Express-Evaluation of Industrial Enterprises Operations in Moscow with Universal Composite Indicator

Recent years were a transformation period of the analytic systems to support management decision-making on continuously available open data away from official periodic reports. In this regard, the system of control and supervision of management objects by the state controlling bodies is changing, new sources of information are included; monitoring of the external environment and media space is introduced. The author proposes an approach to the formation of a generalized key indicator for rapid assessment of the object of management (on the example of an industrial enterprise) on the basis of open data from the Internet. The object of the research is developing universal comprehensive indicator for rapid assessment of the compliance of the economic object of management on the part of regulators or relevant services on the basis of structured and unstructured data from the Internet. Scientific novelty of the study is to propose the concept of building a universal comprehensive indicator (UCI) based on a logical function that uses an extended set of arguments, including both continuous and discrete variables. Transformation into the values of the indicator is proposed using the logical rules, given the requirements for the control object from the regulators. Main results of the work: the concept of constructing universal comprehensive indicator allowing to get an express assessment of the state of the object in control was developed. The algorithm was tested to assess the need and feasibility for the state authorities in the financial assistance of the Moscow industrial enterprise. The approach in this research is applicable to current monitoring of the situation due to official reporting at the tactical level of management.

MVMOO: Mixed variable multi-objective optimisation

In many real-world problems there is often the requirement to optimise multiple conflicting objectives in an efficient manner. In such problems there can be the requirement to optimise a mixture of continuous and discrete variables. Herein, we propose a new multi-objective algorithm capable of optimising both continuous and discrete bounded variables in an efficient manner. The algorithm utilises Gaussian processes as surrogates in combination with a novel distance metric based upon Gower similarity. The MVMOO algorithm was compared to an existing mixed variable implementation of NSGA-II and random sampling for three test problems. MVMOO shows competitive performance on all proposed problems with efficient data acquisition and approximation of the Pareto fronts for the selected test problems.

Attitudes, beliefs and behaviors of religiosity, spirituality, and cultural competence in the medical profession: A cross-sectional survey study

Introduction Religion and spirituality play important roles in the lives of many, including healthcare providers and their patients. The purpose of this study was to examine the relationships between religion, spirituality, and cultural competence of healthcare providers. Methods Physicians, residents, and medical students were recruited through social platforms to complete an electronically delivered survey, gathering data regarding demographics, cultural competency, religiosity, and spirituality. Four composite variables were created to categorize cultural competency: Patient Care Knowledge, Patient Care Skills/Abilities, Professional Interactions, and Systems Level Interactions. Study participants (n = 144) were grouped as Christian (n = 95)/non-Christian (n = 49) and highly religious (n = 62)/not highly religious (n = 82); each group received a score in the four categories. Wilcoxon rank sum and Chi-square tests were used for analysis of continuous and discrete variables. Results A total of 144 individuals completed the survey with the majority having completed medical school (n = 87), identifying as women (n = 108), white (n = 85), Christian (n = 95), and not highly religious (n = 82). There were no significant differences amongst Christian versus non-Christian groups or highly religious versus not highly religious groups when comparing their patient care knowledge (p = .563, p = .457), skills/abilities (p = .423, p = .51), professional interactions (p = .191, p = .439), or systems level interaction scores (p = .809, p = .078). Nevertheless, participants reported decreased knowledge of different healing traditions (90%) and decreased skills inquiring about religious/spiritual and cultural beliefs that may affect patient care (91% and 88%). Providers also reported rarely referring patients to religious services (86%). Conclusions Although this study demonstrated no significant impact of healthcare providers’ religious/spiritual beliefs on the ability to deliver culturally competent care, it did reveal gaps around how religion and spirituality interact with health and healthcare. This suggests a need for improved cultural competence education.

Bayesian Analysis of Finite Populations under Simple Random Sampling

Statistical methods to produce inferences based on samples from finite populations have been available for at least 70 years. Topics such as Survey Sampling and Sampling Theory have become part of the mainstream of the statistical methodology. A wide variety of sampling schemes as well as estimators are now part of the statistical folklore. On the other hand, while the Bayesian approach is now a well-established paradigm with implications in almost every field of the statistical arena, there does not seem to exist a conventional procedure—able to deal with both continuous and discrete variables—that can be used as a kind of default for Bayesian survey sampling, even in the simple random sampling case. In this paper, the Bayesian analysis of samples from finite populations is discussed, its relationship with the notion of superpopulation is reviewed, and a nonparametric approach is proposed. Our proposal can produce inferences for population quantiles and similar quantities of interest in the same way as for population means and totals. Moreover, it can provide results relatively quickly, which may prove crucial in certain contexts such as the analysis of quick counts in electoral settings.

Integrating Modelling of Maintenance Policies within a Stochastic Hybrid Automaton Framework of Dynamic Reliability

The dependability assessment is a crucial activity for determining the availability, safety and maintainability of a system and establishing the best mitigation measures to prevent serious flaws and process interruptions. One of the most promising methodologies for the analysis of complex systems is Dynamic Reliability (also known as DPRA) with models that define explicitly the interactions between components and variables. Among the mathematical techniques of DPRA, Stochastic Hybrid Automaton (SHA) has been used to model systems characterized by continuous and discrete variables. Recently, a DPRA-oriented SHA modelling formalism, known as Stochastic Hybrid Fault Tree Automaton (SHyFTA), has been formalized together with a software library (SHyFTOO) that simplifies the resolution of complex models. At the state of the art, SHyFTOO allows analyzing the dependability of multistate repairable systems characterized by a reactive maintenance policy. Exploiting the flexibility of SHyFTA, this paper aims to extend the tools’ functionalities to other well-known maintenance policies. To achieve this goal, the main features of the preventive, risk-based and condition-based maintenance policies will be analyzed and used to design a software model to integrate into the SHyFTOO. Finally, a case study to test and compare the results of the different maintenance policies will be illustrated.

Rifampin-resistant/multidrug-resistant Tuberculosis in Alberta, Canada: Epidemiology and treatment outcomes in a low-incidence setting

Treatment of rifampin-monoresistant/multidrug-resistant Tuberculosis (RR/MDR-TB) requires long treatment courses, complicated by frequent adverse events and low success rates. Incidence of RR/MDR-TB in Canada is low and treatment practices are variable due to the infrequent experience and challenges with drug access. We undertook a retrospective cohort study of all RR/MDR-TB cases in Alberta, Canada from 2007–2017 to explore the epidemiology and outcomes in our low incidence setting. We performed a descriptive analysis of the epidemiology, treatment regimens and associated outcomes, calculating differences in continuous and discrete variables using Student’s t and Chi-squared tests, respectively. We identified 24 patients with RR/MDR-TB. All patients were foreign-born with the median time to presentation after immigration being 3 years. Prior treatment was reported in 46%. Treatment was individualized. All patients achieved sputum culture conversion within two months of treatment initiation. The median treatment duration after culture conversion was 18 months (IQR: 15–19). The mean number of drugs utilized during the intensive phase was 4.3 (SD: 0.8) and during the continuation phase was 3.3 (SD: 0.9) and the mean adherence to medications was 95%. Six patients completed national guideline-concordant therapy, with many patients developing adverse events (79%). Treatment success (defined as completion of prescribed therapy or cure) was achieved in 23/24 patients and no acquired drug resistance or relapse was detected over 1.8 years of median follow-up. Many cases were captured upon immigration assessment, representing important prevention of community spread. Despite high rates of adverse events and short treatment compared to international guidelines, success in our cohort was very high at 96%. This is likely due to individualization of therapy, frequent use of medications with high effectiveness, intensive treatment support, and early sputum conversion seen in our cohort. There should be ongoing exploration of treatment shortening with well-tolerated, efficacious oral agents to help patients achieve treatment completion.

Development of a Machine Learning Algorithm for Prediction of Complications and Unplanned Readmission Following Reverse Total Shoulder Arthroplasty

Background Reverse total shoulder arthroplasty (rTSA) offers tremendous promise for the treatment of complex pathologies beyond the scope of anatomic total shoulder arthroplasty but is associated with a higher rate of major postoperative complications. We aimed to design and validate a machine learning (ML) model to predict major postoperative complications or readmission following rTSA. Methods We retrospectively reviewed California's Office of Statewide Health Planning and Development database for patients who underwent rTSA between 2015 and 2017. We implemented logistic regression (LR), extreme gradient boosting (XGBoost), gradient boosting machines, adaptive boosting, and random forest classifiers in Python and trained these models using 64 binary, continuous, and discrete variables to predict the occurrence of at least one major postoperative complication or readmission following primary rTSA. Models were validated using the standard metrics of area under the receiver operating characteristic (AUROC) curve, area under the precision–recall curve (AUPRC), and Brier scores. The key factors for the top-performing model were determined. Results Of 2799 rTSAs performed during the study period, 152 patients (5%) had at least 1 major postoperative complication or 30-day readmission. XGBoost had the highest AUROC and AUPRC of 0.681 and 0.129, respectively. The key predictive features in this model were patients with a history of implant complications, protein-calorie malnutrition, and a higher number of comorbidities. Conclusion Our study reports an ML model for the prediction of major complications or 30-day readmission following rTSA. XGBoost outperformed traditional LR models and also identified key predictive features of complications and readmission.

Technical and economic optimization of natural gas transmission network operation to balance node delivery flow rate and operation cost

Natural gas transmission network is the major facility connecting the upstream gas sources and downstream consumers. In this paper, a multi-objective optimization model is built to find the optimum operation scheme of the natural gas transmission network. This model aims to balance two conflicting optimization objective named maximum a specified node delivery flow rate and minimum compressor station power consumption cost. The decision variables involve continuous and discrete variables, including node delivery flow rate, number of running compressors and their rotational speed. Besides, a series of equality and inequality constraints for nodes, pipelines and compressor stations are introduced to control the optimization results. Then, the developed optimization model is applied to a practical large tree-topology gas transmission network, which is 2,229 km in length with 7 compressor stations, 2 gas injection nodes and 20 gas delivery nodes. The ɛ-constraint method and GAMS/DICOPT solver are adopted to solve the bi-objective optimization model. The optimization result obtained is a set of Pareto optimal solutions. To verify the validity of the proposed method, the optimization results are compared with the actual operation scheme. Through the comparison of different Pareto optimal solutions, the variation law of objective functions and decision variables between different optimal solutions are clarified. Finally, sensitivity analyses are also performed to determine the influence of operating parameter changes on the optimization results.

Global Optimization for Mixed–Discrete Structural Design

Several structural design problems that involve continuous and discrete variables are very challenging because of the combinatorial and non-convex characteristics of the problems. Although the deterministic optimization approach theoretically guarantees to find the global optimum, it usually leads to a significant burden in computational time. This article studies the deterministic approach for globally solving mixed–discrete structural optimization problems. An improved method that symmetrically reduces the number of constraints for linearly expressing signomial terms with pure discrete variables is applied to significantly enhance the computational efficiency of obtaining the exact global optimum of the mixed–discrete structural design problem. Numerical experiments of solving the stepped cantilever beam design problem and the pressure vessel design problem are conducted to show the efficiency and effectiveness of the presented approach. Compared with existing methods, this study introduces fewer convex terms and constraints for transforming the mixed–discrete structural problem and uses much less computational time for solving the reformulated problem to global optimality.

Moth flame optimizer-based solution approach for unit commitment and generation scheduling problem of electric power system

This paper proposes a new approach based on the moth flame optimizer algorithm. Moth flame optimizer simulates the natural fervent navigation technique adopted by moths looking for a source of light. The proposed method is further improved by priority list-based ordering; the unit commitment problem (UCP) is a non-linear, non-convex, and combinatorial complex optimization problem. It contains both continuous and discrete variables. This further increases its complexity. Moth flame optimizer is very good at obtaining a commitment pattern: allocation of power on the committed units obtained by mixed-integer quadratic programming method. Heuristic search strategies are used to adopt for the repair of minimum up and downtime, and spinning reserve constraints. MFO effectiveness is tested on the standard UCP reference IEEE model buses 14 and 30, and 10 and 20 units. The efficiency of the projected algorithms is compared to classical PSO, PSOLR, HPSO, PSOSQP, hybrid MPSO, IBPSO, LCA-PSO, NPSO, PSO-GWO, and various other evolutionary algorithms. The comparison result shows that MFO can lead to all methods reported earlier in literature.