Neighboring Links Research Articles

A multi path routing protocol with efficient energy consumption in IoT applications real time traffic

The extensive utilization of IoT applications leads to the aggregation of a substantial volume of data, presenting a crucial challenge in terms of data routing within these networks. RPL intentionally surpasses the limitations sometimes observed in low-power and lossy networks, which are particularly prevalent in IoT networks. The RPL protocol is designed specifically for static networks that do not involve mobility or topological changes. The RPL protocol guarantees continuous connectivity between nodes and mitigates the risk of data loss in stationary IoT applications that do not involve mobility or alterations in network configuration. The article utilizes a mobility aid technology known as network performance stability using the intelligent routing protocol (nPSIR), which expands upon RPL. The Mobility Support Entity (nPSIR) facilitates the displacement of all nodes, with the exception of the root node, and ensures uninterrupted connection during mobility. Moreover, it deals with the situation where there is a physical barrier between two interconnected nodes in a changing environment. In order to achieve this objective, it employs a dynamic trickle timer that operates within two distinct ranges. Furthermore, it utilizes a neighbor link quality table, a mechanism for selecting the most beneficial parent node in the event of migration, a measure of confidence, the identification of crucial regions, and a blacklist. Multiple simulations validate that nPSIR effectively decreases hand-off delay and improves packet delivery, despite the minor drawbacks of increased signaling costs and power consumption. The delivery ratio decreases the quantity of lost data packets and surpasses both RPL as a responsive protocol and mRPL as a proactive protocol in relation to mobility.

Challenges in Protein QM/MM Simulations with Intra-Backbone Link Atoms.

Hybrid quantum mechanical/molecular mechanical (QM/MM) simulations fuel discoveries in many fields of science including computational biochemistry and enzymology. Development of more convenient tools leads to an increase in the number of works in which mechanical insights into enzymes' mode of operation are obtained. Most commonly, these tools feature hydrogen-capping (link atom) approach to provide coupling between QM and MM subsystems across a covalent bond. Extensive studies were conducted to provide a solid foundation for the correctness of such an approach when a bond to a nonpolar MM atom is considered. However, not every task may be accomplished this way. Certain scenarios of using QM/MM in computational enzymology encourage or even necessitate the incorporation of backbone atoms into the QM region. Two out of three backbone atoms are polar, and in QM/MM with electrostatic embedding, a neighboring link atom will be hyperpolarized. Several schemes to mitigate this effect were previously proposed alongside a rigorous assessment of quantitative effects on model systems. However, it was not clear whether they may translate into qualitatively different results and how link atom hyperpolarization may manifest itself in a real-life enzymological scenario. Here, we show that the consequences of such an artifact may be severe and may completely overturn the conclusions drawn from the simulations. Our case advocates for the use of charge redistribution schemes whenever intra-backbone QM/MM boundaries are considered. Moreover, we addressed how different boundary types and charge redistribution schemes influence backbone dynamics. We showed that the results are heavily dependent on which boundary MM terms are retained, with charge alteration being of secondary importance. In the worst case, only three intra-backbone boundaries may be used with relative confidence in the adequacy of resulting simulations, irrespective of the hyperpolarization mitigation scheme. Thus, advances in the field are certainly needed to fuel new discoveries. As of now, we believe that issues raised in this work might encourage authors in the field to report what boundaries, boundary MM terms, and charge redistribution schemes they are using, so their results may be correctly interpreted.

Providing distributed system integrity under spatial grasp technology

In the last decades, we have witnessed an exploding growth of different kinds of sophisticated distributed systems with financial, industrial, ecological, security, military, and many other ap-plications. Providing high integrity of such systems is becoming a key point of their develop-ment, evolution, and usage, especially in various crisis situations and under disastrous and ad-versarial conditions. The paper reviews a number of existing works on the integrity, security, and recovery of distributed systems. It also briefs the main aspects of the Spatial Grasp Model and Technology (SGT), reflecting some general issues of the paradigm, its Spatial Grasp Lan-guage (SGL), and networked SGL interpretation in distributed environments. SGT can dynami-cally establish and keep superior power over large distributed systems, including creating them from scratch. Using a graph-based representation of the distributed system topologies, with nodes having both virtual and physical properties, the paper shows full topology creation start-ing from all nodes in parallel and then from a single node, also copying the existing topology in similar cases. In addition, it demonstrates how to organize distributed systems in such a way so that they can self-recover in any circumstances and after any damages by supplying their nodes with universal genetic-like capabilities by which any self-repairs can be organized. Such recovery may be from missing neighboring nodes and links to the rebuilding of the distributed topologies, which means they cannot be destroyed even in the severest conditions. These features can be particularly useful after IT network damages, environmental and industrial disasters, for crisis management, and on battlefields. The paper confirms the efficiency of the developed distributed control approach for providing high integrity and self-recovery of im-portant distributed systems.

Integrating space syntax with spatial interaction

In this paper, we attempt to compare space syntax with spatial interaction. At one level, these two approaches to urban spatial structure are non-comparable. Space syntax is largely a descriptive technique for visualising spatial relations at the level of connections between places while spatial interaction is a predictive model that forecasts how much travel there will be between places. Space syntax articulates the system in terms of whether or not a physical link, usually at the level of the street, exists while spatial interaction predicts movements between all origins and destinations which are places often anchored in terms of the street network, but which at the level of prediction, assume connections between all places. Space syntax is grounded at a fine spatial scale while spatial interaction defines places as aggregates of activity in larger zones than the scale of the street system. The main output of space syntax is a connectivity matrix of step lengths between streets whereas in spatial interaction, such networks are predetermined, measurable in terms of Euclidean distance or generalised cost of travel, and the output is the volume of travel prior to this being assigned usually to a street network.There is however a fundamental way of relating the implicit network graph of spatial interaction to the explicit planar graph of the street network. We begin by assuming the planar graph of the network is conceived of as a primal problem of spatial interaction while the dual graph linking streets in the planar graph is the graph which is used in space syntax. We exploit this duality and show how we can move easily between spatial interaction as the primal and space syntax as the dual. This is rooted in a more fundamental graph – the bipartite graph which is a list of streets/arcs and their intersections/nodes from which the primal and dual emerge naturally. We explore various accessibility measures and show how they relate and correlate. We then go one step further and consider how various processes of random walking take place in these networks examining the steady states of the primal and dual problems in terms of the likelihood of a random walker visiting any node or street. We thus define primal and dual Markov chains that enable us to generate these probabilities. This provides a basic framework for comparing primal and dual in comparing spatial interaction with space syntax. We illustrate these measures on simple and easy to articulate graphs, extending this to a synthetic network of nearest neighbour links in Greater London based on 699 nodes and 1972 symmetric ‘streets’ between zones. This is a preliminary attack on the problem of linking these two approaches although many challenges remain.

Edge-Assisted Spectrum Sharing for Freshness-Aware Industrial Wireless Networks: A Learning-Based Approach

Information freshness is essential to industrial wireless networks (IWNs) and can be quantified by the age-of-information (AoI) metric. This paper addresses an AoI-aware spectrum sharing (AgeS) problem in IWNs, where multiple device-to-device (D2D) links opportunistically access the spectrum to satisfy their AoI constraints while maximizing primal links’ throughput. Particularly, we orchestrate the access of D2D links in a distributed manner. Since distributed scheduling results in incomplete observation, D2D links share the spectrum with uncertainty on the transmission environment. Therefore, we propose a distributed scheduling scheme, called D-age, to deal with the transmission uncertainty in the AgeS problem, where an adaptation of actor-critic method is adopted with AoI constraints tackled in the dual domain. To address the non-stationary environment and multi-agent credit assignment issue, cooperative multi-agent reinforcement learning (MARL) approach is developed, where multiple local actors are designed to guide D2D links to make real-time decisions via distributed scheduling policies, which are evaluated by an edge-assisted global critic with action-aware advantage functions. Integrated with graph attention networks (GATs), the critic selectively learns contextual information by assigning different importances to neighboring links, which enables the evaluation of scheduling policies in a scalable and computation-efficient manner. Theoretical guarantee of the time-averaged AoI constraints is provided and the effectiveness of D-age in terms of both AoI violation ratio and the capacity of primal links is demonstrated by simulation.

Applying machine learning methods to predict geology using soil sample geochemistry

In this study we compared various machine learning techniques that used soil geochemistry to aid in geologic mapping. We tested six different sampling methods (undersample, oversample, Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN), SMOTE and Edited Nearest Neighbor (SMOTEENN), and SMOTE and Tomek links (SMOTETomek)). SMOTE performed best with ADASYN and SMOTETomek having slightly lower effectiveness. Nine machine learning algorithms (naïve Bayes, logistic regression, quadratic discriminant analysis, nearest neighbors, radial basis function support-vector machine, artificial neural network, random forest, AdaBoost classifier, and gradient boosting classifier) were compared and AdaBoost classifiers and gradient boosting classifiers were found to be most effective. Finally, we experimented with multiple classifier systems (MCS) testing different combinations of algorithms and various combinatorial functions. It was found that MCS can outperform individual models, and the best MCS combined nearest neighbors, radial basis function support-vector machine, artificial neural network, random forest, AdaBoost classifiers, and gradient boosting classifier, then applied a logistic regression to the probabilities output by the models. Ultimately, we created a tool that is able to adequately predict underlying geology in the study area using soil geochemistry.

Low Power FPGA Implementation of a Smart Building Free Space Optical Communication System

Free Space Optical (FSO) communication systems have extensively invaded the speed of smart city evolution due to the current surge in demand for wireless communication spots that can match recent challenges due to high technical leaps in smart city evolution. As the number of users is vastly increasing throughout all networks in the form of machines, devices, and variously distinct objects, FSO is a hugely recommended robust communication system that mitigates a lot of RF disadvantages on the field with no need for licensing, fast rollout time, and low cost. This paper shows an exploit of a Low Power Field Programmable Gate Array (FPGA) based FSO communication system designed for Line of Sight (LOS) Building to Building Communication over a distance of 12 m using a 650 nm Visible Light (VL) red laser source via On-Off Keying (OOK) and higher-level Intensity Modulation (IM)/Pulse Width Modulation (PWM) schemes. The implemented system reached a doubled data rate than OOK of 230 kbps using the IM technique. Traffic monitoring and building security status can be frequently updated between adherent buildings, each scanning its zone real-time conditions and sharing them with the neighboring links.

Non-Markovian temporal networks with auto- and cross-correlated link dynamics.

Many of the biological, social and man-made networks around us are inherently dynamic, with their links switching on and off over time. The evolution of these networks is often observed to be non-Markovian, and the dynamics of their links are often correlated. Hence, to accurately model these networks, predict their evolution, and understand how information and other relevant quantities propagate over them, the inclusion of both memory and dynamical dependencies between links is key. In this article we introduce a general class of models of temporal networks based on discrete autoregressive processes for link dynamics. As a concrete and useful case study, we then concentrate on a specific model within this class, which allows to generate temporal networks with a specified underlying structural backbone, and with precise control over the dynamical dependencies between links and the strength and length of their memories. In this network model the presence of each link is influenced not only by its past activity, but also by the past activities of other links, as specified by a coupling matrix, which directly controls the causal relations, and hence the correlations, among links. We propose a maximum likelihood method for estimating the model's parameters from data, showing how the model allows a more realistic description of real-world temporal networks and also to predict their evolution. Due to the flexibility of maximum likelihood inference, we illustrate how to deal with heterogeneity and time-varying patterns, possibly including also nonstationary network dynamics. We then use our network model to investigate the role that, both the features of memory and the type of correlations in the dynamics of links have on the properties of processes occurring over a temporal network. Namely, we study the speed of a spreading process, as measured by the time it takes for diffusion to reach equilibrium. Through both numerical simulations and analytical results, we are able to separate the roles of autocorrelations and neighborhood correlations in link dynamics, showing that not only is the speed of diffusion nonmonotonically dependent on the memory length, but also that correlations among neighboring links help to speed up the spreading process, while autocorrelations slow it back down. Our results have implications in the study of opinion formation, the modeling of social networks, and the spreading of epidemics through mobile populations.

SDFA: A Service-Driven Fragmentation-Aware Resource Allocation in Elastic Optical Networks

To support the fifth-generation bandwidth-hungry applications, such as the Internet of Things, virtual reality, augmented reality, and cloud computing, elastic optical networks have become the most promising infrastructure that allocates bandwidths for services flexibility. Fragmentation caused by dynamic resource allocation deteriorates the availability of resources in networks, increasing the blocking of requests. The fragmentation occurs not only in the used path but also in the neighboring links that are not included in the used path; they are connected to the used path. This paper proposes a service-driven fragmentation-aware (SDFA) resource allocation scheme to enhance resource utilization by avoiding fragmentation with the joint consideration of the used path and neighboring links. A service-driven fragmentation metric (SDFM) is, for the first time, presented to estimate the fragmentation in the used path and neighboring links. The SDFA scheme prefers to assign services at the spectrum slots, which leads to the minimum value of SDFM. Simulation results indicate that SDFA outperforms four conventional fragmentation-aware resource allocation schemes in terms of blocking probability and resource utilization due to a lower fragmentation in the network.

MSE-RPL: Mobility Support Enhancement in RPL for IoT Mobile Applications

Due to the expansion of IoT applications which causes the generation of a massive amount of data, data routing is one of the most important challenges in these networks. The Routing Protocol for Low-power and Lossy Networks (RPL) was developed to cope with the Low-power and lossy network constraints, which play a significant role in IoT networks. Although most IoT applications involve mobility and topology change that makes mobility support a substantial need to prevent disconnection of nodes and data loss, the RPL is designed for static networks. This article proposes a mobility support method called MSE as an extension of RPL. The MSE supports mobility of all nodes except the root node, and it provides a seamless connection during the mobility. It also manages a situation when a physical obstacle settles between two paired nodes in a dynamic environment. To this end, it uses a dynamic trickle timer with two different ranges, a neighbor link quality table, a function to select the best parent in case of mobility, confidence, critical zones, and a blacklist. Simulations in multiple scenarios indicate that MSE, despite causing a slight increase in signaling cost and power consumption, significantly reduces hand-off delay, increases Packet Delivery Ratio, reduces the number of lost data packets, and outperforms both RPL as a reactive and mRPL as a proactive protocol regarding mobility.

Nonlinearity-induced transition in the nonlinear Su-Schrieffer-Heeger model and a nonlinear higher-order topological system

We study the topological physics in nonlinear Schr\"{o}dinger systems on lattices. We employ the quench dynamics to explore the phase diagram, where a pulse is given to a lattice point and we analyze its time evolution. There are two system parameters $\lambda $ and $\xi $, where $\lambda $ controls the hoppings between the neighboring links and $\xi $ controls the nonlinearity. The dynamics crucially depends on these system parameters. Based on analytical and numerical studies, we derive the phase diagram of the nonlinear Su-Schrieffer-Heeger (SSH) model in the ($\lambda ,\xi $) plane. It consists of four phases. The topological and trivial phases emerge when the nonlinearity $\xi $ is small. The nonlinearity-induced localization phase emerges when $\xi $ is large. We also find a dimer phase as a result of a cooperation between the hopping and nonlinear terms. A similar analysis is made of the nonlinear second-order topological system on the breathing Kagome lattice, where a trimer phase appears instead of the dimer phase.

Design of an arterial signal progression plan for multi-path flows with only intersection turning counts

Most congested urban arterials, connecting multiple major intersections with heavy turning flows, are likely to comprise multiple high-volume path flows in addition to its through traffic. Hence, such arterials, if designed with state-of-the-art models for progressing mainly through traffic flows, cannot achieve the expected level of performance, because those heavy path flows involving turning movements will inevitably suffer from overflows at their turning bays and consequently trigger a mutual blockage between the turning and through traffic flows. Although some recent advances on multi-path signal progression (Yang et al., 2015; Arsava et al., 2016) offer a viable design alternative for arterials accommodating such traffic patterns, their required data such as the arterial’s O-Ds or path volume information cannot be collected with state-of-the-practice traffic sensors, or mathematically estimated at the accuracy level sufficient for design of control strategies. As such, this study proposes a model with the optimized phase sequence and offsets to produce a progression band for each candidate path along the target arterial with only each intersection's volume counts, based on the set of “local bands” for all local paths constituted by each link’s upstream flow-in and downstream flow-out movements. Such identified local progression bands are then optimally connected between two neighboring links to form the progression band for traffic flows on each path to progress over multiple arterial links under the objective of maximizing the total weighted progression bands. To ensure the effectiveness of the produced progression bands, the proposed model has accounted for all factors and geometric constraints that may cause their mutual impedance in design of the offsets and phase sequences. The results of extensive numerical experiments with respect to delay and the number of stops per vehicle (reduced by 5.8% and 9.9%, respectively, compared to the benchmark model) within the entire network have shown the effectiveness of the proposed model.

Machine Learning Assisted Chimera and Solitary States in Networks

Chimera and Solitary states have captivated scientists and engineers due to their peculiar dynamical states corresponding to co-existence of coherent and incoherent dynamical evolution in coupled units in various natural and artificial systems. It has been further demonstrated that such states can be engineered in systems of coupled oscillators by suitable implementation of communication delays. Here, using supervised machine learning, we predict (a) the precise value of delay which is sufficient for engineering chimera and solitary states for a given set of system's parameters, as well as (b) the intensity of incoherence for such engineered states. Ergo, using few initial data points we generate a machine learning model which can then create a more refined phase plot as well as by including new parameter values. We demonstrate our results for two different examples consisting of single layer and multi layer networks. First, the chimera states (solitary states) are engineered by establishing delays in the neighboring links of a node (the interlayer links) in a 2-D lattice (multiplex network) of oscillators. Then, different machine learning classifiers, K-nearest neighbors (KNN), support vector machine (SVM) and multi-layer perceptron neural network (MLP-NN) are employed by feeding the data obtained from the network models. Once a machine learning model is trained using the limited amount of data, it predicts the precise value of critical delay as well as the intensity of incoherence for a given unknown systems parameters values. Testing accuracy, sensitivity, and specificity analysis reveal that MLP-NN classifier is better suited than Knn or SVM classifier for the predictions of parameters values for engineered chimera and solitary states. The technique provides an easy methodology to predict critical delay values as well as intensity of incoherence for that delay value for designing an experimental setup to create solitary and chimera states.

Arterial-Friendly Local Ramp Metering Control Strategy

To prevent local streets being blocked by overflowing on-ramp queues, a standard practice of ramp metering control is to restrain its function when a series of preset conditions are identified by on-ramp queue detectors. Such a trade-off between potential ramp queue spillback and the restraint resulting from the operation of metering control may often fail to either effectively mitigate bottlenecks caused by on-ramp waving or convince arterial users and local traffic agencies of the need for ramp metering operations. This study, therefore, presents an arterial-friendly local ramp metering system (named AF-ramp) that can achieve the target metering rate to produce optimal freeway conditions without ramp queues spilling back onto local streets. This is achieved by concurrently optimizing the signal plans for those intersections that send turning flows to the ramp. At this stage, this system has been developed for time-of-day control. It could also serve as the base module for extending to real-time control, or multi-ramp coordinated operations. The AF-ramp model, with its ability to optimize the arterial signals concurrently with the ramp metering rate, can ensure the best use of the capacity of local intersections and prevent any gridlock caused by overflows from on-ramp queue spillback or arterial turning traffic. With extensive simulation experiments, the evaluation results confirmed the AF-ramp model’s effectiveness in improving traffic conditions on both the freeway and its neighboring arterial links at the same time. This study has also introduced the real-time extension of the proposed model and a framework of a transition from the time-of-day control to fully responsive real-time operations.

Distributed link scheduling in wireless networks

This work investigates distributed transmission scheduling in wireless networks. Due to interference constraints, “neighboring links” cannot be simultaneously activated, otherwise transmissions will fail. Here, we consider any binary model of interference. We use the model described by Bui et al. in [L. X. Bui, S. Sanghavi and R. Srikant, Distributed link scheduling with constant overhead, IEEE/ACM Trans. Netw. 17(5) (2009) 1467–1480; S. Sanghavi, L. Bui and R. Srikant, Distributed link scheduling with constant overhead, in Proc. ACM Sigmetrics (San Diego, CA, USA, 2007), pp. 313–324.]. We assume that time is slotted and during each slot there are two phases: one control phase in which a link scheduling algorithm determines a set of non-interfering links to be activated, and a data phase in which data is sent through these links. We assume random arrivals on each link during each slot, so that a queue is associated to each link. Since nodes do not have a global knowledge of the queues sizes, our aim (like in [L. X. Bui, S. Sanghavi and R. Srikant, Distributed link scheduling with constant overhead, IEEE/ACM Trans. Netw. 17(5) (2009) 1467–1480; S. Sanghavi, L. Bui and R. Srikant, Distributed link scheduling with constant overhead, in Proc. ACM Sigmetrics (San Diego, CA, USA, 2007), pp. 313–324.]) is to design a distributed link scheduling algorithm. To be efficient, the control phase should be as short as possible; this is done by exchanging control messages during a constant number of mini-slots (constant overhead). In this paper, we design the first fully distributed local algorithm with the following properties: it works for any arbitrary binary interference model; it has a constant overhead (independent of the size of the network and the values of the queues), and it does not require any knowledge of the queue-lengths. We prove that this algorithm gives a maximal set of active links, where for any non-active link there exists at least one active link in its interference set. We also establish sufficient conditions for stability under general Markovian assumptions. Finally, the performance of our algorithm (throughput, stability) is investigated and compared via simulations to that of previously proposed schemes.

Fast distributed control design for DC linked microgrids

A centralized controller for a DC linked hybrid microgrid requires costly communication infrastructure. To minimize the costs, in this paper, a sparse distributed controller is formulized by introducing an l1 penalty term to the cost function to enforce at most not more than neighbor-to neighbor links. This makes the design nonlinear which exhausts large amount of computation times. Therefore, by some transformations, the nonlinear cost function is converted to an approximate quadratic one; which has better optimization characteristics, i.e. fast convergence rate and unimodal. The proposed fast distributed controller Design (FDCD) algorithm is employed for a 3-bus and a 69-bus (consist of 7 hybrid microgrid) distribution systems controller design. The results indicate that the obtained sparse distributed controller performs well as if a centralized controller is in operation. It can successfully suppress various types of faults injected to the AC, DC, and DC link sides of the network. The algorithm convergence rate is substantially lower than some other contestant algorithms. A problem that is solved by FDCD in 5 min, it takes 10 min for the others to be completed.

Link utility aware geographic routing for urban VANETs using two-hop neighbor information

Routing in Vehicular Ad Hoc Networks (VANETs) is a challenging problem. While geographic routing protocols are preferred for VANETs due to their scalability, they are focused on finding next-hop nodes closer to the destination without considering their current load or network traffic. In addition, routing decisions based on only one-hop neighbors information might be less optimal since they do not consider the availability of further suitable nodes for forwarding. Thus, such selected next-hop nodes may become overloaded with too much traffic that exceeds their available bandwidth, and may even result in significant packet losses if no other node suitable for forwarding can be found. In this paper, we propose a novel geographic routing protocol named Geo-LU. The proposed protocol improves the routing performance by extending the local view of the network topology at the current forwarder to include two-hop neighbor information. Moreover, it utilizes our proposed link utility (LU) measure. LU considers the utility of a two-hop neighbor link by considering the minimum residual bandwidth on that link and its packet loss rate. By incorporating two-hop neighbor information and our proposed LU measure, the proposed protocol, Geo-LU, can react appropriately to the increased network traffic and to the frequent topology dis-connectivity in VANETs as confirmed by our simulation results.

Dual responsive GG-g-PNPA/PIPAM based novel hydrogels for the controlled release of anti- cancer agent and their swelling and release kinetics

This study includes the synthesis of amino acid graft copolymer from N- Acryloyl-L-phenylalanine amino acid and Guar gum polymer through free radical polymerization. Then the dual responsive (pH & temperature) hydrogels are synthesized by employing free radical cross linking polymerization using graft copolymer, N-isopropyl acrylamide and 2-(dimethylamine) ethylmethacrylate using N,N′-methylenebisacrylamide as a cross-linker and ammonium per sulfate as initiator. The structural properties of the hydrogels are evaluated by the determination of various network properties such as mesh size, crosslink density, Flory-Huggins interaction parameter, volume fraction in the swollen state and the average molecular weight of the polymer chain between two neighboring cross links. Dynamic and equilibrium swelling studies of hydrogels are performed in distilled water, pH 1.20 and pH 7.40 solutions at 25 °C and 37o ± 5 °C, swelling capacity in various salt solutions and corresponding swelling kinetic parameters are also calculated. Grafting, cross linking and structural changes are studied by fourier transform infrared and scanning electron microscope, distribution of imatinib mesylate drug in hydrogel is confirmed by X-ray diffraction, differential scanning calorimetry, thermo gravimetric analysis. Drug loading and encapsulation efficiency of Imatinib mesylate in various formulations of the hydrogels are also studied. The in-vitro drug release studies are performed in pH 1.20 and 7.40 phosphate buffer solutions at different amounts of cross linker, monomer and graft copolymer and temperature. The mode of drug release mechanism is analyzed by various kinetic models such as zero order, First order, Higuchi Square root, Hixson-Crowell cube root and Koresmeyer-Peppas equations.

Examining the robustness of the Ontario truck road network

Robust road transportation networks are essential to facilitate truck movements between regions. Disruptions along certain road links could degrade the network’s robustness and curtail regional commerce. A network is considered robust if the network-wide travel cost does not change significantly before and after disruptions. The network robustness index (NRI) is a well-known measure for examining the effect of reduced capacity on one or more links. It has been mainly applied to urban networks and passenger trips. In this paper, the NRI is applied to assess the robustness of the Ontario-wide truck network. Truck flows between the 49 census divisions within Ontario and 25 external zones for the year 2012 are modeled on the network of 35,254 road links and 14,444 associated nodes. Several scenarios of incrementally reduced capacity on specific critical links are devised and simulated. The NRI is calculated for each scenario to identify the level of network robustness. The locational effects of the capacity reductions were explored with the help of maps to assess the change in traffic volumes of the simulated scenarios relative to the base case. As expected, traffic diffuses or shifts to neighboring links when capacities are reduced. These results can be useful in infrastructure planning and the design of alternative routes to minimize the negative impact on traffic flows when disruptions occur.

A Hybrid Approach Based on Variational Mode Decomposition for Analyzing and Predicting Urban Travel Speed

Predicting travel speeds on urban road networks is a challenging subject due to its uncertainty stemming from travel demand, geometric condition, traffic signals, and other exogenous factors. This uncertainty appears as nonlinearity, nonstationarity, and volatility in traffic data, and it also creates a spatiotemporal heterogeneity of link travel speed by interacting with neighbor links. In this study, we propose a hybrid model using variational mode decomposition (VMD) to investigate and mitigate the uncertainty of urban travel speeds. The VMD allows the travel speed data to be divided into orthogonal and oscillatory sub-signals, called modes. The regular components are extracted as the low-frequency modes, and the irregular components presenting uncertainty are transformed into a combination of modes, which is more predictable than the original uncertainty. For the prediction, the VMD decomposes the travel speed data into modes, and these modes are predicted and summed to represent the predicted travel speed. The evaluation results on urban road networks show that, the proposed hybrid model outperforms the benchmark models both in the congested and in the overall conditions. The improvement in performance increases significantly over specific link-days, which generally are hard to predict. To explain the significant variance of the prediction performance according to each link and each day, the correlation analysis between the properties of modes and the performance of the model are conducted. The results on correlation analysis show that the more variance of nondaily pattern is explained through the modes, the easier it was to predict the speed. Based on the results, discussions on the interpretation on the correlation analysis and future research are presented.