Parameters Standard Deviation Research Articles

Effect of Blood Product Resuscitation on Cefazolin Pharmacokinetics in Trauma Patients.

Background: Antibiotics are frequently administered prophylactically to trauma patients with various injury patterns to prevent infectious complications. Trauma patients may also require large volume resuscitation with blood products. Limited data are available to support antibiotic dosing recommendations in this population. We hypothesized that we would be able to develop a population pharmacokinetic model of cefazolin, a frequently used antibiotic in the trauma scenario, from remnant blood samples by pharmacokinetic analysis of trauma patients. Methods: Remnant plasma from standard of care chemistry/hematology assessments was retrieved within 48 h of collection and assayed to determine cefazolin concentrations. Population pharmacokinetic analyses were conducted in Pmetrics using R. Linear regression was conducted to assess the effect of blood product resuscitation volume on cefazolin pharmacokinetic parameters. Results: Cefazolin concentrations best fitted a two-compartment model (Akaike information criterion: 443.9). The mean ± standard deviation parameters were total body clearance (4.3 ± 1.9L), volume of the central compartment (Vc: 7.7 ± 6.9L), and intercompartment transfer constants (k12: 1.3 ± 0.98 h-1, k21: 0.6 ± 0.45 h-1). No statistical relationships were observed between blood products, volume of blood products, and cefazolin clearance or Vc (R2: 0.0004-0.21, p = 0.08-0.95). Using a 5,000-patient Monte Carlo simulation, 2 g with repeated dosing every 2 h until end of surgery was required to achieve 93.2% probability of 100% free time above the minimum inhibitory concentration (MIC) (fT > MIC) at the ECOFF value for Staphylococcus aureus (2 mg/L). Conclusions: In these 15 trauma patients receiving blood transfusion, no relationship with blood volume resuscitation and cefazolin pharmacokinetics was observed. On the basis of this pharmacokinetic model, frequent cefazolin doses are required to maintain 100% fT > MIC.

Imipenem/relebactam pharmacokinetics in critically ill patients supported on extracorporeal membrane oxygenation.

Extracorporeal membrane oxygenation (ECMO) is a life-saving modality but has the potential to alter the pharmacokinetics (PK) of antimicrobials. Imipenem/cilastatin/relebactam is an antibiotic with utility in treating certain multi-drug resistant Gram-negative infections. Herein, we describe the population pharmacokinetics of imipenem and relebactam in critically ill patients supported on ECMO. Patients with infection supported on ECMO received 4-6 doses of imipenem/cilastatin/relebactam per current prescribing information based on estimated creatinine clearance. Blood samples were collected following the final dose of the antibiotic. Concentrations were determined via LC-MS/MS. Population PK models were fit with and without covariates using Pmetrics. Monte Carlo simulations of 1000 patients assessed joint PTA of fAUC0-24/MIC ≥ 8 for relebactam, and ≥40% fT > MIC for imipenem for each approved dosing regimen. Seven patients supported on ECMO were included in PK analyses. A two-compartment model with creatinine clearance as a covariate on clearance for both imipenem and relebactam fitted the data best. The mean ± standard deviation parameters were: CL0, 15.21 ± 6.52 L/h; Vc, 10.13 ± 2.26 L; K12, 2.45 ± 1.16 h-1 and K21, 1.76 ± 0.49 h-1 for imipenem, and 6.95 ± 1.34 L/h, 9.81 ± 2.69 L, 2.43 ± 1.13 h-1 and 1.52 ± 0.67 h-1 for relebactam. Simulating each approved dose of imipenem/cilastatin/relebactam according to creatinine clearance yielded PTAs of ≥90% up to an MIC of 2 mg/L. Imipenem/cilastatin/relebactam dosed according to package insert in patients supported on ECMO is predicted to achieve exposures sufficient to treat susceptible Gram-negative isolates, including Pseudomonas aeruginosa.

Design and analysis of Cpm and Cpmk indices for uncertainty environment by using two dimensional fuzzy sets

Process capability analysis (PCA) is an important stage to check variability of process by using process capability indices (PCIs) that are very effective statistics to summarize process’ performance. Traditional PCIs can produce some incorrect results and declare misinterpretation about process’ quality if the process includes uncertainties. Additionally, definitions of process’ parameters with exact values is not possible when there are uncertainty caused by measurement errors, sensitivities of measuring instruments or quality engineers’ hesitancies. Although the fuzzy set theory (FST) has been successfully used in PCA, it is the first time to use of Pythagorean fuzzy sets (PFSs) to model uncertainties of process more than traditional fuzzy sets in PCA. Since the PFSs has two-dimensional configurations by defining membership and non-membership values, they also have a huge ability to model uncertainty that arises from the human’s thinking and hesitancies, and has brought flexibility, sensitivity and reality for PCA. In this paper, specification limits (SLs), mean (μp), standard deviation (σ) and target value (T) main parameters of PCIs have been analyzed by using PFSs and Pythagorean fuzzy process capability indices (PFPCIs) for two well-known PCIs such as ( C ˜ pm ) and ( C ˜ pmk ) have been derived. The Pythagorean ( C ˜ pm ) and ( C ˜ pmk ) indices have also been applied and tested on some numerical examples based on real case applications from manufacturing industry. The obtained results show that PFPCIs provide wider knowledge about capability of process and to obtain more realistic results. As a result of considering all possibilities about the process, it has been concluded that the process is incapable. In light of this information, the results obtained using different fuzzy set extensions for (Cpm) and (Cpmk) indices can be compared.

EARLY STAGES OF COLORECTAL CANCER CHARACTERIZATION BY AUTOFLUORESCENCE 3D MICROSCOPY: A PRELIMINARY STUDY.

Colorectal cancer is one of the most prevalent pathologies worldwide whose prognosis is linked to early detection. Colonoscopy is the gold standard for screening, and diagnosis is usually made histologically from biopsies. Aiming to reduce the inspection and diagnostic time as well as the biopsies and resources involved, other techniques are being promoted to conduct accurate in vivo colonoscopy assessments. Optical biopsy aims to detect normal and neoplastic tissues analysing the autofluorescence spectrum based on the changes in the distribution and concentration of autofluorescent molecules caused by colorectal cancer. Therefore, the autofluorescence contribution analysed by image processing techniques could be an approach to a faster characterization of the target tissue. Quantify intensity parameters through digital processing of two data sets of three-dimensional widefield autofluorescence microscopy images, acquired by fresh colon tissue samples from a colorectal cancer murine model. Additionally, analyse the autofluorescence data to provide a characterization over a volume of approximately 50 µm of the colon mucosa for each image, at second (2nd), fourth (4th) and eighth (8th) weeks after colorectal cancer induction. Development of a colorectal cancer murine model using azoxymethane/dextran sodium sulphate induction, and data sets acquisition of Z-stack images by widefield autofluorescence microscopy, from control and colorectal cancer induced animals. Pre-processing steps of intensity value adjustments followed by quantification and characterization procedures using image processing workflow automation by Fiji's macros, and statistical data analysis. The effectiveness of the colorectal cancer induction model was corroborated by a histological assessment to correlate and validate the link between histological and autofluorescence changes. The image digital processing methodology proposed was then performed on the three-dimensional images from control mice and from the 2nd, 4th, and 8th weeks after colorectal cancer chemical induction, for each data set. Statistical analyses found significant differences in the mean, standard deviation, and minimum parameters between control samples and those of the 2nd week after induction with respect to the 4th week of the first experimental study. This suggests that the characteristics of colorectal cancer can be detected after the 2nd week post-induction. The use of autofluorescence still exhibits levels of variability that prevent greater systematization of the data obtained during the progression of colorectal cancer. However, these preliminary outcomes could be considered an approach to the three-dimensional characterization of the autofluorescence of colorectal tissue, describing the autofluorescence features of samples coming from dysplasia to colorectal cancer. • A new digital image processing method was developed to measure intensity in 3D autofluorescence images of colorectal samples using a CRC mouse model. • This method showed that autofluorescence intensity in colon mucosa is similar in healthy tissue but changes significantly in tumor development. • Statistical analysis revealed CRC traits detectable from the second week post-induction, aiding in early CRC detection. • The study provides a basis for 3D autofluorescence characterization in colorectal tissue from dysplasia to cancer, although variability in autofluorescence limits data systematization during cancer progression.

Sensitivity analysis of the PC hyperprior for range and standard deviation components in Bayesian Spatiotemporal high-resolution prediction: An application to PM2.5 prediction in Jakarta, Indonesia

The Gaussian Markov Random Field (GMRF) is widely acknowledged for its remarkable flexibility, especially in the realm of high-resolution prediction, when compared to conventional Kriging methods. Rooted in the fundamental principles of Bayesian estimation, this methodology underscores the importance of a meticulous examination of prior and hyperprior distributions, along with their corresponding parameter values. Sensitivity analyses are crucial for evaluating the potential impact of these distributions and parameter values on prediction results. To determine the most effective values for hyperprior parameters, an iterative trial-and-error approach is commonly employed. In our research, we systematically assessed a variety of parameter values through exhaustive cross-validation. Our study is focused on optimizing hyperprior parameter values, with a particular emphasis on Penalized Complexity (PC). We applied our method to conduct spatiotemporal high-resolution predictions of PM2.5 concentrations in Jakarta province, Indonesia. Achieving accurate predictive modeling of PM2.5 concentrations in Jakarta is contingent upon this optimization. We identified that the optimal values for PC hyperprior parameters, with a range of less than 2,000 and a hyperprior standard deviation greater than 1 with a 0.1 probability, yield the most accurate predictions. These parameter values result in the minimum mean absolute percentage error (MAPE) of 19.35393, along with a deviation information criterion (DIC) of -154.23. Our findings highlight that the standard deviation parameter significantly influences model fit compared to the relatively insignificant impact of the range parameter. When coupled with high-resolution mapping, these optimized parameters facilitate a comprehensive understanding of distribution patterns. This process aids in detecting areas particularly susceptible to risks, thereby enhancing decision-making efficacy regarding air quality management.

SMRF'nin Sinir Ağı ve Artımlı Dinamik Analizin Birleşimine Dayanan Güvenilirlik Tahmini

This paper conducts a comprehensive vulnerability analysis of steel structures, taking into account the stringent HAZUS restrictions. The demand distribution for each mode of failure takes the form of a normal logarithm after extracting the fragility chart. Thus, the two parameters of mean and standard deviation can be used to construct the fragility chart. A total of five modes of failure were used in this paper. Therefore, 10 unknown values were used to derive the fragility curves. Afterward, Incremental Dynamic Analysis (IDA) was used under 40 natural records to obtain the fragility curve. To save time in the analysis and prediction of structural responses, a neural network method was used to select records more efficiently. It was observed that this method is better than the analytical method in considering random uncertainty in steel structures when several acceleration values are used.

Research in Electronic Multi-Sensor Accuracy in the Implementation of Soil Fertility Monitoring System Using LoRA

The use of electronic sensors to track the nutrients in the soil is an interesting tool for farmers. This has led to the sale of many different kinds of electronic sensors with different levels of accuracy. The accuracy of this electronic sensor was figured out by comparing the results of the sensor's measurements with the results of lab tests done in different ways. This study compares the accuracy of electronic devices used to measure soil nutrients like nitrogen, phosphorus, potassium, electrical conductivity, water pH, and humidity to measurements made in the lab using the ICP-OES (Inductively coupled plasma-optical emission spectroscopy) method. We used three electronic sensors and a transmission system based on LoRA (Long Range) to measure the nutrients in the soil and put the results on our website. The similarities between electronic sensors and laboratory test parameters include the standard deviation, accuracy value, and correlation test between sensors and from the sensors to laboratory test results. The standard deviation parameter test showed a big value between the electronic sensor and the lab test results. However, none of the three used electronic sensors had a standard deviation number that differed greatly from the others. Except for the pH value of the soil, the electronic sensor's accuracy tests for the other five parameters were not very good compared to the lab tests. Also, the sensor correlation test showed a high correlation, while the correlation test between sensor data and lab test results showed a low correlation.

Research in Electronic Multi-Sensor Accuracy in the Implementation of Soil Fertility Monitoring System Using LoRA

The use of electronic sensors to track the nutrients in the soil is an interesting tool for farmers. This has led to the sale of many different kinds of electronic sensors with different levels of accuracy. The accuracy of this electronic sensor was figured out by comparing the results of the sensor's measurements with the results of lab tests done in different ways. This study compares the accuracy of electronic devices used to measure soil nutrients like nitrogen, phosphorus, potassium, electrical conductivity, water pH, and humidity to measurements made in the lab using the ICP-OES (Inductively coupled plasma-optical emission spectroscopy) method. We used three electronic sensors and a transmission system based on LoRA (Long Range) to measure the nutrients in the soil and put the results on our website. The similarities between electronic sensors and laboratory test parameters include the standard deviation, accuracy value, and correlation test between sensors and from the sensors to laboratory test results. The standard deviation parameter test showed a big value between the electronic sensor and the lab test results. However, none of the three used electronic sensors had a standard deviation number that differed greatly from the others. Except for the pH value of the soil, the electronic sensor's accuracy tests for the other five parameters were not very good compared to the lab tests. Also, the sensor correlation test showed a high correlation, while the correlation test between sensor data and lab test results showed a low correlation.

The 1991–2020 sea surface temperature normals

AbstractThe 1991–2020 climate normals for sea surface temperature (SST) are computed based on the NOAA Daily Optimum Interpolation SST dataset. This is the first time that high‐resolution SST normals with global coverage can be achieved in the satellite SST era. Normals are one of the fundamental parameters in describing and understanding weather and climate and provide decision‐making information to industry, public, and scientific communities. This product suite includes SST mean, standard deviation, count and extreme parameters at daily, monthly, seasonal and annual time scales on 0.25° spatial grids. The main feature of the SST mean state revealed by the normals is that in the Tropics, the Indo‐Pacific Ocean is dominated by the warm pool (SST ≥ 28°C) while the eastern Pacific is characterized by the cold tongue (SST ≤ 24°C); in the midlatitudes, SSTs are in zonal patterns with high meridional gradients. Daily SST standard deviations are generally small (<1.0°C) except in frontal zones (>1.5°C) mostly associated with ocean currents such as the Gulf Stream, Kuroshio and Equatorial Currents. Compared to the 1982–2011 climatology, the 1991–2020 mean SSTs increased over most global areas but obvious cooling is seen in the Southern Ocean, eastern tropical South Pacific Ocean and North Atlantic warming hole. The Indo‐Pacific warm pool (IPWP) is found to have strengthened in both intensity and coverage since 1982–2011. By a count parameter criterion of ≥300 days annually with SST ≥ 28°C, the IPWP coverage increased 33% from 1982–2011 to 1991–2020. The global mean SST of 1991–2020 is warmer than that of 1982–2011, and the warming rate over 1991–2020 doubles that over 1901–2020.

Presenting a model based on knowledge management in modeling and optimizing the dynamic viscosity of MWCNT (30%) - ZnO (70%) / SAE 40 oil hybrid nano-lubricant with response surface methodology (RSM) for industrial uses

Thermophysical characteristics of MWCNT-ZnO (30–70%)/SAE40 hybrid nanofluid (HNF) using knowledge management cycle and RSM in different solid volume fractions (SVFs), temperature and shear rates (SRs) have been investigated. Emphasis on knowledge management through the use of fast and high-precision methods has led to the use of the RSM for modeling the viscosity of HNF. Different models such as linear model, 2FI, Quadratic, Cubic, Quartic, Fifth and Sixth models are presented. Fifth order model is better than others. Values of R2, Predicted R² and Adjusted R² for all three parameters are 0.9998, which displays good precision of the modeling. The knowledge management cycle is used to evaluate the model to increase sufficient confidence. Examining the matching graphs, deviation coefficient and standard deviation parameters displays good precision of modeling. MOD value is also within ± 2%. With the help of knowledge management in finding the best mode, the optimal viscosity value is equal to 209.53 mPa.sec in temperature, SVF and SR, respectively 31.156 °C, 0.063 % and 933.923 s−1.

Primary moult of adult Laughing Doves Spilopelia senegalensis: introduction of the relative duration index and a comparison of moult indices

A new moult index, the relative duration index (RDI), was developed to quantify progress through moult using the Underhill–Zucchini model of primary avian moult, with the Laughing Dove Spilopelia senegalensis as a case study. The RDI weights individual primaries in accordance with their estimated relative moult durations in the same way that proportion feather mass grown (PFMG) weights primaries in accordance with their relative masses. A sample of 1 893 moult records of adult Laughing Doves was used to study their primary moult. Moult parameters were estimated for three moult indices: the traditional moult score (TMS), PFMG and the RDI, and for each of the 10 primaries. Comparing the moult parameter estimates of each of the three indices to their respective cumulative growth curves obtained from the progression of moult of individual primaries, PFMG had the closest fit. However, any of the three moult indices would give satisfactory results for the Laughing Dove. Laughing Doves had an estimated primary moult duration of 215 days, with mean start and end dates of 3 November and 6 June, respectively. The standard deviation parameter was 66 days, which implies a lack of synchronisation of moult in adult doves. It was hypothesised that the RDI might be an appropriate moult index for species that moult one feather at a time and for which the individual moult durations are not proportional to their mass, as seen in Laughing Doves. However, this was not the case as PFMG provided the best fit. The RDI might also prove to be an appropriate moult index for species that moult multiple feathers simultaneously during part of the moult period and these feathers grow more slowly than when fewer feathers are moulting simultaneously.

On the Effect of Imperfect Reference Signal Phase Recovery on Performance of PSK System Influenced by TWDP Fading.

We examine the effects of imperfect phase estimation of a reference signal on the bit error rate and mutual information over a communication channel influenced by fading and thermal noise. The Two-Wave Diffuse-Power (TWDP) model is utilized for statistical characterization of propagation environment where there are two dominant line-of-sight components together with diffuse ones. We derive novel analytical expression of the Fourier series for probability density function arising from the composite received signal phase. Further, the expression for the bit error rate is presented and numerically evaluated. We develop efficient analytical, numerical and simulation methods for estimating the value of the error floor and identifying the range of acceptable signal-to-noise ratio (SNR) values in cases when the floor is present during the detection of multilevel phase-shift keying (PSK) signals. In addition, we use Monte Carlo simulations in order to evaluate the mutual information for modulation orders two, four and eight, and identify its dependence on receiver hardware imperfections under the given channel conditions. Our results expose direct correspondence between bit error rate and mutual information value on one side, and the parameters of TWDP channel, SNR and phase noise standard deviation on the other side. The results illustrate that the error floor values are strongly influenced by the phase noise when signals propagate over a TWDP channel. In addition, the phase noise considerably affects the mutual information.

Expanding an Education-based Collision Detection System Created on Virtual Reality and Augmented Reality

Virtually enhancing the real environment with augmented reality (AR) has lots of potential but is still in the early stages of research. The definition of appropriate user interfaces (UIs) is complicated by the absence of standards and the growing complexity of interaction opportunities. Several educators have discussed the advantages of XR for students as well as the use of AR and VR in the laboratory. Utilizing AR and VR to create immersive learning experiences is challenging since it takes time and effort to construct instructional AR and VR tools, apps, or educational settings. Because of this, even though these new technologies are said to help today’s students, their implementation in education may be postponed or stopped. In this research, the usage of XR technologies in education has been investigated through the examination of websites, technical papers, reports, and mobile app stores. This research study proposes a collision detection algorithm (CDA) utilizing machine learning. In order to aid in the identification of the meeting of two objects in the virtual environment, the collision detection method is employed in applications that support augmented reality and simulated reality technologies. In this study, mean, standard deviation and error parameters were utilized to analyze competitions that were related to augmented reality and virtual reality.

Development of 3D-Printed Self-Healing Capsules with a Separate Membrane and Investigation of Mechanical Properties for Improving Fracture Strength

Studies on self-healing capsules embedded in cement composites to heal such cracks have recently been actively researched in order to improve the dimensional stability of concrete structures. In particular, capsule studies were mainly conducted to separately inject reactive healing solutions into different capsules. However, with this method, there is an important limitation in that the probability of self-healing is greatly reduced because the two healing solutions must meet and react. Therefore, we propose three-dimensional (3D) printer-based self-healing capsules with a membrane structure that allows two healing solutions to be injected into one capsule. Among many 3D printing methods, we used the fusion deposition modeling (FDM) to design, analyze, and produce new self-healing capsules, which are widely used due to their low cost, precise manufacturing, and high-speed. However, polylactic lactic acid (PLA) extruded in the FDM has low adhesion energy between stacked layers, which causes different fracture strengths depending on the direction of the applied load and the subsequent performance degradation of the capsule. Therefore, the isotropic fracture characteristics of the newly proposed four types of separated membrane capsules were analyzed using finite element method analysis. Additionally, capsules were produced using the FDM method, and the compression test was conducted by applying force in the x, y, and z directions. The isotropic fracture strength was also analyzed using the relative standard deviation (RSD) parameter. As a result, the proposed separated membrane capsule showed that the RSD of isotropic fracture strength over all directions fell to about 18% compared to other capsules.

Gamma radiation detector selection for CT scanner

Three types of gamma radiation detectors associated with distributed electronics namely, NaI (Tl), HPGe and LaBr3(Ce) are compared primarily focusing on electronic noise and scattering noise. Additionally, detectors of same make, material, size and electronics are also compared. A methodology is proposed to select the most suitable detector for computed tomography (CT) among the available options. Standard deviation parameter is employed to estimate electronic noise without performing CT experiment. Kanpur theorem-1(KT-1) is used to estimate the scattering noise quantitatively after verifying its sensitivity to scattering noise. The impact of scattering noise on CT profiles is evaluated using dice similarity dice coefficient. A good resemblance between KT-1 and dice coefficient is observed. A maximum difference of 56% in scattering noise is observed when five detectors used simultaneously instead of single detector whereas a discrepancy of 85% is observed between different types of radiation detectors. As far as ease of handling, operational and capital cost is concern one has to compromise minimum 12% of accuracy in CT reconstruction if NaI (Tl) detector is used with respect to best alternative available.The proposed methodology can be applied to measurement that require minimal scattering interference data other than CT experiments. The manufacturer can add noise level of detector as a characteristic parameter in the data sheet.

Rapid Identification and Location of Defects behind Tunnel Lining Based on Ground-Penetrating Radar

Ground-penetrating radar (GPR) is a mainstream tool to detect defects behind tunnel linings, but the difficulty in interpreting GPR signals limits its application. This paper proposes an intelligent way to differentiate the defects behind tunnel linings by means of a model test and feature parameter analysis, to achieve an efficient and accurate analysis of GPR signals. The model test reveals the differences between defect and non-defect GPR data in amplitude, signal entropy, standard deviation, and other feature parameters. The amplitude of the defect data was on average six times as large as that of non-defect data, and the signal entropy was about 1.2 times larger. The two feature parameters lay the basis for defect detection by GPR. On this basis, a GPR-based automatic identification method was proposed, and the program was compiled on MATLAB. The program achieved a defect recognition rate as high as 96% through a field test at a rapid speed. The program is highly accurate and feasible for detecting defects behind tunnel linings with the aid of GPR.

Estimating default probabilities for no- and low-default portfolios: parameter specification via floor constraints

Abstract For low- and no-default portfolios, financial institutions are confronted with the problem to estimate default probabilities for credit ratings for which no default was observed. The Bayesian approach offers a solution but brings the problem of the parameter assignment of the prior distribution. Sequential Bayesian updating allows to settle the question of the location parameter or mean of the prior distribution. This article proposes to use floor constraints to determine the scale or standard deviation parameter of the prior distribution. The floor constraint can also be used to determine the free parameter γ in the Pluto–Tasche approach.

Application of Time-Frequency Analysis with Selection of Suitable Mother Wavelets for Transient States of HVDC Transmission Lines

Recent developments in power semiconductor devices have made High Voltage Direct Current (HVDC) transmission systems the most crucial technology for future power systems. Today several HVDC projects have been commissioned around the world. The primary concern for such an advanced HVDC grid is to follow standard grid codes to maintain the safety and stability of the electrical equipment during transient and dynamic circumstances. Although the HVDC system has been recognized as the most prominent technology to transmit electric power, some issues must be addressed, such as selective and quick detection of faults. For selective fault detection, it is essential to look into the several fault detection strategies that fortify the HVDC system with time-frequency signal processing techniques. This research uses a time frequency-based method to identify the different HVDC transmission line faults. The most suitable mother wavelet for various transient states is selected by analyzing fault voltage and current signals based on variance, standard deviation, and range parameters. An experimental setup of a thyristor-based HVDC transmission system has been developed in the MATLAB/Simulink environment, and multiple fault scenarios have been analyzed. The simulation outcomes reveal that the most effective mother wavelet for detecting DC line, symmetrical and unsymmetrical faults occurs at the 6th level of fault signal decomposition. By examining current and voltage signals, our work has determined the best mother wavelet for various faults. Using more than one mother wavelet to detect faults is more reliable than using a single mother wavelet to detect all faults. Additionally, it has been noted that each fault's voltage and current signals have two distinct mother wavelets.

Rolling bearing fault feature selection based on standard deviation and random forest classifier using vibration signals

The precise identification of faults is vital for ensuring the reliability of the bearing’s performance, and thus, the functionality of rotary machinery. The focus of our study is on the role that feature selection plays in improving the accuracy of predictive models used for diagnosis. The study combined the Standard Deviation (STD) parameter with the Random Forest (RF) classifier to select relevant features from vibration signals obtained from bearings operating under various conditions. We utilized three databases with different bearings’ health states operating under distinct conditions. The results of the study were promising, indicating that the proposed method was not only effective but also consistent, even under time-varying conditions.

Statistical Characterization and Modeling of Indoor RF-EMF Down-Link Exposure

With the increasing use of wireless communication systems, assessment of exposure to radio-frequency electromagnetic field (RF-EMF) has now become very important due to the rise of public risk perception. Since people spend more than 70% of their daily time in indoor environments, including home, office, and car, the efforts devoted to indoor RF-EMF exposure assessment has also increased. However, assessment of indoor exposure to RF-EMF using a deterministic approach is challenging and time consuming task as it is affected by uncertainties due to the complexity of the indoor environment and furniture structure, existence of multiple reflection, refraction, diffraction and scattering, temporal variability of exposure, and existence of many obstructions with unknown dielectric properties. Moreover, it is also affected by the existence of uncontrolled factors that can influence the indoor RF-EMF exposure such as the constant movement of people and random movement of furniture and doors as people are working in the building. In this study, a statistical approach is utilized to characterize and model the total indoor RF-EMF down-link (DL) exposure from all cellular bands on each floor over the length of a wing since the significance of distance is very low between any two points on each floor in a wing and the variation of RF-EMF DL exposure is mainly influenced by the local indoor environment. Measurements were conducted in three buildings that are located within a few hundred meters vicinity of two base station sites supporting several cellular technologies (2G, 3G, 4G, and 5G). We apply the one-sample Kolmogorov-Smirnov test on the measurement data, and we prove that the indoor RF-EMF DL exposure on each floor over the length of a wing is a random process governed by a Gaussian distribution. We validate this proposition using leave-one-out cross validation technique. Consequently, we conclude that the indoor RF-EMF DL exposure on each floor over the length of a wing can be modeled by a Gaussian distribution and, therefore, can be characterized by the mean and the standard deviation parameters.