Intelligent Vehicle Cyber-Physical System (IVCPS) is an emerging and future transportation system, that is a complex system that integrates cyber space with physical systems. This study conducts a comprehensive review of existing literature to delve into the issues of deconstruction and optimization in IVCPS. Considering the complexity of IVCPS, we draw upon the theory of deconstructionism and propose the concept of deconstructing IVCPS as a whole, analyzing its various functional features. In this research, Fuzzy Soft Sets are utilized to mainly handle the fuzzy and uncertain information in IVCPS, enabling a more accurate description and modeling of IVCPS, and can also provide a solution to the problem of small sample. The Multi-attribute Group Decision-making Method is applied in the decision-making process of system deconstruction, aiming to select the optimal deconstruction strategy and determines the final solution. In conclusion, this study provides a novel perspective and methodology for the deconstruction and optimization of IVCPS, offering valuable insights and references for research and applications in related fields.

The unit dual quaternion (UDQ)-based product-of-exponential (POE) formula has achieved efficient kinematic calibration for serial manipulators. However, due to the presence of unknown passive joint displacements, it is difficult to directly establish explicit forward kinematic models for parallel manipulators (PMs). This forms a barrier to subsequent error modeling and compensation. This work establishes a novel UDQ-based forward kinematic model for a PM by utilizing constraints on the identical pose of the moving platform across all its chains. Furthermore, the adjoint transformation of UDQ's twist is derived for PM's error modeling. Notably, an index matrix is introduced to achieve a unified representation of active or passive joint displacement. Thereby, this UDQ-based kinematic error modeling method is applicable to general PMs. In addition, an error compensation method is proposed for a PM using the UDQ-based local POE formula, which incorporates the developed forward kinematic model to adjust active joint displacements. The proposed method offers significant runtime savings compared to the traditional homogeneous-transformation-matrix-based POE formula due to the compact data structure and reduced arithmetic operations.

In future transportation systems, there is a growing emphasis on integrating macroscopic traffic control and the state of microscopic traffic flow to improve traffic efficiency and safety. And cyber-physical system (CPS) can support integrated, efficient and reliable operations of diverse critical intelligent transportation infrastructure and services. Considering the limitations imposed by the communication range and computing capabilities of single agent, a novel method for combining macro traffic optimization with microscopic traffic flow models from a CPS perspective is presented. This approach takes into account not only exceptional events but also the propagation of congestion waves. Specifically, we analyze macroscopic traffic patterns and transmit optimization results to the microscopic level, as inputs to the microscopic traffic flow model, thereby influencing individual vehicle. Furthermore, the intelligent driver model, incorporating a molecular dynamics-based representation of how multiple leading vehicles impact the ego vehicle is investigated. By leveraging CPS-based macro traffic analysis, we improve the car-following model. To validate the effectiveness of the proposed optimization strategy, we conducted simulation experiments from both microscopic and macroscopic perspectives under mixed traffic environment. The results demonstrate the efficacy of the proposed approach in optimizing vehicle fuel consumption and improving traffic efficiency.

As an important application of Internet of Things (IoT), Internet of Vehicles (IoVs) can provide various valuable services which may require computation-intensive tasks under strict time constraints. Most traditional vehicles may not be able to process all these computation-intensive tasks locally because of the limitation of computing resources. Therefore, task offloading has been proposed, which allows vehicles to offload computation-intensive tasks to Mobile Edge Computing (MEC) servers. With the arising and development of intelligent vehicles, the concept of Vehicle as a Resource (VaaR) has been proposed as an important supplement to MEC, which enables intelligent vehicles to share computation resources with nearby vehicles. Most studies in VaaR generally assume that the transmission data rate of offloading tasks from one vehicle to another is fixed. However, in VaaR, due to the high mobility of vehicles, the communication distance between vehicles may change over time, resulting in changing data rate. Therefore, it is challenging to make offloading decisions (i.e., selecting proper vehicles as computation resource providers) while considering adaptive data rate. In this paper, we study task offloading in vehicular networks while considering adaptive data rate. We propose an Adaptive Data Rate-based Offloading algorithm named ADRO, which can not only achieve minimum energy consumption while satisfying time constraints, but also take adaptive data rate into consideration. Comprehensive experiments have been conducted to demonstrate the efficiency of the ADRO algorithm.

MOTIVATION: Mass cytometry or CyTOF is an emerging technology for high-dimensional multiparameter single cell analysis that overcomes many limitations of fluorescence-based flow cytometry. New methods for analyzing CyTOF data attempt to improve automation, scalability, performance and interpretation of data generated in large studies. Assigning individual cells into discrete groups of cell types (gating) involves time-consuming sequential manual steps, untenable for larger studies. RESULTS: We introduce DeepCyTOF, a standardization approach for gating, based on deep learning techniques. DeepCyTOF requires labeled cells from only a single sample. It is based on domain adaptation principles and is a generalization of previous work that allows us to calibrate between a target distribution and a source distribution in an unsupervised manner. We show that DeepCyTOF is highly concordant (98%) with cell classification obtained by individual manual gating of each sample when applied to a collection of 16 biological replicates of primary immune blood cells, even when measured across several instruments. Further, DeepCyTOF achieves very high accuracy on the semi-automated gating challenge of the FlowCAP-I competition as well as two CyTOF datasets generated from primary immune blood cells: (i) 14 subjects with a history of infection with West Nile virus (WNV), (ii) 34 healthy subjects of different ages. We conclude that deep learning in general, and DeepCyTOF specifically, offers a powerful computational approach for semi-automated gating of CyTOF and flow cytometry data. AVAILABILITY AND IMPLEMENTATION: Our codes and data are publicly available at https://github.com/KlugerLab/deepcytof.git. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Sources of variability in experimentally derived data include measurement error in addition to the physical phenomena of interest. This measurement error is a combination of systematic components, originating from the measuring instrument and random measurement errors. Several novel biological technologies, such as mass cytometry and single-cell RNA-seq (scRNA-seq), are plagued with systematic errors that may severely affect statistical analysis if the data are not properly calibrated. RESULTS: We propose a novel deep learning approach for removing systematic batch effects. Our method is based on a residual neural network, trained to minimize the Maximum Mean Discrepancy between the multivariate distributions of two replicates, measured in different batches. We apply our method to mass cytometry and scRNA-seq datasets, and demonstrate that it effectively attenuates batch effects. AVAILABILITY AND IMPLEMENTATION: our codes and data are publicly available at https://github.com/ushaham/BatchEffectRemoval.git. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Recent years have witnessed intense development of randomized methods for low-rank approximation. These methods target principal component analysis and the calculation of truncated singular value decompositions. The present article presents an essentially black-box, foolproof implementation for Mathworks' MATLAB, a popular software platform for numerical computation. As illustrated via several tests, the randomized algorithms for low-rank approximation outperform or at least match the classical deterministic techniques (such as Lanczos iterations run to convergence) in basically all respects: accuracy, computational efficiency (both speed and memory usage), ease-of-use, parallelizability, and reliability. However, the classical procedures remain the methods of choice for estimating spectral norms and are far superior for calculating the least singular values and corresponding singular vectors (or singular subspaces).

The particularity of service enterprise determines the importance of its innovation on its job environment, which can be carried on through two dimensions: the structure variable and the trend variable of the closer environment. According to the principle idea of stratagem evolution, we can portray three patterns of their innovation and effects: the network integrating innovation which reduces the providing merchants and clients' affective range on service enterprises as well as the uncertainty; the directional cooperative innovation network with the closer environment can found the new value characteristic; the innovation constructed with the environment value network can help find and choose the new providing merchants.