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The iterative process of ethnography not only constructs theory, but its methodology should embody theory. Developing a theoretical framework often demands adjustments in methodology, to leverage previous work and to avoid assumptions compounding through the magnification of blind spots. New theory in policy-engaged ethnography has emphasised the importance of analysing interactions between teachers. However, such analyses have not yet emerged in research on education in Japan, partly because of enduring conceptualisations of teachers as homogeneous. Methodological tools that do exist are grounded in anglosphere data and remain unsuited to research in Japan's schools. As such, participants are positioned amongst, but not in relation to, other teachers and school administrators. Yet, taking teacher heterogeneity seriously enhances the salience of selection bias. The same teachers who are enthusiastic about participating in the research are often those who are enthusiastic about the topic of the research. What is difficult in school ethnography is procuring, and preferably ensuring, participation from teachers who are not enthusiasts toward the topic of the research. Designing research with both the heterogeneity of teachers and the specifics of the local context in mind can provide more diverse data with potential to inform analyses of micro-politics in Japan's schools. In reference to a five-year research project and grounded in data from Japan, the paper proposes some procedures to access a diverse range of teachers based on a theory developed to conceptualise teachers' orientations towards policy work. [ABSTRACT FROM AUTHOR]

In this paper, we present a generalized adaptive Monte Carlo algorithm using the Diagonal and Off-Diagonal Splitting (DOS) iteration method to solve a system of linear algebraic equations (SLAE). The DOS method is a generalized iterative method with some known iterative methods such as Jacobi, Gauss-Seidel, and Successive Overrelaxation methods as its special cases. Monte Carlo algorithms usually use the Jacobi method to solve SLAE. In this paper, the DOS method is used instead of the Jacobi method which transforms the Monte Carlo algorithm into the generalized Monte Carlo algorithm. we establish theoretical results to justify the convergence of the algorithm. Finally, numerical experiments are discussed to illustrate the accuracy and efficiency of the theoretical results. Furthermore, the generalized algorithm is implemented to price options using the finite difference method. We compare the generalized algorithm with standard numerical and stochastic algorithms to show its efficiency. [ABSTRACT FROM AUTHOR]

This study uses a classic fixed point theorem of cone type in a Banach space to identify the eigenvalue intervals of parameters for which an iterative system of a Hadamard fractional boundary value problem has at least one positive solution. To the best of our knowledge, no attempt has been made to obtain such results for Hadamard-type problems in the literature. We provided an example to illustrate the feasibility of our findings in order to show how effective they are. [ABSTRACT FROM AUTHOR]

In this paper, the Fuzzy Sumudu Transform Iterative method (FSTIM) was applied to find the exact fuzzy solution of the fuzzy space-time fractional telegraph equations using the Fuzzy Caputo Fractional Derivative operator. The Telegraph partial differential equation is a hyperbolic equation representing the reaction-diffusion process in various fields. It has applications in engineering, biology, and physics. The FSTIM provides a reliable and efficient approach for obtaining approximate solutions to these complex equations improving accuracy and allowing for fine-tuning and optimization for better approximation results. The work introduces a fuzzy logic-based approach to Sumudu transform iterative methods, offering flexibility and adaptability in handling complex equations. This innovative methodology considers uncertainty and imprecision, providing comprehensive and accurate solutions, and advancing numerical methods. Solving the fuzzy space-time fractional telegraph equation used a fusion of the Fuzzy Sumudu transform and iterative approach. Solution of fuzzy fractional telegraph equation finding analytically and interpreting its results graphically. Throughout the article, whenever we draw graphs, we use Mathematica Software. We successfully employed FSTIM, which is elegant and fast to convergence. [ABSTRACT FROM AUTHOR]

In discrete dynamical systems, the ultimate goal is to understand the asymptotic behavior of all points under iterated compositions of a certain transformation (self-map) of a certain space. In arithmetic dynamics, the asymptotic behavior of points with coordinates of arithmetic interest (algebraic numbers or p-adic numbers) is examined. In connection with arithmetic dynamics, some problems are reduced to the determination of rational points on curves. This article introduces the issues in arithmetic dynamics related to these problems. [ABSTRACT FROM AUTHOR]

The soft SVD is a robust matrix decomposition algorithm and a key component of matrix completion methods. However, computing the soft SVD for large sparse matrices is often impractical using conventional numerical methods for the SVD due to large memory requirements. The Rank-Restricted Soft SVD (RRSS) algorithm introduced by Hastie et al. addressed this issue by sequentially computing low-rank SVDs that easily fit in memory. We analyze the convergence of the standard RRSS algorithm and we give examples where the standard algorithm does not converge. We show that convergence requires a modification of the standard algorithm, and is related to non-uniqueness of the SVD. Our modification specifies a consistent choice of sign for the left singular vectors of the low-rank SVDs in the iteration. Under these conditions, we prove linear convergence of the singular vectors using a technique motivated by alternating subspace iteration. We then derive a fixed point iteration for the evolution of the singular values and show linear convergence to the soft thresholded singular values of the original matrix. This last step requires a perturbation result for fixed point iterations which may be of independent interest. [ABSTRACT FROM AUTHOR]

Wireless sensor networks contain of many sensors that can serve as powerful tools for data collection in environments. A key challenge in these networks is the limited lifetime of sensor batteries. Ideally, all nodes would exhaust their energy simultaneously or through regular scheduling, maximizing the lifetime. Consequently, the primary concern is achieving optimal energy utilization to extend the network's lifetime over a logical duration. Depleting the batteries of the sensors means stopping the operation of the network, because it is practically impossible to replace the batteries of thousands of nodes. To address this issue, the low energy adaptive clustering hierarchical (LEACH) protocol has been widely recognized as one of the prominent solutions for clustering WSNs. However, the random selection of cluster heads in each round under the LEACH protocol fails to guarantee proper convergence. To overcome this limitation, this paper proposes a refined approach by utilizing a genetic algorithm and a novel objective function that incorporates various factors, including energy level and distance. The algorithm employs chromosomes to represent CHs and facilitates the selection of cluster nodes. Notably, the proposed algorithm dynamically performs clustering, meaning that clustering is conducted iteratively, considering identifying dead nodes. By leveraging this approach, the algorithm significantly enhances the clustering quality, ultimately leading to an increased network lifetime. To validate its effectiveness, it is compared with LEACH, LEACH_E and LEACH_EX algorithms, demonstrating its superior capabilities. On average, the proposed algorithm has more alive nodes in the network, and the remaining energy is at least 11% higher than the best other algorithms. [ABSTRACT FROM AUTHOR]

Requirements Agile approaches have transformed engineering. This paper shows how RE in Agile software development has evolved from documentation-heavy to collaborative, adaptable, and customer-focused. Agile was born in the mid-1990s when the industry realized it needed to respond to changing client needs and market volatility. This evolution includes iterative development, client interaction, and emphasizing communication above documentation, as discussed in the paper. By comparing conventional and Agile RE approaches, we demonstrate the benefits of adapting to change, working with customers, and delivering functional software faster. This analysis provides a persuasive description of Agile RE implementation methodologies and resources through a detailed literature review and real-world experiences. User stories and backlog refinement are notable techniques. The research finishes by exploring how these techniques affect team dynamics, project success, and customer satisfaction. RE's Agile difficulties and opportunities are also examined. The findings illuminate RE methods' successful adaptation to Agile projects' dynamic character. Software development is more responsive and effective due to this adaptation. [ABSTRACT FROM AUTHOR]

To calculate the losses of distribution feeders, this paper uses an iterative method that is limited to restricted measurements. The approach presented in this paper uses bill data in addition to output information from a very small number of real-time measurements located on the secondary side of distribution transformers. This method attempts to estimate the load of distribution transformers injected into LV feeders. Energy losses for LV feeders are evaluated by first estimating the power and periodic energy injected to each of the LV feeders and then subtracting the total consumption bills from these estimated values. By using this method, the amount of energy loss is estimated. In this article, a new method called iterative power factor adjustment method is considered as a potential method for estimating losses. The power factor can be increased by repeatedly using evolutionary algorithms and including capacitors in the system. In order to reduce system losses and increase network effectiveness. In this paper, a new method for examining and evaluating Non-Technical Losses (NTL) is proposed. This method considers load estimation and limited measurement to place high priority feeders. [ABSTRACT FROM AUTHOR]

The Index Generalized Minimal RESidual (IGMRES) algo- rithm is designed to compute the Drazin-inverse solution of a linear sys- tem of equations Ax = b, where A is an arbitrary square matrix with index . If = 0, then the this method method coincide with Gener- alized Minimal RESidual (GMRES) method. Also, the kth ideal index generalized minimal residual polynomial of A is introduced and the roots of these polynomials are studied. Moreover, by numerical results the con- vergence rate of these methods are compared by two examples. [ABSTRACT FROM AUTHOR]

Robust approach is a special scalarization method to deal with multiobjective optimization problems in the worst-case. In this paper, we propose a new non-monotone gradient type algorithm for solving unconstrained multiobjective optimization problems by the conjugate technique and the robust approach. The proposed method has a memory gradient property since the search direction is constructed by using the current descent direction and the past multi-step iterative descent directions. For this, the search direction is called a memory gradient search direction. The step-size is computed by the non-monotone linear search. A lower bound of the stepsize is presented under some mild conditions. Then the iterative sequence generated by the proposed method is proved to be convergent to a Pareto critical point of the multiobjective optimization problem under some mild conditions. Numerical experiments are reported to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The intersection of an affine subspace with the cone of positive semidefinite matrices is called a spectrahedron. An orthogonal projection thereof is called a spectrahedral shadow or projected spec-trahedron. Spectrahedra and their projections can be seen as a generalization of polyhedra. This article is concerned with the problem of approximating the recession cones of spectrahedra and spectrahedral shadows via polyhedral cones. We present two iterative algorithms to compute outer and inner approximations to within an arbitrary prescribed accuracy. The first algorithm is tailored to spectrahedra and is derived from polyhedral approximation algorithms for compact convex sets and relies on the fact, that an algebraic description of the recession cone is available. The second algorithm is designed for projected spectrahedra and does not require an algebraic description of the recession cone, which is in general more difficult to obtain. We prove correctness and finiteness of both algorithms and provide numerical examples. [ABSTRACT FROM AUTHOR]

For discrete-time linear systems, by applying the advantages of data-driven methods and iterative learning control, this paper proposes three data-driven learning control algorithms based on the linearity of the system. For iterative learning control, if the initial state shift is zero in each iteration and the system matrix is known, the algorithm presented in this paper can ensure that the system achieve complete tracking in the entire interval through a finite number of iterations. If the initial state shift is arbitrary in each iteration and the system matrix is unknown, the algorithm proposed in this paper can ensure that the system achieve complete tracking at all points except the initial point through finite iterations. Even for systems with non-repetitive processes, if the system matrix is unknown, the method in this paper can ensure that the system achieves complete tracking at all points except a finite number of points in the initial stage. Finally, the effectiveness of the three algorithms is verified by three examples, and the superiority of the proposed algorithms is demonstrated by comparison. [ABSTRACT FROM AUTHOR]

Indoor location services often use Bluetooth low energy (BLE) devices for their low energy consumption and easy implementation. Applications like device monitoring, ranging, and asset tracking utilize the received signal strength (RSS) of the BLE signal to estimate the proximity of a device from the receiver. However, in multipath environments, RSS-based solutions may not provide an accurate estimation. In such environments, receivers with antenna arrays are used to calculate the difference in time of flight (ToF) and therefore calculate the direction of arrival (DoA) of the Bluetooth signal. Other techniques like triangulation have also been used, such as having multiple transmitters or receivers as a network of sensors. To find a lost item, devices like Tile© use an onboard beeper to notify users of their presence. In this paper, we present a system that uses a single-measurement device and describe the method of measurement to estimate the location of a BLE device using RSS. A BLE device is configured as an Eddystone beacon for periodic transmission of advertising packets with RSS information. We developed a smartphone application to read RSS information from the beacon, designed an algorithm to estimate the DoA, and used the phone's internal sensors to evaluate the DoA with respect to true north. The proposed measurement method allows for asset tracking by iterative measurements that provide the direction of the beacon and take the user closer at every step. The receiver application is easily deployable on a smartphone, and the algorithm provides direction of the beacon within a 30° range, as suggested by the provided results. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Education & Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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.)

The variational iteration technique (VIT) is an excellent analytical tool employed in this study to solve the nonlinear 3D-fractional differential equations. The Antagana-Baleanu sense is used to characterize the fractional derivatives (ABFD). To show the applicability of the recommended technique, example is presented. [ABSTRACT FROM AUTHOR]

To solve fractional order fractional order Burger’s equations, a hybrid technique named Atangana- Baleanu fractional variational iteration method (ABVIM) has been applied. Two challenges are overcome to validate and demonstrate the efficacy of the current process. It is also shown that the results acquired using the suggested technique is extremely like those obtained using other strategies. For a range of science and engineering difficulties, the proposed solution has been shown to be efficient, dependable, and simple to implement. [ABSTRACT FROM AUTHOR]

The development of metaknowledge about models (MKM) and metaknowledge about the modelling process (MKP) are important in pre-service science teachers (PSTs) education. MKM refers to knowledge about the model's entities and its purposes, while MKP refers to knowledge about the components and structure of the modelling process. Assessing MKM and MKP is crucial to foster this knowledge in PSTs' education. However, assessment instruments focus on MKM rather than MKP, and the connection between MKM and MKP had not been examined deeply. This study validates and applies a diagram task for assessing PSTs' MKP. For validation, ten experts were surveyed and think-aloud interviews were conducted with ten PSTs. The findings support valid interpretations of PSTs' MKP based on the diagram task. In the main study, the diagram task was administrated to 63 PSTs, along with an MKM questionnaire. PSTs' MKP ranged from simple conceptions of modelling as a linear process including few components to sophisticated conceptions of modelling as an iterative process including multiple components. Furthermore, it was found that knowledge about using models to predict a phenomenon is an aspect connecting sophisticated MKM and MKP, indicating that this aspect is crucial to foster both, PSTs MKM and MKP. [ABSTRACT FROM AUTHOR]

A linear multistep method is transformed into an iterative method based on the Patade and Bhalekar technique for the numerical solution of an optimal control problem modeled for mosquito and insecticide management using forward-backward sweep methods via Pontryagin's principle. Stability and convergence analysis of the iterative method are carried out and it is found to be stable, convergent, and of order four. Results obtained by the method clearly show that the population of mosquitoes can be minimized to a large extent using the new iterative method while reducing the harmful effects of the insecticide, which subsequently reduces the spread of malaria. [ABSTRACT FROM AUTHOR]

SAT solving in large distributed environments has previously led to some famous results and to impressive speedups for selected inputs. However, in terms of general-purpose SAT solving, prior approaches still cannot make efficient use of a large number of processors. We aim to address this issue with a complete and systematic overhaul of the distributed solver HordeSat with a focus on its algorithmic building blocks. In particular, we present a communication-efficient approach to clause sharing, careful buffering and filtering of produced clauses, and effective orchestration of state-of-the-art solver backends. In extensive evaluations, our approach named MallobSat significantly outperforms an updated HordeSat, doubling its mean speedup. Our clause sharing results in effective parallelization even if all threads execute identical solver programs that only differ based on which clauses they import at which times. We thus argue that MallobSat is not a portfolio solver with the added bonus of clause sharing but rather a clause-sharing solver where adding some explicit diversification is useful but not essential. We also discuss the last four iterations of the International SAT Competition (2020–2023), where our system ranked very favorably, and identify several previously unsolved competition problems that MallobSat solved successfully. Last but not least, our approach is malleable, i.e., supports running on a fluctuating set of resources, which allows us to combine parallel job processing and parallel SAT solving in a flexible manner for best resource efficiency. [ABSTRACT FROM AUTHOR]

This paper proposes DeepSynth, a method for effective training of deep reinforcement learning agents when the reward is sparse or non-Markovian, but at the same time progress towards the reward requires achieving an unknown sequence of high-level objectives. Our method employs a novel algorithm for synthesis of compact finite state automata to uncover this sequential structure automatically. We synthesise a human-interpretable automaton from trace data collected by exploring the environment. The state space of the environment is then enriched with the synthesised automaton, so that the generation of a control policy by deep reinforcement learning is guided by the discovered structure encoded in the automaton. The proposed approach is able to cope with both high-dimensional, low-level features and unknown sparse or non-Markovian rewards. We have evaluated DeepSynth’s performance in a set of experiments that includes the Atari game Montezuma’s Revenge, known to be challenging. Compared to approaches that rely solely on deep reinforcement learning, we obtain a reduction of two orders of magnitude in the iterations required for policy synthesis, and a significant improvement in scalability. [ABSTRACT FROM AUTHOR]

Copyright of Polish Statistician / Wiadomości Statystyczne is the property of State Treasury - Statistics Poland 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.)

The global sparse representation method based on compressed sensing fails to capture the local texture and detail structure of an image. To address this, a local dictionary learning method based on the wavelet domain is proposed. The wavelet highfrequency subband sub-block classification and local dictionary learning are implemented using the FCM clustering method and K-L method, respectively. At the reconstruction end, a compressed sampling Matching pursuit algorithm based on iterative update of sparsity is proposed. This adaptive iteration can effectively reconstruct the original image under unknown sparsity conditions. Simulation experiments show that compared to existing reconstruction methods, this approach has the advantages of simple computation, strong adaptive sparse representation ability for the original image, and superior reconstructed image performance. [ABSTRACT FROM AUTHOR]

Electrical Capacitance Tomography (ECT) is a non-invasive, non-intrusive, radiation-free, robust, and cost-efficient measurement system that generates cross-sectional images of industrial operations by measuring differences in dielectric properties within a container. Despite its advantages, the poor quality of reconstructed images limits its applicability. Researchers have explored both non-iterative and iterative methods to address this issue, yet the resulting image quality remains insufficient for high-stakes applications where accurate decision-making is critical. This study proposes a novel algorithm that integrates the ECT model with a diffusion-steered image denoising functional, enhancing the quality of ECT reconstructed images beyond existing methods. Empirical comparisons show that the proposed algorithm outperforms state-of-the-art techniques such as Linear Back Projection and Projected Landweber, with Distribution Error (DE) and Correlation Coefficient (CC) improvements of 62% and 19%, respectively. Qualitative assessments further indicate the superior performance of the proposed algorithm in reconstructing high-quality images. [ABSTRACT FROM AUTHOR]

Image reconstruction is one of the important tasks in the application of electrical capacitance tomography (ECT) systems. Though numerous algorithms have been implemented, it is often challenging to obtain satisfactory images in all imaging regions by the use of a single algorithm due to the soft-field nature of ECT systems. The preferred iterative reconstruction algorithms are highly computationally inefficient. A new iterative reconstruction algorithm is proposed which combine iterative Linear Back Projection and Logistic Regression. In this method, the solution to the ECT forward problem is implemented using logistic regression, whereas the ECT inverse problem is solved using the algebraic reconstruction technique. By doing so, it is possible to obtain high quality images at relatively efficient computational cost. The simulated experimental results shows that the proposed algorithm outperforms the Projected Landweber and Iterative Linear Back Projection in terms of spatial similarity accuracy, quality of reconstruction images, and computational efficiency. There are improvements of 29% spatial similarity accuracy and 58% computational cost relative to Iterative Linear Back Projection algorithm. This is significant improvement toward using ECT system for online industrial operations. [ABSTRACT FROM AUTHOR]

This manuscript puts forward two new generalized families of Jarratt's iterative schemes for deciding the solution of scalar and systems of nonlinear equations. The schemes involve weight functions that are based on bi-variate rational approximation polynomial of degree two in both its numerator and denominator. The convergence study conducted on the schemes, indicated that they have convergence order (CO) four in scalar space and retain the same number of CO in vector space. The numerical experiments conducted on the schemes when used to decide the solutions of some real-life nonlinear models show that they are good challengers of some well-known and robust existing iterative schemes. [ABSTRACT FROM AUTHOR]

In this study, we numerically solve a class of two-point boundary-value-problems with a Riemann-Liouville-Caputo fractional derivative, where the solution might contain a weak singularity. Using the shooting technique based on the secant iterative approach, the boundary value problem is first transformed into an initial value problem, and the initial value problem is then converted into an analogous integral equation. The functions contained in the fractional integral are finally approximated using linear interpolation. An adaptive mesh is produced by equidistributing a monitor function in order to capture the singularity of the solution. A modified Gronwall inequality is used to establish the stability of the numerical scheme. To show the effectiveness of the suggested approach over an equidistributed grid, two numerical examples are provided. [ABSTRACT FROM AUTHOR]

We study the problem of solvability of the equation ϕ (x) = ∫ Ω g (w) ϕ (f (x , ω)) P (d ω) + F (x) , where P is a probability measure on a σ-algebra of subsets of Ω, assuming Hölder continuity of F on the range of f. [ABSTRACT FROM AUTHOR]

A simple and efficient adaptive Markov Chain Monte Carlo (MCMC) method, called the Metropolized Adaptive Subspace (MAdaSub) algorithm, is proposed for sampling from high-dimensional posterior model distributions in Bayesian variable selection. The MAdaSub algorithm is based on an independent Metropolis-Hastings sampler, where the individual proposal probabilities of the explanatory variables are updated after each iteration using a form of Bayesian adaptive learning, in a way that they finally converge to the respective covariates' posterior inclusion probabilities. We prove the ergodicity of the algorithm and present a parallel version of MAdaSub with an adaptation scheme for the proposal probabilities based on the combination of information from multiple chains. The effectiveness of the algorithm is demonstrated via various simulated and real data examples, including a high-dimensional problem with more than 20,000 covariates. [ABSTRACT FROM AUTHOR]

In this paper, by using symmetric first-order divided differences, we introduce a new family of Secant-like iterative methods with quadratic convergence. Afterthought, we analyze its semilocal and local behavior when the nonlinear operator F is not differentiable by imposing appropriate bounding conditions in each case. Theoretical results have also been tested by solving a problem which shows the applicability of our work. [ABSTRACT FROM AUTHOR]

Collin (2022) attempts to break the symmetry between the modal ontological argument for the existence of God and the reverse modal ontological argument against the existence of God by drawing on some Kripkean lessons about a posteriori necessity. He argues that there is an undercutting defeater for taking God's non-existence to be possible. In this paper, I reply that taking the Kripkean considerations about a posteriori necessity into account does not help break the symmetry. For we can argue in a similar way that there is an undercutting defeater for taking God's existence to be possible. [ABSTRACT FROM AUTHOR]

Brian Epstein has argued (in The Ant Trap and 'Anchoring versus grounding') that social kinds 'export' across worlds. Although the conditions for war criminality are not 'fixed' in the Empire, for instance, Darth Vader is a war criminal there. And, according to Epstein, an account of social construction should imply that he is. Ultimately, he argues that 'grounding-only' accounts of social construction – like those proposed by Jonathan Schaffer and Aaron Griffith – imply that social kinds do not export across worlds. As a result, he argues that we need more than grounding; in particular, we need both grounding and what he calls 'anchoring' to account for it. In this paper, I argue that, once we draw a distinction between what I call 'thick' and 'thin' social kinds, Epstein's argument is either invalid or irrelevant. [ABSTRACT FROM AUTHOR]

Both Jago, in his 2020 article 'A short argument for truthmaker maximalism' and his 2021 article 'Which Fitch?', and Loss in his 2021 article 'There are no fundamental facts', employ arguments similar to that familiar from the Church–Fitch Paradox to infer some substantial metaphysical claims from their mere logical possibility. Trueman in his 2022 article 'Truthmaking, grounding and Fitch's paradox' and Nyseth in his 2022 article 'Fitch's paradox and truthmaking' respond by using exactly the same kind of argument to prove contrary theses. Intended as bad company objections, these counterarguments cast doubt on the original arguments, but do not actually identify their flaw. This paper aims to fill this gap. [ABSTRACT FROM AUTHOR]

Rowe has recently argued that pure risk of harm cannot itself be a harm. I respond to Rowe and argue that given an appropriate understanding of objective probabilities, pure objective risk of harm can itself be a harm. [ABSTRACT FROM AUTHOR]

State-of-the-art machine learning systems now routinely exceed benchmarks once thought beyond the ken of artificial intelligence (AI). Often these systems accomplish tasks through novel, insightful processes that remain inscrutable to even their human designers. Taking AlphaGo's 2016 victory over Lee Sedol as a case study, this paper argues that such accomplishments manifest the essential features of achievements as laid out in Bradford's 2015 book Achievement. Achievements like these are directly attributable to AI systems themselves. They are artificial achievements. This opens the door to a challenge that calls out for further inquiry. Since Bradford grounds the intrinsic value of achievements in the exercise of distinctively human perfectionist capacities, the existence of artificial achievements raises the possibility that some achievements might be valueless. [ABSTRACT FROM AUTHOR]

Some philosophers regard it as a serious possibility that we now exist within a simulation. That this hypothesis is somewhat probable has been defended extensively by Nick Bostrom. Notably, Bostrom does not defend the conclusion that we inhabit a simulation, but rather the disjunctive conclusion that the human species is very likely to die out before reaching a 'posthuman stage', that posthuman civilizations are extremely unlikely to run significant numbers of simulations, or that we almost certainly inhabit a simulation. Bostrom argues that there is no basis for strongly favouring one of these alternatives over its counterparts. The upshot is that it is fairly probable that we inhabit a simulation. I argue that posthuman civilizations are likely to be unwilling to run significant numbers of simulations, thereby undermining the case for assigning a somewhat high probability to the hypothesis that we live in a simulation. [ABSTRACT FROM AUTHOR]

When logicians work with multiple-conclusion systems, they use a metalinguistic comma ',' to aggregate premisses and/or conclusions. In this note, I present an analogy between this comma and Prior's infamous connective tonk. The analogy reveals that these expressions have much in common. I argue that, indeed, the comma can be seen as a structural incarnation of tonk. The upshot is that, whatever story one has to tell about tonk , there are good reasons to tell a similar story about the comma in typical multiple-conclusion systems, and vice versa. [ABSTRACT FROM AUTHOR]

Causal decision theory is often motivated as a 'revision of [decision theory] intended to solve Newcomb's problem' (Bacon 2022). In this paper, I give a direct argument for CDT by deriving it from a venerable decision-theoretic principle: the value of knowledge principle. The general framework that I use to deliver this result also supports a new argument for conditionalization. [ABSTRACT FROM AUTHOR]

Let an iterated essentialist statement be a statement of the form 'It lies in the nature of x1,x2,... that it lies in the nature of y1,y2,... that φ'. Let Iteration be the thesis that there are true iterated essentialist statements. Iteration has recently been disputed by Dasgupta (2014) and Glazier (2017). Both authors take the falsity of Iteration to be central to the explanatory role of essentialist truths. An important consequence that is not explicitly noted by them is that the falsity of Iteration would entail that metaphysical necessity is not reducible to essence in the manner suggested by Fine (1994). This paper defends Iteration against these challenges, drawing attention to some features of the collective essences of properties, propositions, and logical operations that have been largely neglected in discussions about essence. [ABSTRACT FROM AUTHOR]

It is often presumed that anti-haecceitists are not committed to the identity of indiscernibles. However, I argue that anti-haecceitism implies a particularly strong thesis about when individuals are indiscernible which motivates the identity of indiscernibles. The argument is first sketched intuitively and then formalized in a system of higher-order modal logic. [ABSTRACT FROM AUTHOR]

In this paper, we consider the Krasnosel'skiĭ-Mann iteration for finding a fixed point of nonexpansive operators in real Hilbert spaces. By introducing new, concise, and self-contained techniques, we propose new inertial factors with desirable upper bounds better than or comparable to existing ones. [ABSTRACT FROM AUTHOR]

For a ship, both synchronous and parametric roll motions can be modelled by nonlinear second-order differential equations with the roll angle as dependent variable and the nonlinearities coming from the damping and restoring moments. In the absence of exact solution techniques, approximate solutions for such equations can be obtained, in principle, analytically or numerically. In this paper, we focused on a fast and accurate recursive scheme based on the segmentation of the time domain into a fine grid of intervals of equal lengths. Due to the smallness of each time subinterval, a reset of the terms of the initial nonlinear equation of motion allows replacing it with a linear one in which the constant coefficients represent approximate values of the former variable coefficients in the middle of each subinterval. The linear equation is solved via the Laplace transform and the resulting solution together with its first derivatives is used to generate the iterative algorithm. This technique was applied to the case of two typical roll equations. The first describes the synchronous roll of a vehicle ferry model equipped or not with bilge keels while the second estimates the parametric roll of a container ship model. In both cases, the analyzed recursive algorithm not only generated results in full agreement with those provided by the ode45 solver in Matlab but also lead to a gain in computation time and offered more flexibility in imposing some restrictive conditions associated, for example, with exceeding some oscillation amplitudes or to the ship capsizing. [ABSTRACT FROM AUTHOR]

In this study, we propose a family of iterative procedures with no derivatives for calculating multiple roots of one-variable nonlinear equations. We also present an iterative technique to approximate the multiplicity of the roots. The new class is optimal since it fits the Kung-Traub hypothesis and has second-order convergence. Derivative-free methods for calculating multiple roots are rarely found in literature, especially in the case of one-step methods, which are the simplest ones in terms of their structure. Moreover, this new family contains almost all the existing single-step derivative-free iterative schemes as its special cases, with an additional degree of freedom. Several results are used to confirm its theoretical order of convergence. Through the complex discrete dynamics analysis, the stability of the suggested class is illustrated, and the most stable methods are found. Several test problems are included to check the performance of the proposed methods, whether the multiplicity of the roots is estimated or known, comparing the numerical results with those obtained by other methods. [ABSTRACT FROM AUTHOR]

A photovoltaic emulator (PVE) is a specialized device designed to mimic the static and dynamic properties of a solar panel. It serves as a crucial tool for testing and validating PV systems. Despite the unpredictable variations in real environmental conditions, the PVE offers a controlled environment, facilitating the smooth implementation and testing of PV subsystems. The PVE consists of two essential components: the reference PV model and the PVE power electronics controller. For simplicity, both these elements must be inherently straightforward. Numerous advanced methods have been proposed in the literature to efficiently integrate the PV model into the PVE system. However, these approaches often involve complex iterative computations of intricate equations related to solar panels, making them less practical. To address these limitations, this paper introduces a novel PVE that aims to simplify the implementation and integration of the PV model. The proposed system employs a simple and non-iterative approach to provide the reference to the PVE, using a straightforward explicit model of the solar panel. In comparison to existing works, the proposed PVE stands out for its high flexibility and simplicity as it does not require the complex iterative computations of implicit equations related to the solar panel model. The overall PVE system is implemented with a basic proportional-integral (PI) controller and a DC-DC buck power electronic converter. It is then validated using a 200 W solar panel through a series of experiments, including the EN 50530 Test. The results of these experiments demonstrate that the proposed PVE efficiently reproduces the static and dynamic characteristics of a solar panel. [ABSTRACT FROM AUTHOR]

For solving nonlinear monotone equations with convex constraints, Liu and Feng (Numer. Algoritm. 82(1):245–262, 2019) suggested a derivative-free iterative technique. Although they assert that the direction d k satisfies inequality (2.1), however, this is not true, as the derivation of the parameter θ k given by equation (2.7) is not correct. This led to Lemma 2.2, Lemma 3.1 and Theorem 3.1 in Liu and Feng (Numer. Algoritm. 82(1):245–262, 2019) not holding. In addition, Theorem 3.1 is still invalid as the bound for ‖ F (x k + α k ′ d k) ‖ was not established by the authors, instead the authors used the bound for ‖ F (x k + α k d k) ‖ as the bound for ‖ F (x k + α k ′ d k) ‖ . In this paper, We first describe the necessary adjustments and establish the bound for ‖ F (x k + α k ′ d k) ‖ , after which the proposed approach by Liu and Feng continues to converge globally. In addition, we provide some numerical results to support the adjustments. [ABSTRACT FROM AUTHOR]

In this paper, we establish new iterative methods for the solution of scalar equations by using the decomposition technique given in [16]. [ABSTRACT FROM AUTHOR]

This paper presents Calderón-Zygmund estimates for the weak solutions of a class of nonuniformly elliptic equations in R n , which are obtained through the use of the iteration-covering method. More precisely, a global Calderón-Zygmund type result | f | p 1 + a (x) | f | p 2 ∈ L s (R n) ⇒ | D u | p 1 + a (x) | D u | p 2 ∈ L s (R n) f o r a n y s > 1 is established for the weak solutions of − d i v A (x , D u) = − d i v F (x , f) i n R n , which are modeled on − d i v (| D u | p 1 − 2 D u + a (x) | D u | p 2 − 2 D u) = − d i v (| f | p 1 − 2 f + a (x) | f | p 2 − 2 f) , where 0 ≤ a (⋅) ∈ C 0 , α (R n) , α ∈ (0 , 1 ] and 1 < p 1 < p 2 < p 1 + α p 1 n . This paper presents Calderón-Zygmund estimates for the weak solutions of a class of nonuniformly elliptic equations in R n , which are obtained through the use of the iteration-covering method. More precisely, a global Calderón-Zygmund type result | f | p 1 + a (x) | f | p 2 ∈ L s (R n) ⇒ | D u | p 1 + a (x) | D u | p 2 ∈ L s (R n) f o r a n y s > 1 is established for the weak solutions of − d i v A (x , D u) = − d i v F (x , f) i n R n , which are modeled on − d i v (| D u | p 1 − 2 D u + a (x) | D u | p 2 − 2 D u) = − d i v (| f | p 1 − 2 f + a (x) | f | p 2 − 2 f) , where 0 ≤ a (⋅) ∈ C 0 , α (R n) , α ∈ (0 , 1 ] and 1 < p 1 < p 2 < p 1 + α p 1 n . [ABSTRACT FROM AUTHOR]

To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators, a min-max-min two-stage robust optimization model is presented, considering the unit commitment, source-network load collaboration, and control of the load demand response. After the constraint functions are linearized, the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method. The minimum-maximum of the original problem was continuously maximized using the iterative method, and the optimal solution was finally obtained. The constraint conditions expressed by the matrix may reduce the calculation time, and the upper and lower boundaries of the original problem may rapidly converge. The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately; otherwise, it is easy to cause excessive accommodation of wind power at some nodes, leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power. Thus, the most economical optimization scheme for the worst scenario of the output power of the generators is obtained, which proves the economy and reliability of the two-stage robust optimization method. [ABSTRACT FROM AUTHOR]

In this paper, we introduce two linearized proximal algorithms for solving DC composite optimization problems. The basic algorithms that we rely are the proximal-linear(ized) methods, which in each iteration solve regularized subproblems formed by linearizing the smooth maps and the concave component, respectively. It is proved that the two proposed algorithms provide descent methods and that if the sequences generated by the algorithms are bounded, every cluster points are critical points of the functions under consideration. Finally, a conclusion is stated and some directions for further research are suggested. [ABSTRACT FROM AUTHOR]