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The successful study of self-healing aqueous micro batteries (AMBs) which inherit the advantages of aqueous batteries and have the ability to automatically repair damage is of great significance for the development of smart wearable and portable electronic devices. However, the rate performance and the related power density of developed self-healing AMBs using metal ions as charge carriers is limited, due to the strong interaction between metal ions and electrode materials. Therefore, there is great potential for developing self-healing NH4+ AMBs, because of the outstanding advantages of NH4+ such as extremely abundant reserves, smaller hydrated ion radius and little molar mass. However, the development of self-healing NH4+ AMBs is still an extremely challenge due to the difficulty in developing self-healing hydrogels and instability of anode materials. Even though, the firstly self-healing NH4+ AMBs based on tailoring hydrogel electrolyte and MXene-integrated perylene anode were successfully assembled. As expected, self-healing NH4+ AMBs exhibit excellent energy density (82.48 µWh·cm−2) and power density (3.09 mW·cm−2), cycle life (81.67% after 3000 GCD cycles), flexibility (95.68% under 180°) and self-healing ability (94.16% after the 10th self-healing cycles).

Abstract Video steganography has the potential to be used to convey illegal information, and video steganalysis is a vital tool to detect the presence of this illicit act. Currently, all the motion vector (MV)‐based video steganalysis algorithms extract feature sets directly from the MVs, but ignoring the embedding operation may perturb the statistical distribution of other video encoding elements, such as the skipped macroblocks (no direct MVs). This paper proposes a novel 11‐dimensional feature set to detect MV‐based video steganography based on the above observation. The proposed feature is extracted based on the skipped macroblocks by recompression calibration. Specifically, the feature consists of two components. The first is the probability distribution of motion vector prediction (MVP) difference, and the second is the probability distribution of partition state transfer. Extensive experiments on different conditions demonstrate that the proposed feature set achieves good detection accuracy, especially in lower embedding capacities. In addition, the loss of detection performance caused by recompression calibration using mismatched quantization parameters (QP) is within the acceptable range, so the proposed method can be used in practical scenarios.

The inadequate automation level in the transit of oil drilling tubular columns has led to significant inefficiencies and safety issues. To address these challenges, a real-time detection algorithm, ECS-YOLOv5s, has been proposed. This algorithm aims to improve the accuracy of drill pipe identification during operational processes, facilitating the automation of tubular column handling It has the potential to reduce drilling cycles and overall drilling costs. ECS-YOLOv5s enhance the detection accuracy of drill pipes by incorporating a Bidirectional Feature Pyramid Network (BiFPN) architecture with an improved multi-scale feature fusion network. The use of EfficientNet as the backbone network reduces the number of parameters and computations while effectively merging features from different layers. Additionally, the Spatial Pyramid Pooling (SPP) structure in the Neck is replaced with SPPF, and a Convolutional Block Attention Module (CBAM) is introduced to improve model robustness, reduce parameters and computations, and enhance the model’s ability to detect dense targets. The ECS-YOLOv5s algorithm exhibits superior performance in drill pipe inspection, achieving a mean Average Precision (mAP) of 90.2%, a frame rate of 125 FPS, and a parameter count of only 37%. It achieves an accuracy of 98.6%, outperforming the original model by 9.2%. The comparative analysis demonstrates that the improved algorithm surpasses traditional models such as YOLOv5s, SSD, Faster-RCNN, and YOLOv7-tiny in both performance and accuracy. These findings provide valuable insights for the research on automated processing of tubular columns in intelligent oil drilling platforms.

Addressing the compatibility challenges surrounding the robustness and reversibility of existing video watermarking techniques, this study introduces a novel video dual-domain blind watermarking algorithm leveraging the Hadamard transform. Specifically tailored for H.264 video copyright protection, the algorithm initially organizes video frames and identifies key frames for watermark embedding. Prior to embedding, the robust watermark undergoes coding preprocessing to optimize its integration. Subsequently, a 4×4 block is expanded based on the selected embedding position within the frame, followed by the application of the Hadamard transform to the enlarged block. The 1-bit robust watermark information is then embedded via the coefficient pair located in the first row of the Hadamard coefficient matrix corresponding to the expanded block. Additionally, a reversible watermark, designed to mitigate the distortions introduced during robust embedding, is generated and embedded into the remaining coefficients of the coefficient matrix using reversible embedding techniques. During watermark extraction, the dual-domain watermark can be retrieved exclusively through reversible extraction methodologies by analyzing the size relationship of coefficient pairs, eliminating the need for access to the original video data. To bolster the algorithm’s robustness, a majority-subordinate voting system is devised and implemented, effectively enhancing its resilience. Experimental findings demonstrate that, compared to similar approaches, this algorithm not only enhances the reversibility of video restoration but also exhibits superior robustness and meets the requirements for imperceptibility.

Abstract The aqueous micro batteries (AMBs) are expected to be one of the most promising micro energy storage devices for its safe operation and cost‐effectiveness. However, the performance of the AMBs is not satisfactory, which is attributed to strong interaction between metal ions and the electrode materials. Here, the first AMBs are developed with NH4+ as charge carrier. More importantly, to solve the low conductivity and the dissolution during the NH4+ intercalation/extraction problem of perylene material represented by perylene‐3,4,9,10‐tetracarboxylic dianhydride (PTCDA), the Ti3C2Tx MXene with high conductivity and polar surface terminals is introduced as a conductive skeleton (PTCDA/Ti3C2Tx MXene). Benefitting from this, the PTCDA/Ti3C2Tx MXene electrodes exhibit ultra‐high cycle life and rate capability (74.31% after 10 000 galvanostatic chargedischarge (GCD) cycles, and 91.67 mAh g−1 at 15.0 A g−1, i.e., capacity retention of 45.2% for a 30‐fold increase in current density). More significantly, the AMBs with NH4+ as charge carrier and PTCDA/Ti3C2Tx MXene anode provide excellent energy density and power density, cycle life, and flexibility. This work will provide strategy for the development of NH4+ storage materials and the design of AMBs.

In intensive care units (ICUs), mortality prediction is performed by combining information from these two sources of ICU patients by monitoring patient health. Respectively, time series data generated from each patient admission to the ICU and clinical records consisting of physician diagnostic summaries. However, existing mortality prediction studies mainly cascade the multimodal features of time series data and clinical records for prediction, ignoring thecross-modal correlation between the underlying features in different modal data. To address theseissues, we propose a multimodal fusion model for mortality prediction that jointly models patients’ time-series data as well as clinical records. We apply a fine-tuned Bert model (Bio-Bert) to the patient’s clinical record to generate a holistic embedding of the text part, which is then combined with the output of an LSTM model encoding the patient’s time-series data to extract valid features. The global contextual information of each modal data is extracted using the improved fusion module to capture the correlation between different modal data. Furthermore, the improved fusion module can be easily added to the fusion features of any unimodal network and utilize existing pre-trained unimodal model weights. We use a real dataset containing 18904 ICU patients to train and evaluate our model, and the research results show that the representations obtained by themodel can achieve better prediction accuracy compared to the baseline.

Magnetoencephalography is a noninvasive neuromagnetic technology to record epileptic activities for the pre-operative localization of epileptogenic zones, which has received increasing attention in the diagnosis and surgery of epilepsy. As reported by recent studies, pathological high frequency oscillations (HFOs), when utilized as a biomarker to localize the epileptogenic zones, result in a significant reduction in seizure frequency, even seizure elimination in around 80% of cases. Thus, objective, rapid, and automatic detection and recommendation of HFOs are highly desirable for clinicians to alleviate the burden of reviewing a large amount of MEG data from a given patient. Despite the advantage, the performance of existing HFOs rarely satisfies the clinical requirement. Consequently, no HFOs have been successfully applied to real clinical applications so far. In this work, we propose a multi-head self-attention-based detector for recommendation, termed MSADR, to detect and recommend HFO signals. Taking advantage of the state-of-the-art multi-head self-attention mechanism in deep learning, the proposed MSADR achieves a more superior accuracy of 88.6% than peer machine learning models in both detection and recommendation tasks. In addition, the robustness of MSADR is also extensively assessed with various ablation tests, results of which further demonstrate the effectiveness and generalizability of the proposed approach.

After long-term ageing, the structure of graphene oxide prepared by the modified Hummers method changed. Because of the desorption of oxygen-containing functional groups, the C/O ratio of graphene oxide increased from 1.96 to 2.76. However, the average interlayer distance decreased from 0.660 to 0.567 nm. The content of -CH- and -CH2- decreased; however, the type of oxygen-containing functional groups did not change. Moreover, ID/IG increased from 0.87 to 0.92, indicating that the defect density decreased because of desorbing oxygen functional groups after ageing. When the temperature exceeded 60°C, CO2 produced by decomposing graphene oxide was detected. The thermal decomposition changed after ageing. The decomposition peak temperature decreased from 216°C to 195°C. The CO2 amount produced remained almost unchanged; however, the amount of CO, SO2 and H2O decreased. After ageing, the apparent activation energy of graphene oxide decreased from 150 to 134 kJ mol−1.

BackgroundThe collagen 3 alpha 1 (COL3A1) rs1800255 polymorphism has been reported to be associated with women pelvic organ prolapse (POP) susceptibility, but the results of these previous studies have been contradictory. The objective of current study is to explore whether COL3A1 rs1800255 polymorphism confers risk to POP.MethodsRelevant literatures were searched by searching databases including Pubmed, Embase, Google academic, the Cochrane library, China National Knowledge Infrastructure (CNKI). Search time is from database foundation to March 2021.ResultsA total of seven literatures were enrolled in the present meta-analysis, including 1642 participants. Overall, no significant association was found by any genetic models. In subgroup analysis based on ethnicity, significant associations were demonstrated in Caucasians by allele contrast (A vs. G: OR = 1.34, 95%CI = 1.03-1.74,), homozygote comparison (AA vs. GG: OR = 3.25, 95%CI = 1.39-7.59), and recessive genetic model (AA vs. GG/GA: OR = 3.22, 95%CI = 1.40-7.42).ConclusionsThe present meta-analysis suggests that the COL3A1 is a candidate gene for POP susceptibility. Caucasian individuals with A allele and AA genotype have a higher risk of POP. The COL3A1 rs1800255 polymorphism may be risk factor for POP in Caucasian population.

The steganographic model based on game theory is improved. A hybrid steganography model that satisfies the Kerckhoffs principle by combining the strategy adaptation of the game theory model and the content adaptation in the cover model is proposed. This model is used to design a high-security steganography algorithm for an H.264 video. The distortion function in UNIWARD algorithm is improved according to the H.264 video coding characteristics. The improved distortion function is used to generate the bias function under the game theory model, and the embedding probability of each position in the cover is calculated. The new distortion function under the hybrid model is generated by combining the embedding probability of the cover position and the distortion cost function under the improved UNIWARD algorithm and using it to embed information in the H.264 residual DCT coefficient block. The experiment proves that the algorithm has higher security than the UNIWARD algorithm. With the same analysis algorithm, the detection error rate is significantly improved compared to the UNIWARD algorithm. The algorithm has a large embedding capacity and good invisibility. When fully embedded, the capacity reaches 1.9% or more, the visual effect does not change significantly, and the PSNR value decreases within 2 dB. After the secret message is embedded in the information, the video bit rate is increased by 9%.

Information technologies such as e-commerce and e-news bring overloaded information as well as convenience to users, cooperatives and companies. Recommender system is a significant technology in solving this information overload problem. Due to the outstanding accuracy performance in top-N recommendation tasks, two-step recommendation algorithms are suitable to generate recommendations. However, their recommendation lists are biased towards popular items. In this paper, we propose a user based two-step recommendation algorithm with popularity normalization to improve recommendation diversity and novelty, as well as two evaluation metrics to measure diverse and novel performance. Experimental results demonstrate that our proposed approach significantly improves the diversity and novelty performance while still inheriting the advantage of two-step recommendation approaches on accuracy metrics.

Autism spectrum disorder (ASD) is a developmental disorder that impacts more than 1.6% of children aged 8 across the United States. It is characterized by impairments in social interaction and communication, as well as by a restricted repertoire of activity and interests. The current standardized clinical diagnosis of ASD remains to be a subjective diagnosis, mainly relying on behavior-based tests. However, the diagnostic process for ASD is not only time consuming, but also costly, causing a tremendous financial burden for patients’ families. Therefore, automated diagnosis approaches have been an attractive solution for earlier identification of ASD. In this work, we set to develop a deep learning model for automated diagnosis of ASD. Specifically, a multichannel deep attention neural network (DANN) was proposed by integrating multiple layers of neural networks, attention mechanism, and feature fusion to capture the interrelationships in multimodality data. We evaluated the proposed multichannel DANN model on the Autism Brain Imaging Data Exchange (ABIDE) repository with 809 subjects (408 ASD patients and 401 typical development controls). Our model achieved a state-of-the-art accuracy of 0.732 on ASD classification by integrating three scales of brain functional connectomes and personal characteristic data, outperforming multiple peer machine learning models in a k-fold cross validation experiment. Additional k-fold and leave-one-site-out cross validation were conducted to test the generalizability and robustness of the proposed multichannel DANN model. The results show promise for deep learning models to aid the future automated clinical diagnosis of ASD.

Background: Mesh exposure after surgery continues to be a clinical challenge for urogynecological surgeons. The purpose of this study was to explore the risk factors for polypropylene (PP) mesh exposure after transvaginal mesh (TVM) surgery. Methods: This study included 195 patients with advanced pelvic organ prolapse (POP), who underwent TVM from January 2004 to December 2012 at the First Affiliated Hospital of Chinese PLA General Hospital. Clinical data were evaluated including patient's demography, TVM type, concomitant procedures, operation time, blood loss, postoperative morbidity, and mesh exposure. Mesh exposure was identified through postoperative vaginal examination. Statistical analysis was performed to identify risk factors for mesh exposure. Results: Two-hundred and nine transvaginal PP meshes were placed, including 194 in the anterior wall and 15 in the posterior wall. Concomitant tension-free vaginal tape was performed in 61 cases. The mean follow-up time was 35.1 ± 23.6 months. PP mesh exposure was identified in 32 cases (16.4%), with 31 in the anterior wall and 1 in the posterior wall. Significant difference was found in operating time and concomitant procedures between exposed and nonexposed groups (F = 7.443, P = 0.007; F = 4.307, P = 0.039, respectively). Binary logistic regression revealed that the number of concomitant procedures and operation time were risk factors for mesh exposure (P = 0.001, P = 0.043). Conclusion: Concomitant procedures and increased operating time increase the risk for postoperative mesh exposure in patients undergoing TVM surgery for POP.