In this paper we study the Buckley-James estimator of accelerated failure time models with auxiliary covariates. Instead of postulating distributional assumptions on the auxiliary covariates, we use a local polynomial approximation method to accommodate them into the Buckley-James estimating equations. The regression parameters are obtained iteratively by minimizing a consecutive distance of the estimates. Asymptotic properties of the proposed estimator are investigated. Simulation studies show that the efficiency gain of using auxiliary information is remarkable when compared to just using the validation sample. The method is applied to the PBC data from the Mayo Clinic trial in primary biliary cirrhosis as an illustration. It is not uncommon to have one or more missing or mismeasured covariates in large cohort epidemiological studies. There are always cases in medical studies, where it is difficult to obtain an accurate measurement for all patients due to a procedure being too expensive or invasive. Alternatively, some auxiliary measurements which are less precise, but highly related to the target procedure, can be easily collected. In some situations, all of the measurements are error prone, while in other cases, a validation subsample, where the measurements are all accurately taken, is made available.The former is a pure measurement error problem. The purpose of this paper is to investigate the inference of the latter cases in a failure time setting. In some cases, the validation sample could be large enough on its own, so one could choose to ignore all data from subjects that have missing or mismeasured values for any of the covariates, with just a minor efficiency loss. However, if the validation sample is relatively small, utilizing the auxiliary information will lead to remarkable efficiency gain, as our simulation results will show.The literature on statistical inference of missing or mismeasured data of failure time regression models is abundant. Ignoring the measurement errors in modeling could lead to severe estimation bias, depending on the magnitude of the measurement error, hence invalidate the whole inference procedure .The conditional expectation of the mis-measured covariate, denoted by The standard deviation of these estimators is estimated using bootstrapping. For Remark 1In order to retain the same quality of information among the replicated estimations, an alternative method of resampling was attempted to keep the proportion of censored observations constant in each replication. We defined In order to solve the estimating equations for the regression parameters, we use the iterative scheme of When the iterations do not converge, a cut-off point has to be determined to stop the iterations. It is advised to select a number of iterations that is slightly greater than the amount required for the convergence when a solution typically does exist. For most of our simulations, we have set this point at When dealing with real data, it is advised to choose an arbitrarily large number for the cut-off point to find the best possible solution.In this section, we investigate the asymptotic properties of our proposed estimator. For that sake, we rewrite the estimating function and the Kaplan-Meier estimator of the residual survival function in the counting process frame work. Define a function The Buckley-James estimator solves the above equation.When some of the covariates are accurately recorded only for the validation sample, but with relevant auxiliary information available for the whole study cohort, the estimating functions involved those mis-measured covariates belonging to the nonvalidation sample. We propose to estimate those terms by using the local polynomial approximation approach. Let The derived estimating equation is thenOur proposed estimator of the regression parameter, accommodating the auxiliary information, For a vector Let Denote further Without loss of generality, let BC] As , whereandis defined asTheorem 2Under assumptions C.0-C.4 and the bandwidth conditions [BC], ifwhere the matrixis defined asand Corollary 1Under assumptions C.0-C.4, the bandwidth conditions [BC] and the assumption that Theorem 3Under assumptions C.0-C.4 and the bandwidth conditions [BC], Let Let DefineThenLet Further,Let By the martingale representation of Kaplan-Meier estimator of the survival function see The Kaplan-Meier estimators of the survival functions of HenceThis term can be further rewritten asWe haveBy assumptions C.0, C.2 and Lenglart's inequality, we haveHence the estimating function can be rewritten asNote that From the equation (U) in the proof of Theorem 3.2, and by Theorem 4.1 of Lai and Ying (1991), we have, for Corollary 1 and Theorem 3 are direct conclusions of Theorems 1 and 2.In this section we examine the small sample performance of our proposed estimator. Let The data for the simulations was generated in the following way. The Various other parameters are controlled over all simulations. Each run calculates 1000 replicates in the bootstrapping to give consistent estimators of the standard deviations. The parameters were chosen as The standard error (SE), standard deviation (SD), and coverage probability (CP) are calculated for each set of simulations. The SE values are the sample standard deviations of the From Estimators Naive estimator The The proposed method removes the estimation bias in The bootstrapping procedure results in good estimates of the standard error for all observed cases over the four estimators and three error distributions.The coverage probabilities for the The model experiences the least variation when the error term follows the standard normal distribution, with standard errors that are approximately half the size as when the error term follows the chosen extreme value or logistic distributions.The efficiency gain is ignorable when To illustrate how to use the smoothing method in practice, we analyze the data from the Mayo Clinic trial in primary biliary cirrhosis (PBC) of the liver. PBC is a chronic liver disease that inflames and slowly destroys the bile ducts in the liver, impairing its ability to function properly. It is believed to be a type of autoimmune disorder where the immune system attacks the bile ducts. PBC occurs primarily in women, with approximately age, patient's age (in years); albumin, serum albumin (in mg/dl); ast, aspartate aminotransferase (in U/ml), once referred to as SGOT; bili, serum bilirunbin (in mg/dl); copper, urine copper (ug/day); edema, equal to 0 if no edema, 0.5 if untreated or successfully treated, or 1 if there exists edema despite diuretic therapy; protime, standardized blood clotting time. Of these, two cases were examined using either ast or copper for our In the Mayo Clinic trial, 418 patients were eligible. Of these patients, mostly complete data was obtained from the first 312 patients. The remaining 106 patients were not part of the actual clinical trial but had some basic measurements taken and were followed for survival. The variables we used in our regression on the logarithm of time were edema05 and edema1, defined asEdema was split into two categorical variables, albumin, ast, bili, copper, and protime, in the interest of making their marginal distributions closer to normal. For the smoothing of the unobserved We also took the log transformation of Examining In this paper we proposed the use of the Buckley-James estimator as a nonparametric method of estimating the regression parameters of an accelerated failure time model with auxiliary covariates. Kernel smoothing was applied using the auxiliary covariates to estimate missing or mismeasured covariates. The Buckley-James method is then applied to the whole study cohort for the inference of the covariates effect. The standard deviations of the estimates of the regression coefficients are estimated through bootstrapping. The proposed estimator is consistent and asymptotically normal.This method was most effective in the case of mis-measured data due to the naturally high correlation between the corresponding The smoothing model is set up in a general format. In applications, we should only choose those variables which are highly related to the mismeasured one. By doing so we can avoid the situations such as the auxiliary covariates only occupy a narrow region, which could cause instability in the local smoothing, hence the whole model.Caution should also be taken when the proposed method is applied to a data with extremely small validation sample. A classic measurement error model might be a better option, where one can estimate the measurement error variance using the validation sample.


To solve the problem of task scheduling in the cloud computing system, this paper proposes a scheduling algorithm for cloud computing based on the driver of dynamic essential path (DDEP). This algorithm applies a predecessor-task layer priority strategy to solve the problem of constraint relations among task nodes. The strategy assigns different priority values to every task node based on the scheduling order of task node as affected by the constraint relations among task nodes, and the task node list is generated by the different priority value. To address the scheduling order problem in which task nodes have the same priority value, the dynamic essential long path strategy is proposed. This strategy computes the dynamic essential path of the pre-scheduling task nodes based on the actual computation cost and communication cost of task node in the scheduling process. The task node that has the longest dynamic essential path is scheduled first as the completion time of task graph is indirectly influenced by the finishing time of task nodes in the longest dynamic essential path. Finally, we demonstrate the proposed algorithm via simulation experiments using Matlab tools. The experimental results indicate that the proposed algorithm can effectively reduce the task Makespan in most cases and meet a high quality performance objective. Due to the high-speed development and popularization of the internet, network resource sharing has emerged; cloud computing is a service related to network resources, and in recent years, cloud computing has become well developed. Because cloud computing exists in a multiple network environment, its resources and services have many features, such as diversification, dynamic behaviour, and pay-per-use, among others. Different resources and services are allocated to meet different user needs. Under these conditions, cloud computing is applied in many different fields, including the finance, manufacturing, medicine, and the electronic industry, etc. Cloud computing is also represents further development of parallel computing, distributed computation and grid computing –6, and tThe cloud computing system creates a shared network resource and service with the user. Due to its diversified, dynamic and flexible nature, different resources and services are offered to different users, which is an advantage of cloud computing. These natures pose a new challenge for development of scheduling algorithms in the cloud computing system –9.The task-scheduling algorithm a branch of scheduling algorithm, which is used to study dependent or independent tasks in a homogeneous or heterogeneous environment. To solve the task-scheduling problem, some researchers have proposed many effective and feasible scheduling algorithms, which can be categorized into list-scheduling algorithms and scheduling algorithms based on task duplication, clustering and random search. According to the scheduling system features, the scheduling algorithms are divided into two classes, namely, scheduling algorithms for homogeneous or heterogeneous environments. The main concept of the list-scheduling algorithm is that the sort order list of task nodes is obtained from the rank value of all task nodes, and the algorithms select task node from the order list in sequence for the corresponding server. The scheduling result is obtained when all task nodes are completed. The classical list-scheduling algorithm includes DLS (dynamic level scheduling) ,HPRV&HURIn recent years, certain researchers have proposed many dependent task scheduling algorithms for heterogeneous systems. These algorithms consider both the task node itself and the communication cost between task nodes, such as HEFT&CPOP ,HEFT-LooThe main contributions of this paper are summarized as follows: we consider first the problem of scheduling order for all task nodes affected by the actual computation time (cost) and communication time (cost) of task node in the scheduling processing. To solve the problem, this paper proposes a scheduling algorithm for the cloud computing system based on the driver of dynamic essential path (DDEP). According to the constraint relations among task nodes in the scheduling model, the algorithm proposes the predecessor-task layer priority strategy to solve the scheduling order relation among task nodes problem. The dynamic essential long path strategy is proposed to compute the dynamic essential path of the pre-scheduling task nodes based on the actual computation cost and communication cost of task nodes in the scheduling process. The task node that has the longest dynamic essential path is scheduled first.The experimental results show that the proposed algorithm can effectively reduce the task Makespan in most cases and also meet a high quality performance objective.The rest of the paper is organized as follows: section 2 reviews some related work and section 3 gives the scheduling model. Section 4 presents the proposed scheduling algorithm, and section 5 illustrates the example analysis. Its time complexity and the experimental result are presented in sections 6 and 7, respectively. Finally, some concluding remarks are given in section 8.Those algorithms that consider both the computational cost of task node itself and the communication cost between task nodes have a common feature, which is that the task-scheduling result is obtained before the task scheduling. In contrast, in the proposed algorithm, the task-scheduling result is obtained after the task scheduling is obtained because the task node sort order is dynamically modified according to the actual scheduling situation. A simple comparative analysis of the proposed algorithm and the existing scheduling algorithms is described as the following sections.O(n2 * k), where the number of task nodes is n; and k is the number of servers.HEFT . The sort order generated by DDEP algorithm is more reasonable than HEFT algorithm. However, the dynamic essential path for each task node is computed twice by DDEP algorithm, which adds to the time complexity compared with HEFT algorithm.HEFT-Lookahead  algorithO(n3), where n is the number of task nodes.The start time for each task node is determined by HEFT-Lookahead algorithm. The current task node and its successor task node are re-scheduled on all servers, and the server is selected that minimizes the current task node and its successor task node finish time. However, the dynamic essential path is computed once, and the start time of the group task nodes is determined by DDEP algorithm. In this way, HEFT-Lookahead algorithm has a higher time complexity than DDEP algorithm, i.e., O(n3 * k), where n is the number of task nodes, and k is the servers number.The main concept of algorithm  divides O(n3), where n is the number of task nodes. Compared with DDEP algorithm, the dynamic essential path is computed once, and the start time of one group task nodes is determined by DDEP algorithm, which gives DDEP algorithm a low time complexity. Furthermore, the DDEP algorithm is simple and viable.DCP  algorithThe cloud computing system is a computer network composed of user applications, the network environment and an easily extensible scheduling algorithm. The users obtain the required resources and services via the scheduling algorithm in the network environment, as shown in To explicitly describe the data model, we define selected terminology:Single entry task node: If there is no parent task node referred to as a single entry task node, then its set of immediate predecessor task nodes is empty.Single exit task node: If there are no child task node known as a single exit task node, its set of immediate successor task nodes is empty.G = {Q,E,S}, where Q is the task node set of DAG graph, Q = {Q1,Q2,…,Qn}, Qi represents the ith task node, n represents the number of task nodes; S is the set of network servers, S = {S1,S2,…, Sm}, Sm represents the mth server, m represents the number of servers, that is the processing machine of task node; SQi is the selectable server set of Qi, SQi⊆S, TQi is the computation time set of Qi on the SQi; TQi = {ti1,ti2,…,tim}; tim represents the computation time of ith task node on the mth server. E is the set of communication costs among task nodes, E = {eij}, and eij represents the precedence constraint relations such that Qi should complete its execution before Qj begins.The main task is task node scheduling and resource allocation problem in the cloud computing system.We first create the scheduling model by converting the cloud computing scheduling problem into the DAG scheduling problem . The appEST represents the earliest start time for Qi on the server Sm; and EFT represents the earliest finish time of Qi on the server Sm. For the single entry task node Qi on the server Sm:T0 represents the application start time. For the other task nodes in the DAG graph:Pre(j)is the set of immediate predecessor task nodes of Qj. After all immediate predecessor task nodes of Qj are finished, the data are transmitted to Qj; where eij represents the communication cost between Qi and Qj. When all data required for Qj have arrived, the server Sm begins to process Qj, eij = data/bnm, where ‘data’ is the size of transmit data, and bnm represents the network bandwidth from Sn to Sm.The DAG scheduling problem is described as follows: Qexit is a single exit task node, and the final objective is to minimize the total time of the application i.e., min(Makespan).The objective functions of all task nodes on the DAG graph are described as:The goal of scheduling algorithm is to minimize the completion time (execution cost) of task graph. The start time and finish time of each task node are the factors that influence the completion time of task graph, which is related to the computation time (cost) and communication time (cost). The sort order of all task nodes is an important factor that influences the start time and finish time of each task node. To solve the sort order of the task node problem, the predecessor-task layer priority strategy and the dynamic essential long path strategy are proposed. According to the sort order list, we select the server that minimizes the start time and finish time of task node.Qexit is the exit task node, and Succe(Qj)is a set of the successor task nodes of Qi. The According to the constraint relations among task nodes in the DAG model, the current task nodes are scheduled after its predecessor task nodes are finished. The predecessor-task layer priority strategy is proposed to address the scheduling order relation among task nodes problem. The entry task nodes can be first scheduled according to the scheduling order of task nodes in the task graph. When the entry task nodes are finished, the successor task nodes can be scheduled such that the entry task nodes have the top priority compared with other task nodes in the task graph. The exit task nodes have the lowest priority values, and the strategy defines the priority value of the exit task node as 1. The priority values of other task nodes in the task graph are equal to the summation of the priority values of its predecessor task node and 1. The priority values of all task nodes are obtained in turn, and the formula is defined by:Because the priority value of Q2 is higher than that of Q3 according to the predecessor-task layer priority strategy, Q2 obtains the first scheduling choice, which shortens the completion time of task graph in Qentry is the entry task node, Pre(Qj)is a set of the predecessor task nodes of Qi.Different priority values are assigned to all task nodes, to address the scheduling order problem for the same priority value of task nodes, the dynamic essential long path strategy is proposed. The main concept of this strategy is to first schedule the task node that has the longest essential path because the completion time of task nodes in the essentially longer path will directly influence the completion time of task graph. The dynamic essential path formula is defined by:eij = 0, when the two task nodes Qi and Qj are scheduled on the same server, and Qi is a predecessor task node of Qj. When some task nodes have been finished, update their actual computation cost, server and the dynamic essential path value. This formula is defined by:tentryk is the computational cost of Qentry on the server Sk. To accurately compute the dynamic essential path of task node, the communication time (cost) is reduced to 0 by the dynamic essential long path strategy,i.e., This paper converts an application into a DAG graph shown in DDEP algorithm is a scheduling algorithm for an application (task) in the cloud computing system and, contains four major data phases: The computation cost phase of all task nodes on all servers, the communication cost phase between task nodes; the task node list phase with different priority values, the dynamic essential path phase of all task nodes.(1)Computation cost phase and communication cost phaseThe two phases are an original array. The user application (task) owns the phase or table that stores the computation cost of each task node on the different servers. The communication cost between task nodes is stored by the adjacent matrix.(2)Task node list with different priority valuesEach task node in the (3)Dynamic essential path (DEP) phaseThe dynamic essential path of all task nodes is initialized as 0. The task node list of highest priority value is sought from the task node list, and the entry task node is found, Task_node_4 = Q1. The DEP of Q1 is abtained by formulas Figs n is the number of task nodes, and k is the number of servers. The specific time complexity analysis is described as follows.The time complexity of DDEP algorithm contains two separate components: one is the time complexity, for which the priority value of all task nodes is obtained by the predecessor-task layer priority strategy, and the other is the time complexity, for which the dynamic essential path of all task nodes is obtained by the dynamic essential long path strategy. It is assumed that n, and the size of adjacent matrix is n * n. The time complexity of searching for the exit task node is O(n). Then search for the predecessor task nodes of the exit task node, the column data of the exit task nodes are traversed, and the maximum number of comparisons is n. The priority value of each task node is obtained in the lookup procedure. Subsequently, the maximum comparison time that the priority value of all task nodes obtains is n * (n − 1), i.e., O(n2). To sum up, the time complexity of the predecessor-task layer priority strategy is O(n2)+O(n).The time complexity of the predecessor-task layer priority strategy. The adjacent matrix is used to store the relationships(communication cost) between task nodes in the task graph. The number of task nodes is n − 1), and the number of computations of the dynamic essential path of these task nodes is (n − 1). Second, the process selects the corresponding server for each task node according to the sort order of the dynamic essential path, with counting time k * (n − 1). The dynamic essential path of each task node is updated, and the counting time is (n − 1). Thus the dynamic essential path of the same priority task node is obtained to require a counting time of 2 * (n − 1) + k * (n − 1). It is assumed that the highest priority value is h. The dynamic essential path of all task nodes is obtained to require the counting times for h * (2 * (n − 1) + k * (n − 1)) = (2 + k)(n * k * h − h). In the worst case, h = n, because the number of servers is relatively smaller than the number of task nodes, and the time complexity of computing the dynamic essential path is O(n2).The time complexity of the dynamic essential long path strategy. First the maximum number that the highest priority value of task node can be obtained is (O(n log n).The time complexity of sorting all task nodes by the dynamic essential path. All task nodes are sorted in descending order by the dynamic essential path, whose time complexity is O(n2)+O(n log n)+O(n2)+O(n), which is approximated as O(n2). The time complexities of the algorithms in references : Computation time is randomly generated in the range. Max_Computation_Cost is the maximum computation time, Min_Computation_Cost is the minimum computation time.MaxOutDegree: Maximum out-degree of task node. The out-degree range of the non-exit task node is 1 to MaxOutDegree. For the exit task node, the out-degree is 0.MaxInDegree: Maximum in-degree of task node. The in-degree range of the non-exit task node is 1 to MaxInDegree. For the entry task node, the in-degree is 0.: Communication cost is randomly generated in the range.The following experiment results are acquired, as generated with scheduling of the randomly generated DAG graph using HEFT, HEFT-Lookahead and CEFT algorithms.SLR(Schedulelengthratio) and Speedup.To evaluate the performance of the proposed algorithm, we adopt the common performance comparison metrics Makespan) to the critical path length with a formula defined by:CPmin is the set of the task nodes on the critical path of DAG graph, and the denominator is the summation of the minimum computation costs of all task nodes on the critical path. The SLR value is not less than 1. If the SLR value is smaller, the algorithm performance is better; if the algorithm performance is worse, the SLR value is larger. The average SLR values over several DAG graphs are used in our experiment.SLR is the main performance measure for a scheduling algorithm on a graph, and is the ratio of the total processing time . The speedup formula is defined by:The sequential execution time is equal to the summation of the computation costs of all task nodes on the single server that minimizes the computation cost. If the Speedup value is smaller, the algorithm performance is worse, whereas if the speedup value is larger, the algorithm performance is better. The average Speedup values over several DAG graphs are used in our experiment.Experimental analysis of the task graph scale. The goal is to verify the influence of the task graph scale on the scheduling algorithm. To show the performance of the proposed algorithm, we adopt different sizes of DAG graph that are scheduled on the same size server to obtain the experimental result. We set the number of task nodes as 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000 respectively, the number of servers is also 5. The computation cost is generated randomly in the interval 5–10, and the communication cost is generated randomly in the interval 5–10. The out-degree and in-degree of task graph are also randomly generated in the interval 1-10. Experimental analysis of the server scale. The goal is to verify the influence of the server scale on the scheduling algorithm. We adopt the same size DAG graph and schedule it on the different sizes of server to obtain the experiment result. We set the number of task nodes to 100, and the number of servers to 3,5,7 and 9. The computation cost is randomly generated from the interval [Experimental analysis of CCR (Communication Cost = CCR*Computation Cost). The goal is to verify the stability of the scheduling algorithm by converting the communication cost. The computation cost is taken from the interval , 20. TheExperimental analysis of robustness. The robustness of the scheduling algorithm is shown as the stability of scheduling a heterogeneous DAG graph. The computation cost is generated from the interval , 20, andConclusion. The performance of the proposed algorithm is verified from four aspects. According to the analysis results shown in Figs To evaluate the performance of the proposed algorithm, we adopt the common performance comparison metric of algorithm running time. We use the proposed algorithm to schedule different sized DAG graphs that are run 100 times on 5 servers to compute the average running time. The experimental result is shown in Finally, for the same-size DAG graph, the running time of the proposed algorithm is longer than that of HEFT algorithm, but the completion time of the proposed algorithm is shorter than those of HEFT algorithm, HEFT-Lookahead algorithm and CEFT algorithm. Because the proposed algorithm fully considers the rationality of the priority value of each task node and because the scheduling order is affected by the change of the dynamic essential path for each task node, a greater amount time is spent on parallel scheduling. In this way, the completion time of the task graph is shortened.O(n2), which is lower than those of the traditional DAG scheduling algorithms. As a result, the proposed method is simple and viable. The robustness of the proposed algorithm is greater than those of the other scheduling algorithms, and thus the proposed algorithm can be used to solve the scheduling problem of the heterogeneous DAG graph. Compared with the other scheduling algorithms, the performance of the proposed algorithm is much better.In this paper, we propose a scheduling algorithm for the cloud computing system based on the driver of dynamic essential path to solve the problem of the scheduling result affected by the scheduling order change of each task node in the scheduling process. Because the scheduling model is a DAG model of parallel computing, the algorithm has universality. The innovative points and significance of this paper are described as follows. The algorithm adopts the predecessor-task layer priority strategy to solve the problem in which the scheduling order of task nodes is affected by the constraint relations among task nodes. Compared with the traditional level priority, the scheduling order of task nodes is more reasonable using the proposed strategy. The algorithm uses the dynamic essential long path strategy to solve the problem in which the scheduling order of all task nodes is affected by the change of the dynamic essential path of each task node in the scheduling process. The reasonable scheduling order shortens the completion time of task graph compared with those algorithms that determine the scheduling order of task nodes prior to scheduling. The time complexity of the proposed algorithm is In conclusion, the proposed algorithm is able to solve the cloud computing scheduling problem and also display a certain reference value for solving the scheduling problem of parallel computing, distributed computation and grid computing.


Global positioning system (GPS) technology is well suited for attitude determination. However, in land vehicle application, low-cost single frequency GPS receivers which have low measurement quality are often used, and external factors such as multipath and low satellite visibility in the densely built-up urban environment further degrade the quality of the GPS measurements. Due to the low-quality receivers used and the challenging urban environment, the success rate of the single epoch ambiguity resolution for dynamic attitude determination is usually quite low. In this paper, a micro-electro-mechanical system (MEMS)—inertial navigation system (INS)-aided ambiguity resolution method is proposed to improve the GPS attitude determination performance, which is particularly suitable for land vehicle attitude determination. First, the INS calculated baseline vector is augmented with the GPS carrier phase and code measurements. This improves the ambiguity dilution of precision (ADOP), resulting in better quality of the unconstrained float solution. Second, the undesirable float solutions caused by large measurement errors are further filtered and replaced using the INS-aided ambiguity function method (AFM). The fixed solutions are then obtained by the constrained least squares ambiguity decorrelation (CLAMBDA) algorithm. Finally, the GPS/MEMS-INS integration is realized by the use of a Kalman filter. Theoretical analysis of the ADOP is given and experimental results demonstrate that our proposed method can significantly improve the quality of the float ambiguity solution, leading to high success rate and better accuracy of attitude determination. In low-cost land vehicle navigation applications, not only are accurate position and velocity information required, but the navigation system also needs to provide accurate attitude ,2,3,4. MWhen a multiple-antenna GPS system is used for attitude determination, the challenge is how to determine the carrier phase integer ambiguity quickly, accurately and reliably. However, due to the loss of lock and noise disturbance, cycle slips often occur in land vehicle applications. Therefore, the single epoch method is usually adopted for dynamic attitude determination in such applications. This is because attitude determination in single epoch is insensitive to cycle slips. In addition, due to the low cost requirement, single frequency GPS receivers are widely used in this type of application, but the carrier phase and code (pseudorange) measurement qualities are both very low for low-cost GPS receivers, especially the code measurement . Moreoveet al. [et al. [The ideas of using inertial sensors to improve GPS ambiguity success rate have been proposed recently ,13,14,15et al.  applied LAMBDA is popular for ambiguity resolution, since it is known to maximize the ambiguity success rate ,16. It hIn this paper, a new method to improve the attitude determination performance by improving the quality of float ambiguity solution is proposed. First, the INS calculated baseline vector is augmented with GPS carrier phase and code measurements. The quality improvement of the float ambiguity solution is verified by theoretical analysis. Second, we use ADOP combined with the INS derived reference value to assess the quality of the float ambiguity solution. The undesirable float solution is replaced with the float solution obtained through the INS-aided AFM, where the INS attitude information is used to optimize the AFM search region. Moreover, some constraints are adopted for the selection of the correct float solution. Then, the fixed solution is obtained by the CLAMBDA method, and the GPS attitude measurement can be calculated. Finally, the GPS/MEMS-INS integrated filter is used to estimate the navigation errors and sensor errors of MEMS-INS, which can effectively improve the quality of the INS attitude measurement for the estimation of the float ambiguity solution. The rest of this paper is organized as follows: a priori given information.For the GPS compass system, two antennas are utilized to provide the observability of yaw (or heading) and pitch (or elevation). The GPS compass model with the baseline constraint is presented as :(1)E(y)If we apply the least squares (LS) estimation principle to solve for the unknown ambiguity vectorBased on the orthogonal decomposition, the minimization problem of (2) can be formulated as:The CLAMBDA method can minimize the following objective function to obtain the fixed ambiguity solution:Equation (4) has no analytic solution, and it should be solved using an efficient searching method. The detailed description of the searching method used here can be found in , which bOur method is based on the orthogonal transformation model, which is a numerically stable approach . Form=non model  of singlThe first step of attitude determination with CLAMBDA is to obtain the float ambiguity solutionsolution  in this S method .Qa^conthe ADOP  is definADOP equals the geometric mean of the conditional standard deviations ofAssuming the expression of the INS calculated baseline vector in the local East-North-Up frame isWhen the baseline length is fixed, the baseline vector can be expressed as the nonlinear function of yaw and pitch ,32. The To simplify the calculation, the upper bounding approach ,34 is adTherefore,DefiningThe vc-matrix of state estimate is:It is easy to prove that the quality of the float solution with the INS calculated baseline vector augmentation is higher than the quality of the float solution without the augmentation. Let:We can obtain the vc-matrix of state estimate:For the float solution without the augmentation, the vc-matrix of state estimate is:AsIt also can be seen from Equations (15), (20) and (21) that more accurate ambiguity float solution can be obtained as long as the number of tracked satellites is not less than two satellites, which is more important for the land vehicle attitude determination.Theoretical analysis results of the ADOP in In order to reduce the effect of large outliers in pseudorange on the float ambiguity solution, a simple quality control (QC) method is adopted here, which is based on the residual chi-square test ,10. As tThen we can perform the residual chi-square test ,10 on psHowever, undetected measurement errors may still result in some low-quality float ambiguity solutions. In order to achieve high success rate, these low-quality float solutions need to be filtered and improved. So the quality of the float solution estimated by Equation (17) should be assessed first. Since the attitude errors of MEMS-INS are estimated and corrected by the GPS/MEMS-INS integrated filter in real time, which will be introduced in bI in the local frame can be obtained by Equation (8). Then according to the carrier phase measurement Equation (5), the INS calculated float ambiguity solutionFirst, the INS attitude calculated baseline vector Second, the ADOP can be calculated with Equations (7) and (18). Then, according to theIf one entry of the float solution estimated by Equation (17) does not satisfy Equation (24), the float solution is marked as low-quality.For the low-quality float solution filtered by Equation (24), we will replace it with the float solution resolved by the INS-aided AFM. The basic idea of AFM is that for the correct attitude, the value of the adaptive function should theoretically have the value 1 as the maximum, since the ambiguity is integer. The maximum of the adaptive function ideally leads to the correct ambiguity . AFM doebk of the vehicle can be calculated by Equation (11), then the float solutionGiven the pitchThe search space is determined by MEMS-INS attitude.However, despite the search space reduction, several candidates of similar magnitudes may still exist in the adaptive function. Therefore, in order to identify the most likely correct candidate, we need the validation procedure with some constrain conditions. Before the validation, for each float solution candidate, the fixed solution is obtained through rounding. The corresponding baseline vectorThe validation procedure is divided into three consecutive steps: (1) Baseline length verificationFor the correct float solution candidate, the error between the estimated and known baseline length is very small, so if the estimated baseline length satisfies(2) Attitude verificationFor the candidate(3) The residual verificationWhen the fixed solution and the estimated baseline vector of each candidate are substituted back to Equation (5), the corresponding residual can be obtained. The correct candidate will make the residual reach the minimum. Thus the remaining candidates are sorted according to the ascending order of their residuals. The float solution of the maximum value of adaptive function is selected from the firstThe flow diagram of the method is shown in The proposed method can significantly improve the quality of the float ambiguity solution. It can not only improve the success rate of attitude determination, but also the computational efficiency. Since when the float solution is accurate, the search time of the fixed solution by CLAMBDA is always very short.After the attitude determination using MEMS-INS aided multiple-antenna GPS, GPS/MEMS-INS integration is also necessary. The integrated Kalman filter estimates the navigation errors and sensor errors using the GPS pseudorange, pseudorange rate and attitude measurements.The states of integrated filter are given by:Continuous state equations are described by:The INS error state equation of  is adoptThe measurements of the integrated filter are the difference of pseudorange, pseudorange rate and attitude between GPS and MEMS-INS, which are given by:The measurement equation of pseudorange is described by :(29)Zρ=The measurement equation of pseudorange rate is described by:The measurement equation of attitude is described by:Then the measurement equations of the integrated filter can be obtained:Based on the discrete forms of Equations (27) and (32), we can implement Kalman filter algorithm to estimate the MEMS-INS errors in real time. The Kalman filter algorithm is composed of prediction and measurement update . Using tField tests in an urban area were conducted to verify the performance of the GPS/MEMS-INS integrated attitude determination system. The test system is shown in The receivers used here are the SUPERSTAR II, which is the NovAtel 12 channels single frequency GPS receiver with 1 Hz output. Its code measurement precision is 0.75 m RMS. The difference carrier phase measurement precision is 0.01 m RMS. The MEMS-INS is the XW-IMU5220 from Beijing Starneto Technology Co. Ltd. . The bias stability of gyro and accelerometer are 0.02°/s and 8 mg, respectively. The output frequency is 100 Hz.The experimental setup of the attitude determination system is shown in i.e., three satellites which have the lowest elevation angles are regarded as invisible. In order to further test the performance of the attitude determination system, three satellites are removed to create a GPS challenged environment, The results of the proposed MEMS-INS aided GPS attitude determination method are compared with the unaided method. The performance improvements are verified through examing the quality of float solution, success rate and attitude accuracy. In the following, the default environment is the actual GPS environment shown in (1) The quality of float solution verificationWe use the ADOP to assess the quality of the float ambiguity solution. i.e., smaller ADOP) when the INS calculated baseline vector is augmented with the GPS carrier phase and code measurements. Since the precisions of the INS calculated baseline vector and GPS carrier phase are higher than that of the pseudorange, the contribution of GPS pseudorange is decreased after using the INS calculated baseline vector augmentation. The improvement is especially significant when additional three satellites with lowest elevation angles are removed. In other words, with the INS calculated baseline vector augmentation, the ADOP is less affected by the number of visible satellites. This can be seen clearly in It can be seen that the quality of the float solution is improved  Success rate verificationThe success rate of the proposed MEMS-INS aided GPS attitude determination method (GPS/INS) is analyzed by comparison with the unaided single frequency single epoch GPS attitude determination method (GPS). In the experiment, since the car moves on a flat road, the pitches of the two baselines can be considered constant in a certain allowable error range, here we use their initial values as the references and set 5° as the error range. The comparison of the attitudes calculated by the two attitude determination methods is shown in From Then, the success rates of the MEMS-INS aided GPS and the unaided GPS attitude determination methods are given in As shown in From the above results, it can be seen that the success rate of GPS/MEMS-INS attitude determination is usually unable to achieve 100%, since the maximum success rate depends on the quality of carrier phase measurement and the performance of MEMS-INS in the land vehicle application. When the integer ambiguity resolution is unsuccessful, correct attitude can not be obtained, and the attitude error may be large. It is no doubt that if the abnormal GPS attitude measurement is used in the integrated filter, the large error will degrade the overall attitude accuracy of the integrated system. To avoid this problem, a simple quality control (QC) method is adopted in the GPS/MEMS-INS integration, which is based on the residual chi-square test ,10.(3) Attitude accuracy verificationThe final attitude results obtained from the integrated filter are shown in As compared with In order to improve the performance of the low-cost GPS/MEMS-INS attitude determination system used in land vehicles, a new integrated attitude determination method is presented. The core issue of the method is how to improve the success rate of the single frequency single epoch GPS ambiguity resolution with low-quality measurements. We adopt the GPS/INS measurements augmentation, the quality assessment of float solution and the undesirable float solution replacement by INS aided AFM to improve the quality of the float solution. Then, the CLAMBDA method is used to obtain the fixed solution, which can maximize the ambiguity success rate for attitude determination. Finally, the GPS/MEMS-INS integrated filter is designed to increase the attitude accuracy. Field test results in urban area demonstrate that our proposed method can significantly improve the quality of the float ambiguity solution, the success rate, and the accuracy of attitude determination especially for GPS challenged environment. The success rate increases to above 97%. Compared with the unaided GPS/MEMS-INS integration, the attitude accuracy is improved at least 35%. The improved low-cost GPS/MEMS-INS attitude determination system can offer a superior performance and efficiently fulfill the task in the land vehicle application.


Cardiovascular disease (CVD) is 1 of the leading causes of death, years of life lost, and disability-adjusted years of life lost worldwide. CVD prevention for children and teens is needed, as CVD risk factors and behaviors beginning in youth contribute to CVD development. In 2012, the National Heart, Lung, and Blood Institute released their “Integrated Guidelines for Cardiovascular Health and Risk Reduction in Children and Adolescents” for clinicians, describing CVD risk factors they should address with patients at primary care preventative visits. However, uptake of new guidelines is slow. Clinical decision support (CDS) tools can improve guideline uptake. In this paper, we describe our process of testing and adapting a CDS tool to help clinicians evaluate patient risk, recommend behaviors to prevent development of risk, and complete complex calculations to determine appropriate interventions as recommended by the guidelines, using a user-centered design approach.The objective of the study was to assess the usability of a pediatric CVD risk factor tool by clinicians.The tool was tested using one-on-one in-person testing and a “think aloud” approach with 5 clinicians and by using the tool in clinical practice along with formal usability metrics with 14 pediatricians. Thematic analysis of the data from the in-person testing and clinical practice testing identified suggestions for change in 3 major areas: user experience, content refinement, and technical deployment. Descriptive statistical techniques were employed to summarize users’ overall experience with the tool.Data from testers showed that general reactions toward the CDS tool were positive. Clinical practice testers suggested revisions to make the application more user-friendly, especially for clinicians using the application on the iPhone, and called for refining recommendations to be more succinct and better tailored to the patient. Tester feedback was incorporated into the design when feasible, including streamlining data entry during clinical visits, reducing the volume of results displayed, and highlighting critical results.This study found support for the usability of our pediatric CVD risk factor tool. Insights shared about this tool may be applicable for designing other mHealth applications and CDS tools. The usability of decision support tools in clinical practice depends critically on receiving  and adapting the tool to meet the needs of clinicians in the practice setting. By 2020, cardiovascular disease (CVD) is projected to rank first in frequency among causes of death, years of life lost, and disability-adjusted years of life lost worldwide . BecauseTo overcome some of the barriers and to support clinicians in implementing the NHLBI CVD guidelines, we developed a comprehensive, multifaceted intervention that includes practice-based clinical champions, monthly collaborative webinars to support practice change, and a tool kit to support guideline implementation. The tool kit comprises a patient and family workbook to support patients in making behavior changes, guideline summary materials that include recommendations for clinicians, and a clinical decision support (CDS) tool -10. CDS The overarching aim of the CDS tool is to provide clinicians at the point of care with actionable, individually relevant recommendations for patients drawn from the comprehensive NHLBI CVD guidelines. We utilized a user-centered design approach in which users participated in pretesting and were involved with refinement of the design throughout the entire development process , includiAfter we created the content for the CDS tool, we developed other elements using key user-interface and user-experience design principles such as giving the user control, empowering the user, and allowing exploration and browsing; providing immediate feedback and the option of help at any point, and defining terminology used in the app; keeping the interface consistent, with active buttons in the same place throughout ; and incThe Pediatric Cardiovascular Risk Reduction CDS Tool consists of a screening (integrated risk) assessment, BMI and BP calculators, and a lipid assessment instrument  and provFor the BMI and BP calculators and the lipid assessment instrument, if the user has already entered information in other modules , then the app will display the relevant, previously entered data in this module . In instances where the user is only interested in using the BMI calculator, BP calculator, or lipid assessment, the user is asked to enter this information. For the lipid assessment, the user is asked to input the type of sample that was drawn  in addition to BMI. For each of the modules, the user is automatically moved to the recommendations screen showing a patient summary of the data entered and the recommendations, after indicating that the user has finished entering the data.The tool is a native Android and iOS app that clinicians can use on smartphones or tablet devices to evaluate patient risk, recommend healthy behaviors to prevent the development of risk, and carry out complex calculations to determine the appropriate interventions, as recommended by the NHLBI guidelines. The development of the app content ,8, impleWe used an iterative process of designing, testing, and revising throughout the design and development life cycle for the screening instrument, the validated BMI and BP calculators, and the lipid assessment instrument . PretestBased on current recommendations from evidence-based user-experience research ,17, we iBecause the CDS tool is designed primarily for use by pediatricians, we recruited participants for in-clinic usability testing through the American Academy of Pediatrics (AAP) Quality Improvement Innovation Network member listserv. Members of this professional group are particularly relevant to sample because participants are board-certified pediatricians who are specifically interested in testing practical tools that improve the quality of care for children and their families. A total of 29 clinicians responded to the recruitment email—which invited clinicians to attend a half-hour webinar detailing the project and CDS app—and agreed to use the app in at least 5 patient-encounter scenarios during a 2-week period. We did not offer any financial incentives to clinicians for their participation. We made the tool available to all interested participants through TestFlight, a Web service through which the research team managed access to prerelease versions of the app. Among the 29 initial clinician responders, 19 clinicians downloaded the app and 14 clinicians provided feedback on their user experiences over the 2-week testing period. We gave participants the opportunity to provide immediate, asynchronous feedback on their user experience in the form of unstructured comments via email, telephone, or short message service during the testing period rather than waiting until the end. We included the telephone number and email address of a research team member in the “About” section of the app so that participants could easily provide such feedback. On completion of testing, we instructed participants to delete the app.A total of 14 participants completed the 10-item System Usability Scale (SUS)  question1. I think that I would like to use this clinical decision support (CDS) app frequently.2. I found the CDS app unnecessarily complex.3. I thought the CDS app was easy to use.4. I think that I would need the support of a technical person to be able to use this CDS app.5. I found the various functions in this CDS app were well integrated.6. I thought there was too much inconsistency in this CDS app.7. I would imagine that most people would learn to use this CDS app very quickly.8. I found the CDS app very cumbersome to use.9. I felt very confident using the CDS app.10. I needed to learn a lot of things before I could get going with this app.In addition to the composite score, we assessed the learnability and usability subscales by analyzing the average responses to learnability  an open-ended question asking “How many times did you use the application during a patient encounter?”; (2) “In what type of patient visit did you use the application?”—with response options of well-child visit, sports physical, weight or obesity follow-up, BP follow-up, lipid follow-up, and other (please specify); and (3) “Which component of the application did you use?”—with response options of integrated assessment, lipid assessment, BP calculator, BMI calculator, and none.We examined the feedback from in-person and clinical practice testers to identify patterns and relevant themes, with the aim of gathering actionable suggestions to revise the CDS tool. We entered the qualitative data into a matrix that segmented clinicians’ comments by comment type, such as positive comment or suggestion for improvement. This common technique in qualitative research enables researchers to organize data to identify commonalities and variations that emerge in comments ,22. In tA total of 5 clinicians (4 outpatient and 1 inpatient) participated in testing the CDS tool. Each clinician had been actively practicing for more than 2 years. All participants practiced at large academic medical centers. The testers were either general pediatricians, internal medicine physicians who saw a large number of pediatric patients, or pediatricians with subspecialties in nephrology or endocrinology.Most of the testers’ comments conveyed during the think-aloud sessions involved suggestions for improvements or changes; a few comments were simply compliments, with 9% (5/58) positive comments; and there were no (0%) negative comments. Thematic analysis of the comments from the in-person testers showed that suggestions for improvements or changes fell into 3 categories: (1) layout, navigation, and/or the user experience ; (2) content refinement ; or (3) technical deployment .Suggestions for changes to the app related to the user experience and reflected individual preferences for the default display and functionality, such as:Does not like that the alphabet is the default keyboard, would prefer number pad.Default to open for the recommendations and supportive action.Content-related suggestions for changes to the app pointed out areas that needed clarification, particularly related to the information conveyed in the recommendations regarding Estimated Energy Requirement (EER) presented in the patient summary and NHLBI recommendations in the obesity risk section.Not sure what the EER is. Would like to calculate that in the app.EER, a pediatrician may not know what this means.For children with out-of-range BMIs, we provided EER language as part of the recommendations. However, whereas nutritionists understand EER, clinicians typically do not. Consequently, we provided support terminology as a design enhancement. Nonactionable user input included specific criticism of the guideline content, which we were not at liberty to revise; for example:Shorten the Overweight/Obesity Recommendations.Consolidate the Tobacco Exposure recommendation with Family History Recommendations.The few deployment suggestions provided meaningful ideas to improve the usability of the CDS tool for clinicians:Would like to be able to print physical activity and nutrition/diet recommendations for the patient to take home.Would like to email the patient [the] patient summary, activity, diet and personal risk factor information.However, some user input was not actionable because it conflicted with the intended design for a freestanding application; for example:This information is redundant to the information available in/entered in the EHR [electronic health record].When considering the screening instrument and the 3 validated calculator modules as well as the functions of the tool, the majority of suggestions for improvement  were in response to the content and display recommendations provided by the app based on the patient’s risk factor information input by the user. Common themes included the length of text displayed and the formatting of text, such as:Supportive action in Family History should only be displayed one time.Change blood pressure to number spinner rather than number data entry.Additionally, 28% (16/58) of comments were suggestions for improvement that could be applied to all modules in the app and spoke to the importance of keeping the interface consistent:When in landscape, the font size changes.Would like to see metric and standard units displayed on the same screen without having to toggle between the two.A small number of comments related to only 1 specific module: only 9% (5/58) of the comments related to the integrated assessment and only 1 comment each (<1%) related to the BMI, BP, and lipid modules.All 14 in-clinic testers were actively practicing pediatricians whose patient population was more than 80% pediatric. Although 2 participants elected to provide limited, asynchronous feedback via email and telephone during the testing period, most of the input was submitted by all clinicians after completion of the 2-week testing period. The app was used between 1 and 20 times per participant during the testing period, with an average of 7 uses. Participants reported using the app most frequently in well-child visits , followed by weight or obesity follow-ups , BP follow-ups , sports physicals , lipid follow-ups , and other types of visits . The most commonly used modules in the app were the integrated assessment  and BP calculator , followed by the BMI calculator , lipid assessment , and none (0%). The results of the SUS data analysis showed that general reactions toward the CDS tool were positive, given the average score of 81, meaning the app was viewed as “above average” with respect to usability  positive comments and no (0%) negative comments; for instance:Great app - would use it at most visits.Users particularly appreciated the app’s feature of calculating the BP percentile based on the NHLBI’s BP tables for children and adolescents :Overall very easy to use and helpful to not have to look up BP values on the chart.Thematic analysis of the comments from the clinical practice testers showed that suggestions for improvements or changes fell into 2 categories: (1) layout, navigation, and/or the user experience , or (2) content . Suggestions for changes to the app related to the user experience and to changing the app to give the user control and empower the user, especially clinicians using the app on the iPhone:When entering numbers on the iPhone, I had to click past the alpha keyboard to get to the numbers. Other apps I have used have the number keypad come up first!The touch screen did not respond easily to touch and many features were very erratic in their scrolling, such as dates.The associated algorithms are great but difficult to properly visualize on the small screens of the smartphones.Content-related suggestions for changes to the app focused on the amount of information conveyed in the recommendations. Sometimes users thought the app provided too much information and they suggested reorganizing the information to make it more succinct:The recommendations should be narrowed down using the answers entered. Otherwise there are too many and it becomes cumbersome to use.The recommendations were too lengthy to be useful in a clinical visit.The recommendation sat the end of the assessment are very wordy. It is a lot of information and the recommendations are important, so streamlining that will mean more people use the app.Nonactionable user input included specific criticism of the guideline content, which we were not at liberty to revise, for example:My only problem with the app was it gave too many repetitive recommendations for healthy children with no risk factors. For example, I would enter data for a child with 25% BMI, enter nonsmoking for child and parents, normal values for parent cholesterol values and normal answers to questions about family risk factors, and it would still recommend I ask about smoking and family risk factors after that.In other instances, testers wanted additional information and information better tailored to the individual patient:I really wanted to know how to intervene when I had abnormal lipids, but that wasn't easily accessible.During the integrated assessment it seemed to give the general guidelines more than telling what to do with this specific patient.Clinical decision support tools can improve clinician adherence to guidelines ,13. ThisHowever, we employed a user-centered approach and systematic process to develop the Pediatric Cardiovascular Risk Reduction CDS Tool that many developers do not implement. Our approach illustrates a field-proven method for soliciting expert user input from a geographically distributed sample of difficult-to-reach participants. Collaboration with the AAP, the credentialing body for all board-certified pediatricians in the United States, enabled access to members of the AAP’s Quality Improvement Innovation Network. This network was established to provide a standard mechanism for developing practical and usable measures, tools, and strategies for the practicing pediatrician in a primary care practice as well as the pediatric hospitalist in the inpatient setting. Engagement with this professional association provided a point of entry to a practical working laboratory for gathering pediatrician input based on their use of the CDS in real-world patient encounters.We incorporated tester feedback into the Pediatric Cardiovascular Risk Reduction CDS Tool design when feasible and applicable. Actionable user input focused mainly on matters of user experience and recommendations to streamline the use of the app during clinical visits. We received feedback that translated into changes in data entry, presentation of recommendations, and presentation of critical results. For example, testers recommended simplifying data entry, which resulted in our asking fewer questions and implementing persistence of forms-based variables across all of the instruments, meaning data entered in 1 tool would carry over to another.Testers also recommended reducing the volume of results displayed, which informed the restructuring of the recommendations layout using design patterns. Consequently, we used faceted navigation and presentation of information to provide an integrated, incremental search-and-browse experience to increase tailoring and refinement of the results presented for an individual patient. The more information—such as social and family history, known risk factors, and clinical observations—that the clinician enters in the CDS tool, the more facets are used progressively to refine results, eliminating extraneous information by narrowing search results. This empowers the user by not forcing clinicians to enter data for all of the variables on a screener page. Consistent with user input regarding the burden of data entry, we did not force data entry and enabled clinicians to figure out how much data they wanted to enter in the screener.User input on the presentation of recommendations also led to refinements to improve readability of the content on the summary results page. Consequently, we reduced the amount of text and used input from testers to make the recommendation language more actionable  rather than providing a more detailed recommendation for follow-up.Additionally, suggestions led to us providing immediate user feedback with the addition of a color-coding feature  to highlight critical or elevated results, such as highlighting when out-of-range or borderline results emerged for BMI, BP percentile, and lipids. Finally, nonactionable user input included specific criticisms of the guideline content, health information technology deployment, and workflow issues, all of which were beyond the scope of this CDS design.One limitation of the study is that we tested the tool using clinicians who were interested in quality improvement and motivated to adopt decision aids. Consequently, participating clinicians may have been more readily amenable to using the CDS tool or more adept at using it than clinicians without this background, experience, or interest. This, and other individual factors that we did not examine , may affect the way clinicians adopt the CDS tool. Certainly testing with more “typical” clinicians who may be less technology savvy would be valuable in future work to identify and address as many usability issues as possible and to ensure that the app is user-friendly for those less familiar with the technology.Another limitation is that the CDS tool was a part of a multifaceted intervention, which limited our ability to assess individual clinician engagement with the app in more detail. During our testing in clinical practice, we did not measure the duration of time spent with the app during the patient visit. Further, we did not ask clinicians to comment on how the CDS tool affected the patient visit and the patient-provider relationship during the clinical practice testing, which would ultimately be a contributing factor to whether or not the CDS is adopted in the clinical setting.We also were unable to retest the refined design after incorporating user feedback because of resource constraints. However, the user feedback described in this paper was applied to changes made in the final version of the Pediatric Cardiovascular Risk Reduction CDS Tool and testers were informed of how their input from usability testing had been incorporated in the final build.Additionally, for the development of this tool, we were responding to a request for a freestanding decision support application. Consequently, we deliberately built it in the context of physician maintenance of certification criteria and to support the trend of individual physicians and staff to use personal mobile phones or other devices. The advantage of this is that the tool is highly portable, which makes it easier to use within the clinical workflow. However, the lack of integration with electronic health records (EHRs) requires additional effort to enter relevant clinical information. This may limit the uptake of this freestanding tool. As EHRs become more prevalent, integration will likely become more of an issue. In the future, developers should consider the relative benefit of building a Web application with an application program interface library, including clear standards for the exchange of clinical variables and bidirectional communication functions between the decision support application and other clinical information systems. Future work should also concentrate on how the CDS will integrate into the broader health care ecosystem.Finally, while this paper focused on the usability testing, rather than the implementation and effectiveness (see ,10,13 foThis study assessed the usability of the Pediatric Cardiovascular Risk Reduction CDS Tool by clinicians. Testing through both one-on-one in-person testing using a "think aloud" approach and in-practice use of the tool along with formal usability metrics revealed ways to optimize the tool related to the user experience, content, and deployment. Although this paper focuses on a CVD tool, the insights that we shared about the reactions to testing and adapting this tool may be applicable to the design of other mobile health apps and CDS tools. Future work should bear in mind the benefits of integration with EHRs as they become more prevalent in the coming years.


The reported clinical case describes the surgical procedure of ridge augmentation by using a “split crest” technique with a partial thickness flap and a subsequent implant-prosthetic rehabilitation aimed at treating a bilateral agenesis of the upper lateral incisors. In such cases with vestibule-palatal and mesial-distal scarce bone thicknesses associated with the need of a proper functional and aesthetic rehabilitation, the split crest technique is particularly suitable. In the case we reported, because of the poor bone thicknesses, we performed a minimally invasive split crest which allowed a correct insertion of the fixtures. This technique allowed us to achieve an optimal functional and aesthetic rehabilitation; moreover, we obtained a good emergency profile, ensuring the vitality of the close teeth and ensuring a good primary stability and the following osseointegration of dental implants. Tooth agenesis is one of the most frequent dental anomalies. Dental agenesis is clinically apparent because of the lack of one or more teeth: for this reason such alteration is one of the best candidates for implant-prosthetic rehabilitation. The literature reports that lateral incisors are affected by agenesis in 2.2% of cases .When the agenesis affects the lateral incisors, besides the functional issues, the greater inconvenience is represented by the aesthetic reasons.The most suitable solution for these clinical conditions is, of course, an implant-prosthetic rehabilitation, but this surgical approach is not always feasible.We well know that the critical condition for a good osseointegration is to have an amount of at least 2 mm of healthy bone around the implant .It is also necessary to ensure a good dental emergence profile, to obtain a correct prosthetic rehabilitation from both aesthetic and functional points of view .These conditions are not always possible and the presence of an exiguous bone thickness forces the surgeon to change the axis of insertion of the implants, exposing the prosthetic restoration to the concrete risk of failure.During the last years, several methods were offered to solve the problem of scant bone thickness in those cases requiring an implant-prosthetic rehabilitation.Indeed, thanks to several GBR (Guided Bone Regeneration) strategies, the idea to place the implants where the bone amount is abundant turned into an inclined bone guided arrangement .Many authors proposed the use of bone grafts taking samples from oral tissues (mandibular branch); others preferred instead the extra-oral tissues  . These mThe first original study by Osborn is dated 1985: it reported the green-stick fracture technique .This technique was furtherly modified by Bruschi and Scipioni who used partial thickness flaps, obtaining great results , 9. SincThe patient here reported was 26 y/o: she showed a bilateral agenesis of the upper lateral incisors .The agenesis of teeth 1.2 and 2.2 caused a mesial shifting of the teeth close to the space left empty by the missing elements.Consequently, an orthodontic treatment was performed to gain the space required to allow the insertion of dental implants. After the orthodontic treatment, a dental “Maryland” bridge was placed as a temporary rehabilitation, in order to ensure the stability of the occlusion, the proper function, and acceptable esthetics .The newly obtained spaces were carefully evaluated to properly plan the implantology in such sites.The bone showed a thin thickness both in vestibular-palatal and mesial-distal directions.After oral and extra-oral clinical inspections and radiography examinations, the bone thickness was about 3 mm in crest in vestibule-lingual direction Figures .The lacking bone depths impeded the right allocation of the implants and thus affected the prognosis.local anesthesia,incision of the partial thickness flap,crestal osteotomy with Beaver 64 blade,distraction of vestibular cortical plate,preparation of the implant site with the proper burs,parallelism verification,implant insertion,suture of the flap with underlying periosteum anchorage.Therefore, it was decided to proceed with a split crest surgical technique, to gain the necessary space for the implants insertion. For both the sites the procedure was performed as follows:After having anesthetized the surgical site, a partial thickness trapezoidal flap was made through the blade Beaver 64. The incision was performed at the bone crest, with mesial and distal releasing cuttings Figures  and 4.The cuts were sharp and divergent to ensure an adequate blood supply and therefore an optimal healing.The Beaver 64 blade was positioned at the top of the crest and was sunk for 4 mm of the cortical bone by a surgical mallet, so as to make a proper bony fracture.In the same way two other fractures involving mesial-distal release were realized, to mobilize the bone fragment and to shape an operculum. The operculum must have smaller dimensions than the flap to ensure an optimal healing and to avoid dehiscence and infections.Using the Bard Parker and then a straight dental elevator, the last bone fragment was displaced Figures  and 6. TAfter the dislocation of the bone fragment, we proceeded to the insertion of a series of burs to prepare the implant site.This operation was performed paying great attention to avoid the risk of injury to the closest dental roots.Soon after the insertion of the first guiding bur, an X-ray examination verified the correct inclination and position of the bur inside the bone Figures , and 9. Three months later, the abutments and the crowns were added Figures , and 15.The choice of the surgical approach detailed above was due to functional and especially esthetic needs concerning the young age of the patient.The orthodontic treatment has led to obtaining the necessary space where the dental implants will be placed; later an orthodontic appliance of Maryland type has been placed, to ensure acceptable stability, function, and aesthetics, as clearly demonstrated in the scientific literature . ReachinWe know indeed that the distance between the implant and the center of the adjacent teeth should be about 7-8 mm.A proper position towards the mesial-distal way enables the formation of an adequate contact-point.The implant should be placed along the oral-lingual direction 1 mm behind the buccal bone in order to obtain a correct distribution of the interdental soft tissues.The use of this procedure has been allowed to expand not only the dimension of the crest along vestibular-palatal direction but also an increased control on the implant insertion, improving the prognosis.Thanks to the split crest approach we also obtained an emergence profile resulting as appropriate to the high esthetic requirements, with a good prosthetic rehabilitation.