergency situations in urban areas contribute to a rising mortality rate and impact society's primary capital. The efficient dispatch and relocation of ambulances pose operational and momentary challenges, necessitating an optimal policy based on the system's real-time status. While previous studies have addressed these concerns, limited attention has been given to the optimal allocation of technicians to respond to emergency situation and minimize overall system costs.

In this paper, a bi-objective mathematical model is proposed to maximize system coverage and enable flexible movement across bases for location, dispatch and relocation of ambulances. Ambulances relocation involves two key decisions: (1) allocating ambulances to bases after completing services and (2) deciding to change the current ambulance location among existing bases to potentially improve response times to future emergencies. The model also considers the varying capabilities of technicians for proper allocation in emergency situations.

The Augmented Epsilon-Constrained (AEC) method is employed to solve the proposed model for small-sized problem. Due to the NP-Hardness of the model, the NSGA-II and MOPSO metaheuristic algorithms are utilized to obtain efficient solutions for large-sized problems. The findings demonstrate the superiority of the MOPSO algorithm.

This study can be useful for emergency medical centers and healthcare companies in providing more effective responses to emergency situations by sending technicians and ambulances.

In this study, a two-objective mathematical model is developed for ambulance location and dispatch and solved by using the AEC method as well as the NSGA-II and MOPSO metaheuristic algorithms. The mathematical model encompasses three primary types of decision-making: (1) Allocating ambulances to bases after completing their service, (2) deciding to relocate the current ambulance among existing bases to potentially enhance response times to future emergencies and (3) considering the diverse abilities of technicians for accurate allocation to emergency situations.

AlSi10Mg alloy is commonly used in laser powder bed fusion due to its printability, relatively high thermal conductivity, low density and good mechanical properties. However, the thermal conductivity of as-built materials as a function of processing (energy density, laser power, laser scanning speed, support structure) and build orientation, are not well explored in the literature. This study aims to elucidate the relationship between processing, microstructure, and thermal conductivity.

The thermal conductivity of laser powder bed fusion (L-PBF) AlSi10Mg samples are investigated by the flash diffusivity and frequency domain thermoreflectance (FDTR) techniques. Thermal conductivities are linked to the microstructure of L-PBF AlSi10Mg, which changes with processing conditions. The through-plane exceeded the in-plane thermal conductivity for all energy densities. A co-located thermal conductivity map by frequency domain thermoreflectance (FDTR) and crystallographic grain orientation map by electron backscattered diffraction (EBSD) was used to investigate the effect of microstructure on thermal conductivity.

The highest through-plane thermal conductivity (136 ± 2 W/m-K) was achieved at 59 J/mm³ and exceeded the values reported previously. The in-plane thermal conductivity peaked at 117 ± 2 W/m-K at 50 J/mm³. The trend of thermal conductivity reducing with energy density at similar porosity was primarily due to the reduced grain size producing more Al-Si interfaces that pose thermal resistance. At these interfaces, thermal energy must convert from electrons in the aluminum to phonons in the silicon. The co-located thermal conductivity and crystallographic grain orientation maps confirmed that larger colonies of columnar grains have higher thermal conductivity compared to smaller columnar grains.

The thermal properties of AlSi10Mg are crucial to heat transfer applications including additively manufactured heatsinks, cold plates, vapor chambers, heat pipes, enclosures and heat exchangers. Additionally, thermal-based nondestructive testing methods require these properties for applications such as defect detection and simulation of L-PBF processes. Industrial standards for L-PBF processes and components can use the data for thermal applications.

To the best of the authors’ knowledge, this paper is the first to make coupled thermal conductivity maps that were matched to microstructure for L-PBF AlSi10Mg aluminum alloy. This was achieved by a unique in-house thermal conductivity mapping setup and relating the data to local SEM EBSD maps. This provides the first conclusive proof that larger grain sizes can achieve higher thermal conductivity for this processing method and material system. This study also shows that control of the solidification can result in higher thermal conductivity. It was also the first to find that the build substrate (with or without support) has a large effect on thermal conductivity.

This study aims to identify the main factors affecting export competitiveness and its barriers, focusing on the minerals industry so that a scale is achieved for measuring export competitiveness in this industry.

The research was conducted with a mixed method approach in the minerals industry. Among the active companies involved in this industry, 34 export companies and export management companies were selected and evaluated. In the qualitative phase, 18 experts and managers of the industry were interviewed to identify the factors affecting the export competitiveness of these companies and the barriers ahead of them. In the quantitative phase, a questionnaire was distributed among 412 managers and experts in this industry to categorize the identified factors and to measure the relationships among them. For data analysis in the qualitative phase, theme analysis was used. For the quantitative phase, factor analysis and structural equation modeling were adopted.

In addition to identifying the main components affecting the competitiveness of companies in exporting minerals as well as the main barriers ahead of them, the findings of the current research categorized these components using factor analysis. These components were categorized into factors, such as manufacturing factors, demand conditions, related and supporting industries, structural factors, competitive strategy and governmental supports. Afterward, their impacts on export competitiveness were measured and supported.

Although some studies have been conducted to examine the competitiveness in different industries, no research has been found that has examined and identified the main factors affecting export competitiveness and their impacts in the minerals industry with a mixed quantitative and qualitative approach. The findings of this research may help managers and policymakers, at the industrial and national levels, to reach a scale for assessing the export companies involved in this industry by identifying the most essential factors of export competitiveness of minerals. Furthermore, the findings of this research can act as a model for future researchers to develop a scale for export competitiveness in other industries.

This study aims to introduce the model of implementation to run the smart production factories. The study also aims to investigate the Industry 4.0 technologies as enablers to deal with challenges in the way of implementation.

This contribution benefits from two teams of experts to evaluate the challenges and technologies of Industry 4.0. The Hanlon method is applied to evaluate, rank and prioritise the challenges which are initially scored by experts’ Team 1. Then, the adjacency matrix among enablers and challenges is extracted through the opinions of experts’ Team 2. The study also uses fuzzy cognitive map (FCM) to evaluate the real weights of technologies and challenges, rank and prioritise subsequently.

A total of 8 challenging obstacles and 24 key technologies have been evaluated. The findings reveals that recruit and retention of experienced managers, undefined return on investment and recruit and retention of multi-skilled workers are the most serious challenges in the way of implementing smart production factories. Furthermore, big data, IT-based management and Internet of Things are the top-ranked key enablers to face the challenges.

To the best of the authors’ knowledge, this study is one of the pioneering studies that uses Hanlon method to evaluate industrial challenges. Integrating Hanlon method and FCM leads to a comprehensive model of evaluation and ranking which is another novelty of this contribution. Although many research studies have been released to implement the smart factories, practical model of implementation for production factories is identified as a literature gap.

The increasing prevalence of coronavirus disease (COVID-19) is a global crisis that leads to physical and psychological outcomes for health-care workers, so this study aims to investigate the mental health outcomes (including general health, generalized anxiety disorder and posttraumatic stress disorder) in health-care workers in Rafsanjan, Iran.

By using convenience sampling, this cross-sectional study was conducted on 332 health-care workers working in public hospitals in southern Iran. Data collection lasted from March to April 2020. General Health Questionnaire (GHQ-28), Generalized Anxiety Disorder 7-item (GAD-7) and Impact of Event Scale were used to collect data. The data were then analyzed by using SPSS 25 and descriptive and inferential statistics (chi-square and multivariate logistic regression).

In total 45.5% of the participants had psychological disorder according to GHQ. In addition, 25.3% of the participants had GAD and 31.6% had posttraumatic stress disorder (PTSD). The results using multivariate logistic regression showed that only income was significantly associated with psychological disorders (95% confidence interval for odds ratio: 1.32–6.45, P = 0.008).

According to the results, the incidence of GAD and PTSD was high among health-care workers. Therefore, it is recommended that the psychological skills of health-care workers be strengthened through counseling and training programs.

This paper provides a novel analysis of mental health in health-care workers in Iran.

The purpose of this paper is to identify the critical factors that impact knowledge sharing (KS) and their importance in technology-intensive service organizations in the United Arab Emirates (UAE).

An extensive literature review was conducted to identify the critical factors for KS in technology-intensive organizations. Then, an analytical hierarchical process (AHP) was applied to prioritize the primary criteria and sub-criteria. This study consists of nine primary criteria and 34 sub-criteria that are relevant to KS in technology-intensive organizations.

The results show that organizational leadership (OL) is the most important factor that impacts KS in technology-intensive organizations, which is followed by organizational culture (OC), organizational strategy (OSY), corporate performance (CP), organizational process (OP), employee engagement (EE) and organizational structure (OST). According to the results, the least impactful factor is human resource management (HRM).

Because the results in this study were only obtained from service organizations, future studies can include manufacturing organizations from different countries and additional success factors. Future studies could also use structural equational modelling methodology for better understanding the relations among these critical factors for KS.

This paper is one of the first in the UAE to examine the broad range of critical success factors for KS in technology-intensive organizations.