Correct Estimation Research Articles

Features of intestinal microbiota composition in cancer patients

Objective: to evaluate and compare the qualitative and quantitative composition of the intestinal microbiota in patients with malignant neoplasms of various localizations. Material and Methods. The study included patients who received different types of treatment in N.N. Blokhin Oncology Research Center, Moscow, Russia in 2023 for gastric cancer, including cardioesophageal adenocarcinoma (group 1), esophageal squamous cell carcinoma (group 2) and metastatic or locally advanced melanoma of the skin (group 3). All patients had to have morphologic verification of the diagnosis at the time of inclusion, be over 18 years old, have an ECOG performance status of ≤1, and have no evidence of intestinal infection, as well as not take antibiotics within 28 days prior to entry into the study. Stool samples were collected during patients’ hospitalization. The quantitative and qualitative composition of microorganisms of 17 taxonomic groups was evaluated. Microorganisms were cultured according to standard microbiological methods, taking into account the growth conditions of a particular group of microorganisms. Species identification of microbial isolates was obtained by matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF) and MALDI Biotyper v.3.0 software (Bruker daltonics, Germany). Descriptive statistics methods from the SPSS Statistics, v.27 software package were used. To quantitatively describe the species diversity of the gut microbiota, calculations were performed using the Margalef species richness index (d) and Shannon’s (H) diversity index. The criterion of uniformity of microbial species distribution according to their abundance in the population community was evaluated using the Pielow index (E). The Hutcheson’s T-criterion was used to test the significance of differences between sample sets of Shannon index values and to obtain statistically correct estimates of differences (p≤0.05). Results. A total of 63 samples of biological material (feces) were investigated. A change in the quantitative composition of intestinal microbiota in all study groups was found, which may have a negative impact on the general condition of the patient and the effectiveness of antitumor treatment. The increase in the proportion of Proteobacteria (Enterobacterales) can be considered as a risk factor for the development of infectious complications caused by Gram-negative microorganisms. The analysis of factors influencing the taxonomic diversity of intestinal microbiota revealed no significant differences in the composition of intestinal microbiota between the groups of patients with malignant tumors of different nosological forms (p>0.05).

Using causal diagrams and superpopulation models to correct geographic biases in biodiversity monitoring data

Abstract Biodiversity monitoring schemes periodically measure species' abundances and distributions at a sample of sites to understand how they have changed over time. Often, the aim is to infer change in an average sense across some wider landscape. Inference to the wider landscape is simple if the species' abundances and distributions are similar at sampled to non‐sampled locations. Otherwise, the data are geographically biased, and some form of correction is desirable. We combine causal diagrams with ‘superpopulation models’ to correct time‐varying geographic biases in biodiversity monitoring data. For a given time‐period, expert‐derived causal diagrams are used to deduce the set of variables that explain the geographic bias, and superpopulation models adjust for these variables to produce a corrected estimate of a landscape‐wide mean of for example abundance or occupancy. Estimating a time trend in the variable of interest is achieved by fitting models for multiple time‐periods and, if the drivers of bias are suspected to change over time, by constructing per period causal diagrams. We test the approach using simulated data then apply it to real data from the UK Butterfly Monitoring Scheme (UKBMS). If the variables that explain the geographic bias are known and measured without error, our method is unbiased. Introducing measurement error reduces the method's efficacy, but it is still an improvement on using the sample mean. When applied to data from the UKBMS, the approach gives different results to the scheme's current method, which assumes no geographic bias. Where the goal is to estimate change in some variable of interest at the landscape level (e.g. biodiversity indicators), models that do not adjust for geographic bias implicitly assume it does not exist. Our approach makes the weaker assumption that there is no geographic bias conditional on the adjustment variables, so it should yield more accurate estimates of time trends in many circumstances. The method does require assumptions about the drivers of bias, but these are codified explicitly in the causal diagrams. Operationalising our approach should be less costly than full probability sampling, which would be needed to satisfy the assumptions of conventional approaches.

Intelligent Saturation Power Limit Load Distribution Algorithm (ISPLLDA) for Cooperative Manipulators Applications

Cooperative manipulators face challenges related to an inadequate distribution of external loads and a decrease in Dynamic Load Carrying Capacity (DLCC). Understanding the impact of optimal load distribution on power consumption, load carrying capacity, and gripper error is crucial. This paper presents the Intelligent Saturation Power Limit Load Distribution Algorithm (ISPLLDA), a novel method that achieves optimal external load distribution. ISPLLDA dynamically distributes the external load among manipulators based on torque-bearing capacity and actuator position. Additionally, nonlinearity in system dynamics introduces uncertainties, leading to incorrect DLCC evaluation, increased error, and higher actuator power consumption. To address this, a Radial Basis Function Neural Network (RBFNN) accurately determines actuator saturation limits in the presence of uncertainty, enabling correct estimation of system dynamics and external disturbances. ISPLLDA ensures near-simultaneous saturation of all manipulators' actuators, maximizing their capacity utilization. The proposed method is validated through simulations and experimental tests on cooperative manipulators. Results demonstrate a 17% increase in load-carrying capacity, as well as more than 35% improvement in error and torque indexes compared to the Lagrange multipliers method.

Forecasting Real Estate Price via Support Vector Machine

This paper focus on the analysis of real estate price using support vector machine. The data set of a local estate price gives the details of the house including the price, date, bedroom numbers and postcode (location), and has been displayed as the line charts. There is comparison about different forecasting methods done by previous researchers including the simple moving average and ARIMA model. The data set is then fed to the model for machine learning and price prediction support vector machine. Moreover, R language is utilised to compute the support vector machine method by e1071 package and graph plotting of different kernel functions inside the support vector machine function. Then the graphs are analysed based on the accuracy of the correct estimation on the diagonal of the matrix generated. Possible improvements including the data collection and survey method are discussed for a higher precision and accuracy result for further study.

Optimized Hybrid Machine Learning Approaches for Stock Market Forecasting and Time Series Analysis

The research introduces an innovative hybrid model of KPCA, ESVM, and TLBO to analyze stock price variation and time series forecasting. To handle the issue of high dimensionality of the financial data and the nonlinear dependencies amongst the variables, the model employs KPCA for feature extraction, thus identifying, and retaining only the feature space that is most relevant. Subsequently, the features extracted are passed through ESVM for regression – aiding in correct estimations on stock prices. To improve the outcome, prediction accuracy and to fine transient parameters of the model TLBO as a metaheuristic algorithm is used. The application of KPCA-ESVM-TLBO establishes optimal characteristics from the above methodologies, producing efficiency in tackling complications and nonlinearity of the data structures. KPCA looks for hidden structure; ESVM does regression with the kernel; and TLBO twiddles appropriate knobs such as λ and kernel coefficients. By using real-world financial data sets, the experimental evaluations presented show that the reported method outperforms the conventional benchmarks in relations of predictive accuracy. MAE, RMSE, and accuracy confirm its relevance: predictive accuracy of 99.99%. This approach to using artificial neural networks in tandem with a nearest neighbor algorithm presents the prospect of a potent weapon for forecasting and decision making in ever complex and volatile market.

Considering Micro/nanostructures at the Surface of Photothermal Materials: A Game Changer in Correct Estimation of Evaporation Rate and Energy Conversion Efficiency in Interfacial Solar Vapor Generation Systems.

Interfacial solar evaporator generation (ISVG) is a new, cost-effective, and eco-friendly emerging method for water desalination. Two main criteria for evaluating ISVG performance are evaporation rate (ṁ) and solar-to-vapor conversion efficiency (η). The main challenge of the previously presented models for the estimation of ṁ and η in 2D systems is that in most cases the calculated values are beyond the theoretical limits, ṁ > 1.47 kg m-2 h-1 and η > 100%, both of which are not acceptable from the thermodynamics viewpoint. Also, the recently presented strategy of reduced vaporization enthalpy for obtaining η < 100% is unacceptable from the thermodynamics approach for ISVG as a two-step continuous process. Therefore, this work aims to present a model and consequently new equations for the correct estimation of evaporation rate and energy conversion efficiency in two-dimensional (2D)-ISVG systems, which are consistent with their corresponding theoretical limits. The basis of the present model is discrimination between the projection area and evaporation area by considering the micro/nanostructures on the surface of interfacial support (photothermal material). This leads to the presentation of new equations for ṁ and η having consistency with thermodynamics laws. The presence of micro/nanostructures on the surface of photothermal material provides a higher evaporation area which is not considered in the previous models and led to theoretically inconsistent results. The results of the present study provide a theoretical basis for the correct estimation of the evaporation rate and energy conversion efficiency in 2D-ISVG systems in future works.

A novel model of age estimation in mixed dentition population in Western India – A retrospective study

Objectives: Age estimation is an integral part of legal investigations for forensic purposes. When the chronological age of the individual is not documented or he/she is under the conflict of the law, age estimation comes into play. The need for age estimation may arise in various legal incidences involving children, and juveniles, for civil aspects such as adoption, child labor, or other criminal proceedings such as rape, kidnapping, and illegal immigration. An accurate and dependable method, that can estimate age with high probability, can aid in narrowing down the list of possible victims or even play a decisive role in such cases. The reliability of the method and the probability of correct age estimation play a decisive role in the court of law. Bedek et al’s model (2019) was recently developed and tested in Croatian and South Indian populations with satisfactory results. As there is no evidence of study in the Western Indian population, looking into the accessibility of the population group, the present study aims to evaluate the validity and reliability of the Bedek method in the Western Indian population. Materials and Methods: Approval was sought from the Institutional Ethical Committee. Five hundred and twenty-five orthopantomographs (OPG) of patients aged 5–15 were obtained. A double-blinded study was done, where the radiographs were analyzed using ImageJ software, independently by two investigators. The data were tabulated and subjected to statistical analysis for accuracy of age estimation and intra- as well as inter-observer reliability. Results: There was an underestimation by a range of −1.3038 to −0.74536. There was underestimation in all the models of Bedek with P &lt; 0.005, for all the teeth models except, the three- and two-teeth model (P &gt; 0.005). Conclusion: In our study, we found that the accuracy of age estimation increases significantly with the number of teeth used. Seven four-teeth models were the most suitable for age estimation on OPG. All models except the three-teeth model and two-teeth model were found to be more accurate.

Human age estimation ability and factors affect the estimation

Number of studies that are focused on estimating age from facial images are increasing every day. These studies are performed largely by automatic systems. Altough these techniques have given better results, they have not reached successful estimation levels as human made, yet. Being able to identify the significant decision-making variables that influence people's estimations is one of the things that can improve these systems. The aim of this study is to examine the success rate of human observers' estimations and to draw attention to what affects those estimations. In this study an age estimation survey was offered; people were asked whether they trust themselves about age estimation and which factors affect their estimations. Participants have been provided with an online survey created using Google Forms. A total of 223 people participated in the study, 66 male and 157 female. In general total 5 images were estimated correctly out of 12, 7 were estimated incorrectly. The ages of all participants (face images of 12 individuals) were estimated correctly with an average of 30.08%. The majority of participants (77,6%) claim to trust their judgement on some level and to make correct estimations overall. When the frequency of factor designation was examined, it was discovered that the majority of participants (65,17%) were focused on the wrinkles on faces (the study includes general face, eyes and mouth.). It is expected that future studies would yield improved results by increasing the number of factors affecting age estimation and including more machine learning studies.

Evaluation of the Duration of Albedo Measurement Campaigns

Bifacial PV modules are increasingly used in commercial solar PV projects. With the use of the solar energy available at the module’s rear surface, the correct estimation of the albedo during the pre-construction phase of commercial projects becomes more important. The intention of the study is to determine the impact of the duration of a short-term albedo measurement campaign to reduce the uncertainty with respect to satellite albedo, thus providing a ‘beneficial campaign’. Albedo measurements from seven sites in the USA have been used to estimate the impact of 1-day and 7-day measurements (short-term measurements) on the simulated annual electrical energy production. Simulations based on monthly albedo data obtained from a recognised satellite provider have been compared with simulations where the albedo has been corrected based on short-term measurements. The study found that a 1-day campaign is often not beneficial, causing an increase in uncertainty with respect to satellite albedo. Measurement campaigns in winter do not generally reduce the error of the annual electrical energy production simulation, however, a 7-day campaign undertaken in summer appears to often reduce the uncertainty. It should be noted that the climate conditions, ground vegetation and measurement setup of the analysed seven sites in the USA may not be representative for other sites in the rest of the world.

Advanced Scanning Technology for Volume Change Measurement of Residual Soil

Weathering processes of rocks lead to the formation of residual soil layers, which are typically characterized by a deep groundwater table and a thick unsaturated zone. Hence, the calculation of a slope’s safety factor under the influences of climatic circumstances is a function of unsaturated characteristics, such as the soil–water characteristic curve (SWCC). To determine the SWCC, the volume of the soil specimen must be determined in order to compute the void ratio and degree of saturation. The drying processes of the soil specimen led to uneven soil volume change during laboratory SWCC testing, demanding the development of a soil shrinkage curve. Several methods for measuring soil volume change have been developed over the years. However, there are significant limitations, and it is rarely used due to the difficulty linked to accurately measuring the soil volume during drying processes. In this study, a revised scanning approach is developed to evaluate residual soil volume change utilizing 3D scanning technology. The proposed method is applied in a case study on residual soil from the Old Alluvium in Singapore. The laboratory data and analysis results suggested that 3D scanning technology should be required to provide a correct estimation of the air-entry value of soil.

Mixture mean residual life model for competing risks data with mismeasured covariates

This paper proposes a mixture regression model for competing risks data, where the logistic regression model is specified for the marginal probabilities of the failure types and the mean residual lifetime (MRL) model is assumed for the failure time given the failure of interest. The estimating equations (EEs) are derived to infer the logistic regression and MRL model separately. We further consider the situation where the covariates are subject to measurement error. The presence of measurement error imposes extra challenges for the analysis of complex time-to-event data. By using the above EEs as the correction-amenable original estimating functions, we propose a corrected score estimation, which does not require specifying the distributions for unobserved error-prone covariates. The proposed estimators are shown to be consistent and asymptotically normally distributed. The performance of the method is investigated by intensive simulation studies and two real examples are presented to illustrate the proposed methods.

Delayed retirement, elderly care and women labor supply

PurposeThis paper believes that while implementing the gradual delay retirement age policy in China, the impact should be considered comprehensively; we should pay attention to impacts brought by the delayed retirement policy and introduce policies to deal with the impacts in a timely manner.Design/methodology/approachThis paper aims to explore the delayed retirement’s impact on women’s labor supply and to clarify the elderly care’s role in it.FindingsThe results found that the delayed retirement has a positive effect on the women’s overall and young women’s labor supply, with a more significant promotion for young women’s labor supply. The mediation results suggest that delayed retirement promotes women’s labor supply by affecting elderly care. Therefore, we believe that while implementing the gradual delay retirement policy in China, it is important to implement it on the correct estimation basis so as to reduce the volatility in the labor market.Originality/valueThis paper may produce marginal contributions in the following two aspects: From the research perspective, we construct a model containing delayed retirement, elderly care and women’s labor supply and illustrate how the delayed retirement promotes women’s labor supply by affecting elderly care. Secondly, from the research content, this paper expands the delayed retirement on macro-employment and further explores the micro-impact on employment.

Estimation of GNSS satellite bias corrections using non-common frequency model with application to PPP-AR

Abstract Corrections of satellite phase biases (SPBs) enable precise point positioning with ambiguity resolution (PPP-AR), which shortens the convergence time and improves positioning precision compared with precise point positioning (PPP). Extending PPP-AR from dual-frequency to multi-frequency can further enhance the positioning performance by correcting satellite code biases (SCBs) at the third frequency and above. In formulating a multi-frequency model to estimate satellite bias corrections (SBCs), including SPBs and SCBs, existing studies use only satellite observations collected at common frequencies (CF). This study proposes a non-common frequency (NCF) model that utilizes dual- and multi-frequency satellites to estimate SBCs simultaneously. To validate the NCF model, we collect data from an Australian network to estimate the SBCs and apply these corrections to the PPP-AR user positioning. The results show that the NCF model can provide SBCs for more satellites and frequencies than the CF model. After correction of SBCs, the proportion of fractional parts of float PPP ambiguities, which are less than 0.1 cycles, has a significant increase from 20% to&amp;#xD;70%, indicating the successful restoration of the integer properties of PPP ambiguities by the&amp;#xD;SBCs. Moreover, a comparable experiment between the NCF and CF PPP-AR models is conducted. The NCF PPP-AR model demonstrates superior performance over the CF model using triple-frequency satellites in convergence speed and positioning precision, achieving improvements of 41.16% and 39.52%, respectively. Meanwhile, compared to the CF model using dual-frequency satellites, the NCF PPP-AR model shows improvements of 17.16% and 7.46% in these aspects. The positioning performance of the NCF PPP-AR model is evaluated across ten user stations sampled over three days. The results indicate the root mean square&amp;#xD;(RMS) positioning errors for the east, north and up components are 0.41, 0.51 and 2.46 cm, respectively. Additionally, the average convergence time for achieving 3D positioning errors within 1 dm is 9.75 min.&amp;#xD;

A study on three commonly used GNSS integer estimators with probability of correct estimation

Integer ambiguity resolution plays a key role in applications related to GNSS precise positioning. This contribution focuses on three commonly used integer estimators (IEs), i.e., integer rounding (IR), integer bootstrapping (IB) and integer least-squares (ILS). Contributions are mainly of four aspects. First, the objective function of IR and IB is given, respectively. Second, an upper bound for ILS is proposed. Third, a sorting technique is introduced to tighten the upper bound of IR/ILS after decorrelation. Fourth, the success-rate approximation for IR and ILS with bounded error is developed, respectively. Finally, real-collected data in PPP validate the following arguments. (i) Applying sorting technique after decorrelation can improve the tightness of IR upper bound a lot, but can only slightly tighten the ILS upper bound. (ii) The proposed ILS upper bound is almost as the same tight as the one in PS-LAMBDA software, much tighter than other known upper bounds. Unlike the PS-LAMBDA upper bound, the proposed ILS upper bound has the advantage of time efficiency for real-time applications. (iii) The proposed approximations produce more accuracy approximated success-rate than the frequently-used ambiguity dilution of precision (ADOP) based approximation.

The role of the Jastrow factor towards the correct estimate of the finite-mass correction

Compact wave functions with explicit correlation are capable of recovering more than seventy percent of the correlation energy of few-electron atoms while some constraints can be imposed to satisfy the cusp conditions. We address the question if it is also possible to describe correctly the first order finite mass correction. To this end, we optimize wave functions free of spin contamination with two-body Jastrow factors. As a playground, we use the first global excited state 1s22p of Li-like atoms because it is the simplest case of open shell and non-zero angular momentum.

ДОСЛІДЖЕННЯ ВПЛИВУ ЧАСТОТНИХ ВЛАСТИВОСТЕЙ СЛУХУ В ПРОЦЕСІ ВИМІРЮВАННЯ ГУЧНОСТІ ЗВУЧАННЯ АУДІОКОНТЕНТУ

Nowadays the problem of correct estimation and measurement of TV- and sound broadcasting programmes loudness is of great importance. The European Broadcasting Union (EBU) has established a special investigation group, dealing with loudness estimation and normalization, similarly, ITU emphasizes on the importance of objective measurement of audio program loudness to facilitate program delivery and exchange, and to provide more comfortable listening experience for audiences. In Recommendation ITU-R BS.1770-5 the modern algorithm of objective loudness measurement is described in detail. This algorithm includes frequency weighting of the measured audio signal to assume frequency properties of human auditory system, but such weighting concerns only the lower part of sound frequency band. In this article, the authors raise the question of the necessity of taking into account the dependence of sensitivity of the human auditory system on upper frequencies of the sound band. A brief overview of the existing representations of the frequency response of hearing sensitivity when listening to different types of sound signals is provided. Also in the article are discussed the results of subjective assessment of appreciability of changes in loudness of audio content of different types (speech and music) while changing the audio signal’s level on the upper frequencies of sound band. In one case the level of audio signal was adjusted according to the equal loudness contour corresponding to 60 phones of loudness level, and in another case, according to one of the variants of the averaged psophometric curve. The results of subjective assessment for these two cases were compared with each other. For all processed sound fragments, a confidence intervals were calculated based on the indicator of appreciability of changes in loudness, percieved by the listeners, and evaluated the accuracy of the results. Based on the results obtained from subjective assessment, further ways of conducting of the research are proposed.

Magnetic polarizability of octet baryons via lattice QCD

Drawing on recent advances in lattice QCD background-field techniques, the magnetic polarizability of octet baryons is calculated from the first principles of QCD. The results are presented in the context of new constituent quark model calculations providing a framework for understanding the lattice results and a direct comparison with simulation results at unphysical quark masses. Using smeared quark sources, low-lying Laplacian eigenmode projection and final-state Landau-mode projection, considerable attention is devoted to ensuring single-state isolation in the lattice correlation functions. We also introduce new weighting methods to reduce the sensitivity to correlation-function fits, averaging over many fits based on merit drawn from the full correlated χ2 of the fits. The techniques are implemented on the 323×64, 2+1-flavor dynamical-fermion lattices provided by the PACS-CS Collaboration following the introduction of uniform magnetic fields quantized to the lowest nontrivial values available. After some scaling of the constituent quark model parameters, we find the model captures the patterns observed in the lattice QCD results very well, providing important insights into the physics underpinning the magnetic polarizabilities. Finally, comparison with the most recent results from experiment proceeds through an effective field theory formalism which incorporates estimates of finite-volume corrections and small electroquenching corrections as the results are brought to the physical point. We find excellent agreement with experiment where available, including the proton and neutron polarizabilities. Published by the American Physical Society 2024

Exploring Soil–Water Characteristic Curves in Transitional Oil Sands Tailings

Soil–water characteristics curves (SWCC) have proved useful in estimating parameters used in modeling unsaturated geotechnical properties of soils including permeability and strength. Either saturation, gravimetric, and instantaneous and initial volumetric water content designation can be used to develop SWCCs. Studies have shown that any of the designations will give good estimates for soils that do not undergo volume change with suction change whereas, for soils that undergo substantial volume change, only saturation and instantaneous volumetric water content designation obtained by incorporating shrinkage curves can give correct estimates. Transition oil sands tailings have fines content that cannot be categorized as sandy or fine materials, and research on volume change with suction change in these materials is limited. In this study, HyProps, Tempe cells, and a chilled-mirror water potential meter were used to measure suction and corresponding water contents for samples that were prepared by mixing coarse sand and Fluid Tailing by ratios that mimic transition zone tailings. Shrinkage tests were also performed to observe the extent of volume change with suction increase. Air Entry Values (AEV) estimated from SWCCs based on gravimetric water content were found to be lower than those estimated from saturation-based SWCCs due to substantial volume changes in these materials with suction increase. The use of saturation water content designation is recommended in estimating AEV for transitional oil sands tailings. This is useful information in predicting the long term unsaturated geotechnical behavior of these materials for environmental management and safety purposes.

Enhanced ovarian cancer survival prediction using temporal analysis and graph neural networks

Ovarian cancer is a formidable health challenge that demands accurate and timely survival predictions to guide clinical interventions. Existing methods, while commendable, suffer from limitations in harnessing the temporal evolution of patient data and capturing intricate interdependencies among different data elements. In this paper, we present a novel methodology which combines Temporal Analysis and Graph Neural Networks (GNNs) to significantly enhance ovarian cancer survival rate predictions. The shortcomings of current processes originate from their disability to correctly seize the complex interactions amongst diverse scientific information units in addition to the dynamic modifications that arise in a affected person`s nation over time. By combining temporal information evaluation and GNNs, our cautioned approach overcomes those drawbacks and, whilst as compared to preceding methods, yields a noteworthy 8.3% benefit in precision, 4.9% more accuracy, 5.5% more advantageous recall, and a considerable 2.9% reduction in prediction latency. Our method’s Temporal Analysis factor uses longitudinal affected person information to perceive good-sized styles and tendencies that offer precious insights into the direction of ovarian cancer. Through the combination of GNNs, we offer a robust framework able to shoot complicated interactions among exclusive capabilities of scientific data, permitting the version to realize diffused dependencies that would affect survival results. Our paintings have tremendous implications for scientific practice. Prompt and correct estimation of the survival price of ovarian most cancers allows scientific experts to customize remedy regimens, manipulate assets efficiently, and provide individualized care to patients. Additionally, the interpretability of our version`s predictions promotes a collaborative method for affected person care via way of means of strengthening agreement among scientific employees and the AI-driven selection help system. The proposed approach not only outperforms existing methods but also has the possible to develop ovarian cancer treatment by providing clinicians through a reliable tool for informed decision-making. Through a fusion of Temporal Analysis and Graph Neural Networks, we conduit the gap among data-driven insights and clinical practice, proposing a capable opportunity for refining patient outcomes in ovarian cancer management operations.

Biweight midcorrelation-based CRITIC method: a case study for electrification of BIPV in Iran

Criteria weights are essential in various multi-criteria decision-making (MCDM) approaches. The amount of weight indicates the criterion's importance, and the performance of MCDM techniques depends on the correct estimation of criteria weights and criteria amount in the decision matrix. The distance correlation-based CRiteria Importance Through Inter-criteria Correlation (CRITIC) technique is a modified version of the CRITIC method. Since Pearson's correlation is sensitive to noisy and outlier data, this paper proposes using the biweight midcorrelation as a robust alternative to Pearson's correlation. The weights obtained from the proposed biweight midcorrelation method make using the full range of MCDM techniques possible. This study examined the performance of the proposed method in comparison with eight criteria weighting techniques on the electric supply for a Building-Integrated PhotoVoltaic (BIPV) problem. Results from three different tests (i.e. distance correlation, Spearman rank-order correlation, and symmetric mean absolute percentage error (sMAPE)) indicated that the proposed method could produce more valid criteria weights and ranks than other methods. The value of the sMAPE criterion for the proposed method is 0.1096. Sensitivity analysis also revealed that the proposed method provides more robust criteria weights and ranks with a larger decision matrix.