Decomposition Scheme Research Articles

What Dictates the α-Effect in Gas-Phase SN2 Reactions? A Density Functional Theory Study.

The α-effect is an important concept in chemistry and biochemistry, namely that for a bimolecular nucleophilic substitution (SN2) reaction, the nucleophilicity of an atom is increased if its adjacent (α) atom has a lone pair of electrons, lowering the reaction barrier height and increasing the reaction rate. However, exceptions exist, even for very similar structural motifs. We investigate what underlies the α-effect in gas-phase SN2 reactions using two total energy decomposition schemes based on density functional theory (DFT) and find that steric effects play an important role, but that there exists a strong linear correlation between the α-effect and electrostatic contribution, suggesting that it is the electrostatic interaction that stabilizes the transition state and leads to the α-effect in gas-phase SN2 reactions. This multifaceted explanation resolves the long-standing uncertainty about the α-effect. We show that the α-effect increases with the branching of the central carbon atom.

Robust adaptive optimization for sustainable water demand prediction in water distribution systems

The advancement of the Internet of Things has positioned intelligent water demand forecasting as a critical component in the quest for sustainable water resource management. Despite the potential benefits, the inherent non-stationarity of water consumption data poses significant hurdles to the predictive accuracy of forecasting models. This study introduces a novel approach, the Robust Adaptive Optimization Decomposition (RAOD) strategy, which integrates a deep neural network to address these challenges. The RAOD strategy leverages the Complete Ensemble Empirical Mode Decomposition (CEEMD) to preprocess the water demand series, mitigating the effects of non-stationarity and non-linearity. To further enhance the model’s robustness, an innovative optimization algorithm is incorporated within the CEEMD process to minimize the variance in multi-scale arrangement entropy among the decomposed components, thereby improving the model’s generalization capabilities. The predictive power of the proposed model is harnessed through the construction of deep neural networks that utilize the decomposed data to forecast minutely water demand. To validate the effectiveness of the RAOD strategy, real-world datasets from four distinct geographical regions are employed for multi-step ahead predictions. The experimental outcomes demonstrate that the RAOD model outperforms existing models across all considered metrics, highlighting its suitability for accurate and reliable water demand forecasting in the context of sustainable energy management.

Hybrid clustering strategies for effective oversampling and undersampling in multiclass classification

Multiclass imbalance is a challenging problem in real-world datasets, where certain classes may have a low number of samples because they correspond to rare occurrences. To address the challenge of multiclass imbalance, this paper introduces a novel hybrid cluster-based oversampling and undersampling (HCBOU) technique. By clustering and separating classes into majority and minority categories, this algorithm retains the most information during undersampling while generating efficient data in the minority class. The classification is carried out using one-vs-one and one-vs-all decomposition schemes. Extensive experimentation was carried out on 30 datasets to evaluate the proposed algorithm's performance. The results were subsequently compared with those of several state-of-the-art algorithms. Based on the results, the proposed algorithm outperforms the competing algorithms under different scenarios. Finally, The HCBOU algorithm demonstrated robust performance across varying class imbalance levels, highlighting its effectiveness in handling imbalanced datasets.

Tucker Decomposition-Enhanced Dynamic Graph Convolutional Networks for Crowd Flows Prediction

Crowd flows prediction is an important problem for traffic management and public safety. Graph Convolutional Network (GCN), known for its ability to effectively capture and utilize topological information, has demonstrated significant advancements in addressing this problem. However, GCN-based models were often based on predefined crowd-flow graphs via historical movement behaviors of human beings and traffic vehicles, which ignored the abnormal changes in crowd flows. In this study, we propose a multi-scale fusion GCN-based framework with Tucker decomposition named mTDNet to enhance dynamic GCN for crowd flows prediction. Following the paradigm of extant methods, we also employ the predefined crowd-flow graphs as a part of mTDNet to effectively capture the historical movement behaviors of crowd flows. To capture the abnormal changes, we propose a Tucker decomposition-based network with the product of the adjacency matrix of historical movement pattern graphs and an Adaptive Learning Tensor ( ALT ) by reconstructing the crowd flows. Particularly, we utilize the Tucker decomposition scheme to decompose ALT , which enhances the dynamic learning of graph structures, allowing for effective capturing of the dynamic changes in crowd flow, including abnormal changes. Furthermore, a multi-scale 3DGCN is utilized to mine and fuse the multi-scale spatio-temporal information from crowd flows, to further boost the mTDNet prediction performance. Experiments conducted on two real-world datasets showed that the proposed mTDNet surpasses other crowd flow prediction methods.

Exploring prototype-guided strategy for domain decomposition in physics-informed neural network

Exploring prototype-guided strategy for domain decomposition in physics-informed neural network

Pentafluoroorthotellurate Uncovered: Theoretical Perspectives on an Extremely Electronegative Group.

The pentafluoroorthotellurate group (-OTeF5, teflate) exhibits high electron-withdrawing properties. Indeed, it is often used as a bulky substitute for fluoride due to its high chemical stability and larger size, which reduces its tendency to act as a bridging ligand. These characteristics make it a valuable ligand in synthetic chemistry, facilitating the preparation of molecular structures analogous to polymeric fluoride-based compounds. In this study, we explore the electronic structure of the teflate group by using advanced Quantum Chemical Topology (QCT) methods to better understand its bonding nature and compare its group electronegativity with that of the halogens. For that, we examine XOTeF5 systems (X = F, Cl, Br, I) and decompose X-OTeF5 interactions into classical (ionic) and exchange-correlation (covalent) contributions by using interacting quantum atoms (IQA) energy decomposition scheme. We also conduct a detailed analysis of electron distribution by utilizing the statistical framework of electron distribution functions (EDFs) and examine the electron localization function (ELF), electron density, and reduced density gradient scalar functions, as well as delocalization indices and QTAIM charges. The results show that the electron-withdrawing properties of the teflate group are comparable to those of fluorine, albeit slightly lower. Moreover, its internal bonding is primarily ionic. Additionally, we compare -OTeF5 with other O-donor groups, demonstrating that the electron-withdrawing properties within OEF5 (E = S, Se, Te) systems are nearly identical, and these groups show a higher group electronegativity than OCF3, OC(CF3)3, and OC6F5.

Uncertainty Optimization of Industrial Production Operations Considering the Stochastic Performance of Control Loops

Process optimization is a highly successful method for achieving optimal efficiency in industrial production. The conventional optimization approach presupposes that the operational parameters should align with the optimization settings. However, it fails to consider that, influenced by the stochastic performance of the control loops, the operating parameters may deviate from the optimal operating settings. Consequently, this results in the violation of constraints in the optimization results and affects production safety. Therefore, this paper proposes an uncertainty optimization method that considers the stochastic performance of control loops to accurately determine the optimal operational performance that can be practically achieved in industrial production. Firstly, a multi-optimization variational mode decomposition strategy is developed to precisely extract the smooth random and trend terms of the control loop output data. Secondly, the random grouping smooths out the random terms and accurately characterizes the uncertainty associated with these terms. Subsequently, a moment uncertainty set with mild mean-zero net condition is then defined to construct an improved distribution robust optimization model considering the stochastic performance of control loops. Finally, the validation of the proposed optimization method in the actual hydrocracking process shows that the optimization error of the proposed method is reduced by more than 10%, and the constraint violation rate is reduced by 14%, which fully proves the effectiveness and applicability of the method.

Detecting memberships in multiplex networks via nonnegative matrix factorization and tensor decomposition

Abstract Multiplex networks provide a powerful data structure for capturing diverse relationships among nodes, and the challenge of community detection within these networks has recently attracted considerable attention. We propose a general and flexible generative model—the Mixed Membership Multilayer Stochastic Block Model (MixMSBM), in which layers with meaningful similarities are grouped together. Within each layer group, the layers share the same mixed membership assignments of nodes to communities, but with distinct community link probability matrices. To address this, we developed NMFTD, a joint clustering approach, to identify cohesive layer groups and determine the mixed memberships of nodes within them. Our method first clusters the layers using matrix factorization with graph regularization, followed by a tensor decomposition strategy enhanced by a corner-finding algorithm to uncover the nodes' mixed memberships in each group. The proposed method is asymptotically consistent, and its effectiveness is validated through experiments on synthetic and real-world multilayer networks. The results show that NMFTD exhibits robustness across various parameter settings, outperforming or competing closely with other methods.

Managing institutional multiplicity: a case of a bilateral development agency’s regional office

PurposeDrawing on institutionalism supplemented by a micro-political perspective, this study explores how a bilateral development agency’s (BDA’s) regional office manages institutional multiplicity, a situation where organisations are embedded into the institutional environments of their headquarters and multiple host countries.Design/methodology/approachI remotely conducted semi-structured in-depth interviews with 20 staff members of a BDA’s regional office in South-East Europe. Reflexive Thematic Analysis was used to analyse the interview data.FindingsTwo themes are developed. One is an institutional decomposition strategy. The subject office decomposes institutional multiplicity into more manageable multiple institutional dualities by deploying local representatives to host countries. The other is the division of duties to demonstrate legitimacy. The division designates who in the office demonstrate legitimacy in which institutional environment. These proactive actions by the office (i.e., the decomposition and the division) question the institutionalist assertion that external institutional conditions determine organisational behaviour.Research limitations/implicationsThe findings may not directly apply to other public sector organisations because BDAs’ overseas offices are “donors” for their host countries. In addition, themes developed in the context of South-East Europe may not be generalisable to other regions.Practical implicationsBDA staff members should understand that institutional decomposition through the deployment of local representatives is a rational strategy to deal with complex conditions of institutional multiplicity. They should also understand that experienced local representatives are required to achieve this strategy.Originality/valueThis is the first empirical study to examine how a public sector organisation’s regional office manages complex institutional multiplicity from a micro-macro combined perspective.

‘같음’과 ‘대체’ 의미에 근거하여 등호의 관계적 이해를 강조한 초등학교 3학년 수업 사례 분석

With the new standards for the “equal sign and equivalence relation” in grades 3 and 4 of the 2022 revised mathematics curriculum, there is a need to specifically explore and implement instructional practices that support a relational understanding of equality. In this study, we implemented three lessons emphasizing the relational understanding of the equal sign for third grade students and analyze the lessons. We developed and implemented teaching methods that emphasize both the sameness and the substitution meaning of the equal sign based on previous research. First, the teacher emphasized the meaning of substitution based on sameness, and students learned that one expression can be substituted for another expression based on the meaning that both sides of the equal sign are equal. Second, the teacher taught relational strategies by paying attention to the structure of equations as the object of reasoning, and students were able to use compensation and decomposition strategies to reason about equations. Finally, the teacher used relational strategies to work with equations with unknowns, such as creating different types of equations, and students were able to justify why the equations they created were true based on the meaning of the equal sign. Based on these findings, we discuss implications for instructional practices that can improve students' relational understanding of the equal sign.

Test Case Minimization with Quantum Annealers

Quantum annealers are specialized quantum computers for solving combinatorial optimization problems with special quantum computing characteristics, e.g., superposition and entanglement. Theoretically, quantum annealers can outperform classic computers. However, current quantum annealers are constrained by a limited number of qubits and cannot demonstrate quantum advantages. Nonetheless, research is needed to develop novel mechanisms to formulate combinatorial optimization problems for quantum annealing (QA). However, QA applications in software engineering remain unexplored. Thus, we propose BootQA , the very first effort at solving test case minimization (TCM) problems on classical software with QA. We provide a novel TCM formulation for QA and utilize bootstrap sampling to optimize the qubit usage. We also implemented our TCM formulation in three other optimization processes: simulated annealing (SA), QA without problem decomposition, and QA with an existing D-Wave problem decomposition strategy, and conducted an empirical evaluation with three real-world TCM datasets. Results show that BootQA outperforms QA without problem decomposition and QA with the existing decomposition strategy regarding effectiveness. Moreover, BootQA ’s effectiveness is similar to SA. Finally, BootQA has higher efficiency in terms of time when solving large TCM problems than the other three optimization processes.

TEC-CNN: Toward Efficient Compressing of Convolutional Neural Nets with Low-rank Tensor Decomposition

Most state-of-the-art convolutional neural networks (CNNs) are characterized by excessive parameterization, leading to a high computational burden. Tensor decomposition has emerged as a model reduction technique for compressing deep neural networks. Previous approaches have predominantly relied on either Tucker decomposition or Canonical Polyadic (CP) decomposition for CNNs. However, CP decomposition exhibits exceptional compression capabilities in comparison to Tucker decomposition, which results in a more pronounced accuracy loss. This article introduces an efficient model compression method, termed TEC-CNN, designed to achieve significant compression while preserving accuracy levels comparable to those of the original models. In TEC-CNN, convolutional layers are identified to obtain convolutional kernels by analyzing given models under the principles of low-rank tensor decomposition, and then, calculating the ranks of convolutional kernels. Furthermore, an efficient decomposition schema for the convolutional kernel is proposed with approximate kernel tensor for reducing parameters. Additionally, a novel format of a convolutional sequence is presented and constructed with a reduced number of parameters to replace the original convolutional layers. Finally, the effectiveness of TEC-CNN is assessed across a range of computer vision tasks. For instance, in CIFAR-100 classification, ResNet18 is compressed to 4.1 MB, while Unext, when applied to image segmentation using the International Skin Imaging Collaboration (ISIC) dataset, is reduced to 3.419 MB. When employed for fire object detection with Yolov7, TEC-CNN achieves a model size reduction of 71.6 MB. Comprehensive experimental results underscore that our approach achieves significant model compression while preserving model performance.

An optimized LSTM model for clean coal ash content prediction in dense medium separation scenarios on the basis of the dual decomposition method

ABSTRACT To increase the accuracy of clean coal ash content prediction during the dense medium separation process and address the time lag issue encountered when measuring clean coal ash content, a prediction model based on WaOA-VMD-SGMD-WaOA-LSTM was proposed. The model adopts the dual decomposition techniques of optimized Variational Mode Decomposition (VMD) and Symplectic Geometric Mode Decomposition (SGMD), which can completely decompose the original clean coal ash data, and uses the Walrus optimization algorithm (WaOA) to optimize the hyperparameters of the Long Short-Term Memory (LSTM) model. In the process of model construction, the characteristic data of ore content ( Z 2 ), raw coal ash ( Z 3 ), heavy mesoporous cyclone pressure ( Z 4 ), heavy mesoporous suspension density ( Z 5 ), and magnetic content ( Z 6 ) were combined with the decomposed cleaned coal ash grouping S-IMF0~S-IMFn, CO-IMF1, and CO-IMF2 as input variables to construct multiple LSTM prediction models. Finally, the prediction value is superimposed to realize the prediction of clean coal ash content. Based on industrial data of a coal preparation plant in Shanxi, China, the results show that the coefficient of determination ( R 2 ) of the model is 0.9974. After adding secondary decomposition technology, the average absolute error was reduced by 60.99% compared with that of the single decomposition strategy.

A Generally Applicable Method for Disentangling the Effect of Individual Noncovalent Interactions on the Binding Energy.

We introduce the fragment-pairwise Local Energy Decomposition (fp-LED) scheme for precise quantification of individual interactions contributing to the binding energy of arbitrary chemical entities, such as protein-ligand binding energies, lattice energies of molecular crystals, or association energies of large biomolecular assemblies. Using fp-LED, we can assess whether the contribution to the binding energy arising from noncovalent interactions between pairs of molecular fragments in any chemical system is attractive or repulsive, and accurately quantify its magnitude at the coupled cluster level - commonly considered as the "gold standard" of computational chemistry. Such insights are crucial for advancing molecular and material design strategies in fields like catalysis and therapeutic development. Illustrative applications across diverse fields demonstrate the versatility and accuracy of this theoretical framework, promising profound implications for fundamental understanding and practical applications.

A Generally Applicable Method for Disentangling the Effect of Individual Noncovalent Interactions on the Binding Energy

We introduce the fragment‐pairwise Local Energy Decomposition (fp‐LED) scheme for precise quantification of individual interactions contributing to the binding energy of arbitrary chemical entities, such as protein‐ligand binding energies, lattice energies of molecular crystals, or association energies of large biomolecular assemblies. Using fp‐LED, we can assess whether the contribution to the binding energy arising from noncovalent interactions between pairs of molecular fragments in any chemical system is attractive or repulsive, and accurately quantify its magnitude at the coupled cluster level ‐ commonly considered as the “gold standard” of computational chemistry. Such insights are crucial for advancing molecular and material design strategies in fields like catalysis and therapeutic development. Illustrative applications across diverse fields demonstrate the versatility and accuracy of this theoretical framework, promising profound implications for fundamental understanding and practical applications.

Conti-Fuse: A novel continuous decomposition-based fusion framework for infrared and visible images

For better explore the relations of inter-modal and inner-modal, even in deep learning fusion framework, the concept of decomposition plays a crucial role. However, the previous decomposition strategies (base & detail or low-frequency & high-frequency) are too rough to present the common features and the unique features of source modalities, which leads to a decline in the quality of the fused images. The existing strategies treat these relations as a binary system, which may not be suitable for the complex generation task (e.g. image fusion). To address this issue, a continuous decomposition-based fusion framework (Conti-Fuse) is proposed. Conti-Fuse treats the decomposition results as few samples along the feature variation trajectory of the source images, extending this concept to a more general state to achieve continuous decomposition. This novel continuous decomposition strategy enhances the representation of complementary information of inter-modal by increasing the number of decomposition samples, thus reducing the loss of critical information. To facilitate this process, the continuous decomposition module (CDM) is introduced to decompose the input into a series continuous components. The core module of CDM, State Transformer (ST), is utilized to efficiently capture the complementary information from source modalities. Furthermore, a novel decomposition loss function is also designed which ensures the smooth progression of the decomposition process while maintaining linear growth in time complexity with respect to the number of decomposition samples. Extensive experiments demonstrate that our proposed Conti-Fuse achieves superior performance compared to the state-of-the-art fusion methods.

Analysis of energy-related carbon dioxide intensity in China's major non-ferrous metal producing regions: Spatio-temporal decomposition and emission reduction strategies

Analysis of energy-related carbon dioxide intensity in China's major non-ferrous metal producing regions: Spatio-temporal decomposition and emission reduction strategies

Reliability analysis of landing gear operation based on improved support vector machine-based decomposed-coordinated

An improved support vector machine-based decomposed-coordinated (ISVM-DC) is proposed to improve the accuracy and efficiency of landing gear operation reliability analysis. The decomposition and coordination strategy decompose the composite failure problem into multiple mathematical models, and then builds the relationship between the total output response and the variable parameters according to the hierarchical relationship. The improved support vector machine aims to optimize the performance of the limit state function in each sub-mathematical model by applying multi-stage adaptive point position control of hyperparameters. Taking the wheel brake temperature difference of main landing gear as an example, the verification of the proposed method in reliability modelling and simulation is carried out. The case results show that the proposed ISVM-DC can accurately decompose and coordinate the architecture of landing gear operational failure, and efficiently construct and solve the reliability performance function.

Characteristics of surface signature generated by an underwater sphere with regular free surface waves

The characteristics of the wake generated by an underwater sphere in the presence of regular free surface waves are investigated. Based on the spectral wave explicit Navier–Stokes equations decomposition strategy, the incident waves are prescribed based on spectral wave models, and the wake characteristics generated by the underwater sphere are determined using a computational fluid dynamics solver. The effects of the wavelength of the surface waves and the Froude number on the wake characteristics are investigated. The results show that the existence of free surface waves can distort the wake patterns, causing them to shift from the classical V-shaped Kelvin wake to an Ω-shape. Analysis of the spectrum indicates that the distortion of wake patterns is mainly determined by the wave–wave interactions.

Model decomposition method for minimizing the consumption of support structure for FFF

Fused filament fabrication (FFF) often requires additional support structures for manufacturing parts with overhanging areas, leading to substantial material and time wastage. Although several model decomposition techniques have been developed to tackle this issue, most focus solely on the constraint of overhang surface area. As a result, these techniques often struggle with parts that have intricate overhang characteristics, causing reduced printing surface accuracy and quality. This study introduces a precise method for detecting overhang regions and support-relevant overhang regions to improve the accuracy of overhang region identification and printing precision. Additionally, a model decomposition strategy leveraging genetic algorithm optimization is proposed to minimize the need for support structures, aiming for nearly support-free printing. Moreover, for intricate structures with a high topology where support structures are unavoidable, a novel projection support based on the distribution of periodic points in triangular form is developed to address this printing challenge. Experimental results demonstrate that the implementation of the algorithm outlined in this work on an FFF printer has achieved nearly support-free printing. Moreover, the proposed method for generating support structures has the potential to reduce material usage by 27 %.