Performance Criteria Research Articles

Prescribed Performance-Based Formation Control for Multiple Autonomous Underwater Helicopters with Complex Dynamic Characteristics

This research addresses the challenge of formation control among multiple homogeneous autonomous underwater helicopters (AUHs) in the presence of external disturbances and complex dynamic characteristics. The study introduces a novel approach by integrating both disturbance and state observers within the control law framework to manage external disturbances and the immeasurability of velocity, respectively. Concurrently, localized radial basis function neural networks (RBFNNs) of identical configurations are incorporated into the formation control law to assimilate model uncertainties. Building upon this integration, an experience-based formation control strategy is developed, leveraging accumulated knowledge to diminish computational demands while maintaining stipulated performance criteria. Furthermore, the incorporation of a finite-time prescribed performance control (FTPPC) technique enhances the learning process’s efficiency by expediting convergence. Numerical simulations are presented to validate the efficacy of the proposed methodology.

A Systematic Literature Review of Routing Protocols in Wireless Sensor Networks: Current Trends and Future Directions

Wireless Sensor Networks (WSNs) have emerged as a key technology in various applications, ranging from environmental monitoring to healthcare and industrial automation. Efficient data communication among sensor nodes is essential for the success of WSNs, and routing protocols play a critical role in determining the overall network performance. This review aims to comprehensively analyze and synthesize the existing literature on routing protocols in WSNs, highlighting their strengths, weaknesses, and applications. The review also aims to highlight various performance indicators (metrics) used by researchers to evaluate the performance of routing protocols in WSN, emerging trends that may influence the future design of routing protocols in WSN, and security concerns in WSN routing protocols. To attain this goal, a systematic literature review process was employed based on Barbara Kitchenham's original guidelines (2007). Relevant papers were retrieved from major academic databases such as Elsevier, Springer, Wiley, IEEE, and the ACM Digital Library, as well as preprints posted on arXiv. The findings demonstrate that various existing routing protocols have been created throughout time and are classified as data-centric, location-based, mobility-based, multi-path-based, heterogeneity-based, and hierarchical routing protocols. The routing protocols in WSNs vary depending on the application and network architecture. The paper also focuses on the performance criteria used to evaluate them, their pros, limitations, areas of applications, emerging trends in WSN, and security challenges. The future of WSN routing protocols is moving toward intelligent, adaptive, and robust protocols that can serve larger, more complex networks.

Are uncrewed aerial spraying systems the future for forestry pesticide application?

Background: Pesticide application is a primary method for managing weeds, insect pests and pathogens in New Zealand’s forests. Apart from some manual spot spraying, most pesticide applications are made using helicopters, with herbicides the most widely used pesticide class. Current aerial application practices have evolved into efficient operations designed to provide a balance between performance criteria, i.e. maximising treatment efficacy, minimising unwanted environmental impacts (e.g. spray drift), and maximising productivity while minimising cost. Over the last decade, there has been a proliferation of relatively small, battery-powered, multi-rotor uncrewed aerial spraying systems (UASS) but their use to date in New Zealand forestry has been limited. This paper assesses the potential role of UASS in forest management and, where opportunities exist, identifies barriers slowing their adoption. Methods: Publications on spray application in New Zealand forestry and use of UASS in both New Zealand and internationally were identified by conducting a Google Scholar literature search using a range of relevant keywords, and the retrieved studies were reviewed systematically. Unpublished reports from the New Zealand Forest Research Institute Ltd and Forest Growers Research Ltd were also considered. Information from the reviews was assessed critically, synthesised, and used to evaluate several potential forestry use cases for UASS. Results: Several potential use cases for UASS were identified along with a set of research and development needs to support and accelerate the adoption of UASS into forest management operations and to provide regulators with the means to apply appropriate risk management measures. Based on the literature analysis, the opportunity for UASS, at least in the near term, is to realise the concept of ‘precision spraying’ rather than to replace conventional aircraft carrying out broadcast applications over large areas. Conclusions: Recent UASS technology improvements have provided the potential for a step-change for at least some pesticide application niches within New Zealand forestry. Significant opportunities for UASS in forestry include herbicide spot spraying, treatment of boundaries close to sensitive areas, low-volume fungicide or insecticide applications, especially for small areas or in pest eradication operations; and applying variable treatments to individual plants or zones within a target area defined by remote sensing tools. A coordinated research and development programme is needed to optimise UASS use and to provide performance data to underpin regulatory processes.

Analysis and selection of the structure of a multiprocessor computing system according to the performance criterion

Objectives. Analysis of the various architectures of computing systems (CSs) used in recent decades has allowed us to identify the most common structures. One of the key features is the use of mass-produced equipment to create data processing subsystems (for example, multicore processors and high-capacity semiconductor memory), as well as network equipment to build communication subsystems. This reduces hardware costs and allows typical or cluster configurations to be created, which is especially important for expensive CSs. The desire to achieve high computational speed and performance in such CSs requires minimizing the time to complete the task and balancing time delays both in data processing subsystems and in the communication subsystem which provides data transmission inside the CS. The aim of this work is to analyze computing modules (CMs) and structures on the basis of which the construction of cluster CSs is carried out.Methods. The main results of the work were obtained using methods of mathematical analysis and modeling.Results. The study considers the structure of modern multicore microprocessors as the basis for building CMs of cluster CSs. As the number of cores in the microprocessor structure increases, the communication network which unites them into a single structure becomes more complicated. It has been shown that in new developments of microprocessors, communication between cores is performed in the form of a network. The microprocessors themselves are MIMD structures in accordance with the well-known Flynn classification.Conclusions. The proposed method of selecting an effective structure of a CS allows us to obtain the optimal structure of a CS according to the criterion of performance.

Optimizing the operation of series and parallel reservoirs using the neutrosophic environment: A case study of the Karun watershed

ABSTRACT In this article, neutrosophic inference is used for the first time as a new fuzzy set theory generalization to improve the operation of series and parallel dam reservoirs. A case study was conducted on the problem of parallel and series use of multiple and multipurpose reservoirs in the dams of the Karun watershed (Iran). The problem of optimizing the operations of dam reservoirs with the objective function of minimizing the shortages and maximizing hydropower production is modeled and solved using training data. The optimization results are applied to model the problem in the adaptive neuro-fuzzy inference system (ANFIS). The behavior of the system in the neutrosophic environment is inferred by ANFIS. Finally, the conventional operation method and the ANFIS method were compared with neutrosophic inference by fuzzy performance criteria. The results indicate that the proposed neutrosophic inference model has a high capacity to infer the behavior of the dam reservoir system. With a higher sustainability index due to increased fuzzy reliability, fuzzy resilience indices, and reduced fuzzy vulnerability index, the proposed model has improved the objective function by 80 and 40% in the training data and 67 and 36% in the test data in comparison to the operation rule curve and ANFIS.

UTILIZATION OF ALUMINA AND STEEL INDUSTRIAL WASTE FOR THE DEVELOPMENT OF CONTROLLED LOW STRENGTH MATERIALS

The study aimed at incorporating Red Mud, a byproduct of bauxite ore extraction, and Ground Granulated Blast Furnace Slag (GGBS), a byproduct of the steel industry, into Controlled Low Strength Materials (CLSM). These materials were combined with M Sand and cement to develop a sustainable and cost-effective construction material. Utilizing Red Mud and GGBS addresses waste disposal challenges while enhancing CLSM properties, making it a more environmentally friendly option. By converting industrial byproducts into valuable construction components, the research contributes to sustainable development and the circular economy. The research methodology involves creating five different dry mix combinations, varying the proportions of Red Mud, GGBS, M Sand, and cement. Each combination was tested with four different water contents to evaluate their engineering and dynamic properties. This methodical approach was focused to identify the optimal mix that balances strength and workability. By analyzing these properties, the study provided a valuable insight into the behavior of CLSM under various conditions, contributing to the advancement of more efficient construction materials. The systematic testing ensured that the developed CLSM mixes met the necessary performance criteria for practical applications. The primary objective of the study was to develop a phenomenological model based on the obtained strength and flow values. This model will serve as a predictive tool for future research, aiding in the design of CLSM mixtures with desired characteristics. By demonstrating the feasibility of using Red Mud in CLSM, the research underscores its potential to offer cost-effective and sustainable solutions for various engineering applications. This innovative approach not only promotes waste utilization but also opens new possibilities for improving construction practices. The developed model will help engineers and researchers predict the performance of new CLSM formulations, streamlining the development process for future projects.

Neuro-Fuzzy: Multivariable Identification of a Pumping System with Variable Demand

Water supply systems (WSS) comprise a set of equipment, works and services aimed at supplying water, covering domestic, industrial and public consumption. WSS face challenges arising from hourly variation in demand, influenced by society's consumption patterns. These variations cause fluctuations in system pressures and energy inefficiencies. As a way of analyzing this aspect, intelligent techniques were used to identify an automated WSS with variable demand for the development of computational models. Two multivariable and non-linear models were developed based on Artificial Neural Networks (ANN) and Neuro-Fuzzy system (NF). The objective is to enable simulations of operation scenarios, analysis, design and implementation of new intelligent control algorithms. To collect the data used in training and validation, experiments were carried out throughout the system's operating region. For cross validation, tests were carried out in operating regions different from those used for training. The models were evaluated using performance criteria, such as: Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), Final Prediction Error (FPE) and Adjustment percentage. The results were obtained using an experimental bench and showed adjustments greater than 99%. The cross-validation results evaluated with performance indicators show the superiority of intelligent models in comparison to parametric and mathematical models in the multivariable and non-linear identification of water pumping systems for simulation and dynamics prediction purposes.

An Artificial Neural Network Prediction Model of GFRP Residual Tensile Strength

This study uses an Artificial Neural Network (ANN) to examine the constitutive relationships of the Glass Fiber Reinforced Polymer (GFRP) residual tensile strength at elevated temperatures. The objective is to develop an effective model and establish fire performance criteria for concrete structures in fire scenarios. Multilayer networks that employ reactive error distribution approaches can determine the residual tensile strength of GFRP using six input parameters, in contrast to previous mathematical models that utilized one or two inputs while disregarding the others. Multilayered networks employing reactive error distribution technology assign weights to each variable influencing the residual tensile strength of GFRP. Temperature exerted the most significant influence at 100%, while sample dimensions had a minimal impact at 17.9%. In addition, the mathematical model closest to the proposed was the Bazli model, because the latter depends on two variables (thickness and temperature). The ANN accurately predicted the residual tensile strength of GFRP at elevated temperatures, achieving a correlation coefficient of 97.3% and a determination coefficient of 94.3%.

Topology Optimization of Periodic Cellular Materials through the Progressive Directional Selection Method and Finite-Volume Theory

This investigation presents the Progressive Directional Selection (PDS) method to optimize the topology of periodic cellular materials, achieving high performance and minimal weight. The homogenized elastic properties of cellular materials are determined using the homogenization method applied to periodic materials based on the unit cell concept as an intermediate step of the topology optimization procedure. The literature often constructs the design domain to conduct a finite element analysis. However, some problems are related to numerical issues, such as the checkerboard pattern and mesh dependence. The checkerboard effect is related to the assumptions of the finite element method, as the satisfaction of equilibrium and continuity conditions at the element nodes, particularly for linear triangular and quadrilateral discretizations in the absence of regularization schemes. This problem can be overcome by the Finite-Volume Theory (FVT), which satisfies the equilibrium equations at the subvolume level, and the compatibility conditions are established through the adjacent subvolume interfaces. The PDS method is inspired by natural selection processes found in biology and employs a strategy to meet the objective function of a discretized analyzed domain subject to a volume constraint. Population selection is based on performance criteria specific to the problem through an iterative process that concludes when the optimized topology ceases to evolve. Numerical examples of topology optimization for materials with periodic cellular microstructures are analyzed using PDS and FVT.

Monovalent selective ion exchange membranes: A review on preparation processes, applications, performance criteria and sustainability aspects

Monovalent selective ion exchange membranes: A review on preparation processes, applications, performance criteria and sustainability aspects

Preconditioned IDR solution method in scientific and industrial applications

Abstract A review of preconditioned solver for large-scale applications in science and industry is presented. The analysis, parallelization, and optimization approach for large unstructured sparse matrices using IDR methods are considered for modern multicore microprocessors. Performance criteria and methods have been revisited, along with consideration of parallelization involving the solver and preconditioner using the OpenMP environment. Results of the successful implementation for efficient parallelization are presented.

Comparative Time Series Analysis of SARIMA, LSTM, and GRU Models for Global SF6 Emission Management System

To maintain the balance of the atmosphere, the amount of change in greenhouse gas emissions must be under control. In order to create a management system and take forward-looking steps in this regard, there is no concrete data other than prediction models today. The success of prediction methods is better understood by comparing multiple methods. This research estimates the changes in the emissions of Sulfur Hexafluoride gas (SF6) in the atmosphere using Seasonal Autoregressive Integrated Moving Average Model (SARIMA), Long-Short Term Memory Neural Network (LSTM) and Gated Recurrent Unit (GRU) forecast models and compares their accuracies. Focusing on monthly SF6 emission values between 1998; 2023, time series analysis was performed to predict future emission figures. The actual values and forecast results were compared and evaluated with performance criteria such as R2, RMSE, NSE, MAE and MAPE%. The findings of this research highlight a continious upward trend in SF6 emissions and project that emission levels could approximately double from current levels by 2050. During the analysis process, all three methods performed well in estimating global SF6 gas emissions. The LSTM model generally outperformed SARIMA and GRU models, having the lowest MAPE (0.003%), MAE (0.0003), RMSE (0.0003), and R2 (1) values. It also exhibited very high predictive success with an NSE value of 0.9991. Therefore, it was determined to be the most suitable estimation method with the least error. The aim of this study is to contribute scientifically to the reduction strategies of SF6 emissions.

Improve the energy efficiency of the fruit freeze-drying through the predictive analysis

The considerable energy expenditure involved in the freeze-drying of foods justifies the development of innovative engineering techniques. Artificial intelligence will facilitate mass balance and energy efficiency in future food freeze-drying processes. This study assessed the energy and manufacturing efficiency of the freeze-drying facility through the application of artificial intelligence. Two distinct artificial neural network models were created utilizing real-time data from a factory located in an industrial zone that processed freeze-dried vegetables and kiwi fruit. Analyzing energy efficiency values and production was done using network models constructed from 20 experimental data sets. The Levenberg-Marquardt approach was employed to train neural networks with a multilayer perceptron architecture. The neural network models' prediction values were compared with the experimentally acquired data, and their performance was examined using several performance criteria. The evaluations carried out for 20 different scenarios revealed overall energy efficiency rates ranging from 25.8 % to 64.5 %. The considerable energy expenditure involved in the freeze-drying of foods justifies the development of innovative engineering techniques. Artificial intelligence will facilitate mass balance and energy efficiency in future food freeze-drying processes.

Phase-locked loop based synchronization schemes for three-phase unbalanced and distorted grid: a review

The rapidly growing dispersion of distributed generation systems into the utility grid needs appropriate control techniques to stay interconnected even under abnormal and distorted grid conditions to ensure the overall grid stability. To avoid the loss of renewable energy sources (RES) based power generation, the disintegration of RES with respect to synchronization issues must be prohibited for efficient operation. RES control highly relies on the synchronization technique as it is faster and accurate enough to detect the utility grid variables in terms of amplitude, phase, and frequency. Mostly, the phase-locked loop (PLL) synchronization schemes are utilized for control of RES and monitoring of grid voltage. The dynamics of the grid side converter (GSC) is directly influenced by the performance and design criteria of the PLLs. This paper proposes an overall review of the performances of three phase PLL based grid synchronization methods under diverse weak grid conditions.

Evaluation of myocardial perfusion imaging techniques and artificial intelligence (AI) tools in coronary artery disease (CAD) diagnosis through multi-criteria decision-making method.

Cardiovascular diseases (CVDs) continue to be the world's greatest cause of death. To evaluate heart function and diagnose coronary artery disease (CAD), myocardial perfusion imaging (MPI) has become essential. Artificial intelligence (AI) methods have been incorporated into diagnostic methods such as MPI to improve patient outcomes in recent years. This study aims to employ a novel approach to examine how parameters/criteria and performance metrics affect the prioritization of selected MPI techniques and AI tools in CAD diagnosis. Identifying the most effective method in these two interconnected areas will increase the CAD diagnosis rate. The study includes an in-depth investigation of popular convolutional neural network (CNN) models, including InceptionV3, VGG16, ResNet50, and DenseNet121, in addition to widely used machine learning (ML) models, including random forests (RF), K-nearest neighbor (KNN), support vector machine (SVM), and Naïve Bayes (NB). In addition, it includes the evaluation of nuclear MPI techniques, including positron emission tomography (PET) and single photon emission computed tomography (SPECT), with the non-nuclear MPI technique of cardiovascular magnetic resonance imaging (CMR). Various performance metrics were used to evaluate AI tools. They are F1-score, recall, specificity, precision, accuracy, and area under the receiver operating characteristic curve (AUC-ROC). For MPI techniques, the evaluation criteria include specificity, sensitivity, radiation dose, cost of scan, and study duration. The analysis was evaluated and compared using the fuzzy-based preference ranking organization method for enrichment evaluation (PROMETHEE), the multi-criteria decision-making method (MDCM). According to the study's findings, considering selected performance metrics or criteria, RF is the most efficient AI tool for SPECT MPI in the diagnosis of CAD with a net flow (Φnet ) of 0.3778, and it's revealed that CMR is the most efficient MPI technique for CAD diagnosis with a net flow of 0.3666. By expanding this study, more comprehensive evaluations can be made in the diagnosis of CAD. It was concluded that CMR outperformed the nuclear MPI techniques. SPECT, as the least advantageous technique, remained below average on other criteria except for the cost of the scan. Integrating the RF algorithm, which stands out as the most effective AI tool in diagnosing CAD, with SPECT MPI may contribute to SPECT becoming a superior alternative.

Integrating ensemble learning and meta bagging techniques for temperature-specific State of Health prediction in Lithium-ion Batteries

Accurately predicting the State of Health (SOH) of lithium-ion batteries is crucial in the field of battery technology to guarantee operational dependability and durability. This study introduces a novel methodology that integrates ensemble learning and meta-bagging techniques to enhance the precision of SOH prediction, particularly in scenarios characterized by fluctuating temperature conditions. We start our methodology by conducting an extensive data-gathering phase, specifically targeting crucial variables: temperature, voltage, current, and capacity. Subsequently, a thorough data preparation stage ensues, encompassing tasks such as cleansing, standardization, and curating relevant features, with particular attention given to temperature characteristics. Next, we employ a temperature-adaptive ensemble learning model that combines different prediction algorithms, such as Linear Regression (LR) and Long Short-Term Memory (LSTM) networks. These algorithms are trained on temperature-stratified data subsets using a Meta-Bagging Estimator (MBE). This strategy enhances the precision of predictions and the ability to overcome the obstacle of temperature changes impacting battery performance. The efficacy of the ensemble model is thoroughly assessed utilizing diverse performance criteria, showcasing its improved predictive capability compared to conventional approaches. Our findings indicate that this customized strategy is better equipped to manage the intricate dynamics of lithium-ion battery behavior, resulting in more dependable and precise assessments of battery SOH. This work substantially contributes to battery health monitoring and can significantly advance battery management systems.

DESIGN AND STATIC ANALYSIS OF PRISMATIC PRESSURE VESSELS ACCORDING TO TS 13445-3 STANDARD

The focus of the presented work is on the design and analysis of prismatic pressure vessels. Engineering calculations, design parameters and performance criteria of pressure vessels are available in the literature in detail, but there are deficiencies in the evaluation of the manufacturing processes of prismatic pressure vessels and the mathematical formulae required by these processes in the light of standards. In this context, it was investigated how a special prismatic pressure vessel, a square profile hyperbaric oxygen chamber, could be optimised in terms of structural strength, stress distribution and safety requirements. The material selection and design of such a chamber has been evaluated using DBA and DBF methods. The performance of the prismatic hyperbaric oxygen chamber in current applications is explained, supported by various engineering analyses and simulations. It was observed that the cabin designed in accordance with TS 13445-3 standards is 2 times safe under operating conditions and the amount of displacement is commercially acceptable. It is shown with SolidWorks analysis.

Noise assessment of multirotor configurations during landing proceduresa).

This study numerically investigates the noise impact of multirotor aerial vehicles with different rotor scales during landing procedures. The operational environments of individual rotors are influenced by rotational speed and wake dynamics, leading to variations in landing noise characteristics. Noise impacts are evaluated across various landing operations from both physical and psychoacoustic perspectives using noise source hemispheres and noise maps. The physical noise impact is quantified using sound exposure level (SEL), while the psychoacoustic impact is assessed through a psychoacoustic annoyance (PA) based on sound quality metrics. Performance contours are established to compare noise impacts alongside other factors, such as energy consumption, landing duration, vehicle attitudes, and safety considerations. The combined effect of noise source strength and landing duration determines SEL, while PA is primarily influenced by acoustic loudness, which follows a similar trend to noise source strength. Consequently, physical and psychoacoustic noise impacts exhibit distinct trends based on the landing operations. This study outlines a process for optimizing landing operations that meet predefined performance goals while minimizing noise impacts. Because operational performance varies significantly across different landing procedures and vehicle types, the study emphasizes the importance of incorporating comprehensive performance criteria in the design of landing operations.

Development of a Rubric using RubiStar

This paper discusses the development of a rubric using RubiStar, an online tool that facilitates the creation and customization of assessment rubrics for educators. The study highlights the importance of rubrics in providing clear expectations and criteria for student performance, thereby enhancing learning outcomes. By utilizing RubiStar, teachers can quickly generate rubrics tailored to specific assignments, saving time while ensuring that assessment criteria are transparent and accessible. Additionally, the paper explores how RubiStar's rubric analysis feature can assist educators in evaluating class performance, identifying areas for improvement, and refining instructional strategies. The development of a rubric using RubiStar offers a streamlined approach to creating effective assessment tools tailored to specific learning objectives. This platform not only simplifies the rubric creation process but also allows teachers to select from various templates and customize criteria based on their unique instructional needs. The user-friendly interface enables educators to modify existing templates or create new rubrics from scratch, promoting efficiency without sacrificing quality. Ultimately, this research underscores the value of integrating technology into the rubric development process to support effective teaching and learning, fostering a culture of continuous improvement in student achievement.

FACTORS ASSOCIATED WITH NURSE PERFORMANCE AT THE INPATIENT INSTALLATION OF THE SOUTH TANGERANG CITY GENERAL HOSPITAL IN 2023

The primary focus of health services is nurse performance since maintaining and enhancing the quality of health services requires assessment. A bridge to guaranteeing the caliber of services rendered to patients is good performance. The fact that performance statistics did not rise by 50% between 2013 and 2015—the target performance criterion is 70–80%—indicates that performance levels among Indonesian nurses are still very poor and have not significantly improved. Data on nurse performance at RSU Kota Tangerang Selatan for the years 2019 (82%), 2020 (85.4%), 2021 (74.3%), and 2022 (75.8%). It is well known that in 2021–2022, nurses' performance did not meet the 80% target. Finding the variables associated with nurses' performance at the South Tangerang City Public Hospital's inpatient installation in 2023 was the aim of this study. This study's methodology combined a cross-sectional study design with a quantitative approach. With 59 respondents overall, complete sampling was the sample strategy employed in this investigation. The data was analyzed using the chi-square test and SPSS software. Age, gender, education, and years of service did not correlate with nursing performance, according to the bivariate analysis (p-value 0.503; 0.481; 0.219; 0.814 > 0.05). However, there was a correlation between nurse performance and the work environment (p-value 0.000 < 0.05) and work discipline (p-value 0.008 <0.05).