Low Convergence Rate Research Articles

Tectonic evolution of the southwestern margin of Pangea and its global implications: Evidence from the mid Permian–Triassic magmatism along the Chilean-Argentine border

The tectonic evolution of the southwestern margin of Pangea supercontinent is represented by the extensive late Paleozoic–Triassic magmatism along the southwestern margin of South America, including the Chilean Frontal Andes batholiths as part of the Choiyoi province. Several models have proposed cessation of subduction as the reason behind the vast amounts of felsic magmatism and apparent lack of typical arc magmas. Here, new U-Pb in zircon ages, and geochemical and isotope analyses (Rb-Sr, Sm-Nd, Re-Os) indicate that mid Permian–Triassic granitic magmatism originated in a subduction-related extensional setting (slab rollback). Subduction and anatexis of lower continental crust were the main magma-generation mechanisms, the latter caused by asthenospheric upwelling, decompression and subsequent accumulation of underplated basalts. A comparison with coeval igneous units along the Chilean-Argentine border allows extension of this model from at least 21° to 40°S. The key elements triggering slab rollback are low subduction plate velocities and convergence rates, which can be attributed to the assembly of Pangea supercontinent (mid Permian–Triassic). Therefore, subduction of the oceanic plate beneath South America has been a continuous process from early Paleozoic times onwards—rather than having a period without subduction before the onset of the Andean cycle as previous models have invoked. New geochronological constraints indicate that the peak of the voluminous crustal-derived magmatism and related explosive volcanism (Choiyoi province) was contemporaneous with the emplacement of the Emeishan and Siberian Traps LIPs, potentially conditioning the Earth system for the environmental collapse and biotic crises related to those LIPs. The observed tectonic changes, magmatism and related environmental implications could potentially be linked to the assembly of Pangea supercontinent.

Convergence and management policy of marine resource utilization efficiency in coastal regions of China

The main purpose of this study was to comprehensively evaluate the marine resource utilization efficiency in China and reveal trends in regional differences. Resource intensity is the amount of resource consumption per unit gross domestic product. It is an important indicator in measuring resource utilization efficiency. We examined marine resource intensity (MRI) convergence for 11 coastal regions in China during 1996–2015 based on resource intensity and economic growth convergence theory. During the study period, MRI in China exhibited a decreasing trend. MRI differed significantly between different coastal provinces, which could be classified into three categories, namely low-, medium-, and high-intensity. Marine economic development, marine industry structure, degree of openness to the outside world, and scientific and technological capability significantly affected the decreasing trend in MRI, whereas the level of material capital investment had a negligible impact. MRI in China followed σ- and absolute β-convergence with a low convergence rate (0.14%). However, no conditional β-convergence trend was observed. Global stochastic convergence could not be applied because a shock will have a continuous impact on MRI in more than 70% of the studied coastal provinces. In summary, policy intervention is essential for reducing the MRI gap between different provinces.

Locally efficient estimation in generalized partially linear model with measurement error in nonlinear function

We investigate the errors in covariates issues in a generalized partially linear model. Different from the usual literature (Ma and Carroll in J Am Stat Assoc 101:1465–1474, 2006), we consider the case where the measurement error occurs to the covariate that enters the model nonparametrically, while the covariates precisely observed enter the model parametrically. To avoid the deconvolution type operations, which can suffer from very low convergence rate, we use the B-splines representation to approximate the nonparametric function and convert the problem into a parametric form for operational purpose. We then use a parametric working model to replace the distribution of the unobservable variable, and devise an estimating equation to estimate both the model parameters and the functional dependence of the response on the latent variable. The estimation procedure is devised under the functional model framework without assuming any distribution structure of the latent variable. We further derive theories on the large sample properties of our estimator. Numerical simulation studies are carried out to evaluate the finite sample performance, and the practical performance of the method is illustrated through a data example.

Differentiation of Continental Subduction Mode: Numerical Modeling

The convergence of the multi-layered continental lithospheres with variable and complex thermal and rheological properties results in various modes of continental collision with distinct deformation behavior of the lithospheric mantle. Using high-resolution thermo-mechanical numerical models, we systematically investigated the effects of crustal rheological strength and the convergence rate on the continental subduction mode. The model results reveal three basic modes of continental subduction, including slab break-off, steep subduction and continental flat-slab subduction. Whether lithospheric mantle of the overriding plate retreats or not during convergence enables the division of the first two modes into two sub-types, which are dominated by the crustal rheological strength. The mode of slab break-off develops under the conditions of low/moderate rheological strength of the continental crust and low convergence rate. In contrast, continental flat-slab subduction favors the strong crust and the high convergence rate. Otherwise, continental steep subduction occurs. The numerical results provide further implications for Geodynamics conditions and physical processes of different modes of continental collision that occur in nature.

Multi-Reservoir System Optimization Based on Hybrid Gravitational Algorithm to Minimize Water-Supply Deficiencies

The growing prevalence of droughts and water scarcity have increased the importance of operating dam and reservoir systems efficiently. Several methods based on algorithms have been developed in recent years in a bid to optimize water release operation policy, in order to overcome or minimize the impact of droughts. However, all of these algorithms suffer from some weaknesses or drawbacks – notably early convergence, a low rate of convergence, or trapping in local optimizations – that limit their effectiveness and efficiency in seeking to determine the global optima for the operation of water systems. Against this background, the present study seeks to introduce and test a Hybrid Algorithm (HA) which integrates the Gravitational Search Algorithm (GSA) with the Particle Swarm Optimization Algorithm (PSOA) with the goal of minimizing irrigation deficiencies in a multi-reservoir system. The proposed algorithm was tested for a specific important multi-reservoir system in Iran: namely the Golestan Dam and Voshmgir Dam system. The results showed that applying the HA could reduce average irrigation deficiencies for the Golestan Dam substantially, to only 2 million cubic meters (MCM), compared to deficiency values for the Genetic Algorithm (GA), PSOA and GSA of 15.1, 6.7 and 5.8 MCM respectively. In addition, the HA performed very efficiently, reducing substantially the computational time needed to achieve the global optimal when compared with the other algorithms tested. Furthermore, the HA showed itself capable of assuring a high volumetric reliability index (VRI) to meet the pattern of water demand downstream from the dams, as well as clearly outperforming the other algorithms on other important indices. In conclusion, the proposed HA seems to offer considerable potential as an optimizer for dam and reservoir operations world-wide.

Heterogeneous strain regime at the west of the Ogasawara Plateau in the Western Pacific Ocean from inversion of earthquake focal mechanisms

The Ogasawara Plateau has a major control on the geology of the region between the Izu-Bonin and Mariana subduction zones. This region is important for understanding active collisional zones. We attempt to illustrate the stress field in the Wadati-Benioff zone of this region through seismic estimations. 49 events (M ≥ 3.5) are chosen for waveform inversion by the ISOLA software to distinguish the stress field. According to available focal mechanism solutions, the heterogeneous stress field parameters are determined for several depth ranges. Based on the epicentral distribution and seismic stress field, the subduction system at the west of the Ogasawara Plateau can be divided into three segments separated by 26.5°N and 24°N. At depths of 0–50 km in the outer-trench slope, the slab is characterized by a varying stress filed. The best-fit stress model with horizontal σ3 and vertical σ1 is related to the plate bending and preexisting faults. For the central segment, the solutions present a compressional stress regime in the outer-trench slope without preexisting faults, induced by the resistance of the buoyant plateau. The solutions for the depth range of 0–50 km in the forearc region, σ1 coincides with the plate convergence and σ3 dips steeply to the west in the northern and central segments, controlled by the friction effect and convergence of the plates. However, this depth range in the southern segment shows an extensional stress range, associated with subduction of older slab segment, relatively low rate of plate convergence and the morphological adjustment of the slab between different segments. At depths of 50–200 km, the inhomogeneous stress field exhibits a complicated extensional stress system with strike-slip focal mechanisms. At depths of 200–400 km, the seismicity presents a pronounced seismic gap and the stress field cannot be determined. The seismic gap may be attributed to the missing or tear of the subducting slab, which is caused by the subduction of the buoyant Ogasawara Plateau. The mechanism solutions are characterized by an inhomogeneous strain regime below 400 km, driven by gravity and strong contortion of the slab and resistance to penetration of the 660-km discontinuity. In contrast, there are little earthquakes below 400 km in the southern region of the Ogasawara Plateau.

Phase-field modelling and computing for a large number of phases

We propose a framework to represent a partition that evolves under mean curvature flows and volume constraints. Its principle follows a phase-field representation for each region of the partition, as well as classical Allen–Cahn equations for its evolution. We focus on the evolution and on the optimization of problems involving high resolution data with many regions in the partition. In this context, standard phase-field approaches require a lot of memory (one image per region) and computation timings increase at least as fast as the number of regions. We propose a more efficient storage strategy with a dedicated multi-image representation that retains only significant phase field values at each discretization point. We show that this strategy alone is unfortunately inefficient with classical phase field models. This is due to non local terms and low convergence rate. We therefore introduce and analyze an improved phase field model that localizes each phase field around its associated region, and which fully benefits of our storage strategy. To demonstrate the efficiency of the new multiphase field framework, we apply it to the famous 3D honeycomb problem and the conjecture of Weaire–Phelan’s tiling.

Research on the Captcha Recognition Method Based On Neural Network

In this paper, the CAPTCHA recognition is studied by using neural network algorithm. Because the traditional BP neural network itself has problems such as uncertain structural parameters, low convergence rate and easy to fall into local minimum, this paper identifies the words in the image according to the pixel value of the verification code. In this paper, the image is divided into individual letters, and then the identified letters are spelled into words. Experimental results show that The accuracy of individual letters was higher, but the accuracy of letters identified from each small image was much lower. The accuracy can be improved by adding dictionaries to the prediction function to find the most matching words.

SCR-Based Tone Reservation Schemes With Fast Convergence for PAPR Reduction in OFDM System

The signal to clipping noise ratio (SCR)-based tone reservation scheme is a promising candidate for peak-to-average ratio (PAPR) reduction of orthogonal frequency division multiplexing signals, but it suffers from low convergence rate caused by the nonoptimal scalar scaling factor. In this letter, we first derive a scaling SCR (S-SCR) scheme, in which the scaling factor is an optimized vector with peak regeneration constraints. To further improve convergence rate and eliminate multiple peaks in each iteration, we then propose the multiple scaling SCR (MS-SCR) scheme with an augmented scaling factor in matrix form. Numerical results demonstrate the superiority of proposed schemes compared with the conventional ones, including better PAPR reduction performance with fewer iterations and comparable bit error ratio performance. Furthermore, the computational complexity reduction ratio of the proposed S-SCR and MS-SCR schemes are increased by 38.52% and 27.36%, respectively.

Dynamics of crustal overthrust versus underthrust in the continental collision zones: Numerical modelling

AbstractTrans‐plate crustal overthrust and underthrust are two unusual crustal architectures in nature, the mechanisms of which remain debated. Here, we construct a series of 2D numerical models to investigate the dynamics of trans‐plate crustal overthrust and underthrust. Model results indicate that the favourable conditions of crustal overthrust include low convergence rate, plastically strong crust, rheologically strong upper crust and hot subducting plate, which meanwhile contribute to the formation of ‘crocodile’ crustal structure. In contrast, a combination of a strong and buoyant subducting plate with a hot overriding plate facilitates crustal underthrust. The development of crustal overthrust shows no correlation with the subduction mode of lithospheric mantle, whereas crustal underthrust normally occurs in an advancing subduction system. Development conditions and crustal structures of the modelled overthrust and underthrust are consistent, on the first order, with the natural Dinarides orogen and the southern Tibetan plateau respectively.

Continental lithospheric-scale subduction versus crustal-scale underthrusting in the collision zone: Numerical modeling

Two distinct styles of continental collision are observed in nature, i.e. lithospheric-scale subduction versus crustal-scale underthrusting. The former refers to a collision scenario where the lower plate subducts below the lithospheric mantle of the overriding plate, whereas the latter is characterized by the underthrusting of the subducting plate beneath the crust of the overriding plate. Systematic numerical models were constructed to study the dynamics of these two different collision modes. The model results indicate that low convergence rate and cold overriding plate contribute to the lithospheric-scale subduction, which incorporates limited shortening of the overriding lithosphere during collision. In contrast, the crustal-scale underthrusting requires relatively high convergence rate and hot/weak overriding plate, in which the thickened lithospheric mantle of the overriding plate is progressively delaminated from the overlying crust and is replaced by the sub-horizontally underthrusted plate. The model results were systematically compared with four ongoing collisional orogens along the Alpine–Himalayan belt, i.e. the central-western Alps, the East Anatolian plateau, the Zagros–Iranian plateau, and the southern Tibetan plateau. It indicates that the lithospheric-scale subduction is widely observed and generally consistent with the natural orogens, for example, the Alps, Anatolia, and Zagros. On the other hand, the crustal-scale underthrusting is reconciled with the India–Asia collision zone, with respect to the geodynamic conditions, crustal-lithospheric structure, as well as the uplift history.

An angular reduced order model for radiative transfer in non grey media

This paper investigates a reduced order model for the angular discretisation of the radiative transfer equation (RTE) when considering non grey participating gases. The key idea is to use a global model for the gas radiative properties and to derive an angular reduced order model, based on the Proper Orthogonal Decomposition (POD) method, for each absorption coefficient class independently. Angular POD basis functions are extracted from high order SN reference solutions. A finite element approach is used to discretised the RTE in space and angle and the POD angular matrices of the reduced system are easily constructed from the SN angular matrices of the reference solutions. The angular POD basis sets are truncated at different levels depending on the absorption coefficient class in order to optimally compute the total radiative power. The method is applied to solve the radiation field associated to an air/H2O mixture flowing in a square differentially heated cavity, with black isothermal walls and diffuse reflecting adiabatic walls. Results show that the POD model is very accurate and efficient for treating the thick classes but it suffers from a low convergence rate for the thin classes. For computing the radiative power, the reduced order model allows to reduce the averaged number of angular basis functions of an order of magnitude and to reduce the CPU time by a factor 2 to 3 to reach a given level of accuracy, compared to a standard SN method.

Robust Novel Affine Projection Sign Subband Adaptive Filter Algorithm

This paper presents a novel affine projection sign subband adaptive filter (NAPSSAF) which could achieve better performance than the conventional APSSAF. The proposed NAPSSAF is obtained by solving the optimal problems regarding the l1-norm of the subband a posteriori error vectors rather than overall a posteriori error vector, which fully uses the subband adaptive filter’s inherent decorrelating property. However, since the NAPSSAF algorithm uses the fixed step size, it has an inherent trade-off between low steady-state error and fast convergence rate. Thus, we also propose a combined step size NAPSSAF (CSS-NAPSSAF) to further improve the performance of the NAPSSAF. Finally, simulations are carried out to exhibit the advantages of the NAPSSAF and CSS-NAPSSAF algorithms. The results of simulations demonstrate that the NAPSSAF is superior to the existing algorithms. Besides, the results of simulations also exhibit the improved performance of the CSS-NAPSSAF compared to NAPSSAF.

Variance and Dimension Reduction Monte Carlo Method for Pricing European Multi-Asset Options with Stochastic Volatilities

Pricing multi-asset options has always been one of the key problems in financial engineering because of their high dimensionality and the low convergence rates of pricing algorithms. This paper studies a method to accelerate Monte Carlo (MC) simulations for pricing multi-asset options with stochastic volatilities. First, a conditional Monte Carlo (CMC) pricing formula is constructed to reduce the dimension and variance of the MC simulation. Then, an efficient martingale control variate (CV), based on the martingale representation theorem, is designed by selecting volatility parameters in the approximated option price for further variance reduction. Numerical tests illustrated the sensitivity of the CMC method to correlation coefficients and the effectiveness and robustness of our martingale CV method. The idea in this paper is also applicable for the valuation of other derivatives with stochastic volatility.

PMT: Opposition-Based Learning Technique for Enhancing Meta-Heuristic Performance

Meta-heuristic algorithms have shown promising performance in solving sophisticated real-world optimization problems. Nevertheless, many meta-heuristic algorithms are still suffering from a low convergence rate because of the poor balance between exploration (i.e., roaming new potential search areas) and exploitation (i.e., exploiting the existing neighbors). In some complex problems, the convergence rate can still be poor owing to becoming trapped in local optima. Addressing these issues, this research proposes a new general opposition-based learning (OBL) technique inspired by a natural phenomenon of parallel mirrors systems called the parallel mirrors technique (PMT). Like existing OBL-based approaches, the PMT generates new potential solutions based on the currently selected candidate. Unlike existing OBL-based techniques, the PMT generates more than one candidate in multiple solution-space directions. To evaluate the PMT’s performance and adaptability, the PMT has been applied to four contemporary meta-heuristic algorithms, differential evolution (DE), particle swarm optimization (PSO), simulated annealing (SA), and whale optimization algorithm (WOA), to solve 15 well-known benchmark functions. The experimentally, the PMT shows promising results by accelerating the convergence rate against the original algorithms with the same number of fitness evaluations.

Scheduling-Efficient Framework for Neural Network on Heterogeneous Distributed Systems and Mobile Edge Computing Systems

As the volume of machine learning training data sets and the quantity of model parameters continue to grow, the pattern in which machine learning models are trained alone can no longer accommodate large-scale data environments. However, distributed systems and mobile edge computing systems are unpredictable and have heterogeneous nodes, resulting in interruptions in training or low convergence rate. In addition, existing distributed machine learning frameworks cannot guarantee a good convergence rate and speedup ratio in a variety of operating environments. Considering the above shortcomings, this paper proposes an adaptive scheduling framework for machine learning based on a heterogeneous distributed system and mobile edge computing system for machine learning model optimization. The framework detects and analyzes the dynamic changes of resources in the distributed system and mobile edge computing system through the resource detection system; then, the task scheduling system adaptively modifies the environmental parameters and schedules calculations. Relevant experiments conducted with the public data set show that the robustness and scalability of the framework are significantly better than the traditional distributed machine learning framework under the premise of ensuring high convergence rate.

Intelligent perturb and observe control based on support vector machine for photovoltaic pumping system

In this paper, an intelligent maximum power point tracking control is proposed for a photovoltaic (PV) water pumping system. This strategy combines the least squares support vector machines (LS-SVM) technique with the exponential adaptive perturb and observe (EAP&O) control. The reason for combining these two techniques is to overcome the steady states oscillations, low convergence rate as well as failure problems in standard P&O. The main purpose of the LS-SVM in this work, is to design an accurate off-line MPP model, which gives back the optimal value of duty cycle at present illumination intensity. These former values serve to initialise the proposed EAP&O in online implementation. To validate and to show the effectiveness of the proposed control, both strategies, EAP&O based on LS-SVM and standard P&O, are applied to the PV pumping system, and finally some important simulation results are presented.

Intelligent perturb and observe control based on support vector machine for photovoltaic pumping system

In this paper, an intelligent maximum power point tracking control is proposed for a photovoltaic (PV) water pumping system. This strategy combines the least squares support vector machines (LS-SVM) technique with the exponential adaptive perturb and observe (EAP&O) control. The reason for combining these two techniques is to overcome the steady states oscillations, low convergence rate as well as failure problems in standard P&O. The main purpose of the LS-SVM in this work, is to design an accurate off-line MPP model, which gives back the optimal value of duty cycle at present illumination intensity. These former values serve to initialise the proposed EAP&O in online implementation. To validate and to show the effectiveness of the proposed control, both strategies, EAP&O based on LS-SVM and standard P&O, are applied to the PV pumping system, and finally some important simulation results are presented.

A Novel Improved Bat Algorithm in UAV Path Planning

Path planning algorithm is the key point to UAV path planning scenario. Many traditional path planning methods still suffer from low convergence rate and insufficient robustness. In this paper, three main methods are contributed to solving these problems. First, the improved artificial potential field (APF) method is adopted to accelerate the convergence process of the bat’s position update. Second, the optimal success rate strategy is proposed to improve the adaptive inertia weight of bat algorithm. Third chaos strategy is proposed to avoid falling into a local optimum. Compared with standard APF and chaos strategy in UAV path planning scenarios, the improved algorithm CPFIBA (The improved artificial potential field method combined with chaotic bat algorithm, CPFIBA) significantly increases the success rate of finding suitable planning path and decrease the convergence time. Simulation results show that the proposed algorithm also has great robustness for processing with path planning problems. Meanwhile, it overcomes the shortcomings of the traditional meta-heuristic algorithms, as their convergence process is the potential to fall into a local optimum. From the simulation, we can see also obverse that the proposed CPFIBA provides better performance than BA and DEBA in problems of UAV path planning.

QSSA: Quantum Evolutionary Salp Swarm Algorithm for Mechanical Design

Salp swarm algorithm is a new meta-heuristic algorithm which has excellent advantages for solving the multidimensional optimization problem. In this paper, a hybrid model assisted evolutionary algorithm for solving engineering design optimization problems is proposed and investigated. The purpose of the optimizer is to improve the potential shortcomings of the basic salp swarm optimization, including trapping in local or deceptive optima easily. On the one hand, quantum behavior can increase an individual's searchability, which can promote the overall optimization trend. On the other hand, the elite opposition-based learning strategy is used to enhance the diversity of the population. Besides, mutation mechanism is introduced to prevent the individuals of the population from being in stagnation behavior. The proposed quantum-behaved and wavelet mutation salp swarm algorithm (QSSA) is applied on twenty-three benchmark functions and three basic constrained engineering problems. Experimental results demonstrate that the algorithm has excellent solution quality, and it can overcome the defect of the low convergence rate.