Chain Network Research Articles

Predicting business processes of the social insurance using recurrent neural network and Markov chain

PurposePredicting the final status of an ongoing process or a subsequent activity in a process is an important aspect of process management. Semi-structured business processes cannot be predicted by precise and mathematical methods. Therefore, artificial intelligence is one of the successful methods. This study aims to propose a method that is a combination of deep learning methods, in particular, the recurrent neural network and Markov chain.Design/methodology/approachThe proposed method applies the BestFirst algorithm for the search section and the Cfssubseteval algorithm for the feature comparison section. This study focuses on the prediction systems of social insurance and tries to present a method that is less costly in providing real-world results based on the past history of an event.FindingsThe proposed method is simulated with real data obtained from Iranian Social Security Organization, and the results demonstrate that using the proposed method increases the memory utilization slightly more than the Markov method; however, the CPU usage time has dramatically decreased in comparison with the Markov method and the recurrent neural network and has, therefore, significantly increased the accuracy and efficiency.Originality/valueThis research tries to provide an approach capable of producing the findings closer to the real world with fewer time and processing overheads, given the previous records of an event and the prediction systems of social insurance.

Trust, quality, and the network collection experience: A tale of two studies on the Democratic Republic of the Congo

Data collection in social network research has advanced to include online questionnaires, digital metadata mining of internet sites, and the use of remote-sensing technologies. Some scholars however call for more attention to nuanced understandings of ties and contexts in studies of social structure and relationships, evoking practices that characterise the field’s foundational works. This article’s two studies reference these earlier efforts, drawing on ethnography and primary data collection. Both were undertaken in conflict-affected eastern Democratic Republic of the Congo (DRC) and broadly aimed to refine understandings of public authority and governance. Such research strives to inform humanitarian interventions to support social structures and actors which benefit their communities – however unexpected and unconventional. The first study (2016) employed a novel link-tracing design to examine personal support networks entwining purportedly hostile sub-populations, from combatants to unaffiliated civilians. The second (2018−20) focussed on access to essential social services across different governance arrangements, areas dominated by tenuous alliances of domestic or foreign militias and other actors. Leveraging an egocentric network design, it yielded multilevel relational network chain data. Each study was rife with obstacles related to accessing participants, sampling, reliability, and validity. We reflect on this network collection experience, foregrounding the interdependence between trust and data quality brought into stark relief by the setting’s instability and insecurity. This interdependency impacts all social network research, especially when it involves precarious contexts or sensitive topics.

State Aggregations in Markov Chains and Block Models of Networks.

We consider state-aggregation schemes for Markov chains from an information-theoretic perspective. Specifically, we consider aggregating the states of a Markov chain such that the mutual information of the aggregated states separated by T time steps is maximized. We show that for T=1 this recovers the maximum-likelihood estimator of the degree-corrected stochastic block model as a particular case, which enables us to explain certain features of the likelihood landscape of this generative network model from a dynamical lens. We further highlight how we can uncover coherent, long-range dynamical modules for which considering a timescale T≫1 is essential. We demonstrate our results using synthetic flows and real-world ocean currents, where we are able to recover the fundamental features of the surface currents of the oceans.

Scalable Orchestration of Service Function Chains in NFV-Enabled Networks: A Federated Reinforcement Learning Approach

Network function virtualization (NFV) is critical to the scalability and flexibility of various network services in the form of service function chains (SFCs), which refer to a set of Virtual Network Functions (VNFs) chained in a specific order. However, the NFV performance is hard to fulfill the ever-increasing requirements of network services mainly due to the static orchestrations of SFCs. To tackle this issue, a novel Scalable SFC Orchestration (SSCO) scheme is proposed in this paper for NFV-enabled networks via federated reinforcement learning. SSCO has three remarkable characteristics distinguishing from the previous work: (1) A federated-learning-based framework is designed to train a global learning model, with time-variant local model explorations, for scalable SFC orchestration, while avoiding data sharing among stakeholders; (2) SSCO allows for parameter update among local clients and the cloud server just at the first and last epochs of each episode to ensure that distributed clients can make model optimization at a low communication cost; (3) SSCO introduces an efficient deep reinforcement learning (DRL) approach, with the local learning knowledge of available resources and instantiation cost, to map VNFs into networks flexibly. Furthermore, a loss-weight-based mechanism is proposed to generate and exploit reference samples in replay buffers for future training, avoiding the strong relevance of samples. Simulation results obtained from different working scenarios demonstrate that SSCO can significantly reduce placement errors and improve resource utilization ratio to place time-variant VNFs compared with the state-of-the-art mechanisms. Furthermore, the results show that the proposed approach can achieve desirable scalability.

Double-network thermocells with extraordinary toughness and boosted power density for continuous heat harvesting

Double-network thermocells with extraordinary toughness and boosted power density for continuous heat harvesting

Modelling the future vulnerability of urban green space for priority-based management and green prosperity strategy planning in Kolkata, India: a PSR-based analysis using AHP-FCE and ANN-Markov model

Changes in land-use and land-cover (LULC) in urban areas affect the natural environment, especially urban green spaces (UGS). The present study examines the loss of UGS due to LULC transformation at different periods to predict the future vulnerable zone of UGS, based on the 'Pressure-State-Response’ framework. To calculate the weight of each factor, a combined Analytical Hierarchical Process and Fuzzy Comprehensive Evaluation method have been used. An integrated multilayer perceptron based artificial neural network and Markov chain (MLP-ANN-MC) model has been employed to predict the UGS vulnerable area in Kolkata. Results indicated that growth rates of built-up area, land-use dynamic degree, change intensity index, and proximity factors are the major responsible for UGS vulnerability. Applying the MLP-ANN-MC model, future vulnerable zones were identified for management and conservation of UGS. The methodology developed and demonstrated in this study expands LULC change analysis and provide a new dimension for UGS vulnerability assessment.

Study on controlling nicotine release from snus by the SIPN membranes

Abstract Snus is one of the types of smokeless tobaccos, which is popular in many countries and regions. The nicotine level in the plasma samples obtained from snus users is similar to the nicotine level obtained from cigarette smokers; hence, the snus users find it difficult to quit. The aim of this study is to develop new semi-interpenetrating polymer (SIPN) membranes that control the stable release of nicotine from snus, achieving the purpose of quitting smoking. Here, the SIPN comprising a polyacrylate network and PEG chains was synthesized through in situ radical polymerization. The SIPN was characterized by Fourier transform infrared spectroscopy, contact angle, differential scanning calorimetry, scanning electron microscopy, cytotoxicity, and in vitro release experiments. Furthermore, this SIPN was used to evaluate the release properties of nicotine in different snus samples varying in moisture, pH, and the tobacco’s particle size. The results indicated that the new SIPN could control release of nicotine at a specific rate, and the release rate could be adjusted according to the customer requirements. Thus, the new SIPN was contributed to build a new nicotine replacement therapy that served the snus users.

Resource Aware Routing for Service Function Chains in SDN and NFV-Enabled Network

Owing to the Network Function Virtualization (NFV) and Software-Defined Networks (SDN), Service Function Chain (SFC) has become a popular service in SDN and NFV-enabled network. However, as the Virtual Network Function (VNF) of each type is generally multi-instance and flows with SFC requests must traverse a series of specified VNFs in predefined orders, it is a challenge for dynamic SFC formation to optimally select VNF instances and construct paths. Moreover, the load balancing and end-to-end delay need to be paid attention to, when routing flows with SFC requests. Additionally, fine-grained scheduling for traffic at flow level needs differentiated routing which should take flow features into consideration. Unfortunately, traditional algorithms cannot fulfill all these requirements. In this paper, we study the Differentiated Routing Problem considering SFC (DRP-SFC) in SDN and NFV-enabled network. We formulate the DRP-SFC as a Binary Integer Programming (BIP) model aiming to minimize the resource consumption costs of flows with SFC requests. Then a novel routing algorithm, Resource Aware Routing Algorithm (RA-RA), is proposed to solve the DRP-SFC. Performance evaluation shows that RA-RA can efficiently solve the DRP-SFC and surpass the performance of other existing algorithms in acceptance rate, throughput, hop count and load balancing.

Design and Research of Wuhan Urban Railway Network Simulation Calculation Information System Utilizing Big Data

Based on the big data-assisted algorithm, the paper uses the dynamic relationship data of railway passenger flow as a research case, adopts the urban network chain model, and uses the mobility and agglomeration of railway passenger flow between cities as the research entry point to analyse the network connection strength of provincial cities and spatial organization structure characteristics. At the same time, it puts forward the idea of using Wuhan’s rich railway resources as the framework of urban railway network passenger flow intensity simulation calculation system, demonstrates the necessity and feasibility of its construction, and puts forward an implementation plan based on the characteristics of the railway network and urban development for the reference of relevant decision makers.

Counseling Acceptance Skills School Counselor In Individual Counseling

The purpose of this study is that researchers want to look at counselee acceptance skills in individual counseling by guidance and counseling teachers in MTsN 10 Agam. This type of research is descriptive qualitative field research. The key informants of this study were 1 guidance and counseling teacher plus a supporting informant of 7 students who had participated in individual counseling using the snowball method, which is a method for identifying, selecting and taking samples in a network or continuous chain of relationships. The information obtained will be developed to the point of saturation so that the longer the more information obtained. Based on the results of research and discussion it can be concluded that as a strategy for counselee acceptance skills by counseling teachers in individual counseling, counseling teachers assume counselee is the closest person, can accept counselees openly, guiding sincerely or voluntarily, sincerity, and loving, keep secrets counselee, not discriminating counselee, and love counseling.

Structural characteristics of complex supply chain networks

AbstractFirms source goods and services in complex, global supply chains. The network structures of these far‐reaching supply chains are believed to influence both firm‐level and supply chain‐level behavior. However, researchers and managers are challenged to manage extended supply chain networks because firms typically do not actively oversee their entire supply chain network. The extant research on the underlying network topologies is limited and relies on untested structural assumptions. This study addresses the latter issue by examining structural topologies in supply chain networks. We draw from the network and supply chain literature and empirically investigate the presence of topologies found in complex networks. In particular, we look for: small‐world, scale‐free, community, and hierarchical network structures. We study 21 extended (five‐tier) supply chain networks of firms representing various industries. Our analysis provides evidence for community, scale‐free, and hierarchical structures within the observed supply chain networks. The identification of network topologies is relevant to understand how to study supply chain networks and provide insights into network behavior and performance.

Thermo-Viscoelastic Response of Protein-Based Hydrogels.

Because of the bioactivity and biocompatibility of protein-based gels and the reversible nature of bonds between associating coiled coils, these materials demonstrate a wide spectrum of potential applications in targeted drug delivery, tissue engineering, and regenerative medicine. The kinetics of rearrangement (association and dissociation) of the physical bonds between chains has been traditionally studied in shear relaxation tests and small-amplitude oscillatory tests. A characteristic feature of recombinant protein gels is that chains in the polymer network are connected by temporary bonds between the coiled coil complexes and permanent cross-links between functional groups of amino acids. A simple model is developed for the linear viscoelastic behavior of protein-based gels. Its advantage is that, on the one hand, the model only involves five material parameters with transparent physical meaning and, on the other, it correctly reproduces experimental data in shear relaxation and oscillatory tests. The model is applied to study the effects of temperature, the concentration of proteins, and their structure on the viscoelastic response of hydrogels.

Decoupling of Mechanical and Transport Properties in Organogels via Solvent Variation.

Organogels have recently been considered as materials for transdermal drug delivery media, wherein their transport and mechanical properties are among the most important considerations. Transport through organogels has only recently been investigated and findings highlight an inextricable link between gels’ transport and mechanical properties based upon the formulated polymer concentration. Here, organogels composed of styrenic triblock copolymer and different aliphatic mineral oils, each with a unique dynamic viscosity, are characterized in terms of their quasi-static uniaxial mechanical behavior and the internal diffusion of two unique solute penetrants. Mechanical testing results indicate that variation of mineral oil viscosity does not affect gel mechanical behavior. This likely stems from negligible changes in the interactions between mineral oils and the block copolymer, which leads to consistent crosslinked network structure and chain entanglement (at a fixed polymer concentration). Conversely, results from diffusion experiments highlight that two penetrants—oleic acid (OA) and aggregated aerosol-OT (AOT)—diffuse through gels at a rate inversely proportional to mineral oil viscosity. The inverse dependence is theoretically supported by the hydrodynamic model of solute diffusion through gels. Collectively, our results show that organogel solvent variation can be used as a design parameter to tailor solute transport through gels while maintaining fixed mechanical properties.

The modified Whitham modulation theory for transmission line with ferroelectric capacitors

Using the Euler-Lagrange dynamics of the Landau-Ginzburg-Devonshire functional for homogeneous ferroelectric chain in the spatial continuous limit, (1 + 1)D-nonlinear Klein-Gordon-Fock equation has been derived. Elementary four-terminal network of this ferroelectric chain has been considered as a Hamiltonian system corresponding to the point mass in the effective nonlinear potential. Expressing all parameters of automodeling solution of the nonlinear Klein-Gordon-Fock via energy of this Hamiltonian system we have extended the Witham modulation theory. Obtained results can be applied to the computer aided design of phase shifters in phased-array antennas for a new generation of information processing and transmission systems based on integrated ferroelectrics.

Soft-hard hybrid covalent-network polymer sponges with super resilience, recoverable energy dissipation and fatigue resistance under large deformation

Energy absorption or dissipation ability has been widely developed in tough hydrogels and 3D nano-structured sponges for a variety of applications. However, fully recoverable energy dissipation and fatigue resistance under large deformation is still challenging yet highly desirable. Polymer network with homogeneous chemical crosslinking structures is an efficient way to construct hydrogels with high resilience and fatigue resistance. Unfortunately, such polymer network usually has poor energy dissipation capability. In this paper, we propose a new approach to build the ability of fully recoverable energy dissipation into covalent-crosslink polymer network by integrating soft and hard chains in a uniform crosslinking network and present the one-pot synthesis method for constructing corresponding polymer sponges by low-temperature phase-separation photopolymerization. The application of such polymer sponges as a tissue engineering scaffold, fabricated by using cyclic acetal units and PEG based monomers in particular is demonstrated. For the first time, we show the feasibility of building a synthetic scaffold with the characteristics of high porosity, super compressibility and resilience, fast recovery, completely recoverable energy dissipation, high fatigue resistance, biodegradability and biocompatibility. Such a scaffold is promising in tissue engineering especially in load-bearing applications.

Towards Large‐Scale Fabrication of Self‐Healable Functional Hydrogel Coatings for Anti‐Fog/Frost Surfaces and Flexible Sensors

AbstractHydrogel coatings have drawn substantial research interest owing to their great potential in developing advanced flexible devices. However, most of them cannot meet the requirement of large‐scale application on diverse substrates and would lose their functionalities under harsh conditions. In this work, a large‐scale and surface‐independent multifunctional hydrogel coating is developed by directly painting and ultraviolet curing hybrid hydrogel precursor containing acrylamide (AM), branched polyethylenimine (PEI), CaCl2, and glycerol. Such a coating shows superior anti‐fog and anti‐frost properties owing to the hydrophilic nature and freezing‐tolerance of the hydrogel. Besides, the hydrogel coating possesses excellent self‐healing performance at both macro‐ and micro‐ scales because of the dynamic hydrogen bonds prevailing between the polymer chains in a gel network. Moreover, this hydrogel coating also exhibits electric conductivity due to the mobile ions present in the gel network. By patterning the hydrogel coating onto an elastic substrate, a foldable tape sensor is fabricated which can sensitively distinguish short‐time click (≈0.1–0.2 s) from long‐time press (≈1–2 s) and detect electrical signals corresponding to cyclic bending deformation. This work provides practical demonstration of hydrogel coatings for engineering applications where anti‐fog, anti‐frosting, self‐healing, and electrically conductive properties are needed.

Rational Design of a High‐Strength Tough Hydrogel from Fundamental Principles

AbstractCurrently, polymer‐based hydrogels have found applications in many fields but are faced with a challenge how to reach an optimum trade‐off between strength and toughness. In this regard, two prototypes (i.e., poly(AAm‐co‐AA)/CS‐Fe3+ and poly(AAm‐co‐AA)/PVA‐Fe3+ double‐network (DN) hydrogels), following the fundamental principles, are developed for the first time through constructing covalent/ionic dual cross‐links in the first networks while inducing polymer entanglement/crystallite‐based hidden chains in the second networks. Through systematic investigation of their comprehensive mechanical properties, the rationality of the proof‐of‐concept strategy is verified but some modifications are needed for further improvement. In summary, the synergy of DN structure and integrated irreversible/reversible dual cross‐links in a sacrificing network can formulate an effective energy‐dissipation system for high toughness, high toughness/strain recoverability, and high antifatigue capability. High‐functionality cross‐links based on polymer crystallites in the second network enhance the strength and elasticity of the as‐prepared hydrogels but destroy the flexibility and softness of the matrix phase, therefore sacrificing their stretchability. For optimal hydrogel design, introducing high‐functionality cross‐links in the matrix network while maintaining its flexibility or constructing hidden chains in the sacrificing network may be an ideal strategy to solve the afore‐mentioned problem.

Chain stiffness boosts active nanoparticle transport in polymer networks.

Recent advances in technologies such as nanomanufacturing and nanorobotics have opened new pathways for the design of active nanoparticles (NPs) capable of penetrating biolayers for biomedical applications, e.g., for drug delivery. The coupling and feedback between active NP motility (with large stochastic increments relative to passive NPs) and the induced nonequilibrium deformation and relaxation responses of the polymer network, spanning scales from the NP to the local structure of the network, remain to be clarified. Using molecular dynamics simulations, combined with a Rouse mode analysis of network chains and position and velocity autocorrelation functions of the NPs, we demonstrate that the mobility of active NPs within cross-linked, concentrated polymer networks is a monotonically increasing function of chain stiffness, contrary to passive NPs, for which chain stiffness suppresses mobility. In flexible networks, active NPs exhibit a behavior similar to passive NPs, with a boost in mobility proportional to the self-propulsion force. These results are suggestive of design strategies for active NP penetration of stiff biopolymer matrices.

Actor analysis for sustainable soil management – A case study from the Netherlands

Soil quality is an important determinant of the productivity, environmental quality and resilience of agricultural ecosystems. In addition to the farmer, there are other actors who may have different interests in soil quality, hampering the implementation of sustainable soil management. To date, these actors have received surprisingly little attention. This study presents an inventory of actors involved in sustainable soil management, including farmers, but also value chain participants (e.g. input suppliers and processors), environmentally engaged actors and policy makers. We applied Analytical Hierarchy Process (AHP) to elicit actors’ priorities for soil sustainability criteria. AHP is a method of multi-criteria analysis that uses pairwise comparisons to assess the relative importance of criteria. Additionally, we differentiated actors based on their involvement and perceived ability to influence decision-making. Based on the results of a survey, actors were placed in a power-interest grid. In this grid, the self-perceived power and interest of actors was differentiated from their power and interest as perceived by other actors. The main findings were that a complex and heterogenous network of actors exists around the farmer. Within this network, farmers and related value chain participants showed a priority for economic soil sustainability criteria. Environmentally engaged actors were confirmed to have a clear priority for environmental criteria. The power-interest grids underscored the prime role of farmers and the relatively high power of value chain participants. The self-assessment of power-interest compared to assessment by others revealed noticeable differences, especially for NGOs and environmentally engaged actors. This study provides an overview of which actors to involve in decision-making on sustainable soil management, which is illustrated for the EU mission “Soil Health and Food”.

Bioinspired hyperbranched protein adhesive based on boronic acid-functionalized cellulose nanofibril and water-soluble polyester

The development of high-performance and sustainable bio-adhesive materials has attracted significant research attention in recent years. However, producing high-performance biopolymer materials with desirable mechanical and antibacterial properties remains a challenge. Herein, a mussel-mimetic strategy is reported to fabricate a soy protein (SP)-based composite film via aqueous co-assembly of 3-aminophenylboronic acid-functionalized cellulose nanofibril (AB@CNF) and hyperbranched polyester (HPE). The water-soluble HPE with numerous hydroxyl end groups was synthesized by using a facile and green method. Through enhanced dynamic covalent interactions and intermolecular sacrificial hydrogen bonds, the incorporated AB@CNF hybrids with conjugated cis-diols strongly interact and entangle with biopolymer chains in the hyperbranched network. As a result of multiple synergistic cross-linking, the toughness and strength of the SP/HPE/AB@CNF film increase by 574% and 346% to 10.05 MJ/m3 and 14.23 MPa, respectively. Additionally, the film offers excellent antibacterial activity, flame retardance, and thermal stability. This sustainable approach should open new avenues for the design and development of biomimetic plant-derived functional materials in biomass adhesive and active packaging.