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The purpose of this work is to present a new method for estimating the parameters of the biomass of agricultural crops based on Earth remote sensing (ERS) data. The method includes mathematical models and algorithms estimation and has been tested on the example of spring wheat sowing. Sowing biomass parameters are the basis for making management decisions aimed at obtaining a given crop yield. Currently, for these purposes, vegetation indices are most widely used. It is impossible to estimate the physical parameters of the crop sowing biomass using these indices, due to their scalar form and lack of dimension. The paper develops a classical approach to the problem of estimating the parameters of the state of agricultural crops, in which remote sensing data are considered as an indirect measurement of the estimated parameters. The basis for the implementation of the estimation method is the dynamic model of biomass parameters and the remote sensing model, which reflects the relationship between the spectral reflection parameters and the estimated parameters of the crop biomass. The parameters of the dynamic model and the remote sensing model are refined by selective ground measurements in separate elementary sections of the field. The difference between this article and previous works of a similar nature lies in the fact that agricultural crops with a more complex morphological structure are considered as the object of evaluation. In addition, such an important feature of agricultural objects as their spatial distribution is considered here. To take it into account, a new type of mathematical models is used, in which spatial coordinates are introduced. Due to the significant complication of modeling and estimation algorithms based on such models, simpler approximation schemes are proposed. The advantage of the proposed approach is that the assessment is considered as a dynamic process that meets the content of the task of monitoring crops. [ABSTRACT FROM AUTHOR]

In this paper, a multiproduct multiperiod capacitated production system that has been organized as a flow shop is considered. The main novelty of the paper is proposing a more efficient mathematical model for the problem of integrating lot sizing and scheduling with sequence-dependent setups. In comparison to the former model, because of fewer continuous and binary decision variables and constraints in the proposed model, this model is very easier to solve. Comparison between two models proves the superiority of the proposed model. Two mixed integer programming-based approaches with rolling horizon framework have been used to solve this model. To evaluate the performance of the proposed model and solution method, problems of different scales have been studied. The used algorithms search the solution space for both lot-sizing and scheduling problems and find a combination of production planning and scheduling that is feasible and close to optimum. [ABSTRACT FROM AUTHOR]

This paper proposes and evaluates a near admissible heuristic search strategy and its application to a kind of flexible manufacturing system (FMS) scheduling in a Petri net framework. Petri nets can concisely model the strict precedence constraint, multiple kinds of resources, and concurrent activities. To cope with the complexities for scheduling of FMS with alternative routings, this paper proposes an admissible heuristic function based on the execution of P-timed Petri nets and presents an improved dynamic weighting A* strategy using the proposed heuristic function. The search scheme does not need to predict the depth of solution in advance and the quality of the search result is also controllable. Some numerical experiments are carried out to demonstrate usefulness of the algorithm. [ABSTRACT FROM AUTHOR]

The end-of-life (EOL) disassembly sequencing problem (DSP) has become increasingly important in the process of handling EOL products. This paper proposes a solution procedure for the “EOL DSP”; the procedure is based on a novel soft-computing algorithm that utilizes modified “simplified swarm optimization,” and the procedure combines the precedence preservative operator, feasible solution generator, self-adaptive parameter control, and repetitive pairwise exchange procedures. By taking into consideration the non-deterministic polynomial time (NP)-complete nature of the problem, the proposed algorithm efficiently seeks the optimal disassembly sequence with a novel approach; this approach involves reducing the initial solution space and using a combination of soft-computing algorithms for achieving higher computational efficiency and solution quality. The results presented in this paper show that the proposed algorithm outperforms the existing algorithms in terms of solution quality achieved in a limited computation time. [ABSTRACT FROM PUBLISHER]

As the location trajectory contains more spatial-temporal information about the user, it will be even dangerous for jeopardizing the privacy of the user. In order to cope with the correlation, an algorithm that utilizes the query division had been proposed. In this algorithm, random blocks of query context was used, so as the adversary was obfuscated and difficult to correlate the real result. However, this algorithm fails to dispose the size of each query block, as once same size blocks were obtained by the adversary continuously, so the adversary can regard them as blocks from the same query context, and then obtains the query context to correlate the discrete locations. In view of above conditions, in this paper we propose a fine granularity block division algorithm based on the conception of granularity measurement as well as granularity layer division, so with the help of collaborative users the location privacy of the user will be protected. In this algorithm, the query context will be divided into fine granularity size of information blocks that difficult to be distinguished with others, and then these blocks will be exchanged with other collaborative users to eliminate the difference in block size. In addition, as each block is divided into fine granularity size, the adversary will be difficult to correlate the discrete locations into location trajectory, so the location privacy will be protected. At last, through security analysis and experimental verification, this granularity indistinguishable algorithm is analyzed and verified at both theoretical and practical levels, which further demonstrate the superiority of the proposed algorithm compared with other similar algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we explore the (in)efficiency of the continuum Bitcoin-USD market in the period ranging from mid 2010 to early 2019. To deal with, we dynamically analyse the evolution of the self-similarity exponent of Bitcoin-USD daily returns via accurate FD4 approach by a 512 day sliding window with overlapping data. Further, we define the memory indicator by the difference between the self-similarity exponent of Bitcoin-USD series and the self-similarity index of its shuffled series. We also carry out additional analyses via FD4 approach by sliding windows of sizes equal to 64, 128, 256, and 1024 days, and also via FD algorithm for values of q equal to 1 and 2 (and sliding windows equal to 512 days). Moreover, we explored the evolution of the self-similarity exponent of actual S&P500 series via FD4 algorithm by sliding windows of sizes equal to 256 and 512 days. In all the cases, the obtained results were found to be similar to our first analysis. We conclude that the self-similarity exponent of the BTC-USD (resp., S&P500) series stands above 0.5. However, this is not due to the presence of significant memory in the series but to its underlying distribution. In fact, it holds that the self-similarity exponent of BTC-USD (resp., S&P500) series is similar or lower than the self-similarity index of a random series with the same distribution. As such, several periods with significant antipersistent memory in BTC-USD (resp., S&P500) series are distinguished. [ABSTRACT FROM AUTHOR]

Computational modeling of engineered gene circuits is an important while challenged task in systems biology. In order to describe and predict the response behaviors of genetic circuits using reliable model parameters, this paper applies an optimal experimental design(OED) method to obtain input signals. In order to obtain informative observations, this study focuses on maximizing Fisher information matrix(FIM)-based optimal criteria and to provide optimal inputs. Furthermore, this paper designs a two-stage optimization with the modified E-optimal criteria and applies harmony search(HS)-based OED algorithm to minimize estimation errors. The proposed optimal identification methodology involves estimation errors and the sample size to pursue a trade-off between estimation accuracy and measurement cost in modeling gene networks. The designed cost function takes two major factors into account, in which experimental costs are proportional to the number of time points. Experiments select two types of synthetic genetic networks to validate the effectiveness of the proposed HS-OED approach. Identification outcomes and analysis indicate the proposed HS-OED method outperforms two candidate OED approaches, with reduced computational effort. [ABSTRACT FROM AUTHOR]

There are differences between the requirements for traffic network for traffic demand in daily and emergency situations. In order to evaluate how the network designed for daily needs can meet the surging demand for emergency evacuation, the concept of emergency reliability and corresponding evaluation method is proposed. This paper constructs a bilevel programming model to describe the proposed problem. The upper level problem takes the maximum reserve capacity multiplier as the optimization objective and considers the influence of reversible lane measures taken under emergency conditions. The lower level model adopts the combined traffic distribution/assignment model with capacity limits, to describe evacuees' path and shelter choice behavior under emergency conditions and take into account the traits of crowded traffic. An iterative optimization method is proposed to solve the upper level model, and the lower level model is transformed into a UE assignment problem with capacity limits over a network of multiple origins and single destination, by adding a dummy node and several dummy links in the network. Then a dynamic penalty function algorithm is used to solve the problem. In the end, numerical studies and results are provided to demonstrate the rationality of the proposed model and feasibility of the proposed solution algorithms. [ABSTRACT FROM AUTHOR]

Establishing causal relations between random variables from observational data is perhaps the most important challenge in today’s science. In remote sensing and geosciences, this is of special relevance to better understand the earth’s system and the complex interactions between the governing processes. In this paper, we focus on an observational causal inference, and thus, we try to estimate the correct direction of causation using a finite set of empirical data. In addition, we focus on the more complex bivariate scenario that requires strong assumptions and no conditional independence tests can be used. In particular, we explore the framework of (nondeterministic) additive noise models, which relies on the principle of independence between the cause and the generating mechanism. A practical algorithmic instantiation of such principle only requires: 1) two regression models in the forward and backward directions and 2) the estimation of statistical independence between the obtained residuals and the observations. The direction leading to more independent residuals is decided to be the cause. We instead propose a criterion that uses the sensitivity (derivative) of the dependence estimator, the sensitivity criterion allows to identify samples most affecting the dependence measure, and hence, the criterion is robust to spurious detections. We illustrate the performance in a collection of 28 geoscience causal inference problems, a database of radiative transfer models simulations and machine learning emulators in vegetation parameter modeling involving 182 problems, and assessing the impact of different regression models in a carbon cycle problem. The criterion achieves the state-of-the-art detection rates in all cases, and it is generally robust to noise sources and distortions. The presented approach confirms the validity in observational bivariate problems in the earth sciences. [ABSTRACT FROM AUTHOR]

In this paper, the application of optimal state estimation and optimal state feedback algorithms for real-time active magnetic bearing control is treated. A linear quadratic Gaussian controller, consisting of an extended Kalman filter and an optimal state feedback regulator, is implemented. It is shown that this controller yields improved rotor positioning accuracy, better system dynamics, higher bearing stiffness, and reduced control energy effort compared to the conventionally used proportional–integral–differential control approaches. In addition, a method for compensating unbalance-caused forces and vibrations of the magnetically levitated rotor is presented which is based on the estimation of unknown disturbance forces. All results achieved in this paper are verified by means of measurements. [ABSTRACT FROM PUBLISHER]

In Part I of this two-part paper a new method of predicting the total lightning flash rate in thunderstorms was introduced. In this paper, the implementation of this method into the convection-permitting Consortium for Small Scale Modeling (COSMO) model is presented. The new approach is based on a simple theoretical model that consists of a dipole charge structure, which is maintained by a generator current and discharged by lightning and, to a small extent, by a leakage current. This approach yields a set of four predictor variables, which are not amenable to direct observations and consequently need to be parameterized (Part I). Using an algorithm that identifies thunderstorm cells and their properties, this approach is applied to determine the flash frequency of every thunderstorm cell in the model domain. With this information, the number of flashes that are accumulated by each cell and during the interval between the activation of the lightning scheme can be calculated. These flashes are then randomly distributed in time and beneath each cell. The output contains the longitude, the latitude, and the time of occurrence of each simulated discharge. Simulations of real-world scenarios are presented, which are compared to measurements with the lightning detection network, LINET. These comparisons are done on the cloud scale as well as in a mesoscale region composing southern Germany (two cases each). The flash rates of individual cumulonimbus clouds at the extreme ends of the intensity spectrum are realistically simulated. The simulated overall lightning activity over southern Germany is dominated by spatiotemporal displacements of the modeled convective clouds, although the scheme generally reproduces realistic patterns such as coherent lightning swaths. [ABSTRACT FROM AUTHOR]

Many methods have been presented for the testing and diagnosis of analog circuits. Each of these methods has its advantages and disadvantages. In this paper we propose a novel sensitivity analysis algorithm for the classical parameter identification method and a continuous fault model for the modern test generation algorithm, and we compare the characteristics of these methods. At present, parameter identification based on the component connection model (CCM) cannot ensure that the diagnostic equation is optimal. The sensitivity analysis algorithm proposed in this paper can choose the optimal set of trees to construct an optimal CCM diagnostic equation, and enhance the diagnostic precision. But nowadays increasing attention is being paid to test generation algorithms. Most test generation algorithms use a single value in the fault model. But the single values cannot substitute for the actual faults that may occur, because the possible faulty values vary over a continuous range. To solve this problem, this paper presents a continuous fault model for the test generation algorithm which has a continuous range of parameters. The test generation algorithm with this model can improve the treatment of the tolerance problem, including the tolerances of both normal and faulty parameters, and enhance the fault coverage rate. The two methods can be applied in different situations. [ABSTRACT FROM AUTHOR]

In this paper, a new method is proposed to identify and classify the data obtained by the sensor network (SN) for the detection of mines. This method is used for the identification of antitank and antipersonnel mines and classification of buried objects within a target region. In this paper, a mobile SN is used to detect mines and some other objects buried and creating magnetic anomaly in and around the region where they are found, with the behavior of the individual sensors swarming onto the area under which a mine or any other object is buried. The process of collecting data by the SN and modeling it mathematically are explained in detail. The SN is modeled as a fictitious two-dimensional spatial impulse sampler. This paper is motivated by clearing the territories of mine fields to open them to agriculture. It is very important because, currently, in some countries, very fertile territories around the borders are covered by buried mines. The approach is basically based on magnetic anomaly measurements, which directly tackles the subregions corresponding to buried objects whether they represent objects that are separately located or occluded by other objects. It is based on a new developed method that is called “the back-most object detection and identification algorithm.” This method is fully automatic, and there is no human intervention throughout the process. In this paper, classification of objects is based on their well-known shapes and dimensions. Therefore, there is no need for sophisticated learning algorithms to achieve classification. The experimental results are given both for detection and identification of a single mine and classification of a number of mines and any other objects that have a potential of giving false alarms in a target region. [ABSTRACT FROM AUTHOR]

High-utility itemset mining (HUIM) is an extension of traditional association-rule mining that can find profitable itemsets for decision-making. It faces, however, a limitation since the utility of an itemset increases along with the size of it. High-average utility itemset mining (HAUIM) provides a fair measure to find the average-utility of an itemset, which is more reasonable to design the sales strategies for making the efficient decision. Traditional algorithms of HAUIM mostly focus on mining high average-utility itemsets (HAUIs) from the static database. When the database size is changed, for example, transaction insertion/deletion, the discovered information is required to be updated, thus the updated database is necessary to be re-scanned for identifying the set of HAUIs in the batch manner. In this paper, we present an updating algorithm called FUP-HAUIMD to maintain the discovered HAUIs with transaction deletion. When some transactions in the database are deleted, the designed FUP-HAUIMD algorithm can easily update the discovered HAUIs without scanning the database all the time. The designed FUP-HAUIMD algorithm divides the itemsets into four cases based on the modified fast updated (MFUP) concept. The average-utility (AU)-list structure is further utilized to keep the necessary ramification for later mining progress. Experiments are then conducted to compare the designed FUP-HAUIMD algorithm with the state-of-the-art baseline algorithm running on the batch mode, and the developed approach shows better performance in terms of runtime, number of examined patterns, and scalability. [ABSTRACT FROM AUTHOR]

This paper presents a theoretical investigation of the general problem of emergent irreducible information in networked populations of computable systems. In particular, we narrow our scope to study this problem in algorithmic networks composed of randomly generated Turing machines that follow a susceptible-infected-susceptible contagion model of imitation of the fittest neighbor. We show that there is a lower bound for the stationary prevalence (i.e., the average density of infected nodes by the fittest nodes) that triggers expected (local) emergent openendedness, that is, that triggers an unlimited increase of the expected local emergent algorithmic complexity (or information) of a node as the population size grows. In addition, we show that static networks with a power-law degree distribution following the Barabási-Albert model satisfy this lower bound and thus display expected (local) emergent open-endedness. [ABSTRACT FROM AUTHOR]

This paper is devoted to mathematical modeling in photoacoustic imaging of small absorbers. We propose a new method for reconstructing small absorbing regions inside a bounded domain from boundary measurements of the induced acoustic signal. We also show the focusing property of the back-propagated acoustic signal. Indeed, we provide two different methods for locating a targeted optical absorber from boundary measurements of the induced acoustic signal. The first method consists of a Multiple Signal Classification (MUSIC)-type algorithm and the second one uses a multifrequency approach. We also show results of computational experiments to demonstrate efficiency of the algorithms. [ABSTRACT FROM AUTHOR]

The article examines model-based research in managerial accounting. The authors express the belief that accounting research has improved in quality and proliferation and that this area of accounting research should become more active in terms of setting theories. Model-based managerial accounting research is defined as an "explicit model" that must be scrutinized "deductively." The article concludes with a discussion of whether or not such literature is useful in accounting research and that the point of this investigation is to develop accounting methods and to simulate accounting scenarios to understand the situation better.

This paper investigates the network resource allocation in multiuser downlink wireless systems where the base station and the mobile stations are equipped with multiple antennas to provide fair and efficient transmission services to the mobile users. We focus on packet scheduling, given that it has a significant impact on the overall performance of a multiple-input-multiple-output (MIMO) system. Most previous schedulers designed at the packet level do not take into account the traffic characteristics (different packet lengths and the arrival process parameters); consequently, they fall short of simultaneously providing fairness and a low average packet transmission delay. We are making use of a flexible packet transmission algorithm at the medium access control (MAC) layer to develop and propose a novel scheduler, which is referred to as MIMO packet-based proportional fairness (MP-PF). The new scheduler is designed with the goal of providing high performance in terms of a low average packet transmission delay and time and service fairness among the users based on the concept of proportional fairness. The scheduler also conserves work and takes into consideration the packet length, the user queue length, the user transmission rate (related to its channel quality), and the service guarantees for heterogeneous users. The well-known ideal service fair scheduler called max-min can also significantly be improved using our framework by taking into consideration the traffic characteristics. We also provide an analysis for the fairness of the new scheduler in terms of time and service allocation, which is the key contribution of this paper. Simulations that consider the traffic characteristics and the mobility of users show the relatively low average packet transmission delay and demonstrate the time and service fairness capabilities of MP-PF, compared with other well-known MIMO schedulers. [ABSTRACT FROM AUTHOR]

This paper solves the variation-aware decoupling capacitance (decap) budgeting problem. Unlike previous works which only consider worst case design, for the first time, we consider the input of both process variation and operation variation for decap budgeting. A novel stochastic current model is proposed that efficiently and accurately captures temporal correlation between clock cycles, logic-induced correlation between ports, and current variation due to process variation with spatial correlation. An iterative alternative programming algorithm that is applicable to a variety of current models is then developed. Compared with the baseline model which assumes maximum current peaks at all ports, the model considering temporal correlation reduces noise by up to 5×, and the model considering both temporal and logic-induced correlations reduces noise by up to 17×. Compared with using deterministic process parameters, considering process variation (in particular Leff variation) reduces the mean noise by up to 4× and 3σ noise by up to 13× when both applying the current model with temporal and logic-induced correlations. Note that stochastic optimization has been used mainly for process variation in the literature, but this paper convincingly demonstrate that stochastic optimization considering operation variation is effective to reduce overdesign introduced by worst case design for power integrity. Such stochastic optimization has a wide scope of applications to design problems. To the best of our knowledge, this is the first in-depth study on decap insertion for power network design considering current correlations including process variation. [ABSTRACT FROM AUTHOR]

This paper presents a new low-power test scheme integrated with the embedded deterministic test environment. The key contribution of this paper is a flexible test cube encoding scheme, which, in conjunction with a continuous flow decompressor, allows one to significantly reduce toggling rates when test patterns are fed into scan chains. The proposed solution requires neither additional design for testability logic nor modifications to the circuit under test. Experimental results obtained for industrial designs indicate that using this nonintrusive technique reduces switching activity to such extent that the resultant scan-in power consumption is similar to that of the functional mode, thus alleviating problems that are related to average and peak power dissipation, overheating, and risk of reliability degradation. Our approach seamlessly integrates with test logic synthesis flow, and it does not compromise compression ratios. Moreover, it fits well into various design paradigms, including modular design flow where modules come with individual decompressors and compactors. [ABSTRACT FROM AUTHOR]

This paper departs from the popular usage of the Backus-Gilbert inversion (BGI) method as a tool for inversion of antenna temperature measurements in microwave radiometry. The BGI method is applied in this paper to enhance the information content of an existing set of oversampled brightness-temperature (TB) data. The purpose is to isolate the inversion process from its resolution enhancement counterpart. The advantage gained is that the resolution enhancement can be performed in a simplified way and in a different level of processing that starts with the scan-mode TB data product and simply requires with it the knowledge of the antenna gain pattern and the sensor's scan geometry. The technique is demonstrated with the 19.35-GHz Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) channel, which provides oversampled TB data. The radiometric resemblance of this channel with that of the 37 GHz and geocollocation of their TB footprints facilitate validation of the enhancement of features. The significance of oversampling the low-frequency (LF) radiometer channels is underscored in the process, which gives the authors the confidence to propose oversampling of the LF data for the forthcoming sensor Microwave Analysis and Detection of Rain and Atmospheric Structures (MADRAS) onboard the Megha-Tropiques mission, which is a joint ISRO-CNES collaboration (due for launch in 2009). [ABSTRACT FROM AUTHOR]

In this paper, the duality in differential form is developed between a 3D primal surface and its dual manifold formed by the surface's tangent planes, that is, each tangent plane of the primal surface is represented as a four-dimensional vector that constitutes a point on the dual manifold. The iterated dual theorem shows that each tangent plane of the dual manifold corresponds to a point on the original 3D surface, that is, the "dual" of the "dual" goes back to the "primal." This theorem can be directly used to reconstruct 3D surface from image edges by estimating the dual manifold from these edges. In this paper, we further develop the work in our original conference papers resulting in the robust differential dual operator. We argue that the operator makes good use of the information available in the image data by using both points of intensity discontinuity and their edge directions; we provide a simple physical interpretation of what the abstract algorithm is actually estimating and why it makes sense in terms of estimation accuracy; our algorithm operates on all edges in the images, including silhouette edges, self occlusion edges, and texture edges, without distinguishing their types (thus, resulting in improved accuracy and handling locally concave surface estimation if texture edges are present); the algorithm automatically handles various degeneracies; and the algorithm incorporates new methodologies for implementing the required operations such as appropriately relating edges in pairs of images, evaluating and using the algorithm's sensitivity to noise to determine the accuracy of an estimated 3D point. Experiments with both synthetic and real images demonstrate that the operator is accurate, robust to degeneracies and noise, and general for reconstructing free-form objects from occluding edges and texture edges detected in calibrated images or video sequences. [ABSTRACT FROM AUTHOR]

Continual scaling of transistors and interconnects has exacerbated the power and thermal management problems in the design of ultralarge-scale integrated (ULSI) circuits. This pa- per presents an efficient thermal-analysis method of O(N lg N) complexity, where N is the number of blocks that discretize the heat-source or temperature-observation regions. The method is named LOTAGre and formulated using the Green's function for heat conduction through multiple-layer materials, which account for the structure of ULSI chips and the accompanying heat sinks and mounting accessories. In addition to analyzing the thermal effects of the distributive heat sources, LOTAGre also considers the ambient temperature effects that are generally excluded in conventional Green's function-based thermal-analysis tools in order to avoid the concomitant analytical complexity. By employing the well-known eigen-expansion technique and classical transmission-line theory, fully analytical and explicit formulas are derived in thus paper for the multilayer Green's function with the inclusion of the s-domain version, the homogeneous and inhomogeneous solutions to the heat-conduction equation. Then, the discrete cosine transform and its inversion are employed to accelerate the numerical computation of the homogeneous and inhomogeneous solutions. This paper includes extensive experimental results to demonstrate that LOTAGre can be as accurate as FLUENT, a sophisticated computational fluid dynamics tool, while speeding up the simulation run time by two to three orders of magnitude in comparison to FLUENT as well as conventional Green's function-based thermal-analysis methods. This paper also discusses the limitations of using the traditional single-layer thermal model in thermal analysis for approximating a multilayer chip structure. [ABSTRACT FROM AUTHOR]

This paper proposes a mixed decision strategy for freight and passenger transportation in metro systems during off-peak hours (MTS-OPH). The definition of the mixed decision strategy is proposed, and fixed and flexible loading modes are considered for different passenger flow volumes. A mathematical model of the MTS-OPH is proposed and solved using an improved variable neighborhood search algorithm. Case studies demonstrate the performance and applicability of the proposed model and algorithm, and the MTS-OPH is discussed for different delivery distances, passenger flows, and metro network types. The proposed strategy is suitable for long-distance delivery, and the proposed model framework can be applied to different types of metro networks with different levels of complexity. The mixed decision strategy provides a decision support tool for metro and freight companies and can propose corresponding solutions according to different passenger flows. [ABSTRACT FROM AUTHOR]

Many classes of engineering problems focus on the process of calibrating mathematical models using observed data. The enormous progress of scientific computation and data-mining techniques has allowed the search for accurate mathematical models from experimental data using algorithms. Among them, the evolutionary polynomial regression (EPR) is an artificial intelligence (AI) technique that merges genetic algorithms (GAs) and regression techniques such as ordinary least square (OLS). This paper presents a robust and well-conditioned EPR technique to remove potential outliers and leverage points included in any biased data set. This hybrid approach combines bisquare, Huber, and Cauchy robust multivariate techniques with GAs and the Akaike weight-based method to assess the optimal polynomial model while limiting the impact of the data bias. The robust techniques will define the parameters, the GAs will determine the exponents, and the Akaike weight-based method will evaluate the relative importance of each observed variable of the proposed model. As a case study, a shear strength data set of RC beams without stirrups is used to compare the standard EPR algorithm with the new proposed hybrid methodology. Furthermore, the optimal robust model is compared with different benchmark formulations to highlight its accuracy and consistency. The proposed hybrid technique can be adopted as a mathematical tool for many engineering problems, providing an unbiased prediction of the observed variable. Furthermore, the shear strength equation that provides the best compromise between accuracy and complexity allows its potential use in many engineering practices and building codes. [ABSTRACT FROM AUTHOR]

The dynamics of the particle size distribution (PSD) of polydispersed fuel spray is important in the evaluation of the combustion process. A better understanding of the dynamics can provide a tool for selecting a PSD that will more effectively meet the needs of the system. In this paper, we present an efficient and elegant method for evaluating the dynamics of the PSD. New insights into the behaviour of polydispersed fuel spray were obtained. A simplified theoretical model was applied to the experimental data and a known approximation of the polydispersed fuel spray. This model can be applied to any distribution, not necessarily an experimental distribution or approximation, and involves a time-dependent function of the PSD. Such simplified models are particularly helpful in qualitatively understanding the effects of various sub-processes. Our main results show that during the self-ignition process, the radii of the droplets decreased as expected, and the number of smaller droplets increased in inverse proportion to the radius. An important novel result (visualised by graphs) demonstrates that the mean radius of the droplets initially increases for a relatively short period of time, which is followed by the expected decrease. Our modified algorithm is superior to the well-known 'parcel' approach because it is much more compact; it permits analytical study because the right-hand sides of the mathematical model are smooth, and thus eliminates the need for a numerical algorithm to transition from one parcel to another. Moreover, the method can provide droplet radii resolution dynamics because it can use step functions that accurately describe the evolution of the radii of the droplets. The method explained herein can be applied to any approximation of the PSD, and involves a comparatively negligible computation time. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop a fixed-wing aircraft that has the abilities of both vertical take-off (VTOL) and a fixed-wing aircraft. To achieve this goal, a prototype of a fixed-wing gyroplane with two propellers is developed and a rotor can maneuver like a drone and also has the ability of vertical take-off and landing similar to a helicopter. This study provides guidance, navigation, and control algorithm for the gyrocopter. Firstly, this study describes the dynamics of the fixed-wing aircraft and its control inputs, i.e., throttle, blade pitch, and thrust vectors. Secondly, the inflow velocity, the forces acting on the rotor blade, and the factors affecting the rotor speed are analyzed. Afterward, the mathematical models of the rotor, dual engines, wings, and vertical and horizontal tails are presented. Later, the flight control strategy using a global processing system (GPS) module is designed. The parameters that are examined are attitude, speed, altitude, turn, and take-off control. Lastly, hardware in the loop (HWIL) based simulations proves the effectiveness and robustness of the navigation guidance and control mechanism. The simulations confirm that the proposed novel mechanism is robust and satisfies mission requirements. The gyrocopter remains stable during the whole flight and maneuvers the designated path efficiently. [ABSTRACT FROM AUTHOR]

In this paper, we present a new structure of the n-dimensional trigonometric cubic B-spline collocation algorithm, which we show in three different formats: one-, two-, and three-dimensional. These constructs are critical for solving mathematical models in different fields. We illustrate the efficiency and accuracy of the proposed method by its application to a few two- and three-dimensional test problems. We use other numerical methods available in the literature to make comparisons. [ABSTRACT FROM AUTHOR]

The paper aims to present an integrated approach to solve the decision-making problem under the probabilistic hesitant fuzzy information (PHFI) features, which is an extension of the hesitant fuzzy set. The considered PHFI not only allows multiple opinions, but also associates occurrence probability to each opinion, which increases the reliability of the information. Motivated by these features of PHFI, an approach is presented to solve the decision problem with partial known information about the attribute and expert weights. In addition, an algorithm for finding some missing values in the preference information is presented and stated their properties. Afterward, the Hamy mean operator has been used to aggregate the different collective information into a single one. Also, we presented a COPRAS method to the PHFI for ranking the given alternatives. The presented algorithm has been demonstrated through a case study of cloud vendor selection and its validity has been revealed by comparing the approach results with the several existing algorithm results. [ABSTRACT FROM AUTHOR]

Diagnosis by comparison is a realistic approach to detect faults of multiprocessor systems. This paper considers a pessimistic diagnostic strategy for hypercube-like multiprocessor systems under the comparison model. The pessimistic strategy is a diagnostic process whereby all faulty nodes can be correctly identified and at most one fault-free node may be misjudged as a faulty node. We propose a pessimistic diagnosis algorithm based on the largest component in the faulty system. For a system with N = 2n nodes and n ≥ 5 , when the number of faulty nodes is bounded by 2n − 2 , the algorithm can correctly identify all nodes except at most one node left undiagnosed. The time complexity of the algorithm is O(Nlog2N). [ABSTRACT FROM PUBLISHER]

In the past decade the humanitarian supply chain (HSC) has attracted the attention of researchers due to the increasing frequency of disasters. The uncertainty in time, location, and severity of disaster during predisaster phase and poor conditions of available infrastructure during postdisaster phase make HSC operations difficult to handle. In order to overcome the difficulties during these phases, we need to assure that HSC operations are designed in an efficient manner to minimize human and economic losses. In the recent times, several mathematical optimization techniques and algorithms have been developed to increase the efficiency of HSC operations. These techniques and algorithms developed for the field of HSC motivate the need of a systematic literature review. Owing to the importance of mathematical modelling techniques, this paper presents the review of the mathematical contributions made in the last decade in the field of HSC. A systematic literature review methodology is used for this paper due to its transparent procedure. There are two objectives of this study: the first one is to conduct an up-to-date survey of mathematical models developed in HSC area and the second one is to highlight the potential research areas which require attention of the researchers. [ABSTRACT FROM AUTHOR]

The Ground-based Radio Navigation System (GRNS) is an alternative/backup navigation system based on time synchronized pseudolites. It has been studied for some years due to the potential vulnerability issue of satellite navigation systems (e.g., GPS or Galileo). In the framework of our study, a periodic pulsed sequence was used instead of the randomized pulse sequence recommended as the RTCM (radio technical commission for maritime services) SC (special committee)-104 pseudolite signal, as a randomized pulse sequence with a long dwell time is not suitable for applications requiring high dynamics. This paper introduces a mathematical model of the post-correlation output in a navigation sensor, showing that the aliasing caused by the additional frequency term of a periodic pulsed signal leads to a false lock (i.e., Doppler frequency bias) during the signal acquisition process or in the carrier tracking loop of the navigation sensor. We suggest algorithms to resolve the frequency false lock issue in this paper, relying on the use of a multi-correlator. A flight test with an unmanned helicopter was conducted to verify the implemented navigation sensor. The results of this analysis show that there were no false locks during the flight test and that outliers stem from bad dilution of precision (DOP) or fluctuations in the received signal quality. [ABSTRACT FROM AUTHOR]

Background: Prognostic studies of time-to-event data, where researchers aim to develop or validate multivariable prognostic models in order to predict survival, are commonly seen in the medical literature; however, most are performed retrospectively and few consider sample size prior to analysis. Events per variable rules are sometimes cited, but these are based on bias and coverage of confidence intervals for model terms, which are not of primary interest when developing a model to predict outcome. In this paper we aim to develop sample size recommendations for multivariable models of time-to-event data, based on their prognostic ability.Methods: We derive formulae for determining the sample size required for multivariable prognostic models in time-to-event data, based on a measure of discrimination, D, developed by Royston and Sauerbrei. These formulae fall into two categories: either based on the significance of the value of D in a new study compared to a previous estimate, or based on the precision of the estimate of D in a new study in terms of confidence interval width. Using simulation we show that they give the desired power and type I error and are not affected by random censoring. Additionally, we conduct a literature review to collate published values of D in different disease areas.Results: We illustrate our methods using parameters from a published prognostic study in liver cancer. The resulting sample sizes can be large, and we suggest controlling study size by expressing the desired accuracy in the new study as a relative value as well as an absolute value. To improve usability we use the values of D obtained from the literature review to develop an equation to approximately convert the commonly reported Harrell's c-index to D. A flow chart is provided to aid decision making when using these methods.Conclusion: We have developed a suite of sample size calculations based on the prognostic ability of a survival model, rather than the magnitude or significance of model coefficients. We have taken care to develop the practical utility of the calculations and give recommendations for their use in contemporary clinical research. [ABSTRACT FROM AUTHOR]

Nowadays, in production environments where the production system is parallel machines, the reliability of the machines is important and the uncertainty of scheduling parameters is common. The focus of this paper is on the unrelated parallel machine scheduling problem using a fuzzy approach with machines maintenance activities and process constraints is of concern. An important application of this problem is in the production of products that the due dates are defined as a time window and the best due date is close to the middle of the time window and the jobs processing times depend on other factors such as operator and their value is not specified and are announced as interval under uncertainty. To begin the study, a fuzzy mathematical model is introduced in which changing between a fuzzy approach and a deterministic model is described. Then, Due to the NP-hard nature of the problem, a fuzzy-based genetic algorithm has been developed to address large instances. In this algorithm, a greedy decoding approach according to fuzzy parameters is developed. Numerical experiments are used assess the efficacy of the developed algorithm. It is concluded that the proposed algorithm shows great performance in large instances and is superior to the proposed mathematical model in small instances too. [ABSTRACT FROM AUTHOR]

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Network function virtualization (NFV) technology can realize on-demand distribution of network resources and improve network flexibility. It has become one of the key technologies for next-generation communications. Virtual network function service chain (VNF-SC) deployment is an important problem faced by network function virtualization technology. In this paper, the problem, VNF deployment for VNF-SC, is investigated. First, a two-objective mathematical model, which maximizes balancing and reliability of SFC, is established. In this model, VNFs are divided into two classes, i.e., part of required VNFs in each VNF-SC is dependent, others are independent. Second, harmony search-based MOEA/D (HS-MOEA/D) is proposed to solve the model effectively. In HS-MOEA/D, Chebyshev decomposition mechanism is used to transform multiobjective optimization problem into a series of single-objective optimization subproblems. A new evolutionary strategy is deeply studied in order to propose a new harmony search (HS) algorithm. Finally, to show high performance of the proposed algorithm, a large number of experiments are conducted. The simulation results show that the proposed algorithm enhances the reliability of SFC and reduces the end-to-end delay. [ABSTRACT FROM AUTHOR]

Energy-saving production is one of the issues that must be paid attention to by today's manufacturing enterprises. Aiming at the problem of integrated process planning and scheduling (IPPS) in the manufacturing process, considering Time-of-Use (TOU) and tiered electricity price, this paper systematically studies the energy-saving scheduling problem in order to reduce the power consumption in processing and production. To establish the multiobjective optimization mathematical model of the problem, the load balancing problem of the equipment is considered, the minimization of the power consumption and the maximum load of the equipment are taken as the optimization objectives. Then, considering the constraints of resource and multiprocess, the switching strategy of the equipment in idle time is introduced, including shutdown and restart operations. In order to solve the model easily, a multiobjective firefly algorithm (MOFA) based on five-layer coding is designed, and the elite strategy is introduced to protect the excellent firefly individuals in the iterative population. Finally, through a specific example, the Pareto solution set is obtained, which provides a reference scheme for decision-makers, and verifies the correctness of the model and the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the problem of temperature distribution for convective straight fins with constant and temperature-dependent thermal conductivity is solved by using artificial neural networks trained by the biogeography-based heterogeneous cuckoo search (BHCS) algorithm. We have solved the integer and noninteger order energy balance equation in order to analyse the temperature distribution in convective straight fins. We have compared our results with homotopy perturbation method (HPM), variational iteration method (VIM), and homotopy perturbation Sumudu transform method (HPSTM). The results show that the ANN–BHCS algorithm gives better results than other analytical techniques. We have further checked the efficiency of the ANN–BHCS algorithm by using the performance metrics MAD, TIC, and ENSE. We have calculated the values of MAD, TIC, and ENSE for case 1 of the problem, and histograms of these metrics show the efficiency of our algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we study the quadratic assignment problem with a rank-one cost matrix (QAP-R1). Four integer-programming formulations are introduced of which three are assumed to have partial integer data. Unlike the standard quadratic assignment problem, some of our formulations can solve reasonably large instances of QAP-R1 with impressive running times and are faster than some metaheuristics. Pairwise relative strength of the LP relaxations of these formulations are also analyzed from theoretical and experimental points of view. Finally, we present a new metaheuristic algorithm to solve QAP-R1 along with its computational analysis. Our study offers the first systematic experimental analysis of integer-programming models and heuristics for QAP-R1. The benchmark instances with various characteristics generated for our study are made available to the public for future research work. Some new polynomially solvable special cases are also introduced. Summary of Contribution: This paper aims to advance our knowledge and ability in solving an important special case of the quadratic assignment problem. It shows how to exploit inherent properties of an optimization problem to achieve computational advantages, a strategy that was followed by researchers in model building and algorithm developments for decades. Our computational results attest to this time-tested general philosophy. The paper presents the first systematic computational study of the rank one quadratic assignment problem, along with new mathematical programming models and complexity analysis. We believe the theoretical and computational results of this paper will inspire further research on the topic and will be of significant value to practitioners using rank one quadratic assignment models. [ABSTRACT FROM AUTHOR]

Slicing is an important step for all layer-based additive manufacturing (AM) processes. This paper proposes an improved and robust slicing algorithm with efficient contour construction to reduce the slicing time and be able to slice a complex STereoLithography (STL) model with millions of triangles (resulting from high-accuracy STL files). Also, another important feature of the proposed method is it can identify outer and inner contours automatically. Test results to demonstrate the improvements in execution time and comparisons with results from published papers have been given to illustrate the algorithm efficiency. The robustness of this slicing algorithm is demonstrated by several complex models with large numbers of nested contours on each slice. [ABSTRACT FROM AUTHOR]

In this paper, an integral feedback and feed-forward (IFF) optimal controller with hard terminal constraints for missile formation reconfiguration is designed. The controller has quadric optimal performance for expected terminal errors, output, and control quantity. From the viewpoint of the kinematics relationship of a formation in the relative coordinate frame, the authors establish a precisely linearized relative motion model by transforming the control variables. This relative motion model can intuitively manifest the relationship of the relative motion in three directions in the relative coordinate frame. In order to solve the designed IFF optimal controller, detailed deductions for deriving the related Lagrange parameters are presented. A precise integration algorithm was adopted instead of using a traditional backward integration algorithm to calculate more precise solutions for the relevant parameters in the IFF optimal controller. A collision avoidance system with four spherical domains was proposed, and a modifying principle to avoid collision during formation reconfiguration was presented. Simulation results demonstrate that the presented IFF optimal controller is capable of implementing missile formation reconfiguration rapidly, stably, and accurately. It can additionally restrain invariant or slowly varying perturbations induced by the velocity of a leader missile. Furthermore, the collision avoidance system developed in this paper can enable missiles to avoid collision during formation reconfiguration. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive study of the computation of square roots over finite extension fields. We propose two novel algorithms for computing square roots over even field extensions of the form \BBF{q^2}, with q = p^n, p an odd prime and n \geq 1. Both algorithms have an associate computational cost roughly equivalent to one exponentiation in \BBF{q^2}. The first algorithm is devoted to the case when q \equiv 1\, mod\, 4, whereas the second one handles the case when q \equiv 3\, mod\,4. Numerical comparisons show that the two algorithms presented in this paper are competitive and in some cases more efficient than the square root methods previously known. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid phased locked loop (HPLL) algorithm for high power master-slave configured (MSC) centralized inverters for large photovoltaic power plants. Proposed PLL technique is a combination of double second order general integral (DSOGI) PLL and enhanced PLL (EPLL) with a zero crossing detector, which enables faster synchronization of slave inverters and enables early export of power into the grid. ${{3}}{\hbox{-}}\phi $ PLL structures such as synchronous reference frame (SRF), double decoupled SRF (DDSRF), EPLL, DSOGI, and DSOGI with frequency locked loop (FLL) are discussed, and a comparative analysis with the proposed PLL technique is presented. The proposed PLL technique is superior as compared with existing PLL techniques in master-slave configurations where it can provide faster synchronization, dynamic interleaving of the hardware timers generating carrier waves for master-slave converters, and low voltage ride through (LVRT) capability. The proposed PLL technique along with the existing techniques is simulated and compared in MATLAB/SIMULINK and digitally implemented in TMS320F2812 based discrete signal controller. The performance of the proposed HPLL technique is experimentally validated with unbalanced and distorted grid signals. Experimental results of proposed HPLL show faster grid synchronization in MSC-based grid-connected inverter, LVRT capability, and dynamic interleaving of MSC inverters. [ABSTRACT FROM AUTHOR]

In business replenishment, discount offers of multi-item may either provide different discount schedules with a single discount type, or provide schedules with multiple discount types. The paper investigates the joint effects of multiple discount schemes on the decisions of multi-item joint replenishment. In this paper, a joint replenishment problem (JRP) model, considering three discount (all-unit discount, incremental discount, total volume discount) offers simultaneously, is constructed to determine the basic cycle time and joint replenishment frequencies of multi-item. To solve the proposed problem, a heuristic algorithm is proposed to find the optimal solutions and the corresponding total cost of the JRP model. Numerical experiment is performed to test the algorithm and the computational results of JRPs under different discount combinations show different significance in the replenishment cost reduction. [ABSTRACT FROM AUTHOR]

The normal studies on air traffic departure scheduling problem (DSP) mainly deal with an independent airport in which the departure traffic is not affected by surrounded airports, which, however, is not a consistent case. In reality, there still exist cases where several commercial airports are closely located and one of them possesses a higher priority. During the peak hours, the departure activities of the lower-priority airports are usually required to give way to those of higher-priority airport. These giving-way requirements can inflict a set of changes on the modeling of departure scheduling problem with respect to the lower-priority airports. To the best of our knowledge, studies on DSP under this condition are scarce. Accordingly, this paper develops a bi-objective integer programming model to address the flight departure scheduling of the partly-restricted (e.g., lower-priority) one among several adjacent airports. An adapted tabu search algorithm is designed to solve the current problem. It is demonstrated from the case study of Tianjin Binhai International Airport in China that the proposed method can obviously improve the operation efficiency, while still realizing superior equity and regularity among restricted flows. [ABSTRACT FROM AUTHOR]