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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]

Randomization procedures that enforce balance in prognostic factors, most commonly stratified randomization, are often employed in clinical trials. When the number of factors or factor levels is large, dynamic allocation procedures, such as the Pocock and Simon's covariate-adaptive randomization (minimization) are preferred. In their ground-breaking work Ye and Shao identified two classes of covariate-adaptive randomization procedures. They have demonstrated theoretically that for these classes, when the model is misspecified, the robust score test (Lin and Wei) as well as the unstratified log-rank test used for analysis of time-to-event endpoints, are valid or conservative (Ye and Shao). This fact, however, was not established for minimization other than through simulations of survival endpoints. In this article, we point out that the results of Ye and Shao can be expanded to a more general class of randomization procedures. We show, in part theoretically, in part through simulations of the within-strata imbalances, that minimization belongs to this class. Along the way we describe the asymptotic correlation matrix of the normalized within-stratum imbalances following minimization with equal prevalence of all strata. We expand the robust tests proposed by Ye and Shao for stratified randomization to minimization and examine their performance through simulations. [ABSTRACT FROM AUTHOR]

Re-randomization inference is used as an alternative approach to more traditional statistical methods. Free from parametric assumptions, its use is particularly suited for studies incorporating covariate-adaptive randomization, and can provide additional evaluation and confirmation of inferences drawn from the original analyses, for example, as a sensitivity analysis. We discuss methodological and computational aspects in the context of a Poisson regression and describe an approach to re-randomization inference. This is tested in a simulation study and then illustrated in a case study in which we evaluate vaccine efficacy data from a previously published influenza vaccine study. Our simulations indicate that re-randomization inference corrects for model misspecification. The case study, which accounted for the minimization factors, shows that the p-value and confidence limits from re-randomization inference agree with the original analysis. In conclusion re-randomization inference is a useful method that can be used to support vaccine clinical development. [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]

Let H = 〈 V , S 〉 be a hypergraph, where V is a set of vertices and S is a set of clusters S 1 , ... , S m , S i ⊆ V , such that the clusters in S are not necessarily disjoint. This paper considers the Clustered Spanning Tree by Paths problem, denoted by CSTP. This problem aims to decide whether a feasible solution tree exists, spanning all vertices of V , such that each cluster induces a path in the solution tree. We introduce the idea of a minimum cardinality feasible removal list and a minimum cardinality feasible insertion list, which removes or inserts vertices from or into clusters, such that using each one of the lists separately creates hypergraphs with feasible solution trees for CSTP. In addition, we decompose the intersection graph of H into smaller instances, for those cases where the intersection graph contains a cut node or a separating edge. We demonstrate how to compose a minimum cardinality feasible removal list or a minimum cardinality feasible insertion list of a given hypergraph from the smaller minimum cardinality feasible removal lists or minimum cardinality feasible insertion lists of the smaller instances. This approach may reduce the size and intricacy of the instances. [ABSTRACT FROM AUTHOR]

Let F \in \mathbb {Z}[x_0, \ldots, x_n] be homogeneous of degree d and assume that F is not a 'nullform', i.e., there is an invariant I of forms of degree d in n+1 variables such that I(F) \neq 0. Equivalently, F is semistable in the sense of Geometric Invariant Theory. Minimizing F at a prime p means to produce T \in Mat(n+1, \mathbb {Z}) \cap GL(n+1, \mathbb {Q}) and e \in \mathbb {Z}_{\ge 0} such that F_1 = p^{-e} F([x_0, \ldots, x_n] \cdot T) has integral coefficients and v_p(I(F_1)) is minimal among all such F_1. Following Kollár [Electron. Res. Announc. Amer. Math. Soc. 3 (1997), pp. 17–27], the minimization process can be described in terms of applying weight vectors w \in \mathbb {Z}_{\ge 0}^{n+1} to F. We show that for any dimension n and degree d, there is a complete set of weight vectors consisting of [0,w_1,w_2,\dots,w_n] with 0 \le w_1 \le w_2 \le \dots \le w_n \le 2 n d^{n-1}. When n = 2, we improve the bound to d. This answers a question raised by Kollár. These results are valid in a more general context, replacing \mathbb {Z} and p by a PID R and a prime element of R. Based on this result and a further study of the minimization process in the planar case n = 2, we devise an efficient minimization algorithm for ternary forms (equivalently, plane curves) of arbitrary degree d. We also describe a similar algorithm that allows to minimize (and reduce) cubic surfaces. These algorithms are available in the computer algebra system Magma. [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]

A node of a Sturm–Liouville problem is an interior zero of an eigenfunction. The aim of this paper is to present a simple and new proof of the result on sharp bounds of the node for the Sturm–Liouville equation with the Dirichlet boundary condition when the L 1 norm of potentials is given. Based on the outer approximation method, we will reduce this infinite-dimensional optimization problem to the finite-dimensional optimization problem. [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.

Intimate partner abuse (IPA) is widespread, and denial, minimization, justifying, and blaming (DMJB) are common among people who have committed IPA. Views on the function of DMJB in IPA are mixed, often based on the theoretical standpoint of the authors. This systematic review brings together the knowledge of how distorted accounts operate in IPA committed by men. A systematic review of primary research related to DMJB in heterosexual men who have committed to IPA was conducted. In all, 31 papers were found to meet the inclusion criteria (adult, male-to-female abuse, in western culture, peer reviewed and published in English) and were quality appraised. Data were extracted and analyzed using narrative synthesis. The findings indicate the way DMJB operates in this group is complex. It can represent facilitators of abusive behavior, a way to protect the individual's identity and self-esteem, and a tool men use instrumentally to achieve goals. Themes were present within and between studies highlighting the complex function of DMJB. A model representing the hypothesized intertwined function of DMJB for IPA is proposed. The limitations of the review are discussed and implications and recommendations for policy, practice, and future research are proposed. [ABSTRACT FROM AUTHOR]

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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]

Nonparametric covariate adjustment is considered for log-rank-type tests of the treatment effect with right-censored time-to-event data from clinical trials applying covariate-adaptive randomization. Our proposed covariate-adjusted log-rank test has a simple explicit formula and a guaranteed efficiency gain over the unadjusted test. We also show that our proposed test achieves universal applicability in the sense that the same formula of test can be universally applied to simple randomization and all commonly used covariate-adaptive randomization schemes such as the stratified permuted block and the Pocock–Simon minimization, which is not a property enjoyed by the unadjusted log-rank test. Our method is supported by novel asymptotic theory and empirical results for Type-I error and power of tests. [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]

A power series being given as the solution of a linear differential equation with appropriate initial conditions, minimization consists in finding a non-trivial linear differential equation of minimal order having this power series as a solution. This problem exists in both homogeneous and inhomogeneous variants; it is distinct from, but related to, the classical problem of factorization of differential operators. Recently, minimization has found applications in Transcendental Number Theory, more specifically in the computation of non-zero algebraic points where Siegel's E-functions take algebraic values. We present algorithms and implementations for these questions, and discuss examples and experiments. [ABSTRACT FROM AUTHOR]

Stalking is a public health concern that affects both heterosexual and non-heterosexual individuals. Consequences to victims include being blamed for their stalking victimization and having their victimization minimized by others. However, less research has been conducted regarding attitudes toward non- heterosexual stalking victims and the fear individuals feel for stalking victims in general. Therefore, using a scenario-based approach, we examined how participants blamed stalking victims, minimized stalking incidents, and felt subjective fear for heterosexual and non-heterosexual stalking victims in hypothetical scenarios. Participants were recruited via Amazon Mechanical Turk for two separate research studies. In Study 1, participants read about heterosexual stalking situations, and in Study 2, participants read about same-gender stalking situations. Participants responded to dependent variable questionnaires that assessed blame, minimization, and subjective fear. Participants also responded to predictor variables questionnaires that assessed gender role ideology, ambivalent sexism, attitudes toward lesbian women, and attitudes toward gay men. MANOVAs and simultaneous multiple regression analyses were conducted to analyze the hypotheses. There were no main effects of participant gender or hypothetical scenarios for either of the two research studies regarding blame, minimization, or subjective fear. However, gender role ideology, ambivalent sexism, and homonegativity were significant predictors of participants' attitudes toward stalking. Explanation for the current studies' findings and implications of these findings were discussed. [ABSTRACT FROM AUTHOR]

For a class of discrete quasi convex functions called semi-strictly quasi M ♮ -convex functions, we investigate fundamental issues relating to minimization, such as optimality condition by local optimality, minimizer cut property, geodesic property, and proximity property. Emphasis is put on comparisons with (usual) M ♮ -convex functions. The same optimality condition and a weaker form of the minimizer cut property hold for semi-strictly quasi M ♮ -convex functions, while geodesic property and proximity property fail. [ABSTRACT FROM AUTHOR]

In this paper, we consider the first eigenvalue λ 1 ⁢ (Ω) of the Grushin operator Δ G := Δ x 1 + | x 1 | 2 ⁢ s ⁢ Δ x 2 with Dirichlet boundary conditions on a bounded domain Ω of ℝ d = ℝ d 1 + d 2 . We prove that λ 1 ⁢ (Ω) admits a unique minimizer in the class of domains with prescribed finite volume, which are the cartesian product of a set in ℝ d 1 and a set in ℝ d 2 , and that the minimizer is the product of two balls Ω 1 * ⊆ ℝ d 1 and Ω 2 * ⊆ ℝ d 2 . Moreover, we provide a lower bound for | Ω 1 * | and for λ 1 ⁢ (Ω 1 * × Ω 2 *) . Finally, we consider the limiting problem as s tends to 0 and to + ∞ . [ABSTRACT FROM AUTHOR]

Randomized controlled trials are widely accepted as the best design for evaluating the efficacy of a treatment, due to the advantages of randomization. The objectives of randomization include removing bias in the assignment of treatments, balancing numbers allocated to arms, and balancing observed and unobserved covariates between arms. Different randomization procedures, each with varying properties of randomness and balance, are available and investigators must be careful to select one that will result in desirable properties. In this article, we use a simulation study, based on two real clinical trials, to empirically test several allocation procedures for multi-arm trials: simple randomization, permuted block randomization, stratified randomization with permuted blocks, urn design, block urn design, stratified block urn design, andminimization.We evaluate properties such as group size balance, covariate balance, loss of precision (i.e., increase in the variance of treatment effect estimates), and predictability of assignment. We also compare different definitions of statistical power relevant inmulti-arm trials:marginal, disjunctive, and conjunctive powers. Based on the simulation results, the considered randomization methods have very little impact on the resultant powers. Consistently strong performance was seen across the performance metrics for stratified block randomization and the stratified block urn design. [ABSTRACT FROM AUTHOR]

The current study focusses on the use of quantum chemistry to elucidate the novel synthetic route for e mrlotinib from methyl 4,5-dihydroxy-2-isocyanobenzoate, which includes oxidative coupling, nucleophilic addition, cyclization and Williamson's ether synthesis. The overall reaction requires three intermediate and produces 13 transition states [TS]. Which are less than the earlier reported synthetic schemes. The energies of each reactant, intermediate and products were calculated using DFT (density functional theory) and B3LYP/6-311+G* as a basis set. The energies diagram obtained indicates the novel proposed scheme could follow the easy path to obtain the product, moreover, the energy barrier required to overcome the transition state is low indicating, very less activation energy is required for every reactant to take part in chemical reaction. [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]

Food waste has been identified as one of the major factors that constitute numerous anthropogenic activities, especially in developing countries. There is a growing problem with food waste that affects every part of the waste management system, from collection to disposal; finding long-term solutions necessitates involving all participants in the food supply chain, from farmers and manufacturers to distributors and consumers. In addition to food waste management, maintaining food sustainability and security globally is crucial so that every individual, household, and nation can always get food. "End hunger, achieve food security and enhanced nutrition, and promote sustainable agriculture" are among the main challenges of global sustainable development (SDG) goal 2. Therefore, sustainable food waste management technology is needed. Recent attention has been focused on global food loss and waste. One-third of food produced for human use is wasted every year. Source reduction (i.e., limiting food losses and waste) and contemporary treatment technologies appear to be the most promising strategy for converting food waste into safe, nutritious, value-added feed products and achieving sustainability. Food waste is also employed in industrial processes for the production of biofuels or biopolymers. Biofuels mitigate the detrimental effects of fossil fuels. Identifying crop-producing zones, bioenergy cultivars, and management practices will enhance the natural environment and sustainable biochemical process. Traditional food waste reduction strategies are ineffective in lowering GHG emissions and food waste treatment. The main contribution of this study is an inventory of the theoretical and practical methods of prevention and minimization of food waste and losses. It identifies the trade-offs for food safety, sustainability, and security. Moreover, it investigates the impact of COVID-19 on food waste behavior. [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.

In this paper, we propose a new method of using the theory of functional connections (TFC) to approximate the solution of fractional differential equations. For functions with one constraint at one point, several constraints at one point, distinct points, and relative constraints, the theoretical approach of the suggested method is investigated. The choice of the basis function is described, and the issue of using monomials is discussed. For the first time in the literature, the suggested method is used to solve fractional differential initial value problems, boundary value problems, and higher-order problems. Wherever the exact solution exists, the numerical results are compared. The numerical findings for the fractional order scenario are compared with the predictor-corrector method and polynomial least squares method. The error plot for the integer order case with the exact solution is also provided. The proposed approach is also used to solve a corneal shape model. [ABSTRACT FROM AUTHOR]

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]

Light attraction is a well-documented phenomenon affecting seabirds worldwide, with most reported incidents involving juvenile birds grounded around urban areas or brightly lit structures at sea immediately after fledging. However, there is little in the literature regarding the impact of light attraction on breeding adult seabirds on land. This paper describes a fallout event of adults of two endangered seabird species due to lights at a single facility on the island of Kaua'i, Hawaii, and the effectiveness of subsequent minimization actions once a new lighting regime was adopted. From 1–16 September 2015, 131 adult endangered seabirds including 123 'a'o (Newell's Shearwaters Puffinus newelli) and six 'ua'u (Hawaiian Petrels Pterodroma sandwichensis) were grounded by lights at the Kōke'e Air Force Station. Most birds had brood patches indicating they were breeders. In response, the facility altered its lighting protocol with a blackout period in effect for the remainder of 2015, and significantly reduced lighting from 2016 onwards. Intensive seabird monitoring was undertaken annually from 2016 onwards. Apart from two Newell's Shearwater adults grounded in 2016 (before the lights were turned out at the start of the seabird season) and one each in 2020 and 2023, no additional grounded birds were found. This fallout event demonstrates that light attraction can be a significant hazard for adult seabirds if bright lights are present near breeding colonies. We provide recommendations for best practice light minimization actions for similar scenarios worldwide where discrete facilities or urban infrastructure are adjacent to breeding colonies of nocturnal Procellariid seabirds. [ABSTRACT FROM AUTHOR]

Order-preserving DAG grammars (OPDGs) is a formalism for representing languages of structurally restricted graphs. As demonstrated in [17], they are sufficiently expressive to model abstract meaning representations in natural language processing, a graph-based form of semantic representation in which nodes encode objects and edges relations. At the same time, they can be parsed in (n 2 + n m) , where m and n are the sizes of the grammar and the input graph, respectively. In this work, we provide an initial algebra semantic for OPDGs, which allows us to view them as regular tree grammars under an equivalence theory. This makes it possible to transfer results from the field of formal tree languages to the domain of OPDGs, both in the unweighted and the weighted case. In particular, we show that deterministic OPDGs can be minimised efficiently, and that they are learnable under the "minimal adequeate teacher" paradigm, that is, by querying an oracle for equivalence between languages, and membership of individual graphs. To conclude, we demonstrate that the languages generated by OPDGs are definable in monadic second-order logic. [ABSTRACT FROM AUTHOR]

Purpose. The purpose of the paper is to provide open-pit mining operations with practical strategies and insights to optimize truck loading and hauling cycles, ultimately leading to enhanced productivity and economic advantages. Methods. The objectives are to minimize loading time, optimize the haul road network, enhance truck performance, and optimize dumping and return time. By diligently implementing these methods and achieving these objectives, open-pit mining operations can significantly reduce the truck cycle times, resulting in increased productivity, lower costs and improved profitability. Findings. In this case, the total loading time of the excavator and shovel is determined to be 3.98 and 2.92 minutes, respectively, while the hauling time for total loading of the open-pit floor depends mainly on the average distance and speed of 239 m and 10.1 km/hour, which results in 1.53 minutes. Originality. As a result, the total cycle time for open-pit mining is 19.765 minutes, resulting from the total loading time, hauling time for total loading, total dumping time, and total return time for empty transport of 4.265, 8.46, 0.86 and 6.18 minutes, respectively. Practical implications. By combining theoretical analysis with practical insights and site-specific considerations, the paper aims to provide a comprehensive and applicable framework for optimizing truck cycle time in open-pit mining, resulting in improved efficiency and profitability of mining operations. [ABSTRACT FROM AUTHOR]

U-bending is one of the common bending processes in which a thin sheet is transformed into a U shape by application of load. In this work, Al 5052 sheet of different thickness was bent to U shape using Box Behnken design to minimize spring-back. The process parameters considered for the experiment are sheet thickness, punch radius and sheet orientation. A quadratic equation was formulated to understand the impact of these process parameters on springback. The sheet thickness followed by punch radius was found to have major influence on the springback. Parameter combination for minimization of springback was obtained using response surface optimizer. The approach adapted in this work is applicable for other sheet metals and springback defect minimization at room temperature. [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.