This study investigates the impact of related and unrelated variety on regional economic resilience in Turkey at the NUTS-3 level, emphasizing the role of sectoral interconnectedness in shaping a region’s capacity to withstand and recover from economic shocks. Departing from conventional employment-based indicators, the analysis adopts a GDP-based framework to capture resilience along two key dimensions: resistance and recovery which the recent literature increasingly interprets as measurable outcomes. Using a pooled OLS approach, resilience indices are constructed for multiple periods, accounting for both financial and epidemic shocks. The findings offer partial and context-dependent evidence: Unrelated variety generally shows a negative association with GDP-based resilience, especially during resistance and early recovery phases, but this relationship is not robust across all model specifications or shock types. Related variety, meanwhile, demonstrates weaker and less consistent effects, with occasional positive contributions observed only in select epidemic recovery models. These results underscore the sensitivity of industrial variety’s role to both the nature of the shock and the chosen resilience metric. Importantly, informal employment consistently exhibits a positive and statistically significant relationship with resilience, suggesting that, in the short term, informality may enhance regional adaptability through labor market flexibility. Overall, the hypothesis that related variety weakens resilience receives limited support, while the stabilizing role of informality appears more robust. The results highlight the need for shock specific and regionally tailored policies. Strengthening the capacity of local institutions and incorporating flexible formal labor arrangements could support more responsive and resilient regional development in Turkey.
This study examines competitive advantage in the aviation sector within the context of resource dependency theory, specifically focusing on the ground handling services sector. The aim of the study is to identify the resources on which ground handling services companies depend within the context of resource dependency theory, to examine the competition experienced in the sector, and to determine the strategies used to achieve competitive advantage. The scope of the study consists of Group A ground handling companies operating in T & uuml;rkiye. A qualitative research method was used in the study, and the data collected using semi-structured interviews were analyzed with the MAXQDA2020 program. The analysis revealed that the ground handling sector in T & uuml;rkiye is costly and volatile, while competition in the sector is balanced and low. In ground handling companies, there is a greater reliance on outsources such as equipment, training, and workforce-human resource. The high cost of resources, regulatory compliance, frequent audits, and lack of alternative suppliers create a situation of forced dependency. Although competition within the sector is balanced and low, the competition between companies cannot be ignored. Ground handling companies generally provide services that are standardized by regulations and do not vary greatly, but they have advantages over their competitors in cargo-warehouse services and platinum services. In addition, ground handling companies have advantages in terms of sustainability, safety, and size. In this regard, ground handling companies implement collaboration-focused, quality-focused, cost-focused, and customer-focused strategies to gain a competitive advantage. Effective management of the procurement process for resources used in the ground handling services sector and dependency relationships related to resources provides companies with a competitive advantage. Companies manage dependency relationships by incorporating available resources into their own structures. However, due to the specialized areas of expertise, the necessity of the relationship, and the high costs involved, companies resort to outsourcing when necessary.
This study was conducted to examine the effects of the Self-Determined Learning Model of Instruction (SDLMI) on the learning and maintenance of self-determination skills in young people with autism spectrum disorder (ASD). Social validity data were also collected from participants and their parents before and after the intervention. A multiple-baseline design was used as a single-subject research design. Young people with ASD not only acquired self-determination skills but also maintained these skills over time. In addition, following the intervention, they successfully performed the independent living skills they had personally selected as their own targets. Both the participants and their parents expressed high levels of satisfaction with the intervention process. Furthermore, compared to pre-intervention reports, notable positive shifts were observed in the perceptions and evaluations of both youth and parents after the study. This study demonstrated that SDLMI can be an effective model for teaching self-determination skills to young people with ASD in natural settings.
In recent years, significant advances in brain imaging technologies, artificial intelligence (AI), and neuroscience have significantly improved our understanding of how the brain functions in the learning process. This research aims to explore the role of neuroscience and AI in designing effective feedback mechanisms by investigating their impact on learning-teaching processes. To achieve this goal, the study addresses the following questions: "1) What are the objective orientations, thematic patterns, methodological approaches, and key findings of neuroscience research investigating feedback processes?" and "2) What are the objective orientations, thematic patterns, methodological approaches, and key findings of AI research investigating feedback processes?" This research adopts a systematic review methodology that encompasses four key stages: planning, searching, selection, and synthesis. Using specific keywords, relevant studies in educational sciences, educational psychology, and neuroscience were systematically identified from the Web of Science (WOS) database, specifically targeting publications indexed in SSCI, ESCI, and SCI-E. Through comprehensive thematic analysis, we systematically mapped the research landscapes in both domains. The systematic review reveals that neuroscience and AI research provide complementary insights into feedback effectiveness. The findings suggest that optimal feedback design requires integrating neurologically-informed principles with AI-enabled delivery systems to create developmentally appropriate and individually adaptive learning environments. This study further highlights a notable deficiency in interdisciplinary research that integrates neuroscience and AI approaches to optimize feedback for performance and learning. Limited collaboration between these fields hinders knowledge exchange and prevents mutual enhancement of feedback methodologies. These fields have developed independently with limited integration and a lack of comprehensive theoretical exploration and investigation into various feedback types and strategies.
Alzheimer's disease is one of the most common neurodegenerative diseases. There is currently no definitive treatment for this disease, which predominantly affects the elderly population. For this reason, discoveries of new molecules are important. Alzheimer's disease is a progressive neurodegenerative disorder involving several pathological mechanisms, notably the reduction of acetylcholine levels and the aggregation of beta-amyloid (A beta) peptides. In this study, novel hybrid compounds derived from donepezil, designed to target both the catalytic active site (CAS) and peripheral anionic site (PAS) of acetylcholinesterase (AChE), were synthesized, and their multifaceted biological activities were evaluated. Among the synthesized derivatives, compounds 4e, 4d, and 4j exhibited remarkable AChE inhibitory potency, with IC50 values of 0.305+0.009 mu M, 0.109+0.004 mu M, and 0.028+0.001 mu M, respectively. Analysis of A beta plaque inhibition profiles indicated that compound 4j showed a pattern highly like that of donepezil. Furthermore, no toxicity problems were observed with any of the compounds. The obtained results indicate that these new donepezil-derived compounds may be potential multi-target agents against Alzheimer's disease.
