Search

Background: BK-polyomavirus (BKpyV) nephropathy (BKVN) is associated with end-stage kidney disease in kidney and non-kidney solid organ transplantation, with no curative treatment. Case presentation: A 45-year-old woman with a past medical history of double lung transplantation subsequently developed end-stage kidney disease, of undetermined origin. One month after receiving a kidney transplant, a diagnosis of early BKVN was suspected, and in retrospect was a reasonable cause for the loss of her native kidneys. Minimisation of immunosuppression, achieved through extracorporeal photopheresis, allowed clearance of BKpyV and so prevented nephropathy. Both lung and kidney grafts had a satisfactory and stable function after one year of follow-up, with no rejection. Conclusions: Extracorporeal photopheresis may have facilitated minimisation of immunosuppression and BKpyV clearance without lung allograft rejection. [ABSTRACT FROM AUTHOR]

Peptides are native binders involved in numerous physiological life procedures, such as cellular signaling, and serve as ready-made regulators of biochemical processes. Meanwhile, small molecules compose many drugs owing to their outstanding advantages of physiochemical properties and synthetic convenience. A novel field of research is converting peptides into small molecules, providing a convenient portable solution for drug design or peptidomic research. Endowing properties of peptides onto small molecules can evolutionarily combine the advantages of both moieties and improve the biological druggability of molecules. Herein, we present eight representative recent cases in this conversion and elaborate on the transformation process of each case. We discuss the innovative technological methods and research approaches involved, and analyze the applicability conditions of the approaches and methods in each case, guiding further modifications of peptides to small molecules. Finally, based on the aforementioned cases, we summarize a general procedure for peptide-to-small molecule modifications, listing the technological methods available for each transformation step and providing our insights on the applicable scenarios for these methods. This review aims to present the progress of peptide-to-small molecule modifications and propose our thoughts and perspectives for future research in this field. This review outlines the transformation of peptides into small molecules, highlighting classical and innovative methods contributing in four stages, providing insights and perspectives for future research in novel drug design. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper presents an altitude and attitude control system for a newly designed rocket-type unmanned aerial vehicle (UAV) propelled by a gimbal-based coaxial rotor system (GCRS) enabling thrust vector control (TVC). The GCRS is the only means of actuation available to control the UAV's orientation, and the flight dynamics identify the primary control difficulty as the highly nonlinear and tightly coupled control distribution problem. To address this, the study presents detailed derivations of attitude flight dynamics and a control strategy to track the desired attitude trajectory. First, a Proportional-Integral-Derivative (PID) control algorithm is developed based on the formulation of linear matrix inequality (LMI) to ensure robust stability and performance. Second, an optimization algorithm using the Levenberg–Marquardt (LM) method is introduced to solve the nonlinear inverse mapping problem between the control law and the actual actuator outputs, addressing the nonlinear coupled control input distribution problem of the GCRS. In summary, the main contribution is the proposal of a new TVC UAV system based on GCRS. The PID control algorithm and LM algorithm were designed to solve the distribution problem of the actuation model and confirm altitude and attitude tracking missions. Finally, to validate the flight properties of the rocket-type UAV and the performance of the proposed control algorithm, several numerical simulations were conducted. The results indicate that the tightly coupled control input nonlinear inverse problem was successfully solved, and the proposed control algorithm achieved effective attitude stabilization even in the presence of disturbances. • A newly designed thrust vector control based rocket-type UAV prototype is presented. • The governing equations of the under-actuated and unstable dynamics system are derived. • A robust PID controller based on the formulation of LMI guaranteeing robust performance in the sense of the Lyapunov is designed. • A configuration of the GCRS is designed to realize the thrust vector control. • A LM-based optimal algorithm is presented to solve the nonlinear force inverse mapping problem induced by the GCRS. [ABSTRACT FROM AUTHOR]

Peptides are native binders involved in numerous physiological life procedures, such as cellular signaling, and serve as ready-made regulators of biochemical processes. Meanwhile, small molecules compose many drugs owing to their outstanding advantages of physiochemical properties and synthetic convenience. A novel field of research is converting peptides into small molecules, providing a convenient portable solution for drug design or peptidomic research. Endowing properties of peptides onto small molecules can evolutionarily combine the advantages of both moieties and improve the biological druggability of molecules. Herein, we present eight representative recent cases in this conversion and elaborate on the transformation process of each case. We discuss the innovative technological methods and research approaches involved, and analyze the applicability conditions of the approaches and methods in each case, guiding further modifications of peptides to small molecules. Finally, based on the aforementioned cases, we summarize a general procedure for peptide-to-small molecule modifications, listing the technological methods available for each transformation step and providing our insights on the applicable scenarios for these methods. This review aims to present the progress of peptide-to-small molecule modifications and propose our thoughts and perspectives for future research in this field.

Abstract Background BK-polyomavirus (BKpyV) nephropathy (BKVN) is associated with end-stage kidney disease in kidney and non-kidney solid organ transplantation, with no curative treatment. Case presentation A 45-year-old woman with a past medical history of double lung transplantation subsequently developed end-stage kidney disease, of undetermined origin. One month after receiving a kidney transplant, a diagnosis of early BKVN was suspected, and in retrospect was a reasonable cause for the loss of her native kidneys. Minimisation of immunosuppression, achieved through extracorporeal photopheresis, allowed clearance of BKpyV and so prevented nephropathy. Both lung and kidney grafts had a satisfactory and stable function after one year of follow-up, with no rejection. Conclusions Extracorporeal photopheresis may have facilitated minimisation of immunosuppression and BKpyV clearance without lung allograft rejection.

In controlled phase II trials, major prognostic factors need to be well balanced between arms. The main procedures used are SPBR (Stratified Permuted Block Randomization) and minimization. First, we provide a systematic review of the treatment allocation procedure used in gastrointestinal oncology controlled phase II trials published in 2019. Second, we performed simulations using data from six phase II studies to measure the impacts of imbalances and bias on the efficacy estimations. From the 40 articles analyzed, all mentioned randomization in both the title and abstract, the median number of patients included was 109, and 77.5% were multicenter. Of the 27 studies that reported at least one stratification variable, 10 included the center as a stratification variable, 10 used minimization, 9 used SBR, and 8 were unspecified. In real data studies, the imbalance increased with the number of centers. The total and marginal imbalances were higher with SBR than with minimization, and the difference increased with the number of centers. The efficiency estimates per arm were close to the original trial estimate in both procedures. Minimization is often used in cases of numerous centers and guarantees better similarity between arms for stratification variables for total and marginal imbalances in phase II trials.

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper introduces a new algorithm designed to address the challenge of distribution network reconfiguration, employing the tabu search metaheuristic in conjunction with the voltage drop concept. Distinguishing itself from existing methods, our proposed approach not only utilizes voltage drop for obtaining the initial solution but also introduces a novel technique for generating a candidate solution neighborhood. This method leverages both randomness and voltage drop, ensuring a smooth and steady descent during algorithm execution. The primary goal of our algorithm is to minimize active power losses within distribution networks. To validate its effectiveness, the proposed method underwent testing on three commonly referenced distribution systems: the 33-Bus, 69-Bus, and 94-Bus systems, widely acknowledged in the literature. A pivotal aspect of our work involves the synergy of the tabu search algorithm with a combination of both random and deterministic methods for generating neighbors. This strategic amalgamation plays a crucial role, enabling rapid execution while consistently yielding high-quality solutions. Additionally, the adoption of the electric distance method for generating the initial solution adds significant value, offering a commendable solution with minimal computational effort. Comparative assessments against other algorithms documented in the literature underscore the superior efficiency of our proposed algorithm. [ABSTRACT FROM AUTHOR]

In controlled phase II trials, major prognostic factors need to be well balanced between arms. The main procedures used are SPBR (Stratified Permuted Block Randomization) and minimization. First, we provide a systematic review of the treatment allocation procedure used in gastrointestinal oncology controlled phase II trials published in 2019. Second, we performed simulations using data from six phase II studies to measure the impacts of imbalances and bias on the efficacy estimations. From the 40 articles analyzed, all mentioned randomization in both the title and abstract, the median number of patients included was 109, and 77.5% were multicenter. Of the 27 studies that reported at least one stratification variable, 10 included the center as a stratification variable, 10 used minimization, 9 used SBR, and 8 were unspecified. In real data studies, the imbalance increased with the number of centers. The total and marginal imbalances were higher with SBR than with minimization, and the difference increased with the number of centers. The efficiency estimates per arm were close to the original trial estimate in both procedures. Minimization is often used in cases of numerous centers and guarantees better similarity between arms for stratification variables for total and marginal imbalances in phase II trials. [ABSTRACT FROM AUTHOR]

The work proves that under conditions of instability of the second derivatives of the function in the minimization region, the estimate of the convergence rate of Newton's method is determined by the parameters of the irreducible part of the conditionality degree of the problem. These parameters represent the degree of difference between eigenvalues of the matrices of the second derivatives in the coordinate system, where this difference is minimal, and the resulting estimate of the convergence rate subsequently acts as a standard. The paper studies the convergence rate of the relaxation subgradient method (RSM) with optimization of the parameters of two-rank correction of metric matrices on smooth strongly convex functions with a Lipschitz gradient without assumptions about the existence of second derivatives of the function. The considered RSM is similar in structure to quasi-Newton minimization methods. Unlike the latter, its metric matrix is not an approximation of the inverse matrix of second derivatives but is adjusted in such a way that it enables one to find the descent direction that takes the method beyond a certain neighborhood of the current minimum as a result of one-dimensional minimization along it. This means that the metric matrix enables one to turn the current gradient into a direction that is gradient-consistent with the set of gradients of some neighborhood of the current minimum. Under broad assumptions on the parameters of transformations of metric matrices, an estimate of the convergence rate of the studied RSM and an estimate of its ability to exclude removable linear background are obtained. The obtained estimates turn out to be qualitatively similar to estimates for Newton's method. In this case, the assumption of the existence of second derivatives of the function is not required. A computational experiment was carried out in which the quasi-Newton BFGS method and the subgradient method under study were compared on various types of smooth functions. The testing results indicate the effectiveness of the subgradient method in minimizing smooth functions with a high degree of conditionality of the problem and its ability to eliminate the linear background that worsens the convergence. [ABSTRACT FROM AUTHOR]

Maximizing efficiency, power density, and reliability stands as paramount objectives in the advancement of power electronic systems. Notably, the dimensions and losses of magnetic components emerge as primary constraints hindering the miniaturization of such systems. Researchers have increasingly focused on the design of loss minimization and size optimization of magnetic devices. In this paper, with the objective of minimizing the loss of magnetic devices, an optimal design method for the winding structure of devices is proposed based on the coupling relationship between the loss prediction model and the design variables. The method examines the decoupling conditions between the design variables and the loss model, deriving optimized design closure equations for the design variables. This approach furnishes a technical foundation for the miniaturized design of miniature apparatuses incorporating magnetic components, offering a straightforward and adaptable design methodology. The finite element method simulation results and experimental measurement data verify the accuracy of the prediction of the proposed method and the validity of the optimal design theory of device loss. [ABSTRACT FROM AUTHOR]

Abstract At present, in order to conduct non‐destructive testing on the grounding grid of substations under the condition of continuous power supply and no excavation, researchers have applied wireless technology based on electrochemical methods to remotely monitor the corrosion state of grounding conductors online. Nevertheless, wireless signals are affected by the environment when they are transmitted underground. In the field of grounding gird wireless monitoring, how to plan the information transmission path of wireless sensor network (WSN) with high accuracy of data transfer and low energy consumption earns growing research attention. To address the problem of WSN path planning in grounding grid area, a path planning method for WSN based on the hybrid algorithm of map‐matching algorithm and double‐pole search algorithm (MM‐DPS) is proposed in this paper. The map‐matching algorithm is employed to calculate the optimal sampling node number of the data transmission path. On the basis of the optimal sampling node number, the double‐pole search algorithm is employed in seeking out each sensor node of the path, and two groups of path plans are obtained. In the simulation experiment, compared with the A‐star algorithm, the MM‐DPS algorithm shortens the data transmission path length by about 39% and reduces the energy consumption by about 57%. The research work brings a method to alleviate the problem of data transmission underground of WSN in grounding grid area. The method not only ensures the accuracy of data transmission, but also shorts the transmission distance and reduces energy consumption.

The aim of this paper is to obtain the sharp estimate for the lowest positive eigenvalue for the Camassa-Holm equation y ″ = 1 4 y + λ m (t) y , with the Neumann-Dirichlet boundary conditions, where potential m admits to change sign. We first study the optimal lower bound for the smallest positive eigenvalue in the measure differential equations. Then based on the relationship between the minimization problem of the smallest positive eigenvalue for the ODE and the one for the MDE, we find the explicit optimal lower bound of the smallest positive eigenvalue for this indefinite Camassa-Holm equation. [ABSTRACT FROM AUTHOR]

One of the classical problems of optimization theory in a finite-dimensional space is to find a minimum of a function on a nonempty set. Usually, finding the precise solution to this task analytically requires a lot of computational resources or is even impossible at all. So, approximate methods are used most often in practical cases. One of the simplest and the most well-known among such approximate methods for unconditional optimization is the method of gradient descent; its generalization for conditional optimization was found in 1964, the method of projected gradient. For some simple sets (line segment, parallelepiped, ball), the projection of the point on the set can be easily found by an explicit formula. However, for more complicated sets (e.g., an ellipse), projecting becomes a separate task. Nevertheless, sometimes computing projection can be simplified by affine transform; e.g., an ellipse can be transformed into a ball by affine (moreover, by linear) transformation. The paper aims to simplify the problem of minimizing function on the set by changing the condition set by affine transform F(x)= Ax+b, where A is a non-degenerated square matrix, and b is a fixed vector of proper dimension. [ABSTRACT FROM AUTHOR]

At present, in order to conduct non‐destructive testing on the grounding grid of substations under the condition of continuous power supply and no excavation, researchers have applied wireless technology based on electrochemical methods to remotely monitor the corrosion state of grounding conductors online. Nevertheless, wireless signals are affected by the environment when they are transmitted underground. In the field of grounding gird wireless monitoring, how to plan the information transmission path of wireless sensor network (WSN) with high accuracy of data transfer and low energy consumption earns growing research attention. To address the problem of WSN path planning in grounding grid area, a path planning method for WSN based on the hybrid algorithm of map‐matching algorithm and double‐pole search algorithm (MM‐DPS) is proposed in this paper. The map‐matching algorithm is employed to calculate the optimal sampling node number of the data transmission path. On the basis of the optimal sampling node number, the double‐pole search algorithm is employed in seeking out each sensor node of the path, and two groups of path plans are obtained. In the simulation experiment, compared with the A‐star algorithm, the MM‐DPS algorithm shortens the data transmission path length by about 39% and reduces the energy consumption by about 57%. The research work brings a method to alleviate the problem of data transmission underground of WSN in grounding grid area. The method not only ensures the accuracy of data transmission, but also shorts the transmission distance and reduces energy consumption. [ABSTRACT FROM AUTHOR]

Wheat is the most important crop and food in the feed consumption pattern. By reducing wheat loss in the harvesting stage, it is possible to significantly increase the production of this crop. Due to the acceptance of farmers in the use of straw collecting combines, the number of this type of combines is increasing, especially for harvesting wheat. This research was conducted in order to investigate the effect of spike density per unit area (340, 350 and 440) and the combine forward speed (1, 2 and 3 km/h) on the percentage of quality loss of wheat in harvesting with a straw collecting combine in Khorramabad city. In this research, Response Surface Methodology (RSM) was used to model and minimize the wheat quality loss. The results showed that process variable was statistically significant as quadratic regression model for response (p

The problem of optimal covering of plane figures with sets of a fixed number of different circles is considered. We suppose that each circle has a radius equal to the sum of the parameter common to all and its individual number. The main aim of the paper is to develop algorithms that allow the construction of a covering with a minimum common parameter. It is proved that the problem can be reduced to minimizing a function of several variables depending on the coordinates of the centers of the circles. The zones of influence of points serving as the centers of circles for a fixed set of individual numbers have been studied. Iterative algorithm for solving the problem is proposed using the concepts of the Chebyshev center and a generalization of the Dirichlet zone. The possibilities of applying the results of the article to the construction of sensor networks are shown.

Proper Generalized Decomposition (PGD) approximates a function by a series of modes, each of them taking a variable-separated form. This allows drastic reduction in numerical complexity, particularly suits high dimensional problems and has the potential to tackle with the curse of dimensionality. In this paper, we formulate residual functionals for stepwise PGD to approximate function, to solve diffusive equation with/without preconditioning, respectively. The discrete counterparts are also presented. We prove that in the discrete counterpart for function approximation, namely, matrix or tensor approximation, the stepwise PGD and multi-modal approximation by Tensor Decomposition (TD) are equivalent. In case of matrix approximation, both give the same result as that by Singular Value Decomposition (SVD). Furthermore, in view of difficulties in convergence and accuracy arising in diffusive equation solving by PGD, we propose to minimize a preconditioned residual functional instead, leading to a Preconditioned PGD (PPGD) approach. Numerical tests for the heat equation in one and two space dimension(s) demonstrate accurate resolution with a small number of modes. It remains effective even at the presence of inhomogeneity or nonlinearity, and is extended to treat linear wave equation. The proposed PPGD may shed insights to design effective PGD algorithms for solving higher dimensional transient partial differential equations. [ABSTRACT FROM AUTHOR]

The construction of reinforced concrete (RC) structures inevitably consumes an excessive number of rebars, leading to significant cutting waste and carbon emissions. Extensive research has been conducted to minimize this issue and its consequences; however, these methods consistently consume a substantial number of rebars. This includes a previous study that utilizes the lap splice position optimization and special-length rebar concept without considering the lapping zone regulation. Moreover, conventional lap splices pose inherent drawbacks that could jeopardize the structural integrity of RC members. In contrast, mechanical couplers eliminate the need for rebar lapping, effectively reducing rebar consumption. This research aims to evaluate the impact of an integrated mechanical coupler and special-length-priority minimization algorithm on the reduction in rebar consumption and cutting waste in RC columns, achieving near-zero cutting waste. To validate the effectiveness of the proposed algorithm, it was applied to the column rebars of an RC building. The results revealed a significant reduction in the ordered rebar consumption by 18.25%, accompanied by substantial reductions in the cutting waste (8.93%), carbon emissions (12.99%), and total costs (9.94%) compared with a previous study. The outcomes provide the industry with insights into further reducing rebar consumption and its related consequences. Applying the proposed algorithm to various construction projects will further amplify the corresponding benefits. [ABSTRACT FROM AUTHOR]

Rebar usage and cutting waste contribute significantly to global greenhouse gas emissions, mainly CO2 and CH4. Researchers have explored various means to minimize cutting waste; however, these studies have yet to address reducing splices and utilizing a single specific special-length rebar. Hence, this study proposed an algorithm to minimize rebar usage and reduce rebar-cutting waste to less than 1% (near-zero rebar-cutting waste). The algorithm involves two main steps: (1) reducing the number of splices by utilizing special-length rebar and (2) adjusting the rebar accordingly based on the obtained special-length rebar. The algorithm was applied to the column rebars of an RC building to validate its effectiveness. The results confirmed a reduction in rebar usage by 3.226 tons (17.76%), a cutting waste rate of 0.83% (near-zero rebar-cutting waste achieved), a reduction of 11.18 tons in CO2 emissions, and a cost reduction of USD 3741. Employing the proposed algorithm in RC building and structure projects will amplify the corresponding benefits and contribute to the achievement of SDGs adopted by the United Nations to ensure sustainable resource usage and the acceleration of sustainable and green construction practices. [ABSTRACT FROM AUTHOR]

In the world, research devoted to adjusting the results of heuristic methods based on forecasting, recognition, classification, and determining the absolute extremum of a multidimensional function is relevant and widely used in such fields as medicine, geology, hydrology, management, and computer technology. In this regard, it is important to construct optimal correctors of heuristic algorithms based on control materials. Therefore, checking the completeness of classes of k-valued logical functions and developing methods and algorithms for minimizing functions in the class of canonical normal forms, estimating the number of monotonic functions of k-valued logic, constructing minimal bases of special classes of correcting functions for correcting incorrect algorithms remains one of the important problems of computational and discrete science. mathematics. Currently, a lot of scientific research is being carried out around the world aimed at expanding the integration of science and industry, in particular the development of the theory of k-valued logical functions for correcting the results of heuristic algorithms. In this case, an important role is played by the construction of formulas in the class of canonical normal forms, the coding of elementary conjunctions and the application of the rules of gluing, absorption and idempotency for them, and checking the completeness of systems of correcting functions. Consequently, the development of effective numerical computational methods and algorithms for constructing correction functions based on k-valued logic to improve the accuracy of the results of heuristic methods is considered a targeted scientific research. The paper considers the representation of k-valued logical functions in the class of disjunctive normal forms. Various classes of monotone functions of k-valued logic are studied. Theorems are proved on the coincidence of abbreviated and shortest disjunctive normal forms of k-valued functions. For a certain class of k-valued monotone functions, we prove an estimate for the number of functions from this class. criteria for the absorption of elementary conjunctions by a first-order neighborhood of disjunctive normal forms of k-valued functions are proved. [ABSTRACT FROM AUTHOR]

The main results of the study of the convergence rate of quasi-Newton minimization methods were obtained under the assumption that the method operates in the region of the extremum of the function, where there is a stable quadratic representation of the function. Methods based on the quadratic model of the function in the extremum area show significant advantages over classical gradient methods. When solving a specific problem using the quasi-Newton method, a huge number of iterations occur outside the extremum area, unless there is a stable quadratic approximation of the function. In this paper, we study the convergence rate of quasi-Newton-type methods on strongly convex functions with a Lipschitz gradient, without using local quadratic approximations of a function based on the properties of its Hessian. We proved that quasi-Newton methods converge on strongly convex functions with a Lipschitz gradient with the rate of a geometric progression, while the estimate of the convergence rate improves with the increasing number of iterations, which reflects the fact that the learning (adaptation) effect accumulates as the method operates. Another important fact discovered during the theoretical study is the ability of quasi-Newton methods to eliminate the background that slows down the convergence rate. This elimination is achieved through a certain linear transformation that normalizes the elongation of function level surfaces in different directions. All studies were carried out without any assumptions regarding the matrix of second derivatives of the function being minimized. [ABSTRACT FROM AUTHOR]

Wheat is the most important crop and food in the feed consumption pattern. By reducing wheat loss in the harvesting stage, it is possible to significantly increase the production of this crop. Due to the acceptance of farmers in the use of straw collecting combines, the number of this type of combines is increasing, especially for harvesting wheat. This research was conducted in order to investigate the effect of spike density per unit area (340, 350 and 440) and the combine forward speed (1, 2 and 3 km/h) on the percentage of quality loss of wheat in harvesting with a straw collecting combine in Khorramabad city. In this research, Response Surface Methodology (RSM) was used to model and minimize the wheat quality loss. The results showed that process variable was statistically significant as quadratic regression model for response (p<0.01). The wheat quality loss is strongly influenced by the combine forward speed and the spike density per unit area. The lowest percentage of wheat quality loss (1.9%) was related to the speed of 1 km/h and the spike density of 340 spikes/m². [ABSTRACT FROM AUTHOR]

Speech enhancement is an important property in today's world because most applications use voice recognition as an important feature for performing operations in it. Perfect recognition of commands is achieved only by recognizing the voice correctly. Hence, the speech signal must be enhanced and free from background noise for the recognition process. In the existing approach, a recurrent convolutional encoder/decoder is used for denoising the speech signal. It utilized the signal-to-noise ratio property for enhancing the speech signal. It removes the noise signal effectively by having a low character error rate. But it does not describe the range of SNR of the noise added to the signal. Hence, in this, optimized deep learning is proposed to enhance the speech signal. AI function deep learning mimics the human brain's ability to analyze data and create patterns for use in making decisions. An optimized convolutional neural network was proposed for enhancing the speech for a different type of signal-to-noise ratio value of noises. Here, the particle swarm optimization process performs tuning the hyper-parameters of the convolutional neural network. The tuning of value is to minimize the character error rate of the signal. The proposed method is realized using MATLAB R2020b software and evaluation takes place by calculating the character error rate, PESQ, and STOI of the signal. Then, the comparison of the proposed and existing method takes place using evaluation metrics with − 5 dB, 0 dB, + 5 dB and + 10 dB. [ABSTRACT FROM AUTHOR]

Relevance. Determined by the importance of minimizing electrical power consumption in industrial enterprises with continuous production, considering the specific characteristics of their technological processes and the requirements to maintain the output volume of their products. Aim. To solve the task of minimizing electrical power consumption based on a mathematical model and gradient method under optimal planning of the production volume of the industrial enterprises with continuous production; to develop a mathematical model for optimal distribution of production over a time cycle (month, quarter, year) across departments, taking into account both simple and functional constraints, derived from the condition of ensuring minimal electrical power consumption in industrial enterprises with continuous production. Methods. When developing the mathematical model for ensuring minimal electrical power consumption while preserving the production volume, classic Lagrange optimization methods were used. To ensure sufficient calculation accuracy, iterative methods were also applied. For the task under consideration, a calculation error margin of ε=0,1 was assumed and established. It is known that the choice of calculation error margin depends on the specifics of the problem at hand and the decision-maker. To verify the adequacy of the developed model, the method of finding the relative extremum of a function of several variables was used. Results. The use of the mathematical model, which takes into account the nature of the technological process and boundary conditions in both simple and integral forms, demonstrated the feasibility of optimal planning of electrical power consumption by the enterprise. The effectiveness of the developed approaches was verified using a metallurgical enterprise as an example of an industrial enterprise with continuous production, in solving the task of minimizing electrical power consumption for products produced during the reporting period. The use of the proposed model allowed for a reduction in annual electrical power consumption by 2.5% while maintaining the same production volume. One of the classic optimization methods – the method of finding the relative extremum of functions of several variables – showed almost identical results upon verification. This serves as further evidence of the adequacy of the proposed model.

The aim of the article is developing and analyse an algorithmic method for solution finding of one inverse problem of 2d vortex fluid dynamics. It is identification and prediction of the flow structure evolution of the based on the data on fluid velocity vectors in a set of reference points. Theoretical analysis of convergence and adequacy of the method is difficult due to the ill-posedness typical of inverse problems, these issues studied experimentally. Methods. The proposed method uses a mathematical model of a point vortex dynamics system for identification and prediction flow structures. The parameters of the model system are found by minimising the functional that evaluates the closeness of the original and model vectors fields at the reference points. The prediction of the vortex structure dynamics is based on the solution of the Cauchy problem for a system of ordinary differential equations with the parameters found in the first stage. Results. As a result of the calculations, we found it out: the algorithm converges to the desired minimum from a wide range of initial approximations; the algorithm converges in all cases when the identified structure consists of sufficiently distant vortices; the forecast of the development of the current gives good results with a steady flow; if the above conditions are violated, the part of successful calculations decreases, false identification and an erroneous forecast may occur; with the convergence of the method, the coordinates and circulation of the eddies of the model system are close to the characteristics of the eddies of the test configurations; the structures of the streamlines of the flows are topologically equivalent; convergence depends more on location than on the number of vectors used for identification. Conclusion. An algorithm for solving the problem of identifying and the evolution forecast of a 2d vortex flow structure is proposed when the fluid velocity vectors in a finite set of reference points are known. The method showed its high efficiency when using from 40 to 200 reference points. The results of the study make it possible to recommend the proposed algorithm for identifying flat vortex structures, which consist of vortices separated from each other.

The paper proposes to consider individual heuristics as unreliably operating parts of the information processing system. In a separate case, several different heuristics are adopted to solve the same problem, and the results obtained are adjusted in a certain way. In this case, problems arise that are close in methodology to the problems of synthesizing reliable circuits from unreliable elements or making a collective expert decision. The work solves the problem of constructing an optimal correction function based on control material; classes of functions of k-valued logic under monotonicity restrictions are studied. A theorem on the completeness of the class of monotonic functions of k-valued logic for arbitrary k is proved, and a basis in the given class is proved and constructed. The problem of constructing an optimal corrector in the class of disjunctive normal forms of k-valued functions is solved.

This paper introduces a new algorithm designed to address the challenge of distribution network reconfiguration, employing the tabu search metaheuristic in conjunction with the voltage drop concept. Distinguishing itself from existing methods, our proposed approach not only utilizes voltage drop for obtaining the initial solution but also introduces a novel technique for generating a candidate solution neighborhood. This method leverages both randomness and voltage drop, ensuring a smooth and steady descent during algorithm execution. The primary goal of our algorithm is to minimize active power losses within distribution networks. To validate its effectiveness, the proposed method underwent testing on three commonly referenced distribution systems: the 33-Bus, 69-Bus, and 94-Bus systems, widely acknowledged in the literature. A pivotal aspect of our work involves the synergy of the tabu search algorithm with a combination of both random and deterministic methods for generating neighbors. This strategic amalgamation plays a crucial role, enabling rapid execution while consistently yielding high-quality solutions. Additionally, the adoption of the electric distance method for generating the initial solution adds significant value, offering a commendable solution with minimal computational effort. Comparative assessments against other algorithms documented in the literature underscore the superior efficiency of our proposed algorithm.

Utilization of lasers and energy-based devices for surgical scar minimization has been substantially evaluated in placebo-controlled trials. The aim of this study was to compare reported measures of efficacy of lasers and energy-based devices in clinical trials in preventing surgical scar formation in a systematic review and network meta-analyses. Five electronic databases, PubMed, Scopus, Embase, ClinicalTrials.gov, and the Cochrane Library, were searched to retrieve relevant articles. The search was limited to randomized controlled trials that reported on clinical outcomes of surgical scars with treatment initiation no later than 6 months after surgery and a follow-up period of at least 3 months. A total of 18 randomized controlled trials involving 482 participants and 671 postsurgical wounds were included in the network meta-analyses. The results showed that the most efficacious treatments were achieved using low-level laser therapy) (weighted mean difference –3.78; 95% confidence interval (95% CI) –6.32, –1.24) and pulsed dye laser (weighted mean difference –2.46; 95% CI –4.53, –0.38). Nevertheless, low-level laser therapy and pulsed dye laser demonstrated comparable outcomes in surgical scar minimization (weighted mean difference –1.32, 95% CI –3.53, 0.89). The findings of this network meta-analyses suggest that low-level laser therapy and pulsed dye laser are both effective treatments for minimization of scar formation following primary closure of surgical wounds with comparable treatment outcomes.

This article presents the optimal placement of electric vehicle (EV) charging stations in an active integrated distribution grid with photovoltaic and battery energy storage systems (BESS), respectively. The increase in the population has enabled people to switch to EVs because the market price for gas-powered cars is shrinking. The fast spread of EVs depends solely on the rapid and coordinated growth of electric vehicle charging stations (EVCSs). Since EVCSs can cause power losses and voltage variations outside the permissible limits, their integration into the current distribution grid can be characterized by the growing penetration of randomly dispersed photovoltaic (PV) and battery energy storage (BESS) systems, which is complicated. This study used genetic algorithm (GA) optimization and load flow (accommodation of anticipated rise in the number of electric cars on the road) analysis with a forward and backward sweep methodology (FBSM) to locate, scale and optimize EVCSs from a distribution grid where distributed PV/BESSs are prevalent. Power optimization was demonstrated to be the objective issue, which included minimizing active and reactive power losses. To verify the proposed optimal objective solutions from the active distribution grid, an IEEE 33 bus distribution grid was considered for EVCSs' optimization under the penetration of photovoltaic and BESS systems. MATLAB simulations for the integrated EVCS-PV-BESS system on the distribution grid for five different zones were performed using detection from zone 1 (ranging from 301.9726 kW to 203.3872 kW), reducing the power losses (accounting for 33%) in the system to a minimum level. [ABSTRACT FROM AUTHOR]

Feature selection methods are widely used in machine learning tasks to reduce the dimensionality and improve the performance of the models. However, traditional feature selection methods based on regression often suffer from a lack of robustness and generalization ability and are easily affected by outliers in the data. To address this problem, we propose a robust feature selection method based on sparse regression. This method uses a non-square form of the L2,1 norm as both the loss function and regularization term, which can effectively enhance the model's resistance to outliers and achieve feature selection simultaneously. Furthermore, to improve the model's robustness and prevent overfitting, we add an elastic variable to the loss function. We design two efficient convergent iterative processes to solve the optimization problem based on the L2,1 norm and propose a robust joint sparse regression algorithm. Extensive experimental results on three public datasets show that our feature selection method outperforms other comparison methods. [ABSTRACT FROM AUTHOR]

At present, the scale of subway construction in Chinese cities has reached a new height, and the shield slag produced by it has also surged year by year. Untreated subway shield slag not only occupies the space resources of the country, but also carries CO2, which causes negative impacts on the environment and which, as a result, is not conducive to the realization of the goal of the national "double-carbon" strategy. Therefore, how to effectively manage the shield slag produced by subway construction has become a scientific problem that needs to be solved urgently. In order to scientifically dispose of metro shield slag and quantify the carbon reduction intensity of its disposal, based on the new shield slag integrated recycling technology, and taking a tunnel interval of Shenzhen Line 13 as an example, this study systematically sorted out the shield slag disposal process, clarified the management path of the on-site resource utilization of slag, and quantitatively compared the carbon emissions before and after the treatment as well as carbon reduction intensity. The results show that the on-site disposal process is basically feasible, and that, it is possible to achieve a shield structure slag reduction of resource products and mud cake water content of less than 40% of the target, in the case of 160,000 m3 of shield structure slag resource utilization after a total carbon reduction of about 4240.13 t CO2, of which each preparation of 1 m3 of recycled bricks can bring about a benefit of carbon reduction of 240.09 kg CO2. Compared with the conventional mud head truck slag disposal, shield structure slag resource utilization can save a utilization cost of about 10.4 million yuan, meaning that, in terms of economic and social levels, this method can achieve good benefits. This case verifies the feasibility of the new technology, and the results of the study can provide experience for other metro projects' shield slag resource utilization, and provide stakeholders with a shield slag recycling management strategy for government departments to scientifically formulate metro shield slag management policy to provide data support. [ABSTRACT FROM AUTHOR]

In this paper, we would first like to promote an interesting idea for identifying the local minimizer of a non-convex optimization problem with the global minimizer of a convex optimization one. Secondly, to give an extension of their sparse regularization model for inverting incomplete Fourier transforms introduced. Thirdly, following the same lines, to develop convergence guaranteed efficient iteration algorithm for solving the resulting nonsmooth and nonconvex optimization problem but here using applied nonlinear analysis tools. These both lead to a simplification of the proofs and to make a connection with classical works in this filed through a startling comment. [ABSTRACT FROM AUTHOR]

This paper deals with the reduction in electrical losses and the minimization of voltage deviation in electrical power grids. To this end, a novel heuristic-based approach is proposed for controlling reactive power injections along an electrical grid with distributed generation using distributed photovoltaic (PV) generation. The proposed approach consists of applying a heuristic, namely a genetic or firefly algorithm, to solve the loss and voltage deviation optimization problem by controlling PV reactive power injections. The proposed method was tested on a radial 100-bus network with an intense penetration of PV generation. The obtained results indicate that the proposed method consistently yields superior grid performance when compared to algorithms previously proposed in the literature. In fact, it led to a reduction in the active power supplied by the reference bus to the grid, thereby alleviating the burden imposed on the most upstream parts of the distribution network. As a consequence, the proposed method contributes to a power-efficient network, enhancing its loss performance and voltage stability. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surfaces (IRSs) have emerged as a revolutionary solution to enhance wireless communications by changing propagation environment in a cost-effective and hardware-efficient fashion. In addition, symbol-level precoding (SLP) has attracted considerable attention recently due to its advantages in converting multiuser interference (MUI) into useful signal energy. Therefore, it is of interest to investigate the employment of IRS in symbol-level precoding systems to exploit MUI in a more effective way by manipulating the multiuser channels. In this article, we focus on joint symbol-level precoding and reflecting designs in IRS-enhanced multiuser multiple-input single-output (MU-MISO) systems. Both power minimization and quality-of-service (QoS) balancing problems are considered. In order to solve the joint optimization problems, we develop an efficient iterative algorithm to decompose them into separate symbol-level precoding and block-level reflecting design problems. An efficient gradient-projection-based algorithm is utilized to design the symbol-level precoding and a Riemannian conjugate gradient (RCG)-based algorithm is employed to solve the reflecting design problem. Simulation results demonstrate the significant performance improvement introduced by the IRS and illustrate the effectiveness of our proposed algorithms.

Maximizing efficiency, power density, and reliability stands as paramount objectives in the advancement of power electronic systems. Notably, the dimensions and losses of magnetic components emerge as primary constraints hindering the miniaturization of such systems. Researchers have increasingly focused on the design of loss minimization and size optimization of magnetic devices. In this paper, with the objective of minimizing the loss of magnetic devices, an optimal design method for the winding structure of devices is proposed based on the coupling relationship between the loss prediction model and the design variables. The method examines the decoupling conditions between the design variables and the loss model, deriving optimized design closure equations for the design variables. This approach furnishes a technical foundation for the miniaturized design of miniature apparatuses incorporating magnetic components, offering a straightforward and adaptable design methodology. The finite element method simulation results and experimental measurement data verify the accuracy of the prediction of the proposed method and the validity of the optimal design theory of device loss.

The work proves that under conditions of instability of the second derivatives of the function in the minimization region, the estimate of the convergence rate of Newton’s method is determined by the parameters of the irreducible part of the conditionality degree of the problem. These parameters represent the degree of difference between eigenvalues of the matrices of the second derivatives in the coordinate system, where this difference is minimal, and the resulting estimate of the convergence rate subsequently acts as a standard. The paper studies the convergence rate of the relaxation subgradient method (RSM) with optimization of the parameters of two-rank correction of metric matrices on smooth strongly convex functions with a Lipschitz gradient without assumptions about the existence of second derivatives of the function. The considered RSM is similar in structure to quasi-Newton minimization methods. Unlike the latter, its metric matrix is not an approximation of the inverse matrix of second derivatives but is adjusted in such a way that it enables one to find the descent direction that takes the method beyond a certain neighborhood of the current minimum as a result of one-dimensional minimization along it. This means that the metric matrix enables one to turn the current gradient into a direction that is gradient-consistent with the set of gradients of some neighborhood of the current minimum. Under broad assumptions on the parameters of transformations of metric matrices, an estimate of the convergence rate of the studied RSM and an estimate of its ability to exclude removable linear background are obtained. The obtained estimates turn out to be qualitatively similar to estimates for Newton’s method. In this case, the assumption of the existence of second derivatives of the function is not required. A computational experiment was carried out in which the quasi-Newton BFGS method and the subgradient method under study were compared on various types of smooth functions. The testing results indicate the effectiveness of the subgradient method in minimizing smooth functions with a high degree of conditionality of the problem and its ability to eliminate the linear background that worsens the convergence.

Soil heavy metal contamination and its enrichment in the edible parts of crops have gained global concern. In this study, a compound bacterial agent possessing the ability to produce the plant hormone, abscisic acid (ABA), was applied to contaminated farmland in Hunan province. Its application reduced the concentration of Cd in radish, cabbage, mustard, and lettuce by 15–144%. Accordingly, the Cd contents in these vegetables were found to be below the maximum limits set by GB 2762–2017. Meanwhile, bacteria agents also led to a significant increase in crops yield by 45–82%. Furthermore, the nutritional indices, including soluble sugar and soluble protein increased by 18–66%, as well as the antioxidant indices, including total phenolic, ascorbate content, and DPPH capacity, enhanced by 12–76%, 10–49% and 50–140%, respectively. In conclusion, the use of ABA-producing bacteria is anticipated to be a novel approach for the safe use of soil with moderate and low pollution.

Simple Summary: The class I ligase is an in-vitro-evolved ribozyme with a high catalytic turnover. In the present study, we considered the conditions under which this ribozyme retains ligation activity by removing the partial structure and by splitting. The ligation activity was maintained even when the structure was split into two molecules of 55 and 39 nucleotides. Our study clarified in several cases the length of the duplexes that is necessary to facilitate activity of the class I ligase ribozyme assembled from multiple fragments. The class I ligase ribozyme consists of 121 nucleotides and shows a high catalytic rate comparable to that found in natural proteinaceous polymerases. In this study, we aimed to identify the smaller active unit of the class I ligase ribozyme comprising ~50 nucleotides, comparable to the estimated length of prebiotically synthesized RNA. Based on the three-dimensional structure of the class I ligase ribozyme, mutants were prepared and their ligation activities were analyzed. Sufficient ligation activity was maintained even when shortening to 94 nucleotides. However, because it would be difficult to approach the target of ~50 nucleotides by removing only the partial structure, the class I ligase ribozyme was then split into two molecules. The ligation activity was maintained even when splitting into two molecules of 55 and 39 nucleotides. Using a system with similar split ribozymes, we analyzed the ligation activity of mutants C30, C47, and A71, which have been previously identified as the positions that contribute to catalytic activity, and discussed the structural basis of the activity of these bases. Our findings suggest the rationale for the class I ligase ribozyme's assembling from multiple fragments that would be achievable with prebiotic synthesis. [ABSTRACT FROM AUTHOR]

This paper is devoted to studying the Cauchy problem and a standing wave for a class of nematic liquid crystal system. This system appears in the recent studies of the propagation of a laser beam in a nematic liquid crystal. The above system couples the Schrödinger evolution equation to a nonlinear elliptic equation which describes the response of the director angle to the laser beam electric field. The global well-posedness will be established by using the Banach fixed point theorem and the continuity argument. Secondly, the existence of standing wave solution is established by using the constrained minimization approach. [ABSTRACT FROM AUTHOR]

Although Günther Anders explicitly confesses in several places that he is an atheist, his writing The Obsolescence of Man lends itself to broad theological readings. We do not understand by this last statement what is commonly accepted in the common mind, but rather we call theological reading the process of going through a given text by a theologian who is able to discover the theological meanings implicit in its content. If secular man has expelled God from the world of ideas, and with this act has degraded theology to the status of a discipline perfectly separate from all the other humanities or sciences, we propose, on the contrary, a restoration. The aim of a theological reading is the constitution of understanding and the elucidation of problems, without any obsession with solutions. The theologian engaged in such an endeavour is a historian of ideas who deals with their past, present and above all future. The right and ability of theology to account for everything is presumed axiomatic. Only in this way can we bring theology out of its imposed isolation and restore it to the world as the queen of the disciplines of the spirit. Therefore, we affirm that the theological reading we shall undertake of the Andersian occasionalism in The Obsolescence of Man will lead to a diagnosis of the degrading state of the world and of man today and will enable us to point out how to deal with the terrible assault to which the whole of humanity is being subjected by the technological revolution. [ABSTRACT FROM AUTHOR]

In free-space optical satellite networks (FSOSNs), satellites connected via laser inter-satellite links (LISLs), latency is a critical factor, especially for long-distance inter-continental connections. Since satellites depend on solar panels for power supply, power consumption is also a vital factor. We investigate the minimization of total network latency (i.e., the sum of the network latencies of all inter-continental connections in a time slot) in a realistic model of a FSOSN, the latest version of the Starlink Phase 1 Version 3 constellation. We develop mathematical formulations of the total network latency over different LISL ranges and different satellite transmission power constraints for multiple simultaneous inter-continental connections. We use practical system models for calculating network latency and satellite optical link transmission power, and we formulate the problem as a binary integer linear program. The results reveal that, for satellite transmission power limits set at 0.5 W, 0.3 W, and 0.1 W, the average total network latency for all five inter-continental connections studied in this work levels off at 339 ms, 361 ms, and 542 ms, respectively. Furthermore, the corresponding LISL ranges required to achieve these average total network latency values are 4500 km, 3000 km, and 1731 km, respectively. Different limitations on satellite transmission power exhibit varying effects on average total network latency (over 100 time slots), and they also induce differing changes in the corresponding LISL ranges. In the absence of satellite transmission power constraints, as the LISL range extends from the minimum feasible range of 1575 km to the maximum feasible range of 5016 km, the average total network latency decreases from 589 ms to 311 ms.

To minimize the objective function in neural networks, first-order methods are usually used, which involve the repeated calculation of the gradient. The number of variables in modern neural networks can be many thousands and even millions. Numerous experiments show that the analytical calculation time of an N variable function’s gradient is approximately N/5 times longer than the calculation time of the function itself. The article considers the possibility of using zero-order methods to minimize the function. In particular, a new zero-order method for function minimization, descent over two-dimensional spaces, is proposed. The convergence rates of three different methods are compared: standard gradient descent with automatic step selection, coordinate descent with step selection for each coordinate, and descent over two-dimensional subspaces. It has been shown that the efficiency of properly organized zero-order methods in the considered problems of training neural networks is not lower than the gradient ones.

Using a variational method, we prove the existence of heteroclinic solutions for a 6-dimensional system of ordinary differential equations. We derive this system from the classical Bénard-Rayleigh problem near the convective instability threshold. The constructed heteroclinic solutions provide first order approximations for domain walls between two orthogonal convective rolls. [ABSTRACT FROM AUTHOR]

In this paper, a compact antenna design method is proposed for microstrip patch antennas using a double-layered defected ground structure (DGS) configuration. While a conventional single-layered defected ground structure yields a lower resonant frequency and Q-factor, a smaller circuit size can be achieved using an additional substrate with a higher dielectric constant. The size reduction obtained from the additional resonant LC elements is analytically explained using the equivalent circuit model. The characteristics of the additional substrates are investigated for various dielectric constants and thicknesses. From the experimental results, the proposed design method leads to a total size reduction of up to 51.7% and a miniaturized design for planar antennas with ground apertures. The proposed design method can be applied to various antenna designs with any DGS pattern. Furthermore, the size reduction method can maintain the structure of the resonant patch element and its radiation characteristics. Therefore, the proposed method is applicable to the design of microwave devices on microstrip-based configurations. [ABSTRACT FROM AUTHOR]

In a dc microgrid, a bidirectional dc–dc converter is an essential link between voltage bus and energy storage system (ESS). When galvanic isolation and high efficiency are required, dual active bridge (DAB) converter is preferred. Due to the instability of renewable energy sources, the DAB converter may transmit high or low power to ESS. To improve the efficiency across wide power range, this article proposes a reactive power minimization and soft switching optimization algorithm that combines the augmented Lagrangian method and triple-phase-shift (TPS) modulation. Unlike an offline algorithm with look-up table, the proposed algorithm can compute phase-shift-angle online, avoiding a large database for various working conditions. A prototype with an 800 W rated power is built, and the efficiency test results validate the efficiency improvement over other TPS-based algorithms. Furthermore, a current regulation experiment demonstrates that the proposed algorithm is suitable for ESS charging, which must deal with varying output voltage and power demand. [ABSTRACT FROM AUTHOR]

This article presents a generic approach to load-current THD minimization in hard-switched Staircase Modulation (SCM) based three-phase (3ɸ) Multilevel Inverters (MLI) with either fixed or variable DC source Ratios (DCR). First, a computer-aided closed-form analytical formulation of Weighted THD (WTHD) applied directly to line-voltage waveforms is derived using Maple software and then verified against numerically-obtained results of previous works. The proposed symbolic analysis of Weighted Line-voltage THD (WLTHD) applies to 3ɸ-MLIs of any topology with Equal Voltage Steps (EVS) or Unequal Voltage Steps (UVS), and any number of voltage levels count (N), either odd or even. Such an analytical approach eliminates the risk of underestimation errors associated with numerical methods. The proposed closed-form WLTHD expression is then utilized for optimal minimization of current-THD in 3ɸ inductive loads with unconnected neutral. This is achieved by introducing a generic UVS-based Optimal Minimization of WLTHD (OMWLTHD), which calculates the optimum Phase Switching Angles (PSA) and the supply DC-Ratios (DCR), given any desired Modulation Index (MI), thus ensuring minimum current-THD across the entire linear MI range. The revealed OMWLTHD approach is fully validated by both digital simulations and controller plus hardware-in-loop (C-HIL) based experiments, using 7-, 8-, and even 13-level 3ɸ-MLIs of both EVS and UVS configurations. Furthermore, to reveal the impact of WLTHD minimization on actual load current-THD, the MLIs were loaded by a Resistive-Inductive-EMF (RLE) load, as well as a 3ɸ squirrel cage induction motor, thus representing a wide range of possible loads, such as motors and ac grids. The conclusive verification results have shown that in the case of 3-wire inductive loads with a power factor up to 0.95, the proposed UVS-based WLTHD optimization is more beneficial for current-THD minimization over other harmonic distortion quantifying parameters, as WLTHD presents the strongest correlation to actual load current THD when tested against any other parameter such as phase and line THD or WTHD. The revealed improvement in current THD was even more pronounced for UVS-based SCM compared to traditional EVS-based SCM. Additionally, MATLAB and Maple source files of the proposed WLTHD formulations and Excel-based pre-calculated optimum PSA and DCR sets for different values of N are provided as downloadable links to further enhance the contribution of the article. [ABSTRACT FROM AUTHOR]

The paper studies randomization rules for a sequential two-treatment, two-site clinical trial in Parkinson’s disease. An important feature is that we have values of responses and five potential prognostic factors from a sample of 144 patients similar to those to be enrolled in the trial. Analysis of this sample provides a model for trial analysis. The comparison of allocation rules is made by simulation yielding measures of loss due to imbalance and of potential bias. A major novelty of the paper is the use of this sample, via a two-stage algorithm, to provide an empirical distribution of covariates for the simulation; sampling of a correlated multivariate normal distribution is followed by transformation to variables following the empirical marginal distributions. Six allocation rules are evaluated. The paper concludes with some comments on general aspects of the evaluation of such rules and provides a recommendation for two allocation rules, one for each site, depending on the target number of patients to be enrolled.