Residual Method Research Articles

Кратък обзор върху микробиологични аспекти на компостирането

Recycling and disposal of urban waste is one of the goals set by the European Union. The process of composting, which is a cost-effective and environmentally friendly method of processing organic residues, is considered to be one of the ways to achieve these goals. Composting is a biological oxidation process in which organic residues are broken down by a large group of different microorganisms. The present publication presents a brief overview on the composting of organic residues and the participation of individual groups of microorganisms in this process. The decay of materials during composting follows the general biochemical pathways of any other degradation process. In the composting processes and in the finished compost, non-sporeforming bacteria and bacilli, which are most actively involved in the initial stages of degradation of organic substances, occupy a major share in the composition of the general microflora. The share of actinomycetes and micromycetes, which are generally more active in the final stages of organic matter decomposition, is smaller. The amount of actinomycetes increases with increasing temperature in the composting phases. The biogenicity of compost depends on the type of biodegradable agricultural waste used.

A warm-start FE-dABCD algorithm for elliptic optimal control problems with constraints on the control and the gradient of the state

In this paper, elliptic control problems with the integral constraint on the gradient of the state and the box constraint on the control are considered. The optimality conditions for the problem are proved. To numerically solve the problem, a finite element duality-based inexact majorized accelerated block coordinate descent (FE-dABCD) algorithm is proposed. Specifically, both the state and the control are discretized by piecewise linear functions. An inexact majorized ABCD algorithm is employed to solve the discretized problem via its dual, which is a multi-block unconstrained convex optimization problem, but the primal variables are also generated in each iteration. Thanks to the inexactness of the FE-dABCD algorithm, the subproblems at each iteration are allowed to be solved inexactly. For the smooth subproblem, we use the preconditioned generalized minimal residual (GMRES) method to solve it. For the two nonsmooth subproblems, one of them has a closed form solution through introducing an appropriate proximal term, and another one is solved by the line search Newton's method. Based on these efficient strategies, we prove that our proposed FE-dABCD algorithm enjoys O(1k2) iteration complexity. Moreover, to make the algorithm more efficient and further reduce its computation cost, based on the mesh-independence of ABCD method, we propose an FE-dABCD algorithm with a warm-start strategy (wFE-dABCD). Some numerical experiments are done and the numerical results show the efficiency of the FE-dABCD algorithm and wFE-dABCD algorithm.

Early pitting fault detection for polymer gears using kurtosis-VMD based condition indicators

The vibration signals of a polymer gear are considerably weak and susceptible to ambient noise at the early stage of the fault, which makes the fault difficult to detect. Efficient detection of an early fault in a polymer gear may improve the operation safety of the machinery system that utilizes it for power transmission. This study introduces an innovative approach for the early detection of pitting faults in polymer gears, utilizing condition indicators (CIs) derived from kurtosis-variational mode decomposition (VMD). First, the vibration signal of the polymer gear is decomposed using VMD into several components. Second, the sensitive components are selected to construct a new signal from the first two largest kurtosis values. Third, the CIs are extracted from newly constructed signals, and envelope spectrum analysis is performed. It is observed from the results that the kurtosis-VMD based CIs are effective in the early pitting fault detection of polymer gears. Finally, it is found that the proposed method performs better in all operating conditions considered in the experiment, compared with raw signal and kurtosis-empirical mode decomposition (EMD) based analysis. The proposed method’s response to noise is also explored. Furthermore, the proposed method is compared with the existing time synchronous averaging (TSA), difference, and residual methods.

Transient Thermal Analysis of Space Rectangular Thin-Walled Structures by Carrera Unified Formulation

The accurate prediction of transient thermal fields of space structures is of paramount significance for the analysis of geometrical stability and thermal management systems. This paper proposes a novel method to solve the transient thermal field of the thin-walled cross section of beams in outer space. High-order beam elements are derived by means of the Carrera unified formulation (CUF), where the longitudinal direction of the beam is discretized by one-dimensional two-node and four-node elements, whereas arbitrary high-order expansion functions are used for the description of the temperature expansion within the cross section. The governing differential equations of the transient temperature field are derived according to the transient thermal conduction theory and the weighted residual method. The solution of the initial value problem for ordinary differential equations is obtained through the application of the Adams–Bashforth method, leading to the determination of the transient thermal field. In order to demonstrate the accuracy of the temperature results, a convergence analysis is conducted. Finally, temperature distributions within the cross section and along the longitudinal direction of the model are discussed to show the effects of several factors, including material properties, angle of solar radiation, and shadows induced by the shelter of other components.

A robust approach for computing solutions of fractional-order two-dimensional Helmholtz equation

The Helmholtz equation plays a crucial role in the study of wave propagation, underwater acoustics, and the behavior of waves in the ocean environment. The Helmholtz equation is also used to describe propagation through ocean waves, such as sound waves or electromagnetic waves. This paper presents the Elzaki transform residual power series method (E\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathbb {E}}$$\\end{document}T-RPSM) for the analytical treatment of fractional-order Helmholtz equation. To develop this scheme, we combine Elzaki transform (E\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathbb {E}}$$\\end{document}T) with residual power series method (RPSM). The fractional derivatives are described in Caputo sense. The E\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathbb {E}}$$\\end{document}T is capable of handling the fractional order and turning the problem into a recurrence form, which is the novelty of our paper. We implement RPSM in such a way that this recurrence relation generates the results in the form of an iterative series. Two numerical applications are considered to demonstrate the efficiency and authenticity of this scheme. The obtained series are determined very quickly and converge to the exact solution only after a few iterations. Graphical plots and absolute error are shown to observe the authenticity of this suggested approach.

Use of Residuals and Rank Product in Detection of Outlier in Survival Analysis with Crimean-Congo Hemorrhagic Fever Data

Purpose: Survival analysis is a statistical method used in many fields, especially in the field of health. It involves modeling the relationship between the survival time of individuals after a treatment or procedure and the event called response. The presence of outliers in the data may cause biased parameter estimations of the established models. Also, this situation causes the proportional hazards assumption to be violated especially in Cox regression analysis. Outlier(s) are identified with the help of residuals, Bootstrap Hypothesis test and Rank product test. Method: In R.4.0.3 software, outlier(s) are determined on a clinical dataset by the Schoenfeld residual, Martingale residual, Deviance residual method and Bootstrap Hypothesis test (BHT) based on Concordance index, and Rank product test. Results: After the cox regression established by the backward stepwise and robust cox regression, it was observed that the established models did not fit. So, the outlier(s) determined by the methods mentioned. Conclusion: It was decided that only one observation could be excluded from the study. As in the survival data, in many data types, outliers can be detected and further analyzes can be applied by using the methods mentioned.

Method of compensating for instrumental uncertainty in measurements using a coordinate measuring ARM

Due to the influence of dynamic factors in various measurement configurations, the degree of uncertainty in measurements using a Coordinate Measuring Arm (CMA) is directly related to the measurement configuration. However, existing models for compensating CMA errors do not account dynamic factors, which impose certain limits for improving the accuracy of CMAs. To solve this issue, a method for residual error correction based on a polynomial model for single-point measurements was proposed. The influence of the CMA configuration on the residual probe error was analyzed. To enhance accuracy, a polynomial model has been developed by studying the relationship between the rotation angles of the CMA's moving elements and the probe coordinates in a cylindrical coordinate system. Experimental results demonstrate that the residual error correction method significantly compensates for instrumental uncertainty, greatly improving the accuracy of measurements using CMAs. Keywords: coordinate measuring arm, measurement error, coordinate measurements, calculation method, single-point residual correction, compensation.

Solving Neutral Delay Differential Equations Using Galerkin Weighted Residual Method Based on Successive Integration Technique and its Residual Error Correction

Objectives: The main objectives of this work are to solve Neutral Delay Differential Equations (NDDEs) using Galerkin weighted residual method based on successive integration technique and to obtain the Estimation of Error using Residual function. Methods: The Galerkin weighted residual method based on successive integration technique is proposed to obtain approximate solutions of the NDDEs. In this study, the most widely used classical orthogonal polynomials, namely, the Bernoulli polynomials, the Chebyshev polynomials, the Hermite polynomials, and the Fibonacci polynomials are considered. Findings: Numerical examples of linear and nonlinear NDDEs have been considered to demonstrate the efficiency and accuracy of the method. Approximate solutions obtained by the proposed method are well comparable with exact solutions. Novelty: From the results it is observed that the accuracy of the numerical solutions by the proposed method increases as N increases. The proposed method is very effective, simple, and suitable for solving the linear and nonlinear NDDEs in real-world problems. Keywords: Galerkin Weighted Residual method, Polynomials, Hermite, Bernoulli, Chebyshev, Fibonacci, Successive integration technique, Neutral Delay Differential Equations

Damage Detection in Members and Connections of Plane Frame with Flexible Connections Using Residual Force Method and Whale Optimization Algorithm

Damage Detection in Members and Connections of Plane Frame with Flexible Connections Using Residual Force Method and Whale Optimization Algorithm

Hybrid Support Vector Machine for Predicting Accuracy of Conflict Flows in Software Defined Networks

Software Defined Networking (SDN) is an infrastructure platform for delivering simplified and compliant services with flexible services. These are the means of centralized maintenance and adaptive functions. SDN is affected by various contention flows and causes network performance issues. In this case, we need to provide efficient solutions to handle conflicting flows with better priority and actions. In this paper, we propose a DeepQ Residue method for analyzing normal and conflicting flow scenarios in the load balancing phase. During simulation, an open SDN network is generated using TensorFlow. We use a Hybrid Support Vector machine with an improved decision tree method to predict accuracy and performance. In this case, we analyze threads from 1000 to 100000 in increments of 10000 threads in each iteration. Here, we train a deep belief network with a decision-free feature for environmental simulation. Based on the simulation results, the accuracy of our proposed method reaches 97 %, and we compare the results with the results of various existing methods. Our proposed algorithm provides a high-performance SDN application with different conflicting load-balanced flows

A green-footprint approach for parallel multiclass analysis of contaminants in roasted coffee via LC-HRMS

The development and validation of a simple, comprehensive, and environment-friendly procedure to determine pesticide residues, naturally occurring and processing contaminants in roasted coffee is presented. A solid-liquid extraction of pesticides and mycotoxins with ethyl acetate and the concurrent partition of acrylamide to an aqueous phase follows a parallel analytical strategy that requires a single analytical portion to determine contaminants that are typically analyzed by dedicated single residue methods. The partition rules the lipids out of the aqueous extract before an “in-tube” dispersive solid phase microextraction (dSPME) for acrylamide retention. This is followed by the elution with buffer prior to injection. This extract is independently introduced into the system front end followed by the injection of the compounds from the organic phase, yet all spotted in the same run. A novel liquid chromatography high-resolution mass spectrometry (LC-HRMS) method setup enables the quantification of 186 compounds at 10 µg/kg, 226 at 5 µg/kg, and the acrylamide at 200 µg/kg for a total of 414 molecules, with acceptable recoveries (70–120%) and precision (RSD < 20%) making this strategy significantly faster and cost-effective than the dedicated single residue methods. Even though the presence of chlorpyrifos, acrylamide, and ochratoxin A was confirmed on samples of different origins, the findings were below the limit of quantification. During the storage of raw coffee, no proof of masking of OTA was found; however, condensation with glucose was evidenced during thermal processing experiments with sucrose by using stable isotope labeling (SIL). No detected conjugates were found in roasted nor in commercial sugar-added torrefacto samples, an industrial processing usually carried out above the decomposition temperature of the disaccharide.Graphical abstract

Residual current detection method based on improved VMD-BPNN.

To further enhance the residual current detection capability of low-voltage distribution networks, an improved adaptive residual current detection method that combines variational modal decomposition (VMD) and BP neural network (BPNN) is proposed. Firstly, the method employs the envelope entropy as the adaptability function, optimizes the [k, ɑ] combination value of the VMD decomposition using the bacterial foraging-particle swarm algorithm (BFO-PSO), and utilizes the interrelation number R as the classification index with the Least Mean Square Algorithm (LMS) to classify, filter, and extract the effective signal from the decomposed signal. Then, the extracted signals are detected by BPNN, and the training data are utilized to predict the residual current signals. Simulation and experimental data demonstrate that the proposed algorithm exhibits strong robustness and high detection accuracy. With an ambient noise of 10dB, the signal-to-noise ratio is 16.3108dB, the RMSE is 0.4359, and the goodness-of-fit is 0.9627 after processing by the algorithm presented in this paper, which are superior to the Variational Modal Decomposition-Long Short-Term Memory (VMD-LSTM) and Normalized-Least Mean Square (N-LMS) detection methods. The results were also statistically analyzed in conjunction with the Kolmogorov-Smirnov test, which demonstrated significance at the experimental data level, indicating the high accuracy of the algorithms presented in this paper and providing a certain reference for new residual current protection devices for biological body electrocution.

Rework and Repair Options for Steam Turbine Components Subject to Flexible Operation

Abstract The increase in renewable energy penetration on the grid has accelerated the need to transition conventional fossil-based energy sources from their traditional base load operation to more flexible operational regimes. The mode of operation changed dramatically in terms of number of starts, operating hours per annum, and variation in load level. This results in greater thermal transients on operational equipment leading to an increase in Low Cycle Fatigue damage. To ensure the continued integrity of the steam turbine components, it is essential to assess the lifetime status by applying residual lifetime analysis methods. Depending on the amount of lifetime consumption and the extend of potential crack findings, different component repair options are possible. The rework or repair options can be divided into two main groups, namely, cold- and hot-rework. These two options can also be carried out consecutively. All rework or repair options provide the opportunity to improve the application of a component by applying profiling with improved stress fields and even superior materials, in the case of hot rework. The aim of the rework/reconditioning is to ensure that the steam turbine component is suitable for future operation. This ensures that plants are well placed to deliver more flexible operation in the energy industry through carefully tailored refurbishments and reworks.

On the shock wave approximation to fractional generalized Burger–Fisher equations using the residual power series transform method

The time-fractional generalized Burger–Fisher equation (TF-GBFE) has various applications across various scientific and engineering disciplines. It is used for investigating various phenomena, including the dynamics of fluid flow, gas dynamics, shock-wave formation, heat transfer, population dynamics, and diffusion transport, among other areas of research. By incorporating fractional calculus into these models, researchers can more effectively represent the non-local and memory-dependent effects frequently observed in natural phenomena. Due to the importance of the family of TF-GBFEs, this work introduces a changed iterative method for analyzing this family analytically to gain a deep understanding of many nonlinear phenomena described by this family (e.g., shock waves). The proposed approach combines two algorithms: the Laplace transform and the residual power series method. The suggested technique is thoroughly discussed. Two numerical problems are discussed to check the effectiveness and accuracy of the proposed method. The approximations for integer and fractional orders are compared with the exact solution for integer-order problems. Finally, to investigate how the fractional order affects these problems, the obtained results are discussed graphically and numerically in the tables.

Numerical solutions to linear transfer problems of polarized radiation

Context. A relevant class of radiative transfer problems for polarized radiation is linear, or can be linearized, and can thus be reframed as linear systems once discretized. In this context, depending on the considered physical models, there are both highly coupled and computationally expensive problems, for which state-of-the-art iterative methods struggle to converge, and lightweight ones, for which solutions can be obtained efficiently. Aims. This work aims to exploit lightweight physical models as preconditioners for iterative solution strategies to obtain accurate and fast solutions for more complex problems. Methods. We considered a highly coupled linear transfer problem for polarized radiation, which we solved iteratively using a matrix-free generalized minimal residual (GMRES) method. Different preconditioners and initial guesses, designed in a physics-based framework, are proposed and analyzed. The action of preconditioners was also computed by applying GMRES. The overall approach thus consists of two nested GMRES iterations, one for the original problem and one for its lightweight version. As a benchmark, we considered the modeling of the intensity and polarization of the solar Ca I 4227 Å line, the Sr II 4077 Å line, and the Mg II h&amp;k lines in a semi-empirical 1D atmospheric model, accounting for partial frequency redistribution effects in scattering processes and considering a general angle-dependent treatment. Results. Numerical experiments show that using tailored preconditioners based on simplified models of the considered problem has a noticeable impact, reducing the number of iterations to convergence by a factor of 10–20. Conclusions. By designing efficient preconditioners in a physics-based context, it is possible to significantly improve the convergence of iterative processes, obtaining fast and accurate numerical solutions to the considered problems. The presented approach is general, requiring only the selection of an appropriate lightweight model, and can be applied to a larger class of radiative transfer problems in combination with arbitrary iterative procedures.

A Novel Residual Magnetism Detection Method of Power Transformer Cores Based on Magnetization Time

A Novel Residual Magnetism Detection Method of Power Transformer Cores Based on Magnetization Time

Evaluation of confirmatory data following the Article 12 MRL review for quizalofop-P-ethyl, quizalofop-P-tefuryl and propaquizafop and modification of the existing maximum residue levels for quizalofop-P-tefuryl.

The applicant Arysta Life Science Great Britain Limited submitted a request to the competent national authority in Croatia to evaluate the confirmatory data that were identified for quizalofop-P-tefuryl in the framework of the maximum residue level (MRL) review under Article 12 of Regulation (EC) No 396/2005 as not available. Since Article 12 data gaps were also set for the two other quizalofop-P variants sharing the same residue definitions for risk assessment and monitoring, EFSA included in the present assessment all quizalofop-P variants: quizalofop-P-ethyl, quizalofop-P-tefuryl and propaquizafop. Moreover, in the application submitted to Croatia, the applicant also included a request to modify the existing MRLs for quizalofop-P-tefuryl in grapes, sunflower seeds and soyabeans in accordance with Article 6 of Regulation (EC) No 396/2005. To address the data gaps, new data on hydrolysis efficiency of quizalofop-P-tefuryl, quizalofop acid, quizalofop-pentanoic acid and quizalofop-P-glycerate in different matrices of animal origin in accordance with the guidance document SANTE/2020/12830 Rev.1 were submitted, along with a validated analytical method for animal commodities. EFSA concluded that the data gap on validation of the efficiency of the extraction and hydrolysis included in the enforcement method of residues in livestock animal commodities was only fully addressed for muscle, poultry liver and eggs. Regarding plant commodities, the remaining data gaps were not addressed. EFSA also considered data gaps for quizalofop-p-ethyl in caraway as sufficiently addressed in the context of a previous MRL application. In general, the new information provided required a revision of the existing MRLs for several commodities of plant and animal origin. Further risk management considerations are required. Based on the risk assessment results, EFSA concluded that the short-term and long-term intake of residues resulting from the use of quizalofop-P-tefuryl according to the reported agricultural practices is unlikely to present a risk to consumer health.

Residual Toxicity of Neem-Based Insecticides against Egg Parasitoid &lt;i&gt;Trichogrammatoidea bactrae&lt;/i&gt; Nagaraja

A study was carried out with neem-based insecticides, viz., azadirachtin (0.03%EC, 0.15%EC, 0.3%EC, 1%EC), neem seed oil (1, 2 and 3%), neem seed extract 5% and dashparni ark (ten plant leaf extract) 4%, on Trichogrammatoidea bactrae Nagaraja (Hymenoptera: Trichogrammatidae) adults using the thin film residue method under laboratory conditions. After coating glass vials with neem-based insecticides, the adults of T. bactrae were released on the first, fifth, and tenth days of coating and the mortality was assessed. Among all neem-based insecticides, azadirachtin 1%EC was observed to be harmful to adults of T. bactrae on the first, fifth and tenth days. In contrast, neem seed extract 5% and dashparni ark 4% were found safe.

Relationship between food group-specific intake and depression among local government employees in Japan

BackgroundWe aimed to examine the relationship between food group-specific intake and depression among workers in Japan.MethodsA questionnaire survey was administered to 568 workers in 2020; 503 workers responded and 423 were included in the study. Information on sex, age, body mass index, overtime hours, sleep duration, marital status, employment position, exercise habits, smoking status, incidence of depression, and intake of energy, proteins, lipids, carbohydrates, alcohol, and specific food groups were collected. The Center for Epidemiologic Studies Depression Scale was used to assess the presence and severity of depression. Food group-specific intake was adjusted for energy intake using the residual method and classified into low, moderate, and high by sex. Logistic regression was used to examine the odds ratios (ORs) and trends according to sex, with the presence/absence of depression as the dependent variable and food group-specific intake as the independent variable.ResultsMen in the eggs low-intake, and women in the other vegetables low- and moderate-intake and eggs moderate-intake groups had significantly higher adjusted ORs (aORs) for depression. Additionally, a dose-response relationship was observed, where the OR for depression was significantly higher in men when the intake of eggs was low (p for trend = 0.024) and in women when the intakes of other vegetables (p for trend = 0.011) and eggs (p for trend = 0.032) were low.ConclusionsThe intake of eggs in men and eggs and vegetables in women may be related to depression.

Substrate-Assisted Laser-Induced Breakdown Spectroscopy Combined with Variable Selection and Extreme Learning Machine for Quantitative Determination of Fenthion in Soybean Oil

The quality and safety of edible vegetable oils are closely related to human life and health, meaning it is of great significance to explore the rapid detection methods of pesticide residues in edible vegetable oils. This study explored the applicability potential of substrate-assisted laser-induced breakdown spectroscopy (LIBS) for quantitatively determining fenthion in soybean oils. First, we explored the impact of laser energy, delay time, and average oil film thickness on the spectral signals to identify the best experimental parameters. Afterward, we quantitatively analyzed soybean oil samples using these optimized conditions and developed a full-spectrum extreme learning machine (ELM) model. The model achieved a prediction correlation coefficient (RP2) of 0.8417, a root mean square error of prediction (RMSEP) of 167.2986, and a mean absolute percentage error of prediction (MAPEP) of 26.46%. In order to enhance the prediction performance of the model, a modeling method using the Boruta algorithm combined with the ELM was proposed. The Boruta algorithm was employed to identify the feature variables that exhibit a strong correlation with the fenthion content. These selected variables were utilized as inputs for the ELM model, with the RP2, RMSEP, and MAPEP of Boruta-ELM being 0.9631, 71.4423, and 10.06%, respectively. Then, the genetic algorithm (GA) was used to optimize the parameters of the Boruta-ELM model, with the RP2, RMSEP, and MAPEP of GA-Boruta-ELM being 0.9962, 11.005, and 1.66%, respectively. The findings demonstrate that the GA-Boruta-ELM model exhibits excellent prediction capability and effectively predicts the fenthion contents in soybean oil samples. It will be valuable for the LIBS quantitative detection and analysis of pesticide residues in edible vegetable oils.