End-to-end image encryption based on deep learning has attracted widespread attention due to its ability to leverage the nonlinear characteristics of generative models. In this work, we propose a joint hierarchical encryption and compression method based on Stable Diffusion and the Denoising Diffusion Implicit Models (DDIM) scheduler, this method enables hierarchical image encryption, decryption, and compression. Specifically, our method first maps plaintext images into a latent space and employs the forward process of a diffusion model to achieve encryption. During the encryption process, we design a multikey modulation mechanism to implement hierarchical encryption and decryption. In addition, to reduce the burden of storage and transmission, we design a compression method based on integer fraction hybrid quantization, where the encrypted latent vectors are quantized and decomposed into integer and fraction components. These components are represented through DCT-based pseudo-image generation and concatenated into a single JPEG ciphertext, thereby achieving the integration of compression and encryption. Experimental results demonstrate that the proposed method not only achieves secure encryption and multi-level decryption, but also ensures robust recovery against common perturbations such as JPEG compression and Gaussian noise, while maintaining high-fidelity semantic reconstruction. Moreover, the method exhibits favorable compression performance, achieving an average compression ratio of approximately 10%.

Cloud services have attracted extensive attention due to low cost, agility and mobility. However, when processing data on cloud servers, users may worry about semi-honest third parties stealing private information from them, hence, data encryption is applied for privacy protection. Inpainting is a technique that reconstructs certain undesirable regions in an image through an imperceptible manner, which can be accomplished by searching for well-matching candidate patches and copying them to to-be-inpainted locations. However, when the image is encrypted, the matched candidate patch searching is a challenging dilemma. Therefore, tackling these data-privacy issues for image inpainting over a cloud infrastructure, we propose an image inpainting scheme using Markov random field (MRF) modeling in encrypted domain. In this scheme, the sender encrypts the to-be-inapinted image by using a homomorphic cryptosystem that supports homomorphic ciphertext comparison. Then, the cloud realizes the MRF-based inpainting for encrypted images through some specific homomorphic operations. In addition, secure context descriptors are utilized to improve the inpainting of textures and structures. Finally, the receiver obtains the inpainted result through image decryption. The proposed scheme is proved to be secure through various cryptographic attacks. Qualitative and quantitative results demonstrate our scheme achieves better inpainted results in structure compared with state-of-the-art schemes in encrypted domain.

The motion of wireless capsule endoscopes (WCE) in the gastrointestinal tract is complex and variable. Measuring its motion patterns accurately is crucial for optimizing diagnostic, therapeutic procedures and improving diagnostic accuracy. To gain a deeper understanding of the motion patterns of WCE in the gastrointestinal tract, particularly its behavior in different regions. A simulation measurement system based on magnetic localization technology is proposed in a laboratory environment. We designed a cylindrical-conical-cylindrical structure simulation device. The free fall motion of soft hydrogel granules is designed to mimic fluid motion in the gastrointestinal tract. A hard-targeted pellet with a permanent magnet simulated the WCE. It measured parameters such as trajectory, vertical velocity, vertical acceleration, and attitude angle of the targeted pellet during its drop at different initial positions in a silo during unloading in a soft granules environment were measured. The experimental results reveal the motion characteristics of a hard pellet in a silo during unloading in a soft granules environment, in the specific wide channel region, as well as in the transition region from the wide channel to the narrow channel. These findings are valuable for understanding the complexity of flow behaviours in different regions of the soft granules environment. And, these findings provide data references for understanding the dynamic behavior of WCE in the gastrointestinal tract, thereby aiding in optimizing WCE design and enhancing its clinical efficacy.

In order to achieve the secure, efficient storage and transmission of medical images, we propose a joint lossless compression and encryption (JLCE) scheme. First, according to the intra-block correlation degree, the original medical image is divided into two non-overlapping regions with strong correlation and weak correlation. Then, a linear prediction method is performed on the strong correlation region to generate prediction errors; while the integer discrete Tchebichef transform (iDTT) with the properties of energy compaction and perfect image reconstruction is exploited to compact the energy of the weak correlation region and produce the transformed coefficients. Finally, a secure arithmetic encoding algorithm is presented to encode the prediction errors and transformed coefficients and output the encrypted and compressed bitstream. Without secret keys, the outputted encoded result can be decoded by our scheme, but the decoded result doesn’t disclose the content of the original medical image. Experimental results show that, the proposed scheme has satisfactory format compatibility and security and also achieves better performances of compression ratio and computational efficiency compared with some state-of-the-art schemes.

Abstract A central goal of disease ecology is to identify the factors that drive the spread of infectious diseases. Changes in vector richness can have complex effects on disease risk, but little is known about the role of vector competence in the relationship between vector richness and disease risk. In this study, we firstly investigated the combined effects of vector competence, interspecific competition, and feeding interference on disease risk through a two‐vector, one‐host SIR‐SI model, and obtained threshold conditions for the occurrence of dilution and amplification effects. Secondly, we extended the above model to the case of N vectors and assumed that all vectors were homogeneous to obtain analytic expressions for disease risk. It was found that in the two‐vector model, disease risk declined more rapidly as interspecific competition of the high‐competence vector increased. When vector richness increases, the positive effects of adding a high‐competence vector species on disease transmission may outweigh the negative effects of feeding interference due to increased vector richness, making an amplification effect more likely to occur. While the addition of a highly competitive vector species may exacerbate the negative effects of feeding interference, making a dilution effect more likely to occur. In the N‐vector model, the effect of increased vector richness on disease risk was fully driven by the strength of feeding interference and interspecific competition, and changes in vector competence only quantitatively but not qualitatively altered the vector richness–disease risk relationship. This work clarifies the role of vector competence in the relationship between vector richness and disease risk and provides a new perspective for studying the diversity–disease relationship. It also provides theoretical guidance for vector management and disease prevention strategies.

Pixel prediction is an important issue in the field of reversible data hiding. Neural networks are gradually used to improve the accuracy of pixel prediction owing to their excellent performance. However, current neural network-based pixel predictors are designed for natural images and do not consider the characteristics of medical images. Therefore, in this paper, we propose a dual-branch neural network-based reversible data hiding scheme for medical images. Detailedly, considering the characteristics of medical images, in which complex and smooth regions are more clearly distinguished, we present a clustering method to classify pixels into three classes according to their complexities, and generate masks to assist pixel prediction. Then, in the prediction stage, a dual-branch neural network-based pixel predictor is designed to extract unique and shared features, and a convolutional block attention module is used to optimize the extracted features. Finally, in the embedding stage, considering the characteristics of region of interest (ROI) and region of non-interest (NROI) in medical images, we design a class-based embedding algorithm, which can prioritize embedding data into NROI with low complexity and then sequentially into low texture complexity region and high texture complexity region of ROI. Experimental results show that our scheme can achieve better performance of pixel prediction and data embedding than existing state-of-the-art works.

Reversible data hiding in encrypted image (RDHEI) has become a research hotspot, which can effectively protect image content privacy. An RDHEI scheme based on the joint encoding of multiple MSBs (most significant bits) and pixel difference is proposed in this paper. A block-based image encryption method is adopted on the content owner side, which can securely protect the image contents while retaining the spatial correlation within each block. By the joint encoding strategy of multiple MSB and pixel difference, the redundancy within the bit plane is sufficiently compressed to accommodate more additional data; thus, a high embedding rate can be achieved. According to different kinds of available keys, image decryption and data extraction can be separably conducted on the receiver side. Experimental results show that our scheme can achieve a higher embedding rate than some state-of-the-art schemes.

为了研究颗粒系统在外界能量驱动下体系内部的运动与结构变化,采用局部密度判断法和粒子图像测速法,测量垂直振动二维圆盘内均值粒径为2 mm的球型颗粒的局部概率密度分布和速度分布,探究气液相变过程中离散颗粒的空间分布特征和动力学特征;通过增加圆盘颗粒的填充密度和振动台的加速度,建立局部概率密度分布的特征参数K值、均值密度φ和加速度Γ的关系模型.结果表明:当垂直振动颗粒系统处于气态时,颗粒系统中颗粒的速度分布满足指数分布,颗粒速度分布指数等于1.52.在改变φ和Γ的过程中,颗粒系统发生气液相变,气液相变过程中颗粒速度速度分布的临界条件是一致的,即当颗粒速度分布指数等于1.40时,垂直振动颗粒系统开始从气态向液态转变.

In recent decades, many perceptual image hashing schemes for content authentication have been proposed. However, existing algorithms cannot provide satisfactory robustness and discrimination in the face of complex manipulations in real scenarios. In this work, we propose a novel perceptual robust image hashing scheme with transformer-based multi-layer constraints. Specifically, we first exploit the Transformer structure into the field of perceptual image hashing, and an integrated loss function is designed to optimize the training of the model. In addition, to solve the issue of the simple content-preserving manipulations used in previous datasets, we construct a more challenging image dataset based on various manipulations, which can deal with complex image authentication scenarios. Experimental results demonstrate that our scheme achieves competitive results compared with existing schemes.

通过回归分析模型分析乙醇转化率、C4 烯烃的选择性与温度的关系,用Matlab拟合效果较好的二次非线性回归模型;最后用二次回归模型分析不同催化剂条件下,温度对究乙醇转化率、C4 烯烃选择性的影响,探索温度与最优的催化剂组合,使烯烃收率达到最值,并适当预测出最优催化剂组合400℃之后的C4 烯烃收率.

Understanding how ecological interactions affect vector-borne disease dynamics is crucial in the context of rapid biodiversity loss and increased emerging vector-borne diseases. Although there have been many studies on the impact of interspecific competition and host competence on disease dynamics, few of them have addressed the case of a vector-borne disease. Using a simple compartment model with two competing host species and one vector, we investigated the combined effects of vector preference, host competence, and interspecific competition on disease risk in a vector-borne system. Our research demonstrated that disease transmission dynamics in multi-host communities are more complex than anticipated. Vector preference and differences in host competence shifted the direction of the effect of competition on community disease risk, yet interspecific competition quantitatively but not qualitatively changed the effect of vector preference on disease risk. Our work also identified the conditions of the dilution effect and amplification effect in frequency-dependent transmission mode, and we discovered that adding vector preference and interspecific competition into a simple two-host-one-vector model altered the outcomes of how increasing species richness affects disease risk. Our work explains some of the variation in outcomes in previous empirical and theoretical studies on the dilution effect.

Speckle blood perfusion imaging is a medical imaging technique that monitors blood perfusion information based on the scattering characteristics of red blood cells to the laser. Its benefits over traditional blood perfusion monitoring technologies include real time imaging, high resolution, cheap cost, and the absence of contrast chemicals, particularly in surface blood flow monitoring. This review focuses on the basic principle of speckle blood perfusion imaging, technical advancements, and its application in monitoring fundus blood flow, cerebral blood flow, body surface microcirculation , and tumor blood perfusion, as well as its new application in monitoring angiogenesis of chicken embryo tumors.

为实现激光散斑血流成像设备的便携化,研究了手机散斑衬比血流成像技术.通过分析和重组彩色滤波阵列的光强信号,解决了彩色图像传感器无法获取散斑信号的问题.提出了基于手机相机的散斑衬比分析方法,使用二维离散小波变换对图像低频区域进行增强,并使用k-means聚类和Ostu算法将背景区域和前景血流区域进行分割和去噪.最后,为验证这两种算法在不同情况下的分割结果,分别进行了血流模拟实验和微循环血流灌注实验.研究结果证明,Ostu算法在不同的血流模型下有更好的分割去噪表现,解决手机自带算法导致血流图像失真问题的同时,提高了图像的可视化效果.

鉴于图像的密文域可逆信息隐藏在安全云计算和隐私保护方面的重要作用,为了提高信息嵌入率,结合医学DICOM图像像素深度高、像素分布连续性高的特点,提出了一种结合两种压缩算法的密文域医学图像可逆信息隐藏算法.首先发送方对图像进行预处理,根据像素中高位比特连续为0的数量嵌入标记信息,腾出高位的冗余空间;然后用一种特殊设计的块加密算法进行加密,在保证明文信息不被泄露的同时保留了部分图像相关性;接着嵌入方根据标记信息,在对应位置嵌入额外信息;此外,为了进一步提高嵌入率,嵌入方还可以利用图像像素的相关性,在块内进行做差,并压缩实施信息嵌入.实验结果表明,该算法不仅具有较高的信息嵌入率,同时还有较好的安全性表现.

Rapid global biodiversity loss and increasing emerging infectious diseases underscore the significance of identifying the diversity-disease relationship. Although experimental evidence supports the existence of dilution effects in several natural ecosystems, we still know very little about the conditions under which a dilution effect will occur. Using a multi-host Susceptible-Infected-Recovered model, we found when disease transmission was density-dependent, the diversity-disease relationship could exhibit an increasing, decreasing, or non-monotonic trend, which mainly depended on the patterns of community assembly. However, the combined effects of the host competence-abundance relationship and species extinction order may reverse or weaken this trend. In contrast, when disease transmission was frequency-dependent, the diversity-disease relationship only showed a decreasing trend, the host competence-abundance relationship and species extinction order did not alter this decreasing trend, but it could reduce the detectability of the dilution effect and affect disease prevalence. Overall, a combination of disease transmission mode, community assembly pattern, and host community composition determines the direction or strength of the diversity-disease relationship. Our work helps explain why previous studies came to different conclusions about the diversity-disease relationship and provides a deeper understanding of the pathogen transmission dynamics in actual communities.

The study of structural defects in particle systems is of great value for studying solid-liquid melting. The volume fraction is a key parameter that can be used to accurately quantify the phase-transition process. The collective behavior and interaction form in a wet particle system are much more complex than that of a dry particle material because of the existence of liquid bridge force between the wet particles. In this paper, the structural defects and the critical value of solid-liquid transformation in the monolayer wet particles during solid-liquid melting under vertical vibration are experimentally studied. The contact model of the wet particle system is constructed according to experimental and theoretical analysis, and the structural changes of the particles in the melting process of the quasi-two-dimensional wet particle system are quantified. The Voronoi tessellation is established to study the phase transition of the particle system, and the local volume fraction is adopted to determine the state of structural defect change during melting. The experimental results indicate that the phase-transition process is caused by structural defects in the solid. The defects appear from the edge of the particle system, and the chain defect pairs spread to the center. The reason for structural defects at the edge of the cluster is that the particles at the edge of the cluster are subjected to less liquid bridge force, and the kinetic energy brought by the collision between the particles and the bottom wall makes the particles become active and begin to explore the available space. The chain defects are caused by the force chain generated by the fluid bridge force, which makes the particles tend to move together in rows. In addition, the local volume fraction of seven-phase defective particles decreases significantly and is much smaller than that of five-phase defective particles and six-phase defective particles when defects occur. Therefore, the evolution and the critical state of the structural defects can be quantified by measuring the change in the minimum local volume fraction (the local volume fraction of particles with 7-fold defects) in the particle system. The local volume fraction of the analysis shows that when the minimum local volume fraction ϕ ≤ 0.6652 defects occur, and when ϕ ≤ 0.4872 particle system transforms from solid to liquid.

本文以应用型本科医学院校上海健康医学院为例,结合上海健康医学院的办学特色和全校通识核心课程架构,通过对数学文化课程的内容梳理,对上海健康医学院的数学文化课程教学内容和教学方法进行了设计,把数学文化中的真,善,美自然融入上海健健康医学院的厚德至善和健行康民的校训,通过数学之旅,数学之美,数学之用,数学之真,数学之趣,数学之问模块融入上海健康医学院的通识课程框架的设计,凸显了健康医学院的办学特色,为医学院校通识课程和课程思政建设提供有益的参考.

Laser speckle contrast imaging (LSCI) technology, a type of blood flow monitoring technology, has broad application prospects and has become an attractive subject of research. In recent years, with the popularization of smart phones, mobile healthcare has gradually entered the lives of the general public. In this paper, a camera system used on smart phones was employed to replace the industrial camera on the traditional laser speckle perfusion imager, and the quality of blood flow imaging was improved. Since the difference between the static and dynamic region of the raw speckle image can be reflected in the subbands of wavelet decomposition, the image processing can be performed while retaining the velocity information. Further, a speckle contrast analysis method based on mobile phone cameras is proposed; the method first processes the raw speckle images with a two-dimensional discrete wavelet transform and image interpolation and then performs a morphological operation. This method improved the speckle image contrast resolution and improved the visualization of images. The results showed that the contrast-to-noise ratio (CNR) of the blood perfusion image processed by the new method can be increased by up to 128%.

高校的立身之本在于立德树人,以立德为根本,以树人为核心,两者需要有机结合,要求高校教师既要保证教学效果又要做好思政育人工作.文章基于以问题为导向(problem-based learning,PBL)的教育理念,针对如何在医用数学建模课程中融入课程思政进行了案例研究.首先,介绍了PBL教育理念及方法;其次,介绍了医用数学建模课程;然后,分析了基于PBL教学法将课程思政融入医用数学建模课程的可行性;最后,文章完整的展示了在PBL案例中融入思政元素的设计过程,通过案例分析验证了该方法能够发挥课程的育人功能,同时激发学生的自学能力和兴趣,将立德与树人有机地结合在一起.

Background Escherichia coli always plays an important role in microbial research, and it has been a benchmark model for the study of molecular mechanisms of microorganisms. Molecular complexes, operons, and functional modules are valuable molecular functional domains of E. coli . The identification of protein complexes and functional modules of E. coli is essential to reveal the principles of cell organization, process, and function. At present, many studies focus on the detection of E. coli protein complexes based on experimental methods. However, based on the large-scale proteomics data set of E. coli, the simultaneous prediction of protein complexes and functional modules, especially the comparative analysis of them is relatively less. Results In this study, the Edge Label Propagate Algorithm (ELPA) of the complex biological network was used to predict the protein complexes and functional modules of two high-quality PPI networks of E. coli , respectively. According to the gold standard protein complexes and function annotations provided by EcoCyc dataset, most protein modules predicted in the two datasets matched highly with real protein complexes, cellular processes, and biological functions. Some novel and significant protein complexes and functional modules were revealed based on ELPA. Moreover, through a comparative analysis of predicted complexes with corresponding functional modules, we found the protein complexes were significantly overlapped with corresponding functional modules, and almost all predicted protein complexes were completely covered by one or more functional modules. Finally, on the same PPI network of E. coli , ELPA was compared with a well-known protein module detection method (MCL) and we found that the performance of ELPA and MCL is comparable in predicting protein complexes. Conclusions In this paper, a link clustering method was used to predict protein complexes and functional modules in PPI networks of E. coli , and the correlation between them was compared, which could help us to understand the molecular functional units of E. coli better.