An Improved Reversible Data Hiding Algorithm Based on Reconstructed Mapping for PVO-k

Reversible Data Hiding (RDH) is a practical and efficient technique for information encryption. Among its methods, the Pixel-Value Ordering (PVO) algorithm and its variants primarily modify prediction errors to embed information. However, both the classic PVO and its improved versions, such as IPVO and PVO-k, share a common limitation: their maximum data embedding capacity for a given grayscale image is relatively low. This poses a challenge when large amounts of data need to be embedded into an image. In response to these issues, this paper proposes an improved design targeting the PVO-k algorithm. We have reconstructed the mapping scheme of the PVO-k algorithm to maximize the number of pixels that can embed encrypted information. Experimental validations show that our proposed scheme significantly surpasses previous algorithms in terms of the maximum data embedding capacity. For instance, when embedding information into a grayscale image of an airplane, our method's capacity exceeds that of PVO-k by 11,207 bits, PVO by 8,004 bits, and IPVO by 4,562 bits. The results demonstrate that our algorithm holds substantial advantages over existing methods and introduces innovative mapping ideas, laying a foundation for future research in reversible data hiding in images

Cascading Hybrid Bandits: Online Learning to Rank for Relevance and Diversity

Relevance ranking and result diversification are two core areas in modern recommender systems. Relevance ranking aims at building a ranked list sorted in decreasing order of item relevance, while result diversification focuses on generating a ranked list of items that covers a broad range of topics. In this paper, we study an online learning setting that aims to recommend a ranked list with $K$ items that maximizes the ranking utility, i.e., a list whose items are relevant and whose topics are diverse. We formulate it as the cascade hybrid bandits (CHB) problem. CHB assumes the cascading user behavior, where a user browses the displayed list from top to bottom, clicks the first attractive item, and stops browsing the rest. We propose a hybrid contextual bandit approach, called CascadeHybrid, for solving this problem. CascadeHybrid models item relevance and topical diversity using two independent functions and simultaneously learns those functions from user click feedback. We conduct experiments to evaluate CascadeHybrid on two real-world recommendation datasets: MovieLens and Yahoo music datasets. Our experimental results show that CascadeHybrid outperforms the baselines. In addition, we prove theoretical guarantees on the $n$-step performance demonstrating the soundness of CascadeHybrid

A Novel Tomato Volume Measurement Method based on Machine Vision

Density is one of the auxiliary indicators for judging the internal quality of tomatoes. However, in the density measurement process, it is often difficult to measure the volume of the tomatoes accurately. To solve this problem, first, this study proposed a novel tomato volume measurement method based on machine vision. The proposed method uses machine vision to measure the geometric feature parameters of tomatoes, and inputs them into the LabVIEW software to convert the calculation of irregular tomato volume into a BP neural network (BPNN) model that calculates the plane pixel area and pixel volume, thereby realizing the modeling, analysis, design and simulation of tomato volume; then, an experimental platform was constructed to compare the results of the proposed method with the results predicted by the 3D wireframe model. When the number of photos taken was n = 5, the average error of the tomato volume prediction results of the 3D wireframe model was 8.22%, and the highest accuracy was 92.93%; while the average error of the tomato volume prediction results of the BPNN was 4.60%, and the highest accuracy was 95.60%. Increasing the number of orthographic projections can improve the accuracy of the model, but when the number of photos was more than 7, the accuracy improvement was not significant. Also, increasing the number of nodes in the hidden layer can improve the accuracy of the model, however, considering that increasing the number of nodes will increase the host operating cost, it is suggested to choose a node number of 12 for the tomato volume measurement. In the end, the final experimental results showed that the proposed method achieved better measurement results. However, the volume measured by the two models is larger than the real volume of tomatoes. For this reason, we added a correction coefficient to the BPNN model, and its highest accuracy has increased by 1.3%

Bioactive conformational generation of small molecules: A comparative analysis between force-field and multiple empirical criteria based methods

<p>Abstract</p> <p>Background</p> <p>Conformational sampling for small molecules plays an essential role in drug discovery research pipeline. Based on multi-objective evolution algorithm (MOEA), we have developed a conformational generation method called Cyndi in the previous study. In this work, in addition to Tripos force field in the previous version, Cyndi was updated by incorporation of MMFF94 force field to assess the conformational energy more rationally. With two force fields against a larger dataset of 742 bioactive conformations of small ligands extracted from PDB, a comparative analysis was performed between pure force field based method (FFBM) and multiple empirical criteria based method (MECBM) hybrided with different force fields.</p> <p>Results</p> <p>Our analysis reveals that incorporating multiple empirical rules can significantly improve the accuracy of conformational generation. MECBM, which takes both empirical and force field criteria as the objective functions, can reproduce about 54% (within 1Å RMSD) of the bioactive conformations in the 742-molecule testset, much higher than that of pure force field method (FFBM, about 37%). On the other hand, MECBM achieved a more complete and efficient sampling of the conformational space because the average size of unique conformations ensemble per molecule is about 6 times larger than that of FFBM, while the time scale for conformational generation is nearly the same as FFBM. Furthermore, as a complementary comparison study between the methods with and without empirical biases, we also tested the performance of the three conformational generation methods in MacroModel in combination with different force fields. Compared with the methods in MacroModel, MECBM is more competitive in retrieving the bioactive conformations in light of accuracy but has much lower computational cost.</p> <p>Conclusions</p> <p>By incorporating different energy terms with several empirical criteria, the MECBM method can produce more reasonable conformational ensemble with high accuracy but approximately the same computational cost in comparison with FFBM method. Our analysis also reveals that the performance of conformational generation is irrelevant to the types of force field adopted in characterization of conformational accessibility. Moreover, post energy minimization is not necessary and may even undermine the diversity of conformational ensemble. All the results guide us to explore more empirical criteria like geometric restraints during the conformational process, which may improve the performance of conformational generation in combination with energetic accessibility, regardless of force field types adopted.</p

A Digital Twin for Geological Carbon Storage with Controlled Injectivity

We present an uncertainty-aware Digital Twin (DT) for geologic carbon storage (GCS), capable of handling multimodal time-lapse data and controlling CO2 injectivity to mitigate reservoir fracturing risks. In GCS, DT represents virtual replicas of subsurface systems that incorporate real-time data and advanced generative Artificial Intelligence (genAI) techniques, including neural posterior density estimation via simulation-based inference and sequential Bayesian inference. These methods enable the effective monitoring and control of CO2 storage projects, addressing challenges such as subsurface complexity, operational optimization, and risk mitigation. By integrating diverse monitoring data, e.g., geophysical well observations and imaged seismic, DT can bridge the gaps between seemingly distinct fields like geophysics and reservoir engineering. In addition, the recent advancements in genAI also facilitate DT with principled uncertainty quantification. Through recursive training and inference, DT utilizes simulated current state samples, e.g., CO2 saturation, paired with corresponding geophysical field observations to train its neural networks and enable posterior sampling upon receiving new field data. However, it lacks decision-making and control capabilities, which is necessary for full DT functionality. This study aims to demonstrate how DT can inform decision-making processes to prevent risks such as cap rock fracturing during CO2 storage operations

Lipocalin-2 variants and their relationship with cardio-renal risk factors

Objectives: To investigate the serum, plasma and urine levels of lipocalin-2 (LCN2) variants in healthy humans and their associations with risk factors for cardiometabolic (CMD) and chronic kidney (CKD) diseases. Methods: Fifty-nine males and 41 females participated in the study. Blood and urine were collected following an overnight fasting. LCN2 variants were analyzed using validated in-house ELISA kits. Heart rate, blood pressure, lipids profile, glucose, adiponectin, high-sensitivity C-reactive protein (hsCRP), creatinine, cystatin C, and biomarkers for kidney function were assessed. Results: The levels of hLcn2, C87A and R81E in serum and urine, but not plasma, were significantly higher in men than women. Increased levels of LCN2 variants, as well as their relative ratios, in serum and plasma were positively associated with body mass index, blood pressure, triglyceride and hsCRP (P \u3c 0.05). No significant correlations were found between these measures and hLcn2, C87A or R81E in urine. However, LCN2 variants in urine, but not plasma or serum, were correlated with biomarkers of kidney function (P \u3c 0.05). Conclusions: Both the serum and plasma levels of LCN2 variants, as well as their ratios are associated with increased cardiometabolic risk, whereas those in urine are correlated with renal dysfunction. LCN2 variants represent promising biomarkers for CMD and CKD