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The time-dependent reliability assessment of ballastless tracks in service life is crucial for ensuring the safe and stability of high-speed railways. This work systematically compiles and summarizes the performance functions of ballastless track structures under different failure modes during service period, and then the all modes are regarded as a mixed series-parallel system, a regularization method for performance functions of multiple failure modes is proposed. Subsequently, the direct probability integral method (DPIM) is firstly introduced for evaluating the time-dependent reliability of the CRTS III slab ballastless track system. The accuracy and computational efficiency of the proposed method are verified through comparison with the traditional Monte Carlo method. Furthermore, the failure modes of the system are categorized into safety and applicability. Numerical examples demonstrate the generality and robustness of the developed method in system reliability analysis. The result indicates that environmental factors are the primary cause of diminished system reliability, both in terms of safety and applicability. Extreme temperature gradients are identified as a primary cause of safety performance degradation in track structures, while negative temperature gradients primarily contribute to serviceability deterioration. The mean of train load below 300 kN is essential for maintaining safety performance. The safety and applicability of the track structure after 30 years of service should be a key concern for the railway department. Especially, the reliability calculation method developed in this work for CRTS III systems can be extended to various static and dynamic system state evaluations.

In spatial domain signal processing, it is necessary to equip more antennas at the receiver to improve spatial demultiplexing capability. However, increasing the number of antennas under restricted space will reduce antenna spacing and raise the channel correlation, making the number of signal streams spatially demultiplexed much smaller than that of antennas. This paper proposes a method to design a space-time-isomeric (SPATIO) array based on metasurface antennas under wireless multipath conditions. Each antenna in this array has a different pattern and varies independently with time, reducing the channel correlation by superposing multipath at distinct positions and moments. Based on the SPATIO array, we present an array parameter design scheme based on infinity norm minimization, which can maximize the received energy of each stream while separating multi-stream received signals. Simulation results illustrate the performance of the SPATIO array for multi-stream signal reception. Compared with conventional multiple-input multiple-output arrays, the proposed array can reduce the bit error rate by one order of magnitude under the same simulation conditions.

In this paper, a novel wideband power amplifier (PA) operating in the 2–6 GHz frequency range is presented. The proposed PA design utilizes a combination technique consisting of a distributed equalization technique, multiplexing the power supply network and matching network technique, an LR dissipative structure, and an RC stability network technique to achieve significant bandwidth while maintaining superior gain flatness, high efficiency, high gain, and compact size. For verification, a three-stage PA using the combination technique is designed and implemented in a 0.25 μm GaN high-electron-mobility transistor (HEMT) process. The fabricated prototype demonstrates a saturated output power of 4 W, a power gain of 21 dB, a gain flatness of ±0.6 dB, a power-added efficiency of 39–46%, and a fractional bandwidth of 100% under the operating conditions of drain voltage 28 V (continuous wave) and gate voltage −2.6 V. Moreover, the chip occupies a compact size of only 2.51 mm × 1.97 mm.

The last decade of phylogenetics has seen the development of many methods that leverage constraints plus dynamic programming. The goal of this algorithmic technique is to produce a phylogeny that is optimal with respect to some objective function and that lies within a constrained version of tree space. The popular species tree estimation method ASTRAL, for example, returns a tree that (1) maximizes the quartet score computed with respect to the input gene trees and that (2) draws its branches (bipartitions) from the input constraint set. This technique has yet to be used for parsimony problems where the input are binary characters, sometimes with missing values. Here, we introduce the clade-constrained character parsimony problem and present an algorithm that solves this problem for the Dollo criterion score in O ( | Σ | 3.726 ( n + k ) + | Σ | 1.726 n k ) time, where n is the number of leaves, k is the number of characters, and Σ is the set of clades used as constraints. Dollo parsimony, which requires traits/mutations to be gained at most once but allows them to be lost any number of times, is widely used for tumor phylogenetics as well as species phylogenetics, for example analyses of low-homoplasy retroelement insertions across the vertebrate tree of life. This motivated us to implement our algorithm in a software package, called Dollo-CDP, and evaluate its utility for analyzing retroelement insertion presence / absence patterns for bats, birds, toothed whales as well as simulated data. Our results show that Dollo-CDP can improve upon heuristic search from a single starting tree, often recovering a better scoring tree. Moreover, Dollo-CDP scales to data sets with much larger numbers of taxa than branch-and-bound while still having an optimality guarantee, albeit a more restricted one. Lastly, we show that our algorithm for Dollo parsimony can easily be adapted to Camin-Sokal parsimony but not Fitch parsimony.

Objection detection is of vital importance to many fields, such as autonomous driving, outdoor robotics, and computer vision. Existing approaches on object detection can hardly run on the resource-constrained edge devices. In order to mitigate this dilemma, we propose to apply TensorFlow Lite to convert Float32 neural network model to unit8 neural network with subtle or even no accuracy loss. Two advantages are here for conversion. First, it reduces the model size to a quarter so that it fits for devices with limited storage. Second, it achieves much faster inference time. I conduct an experiment on MSCOCO dataset. Experimental results show that our proposed method achieves mAP 72.1 and FPS 23 on edge device.

Programmability greatly enhances the degree of freedom to manipulate electromagnetic (EM) waves dynamically and lays crucial foundation for intelligent applications of metasurfaces. However, the traditional programmable metasurfaces need complicated biasing networks to control m×n digital meta‐atoms independently to fulfill the reprogrammable functions in real time, which also results in large power consumption to drive the metasurface. To alleviate this problem, we propose an XOR‐logic phase coding programmable metasurface to reduce the complexity of biasing network from m×n to m+n, which can reduce the power consumption significantly. The XOR‐logic phase coding is achieved by path symmetry of surface currents on a Pancharatnam‐Berry meta‐atom loaded with two PIN diodes. By controlling 2×m×n PIN diodes on the whole metasurface in row‐column manner, only m+n biasing lines are required to switch 0 and 1 states of all meta‐atoms independently. As the proof of concept, a prototype of the XOR‐logic phase coding programmable metasurface is designed and fabricated. Both simulation and measured results verify the reprogrammable functions of beam scanning and multi‐beam scattering. This work provides a new type programmable metasurface with simple architecture and low power consumption, which will find wide applications in intelligent systems such as next‐generation wireless communication, Internet of Things, and radar. By leveraging XOR logic, the biasing‐network complexity of 2D programmable metasurfaces is reduced from m×n to m+n through efficient row‐column control. This enables versatile functions including beam deflection and beamforming, facilitating applications in wireless communications and beyond.

Infrastructure safety is crucial for the rail industry, with signal functionality and track integrity being among essential components. This thesis presents a machine learning framework to predict failures in rail infrastructure assets, focusing on two critical areas: urban rail transit signal failures and broken rails in commuter rail systems. Integrating historical failure data, maintenance data, and track condition data, and operational data, the proposed framework applies machine learning models to identify high-risk locations and predict rail asset failures. Because rail infrastructure asset failures are relatively rare, imbalanced data mining techniques such as SMOTE, ADASYN, and random resampling are also employed to improve predictive accuracy.In the first case study, our model achieves an AUC of 75% and demonstrates the ability to identify approximately one-third of rail signal failures by focusing on 10% of signal locations on the network within the one-month prediction period. Our second case study focused on commuter rail segments, in which our model gives an AUC of 74% and 71% overall accuracy. The results show the potential of this framework to identify high-risk hot spots for prioritized inspection and maintenance, given limited resources.

We present the design, fabrication, and experimental characterization of a modified two-dimensional Luneburg lens based on bulk metamaterials. The lens is composed by a number of concentric layers. By varying the geometric dimensions of unit cells in each layer, the gradient refractive index profile required for the modified Luneburg lens can be achieved. The cylindrical waves generated from a point source at the focus point of the lens could be transformed into plane waves as desired in the microwave frequency. The proposed modified Luneburg lens can realize wide-angle beam scanning when the source moves along the circumferential direction inside the lens. Numerical and experimental results validate the performance of the modified Luneberg lens.

Complex beams hold significant value in radar and communication systems due to their distinctive propagation characteristics. Digital metasurfaces, which can dynamically control electromagnetic (EM) waves, play an important role in realizing complex beams. Conventional analytic and optimization methods face challenges in synthesizing complex beams of low-bit digital metasurfaces due to the quantization error and the high computational complexity. Here, we propose a statistical method to realize complex beams with phase-only digital metasurfaces. To this end, we introduce tailored quantization probabilities to design the discrete random phase distributions, which approximate the continuous excitation coefficients derived from analytic methods. Based on the proposed method, we analyze the error between the realized and target patterns. These findings offer critical insights into the accuracy of random quantization. Complex patterns with cosecant, prescribed null, flat-top, and dual-beam are designed and validated in combination with a 2-bit phase coding digital metasurface. The experimental results are in good agreement with the theoretical analysis. This work pioneers the application of random phase approximation and statistical synthesis in digital metasurfaces, providing a fast and efficient route for realizing complex beams in modern radar and wireless communication technologies.

CRISPR-based lineage tracing, coupled with single-cell RNA sequencing, has emerged as a promising approach for studying cell transformations during development as well as disease progression. However, the high ratio of cells to CRISPR-induced mutations, combined with missing data from silencing or dropout, make cell lineage tree (CLT) reconstruction difficult. As a result, this computational problem has attracted significant attention in recent years, including the introduction of Star Homoplasy Parsimony (SHP) in 2023 to model the specific properties of CRISPR-induced mutations, along with the Startle family of methods based on integer linear programming (ILP) or heuristic search (NNI). Here, we present Star-CDP, the first dynamic programming algorithm for SHP. Star-CDP solves SHP within a constrained search space Σ defined by subsets of cells from which a solution CLT must draw its clades. When Σ is the power set, Star-CDP is an exact exponential algorithm with time complexity O(nm |Σ| 2), where n is the number of cells, m is the number of target sites, and |Σ |= O(2n). We show that it is possible to build clade constraints that are polynomially-sized and effective in practice. Motivated by the technological challenges in producing consistent phylogenetic signal across the tree during lineage tracing, we also present algorithms to efficiently count, sample, and build consensus trees from all solutions to the clade-constrained SHP problem. In simulations, Star-CDP’s strict consensus effectively reduced false positive branches while preserving many more true positives compared to the standard strict consensus implemented by PAUP*, a popular parsimony method from species phylogenetics. Likewise, Star-CDP’s strict consensus achieved the same or higher accuracy (f1-score) on all but one of the 15 model conditions tested, often outperforming leading the methods, Startle-ILP and Startle-NNI, while also scaling to larger data sets than Startle-ILP. Lastly, we analyzed lineage tracing data from the KP-Tracer mouse model of lung adenocarcinoma, finding that Star-CDP produced plausible CLTs, often lowering the number of migration and reseeding events needed to explain metastases compared to Startle. Our analysis also showed, for the first time, that strategies for preprocessing cells with missing data—specifically cell pruning and deduplicating techniques—can have a substantial impact on CLTs reconstructed with the same method, even changing relative performance across methods compared to previously published results. The same was true of postprocessing trees with LAML, a maximum likelihood method designed for mixed-type missing data. By exploring these different pipelines, we recovered the most plausible CLT for the largest KP-Tracer metastatic tumor, reducing the number of reseeding events from 42 to 10 without increasing the number of migrations. Star-CDP is available on Github: https://github.com/molloy-lab/Star-CDP.