Optical Character Recognition (OCR) is a fundamental component of document analysis pipelines, enabling downstream tasks such as information extraction, retrieval, and serving as a key enabler of automation. The recent emergence of multimodal Large Language Models (LLMs) has introduced alternative approaches that integrate implicit OCR capabilities within unified vision-language architectures. Despite their growing prominence, a systematic comparison between traditional OCR systems and multimodal LLMs is still lacking. This paper presents a comprehensive benchmarking study encompassing 16 OCR and multimodal systems, grouped into open-source OCR engines, commercial OCR services, commercial multimodal LLMs, and open-source multimodal LLMs. The evaluation spans four publicly available datasets representing printed, scanned, handwritten, English and Chinese dense-text multicolumn documents. Performance is assessed using character-and word-level accuracy, normalized edit distance, and flexible character accuracy, complemented by latency and cost analyses to provide a holistic view of each system's operational suitability. The results show that no single paradigm excels universally: in our settings, multimodal LLMs achieve stronger performance on unstructured and handwritten inputs, while specialized OCR pipelines remain more reliable for structured and printed layouts. Lightweight domain adaptation through fine-tuning proves beneficial for open-source OCR models, whereas general-purpose LLMs offer competitive accuracy at higher computational costs. All assessment tools and configurations are publicly released to promote reproducibility and facilitate future OCR and LLM research within the document analysis community.
Amid the global push for sustainable construction, bamboo has been widely studied as a rapidly renewable, high-performance building material. This paper reviews the published literature on bamboo’s journey from plant to structural component. The review first examines the fundamental botanical characteristics of bamboo, explaining the relationship between its hierarchical structure—from molecular to cellular levels—and its mechanical properties. It then transitions to an analysis of sustainable forestry, harvesting practices, the structural uses of round bamboo, and the manufacturing processes for engineered bamboo products, such as glued laminated bamboo and bamboo scrimber. Critical performance challenges are addressed by evaluating modern treatment methods that enhance durability, dimensional stability, and fire resistance. Structural applications are explored in detail, covering traditional building typologies, innovative connection systems, and the emerging potential for bamboo in mid- and high-rise as well as long-span structures. The life cycle perspective is completed by examining end-of-life scenarios, including reuse, recycling, and energy recovery. Ultimately, the review situates bamboo within the global context by examining resource distribution, trade, and the policy frameworks required to promote its widespread adoption. By consolidating research across multiple disciplines, this paper highlights the significant potential of bamboo to contribute to a low-carbon built environment. It concludes that a holistic, science-based approach is essential for overcoming remaining challenges and fully integrating this versatile material into modern construction practices.
Coordinating multiple reclaimers for conflict-free route planning across parallel tracks is essential in dry bulk terminal operations. Currently, however, reclaimer scheduling is limited to single-track scenarios without integrated task assignment, which constrains overall handling capacity. To overcome this limitation, this paper employs a time-space network (TSN) model to represent reclaiming operations, incorporating sequencing, task assignment, and non-crossing constraints. The model thereby captures the full complexity of stockyard operations. The reclaiming route planning problem is formulated as a mixed-integer programming (MIP) model, which is computationally intractable. To improve computational efficiency, we propose an innovative two-level metaheuristic framework that separates task sequences from conflict-free route generation, simplifies the encoding process, accelerates the search procedure, and prevents the creation of infeasible solutions. Building on this two-level framework, we develop a centralized two-level metaheuristic algorithm (CTLM) and a parallel two-level metaheuristic algorithm (PTLM). Comprehensive computational experiments show that the proposed CTLM significantly outperforms the Gurobi solver and three commonly used methods regarding solution quality. PTLM achieves an average speedup ratio of 4.07 in large-scale instances with only a very small degree of accuracy loss, making it more suitable for practical terminal operations.
To better satisfy the uneven spatiotemporal distribution of passenger demand on the Y-type metro line, the concept of flexible train composition is explored, where the train compositions can be changed flexibly at a joint station and terminal stations connected to depots. A mixed-integer nonlinear programming model is developed to jointly optimize train timetables, rolling stock circulation plans, and flexible train compositions on a Y-type metro line. Using standard linearization techniques, the model is reformulated as a mixed-integer linear programming (MILP) model with an objective that balances operating cost and passenger service quality. To incorporate short-term demand variations and support real-time operations, the integrated MILP is embedded into a Model Predictive Control (MPC) framework, which updates timetabling decisions dynamically based on demand forecasts. However, solving the resulting MPC-based MILP in real time is computationally challenging due to the large number of binary decision variables. To overcome this limitation, we propose a learning-based optimization framework that integrates offline learning with online mixed-integer optimization. The learning module is trained offline on historical MPC solutions and is designed to selectively predict a subset of high-impact binary decisions, including train composition and routing choices, thereby reducing the combinatorial complexity of the online optimization problem. The predicted decisions are then fixed in an online MILP to optimize the remaining variables while preserving feasibility and consistency across rolling horizons. In addition, a feasibility-efficiency balancing strategy is introduced through selective decision prediction and feasibility-aware penalized training, which reduces infeasible learning outputs. Numerical experiments based on real-world operational data from Guangzhou Metro Line 14 show that the proposed learning-based MPC framework achieves real-time timetabling, while maintaining solution quality comparable to a full MPC-based optimization benchmark, with only a marginal loss in feasibility. Computational results demonstrate the effectiveness of integrating learning and MPC for real-time timetable optimization.
Many governments allocate access to high-rent natural resources through long-term concessions rather than competitive auctions. In Italy, coastal concessions for beach tourism have historically been renewed administratively, in tension with the EU Services (“Bolkestein”) Directive and recent European Court of Justice rulings. We provide an empirical assessment of this system, focusing on how concession fees relate to firms’ performance and local market conditions. We combine administrative data on maritime concessions with balance-sheet information from AIDA at the province and municipality levels, and exploit firm-level identifiers for the Campania region. First, we document strong spatial disparities: fees co-move with revenues but are weakly related to profits, while labor input is the main operational driver. Second, using a Pareto-based framework, we show that the nationally administered fee grid implies substantial under-pricing in high-inequality areas. Third, we construct firm-level valuation proxies in Campania using machine learning and use them to simulate exploratory auction scenarios. The results show that auction-based mechanisms can raise payments among allocated concessions, but aggregate fiscal gains depend critically on reserve prices, competition intensity, and fallback rules for non-allocated lots.
