Doxorubicin (DOX), an anthracycline antibiotic, is widely used to treat a range of solid tumors and hematological malignancies. However, its clinical application in breast cancer is hindered by toxic side effects and the development of multidrug resistance (MDR). Enhancing the selective targeting of DOX and overcoming MDR are critical to improving treatment efficacy. Here, we present a DNA nanoflower (DNF)-based delivery system, designed via rolling circle amplification (RCA) and multi-primer amplification (MCA), which co-delivers antisense oligonucleotides (ASO) and DOX to human breast cancer cells (MCF-7). This system, named DNF-ASO@DOX, effectively promotes gene silencing, enhances drug accumulation, and significantly inhibits cell proliferation. Furthermore, in vivo studies using mouse models of breast cancer demonstrated potent therapeutic effects, highlighting DNF-ASO@DOX as a promising strategy for enhanced anti-tumor therapy.

The intrapulse modulation radar signal detection is essential for contemporary electronic intelligence reconnaissance and other domains. However, there are difficulties in modulated signal recognition due to the complicated electromagnetic environment and poor signal-to-noise ratio. Even though the recognition accuracy has been increased by the current methodologies, the computing pressure are high, and generalization capacity are limited either. In this paper, the Dual-Path Dilated Convolution Attention (DDCA) is proposed. This method is proposed as a lightweight improvement technique to increase the model performance, and a lightweight recognition method by combining denoising convolutional neural networks and improved MobileViT. The simulation results demonstrate that the proposed method effectively reduces the parameter size of network. Meanwhile, a sustainable high recognition accuracy is obtained, the network inference speed is accelerated, and the hardware requirements are alleviated. Based on simulation, the recognition accuracy for ten types of radar signals attains as high as 91.4% when the signal-to-noise ratio is - 16 dB.

This study examines the effect of gate recess depth on the electrical and RF performance of AlGaN/GaN high electron mobility transistors (HEMTs) before and after die-attach. Devices with greater recess depths exhibited more significant changes in transconductance, pinch-off voltage, and RF metrics, such as cut-off frequency (ft) and maximum frequency (fmax), compared to non-recessed devices. However, recessed devices also showed substantial increases in gate and drain leakage currents, indicating adverse effects of packaging-induced stresses.

This letter presents a K-band 4-channel hybrid-packaged phased-array receiver IC for satellite communication (SATCOM). The design utilizes wafer-level chip-scale packaging (WLCSP) technology to integrate four GaAs LNAs and a 4-channel CMOS beamformer into a single package. The GaAs IC features a 2-stage self-biased LNA and a 5th-order band-stop filter, achieving a low cascaded noise figure (NF) and high transmit (TX) rejection. The CMOS beamformer incorporates a novel direct current-mode combination technique based on a parallel switch array attenuator within the passive vector-modulated phase shifter (VMPS), reducing size and phase error. A two-stage digitally controlled current-steering variable gain amplifier (VGA) is optimized to provide a wide gain control range and high resolution. Operating across the SATCOM band of 17.7–21.2 GHz, the proposed phased-array receiver achieves a 360° phase-shifting range with 6-bit resolution and a 15.5-dB gain tuning range with 0.5-dB steps. Measured results show root mean square (rms) phase and amplitude errors of 1.3°–2° and 0.18–0.25 dB, respectively. Each channel demonstrates a state-of-the-art NF of 1.6 dB, a single-path gain of 30 dB, TX rejection of 60 dB, and a TX-band input-referred 1-dB gain compression point (IP1dB) of -10 dBm, while consuming only 46.8 mW of DC power per channel.

A 6-bit asynchronous loop-unrolled (LU) successive approximation register (SAR) analogue-to-digital converter (ADC) with a complementary voltage-to-time converter (CVTC) and the efficient latch technique needed for this structure are proposed. The proposed structure utilises CVTC to reduce the power consumed by the reset operation and halves the operating frequency of the CVTC. Designed with a 500nm CMOS process, the 6-bit 10MS/s LU SAR ADC shows a power saving of 32.6% compared to the VTC-based LU SAR ADC.

This study demonstrates the growth of InAs quantum dots (QDs) on InP substrates using an all group III-arsenide approach in molecular beam epitaxy (MBE) with a low-indium-composition InAlGaAs partial capping layer. Real-time curvature measurements confirm effective strain compensation during multilayer QD growth, enabling precise control of emission wavelength and structural stability. The fabricated lasers exhibited successful operation at a telecommunication C-band.

This paper presents a practical method for allocating inertia and damping in power systems integrated with renewable energy sources (RES). The frequency response model of the power system is first established, with distinct modeling of the dynamics of synchronous generators (SGs) and three types of RES units. A state-space model, which describes the relationship between frequency response and disturbances, is derived through the disturbance allocation process. A fitting method for frequency and power responses is proposed based on modal analysis. Analytical formulas for frequency stability indices (FSIs) and power indices are derived. Furthermore, applying matrix perturbation theory, an iterative optimization method for inertia and damping allocation of RES units is proposed and validated through simulation cases using the modified IEEE 39-bus model.

The identification of vulnerabilities within distribution networks, impacted by complex factors such as ring configurations and the integration of distributed energy resources, remains a significant challenge. This study presents a novel enhanced hierarchical analysis fault chain comprehensive assessment model to address this issue. The proposed model develops four new weak indicators of assessing the nodes of a distribution network based on complex network theory: an improved indicator of node degree, improved indicator of node PageRank, power flow increment impact indicator, and node voltage stability indicator. It introduces the weighting method with the binomial coefficient for optimal distribution of weight in the fault chain framework to further enhance effectiveness in refined hierarchical analysis, aimed at improving objectivity and precision. Besides, it employs an Integrated Analytic Hierarchy Process in which system indicators and operational parameters under various conditions can be integrated and dynamically calculate the fault rate of a weak link in the network. These further enhance the adaptability and precision of the model in solving inherent complexities within modern distribution networks. Simulation case studies performed using IEEE-69 node complex active distribution network have demonstrated the efficacy and superiority of the proposed method for correct identification of weak nodes.

This paper investigates covert wireless transmission in distributed passive Dual-Function Radar and Communication (DFRC) systems. The optimal detection threshold and minimum detection error probability of adversary warden is derived. The closed-form solution for the optimal transmit power for the integrated base station (BS) is calculated to achieve maximum covert rate. Simulation results show that the covert transmission performance can be improved by setting up proper transmit power.

With the development of power electronics technology, AC/DC hybrid distribution networks have gradually become an important form of distribution networks in the future and have attracted widespread attention. The high proportion access of distributed new energy represented by distributed photovoltaic (PV) brings great challenges to the safe and stable operation of AC/DC hybrid distribution networks, meanwhile the randomness and fluctuation of PVs’ power output put forward higher requirements for the real-time and dynamic response of the AC/DC hybrid distribution network optimal voltage control. Firstly, the Q-V voltage control model for PV in AC network and P-V voltage control model for PV in DC network are established. Then, the dynamic optimal voltage control model of AC/DC hybrid distribution network is constructed. After that, the data-driven and model-driven control strategies are combined, the voltage control controller is trained through the Multi-agent Gradient Descent Strategy (MADDPG) to solve the optimal adjustment action value of the PV droop parameter in the real-time state, so as to realize the online dynamic voltage control of AC/DC hybrid distribution network.

Rainfall forecast is generally defined as the prediction of precipitation or severe convective weather in a specific region over a short time interval, recognized as playing an extremely important role in daily meteorological disaster prevention. Traditional precipitation forecasting methods, which primarily rely on numerical weather prediction, have limited the capability to utilize the latest information for short-term precipitation nowcast. A novel deep learning model based on the RFAt- UNet3+ is proposed for precipitation nowcasting, which is composed of the attention and receptive field modules additionally. An accurate precipitation forecast is accomplished by employing a data-driven neural network approach.

Finger vein recognition, like control systems, requires harmonizing local and global dynamics for optimal performance. To address limitations in existing methods, we propose the Wavelet-Transformer algorithm, combining CNNs for local feature extraction, Vision Transformers (ViT) for global dependency modeling, and discrete wavelet transforms (DWT) for time-frequency analysis. This modular design mirrors control theory principles, ensuring stability and adaptability. Experiments on FV210 and FV618 datasets show the algorithm’s superior performance, achieving recognition accuracies of 99.53% and 97.62% with equal error rates of 0.35% and 0.71%, highlighting its robustness for intelligent recognition and control applications.

This study explores the design and evaluation of an economical hardware platform aimed at enhancing load break switches (LBS) in distribution systems. Simulations conducted using DIgSILENT Power Factory and PSCAD validated the proposed method. The Provincial Electricity Authority of Chiang Mai Province successfully implemented this hardware platform in the Ban Khun Pae Smart Microgrid project. The hardware platform underwent rigorous laboratory testing using an overcurrent algorithm to detect fault currents. It successfully identified all potential issues, including saturation currents on feeders, and autonomously transmitted data to operators for timely corrective action. The findings confirmed that single line-to-ground fault currents were the most frequent fault type. The Intelligent Electronic Devices relay and the InHand Wireless Overhead-Line System fault current detectors at the site worked just as well as the improved LBS, showing that they are strong and reliable ways to find faults. This economical and efficient platform not only enhances operational reliability but also paves the way for improved fault monitoring and management in distribution systems, ensuring faster response times, and minimizing downtime during faults. The seamless integration with existing feeder infrastructure further highlights its practical utility and scalability, providing an innovative solution to the challenges faced in modern distribution networks.

The Cyclic Codebook-GRAND (CC-GRAND) algorithm was proposed to address the issue of significant increases in storage capacity with code length in the GRAND algorithm, which uses a cyclic generation codebook to reduce codebook capacity. The simulation results show that the algorithm achieves the same Bit Error Rate (BER) as the Maximum Likelihood Decoding algorithm, while effectively reducing the codebook capacity.

A conventional target detection technique for FMCW millimeter-wave radar applies a two-dimensional (2D) cell-averaging constant false alarm rate (CA-CFAR) detector to all range-Doppler cells in order to suppress noise and clutter. However, this 2D CA-CFAR method has significant drawbacks, particularly its high computational cost due to the large number of additions required, resulting in a time complexity of O(n^{4}). To decrease the computational complexity while ensuring the detection accuracy, a novelty CA-CFAR technique based on the prefix sum algorithm with the complexity O(n^{2}) is proposed in this article. Simulations prove the feasibility of the proposed method. Compared to both conventional and state-of-the-art optimized CA-CFAR techniques, the proposed method reduces the number of addition operations by 95%, lowers CFAR loss by approximately 0.5 dB, and improves the figure of merit (FoM) by about 20% at a fixed false alarm rate of 10^{-6}. This advanced technique offers significant computational efficiency for radar applications.

In order to resolve the contradiction between the rapid growth of wind turbines installed capacity and the lagging operation and maintenance technology. This article uses Supervisory Control And Data Acquisition (SCADA) system data to realize wind turbines fault diagnosis and early warning research. First, this article proposes a method for cleaning abnormal data in SCADA systems. It combines Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Median Absolute Deviation (MAD) to estimate the normal power range. It provides high-quality health data samples for further research, based on the results obtained. Then, for wind turbines fault diagnosis, this article designs a Lightweight Gradient Boosting Machine (LightGBM) model based on a Genetic Algorithm (GA). This model can effectively diagnose different types of faults in wind turbines. Finally, this article proposes a Multivariate State Estimation Technique (MSET) normal model based on the DBSCAN algorithm for fault warning in wind turbines. This article designs two threshold setting methods: fixed threshold and adaptive dynamic threshold. Fault warning is conducted by calculating the residuals between predicted and actual values.

In this paper, a high step-up converter is presented by integration of switched capacitor and coupled inductors with quasi-z-source structure. a switched capacitor along with a pair of coupled inductors are utilized to enhance voltage gain and switch voltage stress. Also, zero current switching for switch at turn-on and for all diodes at turn- off which reduce reverse recovery problem are achieved by this technique. A passive clamp circuit is utilized to absorbing the leakage inductance energy, and improve voltage gain by placing the clamp capacitor in the charge path of the switched capacitor. Also, the main properties of quasi-z-source converter such as common ground between the output and input sides along with continuous input current is maintained. Additionally, these features are realized by proper component count in comparison with other related topologies based on quasi-z-source topology. The experimental results are extracted by a prototype circuit implemented at 300W and 100kHz to confirm the theoretical results and the proposal converter operation.

In remote sensing image segmentation, recognizing buildings is challenging when the visual evidence from pixels is weak or when buildings belong to small, spatially structured objects. To address this issue, we propose a structure-prior guided adaptive context selection network (SGACS-Net) for remote sensing semantic segmentation. The core is to use structure-prior knowledge to dynamically capture prior contextual information and higher-order object structural features, thereby improving the accuracy of remote sensing building segmentation. First, an adaptive context selection module is designed. By dynamically adjusting the spatial sensing field, this module effectively models the global long-range context information dependencies. It captures varying context information of buildings at different scales, thereby enhancing the network’s ability to extract building feature representations. Second, a structure-prior guided variable loss function is proposed. It utilizes the structural features of building points, lines, and surface to identify key regions. By leveraging advanced structure-prior knowledge, it enhances the network’s ability to express structural features. Experimental results on two datasets show that the proposed SGACS-Net outperforms other typical and state-of-the-art methods in terms of remote sensing semantic segmentation performance.