Journal of Research in Multidisciplinary Methods and Applications

Analysis of the Sustainable Development Path of Kindergartens in Guizhou Province Under the Background of Low Fertility Rate

2026-05-18T06:21:31+00:00

In the current population development pattern of China, low fertility rate is a prominent new phenomenon. In the case of the declining birth rate year after year, the decrease in the number of newborns has directly led to the reduction of the scale of preschool education, and the development of kindergartens is also facing new requirements and challenges. Taking Guizhou Province as an example, this paper analyzes the changes in the birth population and the characteristics of the preschool education population in Guizhou Province, studies the impact of low fertility rate on the development of kindergartens in Guizhou Province from the perspectives of enrollment scale, spatial distribution and quality, and explores the countermeasures of kindergarten resource allocation under the background of low fertility rate, in order to create a replicable example for similar regions, and also provides a basis for local governments to formulate local policies for kindergarten resource optimization.

Advances in Research on the Habitat-Disturbing Effects of Ulva Prolifera and Their Consequences for the Ecology of Apostichopus Japonicus Aquaculture Ponds

2026-05-18T06:24:44+00:00

The sustained outbreaks of Ulva prolifera green tides in the Yellow Sea have become one of the most severe ecological disasters in China’s coastal waters. During large-scale senescence and decomposition, substantial amounts of ammonia nitrogen, sulfides, and dissolved organic matter are released, triggering hypoxia, acidification, and sediment deterioration, thereby imposing significant stress on semi-enclosed Apostichopus japonicus aquaculture ponds. At present, systematic studies on how green tides across their entire life cycle—especially during senescence—specifically perturb the pond's benthic environment and, in turn, affect A. japonicus physiological homeostasis and health remain relatively weak. This paper systematically reviews the occurrence characteristics of Yellow Sea green tides, the biogeochemical processes during Ulva senescence and their potential impacts on the aquaculture environment, and integrates findings from A. japonicus ecology and physiological ecology. The aim is to elucidate the stress mechanisms by which green tide disasters affect pond aquaculture systems for A. japonicus, and to provide theoretical support for establishing ecosystem-health-based risk warning and control strategies in aquaculture.

EHL Oil Film Thickness Analysis of VH-CATT Cylindrical Gear

2026-05-18T06:27:52+00:00

Variable hyperbolic circular arc tooth trace ( VH-CATT ) cylindrical gear can be used in high-speed and heavy-duty occasions because of its good transmission performance and bearing capacity. In order to analyze the influence of different working conditions on the oil film thickness, the tooth surface contact mathematical model of VH-CATT cylindrical gear is constructed, and the tooth surface contact point and main curvature of VH-CATT cylindrical gear system are calculated. Based on EHL theory, the minimum oil film thickness and central oil film thickness model of VH-CATT cylindrical gear EHL are constructed, and the distribution of oil film thickness on the tooth surface of VH-CATT cylindrical gear and the variation law of minimum oil film thickness and central oil film thickness in one meshing cycle are analyzed. The results show that as the input speed increases, the entrainment speed increases, and the corresponding oil film thickness increases significantly. When the input torque becomes larger, the decreasing trend of oil film thickness is much smaller than the amplitude of torque change. This study provides a theoretical basis for predicting gear life and calculating wear.

A Review of Road Defect Recognition Based on Unmanned Aerial Vehicles and Deep Learning

2026-05-18T06:31:47+00:00

Real-time monitoring of transportation infrastructure is crucial for ensuring the safety and operational efficiency of road networks. Traditional manual inspections involve high risks and low efficiency, whereas unmanned aerial vehicles (UAVs), with their high maneuverability and broad coverage, have gradually become an important data collection platform in the field of pavement defect detection. However, the large-scale application of UAVs in complex environments still faces technical bottlenecks such as environmental interference, inconsistent data quality, and slow processing of massive images. This paper systematically reviews the frontier progress of UAV-based pavement defect monitoring technologies in recent years: First, it outlines the limitations and challenges during the UAV field data collection phase; Second, it deeply analyzes the algorithmic evolution in office data processing, from image preprocessing and 3D pavement reconstruction to deep learning and semantic segmentation; Building upon this, the paper constructs a multi-dimensional evaluation system ranging from apparent 2D defects to hidden 3D defects and summarizes multi-modal fusion recognition methods, including visible light, LiDAR, infrared thermal imaging, and ground-penetrating radar (GPR). Finally, the paper discusses the limitations of current technologies regarding hardware battery life, legal privacy, and complex background interference, and points out the development potential of edge computing, UAV swarm collaboration, and cyber-physical systems in future intelligent road maintenance.

Cross-Terminal Intelligent Diagnosis and Treatment System Based on Multimodal Large Language Models

2026-05-18T06:36:19+00:00

To address the prominent challenges in current medical auxiliary diagnosis—including inaccurate tongue image feature extraction, poor disease adaptability, lack of cross-terminal collaboration, and high dependence on foreign core technologies—a cross-terminal intelligent diagnosis and treatment system based on multimodal large language models was designed and implemented. The system takes traditional Chinese medicine (TCM) tongue diagnosis as the core application scenario. Using SAM-2 with LoRA lightweight fine-tuning, pixel-level precise segmentation of tongue images is achieved with 97.2% accuracy. A heterogeneous fusion feature extraction architecture combining ResNet and Vision Transformer is proposed, enabling three-layer information fusion of tongue body, tongue coating, and tongue texture, improving disease prediction accuracy to 84%. The Qwen3-VL multimodal large language model integrated with Retrieval-Augmented Generation (RAG) technology constructs an interpretable disease prediction engine with a retrieval precision rate of 61%. Full-stack deployment is completed on the domestic Kunpeng CPU and Ascend NPU hardware platform, achieving an inference speed of 20 Token/s. Experimental results demonstrate that the system achieves significant performance in accuracy, interpretability, and domestic adaptation, validating the feasibility and efficiency of domestic hardware and software systems in handling complex multimodal large model tasks.