System Performance Assessment Research Articles

Enhancing typical Meteorological Year generation for diverse energy systems: A hybrid Sandia-machine learning approach

Accurate performance assessment of energy systems heavily relies on Typical Meteorological Year (TMY) data. The Sandia method, commonly used for TMY generation, is limited by default weighting criteria for meteorological parameters, restricting its suitability for diverse energy system analyses. In response, this study presents a novel framework for generating TMY files customized to various energy systems. Utilizing three tree-based algorithms (CART, RF, XGBoost) and interpretable machine learning techniques, the framework quantifies and personalizes weighting schemes. Validation of the method's applicability is conducted using long-term historical weather data from Beijing and Lhasa, encompassing three distinct energy systems (a full air conditioning building system and two renewable energy systems). Results indicate that the new TMY generation method excels over the original Sandia method for building and photovoltaic systems but encounters limitations with wind power system. Additionally, incorporating meteorological parameters highly relevant to specific energy systems and comprehensively considering their seasonality will contribute to the development of more representative TMY data. The proposed method facilitates precise foundational climate data acquisition, enabling more accurate energy performance analysis and decision-making.

INACIA: Integrating Large Language Models in Brazilian Audit Courts: Opportunities and Challenges

This paper introduces INACIA ( In strução A ssistida c om I nteligência A rtificial), a groundbreaking system designed to integrate Large Language Models (LLMs) into the operational framework of Brazilian Federal Court of Accounts (TCU). The system automates various stages of case analysis, including basic information extraction, admissibility examination, Periculum in mora and Fumus boni iuris analyses, and recommendations generation. Through a series of experiments, we demonstrate INACIA’s potential in extracting relevant information from case documents, evaluating its legal plausibility, and formulating propositions for judicial decision-making. Utilizing a validation dataset alongside LLMs, our evaluation methodology presents a novel approach to assessing system performance, correlating highly with human judgment. These results underscore INACIA’s potential in complex legal task handling while also acknowledging the current limitations. This study discusses possible improvements and the broader implications of applying AI in legal contexts, suggesting that INACIA represents a significant step towards integrating AI in legal systems globally, albeit with cautious optimism grounded in the empirical findings.

Performance Assessment of Different Sustainable Energy Systems Using Multiple-Criteria Decision-Making Model and Self-Organizing Maps

The surging demand for electricity, propelled by the widespread adoption of intelligent grids and heightened consumer interaction with electricity demand and pricing, underscores the imperative for precise prognostication of optimal power plant utilization. To confront this challenge, a dataset centered on issue-centric power plans is meticulously crafted. This dataset encapsulates pivotal facets indispensable for attaining sustainable power generation, including meager gas emissions, installation cost, low maintenance cost, elevated power generation, and copious resource availability. The selection of an optimal power plant entails a multifaceted decision-making process, demanding a systematic approach. Our research advocates the amalgamation of multiple-criteria decision-making (MCDM) models with self-organizing maps to gauge the efficacy of diverse sustainable energy systems. The examination discerns solar energy as the preeminent MCDM criterion, securing the apex position with a score of 83.4%, attributable to its ample resource availability, considerable energy generation, nil greenhouse gas emissions, and commendable efficiency. Wind and hydroelectric power closely trail, registering scores of 75.3% and 74.5%, respectively, along with other energy sources. The analysis underscores the supremacy of the renewable energy sources, particularly solar and wind, in fulfilling sustainability objectives and scrutinizing factors such as cost, resource availability, and the environmental impact. The proposed methodology empowers stakeholders to make judicious decisions, accentuating facets that are required for more sustainable and resilient power infrastructure.

Application of the AHP Method to Assesment of Performance Levels of Production Employees of the Zenbo Division of PT. ABC

In a company, it cannot be separated from the role of human resources who work in it. The quality of human resources is one of the factors that influences the level of company productivity. Therefore, there is a need for an existing employee performance assessment system to determine the level of productivity of each existing employee. The problems that exist at PT. Mertex Indonesia is the absence of an assessment system which results in no knowledge of the performance level of its employees. Based on these problems, this research designed an employee assessment system using the Analytical Hierarchy Process (AHP) method which is used to support decision making for employees which refers to the employee's level of productivity. This research aims to create decision support in evaluating the performance of company employees to be able to maintain the high value of company productivity. The results obtained from weighting using AHP are changes in ranking where operator 2 and operator 3 exchange ranking positions. Highlights : Lack of performance assessment system at PT. ABC impacts productivity. Research designs AHP-based employee assessment system. Results show ranking changes, enhancing decision-making for productivity. Keywords : Human Resources; AHP Method; Performance Assessment

Quantitative measurement of the ureter on three-dimensional magnetic resonance urography images using deep learning.

Accurate measurement of ureteral diameters plays a pivotal role in diagnosing and monitoring urinary tract obstruction (UTO). While three-dimensional magnetic resonance urography (3D MRU) represents a significant advancement in imaging, the traditional manual methods for assessing ureteral diameters are characterized by labor-intensive procedures and inherent variability. In the realm of medical image analysis, deep learning has led to a paradigm shift, yet the development of a comprehensive automated tool for the precise segmentation and measurement of ureters in MR images is an unaddressed challenge. The ureter was quantitatively measured on 3D MRU images using a deep learning model. A retrospective cohort of 445 3D MRU scans (443 patients, 52±18 years; 217 female patients) was collected and split into training, validation, and internal testing cohorts. A 3D V-Net model was trained for urinary tract segmentation, and a post-processing algorithm was developed for ureteral measurements. The accuracy of the segmentation was evaluated using the Dice similarity coefficient (DSC) and volume intraclass correlation coefficient (ICC), with ground truth segmentations provided by experienced radiologists. The external cohort comprised 50 scans (50 patients, 55±21 years; 30 female patients), and the model-predicted ureteral diameter measurements were compared with manual measurements to assess system performance. The various diameter parameters of ureter among the different measurement methods (ground truth, auto-segmentation with automatic diameter extraction, and manual segmentation with automatic diameter extraction) were assessed with Friedman tests and post hoc Dunn test. The effectiveness of the UTO diagnosis was assessed by receiver operating characteristic (ROC) curves and their respective areas under the curve (AUC) between different methods. In both the internal test and external cohorts, the mean DSC values for bilateral ureters exceeded 0.70. The ICCs for the bilateral ureter volume obtained by comparing the model and manual segmentation were all greater than 0.96 (p < 0.05), except for the right ureter in the internal test cohort, for which the ICC was 0.773 (p < 0.05). The mean DSCs for interobserver and intraobserver reliability were all above 0.97. The maximum diameter of the ureter exhibited no statistically significant differences either in the dilated (p=0.08) or in the non-dilated (p=0.32) ureters across the three measurement methods. The AUCs of ground truth, auto-segmentation with automatic diameter extraction, and manual segmentation with automatic diameter extraction in diagnosing UTO were 0.988 (95% CI: 0.934, 1.000), 0.961 (95% CI: 0.893, 0.991), and 0.979 (95% CI: 0.919, 0.998), respectively. There was no statistical difference between AUCs of the different methods (p>0.05). The proposed deep learning model and post-processing algorithm provide an effective means for the quantitative evaluation of urinary diseases using 3D MRU images.

Multi-Task Learning for Design Under Uncertainty With Multi-Fidelity Partially Observed Information

Abstract The assessment of system performance and identification of failure mechanisms in complex engineering systems often requires the use of computation-intensive finite element software or physical experiments, which are both costly and time-consuming. Moreover, when accounting for uncertainties in the manufacturing process, material properties, and loading conditions, the process of reliability-based design optimization (RBDO) for complex engineering systems necessitates the repeated execution of expensive tasks throughout the optimization process. To address this problem, this paper proposes a novel methodology for RBDO. First, a multi-fidelity surrogate modeling strategy is presented, leveraging partially observed information (POI) from diverse sources with varying fidelity and dimensionality to reduce computational cost associated with evaluating expensive high-dimensional complex systems. Second, a multi-task surrogate modeling framework is proposed to address the concurrent evaluation of multiple constraints for each design point. The multi-task framework aids in the development of surrogate models and enhances the effectiveness of reliability analysis and design optimization. The proposed multi-fidelity multi-task machine learning model utilizes a Bayesian framework, which significantly improves the performance of the predictive model and provides uncertainty quantification of the prediction. Additionally, the model provides a highly accurate and efficient framework for reliability-based design optimization through knowledge sharing. The proposed method was applied to two design case studies. By incorporating POI from various sources, the proposed approach improves the accuracy and efficiency of system performance prediction, while simultaneously addressing the cost and complexity associated with the design of complex systems.

Fuzzy logic-based approach for optimal allocation of distributed generation in a restructured power system

Fuzzy logic emerges as a powerful tool for optimizing power flow solutions, particularly in the context of deregulated power systems. By employing fuzzy logic controls, the ideal placement of distribution generators (DGs) can be determined, ensuring the reliability indices are identified through optimal power flow solutions and fuzzy logic controllers to maintain system feasibility. In a deregulated power system, strategic placement of distribution generator units plays a crucial role in minimizing power loss and enhancing overall system performance by mitigating fluctuations. To identify areas of weakness, especially within transmission companies, accessing optimal power flow algorithms becomes essential in a deregulated power system. Both transmission and distribution networks should be appropriately adjusted to alleviate congestion within the respective companies. The aggregator must assess system performance, utilizing data obtained from distribution and transmission companies within the deregulated power system.

Review: update on performance assessment and monitoring systems for ophthalmologists

Review: update on performance assessment and monitoring systems for ophthalmologists

Pedagogical strategies used by an outstanding mathematics teacher: a case study in Region IV of Chile / Estrategias pedagógicas utilizadas por una profesora destacada en matemáticas: un estudio de caso en la IV Región de Chile

How does a maths teacher rated as outstanding according to the ‘DocenteMás’ Performance Assessment System of Chile semiotically mediate her seventh-grade students’ learning? With the goal of answering this question, this article used a qualitative, ethnographic case study method to describe the pedagogical strategies of the teacher and her students when covering the thematic unit on numbers and operations in primary school. The results show that the pedagogical strategies emerge from the communicative interaction in the classroom via a local and a global dynamic. With regard to the local dynamic, the pedagogical strategies discovered include exposition, experiential articulation and arithmetic understanding. In the global dynamic, pedagogical strategies of semiotic mediation like metaphors, question series and joint understanding of knowledge stood out.

Application of Simple Additive Weighting Method To Design an Employee Performance Assessment System in a National Logistics Company

This research aims to discover how an employee performance appraisal system using the website-based SAW method can help managers carry out the employee performance appraisal process. The method used in developing a web-based decision-making system is the prototype method. For data collection, two techniques were used. First, obtain the necessary information through interviews. Second, by collecting performance assessment data previously carried out manually by managers. It can be observed that the system may function effectively by performing its functions as an employee performance assessment utilizing the simple additive weighting calculation technique after creating a prototype decision-making system using the website-based simple additive weighting method. After managers have completed an evaluation, website-based solutions can combine data to make it easier to access, preserve data security, and support decision-making. The results provided yield high accuracy based on the SAW formula. This system uses the SAW calculation method so that employee performance assessments are carried out based on the percentage weight of the criteria that the manager has determined. From the requirements and results of this assessment, SAW can produce a ranking of employees with the best to worst performance. From this ranking, the manager can determine the employee with the best and worst performance, and then managers can make decisions based on that data.

Current scenario and challenges of clinical pharmacists to implement pharmaceutical care in DRG/DIP payment hospitals in China: a qualitative interview study.

The Diagnosis-Related Group (DRG) or Diagnosis-Intervention Packet (DIP) payment system, now introduced in China, intends to streamline healthcare billing practices. However, its implications for clinical pharmacists, pivotal stakeholders in the healthcare system, remain inadequately explored. This study sought to assess the perceptions, challenges, and roles of clinical pharmacists in China following the introduction of the DRG or DIP payment system. Qualitative interviews were conducted among a sample of clinical pharmacists. Ten semi-structured interviews were conducted, either online or face to face. Thematic analysis was employed to identify key insights and concerns related to their professional landscape under the DRG or DIP system. Clinical pharmacists exhibited variable awareness levels about the DRG or DIP system. Their roles have undergone shifts, creating a balance between traditional responsibilities and new obligations dictated by the DRG or DIP system. Professional development, particularly concerning health economics and DRG-based or DIP-based patient care, was highlighted as a key need. There were calls for policy support at both healthcare and national levels and a revised, holistic performance assessment system. The demand for more resources, be it in training platforms or personnel, was a recurrent theme. The DRG or DIP system's introduction in China poses both opportunities and challenges for clinical pharmacists. Addressing awareness gaps, offering robust policy support, ensuring adequate resource allocation, and recognizing the evolving role of pharmacists are crucial for harmoniously integrating the DRG or DIP system into the Chinese healthcare paradigm.

Trends in Avoidable Mortality in Kazakhstan From 2015 to 2021.

The health system performance assessment is a challenging process for decision-makers. In case of Kazakhstan's healthcare system, the calculation of avoidable mortality, which has been underutilized to date, could serve as an additional tool to prioritize areas for improvement. Therefore, the aim of the study is to analyse avoidable mortality in Kazakhstan. The data was retrieved from the Bureau of National Statistics, Kazakhstan. It covers population data by age, mortality rates from disease groups based on the Joint OECD (Organisation for Economic Co-operation and Development)/Eurostat classification of preventable and treatable causes of mortality. The data spans from 2015 to 2021, categorized by gender and 5-year age groups (0, 1-4, 5-9, ..., 70-74). Standardization was performed using the 2015 OECD standard population. We used joinpoint regression analysis to calculate the average annual percentage change (AAPC). From 2015 to 2019, the annual percentage change (APC) in avoidable mortality per 100 000 population was -3.8 (-5.7 to -1.8), and from 2019 to 2021 it increased by 17.6 (11.3 to 24.3). Males exhibited higher avoidable mortality rates compared to females. The preventable mortality rate was consistently higher than the treatable mortality. Both preventable and treatable mortality decreased from 2015 to 2019, with preventable mortality reaching 272.17 before rising to 379.23 per 100 000 population in 2021. Between 2015 and 2021, treatable mortality rates increased from 179.3 (176.93-181.67) to 205.45 (203.08-207.81) per 100 000 population. In Kazakhstan, the leading causes of avoidable mortality were circulatory diseases, respiratory diseases, and cancer. To achieve the goals of universal health coverage (UHC) and improve the overall population health, there is an urgent need to amend the healthcare system and reduce avoidable mortality. While it is important to acknowledge the influence of COVID-19 on these trends, our study's focus on avoidable mortality provides valuable insights that complement the understanding of pandemic-related effects.

Health system performance assessment: embedding resilience through performance intelligence.

Health systems around the world are facing challenges in achieving their goals. In the wake of the coronavirus disease pandemic, the need for resilient health systems has become even more apparent. This article argues that embedding resilience into health system performance assessment (HSPA) frameworks can be a valuable approach for improving health system performance. This perspective examines key challenges threatening health systems and makes a case for the continued relevance of HSPA by embedding resilience-related performance intelligence.

Optimal performance of stand-alone hybrid microgrid systems based on integrated techno-economic-environmental energy management strategy using the grey wolf optimizer.

Recently, global interest in organizing the functioning of renewable energy resources (RES) through microgrids (MG) has developed, as a unique approach to tackle technical, economic, and environmental difficulties. This study proposes implementing a developed Distributable Resource Management strategy (DRMS) in hybrid Microgrid systems to reduce total net percent cost (TNPC), energy loss (Ploss), and gas emissions (GEM) while taking the cost-benefit index (CBI) and loss of power supply probability (LPSP) as operational constraints. Grey Wolf Optimizer (GWO) was utilized to find the optimal size of the hybrid Microgrid components and calculate the multi-objective function with and without the proposed management method. In addition, a detailed sensitivity analysis of numerous economic and technological parameters was performed to assess system performance. The proposed strategy reduced the system's total net present cost, power loss, and emissions by (1.06%), (8.69%), and (17.19%), respectively compared to normal operation. Firefly Algorithm (FA) and Particle Swarm Optimization (PSO) techniques were used to verify the results. This study gives a more detailed plan for evaluating the effectiveness of hybrid Microgrid systems from a technical, economic, and environmental perspective.

Smart Agent-Based Direct Load Control of Air Conditioner Populations in Demand Side Management

The integration of fluctuating renewable resources such as wind and solar into existing power systems poses challenges to grid reliability and the seamless incorporation of these resources. To address the inherent variability in renewable generation, direct load control emerges as a promising method for demand-side management. Thermostatically controlled appliances, like air conditioners, hold a significant role in this approach. However, effective direct load control necessitates accurate load magnitude estimation and the potential for load shifting. In this paper, we introduce a smart-agent architecture that employs a mathematical model to forecast aggregated power consumption behavior, even when changes are introduced by the controller. To assess system performance, a numerical simulator was developed, demonstrating the system’s adaptability to changes, its self-retraining capability, and its continuous improvement in predicting aggregated power consumption.

Application Of Rating Scale Method to Design An Employee Performance Assessment System in a Health Equipment Distributor Company

This research aims to find out how to design and build an employee performance assessment information system using the rating scale method. The data collection methods used in this research were observation, interviews, library research, and a literature study. The research utilized the rating scale as the method for assessing employee performance. The author used the Rapid Application Development (RAD) method in system development. The review and analysis confirmed this research's success in developing an employee performance assessment information system using the rating scale method. The successful implementation of this system has had a positive impact by providing a solution to the challenge of performance assessment that is less than objective. With the existence of an employee performance appraisal information system, companies now have tools that can reduce the level of subjectivity in performance appraisals. Using the rating scale method in this system provides consistent evaluation standards, increases assessment accuracy, and presents a higher element of objectivity. In conclusion, implementing the employee performance assessment information system opens up opportunities for continuous improvement in human resource management. With a focus on objectivity and measurability, this system has the potential to become an invaluable tool in achieving company goals and vision.

Insertion of synthetic lesions on patient data: a method for evaluating clinical performance differences between PET systems

BackgroundPerformance assessment of positron emission tomography (PET) scanners is crucial to guide clinical practice with efficiency. We have already introduced and experimentally evaluated a simulation method allowing the creation of a controlled ground truth for system performance assessment. In the current study, the goal was to validate the method using patient data and demonstrate its relevance to assess PET performances accuracy in clinical conditions.MethodsTwenty-four patients were recruited and sorted into two groups according to their body mass index (BMI). They were administered with a single dose of 2 MBq/kg 18F-FDG and scanned using clinical protocols consecutively on two PET systems: the Discovery-IQ (DIQ) and the Discovery-MI (DMI). For each BMI group, sixty synthetic lesions were dispatched in three subgroups and inserted at relevant anatomical locations. Insertion of synthetic lesions (ISL) was performed at the same location into the two consecutive exams. Two nuclear medicine physicians evaluated individually and blindly the images by qualitatively and semi-quantitatively reporting each detected lesion and agreed on a consensus. We assessed the inter-system detection rates of synthetic lesions and compared it to an initial estimate of at least 1.7 more targets detected on the DMI and the detection rates of natural lesions. We determined the inter-reader variability, evaluated according to the inter-observer agreement (IOA). Adequate inter-reader variability was found for IOA above 80%. Differences in standardized uptake value (SUV) metrics were also studied.ResultsIn the BMI ≤ 25 group, the relative true positive rate (RTPR) for synthetic and natural lesions was 1.79 and 1.83, respectively. In the BMI > 25 group, the RTPR for synthetic and natural lesions was 2.03 and 2.27, respectively. For each BMI group, the detection rate using ISL was consistent to our estimate and with the detection rate measured on natural lesions. IOA above 80% was verified for any scenario. SUV metrics showed a good agreement between synthetic and natural lesions.ConclusionsISL proved relevant to evaluate performance differences between PET scanners. Using these synthetically modified clinical images, we can produce a controlled ground truth in a realistic anatomical model and exploit the potential of PET scanner for clinical purposes.

Simulating Local Biases in Visual Attention in Neurocognitive Performance.

Typical visual perception includes an attention bias toward right hemisphere mediated global, holistic cortical processing. An atypically local, detail-oriented focus of attention is characteristic of left hemisphere processing and is often observed in patients whose field of attention is restricted by certain types of neurocognitive impairment. We designed the present pair of studies to induce a local attentional focus to observe its consequences on neurocognitive measures of visuospatial processing. In Experiment I, participants wore glasses mimicking simultanagnosia, a disorder of visual attention, to induce a narrowed, atypical attentional style while they completed visual neuropsychological tasks. This simulation impaired participants' capacities to visually synthesize and efficiently reproduce Complex Figure stimuli as measured with the Boston Qualitative Scoring System (BQSS), and it induced an atypical attentional style on Rorschach Performance Assessment System (R-PAS) responses. In Experiment II, participants wore glasses designed to provoke differential hemispheric activation, also hypothesized to influence style of visual attention; but this manipulation did not influence neurocognitive task performance. We discuss implications for the interpretation of BQSS and R-PAS scores and offer directions for future research.

Achievement Prediction and Performance Assessment System for Nations in the Asian Games

The profound impact of deep learning technology is poised to revolutionize various industries, marking the fourth industrial revolution. Thus, we combined efficiency and productivity research (data envelopment analysis, DEA), artificial intelligence and deep learning (artificial neural networks, ANN), a system integrating DEA and ANNs, and simultaneous longitudinal research (time series) to determine comprehensive research trends and create relevant applications. We addressed mega-sports events’ performance assessment systems that predict the efficiency of nations participating in the Asian Games from 1990 to 2023 and analyzed the outcomes, applying them to practical issues of national sports policies and development. Performance assessment systems to diagnose, plan, monitor, and revise the impact of implementing measures in Asian nations represent a step forward. The PAS findings point out future research recommendations by addressing national sports policies and development issues, transforming the predictions of performance assessment systems in mega-sports events into practical management recommendations. In this way, the system for enhanced predictive analytics developed in the study can rapidly analyze large, medium, and small datasets, reveal insights that humans may overlook, and refine the likelihood of predicting future events with greater precision and accuracy.

Comprehensive performance assessment and multi-objective optimization of high-power proton exchange membrane fuel cell system under variable load

Due to the advantages of zero emission, high efficiency and low noise, Proton Exchange Membrane Fuel Cell (PEMFC) has grown to be the spotlight in the field of new energy in recent years. In this work, a high-power PEMFC system model is designed, which consists of the fuel feed Subsystem, the air feed subsystem, the thermal management subsystem and the stack. Among them, the material diffusion in the catalyst layer of the stack is intensively studied and derived. For the comprehensive system performance assessment, the thermodynamic, economic and environmental evaluation models have been developed. The mapping relationships between the operating parameters, the structural parameters and the system comprehensive performance under variable load conditions are obtained. The results of the study prove that increasing the operating temperature and decreasing the membrane thickness under high load current conditions are conducive to increase the system net power output, but at the same time increase the levelized cost of energy and the carbon mass specific emission. In addition, a multi-objective optimization strategy based on the NSGA-Ⅱ is designed to obtain the optimal performance of the system under different operating conditions. The optimized net power, the levelized cost of energy and the carbon mass ratio emission of the system for the final optimal operating point are increased by 40.5 %, 10.3 % and 33.3 % respectively. The aforementioned evidence substantiates the effectiveness of the proposed multi-objective optimization strategy in enhancing the overall performance of the PEMFC system.