A practical case: features of the autoimmune insulin syndrome diagnosis

Autoimmune Insulin syndrome (AIS), also named Hirata disease, is a rare condition characterized by hypoglycemic episodes due to the presence of high titers of insulin autoantibodies (IAA). AIS is a form of immune-mediated hypoglycemia, which develops when a triggering factor (medication or a viral infection) acts on an underlying predisposing genetic background. The diagnosis of IAS is challenging, requiring a careful workup aimed at excluding other causes of hyperinsulinemic hypoglucemia. The article presents a case of Hirata disease diagnosing of a patient of the therapy department of the Multidisciplinary Medical Center of the Bank of Russia.Purpose: Determining of the features of the diagnosis of Autoimmune Insulin syndrome (Hirata disease)Materials and methods: the article presents a clinical case of the diagnosis of Autoimmune insulin syndrome (Hirata disease), focuses on the diagnostic significance of laboratory tests. The article is intended for general practitioners, endocrinologists, gastroenterologists.

The Digitalization in the COVID-19 Era

In December 2019, a new viral pneumonia called COVID-19 appeared in Wuhan, China causing a health crisis that then turned into an economic and social crisis. Since then, entire sectors of the economy have been slowing down, forcing them to integrate innovative digital processes to mitigate the negative impact of this crisis. In this regard, the purpose of the chapter is to initiate a reflection on digitization processes based on a review of the literature to help explain the concept. This work allowed spotlighting the existing research on digitalization through practical case studies in business, education, and health systems, and how organizations perceive digitalization during the COVID-19 pandemic. The authors analyze how digital platforms can increase the survival rate of businesses in times of crisis and ensure the continuity of customers' access to the various goods and services. The purpose behind this is to serve as a basis for further empirical research for academics and improve the knowledge base by providing insight into the efforts of the sectors studied.

Knowledge extract and ontology construction method of assembly process text

The actual product assembly process mainly relies on manual assembly by workers, and the personal experience of workers is difficult to effectively reuse. Ontology as a knowledge management and expression tool is gradually applied in the field of assembly. However, the manual construction of the ontology is time-consuming and labor-intensive, and the automatic construction of the ontology requires a large number of corpora for training, both of which are difficult to obtain a good assembly case ontology. This paper proposes a method in which automatically extracts relevant knowledge from case assembly process files to generates case database and integrates ontology framework of assembly domain to construct ontology. It shows that the accuracy can be guaranteed on the basis of the rapid construction of case ontology. The feasibility of this method is proved by a practical case.

Bim Machine Learning and Design Rules to Improve the Assembly Time in Steel Construction Projects

Integrating the knowledge and experience of fabrication during the design phase can help reduce the cost and duration of steel construction projects. Building Information Modeling (BIM) are technologies and processes that reduce the cost and duration of construction projects by integrating parametric digital models as support of information. These models can contain information about the performance of previous projects and allow a classification by linear regression of design criteria with a high impact on the duration of the fabrication. This paper proposes a quantitative approach that applies linear regressions on previous projects’ BIM models to identify some design rules and production improvement points. A case study applied on 55,444 BIM models of steel joists validates this approach. This case study shows that the camber, the weight of the structure, and its reinforced elements greatly influence the fabrication time of the joists. The approach developed in this article is a practical case where machine learning and BIM models are used rather than interviews with professionals to identify knowledge related to a given steel structure fabrication system.

Online Adaptive PID Control for a Multi-Joint Lower Extremity Exoskeleton System Using Improved Particle Swarm Optimization

Robotic exoskeletons have great potential in the medical rehabilitation and augmentation of human performance in a variety of tasks. Proposing effective and adaptive control strategies is one of the most challenging issues for exoskeleton systems to work interactively with the user in dynamic environments and variable tasks. This research, therefore, aims to advance the state of the art of the exoskeleton adaptive control by integrating the excellent search capability of metaheuristic algorithms with the PID feedback mechanism. Specifically, this paper proposes an online adaptive PID controller for a multi-joint lower extremity exoskeleton system by making use of the particle swarm optimization (PSO) algorithm. Significant improvements, including a ‘leaving and re-searching mechanism’, are introduced into the PSO algorithm for better and faster update of the solution and to prevent premature convergence. In this research, a 9-DOF lower extremity exoskeleton with seven controllable joints is adopted as a test-bench, whose first-principle dynamic model is developed, which includes as many uncertain factors as possible for generality, including human–exoskeleton interactions, environmental forces, and joint unilateral constraint forces. Based upon this, to illustrate the effectiveness of the proposed controller, the human–exoskeleton coupled system is simulated in four characteristic scenarios, in which the following factors are considered: exoskeleton parameter perturbations, human effects, walking terrain switches, and walking speed variations. The results indicate that the proposed controller is superior to the standard PSO algorithm and the conventional PID controller in achieving rapid convergence, suppressing the undesired chattering of PID gains, adaptively adjusting PID coefficients when internal or external disturbances are encountered, and improving tracking accuracy in both position and velocity. We also demonstrate that the proposed controller could be used to switch the working mode of the exoskeleton for either performance or an energy-saving consideration. Overall, aiming at a multi-joint lower extremity exoskeleton system, this research proposes a PSO-based online adaptive PID controller that can be easily implemented in applications. Through rich and practical case studies, the excellent anti-interference capability and environment/task adaptivity of the controller are exemplified.

Modeling a 3G Mobile Phone base Radio using Artificial Intelligence Techniques

The main objective of this work is to be able to use artificial intelligence techniques to be able to design a predictive model of the performance of a third-generation mobile phone base radio, using the analysis of KPIs obtained in a statistical data set of the daily behaviour of an RBS. For the realization of these models, various techniques such as Decision Trees, Neural Networks and Random Forest were used. which will allow faster progress in the deep analysis of large amounts of data statistics and get better results. In this part of the work, data was obtained from the behaviour of a third-party mobile phone base radio generation of the Claro operator in Ecuador, it should be noted that. The data are KPIs of the daily and hourly performance of a radio base of third generation mobile telephony, these data were obtained through the operator's remote monitoring and management tool Sure call PRS. To specify this practical case, several models were generated based on in various artificial intelligence technique for the prediction of performance results of a mobile phone base radio of third generation, the same ones that after several tests were creation of a predictive model that determines the performance of a mobile phone base radio. As a conclusion of this work, it was determined that the development of a predictive model based on artificial intelligence techniques is very useful for the analysis of large amounts of data in order to find or predict complex results, more quickly and trustworthy.

Optimizing Locations of Primary Schools in Rural Areas of China

Scientific location selection of schools is an important way to optimize the allocation of educational resources, improve the efficiency of operating schools, and realize the balanced development of education, especially in rural areas. Many studies have considered the location of schools, but most have omitted the impact of transportation network conditions and the time cost differences caused by different travel speeds under different road conditions. The object of this study is to minimize the total transportation costs for students, construction costs for new schools, and the construction and upgrading costs for roads on a traffic network with travel time uncertainty indicated by different travel time scenarios. A mixed-integer programming model for this problem was proposed. Furthermore, a hybrid simulated annealing algorithm was used to solve the problem. Finally, a practical case study was used to illustrate the application of the proposed mathematical model. The results showed that the traffic network has an important influence on the optimization location of rural schools, and the improvement of traffic network conditions can greatly reduce the time required for students to travel to school.

Numerical Simulation of Two-Phase Water-Oil Flow in a Horizontal Pipe Using the Smoothed Particle Hydrodynamics Method

In this work, the numerical code DualPhysics, based on the Lagrangian particle and mesh free method Smoothed Particle Hydrodynamics, has been employed to solve the slightly compressible isothermal two-phase water-oil flow. The continuity and momentum equations are solved, and we used the modified Tait equation of state to determine the pressure. To validate the numerical code, we solved the modified Couette flow of two fluids. As a practical case, we solved the isothermal and two-dimensional two-phase water-oil flow. The mixing of the fluids occurs after passing through a 45 degree Y junction placed at the entrance of the horizontal pipeline. Results showed the potential for using the numerical code, although some modifications and alterations are still necessary to solve practical problems.