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Underwater humanoid robots (UHRs) have emerged as a significant area of interest in robotics, with the potential to overcome the limitations of traditional underwater robots and revolutionize underwater activities. This review examines the development of UHRs, focusing on their perception, decision-making, and execution capabilities within a hierarchical human-machine cooperation framework. The Perception Layer involves gathering information from the environment and human collaborators. The Decision-making Layer explores different levels of robot autonomy and the current status of human-UHR collaborative decision-making. The Execution Layer encompasses modeling, control, and actuation mechanisms to translate high-level intentions into physical actions. Various UHR implementations across research teams are reviewed to provide a comprehensive overview of current advancements. Discussions and challenges surrounding UHR progress are provided as well. Continued research and development efforts of UHR represent a promising avenue for advancing human-machine cooperation and pushing the boundaries of underwater exploration, contributing to scientific discoveries and societal benefits in this captivating realm. [ABSTRACT FROM AUTHOR]

Traditional LSM-tree-based key-value storage systems face significant read amplification issues due to the multi-level structure of LSM-tree, the unordered SSTable files in Level 0, and the lack of an in-memory index structure for key-value pairs. We observed that, under the influence of workloads with locality features, key-value pairs exhibit a range-specific access intensity. Addressing the three reasons for LSM-tree read amplification, we have utilized the range-specific access intensity of workload to propose a multi-tree structure consisting of a B+ tree, a single-level hot tree, and an LSM-tree with partition-based Level 0. This aims to enhance the read performance of LSM-tree-based key-value storage systems. We designed the prototype, MTDB, based on LevelDB. The experimental results show that MTDB's read performance is 1.62× to 2.02× that of LevelDB, and it approaches or exceeds the read performance of KVell and Bourbon while reducing memory overhead by 58.85%–86%. [ABSTRACT FROM AUTHOR]

The log-structured merge tree (LSM-tree) in modern key-value (KV) storage systems needs compactions to move KV pairs from higher level to lower level to keep fully sorted ordering in each level and reclaim invalid KV pairs. Unfortunately, the system performance will be hurt because read–write amplification is caused by each compaction. In this paper, we have observed that read–write amplification exists between consecutive compactions within the same level of the LSM-tree. Motivated by this, we propose the compaction in advance strategy which triggers multiple consecutive compactions ahead of time when a certain level of the LSM-tree reaches its size limit. To improve the write performance, we design a novel pipeline parallel method based on the sorted string table (SST) file granularity. Additionally, we apply the compaction in advance strategy and traditional compaction, with the pipeline method, for low levels and high levels of the LSM-tree, respectively. Finally, we implement a prototype system called PMCKV based on LevelDB. The experimental results show that PMCKV, without increasing storage consumption, achieves a throughput approximately 1.2 - 1.7 times higher than LevelDB, 1.1 - 1.5 times higher than RocksDB, and 1.1 - 1.8 times higher than DiffKV, for write-intensive workloads. Moreover, PMCKV performs similarly to LevelDB and RocksDB for read-intensive workloads. [ABSTRACT FROM AUTHOR]

Deploying LSM-tree-based key-value stores on distributed file systems is a common approach to building distributed key-value storage systems, such as RocksDB on HDFS (RoH). However, due to the inherent read amplification characteristics of the LSM-tree, RoH faces performance degradation issues in read operations, including gets and scans, caused by the transmission of a large amount of irrelevant data between data nodes and the master node. To address these challenges, we firstly propose a computation offloading strategy that shifts get and scan operations from the master node to data nodes to reduce unnecessary data transmission. To boost the read operations, we then design concurrent execution methods: Concurrent-Get and Concurrent-Scan. Finally, we implement our prototype system, CDNRocks, based on RoH, and conduct extensive experiments in a distributed multi-node environment to demonstrate the effectiveness of CDNRocks. The results indicate that CDNRocks outperforms RoH by exhibiting better read performance, less data transmission, and lower CPU utilization on the master node. Furthermore, compared to other distributed key-value storage systems like Cassandra and HBase, CDNRocks excels in read performance within small cluster environments(with node counts up to 9), while achieving a balanced trade-off in load performance and efficient CPU and memory utilization. [ABSTRACT FROM AUTHOR]

Summary: Collaborative cloud applications have become the dominant application mode in the big data era. These applications usually generate plenty of cooperative files, which share their ownerships with all collaborative participants. Data deduplication is a promising solution to improve the storage efficiency and save the user expenditure. However, it remains an open issue on how to securely prove the shared ownerships for the shared files and address the attacks on account of using data deduplication. To tackle the above issue, in this paper, we introduce a novel concept of the Proof of Shared oWnership (PoSW) and construct a secure multi‐server‐aided PoSW (ms‐PoSW) scheme for securing client‐side deduplication for the shared files, which is based on the convergent encryption, secret sharing, and bloom filter. In the ms‐PoSW scheme, we employ a sharing convergent key to avoid the single point of failure, introduce the secret sharing algorithm to implement the shared ownership, and construct a novel interaction protocol between the shared owners and the cloud server to prove the shared ownership. Furthermore, a hybrid PoSW scheme is constructed to address the secure proof of hybrid cloud architectures. Finally, security analysis and performance evaluation show the security and efficiency of the proposed schemes. [ABSTRACT FROM AUTHOR]

• Propose novel score function for PFSs. • Develop novel entropy measure for PFSs. • Put forward novel knowledge measure for PFSs. • Propose novel picture fuzzy MULTIMOORA method. • A practical case of car sharing station site selection in Beijing is given. The car sharing is an effective supplement to public transportation systems. However, how to select an appropriate site for building a car sharing station is a big challenge. Therefore, in this paper, a novel picture fuzzy multi-criteria decision making (MCDM) model is proposed to solve the site selection problem for car sharing stations. To this end, the definition of picture fuzzy sets is adopted to accurately portray the voting information. Then, a concept of knowledge measure is defined for PFNs and an objective method based on knowledge measure is proposed to derive the weight information of criteria. To rank the candidate sites for car sharing stations, an extended MULTIMOORA method based on novel score function and Borda rule is devised. The proposed MCDM model is applied to solve the site selection for car sharing stations in Bejing. Finally, comparative analyses are given to verify the superiority of the proposed MCDM model. [ABSTRACT FROM AUTHOR]

Modern key-value (KV) storage systems adopt append-only writes to update KV pairs with the out-of-place manner, because the performance of sequential accesses is much better than that of random accesses for HDDs. Compaction or GC operations will be deployed by traditional KVs or KV separation schemes due to updating KV pairs via append-only writes. Unfortunately, the system performance will be hurt because extra reads and writes are triggered during those operations, especially under update-intensive workloads.We find that the performance gap for SSDs between sequential and random accesses will get close when the request size becomes large in our experiments. Motivated by this, we propose InPlaceKV built atop SSDs, which adopts an in-place large-update scheme with a hotness-aware method to update KV pairs rather than use append-only writes with the LSM-tree. We further design the working flow of system operations with appropriate data structures. Finally, we compare InPlaceKV with state-of-the-art KV storage systems via extensive experiments under update-intensive workloads, and results validate the effectiveness of our design in improving the system throughput. [ABSTRACT FROM AUTHOR]

This paper proposes three data transmission strategies for resource monitoring in cloud computing platforms, which are dynamic periodic push strategy, window-based event-driven push strategy, and window-based hybrid push strategy, respectively. The dynamic periodic push strategy periodically pushes resource status information at a dynamic time interval that is dynamically updated by considering the change degree of resource status information. The window-based event-driven push strategy pushes resource status information if the change degree of resource status information is larger than a threshold that is dynamically updated by using the exponentially weighted moving average. The window-based hybrid push strategy combines the dynamic periodic push strategy and the window-based event-driven push strategy and then further reduces the communication overhead and improves the data coherence. Experimental results show that the window-based hybrid push strategy performs better than previous data transmission strategies in terms of the number of data transmissions and the data coherence. [ABSTRACT FROM AUTHOR]

As a core paradigm of the evidential clustering algorithm, evidential c -means (ECM) offers a more flexible credal partition to characterize uncertainty and imprecision in cluster assignment, which generalizes hard and fuzzy partitions. Nonetheless, ECM and its derivatives equally consider the importance of each feature in clustering, which can lead to suboptimal clustering performance, especially in high-dimensional datasets where some features are more informative than others. In this paper, we propose a series of new evidential clustering algorithms by integrating feature-weight learning to simultaneously characterize uncertainty and imprecision in cluster assignment and automatically learn the different importance of distinct features in clustering. We consider ECM with vector feature-weight learning and ECM with matrix feature-weight learning, and introduce two different constraints on feature weights, respectively. We develop four objective functions and derive the corresponding optimization process. Extensive experimental results using synthetic and real-world datasets demonstrate the feasibility and effectiveness of the proposed algorithms. • The paper presents evidential c-means algorithms with feature-weight learning. • The algorithms introduce vector and matrix feature-weight learning. • The algorithms create credal partition to represent uncertainty and imprecision. • Extensive experiments show the better performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Highlights: [•] It clearly analyzes inserting and deleting operations in the traditional SB algorithm. [•] It proposes the concept of matching-failed segments due to above operations. [•] It proposes an efficient sliding blocking algorithm with backtracking sub-blocks. [•] SBBS can detect duplicate data as many as possible in matching-failed segments. [ABSTRACT FROM AUTHOR]

The blockchain platforms (BPs) have emerged as important tools for improving enterprise data security. However, the selection of a suitable BP for is a challenging issue for enterprises. In this paper, a novel heterogenous multi-criteria decision-making (MCDM) model is proposed to facilitate the BP selection and evaluation process. First, the evaluation criteria for BPs are defined through an analysis of popular BPs. Second, based on the defined evaluation criteria, multiple types of fuzzy numbers are adopted to accurately represent the evaluation criteria. Then, to deal with multiple types of fuzzy numbers, a hybrid distance measure is proposed to determine the negative and positive ideal separation matrices of the evaluation criteria. In addition, using the axiomatic design's information content metric the as basis, an improved method is proposed to unify the evaluation of multiple types into a single number. An improved weight method is then proposed based on the combination of objective and subjective weighting to obtain the comprehensive weights of the criteria. Finally, a closeness degree metric is introduced to obtain the ranking order of all BP alternatives. An example case is analyzed to illustrate how to evaluate and select a suitable BP. The superiority of the proposed heterogeneous model is verified by comparisons with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

As a critical strategic tool within the framework of the digital economy (DE), artificial intelligence (AI) plays a crucial role in realizing an AI-assisted economy. This novel paradigm has gained widespread acceptance from governments globally, positioning itself as a cornerstone in the practical implementation of the DE. The increasing reliance on digital means for economic transformation, the treatment of data as a critical resource, and the integration of information technologies highlight the synergy between AI and the DE. Against this backdrop, online reviews and SA play a pivotal role, offering rich data streams continually emerging on various platforms. Therefore, this article utilizes a probabilistic linguistic term set (PLTS) to establish a multi-granularity probabilistic linguistic (MG-PL) information system. This approach, through sentiment analysis (SA), enables quantitative analysis of user emotional expressions. The integration of these technological aspects holds the promise of enhancing decision-making processes, facilitating the intellectualization of economic activities, and addressing existing challenges in the application of AI within the DE domain. Initially, SA is conducted on text-based online comment data, forming a probabilistic linguistic incomplete information system. The maximum similarity method is employed to establish a probabilistic linguistic complete information system. Subsequently, an adjustable MG-PL decision-theoretic rough set (DTRS) is developed, followed by the establishment of a three-way group decision model using three-way decisions (3WD) and a directed graph. The model's applicability, stability, and feasibility are demonstrated via a case study centered on the acquisition of new energy vehicles (NEVs). • The sentiment analysis for online reviews within the digital economy is conducted. • An incomplete information system is completed using the maximum similarity method. • A group decision-making method based on 3WD and directed graphs is established. • The model's applicability is shown via the purchase scheme of new energy vehicles. [ABSTRACT FROM AUTHOR]

Four iridium (III) complexes Ir1‐Ir4 were synthesized and characterized. Possessing high singlet oxygen production ability and specific mitochondria‐localization, Ir1 was developed as a mitochondria‐targeting photosensitizer. Ir1 exhibited strong phototoxicity against cancer cell line A549 and its corresponding cisplatin‐resistant one A549R. In contrast, Ir1 showed low cytotoxicity toward normal cell HLF. This selectivity resulted from the different uptake amount. With 405 nm irradiation, Ir1 induced mitochondria‐mediated cell death in A549R cells, achieving the overcome of drug‐resistant. [ABSTRACT FROM AUTHOR]

An algal astaxanthin feeding trial was carried out to investigate the effects of natural astaxanthin from Haematococcus pluvialis as feed additives on growth, pigmenting efficacy and antioxidant capacity in blood parrot (Cichlasoma citrinellum × Cichlasoma. synspilum). Tissue total antioxidant capacity (TAC), superoxide dismutase (SOD), catalase (CAT) and maleic dialdehyde (MDA) were chosen as measures of its antioxidant capacity. All fish which received an astaxanthin (from micro-algal H. pluvialis) supplemented diet with 400 mg/kg of astaxanthin, after 50 days of feeding, the astaxanthin-fed fish displayed a pinkcolored skin and the control-fed fish displayed a grayish skin. For the growth, the weight gains of controlfed fish and astaxanthin-fed fish were 200% and 300%, respectively. Samples of skin and scales were used for analysis of total carotenoids and astaxanthin content, and fish feeding astaxanthin showed significantly (P <0.05) higher concentrations than the control group, indicating that the pigmentation of this fish had been significantly improved by dietary astaxanthin. Compared with the control fish, pigmented fish had lower SOD, CAT and MDA and higher TAC. It can be concluded that supplementation with dietary astaxanthin could effectively enhance growth, skin coloration and the antioxidant capacity of this fish. This study will provide a reference for application of natural astaxanthin from H. pluvialis as feed additives in blood parrot artificial breeding. Our data is also useful in ornamental fish farming, especially when the retentivity of astaxanthin in the skin and scales are involved. It is leading to the possibility of increasing the pigmentation of farmed-fish by adding the powdered form of H. pluvialis to the diet as an effective pigment. [ABSTRACT FROM AUTHOR]

The explosive growth of global data and the gradual evolution of multimedia processing pattern promote us to enter the era of heterogeneous multimedia data. To improve the storage efficiency and reduce the management expenditure of these massive multimedia data in heterogeneous networks, the direct way is multimedia data deduplication. However, it arises serious privacy concerns and poses new security challenges, such as privacy leakage, the side-channel attack and unauthorized access. To solve these issues, we propose a novel role symmetric encryption (RSE) algorithm and a RSE-based proof of ownership (RSE-PoW) scheme for secure deduplication in hierarchical heterogeneous environments, which is based on the role symmetric encryption, proof of ownership and bloom filter. The proposed scheme is the first solution to prevent the privacy leakage and resist against the side-channel attacks, meanwhile, achieve the authorized deduplication effectively for multimedia data in heterogeneous network. Security analysis and performance evaluation show the security and efficiency of our proposed scheme. [ABSTRACT FROM AUTHOR]

• A novel score-based attack is proposed to deceive the DNN models. • Using substitute and target model to evaluate the word importance. • Synonym is used for generating adversarial samples. • Adversarial training can help to enhance the robustness of DNN models. Deep neural networks (DNNs) are vulnerable to adversarial attacks, in which a small perturbation to samples can cause misclassification. However, how to select important words for textual attack models is a big challenge. Therefore, in this paper, an innovative score-based attack model is proposed to solve the important words selection problem for textual attack models. To this end, the generation of semantically adversarial examples in this model is adopted to mislead a text classification model. Then, this model integrates the self-attention mechanism and confidence probabilities for the selection of the important words. Moreover, an alternative model similar to the transfer attack is introduced to reflect the correlation degree of words inside the texts. Finally, adversarial training experimental results demonstrate the superiority of the proposed model. [ABSTRACT FROM AUTHOR]

The selection and utilization of different color spaces significantly impact the recognition performance of deep learning models in downstream tasks. Existing studies typically leverage image information from various color spaces through model integration or channel concatenation. However, these methods result in excessive model size and suboptimal utilization of image information. In this study, we propose the structure-preserved self-attention network (SPSANet) model for efficient fusion of image information from different color spaces. This model incorporates a novel structure-preserved self-attention (SPSA) module that employs a single-head pixel-wise attention mechanism, as opposed to the conventional multihead self-attention (MHSA) approach. Specifically, feature maps from all color space grouping paths are utilized for similarity matching, enabling the model to focus on critical pixel locations across different color spaces. This design mitigates the dependence of the SPSANet model on the choice of color space while enhancing the advantages of integrating multiple color spaces. The SPSANet model also employs channel shuffle operations to facilitate limited interaction between information flows from different color space paths. Experimental results demonstrate that the SPSANet model, utilizing eight common color spaces-RGB, Luv, XYZ, Lab, HSV, YCrCb, YUV, and HLS-achieves superior recognition performance with reduced parameters and computational cost.

The fast-moving field of photodynamic therapy (PDT) has provided fresh opportunities to expand the potential of metallodrugs to combat cancers in a light-controlled manner. As such, in the present study, a series of cyclometalated Ir(III) complexes modified with a tetrazine functional group (namely, Ir-ppy-Tz, Ir-pbt-Tz, and Ir-dfppy-Tz) are developed as potential two-photon photodynamic anticancer agents. These complexes target mitochondria but exhibit low toxicity towards HLF primary lung fibroblast normal cells in the dark. When receiving a low-dose one- or two-photon PDT, they become highly potent towards A549 lung cancer cells (with IC 50 values ranging from 24.0 nM to 96.0 nM) through the generation of reactive oxygen species (ROS) to induce mitochondrial damage and subsequent apoptosis. Our results indicated that the incorporation of tetrazine with cyclometalated Ir(III) matrices would increase the singlet oxygen ( 1 O 2 ) quantum yield ( Φ Δ ) and, meanwhile, enable a type I PDT mechanism. Ir-pbt-Tz, with the largest two-photon absorption (TPA) cross-section ( σ 2 = 102 GM), shows great promise in serving as a two-photon PDT agent for phototherapy.

In recent years, excessive emissions of carbon dioxide have further intensified global warming, which poses a great threat to human society. The best way to reverse this situation is to take action to equalize the "carbon emissions" with the "carbon absorption," i.e., carbon neutrality (CN). To better understand the evolution and display a broad panorama of CN research, this paper provides a comprehensive bibliometric analysis of publications in the field of CN from 2006 to 2021. We use the Web of Science Core Collection as the data source, and a total of 633 publications have been retrieved. In the paper, we measure the productivity and influence of publications through recognized bibliometric metrics, and visually analyze publications using VOS Viewer and CiteSpace. The analysis results show that China is the most productive country/region. The study illustrates that in order to reach the goal of CN, it is necessary to reduce carbon emissions such as full using renewable energy. Simultaneously, it is also essential to enhance the "negative emissions" of carbon such as taking advantage of carbon sink and carbon capture, utilization, and storage. This paper provides some references for scholars who are interested or research in this field., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This paper proposes a novel fuzzy multi-criteria decision-making method based on an improved score function of connection numbers and Choquet integral under interval-valued Pythagorean fuzzy environment. To do so, we first introduce a method to convert interval-valued Pythagorean fuzzy numbers into connection numbers based on the set pair analysis theory. Then an improved score function of connection numbers is proposed to make the ranking order of connection numbers more in line with reality in multi-criteria decision-making process. In addition, some properties of the proposed score function of connection numbers and some examples have been given to illustrate the advantages of conversion method proposed in the paper. Then, considering interactions among different criteria, we propose a fuzzy multi-criteria decision-making approach based on set pair analysis and Choquet integral under interval-valued Pythagorean fuzzy environment. Finally, a case of online learning satisfaction survey and a brief comparative analysis with other existing approaches are studied to show that the proposed method is simple,convenient and easy to implement. Comparing with previous studies, the method in this paper, from a new perspective, effectively deals with multi-criteria decision-making problems that the alternatives cannot be reasonably ranked in the decision-making process under interval-valued Pythagorean fuzzy environment., Competing Interests: Conflict of interestThe authors declare that they have no conflict of interest., (© The Author(s) 2022.)

Since the outbreak of COVID-19, various fields have been damaged to varying degrees, especially in the economic field. According to a comprehensive study of bibliometrics and content analysis, this paper aims at summarizing studies related to the development of the economic field during the COVID-19 epidemic. We search in Web of Science Core Collection using the subjects such as "economics", "economy", "economic", "financial", and then 2274 related documents are collected, which are published from 2020 to 2022. First, this paper uses the mixed qualitative and quantitative analysis methods to analyze the publication status of the countries, institutions, and authors, respectively, and conducts the document co-citation analysis by CiteSpace software. The results showed that the most popular journal is Sustainability, and the most productive research institutions, countries, and authors are primarily located in North American and European countries. Then, it makes an in-depth study of the cooperative network. With the support of Gephi software, this paper employs the social network analysis method to analyze the situation of the country/region cooperation and institutional cooperation. Finally, the content analysis of the related studies is presented to further explore the current challenges. On this basis, this paper analyzes the economic development in the post-epidemic era and draws some conclusions, which provide some references for scholars interested in this field., Competing Interests: The authors declare no conflict of interest., (© 2022 The Author(s).)

Health care systems around the world do not have sufficient medical services to immediately offer elective (e.g., scheduled or non-emergency) services to all patients. The goal of patient admission scheduling (PAS) as a complicated decision making issue is to allocate a group of patients to a limited number of resources such as rooms, time slots, and beds based on a set of preset restrictions such as illness severity, waiting time, and disease categories. This is a crucial issue with multi-criteria group decision making (MCGDM). In order to address this issue, we first conduct an assessment of the admission process and gather four (4) aspects that influence patient admission and design a set of criteria. Even while many of these indicators may be accurately captured by the picture fuzzy set, we use an advanced MCGDM approach that incorporates generalized aggregation to analyze patients' hospitalization. Finally, numerical real-world applications of PAS are offered to illustrate the validity of the suggested technique. The advantages of the proposed approaches are also examined by comparing them to various existing decision methods. The proposed technique has been proved to assist hospitals in managing patient admissions in a flexible manner.

During the COVID-19, colleges organized online education on a massive scale. To make better use of online education in the post-epidemic era, this paper conducts an online education satisfaction survey with four types of colleges and 129,325 students propose a fuzzy TOPSIS (technique for order preference by similarity to ideal solution) method based on the cloud model to rank the satisfaction of different colleges. Firstly, based on the characteristics of online education during the COVID-19, we build an evaluation indicator system from four dimensions: technology, instructor, learner and environment including, 10 indicators and 94 sub-indicators. Secondly, the cloud model is used to quantitatively describe the natural language and uncertainty in a large amount of assessment information. The cloud model generator is used for sub-indicators and achieves an effective and flexible conversion between linguistic information and quantitative values. The cloud model of indicators are presented by integrating the corresponding sub-indicators. The weights of indicators are determined by the entropy method based on the cloud model and possibility degree matrix, which eliminates the judgment of decision-makers and has great power for handling practical problems with unknown weight information. Finally, a fuzzy TOPSIS method based on the cloud model is proposed to rank the satisfaction of online education of different colleges. The proposed method is compared with other existing methods to shown its merits. The experimental result is consistent with the proportion of students who accept online education in the post-epidemic era. According to the second questionnaire, as the qualitative evaluation of the cloud model of indicators increases, the qualitative evaluation of satisfaction of different types of colleges will also increase. It indicates that the method proposed in this paper is practical., (© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.)

Four iridium (III) complexes Ir1-Ir4 were synthesized and characterized. Possessing high singlet oxygen production ability and specific mitochondria-localization, Ir1 was developed as a mitochondria-targeting photosensitizer. Ir1 exhibited strong phototoxicity against cancer cell line A549 and its corresponding cisplatin-resistant one A549R. In contrast, Ir1 showed low cytotoxicity toward normal cell HLF. This selectivity resulted from the different uptake amount. With 405 nm irradiation, Ir1 induced mitochondria-mediated cell death in A549R cells, achieving the overcome of drug-resistant., (© 2021 American Society for Photobiology.)