In the emerging field of urban digital twins (UDTs), advancing intelligent road inspection (IRI) vehicles with automatic road crack detection systems is essential for maintaining civil infrastructure. Over the past decade, deep learning-based road crack detection methods have been developed to detect cracks more efficiently, accurately, and objectively, with the goal of replacing manual visual inspection. Nonetheless, comprehensive comparative analyses of state-of-the-art (SoTA) deep learning techniques, especially data-fusion and label-efficient algorithms for this task, remain limited. This paper thoroughly reviews the SoTA deep learning-based algorithms, including (1) supervised, (2) unsupervised, (3) semi-supervised, and (4) weakly-supervised methods developed for road crack detection. In addition, we establish UDTIRI-Crack, a curated and standardized benchmark suite comprising 2500 images from seven public annotated datasets, and develop an online benchmark platform to provide standardized, reproducible, and extensible evaluation of algorithms. Comprehensive experiments are conducted to compare the detection performance, computational efficiency, and generalizability of public SoTA deep learning-based algorithms for road crack detection. The feasibility of foundation models and vision–language foundation models for road crack detection is further explored. Finally, the existing challenges and future development trends of deep learning-based road crack detection algorithms are discussed. We believe this review, together with the released dataset and platform, can serve as practical guidance for developing intelligent road detection vehicles with the next-generation road condition assessment systems. The benchmark UDTIRI-Crack is available at https://udtiri.com/submission/.
Large Language Models (LLMs) are rapidly transforming intelligent healthcare and medicine. However, their successful clinical deployment depends on reliable and explainable medical reasoning rather than surface-level text generation. This survey reviews LLM reasoning in medicine. It covers foundational datasets, training-time optimization, test-time reasoning, collaborative agents, and reasoning evaluation. We distinguish two complementary design axes. Individual-model optimization improves the reasoning behavior of a model or module through supervised fine-tuning, reinforcement-based fine-tuning, or test-time control. Collaborative-agent design specifies how one or more such modules exchange evidence, invoke tools, and aggregate decisions at the system level. Collaborative-agent design does not replace individual-model optimization. Contemporary agent systems commonly compose prompted or individually trained modules, including SFT- and GRPO-optimized planners, routers, and aggregators. This compositional view clarifies both the promise and the current limitations of agents. Structured collaboration can support transparency, error recovery, and heterogeneous evidence integration in workflow-oriented tasks. However, it does not provide an inherent advantage over a strong single LLM on narrowly scoped questions. Combining individual-model optimization with collaborative-agent design offers substantial potential for advancing future medical intelligence.
Carbon pricing may shift fuel choice without selecting the lowest-emission pathway. We evaluate heavy fuel oil (HFO), liquefied natural gas (LNG), hydrotreated vegetable oil (HVO), green hydrogen, and green ammonia for a 4,253 TEU Panamax ship on the Singapore–Tokyo route, 2026–2050. The framework combines total cost of ownership (TCO), well-to-wake (WtW) emissions, cargo-capacity loss, transition timing, and two-tier fuel-intensity pricing. Without pricing, HFO minimizes TCO and emits 5,206.3 kt CO2e. At Tier 1 and Tier 2 prices of US$100/t CO2e and US$380/t CO2e, LNG Newbuild 2026 minimizes TCO. For a 10-year-old vessel, it reduces emissions by 11.8% but raises TCO by 89.5% over no-policy HFO. HVO switching in 2026 reduces emissions by 78.2%. Raising Tier 1 lowers LNG’s Tier 2 threshold but raises parity TCO. Appraisal should consider thresholds, ownership costs, and lifecycle emissions.
The assessment of equipment health during field operation is at the basis of condition-based and predictive maintenance. However, in many cases, Health Indicators (HIs) can only be measured in controlled environments, such as laboratories. The difference in the conditions of field operation and the controlled environment creates a domain shift that complicates health state estimation. To address this, we propose a novel Physics-informed Domain Adaptation (PIDA) method that uses data collected in controlled environment (source domain) to estimate the equipment HI in the field (target domain). The method assumes that shifts in operating conditions induce localized perturbations in the degradation dynamics, while the physical laws governing the degradation process remain invariant. PIDA first utilizes Sparse Identification of Nonlinear Dynamics with control (SINDYc) to discover the degradation laws governing the source domain data. Subsequently, a model is trained using a composite loss function that combines a Maximum Mean Discrepancy (MMD) term for domain alignment with a physics-informed term that constrains the target domain HI estimates to conform to the discovered law. We validate PIDA on the Aramis Data Challenge and a cross-domain case study involving lithium-ion batteries. PIDA is shown to achieve an average Root Mean Squared Error reduction of 16.95% in HI estimation compared to the best-performing among the state-of-the-art methods considered.
