Sum Of Differences Research Articles

Optimal Estimation of Proton Exchange Membrane Fuel Cells Parameter Based on Coyote Optimization Algorithm

In recent years, the penetration of fuel cells in distribution systems is significantly increased worldwide. The fuel cell is considered an electrochemical energy conversion component. It has the ability to convert chemical to electrical energies as well as heat. The proton exchange membrane (PEM) fuel cell uses hydrogen and oxygen as fuel. It is a low-temperature type that uses a noble metal catalyst, such as platinum, at reaction sites. The optimal modeling of PEM fuel cells improves the cell performance in different applications of the smart microgrid. Extracting the optimal parameters of the model can be achieved using an efficient optimization technique. In this line, this paper proposes a novel swarm-based algorithm called coyote optimization algorithm (COA) for finding the optimal parameter of PEM fuel cell as well as PEM stack. The sum of square deviation between measured voltages and the optimal estimated voltages obtained from the COA algorithm is minimized. Two practical PEM fuel cells including 250 W stack and Ned Stack PS6 are modeled to validate the capability of the proposed algorithm under different operating conditions. The effectiveness of the proposed COA is demonstrated through the comparison with four optimizers considering the same conditions. The final estimated results and statistical analysis show a significant accuracy of the proposed method. These results emphasize the ability of COA to estimate the parameters of the PEM fuel cell model more precisely.

Population genetic structure of Bactrocera dorsalis based on cox1 sequences from Bangladesh and neighboring countries

• High gene flow was found among all the populations, except 4 locations. • The colonization process can be seen as a progressive to rapid expansion. • The sudden expansion of populations was found in BDJ and BDR region of Bangladesh. • B. dorsalis could be the main insect pest of fruit crops in Bangladesh. Oriental fruit fly, Bactrocera dorsalis (Hendel), is a destructive and highly polyphagous invasive fruit fly species of numerous fruit crops in global agriculture. Population genetic structure of this species from five different locations of Bangladesh was examined with other samples (collected from GenBank) from 15 sites of neighboring Asian countries. A fragment of 770 bp mitochondrial DNA cox1 was used to investigate the genetic diversity and the relationship between genetic patterns and geographical distribution of B. dorsalis . A total of 232 variable sites (33.23% of the 698 bp aligned consensus sequences) and 419 unique haplotypes were identified from 710 individuals. Indices of genetic diversity suggested that without exclusion from geographical areas, B. dorsalis retained a relatively high degree of genetic diversity. A demographic assessment [Tajimas’ D test, Fu’s Fs test and sum of square deviation (SSD values)] revealed that both current and historical variables performed a significant role in deciding the weak genetic structure with some exceptions. In Bangladesh, high levels of genetic diversity with a weak genetic structure indicated that the severity of this pest might increase in the future. Proper management techniques should be taken to overcome the future severity of this kind of destructive insect.

Transseptal Puncture Learning Curve for Transcatheter Edge-to-Edge Mitral Valve Repair.

This study examined the learning curve for transseptal puncture (TSP) during transcatheter edge-to-edge mitral valve repair (TEER) performed by a dedicated mitral valve structural heart team. Effective TSP is mandatory for TEER but can be time-consuming and associated with complications including pericardial effusion and cardiac tamponade. TSP was performed on 107 consecutive patients (76 ± 1 years, 52% male) undergoing TEER between 2014 and 2019. TSP was performed by each structural heart team member (1 cardiologist, 2 cardiac surgeons) on a rotating case-by-case basis. No team member had prior independent TSP experience. Data collected included total procedure time, TSP time (time elapsed between procedure start and septal crossing), and number of TSP attempts before successful puncture. Cumulative sum (CUSUM) of deviations from the mean across sequential cases were used to examine learning curves. Median total procedure time was 107 min, and the median TSP time was 14 min. Greater case number was significantly associated with both lower TSP time (r s = -0.22, P = 0.022) and lower total procedure time (r s = -0.29, P = 0.003). The majority of patients required only 1 TSP attempt (79%). There was a significant quadratic relationship between case number and the CUSUM for TSP time, with the learning curve peaking at 49 cases. TSP for TEER has a substantial learning curve, requiring >50 cases to achieve acceptable efficiency. Even once proficiency is demonstrated, TSP remains a time-consuming component of TEER. Improvements in transseptal access technology may significantly decrease the time needed to master TSP and may improve the safety and precision of the procedure.

Solution of the inverse kinetics problem for the catalytic cracking process based on a 16-component kinetic model

This article presents a 16-lump model of catalytic cracking of vacuum gasoil, which allows to take into account the quantity and quality of the main products of the process: gasoline, propane-propylene and butane-butylene fractions; and by-products of the process: light and heavy catalytic gasoil. To solve the direct problem of solving a system of differential equations, the Runge-Kutta method of order 4 is used, to solve the inverse problem of choosing the constants of the rate of chemical transformations, the method of direct search for minimizing the function of the sum of deviations of calculated values from experimental ones is applied. Thus, the reaction rate constants were chosen for the developed model.

Cooperative Stochastic Games with Mean-Variance Preferences

In stochastic games, the player’s payoff is a stochastic variable. In most papers, expected payoff is considered as a payoff, which means the risk neutrality of the players. However, there may exist risk-sensitive players who would take into account “risk” measuring their stochastic payoffs. In the paper, we propose a model of stochastic games with mean-variance payoff functions, which is the sum of expectation and standard deviation multiplied by a coefficient characterizing a player’s attention to risk. We construct a cooperative version of a stochastic game with mean-variance preferences by defining characteristic function using a maxmin approach. The imputation in a cooperative stochastic game with mean-variance preferences is supposed to be a random vector. We construct the core of a cooperative stochastic game with mean-variance preferences. The paper extends existing models of discrete-time stochastic games and approaches to find cooperative solutions in these games.

Optimalizační procedura pro agregaci pořadí v úlohách vícekriteriálního rozhodování

There are many different methods for analysis of multiple criteria decision-making problems. Considering the problems for evaluation of alternatives, a typical situation is that the application of various methods leads to different results, i.e. each method generates different ranking of alternatives. A similar situation occurs in case a certain number of individual decision-makers participates in the analysis of a problem. The paper contains a proposal of optimization procedures that aggregates several individual rankings into one final ranking. As a result, the decision-maker obtains an aggregated ranking that is based either on the minimization of the weighted sum of deviations of individual rankings from the final ranking or on the minimization of the maximum deviation over all individual rankings. The applicability of the procedures is illustrated on a simple numerical example.

Adaptive Multi-Task Human-Robot Interaction Based on Human Behavioral Intention

Learning from demonstrations with Probabilistic Movement Primitives (ProMPs) has been widely used in robot skill learning, especially in human-robot collaboration. Although ProMP has been extended to multi-task situations inspired by the Gaussian mixture model, it still treats each task independently. ProMP ignores the common scenario that robots conduct adaptive switching of the collaborative tasks in order to align with the instantaneous change of human intention. To solve this problem, we proposed an alternate learning-based parameter estimation method and an empirical minimum variation-based decomposition strategy with projection points, combining with linear interpolation strategy for weights, based on a Gaussian mixture model framework. Alternate learning of weights and parameters in multi-task ProMP (MTProMP) allows the robot to obtain a smooth composite trajectory planning which crosses expected via points. Decomposition strategy reflects how the desired via point state is projected onto the individual ProMP component, rendering the minimum total sum of deviations between each projection point with the respective prior. Linear interpolation is used to adjust the weights among sequential via points automatically. The proposed method and strategy are successfully extended to multi-task interaction ProMPs (MTiProMP). With MTProMP and MTiProMP, the robot can be applied to multiple tasks in industrial factories and collaborate with the worker to switch from one task to another according to changing intentions of the human. Classical via points trajectory planning experiments and human-robot collaboration experiments are performed on the Sawyer robot. The results of experiments show that MTProMP and MTiProMP with the proposed method and strategy perform better.

Data envelopment analysis method based on a common set of normalized weights using bargaining game thought

The purpose of this study is to develop a new data envelopment analysis (DEA) method using a common set of normalized weights (CSNW) and the thought of the bargaining game. This method has a secondary objective model established to possibly solve the problem with the non-uniqueness of weights. The secondary objective is to minimize the sum of deviation between the self-evaluation efficiency and the peer-evaluation efficiency of each DMU. This method also involves the transformation of multiplier weights into normalized weights that are comparable and suitable for arithmetic operations and bargaining. The high consensus of CSNW is obtained after multiple bids among DMUs based on a bargaining procedure. The effectiveness of this method for DEA is finally illustrated using a case study.

Robust concurrent topology optimization of multiscale structure under load position uncertainty

Concurrent topology optimization of macrostructure and microstructure has attracted significant interest due to its high structural performance. However, most of the existing works are carried out under deterministic conditions, the obtained design may be vulnerable or even cause catastrophic failure when the load position exists uncertainty. Therefore, it is necessary to take load position uncertainty into consideration in structural design. This paper presents a computational method for robust concurrent topology optimization with consideration of load position uncertainty. The weighted sum of the mean and standard deviation of the structural compliance is defined as the objective function with constraints are imposed to both macro- and micro-scale structure volume fractions. The Bivariate Dimension Reduction method and Gauss-type quadrature (BDRGQ) are used to quantify and propagate load uncertainty to calculate the objective function. The effective properties of microstructure are evaluated by the numerical homogenization method. To release the computation burden, the decoupled sensitivity analysis method is proposed for microscale design variables. The bi-directional evolutionary structural optimization (BESO) method is used to obtain the black-and-white designs. Several 2D and 3D examples are presented to validate the effectiveness of the proposed robust concurrent topology optimization method.

Combining design of experiments, machine learning, and principal component analysis for predicting energy consumption and product quality of a natural gas processing plant

SummaryProcessing natural gas, as a widely used source of energy in our life, is imperative to eliminate the impurities in order to make it consumable. So, appropriate modeling of different units in a real gas processing plant (GPP) is an essential research field. Moreover, high‐dimensional data, with probably unnecessary information, gathered from a real application may lead to complicated models. As a result, the original dataset, obtained through a three‐level design of experiments, should be refined to achieve the most effective observations in a lower dimension vector space. On the other hand, the original dataset needs to be normalized to a standard normal distribution in order to tune the effects of all the variables on the system operation. In this study a radial basis function‐neural network (RBF‐NN) is designed to model the total consumed energy in separation, sweetening, and dehydration units and also the water content in the refined gas in a typical GPP, using a reduced dimension dataset achieved by applying principal component analysis (PCA) on the normalized data. The proposed procedure is evaluated through some well‐known and standard criteria such as error relative deviation, root mean square error, the percentage of the average absolute relative deviation %AARD, sum of squared error, standard deviation, and correlation factor (R2). Simulation and analytical results demonstrate that the designed PCA‐RBF‐NN procedure can precisely model the dynamics of energy consumption and the final water content in a typical GPP with the confidence level of 98.6% through six principal components achieved by PCA technique. Furthermore, small values of the error measurements are obtained while using the developed RBF‐NN model.

Acidity-Driven Bidirectional Room-Temperature Spin-State Switch and Fluorescence Modulation of a Mononuclear Fe(II) Complex

Room-temperature switchable materials showing multiple responses toward external stimuli are highly desired. Herein, we report bidirectional spin-state switch and fluorescence modulation of an Fe(I...

The total reaction cross section of heavy-ion reactions induced by stable and unstable exotic beams: the low-energy regime

In this review paper we present a detailed account of the extraction and the calculation of the total reaction cross section of strongly bound and weakly bound, stable and unstable, exotic, nuclei. We discuss the optical model and the more general coupled channels model of direct reactions. The effect of long-range absorption due to the coupling to excited states in the target and to the breakup continuum in the projectile is also discussed. The generalized optical theorem for charged particle scattering and the resulting sum-of differences method is then discussed. The so-called "quarter-point recipe" is discussed next, and the quarter-point angle is introduced as a simple and rapid mean to obtain the total reaction cross section. The last topic discussed is the reduction of the total reaction cross section that would allow a large body of data to sit on a single universal function. Such a universal function exists in the case of the fusion data, and the aim of this last topic of the review is to extend the fusion case to the total reaction, by adding the direct reaction contribution. Also discussed is the inclusive breakup cross section and how it can be used to extract the total reaction cross section of the interacting fragment with the target. This method is also known as the Surrogate method and represents a case of hybrid reactions. The sum of the integrated inclusive breakup cross section with the complete fusion cross section supplies the total fusion cross section. The main experimental methods to determine the total reaction cross section are also discussed, with emphasis in recent techniques developed to deal with reactions induced by unstable beams.

Rolling Bearing Fault Diagnosis Based on Sensitive Feature Transfer Learning and Local Maximum Margin Criterion under Variable Working Condition

In real industrial scenarios, the working conditions of bearings are variable, and it is therefore difficult for data-driven diagnosis methods based on conventional machine-learning techniques to guarantee the desirable performance of diagnosis models, as the models assume that the distributions of both the training and testing data are the same. To enhance the performance of the fault diagnosis of bearings under different working conditions, a novel diagnosis framework inspired by feature extraction, transfer learning (TL), and feature dimensionality reduction is proposed in this work, and dual-tree complex wavelet packet transform (DTCWPT) is used for signal processing. Additionally, transferable sensitive feature selection by ReliefF and the sum of mean deviation (TSFSR) is proposed to reduce the redundant information of the original feature set, to select sensitive features for fault diagnosis, and to reduce the difference between the marginal distributions of the training and testing feature sets. Furthermore, a modified feature reduction method, the local maximum margin criterion (LMMC), is proposed to acquire low-dimensional mapping for high-dimensional feature spaces. Finally, bearing vibration signals collected from two test rigs are analyzed to demonstrate the adaptability, effectiveness, and practicability of the proposed diagnosis framework. The experimental results show that the proposed method can achieve high diagnosis accuracy and has significant potential benefits in industrial applications.

A New Approach of Minimizing Midas Touch Problem for a Tracer-Free Tongue-Controlled Assistive Technology

Tongue-controlled assistive technologies (ATs) have a great potential to improve the quality of life for people with severe disabilities. Current tongue-controlled ATs, however, require either a tracer to be attached on the tongue or a complicated gesture/mechanism to avoid issuing commands unintendedly during routine intraoral events (e.g., tongue resting, swallowing, and speaking), which poses a high entry barrier to potential users. In this study, we propose a tracer-free tongue-controlled AT using four optical distance sensors and one ambient light sensor with a multi-sensor data fusion algorithm to distinguish the intended tongue contacts from the unintended ones. Eleven healthy human subjects participated in the study to evaluate the performance using the proposed technology in masking unintendedly-issued commands and navigating a mouse cursor on a computer. The proposed multi-sensor data fusion algorithm could mask 100%, 86.99%, and 99.50% of unintendedly-issued commands during tongue resting, swallowing, and speaking, respectively. The efficacy of the tongue commands in mouse navigation was evaluated by command-issuing task and maze-navigation task. The participants could issue tongue commands in a random order with 97.16±0.94% of accuracy. The completion time and the sum of deviation of the maze-navigation task were 31.36±2.74 s and 9.38±0.85 pixel <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively. The subjects' performance on computer-access tasks, using the proposed tracer-free tongue-controlled technology, is at the level of the performance measured with existing tracer-based tongue-controlled technologies, while securing robustness in masking commands, unintendedly issued during routine intraoral events. Results of this preliminary study suggest that the tracer-free implementation is feasible option for the tongue-controlled ATs.

Foam Rolling And Hrv

PURPOSE: The purpose of this study was to determine if a bout of foam rolling could increase parasympathetic activity as measured by heart rate variability (HRV). This study utilized an ultra-short-term HRV finger sensor device and software that records the root mean square sum of standard deviations with the natural log transformation multiplied by 20 (HRV) for easy interpretation of the results. METHODS: Twenty (20) participants were measured pre-activity for HRV using the finger sensor connected to a tablet while lying supine, and then a second measure was recorded with them sitting upright with feet on the ground. The software collects HRV values in 55 seconds, and directs their breathing with an on-screen prompt. Participants then oscillated on a closed-cell cylindrical foam roller using their body weight on the triceps surae, hamstrings, quadriceps, lumbar spine, and pectoral muscles, respectively, for 60 seconds. Participants first massaged the right limb and then repeated the same muscle group on the left before continuing to the next region. A second HRV measurement was recorded with the same procedures. RESULTS: Paired samples t-tests revealed no difference between supine measures (p= 0.56), nor the seated measures (p= 0.59). There was no difference between sexes. CONCLUSIONS: The single bout of foam rolling across major muscle groups do not increase parasympathetic activity via venous return as hypothesized. The change in HRV values averaged 1.55 and 1.30 decreases between supine and seated, respectively, but those changes are not statistically significant. The task of self-massage may be more metabolically demanding than initially believed, as the required muscle activation overpowers the venous return from massage. More research is needed to determine if passive massage would produce the desired parasympathetic neural increase.

Lagrangian tracking of colliding droplets

We introduce a new Lagrangian particle tracking algorithm that tracks particles in three dimensions to separations between trajectories approaching contact. The algorithm also detects low Weber number binary collisions that result in coalescence as well as droplet break-up. Particles are identified in two-dimensional high-resolution digital images by finding sets of circles to describe the edge of each body. This allows identification of particles that overlap in projection by over 80% even for noisy images and without invoking additional temporal data. The algorithm builds trajectories from three-dimensional particle coordinates by minimizing a penalty function that is a weighted sum of deviations from the expected particle coordinates using information from four moments in time. This new hybrid algorithm is validated against synthetic data and found to perfectly reproduce more trajectories than other commonly used methods. Collisions are detected with 95% accuracy for particles that move on average less than one tenth the distance to their nearest neighbor.

Significance of Simultaneous Use of Different Methods for Interpretation of Internal Quality Control Data An Experience of a Tertiary Care Hospital

Background: Quality control is an essential part of quality management in medical laboratory. Various methods are available for the analysis of laboratory quality control data, common ones are Levey Jennings chart and Westgard rules. Westgard multi-rules are a set of rules based on combination of criteria to decide whether an analytical run is acceptable or unacceptable. CUSUM uses the cumulative sum of deviations from a target, however, it is rarely used in current medical laboratory practice. We share our experience of using Westgard rules and CUSUM in the analysis of laboratory quality control data. Material and methods: Internal quality control values of 2-year period was included in the present study. Data for platelet count values of normal level control material run on a fully automated haematology analyser (Sysmex XT-2000i) were analysed. A total of 1825 data points were obtained. The data was interpreted by Westgard rules as well as cumulative sum method. The out of control events was analysed. Results: There was 9 incidence of control value outside 3SD (Westgard Rule 13s) which was picked on Levey Jennings chart but missed by CUSUM method. There were 22 instances of shift in mean (bias) which were only picked by CUSUM method. Conclusion: CUSUM was more sensitive for detection of bias whereas random error was picked-up early by Westgard rules. In conclusion we recommend the use of more than one method for analysis of quality control data.

Grading variation in 2,934 patients with ductal carcinoma in situ of the breast: the effect of laboratory- and pathologist-specific feedback reports

BackgroundHistologic grade of ductal carcinoma in situ of the breast (DCIS) may become the single biomarker that decides whether patients will be treated. Yet, evidence shows that grading variation in daily practice is substantial. To facilitate quality improvement, feedback reports, in which laboratory-specific case-mix adjusted proportions per grade were benchmarked against other laboratories, were sent to the individual laboratories by March 1, 2018. One year later, the effect of these feedback reports on inter-laboratory variation was studied.MethodsSynoptic pathology reports of all pure DCIS resection specimens between March 1, 2017 and March 1, 2019 were retrieved from PALGA (the nationwide Dutch pathology registry). Laboratory-specific proportions per grade were compared to the overall proportion in the year before and after feedback. The absolute deviation for all three grades at once, represented by the overall deviation score (ODS), was calculated as the sum of deviations from the grade-specific overall proportions. Case-mix adjusted, laboratory-specific odds ratios (ORs) for high- (grade III) versus low-grade (grade I-II) DCIS were obtained by multivariable logistic regression.ResultsOverall, 2954 DCIS reports from 31 laboratories were included. After feedback, the range between laboratories decreased by 22 and 6.5% for grades II and III, while an increase of 6.2% was observed for grade I. Both the mean ODS (27.2 to 24.1%) and maximum ODS (87.7 to 59.6%) decreased considerably. However, the range of case-mix adjusted ORs remained fairly stable and substantial (0.39 (95% CI: 0.18–0.86) to 3.69 (95% CI: 1.30–10.51)).ConclusionA promising decrease in grading variation was observed after laboratory-specific feedback for DCIS grades II-III, while this was not observed for DCIS grade I. Overall, grading variation remained substantial which needs to be addressed considering its clinical implications. Nationwide consensus on a classification, and training of (expert breast) pathologists, for example by e-learning, may help to further improve grading standardization.

Development and potential performance of prepolymer in corrosion inhibition for carbon steel in 1.0 M HCl: Outlooks from experimental and computational investigations

Diglycidyl amino benzene (DGAB) epoxy prepolymer was investigated using Fourier transform infrared (FTIR) and nuclear magnetic resonance (NMR) spectroscopy. Then, we highlighted the usefulness of DGAB epoxy prepolymer to improve the resistance of carbon steel (CS) in hydrochloric acid (1.0 M HCl) using weight loss (WL), electrochemical impedance spectroscopy (EIS), potentiodynamic polarization (PDP), scanning electron microscope (SEM), atomic force microscopy (AFM), density functional theory (DFT) and complexation calculations, molecular dynamics (MD) and meansquaredisplacement (MSD) simulations. Highest inhibitory efficiencies for the WL, EIS and PDP methods at 10-3 M of DGAB are 90.8, 96.3 and 95.9%, respectively. SEM and AFM micrographs demonstrated that the epoxy prepolymer could effectively block the acid attack by chemisorption on the surface of the carbon steel, the high correlation coefficient and low Standard Deviation (SD) and low Sum of Squares (SS) value gave the best fit for Langmuir isotherm. PDP data suggested that the epoxy prepolymer could provide excellent corrosion performance and showed a mixed-type inhibitor with predominant cathodic effectiveness. Investigate of the inhibitory layer and the potential mechanism was conceptually evaluated using DFT, MD simulations, radial distribution function (RDF) and mean square displacement (MSD).

Variation in breast cancer grading: the effect of creating awareness through laboratory-specific and pathologist-specific feedback reports in 16 734 patients with breast cancer

AimsHistological grade is widely used to guide the management of invasive breast cancer (IBC). Yet, substantial interlaboratory and intralaboratory grading variations exist in daily pathology practice. To create awareness and...