Abstract
In the modern digital age, organizations face unprecedented challenges and possibilities while managing the intricacies of digital transformation. Accelerated technological developments, changing customer preferences, heightened competition, and dynamic regulatory environments necessitate companies to synchronize their business goals with technological innovations. Leadership is crucial in steering businesses through changes, requiring a deep understanding of change processes and the capacity to adjust leadership accordingly. This research addresses the central question: How does leadership effectively promote organizational digital transformation? The study examines how leaders can effectively promote the adoption of advanced technologies and the promotion of innovation, by first exploring the nature of digital transformation within organizations and then analyzing the evolving dynamics of leadership in this context. An integrative review of the Web of Science (WoS) and Scopus databases was conducted, using the search terms: Leadership and Digital Transformation. The findings emphasize that effective leadership is crucial for managing the minutiae of digital transformation, integrating technology into organizational processes to facilitate learning, collaboration, and agility, enabling companies to adapt to market shifts, reduce uncertainty, and enhance decision-making for sustainable growth. By using the right tools and with the right frequency, leaders may develop team cohesion-even at a distance. Attentive digital-age leaders will know how to leverage the right mechanisms, and herein, we hope to give some indication of how that may be achieved, so that digital transformation increases rather than decreases team motivation levels.

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The increasing global burden of lung diseases necessitates the development of improved diagnostic tools. According to the WHO, hundreds of millions of individuals worldwide are currently affected by various forms of lung disease. The rapid advancement of artificial neural networks has revolutionized lung disease diagnosis, enabling the development of highly effective detection and classification systems. This article presents dual channel neural networks in image feature extraction based on classical CNN and vision transformers for multi-label lung disease diagnosis. Two separate subnetworks are employed to capture both global and local feature representations, thereby facilitating the extraction of more informative and discriminative image features. The global network analyzes all-organ regions, while the local network simultaneously focuses on multiple single-organ regions. We then apply a novel feature fusion operation, leveraging a multi-head attention mechanism to weight global features according to the significance of localized features. Through this multi-channel approach, the framework is designed to identify complicated and subtle features within images, which often go unnoticed by the human eye. Evaluation on the ChestX-ray14 benchmark dataset demonstrates that our hybrid model consistently outperforms established state-of-the-art architectures, including ResNet-50, DenseNet-121, and CheXNet, by achieving significantly higher AUC scores across multiple thoracic disease classification tasks. By incorporating test-time augmentation, the model achieved an average accuracy of 95.7% and a specificity of 99%. The experimental findings indicated that our model attained an average testing AUC of 87%. In addition, our method tackles a more practical clinical problem, and preliminary results suggest its feasibility and effectiveness. It could assist clinicians in making timely decisions about lung diseases.

Abstract
Port wine stocks ameliorate during storage, facilitating product differentiation according to age. This induces a trade-off between immediate revenues and further maturation. Varying climate conditions in the limited supply region lead to stochastic purchase prices for wine grapes. Decision makers must integrate recurring purchasing, production, and issuance decisions. Because stocks from different age classes can be blended to create final products, the solution space increases exponentially in the number of age classes. We model the problem of managing port wine inventory as a Markov decision process, considering decay as an additional source of uncertainty. For small problems, we derive general management strategies from the long-run behavior of the optimal policy. Our solution approach for otherwise intractable large problems, therefore, first aggregates age classes to create a tractable problem representation. We then use machine learning to train tree-based decision rules that reproduce the optimal aggregated policy and the enclosed management strategies. The derived rules are scaled back to solve the original problem. Learning from the aggregated optimum outperforms benchmark rules by 21.4% in annual profits (while leaving a 2.8%-gap to an upper bound). For an industry case, we obtain a 17.4%-improvement over current practices. Our research provides distinct strategies for how producers can mitigate climate risks. The purchasing policy dynamically adapts to climate-dependent price fluctuations. Uncertainties are met with lower production of younger products, whereas strategic surpluses of older stocks ensure high production of older products. Moreover, a wide spread in the age classes used for blending reduces decay risk exposure.

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