Location Problem Research Articles

An Overview Study of Multi-sensor Fusion for Map Building and Localization

This paper reviews the application of multi-sensor fusion in simultaneous localisation and map construction (SLAM) technology. With the development of robotics, autonomous driving and virtual reality, there is an increasing demand for precise localisation and map construction.SLAM technology has emerged to solve the problem of autonomous robot localisation and map construction in unknown environments. However, single-sensor SLAM systems have limitations, such as limited sensing capability and susceptibility to noise interference. Multi-sensor fusion SLAM significantly improves the performance and robustness of the system by integrating the advantages of multiple sensors. The multi-sensor fusion SLAM system includes key components such as sensor data reading, front-end visual odometry, back-end optimisation, loopback detection and map building. The sensor data is first optimised to reduce noise and then further processed according to the task requirements. Sensors are categorised into internal sensors (e.g. IMUs and wheeled odometers) and external sensors (e.g. cameras, LIDAR, UWB sensors, etc.). Common data fusion methods include filter-based fusion (e.g., Extended Kalman Filter, Particle Filter), optimisation-based fusion (e.g., Graph Optimisation, Nonlinear Least Squares) and deep learning-based fusion (e.g., Convolutional Neural Network, Recurrent Neural Network). These methods are able to handle different types of sensor data and improve the performance of SLAM systems. Multi-sensor fusion SLAM technology has a wide range of applications in fields such as robot navigation, autonomous driving, virtual reality, geographic information systems and mapping. In the future, the technology will pay more attention to the combination of multimodal fusion and deep learning, optimising the computational efficiency of the algorithms, improving the real-time and robustness of the system, as well as the fusion of cloud SLAM and edge computing, to promote the development and advancement of the related fields.

The location problem of emergency materials in uncertain environment

The location problem of emergency materials in uncertain environment

Ambulation Protocol for Adult Patients Receiving Extracorporeal Membrane Oxygenation: A Quality Improvement Initiative.

For patients receiving extracorporeal membrane oxygenation, early mobility decreases mechanical ventilation time, delirium incidence, and length of intensive care unit stay and improves physical functioning. Individual centers use institutional guidelines to develop ambulation protocols. Local Problem A quality improvement initiative was used to evaluate an ambulation protocol for adult intensive care unit patients receiving extracorporeal membrane oxygenation. Adult patients receiving extracorporeal membrane oxygenation who walked according to the protocol were compared with a historical control group of patients who walked without the protocol. Data analysis included descriptive statistics and independent t tests. Outcomes included adverse safety events, number of patients and ambulation sessions, standing and ambulation time, and distance. From January to March 2021, 13 of 46 patients receiving extracorporeal membrane oxygenation (28%) walked according to the protocol. In the control group, 14 of 147 patients (10%) walked in 2019; 21 of 144 patients (15%) walked in 2020. Some characteristics of the control group (hospitalized before the COVID-19 pandemic) differed from those of the protocol group (hospitalized during the pandemic). Mean number of ambulation sessions was not significantly different between groups (protocol group, 10; control group, 9). Differences in mean standing time (protocol group, 121.23 minutes; control group, 210.80 minutes), ambulation time (protocol group, 11.77 minutes; control group, 198.70 minutes), and ambulation distance were not significant. Standing time, ambulation time, and distance were not significantly different between the groups. The extracorporeal membrane oxygenation ambulation protocol demonstrated clinical significance by increasing the number of patients walking.

Fast EEG/MEG BEM-based forward problem solution for high-resolution head models.

A fast BEM (boundary element method) based approach is developed to solve an EEG/MEG forward problem for a modern high-resolution head model. The method utilizes a charge-based BEM accelerated by the fast multipole method (BEM-FMM) with an adaptive mesh pre-refinement method (called b-refinement) close to the singular dipole source(s). No costly matrix-filling or direct solution steps typical for the standard BEM are required; the method generates on-skin voltages as well as MEG magnetic fields for high-resolution head models within 90 s after initial model assembly using a regular workstation. The forward method is validated by comparison against an analytical solution on a spherical shell model as well as comparison against a full h-refinement method on realistic 1M facet human head models, both of which yield agreement to within 5 % for the EEG skin potential and MEG magnetic fields. The method is further applied to an EEG source localization (inverse) problem for real human data, and a reasonable source dipole distribution is found.

LP-Rounding Based Algorithm for Capacitated Uniform Facility Location Problem with Soft Penalties

LP-Rounding Based Algorithm for Capacitated Uniform Facility Location Problem with Soft Penalties

The hub location problem with comparisons of compact formulations: A note

The hub location problem with comparisons of compact formulations: A note

Re-locative guided search optimized self-sparse attention enabled deep learning decoder for quantum error correction

Heavy hexagonal coding is a type of quantum error-correcting coding in which the edges and vertices of a low-degree graph are assigned auxiliary and physical qubits. While many topological code decoders have been presented, it is still difficult to construct the optimal decoder due to leakage errors and qubit collision. Therefore, this research proposes a Re-locative Guided Search optimized self-sparse attention-enabled convolutional Neural Network with Long Short-Term Memory (RlGS2-DCNTM) for performing effective error correction in quantum codes. The integration of the self-sparse attention mechanism in the proposed model increases the feature learning ability of the model to selectively focus on informative regions of the input codes. In addition, the use of statistical features computes the statistical properties of the input, thus aiding the model to perform complex tasks effectively. For model tuning, this research utilizes the RIGS nature-inspired algorithm that mimics the re-locative, foraging, and hunting strategies, which avoids local optima problems and improves the convergence speed of the RlGS2-DCNTM for Quantum error correction. When compared with other methods, the proposed RlGS2-DCNTM algorithm offers superior efficacy with a Minimum Mean Squared Error (MSE) of 4.26, Root Mean Squared Error of 2.06, Mean Absolute Error of 1.14 and a maximum correlation and of 0.96 and 0.92 respectively, which shows that the proposed model is highly suitable for real-time error decoding tasks.

Code and Data Repository for The Balanced Facility Location Problem: Complexity and Heuristics

The software and data in this repository are a snapshot of the software and data that were used in the research reported on in the paper The Balanced Facility Location Problem: Complexity and Heuristics by Malena Schmidt and Bismark Singh.

TRADITIONAL GOVERNANCE IN THE COPRODUCTION OF URBAN RESILIENCE: Institutional Enablers and Political Constraints

AbstractBuilding urban resilience in vulnerable global South settlements is a pressing twenty‐first century challenge. Building urban resilience involves addressing institutional deficiencies to mobilize resources for the delivery of urban services and infrastructures. State–civil society partnerships are effective in low‐capacity settings as a step forward in the consolidation of the state, and as an opportunity for the emancipation of vulnerable communities. Coproducing urban resilience requires recognizing marginalized communities (e.g. indigenous groups), their capacity for local problem solving and governance structures for community engagement. In this article, we explore a coalition of five periurban neighborhoods in Oaxaca City (Mexico), which collaborate with the state to address flooding and drought using traditional governance. We argue that, although the recognition and mobilization of traditional governance has enabled the coproduction of public services (adaptation), it has been limited in delivering radical governance transformations. Traditional governance may prevent neighborhood leaders from reaching government positions to secure further resources required for the construction of urban resilience. The article contributes to debates on coproduction, explaining how traditional governance enables the coproduction of infrastructures and service delivery, but is limited in forwarding deep societal transformations necessary for resilience building in vulnerable contexts of the global South.

Reviewing the quality of Environmental Impact Statements of Brazilian landfills: insights for promoting efficiency in final disposal

ABSTRACT Brazil advocates waste hierarchy in its solid waste policy by reducing landfills but still struggles to achieve adequate final disposal. This raises a huge demand for undertaking Environmental Impact Assessment (EIA) processes of landfills. To contribute to achieving efficiency in EIA processes, this work aims to review EIS quality of a representative set of Environmental Impact Statements (EISs) of landfills in the state of São Paulo (Brazil). The quality of a set of 18 landfill EISs prepared by 17 consultancies between 2010 and 2022 was analyzed using two assessment tools with qualitative and quantitative approaches. Both approaches confirm that this set of Brazilian EISs of landfills present good quality but highlight localized problems in specific EIS items. The good quality of this sample of EISs differs greatly from the recent achievements shared in the literature and a previous analysis of one Brazilian landfill EIS. These unexpected positive results of EIS quality can be related to the high experience of consultancies in waste management projects. These EISs reached a minimum quality requirement, and they are enough to support decision-making. The approach proposed herein can be applied to other contexts and used for environmental bodies to assess the quality of EIS.

Improving Timely Evaluation of Newborns at Risk for Hemolytic Disease From a Positive Maternal Antibody

Background: Hemolytic disease of the newborn (HDN) is a rare condition with the potential for high morbidity. Local Problem: Practice at a large maternity care center did not align with the American Academy of Pediatrics (AAP) revised hyperbilirubinemia guidelines. Methods: A pre/post-implementation design with Plan-Do-Study-Act cycles was used. Interventions: A clinical pathway was implemented to improve timely evaluation of newborns at risk for HDN due to a positive maternal antibody using cord blood. Results: Cord blood testing increased among the subset of newborns who did not meet collection criteria prior to the practice change (pre: 0% n = 7, post: 39% n = 23). Missed opportunities to test cord blood occurred less frequently post (35 versus 26 days between missed opportunities). Feasibility data identified the need for educational and clinical tools. Conclusions: A clinical pathway and tailored tools support the AAP recommendation. Electronic health record supports could bolster future efforts.

Out-of-Hospital Cardiac Arrest Ambulance Delay Zones and AED Placement in a Southern Brazilian City

Out-of-hospital cardiac arrests (OHCAs) have high mortality rates, worsened by limited access to automated external defibrillators (AEDs). This study analyzed OHCA response times, identified areas with prolonged ambulance travel times, and proposed optimal AED locations in a medium-sized city in southern Brazil. Data from 278 non-traumatic OHCA cases (2019–2022) in patients over 18 years old, with ambulance response times under 20 min, were included. Spatial survival analysis assessed the probability of exceeding the recommended 5-min (300 s) ambulance response time. The maximal covering location problem identified 100 strategic AED sites within a 150-s reach for bystanders. AED and ambulance travel times were compared using the Wilcoxon test (p < 0.01). Defibrillation occurred in 89 cases (31.01%), and bystander CPR was performed in 149 cases (51.92%). Despite these efforts, 77% of patients died. The median ambulance response time was 11.63 min, exceeding 5 min in most cases, particularly at peak times like 11 a.m. AED placement in selected locations could cover 76% of OHCA occurrences, with a mean AED travel time of 320 s compared to 709 s for ambulances. Strategic AED placement could enhance early defibrillation and improve survival outcomes.

Multiobjective model to optimize charging station location for the decarbonization process in Mexico

AbstractElectric vehicles (EVs) offer significant potential for advancing sustainable environmental goals. However, their widespread adoption has been concentrated in urban areas, raising challenges for interurban travel. In many countries, charging station networks are primarily located within cities, highlighting a key opportunity for expansion to support longer distance journeys. This article addresses the facility location problem for EV charging stations to enable interurban travel. We propose a multiobjective optimization model based on the flow refueling location model with three objectives: maximizing CO2 emissions reduction, minimizing total costs, and reducing user charging time. The model is solved using an epsilon constraint approach, and Mexico's charging station network is used as a case study. Through computational experiments, various scenarios are evaluated, and a comparative analysis is performed between electric and internal combustion vehicles. Results show that deploying 20 strategically located charging stations could mitigate 3.1 million tons of CO2, requiring an investment of nearly USD 3.9 million.

Design of pick-up locations in the crowded road sections

The effective management of urban transport is contingent upon a profound comprehension of the dynamic nature of traffic patterns, alongside the implementation of strategies that are bespoke to the circumstances in question. The growth of urban areas and the increased mobility of individuals have resulted in significant increases in traffic demand, frequently leading to congestion within constrained infrastructure. Cram school districts are particularly susceptible to congestion due to the considerable influx of students and parents, which serves to exacerbate existing traffic issues. The efficacy of traditional methods, such as road expansion, improved public transport, and stricter parking enforcement, in addressing the core causes of congestion has been limited. The objective of this study is to mitigate congestion by identifying optimal pick-up and drop-off (PUDO) zones using the Maximal Covering Location Problem methodology, which considers both pedestrian mobility and network nodes. A social cost analysis of parking facilities and PUDO zones revealed that the latter could reduce social costs by up to 172.3 million KRW. This research is therefore anticipated to alleviate congestion, reduce social costs, and enhance transportation efficiency in cram school districts. The findings may also be applicable to other areas with high demand, thereby offering broader benefits.

Locating stations of shared e-scooters with integrated SWARA and maximal covering location problem

ABSTRACT The increasing usage of shared e-scooters has brought the issue of improper parking to the forefront. Research has shown that when parking areas are provided, the problem of improper parking can be prevented. This study aims to determine optimal parking locations for shared e-scooters in the Kadıköy district of Istanbul. A two-stage integrated approach involving Stepwise Weight Assessment Ratio Analysis (SWARA) and the maximal covering location model was employed to achieve this goal. In the first stage, e-scooter parking demand was identified, and in the second stage, 150 stations were placed that maximize the demand. The model results were compared with real data to validate the model. Decision-makers and other stakeholders can benefit from the suggested method in cases where data is unavailable or when shared e-scooter systems are being set up for the first time.

Localization in quantum field theory for inertial and accelerated observers

Abstract We study the problem of localization in Quantum Field Theory (QFT) from the point of view of inertial and accelerated experimenters. We consider the Newton-Wigner, the Algebraic Quantum Field Theory (AQFT) and the modal localization schemes, which are, respectively, based on the orthogonality condition for states localized in disjoint regions of space, on the algebraic approach to QFT and on the representation of single particles as positive frequency solution of the field equation. We show that only the AQFT scheme obeys causality and physical invariance under diffeomorphisms.

Then, we consider the nonrelativistic limit of quantum fields in the Rindler frame. We demonstrate the convergence between the AQFT and the modal scheme and we show the emergence of the Born notion of localization of states and observables. Also, we study the scenario in which an experimenter prepares states over a background vacuum by means of nonrelativistic local operators and another experimenter carries out nonrelativistic local measurements in a different region. We find that the independence between preparation of states and measurements is not guaranteed when both experimenters are accelerated and the background state is different from Rindler vacuum, or when one of the two experimenters is inertial.

Planning an airport shuttle network with air taxis using choice-based optimization

Abstract Airports and airlines share a common interest in providing passengers with fast and convenient access to airports. With the introduction of air taxis as a new means of transport, the opportunity will soon emerge to introduce air taxi shuttle services to airports. In this context, for each individual airport the question arises where the facilities for landing, ground handling and take-off, also called vertiports, should be located in the area surrounding the airport. We formulate the problem as a hub location problem with multiple allocation, with the objective to maximize the number of passengers who choose to reach the airport by air taxi. In our model, we explicitly incorporate passengers’ choice behavior with respect to available means of transport using a multinomial logit model. We linearize the problem and provide guidance on the steps needed to use the model in practice. In an extensive case study, we apply the model to real data for Munich Airport, perform a stated preference study to parameterize the multinomial logit model, suggest locations for vertiports throughout Bavaria as a result, and investigate to what extent the introduction of air taxi shuttle flights can reduce travel times to Munich Airport.

Model updating method for offshore jacket platforms using improved DNN and OOA considering non-uniform corrosion and structural responses

During the service life of offshore jacket platforms, the harsh marine environment leads to severe structural corrosion and damage, necessitating structural health monitoring. Ensuring the accuracy of numerical finite element models (FEM) requires critical model updating. This study introduces an improved DNN-OOA model updating method by incorporating actual structural responses into the optimization objective function and considering non-uniform corrosion of the structure. We utilized Pyansys to automatically generate large-scale datasets, simplifying the simulation process. An accurate and responsive surrogate model is generated using the improved deep neural network (DNN), and the optimal solution for the parameters to be corrected is sought through the Osprey optimization algorithm (OOA), completing the FEM updating. The main innovation of this study lies in incorporating non-uniform corrosion caused by the real marine physical environment into the model updating process. This phenomenon is employed to determine the updating range for different structural members. Furthermore, the parameters subject to updating include structural damage to the members and changes in the upper mass. Incorporating the structural response under static loading into the optimization objective function allows for a more comprehensive reflection of the structure’s dynamic and static behavior, addressing the regression confusion problem in the optimization process of purely modal frequency updating. Experimental results demonstrate that the proposed improved DNN-OOA model updating method effectively eliminates inaccuracies in simulated structural responses and mitigates the local optimum problem inherent in pure modal frequency updating. In the updated scaled jacket platform FEM, the maximum relative error of the modal frequencies is reduced to 2.624%, and the maximum error in structural response is reduced to 3.510%. This approach provides a more accurate and reliable FEM for the maintenance and safety assessment of offshore jacket platforms.

Inverse boundary value problem for the convection–diffusion equation with local data

We study a local data inverse problem for the time-dependent convection–diffusion equation in a bounded domain where a part of the boundary is treated to be inaccessible. Up on assuming the inaccessible part to be flat, we seek for the unique determination of the time-dependent convection and the density terms from the knowledge of the boundary data measured outside the inaccessible part. In the process, we show that there is a natural gauge in the perturbations, and we prove that this is the only obstruction in the uniqueness result.

Analytical solution of vehicle phase-plane equilibria through the Root-Rational tyre model

Passenger vehicle steady-state behaviour has been investigated for a long time. Among the existing methods, the phase portrait is rather popular and useful. However, the computation of vehicle equilibria may only be done numerically. Furthermore, the numerical procedure must be repeated in case of changes in the inputs (e.g. steering angle). This paper proposes a new methodology to obtain an analytical solution of vehicle equilibria. The key is the definition of a new tyre model, denoted as Root-Rational (RR) tyre model, which allows inverting the vehicle dynamics equations in closed form. Using the proposed tyre model reduces the equilibria location problem to finding the roots of a third-order polynomial. After describing the procedure in detail, comparisons are made between the analytical solution and a classical numerical approach, either using the same RR tyre model or a more classical one. Results show great accuracy when locating the equilibria coordinates and a significantly reduced computational time, paving the way for innovative vehicle stability control algorithms based on the real-time identification of the stability region in the phase plane.