Neighborhood Structure Research Articles

From Local to Nonlocal High-Q Plasmonic Metasurfaces.

The physics of bound states in the continuum (BICs) allows the design and demonstration of optical resonant structures with large values of the quality factor (Q factor) by employing dielectric structures with low losses. However, BIC is a general wave phenomenon that should be observed in many systems, including the metal-dielectric structures supporting surface plasmon polaritons where optical resonances are hindered by losses. Here we suggest and develop a comprehensive strategy to achieve high-Q resonances in plasmonic metasurfaces by effectively tailoring the resonant modes from local to nonlocal regimes, thus transitioning from quasi-isolated localized resonances to extended resonant modes involving strong interaction among neighboring structure metaunits.

Integrated optimization of worker assignment, batch splitting and scheduling for a hybrid assembly line-seru production system

This paper studies an integrated optimization problem of worker assignment, batch splitting and scheduling for a hybrid assembly line-seru production system, which includes multiple serus and a short assembly line. The worker assignment involves assigning each multi-skilled worker to a seru or the assembly line, and assigning the worker tasks to perform. The batch splitting determines the number of subbatches split for each batch, the size of each subbatch, and in which seru the production of each subbatch is performed. The batch scheduling aims to optimize the process sequences of batches in serus and on the assembly line. For the integrated optimization problem, we propose a mixed-integer nonlinear programming model that minimizes the makespan. To solve large-scale instances of the problem, we develop a tailored hybrid variable neighborhood search algorithm with a four-layer solution coding scheme and four novel neighborhood structures. We validate the effectiveness of our model and solution algorithm on two sets of instances. The experimental results indicate that the proposed hybrid metaheuristic achieves a significant reduction in makespan, with an improvement of up to 20.94% compared to the solutions obtained by the Gurobi optimization solver within one hour. In addition, we demonstrate the advantage of optimized batch splitting by comparing it with no batch splitting and equal-size batch splitting strategies in the literature.

A [formula omitted]-Learning based NSGA-II for dynamic flexible job shop scheduling with limited transportation resources

With the widespread adoption of intelligent transportation equipment such as AGVs in the manufacturing field, the flexible job shop scheduling considering limited transportation resources has increasingly attracted attention. However, current research does not consider various dynamic disturbances in real production scenarios, resulting in lower executability of scheduling solutions. To solve this problem, a dynamic flexible job shop scheduling model with limited transportation resources is established, aiming to minimize makespan and total energy consumption. Considering three types of disturbances: job cancellation, machine breakdown, and AGV breakdown, the corresponding event-driven rescheduling strategy is proposed, and a rescheduling instability index is designed to measure the performance of the rescheduling strategy. A Q-Learning-based NSGA-II algorithm (QNSGA-II) is proposed. By learning the feedback historical search experience, it adaptively selects the appropriate neighborhood structures for local search; and a hybrid initialization strategy tailored to the problem characteristics is designed to improve the optimization performance of the algorithm. Through simulation experiments, the effectiveness of the rescheduling strategies and the superiority of the QNSGA-II algorithm in solving such problems are validated.

Multi-view subspace clustering based on multi-order neighbor diffusion

Multi-view subspace clustering (MVC) intends to separate out samples via integrating the complementary information from diverse views. In MVC, since the structural information in the graph is crucial to the graph learning, most of the existing algorithms construct the superficial graph from the original data by directly measuring the similarity between the first-order complementary nearest neighbors. However, the information provided by the superficial graph structure would be influenced by contaminated or absent samples. To address this problem, in the proposed method, the higher-order complementary neighbor graphs are exploited to discover the latent structural information between the samples, and fusing the latent structural information across different orders to achieve the MVC. Specifically, the higher-order neighbor graphs under different views are leveraged to estimate the missing samples. Then, to integrate the neighbor graphs of different orders, the multi-order neighbor diffusion fusion is proposed. Nevertheless, the above problem of diffusion fusion is an intractable non-convex issue. Thus, to address it, the multi-order neighbor diffusion fusion is considered as a combination problem of the solution under different order, and the heuristic algorithm is leveraged to solve it. In this way, not only the data representation under different view and also the neighbor structure under different order can be diffused under a joint optimization framework, thus the consistency and integral information among various perspectives and orders can be utilized effectively and simultaneously. Experiments on both incomplete and complete multi-view dataset demonstrate the convincingness of the high-order neighborhood structure based subspace clustering scheme by comparing with the existing approaches.

Surgical Management of Impacted Supernumerary Tooth and Odontome under General Anesthesia in an Emotionally Immature Child: A Case Report

Abstract The disorder known as hyperdontia is characterized by an excess of teeth or teeth that appear to be added to the normal amount of teeth. With a frequency ranging from 0.1% to 3.9% in permanent dentition and from 0.3% to 1.8% in deciduous dentition, it affects males more often than women. Since children’s cognitive abilities are still developing in comparison to adults’, children freely communicate their worry and anxiety and frequently exhibit uncooperative behavior patterns while undergoing treatment. A 7-year-old child who was emotionally immature reported with a principal complaint of missing tooth in his left upper front tooth area for 2 years. Cone-beam computed tomography was advised to verify location and structural interaction with neighboring structures. Due to his lack of emotional sensitivity and cooperation with others, we planned the surgical extraction under general anesthesia. In the present case, it was deemed preferable to wait for a spontaneous eruption, and the patient was observed for a month on a regular basis.

Prediction of adjacent single pile deformation induced by tunnel excavation based on the Pasternak model

The construction of metro tunnels in soft ground would cause significant settlement of the ground surface and deformation of buildings and their foundations in the vicinity of the tunnels. The deformation laws of the pile of the neighbouring structures induced by the tunnel construction have not yet been fully identified. This paper analyzed the impact mechanism of tunnel construction on piles based on the Pasternak model, deduced the calculation methods of additional horizontal displacement and additional internal force of piles, and introduced three solution methods. The computational method results have been validated through numerical simulations using coupling of discrete element method and finite difference method and compared to select laboratory data. The results show that the model accuracy is better when the tunnel axis burial depth is less than the pile bottom burial depth. The analysis shows that the tunnel burial depth significantly influences the displacement distribution of the adjacent single pile, while the impact on the peak displacement is insignificant. The thickness of the elastic layer (C) is inversely related to the maximum horizontal displacement and internal force, but it does not impact the distribution patterns of the displacement and internal force. For thickness of the elastic layer (C), the bending moment’s point is constant at 2 m above and below the tunnel axis, whereas the shear force’s point remains constant at the buried tunnel axis’s depth and 4 m above the tunnel axis.

Estimate the impact of deep excavation construction in high-rise buildings on neighboring structures

The estimation of diaphragm wall displacements and soil movement around the deep excavation of a high-rise building during the construction of basement floors plays a crucial role in limiting risks that may cause damages to human lives and property during the construction process. This study proposes the use of Finite Element Analysis (FEA) to accurately predict the behavior of soil and diaphragm walls in deep excavations of high-rise buildings. The model is based on advanced soil models and considers the interaction between diaphragm walls and soil, enabling the prediction of adverse impacts during the construction process, as well as negative effects on adjacent buildings. Additionally, the study will analyze and highlight the primary parameters that negatively impact the excavation process and neighboring constructions.

Adaptive variable neighborhood search algorithm with Metropolis rule and tabu list for satellite range scheduling problem

Efficient scheduling of measurement and control resources plays a critical role in successful satellite operations. The tremendous growth in the number of satellites in orbit exacerbates resource scarcity, further enhancing the significance of the satellite range scheduling problem (SRSP). In order to maximize the benefits of measurement and control tasks, we propose an innovative algorithm to solve the SRSP with numerous constraints. Firstly, the SRSP operation flow is analyzed to establish a constraint satisfaction model. A task-resource matching algorithm based on fitness is then designed to generate an initial feasible solution with superior quality by heuristic criteria. Subsequently, an adaptive variable neighborhood search algorithm with Metropolis rule and tabu list (AVNS-MT) is proposed to solve the mathematical model. Three swap neighborhood structures are constructed to explore the solution space efficiently during the variable neighborhood descent search process. Meanwhile, three migration neighborhood structures are devised to enable the shaking of solutions and evasion from the local optimum. Moreover, the incorporation of the Metropolis rule and tabu search strategy will contribute to enhancing the algorithm convergence. Finally, we have conducted extensive simulations to verify the effectiveness and efficiency of the proposed AVNS-MT. Comparison experiments with several state-of-the-art methods validate the superiority of the proposed algorithm.

Joint Projected Fuzzy Neighborhood Preserving C-means Clustering with Local Adaptive Learning

Graph-based dimensionality reduction approaches are extensively applied for preserving the intrinsic structures of high-dimensional data. To capture discriminative information from data clusters that lack a clear manifold structure, previous studies have combined clustering algorithms with local manifold structure preservation. These methods assume that two neighbor data points share the same cluster label. However, the assumption may not hold for Fuzzy C-Means which may assign two nearby data points to different clusters depending on the distance between data points and cluster centroids. This inconsistency degrades the performance in clustering and dimensionality reduction. To tackle this issue, we propose a novel approach for dimensionality reduction, termed joint Projected Fuzzy Neighborhood Preserving C-means Clustering with Local Adaptive Learning (PFNPCM). PFNPCM learns cluster centroids, sparse membership grades, embedded subspaces, and structured graphs simultaneously and extracts both global and local structures of data. Through clustering data that preserves the local neighborhood structure in a low-dimensional space instead of the original one, PFNPCM can effectively capture both global and local information from the inherent manifold structure and cluster structure. The employment of ℓ2,1-norm as the distance metric in the clustering and the preservation of neighborhood structure further ensure the robustness of clustering results against noise or outliers. To achieve the consistency of the captured global and local discriminative information, PFNPCM adopts a novel fuzzy local similarity measure based on the manifold structure, and the manifold assumption thus holds when partitioning unlabeled samples into subgroups. Massive experiments on diverse well-known datasets demonstrate that PFNPCM is superior to some state-of-the-art methods.

HpGraphNEI: A network entity identification model based on heterophilous graph learning

Network entities have important asset mapping, vulnerability, and service delivery applications. In cyberspace, where the network structure is complex and the number of entities is large, effectively obtaining the relevant attributes of entities is a difficult task. Graph neural network-based approaches focus on target IP node messaging from neighboring nodes; however, the graph learning task ignores the heterophilous relationship of network entity identification (NEI) tasks in the graph structure and fails to effectively message from non-neighboring nodes. To address the limitations of the existing task, we propose a NEI model based on heterophilous graph learning (HpGraphNEI); HpGraphNEI converts heterophilous graphs under the NEI task into homophilous graphs and uses the graph learning mechanism to carry out attribute completion task for incomplete entity attributes. First, the acquired dataset is feature-extracted by network measurement, and the clustering algorithm is employed to divide the target nodes into communities. Second, the network topology graph is constructed to embed the node attribute information and neighborhood structure information into the graph in the form of feature vectors. Then, the global attention in the community is calculated according to the attention results, the edges with strong correlation in the network are filtered, the adjacency matrix is reconstructed, and then the updated node information is aggregated to complete the incomplete attribute completion. Fourth, the updated nodes are categorized to output network entity categories and construct network entity portraits based on the attribute completion nodes. We conducted a 2-month data collection in three real regions and successfully identified 6 types of network entities. Compared with the optimal baseline, all the metrics have significantly improved, with NEI accuracy above 93.74% and up to 96.28%, improved 2.27% to 2.69%.

Learning to learn point signature for 3D shape geometry

Point signature is a representation that describes the structural geometry of a point within a neighborhood in 3D shapes. Conventional approaches apply a weight-sharing network, e.g., Graph Neural Network (GNN), to all neighborhoods of all points to directly generate point signatures and gain the generalization ability of the network by extensive training over amounts of samples from scratch. However, such approaches lack the flexibility to rapidly adapt to unseen neighborhood structures and thus cannot generalize well to new point sets. In this paper, we propose a novel meta-learning 3D point signature model, 3D meta point signature (MEPS) network, which is capable of learning robust 3D point signatures. Regarding each point signature learning process as a task, our method obtains an optimized model over the best performance on the distribution of all tasks, generating reliable signatures for new tasks, i.e., signatures of unseen point neighborhoods. Specifically, our MEPS consists of two modules: a base signature learner and a meta signature learner. During training, a base-learner is trained to perform specific signature learning tasks. Meanwhile, a meta-learner is trained to update the base-learner with optimal parameters. During testing, the meta-learner learned with the distribution of all tasks can adaptively change the base-learner parameters to accommodate unseen local neighborhoods. We evaluate our MEPS model on 3D shape correspondence and segmentation. Experimental results demonstrate that our method not only gains significant improvements over the baseline model to achieve state-of-the-art performance, but also is capable of handling unseen 3D geometry. Our implementation is available at https://github.com/hhuang-code/MEPS.

Spatial Clustering of Abandoned Buildings in Historic Environments in Bayındır, Turkey

Abandonment is a major challenge in historical urban areas worldwide, including Turkey and other developing nations, despite strong historical context and legal safeguards. This analogy compares abandonment to cancer metastasis, emphasizing its pervasive and insidious nature. Like cancer, abandonment can spread, impacting neighboring structures and the broader urban fabric. This metaphor highlights the urgent need for a comprehensive analysis of abandonment's spatial characteristics in historic urban areas. A study in Bayindir, Turkey, using GIS, revealed a 20% abandonment rate within its protected urban area, with clusters of low and high abandonment identified. Mapping these clusters showed how they relate to land use types, offering insights for policymakers and planners to preserve and revitalize historically significant urban areas.

Multi-objective evolutionary algorithm based flexible assembly job-shop rescheduling with component sharing for order insertion

This paper addresses a rescheduling problem in an order-driven flexible assembly job-shop, which encompasses a complete production line from machining to assembly. Some products have a multi-level assembly structure, and certain components can be shared among different products. When an order with tight delivery date is inserted, rescheduling is necessary for adjusting original production scheme and tradeoffs among makespan, total tardiness and count of machine changes. To formulate the flexible assembly job-shop rescheduling problem with component sharing (FAJRP-CS), a mixed-integer linear programming model is established. The model involves three categories of decision variables that determine the machine assignment, shared component allocation, and operation sequencing. Meanwhile, solution of FAJRP-CS is analyzed by using graph model, and a neighborhood structure containing two-type move for critical operations is proposed for local optimizing. Then, a hybrid non-dominated sorting genetic algorithm with tabu search (NSGATS) is developed to maintain diversity while improving convergence. The approach is first compared with 8 algorithms on nine test instances, and the effectiveness of the operators, framework and neighborhood structure are discussed separately. Subsequently, NSGATS is applied to solve the multi-stage rescheduling problem in a medical device workshop with 6 products and 56 components. The experimental results demonstrate that NSGATS is effective for solving the FAJRP-CS.

An improved co-evolutionary memetic algorithm based on novel schedule type and unconditional feasibility for hybrid flow-shop scheduling problem

The hybrid flow-shop scheduling problem (HFSP) is used in a wide range of industries and is very hard to solve. There has been a great deal of research on HFSP with the objective of makespan, but related research mainly focused on the algorithmic level, ignoring characters of HFSP. In this paper, through the study of the characters of HFSP itself, together with the design of the algorithm, new records on benchmarks are obtained. Firstly, the property of unconditional feasibility of HFSP (UFH) is formally proven. Based on UFH, the disjunctive-graph-based encoding scheme is considered convenient and accurate to HFSP, and the constraints for feasible neighborhood solutions are superfluous to HFSP. New specialized neighborhood structures are developed for HFSP accordingly. Secondly, traditional schedule types (such as active schedule) are found to be further explored. A novel type of schedule, all-active schedule (AAS) is proposed to tap the potential of one scheduling process. Thirdly, an improved co-evolutionary memetic algorithm (ICMA) based on UFH and AAS is proposed to solve HFSP. To balance exploration and exploitation, the co-evolutionary framework with multiple groups is adopted. Path reconnection is used as a crossover operator, and a non-dominated selection mechanism is also employed. In the end, the effectiveness of ICMA is experimentally demonstrated. 1 new solution on Carlier benchmark (proposed in 2000) and 45 new solutions on Jose benchmark (proposed in 2020) are found by ICMA, outperforming all previous outstanding algorithms.

Old Ground Plan - New Insights: The Central Church Compound in Shivta Revised

ABSTRACT The three monumental churches of the Byzantine site of Shivta in the Negev Desert were fully excavated in the first third of the 20th century. Lacking the original archaeological reports, discussion of them has been based mostly on contemporary observations of the ruins. The Central Church, situated in the center of the village and surrounded by houses, has received the least scholarly attention of the three churches. Recently recovered data, including its ground plan executed by Colt’s expedition in 1937, and archival materials published here for the first time, enabled re-examination of the Central Church structure within the compound, its history, date, and identification of the function of some of the rooms. Analysis of old and new data led us to propose an updated plan of the Central Church complex. Accordingly, new insights are proposed regarding the place of the church within the village, its neighboring domestic structures, its relation to the other two churches of Shivta, and the layout of the site.

Three-way decision based island harmony search algorithm for robust flow-shop scheduling with uncertain processing times depicted by big data

This paper discusses an uncertain two-machine permutation flow-shop scheduling problem (2PFSP) with total weighted tardiness and common due date. Uncertain processing times are described by a large set of discrete scenarios, which is a type of big data. The objective is to minimize the schedule performance under the worst-case scenario. Identifying the worst-case scenario for each evaluated schedule is quite time-consuming in the situation that the scenario set size is large so that the objective evaluation might be computationally expensive. To handle this difficulty, three-way decision is used to preprocess the large-size scenario set to get a reduced scenario set so that the concept of surrogate worst-case scenario is adopted. A hybrid harmony search algorithm of combining three-island framework and the scenario-based local search is developed to solve the discussed problem. Based on the single-scenario knowledge of 2PFSP, a problem-specific scenario-dependent neighborhood structure is constructed under the surrogate worst-case scenario. An extensive experiment was carried out. The computational results show that the application of surrogate worst-case scenario based on three-way decision is effective in reducing the time consuming while keeping schedule performance evaluation. Being compared to the worst-case scenario objective evaluation, for an example in the case of the middle bad-scenario ratio, the surrogate worst-case scenario objective evaluation made the solution algorithm save 12.95 % in average CPU time for all instances while the relative performance difference is only 1.809 % in average. Being compared to possible alternative algorithms derived from the state-of-the-art algorithms, the developed algorithm is advantageous for the addressed problems.

A learning-based granular variable neighborhood search for a multi-period election logistics problem with time-dependent profits

Planning the election campaign for leaders of a political party is a complex problem. The party representatives, running mates, and campaign managers have to design an efficient routing and scheduling plan to visit multiple locations while respecting time and budget constraints. Given the limited time of election campaigns in most countries, every minute should be used effectively, and there is very little room for error. In this paper, we formalize this problem as the multiple Roaming Salesman Problem (mRSP), a new variant of the recently introduced Roaming Salesman Problem (RSP), where a predefined number of political representatives visit a set of cities during a planning horizon to maximize collected rewards, subject to budget and time constraints. Cities can be visited more than once and associated rewards are time-dependent (increasing over time) according to the day of the visit and the recency of previous visits. We develop a compact Mixed Integer Linear Programming (MILP) formulation complemented with effective valid inequalities. Since commercial solvers can obtain optimal solutions only for small-sized instances, we develop a Learning-based Granular Variable Neighborhood Search and demonstrate its capability of providing high-quality solutions in short CPU times on real-world instances. The adaptive nature of our algorithm refers to its ability to dynamically adjust the neighborhood structure based on the progress of the search. Our algorithm generates the best-known results for many instances.

On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem.

We contribute to the efficient approximation of the Pareto-set for the classical NP-hard multiobjective minimum spanning tree problem (moMST) adopting evolutionary computation. More precisely, by building upon preliminary work, we analyze the neighborhood structure of Pareto-optimal spanning trees and design several highly biased sub-graph-based mutation operators founded on the gained insights. In a nutshell, these operators replace (un)connected sub-trees of candidate solutions with locally optimal sub-trees. The latter (biased) step is realized by applying Kruskal's single-objective MST algorithm to a weighted sum scalarization of a sub-graph. We prove runtime complexity results for the introduced operators and investigate the desirable Pareto-beneficial property. This property states that mutants cannot be dominated by their parent. Moreover, we perform an extensive experimental benchmark study to showcase the operator's practical suitability. Our results confirm that the sub-graph-based operators beat baseline algorithms from the literature even with severely restricted computational budget in terms of function evaluations on four different classes of complete graphs with different shapes of the Pareto-front.

Historic redlining, modern neighborhood structures, and mortality in children, adolescents, and young adults with cancer.

11037 Background: Historic redlining, referring to the Home Owners Loan Corporation (HOLC) program’s racially biased risk monitoring maps in the 1930’s, has indelible impacts on modern day neighborhood characteristics and health outcomes. The impact of redlining, a form of structural racism, on children, adolescents, and young adults with cancer (CAYACs) is unknown. This retrospective cohort study evaluates the association between redlining and mortality in CAYACs. Methods: Using the California Cancer Registry, we identified cases <25 years old diagnosed with first primary malignant cancer between 2000-2019. Using merged HOLC maps and U.S. Census data we determined case redlining status (A “Best”, B “Still Desirable”, C “Declining”, D “Hazardous”, and N “Not graded”) by residence at diagnosis. We defined residents of D neighborhoods redlined-exposed and residents of A or B neighborhoods unexposed or referent. Kaplan Meier methods and multivariable Cox proportional hazard models were applied to calculate overall survival. Additional models accounted for census tract-level neighborhood SES (nSES, a composite measure incorporating seven SES domains), neighborhood racial and ethnic composition (or typology) and racialized economic segregation (via index of concentration at the extremes). Results: In total 8,210 cases were HOLC-graded, the median age was 16 years, 46% were female, and 48% had solid tumors. Overall survival at 5 years was inferior for D cases vs. A or B cases (OS 80.2%, 95% CI: 78.5-81.7 vs. OS 88.2, 95% CI: 84.0-91.3). Adjusting for individual clinical characteristics (age, sex, year of diagnosis, and cancer stage), D cases experienced greater mortality (HR 1.32, 95% CI: 1.12-1.55) compared to A or B cases. Additional adjustment for insurance and nSES attenuated the relationship (HR 1.17, 95%CI: 1.00-1.36 and HR 1.07, 95% CI: 0.92-1.24 respectively). Similar patterns were observed in models adjusting for racialized economic segregation (HR 1.07, 95% CI: 0.92-1.24) and typology (HR 1.10, 95% CI: 0.94-1.28) in place of nSES. Conclusions: In this population-based study, redlined-exposed CAYACs experienced greater mortality compared to unexposed CAYACs. Adjusting for insurance and contemporary nSES attenuated the association between redlining and mortality. Findings suggest current neighborhood attributes may be downstream manifestations of structural racism with survival implications for CAYACs.

Revisiting the evolution of ian-Islamic neighborhood structure based on urban development plans: Case study of Sarshoor neighborhood, Mashhad

Revisiting the evolution of ian-Islamic neighborhood structure based on urban development plans: Case study of Sarshoor neighborhood, Mashhad