Values Of Weighting Factors Research Articles

The reputation-based reward mechanism promotes the evolution of fairness

In real life, a good reputation generally brings positive returns to individuals. For example, merchants with numerous good reviews usually gain higher profits. Considering this in the ultimatum game, we propose a reputation-based reward mechanism to investigate the evolution of fairness. Specifically, individuals' reputations evolve dynamically based on the outcomes of games. At the same time, we set a reputation threshold in the population. When individuals' reputations exceed the reputation threshold, they are considered excellent. Otherwise, they are ordinary. The excellent individuals can receive extra rewards compared to the ordinary ones. Finally, individuals' total payoffs determine their fitness within the population. Based on these settings, this paper mainly explores how reputation threshold, weight factor and reward strength affect the evolution of fairness. Through a series of simulations, the reputation-based rewards mechanism is proved to effectively promote the fairness in the population. To be specific, we find that higher reputation thresholds and smaller values of weight factor significantly enhance the promotion effect of reward on fairness. Simultaneously, there is a specific correspondence between the reputation threshold and the weight factor. When reward strength is fixed, for different reputation thresholds, the optimal value of weight factor to achieve maximum fairness levels also varies. Additionally, increasing reward strength can significantly promote fairness.

An artificial fish swarm optimization algorithm for the urban transit routing problem

The Urban Transit Routing Problem (UTRP) is an NP-hard discrete problem that deals with the design of routes for public transport systems. It is a highly complex, multiply constrained problem, while the evaluation of candidate route sets can prove both challenging and time-consuming, with many potential solutions rejected on the grounds of infeasibility. Due to its difficulty, metaheuristic methods, such us swarm intelligence algorithms, are considered highly suitable for the UTRP. The suitability of these methods heavily relies on the correct adaptation of the chosen method for a discrete-space problem, the initialization procedure, and the solution evaluation method. In this context, this study proposes an artificial fish swarm optimization algorithm for the efficient solution of the UTRP, presenting a novel discrete-space adaptation of the former. The results are subsequently compared to 14 other algorithms, including evolutionary, swarm intelligence and hyper-heuristic implementations, using Mandl’s widely used and accepted benchmark. Comparison of the produced solutions with published results on Mandl’s benchmark network, shows that the developed algorithm yields superior results to the existing techniques, yielding very high shares of direct trip coverage, which is vital for transit systems to attract riders and contribute to urban sustainability. A new indicator for operator cost calculation is also developed and integrated into our analysis, offering insights on the trade-offs between user and operator costs. Differences in generated solutions, influenced by the weighting factor value, can result in variations of up to 13% in direct trip coverage and 1.5 minutes in average travel time.

Cell formation and layout design using genetic algorithm and TOPSIS: A case study of Hydraulic Industries State Company.

Cell formation (CF) and machine cell layout are two critical issues in the design of a cellular manufacturing system (CMS). The complexity of the problem has an exponential impact on the time required to compute a solution, making it an NP-hard (complex and non-deterministic polynomial-time hard) problem. Therefore, it has been widely solved using effective meta-heuristics. The paper introduces a novel meta-heuristic strategy that utilizes the Genetic Algorithm (GA) and the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) to identify the most favorable solution for both flexible CF and machine layout within each cell. GA is employed to identify machine cells and part families based on Grouping Efficiency (GE) as a fitness function. In contrast to previous research, which considered grouping efficiency with a weight factor (q = 0.5), this study utilizes various weight factor values (0.1, 0.3, 0.7, 0.5, and 0.9). The proposed solution suggests using the TOPSIS technique to determine the most suitable value for the weighting factor. This factor is critical in enabling CMS to design the necessary flexibility to control the cell size. The proposed approach aims to arrange machines to enhance GE, System Utilization (SU), and System Flexibility (SF) while minimizing the cost of material handling between machines as well as inter- and intracellular movements (TC). The results of the proposed approach presented here show either better or comparable performance to the benchmark instances collected from existing literature.

Fuzzy Use Case Points as a Basis for Effort Estimation

Many software development projects encounter problems related to over- or under-estimation of effort. Accurate effort estimation is crucial for successful project management, but it can be challenging when resources are limited, and little is known about the project. The commonly used method for effort estimation is Use Case Points (UCP), which is mainly used for application-based objects and takes use cases as input. However, UCP has weaknesses, particularly in the high variation of weight factor values for Unadjusted Use Case Weight (UUCW). To address this problem, Fuzzy Use Case Points (FUCP), which is a combination of fuzzy logic and use case points, can be used. By applying fuzzy logic to the UUCW category, FUCP derives new weight factor values for UUCW. The implementation of FUCP to calculate effort estimation in ten government-based projects in this research has shown that FUCP yields the closest value to the actual effort required. It has also been demonstrated that FUCP outperforms UCP in terms of accuracy, with an improvement of 6.51%.

Optimisation of Model Predictive Torque Control Strategy with Standard and Multi-Objective Genetic Algorithms

Abstract In this paper, the flux error-related weighting factor (WF) of the predictive torque control (PTC) strategy for induction motor (IM) control is optimised by a standard genetic algorithm (SGA) through speed errors only and multi-objective genetic algorithm (MOGA) through torque and flux errors. This paper compares the performances of both optimisation methods. Compared to MOGA, SGA offers a straightforward way to select WF and does not need a decision-making method to choose a final solution. But MOGA considers the given problem in a multi-objective way and directly optimises the control objectives of the PTC strate-gy. Comparisons are made over the flux and torque ripples, total harmonic distortion of stator phase current, and average switching frequency for different operating conditions. Simulation results show that both methods choose a close WF value. Consequently, SGA stands out in the optimisation of the PTC strategy with its simple structure.

A Radiomics-Integrated Deep Learning Model for Identifying Radionecrosis Following Brain Metastasis Stereotactic Radiosurgery (SRS)

<h3>Purpose/Objective(s)</h3> To develop a radiomics-integrated deep learning (RIDL) model for identifying radionecrosis in brain metastasis patients with post-SRS radiographic progression. Such a novel model is hypothesized to outperform classic radiomics and deep learning approaches in radionecrosis/recurrence differentiation. <h3>Materials/Methods</h3> The model was developed based on a 51-patient cohort at our institution with post-SRS radiographic progression and known biopsy outcome (37 radionecrosis, 14 recurrence) from Laser Interstitial Thermal Therapy (LITT). Utilizing the 3-month post-SRS high resolution T1+c volume, the RIDL model comprises three key steps: 1) 184 radiomics features (RFs) are extracted from within the SRS planning target volume (PTV) and 60% isodose volume (V₆₀) followed with Z-score normalization; 2) a deep neural network (DNN) mimicking the encoding path of U-net is trained for radionecrosis/recurrence prediction using the 3D volume. Prior to the binary prediction output, latent variables in the DNN are extracted as 512 deep features (DFs); and 3) all extracted features are synthesized as an input of support vector machine (SVM) execution. Key features with higher linear kernel weighting factor values are identified by clustering analysis, and these key features are utilized by SVM to generate the final radionecrosis/recurrence prediction result. During the model training, a 7:3 training/test data sample ratio was adopted, and 50 model versions were acquired with random validation sample assignments. Sensitivity, specificity, accuracy, and ROC of the model were evaluated, and these results were compared with 1) classic radiomics-based prediction (i.e., radiomics features-only for SVM input) and 2) DNN prediction results. Potential associations between the identified key radiomics/deep features and patients' genetic profiles were studied. <h3>Results</h3> As seen in the table, while radiomics-based prediction achieved acceptable accuracy but very low sensitivity, DNN prediction achieved similar accuracy with an improved sensitivity; in contrast, the RIDL achieved the best prediction accuracy and sensitivity results with 32 identified key features (3 RFs+29 DFs), and it also demonstrated superior ROC results. For patients with NSCLC primary disease, 2 RFs extracted in SRS V₆₀ exhibited significant correlation with ALK mutation (R>0.6), and 1 DF exhibited significant correlation with EGFR mutation (R>0.7). <h3>Conclusion</h3> The developed RIDL can accurately differentiate brain metastasis radionecrosis/recurrence using a single post-SRS MR scan. Future work that investigates its integration with genomics as a comprehensive radiogenomic tool is indicated.

High accuracy hybrid CNN classifiers for breast cancer detection using mammogram and ultrasound datasets

Breast cancer is a significant cause of cancer fatality among women all over the world. Hence the detection of this disease at the initial stage works as a boon to the patient so that proper treatment can be provided. We have developed five new deep hybrid convolutional neural network-based breast cancer detection frameworks in this work. The proposed hybrid schemes exhibit better performance than the respective base classifiers keeping the combined benefits of both the networks. In addition, a probability-based weight factor (w) and threshold value (β) play a crucial role in making an efficient hybridization. Experimentally selected optimum threshold value (β) makes the system faster and more accurate. More importantly, unlike traditional deep learning methods, the proposed framework yields excellent performance even in small datasets. The proposed scheme is validated with datasets of two different breast cancer modalities: mini-DDSM (mammogram), BUSI and BUS2 (ultrasound). The experimental results demonstrate the superiority of the proposed ShuffleNet-ResNet scheme over the current state-of-the-art methods in all the mentioned datasets. Moreover, the proposed scheme achieves the accuracy of 99.17%, 98.00% for abnormality and malignancy detection in mini-DDSM respectively, and 96.52%, 93.18% for abnormality and malignancy detection BUSI datasets, respectively. BUS2 delivers 98.13% accuracy for malignancy detection.

FECUNDITY OF GLOSSOGOBIUS GIURIS (HAM.) AND RELATIONSHIP BETWEEN FECUNDITY AND LENGTH-WEIGHT

The fecundity of Glossogobius giuris (Hamilton) was estimated from 60 gravid females collected from local markets of Khulna during May to July 2005. Total length (TL), standard length (SL), total weight (W) and gonad weight (GW) were recorded and standard methods were used to calculate the fecundity and length-weight relationship. The fecundity of the species ranged from 39,173 (total length of 14.4 cm, body weight 22.79 g and egg weight 1.49 g ) to 557,892 (total length of 24.06 cm, body weight 104.56 g and egg weight 11.5 g). The mean fecundity was recorded as 280,315.89±102,223.51. The total gonad weight varied from 1.49 g to 11.5 g where the weight of the left lobe and right lobe of the ovary varied significantly (p&lt;=0.05; t-value= - 2.536, equal variances assumed) from 0.7 g to 4.98 g and 0.71 g to 5.06 g respectively. The mean total egg weight was calculated at 5.99±1.82 g where the mean left lobe was calculated at 2.75±0.91 g and right lobe 3.17±0.90 g. The condition factor varied from 4.47 to 9.69 and the mean value was 8.09±0.72. The regression equation for total length and fecundity of the fish was estimated as F = 39,515.10 T – 531,548.17 (r = 0.683), Standard length and fecundity estimated as F = 50,366.48 S –502,295.20 (r = 0.664), Body weight and fecundity estimated as F = 4,430.22 W – 35,322.55 (r = 0.715), Gonad weight and fecundity estimated as F = 51,556.05 G – 28,762.62 (r =0.918), fecundity and condition factor estimated as F = 213,986.53 + 8,203.75 cf (r = 0.058). The diameter of the eggs of the left lobe varied from 5.3 to 8.6 µm with a mean of 7.4±0.6 µm while the right lobe varied from 5.1 to 8.9 µm with a mean of 7.2±0.8 µm. At 5% level of significance, log value of fecundity-gonad weight and fecundity-condition factor showed the most and the least significant relationship respectively.

Polynomial-time assignment-based cell association with generic utility functions

Cell association is a significant research issue in future mobile communication systems due to the unacceptably large computational time of traditional schemes. This article proposes a polynomial-time cell association scheme which not only completes the association in polynomial time but also fits for a generic optimization objective function. On the one hand, traditional cell association as a non-deterministic polynomial (NP) hard problem with a generic utility function is heuristically transformed into a 2-dimensional assignment optimization and solved by a certain polynomial-time algorithm, which significantly saves computational time. On the other hand, the scheme jointly considers utility maximization and load balancing among multiple base stations (BSs) by maintaining an experience pool storing a set of weighting factor values and their corresponding performances. When an association optimization is required, a suitable weighting factor value is taken from the pool to calculate a long square utility matrix and a certain polynomial-time algorithm will be applied for the association. Comparing with several representative schemes, the proposed scheme achieves large system capacity and high fairness within a relatively short computational time.

An optimal form of the finite element mass matrix in the analysis of longitudinal vibrations of rods

In this paper, an attempt is made to find the optimal form of the mass matrix of a rod finite element, which allows one to obtain the smallest errors in the longitudinal frequency determination of natural vibrations of any boundary conditions within the whole range of determined frequencies. It is assumed that the mass matrix can be treated as a linear combination of the consistent and diagonal matrices. Based on analytical considerations, the optimal values of certain weights for creating a linear combination of the mentioned matrices have been determined. As a result, a mass matrix has been obtained which allows the determination of natural frequencies with the smallest mean error within the possible spectrum of frequencies. It is also shown that the value of the weight coefficient changes depending on the number of natural frequencies within the spectrum one wants to determine. • Determination of the optimal form of a rod finite element mass matrix in the context of minimal errors in calculation of natural frequencies for longitudinal vibration. • Calculation of certain values of weights in order to determine the natural frequencies with the smallest mean error in the range of analysed frequencies. • Determination of optimal values of weight factors.

High-Accuracy Relative Biological Effectiveness Values Following Low-Dose Thermal Neutron Exposures Support Bimodal Quality Factor Response with Neutron Energy.

Theoretical evaluations indicate the radiation weighting factor for thermal neutrons differs from the current International Commission on Radiological Protection (ICRP) recommended value of 2.5, which has radiation protection implications for high-energy radiotherapy, inside spacecraft, on the lunar or Martian surface, and in nuclear reactor workplaces. We examined the relative biological effectiveness (RBE) of DNA damage generated by thermal neutrons compared to gamma radiation. Whole blood was irradiated by 64 meV thermal neutrons from the National Research Universal reactor. DNA damage and erroneous DNA double-strand break repair was evaluated by dicentric chromosome assay (DCA) and cytokinesis-block micronucleus (CBMN) assay with low doses ranging 6–85 mGy. Linear dose responses were observed. Significant DNA aberration clustering was found indicative of high ionizing density radiation. When the dose contribution of both the 14N(n,p)14C and 1H(n,γ)2H capture reactions were considered, the DCA and the CBMN assays generated similar maximum RBE values of 11.3 ± 1.6 and 9.0 ± 1.1, respectively. Consequently, thermal neutron RBE is approximately four times higher than the current ICRP radiation weighting factor value of 2.5. This lends support to bimodal peaks in the quality factor for RBE neutron energy response, underlining the importance of radiological protection against thermal neutron exposures.

Power Management of Hybrid Grid System With Battery Deprivation Cost Using Artificial Neural Network

Continuous power supply in an integrated electric system supplied by solar energy and battery storage can be optimally maintained with the use of diesel generators. This article discusses the optimum setting-point for isolated wind, photo-voltaic, diesel, and battery storage electric grid systems. Optimal energy supply for hybrid grid systems means that the load is sufficient for 24 h. This study aims to integrate the battery deprivation costs and the fuel price feature in the optimization model for the hybrid grid. In order to count charge–discharge cycles and measure battery deprivation, the genetic algorithm concept is utilized. To solve the target function, an ANN-based algorithm with genetic coefficients can also be used to optimize the power management system. In the objective function, a weight factor is proposed. Specific weight factor values are considered for simulation studies. On the algorithm actions, charging status, and its implications for the optimized expense of the hybrid grid, the weight factor effect is measured.

Formulation of indoor air comfort index for office building [A case study in sub-district office

The factor that needs have considered in the design and construction of an office building is worker comfort. This research was conducted to formulate the indoor air comfort index (IAC) by considering temperature, relative humidity, noise, light intensity, and odour. Measurements taken are temperature, noise level, and light intensity, while to measure odour impression, an odour hedonism scale was used. Weighting is carried out to determine the level of influence of each parameter based on 58 respondents. The weighting factors were obtained for temperature 0.28, relative humidity 0.16, noise 0.19, light intensity 0.22, and odour 0.15. The IAC index formula is the total multiplication between the index of each parameter and the weighting factor value. The IAC index is set at five levels, from very comfortable to uncomfortable. Based on the results of the calculation, the Sub-district head’s room and Secretary’s room obtained an IAC index of 4.1 (very comfortable), Government’s room and Economic Development’s room obtained an IAC index of 3.0, and PKK room got an IAC index of 2.6 (quite comfortable), and ten other rooms got an IAC index ranging from 3.1-3.8 (comfortable).

Neural network method of the decision Of the nonlinear problem of optimum distribution of the non-uniform resource

Given article is devoted features of the decision of a problem of integer nonlinear programming, by means of developed neural network method and algorithm of nonlinear optimization of means «decision Search» tabular processor Microsoft Excel. In offered neural network method the task in view decision is made by means of a recurrent neural network (RNN) matrix architecture with m neurons in each line and n neurons in each column. All neurons such network are connected with each other by communications, and the signal from an exit neuron can move on its input. Neural network method is characterized by that on inputs mentioned RNN the entrance vector of values of parameters of optimized nonlinear criterion function of a problem of distribution of a non-uniform resource moves, calculation of values of weight factors connected among themselves neurons is carried out and signal RNN is formed. This signal by means of nonlinear function will be transformed to the discrete target signal characterizing values quasi-optimal of the decision of the mentioned problem which size changes from 0 to 1. The estimation of efficiency of the decision of a considered problem was carried out at its various values of an indicator of efficiency on the basis of developed imitating model RNN. As indicators of efficiency of application offered neural network method were used – an average relative error and time of the decision of a problem. The value received by means of algorithm of nonlinear optimization of means was accepted to the exact decision «decision Search» tabular processor Microsoft Excel. The analysis of the received results of the experimental researches, offered neural network method, has allowed to make the conclusion that in comparison with an existing method of nonlinear optimization of tabular processor Microsoft Excel use offered neural network method allows essentially (in 9,4 times) to lower time of the decision of a problem dimension 10 × 8 (m ×n) and thus to provide accuracy of its decision not less than 99,8 %.

Multi objective optimization of aspirating smoke detector sampling pipeline

This work presents multi-objective optimization of an aspirating smoke detection system which uses the pipeline to transport air sample from the sampling points to the analysing module. On the basis of 3D computational fluid dynamics simulation it has been shown that the smoke transport will not always take place in the centre of the pipe and that one dimensional analysis is not able to provide information in which layer smoke transport will take place. Consequently, velocity from the layer at the distance of 0.95R from the pipe centreline was taken as an input for calculation of the transport time to bind the calculation on the safe side. By employing the multi-objective optimization approach, balancing of specific transport time and volume flow at the sampling points’ location was achieved through pipeline configuration variation with respect to pipe diameters and the position of branches along the main pipe. Objective function was assembled from the flow rate function and transport time function using the weighted sum method. Results for five different values of weight factor have been discussed. After reaching weight factor value of 0.75, configuration reached requested sensitivity, and further increase of weight factor enabled more uniform flow across the network without deterioration of transport time.

Ergonomic Reliability Assessment for Operation Process of Medical Device Interface Design Based on OAT and CREAM

This paper uses OAT (Operator Action Tree) and CREAM (Cognitive Reliability and Error Analysis Method) to analyze the interface design of medical equipment according to human error, discuss how to accurately describe the operator’s failure behavior, determine the development sequence of important human factors events and the feasible calculation method according to the system response, establish a complete ergonomic reliability assessment modeling by example demonstrations. This study can better describe the relationship between software interface reliability and ergonomic reliability. In addition, this paper makes improvement on the cognitive reliability and error analysis. In this paper, based on the basic concept and core idea of CREAM method, MADM (Multi-attribute Decision-making) is used to improve the weighting factor value of CPCs (Common Performance Conditions) provided by CREAM method. After the OAT model building based on the operation process of medical device interface design, the adjusted failure probability prediction analysis method is applied to obtain the failure probability of OAT basic events, which makes CREAM method more suitable for the medical device accident analysis and risk assessment in complex environment.

New adaptive weight formulations for time-cost optimization

Modified Adaptive Weight Approach (MAWA) is one of the simplest methods used for solving time-cost optimization problems. This type of optimization problem, categorized as multi-objective optimization, can be solved with metaheuristic algorithms. Here, metaheuristic algorithms evaluate a randomly generated solution set, referred to as population, within the boundary condition of a solution space. The weight factor values determined by MAWA are applied to all solutions in the population without distinguishing the solutions in that population. However, the potential solutions in the population indicate different fitness properties concerning solution space. In this paper to solve time-cost optimization problems, new adaptive weight formulations are proposed. In contrast to MAWA, the novelty of this study’s formulations is to adaptively detect the weight factor value, depending on the solution‘s fitness in the population. The results obtained from the numerical experiments examined in this study show that the proposed formulations can improve the performance of MAWA and can find identical or slightly different Pareto results for investigated multi-objective optimization problems.

Artificial Intelligence-Based Weighting Factor Autotuning for Model Predictive Control of Grid-Tied Packed U-Cell Inverter

The tuning of weighting factor has been considered as the most challenging task in the implementation of multi-objective model predictive control (MPC) techniques. Thus, this paper proposes an artificial intelligence (AI)-based weighting factor autotuning in the design of a finite control set MPC (FCS-MPC) applied to a grid-tied seven-level packed U-cell (PUC7) multilevel inverter (MLI). The studied topology is capable of producing a seven-level output voltage waveform and inject sinusoidal current to the grid with high power quality while using a reduced number of components. The proposed cost function optimization algorithm ensures auto-adjustment of the weighting factor to guarantee low injected grid current total harmonic distortion (THD) at different power ratings while balancing the capacitor voltage. The optimal weighting factor value is selected at each sampling time to guarantee a stable operation of the PUC inverter with high power quality. The weighting factor selection is performed using an artificial neural network (ANN) based on the measured injected grid current. Simulation and experimental results are presented to show the high performance of the proposed strategy in handling multi-objective control problems.

Global Maximum Power Point Tracking of PV Array Under Non-Uniform Irradiation Condition Using Adaptive Velocity Particle Swarm Optimization

Non-uniform irradiation condition (NUIC) is a condition of differences irradiation level received by each Photovoltaic (PV) on PV array. NUIC of PV array causes the emergence of several power peaks (consisting of several local peaks and one global peak) in the power-voltage (P-V) characteristic curve. This condition can cause several algorithms (hill-climbing / P&amp;O, IC) that are unable to reach the global peak as they are trapped at a local peak. This paper proposes an Adaptive Velocity Particle Swarm Optimization (AVPSO) algorithm to search the global peaks/Global Maximum Power Point (GMPP) of PV arrays under NUIC. The proposed algorithm is a modification of the PSO algorithm. AVPSO algorithm able to adjust its own weight factor values and cognitive acceleration coefficients depend on the distance of the particle's position now with the global best position during the tracking process. Adaptive weight factors can reduce the level of power or voltage oscillation during the tracking process until convergent, while the cognitive acceleration coefficient can prevent particles trapped at the local peak. Thus, the proposed AVPSO algorithm can reach GMPP with faster tracking time and low oscillation rates. In addition, this paper proposed an algorithm that can work both in static and dynamic NUIC patterns; thus, the proposed algorithm can track again when there is a change in global peak value in the PV array.

Infestation rate and impacts of Epipenaeon ingens on growth and reproduction of brown shrimp (Penaeus aztecus)

In this study, 515 Farfantepenaeus aztecus specimens were investigated in the Gulf of Antalya and 304 (59,02%) specimens (167 females and 137 males) infested with Bopyrid isopod parasite Epipenaeon ingens were found. In this study, we aimed to define the rate of infestation and its impact on survival rate and growth rate. Relationship of length- weight and condition factor values were lower in infested specimens compared to non-infested ones. While gonadal development was observed in different stages of non-infested female specimens, no gonadal development was observed in any infested specimens. Moreover, darker color and harder shell structure were observed in parasitized shrimps. During this research, infestation onto P. semisulcatus and P. kerathurus had been seen rarely.Keywords: Gulf of Antalya; Epipenaeon ingens, Farfantepenaeus aztecus; growth; reproduction