Hasil untuk "Ocean engineering"

Ayan Sar, Tanupriya Choudhury, Sampurna Roy et al.

Few-shot multispectral object detection remains a formidable challenge in remote sensing, constrained by the scarcity of annotated data across heterogeneous modalities and environmental conditions. Existing transformer-based detectors, while powerful, often exhibit overfitting in low-sample regimes and fail to preserve cross-spectral consistency between visible and infrared channels. To address these limitations, this article presents few-shot spatial–spectral prototype calibration network (Few-SSPC-Net), a spatial–spectral prototype calibration network designed for efficient and adaptive few-shot multispectral object detection. Unlike conventional transformer-driven pipelines, this framework employs a transformer-free dual-branch convolutional architecture—one branch emphasizing spatial semantics and the other spectral correlations—bridged by a Cross-Scale Interaction Module for fine-grained feature alignment across modalities. Central to this framework is the proposed Spatial–Spectral Prototype Calibration Module, which dynamically refines class prototypes through spectral correlation-guided calibration between support and query samples. This mechanism mitigates prototype drift and enhances generalization by enforcing spectral angular consistency within the embedding space. The entire architecture is trained under an episodic meta-learning paradigm, optimizing a joint objective of classification, localization, and spectral calibration regularization. Extensive experiments on benchmark datasets demonstrate that Few-SSPC-Net achieves consistent gains over state-of-the-art few-shot detectors, with up to +4.7% mAP improvement under five-shot settings, while maintaining competitive inference efficiency. The results affirm the positioning of Few-SSPC-Net as a robust framework for multispectral object detection in complex, data-limited remote sensing scenarios.

Mark Looi

The rapid advance of Generative AI into software development prompts this empirical investigation of perceptual effects on practice. We study the usage patterns of 147 professional developers, examining perceived correlates of AI tools use, the resulting productivity and quality outcomes, and developer readiness for emerging AI-enhanced development. We describe a virtuous adoption cycle where frequent and broad AI tools use are the strongest correlates of both Perceived Productivity (PP) and quality, with frequency strongest. The study finds no perceptual support for the Quality Paradox and shows that PP is positively correlated with Perceived Code Quality (PQ) improvement. Developers thus report both productivity and quality gains. High current usage, breadth of application, frequent use of AI tools for testing, and ease of use correlate strongly with future intended adoption, though security concerns remain a moderate and statistically significant barrier to adoption. Moreover, AI testing tools' adoption lags that of coding tools, opening a Testing Gap. We identify three developer archetypes (Enthusiasts, Pragmatists, Cautious) that align with an innovation diffusion process wherein the virtuous adoption cycle serves as the individual engine of progression. Our findings reveal that organizational adoption of AI tools follows such a process: Enthusiasts push ahead with tools, creating organizational success that converts Pragmatists. The Cautious are held in organizational stasis: without early adopter examples, they don't enter the virtuous adoption cycle, never accumulate the usage frequency that drives intent, and never attain high efficacy. Policy itself does not predict individuals' intent to increase usage but functions as a marker of maturity, formalizing the successful diffusion of adoption by Enthusiasts while acting as a gateway that the Cautious group has yet to reach.

Hao Xu, Yuntian Chen, Rui Cao et al.

Abstract Data-driven discovery of partial differential equations (PDEs) is a promising approach for uncovering the underlying laws governing complex systems. However, purely data-driven techniques face the dilemma of balancing search space with optimization efficiency. This study introduces a knowledge-guided approach that incorporates existing PDEs documented in a mathematical handbook to facilitate the discovery process. These PDEs are encoded as sentence-like structures composed of operators and basic terms, and used to train a generative model, called EqGPT, which enables the generation of free-form PDEs. A loop of “generation–evaluation–optimization” is constructed to autonomously identify the most suitable PDE. Experimental results demonstrate that this framework can recover a variety of PDE forms with high accuracy and computational efficiency, particularly in cases involving complex temporal derivatives or intricate spatial terms, which are often beyond the reach of conventional methods. The approach also exhibits generalizability to irregular spatial domains and higher dimensional settings. Notably, it succeeds in discovering a previously unreported PDE governing strongly nonlinear surface gravity waves propagating toward breaking, based on real-world experimental data, highlighting its applicability to practical scenarios and its potential to support scientific discovery.

Wenli Xie, Bin Cui, Desheng Liu et al.

The rational design of high-performance catalysts for the oxygen evolution reaction (OER) and oxygen reduction reaction (ORR) is essential for the development of clean and renewable energy technologies, particularly in fuel cells and metal-air batteries. Two-dimensional (2D) covalent organic frameworks (COFs) possess numerous hollow sites, which contribute to the stable anchoring of transition metal (TM) atoms and become promising supports for single atom catalysts (SACs). Herein, the OER and ORR catalytic performance of a series of SACs based on TQBQ-COFs were systematically investigated through density functional theory (DFT) calculations, with particular emphasis on the role of the coordination environment in modulating catalytic activity. The results reveal that Rh/TQBQ exhibits the most effective OER catalytic performance, with an overpotential of 0.34 V, while Au/TQBQ demonstrates superior ORR catalytic performance with an overpotential of 0.50 V. A critical mechanistic insight lies in the distinct role of boundary oxygen atoms in TQBQ, which perturb the adsorption energetics of reaction intermediates, thereby circumventing conventional scaling relationships governing OER and ORR pathways. Furthermore, we established the adsorption energy of TM atoms (Ead) as a robust descriptor for predicting catalytic activity, enabling a streamlined screening strategy for SAC design. This study emphasizes the significance of the coordination environment in determining the performance of catalysts and offers a new perspective on the design of novel and effective OER/ORR COFs-based SACs.

Zhanwei Wang, Shidong Fan, Zhiqiang Han

Recently, the IMO has completed the guidelines on the life cycle greenhouse gas intensity of marine fuels to accelerate the application of alternative fuels. Low-carbon fuels may persist for decades and have become a key transitional phase in replacing marine fuels. A more comprehensive methodology for evaluating the carbon emission levels of marine fuels was explored, and the carbon emissions and environmental impacts of a 150,000-ton shuttle tanker under 19 dual-fuel power scenarios were evaluated using the Energy Efficiency Design Index (EEDI) and life cycle assessment (LCA) method. The results show that liquefied natural gas (LNG) has a higher carbon control potential level compared to liquefied petroleum gas (LPG) and methanol (MeOH), while LPG is superior to MeOH based on EEDI evaluation. LCA analysis results show that MeOH (biomass) has the best carbon control potential considering the carbon emissions of the well-to-tank phase of the fuel, followed by LNG, LPG, MeOH (natural gas, NG), and MeOH (coal). However, MeOH (NG) and MeOH (coal) had greater negative environmental impacts. This study provides method support and a direction toward improvement for revising related technical specifications and regulations for dual-fuel vessel performance evaluation, considering the limitations of various maritime regulations.

Guoyong Huang, Weibin Huang, Guangcai Fu et al.

This study investigated the effects of adding lycopene to oxidized fish oil diets on the growth and liver health in Jinhu grouper (Epinephelus fuscoguttatus ♀ × Epinephelus tukula ♂). Fish were fed one of five diets for 8-weeks: (1) FFO: 9 % fresh fish oil (control); (2) OFO: 6 % fresh fish oil and 3 % oxidized fish oil (negative control); (3) LYC1-LYC3: OFO diet supplemented with lycopene (200, 400, 600 mg/kg, respectively). The results showed that the growth performance and whole body crude lipid content were significantly higher in the FFO and LYC groups compared to the OFO group (P < 0.05). Whole body crude lipid was highest in the LYC2 group. In serum biochemical indicators, high-density lipoprotein cholesterol (HDL-C) levels were significantly higher and low-density lipoprotein cholesterol (LDL-C) levels significantly lower in the FFO and LYC2 groups versus the OFO group (P < 0.05). In hepatic antioxidant enzymes, the activities of superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx) were significantly elevated in the FFO and LYC groups compared to the OFO group (P < 0.05). CAT activity was highest in the LYC2 group. In hepatic antioxidant-related and immune-related gene, LYC groups significantly upregulated the sod, cat, gpx genes and down-regulated the pro-inflammatory factors interleukin-1β (il-1β), interleukin-8 (il-8), and tumor necrosis factor-α (tnf-α) compared to the OFO group (P < 0.05). Conclusion: Dietary lycopene effectively attenuated oxidized fish oil-induced oxidative stress and inflammation in grouper by modulating hepatic antioxidant and immune-related gene expression. Under the experimental conditions, 400 mg/kg lycopene (LYC2 group) demonstrated the optimal protective efficacy.

Roberto Verdecchia, Justus Bogner

From its first adoption in the late 80s, qualitative research has slowly but steadily made a name for itself in what was, and perhaps still is, the predominantly quantitative software engineering (SE) research landscape. As part of our regular column on empirical software engineering (ACM SIGSOFT SEN-ESE), we reflect on the state of qualitative SE research with a focus group of experts. Among other things, we discuss why qualitative SE research is important, how it evolved over time, common impediments faced while practicing it today, and what the future of qualitative SE research might look like. Joining the conversation are Rashina Hoda (Monash University, Australia), Carolyn Seaman (University of Maryland, United States), and Klaas Stol (University College Cork, Ireland). The content of this paper is a faithful account of our conversation from October 25, 2025, which we moderated and edited for our column.

Dipin Khati, Yijin Liu, David N. Palacio et al.

Applications of Large Language Models (LLMs) are rapidly growing in industry and academia for various software engineering (SE) tasks. As these models become more integral to critical processes, ensuring their reliability and trustworthiness becomes essential. Consequently, the concept of trust in these systems is becoming increasingly critical. Well-calibrated trust is important, as excessive trust can lead to security vulnerabilities, and risks, while insufficient trust can hinder innovation. However, the landscape of trust-related concepts in LLMs in SE is relatively unclear, with concepts such as trust, distrust, and trustworthiness lacking clear conceptualizations in the SE community. To bring clarity to the current research status and identify opportunities for future work, we conducted a comprehensive review of $88$ papers: a systematic literature review of $18$ papers focused on LLMs in SE, complemented by an analysis of 70 papers from broader trust literature. Additionally, we conducted a survey study with 25 domain experts to gain insights into practitioners' understanding of trust and identify gaps between existing literature and developers' perceptions. The result of our analysis serves as a roadmap that covers trust-related concepts in LLMs in SE and highlights areas for future exploration.

Fei-Lung Huang, Kai-Ying Chen, Wei-Hao Su

Smart city is an area where the Internet of things is used effectively with sensors. The data used by smart city can be collected through the cameras, sensors etc. Intelligent video surveillance (IVS) systems integrate multiple networked cameras for automatic surveillance purposes. Such systems can analyze and monitor video data and perform automatic functions required by users. This study performed main path analysis (MPA) to explore the development trends of IVS research. First, relevant articles were retrieved from the Web of Science database. Next, MPA was performed to analyze development trends in relevant research, and g-index and h-index values were analyzed to identify influential journals. Cluster analysis was then performed to group similar articles, and Wordle was used to display the key words of each group in word clouds. These key words served as the basis for naming their corresponding groups. Data mining and statistical analysis yielded six major IVS research topics, namely video cameras, background modeling, closed-circuit television, multiple cameras, person reidentification, and privacy, security, and protection. These topics can boost the future innovation and development of IVS technology and contribute to smart transportation, smart city, and other applications. According to the study results, predictions were made regarding developments in IVS research to provide recommendations for future research.

Xiaoxing Hu, Yupei Wang, Liang Chen

Semantic segmentation models experience a significant performance degradation due to domain shifts between the source and target domains. This issue is particularly prevalent in remote sensing imagery, where a semantic segmentation model trained on images from one satellite is tested on images from another. Previous research has often overlooked the role of data augmentation in enhancing a model&#x0027;s adaptability to target domains. In contrast, this article proposes a novel self-training framework that incorporates data augmentation at both the input and feature levels, yielding excellent results. Specifically, we introduce a regularized online self-training framework that effectively addresses the challenges of overconfidence and class imbalance inherent in self-training. Based on this framework, we implement two robust data augmentation strategies at the input and feature levels to facilitate the learning of cross-domain invariant knowledge. At the input level, we employ a large-scale domain mixing strategy, termed multidomain mixing, to enhance the model&#x0027;s generalization capability. At the feature level, we introduce masked feature augmentation, a masking-based perturbation technique applied to the semantic features of the target domain. This approach enhances the consistency of teacher&#x2013;student network predictions in the target domain feature space, thereby improving the robustness of the model&#x0027;s recognition of target domain features. The integration of the proposed self-training framework with dual-level data augmentation culminates in our innovative self-training-based dual-level data augmentation (STDA) method. Extensive experimental results on the ISPRS semantic segmentation benchmark demonstrate that STDA outperforms existing state-of-the-art methods, showcasing its effectiveness.

Ebube Alor, Ahmad Abdellatif, SayedHassan Khatoonabadi et al.

Software engineering (SE) chatbots are increasingly gaining attention for their role in enhancing development processes. At the core of chatbots are Natural Language Understanding platforms (NLUs), which enable them to comprehend user queries but require labeled data for training. However, acquiring such labeled data for SE chatbots is challenging due to the scarcity of high-quality datasets, as training requires specialized vocabulary and phrases not found in typical language datasets. Consequently, developers often resort to manually annotating user queries -- a time-consuming and resource-intensive process. Previous approaches require human intervention to generate rules, called labeling functions (LFs), that categorize queries based on specific patterns. To address this issue, we propose an approach to automatically generate LFs by extracting patterns from labeled user queries. We evaluate our approach on four SE datasets and measure performance improvement from training NLUs on queries labeled by the generated LFs. The generated LFs effectively label data with AUC scores up to 85.3% and NLU performance improvements up to 27.2%. Furthermore, our results show that the number of LFs affects labeling performance. We believe that our approach can save time and resources in labeling users' queries, allowing practitioners to focus on core chatbot functionalities rather than manually labeling queries.