Enhancer activity plays a critical role in gene regulation, influencing various biological processes such as development and disease progression. Accurate prediction of enhancer activity is essential for understanding the mechanisms underlying gene regulation and enhancer function. This study introduces a novel deep learning framework, EAP-LSTM (Enhancer Activity Prediction based on Bi-LSTM), to quantitatively predict enhancer activity across different species and cell lines. The model integrates multiple feature modules, including Word2Vec-based representations of DNA sequences, reverse complement k-mer, mismatch k-mer features, and epigenomic data. Evaluated on six cell lines, including five human cell lines (A549, HCT116, HepG2, K562, and MCF-7) and one Drosophila cell line (S2), EAP-LSTM consistently outperforms state-of-the-art models, such as DeepSTARR and HEAP, in all datasets. For example, on the K562 dataset, EAP-LSTM achieves a Pearson correlation coefficient (PCC) of 0.7944, outperforming DeepSTARR and HEAP by 13.65% and 2.73%, respectively. In addition, EAP-LSTM demonstrates strong performance in small-sample learning scenarios, showing clear improvements compared with baseline models. Furthermore, the study investigates the role of transcription factor binding sites (TFBSs) within enhancer regions, identifying critical motifs associated with enhancer activity. These findings not only improve enhancer prediction accuracy but also provide valuable insights into the molecular mechanisms underlying enhancer function.

In this study, we propose a new approach that integrates the Weightless Swarm Algorithm (WSA) with Iterative Learning Control (ILC) to enhance the trajectory-tracking performance of robotic arms. This technique combines the learning capabilities of ILC and particle swarm to optimize the control parameters, effectively reducing tracking errors dynamically. The combination of WSA’s global search capabilities and ILC’s error minimization enables automatic tuning of the control parameters for improved tracking performance and robustness. The algorithm of this paper outperforms the classical ILC and PSO-ILC algorithms, as demonstrated on a model of the 7-degree-of-freedom robotic arm of Barrett Technology. A new performance metric called the "Computing Burden Index" (CBI) is introduced to better assess the different ILC algorithms.

This paper presents an innovative Iterative Learning Control (ILC) strategy for Linear Time-Varying (LTV) systems subject to uncertainties. In a real-world environment, implementing ILC causes the uncertainties to vary concerning both time and iteration. To address this challenge, we introduce a metric to quantify the impact of the uncertainties on the tracking error’s variation. First, an equivalent 2D Roesser model is established for the uncertain ILC system. It has uncertain parameters and is subject to an external disturbance caused by the time-varying model uncertainties of the original system. Then, a Linear Matrix Inequality (LMI) condition is proposed to design the ILC law to provide an upper metric bound. The strategy aims to lower this bound, thereby reducing the impact of uncertainties on the system. Finally, preliminary numerical simulation verifies the effectiveness and robustness of the proposed strategy.

The success of deep learning methods heavily relies on the availability of adequate samples. However, in the task of infrared small target detection (ISTD), the lack of high-quality training samples is a challenging problem due to the confidentiality of the application field and the difficulty of labeling. This limitation often leads to suboptimal detection performance of convolutional neural networks (CNNs). To address this challenge, we propose an adaptive labeling strategy and an ISTD network called the multi-criteria decision network (MCDNet) to achieve higher-quality sample labeling and more accurate detection results. In the adaptive labeling strategy, we propose a second-order differential autocorrelation method to determine the size of fuzzy edge targets accurately. In addition, we introduce local backgrounds to enhance the saliency information in the labels and improve the richness and contrast of training label content. To obtain accurate and robust detection results with limited target feature information, we design MCDNet. In particular, we propose a multi-criteria decision method that can combine the CNN decisions and the infrared small target prior saliency decisions through weighted fusion, and set decision weights based on the importance of different decision criteria in the decision-making process. This method can integrate the advantages of both the CNN decisions and the prior saliency decisions, avoid the one-sidedness of a single criterion, and improve the reliability and stability of the decision-making. The experimental results indicate that our method has a higher accuracy compared to other contrastive methods.

Nowadays, the emerging Internet-of-Things (IoT) emphasize the need for the security of network-connected devices. Additionally, there are two types of services in IoT devices that are easily exploited by attackers, weak authentication services (e.g., SSH/Telnet) and exploited services using command injection. Based on this observation, we propose IoTCMal, a hybrid IoT honeypot framework for capturing more comprehensive malicious samples aiming at IoT devices. The key novelty of IoTC-MAL is three-fold: (i) it provides a high-interactive component with common vulnerable service in real IoT device by utilizing traffic forwarding technique; (ii) it also contains a low-interactive component with Telnet/SSH service by running in virtual environment. (iii) Distinct from traditional low-interactive IoT honeypots[1], which only analyze family categories of malicious samples, IoTCMal primarily focuses on homology analysis of malicious samples. We deployed IoTCMal on 36 VPS1instances distributed in 13 cities of 6 countries. By analyzing the malware binaries captured from IoTCMal, we discover 8 malware families controlled by at least 11 groups of attackers, which mainly launched DDoS attacks and digital currency mining. Among them, about 60% of the captured malicious samples ran in ARM or MIPs architectures, which are widely used in IoT devices.