Search Results (170)

Donkeys (Equus asinus) hold significant agricultural value in China, particularly for their hides and meat, which possess notable medicinal and dietary importance. However, their reproductive efficiency remains suboptimal compared with other livestock. Ovarian function is a key determinant of fertility, yet the molecular mechanisms underlying donkey ovarian biology remain largely unexplored. To address this gap, we performed single-cell RNA sequencing of donkey ovaries, generating a high-resolution transcriptomic atlas comprising 17,423 cells. Cross-species comparative analysis revealed a high degree of evolutionary conservation in core ovarian cell types, including endothelial, epithelial, immune, and smooth muscle cells, among vertebrates. In contrast, granulosa and theca cells exhibited distinct transcriptional profiles across species, reflecting lineage-specific adaptations. Notably, we identified key genes with donkey-specific expression patterns, including NR3C1 in endothelial cells, LIPE in granulosa cells, and DHRS9 in theca interna cells. Furthermore, an in vitro cumulus–oocyte complex model demonstrated the critical role of GATM in mammalian oocyte maturation. Collectively, these findings provide a comprehensive characterization of ovarian cell-type conservation and species-specific adaptations, offering key molecular insights into the mechanisms underlying cross-species differences in reproductive efficiency. Full article

In reversible data hiding, prediction error generation plays a crucial role, with pixel value ordering (PVO) standing out as a prediction method that achieves high fidelity. However, conventional PVO approaches select predicted pixels and their predictions independently, failing to fully exploit the inherent redundancy in ordered pixel sequences. This paper proposes a novel PVO-based prediction method that leverages the continuity and spatial correlation of ordering pixels. We first introduce a new prediction technique that exploits the redundancy of consecutive pixels. Our approach selects the most appropriate prediction method from preset prediction errors, considering both pixel position and value characteristics. Furthermore, we implement an adaptive strategy that dynamically selects multiple iteration parameters based on pixel content to obtain more expandable prediction errors and adjusts the modification of prediction errors accordingly. Unlike traditional fixed-parameter methods, our approach better utilizes the inherent structure and redundancy of image pixels, thereby improving data embedding efficiency while minimizing image distortion. We enhance performance by combining pairwise prediction-error expansion with content-based prediction error analysis. Experimental results demonstrate that the proposed scheme outperforms state-of-the-art solutions in terms of image fidelity while maintaining competitive embedding capacity, confirming the effectiveness of our method for efficient data embedding and image recovery. Full article

This paper defines blind spots in continuous optimization problems as global optima that are inherently difficult to locate due to deceptive, misleading, or barren regions in the fitness landscape. Such regions can mislead the search process, trap metaheuristic algorithms (MAs) in local optima, or hide global optima in isolated regions, making effective exploration particularly challenging. To address the issue of premature convergence caused by blind spots, we propose LTMA+ (Long-Term Memory Assistance Plus), a novel meta-approach that enhances the search capabilities of MAs. LTMA+ extends the original Long-Term Memory Assistance (LTMA) by introducing strategies for handling duplicate evaluations, shifting the search away from over-exploited regions and dynamically toward unexplored areas and thereby improving global search efficiency and robustness. We introduce the Blind Spot benchmark, a specialized test suite designed to expose weaknesses in exploration by embedding global optima within deceptive fitness landscapes. To validate LTMA+, we benchmark it against a diverse set of MAs selected from the EARS framework, chosen for their different exploration mechanisms and relevance to continuous optimization problems. The tested MAs include ABC, LSHADE, jDElscop, and the more recent GAOA and MRFO. The experimental results show that LTMA+ improves the success rates for all the tested MAs on the Blind Spot benchmark statistically significantly, enhances solution accuracy, and accelerates convergence to the global optima compared to standard MAs with and without LTMA. Furthermore, evaluations on standard benchmarks without blind spots, such as CEC’15 and the soil model problem, confirm that LTMA+ maintains strong optimization performance without introducing significant computational overhead. Full article

Traditional reversible data hiding (RDH) methods prioritize the exact recovery of the original cover image; however, this rigidity often hinders both capacity and design flexibility. This study introduces a partial reversible data hiding (PRDH) framework that departs from conventional standards by allowing reversibility relative to a generated cover image rather than the original. The proposed system leverages a dual-image structure and an enhanced HC(7,4) Hamming code to synthesize virtual pixels, enabling efficient and low-distortion syndrome-based encoding. Notably, it achieves embedding rates up to 1.5 bpp with PSNR values exceeding 48 dB. While the proposed method avoids auxiliary data, its reliability hinges on paired image availability, which is a consideration for real-world deployment. Demonstrated resilience to RS-based steganalysis suggests viability in sensitive domains such as embedding structured metadata in diagnostic medical imagery. Nonetheless, further evaluation across more diverse image types and attack scenarios is necessary in order to confirm its generalizability. Full article

With the development of society and the Internet, images have become an important medium for information exchange. To improve the security of image encryption and transmission, a new image encryption algorithm based on bit-plane decomposition, DNA encoding and the 5D Hamiltonian conservative chaotic system is proposed. This encryption scheme is different from the traditional scrambling and diffusion methods at the level of image spatial pixels but encodes images into DNA strands and completely scrambles and diffuses operations on the DNA strands to ensure the security of images and improve the efficiency of image encryption. Firstly, the initial value sequence and convolution kernel of the five-dimensional Hamiltonian conservative chaotic system are obtained using SHA-256. Secondly, the bit-plane decomposition is used to decompose the image into high-bit and low-bit-planes, combine with DNA encoding to generate DNA strands, hide the large amount of valid information contained in the high-bit-planes, and preliminarily complete the hiding of the image information. In order to further ensure the effect of image encryption, seven DNA operation index tables controlling the diffusion process of the DNA strands are constructed based on the DNA operation rules. Finally, the scrambled and diffused DNA strand is decomposed into multiple bit-planes to reconstruct an encrypted image. The experimental results and security analysis show that this algorithm has a large enough key space, strong key sensitivity, high image encryption quality, strong robustness and high encryption efficiency. In addition, it can resist statistical attacks, differential attacks, and common attacks such as cropping attack, noise attack and classical attack. Full article

This paper proposes a reversible data hiding technique based on two cover images. The proposed method enhances performance by utilizing Hamming coding (HC), arithmetic coding (AC), and an improved Exploiting Modification Direction (EMD) technique. Since AC provides lossless compression for binary data, it is widely used in image compression and helps maximize the efficiency of data transmission and storage. The EMD technique is recognized as an efficient data hiding method. However, it has a significant limitation: it does not allow for the restoration of the original cover image after data extraction. Additionally, EMD has a data hiding capacity limit of approximately 1.2 bpp. To address these limitations, an improved reversible data hiding technique is proposed. In this study, HC and AC are integrated with an improved EMD technique to enhance data hiding performance, achieving higher embedding capacity while ensuring the complete restoration of the original cover image. In the proposed method, Hamming coding is applied for data encoding and arithmetic coding is used for compression to increase efficiency. The compressed data are then embedded using the improved EMD technique, enabling the receiver to fully restore the original cover image. Experimental results demonstrate that the proposed method achieves an average PSNR of 66 dB and a data embedding capacity of 1.5 bpp, proving to be a promising approach for secure and efficient data hiding applications. Full article

In smart grids, power monitoring equipment produces large volumes of data that are exchanged between microgrids and the main grid. This data exchange can potentially expose users’ private information, including their living habits and economic status. Therefore, implementing secure and effective data access control mechanisms is crucial. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is a widely used encryption scheme in distributed systems, offering fine-grained access control. However, in CP-ABE systems, malicious users might leak decryption keys to third parties, creating a significant security threat. Thus, there is an urgent need for tracing mechanisms to identify and track these malicious users. Moreover, tracing and user revocation are complementary processes. Although using a binary tree for user revocation is efficient, it limits the number of users. This paper suggests an access control scheme that combines CP-ABE with blockchain to overcome these limitations, leveraging blockchain’s tamper-resistant features. This scheme enables user revocation, tracing, partial policy hiding, and ciphertext searchability, and it has been proven secure. Simulation results show that our approach reduces time overhead by 24% to 68%, compared to other solutions. While some solutions are similar in efficiency to ours, our approach offers more comprehensive functionality and better meets the security requirements of smart grids. Full article

This paper proposes a reversible data hiding (RDH) scheme for images with an improved convolutional neural network (CNN) predictor (ICNNP) that consists of three modules for feature extraction, pixel prediction, and complexity prediction, respectively. Due to predicting the complexity of each pixel with the ICNNP during the embedding process, the proposed scheme can achieve superior performance compared to a CNNP-based scheme. Specifically, an input image is first split into two sub-images, i.e., a “Circle” sub-image and a “Square” sub-image. Meanwhile, each sub-image is applied to predict another one with the ICNNP. Then, the prediction errors of pixels are sorted based on the predicted pixel complexities. In light of this, some sorted prediction errors with less complexity are selected to be efficiently applied for low-distortion data embedding with a traditional histogram-shifting technique. Experimental results show that the proposed ICNNP can achieve better rate-distortion performance than the CNNP, demonstrating its effectiveness. Full article

The substantial waste generated during the processing of hides and skins as well as at other stages of manufacturing is a recurring issue in the leather industry that this article attempts to address. To investigate the mechanical and thermal characteristics of the resultant composites, this study suggests using leather waste from the bovine leather industry, analyzes the tanning process, and assesses the viability of mixing this waste with natural rubber (TSR-20). Without the inclusion of leather waste, the resulting composites had exceptional tensile strength, surpassing 100% of rubber’s strength. The effective interaction of the recycled leather particles with the natural rubber matrix was evidenced using the Lorentz–Park equation. This better performance points to a competitive relationship between rubber and leather waste. The samples’ density was 10% greater than that of polybutadiene elastomers and 10% greater than that of natural leather, while the hardness was comparable to that of PVC, which is frequently utilized in the design of general-purpose soles. This suggests that waste from the leather industry can be efficiently utilized in sustainable applications, particularly in the production of leather goods and shoes, helping to valorize waste that is typically discarded. Furthermore, by encouraging the use of recycled resources in the creation of new compounds, this plan provides the rubber sector with a sustainable option. To optimize this proposal, perhaps will be necessary to identify different vulcanization systems to improve the physical mechanical properties and other uses derived from the optimizations realized. This composite can be applied in the fashion industry in order to develop new trends around the application of waste and residues for a natural design line. Through the research process, it was possible to integrate the residues into the natural rubber matrix, as evidenced in the characterization process. Full article

With the development and widespread application of network information, new technologies led by 5G are emerging, resulting in an increasingly complex network security environment and more diverse attack methods. Unlike traditional networks, 5G networks feature higher connection density, faster data transmission speeds, and lower latency, which are widely applied in scenarios such as smart cities, the Internet of Things, and autonomous driving. The vast amounts of sensitive data generated by these applications become primary targets during the processes of collection and secure sharing, and unauthorized access or tampering could lead to severe data breaches and integrity issues. However, as 5G networks extensively employ encryption technologies to protect data transmission, attackers can hide malicious content within encrypted communication, rendering traditional content-based traffic detection methods ineffective for identifying malicious encrypted traffic. To address this challenge, this paper proposes a malicious encrypted traffic detection method based on reconstructive domain adaptation and adversarial hybrid neural networks. The proposed method integrates generative adversarial networks with ResNet, ResNeXt, and DenseNet to construct an adversarial hybrid neural network, aiming to tackle the challenges of encrypted traffic detection. On this basis, a reconstructive domain adaptation module is introduced to reduce the distribution discrepancy between the source domain and the target domain, thereby enhancing cross-domain detection capabilities. By preprocessing traffic data from public datasets, the proposed method is capable of extracting deep features from encrypted traffic without the need for decryption. The generator utilizes the adversarial hybrid neural network module to generate realistic malicious encrypted traffic samples, while the discriminator achieves sample classification through high-dimensional feature extraction. Additionally, the domain classifier within the reconstructive domain adaptation module further improves the model’s stability and generalization across different network environments and time periods. Experimental results demonstrate that the proposed method significantly improves the accuracy and efficiency of malicious encrypted traffic detection in 5G network environments, effectively enhancing the detection performance of malicious traffic in 5G networks. Full article

In the realm of electronic health (eHealth) services powered by the Internet of Things (IoT), vast quantities of medical images and visualized electronic health records collected by IoT devices must be transmitted daily. Given the sensitive nature of medical information, ensuring the security of transmitted health data is paramount. To address this critical concern, this paper introduces a novel data hiding algorithm tailored for Absolute Moment Block Truncation Coding (AMBTC) in medical images, named HPDH-MI (High Payload Data Hiding for Medical Images). The proposed method embeds secret data into the AMBTC compression code inconspicuously to avoid detection by malicious users. It achieves this by first classifying AMBTC compressed blocks into four categories—flat, smooth, complex I, and complex II—using three predetermined thresholds. A 1-bit indicator, based on the proposed grouping strategy, facilitates efficient and effective block classification. A data embedding strategy is applied to each block type, focusing on block texture and taking into account the symmetric features of the pixels within the block. This approach achieves a balance between data hiding capacity, image quality, and embedding efficiency. Experimental evaluations highlight the superior performance of HPDH-MI. When tested on medical images from the Osirix database, the method achieves an average image quality of 31.22 dB, a payload capacity of 225,911 bits, and an embedding efficiency of 41.78%. These results demonstrate that the HPDH-MI method not only significantly increases the payload for concealing secret data in AMBTC compressed medical images but also maintains high image quality and embedding efficiency. This makes it a promising solution for secure data transmission in telemedicine, addressing the challenges of limited bandwidth while enhancing steganographic capabilities in eHealth applications. Full article

To address the vulnerability of existing hiding algorithms to differential attacks and the limitations of single chaotic systems, such as small key space and low security, a novel algorithm combining audio encryption with information hiding is proposed. First, the original audio is divided into blocks to enhance efficiency. A “one-time pad” mechanism is achieved by associating the key with the plaintext, and a new multidimensional sine-coupled chaotic map is designed, which, in conjunction with multiple chaotic systems, generates the key stream. Next, the block-processed audio signals are matrix-converted and then encrypted using cyclic remainder scrambling, an improved Josephus scrambling, XOR diffusion, and bit diffusion. This results in an encrypted audio information matrix. Finally, the GHM multiwavelet transform is used to select embedding channels, and the least significant bit (LSB) method is employed to hide the information within the carrier image. The algorithm is symmetric, and decryption involves simply reversing the encryption process on the stego image. Experimental results demonstrate that the Structural Similarity Index (SSIM) between the carrier image and the stego image is 0.992540, the Peak Signal-to-Noise Ratio (PSNR) is 49.659404 dB, and the Mean Squared Error (MSE) is 0.708044. These metrics indicate high statistical similarity and indistinguishability in visual appearance. The key space of the encryption algorithm is approximately 2⁸⁵⁰, which effectively resists brute-force attacks. The energy distribution of the encrypted audio approximates noise, with information entropy close to 8, uniform histograms, high scrambling degree, strong resistance to differential attacks, and robustness against noise and cropping attacks. Full article

Theano, TensorFlow, Keras, Torch, PyTorch, and other software frameworks have remarkably stimulated the popularity of deep learning (DL). Apart from all the good they achieve, the danger of such frameworks is that they unintentionally spur a black-box attitude. Some practitioners play around with building blocks offered by frameworks and rely on them, having a superficial understanding of the internal mechanics. This paper constitutes a concise tutorial that elucidates the flows of signals and gradients in deep neural networks, enabling readers to successfully implement a deep network from scratch. By “from scratch”, we mean with access to a programming language and numerical libraries but without any components that hide DL computations underneath. To achieve this goal, the following five topics need to be well understood: (1) automatic differentiation, (2) the initialization of weights, (3) learning algorithms, (4) regularization, and (5) the organization of computations. We cover all of these topics in the paper. From a tutorial perspective, the key contributions include the following: (a) proposition of R and S operators for tensors—rashape and stack, respectively—that facilitate algebraic notation of computations involved in convolutional, pooling, and flattening layers; (b) a Python project named hmdl (“home-made deep learning”); and (c) consistent notation across all mathematical contexts involved. The hmdl project serves as a practical example of implementation and a reference. It was built using NumPy and Numba modules with JIT and CUDA amenities applied. In the experimental section, we compare hmdl implementation to Keras (backed with TensorFlow). Finally, we point out the consistency of the two in terms of convergence and accuracy, and we observe the superiority of the latter in terms of efficiency. Full article

In an AI-immersing age, scholars look for new possibilities of employing AI technology to their fields, and how to strengthen security and protect privacy is no exception. In a coverless data hiding domain, the embedding capacity of an image generally depends on the size of a chosen database. Therefore, choosing a suitable database is a critical issue in coverless data hiding. A novel coverless data hiding approach is proposed by applying deep learning models to generate texture-like cover images or code images. These code images are then used to construct steganographic images to transmit covert messages. Effective mapping tables between code images in the database and hash sequences are established during the process. The cover images generated by a two-dimensional fractional Brownian motion (2D FBM) are simply called fractional Brownian images (FBIs). The only parameter, the Hurst exponent, of the 2D FBM determines the patterns of these cover images, and the seeds of a random number generator determine the various appearances of a pattern. Through the 2D FBM, we can easily generate as many FBIs of multifarious sizes, patterns, and appearances as possible whenever and wherever. In the paper, a deep learning model is treated as a secret key selecting qualified FBIs as code images to encode corresponding hash sequences. Both different seeds and different deep learning models can pick out diverse qualified FBIs. The proposed coverless data hiding scheme is effective when the amount of secret data is limited. The experimental results show that our proposed approach is more reliable, efficient, and of higher embedding capacity, compared to other coverless data hiding methods. Full article

The design of a chaotic system and pseudo-random sequence generation method with excellent performance and its application in image encryption have always been attractive and challenging research fields. In this paper, a new model of parameter-variabled coupled chaotic system (PVCCS) is established by interaction coupling between parameters and states of multiple low-dimensional chaotic systems, and a new way to construct more complex hyperchaotic systems from simple low-dimensional systems is obtained. At the same time, based on this model and dynamical DNA codings and operations, a new pseudo-random sequence generation method (PSGM-3DPVCCS/DNA) is proposed, and it is verified that the generated pseudo-random sequence of PSGM-3DPVCCS/DNA has excellent random characteristics. Furthermore, this paper designs a novel pixel chain diffusion image encryption algorithm based on the proposed parameter-variabled coupled chaotic system (PVCCS) in which the hash value of plaintext image is associated with the initial key to participate in the encryption process so that the encryption key is closely associated with plaintext, which improves the security of the algorithm and effectively resists the differential cryptanalysis risk. In addition, an information hiding method is designed to hide the hash value of plaintext image in ciphertext image so that the hash value does not need to be transmitted in each encryption, and the initial key can be reused, which solves the key management problem in application and improves the application efficiency of the encryption algorithm. The experimental analysis shows that the chaotic system constructed in this paper is creative and universal and has more excellent chaotic characteristics than the original low-dimensional system. The sequence generated by the pseudo-random sequence generation method has excellent pseudo-random characteristics and security, and the image encryption algorithm can effectively resist differential cryptanalysis risk, showing advanced encryption performance. Full article