FFECTIVE USE OF PREVENTIVE DEVELOPMENT RESERVES OF THE PRODUCTION ORGANIZATION DURING DIGITALIZATION

Введение. Современные предприятия серийного машино- и приборостроения, их цеха и участки харктеризуются расширением номеклатуры, высокой скоростью её обновления, развитием цифровизации, совмещением виртуальных процессов с реальными, изменением условий взаимной адаптации производства и продукции, обоснованием снижения потенциальных ошибок и потерь при переходе к лучшему варианту развития и др. Необходимость учета этих факторов объективно обусловливает поиск новых методов повышения эффективности организации действующего производства. Данные и методы. Представлен комплексный подход к развитию организации серийного производства участков и цехов в условиях цифровизации неразрывно связан с мобилизацией внутренних резервов, моделированием параметров парности, виртуальных кластеров, эмерджентности, упреждения, взаимодействия ресурсов, снижения напряженности, согласования перестройки с подстройкой и др. Полученные результаты. Рассмотрено использование задач, входящих в концепцию планирования эффективного развития, включающую совокупность логически вытекающих одно из другого решений, которые ассоциируются с применением системы моделей, необходимых пояснений по работе с ними и конкретной последовательности шагов по реализации. В неё включены модели учета упреждения, локальной оптимизации, взаимодействия резервов, кластеризации, сближения виртуальной среды с реальной. Заключение. Использование рассмотренного подхода и методов оценки перехода от виртуальных кластеров организации к реальному производству в условиях снижения стабильности заказов позволяют в режиме on-line по-новому планировать использование резервов опережающего адаптивного развития эффективной организации производства участков и цехов предприятий. Благодарность проф. В. Димитрову, д-ру А. Бахмутскому и проф. А. Колосову за обсуждение материала статьи Introduction Modern enterprises of serial machine-building and instrument-making, their workshops and sections are characterized by: expansion of the nomenclature, high speed of its updating, development of digitalization, combining virtual processes with real ones, changing conditions for mutual adaptation of production and products, justification of reducing potential errors and losses during the transition to the best variant of development, etc. The need to take into account such factors objectively determines the search for new methods to increase the efficiency of organizing existing production. Data and Methods. An integrated approach to the development of the organization of serial production of sites and workshops in the context of digitalization is presented, which is inextricably linked with the mobilization of internal reserves, modeling of paired parameters, virtual clusters, emergence, anticipation, resource interaction, tension reduction, reconciliation of restructuring with adjustment, etc. Results Obtained. The use of tasks included in the concept of effective development planning, including a set of decisions that logically follow one from the other, which are associated with the use of a system of models, the necessary explanations for working with them and a specific sequence of steps for implementation, are considered. It includes models: accounting for anticipation, local optimization, interaction of reserves, clustering, convergence of the virtual environment with the real one. Conclusion. The use of the considered approach and methods for assessing the transition from virtual clusters of an organization to real production in conditions of decreasing order stability allows on-line to plan in a new way the use of the reserves of proactive adaptive development of the effective organization of production of sites and workshops of enterprises

Can Existing Improvements of Water, Sanitation, and Hygiene (WASH) in Urban Slums Reduce the Burden of Typhoid Fever in These Settings?

Background Sustained investments in water, sanitation, and hygiene (WASH) have lagged in resource-poor settings; incremental WASH improvements may, nonetheless, prevent diseases such as typhoid in disease-endemic populations. Methods Using prospective data from a large cohort in urban Kolkata, India, we evaluated whether baseline WASH variables predicted typhoid risk in a training subpopulation (n = 28 470). We applied a machine learning algorithm to the training subset to create a composite, dichotomous (good, not good) WASH variable based on 4 variables, and evaluated sensitivity and specificity of this variable in a validation subset (n = 28 470). We evaluated in Cox regression models whether residents of “good” WASH households experienced a lower typhoid risk after controlling for potential confounders. We constructed virtual clusters (radius 50 m) surrounding each household to evaluate whether a prevalence of good WASH practices modified the typhoid risk in central household members. Results Good WASH practices were associated with protection in analyses of all households (hazard ratio [HR] = 0.57; 95% confidence interval [CI], .37–.90; P = .015). This protection was evident in persons ≥5 years old at baseline (HR = 0.47; 95% CI, .34–.93; P = .005) and was suggestive, though not statistically significant, in younger age groups (HR = 0.61; 95% CI, .27–1.38; P = .235). The level of surrounding household good WASH coverage was also associated with protection (HR = 0.988; 95% CI, .979–.996; P = .004, for each percent coverage increase). However, collinearity between household WASH and WASH coverage prevented an assessment of their independent predictive contributions. Conclusions In this typhoid-endemic setting, natural variation in household WASH was associated with typhoid risk. If replicated elsewhere, these findings suggest that WASH improvements may enhance typhoid control, short of major infrastructural investments.

Constructing Reliable Computing Environments on Top of Amazon EC2 Spot Instances

Cloud provider Amazon Elastic Compute Cloud (EC2) gives access to resources in the form of virtual servers, also known as instances. EC2 spot instances (SIs) offer spare computational capacity at steep discounts compared to reliable and fixed price on-demand instances. The drawback, however, is that the delay in acquiring spots can be incredible high. Moreover, SIs may not always be available as they can be reclaimed by EC2 at any given time, with a two-minute interruption notice. In this paper, we propose a multi-workflow scheduling algorithm, allied with a container migration-based mechanism, to dynamically construct and readjust virtual clusters on top of non-reserved EC2 pricing model instances. Our solution leverages recent findings on performance and behavior characteristics of EC2 spots. We conducted simulations by submitting real-life workflow applications, constrained by user-defined deadline and budget quality of service (QoS) parameters. The results indicate that our solution improves the rate of completed tasks by almost 20%, and the rate of completed workflows by at least 30%, compared with other state-of-the-art algorithms, for a worse-case scenario.

Large-scale HPC deployment of Scalable CyberInfrastructure for Artificial Intelligence and Likelihood Free Inference (SCAILFIN)

The NSF-funded Scalable CyberInfrastructure for Artificial Intelligence and Likelihood Free Inference (SCAILFIN) project aims to develop and deploy artificial intelligence (AI) and likelihood-free inference (LFI) techniques and software using scalable cyberinfrastructure (CI) built on top of existing CI elements. Specifically, the project has extended the CERN-based REANA framework, a cloud-based data analysis platform deployed on top of Kubernetes clusters that was originally designed to enable analysis reusability and reproducibility. REANA is capable of orchestrating extremely complicated multi-step workflows, and uses Kubernetes clusters both for scheduling and distributing container-based workloads across a cluster of available machines, as well as instantiating and monitoring the concrete workloads themselves. This work describes the challenges and development efforts involved in extending REANA and the components that were developed in order to enable large scale deployment on High Performance Computing (HPC) resources. Using the Virtual Clusters for Community Computation (VC3) infrastructure as a starting point, we implemented REANA to work with a number of differing workload managers, including both high performance and high throughput, while simultaneously removing REANA’s dependence on Kubernetes support at the workers level.