With the rapid advancement of virtual human technology and live-streaming e-commerce, virtual anchors have increasingly become key interactive entities in the digital economy. However, emerging issues such as fake reviews, abnormal tipping, and illegal transactions pose significant threats to platform financial security and user privacy. To address these challenges, a multimodal emotion–finance fusion security recognition framework (MSF-Net) is proposed, which integrates visual, audio, textual, and financial transaction signals to achieve cross-modal feature alignment and multi-signal risk modeling. The framework consists of three core modules: the multimodal alignment transformer (MAT), the fake review detection (FRD) module, and the multi-signal fusion decision module (MSFDM), enabling deep integration of semantic consistency modeling and emotion–behavior collaborative recognition. Experimental results demonstrate that MSF-Net achieves superior performance in virtual live-streaming financial security detection, reaching a precision of 0.932, a recall of 0.924, an F1-score of 0.928, an accuracy of 0.931, and an area under curve (AUC) of 0.956, while maintaining a real-time inference speed of 60.7 FPS, indicating outstanding precision and responsiveness. The ablation experiments further verify the necessity of each module, as the removal of any component leads to an F1-score decrease exceeding 4%, confirming the structural validity of the model’s hierarchical fusion design. In addition, a lightweight version of MSF-Net was developed through parameter distillation and quantization pruning techniques, achieving real-time deployment on mobile devices with an average latency of only 19.4 milliseconds while maintaining an F1-score of 0.923 and an AUC of 0.947. The results indicate that MSF-Net exhibits both innovation and practicality in multimodal deep fusion and security risk recognition, offering a scalable solution for intelligent risk control in data-driven artificial intelligence applications across financial and virtual interaction domains.

Accurate cooling load forecasting in high-efficiency chiller plants with ice storage systems is essential for intelligent control, energy conservation, and maintaining indoor comfort. However, conventional forecasting methods often struggle to model the complex nonlinear dependencies among influencing variables, limiting their predictive performance. To address this, this paper introduces Time-LLM, a novel time series forecasting framework that leverages a frozen large language model (LLM) to improve the accuracy and generalization of cooling load forecasting. Time-LLM extracts features from historical data, reformulates them as natural language prompts, and uses the LLM for temporal sequence modeling; a linear projection layer then maps the LLM output to final predictions. To enable lightweight deployment and improve temporal feature prompting, we propose ETime-LLM, an enhanced variant of Time-LLM. ETime-LLM significantly reduces deployment costs and mitigates the original model’s response lag during trend transitions by focusing on possible turning points. Extensive experiments demonstrate that ETime-LLM consistently outperforms or matches state-of-the-art baselines across short-term, long-term, and few-shot forecasting tasks. Specifically, in the commonly used 24 h forecasting horizon, compared with the original model, ETime-LLM achieves an approximately 17.3% reduction in MAE and a 19.3% reduction in RMSE. It achieves high-quality predictions without relying on costly external data, offering a robust and scalable solution for green and energy-efficient HVAC system management.

Optimizing peanut vine-inversion operations via intelligent control and semantic segmentation

Intelligent emotional learning control system with prescribed performance and dynamic event-triggered mechanism for trajectory tracking of underactuated AUV

Harnessing machine learning for energy optimization and intelligent process control in wastewater treatment

Intelligent monitoring and control of tablet coating processes based on near-infrared spectroscopy

Deep learning-based phenotypic analysis and intelligent environmental control in edible mushrooms: Advances, challenges, and prospects

Multi-objective intelligent flood control operation rules extraction for reservoirs-lake system based on long and short-term memory neural networks coupled with physical constraints

Intelligent hierarchical control strategy for performance optimization and fault-tolerant operation of hybrid power generation systems

Machine learning-based prediction of PAHs thermal desorption efficiency: Model optimization, boundary correction, and mechanistic insights.

A Transformer-based framework with generative spectral augmentation for online monitoring of hyaluronic acid fermentation.

Cross-scale generative adversarial learning networks for intelligent hierarchical control of proton exchange membrane fuel cells systems

Advances in catalysis for biodiesel production: Integrating AI-driven optimization and bibliometric insights into renewable energy technologies.

Improved whale optimization algorithm applied to intelligent control of harvesters to enhance maize harvesting quality

Sodium limitation stimulates Acetobacterium woodii enrichment and boosts CO2 conversion to acetate in self-controlled microbial electrosynthesis systems.

Investigation of An Intelligent Controller For Power Quality Disturbance In A Renewable Source Based Micro Grid With Electric Vehicle Integration

Effective thermal management is pivotal to the performance, safety and lifetime of lithium-ion traction batteries in electric vehicles. This review synthesizes mainstream and emerging heat-dissipation strategies like forced air, indirect liquid cold plates, phase-change materials (PCMs), thermoelectric (TEC) assistance, and two-phase devices such as heat pipes and oscillating heat pipes, then compare them across heat-removal capacity, temperature uniformity (T), energy overhead, packaging complexity, and safety. Evidence shows that natural convection is inadequate for large cells and well-designed forced air can serve moderate C-rates but often leaves persistent non-uniformity. Indirect liquid cooling provides tighter temperature control and scales to fast-charge duty when manifold balance and thermal interfaces are optimized. PCMs excel at transient peak shaving and uniformity but require a recharge path; TECs deliver precise, bidirectional hot-spot control at the cost of electrical power and robust hot-side rejection. Passive two-phase spreaders efficiently relocate heat and suppress gradients when properly integrated. The analysis supports hybrid battery thermal-management systems that combine liquid plates for baseline control, passive spreaders for isothermalization, and selectively engaged boosters (PCM/TEC) coordinated by intelligent control. Such integrated designs are most likely to maintain 2040 C operating bands, hold cell-to-cell T < 5 C, and reduce runaway risk while minimizing lifecycle cost in next-generation EV packs.

In order to solve the problems of thermal management efficiency and temperature control accuracy in the passenger compartment of electric vehicles, the phase change thermal storage design concept and the model-free adaptive control method are applied to the thermal management temperature control system of the passenger compartment. Aiming at the characteristics of waste heat utilization of the whole vehicle and the preheating demand of the passenger compartment, an integrated vehicle thermal management model with a heat exchanger and storage function and an intelligent temperature control system scheme for the passenger compartment is designed. Aiming at the demand for adaptive control of the thermal management system of the passenger compartment of the whole vehicle, a composite strategy of PID control of compressor speed and model-free adaptive control of water pump speed are proposed, and the effect of the application of different control strategies under the demand for temperature control of the passenger compartment is compared and analyzed in simulation. The study shows that the phase change heat storage system and its model-free adaptive control in this paper are more stable, with smaller overshoot and high temperature regulation accuracy; the overshoot of PID control and fuzzy PID control is 14.17% and 8.58%, respectively, while the overshoot of model-free adaptive control is only 0.42%, which verifies the superiority of the designed thermal management system and the effectiveness of the control algorithm, and will effectively enhance the thermal comfort of the passenger compartment of electric vehicles.

This paper presents a human-in-the-loop (HiTL) intelligent adaptive control scheme for unmanned surface vehicles (USVs) that accounts for uncertain dynamics, with the goal of effectively monitoring marine pollutants. To tackle the challenges posed by complex aquatic monitoring environments, this approach integrates human intelligence into the navigation strategy of USVs, allowing for superior path planning through human decision-making. Additionally, a potential field-based obstacle avoidance strategy is developed to ensure the safe operation of USVs within this HiTL framework. To address issues related to system uncertainties, we propose a novel adaptive fuzzy control strategy based on convex optimization, which enhances overall control performance. Finally, stability analysis and simulation results demonstrate the effectiveness of the proposed method. The verification results show that compared with traditional adaptive fuzzy controllers, our control strategy effectively reduces control errors.

The environmental impact of civil aircraft noise has increased, adversely affecting airport operations and the health of nearby residents, and representing a major barrier to the aviation industrys green development. This paper aims to systematically explore the main noise sources and their variation characteristics of civil aircraft during different flight stages such as takeoff, cruise and landing, and to clarify the evolution mechanism of typical sound sources. By reviewing recent research, this paper explores three mainstream noise reduction methods, including airframe noise reduction with passive damping and multi-layer sound absorption, engine noise control via bypass ratio optimization and acoustic liners, and active control systems for cabin and external areas. The results show that, despite success in some frequency ranges, current technologies still face issues with cost, structural integration, and operational energy use. As such, it further explores future development paths like additive manufacturing, intelligent control systems and alternative fuels, and points out that civil aviation noise control will tend towards the integrated evolution direction of multi-system coordination and structural intelligence integration, providing technical references for the design of low-noise green aircraft.