Neighborhood Structure Research Articles

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

Bocio y enfermedad nodular tiroidea

Goiter and thyroid nodular disease are very common conditions of the thyroid gland. Goiter refers to growth of the normal thyroid parenchyma and thyroid nodule when the growth is located in a delimited, distinguishable area of the normal parenchyma. Its etiology is diverse, although the etiopathogenesis is not entirely clear. There tend to be few symptoms and the diagnosis is usually made through incidental findings on imaging tests. When evaluating thyroid nodules, it is essential to conduct a glandular function analysis, with an initial screening by measuring thyroid stimulating hormone (TSH) and describing the gland and possible nodules by means of thyroid ultrasound. Ultrasonography allows for evaluating nodule characteristics and classifying them according to the risk of malignancy by means of EU-TIRADS. In case of asymptomatic goiter, observation and clinical monitoring can be carried out, reserving other interventions for cases with compression of neighboring structures. On the other hand, management of the nodules will depend on symptoms, EU-TIRADS ultrasound features, and the Bethesda histological classification (in case of puncture). Observation and ultrasound monitoring or an invasive approach can be chosen.

Practical Calculation of Diaphragm Wall Deflection using Plaxis 3D Software

In this article, the mathematical models within the Plaxis 3D software were used to simulate and assess the deflection of diaphragm walls, comparing with data collected from a real project. This scientific and effective approach is crucial, as analyzing and simulating the excavation stages before constructing diaphragm walls for underground levels becomes essential for controlling wall displacement and preventing potential soil erosion issues in the surrounding deep excavation, which could have serious consequences for neighboring structures. The numerical modeling results demonstrate that the deflection of the diaphragm wall varies inversely with its thickness, but this variation is relatively small and quite compatible with the observed data

An improved fruit fly optimization algorithm with Q-learning for solving distributed permutation flow shop scheduling problems

The distributed permutation flow shop scheduling problem (DPFSP) is one of the hottest issues in the context of economic globalization. In this paper, a Q-learning enhanced fruit fly optimization algorithm (QFOA) is proposed to solve the DPFSP with the goal of minimizing the makespan. First, a hybrid strategy is used to cooperatively initialize the position of the fruit fly in the solution space and the boundary properties are used to improve the operation efficiency of QFOA. Second, the neighborhood structure based on problem knowledge is designed in the smell stage to generate neighborhood solutions, and the Q-learning method is conducive to the selection of high-quality neighborhood structures. Moreover, a local search algorithm based on key factories is designed to improve the solution accuracy by processing sequences of subjobs from key factories. Finally, the proposed QFOA is compared with the state-of-the-art algorithms for solving 720 well-known large-scale benchmark instances. The experimental results demonstrate the most outstanding performance of QFOA.

Multi-objective fuzzy green scheduling optimization method of special vehicle body-in-white prototype shop considering equipment preventive maintenance

Based on the advantages of non-contact, flexibility, and small heat-affected zone, laser processing technology is widely used in material removal, addition, modification and other manufacturing fields. However, the emission of large amounts of smoke during laser processing of metal materials will result in environmental pollution in the job shop. Additionally, the processing time of laser equipment becomes uncertain as the power decays with prolonged use. Therefore, this paper is mainly focus on a multi-objective flexible job shop fuzzy green scheduling problem to minimize the fuzzy makespan, fuzzy total energy consumption and fuzzy total smoke emission. To solve it, an improved multi-objective particle swarm optimization algorithm has been designed. Firstly, a preventive maintenance strategy is proposed to reduce the makespan and equipment failure frequency by considering periodic power attenuation of laser equipment. Next, different particle updating strategies are employed to enhance particles’ exploration capabilities. Subsequently, five neighborhood structures are introduced to improve the performance of local search. Finally, comparison experiments are conducted with an expanded common benchmark, and a production case study in a special vehicle body-in-white prototype job shop. The results effectively demonstrate the feasibility and effectiveness of the developed model and improved multi-objective particle swarm optimization algorithm.

Retrospective Evaluation of Radiological Findings in Patients with Oral Malignancy

Aim: Oral cancer ranks among the most prevalent cancers worldwide. Imaging methods play a vital role in assessing patients. This study aimed to evaluate the age, sex, and localization distribution as well as panoramic and cone-beam computed tomography (CBCT) image findings in patients with oral malignancy with bone involvement. Materials and Method: Patients who were histopathologically diagnosed with malignant oral lesions at Gazi University, Faculty of Dentistry, Department of Oral Pathology between 2009 and 2023 were reviewed. Patients with oral malignancies whose panoramic images or CBCT images were available in the radiology archive were included in this study. Age-sex distribution was recorded. Localization of lesions and radiological features such as internal structures, borders, and effects on surrounding tissues were evaluated. Results: The mean age of the patients was 58.2 years; nine of them were female and thirteen were male. The incidence of malignancies included in this study was higher in men than in women, and the mandible/maxilla ratio was equal. The most common primary tumor was squamous cell carcinoma (SCC) (n=12). SCC lesions caused destruction in the cortical borders of neighboring structures in the region where they were located and showed the potential to spread toward these regions. Mucoepidermoid carcinoma was observed as an unilocular radiolucent lesion with clear borders, and it was determined that it destroyed the cortical bone. Conclusion: Panoramic radiography and CBCT images are valuable imaging methods in the evaluation of bone involvement of oral malignancies.

A Decision Support Framework for Aircraft Arrival Scheduling and Trajectory Optimization in Terminal Maneuvering Areas

This study introduces a decision support framework that integrates aircraft trajectory optimization and arrival scheduling to facilitate efficient management of descent operations for arriving aircraft within terminal maneuvering areas. The framework comprises three modules designed to tackle specific challenges in the descent process. The first module formulates and solves a trajectory optimization problem, generating a range of candidate descent trajectories for each arriving aircraft. The options for descent operations include step-down descent operation, Continuous Descent Operation (CDO), and CDO with a lateral path stretching strategy. The second module addresses the assignment of conflict-free trajectories to aircraft, determining precise arrival times at each waypoint. This is achieved by solving an aircraft arrival scheduling problem. To overcome computational complexities, a novel variable neighborhood search algorithm is proposed as the solution approach. This algorithm utilizes three neighborhood structures within an extended relaxing and solving framework, and incorporates a tabu search algorithm to enhance the efficiency of the search process in the solution space. The third module focuses on comparing the total cost incurred from flight delays and fuel consumption across the three descent operations, enabling the selection of the most suitable operation for the descent process. The decision support framework is evaluated using real air traffic data from Guangzhou Baiyun International Airport. Experimental results demonstrate that the framework effectively supports air traffic controllers by scheduling more cost-efficient descent operations for arrival aircraft.

The Influence of the Aggregate Configuration on the Seismic Assessment of Unreinforced Masonry Buildings in Historic Urban Areas

Unreinforced masonry (URM) buildings in historic urban areas of European countries are generally clustered in an aggregate configuration and are often characterized by façade walls mutually interconnected with adjacent ones. As a result, the seismic performance of buildings in an aggregate configuration can be affected by the mutual interaction between the adjacent units. This interaction, often called the aggregate effect, could significantly influence the level of the seismic vulnerability of URM buildings in aggregate configuration toward in-plane and out-of-plane mechanisms, the latter being the object of the present paper. Traditional methods for assessing the seismic vulnerability of URM buildings neglect the interactions between adjacent buildings, potentially underestimating the actual vulnerability. This study aims to derive fragility curves specific for UMR buildings in aggregate configuration and proposes an innovative methodology that introduces the aggregate effect into an analytical approach, previously developed by the authors for isolated URM buildings. The aggregate effect is modeled by accounting for the friction forces arising among adjacent facades during the development of out-of-plane overturning mechanisms by considering different scenarios, based on how façade walls interact with neighboring structures (e.g., whether they are connected to transverse and/or lateral coplanar ones). The proposed approach is applied to a real case study of an Italian historical center. The obtained results demonstrate that the aggregate effect significantly influences the fragility curves of URM buildings arranged in aggregate configurations. This highlights the importance of considering this effect and the usefulness of the proposed approach for large-scale assessments of seismic vulnerability in historic urban areas, contributing to sustainable disaster risk prevention.

Subminute thermal damage to cell types present in the skin

Introduction Psoriasis is characterized by an increase in the proliferation of keratinocytes and nerve fiber activity, contributing to the typical skin lesions. Pulsed Dye Laser (PDL) treatment is effective for the treatment of psoriatic lesions but its mechanism remains unclear. One hypothesis is that PDL causes thermal damage by the diffusion of heat to neighboring structures in lesional skin. There is limited information on the thermal sensitivity of these neighboring skin cells when exposed to hyperthermia for durations lasting less than a minute. Our study aimed to investigate the cell-specific responses to heat using sub-minute exposure times and moderate to ablative hyperthermia. Materials and Methods Cultured human endothelial cells, smooth muscle cells, neuronal cells, and keratinocytes were exposed to various time (2–20 sec) and temperature (45–70 °C) combinations. Cell viability was assessed by measuring intracellular ATP content 24 h after thermal exposure and this data was used to calculate fit parameters for the Arrhenius model and CEM43 calculations. Results Our results show significant differences in cell survival between cell types (p < 0.0001). Especially within the range of 50–60 °C, survival of neuronal cells and keratinocytes was significantly less than that of endothelial and smooth muscle cells. No statistically significant difference was found in the lethal dose (LT50) of thermal energy between neuronal cells and keratinocytes. However, CEM43 calculations showed significant differences between all four cell types. Conclusion The results imply that there is a cell-type-dependent sensitivity to thermal damage which suggests that neuronal cells and keratinocytes are particularly susceptible to diffusing heat from laser treatment. Damage to these cells may aid in modulating the neuro-inflammatory pathways in psoriasis. These data provide insight into the potential mechanisms of PDL therapy for psoriasis and advance our understanding of how thermal effects may play a role in its effectiveness.

Tabu search based on novel neighborhood structures for solving job shop scheduling problem integrating finite transportation resources

As advancements in transportation equipment intelligence continue, the job shop scheduling problem integrating finite transportation resources (JSPIFTR) has attracted considerable attention. Within the domain of shop scheduling, the neighborhood structure serves as a cornerstone for enabling intelligent optimization algorithms to effectively navigate and discover optimal solutions. However, current algorithms for JSPIFTR rely on generalized neighborhood structures, which incorporate operators like insertion and swap. While these structures are tailored to the encoding vectors, their utilization often leads to suboptimal optimization efficacy. To address this limitation, this paper introduces novel neighborhood structures specifically designed to the distinctive properties of JSPIFTR. These innovative structures leverage the intrinsic structural information in integrated scheduling, thereby enhancing the optimization effectiveness of the algorithm. Firstly, two theorems are presented to demonstrate the feasibility of the neighborhood solution. Secondly, different neighborhood structures for critical transportation and processing tasks are subsequently designed based on the analysis of the problem properties and constraints. Thirdly, an efficient fast evaluation method is developed to expediently calculate the objective value of the neighborhood solution. Finally, the novel neighborhood structures are combined with the tabu search (TS_NNS) and compared with other state-of-the-art methods on EX and NEX benchmarks. The comparative results demonstrate the remarkable performance of the neighborhood structure, with the TS_NNS enhancing the best solutions across 23 instances.

Inferring gene regulatory networks from single-cell transcriptomics based on graph embedding.

Gene regulatory networks (GRNs) encode gene regulation in living organisms, and have become a critical tool to understand complex biological processes. However, due to the dynamic and complex nature of gene regulation, inferring GRNs from scRNA-seq data is still a challenging task. Existing computational methods usually focus on the close connections between genes, and ignore the global structure and distal regulatory relationships. In this study, we develop a supervised deep learning framework, IGEGRNS, to infer GRNs from scRNA-seq data based on graph embedding. In the framework, contextual information of genes is captured by GraphSAGE, which aggregates gene features and neighborhood structures to generate low-dimensional embedding for genes. Then, the k most influential nodes in the whole graph are filtered through Top-k pooling. Finally, potential regulatory relationships between genes are predicted by stacking CNNs. Compared with nine competing supervised and unsupervised methods, our method achieves better performance on six time-series scRNA-seq datasets. Our method IGEGRNS is implemented in Python using the Pytorch machine learning library, and it is freely available at https://github.com/DHUDBlab/IGEGRNS.

A zero‐inflated Poisson spatial model with misreporting for wildfire occurrences in southern Italian municipalities

AbstractWe propose a Poisson model for zero‐inflated spatial counts contaminated by measurement error: we accommodate the excess of zeroes in the counts, consider the possible under/over reporting of the response and account for the neighboring structure of spatial areal units. Bayesian inferences are provided by MCMC implementation through the R package NIMBLE. To evaluate the model performance, a simulation study is carried out under configurations that allow for structured and unstructured spatial random effects. The proposed model is applied to investigate the distribution of the counts of wildfire occurrences in the municipal areas of two neighboring Italian regions for the summer season 2021. Fire counts are obtained by processing MODIS satellite data, while several socio‐economic and environmental‐driven potential risk factors are also considered in the model formulation. Data from multiple sources with different spatial support are processed in order to comply with the municipal units. Results suggest the appropriateness of the approach and provide some insights on the features of wildfire occurrences.

Multi-objective Evolutionary Algorithm with Variable Neighborhood Search for Optimizing Green Scheduling in a Re-Entrant Hybrid Flow Shop with Dynamic Events

In previous studies on re-entrant hybrid flow shops, the impact of dynamic events was often ignored despite being a common occurrence in practical production. To address this issue and simultaneously reduce energy consumption, a multi-objective evolutionary algorithm with variable neighborhood search (MOEA-VNS) has been proposed to optimize the green scheduling problem in a re-entrant hybrid flow shop with dynamic events (RHFS-GDS).The approach involves creating a green dynamic scheduling optimization model, which aims to minimize the makespan, total energy consumption, and stability of rescheduling solutions. A hybrid green scheduling decoding method is then employed to select machines for each operation and calculate fitness. Additionally, three neighborhood structures are designed to improve the population diversity and optimality of the MOEA-VNS algorithm. Two rescheduling strategies are also adopted to handle dynamic events that may occur during production. Experimental results demonstrate that these approaches are effective in solving the RHFS-GDS problem and can guide actual production. By incorporating dynamic events and rescheduling strategies into the optimization process, the proposed MOEA-VNS algorithm provides a comprehensive solution to the complex challenges faced by re-entrant hybrid flow shops in practical production environments.

A hybrid estimation of distribution algorithm for solving assembly flexible job shop scheduling in a distributed environment

This paper proposes a novel distributed assembly flexible job shop scheduling problem (DAFJSP), which involves three stages: production stage, assembly stage, and delivery stage. The production stage is accomplished in a few flexible job shops, the assembly stage is accomplished in a few single-machine factories, and the delivery stage is to deliver the obtained products to the corresponding customers. To address the problem, a hybrid estimation of distribution algorithm based on differential evolution operator and variable neighborhood search (HEDA-DEV) is proposed with the goal of minimizing the total cost and tardiness. Firstly, a new multidimensional coding method is designed based on the features of the DAFJSP. Secondly, two mutation operators and the similarity coefficient based on the probability matrix are put forward to implement the dynamic mutation. Thirdly, five types of neighborhood structures satisfying cooperative search strategies are employed to adequately improve the local exploitation ability. Finally, the comparison experiment results suggest that the proposed HEDA-DEV has competitive performance compared to the selected efficient algorithms. Moreover, a real case study is used to demonstrate that HEDA-DEV is an effective method for solving DAFJSP.

Normalized analysis of deformation for deep excavation diaphragm walls under different neighboring building conditions

This study investigates the impact of neighboring buildings on diaphragm wall deformation during deep excavation for building foundations. Prior numerical analyses predominantly utilized surface loading models, overlooking the influence of neighboring basements. Employing the finite-element method with PLAXIS 3D AE software, we assess the effects of neighboring structures on deep excavation. Our simulation adopts the top-down construction method, employing plate elements to represent diaphragm walls Temporary retaining support measures are implemented using H-shaped steel assemblies with preloaded anchors to simulate realistic excavation conditions. Neighboring building structures are accurately modeled using plate elements. Our findings reveal that the lateral displacement of the diaphragm wall varies with the depth of neighboring basements. At depths approximately half that of the excavation depth, the displacement significantly increases, reaching approximately 1.7 times the excavation depth and stabilizing thereafter. When neighboring buildings are about twice the excavation depth away, the deformation curve of the wall converges to that of "no loading," suggesting negligible influence on diaphragm wall deformation beyond this distance. Additionally, within one excavation depth, the maximum lateral displacement depth tends to be closer to the surface as the distance to the diaphragm wall decreases. Beyond a distance of one excavation depth from the diaphragm wall, the maximum lateral displacement position is approximately at the excavation face. This study significantly contributes to the comprehension of neighboring structures' influence on diaphragm walls during deep excavation, providing crucial insights for enhancing safety measures.

Sidewall angle tuning in focused electron beam-induced processing.

Structures fabricated using focused electron beam-induced deposition (FEBID) have sloped sidewalls because of the very nature of the deposition process. For applications this is highly undesirable, especially when neighboring structures are interconnected. A new technique combining FEBID and focused electron beam-induced etching (FEBIE) has been developed to fabricate structures with vertical sidewalls. The sidewalls of carbon FEBID structures have been modified by etching with water and it is shown, using transmission electron microscopy imaging, that the sidewall angle can be tuned from outward to inward by controlling the etch position on the sidewall. A surprising under-etching due to the emission of secondary electrons from the deposit was observed, which was not indicated by a simple model based on etching. An analytical model was developed to include continued etching once the deposit has been removed at the exposed pixel. At this stage the secondary electrons from the substrate then cause the adsorbed water molecules to become effective in etching the deposit from below, resulting in under-etched structures. The evolution of the sidewall angle during etching has also been experimentally observed in a scanning electron microscope by continuously monitoring the secondary electron detector signal.

Variable Neighborhood Search for Multi-Port Berth Allocation with Vessel Speed Optimization

This paper delves into the multi-port berth allocation problem (MBAP), enriching the traditional berth allocation problem (BAP) with vessel speed optimization (VSO). In the MBAP, it is assumed that there is cooperation between the port and the shipping companies, and the operation of the vessels and the ports is planned to maximize the overall benefits. Exploring this potential collaboration between ports and shipping entities has the potential to mitigate, or even resolve, the challenges plaguing maritime transportation, e.g., port congestion and suboptimal vessel schedules, to ultimately enhance the efficiency of maritime trade. In this paper, a new mixed-integer linear programming (MILP) model for the MBAP is formulated, which attempts to minimize the total cost incurred during operations, with various constraints such as vessel sailing, the vessel space–time relationship in ports, and the planning period. Meanwhile, an innovative variable neighborhood search (VNS) algorithm is presented, in which the initial solution generation method and neighborhood structures are proposed according to the MBAP characteristics. Furthermore, two sets of MBAP instances are generated to test the proposed MILP and VNS, of which the first set is based on real-world port data and the second on existing studies. The numerical experiments verify that the VNS can efficiently and reliably solve instances of all scales, with each neighborhood structure contributing uniquely to the iterative process. In addition, by analyzing the impact of varying oil prices on the MBAP, the study offers valuable management insights. Finally, a case study based on real data from a port group in the Yangtze River Basin is presented to further demonstrate the necessity of considering vessel service time window and planning period in the MBAP as well as the important role of the VSO in scheduling.

A novel local linear embedding algorithm via local mutual representation for bearing fault diagnosis

The locally linear embedding algorithm (LLE) mainly extracts significant features by mining the local neighborhood structure of the data. However, when the data exhibit strong nonlinearity in high-dimensional space, the single neighborhood structure of the LLE algorithm may not accurately capture the local linear relationships between instances, which degrades the performances of the LLE. Therefore, we propose a multi-structure neighborhood locally linear embedding algorithm via local mutual representation (LMR-LLE). Firstly, in each neighborhood, multiple local neighborhood structures of one instance are mined via local mutual representation to enhance the interconnectivity between the instances. Furthermore, the multiple neighborhood structures are fused in the low-dimensional space to construct a global reconstruction model, and the ultimate significant features are acquired by determining the model’s optimal solution. Finally, the extracted features are fed into a classifier for bearing fault diagnosis. Extensive experiments on two rolling bearing datasets illustrate that the LMR-LLE based diagnosis method has better performance accuracy than conventional LLE-based algorithms.