Search Results (1,200)

How to economically and effectively mobilize remaining oil and achieve carbon sequestration after water flooding in low-permeability, high-water-cut reservoirs is an urgent challenge. This study, focusing on Block Y of the Daqing Oilfield, employs numerical simulation to systematically reveal the synergistic influencing mechanisms of CO₂ flooding and geological storage. A three-dimensional compositional model characterizing this reservoir was constructed, with a focus on analyzing the controlling effects of key geological (depth, heterogeneity, physical properties) and engineering (gas injection rate, gas injection volume, bottom-hole flowing pressure) parameters on the displacement and storage processes. Simulation results indicate that the low-permeability characteristics of Block Y effectively suppress gas channeling, enabling a CO₂ flooding enhanced oil recovery (EOR) increment of 15.65%. Increasing reservoir depth significantly improves both oil recovery and storage efficiency by improving the mobility ratio and enhancing gravity segregation. Parameter optimization is key to achieving synergistic benefits: the optimal gas injection rate is 700–900 m³/d, the economically reasonable gas injection volume is 0.4–0.5 PV, and the optimal bottom-hole flowing pressure is 9–10 MPa. This study confirms that for Block Y and similar high-water-cut, low-permeability reservoirs, CO₂ flooding is a highly promising replacement technology; through optimized design, it can simultaneously achieve significant crude oil production increase and efficient CO₂ storage. Full article

Detecting low-altitude, slow-speed, small (LSS) UAVs is especially challenging in low-visibility scenes (low light, haze, motion blur), where inherent uncertainties in sensor data and object appearance dominate. We propose GAME-YOLO, a novel detector that integrates a Bayesian-inspired probabilistic reasoning framework with Global Attention and Multi-Scale Enhancement to improve small-object perception and sub-pixel-level localization. Built on YOLOv11, our framework comprises: (i) a visibility restoration front-end that probabilistically infers and enhances latent image clarity; (ii) a global-attention-augmented backbone that performs context-aware feature selection; (iii) an adaptive multi-scale fusion neck that dynamically weights feature contributions; (iv) a sub-pixel-aware small-object detection head (SOH) that leverages high-resolution feature grids to model sub-pixel offsets; and (v) a novel Shape-Aware IoU loss combined with focal loss. Extensive experiments on the LSS2025-DET dataset demonstrate that GAME-YOLO achieves state-of-the-art performance, with an AP@50 of 52.0% and AP@[0.50:0.95] of 32.0%, significantly outperforming strong baselines such as LEAF-YOLO (48.3% AP@50) and YOLOv11 (36.2% AP@50). The model maintains high efficiency, operating at 48 FPS with only 7.6 M parameters and 19.6 GFLOPs. Ablation studies confirm the complementary gains from our probabilistic design choices, including a +10.5 pp improvement in AP@50 over the baseline. Cross-dataset evaluation on VisDrone-DET2021 further validates its generalization capability, achieving 39.2% AP@50. These results indicate that GAME-YOLO offers a practical and reliable solution for vision-based UAV surveillance by effectively marrying the efficiency of deterministic detectors with the robustness principles of Bayesian inference. Full article

In dynamic scattering media (such as rain, fog, biological tissues, etc.) environments, scattered light causes severe degradation of target images, directly leading to a sudden drop in the detection confidence of target detection models and a significant increase in the rate of missed detections. This is a key challenge in the intersection of optical imaging and computer vision. Aiming to address the problems of poor generalization and slow reasoning speed of existing schemes, we construct an end-to-end framework of multi-stage preprocessing, customized network reconstruction, and object detection based on the existing network framework. First, we optimize the original degraded image through preprocessing to suppress scattered noise from the source and retain the key features for detection. Relying on a lightweight and customized network (with only 8.20 M of parameters), high-fidelity reconstruction is achieved to further reduce scattering interference and ultimately complete target detection. The reasoning speed of this framework is significantly better than that of the existing network. On RTX4060, the network’s reasoning ability reaches 147.93 frames per second. After reconstruction, the average confidence level of dynamic object detection is 0.95 with a maximum of 0.99, effectively solving the problem of detection failure in dynamic scattering media. It can provide technical support for scenarios such as unmanned aerial vehicle (UAV) monitoring in foggy weather, biomedical target recognition, and low-altitude security. Full article

This work establishes a large language model (LLM) specialized in the domain of thermoelectric generators (TEGs), for deployment on local hardware. Starting with the generalist JanV1-4B model and Qwen3-4B-Thinking-2507 models, an efficient fine-tuning (FT) methodology using quantized low-rank adaptation (QLoRA) was employed, modifying only 3.18% of the total parameters of thee base models. The key to the process is the use of a custom-designed dataset, which merges deep theoretical knowledge with rigorous instruction tuning to refine behavior and mitigate catastrophic forgetting. The dataset employed for FT contains 202 curated questions and answers (QAs), strategically balanced between domain-specific knowledge (48.5%) and instruction-tuning for response behavior (51.5%). Performance of the models was evaluated using two complementary benchmarks: a 16-question multilevel cognitive benchmark (94% accuracy) and a specialized 42-question TEG benchmark (81% accuracy), scoring responses as excellent, correct with difficulties, or incorrect, based on technical accuracy and reasoning quality. The model’s utility is demonstrated through experimental TEG design guidance, providing expert-level reasoning on thermal management strategies. This study validates the specialization of LLMs using QLoRA as an effective and accessible strategy for developing highly competent engineering support tools, eliminating dependence on large-scale computing infrastructures, achieving specialization on a consumer-grade NVIDIA RTX 2070 SUPER GPU (8 GB VRAM) in 263 s. Full article

Intelligent Transportation Systems (ITSs) aim to improve mobility and reduce congestion, yet current solutions still struggle with scalability, sensing bottlenecks, and inefficient computational resource usage. These limitations impede the shift towards environmentally responsible mobility. This work introduces ORQCIAM (Orchestrated Reasoning based on Quantum Computing and Intelligence for Advanced Mobility), a modular framework that combines Quantum Computing (QC) and Large Language Models (LLMs) to enable real-time, energy-aware decision-making in ITSs. Unlike conventional ITS or AI-based approaches that focus primarily on traffic performance, ORQCIAM explicitly incorporates sustainability as a design objective, targeting reductions in travel time, fuel or energy consumption, and CO₂ emissions. The framework unifies cognitive, virtual, and federated sensing to enhance data reliability, while a hybrid decision layer dynamically orchestrates QC–LLM interactions to minimize computational overhead. Scenario-based evaluation demonstrates faster incident screening, more efficient routing, and measurable sustainability benefits. Across tested scenarios, ORQCIAM achieved 9–18% reductions in travel time, 6–14% lower estimated CO₂ emissions, and around a 50–75% decrease in quantum-optimization calls by concealing QC activation during non-critical events. These results confirm that dynamic QC–LLM coordination effectively decreases computational overhead while supporting greener and more adaptive mobility patterns. Overall, ORQCIAM illustrates how hybrid QC–LLM architectures can serve as catalysts for efficient, low-carbon, and resilient transportation systems aligned with sustainable smart-city goals. Full article

Like many modern economies, Latvia experiences demographic decline, which will cause shortages in the labor force in the future. This article aims to characterize the decline in the working-age population and the factors causing it using system dynamics, PEST, and SWOT analysis. First, the article provides two scenarios for the numerical presentation of a long-term change in the population of working age in Latvia due to emigration. Second, the article describes political, economic, social, and technological factors important for a territory to be economically active and attractive for living and working, which, in turn, is a prerequisite for a populous territory. Third, the article characterizes current peculiarities of the labor market in Latvia given findings on political, economic, social, and technological factors, including achievements and issues. As a result of the analysis, the article provides an analysis of a highly illustrative case study of Latvia, with low birth rates and high emigration, on the one hand, and a broad understanding of reasons for demographic decline on the other hand. In combination with the current characteristics of the labor market, the analysis provides knowledge on achievements and issues for the long-term development of the labor force. The article contributes to debates through a multimethod approach to clarify both working-age population projections and factors affecting the economic attractiveness of a territory. The novelty of the research lies in the application of system dynamics for population projections and a combination of PEST and SWOT analysis for macroeconomic issues. The findings may advise policy-making. The main research findings demonstrate that the expected decline in the working-age population in Latvia is alarming. Besides policies for preventing further decline in the working-age population, policy-making should address such issues as the lack of human capital in smart specialization areas, a low interest of society in becoming an entrepreneur, and insufficient activity in high-tech sectors of the economy. At the same time, the realization of smart specialization strategies contributes to labor market resilience. Full article

Algal biofuels have been investigated as an alternative to fossil fuels and first-generation biofuels for transportation in the United States since the 1970s. Yet after five decades of development, scalability and implementation remain limited—largely due to persistent barriers such as low biomass productivity, modest lipid yields, and energy-intensive processing methods. These technical challenges significantly constrain the feasibility of large-scale commercialization despite substantial research and investment. To evaluate progress toward commercial viability, this study harmonized energy inputs and outputs across 508 observations on the production of algal biofuel energy return on energy investment (EROEI) in the United States. While bioethanol achieves an EROEI of (2.8) and oil (8.7), the analysis produced a mean EROEI of 1.01—essentially the break-even point—irrespective of system boundaries. Life-cycle analysis results showed that hydrothermal liquefaction in algal diesel production yielded a slightly higher mean EROEI (0.67) than transesterification (0.51), yet both showed net energy losses. Co-products were found to increase EROEI values, particularly when recycled into production processes. Collectively, these findings indicate that research and development to date has not produced a technology with net energy gains sufficient for commercial viability. For this reason, algal biofuels show little potential to alleviate the ongoing decline in the EROEI of petroleum and are not a promising renewable energy option for reducing greenhouse gas emissions from the transportation sector. They also show little promise for alleviating the land use, food vs. fuel and other controversies that have plagued first and second-generation biofuels. Full article

Steel frame structures have been increasingly widely used in high-rise and multi-story building design. However, traditional rigid welded beam–column joints exhibit poor ductility and high residual stress, which are key reasons for their susceptibility to brittle failure under strong earthquake actions. This study proposes a new type of beam–column joint for steel frames: the corrugated web beam–column joint. In this new joint, the web of the I-beam near the beam flange is partially replaced with a corrugated web that exhibits a folding effect—this modification weakens the plastic bending capacity of the I-beam and promotes the outward movement of plastic hinges. Low-cycle reciprocating loading tests were conducted to verify the performance of two specimens, namely one with the traditional beam–column joint and the other with the corrugated web beam–column joint. Through experimental comparison, it was found that plastic hinges in the new corrugated web joint are generated at the corrugated web, while no damage occurs at the beam-end welds. This indicates that the corrugated web beam–column joint can stably achieve the outward movement of plastic hinges and avoid the location of the beam-end welds, thereby providing theoretical and experimental foundations for the structural design of new ductile steel frames. Full article

This study reports the successful fabrication and application of floatable syntactic foams derived from fine magnesium powder (<45 µm) utilizing expanded perlite (0.25 g/cm³, 0.2–0.4 mm) as the pore former. Sample disks with densities as low as 0.9 g/cm³ were produced via the classical press and sinter process. To ensure reasonable mechanical properties, the specimens were formed under a pressure of 200 MPa in a hardened steel die, followed by high-vacuum sintering (~3 × 10⁻⁶ torr) at 640 °C for 1 h. The resulting foams exhibited sufficient mechanical strength to allow for precision machining into a microboat. We demonstrated their potential use as a Marangoni-induced microswimmer. Spontaneous locomotion was observed when ethanol was used as a propellant, which generates a surface tension gradient between the upper and rear parts of the swimmer. The microboats achieved propulsion speeds of approximately 160 mm/s when propelled by a 95% ethanol + 5% ink mixture. Using a small volume (~4 µL) of the alcohol mixture, the swimmer could cover distances exceeding 350 mm. Full article

Predominant fault diagnosis in industrial robots depends on dedicated vibration or acoustics sensors. However, their practical deployment is often limited by installation constraints, susceptibility to environmental noise, and cost considerations. Applying Energy-Based Maintenance (EBM) principles to achieve enhanced fault diagnosis under practical industrial conditions, we propose a hybrid deep learning framework, the Multi-head Graph Attention Network (MGAT) with Multi-scale CNNBiLSTM Fusion (MGAT-MCNNBiLSTM) for industrial robots. This approach obviates the need for additional dedicated sensors, effectively mitigating associated deployment complexities. The framework embodies four core innovations: (1) Based on the EBM paradigm, motor current is established as the most effective and practical choice for enabling cost-efficient and scalable industrial robot fault diagnosis. A corresponding dataset of motor current has been acquired from industrial robots operating under diverse fault scenarios. (2) An integrated MGAT-MCNNBiLSTM architecture that synergistically models multiscale local features and complex dynamics through its MCNNBiLSTM module while capturing nonlinear interdependencies via MGAT. This comprehensive feature representation enables robust and highly accurate fault detection. (3) The study found that the application of spectral preprocessing techniques yields a marked and statistically significant enhancement in diagnostic performance. A comprehensive and systematic analysis was undertaken to uncover the underlying reasons for this observed performance improvement. (4) To emulate challenging industrial settings and cost-sensitive implementations, noise signal injection was employed to evaluate model robustness in high-electromagnetic-interference environments and low-cost, low-resolution ADC implementations. Experimental validation on real-world industrial robot datasets demonstrates that MGAT-MCNNBiLSTM achieves a superior diagnostic accuracy of 90.7560%. This performance marks a significant absolute improvement of 1.51–8.55% over competing models, including LCNNBiLSTM, SCNNBiLSTM, MCCBiLSTM, GAT, and MGAT. Under challenging noise and low-resolution conditions, the proposed model consistently outperforms CNNBiLSTM variants, GAT, and MGAT with an improvement of 1.37–10.26% and enhanced industrial utility and deployment potential. Full article

While improvements in the spectral and spatial resolution of satellite imagery have opened up new prospects for large-scale environmental monitoring, this potential has remained largely unrealised in dune ecogeomorphology. This is especially true for Mediterranean coastal dunes, where the highly mixed and sparse vegetation requires high resolution satellites and spectral unmixing techniques. To achieve this aim, we employed random forest regressors to predict the fractional cover of dune plant species in two of the sandy barriers of Ria Formosa (S. Portugal) from WorldView-2 imagery (June 2024). The algorithm, tested with spatially upscaled multispectral drone data and satellite imagery, detected the fractional cover of major species (most abundant classes and bushy vegetation) with reasonable to very good accuracy (coefficient of determination, CoD: 0.4 to 0.8) for the former and reasonable to good accuracy (CoD: 0.4 to 0.6) for the latter. Additional tests showed that (a) including the distance to the shoreline can increase model accuracy (CoD by ~0.1); (b) the grouping of species resulted in an insignificant increase in model skill; and (c) testing over independent dune plots showed generalisation beyond the training set and low risk of overfitting or noise. Overall, the approach showed promising results for large-scale observations in highly mixed coastal dunes. Full article

In this study, a machine learning-based quality control algorithm for heavy rainfall was developed by integrating automatic weather station observations with remote sensing data, minute-level data, and metadata. Based on heavy rainfall samples from 1 June 2022 to 31 December 2024, the performances of four gradient boosting models—eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), and Gradient Boosted Regression Trees (GBRT)—significantly outperformed precipitation-threshold-based conventional methods, including regional extreme value checks, temporal consistency checks, and others. Specifically, the XGBoost in particular achieves an increase in precision by 0.110 and recall by 0.162. This translates to a substantial reduction in both false alarms (higher precision) and missed detections (higher recall) of anomalous heavy rainfall events, thereby significantly enhancing the reliability of the quality-controlled data. The radar composite reflectivity, satellite cloud-top temperature, and minute-level precipitation were identified as dominant contributors to model predictions. The integration of multi-sensor observations effectively addressed limitations inherent in conventional threshold-based approaches. Through SHapley Additive exPlanations (SHAP)-based interpretability analysis, the model’s decision logic was shown to align with meteorological physical principles. Characteristic patterns such as combinations of low radar reflectivity and elevated cloud-top temperatures were flagged as anomalous rainfall events, typically corresponding to manual operational errors. Moreover, the model identified anomalous minute-level precipitation extremes to be critical signals for detecting instrument malfunctions, data encoding and transmission errors. The physical consistency of the model’s reasoning enhances its trustworthiness and supports its potential for operational implementation in heavy rainfall quality control. Full article

To extend the near-seabed survey operation duration of deep-sea Autonomous Underwater Vehicles (AUVs), this paper proposes a deep-sea bottom-landing and dwelling technical scheme integrating the drive of a variable buoyancy adjustment mechanism with the support of a “biped” telescopic bottom-landing mechanism. This scheme offers a flexible, low-cost, multi-site repeatable bottom-landing process, and sensitive water area-applicable dwelling solution for marine surveys. Firstly, for hard seabed sediments, the mechanical response of AUVs during hard landing under different driving forces and attitudes is solved through simulation analysis, and the local optimal solution of reasonable driving forces is obtained to provide input for the design of the variable buoyancy mechanism. Secondly, for soft seabeds, the variation law of the bottom-leaving adsorption force with different length-to-width ratios (L/B) under the same bottom-landing plate area is studied to provide design input for the telescopic bottom-landing mechanism. Subsequently, the bottom-landing criteria and calculation formulas for flat and uneven seabeds are established, and the bottom-landing and bottom-leaving control strategies are constructed. Finally, the two sets of mechanisms are integrated into the AUV platform. Verification via pool, lake, and sea tests has demonstrated favorable results, and scientific test data of 56 dives within 1 m of the near-seabed are obtained. Traditional technical solutions primarily rely on jettisonable ballast weights or ballast tanks for operations, enabling only a single dive, bottom-landing, and bottom-leaving process. Their concealment and operational depth are often limited. The technical achievement proposed in this paper supports the ABLUV in performing multiple repeated bottom-landing and bottom-leaving operations in deep-sea environments without the need for jettisoning ballast throughout the entire process. Full article

Construction sites are complex environments where traditional safety monitoring methods often suffer from low detection accuracy and limited interpretability. To address these challenges, this study proposes a modular multimodal agent framework that integrates computer vision, knowledge representation, and large language model (LLM)–based reasoning. First, the CLIP model fine-tuned with Low-Rank Adaptation (LoRA) is combined with YOLOv10 to achieve precise recognition of construction activities and personal protective equipment (PPE). Second, a construction safety knowledge graph integrating Retrieval-Augmented Generation (RAG) is constructed to provide structured domain knowledge and enhance contextual understanding. Third, the FusedChain prompting strategy is designed to guide large language models (LLMs) to perform step-by-step safety risk reasoning. Experimental results show that the proposed approach achieves 97.35% accuracy in activity recognition, an average F1-score of 0.84 in PPE detection, and significantly higher performance than existing methods in hazard reasoning. The modular design also facilitates scalable integration with more advanced foundation models, indicating strong potential for real-world deployment in intelligent construction safety management. Full article

Establishing a mathematical model of a Francis turbine is the foundation for the simulation of hydropower station operation and is of great significance for the analysis of the hydropower station’s transient process. Currently, in engineering practice, the model is often established based on the comprehensive characteristic curves of the Francis turbine provided by the manufacturer, using the external characteristic method. Traditional modeling methods mostly adopt manual reading of points or the use of dedicated numerical software for curve tracing to discretely sample the comprehensive characteristic curves of the turbine. This method is labor-intensive, inefficient, and relies on manual experience, with a small sample size, which, to some extent, affects the accuracy and reliability of the numerical processing results and cannot meet the needs of transient process simulation analysis. To address these shortcomings, this paper proposes a refined modeling method based on image numerical processing and an RBF neural network. Taking the HLA685 Francis turbine as an example, the method first uses image processing to achieve large-scale automated discrete sampling of the turbine’s high-efficiency zone characteristic data, then reasonably extends the small-opening and low-speed regions, and finally uses the RBF neural network method for interpolation and extrapolation to obtain the full characteristic data. This method can effectively improve the efficiency and accuracy of comprehensive characteristic modeling of the turbine and has good reference significance for the comprehensive characteristic modeling of blade-type machinery. Full article