Network flow problems form an important and much‐studied family of combinatorial optimization problems, with a huge array of practical applications. Two network flow problems in particular have received a great deal of attention: the maximum flow and minimum‐cost flow problems. We review the progress that has been made on exact solution algorithms for these two problems, with an emphasis on worst‐case running times.

The effective management of medical records is essential in the ordinary and emergency operations of healthcare providers. This work uses blockchain to develop a smart contract algorithm for users of a medical record platform. This algorithm provides immutable execution and addresses authentication and reliability issues to control access to healthcare platforms. An executable distributed code is used to build the smart contract algorithm. In the proposed algorithm, management operations of the clinical history are carried out and integrated in an automated way in a distributed environment. Solidity is the programming language used to create the algorithm for a private and permissioned architecture with a proposed consensus algorithm requiring significantly less computational power using a 22% faster hash function.

Parking occupancy is difficult in most modern cities because of increases in the accessibility and use of motor vehicles, and users generally take several minutes or even hours to find a place to park. In this work, we propose a smart parking prediction model in order to help users locate in advance the availability of parking near the places they plan to visit. For this it is proposed a fog computing architecture that integrates a machine learning algorithm based on AdaBoost to predict parking places hours or days in advance. Additionally, a user interface was developed, which involves the collection of user inputs through a mobile application where the user is prompted to enter the destination location and the prediction time interval. Through extensive experimentation using real-world parking flow data, our proposed algorithm demonstrated an improved level of accuracy compared with alternative prediction methods. Moreover, a simulation was conducted to evaluate the system’s latency when using cloud computing versus our hybrid approach combining both fog and cloud computing. The results showed that employing the fog module in conjunction with cloud computing significantly reduced response delay in comparison with using cloud computing alone.

Background: Efficient inventory management is critical for sustainability in supply chains. However, maintaining adequate inventory levels becomes challenging in the face of unpredictable demand patterns. Furthermore, the need to disseminate demand-related information throughout a company often relies on cloud services. However, this method sometimes encounters issues such as limited bandwidth and increased latency. Methods: To address these challenges, our study introduces a system that incorporates a machine learning algorithm to address inventory-related uncertainties arising from demand fluctuations. Our approach involves the use of an attention mechanism for accurate demand prediction. We combine it with the Newsvendor model to determine optimal inventory levels. The system is integrated with fog computing to facilitate the rapid dissemination of information throughout the company. Results: In experiments, we compare the proposed system with the conventional demand estimation approach based on historical data and observe that the proposed system consistently outperformed the conventional approach. Conclusions: This research introduces an inventory management system based on a novel deep learning architecture that integrates the attention mechanism with cloud computing to address the Newsvendor problem. Experiments demonstrate the better accuracy of this system in comparison to existing methods. More studies should be conducted to explore its applicability to other demand modeling scenarios.