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High-performance miniature surgical instruments play an important role in complicated minimally invasive surgery (MIS). Based on in-depth analysis of the requirements of MIS and the characteristics of the existing minimally invasive surgical instruments, a multiple degrees of freedom (DOF) robotic surgical instrument with decoupled pose was proposed. Firstly, the design concept of the pose decoupling instrument was described in detail, and its physical structure, transmission structure, and mechanical properties were designed and analyzed. A surgical instrument control algorithm based on the master–slave mode was established. Finally, a physical prototype was developed, and its motion ranges of joints, load capacity, and suture operation performance were comprehensively evaluated, which confirmed the effectiveness of the proposed minimally invasive robotic surgical instrument. [ABSTRACT FROM AUTHOR]

Purpose: There is a paucity of empirically estimated health state utility (HSU) values to estimate health-related quality of life among individuals with sickle cell disease (SCD). This study aims to map the Pediatric Quality of Life Inventory generic core scales (PedsQL GCS) to HSUs for children and adolescents with SCD in the United States, using published algorithms, and to assess the construct validity of these HSUs against SCD-specific PedsQL scores. Methods: We used the published mapping algorithms identified in four published articles, in which the PedsQL GCS was mapped to either the EuroQol-5 Dimension 3-Level, Youth Version or the Child Health Utility 9-Dimension to obtain HSUs. We employed the algorithms to calculate HSUs for a sample of children and adolescents from the Sickle Cell Clinical Research and Intervention Program. To assess the construct validity of the mapped HSUs in SCD patients, we computed Spearman's correlation coefficient comparing the HSUs with the PedsQL SCD total score and separately with each PedsQL SCD dimension-specific score. Results: The mean mapped HSU across published algorithms was 0.792 (95% CI: 0.782–0.801). It was significantly higher among children aged 5–12 years than children aged 13–17 years. The Spearman's correlation coefficient for HSUs versus PedsQL SCD total scores was 0.64 (95% CI: 0.57–0.71). Correlations ranged from 0.40 (95% CI: 0.32–0.48) to 0.60 (95% CI: 0.54–0.66) for HSUs versus PedsQL SCD dimension-specific scores. Conclusions: The existing mapping algorithms show acceptable construct validity in children and adolescents with SCD. Additional algorithms are needed for adults and for specific SCD comorbidities. [ABSTRACT FROM AUTHOR]

Immersed boundary method is a crucial method to deal with particle suspension flow. Particle shapes involved in such flow are usually simple geometry, such as sphere and ellipsoid, which can be conveniently represented by the triangular surface grid. When the number of particles and resolution of the surface grid increase, calculating the hydrodynamic force on the particle surface through integration can be time-consuming. Hence, the present paper establishes a fast mapping method to evaluate immersed boundary hydrodynamic force. Firstly, the particle surface grid is generated by an initial triangular element grid. Subsequently, the initial surface grid is refined by bisection refinement to the desired resolution. The final step is to find the triangular element index on the particle triangular surface grid, which contains the projective point. Test cases show that the present mapping algorithm has good accuracy and efficiency for calculating hydrodynamic forces of particles. [ABSTRACT FROM AUTHOR]

With the advancement in technology, it is now possible to integrate hundreds of cores onto single silicon semiconductor chip or silicon die. In order to provide communication between these cores, large number of resources are required and it leads to the communication problem in System-on- Chip (SoC), which is solved by introduction of Networks-on-Chip (NoC). NoC proves to be most efficient in terms of flexibility, scalability and parallelism. In this paper, the proposed mapping algorithms, Horological Mapping (HorMAP), Rotational Mapping (RtMAP) and Divide and Conquer Mapping (DACMAP) for mapping of tasks onto cores, basically concentrate on the optimization of latency, queuing time, service time and energy consumption of topology at constant bandwidth required. The experimental results discussed in this paper shows the comparison of proposed algorithms with traditional random mapping algorithm. In this paper, 2D mesh topology with XY routing is considered for the simulation of proposed algorithms. [ABSTRACT FROM AUTHOR]

SUMMARY Bulk segregation analysis (BSA) utilizes a strategy of pooling individuals with extreme phenotypes to conduct economical and rapidly linked marker screening or quantitative trait locus (QTL) mapping. With the development of next‐generation sequencing (NGS) technology in the past 10 years, BSA methods and technical systems have been gradually developed and improved. At the same time, the ever‐decreasing costs of sequencing accelerate NGS‐based BSA application in different species, including eukaryotic yeast, grain crops, economic crops, horticultural crops, trees, aquatic animals, and insects. This paper provides a landscape of BSA methods and reviews the BSA development process in the past decade, including the sequencing method for BSA, different populations, different mapping algorithms, associated region threshold determination, and factors affecting BSA mapping. Finally, we summarize related strategies in QTL fine mapping combining BSA. Significance Statement: This paper provides a landscape of BSA methods in the next‐generation sequencing (NGS) era and a guideline to select a proper method for bulk segregation analysis mapping design. In addition, quantitative trait locus fine mapping strategies are summarized for researchers in the field. [ABSTRACT FROM AUTHOR]

Abstract Background Cost-effectiveness models require quality of life utilities calculated from generic preference-based questionnaires, such as EQ-5D. We evaluated the performance of available algorithms for QLQ-C30 conversion into EQ-5D-3L based utilities in a metastatic colorectal cancer (mCRC) patient population and subsequently developed a mCRC specific algorithm. Influence of mapping on cost-effectiveness was evaluated. Methods Three available algorithms were compared with observed utilities from the CAIRO3 study. Six models were developed using 5-fold cross-validation: predicting EQ-5D-3L tariffs from QLQ-C30 functional scale scores, continuous QLQ-C30 scores or dummy levels with a random effects model (RE), a most likely probability method on EQ-5D-3L functional scale scores, a beta regression model on QLQ-C30 functional scale scores and a separate equations subgroup approach on QLQ-C30 functional scale scores. Performance was assessed, and algorithms were tested on incomplete QLQ-C30 questionnaires. Influence of utility mapping on incremental cost/QALY gained (ICER) was evaluated in an existing Dutch mCRC cost-effectiveness model. Results The available algorithms yielded mean utilities of 1: 0.87 ± sd:0.14,2: 0.81 ± 0.15 (both Dutch tariff) and 3: 0.81 ± sd:0.19. Algorithm 1 and 3 were significantly different from the mean observed utility (0.83 ± 0.17 with Dutch tariff, 0.80 ± 0.20 with U.K. tariff). All new models yielded predicted utilities drawing close to observed utilities; differences were not statistically significant. The existing algorithms resulted in an ICER difference of €10,140 less and €1765 more compared to the observed EQ-5D-3L based ICER (€168,048). The preferred newly developed algorithm was €5094 higher than the observed EQ-5D-3L based ICER. Disparity was explained by minimal diffences in incremental QALYs between models. Conclusion Available mapping algorithms sufficiently accurately predict utilities. With the commonly used statistical methods, we did not succeed in developping an improved mapping algorithm. Importantly, cost-effectiveness outcomes in this study were comparable to the original model outcomes between different mapping algorithms. Therefore, mapping can be an adequate solution for cost-effectiveness studies using either a previously designed and validated algorithm or an algorithm developed in this study.

Spiking Neuromorphic systems have been introduced as promising platforms for energy-efficient spiking neural network (SNNs) execution. SNNs incorporate neuronal and synaptic states in addition to the variant time scale into their computational model. Since each neuron in these networks is connected to many others, high bandwidth is required. Moreover, since the spike times are used to encode information in SNN, a precise communication latency is also needed, although SNN is tolerant to the spike delay variation in some limits when it is seen as a whole. The two-dimensional packet-switched network-on-chip was proposed as a solution to provide a scalable interconnect fabric in large-scale spike-based neural networks. The 3D-ICs have also attracted a lot of attention as a potential solution to resolve the interconnect bottleneck. Combining these two emerging technologies provides a new horizon for IC design to satisfy the high requirements of low power and small footprint in emerging AI applications. Moreover, although fault-tolerance is a natural feature of biological systems, integrating many computation and memory units into neuromorphic chips confronts the reliability issue, where a defective part can affect the overall system's performance. This paper presents the design and simulation of R-NASH-a reliable three-dimensional digital neuromorphic system geared explicitly toward the 3D-ICs biological brain's three-dimensional structure, where information in the network is represented by sparse patterns of spike timing and learning is based on the local spike-timing-dependent-plasticity rule. Our platform enables high integration density and small spike delay of spiking networks and features a scalable design. R-NASH is a design based on the Through-Silicon-Via technology, facilitating spiking neural network implementation on clustered neurons based on Network-on-Chip. We provide a memory interface with the host CPU, allowing for online training and inference of spiking neural networks. Moreover, R-NASH supports fault recovery with graceful performance degradation.

Purpose: To develop a mapping algorithm for generating EQ-5D-3L utility scores from the PedsQL Generic Core Scales (PedsQL GCS) in patients with transfusion-dependent thalassemia (TDT). Methods: The algorithm was developed using data from 345 TDT patients. Spearman's rank correlation was used to evaluate the conceptual overlap between the instruments. Model specifications were chosen using a stepwise regression. Both direct and response mapping methods were attempted. Six mapping estimation methods ordinary least squares (OLS), a log-transformed response using OLS, generalized linear model (GLM), two-part model (TPM), Tobit and multinomial logistic regression (MLOGIT) were tested to determine the root mean squared error (RMSE) and mean absolute error (MAE). Other criterion used were accuracy of the predicted utility score, proportions of absolute differences that was less than 0.03 and intraclass correlation coefficient. An in-sample, leave-one-out cross validation was conducted to test the generalizability of each model. Results: The best performing model was specified with three out of the four PedsQL GCS scales—the physical, emotional and social functioning score. The best performing estimation method for direct mapping was a GLM with a RMSE of 0.1273 and MAE of 0.1016, while the best estimation method for response mapping was the MLOGIT with a RMSE of 0.1597 and MAE of 0.0826. Conclusion: The mapping algorithm developed using the GLM would facilitate the calculation of utility scores to inform economic evaluations for TDT patients when EQ-5D data is not available. However, caution should be exercised when using this algorithm in patients who have poor quality of life. [ABSTRACT FROM AUTHOR]

Spiking Neuromorphic systems have been introduced as promising platforms for energy-efficient spiking neural network (SNNs) execution. SNNs incorporate neuronal and synaptic states in addition to the variant time scale into their computational model. Since each neuron in these networks is connected to many others, high bandwidth is required. Moreover, since the spike times are used to encode information in SNN, a precise communication latency is also needed, although SNN is tolerant to the spike delay variation in some limits when it is seen as a whole. The two-dimensional packet-switched network-on-chip was proposed as a solution to provide a scalable interconnect fabric in large-scale spike-based neural networks. The 3D-ICs have also attracted a lot of attention as a potential solution to resolve the interconnect bottleneck. Combining these two emerging technologies provides a new horizon for IC design to satisfy the high requirements of low power and small footprint in emerging AI applications. Moreover, although fault-tolerance is a natural feature of biological systems, integrating many computation and memory units into neuromorphic chips confronts the reliability issue, where a defective part can affect the overall system's performance. This paper presents the design and simulation of R-NASH-a reliable three-dimensional digital neuromorphic system geared explicitly toward the 3D-ICs biological brain's three-dimensional structure, where information in the network is represented by sparse patterns of spike timing and learning is based on the local spike-timing-dependent-plasticity rule. Our platform enables high integration density and small spike delay of spiking networks and features a scalable design. R-NASH is a design based on the Through-Silicon-Via technology, facilitating spiking neural network implementation on clustered neurons based on Network-on-Chip. We provide a memory interface with the host CPU, allowing for online training and inference of spiking neural networks. Moreover, R-NASH supports fault recovery with graceful performance degradation. [ABSTRACT FROM AUTHOR]

Vector extrapolations for fixed-point iterations are shown to converge faster when their step lengths are computed from two or three consecutive maps alternately. Based on this finding, cyclic extrapolation methods are proposed which require few objective function evaluations, no matrix inversion, and little extra memory. They are efficient in high-dimensional contexts and do not require problem-specific adaptation. A convergence analysis is done for symmetric positive definite linear systems and for contraction mappings. The proposed methods rivaled common quasi-Newton alternatives in eight mapping applications that included gradient descent for constrained and unconstrained optimization. • Vector extrapolation methods converge faster when alternating between 2 or 3 maps. • ACX require little computation and converge fast. • ACX accelerate gradient descent. • ACX are used for nonlinear optimization. • ACX accelerate the power method for dominant eigenvalues. [ABSTRACT FROM AUTHOR]

Objective: The objective of this study was to develop algorithms to map the EORTC QLQ-C30 (QLQ-C30) onto EQ-5D-5L in a sample of patients with lymphomas. Methods: An online nationwide survey of patients with lymphoma was carried out in China. Ordinary least squares (OLS), beta-based mixture, adjusted limited dependent variable mixture regression, and a Tobit regression model were used to develop the mapping algorithms. The QLQ-C30 subscales/items, their squared and interaction terms, and respondents' demographic variables were used as independent variables. The root mean square error (RMSE), mean absolute error (MAE), and R-squared (R2) were estimated based on tenfold cross-validation to assess the predictive ability of the selected models. Results: Data of 2222/4068 respondents who self-completed the online survey were elicited for analyses. The mean EQ-5D-5L index score was 0.81 (SD 0.21, range − 0.81–1.0). 19.98% of respondents reported an index score at 1.0. In total, 72 models were generated based on four regression methods. According to the RMSE, MAE and R2, the OLS model including QLQ-C30 subscales, squared terms, interaction terms, and demographic variables showed the best fit for overall and the Non-Hodgkin's lymphoma sample; for Hodgkin's lymphoma, the ALDVMM with 1-component model, including QLQ-C30 subscales, squared terms, interaction terms, and demographic variables, showed a better fit than the other models. Conclusion: The mapping algorithms enable the EQ-5D-5L index scores to be predicted by QLQ-C30 subscale/item scores with good precision in patients living with lymphomas. [ABSTRACT FROM AUTHOR]

In order to effectively map complex application tasks to the network on chip processing unit and achieve the task with less energy consumption, this paper proposed a novel network on chip mapping algorithm that combined genetic algorithm and tabu search algorithm. The method made full use of the powerful global search ability of the genetic algorithm, and combined the local search ability of the tabu search and the prominent mountain features to compensate for the weak local search ability and premature defects of the genetic algorithm, then achieved a better network on chip low power effect. Under the same experimental platform and power consumption model, compared with the early genetic algorithm, the tabu search genetic algorithm has significant energy efficiency improvement, and it has energy efficiency advantages compared with the later improved MGA and AGA algorithms. [ABSTRACT FROM AUTHOR]

Background: Cost-effectiveness models require quality of life utilities calculated from generic preference-based questionnaires, such as EQ-5D. We evaluated the performance of available algorithms for QLQ-C30 conversion into EQ-5D-3L based utilities in a metastatic colorectal cancer (mCRC) patient population and subsequently developed a mCRC specific algorithm. Influence of mapping on cost-effectiveness was evaluated.Methods: Three available algorithms were compared with observed utilities from the CAIRO3 study. Six models were developed using 5-fold cross-validation: predicting EQ-5D-3L tariffs from QLQ-C30 functional scale scores, continuous QLQ-C30 scores or dummy levels with a random effects model (RE), a most likely probability method on EQ-5D-3L functional scale scores, a beta regression model on QLQ-C30 functional scale scores and a separate equations subgroup approach on QLQ-C30 functional scale scores. Performance was assessed, and algorithms were tested on incomplete QLQ-C30 questionnaires. Influence of utility mapping on incremental cost/QALY gained (ICER) was evaluated in an existing Dutch mCRC cost-effectiveness model.Results: The available algorithms yielded mean utilities of 1: 0.87 ± sd:0.14,2: 0.81 ± 0.15 (both Dutch tariff) and 3: 0.81 ± sd:0.19. Algorithm 1 and 3 were significantly different from the mean observed utility (0.83 ± 0.17 with Dutch tariff, 0.80 ± 0.20 with U.K. tariff). All new models yielded predicted utilities drawing close to observed utilities; differences were not statistically significant. The existing algorithms resulted in an ICER difference of €10,140 less and €1765 more compared to the observed EQ-5D-3L based ICER (€168,048). The preferred newly developed algorithm was €5094 higher than the observed EQ-5D-3L based ICER. Disparity was explained by minimal diffences in incremental QALYs between models.Conclusion: Available mapping algorithms sufficiently accurately predict utilities. With the commonly used statistical methods, we did not succeed in developping an improved mapping algorithm. Importantly, cost-effectiveness outcomes in this study were comparable to the original model outcomes between different mapping algorithms. Therefore, mapping can be an adequate solution for cost-effectiveness studies using either a previously designed and validated algorithm or an algorithm developed in this study. [ABSTRACT FROM AUTHOR]

As a success-oriented system reliability and safety-analysis technique, the GO methodology has been applied in a variety of real-world safety-critical industrial fields. Common-cause failure (CCF) is the simultaneous failure of multicomponents within a system due to the same root cause. An enhancement approach for the original GO methodology is proposed in this paper to support CCF modeling and calculation both in graphical modeling aspect and algorithm aspect. First, a new concise and formalized GO operator (named CCO) is introduced to represent complicated CCF event, which makes the CCF modeling process intuitive and concise for analysts. In algorithm aspect, the mapping rule is given and demonstrated to transform new CCO operator with impacted multiple operators to the corresponding Bayesian network (BN) fragment. Second, general mapping programmable process is presented on transforming any CCF enhanced GO model to the corresponding BN. Furthermore, using BN's inference capability, the enhanced GO model with CCF can be calculated efficiently. Nevertheless, the diagnosis process can be performed to investigate the weak points of the modeled system. Finally, a case study is performed to demonstrate the modeling process by means of CCF enhanced GO model. The calculation result shows that CCF has significant influence on the system reliability. Using diagnosis analysis, the CCF event can be confirmed as the major cause leading to system failure. [ABSTRACT FROM AUTHOR]

Power optimization is an important part of network-on-chip(NoC) design. This paper proposes an improved algorithm based on genetic algorithm on how to properly map IP (Intellectual Property) cores to 3D NoC. First, in view of the randomness of the traditional genetic algorithm in individual selection, an improved greedy algorithm is used in the initial population generation stage to make the generated individuals reach the optimal. Secondly, in view of the weak local optimization ability of the traditional genetic algorithm and prone to premature problems, the simulated annealing algorithm is added in the crossover operation stage to make the offspring reach the global optimum. The experimental results show that compared with the traditional genetic algorithm, the algorithm has better convergence and low power consumption performance, which can quickly search for a better solution, in the case of a large number of cores (124 IP cores), the average power consumption can be reduced by 42.2%.

With the aggressive scaling of submicron technology, intermittent faults are becoming one of the limiting factors in achieving high reliability in Network-on-Chip (NoC). Increasing test frequency is necessary to detect intermittent faults, which in turn interrupts the execution of applications. On the other hand, the primary goal of traditional mapping algorithms is to allocate applications to the NoC platform, ignoring the test requirement. In this paper, we propose a novel testing-aware mapping algorithm (TAMA) for NoC, targeting intermittent faults on the paths between crossbars. In this approach, the idle paths are identified, and the components between two crossbars are tested when the application is mapped to the platform. The components can be tested if there is enough time from the time when the application leaves the platform to the time when a new application enters it. The mapping algorithm is tuned to give a higher priority to the tested paths in the next application mapping, which leaves enough time to test the links and the belonging components that have not been tested in the expected time. Experiment results show that the proposed testing-aware mapping algorithm leads to a significant improvement over FF(Fiexitrst Free), NN(Nearest Neighbor), CoNA(Contiguous Neighborhood Allocation), and WeNA(Weighted-based Neighborhood Allocation). • The link utilization is quite low under real applications and the unused links could be tested at runtime. • The components tightly coupled to the link are named "path" and should be tested together. • A testing aware mapping algorithm can reduce the performance loss and increase testing frequency. • The testing frequency has been significantly improved without increasing transmission latency. [ABSTRACT FROM AUTHOR]

Fast aging of components has become one of the major concerns in Systems-on-Chip with further scaling of the submicron technology, which is accelerated when the working conditions are improper, such as unbalanced components' utilization. Moreover, in the process of the applications of mapping algorithm, the imbalance of component utilization is more likely to occur. The reason is that the mapping algorithm in the Network-on-Chip (NoC) domain may frequently select some routers/links for mapping while others are underutilized. Consequently, the highly utilized components may age faster than others that results in disconnecting the related cores from the network. To solve this problem, we have proposed a lifetime-aware mapping algorithm named LBC + LBL, which has simultaneously taken the aging of core and link into account when applying the mapping. In this paper, the lifetime is modeled as a resource consumed over time and a lifetime budget metric is accordingly defined. A suitable node, which has the maximum lifetime budget, is selected for mapping. Experimental results show that the proposed lifetime-aware mapping algorithm (LBC + LBL) could improve the minimal mean time to failure (MTTF) of NoC about 15.1%, compared with the mapping algorithm only considering the aging of cores. • Fast aging of components has become the major concerns in many-core system with scaling of the submicron technology. • This problem accelerates when combined with improper working conditions such as unbalanced components' utilization. • To address this issue, we have proposed a configurable mapping algorithm named LBC + LBL. • The results show that the proposed lifetime-aware mapping algorithm could improve the minimal MTTF of NoC and Cores. [ABSTRACT FROM AUTHOR]

In order to solve the problems of resource storage, resource delivery delay and other issues in the online learning process, it is studied and analyzed from the perspective of process goals. Firstly, a measurable e-learning process capability maturity model (EPCMM) is established. Then, based on the analysis of workflows in EPCMM's five process areas, a mapping relationship between major activities and key objectives is established in the process area. Finally, the mapping algorithm of online learning process features to process goals and the mapping algorithm of online learning metrics to process features are given. The practical results show that compared with the classical algorithm, this algorithm can effectively reduce the average transmission delay of online learning resources and improve the load balance of the system. [ABSTRACT FROM AUTHOR]

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Network-on-chip (NoC) mapping algorithms significantly affect NoC system performance in terms of communication cost and energy consumption. For a specific application represented by a task graph, this paper proposes an energy-efficient mapping algorithm that searches for the mapping decision with best communication locality and therefore lowest energy consumption. To this end, we formulate the concerned mapping problem as an optimization model, and propose an effective meta-heuristic algorithm to solve the formulated optimization model. During the mapping procedure, we employ a simulation-free, communication probability-based energy model to evaluate the quality of each candidate mapping. By iteratively updating the best explored mapping decision using a meta-heuristic search strategy, the mapping procedure can eventually identify an mapping decision with optimal energy efficiency in the search space. The proposed mapping algorithm has been verified on NoC systems of different sizes using a variety of benchmark applications. Simulation results demonstrate that the mapping decision produced by this algorithm achieves an up to 23% energy reduction compared with the traditional round-robin strategy. [ABSTRACT FROM AUTHOR]

Interconnecting networks adopting Fast Optical Switches (FOS) can achieve high bandwidth, low latency, and low power consumption. We propose and demonstrate a novel interconnecting topology based on FOS (FOSquare) with distributed fast flow control which is suitable for HPC infrastructures. We also present an Optimized Mapping (OPM) algorithm that maps the most communication-related processes inside a rack. We numerically investigate and compare the network performance of FOSquare with Leaf-Spine under real traffic traces collected by running multiple applications (CG, MG, MILC, and MINI_MD) in an HPC infrastructure. The numerical results show that the FOSquare can reduce >10% latency with respect to Leaf-Spine under the scenario of 16 available cores.

The cost-effectiveness of sequencing pools of individuals (Pool-Seq) provides the basis for the popularity and widespread use of this method for many research questions, ranging from unraveling the genetic basis of complex traits, to the clonal evolution of cancer cells. Because the accuracy of Pool-Seq could be affected by many potential sources of error, several studies have determined, for example, the influence of sequencing technology, the library preparation protocol, and mapping parameters. Nevertheless, the impact of the mapping tools has not yet been evaluated. Using simulated and real Pool-Seq data, we demonstrate a substantial impact of the mapping tools, leading to characteristic false positives in genome-wide scans. The problem of false positives was particularly pronounced when data with different read lengths and insert sizes were compared. Out of 14 evaluated algorithms novoalign, bwa mem and clc4 are most suitable for mapping Pool-Seq data. Nevertheless, no single algorithm is sufficient for avoiding all false positives. We show that the intersection of the results of two mapping algorithms provides a simple, yet effective, strategy to eliminate false positives. We propose that the implementation of a consistent Pool-Seq bioinformatics pipeline, building on the recommendations of this study, can substantially increase the reliability of Pool-Seq results, in particular when libraries generated with different protocols are being compared.

This research work proposes a novel approach to estimate probabilities of availability states of the energy transfer network in marine energy conversion subsystems, using Bayesian Networks (BNs). The logical interrelationships between units at different level in this network can be understood through qualitative system analysis, which then can be modeled by the fault tree (FT). The FT can be mapped to a corresponding BN, and the condition probabilities of nodes can be determined based on the logic structure. A case study was performed to demonstrate how the mapping is implemented, and the probabilities of availability states were estimated. The results give the probability of each availability state as a function of time, which serves as a basis for choosing the optimal design solution.

Ongoing developments and cost decreases in next-generation sequencing (NGS) technologies have led to an increase in their application, which has greatly enhanced the fields of genetics and genomics. Mapping sequence reads onto a reference genome is a fundamental step in the analysis of NGS data. Efficient alignment of the reads onto the reference genome with high accuracy is very important because it determines the global quality of downstream analyses. In this study, we evaluate the performance of three Burrows-Wheeler transform-based mappers, BWA, Bowtie2, and HISAT2, in the context of paired-end Illumina whole-genome sequencing of livestock, using simulated sequence data sets with varying sequence read lengths, insert sizes, and levels of genomic coverage, as well as five real data sets. The mappers were evaluated based on two criteria, computational resource/time requirements and robustness of mapping. Our results show that BWA and Bowtie2 tend to be more robust than HISAT2, while HISAT2 was significantly faster and used less memory than both BWA and Bowtie2. We conclude that there is not a single mapper that is ideal in all scenarios but rather the choice of alignment tool should be driven by the application and sequencing technology.

Concerning the current situation that net-work fonvarding devices were difficult to deal with the emerging ne"w network protocols, this paper designed a reconfigurable parser for heterogeneous protocols. First of all, by configuring a user-customizable module, this parser could achieve real-time update parse tree in parsing logic, thus implementing the addition and deletion of emerging protocol rapidly. In addition , while the design met traditional Internet parsing , introducing the configurability of data link layer would unify the heterogeneous protocol parsing. Finally , the matching domains were compressed by mapping user-customizable protocol into the hardware structure through the corresponding parse tree mapping algorithm , thus reducing the hardware resources. Experimental result shows that, compared with the existing schemes , the parse speed can achieve 176 Gbps , saving 20% of the slice resources and 17% of the BRAM resources [ABSTRACT FROM AUTHOR]

Federated cloud enables data sharing across different organizations. This paper proposes a distributed search index (DS-index) solution to provide distributed search capability in federated cloud, with the target to support multi-attribute range queries with secondary indexes. DS-index deploys a three-layer architecture with a multi-attribute index overlay, a tree-based P2P network layer, and a federated cloud layer. We present a distributed search algorithm with the three-layer architecture. In order to facilitate distributed search, we propose a dynamic mapping algorithm for DS-index to map between different layers. In addition, a Markov Chain-based cost model is defined to facilitate node selection and mapping. With the dynamic mapping algorithm and cost model, the proposed DS-index solution is more cost-effective than the traditional P2P networks as it can reduce the network cost in terms of index maintenance cost and node selection cost. The experiments demonstrate that, our DS-index solution along with its cost model can save the computation resource and reduce network bandwidth consumption by around 30% comparing to the one without cost model. It can also reduce the number of node splits/merges by around 20% comparing to the state-of-the-art solution. [ABSTRACT FROM AUTHOR]

NOC technology is a solution to cover the communication challenges of complex systems. The important note in the matter of application mapping for those of NoCs who are based on mesh architecture is their NP-hard problem. Also some methods have been proposed trying to overcome the mentioned problem. A low complexity mapping algorithm cannot present the optimal mapping for all applications. Then, adding an optimization phase to mapping algorithms can have an impact on their performance. This study presents an optimization phase based on Cellular Learning Automata to achieve this goal. To evaluate the proposed algorithm, we compare mapping algorithm of Nmap, CastNet, and Onyx before and after optimization. Mathematical analysis and simulation of mapping algorithms for five real applications shows that using the proposed algorithm optimizes efficiency in mapping algorithms.