Hasil untuk "Ocean engineering"

Sundararaman Gopalakrishnan, Krishna K. Osuri, Dev Niyogi et al.

ABSTRACT Over the last decade, tropical cyclone (TC) track and intensity predictions have improved by nearly 50% in the Atlantic and Northern Indian Ocean, driven by advancements in ocean‐coupled numerical models, data assimilation techniques, and an expanding network of observations. However, the prediction of severe weather events driven by convection, particularly those associated with heavy precipitation over land, has not kept pace with these improvements in TC forecasting. While 1–2 km horizontal resolutions are crucial for capturing convection over land and ocean, seamless prediction across scales demands an accurate representation of the coupled evolution of ocean, land, and atmospheric states. To address the complex problem of severe weather across a spectrum of atmospheric motions—including TCs over the ocean and severe convective systems over coastal and inland regions—we have developed the Indian Ocean–Land–Atmosphere (IOLA) Coupled Mesoscale Prediction Framework. This Framework integrates the well‐tested nonhydrostatic model (NMM) dynamical core with advanced nesting techniques from the hurricane weather research and forecast (HWRF) system. It further incorporates ocean coupling from HWRF and physics packages adopted from the WRF community model. This represents the first‐ever coupled modeling system explicitly designed to tackle extreme weather events across multiple domains and scales. Extensive testing of this novel modeling framework demonstrates that a high‐resolution (1–2 km) “all‐purpose” severe weather prediction system can effectively address the challenges of forecasting extreme weather over the Indian region. One of the key focuses of this work is the application of 1‐km horizontal resolution moving nests over the monsoon region, where synoptic‐scale interactions play a critical role in modulating severe weather and heavy precipitation events. With this configuration, the model provides a high equitable threat score (ETS) > 0.18 for heavy to extreme rainfall events for 48 h and above lead times. This framework enables a unified approach to simulating severe weather phenomena accurately and flexibly. Also, it sets a new benchmark for seamless prediction of extreme weather, paving the way for improved resilience against coastal hazards and inland severe weather events.

Patricia G. F. Matsubara, Tayana Conte

Over twenty years ago, the Software Engineering (SE) research community have been involved with Evidence-Based Software Engineering (EBSE). EBSE aims to inform industrial practice with the best evidence from rigorous research, preferably from systematic literature reviews (SLRs). Since then, SE researchers have conducted many SLRs, perfected their SLR procedures, proposed alternative ways of presenting their results (such as Evidence Briefings), and profusely discussed how to conduct research that impacts practice. Nevertheless, there is still a feeling that SLRs' results are not reaching practitioners. Something is missing. In this vision paper, we introduce Evidence to Decision (EtD) frameworks from the health sciences, which propose gathering experts in panels to assess the existing best evidence about the impact of an intervention in all relevant outcomes and make structured recommendations based on them. The insight we can leverage from EtD frameworks is not their structure per se but all the relevant criteria for making recommendations to practitioners from SLRs. Furthermore, we provide a worked example based on an SE SLR. We also discuss the challenges the SE research and practice community may face when adopting EtD frameworks, highlighting the need for more comprehensive criteria in our recommendations to industry practitioners.

Tab, Zhang, Bjorn De Sutter et al.

Empirical research in reverse engineering and software protection is crucial for evaluating the efficacy of methods designed to protect software against unauthorized access and tampering. However, conducting such studies with professional reverse engineers presents significant challenges, including access to professionals and affordability. This paper explores the use of students as participants in empirical reverse engineering experiments, examining their suitability and the necessary training; the design of appropriate challenges; strategies for ensuring the rigor and validity of the research and its results; ways to maintain students' privacy, motivation, and voluntary participation; and data collection methods. We present a systematic literature review of existing reverse engineering experiments and user studies, a discussion of related work from the broader domain of software engineering that applies to reverse engineering experiments, an extensive discussion of our own experience running experiments ourselves in the context of a master-level software hacking and protection course, and recommendations based on this experience. Our findings aim to guide future empirical studies in RE, balancing practical constraints with the need for meaningful, reproducible results.

Sifan Gu, Zhengyu Liu, Ning Zhao et al.

Abstract The rapid CO2 rise during the early deglaciation is often linked to enhanced ventilation by intensified Antarctic Bottom Water (AABW) overturning. The recorded radiocarbon ventilation seesaw during the early deglaciation, which describes improved Southern Ocean and reduced North Atlantic abyssal radiocarbon ventilation, has been interpreted as intensified AABW and reduced North Atlantic Deep Water convections. However, abyssal radiocarbon records also reflect changes in surface reservoir ages and interior water mass mixing. Using isotope-enabled simulations, we show that this seesaw results from weakened AABW overturning and decreased Southern Ocean surface reservoir age. With AABW occupying the abyssal ocean, weakened AABW overturning increases transit time, with the magnitude increasing northward. This transit time increase outpaced the declining $$\Delta ^{14}C_{{atm}}$$ Δ 14 C a t m induced Southern Ocean surface reservoir age decrease in the abyssal North Atlantic, but not in the abyssal Southern Ocean, thus producing a radiocarbon ventilation seesaw. Our results suggest sluggish deep water overturning from both poles during the early deglaciation.

Jiao Pan, Tao Chen, Antonio Plaza

Revealing the spatial-temporal evolution and interactions of ecosystem services (ESs) in mining area is critical for sustainable environmental management. The temporal and spatial characteristics and changing trends of six ESs in Yuzhong mining area from 2000 to 2020 were analyzed. Pearson correlation analysis explored and elucidated the intricate tradeoffs and synergies that manifest across diverse ecosystems. The integrated ecosystem service landscape index (IESLI) was constructed on this basis, and 8 factors (both natural and human) were selected to identify the driving forces. The findings indicated that: 1) Over the past two decades, five categories of ESs have exhibited a declining trend, with water yield experiencing the most significant reduction, reaching 38.7% . 2) Among the 15 ESs pairings, tradeoffs were predominantly negatively correlated. 3) The interaction between land use/land cover and precipitation (54.5% ) emerged as the primary driving force behind the spatial heterogeneity of ESs. 4) The IESLI showed a general downward trend, decreasing from 0.51 in 2005 to 0.44 in 2020. This study provides quantitative evidence of ecosystem degradation and the intricate interrelationships among ESs in mining landscapes, highlighting the critical role of coupled spatial models in uncovering underlying patterns and mechanisms. The findings offer a scientific foundation for ecological restoration and policy-making in mining regions.

Dong-Hee Choi, Hu-Jae Choi, Kwang-Sung Ko et al.

As interest in autonomous maritime technology continues to grow, various collision avoidance algorithms for autonomous vessels have been developed. However, evaluating and comparing the performance of these algorithms presents challenges due to the significant influence of factors such as the number of obstacles, specific encounter scenarios and obstacle arrangements. To address these challenges, the present study employs a Monte Carlo simulation technique to quantitatively evaluate the performance of ship collision avoidance algorithms in port congestion zones. To accurately reflect the conditions of real congested harbor areas with high ship traffic, four major encounter scenarios were defined, with the positions, velocities, and heading angles of obstacles randomly generated within predefined ranges to incorporate randomness into the simulation environment. Using the developed Monte Carlo simulation technique, the performance of the Worst Case Velocity Obstacle (WVO) algorithm and a hybrid algorithm combining WVO with modified Artificial Potential Field algorithm(APF) were qualitatively evaluated. The simulation results revealed the limitations of the WVO algorithm, particularly in scenarios involving crossing and converging encounters, due to frequent heading changes and passive rudder actions. In contrast, the hybrid algorithm, which incorporate WVO algorithm with modified APF method, demonstrated improved collision avoidance performances, including maintaining greater safe distance and reducing collision occurrence through actively using rudder angles.

吴娟娟1，田佳宁1，陈奕蒙1，王成成1，王玉明1,2 , 姜晓明1, 薛长湖1, 张恬恬1 WU Juanjuan1, TIAN Jianing1, CHEN Yimeng1, WANG Chengcheng1, WANG Yuming1,2, JIANG Xiaoming1, XUE Changhu1, ZHANG Tiantian1

为了揭示雌、雄膨腹海马磷脂组成及其差异，用溶剂萃取法分别提取雌、雄膨腹海马脂质，冷丙酮提取其中的磷脂，进一步采用薄层色谱法和硅胶柱层析分离制备各磷脂组分，利用气相色谱技术分析比较雌、雄膨腹海马磷脂及其各组分的脂肪酸组成。结果表明：雌、雄膨腹海马的磷脂含量丰富，分别占总脂的18.11%和18.72%；膨腹海马磷脂包括磷脂酰乙醇胺、磷脂酰胆碱、溶血磷脂酰胆碱和鞘磷脂，其中磷脂酰胆碱含量最高；膨腹海马磷脂中共鉴定出20种脂肪酸，且不饱和脂肪酸含量高于饱和脂肪酸含量；雌、雄膨腹海马磷脂中海洋特征性n-3多不饱和脂肪酸DHA和EPA总含量分别为18.30%、19.61%；膨腹海马的磷脂酰乙醇胺、磷脂酰胆碱和溶血磷脂酰胆碱饱和脂肪酸都以C16∶ 0和C18∶ 0为主，而在鞘磷脂中以C14∶ 0和C16∶ 0为主；膨腹海马4种磷脂组分的单不饱和脂肪酸均以C18∶ 1为主，多不饱和脂肪酸均以EPA和DHA为主；除了雄性膨腹海马溶血磷脂酰胆碱外，其余磷脂组分DHA和EPA总含量均超过16%。综上，膨腹海马磷脂组成多样，海洋特征性n-3多不饱和脂肪酸含量丰富，具有较高的营养价值。 In order to reveal the phospholipids compositions and differences between male and female Hippocampus abdominalis, the lipids of female and male Hippocampus abdominalis were extracted by solvent extraction method, then phospholipids in the lipids were extracted by cold acetone. Moreover, the different phospholipids fractions were separated by thin-layer chromatography and silica gel column chromatography, then the fatty acid composition of phospholipids and the different phospholipids fractions from female and male Hippocampus abdominalis were analyzed by gas chromatography. The results showed that the female and male Hippocampus abdominalis were rich in phospholipids, accounting for 18.11% and 18.72% of the total lipids, respectively. The phospholipids of Hippocampus abdominalis included phosphatidylethanolamine, phosphatidylcholine, lysophosphatidylcholine and sphingomyelin, among which phosphatidylcholine was the most abundant. The phospholipids of Hippocampus abdominalis identified 20 kinds of fatty acids, and the content of unsaturated fatty acids was higher than that of saturated fatty acids. Furthermore, the total content of marine characteristic n-3 polyunsaturated fatty acids DHA and EPA in female and male Hippocampus abdominalis phospholipids were 18.30% and 19.61%, respectively. The saturated fatty acids of phosphatidylethanolamine, phosphatidylcholine and lysophosphatidylcholine in both male and female Hippocampus abdominalis were all dominated by C16∶ 0 and C18∶ 0, whereas those in sphingomyelin were dominated by C14∶ 0 and C16∶ 0. The monounsaturated fatty acids of the four phospholipid fractions of the male and female Hippocampus abdominalis were all dominated by C18∶ 1, and the polyunsaturated fatty acids were all dominated by EPA and DHA. The total contents of DHA and EPA were more than 16%, except for lysophosphatidylcholine in male Hippocampus abdominalis. In conclusion, phospholipids composition of Hippocampus abdominalis is diverse, with abundant marine characteristic n-3 polyunsaturated fatty acids and high nutritional value.

Hayato Ishida, Amal Elsokary, Maria Aslam et al.

Realisation of significant advances in capabilities of sensors, computing, timing, and communication enabled by quantum technologies is dependent on engineering highly complex systems that integrate quantum devices into existing classical infrastructure. A systems engineering approach is considered to address the growing need for quantum-secure telecommunications that overcome the threat to encryption caused by maturing quantum computation. This work explores a range of existing and future quantum communication networks, specifically quantum key distribution network proposals, to model and demonstrate the evolution of quantum key distribution network architectures. Leveraging Orthogonal Variability Modelling and Systems Modelling Language as candidate modelling languages, the study creates traceable artefacts to promote modular architectures that are reusable for future studies. We propose a variability-driven framework for managing fast-evolving network architectures with respect to increasing stakeholder expectations. The result contributes to the systematic development of viable quantum key distribution networks and supports the investigation of similar integration challenges relevant to the broader context of quantum systems engineering.

Tomi Suomi, Petri Ihantola, Tommi Mikkonen et al.

Agile software development relies on self-organized teams, underlining the importance of individual responsibility. How developers take responsibility and build ownership are influenced by external factors such as architecture and development methods. This paper examines the existing literature on ownership in software engineering and in psychology, and argues that a more comprehensive view of ownership in software engineering has a great potential in improving software team's work. Initial positions on the issue are offered for discussion and to lay foundations for further research.

Zhenpeng Chen, Chong Wang, Weisong Sun et al.

Large Language Models (LLMs) are increasingly integrated into software applications, giving rise to a broad class of prompt-enabled systems, in which prompts serve as the primary 'programming' interface for guiding system behavior. Building on this trend, a new software paradigm, promptware, has emerged, which treats natural language prompts as first-class software artifacts for interacting with LLMs. Unlike traditional software, which relies on formal programming languages and deterministic runtime environments, promptware is based on ambiguous, unstructured, and context-dependent natural language and operates on LLMs as runtime environments, which are probabilistic and non-deterministic. These fundamental differences introduce unique challenges in prompt development. In practice, prompt development remains largely ad hoc and relies heavily on time-consuming trial-and-error, a challenge we term the promptware crisis. To address this, we propose promptware engineering, a new methodology that adapts established Software Engineering (SE) principles to prompt development. Drawing on decades of success in traditional SE, we envision a systematic framework encompassing prompt requirements engineering, design, implementation, testing, debugging, evolution, deployment, and monitoring. Our framework re-contextualizes emerging prompt-related challenges within the SE lifecycle, providing principled guidance beyond ad-hoc practices. Without the SE discipline, prompt development is likely to remain mired in trial-and-error. This paper outlines a comprehensive roadmap for promptware engineering, identifying key research directions and offering actionable insights to advance the development of prompt-enabled systems.

Krishna Ronanki, Simon Arvidsson, Johan Axell

The rapid emergence of generative AI models like Large Language Models (LLMs) has demonstrated its utility across various activities, including within Requirements Engineering (RE). Ensuring the quality and accuracy of LLM-generated output is critical, with prompt engineering serving as a key technique to guide model responses. However, existing literature provides limited guidance on how prompt engineering can be leveraged, specifically for RE activities. The objective of this study is to explore the applicability of existing prompt engineering guidelines for the effective usage of LLMs within RE. To achieve this goal, we began by conducting a systematic review of primary literature to compile a non-exhaustive list of prompt engineering guidelines. Then, we conducted interviews with RE experts to present the extracted guidelines and gain insights on the advantages and limitations of their application within RE. Our literature review indicates a shortage of prompt engineering guidelines for domain-specific activities, specifically for RE. Our proposed mapping contributes to addressing this shortage. We conclude our study by identifying an important future line of research within this field.

Zhongze Ma, Chenjie Zhang, Pengcheng Jiao

Summary: Marine activities typically face various risk factors such as marine animal attacks or unexpected collisions. In this paper, we develop underwater smart glasses (USGs) based on visual-tactile fusion for underwater hazard detection in real-time, ensuring operational safety. The proposed USG is composed of the vision module by artificial intelligence (AI)-enabled optical sensing and the tactile module by triboelectric metamaterials-enabled mechanical sensing. The vision module is obtained based on the underwater target detection algorithm you only look once-underwater hazard (YOLO-UH) developed by the dataset to detect toxic marine organisms in the visual field. The tactile module is designed with the kirigami tribo-materials (KTMs) to sensitively detect and warn of collisions outside the visual field. Through numerical simulations, laboratory tests, and real-world experiments, we validated the performance of both modules. The reported USG with its visual-tactile fusion concept enables near-far all-around hazard detection and reduces the danger for divers working underwater.

Yan Wang, Hao Zhang, Wei Huang et al.

The critical nature of passive ship-radiated noise recognition for military and economic security is well-established, yet its advancement faces significant obstacles due to the complex marine environment. The challenges include natural sound interference and signal distortion, complicating the extraction of key acoustic features and ship type identification. Addressing these issues, this study introduces DWSTr, a novel method combining a depthwise separable convolutional neural network with a Transformer architecture. This approach effectively isolates local acoustic features and captures global dependencies, enhancing robustness against environmental interferences and signal variability. Validated by experimental results on the ShipsEar dataset, DWSTr demonstrated a notable 96.5\% recognition accuracy, underscoring its efficacy in accurate ship classification amidst challenging conditions. The integration of these advanced neural architectures not only surmounts existing barriers in noise recognition but also offers computational efficiency for real-time analysis, marking a significant advancement in passive acoustic monitoring and its application in strategic and economic contexts.

Wuwei Feng, Hongya Chen, Qingping Zou et al.

Wireless monitoring systems for the marine environment are important for rapidly growing subsea developments. The power supply of wireless sensor nodes within the monitoring systems, however, is a major challenge. This study proposes a novel piezoelectric wave energy converter (pWEC) device to power the wireless sensing nodes. Unlike previous studies, the proposed device utilizes contactless pWEC technology in which a spring pendulum provides a two-stage frequency amplification of 3.8 times for low-frequency wave environments. The pWEC device consists of a floating body, inner pendulum, spring pendulum, magnets and piezoelectric sheets. In order to harvest the energy from relatively low frequency ocean waves, the pWEC device is designed to have an enhanced energy-capturing frequency. The effects of internal pendulum mass, spring pendulum weight, pendulum length and spring stiffness on wave energy absorption are investigated using theoretical and numerical analysis combined with laboratory experiments. The slider that drives the motion of the piezoelectric sheet vibrates at up to 3.8 times the wave frequency. To test the piezoelectric generators in the laboratory environment, a mechanical structure is set up to simulate the motion of the external floating body and the internal wave energy converter under the action of waves. When the four piezoelectric plates are arranged horizontally, the average output power per plate is increased by 2.4 times, and a single piezoelectric plate can generate an average of 10 mW of power. The proposed piezoelectric wave energy converter device has the potential to provide long-term energy supply for small ocean monitoring platforms at remote locations with reasonable wave energy resources.