Weighted Averaging Method Research Articles

Development of electromagnetic pollution maps utilizing Gaussian process spatial models

The rapid proliferation of wireless technologies in everyday environments demands the quick and precise estimation of electromagnetic field distribution. This distribution is commonly depicted through the electric field strength across various geographical areas. The objective of this research is to determine the most effective geospatial model for generating a national-level electric field strength map within the 30 MHz–6 GHz frequency range. To achieve this, we employed five different methodologies for constructing the electric field strength map. Four of these methodologies are based on Gaussian process regression, while the fifth utilizes the classical weighted-average method of the nearest neighbor. Our study focused on a country with a total area of 9251 km2, using a dataset comprising 3621 measurements. The findings reveal that Gaussian process spatial models, also known as Kriging models, generally outperform other methods when applied to spatial data. However, it was observed that, after excluding some outlier data points, the performance of the classical nearest neighbor models becomes comparable to that of the Gaussian process models. This indicates the potential for both approaches to be effective, depending on the data quality and the presence of outliers.

Mapping and monitoring dense non-aqueous phase liquid source zone by fused surface and cross-borehole electrical resistivity tomography

Effective characterization of dense non-aqueous phase liquid (DNAPL) source zones is crucial for remediating polluted sites. DNAPL often reside as residuals or pools within high-permeability lenses and above impermeable layers due to soil heterogeneity, gravity, and capillary barriers. Given the high cost of drilling, electrical resistivity tomography (ERT) techniques—including surface ERT and cross-borehole ERT, are commonly used for DNAPL source zone mapping and monitoring. However, the low spatial resolution of ERT increases uncertainty in source zone investigations. This study proposes a method for improving DNAPL mapping and monitoring by fusing surface and cross-borehole ERT data. Sandbox experiments were conducted to simulate a heterogeneous DNAPL source zone, employing both ERT methods for static mapping and dynamic monitoring. Reflective light imaging (RLM) was used to visualize DNAPL migration and provide saturation data, allowing for the quantification of ERT's effectiveness in characterizing DNAPL distribution. The results indicate that individual ERT methods face significant challenges in DNAPL source zone mapping due to background interference. Surface ERT alone tends to underestimate the extent of deeper DNAPL source zones. However, fusing surface and cross-borehole ERT results in a complementary enhancement of vertical spatial resolution, thereby improving the characterization of DNAPL source zones. The fusion of static and time-lapse ERT data substantially enhances DNAPL source zone mapping and monitoring capabilities. By calculating the ratio of the ERT-monitored area to the actual area using resistivity change contours (5 %, 10 %, 15 %), it was found that fusing surface and cross-borehole ERT data improved monitoring resolution by 50.48 % compared to surface ERT alone and by 22.95 % compared to cross-borehole ERT. Principal component analysis (PCA) was effective in fusing time-lapse data, while the weighted average method (WAM) outperformed PCA for static resistivity data fusion.

Innovative IoT Threat Detection: Weighted Variational Autoencoder-Based Hunter Prey Search Algorithm for Strengthening Cybersecurity

Cybersecurity threat detection in the Internet of Things (IoT) identifies and mitigates risks associated with connected devices. The IoT devices are vulnerable to attacks as they do not contain any robust security mechanism. Traditional methods for cybersecurity-based threat detection face significant problems such as scalability, privacy concerns, and resource constraints when applied to the dynamic IoT environment. To tackle these challenges, this paper proposed a novel Weighted Variational Autoencoder-based Hunter Prey Search (WVA-HPS) algorithm for enhancing cybersecurity threat detection in IoT. In this study, a weighted Variational Autoencoder is employed for regulating weight mechanism using weight regularization and the weight average ensemble method. The Hunter Prey Search optimization (HPSO) algorithm is utilized for minimizing overfitting issues to enhance the efficiency of the WVA method. The proposed WVA-HPS model comprises five different stages such as data collection, data preprocessing, threat detection, model evaluation, and output. The study is validated on diverse datasets namely BoT-IoT, MQTTset, and IoT-23. The WVA-HPS method's performance is analyzed using the metrics namely precision, accuracy, specificity, F-measure, and recall and its performance is compared with existing methods. The experimental results illustrate the performance of the WVA-HPS method for cybersecurity threat detection in an IoT environment.

Design of Short-Term Power Load Forecasting Model Based on Deep Neural Network

In power system operation and planning, the accuracy of short-term power load forecasting is very important. Because of its powerful data processing and modeling ability, deep neural network has become an effective tool to accurately predict short-term power load. In this study, a short-term power load prediction model based on deep neural network is designed, which adopts deep long short-term memory and threshold period unit model, and combines Boosting algorithm for model fusion. The results show that the average absolute percentage error of the model fused by Boosting algorithm is 0.07%, which is 1.02% lower than the average weight method and 0.59% lower than the reciprocal error method. Boosting fusion model can effectively reduce the overall prediction error and maintain high stability of prediction error at peak, plateau and time sampling points, so as to achieve good prediction effect. Specifically, the MAPE of the model fused using Boosting algorithm is 0.07% (95% confidence), which is 1.14% higher than the average weight method and 0.79% higher than the reciprocal error method. The design of short-term power load forecasting model based on deep neural network can provide more accurate prediction for power system operation and planning, and help to improve the operation efficiency and reliability of power system. At the same time, the design and application of this model also provide a new idea and method for the application of deep learning in power system. The introduction of Boosting algorithm further improves the prediction accuracy and stability of the model, which is a major innovation in model design.

Nuclear mass predictions with the naive Bayesian model averaging method

A naive Bayesian model averaging (NBMA) method is developed to predict nuclear masses. In the NBMA method, the weights of different models may be different for each nucleus, which are sensitive to the model accuracies to describe the nuclear masses of the isotopes and isotones with the same proton and neutron numbers of that nucleus. Therefore, there are remarkable local structures for the weights of different models on the nuclear chart, which well eliminates the local deviations between the model predictions and the experimental masses and thus achieves better accuracy of mass predictions than the traditional arithmetic mean method (AMM) and weighted average method (WAM). Based on the latest atomic mass evaluation of AME2020, the root-mean-square (rms) mass deviation of the NBMA method is 0.293 MeV, while the rms deviations of AMM and WAM are 0.634 and 0.361 MeV, respectively. This accuracy of the NBMA method is even 28% better than the best accuracy of the mass models used in the NBMA method. The extrapolation ability of the NBMA method is also verified with the experimental nuclear masses which are not used in the training of the NBMA method.

Strategic location analysis for offshore wind farms to sustainably fulfill railway energy demand in Turkey

Providing the energy demands of various sectors through the utilization of renewable energy sources offers a more sustainable and long-term approach compared to fossil fuel consumption. In this study, the proposition is made to address Turkey's rail industry energy demand through Offshore Wind Farms (OWF). Accordingly, the spatial analysis for determining the most suitable OWF location is explored. The research methodology is conducted based on a Geographic Information System-Multi-Criteria Decision Making (GIS-MCDM) hybrid approach. Alternative OWF locations are identified using GIS. Location selection criteria are established based on literature review, and a fuzzy-based decision matrix is constructed, incorporating expert opinions. To leverage two distinct fuzzy number sets, Intuitionistic Fuzzy Weighted Averaging (IFWA) and Spherical Fuzzy Stepwise Weight Assessment Ratio Analysis (SF-SWARA) methods are employed for criteria weighting, followed by the aggregation of criterion weights using the Einstein Operations t-norm technique. To ensure comparable OWF alternative rankings, five different alternative ranking methods are employed to obtain alternative rankings. Ultimately, the most suitable OWF location for meeting the energy demand of the rail industry is determined to be the Izmir region. The research findings contribute to managerial perspectives by proposing recommendations concerning strategic location selection, optimized resource allocation, risk mitigation and policy alignment and sustainable long-term planning.

Multisource information fusion considering the weight of focal element’s beliefs: a Gaussian kernel similarity approach

Similarity has been extensively utilized to measure the degree of conflicts between evidences in multisource information fusion. The existent works, however, assumed that the contribution of each focal element’s belief to the similarity measure is the same, and the influence of the weights of focal element’s belief is not considered, which is unreasonable. This article proposes a new Gaussian kernel similarity approach to measure the similarity between evidences. The proposed Gaussian kernel similarity coefficient can effectively take account of the weights of focal element’s beliefs. In addition, it possesses some preferable properties, such as, bounded, consistent, and symmetrical. A multisource information fusion method based on the Gaussian kernel similarity coefficient is, therefore, investigated. The developed method mainly contains three steps: (1) the Gaussian kernel similarity coefficient, as a connection, is leveraged to calculate the weight of evidences based on the weight of focal element’s beliefs; (2) the initial evidences are, thereby, modified based on the weight of evidence via the weight-average method; and (3) the final multisource information fusion can be achieved by the Dempster’s combination rule using the modified evidences. An illustrative example with single-element subset and an application with multi-element subset are presented, and it is verified that the proposed method is effective in dealing with conflicting evidences.

Full-range stress–strain relationship and fracture model for laser cladding additively manufactured 316L stainless steel sheets

Laser cladding (LC) is being used increasingly in civil engineering owing to its high efficiency and minimal distortion since requiring little heat input; especially for repairing or strengthening damaged metal structures. LC stainless steel sheets are characterised by a rounded stress–strain response, with two “knee” regions and no sharply defined yield point. Such behaviour cannot be accurately represented by using previous stress–strain models. Moreover, determining the true stress–strain curve and toughness coefficient for the fracture model is essential to carry out a non-linear finite element analysis for solving elastoplastic problems. In the present study, one simplified full-range engineering stress–strain model for LC 316L stainless steel sheets is proposed based on the three-stage Hradil model and is validated against the experimental results of thirty tensile coupon tests. The proposed model redefines the second and third stages of the stress–strain curve following the experimental observations. All parameters in this simplified model can be derived directly from only four basic material property parameters: the initial elastic modulus, 0.2 % proof stress, ultimate stress and the strain corresponding to the ultimate stress. For defining the fracture model of LC stainless steel sheets, an average weight method is employed to determine the post-necking true stress–strain curves. The recommended values of the weight factor and toughness coefficient for the LC sheets are also obtained based on the available tensile coupon test results. The comparisons between the experimental force–displacement curves of the tensile coupons and the finite element analysis results using the proposed stress–strain relationship and fracture model for the LC sheets shows both results to be in good agreement. Therefore, the stress–strain relationship and fracture model proposed in this paper is suitable for use in the analytical and numerical modelling of structures having LC 316L stainless steel sheets.

Improved geospatial analysis of shoreline modification using a weighted‐average‐based novel formulation

AbstractThis study presents a novel weighted‐average method (WAM) that captures the detailed nuances of shoreline change patterns over long and short periods by considering shorelines belonging to five different time frames. Using the WAM, the average shoreline modification for the longer period (136 years with intervals of ~34 years) and for the shorter period (annual changes over a period of 5 years) have been calculated for the 9 km stretch of the Kollam coast in Kerala, India. Historically, Kollam has been a prominent seaport on the Malabar Coast of India and was extensively mapped due to its importance for political, strategic, and trade reasons. The coastal information available in these older maps is unique as there are no comparable data for the coastline of that period that can be used to assess changes along the coast. The WAM demonstrated the efficacy of the historical maps as pivotal reference points. It reliably captured the different shoreline activities (i.e., growing, stable, and receding regions) of natural and anthropogenic origins within the study area, and the findings are consistent with results obtained by using the linear regression rate (LRR) and the end point rate (EPR) methods. This work presents both qualitative and quantitative analyses to evaluate shoreline change using various statistical and mathematical methods. The findings should serve as baseline information for coastal management, notably mitigating coastal hazards—whether of natural or anthropogenic origin—which could result in the enhancement of ecosystem services for coastal communities.

Post-necking behaviour of Al5052-H32 using weight average method

Post-necking behaviour of Al5052-H32 using weight average method

Weighted-average time-lapse seismic full-waveform inversion

As seismic data can contain information over a large spatial area and are sensitive to changes in the properties of the subsurface, seismic imaging has become the standard geophysical monitoring method for many applications such as carbon capture and storage and reservoir monitoring. The availability of practical tools such as full-waveform inversion (FWI) makes time-lapse seismic FWI a promising method for monitoring subsurface changes. However, FWI is a highly ill-posed problem that can generate artifacts. Because the changes in the earth’s properties are typically small in terms of magnitude and spatial extent, discriminating the true time-lapse signature from noise can be challenging. Different strategies have been proposed to address these difficulties. In this study, we propose a weighted-average (WA) inversion to better control the effects of artifacts and differentiate them from the true 4D changes. We further compare five related strategies with synthetic tests on clean and noisy data. The effects of seawater velocity variation on different strategies also are studied as one of the main sources of nonrepeatability. We tested different strategies of time-lapse FWI (TL-FWI) using the Marmousi and the SEG Advanced Modeling time-lapse models. The results indicate that the WA method can provide the best compromise between accuracy and computation time. This method also provides a range of possible answers of other TL-FWI strategies.

UNav: An Infrastructure-Independent Vision-Based Navigation System for People with Blindness and Low Vision.

Vision-based localization approaches now underpin newly emerging navigation pipelines for myriad use cases, from robotics to assistive technologies. Compared to sensor-based solutions, vision-based localization does not require pre-installed sensor infrastructure, which is costly, time-consuming, and/or often infeasible at scale. Herein, we propose a novel vision-based localization pipeline for a specific use case: navigation support for end users with blindness and low vision. Given a query image taken by an end user on a mobile application, the pipeline leverages a visual place recognition (VPR) algorithm to find similar images in a reference image database of the target space. The geolocations of these similar images are utilized in a downstream task that employs a weighted-average method to estimate the end user's location. Another downstream task utilizes the perspective-n-point (PnP) algorithm to estimate the end user's direction by exploiting the 2D-3D point correspondences between the query image and the 3D environment, as extracted from matched images in the database. Additionally, this system implements Dijkstra's algorithm to calculate a shortest path based on a navigable map that includes the trip origin and destination. The topometric map used for localization and navigation is built using a customized graphical user interface that projects a 3D reconstructed sparse map, built from a sequence of images, to the corresponding a priori 2D floor plan. Sequential images used for map construction can be collected in a pre-mapping step or scavenged through public databases/citizen science. The end-to-end system can be installed on any internet-accessible device with a camera that hosts a custom mobile application. For evaluation purposes, mapping and localization were tested in a complex hospital environment. The evaluation results demonstrate that our system can achieve localization with an average error of less than 1 m without knowledge of the camera's intrinsic parameters, such as focal length.

Assessing Dynamic Load Allowance of the Negative Bending Moment in Continuous Girder Bridges by Weighted Average Method

Accurate acquisition of dynamic load allowance (DLA) based on measurement data is essential to the safety assessment of a bridge. When static load tests cannot be achieved, and filtering fails, the estimated DLAs from the experimental method vary widely due to the choice of a left or right band. In this paper, the proposed weighted average method (WAM) is used to possibly solve the above problem in continuous gird bridges. Two-span and three-span precast concrete box-gird bridges were selected to optimize intercepted segments of WAM for the first time with the assistance of standard deviation and coefficient of variation in statistics. Then, a DLA measurement case of the negative bending moment was utilized to verify the validity of the WAM. The results show that the intercepted segments of 10/16 to 1 times the span length were suitable for the WAM to calculate the DLA of the negative bending moment due to small offset moments and stable variation coefficients. The WAM had a strong anti-interference ability of outliers filtering in “bad data,” which differed significantly from the experimental method. In three measurements of a field bridge, DLAs obtained by the WAM had less dispersion than the experimental and low-pass filtering methods.

INTERNET OF THINGS-ENABLED TOURISM ECONOMIC DATA ANALYSIS AND SUPPLY CHAIN MODELING

The purpose is to cut the costs of Supply Chain enterprises in Ice-Snow Tourism (IST) and improve the intelligence and automation of Supply Chain Management (SCM). First, the spatial-temporal characteristics of economic data of the IST Supply Chain are analyzed based on the Internet of Things (IoT). Second, the annual Online Public Attention (OPA) data to IST in domestic cities and regions are collected. The quarterly concentration index and Gini coefficient are used to analyze their spatial and temporal characteristics. Then, the weighted fusion algorithm used for the Supply Chain scenario modeling is improved to solve data redundancy and improve information accuracy. Finally, the framework of the IST-oriented Supply Chain scenario ontology model is proposed. The experimental results show that Internet users give much attention to IST from 2011 to 2021. OPA to IST increased first and decreased and peaked in 2016. The final fusion value of the proposed data fusion algorithm is 20.0221, and that of the adaptive Weighted Average Method (WAM) is 20.0724. Thus, the proposed algorithm outperforms the adaptive WAM. The traditional scenario-based ontology model takes people as the center. In contrast, the Supply Chain scenario-based ontology model centers around product state and scenario. Therefore, the proposed Supply Chain scenario-based ontology model is entirely new. The proposed scenariobased ontology model using polymorphic IoT lays the foundation for developing an intelligent and automatic SCM. It has great practical significance in realizing efficient tourism industry management and SCM.

Geospatial Technique Integrated with MCDM Models for Selecting Potential Sites for Harvesting Rainwater in the Semi-arid Region

Severe droughts and mismanagement of water resources during the last decades have propelled authorities in the Kurdistan Region to be concerned about better management of precipitation which is considered the primary source of recharging surface and groundwater in the area of interest. The drought cycles in the last decades have stimulated water stakeholders to drill more wells and store uncontrolled runoff in suitable structures during rainy times to fulfill the increased water demands. The optimum sites for rainwater harvesting sites in the Qaradaqh basin, which is considered a water-scarce area, were determined using the analytical hierarchy process (AHP), sum average weighted method (SAWM), and fuzzy-based index (FBI) techniques. The essential thematic layers within the natural and artificial factors were rated, weighted, and integrated via GIS and multi-criteria decision-making (MCDM) approaches. As a consequence of the model results, three farm ponds and four small dams were proposed as future prospective sites for implementing rainwater harvesting structures. The current work shows that the unsuitable ratio over the study area in all methods AHP, SAWM, and FBI occupied 12.6%, 12.7%, and 14.2% respectively. The area under the curve (AUC) and receiver operating characteristics were used to validate the model outcomes. The AUC values range from 0.5 to 1, meaning that all MCDM results are good or are correctly selected. Based on the prediction rate curve for the suitability index map, the prediction accuracy was 72%, 57%, and 59% for AHP, SAWM, and fuzzy overlay, respectively. The final map shows that the potential sites for rainwater harvesting or suitable sites are clustered mainly in the northern and around the basin’s boundary, while unsuitable areas cover northeastern and some scatter zones in the middle due to restrictions of geology, distance to stream with the villages, and slope criteria. The total harvested runoff was 377,260 m3 from all the suggested structures. The proposed sites may provide a scientific and reasonable basis for utilizing this natural resource and minimize the impacts of future drought cycles.

Channel estimation based on superimposed pilot and weighted averaging

Channel estimation based on superimposed pilot (SP) is a challenge in orthogonal frequency division multiplexing (OFDM) systems. To reduce the pilot data interference in the SP and estimate the channel state information accurately, a weighted averaging (WA) channel estimation method based on the superimposed pilot is proposed in this paper. At the transmitter, two signals with data symbols and pilot symbols superimposed at different subcarriers are transmitted. At the receiver, the elimination scheme is proposed to remove the pilot data interference. Based on the temporal correlation of wireless channels, the WA method is used to reduce the interference caused by additive white Gaussian noise in the channel. Simulation results verify that the proposed method can be applied to different channel scenarios. It has better normalized mean square error and bit error rate performance than other existing methods, and the superimposed pilot can improve the throughput of wireless communication systems.

Data Detection Device for Turret Motion Image Using Photoelectric Tracking Control System and Its Performance Analysis

The photoelectric tracking control system is a system composed of photoelectric signal detection, signal processing, servo control system and mechanical structure, which plays a crucial role in the field of target tracking. The turret needs to be tested when it leaves the factory, including high-low direction, horizontal direction, aiming speed and other indicators. Moreover, there are large errors in the manual testing methods such as recorder and recording pen, so the photoelectric tracking device is used for the detection of the turret system. First, the hardware of the photoelectric tracking system is designed, including the main control circuit, peripheral expansion module circuit, intelligent power drive circuit and a detection circuit. The system is connected to the camera. The optical signal sent by the system will hit the fixed target. The upper computer will track the trajectory of the light in real-time and then track the trajectory of the turret to obtain the coordinates of the optical signal and the offset angular velocity of the turret. First, color conversion is conducted on the image data collected by photoelectric tracking through the weighted average method. The complete motion path of the turret is obtained through an image denoising algorithm to obtain the motion speed of the turret. In the test, the photoelectric tracking system is first connected to the motor, and enters a stable state when the motor rotor is 80 ms. The rotor speed is faster. When the rotor speed decreases, the system’s current ripple is large, and then decreases rapidly. After the control system is installed in the turret, its display can be driven by FPGA programming. The VGA display of influence time is set, the editing program is downloaded to the FPGA development board, and the gray image of the turret motion track can be seen through the VGA display.

Assessment of gust factors and wind speed decomposition methods for thunderstorms

This paper examines wind decomposition methods for nonstationary thunderstorms based on the frequency responses. A wide range of methods have been used for this task, including weighted-average methods, Savitzky-Golay filters, and multi-resolution techniques. Two problems arise in the evaluation: (1) how to decide which methods are good, and (2) how to compare the wind properties from one decomposition to another. To determine the most suitable method, the overshoot and sharpness of their frequency responses are evaluated and compared. The half-power cutoff frequency is proposed to connect different methods, so that the wind properties, e.g., gust factor, can be compared. The choice of the cutoff frequency for thunderstorms is also examined with artificially generated nonstationary winds. Records from four thunderstorms are decomposed with the different methods and cutoff frequencies. The gust factors with different gust averaging times are calculated and compared with those for stationary winds. Underlying differences between the gust factor curves are also examined. Finally, the paper provides recommendations for the selection of decomposition method and cutoff frequency for nonstationary winds like those in thunderstorms.

Diatom response to environmental gradients in the high mountain lakes of the Colombia\u2019s Eastern Range

A survey of 60 high mountain lakes of Colombia’s Eastern Range was performed to evaluate the response of surface-sediment diatoms to environmental variables. In each one of these lakes, water samples were taken for physical and chemical characterization, and diatoms were collected from the superficial bottom sediment at the deepest part. Multivariate statistical analyses were made to determine the relationships between environmental and biological data, specifically which environmental variables explain the diatom distribution. For each of these significant environmental variables, optima and ecological tolerances were calculated using the weighted-average method, which allowed for the classification of the species according to their environmental preferences. The lakes showed a wide range of environmental gradients in variables such as pH, alkalinity, and nutrients. In addition, the depth of the lakes was a direct determinant of the light environment of the water column. A total of 339 diatom taxa were identified belonging mainly to the genera Eunotia and Pinnularia. Variables related to pH-alkalinity gradient, trophic condition (nitrates and phosphorus), and physical factors (radiation at the bottom) had a significant effect on diatom composition. Despite the fact that the total organic carbon environmental range was high, the effect of this variable on diatom species composition was not significant. In conclusion, the diatoms of the studied lakes showed a significant ecological relationship with environmental variables which are potentially important in environmental reconstruction. Diatoms in the study sites can provide useful and independent quantitative information to investigate the recent impacts of global change on tropical high mountain ecosystems.

Development and analysis of priority decision rules using MCDM approach for a flexible job shop scheduling: A simulation study

Flexible job shop scheduling problem (FJSSP) is a critical operational planning step in manufacturing that ensures on-time delivery of goods leading to customer satisfaction. The process of assigning jobs to machines has a larger solution space and consumes a huge amount of time and effort. This research focuses on solving FJSSP using the discrete event simulation (DES) model and integrates it with Composite Dispatching Rules (CDRs) and multi-criteria decision making (MCDMs) based priority rules. Performance evaluation of the flexible job shop is carried out to minimize the Makespan, mean Flow-Time, mean Tardiness, and maximum Tardiness. Best performing CDRs and hybrid MCDM techniques are employed for minimizing the objectives considered in this study. The proposed solution framework integrates Fuzzy Analytic Hierarchy Process (FAHP) for assigning weights to the criteria and MCDM approaches namely technique for order of preference by similarity to ideal solution (TOPSIS), The Evaluation based on Distance from Average Solution (EDAS), Compromise Programming (CP), and Weighted Average Method (WAM) separately to prioritize the jobs. Benchmark problems of different sizes are tested using the best performing CDRs available in the literature and proposed MCDM approaches. The proposed approach is implemented in a real-world large-scale flexible job shop (FJS) that produces auto components handling 114 job variants with 245 operations using 28 machines. The performance analysis indicates that no single rule outperforms all the objective measures considered in this study. The MCDMs based rules perform better compared to CDRs for large-scale problems by considering realistic criteria such as demand, due date, setup time, process time, customer priority, and number of operations. The methodology proposed in this study can be tailored in terms of the criteria used for ranking and production parameters to be executable for any real-time instance.