System Performance Indicators Research Articles

Performance analysis of different control models for smart demand–supply energy management system

Several features of innovative grid technologies can be deployed to improve the overall performance of the power system environment. This can be seen from the generation to the consumption of energy. The two-way communication of smart metering introduces the novel functionalities of the energy management system. This paper presents a practical implementation of using the intelligent metering system. It consists of implementing a nanogrid that optimally coordinates the energy from the solar panel, battery storage and utility grid to supply the end user. The developed model is validated with an optimal value of the state of charge of the distributed energy storage to maximise energy from the solar panel and battery storage while minimising the power received from the utility grid. A demand response scheme is employed to formulate the performance index of the energy management system using three optimal control models: adaptive open-loop control, adaptive closed-loop control and model predictive control schemes. The formulation of the performance index of each approach is a function of the energy flow from different resources depending on the power consumption. The three models have given different insights into the performance of the smart nanogrid, which may be used to the advantage of the grid owner or end user. Through the performance of the optimal strategies, it can be observed that energy management is ensured, and real-time monitoring of the entire system is guaranteed. The performance models facilitate the minimisation of the power from the utility, resulting in savings between 23.7% and 39.240% of the total energy demand from the end user. Besides, the system design is validated by an electrical system to form a real-world innovative nanogrid application in residential environments.

УДОСКОНАЛЕННЯ КОНЦЕПТУАЛЬНИХ ЗАСАД ЄВРОІНТЕГРАЦІЇ ТА ФІНАНСОВО-ПРАВОВІ МЕХАНІЗМИ ПРИСКОРЕННЯ ВСТУПУ УКРАЇНИ В ЄС

Introduction. Since 2022, Ukraine has been following the path of “classical accession” to the EU, which involves the compliance with the Copenhagen criteria and adoption of the common EU legislation (acquis) in 33 policy areas/ chapters. But advancement withing this scenario is a long and resource-intensive process. Problem Statement. The urgent task is to find an optimal model of joining the EU with coherence to a reasonable time frame of this course, achieving high results of European integration and identification of the sources of funding for carrying out reforms and overcoming the consequences of the war after reducing the potential of the Ukraine Facility. Purpose - identification of the current financial and legal problems of the European integration process for Ukraine, generalization of the methodological pillars of the concept of staged accession to the EU as a more progressive version over the classic accession model, design of the policy proposals for updating monitoring tools and financial incentives for progressing across the subsequent stages towards the EU membership. Methods. General scientific and special methods are applied: reviews of theoretical sources, advisory and research papers, analysis of statistical data and legal documents, historical method, logical method, methods of comparison and synthesis. Results. The article summarizes the methodological pillars of the concept of staged accession to the EU, systematizes the legal mechanisms of negotiations and obtaining EU membership, shows the financial aspects of the European integration process, including access to the EU cohesion funds, and elaborates practically important proposals for the activity of the EU institutions and national bodies as to the operationalization of the model of staged accession. Conclusions. The staged accession model is the most holistic and logically structured concept that can evolve a predictable progress towards the EU membership. The model should be based on the European Commission's quantitative assessments of countries' preparedness for the membership within the framework of negotiating clusters and chapters. A system of performance indicators is also required that will record progress of the candidate countries across stages and will set the ground for disbursements from the EU funds in line with specified ratios. Since 2028, the new EU medium-term fiscal framework should provide for the formation of new funds to finance the recovery of Ukraine's economy and the application of the EU's cohesion policy towards Ukraine.

Heat flow topology-driven thermo-mass decoupling strategy: Cross-scale regularization modeling and optimization analysis

The liquid desiccant dehumidification (LDD) is both eco-friendly and highly effective in humidity control, widely investigated across diverse angles. This study expands the application of the newly devised heat current method to examine the liquid desiccant dehumidification process. It introduces a new regularized temperature, which includes using a heat flow topology driven thermo-mass decoupling strategy, and constructing the heat current model by defining both the thermal resistance and moisture resistance. It significantly extends the application of the heat current method, clarifying the thermo-mass coupling energy transfer mechanism. The findings indicate that key factors influencing system performance indicators include total thermal conductivity (αA), solution mass flow rate (ms), air-solution mass flow rate ratio (RA/S,D), and heat source temperature (tH). Furthermore, under the influence of distinct thermal conductivities, the optimal output values for performance indicators, including moisture removal rate, exergy efficiency and entransy efficiency, are determined as 8.4 g/s, 71.2%, and 12.3%, respectively. The results of synergistic influence analysis reveal that, for the performance indicator entransy efficiency, msand RA/S,D display a significant degree of synergistic influence, resulting in a strong positive effect and benefiting 68.53% of the total area. Multi-objective optimization indicates a trade-off relationship among the system performance evaluation indicators moisture removal rate, exergy efficiency and entransy efficiency, with optimal solution values of 3.25 g/s, 65.92%, and 12.01%, respectively.

A decision-making approach on control techniques for an inverted pendulum based on, neuro-fuzzy, indirect adaptive and PID controllers

The operation of an inverted pendulum and its respective type of control are affected by the change of the values of its internal parameters. Changes with high uncertainty result in responses with undesirable outputs. In this work, a comparison is presented for the control of an inverted pendulum to determine the operation and characteristics of three types of control systems: Neuro-Fuzzy Control (NFC), Indirect Adaptive Control (IAC) and a Proportional Integral Derivative control (PID). The study considers several indices such as stabilization time, rise time, mean square error, overshoots, convergence, computational load, error, mathematical requirements, and performance indices for control systems. To demonstrate its operation, the controls are implemented in hardware, one for the NFC and another for the IAC under an Arduino UNO platform. The results indicate that the NFC and IAC controls do not generate a transient or impulse response, only a small delay and the rise and stabilization time are minimal. While PID presents a transient response and overshoot, as well as a stabilization time to reach the steady state response.

Impact of early death recording on international comparison of acute myocardial infarction mortality – administrative hospital data study using the example of Germany and the United States

BackgroundIn-hospital mortality from acute myocardial infarction (AMI) is widely used in international comparisons as an indicator of health system performance. Because of the high risk of early death after AMI, international comparisons may be biased by differences in the recording of early death cases in hospital inpatient data. This study examined whether differences in the recording of early deaths affect international comparisons of AMI in-hospital mortality by using the example of Germany and the United States, and explored approaches to address this issue.MethodsThe German Diagnosis-Related Groups Statistics (DRG Statistics), the U.S. National Inpatient Sample (NIS) and the U.S. Nationwide Emergency Department Sample (NEDS) were analysed from 2014 to 2019. Cases with treatment for AMI were identified in German and U.S. inpatient data. AMI deaths occurring in the emergency department (ED) without inpatient admission were extracted from NEDS data. 30-day in-hospital mortality figures were calculated according to the OECD indicator definition (unlinked data) and modified by including ED deaths, or excluding all same-day cases.ResultsGerman age-and-sex standardized 30-day in-hospital mortality was substantially higher compared to the U.S. (in 2019, 7.3% vs. 4.6%). The ratio of German vs. U.S. mortality was 1.6. After inclusion of ED deaths in U.S. data this ratio declined to 1.4. Exclusion of same-day cases in German and U.S. data led to a similar ratio.ConclusionsWhile short-duration treatments due to early death are generally recorded in German inpatient data, in U.S. inpatient data those cases are partially missing. Excluding cases with short-duration treatment from the calculation of mortality indicators could be a feasible approach to account for differences in the recording of early deaths, that might be existent in other countries as well.

Adaptive reinforcement learning-based control using proximal policy optimization and slime mould algorithm with experimental tower crane system validation

This paper presents a novel optimal reference tracking control approach resulted from the combination of a popular policy gradient Reinforcement Learning (RL) algorithm, namely Proximal Policy Optimization (PPO), and a metaheuristic Slime Mould Algorithm (SMA). One of the most important parameters in the PPO-based RL process is the learning rate, which has a big impact on how the parameters of the actor neural network (NN) are iteratively updated. In every episode of the RL process, the weights and the biases of the actor NN are multiplied with the learning rate, determining how much the learning agent will step into a certain direction computed based on previous experiences. The classical PPO algorithm usually relies on fixed values for the learning rates which rarely change, or not at all, during the learning process. However, its main drawback is that the learning agent cannot take advantage of positive momentum in the learning process by accelerating towards good learning experiences or slow down and quickly change the direction in the case of consecutive negative learning experiences. The main objective of the combination proposed in this paper is to create an adaptive SMA-based PPO approach applied to control systems, which instead of using fixed learning rate values, it uses the SMA to compute optimal values of the learning rates in each time step of the learning process based on the progress of the learning agent. This paper investigates if the adaptive SMA-based PPO control approach can be considered as an alternative to the classical PPO version, which employs fixed values of the learning rate. A comparison is carried out using control system performance indices gathered while performing an optimal reference tracking control task on tower crane system laboratory equipment.

A Novel Designation of Solid Oxide Fuel Cell-Integrated System Using LPG as Fuel for Marine Vessels

A Novel Designation of Solid Oxide Fuel Cell-Integrated System Using LPG as Fuel for Marine Vessels

A novel designation of LNG solid oxide fuel cells combined system for marine application

This paper presents a pioneering multigeneration system for marine vessel applications, involving the integration of solid oxide fuel cells (SOFCs) fueled by liquefied natural gas (LNG), coupled with LNG cold energy utilization cycles and exhaust gas heat recovery systems. The system comprises a tandem arrangement of organic Rankine cycle (ORC) and trans-critical carbon dioxide cycle (TCO2), specifically engineered to harness the cold energy released during LNG regasification process. The elevated temperature exhaust gases emanating from the SOFC are efficiently recuperated through a series of components including a gas turbine (GT), regenerative, steam Rankine cycle (SRC), and waste heat boiler (WHB). The proton exchange membrane fuel cells (PEMFC) is designed to enhance the overall operational flexibility and responsiveness of the multigeneration system. The Aspen Hysys V12.1 is used to simulate the propose system and numerical method is employed to parameter optimize of system. The first and second laws of thermodynamics are applied to establish the thermodynamics equations and calculated the system performances. The described system is estimated to obtain energy efficiency of 69.32 % and exergy efficiency of 33.85 %. The high-temperature exhaust gas harvesting integrated systems are sufficiently generated 2091.24 kW which accounted 35.49 % of entire power generate. Furthermore, the parametric researches are implemented to examine the influence of current density, β values to the performance indicators of systems. The WHB is designed to generate 1081 kg/h of superheated vapor at 170 °C and 405 kPa for various purposes of seafarers and equipment's onboard ship. The economic feasibility is performed to view the possibility of investment, maintenance cost and payback period for the described system.

Conjunctive management of groundwater and surface water resources using a hybrid simulation–optimization method

ABSTRACT This study aimed to determine the optimal conjunctive utilization of groundwater and surface water resources in the Halil River basin, one of the most significant study regions in Kerman Province (Iran). Multi-verse optimizer (MVO) and the ANFIS (adaptive neuro-fuzzy inference systems) simulation model, known as the MVO–ANFIS simulation–optimization model, were used for this purpose. Moreover, the optimal exploitation policy for the studied basin was presented. The ANFIS model yielded a coefficient of determination greater than 0.99 in Baft, Rabor, and Jiroft. This model had a high capability to simulate groundwater levels in these three regions. Therefore, the ANFIS model was adopted as the simulation model to predict the aquifer water table in these regions. Regarding the conjunctive utilization of groundwater and surface water resources, the exploitation policy resulting from the MVO–ANFIS simulation–optimization model had a desirable performance by supplying 91.70, 87.75, and 97.58% of the total demands of Baft, Rabor, and Jiroft, respectively. Moreover, results of water system performance indicators, including reliability (82.96, 72.65, 95.07), resiliency (70, 53.47, 80), vulnerability (29.54, 25.64, 17.02), and sustainability (74.24%, 66.10%, 85.78%) in the mentioned regions, respectively, showed the appropriate performance of the proposed model for the simulation–optimization problem.

A networking-economic model to enhance the cultural value in small towns

Purpose The paper aims to provide a decision-support model to ensure a proper use of the limited resources, financial and not, for the enhancement of the cultural heritage and comprehensive development of small towns from sustainable perspective.Design/methodology/approach The assessment model is set up using a multi-criteria method that combines elements of linear planning with a performance indicators system that may represent the complexity of the territory’s cultural identity as a result of existing cultural-historical assets.Findings The model reliability is tested in a case study in a Municipality in southern Italy. The case study’s findings highlight the advantages for the public/private operators, who can consciously choose which preservation and restoration projects to fund while taking into account the effects those decisions will have on the economic, social and environmental context of reference.Research limitations/implications Due to the suggested operational approach and the selection of variables for accounting economic, social and environmental impacts by the renewal project, the research findings may not be generalizable. Therefore, it is recommended that researchers look into the suggested theories in more detail.Practical implications The study offers implications for designing a user-friendly tool to help decision-making processes from a private–public viewpoint in a reasonable allocation of financial resources among investments for cultural property asset enhancement.Originality/value The suggested operational approach provides a reliable information apparatus to depict the decision-making process under small-town development in accordance with sustainability dimensions.

Multi-objective optimization of production system with staggered production

In the manufacturing industry, timely order fulfilment from diverse customers is important for operational profitability, given the constraints of limited production capacity. This paper proposes a novel queueing system for a production system, in which order processing times are categorised as regular or rush based on customer preference. The proposed queueing system employs variable service rates and service strategies in conjunction with staggered production to deal with heterogeneous arrivals. We compute the steady-state probability distribution and develop system performance indicators. The lower and upper bounds of the expected sojourn time for rush and regular orders are derived. Numerical results show that an appropriate switching policy can reduce rush orders by approximately 35% while regular orders increase by less than 10%, demonstrating significant improvement in order fulfilment management. We formulate a multi-objective optimisation problem and implement the NSGA-II algorithm to obtain Pareto optimal solutions. Finally, three regression models are proposed to determine the minimum cost and the associated optimal service rates, given the maximum acceptable value of the expected number of rush orders. The established models can explain at least 80% of the dataset of the optimal solutions and thus our numerical findings provide useful guidance for decision-makers and production managers.

Decomposition of strategic goals and objectives of the Samara region dental

Aim to carry out a scientifically based decomposition of the strategic goals and objectives of the dental service of the Samara region, to determine the necessary basic transformations and opportunities for standardization of the industry model of the separation of powers and responsibilities at various hierarchical levels of management.
 Material and methods. When preparing the article, the conceptual provisions of the National Project, Federal projects, materials of reports on the activities of the dental service of the Samara region in the period 2020 2022 were used. The decomposition was carried out using the main tools of strategic, programtarget and process management; requirements of TQM and PDCA; principles of management by goals, building a balanced system of indicators and key performance indicators.
 Results. The practical task of decomposition of strategic goals and objectives of the development of the dental service of the Samara region has been solved, taking into account the strategic goals of the healthcare of the Russian Federation, a multidimensional decomposition of strategic goals and objectives of the dental service of the region has been carried out; a list of necessary basic transformations with the separation of powers and responsibilities has been determined.

The centre for health informatics: a novel approach to accelerating the field of health data science

Precision Medicine and Precision Public Health are approaches to improve population health. Achieving these goals requires innovation in health informatics. The Centre for Health Informatics (CHI) within the Cumming School of Medicine (CSM) at the University of Calgary (UC), Canada, was created to respond to this need by fostering multidisciplinary collaborations, building capacity by recruiting and training outstanding faculty and students, and harnessing Alberta's rich health data to advance health informatics. To establish CHI as a health informatics leader, CHI has struck partnerships with stakeholders, including Alberta Health Services (AHS), Alberta Health (AH), and the Alberta Strategy for Patient-Oriented Research Unit (AbSPORU) among others. Through these close relationships, the CHI intake team facilitates access to Alberta's rich health data sources and educates researchers on the available health data in Alberta. The concept of a "One Stop Shop" for CSM and UC researchers encourages multidisciplinary collaboration, helps investigators access a wide range of datasets, and receive analytical support. Population-based data sets enable the development of methods to turn raw data into health information, improve health data collection, linkage, analysis, and quality, and applied studies creating clinical decision-support tools, prognostic tools, improved health surveillance methods, and health system performance indicators. CHI's ecosystem of diverse research expertise, cutting-edge technology, and embedded AHS analysts to support data access via a wide-ranging network of partnerships allows our provincial researchers, national and international collaborators tremendous opportunities for empirical research. It paves the way for implementing Precision Medicine in the real world.

THE NOVEL CFRG -BASED COMPLEX FUZZY TRANSFER LEARNING SYSTEM

Today, the rapid development of the internet has led to a data explosion; the complex fuzzy transfer learning (CFTL) model has received increasing attention from the academic community due to its various real-world applications, such as solar activity, digital signal processing, time series forecasting, etc. CFTL combines Transfer learning and Complex Fuzzy Logic in a framework to solve the problem of learning tasks with no prior direct contextual knowledge, which is stored, retrieved, and organized in the data structure. Data structures play an important role in computational intelligence because they are key performance indicators for systems or models. Therefore, to improve the performance of the previous CFTL, this paper investigates a novel complex fuzzy decision tree (CFDT) structure to represent the complex fuzzy rules and provides a transfer learning model for a complex fuzzy inference system. In contrast with prior axis-parallel decision trees in which only a single feature or variable is considered at each node, the node of the proposed decision tree structures includes complex fuzzy inference rules that contain multiple elements. Multiple features for each node help minimize the size. To prove the efficiency of the proposed framework, we carry out extension experiments on numerous instances (datasets). Experimental results demonstrate/exhibit that our offered performs better than the prior framework regarding accuracy and the size of the produced trees.

A novel stability criterion and precise control for linear time delay systems based on regional configuration of the poles

This paper presents the concept of region stability and provides criteria for region stability of linear time delay systems, which can reveal the dynamic and steady-state performance of the systems more precisely. Corresponding design schemes for stabilization and tracking control that can accurately control various performance of time delay systems have also been explored. First, in the light of the connection between the poles and the dynamic properties of the system, the concept of region stability is given to describe the finer dynamic behavior of time delay systems. The criteria for the region stability are also presented. Second, the region stabilization methods are investigated, which can ensure that the system satisfies a certain dynamic performance by setting the eigenvalues in a certain convex region. Third, a precise tracking control of the linear time delay systems is addressed as an application of region stabilization. It can control the steady state performance and transient response of the tracking signal more precisely. Finally, three instances are provided to display the superiority of the new method for the performance indexes of the linear time delay systems.

Methodological provision of analysis of the efficiency of investment activities of enterprises

The paper is devoted to the disclosure of the main provisions of methodological support for the analysis of the investment activity of enterprises and practical recommendations on the use of methods for evaluating the effectiveness of the implementation of investment projects for decision-making. Making investment decisions is based on the use of various formalized and informal methods and models, taking into account the following factors: changes in the value of money over time; alternative value of resources; cash flows generated by the project; uncertainty and risk; inflation, price changes and currency exchange rate fluctuations; investment goals and investor psychology; possible changes in project parameters; synergies between investment projects of the enterprise. When forming the list of monitoring indicators, those aspects of the business entity's investment activity that are most important for owners, investors and management organizers were highlighted. It is proposed to form a system of performance indicators of the enterprise's investment activity in accordance with the sphere of investment: production, marketing, technology, organization. The principles of investment activity analysis and recommendations on the procedures for analyzing the effectiveness of project implementation have been formed, the choice of which depends on the management goals aimed at achieving a stable cash flow and ensuring the financial stability of the enterprise. The essential characteristics, advantages and disadvantages of the existing approaches to evaluating the effectiveness of investment projects are revealed. A system of indicators and criteria for the analysis of the effectiveness of investment activities of indicators characterizing the effectiveness of investments depending on the sphere of investment of funds: production, marketing, technology, organization are proposed. Recommendations for choosing a specific method for evaluating the effectiveness of investment projects in the context of achieving the priority goal - maximizing project profitability, risk acceptability and investors' interests are substantiated.

Health assessment of an electro-hydraulic servo pump control system for servomotor based on LGA deep neural network

Due to its advantages of having a high power-to-weight ratio and being energy-efficient, the electro-hydraulic servo pump control system (abbreviated as EHSPCS) is frequently employed in the industrial field, such as the electro-hydraulic servo pump control (EHSPC) servomotor for steam turbine valve regulation control. However, the EHSPCS has strong nonlinearity and time-varying features, and the factors that cause system performance degradation are complex. Once a system failure occurs, it may lead to serious accidents, causing serious casualties and economic losses. To address the above issues, a system health assessment method based on LSTM-GRNN-ANN (LGA) deep neural network is proposed in this paper. Firstly, with oil volume gas content, servo motor air-gap flux density, and system leakage coefficient as the health assessment performance indicators, a health assessment performance index system for the EHSPCS is built, Furthermore, the system performance index threshold is set. Secondly, an LGA deep neural network is constructed by combining LSTM, GRNN and ANN, and a deep neural network based on the LGA is used to create an EHSPCS health assessment model. Subsequently, system feature parameter extraction, algorithm design, and parameter debugging are carried out. Finally, an EHSPCS experimental platform is established, typical system failure simulation experiments are designed, and comparative experimental analysis is conducted. The experimental findings demonstrate that the average accuracy of the system health assessment model based on the LGA deep neural network suggested in this paper is 96.37%, compared to 89.84%, 87.99% for LSTM and GRNN, which validates the accuracy of the system health assessment model based on the LGA deep neural network.

Hospitalizations for conditions sensitive to primary care and sustainable development objectives

Hospitalizations for Primary Care Sensitive Conditions (ICSAP) are widely recognized as an indicator of health system performance. Considering that the evaluation of health practices is mandatory, monitoring these indicators becomes relevant, with a view to directing actions that can contribute to the Sustainable Development Goals. The objective of this study was to analyze the profile of ICSAP that occurred in the Federal District (DF) in the period from 2018 to 2022. This is an ecological time series study, using Hospital Admission Authorizations as a data source, via the Information System Hospital of the Unified Health System. Data collection was carried out in November 2022 and covered the period from 2018 to 2022. Descriptive statistical analysis was carried out. It was possible to identify the profile of ICSAP in DF. The elderly population and children aged 1 to 14 years were predominant, as well as diagnoses related to anemia, ear, nose and throat infections and asthma. The main causes of deaths and expenses were cerebrovascular diseases, heart failure and bacterial pneumonia.

Quantifying the Effects of Material Input Levels on Jointed Plain Concrete Pavement (JPCP) Performance and Slab Thickness

The mechanistic-empirical pavement design guide (MEPDG) is a commonly accepted design principles guide that aids in jointed plain concrete pavement (JPCP) design and performance analysis. The MEPDG uses three different design parameter input levels, referred to as level one, level two, and level three, providing increasing confidence in the analysis at the lower numbered levels, which use more locally relevant (level two) or project-specific (level one) data. The state-of-the-art pavement ME software (version 2.6.2) uses MEPDG design principles to predict pavement performance. The three performance indicators for JPCP systems (international roughness index (IRI), joint faulting, and transverse cracking) experience significant changes when simulating under a different input level. The IRI and faulting indicator changed by 78 percent when using inputs varying from level one to level three, with the cracking indicator change being more severe at 87 percent. To accommodate the change in performance indicator values between input level one and input level three, increasing the concrete slab thickness is necessary to achieve comparable pavement performance. An increase in the Portland cement concrete (PCC) layer from one inch to two inches is required when input level three simulations are performed, demonstrating the economic and sustainability benefits of using project-specific level one inputs. Understanding the impact of simulation input levels will help to meet design and sustainability goals and improve the lifecycle performance of JPCP systems.

Can Blue-Green Infrastructure enhance resilience in urban drainage systems during failure conditions?

The need to enhance the resilience of urban drainage systems (UDSs) in view of emerging global climate change and urbanisation threats is well recognised. Blue-Green Infrastructure (BGI) provides a suitable strategy for building the resilience of existing UDSs. However, there are limited quantitative studies that provide evidence of their effectiveness for increased uptake in cities. In this research, coupled one-dimensional-two-dimensional (1D-2D) modelling is applied to assess the effectiveness of BGI that include rainwater harvesting systems, infiltration trenches, bioretention cells, and detention ponds using two case study UDSs located in Kampala that experience catastrophic pluvial flooding caused by extreme rainfall. The resulting flooding impacts are quantified considering 'failed' and 'non-failed' UDS initial states, using total flood volume and average flood duration as system performance indicators. The study results suggest that spatially distributed rainwater harvesting systems singularly lead to a reduction in total flood volume and average flood duration of 16-45% and 18-24% in the case study UDSs, respectively. Furthermore, the study results suggest that BGIs are more effective during moderate rainfall (T < 10 years). Based on the study findings, city scale implementation of multifunctional rainwater harvesting systems is recommended as a suitable strategy for enhancing UDSs' resilience.