Parallel Splitting Research Articles

Paleolithic Nguom Culture of Northern Vietnam

Purpose. In North Vietnam, the Nguomian culture has been distinguished, which is characterized by the predominance of flake tools in the techno-typological complex. This industry dates back to the second half of the Upper Pleistocene, preceding the previously identified Sonvian and Hoabinhian. The purpose of this study is to determine the technical and typological characteristics of one of the key monuments of the Nguom industry – the Nguom Rockshelter. For this purpose, a technical and typological analysis of the collection of stone products obtained as a result of excavations in 1981–1982 was carried out.Results. In 2023, 4 589 artifacts of the Nguom Rockshelter were processed, of which 2 437 items were medium and small flakes and 1 284 fragments. The primary splitting of archaeological material is dominated by parallel cores (35 specimens), there are variants of unsystematic (12 specimens) and radial (8 specimens) cores, many core-like fragments (121 specimens). The tool set is represented by a large number of side-scrapers made of flakes and pebbles (22 specimens), retouched flakes with ventral undercut (43 specimens), choppers (25 specimens), adze-shaped objects (6 specimens), and fragmented axes (9 specimens), notched tools (5 copies). There are 25 punctures, 9 checks. Notched tools were mentioned earlier, but a group of artifacts (9 specimens) made of medium flakes should be separately distinguished. The number of single side scrapers is 10 pieces. Of interest is the category of rectangular scrapers with retouching on ¾ of the perimeter, numbering six objects. A series of oval scrapers (10 specimens) is close in shape. End and double scrapers have 52 items. The stone industry of the Nguom Rockshelter can be defined as a flake industry with medium and small tools.Conclusion. In the Late Paleolithic in southern China and Vietnam, both pebble and flake industries coexisted. As in Vietnam, flake complexes are sporadic in South China. These sites are characterized by a large number of tools made of small and medium flakes, simple parallel splitting without preliminary preparation, and the predominance of scrapers in the tool set. The manifestation of the Nguom culture can presumably be considered as a result of the mixing of the alien population in the territory of Southern China and Northern Vietnam with the local original culture in the late Pleistocene.

Adaptive and Parallel Split Federated Learning in Vehicular Edge Computing

Adaptive and Parallel Split Federated Learning in Vehicular Edge Computing

A cooperative resilience-oriented planning framework for integrated distribution energy systems and multi-carrier energy microgrids considering energy trading

Integrated distribution systems (IDSs) and multi-carrier energy microgrids (MCEMs) can play a crucial role in enhancing distribution energy systems’ overall efficiency and flexibility. By cascading energy usage and cooperating through energy trading, IDSs and MCEMs can reduce overall system costs and provide more flexibility for system operators. Adding resilience to the planning problem of IDSs can reduce planning costs in the long term, as proactive preparedness is key to coping with high-impact rare (HR) events. Adding resilience to the planning problem of IDSs can reduce the planning costs in the long term since proactive preparedness is a key necessity to cope with high-impact rare (HR) events. This paper proposes a resilience-oriented stochastic tri-level and two-stage cooperative expansion planning of IDSs and MCEMs, considering energy trading between IDSs and MCEMs. The first stage comprises two levels; the first level minimizes the investment and operation costs of IDSs and MCEMs, while the second level desires to maximize the energy exchange profit for MCEMs and thus reduce the overall costs. The second stage includes the third level problem involving two objective functions: resilience cost minimization and resilience index (RI) maximization. The multi-objective problem in the second stage is converted into a single-objective problem using the min–max regret method. The DC and AC configurations for the power distribution system (PDS) and power microgrids (PMGs) are studied to identify the optimal configuration of these networks in the expansion planning problem. A new framework is proposed based on an aggregator-agent splitting solution using the aggregator coupling coordinator unit (ACC) responsible for coordinating IDNs and MCEMs. The studied large-scale complex optimization problem is efficiently solved computationally by introducing a combined adaptive dynamic programming (ADP) and linearized alternating direction method of multipliers with parallel splitting (LADMMPSAP) algorithm. Three cases are studied to demonstrate the effectiveness of the proposed model and method. The results depict that MCEMs help reduce expansion planning costs and improve the system’s resilience. Adding resilience to the expansion planning problem enhances the resilience of the whole system and simultaneously reduces the costs by 2.7%. The expansion planning costs for the AC and DC configuration are close, and the AC is the optimal choice in all case studies. By increasing the planning horizon from 5 to 10 years, DC will be the optimal solution since network reinforcement costs and power losses are significantly lower.

A proximal fully parallel splitting method with a relaxation factor for separable convex programming

In this paper, we propose a proximal fully parallel splitting method with a relaxation factor for solving separable convex minimization model with linear constraints, where the objective function is the sum of m individual functions without coupled variables. With a full Jacobian decomposition, we decompose the subproblem associated with the augmented Lagrangian method into m smaller subproblems and then add a quadratic proximal term to each decomposed subproblem, which makes the resulting ones easier to solve for many applications. In order to accelerate the numerical performance, we attach a positive relaxation factor to update the Lagrange multiplier, which also allows more flexibility in the design of algorithms. Moreover, we refine the step size of the underrelaxation step, which enlarges several existing ones in the literature. We prove that the proposed method is globally convergent, and show the worst-case O(1/k) convergence rate in a nonergodic sense. Finally, the efficiency and robustness of the proposed method are also demonstrated by solving the ℓ1 norm problem, the ℓ1-regularized least squares problem, the exchange problem and the total variation image restoration problem.

Skeletal and dentoalveolar effects of different types of microimplant-assisted rapid palatal expansion.

To evaluate the following null hypothesis: the skeletal and dentoalveolar expansion patterns in the coronal and axial planes are not different with two different types of microimplant-assisted rapid palatal expansion (MARPE) systems. Pretreatment (T0) and post-MARPE (T1) cone-beam computed tomography (CBCT) images of 32 patients (14 males and 18 females; mean age, 19.37) were analyzed. We compared two different MARPE systems. One MARPE system included the maxillary first premolars, maxillary first molars, and four microimplants as anchors (U46 type, n = 16), while the other included only the maxillary first molars and microimplants as anchors (U6 type, n = 16). In the molar region of the U6 and U46 groups, the transverse expansion at the midnasal, basal, alveolar, and dental levels was 2.64, 3.52, 4.46, and 6.32 mm and 2.17, 2.56, 2.73, and 5.71 mm, respectively. A significant difference was observed in the posterior alveolar-level expansion (p = 0.036) and posterior basal-bone-level expansion (p = 0.043) between the groups, with greater posterior skeletal and alveolar expansion in the U6 group. Compared with the U46 group, the U6 group showed greater posterior expansion at the alveolar and basal-bone levels, with an almost parallel split. Both groups showed a pyramidal expansion pattern in the coronal view.

Sandwich-structured porous concrete manufactured by mortar-extrusion and aggregate-bed 3D printing

To facilitate automation in manufacturing porous concrete and also decrease the cement content of relevant 3D printed concrete, a new type of sandwich-structured porous concrete was proposed in this work by using mortar-extrusion and aggregate-bed 3D printing. Three mixes of mortar made of cement, sand, water, superplasticizer, hydroxypropyl methylcellulose and sodium gluconate with different water-to-cement ratios were evaluated, and one of which was selected to manufacture the sandwich-structured porous concrete by printing the mortar first and then spreading single-sized coarse aggregates on top of the mortar. The printing process went well with the designed mixes and procedures. The porosity and permeability of the printed porous concrete were tested based on the water permeation methods and X-ray computed tomography. And the compressive strength and splitting tensile strength of the specimens were also tested. Results showed the mortar with a water-to-cement ratio of 0.325 was more appropriate for the present printing. And larger aggregate increased the mortar infill in the pores and thus reduced the porosity of the printed porous concrete, which resulted in higher mechanical strengths and lower permeability. On the contrary, smaller aggregates were prone to overlap each other, which facilitated the printing procedures but reduced the strength of the concrete. Generally, the aggregates with size of 1 ∼ 1.5 times of the designed mortar-layer thickness showed better performances on the property tests for the printed porous concrete. Moreover, better buildability resulted in shallower aggregate-embedding depth, and finally increased the permeability but decreased the compressive strengths of the concrete. Larger porosity mainly appeared in smaller-aggregate specimens which reduced the possibility of loading on the aggregate layer during the parallel splitting tensile strength test, and thus increased the measured value. However, larger porosity generally led to smaller compressive and perpendicular splitting tensile strengths but larger permeability. This study may help to increase the manufacturing efficiency and environmental friendliness of porous concrete in future practice.

A rank-two relaxed parallel splitting version of the augmented Lagrangian method with step size in (0,2) for separable convex programming

The augmented Lagrangian method (ALM) is classic for canonical convex programming problems with linear constraints, and it finds many applications in various scientific computing areas. A major advantage of the ALM is that the step for updating the dual variable can be further relaxed with a step size in ( 0 , 2 ) (0,2) , and this advantage can easily lead to numerical acceleration for the ALM. When a separable convex programming problem is discussed and a corresponding splitting version of the classic ALM is considered, convergence may not be guaranteed and thus it is seemingly impossible that a step size in ( 0 , 2 ) (0,2) can be carried on to the relaxation step for updating the dual variable. We show that for a parallel splitting version of the ALM, a step size in ( 0 , 2 ) (0,2) can be maintained for further relaxing both the primal and dual variables if the relaxation step is simply corrected by a rank-two matrix. Hence, a rank-two relaxed parallel splitting version of the ALM with a step size in ( 0 , 2 ) (0,2) is proposed for separable convex programming problems. We validate that the new algorithm can numerically outperform existing algorithms of the same kind significantly by testing some applications.

An Optimized Vertical GaN Parallel Split Gate Trench MOSFET Device Structure for Improved Switching Performance

This work proposes a vertical gallium nitride (GaN) parallel split gate trench MOSFET (PSGT-MOSFET) device architecture suitable for power conversion applications. Wherein two parallel gates, and a field plate are introduced vertically on the sidewalls and connected, respectively, to the gate and source. Technology computer-aided design (TCAD) simulator was used in the design process to achieve a specific on-resistance as low as 0.79 mΩ.cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for the device, which has the capacity of blocking voltages up to 600 V. The peak electric field of the PSGT-MOSFET could well be lowered to 2.95 MV/cm, which is about 17% lower than that of a conventional trench gate MOSFET (TG-MOSFET) near the trench corner with help of suitable design and optimization of trench depth, drift doping, and field plate thickness. The TCAD simulation shows that the higher drift doping on the device performance of PSGT-MOSFET produces ~2× lower switching losses when compared with a similarly rated conventional TG-MOSFET device.

Resilience-constrained expansion planning of integrated power–gas–heat distribution networks

Integrated energy systems (IESs) have attracted wide attention as effective frameworks to develop efficient use of energy resources. Proactive preparedness is a sensible approach for integrated distribution networks (IDNs) to propitiously cope with high-impact rare (HR) events. In this paper, a resilient-constrained two-stage expansion planning methodology is proposed with the aim of simultaneously optimal expansion of the IDNs and resilience improvement against hurricanes in long-term. To maintain the independence of IDN’s operation and to cope with the computational complexity of such large-scale planning problem, a modified linearized alternating direction method of multipliers with parallel splitting and the adaptive penalty (LADMMPSAP) is introduced to convert the centralized model to a decentralized one. Accordingly, a new framework for the interaction between IDNs is proposed with the resilience and energy coordinator unit (RECU), which is responsible for couplings coordination between IDNs. The main goal of stage 1 is to minimize total cost, which includes investment, operation, and resilience costs. The optimal expansion scheme with optimal operation strategies is obtained in this stage. A stochastic resilience maximization under hurricane occurrence scenarios is modeled in stage 2. A normalized resilience index (RI) for evaluating resilience in IDNs suitable for long-term planning is proposed. The priorities of lines hardening and tie-line installation are determined using the proposed resilience importance index (RII) in stage 2 and sent back to stage 1. The effectiveness of the proposed methodology is tested on real-scale IDNs. The simulation results depict the privileges of incorporating resilience in long-term expansion planning from both economic and technical aspects.

AN EFFICIENT NUMERICAL TECHNIQUE FOR SOLVING HEAT EQUATION WITH NONLOCAL BOUNDARY CONDITIONS

A third order parallel algorithm is proposed to solve one dimensional non-homogenous heat equation with integral boundary conditions. For this purpose, we approximate the space derivative by third order finite difference approximation. This parallel splitting technique is combined with Simpson's 1/3 rule to tackle the nonlocal part of this problem. The algorithm develop here is tested on two model problems. We conclude that our method provides better accuracy due to availability of real arithmetic.

Oblique and rotation double random forest

Random Forest is an ensemble of decision trees based on the bagging and random subspace concepts. As suggested by Breiman, the strength of unstable learners and the diversity among them are the ensemble models’ core strength. In this paper, we propose two approaches known as oblique and rotation double random forests. In the first approach, we propose rotation based double random forest. In rotation based double random forests, transformation or rotation of the feature space is generated at each node. At each node different random feature subspace is chosen for evaluation, hence the transformation at each node is different. Different transformations result in better diversity among the base learners and hence, better generalization performance. With the double random forest as base learner, the data at each node is transformed via two different transformations namely, principal component analysis and linear discriminant analysis. In the second approach, we propose oblique double random forest. Decision trees in random forest and double random forest are univariate, and this results in the generation of axis parallel split which fails to capture the geometric structure of the data. Also, the standard random forest may not grow sufficiently large decision trees resulting in suboptimal performance. To capture the geometric properties and to grow the decision trees of sufficient depth, we propose oblique double random forest. The oblique double random forest models are multivariate decision trees. At each non-leaf node, multisurface proximal support vector machine generates the optimal plane for better generalization performance. Also, different regularization techniques (Tikhonov regularization, axis-parallel split regularization, Null space regularization) are employed for tackling the small sample size problems in the decision trees of oblique double random forest. The proposed ensembles of decision trees produce trees with bigger size compared to the standard ensembles of decision trees as bagging is used at each non-leaf node which results in improved performance. The evaluation of the baseline models and the proposed oblique and rotation double random forest models is performed on benchmark 121 UCI datasets and real-world fisheries datasets. Both statistical analysis and the experimental results demonstrate the efficacy of the proposed oblique and rotation double random forest models compared to the baseline models on the benchmark datasets.

New Inexact Parallel Splitting Algorithm for a Class of Separable Convex Programming

In this paper, a new inexact parallel splitting algorithm based on the shrinkage algorithm is proposed. We bring in inexact terms, and new iteration points are obtained by the parallel splitting method. A new descent direction and a proper step length are derived. Under reasonable assumptions, the convergence of the algorithm is demonstrated. Some matrix correction problem experiments show that the algorithm is efficient and easy to implement.

A parallel splitting ALM-based algorithm for separable convex programming

A parallel splitting ALM-based algorithm for separable convex programming

Parallel Normal S-Iteration Methods with Applications to Optimization Problems

A large number of nonlinear and optimization problems can be reduced to altering point problems. This paper aims to introduce the parallel normal S-iteration technique and study its convergence rates for solving such problems in infinite-dimensional Hilbert spaces under practical assumptions. We place particular emphasis on the parallel splitting method for the sum of two maximal monotone operators and that can apply for solving a class of convex composite minimization problems. Moreover, we present applications of our iterative methods to some nonlinear problems, such as a system of variational inequalities and a system of inclusion problems. Finally, to demonstrate the applicability of the altering point technique, the performances of our proposed parallel normal S-iteration methods are presented through numerical experiments in signal recovery problems.

Using Parallel Splits with Self-Report and Other Measures to Enhance Precision in Generalizability Theory Analyses

Although generalizability theory (G-theory) provides indices of reliability that take multiple sources of measurement error into account, those indices are typically conservative in nature because they reflect random rather than classical parallelism. One way to address these shortcomings is to use parallel splits rather than items as the unit of analysis in G-theory designs. In this article, we provide the most extensive treatment to date in how to effectively integrate parallel splits into an extended set of G-theory designs using data from the newly developed version of the Big Five Inventory (BFI-2; Soto & John). Results revealed that properly designed splits approximated classical parallelism while improving overall score consistency and reducing key components of measurement error. Variance components within appropriately chosen G-theory designs also provided effective means to evaluate the quality of splits and determine the best ways to improve score consistency and reduce specific sources of measurement error. To help readers in applying these techniques, we provide a comprehensive instructional supplement with code in R for creating parallel splits, analyzing all illustrated designs, and modifying those designs for other objectively or subjectively scored measures.

A customized low-rank prior model for structured cartoon–texture image decomposition

The mathematical characterization of the texture component plays an instrumental role in image decomposition. In this paper, we are concerned with a low-rank texture prior based cartoon–texture image decomposition model, which utilizes a total variation norm and a global nuclear norm to characterize the cartoon and texture components, respectively. It is promising that our decomposition model is not only extremely simple, but also works perfectly for globally well-patterned images in the sense that the model can recover cleaner texture (or details) than the other novel models. Moreover, such a model can be easily reformulated as a separable convex optimization problem, thereby enjoying a splitting nature so that we can employ a partially parallel splitting method (PPSM) to solve it efficiently. A series of numerical experiments on image restoration demonstrate that PPSM can recover slightly higher quality images than some existing algorithms in terms of taking less iterations or computing time in many cases.

Serial and Parallel Iterative Splitting Methods: Algorithms and Applications to Fractional Convection-Diffusion Equations

The benefits and properties of iterative splitting methods, which are based on serial versions, have been studied in recent years, this work, we extend the iterative splitting methods to novel classes of parallel versions to solve nonlinear fractional convection-diffusion equations. For such interesting partial differential examples with higher dimensional, fractional, and nonlinear terms, we could apply the parallel iterative splitting methods, which allow for accelerating the solver methods and reduce the computational time. Here, we could apply the benefits of the higher accuracy of the iterative splitting methods. We present a novel parallel iterative splitting method, which is based on the multi-splitting methods, The flexibilisation with multisplitting methods allows for decomposing large scale operator equations. In combination with iterative splitting methods, which use characteristics of waveform-relaxation (WR) methods, we could embed the relaxation behavior and deal better with the nonlinearities of the operators. We consider the convergence results of the parallel iterative splitting methods, reformulating the underlying methods with a summation of the individual convergence results of the WR methods. We discuss the numerical convergence of the serial and parallel iterative splitting methods with respect to the synchronous and asynchronous treatments. Furthermore, we present different numerical applications of fluid and phase field problems in order to validate the benefit of the parallel versions.

Mechanism of anisotropic characteristics of layered limestone and a constitutive model for different bedding angles based on a Brazilian tensile test

Layered limestone contains a large number of bedding planes, which leads to the anisotropy of mechanical properties. In this research, a Brazilian tensile test was conducted on limestone samples with seven different bedding dip angles to study the effect of different bedding angles on the tensile mechanical properties. The relationship curves for the horizontal tensile stress and strain were plotted. These curves underwent three stages of compaction, elasticity, and destruction. Furthermore, the variation law of the tensile strength and the tensile modulus with the bedding dip angles were studied, and a directivity index was proposed to quantitatively evaluate both the spatial directionality and the magnitude variability of the tensile strength of laminated rock samples. Additionally, the fracture patterns could be divided into three types, including “perpendicular splitting” in a roughly straight shape (the bedding angle ranged from 0 to 30°), “shearing and splitting” in an arc shape (30 to 75°), and “parallel splitting” in a smoothly straight shape (75 to 90°). The curves of the acoustic emission (AE) ringing counts with the bedding angles of the rock samples exhibited obvious three-stage patterns, and the rock samples had a large variation in the cumulative amplitude of the AE ringing counts because of the anisotropic characteristics. Based on the elastic theory, the anisotropic constitutive model was established to calculate the expression for the axial stress and strain during the test. The theoretically calculated curves could be well matched with the test values. These research results indicated that the tensile strengths, tensile moduli, and fracture patterns of the laminated limestone samples all displayed distinct anisotropy and directionality, which implied the necessity of considering anisotropy in the study of failure mechanisms, the theoretical calculation, and the engineering application of laminated limestone rocks.

Performance and Accuracy Implications of Parallel Split Physics‐Dynamics Coupling in the Energy Exascale Earth System Atmosphere Model

AbstractSimultaneous calculation of atmospheric processes is faster than calculating processes one at a time. This type of parallelism is beneficial or perhaps even necessary to provide good performance on modern supercomputers, which achieve faster performance through increased processor count rather than improved clock speed. The scalability of the Energy Exascale Earth System Model (E3SM) Atmosphere Model (EAM) is limited by the fluid dynamics which scales up to the number of mesh cells in the global mesh. In contrast, the suite of physics parameterizations in EAM is scalable up to the total number of physics columns, which is an order of magnitude greater than the number of mesh cells. A proposed solution to unlocking the greater potential performance from the physics suite is to solve the physics and dynamics in parallel. This work represents a first attempt at parallel splitting of the grid‐scale fluid dynamics model and the subgrid‐scale physics parameterizations in a global atmosphere model. We will demonstrate that switching to parallel physics‐dynamics coupling extends the scalability of the EAM to up to 3 times the previous peak scalability limit and is up to 20% faster than the sequentially split coupling at the highest core counts and the same time step. Decadal simulations of both coupling approaches show very little impact to the model climate. This improved performance does not come without drawbacks, however. Parallel splitting requires a shorter time step and other modifications which largely offset performance gains. A mass fixer is required for conservation. Techniques for mitigating these issues are also discussed.

Optimized Pipelined Fast Fourier Transform Using Split and Merge Parallel Processing Units for OFDM

This paper presents a new optimized design to implement Fast Fourier Transform in orthogonal frequency division multiplexing for real valued signals. The proposed method is a new pipelined design, it is the combination of serial as well as parallel split and merge processing butterfly units. The proposed design initially has a single processing unit, in second stage, processing unit splits into two parallel processing units, and thereafter from stage three onwards, merges into a single processing until the last stage. The proposed design is optimized to reduce the latency as well as the hardware complexity for real inputs and complex inputs, as real valued signals itself reduce half of the computations, redundant data paths are eliminated effectively by this design. The parallel processing unit speeds up the processing at the initial stage, whereas single processing units reduce the hardware complexity. This design is a solution for ambiguity between Latency and hardware. The proposed design is very effective for real valued signals. The proposed design is implemented in Radix-22 model, in which multipliers are required for every two stages. The proposed design reduces Latency parameter more than 50% with a small cost of adders and multipliers, also power consumption for higher order FFT’s is low.