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Gut Microbiota in Familial Mediterranean Fever: Insights into Microbial Patterns and Disease Severity
No full textFamilial Mediterranean Fever (FMF) is a hereditary auto-inflammatory disease endemic to the Middle East and Mediterranean populations. Despite advances in understanding its genetic basis, FMF pathophysiology remains incompletely understood. Emerging evidence suggests the gut microbiota plays a critical role in regulating inflammation and immune responses, raising the possibility that microbiome alterations may contribute to FMF severity. However, research in the Arab region, particularly in Lebanon, remains limited.
This study investigated the gut microbiota composition of 16 FMF patients and 13 healthy controls in Lebanon using 16S rRNA gene sequencing. Clinical data were collected, and disease severity was scored using the International Severity Scoring System for FMF (ISSF). Alpha and beta diversity analyses, as well as differential abundance testing, were conducted to explore microbial differences between groups. Our findings revealed no significant differences in gut microbiota composition or diversity between FMF patients and healthy controls, nor among FMF subgroups stratified by disease severity. Minor compositional variations were observed, such as increased abundance of Synergistota in FMF patients; however, none reached statistical significance. These results contrast with previous studies reporting inflammatory dysbiosis and reduced microbial diversity in FMF cohorts. Multiple factors may account for these discrepancies, including small sample size, moderate sequencing quality, remission status, colchicine treatment, and the difficulty of recruiting both FMF patients and healthy volunteers.
The absence of significant microbial shifts reflects the complexity of host–microbiota interactions in FMF. Future studies should aim to include larger and more diverse cohorts, apply higher-resolution sequencing, and incorporate functional microbiome profiling to better understand the role of the gut microbiota in FMF pathogenesis and progression
Assessing the Nutritional Knowledge of Women Diagnosed with Polycystic Ovary Syndrome (PCOS) and Its Effect on Dietary Quality and Binge Eating: A Cross-Sectional Study
No full textBackground:
Polycystic ovary syndrome (PCOS) affects women’s hormonal and metabolic health and often requires lifestyle changes. In Lebanon, little is known about how women with PCOS manage their eating habits or whether they have the nutritional knowledge to support these changes. This study explored whether nutritional literacy is linked to better diet quality and fewer binge eating behaviors in Lebanese women with PCOS, while also understanding their struggles in managing the condition.
Methods:
A mixed-methods approach was used, combining a cross-sectional survey with semi-structured interviews. The quantitative part involved a cross-sectional survey assessing nutritional literacy, diet quality, and binge eating behaviors among Lebanese women aged 18 to 49 with PCOS. The qualitative part included semi-structured interviews exploring the women’s experience and challenges in dietary management.
Results:
Results showed moderate nutritional literacy among participants, with higher scores in women with advanced education levels. Nutritional literacy was positively associated with vegetable intake and overall diet quality but was not significantly linked to binge eating behaviors. Binge eating was associated with BMI. Qualitative findings revealed that emotional struggles, social influences, and lack of tailored nutritional guidance were major barriers to adopting healthier eating habits.
Conclusion:
This study highlights the importance of nutritional literacy in promoting healthier dietary choices among Lebanese women with PCOS. The findings underscore that improving diet quality in this population calls for comprehensive, personalized support that addresses emotional challenges, social pressures, and the need for culturally sensitive nutritional counseling
Artificial Intelligence in Recruitment and Selection: Questioning Implications
No full textThe rapid evolution of the human resources field has not gone unnoticed.
Artificial Intelligence (AI) and Machine Learning (ML) have gained unprecedented
popularity in today’s business landscape, with many organizations already automating
various processes through these technologies. This research explores the more
unsettling aspects of AI and ML by critically examining their ethical implications.
Specifically, we question the adequacy of existing ethical guidelines, the neutrality
and potential biases in AI-driven decision-making, and the human response to the
integration of AI/ML into the workplace. We delve into the consequences of these
issues and review the current literature relevant to our areas of focus. The originality
of this paper lies in its effort to provide a comprehensive understanding of the risks
and rewards associated with the implementation of AI/ML in organizational settings.
We aim to achieve this by asking the difficult and often uncomfortable questions,
while scrutinizing the limitations and complexities of these technologies
A systematic review of research on just, equitable, responsible, and inclusive smart cities
Full text linkDigital technologies and infrastructure are essential to the development of smart cities. Yet, vulnerable populations often lack equitable access to such resources. In this context, integrating justice into smart city development serves as a crucial foundation for developing just and equitable cities. To explore this issue, we examined 3067 articles and synthesized findings from 67 studies on justice in smart cities. Using deductive content analysis, we categorize justice issues into two distinct groups: types and dimensions. Among the various types of justice, infrastructural justice emerges as the most frequently discussed, appearing in 23 studies and highlighting significant disparities in access to basic urban infrastructure for marginalized communities. In terms of justice dimensions, procedural justice is the most prominent. Discussed in 27 studies, it emphasizes the importance of inclusive decision-making and the challenges posed by limited public awareness and tokenistic participation. The findings reveal that marginalized communities, particularly low-income groups, women, and individuals with disabilities, bear the brunt of exclusion, inequity, and marginalization in smart city developments. These communities are particularly vulnerable to gentrification, displacement, and reduced economic opportunities, further deepening existing inequalities. By positioning justice as a central element in smart city development, this study calls for a fundamental shift in the mindset of practitioners, advocating for policies and governance approaches that promote a just, equitable, responsible, and inclusive smart city ecosystem.Publishe
Sustainable development and investment in artificial intelligence in the USA
Full text linkBusiness practices and government policies are rapidly being reshaped by artificial intelligence (AI) in today’s world. However, it is still unclear whether AI will accelerate or hinder progress on sustainable development goals. Within this perspective, the present study aims to capture the asymmetric effect of investment in AI on the sustainable development goals (SDGs) index in the USA while taking into account green electricity and gross domestic product. Using newly developed estimators, the study explores this relationship by considering non-linearity. The results point to the fact that (i) there is a long-run asymmetric linkage between AI, SDGs, GDP, and green electricity; (ii) AI investment contributes to sustainable development positively in the USA; (iii) green electricity contributes to sustainable development in a positive way; (iv) SDGs index is negatively affected by economic growth.Publishe
Primary school students’ perceptions of artificial intelligence – for good or bad
No full textSince the end of 2022, global discussions on Artificial Intelligence (AI) have surged, influencing diverse societal groups, such as teachers, students and policymakers. This case study focuses on Swedish primary school students aged 11–12. The aim is to examine their cognitive and affective perceptions of AI and their current usage. Data, comprising a pre-test, focus group interviews, and post-lesson evaluation reports, were analysed using a fusion of Mitcham’s philosophical framework of technology with a behavioural component, and the four basic pillars of AI literacy. Results revealed students’ cognitive perceptions encompassing AI as both a machine and a concept with or without human attributes. Affective perceptions were mixed, with students expressing positive views on AI’s support in studies and practical tasks, alongside concerns about rapid development, job loss, privacy invasion, and potential harm. Regarding AI usage, students initially explored various AI tools, emphasising the need for regulations to slow down and contemplate consequences. This study provides insights into primary school students perceptions and use of AI, serving as a foundation for further exploration of AI literacy in education contexts and considerations for policy makers to take into account, listening to children’s voices.
Work Conditions that Promote Teacher Satisfaction and Retention
No full textThis study focuses on teachers in Lebanon who face several challenges. This study aims to identify factors that promote teacher job satisfaction and their perceptions of the work conditions that help in retaining them. The study employed a mixed-methods approach, exploring teachers' views on their work conditions and their impact on their decision to stay. The study was conducted in two schools located in Beirut, with a purposeful sample of 100 teachers who took an online survey (10 of them voluntarily agreed to sit for an interview). The study aimed to provide valuable insights for school administrators whose aim is to lower their turnover rate and retain their qualified teachers. It emphasized the essential role of effective school leadership in promoting an environment that supports teachers and empowers them to reveal the leaders in themselves. The study revealed that elementary and middle school teachers valued the relationship they have with their school leaders who trust them and support them as well as school leaders who demonstrate strong leadership skills. This was evident in both schools. Moreover, the study revealed that teachers are more satisfied and willing to stay in their school when they are supported when dealing with student conduct. For future studies, it is important to widen the range of teachers included to compare the perceptions of teachers who teach different curricula and different levels as well as including a diverse range of schools that reflect diverse cultural and socioeconomic backgrounds in Lebanon that are not only located in the capital, Beirut
Religiosity & Work Dynamics: Understanding Links to Resilience & Productivity
No full textThis study investigates the relationship between religiosity and two key workplace
outcomes: employee resilience and productivity. Using a quantitative survey design, data
was collected from 212 professionals across various sectors. Three theoretical frameworks
were relied on: Job Demands-Resources (JD-R) model, Conservation of Resources (COR)
theory, and Attachment Theory; the study conceptualizes intrinsic religiosity as a personal
resource that enhances coping and performance. Statistical analyses — including
correlations, linear regressions, and moderator testing — revealed that intrinsic religiosity
significantly predicts both resilience and productivity. Gender was examined as a potential
moderator but showed no significant influence. The findings suggest that religiosity
positively contributes to workplace well-being and performance, regardless of gender.
Implications highlight the importance of supporting employees’ internal belief systems
within inclusive and ethically sensitive frameworks. Recommendations for future research
and practical applications are discussed
The Influence of Performance Management Feedback on Employee Performance: The Moderating Role of Organizational Justice and its Dimensions (Procedural, Distributive, Interactional)
No full textIn modern organizations, providing feedback is critical for increasing employee performance; nevertheless, little is known about how performance management feedback quality influences employee performance. Despite the extensive literature on performance management feedback and employee performance, it is not clear which justice predictors best account for greater employee performance. The purpose of this study is to investigate the contribution of performance management feedback in enhancing employee performance. Performance management feedback and employee perception of justice significantly affect employee performance within an organization. In this study the relationships between performance management feedback, employee performance, and procedural, interactional and distributive justice of employees were examined. The literature demonstrated knowledge gaps regarding the relation between the variables which are intended to be filled by this study, although there are limited studies that explore organizational justice in its three dimensions as potential moderators in this relationship. To test the hypothesis set in this study, data was gathered via email and link shared on social media platforms. Using SPSS and AMOS, the data collected was analyzed. The study also presents managerial implications, acknowledges its limitations, and suggests directions for future research.
The empirical data shows that performance management feedback has a positive relationship with employee performance in the three structural equation modeling while distributive, procedural and interactional justices do not moderate this relationship. Managers can increase employee’s performance by giving constructive performance management feedback
Impacts of On-Grid Solar PV on Distribution Networks and Potential Solutions: A Case Study in the Region of Zahle
No full textRecent years have witnessed an increase in the installation of solar photovoltaic-based distributed generators or PV DGs mainly in countries with high solar potential such as Lebanon. In this country among others, the outgrowing trend towards installing on-grid PV-DGs is gradually impacting the safe and reliable operation of distribution networks. This research investigates the impacts of on-grid PV DGs on the power factor, active and reactive power flows, voltage and current profiles in the region of Zahle, Lebanon, and examines solutions to mitigate these impacts using available and affordable resources, infrastructures, and policies. For this purpose, the research develops a model for heavily PV DG-loaded distribution lines in Zahle. Then, power flow simulations are conducted in OpenDSS on these modeled lines with and without the presence of PV DGs to quantify their impacts on voltage, power factor, and power flows. Afterward, the optimal siting and sizing of capacitor banks (CBs) are performed on the modeled network to alleviate the impacts of the PV DGs using both MATLAB and OpenDSS. The siting and sizing methodology is conducted by considering five different optimization algorithms, namely the single-objective genetic algorithm (SOGA), the combined SOGA and loss sensitivity factor algorithm (SOGA-LSF), the multi-objective genetic algorithm (MOGA), the combined MOGA-LSF and the CAPADD algorithm of OpenDSS. The CB allocation solutions obtained from these algorithms are then compared from a technical and economic perspective. The effects of the addition of the CBs on the operation of the distribution network of Zahle are further studied by examining different types of CB control available in OpenDSS (voltage and time control). The results of the impact assessment showed severe problems mainly at the end of Feeder 2. Both the size and location of the PV DGs were concluded to have the most influence on the results. The optimal siting and sizing of the CBs succeeded in reducing the power losses and improving the voltage profile. The MOGA allocation solution achieved the highest power loss reductions and the combined allocation solutions, MOGA-LSF and SOGA-LSF, resulted in the best improvements in the voltage profiles of the endmost buses of Feeder 2. The MOGA and MOGA-LSF were deemed superior in terms of technical performance and cost. The novelty of the research lies in the realistic portrayal of the impacts that PV energy has on distribution networks that are generally forsaken, and on the practicality of the CB allocation methodology and network model proposed. It is expected that the results and conclusions drawn from this research will help distribution utilities deal with the rising issues emanating from PV DGs and predict the impacts they may have before their integration into distribution networks