Location Problem Research Articles

From PERFORM to PERFORM2Scale: Lessons from scaling-up a health management strengthening intervention to support Universal Health Coverage in three African countries.

Strengthening management and leadership competencies among district and local health managers has emerged as a common approach for health systems strengthening and to achieve Universal Health Coverage (UHC). While the literature is rich with localised examples of initiatives that aim to strengthen the capacity of district or local health managers, particularly in sub-Saharan Africa, considerably less attention is paid to the science of how to scale-up these initiatives. The aim of this paper is thus to examine the process of scaling-up a management strengthening intervention (MSI) and identify new knowledge and key lessons learned that can be used to inform the scale-up process of other complex health interventions, in support of UHC. Qualitative methods were used to identify lessons learned from scaling-up the MSI in Ghana, Malawi and Uganda. We conducted 14 interviews with district health management team members, three scale-up assessments with 20 scale-up stakeholders, and three reflection discussions with 11 research team members. We also kept records of activities throughout MSI and scale-up implementation. Data was recorded, transcribed, and analysed against the Theory of Change to identify both scale-up outcomes and the factors affecting these outcomes. The MSI was ultimately scaled-up across 27 districts. Repeated MSI cycles over time were found to foster greater feelings of autonomy among district health management teams (DHMTs) to address longstanding local problems, a more innovative use of existing resources without relying on additional funding, and improved teamwork. The use of 'resource teams' and the emergence of MSI 'champions', were both instrumental in supporting scale-up efforts. Challenges to the sustainability of the MSI include limited government buy-in and lack of sustained financial investment.

Racist and antiracist conspiracy theories

ABSTRACT In conspiracy theory philosophy the primary debate has been epistemic, but the concern is now also shifting to the ethical and political implications of taking conspiracy theories seriously. This shift is epitomized in the work of Quassim Cassam, whose focus has moved away from the epistemic faults of the theories and theorists to the political function of conspiracy theories. This function, he argues, is to ‘express and promote an ideology’, specifically, racist, antisemitic, and extremist ideology. In this essay, I will challenge two of Cassam’s assumptions. First, I will disagree with his claim that racist conspiracy theories ought to be treated dismissively as a class. This is due in part to the difficulties in defining racist conspiracy theories and due in part to what I call the Conspiracy Theory Location Problem, which is the challenge of identifying what constitutes ‘the theory’. In response, I suggest we need to be even more particularistic about ‘instances’ of a theory. Second, I disagree that the only function of conspiracy theories is to promote racist ideology. Instead, I argue that there exists a class of antiracist conspiracy theories that resist white supremacy by uncovering racist conspiracies and challenging racist institutions, systems, and ideologies.

On bias and its reduction via standardization in discretized electromagnetic source localization problems

On bias and its reduction via standardization in discretized electromagnetic source localization problems

Tropical convexity in location problems

AbstractWe investigate location problems where the optimal solution is found within the tropical convex hull of the given input points. Our initial focus is on geodesically star-convex sets, using the asymmetric tropical distance. We introduce the concept of tropically quasiconvex functions, which have sub-level sets with this shape, and are closely related to monotonic functions. Our findings demonstrate that location problems using tropically quasiconvex functions as distance measures will result in an optimal solution within the tropical convex hull of the input points. We also extend this result to cases where the input points are replaced with tropically convex sets. Finally, we explore the applications of our research in phylogenetics, highlighting the properties of consensus methods that arise from our class of location problems.

On crack simulation by mixed‐dimensional coupling in GFEM with global‐local enrichments

AbstractA strategy that combines the global‐local version of the generalized finite element method (GFEM) with a mixed‐dimensional coupling iterative method is proposed to simulate two‐dimensional crack propagation in structures globally represented by Timoshenko‐frame models. The region of interest called the local problem, where the crack propagates, is represented by a 2D elasticity model, where a fine mesh of plane stress/strain elements and special enrichment functions are used to describe this phenomenon accurately. A model of Timoshenko‐frame elements simulates the overall behavior of the structure. A coarse mesh of plane stress/strain elements provides a bridge between these two representation scales. The mixed‐dimensional coupling method imposes displacement compatibility and stress equilibrium at the interface between the two different element types by an iterative procedure based on the principle of virtual work. After establishing the constraint equations for the interface, the 2D elasticity model is related to the small‐scale model by the global‐local enrichment strategy of GFEM. In such a strategy, the numerical solutions of the local problem subjected to boundary conditions derived from the global‐scale problem enrich the approximation of this same global problem in an iterative procedure. Each step of the crack propagation requires a new sequence of this iterative global‐local procedure. On the other hand, the constraint equation for the interface is defined only once. The crack representation in a confined region by the global‐local strategy avoids a remeshing that would require new constraint equations. Two numerical problems illustrate the proposed strategy and assess the influence of the analysis parameters.

Augmented reality navigation method based on image segmentation and sensor tracking registration technology

With the rapid development of modern science and technology, navigation technology provides great convenience for people's life, but the problem of inaccurate localization in complex environments has always been a challenge that navigation technology needs to be solved urgently. To address this challenge, this paper proposes an augmented reality navigation method that combines image segmentation and multi-sensor fusion tracking registration. The method optimizes the image processing process through the GA-OTSU-Canny algorithm and combines high-precision multi-sensor information in order to achieve accurate tracking of positioning and guidance in complex environments. Experimental results show that the GA-OTSU-Canny algorithm has a faster image edge segmentation rate, and the fastest start speed is only 1.8 s, and the fastest intersection selection time is 1.2 s. The navigation system combining the image segmentation and sensor tracking and registration techniques has a highly efficient performance in real-world navigation, and its building recognition rates are all above 99%. The augmented reality navigation system not only improves the navigation accuracy in high-rise and urban canyon environments, but also significantly outperforms traditional navigation solutions in terms of navigation startup time and target building recognition accuracy. In summary, this research not only provides a new framework for the theoretical integration of image processing and multi-sensor data, but also brings innovative technical solutions for the development and application of practical navigation systems.

Fusion of Target and Keypoint Detection for Automated Measurement of Mongolian Horse Body Measurements

Accurate and efficient access to Mongolian horse body size information is an important component in the modernization of the equine industry. Aiming at the shortcomings of manual measurement methods, such as low efficiency and high risk, this study converts the traditional horse body measure measurement problem into a measurement keypoint localization problem and proposes a top-down automatic Mongolian horse body measure measurement method by integrating the target detection algorithm and keypoint detection algorithm. Firstly, the SimAM parameter-free attention mechanism is added to the YOLOv8n backbone network to constitute the SimAM–YOLOv8n algorithm, which provides the base image for the subsequent accurate keypoint detection; secondly, the coordinate regression-based RTMPose keypoint detection algorithm is used for model training to realize the keypoint localization of the Mongolian horse. Lastly, the cosine annealing method was employed to dynamically adjust the learning rate throughout the entire training process, and subsequently conduct body measurements based on the information of each keypoint. The experimental results show that the average accuracy of the SimAM–YOLOv8n algorithm proposed in this study was 90.1%, and the average accuracy of the RTMPose algorithm was 91.4%. Compared with the manual measurements, the shoulder height, chest depth, body height, body length, croup height, angle of shoulder and angle of croup had mean relative errors (MRE) of 3.86%, 4.72%, 3.98%, 2.74%, 2.89%, 4.59% and 5.28%, respectively. The method proposed in this study can provide technical support to realize accurate and efficient Mongolian horse measurements.

Research on optimization algorithms for localization and capacity determination of chargers considering the spatiotemporal distribution of electric vehicles

The optimized layout of electric vehicle (EV) chargers is not only crucial for users' convenience but also a key element in urban sustainable development, energy transition, and the promotion of new energy vehicles. In order to provide a basis for the problem of localization and capacity determination of chargers and compare the merits of several mainstream algorithms, this paper first establishes an optimization model with the objective of minimizing the total investment cost of all the chargers and the constraint of meeting the charging demands of all electric vehicles. Optimizations were performed using genetic algorithm (GA), surrogate optimization algorithm (SOA), and mixed integer linear programming (MILP) algorithm, respectively. In the case of using MILP, the original nonlinear optimization problem was transformed into a linear problem. In the planning of city-level EV chargers, MILP took 14182.57 s to calculate the minimum cost of 34.62 million yuan. After retaining only 10% of the original data amount, SOA took 87651.34 s to calculate the minimum cost of 3.01 million yuan. The results indicate that GA is prone to falling into local optima and is not suitable for large-scale optimization problems. SOA, on the other hand, requires significant memory consumption, so the issue of memory usage needs to be carefully considered when using it directly. Although MILP is only applicable to linear programming problems, it has the advantages of lower memory usage and higher reliability if the problem can be transformed into a linear one.

Capacitated Mobile Facility Location Problem with Mobile Demand: Efficient relief aid provision to en route refugees

As a humanity crisis, the tragedy of forced displacement entails relief aid distribution efforts among en route refugee to alleviate their migration hardships. This study aims to assist humanitarian organizations in cost-efficiently optimizing the logistics of capacitated mobile facilities utilized to deliver relief aid to transiting refugees in a multi-period setting. The problem is referred to as the Capacitated Mobile Facility Location Problem with Mobile Demands (CMFLP-MD). In CMFLP-MD, refugee groups follow specific paths, and meanwhile, they receive relief aid at least once every fixed number of consecutive periods, maintaining continuity of service. To this end, the overall costs associated with capacitated mobile facilities, including fixed, service provision, and relocation costs, are minimized. We formulate a mixed integer linear programming (MILP) model and propose two solution methods to solve this complex problem: an accelerated Benders decomposition approach as an exact solution method and a matheuristic algorithm that relies on an enhanced fix-and-optimize agenda. We evaluate our methodologies by designing realistic instances based on the Honduras migration crisis that commenced in 2018. Our numerical results reveal that the accelerated Benders decomposition excels MILP with a 46% run time improvement on average while acquiring solutions at least as good as the MILP across all instances. Moreover, our matheuristic acquires high-quality solutions with a 2.4% average gap compared to best-incumbents rapidly. An in-depth exploration of the solution properties underscores the robustness of our relief distribution plans under varying migration circumstances. Across several metrics, our sensitivity analyses also highlight the managerial advantages of implementing CMFLP-MD solutions.

A reinforcement learning guided hybrid evolutionary algorithm for the latency location routing problem

The latency location routing problem integrates the facility location problem and the multi-depot cumulative capacitated vehicle routing problem. This problem involves making simultaneous decisions about depot locations and vehicle routes to serve customers while aiming to minimize the sum of waiting (arriving) times for all customers. To address this computationally challenging problem, we propose a reinforcement learning guided hybrid evolutionary algorithm following the framework of the memetic algorithm. The proposed algorithm relies on a diversity-enhanced multi-parent edge assembly crossover to build promising offspring and a reinforcement learning guided variable neighborhood descent to determine the exploration order of multiple neighborhoods. Additionally, strategic oscillation is used to achieve a balanced exploration of both feasible and infeasible solutions. The competitiveness of the algorithm against state-of-the-art methods is demonstrated by experimental results on the three sets of 76 popular instances, including 51 improved best solutions (new upper bounds) for the 59 instances with unknown optima and equal best results for the remaining instances. We also conduct additional experiments to shed light on the key components of the algorithm.

Fairness in accessibility of public service facilities

Our paper studies a fair stochastic facility location problem with congestion under the max-min principle. We analyze the price of fairness in a two-location problem and develop a tractable optimization framework for the general multi-location setting. Evaluating the fair solution against the utilitarian solution on Buffalo's demographic data reveals that implementing a fair solution can substantially improve fairness measures (up to 98%) with relatively limited impact on the overall service quality.

PUBLIC INVESTMENT, ECONOMIC GROWTH AND EFFICIENCY

The definition of public investment is not as clear and precise as it may seem. Often, public investments are described as public expenditures that contribute to the stock of physical capital, encompassing tangible assets such as properties (excluding land), buildings and other structures (e.g., roads, airports, hospitals, schools, telecommunication structures, government buildings, bridges, etc.), machinery, transportation equipment, and intangible assets like intellectual property (OECD, 2016). Therefore, public investment is the allocation of funds that result in the growth of the aforementioned items. Public investment can be viewed as a political tool in the hands of a government, capable of altering or determining the course that a country's economy and other aspects of life will take. Governments utilize public investments to respond to a variety of challenges over time, addressing global issues such as climate change and local problems like periods of economic decline, diverse demographic trends, rapid urbanization, or adaptation to new technologies. As emphasized by the European Investment Bank, "properly functioning infrastructure networks are the backbone of flourishing economies" (European Investment Bank, 2010). Public investments are integral for economic and social prosperity, contributing to the national capital stock by allocating resources to essential basic infrastructure, education, research and development, thereby leading to increased productivity and improved living standards. The aim of this article is to analyze the role of public investments in the economy in addressing contemporary challenges.

О поиске источников шума группой взаимодействующих АНПА с применением генетического алгоритма

Marine unmanned systems are increasingly used in tasks related to search and observation missions. Protection of water areas, survey and inspection operations, tracking of marine animals are examples of such missions. Most of these tasks can be solved more efficiently by use a group of interacting marine drones, compared to single vehicle. The article considers the problem of passive detection and localization of noise objects in a given water area by use a group of autonomous underwater vehicles (AUV). It is assumed that antennas of scalar vector receivers are installed on board the AUV to detect and determine the direction to the noise source. Some ways of organizing the group work of such AUV are considered. A centralized version is investigated, when the collected current information from all AUV is transmitted to one AUV leader. On the base of received information processing, AUV leader forms a group work plan, including the motion parameters for all AUV, to detect and determine the location of noise sources. An approach based on the use of a genetic algorithm is considered, and several versions of its implementation are investigated. The results of model experiments are presented, which demonstrate the efficiency of the proposed algorithms.

On the Local Fourier Uniformity Problem for Small Sets

On the Local Fourier Uniformity Problem for Small Sets

Scientific research entangled in the context of social needs. A practical example of the implementation of the evaluation criterion social impact: original research project Popular Legal Culture

The article addresses the issue of evaluating the scientific activities of universities in Poland and around the world in relation to the new, third evaluation criterion: the qualitatively measured impact of scientific activities on the functioning of society and the economy. The context of the text is a broadly understood critical analysis of universities' policies towards contemporary social problems: exclusion, poverty, helplessness, ignorance of rights and obligations. Social impact, as one of the three criteria for assessing scientific units, has a chance to become a tool for eliminating social inequalities. It is an idea of broadly understood social policy to involve universities in social life. Thanks to the new tasks given to researchers, they have a real chance to improve the world around them and look at local problems and challenges. The text will present the original idea of a research team whose aim is to spread the legal awareness of students of pedagogy and social work, as well as teachers working in schools and social workers working with the socially excluded.

Noel Fitzpatrick

This article discusses the problem of idiomaticity in Bernard Stiegler’s philosophy, showing the connection between the emergence of “idiomatic difference” and what became known as anti-entropy in his late works. In turn, the conditions under which such difference is possible are closely related to the problem of locality. The article shows how idiom can emerge in the localized conditions of a linguistic community, and why it can be considered a key concept in the possibility of developing Stiegler’s project of neganthropology. Key-words: idiom, idiomaticity, idiomatic difference, idiolect

Darboux transformation-based LPNN generating novel localized wave solutions

Darboux transformation method is one of the most essential and important methods for solving localized wave solutions of integrable systems. In this work, we introduce the core idea of Darboux transformation of integrable systems into the Lax pairs informed neural networks (LPNNs), which we proposed earlier. By fully utilizing the data-driven solutions, spectral parameter and spectral function obtained from LPNNs, we present the novel Darboux transformation-based LPNN (DT-LPNN). The notable feature of DT-LPNN lies in its ability to solve data-driven localized wave solutions and spectral problems with high precision, and it also can employ Darboux transformation formulas of integrable systems and non-trivial seed solutions to discover novel localized wave solutions that were previously unobserved and unreported. The numerical results indicate that, by utilizing the single-soliton solutions as the non-trivial seed solutions, we obtain novel localized wave solutions for the Kraenkel–Manna–Merle (KMM) system by employing DT-LPNN, in which solution u changes from original bright single-soliton on zero background wave to new dark single-soliton dynamic behavior on a variable non-zero background wave. Moreover, by treating the two-soliton solutions as the non-trivial seed solutions, DT-LPNN generates novel localized wave solutions for the KMM system that exhibit completely different dynamic behaviors from prior two-soliton solutions. DT-LPNN combines the Darboux transformation theory of integrable systems with deep neural networks, offering a new approach for generating novel localized wave solutions using non-trivial seed solutions.

A hybrid swarm intelligence algorithm for region-based image fusion

This paper proposes a novel multi-hybrid algorithm named DHPN, using the best-known properties of dwarf mongoose algorithm (DMA), honey badger algorithm (HBA), prairie dog optimizer (PDO), cuckoo search (CS), grey wolf optimizer (GWO) and naked mole rat algorithm (NMRA). It follows an iterative division for extensive exploration and incorporates major parametric enhancements for improved exploitation operation. To counter the local optima problems, a stagnation phase using CS and GWO is added. Six new inertia weight operators have been analyzed to adapt algorithmic parameters, and the best combination of these parameters has been found. An analysis of the suitability of DHPN towards population variations and higher dimensions has been performed. For performance evaluation, the CEC 2005 and CEC 2019 benchmark data sets have been used. A comparison has been performed with differential evolution with active archive (JADE), self-adaptive DE (SaDE), success history based DE (SHADE), LSHADE-SPACMA, extended GWO (GWO-E), jDE100, and others. The DHPN algorithm is also used to solve the image fusion problem for four fusion quality metrics, namely, edge-based similarity index (QAB/F\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Q^{AB/F}$$\\end{document}), sum of correlation difference (SCD), structural similarity index measure (SSIM), and artifact measure (NAB/F\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N^{AB/F}$$\\end{document}). The average QAB/F=0.765508\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Q^{AB/F} = 0.765508$$\\end{document}, SCD=1.63185\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$SCD = 1.63185$$\\end{document}, SSIM=0.726317\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$SSIM = 0.726317$$\\end{document}, and NAB/F=0.006617\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N^{AB/F} = 0.006617$$\\end{document} shows the best combination of results obtained by DHPN with respect to the existing algorithms such as DCH, CBF, GTF, JSR and others. Experimental and statistical Wilcoxon’s and Friedman’s tests show that the proposed DHPN algorithm performs significantly better in comparison to the other algorithms under test.

Improving Accessibility to Outpatient Department Through Reduction of Third Next Available Appointment in Tertiary Hospital in Taif, Saudi Arabia

Background The Outpatient Department (OPD) of Al Hada Armed Forces Hospital - Taif Region (AHAFH) plays a critical role in providing specialized medical services to a diverse population in Taif region, Saudi Arabia. The hospital faces the challenge of ensuring timely access to a comprehensive array of 40 specialized services and subspecialties, supported by 120 rooms in the OPD. This article outlines a significant understanding of the patient flow process and queuing mechanism in the context of the OPD and the dynamics of supply and demand. Local Problem Our project was initiated during the last few weeks of fourth quarter 2020, which revealed a concerning trend of increasing the third next available appointment (TNAA), surpassing the optimal target of <14 days. Methods The study used pre-test and post-test monitoring of the TNAA. Patients’ categories of referral were identified as urgent and routine. We also established a referral and discharge criteria for each clinic specialty and reminder messages on patient appointments 24 hours and 48 hours prior to their actual appointment. Interventions The Plan-Do-Study-Act (PDSA) model for improvement was utilized to implement action plans and interventions to address the increase of TNAA (in days) in the OPD. Weekly huddles were conducted to monitor project progress, action plans, data, and challenges. Results During the intervention phase, notable changes were observed in the control chart, including shifts in TNAA mean decreasing from 9.4 days to 5.6 days after the implementation of four PDSA cycles. Moreover, the average TNAA in each quarter showed consistent reduction in TNAA, with an average of six days in Q1 2022. Conclusions The study sheds light on the complexity of managing the patient flow and access in an outpatient setting. By implementing effective strategies and continuous and vigilant monitoring of the outcomes, outpatient departments can strive to ensure that patients receive specialized care in a timely manner.

A Power Method for Computing the Dominant Eigenvalue of a Dual Quaternion Hermitian Matrix

In this paper, we first study the projections onto the set of unit dual quaternions, and the set of dual quaternion vectors with unit norms. Then we propose a power method for computing the dominant eigenvalue of a dual quaternion Hermitian matrix. For a strict dominant eigenvalue, we show the sequence generated by the power method converges to the dominant eigenvalue and its corresponding eigenvector linearly. For a general dominant eigenvalue, we establish linear convergence of the standard part of the dominant eigenvalue. Based upon these, we reformulate the simultaneous localization and mapping problem as a rank-one dual quaternion completion problem. A two-block coordinate descent method is proposed to solve this problem. One block has a closed-form solution and the other block is the best rank-one approximation problem of a dual quaternion Hermitian matrix, which can be computed by the power method. Numerical experiments are presented to show the efficiency of our proposed power method.