Search Results (5)

As sustainability becomes central to tourism, tourists are no longer passive consumers but active stakeholders who influence organizational behavior. This study investigates how green consumer behavior (GCB) shapes expectations for employee green competencies and organizational sustainability strategy (OSS). Data were collected through a structured survey of 326 domestic tourists in Albania. Green skills expectation (GSE) was modeled as a latent construct derived from two observed variables: green loyalty and brand image, and willingness to support sustainability. Statistical analyses included exploratory factor analysis (EFA), K-means clustering and structural equation modeling (SEM). GCB significantly predicted both OSS and GSE, confirming that green tourists influence how organizations structure and communicate their sustainability practices. Cluster analysis identified two consumer profiles: committed eco-tourists and green-adaptive tourists. This study advances current understanding of how tourists act as external agents of internal organizational change. It extends the theoretical discourse on green marketing and sustainable workforce development by positioning tourist expectations as a driver of human resource transformation. The findings offer meaningful implications for tourism operators, educators and policymakers seeking to align employee training and service delivery with the demands of sustainability-oriented travelers. In this way, the study bridges the gap between consumer behavior and workforce development, contributing to a more integrated approach to sustainable tourism. Full article

Despite growing scholarly interest in leadership within entrepreneurial settings, little is known about how relational leadership operates in informal, resource-constrained ecosystems. This study examines how entrepreneurial leadership fosters positive relational dynamics and collaborative resilience within Ecuador’s highly informal entrepreneurial ecosystem. Drawing on entrepreneurial cognition and relational leadership theories, it investigates how entrepreneurs act as informal leaders who cultivate trust, empathy, and mutual support in the absence of formal institutional structures. Using an original mixed-method lexical–clustering design, data were collected from 880 micro and small entrepreneurs in Quito, who categorized 75 entrepreneurial attributes using a forced-choice instrument. Two dominant narratives emerged: collaborative resilience (65%), defined by empathy, adaptability, and social cohesion, and structural vulnerability (35%), marked by bureaucracy, fear, and emotional strain. Gender differences revealed that women emphasize relational stress and communal coping, while men focus on structural barriers and operational constraints. The findings extend leadership research by demonstrating how positive relational processes enable entrepreneurs to transform adversity into collective strength. The study advances relational leadership theory by revealing its cognitive and emotional foundations in nontraditional contexts. It offers policy insights for designing inclusive, trust-based ecosystems that promote psychological safety, collaboration, and sustainable entrepreneurship in emerging economies. Full article

Repairing the central nervous system (CNS) remains one of the most difficult obstacles to overcome in translational neurosciences. This is due to intrinsic growth inhibitors, extracellular matrix issues, the glial scar–form barrier, chronic neuroinflammation, and epigenetic silencing. The purpose of this review is to bring together findings from recent developments in genome editing and computational approaches, which center around the possible convergence of clustered regularly interspaced short palindromic repeats (CRISPR) platforms and artificial intelligence (AI), towards precision neuroregeneration. We wished to outline possible ways in which CRISPR-based systems, including but not limited to Cas9 and Cas12 nucleases, RNA-targeting Cas13, base and prime editors, and transcriptional regulators such as CRISPRa/i, can be applied to potentially reactivate axon-growth programs, alter inhibitory extracellular signaling, reprogram or lineage transform glia to functional neurons, and block oncogenic pathways in glioblastoma. In addition, we wanted to highlight how AI approaches, such as single-cell multi-omics, radiogenomic prediction, development of digital twins, and design of adaptive clinical trials, will increasingly be positioned to act as system-level architects that allow translation of complex datasets into predictive and actionable therapeutic approaches. We examine convergence consumers in spinal cord injury and adaptive neuro-oncology and discuss expanse consumers in ischemic stroke, Alzheimer’s disease, Parkinson’s disease, and rare neurogenetic syndromes. Finally, we discuss the ethical and regulatory landscape around beyond off-target editing and genomic stability of CRISPR, algorithmic bias, explainability, and equitable access to advanced neurotherapies. Our intent was not to provide a comprehensive inventory of possibilities but rather to provide a conceptual tool where CRISPR acts as a molecular manipulator and AI as a computational integrator, converging to create pathways towards precision neuroregeneration, personalized medicine, and adaptive neurotherapeutics that are ethically sound. Full article

Maintaining a high input resolution is crucial for more complex tasks like detection or segmentation to ensure that models can adequately identify and reflect fine details in the output. This study aims to reduce the computation costs associated with high-resolution input by using a variant of transformer, known as the Adaptive Clustering Transformer (ACT). The proposed model is named ACT-FRCNN. Which integrates ACT with a Faster Region-Based Convolution Neural Network (FRCNN) for a detection task head. In this paper, we proposed a method to improve the detection framework, resulting in better performance for out-of-domain images, improved object identification, and reduced dependence on non-maximum suppression. The ACT-FRCNN represents a significant step in the application of transformer models to challenging visual tasks like object detection, laying the foundation for future work using transformer models. The performance of ACT-FRCNN was evaluated on a variety of well-known datasets including BSDS500, NYUDv2, and COCO. The results indicate that ACT-FRCNN reduces over-detection errors and improves the detection of large objects. The findings from this research have practical implications for object detection and other computer vision tasks. Full article

In this study, we propose an optimal cluster expansion-based intrusion-tolerant system (ITS) that can maintain quality of service (QoS) under a massive denial of service (DoS) attack. Our proposed scheme conserves resources while maintaining good QoS by optimally increasing and decreasing cluster size. To evaluate the performance of the proposed scheme, we use a CloudSim simulator and compare our proposed scheme with an existing conventional adaptive cluster transformation (ACT) scheme. Our simulation results show that the proposed scheme outperforms the conventional ACT scheme in terms of better QoS and lower resource consumption. Full article