This study introduces a novel approach to bolstering quantum key distribution (QKD) security by implementing swift classical channel authentication within the SARG04 and BB84 protocols. We propose mono-authentication, a pioneering paradigm employing quantum-resistant signature algorithms—specifically, CRYSTALS-DILITHIUM and RAINBOW—to authenticate solely at the conclusion of communication. Our numerical analysis comprehensively examines the performance of these algorithms across various block sizes (128, 192, and 256 bits) in both block-based and continuous photon transmission scenarios. Through 100 iterations of simulations, we meticulously assess the impact of noise levels on authentication efficacy. Our results notably highlight CRYSTALS-DILITHIUM’s consistent outperformance of RAINBOW, with signature overheads of approximately 0.5% for the QKD-BB84 protocol and 0.4% for the QKD-SARG04 one, when the quantum bit error rate (QBER) is augmented up to 8%. Moreover, our study unveils a correlation between higher security levels and increased authentication times, with CRYSTALS-DILITHIUM maintaining superior efficiency across all key rates up to 10,000 kb/s. These findings underscore the substantial cost and complexity reduction achieved by mono-authentication, particularly in noisy environments, paving the way for more resilient and efficient quantum communication systems. Full article

The previous research has proven that one of the fundamental requirements for ensuring increased profitability and economic competitiveness in offshore-based projects is co-locating different technologies within the same marine space. This paper presents a number of techno-feasibility analyses for floating offshore technologies for the Maltese Islands, located in the central Mediterranean Sea. The first part compares the feasibility between offshore floating solar photovoltaics with onshore-based systems, taking into consideration Malta’s average land rental price per square metre. The second part considers the use of a novel floating breakwater design that integrates energy storage and creates a sheltered water area for a multi-use marine park, thus introducing different revenue streams. The latter includes renting the sheltered marine space out to operators of floating solar farms, aquaculture cages and vessel berthing facilities, as well as the provision of energy storage services. It is found that the combined income from the multiple revenue streams from the multi-use marine park is still insufficient to justify the investment and that financial support from governments is essential to render the floating breakwaters viable. Full article

Abstract: Based on the classical theory of nonlinear Thomson scattering and the single electron model, we performed extensive numerical simulations in MATLAB R2022b to comprehensively investigate how the initial phase of a tightly focused, circularly polarized laser pulse affects the radiation characteristics of high-energy electrons at different energy levels. Our findings indicate that the polar angle corresponding to the maximum radiation energy remains constant as the initial phase of the laser changes from 0 to 2π, while the azimuth angle correspondingly moves from 0 to 2π. Moreover, as the initial phase changes, the pulse width of the electron radiation peak displays a quasi-periodic oscillation with a period of π. Notably, an increase in the initial energy of the electrons results in a significant enhancement in both the peak radiation value and the collimation of the radiation. These results demonstrate that manipulating the initial phase of the driving laser pulse enables effective control over the spatial distribution of radiation light. Full article

Urban parks are considered one of the most significant ecosystems when looking at urban green spaces, but ecological functions and the type of recreation space created can change depending on the park’s age and its vegetation type. Therefore, the effects of the vegetation types present and urban park ages on soil properties and bacterial communities were tested in Yancheng, as it is a typical rapidly urbanizing city in China, and one of the most densely populated metropolises among the central cities of the Yangtze River Delta region. We found that the soil properties and bacterial community composition vary depending on vegetation type and park age. In addition, the pH value of soil planted with Cynodon dactylon is higher, and the available phosphorus concentrations in the old parks are at the highest levels, which are 1.20–2.66 times higher than in the middle-aged and young parks’ soil. Gammaproteobacteria, Alphaproteobacteria, Acidobacteria_6, and Deltaproteobacteria are the predominant bacteria phyla in urban park soil. A higher level of bacterial operational taxonomic units (OTUs) are found in Metasequoia glyptostroboides soil (5479, 69.7%) and middle-aged park soil (5670, 72.2%). Saprospirae, Chloracidobacteria, and Alphaproteobacteria are negatively correlated with pH to a significant extent. Additionally, pH, available potassium, and soil organic carbon were positively correlated with saccharase activity. Available phosphorus and nitrogen are related to soil community composition. These results indicate that both park age and vegetation type contribute to the differences in soil pH, available phosphorus, soil organic carbon, available potassium, available nitrogen, alkaline phosphatase, and soil bacterial composition within urban parks in Yancheng. Full article

Interactive devices such as touch screens have gained widespread usage in daily life; this has directed the attention of researchers to the quality of screen glass. Consequently, defect detection in screen glass is essential for improving the quality of smartphone screens. In recent years, defect detection methods based on deep learning have played a crucial role in improving detection accuracy and robustness. However, challenges have arisen in achieving high-performance detection due to the small size, irregular shapes and low contrast of defects. To address these challenges, this paper proposes CE-SGNet, a Context-Enhanced Network with a Spatial-aware Graph, for smartphone screen defect detection. It consists of two novel components: the Adaptive Receptive Field Attention Module (ARFAM) and the Spatial-aware Graph Reasoning Module (SGRM). The ARFAM enhances defect features by adaptively extracting contextual information to capture the most relevant contextual region of defect features. The SGRM constructs a region-to-region graph and encodes region features with spatial relationships. The connections among defect regions are enhanced during the propagation process through a graph attention network. By enriching the feature representations of defect regions, the CE-SGNet can accurately identify and locate defects of various shapes and scales. Experimental results demonstrate that the CE-SGNet achieves outstanding performance on two public datasets. Full article

Vehicular networks have become a critical component of modern transportation systems by facilitating communication between vehicles and infrastructure. Nonetheless, the security of such networks remains a significant concern, given the potential risks associated with cyberattacks. For this purpose, artificial intelligence approaches have been explored to enhance the security of vehicular networks. Using artificial intelligence algorithms to analyze large datasets can enable the early identification and mitigation of potential threats. However, developing and testing effective artificial-intelligence-based solutions for vehicular networks necessitates access to diverse datasets that accurately capture the various security challenges and attack scenarios in this context. In light of this, the present survey comprehensively examines the vehicular network environment, the associated security issues, and existing datasets. Specifically, we begin with a general overview of the vehicular network environment and its security challenges. Following this, we introduce an innovative taxonomy designed to classify datasets pertinent to vehicular network security and analyze key features of these datasets. The survey concludes with a tailored guide aimed at researchers in the vehicular network domain. This guide offers strategic advice on selecting the most appropriate datasets for specific research scenarios in the field. Full article

International heritage management approaches have developed into a more inclusive process wherein public participation is identified as a pivotal tool. Thus, determining how to assess public interests and include the public’s ideas in heritage protection has become a technical issue, but relevant research still remains limited. This paper aims to test the digital footprints of social media users as a public participatory tool, with the objectives of identifying industrial heritage landscape attributes and assessing associated values. Targeting the Sanxian industrial heritage landscape of Liangdancheng in China as a case study, in this research the data from user-generated content on social media platforms Ctrip, Weibo, and Meituan were collected and processed with ROST CM 6 and NVivo 12, and content analysis (CA) and importance-performance analysis (IPA) were conducted. Results revealed that the industrial heritage landscape of Liangdancheng encompasses various built, cultural, and natural environmental resources, including both tangible and intangible attributes such as architectural constructions, historic artifacts, cultural events, and plants. These attributes were assessed and categorized into four quadrants of importance–performance characteristics, wherein cultural environmental resources show relatively high performance but built environmental resources need further actions to improve their value perception and interpretation among the public. This research demonstrated that the digital footprints of social media users as a participatory tool can work well in terms of data accessibility, value identification, and public representation, advancing the theoretical framework of Chinese industrial heritage management and global practices. Full article

Abstract: Accurate clinical diagnosis of patients presenting to primary care settings with acute sore throat remains challenging, often resulting in the over-prescribing of antibiotics. Using point-of-care tests (POCTs) to differentiate between respiratory infections is well-accepted, yet evidence on the application within primary care is sparse. We assessed the application of testing patients (n = 160) from three family practices with suspected Streptococcal infections using rapid molecular tests (ID NOW Strep A2, Abbott). In addition to comparing clinical evaluation and prescription rates with either usual care or testing, patients and staff completed a questionnaire about their experience of molecular POCT in primary care. The immediate availability of the result was important to patients (100%), and staff (≈90%) stated that molecular testing improved the quality of care. Interestingly, only 22.73% of patients with a Centor score > 2 tested positive for Strep A and, overall, less than 50% of Centor scores 3 and 4 tested positive for Strep A with the ID NOW testing platform. The addition of rapid molecular POCTs to clinical assessment resulted in a 55–65% reduction in immediate and deferred antibiotic prescriptions. The intervention was popular with patients and medical staff but was associated with increased cost and a longer appointment length. Full article

Additive manufacturing has advantages over other traditional manufacturing technologies for the fabrication of complex thin-walled parts. Previous correlation path strategies, when applied to laser metal deposition processes, suffer from contour deposition transboundary and surface “scar” type overstacking. Therefore, this paper proposes a hybrid path generation method for the laser metal deposition process. First, the topological logic of the STL model of the part is restored to reduce redundant calculations at the stage of obtaining the layered contour. Then, the path points are planned on the basis of the offset contours in a helical upward trend to form a globally continuous composite path in space considering the melt channel width. Finally, vectors that adaptively fit to the model surface are generated for the path points as tool orientations and they are optimized by smoothing the rotation angles. The results of experiments conducted on a multi-axis machine equipped with a laser metal deposition module show that the path generated by the proposed method is not only capable of thin-walled structures with overhanging and curved surface features but also improves the surface imperfections of the part due to sudden changes in the angle of rotation while ensuring the boundary dimensions. Full article

The advancement towards utilizing renewable energy sources is crucial for mitigating environmental issues such as air pollution and climate change. Offshore wind turbines, particularly floating offshore wind turbines (FOWTs), are developed to harness the stronger, steadier winds available over deep waters. Accurate metocean data forecasts, encompassing wind speed and wave height, are crucial for offshore wind farms’ optimal placement, operation, and maintenance and contribute significantly to FOWT’s efficiency, safety, and lifespan. This study examines the application of three machine learning (ML) models, including Facebook Prophet, Seasonal Autoregressive Integrated Moving Average with Exogenous Factors (SARIMAX), and long short-term memory (LSTM), to forecast wind speeds and significant wave heights, using data from a buoy situated in the Pacific Ocean. The models are evaluated based on their ability to predict 1-, 3-, and 30-day future wind speed and wave height values, with performances assessed through Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) metrics. Among the models, LSTM displayed superior performance, effectively capturing the complex temporal dependencies in the data. Incorporating exogenous variables, such as atmospheric conditions and gust speed, further refined the predictions.The study’s findings highlight the potential of machine learning (ML) models to enhance the integration and reliability of renewable energy sources through accurate metocean forecasting. Full article

The identification of co-regulated genes and their Transcription-Factor Binding Sites (TFBSs) are the key steps toward understanding transcription regulation. In addition to effective laboratory assays, various bi-clustering algorithms for the detection of the co-expressed genes have been developed. Bi-clustering methods are used to discover subgroups of genes with similar expression patterns under to-be-identified subsets of experimental conditions when applied to gene expression data. By building two fuzzy partition matrices of the gene expression data with the Axiomatic Fuzzy Set (AFS) theory, this paper proposes a novel fuzzy bi-clustering algorithm for the identification of co-regulated genes. Specifically, the gene expression data are transformed into two fuzzy partition matrices via the sub-preference relations theory of AFS at first. One of the matrices considers the genes as the universe and the conditions as the concept, and the other one considers the genes as the concept and the conditions as the universe. The identification of the co-regulated genes (bi-clusters) is carried out on the two partition matrices at the same time. Then, a novel fuzzy-based similarity criterion is defined based on the partition matrices, and a cyclic optimization algorithm is designed to discover the significant bi-clusters at the expression level. The above procedures guarantee that the generated bi-clusters have more significant expression values than those extracted by the traditional bi-clustering methods. Finally, the performance of the proposed method is evaluated with the performance of the three well-known bi-clustering algorithms on publicly available real microarray datasets. The experimental results are in agreement with the theoretical analysis and show that the proposed algorithm can effectively detect the co-regulated genes without any prior knowledge of the gene expression data. Full article

Unbonded flexible risers consist of several helical and cylindrical layers, which can undergo large bending deformation and can be installed in different configurations to adapt to harsh marine environments; thus, they can be applied to transport oil and gas resources from ultra-deep waters (UDW). Due to their special geometric characteristics, they can ensure sufficient axial tensile stiffness while having small bending stiffness, which can undergo large deflection bending deformation. In recent years, the development of unbonded flexible risers has been moving in an intelligent, integrated direction. This paper presents a review of unbonded flexible risers. Firstly, the form and properties of each interlayer of an unbonded flexible riser are introduced, as well as the corresponding performance and configuration characteristics. In recent years, the development of unbonded flexible risers has been evolving, and the development of machine learning on unbonded flexible risers is discussed. Finally, with emphasis on exploring the design characteristics and working principles, three new types of unbonded flexible risers, an integrated production bundle, an unbonded flexible riser with an anti-H₂S layer, and an unbonded flexible riser with a composite armor layer, are presented. The research results show that: (1) the analytical methods of cross-sectional properties of unbonded flexible risers are solved based on ideal assumptions, and the computational accuracy needs to be improved. (2) Numerical methods have evolved from equivalent simplified models to models that account for detailed geometric properties. (3) Compared with ordinary steel risers, the unbonded flexible riser is more suitable for deep-sea resource development, and the structure of each layer can be designed according to the requirements of the actual environment. Full article

Abstract: In this study, we present a novel dual-loop robust trajectory tracking framework for autonomous underwater vehicles, with the objective of enhancing their performance in underwater searching tasks amidst oceanic disturbances. Initially, a real-world AUV experiment is conducted to validate the efficacy of a cross-rudder AUV configuration in maintaining sailing angle stability during the diving stage, which exhibits a strong capability for straight-line sailing. Building upon the experimental findings, we introduce a state-transform-model predictive guide law to compute the desired velocity for the dynamics loop. This guide law dynamically adjusts the controller across varying depths, thereby reducing model predictive control (MPC) computation while optimizing timing without compromising precision or convergence speed. Subsequently, we incorporate a sliding mode controller with a prescribed disturbance observer into the velocity control loop to concurrently enhance the robustness and convergence rate of the system. This innovative amalgamation of controllers significantly improves tracking precision and convergence rate, while also alleviating the computational burden—a pervasive challenge in AUV MPC control. Finally, various condition simulations are conducted to validate the robustness, effectiveness, and superiority of the proposed method. These simulations underscore the enhanced performance and reliability of our proposed trajectory tracking framework, highlighting its potential utility in real-world AUV applications. Full article

Amid the deepening imbalance in the supply and demand of allogeneic organs, xenotransplantation can be a practical alternative because it makes an unlimited supply of organs possible. However, to perform xenotransplantation on patients, the source animals to be used must be free from infectious agents. This requires the breeding of animals using assisted reproductive techniques, such as somatic cell nuclear transfer, embryo transfer, and cesarean section, without colostrum derived in designated pathogen-free (DPF) facilities. Most infectious agents can be removed from animals produced via these methods, but several viruses known to pass through the placenta are not easy to remove, even with these methods. Therefore, in this narrative review, we examine the characteristics of several viruses that are important to consider in xenotransplantation due to their ability to cross the placenta, and investigate how these viruses can be detected. This review is intended to help maintain DPF facilities by preventing animals infected with the virus from entering DPF facilities and to help select pigs suitable for xenotransplantation. Full article

Data are the core element of digital transformation. Data factor marketization (DFM) is critical in the process of digital transformation, which promotes urban green development. This paper analyzes the role of digitization in urban environmental sustainability. We investigate the effects of DFM on environmental pollution (EP) using a difference-in-differences approach and data from 283 cities in China from 2006 to 2019. The findings reveal that cities implementing DFM demonstrate an average reduction in EP of 2.67%. The mechanism behind DFM lowering EP involves fostering green innovation, increasing public awareness of environmental issues, attracting IT professionals, optimizing the industrial structure, and enhancing digital finance capabilities. Large cities, cities in the south, and those not primarily dependent on natural resources exhibit a more pronounced reduction in EP through DFM. Implementing policies related to digital infrastructure and enhancing the protection of urban intellectual property rights further amplifies the effect of DFM in reducing pollution. Additionally, this effect exhibits spatial spillover effects. This study contributes to the existing literature by (1) demonstrating DFM’s role in improving urban environmental quality in China through digital technology and market mechanisms, thereby aligning economic growth with ecological sustainability; (2) emphasizing the importance of public engagement in environmental stewardship through increased awareness and community participation in policymaking, as well as fostering social inclusion and ecological conservation; (3) emphasizing spatial spillover effects, the importance of inter-city collaboration in environmental policies, and advocating for comprehensive strategies to achieve broader environmental improvements across urban areas. Full article

Abstract: This study reports the synthesis of Ni-Co-SiC coatings onto Q235A steel substrates through magnetic-field-induced electrodeposition to improve the surface performances of the machine parts. The microstructure, topology, roughness, corrosion, and wear resistances of the coatings were investigated through X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), atomic force microscopy (AFM), hardness testing, electrochemical analysis, and friction wear testing, respectively. The Ni-Co-SiC coating deposited at 0.4 T (MS1) with a perpendicular magnetic direction showed the maximum SiC content and NiCo grain size (86.5 nm). The surface topology was also fine, dense, and smooth. In addition to that, the images obtained from the AFM characterization showed that the surface roughness of the MS1 coating was 76 nm, which was significantly lower compared to the roughness observed in Ni-Co-SiC coatings fabricated under the magnetic induction of 0 T (MS0) and magnetic field applied in a parallel direction to 0.4 T (MS2). The XRD results revealed that the preferential growth direction of the NiCo grains was changed from the (200) crystal plane to the (111) plane with the introduction of a perpendicular magnetic field. Moreover, MS2, MS1, and MS0 had thickness values of 25.3, 26.7, and 26.3 μm, respectively. Among all the coatings, MS1 showed the lowest friction coefficient and the highest hardness value (914.8 HV), suggesting enhanced wear resistance. Moreover, the MS1 coating revealed a maximum corrosion potential of −257 mV, and the lowest corrosion current of 0.487 μA/cm², suggesting its improved corrosion resistance. Full article

Type 2 diabetes mellitus (T2DM) management and glycemic control in underserved non-Hispanic Black adults presents with multifaceted challenges: balancing the optimal complexity of antihyperglycemic medications prescribed, limited medication access due to socioeconomic status, medication nonadherence, and high prevalence of cardiometabolic comorbidities. This single-center, cross-sectional, retrospective chart analysis evaluated the association of Medication Regimen Complexity (MRC) with cardiometabolic outcomes (glycemic, atherogenic cholesterol, and blood pressure control) among non-Hispanic Black adults with type 2 diabetes. Utilizing 470 independent patient electronic health records, MRC and other covariates were examined to determine their associations with cardiometabolic outcomes. Chi-square tests of independence and multiple logistic regression were performed to identify associations between MRC and cardiometabolic outcomes. Our findings indicate significant negative and positive associations between MRC and glycemic control and atherogenic cholesterol control, respectively. However, there were no associations between MRC and blood pressure control. As diabetes MRC was shown to be associated with poor glycemic control and improved atherogenic cholesterol control, there is a critical need to standardize interdisciplinary diabetes care to include pharmacists and to develop more insurance policy interventions that increase access to newer, efficacious diabetes medications for historically marginalized populations. Full article

Changes in gut microbiome composition have been implicated in the pathogenesis of graft-versus-host disease (GvHD) after allogeneic hematopoietic stem cell transplantation (allo-HSCT). Our objective was to explore the microbial abundance in patients with GvHD after allo-HSCT. We conducted a single-center, prospective study in patients who underwent allo-HSCT and developed grade II or higher acute GvHD and/or moderate or severe chronic GvHD, to explore the microbial abundance of taxa at the phylum, family, genus, and species level, and we utilized alpha and beta diversity indices to further describe our findings. We collected fecal specimens at −2 to +2 (T1), +11 to +17 (T2), +25 to +30 (T3), +90 (T4), and +180 (T5) days to assess changes in gut microbiota, with day 0 being the day of allo-HSCT. We included 20 allo-HSCT recipients in the study. Compared with timepoint T1, at timepoint T4 we found a significant decrease in the abundance of Proteobacteria phylum (14.22% at T1 vs. 4.07% at T4, p = 0.01) and Enterobacteriaceae family (13.3% at T1 vs. <0.05% at T4, p < 0.05), as well as a significant increase in Enterococcus species (0.1% at T1 vs. 12.8% at T4, p < 0.05) in patients who developed acute GvHD. Regarding patients who developed chronic GvHD after allo-HSCT, there was a significant reduction in the abundance of Eurobactereaceae family (1.32% at T1 vs. 0.53% at T4, p < 0.05) and Roseruria genus (3.97% at T1 vs. 0.09% at T4, p < 0.05) at T4 compared with T1. Alpha and beta diversity analyses did not reveal a difference in the abundance of bacteria at the genus level in GvHD patients at T4 compared with T1. Our study reinforces results from previous studies regarding changes in gut microbiota in patients with acute GvHD and provides new data regarding the gut microbiome changes in chronic GvHD. Future studies will need to incorporate clinical parameters in their analyses to establish their association with specific changes in gut microbiota in patients with GvHD after allo-HSCT. Full article

The Reynolds-averaged Navier–Stokes (RANS)-based stochastic multiple mapping conditioning (MMC) approach has been used to study partially premixed jet flames of dimethyl ether (DME) introduced into a vitiated coflowing oxidizer stream. This study investigates DME flames with varying degrees of partial premixing within a fuel jet across different coflow temperatures, delving into the underlying flame structure and stabilization mechanisms. Employing a turbulence k-$\epsilon$ model with a customized set of constants, the MMC technique utilizes a mixture fraction as the primary scalar, mapped to the reference variable. Solving a set of ordinary differential equations for the evolution of Lagrangian stochastic particles’ position and composition, the molecular mixing of these particles is executed using the modified Curl’s model. The lift-off height (LOH) derived from RANS-MMC simulations are juxtaposed with experimental data for different degrees of partial premixing of fuel jets and various coflow temperatures. The RANS-MMC methodology adeptly captures LOH for pure DME jets but exhibits an underestimation of flame LOH for partially premixed jet scenarios. Notably, as the degree of premixing escalates, a conspicuous underprediction in LOH becomes apparent. Conditional scatter and contour plots of OH and CH₂O unveil that the propagation of partially premixed flames emerges as the dominant mechanism at high coflow temperatures, while autoignition governs flame stabilization at lower coflow temperatures in partially premixed flames. Additionally, for pure DME flames, autoignition remains the primary flame stabilization mechanism across all coflow temperature conditions. The study underscores the importance of considering the degree of premixing in partially premixed jet flames, as it significantly impacts flame stabilization mechanisms and LOH, thereby providing crucial insights into combustion dynamics for various practical applications. Full article

With the widespread adoption of battery technology in electric vehicles, there has been significant attention drawn to the increasing frequency of battery fire incidents. However, the jetting behavior and expansion force during the thermal runaway (TR) of batteries represent highly dynamic phenomena, which lack comprehensive quantitative description. This study addresses this gap by employing an enhanced experimental setup that synchronizes the video timing of cameras with a signal acquisition system, enabling the multidimensional quantification of signals, such as images, temperature, voltage, and pressure. It also provides a detailed description of the jetting behavior and expansion force characteristics over time for Li(Ni_0.8Co_0.1Mn_0.1)O₂ batteries undergoing thermal runaway in an open environment. The results from three experiments effectively identify key temporal features, including the timing of the initial jetting spark, maximum jetting velocity, jetting duration, explosion duration, and patterns of flame volume variation. This quantitative analytical approach proves effective across various battery types and conditions. The findings could offer scientific foundations and experimental strategies for parameter identification in fire prevention and thermal runaway model development. Full article

Abstract: Social innovation has emerged as a transformative force in businesses, particularly in the context of cultural and creative hotels. This study aims to explore the relationship between social innovation and value co-creation in cultural and creative hotels, compare the intrinsic characteristics of social innovation and value co-creation within these hotels, and investigate the key factors driving social innovation in this sector. Employing a qualitative research methodology based on the theory of social innovation, this paper examines the process of value co-creation and analyzes the three key drivers within the social innovation ecosystem: establishing interdependence and identity among organizations; enhancing cognitive and value exchanges between organizations; and generating consensus through the role transformation of participants. The findings suggest that the key drivers of the social innovation ecosystem not only enhance the innovation capabilities of businesses but also motivate them to collaboratively create mutually beneficial and symbiotic value. Full article

The Cosmonaut Sea is one of the least accessed regions in the Southern Ocean, and our knowledge about the fish biodiversity in the region is sparse. In this study, we provided a description of demersal fish diversity in the Cosmonaut Sea by analysing cytochrome oxidase I (COI) barcodes of 98 fish samples that were hauled by trawling during the 37th and 38th Chinese National Antarctic Research Expedition (CHINARE) cruises. Twenty-four species representing 19 genera and 11 families, namely, Artedidraconidae, Bathydraconidae, Bathylagidae, Channichthyidae, Liparidae, Macrouridae, Muraenolepididae, Myctophidae, Nototheniidae, Paralepididae and Zoarcidae, were discriminated and identified, which were largely identical to local fish occurrence records and the general pattern of demersal fish communities at high Antarctic shelf areas. The validity of a barcoding gap failed to be detected and confirmed across all species due to the indicative signals of two potential cryptic species. Nevertheless, DNA barcoding still demonstrated to be a very efficient and sound method for the discrimination and classification of Antarctic fishes. In the future, various sampling strategies that cover all geographic sections and depth strata of the Cosmonaut Sea are encouraged to enhance our understanding of local fish communities, within which DNA barcoding can play an important role in either molecular taxonomy or the establishment of a dedicated local reference database for eDNA metabarcoding analyses. Full article