Hasil untuk "Naval Science"

P. Charlot, C. Jacobs, D. Gordon et al.

A new realization of the International Celestial Reference Frame (ICRF) is presented based on the work achieved by a working group of the International Astronomical Union (IAU) mandated for this purpose. This new realization follows the initial realization of the ICRF completed in 1997 and its successor, ICRF2, adopted as a replacement in 2009. The new frame, referred to as ICRF3, is based on nearly 40 years of data acquired by very long baseline interferometry at the standard geodetic and astrometric radio frequencies (8.4 and 2.3 GHz), supplemented with data collected at higher radio frequencies (24 GHz and dual-frequency 32 and 8.4 GHz) over the past 15 years. State-of-the-art astronomical and geophysical modeling has been used to analyze these data and derive source positions. The modeling integrates, for the first time, the effect of the galactocentric acceleration of the solar system (directly estimated from the data) which, if not considered, induces significant deformation of the frame due to the data span. The new frame includes positions at 8.4 GHz for 4536 extragalactic sources. Of these, 303 sources, uniformly distributed on the sky, are identified as “defining sources” and as such serve to define the axes of the frame. Positions at 8.4 GHz are supplemented with positions at 24 GHz for 824 sources and at 32 GHz for 678 sources. In all, ICRF3 comprises 4588 sources, with three-frequency positions available for 600 of these. Source positions have been determined independently at each of the frequencies in order to preserve the underlying astrophysical content behind such positions. They are reported for epoch 2015.0 and must be propagated for observations at other epochs for the most accurate needs, accounting for the acceleration toward the Galactic center, which results in a dipolar proper motion field of amplitude 0.0058 milliarcsecond yr−1 (mas yr−1). The frame is aligned onto the International Celestial Reference System to within the accuracy of ICRF2 and shows a median positional uncertainty of about 0.1 mas in right ascension and 0.2 mas in declination, with a noise floor of 0.03 mas in the individual source coordinates. A subset of 500 sources is found to have extremely accurate positions, in the range of 0.03–0.06 mas, at the traditional 8.4 GHz frequency. Comparing ICRF3 with the recently released Gaia Celestial Reference Frame 2 in the optical domain, there is no evidence for deformations larger than 0.03 mas between the two frames, in agreement with the ICRF3 noise level. Significant positional offsets between the three ICRF3 frequencies are detected for about 5% of the sources. Moreover, a notable fraction (22%) of the sources shows optical and radio positions that are significantly offset. There are indications that these positional offsets may be the manifestation of extended source structures. This third realization of the ICRF was adopted by the IAU at its 30th General Assembly in August 2018 and replaced the previous realization, ICRF2, on January 1, 2019.

LIU Donglin, ZHOU Xia, DAI Jianfeng, XIE Xiangpeng, TANG Yi, LI Juanshi

Integrated energy systems in buildings are an effective means to achieve low-carbon buildings. To further tap into their demand-side flexibility adjustable potential and carbon reduction potential, and reasonably allocate the interests of various entities in the building integrated energy system, a bi-level optimization scheduling strategy for building integrated energy system considering virtual energy storage in buildings under Stackelberg game framework is proposed. First, the thermal inertia of the cooling and heating system inside the building and the flexibility of the cooling and heating load are considered to leverage the virtual energy storage function of the building and improve system flexibility in the game model. Then, the genetic algorithm is used to solve the upper-level pricing model of energy operators, updating the purchase and sale electricity prices set by upper-level leaders, while the CPLEX solver is used to solve the lower-level problem, optimizing equipment output, demand response, and electricity trading plans. Finally, the proposed model is verified by case studies that it can effectively improve the economic performance and low-carbon characteristics of building integrated energy systems.

Peng Dong, Weibing Chen, Kewen Wang et al.

Products with complex structures are structurally intricate and involve multiple professional fields and engineering construction elements, making it difficult for a single contractor to independently develop and manufacture such complex structural products. Therefore, during the research, development, and production of complex products, collaboration between manufacturers and suppliers is essential to ensure the smooth completion of projects. In this process, a complex supply chain network is often formed to achieve collaborative cooperation among all project participants. Within such a complex supply chain network, issues such as delayed delivery, poor product quality, or low resource utilization by any participant may trigger the bullwhip effect. This, in turn, can negatively impact the delivery cycle, product cost, and quality of the entire complex product, causing it to lose favorable competitive positions such as quality advantages and delivery advantages in fierce market competition. Therefore, this paper firstly explores the mechanism of complex product manufacturing and the supply network of complex product manufacturing, in order to grasp the inherent structure of complex product manufacturing with a focus on identifying symmetrical properties among supply chain nodes. Secondly, a complex product supply chain network model is constructed with the Graphical Evaluation and Review Technique (GERT), incorporating symmetry constraints to reflect balanced resource allocation and mutual dependencies among symmetrical nodes. Then, from the perspective of supply chain, we focus on identifying the shortcomings of supply chain suppliers and optimizing the management cost of the whole supply chain in order to improve the quality of complex products, delivery level, and cost saving level. This study constructs a Restricted Grey GERT (RG-GERT) network model with constrained outputs, integrates moment-generating functions and Mason’s Formula to derive transfer functions, and employs a hybrid algorithm (genetic algorithm combined with non-linear programming) to solve the multi-objective optimization problem (MOOP) for joint optimization of delivery time, quality, and cost. Empirical analysis is conducted using simulated data from Y Company’s aerospace equipment supply chain, covering interval parameters such as delivery time [5–30 days], cost [40,000–640,000 CNY], and quality [0.85–1.0], validated with industry-specific constraints. Empirical analysis using Y Company’s aerospace supply chain data shows that the model achieves a maximum customer satisfaction of 0.96, with resource utilization efficiency of inefficient suppliers improved by 15–20% (p < 0.05) after secondary optimization. Key contributions include (1) integrating symmetry analysis to simplify network modeling; (2) extending GERT with grey parameters for non-probabilistic uncertainty; (3) developing a two-stage optimization framework linking customer satisfaction and resource efficiency.

J. Haden Boone, Mathieu Dahan

ABSTRACTIn this article, we investigate a generalized two‐player inspection game between an attacker and a defender who allocates multiple resources across a critical system. Specifically, the attacker targets components of the system while the defender coordinates multiple inspection units to monitor disjoint subsets of components and detect attacks. However, detection is assumed to be imperfect and depends on inspected locations as well as targeted components. This feature permits to model of detection efficacy as a function of the detection technology employed, the system's physical properties, and external environmental factors. The defender (respectively, attacker) aims to minimize (respectively, maximize) the expected number of attacks on the system that remain undetected. We solve this large‐scale zero‐sum game by analytically characterizing the marginal inspection and attack probabilities in equilibrium. Our analysis provides novel insights into the players' behaviors and the criticality of system components, revealing a complex dependence on players' resources and the distribution of detection probabilities across monitoring sets. Using a benchmark water pipeline network, we demonstrate how the proposed solutions can be leveraged to provide recommendations for security agencies regarding the type of detectors to acquire and how to coordinate them based on the characteristics of the system to inspect.

Chenyu WANG, Likun PENG, Jiabao CHEN et al.

To respond to the endurance bottleneck faced by unmanned undersea vehicles(UUVs) in missions such as ocean observation and resource exploration, this paper studied the hydrodynamic performance optimization of a novel foldable solar wing. To balance computational efficiency and optimization accuracy, a parametric model of the wing was established in CAESES software with variables including wing point coordinates, rounding factors of wing edges, wing gaps, and gaps between the wing and the hull. Innovatively, a hybrid optimization framework combining Sobol global sampling and the non-dominatedsorting genetic algorithm II(NSGA-II) optimization algorithm was constructed. Firstly, the Sobol algorithm was used to generate 80 sample points within the threshold space of each variable to fully explore the design space, followed by multi-generation optimization through NSGA-II. To avoid the accuracy degradation of traditional surrogate models, a coupled computational process integrating high-precision hydrodynamic solutions and optimization algorithms was established, enabling automatic co-simulation between CAESES and STAR-CCM + software. Hydrodynamic analyses were conducted on UUVs equipped with wings of different shapes to explore the impact of different parameter combinations on total drag. The optimization results indicate that a certain height difference between the two wing sections protruding from the hull is beneficial for reducing total drag. Flow field analysis shows that the optimized shape effectively suppresses energy dissipation caused by turbulence. The proposed technical route of parametric modeling, intelligent optimization, and high-precision verification not only reduces the straight-line drag of the UUV with a new configuration but also provides a methodological reference for the optimization of complex appendages, possessing significant engineering value for improving the energy utilization efficiency of underwater equipment.

Simone Bozzo, Diego Villa, Simone Mancini

The assessment for the resistance of a new ship under design can be performed through the Experimental Fluid Dynamics (EFD) or the Computational Fluid Dynamics (CFD) approach; both have their own Uncertainty Assessment (UA). In CFD field, the Verification and Validation (V&V) procedures take into account the approximations for numerical issues and the assumptions adopted to describe the physical phenomena to assess UA. Different theoretical approaches have become available over time; nevertheless, a single comprehensive solution to achieve the UA remains still unknown because as the theoretical methodology varies, the outcome changes. In current work, four different literature approaches will be augmented to perform a V&V analysis for two kinds of model hulls, tested at different speeds and compared with the experimental data. The investigations performed among results lead to the division of all the approaches into the three and four solutions families and to define a robust procedure to identify a reasonable value for the numerical uncertainty assessment. Regarding the robustness and the UA of the approaches, the first family proved successful in only 55% of cases with a UA mean value below 2.01%, while the second one always provides a quantification but with a mean value of 6.65%.

Zoe Moorton, Kamlesh Mistry, Rebecca Strachan et al.

Being able to accurately identify litter in a marine environment is crucial to cleaning up our seas and oceans. Research into object detection techniques to support this identification has been underway for over two decades. However, there have been substantial advancements in the past five years due to the implementation of deep learning techniques. Following the PRISMA-ScR guidelines, we provide an in-depth summary and analysis of recent and significant research contributions to the object detection of macro marine debris. From cross-referencing the results of the literature review, we deduce that there is currently no benchmarked framework for evaluating and comparing computer vision techniques for marine environments. Subsequently, we use the results from our analysis to provide a suggested checklist for future researchers in this field. Furthermore, many of the respected researchers in this field have advocated for a comprehensive database of underwater debris to support research developments in intelligent object detection and identification.

Zhenchao Ma, Yuntao Li, Jiawei Zhu et al.

Exposure to a nuclear accident or a radiological attack may cause serious death events due to hematopoietic acute radiation syndrome (H-ARS). While thrombopoietin (TPO) shows promise in mitigating myelosuppression, its clinical use is restricted due to high doses, strict schedules, and systemic toxicity from conventional administration. This study developed a dissolving microneedle patch loaded with engineered activated platelet-derived vesicles encapsulating TPO (TLEVs@MN) for targeted treatment of H-ARS. Activated platelet-derived vesicles were isolated via ultracentrifugation and then modified with glutathione. Glutathione-based anti-ROS modification effectively protected vesicles from radiation-induced oxidative damage, enhancing their stability and targeting efficiency. Using mild sonication, TPO was efficiently encapsulated into engineered vesicles without compromising membrane protein integrity. Further loading into dissolving MNs facilitated minimally invasive transdermal delivery while ensuring long-term vesicle stability during storage. TLEVs@MNs effectively activated the JAK2/STAT3 pathway, restoring mitochondrial function in hematopoietic stem cells. Pharmacokinetic and biodistribution analyses demonstrated that administration of TPO using TLEVs@MNs achieved the precise TPO delivery to bone marrow hematopoietic stem and progenitor cells, significantly improving survival rates and hematopoietic recovery in irradiated animal models. These findings highlighted TLEVs@MN patch as a promosing and robust TPO delivery platform for managing IR-induced hematopoietic injury.

Kebang Lu, Minglei Guan, Zheng Cong et al.

Noise interference and multipath effects in complex marine environments seriously constrain the performance of hydroacoustic positioning systems. Traditional millisecond-level signal application and processing methods are widely used in existing research; however, it is difficult to meet the requirements of centimeter-level positioning accuracy in marine engineering. To address this problem, this study proposes a hydroacoustic positioning method based on a short baseline system for the cooperative reception of multi-channel signals. The method adopts ultra-short pulse signals with microsecond pulse width, and significantly improves the system signal-to-noise ratio and anti-interference capability through multi-channel signal alignment and coherent superposition techniques; meanwhile, a joint energy gradient-phase detection algorithm is designed, which solves the instability problem of the traditional cross-correlation algorithm in the detection of ultra-short pulse signals through the identification of signal stability intervals and accurate phase estimation. Simulation verification shows that the 8-hydrophone × 4-channel configuration can achieve 36.06% signal-to-noise gain under harsh environmental conditions (−10 dB), and the performance of the joint energy gradient-phase detection algorithm is improved by about 19.1% compared with the traditional method in an integrated manner. Marine tests further validate the engineering practicability of the method, with an average SNR gain of 2.27 dB achieved for multi-channel signal reception, and the TDOA estimation stability of the new algorithm is up to 32.0% higher than that of the conventional method, which highlights the significant advantages of the proposed method in complex marine environments. The results show that the proposed method can effectively mitigate the noise interference and multipath effects in complex marine environments, significantly improve the accuracy and stability of hydroacoustic positioning, and provide reliable technical support for centimeter-level accuracy applications in marine engineering.

Zhichao Li, Yinzhuang Yi, Zhuolin Niu et al.

Niels Taubert

This paper reviews research literature on Diamond Open Access (DOA) journals - sometimes also called Platinum Open Access - that was produced after this journal segment started to become a priority in European research policy around 2020. It contextualizes the current science policy debate, critically examines different understandings of DOA, and reviews studies on the role of such journals in scholarly communication. Most existing research consists of quantitative studies focusing on aspects such as the number of DOA journals, their publication output, the diversity of the landscape in terms of subject areas, languages, publishing entities, indexing in major databases, awareness and perception among scholars, cost analyses, as well as insights into the internal operations of DOA journals. The review shows that research on DOA journals is partly influenced by the science policy discourse in at least two ways: first, through the normativity inherent in that discourse, and second, through the temporality of policy-driven research of practical relevance, which leaves important aspects of the phenomenon understudied. Moreover, research on the DOA journal landscape has implications beyond understanding this particular journal segment, as it also challenges established views of the global system of scholarly communication.

Hang Jiang, Tal August, Luca Soldaini et al.

The field of Computer science (CS) has rapidly evolved over the past few decades, providing computational tools and methodologies to various fields and forming new interdisciplinary communities. This growth in CS has significantly impacted institutional practices and relevant research communities. Therefore, it is crucial to explore what specific research values, known as basic and fundamental beliefs that guide or motivate research attitudes or actions, CS-related research communities promote. Prior research has manually analyzed research values from a small sample of machine learning papers. No prior work has studied the automatic detection of research values in CS from large-scale scientific texts across different research subfields. This paper introduces a detailed annotation scheme featuring ten research values that guide CS-related research. Based on the scheme, we build value classifiers to scale up the analysis and present a systematic study over 226,600 paper abstracts from 32 CS-related subfields and 86 popular publishing venues over ten years.

Maria Teresa Rossi, Leonardo Mariani, Oliviero Riganelli

Complex and large industrial systems often misbehave, for instance, due to wear, misuse, or faults. To cope with these incidents, it is important to timely detect their occurrences, localize the sources of the problems, and implement the appropriate countermeasures. This paper reports our experience with a state-of-the-art failure prediction method, PREVENT, and its extension with a troubleshooting module, REACT, applied to naval systems developed by Fincantieri. Our results show how to integrate anomaly detection with troubleshooting procedures. We conclude by discussing a lesson learned, which may help deploy and extend these analyses to other industrial products.

Grace Wolf-Chase, Charles Kerton, Kathryn Devine et al.

We review participatory science programs that have contributed to the understanding of star formation. The Milky Way Project (MWP), one of the earliest participatory science projects launched on the Zooniverse platform, produced the largest catalog of ``bubbles'' associated with feedback from hot young stars to date, and enabled the identification of a new class of compact star-forming regions (SFRs) known as ``yellowballs'' (YBs). The analysis of YBs through their infrared colors and catalog cross-matching led to discovering that YBs are compact photodissociation regions generated by intermediate- and high-mass young stellar objects embedded in clumps that range in mass from 10 - 10,000 solar masses and luminosity from 10 - 1,000,000 solar luminosities. The MIRION catalog, assembled from 6176 YBs identified by citizen scientists, increases the number of candidate intermediate-mass SFRs by nearly two orders of magnitude. Ongoing work utilizing data from the Spitzer, Herschel and WISE missions involves analyzing infrared color trends to predict physical properties and ages of YB environments. Methods include applying summary statistics to histograms and color-color plots as well as SED fitting. Students in introductory astronomy classes contribute toward continued efforts refining photometric measurements of YBs while learning fundamental concepts in astronomy through a classroom-based participatory science experience, the PERYSCOPE project. We also describe an initiative that engaged seminaries, family groups, and interfaith communities in a wide variety of science projects on the Zooniverse platform. This initiative produced important guidance on attracting audiences that are underserved, underrepresented, or apprehensive about science.

Chung Hyeon Lee, Young Kyun Lim, Mungi Kim et al.

The seasonal dynamics of phytoplankton communities in Korean coastal waters (KCWs) are influenced by complex interactions between ocean currents and nearshore human activities. Despite these influences, the understanding of seasonal phytoplankton changes and their environmental relationships in KCWs remains limited. We investigate the influence of the distinct characteristics of the three seas surrounding the KCWs (the Yellow Sea, the South Sea, and the East Sea) on seasonal phytoplankton communities based on field surveys conducted at 23 stations between 2020 and 2021. The East Sea exhibited higher winter temperatures due to the Jeju and Tsushima warm currents, while summer temperatures were lower compared to the other regions, highlighting the role of currents and deeper oceanic waters. The Yellow Sea showed significant freshwater influence with low salinity levels from major rivers, contrasting with the higher salinity in the East Sea. These differences led to a disparity in the productivity of the two regions: the highest value of Chl. <i>a</i> was observed to be 6.05 µg L⁻¹ in the Yellow Sea in summer. Diatoms dominated in nutrient-rich conditions, particularly in the Yellow Sea, where they comprised up to 80–100% of the phytoplankton community in summer, winter, and spring. PCA analysis revealed positive correlations between diatoms and Chl. <i>a</i>, while cryptophytes, which thrive in the absence of diatom proliferation, showed no such correlation, indicating their opportunistic growth in nutrient-limited conditions. This study highlights the significant impact of region-specific hydrographic factors on phytoplankton communities in KCWs, with diatoms dominating in summer and cryptophytes and dinoflagellates showing seasonal and regional variations. Understanding these dynamics is crucial for predicting phytoplankton bloom dynamics and their ecological implications in coastal ecosystems.

ZHANG Haigang, XIE Jinhuai, LIU Jiaqi, GONG Lijia, LI Zhi

The depth distribution characteristics of particle velocity field intensity have had a significant impact on underwater acoustic detection and estimation. In this paper, based on the approximate conditions of the incoherent normal modes sum transformation to angular integration, the angular integration form of incoherent normal modes of particle velocity was derived, which avoided the complex calculations of eigenvalues and eigenfunctions while revealing the physical mechanism behind the significant variations in particle velocity intensity with source depth and symmetrical depth. The numerical results demonstrate that the analytical expression of the angular integration of incoherent particle velocity can facilitate fast computation and effectively characterize the depth distribution characteristics of particle velocity intensity. Additionally, due to the superposition effect of the amplitude function of normal modes, there are notable differences in the depth distribution of vertical and horizontal particle velocity. Subsequently, focusing on the intensity difference of particle velocity, the study analyzed the effects of parameters such as horizontal distance, source frequency, sound speed profile, and water depth on the depth distribution characteristics of particle velocity field intensity. The findings provide a theoretical basis for passive target depth estimation based on vector fields.

Shaolong Zheng, Kungan Zeng, Zhenni Li et al.

Signals from global navigation satellite systems (GNSSs) in urban areas suffer from serious multipath errors caused by building blockages and reflections. The use of deep neural networks offers great potential for predicting and eliminating complex multipath/non-line-of-sight (NLOS) errors. However, existing methods for predicting the original signals face two remaining challenges. The first challenge is an inability to effectively exploit irregular GNSS measurement data caused by an inconsistent number of visible satellites in different epochs. The second challenge is degradation in the generalization performance of the multipath/NLOS prediction model when using data collected from different locations and periods. To address these challenges, this paper proposes a novel graph transformer neural network (GTNN) for predicting satellite visibility that effectively learns environment representations from irregular GNSS measurements to both alleviate multipath interference and improve the generalization performance of the multipath prediction model. To learn from irregular GNSS measurements, a sky satellite graph is constructed as input to a graph neural network by using satellites captured in the same epoch, which can represent the spatial relationships between satellites and enable the model to learn satellite-related features sufficiently well. To improve the generalization ability of our multipath prediction model, a multihead attention mechanism is introduced to aggregate satellite node information by computing the correlation between satellites to extract the environment representation around the receiver. Based on the constructed sky satellite graph and the multihead attention mechanism, our novel GTNN for predicting satellite visibility can not only handle irregular GNSS measurements but can also learn an environment representation via graph attention. Comparative experiments were conducted on real-world GNSS measurement data in urban areas, demonstrating that the proposed method can achieve an accuracy exceeding 96% for satellite visibility prediction and obtain better generalization performance than existing multipath prediction methods. Moreover, the attention weights among satellites were visualized to demonstrate the environment representation learned by the GTNN from the sky satellite graph.

Zanwen Zuo, Zerong Zhou, Yuzhou Chang et al.

Ribonucleotide reductase M2 (RRM2) is a small subunit in ribonucleotide reductases, which participate in nucleotide metabolism and catalyze the conversion of nucleotides to deoxynucleotides, maintaining the dNTP pools for DNA biosynthesis, repair, and replication. RRM2 performs a critical role in the malignant biological behaviors of cancers. The structure, regulation, and function of RRM2 and its inhibitors were discussed. RRM2 gene can produce two transcripts encoding the same ORF. RRM2 expression is regulated at multiple levels during the processes from transcription to translation. Moreover, this gene is associated with resistance, regulated cell death, and tumor immunity. In order to develop and design inhibitors of RRM2, appropriate strategies can be adopted based on different mechanisms. Thus, a greater appreciation of the characteristics of RRM2 is a benefit for understanding tumorigenesis, resistance in cancer, and tumor microenvironment. Moreover, RRM2-targeted therapy will be more attention in future therapeutic approaches for enhancement of treatment effects and amelioration of the dismal prognosis.

Jinyuan LI, Faxin ZHU, Xianbin TENG et al.

ObjectiveAs the traditional ship trajectory prediction method is prone to gradient explosion and long calculation time, this paper seeks to improve its accuracy and calculation efficiency by proposing a ship trajectory prediction model based on an improved Bayesian optimization algorithm (IBOA) and temporal convolution network (TCN). MethodA temporal pattern attention (TPA) mechanism is introduced to extract the weights of each input feature and ensure the timing of the historical flight track data. At the same time, a reversible residual network （RevNet） is introduced to reduce the memory occupied by TCN model training. The IBOA is then used to find the optimality of the hyperparameters in the TCN (size of kernel K, expansion coefficient d). The model is finally validated using a five-fold cross-validation method, and trajectory prediction is carried out after obtaining the optimal model. ResultThe trajectory data is collected by automatic identification system (AIS) and verified. The root mean square error (RMSE) is found to be increased by 5.5×10−5, 3.5×10−4 and 6×10−4 in weak coupling, medium coupling and strong coupling track prediction respectively.ConclusionThe proposed network has good adaptability to complex trajectories and higher accuracy than the traditional model and long short-term memory (LSTM) model, while maintaining high prediction accuracy for trajectories with strong coupling.

Michele N. Maughan, Jenna D. Gadberry, Caitlin E. Sharpes et al.

Since the advent of the Universal Detector Calibrant (UDC) by scientists at Florida International University in 2013, this tool has gone largely unrecognized and under-utilized by canine scent detection practitioners. The UDC is a chemical that enables reliability testing of biological and instrumental detectors. Training a biological detector, such as a scent detection canine, to respond to a safe, non-target, and uncommon compound has significant advantages. For example, if used prior to a search, the UDC provides the handler with the ability to confirm the detection dog is ready to work without placing target odor on site (i.e., a positive control), thereby increasing handler confidence in their canine and providing documentation of credibility that can withstand legal scrutiny. This review describes the UDC, summarizes its role in canine detection science, and addresses applications for UDC within scent detection canine development, training, and testing.