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Medical schools aspire to graduate doctors who are competent in interpreting, conducting and publishing scientific research. Hence, the purpose of the paper is explore the impact of a structured compulsory community-oriented, project-based research program in the undergraduate medical curriculum on medical graduates’ future careers, publications and motivation for scientific research.

An online questionnaire was sent through the Alumni Association of the Arabian Gulf University (AGU) to medical graduates who have valid email addresses, seeking information on their scientific productivity along with demographic and current employment data. Responses were collated and analyzed using a standard statistical software package. The chi-square test and p-value were calculated. P-value <0.05 was considered statistically significant.

Completed forms were received from 91 medical graduates. In this sample, almost a quarter of the respondents have published a research paper out of their undergraduate research project, and about 50% have published at least one paper (as main or co-author) after graduation. Both demographic and medical specialty was not related to the doctor's perception of the benefits they have attained from the compulsory undergraduate research program. However, medical graduates who are at senior levels in their medical careers and those who are employed in secondary healthcare are significantly more likely to publish research papers.

The compulsory community-oriented, project-based research program in AGU has positively contributed to the research productivity of graduates, and it would be worthwhile to include such programs as an integral part of the undergraduate medical curriculum.

This paper aims to propose a system for defect detection in constructed elements that is able to indicate deformity positions. It also evaluates the defects in finishing materials of constructed building elements to support the subjective visual quality investigation of the aesthetics of an architectural work.

This strategy depends on defect features analysis that evaluates the defect value in digital images using digital image processing methods. The research uses the three-dimensional (3D) modeling techniques and image processing algorithms to generate a system that is able to perform some of the monitoring activities by computers. Based on the collected site scans, a 3D model is created for the building. Then, several images can be exported from the 3D model to investigate a specific element. Different image denoizing techniques are compared such as mean filter, median filter, Wiener filter and Split–Bregman iterations. The most efficient technique is implemented in the system. Then, the following six different methods are used for image segmentation to separate the concerned object from the background; color segmentation, region growing segmentation, histogram segmentation, local standard deviation segmentation, adaptive threshold segmentation and mean-shift cluster segmentation.

The proposed system is able to detect the cracks and defected areas in finishing works and calculate the percentage of the defected area compared to the total captured area in the photo with high accuracy.

The proposed system increases the precision of decision-making by decreasing the contribution of human subjective judgment. Investigation of different finishing surfaces is applied to validate the proposed system.

In this manuscript, the authors aimed to demonstrate the influences of influential parameters in mixed-mode crack propagation phenomenon. The authors attempted to cover almost all surrounding issues of this subject as the authors know simulating of propagating cracks as internal strong discontinuity is a complicated issue.

In this manuscript, the authors demonstrated the influences of influential parameters in mixed-mode crack propagation phenomenon. The authors attempted to cover almost all surrounding issues of this subject as the authors know simulating of propagating cracks as internal strong discontinuity is a complicated issue. Furthermore, three different scenarios for crack growth are considered. In reality, edge-cracked plate, center-cracked plate and cracked plate in the presence of void and inclusion are studied. In fact, by designing suitable artificial neural network's (ANN) architectures all the three aforementioned conditions are trained and estimated through those architectures with very good agreement with input data. Also by conducting a series of sensitivity analysis, the most affecting factors in mixed-mode crack propagation in different situations are demonstrated. The obtained results are very interesting and useful for other researchers and also the authors hope the results would be cited by researchers.

The influential parameters on mixed-mode crack propagation were found in this paper.

The computer code using MATLAB was prepared to study the mixed-mode crack paths. Also using ANNs toolbox, the crack path estimation was investigated.

Incipient detection of pavement deterioration (such as crack identification) is critical to optimizing road maintenance because it enables preventative steps to be implemented to mitigate damage and possible failure. Traditional visual inspection has been largely superseded by semi-automatic/automatic procedures given significant advancements in image processing. Therefore, there is a need to develop automated tools to detect and classify cracks.

The literature review is employed to evaluate existing attempts to use Hough transform algorithm and highlight issues that should be improved. Then, developing a simple low-cost crack detection method based on the Hough transform algorithm for pavement crack detection and classification.

Analysis results reveal that model accuracy reaches 92.14% for vertical cracks, 93.03% for diagonal cracks and 95.61% for horizontal cracks. The time lapse for detecting the crack type for one image is circa 0.98 s for vertical cracks, 0.79 s for horizontal cracks and 0.83 s for diagonal cracks. Ensuing discourse serves to illustrate the inherent potential of a simple low-cost image processing method in automated pavement crack detection. Moreover, this method provides direct guidance for long-term pavement optimal maintenance decisions.

The outcome of this research can help highway agencies to detect and classify cracks accurately for a very long highway without a need for manual inspection, which can significantly minimize cost.

Hough transform algorithm was tested in terms of detect and classify a large dataset of highway images, and the accuracy reaches 92.14%, which can be considered as a very accurate percentage regarding automated cracks and distresses classification.

The main purpose of this paper is to investigate the relationships among a set of human resources management (HRM) practices, entrepreneurial traits (ET) and corporate entrepreneurship (CE) in an emerging market such as the Egyptian one.

This research is explanatory in nature where it explains the causal relationships between the variables. Data was collected from 230 human resources (HR) managers and professional serving in top Egyptian financial institutions. Data was analyzed using structural equation modeling with relevant goodness-of-fit statistics.

The results of this study indicate that HRM practices have significant impact on the development of ET of personnel currently operating in the Egyptian financial institutions. Besides, the results confirm the effect of HRM practices on ET, which in turn have significant impact on CE.

This research provides an appropriate direction for HR managers on how to best design HR programs directed toward the development of strong CE orientation. It also highlights the core ET needed to enhance CE in emerging economies. The validated hypothetical relationships of this model serve as the baseline for those managers to plan, execute and measure the consequences of those HRM programs.

Although HRM is widely considered as a critical driving force for CE, there is a remarkable scarcity of empirical research examining the role of ET. The conceptual model tested in this research typically deepens both HRM scholars and managers’ understanding of how they can best connect HRM practices to ET and CE in emerging economies. The findings of this study open the door for a new venue of research in the HRM area of study, particularly in emerging markets that search for a fast-growing rate of economic prosperity. The findings of this study lend support for HRM as an antecedent to CE rather than vice versa.

The purpose of this paper is to examine how a foreign subsidiary operates in emerging markets and integrates market orientation with organizational learning to achieve a competitive lead. It is an attempt to fill an evident gap in the literature of integrating organizational learning into a market-oriented competitive strategy through using a four-step collective learning cycle at General Motors Egypt (GME).

The paper adopts a qualitative case study methodology to thoroughly examine the viewpoints of 90 respondents via in-depth and unstructured interviews with both managers and employees working in a variety of divisions inside GME. An integrative qualitative data analysis approach is used to explore, synthesize, interpret and derive relationships resulting from the collected data.

This work advances the theory of organizational learning by testing the theme of collective learning cycle in a real work setting. It presents a real example of aligning market orientation into a collective learning cycle directed toward achieving competitive advantages.

This study provides scholars and practitioners alike with a real scenario on how and why a four-step organizational learning cycle functions as a building block to generate a competitive advantage. It also discusses the elements of collective learning that are not captured by the four-step collective learning cycle. Factors facilitating market-based organizational learning are also explored. However, the results generated are contingent on the investigated case study circumstances, which are limited in generalizability.

The paper addresses a set of directions through which auto assembly firms leverage both collective learning practices and knowledge-driven strategy to gain competitive advantages. The GME paradigm indicates how a firm can use collective learning not only to respond to an internal need for change but also to react to external market forces and constraints.

This study is the first of its kind to investigate the value of the cyclic learning concept from a strategic viewpoint in a multinational organizational context. It enriches the primarily practitioner literature on aligning collective learning into strategy with rich empirical examination of the learning practices of a leading foreign subsidiary. It resolves a gap in the literature regarding how organizational learning and knowledge management processes are aligned to market-oriented competitive strategy. The paper draws a number of critical research issues that call for refinement of the organizational learning cycle theory.

The purpose of this paper is to develop and empirically examine a comprehensive model that attempts to identify the factors that explain competitive advantage of implementing electronic business (e-business) in an emerging market. It seeks to fulfill an inevitable lack of conducting rigorous and intensive empirical studies on the Middle East and North Africa (MENA) region in which the internet use and e-business applications are on the rise.

This research is descriptive in nature where a quantitative research methodology is deployed. Data are collected using both interviews and e-survey for a sample of 302 Egyptian companies serving in diverse industries. A multivariate partial least squares technique is employed to analyze the collected data.

This study explores various e-business modes and applications widely employed in the MENA region. It also addresses a set of e-business-driven competitive advantages that are mainly generated from supportive ICT environment, major market forces and strategic opportunities, and at last, from electronically driven customers’ relationship initiatives. A number of e-business barriers are claimed to moderate the relationship between those forces and the resulting competitive advantages.

Although this research main framework incorporates important variables based on theoretical and empirical foundations, integrating other factors may extend understanding of how these factors, independently and/or interactively, explain the adoption of e-business and its merit to create distinctive competitive advantage.

This work helps managers and e-business experts alike to comprehend the ways through which firms target the applications of e-business technologies to realize a competitive edge in MENA region. It also helps practitioners and professionals comprehend the interrelationship between the type of forces drive e-business based competitive position and key barriers that deteriorate such a connection in emerging markets.

A model that enables scholars to better understanding the e-business phenomenon in MENA market is developed and validated. This model rests on e-business experts’ perspectives, reflections and it is evidently substantiated by past works in the areas.

The accurate modeling of magnetic hysteresis in electrical steels is important in several electrical and electronic applications. Numerical models have long been known that can correctly reproduce some typical behaviours of these magnetic materials. Among these, the model proposed by Jiles and Atherton must certainly be mentioned. This model is intuitive and fairly easy to implement and identify with relatively few experimental data. Also, for this reason, it has been extensively studied in different formulations. The developments and numerical tests made on this hysteresis model have indicated that it is able to accurately reproduce symmetrical cycles, especially the major loop, but often it fails to reproduce non-symmetrical cycles. This paper aims to show the positive aspects and highlight the defects of the different formulations in predicting the minor loops of electrical steels excited by non-sinusoidal currents.

The different formulations are applied to different electrical steels, and the data coming from the simulations are compared with those measured experimentally. The direct and inverse Jiles–Atherton models, including the introduction of the dissipative factor approach, are presented, and their limitations are proposed and validated using the measurements of three non-grain-oriented materials. Only the measured major loop is used to identify the parameters of the Jiles–Atherton model. Furthermore, the direct and inverse Jiles–Atherton models were used to simulate the minor loops as well as the hysteresis cycles with direct component (DC) bias excitation. Finally, the simulation results are discussed and compared to measurements for each study case.

The paper indicates that both the direct and the inverse Jiles–Atherton model formulations provide a good agreement with the experimental data for the major loop representation; nevertheless, both models can not accurately predict the minor loops even when the modification approaches proposed in the literature were implemented.

The Jiles–Atherton model and its modifications are widely discussed in the literature; however, some limitations of the model and its modification in the case of the distorted current waveform are not completely highlighted. Furthermore, this paper contains an original discussion on the accuracy of the prediction of minor loops from distorted current waveforms, including DC bias.

Patient safety focuses on minimizing risks that might occur to patients during provision of healthcare. The purpose of this study was to explore healthcare practitioners’ attitudes towards patient safety inside different hospital settings in Jeddah, Kingdom of Saudi Arabia.

A descriptive, cross-sectional study was conducted on a sample of healthcare practitioners in main hospitals in Jeddah. Two main hospitals (one governmental and one private) were selected from each region of Jeddah (east, west, north and south), with a total number of eight out of thirty hospitals. Data were collected through the Attitudes to Patient Safety Questionnaire III that was distributed online. The questionnaire used a 5-point scale. Descriptive statistics were used. Comparisons were made by independent t-test and ANOVA. The statistical significance level was set at p < 0.05.

The study included 341 healthcare practitioners of different sexes and specialties in eight major governmental and private hospitals in Jeddah. “Working hours as error cause” subscale had the highest mean score (4.03 ± 0.89), while “Professional incompetence as error cause” had the lowest mean score (3.49 ± 0.97). The total questionnaire had a moderate average score (3.74 ± 0.63). Weak correlations between the average score of the questionnaire and sex, occupation and workplace were found (−0.119, −0.018 and −0.088, respectively).

Hospitals need to develop targeted interventions, including continuing professional development programs, to enhance patient safety culture and practices. Moreover, patient safety training is required at the undergraduate education level, which necessitates health professions education institutions to give more attention to patient safety education in their curricula.

The study contributed to the existing literature on patient safety culture in hospital settings in Jeddah, Saudi Arabia. The insights generated by the study can inform targeted interventions to enhance patient safety culture in hospitals and improve patient outcomes.

Despite considerable growth in literature on Industry 4.0 technologies, the research on the factors influencing the investment on these technologies in pursuit of supply chain 4.0 is yet incipient. The study aims to fill this knowledge void by exploring the perceived drivers and barriers intertwined in the implementation of supply chain 4.0 in the context of food and beverage industry.

Qualitative exploratory research was employed involving 20 semi-structured interviews with senior managers from the Australian food and beverage supply chain. The interviews' data were analysed with VOSViewer software version 1.6.14.

The results unravel that reduction in supply-demand misalignment, fast-changing consumer's needs, threat of legal penalties and cost optimisation are the key drivers; whereas lack of collaboration, organisational inertia and lack of awareness are the critical barriers to implement supply chain 4.0.

The study derives seven propositions and a theoretical framework that need to be empirically corroborated.

Understanding of drivers and barriers will help practitioners to make more informed decision in implementation of supply chain 4.0.

Implementation of supply chain 4.0 can enhance the performance of the food and beverage industry, thus offering more job opportunities and sustained food supply.

This is the first study in exploring drivers and barriers to the implementation of supply chain 4.0; thus, adds new knowledge to the growing body of the literature. The paper introduces a novel method for qualitative data analysis contributing to the methodological development of the supply chain management field.