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Abstract Redundancy elimination techniques are extensively investigated to reduce storage overheads for cloud‐assisted health systems. Deduplication eliminates the redundancy of duplicate blocks by storing one physical instance referenced by multiple duplicates. Delta compression is usually regarded as a complementary technique to deduplication to further remove the redundancy of similar blocks, but our observations indicate that this is disobedient when data have sparse duplicate blocks. In addition, there are many overlapped deltas in the resemblance detection process of post‐deduplication delta compression, which hinders the efficiency of delta compression and the index phase of resemblance detection inquires abundant non‐similar blocks, resulting in inefficient system throughput. Therefore, a multi‐feature‐based redundancy elimination scheme, called MFRE, is proposed to solve these problems. The similarity feature and temporal locality feature are excavated to assist redundancy elimination where the similarity feature well expresses the duplicate attribute. Then, similarity‐based dynamic post‐deduplication delta compression and temporal locality‐based dynamic delta compression discover more similar base blocks to minimise overlapped deltas and improve compression ratios. Moreover, the clustering method based on block‐relationship and the feature index strategy based on bloom filters reduce IO overheads and improve system throughput. Experiments demonstrate that the proposed method, compared to the state‐of‐the‐art method, improves the compression ratio and system throughput by 9.68% and 50%, respectively.

Abstract The potential of the metaverse in the field of education is an area of increasing interest, with many researchers exploring the space to increase the ease and efficacy of student education while reducing time and labor requirements to deliver effective teaching. However, there has been little work into the systematic and technological aspects of delivering education through the metaverse. To fill this gap, we propose a metaverse education system that takes good advantages of virtual reality and Web3 blockchain techologies to create a social learning environment. With this added emphasis on social aspects, learners are able to socialize and engage in collaborative efforts to improve their own knowledge. Using blockchain technology, the system can also help to ensure security and transparency while also keeping progression and grading fair for all participating students.

Abstract Background This study aimed to assess changes in quality of sleep (QoS) in isolated metastatic patients with spinal cord compression following two different surgical treatments and identify potential contributing factors associated with QoS improvement. Methods We reviewed 49 patients with isolated spinal metastasis at our spinal tumor center between December 2017 and May 2021. Total en bloc spondylectomy (TES) and palliative surgery with postoperative stereotactic radiosurgery (PSRS) were performed on 26 and 23 patients, respectively. We employed univariate and multivariate analyses to identify the potential prognostic factors affecting QoS. Results The total Pittsburgh Sleep Quality Index (PSQI) score improved significantly 6 months after surgery. Univariate analysis indicated that age, pain worsening at night, decrease in visual analog scale (VAS), increase in Eastern Cooperative Oncology Group performance score (ECOG-PS), artificial implant in focus, and decrease in epidural spinal cord compression (ESCC) scale values were potential contributing factors for QoS. Multivariate analysis indicated that the ESCC scale score decreased as an independent prognostic factor. Conclusions Patients with spinal cord compression caused by the metastatic disease had significantly improved QoS after TES and PSRS treatment. Moreover, a decrease in ESCC scale value of > 1 was identified as a favorable contributing factor associated with PSQI improvement. In addition, TES and PSRS can prevent recurrence by achieving efficient local tumor control to improve indirect sleep. Accordingly, timely and effective surgical decompression and recurrence control are critical for improving sleep quality.

Breast cancer is one of the most common cancers in the world, especially among women. Breast tumor segmentation is a key step in the identification and localization of the breast tumor region, which has important clinical significance. Inspired by the swin-transformer model with powerful global modeling ability, we propose a semantic segmentation framework named Swin-Net for breast ultrasound images, which combines Transformer and Convolutional Neural Networks (CNNs) to effectively improve the accuracy of breast ultrasound segmentation. Firstly, our model utilizes a swin-transformer encoder with stronger learning ability, which can extract features of images more precisely. In addition, two new modules are introduced in our method, including the feature refinement and enhancement module (RLM) and the hierarchical multi-scale feature fusion module (HFM), given that the influence of ultrasonic image acquisition methods and the characteristics of tumor lesions is difficult to capture. Among them, the RLM module is used to further refine and enhance the feature map learned by the transformer encoder. The HFM module is used to process multi-scale high-level semantic features and low-level details, so as to achieve effective cross-layer feature fusion, suppress noise, and improve model segmentation performance. Experimental results show that Swin-Net performs significantly better than the most advanced methods on the two public benchmark datasets. In particular, it achieves an absolute improvement of 1.4–1.8% on Dice. Additionally, we provide a new dataset of breast ultrasound images on which we test the effect of our model, further demonstrating the validity of our method. In summary, the proposed Swin-Net framework makes significant advancements in breast ultrasound image segmentation, providing valuable exploration for research and applications in this domain.

Mn-based catalysts have great potential in the synergistic removal of NO and chlorobenzene but exhibit serious chlorine poisoning and low catalytic activity at low temperatures. In this study, the performance of MnCeOx for simultaneous catalytic removal of NO and chlorobenzene at low temperatures was optimized by the modification of Sm and Sn. Results showed that the modification of Sm and Sn afforded a loose overall structure of the catalyst with a large specific surface area. The addition of Sm and Sn effectively increased the concentration of Mn4+, Ce3+, chemisorbed oxygen, and the amount of acid on the catalyst surface. This considerably improved the activity of the catalyst for simultaneous removal of NO and chlorobenzene at low temperatures. The intermediates formed by the MCSS-0.086 catalyst during the low-temperature simultaneous catalytic removal of NO and chlorobenzene were CHCl3, CCl4, C2HCl3, and C2Cl4. The presence of SnO2 promoted the deep oxidation of chlorobenzene and inhibited the chlorine poisoning of the catalyst. Thus, the low-temperature stability of the catalyst was improved. The chlorobenzene-oxidation efficiency and the NO-removal efficiency of MCSS-0.086 were more than 90% and 95%, respectively, within the range of 178 °C-300 °C. The NH3-selective catalytic reduction curve of NO removal over the MnCeSmSnOx catalyst followed the Langmuir–Hinshelwood mechanism at low temperatures. The catalytic degradation of chlorobenzene finally yielded small molecules: CO2, H2O, and HCl.

Abstract With modern management of primary liver cancer shifting towards non-invasive diagnostics, accurate tumor classification on medical imaging is increasingly critical for disease surveillance and appropriate targeting of therapy. Recent advancements in machine learning raise the possibility of automated tools that can accelerate workflow, enhance performance, and increase the accessibility of artificial intelligence to clinical researchers. We explore the use of an automated Tree-Based Optimization Tool that leverages a genetic programming algorithm for differentiation of the two common primary liver cancers on multiphasic MRI. Manual and automated analyses were performed to select an optimal machine learning model, with an accuracy of 73–75% (95% CI 0.59–0.85), sensitivity of 70–75% (95% CI 0.48–0.89), and specificity of 71–79% (95% CI 0.52–0.90) on manual optimization, and an accuracy of 73–75% (95% CI 0.59–0.85), sensitivity of 65–75% (95% CI 0.43–0.89) and specificity of 75–79% (95% CI 0.56–0.90) for automated machine learning. We found that automated machine learning performance was similar to that of manual optimization, and it could classify hepatocellular carcinoma and intrahepatic cholangiocarcinoma with an sensitivity and specificity comparable to that of radiologists. However, automated machine learning performance was poor on a subset of scans that met LI-RADS criteria for LR-M. Exploration of additional feature selection and classifier methods with automated machine learning to improve performance on LR-M cases as well as prospective validation in the clinical setting are needed prior to implementation.

The optimal catalyst 10%Ce-Al-Ox combined the advantages of CeO2 and Al2O3, and 10%Ce-Al-Ox exhibited higher than 98% SO2 conversion and higher than 86% sulfur yield in the temperature range of 425–500 °C. VASP calculation showed that the adsorption energy of Ce-Al-Ox catalyst for SO2 was higher than that of Al2O3 for SO2. Finally, for the SO2 reduction by CO over 10%Ce-Al-Ox, the reaction intermediates were bicarbonates, monodentate carbonates and gaseous CO for CO, while weakly adsorbed sulfate, weakly adsorbed SO2 and binuclear bidentate sulfate for SO2.

Abstract Enterprise data typically involves multiple heterogeneous data sources and external data that respectively record business activities, transactions, customer demographics, status, behaviors, interactions and communications with the enterprise, and the consumption and feedback of its products, services, production, marketing, operations, and management, etc. They involve enterprise DNA associated with domain-oriented transactions and master data, informational and operational metadata, and relevant external data. A critical challenge in enterprise data science is to enable an effective ‘whole-of-enterprise’ data understanding and data-driven discovery and decision-making on all-round enterprise DNA. Accordingly, here we introduce a neural encoder Table2Vec for automated universal representation learning of entities such as customers from all-round enterprise DNA with automated data characteristics analysis and data quality augmentation. The learned universal representations serve as representative and benchmarkable enterprise data genomes (similar to biological genomes and DNA in organisms) and can be used for enterprise-wide and domain-specific learning tasks. Table2Vec integrates automated universal representation learning on low-quality enterprise data and downstream learning tasks. Such automated universal enterprise representation and learning cannot be addressed by existing enterprise data warehouses (EDWs), business intelligence and corporate analytics systems, where ‘enterprise big tables’ are constructed with reporting and analytics conducted by specific analysts on respective domain subjects and goals. It addresses critical limitations and gaps of existing representation learning, enterprise analytics and cloud analytics, which are analytical subject, task and data-specific, creating analytical silos in an enterprise. We illustrate Table2Vec in characterizing all-round customer data DNA in an enterprise on complex heterogeneous multi-relational big tables to build universal customer vector representations. The learned universal representation of each customer is all-round, representative and benchmarkable to support both enterprise-wide and domain-specific learning goals and tasks in enterprise data science. Table2Vec significantly outperforms the existing shallow, boosting and deep learning methods typically used for enterprise analytics. We further discuss the research opportunities, directions and applications of automated universal enterprise representation and learning and the learned enterprise data DNA for automated, all-purpose, whole-of-enterprise and ethical machine learning and data science.

Retinal vessel extraction plays an important role in the diagnosis of several medical pathologies, such as diabetic retinopathy and glaucoma. In this article, we propose an efficient method based on a B-COSFIRE filter to tackle two challenging problems in fundus vessel segmentation: (i) difficulties in improving segmentation performance and time efficiency together and (ii) difficulties in distinguishing the thin vessel from the vessel-like noise. In the proposed method, first, we used contrast limited adaptive histogram equalization (CLAHE) for contrast enhancement, then excerpted region of interest (ROI) by thresholding the luminosity plane of the CIELab version of the original RGB image. We employed a set of B-COSFIRE filters to detect vessels and morphological filters to remove noise. Binary thresholding was used for vessel segmentation. Finally, a post-processing method based on connected domains was used to eliminate unconnected non-vessel pixels and to obtain the final vessel image. Based on the binary vessel map obtained, we attempt to evaluate the performance of the proposed algorithm on three publicly available databases (DRIVE, STARE, and CHASEDB1) of manually labeled images. The proposed method requires little processing time (around 12 s for each image) and results in the average accuracy, sensitivity, and specificity of 0.9604, 0.7339, and 0.9847 for the DRIVE database, and 0.9558, 0.8003, and 0.9705 for the STARE database, respectively. The results demonstrate that the proposed method has potential for use in computer-aided diagnosis.

Automatic segmentation of medical images provides a reliable scientific basis for disease diagnosis and analysis. Notably, most existing methods that combine the strengths of convolutional neural networks (CNNs) and Transformers have made significant progress. However, there are some limitations in the current integration of CNN and Transformer technology in two key aspects. Firstly, most methods either overlook or fail to fully incorporate the complementary nature between local and global features. Secondly, the significance of integrating the multiscale encoder features from the dual-branch network to enhance the decoding features is often disregarded in methods that combine CNN and Transformer. To address this issue, we present a groundbreaking dual-branch cross-attention fusion network (DCFNet), which efficiently combines the power of Swin Transformer and CNN to generate complementary global and local features. We then designed the Feature Cross-Fusion (FCF) module to efficiently fuse local and global features. In the FCF, the utilization of the Channel-wise Cross-fusion Transformer (CCT) serves the purpose of aggregatingmulti-scale features, and the Feature FusionModule (FFM) is employed to effectively aggregate dual-branch prominent feature regions from the spatial perspective. Furthermore, within the decoding phase of the dual-branch network, our proposed Channel Attention Block (CAB) aims to emphasize the significance of the channel features between the up-sampled features and the features generated by the FCFmodule to enhance the details of the decoding. Experimental results demonstrate that DCFNet exhibits enhanced accuracy in segmentation performance. Compared to other state-of-the-art (SOTA) methods, our segmentation framework exhibits a superior level of competitiveness. DCFNet's accurate segmentation of medical images can greatly assist medical professionals in making crucial diagnoses of lesion areas in advance. [ABSTRACT FROM AUTHOR]

Automated next-best action recommendation for each customer in a sequential, dynamic and interactive context has been widely needed in natural, social and business decision-making. Personalized next-best action recommendation must involve past, current and future customer demographics and circumstances (states) and behaviors, long-range sequential interactions between customers and decision-makers, multi-sequence interactions between states, behaviors and actions, and their reactions to their counterpart's actions. No existing modeling theories and tools, including Markovian decision processes, user and behavior modeling, deep sequential modeling, and personalized sequential recommendation, can quantify such complex decision-making on a personal level. We take a data-driven approach to learn the next-best actions for personalized decision-making by a reinforced coupled recurrent neural network (CRN). CRN represents multiple coupled dynamic sequences of a customer's historical and current states, responses to decision-makers' actions, decision rewards to actions, and learns long-term multi-sequence interactions between parties (customer and decision-maker). Next-best actions are then recommended on each customer at a time point to change their state for an optimal decision-making objective. Our study demonstrates the potential of personalized deep learning of multi-sequence interactions and automated dynamic intervention for personalized decision-making in complex systems.