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Sensory preconditioning protocols can be used to assess how the brain integrates memories that share common features. In these protocols, animals are first exposed to pairings of two relatively innocuous stimuli, S2 and S1 (stage 1), and then to pairings of one of these stimuli, S1, with an event of motivational significance (stage 2). Following this training, test presentations of S2 elicit responses appropriate to the motivationally significant event, and these responses are taken to indicate formation of distinct S2-S1 and S1-event memories that are integrated in some way to generate that responding. This paper reviews studies of sensory preconditioning in rats, mice, rabbits and people to determine whether S2-S1 and S1-event memories are integrated through a chaining process at the time of their retrieval (i.e., test presentations of S2 trigger retrieval of S1, and thereby, responses appropriate to the event); or “online” at the time of memory formation (i.e., in stage 2, S1 activates a representation of S2 such that both stimuli associate with the motivationally significant event). It finds that the type of integration is determined by the manner in which stimuli are presented in preconditioning as well as their familiarity. When the stimuli in preconditioning are presented repeatedly and/or serially (i.e., one after the other), the S2-S1 and S1-event memories are chained at the time of retrieval/testing. In contrast, when the stimuli in preconditioning are relatively novel and/or presented simultaneously, the S2-S1 and S1-event memories are integrated online. These statements are related to prior claims regarding the circumstances that promote different types of memory integration and, more generally, mechanisms of information processing in the mammalian brain.

The integrated item-sharing and crowdshipping problem arises from the operational planning of sharing platforms that coordinate diverse services offered by individuals to peers. Item-sharing means to rent-out items whereas crowdshipping describes the willingness of private drivers to conduct deliveries on their planned trips. Integrating both concepts on a single platform can lead to higher profits and better service quality by transferring items through crowdshippers. We investigate a multi-period variant of the problem to facilitate more foresighted assignments of items to requests and crowdshippers. Thereby, items can be used to sequentially serve multiple requests without being returned to their initial locations. This so-called ’request chaining’ cuts transportation effort and improves the availability of items to customers. From a modeling perspective, the planning problem is an example for the rarely investigated many-to-many pickup and delivery problems. We present a binary program and we address problem inherent dynamics due to new incoming announcements by both deterministic and stochastic problem solving approaches that are based on a rolling horizon framework. Furthermore, a simulation environment is set up to investigate the effect of a responsive requesting behavior due to word-of-mouth. Our experiments show that some look-ahead in the planning is particularly useful for scenarios with few requests. The consideration of stochastic information can improve operational performance and word-of-mouth can best be dealt with when consumers interact locally. We also derive managerial insights on the number of items that should be provided in such a community, subject to consumer preferences.

As product customization becomes increasingly complex, it calls for a new architecture to enhance collaborations among numerous stakeholders who unnecessarily trust each other. This paper presents a blockchain-based collaborative customization framework, in which, blockchain is exploited as computational infrastructure to moderate, maintain, and manage a decentralized consensus on customization. Collaborative customization is made more transparent by enabling stakeholders to broadcast data, more flexible by fluctuating customization price against requirement fulfillment, more efficient by automating transactions through smart contracts, and more traceable by chaining transactions to constitute product identity. A practical example is presented to apply the framework to elevator customization.

Travel demand for the Metropolitan region Rotterdam - The Hague is predicted using a novel tool chain. Non-classical tools are required to cope with the situation where parking supply is reduced, hubs for trip chaining are made operational and people start to use MaaS including new mobility concepts. An activity based travel plan predictor is combined with a dedicated access-egress model and assignment models. Because the activity based model and access-egress model simulate individuals, they can deal with mode switches at hubs while considering constraints with respect to vehicle ownership, mode availability in the tour, locations where vehicles should be returned and mode availability. Parking capacities, hub and new mobility concepts are included in the assignment models to generated level-of-service matrices that are input to the other models. The tool chain setup, the methods applied, the datasets used and first results are discussed. The new integrated activity based approach proved to be better suited to model impacts of parking, hubs and new mobility concepts than the standard aggregated modelling approach. The results for the base year look promising because they closely resemble observed schedules and the elasticities are within the recommended ranges mentioned in literature.

Mode choice is influenced by a large variety of factors, as for example users’ socio-economic attributes or level of service for the different alternatives. In order to understand better what leads to temporal and spatial variations of modal split, we propose in this paper an analysis of a multi-day travel survey, with a series of descriptive statistics as well as inferential analysis on the correlation between mode choice and tour-specific attributes at both spatial and temporal levels. This paper discusses the importance of considering tour-based mode choice not only because it brings consistency between successive mode choices but also allows the inclusion of relevant tours’ characteristics such as activity types, distances, time of the day, and previous mode choices. A total of 5848 home-based tours done in 2008 are studied in the area of Ghent, Belgium. Identified patterns show the importance of modelling dynamic mode choice with trip chaining and time of the day. The modal share of car drivers differs of more than 40% between hours of the day and about 30% between different activities. Furthermore, the definition of activity spaces by principal mode choice and home-work locations introduces the calibration of probabilistic aggregate Gaussian fit to visited points.

With introduction of Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) technologies, mobile network operators are able to provide on-demand Service Function Chaining (SFC) to meet various needs from users. However, it is challenging to map multiple SFCs to substrate networks efficiently, particularly in a number of key scenarios of forthcoming 5G, where user requests have different priorities and various resource demands. To this end, we first formulate the mapping of multiple SFCs with priorities as a multi-step Linear Integer Programming (ILP) problem, of which the mapping strategy (i.e., the objective function) in each step is configurable to improve overall CPU and bandwidth resource utilization rates. Secondly, to solve the strategy selection problem in each step and alleviate the complexity of ILP, we propose an adaptive deep Q-learning based SFC mapping approach (ADAP), where an agent is learned to make decisions from two low-complexity heuristic SFC mapping algorithms. Finally, we conduct extensive simulations using multiple SFC requests with randomly generated CPU and bandwidth demands in a real-world substrate network topology. Related results demonstrate that compared with a single strategy or random selections of strategies under the ILP-based approach or the proposed heuristic algorithms, our ADAP approach can improve whole-system resource efficiency by scheduling this two simply designed heuristic algorithms properly after limited training episodes.

Cryptography is an important aspect of securing communications, however, the encryption scheme may be known to the attacker(s). Hence, the key can be rewarded as a crucial part of any cryptographic algorithm. With the symmetric key, researchers have developed chaining concepts where the output of one operation becomes an input to the next one. Such concept of modes of operation (Cipher Block Chaining) is integrated in this article to generate multiple key blocks. The generated keys can be provided to an individual block of plaintext for any encryption technique as per the requirements. The proposed Key Block Chaining (KBC) approach can also be referred to as a key derivation approach. The basic concepts of Advanced Encryption Standard and Message Authentication Code have also been incorporated in the proposed model to introduce randomness, and thereby, ensuring security as well as robustness. To assess the randomness of the sequence of generated keys, KBC is implemented and tested using the National Institute of Standards and Technology statistical test suite and reasonable randomness has been successfully achieved. Possible variations and key points of the proposed approach along with statistical analysis and significance of our work have been discussed.

Environmental assessments of e-commerce often focus on the last mile. Collection points are considered more beneficial than home delivery but this largely depends on how consumers’ collection trips are organised. Because this information is lacking, our objective is to find out how consumers make use of and travel to collection points by means of a street intercept surveys in the Brussels-Capital Region. Findings show that the majority of consumers use collection points after an unsuccessful delivery at home. Most consumers travel less than fifteen minutes by car, chaining several activities to the collection trip.

Vehicular traffic between different Origin-Destination (OD) pairs for a typical hourly period may statistically correlate with each other. The covariance mainly generated from the daily variation of travel patterns, network topology, and trip chaining activities of household members can be particularly high during the morning peak hour. With the increasing attention on the OD demand variance and covariance in stochastic road networks, a new criterion is proposed in this paper for measuring the estimation accuracy of OD demand covariance. The mathematical properties of this proposed criterion are analyzed to better understand the relationship between the estimation errors of mean and covariance of OD demands. This paper aims to investigate how to optimize the traffic count locations for minimizing the weighted maximum deviation of estimated mean and covariance of OD demands from the “true” values. To consider the effects of stochastic OD demands on the traffic count location problem in the proposed model, link choice proportions are regarded as stochastic variables and updated by an adapted traffic flow simulator in this study. Both the weighted-sum approach and bi-objective approach are examined with the adaption of the firefly algorithm (FA) to solve the single-objective and bi-objective problems. Numerical examples are presented to demonstrate the effects, with and without considering the covariance of the OD demands for the optimization of traffic count locations.

A two-dimensional incompressible smoothed particle hydrodynamics scheme is used to simulate the interaction of micron-sized particles suspended in quiescent medium. A uniform electric field is applied to the particles, causing them to approach one another due to dielectrophoretic forces and form a chain. Both fluid and particles are assumed to be polarizable and non-conductive where the permittivity of the fluid is assumed to be lower than that of the particles. The numerical scheme is validated by comparing its predictions for simpler case of a pair of particles with results available in the literature. The effects of initial orientation, Reynolds number and differences in particle permittivity on the chaining behavior are studied afterwards. The results show that the particles may follow a convergent or divergent–convergent trajectory, depending on the initial orientation of the particle pair with the electric field. Increasing the field intensity in low-Reynolds regime expedites the chaining process without affecting the particle trajectory. However, the particles may diverge at larger Reynolds numbers. Assigning different permittivities to particles skews the chaining position toward the particle with lower permittivity. Simulating the process for multiple particles results in longer chains branching and encompassing the entire computational domain, much like those observed in experiments.

Currently, studies on constructing integrated modeling solutions in a web-based environment primarily focus on the chaining of different model-services, while the flow of exploring solutions for complex geo-problems – geographically distributed issues – has been less studied. To address this gap, a teamwork-oriented integrated modeling approach to supporting geo-problem solving based on the collaborative work of different modelers is proposed in this article, with a workflow from conceptual, logical, and finally, computational modeling. In this approach, conceptual, logical and computational models are discussed and constructed using a graph-based method, a flowchart-based method and a service-configuration-based method, respectively. By building the corresponding repositories for managing and comparing different versions of conceptual-logical-computational models, dispersed modelers can propose their own ideas and explore appropriate solutions based on existing modeling resources. A prototype modeling system was established, and two experimental modeling cases were conducted to verify the feasibility and capability of the proposed approach.

To assist medical professional in better treatment of diseases, and improve patient outcomes, healthcare has brought about a cognitive computing revolution. The cognitive computing system processes enormous amounts of data instantly to answer specific queries and makes customized intelligent recommendations. Cognitive computing in healthcare links the functioning of human and machines where computers and the human brain truly overlap to improve human decision-making. In regard to this convergence, this systematic literature review (SLR) provides comprehensive information of the prior research related to cognitive computing in healthcare. The SLR focused on methods, algorithms, applications, results, strengths, and limitation using different research articles collected from leading international databases using linear and citation chaining search. The main outcomes of the SLR include proposal on future research direction, challenges faced by researchers, capabilities and the impact of cognitive computing on healthcare outcome and a conceptual model, showcasing the better utilization of cognitive computing in healthcare domain. This study concludes with managerial implications, limitations and scope for future work.

This note extends a result of Mendelson on the supremum of a quadratic process to squared norms of functions taking values in a Banach space. Our method of proof is a reduction by a symmetrization argument and simple observation about the additivity of the generic chaining functional. We demonstrate an application to positive linear functionals of the sample covariance matrix and the apparent variance explained by principal components analysis (PCA).

It is anticipated that future networks support network functions, such as firewalls, load balancers and intrusion prevention systems in a fully automated, flexible, and efficient manner. In cloud computing environments, network functions virtualization (NFV) aims to reduce cost and simplify operations of such network services through the virtualization technologies. To enforce network policies in NFV-based cloud environments, network services are composed of virtualized network functions (VNFs) that are chained together as service function chains (SFCs). All network traffic matching a policy must traverse network functions in the chain in a sequence to comply with it. While SFC has drawn considerable attention, relatively little has been given to dynamic auto-scaling of VNF resources in the service chain. Moreover, most of the existing approaches focus only on allocating computing and network resources to VNFs without considering the quality of service requirements of the service chain such as end-to-end latency. Therefore, in this paper, we define a unified framework for building elastic service chains. We propose a dynamic auto-scaling algorithm called ElasticSFC to minimize the cost while meeting the end-to-end latency of the service chain. The experimental results show that our proposed algorithm can reduce the cost of SFC deployment and SLA violation significantly.

© 2018 Elsevier B.V. Service function chaining (SFC) provisioning is helpful not only for saving the capital expenditure (CAPEX) and operational expenditure (OPEX) of a network provider but also for reducing energy consumption in the substrate network. However, to the best of our knowledge, there has been little research on the problem of energy consumption for orchestrating online SFC requests in multi-domain networks. In this paper, we first formulate the problem of an energy-efficient online SFC request that is orchestrated across multiple clouds as an integer linear programming (ILP) model to find an optimal solution. Then, we analyze the complexity of this ILP model and prove that the problem is NP-hard. Additionally, we propose a low-complexity heuristic algorithm named energy-efficient online SFC request orchestration across multiple domains (EE-SFCO-MD) for near-optimally solving the mentioned problem. Finally, we conduct simulation experiments to evaluate the performance of our algorithm. Simulation results show that EE-SFCO-MD consumes less energy than existing approaches while the online SFC's requirements are met and the privacy of each cloud is effectively guaranteed. The low computational complexity of the heuristic approach makes it applicable for quickly responding to online SFC requests.

This paper presents Silverchain, a tool that generates a fluent embedded domain-specific language (EDSL) from a BNF-style grammar. A fluent EDSL is a class library that allows its users to embed domain-specific sentences into a program written in a general-purpose language by method chaining. A generated EDSL is designed so that its users can find syntactic errors from type errors and make effective use of method chaining in various situations. The first feature is realized by setting the return type of each method based on which methods may be invoked next, and the second feature is achieved by providing subchaining APIs besides regular chaining APIs. The contribution of this paper is the development of a translation method from a grammar to such an EDSL. Our translation method is modeled as the construction of a set of single-state real-time deterministic pushdown automata (RPAs). A fluent EDSL is obtained by encoding those RPAs into class definitions. In the construction of RPAs, Silverchain does not add or remove any non-terminal from the given grammar. This constraint is required to generate subchaining APIs as specified in the grammar.

A new methodology is presented for structuring the multiskilling characteristics of a set of initially single-skilled employees in the context of a multi-department service sector business, more specifically in the retail industry. Assuming stochastic demand under continuous modeling of the workforce, the methodology decomposes the multiskilling problem into three stages that are then solved sequentially. The first stage delivers a novel analytic expression for estimating the number of multiskilled employees required by each store department, the second stage applies constructive heuristics to these estimates to generate a feasible set of closed chaining structures, and the third stage uses Monte Carlo simulation and a linear programming model to evaluate the chains so generated. The results show that in unbalanced systems, the solutions involve closed long chains and closed short chains of different lengths, which are robust to demand variability, minimize the expected total cost of staff shortages, and exhibit the best cost-effective performance. The methodology provides a tool for company decision makers to address the three fundamental multiskilling issues in unbalanced systems, namely, where to add multiskilling, how much to add and how it should be added.

In this study, we propose a conceptual and analytic framework anchored on the concepts of physical and virtual accessibility (the “ease” with which opportunities or activities can be reached in the physical and in the virtual space, respectively) to investigate the rich interplay between virtual and physical activity engagements in multiple activity purposes, while controlling for information and communication technology (ICT) use measures, physical accessibility measures, and demographics. Our framework considers that activity-travel choices are consequences of individual, household, and work characteristics that are mediated by virtual accessibility and physical accessibility. As part of our analysis, we also analyze activity chaining characteristics during travel to study any fragmentation impacts caused by ICT use on activity engagement and scheduling. We use data from the 2011 and 2012 National Travel Survey in Great Britain and apply Bhat’s (2015) generalized heterogeneous data model (GHDM) to jointly model multiple activity and travel outcomes. Our results provide important insights for social welfare, work-life balance, and equity policies, and suggest that decisions regarding virtual activity participations and in-person out-of-home activity participations are determined as a package. Ignoring this package nature of choices can lead to misleading inferences about the effects of virtual activity participations on in-person out-of-home activity participations.

In this paper, we propose and study the performance of two conversion schemes for optical packet switch (OPS) with the objective of reducing packet contention, and hence, improving the overall packet loss ratio (PLR). In both schemes, conversion is performed in two layers. The first layer contains a dedicated wavelength converter (WC) for each input wavelength, while the second conversion layer consists of a dynamic pump-wavelength selection (DPS) of parametric wavelength converters (PWCs) shared in a Feed-Back fashion. The first scheme uses non-circular WCs (NCWCs) with DPS of chained PWCs (called LDPSC, for short). In the LDPSC scheme, an incoming packet is accepted only if it could be assigned to its required output using only the NCWCs in the first layer or more than one PWCs in a chained fashion in the second layer; otherwise it will be dropped. The second scheme uses circular wavelength conversion (CWCs) with DPS of PWCs without chaining (named CDPS, for short). In the CDPS scheme, an incoming packet is accepted only if it can be assigned to its required output using only CWCs in the first layer or only one of the PWCs in the second layer; otherwise it will be dropped. For each of the two schemes, a matching algorithm along with a Dynamic pump conversion (DPC) algorithm is presented. The performances of the proposed schemes are compared to two conventional schemes; namely, the DPSC scheme that uses a shared pool of DPS of chained PWCs, and the LDPS scheme that uses a dedicated NCWC for each input wavelength and a shared pool of DPS of PWCs without chaining. Simulation results show that, the new CDPS scheme achieves the same performance (in terms of PLR) of the LDPS scheme, while reducing the required number of PWCs by more than 44 % and 57 % for conversion distances (that is the maximum number of wavelengths adjacent to any input wavelength on any one of its sides), d = 1 and 2; respectively. Moreover, using conversion distance d = 1 , the new LDPSC scheme reduces the PLR compared to the LDPS scheme by up to 85 % and 22 % for traffic loads, 0.5 and 1; respectively. In addition, for d = 1 , the new LDPSC scheme reduces the PLR compared to the DPSC scheme by up to 99.8 % and 30 % for traffic loads, 0.5 and 1; respectively.

Among various reliability challenges, accelerated aging is of particular importance. The flexibility of high-level synthesis (HLS) can be employed at an affordable cost to increase the reliability, and in particular lifetime, of the systems. In this paper, we extract the aging characteristics of the functional units (FUs), then, a scheduling method based on integer linear programming (ILP) is proposed to increase the lifetime, using the aging characteristics of the FUs. The proposed method extends the lifetime beyond the minimum expected value, with the minimum latency overhead, by applying the operation chaining and multicycling techniques in an aging-aware approach. The constraint matrix of the proposed ILP formulation is totally unimodular. This technique is suitable for both data-flow intensive and control-flow intensive designs and is applicable for large circuits. Experimental results show that the proposed approach increases the lifetime by 2.33×, and increases the latency by an average of 19.8%.

The main issue for any organisational elections is the validity and accessibility of the electoral roll. The electoral roll requires data validation when data is added or updated, and requires effective data selection when data retrieval operations are required. It is an evolving process that needs continuous moderation from both the bureaucrats and the public. Because of their inherent properties, such as non-mutability, multi-level authentication, cryptography, and strict data update laws, Blockchain driven systems have proven to be efficient in terms of security. But due to large data chains, blockchain systems have limited retrieval performance, which slows down the read-cycles, rendering the system ineffective for big data applications. A side-chain-based solution for the efficient storage and recovery of electoral roll data is described in this paper. In comparison to single-chain systems, data updating and insertion into side chains is less reliable. This is due to the fact that as opposed to their single chain counterparts, side chains are very limited in scale, so targeting these side chains is simpler. In order to preserve the security of the side-chain scheme, this paper describes a deep learning cryptosystem (DLC). In order to create a special non-identifiable side chain structure, this DLC utilises a combination of adaptive cryptography and transfer learning. In order to ensure high fidelity and reduce the risk of attacks, this side-chain system uses a hybrid blockchain implementation. The proposed method, while retaining the same degree of accuracy and protection, is found to be 47 percent faster than the current single-chain electoral roll management solutions.

This project studies the driving factors of non-work travel trip chaining in the Mexico City Metropolitan Area (MCMA). It has been argued that urban traveling is increasingly becoming more intricate given that travelers pursue more than one activity in their daily travel. Unlike commuting, non-work travel has been less studied, the literature being even scarcer if we consider the Latin American context as well. Non-work travel is represented by travel to study and other variety of destinations. Thus, using the 2017 Household Origin-Destination Survey (HODS17), this paper analyzes separately the groups of “Students” and the rest of travelers called “Others” in terms of patterns of trip chaining among three modes choices: Transit, Car, and Mixed transportation. Two regression models were applied to study the determinants of 1) number of stops in the daily travel (trip chaining) and 2) tour categories in increasing degree of complexity. A descriptive analysis identified that Car users and Mixed transportation had the highest shares of complex tours together with those in the “Others” group. The associations found between some socioeconomic variables (gender, age, and household size) with trip chaining and tour complexity were different among Students and Others, and discussed in relation to the existing literature. Moreover, most urban form variables supported the hypothesis that dense and diverse urban environments are associated with more trip chaining and tour complexity. These results were discussed in light of the implications to understand the transport disadvantage condition of peripheral low-income neighborhoods typical of big cities in the developing context.

Development of an origin-destination demand matrix is crucial for transit planning. The development process is facilitated by automated transit smart card data, making it possible to mine boarding and alighting patterns on an individual basis. This research proposes a novel trip chaining method which uses Automatic Fare Collection (AFC) and General Transit Feed Specification (GTFS) data to infer the most likely trajectory of individual transit passengers. The method relaxes the assumptions on various parameters used in the existing trip chaining algorithms such as transfer walking distance threshold, buffer distance for selecting the boarding location, time window for selecting the vehicle trip, etc. The method also resolves issues related to errors in GPS location recorded by AFC systems or selection of incorrect sub-route from GTFS data. The proposed trip chaining method generates a set of candidate trajectories for each AFC tag to reach the next tag, calculates the probability of each trajectory, and selects the most likely trajectory to infer the boarding and alighting stops. The method is applied to transit data from the Twin Cities, MN, which has an open transit system where passengers tap smart cards only once when boarding (or when alighting on pay-exit buses). Based on the consecutive tags of the passenger, the proposed algorithm is also modified for pay-exit cases. The method is compared to previous methods developed by the researchers and shows improvement in the number of inferred cases. Finally, results are visualized to understand the route ridership and geographical pattern of trips.

China has recently initialised affordable housing policies to provide low rent housings for medium and low income households aiming to satisfy the growing demand in the housing market. The travel b ...

In this paper we study Hash-based Traffic Steering on Softswitches (HATS) which is a load balancing scheme for chaining virtualized network functions (VNFs), with the aim of mitigating the control and data plane overheads of existing methods. Our method uses a flow-hashing technique applied to softswitches to carry out server and network load balancing without triggering the controller. By exploiting the advantages of HATS, we then derive two algorithms, HATS with Flowcell-based Multipathing (HATS-Flowcell) and Dynamic Weight Adjustment for HATS (D-HATS), to address hash collision problems which downgrade system performance. The first algorithm divides an elephant flow into various equal-size flowcells, which are distributed over network paths as individual flows. The second algorithm periodically updates the hashing weights of VNFs and network paths according to their current load status. Our implementation demonstrates that HATS can be readily deployed on commodity network hardware. Furthermore, our experimental results show that D-HATS has roughly the same load balancing performance with Least Load First (LLF), a controller-based service chaining algorithm; while significantly reducing the number of flow entries and service chaining time by 54% and 93%.

Transportation planners are increasingly looking to reduce drive-alone travel. However, the existence of complex tours could be a major barrier to the shift from drive-alone to public transport. This is particularly problematical for the non-worker segment of the population (most of whom are women and/or older adults). To better understand how socio-demographics, trip attributes, and land use pattern shape tour complexity and mode choices for individuals, this paper introduces a cluster-based approach to model tour complexity, trip chaining, and tour mode choice. Based on daily activity patterns and time-use, five non-worker clusters were identified from the Space-Time Activity Research (STAR) data for Halifax, Canada. Number of tours per day for all clusters was modeled using a Poisson regression model. Trip chaining was then modeled using an Ordered Probit model. Finally, tour mode choice was modeled using a Multinomial Logit (MNL) model. This framework utilizes home-based travel tours, the sequence of trips that begin and end at home, as the basic unit of analysis. Results show that socio-demographic characteristics and tour attributes are strong explanatory factors of travel behavior, consistent with existing literature. Also, urban form characteristics have significant influence on non-workers' travel behaviour and tour complexity. This paper also offers a typology of travel tours to account for different non-work travel purposes. The findings of this study will improve the future evaluation of transportation projects and land-use policymaking.

The speed of vertical electrical impedance testing of a concrete bridge deck using a new rolling probe was demonstrated in this research to be 190 m2/h, which is more than six times faster than the speed at which data were collected using the static probe utilized in earlier research. Furthermore, with impedance measurements obtained at a longitudinal spacing of approximately 0.1 m, the spatial resolution was 12 times greater than that of the static probe. Vertical electrical impedance testing was performed together with half-cell potential testing, chaining, cover depth, and chloride concentration testing, and an interpretation of these data sets was developed that synergistically incorporated the deck maintenance history.

Mobile Edge Cloud (MEC) and Network Function Virtualization (NFV) have been emerging as promising technologies on providing low-latency IoT services in smart cities. However, the MEC can only provide limited number of IoT services because of its finite computing resource. How to deploy VNF instances and route data packets for IoT services in the MEC has been considered as a nontrivial problem. The problem becomes more complicated when taking Quality of Service (QoS) into consideration. In this paper, we study the VNF placement and service chaining problems while considering multi-attribute-based QoS in smart cities. We first formulate the problem as an Integer Linear Programming (ILP) and utilize randomized rounding technique to calculate a solution to the problem. Considering IoT services have different QoS requirements, we design an unsplittable flow based heuristic algorithm to deploy computing resource for each type of IoT service. Finally, large quantities of simulations prove that our algorithms outperform their counterparts.

Segment routing (SR) combines the advantages of source routing powered by software-defined networking (SDN) paradigm and hop-by-hop routing in legacy network infrastructure. The recent applications of SR in multi-domain network and service function chaining (SFC) entail the efficient use of multiple segments. However, because of the computation inefficiency, it is nearly impossible to accurately evaluate whether and to what extent various types of networks will benefit from SR with multiple segments using conventional approaches. In this paper, we propose a flexible Q-SR framework as well as its formulation in order to fully explore the potential of SR from an algorithmic perspective. The framework is highly extensible to design and evaluate algorithms that can be adapted to various network topologies and traffic matrices. For the offline setting, we develop a fully polynomial time approximation scheme (FPTAS) which can find a ( 1 + ω ) -approximation solution for any specified ω > 0 in time that is a polynomial function of the network size. To the best of our knowledge, the proposed FPTAS is the first algorithm that can compute arbitrarily accurate solution. For the online setting, we develop an online primal–dual algorithm that is proven to be O ( 1 ) -competitive and violates link capacities by a factor of O ( log n ) , where n is the node number of the network. For the proposed approximation algorithms, we prove theoretical performance bounds and conduct extensive simulations on synthetic and realistic networks to analyze SR related and algorithmic parameters and validate the computation efficiency in both offline and online scenarios.

Virtual Network Function Placement and Chaining Problem focuses on allocation of customers demand’s on the Substrate Network. Among other factors, an optimal allocation of resources is hampered by nature of the online problem and by its complexity. Since we are dealing with an NP-hard combinatorial problem, while the number of network components grows linearly, the computational processing and runtime increase exponentially. Therefore, an application of Machine Learning techniques to reduce the number of components present in the SN is proposed in this work. In particular, we have developed clustering techniques that aim to find groups of promising components to map customer demands and disregard those that are less promising. Two different clustering models are proposed: (i) based on the Spatial Location of the SN components; and (ii) based on SN’s historical resource consumption data. An Integer Linear Programming model is proposed to evaluate the different simulation scenarios. Both approaches reduced the execution time ( ≈ 75 % ) and the end-to-end delay of virtual requests, in addition to keeping the acceptance rate and profit stable compared to the exact approach.

Location models have been widely used to support locational decisions for various service provision. One common objective of location models has been to ensure maximal accessibility of sited facilities to demand populations. Accessibility evaluation in location models often assumes that trips originate from fixed locations (usually home) and are single purpose. These assumptions contradict the empirical evidence that suggests trips also commonly originate from non-home locations and may involve multiple stops. In this study, a new multi-objective location model is developed that extents the classic p-median problem (PMP) to account for a more realistic assessment of accessibility. Based on the individual accessibility assessment, notions of trip chaining and activity space are incorporated into the model development. In addition to fixed home locations, stops along chained trips are allowed for potential service site visits, and activity space is introduced as an additional dimension to evaluate accessibility of alternative opportunities. The effectiveness of the new model is demonstrated using an application in Tucson, AZ.

Objective To better understand trip-chaining patterns and mode choice of various urban forms, this study examines the effects of land use on travel mode choice and analyzes the concept of neighborhood design in the substitution of short car trips by other transportation modes. Methods A ‘tour’ is defined as a home-to-home loop of individual trips, including all the stops made along the way. This paper develops tour-based, mode-choice models and conducts integrative assessments to determine the relative influences of the various factors associated with tour-based travel and activity space environments for short round trips. Results The short round trips most likely to require a car tended to be either commuting trips, trips involving heavy goods, or trips that link two or more stops. Transit trips for short-distance travel were likely to involve trip chaining, while most shopping trips comprised a single outing with no trip chaining. A key to increasing travel by walking was a concentration of retail shops and service providers near people's homes whereas street networks and a good regional accessibility encouraged cycling and transit use respectively, although potential spurious effects cannot be fully determined. Conclusions Policymakers in LA who hope to increase walking should focus on the concentration of business activity in a compact commercial core in residential areas, while transit agencies in LA should consider trends like chained trip-making and restructure communities and central places with much greater transit accessibility. These strategies for local urban design and regional accessibility are likely to affect people's decisions concerning travel mode mostly in non-work travel without intervening stops; therefore, personal vehicle use can be reduced more easily by focusing on trips for leisure time activities and personal business activities near residential locations rather than on work commutes.

The fast growing development of Network Function Virtualization (NFV) trends and the remarkable progress of Software Defined Networking (SDN) have yielded a synergy between both, towards the provision of convergent networking solutions. Since the traditional networking principles were not designed according to the NFV principles, a work has emerged dedicated to protocol redesign among the academic fellows and the industry representatives. A typical example that has been introduced in such eco-system is the concept of network service chaining, as a composition of virtual network functions stitched together in a logically ordered fashion, to create an integral networking service that can be owned by network administrators, programmers, cloud- and telco-operators. This appoints SDN as an essential technology enabler in administering advanced traffic steering techniques, thanks to the SDN controller's capability to dynamically manage VNFs virtual connections and underlying dataplane flows. Despite the rapid progression, the strategies for traffic steering in NFV environment, are still facing imminent challenges to be addressed. This paper joins NFV and SDN technology into a novel traffic steering solution based on open source reference implementations (such as SDK for SDN, virtual Traffic Classifier, virtual Media Transcoder, and WAN Infrastructure Connection Manager) designed with performance and network optimizations in mind. We have successfully deployed and tested the system prototype in a real datacenter. The evaluation results of the prototype system: (1) validated the presented chain use cases, (2) affirmed an efficient performance and scalability of the chaining method, and (3) certified good quality of video traffic transmission and transcoding. HighlightsHolistic approach in SFC delivery and OpenFlow-based traffic steering mechanism in OpenDaylight.Set of four components/services delivered, each of them contributing to the open source community.Implementation reference and support for all components.SFC solution deployed in SDN-NFV environment and proof of concept validated.End-to-end service chaining tests over different traffic types, to showcase a chained traffic characterization and performance implications of the same.A thorough State of the Art in standards, academic and industry implementations of SFC.

Polycentric urban growth with dense, diverse, and accessible centers is assumed to have multiple economic, social, and environmental benefits for the region. Yet, evidence on the benefits of polycentricity in the transportation sector remains less studied, and previous research has found mixed results. Besides, to our knowledge, none of the previous research focuses on trip chains or tours (sequence of trips that begins and ends at home) in centers. This paper investigates trip chaining efficiency in 28 U.S. regions, comparing three different types of tours, i.e., tours that are within a center, tours that are partially inside a center, and tours that are completely outside a center. I consider a tour to be efficient if it comprises travel modes other than the personal automobile. The results of parametric tests show that tours within a center are associated with higher walk, bike, and transit mode shares and lower car share. Effect sizes are larger for walk and car shares than for bike and transit shares. The findings of this research can encourage and guide planners to design environments that promote active transportation while maximizing resident access to services and amenities and minimizing their expenditure on mobility and travel time.

In the last decade, the automotive industry incorporated multiple electronic components into vehicles introducing various capabilities for adversaries to generate diverse types of attacks. In comparison to older types of vehicles, where the biggest concern was physical security, modern vehicles might be targeted remotely. As a result, multiple attack vectors aiming to disrupt different vehicle components emerged. Research and practice lack a comprehensive attack taxonomy for the automotive domain. In this regard, we conduct a systematic literature study, wherein 48 different attacks were identified and classified according to the proposed taxonomy of attack mechanisms. The taxonomy can be utilized by penetration testers in the automotive domain as well as to develop more sophisticated attacks by chaining multiple attack vectors together. In addition, we classify the identified attack vectors based on the following five dimensions: (1) AUTOSAR layers, (2) attack domains, (3) information security principles, (4) attack surfaces, and (5) attacker profile. The results indicate that the most applied attack vectors identified in literature are GPS spoofing, message injection, node impersonation, sybil, and wormhole attack, which are mostly applied to application and services layers of the AUTOSAR architecture. © 2021 The Author(s) open access

Studying trip chaining behavior has been a challenging endeavor which requires the support of microscopic travel data. New insights into trip chaining can be gained from real-time GPS travel data. This research introduces a framework that considers two-destination choice in the context of home-based trip chains. We propose and empirically compare three alternatives of building choice sets where we consider various relationships of the two destinations (such as major–minor destinations, selecting one first, and selecting two concurrently). Our choice set formation alternatives use survival models to determine the selection probability of a destination. Our results reveal that trip chaining behavior is shaped by the features of retail clusters, spatial patterns of clusters, transportation networks, and the axis of travel. This research reveals that not only the spatial relationship but also the land use relationship of the destinations in a trip chain affect the decision making process.

This paper presents an innovative methodology to comprehensively model two dimensions of commercial vehicle travel activity using disaggregate level commercial vehicle daily travel data. Two dimensions of freight activity considered in this model include the daily tour-chaining pattern(s), and the number of trips made in the tour-chains. The results of this study indicate that a host of factors including cargo characteristics, trip characteristics, activity features, base location of commercial vehicle trips, and the attributes of intermediate stop locations affect the choice of tour chain strategy and the associated number of daily trips.

Rule-based argumentation systems are developed for reasoning about defeasible information. They take as input a theory made of a set of facts , a set of strict rules , which encode strict information, and a set of defeasible rules which describe general behavior with exceptional cases. They build arguments by chaining such rules, define attacks between them, use a semantics for evaluating the arguments, and finally identify the plausible conclusions that follow from the theory. Undercutting is one of the main attack relations of such systems. It consists of blocking the application of defeasible rules when their exceptional cases hold. In this paper, we consider this relation for capturing all the different conflicts in a theory. We present the first argumentation system that uses only undercutting, and show that it satisfies the rationality postulates proposed in the literature. Finally, we fully characterize both its extensions and its plausible conclusions under various acceptability semantics. Indeed, we show full correspondences between extensions and sub-theories of the theory under which the argumentation system is built.

Network Function Virtualization (NFV) is a novel concept that is reshaping the middlebox arena, shifting network functions (e.g. firewall, gateways, proxies) from specialized hardware appliances to software images running on commodity hardware. This concept has potential to make network function provision and operation more flexible and cost-effective, paramount in a world where deployed middleboxes may easily reach the order of hundreds. In spite of recent research activity in the field, little has been done towards efficient and scalable placement & chaining of virtual network functions (VNFs) a key feature for the effective success of NFV. More specifically, existing strategies have either neglected the chaining aspect of NFV, focusing on efficient placement only, or failed to scale to hundreds of network functions. In this paper, we approach VNF placement and chaining as an optimization problem, and propose a fix-and-optimize-based heuristic algorithm for tackling it. Our algorithm incorporates a Variable Neighborhood Search (VNS) meta-heuristic, for efficiently exploring the placement and chaining solution space. The goal is to minimize required resource allocation, while meeting network flow requirements and constraints. We provide evidence that our algorithm is able to find feasible, high quality solutions efficiently, even in scenarios scaling to hundreds of VNFs.

Service Function Chaining (SFC) is the problem of deploying various network service instances over geographically distributed data centers and providing inter-connectivity among them. The goal is to enable the network traffic to flow smoothly through the underlying network, resulting in an optimal quality of experience to the end-users. Proper chaining of network functions leads to optimal utilization of distributed resources. This has been a de-facto model in the telecom industry with network functions deployed over underlying hardware. Though this model has served the telecom industry well so far, it has been adapted mostly to suit the static behavior of network services and service demands due to the deployment of the services directly over physical resources. This results in network ossification with larger delays to the end-users, especially with the data-centric model in which the computational resources are moving closer to end users. A novel networking paradigm, Network Function Virtualization (NFV), meets the user demands dynamically and reduces operational expenses (OpEx) and capital expenditures (CapEx), by implementing network functions in the software layer known as virtual network functions (VNFs). VNFs are then interconnected to form a complete end-to-end service, also known as service function chains (SFCs). In this work, we study the problem of deploying service function chains over network function virtualized architecture. Specifically, we study virtual network function placement problem for the optimal SFC formation across geographically distributed clouds. We set up the problem of minimizing inter-cloud traffic and response time in a multi-cloud scenario as an ILP optimization problem, along with important constraints such as total deployment costs and service level agreements (SLAs). We consider link delays and computational delays in our model. The link queues are modeled as M/D/1 (single server/Poisson arrival/deterministic service times) and server queues as M/M/1 (single server/Poisson arrival/exponential service times) based on the statistical analysis. In addition, we present a novel affinity-based approach (ABA) to solve the problem for larger networks. We provide a performance comparison between the proposed heuristic and simple greedy approach (SGA) used in the state-of-the-art systems. Greedy approach has already been widely studied in the literature for the VM placement problem. Especially we compare our proposed heuristic with a greedy approach using first-fit decreasing (FFD) method. By observing the results, we conclude that the affinity-based approach for placing the service functions in the network produces better results compared against the simple greedy (FFD) approach in terms of both, total delays and total resource cost. We observe that with a little compromise (gap of less than 10% of the optimal) in the solution quality (total delays and cost), affinity-based heuristic can solve the larger problem more quickly than ILP. This work has been supported under the grant ID NPRP 6 - 901 - 2 - 370 for the project entitled "Middleware Architecture for Cloud Based Services Using Software Defined Networking (SDN)", which is funded by the Qatar National Research Fund (QNRF) and a grant from Huawei. H. Anthony Chan was supported by Huawei. The statements made herein are solely the responsibility of the authors. Scopus

International audience; We focus on the placement of Virtualized Network Functions (VNFs) chains in the Network Function Virtualization (NFV) context where NFV Infrastructures (NFVIs) are used to host the VNFs. The optimal placement of VNF (service) chains in hosting infrastructures is one of the key issues in the deployment of service functions in large-scale environments. The Virtualized Network Functions Chain Placement Problem VNF-CPP is NP-Hard and there is a need for placement approaches that can scale with problem size and find good solutions in acceptable times. We propose a matrix-based optimization and a multi-stage graph method that are cost efficient and improve scalability by finding solutions in polynomial times. These algorithms are compared with an exact formulation given by the Perfect 2-Matching leading to a polynomial variant of the VNF-CPP for VNF chains with 2 arcs. Simulation results for longer and more complex chains confirm the efficiency and scalability of the proposed methods and their ability to find good suboptimal solutions

Analysis of traveler behaviors generally includes multiple activities en route among different origin and destination pairs, which are presented as trip chaining. These activities can be decomposed as a single trip into a chain of a primary activity and secondary activities. Because the sequence of different activities is associated with different travel cost during a trip-chain, and some of secondary activities are not necessary to go to the specific nodes to complete these activities. That is, the trip-chain based travelers’ trip-chain route choice behaviors and secondary activity nodes choice should be considered simultaneously. In this study, a combined activity nodes choice and trip-chain based user equilibrium traffic assignment model is formulated. A new solution algorithm is also developed, and the model performance was verified using the modified UTown test network. The numerical analysis result has an implication for urban traffic management and sustainable mobility.

Morphological switch-reference systems have been described as complex structural phenomena with specific marking for chain-medial cataphoric referential continuity (SS) and discontinuity (DS). This paper attempt to place these synchronic structural systems in two explanatory contexts: First, the functional context of discourse structure and the management of nominal reference, with special attention given to the universal functions of zero, independent pronouns, anaphoric pronouns, pronominal agreement, conjunctions and non-finite verbal structures. And second, the diachronic processes via which complex synchronic switch-reference systems arise gradually from their simple building blocks.

IPv6 Segment Routing is a recent IPv6 extension that is generating a lot of interest among researchers and in industry. Thanks to IPv6 SR, network operators can better control the paths followed by packets inside their networks. This provides enhanced traffic engineering capabilities and is key to support Service Function Chaining (SFC). With SFC, an end-to-end service is the composition of a series of in-network services. Simple services such as NAT, accounting or stateless firewalls can be implemented on a per-packet basis. However, more interesting services like transparent proxies, transparent compression or encryption, transcoding, etc. require functions that operate on the bytestream. In this paper, we extend the IPv6 implementation of Segment Routing in the Linux kernel to enable network functions that operate on the bytestream and not on a per-packet basis. Our SRv6Pipes enable network architects to design end-to-end services as a series of in-network functions. We evaluate the performance of our implementation with different microbenchmarks.

Time-dependent Origin-Destination (OD) demand flows are fundamental inputs for Dynamic Traffic Assignment (DTA) systems and real-time traffic management. This work introduces a novel state-space framework to estimate these demand flows in an online context. Specifically, we propose to explicitly include trip-chaining behavior within the state-space formulation, which is solved using the well-established Kalman Filtering technique. While existing works already consider structural information and recursive behavior within the online demand estimation problem, this information has been always considered at the OD level. In this study, we introduce this structural information by explicitly representing trip-chaining within the estimation framework. The advantage is twofold. First, all trips belonging to the same tour can be jointly calibrated. Second, given the estimation during a certain time interval, a prediction of the structural deviation over the whole day can be obtained without the need to run additional simulations. The effectiveness of the proposed methodology is demonstrated first on a toy network and then on a large real-world network. Results show that the model improves the prediction performance with respect to a conventional Kalman Filtering approach. We also show that, on the basis of the estimation of the morning commute, the model can be used to predict the evening commute without need of running additional simulations., copyright 2019. This manuscript version is made available under the CC-BY-NC-ND 2.0 license http://creativecommons.org/licenses/by-nc-nd/2.0 Article presented at 23rd International Symposium on Transportation and Traffic Theory, ISTTT 23, 24-26 July 2019, Lausanne, Switzerland

Group A Streptococcal (GAS) biofilm formation is an important pathological feature contributing to the antibiotic tolerance and progression of various GAS infections. Although a number of bacterial factors have been described to promote in vitro GAS biofilm formation, the relevance of in vitro biofilms to host-associated biofilms requires further understanding. In this study, we demonstrate how constituents of the host environment, such as lysozyme and NaCl, can modulate GAS bacterial chain length and, in turn, shape GAS biofilm morphology and structure. Disruption of GAS chains with lysozyme results in biofilms that are more stable. Based on confocal microscopy, we attribute the increase in biofilm stability to a dense and compact three-dimensional structure produced by de-chained cells. To show that changes in biofilm stability and structure are due to the shortening of bacterial chains and not specific to the activity of lysozyme, we demonstrate that augmented chaining induced by NaCl or deletion of the autolysin gene mur1.2 produced defects in biofilm formation characterized by a loose biofilm architecture. We conclude that GAS biofilm formation can be directly influenced by host and environmental factors through the modulation of bacterial chain lengths, potentially contributing to persistence and colonization within the host. Further studies of in vitro biofilm models incorporating physiological constituents such as lysozyme may uncover new insights into the physiology of in vivo GAS biofilms.

Multitask (multi-target or multi-output) learning (MTL) deals with simultaneous prediction of several outputs. MTL approaches rely on the optimization of a joint score function over the targets. However, defining a joint score in global models is problematic when the target scales are different. To address such problems, single target (i.e. local) learning strategies are commonly employed. Here we propose alternative tree-based learning strategies to handle the issue with target scaling in global models, and to identify the learning order for chaining operations in local models. In the first proposal, the problems with target scaling are resolved using alternative splitting strategies which consider the learning tasks in a multi-objective optimization framework. The second proposal deals with the problem of ordering in the chaining strategies. We introduce an alternative estimation strategy, minimum error chain policy, that gradually expands the input space using the estimations that approximate to true characteristics of outputs, namely out-of-bag estimations in tree-based ensemble framework. Our experiments on benchmark datasets illustrate the success of the proposed multitask extension of trees compared to the decision trees with de facto design especially for datasets with large number of targets. In line with that, minimum error chain policy improves the performance of the state-of-the-art chaining policies.

This research investigated the performance of an Electric Taxi (ET) fleet that catered solely for customers with advance reservations. In a previously related research, a customized Paired Pickup and Delivery Problem with Time Window and Charging Station (PPDPTWCS) had been formulated to solve for the minimum number of taxis that would serve a fixed set of customer demand. The concept behind this fleet optimization was to chain multiple customer trips and trips to Charging Stations (CSs) to form a route and assigned to a taxi driver. In this paper the sensitivity of the ET fleet’s operations with respect to network sizes, customer demand densities and number of CSs have been investigated. It also analyzed the market shares of the CSs and the occupancy of a CS over time. The results showed that, (1) the expansion of network size or the increase in customer demand density led to increase in fleet size, number of trips to the CSs and maximum occupancies at the CSs but these performance measures grew at different rates; (2) when the network size and number of CSs were fixed, an increase in customer demand density led to a better utilization of taxis in terms of more customers served per taxi and higher average revenue per taxi; (3) given the same network size and demand density, the ET fleet’s performance was relatively insensitive to the number of CSs; and (4) the usage of individual CS was affected by the number of CS and their locations; and (5) when all the ETs were fully charged at the beginning of the same shift hour, they visited the CSs in bunches when their batteries were about to run out. These findings contribute to a better understanding of the operations of the ET fleet and the CSs. They could be used for making better decisions in the planning of ET operations.

We investigate a lean inventory sharing strategy, called “Circular Bidirectional Chaining” (BDC), in a single period setting, and quantify the difference between the performance of BDC and the performance of other inventory sharing strategies for normally distributed demands. Under BDC all the locations, each facing stochastic demand, are connected in a closed loop, such that each location is allowed to cooperate laterally with exactly two adjacent locations. A location is not allowed to serve as a source and a sink of material at the same time. To consider BDC vis-a-vis other strategies, one must first optimize the proposed BDC strategy. Managing the BDC consists of two problems: determining the optimal order quantities, and, for given order quantities and demand realizations, determining how should items be transshipped. The former is a stochastic planning problem with recourse, solved via simulation-based optimization, while the latter, which is the recourse part of the former, can be interpreted as a transportation problem. Sensitivity analysis with respect to problem parameters is provided. It turns out that BDC can achieve a considerable portion of the benefits of complete pooling in around 65% of the cases, while the cost required to enable cooperation via BDC is lower than that of complete pooling.

Variability and seasonality is an inherent characteristic in many service industries. This leads to the problem of mismatch between employee supply and demand. In this work we present a mixed integer linear programming model and a constructive heuristic to address this problem. The proposed methodology applies the concept of multiskilling, an attractive source of flexibility for improving service levels and reducing the costs to firms of staff shortages and surpluses induced by employee demand variability. The model structures the multiskilling characteristics of a set of employees. Initially deterministic, the model is then modified to incorporate a robust optimization approach in which demand uncertainty is explicitly included. A Monte Carlo simulation is conducted to evaluate the model's robust solutions for different levels of demand variability and decision-maker risk aversion. The methodology creates closed-chain multiskilling structures of different lengths with excellent cost-effective performance for each variability level. Finally, some user guidelines are defined for choosing the appropriate risk aversion level and generating a skills training plan that will ensure most of the total potential benefits of multiskilling are actually obtained with an investment much less than what would be required by the most conservative (i.e., worst case) robust solution.