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Computer programming courses are known to challenge many students, especially in their first programming courses. Some topics in computer programming are more challenging than others due to the complexity of the logic underlying the concepts. One such topic that is considered challenging is recursive programming or recursion. Recursion as a teaching topic has long been a point of discussion, especially in relation to how it can be taught effectively. This paper delves into the topic of recursive programming and strategies applied to teach it recursively in a four-year institution. It examines the factors that make recursion challenging to learn and some strategies to teach recursion. The strategy pursued here stems from previous studies using data recursion and functional programming, which uses recursion by default. The data structure of 'list' from the LISP programming language, which is apt to data recursion, is implemented in Python and used for solving problems. The effectiveness of the strategy is assessed. Results indicate that the use and understanding of recursive programming can be enhanced by using recursive data structures and having the students use them in more practical applications. This hopefully will encourage other instructors to use the same/similar strategies to teach recursion more effectively. [ABSTRACT FROM AUTHOR]

The Euclidean Discus Toss is an active and tactile learning activity that models the extended Euclidean algorithm with a frisbee relay. The extended Euclidean algorithm involves both iterative and recursive programming and is regularly taught throughout the mathematics and computer science curricula. The Euclidean Discus Toss invites students to toss and catch frisbees in a collaborative and hands-on effort designed to sharpen modular arithmetic skills, enhance familiarity with iterative and recursive algorithms, and strengthen classroom community. The activity is fun, low-stakes, and can be customized to meet a variety of pedagogical objectives. [ABSTRACT FROM AUTHOR]

This paper examines the application of Neural Network architectures and training algorithms in the rental application screening within the Nigerian property market. The study compares the performance of two ANN architectures, Feedforward Neural Network (FFNN) and Recursive Neural Network (RNN), using ten selected training algorithms. The research utilizes 724 datasets obtained from 53 professional property managers operating in the Lagos Metropolitan rental market. The datasets are divided into training (70%), validation (15%), and testing (15%) sets. Performance measurements used include, sensitivity, specificity, precision, geometric mean, F1 score, F2 score, Adjusted F2 score, Mathews Correction Coefficient, and Area under the curve (AUC). The results indicate that both FFNN and RNN, trained by the selected algorithms, demonstrate satisfactory performance. However, the Bayesian regularization (BR) training algorithm outperforms the other nine algorithms. On the other hand, the Gradient Descent Algorithm (GD), Scaled Conjugate Gradient Backpropagation (SCG), and Resilient Backpropagation (RP) show the least performance in training FFNN and RNN. The study concludes that a BR trained FFNN and RNN are more suitable for rental application selection in the Nigerian property market. The findings of this study provide valuable insights and decisionmaking models for property management professionals and investors in the Nigerian rental market. [ABSTRACT FROM AUTHOR]

This paper presents a computational account of nonderived environment blocking (NDEB) that indicates the challenges it has posed for phonological theory do not stem from any inherent complexity of the patterns themselves. Specifically, it makes use of input strictly local (ISL) functions, which are among the most restrictive (i.e., lowest computational complexity) classes of functions in the subregular hierarchy (Heinz 2018) and shows that NDEB is ISL provided the derived and nonderived environments correspond to unique substrings in the input structure. Using three classic examples of NDEB from Finnish, Polish, and Turkish, it is shown that the distinction between derived and nonderived sequences is fully determined by the input structure and can be achieved without serial derivation or intermediate representations. This result reveals that such cases of NDEB are computationally unexceptional and lends support to proposals in rule- and constraint-based theories that make use of its input-oriented nature. [ABSTRACT FROM AUTHOR]

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We motivate and develop a natural bicriteria measure for assessing the quality of a clustering that avoids the drawbacks of existing measures. A simple recursive heuristic is shown to have poly-logarithmic worst-case guarantees under the new measure. The main result of the article is the analysis of a popular spectral algorithm. One variant of spectral clustering turns out to have effective worst-case guarantees; another finds a "good" clustering, if one exists. [ABSTRACT FROM AUTHOR]

With recent research in sensor networks and IoT technologies, algorithms for broadcast networks should not only seek to minimise runtime but also energy utilisation. We consider the scenario where processors conserve energy by going into a sleep state for the vast majority of the time. Algorithms under these conditions face additional challenges. Specifically, hibernating processors will not know when tasks that have a random runtime complete. This problem is compounded in recursive algorithms where each task's runtime depends upon the runtime of the recursive calls. We provide a framework, breadth-first recursion, that supports recursive algorithms in this setting. As a simple example, we provide a quick-sort algorithm which is optimal for both time and energy utilisation. We then provide an energy-efficient convex hull algorithm with output sensitive runtime that is sublinear for many realistic distributions for processor placement. Further, we provide simulations that show that our algorithms are practical and significantly faster than previous results. The left side of the image represents our algorithmic technique of breadth first recursion. Inside each box is filled with code and represents the recursive calls of a recursive algorithm. They are arranged in a recursion tree and there is an arrow indicating the order that they are executed. On the right side is a representation of the basic components of our model. They represent low-energy broadcast devices in a single-hop network. [ABSTRACT FROM AUTHOR]

This paper analyzes the strategy that minimizes the initial cost of replicating a contingent claim in a market with transactions costs and trading constraints. The linear programming and two-stage backward recursive models developed are applicable to the replication of convex as well as nonconvex payoffs and to a portfolio of options with different maturities. The paper's formulation conveniently accounts for fixed and variable transactions costs, lot size constraints, and position limits on trading. The article shows that in the presence of trading frictions, it is no longer optimal to revise one's portfolio in each period. At the optimum, cash flows in excess of the desired ones may be generated. The optimal policy trades off the curvature of the payoff that is generated against the terminal slack. [ABSTRACT FROM AUTHOR]

In this paper a recursive programming model is constructed and optimized so that a mix of bank assets may be selected. The purpose is to link optimization over time with a commercial bank asset management model. In recursive programming models an optimal decision is to be determined for each period. In any period the decision depends upon the data and parameters for the model and the allocations in the previous period. A recursive programming model may be a linear, quadratic, or nonlinear programming problem; a linear model will be presented here. For any particular period, a bank will be considered to have a fixed level of total assets which are allocated among cash, loans, investments, fixed assets, and other assets. For most banks, total assets are increasing over time since their liabilities, mainly demand and time-and-savings deposits, and their total capital are growing. Most of this growth is accounted for by increases in deposit liabilities. As a reaction to changing interest rates, liquidity needs, and growth, banks usually find it desirable to rearrange asset levels from one period to the next. Therefore, it seems appropriate to analyze bank asset management over time by employing a recursive programming model. The dynamic solutions to the programming model provide normative conclusions for the banking firm. The constraints of the programming problem constitute a model that describes the bank activities completed as managerial choices evolve. After the model is delineated, a linear profit function is imposed as an objective function, and the profit function is maximized subject to the behavioral constraints. [ABSTRACT FROM AUTHOR]

We investigate a modality for controlling the behaviour of recursive functional programs on infinite structures which is completely silent in the syntax. The latter means that programs do not contain "marks" showing the application of the introduction and elimination rules for the modality. This shifts the burden of controlling recursion from the programmer to the compiler. To do this, we introduce a typed lambda calculus à la Curry with a silent modality and guarded recursive types. The typing discipline guarantees normalisation and can be transformed into an algorithm which infers the type of a program. [ABSTRACT FROM AUTHOR]

Abstract The single-image super-resolution techniques (SISR) have been significantly promoted by deep networks. However, the storage and computation complexities of deep models increase dramatically alongside with the reconstruction performance. This paper proposes a densely connected recursive network (DCRN) to trade off the performance and complexity. We introduce an enhanced dense unit by removing the batch normalization (BN) layers and employing the squeeze-and-excitation (SE) structure. A recursive architecture is also adopted to control the parameters of deep networks. Moreover, a de-convolution based residual learning method is proposed to accelerate the residual feature extraction process. The experimental results validate the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

For an orthogonal frequency-division multiplexing (OFDM) system in high-mobility applications, channel suffers from both frequency-selective and time-selective fading introduced by Doppler shift. Large pilot overhead is needed to estimate the numerous parameters of doubly-selective channel, resulting in low-spectral efficiency. In this paper, considering the correlation of practical wireless channels in high-dimensional signal spaces, a recursive channel estimation scheme based on compressed sensing (CS) is proposed for high-mobility OFDM systems to reduce the pilot overhead. Specifically, by exploiting the sparsity of OFDM channel in basis expansion model (BEM), the sparse BEM coefficients is estimated instead of numerous channel taps. Then we theoretically verify that the BEM coefficients corresponding successive OFDM symbols also share a common support. To utilize this temporal correlation of BEM coefficients, a recursive channel estimation algorithm derived from the classical modified CS algorithm is proposed to improve the present estimation by prior channel information from previous estimation. Theoretical and simulation results demonstrate that the proposed recursive channel estimation scheme preforms better than conventional schemes in various scenarios, even with less pilot overhead. [ABSTRACT FROM AUTHOR]

Reversible computing is both forward and backward deterministic. This means that a uniquely determined step exists from the previous computational configuration (backward determinism) to the next one (forward determinism) and vice versa. We present the reversible primitive recursive functions (RPRF), a class of reversible (endo-)functions over natural numbers which allows to capture interesting extensional aspects of reversible computation in a formalism quite close to that of classical primitive recursive functions. The class RPRF can express bijections over integers (not only natural numbers), is expressive enough to admit an embedding of the primitive recursive functions and, of course, its evaluation is effective. We also extend RPRF to obtain a new class of functions which are effective and Turing complete, and represent all Kleene’s μ-recursive functions. Finally, we consider reversible recursion schemes that lead outside the reversible endo-functions. [ABSTRACT FROM AUTHOR]

The Knowledge in Pieces (KiP) framework offers a powerful perspective to understand the dynamic nature of knowledge and knowledge development. However, there is still much we do not know about the mechanisms underlying the moment-to-moment knowledge construction process. The purpose of this study was to explore the ways in which contextual knowledge elements support that process. According to KiP, a knowledge element, once activated, becomes part of the context that shapes the activation, configuration, and operation of other elements. Based on this assumption, we hypothesized that when the structural and operational characteristics of these contextual knowledge elements are compatible with those of the target concept, the contextual knowledge elements can support the knowledge construction process. This hypothesis was investigated in the domain of recursion, a fundamental but challenging programming concept. Sixty undergraduate computer science students completed four recursion evaluation tasks with varying likelihood to induce compatible contextual elements. Results showed that students performed better on high-compatibility tasks than on low-compatibility tasks at both the group level and individual level. Further qualitative analysis identified the knowledge elements involved and elaborated and refined the explanatory model for the contextual support effect. [ABSTRACT FROM AUTHOR]

Cotton production substantially contributes to the GDP of Uzbekistan. It is produced under a state procurement policy, according to which farmers have to allocate half of their land for cotton, fulfill cotton output levels and sell the entire harvest of cotton to the state. Land is state owned and a land consolidation process is in place, where land allocated to a farm can be reduced if cotton production targets are not met. However, low production levels may not only arise due to bad management practices but also due to production risks, e.g., due to climatic variability or limited access to inputs such as irrigation water. In this study, we aim to assess policies that can improve cotton production levels and farm profits under land consolidation and agricultural risks. We use a recursive programming model, which considers risk-averse farmers and their farm size adjustments, together with irrigation water-crop yield response functions on different land productivity classes. We consider production, market and institutional risks and find that such risks affect resource allocation, production levels and thus also farm size developments. The farm land consolidation improves overall cotton output, yet, it reduces farm incomes as the state cotton procurement price is low. Under conditions of the state cotton procurement and land consolidation, increasing the cotton price can improve cotton production and farm profits and reduce farm size changes, but farm size changes still occur among farmers. Having secure land tenure together with land use flexibility and higher cotton prices allows farmers to improve incomes, reduce income disparity, diversify land uses, but results in lower cotton output than under the state cotton procurement policy. [ABSTRACT FROM AUTHOR]

The aim of this paper is to evaluate OpenMP, TBB and Cilk Plus as basic language-based tools for simple and efficient parallelization of recursively defined computational problems and other problems that need both task and data parallelization techniques. We show how to use these models of parallel programming to transform a source code of Adaptive Simpson’s Integration to programs that can utilize multiple cores of modern processors. Using the example of Belman-Ford algorithm for solving single-source shortest path problems, we advise how to improve performance of data parallel algorithms by tuning data structures for better utilization of vector extensions of modern processors. Manual vectorization techniques based on Cilk array notation and intrinsics are presented. We also show how to simplify such optimization using Intel SIMD Data Layout Template containers. [ABSTRACT FROM AUTHOR]

In this paper, a novel baseline free approach for continuous online damage detection of multi degree of freedom vibrating structures using Recursive Principal Component Analysis (RPCA) in conjunction with Time Varying Auto-Regressive Modeling (TVAR) is proposed. In this method, the acceleration data is used to obtain recursive proper orthogonal components online using rank-one perturbation method, followed by TVAR modeling of the first transformed response, to detect the change in the dynamic behavior of the vibrating system from its pristine state to contiguous linear/non-linear-states that indicate damage. Most of the works available in the literature deal with algorithms that require windowing of the gathered data owing to their data-driven nature which renders them ineffective for online implementation. Algorithms focussed on mathematically consistent recursive techniques in a rigorous theoretical framework of structural damage detection is missing, which motivates the development of the present framework that is amenable for online implementation which could be utilized along with suite experimental and numerical investigations. The RPCA algorithm iterates the eigenvector and eigenvalue estimates for sample covariance matrices and new data point at each successive time instants, using the rank-one perturbation method. TVAR modeling on the principal component explaining maximum variance is utilized and the damage is identified by tracking the TVAR coefficients. This eliminates the need for offline post processing and facilitates online damage detection especially when applied to streaming data without requiring any baseline data. Numerical simulations performed on a 5-dof nonlinear system under white noise excitation and El Centro (also known as 1940 Imperial Valley earthquake) excitation, for different damage scenarios, demonstrate the robustness of the proposed algorithm. The method is further validated on results obtained from case studies involving experiments performed on a cantilever beam subjected to earthquake excitation; a two-storey benchscale model with a TMD and, data from recorded responses of UCLA factor building demonstrate the efficacy of the proposed methodology as an ideal candidate for real time, reference free structural health monitoring. [ABSTRACT FROM AUTHOR]

The aim of this paper is to provide mathematical foundations of a graph transformation language, called UnCAL, using categorical semantics of type theory and fixed points. About 20 years ago, Buneman et al. developed a graph database query language UnQL on the top of a functional meta-language UnCAL for describing and manipulating graphs. Recently, the functional programming community has shown renewed interest in UnCAL, because it provides an efficient graph transformation language which is useful for various applications, such as bidirectional computation.In order to make UnCAL more flexible and fruitful for further extensions and applications, in this paper, we give a more conceptual understanding of UnCAL using categorical semantics. Our general interest of this paper is to clarify what is the algebra of UnCAL. Thus, we give an equational axiomatisation and categorical semantics of UnCAL, both of which are new. We show that the axiomatisation is complete for the original bisimulation semantics of UnCAL. Moreover, we provide a clean characterisation of the computation mechanism of UnCAL called ‘structural recursion on graphs’ using our categorical semantics. We show a concrete model of UnCAL given by the λG-calculus, which shows an interesting connection to lazy functional programming. [ABSTRACT FROM AUTHOR]

We develop a recursive dual method for solving dynamic economic problems. The method uses a Lagrangian to pair a dynamic recursive economic problem with a dual problem. We show that such dual problems can be recursively decomposed with costates (i.e., Lagrange multipliers on laws of motion) functioning as state variables. In dynamic contracting and policy settings, the method often replaces an endogenous state space of forward‐looking utilities with an exogenously given state space of costates. We provide a principle of optimality for dual problems and give conditions under which the dual Bellman operator is a contraction with the optimal dual value function its unique fixed point. We relate economic problems to their duals, address computational issues, and give examples. [ABSTRACT FROM AUTHOR]

This paper aims to develop a hybrid genetic algorithm (HGA) to solve the resource constrained assembly line balancing problem (RCALBP) in the sewing line of a footwear manufacturing plant. Sewing, which is the most critical process in footwear manufacturing, has a series of processes, such as punching, trimming, attaching shoelaces. RCALBP in the sewing line considers not only the precedence constraints of product assembly but also the resource constraints, such as operators and equipment. A novel HGA that includes two stages is proposed to optimize the resources in the sewing line. The first stage uses the priority rule-based method (PRBM) to determine the feasible solutions of assigning tasks and machines to workstations. The solutions of PRBM are used to construct the initial population of genetic algorithm (GA) in the second stage. To ensure that the solution of GA is feasible, a two-point-order crossover with the new technique of searching feasible solution patterns is proposed. Moreover, the mutation procedure of GA is modified to avoid the building block from breaking, which may cause unfeasible solutions in RCALBP. A self-tuning method is also applied recursively to exclude unfeasible solutions. The proposed HGA is compared with the manual procedure adopted practically in factories, the existing heuristic model in the literature, and the traditional GA. Based on actual data from a footwear factory, computational results demonstrate that the proposed HGA can achieve better results than the other algorithms. [ABSTRACT FROM AUTHOR]

With the increasing complexity of the problems and systems arising nowadays, the use of multi-level models is becoming more frequent in practice. However, there are still few reports in the literature concerning methods for analyzing such models without flattening the multi-level structure. For instance, several variants of multi-level Petri nets have been applied for modeling interaction protocols and mobility in multi-agent systems and coordination of cross-organizational workflows. But there are few automated tools for analyzing the behavior of these nets. In this paper we explain how to detect faults in models based on a representative class of multi-level nets: the nested Petri nets. We translate a nested net into a verifiable model that preserves its modular structure, a PROMELA program. This allows the use of SPIN model checker to verify properties related to termination, boundedness and reachability. [ABSTRACT FROM AUTHOR]

We show how to underapproximate the procedure summaries of recursive programs over the integers using off-the-shelf analyzers for non-recursive programs. The novelty of our approach is that the non-recursive program we compute may capture unboundedly many behaviors of the original recursive program for which stack usage cannot be bounded. Moreover, we identify a class of recursive programs on which our method terminates and returns the precise summary relations without underapproximation. Doing so, we generalize a similar result for non-recursive programs to the recursive case. Finally, we present experimental results of an implementation of our method applied on a number of examples. [ABSTRACT FROM AUTHOR]

Recent developments in recognition of cursive scripts rely on implicit feature extraction methods that provide better results as compared to traditional hand-crafted feature extraction approaches. We present a hybrid approach based on explicit feature extraction by combining convolutional and recursive neural networks for feature learning and classification of cursive Urdu Nastaliq script. The first layer extracts low-level translational invariant features using Convolutional Neural Networks (CNN) which are then forwarded to Multi-dimensional Long Short-Term Memory Neural Networks (MDLSTM) for contextual feature extraction and learning. Experiments are carried out on the publicly available Urdu Printed Text-line Image (UPTI) dataset using the proposed hierarchical combination of CNN and MDLSTM. A recognition rate of up to 98.12% for 44-classes is achieved outperforming the state-of-the-art results on the UPTI dataset. [ABSTRACT FROM AUTHOR]

Data Nets are a version of colored Petri nets in which tokens carry data from an infinite and linearly ordered domain. This is a very expressive class, though coverability and termination remain decidable. Those problems have recently been proven complete for the F ω ω ω class in the fast growing complexity hierarchy. We characterize the exact complexity of Unordered Data Nets ( UDN ), a subclass of Data Nets with unordered data. We bound the length of bad sequences in well-quasi orderings of multisets over tuples of naturals by adapting the analogous result by Schmitz and Schnoebelen for words over a finite alphabet. These bounds imply that both problems are in F ω ω . We prove that this result is tight by constructing UDN that weakly compute fast-growing functions and their inverses. This is the first complete problem for F ω ω with an underlying wqo not based on finite words over a finite alphabet. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a recursive construction of simple t-designs for arbitrary t. The construction is of purely combinatorial nature and it requires finding solutions for the indices of the ingredient designs that satisfy a certain set of equalities. We give a small number of examples to illustrate the construction, whereby we have found a large number of new t-designs, which were previously unknown. This indicates that the method is useful and powerful. [ABSTRACT FROM AUTHOR]

We introduce a new, demand-driven variant of Spector's bar recursion in the spirit of the Berardi–Bezem–Coquand functional of [4] . The recursion takes place over finite partial functions u , where the control parameter ω , used in Spector's bar recursion to terminate the computation at sequences s satisfying ω ( s ˆ ) < | s | , now acts as a guide for deciding exactly where to make bar recursive updates, terminating the computation whenever ω ( u ˆ ) ∈ dom ( u ) . We begin by exploring theoretical aspects of this new form of recursion, then in the main part of the paper we show that demand-driven bar recursion can be directly used to give an alternative functional interpretation of classical countable choice. We provide a short case study as an illustration, in which we extract a new bar recursive program from the proof that there is no injection from N → N to N , and compare this with the program that would be obtained using Spector's original variant. We conclude by formally establishing that our new bar recursor is primitive recursively equivalent to the original Spector bar recursion, and thus defines the same class of functionals when added to Gödel's system T . [ABSTRACT FROM AUTHOR]

To address the sparse system identification problem in a non-Gaussian impulsive noise environment, the recursive generalized maximum correntropy criterion (RGMCC) algorithm with sparse penalty constraints is proposed to combat impulsive-inducing instability. Specifically, a recursive algorithm based on the generalized correntropy with a forgetting factor of error is developed to improve the performance of the sparsity aware maximum correntropy criterion algorithms by achieving a robust steady-state error. Considering an unknown sparse system, the l 1-norm and correntropy induced metric are employed in the RGMCC algorithm to exploit sparsity as well as to mitigate impulsive noise simultaneously. Numerical simulations are given to show that the proposed algorithm is robust while providing robust steady-state estimation performance. [ABSTRACT FROM AUTHOR]

In this paper, a novel design of third and fifth order differentiator based on lattice wave digital filter (LWDF), established on optimizing L1-error approximation function using cuckoo search algorithm (CSA) is proposed. We present a novel realization of minimum multiplier differentiator using LWD structure leading to requirement of optimizing only N coefficients for N th order differentiator. The γ coefficients of lattice wave digital differentiator (LWDD) are computed by minimizing the L1 -norm fitness function leading to a flat response. The superiority of the proposed LWDD is evident by comparing it with other differentiators mentioned in the literature. The magnitude response of the designed LWDD is found to be of high accuracy with flat response in a wide frequency range. The simulation and statistical results validates that the designed minimum multiplier LWDD circumvents the existing one in terms of minimum absolute magnitude error, mean relative error (dB) and efficient structural realization, thereby making the proposed LWDD a promising approach to digital differentiator design. [ABSTRACT FROM AUTHOR]

Location of poles and zeroes greatly affect phase response and magnitude response of a system. Recently, pole-zero optimization emerged as an effective approach to approximately match magnitude response of a system with that of an ideal one. In this brief, a methodology for the design of linear phase integrators and ones with constant phase of -90 degree is proposed. The aim of this method is to simultaneously attain multiple objectives of magnitude and phase optimization. In this method, magnitude response error is minimized under the constraint that the maximum passband phase-response error is below a prescribed level. Examples are included to illustrate the proposed design technique. [ABSTRACT FROM AUTHOR]

This paper deals with the self-tuning control problem of linear systems described by autoregressive exogenous (ARX) mathematical models in the presence of unmodelled dynamics. An explicit scheme of control is described, which we use a recursive algorithm on the basis of the robustness σ-modification approach to estimate the parameters of the system, to solve the problem of regulation tracking of the system. This approach was designed with the assumptions that the norm of the vector of the parameters is well-known. A new quadratic criterion is proposed to develop a modified recursive least squares (M-RLS) algorithm with σ-modification. The stability condition of the proposed estimation scheme is proved using the concepts of the small gain theorem. The effectiveness and reliability of the proposed M-RLS algorithm are shown by an illustrative simulation example. The effectiveness of the described explicit self-tuning control scheme is demonstrated by simulation results of the cruise control system for a vehicle. [ABSTRACT FROM AUTHOR]

We propose an extended recursive Cramér–Rao lower bound (ER-CRLB) method as a fundamental tool to analyze the performance of wireless indoor localization systems. The ER-CRLB fully models the complicated indoor environment, e.g., the sequential position state propagation, the target-anchor geometry effect, the non-line-of-sight (NLOS) identification, and the related prior information. First, we use an abstract function to represent the entire wireless localization system model. Then, the unknown vector of the ER-CRLB consists of two parts: The first part is the estimated vector, and the second part is the auxiliary vector that helps improve the estimation accuracy. Accordingly, the Fisher information matrix (FIM) of the ER-CRLB is divided into two parts, namely, the state matrix and the auxiliary matrix. Based on this idea, ER-CRLB can be a practical fundamental limit to denote the system which fuses multiple information in the complicated environment, e.g., recursive Bayesian estimation based on the hidden Markov model, the map matching method and NLOS identification and mitigation methods. When only a small set of unknown vectors is estimated in the system, the ER-CRLB is equivalent to the other CRLBs of the wireless indoor localization system as well. However, the ER-CRLB is more adaptable than other CRLBs when considering more unknown important factors. We employ the ER-CRLB to analyze the time-of-arrival (TOA) range-based indoor localization system. The influence of the hybrid line-of-sight (LOS)/NLOS channels, the building layout information, and the relative height differences between the target and anchors are analyzed. It is demonstrated that the ER-CRLB exploits all the available information for the indoor localization systems and serves as a fundamental limit of the unbiased estimation accuracy. [ABSTRACT FROM PUBLISHER]

The growing interest in intensive production practices makes it essential for catfish farmers to understand the best investment plan to transition an entire farm to new technologies. The 6-year mixed-integer-recursive programming model developed included traditional ponds, re-working existing ponds to make them more productive, converting traditional ponds to split ponds or adding additional aeration. Farm scenarios were developed that differed in the percentages of ponds that required reworking. The model selected primarily adoption of split ponds as the profit-maximizing strategy under current prices and unlimited availability of investment capital. Restricted availability of investment capital had a strong negative effect on those farms with greater percentages of older ponds that required re-working. Farmers that had reworked ponds more recently were able to invest the greater profits from the higher yields to transition more rapidly to new technologies. Larger farms could transition greater proportions of the farm to new technologies than could smaller farms. Higher feed prices and lower fish prices shifted the profit-maximization strategies from split ponds to intensively aerated systems and traditional single-batch systems. Results provide guidance to farms to make decisions related to the optimal investment pathway to transition farms to more productive and profitable technologies. [ABSTRACT FROM AUTHOR]

In this essay, we propose a recursive model of institutional change building on the Annales School, one of the 20th century’s most influential streams of historical research. Our model builds upon three concepts from the Annales—mentalities, levels of time, and critical events—to explore how critical events affect different dimensions of institutional logics and exert short- or long-range influences. On these bases, organizations make choices, from decoupling to radical shifts in logics, leading to severe institutional changes that become the matter of history. As much as organizations are influenced by events and the prevalent institutional logics, their choices trigger macro-level changes in a recursive manner. More broadly, we comment on how fruitful is our approach to historicize organization studies. [ABSTRACT FROM AUTHOR]

We consider the problem of finding the point in the transportation polytope which is closest to the origin. Recursive formulas to solve it are provided, explaining how they arise from geometric considerations, via projections, and we derive solution algorithms with linear computational complexity in the number of variables. [ABSTRACT FROM AUTHOR]