Computational models of neural networks can be based on a variety of different parameters. These parameters include, for example, the 3d shape of neuron layers, the neurons' spatial projection patterns, spiking dynamics and neurotransmitter systems. While many well-developed approaches are available to model, for example, the spiking dynamics, there is a lack of approaches for modeling the anatomical layout of neurons and their projections. We present a new method, called Parametric Anatomical Modeling (PAM), to fill this gap. PAM can be used to derive network connectivities and conduction delays from anatomical data, such as the position and shape of the neuronal layers and the dendritic and axonal projection patterns. Within the PAM framework, several mapping techniques between layers can account for a large variety of connection properties between pre- and post-synaptic neuron layers. PAM is implemented as a Python tool and integrated in the 3d modeling software Blender. We demonstrate on a 3d model of the hippocampal formation how PAM can help reveal complex properties of the synaptic connectivity and conduction delays, properties that might be relevant to uncover the function of the hippocampus. Based on these analyses, two experimentally testable predictions arose: (i) the number of neurons and the spread of connections is heterogeneously distributed across the main anatomical axes, (ii) the distribution of connection lengths in CA3-CA1 differ qualitatively from those between DG-CA3 and CA3-CA3. Models created by PAM can also serve as an educational tool to visualize the 3d connectivity of brain regions. The low-dimensional, but yet biologically plausible, parameter space renders PAM suitable to analyse allometric and evolutionary factors in networks and to model the complexity of real networks with comparatively little effort. Computational simulations of neural networks have become an important tool to untangle the relationship between the function of a network and its structural properties. There are several levels on which artificial neural network can capture properties of the biological ideal. At the neuronal level, these are, for example, the spiking dynamics . Topological relations between CA1 and entorhinal layers remain roughly preserved. However, connection distances between those layers vary widely as different parts of CA1 and entorhinal cortex have different non-linear tracks and therefore varying distances to each other , we propose a technique and a Python implementation to close this gap. The basic idea of PAM is to trace neural, synaptic and intermediate layers from anatomical data and relate those layers to each other. With a set of mapping techniques, complex relationships between those layers can be defined to determine how axonal and dendritic projections traverse through space and where synapses are formed. A powerful feature of PAM is that spatial relations between and within layers can be combined to derive connections and distances between neurons. Furthermore, two- and three-dimensional experimental data  can be integrated in the model to describe neural density or functional properties of neurons. As a side effect, neural networks created using PAM are of high educational value as the depiction of neural layers created from anatomical data along with the selective visualization of axons, dendrites and synapses can be explored in 3d and clearly demonstrate how the layers are wired.In the following, we first introduce the principles that PAM is built on and then an implementation of PAM in the 3d software Blender. Subsequently, we apply PAM to build a model of the hippocampal formation in the rat. Finally, we find that these models can lead to new insights about brain structures and, potentially, functions.The most important concept in PAM is the “layer” Figure . A layerNote, that the wiring of the network is defined solely on the level of layers and not for single neurons. This approach corresponds to the notion that in real networks 3d patterns define where in space precursor cells proliferate and in which directions axon and dendrite cones grow. PAM uses these low-dimensional but biological plausible categories to define the architecture of neural networks. Groups of identical neurons are then distributed over the layer with a given density. Their connections to other neurons is a result of their relative location to other neurons and their projection direction across intermediate and SLs. PAM does not include a developmental component such as structural development through gene regulatory networks or cell migration. Instead, our approach rather aims to understand the functional implications of the developed structure.A network is defined by mappings between pre-synaptic and a post-synaptic neural layer , for any point on the first layer its corresponding position on the second layer can be directly computed. This mapping technique is useful whenever topological relations between neurons should be preserved independent from the origin and target location of axons and dendrites in space. The most obvious example for this is the mapping between photoreceptor cells in the retina to V1, where intermediate layers could be used to layout the realistic trajectory of the fibers to the visual cortex. But also the mapping of the dentate gyrus layer on a SL around CA3 in the hippocampus could make use of topological similarities to constrain the axonal projections along the septo-temporal axis.p on a layer X is mapped on another layer Y by computing the intersection between the line normal to X through the point p and layer Y. If there is no intersection, there is no connection. This mapping technique can be used when the projection direction of neurons solely depends on the layer it is located in . Furthermore, this mapping technique can be helpful to selectively map subareas of a layer onto certain target regions .Any point p on the first layer the closest point on the next layer. Such a mapping can be useful, when the relative position of neurons on the first layer and its proximity to the target layer determine their entry direction on the target layer. This can be helpful if the curvature of layers in space do not allow a reliable mapping between layers based on normal mapping.Euclidean mapping computes for a given point p on one layer to an arbitrary location on the next layer. This mapping is useful when the projection kernels of neurons are well-defined while the axonal or dendritic projections through space are randomly distributed across brains.The random mapping maps a point The connection distance between a pre- and a post-synaptic neuron along the axon and dendrite is an important piece of information, for instance, when conduction latencies should be part of the network simulation. PAM includes several methods to measure the distance between a neuron and a synapse incorporating spatial distances on a layer and between layers Figure . UV-distEuclidean distances between two layers correspond to connections along the shortest paths in space. These can be connections through the whole nervous system, like thalamo-cortical connections or sensory pathways but also more direct connections between cortical layers.Note that the distances computed by any of these methods represent estimates of the lower bounds since the convoluted morphology of real dendrites and axons may result in longer pathways and therefore longer latencies between two endpoints.Electrophysiological studies have shown some variability in the conduction latency per mm  that determines the probability of a neuron forming synapses in location z  can be integrated in the definition of the kernel (see Discussion for more details).n pre-synaptic and m post-synaptic neurons is evaluated at c spatial locations, the computational effort scales as O(cnm), a large number even in a small rat brain. In some cases where modeling the connectivity precisely is important, there might be no other alternative. If, on the other hand, the details are less important than the gross features of the connectivity, we can use an approximate sampling algorithm that provides a trade-off between mathematical accuracy and computational efficiency.However, in a network with realistic numbers of neurons and synapses, the computation of synaptic connections can be very time consuming. If every potential connection between If synaptic connections are formed sparsely, we can save computational time by systematically skipping partners that have a very low connection probability. The specific algorithm is as follows.c bins  ≥ p0 (see Section Methods). The values p (iz|zpost) are stored with the neuron id in the bin for later use.Step 2: Each post-synaptic neuron is mapped onto the SL and added to every bin p (z|zpre) as we need to generate synapses for this pre-synaptic neuron ≤ p1.Step 3: Each pre-synaptic neuron is mapped on the SL and we sample as many times from its connectivity kernel n Figure . Each sap (zpost|jz). These probabilities are related to the probabilities stored in Step 2 through Bayes' theoremStep 4: From each bin in the previous step, we determine the post-synaptic neuron to connect by sampling from p (zpost) is the same for every post-synaptic neuron p (zpost|jz)∝ p (jz|zpost).Since s = αnm, which is significantly better than the exact algorithm because for large networks α is generally a small value.The computational costs for this algorithm scales with the number of synapses PAM is a general approach to generate artificial neural networks based on anatomical data. To apply this technique, tools are needed to model and define the relationships between the layers. Therefore, we developed the functionality for defining parametric anatomical models (PAMs) in the open source 3d software BlenderThere were additional reasons for implementing PAM in the Blender environment. Because of its strong support for Python and its open application programming interface (API), Blender can be used as an integrated development environment for creating new tools and amending existing tools such as NEST. PAM for Blender consists of a set of add-ons and Python modules that extend the functionality of Blender to generate and relate anatomical layers to each other and to create neural networks for the networks simulator NEST. These tools along with example files and video tutorials are freely available First, layers need to be created that define the location for the cell bodies of neurons and for their synapses. Depending on the brain region, intermediate layers might be included to describe important landmarks for the trajectories of neurites. Since 2d and 3d images can be imported into Blender, atlas data or anatomical data, such as histological images or 3d data acquired through computer tomography or magnetic resonance imaging, can be used to support the modeling process. The depiction of metric units within the modeling environment allows to model the 3d structures with the correct scaling. All layers can be automatically unfolded to make uv-coordinates for the layers available. This part relies on Blender's internal tools and requires some modeling skills. However, once a brain region has been modeled as layers, it can serve as a template for a variety of neural network models.2. For each neuron group in a layer, the number of neurons that should be used in the simulation, can be defined. The PAM add-on for Blender provides a user interface for calculating the surface area, number of neurons and for visualizing the connectivity kernels on the SL the neuron types which are connectedthe mapping between successive layersthe way distances between successive layers should be calculatedthe connectivity kernels for pre- and post-synaptic neuronsthe number of outgoing (or incoming) connectionshttp://cns.mrg1.rub.de/index.php/software).Note, that a 3d-layer can have multiple roles in the definition of a mapping. For example, it can be pre- and post-SL, and technically even the SL at the same time. Therefore, recurrent connections can be described using the same syntax as feedforward connections. We provide PAM modules for defining connections and computing the mapping, the synapses between neurons and their connection lengths. Furthermore, neurons and connections can be visualized to obtain a qualitative impression of the setup and to manually adjust the connectivity kernels. Several video tutorials and a wiki on the project website document how connections in PAM can be defined to rebuild connectivity patterns of real neural networks .In the following, we demonstrate how PAM can be used to model connectivity patterns and distances between neurons based on neuroanatomical data. Note that we make no claim that this hippocampus model is complete. In our analyses, we focus on the connections between DG and CA3, CA3 and CA3, and CA3 and CA1 to reveal that with PAM structurally important features of the anatomy can be identified and incorporated in a computational model.The projection patterns of the connections between entorhinal cortex and the hippocampal formation are also a very good example to demonstrate the benefits of PAM , CA3, CA1 and subiculum] and the entorhinal cortex  were modeled based on publicly available data. The neural layers were traced in alignment with the atlas data of the Rat Hippocampus Atlas Subsequently, synaptic and intermediate layers were created to define the connections. We based the model on the following reference: for the overall picture  project to DG (and to CA3 and CA1). More specifically, the lateral and caudiomedial part of the LEC/MEC network projects to the septal half of DG and two more rostral areas project to the third and fourth quarter of the dentate gyrus in the temporal half . First, we created a layer (IL1) that served as a mask for the caudal lateral part of LEC/MEC, which we wanted to map to the septal portion of DG. Between LEC/MEC and IL1, we used normal mapping to ensure that only those neurons located in the caudal lateral part of LEC/MEC will project to the SL. In general, normal mapping is a helpful tool to project only a subgroup of neurons on another layer or to change the mesh structure of the layer. Additional intermediate layers (IL2 and IL3) were added to define the geometry of the perforant path. The mesh topology of IL2 and IL3 were identical to that of IL1, while their shapes were deformed to match the connection pathways of the LEC/MEC neurons. The mesh topology is used to define the relative position of the neuron projections in each layer, and the shape of the layer defines the position in 3d space. The mapping between layers IL1, IL2, and IL3 was topological since they all had the same mesh topology. From IL3, neuronal projections enter the SL using Euclidean mapping. Topological mapping could not be used here, as SL is a copy of the DG layer and, therefore, does not have the same mesh topology as IL3. Instead, using Euclidean mapping, neuronal projections on IL3, which are distributed along the septo-temporal axis, enter SL at the most posterior point of DG. From there, the connectivity kernel defines that synapses for a particular neuron can be generated along the whole proximo-distal axis of the SL . These exceptionally low p-values are largely due to the large sample size and belies the comparatively small differences in some of the pairwise comparisons. However, for the CA3-CA1 connections, the distributions of distances are qualitatively different. The advantage of layer-based distance calculation becomes the most apparent for these connections as the axons of CA3 pyramidal cells project along the proximal-distal axis of the cornu ammonis regions rather than traversing directly to the CA1 target neurons  without STDP or any other sort of adaptation. Connection weights were manually adjusted to match activity levels observed in experimental studies , DG, CA3, CA1, Sub, deep EC (dEC)] into a NEST simulation  of the rodent hippocampus (Thompson et al., An intriguing possibility that PAM offers is to study the precise functional effect of brain lesions. Since the network models are derived from spatial anatomical data, any kind of local modification in the biological network can be reproduced in the virtual network, and vice versa. For example, controlled or known brain lesions can be simulated anatomically correctly in a virtual network. Alternatively, insights about a virtual lesion in a network simulation based on PAM can serve as prediction for The anatomical reference frame provided by PAM might prove useful in investigating the relationship between size and form of a brain structure on the one side and its impact on connectivity patterns, conduction distances, self-organizing processes and function on the other hand. This is in particular relevant to understanding the emergence of certain networks from an evolutionary point of view. For example, allometric factors can be incorporated into the analysis of networks in scaled up versions of brain structures recreated with PAM. By recreating homologous regions from different species, the functional influence of the scale and the form of a network could be dissociated with the help of computational models.PAM could be used as a powerful educational and documentation tool. The possibility to visually explore and manipulate the reconstructed model of the hippocampus has helped us tremendously to better understand the structure of the hippocampus and the projection patterns of its neurons. In Blender, it is possible to rotate the 3d model in all three directions, remove certain layers, color layers, including making them partially transparent, and to do many more things. In addition, the Python implementation of PAM includes tools that can facilitate the understanding of the synaptic connectivity patterns. It provides functions to visualize connections and synapse locations. The description of anatomical layers and mappings between those layers as provided by PAM, could serve to collect and document knowledge about neuron locations, densities and axonal and dendritic projections.We have proposed a new modeling technique, PAM, that can generate neural networks with connectivity patterns and connection distances that are consistent with experimentally measured layer morphologies and complex projection patterns. PAM can also serve as a tool for collecting, systemizing, and visualizing anatomical data. Using a common reference frame for anatomical data would greatly facilitates the transfer of such data and increase their potential impact. It is therefore our hope that computational and experimental neuroscientists alike will find PAM a useful tool for their research.http://cns.mrg1.rub.de/index.php/software. We invite the reader to examine the code and contribute to the project.The Python implementation of PAM, reported in this article, along with some example files of the hippocampal model and the exported data for NEST can be found in the Supplemental Materials. PAM is further under development. The most recent version, a Wiki, and videos can be found at Martin Pyka developed the PAM technique, created the hippocampal model, Python implementation of PAM, data analyses, writing the manuscript. Sebastian Klatt Python implementation of PAM, writing the manuscript. Sen Cheng developed the PAM technique, data analyses, writing the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Pedestrian injuries and fatalities present a problem all over the world. Pedestrian conformity violation behaviors, which lead to many pedestrian crashes, are common phenomena at the signalized intersections in China. The concepts and metrics of complex networks are applied to analyze the structural characteristics and evolution rules of pedestrian network about the conformity violation crossings. First, a network of pedestrians crossing the street is established, and the network's degree distributions are analyzed. Then, by using the basic idea of SI model, a spreading model of pedestrian illegal crossing behavior is proposed. Finally, through simulation analysis, pedestrian's illegal crossing behavior trends are obtained in different network structures and different spreading rates. Some conclusions are drawn: as the waiting time increases, more pedestrians will join in the violation crossing once a pedestrian crosses on red firstly. And pedestrian's conformity violation behavior will increase as the spreading rate increases. Traffic accidents involving pedestrians have become a major safety problem all over the world, particularly in developing countries. Several studies have examined gender and age differences in pedestrian behavior , 2. PhysConformity behavior, also known as herding behavior, refers to the individual's behavior tendency of following the group. This psychology phenomenon has been found prevalent in social life . Most scComplex networks have been successfully applied in many engineering fields. In this paper, complex network theory is used to analyze the structural characteristics and evolution rules of pedestrian network about the conformity violation crossings.N nodes and P × N × (N − 1)/2 edges, and P is the link probability between each pair of nodes. This classic mathematical theory can be seen as the foundation for complex network theory. The found of small-world and scale-free properties brings a new start for complex network study. When represented as graphs, some networks reveal a relatively small distance between each pair of nodes; that is, on average a small number of nodes separate them. This type of complex network, known as small-world, also shows high clustering; that is, if two nodes are connected to the same node, the probability that they are connected to each other is high [In the 1960s, Erdos and Renyi proposed random graph theory to analyze the complexity of network topology. Random network is composed of  is high . Some otComplex networks have attracted a great deal of attention in recent years. One of the main reasons why complex networks have become so popular is the flexibility and generality in representing virtually any natural structures, including those undergoing dynamic changes of topology . Taking Recently, the flow characteristics of the transportation system (such as traffic flows and pedestrian flow) become of primary interest in complex networks. In particular, traffic congestion and its dynamical relation to network structures have become a hot topic. But few studies combining the complex network and pedestrian behavior are presented in the reported literatures.Inspired by the above research results, this paper would apply complex network theory to simulate pedestrian violation behavior.Pedestrian safety in urban areas is an issue of growing concern. Pedestrian behavior modeling is an important topic in the pedestrian safety research field. Researchers have built models to describe and simulate pedestrian movement characteristics since the 1970s. Previous methods for pedestrian behavior modeling can be classified into two main categories: microscopic and macroscopic models. In the last years, much more attention has focused on microscopic modeling, where each pedestrian is modeled as an agent. Microscopic models include social forces models, lattice gas (LG) model, cellular automata (CA) model, and artificial-intelligence-based models , 12. TheCA model uses a discrete space structure to simulate pedestrian walking behaviors including way change, step forward, and gap computation. In the model, each cell in the grid is represented by a state variable. A set of rules defines the cell's state according to the neighborhood of the cells, and a transition matrix is used to update the cell states in successive time steps. According to the rules, the lane which promotes forward movement best is chosen for sidestep movement. And the movement space of each pedestrian is based on the desired speed and the available gap ahead for forward moving. CA model is capable of effectively capturing collective behaviors of pedestrians who are autonomous at a microlevel , 14. SimThe core idea of complex network is to describe a system's macroscopic phenomena through exploring the microscopic individual's activities as well as the interactions between the individuals. Accordingly, complex network can be regarded as a bridge between microscopic individuals and macroscopic phenomena. In this paper, the theory of complex network is applied to capture pedestrian crossing behaviors at signalized intersections, especially when pedestrians are in a conformity situation.Aims of this paper are to examine the pedestrian's conformity phenomena during the red signal time at intersections and to find out the spread rule of herding behaviors. The overall study process includes the following four steps: (1) use motion capture technology to collect the basic behavior data for constructing pedestrian network, (2) construct a pedestrian network and analyze the statistical parameters of the pedestrian network, (3) build a spread model of pedestrian's violation behavior using the approach of SI model, and (4) analyze the spread process of pedestrian's violation behavior based on the simulation results.Illegal pedestrians at signalized intersections can be well described by complex networks, where nodes represent the pedestrians, and links denote the relations or interactions among these pedestrians. According to their crossing behavior, illegal pedestrians can be divided into leaders and herding pedestrians. Leaders refer to the pedestrians who walk across the intersection firstly during the red light. Influenced by other illegal pedestrians, the pedestrians who commit violation accordingly are regarded as herding pedestrians. Based on the built pedestrian network, the mechanism of pedestrian dynamics when they are in conformity situation can be seen.Pedestrian network has the following characteristics. (1) Directional: the relations or interactions among the pedestrians have direction, as pedestrians are only influenced by the front pedestrians and would not observe the behavior of pedestrians behind them in most cases. (2) Complex: nodes are complex, as pedestrian itself is a complex individual, whose behavior is influenced by personal factors, other pedestrian's behavior, and traffic environment. And the links are also complex, referring to the complexity, variability, and randomness of pedestrian behavior, as the relationship between the pedestrians is represented by their behaviors. (3) Increasing: under the impact of conformity psychology, pedestrian's violation behavior will increase constantly. The pedestrian network's nodes will grow dynamically.Signalized intersections with large pedestrian volume and many illegal pedestrians are selected as the study sites in this paper. Pedestrian crossing behavior is studied through the data collected by a direct observation of pedestrian activities using two video cameras set up beside the crosswalks. Cameras are placed in relatively concealed locations, such as the nearby billboards and street trees, so that the presence of the camera would not affect the pedestrian's normal crossing behavior. In spite of pedestrian's crossing behavior such as violating or not, cameras also could film pedestrian's microscopic moving activities such as head movement, turning aside, looking, and saccade.After the field study, researchers from Nanjing University of Science and Technology record the information obtained from the video. The detailed procedures to determine the behavior relationship between different pedestrians are presented as follows. Firstly, the pedestrian interaction region from the video is judged. It is shown in the literature  that obsThe survey is carried out in the morning and evening peak hours. Video camera is placed at each crosswalk to record the expression and action of the pedestrian clearly. According to the basic method of the behavioral effects of relationship determination, the relationships between the pedestrians in different red light stage, different genders are recorded. In order to study the dynamics characteristics of the pedestrian's conformity violation behavior, the basic indicators of the networks need to be analyzed and calculated firstly. Given the proliferation mechanism and that the dissemination research goal is to explore the evolution of group behavior of pedestrians, pedestrians quantification of different types of individuals in the network status, and key individuals screened pedestrians group behavior, this paper intends to calculate the degree to analyze the topological characteristics of the conformity violation behavior.Degree refers to the number of nodes connected to the other nodes. In the network, the degree of the node includes the out-degree and in-degree. Out-degree means the number of the nodes pointing to the others and in-degree means the number of other nodes pointing to node. And the average of all the nodes in the network is called the average degree of the network.1) Average Out-Degrees in the Different Red Light Stage. Through calculating the pedestrian average out-degree in 500 different signal cycles, the pedestrian average out-degree in stage one (0–10 s) is obtained. The average out-degree is 1.5, which means that the behavior of each pedestrian crossing street illegally could attract 1.5 other pedestrians following him. As the waiting time increases, the pedestrian average out-degree gradually increases. In stage four (50 s or more), the average out-degree of the pedestrian network is 2.8, indicating that pedestrians wait longer; the waiting pedestrians are more likely to commit violation when someone else does it firstly. R2 is 0.84, indicating that the two variables are highly correlated. Therefore, in order to reduce the herd groups of illegal pedestrians, pedestrian signal should be set reasonable. For example, the time of red light should not be set too long.(2) Average Out-Degrees of Female and Male Pedestrians. Through the average calculation of the out-degree and in-degree of male and female illegal pedestrians, the effect of gender factor on pedestrian's conformity behavior can be judged. It can be seen in Figures  and the pedestrians are still waiting . Once one of the crowded pedestrians crosses the street illegally, affected by the other's violation behavior, the pedestrians waiting to cross the street will think to choose crossing on red or not. These pedestrians are called in a “wait state.” Under the conformity mentality, part of the waiting pedestrians may follow the leader illegal pedestrian, while another part of the pedestrians follows the traffic laws and continues waiting until the pedestrian light turns green.Therefore, the pedestrians on crosswalk intersection could be divided into four categories by their behavior: the leader, the herding illegal pedestrians, the watching pedestrians, and the waiting pedestrians. The leader is the pedestrian who crosses on red firstly. Leader's illegal behavior begins to spread in the crowd. Pedestrians who receive illegal crossing street behavior information change into the watching state. Pedestrians in watching state may choose to commit violation or are still waiting for the green light following the impact forces such as traffic environment, psychological, social constraints. The detailed changing process is shown in p from watching state to crossing street illegal state and then have a certain probability 1 − p from watching state to waiting state;pedestrians in a watching state have a certain probability for pedestrians crossing the street if they follow the leader, then the pedestrians' waiting state transforms to the crossing street illegal state;t time, the proportion number of pedestrians in crossing street illegal state and pedestrians in watching state is i(t) and o(t), respectively;at k neighbors, each illegal pedestrian can be observed by k pedestrians around him and impact the other pedestrians. k is a constant.based on the principle of In this model, it is assumed thatkl(t) pedestrians around him. Because the number of pedestrians who are in the watching state is No(t), there are pkNo(t)i(t) watching pedestrians who will cross the street illegally. As a result, the increasing rate of pedestrian crossing the street illegally is pkNoi:i(t) + o(t) = 1, at the initial time (t = 0), the proportion of illegal pedestrians is i0; thenAccording to the assumption, each illegal pedestrian's behavior can be observed by it = 1/(1 + (1/i0 − 1)epkt−).Solve the equation, the results can be A complex system simulation software “NetLogo” is applied to simulate the spread model of pedestrian's violation crossing behavior. The pedestrian violation behavior spread model based on improved SI is established. In the simulation process, to analyze the influencing factors of the behavior spreading, two key parameters are changed: the average degree of the network and the spreading rate. Spreading rate is set as 10%, and the spreading characteristics of violation behavior are simulated in the network when the average degree of the network is 2, 3, 5, 6, and 8. In addition, to analyze the factor of spreading rate, the spreading characteristics of pedestrian violation behavior are simulated when the average degree of the network is 6 and spreading rates are 10% and 15%.According to the simulation results, when the average degree of the network is less than 3, illegal behavior could not be spread on the pedestrian network. The leader's illegal behavior only can attract several pedestrians around him to cross the street illegally, but the illegal behavior could not be spread further. And other pedestrians crossing the street will not join in the illegal group. When the average degree of the network is close to or larger than 5, the crossing street illegal behavior can be spread in the network, which has produced the conformity effect.In addition, detailed analysis shows that when the average degree of the network is 5, other pedestrians will gradually join into the ranks of crossing the street illegally when there is a leader illegal pedestrian. And about 50% of the pedestrians will cross the street illegally because of the conformity effect. When the average degree of the network is 6 or 8, the rate of pedestrians crossing the street illegally stabilized at 60% or 80%. It can be seen that (1) the spreading rule of illegal behavior has a steady effect, so the behavior spread of impact scope will eventually reach a steady state; (2) when the pedestrians' connectivity is much stronger (average degree is higher), the illegal behavior will spread more widely and fast.Spreading rate is defined as the probability of the pedestrian whose illegal behavior is impacted by other pedestrians. Spreading rate can represent the amount of a person's conformity probability. Different simulation results when the average degree is 6 and the spreading rates are 10% see  and 15% Pedestrian conformity violation behaviors are common phenomena at the signalized intersections in China. Based on the theory of complex networks, the pedestrian's conformity violation behavior is studied in the paper. First, the network of pedestrians crossing the street is constructed and the degree distribution of the pedestrian network is analyzed. Then, using the basic ideas of SI model, pedestrians crossing street illegal behavior spread model is established. In addition, simulation method is applied to get the communication trends of pedestrian violation behavior in different network structures and different spreading rates.It is concluded that as waiting time increases, more pedestrians will commit violation when the first pedestrian crosses the street illegally in the group. In addition, in both the in-degree and the out-degree, male pedestrians are higher than females. It means that male pedestrians are more likely to follow others than females, and male pedestrians' behavior is more likely to attract other pedestrians. Through analyzing the pedestrians illegal behavior spread rule, an important conclusion is found: as the spreading rate increases, the violation rate of pedestrians crossing street is higher. Therefore, improving pedestrians' safety awareness and compliance awareness can reduce the probability of pedestrian violations and improve pedestrian safety at signalized intersections.


Over the last few years high-resolution geophysical techniques, in particular ground-penetrating radar (GPR), have been used in agricultural applications for assessing soil water content variation in a non-invasive way. However, the wide use of GPR is greatly limited by the data processing complexity. In this paper, a quantitative analysis of GPR data is proposed. The data were collected with 250, 600 and 1600 MHz antennas in a gravelly soil located in south-eastern Italy. The objectives were: (1) to investigate the impact of data processing on radar signals; (2) to select a quick, efficient and error-effective data processing for detecting subsurface features; (3) to examine the response of GPR as a function of operating frequency, by using statistical and geostatistical techniques. Six data processing sequences with an increasing level of complexity were applied. The results showed that the type and range of spatial structures of GPR data did not depend on data processing at a given frequency. It was also evident that the noise tended to decrease with the complexity of processing, then the most error-effective procedure was selected. The results highlight the critical importance of the antenna frequency and of the spatial scale of soil/subsoil processes being investigated. The model is authorized if the mathematical functions ug(h) are mathematically authorized semivariogram models under the constrain of positive semi-definiteness of each Bu, being assumed to represent a variance-covariance matrix at the spatial scale u. Fitting of LMC is performed by weighed least-squares approximation under the constraint of positive semi-definiteness of the Bu, using an iterative approach developed by Rivoirard [As for variogram fitting, a Linear Model of Coregionalization (LMC) was applied. LMC, developed by Journel and Huijbregts , consideivoirard . GoodnesIn LMC, total variance can be decomposed into spatially structured variance at different spatial scales and spatially unstructured variance (nugget). The nugget effect represents unexplained spatially dependent variation  or purely random variance (like measurement or sampling error). The proportion of total variation can warn us of large measurement errors or of the need to sample more densely, or both  and for ®, release 2014 [The GPR data were interpolated with block cokriging in order to reduce the variability, using a regular 5 × 5 discretization of each 0.5 m × 0.5 m block; finally the estimates were back-transformed to amplitude of the radar signal. Geostatistical procedures were separately applied to the data of each antenna frequency, by using the software package ISATISase 2014 . After iIn order to make the comparison among the maps more objective and to choose the most efficient GPR processing in a less subjective way, the spatial association between paired GPR maps was calculated using confusion matrix and k statistic. The efficiency of data processing was evaluated in terms of maps quality and computer time consuming. The maps were preventively classified in ten isofrequency classes of radar signal amplitude and the classes were then compared in pairs by using a two-enter table (confusion matrix). Each cell of the matrix gave the absolute frequency of the occurrence of the two corresponding classes . The ove−1), visible only in the radar sections acquired with 1600 MHz frequency (an example is reported −1) and a third layer between 20 ns and 22 ns (0.6–0.66 m depth with an average velocity of 0.1 mns−1), detectable from all the radar sections acquired with 250 and 600 MHz frequencies and from CMP data  for 250 and 600 MHz frequencies and at about 5.5 ns (about 0.15 m) for 1600 MHz frequency; therefore, all GPR data coming from the longer travel times (deeper depths) will not be treated from now onwards.Statistical analysis highlighted that GPR data at any frequency and for all procedures showed clear departure from normal distribution, however the addition of further steps in the processing (after the third procedure) caused a more symmetric distribution. In addition, Person’s correlations allowed you to disclose the main reflections observed visually in the radar sections and to evaluate the strength of spatial association between the data at the different depths as function of the processing procedure. The GPR amplitudes at the different times were strongly correlated within an interval of 10–14 ns, for 250 MHz antenna  and the computer processing time demand (the computer time for the fourth procedure was longer) for any frequency.All GPR data at any time interval for each antenna were correlated so to justify the application of a multivariate approach. The data, being generally skewed, were previously transformed into standard Gaussian variables and a linear isotropic model of coregionalization (LMC) was fitted to the all experimental direct and cross-variograms separately for each processing procedure and each antenna.Independently of the kind of processing, the basic structures were identical for any antenna, showing that the procedures did not alter the intrinsic spatial structures. Geostatistical modelling of radar amplitude was affected by data processing only for the estimated parameters of nugget effect and partial sill. The LMCs consisted of the following basic structures: (1) for 250 MHz: Nugget effect, Spherical model (Range = 3 m) and Spherical model (Range = 12 m); (2) for 600 MHz: Nugget effect, Spherical model (Range = 1.2 m) and K-Bessel model (Range = 10 m); (3) for 1600 MHz: Nugget effect, Spherical model (Range = 0.5 m) and Exponential model (Range = 12 m). The goodness of LMC fitting was satisfactory because mean error was quite close to 0 and the mean square standardised error was almost 1 for all data sets.As regards the first procedure at 250 MHz frequency, the visual inspection of the estimated normalized amplitude maps at the different times showed that the processing did not modify sensibly the signal at any depth, preserving the raw information. However, the signal in depth was not usable because of the high attenuation a. For boFor the second processing, the maps at 250 MHz frequency and corresponding to the longer time slices (16–18 ns) appeared well structured and more associated with the ones observed in the shallower depths. Therefore, the addition of a further step in the GPR processing improved the signal, recovering more information from the deeper depths. This characteristic was not valid for the other frequencies, suggesting that no improvement was obtained with the addition of dewow, as expected for the higher sampling frequencies used. As for the third procedure, all the maps of the estimated amplitude at different times  displayeAn improvement was also visible for the maps of the other frequencies  which loNo significant improvement was observed in the maps obtained with the application of running average (fourth and fifth processing) even if the maps looked slightly more smoothed. Finally, the maps obtained with the sixth procedure were quite similar to the previous maps, though the quality has worsened for some of them, in particular for the deeper maps , becauseSince the best processing should be selected on its capability of filtering spatially uncorrelated error , the proportion of the total variance associated with nugget component was calculated .It is evident that the error has a tendency to decrease with the complexity of processing, with the exception of the last procedure, confirming in objective way most of the previous considerations.As for 250 MHz frequency, the main differences were between the first two processing procedures and the third one (about 16%), while the successive steps did not reduce sensibly the measurement error, with the exception of the last one that caused an increase. As explained previously, a reason, is that migration relies on the knowledge of velocity; therefore, an uncertain estimation may cause error in prediction of signal. An improvement in the results may be achieved by using an accurate subsurface velocity model obtainable with CMP semblance technique, which requires a longer time of signal acquisition and processing. For the other antenna frequencies , the nugFor each frequency, all maps corresponding to the different types of processing were compared with the one of the third processing procedure, assumed as reference. In the interest of conciseness, only the results at 250 MHz frequency will be presented. As regards the shallower depth, the maps corresponding to the first two procedures generally showed lower overall accuracy and weighted kappa coefficient, which is indicative of poor spatial association with the reference map. The use of these procedures then changed the signal structure in the shallower layers, though at a visual inspection the maps appeared very similar. Moderate association (about 50%) was obtained with the fourth procedure, indicating no actual improvement. Moreover, the poor agreement (about 20%) with the procedure after migration confirmed the result obtained through a visual comparison of the maps. On the contrary, the overall accuracy for the maps corresponding to 10 ns  was highIn conclusion, the traditional statistical analysis allowed you to objectively state that the spatial association with the map of the third procedure decreased with the complexity of processing. However, geostatistics with nugget effect estimation provided a tool of testing the quality of maps and then a criterion for selecting the optimal processing.In order to investigate the response of GPR as a function of operating frequency, we restricted the geostatistical analysis only to the data processed according to the third procedure. The estimated structures  of GPR data are expected to depend on the antenna frequency, since the radar signal wavelength affects spatial resolution, which is determined by the area illuminated by a GPR antenna, often referred to as the Fresnel zone or antenna footprint  or suppoThe LMCs  was quite close to the wavelength calculated for propagation velocity of 0.06 m/ns , which is directly proportional to the measurement resolution. The short-range structure was probably more sensitive to the type of equipment used, whereas the longer range was more influenced by the material properties. Some researchers obtained similar results using different antenna frequency but they asserted that they depend not only on the material and its intrinsic structure but also on data processing and signal frequency . The lonThe approach presented in this paper aims to investigate the effects of data processing on radar signal and to select a data processing procedure which is quick and efficient in terms of both computer time and quality of maps. Geostatistical analysis applied to three different frequencies, stressed the critical importance of the scale of survey and the need to utilise a proper equipment to capture the scale-dependent variability of soil/subsoil.The processing procedure, based on the instantaneous amplitude or envelope, was evaluated with statistical and geostatistical techniques. Statistical analysis allowed you to detect the major reflections of the radar signal, and other useful information not evident at visual interpretation of radar sections. Geostatistical analysis provided also a criterion to select the most efficient processing on the basis of its capability of reducing spatially uncorrelated error. The selected error-effective procedure included time zero correction, dewow filter, bandpass filter and envelope. The main limitation of the proposed procedure consisted in the extremely heterogeneous subsurface conditions of study site because the received signal was the result of multiple interactions. Further studies should be conducted to improve the interpretation of such type of data. In particular the obtained results indicated that geostatistical tools, like geostatistical component filtering , could bSignal frequency exhibited a drastic influence on the spatial structures of geostatistical modelling, because of its relation to spatial resolution. The pattern of spatial structures estimated by geostatistics can be related to both radar equipment (short range) and soil properties (long range). Disclosing which soil physical properties are mainly responsible for the observed spatial structures of GPR signal remains an interesting and challenging research topic for future investigations.


A successful implant-supported restoration must provide adequate function and esthetics. Osseointegrated implants have given an alternative choice for patients who have lost their teeth. Most commonly encountered problems while doing a transfer from patient to the master cast in restoring implant-supported crowns are an uneven distribution of occlusal loads and undue torquing forces on the various elements of implant. This is caused due to poor fit of frameworks connected to implant, which further leads to marginal bone loss, loosening of screws, fatigue fracture of implant components, and ultimately implant failure. This paper presents a simplified and easy solution to overcome such problems by introducing an innovative gingival retraction system for restoring implant-supported crowns to achieve superior and predictable long-term outcomes. Osseointegrated implants have proven predictable long-term alternative treatment options to traditional prostheses for patients who lost their teeth , 2. For The poor fit of frameworks connected to implant may cause uneven distribution of occlusal loads and torquing stresses. These problems may lead to marginal bone loss, loosening of screws, fatigue fractures of implant components, and failure of implants –10. The However, the inaccuracy in dental implant impression is a major and unsolved problem. This can drastically affect the osseointegration of the majority of implants. This case report describes a solution for such problems, by using a newly introduced gingival retraction system for making a direct abutment-level impression in routine clinical practice.A 37-year old female patient reported to the department of prosthodontics with a request to discuss the options for prosthetic replacement of mandibular right first molar which was extracted 6 months back. She was given an option of implant-supported crown. The patient agreed to proceed with an implant-supported crown with 36. Clinical and radiological assessment showed an adequate bone support with 36. The benefits and the risks of the planned treatment were explained to the patient and a signature for consent was obtained.Recently, a Canadian company, Stomatotech, came up with a simple idea to retract the gingival tissue using a disposable plastic collar that is inserted on the apical end of the abutment before the abutment is engaged to the implant. Following the abutment's engagement to the implant, the plastic collar is found between the apical part of the abutment and the gingival soft tissue. Shortly after the removal of the impression from the mouth, the plastic collar is pulled out and removed permanently. The plastic collar creates a perfect gingival retraction with a valve factor preventing the liquids from contaminating the area of the finish line of the abutment.The patient was called after three months for prosthodontic intervention. The healing abutment was removed  and the The utmost care is needed while doing implant transfer to obtain master casts and also while achieving a passive fit of the prosthetic superstructure to the implant to maintain the osseointegration around the implant –19. EameThe transfer coping while impressing is mechanically caught in the impression material. It does not become an integral part of the impression and can be easily moved.Also, when the lab technician engages analogs into the impression, its displacement cannot be avoided.The displacement of the transfer coping can also happen due to the gravity forces of the impression tray . An impression tray that weighs 100 grams can generate a torque of 5.8 Ncm by its own weight, which is sufficient to shift the transfer.Splinting with acrylic resins may lead to displacement of the transfers due to the shrinkage of the acrylic materials. Even a splinted assembly of impression transfers does not become an integral part of the impression.Due to the expansion of the dental stone during its setting, a serious shifting of the analog from its original position is caused. Hence, the analog becomes loose and mobile.The sectioning of an implant stone model is very difficult because of the presence of the hard steel analogs in the body of the model. Also, a small amount of the stone around the analogs often leads to breakage of the die. This needs either a redo of the master cast or working on an unsectioned cast. All these difficulties prevent precise fabrication of the implant-supported restoration.The problems encountered while doing the transfer can be enlisted as follows.The prime concern with the direct impression is the abutment's subgingival area registration. Dr. Vincent Bennani  discusseChang et al.  studied The main purpose of G-Cuff is to support soft tissue that surrounds the implant abutment allowing the impression means  to have an access to the surface of the abutment needed for the optimal restoration. The restoration with G-Cuff is a two-appointment procedure which completely eliminates a “try-in” visit. It renders a simple, affordable, and universal solution for any bridge and crown restoration.As accuracy is the main factor in a restoration procedure especially for bridges or splinted crowns, the impression with G-Cuff is way more reliable than any other method, either “open tray” or “close tray.” Due to the significant shortening of the lab procedure, the risk of impression distortion is dramatically lowered.The G-Cuff system eliminates the need of materials such as impression copings, implant analogs, temporary abutments, and custom impression trays.In addition, the system solves a problem of the U.D.I . The original abutment and a G-Cuff are enough to take a new impression and then to complete the restoration without even knowing the brand of the implant.The list of the advantages also includes reduction of redo rate, universality, chair-time reduction, precision of a framework, and the fact it works with custom/stock abutments.It also acts as a cement barrier making the peri-implant area free of cement remains. G-Cuff keeps the occlusal part of the gingival margin open, letting cement flow outside, as well as seals the apical part of the gingival margin, thus preventing cement from penetrating the Peri-implant tissue.It maximizes infection control due to the minimal manipulation of the abutment, and also numbers are printed on each G-cuff (8 different sizes) for easy recognition.Unlike the retraction cord, G-Cuff does not traumatize the tissue around the implant.It allows sectioning of any stone model creating a perfect working condition for the lab. In many cases transfers sectioning of the stone model is just impossible or very hard. G-Cuff eliminates any metal parts  in the stone model allowing an easy sectioning like for regular crowns and bridges.Due to a special design of the apical margin of the cuff when the abutment is tightened, the cuff stretches and slides out of the AIJ (abutment implant junction) and is never trapped.In the cases where the aesthetic demand is extremely high, only G-Cuff allows the technician to access the entire restoration to create the utmost aesthetics.The advantages of this simple and efficient system are both economical and clinical as follows.The problems while making transfer of implant impression from patient's mouth to the definitive cast are numerous, which cause poor fit of framework and finally implant failure. A slight shift or rotation of transfer is unavoidable causing an obstacle in the success of implant-supported restorations. A newly introduced gingival retraction system (G-Cuff) was used in the described case report which yielded a successful and predictable restoration for the patient. This retraction system can be advocated to single crowns and bridges for the long-term outcomes.The G-Cuff kit is a must in every dental office because it is the only solution for UDI , which recently have become more and more frequent.


In this paper we propose a modification of the polycrystalline shape memory alloy constitutive model originally proposed by Souza. By introducing a transformation strain energy with two different hardening coefficients, we are able to take into account the effect of the martensitic transformation of unfavorably oriented grains occurring after the main plateau. By choosing a proper second hardening coefficient, it is possible to reproduce the correct stress strain behavior of the material after the plateau without the need of introducing a much smaller Young modulus for martensite. The proposed modification is introduced in the model comprising permanent deformation effects. Model results for uniaxial stress tests are compared to experimental results showing good agreement. Nitinol, a nearly equiatomic NiTi alloy, has been widely utilized in biomedical applications for more than 20 years. Peripheral stents, fixators, bone connectors are examples of Nitinol implants. Guide wires, endodontic files are examples of Nitinol medial or chirurgical tools.Currently, more demanding applications of Nitinol are emerging: cardiac valves are an example.Engineers, for the design of such devices, are more and more interested in constitutive models robust and easy to use but also able to accurately describe the material behavior. Typical loading paths contemplate a first string deformations, during which the material may overcome locally the plateau deformation and undergo permanent inelasticity, a deployment and a smaller deformation during the operating lifetime of the device. An accurate evaluation of stresses during the first deformations and the quantification of permanent inelasticity are needed for an accurate design of future Nitinol devices.et al. [et al. [et al. [ab initio calculations . Howeves e.g., ,8) have  have et Recent investigations ,10 have Taking advantage of these findings, here we propose a slight modification of the original Souza-Auricchio model consisting in the introduction of a double hardening coefficient in the transformation strain energy components of the pseudo potential. The first hardening coefficient is responsible for the main transformation plateau or temperature induced transformation strain. The second coefficient, which is much higher, describes the material behavior after the main transformation is completed.After presenting the main model equations, we will compare the traditional model and the modified model in uniaxial stress conditions.The proposed model variation will be subsequently introduced in the model with permanent deformation described in  and simuϵ and the absolute temperature, T as control variables and the second-order traceless transformation strain tensor etr as internal variable. The quantity etr has the role of describing the strain associated to the phase transformations.The model assumes the strain, Assuming a small strain regime, we express the free energy function for a polycrystalline SMA material through the following convex potential:I the second-order identity tensor and introducing the standard decomposition:e are, respectively, the volumetric and the deviatoric components of the total strain, we describe the terms of Equation (1) as follows.Indicating with K the bulk modulus, G the shear modulus, α the thermal expansion coefficient, and T0 the reference temperature. The operator ‖∗‖ indicates the Euclidean norm.The elastic strain energy, due to thermo-elastic material deformation is:T) a function related to the dependence of the critical stress on temperature and The chemical energy is:Finally the transformation strain energy, due to the transformation-induced hardening, is set equal to:Starting from the pseudo-potential and following standard arguments, we obtain the model constitutive equations:The model is then completed by introducing the associative evolution law:For the sake of simplicity, here we choose a symmetric yield function:In the present model no limit to the transformation strain is considered. This limitation could be easily overcome by introducing an indicator function as in the original model. It must considered, however, that reaching the full transformation would cause an increase of the slope in a monotonic stress strain test before plasticity takes place. This is not observed experimentally, suggesting that plasticity occurs before the complete martensite transformation occurs.The standard model and the modified model proposed are compared by simulating a uniaxial stress strain test. The results are shown in L, the standard model behaves elastically with a young modulus identical to the one of the parent phase. On the contrary, in the modified model the transformation continues, but with a much higher hardening. This section of the stress-strain characteristic can be described by the following equation:Starting from the rest condition in the parent phase the two models behave identically up to the end of the plateau. After reaching the maximum transformations strain ϵL, the reverse transformation proceeds with the lower hardening coefficient. At this point the two models again provide an identical solution.During unloading, the modified model firstly behaves elastically. Like the original model, the proposed modified model assumes identical elastic modulus of the austenite and the martensite phase. When the critical stress is reached, the reverse transformation starts, initially with the higher hardening, assuming that the reverse transformation of the unfavorably oriented grains is the first to take place. When the transformation modulus is lower than ϵtr described in Equation (4), can be easily introduced in the model contemplating permanent inelasticity described in [The proposed model variation, consisting in the modified term of the pseudo-potential Ψribed in .The model equations will not be re-proposed here. We will only recall the new quantities introduced in the model to account for permanent inelasticity, and the additional parameters.epl, the functional fatigue tensor q, modeling the irreversible processes induced by microscopic and macroscopic stresses. Increasing q prevents the material from fully recovering generic external deformations. The thermodynamic force associated to the functional fatigue tensor is identified by the tensor Q. For the model definition it is necessary to define the limit function ξ, that now must comprehend both the thermodynamic force X and Q, the functional fatigue hardening H and the coefficient a, describing the ratio between the plasticity tensor and the functional fatigue tensor.Permanent inelasticity is described by two new internal variables, the plasticity tensor For the simulations with permanent inelasticity we have adopted the limit function:antities  completeThe adopted parameters are not obtained with a rigorous fitting procedure but provide a reasonable matching with experimental results. A comparison is shown in Experiments have been carried on a standard 0.3 mm diameter superelastic wire provided by Memry. Stress strain test and temperature loops at constant load have been performed on SMAq, an internally constructed characterization tools .Several loading conditions are provided. In order to understand the internal variable interplay, we start analyzing a stress strain test at 50 °C up to 9% deformation b, with tQ component of the force is lower, due to accumulated hardening, for the same total deformation conditions.Upon loading, when the plateau stress is reached the thermodynamic forces are located on the limit boundary , the model correctly predicts a decrease in the plateau stress. With the same parameter set, we have performed temperature loops at constant load simulations and these are compared to experimental results in We have described a modification of the Souza-Auricchio model accounting for the residual phase transformation occurring after the main plateau of polycrystalline Nitinol. This phase transformation involves unfavorably oriented grains and it is rendered by introducing a second hardening coefficient in the transformation energy component of the pseudo-potential.This modification can be easily implemented also in the model version contemplating permanent inelasticity. The modified model has been tested by comparing simulated and experimental uniaxial tests, with satisfactory agreement.Further investigations are needed in order to investigate the model behavior in more complex loading conditions.


Despite persistent calls to measure the effectiveness of educational interventions on patient outcomes, few studies have been conducted. Within musculoskeletal physiotherapy, the effects of postgraduate clinical mentoring on physiotherapist performance have been assessed, but the impact of this mentoring on patient outcomes remains unknown. The objective of this trial is to assess the effectiveness of a work-based mentoring programme to facilitate physiotherapist clinical reasoning on patient outcomes in musculoskeletal physiotherapy.A stepped wedge cluster randomised controlled trial (CRCT) has been designed to recruit a minimum of 12 senior physiotherapists who work in musculoskeletal outpatient departments of a large National Health Service (NHS) organization. Participating physiotherapists will be randomised by cluster to receive the intervention at three time periods. Patients will be blinded to whether their physiotherapist has received the intervention. The primary outcome measure will be the Patient-Specific Functional Scale; secondary outcome measures will include the EQ-5D, patient activation, patient satisfaction and physiotherapist performance. Sample size considerations used published methods describing stepped wedge designs, conventional values of 0.80 for statistical power and 0.05 for statistical significance, and pragmatic groupings of 12 participating physiotherapists in three clusters. Based on an intergroup difference of 1.0 on the PSFS with a standard deviation of 2.0, 10 patients are required to complete outcome measures per physiotherapist, at time period 1 (prior to intervention roll-out) and at each of time periods 2, 3 and 4, giving a sample size of 480 patients. To account for the potential loss to follow-up of 33%, 720 sets of patient outcomes will be collected.All physiotherapist participants will receive 150 hours of mentored clinical practice as the intervention and usual in-service training as control. Consecutive, consenting patients attending treatment by the participating physiotherapists during data collection periods will complete outcome measures at baseline, discharge and 12 months post-baseline. The lead researcher will be blinded to the allocation of the physiotherapist when analyzing outcome data; statistical analysis will involve classical linear models incorporating both an intervention effect and a random intercept term to reflect systematic differences among clusters.ISRCTN79599220.Assigned 31 July 2012: The online version of this article (doi:10.1186/1745-6215-15-409) contains supplementary material, which is available to authorized users. Outcomes research has been defined as the assessment of what does and does not work in the delivery of healthcare . While ath to 55th percentile mean outcome scores) were identified. No difference in the years of clinical experience was found between these groups. The same authors have argued that the key behaviours of expertise can be identified, nurtured and taught [A key focus of postgraduate healthcare and medical education is that of expertise development –28. The d taught , 37–40, d taught –38, 41 id taught , 42, 43.Clinical reasoning has been defined as a context-dependent way of thinking and decision making in professional practice that is used to guide practice actions , and reshave employed educational interventions and assessed the clinical outcomes of patients treated by the participating clinicians. These studies can be divided into two groups. The first group of studies consist of cluster randomised controlled trials (CRCTs) evaluating the effect of implementation of a specific treatment approach  compared to usual care. The interventions all included education of the clinician but did not measure the efficacy of the educational programme per se; therefore, the evaluation of patient outcome was a measure of the combination of the treatment approach and the educational intervention. These cluster RCTs also explore the strengths and weaknesses of the educational interventions utilised. Interestingly, the findings of this group of studies are consistently disappointing in terms of impact on patient outcomes, and the authors frequently cite potential shortcomings in the education of the clinicians as a possible reason for the failure of the intervention to be more effective than usual care [Education in the clinical context is highlighted from the small number of musculoskeletal studies that ual care –64.P =0.019). The patients in the ongoing training group required a mean of 1.5 fewer visits during the post-training period, which was also statistically significant (P <0.001). While Cleland’s study [et al.’s successful approach are that the education programme was ongoing and included an outreach visit with 1-hour co-treatment of an actual patient with neck pain in the physiotherapist’s own clinical practice setting. Overmeer and colleagues [The second group of studies consists of two recent trials that specifically evaluated educational approaches using patient outcomes. Overmeer and colleagues , 66 inve’s study  did not ’s study , with ti’s study  to 8 day’s study . Cleland’s study  employedlleagues  propose This call for the delivery of education in the clinical context has also been argued elsewhere within the physiotherapy literature , in partThe objective of this trial is to assess the effectiveness and cost-effectiveness of a work-based mentoring programme for physiotherapists to facilitate clinical reasoning on patient outcomes in the field of musculoskeletal physiotherapy on patient clinical outcome.A stepped-wedge design, a type of CRCT, has been designed in line with the SPIRIT 2013 statement . A CRCT CRCTs can use parallel, crossover or stepped-wedge designs , and theThe first step (time period 1) corresponds to a baseline measurement where none of the clusters receive the intervention. At each subsequent step, a cluster of participating physiotherapists  will cross over from control to receive the intervention.All qualified physiotherapists working in the selected National Health Service (NHS) organization whose majority of time is practising in the musculoskeletal outpatient context will be eligible to participate. Exclusion criteria are physiotherapists who have already undertaken work-based placements as part of Master’s education, and rotational members of staff who will not be present for the duration of the study. Informed consent will be gained by approaching eligible physiotherapists and issuing detailed written information; those wishing to participate will give written consent, and they will be free to withdraw from the study at any time without giving reason.Consecutive consenting patients attending the outpatient musculoskeletal physiotherapy service for treatment by the participating physiotherapists during data collection periods will be eligible to participate. Exclusion criteria are patients under 18 years of age, and patients who are not English-literate due to the validity of some of the selected outcome measures being established only for the English language and for adults –92. PatiPatient outcome data will be collected at the first and last (discharge) appointment for physiotherapy treatment at six outpatient sites for musculoskeletal physiotherapy service delivery. The six sites will be organized into three pairs for training purposes, and these pairs will form the three clusters that will be the unit of randomisation as illustrated below. While the outcome at discharge will be the primary end point, patient outcome data at 12 months post-discharge will inform long-term impact on patient outcomes and assist in analysis of the cost-effectiveness of the intervention.The intervention is a 150-hour clinical mentorship programme, aimed at facilitating clinical reasoning, based on established practice in Master’s level courses in musculoskeletal physiotherapy, in line with the educational standards document of IFOMPT . The focDuring the control steps of the study design, participants will receive their usual training allocation. Usual training for staff in the selected NHS organization involves monthly in-service training on current evidence applied to physiotherapy practice (4 hours per month), weekly technique sessions on the technical and practical skills of physiotherapy practice (30 minutes per week), as well as monthly mentoring sessions (1½ hours per month). While usual training does contain mentoring, the content, delivery and volume of this mentoring differs substantially from that of the intervention in that while patient interactions may be observed, they frequently take other forms, including practical skill teaching, tutorials, and retrospective reviews of patients using notes. Usual training allocates 1½ hours to mentoring, delivered once a month. In comparison, the intervention will allocate a much more intensive 150 hours of mentorship.Clusters of patients will receive their physiotherapy treatment after the participating physiotherapists have received either intervention or control educational intervention. Patient outcome data will be collected at baseline (time period 1) and at the end of each of the subsequent three time periods (time periods 2 to 4) to allow for the consolidation and application of the training (which will have been delivered at the start of the time period).Patient outcomes will be measured at the first visit, final discharge visit (primary end point), and at 12 months post-discharge, using four patient reported outcome measures. The primary outcome measure is health-related quality of life (HRQL) measured with a patient-specific tool, the Patient Specific Functional Scale (PSFS). Secondary outcome measures include a generic measure of HRQL (EQ-5D-5L), a measure of the patients’ ability to self-manage (the Patient Activation Measure (PAM)), and patient satisfaction ). In selecting appropriate outcome measures, three important factors were considered  - selectThe PSFS, a patient-specific HRQL measure, was selected as the primary outcome measure for its responsiveness , 102 andPhysiotherapist performance will be assessed by an independent, blinded assessor who works outside of the NHS organization where the research is being conducted. This assessment of performance will be made using the criteria used by a UK academic institution experienced in the assessment of Master’s level postgraduate student performance in the musculoskeletal context. The independent assessor is educated to Master’s level and is MACP qualified, with several years experience of Master’s level mentoring, as well as experience in using the same criteria for assessment purposes.Sample size calculations, using conventional values of 0.80 for statistical power and 0.05 for statistical significance, were performed following the approached outlined for stepped-wedge designs in Hussey and Hughes . For a rIn stepped-wedge trials, the timing of intervention rollout is the unit of randomisation. The participating physiotherapists and clinical mentors implementing the mentoring interventions will be aware of which cluster is receiving the intervention , 82. TheThe key code will only be opened once all outcome data have been collected and analyzed from each of the four time points. These processes will ensure allocation concealment and the blinding of the lead researcher to the sequence generation and intervention allocation. The independent assessor of physiotherapist performance will be blinded as to whether the physiotherapist has received the intervention during their assessment. Patients will also be blinded to whether or not their treating physiotherapist has received the intervention.The statistical analysis of the stepped-wedge design has been an area of debate in the literature as analysis is said to be complex due to the need to account for repeated measures on the same individual and control for trends in outcome variables due to passage of time . Indeed,The use of aggregate differences in PSFS scores has been advocated and used in studies , 138, buEthical approval for the study was sought and obtained from the South East Wales Research Ethics Committee C on 20/04/2012 (ref: 12/WA/0078).Stepped-wedge designs afford many advantages as outlined above. There are, however, limitations to this design, which are strongly emphasised by one group of authors in making their case for the superiority of standard parallel CRCTs . The undThe first point about the duration of the trial, and the associated point of attrition as a result of this, is well made. However, this trial is taking place in the real world of clinical practice, where normal service delivery and targets must still be met. While a standard parallel CRCT would reduce the time and potential attrition, it would be impractical for two reasons related to the intervention being delivered. First, the time factors involved in the intervention delivery would make the impact of introducing the intervention simultaneously across multiple clusters far greater on the capacity of the musculoskeletal physiotherapy service to deliver usual care across different clinics and venues. By rolling out the intervention across different time points, greater flexibility is afforded to organize clinic cover, allowing the participants to receive the intervention without compromise to the delivery of usual musculoskeletal service delivery commitments. Indeed, when negotiating the implementation of the RCT, the senior management within the organization made clear that the loss of the agreed clinical commitments required by a parallel CRCT would be unacceptable. Secondly, the delivery of the intervention is by mentors who have qualified at Master’s level, having received such mentoring as part of their postgraduate education, as well as delivering mentoring in this format to Master’s level students. Such a finite resource makes a standard parallel CRCT impractical, whereas the rolling out of the intervention in the stepped-wedge design utilises the available mentors in a way that makes this CRCT feasible.Although the repeated measurements of a stepped-wedge design may create a burden on participants, similar measures are already collected regularly by the participants as standard clinical practice within the musculoskeletal physiotherapy service for its annual service evaluation process. The final point - that an intervention is implemented in all clusters of the trial when it has not yet been proven to be effective - is recognised as being valid in certain contexts. However, in the context of this trial, patients will receive their usual physiotherapy, and participating physiotherapists will receive an intervention for which there is already qualitative research supporting the impact of change in physiotherapist performance. Indeed, it could be argued that in a parallel CRCT, attrition from the control group could be greater than in the stepped-wedge CRCT design, as participating physiotherapists would in all likelihood concur that it is rational to believe that the intervention is likely to result in good rather than harm as discussed above.In addition, the two systematic reviews raised further issues regarding the reporting and analysis of stepped-wedge CRCTs. A lack of fulfilment of the methodological requirements for a controlled trial, a lack of blinding of those assessing outcomes and heterogeneity of analytical methods applied were the key weaknesses identified. This led to the reviewers making the following recommendations for the reporting and analysis of stepped wedge CRCTs. First, authors should register their trial on the Controlled Clinical Trials Register and follow appropriate reporting guidelines; second, ways should be explored for enhancing internal validity through blinding of outcome assessors where possible and for the use of adequate sequence generation and allocation concealment; and third, standard methods of analysis should be used. The current study has been registered with Current Controlled Trials, and CONSORT and SPIRIT guidelines have been used in the protocol write-up. Sequence generation will be performed by one of the clinical mentors, randomly allocating the clusters of physiotherapists to the sequence of intervention by computer programme . The allThrough this RCT, the effectiveness of this mentoring programme for physiotherapists in the workplace to achieve better clinical outcomes for their patients will be evaluated. Specifically, this will allow the exploration of whether there are potential benefits to using such a training programme on a larger scale, as well as contributing to the body of literature on education and outcomes.At the time of manuscript submission, the RCT had begun patient recruitment, which will not be complete until 2015.AWi is clinical lead for Musculoskeletal Physiotherapy at Cardiff and Vale University Health Board. CP is Director of Research at the College of Human and Health Sciences and Professor of Health Economics at Swansea Centre for Health Economics, Swansea University. AWa is Senior Trial Statistician at the College of Medicine, Swansea University. AR is Academic Lead for Physiotherapy and Programme Leader of the MSc Exercise and Sport Medicine  programme at the School of Sport, Exercise and Rehabilitations Sciences, University of Birmingham.


The complexities of the function and structure of human fingers have long been recognised. The in vivo forces in the human finger tendon network during different activities are critical information for clinical diagnosis, surgical treatment, prosthetic finger design, and biomimetic hand development. In this study, we propose a novel method for in vivo force estimation for the finger tendon network by combining a three-dimensional motion analysis technique and a novel biomechanical tendon network model. The extensor mechanism of a human index finger is represented by an interconnected tendinous network moving around the phalanx's dorsum. A novel analytical approach based on the “Principle of Minimum Total Potential Energy” is used to calculate the forces and deformations throughout the tendon network of the extensor mechanism when subjected to an external load and with the finger posture defined by measurement data. The predicted deformations and forces in the tendon network are in broad agreement with the results obtained by previous experimental in vitro studies. The proposed methodology provides a promising tool for investigating the biomechanical function of complex interconnected tendon networks in vivo. The complexities of the function and structure of human finger have long been recognised –4. EffecOver recent decades, a large number of biomechanical studies have been conducted to investigate the forces and moments transmitted in the musculoskeletal system of the human finger using analytical functional representations of the tendon network, which are all based on the conventional equilibrium method and do not explicitly represent the tendon network's geometry –12. AlthTherefore, in this study, we developed a novel method to assess the in vivo forces in the finger extensor mechanism by combining a three-dimensional motion analysis technique and a novel biomechanical tendon network model. The finger extensor mechanism is modelled using an interconnected tendinous network moving around the phalanx's dorsum. A novel analytical approach based on the “Principle of Minimum Total Potential Energy” is used to calculate the forces and deformations throughout the tendon network of the extensor mechanism when subjected to a measured external load and finger posture.Ki satisfies the following conditions:Ki is the spring stiffness determined by the material characteristic of the tendon, li is the length of the individual tendon component, and li0 is the slack length of the tendon component. The length li is determined by the tendon component's path, over the bone surface, between two of the tendon network nodes  = 0 is the equality constraint that “keeps the node on the line between the node itself and a vertex,” and λs is the Lagrange multiplier:In this study, the finger extensor mechanism is modelled as an interconnected tendon network moving over the phalanges see . Each inditions:ki(l)={Kiwork see :(4)∂(∑iorce see . Thereafponds toδ∏=δ(U+V)fined in , where L pjk)Minimize Π( Ls(pjk) = 0Subject to This leads to the following set of equations, which are used to solve for the coordinates of the nodes in the tendon network:FTE, FES, FRB, FUB, FLE, FUI, FRI, and FLU are the forces exerted by the major muscles and tendons of the finger extensor mechanism. FFDP is the force of the finger flexor muscle that spans the three joints. The force of the other flexor muscle FDS has been merged with FFDP due to its relatively small contribution to index finger pinching [aFDP_DIP_FL and aTE_DIP_FL are the flexion/extension moment arms around the DIP joint. aES_PIP_FL, aUB_PIP_FL, and aRB_PIP_FL are the flexion/extension moment arms around the PIP joint. aLE_MCP_FL, aRI_MCP_FL, aUI_MCP_FL, and aLU_MCP_FL are the flexion/extension moment arms around the MCP joint. aRI_MCP_AD, aUI_MCP_AD, and aLU_MCP_AD are the adduction/abduction moment arms around MCP joint. The external load P exerted at the fingertip during pressing, the angles of the phalanx segments  with respect to the X axis of the global coordinate system  are determined from measurement data (see following section).The external loads applied to the tendon network acted along the lines representing the muscles' RI, LU, LE, and UI. To provide kinematic and kinetic inputs for the tendon network model, a multisegment finger model was constructed . Considepinching . aFDP_Dstem see , and alsFRI, FLU, FLE, and FUI are the external forces applied to the finger tendon network at nodes H, G, A, and N, respectively. Then, after all of the tendon component forces have been obtained by minimising the total potential energy, the bone-to-bone contact forces FDIP, FPIP, and FMCP are calculated from the force equilibrium for each joint. The entire biomechanical analysis described above was implemented in the Matlab programming environment .In order to solve this statically indeterminate problem, the following set of validated constraint equations based on an in vitro anatomical study was used –20:(8)FThe 3D finger postures and the external forces at the fingertip during static pressing by one healthy male subject  were measured to provide kinematic and kinetic inputs for the finger model. The subject provided informed consent in accordance with the policies of the institute's ethical advisory committee. The subject was instructed to press the force plate surface using his index finger for approximately 3 seconds with maximum voluntary isometric force. Static pressing measurements were taken at three different finger postures, from very flexed to fully extended see . Each exP and the phalanx angles θ1, θ2, θ3, and θ4 at a representative instant of time were used as inputs to the multisegment finger model and the tendon network model for in vivo force calculation.The raw marker data was processed using customised Matlab codes. All trials with more than 10 consecutive missing frames were discarded. After fill-gap processing, the data were filtered using a low-pass zero-lag fourth-order Butterworth digital filter with a cut-off frequency of 6.0 Hz. For both marker and force plate records, only the data in the middle of the trial when the subject had reached a steady isometric pressing condition were used. After data processing, the measured external load The measured 3D fingertip forces and positions of the phalanges from three representative static pressing trials, each for a different finger posture varying from a flexed position to a fully extended position see , were usPx, Py, Pz) and the phalanx angles  for each of the three finger postures. The calculated muscle forces for the four major finger muscles  and also the bone-to-bone contact forces at each finger joint during pressing are listed in  MN experiences the maximum tendon length change for all three postures. This is probably due to the action of the major extensor muscle UI, which generated the maximum force among the four finger extensor muscles  obtaine E0 and D attach to the bone while the other nodes move around the bone surfaces. In this study, some rather simple constraint equations are used to define the interaction between tendons and bones. The nodes of the tendon network are constrained to only move on the lines containing vertexes of the 3D surfaces of phalanx's bones. Constraint equations were defined based on the geometry of the facet in contact with the tendon component. However, practically, the nodes can move around the entire surface of the contact area rather than move along some particular predefined lines on the surface. In addition, only nodes being constrained to move on the bone surface are not enough. Therefore, an effective bone wrapping algorithm is needed to further improve the tendon network model by representing more realistic geometrical changes of the tendon components whilst moving around bones.To simplify the modelling process, some noteworthy assumptions were employed in the multisegment finger model and in the tendon network model. The tendons are represented as line springs  that caAlthough the extensor mechanism of a single index finger was modelled in this study, the proposed energy-based method can be applied to the other human fingers and to the whole human hand complex as well. This would allow us to predict the structure deformation and the force distribution of the finger extensor mechanisms at different hand activities, for instance, precision grip  or fingeA subject-specific experimental validation of the predicted deformations and forces in the extensor tendon network appears impossible at present due to the limitations of the current measurement techniques available to quantify the internal mechanical state of the musculoskeletal system in vivo. However, good agreement with in vitro experimental studies in terms of the general trends and the ranges of the predicted deformations and forces in the extensor tendon network suggest that the proposed method is a promising tool for investigating the in vivo biomechanics of complex interconnected musculoskeletal systems.In this study, we proposed a novel method for in vivo force evaluation of the finger tendon network by integrating the three-dimensional motion analysis technique and a novel biomechanical tendon network model. The Principle of Minimum Total Potential Energy is used to assess the force transmissions throughout the tendon network of the extensor mechanism when subjected to the external load and the finger posture defined by the measured data. The proposed method can not only evaluate the forces acting on each individual tendon components, but also can work out the bone-to-bone contact forces at each finger joint and, hence, may provide a comprehensive biomechanical database of the in vivo loading condition of the human finger musculoskeletal complex.