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In urban rail traction power supply system, DC catenary grounding is a common fault. As IGBT devices of PWM rectifier have no ability to withstand short-circuit current, controllable traction power supply rectifier equipment usually adopts PWM rectifier and diode rectifier in parallel mode to improve short-circuit withstand ability, but PWM rectifier and diode rectifier in direct parallel connection will introduce circulating current, resulting in damage to the diode rectifier. In order to solve the circulating current problem, firstly, the advantages and disadvantages of the common scheme of circulating current between PWM rectifier and diode rectifier are analyzed. Then, a new topology is proposed, which requires no additional hardware investment, and the circulating current is suppressed within the safe range by means of a specific angle carrier phase shift modulation algorithm between multiple PWM rectifiers. Finally, a simulation model of the traction power supply system is established based on the proposed rectifier equipment, and the system simulation and experimental verification are carried out. Experimental results show that the diode rectifier branch current is only 6.7 A when the equipment is running. The simulation results are consistent with the experimental results, and the rectifier equipment and its control strategy proposed in this paper can effectively suppress the circulating current, which verifies the effectiveness and feasibility of the scheme.

Electromagnetic vortex carries the orbital angular momentum, one of the most fundamental properties of waves. The order of such vortex can be unbounded in principle, thus facilitating high-capability wave technologies for optical communications, photonic integrated circuits and others. However, it remains a key challenge to generate the high-order vortex beams in a reconfigurable, broadband and cost-effective manner. Here, inspired by the balanced-ternary concept, we demonstrate the reconfigurable generation of order-controllable vortices via cascaded N-layer metasurfaces. We theoretically showed that 3N−1${3}^{N}-1$ different vortex modes can be generated by cascading N metasurfaces, each one serving as an individual vortex beam generator for the order of 3k${3}^{k}$ (k = 0,1,2 …, N−1$N-1$). As a proof-of-concept demonstration, a reconfigurable generation of 26 different vortex beams, with orders from 1 to 13 and from −1 to −13, is showcased in a broad millimeter-wave region by a cascade of 3 metasurfaces. Our method can be easily extended to vortex beam generator of arbitrary orders in a reconfigurable and easily implementable manner, paving a new avenue towards tremendous practical applications.

Based on the zig-zag theory and Hamiltonian principle, the electromechanical coupling dynamic finite element(FE) model of piezoelectric damping laminated structure is established, the natural frequency and loss factor of the structure is accurately calculated. The eight nodes quadratic element is adopt for FE modeling, each node with seven DOFs, which considers the complex elastic modulus of damping layer. The correctness of model is verified by the calculation and simulation of an example in the literature. The effects of reinforcement orientation angle, thickness of damping layer and structure curvature on the frequency and loss factor of piezoelectric damping laminated structure are investigated. This study can provide references for vibration control and optimization design of piezoelectric damping laminated structures.

Objective Methods Results Conclusions Hip fracture (HF) has been described as the “last fracture of life” in the elderly, so the assessment of HF risk is extremely important. Currently, few studies have examined the relationship between imaging data from chest computed tomography (CT) and HF. This study demonstrated that pectoral muscle index (PMI) and vertebral body attenuation values could predict HF, aiming to opportunistically assess the risk of HF in patients without bone mineral density (BMD) based on chest CT for other diseases.In the retrospective study, 800 participants who had both BMD and chest CT were enrolled from January 2021 to January 2024. After exclusion, 472 patients were finally enrolled, divided into the healthy control (HC) group and the HF group. Clinical data were collected, and differences between the two groups were compared. A predictive model was constructed based on the PMI and CT value of the fourth thoracic vertebra (T4HU) by logistic regression analysis, and the predictive effect of the model was analyzed by using the receiver operating characteristic (ROC) curve. Finally, the clinical utility of the model was analyzed using decision curve analysis (DCA) and clinical impact curves.Both PMI and T4HU were lower in the HF group than in the HC group (p < 0.05); low PMI and low T4HU were risk factors for HF. The predictive model incorporating PMI and T4HU on the basis of age and BMI had excellent diagnostic efficacy with an area under the curve (AUC) of 0.865 (95% confidence interval [CI]: 0.830–0.894, p < 0.01), sensitivity and specificity of 0.820 and 0.754, respectively. The clinical utility of the model was validated using calibration curves and DCA. The AUC of the predictive model incorporating BMD based on age and BMI was 0.865 (95% CI: 0.831–0.895, p < 0.01), with sensitivity and specificity of 0.698 and 0.711, respectively. There was no significant difference in diagnostic efficacy between the two models (p = 0.967).PMI and T4HU are predictors of HF in patients. In the absence of dual‐energy x‐ray absorptiometry (DXA), the risk of HF can be assessed by measuring the PMI and T4HU on chest CT examination due to other diseases, and further treatment can be provided in time to reduce the incidence of HF. [ABSTRACT FROM AUTHOR]

The Dung Beetle Optimization (DBO) algorithm, a well-established swarm intelligence technique, has shown considerable promise in solving complex engineering design challenges. However, it is hampered by limitations such as suboptimal population initialization, sluggish search speeds, and restricted global exploration capabilities. To overcome these shortcomings, we propose an enhanced version termed Adaptive Spiral Strategy Dung Beetle Optimization (ADBO). Key enhancements include the application of the Gaussian Chaos strategy for a more effective population initialization, the integration of the Whale Spiral Search Strategy inspired by the Whale Optimization Algorithm, and the introduction of an adaptive weight factor to improve search efficiency and enhance global exploration capabilities. These improvements collectively elevate the performance of the DBO algorithm, significantly enhancing its ability to address intricate real-world problems. We evaluate the ADBO algorithm against a suite of benchmark algorithms using the CEC2017 test functions, demonstrating its superiority. Furthermore, we validate its effectiveness through applications in diverse engineering domains such as robot manipulator design, triangular linkage problems, and unmanned aerial vehicle (UAV) path planning, highlighting its impact on improving UAV safety and energy efficiency. [ABSTRACT FROM AUTHOR]

Object detection in aerial images plays a crucial role across diverse domains such as agriculture, environmental monitoring, and security. Aerial images present several challenges, including dense small objects, intricate backgrounds, and occlusions, necessitating robust detection algorithms. This paper addresses the critical need for accurate and efficient object detection in aerial images using a Transformer-based approach enhanced with specialized methodologies, termed DFS-DETR. The core framework leverages RT-DETR-R18, integrating the Cross Stage Partial Reparam Dilation-wise Residual Module (CSP-RDRM) to optimize feature extraction. Additionally, the introduction of the Detail-Sensitive Pyramid Network (DSPN) enhances sensitivity to local features, complemented by the Dynamic Scale Sequence Feature-Fusion Module (DSSFFM) for comprehensive multi-scale information integration. Moreover, Multi-Attention Add (MAA) is utilized to refine feature processing, which enhances the model's capacity for understanding and representation by integrating various attention mechanisms. To improve bounding box regression, the model employs MPDIoU with normalized Wasserstein distance, which accelerates convergence. Evaluation across the VisDrone2019, AI-TOD, and NWPU VHR-10 datasets demonstrates significant improvements in the mean average precision (mAP) values: 24.1%, 24.0%, and 65.0%, respectively, surpassing RT-DETR-R18 by 2.3%, 4.8%, and 7.0%, respectively. Furthermore, the proposed method achieves real-time inference speeds. This approach can be deployed on drones to perform real-time ground detection. [ABSTRACT FROM AUTHOR]

Considering the dilemma of obtaining economic and high-performance composites based on non-polar and main-chain-saturated ethylene propylene diene monomer rubber (EPDM), we proposed an effective and universal filler modification and nanocomposite preparation method. Specifically, the montmorillonite (MMT) surface was coated with polydopamine (PDA) to obtain DMMT, which was confirmed by XRD, XPS, FTIR, and TGA. After compounding DMMT gel with the solid EPDM via the gel compounding method, a silane coupling agent, vinyltrimethoxysilane, was introduced to construct covalent interactions between rubber and filler. Compared with the unmodified MMT filler EPDM, the EPDM/DMMT nanocomposite showed much fewer filler aggregates in the matrix. The highest tensile strength of the composites reached 6.5 MPa with 10 phr DMMT, almost 200% higher than that of pure EPDM. [ABSTRACT FROM AUTHOR]

In order to provide a transforming way of knowing for teachers' professional learning, this paper uses narrative to present the knowledge emerging from teachers as participants in a concept study derived from complexity science. There are three components in the narrative: what knowledge emerged from concept study; what has been changed for the emerged knowledge; and where we are going with complexity. The narrative provides not only a lived example but a framework for the action towards the transforming way of knowing for teachers' professional learning.

In recent months, the B.1.617.2 (Delta) variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread globally and has become the predominant circulating variant within the United States (1). [...]

In this paper, we propose the Programmable LInk Metric Identification (ProgLIMI) infrastructure for software-defined networking (SDN) networks. ProgLIMI identifies round-trip link metrics (RTLMs) from accumulated end-to-end metrics of selected measurement paths by leveraging the flexible routing control capability of SDN networks. ProgLIMI mainly solves three sub-problems: 1) monitor placement; 2) linearly independent measurement path construction; and 3) flow rule design. To reduce measurement cost, ProgLIMI tries to minimize the number of required monitors and flow rules. In this paper, we address the three sub-problems for both full and hybrid SDN networks. For full SDN networks, ProgLIMI can achieve full RTLM identification using only one monitor and two flow rules in each SDN switch. In contrast, the RTLM identification in hybrid SDN networks is more complicated due to the routing constraint of hybrid SDN networks. We first prove that the monitor placement problem in hybrid SDN networks is NP-hard. We then formulate the monitor placement and measurement path selection problem in hybrid SDN networks and propose a greedy heuristic algorithm to solve the problem efficiently. Our evaluations on both physical testbed and simulation platform reveal that ProgLIMI can accurately identify the RTLMs (delay and loss rate). Besides, ProgLIMI is also resource efficient, i.e., it only requires two flow rules in each SDN switch and a small number of monitors, and the extra probing traffic load incurred by ProgLIMI is also low.

A key requirement for network management is the accurate and reliable monitoring of relevant network characteristics. In today's large-scale networks, this is a challenging task due to the scarcity of network measurement resources and the hard constraints that this imposes. This paper proposes a new framework, called SNIPER, which leverages the flexibility provided by Software-Defined Networking (SDN) to design the optimal observation or measurement matrix that can lead to the best achievable estimation accuracy using Matrix Completion (MC) techniques. To cope with the complexity of designing large-scale optimal observation matrices, we use the Evolutionary Optimization Algorithms (EOA) which directly target the ultimate estimation accuracy as the optimization objective function. We evaluate the performance of SNIPER using both synthetic and real network measurement traces from different network topologies and by considering two main applications for per-flow size and delay estimations. Our results show that SNIPER can be applied to a variety of network performance measurements under hard resource constraints. For example, by measuring only 8.8% of all per-flow path delays in Harvard network [1], congested paths can be detected with probability of 0.94. To demonstrate the feasibility of our framework, we also have implemented a prototype of SNIPER in Mininet.