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This paper proposes a novel soft sensor modeling approach, MIC-TCA-INGO-LSSVM, to address the decline in performance of soft sensor models during the fermentation process of Pichia pastoris , caused by changes in working conditions. Initially, the transfer component analysis (TCA) method is utilized to minimize the differences in data distribution across various working conditions. Subsequently, a least squares support vector machine (LSSVM) model is constructed using the dataset adapted by TCA, and strategies for improving the northern goshawk optimization (INGO) algorithm are proposed to optimize the parameters of the LSSVM model. Finally, to further enhance the model's generalization ability and prediction accuracy, considering the transfer of knowledge from multiple-source working conditions, a sub-model weighted ensemble scheme is proposed based on the maximum information coefficient (MIC) algorithm. The proposed soft sensor model is employed to predict cell and product concentrations during the fermentation process of Pichia pastoris . Simulation results indicate that the RMSE of the INGO-LSSVM model in predicting cell and product concentrations is reduced by 47.3% and 42.1%, respectively, compared to the NGO-LSSVM model. Additionally, TCA significantly enhances the model's adaptability when working conditions change. Moreover, the soft sensor model based on TCA and the MIC-weighted ensemble method achieves a reduction of 41.6% and 31.3% in the RMSE for predicting cell and product concentrations, respectively, compared to the single-source condition transfer model TCA-INGO-LSSVM. These results demonstrate the high reliability and predictive performance of the proposed soft sensor method under varying working conditions.

This study introduces YOLOv8n-vegetable, a model designed to address challenges related to imprecise detection of vegetable diseases in greenhouse plant environment using existing network models. The model incorporates several improvements and optimizations to enhance its effectiveness. Firstly, a novel C2fGhost module replaces partial C2f. with GhostConv based on Ghost lightweight convolution, reducing the model's parameters and improving detection performance. Second, the Occlusion Perception Attention Module (OAM) is integrated into the Neck section to better preserve feature information after fusion, enhancing vegetable disease detection in greenhouse settings. To address challenges associated with detecting small-sized objects and the depletion of semantic knowledge due to varying scales, an additional layer for detecting small-sized objects is included. This layer improves the amalgamation of extensive and basic semantic knowledge, thereby enhancing overall detection accuracy. Finally, the HIoU boundary loss function is introduced, leading to improved convergence speed and regression accuracy. These improvement strategies were validated through experiments using a self-built vegetable disease detection dataset in a greenhouse environment. Multiple experimental comparisons have demonstrated the model's effectiveness, achieving the objectives of improving detection speed while maintaining accuracy and real-time detection capability. According to experimental findings, the enhanced model exhibited a 6.46% rise in mean average precision (mAP) over the original model on the self-built vegetable disease detection dataset under greenhouse conditions. Additionally, the parameter quantity and model size decreased by 0.16G and 0.21 MB, respectively. The proposed model demonstrates significant advancements over the original algorithm and exhibits strong competitiveness when compared with other advanced object detection models. The lightweight and fast detection of vegetable diseases offered by the proposed model presents promising applications in vegetable disease detection tasks., (© 2024. The Author(s).)

The scoring models used for protein structure modeling and ranking are mainly divided into unified field and protein-specific scoring functions. Although protein structure prediction has made tremendous progress since CASP14, the modeling accuracy still cannot meet the requirements to a certain extent. Especially, accurate modeling of multi-domain and orphan proteins remains a challenge. Therefore, an accurate and efficient protein scoring model should be developed urgently to guide the protein structure folding or ranking through deep learning. In this work, we propose a protein structure global scoring model based on equivariant graph neural network (EGNN), named GraphGPSM, to guide protein structure modeling and ranking. We construct an EGNN architecture, and a message passing mechanism is designed to update and transmit information between nodes and edges of the graph. Finally, the global score of the protein model is output through a multilayer perceptron. Residue-level ultrafast shape recognition is used to describe the relationship between residues and the overall structure topology, and distance and direction encoded by Gaussian radial basis functions are designed to represent the overall topology of the protein backbone. These two features are combined with Rosetta energy terms, backbone dihedral angles and inter-residue distance and orientations to represent the protein model and embedded into the nodes and edges of the graph neural network. The experimental results on the CASP13, CASP14 and CAMEO test sets show that the scores of our developed GraphGPSM have a strong correlation with the TM-score of the models, which are significantly better than those of the unified field score function REF2015 and the state-of-the-art local lDDT-based scoring models ModFOLD8, ProQ3D and DeepAccNet, etc. The modeling experimental results on 484 test proteins demonstrate that GraphGPSM can greatly improve the modeling accuracy. GraphGPSM is further used to model 35 orphan proteins and 57 multi-domain proteins. The results show that the average TM-score of the models predicted by GraphGPSM is 13.2 and 7.1% higher than that of the models predicted by AlphaFold2. GraphGPSM also participates in CASP15 and achieves competitive performance in global accuracy estimation., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Domain boundary prediction is one of the most important problems in the study of protein structure and function, especially for large proteins. At present, most domain boundary prediction methods have low accuracy and limitations in dealing with multi-domain proteins. In this study, we develop a sequence-based protein domain boundary prediction, named DomBpred. In DomBpred, the input sequence is first classified as either a single-domain protein or a multi-domain protein through a designed effective sequence metric based on a constructed single-domain sequence library. For the multi-domain protein, a domain-residue clustering algorithm inspired by Ising model is proposed to cluster the spatially close residues according inter-residue distance. The unclassified residues and the residues at the edge of the cluster are then tuned by the secondary structure to form potential cut points. Finally, a domain boundary scoring function is proposed to recursively evaluate the potential cut points to generate the domain boundary. DomBpred is tested on a large-scale test set of FUpred comprising 2549 proteins. Experimental results show that DomBpred better performs than the state-of-the-art methods in classifying whether protein sequences are composed by single or multiple domains, and the Matthew's correlation coefficient is 0.882. Moreover, on 849 multi-domain proteins, the domain boundary distance and normalised domain overlap scores of DomBpred are 0.523 and 0.824, respectively, which are 5.0% and 4.2% higher than those of the best comparison method, respectively. Comparison with other methods on the given test set shows that DomBpred outperforms most state-of-the-art sequence-based methods and even achieves better results than the top-level template-based method. The executable program is freely available at https://github.com/iobio-zjut/DomBpred and the online server at http://zhanglab-bioinf.com/DomBpred/.

Human Action Recognition (HAR) aims to understand human behavior and assign a label to each action. It has a wide range of applications, and therefore has been attracting increasing attention in the field of computer vision. Human actions can be represented using various data modalities, such as RGB, skeleton, depth, infrared, point cloud, event stream, audio, acceleration, radar, and WiFi signal, which encode different sources of useful yet distinct information and have various advantages depending on the application scenarios. Consequently, lots of existing works have attempted to investigate different types of approaches for HAR using various modalities. In this article, we present a comprehensive survey of recent progress in deep learning methods for HAR based on the type of input data modality. Specifically, we review the current mainstream deep learning methods for single data modalities and multiple data modalities, including the fusion-based and the co-learning-based frameworks. We also present comparative results on several benchmark datasets for HAR, together with insightful observations and inspiring future research directions.

Protein model quality assessment plays an important role in protein structure prediction, protein design and drug discovery. In this work, DeepUMQA2, a substantially improved version of DeepUMQA for protein model quality assessment, is proposed. First, sequence features containing protein co-evolution information and structural features reflecting family information are extracted to complement model-dependent features. Second, a novel backbone network based on triangular multiplication update and axial attention mechanism is designed to enhance information exchange between inter-residue pairs. On CASP13 and CASP14 datasets, the performance of DeepUMQA2 increases by 20.5 and 20.4% compared with DeepUMQA, respectively (measured by top 1 loss). Moreover, on the three-month CAMEO dataset (11 March to 04 June 2022), DeepUMQA2 outperforms DeepUMQA by 15.5% (measured by local AUC0,0.2) and ranks first among all competing server methods in CAMEO blind test. Experimental results show that DeepUMQA2 outperforms state-of-the-art model quality assessment methods, such as ProQ3D-LDDT, ModFOLD8, and DeepAccNet and DeepUMQA2 can select more suitable best models than state-of-the-art protein structure methods, such as AlphaFold2, RoseTTAFold and I-TASSER, provided themselves., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Fluorescence microscopy has been widely used in the field of biological imaging, but the disturbance of background noise has always been an unavoidable phenomenon. To obtain a background free image, a virtual HiLo based on edge detection (V-HiLo-ED) method for background removing is proposed, which is different from the existing popular software algorithms that obtain the background-free image by subtracting the estimated background, but the background-free image is directly reconstructed by estimating the foreground. Compared with two other popular software-based methods, the wavelet-based background and noise subtraction algorithm (WBNS) and the rolling ball algorithm (RBA), the V-HiLo-ED owns a better quality on achieving background-free imaging. Compared with hardware-based method such as HiLo method, V-HiLo-ED exhibits almost the same performance but faster speed. In combination with light sheet microscopy, the V-HiLo-ED further improves the signal-to-noise ratio of images with thick light-sheet. These experiment results indicate that the V-HiLo-ED owns the potentiality in many other image applications such as endoscopy., (© 2022 Wiley-VCH GmbH.)

Automatic electrocardiogram (ECG) classification is a promising technology for the early screening and follow-up management of cardiovascular diseases. It is, by nature, a multi-label classification task owing to the coexistence of different kinds of diseases, and is challenging due to the large number of possible label combinations and the imbalance among categories. Furthermore, the task of multi-label ECG classification is cost-sensitive, a fact that has usually been ignored in previous studies on the development of the model. To address these problems, in this work, we propose a novel deep learning model-based learning framework and a thresholding method, namely category imbalance and cost-sensitive thresholding (CICST), to incorporate prior knowledge about classification costs and the characteristic of category imbalance in designing a multi-label ECG classifier. The learning framework combines a residual convolutional network with a class-wise attention mechanism. We evaluate our method with a cost-sensitive metric on multiple realistic datasets. The results show that CICST achieved a cost-sensitive metric score of 0.641 ± 0.009 in a 5-fold cross-validation, outperforming other commonly used thresholding methods, including rank-based thresholding, proportion-based thresholding, and fixed thresholding. This demonstrates that, by taking into account the category imbalance and predefined cost information, our approach is effective in improving the performance and practicability of multi-label ECG classification models.

Arbitrary image style transfer is the process of inputting any set of images to generate images with a certain artistic style. Aiming at the problem of how to adapt both global style and local style and maintain spatial consistency based on the arbitrary style transfer algorithm. This paper proposed a multi-level adaptive arbitrary style transfer network and adopted a multi-level strategy to integrate multi-level context information in a progressive manner. First, the convolution block attention module C B A M is referenced to the encoder to improve the semantic matching of the algorithm and maintain spatial consistency. Secondly, the multi-branch content is integrated with the style features, quantifying the local similarity between content and style features in a non-local way, rearranges the distribution of style representation according to the content representation. Finally, the multi-layer features after alignment are provided to the decoder module by the Adaptive Weight Skip Connection A W S C , which can integrate local and global styles efficiently and flexibly. In addition, the identity loss is used to eliminate image artifacts and better retain the content structure information. Qualitative and quantitative experiments show that the proposed method is superior to the most advanced CNN-based method, and can generate high-quality stylized images with arbitrary styles and better visual effects. [ABSTRACT FROM AUTHOR]

Near infrared (NIR) spectroscopy is sensitive to physical conditions such as sample temperature, meaning that rapid detection methods based on NIR spectroscopy are significantly influenced by temperature. To address this challenge, symbolic regression was employed to mitigate the effects of temperature. The Weighted Windowed Adaptive Optimization algorithm was proposed and combined with the Sequential Projection Algorithm to extract temperature-related feature points and remove redundant data. Subsequent 3D modeling of these feature points revealed that absorbance alterations due to temperature comprised two distinct segments. Consequently, based on symbolic regression, the temperature standardization algorithm was devised to generate piecewise equations. This algorithm surpassed genetic programming and non-segmented methods in performance metrics. The piecewise function equations generated by the algorithm were used to regress the absorbance at different temperatures to the standard temperature. Non-dairy cream, with different indigo pigment contents, was temperature standardized using a piecewise function to obtain spectra at two standard temperatures; 18°C and 28°C. The r2 for the quantitative regression model improved from 0.71 to 0.95 at 18°C and from 0.63 to 0.85 at 28°C. The temperature standardization method offers interpretable equations for spectra that model the complex changes with temperature, factoring out the temperature variation, thereby facilitating the practical use of NIR spectroscopy in rapid detection applications. [ABSTRACT FROM AUTHOR]

Operational amplifiers (Op-Amps) are critical to sensor systems because they enable precise, reliable, and flexible signal processing. Current automated Op-Amp generation methods suffer from extremely low efficiency because the time-consuming SPICE-in-the-loop sizing is normally involved as its inner loop. In this paper, we propose an efficiently automated Op-Amp generation tool using a hybrid sizing method, which combines the merits together from a deterministic optimization algorithm and differential evolution algorithm. Thus, it can not only quickly find a decent local optimum, but also eventually converge to a global optimum. This feature is well fit to be serving as an acute filter in the circuit structure evaluation flow to efficiently eliminate any undesirable circuit structures in advance of detailed sizing. Our experimental results demonstrate its superiority over traditional sizing approaches and show its efficacy in highly boosting the efficiency of automated Op-Amp structure generation. [ABSTRACT FROM AUTHOR]

In this paper, a brain-like navigation scheme based on fuzzy kernel C-means (FKCM) clustering assisted pyramid Lucas Kanade (LK) optical flow algorithm is developed to measure the position of vehicle. The Speed Cell and Place Cell in animals' brain are introduced to construct the brain-like navigation mechanism which involves the optical flow method and image template matching to imitate the cells above-mentioned separately. To eliminate the singular values during optical flow calculation, the output of pyramid LK algorithm is clustered by FKCM algorithm firstly. Then, the velocity is calculated and integrated to get the position of the vehicle, and the brain-like navigation scheme is introduced to correct the position measurement errors by eliminating the accumulated errors resulting from velocity integration. The prominent advantages of the presented method are: (i) a pure visual brain-like position measurement method based on the concept of speed cells and place cells is proposed, making visual navigation more accurate and intelligent; (ii) the FKCM algorithm is used to eliminate the singular value of the pyramid LK algorithm, which improves the calculated velocity accuracy. Also, experimental comparison with classical pyramid LK algorithm is given to illustrate the superiority of the proposed method in position measurement. • Analyzed impact of clustering algorithms on optical flow precision. • Achieved notable reduction in optical flow singular value. • Brain-like navigation minimizes velocity integral errors. [ABSTRACT FROM AUTHOR]

Reinforcement learning (RL) is a powerful technique that allows agents to learn optimal decision-making policies through interactions with an environment. However, traditional RL algorithms suffer from several limitations such as the need for large amounts of data and long-term credit assignment, i.e. the problem of determining which actions actually produce a certain reward. Recently, Transformers have shown their capacity to address these constraints in this area of learning in an offline setting. This paper proposes a framework that uses Transformers to enhance the training of online off-policy RL agents and address the challenges described above through self-attention. The proposal introduces a hybrid agent with a mixed policy that combines an online off-policy agent with an offline Transformer agent using the Decision Transformer architecture. By sequentially exchanging the experience replay buffer between the agents, the agent's learning training efficiency is improved in the first iterations and so is the training of Transformer-based RL agents in situations with limited data availability or unknown environments. [ABSTRACT FROM AUTHOR]

Cellular image analysis system is a complex system that plays a critical role in disease diagnosis and pharmaceutical research. The analysis of image data is one of the most critical aspects of the system. However, there are differences in the distribution of cellular images, including cell morphology, cell density etc. This often requires careful algorithm customization, strict parameter tuning, or even inefficient manual processing, leading to low levels of automation. In this work, an efficient end‐to‐end cell segmentation algorithm, ECS‐Net, is proposed that can handle detection, segmentation, and counting tasks simultaneously. Two modules, proposal focus module (PFM) and enhance mask feature head (EMFH), are introduced to improve the segmentation accuracy. The proposed algorithm achieves better detection and segmentation accuracy with fewer parameters and computational cost, thus improving cellular image analysis systems. Furthermore, considering the medical IoT scenario, the scaled‐down model with only 5.8M parameters has only a small decrease in accuracy which has significant application value. [ABSTRACT FROM AUTHOR]

Due to atmospheric scattering, solar radiation, and other factors, the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) product data suffer from a substantial amount of background noise. This poses a significant challenge when attempting to directly utilize the raw data. Consequently, data denoising becomes an indispensable preprocessing step for its subsequent applications, such as the extraction of forest structure parameters and ground elevation data. While the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is currently the most widely used method, it remains susceptible to complexities arising from terrain, low signal-to-noise ratio (SNR), and input parameter variations. This paper proposes an efficient Multi-Level Auto-Adaptive Noise Filter (MLANF) algorithm based on photon spatial density. Its purpose is to extract signal photons from ICESat-2 terrestrial data of different ground cover types. The algorithm follows a two-step process. Firstly, random noise photons are removed from the upper and lower regions of the signal photons through a coarse denoising process. Secondly, in the fine denoising step, the K-Nearest Neighbor (KNN) algorithm selects the K photons to calculate the slope along the track. The calculated slope is then used to rotate the direction of the searching neighborhood in the DBSCAN algorithm. The proposed algorithm was tested in eight datasets of four surface types: forest, grassland, desert, and urban, and the extraction results were compared with those from the ATL08 datasets and the DBSCAN algorithm. Based on the ground-truth signal photons obtained by visual inspection, the classification precision, recall, and F-score of our algorithm, as well as two other algorithms, were calculated. The MLANF could achieve a good balance between classification precision (97.48% averaged) and recall (97.96% averaged). Its F-score (97.69% averaged) was higher than that of the other two methods. This demonstrates that the MLANF algorithm successfully obtained a continuous surface profile from ICESat-2 datasets with different surface cover types, significant topographic relief, and low SNR. [ABSTRACT FROM AUTHOR]

Summary: The tasks of a space‐based information network are complex and diverse, but the resources of a space‐based environment are minimal. The existing methods are challenging to match task demand to resource supply accurately. Aiming at the problem of accurate prediction from task to resource, we propose a resource prediction adjustment strategy. First, we propose a multidimensional resource prediction algorithm based on improved particle swarm optimization and back propagation (IPSO‐BP) neural network. The improved PSO is used to optimize the weight and threshold of BP neural network to make up for the defects that BP neural network is easy to fall into local minimum and the predicted output value is not unique. Second, to meet the quality of service (QoS) of tasks, we propose a density‐based performance evaluation algorithm (DPEA) to adjust resources. This method uses the idea of local sensitive hash to select the evaluation subset for the configuration task, then dynamically selects the k nearest neighbors of the configuration task, and uses the idea of weighted average to evaluate the QoS performance index of the configuration task. Simulation results show that the proposed resource prediction and adjustment strategy effectively reduces the scheduling time overhead and improves resource utilization. [ABSTRACT FROM AUTHOR]

The cloud computing center can dynamically respond to various needs, schedule computing resources, and provide users with convenient IT services. As the demand for cloud computing services continues to increase, the scale of the data center is getting larger and larger, and the problem of high energy consumption of equipment is becoming more and more prominent. Therefore, building a green data center is key to ensuring the development of the technology industry. Virtual machine online migration technology has been widely used in energy consumption management, which plays an important role in the energy‐saving management of large‐scale data centers. Considering the problem of energy consumption in a multi‐data center environment, a cross‐data center virtual machine migration strategy is proposed, EVMA. First, the target data center of the virtual machine migration is determined according to the bandwidth between data centers, and then the overload host and virtual machine selection strategy is determined according to the historical CPU load. The experimental results showed that the algorithm had a good performance in reducing the energy consumption of the data center and ensuring the quality of service. [ABSTRACT FROM AUTHOR]

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The efficiency of train transportation in the port environment directly restricts the production efficiency of the entire port. In order to coordinate the productivity of the entire port and further improve the loading and unloading efficiency of the train automation system, this paper proposes a material segmentation and data statistics algorithm for the problem of material identification in the train compartment. The terrestrial laser scanning system was established under the experimental scenario of CHN ENERGY Tianjin Port. By fusing the laser point cloud data, and then performing pre-processing operations such as box filtering, down-sampling, and radius filtering, the pre-processed point cloud data is projected onto a two-dimensional plane as a two-dimensional image, and the canny operator is used to extract the contour of the carriage on the image. Further fitting to the plane of the point cloud of the carriage wall, and the point cloud of materials in the carriage is segmented. Through the method of slicing the compartment, completing the statistical analysis of the data in the compartment from the whole to the part. The database fields of the main carriage types are established to realize the information interaction between the database and the recognition algorithm. The final experimental results show that the error rate of the carriage length recognition is 5.89%, and the width error rate is 7.25%, which verifies that the algorithm has good recognition accuracy which fully meets engineering needs. [ABSTRACT FROM AUTHOR]

This paper proposes a novel Non-linear Camera Assisted-Received Signal Strength (nCA-RSS) algorithm. The basic idea of nCA-RSS is to simultaneously utilize camera and Photo-Diode (PD) to capture visual information and signal strength in- formation to achieve both high coverage and accurate positioning. First, based on single-view geometry theorem, nCA-RSS algorithm estimates incidence angle of visible light based on visual information. Then, based on the estimated incidence angle, nCA-RSS algorithm utilizes signal strength to determine the location of the receiver. Due to the use of visual formation, nCA- RSS no longer has orientation limitations. Based on Levenberg-Marquardt, nCA-RSS can use three Light Emitting Diode (LED) to achieve precise 3-Dlmensional (3D) positioning. Therefore, compared with perspective n point algorithm, nCA-RSS can achieve high coverage. Simulation results show that nCA-RSS can achieve 80th percent!! accuracies of less than 2. 5 cm for positioning regardless of the orientation of the receiver. [ABSTRACT FROM AUTHOR]

When heart rate is extracted by wearable PPG, motion artifact noise causes serious interference. Since the accuracy of PPG heart rate extraction tends to be positively correlated with the complexity of heart rate extraction algorithm, it is challenging to extract heart rate via wearable PPG throughout the day by taking into account both algorithmic complexity and the accuracy of heart rate extraction. In this paper, a PPG heart rate extraction algorithm is proposed under the motion artifact intensity classification and removal (MAICR) framework. Firstly, it evaluates the motion artifact intensity using acceleration time-domain information and PPG frequency-domain information. Then, different heart rate estimation methods are selected according to motion artifact intensity, in ascending order. This corresponds to the use of the frequency-domain direct extraction method, the MNLMS adaptive filtering method, and the joint sparse spectral reconstruction method. Additionally, the overall framework of the algorithm (MAICR) is developed by considering the performance of various methods. Finally, experiments are conducted on the public dataset to demonstrate that the proposed algorithm is applicable to reduce the algorithmic complexity effectively while maintaining the accuracy in heart rate extraction at a certain degree. [ABSTRACT FROM AUTHOR]

• This paper proposes a re-identification method based on visual common attention mechanism • This paper proposes a joint feature method for pedestrian re-identification • This paper proposes a new strategy for generating difficult positive samples. In view of the strong generalizability and self-learning capabilities of deep learning models, many scholars have studies how to apply deep learning theory in the pedestrian re-identification field. However, a number of problems persist in practically applying deep learning in this field, including determining how to make full use of the features of the sequence information in the salient region of an image and addressing the data gap between data-driven deep learning models and pedestrian re-identification tasks. In view of these problems, in this paper, a re-identification method is proposed based on a visual common attention mechanism. Initially, the method focuses on the local area of the image at the location specified by the given coordinates. Next, under the constraint of pedestrian image pairing tags, it focuses on the sequence of salient regions of image pairs based on deep learning techniques. Then, the global features and the local attention features are cascaded into joint features for use in pedestrian re-identification. To address the data gap between deep learning models and pedestrian re-identification, a new strategy for generating difficult positive samples is proposed primarily through a positive sample that mainly involves a positive sample generation network, a difficult positive sample conversion network, and a dual-stream twin network. We using this network, a large number of positive samples can be obtained to train the data-driven neural network and solve the re-identification task. The above ideas are combined to propose a pedestrian re-identification algorithm based on a visual attention-positive sample generation network deep learning model. The experimental results show that the method proposed in this paper not only achieves better recognition results than other deep learning methods, but also adapts well to a variety of databases. In addition, the method proposed in this paper is more robust to occluded pedestrian images than other deep learning methods. In addition, the method proposed in this paper is more robust than other deep learning methods for occluding pedestrian images. [ABSTRACT FROM AUTHOR]

Background DSA is an irreplaceable diagnostic method for cerebral vascular disease. It is sensitive to intracranial macrovascular disease, but not to distal blood perfusion. In order to quantitatively assess the perfusion area of the feeding artery, this study is the first to propose an imaging method of the internal carotid artery (ICA) perfusion area relying on deconvolution algorithm and cross - correlation (CC) algorithm. Methods Four patients with cerebral vascular disease and 2 healthy subjects who underwent DSA examination in Luwan Branch of Ruijin Hospital Affiliated to Shanghai Jiaotong University School of Medicine from June 2020 to May 2021 were enrolled. The arterial input function (AIF) of the ICA was extracted, and a deconvolution algorithm was used to calculate the time to peak (Tmax) for each pixel. Then the CC algorithm was used to obtain the CC map related to the perfusion of the ICA at each pixel point of the whole brain. Area of interest (ROI) of AIF were randomly selected for 2 healthy subjects, calculated the root mean square error (RMSE) of the CC map for reliability analysis. The CC map was drawn for 4 patients with cerebral vascular disease for validity analysis. Results Reliability analysis showed that the RMSE values of Towne's and lateral views of the CC map and Tmax map of the left and right ICA in a healthy subject were 0.008 ±0.011, 0.022 ± 0.002, 0.015 ±0.007, 0.004 ±0.008, and (0.108 ± 0.181) s, (0.181 ± 0.214) s, (0.301 ± 0.230) s, (0.035 ± 0.092) s; another healthy subject were 0.015 ± 0.023, 0.007 ±0.011, 0.007 ±0.011, 0.005 ±0.012, and (0.172±0.275) s, (0.092 ± 0.174) s, (0.087±0.156) s, (0.079± 0.153) s. The averaged RMSE value of Tmax map was lower than the temporal resolution of DSA, indicating that the reliability of the CC map of the AIF value box in different ROIs of the ICA was high. Validity analysis showed that the CC map and Tmax map of the patient with acute ischemic stroke treated by intravenous thrombolysis bridging to mechanical thrombectomy can fully display the perfusion changes of the ICA after successful thrombectomy. Postoperative DSA in the patient with subacute ischemic stroke after endovascular therapy showed recanalization of the occluded artery, while the CC map showed no significant change in the perfusion of the ICA. The DSA of the patient with middle cerebral artery (MCA) stenosis without related ischemic events showed severe MCA stenosis, but the CC map showed that the ICA was well perfused, and no endovascular treatment was required. The DSA of the patient with acute ischemic stroke without MCA stenosis showed no MCA stenosis, but only sparse ICA-related perfusion was seen on the CC map. Conclusions The CC map and Tmax map based on DSA images have good reliability in different ROIs of AIF. The CC map can provide quantitative information on the perfusion area of the feeding arteries, which is helpful for accurate diagnosis and appropriate treatment decisions. [ABSTRACT FROM AUTHOR]

In this paper, we propose a modified hybrid Salp Swarm Algorithm (SSA) and Aquila Optimizer (AO) named IHSSAO for UAV path planning in complex terrain. The primary logic of the proposed IHSSAO is to enhance the performance of AO by introducing the leader mechanism of SSA, tent chaotic map, and pinhole imaging opposition-based learning strategy. Firstly, the tent chaotic map is utilized to substitute the randomly generated initial population in the original algorithm to increase the diversity of the initial individuals. Secondly, we integrate the leader mechanism of SSA into the position update formulation of the basic AO, which enables the search individuals to fully utilize the optimal solution information and enhances the global search capability of AO. Thirdly, we introduce the pinhole imaging opposition-based learning in the proposed IHSSAO to enhance the capability to escape from the local optimization. To verify the effectiveness of the proposed IHSSAO algorithm, we tested it against SSA, AO, and five other advanced meta-heuristic algorithms on 23 classical benchmark functions and 17 IEEE CEC2017 test functions. The experimental results indicate that the proposed IHSSAO is superior to the other seven algorithms in most cases. Eventually, we applied the IHSSAO, SSA, and AO to solve the UAV path planning problem. The experimental results verify that the IHSSAO is superior to the basic SSA and AO for solving the UAV path planning problem in complex terrain. [ABSTRACT FROM AUTHOR]

The standard RSA relies on multiple big-number modular exponentiation operations and a longer key-length is required for better protection. This imposes a hefty time penalty for encryption and decryption. In this study, we analysed and developed an improved parallel algorithm (PMKRSA) based on the idea of splitting the plaintext into multiple chunks and encrypt the chunks using multiple key-pairs. The algorithm in our new scheme is so natural for parallelised implementation that we also investigated its parallelisation in a GPU environment. In the following, the structure of our new scheme is outlined and its correctness is proved mathematically. Then, with the algorithm implemented and optimised on both CPU and CPU+GPU platforms, we showed that our algorithm shortens the computational time considerably, and it has a security advantage over the standard RSA as it is invulnerable to the common attacks. Finally, we also proved the feasibility of using our algorithm to encrypt large files through simulation. The results show that over the set of file size: 1 MB, 10 MB, 25 MB, 50 MB, 100 MB, the average encryption and decryption time of the CPU version is 0.2476 and 9.4476 s, and for the CPU+GPU version, it is 0.0009 and 0.0618 s, respectively. [ABSTRACT FROM AUTHOR]

In many scientific fields, the continuous operation of ultra-stable lasers is crucial for applications. To speed up the frequency stabilization process in case of the occurence of unexpected interruptions, a prompt frequency stabilization approach based on an improved mean shift algorithm is proposed and verified with a homemade laser system. We developed a double-loop feedback controller to steer the laser frequency with fast and slow channels, respectively. In this study, an improved mean shift algorithm is utilized to intelligently search for the transmission signal, which involves adaptively updating the sliding window radius and incorporating a Gaussian kernel function to update the shift vector. The number of lock points on the left and right sides of the central point determines the scanning direction to search for the transmission signal quickly. The laser is intentionally interrupted 306 times within 10,000 s to evaluate the relocking performance. The median auto-locking time of the laser is improved from 16 s to 4 s. By beating with another ultra-stable laser system, the laser frequency instability is measured to be less than 2.1 × 10 − 14 and the linewidth is 5 Hz. This work improves the adaptation and relocking ability of the ultra-stable laser in a complex environment. [ABSTRACT FROM AUTHOR]

Background: Acetowhite (AW) region is a critical physiological phenomenon of precancerous lesions of cervical cancer. An accurate segmentation of the AW region can provide a useful diagnostic tool for gynecologic oncologists in screening cervical cancers. Traditional approaches for the segmentation of AW regions relied heavily on manual or semi-automatic methods.Objective: To automatically segment the AW regions from colposcope images.Methods: First, the cervical region was extracted from the original colposcope images by k-means clustering algorithm. Second, a deep learning-based image semantic segmentation model named DeepLab V3+ was used to segment the AW region from the cervical image.Results: The results showed that, compared to the fuzzy clustering segmentation algorithm and the level set segmentation algorithm, the new method proposed in this study achieved a mean Jaccard Index (JI) accuracy of 63.6% (improved by 27.9% and 27.5% respectively), a mean specificity of 94.9% (improved by 55.8% and 32.3% respectively) and a mean accuracy of 91.2% (improved by 38.6% and 26.4% respectively). A mean sensitivity of 78.2% was achieved by the proposed method, which was 17.4% and 10.1% lower respectively. Compared to the image semantic segmentation models U-Net and PSPNet, the proposed method yielded a higher mean JI accuracy, mean sensitivity and mean accuracy.Conclusion: The improved segmentation performance suggested that the proposed method may serve as a useful complimentary tool in screening cervical cancer. [ABSTRACT FROM AUTHOR]

Sequential Monte Carlo algorithms are widely accepted as powerful computational tools for making inference with dynamical systems. A key step in sequential Monte Carlo is resampling, which plays the role of steering the algorithm towards the future dynamics. Several strategies have been used in practice, including multinomial resampling, residual resampling, optimal resampling, stratified resampling and optimal transport resampling. In one-dimensional cases, we show that optimal transport resampling is equivalent to stratified resampling on the sorted particles, and both strategies minimize the resampling variance as well as the expected squared energy distance between the original and resampled empirical distributions. For general |$d$| -dimensional cases, we show that if the particles are first sorted using the Hilbert curve, the variance of stratified resampling is |$O(m^{-(1+2/d)})$|⁠ , an improvement over the best previously known rate of |$O(m^{-(1+1/d)})$|⁠ , where |$m$| is the number of resampled particles. We show that this improved rate is optimal for ordered stratified resampling schemes, as conjectured in Gerber et al. (2019). We also present an almost-sure bound on the Wasserstein distance between the original and Hilbert-curve-resampled empirical distributions. In light of these results, we show that for dimension |$d>1$| the mean square error of sequential quasi-Monte Carlo with |$n$| particles can be |$O(n^{-1-4/\{d(d+4)\}})$| if Hilbert curve resampling is used and a specific low-discrepancy set is chosen. To our knowledge, this is the first known convergence rate lower than |$o(n^{-1})$|⁠. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) has been widely used in various fields, and meeting practical high-quality flight paths is one of the crucial functions of UAV. Many algorithms have the problem of too fast convergence and premature in UAV path planning. This study proposed a sparrow particle swarm algorithm for UAV path planning, the SPSA. The algorithm selects a suitable model for path initialization, changes the discoverer position update, and reinforces the influence of start-end line on path search, which can significantly reduce blind search. The number of target points reached is increased by adaptive variable speed escapes in areas of deadlock. In this case, the planned trajectory will fluctuate, and adaptive oscillation optimization can effectively reduce the fluctuation of the path. Finally, the optimal path is simplified, and the path nodes are interpolated with cubic splines to improve the smoothness of the path, which improves the smoothness of the UAV flight trajectory and makes it more suitable for use as the UAV real flight trajectory. By comparison, it can be concluded that the improved SPSA has good convergence speed and better search results and can avoid local optimality. [ABSTRACT FROM AUTHOR]

This work was aimed to explore the application of the L2-block-matching and 3-dimentional filtering (BM3D) (L2-BM3D) denoising algorithm in the treatment of lumbar degeneration with long- and short-segment fixation of posterior decompression. 120 patients with degenerative lumbar scoliosis were randomly divided into group A (MRI images were not processed), group B (MRI images were processed by the BM3D denoising algorithm), and group C (MRI images were processed by the BM3D denoising algorithm based on weighted norm L2). This denoising algorithm was comprehensively evaluated in terms of mean square error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and running time. Besides, the results of surgeries based on different denoising methods were assessed through the surgical time, intraoperative blood loss, postoperative drainage, and postoperative follow-up. The results showed the following: (1) PSNR (peak signal-to-noise ratio) and SSIM (structural similarity index measure) of the L2-BM3D algorithm are better than those of the BM3D algorithm (31.21 dB versus 29.33 dB, 0.83 versus 0.72), while mean square error (MSE) was less than that of the BM3D algorithm (P < 0.05). (2) The operation time, intraoperative bleeding, and postoperative drainage volume in group C were lower than those in group B and group A (P < 0.05). The postoperative follow-up results showed that, in group C, the postoperative VAS (visual analysis scale) score (1.03 ± 0.29) and ODI (Oswestry disability index) (9.29 ± 0.32) were lower, indicating that the postoperative recovery effect of patients was better. Therefore, the patient's postoperative recovery effect was better. In conclusion, the L2-BM3D algorithm had an ideal denoising effect on MRI images of lumbar degeneration and was worthy of clinical promotion. [ABSTRACT FROM AUTHOR]

Stochastic local search (SLS) algorithms are well known for their ability to efficiently find models of random instances of the SAT problem, especially for uniform random k‐SAT instances. Two processes affect most SLS solvers—the initial assignment of the variables and the heuristics that select which variable to flip. In the last few years, the work on generating the appropriate initial assignment has not been paid much attention or seen much progress, while most SLS solvers focused on the heuristic algorithm. The present work aims to improve SLS algorithms on uniform random k‐SAT instances by developing effective methods for generating the initial assignment of variables in a controlled way. First, the allocation strategy introduced recently for 3‐SAT instances is extended to initialize the initial assignment on random k‐SAT instances. Then a concept of an initial probability distribution of the clause‐to‐variable ratio of the instance is introduced to determine the parameters of the allocation strategy. This combined method is added to the beginning of six state‐of‐the‐art SLS algorithms in order to generate initial assignments of variables in a controlled way instead of generating them randomly, resulting in six extended SLS algorithms named WalkSATlm_E, DCCASat_E, Score2SAT_E, CSCCSat_E, Probsat_E, and Sparrow_E, respectively. They are then evaluated in terms of their capabilities and efficiency on uniform random k‐SAT instance from the random track of SAT Competitions in 2016, 2017, and 2018. Experimental results show that these improved SLS solvers outperform their original performance, especially WalkSAT_E, Score2SAT_E, and CSCCSat_E outperform the winner of the random track of SAT competition in 2017. In addition, based on the initial probability distribution method, the present work proposes a parameter tuning and analysis of random 3‐SAT instances and provides an additional comparative analysis with the state‐of‐the‐art random SLS solvers based on large‐scale experiments. [ABSTRACT FROM AUTHOR]

In this paper, we numerically compare several well-established and recent qualitative algorithms for the shape reconstruction of obstacles in elastic scattering based on the measured noisy full far-field data. The compared algorithms include linear sampling method, factorization method and its variant (i.e., F ♯ method), direct sampling method, and direct factorization method. To regularize the ill-conditioned far-field integral operator used in linear sampling method and factorization method, the Tikhonov regularization is used, where we compared two different regularization parameter choice technique: the generalized discrepancy principle (GDP) and the improved maximum product criterion (IMPC). The GDP requires a priori knowledge of the noise level in the far-field data, while the IMPC does not have such an impractical requirement. Extensive numerical examples are provided to illustrate the difference, similarity, advantage, and disadvantage of the tested qualitative methods under various settings. The direct sampling method and direct factorization method outperform the others in computational efficiency while achieving comparable reconstruction accuracy. We find the F ♯ method is numerically less sensitive to noise. [ABSTRACT FROM AUTHOR]

Coevolutionary algorithms have demonstrated high performance on many constrained multi-objective problems. However, on some problems with fraudulent constraints or small feasible regions, they may fail to converge to the Pareto front (PF) or even fail to find a feasible solution. To search for feasible solutions robustly for various problems and obtain solutions approaching the PF as possible, this paper proposes a coevolutionary algorithm assisted by two archives. To be specific, a population for minimizing constraint violation and a population for optimizing objectives without constraints coevolve to find a feasible solution first. Then, the feasible solutions are improved by the population with constrained dominance principle and an archive consisting of inversely-updated infeasible solutions. In addition to searching in the feasible regions found so far, the unconstrained population continues searching the objectives without constraints, expecting that new feasible regions can be spotted in the promising regions. To mitigate the issues that small feasible regions may be missed by the unconstrained population, a diversity archive is updated in a larger objective space than the unconstrained population to enhance exploration. In the experiments, the proposed method is compared with 11 state-of-the-art algorithms on 67 problems to demonstrate its effectiveness. The results show that the proposed method is very robust in finding feasible solutions and obtains better or competitive performance on most problems. • Four sources of parents are maintained in the process to achieve the robustness of the algorithm. • The optimization is divided into two stages to benefit from single-objective optimization. • Extensive experiments are conducted to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• Classification methods can influence the performance of integrated modeling. • Clustering algorithms may improve accuracy of integrated modeling. • Compared to NBC, ISOMax may make the hybrid models more excellent. Flash flood is one of the most severe natural disasters around the world, and has caused sizeable economic losses and countless death. Assessing flash flood susceptibility by hybrid models of statistical and machine learning methods is essential for flood mitigation strategies and disaster preparedness. Although classifying the flash flood conditioning factors becomes a crucial step before applying these hybrid models, their impact on the accuracy of integrated modeling is still unclear. Most previous studies used natural break classification (NBC) and quantile classification methods by default to conduct the classification, but more classification methods have not been tried. In this context, this study introduced three clustering algorithms of K-Means, Expectation Maximization, and ISOMaximum likelihood algorithm (ISOMax) into the classification of factors, and compared them to NBC and quantile classification. To test the impact of classification methods on integrated modeling, these classification results were applied into the construction of three hybrid models (i.e., the integrating of frequency ratio with support vector machines, random forest, and bayesian-regularization neural networks). Then, the accuracy of these hybrid models was evaluated by using ROC curves and statistical indicators. The classification results show that the clustering intervals in the same factor varied with classification algorithms. It can be found from the model performance evaluation results that different classification algorithms will lead to discrepancies in accuracy of integrated modeling. Compared to NBC, the ISOMax allows a better fitting and prediction ability of hybrid models in this study. The application of clustering algorithm provides a new perspective for improving the accuracy of integrated modeling. [ABSTRACT FROM AUTHOR]

We propose and analyze a Vanka-type multigrid solver for solving a sequence of complex-shifted Laplacian systems arising in diagonalization-based parallel-in-time algorithms for evolutionary equations. Under suitable assumption, local Fourier analysis shows the proposed Vanka-type smoother achieves a uniform smoothing factor, which is verified by several numerical examples. [ABSTRACT FROM AUTHOR]

• A ROM-accelerated parallel-in-time preconditioner is developed for solving evolutionary PDEs. • Low dimensional approximation is introduced to the ParaDIAG preconditioning procedure. • More than an order of magnitude speed-up has been achieved by the new algorithm. In this paper we propose a model reduction technique to speed up the diagonalization-based parallel-in-time (ParaDIAG) preconditioner, for iteratively solving all-at-once systems from evolutionary PDEs. In particular, we use the reduced basis method to seek a low-dimensional approximation to the sequence of complex-shifted systems arising from Step-(b) of the ParaDIAG preconditioning procedure. Different from the standard reduced order modeling that uses the separation of offline and online stages, we have to build the reduced order model (ROM) online for the considered systems at each iteration. Therefore, several heuristic acceleration techniques are introduced in the greedy basis generation algorithm, that is built upon a residual-based error indicator, to further boost up its computational efficiency. Several numerical experiments are conducted, which illustrate the favorable computational efficiency of our proposed ROM-accelerated ParaDIAG preconditioner, in comparison with the multigrid-based one. [ABSTRACT FROM AUTHOR]

l To propose a novel coupling dynamics model of an asymmetrical dual-rotor system. l Natural vibration characteristics and coupled phenomena of the model to be analyzed systematically. l To appear harmonic resonances, super harmonic resonances and three unstable regions. l To verify natural vibration characteristics of the system through using experimental results. l To prove the correctness of the proposed novel model. The asymmetrical dual-rotor can represent special fault cases of an aero-engine, such as cracked rotors or lost blades, and to understand characteristics of natural vibrations of the system are very important on the design or maintenance stages of the aero-engine. The novel asymmetrical dual-rotor model is put forward based on special fault cases of the aero-engine. In the coupled dynamics model, equations of motion and natural vibration are derived by using Lagrange's equation and the harmonic balance algorithm, systematically. Natural vibration frequencies are studied by using equations of natural vibrations of the model, and coupled natural vibration frequencies between rotors are investigated too. Response curves of coupled phenomena of the model are analyzed in detail. Due to effects of an asymmetry and coupling actions between rotors, complex natural vibration phenomena, such as three unstable regions and two super harmonic resonances on the low pressure (LP) rotor, are firstly investigated on the proposed novel dual-rotor model. In addition, solutions of theoretical analyses are good consistent with results of numerical simulations, and results of experiments can further illustrate natural vibration characteristics of the dynamics model. Research results prove the correctness of the proposed coupled dynamics model. [ABSTRACT FROM AUTHOR]

l An SLD and ASCS model of a preloaded DNBS considering geometric errors are proposed. l An SLD algorithm is proposed for a preloaded DNBS considering non-loaded balls. l The critical external axial force increases with the geometric error l The initial ASCS of a preloaded DNBS decreases with increasing geometric error. l The ASCS can be kept more stable by selecting the appropriate preload. The preloaded double-nut ball screw (DNBS) is widely used in precision transmission for its high load capacity and contact stiffness. However, few researches have been done on the influence of load distribution on the loading performance of a DNBS, and no solution has been found for the problem of non-loaded balls in the raceway. Therefore, we propose a static load distribution (SLD) model of a preloaded DNBS considering geometric errors, which takes into account the influence of non-loaded balls and the interaction between the elastic deformation of a screw/nut and the Hertz contact forces of screw–ball/nut–ball contact areas. This model is verified experimentally. The maximum deviation between the theoretical and experimental values is 6.21%. We numerically simulate the effects of geometric errors on the SLD and axial static contact stiffness (ASCS) of a preloaded DNBS. The SLD varies significantly with the geometric error, preload and external axial forces, and the critical external axial force on the preloaded DNBS increases with the preload and geometric error. For a preloaded DNBS with a given geometric error, the ASCS can be kept more stable by selecting the appropriate preload. [ABSTRACT FROM AUTHOR]

Automated theorem prover (ATP) for first-order logic (FOL), as a significant inference engine, is one of the hot research areas in the field of knowledge representation and automated reasoning. E prover, as one of the leading ATPs, has made a significant contribution to the development of theorem provers for FOL, particularly equality handling, after more than two decades of development. However, there are still a large number of problems in the TPTP problem library, the benchmark problem library for ATPs, that E has yet to solve. The standard contradiction separation (S-CS) rule is an inference method introduced recently that can handle multiple clauses in a synergized way and has a few distinctive features which complements to the calculus of E. Binary clauses, on the other hand, are widely utilized in the automated deduction process for FOL because they have a minimal number of literals (typically only two literals), few symbols, and high manipulability. As a result, it is feasible to improve a prover's deduction capability by reusing binary clause. In this paper, a binary clause reusing algorithm based on the S-CS rule is firstly proposed, which is then incorporated into E with the objective to enhance E's performance, resulting in an extended E prover. According to experimental findings, the performance of the extended E prover not only outperforms E itself in a variety of aspects, but also solves 18 problems with rating of 1 in the TPTP library, meaning that none of the existing ATPs are able to resolve them. [ABSTRACT FROM AUTHOR]

• The backpropagation with gradient accumulation (BP-GA) algorithm is proposed. • The BP-GA algorithm can tolerate significant memristor non-idealities. • The BP-GA algorithm is insensitive to learning rate. • The robustness of the BP-GA algorithm is attributed to the gradient accumulation. Memristive neural network (MNN) has emerged as a new computing architecture with high speed and low power consumption, but its hardware implementation is hampered mainly by the device non-idealities of memristors. Here, we propose a backpropagation with gradient accumulation (BP-GA) algorithm which can effectively tolerate the memristor non-idealities. We first show that a memristor-based single-layer perceptron trained with BP-GA achieves high image recognition accuracies (>85% on the MNIST dataset) with a wide range of learning rates (0.01–50), large nonlinearities (>6), a small number of conductance states (even down to 3 states), a broad spectrum of ON/OFF ratios (across 3 orders of magnitude), and large noises (up to 40%). The origin for such good robustness against the learning rate and memristor non-idealities is then investigated and revealed to be associated with the operation mechanism of BP-GA. In this algorithm, the weights can keep increasing in magnitude due to the gradient accumulation and therefore become movable even at a small learning rate (or a large ON/OFF ratio equivalently). Moreover, the weights corresponding to the foreground and background of the input image are appropriately moved to the positive and negative boundaries of the weight range, respectively, thus allowing the learning of the image features using only a small number of conductance states. These boundary weights are trapped there without significant oscillation under the effects of accumulated gradients, resulting in immunity to large nonlinearity and noise. Therefore, BP-GA enables the MNN to be insensitive to the learning rate and robust against the memristor non-idealities. The performance of BP-GA is further evaluated on the memristor-based multilayer perceptron (on the MNIST dataset) and convolutional neural network (on the Cifar-10 dataset). For both MNNs, BP-GA exhibits relatively high accuracies and good tolerance against memristor non-idealities, demonstrating its applicability to complex networks and problems. This study provides a viable approach at the algorithm level for addressing some important hardware implementation issues of MNNs using realistic memristors. [ABSTRACT FROM AUTHOR]

Precise target distribution is a key issue for further appropriate applications of fangjis (formulas) with similar efficacy and herbal constituents to maximize efficacy and minimize toxicity. To develop an algorithm for investigating the differential target distributions and characteristic mechanisms of fangjis. In this study, we proposed a Modulome-Fangjiome Association Study (MoFAS) for comparing fangjis from qi-invigorating and xue-nourishing fangjiome (represented by four fangjis: FEJ, SDT, LYG and QOL). Firstly, the database-driven target network of these 4 fangjis was constructed as qi-xue network and decomposed into modules. Then, the modular map with functional landscape were constructed based on consistency score and enrichment analysis. Finally, we employed a targeting rate (TR) matrix to assess the contribution of this fangjiome to modulome (a set of modules) and compared characteristic effect of fangjis by principal component analysis (PCA). A qi-xue network constituted by 579 proteins and 23 modules were constructed. In the functional landscape, 3 primary modules were mainly involved in the endocrine system and environmental adaptation. For the target distribution, SDT and QOL were more similar; the FEJ and LYG were located distant from other fangjis according to PCA. The common effects of FEJ, SDT, and QOL focused on stress response and organism development in environmental perturbation, but the FEJ was superior in regulating critical targets, primarily focusing on hormone and neurotransmitter processes. SDT and QOL were concentrated on the majority scale of the qi-xue network, especially for the mitotic cell cycle and development. LYG only targeted lymphocyte costimulation and icosanoid biosynthetic processes. In this study, for the first time, we investigated the difference in the target distribution of qi-invigorating and xue-nourishing fangjiome and provided direct evidence for the characteristic therapeutic effect of these fangjis, which may promote the precise application of fangjis and support the identification of appropriate populations. [Display omitted] • This strategy provided a systematical exploration of target distribution for multiple-target and multiple-herb fangjis. • This strategy promoted precise application of fangjis from identical fangjiome and identification of appropriate populations. • This paper is a paradigm for precision medicine of fangjis and network-based drug discovery. [ABSTRACT FROM AUTHOR]

In real-world applications, the ability to encrypt a large quantity of images is highly desirable. A branch of works resort chaotic encryption to encrypt images but mostly aim at one image for one-time encryption. Besides, encryption algorithms that can support processing batch images are strongly needed for acceleration in the scenario of massive data. To this end, we propose a fast and more secure algorithm for protecting batch images by employing chaotic intrinsic properties, reversible steganography, and parallel computing. First, our algorithm assigns batch images evenly to each thread, in which Cipher Block Chaining (CBC) mode is applied among neighboring images for encryption. Later, the identifier number of each thread and its CBC indexes are encrypted and embedded into the corresponding cipher-images using reversible steganography, which enhances the security of existing chaotic encryption. Besides, keystreams are associated with the plaintext for the goal of resisting chosen-plaintext attack. The experimental results and security analyses show that our algorithm can achieve superior results with high efficiency. • A fast parallel batch image encryption algorithm using intrinsic properties of chaos is proposed. • The parallelism and keystream reuse improve the encryption efficiency. • The use of extended CBC mode and the plaintext-dependent diffusion strategy enhance the ability to resist chosen-plaintext attack. • The embedding of auxiliary decryption key enhances the protection. [ABSTRACT FROM AUTHOR]

• MEMS-INS, GPS and polarization compass are integrated together for navigation. • A multi-rate algorithm is proposed to solve the problem caused by different sampling frequencies. • Singular value decomposition is used to improve the numerical stability of the algorithm. The microelectromechanical system-inertial navigation system (MEMS-INS) is one of the most widely used sensor systems in navigation but must be used with other sensors because of its error accumulation characteristics. The information fusion method for integrated navigation systems based on filtering technology is thus very important. This paper introduces a new adaptive Kalman filter for nonlinear integrated systems. An improved multi-rate strong tracking square-root cubature Kalman filter (MR-STSCKF) for a MEMS-INS/Global Positioning System (GPS)/polarization compass integrated navigation system is proposed. The proposed filter is used to estimate the system covariance adaptively. The proposed approach can overcome the problem of the inconsistency between the sampling frequencies of different sensors while maintaining the high precision of the integrated navigation results. Experimental results demonstrate that the proposed MR-STSCKF algorithm is effective in improving the accuracy of the MEMS-INS/GPS/polarization compass integrated navigation system with a high sampling frequency. [ABSTRACT FROM AUTHOR]