Multilayer Problem Research Articles

“You have to believe the patient”: What do people with fibromyalgia find helpful (and hindering) when accessing health care?

ABSTRACT Background Fibromyalgia (FM) is a complex, still poorly understood, and difficult-to-treat chronic pain condition for which many people struggle to find adequate care. Aims This study investigated the research question, “What do people accessing health care services for fibromyalgia perceive as helpful, hindering, and absent but desired?” with the aim of identifying clear, implementable changes for clinical practice. Materials and methods This study used the enhanced critical incident technique (ECIT), a qualitative research method that focuses on helping, hindering, and desired factors, to explore the health care experiences of 14 individuals (12 women and 2 men) diagnosed with FM. Results Using qualitative data analysis, results identified three categories of health care experiences: (1) systemic navigation, including financial and economic security; accessibility, flexibility, and continuity of care; and diversity of treatment options; (2) clinician–patient alliance, including invalidation and prejudice; therapeutic bond; and clinician–patient alignment on treatment plan; and (3) patient self-management strategies, including information-seeking and education, self-advocacy, social supports, symptom management strategies, and other coping strategies. Participants tended to conceptualize their health care concerns as a multilayered systemic problem. Conclusions Participants described a medical system they perceived as poorly equipped to support their needs and tended to invalidate their health concerns. Helping experiences tended to be the result of unique efforts on the part of individual clinicians. Findings emphasize the importance of recognizing the complexities and psychological impact of pain, trusting clinician–patient relationships, multidisciplinary/interdisciplinary care within a biopsychosocial framework, and improved education and awareness around psychosocial aspects of FM and effective management of chronic pain.

ADMIRE: collaborative data-driven and model-driven intelligent routing engine for traffic grooming in multi-layer X-Haul networks

X-Haul aims to provide a unified transport network for integrating mobile fronthaul/midhaul/backhaul. Its architecture is inherited from the IP-over-WDM paradigm, in which the upper layer is an electrical packet-switched network and the bottom layer is an optical circuit-switched network. Traffic grooming in X-Haul poses a big challenge, as the mobile traffic load has shown. As a multi-layer routing problem, traffic grooming has been well studied through some classic mathematical models, such as the auxiliary graph (AG). Recently, a new approach based on machine learning has been attracting much attention for its network routing decision making. According to previous studies, either of the two approaches has its limitations, but the combination of the two may achieve profit maximization. To this end, we propose and demonstrate, for the first time, to our knowledge, a collaborative data-driven (using machine learning) and model-driven (using experiential knowledge) intelligent routing engine (ADMIRE) for traffic grooming in an X-Haul testbed with a real mobile dataset. The principle of ADMIRE is to exploit the capability of machine learning for an accurate AG model in a dynamic network environment. We compare ADMIRE with a traditional model-driven approach (i.e., the AG), and the evaluation results show that ADMIRE can achieve good performance and a strong generalization ability. In addition, we also verify the influence of data correlation on ADMIRE.

Decision-making for Product Life Cycle Design Solution Based on Multi-layer Constraints Satisfaction Problem

Decision-making for Product Life Cycle Design Solution Based on Multi-layer Constraints Satisfaction Problem

Renewable-based microgrids’ energy management using smart deep learning techniques: Realistic digital twin case

In this research study, a novel demand response program (DRP) has been proposed for renewable-based microgrids (MGs) which takes into account the high penetration of tidal units and solar energy as important, prevalent renewable resources in the power systems. To this end, multi-objective problem (MOP) structure is a promising solution to reduce the whole operating costs of the scheduling problem as well as decrease the high risk of failure in electrical power transmission because of component increasing failure rates and extended repair time. The complexities and nonlinearity of the problem necessitate an innovative heuristic solution which is derived from the Grey Wolf optimization algorithm to help for resolving the problem without making any assumptions or compromising precision. This paper also proposes the dynamic 3-phase correction (DPC) formulation to boost the layout convergence ability by increasing the global search features. Through such a modification, the diversity of the members in the algorithm population increases which would result in low computational burden and low possibility of trapping in local optima. Moreover, it is necessary to have a clear and accurate estimation of the output power of renewable sources in the system. Considering that solar irradiance is hard to anticipate, this paper develops a deep learning layout that uses generative adversarial networks (GAN) to forecast the hourly power generation of the tidal and solar agents. GAN model consists of two competing networks which help to enhance the training process by increasing the accuracy of distinguishing real data from fake data. At the end, IEEE standard test system is used to evaluate the efficiency and effectiveness of the suggested multi-layer problem. The simulation results display that the suggested deep model outperforms other well-known algorithms in smart microgrids.

Theoretical analysis of a two-dimensional multilayer diffusion problem with general convective boundary conditions normal to the layered direction

Theoretical analysis of transient thermal conduction in a two-dimensional multilayer structure has been limited to problems with isothermal or adiabatic boundary conditions along the walls normal to the layered direction. Due to mathematical difficulties pointed out in past work, an analytical solution has not been possible so far for the general case where each layer has a distinct convective boundary condition. This work presents an analytical technique to solve this general problem using Laplace transforms followed by derivation of a sufficient number of linear algebraic equations based on given boundary conditions to determine the coefficients of an eigenfunction-based series solution. This technique makes it possible to solve the problem when each layer may have a different convective heat transfer coefficient along the walls normal to the layered direction. Results from this general analysis are shown to correctly reduce to past work for the special cases of very small or very large Biot number. Good agreement with specific results presented in a past paper is also demonstrated. The technique is used to investigate the impact of key dimensionless parameters on the temperature field. This work significantly generalizes past work that was limited only to adiabatic or isothermal boundary conditions. Results presented here may help improve the theoretical understanding of multilayer diffusion problems, and make theoretical models much more representative of realistic conditions.

Analytical method for mechanical analysis of multiple shallow tunnels with concrete linings

The mechanical evaluation of multiple shallow tunnels is essential for predicting the potential risk. In previous analytical studies, the Schwarz alternating method is the unique tool to calculate the mechanical field of multiple shallow tunnels through a complicated iterative process. This work will develop a novel analytical method to calculate the ground movement and stress caused by excavating multiple tunnels. The proposed method introduces an enhanced potential function to reformulate the mechanical expressions of multiply-connected domains. And then, solutions of high accuracy can be obtained directly without any iteration. The key advantage of our method is that it can analyze the mechanical response of arbitrary-shaped shallow tunnels, including multiple excavations, complicated lining structures and multi-layer soil problems. Finally, our analytical solutions are verified by the field measuring data, existing analytical method, and numerical simulation. The results turned out that our method has high precision and good practicability.

Evolutionary Multitasking Multilayer Network Reconstruction

Due to the multilayer nature of real-world systems, the problem of inferring multilayer network structures from nonlinear and complex dynamical systems is prominent in many fields, including engineering, biological, physical, and computer sciences. Many network reconstruction methods have been proposed to address this problem, but none of them consider the similarities among network reconstruction tasks at different component layers, which are inspired by topology correlations and dynamic couplings among different component layers. This article develops an evolutionary multitasking multilayer network reconstruction framework to make use of the correlations among different component layers to improve the reconstruction performance; we refer to this framework as EM2MNR. In EM2MNR, the multilayer network reconstruction problem is first established as a multitasking multilayer network reconstruction problem, where the goal of each task is to reconstruct the network structure of a component layer. In addition, multitasking multilayer network reconstruction problems are high dimensional, but existing evolutionary multitasking algorithms may have poor performance when dealing with optimization problems with a high-dimensional search space. Inspired by the sparsity of multilayer networks, EM2MNR employs the restricted Boltzmann machine to extract low effective features from the original decision space and then decides whether to conduct knowledge transfer on these features. To verify the performance of EM2MNR, this article also designs a test suite for multilayer network reconstruction problems. The experimental results demonstrate the significant improvement obtained by the proposed EM2MNR framework on 96 multilayer network reconstruction problems.

Evaluation of China's regional innovation capability based on simulated annealing projection pursuit model and nested fuzzy evaluation model

AbstractRegional innovation capability is an important indicator of both regional innovative and long‐term development. The purpose of this study is to build an evaluation index system for regional innovation capability in order to identify regional differences and support innovation more effectively. After establishing a reasonable evaluation value for regional innovation capability, a combination of simulated annealing optimized projection pursuit (SA–PP) and N‐layer nested fuzzy comprehensive evaluation models is used to assess China's regional innovation capabilities. The results show that the SA–PP model effectively mitigates the risk of erroneous evaluation results caused by index weight uncertainty, resulting in a more reasonable, robust, and intelligent assessment of regional innovation capability. Furthermore, the nested fuzzy comprehensive evaluation model is capable of easily resolving the evaluation factor set's heterogeneity and multilayer problems. The most significant influences on China's regional innovation capability are knowledge acquisition and enterprise innovation. The comprehensive score of the proposed combinational evaluation model manifests that provinces with strong regional innovation capabilities are mainly concentrated in the southeast coastal regions. The research results allow for precise weight determination and object ranking.

Cloud-Edge-End Collaboration in Air–Ground Integrated Power IoT: A Semidistributed Learning Approach

The combination of air–ground integrated power Internet of Things (AGI-PIoT) and cloud-edge-end collaboration enables flexible coverage and real-time data processing. However, how to achieve intelligent cloud-edge-end collaboration in AGI-PIoT faces several challenges such as dynamics of aerial networks, coupling of resource allocation in multiple layers, timescales, and dimensions, incomplete information, and dimensionality curse. In this article, we propose a FEderated Deep rEinforcement leaRning-based multi-lAyer multi-Timescale multi-dImensional resOurce allocatioN algorithm (FEDERATION). The multilayer multitimescale multidimensional resource allocation problem is decomposed into three subproblems based on Lyapunov optimization. For the subproblem of joint task offloading and power control, a federated deep actor-critic-based semidistributed algorithm is developed. The subproblem of admission control is solved by quadratic programming. The third subproblem is addressed through smooth approximation and Lagrange dual decomposition. Simulation results indicate that FEDERATION outperforms existing algorithms in queuing delay, energy consumption, and convergence.

SPH modeling and investigation of cold spray additive manufacturing with multi-layer multi-track powders

Cold spray (CS), as a new solid-state additive manufacturing technique, involves complex phenomena like high-speed impacting of micro-powders, large material deformations, phase change and jet generation. The CS process is difficult to be accurately simulated, and the underlying coating mechanisms, especially for multi-powders CS, have not been well understood. As a Lagrangian and particle method, the smoothed particle hydrodynamics (SPH) method has natural advantages in treating large deformations and capturing moving interfaces. In this work, we present an improved SPH approach to implement three-dimensional particle modeling and study of the multi-layer multi-track CS problem. The kernel gradient correction (KGC), adaptive smoothing length and the constitutive model are integrated in the improved SPH. The contour profile and width thickness ratio of the deformed powder obtained by the present simulation are very close to the experimental results. The bonding behaviors in a single powder CS with different materials are first explained where the critical velocities for different materials are obtained. Furthermore, coating behaviors in CS with different layers and tracks of powders are comprehensively analyzed. Some typical deformation parameters of the powders track with regard to an increasing impacting velocity are attained. With the present numerical model, a larger scale multi-layer multi-track CS is promising to be reproduced with some parameter windows predicted, which is greatly helpful to CS operations.

In-Work Poverty: A Multi-Layered Problem across European Union Countries

The article’s primary purpose is to present an understanding of in-work poverty by defining the mentioned phenomenon uniformly in all European Union countries and by demonstrating its main determinants. First of all, it is necessary to present a complex definition of the term in-work poverty and refer it to the concept of precarious work functioning in both the legal circuit and the literature. The analysis starts by presenting the existing indicators and measurements of this phenomenon as a basis for conclusions on legal, economic, and social conditions of in-work poverty. The critical role in this regard is attributed to labour law and social security regulations and their continuous changes caused by adapting to new forms of organizations of work and to expectations coming from market competition. The broad range of analyses of phenomena in the work-poverty results from studies delivered under a project of the same name “Working Yet Poor.” The project aims to examine the regulatory structures affecting the working conditions and to assess different aspects of regulations that can have a direct and indirect impact on the occurrence of this phenomenon.

Can MCDA Serve Ex-Post to Indicate ‘Winners and Losers’ in Sustainability Dilemmas? A Case Study of Marine Spatial Planning in Germany

Multi-criteria decision analyses (MCDAs) have been developed to support and evaluate decision-making on multi-layered problems. The benefit lies in creating transparency, among other benefits, especially in tackling divergent stakeholder interests. Within the energy transition, area shortage can lead to sustainability trade-offs, calling for the reconciliation of planning processes and satisfactory compromises. While ex ante MCDAs complement planning, the ex post consideration of processes has been less widely studied. Using a case study of offshore wind energy (OWP) within German marine spatial planning, we investigated the shifting weights of sustainability criteria and stakeholder interests. A multi-criteria approach (Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE)) addressed how decision-making can be iteratively traced, and the winners and losers indicated in sustainability dilemmas, such as between climate and biodiversity implications. Findings illustrate that stakeholders are divided in the green-on-green dilemma. The ‘winners’ embrace the branches of energy and climate protection. It remains a question though for ‘losers’ how weighting decisions of sustainability goals can be detrimental, such as ‘good environmental status’, and what kind of balancing occurs. How compromises are found, such as through transparency and solid justification, is crucial in satisfactorily solving trade-offs for public interests. PROMETHEE makes revealing stakeholder constellations within policy dynamics feasible, though assuming there is the will to work multidisciplinarily within future planning decisions.

A DRL-Driven Intelligent Optimization Strategy for Resource Allocation in Cloud-Edge-End Cooperation Environments

Complex dynamic services and heterogeneous network environments make the asymmetrical control a curial issue to handle on the Internet. With the advent of the Internet of Things (IoT) and the fifth generation (5G), the emerging network applications lead to the explosive growth of mobile traffic while bringing forward more challenging service requirements to future radio access networks. Therefore, how to effectively allocate limited heterogeneous network resources to improve content delivery for massive application services to ensure network quality of service (QoS) becomes particularly urgent in heterogeneous network environments. To cope with the explosive mobile traffic caused by emerging Internet services, this paper designs an intelligent optimization strategy based on deep reinforcement learning (DRL) for resource allocation in heterogeneous cloud-edge-end collaboration environments. Meanwhile, the asymmetrical control problem caused by complex dynamic services and heterogeneous network environments is discussed and overcome by distributed cooperation among cloud-edge-end nodes in the system. Specifically, the multi-layer heterogeneous resource allocation problem is formulated as a maximal traffic offloading model, where content caching and request aggregation mechanisms are utilized. A novel DRL policy is proposed to improve content distribution by making cache replacement and task scheduling for arriving content requests in accordance with the information about users’ history requests, in-network cache capacity, available link bandwidth and topology structure. The performance of our proposed solution and its similar counterparts are analyzed in different network conditions.

Diffusion and reaction in a two-dimensional multilayer body: Analytical solution and imaginary eigenvalue analysis

Heat and mass transfer in multilayer bodies occur commonly in a number of engineering problems, such as thermal management, manufacturing and reacting systems. Much of the past literature on theoretical analysis of multilayer diffusion-reaction problems is based on the assumption of one-dimensional transport. Given the geometrical complexity of practical engineering systems, two- or three-dimensional analysis may be needed for improved accuracy. This work presents theoretical analysis of coupled diffusion and reaction occurring in a general, two-dimensional multilayer body. The transient temperature distribution is written in the form of an infinite series, and the eigenequation for this problem is derived. Adiabatic or isothermal conditions along the side walls are accounted for by appropriate choice of eigenvalues in that direction. Results are shown to be in good agreement with numerical simulations. It is shown that the temperature distribution may converge or diverge, depending on the specific values of various problem parameters, such as reaction coefficients and diffusivities in each layer, as well as the nature of the boundary conditions in both directions. An analysis for the existence of imaginary eigenvalues, which are related to divergence at large times is presented. The theoretical model is used to predict the limits of design parameters to prevent thermal runaway in a two-layer Li-ion cell. This work improves upon the limited past work on multi-dimensional multilayer transport by accounting for the reaction term, and by identifying the possibility of imaginary eigenvalues in such problems. Due to the close relationship between imaginary eigenvalues and thermal runaway, these results may contribute towards improved safety and reliability of practical engineering systems.

Spectral clustering of single-cell multi-omics data on multilayer graphs.

Single-cell sequencing technologies that simultaneously generate multimodal cellular profiles present opportunities for improved understanding of cell heterogeneity in tissues. How the multimodal information can be integrated to obtain a common cell type identification, however, poses a computational challenge. Multilayer graphs provide a natural representation of multi-omic single-cell sequencing datasets, and finding cell clusters may be understood as a multilayer graph partition problem. We introduce two spectral algorithms on multilayer graphs, spectral clustering on multilayer graphs and the weighted locally linear (WLL) method, to cluster cells in multi-omic single-cell sequencing datasets. We connect these algorithms through a unifying mathematical framework that represents each layer using a Hamiltonian operator and a mixture of its eigenstates to integrate the multiple graph layers, demonstrating in the process that the WLL method is a rigorous multilayer spectral graph theoretic reformulation of the popular Seurat weighted nearest neighbor (WNN) algorithm. Implementing our algorithms and applying them to a CITE-seq dataset of cord blood mononuclear cells yields results similar to the Seurat WNN analysis. Our work thus extends spectral methods to multimodal single-cell data analysis. The code used in this study can be found at https://github.com/jssong-lab/sc-spectrum. All public data used in the article are accurately cited and described in Materials and Methods and in Supplementary Information. Supplementary data are available at Bioinformatics online.

Multilayer cellular learning automata: A computational model to solve multilayer infrastructure problems with its application in community detection for multilayer networks

Multilayer networks are one type of complex systems that have multiple types of interactions among their nodes. Through the development and extension of the real-world networks, especially social and biological networks, understanding the structure and organization of multilayer systems become more significant. In this paper, we propose a new computational model based on learning automata to overcome the problems related to multilayer networks. In our proposed model, the whole network is modeled as a set of layers in which an irregular cellular learning automata (ICLA) is assigned to each node in a layer. The functionality of cellular learning automata (CLA) is dependent on two important features: local and global environments. In this model, the information from the other layers is evaluated as the global environments, and update rules are performed based on the information from the other layers. Finally, by the convergence of CLA in each layer the global environments cause the multilayer cellular learning automata (MLCLA) to cover all possible states in all nodes of layers. We apply this model to community detection in multilayer networks as an example of the application of the proposed model. To solve this, each CLA by the depth-first search algorithm detects the optimal community in a layer and then sends the information about the identified community to the next layer as a feature vector. The simulation results on multiple well-known multilayer datasets demonstrate the effectiveness and superiority of the proposed algorithm in terms of similarity and modularity measures, and the analysis results of the proposed algorithm in terms of Precision, Recall, and F1-score are higher than the other available community detection methods.

Bioshock: Infinite as the Mirror of America?

Few mainstream computer games have caused such controversy as Bioshock: Infinite (2013). The third instalment of the Bioshock series is set in the fictional city of Columbia in an alternate history of the early twentieth century, which at first glance appears to be a perfect social utopia. After a while, however, the narrative begins to uncover the multilayered problems of society oppressed by a fraction of the white elite and religious fanaticism. The popularity of the game is not only due to the attractive audiovisual processing and complex game mechanics. It is primarily a story that uses an unprecedented amount of religious symbolism – especially Christian symbols, historical references, polysemic story elements, and a story based on the concepts of frontier myth and American exceptionalism. This work will deal with analysing these phenomena, especially those directly related to the religious and nationalistic topics in the United States.

Generalized Laplace-Type Transform Method for Solving Multilayer Diffusion Problems

Multilayer diffusion problems have found significant importance that they arise in many medical, environmental, and industrial applications of heat and mass transfer. In this article, we study the solvability of a one-dimensional nonhomogeneous multilayer diffusion problem. A new generalized Laplace-type integral transform is used, namely, the M ρ , m -transform. First, we reduce the nonhomogeneous multilayer diffusion problem into a sequence of one-layer diffusion problems including time-varying given functions, followed by solving a general nonhomogeneous one-layer diffusion problem via the M ρ , m -transform. Hence, by means of general interface conditions, a renewal equations’ system is determined. Finally, the M ρ , m -transform and its analytic inverse are used to obtain an explicit solution to the renewal equations’ system. Our results are of general attractiveness and comprise a number of previous works as special cases.

A multi-product multi-layer urban freight distribution problem solved using a hybrid metaheuristic procedure

The pick-up and delivery routing problem has received special attention thanks to its application to urban freight distribution processes. However, due to the multiple levels involved in those processes, modeling and analyzing urban distribution networks in urban contexts are complex tasks. As a result, efficient and robust solution methods should be proposed according to the dynamic and uncertain conditions that characterize this type of problems. This article presents a new formulation for the pick-up and delivery problem in a logistics distribution network composed of 3 levels: n: 1: m (n suppliers, 1 urban consolidation center, and m customers). In addition, an algorithm based on GRASP heuristic and 2-opt algorithm was implemented here to find solutions to problem, which were compared with the results of the same algorithm for a two-layer vehicle routing problem in several instances. Thus, the proposed procedure achieved a 22% improvement over such algorithm.

Verification of multi-layered assignment problems

The class of assignment problems is a fundamental and well-studied class in the intersection of Social Choice, Computational Economics and Discrete Allocation. In a general assignment problem, a group of agents expresses preferences over a set of items, and the task is to allocate items to agents in an “optimal” way. A verification variant of this problem includes an allocation as part of the input, and the question becomes whether this allocation is “optimal”. In this paper, we generalize the verification variant to the setting where each agent is equipped with multiple incomplete preference lists: Each list (called a layer) is a ranking of items in a possibly different way according to a different criterion. In particular, we introduce three multi-layer verification problems, each corresponds to an optimality notion that weakens the notion of global optimality (that is, pareto optimality in multiple layers) in a different way. Informally, the first notion requires that, for each group of agents whose size is exactly some input parameter k, the agents in the group will not be able to trade their assigned items among themselves and benefit in at least \(\alpha \) layers; the second notion is similar, but it concerns all groups of size at most k rather than exactly k; the third notion strengthens these notions by requiring that groups of k agents will not be part of possibly larger groups that benefit in at least \(\alpha \) layers. We study the three problems from the perspective of parameterized complexity under several natural parameterizations such as the number of layers, the number of agents, the number of items, the number of allocated items, the maximum length of a preference list, and more. We present an almost comprehensive picture of the parameterized complexity of the problems with respect to these parameters.